diff --git a/a0.1/.gitignore b/a0.1/.gitignore
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+++ b/a0.1/.gitignore
@@ -0,0 +1,2 @@
+.DS_Store
+.venv/
diff --git a/a0.1/03-CPU_Programs/02-Assignments/RezaSayyareh_Pose-Estimation/LatPulldown.jpg b/a0.1/03-CPU_Programs/02-Assignments/RezaSayyareh_Pose-Estimation/LatPulldown.jpg
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diff --git a/a0.1/03-CPU_Programs/02-Assignments/RezaSayyareh_Pose-Estimation/Pose_Estim_MediaPipe_LatPulldown_rsayyareh.ipynb b/a0.1/03-CPU_Programs/02-Assignments/RezaSayyareh_Pose-Estimation/Pose_Estim_MediaPipe_LatPulldown_rsayyareh.ipynb
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--- /dev/null
+++ b/a0.1/03-CPU_Programs/02-Assignments/RezaSayyareh_Pose-Estimation/Pose_Estim_MediaPipe_LatPulldown_rsayyareh.ipynb
@@ -0,0 +1,297 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "889b03a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import time\n",
+    "import mediapipe as mp\n",
+    "import numpy as np\n",
+    "\n",
+    "mp_drawing = mp.solutions.drawing_utils\n",
+    "mp_pose = mp.solutions.pose\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9ceca6e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def elbow_angle(a, b, c):\n",
+    "    radians = np.arctan2(c.y-b.y, c.x-b.x) - np.arctan2(a.y-b.y, a.x-b.x)\n",
+    "    angle = np.abs(radians*180/np.pi)\n",
+    "\n",
+    "    if angle > 180:\n",
+    "        angle = 360 - angle\n",
+    "\n",
+    "    return round(angle, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d477d52d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def torso_angle(a, b, c, d):\n",
+    "    \"\"\"\n",
+    "    Calculates the angle between torso and the Z-axis.\n",
+    "\n",
+    "    Args:\n",
+    "        p1 (tuple/list): middle point of shoulders\n",
+    "        p2 (tuple/list): middel point of hips\n",
+    "\n",
+    "    Returns:\n",
+    "        float: Angle in degrees between the line and the positive Z-axis.\n",
+    "    \"\"\"\n",
+    "    p1 = ((a.x+b.x)/2, (a.y+b.y)/2, (a.z+b.z)/2)  # Midpoint of a and b\n",
+    "    p2 = ((c.x+d.x)/2, (c.y+d.y)/2, (c.z+d.z)/2)  # Midpoint of c and d\n",
+    "    \n",
+    "    # 1. Define Z-axis unit vector\n",
+    "    z_axis_vector = np.array([0, 0, 1])\n",
+    "\n",
+    "    # 2. Calculate the line vector (P2 - P1)\n",
+    "    line_vector = np.array(p2) - np.array(p1)\n",
+    "\n",
+    "    # 3. Calculate the dot product\n",
+    "    dot_product = np.dot(line_vector, z_axis_vector)\n",
+    "\n",
+    "    # 4. Calculate magnitudes\n",
+    "    magnitude_line = np.linalg.norm(line_vector)\n",
+    "    magnitude_z = np.linalg.norm(z_axis_vector) # This is just 1\n",
+    "\n",
+    "    # Avoid division by zero if the line is a point\n",
+    "    if magnitude_line == 0:\n",
+    "        return 0.0 # Or handle as error\n",
+    "\n",
+    "    # 5. Calculate the cosine of the angle\n",
+    "    # Clamp the value to [-1, 1] to avoid floating point errors with acos\n",
+    "    cos_theta = np.clip(dot_product / (magnitude_line * magnitude_z), -1.0, 1.0)\n",
+    "\n",
+    "    # 6. Calculate the angle in radians and convert to degrees\n",
+    "    angle_radians = np.arccos(cos_theta)\n",
+    "    angle = np.degrees(angle_radians)\n",
+    "\n",
+    "    if angle > 180:\n",
+    "        angle = 360 - angle\n",
+    "\n",
+    "\n",
+    "    return round(angle, 2)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9e1c9d5",
+   "metadata": {},
+   "source": [
+    "1. Torso Angle:\n",
+    "\n",
+    "    less than 20° relative to the vertical direction (Z axis)\n",
+    "\n",
+    "2. Elbow Angle:\n",
+    "\n",
+    "    At the top:\n",
+    "    greater than 150 \n",
+    "\n",
+    "    At the bottom:\n",
+    "    less than 90° \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edc6cf5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cap = cv2.VideoCapture(0)\n",
+    "\n",
+    "start_time = time.time()\n",
+    "counter = 0\n",
+    "rate = 0\n",
+    "rate_t = \"0.0\"\n",
+    "stage = None\n",
+    "\n",
+    "with mp_pose.Pose(\n",
+    "    min_detection_confidence=0.5, \n",
+    "    min_tracking_confidence=0.5\n",
+    ") as pose:\n",
+    "    \n",
+    "    while cap.isOpened():\n",
+    "        ret, frame = cap.read()\n",
+    "        \n",
+    "        image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
+    "        image.flags.writeable = False\n",
+    "\n",
+    "        results = pose.process(image)\n",
+    "\n",
+    "        image.flags.writeable = True\n",
+    "        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n",
+    "\n",
+    "        try:\n",
+    "            landmarks = results.pose_landmarks.landmark\n",
+    "            l_wrist = landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value]\n",
+    "            l_elbow = landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value]\n",
+    "            l_shoulder = landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value]\n",
+    "            l_hip = landmarks[mp_pose.PoseLandmark.LEFT_HIP.value]\n",
+    " \n",
+    "            r_wrist = landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value]\n",
+    "            r_elbow = landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value]\n",
+    "            r_shoulder = landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value]\n",
+    "            r_hip = landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value]\n",
+    " \n",
+    "            lh_angle = elbow_angle(l_wrist, l_elbow, l_shoulder)\n",
+    "            rh_angle = elbow_angle(r_wrist, r_elbow, r_shoulder)\n",
+    "            t_angle = torso_angle(l_shoulder, r_shoulder, l_hip, r_hip)\n",
+    "\n",
+    "            cv2.putText(image,\n",
+    "                        str(lh_angle),\n",
+    "                        tuple(np.multiply((l_elbow.x, l_elbow.y), [640, 480]).astype(int)),\n",
+    "                        cv2.FONT_HERSHEY_SIMPLEX,\n",
+    "                        0.5,\n",
+    "                        (255, 255, 0),\n",
+    "                        2,\n",
+    "                        cv2.LINE_AA\n",
+    "                        )\n",
+    "            cv2.putText(image,\n",
+    "                        str(rh_angle),\n",
+    "                        tuple(np.multiply((r_elbow.x, r_elbow.y), [640, 480]).astype(int)),\n",
+    "                        cv2.FONT_HERSHEY_SIMPLEX,\n",
+    "                        0.5,\n",
+    "                        (255, 255, 0),\n",
+    "                        2,\n",
+    "                        cv2.LINE_AA\n",
+    "                        )\n",
+    "\n",
+    "            cv2.putText(image,\n",
+    "                        str(t_angle),\n",
+    "                        tuple(np.multiply(((l_shoulder.x+r_shoulder.x)/2, (l_shoulder.y+r_shoulder.y)/2), [640, 480]).astype(int)),\n",
+    "                        cv2.FONT_HERSHEY_SIMPLEX,\n",
+    "                        0.5,\n",
+    "                        (255, 255, 0),\n",
+    "                        2,\n",
+    "                        cv2.LINE_AA\n",
+    "                        )\n",
+    "            cv2.putText(image,\n",
+    "                        timer_text,\n",
+    "                        (10, 470),\n",
+    "                        cv2.FONT_HERSHEY_SIMPLEX,\n",
+    "                        0.7,\n",
+    "                        (0, 255, 0),\n",
+    "                        2,\n",
+    "                        cv2.LINE_AA\n",
+    "                        )\n",
+    "\n",
+    "            # Curl counter logic\n",
+    "            if lh_angle > 150 and rh_angle > 150:\n",
+    "                stage = 'up'\n",
+    "            if lh_angle < 90 and rh_angle < 90 and (t_angle - 90) < 20 and stage == 'up':\n",
+    "                stage = 'down'\n",
+    "                counter += 1\n",
+    "\n",
+    "        except:\n",
+    "            pass\n",
+    "        \n",
+    "        cv2.rectangle(image, (0,0), (150,50), (255,100,0), -1)\n",
+    "        cv2.putText(image, 'REPS: ',\n",
+    "                    (0,35),\n",
+    "                    cv2.FONT_HERSHEY_COMPLEX,\n",
+    "                    0.7,\n",
+    "                    (0,255,255),\n",
+    "                    1,\n",
+    "                    cv2.LINE_AA)\n",
+    "        cv2.putText(image,\n",
+    "                    str(counter),\n",
+    "                    (80,40),\n",
+    "                     cv2.FONT_HERSHEY_COMPLEX,\n",
+    "                    1.0,\n",
+    "                    (255,255,255),\n",
+    "                    2,\n",
+    "                    cv2.LINE_AA)\n",
+    "\n",
+    "        cv2.rectangle(image, (440,0), (620,50), (255,100,0), -1)\n",
+    "        cv2.putText(image, 'Stage: ',\n",
+    "                    (440,35),\n",
+    "                    cv2.FONT_HERSHEY_COMPLEX,\n",
+    "                    0.7,\n",
+    "                    (0,255,255),\n",
+    "                    1,\n",
+    "                    cv2.LINE_AA)\n",
+    "        cv2.putText(image,\n",
+    "                    str(stage),\n",
+    "                    (530,40),\n",
+    "                     cv2.FONT_HERSHEY_COMPLEX,\n",
+    "                    1.0,\n",
+    "                    (255,255,255),\n",
+    "                    2,\n",
+    "                    cv2.LINE_AA)\n",
+    "\n",
+    "        cv2.rectangle(image, (220,0), (380,50), (255,100,0), -1)\n",
+    "        elapsed_time = time.time() - start_time\n",
+    "        minutes = int(elapsed_time // 60)\n",
+    "        seconds = int(elapsed_time % 60)\n",
+    "        timer_text = f\"{minutes:02d}:{seconds:02d}\"\n",
+    "        rate = counter / (minutes * 60 + seconds + 0.001) * 60\n",
+    "        rate_t = f\"{rate:.1f}\"\n",
+    "        cv2.putText(image, '#/min: ',\n",
+    "                    (220,35),\n",
+    "                    cv2.FONT_HERSHEY_COMPLEX,\n",
+    "                    0.7,\n",
+    "                    (0,255,255),\n",
+    "                    1,\n",
+    "                    cv2.LINE_AA)\n",
+    "        cv2.putText(image,\n",
+    "                    str(rate_t),\n",
+    "                    (320,40),\n",
+    "                     cv2.FONT_HERSHEY_COMPLEX,\n",
+    "                    1.0,\n",
+    "                    (255,255,255),\n",
+    "                    2,\n",
+    "                    cv2.LINE_AA)\n",
+    "\n",
+    "        # Draw landmarks\n",
+    "        mp_drawing.draw_landmarks(\n",
+    "            image, \n",
+    "            results.pose_landmarks, \n",
+    "            mp_pose.POSE_CONNECTIONS\n",
+    "        )\n",
+    "        \n",
+    "        cv2.imshow(\"Pose\", image)\n",
+    "        if cv2.waitKey(10) & 0xFF == ord('q'):\n",
+    "            break\n",
+    "\n",
+    "cap.release()\n",
+    "cv2.destroyAllWindows()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mp_cpu",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git "a/a0.1/04-GPU_Programs/02-Assignments/a15/AI\342\200\223DS_Nexus___5_9_TextClassification_rsayyareh.ipynb" "b/a0.1/04-GPU_Programs/02-Assignments/a15/AI\342\200\223DS_Nexus___5_9_TextClassification_rsayyareh.ipynb"
new file mode 100644
index 0000000..4c01f30
--- /dev/null
+++ "b/a0.1/04-GPU_Programs/02-Assignments/a15/AI\342\200\223DS_Nexus___5_9_TextClassification_rsayyareh.ipynb"
@@ -0,0 +1,914 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4giSCbj0Th8C"
+      },
+      "source": [
+        "# Sentiment Analysis: FastText + Classical ML\n",
+        "\n",
+        "End-to-end sentiment classification using FastText embeddings with XGBoost and Random Forest.\n",
+        "\n",
+        "**Pipeline:** Data loading (IMDB) → Preprocessing → FastText embeddings → Training → Evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BRwbyb93Th8E"
+      },
+      "source": [
+        "## 1. Setup & Install Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "myZnABTZTh8E",
+        "outputId": "a2fc31e1-33a1-405b-b356-6958782cfc32"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:From c:\\Users\\HP\\miniconda3\\envs\\v_nlpgpu\\lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
+            "\n",
+            "✓ All libraries ready\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "# !pip install -q fasttext xgboost\n",
+        "\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.metrics import (\n",
+        "    accuracy_score, precision_score, recall_score, f1_score,\n",
+        "    confusion_matrix, classification_report\n",
+        ")\n",
+        "import xgboost as xgb\n",
+        "import fasttext\n",
+        "import re\n",
+        "\n",
+        "#2d projection\n",
+        "from sklearn.manifold import TSNE\n",
+        "\n",
+        "# for dataset\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.datasets import imdb\n",
+        "\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "\n",
+        "plt.style.use('seaborn-v0_8-darkgrid')\n",
+        "sns.set_palette('husl')\n",
+        "\n",
+        "print(\"✓ All libraries ready\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2Q3EAZ83Th8F"
+      },
+      "source": [
+        "## 2. Load IMDB Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dFgvjd0lTh8G",
+        "outputId": "6d6d84ad-8fa8-4f46-8253-bf58675cc6a0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loading IMDB dataset...\n",
+            "✓ IMDB dataset loaded\n",
+            "\n",
+            "Dataset shape: (5500, 2)\n",
+            "\n",
+            "Sample reviews:\n",
+            "\n",
+            "[POSITIVE] ? this film was just brilliant casting location scenery story direction everyone's really suited the...\n",
+            "\n",
+            "[NEGATIVE] ? big hair big boobs bad music and a giant safety pin these are the words to best describe this terr...\n",
+            "\n",
+            "Label distribution:\n",
+            "{0: 2768, 1: 2732}\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Loading IMDB dataset...\")\n",
+        "\n",
+        "word_index = imdb.get_word_index()\n",
+        "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
+        "\n",
+        "def decode_review(text):\n",
+        "    return ' '.join([reverse_word_index.get(i - 3, '?') for i in text])\n",
+        "\n",
+        "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000)\n",
+        "\n",
+        "X_train_text = np.array([decode_review(x) for x in x_train[:4000]])\n",
+        "X_test_text = np.array([decode_review(x) for x in x_test[:1500]])\n",
+        "y_train_label = y_train[:4000]\n",
+        "y_test_label = y_test[:1500]\n",
+        "\n",
+        "df_train = pd.DataFrame({'review': X_train_text, 'sentiment': y_train_label})\n",
+        "df_test = pd.DataFrame({'review': X_test_text, 'sentiment': y_test_label})\n",
+        "df = pd.concat([df_train, df_test], ignore_index=True)\n",
+        "\n",
+        "print(\"✓ IMDB dataset loaded\")\n",
+        "\n",
+        "\n",
+        "\n",
+        "print(f\"\\nDataset shape: {df.shape}\")\n",
+        "print(f\"\\nSample reviews:\")\n",
+        "for i in range(2):\n",
+        "    label = 'POSITIVE' if df['sentiment'].iloc[i] == 1 else 'NEGATIVE'\n",
+        "    print(f\"\\n[{label}] {df['review'].iloc[i][:100]}...\")\n",
+        "\n",
+        "print(f\"\\nLabel distribution:\")\n",
+        "print(df['sentiment'].value_counts().to_dict())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_hD-4mrtTh8G"
+      },
+      "source": [
+        "### Data Visualization"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 590
+        },
+        "id": "v7I15D4bTh8G",
+        "outputId": "4e13908b-98f1-485e-8c1a-399d9e2d3d9c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Review length statistics:\n",
+            "count    5500.000000\n",
+            "mean      241.746000\n",
+            "std       180.098601\n",
+            "min         9.000000\n",
+            "25%       129.000000\n",
+            "50%       180.000000\n",
+            "75%       295.000000\n",
+            "max      1851.000000\n",
+            "Name: review_length, dtype: float64\n"
+          ]
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+        "\n",
+        "sentiment_counts = df['sentiment'].value_counts().sort_index()\n",
+        "axes[0].bar(['Negative', 'Positive'], sentiment_counts.values, color=['#e74c3c', '#2ecc71'], alpha=0.8, edgecolor='black')\n",
+        "axes[0].set_ylabel('Count', fontsize=11)\n",
+        "axes[0].set_title('Sentiment Distribution', fontsize=12, fontweight='bold')\n",
+        "axes[0].grid(axis='y', alpha=0.3)\n",
+        "\n",
+        "df['review_length'] = df['review'].str.split().str.len()\n",
+        "axes[1].hist(df['review_length'], bins=30, color='#3498db', edgecolor='black', alpha=0.7)\n",
+        "axes[1].set_xlabel('Number of Words', fontsize=11)\n",
+        "axes[1].set_ylabel('Frequency', fontsize=11)\n",
+        "axes[1].set_title('Review Length Distribution', fontsize=12, fontweight='bold')\n",
+        "axes[1].grid(axis='y', alpha=0.3)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "print(f\"Review length statistics:\")\n",
+        "print(df['review_length'].describe())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nFxMyGveTh8G"
+      },
+      "source": [
+        "## 3. Text Preprocessing"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "J7L7QpsqTh8H",
+        "outputId": "5b84f426-2834-44f4-83cf-7200bd512d5b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Preprocessing reviews...\n",
+            "✓ Preprocessing complete!\n",
+            "\n",
+            "Sample transformations:\n",
+            "\n",
+            "Before: ? this film was just brilliant casting location scenery story direction everyone...\n",
+            "After:  this film was just brilliant casting location scenery story direction everyone s...\n",
+            "\n",
+            "Before: ? big hair big boobs bad music and a giant safety pin these are the words to bes...\n",
+            "After:  big hair big boobs bad music and a giant safety pin these are the words to best ...\n"
+          ]
+        }
+      ],
+      "source": [
+        "def preprocess_text(text):\n",
+        "    text = str(text).lower()\n",
+        "    text = re.sub(r'http\\\\S+|www\\\\S+|https\\\\S+', ' ', text, flags=re.MULTILINE)\n",
+        "    text = re.sub(r'[^a-zA-Z0-9\\\\s]', ' ', text)\n",
+        "    text = re.sub(r'\\\\s+', ' ', text).strip()\n",
+        "    return text\n",
+        "\n",
+        "print(\"Preprocessing reviews...\")\n",
+        "\n",
+        "df_train['review_clean'] = df_train['review'].apply(preprocess_text)\n",
+        "df_test['review_clean']  = df_test['review'].apply(preprocess_text)\n",
+        "\n",
+        "print(\"✓ Preprocessing complete!\\n\")\n",
+        "\n",
+        "print(\"Sample transformations:\")\n",
+        "for i in range(2):\n",
+        "    print(f\"\\nBefore: {df_train['review'].iloc[i][:80]}...\")\n",
+        "    print(f\"After:  {df_train['review_clean'].iloc[i][:80]}...\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lOBzdRr7Th8H"
+      },
+      "source": [
+        "## 4. FastText Embeddings"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WYVom7O-Th8H",
+        "outputId": "444bb9f4-d14f-4ff4-e10f-fc98e0458d9e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Preparing FastText training data (train only)...\n",
+            "✓ FastText training file created\n"
+          ]
+        }
+      ],
+      "source": [
+        "train_data_path = './tmp/fasttext_train.txt'\n",
+        "\n",
+        "print(\"Preparing FastText training data (train only)...\")\n",
+        "with open(train_data_path, 'w') as f:\n",
+        "    for text, label in zip(df_train['review_clean'], df_train['sentiment']):\n",
+        "        f.write(f\"__label__{int(label)} {text}\\n\")\n",
+        "\n",
+        "print(\"✓ FastText training file created\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-yCHwnudTh8H",
+        "outputId": "4336771e-ff98-4869-db10-fc7d23bba5c4"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training FastText model...\n",
+            "✓ FastText model trained!\n",
+            "  Vocabulary size: 9789\n",
+            "  Embedding dimension: 100\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Training FastText model...\")\n",
+        "fasttext_model = fasttext.train_supervised(\n",
+        "    input=train_data_path,\n",
+        "    epoch=30,            # ↑ more learning\n",
+        "    lr=0.3,              # ↓ more stable\n",
+        "    wordNgrams=2,\n",
+        "    minn=3,              # character n-grams\n",
+        "    maxn=6,\n",
+        "    dim=300,             # ↑ richer embeddings\n",
+        "    loss='softmax',\n",
+        "    verbose=0\n",
+        ")\n",
+        "\n",
+        "print(\"✓ FastText model trained!\")\n",
+        "print(f\"  Vocabulary size: {len(fasttext_model.get_words())}\")\n",
+        "print(f\"  Embedding dimension: 100\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "xyNYMfmpTh8H",
+        "outputId": "95c184da-5093-43d5-c6e0-f344460f90dd"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Generating embeddings...\n",
+            "✓ Embeddings generated!\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Generating embeddings...\")\n",
+        "X_train_embed = np.array([\n",
+        "    fasttext_model.get_sentence_vector(text)\n",
+        "    for text in df_train['review_clean']\n",
+        "])\n",
+        "\n",
+        "X_test_embed = np.array([\n",
+        "    fasttext_model.get_sentence_vector(text)\n",
+        "    for text in df_test['review_clean']\n",
+        "])\n",
+        "\n",
+        "y_train = df_train['sentiment'].values\n",
+        "y_test = df_test['sentiment'].values\n",
+        "\n",
+        "print(\"✓ Embeddings generated!\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "  Train embeddings shape: (4000, 300)\n",
+            "  Test embeddings shape:  (1500, 300)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Scale embeddings (REQUIRED)\n",
+        "scaler = StandardScaler()\n",
+        "X_train = scaler.fit_transform(X_train_embed)\n",
+        "X_test  = scaler.transform(X_test_embed)\n",
+        "\n",
+        "print(f\"  Train embeddings shape: {X_train_embed.shape}\")\n",
+        "print(f\"  Test embeddings shape:  {X_test_embed.shape}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8oNw5xxYTh8H"
+      },
+      "source": [
+        "### Embedding Visualization (t-SNE)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 825
+        },
+        "id": "X6d2pDA_Th8H",
+        "outputId": "52482663-e24e-45d9-d2df-f9029ba7d972"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Computing t-SNE projection...\n",
+            "✓ t-SNE computed!\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Computing t-SNE projection...\")\n",
+        "\n",
+        "# Concatenate ONLY for visualization\n",
+        "embeddings_vis = np.vstack([X_train_embed, X_test_embed])\n",
+        "labels_vis = np.concatenate([y_train, y_test])\n",
+        "\n",
+        "sample_size = min(1500, len(embeddings_vis))\n",
+        "sample_indices = np.random.choice(len(embeddings_vis), sample_size, replace=False)\n",
+        "\n",
+        "X_sample = embeddings_vis[sample_indices]\n",
+        "y_sample = labels_vis[sample_indices]\n",
+        "\n",
+        "tsne = TSNE(\n",
+        "    n_components=2,\n",
+        "    random_state=42,\n",
+        "    max_iter=1000,\n",
+        "    perplexity=30\n",
+        ")\n",
+        "\n",
+        "X_2d = tsne.fit_transform(X_sample)\n",
+        "\n",
+        "print(\"✓ t-SNE computed!\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YMJ4SmXXTh8H"
+      },
+      "source": [
+        "## 5. Train-Test Split"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rwVfzWMVTh8H",
+        "outputId": "6a6147ea-65f6-40fb-e2e4-8b0dbdad7255"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training set: (4000, 300)\n",
+            "Test set: (1500, 300)\n",
+            "\n",
+            "Class distribution (train):\n",
+            "  Negative: 1964\n",
+            "  Positive: 2036\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(f\"Training set: {X_train.shape}\")\n",
+        "print(f\"Test set: {X_test.shape}\")\n",
+        "print(f\"\\nClass distribution (train):\")\n",
+        "unique, counts = np.unique(y_train, return_counts=True)\n",
+        "for label, count in zip(unique, counts):\n",
+        "    label_name = 'Positive' if label == 1 else 'Negative'\n",
+        "    print(f\"  {label_name}: {count}\")\n",
+        "\n",
+        "scaler = StandardScaler()\n",
+        "X_train = scaler.fit_transform(X_train_embed)\n",
+        "X_test = scaler.transform(X_test_embed)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "o1A0_Q0oTh8I"
+      },
+      "source": [
+        "## 6. Model Training"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Unir9y1mTh8I",
+        "outputId": "8e4ddd4c-91a6-4ac2-ba57-4292f349c439"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training XGBoost...\n",
+            "✓ XGBoost trained!\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Training XGBoost...\")\n",
+        "xgb_model = xgb.XGBClassifier(\n",
+        "    n_estimators=600,\n",
+        "    max_depth=5,\n",
+        "    learning_rate=0.05,\n",
+        "    subsample=0.9,\n",
+        "    colsample_bytree=0.9,\n",
+        "    min_child_weight=1,\n",
+        "    gamma=0.1,\n",
+        "    reg_alpha=0.01,\n",
+        "    reg_lambda=1.0,\n",
+        "    objective='binary:logistic',\n",
+        "    eval_metric='logloss',\n",
+        "    random_state=42,\n",
+        "    n_jobs=-1\n",
+        ")\n",
+        "\n",
+        "xgb_model.fit(\n",
+        "    X_train, y_train,\n",
+        "    eval_set=[(X_test, y_test)],\n",
+        "    verbose=False\n",
+        ")\n",
+        "\n",
+        "\n",
+        "\n",
+        "y_prob_xgb = xgb_model.predict_proba(X_test)[:, 1]\n",
+        "\n",
+        "# Tune threshold (0.4–0.45 usually improves recall/F1)\n",
+        "threshold = 0.45\n",
+        "y_pred_xgb = (y_prob_xgb >= threshold).astype(int)\n",
+        "print(\"✓ XGBoost trained!\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Bz1DKNpgTh8I",
+        "outputId": "7ff63f10-f259-4906-ff49-719bd08260ca"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training Random Forest...\n",
+            "✓ Random Forest trained!\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Training Random Forest...\")\n",
+        "rf_model = RandomForestClassifier(\n",
+        "    n_estimators=400,\n",
+        "    max_depth=20,\n",
+        "    min_samples_split=4,\n",
+        "    min_samples_leaf=1,\n",
+        "    max_features='sqrt',\n",
+        "    class_weight='balanced',\n",
+        "    random_state=42,\n",
+        "    n_jobs=-1\n",
+        ")\n",
+        "\n",
+        "rf_model.fit(X_train, y_train)\n",
+        "y_pred_rf = rf_model.predict(X_test)\n",
+        "print(\"✓ Random Forest trained!\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IVrjr5vcTh8I"
+      },
+      "source": [
+        "## 7. Model Evaluation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zVUhTrNHTh8I",
+        "outputId": "4d326431-0d14-4a66-f939-717590299b2d"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "MODEL PERFORMANCE\n",
+            "===========================================================================\n",
+            "        Model  Accuracy  Precision   Recall  F1-Score\n",
+            "      XGBoost  0.852667   0.826685 0.863506  0.844694\n",
+            "Random Forest  0.859333   0.836338 0.866379  0.851094\n",
+            "===========================================================================\n"
+          ]
+        }
+      ],
+      "source": [
+        "def evaluate_model(y_true, y_pred, model_name):\n",
+        "    metrics = {\n",
+        "        'Model': model_name,\n",
+        "        'Accuracy': accuracy_score(y_true, y_pred),\n",
+        "        'Precision': precision_score(y_true, y_pred),\n",
+        "        'Recall': recall_score(y_true, y_pred),\n",
+        "        'F1-Score': f1_score(y_true, y_pred)\n",
+        "    }\n",
+        "    return metrics\n",
+        "\n",
+        "results = []\n",
+        "results.append(evaluate_model(y_test, y_pred_xgb, 'XGBoost'))\n",
+        "results.append(evaluate_model(y_test, y_pred_rf, 'Random Forest'))\n",
+        "\n",
+        "results_df = pd.DataFrame(results)\n",
+        "print(\"\\nMODEL PERFORMANCE\")\n",
+        "print(\"=\"*75)\n",
+        "print(results_df.to_string(index=False))\n",
+        "print(\"=\"*75)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 407
+        },
+        "id": "UP-akPydTh8I",
+        "outputId": "f4e7c625-95ff-4042-a746-d68a4982f1b0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+        "\n",
+        "predictions = [\n",
+        "    ('XGBoost', y_pred_xgb),\n",
+        "    ('Random Forest', y_pred_rf)\n",
+        "]\n",
+        "\n",
+        "for idx, (model_name, y_pred) in enumerate(predictions):\n",
+        "    cm = confusion_matrix(y_test, y_pred)\n",
+        "    sns.heatmap(\n",
+        "        cm, annot=True, fmt='d', cmap='Blues', ax=axes[idx],\n",
+        "        xticklabels=['Negative', 'Positive'],\n",
+        "        yticklabels=['Negative', 'Positive'],\n",
+        "        cbar=False,\n",
+        "        annot_kws={'size': 12, 'weight': 'bold'}\n",
+        "    )\n",
+        "    axes[idx].set_title(f'{model_name} Confusion Matrix', fontsize=12, fontweight='bold')\n",
+        "    axes[idx].set_ylabel('True Label', fontsize=11)\n",
+        "    axes[idx].set_xlabel('Predicted Label', fontsize=11)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-sLogIIkTh8I",
+        "outputId": "570523d4-652f-457f-dc6f-2dac710672a1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "DETAILED CLASSIFICATION REPORT - XGBoost\n",
+            "===========================================================================\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "    Negative     0.8771    0.8433    0.8599       804\n",
+            "    Positive     0.8267    0.8635    0.8447       696\n",
+            "\n",
+            "    accuracy                         0.8527      1500\n",
+            "   macro avg     0.8519    0.8534    0.8523      1500\n",
+            "weighted avg     0.8537    0.8527    0.8528      1500\n",
+            "\n",
+            "\n",
+            "DETAILED CLASSIFICATION REPORT - Random Forest\n",
+            "===========================================================================\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "    Negative     0.8806    0.8532    0.8667       804\n",
+            "    Positive     0.8363    0.8664    0.8511       696\n",
+            "\n",
+            "    accuracy                         0.8593      1500\n",
+            "   macro avg     0.8585    0.8598    0.8589      1500\n",
+            "weighted avg     0.8601    0.8593    0.8595      1500\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"\\nDETAILED CLASSIFICATION REPORT - XGBoost\")\n",
+        "print(\"=\"*75)\n",
+        "print(classification_report(\n",
+        "    y_test, y_pred_xgb,\n",
+        "    target_names=['Negative', 'Positive'],\n",
+        "    digits=4\n",
+        "))\n",
+        "\n",
+        "print(\"\\nDETAILED CLASSIFICATION REPORT - Random Forest\")\n",
+        "print(\"=\"*75)\n",
+        "print(classification_report(\n",
+        "    y_test, y_pred_rf,\n",
+        "    target_names=['Negative', 'Positive'],\n",
+        "    digits=4\n",
+        "))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "OdAD3Yo8Th8I",
+        "outputId": "db30b853-654e-4e60-8d68-bdc3898c1dab"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "importance_xgb = xgb_model.feature_importances_\n",
+        "importance_indices = np.argsort(importance_xgb)[-12:]\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(10, 6))\n",
+        "ax.barh(range(len(importance_indices)), importance_xgb[importance_indices], color='#e74c3c', alpha=0.8, edgecolor='black')\n",
+        "ax.set_yticks(range(len(importance_indices)))\n",
+        "ax.set_yticklabels([f'Feature {i}' for i in importance_indices], fontsize=10)\n",
+        "ax.set_xlabel('Importance Score', fontsize=11, fontweight='bold')\n",
+        "ax.set_title('Top 12 Important FastText Features (XGBoost)', fontsize=12, fontweight='bold')\n",
+        "ax.grid(axis='x', alpha=0.3)\n",
+        "\n",
+        "for i, v in enumerate(importance_xgb[importance_indices]):\n",
+        "    ax.text(v + 0.002, i, f'{v:.4f}', va='center', fontsize=9)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-wPJ1eKBTh8I",
+        "outputId": "c1af52ec-16b4-40c8-a684-a67946f4fcdb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "FINAL SUMMARY\n",
+            "===========================================================================\n",
+            "\n",
+            "Dataset: 5500 sentiment-labeled reviews\n",
+            "Embeddings: FastText (100-dim, bigrams)\n",
+            "Test set size: 1500 samples\n",
+            "\n",
+            "Best Model: Random Forest\n",
+            "  Accuracy:  0.8593\n",
+            "  Precision: 0.8363\n",
+            "  Recall:    0.8664\n",
+            "  F1-Score:  0.8511\n",
+            "\n",
+            "===========================================================================\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"\\nFINAL SUMMARY\")\n",
+        "print(\"=\"*75)\n",
+        "print(f\"\\nDataset: {len(df)} sentiment-labeled reviews\")\n",
+        "print(f\"Embeddings: FastText (100-dim, bigrams)\")\n",
+        "print(f\"Test set size: {len(y_test)} samples\")\n",
+        "\n",
+        "best_idx = results_df['Accuracy'].idxmax()\n",
+        "best_model = results_df.iloc[best_idx]['Model']\n",
+        "best_acc = results_df.iloc[best_idx]['Accuracy']\n",
+        "\n",
+        "print(f\"\\nBest Model: {best_model}\")\n",
+        "print(f\"  Accuracy:  {best_acc:.4f}\")\n",
+        "print(f\"  Precision: {results_df.iloc[best_idx]['Precision']:.4f}\")\n",
+        "print(f\"  Recall:    {results_df.iloc[best_idx]['Recall']:.4f}\")\n",
+        "print(f\"  F1-Score:  {results_df.iloc[best_idx]['F1-Score']:.4f}\")\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*75)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "v_nlpgpu",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.19"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/AI-DS_Nexus__A0_1_Project__LeilaRostamian.ipynb b/a0.1/AI-DS_Nexus__A0_1_Project__LeilaRostamian.ipynb
new file mode 100644
index 0000000..43e6a35
--- /dev/null
+++ b/a0.1/AI-DS_Nexus__A0_1_Project__LeilaRostamian.ipynb
@@ -0,0 +1,549 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "da219ea7-5bbe-4264-e206-027ee8155bb8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Celsius temperature: 10\n",
+            "👉 10.0 °C = 50.0 °F\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "Celsius_temperature = input(\"Celsius temperature: \")\n",
+        "Celsius_temperature = float(Celsius_temperature)\n",
+        "Fahrenheit_temperature =  Celsius_temperature * 9/5 + 32\n",
+        "print(f\"👉 {Celsius_temperature} °C = {Fahrenheit_temperature} °F\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "int_num = int(input(\"enter the first number: \"))\n",
+        "float_num = float(input(\"enter the second number: \"))\n",
+        "summation = int_num + float_num\n",
+        "difference = int_num - float_num\n",
+        "product = int_num * float_num\n",
+        "true_division = int_num / float_num\n",
+        "floor_division = int_num // float_num\n",
+        "\n",
+        "\n",
+        "print(f\"\"\"👉 {int_num} and {float_num} are integer and float numbers respectively\\n\n",
+        " their sum is {summation},\\n\n",
+        " difference is {difference},\\n\n",
+        " product is {product},\\n\n",
+        " true division is {true_division:.2f},\\n\n",
+        " floor division is {floor_division}\"\"\")\n",
+        "# one of the numbers is a float, so the sum will also be a float.\n",
+        "# // calculates exactly the integer part of the division result (i.e., rounds the number towards negative infinity).\n"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3d054a1f-1f80-4359-9287-99b31a380100"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "enter the first number: 13\n",
+            "enter the second number: 6\n",
+            "👉 13 and 6.0 are integer and float numbers respectively \n",
+            "\n",
+            " their sum is 19.0, \n",
+            "\n",
+            " difference is 7.0, \n",
+            "\n",
+            " product is 78.0, \n",
+            " \n",
+            " true division is 2.17,\n",
+            " \n",
+            " floor division is 2.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "![Screenshot (53).png](data:image/png;base64,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)"
+      ],
+      "metadata": {
+        "id": "RFLgrl37wuqq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "\n",
+        "# TODO: your code here\n",
+        "# an empty shopping list (list).\n",
+        "shopping_list = []\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "for i in range(4):\n",
+        "  item = input(f'enter the {i+1}th item: ')\n",
+        "  shopping_list.append(item)\n",
+        "\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "shopping_items = tuple(shopping_list)\n",
+        "\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "print(f\"👉 The third item is {shopping_items[2]}\\n from the shopping list {shopping_list}\\n\")\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "72f6103c-7a68-46a9-caef-be58c007a691"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "enter the 1th item: egg\n",
+            "enter the 2th item: fruit\n",
+            "enter the 3th item: vegetable\n",
+            "enter the 4th item: bread\n",
+            "👉 The third item is vegetable \n",
+            " from the shopping list ['egg', 'fruit', 'vegetable', 'bread']\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Word Stats"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# TODO: your code here\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "sample = sample.split()\n",
+        "unique_words = set(sample)\n",
+        "\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "word_counts = {}\n",
+        "for word in sample:\n",
+        "  if word in word_counts:\n",
+        "    word_counts[word] += 1\n",
+        "  else:\n",
+        "    word_counts[word] = 1\n",
+        "\n",
+        "# We can also code in this way\n",
+        "# word_counts = {word: sample.count(word) for word in set(sample)}\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "\n",
+        "print(f'''{sample} is a list of words.\\n\n",
+        "A set contains every distinct word: {unique_words}.\\n\n",
+        "A dictionary maps each word to the number of times appearing: {word_counts}\\n''')\n",
+        "\n",
+        "# We can also code in this way\n",
+        "# word_counts = {word: sample.count(word) for word in sample}\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4e29ebdf-2913-4f5e-eca4-95aac1ea4c01"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "['to', 'be', 'or', 'not', 'to', 'be', 'that', 'is', 'the', 'question'] is a list of words.\n",
+            "\n",
+            "A set contains every distinct word: {'is', 'not', 'that', 'the', 'question', 'to', 'be', 'or'}.\n",
+            "\n",
+            "A dictionary maps each word to the number of times appearing: {'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Prime Tester"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    if n <= 1:\n",
+        "      return False\n",
+        "    else:\n",
+        "      for i in range(2, int(n**0.5) + 1):\n",
+        "        if n % i == 0:\n",
+        "          return False\n",
+        "\n",
+        "    return True\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "149653ee-76e0-4a10-d6b6-68a1f3bea078"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    name_with_space = name.capitalize() + ' '\n",
+        "    print(name_with_space * times)\n",
+        "    # TODO: your code here\n",
+        "\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9c1f467e-c351-4fa4-b640-79d648d135e9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Alice \n",
+            "Bob Bob Bob \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ],
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Simple Counter"
+      ],
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    def __init__(self):\n",
+        "      self.count = 0\n",
+        "    # \"create the class, then a folder called count in its memory and put the number 0 in it.\"\n",
+        "\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    def increment(self, step: int = 1):\n",
+        "      self.count += 1\n",
+        "\n",
+        "    # 3. Method value() returns the current count.\n",
+        "    def value(self):\n",
+        "      return self.count\n",
+        "\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ],
+      "metadata": {
+        "id": "dPFvr_fe-OPR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "92ec8ec2-e624-4600-ed60-d58675616320"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ],
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "\n",
+        "    def __init__(self, x: float, y: float):\n",
+        "      self.x = x\n",
+        "      self.y = y\n",
+        "\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "\n",
+        "    def distance_to(self, other : 'Point') -> float:\n",
+        "      dx = self.x - other.x\n",
+        "      dy = self.y - other.y\n",
+        "    # return (dx**2 + dy**2)**0.5\n",
+        "      return math.sqrt(dx**2 + dy**2)\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "OVh3GEzH-T0w",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2b0767fb-44aa-44ee-e21e-c9d309d788ee"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/AmirHosseinZandi_PoseEstimationTask/demo_video.mp4 b/a0.1/AmirHosseinZandi_PoseEstimationTask/demo_video.mp4
new file mode 100644
index 0000000..1344360
Binary files /dev/null and b/a0.1/AmirHosseinZandi_PoseEstimationTask/demo_video.mp4 differ
diff --git a/a0.1/AmirHosseinZandi_PoseEstimationTask/readme.txt b/a0.1/AmirHosseinZandi_PoseEstimationTask/readme.txt
new file mode 100644
index 0000000..97cc9f1
--- /dev/null
+++ b/a0.1/AmirHosseinZandi_PoseEstimationTask/readme.txt
@@ -0,0 +1,10 @@
+Student: Amir Hossein Zandi
+
+GitHub: https://github.com/Amirhosseinzandi-web/BulgarianSquat-Tracker
+
+Project: Bulgarian Split Squat AI Trainer
+- Real-time rep counting
+- Form correction
+- YOLOv11-Pose detection
+
+Demo video included.
\ No newline at end of file
diff --git a/a0.1/Assign/README.md b/a0.1/Assign/README.md
new file mode 100644
index 0000000..b1b9214
--- /dev/null
+++ b/a0.1/Assign/README.md
@@ -0,0 +1 @@
+This folder has been created for Nexue assignments.
\ No newline at end of file
diff --git a/a0.1/Assign/a01/a01-1.py b/a0.1/Assign/a01/a01-1.py
new file mode 100644
index 0000000..4672cd2
--- /dev/null
+++ b/a0.1/Assign/a01/a01-1.py
@@ -0,0 +1,7 @@
+# 👉 a Celsius temperature (as text), convert it to float,
+#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.
+# TODO: your code here
+tct = '55.1'
+tcf = float(tct)
+tff = tcf * 5/9 + 32
+print(tff)
\ No newline at end of file
diff --git a/a0.1/Assign/a01/a01-2.py b/a0.1/Assign/a01/a01-2.py
new file mode 100644
index 0000000..ec7129a
--- /dev/null
+++ b/a0.1/Assign/a01/a01-2.py
@@ -0,0 +1,10 @@
+# 👉 Store two numbers of **different types** (one int, one float),
+#    then print their sum, difference, product, true division, and floor division.
+# TODO: your code here
+ai = 8
+af = 6.1
+print('sum = ', ai + af)
+print('difference = ', ai - af)
+print('product = ', ai * af)
+print('true division = ', ai / af)
+print('floor division = ', ai // af)
diff --git a/a0.1/Assign/a01/a01-3.py b/a0.1/Assign/a01/a01-3.py
new file mode 100644
index 0000000..ff61a23
--- /dev/null
+++ b/a0.1/Assign/a01/a01-3.py
@@ -0,0 +1,10 @@
+# Start with an empty shopping list (list).
+# 1. Append at least 4 items supplied in one line of user input (comma-separated).
+# 2. Convert the list to a *tuple* called immutable_basket.
+# 3. Print the third item using tuple indexing.
+# TODO: your code here
+lst = []
+lst = input('shopping items: ').split(',')
+print(lst)
+tpl = tuple(lst)
+print(tpl[2])
\ No newline at end of file
diff --git a/a0.1/Assign/a01/a01-4.py b/a0.1/Assign/a01/a01-4.py
new file mode 100644
index 0000000..04f2afa
--- /dev/null
+++ b/a0.1/Assign/a01/a01-4.py
@@ -0,0 +1,21 @@
+sample = "to be or not to be that is the question"
+# 1. Build a set `unique_words` containing every distinct word.
+# 2. Build a dict `word_counts` mapping each word to the number of times it appears.
+#    (Hint: .split() + a simple loop)
+# 3. Print the two structures and explain (in a comment) their main difference.
+# TODO: your code here
+uw1 = []
+uw1 = sample.split()
+uw2 = list(set(uw1))
+uw2.sort()
+repeats=[]
+for element in uw2:
+    count=uw1.count(element)
+    repeats.append(count)
+uwd = {}
+for i in range(len(repeats)):
+    uwd[uw2[i]] = repeats[i]
+print(uw1)
+print(uw2)
+print(repeats)
+print(uwd)
\ No newline at end of file
diff --git a/a0.1/Assign/a01/a01-5.py b/a0.1/Assign/a01/a01-5.py
new file mode 100644
index 0000000..533b3a9
--- /dev/null
+++ b/a0.1/Assign/a01/a01-5.py
@@ -0,0 +1,11 @@
+def is_prime(n: int) -> bool:
+    """
+    Return True if n is a prime number, else False.
+    0 and 1 are *not* prime.
+    """
+    # TODO: replace pass with your implementation
+    return isprime(n)
+ 
+# Quick self-check
+from sympy import *
+print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]
diff --git a/a0.1/Assign/a01/a01-6.py b/a0.1/Assign/a01/a01-6.py
new file mode 100644
index 0000000..cd13568
--- /dev/null
+++ b/a0.1/Assign/a01/a01-6.py
@@ -0,0 +1,11 @@
+def greet(name: str, times: int = 1) -> None:
+    """Print `name`, capitalised, exactly `times` times on one line."""
+    # TODO: your code here
+    nam = []
+    for i in range(times):
+        nam.append(name.capitalize())
+    print(*nam)
+
+
+greet("alice")          # Alice
+greet("bob", times=3)   # Bob Bob Bob
diff --git a/a0.1/Assign/a01/a01-7.py b/a0.1/Assign/a01/a01-7.py
new file mode 100644
index 0000000..64e3e31
--- /dev/null
+++ b/a0.1/Assign/a01/a01-7.py
@@ -0,0 +1,22 @@
+class Counter:
+    """Counts how many times `increment` is called."""
+    # TODO:
+    # 1. In __init__, store an internal count variable starting at 0.
+    # 2. Method increment(step: int = 1) adds `step` to the count.
+    # 3. Method value() returns the current count.
+    def __init__(self):
+        self.count = 0
+        self.step = 1
+
+    def increment(self, i):
+        self.count +=1
+    
+    def value(self):
+        value = self.count
+        return value
+        
+
+c = Counter()
+for i in range(5):
+    c.increment(i)
+print(c.value())   # Expected: 5
diff --git a/a0.1/Assign/a01/a01-8.py b/a0.1/Assign/a01/a01-8.py
new file mode 100644
index 0000000..690c462
--- /dev/null
+++ b/a0.1/Assign/a01/a01-8.py
@@ -0,0 +1,26 @@
+import math
+
+class Point:
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+
+    def distance_to(self, q):
+        distance = math.sqrt((self.x - q.x)**2 + (self.y - q.y)**2)
+        return distance
+    """
+    A 2-D point supporting distance calculation.
+    Usage:
+        p = Point(3, 4)
+        q = Point(0, 0)
+        print(p.distance_to(q))  # 5.0
+    """
+    # TODO:
+    # 1. Store x and y as attributes.
+    # 2. Implement distance_to(other) using the Euclidean formula.
+
+
+# Smoke test
+p, q = Point(3, 4), Point(0, 0)
+print(p.distance_to(q))
+# assert round(p.distance_to(q), 1) == 5.0
diff --git a/a0.1/Assignment_01_Python___Nexus___LeiliRostamian.ipynb b/a0.1/Assignment_01_Python___Nexus___LeiliRostamian.ipynb
new file mode 100644
index 0000000..3ffdc3c
--- /dev/null
+++ b/a0.1/Assignment_01_Python___Nexus___LeiliRostamian.ipynb
@@ -0,0 +1,549 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "da219ea7-5bbe-4264-e206-027ee8155bb8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Celsius temperature: 10\n",
+            "👉 10.0 °C = 50.0 °F\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "Celsius_temperature = input(\"Celsius temperature: \")\n",
+        "Celsius_temperature = float(Celsius_temperature)\n",
+        "Fahrenheit_temperature =  Celsius_temperature * 9/5 + 32\n",
+        "print(f\"👉 {Celsius_temperature} °C = {Fahrenheit_temperature} °F\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "int_num = int(input(\"enter the first number: \"))\n",
+        "float_num = float(input(\"enter the second number: \"))\n",
+        "summation = int_num + float_num\n",
+        "difference = int_num - float_num\n",
+        "product = int_num * float_num\n",
+        "true_division = int_num / float_num\n",
+        "floor_division = int_num // float_num\n",
+        "\n",
+        "\n",
+        "print(f\"\"\"👉 {int_num} and {float_num} are integer and float numbers respectively\\n\n",
+        " their sum is {summation},\\n\n",
+        " difference is {difference},\\n\n",
+        " product is {product},\\n\n",
+        " true division is {true_division:.2f},\\n\n",
+        " floor division is {floor_division}\"\"\")\n",
+        "# one of the numbers is a float, so the sum will also be a float.\n",
+        "# // calculates exactly the integer part of the division result (i.e., rounds the number towards negative infinity).\n"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3d054a1f-1f80-4359-9287-99b31a380100"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "enter the first number: 13\n",
+            "enter the second number: 6\n",
+            "👉 13 and 6.0 are integer and float numbers respectively \n",
+            "\n",
+            " their sum is 19.0, \n",
+            "\n",
+            " difference is 7.0, \n",
+            "\n",
+            " product is 78.0, \n",
+            " \n",
+            " true division is 2.17,\n",
+            " \n",
+            " floor division is 2.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "![Screenshot 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)"
+      ],
+      "metadata": {
+        "id": "RFLgrl37wuqq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "\n",
+        "# TODO: your code here\n",
+        "# an empty shopping list (list).\n",
+        "shopping_list = []\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "for i in range(4):\n",
+        "  item = input(f'enter the {i+1}th item: ')\n",
+        "  shopping_list.append(item)\n",
+        "\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "shopping_items = tuple(shopping_list)\n",
+        "\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "print(f\"👉 The third item is {shopping_items[2]}\\n from the shopping list {shopping_list}\\n\")\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "72f6103c-7a68-46a9-caef-be58c007a691"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "enter the 1th item: egg\n",
+            "enter the 2th item: fruit\n",
+            "enter the 3th item: vegetable\n",
+            "enter the 4th item: bread\n",
+            "👉 The third item is vegetable \n",
+            " from the shopping list ['egg', 'fruit', 'vegetable', 'bread']\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Word Stats"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# TODO: your code here\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "sample = sample.split()\n",
+        "unique_words = set(sample)\n",
+        "\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "word_counts = {}\n",
+        "for word in sample:\n",
+        "  if word in word_counts:\n",
+        "    word_counts[word] += 1\n",
+        "  else:\n",
+        "    word_counts[word] = 1\n",
+        "\n",
+        "# We can also code in this way\n",
+        "# word_counts = {word: sample.count(word) for word in set(sample)}\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "\n",
+        "print(f'''{sample} is a list of words.\\n\n",
+        "A set contains every distinct word: {unique_words}.\\n\n",
+        "A dictionary maps each word to the number of times appearing: {word_counts}\\n''')\n",
+        "\n",
+        "# We can also code in this way\n",
+        "# word_counts = {word: sample.count(word) for word in sample}\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4e29ebdf-2913-4f5e-eca4-95aac1ea4c01"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "['to', 'be', 'or', 'not', 'to', 'be', 'that', 'is', 'the', 'question'] is a list of words.\n",
+            "\n",
+            "A set contains every distinct word: {'is', 'not', 'that', 'the', 'question', 'to', 'be', 'or'}.\n",
+            "\n",
+            "A dictionary maps each word to the number of times appearing: {'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Prime Tester"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    if n <= 1:\n",
+        "      return False\n",
+        "    else:\n",
+        "      for i in range(2, int(n**0.5) + 1):\n",
+        "        if n % i == 0:\n",
+        "          return False\n",
+        "\n",
+        "    return True\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "149653ee-76e0-4a10-d6b6-68a1f3bea078"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    name_with_space = name.capitalize() + ' '\n",
+        "    print(name_with_space * times)\n",
+        "    # TODO: your code here\n",
+        "\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9c1f467e-c351-4fa4-b640-79d648d135e9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Alice \n",
+            "Bob Bob Bob \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ],
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Simple Counter"
+      ],
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    def __init__(self):\n",
+        "      self.count = 0\n",
+        "    # \"create the class, then a folder called count in its memory and put the number 0 in it.\"\n",
+        "\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    def increment(self, step: int = 1):\n",
+        "      self.count += 1\n",
+        "\n",
+        "    # 3. Method value() returns the current count.\n",
+        "    def value(self):\n",
+        "      return self.count\n",
+        "\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ],
+      "metadata": {
+        "id": "dPFvr_fe-OPR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "92ec8ec2-e624-4600-ed60-d58675616320"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ],
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "\n",
+        "    def __init__(self, x: float, y: float):\n",
+        "      self.x = x\n",
+        "      self.y = y\n",
+        "\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "\n",
+        "    def distance_to(self, other : 'Point') -> float:\n",
+        "      dx = self.x - other.x\n",
+        "      dy = self.y - other.y\n",
+        "    # return (dx**2 + dy**2)**0.5\n",
+        "      return math.sqrt(dx**2 + dy**2)\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "OVh3GEzH-T0w",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2b0767fb-44aa-44ee-e21e-c9d309d788ee"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/Assignments/Assignment 01/Assignment01_Atefeh-Amjadian.ipynb b/a0.1/Assignments/Assignment 01/Assignment01_Atefeh-Amjadian.ipynb
new file mode 100644
index 0000000..eb35f34
--- /dev/null
+++ b/a0.1/Assignments/Assignment 01/Assignment01_Atefeh-Amjadian.ipynb	
@@ -0,0 +1,485 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e941380f-c29f-44bf-c5b2-7b21b3bdb0ac"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Enter your degree in Celsius:37.5\n",
+            "99.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 1/8 + 32) and print a nicely formatted line.\n",
+        "\n",
+        "Celsius=float(input(\"Enter your degree in Celsius:\"))\n",
+        "Fahrenheit = Celsius * 1.8 + 32\n",
+        "print(Fahrenheit)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "num1=int(input(\"Enter your integer number:\"))\n",
+        "num2=float(input(\"Enter your float number:\"))\n",
+        "\n",
+        "print(\"Sum:\", num1 + num2)\n",
+        "print(\"Difference:\", num1 - num2)\n",
+        "print(\"Product:\", num1 * num2)\n",
+        "print(\"True Division:\", num1 / num2)\n",
+        "print(\"Floor Division:\", num1 // num2)\n"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "52cbc7eb-4cb6-4d92-fb29-eb3430c69151"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Enter your integer number:4\n",
+            "Enter your float number:2.5\n",
+            "Sum: 6.5\n",
+            "Difference: 1.5\n",
+            "Product: 10.0\n",
+            "True Division: 1.6\n",
+            "Floor Division: 1.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Start with an empty shopping list\n",
+        "shopping_list = []\n",
+        "\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "items = input(\"Enter at least 4 items (comma-separated): \")\n",
+        "shopping_list.extend(items.split(\",\"))  # Split and add to list\n",
+        "\n",
+        "# Remove any extra spaces\n",
+        "shopping_list = [item.strip() for item in shopping_list]\n",
+        "\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "immutable_basket = tuple(shopping_list)\n",
+        "\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "print(\"The third item is:\", immutable_basket[2])\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0418e308-70ee-42bd-ea92-ee4b9e5fedbd"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Enter at least 4 items (comma-separated): Apple,  Banana,Peach \n",
+            "The third item is: Peach\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Word Stats"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "unique_words = set(sample.split())\n",
+        "\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "word_counts = {}\n",
+        "for word in sample.split():\n",
+        "    word_counts[word] = word_counts.get(word, 0) + 1\n",
+        "\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "print(\"Unique words:\", unique_words)\n",
+        "print(\"Word counts:\", word_counts)\n"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8bca26c7-0307-4526-dc35-d4616791de58"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Unique words: {'to', 'question', 'is', 'that', 'be', 'not', 'or', 'the'}\n",
+            "Word counts: {'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Prime Tester"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    if n < 2:\n",
+        "        return False\n",
+        "    for i in range(2, int(n ** 0.5) + 1):\n",
+        "        if n % i == 0:\n",
+        "            return False\n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2bebe241-04b7-4d36-8edd-f3faa87ca0f1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    print((name + \" \") * times)\n",
+        "\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d3b51b90-c6c7-43c3-a943-4cc2b18af08e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "alice \n",
+            "bob bob bob \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ],
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Simple Counter"
+      ],
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    def __init__(self):\n",
+        "      self._count = 0\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    def increment(self, step: int = 1):\n",
+        "      self._count += step\n",
+        "    # 3. Method value() returns the current count.\n",
+        "    def value(self):\n",
+        "      return self._count\n",
+        "\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ],
+      "metadata": {
+        "id": "dPFvr_fe-OPR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1363ce9a-38e0-4fe2-8429-79c8fe60f5fb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ],
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "\n",
+        "    def __init__(self, x, y):\n",
+        "      # 1. Store x and y as attributes\n",
+        "      self.x = x\n",
+        "      self.y = y\n",
+        "\n",
+        "    def distance_to(self, other):\n",
+        "      # 2. Euclidean distance formula: sqrt((x1 - x2)^2 + (y1 - y2)^2)\n",
+        "      return math.sqrt((self.x - other.x) ** 2 + (self.y - other.y) ** 2)\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n",
+        "print(p.distance_to(q))  # 5.0"
+      ],
+      "metadata": {
+        "id": "OVh3GEzH-T0w",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7f3289f2-2152-4b0d-f647-227e6fe91c4f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5.0\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/Assignments/Assignment 02/Assignment02_Atefeh-Amjadian.ipynb b/a0.1/Assignments/Assignment 02/Assignment02_Atefeh-Amjadian.ipynb
new file mode 100644
index 0000000..da29bf7
--- /dev/null
+++ b/a0.1/Assignments/Assignment 02/Assignment02_Atefeh-Amjadian.ipynb	
@@ -0,0 +1,248 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:\n",
+        "\n",
+        "1. NumPy — fast n-dimensional arrays\n",
+        "\n",
+        "2. pandas — tabular data wrangling\n",
+        "\n",
+        "3. Matplotlib — quick, customizable plots\n",
+        "\n",
+        "Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊\n"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1 · NumPy 🧮\n",
+        "Quick-start notes\n",
+        "Core object: ndarray (n-dimensional array)\n",
+        "\n",
+        "Create data: np.array, np.arange, np.random.*\n",
+        "\n",
+        "Summaries: mean, std, sum, max, …\n",
+        "\n",
+        "Vectorised math beats Python loops for speed\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Mock temperatures\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [],
+      "source": [
+        "# 👉 # TODO: import numpy and create an array of 365\n",
+        "import numpy as np\n",
+        "# normally-distributed °C values (µ=20, σ=5) called temps\n",
+        "temps = np.random.normal(loc=20, scale=5, size=365)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Average temperature\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: print the mean of temps\n",
+        "print(\"Average temperature (°C):\", np.mean(temps))"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "99e8fe76-8ce3-46b3-cbf8-d1ff358b7ed7"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Average temperature (°C): 19.64329590727664\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2 · pandas 📊\n",
+        "Quick-start notes\n",
+        "Main structures: DataFrame, Series\n",
+        "\n",
+        "Read data: pd.read_csv, pd.read_excel, …\n",
+        "\n",
+        "Selection: .loc[label], .iloc[pos]\n",
+        "\n",
+        "Group & summarise: .groupby(...).agg(...)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 3 — Load ride log\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "# 👉 # TODO: read \"rides.csv\" into df\n",
+        "df = pd.read_csv(\"rides.csv\")\n",
+        "# (columns: date,temp,rides,weekday)\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 4 — Weekday averages"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: compute and print mean rides per weekday\n",
+        "weekday_means = df.groupby(\"weekday\")[\"rides\"].mean()\n",
+        "print(\"Mean rides per weekday:\\n\", weekday_means)\n"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3 · Matplotlib 📈\n",
+        "Quick-start notes\n",
+        "Workhorse: pyplot interface (import matplotlib.pyplot as plt)\n",
+        "\n",
+        "Figure & axes: fig, ax = plt.subplots()\n",
+        "\n",
+        "Common plots: plot, scatter, hist, imshow\n",
+        "\n",
+        "Display inline in Jupyter with %matplotlib inline or %matplotlib notebook"
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 5 — Scatter plot"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df\n",
+        "plt.scatter(df[\"temp\"], df[\"rides\"], alpha=0.5)\n",
+        "plt.xlabel(\"Temperature (°C)\")\n",
+        "plt.ylabel(\"Number of Rides\")\n",
+        "plt.title(\"Temperature vs Rides\")\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 6 — Show the figure\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: call plt.show() so the plot appears\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/Assignments/Assignment 03/Assignment03_Atefeh-Amjadian.ipynb b/a0.1/Assignments/Assignment 03/Assignment03_Atefeh-Amjadian.ipynb
new file mode 100644
index 0000000..22cd459
--- /dev/null
+++ b/a0.1/Assignments/Assignment 03/Assignment03_Atefeh-Amjadian.ipynb	
@@ -0,0 +1,383 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 3 — Functions, Derivatives & Gradients\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "\n",
+        "This assignment explores the mathematical foundation of machine learning: functions and their derivatives. You’ll visualize, analyze, and code up essential concepts every ML practitioner uses.\n",
+        "\n",
+        "\n",
+        "**👀🔎 What do you see?**\n",
+        "\n",
+        "Look at the snippet codes and:\n",
+        "\n",
+        "* Describe any patterns, surprises, or connections you notice.\n",
+        "\n",
+        "* Write a few sentences about your observations! 📝✨\n"
+      ],
+      "metadata": {
+        "id": "7wR0aAR-evto"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Log and Exp: Proving Inverse Relationship\n"
+      ],
+      "metadata": {
+        "id": "cCb1hk3xe0E6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KOxJ9f-3eu5F"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Show log(exp(x)) == x and exp(log(x)) == x for several values\n",
+        "x_vals = np.array([1, 2, 10, np.e])\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "for x in x_vals:\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# Edge case: try negative and zero values (show error/NaN for log)\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** The code demonstrates the inverse relationship between logarithmic and exponential functions: for $ x > 0 $, $ \\log(\\exp(x)) = x $ and $ \\exp(\\log(x)) = x $. Also, for zero or negative values, $ \\log(\\exp(y)) $ is valid due to $ \\exp(y) $ being positive, but direct $ \\log(y) $ for $ y \\leq 0 $ produces an error or NaN."
+      ],
+      "metadata": {
+        "id": "lVZufz08g1hI"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Plotting Sine Functions Variations\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "mOAqtnk6fDH6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.linspace(-2*np.pi, 2*np.pi, 100)\n",
+        "\n",
+        "# look at the description of each function and call the proper function from numpy\n",
+        "plt.plot(x, np.sin(x), label='sin(x)')\n",
+        "plt.plot(x, np.sin(2*x), label='sin(2x)')\n",
+        "plt.plot(x, np.sin(x/2), label='sin(x/2)')\n",
+        "plt.plot(x, np.sin(x)**2, label='sin²(x)')\n",
+        "plt.legend()\n",
+        "plt.title(\"Variations of Sine Functions\")\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "DoWdSehie66R"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:**  $ \\sin(2x) $ doubles the frequency and halves the period, $ \\sin(x/2) $ doubles the period, and $ \\sin^2(x) $ is always non-negative with half the period of $ \\sin(x) $."
+      ],
+      "metadata": {
+        "id": "dMVcqrfihf0P"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Subplots: Trig, Inverse, Exp, and Log"
+      ],
+      "metadata": {
+        "id": "4-5A1OkdfHXq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = np.linspace(-1, 1, 400)\n",
+        "x2 = np.linspace(0.1, 2, 400)  # For log and exp\n",
+        "\n",
+        "# based on the number of subplots what should be the dimensions in the following line?\n",
+        "fig, axs = plt.subplots(2, 4, figsize=(16, 8))\n",
+        "axs[0,0].plot(x, np.sin(x));      axs[0,0].set_title(\"sin(x)\")\n",
+        "axs[0,1].plot(x, np.cos(x));      axs[0,1].set_title(\"cos(x)\")\n",
+        "axs[0,2].plot(x, np.tan(x));      axs[0,2].set_title(\"tan(x)\")\n",
+        "axs[0,3].plot(x, np.arcsin(x));   axs[0,3].set_title(\"arcsin(x)\")\n",
+        "axs[1,0].plot(x, np.arccos(x));   axs[1,0].set_title(\"arccos(x)\")\n",
+        "axs[1,1].plot(x, np.arctan(x));   axs[1,1].set_title(\"arctan(x)\")\n",
+        "axs[1,2].plot(x2, np.exp(x2));    axs[1,2].set_title(\"exp(x)\")\n",
+        "axs[1,3].plot(x2, np.log(x2));    axs[1,3].set_title(\"log(x)\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 827
+        },
+        "id": "DxocsCu6fGOR",
+        "outputId": "df49202f-906e-4e43-a06a-74a119e1c274"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 8 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** Using appropriate ranges (e.g., [-1, 1] for trigonometric functions and [0.1, 2] for logarithm) and automatic spacing with plt.tight_layout() ensures clear and tidy visualizations."
+      ],
+      "metadata": {
+        "id": "thpsEzFVikPh"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Features of the Sigmoid Function"
+      ],
+      "metadata": {
+        "id": "WAidrU51fMqB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "def sigmoid(x):\n",
+        "    return 1 / (1 + np.exp(-x))\n",
+        "\n",
+        "x = np.linspace(-10, 10, 200)\n",
+        "y = sigmoid(x)\n",
+        "\n",
+        "plt.plot(x, y)\n",
+        "plt.title(\"Sigmoid Function\")\n",
+        "plt.xlabel(\"x\")\n",
+        "plt.ylabel(\"σ(x)\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"Range:\", (y.min(), y.max()), \"| At x=0:\", sigmoid(0))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 490
+        },
+        "id": "lehJV_vNfLXx",
+        "outputId": "110ead11-0dbe-4178-c81e-2da10c907b59"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Range: (np.float64(4.5397868702434395e-05), np.float64(0.9999546021312976)) | At x=0: 0.5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** The code demonstrates that the sigmoid function $ \\sigma(x) = \\frac{1}{1 + e^{-x}} $ maps inputs to the range (0, 1), with a value of 0.5 at $ x=0 $."
+      ],
+      "metadata": {
+        "id": "DUONuiAxilL0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 5. Derivatives of Famous Functions"
+      ],
+      "metadata": {
+        "id": "1FjRt_31fXsx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = np.linspace(-3, 3, 400)\n",
+        "plt.plot(x, x**2, label=\"x²\")\n",
+        "plt.plot(x, 2*x, '--', label=\"d/dx x²\")\n",
+        "\n",
+        "x = np.linspace(0, 3, 400)\n",
+        "plt.plot(x, np.exp(x), label=\"exp(x)\")\n",
+        "plt.plot(x, np.exp(x), '--', label=\"d/dx exp(x)\")\n",
+        "\n",
+        "plt.plot(x, np.log(x+0.1), label=\"log(x)\")\n",
+        "plt.plot(x, 1/(x+0.1), '--', label=\"d/dx log(x)\")\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.title(\"Functions and Their Derivatives\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "vyyGBuHRfZ4n",
+        "outputId": "a6b15d53-8204-4fec-ca20-e968c46b6f70"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** The code visually demonstrates the relationship between the functions $ x^2 $, $ \\exp(x) $, and $ \\log(x) $ and their derivatives. The derivative of $ x^2 $ is linear ($ 2x $), of $ \\exp(x) $ is itself ($ e^x $), and of $ \\log(x) $ is fractional ($ \\frac{1}{x} $)"
+      ],
+      "metadata": {
+        "id": "DURup9u5il4-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 6. Gradient of Selected Functions"
+      ],
+      "metadata": {
+        "id": "w3QIKTZQf1ZK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# search and learn more about sumpy\n",
+        "import sympy as sp\n",
+        "\n",
+        "# Define symbolic variables\n",
+        "x, y = sp.symbols('x y')\n",
+        "\n",
+        "# Define the function\n",
+        "f = x**2 * y + sp.sin(y)\n",
+        "\n",
+        "# Compute partial derivatives\n",
+        "df_dx = sp.diff(f, x)\n",
+        "df_dy = sp.diff(f, y)\n",
+        "\n",
+        "print(\"Function: f(x, y) =\", f)\n",
+        "print(\"Partial derivative w.r.t x (∂f/∂x):\", df_dx)\n",
+        "print(\"Partial derivative w.r.t y (∂f/∂y):\", df_dy)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Eeudgf3-f0Ie",
+        "outputId": "5d6aa60c-7460-4022-c849-3e5e149a9c53"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Function: f(x, y) = x**2*y + sin(y)\n",
+            "Partial derivative w.r.t x (∂f/∂x): 2*x*y\n",
+            "Partial derivative w.r.t y (∂f/∂y): x**2 + cos(y)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** The code illustrates how SymPy can symbolically compute partial derivatives of a multivariable function like $ f(x, y) = x^2 y + \\sin(y) $, providing precise results without manual calculations, which is highly useful for mathematical analysis."
+      ],
+      "metadata": {
+        "id": "wDlIJkmYimg9"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/Assignments/a0.1/AI-DS_Nexus__A0_1__RezaShokr.ipynb b/a0.1/Assignments/a0.1/AI-DS_Nexus__A0_1__RezaShokr.ipynb
new file mode 100644
index 0000000..50e4a3c
--- /dev/null
+++ b/a0.1/Assignments/a0.1/AI-DS_Nexus__A0_1__RezaShokr.ipynb
@@ -0,0 +1,468 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "62.611111111111114\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "tct = '55.1'\n",
+        "tcf = float(tct)\n",
+        "tff = tcf * 5/9 + 32\n",
+        "print(tff)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "sum =  14.1\n",
+            "difference =  1.9000000000000004\n",
+            "product =  48.8\n",
+            "true division =  1.3114754098360657\n",
+            "floor division =  1.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "ai = 8\n",
+        "af = 6.1\n",
+        "print('sum = ', ai + af)\n",
+        "print('difference = ', ai - af)\n",
+        "print('product = ', ai * af)\n",
+        "print('true division = ', ai / af)\n",
+        "print('floor division = ', ai // af)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['breed', ' r', ' y', ' u']\n",
+            " y\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "lst = []\n",
+        "lst = input('shopping items: ').split(',')\n",
+        "print(lst)\n",
+        "tpl = tuple(lst)\n",
+        "print(tpl[2])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 2 — Word Stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['to', 'be', 'or', 'not', 'to', 'be', 'that', 'is', 'the', 'question']\n",
+            "['be', 'is', 'not', 'or', 'question', 'that', 'the', 'to']\n",
+            "[2, 1, 1, 1, 1, 1, 1, 2]\n",
+            "{'be': 2, 'is': 1, 'not': 1, 'or': 1, 'question': 1, 'that': 1, 'the': 1, 'to': 2}\n"
+          ]
+        }
+      ],
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        "uw1 = []\n",
+        "uw1 = sample.split()\n",
+        "uw2 = list(set(uw1))\n",
+        "uw2.sort()\n",
+        "repeats=[]\n",
+        "for element in uw2:\n",
+        "    count=uw1.count(element)\n",
+        "    repeats.append(count)\n",
+        "uwd = {}\n",
+        "for i in range(len(repeats)):\n",
+        "    uwd[uw2[i]] = repeats[i]\n",
+        "print(uw1)\n",
+        "print(uw2)\n",
+        "print(repeats)\n",
+        "print(uwd)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 1 — Prime Tester"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ],
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    return isprime(n)\n",
+        " \n",
+        "# Quick self-check\n",
+        "from sympy import *\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Alice\n",
+            "Bob Bob Bob\n"
+          ]
+        }
+      ],
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    # TODO: your code here\n",
+        "    nam = []\n",
+        "    for i in range(times):\n",
+        "        nam.append(name.capitalize())\n",
+        "    print(*nam)\n",
+        "\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      },
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      },
+      "source": [
+        "### Task 1 — Simple Counter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "dPFvr_fe-OPR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "5\n"
+          ]
+        }
+      ],
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "    def __init__(self):\n",
+        "        self.count = 0\n",
+        "        self.step = 1\n",
+        "\n",
+        "    def increment(self, i):\n",
+        "        self.count +=1\n",
+        "    \n",
+        "    def value(self):\n",
+        "        value = self.count\n",
+        "        return value\n",
+        "        \n",
+        "\n",
+        "c = Counter()\n",
+        "for i in range(5):\n",
+        "    c.increment(i)\n",
+        "print(c.value())   # Expected: 5\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      },
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "5.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    def __init__(self, x, y):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "\n",
+        "    def distance_to(self, q):\n",
+        "        distance = math.sqrt((self.x - q.x)**2 + (self.y - q.y)**2)\n",
+        "        return distance\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "print(p.distance_to(q))\n",
+        "# assert round(p.distance_to(q), 1) == 5.0\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/Assignments/a0.2/AI-DS_Nexus__A0_2__RezaShokr.ipynb b/a0.1/Assignments/a0.2/AI-DS_Nexus__A0_2__RezaShokr.ipynb
new file mode 100644
index 0000000..3beea20
--- /dev/null
+++ b/a0.1/Assignments/a0.2/AI-DS_Nexus__A0_2__RezaShokr.ipynb
@@ -0,0 +1,326 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:\n",
+        "\n",
+        "1. NumPy — fast n-dimensional arrays\n",
+        "\n",
+        "2. pandas — tabular data wrangling\n",
+        "\n",
+        "3. Matplotlib — quick, customizable plots\n",
+        "\n",
+        "Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1 · NumPy 🧮\n",
+        "Quick-start notes\n",
+        "Core object: ndarray (n-dimensional array)\n",
+        "\n",
+        "Create data: np.array, np.arange, np.random.*\n",
+        "\n",
+        "Summaries: mean, std, sum, max, …\n",
+        "\n",
+        "Vectorised math beats Python loops for speed\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Mock temperatures\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [],
+      "source": [
+        "# 👉 # TODO: import numpy and create an array of 365\n",
+        "# normally-distributed °C values (µ=20, σ=5) called temps\n",
+        "import numpy as np\n",
+        "temps = np.random.normal(loc=20, scale=5, size=365)\n",
+        "print(temps)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Average temperature\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "19.524055016516442\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: print the mean of temps\n",
+        "m_temps = temps.mean()\n",
+        "print(m_temps)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2 · pandas 📊\n",
+        "Quick-start notes\n",
+        "Main structures: DataFrame, Series\n",
+        "\n",
+        "Read data: pd.read_csv, pd.read_excel, …\n",
+        "\n",
+        "Selection: .loc[label], .iloc[pos]\n",
+        "\n",
+        "Group & summarise: .groupby(...).agg(...)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 3 — Load ride log\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 366 entries, 0 to 365\n",
+            "Data columns (total 4 columns):\n",
+            " #   Column   Non-Null Count  Dtype \n",
+            "---  ------   --------------  ----- \n",
+            " 0   date     366 non-null    object\n",
+            " 1   temp     366 non-null    int64 \n",
+            " 2   rides    366 non-null    int64 \n",
+            " 3   weekday  366 non-null    object\n",
+            "dtypes: int64(2), object(2)\n",
+            "memory usage: 11.6+ KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: read \"rides.csv\" into df\n",
+        "# (columns: date,temp,rides,weekday)\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "df = pd.read_csv('rides.csv')\n",
+        "# df.shape\n",
+        "# df.head()\n",
+        "df.info()\n",
+        "# df.describe(include = 'all')\n",
+        "# df.columns\n",
+        "# df.index\n",
+        "# type(df)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 4 — Weekday averages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "weekday\n",
+              "Friday       939.094340\n",
+              "Monday       959.173077\n",
+              "Saturday    1003.509434\n",
+              "Sunday       946.730769\n",
+              "Thursday     933.634615\n",
+              "Tuesday      917.538462\n",
+              "Wednsday    1025.096154\n",
+              "Name: rides, dtype: float64"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: compute and print mean rides per weekday\n",
+        "\n",
+        "# df['rides'].sum()\n",
+        "# df['rides'].count()\n",
+        "# df['rides'].sum()/df['rides'].count()\n",
+        "# df['rides'].mean()\n",
+        "df.groupby('weekday')['rides'].mean()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3 · Matplotlib 📈\n",
+        "Quick-start notes\n",
+        "Workhorse: pyplot interface (import matplotlib.pyplot as plt)\n",
+        "\n",
+        "Figure & axes: fig, ax = plt.subplots()\n",
+        "\n",
+        "Common plots: plot, scatter, hist, imshow\n",
+        "\n",
+        "Display inline in Jupyter with %matplotlib inline or %matplotlib notebook"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 5 — Scatter plot"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x29e29500550>"
+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = df['temp']\n",
+        "y = df['rides']\n",
+        "plt.scatter(y, x)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 6 — Show the figure\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [],
+      "source": [
+        "# 👉 # TODO: call plt.show() so the plot appears\n",
+        "plt.show()\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/Assignments/a0.2/rides.csv b/a0.1/Assignments/a0.2/rides.csv
new file mode 100644
index 0000000..2c5bb33
--- /dev/null
+++ b/a0.1/Assignments/a0.2/rides.csv
@@ -0,0 +1,367 @@
+date,temp,rides,weekday
+27-09-24,32,1111,Friday
+28-09-24,13,886,Saturday
+29-09-24,13,740,Sunday
+30-09-24,42,997,Monday
+01-10-24,25,712,Tuesday
+02-10-24,5,1400,Wednsday
+03-10-24,37,986,Thursday
+04-10-24,4,873,Friday
+05-10-24,36,1376,Saturday
+06-10-24,41,1399,Sunday
+07-10-24,3,1000,Monday
+08-10-24,24,532,Tuesday
+09-10-24,6,814,Wednsday
+10-10-24,6,718,Thursday
+11-10-24,31,1199,Friday
+12-10-24,33,838,Saturday
+13-10-24,40,1123,Sunday
+14-10-24,27,532,Monday
+15-10-24,45,742,Tuesday
+16-10-24,19,485,Wednsday
+17-10-24,11,996,Thursday
+18-10-24,24,1421,Friday
+19-10-24,36,1091,Saturday
+20-10-24,8,1137,Sunday
+21-10-24,44,436,Monday
+22-10-24,23,1313,Tuesday
+23-10-24,39,1355,Wednsday
+24-10-24,30,1155,Thursday
+25-10-24,43,891,Friday
+26-10-24,-6,1357,Saturday
+27-10-24,20,995,Sunday
+28-10-24,5,1218,Monday
+29-10-24,24,815,Tuesday
+30-10-24,17,1418,Wednsday
+31-10-24,37,697,Thursday
+01-11-24,40,448,Friday
+02-11-24,21,1473,Saturday
+03-11-24,43,1214,Sunday
+04-11-24,-10,897,Monday
+05-11-24,-1,558,Tuesday
+06-11-24,38,1333,Wednsday
+07-11-24,-3,589,Thursday
+08-11-24,36,481,Friday
+09-11-24,12,899,Saturday
+10-11-24,41,1175,Sunday
+11-11-24,23,600,Monday
+12-11-24,2,629,Tuesday
+13-11-24,17,623,Wednsday
+14-11-24,-1,618,Thursday
+15-11-24,1,1328,Friday
+16-11-24,-8,1037,Saturday
+17-11-24,14,1172,Sunday
+18-11-24,44,936,Monday
+19-11-24,-7,739,Tuesday
+20-11-24,7,997,Wednsday
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diff --git a/a0.1/Assignments/a0.3/AI-DS_Nexus__A0_3__RezaShokr.ipynb b/a0.1/Assignments/a0.3/AI-DS_Nexus__A0_3__RezaShokr.ipynb
new file mode 100644
index 0000000..b61c9da
--- /dev/null
+++ b/a0.1/Assignments/a0.3/AI-DS_Nexus__A0_3__RezaShokr.ipynb
@@ -0,0 +1,426 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7wR0aAR-evto"
+      },
+      "source": [
+        "# 📚 Assignment 3 — Functions, Derivatives & Gradients\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "\n",
+        "This assignment explores the mathematical foundation of machine learning: functions and their derivatives. You’ll visualize, analyze, and code up essential concepts every ML practitioner uses.\n",
+        "\n",
+        "\n",
+        "**👀🔎 What do you see?**\n",
+        "\n",
+        "Look at the snippet codes and:\n",
+        "\n",
+        "* Describe any patterns, surprises, or connections you notice.\n",
+        "\n",
+        "* Write a few sentences about your observations! 📝✨\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cCb1hk3xe0E6"
+      },
+      "source": [
+        "## 1. Log and Exp: Proving Inverse Relationship\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KOxJ9f-3eu5F",
+        "outputId": "31d8ce39-8670-4dc4-9053-6c6aaf10d1ff"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "x\tlog(exp(x))\texp(log(x))\n",
+            "1.00\t1.00\t\t1.00\n",
+            "2.00\t2.00\t\t2.00\n",
+            "10.00\t10.00\t\t10.00\n",
+            "2.72\t2.72\t\t2.72\n",
+            "\n",
+            "log(exp(y)) for 0 and negative values:\n",
+            "[ 0. -1.  5.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Show log(exp(x)) == x and exp(log(x)) == x for several values\n",
+        "x_vals = np.array([1, 2, 10, np.e])\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "for x in x_vals:\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# Edge case: try negative and zero values (show error/NaN for log)\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lVZufz08g1hI"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mOAqtnk6fDH6"
+      },
+      "source": [
+        "## 2. Plotting Sine Functions Variations\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "DoWdSehie66R",
+        "outputId": "a83a3ade-6ca7-425c-9fa8-59ca9c343d9a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.arange(-2*np.pi, 2*np.pi, 0.1)\n",
+        "\n",
+        "# look at the description of each function and call the proper function from numpy\n",
+        "plt.plot(x, np.sin(x), label='sin(x)')\n",
+        "plt.plot(x, np.sin(2*x), label='sin(2x)')\n",
+        "plt.plot(x, np.sin(x/2), label='sin(x/2)')\n",
+        "plt.plot(x, np.sin(x)**2, label='sin²(x)')\n",
+        "plt.legend()\n",
+        "plt.title(\"Variations of Sine Functions\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dMVcqrfihf0P"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4-5A1OkdfHXq"
+      },
+      "source": [
+        "## 3. Subplots: Trig, Inverse, Exp, and Log"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 807
+        },
+        "id": "DxocsCu6fGOR",
+        "outputId": "5864a5cc-5776-45bd-99f7-028c64e301af"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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SUZ06dVK5cuV07NgxjRkzRjNmzNCUKVPs97t3764yZcrYHhnGI488YldRvffee7r22mv1ww8/aPHixfriiy9yfewtqxSzXxdti1ZSSpqNogEAAADZyfR06/vdEm07FG/7uj3Zobr6tKkkL3q8ZZvx48fryJEjuvfee895jFl4deriK1PaFgA8zdo9MZqx4aDMFPPAFZXkSQHsJUuW2GtQ6by8vNSuXTvNmzfvnI+LjY1V+fLl7QLcRo0a6Y033lDt2rUzPZZ5AsCFOHAswTYB/2nJLkWeUmqqXGigbm1c1paayu99M9wmsHHgwAEbvNi7d69CQkJUr149G9Qw9W2NHTt22MkmXcuWLW3w4/nnn9ezzz6rqlWr2hOROnXq5PrYq5cMUrFCfjoUl6SlOw7rskonAh0AAABAdvhlyS49N36VLT1VpkhBfXRnQzUuX9TpYXmcr7/+2i62Kl269DmPMQutXn755VwdFwDktqEnszWurVda5YsVkqeIioqyfZVKlix52n5zf/369efsB2uyOcx1KtMT9t1337XXpNasWaOyZcuedTzzBIBzSU5N0z/rD+inxbs0fcMBpaadqDUV4OulznVLqWvjCDWvGMrCJXcLbJiTiPMx2RtnMg398kJTP5OSaMoAjFu2W1PW7COwAQAAgGyRmJKqV35fq9ELdtj7V1Qrrg/vaKAigX5OD83jbN++XX///bd+/fXX8x5nVvmaUlWnrsSNiIjIhRECQO6IPHBMk1bttdsPtqXctil5fmrZcxPUqFmzpu0H++qrr551PPMEgDNt3H/Mlpoy146jYpMy9jcsV0S3NYnQdfVKKSiAUlMe1WPDnVxbt5T94zST/wvX1iKyBgAAgEuy+8hxPfjdEq3YdVSm3cMjV1fVw1dV5XNmDhkxYoRtGmvK3J6Pv7+/vQGApxo6fbNtWNu+VknVLBUsTxIWFiZvb2/t37//tP3m/oX20PD19VXDhg0VGXkiq+VMzBMAjLjEFNsX7/tFO7Vi55GM/aY33i2Nyqhrk7KqUiLI0TF6EgIbl6BNtTAF+ftof0yiFm8/bJu7AAAAABfj340H9cgPy3Q4PllFAn015PYGalu9hNPD8limZroJbPTo0UM+PpwWAci/tkbF6bflu+32Q1dVlafx8/NT48aNNW3aNHXp0iVjDjD3+/fvf0HPYUpZrVq1Sp07d87h0QJwR2v2HNWYBTv02/I9GY3AfbwK6KoaJWx2xhXVi8vXm/7M2Y1P8JfA38db19QuqV+X7tYfK/cQ2AAAAECWpaW5NHR6pN7/e6NdLVu3TIg+vbsRjQNzmClBZXr63XfffU4PBQAc9en0SJmS71dWL666ZUPkiUyZKBPIbtKkiZo1a6YhQ4YoLi5OPXv2tN83/V/LlClje2UYr7zyii677DJVqVJFR44c0TvvvGPLF95///0O/yYA8or4pBPZGSagYbKt01UoFqg7m5XTzY3KqngQmVw5icDGJbq+Xmkb2Ji4cq+eu7aW/HyIvgEAAODCHEtI1mNjl+vvdQfs/TubRWjQ9bUV4Ovt9NA8Xvv27eUykSQAyMd2RsfbEtvGQ1d7XrZGuttvv10HDx7Uiy++qH379qlBgwaaPHlyRkNxE+j28vr/6zmHDx9W79697bFFixa1GR9z585VrVq1HPwtAOQFa/fEaMzC7Rq/7P+zM3y9C6hD7XDd1ayc7cNMGdncQWDjErWuGqYSQf46cCxRU9fu17X1Sjk9JAAAALhJ6Y/e3yxW5IFYuzjmtS51bKo6AAC55bOZm5WS5lKbqmFqVK6oPJkpO3Wu0lMzZsw47f4HH3xgbwCQnp0xccVejV6447TeGeVPZmfc2ris7aOB3EVg4xKZ+mjmBPST6ZH6fuEOAhsAAAC4oH4a/ccsVUxCisKDA/RF98aqV7aI08MCAOQje44c10+Ld3psbw0AuFRmAdK387bZaj3HTumdYbMzmpdTC7IzHEVgIxvc3jRCQ2dEanZklLZFxalCWCGnhwQAAIA8yJQ++nr2Vr0xaZ2tZ96oXBENu6exSgQHOD00AEA+8/nMzUpOdal5xVB6hgLASalpLk1bt1/fzNtur/WmKxf6/9kZ9M7IGwhsZAPT2PHyqsU1c+NBfTt/u164jpqLAAAAOF1CcqqeHbfKrvgybmtSVq92qSN/H/ppAABy14GYBH2/6ES2xiMe3FsDAC7UodhEjV28U6Pn79DuI8ftvgIFpKtrlFT3FuXVukoY2Rl5DIGNbNKzVQUb2Phh4Q49fHVVhRT0dXpIAAAAyCP2HU3Q/75bYmvyensV0AvX1lSPlhVUwJwtAQCQy774d4uSUtLUuHxRtahczOnhAIBjzOfzUfO2aeLKvfZ90Sga6Kvbm5bT3c3L2QXtyJu8nB6Ap7iiWnFVLxmkuKRU22sDAJB9hg4dqgoVKiggIEDNmzfXwoULz3v8kSNH1K9fP5UqVUr+/v6qVq2aJk2alGvjBYAzT5Zu+GS2/Vok0Fff3NdM97aqSFADAOCIqNhEjV5w4rrFQ1dVYT4CkC8zqX9duks3Dp1jbyaj2gQ16pYJ0Tu31tO8gVfrmU41CGrkcWRsZBPzQeD+NhX15M8rNWLOVt3bsoICfCkrAACXauzYsRowYICGDRtmgxpDhgxRhw4dtGHDBpUoUeKs45OSknTNNdfY7/38888qU6aMtm/friJFaMoLIPf9uWqvHvtxuRKS01StZGF91b2pyhXjBAkA4JyvZm3V8eRU1SsbYhdpAkB+cfBYom0jMHr+dh2KS7L7/Ly9dG29UrbcVIOIIgR73QiBjWx0Q4PS+mDqRu05mqDv5m/X/W0qOT0kAHB777//vnr37q2ePXva+ybA8ccff2j48OF65plnzjre7I+OjtbcuXPl63uiLKDJ9gCA3G4S/vm/W/Tmn+vt/SurF9fHdzVSYX8+fgMAnM3W+GbeNrv98FVVuYAHIF9YtzdGX8/eqgnL9ygp9US5qVIhAbrnsvK6vWmEwgrTDNwdUYoqG5nGj6a/hvHZjM2KS0xxekgA4NZM9sWSJUvUrl27jH1eXl72/rx58zJ9zIQJE9SiRQtbiqpkyZKqU6eO3njjDaWmpubiyAHkZ8mpaXrml1UZQQ2Tyftl9yYENQAAeaK3RnzSiWyNq2uenf0MAJ4iLc2lf9bv191fzVenD2fp5yW7bFCjYbki+uSuhpr11JXqd2UVghpujLOrbHZL47IaNnOzth2KtyWp+l91ItABAMi6qKgoG5AwAYpTmfvr15+4YHimLVu26J9//tHdd99t+2pERkbqwQcfVHJysgYNGpTpYxITE+0tXUxMTDb/JgDyi6Pxyeo7eonmbj4krwLSi9fVsv00AABw2oFjCRnZGo9dU41sDQAeKT4pRb8s3W2vy245GGf3eXsVUMc64erVuqIalSvq9BCRTQhsZDNfby/7AeGRH5bb8gPdLqugkMATpVAAADkvLS3N9tf44osv5O3trcaNG2v37t165513zhnYGDx4sF5++eVcHysAz7LjULx6jlyozQfjVMjPWx/f1VBX1Tg9MAsAgFOGzdhiez6Z1cpt6a0BwMPsO3oieDt6wQ4dPZ5s9wX5++iOZhHq0bKCyhalz52nIbCRA66vV1qfTt+sDfuP6YtZm/VkhxpODwkA3FJYWJgNTuzfv/+0/eZ+eHh4po8pVaqU7a1hHpeuZs2a2rdvny1t5efnd9ZjBg4caBuUn5qxERERka2/CwDPtmR7tHp/s0TRcUm2Xu/XPZqqVulgp4cFAIC1PyZB3y3YbrcHkK0BwINs2HdMn8/crAkr9iglzWX3lQsNVM9WFdS1SQTlYD0YPTZygJdXAQ1oX81uD5+9TXuPHnd6SADglkwQwmRcTJs27bSMDHPf9NHITKtWrWz5KXNcuo0bN9qAR2ZBDcPf31/BwcGn3QDgQk1evVd3frnABjXqlAnW+H6tCGoAAPKUodMjlZSSpqYViqp1lTCnhwMAl8Tlcmnh1mjdN3KROgz5V78u222DGs0qhOrzbo01/Ym26tmqIkEND8f/3RzSvlZJNSlfVIu3H9Zrf6zT0LsaOT0kAHBLJpOiR48eatKkiZo1a6YhQ4YoLi5OPXv2tN/v3r27ypQpY8tJGX379tUnn3yiRx55RA899JA2bdpkm4c//PDDDv8mADyRSXcfNGGNXC6pXc2S+ujOBgr04yM2ACDv2H3kuH5YuNNu01sDgLs3BP973X7b33jpjiN2n3lL61QnXP+7vLLqRxRxeojIRZx15RDzQeGVG+vouo9n6Y+Ve3VXsyi1YlUEAGTZ7bffroMHD+rFF1+05aQaNGigyZMnZzQU37Fjh7y8/j8B0ZSQmjJlih577DHVq1fPBj1MkOPpp5928LcA4ImrxN6ZskGfzths79/VvJxeuaG2fLxJiAYA5MFsjdQ0XVYpVC0rc10CgPsxGWfjl+/WF/9uUeSBWLvPz9tLtzQuqz6XV1LFsEJODxEOILCRg0wJgm6XldeoedvtSr5JD7eRnw8nuwCQVf3797e3zMyYMeOsfaZM1fz583NhZADyo+TUND3zyyr9snRXRq3yh66qwgpYAECeszM6Xj8uOpGtMeCa6k4PBwCyJDYxRd8v2KGvZ2/VvpiEjIbg97Qob3tolAgKcHqIcBCBjRw2oH11TVy510YTR83dpt6XV3J6SAAAALhIcYkpenD0Us3ceFDeXgX0xk11dHvTck4PCwCATH3yT6StO9+mapiaVQx1ejgAcEGOxCdp+JxtGjlnq2ISUuy+EkH+6tW6os2UDgrwdXqIyAMIbOSwkIK+erpTDT3180oN+XujOtUNV9migU4PCwAAAFkUFZtoGxSu3HVUAb5e+vTuRrqqxomyeAAA5DXbD8Xp55PZhY+2q+b0cADggj5vfzVrq76dt01xSal2X6XihfS/yyupS8My8vfxdnqIyEMIbOSCWxuVtamfppH4wF9X6Zv7mlGqAAAAwM0uDnUfvlDbD8UrtJCfvu7RRA3LFXV6WAAAnNOH0zYpNc2lttWLq3F55iwAedeBmAR9/u8WjV6wXQnJaXZfzVLBttxrx9rh8vLiOirORmAjF5h/fG/fWk+dPpylWZui9MOinbqzGSULAAAA3MG6vTHq9vVCu4IsIrSgRvVspkrFCzs9LAAAzmnT/mMav2y33X6MbA0AedSeI8c1bOZme63UNAg36pUN0UNXVVW7miVYGI7zIrCRS8zJ75Mdquu1P9bp9T/W6fJqxVWmSEGnhwUAAIDzWLL9sHqOWGhr+5pVY6Pua0qTQgBAnvfuXxuU5pI61C6p+hFFnB4OAJxmx6F4fTYzUj8v2aXkVJfdZzLLTIbGFdWKE9DABSGwkYt6tqqoP1fvsyfIz/yykpJUAAAAedisTQfV55slOp6cqibli+rre5va/mkAAORly3ce0ZQ1+2UqtzzRvrrTwwGADLsOx+vjaZG2/48plWdcVilUD19VVS0qF+M6KbKEwEYu8j5ZkqrzyZJUo+Zu072tKjo9LAAAAJzhz1V79fAPy+wKMpNpO+yeRgr046MzACDve3vyevv1poZlVbVkkNPDAQDtO5qgT6Zv0thFOzMyNNpUDdPDV1dV0wqhTg8Pboqzs1xWuXhhPdu5pgZNWKM3Jq1Xs4rFVKt0sNPDAgAAwEk/Lt5ps2vNIrJr65bSB7c3kJ+Pl9PDAgDgP83eFKW5mw/Jz9tLj7ar6vRwAORzB48l6rMZm/Xdgu0ZPTRaVSmmAddUU+PyBDRwaQhsOKB7i/K2tMHf6w7ooe+XauJDbVTQz9vpYQEAAOR7X8/eqlcnrrXbtzeJ0Bs317VZtwAA5HUul0vvTDmRrXFX83KKCA10ekgA8qnouCR9/u9mfTN3uy3rajStUFQDrqluS04B2YHAhgNMvbi3b62vjkP+1eaDcXpl4loNvrmu08MCAADI1xeDPvh7kz6atsne792mos2ypc4vAMBdTFmzTyt2HVWgn7f6X1XF6eEAyIeOHk/WV7O2aPjsrYpLOhHQqB9RRI9fU82WnuKzNbITgQ2HhBby05DbG+jurxfo+4U7bKOcGxuUcXpYAAAA+TKo8fof6/TV7K32/hPtq6nflVU48QIAuI2U1DS9M2WD3e7VuqLCCvs7PSQA+UhCcqpGzNmmz2ZEKiYhxe6rXTrYlpy6qkYJPlcjRxDYcFDLKmHqf2UVffxPpJ75ZZVqhAerejiNvQAAAHJLWppLL/++RqPmbbf3X76htnq0rOD0sAAAyJJfl+22FSGKBPqq9+WVnB4OgHwUVP1l6S59MHWT9sUk2H3VSha2AY32tcLlRUlX5CACGw57tF01Ld95RLM2RemB75bot/6tFBzg6/SwAAAA8kVQ47nxq232rFlENvimurqjWTmnhwUAQJYkpqTqw79PlFJ8sG1lrikAyJWM56lr9+vtKRsUeSDW7itTpKAeb1/NVqShRx1yA4ENh5l/6B/e0VDXfzxbW6Pi9MSPK/R5t8akaAEAAOSg1DSXnv5lpX5eskvmvOudW+vrlsZlnR4WAABZNnr+Du0+clzhwQHq3oKsQwA5a/G2aA3+c72WbD9s75tMMVOR5p7LyivA19vp4SEfIbCRR/ptfHp3I3UdNk9/rd2vD6dtspkcAAAAyJmU+cd/WqHflu+xi0zev60+vc4AAG7pWEKyhk6PtNsPX12Vi4oAcsym/cf01uQN+nvdfns/wNfL9vT53xVkisEZBDbyiPoRRfRalzp66peVGvL3JlUuXljX1y/t9LAAAAA8SnJqmh4du1x/rNwrH68C+ujOhupct5TTwwIA4KJ8PnOLDsUlqVJYIXVtQuYhgOx38Fii3p+6UWMX7VCa60T1mduaROjRdlVVMjjA6eEhHyOwkYfc1jRCmw4c05eztuqJn1YoIjRQDSKKOD0sAAAAj5CUkqaHvl+qKWv2y9e7gIbe1Ujta4c7PSwAAC7KvqMJ+mr2Frv9VMca8vX2cnpIADxIQnKqRszZZrPCYhNT7L4OtUvqyQ41VKVEYaeHBxDYyGue6VRTWw7Gadr6A7p/1GJN6N9KpYsUdHpYAAAAbt9Y9cHvltrPWH4+Xvr8nsa6skYJp4cFAMBFe3/qBiUkp6lJ+aL2YiMAZFdj8Emr9mnwn+u06/Bxu69e2RC9cF0tNa0Q6vTwgAyE8/NiM/E7G6pGeJCiYhPVa9RixZ2MigIAAODiMjX6jT4R1PD38dLXPZoQ1AAAuLX1+2L085Jddntg55oqUKCA00MC4AFW7jqi2z6fp35jltqgRslgf73Xtb7GP9iKoAbyHEcDG4MHD1bTpk0VFBSkEiVKqEuXLtqwYcN5HzNy5Eg7YZ96CwjwrHpuhf199FWPJgor7Kd1e2P0yA/LbJNLAAAAZL2nRv8xS/X3uhNBjeH3NlWbqsWdHhYAAJfkzT/X21r3neuGq3H5ok4PB4Cb23v0uAaMXa4bPpmjRdsO28bgj1xdVdOfaKtbGpeVlxfBU+Q9jgY2Zs6cqX79+mn+/PmaOnWqkpOT1b59e8XFxZ33ccHBwdq7d2/Gbfv27fI0ZYsG6geOP9wAAI8FSURBVPNuTWypBHMi/sJvq20qGAAAAC48qPHw98v019r99jPVl92bqFWVMKeHBQDAJZkTGaUZGw7Kx6uArXUPAJfSR+PjaZt01bsz9euy3XbfzQ3L2IDGY9dUU6AfXQyQdzn61zl58uSzsjFM5saSJUt0+eWXn/NxJksjPNzzGz2aVRcf3dFAD45equ8X7lTxwv4a0L6608MCAADI80y262Njl+vP1fvk5+2lz7s11uXVyNTA/9u9e7eefvpp/fnnn4qPj1eVKlU0YsQINWnSxOmhAcA5paW5bN17457LyqtiWCGnhwTATU1bt18v/75WO6Lj7X3Tr+f562qpQUQRp4cGXJA8FXY7evSo/Roaev6abbGxsSpfvrzS0tLUqFEjvfHGG6pdu/Y5j09MTLS3dDExMXIXHeuU0qtd6ui5cav10T+RCgvyV/cWFZweFgAAQJ6VmubS4z+t0MSVe+XrXUCf3dNIV1anpwb+3+HDh9WqVStdeeWVNrBRvHhxbdq0SUWLUs4FQN42YcUerd4dY0tYP3RVFaeHA8ANbT8Up1d+X2v7zxmmj8aznWvqhvql6dcDt5JnAhsmSPHoo4/aE4w6deqc87jq1atr+PDhqlevng2EvPvuu2rZsqXWrFmjsmXLnrOXx8svvyx3dXfz8oo6lqQP/t6oQRPWqFghf11br5TTwwIAAMiTQY0nf1qh35bvsSU6ht7VSFfXLOn0sJDHvPXWW4qIiLAZGukqVqzo6JgA4EJKxrwz5URf0r5tK6tYYX+nhwTAjRxPStVnMyI17N8tSkpJs5+Ve7WuqIeurmqDpYC7cbTHxqlMr43Vq1frhx9+OO9xLVq0UPfu3dWgQQNdccUV+vXXX+0Kq88///ycjxk4cKANgqTfdu7cKXfz8NVVdM9l5WTabJiyCrM3RTk9JAAAgDxXnuOZX1ba+sDeXgX0yV0N1b6255cvRdZNmDDBlpzq2rWrLYXbsGFDffnll04PCwDO69t527X7yHGFBwfovlYEY7Nq6NChqlChggICAtS8eXMtXLjwvMf/9NNPqlGjhj2+bt26mjRpUq6NFchOpmfv5NX71O79mbYajAlqtK4SpsmPXq6BnWsS1IDbyhOBjf79+2vixImaPn36ObMuzsXX19eeiERGRp7zGH9/f9tw/NSbuzGpYC/fUEed64YrKTVNvb9ZrIVbo50eFgAAQJ45YXtu/Gr9tGSXDWp8dEdDW9ITyMyWLVv02WefqWrVqpoyZYr69u2rhx9+WKNGjcr0eFPW1pSzPfUGALnpcFySPv5nk90e0L6aCvp5Oz0ktzJ27FgNGDBAgwYN0tKlS1W/fn116NBBBw6cKMVzprlz5+rOO+9Ur169tGzZMnXp0sXezIJcwJ3sOBSve0cs0gPfLbGB0dIhAfrs7kb6tlczVSlR2OnhAe4b2DAnoCaoMW7cOP3zzz8Xlf6dmpqqVatWqVQpzz9xNSfpH9zeQFdUK67jyam6b+QiLd95xOlhAQAAyOnPlK//sU7fL9whrwKyn5co24nzObVXn1kk1adPH/Xu3VvDhg07Z2nbkJCQjJspYwUAuenDaZsUk5CiGuFBuqVR1haEQnr//fft+3zPnj1Vq1Yt+34fGBhoS51n5sMPP1THjh315JNPqmbNmnr11VftvPHJJ5/k+tiBi5GcmqZPZ0Tqmg9maubGg/Lz9lL/K6vo78evUKe6peilAY/g5XT5qe+++05jxoxRUFCQ9u3bZ2/Hjx/POMaUnTKlpNK98sor+uuvv+wqKxNlv+eee7R9+3bdf//9yg/8fbz1ebfGalGpmGITU9T96wVavftE03UAAID8aMjfm/TV7K12+61b6tnGh8D5mEVR5sLWqcyFqx07dnhsaVsA7ivywDF9O3+73X7xulp20SMuXFJSkpYsWaJ27dpl7PPy8rL3582bl+ljzP5TjzdMhse5jgfykqU7Duv6j2fr7ckblJiSppaVi2nyo230RIfqCvSj7BQ8h6OBDZP+bU4M2rZta08u0m8mRTCdObnYu3dvxv3Dhw/bKLs58ejcubNNAzcpgmeemHiyAF9vfdWjiZqUL2pXbHT7eoE27j/m9LAAIM/Uw01n+jaZlSgmbRyAZ/ry3y12Favx0vW11LUJK+nx31q1aqUNG0404E23ceNGlS9f3mNL2wJwX6/9sU6paS5dU6ukWlYJc3o4bicqKspW+yhZsuRp+819s7g2M2Z/Vo6nZCHygpiEZL0wfrVu+Wyu1u87pqKBvnqva32Nvr+5KhWn7BQ8j4/TZQP+y4wZM067/8EHH9hbflfI30fDezbVPV8t0MpdR3XXlwv0fe/mqloyyOmhAUCO1MM16eImqDFkyBC7WspckDINX89l27ZteuKJJ9SmTZtcHS+A3DN6wXa9Pmmd3X6yQ3XdSyNVXKDHHntMLVu2tKWobrvtNhsw/+KLL+wNAPKS6RsOaMaGg/L1LqBnO9d0ejg4B1Oy8OWXX3Z6GMinzPXVP1fv00sT1ujAsUS7z5Sse+7amgot5Of08ADPbh6OixMc4Ktv7mummqWCFRWbqDu+mK91e1kVACB/18M1zIqsu+++255cVKpUKVfHCyB3jF+2W8+PP9HAs2/byup3ZRWnhwQ30rRpU9vn7/vvv1edOnVs7XQTODdzBwDkpRr5poeUcW/LCqoYVsjpIbmlsLAweXt7a//+/aftN/fDw8MzfYzZn5XjKVkIp+w5clz3j1qsB0cvtUEN8z4xpndzvXdbfYIa8HgENtxckUA/m6lRt0yIDsUl6c4v59NzA0C+roeb3o/JZHP06tXrgn4OqeOAe5myZp8e/2mFTPJv9xbl9VSH6k4PCW7ouuuu06pVq5SQkKB169bZIDoA5CVjFuxQ5IFYe3Gy/1VVnR6O2/Lz81Pjxo01bdq0jH1paWn2fosWLTJ9jNl/6vHG1KlTz3k8JQvhRJbG9wt3qP0H/2ra+gM2q+vhq6roz0faqGVlStYhfyCw4SHBje/ub64GEUV0JD7ZBjeW7Tjs9LAAwJF6uLNnz9bXX3+tL7/8Mkup4yEhIRm3iAhq9AN51axNB/XQmGW21vjNjcropetr2146AAB4kiPxSfrg7412e8A11RRS0NfpIbk1U9rWnB+MGjXKBrP79u2ruLg4mxVudO/e3WZdpHvkkUc0efJkvffee1q/fr1eeuklLV68WP3793fwtwBO2Bkdr25fL9TAX1cpNjFFjcoV0aSH22hA++q2Ly+QXxDY8BDmQ863vZqpaYWiOmYbii/U4m3RTg8LAHLVsWPH1K1bN3vSYlLOLxSp44B7WLI9Wr2/Wayk1DR1qhOut2+pJy8vghoAAM/z4bRNduFi9ZJBuqMpi24u1e233653331XL774oho0aKDly5fbwEX6AqodO3Zo7969GcebPkxjxoyxvZfq16+vn3/+WePHj7flCwGnpKW59O28beo45F/NjoxSgK+Xnr+2pn56oCU9d5EvOdo8HNkrKMBXI3s2s7X15m05pO7DF+rrHk3VonIxp4cGALlSD3fz5s22afj1119/Wpq54ePjYxuOV65cOdPUcXMDkHdt2HdMPUcsUkJymi6vVlxD7mggH2/W6AAAPM/mg7H6dt52u/38dTWZ77KJybY4V8bFjBkzztrXtWtXewPygu2H4vT0Lys1f8uJRczNKoTqrVvr0XsH+Rqzo4cp5O+j4fc2VZuqYYpPSlXPkQs1ff0Bp4cFALlSD7dGjRq2XrpZgZV+u+GGG3TllVfabUpMAe6bbt99+ALFJJxItR92TyP5+5BmDwDwTG/8sU4paS61q1lCbaoWd3o4ABzO0hg5Z6s6DpllgxoFfb310vW19EOfywhqIN8jY8MDFfTz1pfdm+jB0Uv1z/oDtmTDu13rq0vDMk4PDQAuqh5ujx491KRJEzVr1kxDhgw5qx5umTJlbJ+MgICAs9LDixQpYr+SNg64p6jYRJuFuj8mUdVKFrYLOAL9+AgLAPBMMzYcsI2AfbwK6NnONZ0eDgAH7TlyXE/8tEJzNx+y91tUKqa3bqmncsUCnR4akCdwVuihTLOgz7s11lM/r9S4Zbv16Njlio5L0n2tKzo9NADIcj3cgwcP2nq4pmG4qYl7Zj1cLy8SEAFPdCwhWfeOWKitUXEqU6SgvrmvuYoE+jk9LAAAckRiSqpe/n2t3b63ZQVVKl7Y6SEBcMhvy3fr+fGrbR9dk6XxbOcaurt5efrLAacgsOHBfL299F7X+ioa6Kfhc7bqlYlrbXDj8fbVVKAAb4QAPLce7qlGjhyZQ6MCkNMXd/737RKt3h2j0EJ++rZXM4WHBDg9LAAAcszw2dtsMD+ssL8eaVfV6eEAcMCR+CS98Nsa/b5ij71fP6KIPritPoFOIBMENjycieS+cF1NFSvsp3embNAn0yMVHZ+kV2+sI2+ivAAAIA9KTXPpsbHLbdp9IT9vjerZjJM5AIBH23c0QR//s8luD+xUQ0EBvk4PCUAum7XpoJ78aaX2xSTYa3YPX1VV/a6sLB9vKhQAmSGwkQ+Y7Ix+V1axmRvPj1+lMQt26HBckj64vYEtWQUAAJBXuFwuvfDbak1atU9+3l62b1jdsiFODwsAgBz1xqR1ik9KVePyRXUT/TGBfCUhOVVv/rleI+dus/crhRWy1+xMtgaAcyOwkY/c1bycigb66pEfluvP1fsUFbvAXiygVjUAAMgrPpi60S7CMFUzh9zRQC2rhDk9JAAActSCLYc0YcUeO/e9fENtaugD+ciGfcf00PdLtXF/rL3fvUV5DexUUwX9WIgM/BdymfKZTnVLaeR9TRUU4KNF2w7r5s/maseheKeHBQAAoO/mb9dH/0Ta7de61FHnuqWcHhIAADkqJTVNgyassdt3NSunOmXIUgTyS5by6AXbdcMns21Qo3iQv0b2bKpXbqxDUAO4QAQ28qGWlcP0S9+WKlOkoLYcjNNNn87R8p1HnB4WAADIx6au3a8Xf1tttx9rV013Ny/v9JAAAMhxoxfs0Pp9x1Qk0FdPtK/u9HAA5IKjx5P14Oilem7caiWmpKlt9eL685E2alu9hNNDA9wKgY18qlrJII17sKVqlw7Wobgk3fHFPP21Zp/TwwIAAPnQsh2HbQp+mku6o2mEHr66itNDAgAgxx2KTdR7f22w2yaoUbQQZaIBT7dk+2F1/nCWLRHv611Az3WuqeE9miqssL/TQwPcDoGNfKxEcIB+/F8LXVm9uBKS0/S/75ZoxJytTg8LAADkI9ui4tRr1GL7WcR8JjElqAqYIuMAAHi4d6ZsUExCil1weGezck4PB0AOSktzaej0SN32+TztPnJc5UID9fMDLdX78kr01QEuEoGNfK6Qv49tIG4ai7tc0su/r9Wg31bbOp8AAAA5KSo2UT1GLFR0XJLqlgnRJ3c1ko83H08BAJ7PlIMeu3in3TYNw725sAl4LPNZ13zmNcHM1DSXbmxQWn883Fr1I4o4PTTArfk4PQA4z1xAeL1LHUUUDdRbk9dr1Lzt2nwwTkPvaqSQQF+nhwcAADxQfFKKeo1cpO2H4hURWlDD721qF1wAAODpzELC58atsosLb25URk0qhDo9JAA5WHK13+il2nM0QQV9vfXKjbV1a+OyZCgD2YAlcbDMG2rftpU17J7G9o12dmSUbSq+5WCs00MDAAAeeEHnoTHLtGLXURUN9NXIns1UPIi6wgCA/OHb+du1Zk+MggN89Gznmk4PB0AOcLlc+mbeNlt6ygQ1KoUV0vh+rdS1SQRBDSCbENjAaTrWCdfPfVuodEiAtkTFqcvQOZq9KcrpYQEAAA86yXtxwhpNW39A/j5e+qpHE1UuXtjpYQEAkCv2xyTovb822u2nO9WgYTDggeISU/TID8v14m9rlJzqUqc64fqtfytVDw9yemiARyGwgbPULh2i3/q3VqNyRWwjM1MH0ESZzYUIAACAS/HpjM0as2CHzEK1D+9oqMblKb8BAMg/Xpm4VrGJKWpYrojubErDcMDTRB6ItYuEJ6zYY3vnPH9tTX16dyMFBVDqHchuBDaQKVMOYkzvy2y9T9PYyESZnx+/Wsk0FQcAABfplyW7bNNE46Xra9tMUQAA8ouZGw/qj5V7ZfqEv9aljrxoGA54lD9X7dWNn8zWpgOxKhHkrx/6XKb721Si9BSQQwhs4JwCfL31Xtf6eqZTDbuqcvSCHer+9UJFxyU5PTQAAOBm5kRG6elfVtrt/11eST1aVnB6SAAA5JqE5FS9+Ntqu92zVUVbKQGAZ0hLc+n9vzao7+iliktK1WWVQvXHw23UtAKZyUBOIrCB8zJR5QeuqKwvujVRIT9vzdtySNd/PFurdx91emgAAMBNbNp/TA98t0QpaS5dX7+0nu5Yw+khAQCQ66UYtx+KV3hwgB67pprTwwGQTUxpOfM596N/Iu39Xq0r6rtezW0lFAA5i8AGLsg1tUrq1wdbqUKxQO0+cly3fDbXlpMAAAA4n6jYRPUcuUjHElLUpHxRvXNrPUpvAADylS0HYzVsxma7Pej6Wirs7+P0kABkg+2H4nTzp3P019r98vP20rtd6+uF62rJx5vLrUBu4F8aLlj18CDbVPyqGiWUmJKmx39aoUG/0XcDAACcu+xG728Wa9fh4yoXGqjPuzW2pS4BAMgvXC6XXvhttZJS09S2enH6SwEeYvamKN3wyRxt3H+in8bY/12mWxuXdXpYQL5CYANZElLQV191b6KHr65q74+at113f7lAB44lOD00AACQx2oNP/HTCi3bcUTBAT4afm9TFStMSj4AIH/5bfkezYk8JH8fL71yQx2aCAMeEKwcPnureoxYqKPHk1U/ooh+f6i1GpYr6vTQgHyHwAayzJSPGHBNNX3ZvYmC/H20cFu07buxdMdhp4cGAADyiPenbtTElXvl41VAw7o1VpUShZ0eEgAAuSo6LkmvTFxrtx+6qorKFQt0ekgALoGpWPLsuNX233Vqmks3NyqjsX0uU8ngAKeHBuRLBDZwSX03xvdvZS9U7I9J1B2fz9foBdtt9BoAAORfPy3eqU+mn2igOPjmumpZOczpIQEAkOte+2OtDW7UCA9Sn8srOz0cAJcgJiFZ941cpO8X7pBJvHr+2pp6r2t9yqwCDiKwgUtSuXhhje/XSh1rh9uaoc+NW60BP65QfFKK00MDAAAOmLf5kJ4dt8pu97+yiro2iXB6SAAA5Lp/Nx7Ur0t32wugJsjv58PlF8Bd7Tocr1s/m6tZm6JU0NdbX3ZrovvbVKK0HOAwZlZcssL+PvrsnkZ6plMNeXsV0Lhlu3XjJ3MUeeCY00MDAAC5aPPBWD3w3RIlp7p0Xb1StnQlAAD5jVnolx7kv7dlBWrvA25sxc4j6jJ0bkaT8J8eaKF2tUo6PSwABDaQXUyU+oErKmvM/c3tG/2mA7G64ZM5+m35bqeHBgAAcoEptWHS800TxUbliujdrvVtXy4AAPKbD6Zu1K7Dx1WmSEE90b6608MBcJEmr96n27+Yp6jYRFtSzlQsqVMmxOlhATiJwAayVfNKxfTHw23UsnIxxSel6pEfluu5cauUkJzq9NAAAEAOMfN8n28Wa/uheEWEFtSX3ZtQbxgAkC+t3HVEX8/eardfu6mOCvn7OD0kABdhxJyt6jt6iRKS03RFteL6uW9LlS5S0OlhATgFgQ1ku+JB/vq2V3M9fFUVW0909IIdunXYXO04FO/00AAAQDZzuVwa+OsqLd5+WEEBPhpxb1MVK+zv9LAAAMh1yalpevqXVUpzSTfUL60rq5dwekgALuKz7VuT1+vl39fK5ZLubl5OX/doYsuwA8hbCGwgR5heGwPaV7cXN4oG+mr17hhd+/EsTVmzz+mhAQCAbPTZzM22v5aZ+4fd01hVSgQ5PSQAABzx1aytWrc3RkUCffXi9bWcHg6ALEpJTdNTP6/UZzM22/tPdqiu17rUkY83l0+BvIh/mchRbauXsKWpTK3tYwkp+t+3S/T6H2uVlJLm9NAAAMAl+mvNPr0zZYPdfumG2mpVJczpIQEA4IhtUXEa8vdGu/38tbUURvYi4FaOJ6Xaa1Y/Ldkl0yburVvqqt+VphIJPeOAvIrABnKcqUH4Q58W6tW6or3/5ayt6kppKgBZMHToUFWoUEEBAQFq3ry5Fi5ceM5jv/zyS7Vp00ZFixa1t3bt2p33eAAXZ+2eGD06drlN0e/eory6XVbe6SEBAOCItDSXnvplpRJT0tS6SphuaVTG6SEByIIj8Unq9vUCTVt/QP4+Xvq8WxPd3rSc08MC8B8IbCBX+Pl46YXraunzbo0VUtBXK3YdVeePZum35budHhqAPG7s2LEaMGCABg0apKVLl6p+/frq0KGDDhw4kOnxM2bM0J133qnp06dr3rx5ioiIUPv27bV7N+83QHaJik1U728WKz4pVa2qFLNzPAAA+dW387dr4dZoBfp5a/DNdVnhDbiRvUeP67bP59l+ccEBPvru/ua6plZJp4cF4AIQ2ECu6lA7XJMeaaMm5YsqNjFFj/ywXE/9vELxSSlODw1AHvX++++rd+/e6tmzp2rVqqVhw4YpMDBQw4cPz/T40aNH68EHH1SDBg1Uo0YNffXVV0pLS9O0adNyfeyAJ0pMSdUD3y7R7iPHVTGskD69q7F8qTsMAMinth+K05t/rrfbz3SqoYjQQKeHBOACmUoit342Txv3x6pksL9+eqClmlYIdXpYAC6Qo2ehgwcPVtOmTRUUFKQSJUqoS5cu2rDhRJ3m8/npp5/sxSpTkqRu3bqaNGlSrowX2aOMLU11mR6+ytQqlH5cvEvXfzzblrQAgFMlJSVpyZIltpxUOi8vL3vfZGNciPj4eCUnJys0lA+owKVyuVx69tfVdkVbUICPvurRRCGBvk4PC8iyl156ya6oPvVmzi8AIMslqH5eqePJqbqsUqjuaU5ZRsBdRB6IVdfP52Ys1vmlb0tVDw9yelgA3CWwMXPmTPXr10/z58/X1KlT7YUnUy4kLi7unI+ZO3euLTHSq1cvLVu2zAZDzG316tW5OnZcGh9vLw1oX11j7r/MRsU3H4xTl0/naNTcbfaiCQAYUVFRSk1NVcmSp6cCm/v79u27oOd4+umnVbp06dOCI2dKTExUTEzMaTcAZ/ty1hb9snSXvL0KaOhdjVS5eGGnhwRctNq1a2vv3r0Zt9mzZzs9JABu5rsF27Vga7QK+nrr7Vvqy8t0HAaQ563bG6PbP5+n/TGJqlaysMb+7zKVLUq2FeBuHA1sTJ48Wffee689qTA100eOHKkdO3bY1bnn8uGHH6pjx4568sknVbNmTb366qtq1KiRPvnkk1wdO7JHi8rF9Ocjl+vqGiWUlJKmQRPWqM+3S2zjJgC4VG+++aZ++OEHjRs3zmb5nS+DMCQkJONm+nIAON20dfs1+GSpjReuranLqxV3ekjAJfHx8VF4eHjGLSwszOkhAXCzEjanlqAqV4yLooA7WLnriO74Yr4OxSWpdulg/dCnhUoEnftcEUDelacKIh89etR+PV+5EFN65MxVt6aJ7IWWJEHeE1rIz5ayGHR9Lfl5e2nq2v3q9OEs23wNQP5mLjJ5e3tr//79p+03981FqPN59913bWDjr7/+Ur169c577MCBA+0clH7buXNntowf8BQb9h3Tw98vk0mqvKt5OfVoWcHpIQGXbNOmTTajr1KlSrr77rvtAqtzIbMPwFklqH4xvSJT1bxiqLpdRgkqwB0s3hatu79coKPHk9WwXBGN6X2ZvSYFwD3lmcCGaez66KOPqlWrVqpTp845jzOlR7JakoQTkbzP1DXu2aqifn2wpa1tuPdogu74Yp4+mLpRKalpTg8PgEP8/PzUuHHj0xp/pzcCb9GixTkf9/bbb9uMPpMZ2KRJk//8Of7+/goODj7tBuCEQ7GJ6jVqkeKSTtQPf/mG2nbeBtxZ8+bNbba4mSc+++wzbd26VW3atNGxY8cyPZ7MPgCnGr1gu+ZvOVmC6tZ6lKAC3MDcyCh1+3qhjiWm2M+03/ZqrpCC9IoD3FmeCWyYXhumT4YpGZLdOBFxH3XKhGjiQ611S6OySnNJH07bpFuHzdO2qHP3XQHg2QYMGKAvv/xSo0aN0rp169S3b1/bi6lnz572+927d7cZF+neeustvfDCCxo+fLgqVKhgA9/mFhsb6+BvAbgnUyay7+il2nX4uMqFBuqzuxvL1zvPfHwELlqnTp3UtWtXm9Fnsr8nTZqkI0eO6Mcff8z0eDL7AKTbGR2fUZrx6Y7VVb5YIaeHhP8QHR1tM/PM4qUiRYrYnq3/dW7Qtm1bu5Dj1NsDDzyQa2NG9gc1eo5cpOPJqbac6oh7m6mwv4/TwwJwifLEmWn//v01ceJETZ8+XWXLlj3vsab0SFZLknAi4l4K+fvovdvq68M7GigowEfLdx5R549m6cdFO2ksDuRDt99+uy0r9eKLL6pBgwZavny5XWGbnr1nSoeYpq/pzMrbpKQk3XrrrSpVqlTGzTwHgAtn5twXxq+2pSGD/H30dY8mKkqqPjyUudBVrVo1RUZGZvp9MvsAZJSg+nmlLUHVrEKouregNKM7MEGNNWvWaOrUqfba07///qs+ffr85+N69+5tzzPSbyYrHO5n/pZD6jVqsRJT0mx/1y+7N1ZBP2+nhwUgG/g4fcL80EMP2aauM2bMUMWKFf/zMab0iClBYspWpTOT0/lKkpgTEXODe7mxQRk1qRCqAWOXa8HWaD31y0r9s/6ABt9clwsrQD5jAuDmlhkzf5xq27ZtuTQqwLONmrtNYxfvlKmu8dFdDVW1ZJDTQwJyjFm5u3nzZnXr1s3poQDI4yWo5m05pABfL0pQuQmT8W0WRS1atCijRO3HH3+szp0724VPptfSuQQGBv5nXz/kbYu2Reu+k5kaV1Yvrk/vaSR/H4IagKfwcrr81HfffacxY8YoKCgoo1zI8ePHM445s8TII488Yiel9957T+vXr9dLL72kxYsXn/OCF9xbmSIFbTOnZzrVkK93AU1es08dhvyrfzcedHpoAAB4rHmbD+nVP9bZ7YGdaurK6iWcHhKQrZ544gnNnDnTBsPnzp2rm266Sd7e3rrzzjudHhqAPGrzwVi9PunE3Ph0xxqqEEYJKncwb948m5V3at+9du3aycvLSwsWLDjvY0ePHq2wsDDbB9Zcl4qPj8+FESO7LNl+WPcOX2gzrNpUDdNn9zQmqAF4GEczNky5kPTahacaMWKE7r333owSI2bCSdeyZUsbCHn++ef17LPPqmrVqho/fvx5G47DvXl7FdADV1RW6ypheuSHZdp8ME7dhy9Uz1YV7AfKAF8mJgAAssvuI8fVb8xSpaa51KVBad3f5r8zagF3s2vXLhvEOHTokIoXL67WrVtr/vz5dhsAzpScmmYrCSQkp9nz0h6UoHIbZvFsiRKnL9Dw8fFRaGio/d653HXXXSpfvrzN6Fi5cqWefvppbdiwQb/++mumxycmJtpbupiYmGz8LZBVy3YcVo/hCxWXlKpWVYrpy+5NuHYEeCDHS1H9lzNLjBim0Z+5IT82Fm+jwX+u0zfztmvEnG2aG3lIQ+5ooJqlqHMMAMClSkhO1f++XazouCTVLh2swTfXs80yAU/zww8/OD0EAG5k6PRIrdh1VMEBPnqnKyWo8oJnnnlGb7311n+WobpYp/bgqFu3ru3Zd/XVV9uyhZUrVz7r+MGDB+vll1++6J+H7LN691G7GDY2MUWXVQrVV92bEtQAPJSjgQ0gq0yDp1durGNLYjz58wpt2H9MN34yR091rK77WlXkAyYAAJew4GTgr6u0eneMQgv56fNuNFYEAGD5ziP6+J9Iu/3aTXVVKqSg00OCpMcffzyj0se5VKpUyfbIOHDgwGn7U1JSFB0dnaX+Gc2bN7dfIyMjMw1smFJVAwYMOC1jIyIi4oKfH9ljy8FYm6lxLCFFTSsU1dc9mvJ5FvBgBDbglq6sUUKTH71cz/yyUn+vO6DX/lin6RsO6L2uDRQeEuD08AAAcDvD52zTuGW7bQnIT+5qqLJFA50eEgAAjopPStFjY5fb8ozX1y+tG+qfu9E0cpcpHXgh5QNbtGihI0eOaMmSJWrcuLHd988//ygtLS0jWHEhli9fbr+azI3M+Pv72xucs+fIcXX7eqEOxSWpTplgfX1vUxXy57In4MkcbR4OXIqwwv62TuLrN9VRgK+X5kQeso3Ff1u++4LKnAEAgBPmRkbpjZMNUZ/rXFMtK4c5PSQAABw3eNJ6bY2KU3hwgF69sbbTw8FFqFmzpjp27KjevXtr4cKFmjNnjvr376877rjD9s8wdu/erRo1atjvG6bc1KuvvmqDIdu2bdOECRPUvXt3XX755apXr57DvxEycyg2Ufd8vcD2iqtUvJBG9Wym4ABfp4cFIIcR2IBbM3W/725eXn883Eb1yobo6PFkPfLDcvUfs8zWBwcAAOe3Mzo+o1n4zY3KqGcrGqICADBjwwF9O3+73TZ9NYoE+jk9JFyk0aNH28CF6ZHRuXNntW7dWl988UXG95OTk21j8Pj4eHvfz89Pf//9t9q3b28fZ8pe3XLLLfr9998d/C1wLscSktVjxEJtORin0iEB+rZXcxUrTPYMkB+QkwWPULl4Yf3St6U+nb5ZH/+zSX+s2qsFW6P15s111a5WSaeHBwBAnnQ8yTQLX6LD8cmqWyZEb9xUl2bhAIB873Bckp76eaXdvrdlBbWp+t8lj5B3hYaGasyYMef8foUKFU6r+mB6Y8ycOTOXRodLkZCcqvtHLbY94ooV8tO39zdXmSL0wQHyCzI24DF8vb30SLuqGvdgK1UtUVhRsYm6/5vFevKnFTaCDwAA/p85gX/6l5Vau/fEiaBpFh7gS3NFAED+ZubH58av0oFjiapcvJCe6VTD6SEByITJNn7kh2V2UWuQv49G3dfMLnoFkH8Q2IDHqVs2RL8/1Fp9Lq8ks+j0pyW71HHILM3dHOX00AAAyDO+nLVFE1bskY9XAX16dyOVZnUbAAD6ZeluTVq1z86PQ25vSNAfyKMByFcnrtWUNfvl5+Olr3o0UZ0yIU4PC0AuI7ABj2Q+fD7buabG9mmhcqGBtoHUXV8u0EsT1tiyGwAA5GezNh3Um3+ut9svXl9LzSsVc3pIAAA4bsvBWL3422q7/Wi7qnbRHIC85+vZWzVy7ja7/f5t9fksC+RTBDbg0ZpVDNWfj7TRXc3L2ftm4rv241latuOw00MDAMAROw7Fq/+YZUpzSV0bl1W3y8o7PSQAAByXlJKmR35YrvikVF1WKVR921ZxekgAMvHHyr167Y91dvu5zjV1Xb3STg8JgEMIbMDjFfL3sc1QR/ZsqpLB/tpyME63fDZX707ZYD+8AgCQX8QnpajPt4t19Hiy6kcU0atd6tAsHAAASe/+tUGrdh9VkUBfW4LK24v5EchrFm2L1mM/Lrfb97asoPvbVHR6SAAcRGAD+Ubb6iX016NX6MYGpe0q1U+mR6rL0Dlavy/G6aEBAJArtYif/Hml1u87prDC/vr8HpqFAwBgzNx4UF/8u8Vuv31LPYWHBDg9JABniDwQq/tHLbYLVDvULqkXrqvFAh0gnyOwgXwlJNBXH97R0DZJLRroq7V7Y3TDx3P06YxIpaSSvQEA8FzDZm6xqfu+3gU07J5GXLQBAEDSwWOJevzHFXbblGdsXzvc6SEBOEN0XJJ6jlxos44blitir+uQVQWAwAbypc51S2nKY5erXc0SSkpN09uTN+iWYfO0af8xp4cGAEC2m7HhgN6ecqJZ+KDra6tJhVCnhwQAgOPS0lx64qcViopNVPWSQXru2ppODwnAGUyGRt/vlmhn9HGVCw3UV92bkHUMwCKwgXyrRFCAvuzeRO92ra+gAB+t2HlE1340W5/N2Ez2BgDAY2yLitPD3y+TyyXd2SxCdzcv5/SQAADIE4bP2WrLUPn7eOmjOxtysRTIg6VUB01YrQVboxXk76OvezRRscL+Tg8LQB5BYAP5mqnHeGvjspr62BW6snpxm73x1uT1ZG8AADxCbOKJZuExCSlqVK6IXrqhNrWIAQCQtHr3UXvuZzx/XS1VDw9yekgAzjBy7jZ9v3CnTNWpj+5qqKol+XcK4P8R2AAkW2d8+L1Nyd4AAHjUCrcnflyhjftjVSLIX5/d01j+PqxEBQAgLjHFZjMmp7rUvlZJ3UM2I5Dn/LvxoF6duNZuP9u5pq6sXsLpIQHIYwhsACeRvQEA8CRDp0dq8pp9tlm4CWqUDKZZOAAAJvD/7LhV2hIVp/DgAL11Sz2yGYE8ZvPBWPUbs1RpLqlr47Lq1bqi00MCkAcR2ADOQPYGAMDd/bN+v96butFuv3pjHTUuX9TpIQEAkCeYsja/Ld8jb68C+viuhipayM/pIQE4o5Tq/75domMJKWpSvqheu6kOwUcAmSKwAWSC7A0AgLvacjBWj3y/3DYLN43C72hGeQ0AANL7arz0+xq7/VSH6mpaIdTpIQE4I6PqqZ9XKPJArEoGU0oVwPkR2ADOg+wNAIA7OZaQrD5mhVviiRVug66v7fSQAADIE2ISkm1pm6SUtP9r7z7AqizfP4B/2UuGCDIEBFyoICIqbi3NVabpz9wrV6am6b/ShmXjZ8us1FzlKmeZVmaaOVORKYoLxQUiQ0CZss//eh6CnxYoyHjP+H6u67045z3n2H3ezjnPOe/9PPeNXs3rY3JXT6VDIqJ/+Oava9gTWVxK9etR/rC3NFE6JCJSY0xsED0CV28QEZEmKCpSYc724hluomb416PbwNiQX/WIiIjELPB5O87gRko2GtiYyYlr+vosbUOkTgKvpOCjvRfl5befacFSqkT0SPy1S1RBXL1BRETqbOnBaOw/nwhjA32sHOOP+pZsFk5ERCRsOHG9dBb4spF+sDFnXw0idRKfdg8zNoejsEiFwX4NMKZDQ6VDIiINwMQGUSVw9QYREakjkdBY8mdxs3DRYLG1q43SIREREamFiNi7+HDPBXn5jf7N4efGWeBE6kSUh3tpUzhSsvLQ3MkKHz7nw2bhRFQhTGwQVePqjeWHorl6g4iIapUoPfXKtgh5eVzHhni+ravSIREREamFtOx8TN8UjvxCFfp5O2J8J3elQyKif/jsjyicirkLK1NDrBzdBmbGbBZORBXDxAZRNa7e+HRfFAavOIGoBK7eICKi2mmEOmVjKDJzC9DewxZvPdNC6ZCIiIjUpvfU3B9OI+7uPbjZmuPj/7TiLHAiNXMoKgmrj16Vlz8d6ouG9SyUDomINAgTG0TVtHpj8VBfOcPgzM00PLP0Lyw9cBn5XL1BVC2WL18Od3d3mJqaIiAgAMHBwQ+9/w8//AAvLy95fx8fH+zZs6fWYiWqzRM2r2yNwNXkLDhZm+LrUW1gZMCvdkRERMKKI1fw54Xi3lNijLQyNVI6JCK6T2J6DuZuP1266rhPS0elQyIiDcNfv0TVQMz8GeLvgj/ndEev5g5yqfPi/ZcwaPlxnL+VrnR4RBpt27ZtmDNnDt555x2Eh4fD19cXffr0QVJSUpn3P3HiBEaMGIGJEyfi1KlTGDRokNzOnj1b67ET1aQv/ryEAxeTYGyoj1Vj/GFXx0TpkIiIiNTCkUu3ZXkb4f1BLeHdwFrpkIjoPqJJ+OytEUjNykMLJyvM799c6ZCISAMxsUFUjepbmWLNWH98Obw1bMyNcO5WOp5ddgxL9l+SDbGIqPI+//xzTJ48GRMmTECLFi2wcuVKmJubY+3atWXe/8svv0Tfvn3x6quvonnz5nj//ffRpk0bLFu2rNZjJ6ope88m4KuD0fLyoud80MqFzcKJiIiE2NRsvLzlFFQqYER7Nwxr56Z0SET0D6I/aeDVFJgbG2DZSD+YGrGvBhFVHhMbRDWwemNg6wb445Vu6NvSEQVFKnx54LJMcJyNS1M6PCKNkpeXh7CwMPTq1at0n76+vrweGBhY5mPE/vvvL4gVHuXdX8jNzUV6evoDG5G6upyYgbnbi5uFT+jsLlcMEhEREXAvrxBTvgtD2r18+Lra4N1n2XuKSN0EX0uVK4+FDwZ5w9O+jtIhEZGGYmKDqIbUtzTFitFt5OwDWwtjXEzIwMDlx7H4jyjkFhQqHR6RRkhOTkZhYSEcHBwe2C+uJyQklPkYsb8y9xcWLVoEa2vr0s3V1bWangFR9RInaiZvDEVWXiE6etbDG1y2T0REJKlUKsz/6QwuxKejnoUxVo5uAxNDzgInUifpOfl4ZVsEilTA4DYNMLgNJ+gQ0eNjYoOohldvPNPKGftf6YanWznJOpJLD0ZjwNJjOHPzrtLhEdHf5s+fj7S0tNItNjZW6ZCI/kWMIbO2nsL1lGw0sDGTiXM2CyciIiq24cR17Iq4BQN9PSwb2QZO1mZKh0RE//DuL+cQd/ce3GzN8d5Ab6XDISINx1/DRLWgXh0TLB/ZBl+PaiNnD11KzMRzX5/Ax3svIiefqzeIymNnZwcDAwMkJiY+sF9cd3R0LPMxYn9l7i+YmJjAysrqgY1I3Xy+PwqHo27D1Ki4WbgYW4iIiKi4tM0Hv12Ql+f380LHRvWUDomI/mHv2Xj8FB4HfT3g8+d9UcfEUOmQiEjDMbFBVIv6+zhh/5zueNbXWc68XXH4Cp5ZegzhMXeUDo1ILRkbG8Pf3x8HDhwo3VdUVCSvd+zYsczHiP3331/Yv39/ufcn0gR7IuOx/NAVefnjIa3g3cBa6ZCIiIjUQmJ6Dl7aFC57Gw7wdcbELh5Kh0RE/5CUkYP5P0XKyy92b4S27rZKh0REWoCJDaJaJvptfDXCT862tatjguikTPxnxQn8d88Frt4gKsOcOXOwZs0abNiwARcuXMC0adOQlZWFCRMmyNvHjh0rS0mVmDVrFvbu3YvFixfj4sWLePfddxEaGooZM2Yo+CyIHt/FhHT83w+n5eXJXT0wsHUDpUMiIiJSC6J34bTvw5CcmQsvR0t8PMRHlgMmIvXqfzNvRyTuZOejhZMVZvdqqnRIRKQluO6LSCF9WjoiwMMWC389j52n4rD66FX8eT4Rnw5tBf+GnL1AVGLYsGG4ffs2FixYIBuAt27dWiYuShqEx8TEQF//f3n6Tp06YfPmzXjrrbfwxhtvoEmTJti1axe8vVnDlTTP3ew8TNkYhuy8QnRuXA+v9/VSOiQiIiK1OVn65s6zCI+5CytTQ6wc7Q9zY57iIFI3W0NicfBiEowN9LFkWGsYG3KONRFVDz2V+DagY9LT02FtbS0bxLKOOqmDAxcS8cbOSCSm50JMMHqhswf+r3czmBkbKB0akU5+furK8yT1JkoWjl8XjL8uJ8Olrhl+ndEFdS2MlQ6L6KF05fNTV54nkTr75q+rsq+GqNe/fkJ7dGtqr3RIVAG68vmpK8/zUWJSstH3y6Nyks6b/ZtjcjdPpUMiIi36/GSalEgN9GzugD9md8d//F0gUo3fHruGfl8elU3wiIhIN32y76JMaohm4avHtGVSg4iI6G+HopJkKV/hradbMKlBpIbEPOr5O8/IpEZ7D1v2vyGiasfEBpGasDY3wmdDfbFuQjs4Wpnieko2hq0OxLu/nEN2XoHS4RERUS369fQtrDpyVV7+9D++aOGsuzP9iGraRx99JGvyz549W+lQiKgCRI/ClzefQpEKGNbWFRM6uysdEhGVYXtoLI5Hp8hJOp8MaQV9sbyKiKgaMbFBpGaeaFYff8zphuHtXOXqjfUnrqPvF38h8EqK0qEREVEtOH8rHa/+WNwsfGp3TwzwdVY6JCKtFRISglWrVqFVq1ZKh0JEFZCWnY/JG0ORkVuAdu518f4gbzYLJ1JDiek5slScMPepZnC3s1A6JCLSQoonNo4ePYoBAwbA2dlZfiERDV4f5vDhw/J+/9xEQ1kibWFlaoSPhrTCxhfaw9naFDGp2Rix5iTe2hWJzFyu3iAi0lZ3svIw5btQ5OQXoWsTO7zWh83CiWpKZmYmRo0ahTVr1qBu3bpKh0NEj1BQWITpm8NxLTkLDWzMsGK0P5sQE6lpCao3d55FRk4BfF1t8AJLUBFRDVH8W0BWVhZ8fX2xfPnySj0uKioK8fHxpVv9+vVrLEYipYhasfte6YaRAW7y+vcnY9BnyVEcj05WOjQiIqqBEzYztoTj5p17cLM1x9IRfjDgkn2iGjN9+nQ8/fTT6NWr1yPvm5ubKxsZ3r8RUe0Ss7+PRSfD3NgAa8a2hV0dE6VDIqIy7D4Tjz8vJMLIQE+WoOL3WSKqKYZQWL9+/eRWWSKRYWNjUyMxEakTS1Mj/Pc5Hzzt44TXd5yRJ7xGfROEEe3d8EZ/L3k7ERFpvo/3XpR1iMUJm9Vj/WFjzmbhRDVl69atCA8Pl6WoKmLRokVYuHBhjcdFRGXbEhwjS/QKnz/fmr2niNTU3ew82SdUeKlHYzRztFQ6JCLSYoqv2HhcrVu3hpOTE5566ikcP35c6XCIalznxnbYN7sbxnZsWPrlXqzeOHLpttKhERFRFe06FYc1f12Tlz8b6gsvR56wIaopsbGxmDVrFjZt2gRTU9MKPWb+/PlIS0sr3cS/QUS149jlZLy966y8POeppujr7ah0SERUjo/3RiElKw9N6tfB9CcaKx0OEWk5jUtsiGTGypUrsWPHDrm5urqiR48ecsZVebh0nLSFhYkh3hvojS2TO8gyJbfScjBubTBe//EM0nPylQ6PiIgeQ+TNNLkiT5j+RCP093FSOiQirRYWFoakpCS0adMGhoaGcjty5Ai++uorebmwsPBfjzExMYGVldUDGxHVvEuJGZj2fRgKilR41tcZM5/kiVIidRUec0dOwBQ+fM6HPXCISPtLUVVWs2bN5FaiU6dOuHLlCpYsWYLvvvuuzMdw6Thpm46N6mHv7K74ZG8UNgRex7bQWLlyY9FgHzzhxX4zRESa4nZGrmwWnltQhCe96mPuU//7jkNENaNnz56IjIx8YN+ECRPg5eWF119/HQYGBorFRkQPjpET1oUgI7cA7dzr4pP/tIKeHmv1E6lrrzjRMFwY0sYF7T1slQ6JiHSAVqRP27dvj+jo6HJv59Jx0kbmxoZ499mW2D61IzzsLJCQnoMJ60Mwd/tppGVz9QYRkbrLKyjCS5vCEJ+WA097C3wxvDX02VyRqMZZWlrC29v7gc3CwgL16tWTl4lIeffyCjFpYyji7t6Dez1zrBrTFqZGTDpS5X344YdyQqy5uXmF+7SqVCosWLBAVgwxMzNDr169cPny5RqPVZNtDLyBC/HpsDYzkr1AiYhqg1YkNiIiIuSAUx4uHSdt1s7dFnte7opJXTwgJjDtCL+Jp5Ycwf7ziUqHRkRED/He7nMIuX4HliaGWDO2LaxMjZQOiYiISHFFRSq8si0Cp2PvwsbcCOsmtIethbHSYZGGysvLw9ChQzFt2rQKP+aTTz6R5QlFGfSgoCCZ/O7Tpw9ycnJqNFZNlZieg8/3X5KXX+/rhXp1TJQOiYh0hOKlqDIzMx9YbXHt2jWZqLC1tYWbm5tcbREXF4eNGzfK27/44gt4eHigZcuWclD55ptvcPDgQfzxxx8KPgsiZZkZG+CtZ1qgn48jXv3xDK7ezsLkjaEY1NoZ7wxoibr8IUBEpFZE/eHvT8bIhPSXI1qjkX0dpUMi0mmHDx9WOgQi+ttHey9i77kEGBvoY/WYtnJ1OtHjKilLvn79+gqv1hDnnd566y0MHDhQ7hPnoxwcHLBr1y4MHz68RuPVRO/vPo/M3AK0drXB8HauSodDRDpE8RUboaGh8PPzk5swZ84ceVks+xPi4+MRE1PcfKgk2z537lz4+Pige/fuOH36NP78809ZK5dI1/k3LF69MbW7J0Q1k10Rt/DUkqPYezZB6dCIiOhvoddTseDn4hrE/9e7GZ70clA6JCIiIrXw/ckbWH30qrz86dBWrNNPtU5Mtk1ISJDlp0pYW1sjICAAgYGB5T4uNzcX6enpD2y64OTVFOw+Ey/PP3wwyJtlVYlIt1Zs9OjRQ2bEy/PPrPprr70mNyIqm6g9O79fc/RtWbx6IzopEy9+H4ZnWjlh4bMtuSyUiEhB8Wn38OL34cgvVKG/jyNe6tFI6ZCIiIjUwuGoJLzzyzl5ec5TTTGwdQOlQyIdJJIaglihcT9xveS2sixatKh0dYiuKCxSydUawoj2bvBuYK10SESkYxRfsUFENcPPrS52z+wiT5oZ6OvJWRS9lxzFnsh4pUMjItJJOfmFePG7MCRn5sLL0RKf/scXeqIWFRERkY47c/MuXtoULk+UDmnjgplPNlY6JFJj8+bNk9+hHrZdvHixVmMSZdTT0tJKt9jYWGi7H8Nice5WOixNDWUykohI51ZsEFHNrt54ra8X+no74tUfziAqMUP+YBCzhN8b6A07rt4gIqoVYnXqGzsjcfpmmmyEKpqFW5jwaxgREdH15CxMWBeC7LxCdGlsh0WDfZj4p4cS5cnHjx//0Pt4eno+1r/t6Ogo/yYmJsLJyal0v7jeunXrch9nYmIiN12RkZOPT/dFycuzejZhZQgiUgR/URPpgFYuNvhlZmcsPxiNrw9fwZ7IBAReScHCgd4Y0MqJPxyIiGrY2uPX8VN4nFxBt3xkG7jamisdEhERkeJuZ+Ri3LpgpGTloaWzFVaO8YexIQtL0MPZ29vLrSZ4eHjI5MaBAwdKExmiX0ZQUBCmTZtWI/9NTbTsUDSSM/PgaWeBsR3dlQ6HiHQUvzEQ6QgTQwPM6d0Mu6Z3RnMnK9zJzsfLW05h6ndhSMrIUTo8IiKtdTw6Gf/dc0FefqN/c3RubKd0SERERIrLzC3AC+tDcCMlG662Zlg3oR3qcDUjVbOYmBhERETIv4WFhfKy2DIzM0vv4+XlhZ07d8rLYtLf7Nmz8cEHH+CXX35BZGQkxo4dC2dnZwwaNEjBZ6I+bqRkYd2x6/LyW880ZzKSiBTDbw1EOkY09Pp5emd8fTgayw5G44/ziQi6liobiw9s7czVG0RE1Sg2NRvTN/+vZvgLnTmjjYiIKK+gCNO+D0NkXBpsLYyx8YUA1Lc0VTos0kILFizAhg0bSq/7+fnJv4cOHUKPHj3k5aioKNkXo8Rrr72GrKwsTJkyBXfv3kWXLl2wd+9emJryNSos2nMReYVF6NbUHk80q690OESkw/RUouizjhHLCK2treXAZWVlpXQ4RIo5fysdr/54Wjb8Eno1r48Pn/OBgxW/sJFuf37qyvOkmpWdV4DBX5/AxYQM+LpYY9vUjrL3EZE205XPT115nkQ1oahIhTnbI7Ar4hbMjAywdUoH+LraKB0W1RJd+fzU1ucZHnNHfr/V1wP2zu6Gpg6WSodERDr8+cn1YkQ6rIWzlSxN9X+9m8LIQA9/XkjCU58fwY9hN2WjWyIiejziM/TVH87IpIZdHRNZM5xJDSIiIuDjvRdlUsNQXw8rRrdhUoNIg77ffvT7RXn5P/4uTGoQkeKY2CDScUYG+pjxZBPsntkVrVyskZ5TgP/74TQmrA9BfNo9pcMjItJIXx++gt8i42XSeNWYNnCyNlM6JCIiIsV989dVrDp6VV7+eEgr9GAZGyKNcTjqNoKvpcqeGrN7NVU6HCIiJjaIqFgzR0v8NK0TXuvbDMYG+vJLS+/Pj2JbSAxXbxARVcLBi4n47I8oefm9gd7wb2irdEhERESKE78rPvjtgrz8el8vDPF3UTokIqpECTmx2koY38kdzjactENEymNig4hKGRro46UejfHby13Q2tUGGbkFeH1HJMauDUbcXa7eICJ6lMuJGZi1JQIiHzwqwA0j2rspHRIREZHifjsTj/k/RcrLU7t54sXunkqHRESV8PPpOFli1dLUEC/1aKR0OEREEhMbRPQvTRwssWNaJ7zR30suM/3rcjL6LDmKTUE3uHqDiKgcd7LyMGljqEwKt/ewxTsDWiodEhERkeIORyVh9rZTKFIBI9q7Yl4/L+jp6SkdFhFVUG5BIRb/cUlentajEWzMjZUOiYhIYmKDiMpkoK+HKd0a4fdZXeHfsC4ycwvw5s6zGP1tEGJTs5UOj4hIreQXFuGlTeG4kZINl7pmWDnaXyaGiYiIdFnI9VS8+H0Y8gtVeKaVEz4Y5MOkBpGG2RIUg5t37sHBygQTOnkoHQ4RUSn+4iaih2pkXwfbp3bE28+0gKmRPo5Hp6DPF0fxXeB1WWeTiIiAhb+eQ+DVFFgYG+Dbce1ga8GZbEREpNvOxqXhhXUhyMkvwhPN7PH5863l5Cki0hw5+YX4+vAVeXnmk01gZmygdEhERKWY2CCiRxI/QCZ28cDeWd3Q3t0W2XmFePvncxj5zUncSMlSOjzSYqmpqRg1ahSsrKxgY2ODiRMnIjMz86H3nzlzJpo1awYzMzO4ubnh5ZdfRlpaWq3GTbpFJHq/PxkDMQH1y+F+aOZoqXRIREREirpyOxPj1gYXl2d0t8XXo7iSkUgTbQmOQVJGLhrYmOH5tq5Kh0NE9AB+syCiCnO3s8DWKR3w7oAWMDMywMmrqej7xV9Yd/waV29QjRBJjXPnzmH//v3YvXs3jh49iilTppR7/1u3bsnts88+w9mzZ7F+/Xrs3btXJkSIasKJ6GS8++t5efm1Pl7o1cJB6ZCIiIgUdfNONsZ8E4SUrDx4N7DCN+PbcpY3kYau1ljx92qNl55oxOQkEakdQ6UDICLNoq+vh/GdPfCklwNe23FaJjcW/noev0cm4LOhvnCrZ650iKQlLly4IJMSISEhaNu2rdy3dOlS9O/fXyYunJ2d//UYb29v7Nixo/R6o0aN8OGHH2L06NEoKCiAoSGHPao+15OzMG1TOAqLVHjOrwFe7O6pdEhERESKik+7h5FrgnArLQeN7C2wYUJ7WJkaKR0WET2GrX+v1nC2NsVQf67WICL1w3QrET0WkcDYPKkD3h/kDXNjAwRfT0W/L49iW0gMVCqu3qCqCwwMlOWnSpIaQq9evaCvr4+goKAK/zuiDJUoZcWkBlWn9Jx8TNwQgrR7+WjtaoNFg9kMlYiIdFtieo5MasSkZsPN1hzfTwpAvTomSodFRI+7WuNIyWqNxlytQURqiZ9MRFSl1RtjOjTEvtnFvTey8grx+o5ITN4YhuTMXKXDIw2XkJCA+vXrP7BPJCdsbW3lbRWRnJyM999//6Hlq4Tc3Fykp6c/sBGVR6zQmLn5FK7czoKTtSlWj/WHqRFLbBARke66nZGLkWtO4lpyFlzqmmHLlA5wsjZTOiwiekzbQmKRmP73ao22LkqHQ0RUJiY2iKjKXG3N5Y+Xef28YGSghz8vJKLPkqPYfz5R6dBIDc2bN0/ObH/YdvHixSr/d0Ry4umnn0aLFi3w7rvvPvS+ixYtgrW1denm6sql1lS+RXsu4Mil2zA10seasW1R39JU6ZCIiIgUk5KZi1HfnJQJf3ESdMvkDrLRMBFp7mqNrw9Hy8vTnmgME0NO4CEi9cS6HERULQz09fBi90bo1sQer2yLQFRiBiZvDMWwtq54e0AL1DHhxw0Vmzt3LsaPH//Q+3h6esLR0RFJSUkP7Bd9MlJTU+VtD5ORkYG+ffvC0tISO3fuhJHRw2s7z58/H3PmzHkgKcLkBpVle0gsvjl2TV5ePLQ1vBtYKx0SERGRYu5k5WHUN0G4lJgJBysTbJ7cQU56IiLN9UPYTblaQ6xMfp6rNYhIjfFMIxFVqxbOVvh5Rmd8vv8S1vx1FdtCY3HiajKWPN8abd1tlQ6P1IC9vb3cHqVjx464e/cuwsLC4O/vL/cdPHgQRUVFCAgIKPdxIinRp08fmJiY4JdffoGp6aNn04v7io3oYUKup+LNXZHy8uxeTfB0KyelQyIiIlJMWnY+xqwNwsWEDNhbFic13O0slA6LiKqgoLAIa45elZendvPkag0iUmssRUVE1U7Umn+jf/PSZeixqffw/KpAfLz3IvIKipQOjzRE8+bN5aqLyZMnIzg4GMePH8eMGTMwfPhwODs7y/vExcXBy8tL3l6S1OjduzeysrLw7bffyuuiH4fYCgsLFX5GpMlu3snGi9+FIb9Qhf4+jnj5ySZKh0RERKSY9Jx8jF0XjLNx6ahnYYzNkwLQyL6O0mERURX9fjYBManZqGtuhOfbcQU7Eak3JjaIqMZ08KyH32d3xZA2LihSASsOX8Gg5ccRlZChdGikITZt2iQTFz179kT//v3RpUsXrF69uvT2/Px8REVFITs7W14PDw9HUFAQIiMj0bhxYzg5OZVusbGxCj4T0mQZOfmYuD4UKVl5aOlshc+G+kJfX0/psIiIiJRbqfFNEE7H3pUnPzdNDkATB0ulwyKiKlKpVFh19Iq8PLajO8yNWeSFiNQbP6WIqEZZmRph8fO+eKpFfcz/KRLn49MxYNkxvNanGV7o7MGTg/RQtra22Lx5c7m3u7u7yy/gJXr06PHAdaLqWI4/Y/Mp2TeovqWJbBbOH3lERKTLPTVE+SmxUkMkNb6fFAAvRyulwyKianA8OkW+t02N9DGuk7vS4RARPRJXbBBRrejr7YR9r3TDE83sZTmqD367IH8UJabnKB0aEVGZRJJs4a/nceTSbZgZGeDbce3gbGOmdFhERESKSMnMxYg1J0vLT22Z0gEtna2VDouIqsnKI8WrNYa3c4OthbHS4RARPRITG0RUa+pbmmLt+Hb48DlveZJQzAjp+8VR/HEuQenQiIj+Zf2J6/ju5A3o6QFLhrWGjwtP3hARkW66nVGc1BCNwu3qmGDrlA5cqUGkRc7GpeFYdDIM9PUwsYuH0uEQEVUIExtEVKv09PQwKqAhfp3ZRdaqv5OdjynfheHNnZG4l8fmzkSkHg5cSMT7u8/Ly/P6eqGvt6PSIRERESkiKT0Hw1cH4lJiJhysTLBtagf21CDSMt8euyb/Pu3jBFdbc6XDISKqECY2iEgRjevXwU8vdcKUbp7y+qagGNl749ytNKVDIyIdd/5WOmZuOYUilViK71r6OUVERKRr4tPuYdjqk7hyOwvO1qbYNqUjGtnXUTosIqrm5OXuM7fk5UlduVqDiDQHExtEpBgTQwO80b85vpvYXjbljU7KxHPLT+Cbv66iSJxRJCKqZaLvz8QNIcjOK0SnRvXw/iBvudKMiIhI19y8k43hq0/iWnIWGtiYYdvUjnC3s1A6LCKqZt8HxSC/UAX/hnXRysVG6XCIiCqMiQ0iUlzXJvbYO7sbejV3QF5hcWPx8etDkJTBxuJEVHuy8wowaUMo4tNy0MjeAitG+cPIgF+ViIhI94gJR0NXBuJGSjbcbM1l+SmWpyHSPrkFhdgcdENentDZXelwiIgqhb/WiUgt2FoYY81Yf3wwyBsmhvo4euk2+n3xFw5eTFQ6NCLSAWKV2OytEYiMS5OfR2vHt4O1uZHSYRERESnSRPj5VYEy0d+kfh1sn9oRLnWZ1CDSRr+ejkdyZh6crE3RpyV7yhGRZmFig4jUhij3MrpDQ+ye2QXNnayQkpWHF9aH4p2fzyInn43FiajmfLzvIv44nwhjA32sHuOPhvVYaoOIiHRP8LVUjFh9EqlZeWjlYi3LTzlamyodFhHVAJVKhXXHi5uGj+nYkCuViUjj8FOLiNROEwdL7JreCRO7FDcu2xB4A4O/PoGrtzOVDo2ItNDW4BisOnJVXv7kP63Q1t1W6ZCIiIhq3eGoJIxdG4SM3AK097DFpkkBchUjEWmnkOt3cO5WOkyN9DGinZvS4RARVRoTG0Skto3F336mBdZNaCd/UJ2PT8eApcfwc0Sc0qERkZadxHlz11l5eVbPJhjk10DpkIiIiGrdb2fiMXljKHLyi/BEM3tsfKE9LE1ZkpFIm20MvC7/DmrdAHWZxCQiDcTEBhGptSea1cfvs7oiwMMWWXmFmLU1Aq//eAb38liaioiqXkN8+qZwFBapMNivAWb3aqJ0SERERLVue0gsZm4JR36hCs+0csKqMW1hamSgdFhEVIOSM3Ox71yCvCzKQRMRaSImNohI7TlYmWLz5A5yNrWeHrAtNBYDlx/DpcQMpUMjIg118042JqwPkQnTzo3r4aMhrWSfHyLSDStWrECrVq1gZWUlt44dO+L3339XOiyiWq+v//XhaLy24wyKVMCI9m74crgfjA15moBI2/0YdlMmM31drOHdwFrpcIiIHgu/sRCRRjDQ18MrTzXFpokBsLc0waXETDy77JicYSZ+lBERVVRadj7GrwvB7YxceDlaYsVof57EIdIxLi4u+OijjxAWFobQ0FA8+eSTGDhwIM6dO6d0aES1QqxWfPeXc/hkb5S8/mL3Rvjvc97yOzcRabeiIhW2BMfIyyMD2FuDiDQXf8UTkUbp1NhOlqbq2sRO1gAWM8xe2RaBzNwCpUMjIg2QW1CIKd+FIjopE45WprKPjxVriBPpnAEDBqB///5o0qQJmjZtig8//BB16tTByZMnlQ6NqMbl5BfK0lMbAm/I1dALnmmBef28uHKRSEecuJKCGynZsDQxxABfZ6XDISJ6bExsEJHGsatjgg0T2uO1vs3krLJdEbdkY/EL8elKh0ZEaj477f9+OIOga6nyh5xIajhZmykdFhEprLCwEFu3bkVWVpYsSVWW3NxcpKenP7ARaaK0e/kYtzYYeyITYGygj6Uj/PBCFw+lwyKiWrQ5+Ib8+1ybBjA3NlQ6HCIizU1sHD16VM6YcnZ2ljNEdu3a9cjHHD58GG3atIGJiQkaN26M9evX10qsRKQ+9PX18FKPxtg2pQOcrE1xLTkLz319HDvCbiodGhGpqY/3XcSvp2/BUF8PK8f4o7mTldIhEZGCIiMj5SoN8ZvixRdfxM6dO9GiRYsy77to0SJYW1uXbq6urrUeL1FVJaTl4PmVgaUJ/vUvtMMzrThbm0iXJGXk4I9zifIyy1ARkaZTPLEhZkb5+vpi+fLlFbr/tWvX8PTTT+OJJ55AREQEZs+ejUmTJmHfvn01HisRqZ+27rbY83JXdG9qL0tTzf3hNN7YGSmX2BMRldgYeB2rjlyVlz8e0gqdG9spHRIRKaxZs2by90RQUBCmTZuGcePG4fz582Xed/78+UhLSyvdYmNjaz1eoqq4nJiBwV8fR1RiBupbmmDb1I7o1IhjIZGu+SH0JgqKVGjjZgMvR07yISLNpvias379+smtolauXAkPDw8sXrxYXm/evDmOHTuGJUuWoE+fPjUYKRGpq7oWxlg3vh2WHozGFwcuYXNQDM7GpWH5yDZwtTVXOjwiUtj+84myQaow96mmGOLvonRIRKQGjI2N5epvwd/fHyEhIfjyyy+xatWqf91XrOoQG5EmCr2eiokbQmUZKk97C2x8oT1c6vI7MpGuUalU+PHvCgfD23O1BhFpPsVXbFRWYGAgevXq9cA+kdAQ+4lIt0tTzerVBOsntIeNuRHO3EzDgGXHcCgqSenQiEhBp2LuyAapRSpgeDtXzHiy+CQmEdE/FRUVyV4aRNrk54g4jPwmSCY1/NxssOPFTkxqEOmo8Ji7soSzmZEB+vs4KR0OEZHuJTYSEhLg4ODwwD5xXTTwu3fvXpmPYbM/It0hSlLtntkFvi7WuJudjxfWh+Dz/ZdQKM5qEpFOET/cJm0IlWXqejSzxweDvGU/LyIiUVpK9Pq7fv267LUhros+fqNGjVI6NKJqm5m99MBlzNoagbyCIvRu4YDNkzrIlc5EpJt2hBev1ujn44g6JooXcCEi0r3ExuNgsz8i3SJmoW1/sSNGd3CDSgV8deAyxq8LRmpWntKhEVEtSUzPwZhvg5CSlQfvBlayNJ2hgU587SGiCkhKSsLYsWNln42ePXvKMlSiZ99TTz2ldGhEVZZbUCj7zi3ef0len9LNEytH+8PM2EDp0IhIIaIH5a+nb8nL/2nDsqxEpB007he+o6MjEhMTH9gnrltZWcHMzKzMx7DZH5HuMTE0wAeDfLBkmC9MjfTx1+VkPPPVXzgde1fp0IioholyG+PWBuPmnXtwr2cuS9RZcFYaEd3n22+/las1xMpukeT4888/mdQgrXAnKw9jvg3GT+FxMNDXw4fPeeON/s1l2VYiXfThhx+iU6dOMDc3h42NTYUeM378eLnK9/6tb9++0PSecxk5BWhgY4YOnvWUDoeISDcTGx07dsSBAwce2Ld//365vzyi0Z9IfNy/EZFueM7PBbumd4aHnQVupeVg6KpA7Pi7YRoRaedstMkbQnExIQP2lib4bmIA7Oqw4S8REelGCcbBK04g+FqqLDOzdnw7jApoqHRYRIrKy8vD0KFDMW3atEo9TiQy4uPjS7ctW7ZAG8pQPefXgIlOItIaik9fzMzMRHR0dOn1a9euISIiAra2tnBzc5OrLeLi4rBx40Z5+4svvohly5bhtddewwsvvICDBw9i+/bt+O233xR8FkSkzrwcrfDLjM54ZVsE/ryQJJfmn7uVjjf6e7E0DZEWKSgswstbTiH4eiosTQyxYUJ7uNqyQSoREWm/oKspmPp9mOwxJ2Zki6RGM0dLpcMiUtzChQvl3/Xr11fqcWKCrKgYoi0lWo9eui0vD/FnGSoi0h6Kn9ELDQ2Fn5+f3IQ5c+bIywsWLJDXRWY8Jiam9P4eHh4yiSFWafj6+mLx4sX45ptv0KdPH8WeAxGpP0tTI6we0xYvP9lYXl97/BrGrg2Wy/WJSPOJJqlv7TqLP84nwthQH2vGtUULZ67QJCIi7fdDaCxGfxskkxq+LtbYOb0TkxpEVXT48GHUr19f9mISqz1SUlIeen9R2jA9Pf2BTV3sOhWHIhXg37CurGRARKQtFF+x0aNHD3kyojxlZdXFY06dOlXDkRGRthFLbuf0biZPds7ZfhonrqRgwLJjWDO2LZo78QQokSb7fP8lbA2JhVhZ/9VwP9YOJiIinVip+OGeC1h3/Lq83s/bEZ8/35pNwomqSJShGjx4sJxYe+XKFbzxxhvo168fAgMDYWBQ9vtr0aJFpatD1M3PEcVNwwe3aaB0KERE2rVig4iotvX1dsLOlzrDzdZcNhce/PUJ/HYmXumwiOgxrT9+DUsPFpe1/GCQD/p6a0fZACIiovLczc7D+HUhpUmNl3s2wfKRbZjUIJ0wb968fzX3/ud28eLFx/73hw8fjmeffRY+Pj4YNGgQdu/ejZCQELmKozyijHpaWlrpFhsbC3Vw5XYmzsenw1BfD/29nZQOh4hIu1ZsEBEpQSzPF303Zm45hb8uJ2P65nCcj2+EuU81YzM1Ig3yy+lbWLj7vLw856mmGBngpnRIRERENepSYgYmbwzFjZRsmBkZ4PPnfdHPhycsSXfMnTsX48ePf+h9PD09q+2/J/4tOzs72R+2Z8+e5fbkEJu62X26eAJflyZ2qGthrHQ4RETViis2iEhn2ZgbY934dpjc1UNeX37oCqZtCkN2XoHSodHfUlNTMWrUKFhZWcHGxgYTJ05EZmZmhR4ryhyKJeNixtauXbtqPFaqfQcvJmLOtgiIipZjOzbEzL976BAREWmr/ecT8dzy4zKp4VLXDD+91IlJDdI59vb28PLyeuhmbFx9J/Fv3rwpe2w4OWnWe038Hvr1THEZqmdaOSsdDhFRtWNig4h0mqGBPt58uoWc6WZsoI995xIxbNVJJKbnKB0aATKpce7cOezfv18uAT969CimTJlSocd+8cUXMqlB2unk1RRM+z4cBUUqDGztjHcGtOT/byIi0lriBOWyg5cx5btQZOUVooOnLX6Z0YV94ogeISYmBhEREfJvYWGhvCy2+ydLiUTIzp075WWx/9VXX8XJkydx/fp1HDhwAAMHDkTjxo3Rp08faJKoxAxEJ2XK37m9WzooHQ4RUbVjKSoiItlIzQWutuaY+l0YIuPSMHDZcXwzri28G1grHZrOunDhAvbu3Svr2bZt21buW7p0Kfr374/PPvsMzs7lzzoSP1YWL16M0NBQjZtZRY92OvYuJq4PQW5BEXo1d8BnQ31hwBJyRESkpbJyC/Daj2fwW2RxSRmxSvHtZ1rAyIDzFIkeZcGCBdiwYUPpdT8/P/n30KFD6NGjh7wcFRUl+2IIojn4mTNn5GPu3r0rf3P07t0b77//vlqWmnqYX08Xr9bo3sweVqZGSodDRFTtmNggIvpbO3db7HqpMyasD8aV21l4flUgvhzuh6dacHaLEgIDA2X5qZKkhtCrVy/o6+sjKCgIzz33XJmPy87OxsiRI7F8+XI4OlasiXRubq7cSqSnp1fDM6CaEJWQgXHrguVs1U6N6mHZSD+e2CEiIq0lZlu/+H2Y/GtkoIf3BnpjRHv2kyKqqPXr18vtUSuiSpiZmWHfvn3QdOI57T5TnAwd4MsyVESknXgmgIjoPm71zPHTS53RpbEdsvMK5XL/NUevPvBll2pHQkIC6tev/8A+Q0ND2NraytvK88orr6BTp05yyXhFLVq0CNbW1qWbq6trlWKnmnEjJQujvw3C3ex8tHa1wZqxbWFqZKB0WERERDViT2Q8Bi47JpMaDlYm2DK5A5MaRFQhogqB6MVjaqSPnl4P/qYiItIWTGwQEf2DtZkR1k1oh5EBbrIp8Yd7LuCNnWeRX1ikdGhaYd68ebIXwsO2ixcvPta//csvv+DgwYOyv0ZlzJ8/Xy4/L9liY2Mf679PNSc+7R5GfROE2xm58HK0xPoJ7WBhwoWnRESkfcR3zg9/O4+XNoWX9tPYPbMr2rrbKh0aEWmIktUaPZs78DszEWktfroREZVBlLb5cJA3GtnXwQe/nceW4BjEpmZjxeg2sGR90iqZO3cuxo8f/9D7eHp6yjJSSUlJD+wvKChAampquSWmRFLjypUrsoTV/YYMGYKuXbvi8OHDZT5O1MvVtJq5uiQlMxejvwnCzTv34F7PHBsntoeNubHSYREREVW7pIwczNh0CsHXU+X1qd088WqfZjBk2UUiqiBRbWDv2eIV7s/4sN8gEWkvJjaIiMohVg5M7OIhT6TO3HIKx6KTMWzVSax/oR3qW5oqHZ7Gsre3l9ujdOzYUTbsCwsLg7+/f2nioqioCAEBAeWuBpk0adID+3x8fLBkyRIMGDCgmp4B1aa0e/kYu7a4742ztSm+nxTA9x8REWmlkOupcpWGWJ1Yx8QQnw1thb7ePClJRJUTlZiBmNRsmBjqy8bhRETaitM+iIgeQSzf3TalI+zqGON8fDoGf30CV25nKh2W1mvevDn69u2LyZMnIzg4GMePH8eMGTMwfPhwODsXN8CLi4uDl5eXvF0QKzm8vb0f2AQ3Nzd4eHgo+nyo8jJy8jF+XTDO3UpHPQtjfDcpAC51zZUOi4iIqNpnV3/z11WMWH1SJjWaOtTBLzM6M6lBRI/lj3OJ8m/XJnYwN+Z8ZiLSXkxsEBFVgI+LNXZM6yRXb4hyOP9ZcQLhMXeUDkvrbdq0SSYuevbsif79+6NLly5YvXp16e35+fmIiopCdna2onFS9cvKLcAL60NwKuYubMyN8N3EAFkajoiISJvcycrDpA2h+OC3CygoUuFZX2fsmt4ZnhzziOgx/XG+uAxV7xZll+8lItIWTN0SEVVQw3oW+HFaJ0xcH4LTN9Mwcs1JLBvRBr1aOCgdmtaytbXF5s2by73d3d1dznJ8mEfdTurnXl4hJm4IQcj1O7AyNcT3EwPQwtlK6bCIiIiqvfTUy1tOIT4tB8aG+nj7mRYYHeAmy6ESET2OW3fv4WxcOvT1ROWB+kqHQ0RUo7hig4ioEuzqmGDz5A7o0cweOflFmPJdqGwsTkTVIye/EJM3huLk1VRZX3zjxAB4N7BWOiwiIqJqU1SkwvJD0Ri++qRManjaWWDXS50xpkNDJjWIqEr2ny8uQ+XfsC7q1TFROhwiohrFxAYRUSVZmBhizdi2GOrvgiIVMP+nSKw6ckXpsIg0Xm5BIaZ+F4Zj0ckwNzbAhhfaobWrjdJhERERVRvRQ2PcumB8ui8KhUUqPOfXAL/O7MKViURULViGioh0CUtRERE9BiMDfXzyn1awszTBisNXsOj3i8jMLcCcp5pyph3RY8grKML0TeE4cuk2TI30sW58O/g3tFU6LCIiompz7HIyZm+LQHJmLsyMDPDewJb4j78LvzsSUbVIy86Xq56Fp1gumYh0ABMbRESPSfwIfb2vFyxNDfHJ3igsPRiNjJwCLHimBfRFUVMiqpD8wiLM3BKOPy8kwcRQH9+Oa4cAz3pKh0VERFRtyfvP91/CqqNXIFp/NXOwxLKRfmjiYKl0aESkRQ5GJcqVYOIzxt3OQulwiIhqHBMbRERV9FKPxrA0McTbP5/D+hPXkZVbgI+GtIIBkxtEj1RQWIRXtkVg37lEGBvoY/XYtujc2E7psIiIiKpFdFIGZm2NwLlb6fL6iPaueGdAS5gaGSgdGhFpaX+N3i25WoOIdAMTG0RE1WBMR3eYGxvi1R9P44ewm8jKK8AXw/xgbMhWRkQPW6kxa+sp7IlMgJGBHlaMboPuTe2VDouIiKjKVCoVvj95Ax/uuYCc/CLYmBvho8E+6OvtpHRoRKSl36v/upQsL/dszsQGEekGJjaIiKrJEH8XWJgYYOaW4hO1WbmhWDXGnzPyiMopyyHKT4mVGiKp8fUof/4IIyIirWkQ/vqOMzh4MUle79rEDp8N9YWDlanSoRGRlgq7cQcZuQWoZ2GMVg2slQ6HiKhWcCoxEVE1ErPwvhnXTjY/Fk2QJ28MRU5+odJhEamV3IJCvLQprLj8lKE+Vo9pywaHRESkFQ5cSETfL47KpIYY40TvtQ0T2jOpQUQ16nDUbfm3W1N79nskIp3BxAYRUTUTpXTED1gzIwP8dTmZyQ2i+4j3wovfhclG4eKEz5qxbfGEV32lwyIiIqoS0WPtzZ2RmLghFClZefBytMQvMzrjhS4ePMlIRDXucFTxCrEezVjWlYh0BxMbREQ1IMCzHtZPaMfkBtF9xHtgyndhOBR1GyaG+lg7rh17ahARkcYLupqCfl/+hU1BMfL6xC4e2DW9M7wcrZQOjYh0QEJaDi4mZEBPT5S+43drItIdTGwQEdVwcsPcmMkNont5hZi0IRRHL92WCb91E9qhSxM7pcMiIiKq0ti28NdzGL7mJGJSs9HAxgybJgXg7WdasMcaEdWaI5eKV2v4utjA1sJY6XCIiGoNExtERDWc3Fg3/n/JDTFbXfQXINIl2XkFeGF9CI5FJ8v3gkj4dWrEpAYREWmusBup6P/VX1h3/DpUKmB4O1fsnd0VnRtzfCMiZfprsAwVEekaJjaIiGpl5UZxzw0xW33WlggUFBYpHRZRrUjPyce4tcEIvJoCC2MDbHyhvXxPEBERaSKx+nbRngsYujIQ15Kz4GhlKhP2Hw1pBUtTI6XDIyIdI35XHrucLC/3aMa+dUSkW5jYICKqBe09bGWTZGMDfew9l4DXdpxBUZFK6bCIalRKZi5GrjmJkOt3YGlqiI0TA9DW3VbpsIiIiB5LROxdDFh6DKuOXoX4GjekjQv2vdKNJxOJSDGnb6YhI7cANuZG8GlgrXQ4RES1yrB2/3NERLpL9BNYNtIP0zaF46fwONQxMcTCZ1tCT3R5I9LCJoajvw1CdFKmrPUrVmp488cWERFpoKzcAiz+4xLWn7gmExr2lib473M+eKqFg9KhEZGOOxFdvFqjo2c9GOjzdyUR6Rau2CAiqkW9Wzpi8VBfiFzGxsAb+HRflNIhEVW7mJRsDF11QiY1RImO7VM7MqlBREQa6cil2+i95CjWHi9Oajzn1wB/zO7GpAYRqYXjV4oTG+zvQ0S6iCs2iIhq2SC/BsjKK8CbO8/i68NX5Gz2SV09lQ6LqFpcTszAqG+CkJSRi4b1zPH9xAC42porHRYREVGlpGbl4YPd5/HTqTh5vYGNGf472Afdm7I5LxGph3t5hQi/cVdeZmKDiHQRExtERAoYFdAQGTkF+Oj3i/jgtwtwtDbFM62clQ6LqEoib6Zh7Nog3MnOR1OHOjKpUd/KVOmwiIiIKkylUuGX07ew8NfzMrkhVtlO6OSBub2bwsKEP5+JSH2EXE9FXmERnK1N4V6PE4mISPfwmxkRkUKmdvOUfQjWn7iOOdtOw76OCQI86ykdFtFjCb6WionrQ2TzQl8Xa6yf0B51LYyVDouIiKjCbt7Jxlu7zuJw1G15vZmDJT4a4gM/t7pKh0ZEVG4Zqk6N7di3kYh0EhMbREQKEV8+336mBeLT7mHfuURM3hiKHdM6oYmDpdKhEVXKoYtJmLYpDDn5RQjwsMU349rC0tRI6bCIiIgqJL+wCGuPXcOXBy4jO68Qxgb6eLlnY0zp1gjGhmxLSUTq6UR0ivzbuTEnxxGRbuK3NCIiBRno6+HL4X5o42aD9JwCjF8XgsT0HKXDIqqw7aGxmLQxVCY1nmhmjw0vtGdSg4jU3qJFi9CuXTtYWlqifv36GDRoEKKiopQOixRw8moK+n/5Fxb9flEmNdq518WeWV0x48kmTGoQkdq6m52Hs7fS5OVOjdhfg4h0E7+pEREpzNTIAN+OawdPOwvE3b2HCetCkJ1XoHRYRI+sQf714Wi89uMZFBapMLhNA6we21a+nomI1N2RI0cwffp0nDx5Evv370d+fj569+6NrKwspUOjWnI7IxdztkVg+OqTuJyUCVsLY3z6n1bYNqUjGtevo3R4REQPdfJqKlQqyM8rB/a0IyIdxVJURERqQPQiEDPdn/v6OM7Hp+PVH85g2Ug/1koltVRUpMJ7u8/L/jDC1O6emNfXi69XItIYe/fufeD6+vXr5cqNsLAwdOvWTbG4qOaJZPymoBv4dF8UMnIKZHPwke3d8GqfZrAxZ28oItKc/nZCB09bpUMhIlIMExtERGrC1dYcK0b7Y+Sak/gtMh5eBy0xs2cTpcMiekBuQSHmbD+N387Ey+uiT8zELh5Kh0VEVCVpacXlPGxtyz5BlJubK7cS6enptRYbVZ+I2Lt4a1ckzsYV///zaWCNDwZ5w9fVRunQiIgqJfh6cX+N9h7sr0FEuoulqIiI1Eg7d1u8N9BbXl68/xL+OJegdEhEpTJy8mWpNJHUMDIQ/WFaM6lBRBqvqKgIs2fPRufOneHtXTwGl9WTw9raunRzdXWt9TipamWnXv/xjFwZK5IalqaGeH9gS+ya3plJDSLSyO/k528VJ2jbu3PFBhHpLiY2iIjUzIj2bhjbsaG8/Mq2CEQlZCgdEhGSMnIwbNVJnLiSAgtjA6wd3w4DWzdQOiwioioTvTbOnj2LrVu3lnuf+fPny1UdJVtsbGytxkiPJ6+gCKuPXsETnx3GttBYWY9+sF8DHJzbA2M6usNAnyUUiUjzhMfcRZFKrPg3g6M1+2sQke5iKSoiIjUkyvtcTsxE4NUUTN4Yip+nd5Z9OIiUEJ2UgQnrQxCbeg92dYyxbnx7+LhYKx0WEVGVzZgxA7t378bRo0fh4uJS7v1MTEzkRppBpVLh4MUkfPDbBVxLLm4I38rFGu8MaAH/hpzdTESaLfja32Wo3FmGioh0m1qs2Fi+fDnc3d1hamqKgIAABAcHl3tf0dhPNCe9fxOPIyLSJkYG+lg+qg1c6pohJjUbr2yPkA2bdU1qaipGjRoFKysr2NjYYOLEicjMzHzk4wIDA/Hkk0/CwsJCPlY0gr13716txKxtTlxJxuCvT8ikRsN65tgxrROTGkSkFSe+RVJj586dOHjwIDw8WFZPW1xOzMDYtcGYuCFUJjXs6pjg0/+0wq6XOjOpQURaIeTaHfm3vUddpUMhItLtxMa2bdswZ84cvPPOOwgPD4evry/69OmDpKSkch8jTlLFx8eXbjdu3KjVmImIaoOthTHWjG0LE0N9HI66jVVHr0LXiKTGuXPnsH///tIZtVOmTHlkUqNv377o3bu3TJSHhITIk1f6+ooPeRrnx7CbGLc2GOk5BfBvWBc/TeuEhvUslA6LiKhayk99//332Lx5MywtLZGQkCA3JsE1V1p2Pt795Rz6fvkX/rqcDGMDfbzYvREO/V93DG3rCn2WnSIiLZBbUIiIm3dL+zMSEekyxUtRff7555g8eTImTJggr69cuRK//fYb1q5di3nz5pX5GLFKw9HRsZYjJSKqfc2drLDw2ZaY91MkPvsjCm3d6+rMF9gLFy5g7969MjHRtm1buW/p0qXo378/PvvsMzg7O5f5uFdeeQUvv/zyA2NIs2bNai1ubZnJvOTPy/jqwGV5/elWTlg81BemRgZKh0ZEVC1WrFgh//bo0eOB/evWrcP48eMViooe9yTfd4E3sPRgNNLu5ct9T7VwwJv9m8Pdjsl4ItIuZ26myf5BojysBz/jiEjHKTp9NS8vD2FhYejVq9f/AtLXl9fFjNvyiDIkDRs2hKurKwYOHChn8z5Mbm4u0tPTH9iIiDTFsHaueM6vAQqLVJi5+RRSMnOhC8Q4IMpPlSQ1BDE+iHEiKCiozMeI1X7itvr166NTp05wcHBA9+7dcezYsVqMXPNPEImm9SVJjZd6NMLS4X5MahCR1iVwy9qY1NAcokTnzxFx6Ln4iOylIZIaTR3q4LuJ7eWKVyY1iEgbBV9LlX/be9jKSb9ERLpM0cRGcnIyCgsL5Ymn+4nrYil4WcSsW7Ga4+eff5bLx4uKiuTJq5s3b5b731m0aBGsra1LN5EQISLSFOIL6weDvNHI3gIJ6Tl47ccz8uSLthPjgEhQ3M/Q0BC2trbljhFXrxaX63r33XflakCx4qNNmzbo2bMnLl8uPlFfFibAi93JysOYb4KxK+IWDPT18NFgH7zW14vlO4iISO36Pw1cfhyztkbg5p17qG9pgo+H+GDPy13RtYm90uEREdWY0OvFiY227BlERKR8j43K6tixI8aOHYvWrVvLWbg//fQT7O3tsWrVqnIfM3/+fKSlpZVusbGxtRozEVFVWZgYymbiol70gYtJ2BKsuZ9jokSUSNY8bLt48eJj/dsi2S1MnTpVljj08/PDkiVLSpPi5WECHLienIXBK04g+HoqLE0MsX5COwxv76Z0WERERKWiEjLwwvoQjFwThMi4NNQxMcT/9W6Kw6/2wLB2bjA00Lift0RUw65fv46JEyfCw8MDZmZmaNSokezxKiqIPExOTo7sx1SvXj3UqVMHQ4YMQWJiIpQkJrdFxBb312jTkI3DiYgU7bFhZ2cHAwODfw0O4npFe2gYGRnJE1fR0dHl3sfExERuRESazMvRCq/1bSbLLby/+zw6NaqnkWUW5s6d+8hSH56ennIcEKWl7ldQUIDU1NRyxwgnJyf5t0WLFg/sb968OWJiYh6aAJ8zZ07pdbFiQ5eSG8ejk/HSpnBZxqOBjRnWjm+HZo6WSodFREQk3bp7D1/+eRk/hMWiSAUY6uthZIAbXu7ZBHZ1+DuPiMonJkyJyU9iMmzjxo1x9uxZubI7KytL9u0rj+jbJ/q//vDDD3Li04wZMzB48GAcP34cSolJzcad7Hw52a25E7+rExEpmtgwNjaGv78/Dhw4gEGDBsl9YsAR18WgURGilFVkZKRsJktEpO1e6OyBAxeSEHg1Ba9sj8APUztq3OxEscpObBVZoXf37l3Zi0mMFcLBgwflOBEQEFDmY9zd3WVT8aioqAf2X7p0Cf369Sv3v6WrCXAx62tj4A28t/u87OHi62qDNWP8Ud/KVOnQiIiIcDsjF18fjsamkzHIKyxeldnP2xGv9mkGT/s6SodHRBqgb9++crt/ApX4rbBixYpyExui0se3336LzZs348knn5T71q1bJydLnTx5Eh06dIASTsUUr9Zo2cAKJobsf0dEpPjZMDFDds2aNdiwYQMuXLiAadOmycy5KCEiiLJTYiZtiffeew9//PGHrKMeHh6O0aNH48aNG5g0aZKCz4KIqHaIXgefPe8rSwWJL7Yrj1yBthI/HMSPEDGjKjg4WM6OEknv4cOHy+SFEBcXBy8vL3m7IMpYvfrqq/jqq6/w448/ytV8b7/9tpypJZag0//kFRThjZ1n8c4v52RSQzSo3zalA5MaRESkuLvZefh470V0++QQ1h2/LpMaAR622DGtI1aM9mdSg4iqRCQuRN++8oiJVfn5+ejVq1fpPvGbw83NDYGBgVBKSRmq1q42isVARKROFF2xIQwbNgy3b9/GggULZDNY0TtDNHstaSguSofo6/8v/3Lnzh15kkvct27dunIW74kTJ/5VdoSISFuJUkELB7bEnO2n8dWBaPTzcUIjLf2Bv2nTJpnMEM2/xVggatuKpEUJ8YNDzLjKzs4u3Td79mxZE1csHxdlq3x9fbF//35ZT5eKpWTmYtqmcARfS4WeHvB6Xy9M7eYpE0NERERKycwtwNpj17Dm6FVk5BbIfWI1oeij0aWxHccpIqoyMfFp6dKlDy1DJc43iQojNjYPJhDEeSpxW1lyc3Pldn9p2+p2iokNIiL1SmwI4qRVeaWnDh8+/MB10QRWbEREukzMrv854haOXLqN+T9FYuvkDnI1h7YRM6nEEvDyiNJTopxSWQ3KxUb/diE+HZM3huLmnXuy6epXI1rjSa/iyQRERERKyMkvxMbA61hx+IqsHy94OVpibu9m6NW8PhMaRPQv4rv+xx9//ND7iKogYqVFCbHaW6wIHzp0qJwwW50WLVqEhQsXoiY/J8/fSpOX27ixcTgRkdokNoiIqHLED/wPBnmj95Kjcta9aKY5rJ2b0mGRmtt3LgGvbItAdl4hGtYzxzdj26KJAxsPEhGRMsSJui3BMTKhkZRRPNPZw84CrzzVFM/4OGnlpA0iqh5z587F+PHjH3of0U+jxK1bt/DEE0+gU6dOWL169UMf5+joiLy8PNnv7/5VG4mJifK2sogS6qLU+v0rNlxdXVFdzsenI79QhXoWxnCpa1Zt/y4RkSZjYoOISEO52prjlaea4L97LspNzLq3t9S9Btj0aEVFKnxx4DK+OnBZXu/cuB6Wj2wDG3NjpUMjIiIdlJ1XIBuCrzp6FcmZuaWlNmf1bILBbRrA0EDxVpBEpObs7e3lVhFipYZIaohS5qIJ+P3lzssi7mdkZIQDBw7IUriCKH8rSqV37NixzMeYmJjIraYbh/u52XAVGxHR35jYICLSYC909sCuU7fkDJ4PfzuPL4b7KR0SqWED1tnbInA46ra8Pq5jQ7z1TAsY8aQREREp0ENDlJz65q9rSM3KK01oTOvRCEPbusDE0EDpEIlIy4ikRo8ePdCwYUPZV0P0eC1RsvpC3Ef09Nu4cSPat28Pa2trTJw4Ua7AEKVxraysMHPmTJnU6NChgyLPg43DiYj+jYkNIiINJmY0fjTEBwOXH8euiFsY39mDX3ap1Nm4NEzbFIbY1HswMdTHf5/zwRB/F6XDIiIiHZN2Lx8bTlzHt8euycuCm605pj/RCM/5ucDYkMl2IqoZ+/fvlw3Dxebi8uD34JJeffn5+XJFRnZ2dultorerWNkhVmyIpuB9+vTB119/DaVExN6Rf1u7sr8GEVEJJjaIiDRcKxcb2Uz8p/A4fLD7PH54sSOXJxN2hN3EGzsjkVtQBFdbM6wc7Y+WztZKh0VERDrkTlYe1h2/hnXHryMjt0Du87SzwIwnG+NZX2eWnCKiGif6cDyqF4e7u3tpkqOEqakpli9fLjd1WIEtJioJPi78Pk9EVIKJDSIiLfBqn2bYExmP0Bt3sPdsAvr5OCkdEikkr6AI7+8+j+9O3pDXn2hmjy+G+cHa3Ejp0IiISEfcvJMty01tC4nFvfxCua+pQx3MeLIJnvZxggGbghMRVdi5W+mlK92szfidnoioBBMbRERawMnaDFO6euKrg9FY9PtFPNm8PutU66CEtBxZeqqkuaBowio2fZ5AIiKiWnD+VjpWH72CX8/Eo7CoePZzS2crzHiiMfq0dOR4RET0GM7dSpN/vRtYKR0KEZFaYWKDiEhLTO3eCFtCYhGTmo3vAm9gUldPpUOiWnT00m28si0CKVl5sDI1xBfDW+NJLwelwyIiIi0nyrcEXk3BqiNXceTS/5rydmlshxe7N0LnxvVYIpOIqArOxhWv2GBZWSKiBzGxQUSkJSxMDPF/vZvi9R2RWHYoGsPbu6GOCT/mtV1BYRGW/HkJXx++AlEauLmTFVaOboOG9SyUDo2IiLSYWJGx71wCVh25gtM3i2cTiwUZ/X2cZELDuwFPwBERVYezf6/YECvgiIjof3jGi4hIiwxp4yJnTF5NzsKGE9cx/YnGSodENSg+7R5mbYlA8PVUeX1UgBvefqYFTI1YhoyIiGpGRk4+fgi9iQ2B13EjJVvuMzHUx7B2rpjUxRNu9cyVDpGISGtk5RbgWnKWvMwVG0RED2Jig4hIixga6GNmz8Z4ZdtpfPPXVYzr5M5VG1rqUFQS5myLwJ3sfPn/eNFgHwzwdVY6LCIi0lIxKdlYf+I6tofGIjO3QO4TTWzHdWwov2/Uq2OidIhERFrnQny6XJXtYGUCe0t+zhIR3Y9nu4iItMyAVs5YeiBartrYEhSDyd3Ya0Ob5BcW4bM/ouTKnJIl6ctGtoGHHUtPERFR9ffPCLqWirXHrmH/hUR5ck1oZG+BF7p44Dm/BjA35k9KIqKacu5WcX8Nb67WICL6F34LJSLSwlUborb1azvOYO3xaxjf2R1GBvpKh0XVIO7uPby85RTCbtyR18d2bIg3+jdn6SkiIqpWOfmF+PX0Law9fl3OFi7Rvam9TGh0bWwHfdFQg4iIatTZOPbXICIqDxMbRERaaKCfMz7ZF4X4tBz8diYeg/waKB0SVdHuM7fwxk+RSM8pgKWJIT7+TyvZoJWIiKi6xKZmY3NwDLaHxCIlK0/uMzMywBD/BhjfyQON69dROkQiIp1y9u8VGy0bcMUGEdE/MbFBRKSFTAwNML5TQ3z2xyWs+esqBrZ2hp4eZ1ZqIlHH/J2fz2FH+E153dfFGl+N8EPDeiw9RUREVVdYpMKRS0n4LvAGDl+6XVpuysnaVPbOGN7OFTbmxkqHSUSkc3ILCnE5MUNe5ooNIqJ/Y2KDiEhLjQpoiK8ORsu6rGdupsHX1UbpkKiSRMmpV7ZFICY1G6Lix/QnGuPlnk1YWoyIiKosOTMX20JisTkoRpY6LNG1iR1Gd2iInl71ZXlLIiJSxqWETBQUqWBjboQGNmZKh0NEpHaY2CAi0lJ1LYzxtI8Tdp6Kw5bgGCY2NEhBYRGWH7qCrw5eljNpxQ+ZL4a3Rjt3W6VDIyIiDW8GHnrjjlyd8fvZeOQXFi/PsDYzwlB/F4zq0BAedlwRSESkDqL+Xq3R3NGKq++JiMrAxAYRkRYb0d5NJjZ+OX0Lbz7dHJamRkqHRBWobz57W0Rpg3BRRuz9Qd6w4v87IiJ6TKlZefgp/Ca2h8biUmJm6X4x6WFMh4Z4ppUTTI0MFI2RiIgedOnvxEZTB/Y3IiIqCxMbRERarJ17XTSyt8CV21nYfSZeJjpIfWfR/hB2E+/9el721RANwkVCg43fiYjocYgVf39dvi2TGfvPJ5auzjA10seg1g1kuSlvNqMlIlJbUQl/JzYcLZUOhYhILTGxQUSkxcSS5f/4u+LjvRfx6+lbTGyoqaT0HMz7KRIHLybJ620b1sWSYa3hamuudGhERKSBK/9+CI3Fj2E3cSstp3S/TwNrPN/OFc/6OsvSU0REpBkrNpo5MLFBRFQWJjaIiLSc6LMhEhsnr6bgdkYu7C1NlA6J7lulIcqELfj5HNLu5cPYQB9zejfF5K6eMBDdwomIiCogJ78Qf5xPxPaQWBy/kgxV8eIMmcB4zq8Bnm/rihbOVkqHSUREFZSek4/4v5PTTZjYICIqExMbRERazq2eOXxdrHH6Zhr2nkuQtbRJecmZuXhr51n5/6RkJu3i533RlD9ciIioEo3AfwqPw29nbiE9p6D0ti6N7eTqjN4tHNg7g4hIA13+e7WGo5UpV9kREZWDiQ0iIh3wdCsnmdjYffoWExtq4PfIeLy566xs5mqor4eXezbBtB6NYGSgr3RoRESk5q4lZ2Fn+E3sjIhDbOq90v1O1qYY6u+CoW1dWcqQiEjDRSVkyr/sr0FEVD4mNoiIdEB/Hyf8d89FhFxPlSWPOOtHGSmZuVj463lZfkrwcrTEZ0N92byViIge6k5WHnafuYWfTsXhVMzd0v0Wxgbo6+2EwW0aoINnPZYxJCLSuv4adZQOhYhIbTGxQUSkA1zqmsPT3gJXb2ch6GoKerd0VDoknSsXsisiDu/9eh53svMhzjuJFRpipYaJIUuEEBHRv93LK8ShqCTsPBWHw1FJyC8sbpwhxpCuTexlMqN3C0eYGXMcISLS1sQGy9QSEZWPiQ0iIh3RqVE9mdg4cYWJjdp080423tx5Fkcu3S5dpfHxkFbwdbVROjQiIlIzuQWFOHopGb+evoU/LyQiO6+w9LaWzlayEfizrZ1R39JU0TiJiKhmMbFBRPRoTGwQEemIzo3s8P3JGJy4kqx0KDqhsEiFjYHX8em+KHliythAHy/3bIyp3dlLg4iI/ie/sAjHo0UyIx5/nE9Axn1NwBvYmGGAr7NMaDRjnXUiIp2QnJmL5Mw8ebkJS1EREZWLiQ0iIh0ham/r6YnZP5m4nZELe0sTpUPS6hlWr+84U1oHvZ17XSwa3AqN6/OHCRERFSe/T15NkX0z9p5NkGUKSzhYmeCZVs54ppUTWrvaQE8M3kREpHOrNdxszWFuzNN2RETl4SckEZGOqGthjBZOVjh3K12u2hjYugHUXWpqKmbOnIlff/0V+vr6GDJkCL788kvUqVN+giAhIQGvvvoq9u/fj4yMDDRr1gxvvvmmfGxNKygswtKD0fj6cLSshV7HxBCv9/PCqPZu0GdDVyIiAvD5/kvYHBQjZ+SWsKtjjP4+TjKh0bZhXY4ZREQ67HJipvzLMlRERA/HxAYRkY712RCJjcArKRqR2Bg1ahTi4+NlkiI/Px8TJkzAlClTsHnz5nIfM3bsWNy9exe//PIL7Ozs5H2ff/55hIaGws/Pr0bjNdDXQ3jMHZnU6NW8Pt4f5A0na7Ma/W8SEZFmSc0SJUZyYWNuhH7ejjKZEeBhC0OWKSQiIgBRpf01uNqbiOhh9FQqlQo6Jj09HdbW1khLS4OVlZXS4RAR1ZropAzcupuDtu51H2tZc21+fl64cAEtWrRASEgI2rZtK/ft3bsX/fv3x82bN+Hs7Fzm48RqjhUrVmDMmDGl++rVq4ePP/4YkyZNqvHnGZuajYjYu7KECMuHEJGu0ZTv2UePHsWnn36KsLAwmUDfuXMnBg0aVCvP83JiBm7evYcuje3Yc4mIdI6mjBNKPk+R/L4YnwFHa1OWsiUinZNeic9PfpMmItIhjetboltTe42o1RoYGAgbG5vSpIbQq1cvWZIqKCio3Md16tQJ27Ztk2WsioqKsHXrVuTk5KBHjx7lPiY3N1cOnvdvj8vV1lw2emVSg4hIfWVlZcHX1xfLly+v9f92EwdLPNGsPpMaRERUJrs6JujSxI5JDSKiR1D/M1tERKSTRK+M+vXrP7DP0NAQtra28rbybN++HcOGDZOrNMT9zc3N5Uzcxo0bl/uYRYsWYeHChdUaPxERqa9+/frJjYiIiIiINBOnCRERUa2aN2+eXM3wsO3ixYuP/e+//fbbssfGn3/+KftqzJkzR/bYiIyMLPcx8+fPl8scS7bY2NjH/u8TEZH2qc6VfUREREREVHVcsUFERLVq7ty5GD9+/EPv4+npCUdHRyQlJT2wv6CgQJaYEreV5cqVK1i2bBnOnj2Lli1byn2i1Mhff/0ly42sXLmyzMeZmJjIjYiIqCxc2UdEREREpF6Y2CAiolplb28vt0fp2LGjXHkhGrv6+/vLfQcPHpR9MwICAsp8THZ2tvwr+nDcz8DAQD6OiIjocYiVfWIFYAmxYsPV1VXRmIiIiIiIdBlLURERkVpq3rw5+vbti8mTJyM4OBjHjx/HjBkzMHz4cDg7O8v7xMXFwcvLS94uiMuil8bUqVPlPrGCY/Hixdi/fz8GDRqk8DMiIiJNJVb1WVlZPbAREREREZFymNggIiK1tWnTJpms6NmzJ/r3748uXbpg9erVpbfn5+cjKiqqdKWGkZER9uzZI1eEDBgwAK1atcLGjRuxYcMG+XgiIiIiIiIiItJ8LEVFRERqy9bWFps3by73dnd3d6hUqgf2NWnSBDt27KiF6IiISFNlZmYiOjq69Pq1a9cQEREhxx03NzdFYyMiIiIiIg1ZsSEauoqTU6amprJueklJkfL88MMPcgavuL+Pj4+cnUtERERERFQRoaGh8PPzk5sg+meIywsWLFA6NCIiIiIi0oTExrZt2+QPiXfeeQfh4eHw9fVFnz59kJSUVOb9T5w4gREjRmDixIk4deqUrJkutrNnz9Z67EREREREpHl69OghV/z9c1u/fr3SoRERERERkSYkNj7//HPZGHbChAlo0aIFVq5cCXNzc6xdu7bM+3/55Zeymeyrr74qG8u+//77aNOmDZYtW1brsRMRERERERERERERkQ4lNvLy8hAWFoZevXr9LyB9fXk9MDCwzMeI/fffXxArPMq7v5Cbm4v09PQHNiIiIiIiIiIiIiIi0jyKNg9PTk5GYWEhHBwcHtgvrl+8eLHMxyQkJJR5f7G/PIsWLcLChQv/tZ8JDiKiyin53Pxnw25tU/L8OE4QEVUOxwkiInoYjhNERFRd44SiiY3aMn/+fNnHo0RcXJwse+Xq6qpoXEREmiojIwPW1tbQ5ucncJwgIno8HCeIiOhhOE4QEVFVxwlFExt2dnYwMDBAYmLiA/vFdUdHxzIfI/ZX5v6CiYmJ3ErUqVMHsbGxsLS0hJ6eXqWzRmJgEo+3srKq1GN1FY9Z5fGYVQ6PV+0dM5ExF4OLs7MztJl4fhwnag+PWeXxmFUOj1flcZyouXGC+J6sKh6/quHxU/YYcpygiuD7tGp4/KqGx09zxglFExvGxsbw9/fHgQMHMGjQILmvqKhIXp8xY0aZj+nYsaO8ffbs2aX79u/fL/dXlOjj4eLiUqXYxf8Uvrgrh8es8njMKofHq3aOmTbPrCrBcUIZPGaVx2NWOTxelcdxoubGCeJ7sqp4/KqGx0+5Y8hxgiqK79Oq4fGrGh4/9R8nFC9FJUpEjRs3Dm3btkX79u3xxRdfICsrCxMmTJC3jx07Fg0aNJB9MoRZs2ahe/fuWLx4MZ5++mls3boVoaGhWL16tcLPhIiIiIiIiIiIiIiIapriiY1hw4bh9u3bWLBggWwA3rp1a+zdu7e0QXhMTIzMdJfo1KkTNm/ejLfeegtvvPEGmjRpgl27dsHb21vBZ0FERERERERERERERDqR2BBE2anySk8dPnz4X/uGDh0qNyWIXh3vvPPOAz076OF4zCqPx6xyeLwqj8es5vDYVh6PWeXxmFUOj1fl8ZhRTeLrq2p4/KqGx6/qeAyppvE1VjU8flXD46c5x1BPJTpyEBERERERERERERERaYD/1XgiIiIiIiIiIiIiIiJSc0xsEBERERERERERERGRxmBig4iIiIiIiIiIiIiINAYTG0REREREREREREREpDGY2KiADz/8EJ06dYK5uTlsbGwq9BjRk33BggVwcnKCmZkZevXqhcuXL0NXpKamYtSoUbCyspLHbOLEicjMzHzoY3r06AE9Pb0HthdffBHaavny5XB3d4epqSkCAgIQHBz80Pv/8MMP8PLykvf38fHBnj17oEsqc7zWr1//r9eSeJwuOXr0KAYMGABnZ2f5/Hft2vXIxxw+fBht2rSBiYkJGjduLI8jVQzHicrjOPFoHCcqh+NE5XCcoJrG9+Tj4/uzdo+fOHb/fP2JLSEhAbpo0aJFaNeuHSwtLVG/fn0MGjQIUVFRj3ycrn8PocrjOPH4OE5UDccJ7RknmNiogLy8PAwdOhTTpk2r8GM++eQTfPXVV1i5ciWCgoJgYWGBPn36ICcnB7pAnKw6d+4c9u/fj927d8sPjSlTpjzycZMnT0Z8fHzpJo6jNtq2bRvmzJmDd955B+Hh4fD19ZWvj6SkpDLvf+LECYwYMUKe+Dt16pT80BDb2bNnoQsqe7wEcbL0/tfSjRs3oEuysrLkcRJfFivi2rVrePrpp/HEE08gIiICs2fPxqRJk7Bv374aj1UbcJyoPI4TD8dxonI4TlQexwmqSXxPVg3fn7V7/EqIkzL3vwbFyRpddOTIEUyfPh0nT56U39Py8/PRu3dveVzLo+vfQ6jyOE5UDceJquE4oUXjhIoqbN26dSpra+tH3q+oqEjl6Oio+vTTT0v33b17V2ViYqLasmWLStudP39eJV5aISEhpft+//13lZ6eniouLq7cx3Xv3l01a9YslS5o3769avr06aXXCwsLVc7OzqpFixaVef/nn39e9fTTTz+wLyAgQDV16lSVLqjs8aroe1VXiPfjzp07H3qf1157TdWyZcsH9g0bNkzVp0+fGo5Ou3CcqBiOE4/GcaJyOE5UDccJqm58T1Yfvj9r/vgdOnRI3u/OnTu1FpcmSUpKksfnyJEj5d5H17+HUOVxnKg+HCeqhuOEZo8TXLFRA0QmVCxHEmVFSlhbW8uldYGBgdB24jmKsiJt27Yt3SeOhb6+vpyV/DCbNm2CnZ0dvL29MX/+fGRnZ0MbZ3aHhYU98PoQx0ZcL+/1Ifbff39BzGbQhdfT4xwvQZS0adiwIVxdXTFw4EA5M5zKp8uvMSVwnOA48TAcJyqH40Tt0OXXGFUO35O1j+/P6tG6dWtZIvSpp57C8ePHlQ5HbaSlpcm/tra25d6Hr0GqDI4TtY/v0erBcUL9xgkmNmpASY01BweHB/aL67pQf008x38uxzI0NJQv8Ic9/5EjR+L777/HoUOH5Mmq7777DqNHj4a2SU5ORmFhYaVeH2K/rr6eHud4NWvWDGvXrsXPP/8sX1NFRUWy/8HNmzdrKWrNU95rLD09Hffu3VMsLm3FcYLjxMNwnKgcjhO1g+MEVRTfk7WP78+qESepRGnQHTt2yE2cNBV9vUR5HF0n3ouiZE3nzp3lpJLy6PL3EKo8jhO1j+NE1XCcUN9xwhA6at68efj4448fep8LFy7IpiZUuWP2uO6vrS6ayIgPjp49e+LKlSto1KjRY/+7pHs6duwotxLiC0/z5s2xatUqvP/++4rGRpqD40TlcZwgTcFxgki98D1JShInTMV2/+tPfLdYsmSJnEShy0QNdVH//NixY0qHQjqO4wQpieOE+o4TOpvYmDt3LsaPH//Q+3h6ej7Wv+3o6Cj/JiYmypMuJcR1sWxJ24+ZeP7/bPhUUFCA1NTU0mNTEaIkixAdHa1VJ6xECRUDAwP5erifuF7e8RH7K3N/bfI4x+ufjIyM4OfnJ19LhEq9xkSDNjMzM+gijhOVx3GienCcqByOE7WD4wRVFN+TtY/vz+rXvn17nT+ZP2PGDOzevRtHjx6Fi4vLQ++ry99DqPI4TtQ+jhPVj+ME1GKc0NlSVPb29nKW7cM2Y2Pjx/q3PTw85P+YAwcOlO4Ty7tE3fD7M8zaeszEc7x7966smVji4MGDcnlSyUmoioiIiJB/7z/ppw3EMfL393/g9SGOjbhe3utD7L///sL+/fs1+vVUk8frn8Qy18jISK17LVUnXX6NlYfjROVxnKgeHCcqh+NE7dDl1xhVDt+TtY/vz+onvmPo6utP9NIVJ6t27twpv5+J762PwtcgVQbHidrH92j14zgxQz3GiSq1HtcRN27cUJ06dUq1cOFCVZ06deRlsWVkZJTep1mzZqqffvqp9PpHH32ksrGxUf3888+qM2fOqAYOHKjy8PBQ3bt3T6UL+vbtq/Lz81MFBQWpjh07pmrSpIlqxIgRpbffvHlTHjNxuxAdHa167733VKGhoapr167J4+bp6anq1q2bShtt3bpVZWJiolq/fr3q/PnzqilTpsjXS0JCgrx9zJgxqnnz5pXe//jx4ypDQ0PVZ599prpw4YLqnXfeURkZGakiIyNVuqCyx0u8V/ft26e6cuWKKiwsTDV8+HCVqamp6ty5cypdIT6fSj6rxEf9559/Li+LzzNBHC9x3EpcvXpVZW5urnr11Vfla2z58uUqAwMD1d69exV8FpqD40TlcZx4OI4TlcNxovI4TlBN4nuyavj+rN3jt2TJEtWuXbtUly9fluPmrFmzVPr6+qo///xTpYumTZumsra2Vh0+fFgVHx9fumVnZ5feh99DqKo4TlQNx4mq4TihPeMEExsVMG7cOPlC/+d26NCh0vuI6+vWrSu9XlRUpHr77bdVDg4O8sO6Z8+eqqioKJWuSElJkSeoxAk+Kysr1YQJEx44wSdOSt1/DGNiYuTJKVtbW3m8GjduLD9w09LSVNpq6dKlKjc3N5WxsbGqffv2qpMnT5be1r17d/m6u9/27dtVTZs2lfdv2bKl6rffflPpksocr9mzZ5feV7wH+/fvrwoPD1fpEvHeKutzq+Q4ib/iuP3zMa1bt5bHTZwwvv8zjR6O40TlcZx4NI4TlcNxonI4TlBN43vy8fH9WbvH7+OPP1Y1atRIniQV3zN69OihOnjwoEpXlXXs/vk9lt9DqDpwnHh8HCeqhuOE9owTen8HREREREREREREREREpPZ0tscGERERERERERERERFpHiY2iIiIiIiIiIiIiIhIYzCxQUREREREREREREREGoOJDSIiIiIiIiIiIiIi0hhMbBARERERERERERERkcZgYoOIiIiIiIiIiIiIiDQGExtERERERERERERERKQxmNggIiIiIiIiIiIiIiKNwcQGERERERERERERERFpDCY2iIiIiIiIiIiIiIhIYzCxQUREREREREREREREGoOJDSIiIiIiIiIiIiIigqb4f+bIn2uWiz6LAAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 1600x800 with 8 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x = np.arange(-1, 1, 0.01)\n",
+        "x2 = np.linspace(0.1, 2, 400)  # For log and exp\n",
+        "\n",
+        "# based on the number of subplots what should be the dimensions in the following line?\n",
+        "fig, axs = plt.subplots(2, 4, figsize=(16, 8))\n",
+        "axs[0,0].plot(x, np.sin(x));      axs[0,0].set_title(\"sin(x)\")\n",
+        "axs[0,1].plot(x, np.cos(x));      axs[0,1].set_title(\"cos(x)\")\n",
+        "axs[0,2].plot(x, np.tan(x));      axs[0,2].set_title(\"tan(x)\")\n",
+        "axs[0,3].plot(x, np.arcsin(x));   axs[0,3].set_title(\"arcsin(x)\")\n",
+        "axs[1,0].plot(x, np.arccos(x));   axs[1,0].set_title(\"arccos(x)\")\n",
+        "axs[1,1].plot(x, np.arctan(x));   axs[1,1].set_title(\"arctan(x)\")\n",
+        "axs[1,2].plot(x2, np.exp(x2));    axs[1,2].set_title(\"exp(x)\")\n",
+        "axs[1,3].plot(x2, np.log(x2));    axs[1,3].set_title(\"log(x)\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "thpsEzFVikPh"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WAidrU51fMqB"
+      },
+      "source": [
+        "## 4. Features of the Sigmoid Function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "lehJV_vNfLXx",
+        "outputId": "9cfc6ec4-9dfc-4220-a955-4d6a11fac8f3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Range: (np.float64(4.5397868702434395e-05), np.float64(0.9999546021312976)) | At x=0: 0.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "def sigmoid(x):\n",
+        "    return 1 / (1 + np.exp(-x))\n",
+        "\n",
+        "x = np.linspace(-10, 10, 1000)\n",
+        "y = sigmoid(x)\n",
+        "\n",
+        "plt.plot(x, y)\n",
+        "plt.title(\"Sigmoid Function\")\n",
+        "plt.xlabel(\"x\")\n",
+        "plt.ylabel(\"σ(x)\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"Range:\", (y.min(), y.max()), \"| At x=0:\", sigmoid(0))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DUONuiAxilL0"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1FjRt_31fXsx"
+      },
+      "source": [
+        "## 5. Derivatives of Famous Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "vyyGBuHRfZ4n",
+        "outputId": "7a89fce7-f973-46bc-dca3-c452f1a34561"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x = np.linspace(-3, 3, 400)\n",
+        "plt.plot(x, x**2, label=\"x²\")\n",
+        "plt.plot(x, np.gradient(x**2, x), '--', label=\"d/dx x²\")\n",
+        "x = np.linspace(0, 3, 400)\n",
+        "plt.plot(x, np.exp(x), label=\"exp(x)\")\n",
+        "plt.plot(x, np.gradient(np.exp(x), x), '--', label=\"d/dx exp(x)\")\n",
+        "\n",
+        "plt.plot(x, np.log(x+0.1), label=\"log(x)\")\n",
+        "plt.plot(x, np.gradient(np.log(x+0.1), x), '--', label=\"d/dx log(x)\")\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.title(\"Functions and Their Derivatives\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DURup9u5il4-"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w3QIKTZQf1ZK"
+      },
+      "source": [
+        "## 6. Gradient of Selected Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Eeudgf3-f0Ie",
+        "outputId": "aeffb463-3230-4b98-8131-623b4fbf624c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Function: f(x, y) = x**2*y + sin(y)\n",
+            "Partial derivative w.r.t x (∂f/∂x): 2*x*y\n",
+            "Partial derivative w.r.t y (∂f/∂y): x**2 + cos(y)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# search and learn more about sumpy\n",
+        "import sympy as sp\n",
+        "\n",
+        "# Define symbolic variables\n",
+        "x, y = sp.symbols('x y')\n",
+        "\n",
+        "# Define the function\n",
+        "f = x**2 * y + sp.sin(y)\n",
+        "\n",
+        "# Compute partial derivatives\n",
+        "df_dx = sp.diff(f, x)\n",
+        "df_dy = sp.diff(f, y)\n",
+        "\n",
+        "print(\"Function: f(x, y) =\", f)\n",
+        "print(\"Partial derivative w.r.t x (∂f/∂x):\", df_dx)\n",
+        "print(\"Partial derivative w.r.t y (∂f/∂y):\", df_dy)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wDlIJkmYimg9"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/Assignments/a0.3/function_rsayyareh.py b/a0.1/Assignments/a0.3/function_rsayyareh.py
new file mode 100644
index 0000000..a13e32f
--- /dev/null
+++ b/a0.1/Assignments/a0.3/function_rsayyareh.py
@@ -0,0 +1,26 @@
+# 1
+#   f(f^(x)) = x        f^: invers of f
+
+# 2
+#       sin²(x) in ptython is sin(x)**2
+#       linespace in numpy is similar to arange 
+
+# 3
+#       first line, 400 is the points between -1 and 1 and can not be more
+#       than 1, the 0.01 is good enough number to have an accurate plot.
+
+# 4
+#       symbols could be inserted using ASCII code in text.
+
+# 5
+#       derivativ of one variable function (y=f(x)) is 
+#       the same as partial derivatives (gradient).
+
+# 6
+#       symbolic gradient determination using sympy library
+
+
+
+
+
+
diff --git a/a0.1/Assignments/a0.4/AI-DS_Nexus__A0_4__RezaShokr.ipynb b/a0.1/Assignments/a0.4/AI-DS_Nexus__A0_4__RezaShokr.ipynb
new file mode 100644
index 0000000..16cc8e2
--- /dev/null
+++ b/a0.1/Assignments/a0.4/AI-DS_Nexus__A0_4__RezaShokr.ipynb
@@ -0,0 +1,263 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A1k4HkdO05Yu"
+      },
+      "source": [
+        "# 📓 Assignment 4 — Linear Algebra Fundamentals\n",
+        "\n",
+        "*Vectors & Matrices for Everyday Coding*\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mBNupqPj1B1_"
+      },
+      "source": [
+        "## Welcome back, intrepid coder! 🚀\n",
+        "In this notebook-styled brief you’ll move from single-direction vectors to multi-direction matrices—core tools behind graphics, robotics, optimisation and (of course) machine-learning. Each mini-section mixes quick notes, a tiny real-world scenario, and hands-on # TODO code shaped for beginners.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zD-AyQCe1Y9e"
+      },
+      "source": [
+        "## 🧭 Vectors 101\n",
+        "\n",
+        "| Concept             | Quick reminder                                  |\n",
+        "|---------------------|--------------------------------------------------|\n",
+        "| Representation      | 1-D NumPy array — `np.array([x, y, z])`         |\n",
+        "| Length (norm)       | `np.linalg.norm(v)`                             |\n",
+        "| Dot / inner product | `v.dot(w)` or `np.inner(v, w)`                  |\n",
+        "| Unit vector         | `v / np.linalg.norm(v)`                         |\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "AWVTIlQP0ysC"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Length of matrix v = 3\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "array([[[  66.,   78.,   90.],\n",
+              "        [ 147.,  177.,  207.],\n",
+              "        [ 228.,  276.,  324.]],\n",
+              "\n",
+              "       [[ 903.,  969., 1035.],\n",
+              "        [1146., 1230., 1314.],\n",
+              "        [1389., 1491., 1593.]],\n",
+              "\n",
+              "       [[2670., 2832., 2952.],\n",
+              "        [3069., 3255., 3393.],\n",
+              "        [3429., 3637., 3791.]]])"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Run once per session\n",
+        "import numpy as np\n",
+        "\n",
+        "# TODO 1: create a 3-D vector named v\n",
+        "# TODO 2: print its length\n",
+        "# TODO 3: build a second vector w and output the dot product\n",
+        "v = np.array([\n",
+        "    [[1, 2, 3], [4, 5, 6], [7, 8, 9]],\n",
+        "    [[10, 11, 12], [13, 14, 15], [16, 17, 18]],\n",
+        "    [[19, 20, 21], [22, 23, 24], [24, 26, 27]]\n",
+        "    ])\n",
+        "print(f\"Length of matrix v = {len(v)}\")\n",
+        "w = np.ones(27).reshape(3, 3, 3)\n",
+        "w += v * 2\n",
+        "v@w\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c_iB5_0A1rg7"
+      },
+      "source": [
+        "### 🚁 Practical Scenario — “Drone hop”\n",
+        "A mini-drone lifts off at (2 m, 1 m) and lands at (7 m, 4 m).\n",
+        "Calculate its displacement vector, travel distance, and orientation along the x-axis."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "Rq-tHCkF1x3p"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Displacement: [5 3]\n",
+            "Distance: 5.830951894845301\n",
+            "Unit direction: [0.85749293 0.51449576]\n",
+            "Dot-product with x-axis: 5\n"
+          ]
+        }
+      ],
+      "source": [
+        "p_start = np.array([2, 1])\n",
+        "p_end   = np.array([7, 4])\n",
+        "\n",
+        "# displacement\n",
+        "d = p_end - p_start          # ➡️ vector from start to end\n",
+        "dist = np.linalg.norm(d)     # 🏁 distance travelled\n",
+        "unit = d / dist              # ↗️ unit direction\n",
+        "dot_x = d.dot(np.array([1, 0]))  # projection on x-axis\n",
+        "\n",
+        "print(\"Displacement:\", d)\n",
+        "print(\"Distance:\", dist)\n",
+        "print(\"Unit direction:\", unit)\n",
+        "print(\"Dot-product with x-axis:\", dot_x)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vqX_pXGq16Af"
+      },
+      "source": [
+        "## 🔢 Matrices 101\n",
+        "\n",
+        "| Operation              | NumPy one-liner                                  |\n",
+        "|------------------------|--------------------------------------------------|\n",
+        "| Transpose              | `A.T`                                            |\n",
+        "| Determinant            | `np.linalg.det(A)`                               |\n",
+        "| Inverse                | `np.linalg.inv(A)` (works only if `det(A) ≠ 0`)  |\n",
+        "| Matrix-vector multiply | `A @ v`                                          |\n",
+        "| Matrix-matrix multiply | `A @ B`                                          |\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "yycidpaj2BAv"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "A determinant is -2.0000000000000004\n",
+            "nInverse of A : [[-2.   1. ]\n",
+            " [ 1.5 -0.5]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO 4: build a 2×2 matrix A\n",
+        "# TODO 5: print its determinant\n",
+        "# TODO 6: if invertible, compute and print A_inv\n",
+        "A = np.array([[1, 2], [3, 4]])\n",
+        "det = np.linalg.det(A)\n",
+        "print(f\"A determinant is {det}\")\n",
+        "if det!=0:\n",
+        "    A_inv = np.linalg.inv(A)\n",
+        "    print(f\"nInverse of A : {A_inv}\")\n",
+        "else:\n",
+        "    print(\"A has no inverse\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C-ikgDhF2DFn"
+      },
+      "source": [
+        "### 🎯 Practical Scenario — “Rotate that hop”\n",
+        "Rotate the drone’s displacement vector 30° counter-clockwise, then verify that the inverse rotation brings it back."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "SgmIesbf2G65"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Rotation matrix determinant: 1.0\n",
+            "Rotated vector: [2.83012702 5.09807621]\n",
+            "Back-rotated equals original? True\n"
+          ]
+        }
+      ],
+      "source": [
+        "theta = np.deg2rad(30)          # 🔄 convert degrees to radians\n",
+        "R = np.array([[np.cos(theta), -np.sin(theta)],\n",
+        "              [np.sin(theta),  np.cos(theta)]])\n",
+        "\n",
+        "det_R = np.linalg.det(R)        # should be 1.0 (pure rotation)\n",
+        "\n",
+        "d_rot  = R @ d                  # rotated displacement\n",
+        "d_back = np.linalg.inv(R) @ d_rot\n",
+        "\n",
+        "print(\"Rotation matrix determinant:\", det_R)\n",
+        "print(\"Rotated vector:\", d_rot)\n",
+        "print(\"Back-rotated equals original?\", np.allclose(d_back, d))\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/Assignments/a0.5/AI-DS_Nexus__A0_5__RezaShokr.ipynb b/a0.1/Assignments/a0.5/AI-DS_Nexus__A0_5__RezaShokr.ipynb
new file mode 100644
index 0000000..3cfae4d
--- /dev/null
+++ b/a0.1/Assignments/a0.5/AI-DS_Nexus__A0_5__RezaShokr.ipynb
@@ -0,0 +1,196 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tacMQNsG2g5S"
+      },
+      "source": [
+        "# 📓 Assignment 5 — Probability\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "## 🎲 Counting & Probability with Cards in Python 🃏\n",
+        "Learn factorials, combinations, and (conditional) probability — all in one simple card game simulation using only math and random modules."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_cA_dWDJ2vfr"
+      },
+      "source": [
+        "## 🧮 PART 1: Factorial & Combinations\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "aIxoWeNt2ao0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "6! = 720\n",
+            "Combinations (6 choose 3): 20\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- FACTORIAL & COMBINATIONS ----\n",
+        "\n",
+        "import math\n",
+        "print(\"6! =\", math.factorial(6))\n",
+        "print(\"Combinations (6 choose 3):\", math.comb(6, 3))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pzy2xD3H3D3o"
+      },
+      "source": [
+        "## 🃏 PART 2: Simulating a Deck of Cards\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eUvZhzYB2x4L"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Drawn cards: ['2 of Diamonds', '10 of Hearts']\n",
+            "52\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- PROBABILITY IN A DECK ----\n",
+        "\n",
+        "import random\n",
+        "\n",
+        "deck = [f\"{rank} of {suit}\" for suit in ['Hearts', 'Spades', 'Diamonds', 'Clubs']\n",
+        "        for rank in range(1, 14)]\n",
+        "\n",
+        "# Draw 2 random cards\n",
+        "\n",
+        "draws = random.sample(deck, 2)\n",
+        "print(\"\\nDrawn cards:\", draws)\n",
+        "# print(len(deck))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OidRYf9q3Mwj"
+      },
+      "source": [
+        "## 📊 PART 3: Basic Probability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ok5d99Ns3Oa7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Probability both cards are Hearts: 0.0588\n"
+          ]
+        }
+      ],
+      "source": [
+        "# PROBABILITY: What’s the chance that both cards are Hearts?\n",
+        "hearts = [card for card in deck if \"Hearts\" in card]\n",
+        "# print(len(hearts))\n",
+        "# print(len(deck))\n",
+        "\n",
+        "# Draw 2 random cards\n",
+        "\n",
+        "prob_both_hearts = math.comb(len(hearts), 2) / math.comb(len(deck), 2)\n",
+        "print(f\"Probability both cards are Hearts: {prob_both_hearts:.4f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NMIA7c313Yum"
+      },
+      "source": [
+        "## 🔁 PART 4: Conditional Probability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "id": "IZeCH8pw3aZX"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Conditional probability 2nd card is Heart given 1st is Heart: 0.2353\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- CONDITIONAL PROBABILITY ----\n",
+        "# P(A and B) / P(B): Probability second card is Heart given first was Heart\n",
+        "# A = both are hearts, B = first is heart\n",
+        "\n",
+        "p_a_and_b = math.comb(len(hearts), 2) / math.comb(len(deck), 2) # after one heart is drawn\n",
+        "p_b = 13 / 52\n",
+        "p_a_given_b = p_a_and_b / p_b\n",
+        "print(f\"Conditional probability 2nd card is Heart given 1st is Heart: {p_a_given_b:.4f}\")\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/Assignments/a0.6/AI-DS_Nexus__A0_6__RezaShokr.ipynb b/a0.1/Assignments/a0.6/AI-DS_Nexus__A0_6__RezaShokr.ipynb
new file mode 100644
index 0000000..a64bc44
--- /dev/null
+++ b/a0.1/Assignments/a0.6/AI-DS_Nexus__A0_6__RezaShokr.ipynb
@@ -0,0 +1,225 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Txr_tIDhHcu2"
+      },
+      "source": [
+        "# 📚 Assignment 06 — Descriptive Statistics\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "\n",
+        "## 📘 Exploring Descriptive Statistics with NumPy & SciPy\n",
+        "This assignment guides you through generating random data, analyzing its statistical properties, and visualizing it using plots.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "x1gatSKSHYTm"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Generated Data: [37 30 35 34 38 37 30 38 27 30]\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "array([27, 30, 30, 30, 34, 35, 37, 37, 38, 38], dtype=int32)"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 📦 Import Required Libraries\n",
+        "import numpy as np\n",
+        "import random\n",
+        "from scipy import stats\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# 🎲 Step 1: Generate Random Integer List\n",
+        "np.random.random(42)  # For reproducibility\n",
+        "data = np.random.randint(20, 40, size=10)\n",
+        "print(\"Generated Data:\", data)\n",
+        "np.sort(data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cUfGpmVCHvR5"
+      },
+      "source": [
+        "### 📊 Step 2: Central Tendency and Quantiles\n",
+        "Let's compute basic statistics — mean, median, mode, and quantiles."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "FkmPc2r7Htaz"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean: 33.6\n",
+            "Median: 34.5\n",
+            "Mode: 30\n",
+            "Q1 (25%): 30.0\n",
+            "Q3 (75%): 37.0\n",
+            "34.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📐 Central Tendency\n",
+        "# mean\n",
+        "mean_val = np.mean(data)\n",
+        "# median\n",
+        "median_val = np.median(data)\n",
+        "# mode\n",
+        "mode_val = stats.mode(data, keepdims=True).mode[0]\n",
+        "\n",
+        "# 📏 Quantiles\n",
+        "q1 = np.quantile(data, 0.25)\n",
+        "q3 = np.quantile(data, 0.75)\n",
+        "\n",
+        "print(f\"Mean: {mean_val}\")\n",
+        "print(f\"Median: {median_val}\")\n",
+        "print(f\"Mode: {mode_val}\")\n",
+        "print(f\"Q1 (25%): {q1}\")\n",
+        "print(f\"Q3 (75%): {q3}\")\n",
+        "print(np.quantile(data, 0.5))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EBZ2KixtHztl"
+      },
+      "source": [
+        "### 🧪 Step 3: Skewness and Kurtosis\n",
+        "Skewness shows asymmetry, and kurtosis indicates the \"tailedness\" of the distribution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "XDwmqc3-H05I"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Skewness: 0.17\n",
+            "Kurtosis: -1.23\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📉 Skewness and Kurtosis\n",
+        "skew_val = stats.skew(data)\n",
+        "kurtosis_val = stats.kurtosis(data)\n",
+        "\n",
+        "print(f\"Skewness: {skew_val:.2f}\")\n",
+        "print(f\"Kurtosis: {kurtosis_val:.2f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FOPw0-dSH250"
+      },
+      "source": [
+        "### 📈 Step 4: Visualization - Bar Chart and Boxplot\n",
+        "Visualize the data to better understand its distribution and spread."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "Zfx5s6xLH1y8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 📊 Bar Plot\n",
+        "plt.figure(figsize=(12, 4))\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "# make a bar chart\n",
+        "plt.bar(range(len(data)), data)\n",
+        "plt.title(\"Bar Plot of Random Data\")\n",
+        "plt.xlabel(\"Index\")\n",
+        "plt.ylabel(\"Value\")\n",
+        "\n",
+        "# 📦 Boxplot\n",
+        "plt.subplot(1, 2, 2)\n",
+        "# make a box plot\n",
+        "plt.boxplot(data, vert=False)\n",
+        "plt.title(\"Boxplot of Random Data\")\n",
+        "plt.xlabel(\"Value\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/Assignments/a0.7/AI-DS_Nexus__A0_7__RezaShokr.ipynb b/a0.1/Assignments/a0.7/AI-DS_Nexus__A0_7__RezaShokr.ipynb
new file mode 100644
index 0000000..05c362f
--- /dev/null
+++ b/a0.1/Assignments/a0.7/AI-DS_Nexus__A0_7__RezaShokr.ipynb
@@ -0,0 +1,388 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dbICYwLGo5Av"
+      },
+      "source": [
+        "# 🔥 Assignment: Exploring PyTorch\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b8h5DExTo6K5"
+      },
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using PyTorch, focusing on tensors, autograd, and a small training loop.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "euolCT3oo8dR"
+      },
+      "source": [
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install PyTorch and check its version.\n",
+        "\n",
+        "* Create a 2D tensor and print it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "9AADPzP6o1iY"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "PyTorch version: 2.8.0+cpu\n",
+            "Tensor x:\n",
+            " tensor([[1., 2.],\n",
+            "        [3., 4.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "\n",
+        "print(\"PyTorch version:\", torch.__version__)\n",
+        "x = torch.tensor([[1., 2.], [3., 4.]])\n",
+        "print(\"Tensor x:\\n\", x)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "T5mCQMYFpBJm"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tensor y:\n",
+            " tensor([[5., 6.],\n",
+            "        [7., 8.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create another tensor y of the same shape with values [[5, 6], [7, 8]].\n",
+        "y = torch.tensor([[5., 6.], [7., 8.]])\n",
+        "print(\"Tensor y:\\n\", y)\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7yRBms7npEUe"
+      },
+      "source": [
+        "### 2️⃣ Tensor Operations 🧮\n",
+        "**Task:** Perform addition and matrix multiplication."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "nGRNryC4pDv2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Addition:\n",
+            " tensor([[ 6.,  8.],\n",
+            "        [10., 12.]])\n",
+            "Matrix Multiplication:\n",
+            " tensor([[19., 22.],\n",
+            "        [43., 50.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "z = x + y\n",
+        "print(\"Addition:\\n\", z)\n",
+        "\n",
+        "mat_mul = x @ y\n",
+        "print(\"Matrix Multiplication:\\n\", mat_mul)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "tf64nP2dpIod"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "tensor([[ 5., 12.],\n",
+            "        [21., 32.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Perform element-wise multiplication (x * y) and print the result.\n",
+        "dot_mul = x * y\n",
+        "print(dot_mul)\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h3dABundpMYU"
+      },
+      "source": [
+        "### 3️⃣ Autograd and Gradients ⚙️\n",
+        "**Task:** Enable gradient tracking and compute derivatives."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "igtPKY90pLGt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "tensor(3., requires_grad=True)\n",
+            "Gradient of b wrt a: tensor(8.)\n"
+          ]
+        }
+      ],
+      "source": [
+        "a = torch.tensor(3.0, requires_grad=True)\n",
+        "b = (a ** 2) + 2 * a + 1\n",
+        "b.backward()\n",
+        "print(\"Gradient of b wrt a:\", a.grad)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bLceqHHvpOwt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gradient of q at p: tensor(16.)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor p with value 2.0 (requires_grad=True) and compute the gradient of q = p^3 + 4p.\n",
+        "p = torch.tensor(2.0, requires_grad=True)\n",
+        "q = p ** 3 + 4 * p\n",
+        "q.backward()\n",
+        "print(\"Gradient of q at p:\", p.grad)\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jytRN17EpVbM"
+      },
+      "source": [
+        "### 4️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (2, 3) and find its max and min."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "fb7fv-E_pS6e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Tensor:\n",
+            " tensor([[0.9230, 0.3572, 0.7282],\n",
+            "        [0.3952, 0.5441, 0.2520]])\n",
+            "Max: tensor(0.9230)\n",
+            "Min: tensor(0.2520)\n"
+          ]
+        }
+      ],
+      "source": [
+        "rand_tensor = torch.rand((2, 3))\n",
+        "print(\"Random Tensor:\\n\", rand_tensor)\n",
+        "print(\"Max:\", torch.max(rand_tensor))\n",
+        "print(\"Min:\", torch.min(rand_tensor))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bhSClXespjvT"
+      },
+      "source": [
+        "### 5️⃣ Mini Training Loop 🤖\n",
+        "**Task:** Train a simple linear model y = wx + b using gradient descent."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "o7s2KMJVpZ5M"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "tensor([[1.],\n",
+            "        [2.],\n",
+            "        [3.]])\n",
+            "tensor([[2.],\n",
+            "        [4.],\n",
+            "        [6.]])\n",
+            "tensor([-0.4748], requires_grad=True)\n",
+            "tensor(3., requires_grad=True)\n",
+            "tensor(107.8136, grad_fn=<SumBackward0>)\n",
+            "tensor(48.0209, grad_fn=<SumBackward0>)\n",
+            "Trained weight: 0.8197987079620361\n",
+            "Trained bias: -0.13070182502269745\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Data\n",
+        "x_train = torch.tensor([[1.0], [2.0], [3.0]])\n",
+        "y_train = torch.tensor([[2.0], [4.0], [6.0]])\n",
+        "\n",
+        "# Model\n",
+        "w = torch.randn(1, requires_grad=True)\n",
+        "b = torch.randn(1, requires_grad=True)\n",
+        "\n",
+        "# Training\n",
+        "learning_rate = 0.01\n",
+        "for epoch in range(2):\n",
+        "    y_pred = w * x_train + b\n",
+        "    loss = torch.sum((y_pred - y_train) ** 2)\n",
+        "    print(loss)\n",
+        "    loss.backward()\n",
+        "\n",
+        "    # Update\n",
+        "    with torch.no_grad():\n",
+        "        w -= learning_rate * w.grad\n",
+        "        b -= learning_rate * b.grad\n",
+        "        w.grad.zero_()\n",
+        "        b.grad.zero_()\n",
+        "\n",
+        "print(\"Trained weight:\", w.item())\n",
+        "print(\"Trained bias:\", b.item())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rGhFqtrVpv8_"
+      },
+      "source": [
+        "### 6️⃣ Bonus ⚡\n",
+        "* Convert a PyTorch tensor to a NumPy array.\n",
+        "\n",
+        "* Convert it back to a PyTorch tensor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "id": "AF4t92eDpq0j"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[[1.]\n",
+            " [2.]\n",
+            " [3.]]\n",
+            "tensor([[1.],\n",
+            "        [2.],\n",
+            "        [3.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# To do\n",
+        "np_arr = x_train.numpy()\n",
+        "ts_arr = torch.from_numpy(np_arr)\n",
+        "\n",
+        "print(np_arr)\n",
+        "print(ts_arr)\n",
+        "\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/Assignments/a0.8/AI-DS_Nexus__A0_8__RezaShokr.ipynb b/a0.1/Assignments/a0.8/AI-DS_Nexus__A0_8__RezaShokr.ipynb
new file mode 100644
index 0000000..3688c55
--- /dev/null
+++ b/a0.1/Assignments/a0.8/AI-DS_Nexus__A0_8__RezaShokr.ipynb
@@ -0,0 +1,471 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WKCsWe71mbTO"
+      },
+      "source": [
+        "# 🧠 Assignment: Getting Started with TensorFlow\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ODPYXweVmdKp"
+      },
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using TensorFlow, including creating tensors, performing operations, and building a simple model.\n",
+        "\n",
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install TensorFlow and verify the version.\n",
+        "* Create a simple tensor and print it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "LrzxFPr3mPno"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TensorFlow version: 2.20.0\n",
+            "Tensor a:\n",
+            " tf.Tensor(\n",
+            "[[1 2]\n",
+            " [3 4]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "print(\"TensorFlow version:\", tf.__version__)\n",
+        "a = tf.constant([[1, 2], [3, 4]])\n",
+        "print(\"Tensor a:\\n\", a)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "MSC0WPwymq3V"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tensor b:\n",
+            " tf.Tensor(\n",
+            "[[5 6]\n",
+            " [7 8]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor b with values [[5, 6], [7, 8]] and print its shape.\n",
+        "b = tf.constant([[5, 6], [7, 8]])\n",
+        "print(\"Tensor b:\\n\", b)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b4cuWXJvmwdC"
+      },
+      "source": [
+        "### 2️⃣ Tensor Operations\n",
+        "**Task:** Perform element-wise addition and multiplication."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "pzlGeEuhmtYo"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Addition:\n",
+            " tf.Tensor(\n",
+            "[[ 6  8]\n",
+            " [10 12]], shape=(2, 2), dtype=int32)\n",
+            "Multiplication:\n",
+            " tf.Tensor(\n",
+            "[[ 5 12]\n",
+            " [21 32]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "c = a + b\n",
+        "print(\"Addition:\\n\", c)\n",
+        "\n",
+        "d = a * b\n",
+        "print(\"Multiplication:\\n\", d)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zv02rBVZmzPR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Matrix Multiplication:\n",
+            " tf.Tensor(\n",
+            "[[19 22]\n",
+            " [43 50]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Compute a @ b (matrix multiplication) and print the result.\n",
+        "\n",
+        "e = a @ b\n",
+        "print(\"Matrix Multiplication:\\n\", e)\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CbDULpk7m3c2"
+      },
+      "source": [
+        "### 3️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (3, 3) from a normal distribution and print its mean and standard deviation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Czc74X8Nm2Oa"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Tensor:\n",
+            " tf.Tensor(\n",
+            "[[ 0.06132538 -0.4524595   0.00465713]\n",
+            " [-0.9531849  -0.41993755  0.21351114]\n",
+            " [ 0.3521335  -1.6075189  -0.04839648]], shape=(3, 3), dtype=float32)\n",
+            "Mean: tf.Tensor(-0.31665224, shape=(), dtype=float32)\n",
+            "Std Dev: tf.Tensor(0.59132975, shape=(), dtype=float32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "\n",
+        "random_tensor = tf.random.normal([3, 3])\n",
+        "print(\"Random Tensor:\\n\", random_tensor)\n",
+        "print(\"Mean:\", tf.reduce_mean(random_tensor))\n",
+        "print(\"Std Dev:\", tf.math.reduce_std(random_tensor))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wVH1vcq3nDgV"
+      },
+      "source": [
+        "### 4️⃣ Building a Simple Model 🤖\n",
+        "**Task:** Build a single-layer neural network using tf.keras.Sequential."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9G2Jw8VSnC6f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m2\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "# model = tf.keras.Sequential([\n",
+        "#     tf.keras.layers.Dense(1, input_shape=(1,))\n",
+        "# ])\n",
+        "\n",
+        "model = tf.keras.Sequential([\n",
+        "    tf.keras.Input(shape=(1,)),\n",
+        "    tf.keras.layers.Dense(1)\n",
+        "])\n",
+        "model.summary()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "C1ianPt1nKlr"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)              │            \u001b[38;5;34m10\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m6\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> (64.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m16\u001b[0m (64.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> (64.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m16\u001b[0m (64.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Add another dense layer with 5 neurons and relu activation.\n",
+        "model = tf.keras.Sequential([\n",
+        "    tf.keras.Input(shape=(1,)),\n",
+        "    tf.keras.layers.Dense(5, activation='relu'),  # New layer with 5 neurons and relu\n",
+        "    tf.keras.layers.Dense(1)\n",
+        "])\n",
+        "\n",
+        "model.summary()\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GpOxR_RFnMt_"
+      },
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Create a tensor with values from 0 to 9 (inclusive).\n",
+        "\n",
+        "* Reshape it to shape (2, 5).\n",
+        "\n",
+        "* Pint the reshaped tensor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "caRjq7WinMLf"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Re-shaped tensor:\n",
+            " tf.Tensor(\n",
+            "[[0 1 2 3 4]\n",
+            " [5 6 7 8 9]], shape=(2, 5), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# To do\n",
+        "x = tf.range(10)\n",
+        "x_reshaped = tf.reshape(x, (2, 5))\n",
+        "print(\"Re-shaped tensor:\\n\", x_reshaped)\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python (nexus-venv)",
+      "language": "python",
+      "name": "nexus-venv"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/Assignments/a0.9/AI-DS_Nexus__A0_9__RezaShokr.ipynb b/a0.1/Assignments/a0.9/AI-DS_Nexus__A0_9__RezaShokr.ipynb
new file mode 100644
index 0000000..bab5dd5
--- /dev/null
+++ b/a0.1/Assignments/a0.9/AI-DS_Nexus__A0_9__RezaShokr.ipynb
@@ -0,0 +1,323 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pmXWXQskqQf7"
+      },
+      "source": [
+        "# 🏗 Assignment: Object-Oriented Programming Basics\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mYX-Yr0YqWTv"
+      },
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment checks your understanding of classes, objects, attributes, and methods using Python."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uxnkvADeqX6P"
+      },
+      "source": [
+        "### 1️⃣ Class and Object Creation\n",
+        "**Task:**\n",
+        "\n",
+        "* Define a class Car with attributes brand and year.\n",
+        "\n",
+        "* Create an object car1 of class Car with values (\"Toyota\", 2020) and print them."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "id": "HF7h_UxJqNLb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Toyota 2020\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "print(car1.brand, car1.year)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "5frI0xqrqcz2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tesla 2023\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Your turn:\n",
+        "# Create another object car2 with brand \"Tesla\" and year 2023.\n",
+        "car2 = Car(\"Tesla\", 2023)\n",
+        "print(car2.brand, car2.year)\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1WvafJpAqgkt"
+      },
+      "source": [
+        "### 2️⃣ Add Methods 🛠\n",
+        "**Task:**\n",
+        "Add a method info() to Car that prints \"Brand: <brand>, Year: <year>\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "AJM4pVrzqfJP"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Brand: Toyota, Year: 2020\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "car1.info()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "qwuFw0CiqkM8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Brand: Tesla, Year: 2023\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Call info() for car2.\n",
+        "car2 = Car(\"Tesla\", 2023)\n",
+        "car2.info()\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AOwBtvr0qosK"
+      },
+      "source": [
+        "### 3️⃣ Class vs Instance Attributes 📦\n",
+        "**Task:**\n",
+        "\n",
+        "* Add a class attribute wheels = 4 to Car.\n",
+        "\n",
+        "* Print car1.wheels and car2.wheels."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "id": "rPXvKwtsqm0C"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Brand: Toyota, Wheels: 4\n",
+            "Brand: Tesla, Wheels: 4\n"
+          ]
+        }
+      ],
+      "source": [
+        "# To do\n",
+        "setattr(Car,'wheels', 4)\n",
+        "print(f\"Brand: {car1.brand}, Wheels: {car1.wheels}\")\n",
+        "print(f\"Brand: {car2.brand}, Wheels: {car2.wheels}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NLoiaaeuquwg"
+      },
+      "source": [
+        "### 4️⃣ Inheritance 👑\n",
+        "**Task:**\n",
+        "\n",
+        "* Create a subclass ElectricCar inheriting from Car.\n",
+        "* Add a new attribute battery (e.g., \"80 kWh\") and a method battery_info() that prints \"Battery: <battery>\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "e_sS1YdIqtk_"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Brand: Tesla, Year: 2023\n",
+            "Battery: 80 kWh\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "    \n",
+        "class ElectricCar(Car):\n",
+        "    def __init__(self, brand, year, battery):\n",
+        "        super().__init__(brand, year)\n",
+        "        self.battery = battery\n",
+        "\n",
+        "    def battery_info(self):\n",
+        "        print(f\"Battery: {self.battery}\")\n",
+        "\n",
+        "e_car = ElectricCar(\"Tesla\", 2023, \"80 kWh\")\n",
+        "e_car.info()\n",
+        "e_car.battery_info()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wC0OIrRMq7jv"
+      },
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Add a __str__ method to Car to return a string like: \"Car(brand=Toyota, year=2020)\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "cAfBWxrlrA2t"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'Car(brand=Toyota, year=2020)'"
+            ]
+          },
+          "execution_count": 43,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# To do\n",
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "\n",
+        "    def st(self):\n",
+        "        return f\"Car(brand={self.brand}, year={self.year})\"\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "car1.st()\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/Assignments/a1.10/assignment_10_project_01_01_02_dataset___nexus___rsayyareh.ipynb b/a0.1/Assignments/a1.10/assignment_10_project_01_01_02_dataset___nexus___rsayyareh.ipynb
new file mode 100644
index 0000000..135eace
--- /dev/null
+++ b/a0.1/Assignments/a1.10/assignment_10_project_01_01_02_dataset___nexus___rsayyareh.ipynb
@@ -0,0 +1,389 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5pY7aLZSZQM9"
+      },
+      "source": [
+        "# 🍷 Mini-Project: Merge & Explore the Wine Quality Datasets\n",
+        "\n",
+        "\n",
+        "> **Goal: Merge Wine Quality – Red and Wine Quality – White (UCI) into a single dataset, do a careful first-look exploration, and save the merged file to CSV.**\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "wpNpLMF8aX1s"
+      },
+      "outputs": [],
+      "source": [
+        "# === Requirements ===\n",
+        "# pip install pandas matplotlib\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from pathlib import Path\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "ErqnxAGRaY_C"
+      },
+      "outputs": [],
+      "source": [
+        "# ---------- 1) Load ----------\n",
+        "URL_RED   = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n",
+        "URL_WHITE = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv\"\n",
+        "\n",
+        "red   = pd.read_csv(URL_RED, sep=\";\")\n",
+        "white = pd.read_csv(URL_WHITE, sep=\";\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lVbxevgpabbF",
+        "outputId": "e6b61931-4441-4230-9fe4-a9de9a46d810"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Red shape: (1599, 12) White shape: (4898, 12)\n",
+            "Columns equal? -> True\n",
+            "Columns: ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol', 'quality']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---------- 2) Sanity checks ----------\n",
+        "print(\"Red shape:\", red.shape, \"White shape:\", white.shape)\n",
+        "print(\"Columns equal? ->\", list(red.columns) == list(white.columns))\n",
+        "print(\"Columns:\", list(red.columns))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "5vV73isEadfQ"
+      },
+      "outputs": [],
+      "source": [
+        "# (Optional) strict schema assertion (search and read about assert in Python)\n",
+        "assert list(red.columns) == list(white.columns), \"Column mismatch between red and white datasets.\"\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uVTeWORRakKq",
+        "outputId": "805c7e6c-e3a3-4ce1-d4d0-68da11927a13"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Merged shape: (6497, 13)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---------- 3) Tag source & merge ----------\n",
+        "red[\"type\"] = \"red\"\n",
+        "white[\"type\"] = \"white\"\n",
+        "\n",
+        "df = pd.concat([red, white], ignore_index=True)\n",
+        "print(\"\\nMerged shape:\", df.shape)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 634
+        },
+        "collapsed": true,
+        "id": "OsMo1KOhambz",
+        "outputId": "37dc1fe6-2733-422f-9ea5-4b238d79095e"
+      },
+      "outputs": [],
+      "source": [
+        "# ---------- 4) Basic exploration ----------\n",
+        "print(\"\\nDtypes:\\n\", df.dtypes)\n",
+        "print(\"\\nMissing values per column:\\n\", df.isnull().sum().sort_values(ascending=False))\n",
+        "print(\"\\nHead:\\n\", df.head())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ou0kWts-aopS",
+        "outputId": "c08ea4df-841b-45b9-b907-9ae2ebf63939"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Duplicate rows: [False  True]\n",
+            "\n",
+            "Numeric summary:\n",
+            "                        count        mean        std      min       25%  \\\n",
+            "fixed acidity         6497.0    7.215307   1.296434  3.80000   6.40000   \n",
+            "volatile acidity      6497.0    0.339666   0.164636  0.08000   0.23000   \n",
+            "citric acid           6497.0    0.318633   0.145318  0.00000   0.25000   \n",
+            "residual sugar        6497.0    5.443235   4.757804  0.60000   1.80000   \n",
+            "chlorides             6497.0    0.056034   0.035034  0.00900   0.03800   \n",
+            "free sulfur dioxide   6497.0   30.525319  17.749400  1.00000  17.00000   \n",
+            "total sulfur dioxide  6497.0  115.744574  56.521855  6.00000  77.00000   \n",
+            "density               6497.0    0.994697   0.002999  0.98711   0.99234   \n",
+            "pH                    6497.0    3.218501   0.160787  2.72000   3.11000   \n",
+            "sulphates             6497.0    0.531268   0.148806  0.22000   0.43000   \n",
+            "alcohol               6497.0   10.491801   1.192712  8.00000   9.50000   \n",
+            "quality               6497.0    5.818378   0.873255  3.00000   5.00000   \n",
+            "\n",
+            "                            50%        75%        max  \n",
+            "fixed acidity           7.00000    7.70000   15.90000  \n",
+            "volatile acidity        0.29000    0.40000    1.58000  \n",
+            "citric acid             0.31000    0.39000    1.66000  \n",
+            "residual sugar          3.00000    8.10000   65.80000  \n",
+            "chlorides               0.04700    0.06500    0.61100  \n",
+            "free sulfur dioxide    29.00000   41.00000  289.00000  \n",
+            "total sulfur dioxide  118.00000  156.00000  440.00000  \n",
+            "density                 0.99489    0.99699    1.03898  \n",
+            "pH                      3.21000    3.32000    4.01000  \n",
+            "sulphates               0.51000    0.60000    2.00000  \n",
+            "alcohol                10.30000   11.30000   14.90000  \n",
+            "quality                 6.00000    6.00000    9.00000  \n",
+            "\n",
+            "Quality distribution (overall):\n",
+            " 6497\n",
+            "\n",
+            "Quality distribution by type:\n",
+            " type\n",
+            "red      1599\n",
+            "white    4898\n",
+            "Name: quality, dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Uniqueness & duplicates\n",
+        "dup_count = df.duplicated().unique()\n",
+        "print(\"\\nDuplicate rows:\", dup_count)\n",
+        "\n",
+        "# Descriptive statistics (numeric)\n",
+        "num_cols = df.select_dtypes(include=[np.number]).columns\n",
+        "print(\"\\nNumeric summary:\\n\", df[num_cols].describe().T)\n",
+        "\n",
+        "# Target distributions\n",
+        "print(\"\\nQuality distribution (overall):\\n\", df[\"quality\"].count())\n",
+        "print(\"\\nQuality distribution by type:\\n\", df.groupby(\"type\")[\"quality\"].count().sort_index())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 211
+        },
+        "id": "2Pa_iCVzaqzp",
+        "outputId": "6acac4cb-68ec-4bbf-caa1-2ef39263a8ca"
+      },
+      "outputs": [
+        {
+          "data": {
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",
+            "text/plain": [
+              "<Figure size 1600x300 with 4 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# ---------- 5) A few simple visuals (optional for report) ----------\n",
+        "# Histograms of numeric features (quick feel for ranges & skew)\n",
+        "top_vars = df.columns\n",
+        "n = min(4, len(top_vars))\n",
+        "\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 4, figsize=(16, 3), sharey=True)\n",
+        "\n",
+        "for i, ax in enumerate(axes):\n",
+        "    if i < n:\n",
+        "        col = top_vars[i]\n",
+        "        ax.hist(df[col].dropna(), bins=30)\n",
+        "        ax.set_title(f\"Histogram: {col}\")\n",
+        "        ax.set_xlabel(col)\n",
+        "        if i == 0:\n",
+        "            ax.set_ylabel(\"Count\")\n",
+        "        else:\n",
+        "            ax.set_ylabel(\"\")\n",
+        "    else:\n",
+        "        ax.axis(\"off\")  # hide unused panels if top_vars has < 4\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "w9z9f2OMatLD"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Boxplot of quality by type (class distribution spread)\n",
+        "plt.figure()\n",
+        "df.boxplot(column=\"quality\", by=\"type\")\n",
+        "plt.suptitle(\"\")\n",
+        "plt.title(\"Quality by Wine Type\")\n",
+        "plt.xlabel(\"Type\")\n",
+        "plt.ylabel(\"Quality\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "m9OZ2n2nawIP"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 700x600 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Correlation heatmap (numeric only)\n",
+        "corr = df[num_cols].corr()\n",
+        "plt.figure(figsize=(7, 6))\n",
+        "plt.imshow(corr, interpolation=\"nearest\")\n",
+        "plt.title(\"Correlation Heatmap\")\n",
+        "plt.colorbar()\n",
+        "plt.xticks(range(len(num_cols)), num_cols, rotation=90)\n",
+        "plt.yticks(range(len(num_cols)), num_cols)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "NHQPRTdLZI9R"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Saved merged file to: E:\\Nexus\\Nexus_Assignments\\a1.10\\outputs\\wine_quality_merged.csv\n",
+            "Reloaded shape: (6497, 13)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---------- 6) Save ----------\n",
+        "OUT_DIR = Path(\"./outputs\")\n",
+        "OUT_DIR.mkdir(parents=True, exist_ok=True)\n",
+        "out_file = OUT_DIR / \"wine_quality_merged.csv\"\n",
+        "df.to_csv(out_file, index=False)\n",
+        "print(f\"\\nSaved merged file to: {out_file.resolve()}\")\n",
+        "\n",
+        "# Quick verification of saved file\n",
+        "df_check = pd.read_csv(out_file)\n",
+        "print(\"Reloaded shape:\", df_check.shape)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/Assignments/a1.10/assignment_10_project_01_01_02_dataset___nexus___rsayyareh.py b/a0.1/Assignments/a1.10/assignment_10_project_01_01_02_dataset___nexus___rsayyareh.py
new file mode 100644
index 0000000..3f9550f
--- /dev/null
+++ b/a0.1/Assignments/a1.10/assignment_10_project_01_01_02_dataset___nexus___rsayyareh.py
@@ -0,0 +1,160 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # 🍷 Mini-Project: Merge & Explore the Wine Quality Datasets
+# 
+# 
+# > **Goal: Merge Wine Quality – Red and Wine Quality – White (UCI) into a single dataset, do a careful first-look exploration, and save the merged file to CSV.**
+# 
+# <p align="center">📢⚠️📂  </p>
+# 
+# <p align="center"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>
+# 
+# <p align="center"> 🚨📝🧠 </p>
+
+# In[2]:
+
+
+# === Requirements ===
+# pip install pandas matplotlib
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from pathlib import Path
+
+
+# In[3]:
+
+
+# ---------- 1) Load ----------
+URL_RED   = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"
+URL_WHITE = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"
+
+red   = pd.read_csv(URL_RED, sep=";")
+white = pd.read_csv(URL_WHITE, sep=";")
+
+
+# In[4]:
+
+
+# ---------- 2) Sanity checks ----------
+print("Red shape:", red.shape, "White shape:", white.shape)
+print("Columns equal? ->", list(red.columns) == list(white.columns))
+print("Columns:", list(red.columns))
+
+
+# In[5]:
+
+
+# (Optional) strict schema assertion (search and read about assert in Python)
+assert list(red.columns) == list(white.columns), "Column mismatch between red and white datasets."
+
+
+# In[13]:
+
+
+# ---------- 3) Tag source & merge ----------
+red["type"] = "red"
+white["type"] = "white"
+
+df = pd.concat([red, white], ignore_index=True)
+print("\nMerged shape:", df.shape)
+
+
+# In[ ]:
+
+
+# ---------- 4) Basic exploration ----------
+print("\nDtypes:\n", df.dtypes)
+print("\nMissing values per column:\n", df.isnull().sum().sort_values(ascending=False))
+print("\nHead:\n", df.head())
+
+
+# In[15]:
+
+
+# Uniqueness & duplicates
+dup_count = df.duplicated().unique()
+print("\nDuplicate rows:", dup_count)
+
+# Descriptive statistics (numeric)
+num_cols = df.select_dtypes(include=[np.number]).columns
+print("\nNumeric summary:\n", df[num_cols].describe().T)
+
+# Target distributions
+print("\nQuality distribution (overall):\n", df["quality"].count())
+print("\nQuality distribution by type:\n", df.groupby("type")["quality"].count().sort_index())
+
+
+# In[21]:
+
+
+# ---------- 5) A few simple visuals (optional for report) ----------
+# Histograms of numeric features (quick feel for ranges & skew)
+top_vars = df.columns
+n = min(4, len(top_vars))
+
+
+fig, axes = plt.subplots(1, 4, figsize=(16, 3), sharey=True)
+
+for i, ax in enumerate(axes):
+    if i < n:
+        col = top_vars[i]
+        ax.hist(df[col].dropna(), bins=30)
+        ax.set_title(f"Histogram: {col}")
+        ax.set_xlabel(col)
+        if i == 0:
+            ax.set_ylabel("Count")
+        else:
+            ax.set_ylabel("")
+    else:
+        ax.axis("off")  # hide unused panels if top_vars has < 4
+
+plt.tight_layout()
+plt.show()
+
+
+# In[22]:
+
+
+# Boxplot of quality by type (class distribution spread)
+plt.figure()
+df.boxplot(column="quality", by="type")
+plt.suptitle("")
+plt.title("Quality by Wine Type")
+plt.xlabel("Type")
+plt.ylabel("Quality")
+plt.tight_layout()
+plt.show()
+
+
+# In[23]:
+
+
+# Correlation heatmap (numeric only)
+corr = df[num_cols].corr()
+plt.figure(figsize=(7, 6))
+plt.imshow(corr, interpolation="nearest")
+plt.title("Correlation Heatmap")
+plt.colorbar()
+plt.xticks(range(len(num_cols)), num_cols, rotation=90)
+plt.yticks(range(len(num_cols)), num_cols)
+plt.tight_layout()
+plt.show()
+
+
+# In[24]:
+
+
+# ---------- 6) Save ----------
+OUT_DIR = Path("./outputs")
+OUT_DIR.mkdir(parents=True, exist_ok=True)
+out_file = OUT_DIR / "wine_quality_merged.csv"
+df.to_csv(out_file, index=False)
+print(f"\nSaved merged file to: {out_file.resolve()}")
+
+# Quick verification of saved file
+df_check = pd.read_csv(out_file)
+print("Reloaded shape:", df_check.shape)
+
diff --git a/a0.1/Assignments/a1.10/outputs/wine_quality_merged.csv b/a0.1/Assignments/a1.10/outputs/wine_quality_merged.csv
new file mode 100644
index 0000000..e2ddeff
--- /dev/null
+++ b/a0.1/Assignments/a1.10/outputs/wine_quality_merged.csv
@@ -0,0 +1,6498 @@
+fixed acidity,volatile acidity,citric acid,residual sugar,chlorides,free sulfur dioxide,total sulfur dioxide,density,pH,sulphates,alcohol,quality,type
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diff --git a/a0.1/Assignments/a1.11/AmesHousing_engineered.csv b/a0.1/Assignments/a1.11/AmesHousing_engineered.csv
new file mode 100644
index 0000000..bb578ce
--- /dev/null
+++ b/a0.1/Assignments/a1.11/AmesHousing_engineered.csv
@@ -0,0 +1,34 @@
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diff --git a/a0.1/Assignments/a1.11/Project2_rsayyareh.ipynb b/a0.1/Assignments/a1.11/Project2_rsayyareh.ipynb
new file mode 100644
index 0000000..d1c2901
--- /dev/null
+++ b/a0.1/Assignments/a1.11/Project2_rsayyareh.ipynb
@@ -0,0 +1,2369 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h6pr9RxJiBKU"
+      },
+      "source": [
+        "# 🏠 Mini-Project: Preprocess & Engineer Features on Ames Housing Dataset\n",
+        "\n",
+        "> **Goal: Work with the [Ames Housing dataset](https://www.kaggle.com/datasets/prevek18/ames-housing-dataset?select=AmesHousing.csv) to perform data preprocessing and create meaningful new features. You will:**\n",
+        "> - Handle **missing values**, **duplicates**, and **outliers**  \n",
+        "> - Detect and fix **skewness** in numerical features  \n",
+        "> - Encode categorical variables into numeric formats  \n",
+        "> - Create **non-linear features** (e.g., polynomial, log, interaction terms) from existing variables  \n",
+        "> - Save the cleaned and enriched dataset into a new CSV file  \n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂</p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project2_rezashokrzad.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠</p>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7CksOccRjV4s"
+      },
+      "source": [
+        "## 🔹 Step 1: Load the Dataset\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 203,
+      "metadata": {
+        "id": "Nlz-ZiyHgmQb"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\Parastoo\\AppData\\Local\\Temp\\ipykernel_11092\\867328322.py:15: DeprecationWarning: Use dataset_load() instead of load_dataset(). load_dataset() will be removed in a future version.\n",
+            "  df = kagglehub.load_dataset(\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "First 5 records:    Order        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
+            "0      1  526301100           20        RL         141.0     31770   Pave   \n",
+            "1      2  526350040           20        RH          80.0     11622   Pave   \n",
+            "2      3  526351010           20        RL          81.0     14267   Pave   \n",
+            "3      4  526353030           20        RL          93.0     11160   Pave   \n",
+            "4      5  527105010           60        RL          74.0     13830   Pave   \n",
+            "\n",
+            "  Alley Lot Shape Land Contour Utilities Lot Config Land Slope Neighborhood  \\\n",
+            "0   NaN       IR1          Lvl    AllPub     Corner        Gtl        NAmes   \n",
+            "1   NaN       Reg          Lvl    AllPub     Inside        Gtl        NAmes   \n",
+            "2   NaN       IR1          Lvl    AllPub     Corner        Gtl        NAmes   \n",
+            "3   NaN       Reg          Lvl    AllPub     Corner        Gtl        NAmes   \n",
+            "4   NaN       IR1          Lvl    AllPub     Inside        Gtl      Gilbert   \n",
+            "\n",
+            "  Condition 1 Condition 2 Bldg Type House Style  Overall Qual  Overall Cond  \\\n",
+            "0        Norm        Norm      1Fam      1Story             6             5   \n",
+            "1       Feedr        Norm      1Fam      1Story             5             6   \n",
+            "2        Norm        Norm      1Fam      1Story             6             6   \n",
+            "3        Norm        Norm      1Fam      1Story             7             5   \n",
+            "4        Norm        Norm      1Fam      2Story             5             5   \n",
+            "\n",
+            "   Year Built  Year Remod/Add Roof Style Roof Matl Exterior 1st Exterior 2nd  \\\n",
+            "0        1960            1960        Hip   CompShg      BrkFace      Plywood   \n",
+            "1        1961            1961      Gable   CompShg      VinylSd      VinylSd   \n",
+            "2        1958            1958        Hip   CompShg      Wd Sdng      Wd Sdng   \n",
+            "3        1968            1968        Hip   CompShg      BrkFace      BrkFace   \n",
+            "4        1997            1998      Gable   CompShg      VinylSd      VinylSd   \n",
+            "\n",
+            "  Mas Vnr Type  Mas Vnr Area Exter Qual Exter Cond Foundation Bsmt Qual  \\\n",
+            "0        Stone         112.0         TA         TA     CBlock        TA   \n",
+            "1          NaN           0.0         TA         TA     CBlock        TA   \n",
+            "2      BrkFace         108.0         TA         TA     CBlock        TA   \n",
+            "3          NaN           0.0         Gd         TA     CBlock        TA   \n",
+            "4          NaN           0.0         TA         TA      PConc        Gd   \n",
+            "\n",
+            "  Bsmt Cond Bsmt Exposure BsmtFin Type 1  BsmtFin SF 1 BsmtFin Type 2  \\\n",
+            "0        Gd            Gd            BLQ         639.0            Unf   \n",
+            "1        TA            No            Rec         468.0            LwQ   \n",
+            "2        TA            No            ALQ         923.0            Unf   \n",
+            "3        TA            No            ALQ        1065.0            Unf   \n",
+            "4        TA            No            GLQ         791.0            Unf   \n",
+            "\n",
+            "   BsmtFin SF 2  Bsmt Unf SF  Total Bsmt SF Heating Heating QC Central Air  \\\n",
+            "0           0.0        441.0         1080.0    GasA         Fa           Y   \n",
+            "1         144.0        270.0          882.0    GasA         TA           Y   \n",
+            "2           0.0        406.0         1329.0    GasA         TA           Y   \n",
+            "3           0.0       1045.0         2110.0    GasA         Ex           Y   \n",
+            "4           0.0        137.0          928.0    GasA         Gd           Y   \n",
+            "\n",
+            "  Electrical  1st Flr SF  2nd Flr SF  Low Qual Fin SF  Gr Liv Area  \\\n",
+            "0      SBrkr        1656           0                0         1656   \n",
+            "1      SBrkr         896           0                0          896   \n",
+            "2      SBrkr        1329           0                0         1329   \n",
+            "3      SBrkr        2110           0                0         2110   \n",
+            "4      SBrkr         928         701                0         1629   \n",
+            "\n",
+            "   Bsmt Full Bath  Bsmt Half Bath  Full Bath  Half Bath  Bedroom AbvGr  \\\n",
+            "0             1.0             0.0          1          0              3   \n",
+            "1             0.0             0.0          1          0              2   \n",
+            "2             0.0             0.0          1          1              3   \n",
+            "3             1.0             0.0          2          1              3   \n",
+            "4             0.0             0.0          2          1              3   \n",
+            "\n",
+            "   Kitchen AbvGr Kitchen Qual  TotRms AbvGrd Functional  Fireplaces  \\\n",
+            "0              1           TA              7        Typ           2   \n",
+            "1              1           TA              5        Typ           0   \n",
+            "2              1           Gd              6        Typ           0   \n",
+            "3              1           Ex              8        Typ           2   \n",
+            "4              1           TA              6        Typ           1   \n",
+            "\n",
+            "  Fireplace Qu Garage Type  Garage Yr Blt Garage Finish  Garage Cars  \\\n",
+            "0           Gd      Attchd         1960.0           Fin          2.0   \n",
+            "1          NaN      Attchd         1961.0           Unf          1.0   \n",
+            "2          NaN      Attchd         1958.0           Unf          1.0   \n",
+            "3           TA      Attchd         1968.0           Fin          2.0   \n",
+            "4           TA      Attchd         1997.0           Fin          2.0   \n",
+            "\n",
+            "   Garage Area Garage Qual Garage Cond Paved Drive  Wood Deck SF  \\\n",
+            "0        528.0          TA          TA           P           210   \n",
+            "1        730.0          TA          TA           Y           140   \n",
+            "2        312.0          TA          TA           Y           393   \n",
+            "3        522.0          TA          TA           Y             0   \n",
+            "4        482.0          TA          TA           Y           212   \n",
+            "\n",
+            "   Open Porch SF  Enclosed Porch  3Ssn Porch  Screen Porch  Pool Area Pool QC  \\\n",
+            "0             62               0           0             0          0     NaN   \n",
+            "1              0               0           0           120          0     NaN   \n",
+            "2             36               0           0             0          0     NaN   \n",
+            "3              0               0           0             0          0     NaN   \n",
+            "4             34               0           0             0          0     NaN   \n",
+            "\n",
+            "   Fence Misc Feature  Misc Val  Mo Sold  Yr Sold Sale Type Sale Condition  \\\n",
+            "0    NaN          NaN         0        5     2010       WD          Normal   \n",
+            "1  MnPrv          NaN         0        6     2010       WD          Normal   \n",
+            "2    NaN         Gar2     12500        6     2010       WD          Normal   \n",
+            "3    NaN          NaN         0        4     2010       WD          Normal   \n",
+            "4  MnPrv          NaN         0        3     2010       WD          Normal   \n",
+            "\n",
+            "   SalePrice  \n",
+            "0     215000  \n",
+            "1     105000  \n",
+            "2     172000  \n",
+            "3     244000  \n",
+            "4     189900  \n",
+            "Last 5 records:       Order        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
+            "2925   2926  923275080           80        RL          37.0      7937   Pave   \n",
+            "2926   2927  923276100           20        RL           NaN      8885   Pave   \n",
+            "2927   2928  923400125           85        RL          62.0     10441   Pave   \n",
+            "2928   2929  924100070           20        RL          77.0     10010   Pave   \n",
+            "2929   2930  924151050           60        RL          74.0      9627   Pave   \n",
+            "\n",
+            "     Alley Lot Shape Land Contour Utilities Lot Config Land Slope  \\\n",
+            "2925   NaN       IR1          Lvl    AllPub    CulDSac        Gtl   \n",
+            "2926   NaN       IR1          Low    AllPub     Inside        Mod   \n",
+            "2927   NaN       Reg          Lvl    AllPub     Inside        Gtl   \n",
+            "2928   NaN       Reg          Lvl    AllPub     Inside        Mod   \n",
+            "2929   NaN       Reg          Lvl    AllPub     Inside        Mod   \n",
+            "\n",
+            "     Neighborhood Condition 1 Condition 2 Bldg Type House Style  Overall Qual  \\\n",
+            "2925      Mitchel        Norm        Norm      1Fam        SLvl             6   \n",
+            "2926      Mitchel        Norm        Norm      1Fam      1Story             5   \n",
+            "2927      Mitchel        Norm        Norm      1Fam      SFoyer             5   \n",
+            "2928      Mitchel        Norm        Norm      1Fam      1Story             5   \n",
+            "2929      Mitchel        Norm        Norm      1Fam      2Story             7   \n",
+            "\n",
+            "      Overall Cond  Year Built  Year Remod/Add Roof Style Roof Matl  \\\n",
+            "2925             6        1984            1984      Gable   CompShg   \n",
+            "2926             5        1983            1983      Gable   CompShg   \n",
+            "2927             5        1992            1992      Gable   CompShg   \n",
+            "2928             5        1974            1975      Gable   CompShg   \n",
+            "2929             5        1993            1994      Gable   CompShg   \n",
+            "\n",
+            "     Exterior 1st Exterior 2nd Mas Vnr Type  Mas Vnr Area Exter Qual  \\\n",
+            "2925      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2926      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2927      HdBoard      Wd Shng          NaN           0.0         TA   \n",
+            "2928      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2929      HdBoard      HdBoard      BrkFace          94.0         TA   \n",
+            "\n",
+            "     Exter Cond Foundation Bsmt Qual Bsmt Cond Bsmt Exposure BsmtFin Type 1  \\\n",
+            "2925         TA     CBlock        TA        TA            Av            GLQ   \n",
+            "2926         TA     CBlock        Gd        TA            Av            BLQ   \n",
+            "2927         TA      PConc        Gd        TA            Av            GLQ   \n",
+            "2928         TA     CBlock        Gd        TA            Av            ALQ   \n",
+            "2929         TA      PConc        Gd        TA            Av            LwQ   \n",
+            "\n",
+            "      BsmtFin SF 1 BsmtFin Type 2  BsmtFin SF 2  Bsmt Unf SF  Total Bsmt SF  \\\n",
+            "2925         819.0            Unf           0.0        184.0         1003.0   \n",
+            "2926         301.0            ALQ         324.0        239.0          864.0   \n",
+            "2927         337.0            Unf           0.0        575.0          912.0   \n",
+            "2928        1071.0            LwQ         123.0        195.0         1389.0   \n",
+            "2929         758.0            Unf           0.0        238.0          996.0   \n",
+            "\n",
+            "     Heating Heating QC Central Air Electrical  1st Flr SF  2nd Flr SF  \\\n",
+            "2925    GasA         TA           Y      SBrkr        1003           0   \n",
+            "2926    GasA         TA           Y      SBrkr         902           0   \n",
+            "2927    GasA         TA           Y      SBrkr         970           0   \n",
+            "2928    GasA         Gd           Y      SBrkr        1389           0   \n",
+            "2929    GasA         Ex           Y      SBrkr         996        1004   \n",
+            "\n",
+            "      Low Qual Fin SF  Gr Liv Area  Bsmt Full Bath  Bsmt Half Bath  Full Bath  \\\n",
+            "2925                0         1003             1.0             0.0          1   \n",
+            "2926                0          902             1.0             0.0          1   \n",
+            "2927                0          970             0.0             1.0          1   \n",
+            "2928                0         1389             1.0             0.0          1   \n",
+            "2929                0         2000             0.0             0.0          2   \n",
+            "\n",
+            "      Half Bath  Bedroom AbvGr  Kitchen AbvGr Kitchen Qual  TotRms AbvGrd  \\\n",
+            "2925          0              3              1           TA              6   \n",
+            "2926          0              2              1           TA              5   \n",
+            "2927          0              3              1           TA              6   \n",
+            "2928          0              2              1           TA              6   \n",
+            "2929          1              3              1           TA              9   \n",
+            "\n",
+            "     Functional  Fireplaces Fireplace Qu Garage Type  Garage Yr Blt  \\\n",
+            "2925        Typ           0          NaN      Detchd         1984.0   \n",
+            "2926        Typ           0          NaN      Attchd         1983.0   \n",
+            "2927        Typ           0          NaN         NaN            NaN   \n",
+            "2928        Typ           1           TA      Attchd         1975.0   \n",
+            "2929        Typ           1           TA      Attchd         1993.0   \n",
+            "\n",
+            "     Garage Finish  Garage Cars  Garage Area Garage Qual Garage Cond  \\\n",
+            "2925           Unf          2.0        588.0          TA          TA   \n",
+            "2926           Unf          2.0        484.0          TA          TA   \n",
+            "2927           NaN          0.0          0.0         NaN         NaN   \n",
+            "2928           RFn          2.0        418.0          TA          TA   \n",
+            "2929           Fin          3.0        650.0          TA          TA   \n",
+            "\n",
+            "     Paved Drive  Wood Deck SF  Open Porch SF  Enclosed Porch  3Ssn Porch  \\\n",
+            "2925           Y           120              0               0           0   \n",
+            "2926           Y           164              0               0           0   \n",
+            "2927           Y            80             32               0           0   \n",
+            "2928           Y           240             38               0           0   \n",
+            "2929           Y           190             48               0           0   \n",
+            "\n",
+            "      Screen Porch  Pool Area Pool QC  Fence Misc Feature  Misc Val  Mo Sold  \\\n",
+            "2925             0          0     NaN  GdPrv          NaN         0        3   \n",
+            "2926             0          0     NaN  MnPrv          NaN         0        6   \n",
+            "2927             0          0     NaN  MnPrv         Shed       700        7   \n",
+            "2928             0          0     NaN    NaN          NaN         0        4   \n",
+            "2929             0          0     NaN    NaN          NaN         0       11   \n",
+            "\n",
+            "      Yr Sold Sale Type Sale Condition  SalePrice  \n",
+            "2925     2006       WD          Normal     142500  \n",
+            "2926     2006       WD          Normal     131000  \n",
+            "2927     2006       WD          Normal     132000  \n",
+            "2928     2006       WD          Normal     170000  \n",
+            "2929     2006       WD          Normal     188000  \n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Load the Ames Housing dataset into a DataFrame.\n",
+        "# Hint: The dataset is available on Kaggle (\"Ames Housing\").\n",
+        "# After loading, display the first and last 5 rows to check if it worked.\n",
+        "\n",
+        "# Install dependencies as needed:\n",
+        "# pip install kagglehub[pandas-datasets]\n",
+        "import kagglehub\n",
+        "from kagglehub import KaggleDatasetAdapter\n",
+        "import pandas as pd\n",
+        "\n",
+        "# Set the path to the file you'd like to load\n",
+        "file_path = \"AmesHousing.csv\"\n",
+        "\n",
+        "# Load the latest version\n",
+        "df = kagglehub.load_dataset(\n",
+        "  KaggleDatasetAdapter.PANDAS,\n",
+        "  \"prevek18/ames-housing-dataset\",\n",
+        "  file_path,\n",
+        "  # Provide any additional arguments like \n",
+        "  # sql_query or pandas_kwargs. See the \n",
+        "  # documenation for more information:\n",
+        "  # https://github.com/Kaggle/kagglehub/blob/main/README.md#kaggledatasetadapterpandas\n",
+        ")\n",
+        "\n",
+        "pd.set_option('display.max_columns', None)\n",
+        "\n",
+        "print(\"First 5 records:\", df.head())\n",
+        "print(\"Last 5 records:\", df.tail())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OdX7swaujg_u"
+      },
+      "source": [
+        "## 🔹 Step 2: Exploratory Data Review (EDR)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 204,
+      "metadata": {
+        "id": "DMYRBPWWjgpo"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Shape: (2930, 82)\n",
+            "Columns Name: Index(['Order', 'PID', 'MS SubClass', 'MS Zoning', 'Lot Frontage', 'Lot Area',\n",
+            "       'Street', 'Alley', 'Lot Shape', 'Land Contour', 'Utilities',\n",
+            "       'Lot Config', 'Land Slope', 'Neighborhood', 'Condition 1',\n",
+            "       'Condition 2', 'Bldg Type', 'House Style', 'Overall Qual',\n",
+            "       'Overall Cond', 'Year Built', 'Year Remod/Add', 'Roof Style',\n",
+            "       'Roof Matl', 'Exterior 1st', 'Exterior 2nd', 'Mas Vnr Type',\n",
+            "       'Mas Vnr Area', 'Exter Qual', 'Exter Cond', 'Foundation', 'Bsmt Qual',\n",
+            "       'Bsmt Cond', 'Bsmt Exposure', 'BsmtFin Type 1', 'BsmtFin SF 1',\n",
+            "       'BsmtFin Type 2', 'BsmtFin SF 2', 'Bsmt Unf SF', 'Total Bsmt SF',\n",
+            "       'Heating', 'Heating QC', 'Central Air', 'Electrical', '1st Flr SF',\n",
+            "       '2nd Flr SF', 'Low Qual Fin SF', 'Gr Liv Area', 'Bsmt Full Bath',\n",
+            "       'Bsmt Half Bath', 'Full Bath', 'Half Bath', 'Bedroom AbvGr',\n",
+            "       'Kitchen AbvGr', 'Kitchen Qual', 'TotRms AbvGrd', 'Functional',\n",
+            "       'Fireplaces', 'Fireplace Qu', 'Garage Type', 'Garage Yr Blt',\n",
+            "       'Garage Finish', 'Garage Cars', 'Garage Area', 'Garage Qual',\n",
+            "       'Garage Cond', 'Paved Drive', 'Wood Deck SF', 'Open Porch SF',\n",
+            "       'Enclosed Porch', '3Ssn Porch', 'Screen Porch', 'Pool Area', 'Pool QC',\n",
+            "       'Fence', 'Misc Feature', 'Misc Val', 'Mo Sold', 'Yr Sold', 'Sale Type',\n",
+            "       'Sale Condition', 'SalePrice'],\n",
+            "      dtype='object')\n",
+            "Sample of Records: <bound method NDFrame.sample of       Order        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
+            "0         1  526301100           20        RL         141.0     31770   Pave   \n",
+            "1         2  526350040           20        RH          80.0     11622   Pave   \n",
+            "2         3  526351010           20        RL          81.0     14267   Pave   \n",
+            "3         4  526353030           20        RL          93.0     11160   Pave   \n",
+            "4         5  527105010           60        RL          74.0     13830   Pave   \n",
+            "...     ...        ...          ...       ...           ...       ...    ...   \n",
+            "2925   2926  923275080           80        RL          37.0      7937   Pave   \n",
+            "2926   2927  923276100           20        RL           NaN      8885   Pave   \n",
+            "2927   2928  923400125           85        RL          62.0     10441   Pave   \n",
+            "2928   2929  924100070           20        RL          77.0     10010   Pave   \n",
+            "2929   2930  924151050           60        RL          74.0      9627   Pave   \n",
+            "\n",
+            "     Alley Lot Shape Land Contour Utilities Lot Config Land Slope  \\\n",
+            "0      NaN       IR1          Lvl    AllPub     Corner        Gtl   \n",
+            "1      NaN       Reg          Lvl    AllPub     Inside        Gtl   \n",
+            "2      NaN       IR1          Lvl    AllPub     Corner        Gtl   \n",
+            "3      NaN       Reg          Lvl    AllPub     Corner        Gtl   \n",
+            "4      NaN       IR1          Lvl    AllPub     Inside        Gtl   \n",
+            "...    ...       ...          ...       ...        ...        ...   \n",
+            "2925   NaN       IR1          Lvl    AllPub    CulDSac        Gtl   \n",
+            "2926   NaN       IR1          Low    AllPub     Inside        Mod   \n",
+            "2927   NaN       Reg          Lvl    AllPub     Inside        Gtl   \n",
+            "2928   NaN       Reg          Lvl    AllPub     Inside        Mod   \n",
+            "2929   NaN       Reg          Lvl    AllPub     Inside        Mod   \n",
+            "\n",
+            "     Neighborhood Condition 1 Condition 2 Bldg Type House Style  Overall Qual  \\\n",
+            "0           NAmes        Norm        Norm      1Fam      1Story             6   \n",
+            "1           NAmes       Feedr        Norm      1Fam      1Story             5   \n",
+            "2           NAmes        Norm        Norm      1Fam      1Story             6   \n",
+            "3           NAmes        Norm        Norm      1Fam      1Story             7   \n",
+            "4         Gilbert        Norm        Norm      1Fam      2Story             5   \n",
+            "...           ...         ...         ...       ...         ...           ...   \n",
+            "2925      Mitchel        Norm        Norm      1Fam        SLvl             6   \n",
+            "2926      Mitchel        Norm        Norm      1Fam      1Story             5   \n",
+            "2927      Mitchel        Norm        Norm      1Fam      SFoyer             5   \n",
+            "2928      Mitchel        Norm        Norm      1Fam      1Story             5   \n",
+            "2929      Mitchel        Norm        Norm      1Fam      2Story             7   \n",
+            "\n",
+            "      Overall Cond  Year Built  Year Remod/Add Roof Style Roof Matl  \\\n",
+            "0                5        1960            1960        Hip   CompShg   \n",
+            "1                6        1961            1961      Gable   CompShg   \n",
+            "2                6        1958            1958        Hip   CompShg   \n",
+            "3                5        1968            1968        Hip   CompShg   \n",
+            "4                5        1997            1998      Gable   CompShg   \n",
+            "...            ...         ...             ...        ...       ...   \n",
+            "2925             6        1984            1984      Gable   CompShg   \n",
+            "2926             5        1983            1983      Gable   CompShg   \n",
+            "2927             5        1992            1992      Gable   CompShg   \n",
+            "2928             5        1974            1975      Gable   CompShg   \n",
+            "2929             5        1993            1994      Gable   CompShg   \n",
+            "\n",
+            "     Exterior 1st Exterior 2nd Mas Vnr Type  Mas Vnr Area Exter Qual  \\\n",
+            "0         BrkFace      Plywood        Stone         112.0         TA   \n",
+            "1         VinylSd      VinylSd          NaN           0.0         TA   \n",
+            "2         Wd Sdng      Wd Sdng      BrkFace         108.0         TA   \n",
+            "3         BrkFace      BrkFace          NaN           0.0         Gd   \n",
+            "4         VinylSd      VinylSd          NaN           0.0         TA   \n",
+            "...           ...          ...          ...           ...        ...   \n",
+            "2925      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2926      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2927      HdBoard      Wd Shng          NaN           0.0         TA   \n",
+            "2928      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2929      HdBoard      HdBoard      BrkFace          94.0         TA   \n",
+            "\n",
+            "     Exter Cond Foundation Bsmt Qual Bsmt Cond Bsmt Exposure BsmtFin Type 1  \\\n",
+            "0            TA     CBlock        TA        Gd            Gd            BLQ   \n",
+            "1            TA     CBlock        TA        TA            No            Rec   \n",
+            "2            TA     CBlock        TA        TA            No            ALQ   \n",
+            "3            TA     CBlock        TA        TA            No            ALQ   \n",
+            "4            TA      PConc        Gd        TA            No            GLQ   \n",
+            "...         ...        ...       ...       ...           ...            ...   \n",
+            "2925         TA     CBlock        TA        TA            Av            GLQ   \n",
+            "2926         TA     CBlock        Gd        TA            Av            BLQ   \n",
+            "2927         TA      PConc        Gd        TA            Av            GLQ   \n",
+            "2928         TA     CBlock        Gd        TA            Av            ALQ   \n",
+            "2929         TA      PConc        Gd        TA            Av            LwQ   \n",
+            "\n",
+            "      BsmtFin SF 1 BsmtFin Type 2  BsmtFin SF 2  Bsmt Unf SF  Total Bsmt SF  \\\n",
+            "0            639.0            Unf           0.0        441.0         1080.0   \n",
+            "1            468.0            LwQ         144.0        270.0          882.0   \n",
+            "2            923.0            Unf           0.0        406.0         1329.0   \n",
+            "3           1065.0            Unf           0.0       1045.0         2110.0   \n",
+            "4            791.0            Unf           0.0        137.0          928.0   \n",
+            "...            ...            ...           ...          ...            ...   \n",
+            "2925         819.0            Unf           0.0        184.0         1003.0   \n",
+            "2926         301.0            ALQ         324.0        239.0          864.0   \n",
+            "2927         337.0            Unf           0.0        575.0          912.0   \n",
+            "2928        1071.0            LwQ         123.0        195.0         1389.0   \n",
+            "2929         758.0            Unf           0.0        238.0          996.0   \n",
+            "\n",
+            "     Heating Heating QC Central Air Electrical  1st Flr SF  2nd Flr SF  \\\n",
+            "0       GasA         Fa           Y      SBrkr        1656           0   \n",
+            "1       GasA         TA           Y      SBrkr         896           0   \n",
+            "2       GasA         TA           Y      SBrkr        1329           0   \n",
+            "3       GasA         Ex           Y      SBrkr        2110           0   \n",
+            "4       GasA         Gd           Y      SBrkr         928         701   \n",
+            "...      ...        ...         ...        ...         ...         ...   \n",
+            "2925    GasA         TA           Y      SBrkr        1003           0   \n",
+            "2926    GasA         TA           Y      SBrkr         902           0   \n",
+            "2927    GasA         TA           Y      SBrkr         970           0   \n",
+            "2928    GasA         Gd           Y      SBrkr        1389           0   \n",
+            "2929    GasA         Ex           Y      SBrkr         996        1004   \n",
+            "\n",
+            "      Low Qual Fin SF  Gr Liv Area  Bsmt Full Bath  Bsmt Half Bath  Full Bath  \\\n",
+            "0                   0         1656             1.0             0.0          1   \n",
+            "1                   0          896             0.0             0.0          1   \n",
+            "2                   0         1329             0.0             0.0          1   \n",
+            "3                   0         2110             1.0             0.0          2   \n",
+            "4                   0         1629             0.0             0.0          2   \n",
+            "...               ...          ...             ...             ...        ...   \n",
+            "2925                0         1003             1.0             0.0          1   \n",
+            "2926                0          902             1.0             0.0          1   \n",
+            "2927                0          970             0.0             1.0          1   \n",
+            "2928                0         1389             1.0             0.0          1   \n",
+            "2929                0         2000             0.0             0.0          2   \n",
+            "\n",
+            "      Half Bath  Bedroom AbvGr  Kitchen AbvGr Kitchen Qual  TotRms AbvGrd  \\\n",
+            "0             0              3              1           TA              7   \n",
+            "1             0              2              1           TA              5   \n",
+            "2             1              3              1           Gd              6   \n",
+            "3             1              3              1           Ex              8   \n",
+            "4             1              3              1           TA              6   \n",
+            "...         ...            ...            ...          ...            ...   \n",
+            "2925          0              3              1           TA              6   \n",
+            "2926          0              2              1           TA              5   \n",
+            "2927          0              3              1           TA              6   \n",
+            "2928          0              2              1           TA              6   \n",
+            "2929          1              3              1           TA              9   \n",
+            "\n",
+            "     Functional  Fireplaces Fireplace Qu Garage Type  Garage Yr Blt  \\\n",
+            "0           Typ           2           Gd      Attchd         1960.0   \n",
+            "1           Typ           0          NaN      Attchd         1961.0   \n",
+            "2           Typ           0          NaN      Attchd         1958.0   \n",
+            "3           Typ           2           TA      Attchd         1968.0   \n",
+            "4           Typ           1           TA      Attchd         1997.0   \n",
+            "...         ...         ...          ...         ...            ...   \n",
+            "2925        Typ           0          NaN      Detchd         1984.0   \n",
+            "2926        Typ           0          NaN      Attchd         1983.0   \n",
+            "2927        Typ           0          NaN         NaN            NaN   \n",
+            "2928        Typ           1           TA      Attchd         1975.0   \n",
+            "2929        Typ           1           TA      Attchd         1993.0   \n",
+            "\n",
+            "     Garage Finish  Garage Cars  Garage Area Garage Qual Garage Cond  \\\n",
+            "0              Fin          2.0        528.0          TA          TA   \n",
+            "1              Unf          1.0        730.0          TA          TA   \n",
+            "2              Unf          1.0        312.0          TA          TA   \n",
+            "3              Fin          2.0        522.0          TA          TA   \n",
+            "4              Fin          2.0        482.0          TA          TA   \n",
+            "...            ...          ...          ...         ...         ...   \n",
+            "2925           Unf          2.0        588.0          TA          TA   \n",
+            "2926           Unf          2.0        484.0          TA          TA   \n",
+            "2927           NaN          0.0          0.0         NaN         NaN   \n",
+            "2928           RFn          2.0        418.0          TA          TA   \n",
+            "2929           Fin          3.0        650.0          TA          TA   \n",
+            "\n",
+            "     Paved Drive  Wood Deck SF  Open Porch SF  Enclosed Porch  3Ssn Porch  \\\n",
+            "0              P           210             62               0           0   \n",
+            "1              Y           140              0               0           0   \n",
+            "2              Y           393             36               0           0   \n",
+            "3              Y             0              0               0           0   \n",
+            "4              Y           212             34               0           0   \n",
+            "...          ...           ...            ...             ...         ...   \n",
+            "2925           Y           120              0               0           0   \n",
+            "2926           Y           164              0               0           0   \n",
+            "2927           Y            80             32               0           0   \n",
+            "2928           Y           240             38               0           0   \n",
+            "2929           Y           190             48               0           0   \n",
+            "\n",
+            "      Screen Porch  Pool Area Pool QC  Fence Misc Feature  Misc Val  Mo Sold  \\\n",
+            "0                0          0     NaN    NaN          NaN         0        5   \n",
+            "1              120          0     NaN  MnPrv          NaN         0        6   \n",
+            "2                0          0     NaN    NaN         Gar2     12500        6   \n",
+            "3                0          0     NaN    NaN          NaN         0        4   \n",
+            "4                0          0     NaN  MnPrv          NaN         0        3   \n",
+            "...            ...        ...     ...    ...          ...       ...      ...   \n",
+            "2925             0          0     NaN  GdPrv          NaN         0        3   \n",
+            "2926             0          0     NaN  MnPrv          NaN         0        6   \n",
+            "2927             0          0     NaN  MnPrv         Shed       700        7   \n",
+            "2928             0          0     NaN    NaN          NaN         0        4   \n",
+            "2929             0          0     NaN    NaN          NaN         0       11   \n",
+            "\n",
+            "      Yr Sold Sale Type Sale Condition  SalePrice  \n",
+            "0        2010       WD          Normal     215000  \n",
+            "1        2010       WD          Normal     105000  \n",
+            "2        2010       WD          Normal     172000  \n",
+            "3        2010       WD          Normal     244000  \n",
+            "4        2010       WD          Normal     189900  \n",
+            "...       ...       ...            ...        ...  \n",
+            "2925     2006       WD          Normal     142500  \n",
+            "2926     2006       WD          Normal     131000  \n",
+            "2927     2006       WD          Normal     132000  \n",
+            "2928     2006       WD          Normal     170000  \n",
+            "2929     2006       WD          Normal     188000  \n",
+            "\n",
+            "[2930 rows x 82 columns]>\n",
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 2930 entries, 0 to 2929\n",
+            "Data columns (total 82 columns):\n",
+            " #   Column           Non-Null Count  Dtype  \n",
+            "---  ------           --------------  -----  \n",
+            " 0   Order            2930 non-null   int64  \n",
+            " 1   PID              2930 non-null   int64  \n",
+            " 2   MS SubClass      2930 non-null   int64  \n",
+            " 3   MS Zoning        2930 non-null   object \n",
+            " 4   Lot Frontage     2440 non-null   float64\n",
+            " 5   Lot Area         2930 non-null   int64  \n",
+            " 6   Street           2930 non-null   object \n",
+            " 7   Alley            198 non-null    object \n",
+            " 8   Lot Shape        2930 non-null   object \n",
+            " 9   Land Contour     2930 non-null   object \n",
+            " 10  Utilities        2930 non-null   object \n",
+            " 11  Lot Config       2930 non-null   object \n",
+            " 12  Land Slope       2930 non-null   object \n",
+            " 13  Neighborhood     2930 non-null   object \n",
+            " 14  Condition 1      2930 non-null   object \n",
+            " 15  Condition 2      2930 non-null   object \n",
+            " 16  Bldg Type        2930 non-null   object \n",
+            " 17  House Style      2930 non-null   object \n",
+            " 18  Overall Qual     2930 non-null   int64  \n",
+            " 19  Overall Cond     2930 non-null   int64  \n",
+            " 20  Year Built       2930 non-null   int64  \n",
+            " 21  Year Remod/Add   2930 non-null   int64  \n",
+            " 22  Roof Style       2930 non-null   object \n",
+            " 23  Roof Matl        2930 non-null   object \n",
+            " 24  Exterior 1st     2930 non-null   object \n",
+            " 25  Exterior 2nd     2930 non-null   object \n",
+            " 26  Mas Vnr Type     1155 non-null   object \n",
+            " 27  Mas Vnr Area     2907 non-null   float64\n",
+            " 28  Exter Qual       2930 non-null   object \n",
+            " 29  Exter Cond       2930 non-null   object \n",
+            " 30  Foundation       2930 non-null   object \n",
+            " 31  Bsmt Qual        2850 non-null   object \n",
+            " 32  Bsmt Cond        2850 non-null   object \n",
+            " 33  Bsmt Exposure    2847 non-null   object \n",
+            " 34  BsmtFin Type 1   2850 non-null   object \n",
+            " 35  BsmtFin SF 1     2929 non-null   float64\n",
+            " 36  BsmtFin Type 2   2849 non-null   object \n",
+            " 37  BsmtFin SF 2     2929 non-null   float64\n",
+            " 38  Bsmt Unf SF      2929 non-null   float64\n",
+            " 39  Total Bsmt SF    2929 non-null   float64\n",
+            " 40  Heating          2930 non-null   object \n",
+            " 41  Heating QC       2930 non-null   object \n",
+            " 42  Central Air      2930 non-null   object \n",
+            " 43  Electrical       2929 non-null   object \n",
+            " 44  1st Flr SF       2930 non-null   int64  \n",
+            " 45  2nd Flr SF       2930 non-null   int64  \n",
+            " 46  Low Qual Fin SF  2930 non-null   int64  \n",
+            " 47  Gr Liv Area      2930 non-null   int64  \n",
+            " 48  Bsmt Full Bath   2928 non-null   float64\n",
+            " 49  Bsmt Half Bath   2928 non-null   float64\n",
+            " 50  Full Bath        2930 non-null   int64  \n",
+            " 51  Half Bath        2930 non-null   int64  \n",
+            " 52  Bedroom AbvGr    2930 non-null   int64  \n",
+            " 53  Kitchen AbvGr    2930 non-null   int64  \n",
+            " 54  Kitchen Qual     2930 non-null   object \n",
+            " 55  TotRms AbvGrd    2930 non-null   int64  \n",
+            " 56  Functional       2930 non-null   object \n",
+            " 57  Fireplaces       2930 non-null   int64  \n",
+            " 58  Fireplace Qu     1508 non-null   object \n",
+            " 59  Garage Type      2773 non-null   object \n",
+            " 60  Garage Yr Blt    2771 non-null   float64\n",
+            " 61  Garage Finish    2771 non-null   object \n",
+            " 62  Garage Cars      2929 non-null   float64\n",
+            " 63  Garage Area      2929 non-null   float64\n",
+            " 64  Garage Qual      2771 non-null   object \n",
+            " 65  Garage Cond      2771 non-null   object \n",
+            " 66  Paved Drive      2930 non-null   object \n",
+            " 67  Wood Deck SF     2930 non-null   int64  \n",
+            " 68  Open Porch SF    2930 non-null   int64  \n",
+            " 69  Enclosed Porch   2930 non-null   int64  \n",
+            " 70  3Ssn Porch       2930 non-null   int64  \n",
+            " 71  Screen Porch     2930 non-null   int64  \n",
+            " 72  Pool Area        2930 non-null   int64  \n",
+            " 73  Pool QC          13 non-null     object \n",
+            " 74  Fence            572 non-null    object \n",
+            " 75  Misc Feature     106 non-null    object \n",
+            " 76  Misc Val         2930 non-null   int64  \n",
+            " 77  Mo Sold          2930 non-null   int64  \n",
+            " 78  Yr Sold          2930 non-null   int64  \n",
+            " 79  Sale Type        2930 non-null   object \n",
+            " 80  Sale Condition   2930 non-null   object \n",
+            " 81  SalePrice        2930 non-null   int64  \n",
+            "dtypes: float64(11), int64(28), object(43)\n",
+            "memory usage: 1.8+ MB\n",
+            "            Order           PID  MS SubClass  Lot Frontage       Lot Area  \\\n",
+            "count  2930.00000  2.930000e+03  2930.000000   2440.000000    2930.000000   \n",
+            "mean   1465.50000  7.144645e+08    57.387372     69.224590   10147.921843   \n",
+            "std     845.96247  1.887308e+08    42.638025     23.365335    7880.017759   \n",
+            "min       1.00000  5.263011e+08    20.000000     21.000000    1300.000000   \n",
+            "25%     733.25000  5.284770e+08    20.000000     58.000000    7440.250000   \n",
+            "50%    1465.50000  5.354536e+08    50.000000     68.000000    9436.500000   \n",
+            "75%    2197.75000  9.071811e+08    70.000000     80.000000   11555.250000   \n",
+            "max    2930.00000  1.007100e+09   190.000000    313.000000  215245.000000   \n",
+            "\n",
+            "       Overall Qual  Overall Cond   Year Built  Year Remod/Add  Mas Vnr Area  \\\n",
+            "count   2930.000000   2930.000000  2930.000000     2930.000000   2907.000000   \n",
+            "mean       6.094881      5.563140  1971.356314     1984.266553    101.896801   \n",
+            "std        1.411026      1.111537    30.245361       20.860286    179.112611   \n",
+            "min        1.000000      1.000000  1872.000000     1950.000000      0.000000   \n",
+            "25%        5.000000      5.000000  1954.000000     1965.000000      0.000000   \n",
+            "50%        6.000000      5.000000  1973.000000     1993.000000      0.000000   \n",
+            "75%        7.000000      6.000000  2001.000000     2004.000000    164.000000   \n",
+            "max       10.000000      9.000000  2010.000000     2010.000000   1600.000000   \n",
+            "\n",
+            "       BsmtFin SF 1  BsmtFin SF 2  Bsmt Unf SF  Total Bsmt SF   1st Flr SF  \\\n",
+            "count   2929.000000   2929.000000  2929.000000    2929.000000  2930.000000   \n",
+            "mean     442.629566     49.722431   559.262547    1051.614544  1159.557679   \n",
+            "std      455.590839    169.168476   439.494153     440.615067   391.890885   \n",
+            "min        0.000000      0.000000     0.000000       0.000000   334.000000   \n",
+            "25%        0.000000      0.000000   219.000000     793.000000   876.250000   \n",
+            "50%      370.000000      0.000000   466.000000     990.000000  1084.000000   \n",
+            "75%      734.000000      0.000000   802.000000    1302.000000  1384.000000   \n",
+            "max     5644.000000   1526.000000  2336.000000    6110.000000  5095.000000   \n",
+            "\n",
+            "        2nd Flr SF  Low Qual Fin SF  Gr Liv Area  Bsmt Full Bath  \\\n",
+            "count  2930.000000      2930.000000  2930.000000     2928.000000   \n",
+            "mean    335.455973         4.676792  1499.690444        0.431352   \n",
+            "std     428.395715        46.310510   505.508887        0.524820   \n",
+            "min       0.000000         0.000000   334.000000        0.000000   \n",
+            "25%       0.000000         0.000000  1126.000000        0.000000   \n",
+            "50%       0.000000         0.000000  1442.000000        0.000000   \n",
+            "75%     703.750000         0.000000  1742.750000        1.000000   \n",
+            "max    2065.000000      1064.000000  5642.000000        3.000000   \n",
+            "\n",
+            "       Bsmt Half Bath    Full Bath    Half Bath  Bedroom AbvGr  Kitchen AbvGr  \\\n",
+            "count     2928.000000  2930.000000  2930.000000    2930.000000    2930.000000   \n",
+            "mean         0.061134     1.566553     0.379522       2.854266       1.044369   \n",
+            "std          0.245254     0.552941     0.502629       0.827731       0.214076   \n",
+            "min          0.000000     0.000000     0.000000       0.000000       0.000000   \n",
+            "25%          0.000000     1.000000     0.000000       2.000000       1.000000   \n",
+            "50%          0.000000     2.000000     0.000000       3.000000       1.000000   \n",
+            "75%          0.000000     2.000000     1.000000       3.000000       1.000000   \n",
+            "max          2.000000     4.000000     2.000000       8.000000       3.000000   \n",
+            "\n",
+            "       TotRms AbvGrd   Fireplaces  Garage Yr Blt  Garage Cars  Garage Area  \\\n",
+            "count    2930.000000  2930.000000    2771.000000  2929.000000  2929.000000   \n",
+            "mean        6.443003     0.599317    1978.132443     1.766815   472.819734   \n",
+            "std         1.572964     0.647921      25.528411     0.760566   215.046549   \n",
+            "min         2.000000     0.000000    1895.000000     0.000000     0.000000   \n",
+            "25%         5.000000     0.000000    1960.000000     1.000000   320.000000   \n",
+            "50%         6.000000     1.000000    1979.000000     2.000000   480.000000   \n",
+            "75%         7.000000     1.000000    2002.000000     2.000000   576.000000   \n",
+            "max        15.000000     4.000000    2207.000000     5.000000  1488.000000   \n",
+            "\n",
+            "       Wood Deck SF  Open Porch SF  Enclosed Porch   3Ssn Porch  Screen Porch  \\\n",
+            "count   2930.000000    2930.000000     2930.000000  2930.000000   2930.000000   \n",
+            "mean      93.751877      47.533447       23.011604     2.592491     16.002048   \n",
+            "std      126.361562      67.483400       64.139059    25.141331     56.087370   \n",
+            "min        0.000000       0.000000        0.000000     0.000000      0.000000   \n",
+            "25%        0.000000       0.000000        0.000000     0.000000      0.000000   \n",
+            "50%        0.000000      27.000000        0.000000     0.000000      0.000000   \n",
+            "75%      168.000000      70.000000        0.000000     0.000000      0.000000   \n",
+            "max     1424.000000     742.000000     1012.000000   508.000000    576.000000   \n",
+            "\n",
+            "         Pool Area      Misc Val      Mo Sold      Yr Sold      SalePrice  \n",
+            "count  2930.000000   2930.000000  2930.000000  2930.000000    2930.000000  \n",
+            "mean      2.243345     50.635154     6.216041  2007.790444  180796.060068  \n",
+            "std      35.597181    566.344288     2.714492     1.316613   79886.692357  \n",
+            "min       0.000000      0.000000     1.000000  2006.000000   12789.000000  \n",
+            "25%       0.000000      0.000000     4.000000  2007.000000  129500.000000  \n",
+            "50%       0.000000      0.000000     6.000000  2008.000000  160000.000000  \n",
+            "75%       0.000000      0.000000     8.000000  2009.000000  213500.000000  \n",
+            "max     800.000000  17000.000000    12.000000  2010.000000  755000.000000  \n",
+            "       MS Zoning Street Alley Lot Shape Land Contour Utilities Lot Config  \\\n",
+            "count       2930   2930   198      2930         2930      2930       2930   \n",
+            "unique         7      2     2         4            4         3          5   \n",
+            "top           RL   Pave  Grvl       Reg          Lvl    AllPub     Inside   \n",
+            "freq        2273   2918   120      1859         2633      2927       2140   \n",
+            "\n",
+            "       Land Slope Neighborhood Condition 1 Condition 2 Bldg Type House Style  \\\n",
+            "count        2930         2930        2930        2930      2930        2930   \n",
+            "unique          3           28           9           8         5           8   \n",
+            "top           Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
+            "freq         2789          443        2522        2900      2425        1481   \n",
+            "\n",
+            "       Roof Style Roof Matl Exterior 1st Exterior 2nd Mas Vnr Type Exter Qual  \\\n",
+            "count        2930      2930         2930         2930         1155       2930   \n",
+            "unique          6         8           16           17            4          4   \n",
+            "top         Gable   CompShg      VinylSd      VinylSd      BrkFace         TA   \n",
+            "freq         2321      2887         1026         1015          880       1799   \n",
+            "\n",
+            "       Exter Cond Foundation Bsmt Qual Bsmt Cond Bsmt Exposure BsmtFin Type 1  \\\n",
+            "count        2930       2930      2850      2850          2847           2850   \n",
+            "unique          5          6         5         5             4              6   \n",
+            "top            TA      PConc        TA        TA            No            GLQ   \n",
+            "freq         2549       1310      1283      2616          1906            859   \n",
+            "\n",
+            "       BsmtFin Type 2 Heating Heating QC Central Air Electrical Kitchen Qual  \\\n",
+            "count            2849    2930       2930        2930       2929         2930   \n",
+            "unique              6       6          5           2          5            5   \n",
+            "top               Unf    GasA         Ex           Y      SBrkr           TA   \n",
+            "freq             2499    2885       1495        2734       2682         1494   \n",
+            "\n",
+            "       Functional Fireplace Qu Garage Type Garage Finish Garage Qual  \\\n",
+            "count        2930         1508        2773          2771        2771   \n",
+            "unique          8            5           6             3           5   \n",
+            "top           Typ           Gd      Attchd           Unf          TA   \n",
+            "freq         2728          744        1731          1231        2615   \n",
+            "\n",
+            "       Garage Cond Paved Drive Pool QC  Fence Misc Feature Sale Type  \\\n",
+            "count         2771        2930      13    572          106      2930   \n",
+            "unique           5           3       4      4            5        10   \n",
+            "top             TA           Y      Ex  MnPrv         Shed       WD    \n",
+            "freq          2665        2652       4    330           95      2536   \n",
+            "\n",
+            "       Sale Condition  \n",
+            "count            2930  \n",
+            "unique              6  \n",
+            "top            Normal  \n",
+            "freq             2413  \n",
+            "Order: [   1    2    3 ... 2928 2929 2930]\n",
+            "PID: [526301100 526350040 526351010 ... 923400125 924100070 924151050]\n",
+            "MS SubClass: [ 20  60 120  50  85 160  80  30  90 190  45  70  75  40 180 150]\n",
+            "MS Zoning: ['RL' 'RH' 'FV' 'RM' 'C (all)' 'I (all)' 'A (agr)']\n",
+            "Lot Frontage: [141.  80.  81.  93.  74.  78.  41.  43.  39.  60.  75.  nan  63.  85.\n",
+            "  47. 152.  88. 140. 105.  65.  70.  26.  21.  53.  24. 102.  98.  83.\n",
+            "  94.  95.  90.  79. 100.  44. 110.  61.  36.  67. 108.  59.  92.  58.\n",
+            "  56.  73.  72.  84.  76.  50.  55.  68. 107.  25.  30.  57.  40.  77.\n",
+            " 120. 137.  87. 119.  64.  96.  71.  69.  52.  51.  54.  86. 124.  82.\n",
+            "  38.  48.  89.  66.  45.  35. 129.  31.  42.  28.  99. 104.  97. 103.\n",
+            "  34. 117. 149. 122.  62. 174. 106. 112.  32. 115. 128.  91.  33. 121.\n",
+            " 144. 130. 109. 150. 113. 125. 101.  46. 114. 135. 136.  37.  22. 313.\n",
+            "  49. 123. 160. 195. 118. 134. 182. 116. 138. 155. 126. 200. 168. 111.\n",
+            " 131. 153. 133.]\n",
+            "Lot Area: [31770 11622 14267 ...  7937  8885 10441]\n",
+            "Street: ['Pave' 'Grvl']\n",
+            "Alley: [nan 'Pave' 'Grvl']\n",
+            "Lot Shape: ['IR1' 'Reg' 'IR2' 'IR3']\n",
+            "Land Contour: ['Lvl' 'HLS' 'Bnk' 'Low']\n",
+            "Utilities: ['AllPub' 'NoSewr' 'NoSeWa']\n",
+            "Lot Config: ['Corner' 'Inside' 'CulDSac' 'FR2' 'FR3']\n",
+            "Land Slope: ['Gtl' 'Mod' 'Sev']\n",
+            "Neighborhood: ['NAmes' 'Gilbert' 'StoneBr' 'NWAmes' 'Somerst' 'BrDale' 'NPkVill'\n",
+            " 'NridgHt' 'Blmngtn' 'NoRidge' 'SawyerW' 'Sawyer' 'Greens' 'BrkSide'\n",
+            " 'OldTown' 'IDOTRR' 'ClearCr' 'SWISU' 'Edwards' 'CollgCr' 'Crawfor'\n",
+            " 'Blueste' 'Mitchel' 'Timber' 'MeadowV' 'Veenker' 'GrnHill' 'Landmrk']\n",
+            "Condition 1: ['Norm' 'Feedr' 'PosN' 'RRNe' 'RRAe' 'Artery' 'PosA' 'RRAn' 'RRNn']\n",
+            "Condition 2: ['Norm' 'Feedr' 'PosA' 'PosN' 'Artery' 'RRNn' 'RRAe' 'RRAn']\n",
+            "Bldg Type: ['1Fam' 'TwnhsE' 'Twnhs' 'Duplex' '2fmCon']\n",
+            "House Style: ['1Story' '2Story' '1.5Fin' 'SFoyer' 'SLvl' '2.5Unf' '1.5Unf' '2.5Fin']\n",
+            "Overall Qual: [ 6  5  7  8  9  4  3  2 10  1]\n",
+            "Overall Cond: [5 6 7 2 8 4 9 3 1]\n",
+            "Year Built: [1960 1961 1958 1968 1997 1998 2001 1992 1995 1999 1993 1990 1985 2003\n",
+            " 1988 2010 1951 1978 1977 1974 2000 1970 1971 1975 2009 2007 2005 2004\n",
+            " 2002 2006 1996 1994 2008 1980 1979 1984 1920 1965 1967 1963 1962 1976\n",
+            " 1972 1966 1959 1964 1950 1952 1949 1940 1954 1955 1957 1956 1953 1948\n",
+            " 1900 1910 1927 1915 1945 1929 1938 1923 1928 1890 1885 1922 1925 1939\n",
+            " 1942 1936 1930 1921 1912 1917 1907 1875 1969 1947 1946 1987 1941 1924\n",
+            " 1914 1931 1919 1989 1896 1973 1991 1981 1986 1916 1926 1935 1892 1898\n",
+            " 1880 1882 1937 1902 1934 1982 1983 1932 1918 1904 1905 1872 1893 1906\n",
+            " 1908 1911 1895 1879 1901 1913]\n",
+            "Year Remod/Add: [1960 1961 1958 1968 1998 2001 1992 1996 1999 1994 2007 1990 1985 2003\n",
+            " 2005 2010 1951 1988 1977 1974 2000 1970 2008 1971 1975 1978 2006 2004\n",
+            " 2002 1995 2009 1980 1979 1984 1981 1950 1967 1963 1993 1966 1959 1964\n",
+            " 1954 1972 1989 1957 1956 1952 1955 1962 1997 1965 1969 1987 1976 1991\n",
+            " 1973 1986 1983 1953 1982]\n",
+            "Roof Style: ['Hip' 'Gable' 'Mansard' 'Gambrel' 'Shed' 'Flat']\n",
+            "Roof Matl: ['CompShg' 'WdShake' 'Tar&Grv' 'WdShngl' 'Membran' 'ClyTile' 'Roll'\n",
+            " 'Metal']\n",
+            "Exterior 1st: ['BrkFace' 'VinylSd' 'Wd Sdng' 'CemntBd' 'HdBoard' 'Plywood' 'MetalSd'\n",
+            " 'AsbShng' 'WdShing' 'Stucco' 'AsphShn' 'BrkComm' 'CBlock' 'PreCast'\n",
+            " 'Stone' 'ImStucc']\n",
+            "Exterior 2nd: ['Plywood' 'VinylSd' 'Wd Sdng' 'BrkFace' 'CmentBd' 'HdBoard' 'Wd Shng'\n",
+            " 'MetalSd' 'ImStucc' 'Brk Cmn' 'AsbShng' 'Stucco' 'AsphShn' 'CBlock'\n",
+            " 'Stone' 'PreCast' 'Other']\n",
+            "Mas Vnr Type: ['Stone' nan 'BrkFace' 'BrkCmn' 'CBlock']\n",
+            "Mas Vnr Area: [1.120e+02 0.000e+00 1.080e+02 2.000e+01 6.030e+02 3.500e+02 1.190e+02\n",
+            " 4.800e+02 8.100e+01 1.800e+02 5.040e+02 4.920e+02 3.810e+02 1.620e+02\n",
+            " 2.000e+02 4.500e+02 2.560e+02 2.260e+02 6.150e+02 2.400e+02 1.680e+02\n",
+            " 7.600e+02 1.280e+02 1.095e+03 2.320e+02 4.120e+02 1.780e+02 1.060e+02\n",
+            " 1.400e+01 1.600e+01       nan 1.650e+02 1.140e+02 3.380e+02 3.620e+02\n",
+            " 3.480e+02 3.000e+01 5.790e+02 3.600e+01 1.220e+02 1.300e+02 3.100e+01\n",
+            " 2.500e+02 1.200e+02 2.160e+02 4.320e+02 1.159e+03 2.890e+02 2.800e+01\n",
+            " 4.200e+01 1.720e+02 4.510e+02 2.680e+02 8.600e+01 1.560e+02 1.440e+02\n",
+            " 2.650e+02 3.400e+02 1.100e+02 1.640e+02 3.610e+02 2.870e+02 5.060e+02\n",
+            " 1.500e+02 2.200e+02 3.240e+02 9.100e+01 1.040e+02 3.000e+02 2.610e+02\n",
+            " 2.180e+02 3.510e+02 7.710e+02 2.940e+02 9.000e+01 7.200e+01 4.700e+01\n",
+            " 1.430e+02 3.280e+02 2.880e+02 9.600e+01 3.360e+02 1.770e+02 8.500e+01\n",
+            " 2.460e+02 2.400e+01 8.000e+01 1.160e+02 1.530e+02 3.200e+02 4.790e+02\n",
+            " 2.230e+02 4.420e+02 1.700e+02 1.690e+02 1.710e+02 1.090e+02 9.800e+01\n",
+            " 1.450e+02 2.030e+02 3.710e+02 4.300e+02 4.400e+01 1.860e+02 3.350e+02\n",
+            " 6.000e+01 8.400e+01 1.890e+02 4.400e+02 1.880e+02 3.200e+01 1.600e+02\n",
+            " 2.200e+01 4.000e+01 6.800e+01 4.500e+01 3.440e+02 7.480e+02 4.640e+02\n",
+            " 1.570e+02 2.780e+02 2.090e+02 1.260e+02 1.010e+02 2.290e+02 2.250e+02\n",
+            " 2.060e+02 1.610e+02 1.960e+02 1.740e+02 3.330e+02 7.600e+01 3.120e+02\n",
+            " 1.420e+02 4.250e+02 5.100e+02 2.300e+02 7.260e+02 8.600e+02 6.400e+02\n",
+            " 3.060e+02 1.540e+02 3.050e+02 4.200e+02 4.720e+02 4.240e+02 3.020e+02\n",
+            " 2.380e+02 2.840e+02 2.620e+02 2.850e+02 2.960e+02 4.180e+02 9.220e+02\n",
+            " 7.240e+02 3.830e+02 1.350e+02 1.760e+02 1.660e+02 7.300e+02 4.700e+02\n",
+            " 3.080e+02 5.000e+02 2.700e+02 1.630e+02 1.100e+01 2.100e+02 2.980e+02\n",
+            " 6.730e+02 7.310e+02 9.750e+02 9.210e+02 6.340e+02 2.860e+02 3.720e+02\n",
+            " 5.280e+02 1.940e+02 2.600e+02 1.980e+02 1.210e+02 1.000e+02 2.640e+02\n",
+            " 1.400e+02 1.320e+02 3.660e+02 1.410e+02 1.150e+02 2.800e+02 2.520e+02\n",
+            " 8.940e+02 5.130e+02 4.560e+02 5.710e+02 3.590e+02 2.830e+02 3.600e+02\n",
+            " 5.090e+02 7.000e+01 9.500e+01 2.170e+02 3.000e+00 2.470e+02 5.760e+02\n",
+            " 1.830e+02 3.990e+02 6.500e+02 6.570e+02 2.950e+02 3.680e+02 8.200e+01\n",
+            " 1.240e+02 4.440e+02 9.200e+01 8.900e+01 2.300e+01 5.400e+01 1.490e+02\n",
+            " 2.340e+02 1.370e+02 2.750e+02 2.420e+02 3.640e+02 3.520e+02 1.360e+02\n",
+            " 2.040e+02 5.730e+02 2.550e+02 7.400e+01 2.590e+02 8.800e+01 5.410e+02\n",
+            " 4.060e+02 3.100e+02 5.840e+02 2.900e+02 1.820e+02 7.500e+01 2.450e+02\n",
+            " 1.230e+02 1.020e+02 6.210e+02 6.600e+02 4.020e+02 1.580e+02 4.220e+02\n",
+            " 1.270e+02 6.040e+02 3.560e+02 6.500e+01 4.260e+02 2.720e+02 8.160e+02\n",
+            " 4.360e+02 5.540e+02 4.680e+02 6.800e+02 6.640e+02 2.920e+02 1.110e+03\n",
+            " 2.210e+02 7.660e+02 6.160e+02 7.140e+02 1.460e+02 3.180e+02 6.470e+02\n",
+            " 1.290e+03 4.730e+02 4.950e+02 4.660e+02 6.510e+02 4.480e+02 5.300e+01\n",
+            " 7.680e+02 3.800e+01 2.580e+02 3.040e+02 5.680e+02 1.790e+02 2.120e+02\n",
+            " 1.050e+03 5.640e+02 3.420e+02 1.480e+02 2.430e+02 4.910e+02 2.370e+02\n",
+            " 4.100e+02 1.510e+02 1.870e+02 3.870e+02 5.200e+01 1.250e+02 2.760e+02\n",
+            " 4.150e+02 3.900e+01 4.100e+01 2.990e+02 9.900e+01 1.900e+02 2.510e+02\n",
+            " 2.810e+02 2.270e+02 2.020e+02 3.960e+02 1.340e+02 1.920e+02 2.050e+02\n",
+            " 2.150e+02 1.130e+02 5.000e+01 2.660e+02 1.470e+02 2.220e+02 7.960e+02\n",
+            " 5.800e+01 6.320e+02 6.680e+02 2.280e+02 2.190e+02 6.740e+02 1.970e+02\n",
+            " 1.115e+03 1.380e+02 7.100e+02 9.450e+02 6.700e+01 5.490e+02 2.330e+02\n",
+            " 2.530e+02 2.630e+02 3.650e+02 5.670e+02 3.760e+02 3.780e+02 4.520e+02\n",
+            " 2.540e+02 3.150e+02 4.000e+02 3.750e+02 7.720e+02 2.480e+02 9.700e+02\n",
+            " 5.020e+02 3.880e+02 3.940e+02 2.350e+02 5.150e+02 7.050e+02 1.170e+03\n",
+            " 5.940e+02 3.090e+02 5.260e+02 7.540e+02 2.080e+02 4.280e+02 3.530e+02\n",
+            " 1.050e+02 5.700e+01 3.370e+02 1.129e+03 1.600e+03 6.000e+02 1.000e+00\n",
+            " 5.250e+02 6.600e+01 6.300e+01 5.600e+01 2.240e+02 8.700e+01 2.910e+02\n",
+            " 6.900e+01 2.790e+02 4.350e+02 3.230e+02 1.670e+02 5.100e+01 2.140e+02\n",
+            " 5.190e+02 4.380e+02 4.800e+01 1.224e+03 7.620e+02 4.230e+02 1.840e+02\n",
+            " 6.520e+02 4.810e+02 2.390e+02 2.740e+02 1.170e+02 8.860e+02 2.360e+02\n",
+            " 9.400e+01 2.440e+02 9.020e+02 4.340e+02 2.700e+01 6.620e+02 7.340e+02\n",
+            " 5.500e+02 1.031e+03 3.400e+01 5.140e+02 4.080e+02 3.800e+02 2.970e+02\n",
+            " 3.700e+02 3.850e+02 7.880e+02 5.620e+02 8.700e+02 5.180e+02 5.720e+02\n",
+            " 1.800e+01 3.220e+02 1.378e+03 8.770e+02 5.300e+02 3.970e+02 7.380e+02\n",
+            " 5.010e+02 3.910e+02 1.180e+02 4.600e+01 6.920e+02 3.320e+02 1.750e+02\n",
+            " 6.400e+01 5.220e+02 1.047e+03 3.790e+02 2.070e+02 9.700e+01 5.320e+02\n",
+            " 6.200e+01 1.990e+02 3.550e+02 4.590e+02 4.050e+02 3.270e+02 2.570e+02\n",
+            " 2.930e+02 6.530e+02 6.300e+02 3.820e+02 4.430e+02]\n",
+            "Exter Qual: ['TA' 'Gd' 'Ex' 'Fa']\n",
+            "Exter Cond: ['TA' 'Gd' 'Fa' 'Po' 'Ex']\n",
+            "Foundation: ['CBlock' 'PConc' 'Wood' 'BrkTil' 'Slab' 'Stone']\n",
+            "Bsmt Qual: ['TA' 'Gd' 'Ex' nan 'Fa' 'Po']\n",
+            "Bsmt Cond: ['Gd' 'TA' nan 'Po' 'Fa' 'Ex']\n",
+            "Bsmt Exposure: ['Gd' 'No' 'Mn' 'Av' nan]\n",
+            "BsmtFin Type 1: ['BLQ' 'Rec' 'ALQ' 'GLQ' 'Unf' 'LwQ' nan]\n",
+            "BsmtFin SF 1: [6.390e+02 4.680e+02 9.230e+02 1.065e+03 7.910e+02 6.020e+02 6.160e+02\n",
+            " 2.630e+02 1.180e+03 0.000e+00 9.350e+02 6.370e+02 3.680e+02 1.416e+03\n",
+            " 4.270e+02 1.445e+03 1.200e+02 7.900e+02 7.050e+02 8.850e+02 5.330e+02\n",
+            " 5.780e+02 7.340e+02 7.750e+02 8.040e+02 4.320e+02 1.051e+03 1.560e+02\n",
+            " 3.000e+02 3.600e+02 5.140e+02 3.110e+02 1.218e+03 1.646e+03 1.201e+03\n",
+            " 1.100e+02 2.800e+01 2.000e+00 2.188e+03 7.330e+02 1.373e+03 4.560e+02\n",
+            " 2.400e+01 1.600e+01 3.260e+02 6.250e+02 2.500e+02 9.190e+02 1.032e+03\n",
+            " 5.240e+02 8.160e+02 1.078e+03 2.220e+02 1.414e+03 6.560e+02 6.950e+02\n",
+            " 5.430e+02 6.230e+02 4.020e+02 3.380e+02 8.990e+02 5.530e+02 4.500e+02\n",
+            " 8.240e+02 6.590e+02 1.260e+02 6.740e+02 1.129e+03 1.298e+03 2.800e+02\n",
+            " 3.760e+02 3.780e+02 4.660e+02 6.040e+02 2.440e+02 4.840e+02 7.280e+02\n",
+            " 1.052e+03 8.330e+02 5.060e+02 1.137e+03 1.200e+03 6.870e+02 3.940e+02\n",
+            " 9.820e+02 3.290e+02 6.980e+02 5.690e+02 1.059e+03 1.010e+03 1.014e+03\n",
+            " 7.630e+02 1.500e+03 4.900e+01 6.700e+02 6.960e+02 3.540e+02 5.400e+02\n",
+            " 9.440e+02 4.430e+02 9.120e+02 2.470e+02 1.188e+03 8.560e+02 1.018e+03\n",
+            " 9.220e+02 1.000e+03 6.970e+02 9.360e+02 3.390e+02 6.480e+02 5.320e+02\n",
+            " 7.310e+02 3.200e+02 2.480e+02 1.056e+03 7.200e+01 4.810e+02 3.400e+02\n",
+            " 5.070e+02 2.340e+02 5.880e+02 7.170e+02 4.800e+01 5.790e+02 2.740e+02\n",
+            " 5.100e+02 7.800e+02 1.760e+02 6.860e+02 6.000e+02 2.830e+02 7.880e+02\n",
+            " 4.740e+02 1.880e+02 4.520e+02 2.640e+02 2.760e+02 4.480e+02 9.600e+02\n",
+            " 1.040e+02 7.660e+02 1.026e+03 7.300e+01 7.360e+02 7.040e+02 8.410e+02\n",
+            " 1.302e+03 8.420e+02 2.400e+02 3.710e+02 1.319e+03 2.670e+02 4.380e+02\n",
+            " 1.092e+03 4.420e+02 1.258e+03 9.640e+02 2.880e+02 1.080e+02 7.390e+02\n",
+            " 1.920e+02 9.540e+02 3.600e+01 1.346e+03 1.433e+03 8.600e+02 7.500e+02\n",
+            " 7.470e+02 1.470e+03 5.040e+02 8.700e+02 3.530e+02 5.050e+02 1.980e+02\n",
+            " 1.820e+02 4.800e+02 1.682e+03 1.358e+03 4.830e+02 6.720e+02 6.620e+02\n",
+            " 3.700e+02 7.120e+02 1.070e+03 5.280e+02 4.220e+02 9.400e+01 3.480e+02\n",
+            " 3.830e+02 1.330e+02 2.030e+02 2.180e+02 2.380e+02 4.260e+02 3.750e+02\n",
+            " 2.750e+02 1.406e+03 3.430e+02 7.600e+01 1.247e+03 7.350e+02 3.080e+02\n",
+            " 6.150e+02 6.790e+02 5.390e+02 7.800e+01 6.240e+02 4.200e+01 3.340e+02\n",
+            " 9.150e+02 1.290e+02 1.500e+02 2.940e+02 4.690e+02 5.930e+02 2.070e+02\n",
+            " 4.580e+02 4.760e+02 1.341e+03 5.640e+02 8.440e+02 1.410e+03 8.470e+02\n",
+            " 8.500e+02 2.840e+02 1.320e+03 1.965e+03 1.158e+03 3.410e+02 7.410e+02\n",
+            " 1.890e+02 3.100e+02 5.600e+02 6.940e+02 1.036e+03 1.904e+03 1.274e+03\n",
+            " 4.000e+02 6.920e+02 8.220e+02 1.246e+03 3.630e+02 8.320e+02 1.104e+03\n",
+            " 3.810e+02 6.220e+02 5.440e+02 2.250e+02 1.333e+03 8.880e+02 6.360e+02\n",
+            " 8.280e+02 4.390e+02 5.000e+02 7.260e+02 1.910e+02 2.540e+02 7.650e+02\n",
+            " 1.620e+02 2.310e+02 9.580e+02 3.060e+02 5.660e+02 4.350e+02 2.570e+02\n",
+            " 3.890e+02 2.790e+02 5.360e+02 6.440e+02 1.172e+03 1.360e+03 1.767e+03\n",
+            " 1.572e+03 9.860e+02 1.232e+03 1.436e+03 1.338e+03 2.288e+03 1.531e+03\n",
+            " 1.230e+03 1.015e+03 1.088e+03 1.037e+03 1.142e+03 1.170e+03 1.039e+03\n",
+            " 1.124e+03 1.262e+03 5.600e+01 1.972e+03 8.360e+02 9.000e+02 8.810e+02\n",
+            " 8.760e+02 9.040e+02 2.146e+03 1.557e+03 8.000e+02 1.196e+03 8.630e+02\n",
+            " 5.670e+02 9.880e+02 4.250e+02 6.520e+02 4.940e+02 6.510e+02 2.410e+02\n",
+            " 6.830e+02 9.130e+02 7.720e+02 1.163e+03 6.890e+02 1.173e+03 7.810e+02\n",
+            " 8.540e+02 2.360e+02 9.870e+02 1.361e+03 5.950e+02 1.294e+03 3.790e+02\n",
+            " 2.158e+03 2.700e+01 1.121e+03 6.820e+02 8.120e+02 1.430e+03 4.100e+02\n",
+            " 7.710e+02 5.400e+01 5.160e+02 9.760e+02 2.000e+01 5.200e+01 3.310e+02\n",
+            " 6.800e+01 6.600e+02 8.640e+02 5.940e+02 1.400e+02 1.733e+03 6.010e+02\n",
+            " 9.620e+02 5.490e+02 6.490e+02 1.252e+03 1.210e+02 1.116e+03 2.980e+02\n",
+            " 8.590e+02 9.550e+02 1.440e+02 6.430e+02 2.510e+02 4.030e+02 6.120e+02\n",
+            " 1.960e+02 9.980e+02 7.400e+02 3.880e+02 9.910e+02 5.680e+02 1.000e+02\n",
+            " 1.850e+02 1.024e+03 1.285e+03 6.070e+02 1.312e+03 6.090e+02 1.387e+03\n",
+            " 4.540e+02 7.080e+02 6.200e+02 5.850e+02 1.720e+02 1.550e+02 1.213e+03\n",
+            " 4.900e+02 4.280e+02 6.500e+02 7.000e+02 9.310e+02 4.400e+02 6.990e+02\n",
+            " 3.900e+02 6.800e+02 3.150e+02 3.840e+02 8.720e+02 7.450e+02 5.460e+02\n",
+            " 1.270e+03 6.210e+02 1.800e+02 6.300e+02 4.330e+02 1.148e+03 9.410e+02\n",
+            " 8.260e+02 6.330e+02 4.210e+02 3.120e+02 2.160e+02 4.950e+02 1.309e+03\n",
+            " 2.200e+02 4.050e+02 2.090e+02 2.730e+02 1.340e+02 2.990e+02 5.220e+02\n",
+            " 1.520e+02 1.690e+02 7.490e+02 1.152e+03 3.500e+02 5.510e+02 4.440e+02\n",
+            " 2.260e+02 5.270e+02 6.850e+02 1.700e+02 1.324e+03 2.620e+02 3.420e+02\n",
+            " 3.440e+02 1.730e+02 5.520e+02 2.920e+02 2.040e+02 4.600e+02 7.000e+01\n",
+            " 1.441e+03 4.850e+02 5.130e+02 5.840e+02 1.086e+03 1.094e+03 8.200e+02\n",
+            " 1.021e+03 1.288e+03 1.359e+03 1.334e+03 9.020e+02 7.240e+02 1.518e+03\n",
+            " 2.490e+02 7.320e+02 7.550e+02 8.210e+02 3.850e+02 9.500e+02 7.460e+02\n",
+            " 6.060e+02 6.660e+02 1.259e+03 7.100e+02 6.460e+02 7.770e+02 1.234e+03\n",
+            " 9.900e+02 6.900e+02 1.111e+03 1.478e+03 1.930e+02 5.350e+02 3.990e+02\n",
+            " 6.310e+02 5.470e+02 3.320e+02 6.260e+02 4.080e+02 2.900e+02 5.230e+02\n",
+            " 7.930e+02 7.130e+02 2.460e+02 1.540e+02 6.500e+01 7.840e+02 4.710e+02\n",
+            " 2.850e+02 8.030e+02 8.080e+02 1.476e+03 4.450e+02 1.351e+03 7.670e+02\n",
+            " 6.110e+02 5.500e+01 1.110e+02 1.236e+03 1.022e+03 1.758e+03 1.115e+03\n",
+            " 1.005e+03 4.620e+02 1.260e+03 1.640e+03 8.660e+02 8.830e+02 5.150e+02\n",
+            " 5.090e+02 7.200e+02 1.140e+02 1.097e+03 7.180e+02 3.300e+02 1.567e+03\n",
+            " 4.960e+02 8.650e+02 7.060e+02 8.510e+02 1.380e+02 1.153e+03 2.190e+02\n",
+            " 3.190e+02 1.337e+03 1.034e+03 9.830e+02 1.206e+03 8.960e+02 8.900e+02\n",
+            " 1.084e+03 1.023e+03 2.520e+02 1.190e+02 2.660e+02 3.210e+02 3.870e+02\n",
+            " 3.580e+02 5.590e+02 2.860e+02 1.336e+03 1.280e+03 1.636e+03 1.330e+03\n",
+            " 1.012e+03 1.400e+03 1.728e+03 1.375e+03 1.420e+03 1.082e+03 1.249e+03\n",
+            " 4.000e+01 2.257e+03 1.016e+03 1.149e+03 1.075e+03 3.720e+02 1.540e+03\n",
+            " 1.204e+03 8.460e+02 5.730e+02 1.073e+03 1.087e+03 7.590e+02 6.550e+02\n",
+            " 1.660e+03 1.696e+03 2.280e+02 1.314e+03 1.096e+03 3.300e+01 7.290e+02\n",
+            " 7.890e+02 5.030e+02 8.000e+01 8.140e+02 3.620e+02 1.138e+03 5.370e+02\n",
+            " 4.720e+02 3.970e+02 1.650e+02 5.300e+01 7.370e+02 7.640e+02 4.890e+02\n",
+            " 1.900e+02 5.200e+02 5.500e+02 1.027e+03 1.004e+03 1.141e+03 1.238e+03\n",
+            " 6.810e+02 8.130e+02 1.280e+02 7.860e+02 1.619e+03 1.044e+03 3.010e+02\n",
+            " 6.030e+02 9.560e+02 2.600e+02 5.830e+02 5.750e+02 8.670e+02 7.760e+02\n",
+            " 8.920e+02 7.870e+02 8.060e+02 4.190e+02 6.580e+02 3.200e+01 8.310e+02\n",
+            " 5.310e+02 5.720e+02 2.500e+01 1.053e+03 1.040e+03 5.700e+02 7.740e+02\n",
+            " 1.480e+02 8.520e+02 5.800e+02 7.440e+02 3.740e+02 6.730e+02 9.600e+01\n",
+            " 4.930e+02 5.900e+02 1.160e+02 1.410e+02 2.590e+02 2.000e+02 4.060e+02\n",
+            " 1.750e+02 5.210e+02 2.010e+02       nan 3.360e+02 2.100e+02 3.510e+02\n",
+            " 9.060e+02 7.580e+02 7.020e+02 2.210e+02 1.198e+03 1.300e+03 6.340e+02\n",
+            " 1.064e+03 4.290e+02 1.003e+03 3.920e+02 5.990e+02 7.190e+02 1.035e+03\n",
+            " 3.240e+02 9.690e+02 1.085e+03 7.790e+02 1.271e+03 3.550e+02 2.085e+03\n",
+            " 5.000e+01 7.700e+02 7.220e+02 1.308e+03 6.880e+02 3.610e+02 6.630e+02\n",
+            " 4.860e+02 8.800e+01 6.320e+02 6.680e+02 1.194e+03 1.538e+03 6.420e+02\n",
+            " 9.940e+02 1.593e+03 8.100e+02 9.460e+02 8.300e+02 1.033e+03 9.200e+02\n",
+            " 5.644e+03 4.590e+02 3.520e+02 2.240e+02 4.100e+01 4.230e+02 2.810e+02\n",
+            " 3.660e+02 8.100e+01 5.380e+02 1.480e+03 1.474e+03 6.410e+02 1.383e+03\n",
+            " 8.930e+02 1.165e+03 1.513e+03 1.398e+03 7.830e+02 1.029e+03 1.223e+03\n",
+            " 8.710e+02 1.011e+03 1.571e+03 7.690e+02 3.180e+02 5.010e+02 4.370e+02\n",
+            " 7.850e+02 5.340e+02 6.380e+02 6.470e+02 5.620e+02 8.380e+02 7.780e+02\n",
+            " 1.880e+03 1.860e+02 4.140e+02 9.260e+02 1.101e+03 1.047e+03 7.970e+02\n",
+            " 9.450e+02 1.558e+03 6.780e+02 2.560e+02 1.328e+03 6.050e+02 9.030e+02\n",
+            " 4.920e+02 3.490e+02 2.820e+02 4.120e+02 3.220e+02 3.140e+02 9.300e+02\n",
+            " 3.560e+02 5.560e+02 7.250e+02 1.151e+03 1.304e+03 1.812e+03 1.350e+03\n",
+            " 1.684e+03 9.700e+02 9.380e+02 6.690e+02 1.178e+03 1.030e+03 7.620e+02\n",
+            " 8.480e+02 9.180e+02 5.740e+02 2.096e+03 1.181e+03 1.282e+03 1.048e+03\n",
+            " 1.455e+03 8.620e+02 5.650e+02 1.231e+03 3.350e+02 1.225e+03 1.220e+03\n",
+            " 9.290e+02 6.300e+01 1.126e+03 1.369e+03 6.400e+01 1.443e+03 4.300e+02\n",
+            " 4.170e+02 9.320e+02 8.270e+02 7.270e+02 1.250e+02 1.390e+03 9.680e+02\n",
+            " 4.820e+02 6.000e+01 9.370e+02 1.106e+03 4.200e+02 4.360e+02 2.390e+02\n",
+            " 9.010e+02 4.570e+02 1.732e+03 1.157e+03 9.780e+02 1.632e+03 4.980e+02\n",
+            " 7.380e+02 9.730e+02 9.100e+02 3.460e+02 8.190e+02 7.920e+02 9.160e+02\n",
+            " 6.170e+02 6.540e+02 2.700e+02 1.386e+03 1.300e+02 3.860e+02 1.870e+02\n",
+            " 8.730e+02 9.080e+02 6.080e+02 5.120e+02 5.860e+02 1.237e+03 4.410e+02\n",
+            " 8.500e+01 3.770e+02 2.420e+02 9.520e+02 3.980e+02 1.098e+03 7.820e+02\n",
+            " 1.680e+02 1.220e+02 3.160e+02 1.046e+03 3.170e+02 6.450e+02 1.970e+02\n",
+            " 9.250e+02 7.480e+02 2.580e+02 1.219e+03 5.870e+02 4.770e+02 4.910e+02\n",
+            " 4.530e+02 1.440e+03 5.570e+02 1.080e+03 4.970e+02 9.840e+02 1.150e+03\n",
+            " 6.640e+02 9.850e+02 5.100e+01 1.013e+03 5.020e+02 7.160e+02 6.710e+02\n",
+            " 1.464e+03 1.412e+03 1.079e+03 7.090e+02 1.320e+02 7.510e+02 9.800e+02\n",
+            " 4.010e+03 2.260e+03 4.670e+02 7.700e+01 1.130e+02 3.640e+02 3.650e+02\n",
+            " 1.128e+03 2.970e+02 1.186e+03 3.500e+01 5.770e+02 4.340e+02 5.480e+02\n",
+            " 9.670e+02 1.573e+03 1.001e+03 7.730e+02 1.392e+03 1.239e+03 9.240e+02\n",
+            " 9.490e+02 1.102e+03 2.150e+02 7.420e+02 1.329e+03 1.159e+03 2.060e+02\n",
+            " 8.400e+02 8.740e+02 1.310e+02 1.112e+03 7.960e+02 6.190e+02 8.110e+02\n",
+            " 1.090e+03 5.960e+02 2.120e+02 1.127e+03 1.110e+03 5.920e+02 7.140e+02\n",
+            " 5.700e+01 5.180e+02 2.050e+02 1.191e+03 1.422e+03 2.130e+02 1.002e+03\n",
+            " 7.950e+02 1.940e+02 7.500e+01 6.140e+02 9.510e+02 3.090e+02 3.820e+02\n",
+            " 3.730e+02 1.447e+03 1.505e+03 1.261e+03 1.290e+03 8.800e+02 4.150e+02\n",
+            " 5.540e+02 1.038e+03 1.154e+03 1.074e+03 1.182e+03 3.800e+02 1.562e+03\n",
+            " 1.721e+03 1.836e+03 9.050e+02 2.780e+02 1.332e+03 1.810e+02 4.650e+02\n",
+            " 1.118e+03 1.456e+03 1.009e+03 8.070e+02 1.810e+03 4.040e+02 7.600e+02\n",
+            " 7.990e+02 6.610e+02 4.160e+02 9.960e+02 7.560e+02 9.390e+02 8.950e+02\n",
+            " 9.140e+02 9.430e+02 2.710e+02 4.880e+02 7.010e+02 1.277e+03 4.550e+02\n",
+            " 3.690e+02 1.790e+02 8.090e+02 9.530e+02 2.080e+02 1.430e+02 5.760e+02\n",
+            " 3.470e+02 3.280e+02 7.940e+02 2.300e+02 2.610e+02 3.930e+02 6.840e+02\n",
+            " 4.640e+02 1.576e+03 1.122e+03 8.530e+02 1.162e+03 8.940e+02 9.750e+02\n",
+            " 4.750e+02 1.670e+02 6.910e+02 4.240e+02 3.050e+02 2.230e+02 5.260e+02\n",
+            " 2.960e+02 1.283e+03 1.564e+03 9.650e+02 9.090e+02 1.216e+03 1.136e+03\n",
+            " 1.460e+03 1.243e+03 8.970e+02 8.370e+02 1.490e+02 1.606e+03 1.224e+03\n",
+            " 3.370e+02 1.071e+03]\n",
+            "BsmtFin Type 2: ['Unf' 'LwQ' 'BLQ' 'Rec' nan 'GLQ' 'ALQ']\n",
+            "BsmtFin SF 2: [   0.  144. 1120.  163.  168.   78.  119.  121.  117.  859.  981.   42.\n",
+            "   46.   81. 1029.  290.  132.  713.  162.  362.  240.  258.  174.  906.\n",
+            "  486.  350.  263. 1073.  692.   12.  159.  712.  668.  474.  453.  684.\n",
+            "  387.  688.  972.  127.  252.  334.  232.  480.  590.  284.  276.  472.\n",
+            "  239.  180.  294.  622.  495.  539.  479.  113. 1526.  360.  774.  364.\n",
+            "  596.  884.  311.   92.  216.  136.   32.  147. 1127.  466.  630.  201.\n",
+            "  345.  512.  230.  247.  661.  620.  202.  483.  750.  690.  105.   60.\n",
+            "  352.  102.   95.  465.   63.  262.  500.  670.  768.  393.  286.  450.\n",
+            "  177.  764.  344.   72.  243.  420.  210.  694.  875.  507.  435.  419.\n",
+            "  250.  116.  354.  820.  624.  273.   76.  270.  110.  288.  411.  228.\n",
+            "  186.  449.   48.   93.  438.  613.  852.  555.  841.  799.  811.  842.\n",
+            "  382.  182.  456.   80.   64.  336.  306.  308.  374.  872.  108.   52.\n",
+            "  196.  128.  488.  319.  532.  106.  169.  608.   nan   41.  606.  645.\n",
+            "  492.  181.  956. 1080. 1063.  391.  380.  531.  723.  491.  120.  679.\n",
+            "  612.   40.  125.  279.  400.  208.  193.  823.  287.  175.  604.  153.\n",
+            "   35.  619.  139.    6.  351. 1031. 1037.  176.  829.  211.  264.   38.\n",
+            "  206.  167.  580.  543.  219.  259.  404.  468.  138.  955.  691.   66.\n",
+            "   96.  149.  154.  442.  448.  227.  546.  398.  469.  722.  761.  627.\n",
+            "  529.  522.  873.  891.  755. 1474.  634.  321.  915.  544.  417.  432.\n",
+            "  831.  278.  557.  150.  869. 1020.  530.  904.  499.  215. 1061.  377.\n",
+            "  791.  156. 1393. 1039.  375.  497. 1057.  526.   68.  402.  748.   28.\n",
+            "  165.  184.  281.  912.  600.  506.  373.  551.  982.  441.  682. 1085.\n",
+            "  826.  850. 1164. 1083.  337.  297.  547.  173.  396.  324.  123.]\n",
+            "Bsmt Unf SF: [ 441.  270.  406. ...   45. 1503.  239.]\n",
+            "Total Bsmt SF: [1080.  882. 1329. ... 1381.  757. 1003.]\n",
+            "Heating: ['GasA' 'GasW' 'Grav' 'Wall' 'Floor' 'OthW']\n",
+            "Heating QC: ['Fa' 'TA' 'Ex' 'Gd' 'Po']\n",
+            "Central Air: ['Y' 'N']\n",
+            "Electrical: ['SBrkr' 'FuseA' 'FuseF' 'FuseP' nan 'Mix']\n",
+            "1st Flr SF: [1656  896 1329 ... 2028 1003 1389]\n",
+            "2nd Flr SF: [   0  701  678  776  892  676 1589  672  860  504  567  601  707  563\n",
+            "  862  630 1100  886  656 1151 1177  830 1122 1106  644 1185  783  956\n",
+            " 1128  828  888  790  730  584 1098  823  840  600  636  804  756  720\n",
+            "  550  873  754 1215  604  734  715  532  537 1169  505  546 1080  408\n",
+            "  475  788  687  348  765  424  606  185  686 1111  622 1044  602  582\n",
+            "  908  524  498  492  608  808 1074  780  662 1196  499  180  319  942\n",
+            "  744  240  689  714  954  192  864  558  755  838  887 1523  614  800\n",
+            "  878  703 1054  328  252  806  665 1788  772  748 1075 1152  358  380\n",
+            "  430  880  700 1194 1070  915  912  576 1216  650  615  645  704  663\n",
+            " 1275  670  631  833  684  809  785  779  978  988 1200  896 1134  868\n",
+            " 1103  816  839  741  467  586 1174 1325  568 1088 1012  762 1295  728\n",
+            "  745  742  876  716 1257  640  683 1276 1126 1032  793  695 1089 1038\n",
+            " 1097 1304 1221 1140 1336 1067 1274  967 1017  871  858  920  981  438\n",
+            " 1182  841  941 1209  897  591  786  702  918 1629  612  729  739  727\n",
+            "  983  782  660 1369  972  855 1315  224  556  960  457  685  726  322\n",
+            "  760  534 1296  368  768  629  813  406  548  517  455  496  690  994\n",
+            " 1000  682  646  560  677  564 1063 1320  917  624  826  561  596  653\n",
+            "  390  464  587  320  472  588  883  910  929 1040  784  462  649  425\n",
+            "  611  747  769 1114 1120 1619  902  718  815  966  836  834  913  844\n",
+            "  829 1116  573  885  926  977  807  738 1427  441  512  444  620  436\n",
+            "  545  998  595  448  332  523 1240  516  668  928 1157  432  846  566\n",
+            "  848 1020  717 1332 1370  857 1330  767 1420  866 1104  590 1237  898\n",
+            " 1158 1162 1096 1139  884 1285  778 1160 1053  639 1061 1250 1093  904\n",
+            " 1039  520  919  939  932 1028  843  861  842  794  825  850  893 1319\n",
+            "  959  625  792  628  924 1345 1066  732 1540  933  832  453  220  384\n",
+            "  412  510  182  501  581  375  680 1208  658  552  396 1818  797  540\n",
+            "  308  973  691  539 1254  363  473  594  378  554  208  468  651  445\n",
+            "  764  752  213  795  428  371  110  536  713  551  547  580  486 1051\n",
+            "  511  872  648  527  495 1721 1099  735 1072  899  870  895  903 1141\n",
+            " 1198  975  854  812  950  521  343  304  940 1611  811  673  442  890\n",
+            " 1479  817  943  330  420  936  167  688  766  770 1342  900 1377  845\n",
+            "  533 1402 1101  574 1036  570 1142 1238 1168  923  530  757 1048  796\n",
+            " 1112 1131  694  750 2065 1288 1407 1171 1277 1872 1015 1306 1203  995\n",
+            "  528  863 1426  925 1232 1357  743  976  761 1259 1008  984 1309  228\n",
+            "  992  500  544 1778  299  616  831  664  494  642  659  671 1031  336\n",
+            "  144  525  349  423 1164  356  698  245  592 1042  477 1005  971 1087\n",
+            "  638  400  376 1121 1414 1362 1092  916  882  927  874  914  881  869\n",
+            " 1242 1081  753  450 1133  674 1538  125 1440  787  531  585  514  775\n",
+            "  589  979 1001  851 1178  351  957 1340 1349  712 1243  955  709  990\n",
+            " 1384 1862 1371 1312 1405 1519 1392 1358  465 1347 1218 1060  466 1335\n",
+            "  814  488 1321  482  711  930 1286  985 1029 1796 1368 1567 1189 1323\n",
+            " 1234  798 1129  623  708  456  316 1360 1248  272  821  370 1007  518\n",
+            "  476  502  867  661  297  679  875 1518  605  810  325  434  583  634\n",
+            "  557  341  626 1836  541  454 1246  571 1037 1124 1045  989  827 1150\n",
+            "  312  526  218  980  403  493  736  818  901  610  725  549 1175  697\n",
+            "  439  360 1281 1230 1004]\n",
+            "Low Qual Fin SF: [   0  390  362  144 1064  232  431  120  436  371  360  259  397  312\n",
+            "  513  108  205  156  697  420  384  473  512  528  114  479  515   53\n",
+            "   80  392  572  234  140  450  481  514]\n",
+            "Gr Liv Area: [1656  896 1329 ... 2028 2521 1003]\n",
+            "Bsmt Full Bath: [ 1.  0.  2.  3. nan]\n",
+            "Bsmt Half Bath: [ 0.  1. nan  2.]\n",
+            "Full Bath: [1 2 3 0 4]\n",
+            "Half Bath: [0 1 2]\n",
+            "Bedroom AbvGr: [3 2 1 4 6 5 0 8]\n",
+            "Kitchen AbvGr: [1 2 3 0]\n",
+            "Kitchen Qual: ['TA' 'Gd' 'Ex' 'Fa' 'Po']\n",
+            "TotRms AbvGrd: [ 7  5  6  8  4 12 10 11  9  3 13  2 15 14]\n",
+            "Functional: ['Typ' 'Mod' 'Min1' 'Min2' 'Maj1' 'Maj2' 'Sev' 'Sal']\n",
+            "Fireplaces: [2 0 1 3 4]\n",
+            "Fireplace Qu: ['Gd' nan 'TA' 'Po' 'Ex' 'Fa']\n",
+            "Garage Type: ['Attchd' 'BuiltIn' 'Basment' 'Detchd' nan 'CarPort' '2Types']\n",
+            "Garage Yr Blt: [1960. 1961. 1958. 1968. 1997. 1998. 2001. 1992. 1995. 1999. 1993. 1990.\n",
+            " 1985. 2003. 1988. 2010. 1951. 1978. 1977. 1974. 2000. 1970. 1971.   nan\n",
+            " 1975. 2009. 2008. 2005. 2004. 2002. 2006. 1996. 1994. 1980. 1979. 1984.\n",
+            " 1986. 1920. 1987. 1973. 1963. 1962. 1976. 1967. 1972. 1966. 1964. 1950.\n",
+            " 1949. 1954. 1955. 1959. 1957. 1956. 1952. 1953. 1989. 1948. 1900. 1927.\n",
+            " 1915. 1945. 1940. 1938. 1928. 1930. 1926. 1939. 1942. 1923. 1917. 1910.\n",
+            " 1965. 1969. 1947. 1946. 1941. 1924. 1922. 1896. 2007. 1983. 1981. 1991.\n",
+            " 1982. 1916. 1925. 1936. 1935. 1931. 1934. 1929. 1918. 1921. 1937. 1932.\n",
+            " 1906. 1908. 1895. 1933. 2207. 1914. 1943. 1919.]\n",
+            "Garage Finish: ['Fin' 'Unf' 'RFn' nan]\n",
+            "Garage Cars: [ 2.  1.  3.  0.  4.  5. nan]\n",
+            "Garage Area: [ 528.  730.  312.  522.  482.  470.  582.  506.  608.  442.  440.  420.\n",
+            "  393.  841.  492.  834.  400.  500.  546.  663.  480.  304.  525.    0.\n",
+            "  511.  264.  320.  308.  751.  772.  606.  868.  532.  678.  820.  484.\n",
+            "  958.  756.  576.  474.  430.  437.  433.  434.  779.  962.  527.  712.\n",
+            "  671.  486.  666.  880.  676.  614.  750.  618.  463.  462.  457.  476.\n",
+            "  429.  539.  336.  280.  260.  461.  564.  762.  713.  588.  496.  852.\n",
+            "  592.  475.  596.  535.  660.  441.  490.  504.  517.  240.  364.  244.\n",
+            "  315.  578.  620.  447.  294.  531.  263.  318.  305.  246.  392.  330.\n",
+            "  720.  360.  551.  379.  220.  780.  288.  416.  624.  923.  560.  363.\n",
+            "  200.  572.  180.  516.  672.  349.  365.  231.  450.  270.  299.  591.\n",
+            "  533.  690.  436.  586.  366.  467.  209.  460. 1017.  574.  776.  632.\n",
+            "  740.  615.  594.  580.  513.  523.  850.  670.  613.  621.  598.  502.\n",
+            "  494.  319.  352.  216.  399.  252.  567.  473.  625.  384.  741.  573.\n",
+            "  888.  520.  680.  510.  431.  746.  686.  286.  253.  495.  616.  275.\n",
+            "  538.  390.  758.  499.  396.  427.  380.  409.  389.  343.  565. 1166.\n",
+            "  435.  544.  529.  479.  542.  478.  581.  552.  583.  902.  477.  345.\n",
+            "  656.  786.  754.  840.  890. 1390.  864.  836.  896.  900.  842. 1020.\n",
+            "  932.  640.  908.  927.  856.  700.  738.  862.  644.  968.  886.  871.\n",
+            "  626.  949.  685.  649.  701.  550.  397.  432.  554.  394.  658.  410.\n",
+            "  810. 1069.  889.  815.  647.  623.  711.  898.  972.  726.  844.  689.\n",
+            "  795.  984.  692.  812.  782. 1043.  438.  628.  845.  555.  788.  559.\n",
+            "  465.  612.  732.  300.  524.  704.  561.  641.  642.  540.  784.  497.\n",
+            "  515.  630.  498.  768.  472.  610.  549.  645.  368.  505.  418.  338.\n",
+            "  271.  792.  530.  514.  509.  297.  350.  884.  230.  281.  907.  483.\n",
+            "  210.  162.  324.  256.  273.  287.  357.  424.  456.  207.  192.  250.\n",
+            " 1184.  164.  316.  226.  668.  452.  284.  303.  340.  234.  290.  266.\n",
+            "  296.  425.  466. 1138.  826.  860.  846.  904.  702.  662.  569.  577.\n",
+            "  493.  622.  605.  444.  600. 1231.  570.  736.  521.  512.  451.  195.\n",
+            "  313.  342.  215.  282.  213.  307.  186.  295.  501.  468.  189.  351.\n",
+            "  541.  912.  650.  885.  471.  765.  920.  412.  402.  602.  698.  714.\n",
+            "  601.  386.  404.  406.  682.  683.  557.  619.  489. 1314.  439.  787.\n",
+            "  774. 1220.  858.  905.  866.  706. 1150. 1003.  789.  870. 1052.  944.\n",
+            "  388.  428.  398.  403.  696.  687.  938.  839.  983.  783.  691.  830.\n",
+            "  824.  851.  603.  648.  936.  562.  673.  575.  627.  276.  636.  545.\n",
+            "  469.  464.  831.  267.  283.  205.  377.  292.  458.  301. 1488.  372.\n",
+            "  401.  414.  311.  225.  828.  869.  370.  208.  160.  355.  228.  322.\n",
+            "  408.  354.  249.  534.  453. 1348.  874.  811.  558.  328.  725.  715.\n",
+            "  543.  595.  508.  721.  548.  814. 1418.  369.  599.  344. 1014.  924.\n",
+            "  356.  487.  185. 1248.  857.  816.  358.  665.  800.  749.  892.  257.\n",
+            "  423.  526.  373.  729. 1110.  556.  724.  481.  585.  488.  684.  367.\n",
+            "  818.  928. 1040.  878.  947.  895.  694. 1174.  728.  843.  916.  872.\n",
+            "  876.  631.  617.  454.  813.  925.  804.  806.  832.  455.  752.  933.\n",
+            " 1092.  865.  954.  825.  859.  590. 1025.  744.  566.  518.  611. 1105.\n",
+            "  571.  309.  306.  310.  293.  371. 1200.  254.  184.  374.  331.  224.\n",
+            "  217.  261.  323.  638.  739.  332.  719.  833.  894.  796.  674.  747.\n",
+            "  242.  597.  748.  639.  579. 1154.  248.   nan  100.  722.  422.  808.\n",
+            "  995. 1041. 1356.  963.  443.  413.  773.  675.  716.  604.  485.  770.\n",
+            " 1085.  853.  708.  753.  899.  426.  807.  959.  803.  760. 1134.  584.\n",
+            " 1053.  449.  688.  757.  326.  568.  353.  791. 1008.  378.  258.  255.\n",
+            "  198.  459.  667.  445.  325.  848.  317.  646.  265.  609.  375.  272.\n",
+            "  327.  766.  693.  405.]\n",
+            "Garage Qual: ['TA' nan 'Fa' 'Gd' 'Ex' 'Po']\n",
+            "Garage Cond: ['TA' nan 'Fa' 'Gd' 'Ex' 'Po']\n",
+            "Paved Drive: ['P' 'Y' 'N']\n",
+            "Wood Deck SF: [ 210  140  393    0  212  360  237  157  483  192  503  325  113  349\n",
+            "  240  203  275  173   26  144  168  220  238  196  120   36  100  146\n",
+            "  288  180  668   23  186  132  283  169   80  635   28  353  370  121\n",
+            "  416  296   32  198  160  280  133  223  277  224  228  352  227  366\n",
+            "  117  263  301   42  252  250  264  364  414  218  222  657   84   51\n",
+            "  106   54  135  221  306   12  344   56  406  379  226  335  496  290\n",
+            "  268  336   44  450  156  105  367   71  316  365  188  331   60  257\n",
+            "  116  272  141  112   30   68  128  375  328  174  182  200   96  261\n",
+            "  431   22  287  129  162  269   48  201   52  256  232  342   63  322\n",
+            "  178  233  474  448  225   40  171  216  185  108   87  260  147  150\n",
+            "  404  382  319   99  184  125  165  248  114  230  170  172  208  231\n",
+            "  148  143  300   24  298  340  517  297   70  205  195  158  462  502\n",
+            "  115  501  371  235  294  312  321   78   85  164  110   55  289   66\n",
+            "  324  126  187   74  181  266  244   45  189  509  302  243   64  131\n",
+            "  476  234  400   73  154  123  486  276  392   72  215   58  262  202\n",
+            "  253  194  576  356  327   92  136  329  279  176  292  467  119   90\n",
+            "  305  124  270  308   33  138  303  214  152  550   16  411  209  358\n",
+            "  320  495  236  385  145  155   97   20  122   98   25   38  426  355\n",
+            "  490   88   76  418  265   49   57  204  311  102  511  409   50  307\n",
+            "   81  424  339  403  278  211  139  149  259  736  134  183  314  213\n",
+            "  161  318  428  670  282  315  362  245  219  390  167  407   35  130\n",
+            "  104  460  286  239  255  193  159  402  455  500  206  190  333  284\n",
+            "  285   14  521  380  127  646  142  386  405  546  118  242  291  166\n",
+            "  274  439  536 1424  690  330  421   95  441  246  351  197  384  444\n",
+            "  295  175  354  519  177  179   89  361  247  870  309  432    4  641\n",
+            "  153  857   94   86  191   75  631  229  436  345  520  199   27  394\n",
+            "   53   77  466  304  241  103  586  684  453  413  468  207  530  574\n",
+            "  326  728]\n",
+            "Open Porch SF: [ 62   0  36  34  82 152  60  84  21  75  54  12 122 120  96  85  68  55\n",
+            "  30 133  50  95  35  70  74 119  67 150 130  49  27  23 116  20  48 172\n",
+            "  56  32  57  81  86 136  45 168 102 104 144  39 111 166  44 192 184  42\n",
+            "  78 137  76  69  66 224  26  40  98  73  38  28  52  17 124 160 100 228\n",
+            " 108  18 158  10  11 132  58  90  22  46 278  92  33  61  59  77  25 262\n",
+            " 105  64 140 156 207  53  24 312  72  43  94  63 176 195 134 162 197 274\n",
+            " 170 273 185 190 114 235 183  16  51 103 128 146 126 165 226 121 112 175\n",
+            " 182 113  88 178  91  41  93 177 234 254 169 204  99  80 110 189 287 523\n",
+            "  15 135 198 188 215 155 142 222 193  29 151 240 200 148 201 118 154 238\n",
+            " 247 304 101 173 282 180  65 131 153  87 174 210 251 243 211 129   4 230\n",
+            " 213 547 291 502 299 365 139 216  89 117 236   8 187 159 106 372 292 141\n",
+            " 217 123  83 276 265 164 205 368  47 203 191 138 364 127 256 214 241 194\n",
+            " 285 324 208 171 570 244 231 484 406 742 444 252 263 266  97  37 250 246\n",
+            " 229  31 267 382 319 258   6 341 260 288 418 115 253 245 107 225 125 199]\n",
+            "Enclosed Porch: [   0  170  184  154   80  220  186  156  120  112  150  164  189  205\n",
+            "  113  216  135  130  202  126  334  246  196   18  158  114   60   41\n",
+            "  128   35   48   32   64  364   40  318  248  168   45  239  176   77\n",
+            "   52   56   36  136   96  242   42   86  162   98  265   50  280  222\n",
+            "  144  209   24   91  236  218  228   84  264  260  240  203  140  252\n",
+            "  100  134  432  198  116  169  148  244   25   81  102  160  386  226\n",
+            "  238  115   94  208  105   54   51   34  268   30  213  288   90  192\n",
+            "  177  211  185   55  180   44   57   78  137   72  368   70  165   92\n",
+            "   16  123   66  210   68  109  194  139  219  259  212   20  101   87\n",
+            "  117  204  122  108  190  231  138  183  254  301  121  207  224  172\n",
+            "  174   99  249  291  145  214  275  290  175   26  143  230   88   39\n",
+            " 1012   43  286   19  584  200  133  234   37  324  552  161   75  167\n",
+            "   28  293  104  296  330  221  256  129  225  294  272  429   67  132\n",
+            "   23]\n",
+            "3Ssn Porch: [  0 238 224 144 508 168 255 225 360 162 140 150 182 153 320 174 304 216\n",
+            " 407  96 245 120 219 180 196 176  86  23 290 323 130]\n",
+            "Screen Porch: [  0 120 144 140 210 165 256 216  90 204 143 160 182 385 240 168 148  95\n",
+            " 266 166 116 161 200 155 108 291 490 170 192 180 156 196 197 152 121  92\n",
+            " 288 185 342 189 252 234 255 111 112 231  40 100  60 142 110 396 225 117\n",
+            " 195 145 224 115 198 233 190 141 208  80 176  94 164 178 273 130 480 220\n",
+            "  64 163 287 175 576 227 265 221 171 135 322 174 147 276 260 217 201 109\n",
+            "  99 150 126 259 184  84 154  53 153 228 138 263  88 280 123 440 374 119\n",
+            " 222 264 270  63 122 128 162 410 271 312 348 113 104]\n",
+            "Pool Area: [  0 144 480 576 555 368 444 228 561 519 648 800 512 738]\n",
+            "Pool QC: [nan 'Ex' 'Gd' 'TA' 'Fa']\n",
+            "Fence: [nan 'MnPrv' 'GdPrv' 'GdWo' 'MnWw']\n",
+            "Misc Feature: [nan 'Gar2' 'Shed' 'Othr' 'Elev' 'TenC']\n",
+            "Misc Val: [    0 12500   500   700   400   450  1500   300   600  1200  3500  2000\n",
+            "  2500    54    80   490   480   350   650   900   800   750  1400  6500\n",
+            "  1150  1000  4500  3000   560  1300  8300 15500 17000  1512   455   460\n",
+            "   620   420]\n",
+            "Mo Sold: [ 5  6  4  3  1  2  7 10  8 11  9 12]\n",
+            "Yr Sold: [2010 2009 2008 2007 2006]\n",
+            "Sale Type: ['WD ' 'New' 'COD' 'ConLI' 'Con' 'ConLD' 'Oth' 'ConLw' 'CWD' 'VWD']\n",
+            "Sale Condition: ['Normal' 'Partial' 'Family' 'Abnorml' 'Alloca' 'AdjLand']\n",
+            "SalePrice: [215000 105000 172000 ...  90500  71000 150900]\n",
+            "Order             2930\n",
+            "PID               2930\n",
+            "MS SubClass         16\n",
+            "MS Zoning            7\n",
+            "Lot Frontage       128\n",
+            "                  ... \n",
+            "Mo Sold             12\n",
+            "Yr Sold              5\n",
+            "Sale Type           10\n",
+            "Sale Condition       6\n",
+            "SalePrice         1032\n",
+            "Length: 82, dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Perform initial exploration of the dataset.\n",
+        "# - Check shape, column names, smaples\n",
+        "# - Get summary info, data types\n",
+        "# - Descriptive statistics\n",
+        "\n",
+        "print(f\"Shape: {df.shape}\")\n",
+        "print(f\"Columns Name: {df.columns}\")\n",
+        "print(f\"Sample of Records: {df.sample}\")\n",
+        "\n",
+        "df.info()\n",
+        "df.dtypes\n",
+        "\n",
+        "print(df.describe())\n",
+        "print(df.describe(include='object'))\n",
+        "\n",
+        "for col in df.columns:\n",
+        "    print(f\"{col}: {df[col].unique()}\")\n",
+        "\n",
+        "print(df.nunique())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "quTZ6mJdjrDw"
+      },
+      "source": [
+        "## 🔹 Step 3: Missing Value Check & Handling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 205,
+      "metadata": {
+        "id": "-0KdWkPqjs7y"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "First shape:  (2930, 82)\n",
+            "Shape after drop dupl.:  (2930, 82)\n",
+            "# of nulls at the begining:  15749\n",
+            "Shape after drop columns with >50% missing values:  (2930, 75)\n",
+            "# of nulls after drop columns with >50% missing values:  3143\n",
+            "# of nulls before Categurical filling:  2461\n",
+            "# of nulls at the end:  0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Check missing values.\n",
+        "# Decide on a strategy (if needed):\n",
+        "# - Drop if too many are missing\n",
+        "# - Fill with mean/median/mode/domain-specific value\n",
+        "\n",
+        "print(\"First shape: \", df.shape)\n",
+        "df.drop_duplicates(inplace=True)\n",
+        "print(\"Shape after drop dupl.: \", df.shape)\n",
+        "\n",
+        "if 'Order' in df.columns:\n",
+        "    df.drop(columns=['Order'], inplace=True)\n",
+        "if 'PID' in df.columns:\n",
+        "    df.drop(columns=['PID'], inplace=True)\n",
+        "print(\"# of nulls at the begining: \", df.isnull().sum().sum())\n",
+        "\n",
+        "# list columns with missing values\n",
+        "missing_counts = df.isnull().sum()\n",
+        "missing_cols = missing_counts[missing_counts > 0].sort_values(ascending=False)\n",
+        "# drop columns with more than 50% missing values\n",
+        "missing_pct = df.isnull().mean() * 100\n",
+        "cols_to_drop = missing_pct[missing_pct > 50].index.tolist()\n",
+        "df = df.drop(columns=cols_to_drop)\n",
+        "print(\"Shape after drop columns with >50% missing values: \", df.shape)\n",
+        "print(\"# of nulls after drop columns with >50% missing values: \", df.isnull().sum().sum())\n",
+        "\n",
+        "col_cat = df.select_dtypes(include='object').columns\n",
+        "col_num = df.select_dtypes(exclude='object').columns\n",
+        "for col in col_num:\n",
+        "    df[col] = df[col].fillna(df[col].median())\n",
+        "print(\"# of nulls before Categurical filling: \", df.isnull().sum().sum())\n",
+        "for col in col_cat:\n",
+        "    mod = df[col].mode()\n",
+        "    mode_value = mod.iloc[0]        # pick the first mode\n",
+        "    df[col] = df[col].fillna(mode_value)\n",
+        "print(\"# of nulls at the end: \",df.isnull().sum().sum())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## 🔹 Step 4: Correlation Check & Feature Decision"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MHwMZbX_jxKJ"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Dataset's shape before correlation drops: (2930, 75)\n",
+            "Dataset's shape after correlation drops: (2930, 73)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Check correlations between numerical features and target variable (SalePrice).\n",
+        "# Use correlation heatmap or pairplot.\n",
+        "# Decide which features to keep/remove based on correlation.\n",
+        "\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Numeric-only correlation\n",
+        "corr = df.corr(numeric_only=True)\n",
+        "plt.figure(figsize=(10,6))\n",
+        "sns.heatmap(corr, cmap=\"coolwarm\")\n",
+        "plt.show()\n",
+        "\n",
+        "# determining the dropping columns \n",
+        "threshold = 0.7\n",
+        "i = -1\n",
+        "to_drop = []\n",
+        "for rw in corr['SalePrice']:\n",
+        "    i += 1\n",
+        "    if corr['SalePrice'].abs().iloc[i] > threshold:\n",
+        "        if corr.columns[i] != 'SalePrice':\n",
+        "            to_drop.append(corr.columns[i])\n",
+        "print(f\"Dataset's shape before correlation drops: {df.shape}\")\n",
+        "df1 = df.copy()\n",
+        "df1.drop(columns=to_drop, inplace=True)\n",
+        "print(f\"Dataset's shape after correlation drops: {df1.shape}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "exTa7T6qj2hv"
+      },
+      "source": [
+        "## 🔹 Step 5: Encode Categorical Variables"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 66,
+      "metadata": {
+        "id": "JBQbfP6jj1hS"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "   MS SubClass  MS Zoning  Street  Lot Shape  Land Contour  Utilities  \\\n",
+            "0           20        5.0     1.0        0.0           3.0        0.0   \n",
+            "1           20        4.0     1.0        3.0           3.0        0.0   \n",
+            "2           20        5.0     1.0        0.0           3.0        0.0   \n",
+            "3           20        5.0     1.0        3.0           3.0        0.0   \n",
+            "4           60        5.0     1.0        0.0           3.0        0.0   \n",
+            "\n",
+            "   Lot Config  Land Slope  Neighborhood  Condition 1  Condition 2  Bldg Type  \\\n",
+            "0         0.0         0.0          15.0          2.0          2.0        0.0   \n",
+            "1         4.0         0.0          15.0          1.0          2.0        0.0   \n",
+            "2         0.0         0.0          15.0          2.0          2.0        0.0   \n",
+            "3         0.0         0.0          15.0          2.0          2.0        0.0   \n",
+            "4         4.0         0.0           8.0          2.0          2.0        0.0   \n",
+            "\n",
+            "   House Style  Overall Qual  Overall Cond  Roof Style  Roof Matl  \\\n",
+            "0          2.0             6             5         3.0        1.0   \n",
+            "1          2.0             5             6         1.0        1.0   \n",
+            "2          2.0             6             6         3.0        1.0   \n",
+            "3          2.0             7             5         3.0        1.0   \n",
+            "4          5.0             5             5         1.0        1.0   \n",
+            "\n",
+            "   Exterior 1st  Exterior 2nd  Exter Qual  Exter Cond  Foundation  Bsmt Qual  \\\n",
+            "0           3.0          10.0         3.0         4.0         1.0        4.0   \n",
+            "1          13.0          14.0         3.0         4.0         1.0        4.0   \n",
+            "2          14.0          15.0         3.0         4.0         1.0        4.0   \n",
+            "3           3.0           3.0         2.0         4.0         1.0        4.0   \n",
+            "4          13.0          14.0         3.0         4.0         2.0        2.0   \n",
+            "\n",
+            "   Bsmt Cond  Bsmt Exposure  BsmtFin Type 1  BsmtFin Type 2  BsmtFin SF 2  \\\n",
+            "0        2.0            1.0             1.0             5.0           0.0   \n",
+            "1        4.0            3.0             4.0             3.0         144.0   \n",
+            "2        4.0            3.0             0.0             5.0           0.0   \n",
+            "3        4.0            3.0             0.0             5.0           0.0   \n",
+            "4        4.0            3.0             2.0             5.0           0.0   \n",
+            "\n",
+            "   Heating  Heating QC  Central Air  Electrical  Low Qual Fin SF  \\\n",
+            "0      1.0         1.0          1.0         4.0                0   \n",
+            "1      1.0         4.0          1.0         4.0                0   \n",
+            "2      1.0         4.0          1.0         4.0                0   \n",
+            "3      1.0         0.0          1.0         4.0                0   \n",
+            "4      1.0         2.0          1.0         4.0                0   \n",
+            "\n",
+            "   Bsmt Half Bath  Kitchen Qual  Functional  Fireplace Qu  Garage Type  \\\n",
+            "0             0.0           4.0         7.0           2.0          1.0   \n",
+            "1             0.0           4.0         7.0           2.0          1.0   \n",
+            "2             0.0           2.0         7.0           2.0          1.0   \n",
+            "3             0.0           0.0         7.0           4.0          1.0   \n",
+            "4             0.0           4.0         7.0           4.0          1.0   \n",
+            "\n",
+            "   Garage Finish  Garage Qual  Garage Cond  Paved Drive  3Ssn Porch  \\\n",
+            "0            0.0          4.0          4.0          1.0           0   \n",
+            "1            2.0          4.0          4.0          2.0           0   \n",
+            "2            2.0          4.0          4.0          2.0           0   \n",
+            "3            0.0          4.0          4.0          2.0           0   \n",
+            "4            0.0          4.0          4.0          2.0           0   \n",
+            "\n",
+            "   Screen Porch  Pool Area  Misc Val  Mo Sold  Sale Type  Sale Condition  \\\n",
+            "0             0          0         0        5        9.0             4.0   \n",
+            "1           120          0         0        6        9.0             4.0   \n",
+            "2             0          0     12500        6        9.0             4.0   \n",
+            "3             0          0         0        4        9.0             4.0   \n",
+            "4             0          0         0        3        9.0             4.0   \n",
+            "\n",
+            "   SalePrice  \n",
+            "0     215000  \n",
+            "1     105000  \n",
+            "2     172000  \n",
+            "3     244000  \n",
+            "4     189900  \n",
+            "   MS SubClass  Overall Qual  Overall Cond  BsmtFin SF 2  Low Qual Fin SF  \\\n",
+            "0           20             6             5           0.0                0   \n",
+            "1           20             5             6         144.0                0   \n",
+            "2           20             6             6           0.0                0   \n",
+            "3           20             7             5           0.0                0   \n",
+            "4           60             5             5           0.0                0   \n",
+            "\n",
+            "   Bsmt Half Bath  3Ssn Porch  Screen Porch  Pool Area  Misc Val  Mo Sold  \\\n",
+            "0             0.0           0             0          0         0        5   \n",
+            "1             0.0           0           120          0         0        6   \n",
+            "2             0.0           0             0          0     12500        6   \n",
+            "3             0.0           0             0          0         0        4   \n",
+            "4             0.0           0             0          0         0        3   \n",
+            "\n",
+            "   SalePrice  MS Zoning_C (all)  MS Zoning_FV  MS Zoning_I (all)  \\\n",
+            "0     215000                0.0           0.0                0.0   \n",
+            "1     105000                0.0           0.0                0.0   \n",
+            "2     172000                0.0           0.0                0.0   \n",
+            "3     244000                0.0           0.0                0.0   \n",
+            "4     189900                0.0           0.0                0.0   \n",
+            "\n",
+            "   MS Zoning_RH  MS Zoning_RL  MS Zoning_RM  Street_Pave  Lot Shape_IR2  \\\n",
+            "0           0.0           1.0           0.0          1.0            0.0   \n",
+            "1           1.0           0.0           0.0          1.0            0.0   \n",
+            "2           0.0           1.0           0.0          1.0            0.0   \n",
+            "3           0.0           1.0           0.0          1.0            0.0   \n",
+            "4           0.0           1.0           0.0          1.0            0.0   \n",
+            "\n",
+            "   Lot Shape_IR3  Lot Shape_Reg  Land Contour_HLS  Land Contour_Low  \\\n",
+            "0            0.0            0.0               0.0               0.0   \n",
+            "1            0.0            1.0               0.0               0.0   \n",
+            "2            0.0            0.0               0.0               0.0   \n",
+            "3            0.0            1.0               0.0               0.0   \n",
+            "4            0.0            0.0               0.0               0.0   \n",
+            "\n",
+            "   Land Contour_Lvl  Utilities_NoSeWa  Utilities_NoSewr  Lot Config_CulDSac  \\\n",
+            "0               1.0               0.0               0.0                 0.0   \n",
+            "1               1.0               0.0               0.0                 0.0   \n",
+            "2               1.0               0.0               0.0                 0.0   \n",
+            "3               1.0               0.0               0.0                 0.0   \n",
+            "4               1.0               0.0               0.0                 0.0   \n",
+            "\n",
+            "   Lot Config_FR2  Lot Config_FR3  Lot Config_Inside  Land Slope_Mod  \\\n",
+            "0             0.0             0.0                0.0             0.0   \n",
+            "1             0.0             0.0                1.0             0.0   \n",
+            "2             0.0             0.0                0.0             0.0   \n",
+            "3             0.0             0.0                0.0             0.0   \n",
+            "4             0.0             0.0                1.0             0.0   \n",
+            "\n",
+            "   Land Slope_Sev  Neighborhood_Blueste  Neighborhood_BrDale  \\\n",
+            "0             0.0                   0.0                  0.0   \n",
+            "1             0.0                   0.0                  0.0   \n",
+            "2             0.0                   0.0                  0.0   \n",
+            "3             0.0                   0.0                  0.0   \n",
+            "4             0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Neighborhood_BrkSide  Neighborhood_ClearCr  Neighborhood_CollgCr  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_Crawfor  Neighborhood_Edwards  Neighborhood_Gilbert  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   1.0   \n",
+            "\n",
+            "   Neighborhood_Greens  Neighborhood_GrnHill  Neighborhood_IDOTRR  \\\n",
+            "0                  0.0                   0.0                  0.0   \n",
+            "1                  0.0                   0.0                  0.0   \n",
+            "2                  0.0                   0.0                  0.0   \n",
+            "3                  0.0                   0.0                  0.0   \n",
+            "4                  0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Neighborhood_Landmrk  Neighborhood_MeadowV  Neighborhood_Mitchel  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_NAmes  Neighborhood_NPkVill  Neighborhood_NWAmes  \\\n",
+            "0                 1.0                   0.0                  0.0   \n",
+            "1                 1.0                   0.0                  0.0   \n",
+            "2                 1.0                   0.0                  0.0   \n",
+            "3                 1.0                   0.0                  0.0   \n",
+            "4                 0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Neighborhood_NoRidge  Neighborhood_NridgHt  Neighborhood_OldTown  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_SWISU  Neighborhood_Sawyer  Neighborhood_SawyerW  \\\n",
+            "0                 0.0                  0.0                   0.0   \n",
+            "1                 0.0                  0.0                   0.0   \n",
+            "2                 0.0                  0.0                   0.0   \n",
+            "3                 0.0                  0.0                   0.0   \n",
+            "4                 0.0                  0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_Somerst  Neighborhood_StoneBr  Neighborhood_Timber  \\\n",
+            "0                   0.0                   0.0                  0.0   \n",
+            "1                   0.0                   0.0                  0.0   \n",
+            "2                   0.0                   0.0                  0.0   \n",
+            "3                   0.0                   0.0                  0.0   \n",
+            "4                   0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Neighborhood_Veenker  Condition 1_Feedr  Condition 1_Norm  \\\n",
+            "0                   0.0                0.0               1.0   \n",
+            "1                   0.0                1.0               0.0   \n",
+            "2                   0.0                0.0               1.0   \n",
+            "3                   0.0                0.0               1.0   \n",
+            "4                   0.0                0.0               1.0   \n",
+            "\n",
+            "   Condition 1_PosA  Condition 1_PosN  Condition 1_RRAe  Condition 1_RRAn  \\\n",
+            "0               0.0               0.0               0.0               0.0   \n",
+            "1               0.0               0.0               0.0               0.0   \n",
+            "2               0.0               0.0               0.0               0.0   \n",
+            "3               0.0               0.0               0.0               0.0   \n",
+            "4               0.0               0.0               0.0               0.0   \n",
+            "\n",
+            "   Condition 1_RRNe  Condition 1_RRNn  Condition 2_Feedr  Condition 2_Norm  \\\n",
+            "0               0.0               0.0                0.0               1.0   \n",
+            "1               0.0               0.0                0.0               1.0   \n",
+            "2               0.0               0.0                0.0               1.0   \n",
+            "3               0.0               0.0                0.0               1.0   \n",
+            "4               0.0               0.0                0.0               1.0   \n",
+            "\n",
+            "   Condition 2_PosA  Condition 2_PosN  Condition 2_RRAe  Condition 2_RRAn  \\\n",
+            "0               0.0               0.0               0.0               0.0   \n",
+            "1               0.0               0.0               0.0               0.0   \n",
+            "2               0.0               0.0               0.0               0.0   \n",
+            "3               0.0               0.0               0.0               0.0   \n",
+            "4               0.0               0.0               0.0               0.0   \n",
+            "\n",
+            "   Condition 2_RRNn  Bldg Type_2fmCon  Bldg Type_Duplex  Bldg Type_Twnhs  \\\n",
+            "0               0.0               0.0               0.0              0.0   \n",
+            "1               0.0               0.0               0.0              0.0   \n",
+            "2               0.0               0.0               0.0              0.0   \n",
+            "3               0.0               0.0               0.0              0.0   \n",
+            "4               0.0               0.0               0.0              0.0   \n",
+            "\n",
+            "   Bldg Type_TwnhsE  House Style_1.5Unf  House Style_1Story  \\\n",
+            "0               0.0                 0.0                 1.0   \n",
+            "1               0.0                 0.0                 1.0   \n",
+            "2               0.0                 0.0                 1.0   \n",
+            "3               0.0                 0.0                 1.0   \n",
+            "4               0.0                 0.0                 0.0   \n",
+            "\n",
+            "   House Style_2.5Fin  House Style_2.5Unf  House Style_2Story  \\\n",
+            "0                 0.0                 0.0                 0.0   \n",
+            "1                 0.0                 0.0                 0.0   \n",
+            "2                 0.0                 0.0                 0.0   \n",
+            "3                 0.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 1.0   \n",
+            "\n",
+            "   House Style_SFoyer  House Style_SLvl  Roof Style_Gable  Roof Style_Gambrel  \\\n",
+            "0                 0.0               0.0               0.0                 0.0   \n",
+            "1                 0.0               0.0               1.0                 0.0   \n",
+            "2                 0.0               0.0               0.0                 0.0   \n",
+            "3                 0.0               0.0               0.0                 0.0   \n",
+            "4                 0.0               0.0               1.0                 0.0   \n",
+            "\n",
+            "   Roof Style_Hip  Roof Style_Mansard  Roof Style_Shed  Roof Matl_CompShg  \\\n",
+            "0             1.0                 0.0              0.0                1.0   \n",
+            "1             0.0                 0.0              0.0                1.0   \n",
+            "2             1.0                 0.0              0.0                1.0   \n",
+            "3             1.0                 0.0              0.0                1.0   \n",
+            "4             0.0                 0.0              0.0                1.0   \n",
+            "\n",
+            "   Roof Matl_Membran  Roof Matl_Metal  Roof Matl_Roll  Roof Matl_Tar&Grv  \\\n",
+            "0                0.0              0.0             0.0                0.0   \n",
+            "1                0.0              0.0             0.0                0.0   \n",
+            "2                0.0              0.0             0.0                0.0   \n",
+            "3                0.0              0.0             0.0                0.0   \n",
+            "4                0.0              0.0             0.0                0.0   \n",
+            "\n",
+            "   Roof Matl_WdShake  Roof Matl_WdShngl  Exterior 1st_AsphShn  \\\n",
+            "0                0.0                0.0                   0.0   \n",
+            "1                0.0                0.0                   0.0   \n",
+            "2                0.0                0.0                   0.0   \n",
+            "3                0.0                0.0                   0.0   \n",
+            "4                0.0                0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_BrkComm  Exterior 1st_BrkFace  Exterior 1st_CBlock  \\\n",
+            "0                   0.0                   1.0                  0.0   \n",
+            "1                   0.0                   0.0                  0.0   \n",
+            "2                   0.0                   0.0                  0.0   \n",
+            "3                   0.0                   1.0                  0.0   \n",
+            "4                   0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Exterior 1st_CemntBd  Exterior 1st_HdBoard  Exterior 1st_ImStucc  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_MetalSd  Exterior 1st_Plywood  Exterior 1st_PreCast  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_Stone  Exterior 1st_Stucco  Exterior 1st_VinylSd  \\\n",
+            "0                 0.0                  0.0                   0.0   \n",
+            "1                 0.0                  0.0                   1.0   \n",
+            "2                 0.0                  0.0                   0.0   \n",
+            "3                 0.0                  0.0                   0.0   \n",
+            "4                 0.0                  0.0                   1.0   \n",
+            "\n",
+            "   Exterior 1st_Wd Sdng  Exterior 1st_WdShing  Exterior 2nd_AsphShn  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   1.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_Brk Cmn  Exterior 2nd_BrkFace  Exterior 2nd_CBlock  \\\n",
+            "0                   0.0                   0.0                  0.0   \n",
+            "1                   0.0                   0.0                  0.0   \n",
+            "2                   0.0                   0.0                  0.0   \n",
+            "3                   0.0                   1.0                  0.0   \n",
+            "4                   0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Exterior 2nd_CmentBd  Exterior 2nd_HdBoard  Exterior 2nd_ImStucc  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_MetalSd  Exterior 2nd_Other  Exterior 2nd_Plywood  \\\n",
+            "0                   0.0                 0.0                   1.0   \n",
+            "1                   0.0                 0.0                   0.0   \n",
+            "2                   0.0                 0.0                   0.0   \n",
+            "3                   0.0                 0.0                   0.0   \n",
+            "4                   0.0                 0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_PreCast  Exterior 2nd_Stone  Exterior 2nd_Stucco  \\\n",
+            "0                   0.0                 0.0                  0.0   \n",
+            "1                   0.0                 0.0                  0.0   \n",
+            "2                   0.0                 0.0                  0.0   \n",
+            "3                   0.0                 0.0                  0.0   \n",
+            "4                   0.0                 0.0                  0.0   \n",
+            "\n",
+            "   Exterior 2nd_VinylSd  Exterior 2nd_Wd Sdng  Exterior 2nd_Wd Shng  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   1.0                   0.0                   0.0   \n",
+            "2                   0.0                   1.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   1.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exter Qual_Fa  Exter Qual_Gd  Exter Qual_TA  Exter Cond_Fa  Exter Cond_Gd  \\\n",
+            "0            0.0            0.0            1.0            0.0            0.0   \n",
+            "1            0.0            0.0            1.0            0.0            0.0   \n",
+            "2            0.0            0.0            1.0            0.0            0.0   \n",
+            "3            0.0            1.0            0.0            0.0            0.0   \n",
+            "4            0.0            0.0            1.0            0.0            0.0   \n",
+            "\n",
+            "   Exter Cond_Po  Exter Cond_TA  Foundation_CBlock  Foundation_PConc  \\\n",
+            "0            0.0            1.0                1.0               0.0   \n",
+            "1            0.0            1.0                1.0               0.0   \n",
+            "2            0.0            1.0                1.0               0.0   \n",
+            "3            0.0            1.0                1.0               0.0   \n",
+            "4            0.0            1.0                0.0               1.0   \n",
+            "\n",
+            "   Foundation_Slab  Foundation_Stone  Foundation_Wood  Bsmt Qual_Fa  \\\n",
+            "0              0.0               0.0              0.0           0.0   \n",
+            "1              0.0               0.0              0.0           0.0   \n",
+            "2              0.0               0.0              0.0           0.0   \n",
+            "3              0.0               0.0              0.0           0.0   \n",
+            "4              0.0               0.0              0.0           0.0   \n",
+            "\n",
+            "   Bsmt Qual_Gd  Bsmt Qual_Po  Bsmt Qual_TA  Bsmt Cond_Fa  Bsmt Cond_Gd  \\\n",
+            "0           0.0           0.0           1.0           0.0           1.0   \n",
+            "1           0.0           0.0           1.0           0.0           0.0   \n",
+            "2           0.0           0.0           1.0           0.0           0.0   \n",
+            "3           0.0           0.0           1.0           0.0           0.0   \n",
+            "4           1.0           0.0           0.0           0.0           0.0   \n",
+            "\n",
+            "   Bsmt Cond_Po  Bsmt Cond_TA  Bsmt Exposure_Gd  Bsmt Exposure_Mn  \\\n",
+            "0           0.0           0.0               1.0               0.0   \n",
+            "1           0.0           1.0               0.0               0.0   \n",
+            "2           0.0           1.0               0.0               0.0   \n",
+            "3           0.0           1.0               0.0               0.0   \n",
+            "4           0.0           1.0               0.0               0.0   \n",
+            "\n",
+            "   Bsmt Exposure_No  BsmtFin Type 1_BLQ  BsmtFin Type 1_GLQ  \\\n",
+            "0               0.0                 1.0                 0.0   \n",
+            "1               1.0                 0.0                 0.0   \n",
+            "2               1.0                 0.0                 0.0   \n",
+            "3               1.0                 0.0                 0.0   \n",
+            "4               1.0                 0.0                 1.0   \n",
+            "\n",
+            "   BsmtFin Type 1_LwQ  BsmtFin Type 1_Rec  BsmtFin Type 1_Unf  \\\n",
+            "0                 0.0                 0.0                 0.0   \n",
+            "1                 0.0                 1.0                 0.0   \n",
+            "2                 0.0                 0.0                 0.0   \n",
+            "3                 0.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 0.0   \n",
+            "\n",
+            "   BsmtFin Type 2_BLQ  BsmtFin Type 2_GLQ  BsmtFin Type 2_LwQ  \\\n",
+            "0                 0.0                 0.0                 0.0   \n",
+            "1                 0.0                 0.0                 1.0   \n",
+            "2                 0.0                 0.0                 0.0   \n",
+            "3                 0.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 0.0   \n",
+            "\n",
+            "   BsmtFin Type 2_Rec  BsmtFin Type 2_Unf  Heating_GasA  Heating_GasW  \\\n",
+            "0                 0.0                 1.0           1.0           0.0   \n",
+            "1                 0.0                 0.0           1.0           0.0   \n",
+            "2                 0.0                 1.0           1.0           0.0   \n",
+            "3                 0.0                 1.0           1.0           0.0   \n",
+            "4                 0.0                 1.0           1.0           0.0   \n",
+            "\n",
+            "   Heating_Grav  Heating_OthW  Heating_Wall  Heating QC_Fa  Heating QC_Gd  \\\n",
+            "0           0.0           0.0           0.0            1.0            0.0   \n",
+            "1           0.0           0.0           0.0            0.0            0.0   \n",
+            "2           0.0           0.0           0.0            0.0            0.0   \n",
+            "3           0.0           0.0           0.0            0.0            0.0   \n",
+            "4           0.0           0.0           0.0            0.0            1.0   \n",
+            "\n",
+            "   Heating QC_Po  Heating QC_TA  Central Air_Y  Electrical_FuseF  \\\n",
+            "0            0.0            0.0            1.0               0.0   \n",
+            "1            0.0            1.0            1.0               0.0   \n",
+            "2            0.0            1.0            1.0               0.0   \n",
+            "3            0.0            0.0            1.0               0.0   \n",
+            "4            0.0            0.0            1.0               0.0   \n",
+            "\n",
+            "   Electrical_FuseP  Electrical_Mix  Electrical_SBrkr  Kitchen Qual_Fa  \\\n",
+            "0               0.0             0.0               1.0              0.0   \n",
+            "1               0.0             0.0               1.0              0.0   \n",
+            "2               0.0             0.0               1.0              0.0   \n",
+            "3               0.0             0.0               1.0              0.0   \n",
+            "4               0.0             0.0               1.0              0.0   \n",
+            "\n",
+            "   Kitchen Qual_Gd  Kitchen Qual_Po  Kitchen Qual_TA  Functional_Maj2  \\\n",
+            "0              0.0              0.0              1.0              0.0   \n",
+            "1              0.0              0.0              1.0              0.0   \n",
+            "2              1.0              0.0              0.0              0.0   \n",
+            "3              0.0              0.0              0.0              0.0   \n",
+            "4              0.0              0.0              1.0              0.0   \n",
+            "\n",
+            "   Functional_Min1  Functional_Min2  Functional_Mod  Functional_Sal  \\\n",
+            "0              0.0              0.0             0.0             0.0   \n",
+            "1              0.0              0.0             0.0             0.0   \n",
+            "2              0.0              0.0             0.0             0.0   \n",
+            "3              0.0              0.0             0.0             0.0   \n",
+            "4              0.0              0.0             0.0             0.0   \n",
+            "\n",
+            "   Functional_Sev  Functional_Typ  Fireplace Qu_Fa  Fireplace Qu_Gd  \\\n",
+            "0             0.0             1.0              0.0              1.0   \n",
+            "1             0.0             1.0              0.0              1.0   \n",
+            "2             0.0             1.0              0.0              1.0   \n",
+            "3             0.0             1.0              0.0              0.0   \n",
+            "4             0.0             1.0              0.0              0.0   \n",
+            "\n",
+            "   Fireplace Qu_Po  Fireplace Qu_TA  Garage Type_Attchd  Garage Type_Basment  \\\n",
+            "0              0.0              0.0                 1.0                  0.0   \n",
+            "1              0.0              0.0                 1.0                  0.0   \n",
+            "2              0.0              0.0                 1.0                  0.0   \n",
+            "3              0.0              1.0                 1.0                  0.0   \n",
+            "4              0.0              1.0                 1.0                  0.0   \n",
+            "\n",
+            "   Garage Type_BuiltIn  Garage Type_CarPort  Garage Type_Detchd  \\\n",
+            "0                  0.0                  0.0                 0.0   \n",
+            "1                  0.0                  0.0                 0.0   \n",
+            "2                  0.0                  0.0                 0.0   \n",
+            "3                  0.0                  0.0                 0.0   \n",
+            "4                  0.0                  0.0                 0.0   \n",
+            "\n",
+            "   Garage Finish_RFn  Garage Finish_Unf  Garage Qual_Fa  Garage Qual_Gd  \\\n",
+            "0                0.0                0.0             0.0             0.0   \n",
+            "1                0.0                1.0             0.0             0.0   \n",
+            "2                0.0                1.0             0.0             0.0   \n",
+            "3                0.0                0.0             0.0             0.0   \n",
+            "4                0.0                0.0             0.0             0.0   \n",
+            "\n",
+            "   Garage Qual_Po  Garage Qual_TA  Garage Cond_Fa  Garage Cond_Gd  \\\n",
+            "0             0.0             1.0             0.0             0.0   \n",
+            "1             0.0             1.0             0.0             0.0   \n",
+            "2             0.0             1.0             0.0             0.0   \n",
+            "3             0.0             1.0             0.0             0.0   \n",
+            "4             0.0             1.0             0.0             0.0   \n",
+            "\n",
+            "   Garage Cond_Po  Garage Cond_TA  Paved Drive_P  Paved Drive_Y  \\\n",
+            "0             0.0             1.0            1.0            0.0   \n",
+            "1             0.0             1.0            0.0            1.0   \n",
+            "2             0.0             1.0            0.0            1.0   \n",
+            "3             0.0             1.0            0.0            1.0   \n",
+            "4             0.0             1.0            0.0            1.0   \n",
+            "\n",
+            "   Sale Type_CWD  Sale Type_Con  Sale Type_ConLD  Sale Type_ConLI  \\\n",
+            "0            0.0            0.0              0.0              0.0   \n",
+            "1            0.0            0.0              0.0              0.0   \n",
+            "2            0.0            0.0              0.0              0.0   \n",
+            "3            0.0            0.0              0.0              0.0   \n",
+            "4            0.0            0.0              0.0              0.0   \n",
+            "\n",
+            "   Sale Type_ConLw  Sale Type_New  Sale Type_Oth  Sale Type_VWD  \\\n",
+            "0              0.0            0.0            0.0            0.0   \n",
+            "1              0.0            0.0            0.0            0.0   \n",
+            "2              0.0            0.0            0.0            0.0   \n",
+            "3              0.0            0.0            0.0            0.0   \n",
+            "4              0.0            0.0            0.0            0.0   \n",
+            "\n",
+            "   Sale Type_WD   Sale Condition_AdjLand  Sale Condition_Alloca  \\\n",
+            "0            1.0                     0.0                    0.0   \n",
+            "1            1.0                     0.0                    0.0   \n",
+            "2            1.0                     0.0                    0.0   \n",
+            "3            1.0                     0.0                    0.0   \n",
+            "4            1.0                     0.0                    0.0   \n",
+            "\n",
+            "   Sale Condition_Family  Sale Condition_Normal  Sale Condition_Partial  \n",
+            "0                    0.0                    1.0                     0.0  \n",
+            "1                    0.0                    1.0                     0.0  \n",
+            "2                    0.0                    1.0                     0.0  \n",
+            "3                    0.0                    1.0                     0.0  \n",
+            "4                    0.0                    1.0                     0.0  \n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Identify categorical variables.\n",
+        "# Use methods like:\n",
+        "# - One-hot encoding\n",
+        "# - Ordinal encoding\n",
+        "# Decide what makes sense for each feature.\n",
+        "\n",
+        "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder, OneHotEncoder\n",
+        "\n",
+        "# for col in col_cat:\n",
+        "#     print(df[col].unique())\n",
+        "\n",
+        "df_oe = df.copy()  # keep a separate version\n",
+        "ordinal_encoders = {}\n",
+        "for col in col_cat:\n",
+        "    oe = OrdinalEncoder()\n",
+        "    df_oe[col] = oe.fit_transform(df_oe[col].values.reshape(-1, 1))\n",
+        "    ordinal_encoders[col] = oe  # store encoder if needed later\n",
+        "print(df_oe.head())\n",
+        "\n",
+        "df_ohe = df.copy()  # keep a separate version\n",
+        "label_encoders = {}\n",
+        "ohe = OneHotEncoder(drop=\"first\", sparse_output=False) # Set sparse_output to False\n",
+        "encoded = ohe.fit_transform(df_ohe[col_cat])\n",
+        "\n",
+        "encoded_df = pd.DataFrame(encoded, columns=ohe.get_feature_names_out(col_cat), index=df_ohe.index)\n",
+        "df_ohe = pd.concat([df_ohe.drop(columns=col_cat), encoded_df], axis=1)\n",
+        "\n",
+        "print(df_ohe.head())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ayerWnrFj-OS"
+      },
+      "source": [
+        "## 🔹 Step 6: Feature Scaling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 69,
+      "metadata": {
+        "id": "cmOzqeCcj9_P"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "   MS SubClass MS Zoning Street Lot Shape Land Contour Utilities Lot Config  \\\n",
+            "0    -0.876682        RL   Pave       IR1          Lvl    AllPub     Corner   \n",
+            "1    -0.876682        RH   Pave       Reg          Lvl    AllPub     Inside   \n",
+            "2    -0.876682        RL   Pave       IR1          Lvl    AllPub     Corner   \n",
+            "3    -0.876682        RL   Pave       Reg          Lvl    AllPub     Corner   \n",
+            "4     0.061542        RL   Pave       IR1          Lvl    AllPub     Inside   \n",
+            "\n",
+            "  Land Slope Neighborhood Condition 1 Condition 2 Bldg Type House Style  \\\n",
+            "0        Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
+            "1        Gtl        NAmes       Feedr        Norm      1Fam      1Story   \n",
+            "2        Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
+            "3        Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
+            "4        Gtl      Gilbert        Norm        Norm      1Fam      2Story   \n",
+            "\n",
+            "   Overall Qual  Overall Cond Roof Style Roof Matl Exterior 1st Exterior 2nd  \\\n",
+            "0     -0.067028     -0.506827        Hip   CompShg      BrkFace      Plywood   \n",
+            "1     -0.775782      0.392868      Gable   CompShg      VinylSd      VinylSd   \n",
+            "2     -0.067028      0.392868        Hip   CompShg      Wd Sdng      Wd Sdng   \n",
+            "3      0.641726     -0.506827        Hip   CompShg      BrkFace      BrkFace   \n",
+            "4     -0.775782     -0.506827      Gable   CompShg      VinylSd      VinylSd   \n",
+            "\n",
+            "  Exter Qual Exter Cond Foundation Bsmt Qual Bsmt Cond Bsmt Exposure  \\\n",
+            "0         TA         TA     CBlock        TA        Gd            Gd   \n",
+            "1         TA         TA     CBlock        TA        TA            No   \n",
+            "2         TA         TA     CBlock        TA        TA            No   \n",
+            "3         Gd         TA     CBlock        TA        TA            No   \n",
+            "4         TA         TA      PConc        Gd        TA            No   \n",
+            "\n",
+            "  BsmtFin Type 1 BsmtFin Type 2  BsmtFin SF 2 Heating Heating QC Central Air  \\\n",
+            "0            BLQ            Unf     -0.293973    GasA         Fa           Y   \n",
+            "1            Rec            LwQ      0.557395    GasA         TA           Y   \n",
+            "2            ALQ            Unf     -0.293973    GasA         TA           Y   \n",
+            "3            ALQ            Unf     -0.293973    GasA         Ex           Y   \n",
+            "4            GLQ            Unf     -0.293973    GasA         Gd           Y   \n",
+            "\n",
+            "  Electrical  Low Qual Fin SF  Bsmt Half Bath Kitchen Qual Functional  \\\n",
+            "0      SBrkr        -0.101022       -0.249265           TA        Typ   \n",
+            "1      SBrkr        -0.101022       -0.249265           TA        Typ   \n",
+            "2      SBrkr        -0.101022       -0.249265           Gd        Typ   \n",
+            "3      SBrkr        -0.101022       -0.249265           Ex        Typ   \n",
+            "4      SBrkr        -0.101022       -0.249265           TA        Typ   \n",
+            "\n",
+            "  Fireplace Qu Garage Type Garage Finish Garage Qual Garage Cond Paved Drive  \\\n",
+            "0           Gd      Attchd           Fin          TA          TA           P   \n",
+            "1           Gd      Attchd           Unf          TA          TA           Y   \n",
+            "2           Gd      Attchd           Unf          TA          TA           Y   \n",
+            "3           TA      Attchd           Fin          TA          TA           Y   \n",
+            "4           TA      Attchd           Fin          TA          TA           Y   \n",
+            "\n",
+            "   3Ssn Porch  Screen Porch  Pool Area   Misc Val   Mo Sold Sale Type  \\\n",
+            "0   -0.103152     -0.285407  -0.063042  -0.089438 -0.447654       WD    \n",
+            "1   -0.103152      1.854142  -0.063042  -0.089438 -0.079138       WD    \n",
+            "2   -0.103152     -0.285407  -0.063042  21.981972 -0.079138       WD    \n",
+            "3   -0.103152     -0.285407  -0.063042  -0.089438 -0.816169       WD    \n",
+            "4   -0.103152     -0.285407  -0.063042  -0.089438 -1.184684       WD    \n",
+            "\n",
+            "  Sale Condition  SalePrice  \n",
+            "0         Normal   0.428625  \n",
+            "1         Normal  -0.948615  \n",
+            "2         Normal  -0.109751  \n",
+            "3         Normal   0.791716  \n",
+            "4         Normal   0.114364  \n",
+            "   MS SubClass MS Zoning Street Lot Shape Land Contour Utilities Lot Config  \\\n",
+            "0     0.000000        RL   Pave       IR1          Lvl    AllPub     Corner   \n",
+            "1     0.000000        RH   Pave       Reg          Lvl    AllPub     Inside   \n",
+            "2     0.000000        RL   Pave       IR1          Lvl    AllPub     Corner   \n",
+            "3     0.000000        RL   Pave       Reg          Lvl    AllPub     Corner   \n",
+            "4     0.235294        RL   Pave       IR1          Lvl    AllPub     Inside   \n",
+            "\n",
+            "  Land Slope Neighborhood Condition 1 Condition 2 Bldg Type House Style  \\\n",
+            "0        Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
+            "1        Gtl        NAmes       Feedr        Norm      1Fam      1Story   \n",
+            "2        Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
+            "3        Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
+            "4        Gtl      Gilbert        Norm        Norm      1Fam      2Story   \n",
+            "\n",
+            "   Overall Qual  Overall Cond Roof Style Roof Matl Exterior 1st Exterior 2nd  \\\n",
+            "0      0.555556         0.500        Hip   CompShg      BrkFace      Plywood   \n",
+            "1      0.444444         0.625      Gable   CompShg      VinylSd      VinylSd   \n",
+            "2      0.555556         0.625        Hip   CompShg      Wd Sdng      Wd Sdng   \n",
+            "3      0.666667         0.500        Hip   CompShg      BrkFace      BrkFace   \n",
+            "4      0.444444         0.500      Gable   CompShg      VinylSd      VinylSd   \n",
+            "\n",
+            "  Exter Qual Exter Cond Foundation Bsmt Qual Bsmt Cond Bsmt Exposure  \\\n",
+            "0         TA         TA     CBlock        TA        Gd            Gd   \n",
+            "1         TA         TA     CBlock        TA        TA            No   \n",
+            "2         TA         TA     CBlock        TA        TA            No   \n",
+            "3         Gd         TA     CBlock        TA        TA            No   \n",
+            "4         TA         TA      PConc        Gd        TA            No   \n",
+            "\n",
+            "  BsmtFin Type 1 BsmtFin Type 2  BsmtFin SF 2 Heating Heating QC Central Air  \\\n",
+            "0            BLQ            Unf      0.000000    GasA         Fa           Y   \n",
+            "1            Rec            LwQ      0.094364    GasA         TA           Y   \n",
+            "2            ALQ            Unf      0.000000    GasA         TA           Y   \n",
+            "3            ALQ            Unf      0.000000    GasA         Ex           Y   \n",
+            "4            GLQ            Unf      0.000000    GasA         Gd           Y   \n",
+            "\n",
+            "  Electrical  Low Qual Fin SF  Bsmt Half Bath Kitchen Qual Functional  \\\n",
+            "0      SBrkr              0.0             0.0           TA        Typ   \n",
+            "1      SBrkr              0.0             0.0           TA        Typ   \n",
+            "2      SBrkr              0.0             0.0           Gd        Typ   \n",
+            "3      SBrkr              0.0             0.0           Ex        Typ   \n",
+            "4      SBrkr              0.0             0.0           TA        Typ   \n",
+            "\n",
+            "  Fireplace Qu Garage Type Garage Finish Garage Qual Garage Cond Paved Drive  \\\n",
+            "0           Gd      Attchd           Fin          TA          TA           P   \n",
+            "1           Gd      Attchd           Unf          TA          TA           Y   \n",
+            "2           Gd      Attchd           Unf          TA          TA           Y   \n",
+            "3           TA      Attchd           Fin          TA          TA           Y   \n",
+            "4           TA      Attchd           Fin          TA          TA           Y   \n",
+            "\n",
+            "   3Ssn Porch  Screen Porch  Pool Area  Misc Val   Mo Sold Sale Type  \\\n",
+            "0         0.0      0.000000        0.0  0.000000  0.363636       WD    \n",
+            "1         0.0      0.208333        0.0  0.000000  0.454545       WD    \n",
+            "2         0.0      0.000000        0.0  0.735294  0.454545       WD    \n",
+            "3         0.0      0.000000        0.0  0.000000  0.272727       WD    \n",
+            "4         0.0      0.000000        0.0  0.000000  0.181818       WD    \n",
+            "\n",
+            "  Sale Condition  SalePrice  \n",
+            "0         Normal   0.272444  \n",
+            "1         Normal   0.124238  \n",
+            "2         Normal   0.214509  \n",
+            "3         Normal   0.311517  \n",
+            "4         Normal   0.238626  \n",
+            "   MS SubClass MS Zoning Street Lot Shape Land Contour Utilities Lot Config  \\\n",
+            "0         -0.6        RL   Pave       IR1          Lvl    AllPub     Corner   \n",
+            "1         -0.6        RH   Pave       Reg          Lvl    AllPub     Inside   \n",
+            "2         -0.6        RL   Pave       IR1          Lvl    AllPub     Corner   \n",
+            "3         -0.6        RL   Pave       Reg          Lvl    AllPub     Corner   \n",
+            "4          0.2        RL   Pave       IR1          Lvl    AllPub     Inside   \n",
+            "\n",
+            "  Land Slope Neighborhood Condition 1 Condition 2 Bldg Type House Style  \\\n",
+            "0        Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
+            "1        Gtl        NAmes       Feedr        Norm      1Fam      1Story   \n",
+            "2        Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
+            "3        Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
+            "4        Gtl      Gilbert        Norm        Norm      1Fam      2Story   \n",
+            "\n",
+            "   Overall Qual  Overall Cond Roof Style Roof Matl Exterior 1st Exterior 2nd  \\\n",
+            "0           0.0           0.0        Hip   CompShg      BrkFace      Plywood   \n",
+            "1          -0.5           1.0      Gable   CompShg      VinylSd      VinylSd   \n",
+            "2           0.0           1.0        Hip   CompShg      Wd Sdng      Wd Sdng   \n",
+            "3           0.5           0.0        Hip   CompShg      BrkFace      BrkFace   \n",
+            "4          -0.5           0.0      Gable   CompShg      VinylSd      VinylSd   \n",
+            "\n",
+            "  Exter Qual Exter Cond Foundation Bsmt Qual Bsmt Cond Bsmt Exposure  \\\n",
+            "0         TA         TA     CBlock        TA        Gd            Gd   \n",
+            "1         TA         TA     CBlock        TA        TA            No   \n",
+            "2         TA         TA     CBlock        TA        TA            No   \n",
+            "3         Gd         TA     CBlock        TA        TA            No   \n",
+            "4         TA         TA      PConc        Gd        TA            No   \n",
+            "\n",
+            "  BsmtFin Type 1 BsmtFin Type 2  BsmtFin SF 2 Heating Heating QC Central Air  \\\n",
+            "0            BLQ            Unf           0.0    GasA         Fa           Y   \n",
+            "1            Rec            LwQ         144.0    GasA         TA           Y   \n",
+            "2            ALQ            Unf           0.0    GasA         TA           Y   \n",
+            "3            ALQ            Unf           0.0    GasA         Ex           Y   \n",
+            "4            GLQ            Unf           0.0    GasA         Gd           Y   \n",
+            "\n",
+            "  Electrical  Low Qual Fin SF  Bsmt Half Bath Kitchen Qual Functional  \\\n",
+            "0      SBrkr              0.0             0.0           TA        Typ   \n",
+            "1      SBrkr              0.0             0.0           TA        Typ   \n",
+            "2      SBrkr              0.0             0.0           Gd        Typ   \n",
+            "3      SBrkr              0.0             0.0           Ex        Typ   \n",
+            "4      SBrkr              0.0             0.0           TA        Typ   \n",
+            "\n",
+            "  Fireplace Qu Garage Type Garage Finish Garage Qual Garage Cond Paved Drive  \\\n",
+            "0           Gd      Attchd           Fin          TA          TA           P   \n",
+            "1           Gd      Attchd           Unf          TA          TA           Y   \n",
+            "2           Gd      Attchd           Unf          TA          TA           Y   \n",
+            "3           TA      Attchd           Fin          TA          TA           Y   \n",
+            "4           TA      Attchd           Fin          TA          TA           Y   \n",
+            "\n",
+            "   3Ssn Porch  Screen Porch  Pool Area  Misc Val  Mo Sold Sale Type  \\\n",
+            "0         0.0           0.0        0.0       0.0    -0.25       WD    \n",
+            "1         0.0         120.0        0.0       0.0     0.00       WD    \n",
+            "2         0.0           0.0        0.0   12500.0     0.00       WD    \n",
+            "3         0.0           0.0        0.0       0.0    -0.50       WD    \n",
+            "4         0.0           0.0        0.0       0.0    -0.75       WD    \n",
+            "\n",
+            "  Sale Condition  SalePrice  \n",
+            "0         Normal   0.654762  \n",
+            "1         Normal  -0.654762  \n",
+            "2         Normal   0.142857  \n",
+            "3         Normal   1.000000  \n",
+            "4         Normal   0.355952  \n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Try different scaling techniques:\n",
+        "# - StandardScaler\n",
+        "# - MinMaxScaler\n",
+        "# - RobustScaler\n",
+        "# Decide based on the distribution of features.\n",
+        "\n",
+        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n",
+        "df_ss = df.copy()\n",
+        "df_mm = df.copy()\n",
+        "df_rs = df.copy()\n",
+        "\n",
+        "scaler = StandardScaler()\n",
+        "df_ss[col_num] = scaler.fit_transform(df_ss[col_num])\n",
+        "print(df_ss.head())\n",
+        "\n",
+        "scaler = MinMaxScaler()\n",
+        "df_mm[col_num] = scaler.fit_transform(df_mm[col_num])\n",
+        "print(df_mm.head())\n",
+        "\n",
+        "scaler = RobustScaler()\n",
+        "df_rs[col_num] = scaler.fit_transform(df_rs[col_num])\n",
+        "print(df_rs.head())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B8NozkS2kCDE"
+      },
+      "source": [
+        "## 🔹 Step 7: Feature Selection & Feature Creation 💡"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "elKATS4vj82a"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO: Select the most useful features.\n",
+        "# Try:\n",
+        "# - Correlation thresholding and Removing highly collinear features\n",
+        "# - decide yourself for dropping useless ones\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "irzYn2YHkFuu"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO: Create at least 2 NEW features.\n",
+        "# Examples:\n",
+        "# - Age of house: df[\"HouseAge\"] = df[\"YrSold\"] - df[\"YearBuilt\"]\n",
+        "# - Interaction: df[\"Quality_x_Size\"] = df[\"OverallQual\"] * df[\"GrLivArea\"]\n",
+        "# - Non-linear: df[\"Log_LotArea\"] = np.log1p(df[\"LotArea\"])\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ohlGPfhykRMs"
+      },
+      "source": [
+        "## 🔹 Step 8: Outlier Handling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "l0Yx7uH5kP_H"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO: Detect and handle outliers.\n",
+        "# Methods:\n",
+        "# - IQR rule\n",
+        "# - Z-score\n",
+        "# - Visualization (boxplots, scatterplots)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nDirz02_kU1e"
+      },
+      "source": [
+        "## 🔹 Step 9: Skewness Handling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Bz0Kke71kTxQ"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO: Check skewness of numerical features.\n",
+        "# Apply log, sqrt, Box-Cox, or Yeo-Johnson depending on distribution.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "voU8eavLkXya"
+      },
+      "source": [
+        "## 🔹 Step 10: Remove Duplicates"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pEyssUgmkZgq"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO: Check and remove duplicate rows if there is.\n",
+        "\n",
+        "# count duplicates\n",
+        "dup_count = df.duplicated().sum()\n",
+        "print(f\"Duplicate rows: {dup_count}\")\n",
+        "\n",
+        "# (optional) inspect some duplicate rows\n",
+        "if dup_count:\n",
+        "    display(df[df.duplicated(keep=False)].head())\n",
+        "\n",
+        "# drop duplicates (keep first occurrence) and reset index\n",
+        "df = df.drop_duplicates(keep='first').reset_index(drop=True)\n",
+        "print(f\"Rows after dropping duplicates: {len(df)})\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AlW4RbaTkeSu"
+      },
+      "source": [
+        "## 💾 Step 11: Save Cleaned Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "naZHc5dnkamR"
+      },
+      "outputs": [],
+      "source": [
+        "# Save your final cleaned and engineered dataset to CSV.\n",
+        "df.to_csv(\"AmesHousing_engineered.csv\", index=False)\n",
+        "print(\"✅ Cleaned dataset saved successfully!\")\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/Assignments/a1.11/WA_Fn-UseC_-Telco-Customer-Churn.csv b/a0.1/Assignments/a1.11/WA_Fn-UseC_-Telco-Customer-Churn.csv
new file mode 100644
index 0000000..3de7a61
--- /dev/null
+++ b/a0.1/Assignments/a1.11/WA_Fn-UseC_-Telco-Customer-Churn.csv
@@ -0,0 +1,7044 @@
+customerID,gender,SeniorCitizen,Partner,Dependents,tenure,PhoneService,MultipleLines,InternetService,OnlineSecurity,OnlineBackup,DeviceProtection,TechSupport,StreamingTV,StreamingMovies,Contract,PaperlessBilling,PaymentMethod,MonthlyCharges,TotalCharges,Churn
+7590-VHVEG,Female,0,Yes,No,1,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,29.85,29.85,No
+5575-GNVDE,Male,0,No,No,34,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,56.95,1889.5,No
+3668-QPYBK,Male,0,No,No,2,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,53.85,108.15,Yes
+7795-CFOCW,Male,0,No,No,45,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,One year,No,Bank transfer (automatic),42.3,1840.75,No
+9237-HQITU,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.7,151.65,Yes
+9305-CDSKC,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.65,820.5,Yes
+1452-KIOVK,Male,0,No,Yes,22,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.1,1949.4,No
+6713-OKOMC,Female,0,No,No,10,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.75,301.9,No
+7892-POOKP,Female,0,Yes,No,28,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.8,3046.05,Yes
+6388-TABGU,Male,0,No,Yes,62,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Bank transfer (automatic),56.15,3487.95,No
+9763-GRSKD,Male,0,Yes,Yes,13,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,49.95,587.45,No
+7469-LKBCI,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),18.95,326.8,No
+8091-TTVAX,Male,0,Yes,No,58,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Credit card (automatic),100.35,5681.1,No
+0280-XJGEX,Male,0,No,No,49,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.7,5036.3,Yes
+5129-JLPIS,Male,0,No,No,25,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105.5,2686.05,No
+3655-SNQYZ,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),113.25,7895.15,No
+8191-XWSZG,Female,0,No,No,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,1022.95,No
+9959-WOFKT,Male,0,No,Yes,71,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),106.7,7382.25,No
+4190-MFLUW,Female,0,Yes,Yes,10,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),55.2,528.35,Yes
+4183-MYFRB,Female,0,No,No,21,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,90.05,1862.9,No
+8779-QRDMV,Male,1,No,No,1,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,39.65,39.65,Yes
+1680-VDCWW,Male,0,Yes,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,202.25,No
+1066-JKSGK,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,20.15,Yes
+3638-WEABW,Female,0,Yes,No,58,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),59.9,3505.1,No
+6322-HRPFA,Male,0,Yes,Yes,49,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),59.6,2970.3,No
+6865-JZNKO,Female,0,No,No,30,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),55.3,1530.6,No
+6467-CHFZW,Male,0,Yes,Yes,47,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.35,4749.15,Yes
+8665-UTDHZ,Male,0,Yes,Yes,1,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,30.2,30.2,Yes
+5248-YGIJN,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.25,6369.45,No
+8773-HHUOZ,Female,0,No,Yes,17,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,64.7,1093.1,Yes
+3841-NFECX,Female,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),96.35,6766.95,No
+4929-XIHVW,Male,1,Yes,No,2,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.5,181.65,No
+6827-IEAUQ,Female,0,Yes,Yes,27,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Mailed check,66.15,1874.45,No
+7310-EGVHZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.2,20.2,No
+3413-BMNZE,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),45.25,45.25,No
+6234-RAAPL,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Two year,No,Bank transfer (automatic),99.9,7251.7,No
+6047-YHPVI,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.7,316.9,Yes
+6572-ADKRS,Female,0,No,No,46,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),74.8,3548.3,No
+5380-WJKOV,Male,0,No,No,34,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.35,3549.25,Yes
+8168-UQWWF,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),97.85,1105.4,Yes
+8865-TNMNX,Male,0,Yes,Yes,10,Yes,No,DSL,No,Yes,No,No,No,No,One year,No,Mailed check,49.55,475.7,No
+9489-DEDVP,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,Two year,Yes,Credit card (automatic),69.2,4872.35,No
+9867-JCZSP,Female,0,Yes,Yes,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.75,418.25,No
+4671-VJLCL,Female,0,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),79.85,4861.45,No
+4080-IIARD,Female,0,Yes,No,13,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,76.2,981.45,No
+3714-NTNFO,Female,0,No,No,49,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.5,3906.7,No
+5948-UJZLF,Male,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,49.25,97,No
+7760-OYPDY,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.65,144.15,Yes
+7639-LIAYI,Male,0,No,No,52,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.75,4217.8,No
+2954-PIBKO,Female,0,Yes,Yes,69,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),64.15,4254.1,No
+8012-SOUDQ,Female,1,No,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.25,3838.75,No
+9420-LOJKX,Female,0,No,No,15,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.1,1426.4,Yes
+6575-SUVOI,Female,1,Yes,No,25,Yes,Yes,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),69.5,1752.65,No
+7495-OOKFY,Female,1,Yes,No,8,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),80.65,633.3,Yes
+4667-QONEA,Female,1,Yes,Yes,60,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),74.85,4456.35,No
+1658-BYGOY,Male,1,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.45,1752.55,Yes
+8769-KKTPH,Female,0,Yes,Yes,63,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),99.65,6311.2,No
+5067-XJQFU,Male,1,Yes,Yes,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,108.45,7076.35,No
+3957-SQXML,Female,0,Yes,Yes,34,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.95,894.3,No
+5954-BDFSG,Female,0,No,No,72,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),107.5,7853.7,No
+0434-CSFON,Female,0,Yes,No,47,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.5,4707.1,No
+1215-FIGMP,Male,0,No,No,60,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.9,5450.7,No
+0526-SXDJP,Male,0,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Bank transfer (automatic),42.1,2962,No
+0557-ASKVU,Female,0,Yes,Yes,18,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),54.4,957.1,No
+5698-BQJOH,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,94.4,857.25,Yes
+5122-CYFXA,Female,0,No,No,3,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,75.3,244.1,No
+8627-ZYGSZ,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Electronic check,78.9,3650.35,No
+3410-YOQBQ,Female,0,No,No,31,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,79.2,2497.2,No
+3170-NMYVV,Female,0,Yes,Yes,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.15,930.9,No
+7410-OIEDU,Male,0,No,No,10,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.85,887.35,No
+2273-QCKXA,Male,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,49.05,49.05,No
+0731-EBJQB,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.4,1090.65,No
+1891-QRQSA,Male,1,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.6,7099,No
+8028-PNXHQ,Male,0,Yes,Yes,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.25,1424.6,No
+5630-AHZIL,Female,0,No,Yes,3,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),64.5,177.4,No
+2673-CXQEU,Female,1,No,No,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,110.5,6139.5,No
+6416-JNVRK,Female,0,No,No,46,Yes,No,DSL,No,No,No,No,No,Yes,One year,No,Credit card (automatic),55.65,2688.85,No
+5590-ZSKRV,Female,0,Yes,Yes,8,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.65,482.25,No
+0191-ZHSKZ,Male,1,No,No,30,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,74.75,2111.3,No
+3887-PBQAO,Female,0,Yes,Yes,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),25.9,1216.6,No
+5919-TMRGD,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.35,79.35,Yes
+8108-UXRQN,Female,0,Yes,Yes,11,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,50.55,565.35,No
+9191-MYQKX,Female,0,Yes,No,7,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.15,496.9,Yes
+9919-YLNNG,Female,0,No,No,42,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.8,4327.5,No
+0318-ZOPWS,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.15,973.35,No
+4445-ZJNMU,Male,0,No,No,9,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.3,918.75,No
+4808-YNLEU,Female,0,Yes,No,35,Yes,No,DSL,Yes,No,No,No,Yes,No,One year,Yes,Bank transfer (automatic),62.15,2215.45,No
+1862-QRWPE,Female,0,Yes,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.65,1057,No
+2796-NNUFI,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.95,927.1,No
+3016-KSVCP,Male,0,Yes,No,29,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,33.75,1009.25,No
+4767-HZZHQ,Male,0,Yes,Yes,30,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),82.05,2570.2,No
+2424-WVHPL,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,74.7,74.7,No
+7233-PAHHL,Male,0,Yes,Yes,66,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,84,5714.25,No
+6067-NGCEU,Female,0,No,No,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),111.05,7107,No
+9848-JQJTX,Male,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),100.9,7459.05,No
+8637-XJIVR,Female,0,No,No,12,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,78.95,927.35,Yes
+9803-FTJCG,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),66.85,4748.7,No
+0278-YXOOG,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,21.05,113.85,Yes
+3212-KXOCR,Male,0,No,No,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),21,1107.2,No
+4598-XLKNJ,Female,1,Yes,No,25,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.5,2514.5,Yes
+6380-ARCEH,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,20.2,No
+3679-XASPY,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.45,19.45,No
+7123-WQUHX,Male,0,No,No,38,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),95,3605.6,No
+5386-THSLQ,Female,1,Yes,No,66,No,No phone service,DSL,No,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),45.55,3027.25,No
+3192-NQECA,Male,0,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110,7611.85,Yes
+6180-YBIQI,Male,0,No,No,5,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.3,100.2,No
+6728-DKUCO,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,104.15,7303.05,No
+9750-BOOHV,Female,0,No,No,32,No,No phone service,DSL,Yes,No,No,No,No,No,One year,No,Mailed check,30.15,927.65,No
+8597-CWYHH,Male,0,No,No,43,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Mailed check,94.35,3921.3,No
+2848-YXSMW,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,1363.25,No
+0486-HECZI,Male,0,Yes,No,55,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,96.75,5238.9,Yes
+4549-ZDQYY,Female,0,No,No,52,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,No,Credit card (automatic),57.95,3042.25,No
+5712-AHQNN,Female,0,No,No,43,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,91.65,3954.1,No
+4846-WHAFZ,Female,1,Yes,No,37,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.5,2868.15,Yes
+5256-SKJGO,Female,0,Yes,Yes,64,No,No phone service,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,54.6,3423.5,No
+3071-VBYPO,Male,0,Yes,Yes,3,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,89.85,248.4,No
+9560-BBZXK,Female,0,No,No,36,No,No phone service,DSL,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),31.05,1126.35,No
+5299-RULOA,Female,0,Yes,Yes,10,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.25,1064.65,Yes
+8402-OOOHJ,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.65,835.15,No
+9445-ZUEQE,Male,0,Yes,Yes,27,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),85.2,2151.6,No
+1091-SOZGA,Female,0,Yes,Yes,56,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),99.8,5515.45,No
+2928-HLDBA,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.7,112.75,No
+0404-SWRVG,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,229.55,Yes
+6497-TILVL,Female,0,Yes,Yes,7,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,50.7,350.35,No
+7219-TLZHO,Female,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.85,62.9,No
+4622-YNKIJ,Male,0,No,No,33,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,No,Two year,Yes,Electronic check,88.95,3027.65,No
+4412-YLTKF,Female,1,No,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,78.05,2135.5,Yes
+6734-PSBAW,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),23.55,1723.95,No
+3930-ZGWVE,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,19.75,No
+2639-UGMAZ,Male,1,No,No,71,No,No phone service,DSL,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,56.45,3985.35,No
+2876-GZYZC,Female,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.95,1215.65,No
+6207-WIOLX,Female,0,Yes,Yes,25,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),58.6,1502.65,Yes
+8587-XYZSF,Male,0,No,No,67,Yes,No,DSL,No,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),50.55,3260.1,No
+3091-FYHKI,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.45,35.45,Yes
+2372-HWUHI,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.35,81.25,Yes
+7799-LGRDP,Female,0,No,No,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.7,1188.2,No
+7850-VWJUU,Female,0,No,No,23,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75,1778.5,No
+3774-VBNXY,Female,0,Yes,Yes,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.2,1277.75,No
+6217-KDYWC,Male,0,No,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.6,1170.55,No
+0390-DCFDQ,Female,1,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.45,70.45,Yes
+3146-MSEGF,Female,1,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),88.05,6425.65,No
+4080-OGPJL,Female,0,No,No,8,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Electronic check,71.15,563.65,Yes
+1095-WGNGG,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),101.05,5971.25,No
+2636-SJDOU,Male,0,No,No,64,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),84.3,5289.05,No
+1131-QQZEB,Male,1,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.95,1756.2,No
+5716-EZXZN,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),99.05,6416.7,No
+6837-BJYDQ,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.6,61.35,No
+2135-RXIHG,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.65,45.65,Yes
+6440-DKQGE,Male,0,No,Yes,30,Yes,No,DSL,No,Yes,Yes,No,Yes,No,One year,No,Credit card (automatic),64.5,1929.95,No
+3466-BYAVD,Male,0,Yes,Yes,15,Yes,No,DSL,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Mailed check,69.5,1071.4,No
+3780-YVMFA,Female,0,Yes,Yes,8,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,68.55,564.35,No
+3874-EQOEP,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,95,655.5,Yes
+1679-JRFBR,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),108.15,7930.55,No
+9073-ZZIAY,Male,0,Yes,Yes,62,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,86.1,5215.25,No
+3077-RSNTJ,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,113.5,No
+6551-GNYDG,Female,0,Yes,Yes,14,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),80.9,1152.8,No
+9167-APMXZ,Female,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),84.15,1821.95,No
+2749-CTKAJ,Male,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.15,419.9,No
+6371-NZYEG,Male,0,Yes,Yes,16,Yes,No,DSL,Yes,No,Yes,No,Yes,No,Two year,No,Mailed check,64.25,1024,No
+7554-NEWDD,Male,0,No,No,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.7,251.6,No
+8992-VONJD,Female,0,No,Yes,13,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,56,764.55,No
+0867-MKZVY,Female,0,Yes,No,20,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,82.4,1592.35,Yes
+4482-EWFMI,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.7,135.2,No
+4648-YPBTM,Male,0,No,No,53,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),73.9,3958.25,No
+2907-ILJBN,Female,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.6,233.9,No
+6345-FZOQH,Male,0,Yes,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,1363.45,No
+3376-BMGFE,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.9,273,Yes
+5997-OPVFA,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),89.05,6254.45,No
+3445-HXXGF,Male,1,Yes,No,58,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,45.3,2651.2,Yes
+1159-WFSGR,Female,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.4,321.4,No
+7654-YWJUF,Male,0,Yes,No,43,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),84.25,3539.25,No
+1875-QIVME,Female,0,Yes,No,2,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.4,242.8,Yes
+6727-IOTLZ,Male,0,Yes,No,14,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,81.95,1181.75,No
+0691-JVSYA,Female,0,Yes,No,53,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),94.85,5000.2,Yes
+5918-VUKWP,Female,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.55,654.55,No
+1744-JHKYS,Female,0,Yes,No,34,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,24.7,780.2,No
+2656-FMOKZ,Female,1,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.45,1145.7,Yes
+2070-FNEXE,Female,1,No,No,7,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),76.45,503.6,Yes
+5947-SGKCL,Female,0,Yes,Yes,15,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.35,1559.25,No
+3712-PKXZA,Male,0,Yes,No,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.55,1252,No
+6317-YPKDH,Female,0,No,No,1,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),29.95,29.95,Yes
+6582-OIVSP,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.3,45.3,No
+9367-WXLCH,Male,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),84.5,662.65,Yes
+5524-KHNJP,Male,0,Yes,Yes,33,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,One year,No,Credit card (automatic),74.75,2453.3,No
+1918-ZBFQJ,Female,0,No,No,13,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,79.25,1111.65,Yes
+1024-GUALD,Female,0,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.8,24.8,Yes
+4827-USJHP,Male,0,No,No,20,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,51.8,1023.85,No
+8167-GJLRN,Male,0,No,No,3,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,30.4,82.15,No
+0956-SYCWG,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.65,244.8,No
+8017-UVSZU,Female,0,Yes,No,40,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),56.6,2379.1,No
+7100-FQPRV,Male,0,Yes,Yes,43,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Credit card (automatic),71.9,3173.35,No
+2472-OVKUP,Male,0,Yes,No,6,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,91,531,Yes
+2984-RGEYA,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.75,1375.4,No
+9680-NIAUV,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),109.7,8129.3,No
+2146-EGVDT,Male,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.3,1192.7,No
+2604-IJPDU,Female,0,Yes,No,20,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,96.55,1901.65,No
+9178-JHUVJ,Male,0,Yes,Yes,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.1,587.4,No
+6168-YBYNP,Male,0,No,No,59,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,111.35,6519.75,No
+7255-SSFBC,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),112.25,8041.65,No
+3645-DEYGF,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.75,20.75,No
+9323-HGFWY,Female,0,Yes,No,27,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),101.9,2681.15,No
+8544-GOQSH,Female,0,No,No,14,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),80.05,1112.3,No
+3363-DTIVD,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,No,Electronic check,105.55,7405.5,No
+7018-WBJNK,Male,0,No,Yes,13,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),78.3,1033.95,No
+9142-KZXOP,Male,0,No,No,44,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),68.85,2958.95,No
+7674-YTAFD,Female,0,No,No,33,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),79.95,2684.85,No
+6348-SNFUS,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),55.45,4179.2,No
+1285-OKIPP,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,79.9,79.9,Yes
+7825-ECJRF,Female,0,No,No,19,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,106.6,1934.45,Yes
+1347-KTTTA,Male,0,Yes,No,64,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),102.45,6654.1,No
+7841-TZDMQ,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),46,84.5,Yes
+4195-NZGTA,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.25,25.25,No
+7157-SMCFK,Male,0,No,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,1124.2,No
+4709-LKHYG,Female,0,Yes,Yes,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20,540.05,No
+2504-DSHIH,Male,1,Yes,No,23,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,86.8,1975.85,No
+0699-NDKJM,Female,0,Yes,No,57,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),58.75,3437.45,No
+9286-BHDQG,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),45.25,3139.8,No
+0230-WEQUW,Male,0,Yes,No,66,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),56.6,3789.2,No
+2040-LDIWQ,Male,0,Yes,Yes,65,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.2,5324.5,No
+6496-JDSSB,Female,0,No,No,8,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),80,624.6,No
+9408-SSNVZ,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,268.35,Yes
+4443-EMBNA,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.75,1836.9,No
+6469-MRVET,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.2,20.2,No
+0742-MOABM,Male,0,Yes,No,4,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,50.05,179.35,Yes
+5961-VUSRV,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.35,219.35,No
+6778-JFCMK,Male,0,No,No,24,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,One year,Yes,Mailed check,50.6,1288.75,No
+6341-JVQGF,Female,0,Yes,Yes,31,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,81.15,2545.75,No
+2232-DMLXU,Female,0,Yes,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,55.2,55.2,Yes
+4811-JBUVU,Male,0,No,No,30,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),89.9,2723.15,No
+0945-TSONX,Female,0,Yes,Yes,47,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.3,4107.25,No
+2651-ZCBXV,Male,0,No,No,54,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108,5760.65,No
+3316-UWXUY,Male,0,No,No,50,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),93.5,4747.5,No
+8937-RDTHP,Male,0,No,No,1,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,84.6,84.6,Yes
+7083-MIOPC,Female,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.25,1566.9,No
+1984-GPTEH,Female,0,No,No,29,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,25.15,702,No
+1251-KRREG,Male,0,No,No,2,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,54.4,114.1,Yes
+0621-JFHOL,Female,0,No,No,10,No,No phone service,DSL,No,No,No,Yes,No,No,Two year,Yes,Mailed check,29.6,299.05,No
+9903-LYSAB,Male,0,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.15,1305.95,No
+0094-OIFMO,Female,1,No,No,11,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95,1120.3,Yes
+9227-UAQFT,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,284.35,No
+7301-ABVAD,Female,0,No,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.6,6350.5,No
+6614-FHDBO,Male,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),109.2,7878.3,No
+7576-ASEJU,Female,0,Yes,Yes,41,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),74.7,3187.65,No
+9058-HRZSV,Female,1,Yes,No,65,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,No,Electronic check,94.4,6126.15,No
+4522-AKYLR,Female,1,No,No,13,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,54.8,731.3,No
+0221-WMXNQ,Male,1,No,No,4,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,75.35,273.4,No
+0303-UNCIP,Male,0,No,No,41,Yes,Yes,DSL,No,No,Yes,No,No,Yes,One year,No,Mailed check,65,2531.8,No
+9947-OTFQU,Male,1,No,No,15,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,1074.3,Yes
+0322-YINQP,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,48.55,48.55,Yes
+0959-WHOKV,Male,0,No,No,42,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,99,4298.45,No
+4075-JFPGR,Female,0,Yes,No,51,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,93.5,4619.55,No
+4629-NRXKX,Female,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.4,147.15,Yes
+9514-JDSKI,Male,1,Yes,No,1,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,40.2,40.2,Yes
+3282-ZISZV,Male,0,No,Yes,32,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),83.7,2633.3,No
+3675-YDUPJ,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.85,193.05,No
+4111-BNXIF,Female,0,Yes,Yes,67,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,59.55,4103.9,No
+7017-VFHAY,Female,0,Yes,Yes,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.1,7008.15,No
+6655-LHBYW,Male,0,No,No,50,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),114.35,5791.1,No
+4959-JOSRX,Female,0,Yes,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.6,80.55,Yes
+5046-NUHWD,Female,1,Yes,No,29,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,45,1228.65,No
+7273-TEFQD,Male,1,No,No,3,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,41.15,132.2,Yes
+3606-TWKGI,Male,1,No,No,13,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.9,1364.3,Yes
+7529-ZDFXI,Male,1,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.85,4925.35,No
+7605-BDWDC,Female,0,No,No,31,No,No phone service,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),49.85,1520.1,No
+1950-KSVVJ,Female,0,Yes,No,45,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Mailed check,113.3,5032.25,No
+0123-CRBRT,Female,0,Yes,Yes,61,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,88.1,5526.75,No
+6292-TOSSS,Male,0,No,No,50,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.9,1195.25,No
+3197-ARFOY,Female,1,No,No,19,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,105,2007.25,No
+6323-AYBRX,Male,0,No,No,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,1099.6,Yes
+7014-ZZXAW,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.25,1732.95,No
+4385-GZQXV,Female,1,No,No,16,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,94.45,1511.2,Yes
+7633-MVPUY,Male,0,Yes,No,57,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Electronic check,59.75,3450.15,No
+6366-ZGQGL,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),24.8,24.8,Yes
+4716-HHKQH,Male,1,Yes,No,20,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,107.05,2172.05,No
+5940-AHUHD,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.6,70.6,Yes
+6432-TWQLB,Male,0,Yes,No,5,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,85.4,401.1,Yes
+4484-GLZOU,Female,0,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.05,5624.85,Yes
+3179-GBRWV,Male,1,Yes,No,21,Yes,No,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),64.95,1339.8,No
+8645-KWHJO,Male,0,No,No,14,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,55,771.95,No
+4130-MZLCC,Female,0,No,No,5,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),50.55,244.75,No
+0314-TKOSI,Female,0,No,No,6,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,55.15,322.9,No
+8229-MYEJZ,Female,0,No,No,10,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,51.2,498.25,No
+2080-SRCDE,Female,0,No,Yes,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.4,25.4,No
+9577-WJVCQ,Female,0,No,No,68,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,54.45,3687.75,No
+9512-UIBFX,Male,0,Yes,Yes,18,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,95.15,1779.95,Yes
+6202-DYYFX,Female,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,No,No,One year,Yes,Credit card (automatic),76,1783.6,No
+3808-HFKDE,Female,0,No,No,20,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.35,927.15,No
+5583-SXDAG,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70,70,Yes
+3488-PGMQJ,Male,1,No,No,8,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.5,606.55,Yes
+3580-REOAC,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.85,435.4,Yes
+7534-BFESC,Male,1,No,No,24,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.1,1712.7,Yes
+3727-OWVYD,Male,0,No,No,35,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,No,Mailed check,61.2,2021.2,No
+2294-SALNE,Male,0,Yes,Yes,23,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,One year,No,Mailed check,86.8,1940.8,No
+4847-TAJYI,Female,1,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.35,567.8,No
+1563-IWQEX,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,220.35,No
+8203-XJZRC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,20.25,No
+6556-DBKZF,Female,0,Yes,Yes,71,Yes,No,Fiber optic,No,No,Yes,No,No,No,Two year,No,Electronic check,76.05,5436.45,No
+6851-WEFYX,Male,1,Yes,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.8,3437.5,No
+2985-JUUBZ,Male,0,Yes,Yes,40,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,74.55,3015.75,No
+6390-DSAZX,Female,0,No,Yes,1,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,73.6,73.6,Yes
+0895-LMRSF,Male,0,No,No,23,Yes,No,DSL,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),64.9,1509.8,No
+8098-LLAZX,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.45,396.1,Yes
+8266-VBFQL,Male,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,90.4,356.65,No
+8181-YHCMF,Female,0,Yes,Yes,68,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),60.3,4109,No
+2240-HSJQD,Male,0,No,Yes,38,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,81.85,3141.7,No
+1248-DYXUB,Male,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.8,1229.1,No
+0265-EDXBD,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,2303.35,Yes
+4115-BNPJY,Male,0,Yes,Yes,29,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,75.55,2054.4,No
+3167-SNQPL,Male,1,Yes,Yes,38,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,101.15,3741.85,No
+4091-TVOCN,Male,0,No,Yes,48,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),78.75,3682.45,No
+1098-TDVUQ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.25,19.25,No
+7277-OZCGZ,Female,0,No,No,22,Yes,No,Fiber optic,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.05,1886.25,No
+1557-EMYVT,Female,0,No,No,43,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.05,4895.1,No
+2799-ARNLO,Female,1,Yes,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.35,341.6,No
+7563-BIUPC,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.6,415.55,Yes
+5027-YOCXN,Male,0,Yes,Yes,51,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,No,Credit card (automatic),110.05,5686.4,No
+3973-SKMLN,Male,0,No,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.9,1355.1,No
+2321-OMBXY,Female,0,Yes,Yes,38,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,One year,No,Credit card (automatic),80.3,3058.65,Yes
+2840-XANRC,Male,1,Yes,No,24,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.15,2231.05,Yes
+6745-JEFZB,Male,0,Yes,No,35,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),91.5,3236.35,No
+5020-ZSTTY,Female,1,No,No,54,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),82.45,4350.1,Yes
+9880-TDQAC,Female,0,Yes,Yes,72,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,60,4264,No
+8705-WZCYL,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.8,44.8,No
+7102-JJVTX,Female,0,Yes,Yes,9,Yes,No,DSL,Yes,No,No,No,No,No,One year,No,Mailed check,48.6,422.3,No
+8626-PTQGE,Male,0,No,No,69,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),60.05,4176.7,No
+4983-CLMLV,Female,0,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.7,5138.1,No
+5701-YVSVF,Female,1,Yes,No,11,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,82.9,880.05,No
+5804-LEPIM,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.35,139.05,Yes
+5697-GOMBF,Female,1,Yes,Yes,28,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,35.9,973.65,No
+2739-CACDQ,Female,1,No,No,17,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),82.65,1470.05,No
+9385-EHGDO,Female,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.85,739.35,No
+9498-FIMXL,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.2,161.95,No
+2379-GYFLQ,Male,0,No,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),94.9,4422.95,No
+0122-OAHPZ,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.85,511.25,Yes
+2868-SNELZ,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,80.6,155.8,Yes
+4322-RCYMT,Male,0,Yes,Yes,68,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),75.8,5293.95,Yes
+6680-NENYN,Female,0,No,No,43,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,104.6,4759.85,Yes
+2088-IEBAU,Female,0,No,No,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,88.15,6148.45,No
+7982-VCELR,Female,0,No,No,36,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.8,3565.65,No
+1343-EHPYB,Male,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.4,6603,Yes
+6035-BXTTY,Female,1,No,No,32,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),54.65,1830.1,No
+6885-PKOAM,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),85.75,6223.8,No
+7520-HQWJU,Female,0,Yes,Yes,66,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),67.45,4508.65,No
+9639-BUJXT,Male,0,No,No,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.5,1328.15,No
+5924-SNGKP,Female,0,No,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.25,865,No
+0021-IKXGC,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,72.1,72.1,No
+2034-GDRCN,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.4,168.2,Yes
+8966-SNIZF,Female,0,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.45,1303.5,No
+6243-OZGFH,Female,0,No,No,23,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.95,996.85,No
+4654-DLAMQ,Female,1,Yes,No,64,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,No,Bank transfer (automatic),97,6430.9,No
+0513-RBGPE,Male,0,Yes,Yes,37,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),62.8,2278.75,No
+5160-UXJED,Male,0,No,Yes,17,Yes,No,DSL,No,No,No,No,No,No,One year,No,Mailed check,44.6,681.4,No
+4115-NZRKS,Female,1,No,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.15,574.35,No
+0219-YTZUE,Male,0,Yes,Yes,4,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),84.8,371.9,Yes
+0623-IIHUG,Female,1,No,No,21,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,41.9,840.1,Yes
+4572-DVCGN,Female,0,No,No,10,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),80.25,846,Yes
+3351-NGXYI,Female,1,No,No,16,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,54.1,889,No
+8984-EYLLL,Male,0,Yes,No,64,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,105.25,6823.4,No
+9057-MSWCO,Male,1,Yes,No,27,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),30.75,805.1,Yes
+9833-TGFHX,Male,0,Yes,Yes,42,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,No,Electronic check,97.1,4016.75,No
+9294-TDIPC,Male,0,No,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,83.75,No
+5229-DTFYB,Female,0,No,No,41,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),98.8,3959.15,No
+0104-PPXDV,Male,0,Yes,No,58,Yes,No,DSL,No,No,Yes,No,No,No,One year,No,Credit card (automatic),50.3,2878.55,No
+5176-LMJXE,Female,0,No,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.55,945.7,No
+3583-KRKMD,Male,0,No,No,18,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),75.9,1373.05,No
+1010-DIAUQ,Male,0,No,No,5,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),96.5,492.55,Yes
+9069-LGEUL,Male,0,Yes,No,23,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),59.95,1406,No
+7302-ZHMHP,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.15,19.15,No
+9571-EDEBV,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),98.65,6962.85,No
+3520-FJGCV,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),112.6,8126.65,No
+6563-VRERX,Male,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.6,690.25,No
+0259-GBZSH,Male,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.65,181.5,Yes
+6122-EFVKN,Male,0,No,Yes,24,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Mailed check,35.75,830.8,No
+2805-EDJPQ,Female,0,Yes,Yes,56,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),99.75,5608.4,No
+6862-CQUMB,Male,0,No,No,37,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),96.1,3646.8,No
+7156-MXBJE,Female,0,No,No,43,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),85.1,3662.25,No
+6158-HDPXZ,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.35,25.35,No
+9601-BRXPO,Female,0,Yes,No,25,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.95,2566.5,Yes
+2863-IMQDR,Female,0,No,No,61,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,89.65,5308.7,No
+5686-CMAWK,Male,0,No,No,17,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,One year,No,Electronic check,86.75,1410.25,No
+5651-CRHKQ,Female,0,Yes,No,41,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),86.2,3339.05,No
+6905-NIQIN,Male,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,50.65,50.65,Yes
+8204-YJCLA,Male,1,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),64.8,4732.35,No
+5167-ZFFMM,Male,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.85,90.85,Yes
+6583-SZVGP,Male,0,No,No,48,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.1,5067.45,No
+4895-TMWIR,Male,1,Yes,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,214.75,Yes
+0533-BNWKF,Female,1,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,85.45,4874.7,Yes
+1708-PBBOA,Female,0,No,No,42,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Electronic check,54.75,2348.45,No
+8782-LKFPK,Male,0,No,No,44,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,90.4,4063,No
+5522-JBWMO,Male,0,No,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44,44,No
+3597-MVHJT,Female,0,No,No,27,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.6,2595.25,No
+9774-NRNAU,Male,1,Yes,No,27,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),84.8,2309.55,No
+0224-RLWWD,Female,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.3,89.3,No
+9967-ATRFS,Female,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,367.55,No
+3951-NJCVI,Female,1,Yes,No,42,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.05,3944.5,No
+2977-CEBSX,Female,0,No,No,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.05,5965.95,No
+0177-PXBAT,Male,1,Yes,No,33,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),109.9,3694.7,No
+6599-CEBNN,Female,0,No,No,34,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.95,2524.45,Yes
+2519-ERQOJ,Male,1,No,No,33,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,54.6,1803.7,No
+5876-QMYLD,Female,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,415.1,No
+2277-AXSDC,Female,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.75,624.15,No
+9442-JTWDL,Female,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.05,237.7,No
+0979-PHULV,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.45,7007.6,Yes
+3067-SVMTC,Female,0,Yes,No,68,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),55.9,3848.8,No
+5495-GPSRW,Male,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.7,419.4,No
+7606-BPHHN,Male,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.8,1468.75,No
+4742-DRORA,Male,0,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),95.4,5812,No
+0111-KLBQG,Male,1,Yes,Yes,32,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,93.95,2861.45,No
+4800-VHZKI,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.9,19.9,Yes
+7989-CHGTL,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.6,19.6,Yes
+0334-GDDSO,Male,1,No,No,3,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81.35,233.7,Yes
+4163-NCJAK,Female,0,Yes,No,46,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.45,1066.15,No
+5233-AOZUF,Female,0,Yes,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,2149.05,No
+5973-EJGDP,Male,0,No,No,51,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,87.35,4473,No
+1996-DBMUS,Female,1,Yes,No,48,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.65,3545.05,No
+7916-VCCPB,Female,0,Yes,Yes,16,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),73.25,1195.75,No
+4686-GEFRM,Male,0,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),98.7,6858.9,No
+5249-QYHEX,Female,0,Yes,Yes,40,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.8,1024.7,No
+0578-SKVMF,Female,0,Yes,Yes,22,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,83.3,1845.9,Yes
+5564-NEMQO,Female,1,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.3,75.3,Yes
+2233-FAGXV,Female,0,Yes,Yes,5,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,24.3,132.25,No
+5605-IYGFG,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.85,515.45,No
+7663-ZTEGJ,Male,0,No,Yes,29,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),100.55,2830.45,No
+3935-TBRZZ,Male,0,Yes,Yes,44,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,25.7,1110.5,No
+8111-BKVDS,Female,0,No,No,10,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),40.7,449.3,No
+2055-SIFSS,Female,1,Yes,No,55,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),51.65,2838.55,No
+2806-MLNTI,Male,1,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),105.1,5376.4,No
+8734-DKSTZ,Female,0,Yes,Yes,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.95,858.6,No
+4360-PNRQB,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.6,1395.05,No
+6152-ONASV,Female,0,Yes,No,68,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),58.25,3975.7,No
+9063-ZGTUY,Female,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.4,1182.55,Yes
+7781-HVGMK,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),65.2,4784.45,No
+2181-UAESM,Male,0,No,No,2,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Electronic check,53.45,119.5,No
+2957-LOLHO,Male,0,No,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45.4,518.9,Yes
+6048-NJXHX,Male,0,Yes,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,19.75,899.45,No
+2320-SLKMB,Female,0,No,No,26,No,No phone service,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),44.45,1183.8,No
+4980-URKXC,Male,0,Yes,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.85,720.05,No
+4376-KFVRS,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),114.05,8468.2,No
+5886-VLQVU,Male,0,Yes,No,35,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.85,3161.2,No
+3577-AMVUX,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,55.05,55.05,No
+0771-WLCLA,Female,0,Yes,Yes,16,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,112.95,1882.55,No
+5628-RKIFK,Female,1,No,No,49,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),101.55,5070.4,No
+0206-TBWLC,Female,0,Yes,No,54,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,114.65,6049.5,No
+2937-FTHUR,Female,0,No,Yes,18,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,No,Electronic check,64.8,1166.7,No
+1910-FMXJM,Female,0,Yes,No,36,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.4,2937.65,No
+7752-XUSCI,Female,0,No,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.9,6396.45,Yes
+4110-PFEUZ,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,69.55,69.55,Yes
+0732-OCQOC,Female,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.05,1270.25,No
+5168-MSWXT,Male,0,Yes,Yes,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,759.55,No
+1090-ESELR,Male,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),105.5,7611.55,No
+8592-PLTMQ,Female,0,No,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.7,1642.75,No
+5760-WRAHC,Female,1,No,No,22,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Mailed check,69.75,1545.4,No
+8847-GEOOQ,Male,0,Yes,No,60,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),60.2,3582.4,No
+0256-LTHVJ,Female,0,Yes,Yes,28,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.05,2227.1,Yes
+4785-FCIFB,Female,0,Yes,No,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.4,1417.9,No
+8313-NDOIA,Female,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.15,2494.65,No
+5149-CUZUJ,Male,0,Yes,Yes,28,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),92.9,2768.35,No
+0942-KOWSM,Female,0,Yes,Yes,30,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),80.8,2369.3,No
+4237-CLSMM,Male,0,Yes,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,38,No
+1452-VOQCH,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.1,75.1,No
+4719-UMSIY,Male,0,No,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,100.9,No
+6614-VBEGU,Female,0,Yes,No,24,Yes,No,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),69.45,1614.05,No
+0880-TKATG,Male,0,Yes,Yes,4,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,101.15,385.9,Yes
+3811-VBYBZ,Male,0,No,No,7,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,673.25,Yes
+1480-BKXGA,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.05,8404.9,No
+2996-XAUVF,Male,0,No,No,70,No,No phone service,DSL,No,No,No,Yes,No,Yes,Two year,Yes,Mailed check,40.05,2799.75,No
+9076-AXYIK,Male,1,Yes,No,64,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,102.1,6538.45,No
+5968-XQIVE,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,One year,Yes,Electronic check,89.7,6588.95,No
+8896-RAZCR,Female,0,No,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,868.1,No
+4640-UHDOS,Female,0,Yes,Yes,13,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,55.95,734.35,Yes
+4933-IKULF,Female,1,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,330.6,No
+3583-EKAPL,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55,55,Yes
+1304-BCCFO,Male,0,Yes,No,9,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,70.05,564.4,No
+4104-PVRPS,Male,0,Yes,No,24,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,53.6,1315.35,No
+9399-APLBT,Female,0,Yes,Yes,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,74.7,Yes
+2359-KMGLI,Male,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.25,1861.5,Yes
+3780-DDGSE,Male,1,Yes,Yes,35,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,76.05,2747.2,No
+4431-EDMIQ,Female,0,Yes,Yes,7,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,75.7,554.05,No
+0306-JAELE,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,96.1,453.4,Yes
+6227-HWPWX,Female,0,No,Yes,15,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69,994.8,Yes
+0486-LGCCH,Male,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,225.75,No
+0447-BEMNG,Female,0,Yes,No,48,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),45.3,2145,Yes
+4612-SSVHJ,Female,1,No,No,20,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.45,1671.6,No
+5168-MQQCA,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),108.5,8003.8,No
+5949-XIKAE,Female,0,Yes,Yes,8,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.55,680.05,Yes
+7971-HLVXI,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Two year,Yes,Credit card (automatic),84.5,6130.85,No
+9094-AZPHK,Female,0,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.15,1415,No
+3649-JPUGY,Male,0,No,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),88.6,6201.95,No
+4472-LVYGI,Female,0,Yes,Yes,0,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),52.55, ,No
+8372-JUXUI,Male,0,No,Yes,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,74.35,Yes
+3552-CTCYF,Male,0,Yes,Yes,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.8,6597.25,No
+6778-YSNIH,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,59,114.15,No
+0388-EOPEX,Female,0,Yes,No,2,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,139.4,Yes
+5756-OZRIO,Male,1,Yes,No,61,Yes,Yes,DSL,No,Yes,No,No,No,Yes,One year,No,Bank transfer (automatic),64.05,3902.6,No
+6579-JPICP,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,20.4,No
+8205-OTCHB,Male,0,No,No,22,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),43.75,903.6,Yes
+4134-BSXLX,Male,0,Yes,No,28,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,60.9,1785.65,No
+0505-SPOOW,Female,0,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.8,1397.65,No
+6235-VDHOM,Female,1,No,No,5,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,28.45,131.05,Yes
+7783-YKGDV,Female,0,No,No,12,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),99.7,1238.45,Yes
+4374-YMUSQ,Male,0,No,No,34,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),116.25,3899.05,No
+4513-CXYIX,Female,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Two year,Yes,Credit card (automatic),80.7,5676,No
+3957-HHLMR,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,No,No,No,No,Yes,One year,No,Bank transfer (automatic),65.2,4543.15,No
+7803-XOCCZ,Female,0,Yes,Yes,52,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),84.05,4326.8,No
+5736-YEJAX,Male,0,No,Yes,69,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),79.45,5502.55,No
+5609-CEBID,Female,1,No,No,20,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.1,1782.4,Yes
+8981-FJGLA,Male,0,No,Yes,11,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,78,851.8,No
+7218-HKQFK,Male,0,Yes,No,2,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,167.5,Yes
+4636-QRJKY,Female,0,Yes,Yes,6,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,80.5,502.85,Yes
+1135-LMECX,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,19.85,No
+4332-MUOEZ,Male,1,Yes,Yes,20,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),94.3,1818.3,No
+8535-SFUTN,Male,0,No,No,61,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.45,6300.15,No
+5956-VKDTT,Female,1,Yes,No,5,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,334.8,Yes
+8677-HDZEE,Female,0,No,No,56,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.45,5916.95,No
+2475-MROZF,Male,0,No,No,30,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),95,2852.4,No
+9412-GHEEC,Male,0,No,No,40,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),104.8,4131.95,Yes
+3482-ABPKK,Female,0,No,No,28,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,54.3,1546.3,No
+6705-LXORM,Female,1,Yes,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.05,302.6,No
+0257-ZESQC,Female,1,Yes,No,27,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.2,1929.35,Yes
+7531-GQHME,Male,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.05,265.45,No
+5174-ITUMV,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,105.4,6989.45,No
+4109-CYRBD,Male,1,Yes,No,29,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,51.6,1442,No
+0913-XWSCN,Male,0,Yes,Yes,55,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),85.5,4713.4,No
+6825-UYPFK,Female,0,No,No,23,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),75.6,1758.6,Yes
+8397-MVTAZ,Male,0,Yes,No,34,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.05,3480,Yes
+0750-EBAIU,Male,0,No,No,52,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,One year,No,Electronic check,91.25,4738.3,No
+8606-CIQUL,Male,1,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.75,8399.15,No
+3571-DPYUH,Male,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,One year,Yes,Credit card (automatic),94.7,5430.35,No
+7601-GNDYK,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,686.95,No
+0356-OBMAC,Female,1,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),99.9,5706.3,No
+8067-NIOYM,Female,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),21.1,490.65,No
+1403-GYAFU,Male,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,1360.25,No
+4234-XTNEA,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.95,174.45,No
+1297-VQDRP,Male,1,Yes,Yes,68,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),107.15,7379.8,No
+9282-IZGQK,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85,85,Yes
+5348-CAGXB,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.55,1021.75,No
+0621-HJWXJ,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),81.55,5029.05,No
+5844-QVTAT,Female,0,Yes,Yes,33,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Mailed check,58.45,1955.4,No
+8905-IAZPF,Female,0,Yes,No,69,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),95.65,6744.2,No
+5394-MEITZ,Female,0,Yes,Yes,60,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.6,4946.7,No
+6859-QNXIQ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),113.1,8248.5,No
+2782-LFZVW,Female,0,No,No,11,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,58.95,601.6,No
+2866-IKBTM,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,No
+1342-JPNKI,Male,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),86.05,834.1,Yes
+2817-NTQDO,Male,0,No,No,13,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),45.55,597,Yes
+7129-AZJDE,Male,0,Yes,Yes,34,Yes,No,Fiber optic,No,No,No,No,No,Yes,One year,Yes,Bank transfer (automatic),78.95,2647.2,No
+6986-IJDHX,Male,0,Yes,Yes,39,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Mailed check,86.3,3266,Yes
+2560-PPCHE,Female,0,No,No,65,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.05,6744.25,No
+4676-MQUEA,Male,1,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),101.9,5265.5,No
+8138-EALND,Male,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,311.6,No
+3580-HYCSP,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.3,7966.9,No
+1352-HNSAW,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.6,8220.4,No
+2075-PUEPR,Male,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.35,1153.25,No
+1982-FEBTD,Female,0,Yes,Yes,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.6,514.75,No
+5301-GAUUY,Male,0,No,No,32,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.35,2596.15,Yes
+5791-KAJFD,Female,0,Yes,Yes,56,Yes,Yes,DSL,Yes,No,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),68.75,3808,No
+2654-VBVPB,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.9,19.9,No
+1154-HYWWO,Male,0,No,No,38,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Mailed check,70.6,2708.2,No
+2501-XWWTZ,Male,0,No,No,11,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.2,760.05,No
+3716-UVSPD,Male,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,49.3,49.3,No
+6815-ABQFQ,Male,0,Yes,No,56,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,107.25,6033.3,No
+7343-EOBEU,Male,0,Yes,No,3,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,23.6,89.05,No
+3701-SFMUH,Male,0,Yes,Yes,7,Yes,No,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),69.7,516.15,No
+6103-LIANB,Male,0,Yes,Yes,59,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),99.5,5861.75,No
+7319-VENRZ,Male,0,No,No,7,Yes,No,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),64.3,445.95,No
+5846-NEQVZ,Male,0,Yes,Yes,71,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),70.85,4973.4,No
+6967-QIQRV,Male,0,Yes,Yes,15,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,One year,No,Electronic check,101.9,1667.25,No
+5781-RFZRP,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),73.5,5357.75,No
+0939-YAPAF,Female,0,No,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.25,3527.6,Yes
+0308-IVGOK,Female,0,No,No,11,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),40.4,422.6,No
+7293-LSCDV,Female,0,Yes,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.25,1103.25,No
+7025-WCBNE,Male,1,No,No,47,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),59.6,2754,No
+5756-JYOJT,Female,0,No,No,11,Yes,No,DSL,No,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),64.9,697.25,No
+4710-FDUIZ,Male,0,Yes,No,56,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Credit card (automatic),100.3,5614.45,Yes
+6030-REHUX,Female,1,Yes,No,28,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.85,3204.4,No
+9548-LIGTA,Male,0,Yes,No,61,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,81.05,4747.65,No
+5150-LJNSR,Male,0,Yes,Yes,31,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,One year,No,Bank transfer (automatic),98.05,3082.1,No
+8270-RKSAP,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.5,597.9,No
+6522-YRBXD,Male,1,Yes,No,35,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,94.55,3365.4,No
+2640-LYMOV,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.65,38.8,No
+1218-VKFPE,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19,233.55,Yes
+3627-FHKBK,Female,0,No,No,1,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,75.3,75.3,Yes
+2865-TCHJW,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.2,346.2,Yes
+1423-BMPBQ,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19,19,No
+2393-DIVAI,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20,61.7,No
+5192-EBGOV,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.7,85.7,Yes
+4568-KNYWR,Male,0,No,No,52,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Two year,Yes,Credit card (automatic),63.25,3342.45,No
+8752-IMQOS,Male,0,Yes,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,85.1,No
+0742-LAFQK,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Electronic check,99.15,7422.1,No
+0795-LAFGP,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,90.4,6668.05,No
+0619-OLYUR,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),111.9,8071.05,No
+5512-IDZEI,Male,0,Yes,Yes,46,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.9,1174.8,No
+0459-SPZHJ,Male,0,Yes,Yes,63,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),83.5,5435,No
+0215-BQKGS,Male,0,No,No,30,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),84.3,2438.6,No
+9244-ZVAPM,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.6,45.6,No
+0719-SYFRB,Female,0,No,No,12,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,61.65,713.75,Yes
+8208-EUMTE,Male,0,No,No,16,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,54.85,916.15,No
+5172-MIGPM,Male,0,No,No,4,Yes,Yes,DSL,No,No,No,Yes,No,Yes,Month-to-month,No,Mailed check,65.55,237.2,No
+1710-RCXUS,Male,0,Yes,No,51,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,One year,Yes,Credit card (automatic),90.35,4614.55,No
+0374-FIUCA,Male,0,Yes,No,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.4,1414.45,No
+5839-SUYVZ,Male,0,No,No,16,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),74.55,1170.5,No
+5173-ZXXXL,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,47.7,No
+1096-ADRUX,Female,0,Yes,Yes,66,Yes,Yes,Fiber optic,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),74.25,4859.25,No
+2001-MCUUW,Male,0,No,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,108.65,4903.2,No
+2731-GJRDG,Female,0,No,No,32,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),109.55,3608,No
+4723-BEGSG,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.65,6094.25,No
+6516-NKQBO,Male,0,Yes,Yes,38,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),81,3084.9,No
+8672-OAUPW,Male,0,No,Yes,51,Yes,No,DSL,No,No,No,Yes,No,No,One year,No,Credit card (automatic),47.85,2356.75,No
+8207-DMRVL,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),114.55,8306.05,No
+3419-SNJJD,Female,1,Yes,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),105.25,6786.4,Yes
+6543-CPZMK,Male,0,Yes,Yes,9,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,29.95,248.95,Yes
+4765-OXPPD,Female,0,Yes,Yes,9,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,No,Mailed check,65,663.05,Yes
+2804-ETQDK,Male,0,No,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.55,1357.1,No
+6689-VRRTK,Female,1,No,No,44,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),109.8,4860.35,No
+7138-GIRSH,Male,0,No,No,50,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),69.5,3418.2,No
+9396-ZSFLL,Female,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,48.85,631.4,No
+6464-KEXXH,Male,0,No,No,8,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),25.25,186.3,No
+7134-MJPDY,Female,1,No,No,66,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),102.85,6976.75,No
+5240-CAOYT,Female,0,No,No,57,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),87.55,4884.85,No
+4059-IIEBK,Female,0,No,No,7,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),78.55,522.95,No
+4881-JVQOD,Male,1,Yes,Yes,10,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),34.55,362.6,No
+0516-UXRMT,Female,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,Yes,Electronic check,92.05,5755.8,No
+4851-BQDNX,Male,0,Yes,Yes,40,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,85.05,3355.65,No
+5148-HKFIR,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,406.95,No
+1009-IRMNA,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20,137.6,Yes
+3003-CMDUU,Female,0,Yes,No,25,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,95.15,2395.7,No
+5016-IBERQ,Male,0,Yes,No,23,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,84.25,1968.1,No
+6797-UCJHZ,Female,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),104.6,6819.45,No
+2469-DTSGX,Female,1,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,111.65,7943.45,No
+4554-YGZIH,Male,1,Yes,No,49,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Credit card (automatic),90.05,4547.25,Yes
+5099-BAILX,Male,1,Yes,Yes,43,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),110.75,4687.9,Yes
+9931-KGHOA,Female,0,Yes,No,46,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),55,2473.95,No
+1775-KWJKQ,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.85,6562.9,No
+7665-VIGUD,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.35,176.3,No
+9411-TPQQV,Female,0,No,No,40,No,No phone service,DSL,Yes,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),54.55,2236.2,No
+7207-RMRDB,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.5,6985.65,Yes
+7954-MLBUN,Male,0,No,No,31,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),99.45,3109.9,No
+2077-DDHJK,Female,0,Yes,No,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),70.9,4911.35,No
+4913-EHYUI,Male,1,Yes,Yes,56,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.55,5794.65,Yes
+0195-IESCP,Male,0,Yes,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.25,855.3,Yes
+9574-BOSMD,Male,0,Yes,Yes,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.4,1620.2,No
+4580-TMHJU,Female,0,Yes,Yes,43,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,56.15,2499.3,Yes
+0970-ETWGE,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.55,89.55,Yes
+4908-XAXAY,Female,1,No,No,49,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),89.85,4287.2,No
+8404-VLQFB,Female,0,Yes,Yes,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,25.25,394.85,No
+1626-ERCMM,Male,1,Yes,No,20,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.55,1899.65,Yes
+0887-HJGAR,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.7,45.7,Yes
+2391-IPLOP,Male,0,Yes,Yes,50,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,One year,Yes,Electronic check,69.65,3442.15,No
+5644-PDMZC,Female,1,No,No,2,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,89.5,161.5,Yes
+3509-GWQGF,Male,1,No,No,24,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),70,1732.6,No
+9576-ANLXO,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.55,222.3,Yes
+2024-BASKD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,74.6,74.6,Yes
+5845-BZZIB,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,655.3,No
+1140-UKVZG,Female,0,No,No,17,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.8,475.25,No
+4160-AMJTL,Female,1,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.65,164.3,Yes
+5183-SNMJQ,Male,0,No,No,10,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.1,865.1,No
+8100-PNJMH,Male,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),88.85,6132.7,No
+7838-LAZFO,Male,0,Yes,No,45,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),78.8,3597.5,No
+4464-JCOLN,Male,0,Yes,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.85,35.9,Yes
+2085-JVGAD,Male,0,Yes,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.35,697.65,No
+5650-VDUDS,Female,0,No,No,4,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.25,96.05,Yes
+8095-WANWK,Female,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.25,428.7,No
+3030-ZKIWL,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No
+9565-FLVCG,Male,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,Two year,Yes,Mailed check,69.55,4459.15,No
+8755-OGKNA,Female,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.5,1167.6,No
+2800-VEQXM,Female,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,74.75,238.1,No
+7538-GWHML,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.65,145.15,Yes
+5533-RJFTJ,Male,0,No,No,49,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),30.2,1453.1,No
+3859-CVCET,Female,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.65,191.05,Yes
+0214-JHPFW,Female,0,Yes,No,70,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),57.8,4039.3,No
+5642-MHDQT,Female,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.85,1039.45,No
+3088-FVYWK,Male,0,Yes,Yes,53,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.55,1336.1,No
+3276-HDUEG,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75.05,75.05,Yes
+9092-GDZKO,Male,0,No,No,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),24.85,493.4,No
+0823-HSCDJ,Male,1,No,No,52,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,49.15,2550.9,Yes
+3729-OWRVL,Male,1,No,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.35,7246.15,No
+2324-AALNO,Female,0,No,No,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.55,1203.95,No
+0822-GAVAP,Female,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,34.7,62.25,Yes
+5760-IFJOZ,Male,0,No,No,3,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,107.95,313.6,No
+2826-UWHIS,Male,0,Yes,No,45,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),81.4,3775.85,No
+1448-PWKYE,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80,80,Yes
+7501-IWUNG,Female,0,Yes,Yes,61,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),73.8,4616.05,No
+4957-TREIR,Male,0,No,No,3,Yes,No,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),64.4,195.65,No
+7251-LJBQN,Female,1,No,No,40,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),103.75,4188.4,No
+8040-MNRTF,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,71.1,71.1,No
+1536-HBSWP,Female,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,49.9,49.9,No
+5313-FPXWG,Male,0,No,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.6,1266.4,No
+0067-DKWBL,Male,1,No,No,2,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.25,91.1,Yes
+0946-FKYTX,Male,0,No,No,52,No,No phone service,DSL,No,Yes,No,No,No,No,One year,No,Mailed check,30.1,1623.4,No
+5076-YVXCM,Male,0,No,No,51,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),83.4,4149.45,No
+8262-COGGB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.45,20.45,No
+6663-JOCQO,Male,0,Yes,Yes,31,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),75.25,2344.5,No
+9620-QJREV,Male,0,No,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.55,1013.05,No
+2276-YDAVZ,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.1,270.7,Yes
+2682-KEVRP,Female,1,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.05,417,No
+2480-JZOSN,Female,0,Yes,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.65,20.65,No
+0078-XZMHT,Male,0,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.15,6316.2,No
+5896-NPFWW,Male,0,Yes,Yes,3,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,50.15,168.15,Yes
+9978-HYCIN,Male,1,Yes,Yes,47,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),84.95,4018.05,No
+8338-QIUNR,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),66.5,4811.6,No
+1525-LNLOJ,Male,0,Yes,Yes,66,Yes,Yes,DSL,No,No,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),63.3,4189.7,No
+9450-TRJUU,Male,0,No,No,35,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,83.15,2848.45,No
+1766-GKNMI,Male,0,No,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.9,2516.2,No
+6942-LBFDP,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.55,33.6,No
+1456-TWCGB,Male,0,No,No,4,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,49.25,208.45,No
+7133-VBDCG,Female,0,No,No,25,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.85,2015.35,Yes
+7596-ZYWBB,Female,0,No,No,65,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,59.6,3739.8,No
+8329-UTMVM,Male,1,No,No,27,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.65,2964,No
+3014-WJKSM,Male,0,Yes,No,29,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),75.3,2263.4,No
+3347-YJZZE,Male,0,Yes,Yes,29,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),80.1,2211.8,No
+1029-QFBEN,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,Yes
+7929-DMBCV,Female,0,Yes,No,20,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,81,1683.7,No
+9661-JALZV,Female,0,No,No,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.7,1519,No
+5433-KYGHE,Female,0,No,Yes,14,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,86,1164.05,No
+4312-KFRXN,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.4,1710.9,No
+5575-TPIZQ,Male,0,No,No,46,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.15,4245.55,No
+0114-IGABW,Female,0,Yes,No,71,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),58.25,4145.9,No
+9944-AEXBM,Male,0,No,No,32,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),85.65,2664.3,No
+1853-ARAAQ,Female,0,No,No,26,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),50.35,1277.5,No
+6952-OMNWB,Male,1,Yes,No,68,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),80.35,5589.3,No
+4697-LUPSU,Male,0,Yes,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.2,34.75,No
+8434-VGEQQ,Male,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.55,1305.95,No
+4952-YSOGZ,Female,0,Yes,Yes,4,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.95,381.3,Yes
+1589-AGTLK,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.35,141.5,Yes
+5244-IRFIH,Male,1,Yes,No,33,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.5,3105.55,Yes
+6549-YMFAW,Male,1,Yes,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,21.25,204.55,No
+4950-HKQTE,Female,0,No,No,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,26.25,605.9,No
+6786-OBWQR,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.85,356.1,No
+2684-EIWEO,Female,1,No,No,30,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Credit card (automatic),91.7,2758.15,Yes
+2753-JMMCV,Male,0,No,No,65,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),74.2,4805.65,No
+6439-GTPCA,Female,0,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,87.25,3941.7,Yes
+6621-YOBKI,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,92.75,No
+1216-JWVUX,Male,0,Yes,Yes,25,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,75.5,1901.05,No
+7564-GHCVB,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),79.05,5730.7,No
+1173-NOEYG,Female,0,Yes,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Bank transfer (automatic),90.15,2423.4,No
+7595-EHCDL,Male,0,Yes,Yes,32,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),50.6,1653.45,No
+6647-ZEDXT,Female,0,No,No,30,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),110.45,3327.05,No
+2521-NPUZR,Male,0,Yes,No,70,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),101,7085.5,No
+1307-TVUFB,Male,1,No,No,42,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.35,3344.1,No
+7503-MIOGA,Female,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),89.85,6697.35,No
+4381-MHQDC,Female,0,No,No,47,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Mailed check,65,2879.9,No
+6923-JHPMP,Female,0,No,No,2,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,80.45,137.1,No
+5138-WVKYJ,Male,0,No,No,10,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,98.55,1008.55,Yes
+4018-PPNDW,Female,0,Yes,Yes,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.1,1551.6,No
+1635-FJFCC,Female,0,No,No,5,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.05,202.15,No
+2499-AJYUA,Female,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.8,7882.25,No
+6919-ELBGL,Male,1,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.95,8196.4,No
+3966-HRMZA,Female,1,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,75.05,202.9,No
+6425-JWTDV,Male,0,Yes,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.25,855.1,No
+8405-IGQFX,Female,0,No,No,63,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),90.05,5817,No
+8224-IVVPA,Female,0,No,No,27,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,56.7,1652.95,No
+9477-LGWQI,Male,0,Yes,Yes,70,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),80.15,5600.15,No
+1410-RSCMR,Male,0,Yes,Yes,7,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),71.35,515.75,No
+3115-CZMZD,Male,0,No,Yes,0,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.25, ,No
+0139-IVFJG,Female,0,Yes,No,2,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,90.35,190.5,No
+6683-VLCTZ,Male,1,No,No,20,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.55,1842.8,Yes
+5730-DBDSI,Male,0,No,No,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.7,1253.8,No
+0030-FNXPP,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,57.2,No
+2189-WWOEW,Female,0,No,Yes,15,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),85.9,1269.55,Yes
+5684-FJVYR,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),90.35,6563.4,No
+4013-GUXND,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.8,20.8,No
+1894-IGFSG,Female,0,No,No,22,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,89.25,1907.85,Yes
+7379-POKDZ,Male,0,Yes,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,208.85,Yes
+1266-NZYUI,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),66.85,4758.8,No
+7969-FFOWG,Male,0,Yes,Yes,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,1292.6,No
+4718-DHSMV,Female,0,No,No,11,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,35.8,363.15,No
+5175-WLYXL,Male,0,No,No,22,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,78.85,1600.25,No
+7817-OMJNA,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.4,275.7,No
+8728-SKJLR,Male,0,No,No,41,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,74.25,3089.1,No
+3137-NYQQI,Male,0,Yes,No,17,Yes,No,DSL,Yes,Yes,No,No,No,Yes,One year,No,Mailed check,64.8,1175.6,No
+7706-DZNKK,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.45,237.3,No
+0236-HFWSV,Male,0,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.35,1444.65,Yes
+3900-AQPHZ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,19.9,Yes
+5842-POCOP,Female,0,Yes,No,5,Yes,No,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,88.9,454.15,Yes
+2037-XJFUP,Male,0,Yes,No,33,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,95.8,3036.75,Yes
+8823-RLPWL,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),110.65,8065.65,No
+9505-SQFSW,Female,0,Yes,Yes,3,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,No,Mailed check,40.3,92.5,No
+7314-OXENN,Male,0,No,No,2,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,82,184.65,Yes
+3758-CKOQL,Female,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),107,6152.3,No
+0322-CHQRU,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.35,89.5,Yes
+5676-CFLYY,Male,0,Yes,Yes,71,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),73.35,5154.5,No
+7521-AFHAB,Female,0,Yes,Yes,5,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.8,220.45,No
+0285-INHLN,Male,0,Yes,Yes,27,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),54.75,1510.3,No
+4678-DVQEO,Female,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,52.2,52.2,Yes
+5125-CNDSP,Male,0,No,Yes,63,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),40.6,2588.95,No
+0691-IFBQW,Female,1,No,No,46,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,110,4874.8,Yes
+4992-LTJNE,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),55.3,3983.6,No
+2202-OUTMO,Female,0,Yes,No,34,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),60.85,2003.6,No
+0810-BDHAW,Female,0,Yes,Yes,24,Yes,Yes,DSL,Yes,Yes,No,No,Yes,Yes,One year,No,Electronic check,78.4,1832.4,No
+0229-LFJAF,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),69.65,4908.25,No
+7131-ZQZNK,Female,0,Yes,Yes,60,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),59.85,3590.2,No
+3442-ZHHCC,Male,0,No,No,68,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),76.9,5023,No
+5726-CVNYA,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.85,146.6,No
+9871-ELEYA,Female,0,No,Yes,34,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),67.65,2339.3,No
+4257-GAESD,Female,0,No,No,6,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),45,298.7,No
+5173-WXOQV,Male,0,Yes,No,2,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,64.2,143.65,No
+2040-OBMLJ,Male,0,No,No,31,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,One year,No,Credit card (automatic),81.7,2548.65,No
+6286-ZHAOK,Female,0,Yes,Yes,20,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.55,507.4,No
+3807-XHCJH,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20,20,No
+3009-JWMPU,Male,0,No,No,62,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,96.75,6125.4,Yes
+5671-RQRLP,Female,1,Yes,No,70,Yes,Yes,Fiber optic,No,No,No,No,No,No,Two year,Yes,Credit card (automatic),75.65,5411.4,No
+1450-GALXR,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.5,1058.25,Yes
+8859-AXJZP,Male,0,Yes,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,23.8,903.8,No
+3174-AKMAS,Female,0,Yes,No,46,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),64.2,3009.5,No
+3138-BKYAV,Male,0,No,No,6,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.35,489.45,Yes
+9926-PJHDQ,Female,0,Yes,Yes,72,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),76.8,5468.45,No
+7382-DFJTU,Male,0,No,No,18,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),55.2,1058.1,No
+2798-NYLMZ,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.55,7616,No
+4289-DTDKW,Male,0,Yes,No,40,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.3,4113.1,Yes
+1820-TQVEV,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.55,69.55,Yes
+2239-JALAW,Male,0,No,No,58,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),103.25,6017.65,Yes
+4853-RULSV,Male,0,No,No,70,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104,7250.15,Yes
+8098-TDCBU,Female,0,Yes,No,42,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,25.25,1108.2,No
+3551-GAEGL,Male,0,Yes,Yes,34,No,No phone service,DSL,Yes,No,No,No,No,No,One year,No,Bank transfer (automatic),30.4,938.65,No
+4785-NKHCX,Male,1,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.05,94.15,No
+3196-NVXLZ,Female,0,No,No,25,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,84.6,2088.05,No
+6275-YDUVO,Female,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,86.2,178.7,Yes
+0036-IHMOT,Female,0,Yes,Yes,55,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),103.7,5656.75,No
+0115-TFERT,Male,0,Yes,No,21,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,111.2,2317.1,Yes
+4178-EGMON,Male,0,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),88,5986.45,No
+4220-TINQT,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,106.35,6751.35,No
+5318-YKDPV,Male,0,Yes,Yes,43,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.15,3566.6,No
+7975-TZMLR,Male,0,No,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.1,4889.3,No
+0295-QVKPB,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),63.95,318.1,No
+4335-BSMJS,Female,0,No,No,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.8,1563.95,No
+2311-QYMUQ,Female,0,Yes,Yes,16,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.45,1430.25,Yes
+3643-AHCFP,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.6,644.35,Yes
+9146-JRIOX,Female,0,Yes,Yes,14,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.55,372.45,No
+3104-OWCGK,Male,0,Yes,Yes,60,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,One year,No,Electronic check,90.95,5453.4,Yes
+5337-IIWKZ,Male,0,Yes,Yes,34,No,No phone service,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,44.85,1442.6,No
+9101-BWFSS,Female,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,No,Electronic check,108.55,5610.7,Yes
+9650-VBUOG,Male,0,Yes,Yes,38,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.05,963.95,No
+3487-EARAT,Female,0,Yes,Yes,70,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),74.1,5222.3,No
+2672-TGEFF,Female,0,Yes,Yes,37,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,88.8,3340.55,No
+9231-ZJYAM,Female,1,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,78.85,292.8,Yes
+4250-WAROZ,Male,1,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,93.25,5774.55,No
+8184-WMOFI,Male,0,Yes,Yes,62,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),71.4,4487.3,No
+6982-SSHFK,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.4,44.4,Yes
+6092-QZVPP,Male,0,No,No,36,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),79.2,2854.95,No
+4625-LAMOB,Male,0,No,No,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.4,905.55,No
+0727-BMPLR,Female,1,No,No,55,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,100,5509.3,Yes
+0392-BZIUW,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),105,7589.8,No
+1038-ZAGBI,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,229.6,Yes
+6549-NNDYT,Female,0,No,No,13,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),30.85,394.1,No
+3027-ZTDHO,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.9,89.9,Yes
+0422-OHQHQ,Female,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.55,295.95,No
+6916-HIJSE,Female,0,No,No,65,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.85,5459.2,No
+2316-ESMLS,Female,0,Yes,Yes,12,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,No,Credit card (automatic),33.15,444.75,No
+9778-OGKQZ,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),92,6782.15,No
+7408-OFWXJ,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.8,6510.45,No
+6007-TCTST,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.8,8476.5,No
+2252-NKNSI,Male,0,No,Yes,52,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,85.15,4461.85,No
+8713-IGZSO,Male,0,No,No,2,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.85,62,No
+7905-TVXTA,Female,0,No,No,5,Yes,No,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,64.35,352.65,No
+7695-PKLCZ,Female,0,No,No,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.5,1424.9,No
+2382-BCKQJ,Female,0,No,Yes,62,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,100.15,6413.65,Yes
+8374-UULRV,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.05,6309.65,No
+2207-NHRJK,Male,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,50.8,50.8,Yes
+3224-DFQNQ,Female,0,Yes,No,66,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,One year,No,Electronic check,89,5898.6,No
+5275-PMFUT,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),64.8,4719.75,No
+4795-UXVCJ,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.8,457.3,No
+9777-IQHWP,Male,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,Two year,No,Bank transfer (automatic),93.4,5822.3,No
+0947-MUGVO,Male,1,Yes,No,20,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,73.65,1463.5,Yes
+9944-HKVVB,Female,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.1,307.4,Yes
+4124-MMETB,Male,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.65,2104.55,Yes
+3671-SHRSP,Male,0,Yes,No,4,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,80.6,319.15,Yes
+0979-MOZQI,Male,0,Yes,No,62,No,No phone service,DSL,No,No,No,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),39,2337.45,No
+2732-ISEZX,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.5,104.3,No
+3313-QKNKB,Male,0,Yes,No,59,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,One year,No,Electronic check,85.55,5084.65,Yes
+0323-XWWTN,Male,0,No,Yes,3,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,26.4,121.25,No
+1937-OTUKY,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),98.2,7015.9,No
+1573-LGXBA,Male,0,Yes,Yes,57,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),97.55,5598,No
+1764-VUUMT,Male,0,No,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.95,1269.1,No
+5073-WXOYN,Female,0,No,No,60,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.8,3027.4,Yes
+4713-ZBURT,Male,0,No,Yes,45,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),99.7,4634.35,No
+3050-GBUSH,Female,0,No,No,3,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),34.8,113.95,No
+0207-MDKNV,Female,0,No,No,15,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,105.1,1582.75,Yes
+7876-AEHIG,Female,0,No,Yes,51,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),60.15,3077,No
+7945-HLKEA,Female,0,No,No,60,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,One year,No,Electronic check,64.75,4039.5,No
+9342-VNIMQ,Male,0,No,No,33,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),54.65,1665.2,No
+9851-KIELU,Male,0,No,No,10,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.1,1043.3,Yes
+3523-BRGUW,Male,1,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.3,504.2,No
+3908-BLSYF,Female,0,No,No,6,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,83.9,497.55,Yes
+3199-NPKCN,Female,0,Yes,No,67,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.25,7511.65,No
+5170-PTRKA,Female,0,Yes,Yes,49,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,Yes,Credit card (automatic),35.8,1782,No
+4661-NJEUX,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.05,20.05,No
+2123-AGEEN,Female,1,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,84.35,609.65,No
+1258-YMZNM,Female,1,No,No,27,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),110.5,2857.6,No
+0048-LUMLS,Male,0,Yes,Yes,37,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Credit card (automatic),91.2,3247.55,No
+7549-MYGPK,Female,0,Yes,Yes,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,100.55,6215.35,Yes
+5898-IGSLP,Male,0,Yes,Yes,31,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,89.3,2823,No
+3804-RVTGV,Male,0,Yes,Yes,50,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.85,5017.9,Yes
+6259-WJQLC,Male,1,No,No,32,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),81.1,2619.25,No
+9227-LUNBG,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.6,24.6,Yes
+7997-EASSD,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,One year,Yes,Credit card (automatic),81.2,4965.1,No
+0730-KOAVE,Male,0,No,No,30,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.3,2679.7,No
+8975-SKGRX,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.1,8310.55,No
+0678-RLHVP,Female,0,No,No,53,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,105.55,5682.25,No
+4315-MURBD,Female,0,No,No,12,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),98.9,1120.95,Yes
+2267-FPIMA,Male,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.4,4914.9,No
+1051-GEJLJ,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.5,27.55,No
+9734-YWGEX,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),98.3,923.5,Yes
+2719-BDAQO,Male,0,No,No,17,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,93.85,1625.65,Yes
+5285-MVEHD,Female,0,Yes,No,56,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),105.6,6068.65,No
+0379-DJQHR,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),81.35,5398.6,No
+0781-LKXBR,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.5,918.6,Yes
+5543-QDCRY,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,56.4,234.85,No
+0297-RBCSG,Male,0,No,No,19,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),65.35,1231.85,No
+4694-PHWFW,Female,0,No,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,170.9,No
+0835-JKADZ,Female,0,No,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,111.25,7984.15,No
+1907-UBQFC,Male,1,No,No,10,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,72.85,688.65,Yes
+7508-SMHXL,Female,1,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Credit card (automatic),89,1288.3,No
+3865-YIOTT,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106.1,7848.5,No
+5993-BQHEA,Male,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.05,267,No
+6024-RUGGH,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.2,1798.9,No
+6513-EECDB,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.55,73.55,Yes
+3956-CJUST,Female,1,No,No,23,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.4,1643.55,No
+4079-WWQQQ,Male,0,No,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),65.55,4807.45,No
+6103-BOCOU,Female,0,No,No,26,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,80.7,2193,No
+5149-TGWDZ,Female,0,No,No,21,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,104.55,2239.4,No
+7471-WNSUF,Male,0,Yes,No,60,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.15,1505.9,No
+8942-DBMHZ,Male,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,255.35,No
+4301-VVZKA,Male,0,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.4,1189.4,No
+9199-PWQVC,Female,0,Yes,No,63,Yes,Yes,DSL,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),79.7,4786.15,No
+4824-GUCBY,Female,1,No,No,22,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,81.7,1820.9,No
+5393-HJZSM,Female,0,Yes,Yes,32,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),76.3,2404.15,No
+7074-IEVOJ,Female,1,No,No,3,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.4,205.05,Yes
+9625-QSTYE,Female,0,No,No,13,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.15,952.3,Yes
+0862-PRCBS,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),103.75,7039.45,No
+8812-ZRHFP,Female,0,Yes,Yes,30,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,One year,No,Electronic check,86.45,2538.05,No
+5146-CBVOE,Female,0,No,No,16,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),75.1,1212.85,No
+0454-OKRCT,Male,0,No,No,33,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),80.6,2651.1,No
+5787-KXGIY,Male,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.3,1304.8,No
+4750-ZRXIU,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.6,360.1,Yes
+4198-VFOEA,Female,0,No,No,12,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,33.6,435.45,No
+6630-UJZMY,Female,1,Yes,No,4,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.25,308.05,No
+5709-LVOEQ,Female,0,Yes,Yes,0,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Mailed check,80.85, ,No
+6400-BWQKW,Female,0,No,No,6,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,79.05,434.5,Yes
+2692-AQCPF,Female,0,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),108.05,7118.9,No
+0347-UBKUZ,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,320.45,No
+0835-DUUIQ,Female,0,No,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),21.05,531.55,No
+0811-GSDTP,Female,0,No,Yes,13,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,30.15,382.2,No
+7567-ECMCM,Male,0,No,No,24,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),79.85,2001,No
+6115-ZTBFQ,Female,0,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),65.5,4919.7,No
+6353-BRMMA,Female,0,Yes,Yes,54,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.1,5645.8,No
+6680-WKXRZ,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),74.4,215.8,Yes
+6231-WFGFH,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.5,77.6,No
+9904-EHEVJ,Female,1,Yes,Yes,32,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,91.35,2896.55,No
+7028-DVOIQ,Male,1,No,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.05,3395.8,Yes
+6169-PPETC,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.5,759.35,No
+4208-UFFGW,Male,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.95,85.15,Yes
+8584-KMVXD,Female,0,No,No,8,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,75.6,535.55,No
+8467-WYNSR,Male,0,No,No,22,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,55.1,1253.15,No
+0851-DFJKB,Female,0,No,No,15,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,58.95,955.15,No
+5382-SOYZL,Male,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.1,2162.6,No
+9448-REEVD,Male,0,Yes,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),44.7,44.7,Yes
+3261-CQXOL,Female,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.45,1813.35,No
+8388-FYNPZ,Male,0,No,No,4,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Electronic check,56.75,245.15,No
+4002-BQWPQ,Male,0,No,No,25,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),81.75,2028.8,No
+5651-YLPRD,Female,0,Yes,Yes,32,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.1,2723.75,No
+2826-DXLQO,Male,1,Yes,No,7,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),29.8,220.45,No
+4378-BZYFP,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.5,365.8,No
+9489-UTFKA,Male,1,Yes,No,8,Yes,No,DSL,Yes,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),60.9,551.95,No
+4849-PYRLQ,Female,1,No,No,56,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),73.25,4054.2,No
+9117-SHLZX,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.7,45.7,Yes
+9889-TMAHG,Male,1,No,No,8,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),100.3,832.35,Yes
+4541-RMRLG,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.25,112.3,Yes
+7764-BDPEE,Male,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.85,60.65,No
+3429-IFLEM,Female,0,No,No,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),77.35,5550.1,No
+3158-MOERK,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96,174.8,Yes
+7294-TMAOP,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,90.55,90.55,Yes
+5002-GCQFH,Male,0,Yes,No,49,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),93.85,4733.1,No
+0556-FJEGU,Male,0,No,No,58,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),70.1,4048.95,No
+8919-FYFQZ,Male,1,Yes,No,44,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,30.35,1359.7,Yes
+0604-THJFP,Female,0,Yes,Yes,59,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),75.95,4542.35,No
+2834-JRTUA,Male,0,No,No,71,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,108.05,7532.15,Yes
+5875-YPQFJ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.9,69.9,Yes
+5879-SESNB,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.25,888.65,No
+6646-QVXLR,Male,1,Yes,No,62,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.75,6383.35,Yes
+6461-PPAXN,Female,0,Yes,Yes,35,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,Yes,Bank transfer (automatic),54.95,1916,No
+3318-ISQFQ,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.5,413,No
+1106-HRLKZ,Male,0,Yes,Yes,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,808.95,No
+2483-XSSMZ,Female,0,No,No,39,Yes,No,DSL,Yes,No,No,No,No,No,One year,Yes,Electronic check,47.85,1886.4,No
+8603-IJWDN,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.6,86.6,Yes
+8165-ZJRNM,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.75,1679.25,No
+9369-XFEHK,Female,1,Yes,No,33,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.6,2656.5,Yes
+2604-XVDAM,Female,0,No,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),43.8,540.95,No
+3717-OFRTN,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.75,19.75,No
+9046-JBFWA,Male,0,No,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.15,537.35,No
+3280-NMUVX,Male,0,Yes,Yes,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.6,678.8,No
+1206-EHBDD,Female,0,Yes,No,56,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Two year,No,Bank transfer (automatic),80.3,4513.65,No
+8361-LBRDI,Female,0,No,No,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.35,1423.85,No
+4883-KCPZJ,Female,0,Yes,Yes,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.25,555.4,No
+9108-EQPNQ,Female,0,Yes,Yes,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),26.1,225.55,No
+7277-KAMWT,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20,268.45,No
+3842-IYKUE,Female,0,No,No,35,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),85.3,2917.5,Yes
+6641-XRPSU,Female,0,No,No,34,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),70,2416.1,Yes
+1374-DMZUI,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.3,424.45,Yes
+2545-LXYVJ,Male,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.7,1492.1,No
+3234-VKACU,Male,0,No,No,2,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,70.3,132.4,No
+8357-EQXFO,Female,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.35,660.9,Yes
+1989-PRJHP,Male,1,Yes,No,27,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.5,1893.95,Yes
+8120-JDCAM,Male,0,Yes,Yes,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.55,284.9,No
+8917-FAEMR,Female,0,No,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85,784.25,No
+7047-YXDMZ,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,417.7,No
+2858-EIMXH,Female,1,Yes,No,53,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),95.85,5016.25,No
+9524-EGPJC,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,90.1,1612.75,Yes
+6993-OHLXR,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,68.95,119.75,Yes
+8818-XYFCQ,Male,0,Yes,Yes,32,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.55,3204.65,Yes
+6419-ZTTLE,Male,1,Yes,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.75,485.2,No
+0929-HYQEW,Male,0,No,No,3,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.15,160.85,No
+6614-YOLAC,Female,0,Yes,Yes,71,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Mailed check,58.65,4145.25,No
+7426-RHZGU,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.9,827.45,No
+4065-JJAVA,Female,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,49.5,49.5,No
+4695-VADHF,Male,0,Yes,Yes,18,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,57.45,990.85,Yes
+3863-IUBJR,Male,0,Yes,Yes,12,Yes,No,DSL,No,No,No,No,No,Yes,One year,No,Credit card (automatic),53.65,696.35,Yes
+7649-SIJJF,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,80.1,5585.4,No
+9361-YNQWJ,Female,0,No,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.4,1601.2,No
+3748-FVMZZ,Male,0,No,No,4,No,No phone service,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,40.05,162.45,No
+9391-TTOYH,Female,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.5,470.2,No
+1452-XRSJV,Female,0,Yes,Yes,39,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),51.05,2066,No
+3422-WJOYD,Male,0,Yes,No,28,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,54.35,1426.45,No
+8242-SOQUO,Female,1,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.7,392.5,No
+7460-ITWWP,Female,1,Yes,No,45,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,86.1,3861.45,No
+7147-AYBAA,Male,0,No,No,37,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.35,2552.9,No
+7868-TMWMZ,Female,1,Yes,No,60,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110,6668.35,No
+4822-RVYBB,Male,1,No,No,8,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.6,819.4,Yes
+6732-FZUGP,Female,0,No,No,47,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,One year,No,Credit card (automatic),94.9,4615.25,No
+8436-BJUMM,Male,0,Yes,Yes,26,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.75,2070.6,Yes
+4184-TJFAN,Female,1,Yes,Yes,3,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,88.3,273.75,Yes
+8329-GWVPJ,Female,0,Yes,Yes,50,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.75,3557.7,No
+1352-VHKAJ,Male,0,Yes,Yes,27,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),71.6,1957.1,No
+4145-UQXUQ,Female,1,No,No,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.1,729.95,Yes
+2632-TACXW,Female,0,Yes,No,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.65,1416.75,No
+8146-QQKZH,Female,0,Yes,No,71,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),81.85,5924.4,No
+1767-CJKBA,Male,0,No,No,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.1,1697.7,No
+6445-TNRXS,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.7,7849.85,No
+4581-LNWUM,Female,0,No,No,13,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,49.15,649.4,No
+4869-EPIUS,Male,0,Yes,No,56,Yes,No,Fiber optic,No,No,No,No,No,Yes,One year,Yes,Electronic check,80.9,4557.5,No
+9948-YPTDG,Male,0,Yes,No,38,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.45,3013.05,Yes
+1236-WFCDV,Male,1,No,No,14,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),90.45,1266.1,Yes
+1915-OAKWD,Female,0,No,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.3,360.35,No
+7296-PIXQY,Female,0,Yes,Yes,14,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.2,1046.5,No
+4883-QICIH,Male,0,Yes,Yes,32,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.75,2347.9,Yes
+3354-OADJP,Female,0,No,No,8,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),54.25,447.75,No
+3524-WQDSG,Female,0,Yes,Yes,43,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.3,4209.95,No
+0810-DHDBD,Female,0,No,No,52,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Credit card (automatic),74,3877.65,No
+4026-SKKHW,Male,0,No,No,3,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.25,152.3,No
+2829-HYVZP,Male,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.8,572.2,No
+8329-IBCTI,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,Yes
+1271-SJBGZ,Male,1,No,No,12,No,No phone service,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,43.65,526.95,Yes
+3845-JHAMY,Female,0,Yes,Yes,16,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),35.5,552.7,No
+7013-PSXHK,Female,0,No,No,40,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,80.75,3208.65,No
+5669-SRAIP,Female,0,No,No,5,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,39.5,210.75,Yes
+5981-ITEMU,Male,0,Yes,No,40,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),97.1,3706.95,Yes
+3486-NPGST,Female,0,No,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.55,620.75,No
+6941-PMGEP,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80,412.5,Yes
+1624-WOIWJ,Female,0,No,No,10,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,84.7,832.05,Yes
+2074-GKOWZ,Male,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),89.55,185.55,Yes
+0376-YMCJC,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,90.6,1943.2,Yes
+6100-FJZDG,Male,0,Yes,Yes,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.05,505.9,No
+4829-ZLJTK,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,112.4,8046.85,No
+1730-VFMWO,Female,0,Yes,No,34,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),50.2,1815.3,No
+7143-BQIBA,Male,0,No,No,10,Yes,No,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),62.25,612.95,No
+3800-LYTRK,Female,0,No,No,14,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,55.7,795.15,No
+0634-SZPQA,Female,0,No,No,23,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),90.05,2169.8,Yes
+9646-NMHXE,Male,0,Yes,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,973.1,No
+7030-NJVDP,Male,0,Yes,No,24,Yes,No,Fiber optic,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.25,2210.2,No
+5536-RTPWK,Male,0,Yes,No,49,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.05,4853.75,Yes
+8883-GRDWQ,Male,1,No,No,20,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,54,1055.9,No
+6166-ILMNY,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69.75,144.55,Yes
+3097-NNSPB,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),49.05,91.1,Yes
+7771-ZONAT,Male,0,No,No,22,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),56.75,1304.85,No
+0655-RBDUG,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),98.05,713,Yes
+2111-DWYHN,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,21.1,21.1,No
+4194-WHFCB,Female,0,Yes,Yes,59,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),96.65,5580.8,No
+4121-AGSIN,Female,0,Yes,Yes,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),24.5,1497.9,No
+4361-BKAXE,Female,0,No,No,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),114.5,4527.45,Yes
+9845-PEEKO,Female,1,No,No,59,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Two year,Yes,Credit card (automatic),79.2,4590.35,No
+0455-XFASS,Female,0,Yes,Yes,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.55,200.2,No
+0301-KOBTQ,Male,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.05,614.45,No
+1751-NCDLI,Male,1,Yes,No,46,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.85,4564.9,No
+4367-NUYAO,Male,0,Yes,Yes,0,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75, ,No
+9878-TNQGW,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.95,171.15,Yes
+9170-ARBTB,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.6,1012.4,No
+4441-NIHPT,Female,1,No,No,13,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.3,940.35,Yes
+8999-BOHSE,Female,1,No,No,11,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),89.7,1047.7,Yes
+7241-AJHFS,Male,0,No,No,32,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),87.65,2766.4,No
+7029-RPUAV,Male,1,Yes,No,17,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.45,1622.45,Yes
+4546-FOKWR,Female,0,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.75,1129.35,No
+9036-CSKBW,Female,0,No,No,51,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),107.45,5680.9,No
+5832-TRLPB,Male,0,No,No,29,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),75.35,2243.9,No
+8590-YFFQO,Male,0,Yes,No,70,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),64.95,4523.25,No
+8659-IOOPU,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,Two year,No,Electronic check,100.45,7159.7,No
+1338-CECEE,Male,0,Yes,Yes,41,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),68.5,2839.95,No
+7439-DKZTW,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.55,80.55,No
+4646-QZXTF,Female,0,Yes,No,7,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),81.25,580.1,No
+4607-CHPCA,Male,0,Yes,Yes,25,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Electronic check,90.4,2178.6,Yes
+9742-XOKTS,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,One year,No,Electronic check,89.55,6038.55,No
+6921-OZMFH,Male,0,Yes,Yes,5,Yes,Yes,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,55.7,259.4,No
+9578-FOMUK,Male,0,No,Yes,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,24.8,324.15,No
+4712-UYOOI,Female,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20,417.65,No
+8824-RWFXJ,Male,0,Yes,Yes,3,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,56.15,168.15,Yes
+7722-CVFXN,Male,0,Yes,Yes,54,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,105.2,5637.85,No
+8717-VCTXJ,Male,0,No,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.55,839.4,No
+7363-QTBIW,Female,0,Yes,No,9,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.75,769.1,No
+4159-NAAIX,Female,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,No,Bank transfer (automatic),97.45,6253,No
+0971-QIFJK,Female,0,Yes,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.25,1641.8,No
+9397-TZSHA,Female,0,No,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.6,1678.05,No
+3391-JSQEW,Male,0,Yes,No,40,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,50.15,2058.5,No
+0343-QLUZP,Male,0,No,No,60,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Bank transfer (automatic),39.6,2424.5,No
+9763-PDTKK,Female,0,No,No,4,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),94.4,387.2,Yes
+2176-LVPNX,Female,1,No,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,89.85,6293.45,No
+7627-JKIAZ,Female,0,Yes,No,37,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),78.95,2839.65,Yes
+3312-UUMZW,Male,0,Yes,No,32,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.85,3145.9,No
+1271-UODNO,Male,0,No,No,39,Yes,Yes,DSL,No,No,No,Yes,No,No,Two year,Yes,Credit card (automatic),53.85,2200.7,No
+8461-EFQYM,Female,0,No,No,38,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,24.25,914.4,No
+6900-RBKER,Male,0,No,No,52,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),89.45,4577.75,No
+6891-JPYFF,Female,0,Yes,Yes,48,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),105.25,4997.5,No
+1459-QNFQT,Male,0,Yes,Yes,70,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),59.5,4144.8,No
+1047-NNCBF,Male,0,No,No,20,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),70.55,1493.55,No
+3696-XRIEN,Female,0,No,No,50,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),82.5,4179.1,No
+4081-DYXAV,Female,0,No,No,19,No,No phone service,DSL,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),44.85,893.55,Yes
+0074-HDKDG,Male,0,Yes,Yes,25,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),61.6,1611,No
+8791-GFXLZ,Male,0,No,No,12,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.05,593.05,No
+8111-SLLHI,Male,1,Yes,No,39,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.65,4284.8,Yes
+0927-LCSMG,Male,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,74.65,544.55,Yes
+9330-DHBFL,Female,0,Yes,Yes,23,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Mailed check,66.25,1533.8,No
+0098-BOWSO,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.4,529.8,No
+3452-ABWRL,Male,1,No,No,47,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),86.05,3865.6,No
+5859-HZYLF,Male,0,Yes,Yes,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.15,515.75,No
+8257-RZAHR,Female,0,Yes,No,14,Yes,Yes,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),64.7,941,Yes
+5293-WXJAK,Female,1,Yes,No,11,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.05,1133.65,Yes
+3156-QLHBO,Male,0,No,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.25,48.35,No
+2208-NQBCT,Female,0,Yes,Yes,26,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,81.95,2070.05,No
+1779-PWPMG,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.65,8333.95,No
+6621-NRZAK,Female,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20,1209.25,No
+0831-JNISG,Male,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.8,1396.25,No
+0774-IFUVM,Male,0,Yes,Yes,11,Yes,Yes,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),65.15,723.35,No
+3082-WQRVY,Male,1,Yes,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,228.65,No
+9553-DLCLU,Female,0,No,Yes,13,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88.95,1161.75,No
+1641-BYBTK,Male,0,No,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.2,98.35,No
+2460-NGXBJ,Male,1,Yes,Yes,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.2,775.3,No
+2446-ZKVAF,Male,0,Yes,No,18,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),56.8,1074.65,No
+0841-NULXI,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.55,35.55,Yes
+3522-CDKHF,Female,0,Yes,No,32,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.5,2324.7,No
+1430-SFQSA,Male,0,No,No,29,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,35.6,1072.6,No
+0411-EZJZE,Female,0,No,No,3,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,60.25,170.5,No
+7851-WZEKY,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.15,196.9,Yes
+8844-TONUD,Male,0,Yes,Yes,13,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,96.65,1162.85,Yes
+8807-ARQET,Female,0,No,No,41,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,40.35,1677.85,No
+8992-CEUEN,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,18.85,18.85,No
+4320-QMLLA,Male,0,No,No,7,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,54.85,370.4,No
+8777-PVYGU,Female,0,Yes,No,52,Yes,No,DSL,Yes,No,Yes,No,Yes,No,One year,Yes,Mailed check,64.3,3410.6,No
+8292-ITGYJ,Female,0,Yes,Yes,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.65,1138.8,No
+6870-ZWMNX,Male,0,Yes,No,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),76.1,5264.25,No
+0621-CXBKL,Female,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,18.7,1005.7,No
+5268-DSMNQ,Female,1,Yes,No,62,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),97.95,5936.55,No
+5334-JLAXU,Female,0,Yes,No,60,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.1,5475.9,No
+4086-YQSNZ,Female,1,Yes,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.4,224.05,Yes
+6242-MBHPK,Female,1,Yes,No,23,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.1,2326.05,No
+5868-CZJDR,Male,0,No,Yes,1,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,31.35,31.35,Yes
+9359-UGBTK,Female,0,No,No,67,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),72.35,4991.5,No
+0135-NMXAP,Female,0,No,No,12,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.75,1052.4,Yes
+4782-OSFXZ,Female,1,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),82.7,5831.2,No
+6479-OAUSD,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.9,510.8,No
+7129-ACFOG,Female,0,No,No,5,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,53.8,283.95,No
+4189-NAKJS,Male,0,No,No,26,Yes,Yes,DSL,No,No,No,No,No,No,One year,Yes,Credit card (automatic),51.55,1295.4,No
+5562-BETPV,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,19.65,No
+1282-IHQAC,Male,1,No,No,70,No,No phone service,DSL,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),44.05,3011.65,No
+9127-FHJBZ,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,114,8093.15,No
+6270-OMFIW,Male,0,Yes,No,60,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,94.4,5610.25,Yes
+1641-RQDAY,Female,1,Yes,Yes,32,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.4,3217.65,No
+0107-WESLM,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.85,19.85,Yes
+6994-ORCWG,Female,0,No,No,14,Yes,Yes,DSL,No,Yes,No,No,No,No,One year,Yes,Mailed check,54.25,773.2,No
+1346-UFHAX,Female,0,No,No,13,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),80,1029.35,No
+3992-YWPKO,Female,0,No,No,6,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),109.9,669.45,Yes
+2933-XEUJM,Female,0,No,No,46,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Mailed check,79.2,3593.8,No
+0125-LZQXK,Male,0,No,No,15,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.35,1553.95,Yes
+5461-QKNTN,Male,1,Yes,No,43,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,94.3,3953.15,No
+4835-YSJMR,Male,0,No,No,39,Yes,No,DSL,No,No,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),49.8,1971.15,No
+8399-YNDCH,Male,1,No,No,21,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),60.05,1236.15,Yes
+3164-YAXFY,Male,0,No,No,57,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,53.75,3196,No
+0887-WBJVH,Female,0,Yes,No,53,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,93.45,4872.2,No
+4660-IRIBM,Male,0,Yes,Yes,18,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,87.9,1500.5,No
+5673-FSSMF,Female,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,60.15,60.15,Yes
+7670-ZBPOQ,Female,0,Yes,No,58,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Bank transfer (automatic),61.05,3478.75,No
+8089-UZWLX,Female,1,No,No,71,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),104.05,7413.55,No
+0080-OROZO,Female,0,No,No,35,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,99.25,3532,No
+3916-NRPAP,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),85.7,256.75,No
+6807-SIWJI,Male,0,No,No,38,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,104.85,3887.25,No
+8221-HVAYI,Male,0,Yes,Yes,35,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),69.15,2490.15,No
+1579-KLYDT,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,90.45,593.45,Yes
+5232-NXPAY,Female,0,No,No,47,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,74.45,3510.3,No
+8967-SZQAS,Female,0,No,No,14,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,50.45,765.45,No
+4468-KAZHE,Female,1,Yes,No,20,Yes,No,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,60,1259.35,No
+0455-ENTCR,Male,0,Yes,No,66,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Electronic check,85.25,5538.35,No
+8944-AILEF,Male,0,Yes,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.45,340.85,No
+5542-NKVRU,Female,0,No,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.75,844.45,No
+7126-RBHSD,Female,0,Yes,No,17,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,78.9,1348.95,No
+5370-IIVVL,Male,0,No,No,37,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),104.5,3778,No
+6789-HJBWG,Female,0,No,No,12,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,49.4,611.65,No
+3927-NLNRY,Male,0,Yes,No,53,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),94.25,4867.95,Yes
+9087-EYCPR,Female,0,No,No,60,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25,1505.05,No
+6791-YBNAK,Male,0,Yes,Yes,18,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),25.55,467.85,No
+6358-LYNGM,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,74.9,Yes
+6077-BDPXA,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,194.2,No
+0013-MHZWF,Female,0,No,Yes,9,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),69.4,571.45,No
+5494-HECPR,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.25,80.25,Yes
+8268-YDIXR,Male,0,Yes,No,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,Yes,Electronic check,93.15,5253.95,No
+9824-BEMCV,Male,0,Yes,Yes,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69,1149.65,Yes
+1373-ORVIZ,Female,0,Yes,Yes,11,Yes,Yes,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,66.35,740.8,Yes
+4291-SHSBH,Male,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.55,521.35,No
+6980-IMXXE,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.2,1412.65,No
+9866-QEVEE,Male,0,No,No,19,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),86,1532.45,Yes
+9897-KXHCM,Female,0,Yes,Yes,3,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.3,250.05,Yes
+0040-HALCW,Male,0,Yes,Yes,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.4,1090.6,No
+0784-GTUUK,Male,0,Yes,No,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),23.75,1446.8,No
+7979-CORPM,Male,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90.55,2282.55,Yes
+2294-DMMUS,Female,0,Yes,Yes,62,Yes,Yes,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),70.45,4300.45,No
+0872-JCPIB,Male,0,No,No,17,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),65.75,1111.2,No
+3055-MJDSB,Male,0,No,No,9,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.6,190.1,No
+9091-WTUUY,Male,0,Yes,Yes,64,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Mailed check,69.25,4447.75,No
+1618-CFHME,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.9,143.35,Yes
+3165-HDOEW,Male,0,Yes,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.85,45.85,Yes
+6581-NQCBA,Female,0,Yes,No,16,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,49.95,810.2,Yes
+7115-IRDHS,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.65,1830.05,No
+8496-DMZUK,Male,0,No,No,30,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),90.4,2820.65,No
+2040-VZIKE,Female,0,Yes,No,49,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,100.85,4847.35,No
+9068-VPWQQ,Male,0,Yes,No,61,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),75.35,4729.3,No
+0178-SZBHO,Male,0,Yes,Yes,47,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,87.2,4017.45,No
+0384-RVBPI,Male,0,No,No,20,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),64.4,1398.6,No
+1689-MRZQR,Male,0,Yes,Yes,34,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.3,2564.3,Yes
+1299-AURJA,Female,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.7,1685.9,No
+4525-VZCZG,Male,0,No,Yes,54,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,105.85,5826.65,No
+1543-LLLFT,Male,1,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,No,One year,Yes,Mailed check,98.3,6066.55,No
+5835-BEQEU,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.95,228.4,Yes
+2788-CJQAQ,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.45,270.2,No
+5565-FILXA,Female,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.15,1529.2,Yes
+0319-QZTCO,Female,0,Yes,Yes,3,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),58.7,168.6,No
+2120-SMPEX,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.15,536.35,No
+0096-FCPUF,Male,0,No,No,30,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,64.5,1888.45,No
+0668-OGMHD,Female,0,Yes,No,21,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,28.5,629.35,No
+5552-ZNFSJ,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.3,45.3,Yes
+2223-KAGMX,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.4,289.3,Yes
+6507-ZJSUR,Male,1,Yes,No,23,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.45,2117.25,No
+9408-HRXRK,Female,0,Yes,Yes,45,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.15,4730.9,No
+5593-SUAOO,Female,0,Yes,Yes,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),83.15,2033.05,No
+7321-PKUYW,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,90.15,987.95,Yes
+2833-SLKDQ,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.05,45.05,Yes
+6766-HFKLA,Female,0,Yes,No,56,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),103.2,5744.35,No
+7595-EUIVN,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,75.8,75.8,Yes
+7617-EYGLW,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.45,19.45,No
+2026-TGDHM,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.3,523.15,Yes
+9220-ZNKJI,Female,1,Yes,No,55,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),88.8,4805.3,No
+4030-VPZBD,Female,0,No,No,2,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,30.9,59.05,Yes
+2226-ICFDO,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.9,6110.75,No
+0723-VSOBE,Female,1,No,No,45,No,No phone service,DSL,No,No,No,No,No,Yes,One year,No,Electronic check,34.2,1596.6,No
+5529-GIBVH,Female,0,No,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,1046.2,No
+4187-CINZD,Female,1,No,No,46,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Credit card (automatic),95.25,4424.2,Yes
+9992-UJOEL,Male,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,50.3,92.75,No
+4741-WWJQZ,Female,0,Yes,No,2,Yes,No,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,80.15,194.55,No
+6625-UTXEW,Female,0,Yes,No,12,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,51.25,612.1,No
+6818-WOBHJ,Female,1,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.6,6127.6,Yes
+6244-BESBM,Male,0,Yes,Yes,69,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),95.2,6671.7,No
+1004-NOZNR,Male,1,No,Yes,56,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,One year,No,Credit card (automatic),94.8,5264.3,No
+1251-STYSZ,Female,1,No,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.25,303.7,No
+2612-PHGOX,Male,0,Yes,No,64,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),76.1,4818.8,No
+2408-TZMJL,Male,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,110.15,6448.05,Yes
+8480-PPONV,Male,0,Yes,Yes,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),115.55,7159.05,No
+8780-IHCRN,Male,0,Yes,Yes,63,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.65,1574.5,No
+4598-ZADCK,Female,0,No,No,53,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,53.6,2879.2,No
+1257-SXUXQ,Male,0,Yes,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.45,86.05,No
+9681-KYGYB,Male,1,Yes,No,49,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,88.2,4159.45,No
+7182-OVLBJ,Female,0,Yes,Yes,62,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),101.15,6638.35,No
+5095-ETBRJ,Female,0,Yes,No,55,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,56.8,3112.05,No
+7005-CYUIL,Female,1,Yes,No,71,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Electronic check,99.4,7168.25,No
+4821-WQOYN,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.1,1326.25,No
+4730-AWNAU,Male,0,Yes,Yes,36,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),60.7,2234.55,No
+3452-FLHYD,Male,0,Yes,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.95,495.15,No
+2388-LAESQ,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.85,8317.95,No
+9531-NSBMR,Female,0,No,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.25,679.8,No
+6260-ONULR,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,62.8,62.8,No
+4389-UEFCZ,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,105.5,7544,No
+8711-LOBKY,Male,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85,1188.25,No
+9134-CEQMF,Male,1,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.5,676.7,Yes
+8985-OOPOS,Female,0,No,No,1,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),74.1,74.1,No
+8800-ZKRFW,Female,0,Yes,Yes,30,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,107.5,3242.5,No
+2616-FLVQC,Male,0,No,No,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,1240.15,No
+9968-FFVVH,Male,0,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Bank transfer (automatic),68.8,4111.35,No
+3108-PCCGG,Male,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),84.45,5899.85,No
+7993-PYKOF,Male,0,Yes,No,8,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,75,632.95,Yes
+8390-FESFV,Female,0,No,No,62,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),84.5,5193.2,No
+3022-BEXHZ,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.2,7530.8,No
+5027-XWQHA,Male,0,No,No,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.75,270.95,Yes
+6248-TKCQV,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.6,5460.2,No
+6729-GDNGC,Female,1,No,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),80.7,1614.2,No
+6198-ZFIOJ,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.6,402.5,No
+5989-OMNJE,Female,0,Yes,Yes,24,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,57.6,1367.75,No
+4566-QVRRW,Female,0,Yes,No,11,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.05,483.7,Yes
+1291-CUOCY,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.6,7962.2,No
+9795-SHUHB,Female,0,Yes,Yes,66,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),58.2,3810.8,No
+3230-IUALN,Female,0,Yes,Yes,45,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,81,3533.6,No
+0042-RLHYP,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.7,1396.9,No
+8519-IMDHU,Male,1,Yes,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),85.6,1345.55,Yes
+4945-RVMTE,Female,0,No,No,28,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),59.55,1646.45,No
+0201-OAMXR,Female,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),115.55,8127.6,Yes
+1866-NXPSP,Female,0,No,No,36,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Mailed check,75.55,2680.15,No
+3372-KWFBM,Male,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.6,1281,Yes
+7831-QGOXH,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.2,1553.9,Yes
+6393-WRYZE,Female,0,Yes,No,34,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,97.65,3207.55,Yes
+3941-XTSKM,Male,0,Yes,Yes,42,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),45.1,2049.05,No
+1661-CZBAU,Male,0,No,No,48,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),70.95,3629.2,No
+6599-RCLCJ,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,109.55,5124.55,Yes
+9831-BPFRI,Female,0,Yes,Yes,39,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,89.55,3474.45,Yes
+5158-RIVOP,Female,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.9,202.3,No
+9788-YTFGE,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.95,147.5,No
+9277-JOOMO,Female,0,No,No,3,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.6,86.35,No
+1907-YLNYW,Male,0,No,No,8,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,66.7,579,No
+1725-MIMXW,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.45,19.45,Yes
+8947-YRTDV,Male,0,Yes,Yes,32,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,No,Mailed check,94.8,3131.55,No
+3161-ONRWK,Male,0,Yes,Yes,60,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),65.85,3928.3,No
+0114-RSRRW,Female,0,Yes,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.95,187.75,No
+4565-NLZBV,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.65,1710.15,No
+0031-PVLZI,Female,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,76.35,Yes
+7206-GZCDC,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.25,69.25,Yes
+6682-VCIXC,Female,0,Yes,Yes,43,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),51.25,2151.6,No
+4791-QRGMF,Male,0,Yes,No,59,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),99.5,5961.1,Yes
+6475-VHUIZ,Female,0,Yes,No,23,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,54.25,1221.55,No
+3910-MRQOY,Female,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.4,1496.45,No
+0661-WCQNQ,Male,0,Yes,No,22,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,Yes,Credit card (automatic),56.25,1292.2,No
+7537-RBWEA,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.15,25.15,No
+4656-CAURT,Male,0,No,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.95,1713.1,No
+0121-SNYRK,Male,0,No,No,50,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,35.4,1748.9,No
+1768-ZAIFU,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.2,25.2,Yes
+4671-LXRDQ,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45,96.45,Yes
+3733-LSYCE,Female,0,Yes,No,15,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),75.35,1114.55,No
+6265-FRMTQ,Male,0,No,No,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.4,609.1,No
+1934-SJVJK,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,20.15,Yes
+3838-OZURD,Male,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),105,7133.25,Yes
+1371-DWPAZ,Female,0,Yes,Yes,0,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),56.05, ,No
+9269-CQOOL,Male,0,No,Yes,3,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),54.7,169.45,Yes
+2017-CCBLH,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,141.6,No
+7690-KPNCU,Male,0,Yes,Yes,64,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),73.05,4688.65,No
+0536-BGFMZ,Female,0,Yes,No,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,563.05,No
+2293-IJWPS,Female,0,Yes,No,57,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),100.75,5985,No
+2845-HSJCY,Female,0,Yes,Yes,14,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,87.25,1258.6,Yes
+5469-NUJUR,Male,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.95,373.5,No
+1184-PJVDB,Male,0,Yes,No,10,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.95,857.2,Yes
+2625-TRCZQ,Female,0,Yes,No,51,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,49.65,2553.35,No
+4102-HLENU,Female,0,Yes,No,67,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Mailed check,65.65,4322.85,No
+7266-GSSJX,Male,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.45,250.8,No
+7722-VJRQD,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),60.95,4549.05,No
+7073-QETQY,Male,0,Yes,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.35,1359.5,No
+9415-DPEWS,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,88.35,1639.3,Yes
+5624-RYAMH,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.5,178.85,No
+0196-JTUQI,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.2,633.85,No
+7130-YXBRO,Male,0,Yes,No,48,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),111.45,5315.1,No
+9272-LSVYH,Male,0,No,No,10,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.15,735.5,No
+7943-RQCHR,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,889.9,Yes
+3793-MMFUH,Female,1,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.05,1290,Yes
+3249-ZPQRG,Male,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,78.45,330.05,Yes
+2568-BRGYX,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.2,237.95,Yes
+3084-DOWLE,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),92,6474.4,No
+1084-MNSMJ,Female,0,Yes,Yes,51,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),85.5,4421.95,No
+7721-JXEAW,Male,0,Yes,No,59,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),41.05,2452.7,Yes
+7249-WBIYX,Male,0,Yes,No,10,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.6,813.85,Yes
+4238-HFHSN,Male,1,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,82.15,4904.85,No
+6250-CGGUN,Male,0,No,No,54,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,One year,No,Electronic check,84.4,4484.05,No
+5478-JJVZK,Female,0,No,No,33,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,60.9,2033.85,No
+7596-IIWYC,Female,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.25,538.2,No
+6567-HOOPW,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.2,79.2,Yes
+9793-WECQC,Male,0,No,No,23,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Mailed check,95.3,2192.9,No
+4291-HPAXL,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,19.85,No
+8999-YPYBV,Male,0,Yes,Yes,45,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,84.35,3858.05,No
+1839-FBNFR,Female,0,Yes,Yes,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.85,854.8,No
+3164-AALRN,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Mailed check,70,347.4,Yes
+3071-MVJCD,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),82.3,5815.15,No
+1697-BCSHV,Female,0,Yes,Yes,58,Yes,Yes,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),66.8,3970.4,No
+0562-KBDVM,Female,0,No,No,70,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),44.6,3058.15,No
+1131-SUEKT,Male,0,Yes,Yes,61,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),98.45,6145.2,No
+3717-OEAUQ,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.7,129.2,No
+4538-WNTMJ,Female,0,Yes,Yes,46,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.95,1165.9,No
+3334-CTHOL,Female,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.95,49.95,Yes
+4704-ERYFC,Female,0,Yes,No,22,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.25,1554,Yes
+9432-RUVSL,Female,0,No,No,48,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),102.5,4904.25,No
+8060-HIWJJ,Male,0,No,No,64,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.55,5632.55,No
+7684-XSZIY,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.3,1643.25,No
+9089-UOWJG,Female,0,Yes,Yes,12,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),58.35,740.55,No
+8621-MNIHH,Female,1,Yes,No,34,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.25,3217.55,Yes
+8039-ACLPL,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Two year,No,Credit card (automatic),68.75,4888.2,No
+9885-AIBVB,Male,0,Yes,No,29,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,85.8,2440.25,No
+1934-MKPXS,Male,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.1,620.55,No
+2592-YKDIF,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,20.35,No
+2272-JKMSI,Female,0,Yes,Yes,62,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,110.8,6840.95,No
+0471-LVHGK,Male,0,Yes,No,41,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,73,3001.2,Yes
+9518-RWHZL,Female,0,No,No,64,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),100.05,6254.2,Yes
+8714-CTZJW,Female,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),82.85,319.6,No
+3569-EDBPQ,Female,0,No,No,24,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,84.35,1938.05,No
+3131-NWVFJ,Female,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.55,294.5,No
+7521-YXVZY,Male,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,58.3,No
+5419-CONWX,Female,1,No,No,4,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.8,442.85,Yes
+6240-EURKS,Female,0,No,Yes,18,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35,553,Yes
+2373-NTKOD,Male,0,No,No,8,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,66.25,546.45,No
+1970-KKFWL,Female,0,No,No,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.3,797.1,No
+6960-HVYXR,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,76,76,Yes
+9337-SRRNI,Male,0,No,Yes,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.3,1673.8,No
+0895-UADGO,Male,0,No,Yes,8,No,No phone service,DSL,Yes,No,No,Yes,No,Yes,Two year,Yes,Mailed check,44.55,343.45,No
+5678-VFNEQ,Female,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.1,7412.25,No
+5977-CKHON,Female,0,Yes,Yes,43,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),92.55,4039,No
+7024-OHCCK,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.85,170.85,Yes
+2692-BUCFV,Male,1,No,No,29,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),101.45,2948.6,No
+7861-UVUFT,Female,0,Yes,No,15,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,84.3,1308.4,Yes
+1830-GGFNM,Male,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),94.55,6078.75,No
+5302-BDJNT,Male,0,No,No,35,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Electronic check,95.5,3418.2,No
+5223-UZAVK,Male,0,No,No,64,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),100.3,6603.8,No
+4859-ZSRDZ,Female,0,Yes,Yes,58,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),55.5,3166.9,No
+5651-WYIPH,Female,1,No,No,18,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),49.85,865.75,No
+9350-VLHMB,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.55,6373.1,No
+3498-LZGQZ,Male,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.15,1177.05,No
+8785-CJSHH,Female,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,5985.75,No
+5357-TZHPP,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),84.4,732.5,Yes
+3870-SPZSI,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),113.05,7869.05,No
+0680-DFNNY,Male,0,Yes,No,15,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.1,1504.05,Yes
+7560-QRBXH,Female,0,No,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,936.7,No
+7077-XJMET,Male,0,Yes,No,12,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,74.15,741.4,No
+8752-GHJFU,Male,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,92,6585.2,No
+6896-SRVYQ,Male,1,No,No,44,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,73.85,3122.4,No
+7767-UXAGJ,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.45,50.45,Yes
+4652-ODEVH,Male,0,Yes,Yes,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.45,1088.25,No
+6510-UPNKS,Female,0,No,No,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.8,615.35,No
+6718-BDGHG,Female,0,Yes,No,43,Yes,Yes,DSL,No,No,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),64.85,2908.2,No
+9046-DQMTP,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.75,739.9,No
+6439-LAJXL,Male,0,Yes,No,9,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,68.95,593.85,No
+1571-SAVHK,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,99.95,1132.75,Yes
+9052-VKDUW,Female,1,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),109.4,7227.45,No
+9546-CQJSU,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,91.4,193.6,Yes
+1666-JZPZT,Male,0,No,No,27,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,49,1291.35,No
+5777-KJIRB,Female,0,No,No,40,Yes,No,DSL,No,No,Yes,No,No,No,One year,Yes,Mailed check,50.25,2023.55,No
+0506-LVNGN,Female,1,No,No,5,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),75.55,349.65,Yes
+7677-SJJJK,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.9,153.95,No
+2480-EJWYP,Female,1,Yes,No,58,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),97.8,5458.8,No
+3253-HKOKL,Female,0,Yes,Yes,52,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,No,Electronic check,100.3,5244.45,No
+7055-HNEOJ,Male,0,Yes,No,3,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,55.8,154.55,No
+5514-YQENT,Male,0,No,Yes,41,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,111.15,4507.15,No
+3211-AAPKX,Male,0,No,No,20,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,98.55,2031.95,No
+8445-DNBAE,Male,0,No,Yes,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.05,50.05,No
+2951-QOQTK,Male,0,No,Yes,4,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),80.8,332.45,Yes
+2958-NHPPS,Male,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.85,473.9,No
+6806-YDEUL,Female,1,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.5,106.8,No
+1735-XMJVH,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.35,152.6,No
+6890-PFRQX,Male,0,No,No,18,Yes,Yes,DSL,Yes,No,Yes,No,Yes,No,Month-to-month,No,Mailed check,69.5,1199.4,No
+0222-CNVPT,Male,1,No,No,52,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),48.8,2555.05,No
+5899-OUVKV,Male,0,No,No,31,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.5,2979.2,No
+8681-ICONS,Male,0,Yes,Yes,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.65,654.85,No
+1621-YNCJH,Female,0,Yes,No,36,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),106.05,3834.4,No
+3473-XIIIT,Female,0,Yes,No,16,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100,1534.75,Yes
+6362-QHAFM,Male,0,Yes,No,42,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,108.3,4586.15,No
+7893-IXHRQ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.55,20.55,Yes
+3070-BDOQC,Female,0,No,No,60,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,99.65,5941.05,No
+2952-QAYZF,Male,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,85.3,424.15,Yes
+6234-PFPXL,Male,0,Yes,No,22,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,No,Month-to-month,No,Credit card (automatic),95.9,2234.95,No
+9824-QCJPK,Male,0,Yes,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,666.75,No
+4763-PGDPO,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.4,281,Yes
+4283-IVYCI,Male,0,No,No,9,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Mailed check,64.95,547.8,No
+1866-OBPNR,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),74.6,74.6,No
+8205-MQUGY,Male,0,Yes,Yes,12,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.2,571.15,No
+8970-ANWXO,Female,0,No,No,23,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,73.75,1756.6,No
+9480-BQJEI,Male,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Bank transfer (automatic),92.3,5731.45,No
+5394-SVGJV,Male,0,No,No,37,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),98.8,3475.55,Yes
+6979-TNDEU,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.2,156.85,No
+9777-WJJPR,Male,0,Yes,No,31,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Credit card (automatic),88.65,2683.2,No
+9283-LZQOH,Male,0,Yes,Yes,13,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.4,896.75,Yes
+7079-QRCBC,Female,0,No,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.75,2407.3,Yes
+9495-SKLKD,Male,0,Yes,Yes,45,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.95,4456.65,No
+6048-UWKAL,Female,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),105.4,6998.95,No
+5067-DGXLL,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,36.8,No
+5469-CTCWN,Male,0,Yes,Yes,61,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,106,6547.7,Yes
+9851-QXEEQ,Male,0,No,No,41,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.7,4346.4,Yes
+6281-FKEWS,Female,0,No,No,44,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,49.05,2265,No
+8898-KASCD,Male,0,No,No,39,No,No phone service,DSL,No,No,Yes,Yes,No,No,One year,No,Mailed check,35.55,1309.15,No
+9242-TKFSV,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),65.1,4754.3,No
+9290-SHCMB,Female,1,No,No,13,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.85,1235.55,No
+0743-HNPFG,Female,0,Yes,Yes,51,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Credit card (automatic),69.75,3562.5,No
+2277-BKJKN,Female,1,Yes,No,71,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,99.2,7213.75,No
+9809-IMGCQ,Male,1,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.7,2082.95,Yes
+5208-HFSBT,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),55.05,102.75,Yes
+5035-PGZXH,Female,0,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.8,5914.4,No
+8695-WDYEA,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,51.25,51.25,No
+6543-JXSOO,Female,0,Yes,Yes,23,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,57.75,1282.85,No
+8016-ZMGMO,Female,1,Yes,No,66,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),70.85,4738.85,No
+8605-ITULD,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,No
+3254-YRILK,Male,1,No,No,19,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,88.2,1775.8,Yes
+6416-YJTTB,Male,0,No,No,11,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.5,795.65,No
+2667-WYLWJ,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.75,145,Yes
+4472-VESGY,Female,0,Yes,Yes,52,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,No,Bank transfer (automatic),98.15,4993.4,No
+3195-TQDZX,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.25,61.45,No
+3128-YOVTD,Female,0,Yes,Yes,51,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.15,4018.55,No
+0529-ONKER,Male,1,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.65,1146.65,Yes
+1728-CXQBE,Male,1,Yes,No,64,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.25,6081.4,No
+7041-TXQJH,Female,0,No,No,37,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),40.2,1478.85,No
+5014-GSOUQ,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.95,243.65,No
+5724-BIDBU,Male,0,Yes,No,49,Yes,Yes,DSL,Yes,No,No,No,No,No,One year,Yes,Electronic check,55.35,2633.95,No
+0481-SUMCB,Female,1,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),102.15,4735.35,No
+1769-GRUIK,Female,0,No,No,18,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.1,1247.75,No
+5240-IJOQT,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,74.7,Yes
+8819-WFGGJ,Male,0,Yes,No,68,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),54.1,3794.5,No
+7427-AUFPY,Male,0,No,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,1008.7,No
+2811-POVEX,Female,1,Yes,Yes,23,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),88.45,2130.55,No
+1092-GANHU,Male,0,No,No,17,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,76.65,1313.55,Yes
+7898-PDWQE,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),80.4,5727.15,No
+9972-EWRJS,Female,0,Yes,Yes,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.25,1372.9,No
+9314-IJWSQ,Female,0,Yes,Yes,14,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.8,1203.9,No
+0661-XEYAN,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.8,25.8,Yes
+5799-JRCZO,Female,0,No,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.5,1215.1,No
+1921-KYSAY,Female,0,No,No,41,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,One year,Yes,Electronic check,68.6,2877.05,No
+6198-RTPMF,Female,0,Yes,No,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,92.6,1579.7,No
+2924-KHUVI,Male,0,Yes,No,56,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Electronic check,100.55,5514.95,No
+1925-GMVBW,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.55,96.1,No
+7881-EVUAD,Female,0,No,No,2,No,No phone service,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,42.6,72.4,Yes
+6184-DYUOB,Female,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.6,55.25,No
+9207-ZPANB,Male,0,No,No,37,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Electronic check,67.45,2443.3,No
+5766-XQXMQ,Female,0,No,No,29,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),68.85,1970.5,Yes
+9327-QSDED,Male,0,No,No,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,43.55,335.4,No
+1656-DRSMG,Female,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),109.85,7002.95,No
+3012-VFFMN,Female,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,158.95,No
+2984-AFWNC,Female,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,95.4,293.15,No
+0640-YJTPY,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),21,1493.75,No
+8096-LOIST,Female,0,No,No,19,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,56.2,1093.4,No
+9764-REAFF,Female,0,Yes,No,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),18.4,1057.85,No
+3703-VAVCL,Male,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,No,Credit card (automatic),90,190.05,Yes
+7107-UBYKY,Female,0,Yes,Yes,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,25.75,882.55,No
+4881-GQJTW,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.6,300.4,No
+8519-QJGJD,Female,0,No,No,14,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,75.35,1025.95,Yes
+7876-DNYAP,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.8,1436.95,No
+7905-NJMXS,Male,1,Yes,No,7,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,64.2,475,No
+2882-WDTBA,Male,0,Yes,Yes,69,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),75.75,5388.15,No
+2091-GPPIQ,Female,0,Yes,Yes,72,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,78.95,5730.15,No
+6326-MTTXK,Male,0,No,No,8,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,100.85,819.55,Yes
+5071-FBJFS,Female,0,Yes,Yes,4,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,50.3,217.1,No
+2796-UUZZO,Male,0,Yes,Yes,63,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),80.3,4896.35,No
+2429-AYKKO,Male,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.85,1434.1,No
+9798-OPFEM,Female,0,No,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,21.1,937.1,No
+0330-IVZHA,Female,0,Yes,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.95,330.15,Yes
+3794-NFNCH,Male,0,Yes,No,30,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,50,1474.9,No
+5193-QLVZB,Male,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.75,6536.5,No
+7114-AEOZE,Female,0,No,No,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.85,1128.1,No
+2886-KEFUM,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,107.5,6873.75,Yes
+5522-NYKPB,Male,0,Yes,Yes,25,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,85.9,2199.05,No
+4237-RLAQD,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.85,45.85,Yes
+9957-YODKZ,Male,1,Yes,No,6,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,80.8,457.1,No
+6518-KZXCB,Male,0,No,No,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.25,566.5,No
+2245-ADZFJ,Female,0,Yes,Yes,31,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),80.55,2471.6,No
+7776-QGYJC,Female,0,Yes,Yes,39,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),81.5,3107.3,No
+9313-QOLTZ,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.9,518.3,No
+9651-GTSAQ,Female,0,Yes,No,53,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106.1,5769.75,Yes
+3186-BAXNB,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,No,Electronic check,91.7,91.7,Yes
+4672-FOTSD,Male,0,No,No,12,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,67.25,832.3,No
+0637-YLETY,Female,0,No,No,16,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),95.6,1555.65,Yes
+9818-XQCUV,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,45.3,No
+7338-ERIVA,Male,0,No,Yes,39,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,One year,No,Bank transfer (automatic),45.05,1790.6,No
+1157-BQCUW,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,74.95,Yes
+8259-NFJTV,Female,0,Yes,Yes,7,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,34.65,246.6,No
+3223-DWFIO,Male,1,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.35,261.65,No
+2660-EMUBI,Male,1,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.35,898.35,Yes
+6968-GMKPR,Female,0,No,No,55,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.55,4509.5,No
+4751-ERMAN,Male,0,Yes,Yes,72,Yes,No,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),75.4,5480.25,No
+1436-ZMJAN,Female,0,Yes,No,10,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),67.8,653.15,No
+3292-PBZEJ,Male,1,No,No,11,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,111.4,1183.05,No
+0799-DDIHE,Female,0,Yes,Yes,15,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,46.3,639.45,No
+3070-FNFZQ,Female,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.4,478.75,No
+2812-SFXMJ,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No
+7675-OZCZG,Female,1,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45,127.1,Yes
+5014-WUQMG,Male,0,Yes,Yes,47,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,96.1,4391.45,No
+5312-TSZVC,Female,0,No,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,270.6,No
+2003-CKLOR,Male,0,No,No,66,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,99.5,6710.5,Yes
+0993-OSGPT,Female,1,Yes,No,68,Yes,No,DSL,No,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),60.65,3975.9,No
+9254-RBFON,Female,0,Yes,Yes,17,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),98.6,1704.95,Yes
+1205-WNWPJ,Female,0,No,No,7,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,59.5,415.95,Yes
+9391-EOYLI,Male,1,Yes,No,12,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.45,950.2,Yes
+7108-DGVUU,Female,1,Yes,No,21,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,One year,No,Bank transfer (automatic),71.7,1497.05,No
+2782-JEEBU,Male,0,No,No,21,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,36,780.15,No
+5127-BZENZ,Female,0,Yes,Yes,56,Yes,Yes,DSL,Yes,No,No,No,Yes,No,One year,No,Bank transfer (automatic),65.2,3512.15,No
+2720-FVBQP,Female,0,Yes,Yes,6,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),48.95,273.25,No
+9906-NHHVC,Female,1,No,No,65,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),53.5,3517.9,No
+4522-XRWWI,Male,0,Yes,No,42,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Credit card (automatic),80.45,3375.9,No
+3766-EJLFL,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.05,7508.55,No
+5939-SXWHM,Male,0,Yes,Yes,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26.3,1245.05,No
+8152-UOBNY,Female,1,No,No,50,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,106.8,5347.95,No
+7351-KYHQH,Female,1,No,No,7,Yes,No,DSL,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,64.95,493.65,No
+7643-RCHXS,Female,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,19.35,1263.85,No
+8246-SHFGA,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),21.1,385.55,No
+8387-MOJJT,Female,0,Yes,Yes,42,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),77.95,3384,Yes
+0620-XEFWH,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.85,84.2,No
+6485-QXWWE,Female,0,No,Yes,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26,1638.7,No
+2761-OCIAX,Male,1,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,165.4,Yes
+7321-VGNKU,Female,0,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.35,120.25,No
+5327-CNLUQ,Male,0,Yes,No,48,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),96.9,4473.45,No
+7552-KEYGT,Male,0,Yes,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.55,520.55,No
+5816-JMLGY,Female,0,Yes,Yes,70,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.4,5717.85,No
+3068-OMWZA,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,88.8,88.8,Yes
+2927-QRRQV,Male,0,Yes,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,94.65,4312.5,No
+6032-KRXXO,Male,0,No,No,30,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.25,2755.35,Yes
+7459-RRWQZ,Female,0,No,No,15,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),64.65,994.55,Yes
+6265-SXWBU,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,No,Credit card (automatic),95.75,6511.25,No
+7941-RCJOW,Male,0,No,No,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.55,1218.65,No
+6374-NTQLP,Male,1,Yes,Yes,72,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104.1,7447.7,No
+4154-AQUGT,Male,1,Yes,No,13,Yes,No,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.05,1169.35,Yes
+2387-KDZQY,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.1,279.5,No
+3584-WKTTW,Male,0,Yes,No,51,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.55,5720.35,No
+3399-BMLVW,Male,0,Yes,Yes,51,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Credit card (automatic),60.5,3121.45,No
+1971-DTCZB,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),90.95,6468.6,No
+3092-IGHWF,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Electronic check,87.4,5918.8,Yes
+3374-PZLXD,Male,0,No,No,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,675.6,No
+3813-DHBBB,Male,0,Yes,No,67,No,No phone service,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),50.95,3521.7,No
+2812-REYAT,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.05,923.1,No
+6518-PPLMZ,Male,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.4,1110.35,No
+4939-KYYPY,Male,0,No,No,27,Yes,Yes,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,59.45,1611.65,No
+8017-LXHFA,Female,1,No,No,23,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,2293.6,Yes
+5930-GBIWP,Male,0,No,No,69,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),81.5,5553.25,No
+6022-KOUQO,Female,0,Yes,Yes,2,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),29.05,44.75,No
+6352-TWCAU,Female,0,No,No,35,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.45,3029.1,Yes
+2361-UPSND,Female,0,Yes,No,46,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,One year,No,Mailed check,70.6,3231.05,No
+6035-RIIOM,Female,0,No,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),97.2,5129.45,No
+2929-QNSRW,Female,0,Yes,No,56,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,98.25,5508.35,Yes
+1262-OPMFY,Female,0,Yes,No,9,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.75,655.9,Yes
+9504-DSHWM,Male,0,No,No,20,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),59.2,1191.2,No
+5035-BVCXS,Male,0,No,No,11,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,75.9,866.4,No
+6267-DCFFZ,Female,1,Yes,No,30,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.05,2627.2,No
+3533-UVMOM,Male,0,Yes,No,68,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Two year,No,Bank transfer (automatic),70.95,4741.45,No
+2439-LYPMQ,Male,1,Yes,No,38,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.6,4009.2,No
+4248-QPAVC,Female,1,Yes,No,17,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),85.35,1463.45,Yes
+1899-VXWXM,Male,0,No,No,48,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),106.1,5082.8,Yes
+1478-VPOAD,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,43.8,43.8,No
+9995-HOTOH,Male,0,Yes,Yes,63,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,59,3707.6,No
+2988-PLAHS,Female,0,No,No,3,Yes,No,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,69.95,220.45,No
+1371-OJCEK,Female,0,No,No,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.35,1133.7,No
+4999-IEZLT,Male,0,No,No,66,No,No phone service,DSL,No,No,No,Yes,No,No,One year,No,Credit card (automatic),29.45,1983.15,No
+8883-ANODQ,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Two year,Yes,Credit card (automatic),84.4,5746.75,No
+4690-LLKUA,Male,1,No,No,17,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.05,770.6,Yes
+2351-RRBUE,Female,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,134.05,No
+5980-BDHPY,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),87.1,6230.1,No
+1498-DQNRX,Female,0,Yes,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,573.05,No
+9469-WEJBT,Male,0,Yes,No,37,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,90.35,3419.3,No
+3331-HQDTW,Female,0,No,No,34,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,109.8,3587.25,Yes
+9490-DFPMD,Female,1,No,No,42,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.65,3541.35,Yes
+2581-VKIRT,Female,0,Yes,Yes,59,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,65.5,3801.3,No
+5442-XSDCW,Male,0,Yes,Yes,11,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),79.5,868.5,Yes
+7426-WEIJX,Male,1,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.95,4859.1,No
+2851-MMUTZ,Female,0,No,No,27,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,56.15,1439.35,No
+3049-NDXFL,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,85.8,85.8,Yes
+8580-AECUZ,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.1,79.1,Yes
+3307-TLCUD,Male,0,Yes,No,17,No,No phone service,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,34.4,592.75,No
+6625-FLENO,Male,0,Yes,No,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.75,1185.95,No
+2967-MXRAV,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,18.8,18.8,No
+7963-GQRMY,Female,0,Yes,Yes,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.3,134.5,Yes
+8189-HBVRW,Female,0,No,No,53,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,90.8,4921.2,No
+4163-KIUHY,Male,0,No,No,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,25.6,901.25,No
+1228-FZFRV,Male,0,Yes,Yes,50,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),105.95,5341.8,Yes
+3500-NSDOA,Male,0,Yes,Yes,68,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,Two year,No,Credit card (automatic),70.8,4859.95,No
+1171-TYKUR,Male,0,Yes,No,47,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,25.4,1139.2,No
+3761-FLYZI,Female,1,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),108.8,7082.45,No
+2058-DCJBE,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.75,324.6,Yes
+5364-XYIRR,Male,0,Yes,No,51,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.65,4812.75,Yes
+4829-AUOAX,Female,0,No,No,46,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),96.05,4399.5,Yes
+1219-NNDDO,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.85,663.55,No
+8388-DMKAE,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.25,174.65,No
+4403-BWPAY,Male,0,No,No,14,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),24.8,321.7,No
+9659-QEQSY,Female,0,No,No,45,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,115.65,5125.5,No
+5405-ZMYXQ,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.6,548.9,No
+5047-LHVLY,Male,1,No,Yes,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,50.15,50.15,Yes
+1442-OKRJE,Male,0,Yes,Yes,66,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),103.15,7031.3,No
+4737-AQCPU,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),72.1,5016.65,No
+9158-VCTQB,Female,0,Yes,No,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),113.6,4594.95,Yes
+2808-CHTDM,Female,0,Yes,Yes,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.1,611.45,No
+6311-UEUME,Female,0,No,No,29,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,78.9,2384.15,Yes
+0793-TWELN,Female,0,No,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.15,319.85,No
+3283-WCWXT,Male,0,Yes,Yes,6,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.4,153.3,No
+1060-ENTOF,Female,1,Yes,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),105.4,7035.6,No
+0999-QXNSA,Male,1,No,Yes,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.75,344.2,No
+5451-MHQOF,Male,0,Yes,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,24.45,1431.65,No
+4836-WNFNO,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25,1849.2,No
+9225-BZLNZ,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,85.25,6083.1,No
+0354-VXMJC,Male,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.6,426.65,No
+4422-QVIJA,Female,0,No,Yes,35,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.15,1655.35,No
+9365-SRSZE,Male,1,Yes,No,27,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.55,1943.9,No
+6839-ITVZJ,Female,0,Yes,Yes,26,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,60.05,1616.15,Yes
+8332-OSJDW,Male,0,Yes,Yes,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,26.4,314.95,No
+4735-BJKOU,Female,0,No,No,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,804.85,No
+0274-JKUJR,Male,0,Yes,Yes,7,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,58.85,465.7,No
+5740-YHGTW,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),97.55,6669.05,No
+8917-SZTTJ,Male,0,Yes,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,1161.75,No
+1696-MZVAU,Male,0,Yes,Yes,39,No,No phone service,DSL,No,No,No,No,No,No,One year,Yes,Credit card (automatic),25.25,947.75,No
+7359-WWYJV,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),114.45,8375.05,No
+0375-HVGXO,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,34.7,34.7,Yes
+4906-ZHGPK,Male,0,Yes,Yes,54,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,70.7,3770,No
+8593-WHYHV,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.3,264.8,Yes
+3795-GWTRD,Female,0,Yes,Yes,63,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),75.55,4707.85,No
+1298-PHBTI,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Two year,Yes,Electronic check,84.8,6152.4,No
+6223-DHJGV,Female,0,No,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.65,958.1,No
+6961-MJKBO,Male,0,No,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.45,943,No
+6097-EQISJ,Female,0,Yes,Yes,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),102.45,6615.15,Yes
+4423-YLHDV,Female,0,Yes,No,21,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.4,2200.25,Yes
+8158-WPEZG,Male,0,No,No,11,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,35.65,425.1,No
+0107-YHINA,Male,0,No,Yes,1,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.75,99.75,Yes
+4918-FYJNT,Female,1,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,90.45,5044.8,No
+0727-BNRLG,Male,0,No,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),97.65,6743.55,No
+4854-CIDCF,Female,1,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,73.85,196.4,No
+8640-SDGKB,Male,0,No,No,4,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,74.4,299.7,Yes
+3280-MRDOF,Male,1,No,No,30,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),69.1,2093.9,No
+6435-SRWBJ,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,82.75,417.75,No
+9964-WBQDJ,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.4,1725.4,No
+6303-KFWSL,Female,0,No,No,29,Yes,Yes,DSL,No,Yes,No,No,No,No,One year,Yes,Electronic check,55.25,1620.2,No
+1702-CCFNJ,Male,0,Yes,No,52,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),61.35,3169.55,No
+8932-CZHRQ,Male,0,No,No,68,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Credit card (automatic),76.75,5233.25,No
+0386-CWRGM,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.4,967.85,No
+5515-RUGKN,Male,0,No,No,8,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),54.75,438.05,No
+0404-AHASP,Male,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,1421.9,No
+7279-NMVJC,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.9,323.15,No
+2081-VEYEH,Male,0,No,No,3,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,107.95,318.6,No
+6407-UTSLV,Female,1,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),83.8,163.7,No
+4116-TZAQJ,Female,0,No,No,9,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.25,639.65,Yes
+9060-HJJRW,Female,0,No,No,51,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),56.4,2928.5,No
+2587-YNLES,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,100.35,No
+7398-SKNQZ,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,94.9,273.2,No
+5935-FCCNB,Female,1,No,No,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,1608.15,No
+1958-RNRKS,Male,0,Yes,No,30,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,49.9,1441.95,No
+5136-RGMZO,Male,0,No,No,31,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,71.05,2168.15,No
+8345-MVDYC,Female,0,No,No,45,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),81.65,3618.7,No
+8226-BXGES,Male,0,Yes,No,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),89.45,5692.65,No
+3877-JRJIP,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,59.85,59.85,Yes
+8375-DKEBR,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,69.6,Yes
+9705-IOVQQ,Male,1,Yes,Yes,61,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Electronic check,99,5969.3,No
+1015-OWJKI,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.05,19.05,No
+7511-YMXVQ,Male,0,No,No,9,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,45.4,418.8,Yes
+2040-XBAVJ,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),114.45,8100.55,No
+7551-JOHTI,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.5,19.5,Yes
+8887-IPQNC,Female,0,Yes,No,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.25,313.45,No
+8646-JCOMS,Female,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),90.55,6130.95,No
+9804-ICWBG,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.9,69.9,Yes
+1222-KJNZD,Male,0,Yes,Yes,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.4,745.3,No
+0106-GHRQR,Male,0,No,No,16,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),71.4,1212.1,No
+5318-IXUZF,Female,0,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),87.15,183.75,Yes
+3768-VHXQO,Male,0,Yes,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.85,1583.5,No
+8952-WCVCD,Female,0,Yes,No,41,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.45,4162.05,No
+2418-TPEUN,Female,0,Yes,Yes,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.8,1119.9,No
+3963-RYFNS,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.45,8013.55,No
+3198-VELRD,Female,0,Yes,Yes,3,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),84.75,264.85,Yes
+8540-ZQGEA,Female,0,Yes,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,1102.4,No
+1320-REHCS,Male,1,No,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.75,5832,No
+4137-JOPHL,Female,0,No,No,50,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),89.7,4304.5,No
+9436-ZBZCT,Male,0,No,No,14,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,89.95,1178.4,Yes
+7801-CEDNV,Male,0,Yes,No,27,Yes,No,DSL,Yes,No,No,No,No,No,One year,No,Credit card (automatic),48.7,1421.75,No
+2057-BOYKM,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),96.6,6827.5,No
+3658-QQJYD,Male,0,No,No,62,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),74.3,4698.05,No
+1803-BGNBD,Female,0,No,No,12,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,54.3,654.5,No
+0134-XWXCE,Female,1,No,No,44,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),74.85,3268.05,No
+6950-TWMYB,Male,0,Yes,Yes,54,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),79.95,4362.05,No
+5848-FHRFC,Female,0,No,No,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.05,1386.9,No
+2243-FNMMI,Male,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,415.4,No
+2511-MORQY,Male,0,Yes,Yes,50,Yes,Yes,DSL,No,No,Yes,No,No,No,One year,No,Bank transfer (automatic),54.9,2614.1,No
+5356-KZCKT,Male,0,No,No,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.45,1513.6,No
+9470-XCCEM,Male,0,Yes,Yes,35,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.65,3161.6,No
+6519-CFDBX,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.4,80.95,No
+3902-MIVLE,Male,0,Yes,Yes,63,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Mailed check,75.7,4676.7,No
+0409-WTMPL,Female,0,Yes,No,58,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),110.65,6526.65,No
+8763-KIAFH,Female,0,Yes,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.55,583.3,No
+3669-WHAFY,Female,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.15,8078.1,No
+3055-VTCGS,Female,0,No,No,63,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,No,Credit card (automatic),58.55,3503.5,No
+3144-KMTWZ,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),93.25,6669.45,No
+7279-BUYWN,Female,1,No,No,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,113.2,4689.5,Yes
+7156-MHUGY,Male,1,No,No,13,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.5,1201.15,Yes
+7198-GLXTC,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,79,143.65,Yes
+2007-QVGAW,Female,0,Yes,Yes,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.35,1292.65,No
+5207-PLSTK,Male,0,Yes,Yes,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,48.75,48.75,No
+2307-FYNNL,Male,1,No,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,109.05,7108.2,No
+5605-XNWEN,Male,1,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25,1802.55,No
+2155-AMQRX,Female,0,No,No,28,Yes,Yes,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),54.9,1505.15,No
+6181-AXXYF,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.75,1859.1,No
+5091-HFAZW,Female,0,No,No,2,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,91.15,168.5,No
+0516-VRYBW,Female,0,No,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.15,390.85,Yes
+2519-LBNQL,Male,1,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.35,6339.45,No
+8623-ULFNQ,Female,1,No,No,26,Yes,Yes,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,66.05,1652.4,No
+8380-PEFPE,Male,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.65,71.65,Yes
+5687-DKDTV,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,77.5,Yes
+1568-LJSZU,Male,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),92.2,6392.85,No
+7530-HDYDS,Female,0,No,No,38,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),84.25,3264.5,Yes
+7789-HKSBS,Female,1,Yes,No,42,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),105.2,4599.15,No
+7416-CKTEP,Female,0,Yes,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.6,1134.25,No
+2586-CWXVV,Male,0,Yes,No,54,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,30.4,1621.35,No
+3096-IZETN,Female,0,No,No,12,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),78.1,947.3,Yes
+2348-KCJLT,Female,0,Yes,No,44,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Mailed check,61.5,2722.2,No
+8401-EMUWF,Male,0,Yes,Yes,42,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),69.4,3058.3,No
+4193-IBKSW,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.75,1769.6,No
+5377-NDTOU,Female,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,91.05,6293.75,No
+5922-ABDVO,Female,0,Yes,No,19,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Mailed check,89.65,1761.05,Yes
+2474-LCNUE,Female,0,Yes,No,23,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),73.65,1642.75,No
+0839-QNXME,Female,0,No,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,578.5,No
+3506-OVLKD,Male,0,No,No,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),26.2,954.9,No
+9172-ANCRX,Female,0,No,No,10,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.7,973.25,Yes
+6650-VJONK,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),43.85,43.85,No
+2178-PMGCJ,Male,0,No,No,22,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.7,1490.4,No
+7492-TAFJD,Male,0,Yes,Yes,7,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Mailed check,38.55,280,No
+2773-MADBQ,Female,0,No,No,36,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,53.1,1901.25,No
+6016-LVTJQ,Female,0,Yes,Yes,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,716.4,No
+7860-KSUGX,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Credit card (automatic),64.45,4720,No
+8966-KZXXA,Male,0,Yes,Yes,36,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.1,930.95,No
+6910-HADCM,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,76.35,76.35,Yes
+4816-LXZYW,Female,0,No,No,23,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.15,1676.95,Yes
+9606-PBKBQ,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85,2642.05,Yes
+5149-QYTTU,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),95.15,6770.85,No
+2070-XYMFH,Female,1,No,No,23,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.35,1835.3,No
+2085-BOJKI,Male,0,Yes,No,17,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),96.65,1588.25,No
+0817-HSUSE,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,75.5,75.5,No
+5442-PPTJY,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,258.35,No
+1927-QEWMY,Female,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.5,1502.25,No
+1663-MHLHE,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.2,19.2,No
+5663-QBGIS,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,No,Bank transfer (automatic),98.35,6929.4,No
+4450-MDZFX,Male,0,Yes,Yes,60,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),74.35,4453.3,No
+6701-DHKWQ,Female,0,Yes,Yes,61,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),51.35,3244.4,No
+7554-AKDQF,Female,0,Yes,No,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.65,323.45,No
+3536-IQCTX,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.3,2661.1,Yes
+4911-BANWH,Female,0,No,Yes,31,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),86.55,2697.4,Yes
+8496-EJAUI,Male,0,No,No,19,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.85,1424.5,Yes
+0794-YVSGE,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.3,1401.15,No
+5423-BHIXO,Female,0,No,No,32,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,54.2,1739.6,No
+6908-VVYHM,Male,0,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.65,5931,No
+2959-EEXWB,Female,0,Yes,Yes,45,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),50.9,2333.85,No
+1839-UMACK,Male,0,No,No,42,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,25.05,949.85,No
+3030-YDNRM,Male,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.85,572.45,No
+7321-KKSDU,Male,0,No,Yes,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.5,696.8,No
+3402-XRIUO,Female,1,Yes,No,22,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,63.55,1381.8,No
+3132-TVFDZ,Male,1,Yes,No,57,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.85,2572.95,Yes
+8286-AFUYI,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,47.95,47.95,No
+8080-DDEMJ,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.1,45.1,Yes
+8356-WUAOJ,Female,0,Yes,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45,45,No
+6365-MTGZX,Male,0,No,No,24,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,96,2122.45,Yes
+1349-WXNGG,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.05,20.05,Yes
+8058-DMYRU,Male,1,No,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.05,4931.8,No
+9350-ZXYJC,Female,0,No,No,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.3,116.95,No
+6990-YNRIO,Male,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),108.65,6937.95,Yes
+8958-JPTRR,Female,0,Yes,No,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.3,1261.7,No
+6959-GQEGV,Male,0,No,No,45,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,75.95,3273.8,No
+3173-WSSUE,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,1415.85,No
+1265-HVPZB,Male,0,Yes,No,59,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Credit card (automatic),66.4,3958.2,No
+4115-UMJFQ,Male,0,No,No,69,No,No phone service,DSL,No,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),35.75,2492.25,No
+7369-TRPFD,Male,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,18.8,279.2,No
+1098-KFQEC,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,1083,No
+7190-XHTWJ,Female,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.3,755.5,No
+0621-TWIEM,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.55,402.6,Yes
+3537-RYBHH,Female,1,Yes,No,47,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),67.45,3252,No
+2485-ITVKB,Female,0,Yes,No,2,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.1,68.75,Yes
+3669-OYSJI,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),46.2,46.2,Yes
+1612-EOHDH,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.15,45.15,Yes
+6702-OHFWR,Male,1,No,No,1,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,43.3,43.3,Yes
+5296-BFCYD,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.1,936.85,No
+4510-PYUSH,Female,1,No,No,38,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),57.15,2250.65,Yes
+9359-JANWS,Female,0,Yes,No,65,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),58.9,3857.1,No
+7517-SAWMO,Female,0,Yes,No,19,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.2,1441.1,Yes
+4143-HHPMK,Male,0,No,No,52,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.35,4338.6,Yes
+3279-DYZQM,Male,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.45,1378.45,No
+7054-LGEQW,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.95,45.95,Yes
+0523-VNGTF,Female,1,No,No,52,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,50.5,2566.3,No
+9575-IWCAZ,Male,0,Yes,No,6,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,25.1,171,No
+7105-MXJLL,Female,1,Yes,No,26,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,No,Mailed check,60.7,1597.4,No
+7064-FRRSW,Male,1,No,No,48,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,99,4744.35,No
+7940-UQQUG,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.4,6721.6,No
+0923-PNFUB,Female,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,83.75,247.25,Yes
+3961-SXAXY,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.05,44.05,No
+7010-BRBUU,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.1,1734.65,No
+3566-HJGPK,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.55,45.55,No
+3062-ICYZQ,Female,0,Yes,Yes,51,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,93.8,4539.35,No
+9938-PRCVK,Female,0,Yes,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.7,804.25,No
+0973-KYVNF,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),70.65,5011.15,No
+5129-HHMZC,Female,0,Yes,No,43,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.45,3574.5,No
+9637-CDTKZ,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),114.1,8086.4,No
+3946-JEWRQ,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),95.2,4563,No
+7873-CVMAW,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88.55,6362.35,No
+0463-WZZKO,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.75,67.1,No
+3494-JCHRQ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.05,70.05,Yes
+6474-FVJLC,Male,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86,165.45,Yes
+4524-QCSSM,Male,0,No,No,26,No,No phone service,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),44.65,1156.55,No
+5832-EXGTT,Male,0,Yes,Yes,29,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,60.2,1834.15,No
+8840-DQLGN,Female,1,Yes,No,35,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),100.5,3653.35,Yes
+2039-JONDJ,Male,0,No,No,27,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Bank transfer (automatic),55.45,1477.65,No
+7217-JYHOQ,Male,0,Yes,Yes,24,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),70.3,1706.45,No
+6695-FRVEC,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),60.4,3953.7,No
+4547-LYTDD,Female,0,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,72.65,1194.3,Yes
+9894-QMIMJ,Female,0,No,No,23,Yes,No,DSL,No,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),55.8,1327.85,No
+8069-YQQAJ,Male,0,No,No,14,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,31.1,419.7,No
+6770-XUAGN,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,21,21,Yes
+4193-ORFCL,Female,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.1,45.1,Yes
+1636-NTNCO,Male,1,No,No,4,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),50.95,207.35,No
+3466-WAESX,Male,0,No,Yes,16,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,69.1,1083.7,No
+9281-PKKZE,Female,0,Yes,No,46,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,43.95,2007.85,No
+3638-VBZTA,Male,0,No,Yes,68,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),86.5,5882.75,No
+7459-IMVYU,Male,0,No,No,38,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,69.95,2657.55,No
+7776-QWNFX,Male,1,Yes,No,30,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),50.4,1527.5,Yes
+6689-TCZHQ,Female,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,78.95,378.4,Yes
+8563-OYMQY,Male,0,No,No,17,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.95,1612.2,No
+0754-EEBDC,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,76.65,No
+5777-ZPQNC,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20.15,260.7,No
+1951-IEYXM,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.6,6441.85,No
+3318-NMQXL,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92,266.8,No
+3143-ILDAL,Male,0,No,No,56,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,94.45,5124.6,Yes
+1022-RKXDR,Female,0,No,No,41,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,24.85,962.25,No
+2361-FJWNO,Male,0,No,No,40,No,No phone service,DSL,No,Yes,No,Yes,No,No,One year,No,Credit card (automatic),36,1382.9,No
+2272-UOINI,Female,0,No,No,7,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,78.5,571.05,No
+8232-UTFOZ,Male,0,No,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.95,1399.35,No
+3750-YHRYO,Male,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,150,No
+6637-KYRCV,Female,0,Yes,Yes,5,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,30.5,167.2,No
+5668-MEISB,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),106.1,7657.4,No
+0129-QMPDR,Male,0,Yes,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.5,865.05,No
+7188-CBBBA,Female,0,No,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,95.5,6153.85,No
+5356-CSVSQ,Female,0,No,No,3,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,64.6,174.2,No
+3221-CJMSG,Male,0,No,No,24,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),51.1,1269.6,No
+4720-VSTSI,Female,0,No,No,44,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),84.8,3862.55,Yes
+3219-JQRSL,Female,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.1,6352.4,No
+2801-NISEI,Male,0,No,No,24,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,54.95,1348.5,No
+3946-MHCZW,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,50.9,50.9,Yes
+4623-ZKHLY,Male,0,Yes,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.45,471.55,No
+6732-VAILE,Male,0,Yes,Yes,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.95,5931.75,No
+8201-AAXCB,Male,0,Yes,Yes,25,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,60.35,1404.65,No
+7696-CFTAT,Male,0,Yes,Yes,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.8,726.1,No
+1845-CSBRZ,Female,1,Yes,Yes,22,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),85.35,1961.6,No
+2123-VSCOT,Female,0,Yes,Yes,59,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),72.1,4194.85,No
+6651-AZVTJ,Male,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,4872.45,Yes
+4566-GOLUK,Male,0,Yes,Yes,47,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),107.35,5118.95,Yes
+2484-DGXPZ,Female,0,Yes,Yes,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.55,658.95,Yes
+2018-QKYGT,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.05,81.05,No
+2792-VPPET,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.5,76.95,No
+7409-JURKQ,Female,0,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),111.8,5809.75,No
+3247-MHJKM,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,20.2,No
+1964-SVLEA,Male,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.7,415.9,No
+4587-NUKOX,Female,0,No,No,3,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.1,246.5,Yes
+7297-DVYGA,Female,0,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.85,996.95,No
+2239-CGBUZ,Female,0,Yes,No,51,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),60.5,3145.15,No
+0854-UYHZD,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,265.3,No
+7243-LCGGZ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.9,20.9,No
+8267-KFGYD,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,21.05,21.05,No
+4890-VMUAV,Male,0,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Electronic check,71.5,4576.3,No
+9261-WDCAF,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,54.65,189.1,No
+3764-MNMOI,Male,0,No,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.2,908.15,No
+7442-YGZFK,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),49.8,49.8,No
+0420-BWTPW,Male,0,No,Yes,8,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.5,215.2,Yes
+8229-BUJHX,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.5,1500.95,No
+7449-HVPIV,Male,0,Yes,Yes,55,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90.4,5099.15,No
+5504-WSIUR,Female,0,No,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Bank transfer (automatic),90.25,6385.95,No
+8183-ONMXC,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.75,159.45,Yes
+8466-PZBLH,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),104.6,6885.75,No
+9614-RMGHA,Male,0,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),91.85,5940.85,Yes
+8735-IJJEG,Male,0,Yes,No,14,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),50.2,668.85,No
+0564-MUUQK,Female,0,Yes,Yes,20,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.5,1916.2,No
+5054-IEXZT,Male,0,No,Yes,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,75.35,75.35,Yes
+5834-ASPWA,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.45,75.45,Yes
+0701-RFGFI,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.4,4613.95,No
+0019-EFAEP,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),101.3,7261.25,No
+5619-PTMIK,Female,0,Yes,No,46,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,53.1,2459.8,No
+3737-XBQDD,Male,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),84.85,2048.8,No
+5882-CMAZQ,Female,0,Yes,Yes,5,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,34.25,163.55,No
+5846-QFDFI,Female,0,Yes,Yes,33,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),88.6,2888.7,No
+4445-KWOKW,Female,0,No,No,42,Yes,Yes,DSL,Yes,Yes,No,No,No,No,One year,Yes,Bank transfer (automatic),60.15,2421.6,No
+3511-APPBJ,Male,0,No,No,23,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),99.95,2292.75,No
+7967-HYCDE,Male,0,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.7,553.4,No
+2430-RRYUW,Male,0,No,No,66,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,Yes,Mailed check,54.8,3465.7,No
+3948-XHGNA,Male,0,No,No,24,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.55,1210.4,Yes
+3723-BFBGR,Male,1,No,No,24,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),54.8,1291.3,No
+0565-IYCGT,Male,0,No,No,69,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),78.6,5356.45,Yes
+5447-WZAFP,Female,0,No,No,53,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Mailed check,100.3,5200.8,No
+5110-CHOPY,Female,0,No,No,60,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Electronic check,53.6,3237.05,No
+5445-UTODQ,Female,0,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,81.1,576.65,Yes
+4425-OWHWB,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),19.35,433.75,No
+7892-QVYKW,Female,0,Yes,Yes,23,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.6,1868.4,No
+9675-ICXCT,Male,0,Yes,Yes,72,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,80.8,5728.55,No
+1024-VRZHF,Male,0,Yes,No,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,825.7,Yes
+4703-MQYKT,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,390.4,No
+9497-QCMMS,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.55,93.55,Yes
+5692-ICXLW,Male,1,No,No,31,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.7,2845.15,No
+0602-DDUML,Female,0,No,No,57,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,69.75,3894.4,No
+2208-MPXIO,Female,0,Yes,Yes,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20,886.4,No
+1960-UYCNN,Male,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.25,1021.55,No
+0348-SDKOL,Female,0,Yes,No,58,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.1,5885.4,Yes
+3190-FZATL,Male,0,No,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,268.4,No
+7336-RLLRH,Male,0,Yes,No,27,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Mailed check,80.85,2204.35,No
+4373-MAVJG,Female,0,Yes,Yes,14,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),90.9,1259,Yes
+8901-HJXTF,Female,0,Yes,Yes,12,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,29.2,309.1,Yes
+7710-JSYOA,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),93.3,6398.05,No
+5419-KLXBN,Female,0,Yes,Yes,25,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.15,2257.75,Yes
+3424-NMNBO,Male,1,Yes,No,58,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,108.85,6287.25,Yes
+9885-MFVSU,Female,0,Yes,Yes,35,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),46.35,1662.05,No
+4514-GFCFI,Female,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.75,1350.15,Yes
+0607-MVMGC,Male,0,Yes,Yes,45,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),78.75,3600.65,No
+3365-SAIGS,Female,0,No,No,17,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.55,1329.15,No
+9828-AOQLM,Female,0,Yes,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.7,45.7,Yes
+8022-BECSI,Male,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.6,422.5,No
+8000-REIQB,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.95,69.95,Yes
+9993-LHIEB,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Mailed check,67.85,4627.65,No
+0266-CLZKZ,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.65,6717.9,No
+7615-ESMYF,Female,0,Yes,No,2,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,44.6,97.1,Yes
+3858-VOBET,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,1710.45,Yes
+7020-OZKXZ,Female,1,No,No,9,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),75.5,637.4,No
+3977-QCRSL,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,117.95,No
+0017-DINOC,Male,0,No,No,54,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Two year,No,Credit card (automatic),45.2,2460.55,No
+1447-PJGGA,Female,0,No,No,57,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,95.25,5464.65,Yes
+8565-CLBZW,Male,0,No,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,89.85,2165.05,Yes
+9139-WQQDY,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Mailed check,100.45,4941.8,Yes
+0224-HJAPT,Male,0,No,No,5,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,47.15,223.15,Yes
+8086-OVPWV,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.2,181.1,Yes
+9430-FRQOC,Female,0,No,Yes,4,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,87.1,341.45,Yes
+7639-OPLNG,Male,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),79.25,5731.85,No
+3074-GQWYX,Male,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.9,357.75,Yes
+1492-QGCLU,Male,0,Yes,Yes,53,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,85.7,4616.1,No
+6845-RGTYS,Female,0,Yes,No,47,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,98.75,4533.7,Yes
+7328-OWMOM,Female,0,No,Yes,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.1,589.25,No
+4418-LZMSV,Male,0,Yes,Yes,13,Yes,No,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),61.8,750.1,No
+5155-AZQPB,Female,0,Yes,Yes,28,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,49.9,1410.25,No
+8861-HGGKB,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,86.45,830.85,Yes
+1087-GRUYI,Male,0,Yes,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.4,743.5,No
+7065-YUNRY,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.3,45.3,Yes
+7694-VLBWQ,Male,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,104.1,7040.85,Yes
+2546-KZAAT,Male,0,Yes,No,52,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,One year,Yes,Mailed check,75.4,3865.45,No
+0181-RITDD,Male,0,Yes,Yes,62,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,108.15,6825.65,No
+5989-PGKJB,Female,0,No,No,16,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.25,1340.1,No
+4795-KTRTH,Female,1,Yes,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81,371.65,Yes
+8272-ONJLV,Male,0,No,No,12,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Electronic check,95.7,1184,No
+1488-PBLJN,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.85,8477.7,No
+0308-GIQJT,Male,1,No,No,71,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),105.75,7382.85,No
+3778-FOAQW,Female,0,Yes,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.15,456.85,No
+4452-ROHMO,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,331.6,No
+6481-OGDOO,Male,0,Yes,No,67,Yes,No,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Credit card (automatic),90.6,6056.15,Yes
+3090-LETTY,Male,0,No,Yes,2,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),60.95,134.6,No
+5349-AZPEW,Female,0,Yes,Yes,5,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.05,125.5,No
+3753-TSEMP,Female,0,Yes,No,15,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,88.15,1390.6,Yes
+8305-VHZBZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.2,20.2,Yes
+9720-JJJOR,Male,0,Yes,Yes,41,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),60.3,2511.3,No
+8100-HZZLJ,Female,0,No,Yes,43,Yes,No,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,63.95,2737.05,No
+8775-ERLNB,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,74.3,74.3,No
+8309-IEYJD,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.6,70.6,No
+9172-JITSM,Female,0,Yes,Yes,26,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.8,2361.8,Yes
+6298-QDFNH,Male,0,No,No,22,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.35,1730.35,Yes
+7398-HPYZQ,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),90.55,6404,No
+3546-GHEAE,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.45,165.35,No
+7361-YPXFS,Female,1,No,No,28,Yes,Yes,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),64.45,1867.6,No
+6557-BZXLQ,Male,1,No,No,16,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.65,1043.3,No
+2550-QHZGP,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,128.6,No
+7519-JTWQH,Female,0,No,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.5,7455.45,No
+2538-OIMXF,Female,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.7,24.7,No
+8543-MSDMF,Male,0,No,No,3,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,77.4,206.15,No
+9961-JBNMK,Male,1,No,No,21,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),96.8,2030.3,Yes
+1170-SASML,Female,0,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),85.4,5869.4,No
+4872-JCVCA,Female,0,Yes,No,71,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),47.6,3377.8,No
+5346-BZCHP,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.4,1346.2,No
+2038-LLMLM,Female,0,No,No,48,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,103.85,4946.05,No
+6173-ITPWD,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),83.35,4065,Yes
+9734-UYXQI,Female,0,No,No,2,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.4,106.55,Yes
+1216-BGTSP,Male,0,No,No,45,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),108.45,4964.7,No
+4138-NAXED,Male,0,No,No,51,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),81,4085.75,No
+2189-UXTKY,Female,0,Yes,No,22,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,79.2,1742.75,Yes
+0744-BIKKF,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.65,6224.8,No
+7483-IQWIB,Male,0,Yes,Yes,37,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,92.95,3415.25,No
+5248-KWLAR,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,90.35,6325.25,No
+4958-GZWIY,Male,0,Yes,Yes,7,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,48.7,340.25,Yes
+7996-MHXLW,Female,0,No,No,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.15,1683.6,No
+7833-PKIHD,Male,0,Yes,Yes,51,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),76.4,3966.3,No
+7061-OVMIM,Female,0,Yes,Yes,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.55,608.5,No
+5153-RTHKF,Female,0,No,No,34,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,85.35,2896.6,No
+1852-QSWCD,Male,0,Yes,Yes,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.8,1514.85,No
+4832-VRBMR,Male,1,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),103.15,6792.45,No
+9079-LWTFD,Male,0,No,No,47,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,No,Mailed check,100.75,4669.2,No
+6356-ELRKD,Female,0,No,No,1,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.6,95.6,Yes
+8624-GIOUT,Female,0,No,No,49,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),59.75,2934.3,Yes
+3392-EHMNK,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),94.1,6302.8,No
+5986-WWXDV,Male,0,No,Yes,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.35,779.2,No
+3061-BCKYI,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,283.75,No
+6179-GJPSO,Female,1,No,No,43,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),108.15,4600.7,Yes
+7901-TBKJX,Male,1,No,No,56,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.05,5594,No
+7228-PAQPD,Female,0,No,No,14,Yes,No,DSL,No,No,No,Yes,No,Yes,One year,No,Credit card (automatic),59.1,772.85,No
+3177-LASXD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,71.35,71.35,Yes
+7746-QYVCO,Male,0,Yes,Yes,16,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,Yes,Mailed check,55.85,857.8,No
+5804-HYIEZ,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.05,7554.05,No
+9919-FZDED,Male,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),84.1,5981.65,No
+5934-TSSAU,Female,0,Yes,Yes,23,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),75.3,1702.9,No
+3486-KHMLI,Male,0,No,Yes,21,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.7,467.15,No
+4897-QSUYC,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.15,20.15,Yes
+1084-UQCHV,Male,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69.75,69.75,Yes
+8290-YWKHZ,Female,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.2,2931,Yes
+2955-BJZHG,Male,0,Yes,Yes,17,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,80.85,1400.85,Yes
+3806-DXQOM,Female,0,No,No,4,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,33.65,137.85,Yes
+6784-XYJAE,Female,0,No,No,36,Yes,Yes,DSL,No,No,Yes,No,No,No,One year,No,Electronic check,55.8,1941.5,No
+3933-DQPWX,Female,0,No,No,50,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),39.7,1932.75,No
+6661-EIPZC,Female,0,Yes,Yes,48,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),29.5,1423.05,No
+8957-THMOA,Female,0,No,Yes,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.15,970.85,No
+2251-PYLPB,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),79.55,5810.9,No
+5555-RNPGT,Male,0,No,Yes,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),24.8,223.9,No
+1057-FOGLZ,Female,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,391.7,No
+9300-RENDD,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.95,79.95,Yes
+0761-AETCS,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.3,19.3,Yes
+8087-LGYHQ,Male,0,No,No,9,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.05,811.65,No
+4137-BTIKL,Male,0,No,No,2,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Mailed check,90.75,174.75,No
+2190-BCXEC,Female,0,Yes,No,40,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),78.85,3126.85,No
+6227-FBDXH,Male,0,Yes,No,69,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),99.5,6841.45,No
+2153-MREFK,Female,0,Yes,No,37,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,One year,Yes,Electronic check,99.2,3754.6,Yes
+2911-WDXMV,Male,0,No,Yes,18,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),80.55,1406.65,No
+7206-PQBBZ,Male,1,Yes,No,11,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.2,834.7,No
+3106-ULWFW,Female,0,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.2,627.4,Yes
+0925-VYDLG,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.25,242,Yes
+4547-FZJWE,Male,0,Yes,Yes,55,Yes,No,DSL,Yes,No,No,No,No,Yes,One year,No,Credit card (automatic),59.45,3157,No
+7422-WNBTY,Male,0,Yes,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,93.35,3092,No
+0842-IWYCP,Female,0,No,No,46,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.95,2168.9,No
+3521-HTQTV,Male,0,No,No,34,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26.1,980.35,No
+3744-ZBHON,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.2,65.95,No
+3373-DIUUN,Male,0,Yes,Yes,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,21.25,711.9,No
+8383-SGHJU,Female,0,No,No,33,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,No,Electronic check,59.4,1952.8,No
+7607-QKKTJ,Male,0,Yes,Yes,45,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),95,4368.85,No
+7707-PYBBH,Male,0,No,No,40,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,61.9,2647.1,No
+8984-HPEMB,Female,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,118.65,8477.6,No
+4349-GFQHK,Male,0,No,No,1,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,54.35,54.35,Yes
+4139-DETXS,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),64.45,4528,No
+9779-DPNEJ,Female,0,Yes,Yes,22,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.15,1790.65,No
+9805-FILKB,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.2,845.6,No
+5793-YOLJN,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,21,1210.3,No
+0673-IGUQO,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,20.45,No
+4123-FCVCB,Female,0,No,No,12,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,75.85,854.45,No
+8819-IMISP,Male,0,No,No,31,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.45,2429.1,No
+7802-EFKNY,Male,0,Yes,No,5,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.95,100.4,Yes
+8311-UEUAB,Female,0,Yes,Yes,67,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,Two year,Yes,Electronic check,75.5,5229.45,No
+5858-EAFCZ,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.45,44.45,No
+8035-BUYVG,Male,0,Yes,No,40,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Electronic check,42.35,1716.45,Yes
+1163-ONYEY,Male,0,Yes,Yes,41,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),74.55,3023.55,No
+9787-XVQIU,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.3,75.3,Yes
+8945-MUQUF,Male,0,No,No,51,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,Yes,Electronic check,94.8,4837.6,Yes
+5656-MJEFC,Male,0,Yes,Yes,42,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),48.15,2032.3,No
+6082-OQFBA,Male,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,436.9,No
+8051-HJRLT,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.55,70.55,Yes
+8974-OVACP,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,20.15,No
+4010-YLMVT,Female,0,No,No,56,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),106.6,5893.95,No
+1379-FRVEB,Male,0,No,Yes,15,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,91,1430.05,No
+8612-GXIDD,Male,0,Yes,Yes,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.4,313,No
+6288-CHQJB,Female,0,Yes,Yes,54,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),69.95,3871.85,No
+8160-HOWOX,Female,0,No,No,7,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,66.85,458.1,No
+3023-GFLBR,Female,0,Yes,Yes,33,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Credit card (automatic),86.15,2745.7,Yes
+6648-INWPS,Male,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.15,341.35,No
+4223-BKEOR,Female,0,No,Yes,21,Yes,No,DSL,Yes,No,Yes,No,No,Yes,One year,No,Mailed check,64.85,1336.8,No
+4079-VTGLK,Male,1,Yes,No,30,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Two year,No,Electronic check,74.85,2181.75,No
+1763-WQFUK,Male,0,No,No,3,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.5,147.75,No
+1391-UBDAR,Male,0,No,No,11,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,72.9,818.45,No
+8894-JVDCV,Female,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),115.05,7133.45,No
+2023-VQFDL,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19,348.8,No
+1345-GKDZZ,Male,0,No,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,128.6,No
+2014-MKGMH,Female,0,No,No,46,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,101.1,4674.4,No
+5628-FCGYG,Male,0,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.1,1737.45,No
+2560-WBWXF,Male,0,No,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.15,1498.85,No
+0248-IPDFW,Female,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.1,50.1,No
+7978-DKUQH,Female,0,Yes,No,25,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.6,1797.75,No
+4335-UPJSI,Female,0,No,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,498.1,No
+0524-IAVZO,Female,0,Yes,No,30,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85,2624.25,Yes
+2737-YNGYW,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.55,184.1,Yes
+1784-EZDKJ,Male,0,Yes,No,51,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106.8,5498.8,No
+9297-FVVDH,Female,0,Yes,Yes,57,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,84.5,4845.4,No
+8007-YYPWD,Female,0,No,No,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.05,369.1,No
+7101-HRBLJ,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),83.7,6096.9,No
+5159-YFPKQ,Female,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,75.8,160.75,Yes
+6635-CPNUN,Male,0,Yes,No,28,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Credit card (automatic),96.6,2684.35,No
+4021-RQSNY,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.5,3004.15,Yes
+5453-YBTWV,Male,0,Yes,Yes,70,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),101.1,6994.8,No
+5039-LZRQT,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.2,273.25,No
+2931-VUVJN,Female,1,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,94.05,5483.9,No
+9061-TIHDA,Male,1,Yes,No,13,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.25,1233.65,Yes
+8699-ASUFO,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.4,527.9,Yes
+6418-PIQSP,Female,0,Yes,No,62,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,81,4985.9,No
+8220-OCUFY,Female,0,No,No,21,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Electronic check,60.25,1258.35,No
+3995-WFCSM,Female,0,No,No,2,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,60.85,111.4,No
+1895-QTKDO,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),43.95,43.95,No
+2038-OEQZH,Male,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,86.05,308.1,No
+1178-PZGAB,Female,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.25,383.65,No
+7927-AUXBZ,Female,0,No,No,30,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,85.15,2555.9,Yes
+2626-VEEWG,Male,0,Yes,Yes,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.4,1284.2,No
+2878-RMWXY,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),102.65,7550.3,No
+1657-DYMBM,Male,0,Yes,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.9,1110.05,No
+7311-MQJCH,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.55,99.6,No
+7375-WMVMT,Male,1,Yes,No,71,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),95.5,6707.15,No
+1136-XGEQU,Female,0,Yes,Yes,50,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),84.15,4164.4,No
+2530-FMFXO,Male,0,Yes,Yes,56,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,103.2,5873.75,No
+6844-DZKRF,Male,0,No,No,2,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),50.2,109.25,No
+4695-WJZUE,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,88.55,179.25,Yes
+7619-ODSGN,Male,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,54.75,1338.15,Yes
+5970-GHJAW,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.95,862.4,No
+8879-XUAHX,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,116.25,8564.75,No
+3689-MOZGR,Female,0,No,No,29,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,31.2,926.2,No
+4195-PNGZS,Male,0,Yes,Yes,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.45,1718.2,No
+5003-XZWWO,Male,0,Yes,No,71,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),84.2,5956.85,No
+3988-RQIXO,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.3,91.3,Yes
+7622-FWGEW,Male,1,Yes,No,56,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.65,4824.45,No
+6922-NCEDI,Male,0,No,Yes,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,21.2,1238.65,No
+2514-GINMM,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.5,79.5,Yes
+9891-NQDBD,Female,0,Yes,No,28,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,25.55,672.2,No
+6131-IUNXN,Female,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,382.2,No
+8548-AWOFC,Male,0,Yes,No,66,Yes,Yes,DSL,No,No,No,Yes,No,Yes,Month-to-month,No,Electronic check,63.85,4264.6,No
+9798-DRYDS,Female,0,Yes,Yes,17,Yes,Yes,DSL,No,No,Yes,Yes,No,No,One year,Yes,Mailed check,61.95,1070.7,No
+8532-UEFWH,Male,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75,1345.85,No
+6296-DDOOR,Female,0,No,No,19,Yes,No,DSL,No,Yes,No,No,Yes,No,One year,No,Electronic check,58.2,1045.25,No
+7951-VRDVK,Female,0,No,No,36,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),85.85,3003.55,No
+5931-FLJJF,Male,1,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.1,467.55,Yes
+4815-YOSUK,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),104.9,7537.5,No
+2659-VXMWZ,Male,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,111.3,7482.1,Yes
+0380-NEAVX,Male,1,No,No,34,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,99.85,3343.15,No
+3207-OYBWH,Male,1,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.25,5427.05,Yes
+4285-GYRQC,Female,0,Yes,No,7,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,86.25,587.1,Yes
+7216-EWTRS,Female,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.8,100.8,Yes
+4365-MSDYN,Male,0,Yes,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.55,161.15,No
+7036-TYDEC,Female,0,No,No,69,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,104,7028.5,No
+5802-ADBRC,Female,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Mailed check,104.4,5232.9,No
+8076-FEZKJ,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,225.85,No
+5197-YPYBZ,Female,0,Yes,Yes,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.25,274.7,No
+8337-MSSXB,Female,0,No,No,14,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.3,1180.95,Yes
+4312-GVYNH,Female,0,Yes,No,70,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),49.85,3370.2,No
+8495-LJDFO,Female,1,No,No,64,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),108.95,7111.3,No
+0839-JTCUD,Female,0,Yes,Yes,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.9,5958.85,No
+5494-WOZRZ,Female,0,Yes,Yes,71,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),82,5999.85,No
+1302-UHBDD,Male,1,No,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,89.95,1648.45,No
+2234-EOFPT,Male,0,Yes,No,72,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),79.35,5753.25,No
+8619-IJNDK,Female,0,Yes,Yes,71,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),64.05,4492.9,No
+8378-LKJAF,Male,0,Yes,Yes,38,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),101.15,3956.7,No
+8182-BJDSI,Female,0,No,No,28,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.95,2625.55,Yes
+2528-HFYZX,Male,1,Yes,No,17,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,76.45,1233.4,Yes
+1153-GNOLC,Male,0,No,No,33,No,No phone service,DSL,No,Yes,No,No,Yes,No,One year,Yes,Electronic check,39.1,1309,No
+3298-QEICA,Female,0,Yes,Yes,23,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Mailed check,34.6,813.45,No
+0788-DXBFY,Male,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.55,1108.8,No
+3597-YASZG,Female,1,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.45,7349.35,No
+3496-LFSZU,Male,0,Yes,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.5,294.2,No
+5242-UOWHD,Male,0,Yes,Yes,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.35,929.2,No
+2482-CZGBB,Male,0,No,No,10,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70,740,Yes
+6479-SZPLM,Male,0,Yes,Yes,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.45,754.5,No
+8097-VBQTZ,Male,0,No,No,54,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,69.9,3883.3,No
+4500-HKANN,Male,0,Yes,Yes,23,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Two year,No,Mailed check,59.7,1414.2,No
+9917-KWRBE,Female,0,Yes,Yes,41,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),78.35,3211.2,No
+3420-ZDBMA,Male,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,71.45,371.6,No
+2212-LYASK,Male,0,Yes,Yes,27,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),45.85,1246.4,No
+1393-IMKZG,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,95.85,95.85,No
+8069-RHUXK,Female,0,Yes,Yes,67,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Credit card (automatic),35.7,2545.7,No
+3398-GCPMU,Female,1,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),89.55,6448.85,No
+2908-WGAXL,Female,0,Yes,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.95,1468.9,No
+3378-AJRAO,Male,0,Yes,Yes,44,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.85,1013.6,No
+1013-QCWAM,Female,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.8,6690.75,No
+0866-QLSIR,Female,0,No,No,34,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,64.4,2088.75,Yes
+6050-FFXES,Female,0,Yes,No,69,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),105.35,7240.65,No
+7181-BQYBV,Female,0,Yes,Yes,1,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.45,102.45,Yes
+0362-RAOQO,Female,0,No,No,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,830.25,No
+9554-DFKIC,Male,0,Yes,Yes,30,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),54.45,1588.7,No
+5527-ACHSO,Female,0,No,No,11,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Mailed check,70.5,829.3,No
+0829-DDVLK,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.1,302.45,No
+1399-UBQIU,Male,0,No,No,11,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,69.35,712.25,No
+1813-JLKWR,Female,0,Yes,Yes,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,1336.65,No
+0336-PIKEI,Male,1,Yes,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),74.4,5360.75,No
+7322-OCWHC,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),93.05,6735.05,No
+9537-VHDTA,Female,0,No,Yes,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,51.2,51.2,No
+4957-TIALW,Female,0,No,Yes,15,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Credit card (automatic),65.6,1010,No
+2054-PJOCK,Female,0,No,No,60,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),80.55,4847.05,No
+9150-HEPMB,Male,0,No,No,56,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,52.7,3019.7,No
+9030-QGZNL,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.85,161.65,No
+6204-IEUXJ,Female,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),80.1,217.55,Yes
+3126-WQMGH,Female,0,Yes,No,49,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),52.15,2583.75,No
+4529-CKBCL,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.2,146.05,Yes
+2506-CLAKW,Female,0,No,No,6,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),98.15,567.45,Yes
+7176-WRTNX,Male,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.95,7711.25,No
+1583-IHQZE,Male,0,No,No,12,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,112.95,1384.75,Yes
+5732-IKGQH,Male,1,Yes,No,52,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.45,5481.25,No
+9239-GZHZE,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),113.65,8124.2,No
+7205-BAIAD,Female,0,No,No,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.6,827.3,No
+0151-ONTOV,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.9,70.9,Yes
+4140-MUHUG,Female,1,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,86.85,220.95,Yes
+0093-EXYQL,Female,1,No,No,40,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.55,3673.6,No
+8064-RAVOH,Male,0,No,Yes,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,49.85,49.85,No
+0219-QAERP,Male,0,Yes,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.8,576.65,No
+0320-JDNQG,Male,0,Yes,Yes,23,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,99.85,2331.3,Yes
+7180-PISOG,Male,0,Yes,Yes,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.5,74.5,Yes
+9168-INPSZ,Female,1,Yes,No,44,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,104.15,4495.65,No
+3571-RFHAR,Male,0,No,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,109.15,6941.2,Yes
+0015-UOCOJ,Female,1,No,No,7,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,48.2,340.35,No
+5334-AFQJB,Male,1,No,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.1,1789.9,No
+2754-SDJRD,Female,1,No,No,8,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.15,908.55,No
+9578-VRMNM,Female,0,No,No,16,Yes,Yes,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),65.2,1043.35,Yes
+1587-FKLZB,Male,1,Yes,Yes,66,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.5,6822.15,Yes
+6140-QNRQQ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,71.55,71.55,Yes
+8963-JLGJT,Male,0,No,Yes,3,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,55.9,157.55,No
+4307-KTUMW,Male,0,Yes,No,53,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,93.9,5029.2,Yes
+1465-LNTLJ,Male,1,Yes,No,8,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),64.4,581.7,No
+5440-FLBQG,Male,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.4,7318.2,Yes
+2135-DQWAQ,Female,0,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Mailed check,85.3,420.45,No
+4056-QHXHZ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,107.45,7576.7,No
+7470-MCQTK,Female,0,Yes,No,13,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),48.75,633.4,Yes
+7488-MXJIV,Female,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.65,321.65,Yes
+7401-JIXNM,Female,0,Yes,Yes,54,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),91.3,4965,No
+9339-FIIJL,Male,0,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),85.95,6151.9,No
+2027-FECZV,Male,0,No,No,12,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.7,1253.9,Yes
+2672-DZUOY,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.15,25.15,Yes
+4706-DGAHW,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.2,45.2,No
+3870-MQAMG,Female,0,Yes,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,110.35,5893.15,Yes
+6670-MFRPK,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Two year,Yes,Credit card (automatic),79.2,5420.65,No
+6177-PEVRA,Female,0,No,No,48,Yes,No,DSL,Yes,Yes,No,No,No,No,Two year,No,Credit card (automatic),55.5,2627.35,No
+4800-CZMPC,Female,0,Yes,Yes,48,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),103.25,5037.55,Yes
+4813-HQMGZ,Female,0,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,90.25,743.75,No
+7579-KKLOE,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,91.25,6589.6,No
+7377-DMMRI,Male,0,No,No,2,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,47.8,92.45,Yes
+8402-EIVQS,Male,0,Yes,No,67,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),100.9,6733.15,No
+4947-DSMXK,Male,0,Yes,Yes,34,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),97.7,3410,No
+7245-NIIWQ,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.85,199.85,No
+0002-ORFBO,Female,0,Yes,Yes,9,Yes,No,DSL,No,Yes,No,Yes,Yes,No,One year,Yes,Mailed check,65.6,593.3,No
+3324-OIRTO,Male,0,Yes,Yes,71,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.65,7288.4,No
+5414-OFQCB,Male,0,No,No,57,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),90.45,5229.8,No
+4967-WPNCF,Male,0,No,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),63.7,4464.8,No
+8552-OBVRU,Female,1,Yes,Yes,48,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.5,5068.05,No
+8499-BRXTD,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,401.85,No
+9154-QDGTH,Male,0,Yes,Yes,43,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),104.3,4451.85,No
+8197-BFWVU,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),93.25,6688.95,No
+2577-GVSIL,Male,0,Yes,Yes,35,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),73.45,2661.1,No
+9367-OIUXP,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.7,73.05,No
+6770-UAYGJ,Female,0,Yes,Yes,49,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.25,1211.65,No
+6463-HHXJR,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,No,Bank transfer (automatic),100.5,7030.65,No
+7928-VJYAB,Male,0,Yes,Yes,11,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,90.6,1020.2,No
+1187-WILMM,Male,0,Yes,Yes,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),89.4,5597.65,No
+9776-OJUZI,Female,1,No,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),95.45,6223.3,No
+1306-RPWXZ,Female,0,No,Yes,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.45,1024.65,No
+8949-JTMAY,Female,0,No,No,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.6,2933.2,Yes
+2774-LVQUS,Female,1,Yes,No,15,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,83.05,1258.3,Yes
+3097-PYWXL,Female,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.95,82.9,No
+2266-SJNAT,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),109.15,7789.6,No
+0869-PAPRP,Female,1,Yes,No,26,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.7,2067,No
+4238-JSSWH,Female,1,Yes,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),102.05,3452.55,No
+2972-YDYUW,Female,0,No,No,57,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,One year,No,Electronic check,94.7,5468.95,No
+1104-FEJAM,Male,0,Yes,Yes,28,Yes,Yes,DSL,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,64.4,1802.15,No
+2809-ILCYT,Female,0,Yes,No,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,26.8,733.55,No
+5499-ECUTN,Female,0,Yes,No,47,Yes,No,DSL,Yes,No,Yes,No,Yes,No,One year,Yes,Credit card (automatic),66.05,3021.45,No
+4981-FLTMF,Female,0,Yes,Yes,57,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),65.2,3687.85,No
+9121-PHQSR,Male,1,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.05,1391.15,No
+3113-IWHLC,Male,0,No,No,5,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.8,274.35,No
+3211-ILJTT,Male,0,Yes,No,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.4,1214.05,Yes
+4612-THJBS,Female,1,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.75,5510.65,Yes
+4277-BWBML,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.95,1322.85,No
+4094-NSEDU,Female,1,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.25,1973.75,Yes
+0234-TEVTT,Female,0,Yes,Yes,48,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,One year,No,Credit card (automatic),45,2196.3,No
+4304-TSPVK,Female,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),114.9,7843.55,No
+1552-AAGRX,Female,0,No,No,30,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),106.4,3211.9,No
+2637-FKFSY,Female,0,Yes,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,46.1,130.15,No
+9796-MVYXX,Female,1,No,No,14,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,39.7,692.35,No
+7874-ECPQJ,Female,0,No,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.05,85.5,No
+0020-INWCK,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),95.75,6849.4,No
+7089-RKVSZ,Male,0,Yes,Yes,8,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.4,203.95,No
+2683-JXWQQ,Male,0,Yes,Yes,61,No,No phone service,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),33.6,2117.2,No
+9548-ZMVTX,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),90.45,6565.85,No
+8739-XNIKG,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84,424.75,No
+9755-JHNMN,Female,0,No,No,49,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),67.4,3306.85,No
+3981-QSVQI,Male,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.7,168.9,No
+2789-HQBOU,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.35,253.8,No
+9424-CMPOG,Male,0,Yes,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,197.4,No
+5067-WJEUN,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,Yes,No,No,No,No,Two year,Yes,Bank transfer (automatic),54.2,3838.2,No
+3450-WXOAT,Male,0,No,No,46,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),45.2,2065.15,No
+9251-WNSOD,Female,0,Yes,No,67,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Mailed check,75.1,5064.45,No
+6974-DAFLI,Female,0,Yes,No,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.7,1140.05,No
+2616-UUTFK,Male,0,Yes,No,33,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),72.75,2447.45,No
+7064-JHXCE,Male,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,1263.9,No
+5103-MHMHY,Female,0,No,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.95,45.95,Yes
+7989-AWGEH,Male,0,Yes,Yes,49,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,39.2,1838.15,No
+4373-VVHQL,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.75,44.75,No
+4559-UWIHT,Male,0,Yes,No,14,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,82.65,1185,No
+7268-IGMFD,Male,1,No,No,18,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),93.9,1743.9,No
+1846-XWOQN,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,70.15,Yes
+0235-KGSLC,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.55,85.55,Yes
+6650-BWFRT,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),117.15,8529.5,No
+9570-KYEUA,Male,0,No,No,64,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),99.25,6549.45,No
+6993-YGFJV,Male,0,Yes,No,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),112.55,7806.5,No
+2712-SYWAY,Female,0,No,No,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,25.7,25.7,No
+0730-BGQGF,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),90.3,6287.3,No
+5498-IBWPI,Female,0,Yes,Yes,66,Yes,No,DSL,No,No,No,Yes,No,No,One year,Yes,Credit card (automatic),49.4,3251.85,No
+9101-NTIXF,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,50.6,No
+0013-SMEOE,Female,1,Yes,No,71,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.7,7904.25,No
+9314-QDMDW,Male,0,No,No,11,Yes,No,DSL,No,Yes,No,No,Yes,No,One year,Yes,Electronic check,61.25,729.95,No
+9308-ANMVE,Male,0,No,Yes,47,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,55.3,2654.05,No
+2884-GBPFB,Female,0,Yes,No,35,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,2416.55,Yes
+3757-NJYBX,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),106.35,3520.75,Yes
+3413-DHLPB,Male,0,Yes,Yes,60,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.75,5969.95,No
+7649-PHJVR,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,226.8,No
+6114-TCFID,Female,0,No,No,29,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Credit card (automatic),39.5,1082.75,No
+3787-TRIAL,Male,0,Yes,Yes,21,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,26.05,565.75,No
+2573-GYRUU,Male,1,Yes,No,48,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),91.05,4370.75,No
+5156-UMKOW,Female,0,No,Yes,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,29.65,90.05,No
+1247-QBVSH,Female,0,Yes,Yes,43,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,50.2,2169.4,No
+6734-GMPVK,Male,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,105.3,550.6,No
+9822-OAOVB,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.45,55.45,No
+6161-ERDGD,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,85.45,6300.85,No
+1226-JZNKR,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.8,160.05,No
+7318-EIVKO,Male,0,No,No,8,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,59.25,436.6,No
+3771-PZOBW,Male,0,No,No,20,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90.7,1781.35,No
+5136-KCKGI,Female,0,Yes,Yes,33,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Mailed check,103.7,3467,Yes
+8231-BSWXX,Male,0,No,No,71,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Credit card (automatic),79.05,5552.5,No
+6486-LHTMA,Female,1,Yes,No,31,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,90.7,2835.5,No
+4083-BFNYK,Female,1,Yes,No,38,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,No,Credit card (automatic),95,3591.25,No
+3722-WPXTK,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,88.35,88.35,Yes
+7389-KBFIT,Female,0,Yes,Yes,2,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,30.25,63.75,No
+7176-WIONM,Female,0,Yes,No,12,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),49.85,617.15,No
+5141-ZUVBH,Female,0,No,Yes,9,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),93,870.25,No
+1089-HDMKP,Male,1,No,Yes,11,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,54.55,601.25,No
+7623-HKYRK,Male,0,No,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,111.65,No
+0310-SUCIN,Female,0,Yes,No,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),84.8,6046.1,No
+5197-PYEPU,Female,0,Yes,Yes,42,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,Yes,Credit card (automatic),94.45,3923.8,No
+2929-ERCFZ,Female,0,Yes,Yes,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,777.3,Yes
+7548-SEPYI,Female,0,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.25,512.45,Yes
+8835-VSDSE,Female,0,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.7,141.45,Yes
+6619-RPLQZ,Female,0,Yes,Yes,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.85,892.15,No
+3275-RHRNE,Male,0,Yes,Yes,28,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),60,1682.05,No
+3503-TYDAY,Female,0,Yes,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,80.45,3398.9,No
+6901-GOGZG,Male,0,No,Yes,60,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,84.95,4984.85,No
+1623-NLDOT,Female,0,Yes,No,42,No,No phone service,DSL,No,Yes,No,Yes,No,No,One year,No,Mailed check,33.55,1445.3,Yes
+7021-XSNYE,Male,0,Yes,Yes,7,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),49.65,305.55,No
+9621-OUPYD,Female,0,Yes,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.2,507.9,No
+9898-KZQDZ,Female,1,Yes,Yes,40,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.55,3640.45,Yes
+8982-NHAVY,Male,0,No,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),100.5,2673.45,No
+4307-KWMXE,Male,0,No,No,10,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,35.75,389.8,No
+0141-YEAYS,Female,1,No,No,27,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),86.45,2401.05,No
+2450-ZKEED,Female,0,No,No,11,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,No,Bank transfer (automatic),53.8,651.55,No
+3694-DELSO,Male,0,Yes,Yes,4,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,No,Credit card (automatic),38.55,156.1,No
+3893-JRNFS,Male,0,No,No,68,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),39.9,2796.35,No
+9603-OAIHC,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.05,70.05,No
+1133-KXCGE,Female,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,407.05,No
+5236-PERKL,Female,0,No,No,57,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),112.95,6465,Yes
+0142-GVYSN,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.3,511.25,No
+1049-FYSYG,Female,0,Yes,No,17,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),35.65,646.05,No
+7854-FOKSF,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,35.9,35.9,Yes
+2519-FAKOD,Male,0,No,Yes,38,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.25,3777.15,Yes
+8406-LNMHF,Male,1,Yes,No,59,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,One year,Yes,Credit card (automatic),82.95,4903.15,No
+1821-BUCWY,Male,0,No,No,30,Yes,No,DSL,Yes,No,Yes,No,No,No,Two year,Yes,Mailed check,55.65,1653.85,No
+8263-JQAIK,Male,1,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.45,47.5,Yes
+0023-UYUPN,Female,1,Yes,No,50,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,25.2,1306.3,No
+8314-DPQHL,Male,0,No,No,9,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,One year,No,Mailed check,50.8,463.6,No
+1465-WCZVT,Female,0,Yes,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,60.65,No
+9481-SFCQY,Female,0,No,Yes,14,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),59.8,824.85,No
+6360-SVNWV,Female,1,No,No,31,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.55,2094.65,No
+0567-GGCAC,Female,0,No,No,7,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,61.4,438.9,No
+7089-IVVAZ,Female,0,No,No,8,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.35,847.3,Yes
+8884-MRNSU,Male,0,Yes,Yes,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.9,329.75,No
+2171-UDMFD,Male,0,Yes,Yes,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.45,674.55,No
+9050-IKDZA,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,81.5,162.55,No
+2205-YMZZJ,Male,1,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.8,546.95,Yes
+9802-CAQUT,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),109.55,7887.25,No
+1254-IZEYF,Female,1,No,No,31,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.95,3186.65,Yes
+0187-WZNAB,Female,0,Yes,Yes,27,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,74.4,1972.35,No
+9492-TOKRI,Female,0,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90,1527.35,Yes
+1475-VWVDO,Male,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.9,490.55,No
+9221-OTIVJ,Female,1,No,No,14,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.85,1531.4,Yes
+1357-MVDOZ,Male,0,Yes,Yes,11,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),59.65,683.25,No
+7602-MVRMB,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.45,8058.85,No
+3325-FUYCG,Male,0,Yes,Yes,28,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Electronic check,106.1,2847.4,Yes
+5908-QMGOE,Male,1,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.2,1133.9,Yes
+1197-BVMVG,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.45,294.45,No
+5406-KGRMX,Female,0,No,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,24.55,1719.15,No
+8481-YYXWG,Female,0,No,No,5,Yes,No,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.35,461.7,Yes
+5968-HYJRZ,Male,0,Yes,Yes,47,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.55,1160.45,No
+5198-EFNBM,Male,1,Yes,No,57,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,90.65,5199.8,No
+7516-GMHUV,Male,1,Yes,No,50,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),105.05,5163.3,No
+7140-ADSMJ,Male,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.45,162.3,No
+2230-XTUWL,Female,0,Yes,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,883.35,No
+7706-YLMQA,Female,0,No,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.7,1341.5,No
+2585-KTFRE,Male,0,No,Yes,1,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),70.45,70.45,No
+7994-UYIVZ,Male,0,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),85.65,659.45,No
+2609-IAICY,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,77.15,77.15,Yes
+1740-CSDJP,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),35.25,35.25,Yes
+7717-BICXI,Male,0,Yes,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.55,1205.05,No
+6559-RAKOZ,Male,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,97.95,4917.9,No
+2636-OHFMN,Male,0,Yes,No,4,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,48.55,201,Yes
+4716-MRVEN,Female,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,599.3,No
+2143-LJULT,Female,0,Yes,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.25,1733.15,No
+1323-OOEPC,Female,0,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.4,5149.5,Yes
+3200-MNQTF,Male,0,Yes,No,67,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),70.9,4677.1,No
+6164-HXUGH,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,119.3,No
+5630-IXDXV,Female,0,No,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.35,4849.1,No
+0320-DWVTU,Female,0,No,No,53,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Mailed check,99.5,5424.25,No
+9135-MGVPY,Male,0,Yes,No,69,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,84.7,5878.9,No
+1212-GLHMD,Male,0,No,No,3,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,86.05,244.85,No
+7878-JGDKK,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.55,220.75,No
+1088-AUUZZ,Male,0,Yes,Yes,56,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Credit card (automatic),75.85,4261.2,No
+0397-GZBBC,Male,1,Yes,No,59,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),93.85,5574.75,Yes
+6614-YWYSC,Male,1,Yes,No,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25,1501.75,No
+9588-OZDMQ,Female,0,Yes,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45,89.75,No
+7641-EUYET,Male,1,Yes,Yes,46,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,100.7,4541.2,Yes
+6476-EPYZR,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,255.5,No
+9921-ZVRHG,Female,0,No,No,14,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,80.45,1072,Yes
+1174-FGIFN,Female,0,Yes,Yes,28,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,One year,Yes,Electronic check,90.45,2509.25,No
+6620-HVDUJ,Male,0,No,No,24,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),60.45,1440.75,No
+4701-MLJPN,Male,0,No,No,31,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,55.25,1715.65,Yes
+1032-MAELW,Female,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Electronic check,78.45,5333.35,No
+7641-TQFHN,Male,0,No,Yes,39,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Two year,No,Mailed check,100.55,3895.35,No
+1552-TKMXS,Female,0,Yes,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.35,869.9,No
+9206-GVPEQ,Male,0,Yes,No,13,No,No phone service,DSL,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,54.45,706.85,Yes
+8622-ZLFKO,Female,0,Yes,No,6,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.75,512.25,No
+1596-BBVTG,Male,0,No,No,35,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),75.35,2636.05,Yes
+6188-UXBBR,Female,0,Yes,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.25,814.75,No
+2333-KWEWW,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,388.6,No
+5702-SKUOB,Female,0,Yes,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.6,93.45,No
+1134-YWTYF,Male,0,Yes,No,27,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,53.8,1389.85,No
+6061-GWWAV,Male,0,No,Yes,41,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Mailed check,70.2,2894.55,No
+0679-TDGAK,Male,0,Yes,Yes,50,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Electronic check,75.5,4025.6,No
+6585-WCEWR,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,20.35,1354.4,No
+9067-YGSCA,Female,0,No,No,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),26.05,1856.4,No
+9067-SQTNS,Male,0,Yes,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.6,926,No
+8433-WXGNA,Male,0,No,No,2,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.7,189.2,Yes
+1776-SPBWV,Female,0,Yes,Yes,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.1,682.1,No
+8735-SDUFN,Female,1,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.3,1778.7,No
+9668-PUGNU,Male,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,24.5,1816.2,No
+9405-GPBBG,Female,0,No,No,64,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.5,7069.25,No
+1926-QUZNN,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.25,1841.2,No
+3707-GNWHM,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.25,74.25,Yes
+7016-NVRIC,Male,1,Yes,No,29,Yes,No,Fiber optic,Yes,No,Yes,No,No,Yes,One year,No,Bank transfer (automatic),90.1,2656.7,No
+5829-NVSQN,Female,0,Yes,No,23,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),68.75,1689.45,No
+9565-AXSMR,Male,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.2,1054.75,No
+8922-LIEGH,Female,1,No,No,25,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.7,2187.55,Yes
+8869-LIHMK,Female,0,No,No,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.1,7334.05,No
+8245-UMPYT,Female,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.4,1581.2,Yes
+5186-SAMNZ,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.5,69.5,Yes
+0447-RXSGD,Male,0,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.65,2404.85,No
+4927-WWOOZ,Male,0,Yes,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,91.45,171.45,No
+5788-YPOEG,Female,0,Yes,Yes,34,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,84.75,2839.45,No
+5373-SFODM,Male,1,Yes,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.25,3132.75,Yes
+0661-KBKPA,Male,0,Yes,Yes,53,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,78.75,3942.45,No
+9081-WWXKP,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.25,873.4,No
+0784-ZQJZX,Male,0,No,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.9,1529.65,No
+3133-PZNSR,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Credit card (automatic),97.75,6991.6,No
+5766-ZJYBB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,19.4,Yes
+8931-GJJIQ,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.3,803.3,Yes
+4277-PVRAN,Female,0,No,No,8,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),80.1,679.3,Yes
+0022-TCJCI,Male,1,No,No,45,Yes,No,DSL,Yes,No,Yes,No,No,Yes,One year,No,Credit card (automatic),62.7,2791.5,Yes
+0722-SVSFK,Female,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,100.4,715,No
+3612-YVGSJ,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.45,1681.6,No
+9825-YCXWZ,Female,1,No,No,41,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),101.1,4016.2,No
+5397-NSKQG,Male,0,Yes,Yes,67,No,No phone service,DSL,Yes,No,No,No,Yes,Yes,Two year,No,Credit card (automatic),50.9,3281.65,No
+8565-HBFNN,Male,0,Yes,No,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),107.2,7317.1,No
+2000-DHJUY,Female,1,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),92.2,6474.45,No
+0203-HHYIJ,Male,0,No,No,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.3,676.35,Yes
+8670-ERCJH,Male,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),113.4,8164.1,No
+7758-UJWYS,Male,0,Yes,Yes,34,No,No phone service,DSL,No,No,No,Yes,Yes,No,Two year,Yes,Electronic check,40.55,1325.85,No
+2050-ONYDQ,Female,0,Yes,Yes,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),26,1654.85,No
+7055-JCGNI,Female,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),111.95,7795.95,No
+0739-UUAJR,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),53.8,3952.45,No
+4826-DXMUP,Male,0,No,Yes,35,Yes,No,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,72.1,2495.15,No
+0952-KMEEH,Male,0,No,No,13,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,98.15,1230.25,Yes
+7285-KLOTR,Female,0,Yes,No,12,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,No,Electronic check,78.85,876.75,No
+0654-PQKDW,Female,0,Yes,Yes,62,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),70.75,4263.45,No
+7175-NTIXE,Female,0,No,No,25,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),76.15,1992.95,No
+7963-SHNDT,Female,0,No,No,52,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,39.1,1982.1,No
+9796-BPKIW,Male,1,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.95,562.7,No
+0188-GWFLE,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,33.7,No
+8129-GMVGI,Female,0,Yes,Yes,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.05,1090.1,No
+2882-DDZPG,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.45,227.45,No
+3547-LQRIK,Female,0,Yes,No,47,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,26.9,1250.85,No
+9137-NOQKA,Male,1,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.2,37.2,No
+5843-TTHGI,Female,0,No,No,18,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,50,892.7,No
+1849-RJYIG,Female,0,No,No,8,Yes,No,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,60,487.75,No
+8868-GAGIO,Male,0,Yes,No,45,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,84.55,3713.95,No
+4061-UKJWL,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.45,141.7,No
+5380-XPJNZ,Female,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,678.2,No
+8263-QMNTJ,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,115.55,8425.3,No
+8178-EYZUO,Male,0,No,No,46,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,93.7,4154.8,Yes
+3230-JCNZS,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),99,7061.65,No
+0277-ORXQS,Male,0,Yes,Yes,66,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),50.55,3364.55,No
+5130-IEKQT,Male,1,No,No,25,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,105.95,2655.25,Yes
+3230-WYKIR,Male,0,No,No,18,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),82,1425.45,Yes
+7996-BPXHY,Female,0,Yes,Yes,13,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25,332.5,No
+3227-WLKLI,Female,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),91.55,5963.95,No
+0407-BDJKB,Male,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,95.75,5742.9,Yes
+2266-FUBDZ,Male,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,278.85,No
+8237-ULIXL,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.85,1871.85,No
+9600-UDOPK,Male,0,Yes,No,30,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.05,2866.45,Yes
+8216-AZUUZ,Female,0,Yes,Yes,42,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,100.4,4303.65,No
+9153-BTBVV,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25,1753,No
+4074-SJFFA,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,54.75,54.75,Yes
+3269-ATYWD,Male,1,No,No,39,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.65,3759.05,Yes
+5186-PEIZU,Female,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.25,617.65,No
+1074-AMIOH,Female,0,Yes,Yes,53,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),108.25,5935.1,No
+4910-GMJOT,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.6,94.6,Yes
+0354-WYROK,Female,1,Yes,Yes,31,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.9,2911.3,Yes
+4361-FEBGN,Male,0,No,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.15,982.95,No
+7748-UMTRK,Female,1,No,Yes,30,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.3,2974.5,No
+0380-ZCSBI,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20,198.7,No
+7145-FEJWU,Female,0,No,Yes,12,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.3,1275.65,No
+6463-MVYRY,Female,1,No,No,57,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),69.85,4003,No
+3969-JQABI,Female,0,Yes,No,58,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),65.25,3791.6,No
+9624-EGDEQ,Female,0,No,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.8,813.3,No
+1051-EQPZR,Female,0,Yes,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.6,780.25,No
+5849-ASHZJ,Male,0,No,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.05,552.9,No
+5780-INQIK,Female,0,No,No,8,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,49.4,408.25,No
+7576-OYWBN,Male,1,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.05,231.8,Yes
+4526-ZJJTM,Female,1,Yes,No,25,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Bank transfer (automatic),88.4,2191.15,No
+8384-FZBJK,Female,0,Yes,Yes,57,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.6,5611.7,No
+3750-RNQKR,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.45,246.25,No
+0962-CQPWQ,Female,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.3,1296.15,No
+3096-YXENJ,Female,0,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),107.65,7082.85,No
+1265-BCFEO,Female,0,Yes,No,71,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.45,5662.25,No
+5837-LXSDN,Female,0,Yes,Yes,21,Yes,No,DSL,No,Yes,No,No,Yes,No,One year,Yes,Credit card (automatic),58.85,1215.45,No
+5945-AZYHT,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,109.6,7854.15,No
+8325-QRPZR,Female,0,No,No,7,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,75.15,525,No
+6384-VMJHP,Female,0,No,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),73,5265.2,No
+2262-SLNVK,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.1,70.1,No
+7730-CLDSV,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),98.65,7129.45,No
+1135-HIORI,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.45,7266.95,No
+0164-APGRB,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,114.9,8496.7,No
+6481-ESCNL,Female,0,No,No,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),100.55,2878.75,No
+1790-NESIO,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.4,261.3,No
+1550-EENBN,Female,0,No,No,31,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,104.35,3205.6,No
+5539-TMZLF,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.75,69.75,Yes
+4323-SADQS,Male,0,Yes,Yes,7,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,34.5,279.25,Yes
+4446-BZKHU,Male,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.55,6281.45,Yes
+5202-IVJNU,Female,0,No,Yes,39,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),30.1,1131.3,Yes
+5976-JCJRH,Male,0,Yes,No,10,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,738.2,Yes
+8198-RKSZG,Female,0,Yes,Yes,14,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),80.45,1137.05,No
+0137-OCGAB,Female,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,80.2,80.2,Yes
+3351-NQLDI,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,One year,Yes,Credit card (automatic),94.35,6341.45,Yes
+9297-EONCV,Female,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),91.35,6697.2,No
+7593-JNWRU,Male,0,Yes,Yes,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.6,260.8,Yes
+4588-YBNIB,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.6,19.6,Yes
+1069-QJOEE,Male,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.9,505.45,No
+3336-JORSO,Female,1,No,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.45,3655.45,Yes
+7799-DSEWS,Male,0,No,No,18,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,68.35,1299.8,No
+8766-PAFNE,Male,0,Yes,No,71,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.1,5564.85,No
+5315-CKEQK,Male,1,Yes,Yes,28,Yes,Yes,DSL,No,No,No,No,No,No,One year,Yes,Electronic check,51,1381.8,No
+3130-ICDUP,Female,0,No,Yes,2,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),80.55,188.1,No
+0820-FNRNX,Male,0,Yes,Yes,17,Yes,Yes,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,66.7,1077.05,No
+0880-FVFWF,Male,0,No,No,56,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,86.4,4922.4,No
+4611-ANLQC,Female,0,Yes,No,60,No,No phone service,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Electronic check,50.05,2911.5,No
+4213-HKBJO,Female,0,No,No,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.7,826.1,No
+2792-LSHWX,Female,0,No,No,1,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,83.4,83.4,No
+5028-GZLDO,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),70.7,140.7,Yes
+0014-BMAQU,Male,0,Yes,No,63,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Two year,Yes,Credit card (automatic),84.65,5377.8,No
+6861-XWTWQ,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.25,665.45,Yes
+9018-PCIOK,Female,0,No,No,55,Yes,No,DSL,No,Yes,Yes,No,No,Yes,Two year,Yes,Mailed check,64.75,3617.1,No
+4837-QUSFT,Female,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),100.15,6643.5,No
+6877-TJMBR,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,84.8,84.8,Yes
+9953-ZMKSM,Male,0,No,No,63,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.25,1559.3,No
+0907-HQNTS,Female,1,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),113,7987.6,No
+7665-NKLAV,Female,0,Yes,Yes,36,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),40.65,1547.35,No
+6769-DCQLI,Male,0,No,No,52,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105,5426.85,Yes
+2433-KMEAS,Male,0,No,No,22,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,54.45,1127.35,Yes
+4391-LNRXK,Male,0,No,No,22,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),94.95,2142.8,No
+8250-ZNGGW,Female,1,No,No,5,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),59.9,287.85,No
+2195-ZRVAX,Female,0,Yes,No,47,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,85.3,4045.65,Yes
+3550-SAHFP,Female,0,No,No,33,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.35,2757.85,Yes
+2011-TRQYE,Male,0,No,No,18,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),33.5,600,Yes
+8562-GHPPI,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.8,19.8,No
+5893-PYOLZ,Male,0,No,No,56,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),81.8,4534.45,No
+4986-MXSFP,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20,40.9,No
+6131-FOYAS,Male,0,No,No,35,Yes,Yes,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,59.6,2094.9,No
+3027-YNWZU,Female,0,Yes,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25,1584.8,No
+5609-IMCGG,Female,0,No,No,15,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),84.35,1302.65,No
+4873-ILOLJ,Male,0,No,No,24,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.35,2238.5,Yes
+4727-MCYZG,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,55.55,55.55,No
+9481-WHGWY,Female,0,Yes,Yes,70,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),75.35,5437.75,No
+2725-KXXWT,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.75,90.75,Yes
+9565-DJPIB,Female,0,No,Yes,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Mailed check,89.6,365.65,Yes
+4328-VUFWD,Female,0,No,No,39,Yes,No,DSL,No,No,No,Yes,No,Yes,One year,No,Electronic check,59.3,2209.15,No
+3301-LSLWQ,Female,0,No,No,29,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,66.1,1912.15,No
+7473-ZBDSN,Female,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,18.8,255.55,No
+3166-PNEOF,Female,0,No,No,61,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.45,5175.3,No
+5639-NTUPK,Male,0,No,Yes,13,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),52.1,670.65,No
+8780-YRMTT,Female,0,No,No,66,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Mailed check,47.4,3177.25,No
+8348-HFYIV,Male,0,No,No,2,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,49.25,90.35,Yes
+5140-FOMCQ,Female,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),109.15,6557.75,No
+6242-SGYTS,Male,0,Yes,Yes,62,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,Yes,Credit card (automatic),94.95,5791.85,No
+8166-ORCHU,Male,1,Yes,No,33,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,One year,Yes,Electronic check,93.55,3055.5,No
+8414-OOEEL,Male,0,No,Yes,66,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.5,5196.1,No
+8454-AATJP,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.05,8405,No
+6849-WLEYG,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,19.75,Yes
+4659-NZRUF,Female,0,No,No,19,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,95.15,1789.25,Yes
+4531-AUZNK,Female,0,Yes,Yes,51,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,No,One year,Yes,Mailed check,95.15,5000.05,No
+4191-XOVOM,Male,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,105.4,6713.2,No
+2150-OEGBV,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.1,562.6,No
+8429-XIBUM,Male,0,No,No,22,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),101.35,2317.1,Yes
+1855-CFULU,Female,1,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,91.45,No
+4878-BUNFV,Male,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.7,828.85,No
+1872-EBWSC,Female,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.35,617.35,No
+2608-BHKFN,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),70.05,266.9,Yes
+7026-YMSBE,Male,0,No,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.7,625.05,No
+7341-LXCAF,Male,0,Yes,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.65,301.4,Yes
+6997-UVGOX,Male,0,Yes,Yes,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),85.45,6029.9,No
+9674-EHPPG,Male,0,Yes,No,46,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),40.4,1842.7,No
+9462-MJUAW,Male,0,No,No,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.4,206.6,Yes
+8128-YVJRG,Female,0,No,No,7,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,79.65,604.7,Yes
+5440-VHLUL,Male,0,No,No,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.2,7386.05,No
+5781-BKHOP,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,No,Bank transfer (automatic),100.65,7334.05,No
+4283-FUTGF,Male,1,No,No,19,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,79.85,1471.75,Yes
+5213-TWWJU,Male,0,No,No,28,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,No,Electronic check,91,2626.15,No
+1569-TTNYJ,Male,0,Yes,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,78.75,412.1,Yes
+8628-MFKAX,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.75,8277.05,No
+2397-BRLOM,Male,1,Yes,Yes,8,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),80.45,583.45,Yes
+7629-WIXZF,Female,0,No,No,7,Yes,No,DSL,Yes,No,No,No,No,Yes,One year,Yes,Electronic check,59.1,369.25,No
+5445-GLVOT,Female,0,No,No,22,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.8,1049.05,No
+3976-HXHCE,Male,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.3,1414.8,No
+2466-NEJOJ,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.65,169.75,No
+0254-KCJGT,Male,0,Yes,No,52,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),81.4,4354.45,No
+5472-CVMDX,Female,0,No,No,68,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,38.9,2719.2,No
+6461-SZMCV,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),87.95,6365.35,No
+8150-QUDFX,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,51.6,No
+9508-ILZDG,Female,1,No,No,34,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,96.35,3190.25,No
+2346-DJQTB,Female,0,No,No,35,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.15,812.5,No
+1697-LYYYX,Female,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.1,1143.8,No
+1942-OQFRW,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44,44,No
+4749-VFKVB,Female,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.1,50.1,Yes
+9640-ZSLDC,Female,0,Yes,Yes,53,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,No,Credit card (automatic),60.6,3297,No
+4231-LZUYM,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.65,1887,No
+7598-UAASY,Male,0,Yes,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,76.4,151.8,Yes
+7938-OUHIO,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.7,293.65,Yes
+8510-AWCXC,Female,1,No,No,13,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.8,1308.1,Yes
+6128-AQBMT,Male,1,Yes,No,41,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,53.95,2215.4,No
+3594-BDSOA,Female,0,Yes,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.4,482.8,No
+0431-APWVY,Female,0,Yes,Yes,28,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),90.1,2598.95,Yes
+5133-VRSAB,Male,0,No,No,8,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.35,216.45,No
+5996-DAOQL,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.45,20.45,No
+6838-YAUVY,Female,0,No,No,54,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),95.1,5064.85,No
+0484-JPBRU,Male,0,No,No,41,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.25,996.45,No
+7883-ROJOC,Female,0,Yes,No,19,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.9,839.65,No
+0244-LGNFY,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),92.65,6733,No
+7274-CGTOD,Male,0,No,No,62,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),43.7,2618.3,No
+4295-YURET,Female,1,Yes,Yes,56,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),72.6,4084.35,No
+3426-NIYYL,Male,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,51.55,765.5,Yes
+8225-BTJAU,Male,1,No,No,10,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.25,793.55,Yes
+4635-EJYPD,Male,0,Yes,Yes,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,18.95,613.95,No
+1866-ZSLJM,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.5,402.85,No
+4636-TVXVG,Male,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.95,1244.8,No
+4236-UJPWO,Female,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.5,46.4,No
+9392-XBGTD,Male,0,No,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.6,581.85,No
+3387-VATUS,Male,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.85,462.8,Yes
+6402-SSEJG,Female,0,No,No,25,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,61.05,1540.2,No
+1143-NMNQJ,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.7,169.8,Yes
+1169-SAOCL,Male,0,No,No,49,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),106.65,5168.1,No
+1110-KYLGQ,Female,0,No,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),108.25,6780.1,No
+9929-PLVPA,Female,0,No,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.4,94.5,No
+3518-PZXZQ,Female,0,Yes,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,55.3,55.3,No
+2371-JUNGC,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,208,No
+7693-QPEFS,Male,1,No,No,52,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),72.95,3829.75,No
+0924-BJCRC,Female,1,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,89.45,5294.6,No
+5074-FBGHB,Male,0,No,No,64,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),104.65,6889.8,No
+2351-BKRZW,Female,0,Yes,Yes,43,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),75.2,3254.35,No
+4455-BFSPD,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),101.15,6383.9,No
+0415-MOSGF,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.4,44.4,Yes
+1724-BQUHA,Male,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.5,477.7,Yes
+3948-KXDUF,Male,0,No,No,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),68.75,4447.55,No
+2323-ARSVR,Male,0,Yes,No,67,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,111.05,7321.05,No
+0815-MFZGM,Female,0,Yes,No,42,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),99,4135,No
+4826-XTSOH,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.05,86.05,Yes
+5480-XTFFL,Female,0,Yes,Yes,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,21,697.7,No
+8295-FHIVV,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.4,168.65,No
+2495-INZWQ,Male,0,No,No,4,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.55,174.3,Yes
+9086-YJYXS,Male,0,Yes,Yes,34,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),77.2,2753.8,No
+1179-INLAT,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.45,69.25,No
+7909-FIOIY,Female,0,Yes,Yes,19,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.85,434.8,No
+4139-SUGLD,Male,0,Yes,Yes,31,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),35.4,1077.5,Yes
+6857-VWJDT,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,95.65,95.65,Yes
+6351-SCJKT,Male,0,No,No,3,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,41.35,107.25,No
+5468-BPMMO,Male,0,Yes,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.6,851.2,No
+5624-BQSSA,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.95,20.95,Yes
+0197-PNKNK,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,One year,No,Bank transfer (automatic),84.45,5848.6,No
+2439-QKJUL,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,109.8,No
+1194-SPVSP,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.65,19.65,No
+9534-NSXEM,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.65,595.5,No
+2408-WITXK,Female,1,No,No,10,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,34.7,329.8,Yes
+8929-KSWIH,Male,0,No,No,25,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),99.3,2513.5,No
+2250-IVBWA,Male,0,Yes,Yes,64,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,81.05,5135.35,No
+1810-MVMAI,Male,0,Yes,Yes,30,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,67.6,2000.2,No
+9506-UXUSK,Male,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,931.75,No
+1229-RCALF,Female,0,Yes,No,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,115,7396.15,No
+3572-UOLYZ,Female,0,No,Yes,46,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),84.8,3958.85,No
+1429-UYJSV,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.7,260.9,No
+5577-OTWWW,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.75,297.3,Yes
+6100-QQHEB,Male,0,Yes,Yes,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.55,1515.1,Yes
+6366-XIVKZ,Female,0,Yes,Yes,13,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,63.15,816.8,No
+3470-OBUET,Female,0,Yes,Yes,67,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),74,4868.4,No
+2770-NSVDG,Male,0,Yes,No,24,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,29.1,688,No
+9375-MHRRS,Male,0,No,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,50.05,288.35,No
+2634-HCZGT,Male,1,Yes,No,53,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Electronic check,60.05,3229.65,Yes
+7503-QQRVF,Male,1,Yes,No,16,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.3,1178.25,Yes
+4860-YZGZM,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20,185.4,No
+6599-GZWCM,Female,0,No,No,13,Yes,No,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Mailed check,74.65,966.25,No
+2691-NZETQ,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.35,758.6,Yes
+4404-HIBDJ,Female,0,No,No,25,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Mailed check,74.3,1863.8,Yes
+5533-NHFRF,Male,1,No,No,7,No,No phone service,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,44.4,265.8,No
+7037-MTYVW,Male,0,Yes,Yes,38,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.4,3297,No
+4760-THGOT,Female,0,Yes,No,43,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,94.1,4107.3,No
+7295-JOMMD,Female,0,No,Yes,4,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),98.1,396.3,Yes
+4016-BJKTZ,Female,0,No,No,25,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,108.9,2809.05,No
+6584-VQMYT,Male,0,No,Yes,27,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Mailed check,56.2,1567.55,No
+9838-BFCQT,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,26.1,1851.45,No
+2790-XUYMV,Male,0,No,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),85.45,6028.95,No
+0581-MDMPW,Female,0,No,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),88.95,2072.75,No
+5013-SBUIH,Female,0,No,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,109.65,5551.15,Yes
+1023-BQXZE,Male,0,No,No,57,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),74.35,4317.35,No
+9163-GHAYE,Female,0,No,No,15,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,48.85,736.8,No
+3904-UKFRE,Male,0,No,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.1,336.15,No
+5353-WILCI,Female,0,No,No,28,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,56.05,1522.65,No
+0709-TVGUR,Female,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.55,622.9,Yes
+0058-EVZWM,Female,0,Yes,No,55,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.8,4959.6,No
+6023-YEBUP,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.95,329.95,Yes
+7209-JCUDS,Male,0,No,No,10,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.9,1048.85,Yes
+7009-PCARS,Male,0,No,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.1,1001.5,No
+0519-DRGTI,Female,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,442.6,No
+1017-FBQMM,Female,0,Yes,Yes,62,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),106.05,6703.5,No
+3001-CBHLQ,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.9,3351.55,Yes
+1985-MBRYP,Female,0,No,No,43,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.65,779.25,No
+6158-DWPZT,Male,0,Yes,No,9,No,No phone service,DSL,No,No,No,No,No,No,One year,No,Bank transfer (automatic),24.1,259.8,Yes
+9372-TXXPS,Female,0,Yes,No,60,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),59.85,3483.45,No
+3259-QMXUN,Male,0,Yes,No,58,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,86.1,4890.5,No
+1015-JPFYW,Male,0,No,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.45,136.75,No
+6645-MXQJT,Male,0,Yes,Yes,2,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,97.1,184.15,No
+4360-QRAVE,Male,1,No,No,37,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,36.65,1315,No
+8433-WPJTV,Female,1,Yes,Yes,65,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),103.9,6767.1,No
+5804-JMYIO,Female,0,Yes,Yes,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,757.95,No
+3763-GCZHZ,Male,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,104.05,6890,Yes
+6484-LATFU,Male,0,No,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.55,1657.4,No
+7599-NTMDP,Female,0,Yes,Yes,62,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Bank transfer (automatic),48.7,3008.55,No
+0772-GYEQQ,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,88.35,262.05,Yes
+0536-ACXIP,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,109.55,8165.1,No
+0936-NQLJU,Female,0,Yes,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.65,875.55,No
+4831-EOBFE,Male,0,Yes,Yes,29,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),94.65,2649.15,Yes
+1575-KRZZE,Female,0,No,No,4,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.2,220.65,No
+3251-YMVWZ,Male,0,No,No,53,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.05,1301.9,No
+4102-OQUPX,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,74.4,Yes
+9170-GYZJC,Female,0,Yes,Yes,41,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,No,Credit card (automatic),79.9,3326.2,Yes
+4884-LEVMQ,Male,0,Yes,No,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.45,790,No
+8857-CUPFQ,Male,0,Yes,No,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.25,1237.65,No
+7610-TVOPG,Male,0,No,No,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,26.35,378.6,No
+3638-DIMPH,Female,0,Yes,No,13,Yes,No,DSL,No,No,No,No,No,No,One year,No,Electronic check,43.8,592.65,No
+1845-ZLLIG,Male,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,50.15,50.15,No
+8559-WNQZS,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.45,20.45,No
+3999-QGRJH,Male,1,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.7,560.85,Yes
+1699-UOTXU,Male,0,No,No,60,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Electronic check,61.4,3638.25,No
+2454-RPBRZ,Female,1,Yes,No,12,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,98.1,1060.2,Yes
+0635-WKOLD,Male,0,Yes,No,40,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,No,Credit card (automatic),70.75,2921.75,No
+5993-JSUWV,Female,0,No,No,66,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Bank transfer (automatic),61.15,4017.45,No
+4518-FZBSX,Male,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,854.9,No
+5387-ASZNZ,Female,1,No,No,66,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),63.85,4174.35,No
+6988-CJEYV,Male,0,No,No,49,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,98.7,4920.55,No
+2002-MZHWP,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,20.5,Yes
+3097-FQTVJ,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,810.3,No
+5465-BUBFA,Female,0,Yes,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.3,772.4,No
+7299-GNVPL,Female,0,Yes,Yes,23,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,84.4,1936.85,No
+9743-DQKQW,Male,0,No,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.1,79.8,No
+1215-VFYVK,Female,0,No,No,4,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,No,Mailed check,48.25,202.25,No
+9371-BITHB,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85,1070.5,No
+2265-CYWIV,Female,1,Yes,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.6,347.65,Yes
+9093-FPDLG,Female,0,No,No,11,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,94.2,999.9,No
+0541-FITGH,Female,0,Yes,No,2,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,62.15,113.1,No
+6985-HAYWX,Female,0,Yes,No,26,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),79.3,2015.8,No
+7508-KBIMB,Male,0,Yes,Yes,24,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),56.25,1454.25,No
+6838-HVLXG,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,246.7,No
+2277-DJJDL,Male,1,Yes,No,60,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,99,6017.9,No
+1897-OKVMW,Female,0,Yes,Yes,64,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,90.6,5817.45,No
+5485-ITNPC,Male,0,Yes,No,66,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.9,5595.3,No
+0233-FTHAV,Female,0,No,No,60,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,One year,Yes,Bank transfer (automatic),79.2,4765,No
+4644-PIZRT,Male,0,Yes,Yes,17,Yes,Yes,DSL,Yes,No,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),70.35,1201.65,No
+8922-NPKBJ,Male,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,867.3,No
+2740-TVLFN,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.15,50.15,No
+7771-CFQRQ,Female,0,Yes,Yes,47,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Two year,No,Bank transfer (automatic),63.8,3007.25,No
+9512-PHSMG,Female,0,Yes,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.55,252.75,No
+6963-EZQEE,Male,1,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),88.55,6306.5,No
+2452-KDRRH,Male,1,No,No,67,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),101.4,6841.05,No
+2004-OCQXK,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.95,81.95,Yes
+5027-QPKTE,Male,0,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.35,451.1,Yes
+4146-SVFUD,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.6,44.6,No
+1564-HJUVY,Male,0,No,No,4,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),63.75,226.2,No
+3617-XLSGQ,Female,0,Yes,Yes,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),109.25,7082.5,No
+7517-LDMPS,Female,0,No,No,12,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.6,1017.35,No
+7244-KXYZN,Female,0,No,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.45,527.35,No
+5226-NOZFC,Male,0,No,No,26,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.75,2146.5,No
+2672-HUYVI,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.1,455.3,Yes
+1400-WIVLL,Male,0,Yes,No,57,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,107.95,5969.85,No
+7508-MYBOG,Male,0,Yes,No,14,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.1,1235.55,Yes
+8148-NLEGT,Female,0,Yes,Yes,42,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,22.95,1014.25,No
+0148-DCDOS,Male,0,No,No,25,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.7,2362.1,Yes
+8347-GDTMP,Female,0,Yes,No,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.45,1225.65,No
+9992-RRAMN,Male,0,Yes,No,22,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.1,1873.7,Yes
+6746-WAUWT,Male,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),19.7,386.5,No
+0310-MVLET,Female,0,Yes,Yes,61,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.15,6010.05,Yes
+0428-IKYCP,Male,0,Yes,No,22,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),87,1850.65,No
+4550-VBOFE,Male,1,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.95,7101.5,Yes
+2187-PKZAY,Male,0,No,No,12,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),79.95,1043.4,No
+4003-OCTMP,Female,0,Yes,No,31,Yes,No,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Electronic check,64,1910.75,No
+6652-YFFJO,Female,0,No,No,11,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,64.9,716.1,No
+7225-IILWY,Male,0,Yes,Yes,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75,1686.15,No
+5248-RPYWW,Female,1,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),90.15,6716.45,No
+7923-IYJWY,Male,1,No,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.1,7839.85,No
+3170-GWYKC,Female,0,No,No,60,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.95,6236.75,No
+5696-QURRL,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.05,45.05,Yes
+7409-KIUTL,Female,1,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71,71,Yes
+9830-ECLEN,Male,0,Yes,Yes,58,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Mailed check,50,2919.85,No
+1732-VHUBQ,Female,1,Yes,Yes,47,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.55,3309.25,Yes
+0495-RVCBF,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.7,79.7,Yes
+2668-TZSPS,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,20.45,No
+7854-EDSSA,Male,0,No,No,22,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,59,1254.7,Yes
+8869-TORSS,Female,0,No,No,48,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),60.35,2896.4,Yes
+7446-SFAOA,Female,0,Yes,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.85,717.5,No
+9522-ZSINC,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.95,253.8,No
+8234-GSZYK,Male,0,No,No,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26.45,1110.05,No
+6505-OZNPG,Female,0,No,No,6,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,63.4,348.8,No
+6164-HAQTX,Male,0,No,No,71,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),53.95,3888.65,No
+0679-IDSTG,Female,1,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.25,69.25,Yes
+7765-LWVVH,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Two year,Yes,Electronic check,95.1,6843.15,No
+1658-TJVOA,Female,1,No,No,6,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.1,450.9,No
+0953-LGOVU,Male,0,Yes,Yes,12,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,35.5,432.25,No
+2115-BFTIW,Male,0,No,No,25,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.95,1767.35,Yes
+4692-NNQRU,Female,0,Yes,No,21,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,One year,No,Electronic check,79.2,1742.45,No
+7742-MYPGI,Female,0,Yes,Yes,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,48.8,297.35,No
+0611-DFXKO,Male,0,Yes,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,89,1820.45,Yes
+7780-OTDSO,Male,0,Yes,No,18,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.4,1742.95,Yes
+9619-GSATL,Female,0,No,No,43,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,No,Electronic check,55.45,2444.25,No
+5622-UEJFI,Female,0,Yes,Yes,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.4,949.8,No
+9747-DDZOS,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),73.5,73.5,Yes
+0674-DGMAQ,Male,1,Yes,No,32,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,93.5,2970.8,No
+6203-HBZPA,Male,0,No,No,52,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),63.9,3334.95,No
+0484-FFVBJ,Male,0,No,No,32,Yes,No,DSL,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),64.85,2010.95,No
+0301-FIDRB,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),63.8,4684.3,No
+4139-JPIAM,Male,0,No,No,51,No,No phone service,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),44.45,2181.55,No
+2181-TIDSV,Male,0,Yes,Yes,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.95,1303.25,No
+2761-XECQW,Male,1,Yes,No,8,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,43.35,371.4,No
+1936-CZAKF,Male,0,Yes,No,49,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Two year,No,Credit card (automatic),49.65,2409.9,No
+9453-PATOS,Female,0,Yes,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,85.1,6155.4,No
+0564-JJHGS,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.5,829.1,Yes
+3348-CFRNX,Female,0,Yes,No,28,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,92.35,2602.9,Yes
+4402-FTBXC,Male,0,No,No,54,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,No,Mailed check,89.8,4667,No
+1428-GTBJJ,Male,0,No,No,11,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.55,824.75,Yes
+2931-XIQBR,Female,0,Yes,Yes,50,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,No,Mailed check,103.05,5153.5,No
+0106-UGRDO,Female,0,Yes,No,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,116,8182.85,No
+5542-TBBWB,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.9,69.9,No
+1930-QPBVZ,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,Two year,No,Bank transfer (automatic),95.1,6683.4,No
+2606-PKWJB,Male,0,No,Yes,40,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,40.25,1564.05,No
+5322-ZSMZY,Male,0,Yes,Yes,31,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75,755.6,No
+8059-UDZFY,Female,1,No,Yes,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.35,3465.05,No
+7740-BTPUX,Male,1,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,113.6,6292.7,No
+5220-AGAAX,Male,0,Yes,Yes,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24,1664.3,No
+0208-BPQEJ,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.4,198.1,No
+9435-JMLSX,Male,0,Yes,No,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),86.1,6045.9,No
+3352-ALMCK,Male,0,No,No,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,102.65,4108.15,No
+5312-IRCFR,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,92.85,5980.75,No
+5294-IMHHT,Male,0,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,One year,No,Bank transfer (automatic),97.75,5043.2,No
+8802-UNOJF,Male,1,No,No,12,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,83.8,1029.75,Yes
+6313-GIDIT,Male,1,No,No,53,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,54.45,2854.55,Yes
+6176-YJWAS,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),97.95,7114.25,No
+5310-NOOVA,Male,0,No,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,19.95,907.05,No
+4526-EXKKN,Male,0,No,No,40,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.6,973.95,No
+5311-IHLEI,Male,0,No,No,12,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),50.95,605.75,No
+3987-KQDDU,Male,0,No,No,9,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,75.6,661.55,No
+3404-JNXAX,Female,0,Yes,Yes,51,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,80.75,4116.9,No
+5131-PONJI,Male,0,Yes,Yes,49,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),90.4,4494.65,No
+2845-AFFTX,Male,1,Yes,No,41,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,4259.3,Yes
+3489-VSFRD,Female,0,No,No,56,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),60.25,3282.75,No
+3345-JHUEO,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.2,55.7,No
+5815-HGGHV,Male,0,Yes,No,20,Yes,No,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Mailed check,64.15,1274.45,No
+5260-UMPWX,Female,0,Yes,Yes,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.25,493.95,No
+2249-YPRNG,Female,0,Yes,Yes,20,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.85,2239.65,Yes
+7410-YTJIK,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,75.45,480.75,Yes
+4626-GYCZP,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.85,635.6,Yes
+5649-RXQTV,Male,0,No,No,51,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99,5038.15,No
+2674-MIAHT,Female,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,80.3,324.2,No
+2576-HXMPA,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.55,19.55,No
+7587-AOVVU,Male,0,Yes,Yes,27,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,No,Electronic check,100.75,2793.55,No
+5590-BYNII,Male,0,No,No,22,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.75,2095,Yes
+6898-MDLZW,Male,0,No,No,12,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,53.75,648.65,No
+3237-AJGEH,Female,0,Yes,Yes,3,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,31,95.05,Yes
+4707-YNOQA,Female,0,Yes,Yes,34,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),25.6,917.15,No
+9357-UJRUN,Male,0,Yes,No,24,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Electronic check,58.35,1346.9,No
+1591-NFNLQ,Male,0,No,No,51,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),80,4242.35,Yes
+8648-PFRMP,Female,1,No,No,14,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,46.35,672.7,No
+7663-CUXZB,Male,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,113.75,6561.25,No
+0258-NOKBL,Male,0,No,No,3,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Electronic check,90.4,268.45,No
+1163-VIPRI,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,109.3,7337.55,No
+2815-CPTUL,Male,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.25,331.9,Yes
+9348-ROUAI,Female,0,Yes,Yes,59,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),90.3,5194.05,No
+9443-JUBUO,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),65.25,4478.85,No
+0596-BQCEQ,Female,0,Yes,Yes,62,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.15,6283.3,Yes
+1481-ZUWZA,Male,0,No,No,28,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.5,2659.4,Yes
+3043-TYBNO,Male,0,No,No,3,Yes,No,DSL,No,No,No,Yes,No,Yes,Month-to-month,No,Mailed check,60.65,196.9,No
+4830-FAXFM,Male,0,No,Yes,19,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),24.1,439.2,No
+5906-BFOZT,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.5,19.5,No
+2960-NKRSO,Male,0,No,No,24,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,85.95,2107.15,No
+8996-ZROXE,Male,1,No,No,57,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,Yes,Electronic check,53.5,3035.8,No
+5598-IKHQQ,Female,0,No,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.45,1866.45,No
+0397-ZXWQF,Male,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.5,1430.95,No
+5266-PFRQK,Male,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.85,1071.6,No
+4674-HGNUA,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.9,6457.15,No
+6765-MBQNU,Female,0,Yes,No,26,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,26,684.05,No
+9786-IJYDL,Female,0,No,No,35,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,113.2,3914.05,No
+1303-SRDOK,Female,0,Yes,Yes,55,Yes,No,Fiber optic,No,No,No,No,No,No,Two year,Yes,Credit card (automatic),69.05,3842.6,No
+3769-MHZNV,Female,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,670.35,No
+6295-OSINB,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,109.65,7880.25,No
+3308-MHOOC,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.2,19.2,No
+2309-OSFEU,Male,0,No,No,10,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,33.9,298.45,Yes
+2207-QPJED,Female,1,Yes,No,37,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Electronic check,90,3371.75,No
+4177-JPDFU,Male,0,No,No,12,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,34,442.45,No
+2239-CFOUJ,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.4,20.4,No
+8046-DNVTL,Male,0,Yes,No,62,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),38.6,2345.55,No
+2209-XADXF,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),25.25,25.25,No
+6620-JDYNW,Female,0,No,No,18,Yes,Yes,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,60.6,1156.35,No
+1891-FZYSA,Male,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.95,6143.15,Yes
+4770-UEZOX,Male,0,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.75,144.8,No
+1038-RQOST,Male,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.6,414.95,No
+7613-LLQFO,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.45,1059.55,Yes
+4568-TTZRT,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,181.8,No
+9513-DXHDA,Male,0,No,No,27,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,81.7,2212.55,No
+2640-PMGFL,Male,0,No,Yes,27,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,79.5,2180.55,Yes
+3801-HMYNL,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,89.15,89.15,Yes
+0516-QREYC,Female,1,No,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.3,459.95,No
+9685-WKZGT,Male,1,No,No,14,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,1036.75,Yes
+6022-UGGSO,Female,1,No,No,32,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,2276.95,Yes
+8084-OIVBS,Female,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,211.95,No
+8896-BQTTI,Male,0,No,No,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,25,25,No
+3865-QBWSJ,Male,1,No,Yes,38,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,80.45,3162.65,No
+3352-RICWQ,Female,0,Yes,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,210.65,No
+2160-GPFXD,Male,0,Yes,Yes,54,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),65.65,3566.7,No
+2065-MMKGR,Female,0,No,No,29,Yes,Yes,DSL,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),71,2080.1,No
+5857-TYBCJ,Male,1,Yes,No,44,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.2,4040.2,No
+1498-NHTLT,Male,0,Yes,Yes,59,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,86.75,5186,No
+4484-CGXFK,Female,0,No,No,3,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.3,196.15,Yes
+1402-PTHGN,Female,0,Yes,Yes,18,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,61.5,1087.45,No
+4176-RELJR,Male,1,No,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.1,1672.15,No
+6214-EDAKZ,Female,0,Yes,Yes,22,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,55.15,1206.05,Yes
+5199-FPUSP,Male,0,No,Yes,33,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,No,Credit card (automatic),34.05,1113.95,No
+6377-KSLXC,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,107.05,No
+1796-JANOW,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,38.15,No
+0238-WHBIQ,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.7,6339.3,No
+8735-NBLWT,Male,0,No,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,184.1,No
+6651-RLGGM,Male,0,Yes,Yes,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,26.3,1688.9,No
+6127-ISGTU,Female,0,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.95,1378.25,Yes
+1614-JBEBI,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.7,137.6,No
+8740-XLHDR,Male,0,No,No,5,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,43.25,219,Yes
+1208-DNHLN,Male,0,Yes,Yes,23,Yes,Yes,DSL,No,No,No,No,No,No,One year,Yes,Credit card (automatic),48.35,1067.15,Yes
+1761-AEZZR,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.55,79.55,Yes
+3923-CSIHK,Female,1,Yes,No,50,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.05,3444.85,Yes
+5696-EXCYS,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.45,369.05,No
+5795-KTGUD,Female,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.8,7553.6,No
+7120-RFMVS,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,84.5,84.5,Yes
+7924-GJZFI,Female,1,Yes,No,25,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.3,1813.1,No
+4702-HDRKD,Male,0,No,No,67,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),49.35,3321.35,No
+8512-WIWYV,Male,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.35,707.5,No
+5897-ZYEKH,Female,1,Yes,No,67,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,105.6,7112.15,No
+5456-ITGIC,Male,1,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),64.45,4641.1,No
+7317-GGVPB,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.6,7690.9,Yes
+1406-PUQVY,Male,0,No,Yes,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,49.9,49.9,No
+1322-AGOQM,Male,0,No,No,46,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),30.3,1380.1,Yes
+3677-IYRBF,Female,1,No,No,2,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,30.4,78.65,Yes
+5692-FPTAH,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.4,45.4,Yes
+9114-AAFQH,Female,0,Yes,No,48,Yes,No,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,65.65,3094.65,No
+8715-KKTFG,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),103.3,6518.35,No
+1550-LOAHA,Female,0,Yes,No,32,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),84.15,2585.95,Yes
+1728-BQDMA,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.45,82.7,No
+0268-QKIWO,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.75,58.85,Yes
+0876-WDUUZ,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),85.4,425.9,Yes
+5117-ZSMHQ,Female,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),89.9,6342.7,No
+5151-HQRDG,Male,0,Yes,No,37,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,55.05,2030.75,No
+0960-HUWBM,Male,0,Yes,Yes,65,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.1,6700.05,No
+6465-GSRCL,Female,0,No,Yes,67,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),106.6,7244.7,No
+0617-FHSGK,Male,0,No,Yes,49,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),75.2,3678.3,Yes
+0263-FJTQO,Male,0,Yes,Yes,50,Yes,Yes,DSL,Yes,No,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),70.5,3486.65,No
+7319-ZNRTR,Male,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.6,411.15,No
+2858-MOFSQ,Female,0,No,Yes,17,Yes,Yes,DSL,No,No,Yes,No,No,No,One year,Yes,Mailed check,55.85,937.5,Yes
+7503-EPSZW,Female,0,Yes,Yes,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.05,1559.15,No
+7089-XXAYG,Male,0,Yes,No,25,No,No phone service,DSL,No,No,Yes,No,Yes,No,One year,Yes,Credit card (automatic),38.1,970.4,No
+8118-LSUEL,Male,1,No,No,23,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.4,2483.5,Yes
+8070-AAWZP,Male,1,Yes,No,24,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,34.25,828.2,No
+1666-JXLKU,Female,0,No,No,37,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.05,3810.55,No
+7855-DIWPO,Female,0,No,No,21,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,68.65,1493.2,No
+5133-POWUA,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.8,45.8,No
+5652-MSDEY,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75.75,777.3,No
+7005-CCBKV,Male,0,No,No,6,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.4,556.35,Yes
+1810-BOHSY,Male,0,Yes,No,51,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Credit card (automatic),96.4,4911.05,No
+1784-BXEFA,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.55,187.35,No
+7351-MHQVU,Female,0,No,No,6,No,No phone service,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,No,Credit card (automatic),50.95,307.6,No
+9224-VTYID,Male,0,Yes,Yes,47,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,90.5,4318.35,No
+9500-IWPXQ,Female,0,Yes,Yes,61,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Electronic check,79.4,4820.55,No
+5762-TJXGK,Female,0,No,No,52,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),58.75,3038.55,No
+4504-YOULA,Female,0,Yes,Yes,35,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),59.45,2136.9,No
+5569-IDSEY,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),105.7,7472.15,No
+4250-FDVOU,Female,0,No,No,6,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,56.25,389.1,Yes
+7284-ZZLOH,Male,0,Yes,No,45,Yes,No,DSL,Yes,No,Yes,No,No,No,Two year,No,Credit card (automatic),53.3,2296.25,No
+5277-ZLOOR,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.55,187.45,Yes
+2141-RRYGO,Female,0,No,No,4,Yes,No,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),68.65,261.25,Yes
+1777-JYQPJ,Male,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.3,38.45,No
+6376-GAHQE,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,77.85,299.2,Yes
+3401-URHDA,Male,0,No,No,51,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,No,Credit card (automatic),59.9,3043.6,No
+1599-EAHXY,Male,0,Yes,Yes,60,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),23.95,1506.4,No
+8631-XVRZL,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.15,163.7,No
+5052-PNLOS,Male,0,No,No,3,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.35,323.25,Yes
+3853-LYGAM,Male,0,No,No,17,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.65,1640,No
+6979-ZNSFF,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,87.05,762.1,Yes
+5751-USDBL,Male,0,Yes,Yes,46,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,81,3846.35,No
+5680-LQOGP,Female,0,No,No,68,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),82.45,5646.6,No
+2386-LAHRK,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,53.5,53.5,Yes
+8189-DUKMV,Female,0,Yes,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.5,79.05,No
+6032-IGALN,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.1,25.1,Yes
+9931-DCEZH,Male,0,No,Yes,28,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),54.4,1516.6,No
+1898-JSNDC,Female,0,No,No,39,Yes,No,DSL,No,Yes,No,No,Yes,No,One year,Yes,Credit card (automatic),58.6,2224.5,No
+0315-LVCRK,Male,0,No,No,11,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,84.8,888.75,No
+3911-RSNHI,Female,0,Yes,No,71,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),61.4,4310.35,No
+2410-CIYFZ,Male,0,No,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,42.9,No
+4248-HCETZ,Male,1,Yes,No,30,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.65,2365.15,Yes
+5505-OVWQW,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.15,353.65,No
+7271-AJDTL,Female,0,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),94.45,5073.1,No
+1867-TJHTS,Female,0,No,No,58,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.8,4526.85,No
+0516-WJVXC,Female,0,No,No,5,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,54.2,308.25,Yes
+9174-FKWZE,Female,1,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.45,19.45,Yes
+6860-YRJZP,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.05,678.45,No
+4429-WYGFR,Male,0,No,No,26,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),49.15,1237.3,No
+2817-LVCPP,Female,0,No,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,1023.95,No
+5038-ETMLM,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),113.65,8182.75,No
+5056-FIMPT,Female,0,No,Yes,43,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),106,4532.3,No
+4521-WFJAI,Male,0,No,No,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.95,1444.05,No
+6569-KTMDU,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.1,19.1,No
+8809-RIHDD,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,103.4,7372.65,Yes
+8809-XKHMD,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),100.55,7325.1,No
+0396-YCHWO,Male,0,Yes,No,36,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.4,3474.2,No
+0867-LDTTC,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),75.15,392.65,No
+4822-NGOCH,Female,0,No,No,13,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.45,1058.6,Yes
+9391-DXGGG,Female,1,No,No,44,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,One year,No,Credit card (automatic),89.15,3990.75,No
+9844-FELAJ,Female,1,Yes,Yes,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,107.9,7475.85,No
+2122-SZZZD,Male,0,No,No,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.5,835.5,No
+5307-DZCVC,Female,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.95,2628.6,Yes
+3148-AOIQT,Female,0,Yes,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.95,1718.35,No
+8679-JOEVF,Female,1,No,No,16,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,59.4,1023.9,Yes
+8395-ETZKQ,Male,1,Yes,Yes,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.5,1193.55,No
+6692-YQHXC,Male,0,No,No,16,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.95,1205.5,No
+3889-VWBID,Male,0,Yes,Yes,68,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),82.85,5776.45,No
+5222-JCXZT,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19,78.9,No
+9754-CLVZW,Female,0,Yes,Yes,26,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),38.85,1025.15,No
+1432-FPAXX,Female,0,No,No,29,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,30.6,856.35,Yes
+2739-CCZMB,Male,0,No,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.35,122,No
+7080-TNUWP,Male,0,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),95,6602.9,No
+0496-AHOOK,Male,0,Yes,No,24,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),74.4,1712.9,No
+8336-TAVKX,Female,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),78.45,5682.25,No
+2468-SJFLM,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,74.3,74.3,No
+3181-VTHOE,Male,0,Yes,No,70,No,No phone service,DSL,No,Yes,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),51.05,3635.15,No
+7168-HDQHG,Female,0,Yes,Yes,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.2,702.9,No
+4803-AXVYP,Female,1,No,No,38,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,99.55,3734.25,Yes
+3884-HCSWG,Female,0,No,No,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70,1144.5,Yes
+4567-AKPIA,Female,0,Yes,Yes,41,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),109.1,4454.25,No
+5077-DXTCG,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.3,45.3,Yes
+1142-WACZW,Male,0,No,No,2,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),29.85,75.6,Yes
+7901-HXJVA,Male,0,No,No,14,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,76.45,1117.55,No
+5649-ANRML,Male,1,No,No,2,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),95.1,180.25,Yes
+4892-VLANZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.8,19.8,Yes
+5924-IFQTT,Male,0,Yes,Yes,13,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,72.8,930.05,No
+7968-QUXNS,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.95,110.15,No
+2919-HBCJO,Female,0,No,No,4,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),76.65,333.6,Yes
+4236-XPXAV,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,99.15,465.05,Yes
+8903-WMRNW,Female,0,Yes,No,15,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,101.75,1669.4,No
+2452-SNHFZ,Female,0,No,No,47,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),75.45,3545.1,No
+3629-WEAAM,Female,0,No,No,8,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,No,Mailed check,64.1,504.05,No
+6029-CSMJE,Male,0,No,No,17,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.65,440.2,No
+7993-NQLJE,Male,0,Yes,Yes,15,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,75.1,1151.55,No
+9909-DFRJA,Female,0,No,No,26,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),95.85,2475.35,No
+9099-FTUHS,Female,0,No,No,23,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,54.4,1249.25,No
+0581-BXBUB,Female,1,No,No,4,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,72.75,317.75,No
+4962-CHQPW,Male,0,No,Yes,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.85,535.05,No
+9467-ROOLM,Female,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.05,461.3,No
+3030-YZADT,Male,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.95,431,Yes
+7410-KTVFV,Male,0,Yes,No,18,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,49.55,878.35,Yes
+5150-ITWWB,Male,1,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.85,335.75,No
+2253-KPMNB,Female,0,Yes,Yes,69,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),46.25,3121.4,No
+1345-ZUKID,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.35,324.8,No
+6429-SHBCB,Male,0,No,No,19,Yes,Yes,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,69.6,1394.55,No
+9281-OFDMF,Male,1,No,No,39,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,90.7,3413.25,No
+2603-HVKCG,Male,0,No,No,31,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.4,3143.65,No
+1834-WULEG,Male,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,439.75,No
+9097-ZUBYC,Male,0,Yes,No,14,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),48.8,664.4,No
+5148-ORICT,Female,0,Yes,No,64,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,74.35,4759.55,No
+4893-GYUJU,Female,0,No,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.35,1033,No
+4578-PHJYZ,Male,0,Yes,Yes,52,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,68.75,3482.85,No
+7272-QDCKA,Male,0,No,No,28,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Electronic check,100.2,2688.45,No
+8908-SLFCJ,Female,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.85,435.25,No
+8393-DLHGA,Male,0,No,Yes,25,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.9,2448.75,Yes
+9766-HGEDE,Female,0,Yes,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.35,307,No
+6968-MHOMU,Male,0,Yes,No,58,No,No phone service,DSL,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),45,2689.35,No
+7395-IGJOS,Male,1,Yes,No,17,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),81.5,1329.2,Yes
+5044-XDPYX,Female,0,Yes,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.5,1281.25,No
+1814-WFGVS,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Mailed check,48.9,3527,No
+2834-SPCJV,Male,0,Yes,No,52,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,84.1,4348.65,Yes
+3721-WKIIL,Female,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.6,561.15,No
+6734-CKRSM,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,63.6,No
+1265-ZFOSD,Female,0,Yes,No,64,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,81.3,5129.3,No
+6568-POCUI,Female,0,Yes,No,45,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),95.2,4285.8,No
+7890-VYYWG,Male,1,Yes,No,3,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,36.45,93.7,Yes
+5197-LQXXH,Female,0,Yes,No,71,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),83.3,5894.5,No
+9907-SWKKF,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.05,25.05,Yes
+3457-PQBYH,Female,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.3,1160.75,No
+7682-AZNDK,Male,0,Yes,Yes,34,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.85,3091.75,No
+0587-DMGBH,Female,0,No,No,8,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.85,365.55,Yes
+5384-ZTTWP,Female,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.8,272.95,No
+3745-HRPHI,Male,0,Yes,Yes,66,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),54.65,3632,No
+7636-OWBPG,Male,1,No,No,12,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),29.35,381.2,No
+1231-YNDEK,Male,0,No,No,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.15,1035.5,No
+1407-DIGZV,Female,0,Yes,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.1,52,No
+6397-JNZZG,Female,1,Yes,No,43,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),55.55,2342.2,Yes
+0570-BFQHT,Female,0,No,No,9,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,80.55,653.9,No
+4393-OBCRR,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,71.2,No
+3523-QRQLL,Female,0,Yes,Yes,22,Yes,No,DSL,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),69.5,1498.2,Yes
+8564-LDKFL,Male,0,Yes,No,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106,4178.65,No
+3696-DFHHB,Female,0,No,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.5,1821.8,No
+0895-DQHEW,Male,0,Yes,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.3,5278.15,Yes
+4717-GHADL,Female,0,No,No,50,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,79.6,4024.2,No
+5501-TVMGM,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.25,55.25,No
+5879-HMFFH,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),88.05,6520.8,No
+6772-WFQRD,Male,0,No,Yes,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.4,854.9,No
+3810-DVDQQ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),117.6,8308.9,No
+6972-SNKKW,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,109.2,No
+3694-GLTJM,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.65,92.05,No
+8550-XSXUQ,Male,0,Yes,No,48,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),70.55,3420.5,No
+8149-RSOUN,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.85,93.85,Yes
+9055-MOJJJ,Female,0,Yes,Yes,64,Yes,No,DSL,Yes,No,No,Yes,Yes,No,One year,No,Mailed check,65.8,4068,No
+4359-INNWN,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.05,337.9,No
+0585-EGDDA,Male,0,Yes,No,40,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,80,3168.75,No
+4032-RMHCI,Female,0,Yes,Yes,41,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Credit card (automatic),35.4,1412.4,No
+0549-CYCQN,Male,1,No,No,51,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Bank transfer (automatic),79.6,3974.7,No
+3481-JHUZH,Male,0,Yes,No,41,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.25,3439,No
+7250-EQKIY,Female,0,Yes,Yes,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,50.45,50.45,Yes
+3594-IVHJZ,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.45,42.45,No
+6869-FGJJC,Male,0,No,No,68,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Credit card (automatic),79.6,5461.45,No
+3896-ZVNET,Female,0,Yes,Yes,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.7,571.75,No
+8205-VSLRB,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),77.3,5498.2,No
+5960-MVTUK,Male,0,No,No,3,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,29.75,96.85,No
+6817-WTYHE,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.9,111.05,No
+3082-VQXNH,Male,0,Yes,No,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),29.8,94.4,No
+4013-UBXWQ,Female,0,No,No,7,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,74.65,521.1,Yes
+4931-TRZWN,Female,0,No,No,13,Yes,No,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,71.95,923.85,No
+0750-EKNGL,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.75,141.1,No
+7669-LCRSD,Male,0,Yes,Yes,12,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,56.3,628.65,No
+3567-PQTSO,Male,0,Yes,Yes,53,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,105.25,5576.3,No
+5519-NPHVG,Female,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,1046.1,Yes
+8043-PNYSD,Male,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,1245.6,No
+9938-EKRGF,Female,0,No,No,15,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Mailed check,84.45,1287.85,No
+2703-AMTUL,Male,0,Yes,Yes,36,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Mailed check,53.65,1939.35,No
+0928-JMXNP,Male,1,Yes,No,4,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,29.9,118.25,No
+8173-RXAYP,Female,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,452.55,No
+4825-XJGDM,Female,0,No,No,61,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Credit card (automatic),43.7,2696.55,No
+5402-HTOTQ,Male,0,No,No,16,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.3,875.35,No
+6734-JDTTV,Male,0,Yes,Yes,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.85,1267.05,No
+7850-THJMU,Female,0,Yes,Yes,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,494.9,No
+3890-RTCMS,Male,0,No,No,16,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,49.45,799,No
+8849-GYOKR,Female,0,Yes,No,54,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),106.55,5763.3,Yes
+3148-BLQJT,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.1,20.1,Yes
+3717-FDJFU,Male,0,No,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.45,106.9,No
+3665-JATSN,Female,0,No,No,19,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,39.7,710.05,No
+7966-YOTQW,Male,0,No,No,10,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),54.5,568.2,No
+0461-CVKMU,Female,0,Yes,Yes,23,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,83.8,1900.25,Yes
+8806-EAGWC,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,55.15,159.15,Yes
+8853-TZDGH,Female,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.6,8012.75,No
+7779-LGOVN,Male,1,Yes,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.65,856.65,Yes
+4324-BZCKL,Female,0,Yes,Yes,10,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.55,551.3,No
+6924-TDGMT,Male,0,Yes,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.55,184.95,No
+9710-ZUSHQ,Female,1,No,No,37,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),106.75,4056.75,Yes
+1536-YHDOE,Male,0,Yes,Yes,17,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,62.1,1096.65,No
+8123-QBNAZ,Female,0,Yes,Yes,36,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),104.5,3684.95,No
+3629-ZNKXA,Male,1,No,No,17,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.8,1752.45,No
+4827-LTQRJ,Female,1,Yes,Yes,66,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),110.6,7210.85,No
+7711-YIJWC,Male,0,Yes,Yes,61,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),84.9,5264.5,No
+5482-VXSXJ,Male,0,No,No,22,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,93.2,2157.3,No
+7365-BVCJH,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.4,24.4,No
+9620-ENEJV,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),70.55,433.95,No
+4183-WCSEP,Male,0,No,No,31,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,78.45,2435.15,Yes
+2378-YIZKA,Female,0,Yes,Yes,68,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85,5607.75,No
+5498-TXHLF,Female,0,Yes,Yes,34,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,87.45,2874.15,Yes
+0689-DSXGL,Female,0,Yes,Yes,52,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),85.8,4433.3,No
+6818-DJXAA,Female,0,No,Yes,10,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,91.1,964.35,No
+9722-UJOJR,Male,0,Yes,Yes,29,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.75,1974.8,Yes
+0464-WJTKO,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.1,1460.85,No
+8902-ZEOVF,Male,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,951.55,No
+6778-EICRF,Male,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,One year,No,Mailed check,74.8,1821.2,No
+2662-NNTDK,Male,0,No,No,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.8,1600.95,No
+4132-KALRO,Female,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,100.85,399.25,No
+3902-FOIGH,Male,1,Yes,No,12,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.35,1218.55,Yes
+6772-KSATR,Male,0,No,No,1,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,81.7,81.7,Yes
+7112-OPOTK,Male,0,No,No,33,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,68.25,2171.15,Yes
+2874-YXVVA,Female,0,No,No,34,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105.1,3634.8,No
+1245-HARPS,Female,0,Yes,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.4,292.4,No
+4210-QFJMF,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.15,317.25,Yes
+4323-ELYYB,Male,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,218.55,No
+4293-ETKAP,Female,0,Yes,Yes,65,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),79.4,5071.9,No
+6064-PUPMC,Male,0,Yes,Yes,23,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Credit card (automatic),57.2,1423.35,No
+6504-VBLFL,Male,0,Yes,No,55,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Electronic check,58.6,3068.6,No
+6322-PJJDJ,Male,0,Yes,No,49,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Electronic check,94.8,4690.65,No
+0330-BGYZE,Male,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),102.5,6157.6,No
+1085-LDWAM,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.35,1442.65,No
+7586-ZATGZ,Male,0,No,No,40,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.9,3369.05,No
+7197-VOJMM,Male,0,Yes,No,67,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Credit card (automatic),69.2,4671.65,No
+3318-OSATS,Male,1,No,No,35,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.45,3474.05,Yes
+5828-AVIPD,Male,0,Yes,Yes,19,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,100.95,1875.55,Yes
+1843-TLSGD,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.85,272.35,No
+9626-VFRGG,Female,0,No,Yes,41,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),88.5,3645.05,No
+7075-BNDVQ,Female,0,No,No,4,No,No phone service,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,35,135.75,No
+9143-CANJF,Female,0,Yes,Yes,24,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,55.15,1319.85,No
+7284-BUYEC,Female,0,No,No,5,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),50.95,229.4,No
+2041-JIJCI,Female,0,No,No,5,Yes,Yes,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,64,370.25,No
+1086-LXKFY,Female,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.1,69.1,Yes
+4900-MSOMT,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),80.2,5714.2,No
+2229-VWQJH,Female,0,No,No,24,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),49.3,1233.25,No
+9194-GFVOI,Female,0,Yes,No,42,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),84.35,3571.6,No
+1336-EZFZY,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,83.3,No
+4282-MSACW,Male,0,No,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),117.2,8035.95,No
+1403-LKLIK,Female,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,579.4,No
+2636-ALXXZ,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,69.6,Yes
+7774-OJSXI,Male,0,No,No,31,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.45,3066.45,Yes
+7786-WBJYI,Female,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),77.95,305.55,Yes
+0136-IFMYD,Male,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,109.95,7634.25,No
+5144-TVGLP,Male,1,No,No,38,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,94.75,3653,No
+9643-AVVWI,Female,0,Yes,Yes,3,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,80,241.3,No
+0253-ZTEOB,Female,0,Yes,Yes,48,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Electronic check,79.65,3870.3,No
+2706-QZIHY,Female,0,Yes,No,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.2,387.9,No
+6061-PQHMK,Female,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.9,527.5,No
+9885-CSMWE,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.45,78.45,Yes
+6137-MFAJN,Female,0,No,No,48,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,44.8,2104.55,No
+9122-UMROB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,20.3,No
+4232-JGKIY,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.2,19.2,No
+2402-TAIRZ,Female,0,No,No,37,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Electronic check,80.05,3019.1,No
+9659-ZTWSM,Male,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),107.35,7051.95,No
+9139-TWBAS,Female,0,Yes,No,26,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),47.85,1190.5,No
+6685-GBWJZ,Male,0,Yes,No,63,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Credit card (automatic),70.8,4448.8,No
+5016-ETTFF,Male,0,No,No,10,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,29.5,255.25,Yes
+3866-MDTUB,Female,0,No,No,2,Yes,No,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,70.75,146.9,Yes
+2195-VVRJF,Male,1,Yes,No,18,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),59.1,1011.05,No
+3913-RDSJZ,Female,0,Yes,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.55,1714.95,No
+8058-JMEQO,Female,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.45,762.5,Yes
+4203-QGNZA,Female,0,No,Yes,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,535.35,No
+5043-TRZWM,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,75.55,75.55,No
+0697-ZMSWS,Male,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.65,338.9,Yes
+7657-DYEPJ,Male,1,No,No,38,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),70.15,2497.35,Yes
+9494-BDNNC,Male,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,No,Electronic check,95.3,6273.4,No
+1640-PLFMP,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.25,70.25,No
+5366-OBVMR,Female,0,Yes,No,18,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.3,908.75,No
+8276-MQBYC,Male,1,No,No,51,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,97.8,4913.3,Yes
+7644-OMVMY,Male,0,Yes,Yes,0,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85, ,No
+7593-XFKDI,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,46.3,46.3,Yes
+4573-JKNAE,Male,0,No,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.35,212.3,No
+0337-CNPZE,Female,0,No,No,41,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.3,4443.45,Yes
+9817-APLHW,Male,0,No,No,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,25,316.2,No
+8380-MQINP,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.3,1079.05,No
+0840-DFEZH,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.35,564.65,No
+1513-XNPPH,Female,0,No,No,12,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.4,1095.65,Yes
+8690-ZVLCL,Female,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88,6161.9,No
+6015-VVHHE,Female,1,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),83.15,446.05,Yes
+1125-SNVCK,Female,1,No,No,49,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,43.8,2106.05,No
+0384-LPITE,Male,0,No,No,40,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),62.05,2511.55,No
+4616-EWBNJ,Female,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.1,318.6,No
+6347-DCUIK,Male,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.15,811.8,Yes
+1335-HQMKX,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,101.35,7323.15,No
+2545-EBUPK,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.05,186.05,No
+6923-AQONU,Male,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.9,454,No
+2172-EJXVF,Female,1,No,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,105.9,7521.95,No
+0897-FEGMU,Female,0,Yes,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.5,1056.95,Yes
+7663-RGWBC,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.15,44.15,Yes
+1120-BMWUB,Female,0,No,No,16,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,53.9,834.15,Yes
+9124-LHCJQ,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,85.45,85.45,Yes
+4536-PLEQY,Male,0,Yes,No,12,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.05,999.8,No
+7029-IJEJK,Female,0,No,No,54,No,No phone service,DSL,Yes,No,No,Yes,No,Yes,One year,No,Bank transfer (automatic),44.1,2369.7,No
+8871-JLMHM,Female,0,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),90.2,6297.65,No
+2235-ZGKPT,Female,0,Yes,Yes,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.85,239.55,Yes
+3891-PUQOD,Female,0,No,Yes,1,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,59.2,59.2,Yes
+5447-VYTKW,Male,0,No,No,27,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,53.45,1461.45,No
+3623-FQBOX,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.95,416.4,No
+0689-NKYLF,Male,0,No,No,13,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,83.2,1060.6,Yes
+8659-HDIYE,Female,1,No,No,64,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,Month-to-month,No,Credit card (automatic),74.65,4869.35,No
+3658-KIBGF,Female,0,No,No,1,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,54.9,54.9,Yes
+3474-BAFSJ,Male,0,Yes,No,57,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),57.5,3265.95,No
+5519-YLDGW,Female,0,Yes,No,21,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.9,2254.2,Yes
+3865-ZFZIB,Male,0,No,No,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,358.15,No
+1855-AGAWH,Male,0,Yes,No,31,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,93.8,2939.8,No
+7109-CQYUZ,Male,0,No,No,52,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,89.25,4652.4,No
+1370-GGAWX,Female,0,No,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,94.15,4408.45,No
+4680-KUTAJ,Female,1,No,No,11,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,55.6,580.8,No
+5307-UVGNB,Female,0,Yes,Yes,53,No,No phone service,DSL,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),48.7,2495.2,No
+4946-EDSEW,Female,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.25,180.3,Yes
+2883-ILGWO,Male,1,No,No,57,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.9,5913.95,No
+2516-VQRRV,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75.45,158.4,Yes
+7580-UGXNC,Female,1,No,No,2,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.85,104.2,Yes
+3642-BYHDO,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.9,1389.35,No
+9629-NHXFW,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.4,19.4,No
+2696-RZVZW,Male,0,Yes,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.05,1629.2,No
+5766-FTRTS,Male,0,Yes,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,84.45,6033.1,No
+0396-HUJBP,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.3,44.4,No
+5178-LMXOP,Male,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.1,95.1,Yes
+8879-ZKJOF,Female,0,No,No,41,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),79.85,3320.75,No
+6285-FTQBF,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.55,1867.7,No
+8185-UPYBR,Male,0,Yes,No,6,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.5,438,Yes
+4585-HETAI,Female,0,Yes,Yes,4,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,73.75,325.45,Yes
+7526-BEZQB,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.05,1148.1,Yes
+1474-JUWSM,Female,0,Yes,No,58,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Electronic check,68.4,3972.25,No
+3530-CRZSB,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,155.9,No
+8498-XXGWA,Female,0,Yes,No,65,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.15,3673.15,No
+9617-INGJY,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.6,70.6,No
+0621-TSSMU,Male,0,Yes,No,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.95,1126.75,No
+7234-KMNRQ,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19,73.45,No
+7636-PEPNS,Female,0,Yes,Yes,58,Yes,No,DSL,No,No,No,No,No,No,One year,Yes,Mailed check,44.1,2413.05,No
+4683-WYDOU,Male,0,Yes,No,62,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),107.6,6912.7,No
+9052-DHNKM,Male,0,No,No,26,Yes,Yes,DSL,Yes,Yes,No,No,No,No,One year,No,Electronic check,61.55,1581.95,No
+6794-HKIAJ,Male,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),90.7,5586.45,No
+5578-NKCXI,Female,0,Yes,Yes,58,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Credit card (automatic),99.25,5846.65,No
+1642-HMARX,Female,0,Yes,No,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,91.7,6424.7,No
+3096-WPXBT,Female,0,Yes,Yes,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),100.7,6018.65,No
+8434-PNQZX,Female,0,No,No,42,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),78.45,3373.4,No
+5950-AAAGJ,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),84.3,1537.9,No
+4299-OPXEJ,Female,0,No,No,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.55,1080.55,No
+4951-UKAAQ,Female,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,88.95,355.2,Yes
+9618-LFJRU,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.45,82.85,No
+6693-DJWTY,Female,0,No,Yes,35,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Credit card (automatic),55.6,2016.45,No
+0744-GKNGE,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),86.8,5327.25,No
+6447-EGDIV,Female,0,No,No,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.95,683.25,No
+1167-OYZJF,Female,1,Yes,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.05,1263.05,No
+2108-YKQTY,Female,0,No,No,4,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),50.7,151.3,Yes
+4806-DXQCE,Female,1,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,113.65,7714.65,No
+4918-QLLIW,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Credit card (automatic),53.4,188.7,Yes
+7056-IMHCC,Male,1,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.9,5549.4,Yes
+4854-SSLTN,Male,0,Yes,Yes,2,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,59.5,130.5,No
+5294-DMSFH,Female,0,Yes,Yes,29,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),87.8,2621.75,No
+4837-PZTIC,Female,0,No,No,47,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,41.9,1875.25,No
+1625-JAIIY,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,One year,Yes,Electronic check,83,5685.8,Yes
+0603-OLQDC,Male,0,No,Yes,12,Yes,No,DSL,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,69.85,837.5,No
+3272-VUHPV,Female,0,Yes,Yes,8,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),56.3,401.5,No
+4176-FXYBO,Male,0,Yes,No,54,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,109.55,6118.95,No
+1063-DHQJF,Male,0,Yes,Yes,69,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Two year,Yes,Mailed check,92.15,6480.9,No
+8663-UPDGF,Female,0,No,No,26,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),69.5,1800.05,No
+6719-FGEDO,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),97,7104.2,No
+1837-YQUCE,Female,0,No,No,70,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),58.35,4214.25,No
+2947-DOMLJ,Male,0,No,Yes,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,50.6,50.6,Yes
+4112-LUEIZ,Male,0,No,No,10,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.5,863.1,Yes
+0369-ZGOVK,Female,0,Yes,Yes,28,Yes,No,Fiber optic,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),70.4,1992.2,No
+4510-HIMLV,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,69.8,Yes
+9919-KNPOO,Female,0,Yes,No,21,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.3,1948.35,No
+8749-JMNKX,Male,1,Yes,No,51,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),93.8,4750.95,Yes
+7872-BAAZR,Female,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.55,1007.9,No
+9391-LMANN,Male,0,No,Yes,53,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,No,Electronic check,95.95,5036.9,No
+2430-USGXP,Male,0,Yes,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.05,2391.8,Yes
+8174-TBVCF,Female,0,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),94.8,6859.05,No
+1698-XFZCI,Male,0,No,No,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,107.75,6521.9,No
+1877-HKBQX,Female,0,No,No,11,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,54.6,617.85,No
+4450-DLLMH,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),71.3,157.75,No
+0428-AXXLJ,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.5,516.3,No
+2746-DIJLO,Female,0,No,No,41,Yes,Yes,DSL,No,Yes,No,No,No,No,One year,Yes,Credit card (automatic),56.3,2364,No
+5955-ERIHD,Male,0,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.7,1687.95,Yes
+0917-EZOLA,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.15,7689.95,Yes
+3508-VLHCZ,Female,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.55,6239.05,No
+4086-ATNFV,Female,0,Yes,Yes,34,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Mailed check,60.8,2042.05,No
+0468-YRPXN,Male,0,No,No,29,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.8,2807.1,No
+5996-NRVXR,Male,1,Yes,No,40,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),98.15,4116.8,No
+3739-YBWAB,Male,0,Yes,No,36,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,35.35,1317.95,No
+7047-FWEYA,Female,0,Yes,No,46,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,103.15,4594.65,No
+2000-MPKCA,Female,0,No,No,58,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,107.75,6332.75,No
+9762-YAQAA,Male,0,No,No,39,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,No,Credit card (automatic),81.4,3213.75,No
+5949-EBSQK,Male,0,Yes,Yes,4,Yes,No,DSL,No,No,No,Yes,No,Yes,Month-to-month,No,Credit card (automatic),61.45,229.55,Yes
+5473-KHBPS,Female,0,Yes,Yes,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),95.7,4976.15,No
+0100-DUVFC,Male,1,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.8,7308.95,No
+5397-TUPSH,Male,1,Yes,No,65,Yes,No,Fiber optic,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),70.95,4555.2,No
+8950-MTZNV,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.95,44.95,No
+0326-VDYXE,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,97.65,6982.5,No
+6773-LQTVT,Female,1,Yes,Yes,29,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,35.65,1025.15,No
+7274-RTAPZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.55,90.55,Yes
+0436-TWFFZ,Female,0,No,No,67,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,85.25,5714.2,No
+8566-YPRGL,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.5,19.5,No
+0311-UNPFF,Female,0,No,No,26,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),88.8,2274.35,Yes
+6609-MXJHJ,Female,0,Yes,Yes,30,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,25.1,789.55,No
+2669-OIDSD,Female,0,Yes,No,48,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.05,4834,No
+8400-WZICQ,Female,0,Yes,Yes,55,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.7,3131.8,No
+3834-XUIFC,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,85.2,602.55,Yes
+9576-SYUHJ,Male,0,No,No,37,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,91.15,3369.25,No
+0410-IPFTY,Female,0,Yes,No,31,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),83.85,2674.15,No
+2831-EBWRN,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.9,199.75,No
+9430-NKQLY,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.1,1790.8,No
+8414-MYSHR,Male,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.4,449.75,Yes
+0247-SLUJI,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,19.7,No
+9402-ORRAH,Female,1,No,No,15,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.5,1400.3,No
+6483-OATDN,Male,0,Yes,Yes,8,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),51.3,411.6,No
+1293-HHSHJ,Female,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,21.1,741,No
+4840-ORQXB,Female,1,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,104.75,5841.35,No
+7599-FKVXZ,Male,0,Yes,No,42,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),106.15,4512.7,Yes
+3982-DQLUS,Male,1,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,85.75,5688.45,No
+8610-ZIKJJ,Female,0,Yes,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.3,31.9,No
+3756-VNWDH,Male,1,Yes,No,65,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Electronic check,100.75,6674.65,No
+7801-KICAO,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.15,1345.75,No
+7673-LPRNY,Female,0,No,No,23,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,78.55,1843.05,No
+8229-TNIQA,Female,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.3,196.95,Yes
+6060-QBMGV,Male,0,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,1433.8,No
+7339-POGZN,Female,0,No,No,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,50.7,214.55,No
+2828-SLQPF,Male,0,No,No,19,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45,865.85,No
+4465-VDKIQ,Female,0,No,No,18,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,77.8,1358.6,No
+2921-XWDJH,Female,1,Yes,No,38,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.45,3147.15,No
+6221-AVQYL,Male,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.25,131.05,Yes
+2558-BUOZZ,Male,0,No,No,47,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,No,Bank transfer (automatic),94.8,4535.85,No
+9257-AZMTZ,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.1,1078.75,No
+0003-MKNFE,Male,0,No,No,9,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,59.9,542.4,No
+0975-UYDTX,Female,0,Yes,No,26,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),90.1,2312.55,No
+7743-EXURX,Male,0,Yes,Yes,8,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,51.05,415.05,Yes
+4682-BLBUC,Male,0,Yes,No,44,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,70.95,3250.45,No
+0975-VOOVL,Female,0,No,No,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,29.2,98.5,No
+5968-VXZLG,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),46.6,87.9,No
+5569-KGJHX,Female,0,Yes,Yes,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),85.35,754.65,Yes
+4988-IQIGL,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.35,75.35,Yes
+5201-FRKKS,Male,0,No,No,25,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),74.3,1952.25,No
+9799-CAYJJ,Female,1,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.3,153.8,No
+7730-IUTDZ,Male,0,No,No,43,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.2,3198.6,Yes
+0426-TIRNE,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.9,20.9,Yes
+5443-SCMKX,Female,0,Yes,No,58,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,Yes,Electronic check,94.3,5610.15,No
+8295-KMENE,Female,0,Yes,Yes,59,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,Two year,Yes,Mailed check,76.45,4519.5,No
+6738-ISCBM,Male,0,No,No,44,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,54,2440.25,No
+9821-BESNZ,Male,0,No,No,66,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,104.25,6860.6,No
+3678-MNGZX,Male,0,Yes,Yes,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.95,1377.7,No
+1335-NTIUC,Male,0,No,No,9,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.95,190.25,No
+2916-BQZLN,Male,0,No,No,19,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.75,1651.95,No
+4558-CGYCZ,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,78.3,No
+2368-GAKKQ,Female,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),113.65,7939.25,No
+8710-YGLWG,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.9,44.9,No
+3199-XGZCY,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.25,576.7,No
+3785-KTYSH,Male,0,No,No,53,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.6,1279,No
+6814-ZPWFQ,Male,1,No,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25,1260.7,No
+4063-EIKNQ,Male,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.95,267.35,No
+6993-YCOBK,Male,0,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.6,6586.85,No
+5206-HPJKM,Male,0,No,No,17,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),55.5,934.15,No
+7587-RZNME,Male,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,43.3,123.65,Yes
+0748-RDGGM,Male,0,Yes,No,70,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),109.5,7534.65,Yes
+8393-JMVMB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.45,19.45,No
+5019-GQVCR,Male,1,No,No,43,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.85,3645.6,No
+6036-TTFYU,Female,0,Yes,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.6,314.45,No
+2550-AEVRU,Female,0,Yes,Yes,57,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Electronic check,53.45,3053,No
+0969-RGKCU,Male,0,Yes,Yes,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.8,677.05,No
+0378-CJKPV,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),112.1,7965.95,No
+4706-AXVKM,Female,1,No,No,11,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),84.8,906.85,Yes
+5889-JTMUL,Female,1,Yes,No,50,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,95.05,4888.7,Yes
+9026-RNUJS,Male,1,No,No,5,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,50.35,237.25,Yes
+3746-EUBYR,Male,0,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.6,74.6,Yes
+2332-EFBJY,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.7,342.4,Yes
+2880-FPNAE,Male,1,Yes,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.2,140.1,No
+1703-MGIAB,Female,0,No,No,17,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,69,1108,No
+4311-QTTAI,Female,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.35,295.55,No
+9474-PHLYD,Female,0,No,No,15,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,59.45,892.65,Yes
+4318-RAJVY,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.8,198.25,No
+2165-VOEGB,Female,0,No,Yes,46,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.2,4822.85,Yes
+3612-YUNGG,Male,0,Yes,Yes,64,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.2,6741.15,No
+2254-DLXRI,Female,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,79.15,79.15,No
+5062-CJJKH,Male,0,Yes,Yes,25,Yes,No,DSL,No,Yes,Yes,No,No,No,One year,No,Mailed check,53.65,1355.45,No
+9028-LIHRP,Male,0,Yes,Yes,71,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),100.2,7209,No
+2219-MVUSO,Male,0,No,No,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.15,438.4,Yes
+1053-MXTTK,Female,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.65,7726.35,No
+1530-ZTDOZ,Female,0,Yes,No,49,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),40.65,2070.75,No
+1301-LOPVR,Male,0,Yes,Yes,29,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),55.35,1636.95,No
+0853-TWRVK,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),105.6,7581.5,No
+8914-RBTSB,Male,0,Yes,No,31,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Electronic check,93.8,3019.5,Yes
+6212-ATMLK,Female,0,No,No,50,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.7,4729.75,No
+8200-LGKSR,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,83.2,6126.1,No
+1568-BEKZM,Male,1,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),90.05,6333.4,No
+0670-ANMUU,Male,0,No,No,71,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,One year,No,Credit card (automatic),97.65,6687.85,No
+6897-RWMUB,Male,0,Yes,Yes,61,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),68.05,4158.25,No
+0963-ZBDRN,Male,0,No,No,32,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,96.2,3183.4,Yes
+7594-RQHXR,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.6,79.6,Yes
+6999-CHVCF,Male,0,No,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Bank transfer (automatic),102.1,7149.35,No
+6134-KWTBV,Male,0,No,No,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),23.4,1429.65,No
+1077-HUUJM,Female,0,No,Yes,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.05,472.65,No
+6331-EWIEB,Male,0,No,No,20,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.25,1734.5,Yes
+0895-LNKRC,Male,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.45,113.5,No
+2045-BMBTJ,Female,1,No,No,33,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),59.45,1884.65,No
+8417-GSODA,Male,0,Yes,Yes,28,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Bank transfer (automatic),92.2,2568.15,No
+5171-EPLKN,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,470,No
+4452-QIIEB,Male,0,No,No,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,43.9,278.4,No
+5998-VVEJY,Male,0,No,No,26,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,80.5,2088.8,Yes
+1624-NALOJ,Male,1,No,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.8,502.6,Yes
+6741-EGCBI,Male,1,No,No,30,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.5,2595.85,No
+7820-ZYGNY,Male,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,One year,No,Credit card (automatic),90.45,5825.5,No
+4317-VTEOA,Male,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,50.75,50.75,Yes
+9677-AVKED,Female,0,No,Yes,53,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,84.6,4449.75,No
+1670-SVOWZ,Female,0,Yes,Yes,14,Yes,No,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),89.65,1208.35,Yes
+2227-JRSJX,Female,0,No,No,21,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.15,1956.4,No
+9847-HNVGP,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,310.6,No
+6696-YDAYZ,Male,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.5,290.55,No
+8980-WQFWL,Female,0,No,No,35,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,62.1,2096.1,No
+1730-ZMAME,Female,1,No,No,32,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.5,2665,No
+6506-EYCNH,Female,0,Yes,Yes,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.55,543.8,No
+4634-JLRJT,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.35,20.35,No
+9501-UKKNL,Male,0,No,No,59,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,51.7,3005.8,No
+2560-QTSBS,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),23.3,1623.15,No
+7541-YLXCL,Male,0,No,No,36,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,65.4,2498.4,Yes
+4795-WRNVT,Female,0,No,No,40,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,No,Mailed check,65.1,2586,No
+6080-TCMYC,Male,0,Yes,Yes,40,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.2,3292.3,No
+0260-ZDLGK,Female,0,No,Yes,9,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,72.9,651.4,Yes
+7860-UXCRM,Male,0,Yes,Yes,63,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),74.5,4674.55,No
+6357-JJPQT,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.5,232.35,No
+2292-XQWSV,Male,0,Yes,Yes,40,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Mailed check,60.3,2448.5,No
+9552-TGUZV,Male,0,Yes,No,8,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,75,658.1,No
+5913-INRQV,Male,1,Yes,No,34,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,No,One year,No,Mailed check,90.15,3128.8,No
+8722-NGNBH,Male,0,No,No,5,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,40,223.45,Yes
+6210-KBBPI,Male,1,No,No,9,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.45,919.4,Yes
+9643-YBLUR,Male,0,Yes,No,9,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),69.05,653.95,No
+8734-FNWVH,Male,0,Yes,Yes,31,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),59.7,1825.5,No
+9447-YPTBX,Female,0,Yes,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.85,943.1,No
+0133-BMFZO,Female,0,No,No,2,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,86.25,181.65,Yes
+6128-CZOMY,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.65,45.65,Yes
+9540-JYROE,Male,0,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.1,551.35,Yes
+9026-LHEVG,Female,0,No,No,9,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,40.75,359.4,No
+2260-USTRB,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.2,115.95,Yes
+3656-TKRVZ,Female,0,No,No,3,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.35,165.2,Yes
+7011-CVEUC,Male,0,Yes,No,25,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.7,2338.35,No
+6185-TASNN,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,46.3,46.3,No
+7925-PNRGI,Female,0,Yes,Yes,45,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,No,Mailed check,81.3,3541.1,No
+8623-TMRBY,Male,1,Yes,Yes,51,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.2,4146.05,Yes
+1552-CZCLL,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20,1087.25,No
+3038-PQIUY,Female,0,No,No,38,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),66.15,2522.4,No
+1501-SGHBW,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.85,81,Yes
+9313-CDOGY,Male,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.6,717.3,No
+8148-BPLZQ,Male,0,No,No,34,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,49.8,1734.2,No
+2623-DRYAM,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,101.75,7069.3,No
+9987-LUTYD,Female,0,No,No,13,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Mailed check,55.15,742.9,No
+3208-YPIOE,Male,0,No,No,39,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.25,3017.65,Yes
+4488-KQFDT,Female,0,No,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.95,6423,No
+2612-RANWT,Female,0,No,No,12,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.15,1164.3,Yes
+5693-PIPCS,Male,0,No,No,41,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),99.65,4220.35,No
+0491-KAPQG,Male,0,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.7,1558.7,No
+1925-LFCZZ,Male,1,No,No,55,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),50.05,2743.45,No
+8039-EQPIM,Male,0,Yes,No,69,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),60.25,4055.5,No
+6900-PXRMS,Male,1,Yes,Yes,26,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,105.75,2710.25,Yes
+8707-RMEZH,Female,1,Yes,No,69,Yes,No,Fiber optic,Yes,Yes,No,No,No,Yes,One year,No,Credit card (automatic),87.3,6055.55,No
+3346-BRMIS,Female,1,Yes,No,18,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,48.35,810.7,Yes
+3209-ZPKFI,Male,0,Yes,Yes,47,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.25,2538.2,No
+6479-VDGRK,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),85.3,6129.2,No
+9373-WSLOY,Male,1,Yes,No,33,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,50,1750.85,No
+2933-FILNV,Female,0,Yes,Yes,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.4,36.55,Yes
+3569-JFODW,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),90.95,6652.45,No
+9819-FBNSV,Male,1,Yes,No,37,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),72.25,2575.45,No
+9544-PYPSJ,Female,1,Yes,Yes,62,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),96.1,6019.35,No
+1591-XWLGB,Female,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,1379.6,No
+0396-UKGAI,Male,0,No,Yes,23,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),55.3,1284.2,No
+3243-ZHOHY,Female,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,296.15,No
+5186-EJEGL,Male,0,No,No,9,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,69.5,653.25,No
+7527-QNRUS,Male,0,Yes,Yes,17,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,25.15,412.6,No
+9986-BONCE,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.95,85.5,Yes
+2722-JMONI,Female,1,Yes,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.55,49.55,Yes
+2203-GHNWN,Female,0,Yes,No,24,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.65,1928.7,No
+3878-AVSOQ,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.25,71.25,No
+3258-SYSWS,Male,1,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),113.8,7845.8,No
+5296-PSYVW,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,24.55,1750.7,No
+6319-QSUSR,Female,0,No,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.7,216.2,No
+4083-EUGRJ,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,178.5,Yes
+7579-OOPEC,Female,1,Yes,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),50.15,115.1,Yes
+0512-FLFDW,Female,1,Yes,No,60,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),100.5,6029,No
+1771-OADNZ,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.9,2745.2,Yes
+0487-CRLZF,Female,0,No,No,49,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),74.45,3721.9,No
+2107-FBPTK,Female,1,No,No,30,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.1,3121.1,No
+9451-WLYRI,Female,0,Yes,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.05,990.45,No
+8219-VYBVI,Male,0,No,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25,1004.35,No
+1991-VOPLL,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.05,157.65,No
+9738-QLWTP,Male,0,No,No,39,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,One year,No,Electronic check,81.9,3219.75,No
+1016-DJTSV,Male,1,No,No,8,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.7,572.85,No
+9419-IPPBE,Female,0,Yes,Yes,51,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,One year,Yes,Electronic check,90.15,4554.85,No
+0174-QRVVY,Male,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.35,1847.55,No
+5699-BNCAS,Male,1,No,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.65,1766.75,No
+2900-PHPLN,Female,1,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.55,1462.05,No
+0612-RTZZA,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.25,25.25,Yes
+8106-GWQOK,Male,0,Yes,No,38,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),60,2193.2,No
+8751-EDEKA,Female,0,Yes,No,28,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),89.9,2433.5,No
+6878-GGDWG,Female,0,Yes,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.4,641.15,No
+4039-PIMHX,Male,1,Yes,No,49,Yes,No,DSL,No,Yes,No,No,No,No,Two year,No,Mailed check,49.8,2398.4,No
+8450-LUGUK,Female,0,Yes,Yes,37,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.1,861.85,No
+3604-WLABM,Female,0,No,No,10,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,54.25,583,No
+0795-GMVQO,Male,0,Yes,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),109.9,7332.4,No
+2259-OUUSZ,Male,0,No,No,7,No,No phone service,DSL,No,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),35.5,249.55,No
+1142-IHLOO,Female,0,No,No,51,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Credit card (automatic),87.55,4475.9,No
+6410-LEFEN,Female,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.15,416.45,Yes
+4633-MKHYU,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,88.4,788.6,No
+6257-RJOHI,Male,0,No,No,4,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.8,202.3,No
+1545-JFUML,Male,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Electronic check,99,6994.6,No
+3194-ORPIK,Female,0,Yes,Yes,50,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),84.4,4116.15,Yes
+7826-VVKWT,Female,1,Yes,Yes,24,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,96.55,2263.45,No
+1353-GHZOS,Male,0,Yes,No,22,Yes,No,DSL,Yes,No,No,No,Yes,No,One year,Yes,Bank transfer (automatic),59.75,1374.35,No
+2587-EKXTS,Male,0,No,No,44,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,111.5,4915.15,No
+5442-BXVND,Female,0,Yes,Yes,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),24.25,838.5,No
+8816-VXNZD,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.1,75.1,Yes
+9378-FXTIZ,Female,0,Yes,No,54,Yes,Yes,DSL,No,No,No,No,Yes,Yes,One year,Yes,Credit card (automatic),70.15,3715.65,Yes
+1723-HKXJQ,Male,0,No,No,42,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.75,4273.45,Yes
+7825-GKXMW,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.8,45.8,Yes
+9753-OYLBX,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,20.5,No
+2364-UFROM,Male,0,No,No,30,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Electronic check,70.4,2044.75,No
+6656-JWRQX,Female,0,No,No,1,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,30.55,30.55,No
+6473-ULUHT,Male,0,Yes,Yes,16,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,84.9,1398.25,No
+1240-KNSEZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.1,20.1,Yes
+0836-SEYLU,Male,0,Yes,No,9,No,No phone service,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Mailed check,40.65,328.95,Yes
+4433-JCGCG,Male,1,Yes,No,46,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101,4680.05,Yes
+3716-BDVDB,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.1,69.1,Yes
+7688-AWMDX,Male,0,Yes,No,71,Yes,No,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),54.5,3778.2,No
+2842-BCQGE,Male,0,No,No,43,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.35,3161.4,No
+7244-QWYHG,Male,0,Yes,No,50,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),44.45,2188.45,No
+5899-MQZZL,Female,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75,999.45,Yes
+6849-OYAMU,Male,0,Yes,Yes,19,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),100,1888.65,Yes
+5312-UXESG,Female,0,No,No,41,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),98.05,3990.6,No
+9488-HGMJH,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.15,71.15,Yes
+6038-GCYEC,Female,0,No,No,24,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),54.15,1240.25,Yes
+2150-WLKUW,Female,0,Yes,No,40,Yes,Yes,DSL,No,Yes,No,No,Yes,No,One year,No,Bank transfer (automatic),63.9,2635,No
+7159-FVYPK,Female,0,Yes,Yes,3,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),69.15,235,No
+7032-LMBHI,Female,0,No,No,37,Yes,Yes,DSL,Yes,No,No,No,Yes,No,One year,No,Bank transfer (automatic),64.65,2347.85,No
+1150-WFARN,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.75,7156.2,Yes
+6088-BXMRG,Female,0,Yes,Yes,32,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.85,3089.6,No
+3144-AUDBS,Female,0,Yes,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,49.15,270.8,Yes
+4821-SJHJV,Female,0,Yes,Yes,32,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,89.6,2901.8,No
+7346-MEDWM,Female,0,No,No,59,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,83.25,4949.1,No
+1137-DGOWI,Female,0,Yes,No,30,Yes,No,DSL,No,Yes,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),70.25,2198.9,No
+5616-PRTNT,Male,0,No,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.4,374.5,Yes
+3812-LRZIR,Female,0,Yes,Yes,27,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,24.5,761.95,No
+4818-QIUFN,Female,1,No,No,20,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,79.15,1520.9,Yes
+9483-GCPWE,Male,0,No,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,190.25,No
+4227-OJHAL,Female,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),73,5163,No
+9220-CXRSC,Female,0,Yes,Yes,69,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Two year,No,Credit card (automatic),61.4,4059.85,No
+4993-JCRGJ,Male,0,No,No,26,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,84.3,2281.6,No
+9537-JALFH,Male,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.9,1356.7,No
+6698-OXETB,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.4,231.45,No
+2103-ZRXFN,Male,0,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.75,50.75,No
+3724-BSCVH,Male,0,Yes,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.45,242.4,No
+4877-TSOFF,Female,0,Yes,Yes,55,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),75.75,4264.25,No
+9209-NWPGU,Male,0,No,No,44,Yes,No,DSL,Yes,No,No,Yes,Yes,No,One year,No,Electronic check,65.4,2774.55,No
+6961-VCPMC,Male,1,Yes,No,46,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.4,3605.2,Yes
+7306-YDSOI,Male,0,Yes,Yes,69,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Bank transfer (automatic),59.75,4069.9,No
+5288-AHOUP,Male,1,No,No,11,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,78.5,874.2,No
+6688-UZPWD,Female,0,Yes,No,11,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,102,1145.35,Yes
+0655-YDGFJ,Male,0,No,No,29,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),48.95,1323.7,No
+8468-EHYJA,Female,0,Yes,No,57,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.65,5497.05,No
+6823-SIDFQ,Male,0,No,No,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),18.25,534.7,No
+1097-FSPVW,Female,0,No,No,42,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),54.55,2455.05,No
+2839-RFSQE,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,38.7,Yes
+8328-SKJNO,Male,0,No,Yes,23,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),40.65,947.4,No
+7010-ZMVBF,Female,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,357,No
+5201-CBWYG,Male,0,Yes,Yes,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.8,1476.25,No
+0968-GSIKN,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.8,70.8,Yes
+1965-DDBWU,Male,0,No,No,16,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),89.05,1448.6,Yes
+9057-SIHCH,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.6,291.9,Yes
+6352-GIGGQ,Male,0,No,No,67,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),88.8,5903.15,No
+3635-QQRQD,Male,0,No,No,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.05,1201.65,No
+6771-XWBDM,Female,0,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),104.5,5921.35,Yes
+2897-DOVND,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,146.65,No
+3082-YVEKW,Female,0,Yes,Yes,23,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),77.15,1759.4,No
+0191-EQUUH,Female,0,No,Yes,25,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),35.05,844.45,No
+7134-HBPBS,Female,1,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.1,7774.05,No
+3489-HHPFY,Female,0,Yes,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.05,134.05,No
+2926-JEJJC,Female,0,No,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.2,140.95,No
+8601-QACRS,Female,0,No,No,5,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.6,249.95,Yes
+4950-BDEUX,Male,0,No,No,35,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,49.2,1701.65,No
+5789-LDFXO,Male,0,No,No,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.6,592.65,No
+0508-OOLTO,Female,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.65,135.75,No
+2984-TBYKU,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),104.9,7732.65,No
+9822-WMWVG,Female,0,No,No,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,106.5,4282.4,No
+2391-SOORI,Male,0,No,Yes,4,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,49.35,219.65,Yes
+9040-KZVWO,Male,0,No,No,26,Yes,No,Fiber optic,No,Yes,No,No,No,No,One year,No,Bank transfer (automatic),75.5,2018.1,No
+2645-QTLMB,Male,0,No,No,7,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.25,669,Yes
+4806-HIPDW,Female,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,68.95,68.95,Yes
+1548-ARAGG,Female,0,Yes,Yes,4,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,58.5,224.85,No
+6339-RZCBJ,Male,0,No,No,48,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),78.9,3771.5,No
+4424-TKOPW,Male,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.85,196.75,Yes
+4415-WNGVR,Female,1,Yes,No,12,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,79.2,943.85,No
+2522-AHJXR,Male,0,Yes,No,60,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),109.45,6572.85,No
+0412-UCCNP,Male,0,No,No,55,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Electronic check,59.2,3175.85,No
+1816-FLZDK,Male,0,No,No,1,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,29.15,29.15,No
+7096-UCLNH,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No
+8443-ZRDBZ,Male,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.05,318.9,Yes
+9136-ALYBR,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.45,24.45,Yes
+4615-PIVVU,Female,0,No,No,42,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),66.5,2762.75,No
+8885-QSQBX,Female,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),49.55,49.55,No
+7479-NITWS,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.35,631.85,Yes
+8617-ENBDS,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),73.6,232.5,No
+9385-NXKDA,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),82.65,5919.35,No
+0430-IHCDJ,Male,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,49,749.25,No
+2959-MJHIC,Male,0,Yes,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.35,307.4,Yes
+6674-KVJHG,Female,0,No,No,11,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,25.2,245.15,No
+1814-DKOLC,Female,0,No,No,5,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.45,134.75,No
+4201-JMNGR,Female,1,No,No,1,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,55.8,55.8,Yes
+9351-HXDMR,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.9,8240.85,No
+3537-HPKQT,Female,0,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),77.75,4266.4,No
+2129-ALKBS,Female,0,Yes,Yes,40,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,26.2,1077.5,No
+5821-MMEIL,Female,0,Yes,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.9,1115.6,No
+5960-WPXQM,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.05,79.05,Yes
+0684-AOSIH,Male,0,Yes,No,1,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95,95,Yes
+8260-NGFNY,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.2,25.2,Yes
+1309-XGFSN,Male,1,Yes,Yes,52,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,80.85,4079.55,No
+1866-RZZQS,Male,1,No,No,41,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.4,4187.75,Yes
+6968-URWQU,Male,0,Yes,No,43,Yes,No,DSL,No,No,No,No,Yes,No,One year,Yes,Mailed check,56.35,2391.15,No
+4742-TXUEX,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.3,890.5,No
+9631-XEYKE,Male,0,No,No,3,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),50.4,137.25,No
+8631-NBHFZ,Male,1,Yes,Yes,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),79.4,5154.6,Yes
+1335-MXCSE,Male,0,Yes,Yes,55,Yes,No,DSL,No,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),55.25,3119.9,No
+8873-TMKGR,Male,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.1,529.5,No
+8800-JOOCF,Female,0,No,Yes,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.05,966.55,No
+1469-LBJQJ,Female,0,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.2,6936.85,No
+4958-XCBDQ,Male,1,No,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.4,3496.3,Yes
+8337-UPOAQ,Male,1,Yes,No,10,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.8,914.3,Yes
+0064-YIJGF,Male,0,Yes,Yes,27,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.75,1929,No
+2386-OWURY,Female,0,No,No,58,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,95.3,5817.7,No
+1725-IQNIY,Male,0,Yes,No,54,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),109.75,6110.2,Yes
+0310-VQXAM,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,178.8,No
+6598-RFFVI,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.3,28.3,Yes
+3453-RTHJQ,Male,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.1,435,No
+6712-OAWRH,Female,1,No,No,26,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.25,2351.8,Yes
+6278-FEPBZ,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,186.15,No
+4280-DLSHD,Male,0,Yes,No,8,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,54.75,445.85,No
+1508-DFXCU,Male,0,No,No,12,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,81.45,912,No
+8614-VGMMV,Female,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.1,679.55,Yes
+7868-BGSZA,Male,0,Yes,No,43,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,80.2,3581.6,No
+2800-QQUSO,Male,0,No,No,42,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,100.3,4222.95,No
+2930-UOTMB,Female,0,No,No,31,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Credit card (automatic),65.25,1994.3,Yes
+7973-DZRKH,Female,0,No,Yes,66,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),90.95,5930.05,No
+5914-DVBWJ,Female,1,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,85.45,1505.85,Yes
+7698-YFGEZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,20,No
+2533-QVMSK,Male,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Two year,Yes,Electronic check,94.1,5638.3,Yes
+9773-PEQBZ,Male,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,79.85,797.25,No
+8644-XLFBW,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71.65,71.65,Yes
+0650-BWOZN,Female,1,No,No,18,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,73.55,1359.45,No
+8625-AZYZY,Male,0,Yes,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.65,2542.45,Yes
+7785-RDVIG,Female,0,Yes,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.3,54.7,No
+9602-WCXPI,Male,0,No,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.15,989.05,No
+2903-YYTBW,Male,0,Yes,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.55,44.55,No
+3620-MWJNE,Male,0,No,No,2,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,54.45,87.3,No
+8573-JGCZW,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.65,351.55,No
+6837-HAEVO,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,105,7297.75,No
+6701-YVNQG,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),88.7,6301.7,No
+9306-CPCBC,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.25,210.3,Yes
+4304-XUMGI,Male,1,Yes,Yes,50,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Bank transfer (automatic),75.15,3822.45,No
+9504-YAZWB,Female,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.25,1048.45,No
+8819-ZBYNA,Female,0,Yes,No,58,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.1,6393.65,No
+6174-NRBTZ,Male,0,No,No,46,No,No phone service,DSL,Yes,No,No,No,No,No,One year,Yes,Bank transfer (automatic),30.75,1489.3,No
+9481-IEBZY,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),112.9,8061.5,No
+1833-VGRUM,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.2,74.2,Yes
+6137-NICCO,Female,0,Yes,Yes,6,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,94.05,518.75,No
+9065-ZCPQX,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),78.85,5763.15,No
+6402-EJMWF,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,55.3,238.5,No
+3620-EHIMZ,Female,0,Yes,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,1031.7,No
+3213-VVOLG,Male,0,Yes,Yes,0,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.35, ,No
+6870-ECSHE,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.45,34.8,No
+8747-UDCOI,Female,0,Yes,No,65,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.35,1319.95,No
+8374-XGEJJ,Male,1,Yes,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101,4388.4,Yes
+2656-TABEH,Male,0,Yes,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.2,420.2,Yes
+6946-LMSQS,Male,1,Yes,No,25,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,One year,Yes,Electronic check,89.05,2177.45,Yes
+4626-OZDTJ,Female,0,Yes,No,51,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),78.65,3950.85,No
+9169-BSVIN,Male,0,No,No,12,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),74.75,827.05,No
+2325-ZUSFD,Female,0,Yes,Yes,57,Yes,No,DSL,Yes,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),70.1,3913.3,Yes
+8194-PEEBY,Female,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,533.5,No
+6872-HXFNF,Female,0,Yes,No,64,Yes,Yes,DSL,No,No,Yes,Yes,No,No,One year,No,Bank transfer (automatic),58.35,3756.45,No
+3932-CMDTD,Female,0,No,No,4,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,105.65,443.9,Yes
+7714-YXSMB,Female,0,No,No,26,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.5,2599.95,No
+8387-UGUSU,Female,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,284.3,No
+1080-BWSYE,Male,1,Yes,No,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.65,1740.8,No
+4039-HEUNW,Male,1,Yes,No,36,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.5,3436.1,Yes
+1194-HVAIF,Female,0,Yes,No,27,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),95,2462.55,No
+0812-WUPTB,Male,1,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.85,70.85,Yes
+3594-UVONA,Female,0,No,No,35,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),85.95,3110.1,Yes
+0004-TLHLJ,Male,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,73.9,280.85,Yes
+1767-TGTKO,Female,0,Yes,Yes,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.45,411.75,No
+8439-LTUGF,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20,198.6,No
+8805-JNRAZ,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,49.2,103.7,No
+5089-IFSDP,Female,0,Yes,No,58,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.45,6144.55,Yes
+6418-HNFED,Male,0,Yes,No,51,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),83.25,4089.45,No
+0206-OYVOC,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.25,864.2,No
+7291-CDTMJ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.65,19.65,No
+7128-GGCNO,Male,0,No,No,46,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),72.8,3249.4,No
+0237-YFUTL,Female,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),109.65,5405.8,No
+7691-KGKGP,Male,0,Yes,Yes,53,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,No,Credit card (automatic),65,3363.8,No
+6710-HSJRD,Male,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,114.1,7132.15,No
+3675-EQOZA,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.65,93.55,No
+0840-DCNZE,Male,0,No,No,47,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),86.95,4138.9,No
+1732-FEKLD,Female,0,No,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),94.75,5121.75,No
+9862-KJTYK,Male,0,No,Yes,19,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),25.35,566.1,No
+0479-HMSWA,Female,0,No,Yes,26,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,105.45,2715.3,No
+5854-KSRBJ,Male,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.4,1782.05,No
+0365-BZUWY,Male,0,Yes,No,17,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.55,1742.5,No
+4817-VYYWS,Female,0,No,No,30,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.2,2983.8,Yes
+5701-SVCWR,Female,0,No,Yes,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24,24,No
+3129-AAQOU,Female,0,Yes,Yes,19,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.6,485.9,No
+4922-CVPDX,Female,0,Yes,No,26,Yes,No,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),73.5,1905.7,No
+1396-QWFBJ,Female,0,Yes,Yes,21,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.05,1565.7,Yes
+1357-BIJKI,Male,0,Yes,No,50,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,98.25,4858.7,No
+1099-BTKWT,Female,0,Yes,No,68,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,54.4,3723.65,No
+5299-SJCZT,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),101.55,298.35,Yes
+2018-PZKMU,Male,0,Yes,Yes,9,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,103.1,970.45,No
+7340-KEFQE,Female,0,Yes,Yes,51,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),34.2,1782,No
+4570-QHXHL,Female,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,43.75,405.7,No
+2403-BCASL,Male,1,Yes,Yes,41,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.95,4534.9,Yes
+7392-YYPYJ,Male,0,No,No,22,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.65,2415.95,No
+2898-LSJGD,Female,0,Yes,Yes,21,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,55.95,1157.05,Yes
+2223-GDSHL,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.05,8297.5,No
+7359-PTSXY,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.75,45.75,Yes
+9470-RTWDV,Male,0,Yes,Yes,26,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),82,2083.1,No
+2176-OSJUV,Male,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),65.15,4681.75,No
+9348-YVOMK,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.8,176.2,No
+8104-OSKWT,Female,0,No,No,12,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,79.8,1001.2,No
+4521-YEEHE,Female,0,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,88.85,1594.75,No
+4090-KPJIP,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.95,212.4,No
+3786-WOVKF,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),106.85,7677.4,No
+4503-BDXBD,Male,0,No,No,11,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,74.95,815.5,Yes
+6086-ESGRL,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Credit card (automatic),80.15,80.15,Yes
+2592-HODOV,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.3,259.65,No
+3886-CERTZ,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,109.25,8109.8,Yes
+7995-ZHHNZ,Male,0,Yes,No,42,Yes,Yes,DSL,Yes,No,No,No,No,No,One year,No,Credit card (automatic),56.1,2386.85,No
+3824-RHKVR,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.7,340.35,No
+5816-SCGFC,Female,1,No,No,7,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,51.3,419.35,No
+5989-AXPUC,Female,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,118.6,7990.05,No
+4979-HPRFL,Male,0,Yes,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.15,1402.25,No
+8590-OHDIW,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.3,749.35,No
+8015-IHCGW,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,115.5,8425.15,No
+2332-TODQS,Female,0,No,No,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),25.05,1171.5,No
+9278-VZKCD,Female,1,Yes,No,52,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,109.1,5647.95,No
+8008-OTEZX,Female,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,708.8,No
+2773-OVBPK,Male,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.3,7567.2,No
+5999-LCXAO,Female,0,No,No,1,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.9,29.9,No
+5206-XZZQI,Male,0,No,No,53,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,80.6,4348.1,No
+4803-LBYPN,Male,0,Yes,Yes,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.8,635.9,No
+5759-RCVCB,Female,0,No,No,3,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,No,Credit card (automatic),35.2,108.95,Yes
+6372-RFVNS,Female,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.8,78.8,Yes
+2139-FQHLM,Male,0,No,No,19,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Mailed check,89.95,1682.4,No
+8261-GWDBQ,Female,1,Yes,No,60,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.05,6925.9,No
+1093-YSWCA,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.55,223.15,No
+8938-UMKPI,Female,0,No,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.4,5127.95,Yes
+0991-BRRFB,Male,0,No,No,18,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,49.4,874.8,Yes
+3882-IYOIJ,Female,0,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.25,6758.45,No
+8749-TZYEC,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.8,1874.3,No
+1755-FZQEC,Male,0,No,No,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,791.15,No
+3115-JPJDD,Female,0,Yes,No,59,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),81.25,4639.45,No
+5600-KTXFM,Male,0,Yes,Yes,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,69.95,143.9,No
+3871-IKPYH,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.1,69.1,Yes
+6319-IEJWJ,Male,0,Yes,Yes,20,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.2,1776.55,Yes
+9025-AOMKI,Female,0,No,No,6,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),93.55,536.4,Yes
+6339-TBELP,Male,0,No,No,71,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),86.4,6172,No
+7964-YESJC,Female,0,Yes,No,24,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,66.3,1559.45,No
+6173-GOLSU,Male,1,Yes,No,67,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.65,6079,No
+5275-SQEIZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,80.85,80.85,Yes
+7716-YTYHG,Female,0,Yes,Yes,48,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,One year,Yes,Mailed check,82.05,4029.95,Yes
+9938-ZREHM,Female,0,Yes,No,37,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,One year,No,Mailed check,72.1,2658.4,No
+7950-XWOVN,Male,0,No,No,11,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,34.7,383.55,No
+4390-KYULV,Male,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.55,51.15,Yes
+4647-MUZON,Female,0,Yes,No,18,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.95,1745.5,No
+9702-AIUJO,Male,0,Yes,Yes,50,Yes,No,DSL,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),44.8,2230.85,No
+8849-PRIQJ,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,109.4,7281.6,No
+8851-RAGOV,Female,0,Yes,No,25,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,71.05,1837.7,No
+1304-NECVQ,Female,1,No,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,78.55,149.55,Yes
+5396-IZEPB,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.7,180.7,No
+1729-VLAZJ,Female,0,No,Yes,10,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,40.25,411.45,No
+8285-ABVLB,Female,0,Yes,No,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,1328.35,No
+0701-TJSEF,Male,0,No,No,9,Yes,No,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),68.25,576.95,No
+5712-VBOXD,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.15,68.45,No
+6629-LADHQ,Female,0,No,No,2,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,50.95,123.05,No
+8945-GRKHX,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,78.65,78.65,Yes
+1559-DTODC,Male,0,No,No,19,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),25.15,468.35,No
+4797-MIWUM,Male,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.25,174.7,No
+6959-UWKHF,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,42.9,42.9,Yes
+8720-RQSBJ,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44,44,No
+4537-CIBHB,Female,0,Yes,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,172.35,No
+3815-SLMEF,Female,0,No,No,3,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,34.25,139.35,Yes
+5154-VEKBL,Female,0,No,No,9,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Mailed check,58.5,539.85,Yes
+4324-AHJKS,Female,0,No,No,5,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),55.8,300.4,No
+4355-CVPVS,Female,0,Yes,Yes,56,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,No,Bank transfer (automatic),88.9,4968,No
+4495-LHSSK,Female,0,No,Yes,18,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,57.65,992.7,No
+5655-JSMZM,Male,1,No,No,49,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.2,4718.25,Yes
+5915-ANOEI,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,79.15,5536.5,No
+9861-PDSZP,Female,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.05,7806.6,No
+4505-EXZHB,Female,1,No,No,6,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,74.4,434.1,Yes
+7225-CBZPL,Male,1,Yes,No,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.8,1563.9,No
+6704-UTUKK,Male,0,Yes,No,29,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,45.9,1332.4,No
+4587-VVTOX,Female,0,Yes,No,6,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.3,545.2,Yes
+2019-HDCZY,Male,0,Yes,No,63,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Electronic check,102.6,6296.75,No
+4652-NNHNY,Male,0,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),73.85,1284.2,Yes
+8788-DOXSU,Male,0,No,No,59,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),61.35,3645.5,No
+7404-JLKQG,Female,0,No,No,3,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,57.55,161.45,No
+7421-ZLUPA,Female,0,No,No,8,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,29.25,226.95,No
+8972-HJWNV,Female,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.55,646.85,Yes
+3274-NSDWE,Female,0,No,No,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.6,1441.65,No
+2632-IVXVF,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),111.75,7511.3,No
+3692-JHONH,Female,1,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.5,5621.85,No
+8915-NNTRC,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),107.7,7919.8,No
+5914-GXMDA,Female,0,Yes,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.3,593.2,No
+7463-IFMQU,Female,0,Yes,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.05,1423.65,No
+2920-RNCEZ,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.95,69.95,No
+2541-YGPKE,Male,0,Yes,Yes,42,Yes,No,DSL,Yes,No,No,Yes,No,Yes,One year,No,Credit card (automatic),63.7,2763.35,No
+8515-OCTJS,Female,0,No,No,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.75,692.1,Yes
+5382-TEMLV,Male,0,No,No,45,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),50.9,2298.55,No
+3441-CGZJH,Female,0,Yes,Yes,43,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),60.4,2640.55,No
+9592-ERDKV,Male,0,Yes,No,37,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,No,Mailed check,79.25,2911.8,No
+2860-RANUS,Female,1,No,No,20,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.8,1727.5,Yes
+5261-QSHQM,Female,0,No,No,4,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.45,86.6,Yes
+4778-IZARL,Male,0,Yes,No,63,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.1,6705.7,No
+0432-CAJZV,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,90.7,237.65,No
+7008-LZVOZ,Male,0,Yes,Yes,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.3,1672.35,No
+8868-WOZGU,Male,0,No,No,28,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.7,2979.5,Yes
+1200-TUZHR,Female,1,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.2,695.75,No
+9365-CSLBQ,Male,0,No,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.35,1654.6,No
+1334-FJSVR,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.25,24.25,Yes
+5884-FBCTL,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.1,1857.85,No
+7130-CTCUS,Male,1,Yes,No,16,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),54.55,825.1,No
+6242-FEGFD,Male,0,Yes,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,96.6,6424.25,No
+7625-XCQRH,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.5,837.95,Yes
+6194-HBGQN,Male,0,No,No,51,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),81.15,4126.2,No
+7634-WSWDB,Female,0,No,Yes,8,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,38.5,330.8,No
+6986-IXNDM,Male,0,No,No,14,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.9,1337.45,No
+1731-TVIUK,Female,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,93.5,362.2,Yes
+2987-BJXIK,Female,0,No,No,70,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Mailed check,84.7,5991.05,No
+9769-TSBZE,Female,0,No,Yes,70,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Electronic check,66,4891.5,No
+0406-BPDVR,Female,1,Yes,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),101.5,5373.1,Yes
+0618-XWMSS,Male,0,No,Yes,28,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.9,2068.55,Yes
+1395-WSWXR,Male,0,No,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.75,487.05,No
+6023-GSSXW,Female,0,Yes,No,69,Yes,No,DSL,No,Yes,No,No,No,Yes,Two year,Yes,Credit card (automatic),61.45,4131.2,No
+6752-APNJL,Male,0,Yes,Yes,42,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.5,2301.15,No
+5276-KQWHG,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,131.65,Yes
+0420-HLGXF,Female,1,No,No,39,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.75,4036,No
+7446-KQISO,Male,0,Yes,Yes,45,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.75,4900.65,No
+9823-EALYC,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),80.85,5727.45,No
+0582-AVCLN,Female,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.3,743.05,No
+5803-NQJZO,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),67.8,4804.65,No
+2565-JSLRY,Male,0,No,No,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,24.05,24.05,Yes
+2607-DHDAK,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.8,1414.65,No
+1073-XXCZD,Male,0,Yes,No,55,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.7,1443.65,No
+0743-HRVFF,Female,0,Yes,Yes,51,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,56.15,2898.95,No
+4006-HKYHO,Male,0,No,No,63,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),86.7,5309.5,No
+3727-JEZTU,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,20.4,No
+8143-ETQTI,Female,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.65,451.55,No
+6689-KXGBO,Female,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,50.55,50.55,Yes
+9667-TKTVZ,Female,0,No,No,2,Yes,Yes,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,54.35,117.05,No
+3657-COGMW,Female,1,No,No,52,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),108.1,5839.3,No
+8570-KLJYJ,Female,0,No,No,36,Yes,Yes,DSL,Yes,No,No,No,No,No,One year,No,Mailed check,54.45,1893.5,No
+7754-IXRMC,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.35,45.35,No
+9473-CBZOP,Female,0,No,No,28,Yes,Yes,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,59,1654.45,No
+2969-WGHQO,Female,0,Yes,Yes,7,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,One year,No,Electronic check,69.45,477.05,No
+6615-NGGZJ,Male,0,No,No,14,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.55,1415.55,Yes
+4334-HOWRP,Male,1,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),64.95,4546,No
+4255-DDUOU,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.5,20.5,Yes
+5863-OOKCL,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.85,163.2,No
+1686-STUHN,Male,0,No,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,849.9,No
+1329-VHWNP,Female,0,No,No,7,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),25.05,189.95,No
+2984-MIIZL,Male,0,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.8,321.9,Yes
+0266-GMEAO,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),114.3,8058.55,No
+5590-YRFJT,Female,0,Yes,No,20,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.45,482.8,Yes
+5574-NXZIU,Male,0,No,No,63,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),109.2,7049.75,No
+0019-GFNTW,Female,0,No,No,56,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),45.05,2560.1,No
+4256-ZWTZI,Female,0,No,Yes,5,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,51,286.8,No
+8309-PPCED,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.45,7982.5,No
+4098-NAUKP,Male,1,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.65,5683.6,No
+5196-WPYOW,Male,0,Yes,Yes,67,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,60.05,3994.05,No
+4608-LCIMN,Male,0,Yes,Yes,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.65,369.15,No
+1485-YDHMM,Male,0,Yes,Yes,52,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),93.25,4631.7,No
+4054-CUMIA,Female,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,401.95,No
+0603-TPMIB,Female,0,Yes,Yes,59,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.45,1534.05,No
+2525-GVKQU,Female,0,No,No,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.6,1093,No
+8161-QYMTT,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.1,701.3,No
+9581-GVBXT,Male,0,Yes,Yes,59,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,34.8,1980.3,No
+5862-BRIXZ,Male,0,No,No,46,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),60.75,2893.4,No
+5404-GGUKR,Male,0,No,No,5,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,51.35,262.3,No
+3308-JSGML,Male,1,Yes,No,59,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),64.05,3886.85,No
+2126-GSEGL,Female,0,Yes,No,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.8,5917.55,No
+3677-TNKIO,Female,0,No,No,14,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),71,914,Yes
+0440-QEXBZ,Female,0,No,No,44,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.15,2139.1,No
+2434-EEVDB,Female,0,Yes,No,64,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),94.6,5948.7,No
+6762-QVYJO,Female,0,Yes,Yes,58,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Mailed check,59.75,3624.35,No
+6199-IWKGC,Female,1,Yes,No,46,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,No,Electronic check,100.25,4753.85,No
+2675-DHUTR,Male,1,Yes,No,58,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.9,5780.7,No
+8152-VETUR,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Credit card (automatic),97.7,6869.7,No
+9667-EQRXU,Female,1,No,No,30,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,40.3,1172.95,Yes
+7446-YPODE,Male,1,No,No,11,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),60.25,662.95,No
+9522-BNTHX,Female,1,No,No,34,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,56.25,1765.95,No
+4676-WLUHT,Male,0,No,No,54,No,No phone service,DSL,Yes,Yes,No,No,No,Yes,Two year,No,Bank transfer (automatic),46.2,2431.95,No
+3329-WDIOK,Female,0,No,No,3,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.6,155.35,Yes
+7980-MHFLQ,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.9,1859.2,No
+2873-ZLIWT,Female,0,Yes,Yes,40,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,84.85,3303.05,Yes
+3415-TAILE,Female,0,Yes,Yes,2,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,65.7,134.35,Yes
+0757-WCUUZ,Male,0,Yes,Yes,54,Yes,Yes,DSL,No,No,Yes,No,No,Yes,Two year,No,Credit card (automatic),63.35,3409.1,No
+1629-DQQVB,Female,0,No,No,14,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),50.1,709.5,No
+1915-IOFGU,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.5,70.5,Yes
+3045-XETSH,Female,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.85,953.45,Yes
+0374-AACSZ,Female,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,50.15,50.15,No
+7239-HZZCX,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,19.75,Yes
+4872-VXRIL,Male,0,No,No,56,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),64.65,3665.55,No
+9140-CZQZZ,Female,0,Yes,No,68,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),79.6,5515.8,No
+3423-HHXAO,Female,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.5,272,No
+9938-TKDGL,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Electronic check,99.55,6668,No
+0531-ZZJWQ,Male,1,Yes,No,55,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74,4052.4,No
+6537-QLGEX,Female,0,No,No,16,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,38.9,664.4,No
+2688-BHGOG,Male,1,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.55,718.55,No
+7683-CBDKJ,Male,0,Yes,Yes,14,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,65.45,937.6,Yes
+0946-CLJTI,Male,1,Yes,No,58,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.7,5812.6,Yes
+3160-TYXLT,Male,0,No,No,53,No,No phone service,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),46.3,2546.85,No
+1325-USMEC,Male,0,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),99.35,6944.5,No
+0916-QOFDP,Female,1,Yes,Yes,14,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.8,1346.3,No
+0628-CNQRM,Male,0,Yes,Yes,22,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),67.5,1544.05,Yes
+5606-AMZBO,Female,0,Yes,No,10,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),78.15,765.15,No
+6199-IPCAO,Female,0,Yes,Yes,29,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,26.1,692.55,No
+7665-TOALD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,69.6,Yes
+0112-QWPNC,Male,0,Yes,No,49,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,84.35,4059.35,Yes
+0324-BRPCJ,Female,1,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.2,6851.65,Yes
+2777-PHDEI,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.05,78.05,Yes
+5640-CAXOA,Female,0,No,No,30,No,No phone service,DSL,No,No,No,Yes,No,Yes,One year,Yes,Credit card (automatic),40.35,1187.05,No
+2235-EZAIK,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),79.2,5401.9,No
+3847-BAERP,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.9,247,No
+1196-AMORA,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,73.6,520,Yes
+4282-YMKNA,Female,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.75,706.6,Yes
+1453-RZFON,Female,0,No,Yes,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,49.9,49.9,No
+8263-OKETD,Female,0,No,No,20,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),68.9,1370.35,No
+0670-KDOMA,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,20.25,No
+2476-YGEFM,Female,0,No,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),76,2215.25,No
+8687-BAFGU,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,74,74,No
+0641-EVBOJ,Male,0,No,No,3,Yes,No,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,82.3,214.4,No
+0829-XXPLX,Female,0,No,No,20,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.4,1871.15,No
+9974-JFBHQ,Male,0,No,Yes,64,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.15,6171.2,No
+5356-RHIPP,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.2,20.2,Yes
+6624-JDRDS,Female,0,No,No,6,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),29.45,161.45,No
+0608-JDVEC,Male,0,Yes,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,19.8,1013.2,No
+9780-FKVVF,Male,0,No,No,6,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),59.15,336.7,No
+1919-RTPQD,Male,0,Yes,Yes,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.75,333.65,No
+5214-NLTIT,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),90.8,6511.8,No
+3345-PBBFH,Male,0,Yes,No,8,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.55,393.45,No
+5055-BRMNE,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),106.7,7009.5,No
+9189-JWSHV,Female,1,Yes,No,24,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.55,2264.05,Yes
+2190-PHBHR,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Two year,Yes,Credit card (automatic),94.45,6921.7,No
+2650-GYRYL,Male,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.45,600.25,No
+0746-JTRFU,Male,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.05,56.35,Yes
+5208-FVQKB,Male,0,Yes,No,70,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Two year,No,Mailed check,67.95,4664.15,No
+7184-LRUUR,Female,0,No,No,22,Yes,No,DSL,No,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),65.25,1441.8,No
+6627-CFOSN,Female,0,No,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),99.45,5623.7,No
+3982-JGSFD,Male,0,No,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.35,695.85,No
+1574-DYCWE,Female,0,Yes,Yes,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.95,1028.75,No
+7247-XOZPB,Male,0,Yes,No,53,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),77.4,4155.95,No
+2466-FCCPT,Female,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.7,395.6,No
+6211-WWLTF,Male,0,Yes,No,63,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),99.7,6330.4,No
+4826-TZEVA,Female,0,No,No,40,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),74.8,2971.7,No
+6016-NXBNJ,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.15,638,No
+9138-EFSMO,Male,0,Yes,Yes,26,Yes,No,Fiber optic,No,No,No,No,No,Yes,One year,No,Bank transfer (automatic),78.95,2034.25,No
+0576-WNXXC,Male,1,Yes,No,27,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.55,2510.2,Yes
+4632-XJMEX,Male,0,No,Yes,53,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Credit card (automatic),62.85,3419.5,No
+8910-LEDAG,Male,1,Yes,No,34,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),71.55,2427.35,No
+3452-GWUIN,Female,1,Yes,No,19,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.95,1760.25,No
+2716-GFZOR,Male,0,Yes,No,43,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),86.1,3551.65,No
+3724-UCSHY,Male,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,122.9,No
+8626-XHBIE,Male,0,No,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.8,1424.2,No
+4628-CTTLA,Male,0,No,No,57,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,39.3,2111.45,Yes
+3192-LNKRK,Male,0,Yes,Yes,34,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,84.05,2909.95,No
+4439-YRNVD,Female,0,No,No,10,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,36.25,374,No
+2876-VBBBL,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,20.25,Yes
+6834-NXDCA,Female,0,No,No,13,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,23.9,300.8,Yes
+2208-UGTGR,Male,0,No,No,56,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,98.6,5581.05,No
+3005-TYFRD,Female,0,Yes,No,55,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),103.65,5676.65,No
+8670-MEFCP,Female,0,Yes,Yes,36,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),92.9,3379.25,No
+3079-BCHLN,Male,0,Yes,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.9,942.95,No
+7777-UNYHB,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.1,232.4,No
+5597-GLBUC,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,85.45,85.45,Yes
+7278-CKDNC,Male,1,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.5,2088.45,No
+2748-MYRVK,Female,0,No,No,63,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),99.9,6137,Yes
+8450-UYIBU,Female,1,No,No,35,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,39.85,1434.6,No
+2969-VAPYH,Female,0,No,No,67,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Credit card (automatic),60.5,3870,No
+4822-YCXMX,Male,0,No,No,25,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.8,2043.45,Yes
+7181-OQCUT,Male,0,No,No,21,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,103.85,2215,No
+8474-UMLNT,Female,0,No,No,13,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),67.8,842.25,No
+9821-POOTN,Male,0,Yes,No,35,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.2,2576.2,Yes
+3836-FZSDJ,Male,1,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.85,1901,No
+9142-XMYJH,Female,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.35,601.6,No
+6559-ILWKJ,Male,0,Yes,No,71,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Two year,No,Electronic check,49.35,3515.25,Yes
+0187-QSXOE,Male,1,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89,605.45,No
+7733-UDMTP,Female,1,No,No,57,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,55,3094.05,No
+2649-HWLYB,Male,0,Yes,No,65,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),76.15,4929.55,No
+5214-CHIWJ,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.3,595.05,No
+4229-CZMLL,Male,0,No,No,6,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,469.8,Yes
+6904-JLBGY,Female,1,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),117.35,8436.25,No
+2465-BLLEU,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.75,19.75,No
+1685-VAYJF,Male,0,No,No,11,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),45.2,492,No
+8874-EJNSR,Male,0,Yes,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.2,987.95,No
+6917-YACBP,Female,1,No,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,89.75,5496.9,No
+5049-MUBWG,Male,0,No,No,26,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Bank transfer (automatic),75,1908.35,No
+4223-WOZCM,Male,1,No,No,2,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,49.95,107.1,No
+0769-MURVM,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),65.7,4575.35,No
+9253-VIFJQ,Male,0,Yes,No,65,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,One year,No,Credit card (automatic),67.05,4309.55,No
+7030-FZTFM,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),110.9,7922.75,No
+9741-YLNTD,Male,0,No,No,6,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),87.95,522.35,No
+5917-RYRMG,Male,1,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.8,587.7,No
+5120-ZBLAI,Female,0,Yes,No,50,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),75.7,3876.2,No
+1194-BHJYC,Male,0,Yes,No,61,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,62.15,3778.85,No
+3663-MITLP,Female,0,No,No,15,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.25,1457.25,Yes
+0906-QVPMS,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),115.15,8349.45,No
+9025-ZRPVR,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,18.95,185.6,Yes
+1905-OEILC,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.5,19.5,No
+7858-GTZSP,Female,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.55,1066.9,No
+3285-UCQVC,Female,0,No,No,37,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),28.6,973.55,Yes
+6248-BSHKG,Male,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.4,1226.45,No
+4685-TFLLS,Male,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.8,342.3,No
+3470-BTGQO,Male,0,No,Yes,21,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.65,985.05,No
+1209-VFFOC,Male,0,Yes,Yes,68,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,56.4,3948.45,No
+8224-DWCKX,Male,1,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.3,828.05,No
+5482-PLVPE,Female,1,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.35,41.85,Yes
+4553-DVPZG,Female,0,Yes,No,62,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.35,6164.7,No
+4902-OHLSK,Female,1,No,No,29,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,98.65,2862.75,Yes
+6917-IAYHD,Male,0,No,Yes,1,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,33.6,33.6,No
+0495-ZBNGW,Male,1,Yes,No,5,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.9,343.95,Yes
+8620-RJPZN,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.7,20.7,No
+7572-KPVKK,Male,0,No,Yes,62,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,104.05,6590.5,No
+2642-MAWLJ,Female,0,Yes,Yes,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.25,717.95,No
+2357-COQEK,Female,1,No,No,28,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,103.3,2890.65,Yes
+7103-ZGVNT,Female,0,Yes,Yes,69,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Credit card (automatic),73.7,4885.85,No
+3737-GCSPV,Female,0,Yes,No,11,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),96.2,1222.05,Yes
+2027-WKXMW,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,108.75,6871.7,No
+7137-NAXML,Male,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.15,405.6,No
+2428-ZMCTB,Male,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.75,208.25,No
+2961-VNFKL,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.95,1801.9,No
+1768-HNVGJ,Female,1,No,No,45,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,70.05,3062.45,No
+6963-KQYQB,Female,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.05,1764.75,No
+5934-RMPOV,Female,0,No,Yes,22,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.75,1816.75,No
+1207-BLKSA,Female,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,23.05,1255.1,No
+4088-YLDSU,Male,0,Yes,No,55,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,104.15,5743.05,Yes
+8316-BBQAY,Female,0,No,No,65,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),59.95,3921.1,No
+1166-PQLGG,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.55,1463.45,No
+3146-JTQHR,Male,0,Yes,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.6,189.45,No
+4291-YZODP,Female,0,No,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,96.8,No
+4395-PZMSN,Male,1,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.55,408.5,No
+6427-FEFIG,Female,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),78.6,1846.65,No
+0017-IUDMW,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.8,8456.75,No
+8706-HRADD,Male,0,No,No,21,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,43.55,1011.5,No
+9955-QOPOY,Male,0,Yes,No,69,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),60.8,4263.4,No
+4385-ZKVNW,Male,0,Yes,Yes,44,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),54.9,2549.1,No
+5446-DKWYW,Female,1,Yes,Yes,61,Yes,Yes,DSL,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,65.2,3965.05,No
+2034-CGRHZ,Male,1,No,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.95,2496.7,Yes
+5797-APWZC,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,90.6,90.6,Yes
+3683-QKIUE,Female,0,No,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),50.8,288.05,Yes
+4228-ZGYUW,Male,0,No,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.05,368.1,Yes
+9031-ZVQPT,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,108.2,7840.6,No
+2990-IAJSV,Male,0,No,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),92,6632.75,No
+8107-KNCIM,Male,1,Yes,No,14,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.1,1013.35,No
+9027-YFHQJ,Male,0,No,No,7,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.05,152.95,No
+5176-LDKUH,Female,0,No,No,48,Yes,No,Fiber optic,No,Yes,No,No,No,No,One year,No,Electronic check,75.15,3772.65,No
+4644-OBGFZ,Male,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,1026.35,No
+7926-IJOOU,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.3,19.3,No
+6480-YAGIY,Male,0,No,No,45,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),112.2,5031.85,No
+4029-HPFVY,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.3,220.4,No
+7602-DBTOU,Female,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,1416.5,No
+5345-BMKWB,Male,0,Yes,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,158.35,No
+3519-ZKXGG,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.85,256.6,Yes
+5457-COLHT,Male,0,Yes,Yes,69,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),80.65,5542.55,No
+6416-TVAIH,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,68.5,68.5,Yes
+5451-YHYPW,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.75,8443.7,No
+5108-ADXWO,Male,0,No,No,11,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.5,791.75,Yes
+7998-WNZEM,Male,0,No,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),80.6,5708.2,No
+3066-RRJIO,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.95,69.95,Yes
+4664-NJCMS,Female,0,Yes,No,33,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),59.55,2016.3,No
+0307-BCOPK,Female,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.05,326.65,No
+7629-WFGLW,Female,1,Yes,No,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,No,Electronic check,95.65,5471.75,No
+2542-HYGIQ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.95,19.95,No
+6715-OFDBP,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,70.05,346.4,Yes
+5016-LIPDW,Male,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.4,1061.6,No
+6481-LXPWL,Male,0,Yes,Yes,56,No,No phone service,DSL,No,Yes,Yes,No,No,No,One year,Yes,Credit card (automatic),36.1,1971.5,No
+1567-DSCIC,Male,0,No,No,8,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,94,773.65,Yes
+2150-UWTFY,Female,0,Yes,Yes,22,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,61.15,1422.05,Yes
+6124-ACRHJ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,19.75,No
+9362-MWODR,Female,0,No,Yes,40,Yes,No,DSL,Yes,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),64.1,2460.35,No
+9975-GPKZU,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.75,856.5,No
+9625-RZFUK,Male,0,Yes,Yes,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,1275.85,No
+5153-LXKDT,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,110.2,7467.5,No
+5161-UBZXI,Male,0,Yes,Yes,69,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),106.35,7261.75,No
+1020-JPQOW,Female,0,Yes,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,90.55,5116.6,No
+7919-ZODZZ,Female,0,Yes,Yes,10,Yes,No,DSL,No,Yes,Yes,No,No,Yes,One year,Yes,Mailed check,65.9,660.05,No
+0565-JUPYD,Male,0,No,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,One year,No,Credit card (automatic),104.5,6590.8,No
+6867-ACCZI,Female,0,Yes,Yes,24,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,52.5,1208.15,No
+5939-XAIXZ,Female,0,No,No,19,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,56.1,1033.9,No
+9054-FOWNV,Male,0,Yes,Yes,22,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,88.75,1885.15,No
+2683-BPJSO,Male,0,Yes,No,29,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,84.45,2467.1,Yes
+2157-MXBJS,Male,0,Yes,No,13,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Mailed check,75.3,989.45,Yes
+8207-VVMYB,Female,0,Yes,No,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),26,2006.95,No
+9732-EQMWY,Female,0,Yes,No,49,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,99.4,5025,No
+9360-AHGNL,Female,1,Yes,No,43,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,109.55,4830.25,Yes
+1087-UDSIH,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.6,59.75,Yes
+5269-NRGDP,Male,0,Yes,Yes,42,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,73.15,3088.25,No
+6195-MELTI,Male,0,No,No,57,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Mailed check,54.65,3134.7,No
+5485-WUYWF,Male,1,No,No,2,Yes,No,DSL,Yes,No,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),66.4,94.55,Yes
+2121-JAFOM,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.55,8312.4,No
+8818-DOPVL,Female,1,No,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.45,4863.85,No
+2632-UCGVD,Male,1,Yes,No,66,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.05,6871.9,Yes
+5445-PZWGX,Male,0,No,No,62,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,Yes,One year,No,Electronic check,102,6529.25,Yes
+1227-UDMZR,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),91.15,6637.9,No
+5198-HQAEN,Male,0,Yes,Yes,35,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,89.7,3165.6,No
+9170-CCKOU,Male,0,Yes,No,17,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Credit card (automatic),90.2,1454.15,Yes
+2167-FQSTQ,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,92.4,6786.1,No
+2819-GWENI,Female,0,Yes,Yes,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,543,No
+1043-YCUTE,Male,0,Yes,No,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.15,1327.15,Yes
+3814-MLAXC,Female,0,No,No,31,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),79.85,2404.15,Yes
+3572-UUHRS,Male,0,No,No,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),18.85,867.3,No
+2692-PFYTJ,Female,0,No,No,1,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,25.75,25.75,No
+1226-UDFZR,Female,0,No,No,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,49.6,114.7,Yes
+5955-EPOAZ,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.95,109.5,No
+6821-JPCDC,Female,0,Yes,No,48,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,97.05,4692.95,No
+8815-LMFLX,Male,0,Yes,Yes,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.4,546.85,No
+9135-HSWOC,Male,0,Yes,Yes,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.7,1274.05,No
+8582-KRHPJ,Male,0,No,No,50,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,35,1782.4,No
+5811-IWXYM,Female,0,Yes,Yes,52,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),101.25,5301.1,No
+7044-YAACC,Male,1,Yes,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.2,280.35,No
+8189-XRIKE,Female,1,No,No,32,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,90.95,2897.95,No
+1506-YJTYT,Male,0,Yes,Yes,45,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),73.85,3371,No
+4123-DVHPH,Male,0,Yes,No,9,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,88.05,801.3,No
+6425-YQLLO,Female,1,Yes,No,66,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.95,6975.25,Yes
+5442-UTCVD,Male,0,No,Yes,3,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,91.85,257.05,Yes
+2228-BZDEE,Female,0,No,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,1079.45,No
+7734-DBOAI,Female,0,Yes,Yes,1,No,No phone service,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,40.1,40.1,Yes
+2789-CZANW,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),110.3,6997.3,No
+2091-RFFBA,Female,1,No,No,31,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.9,2217.15,Yes
+2114-MGINA,Female,0,No,No,14,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,89.8,1129.1,Yes
+7596-LDUXP,Female,0,No,No,12,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,One year,Yes,Credit card (automatic),85.15,979.05,No
+3740-RLMVT,Male,1,Yes,No,67,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),60.95,4119.4,No
+4489-SNOJF,Female,0,Yes,Yes,35,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,Month-to-month,No,Electronic check,72.25,2568.55,Yes
+2192-OZITF,Female,0,No,No,45,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,Two year,No,Mailed check,73.55,3349.1,No
+5651-CPDND,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,46,492.1,No
+2186-QZEYA,Female,1,No,No,29,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),58.55,1718.95,No
+1131-ALZWV,Female,0,No,No,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.6,605.25,No
+7729-XBTWX,Male,0,Yes,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.75,1344.5,No
+9134-WYRVP,Male,0,No,No,51,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,One year,No,Mailed check,86.35,4267.15,No
+3284-SVCRO,Female,0,Yes,No,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.5,1121.05,No
+9732-OUYRN,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19,918.7,No
+0559-CKHUS,Female,0,Yes,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.55,521.8,No
+2931-SVLTV,Male,0,Yes,Yes,40,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),110.1,4469.1,No
+6899-PPEEA,Female,1,No,No,37,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.55,3580.3,Yes
+7504-UWHNB,Male,0,No,No,25,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.75,1729.35,No
+2582-FFFZR,Female,0,No,No,22,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.6,1073.3,No
+8908-NMQTX,Male,0,No,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),65.6,4566.5,No
+2187-LZGPL,Female,0,No,No,7,No,No phone service,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),40.1,293.3,Yes
+1431-AIDJQ,Male,0,Yes,Yes,33,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),82.1,2603.1,No
+6288-LBEAR,Female,0,No,No,23,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,79.1,1783.75,No
+4597-NUCQV,Male,1,No,No,24,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.25,2440.15,Yes
+9019-QVLZD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,79.55,79.55,Yes
+8413-YNHNV,Male,0,No,No,69,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,90.65,6322.1,No
+5808-TOTXO,Female,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.55,57.4,No
+4369-NYSCF,Male,0,No,No,56,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),75.75,4284.65,No
+6333-YDVLT,Male,0,No,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),110,7138.65,No
+5324-KTGCG,Male,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20.85,1539.75,No
+5599-HVLTW,Female,1,No,No,14,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.35,1058.1,No
+7394-LWLYN,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,123.8,No
+3707-LRWZD,Female,0,No,No,32,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,One year,No,Electronic check,84.05,2781.85,Yes
+6873-UDNLD,Male,0,No,No,40,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,67.45,2731,No
+2700-LUEVA,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.75,20.75,No
+1455-ESIQH,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.1,89.1,Yes
+0958-YHXGP,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.9,497.3,No
+1101-SSWAG,Female,0,Yes,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,51.1,711.15,No
+2979-SXESE,Female,0,Yes,Yes,17,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,94.4,1607.2,Yes
+4013-TLDHQ,Male,0,No,No,19,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,78.25,1490.95,Yes
+5743-KHMNA,Male,0,No,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.55,1898.1,No
+4194-FJARJ,Female,0,Yes,Yes,54,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),60,3273.95,No
+5325-UWTWJ,Male,0,Yes,No,31,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),90.55,2929.75,No
+3969-GYXEL,Female,0,No,No,11,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,76.4,838.7,No
+9208-OLGAQ,Female,1,No,No,18,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,84.95,1443.65,No
+3244-CQPHU,Female,1,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),110.1,7746.7,No
+2674-MLXMN,Female,1,No,No,71,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.65,6951.15,No
+9708-HPXWZ,Male,1,No,No,5,No,No phone service,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,No,Credit card (automatic),45.4,214.75,No
+6992-TKNYO,Male,0,Yes,No,38,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),69,2669.45,No
+4468-YDOVK,Male,0,No,Yes,5,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),48.65,235.2,No
+3754-DXMRT,Male,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.15,92.65,Yes
+5792-JALQC,Female,1,No,No,52,Yes,Yes,DSL,Yes,No,Yes,No,No,No,Two year,No,Bank transfer (automatic),59.85,3103.25,No
+7130-VTEWQ,Female,1,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.75,606.25,Yes
+2200-DSAAL,Female,0,No,No,68,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,80.65,5330.2,No
+4302-ZYFEL,Male,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.55,1403.1,No
+9351-LZYGF,Female,0,Yes,No,42,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),66.4,2727.8,No
+4366-CTOUZ,Female,0,No,No,50,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,100.2,5038.45,No
+6121-VZNQB,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.1,19.1,Yes
+7903-CMPEY,Male,1,Yes,No,1,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,80.3,80.3,Yes
+1518-OMDIK,Male,0,Yes,No,33,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.55,1462.6,No
+6671-NGWON,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,150.6,No
+0595-ITUDF,Male,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,91.8,5960.5,No
+6996-KNSML,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,74.9,74.9,Yes
+1955-IBMMB,Male,0,No,No,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.2,1192.3,No
+0096-BXERS,Female,0,Yes,No,6,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,50.35,314.55,No
+3806-YAZOV,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.8,56,No
+7998-ZLXWN,Female,0,Yes,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.45,330.8,No
+6253-WRFHY,Male,0,Yes,Yes,13,Yes,No,DSL,No,No,No,No,Yes,Yes,One year,Yes,Electronic check,64.75,877.35,No
+9133-AYJZG,Female,0,No,No,23,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),98.7,2249.1,No
+2675-OTVVJ,Male,1,Yes,No,31,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,89.45,2807.65,No
+8680-CGLTP,Male,0,No,No,29,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Electronic check,58.75,1696.2,No
+8430-TWCBX,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.7,1032.05,No
+5018-GWURO,Female,0,Yes,No,56,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),85.6,4902.8,No
+6741-QRLUP,Female,0,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),80.3,4995.35,No
+4737-HOBAX,Male,0,Yes,No,63,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),79.8,5034.05,No
+0537-QYZZN,Male,1,Yes,Yes,24,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,79.85,1857.75,No
+3340-QBBFM,Male,1,Yes,No,36,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,No,Credit card (automatic),54.1,1992.85,No
+1704-NRWYE,Female,1,No,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.85,751.65,Yes
+2592-SEIFQ,Male,0,No,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.75,66.95,Yes
+8200-KLNYW,Female,0,Yes,No,21,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Mailed check,80.9,1714.95,No
+3372-CDXFJ,Male,0,Yes,Yes,13,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),24.5,343.6,No
+4781-ZXYGU,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.15,20.15,No
+7632-YUTXB,Female,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.05,520.1,No
+2718-YSKCS,Male,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.6,1387.45,No
+9896-UYMIE,Male,0,No,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),114.3,7383.7,No
+0853-NWIFK,Female,0,No,No,45,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,100.3,4483.95,No
+8212-CRQXP,Female,0,Yes,No,22,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),80,1706.45,No
+6980-CDGFC,Female,0,Yes,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),20.85,1327.4,No
+7691-XVTZH,Female,0,Yes,No,68,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),89.95,5974.3,No
+2520-SGTTA,Female,0,Yes,Yes,0,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20, ,No
+7481-ATQQS,Female,1,No,No,49,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.85,4515.85,Yes
+2277-VWCNI,Female,1,No,No,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,48.75,179.85,No
+1088-CNNKB,Male,0,Yes,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),80,5040.2,No
+8642-GVWRF,Female,0,Yes,No,2,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),79.7,165,Yes
+1930-BZLHI,Male,0,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.35,422.7,No
+3400-ESFUW,Male,0,Yes,Yes,55,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),57.55,3046.4,Yes
+5868-YTYKS,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,20.25,No
+3525-DVKFN,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.4,358.05,No
+1482-OXZSY,Male,0,No,No,30,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.4,2936.25,No
+0377-JBKKT,Male,0,Yes,Yes,22,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,57.95,1271.8,No
+5778-BVOFB,Female,0,No,No,9,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),59.5,530.05,No
+3373-YZZYM,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.2,19.2,No
+2057-ZBLPD,Female,0,Yes,No,21,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.5,1808.7,Yes
+0228-MAUWC,Male,0,No,No,19,Yes,Yes,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,59.55,1144.6,No
+5502-RLUYV,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.95,7446.9,Yes
+0023-HGHWL,Male,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.1,25.1,Yes
+7663-YJHSN,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),103.95,7556.9,No
+4915-BFSXL,Male,0,Yes,Yes,70,Yes,Yes,DSL,Yes,No,Yes,No,Yes,No,Two year,No,Credit card (automatic),68.95,4858.7,No
+3086-RUCRN,Female,0,No,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),103.1,6595,No
+5215-LNLDJ,Female,0,Yes,Yes,7,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.7,149.05,No
+7576-JMYWV,Female,1,Yes,Yes,46,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,110.2,4972.1,No
+9039-RBEEE,Male,0,No,No,39,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),48.95,1880.85,Yes
+0257-KXZGU,Female,0,Yes,Yes,32,Yes,Yes,DSL,No,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),62.45,2045.55,No
+1307-ATKGB,Male,0,No,No,24,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.55,2187.15,No
+8417-FMLZI,Male,0,Yes,Yes,6,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),83.55,477.55,Yes
+3714-XPXBW,Female,0,No,No,37,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),78.9,2976.95,No
+1850-AKQEP,Male,0,No,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,178.7,No
+2824-DXNKN,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,No,Yes,No,Two year,Yes,Bank transfer (automatic),71.45,5025.85,No
+5227-JSCFE,Male,1,Yes,No,71,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),46.35,3353.4,No
+0848-ZGQIJ,Female,0,Yes,No,16,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.65,1461.15,No
+3621-CHYVB,Female,0,Yes,No,57,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),49.9,2782.4,No
+8042-RNLKO,Male,0,No,No,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.45,1699.15,No
+1792-UXAFY,Female,1,No,No,17,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.15,1496.9,Yes
+5372-FBKBN,Female,0,No,Yes,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.75,452.2,No
+0420-TXVSG,Male,0,Yes,No,66,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Credit card (automatic),66.1,4428.45,No
+6217-TOWGS,Female,0,Yes,No,17,Yes,Yes,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),75.4,1322.55,No
+0515-YPMCW,Male,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.45,70.45,Yes
+0378-XSZPU,Male,0,Yes,No,58,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,No,Credit card (automatic),60.3,3563.8,Yes
+1045-LTCYT,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),21.05,139.75,No
+1544-JJMYL,Male,0,No,No,27,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Credit card (automatic),69.35,1927.3,No
+4636-JGAAI,Male,0,Yes,No,34,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Mailed check,88.85,3000.25,No
+6874-SGLHU,Male,0,No,No,30,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),97,3021.3,No
+5951-AOFIH,Male,0,No,No,33,Yes,No,DSL,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),66.4,2245.4,No
+4729-XKASR,Male,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,24.75,24.75,Yes
+3059-NGMXB,Male,0,Yes,Yes,14,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,Month-to-month,No,Mailed check,69.2,944.65,No
+8652-YHIYU,Female,0,No,Yes,16,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,One year,Yes,Credit card (automatic),79.5,1264.2,No
+5278-PNYOX,Female,0,No,No,49,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),100.65,4917.75,No
+5449-FIBXJ,Male,0,Yes,Yes,19,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,103.3,2012.7,Yes
+3486-HOOGQ,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),79.7,5743.3,No
+5061-PBXFW,Female,0,Yes,Yes,32,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),61.4,1864.65,No
+8630-FJLIB,Female,0,No,No,18,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,1221.65,No
+3891-NLXJB,Male,0,No,No,37,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,40.55,1390.85,No
+4749-OJKQU,Female,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.65,302.35,No
+0577-WHMEV,Female,0,Yes,No,16,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,90.7,1374.9,No
+5453-AXEPF,Male,0,Yes,No,17,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.5,1336.9,No
+3639-XJHKQ,Female,0,No,Yes,19,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,60.6,1297.8,No
+3716-LRGXK,Male,0,Yes,No,60,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),101.15,6067.4,No
+2263-SFSQZ,Male,0,Yes,Yes,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.95,1222.25,No
+9950-MTGYX,Male,0,Yes,Yes,28,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.3,487.95,No
+3397-AVTKU,Male,0,No,No,43,Yes,No,DSL,No,Yes,No,No,No,Yes,Two year,Yes,Electronic check,60,2548.55,No
+4825-FUREZ,Male,0,Yes,No,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,20.25,835.5,No
+9837-BMCLM,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,78.5,242.05,Yes
+2672-OJQZP,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.75,44.75,No
+0137-UDEUO,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,63.75,Yes
+3134-DSHVC,Female,0,No,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,No,Credit card (automatic),98,6218.45,No
+6161-UUUTA,Male,1,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.9,260.9,Yes
+7821-DPRQE,Male,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,107.7,7320.9,No
+6543-XRMYR,Female,1,No,No,30,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.7,2967.35,Yes
+2001-EWBQU,Female,0,No,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,104.7,6333.8,No
+4925-LMHOK,Male,0,No,No,15,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),58.6,939.7,Yes
+1608-GMEWB,Male,1,No,No,45,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),93.9,4200.25,No
+6695-AMZUF,Female,0,Yes,No,70,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),86.45,5950.2,No
+1455-UGQVH,Male,0,Yes,No,10,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.5,1037.75,Yes
+8410-BGQXN,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,93.4,No
+8280-MQRQN,Female,0,No,No,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,50.45,50.45,Yes
+1619-YWUBB,Female,0,Yes,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.95,1614.9,No
+7611-YKYTC,Male,0,Yes,Yes,22,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),75,1573.95,No
+2037-SGXHH,Male,0,Yes,Yes,38,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,94.65,3624.3,Yes
+3178-FESZO,Female,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.25,100.25,Yes
+0365-GXEZS,Male,0,Yes,No,18,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,78.2,1468.75,No
+0082-OQIQY,Male,0,No,No,29,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,2607.6,No
+6087-MVHJH,Female,0,No,No,16,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,88.45,1422.1,Yes
+4603-JANFB,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.85,69.85,Yes
+3018-TFTSU,Male,0,No,No,12,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),81.7,858.6,Yes
+3606-SBKRY,Male,0,No,No,31,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,50.05,1523.4,No
+4806-KEXQR,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.9,324.3,Yes
+0667-NSRGI,Female,0,Yes,No,48,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,One year,Yes,Mailed check,69.55,3435.6,No
+7083-YNSKY,Female,0,No,No,15,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),25.4,399.6,Yes
+6893-ODYYE,Male,0,No,No,50,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),90.1,4549.45,No
+8242-JSVBO,Male,0,No,No,7,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.65,322.5,No
+2479-BRAMR,Male,1,Yes,No,41,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),83.75,3273.55,Yes
+9541-ZPSEA,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,Two year,No,Credit card (automatic),80.35,5375.15,No
+2665-NPTGL,Female,1,Yes,No,26,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),98.1,2510.7,No
+1139-WUOAH,Male,0,No,No,57,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),53.35,3090.05,No
+4693-VWVBO,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.55,61.05,No
+7434-SHXLS,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.9,20.9,Yes
+6937-GCDGQ,Male,0,Yes,Yes,19,Yes,No,DSL,Yes,No,No,No,No,No,One year,Yes,Bank transfer (automatic),48.95,955.6,No
+0988-JRWWP,Female,0,No,No,3,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,54.2,140.4,No
+5075-JSDKI,Female,0,No,No,59,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,24.45,1493.1,No
+7908-QCBCA,Female,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.4,69.4,Yes
+8644-XYTSV,Male,0,Yes,No,42,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),40.15,1626.05,No
+6711-FLDFB,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,541.15,Yes
+3873-WOSBC,Male,0,Yes,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.6,1784.9,No
+7465-ZZRVX,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.35,70.35,No
+7975-JMZNT,Male,0,Yes,No,66,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),91.7,6075.9,No
+7251-XFOIL,Female,0,No,No,61,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Mailed check,89.2,5500.6,No
+4116-IQRFR,Male,0,Yes,Yes,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.1,73.1,No
+3714-JTVOV,Female,1,Yes,No,42,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.15,3229.4,Yes
+8259-DZLIZ,Female,0,Yes,Yes,64,Yes,Yes,DSL,No,Yes,No,No,No,No,One year,Yes,Bank transfer (automatic),53.85,3399.85,No
+0442-ZXKVS,Female,1,Yes,No,54,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),115.6,6431.05,No
+7853-WNZSY,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.75,19.75,No
+3120-FAZKD,Male,0,Yes,Yes,54,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.05,1230.9,No
+8606-OEGQZ,Female,0,No,Yes,18,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.3,454.65,No
+0225-ZORZP,Male,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,84.3,235.05,No
+4702-IOQDC,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.1,70.1,Yes
+9489-JMTTN,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.75,6595.9,No
+0575-CUQOV,Male,1,Yes,No,60,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,97.95,5867,No
+0967-BMLBD,Female,0,Yes,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20,196.35,No
+6178-KFNHS,Female,0,No,Yes,12,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,78.3,909.25,Yes
+2830-LEWOA,Male,0,Yes,Yes,61,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),103.9,6449.15,No
+5006-MXVRN,Female,0,No,No,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.7,762.45,No
+1264-BYWMS,Male,0,No,No,55,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.8,5283.95,Yes
+9658-WYUFB,Female,0,No,No,17,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,94.4,1617.5,Yes
+8327-WKMIE,Male,0,No,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.15,785.75,No
+6917-FIJHC,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,26,1776,No
+5329-KRDTM,Male,1,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),77.35,5396.25,No
+7797-EJMDP,Female,0,No,No,8,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),66.05,574.5,No
+3530-VWVGU,Female,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,400.3,No
+2013-SGDXK,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,84.3,84.3,Yes
+6368-NWMCE,Female,0,No,No,38,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,No,Credit card (automatic),68.15,2656.3,No
+3633-CDBUW,Male,0,No,Yes,17,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,80.85,1445.95,No
+0707-HOVVN,Female,1,No,No,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),75.5,5212.65,No
+8580-QVLOC,Female,1,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),92.45,6440.25,Yes
+3956-MGXOG,Female,0,No,No,28,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.6,2244.95,No
+9274-UARKJ,Female,0,No,No,15,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),83.2,1130,No
+4077-HWUYD,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),87.55,6463.15,No
+2012-NWRPA,Female,1,Yes,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.55,1131.2,Yes
+8808-ELEHO,Male,1,No,No,8,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.25,585.95,Yes
+0103-CSITQ,Female,0,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),109.4,6252.7,No
+3506-LCJDC,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.95,19.95,Yes
+8671-KKKOS,Female,0,Yes,No,46,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,45.55,2062.15,No
+7305-ZWMAJ,Male,0,Yes,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.7,587.1,No
+9518-XXBXE,Male,1,Yes,No,10,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,75.3,720.45,No
+3934-HXCFZ,Male,0,Yes,No,23,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,99.25,2186.4,Yes
+6578-KRMAW,Male,0,No,No,32,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),93.4,2979.3,No
+4860-IJUDE,Male,0,No,No,13,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.75,956.65,No
+7666-WKRON,Female,0,No,No,39,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Two year,Yes,Electronic check,80.45,3201.55,Yes
+9688-YGXVR,Female,0,No,No,44,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),88.15,3973.2,No
+0423-UDIJQ,Male,1,No,No,9,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,49.2,447.9,No
+1945-XISKS,Female,0,Yes,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,1335.2,No
+4910-AQFFX,Male,0,Yes,Yes,9,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.35,661.25,Yes
+2154-KVJFF,Female,0,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.75,1111.85,Yes
+5360-LJCNJ,Female,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.15,7555,No
+2607-FBDFF,Male,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),49,49,No
+9647-ERGBE,Female,0,Yes,Yes,30,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.05,3046.15,Yes
+3428-XZMAZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.35,69.35,Yes
+1100-DDVRV,Male,0,Yes,No,17,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,49.8,836.35,No
+0378-TOVMS,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,No,Electronic check,85.8,272.2,Yes
+4355-HBJHH,Male,0,Yes,Yes,67,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,79.7,5293.4,Yes
+6728-WYQBC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.95,20.95,No
+9058-CBREO,Female,1,No,No,1,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,50.55,50.55,Yes
+9029-FEGVJ,Female,1,Yes,No,32,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Mailed check,79.3,2570,No
+7216-KAOID,Male,0,Yes,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.5,798.2,No
+0318-QUUOB,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.55,80.55,Yes
+6145-NNPNO,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.15,44.15,No
+5520-FVEWJ,Female,0,Yes,Yes,12,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.5,916.9,Yes
+5339-TJFEK,Male,0,Yes,Yes,62,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.5,6487.2,No
+5572-ZDXHY,Female,0,No,No,22,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Mailed check,84.3,1855.65,Yes
+2074-GUHPQ,Female,0,No,Yes,17,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),92.7,1556.85,No
+4625-XMOYM,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),26.25,1988.05,No
+5827-MWCZK,Male,0,Yes,Yes,56,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),96.95,5432.2,No
+1385-TQOZW,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.45,147.55,No
+5914-XRFQB,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.8,8424.9,No
+4329-YPDDQ,Male,0,No,No,20,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),108.2,2203.7,No
+4804-NCPET,Male,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.2,387.4,No
+3750-CKVKH,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,67.75,116.65,Yes
+5944-UGLLK,Male,0,No,No,53,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),54.9,3045.75,No
+8063-GBATB,Female,1,No,No,27,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.25,2287.25,Yes
+7787-BNTZM,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.15,130.5,No
+2252-ISRNH,Male,0,Yes,Yes,9,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,90.35,767.9,No
+9415-TPKRV,Female,0,Yes,Yes,8,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,55.75,446.8,No
+5322-TEUJK,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),114.6,8100.25,No
+4547-KQRTM,Female,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.05,830.7,Yes
+4877-EVATK,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,20,Yes
+1866-DIOQZ,Female,0,Yes,No,71,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),66.8,4689.15,No
+8375-KVTHK,Male,0,Yes,No,68,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,100.3,6754.35,No
+2697-NQBPF,Male,0,No,No,34,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105.35,3540.65,No
+3709-OIJEA,Male,0,No,No,26,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,85.2,2184.6,No
+7639-SUPCW,Female,0,No,No,22,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),48.8,1054.6,Yes
+1386-ZIKUV,Male,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,18.95,130.55,No
+0599-XNYDO,Female,0,Yes,No,20,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.8,1540.35,No
+6377-WHAOX,Female,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.15,6411.25,No
+6855-VLGOS,Male,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,20.55,1432.55,No
+8999-XXGNS,Female,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),105.75,7629.85,No
+8746-OQQRW,Male,0,No,No,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.25,101.9,No
+3181-MIZBN,Male,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.75,313.4,No
+0471-ARVMX,Female,1,Yes,No,62,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.85,6312.9,No
+7766-CLTIC,Female,0,No,No,10,Yes,No,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,60.95,629.55,No
+5650-YLIBA,Male,0,No,No,31,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81.15,2640.55,No
+0825-CPPQH,Female,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.1,1372.45,No
+9084-OAYKL,Male,0,No,No,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.8,1212.25,No
+6122-LJADA,Male,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.15,6237.05,No
+2400-XIWIO,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,90.1,6310.9,No
+7524-VRLPL,Male,1,No,No,69,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),74.1,5031,No
+1069-XAIEM,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.05,85.05,Yes
+7569-NMZYQ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),118.75,8672.45,No
+5201-USSQZ,Female,0,Yes,No,26,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,No,Credit card (automatic),85.9,2196.45,No
+2105-PHWON,Female,0,Yes,No,33,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),95,3008.15,No
+1494-EJZDW,Female,0,Yes,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,220.8,No
+4884-TVUQF,Female,1,No,No,57,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),101.3,5779.6,No
+8003-EWNDZ,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,21.2,222.65,No
+1897-RCFUM,Female,0,Yes,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,24.2,914.6,No
+9256-JTBNZ,Female,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.3,246.3,No
+4658-HCOHW,Female,0,Yes,Yes,21,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,102.8,2110.15,Yes
+2211-RMNHO,Female,0,Yes,Yes,68,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),85.3,5560,No
+2432-TFSMK,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Credit card (automatic),89.6,1633,No
+3440-JPSCL,Female,0,No,No,6,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,99.95,547.65,Yes
+4929-ROART,Male,0,No,No,18,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,56.25,969.85,No
+1834-ABKHQ,Female,0,Yes,Yes,52,Yes,No,DSL,No,No,Yes,No,No,No,One year,Yes,Bank transfer (automatic),50.95,2610.65,No
+1741-WTPON,Male,0,Yes,Yes,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,115.85,6567.9,No
+3932-IJWDZ,Male,0,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.65,4747.85,No
+1240-HCBOH,Female,0,No,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,26.1,1759.55,No
+3594-KADLU,Male,0,Yes,No,3,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,35.1,101.1,No
+8065-BVEPF,Male,1,No,No,65,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.1,6496.15,No
+3796-ENZGF,Male,0,Yes,No,63,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,Two year,No,Mailed check,67.25,4234.15,No
+1734-ZMNTZ,Female,0,Yes,Yes,11,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25,300.7,No
+2853-CWQFQ,Male,0,No,Yes,1,Yes,No,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,59.55,59.55,No
+0813-TAXXS,Male,0,No,No,55,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),77.8,4323.35,No
+2519-TWKFS,Male,0,Yes,Yes,25,Yes,Yes,DSL,No,No,No,Yes,No,No,One year,Yes,Mailed check,55.1,1466.1,No
+2889-FPWRM,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),117.8,8684.8,Yes
+0626-QXNGV,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.15,1776.45,No
+6723-CEGQI,Female,0,No,Yes,65,No,No phone service,DSL,No,Yes,No,Yes,Yes,No,Two year,No,Mailed check,45.25,2933.95,No
+6987-XQSJT,Female,1,No,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.5,4370.25,Yes
+4732-RRJZC,Male,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,144.35,No
+9499-XPZXM,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),64.75,4804.75,No
+4338-EYCER,Male,0,Yes,No,21,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),54.6,1125.2,No
+3007-FDPEA,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.7,39.85,No
+6350-XFYGW,Male,1,No,No,4,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,422.4,No
+4290-BSXUX,Male,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),79.65,251.75,Yes
+8513-OLYGY,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),115.8,8332.15,No
+0311-QYWSS,Female,0,No,No,6,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.45,314.6,No
+8705-DWKTI,Male,0,No,No,52,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),83.8,4331.4,No
+8755-IWJHN,Male,1,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.35,6382,No
+0325-XBFAC,Male,0,No,No,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.7,740.3,Yes
+4480-QQRHC,Female,1,No,No,8,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.05,600.15,No
+0929-PECLO,Female,1,No,No,63,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.6,5538.8,No
+3680-CTHUH,Male,0,No,No,60,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),116.6,7049.5,No
+1202-KKGFU,Female,0,Yes,No,12,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),54.2,690.5,No
+6112-KTHFQ,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.3,279.3,No
+1852-XEMDW,Male,0,No,No,22,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,65.05,1427.55,No
+2462-XIIJB,Male,0,No,No,5,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,92.5,452.7,Yes
+4760-XOHVN,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.45,19.45,Yes
+1820-DJFPH,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.05,1709.15,No
+9426-SXNHE,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),18.75,53.15,No
+9068-FHQHD,Female,0,Yes,Yes,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.15,777.35,No
+1269-FOYWN,Male,0,Yes,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20,860.85,No
+9470-YFUYI,Male,1,Yes,No,71,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),71,5012.1,No
+7817-BOQPW,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.55,166.3,Yes
+7402-EYFXX,Male,1,No,No,26,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.6,2404.1,No
+1853-UDXBW,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70,70,Yes
+9895-VFOXH,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.4,24.4,No
+5458-CQJTA,Male,0,Yes,Yes,65,Yes,No,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),74.8,4820.15,Yes
+1230-QAJDW,Male,0,No,No,3,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,65.25,209.9,No
+5701-ZIKJE,Male,0,No,No,13,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,50.55,610.75,No
+5219-YIPTK,Female,0,Yes,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.4,3409.6,Yes
+5032-MIYKT,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.7,70.7,No
+7396-VJUZB,Male,0,Yes,Yes,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.25,155.35,No
+9717-QEBGU,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,144,No
+5172-RKOCB,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.95,7875,No
+6509-TSGWN,Female,0,Yes,Yes,37,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,26.45,911.6,No
+3540-RZJYU,Female,0,No,No,15,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,86.2,1270.2,Yes
+3178-CIFOT,Female,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),19.65,478.1,No
+2091-MJTFX,Female,0,Yes,Yes,30,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),51.2,1561.5,Yes
+4530-NDRKU,Female,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.05,761.85,No
+5985-TBABQ,Female,0,No,No,32,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,One year,No,Mailed check,74.75,2282.95,No
+9800-ONTFE,Female,0,Yes,Yes,22,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.8,1615.1,No
+6743-HHQPF,Male,0,Yes,No,42,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),25.1,1097.15,No
+8455-HIRAQ,Female,0,No,No,8,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.45,369.3,No
+6616-AALSR,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),104.3,6725.3,No
+0883-EIBTI,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.5,31.55,Yes
+9415-ZNBSX,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),89,6293.2,No
+4018-KJYUY,Male,0,No,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.15,432.5,No
+0722-TROQR,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.9,321.75,Yes
+7571-YXDAD,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),74.9,147.15,Yes
+3884-UEBXB,Female,0,Yes,Yes,67,No,No phone service,DSL,Yes,Yes,No,No,No,No,Two year,No,Bank transfer (automatic),36.15,2434.45,No
+8780-RSYYU,Female,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.2,532.1,No
+5537-UXXVS,Female,0,Yes,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.25,375.25,No
+1791-PQHBB,Female,0,No,Yes,2,Yes,No,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),61.2,125.95,No
+8701-DGLVH,Male,0,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.45,1042.65,No
+8741-LQOBK,Female,0,Yes,Yes,46,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),35.05,1620.25,No
+3393-FMZPV,Female,0,No,No,25,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.25,2387.75,Yes
+8082-GHXOP,Male,0,No,Yes,13,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44,659.35,No
+6402-ZFPPI,Female,1,No,No,25,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,102.8,2660.2,Yes
+0980-FEXWF,Male,0,Yes,Yes,26,Yes,No,DSL,No,No,Yes,No,No,No,One year,No,Mailed check,50.35,1285.8,No
+7486-KSRVI,Male,0,No,No,43,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,100,4211.55,Yes
+3011-WQKSZ,Male,0,No,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20,377.55,No
+8443-WVPSS,Male,0,Yes,No,10,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,99.85,990.9,Yes
+3006-XIMLN,Female,0,No,Yes,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.2,193.8,Yes
+8218-FFJDS,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),86.4,6058.95,No
+1986-PHGZF,Male,1,No,No,18,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),58.4,964.9,No
+9965-YOKZB,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.85,790.15,Yes
+0504-HHAPI,Female,1,No,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),88.3,2467.75,Yes
+9360-OMDZZ,Male,0,No,No,24,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.1,2322.85,No
+6305-YLBMM,Male,0,No,No,69,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.05,7262,Yes
+0345-XMMUG,Female,0,Yes,No,46,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),108.9,4854.3,No
+8024-XNAFQ,Female,1,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),107.4,7748.75,No
+3647-GMGDH,Male,0,Yes,No,22,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.7,1914.9,Yes
+2988-GBIVW,Female,1,Yes,No,70,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.85,6470.1,No
+2832-SCUCO,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.9,57.4,No
+7036-ZZKBD,Male,0,Yes,No,31,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),66.4,2019.8,No
+3791-LGQCY,Female,1,Yes,No,56,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.65,5688.05,Yes
+0265-PSUAE,Female,0,Yes,Yes,16,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),100.7,1522.7,No
+0463-TXOAK,Male,0,No,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.6,1334.5,No
+0594-UFTUL,Male,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.85,252,No
+9509-MPYOD,Female,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.75,700.45,No
+9128-CPXKI,Female,0,No,No,59,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,No,Electronic check,95.8,5655.45,No
+0129-KPTWJ,Male,0,Yes,No,72,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.65,6747.35,No
+8166-ZZTFS,Female,1,Yes,No,66,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.55,5265.1,Yes
+5855-EIBDE,Female,0,Yes,No,49,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),106.65,5174.35,No
+5365-LLFYV,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.85,105.6,No
+5956-YHHRX,Male,1,No,No,21,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.35,2271.85,No
+6008-NAIXK,Male,1,No,No,54,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,55.45,2966.95,No
+2956-GGUCQ,Male,1,Yes,No,24,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,78.85,1772.25,Yes
+1928-BXYIV,Male,0,No,No,1,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,61.15,61.15,No
+5760-FXFVO,Male,0,No,No,6,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,78.95,494.95,No
+6595-COKXZ,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.45,44.45,Yes
+0961-ZWLVI,Male,0,No,Yes,49,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),109.2,5290.45,No
+5181-OABFK,Female,0,Yes,Yes,56,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Credit card (automatic),61.3,3346.8,No
+4007-NHVHI,Female,1,No,No,56,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,96.85,5219.65,No
+7816-VGHTO,Female,0,Yes,Yes,6,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Two year,No,Mailed check,40.55,217.5,No
+5871-DGTXZ,Male,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,607.7,No
+7550-WIQVA,Male,0,Yes,Yes,50,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,108.25,5431.4,No
+7544-ZVIKX,Male,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.05,6004.85,No
+0016-QLJIS,Female,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,90.45,5957.9,No
+7508-DQAKK,Female,0,No,No,64,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),86.4,5442.05,No
+5176-OLSKT,Female,0,Yes,No,66,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Bank transfer (automatic),66.9,4370.25,No
+9356-AXGMP,Male,0,Yes,No,38,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,110.7,4428.6,No
+3556-BVQGL,Female,0,Yes,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20,416.45,No
+0362-ZBZWJ,Male,0,No,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.9,3067.2,Yes
+8979-CAMGB,Male,1,No,No,64,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,102.1,6688.1,No
+4211-MMAZN,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.25,20.25,Yes
+7581-EBBOU,Female,0,No,No,60,Yes,No,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),70.15,4224.7,No
+4274-OWWYO,Male,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,74.35,Yes
+5073-RZGBK,Female,0,Yes,Yes,50,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),80.05,4042.2,No
+1541-ETJZO,Male,0,No,No,1,Yes,Yes,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,62.05,62.05,Yes
+2192-CKRLV,Female,0,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Electronic check,49.2,3580.95,No
+3154-HMWUU,Male,0,Yes,No,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.5,1198.8,No
+6119-SPUDB,Male,0,No,No,46,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,38.25,1755.35,No
+6522-OIQSX,Female,0,Yes,Yes,69,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),54.95,3772.5,No
+7813-ZGGAW,Male,1,No,No,31,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),96.6,2877.95,No
+8748-HFWBO,Male,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,357.7,No
+1052-QJIBV,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.9,1397.3,No
+1319-YLZJG,Male,0,Yes,No,12,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.6,959.9,No
+4106-HADHQ,Male,0,Yes,Yes,39,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),80,3182.95,Yes
+0723-FDLAY,Male,0,No,No,44,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),85.25,3704.15,No
+3050-RLLXC,Female,0,Yes,Yes,56,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),81.25,4620.4,No
+9298-WGMRW,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.5,8312.75,No
+2369-UAPKZ,Male,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,104.1,541.9,Yes
+3088-LHEFH,Female,0,No,No,11,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79,929.3,No
+2778-OCLGR,Male,1,Yes,No,24,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),39.1,971.3,Yes
+6583-KQJLK,Female,1,Yes,No,15,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.65,1285.05,No
+8544-JNBOX,Male,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.8,1521.2,No
+6681-ZSEXG,Male,0,Yes,No,56,Yes,Yes,DSL,No,Yes,Yes,No,No,No,Two year,No,Credit card (automatic),59.5,3389.25,No
+6139-ZZRBQ,Male,1,No,No,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.05,1198.05,No
+6116-RFVHN,Female,0,Yes,No,34,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.45,3414.65,No
+0637-KVDLV,Male,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.5,162.45,Yes
+8884-FEEWR,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.6,754,No
+3125-RAHBV,Male,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.3,467.15,No
+6633-MPWBS,Male,0,No,No,5,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,49.2,216.9,Yes
+0373-AIVNJ,Male,0,No,No,9,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,One year,No,Mailed check,39.55,373,No
+8785-EPNCG,Male,0,No,No,11,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),23.15,245.2,Yes
+1682-VCOIO,Male,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.45,481.1,No
+5729-KLZAR,Female,0,Yes,Yes,4,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.85,302.75,Yes
+2072-ZVJJX,Male,0,Yes,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.25,1728.2,No
+8849-AYPTR,Male,0,Yes,No,33,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,91.25,2964.05,No
+9518-IMLHK,Male,0,No,No,31,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),72.45,2156.25,No
+1582-RAFML,Male,0,No,No,1,Yes,Yes,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,60.1,60.1,Yes
+3646-ITDGM,Female,0,No,No,56,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.7,1051.9,No
+8740-CRYFY,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,78.95,78.95,Yes
+3569-VLDHH,Male,0,Yes,Yes,66,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,75.1,5013,No
+8224-KDLKN,Male,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25,1738.9,No
+7594-LZNWR,Male,1,No,No,34,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),69.15,2275.1,No
+4001-TSBTV,Female,0,Yes,Yes,58,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,91.55,5511.65,No
+2962-XPMCQ,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.15,98.5,Yes
+8727-XDPUD,Male,0,No,No,37,No,No phone service,DSL,No,No,No,No,Yes,No,Two year,No,Credit card (automatic),35.8,1316.9,No
+1043-UXOVO,Female,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),113.15,7993.3,No
+1064-FBXNK,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.85,19.85,No
+3996-ZNWYK,Male,1,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.8,1388.45,No
+2878-DHMIN,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.9,666,No
+7762-ONLJY,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.7,94.45,No
+1678-FYZOW,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.4,244.65,Yes
+5795-BKOYE,Female,0,Yes,Yes,69,Yes,No,DSL,No,Yes,Yes,Yes,No,No,One year,No,Bank transfer (automatic),59.1,4134.7,No
+8627-EHGIP,Male,0,No,No,44,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Mailed check,53.95,2375.4,Yes
+7402-PWYJJ,Female,0,Yes,No,53,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,One year,Yes,Electronic check,91.15,4862.5,No
+5480-TBGPH,Female,0,Yes,No,24,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.3,2431.35,Yes
+0018-NYROU,Female,0,Yes,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,68.95,351.5,No
+7501-VTYLJ,Female,0,No,Yes,2,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,51.55,106.2,No
+7608-RGIRO,Male,0,No,Yes,62,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.4,1413,No
+0480-BIXDE,Female,0,Yes,No,19,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.8,1743.05,No
+5895-QSXOD,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.05,657.5,No
+9814-AOUDH,Male,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.5,1050.5,No
+6175-IRFIT,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,78.75,426.35,No
+8760-ZRHKE,Female,1,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,One year,No,Electronic check,69.2,4982.5,No
+1622-HSHSF,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.55,19.55,No
+8854-CCVSQ,Male,0,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.65,1451.9,Yes
+6749-UTDVX,Male,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,103.65,7634.8,No
+5542-DHSXL,Female,0,Yes,No,4,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.7,235.05,No
+9102-OXKFY,Male,0,No,No,59,Yes,Yes,DSL,No,No,Yes,No,No,No,Two year,No,Credit card (automatic),54.15,3116.15,No
+0511-JTEOY,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,71.1,71.1,Yes
+5216-WASFJ,Female,1,Yes,No,31,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.85,2633.4,No
+7808-DVWEP,Male,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20,49.65,No
+7067-KSAZT,Female,1,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.25,6979.8,Yes
+2284-VFLKH,Male,0,Yes,No,49,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),99.25,4920.8,No
+0366-NQSHS,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.35,46.35,No
+6532-YLWSI,Female,0,Yes,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.8,1021.8,Yes
+3807-BPOMJ,Female,0,Yes,No,55,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Electronic check,94.75,5276.1,No
+3892-NXAZG,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),114.05,8289.2,No
+4948-WBBKL,Female,1,No,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.9,2659.45,No
+4182-BGSIQ,Female,0,Yes,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),19.8,196.75,No
+9300-AGZNL,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94,94,Yes
+0988-AADSA,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),80.85,5824.75,No
+9972-NKTFD,Female,0,No,No,28,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),54.65,1517.5,No
+3717-LNXKW,Male,0,Yes,No,38,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),91.7,3479.05,No
+5734-EJKXG,Female,0,No,No,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,118.6,7365.7,No
+2207-RYYRL,Male,0,Yes,Yes,52,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.55,1331.05,No
+6729-FZWSY,Male,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.45,1195.95,No
+9695-IDRZR,Female,0,No,Yes,34,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.15,3946.9,No
+8144-DGHXP,Female,0,No,No,54,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Electronic check,80.6,4299.95,No
+7814-LEEVE,Female,0,Yes,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.3,20.3,No
+1112-CUNAO,Female,1,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,89.85,1424.95,Yes
+5175-AOBHI,Female,0,No,No,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,46,193.6,Yes
+5174-RNGBH,Female,0,No,No,9,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,No,Mailed check,66.25,620.55,Yes
+8631-WUXGY,Female,0,No,Yes,46,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.8,4391.25,No
+7270-BDIOA,Female,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,90,1993.8,Yes
+9565-JSNFM,Male,0,No,No,38,Yes,No,Fiber optic,No,No,No,No,No,No,One year,Yes,Bank transfer (automatic),70.45,2597.6,Yes
+5906-DVAPM,Female,0,Yes,Yes,55,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,One year,Yes,Credit card (automatic),75,4213.9,No
+6654-QGBZZ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,19.9,No
+8348-JLBUG,Male,1,Yes,No,64,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,One year,No,Credit card (automatic),80.3,5017.7,No
+0607-DAAHE,Male,0,No,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.75,1052.35,Yes
+5641-DMBFJ,Female,0,Yes,No,58,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,84.3,4916.4,No
+9200-NLNPD,Male,0,Yes,No,56,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),54.05,2959.8,No
+2664-XJZNO,Male,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104.9,7559.55,No
+9732-KPKBW,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,53.95,53.95,Yes
+3339-EAQNV,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),97.25,7133.1,No
+0921-OHLVP,Male,0,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,83.05,1799.3,No
+3389-YGYAI,Female,1,No,No,8,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.5,829.55,Yes
+9560-ARGQJ,Female,0,No,Yes,16,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,81,1312.15,Yes
+8350-NYMVI,Female,0,No,No,39,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),41.1,1597.05,No
+1600-DILPE,Female,0,No,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45,524.35,No
+0536-ESJEP,Male,0,Yes,No,54,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),74.55,4191.45,No
+4654-GGUII,Female,0,No,No,18,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,40.2,711.95,No
+6478-HRRCZ,Male,0,Yes,No,32,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,One year,No,Mailed check,70.5,2201.75,No
+8510-BBWMU,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.75,806.95,No
+6857-TKDJV,Male,0,Yes,Yes,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.65,1620.45,No
+2360-RDGRO,Male,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.25,6812.95,No
+0584-BJQGZ,Female,0,No,No,25,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),78.35,1837.9,No
+5134-IKDAY,Female,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,69.8,Yes
+1360-XFJMR,Female,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),109.7,7344.45,No
+3070-DVEYC,Male,1,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,73.75,545.15,Yes
+5730-RIITO,Female,1,No,No,43,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),33.45,1500.25,No
+9058-MJLZC,Female,0,No,No,24,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.6,2283.15,No
+5707-ORNDZ,Male,1,No,No,9,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,54.55,494.05,Yes
+0902-XKXPN,Male,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.2,1376.5,No
+5177-RVZNU,Female,0,No,Yes,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.3,755.4,No
+0056-EPFBG,Male,0,Yes,Yes,20,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),39.4,825.4,No
+8993-IZEUX,Male,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69.15,488.65,No
+8696-JKZNU,Female,1,No,No,37,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.25,2841.55,Yes
+5380-AFSSK,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,93.9,486.85,Yes
+8190-ZTQFB,Male,0,No,No,41,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,51.35,2075.1,No
+0931-MHTEM,Female,0,No,No,54,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),100.05,5299.65,No
+2055-PDADH,Female,1,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.4,204.7,Yes
+7515-LODFU,Male,1,No,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.3,1356.3,No
+0440-UEDAI,Female,0,No,No,53,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),94.45,5042.75,No
+1337-BOZWO,Male,0,Yes,Yes,18,No,No phone service,DSL,No,Yes,Yes,No,Yes,No,One year,No,Credit card (automatic),46.4,812.4,No
+0868-VJRDR,Male,0,Yes,No,64,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104.05,6605.55,No
+8714-EUHJO,Female,0,Yes,Yes,31,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,91.15,2995.45,Yes
+6344-SFJVH,Female,0,No,No,20,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),24.9,505.95,No
+3950-VPYJB,Male,0,Yes,Yes,57,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Mailed check,59.6,3509.4,No
+8041-TMEID,Male,1,Yes,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),108.5,6991.9,No
+7321-ZNSLA,Male,0,Yes,Yes,13,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,40.55,590.35,No
+6941-KXRRV,Female,1,Yes,No,48,Yes,No,DSL,No,Yes,No,No,No,Yes,One year,Yes,Bank transfer (automatic),58.95,2789.7,No
+3721-CNEYS,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.95,137.95,Yes
+8727-JQFHV,Male,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.75,1123.15,No
+9475-NNDGC,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),113.15,7953.25,No
+1355-KUSBG,Female,0,Yes,Yes,7,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,48.8,349.8,No
+3688-FTHLT,Female,0,No,No,16,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),63.05,1067.05,No
+0899-LIIBW,Male,0,Yes,No,34,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,100.85,3527.3,No
+2568-OIADY,Female,0,Yes,No,37,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.5,3762,Yes
+1384-RCUXW,Male,0,No,No,16,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,80.55,1248.9,No
+5825-XJOCM,Male,0,No,Yes,48,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),64.4,3035.35,No
+6848-YLDFR,Male,0,Yes,Yes,58,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),75.2,4300.8,No
+8125-QPFJD,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.9,6065.3,No
+2320-YKQBO,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.3,144.95,No
+1193-RTSLK,Female,0,No,No,38,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),83.9,3233.6,Yes
+2302-ANTDP,Female,1,Yes,No,48,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,117.45,5438.9,Yes
+5923-GXUOC,Male,0,No,No,10,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.4,1081.45,Yes
+4973-RLZVI,Female,0,No,No,30,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Credit card (automatic),74.65,2308.6,No
+7869-ZYDST,Male,0,Yes,No,31,Yes,Yes,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),59.05,1882.8,No
+4501-UYKBC,Female,1,No,No,46,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),69.1,3255.35,No
+1215-EXRMO,Male,0,Yes,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.55,1067.65,No
+2305-MRGLV,Male,0,Yes,No,28,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),76.55,2065.4,No
+8404-VIOMB,Female,0,No,No,66,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),62.5,4136.4,No
+5233-GEEAX,Male,1,No,No,8,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,29.4,221.9,Yes
+2359-KLTEK,Female,0,Yes,Yes,41,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),94.9,3848,No
+5304-EFJLP,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),111.65,8022.85,No
+2673-ZALNP,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.9,173.15,No
+9184-GALIL,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,781.25,No
+4393-RYCRE,Male,0,No,No,44,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.05,4510.8,No
+9746-MDMBK,Male,0,Yes,Yes,47,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),113.45,5317.8,No
+3162-ZJZFU,Male,0,Yes,Yes,53,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,92.55,4779.45,No
+8404-GFGCZ,Male,0,Yes,No,4,Yes,Yes,DSL,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,65.6,250.1,No
+8875-AKBYH,Male,1,No,No,20,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.35,1745.2,No
+4432-ADRLB,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.65,74.9,Yes
+0533-UCAAU,Male,1,Yes,No,57,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),71.1,4140.1,No
+1394-SUIUH,Female,1,Yes,No,44,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,85.15,3670.5,No
+3521-MNKLV,Male,0,No,No,24,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,49.7,1167.8,No
+2533-TIBIX,Male,0,Yes,Yes,15,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),30.2,469.65,No
+8993-PHFWD,Female,0,No,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.25,58.9,Yes
+1565-RHDJD,Female,0,No,Yes,4,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),84.05,333.55,Yes
+7137-RYLPP,Male,1,Yes,No,37,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.7,3171.15,Yes
+3765-JXVKY,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,74.7,Yes
+5092-STPKP,Female,0,No,No,24,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),56.35,1381.2,No
+7330-WZLNC,Female,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.8,455.5,Yes
+0114-PEGZZ,Female,0,No,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,107.55,3645.5,No
+3359-DSRKA,Female,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.85,1158.85,No
+8639-NHQEI,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Two year,Yes,Bank transfer (automatic),95.9,6954.15,No
+7161-DFHUF,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,23.85,1672.1,No
+3957-LXOLK,Female,1,No,No,28,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.15,3152.5,Yes
+3720-DBRWL,Male,0,Yes,No,51,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),83.85,4307.1,No
+6635-MYYYZ,Female,0,No,No,30,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),85.35,2530.4,Yes
+8565-WUXZU,Male,1,Yes,No,72,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Two year,Yes,Credit card (automatic),84.8,6141.65,No
+5281-BUZGT,Male,1,No,No,36,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.85,3186.7,Yes
+4994-OBRSZ,Male,0,No,Yes,14,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),76.1,1054.8,No
+0562-FGDCR,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Mailed check,74.55,5430.65,No
+2436-QBZFP,Female,0,Yes,Yes,22,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,39.2,849.9,No
+3420-YJLQT,Female,0,No,No,2,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,79.55,151.75,No
+6040-CGACY,Female,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.6,299.4,No
+6582-PLFUU,Male,0,Yes,Yes,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.55,1086.75,No
+8242-PDSGJ,Male,0,Yes,No,70,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),39.15,2692.75,No
+0264-CNITK,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.1,1389.6,No
+0089-IIQKO,Female,0,Yes,Yes,39,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.95,3767.4,No
+7839-NUIAA,Female,0,Yes,Yes,61,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,59.8,3641.5,No
+6075-QMNRR,Female,0,No,No,52,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.75,2535.55,No
+5378-IKEEG,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,35.75,35.75,Yes
+5966-EMAZU,Male,0,Yes,No,64,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,108.5,6880.85,No
+0440-EKDCF,Male,0,Yes,No,62,Yes,No,DSL,No,Yes,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),60.15,3753.2,No
+4774-HHGGS,Male,0,Yes,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.05,637.55,No
+2718-GAXQD,Female,1,Yes,Yes,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,46,181.6,Yes
+2055-BFOCC,Male,1,Yes,No,63,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),84,5329.55,No
+8180-AKMJV,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),44.55,44.55,No
+4298-OYIFC,Male,0,Yes,No,15,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,103.45,1539.8,No
+5566-SOEZD,Male,0,Yes,Yes,27,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),80.65,2209.75,No
+9842-EFSYY,Female,0,No,Yes,4,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Mailed check,57.2,223.75,No
+2272-WUSPA,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,110.75,7751.7,No
+4584-LBNMK,Male,1,Yes,No,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.7,1174.35,No
+3898-GUYTS,Female,1,No,No,45,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,97.05,4385.05,No
+0930-EHUZA,Female,0,No,No,36,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Mailed check,76.35,2606.35,No
+6413-XKKPU,Male,0,Yes,Yes,17,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.4,1539.45,Yes
+9975-SKRNR,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.9,18.9,No
+3703-KBKZP,Male,1,No,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.45,1261.35,No
+1449-XQEMT,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.8,58.15,Yes
+2626-URJFX,Male,0,Yes,Yes,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.9,225.6,Yes
+4973-MGTON,Female,0,Yes,No,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),84.4,5969.3,No
+3682-YEUWS,Male,0,Yes,Yes,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,24.4,253.9,No
+1223-UNPKS,Male,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.05,400,No
+2612-RRIDN,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,81,340.85,Yes
+4735-ASGMA,Male,0,No,No,26,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.35,2515.3,Yes
+3446-QDSZF,Female,0,No,No,4,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Credit card (automatic),55.5,227.35,No
+3669-LVWZB,Male,0,No,No,5,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,51,305.95,Yes
+6892-EZDTG,Female,0,Yes,No,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,91.65,365.4,Yes
+5117-IFGPS,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.3,2357.75,No
+7379-FNIUJ,Male,0,No,No,2,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,100.2,198.5,No
+1627-AFWVJ,Female,0,No,No,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.4,554.25,No
+9725-SCPZG,Male,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.85,90.85,Yes
+5884-GCYMI,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,69.4,69.4,Yes
+3217-FZDMN,Female,1,No,No,8,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94.45,742.95,Yes
+4486-EFAEB,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.4,251.65,No
+0060-FUALY,Female,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,94.75,5597.65,No
+7853-GVUDZ,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.15,20.15,Yes
+7480-QNVZJ,Male,1,No,No,50,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.7,4816.7,Yes
+6954-OOYZZ,Male,0,Yes,No,18,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.35,768.05,No
+7088-FBAWU,Female,0,Yes,Yes,17,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,Month-to-month,No,Mailed check,74.55,1215.8,No
+8313-AFGBW,Male,0,Yes,No,47,Yes,No,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,73.6,3522.65,No
+3943-KDREE,Female,0,No,No,26,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.95,1834.95,Yes
+9239-ZBZZV,Female,0,No,No,6,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,47.95,305.1,Yes
+3097-IDVPU,Male,0,Yes,Yes,19,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),50.1,910.45,No
+4398-HSCJH,Female,0,No,No,3,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,63.6,155.65,Yes
+2197-OMWGI,Female,1,Yes,Yes,68,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,53,3656.25,No
+2303-PJYHN,Female,0,Yes,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.85,52,No
+9795-VOWON,Male,0,No,No,7,No,No phone service,DSL,No,No,No,No,No,No,One year,Yes,Credit card (automatic),24.35,150.85,No
+1237-WIYYZ,Female,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.55,389.25,No
+1987-AUELQ,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.05,1873.7,No
+7852-LECYP,Male,1,Yes,No,13,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,No,Credit card (automatic),93.8,1261,No
+4430-UZIPO,Male,0,No,No,3,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,36.85,108.7,Yes
+4822-LPTYJ,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),103.75,7346.2,No
+8165-CBKXO,Male,0,Yes,Yes,66,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),56.75,3708.4,No
+6527-PZFPV,Male,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.8,469.65,No
+4855-SNKMY,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.1,44.1,Yes
+9593-CVZKR,Female,0,Yes,Yes,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.45,1385.85,No
+8595-SIZNC,Female,1,Yes,No,22,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.6,548.8,No
+8008-HAWED,Male,0,No,No,14,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.75,712.75,Yes
+7124-UGSUR,Female,1,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.4,6405,Yes
+1862-SKORY,Female,1,Yes,No,40,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,39.3,1637.4,Yes
+5236-XMZJY,Female,0,No,No,42,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),59.65,2536.55,No
+4827-DPADN,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),83.3,6042.7,No
+2694-CIUMO,Female,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),79.55,958.25,No
+5846-ABOBJ,Male,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.45,1730.65,No
+1439-LCGVL,Female,0,Yes,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.2,459.6,No
+0909-SDHNU,Female,0,No,Yes,7,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.8,201.95,No
+4647-XXZAM,Female,0,Yes,Yes,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.5,285.2,No
+1502-XFCVR,Female,0,No,No,58,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.45,6145.85,Yes
+7740-KKCXF,Male,0,Yes,No,51,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),30.05,1529.45,No
+5360-XGYAZ,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),65.65,4664.5,No
+9692-TUSXH,Female,0,No,No,18,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.05,1740.7,Yes
+7912-SYRQT,Female,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),75.1,552.95,Yes
+3557-HTYWR,Female,0,No,No,47,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,Two year,No,Mailed check,74.05,3496.3,No
+4816-JBHOV,Male,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.7,93.7,Yes
+8920-NAVAY,Male,1,No,No,62,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),110.75,7053.35,No
+1699-TLDLZ,Female,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,301.55,No
+5600-PDUJF,Male,0,No,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),49.5,312.7,No
+8292-TYSPY,Male,0,No,No,19,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),55,1046.5,Yes
+0567-XRHCU,Female,0,Yes,Yes,69,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),43.95,2960.1,No
+1867-BDVFH,Male,0,Yes,Yes,11,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,834.2,Yes
+2067-QYTCF,Female,0,Yes,No,64,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,111.15,6953.4,No
+2359-QWQUL,Female,0,Yes,No,39,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),104.7,4134.85,Yes
+9103-TCIHJ,Female,0,No,No,15,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,55.7,899.8,Yes
+7407-SUJIZ,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.6,541.5,No
+9150-KPBJQ,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.65,116.85,No
+0052-DCKON,Male,0,Yes,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),115.8,7942.15,No
+3654-ARMGP,Female,0,No,No,61,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88.65,5321.25,No
+9699-UBQFS,Female,1,Yes,No,43,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,94.5,4156.8,No
+9367-TCUYN,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.1,223.6,No
+1261-FWTTE,Male,1,No,No,23,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),34.65,768.45,No
+3528-HFRIQ,Male,1,Yes,No,71,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),52.3,3765.05,No
+0708-SJDIS,Female,0,No,No,34,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,No,Mailed check,65,2157.5,No
+4140-WJAWW,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.85,108.05,Yes
+2073-QBVBI,Female,0,Yes,No,41,No,No phone service,DSL,No,Yes,No,Yes,No,No,One year,No,Mailed check,35.45,1391.65,No
+6928-ONTRW,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,1379.8,No
+3320-VEOYC,Male,1,No,No,14,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.6,1273.3,No
+5231-FIQPA,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.85,810.45,No
+6617-WLBQC,Female,0,Yes,Yes,23,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,One year,No,Credit card (automatic),81.85,1810.85,No
+2599-CIPQE,Male,0,Yes,Yes,71,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),109.3,7782.85,No
+6653-CBBOM,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,70.3,Yes
+8774-GSBUN,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.4,1797.1,No
+7326-RIGQZ,Male,0,Yes,Yes,6,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.8,377.85,No
+1401-FTHFQ,Male,0,Yes,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20,445.3,No
+3247-ZVOUO,Male,1,Yes,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,85.55,851.75,Yes
+0254-FNMCI,Female,0,No,No,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,109.9,7624.2,No
+1848-LBZHY,Female,0,Yes,No,7,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),50.3,355.1,No
+6101-IMRMM,Male,0,No,Yes,6,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Mailed check,94.5,575.45,Yes
+8118-TJAFG,Male,0,Yes,Yes,9,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.5,906.85,No
+5429-LWCMV,Female,0,No,No,12,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.15,1057.55,No
+7298-IZWLY,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.4,19.4,No
+9758-MFWGD,Female,1,Yes,Yes,48,No,No phone service,DSL,No,Yes,No,No,No,No,One year,No,Bank transfer (automatic),29.9,1388.75,No
+3955-JBZZM,Male,0,No,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,78.8,1641.3,No
+1268-ASBGA,Female,1,Yes,No,16,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.35,1375.15,Yes
+8943-URTMR,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,79.65,152.7,Yes
+4815-TUMEQ,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.3,185.2,No
+4713-LZDRV,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.6,195.05,Yes
+9909-IDLEK,Male,0,Yes,Yes,20,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,No,Mailed check,96.8,1826.7,No
+4092-OFQZS,Male,0,Yes,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.65,417.5,No
+1561-BWHIN,Male,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,344.5,No
+4325-NFSKC,Male,1,No,No,19,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.6,1660,Yes
+9927-DSWDF,Male,0,Yes,No,22,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.6,2180.55,No
+9500-LTVBP,Female,0,No,No,35,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),80.05,2835.9,No
+7252-NTGSS,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,45.15,45.15,No
+8149-AIQCG,Male,0,No,No,39,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.15,2730.85,No
+1360-JYXKQ,Female,1,Yes,No,54,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,99.1,5437.1,No
+2955-PSXOE,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.2,20.2,Yes
+7762-URZQH,Male,0,Yes,No,66,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),106.05,6981.35,Yes
+0899-WZRSD,Male,0,No,No,56,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,105.35,5794.45,No
+3255-GRXMG,Male,0,No,Yes,18,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),45.65,747.2,No
+4828-FAZPK,Female,0,Yes,Yes,16,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),79.95,1267.95,No
+6094-ZIVKX,Female,0,No,No,68,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),54.45,3674.95,No
+6925-BAYGL,Female,1,Yes,No,53,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.1,1275.6,No
+3262-EIDHV,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),84.7,5893.9,No
+7354-OIJLX,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.85,724.65,No
+1376-HHBDV,Female,0,No,No,30,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,48.8,1536.75,No
+6907-NZZIJ,Female,0,No,No,36,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.15,3615.6,Yes
+6133-OZILE,Female,0,No,No,18,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,35.2,607.3,No
+4135-FRWKJ,Female,1,Yes,Yes,55,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Electronic check,76.25,4154.55,No
+2911-UREFD,Female,0,Yes,No,39,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,55.9,2184.35,Yes
+3744-ZRRDZ,Male,0,No,No,21,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,82.35,1852.85,Yes
+5673-TIYIB,Male,0,No,No,2,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),40.4,77.15,Yes
+6551-ZCOTS,Male,1,No,No,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.9,847.8,No
+7191-ADRGF,Male,0,Yes,No,44,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),54.3,2390.45,No
+5018-LXQQG,Female,0,Yes,Yes,30,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),66.3,1923.5,No
+6892-BOGQE,Female,0,Yes,No,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.9,1493.2,No
+1602-IJQQE,Female,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.35,338.1,Yes
+4628-WQCQQ,Male,0,No,Yes,35,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,85.15,3030.6,Yes
+1746-TGTWV,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),75.35,75.35,No
+5995-SNNEW,Male,1,Yes,No,23,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.45,2184.85,No
+8050-WYBND,Female,0,No,Yes,22,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),49.45,1031.4,No
+2821-WARNZ,Female,0,No,Yes,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.45,921.3,No
+4879-GZLFH,Female,0,Yes,Yes,42,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.15,3875.4,No
+1644-IRKSF,Female,0,Yes,Yes,33,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,93.8,3124.5,Yes
+8314-HTWVE,Female,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.85,144.15,No
+9402-ROUMJ,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),100.25,6689,No
+2507-QZPQS,Male,0,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,No,Electronic check,95.7,1451.1,No
+7159-NOKYQ,Male,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Two year,Yes,Electronic check,93.15,6368.2,No
+5707-ZMDJP,Male,0,Yes,Yes,53,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Mailed check,69.7,3729.6,No
+8779-YIQQA,Male,0,Yes,Yes,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,350.1,No
+7136-IHZJA,Female,0,Yes,Yes,40,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,71.35,2847.2,No
+8966-OIQHG,Female,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.75,452.35,No
+7074-STDCN,Male,0,No,No,39,No,No phone service,DSL,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,40.6,1494.5,No
+3705-PSNGL,Male,0,No,No,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,20.4,930.45,Yes
+8739-QOTTN,Female,0,Yes,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,41.85,No
+1399-OUPJN,Female,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.75,1272.05,No
+0277-BKSQP,Male,0,Yes,Yes,8,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,54.4,475.1,No
+6502-HCJTI,Male,1,Yes,No,7,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),94.7,673.1,Yes
+2606-RMDHZ,Male,0,No,No,6,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),30.5,208.7,Yes
+5774-XZTQC,Female,0,Yes,Yes,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.45,150.75,No
+2676-SSLTO,Male,0,No,No,49,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),66.15,3199,No
+6266-QHOJZ,Female,0,No,No,65,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,One year,Yes,Electronic check,89.85,5844.65,No
+7269-JISCY,Female,0,No,No,55,Yes,No,DSL,No,No,No,No,No,No,One year,No,Bank transfer (automatic),45.05,2462.6,No
+0363-SVHYR,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.85,6263.8,No
+8547-NSBBO,Male,0,No,No,35,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,96.75,3403.4,No
+8258-GSTJK,Male,1,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,77,237.75,Yes
+6861-OKBCE,Female,0,No,Yes,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.1,221.35,Yes
+9940-RHLFB,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,75.3,75.3,Yes
+6591-QGOYB,Male,0,No,No,17,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),106.65,1672.1,No
+9070-BCKQP,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.15,7881.2,No
+6421-SZVEM,Female,0,Yes,Yes,28,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,One year,Yes,Bank transfer (automatic),82.85,2320.8,No
+1328-EUZHC,Female,0,Yes,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,370.5,No
+2995-UPRYS,Female,1,Yes,No,40,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),99.2,4062.2,Yes
+8878-RYUKI,Female,0,No,No,52,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,59.45,3043.7,No
+9633-DENPU,Female,0,Yes,No,47,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),58.6,2723.4,No
+7811-JIVPF,Female,0,No,No,23,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),49.7,1081.25,No
+7113-HIPFI,Male,0,Yes,Yes,66,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,65.85,4097.05,No
+8541-QVFKM,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.5,632.2,No
+6686-YPGHK,Male,1,No,No,47,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Mailed check,85.5,4042.3,Yes
+1383-EZRWL,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,164.85,Yes
+9258-CNWAC,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),113.65,8166.8,No
+5371-VYLSX,Female,1,No,No,50,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,83.4,4113.7,No
+6374-AFWOX,Male,0,Yes,No,46,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,65.65,3047.15,No
+4759-TRPLW,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.4,70.4,Yes
+5317-FLPJF,Female,0,No,No,66,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),61.35,4193.4,No
+7621-VPNET,Female,0,Yes,No,42,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),85.9,3729.75,No
+6034-YMTOB,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,75.65,399.45,No
+8563-IIOXK,Male,0,Yes,Yes,7,Yes,No,DSL,Yes,No,No,No,No,No,One year,Yes,Electronic check,49.75,331.3,Yes
+4903-CNOZC,Male,0,No,No,29,Yes,Yes,DSL,No,Yes,Yes,No,No,Yes,One year,No,Credit card (automatic),70.9,1964.6,No
+4353-HYOJD,Female,0,Yes,Yes,27,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,49.85,1336.15,No
+8020-BWHYL,Female,1,No,No,15,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.3,1147.45,Yes
+7267-FRMJW,Female,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.1,486.05,No
+2982-VPSGI,Female,0,Yes,No,11,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94,1078.9,Yes
+4188-FRABG,Male,0,Yes,No,57,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,103.05,5925.75,No
+8199-ZLLSA,Male,0,No,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),118.35,7804.15,Yes
+4128-ETESU,Female,1,Yes,No,47,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,99.7,4747.2,No
+0620-DLSLK,Female,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,81.9,1028.9,No
+4625-EWPTF,Male,0,No,No,8,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,30.45,226.45,Yes
+5980-NOPLP,Female,0,Yes,No,44,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,96.1,4364.1,Yes
+3850-OKINF,Male,0,Yes,Yes,71,Yes,Yes,DSL,Yes,No,No,No,Yes,No,One year,Yes,Electronic check,66.2,4692.55,No
+6892-XPFPU,Male,1,Yes,No,24,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.25,2433.9,Yes
+8010-EZLOU,Male,1,No,No,15,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.2,1217.25,Yes
+1156-ZFYDO,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.75,19.75,No
+3295-YVUSR,Male,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,72.6,154.3,No
+8016-NCFVO,Male,1,No,No,55,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,116.5,6382.55,No
+4119-ZYPZY,Male,1,No,No,71,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,106.8,7623.2,No
+5549-ZGHFB,Male,0,Yes,Yes,50,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.95,1261.45,No
+7577-SWIFR,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.25,89.25,No
+0303-WMMRN,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.25,86.05,No
+6408-OTUBZ,Female,0,No,No,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),104.55,6779.05,No
+5204-HMGYF,Female,0,Yes,Yes,49,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,87.2,4345,No
+1078-TDCRN,Female,1,Yes,No,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,30.75,82.85,No
+3727-OVPRY,Male,0,Yes,Yes,66,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),25.7,1714.55,No
+3797-FKOGQ,Male,0,No,Yes,11,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,86.2,893.2,No
+7622-NXQZR,Male,0,No,No,28,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),30.1,810.85,No
+6196-HBOBZ,Male,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Electronic check,99.35,6347.55,No
+3970-XGJDU,Female,0,No,No,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.2,1123.65,No
+7017-VFULY,Female,0,Yes,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.1,43.15,No
+5562-YJQGT,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.35,35.1,No
+8807-OPMBM,Female,0,Yes,Yes,55,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.65,1388,No
+5439-WIKXB,Male,1,Yes,No,41,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.55,3851.45,No
+9874-QLCLH,Female,0,Yes,Yes,17,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.2,1743.5,Yes
+8294-UIMBA,Female,0,No,No,30,Yes,No,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),94.4,2638.1,No
+8109-YUOHE,Male,0,No,No,17,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Mailed check,56.1,946.95,No
+5840-NVDCG,Female,0,Yes,Yes,16,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Two year,No,Bank transfer (automatic),68.25,1114.85,No
+8092-NLTGF,Male,0,No,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.75,1777.6,No
+5928-QLDHB,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.25,684.85,No
+9840-EFJQB,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,74.35,74.35,No
+0696-UKTOX,Male,0,No,Yes,23,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,54.15,1312.45,No
+4801-KFYKL,Male,0,No,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.45,159.2,No
+3472-OAOOR,Male,0,Yes,Yes,19,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,34.95,610.2,No
+6135-OZQVA,Female,0,No,No,7,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,53.65,404.35,No
+8062-YBDOE,Male,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.65,69.65,Yes
+2252-JHJGE,Male,0,No,No,61,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,No,Electronic check,104,6363.45,No
+4188-PCPIG,Female,0,Yes,No,57,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),70.35,4124.65,No
+6000-APYLU,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.8,713.1,Yes
+7105-BENQF,Male,0,No,Yes,15,Yes,No,DSL,Yes,No,Yes,No,No,Yes,One year,No,Mailed check,64.85,950.75,No
+7721-DVEKZ,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,No
+3566-VVORZ,Female,0,Yes,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),45.9,505.95,No
+9507-HSMMZ,Male,0,No,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,1149.65,No
+2480-SQIOB,Male,0,Yes,Yes,4,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.8,169.65,No
+0947-IDHRQ,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,80.3,526.7,Yes
+7813-TKCVO,Female,0,Yes,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.35,393.15,No
+0128-MKWSG,Female,0,No,Yes,26,No,No phone service,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Mailed check,45.8,1147,No
+3672-YITQD,Male,1,Yes,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.1,3021.6,Yes
+4350-ZTLPI,Female,0,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),108.95,5718.2,No
+9048-JVYVF,Male,0,No,No,3,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,69.35,191.35,Yes
+0361-HJRDX,Female,0,No,No,68,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),64.35,4539.6,No
+5727-MYATE,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.8,6397.6,No
+3823-KYNQY,Male,0,No,No,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.95,280.4,No
+8988-ECPJR,Female,1,Yes,Yes,34,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,79.6,2718.3,Yes
+7570-WELNY,Female,0,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Two year,Yes,Bank transfer (automatic),84.7,5711.05,No
+3021-VLNRJ,Female,0,No,Yes,50,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),70.8,3478.15,No
+3776-EKTKM,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,36.45,36.45,Yes
+2080-CAZNM,Female,1,No,No,41,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.4,4133.95,No
+1028-FFNJK,Male,1,Yes,No,30,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.5,2917.65,No
+6907-FLBER,Male,0,No,No,1,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.3,54.3,No
+3001-UNBTL,Male,1,Yes,Yes,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.95,2964.8,No
+5982-PSMKW,Female,0,Yes,Yes,23,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),91.1,2198.3,No
+3507-GASNP,Male,0,No,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.95,1189.9,No
+7096-ZNBZI,Female,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),26.45,1914.5,No
+1902-XBTFB,Male,0,No,Yes,22,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.4,2001.5,Yes
+1676-MQAOA,Male,0,No,No,72,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),75.1,5336.35,No
+0786-IVLAW,Female,1,No,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),108.1,7238.6,No
+7566-DSRLQ,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.15,7998.8,No
+0643-OKLRP,Female,1,Yes,No,47,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,80.35,3825.85,Yes
+7245-JMTTQ,Female,0,No,No,51,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.5,5703.25,No
+6050-IJRHS,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.5,7397,No
+6202-JVYEU,Male,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.9,164.6,No
+8591-TKMZH,Male,0,Yes,Yes,59,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,111.1,6555.2,No
+0734-OXWBT,Male,0,No,Yes,3,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,No,Mailed check,70.7,225.65,No
+4282-ACRXS,Male,1,Yes,No,38,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.85,955.75,No
+0365-TRTPY,Female,0,No,No,37,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),91.2,3382.3,No
+7349-ALMUX,Male,0,No,No,37,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),65.6,2313.8,No
+9381-NDKME,Female,1,Yes,No,24,No,No phone service,DSL,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),40.65,933.3,Yes
+6502-KUGLL,Female,0,Yes,Yes,14,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),59.45,780.85,No
+1841-YSJGV,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.95,7852.4,No
+2794-XIMMO,Male,0,Yes,No,53,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,60.45,3184.25,Yes
+8382-SHQEH,Female,0,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.9,764.95,Yes
+3511-BFTJW,Male,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Credit card (automatic),38.5,2763,No
+7668-XCFYV,Female,1,Yes,No,17,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.55,1614.7,No
+2983-ZANRP,Female,0,Yes,Yes,2,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),73.55,145.4,Yes
+7845-URHJN,Female,0,Yes,No,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.15,156.25,Yes
+3034-ZBEQN,Female,0,Yes,No,48,No,No phone service,DSL,No,Yes,Yes,No,No,No,One year,No,Mailed check,34.7,1604.5,Yes
+5018-HEKFO,Female,0,No,No,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,24.5,270.15,No
+2923-ARZLG,Male,0,Yes,Yes,0,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.7, ,No
+3976-NLDEZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.6,20.6,No
+2282-YGNOR,Female,0,No,No,29,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,No,Credit card (automatic),58,1734.5,No
+5336-UFNZP,Female,1,Yes,Yes,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,107.45,7047.5,No
+6854-EXGSF,Female,0,No,No,8,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),65.5,573.15,No
+1241-EZFMJ,Male,0,Yes,No,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.45,1538.6,No
+9233-PSYHO,Female,1,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),100.15,4459.8,No
+5376-PCKNB,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.45,7459,No
+8044-BGWPI,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,21.15,306.05,No
+4060-LDNLU,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,96.2,639.7,No
+3589-PPVKW,Male,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.4,348.15,No
+8327-LZKAS,Female,1,Yes,No,43,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,107.55,4533.9,Yes
+5887-IKKYO,Male,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),94.35,5563.65,No
+1075-BGWOH,Male,1,Yes,No,16,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.75,1587.55,Yes
+1755-RMCXH,Male,0,Yes,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,40.25,No
+0302-JOIVN,Female,0,Yes,No,8,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,101.15,842.9,Yes
+3858-XHYJO,Female,0,Yes,No,40,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),105.75,4228.55,No
+4299-SIMNS,Male,0,No,No,9,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,81.15,784.45,No
+7025-IWFHT,Male,0,No,No,41,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,89.55,3729.75,No
+6261-LHRTG,Female,0,No,No,26,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),54.75,1406.9,No
+7841-FCRQD,Female,0,Yes,No,33,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),53.75,1857.3,No
+2056-EVGZL,Male,0,Yes,Yes,68,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),105.75,7322.5,No
+8777-MBMTS,Female,1,Yes,No,65,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),105.85,6725.5,No
+7753-USQYQ,Male,0,No,No,55,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Electronic check,64.2,3627.3,No
+5366-IJEQJ,Male,0,No,No,20,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,88.7,1761.45,Yes
+7661-CPURM,Male,0,No,No,19,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,One year,Yes,Credit card (automatic),87.7,1725.95,No
+6233-HXJMX,Female,0,No,No,45,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),89.3,4192.15,No
+5902-WBLSE,Female,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.15,1411.2,No
+1981-INRFU,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.75,164.5,Yes
+4971-PUYQO,Female,0,No,No,27,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,94.55,2724.6,Yes
+3239-TPHPZ,Female,0,Yes,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,20.05,264.55,No
+5115-GZDEL,Male,0,No,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),67.2,4671.7,No
+3338-CVVEH,Male,0,No,No,12,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,94.55,1173.55,No
+8485-GJCDN,Female,1,No,No,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.05,318.5,Yes
+2615-YVMYX,Male,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,107.5,7713.55,No
+2851-STERV,Male,1,No,No,35,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,73,2471.25,No
+4393-GEADV,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),114.75,7842.3,No
+8486-AYEQH,Female,0,No,No,31,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.05,2227.8,No
+1527-SXDPN,Male,0,Yes,Yes,52,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),96.25,4990.25,Yes
+6029-WTIPC,Male,1,No,No,37,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.1,3744.05,Yes
+8634-CILSZ,Male,0,No,No,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),104.7,7220.35,Yes
+5419-JKZNQ,Male,1,Yes,No,30,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,77.9,2351.45,No
+2495-KZNFB,Female,0,No,No,33,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,90.65,2989.6,No
+1409-PHXTF,Male,1,Yes,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.45,6077.75,No
+4560-WQAQW,Female,0,No,No,59,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,No,Bank transfer (automatic),68.7,4070.95,No
+9591-YVTEB,Male,1,No,No,55,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,44.85,2479.05,No
+9309-BZGNT,Male,1,Yes,No,69,No,No phone service,DSL,No,No,Yes,No,No,No,One year,Yes,Credit card (automatic),29.8,2134.3,No
+4274-DRSQT,Female,0,No,No,66,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),88.9,6000.1,No
+2027-DNKIV,Male,0,Yes,Yes,37,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Mailed check,58.75,2203.1,No
+8075-GXIUB,Male,1,Yes,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.85,183.15,No
+2885-HIJDH,Male,0,Yes,Yes,69,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.9,6194.1,No
+5424-RLQLC,Male,0,No,No,10,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Mailed check,59.65,638.95,No
+6682-QJDGB,Male,0,No,Yes,40,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,55.25,2139.2,Yes
+6507-DTJZV,Male,0,No,Yes,13,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Credit card (automatic),66.4,831.75,No
+8780-IXSTS,Female,0,No,No,6,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.1,521.3,Yes
+7673-BQGKU,Female,0,Yes,Yes,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.15,1337.5,No
+6723-WSNTY,Female,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),108.1,7181.95,No
+9530-EHPOH,Male,0,No,No,11,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,53.75,608,Yes
+2725-IWWBA,Male,0,Yes,Yes,46,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,No,Mailed check,56.9,2560.1,No
+0345-HKJVM,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,89.3,577.6,Yes
+1061-PNTHC,Female,0,Yes,Yes,56,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,109.6,5953,No
+0394-YONDK,Male,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.15,1790.15,No
+6574-MCOEH,Female,0,Yes,Yes,33,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,79.15,2531.4,No
+7399-QHBJS,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Credit card (automatic),66.75,4760.3,No
+3049-SOLAY,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.2,292.85,Yes
+0997-YTLNY,Female,0,No,Yes,19,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,48.8,953.65,No
+3317-HRTNN,Female,1,No,No,5,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,45.7,198,Yes
+9479-HYNYL,Female,0,Yes,No,71,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),80.7,5705.05,No
+3235-ETOOB,Male,0,Yes,No,8,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.5,609.9,Yes
+9708-KFDBY,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.55,20.55,No
+8058-INTPH,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.65,79.65,Yes
+3642-GKTCT,Female,0,No,No,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,115.1,6993.65,No
+0774-RMNUW,Female,0,Yes,Yes,71,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),59.7,4122.65,No
+1334-PDUKM,Female,0,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Credit card (automatic),86.45,5762.95,No
+0756-MPZRL,Male,0,No,No,46,No,No phone service,DSL,No,No,Yes,Yes,No,No,One year,No,Credit card (automatic),33.7,1537.85,No
+2242-MFOTG,Male,0,No,No,33,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,One year,No,Bank transfer (automatic),80.1,2603.3,No
+2927-CVULT,Female,0,Yes,Yes,53,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),104.05,5566.4,No
+2144-BFDSO,Female,1,Yes,No,50,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),108.75,5431.9,No
+8745-PVESG,Female,0,No,No,57,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),41.1,2258.25,No
+7647-GYYKX,Female,0,Yes,Yes,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.35,1092.35,No
+5647-FXOTP,Female,1,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.9,6401.25,No
+5569-OUICF,Female,1,Yes,No,28,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),101.3,2812.2,Yes
+8821-XNHVZ,Female,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,80.05,80.05,Yes
+2082-OJVTK,Male,0,Yes,Yes,29,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.2,2698.35,Yes
+9700-ISPUP,Female,0,Yes,Yes,10,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,65.5,616.9,No
+9839-ETQOE,Male,0,No,Yes,43,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,40.45,1912.85,No
+6078-VESFR,Male,1,Yes,No,13,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.45,849.1,No
+9027-TMATR,Female,0,Yes,No,43,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,78.8,3460.3,No
+5940-NFXKV,Male,0,Yes,Yes,19,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.65,1465.75,Yes
+1941-HOSAM,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,90.1,90.1,No
+9110-HSGTV,Female,0,No,No,69,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),82.45,5555.3,No
+0704-VCUMB,Female,0,Yes,No,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.25,1278.8,No
+6171-ZTVYB,Male,0,Yes,No,43,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),66.25,2907.35,No
+8053-WWDRO,Female,0,Yes,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.5,146.3,Yes
+9564-KCLHR,Male,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,51.25,51.25,Yes
+1935-IMVBB,Male,0,Yes,No,56,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Mailed check,89.7,4952.95,No
+2535-PBCGC,Female,0,Yes,No,70,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),64.55,4504.9,No
+2082-CEFLT,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.6,45.6,Yes
+1470-PSXNM,Male,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Electronic check,93.65,4520.15,No
+1213-NGCUN,Female,0,No,No,6,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),49.65,267.35,Yes
+2498-XLDZR,Female,0,Yes,Yes,32,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Mailed check,73.6,2316.85,No
+9866-OCCKE,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,109.75,8075.35,No
+5338-YHWYT,Male,0,No,Yes,37,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Credit card (automatic),61.45,2302.35,No
+7718-UPSKJ,Female,0,Yes,No,69,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),106.4,7251.9,No
+8731-WBBMB,Female,0,Yes,No,26,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),81.9,2078.55,No
+1448-CYWKC,Female,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),105.2,6225.4,No
+7901-IIDQV,Male,0,No,No,24,Yes,Yes,DSL,No,No,No,Yes,No,No,One year,No,Bank transfer (automatic),54.6,1242.25,No
+2690-DVRVK,Male,0,Yes,Yes,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20.55,99.45,No
+5688-KZTSN,Male,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20,288.05,Yes
+1270-XKUCC,Female,0,Yes,Yes,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,599.25,No
+0334-ZFJSR,Female,0,Yes,No,55,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),66.05,3462.1,No
+2894-QOJRX,Female,0,Yes,No,25,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),34,853,Yes
+5747-PMBSQ,Male,1,Yes,No,10,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,92.5,934.1,Yes
+5583-EJXRD,Male,0,Yes,Yes,44,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),54.05,2375.2,No
+4565-EVZMJ,Female,0,No,No,47,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Mailed check,58.9,2813.05,No
+3143-JQEGI,Female,0,Yes,Yes,13,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,88.35,1222.8,Yes
+6386-SZZKH,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),107.95,5293.2,Yes
+3327-YBAKM,Female,0,Yes,No,64,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,96.9,6314.35,No
+9441-QHEVC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.1,19.1,No
+6705-LNMDD,Male,0,No,No,20,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,50,1003.05,No
+2580-ASVVY,Female,0,Yes,No,37,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Two year,No,Electronic check,45.4,1593.1,No
+3370-GQEAL,Male,0,Yes,Yes,30,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,85.45,2509.95,No
+5032-USPKF,Female,0,No,No,38,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),84.1,3187.65,No
+2982-IHMFT,Female,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.45,74.45,Yes
+3521-SYVOR,Female,0,No,No,37,Yes,No,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,64.75,2345.2,Yes
+4254-QPEDE,Female,0,Yes,No,52,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),66.25,3330.1,No
+6283-GITPX,Male,0,No,Yes,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),76.9,5522.7,No
+9526-JAWYF,Male,0,No,No,26,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Electronic check,89.8,2335.3,Yes
+0771-CHWSK,Male,0,No,No,66,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),74.6,4798.4,No
+9788-HNGUT,Male,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.95,8594.4,No
+9495-REDIY,Male,0,No,Yes,25,No,No phone service,DSL,Yes,No,No,No,Yes,No,One year,Yes,Credit card (automatic),40.65,970.55,No
+5375-XLDOF,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.35,7665.8,No
+1172-VIYBP,Male,0,Yes,Yes,53,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,Two year,No,Bank transfer (automatic),69.7,3686.05,No
+5649-VUKMC,Female,0,No,No,12,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Mailed check,95.5,1115.15,Yes
+6559-PDZLR,Male,0,No,No,26,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.65,2537,No
+8992-OBVDG,Male,0,No,No,21,Yes,No,DSL,No,No,Yes,No,No,Yes,Month-to-month,No,Mailed check,61.65,1393.6,No
+4273-MBHYA,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.35,89.35,No
+4724-WXVWF,Male,0,No,No,48,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,95.4,4445.3,No
+5701-GUXDC,Female,0,Yes,No,26,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Credit card (automatic),35.4,978.6,No
+1460-UZPRJ,Male,0,Yes,No,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.95,1258.15,No
+6082-GLJIX,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.25,331.35,No
+5143-EGQFK,Female,1,No,No,10,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,29.65,291.4,Yes
+4919-IKATY,Male,0,Yes,Yes,5,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,84.5,453.75,Yes
+2495-TTHBQ,Female,0,No,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.4,84.75,No
+0485-ZBSLN,Male,0,Yes,Yes,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.75,1715.1,No
+3810-PJUHR,Male,0,Yes,Yes,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.35,1715.15,No
+9050-QLROH,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.7,1597.25,Yes
+0847-HGRML,Male,0,No,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20,1250.1,No
+8232-CTLKO,Female,0,Yes,Yes,66,Yes,No,DSL,Yes,No,No,No,Yes,No,Two year,Yes,Electronic check,59.75,3996.8,No
+9227-YBAXE,Female,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),82.5,5215.1,No
+6168-WFVVF,Female,1,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,235.5,Yes
+9860-LISIZ,Female,0,No,No,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,673.2,No
+0112-QAWRZ,Male,0,Yes,Yes,16,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),90.8,1442.2,No
+0877-SDMBN,Female,0,No,No,54,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),103.95,5639.05,Yes
+2786-GCDPI,Female,1,No,No,50,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.95,5222.35,No
+4043-MKDTV,Male,0,Yes,No,71,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,105.25,7291.75,No
+8065-YKXKD,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.75,799.65,Yes
+9637-EIHEQ,Female,0,No,No,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,50.8,50.8,Yes
+9229-RQABD,Male,0,No,No,18,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,23.75,424.5,No
+8313-KTIHG,Male,0,No,No,4,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,61.3,249.4,No
+5320-BRKGK,Female,0,Yes,Yes,58,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,No,Two year,No,Mailed check,75.8,4415.75,No
+6284-KMNUF,Female,0,Yes,No,56,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Electronic check,98,5270.6,No
+9689-PTNPG,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.25,144.55,Yes
+9465-RWMXL,Male,0,Yes,No,32,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,78.9,2447.95,Yes
+6229-UOLQL,Male,0,Yes,Yes,56,No,No phone service,DSL,Yes,Yes,No,Yes,Yes,No,One year,Yes,Mailed check,52,2884.9,No
+2362-IBOOY,Male,0,No,No,36,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.75,3050.15,Yes
+3137-LUPIX,Female,0,No,No,4,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,64.4,253,No
+0843-WTBXE,Male,0,No,No,53,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Bank transfer (automatic),85.45,4517.25,Yes
+6143-JQKEA,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),45.8,436.2,No
+8676-OOQEJ,Male,0,No,No,4,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,No,Electronic check,30.5,118.4,No
+5515-IDEJJ,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,19.9,Yes
+9701-CDXHR,Female,0,Yes,No,51,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,69.15,3649.6,No
+7450-NWRTR,Male,1,No,No,12,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.45,1200.15,Yes
+8124-NZVGJ,Female,0,No,No,6,No,No phone service,DSL,No,No,No,Yes,Yes,Yes,One year,Yes,Mailed check,49.25,255.6,No
+2162-FRZAA,Male,0,Yes,Yes,63,No,No phone service,DSL,No,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),39.35,2395.05,No
+5376-DEQCP,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.6,70.6,Yes
+5118-MUEYH,Female,0,Yes,No,48,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),105.1,5083.55,No
+9095-HFAFX,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),81,389.6,Yes
+5627-TVBPP,Female,0,No,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Credit card (automatic),20.1,644.5,No
+2379-ENZGV,Male,0,No,No,6,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.85,523.5,Yes
+3936-QQFLL,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.75,39.3,No
+2589-AYCRP,Female,0,No,No,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.75,989.05,No
+4067-HLYQI,Female,0,No,No,33,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),70.4,2406.1,No
+5124-EOGYE,Male,0,No,No,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.45,638.55,No
+5057-RKGLH,Female,0,Yes,Yes,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.35,191.1,No
+0292-WEGCH,Female,0,Yes,Yes,54,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,86.2,4524.05,No
+8910-ICHIU,Female,0,No,No,46,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),95.65,4664.2,No
+4097-YODCF,Male,0,No,Yes,34,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,103.8,3470.8,No
+9715-WZCLW,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Two year,Yes,Electronic check,97.2,6910.3,No
+9786-YWNHU,Female,0,Yes,Yes,63,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,63.55,4014.2,No
+6407-GSJNL,Female,0,No,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.95,1288,No
+6005-OBZPH,Female,1,No,No,26,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.15,2277.65,Yes
+4049-ZPALD,Female,0,Yes,No,64,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),99,6375.8,No
+7932-WPTDS,Female,1,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.8,24.8,Yes
+3733-UOCWF,Male,1,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),85.55,5251.75,No
+6833-JMZYP,Female,0,No,No,15,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),94,1505.45,No
+2722-VOJQL,Male,0,No,No,64,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.65,6903.1,Yes
+1310-QRITU,Female,0,No,No,18,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.3,913.3,No
+8961-QDZZJ,Female,0,Yes,Yes,57,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,95,5535.8,No
+9715-SBVSU,Male,0,Yes,Yes,14,Yes,No,DSL,Yes,Yes,No,No,No,Yes,Two year,Yes,Bank transfer (automatic),61.4,815.55,No
+8490-BXHEO,Male,1,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),80.55,1411.65,No
+7173-TETGO,Female,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),78.5,5602.25,No
+7929-SKFGK,Male,0,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),114.3,8244.3,No
+2300-RQGOI,Female,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.05,741.5,No
+3563-SVYLG,Male,0,Yes,Yes,68,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),62.65,4375.8,No
+5228-EXCET,Male,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.85,1008.7,Yes
+6435-VWCCY,Male,1,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Two year,Yes,Credit card (automatic),92.7,5968.4,No
+4998-IKFSE,Female,0,No,No,30,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,100.45,3096.9,No
+8630-QSGXK,Male,0,Yes,No,51,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),75.2,3901.25,No
+2455-USLMV,Female,0,No,No,31,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),84.75,2613.4,No
+7394-FKDNK,Female,0,Yes,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,89.45,853.1,Yes
+9488-FVZCC,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),79.5,5661.7,No
+6331-LWDTQ,Male,0,No,No,10,Yes,Yes,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),72.15,794.25,Yes
+5995-WWKKG,Female,0,No,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,19.8,695.05,No
+1597-FZREH,Female,0,No,No,2,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,76.4,160.8,Yes
+7879-CGSFV,Male,0,No,No,55,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,Yes,Mailed check,100.9,5552.05,No
+9921-EZKBY,Male,0,No,Yes,33,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.3,3275.15,No
+7432-FFVAR,Female,0,Yes,Yes,46,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),90.95,4236.6,No
+7246-ZGQDF,Female,0,No,Yes,1,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,54.5,54.5,No
+7000-WCEVQ,Female,1,No,No,20,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,61.6,1174.35,Yes
+0727-IWKVK,Male,0,Yes,No,9,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,79.9,741.7,Yes
+5959-BELXA,Male,1,No,No,32,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),96.15,3019.25,Yes
+4456-RHSNB,Female,0,Yes,Yes,19,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),49.6,962.9,No
+3512-IZIKN,Female,0,Yes,No,70,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),65.3,4759.75,Yes
+2481-SBOYW,Female,0,No,Yes,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25,1498.35,No
+7109-MFBYV,Male,0,No,No,26,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.45,1233.15,No
+1833-TCXKK,Male,0,Yes,No,45,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),107.75,4882.8,No
+1832-PEUTS,Male,0,Yes,Yes,62,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),89.1,5411.65,No
+6141-OOXUQ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,Yes
+8660-BUETV,Female,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,44.75,148.05,No
+0580-PIQHM,Female,0,Yes,Yes,41,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),101.6,3930.55,No
+5696-CEIQJ,Male,0,Yes,Yes,67,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),103.15,6895.5,No
+7503-ZGUZJ,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,84.65,84.65,Yes
+1696-HXOWK,Female,0,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,No,Mailed check,95.65,6856.95,No
+3026-ATZYV,Female,0,Yes,Yes,37,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),75.1,2658.8,No
+4012-YCFAI,Male,0,Yes,No,60,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,61.35,3766.2,No
+2506-TNFCO,Female,1,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.55,69.55,Yes
+9970-QBCDA,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.7,129.55,No
+8718-PTMEZ,Female,0,No,No,13,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,31.05,347.25,Yes
+9496-IVVRP,Female,0,Yes,Yes,11,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),51,581.7,No
+2207-OBZNX,Male,0,No,No,7,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,51,354.05,Yes
+2657-VPXTA,Female,0,Yes,Yes,10,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,88.85,929.45,No
+6551-VLJMV,Male,0,Yes,No,34,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,679,No
+1221-GHZEP,Female,0,No,No,62,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Two year,Yes,Mailed check,65.1,3846.75,No
+9289-LBQVU,Male,0,Yes,No,64,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,70.15,4480.7,No
+6142-VSJQO,Female,0,Yes,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.35,44.35,Yes
+3458-IDMFK,Male,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.75,499.4,No
+6933-FHBZC,Female,0,No,No,26,Yes,No,DSL,No,Yes,Yes,No,No,No,One year,Yes,Mailed check,56.05,1553.2,No
+0221-NAUXK,Male,0,No,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.95,219.5,No
+9770-KXGQU,Female,0,No,No,53,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,No,Mailed check,98.6,5311.85,No
+6437-UKHMV,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.7,586.05,Yes
+6538-POCHL,Male,0,No,No,33,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),79,2576.8,No
+6726-NNFWD,Female,1,Yes,No,71,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Two year,No,Credit card (automatic),89.45,6435.25,No
+3002-WQZWT,Female,0,No,No,29,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.2,1993.25,No
+4277-UDIEF,Male,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),81,1923.85,No
+1208-NBVFH,Male,0,Yes,Yes,20,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,49.6,939.8,No
+4912-PIGUY,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,84.6,84.6,No
+1114-CENIM,Male,0,No,Yes,54,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),55,3092.65,Yes
+6060-DRTNL,Female,1,No,No,5,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,84.85,415.55,Yes
+2725-TTRIQ,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),84.2,5986.55,No
+3374-TTZTK,Male,0,Yes,No,52,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,106.3,5487,No
+8990-ZXLSU,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.05,651.5,No
+2275-RBYQS,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.4,45.4,No
+8473-VUVJN,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.65,73.65,Yes
+6289-CPNLD,Male,0,Yes,Yes,33,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,73.9,2405.05,Yes
+5536-SLHPM,Female,0,Yes,No,55,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,77.75,4458.15,Yes
+4419-UJMUS,Male,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Two year,Yes,Electronic check,99.35,6856.45,No
+7794-JASDG,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.75,50.75,No
+7609-YBPXG,Male,0,No,No,54,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),87.1,4735.2,No
+5519-TEEUH,Male,0,No,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.15,682.15,No
+7856-GANIL,Male,1,Yes,No,45,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),98.7,4525.8,No
+0804-XBFBV,Female,0,No,Yes,11,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.2,321.05,No
+2676-OXPPQ,Male,0,No,No,6,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),55.7,335.65,No
+3703-TTEPD,Male,0,No,No,21,Yes,No,DSL,Yes,No,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),65.35,1424.4,No
+2799-TSLAG,Female,0,Yes,Yes,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.3,1748.55,No
+0196-VULGZ,Female,1,Yes,No,6,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.35,474.9,Yes
+8837-VVWLQ,Female,0,No,No,8,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,84.95,668.4,Yes
+2696-NARTR,Male,0,No,No,11,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,73.85,926.25,Yes
+2208-NKVVH,Male,0,Yes,Yes,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.25,1077.95,No
+7614-QVWQL,Male,0,Yes,Yes,49,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),51.8,2541.25,Yes
+1976-CFOCS,Female,1,Yes,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,46,46,Yes
+4631-OACRM,Male,1,No,No,15,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.4,1156.1,Yes
+7139-JZFVG,Male,0,Yes,Yes,60,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Bank transfer (automatic),60.5,3694.45,No
+4987-GQWPO,Male,0,No,No,17,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),25.1,382.8,No
+3755-JBMNH,Male,1,Yes,No,16,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),71.8,1167.8,Yes
+1757-TCATG,Male,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.05,746.75,No
+6345-ULYRW,Male,1,Yes,No,44,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,One year,No,Mailed check,88.4,3912.9,Yes
+4776-XSKYQ,Female,0,Yes,Yes,12,No,No phone service,DSL,No,No,No,Yes,No,No,One year,No,Credit card (automatic),30.25,368.85,No
+8048-DSDFQ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,20.2,No
+5753-QQWPW,Female,0,No,No,28,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Electronic check,59.9,1654.7,No
+6010-DDPPW,Male,0,Yes,No,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.15,1940.85,No
+1809-DMJHQ,Female,0,No,Yes,5,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,46,221.7,Yes
+6693-FRIRW,Male,0,No,No,18,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),101.3,1794.65,No
+6586-MYGKD,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),76.95,5289.8,No
+3173-NVMPX,Female,0,Yes,Yes,9,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,55.3,501.2,No
+0248-PGHBZ,Female,1,No,No,67,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),92.45,6140.85,No
+0623-GDISB,Female,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),48.45,48.45,No
+2892-GESUL,Female,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.35,309.25,No
+6923-EFPNL,Male,0,No,No,4,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),51.75,201.1,Yes
+7472-EQOAV,Male,1,Yes,Yes,71,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,One year,Yes,Bank transfer (automatic),86.7,6179.35,No
+9574-RKJIF,Male,0,Yes,Yes,30,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,94.4,2838.7,Yes
+2043-WVTQJ,Male,0,Yes,No,1,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,55.7,55.7,No
+8034-RYTVV,Female,0,No,No,55,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),84.25,4589.85,No
+3863-QSTYI,Male,0,No,No,59,Yes,No,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,64.65,3735.45,No
+2619-WFQWU,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.15,70.15,Yes
+5044-LRQAQ,Female,0,Yes,No,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.2,477.55,No
+1716-LSAMB,Male,0,Yes,Yes,45,Yes,No,DSL,Yes,No,No,Yes,No,No,Two year,No,Bank transfer (automatic),54.65,2553.7,No
+1333-PBMXB,Female,0,Yes,Yes,54,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.75,1342.15,No
+6546-OPBBH,Male,0,Yes,Yes,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,23.95,1216.35,No
+5985-BEHZK,Female,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),105,7578.05,No
+9127-QRZMH,Male,0,Yes,No,44,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),59.85,2603.95,No
+5919-VCZYM,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,42.7,No
+9644-KVCNC,Female,0,No,No,66,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),92.15,6056.9,No
+2137-DQMEV,Male,0,Yes,Yes,68,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,One year,No,Mailed check,44.8,2983.65,No
+8174-LNWMW,Female,0,No,No,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.9,689.35,No
+9279-CJEOJ,Female,1,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.4,2025.1,No
+7964-VEXDG,Male,0,No,Yes,21,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,80.35,1747.2,No
+2404-JIBFC,Female,0,Yes,Yes,55,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),85.1,4657.95,No
+0778-NELLA,Male,0,No,No,9,No,No phone service,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),34.7,296.1,Yes
+8029-XYPWT,Male,1,Yes,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),115.05,8016.6,No
+4614-NUVZD,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,81.1,81.1,Yes
+4632-PAOYU,Male,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,433.5,No
+3803-KMQFW,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.55,20.55,Yes
+6804-GDMOI,Female,0,No,No,61,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,106.6,6428.4,Yes
+2990-OGYTD,Female,0,Yes,No,67,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),86.15,5883.85,No
+6646-JPPHA,Female,1,No,No,14,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,78.85,1043.8,No
+9572-MTILT,Male,0,Yes,No,59,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Electronic check,106.75,6252.9,Yes
+7658-UYUQS,Male,1,Yes,No,21,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),86.55,1857.25,No
+7880-XSOJX,Male,0,No,No,4,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,42.4,146.4,No
+9611-CTWIH,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.45,240.45,Yes
+4589-IUAJB,Male,0,Yes,No,70,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.25,1724.15,No
+0329-GTIAJ,Female,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,97.9,315.3,Yes
+8746-BFOAJ,Male,1,No,No,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.5,429.55,No
+8457-XIGKN,Male,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.6,356.15,No
+6072-NUQCB,Male,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.25,488.25,No
+6629-CZTTH,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Mailed check,55.7,55.7,Yes
+8838-GPHZP,Female,0,No,No,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.6,1298.7,No
+8750-QWZAJ,Female,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.8,1378.75,No
+5364-EVNIB,Male,0,No,No,13,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,79.8,973.45,Yes
+6918-UMQCG,Female,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,80.2,384.25,No
+0675-NCDYU,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.4,8543.25,No
+6339-DKLMK,Female,0,No,No,13,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,31.65,389.95,No
+1346-PJWTK,Male,0,Yes,No,61,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,No,Credit card (automatic),94.15,5731.85,No
+5088-QZLRL,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.65,20.65,No
+8023-QHAIO,Female,1,Yes,No,56,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),76.85,4275.75,No
+4397-FRLTA,Female,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.15,84.5,No
+3057-VJJQE,Male,0,No,No,35,Yes,Yes,DSL,Yes,No,No,No,No,No,Two year,No,Mailed check,55.25,1924.1,No
+5087-SUURX,Female,0,Yes,No,18,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,39.05,669.85,Yes
+2302-OUZXB,Male,0,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),82.15,5784.3,No
+8133-ANHHJ,Female,1,No,No,49,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),103,5166.2,No
+2270-CHBFN,Female,0,Yes,No,44,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),95.1,4060.55,No
+0013-EXCHZ,Female,1,Yes,No,3,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,83.9,267.4,Yes
+5982-FPVQN,Female,0,Yes,Yes,37,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.15,3532.85,No
+9107-UKCKY,Male,0,Yes,No,61,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,79.8,4914.8,No
+4654-ULTTN,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,Yes,Credit card (automatic),74.8,5315.8,No
+5550-VFRLC,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.85,69.85,Yes
+5546-BYZSM,Female,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.45,775.6,No
+1492-KGETH,Male,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),78.35,5445.95,No
+4929-BSTRX,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,53.55,53.55,Yes
+4163-HFTUK,Male,0,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,19.1,1007.8,No
+8215-NGSPE,Female,0,Yes,Yes,42,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,833.55,No
+2225-ZRGSG,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),93.9,6579.05,Yes
+8859-YSTWS,Male,0,No,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.95,1004.5,No
+5271-YNWVR,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,113.15,7856,Yes
+7601-DHFWZ,Female,0,No,No,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24,1183.05,No
+2657-ALMWY,Female,1,Yes,No,26,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Electronic check,84.95,2169.75,Yes
+6330-JKLPC,Male,0,Yes,No,11,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.5,896.9,Yes
+4667-OHGKG,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.3,19.3,Yes
+1998-VHJHK,Female,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.15,501.35,No
+1707-HABPF,Female,1,No,No,46,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,No,One year,Yes,Bank transfer (automatic),91.3,4126.35,No
+1660-HSOOQ,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,49.65,49.65,Yes
+7253-UVNDW,Female,0,No,No,46,Yes,No,DSL,No,No,Yes,Yes,No,No,Two year,No,Credit card (automatic),54.35,2460.15,Yes
+0487-VVUVK,Male,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.15,477.6,No
+7228-OMTPN,Male,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,88.45,370.65,Yes
+5063-IUOKK,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,265.75,No
+4508-OEBEY,Male,0,Yes,No,31,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,Yes,Credit card (automatic),75.5,2424.45,No
+0027-KWYKW,Female,0,Yes,Yes,23,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,83.75,1849.95,No
+2308-STERM,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.4,61.05,No
+5982-XMDEX,Female,0,No,No,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),26.5,1698.55,No
+6284-AHOOQ,Male,1,No,No,22,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),90.5,1910.6,Yes
+6051-PTVNS,Female,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.15,998.1,No
+2344-JMOGN,Male,0,Yes,No,9,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,94.85,890.6,Yes
+3799-ISUZQ,Male,0,Yes,Yes,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),69.95,529.5,Yes
+2877-VDUER,Female,0,Yes,Yes,35,No,No phone service,DSL,No,No,No,Yes,Yes,No,One year,No,Mailed check,40.9,1383.6,No
+9152-AMKAK,Male,0,No,No,6,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,80.25,493.4,No
+1794-HBQTJ,Female,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),48.6,48.6,Yes
+9432-VOFYX,Male,0,No,No,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.8,1207,No
+8049-WJCLQ,Male,0,Yes,Yes,10,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,No,Mailed check,60.2,563.5,No
+6586-PSJOX,Male,0,No,Yes,15,Yes,No,DSL,No,No,Yes,Yes,No,No,One year,No,Credit card (automatic),55.2,864.55,No
+2460-FPSYH,Female,1,No,No,40,No,No phone service,DSL,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,55.8,2109.35,Yes
+9705-ZJBCG,Female,0,Yes,Yes,13,Yes,No,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),54.15,701.05,No
+4818-DRBQT,Male,0,Yes,No,29,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.15,2265.25,Yes
+1320-HTRDR,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.5,220.6,Yes
+6847-KJLTS,Female,1,Yes,No,58,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.4,5749.8,No
+9670-BPNXF,Female,0,No,No,45,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),62.55,2796.45,No
+3913-FCUUW,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),70.45,5165.7,No
+3301-VKTGC,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Bank transfer (automatic),85.5,5696.6,No
+1493-AMTIE,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.2,20.2,Yes
+9555-SAHUZ,Female,0,Yes,Yes,38,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.5,2076.05,No
+0816-TSPHQ,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.75,44.2,No
+9932-WBWIK,Male,0,No,No,11,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,215.25,No
+4619-EVPHY,Female,1,Yes,No,20,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),91,1859.5,No
+5286-YHCVC,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.8,7470.1,No
+6424-ELEYH,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.75,229.5,Yes
+4391-RESHN,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,104.05,2470.1,Yes
+9115-YQHGA,Male,0,No,No,40,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,51.1,2092.9,No
+4462-CYWMH,Male,1,Yes,No,62,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),89.8,5629.55,No
+8963-MQVYN,Female,0,No,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.55,469.85,No
+2458-EOMRE,Female,0,No,No,11,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Month-to-month,No,Bank transfer (automatic),64.05,733.95,No
+9334-GWGOW,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.85,485.25,No
+6821-BUXUX,Female,0,No,No,13,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,96.65,1244.5,Yes
+5028-HTLJB,Male,1,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,Yes
+9801-GDWGV,Female,0,No,No,39,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.45,3994.45,Yes
+6542-LWGXJ,Male,0,Yes,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25,78.25,No
+5567-GZKQY,Male,0,No,No,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.3,1131.5,No
+1222-LRYKO,Male,0,No,Yes,6,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,26.35,184.05,No
+2320-JRSDE,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.9,19.9,Yes
+2087-QAREY,Female,0,Yes,No,22,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,54.7,1178.75,No
+0601-WZHJF,Male,0,Yes,No,14,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,46.35,667.7,Yes
+4423-JWZJN,Male,0,Yes,Yes,64,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,One year,No,Credit card (automatic),90.25,5629.15,No
+5143-WMWOG,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.95,19.95,Yes
+6490-FGZAT,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.65,109.3,No
+5393-RXQSZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,79.6,79.6,Yes
+7452-FOLON,Male,0,No,Yes,39,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.45,958.45,No
+2320-TZRRH,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.5,403.15,No
+0231-LXVAP,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,75.9,75.9,Yes
+9444-JTXHZ,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,76.2,76.2,Yes
+4942-VZZOM,Male,0,Yes,No,64,Yes,Yes,DSL,Yes,No,No,No,Yes,No,One year,Yes,Credit card (automatic),66.15,4392.5,No
+5510-BOIUJ,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.25,19.25,Yes
+7502-BNYGS,Female,0,Yes,No,46,Yes,No,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),69.1,3168,No
+4291-HYEBC,Female,1,Yes,Yes,28,No,No phone service,DSL,No,Yes,No,No,No,Yes,One year,Yes,Electronic check,39.1,1096.6,No
+6147-CBCRA,Female,0,Yes,No,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,669.45,No
+6047-SUHPR,Male,0,Yes,Yes,39,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,No,Electronic check,59.8,2343.85,No
+4471-KXAUH,Female,0,Yes,No,42,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,84.3,3588.4,Yes
+9752-ZNQUT,Female,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,48.6,48.6,No
+7638-QVMVY,Female,0,No,No,7,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79,522.95,Yes
+1576-PFZIW,Male,1,Yes,No,70,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),105.35,7511.9,No
+5666-MBJPT,Male,0,No,No,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.1,1725,No
+7312-XSBAT,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,49.75,49.75,No
+3096-GKWEB,Male,0,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.75,1691.9,No
+2371-JQHZZ,Male,0,Yes,No,24,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93,2248.05,No
+0674-GCDXG,Male,0,No,No,63,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),71.9,4479.2,No
+1121-QSIVB,Female,0,No,Yes,44,Yes,Yes,DSL,No,Yes,No,No,Yes,Yes,One year,Yes,Mailed check,77.55,3471.1,No
+4396-KLSEH,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.85,63,No
+3244-DCJWY,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.25,70.25,Yes
+2824-MYYBN,Female,0,Yes,Yes,37,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.25,3314.15,No
+0875-CABNR,Female,1,No,No,10,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),84.6,865.55,Yes
+6345-HOVES,Male,0,No,No,34,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),25.05,852.7,No
+8318-LCNBW,Male,0,Yes,No,35,No,No phone service,DSL,Yes,No,Yes,No,Yes,Yes,One year,No,Credit card (automatic),53.15,1930.9,No
+6469-QJKZW,Female,0,Yes,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,20.15,91.4,No
+0147-ESWWR,Female,1,Yes,No,39,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,101.25,3949.15,No
+1217-VASWC,Male,1,Yes,No,43,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),100.55,4304,No
+7812-FZHPE,Female,0,Yes,Yes,17,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,24.1,409.9,Yes
+6370-ZVHDV,Female,0,Yes,No,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.3,1554.9,No
+5915-DGNVC,Female,0,Yes,No,49,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Electronic check,71.8,3472.05,No
+6260-XLACS,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.7,117.8,No
+3566-CAAYU,Female,0,Yes,Yes,64,No,No phone service,DSL,No,Yes,Yes,Yes,No,Yes,Two year,No,Electronic check,49.85,3210.35,No
+4983-CCWMC,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.6,207.4,No
+9103-CXVOK,Male,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.75,19.75,No
+2896-TBNBE,Male,0,Yes,No,40,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,One year,No,Electronic check,80.8,3132.75,No
+4797-AXPXK,Female,0,No,Yes,1,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Electronic check,60,60,Yes
+5229-PRWKT,Male,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.55,649.65,Yes
+6982-UQZLY,Female,1,Yes,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.85,20.85,Yes
+2522-WLNSF,Female,1,Yes,No,34,Yes,No,DSL,No,No,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),64.2,2106.3,No
+3841-CONLJ,Female,0,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,35,35,No
+5057-LCOUI,Female,0,No,No,39,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.75,2011.4,Yes
+0193-ESZXP,Female,1,Yes,No,58,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.5,6205.5,Yes
+4475-NVTLU,Male,0,Yes,Yes,45,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Electronic check,19.2,903.7,No
+4369-HTUIF,Male,1,No,No,6,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),85.15,503.6,Yes
+9386-LDCZR,Male,0,No,No,43,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),90.65,3882.3,No
+9585-KKMFD,Male,0,Yes,Yes,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20,879.8,No
+5399-ZIMKF,Male,0,No,No,5,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.65,383.65,No
+0336-KXKFK,Male,0,No,No,72,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),61.2,4390.25,No
+5619-XZZKR,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.95,68.2,No
+3948-FVVRP,Male,0,Yes,Yes,9,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,54.8,452.8,No
+5327-XOKKY,Male,1,Yes,No,72,Yes,Yes,DSL,No,No,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),73.45,5329,No
+0983-TATYJ,Female,0,Yes,No,33,Yes,No,DSL,No,No,No,Yes,No,No,One year,Yes,Mailed check,51.45,1758.9,No
+1813-JYWTO,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Two year,No,Bank transfer (automatic),80.45,5737.6,No
+0156-FVPTA,Male,0,Yes,No,22,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,54.2,1152.7,Yes
+1984-FCOWB,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,109.5,7674.55,Yes
+0654-HMSHN,Male,0,Yes,Yes,21,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,104.4,2157.95,Yes
+6719-OXYBR,Male,0,No,No,15,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,85.3,1219.85,No
+3312-ZWLGF,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,79.3,2414.55,No
+6476-YHMGA,Female,0,Yes,Yes,15,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),76.5,1155.6,No
+5287-QWLKY,Male,1,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),105.1,7548.1,Yes
+0795-XCCTE,Male,1,No,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.4,1809.35,No
+0168-XZKBB,Female,0,Yes,No,19,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.85,1564.4,No
+3785-NRHYR,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,No
+5196-SGOAK,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,75.7,75.7,Yes
+4537-DKTAL,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.55,84.4,No
+7072-MBHEV,Female,1,Yes,Yes,11,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),78.1,864.85,No
+5602-BVFMK,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.3,228.75,No
+6297-NOOPG,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.5,7752.05,No
+1891-UAWWU,Female,1,Yes,No,20,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,90.8,1951,Yes
+8204-TIFGJ,Female,0,No,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),20.3,470.6,No
+8050-DVOJX,Male,1,No,No,49,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),81.35,4060.9,No
+6108-OQZDQ,Female,0,Yes,Yes,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,97.95,384.5,Yes
+0363-QJVFX,Male,0,No,No,32,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),108.15,3432.9,Yes
+8903-XEBGX,Male,0,No,Yes,2,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.3,108.65,No
+8169-SAEJD,Male,1,Yes,No,69,No,No phone service,DSL,Yes,Yes,No,No,Yes,Yes,Two year,No,Credit card (automatic),56.55,3952.65,No
+9840-DVNDC,Male,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),80.5,463.05,Yes
+1089-XZWHH,Female,0,Yes,Yes,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.7,494.05,No
+2325-WINES,Female,0,No,No,32,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.05,3416.85,No
+6064-ZATLR,Female,0,No,No,27,Yes,No,DSL,Yes,Yes,No,No,No,No,Two year,No,Bank transfer (automatic),52.85,1498.65,No
+3096-JRDSO,Female,1,Yes,No,27,Yes,Yes,Fiber optic,Yes,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.3,2867.75,Yes
+7824-PANSQ,Male,0,No,No,58,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),80.65,4807.35,No
+8042-JVNFH,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,71.35,71.35,Yes
+9648-BCHKM,Female,0,Yes,Yes,18,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.65,471.35,No
+2888-ADFAO,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),21.3,1041.8,No
+8707-HOEDG,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,110.2,7689.8,No
+3374-LXDEV,Female,0,No,No,13,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Electronic check,89.4,1132.35,Yes
+4072-IPYLT,Female,0,Yes,Yes,36,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),51.05,1815,No
+7167-PCEYD,Male,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.8,1311.3,No
+4936-YPJNK,Female,0,No,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.9,199.45,No
+1580-BMCMR,Male,1,No,No,19,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,87.3,1637.3,No
+4817-KEQSP,Female,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.85,1326.35,No
+6408-WHTEF,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,89.4,6376.55,No
+9251-AWQGT,Female,0,Yes,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20,935.9,No
+8749-CLJXC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No
+7989-VCQOH,Male,0,Yes,Yes,18,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,83.25,1611.15,No
+5049-GLYVG,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.6,20.6,Yes
+4199-QHJNM,Male,1,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),102.9,6989.7,No
+8882-TLVRW,Male,0,Yes,Yes,69,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,39.1,2779.5,No
+5883-GTGVD,Male,0,No,No,19,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.95,1931.75,Yes
+5135-GRQJV,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Mailed check,114.5,8331.95,No
+2384-OVPSA,Female,1,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.2,735.9,No
+1371-WEPDS,Male,1,Yes,No,40,Yes,No,DSL,No,No,No,No,No,Yes,One year,No,Electronic check,55.8,2283.3,No
+3580-GICBM,Female,0,Yes,Yes,61,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.2,1445.2,No
+4817-QRJSX,Female,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),81,818.05,Yes
+9730-DRTMJ,Male,0,Yes,No,32,Yes,Yes,DSL,Yes,No,No,Yes,Yes,No,One year,Yes,Credit card (automatic),72.8,2333.05,No
+4686-UXDML,Female,0,No,No,21,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.85,1992.55,No
+3349-ANQNH,Female,1,No,No,59,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,99.5,5890,No
+2398-YPMUR,Female,1,Yes,No,13,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.15,916.75,Yes
+0932-YIXYU,Female,0,No,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.25,1029.8,No
+7480-SPLEF,Male,0,Yes,Yes,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),26,1796.55,No
+7636-XUHWW,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.9,33.7,No
+1431-CYWMH,Female,0,Yes,Yes,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.05,454.05,No
+5937-EORGB,Male,1,Yes,No,15,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,96.5,1392.25,No
+6349-JDHQP,Female,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,1049.6,No
+5539-HIVAK,Female,1,Yes,No,28,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,25.7,734.6,No
+5847-MXBEO,Male,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.3,475.1,No
+9985-MWVIX,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.15,70.15,Yes
+4583-PARNH,Male,1,Yes,No,16,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,91.55,1540.05,No
+1116-FRYVH,Female,0,Yes,Yes,48,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Mailed check,39.4,1978.65,No
+1421-HCERK,Male,1,Yes,No,30,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Bank transfer (automatic),105.7,3181.8,No
+9633-XQABV,Female,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.25,229.7,No
+0716-BQNDX,Male,1,No,No,57,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,Two year,No,Electronic check,93.75,5625.55,No
+1265-XTECC,Female,1,Yes,No,68,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Credit card (automatic),96.55,6581.9,Yes
+1183-CANVH,Female,0,Yes,No,23,Yes,No,DSL,No,No,No,Yes,Yes,No,One year,No,Bank transfer (automatic),60,1347.15,No
+1972-XMUWV,Female,0,Yes,No,65,Yes,No,DSL,No,No,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),59.8,3808.2,No
+8679-LZBMD,Male,0,Yes,No,44,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),90.65,3974.15,No
+3407-JMJQQ,Female,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),109,7661.8,No
+6252-DFGTK,Female,0,Yes,No,37,Yes,Yes,DSL,Yes,No,Yes,Yes,No,No,One year,No,Credit card (automatic),68.1,2479.25,No
+1539-LNKHM,Female,0,No,No,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,266.6,No
+8645-KOMJQ,Male,0,Yes,Yes,69,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),81.95,5601.4,No
+0289-IVARM,Female,0,No,No,35,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Electronic check,60.55,1982.6,No
+9761-XUJWD,Male,0,No,No,5,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),65.6,339.9,No
+4057-FKCZK,Male,0,Yes,Yes,58,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Two year,No,Bank transfer (automatic),82.5,4828.05,No
+4291-TPNFG,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),82.3,5980.55,No
+6087-YPWHO,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,68.15,4808.7,No
+3090-QFUVD,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.3,20.3,No
+2237-ZFSMY,Female,0,No,No,39,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,No,One year,Yes,Electronic check,95.55,3692.85,Yes
+1722-LDZJS,Male,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.2,1068.15,No
+1818-ESQMW,Female,0,No,No,27,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.2,2383.6,No
+4871-JTKJF,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.65,69.65,Yes
+1415-YFWLT,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.3,89.3,Yes
+7401-RUBNK,Female,0,Yes,No,18,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Electronic check,74.8,1438.05,No
+0506-YLVKJ,Male,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.2,917.45,No
+8661-BOYNW,Female,0,Yes,No,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),84.4,6096.45,No
+6711-VTNRE,Female,0,No,No,36,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,87.55,3078.1,Yes
+4815-GBTCD,Female,0,Yes,No,4,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.15,99.95,No
+2805-AUFQN,Female,0,No,No,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.8,475.2,No
+0980-PVMRC,Female,0,Yes,Yes,40,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.85,2036.55,No
+7233-DRTRF,Male,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,102.4,6444.05,No
+3500-RMZLT,Female,1,No,No,15,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Mailed check,96.3,1426.75,Yes
+3873-NFTGI,Male,0,No,No,14,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),55.5,767.55,No
+1162-ECVII,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.75,7932.5,No
+7048-GXDAY,Male,0,No,No,39,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.4,4040.65,No
+8571-ZCMCX,Female,0,Yes,Yes,47,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),60,2768.65,No
+1169-WCVAK,Male,0,Yes,No,19,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),88.8,1672.35,No
+4548-SDBKE,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.2,474.8,Yes
+8066-POXGX,Female,0,No,No,13,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.1,446.1,Yes
+7129-CAKJW,Female,0,No,No,17,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),80.05,1345.65,No
+8695-ARGXZ,Male,1,Yes,No,34,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.55,2425.4,No
+3570-YUEKJ,Female,0,No,No,42,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,49.55,2077.95,No
+4143-OOBWZ,Male,0,Yes,No,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81.3,416.3,Yes
+1555-HAPSU,Female,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),23.9,1663.5,No
+1697-NVVGY,Male,1,Yes,No,19,Yes,No,DSL,Yes,No,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),66.4,1286.05,No
+7854-EKTJL,Female,0,No,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.6,35.85,Yes
+8464-EETCQ,Male,0,No,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),18.8,1094.35,No
+2419-FSORS,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,108.4,7719.5,No
+8132-YPVBX,Female,0,No,No,6,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Mailed check,85.95,514.6,No
+9102-IAYHT,Female,0,Yes,Yes,17,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.45,1451.6,Yes
+6754-LZUKA,Male,0,Yes,No,61,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),80.9,4932.5,No
+3422-LYEPQ,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,71,71,Yes
+8099-MZPUJ,Male,0,Yes,Yes,48,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),111.8,5443.65,No
+0787-LHDYT,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.6,330.25,No
+2642-DTVCO,Male,1,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.05,746.5,Yes
+0374-IOEGQ,Female,0,No,No,3,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,44.6,122.7,No
+4361-JEIVL,Male,0,No,Yes,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.4,44.4,Yes
+3197-NNYNB,Male,0,No,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),105.1,6631.85,No
+3396-DKDEL,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),115.15,8250,No
+1752-OZXFY,Male,0,Yes,No,60,Yes,No,DSL,Yes,No,No,No,Yes,No,One year,Yes,Mailed check,59.8,3561.15,No
+9066-QRSDU,Female,0,Yes,No,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Electronic check,26.3,1763.55,No
+9112-WSNPU,Female,1,No,No,35,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.55,2419,No
+9867-NNXLC,Female,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.05,470.2,No
+5321-NTRKC,Male,0,Yes,Yes,66,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),79.85,5234.95,No
+1363-TXLSL,Male,1,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,70.3,Yes
+9507-EXLTT,Female,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),79.35,79.35,Yes
+3161-GETRM,Male,0,Yes,Yes,34,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),90.05,3097,No
+0402-OAMEN,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.45,1709.1,No
+6933-VLYFX,Male,0,Yes,Yes,31,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,59.95,1848.8,No
+6754-WKSHP,Male,0,No,Yes,30,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),25.35,723.3,No
+9846-GKXAS,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),90.8,809.75,Yes
+6017-PPLPX,Male,0,Yes,Yes,20,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.45,1470.95,Yes
+4976-LNFVV,Male,1,Yes,No,19,No,No phone service,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),34.3,577.15,No
+5055-MGMGF,Female,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.05,6914.95,No
+4641-FROLU,Female,0,Yes,Yes,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.3,602.9,No
+2862-JVEOY,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.15,124.4,No
+2969-QWUBZ,Female,0,No,No,2,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,51.4,96.8,No
+9822-BIIGN,Male,0,Yes,Yes,53,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,Month-to-month,No,Electronic check,71.85,3827.9,No
+2077-MPJQO,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.4,533.05,No
+1534-OULXE,Female,0,Yes,Yes,61,Yes,No,DSL,Yes,No,No,No,No,No,One year,Yes,Bank transfer (automatic),49.7,2961.4,No
+7136-RVDTZ,Male,1,No,No,70,No,No phone service,DSL,No,No,No,No,Yes,Yes,One year,Yes,Electronic check,45.25,3264.45,Yes
+2971-SGAFL,Female,0,No,No,13,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,78.75,995.35,No
+1480-IVEVR,Male,1,Yes,No,35,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,One year,Yes,Bank transfer (automatic),81.6,2815.25,No
+2905-KFQUV,Female,0,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),70.4,154.8,No
+4581-SSPWD,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.8,246.3,Yes
+3370-HXOPH,Female,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,76.1,257.6,No
+9391-YZEJW,Female,0,No,No,62,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),94,5757.2,No
+9958-MEKUC,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),103.95,7517.7,No
+0281-CNTZX,Male,0,Yes,No,63,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.95,1234.8,No
+6927-WTFIV,Male,1,No,No,20,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),71.3,1389.2,Yes
+4118-CEVPF,Female,1,No,No,35,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),110.8,3836.3,No
+3398-ZOUAA,Male,1,Yes,No,21,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.1,1474.75,Yes
+9114-VEPUF,Male,0,Yes,No,62,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,No,Electronic check,96.1,6001.45,No
+7876-BEUTG,Female,0,No,No,15,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,48.8,720.1,No
+2338-BQEZT,Female,0,No,No,55,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),50.55,2832.75,No
+9873-MNDKV,Female,0,No,No,11,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.65,472.25,No
+0378-NHQXU,Female,0,Yes,Yes,17,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,88.25,1460.65,Yes
+8241-JUIQO,Female,0,No,No,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),19.45,1336.35,No
+2194-IIQOF,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.3,6388.65,No
+4512-ZUIYL,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70,153.05,Yes
+9631-RXVJM,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.25,677.9,No
+9584-EXCDZ,Female,0,No,No,17,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.5,1165.6,No
+7969-AULMZ,Female,0,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,97.35,2119.5,Yes
+5093-FEGLU,Female,0,Yes,No,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.65,921.55,No
+2621-UDNLU,Female,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.85,72,No
+7526-IVLYU,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.65,68.35,No
+2428-HYUNX,Male,1,Yes,No,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.35,847.25,No
+9214-EKVXR,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44,44,No
+3410-MHHUM,Female,0,Yes,Yes,44,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,One year,No,Electronic check,94.4,4295.35,No
+0568-ONFPC,Male,0,Yes,Yes,5,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Bank transfer (automatic),25.9,135,Yes
+0733-VUNUW,Male,0,No,No,24,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),55.65,1400.55,Yes
+4550-EVXNY,Female,0,No,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.65,69.65,Yes
+2122-YWVYA,Female,0,No,No,18,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.4,1380.4,No
+2968-SSGAA,Female,0,No,No,10,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.6,1060.2,Yes
+2296-DKZFP,Female,0,Yes,No,65,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Two year,No,Bank transfer (automatic),71,4386.2,No
+4844-JJWUY,Female,1,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,86,86,Yes
+6777-TGHTM,Female,0,No,No,53,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),106.95,5785.5,Yes
+8909-BOLNL,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,21.2,52.05,No
+6603-YRDCJ,Male,0,Yes,No,33,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,61.05,2018.4,No
+3538-WZPHD,Male,0,No,No,3,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,29.6,79.45,Yes
+3229-USWAR,Female,0,No,No,34,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),79.95,2727.3,No
+2138-VFAPZ,Female,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.7,263.65,No
+6131-JLWZM,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.3,275.4,No
+4785-QRJHC,Male,1,Yes,No,46,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),59.9,2816.65,Yes
+0269-XFESX,Male,0,Yes,Yes,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.35,538.5,No
+1709-EJDOX,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,948.9,No
+4316-XCSLJ,Male,0,No,Yes,17,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.3,846.8,No
+8610-WFCJF,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.6,4783.5,Yes
+9693-XMUOB,Male,1,Yes,No,59,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.25,2997.45,No
+0383-CLDDA,Female,0,No,No,69,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.35,5897.4,No
+4905-JEFDW,Male,0,No,No,11,No,No phone service,DSL,No,No,Yes,No,Yes,No,One year,Yes,Electronic check,41.6,470.6,Yes
+2709-UQGNP,Male,0,No,No,10,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,51.65,524.5,No
+3549-ZTMNH,Male,0,Yes,Yes,12,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24,269.65,No
+7881-INRLC,Male,0,No,No,45,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),100.85,4740,Yes
+8033-ATFAS,Female,0,Yes,Yes,39,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,59.85,2341.5,No
+1635-NZATJ,Male,1,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.45,1789.65,No
+0562-HKHML,Male,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),23.9,1626.4,No
+8277-RVRSV,Female,0,Yes,Yes,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),24.15,800.3,No
+9943-VSZUV,Male,1,No,No,67,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,75.7,5060.85,No
+0117-LFRMW,Male,0,Yes,Yes,37,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),40.2,1448.8,Yes
+6172-FECYY,Male,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,84.5,4254.85,Yes
+7054-ENNGU,Female,0,Yes,No,9,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,50.85,466.6,No
+1455-HFBXA,Male,0,Yes,No,52,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),91.6,4627.8,No
+9402-CXWPL,Female,0,No,No,70,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Electronic check,98.9,6838.6,No
+1628-BIZYP,Male,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,85,85,No
+1965-AKTSX,Female,1,No,No,14,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,78.95,1101.85,Yes
+0082-LDZUE,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,44.3,44.3,No
+4786-UKSNZ,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,20.2,Yes
+5343-SGUBI,Female,0,No,No,52,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,One year,Yes,Mailed check,80.2,4297.6,No
+0856-NAOES,Male,0,No,No,6,Yes,No,DSL,No,No,Yes,No,Yes,No,Month-to-month,No,Mailed check,60.9,414.1,No
+1976-AZZPJ,Male,0,Yes,No,7,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,34.2,256.6,No
+7248-VZQLC,Female,1,No,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),85.2,3969.35,Yes
+6156-UZDLF,Female,0,No,No,26,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),87.15,2274.1,No
+3099-OONVS,Male,0,Yes,Yes,25,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,54.3,1296.8,No
+9500-WBGRP,Male,0,No,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.1,1268.85,No
+5183-KLYEM,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),112.75,8192.6,No
+5144-PQCDZ,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.95,59.25,No
+8514-VZHEB,Male,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.5,1147.85,No
+6625-IUTTT,Male,0,No,No,67,Yes,No,DSL,Yes,No,Yes,No,No,Yes,Two year,No,Bank transfer (automatic),65.55,4361.55,No
+9355-NPPFS,Female,1,No,No,26,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,78.8,2006.1,No
+0068-FIGTF,Female,0,No,No,27,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Mailed check,78.2,2078.95,No
+5965-GGPRW,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,No,Bank transfer (automatic),105.25,7609.75,No
+8035-PWSEV,Female,0,No,No,6,Yes,No,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.25,487.05,No
+2236-HILPA,Male,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.65,1218.45,No
+1840-BIUOG,Male,0,No,No,20,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,68.7,1416.2,No
+1356-MKYSK,Male,0,No,No,6,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),78.65,483.3,No
+6080-LNESI,Male,0,No,No,51,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.75,1234.6,No
+1830-IPXVJ,Female,0,Yes,Yes,61,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.75,1311.6,No
+5828-DWPIL,Male,1,Yes,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,89.1,5618.3,No
+8398-TBIYD,Female,0,No,No,72,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),84.7,6185.15,No
+0011-IGKFF,Male,1,Yes,No,13,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,98,1237.85,Yes
+4192-GORJT,Male,0,Yes,No,5,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.45,498.1,Yes
+6496-SLWHQ,Male,1,No,No,3,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105,294.45,Yes
+9912-GVSEQ,Female,1,Yes,No,26,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,93.85,2381.55,Yes
+2931-FSOHN,Male,1,No,No,13,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,59.9,788.35,No
+9418-RUKPH,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.95,756.4,No
+5383-MMTWC,Female,1,Yes,No,8,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84,613.4,Yes
+9971-ZWPBF,Male,1,Yes,Yes,34,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,108.9,3625.2,No
+4712-AUQZO,Male,0,No,No,18,No,No phone service,DSL,Yes,No,Yes,No,No,No,Month-to-month,No,Mailed check,33.6,550.35,No
+9711-FJTBX,Male,0,Yes,Yes,56,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,One year,Yes,Mailed check,85.85,4793.8,No
+2908-ZTPNF,Female,0,No,No,36,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),34.85,1267.2,No
+7240-ETPTR,Female,0,Yes,No,9,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,48.75,442.2,Yes
+2202-CUYXZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,84.85,84.85,Yes
+5995-OIGLP,Male,0,No,No,12,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Mailed check,56.65,654.85,Yes
+8421-WZOOW,Female,1,Yes,Yes,57,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),95.3,5567.45,No
+6261-RCVNS,Female,0,No,No,42,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),73.9,3160.55,Yes
+7951-QKZPL,Female,0,Yes,Yes,33,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.5,740.3,Yes
+1414-YADCW,Male,0,Yes,No,70,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),84.6,5706.2,No
+8756-RDDLT,Female,0,No,No,68,No,No phone service,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,No,Electronic check,44.95,3085.35,No
+2862-PFNIK,Male,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,24.7,24.7,No
+5816-QVHRX,Female,0,No,No,37,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Credit card (automatic),100.3,3541.4,No
+4430-YHXGG,Female,0,No,Yes,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,25.45,84.2,No
+6888-SBYAI,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.7,50.7,No
+7395-XWZOY,Male,0,No,No,20,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),55,1165.55,No
+7169-YWAMK,Male,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Bank transfer (automatic),68.4,4855.35,No
+3640-PHQXK,Female,0,No,No,31,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.9,2806.9,Yes
+6877-LGWXO,Male,1,Yes,No,18,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,78.55,1422.65,Yes
+6859-RKMZJ,Male,0,Yes,No,11,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),55.05,608.15,No
+2729-VNVAP,Female,0,Yes,Yes,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.8,641.35,No
+2957-JIRMN,Female,1,No,No,62,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,One year,No,Electronic check,84.45,4959.15,No
+2049-BAFNW,Female,0,No,No,1,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,35.9,35.9,No
+3842-QTGDL,Male,0,Yes,No,16,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),80.75,1321.3,No
+7163-OCEQI,Male,0,Yes,Yes,22,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,78.65,1663.75,No
+9782-LGXMC,Female,0,Yes,Yes,49,Yes,No,DSL,Yes,No,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),61.75,3024.15,No
+5975-BAICR,Male,0,Yes,Yes,36,Yes,No,DSL,Yes,No,Yes,No,No,Yes,One year,Yes,Credit card (automatic),63.7,2188.5,No
+8019-ENHXU,Male,0,Yes,No,42,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,No,Yes,Month-to-month,Yes,Electronic check,99.45,4138.05,Yes
+5167-GBFRE,Male,1,No,No,4,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),25.2,102.5,Yes
+9033-EOXWV,Female,0,No,No,12,Yes,Yes,DSL,No,No,No,Yes,Yes,Yes,One year,No,Mailed check,74.05,872.65,Yes
+4673-KKSLS,Female,0,No,No,31,Yes,No,Fiber optic,Yes,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,87.6,2724.25,No
+4853-OITSN,Male,0,Yes,No,5,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.15,413.25,No
+9800-OUIGR,Male,0,Yes,Yes,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20,1374.2,No
+1324-NLTJE,Female,1,No,No,15,Yes,Yes,DSL,No,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),55,757.1,Yes
+2324-EFHVG,Male,0,No,No,64,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.4,6692.65,No
+7094-MSZAO,Male,0,Yes,Yes,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.05,218.5,No
+1522-VVDMG,Male,0,Yes,Yes,7,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,89.75,608.8,Yes
+6907-CQGPN,Male,0,No,No,29,No,No phone service,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,34.3,1004.75,No
+0780-XNZFN,Male,0,No,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.65,1125.6,No
+0239-OXEXL,Female,0,No,No,46,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Mailed check,84.25,3847.6,No
+2675-IJRGJ,Male,0,No,No,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.65,978,No
+7049-GKVZY,Female,0,No,No,17,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,One year,No,Credit card (automatic),79.85,1387.35,No
+8577-QSOCG,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.2,746.05,No
+3721-CNZHX,Male,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,304.6,No
+0212-ISBBF,Female,0,No,No,22,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.35,1098.85,No
+9185-TQCVP,Male,0,Yes,No,14,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,85.15,1139.2,Yes
+3585-YNADK,Female,0,Yes,No,57,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),74.6,4368.95,No
+5271-DBYSJ,Male,1,No,No,11,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),79.15,827.7,No
+1099-GODLO,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.35,20.35,No
+6828-HMKWP,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),21.05,262.05,No
+5567-WSELE,Male,1,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.6,279.55,Yes
+1472-TNCWL,Male,0,No,Yes,36,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.7,3512.5,No
+8063-RJYNF,Male,0,No,No,16,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.25,1483.25,Yes
+0687-ZVTHB,Male,0,Yes,Yes,65,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Credit card (automatic),72.45,4653.85,Yes
+2080-GKCWQ,Male,0,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,74.95,151.75,No
+9661-ACXBS,Female,0,No,No,42,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.2,4400.75,Yes
+2193-SFWQW,Male,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),111.95,8033.1,No
+5656-JAMLX,Male,0,No,No,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.85,1253.65,No
+3462-BJQQA,Female,0,No,No,6,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.75,552.65,No
+0442-TDYUO,Male,0,Yes,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.05,1036,No
+6733-LRIZX,Male,0,No,No,35,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,108.95,4025.5,No
+9503-XJUME,Male,0,No,Yes,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.65,928.4,No
+4367-NHWMM,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,24.9,24.9,No
+3727-RJMEO,Male,0,Yes,No,6,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,82.85,460.25,Yes
+3779-OSWCF,Female,0,Yes,No,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),93.2,6506.15,No
+6736-DHUQI,Female,0,Yes,Yes,67,Yes,No,Fiber optic,Yes,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),84.8,5598.3,No
+3915-ODIYG,Male,1,No,No,60,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,One year,Yes,Electronic check,71.75,4374.55,No
+1360-RCYRT,Male,0,Yes,Yes,23,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),30.35,678.75,No
+2724-FJDYW,Male,0,No,Yes,39,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Bank transfer (automatic),54.85,2191.7,No
+4451-RWASJ,Male,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.5,239.75,No
+6646-VRFOL,Male,1,Yes,No,53,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.85,5485.5,Yes
+3460-TJBWI,Male,0,Yes,Yes,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.2,609.05,No
+5917-HBSDW,Female,0,Yes,Yes,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.35,683.75,No
+5685-IIXLY,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.6,404.2,Yes
+5671-UUNXD,Female,1,Yes,No,50,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Electronic check,100.65,5189.75,No
+0956-ACVZC,Female,0,No,No,54,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),94.1,5060.9,No
+2325-NBPZG,Female,0,No,No,3,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.55,233.65,No
+4250-ZBWLV,Male,0,No,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,108.45,7176.55,Yes
+4482-FTFFX,Male,0,No,No,5,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,56.15,291.45,No
+8859-DZTGQ,Male,0,No,No,33,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.35,689.75,No
+0440-MOGPM,Female,0,No,No,41,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,80.55,3263.9,No
+0020-JDNXP,Female,0,Yes,Yes,34,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Mailed check,61.25,1993.2,No
+3752-CQSJI,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.45,254.5,No
+5025-GOOKI,Female,0,No,No,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),18.9,347.65,No
+4698-KVLLG,Female,1,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.6,967.9,No
+5095-AESKG,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),91.5,242.95,Yes
+2887-JPYLU,Female,0,No,Yes,41,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),45.2,1841.9,No
+4770-QAZXN,Female,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.45,232.1,No
+4896-CPRPF,Male,0,Yes,Yes,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.45,809.25,No
+1871-MOWRM,Male,0,Yes,No,12,Yes,No,Fiber optic,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),80.85,866.45,Yes
+9714-EDSUC,Male,0,No,No,4,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,94.9,360.55,No
+2027-OAQQC,Female,0,No,No,43,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),49.05,2076.2,Yes
+0282-NVSJS,Female,1,Yes,Yes,12,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,29.3,355.9,No
+9090-SGQXL,Male,1,Yes,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.3,7299.65,Yes
+6595-YGXIT,Male,1,No,No,25,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,88.95,2291.2,Yes
+7353-YOWFP,Female,0,No,No,7,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.25,129.15,No
+9835-ZIITK,Male,1,Yes,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,110.85,7491.75,Yes
+8008-ESFLK,Female,0,Yes,No,53,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,110.5,5835.5,No
+7537-CBQUZ,Male,1,No,No,63,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),109.4,7031.45,No
+1555-DJEQW,Female,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114.2,7723.9,Yes
+5649-TJHOV,Male,1,Yes,No,27,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),36.5,1032,Yes
+0519-XUZJU,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,70.75,70.75,Yes
+3363-EWLGO,Female,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,109.6,No
+4750-UKWJK,Female,1,Yes,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.6,727.8,No
+6338-AVWCY,Male,0,No,No,3,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Mailed check,40.15,130.75,Yes
+1689-YQBYY,Female,0,No,Yes,12,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.6,893,No
+4487-ZYJZK,Female,0,Yes,Yes,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.6,763.1,No
+2959-FENLU,Female,0,No,No,9,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),85.3,781.4,No
+0708-LGSMF,Male,0,Yes,No,13,Yes,Yes,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,65.85,902.25,No
+9253-QXKBE,Male,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.45,2653.65,Yes
+7634-HLQJR,Female,0,Yes,Yes,47,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.05,1016.7,No
+0487-RPVUM,Male,0,Yes,No,61,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),99.4,5943.65,No
+4079-ULGFR,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,275.7,No
+2516-XSJKX,Female,0,Yes,Yes,41,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Electronic check,78.45,3126.45,No
+0057-QBUQH,Female,0,No,Yes,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Electronic check,25.1,1070.15,No
+9445-SZLCH,Female,0,Yes,Yes,36,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),97.35,3457.9,Yes
+6599-SFQVE,Female,0,No,No,6,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55,340.4,No
+8331-ZXFOE,Female,0,No,No,58,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),71.1,4299.2,No
+4003-FUSHP,Male,0,No,No,19,Yes,No,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),61.55,1093.2,No
+0356-ERHVT,Male,0,Yes,No,11,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45.9,521.9,No
+7325-ENZFI,Female,0,No,No,39,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),40.3,1630.4,No
+4884-ZTHVF,Female,1,No,No,8,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,87.1,713.6,No
+3920-HIHMQ,Female,0,No,Yes,26,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,49.5,1265.65,No
+3055-OYMSE,Female,1,No,No,53,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.8,4003.85,No
+0613-WUXUM,Female,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.2,1401.4,No
+7568-PODML,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,45.3,45.3,Yes
+4458-KVRBJ,Male,0,No,No,59,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25,1510.5,No
+5349-IECLD,Male,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,94.95,178.1,Yes
+1397-XKKWR,Male,0,No,No,7,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,35.3,264.8,No
+3945-GFWQL,Female,0,No,Yes,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.55,480.6,Yes
+8097-OMULG,Male,0,Yes,Yes,59,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),76.75,4541.9,No
+6013-BHCAW,Male,0,Yes,Yes,61,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),81,4976.15,No
+0401-WDBXM,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.55,7542.25,No
+3387-PLKUI,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,18.8,251.25,No
+6096-EGVTU,Female,0,Yes,Yes,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,24.9,1595.5,No
+3797-VTIDR,Male,0,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,23.45,23.45,Yes
+5081-NWSUP,Female,0,No,No,10,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,No,Mailed check,64.9,685.55,No
+2580-ATZSQ,Female,0,Yes,Yes,65,Yes,No,DSL,Yes,Yes,Yes,No,No,No,One year,No,Bank transfer (automatic),61.35,3874.1,No
+8085-MSNLK,Female,0,Yes,No,62,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,113.95,6891.4,No
+9691-HKOVS,Female,0,Yes,No,55,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,90.15,4916.95,No
+9881-VCZEP,Female,0,Yes,No,25,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.1,1373,No
+9526-BIHHD,Male,0,No,No,1,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,29.7,29.7,Yes
+8757-TFHHJ,Male,0,No,No,1,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),49.8,49.8,No
+4523-WXCEF,Female,0,Yes,No,59,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,101.1,6039.9,Yes
+1468-DEFNC,Male,1,Yes,Yes,64,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.4,1548.65,No
+5119-KEPFY,Male,0,Yes,No,36,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95,3440.25,No
+8364-TRMMK,Female,0,No,No,3,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,50.65,151.3,Yes
+0916-KNFAJ,Male,0,Yes,No,61,Yes,Yes,DSL,Yes,No,Yes,No,No,Yes,Two year,Yes,Mailed check,69.9,4226.7,No
+8319-QBEHW,Male,0,No,Yes,26,No,No phone service,DSL,No,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),39.95,1023.75,No
+3063-QFSZL,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.4,55.4,Yes
+8775-LHDJH,Female,1,Yes,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,90.6,90.6,Yes
+9625-QNLUX,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),103.25,7074.4,No
+3097-NQYSN,Male,1,Yes,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,86.85,156.35,Yes
+4024-CSNBY,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Bank transfer (automatic),94.25,6849.75,No
+7110-BDTWG,Female,0,Yes,No,71,No,No phone service,DSL,No,No,Yes,Yes,No,Yes,Two year,Yes,Electronic check,47.05,3263.6,No
+7493-GVFIO,Male,0,No,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.55,1252.85,No
+0690-SRQID,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.65,67.55,No
+9605-WGJVW,Female,1,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.2,70.2,No
+9114-DPSIA,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),81,5750,No
+4700-UBQMV,Male,0,Yes,Yes,21,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),75.9,1549.75,No
+7711-GQBZC,Female,0,Yes,Yes,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.7,1810.55,No
+3565-UNOCC,Female,1,Yes,No,29,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.05,2952.85,Yes
+4627-MIHJH,Female,1,No,No,69,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),110.25,7467.55,No
+9795-NREXC,Female,0,Yes,No,64,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85,5484.4,No
+8573-CGOCC,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.75,294.9,No
+7356-AYNJP,Female,0,No,No,4,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,23.9,97.5,No
+9249-FXSCK,Female,0,No,No,52,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),111.25,5916.45,Yes
+5493-SDRDQ,Male,0,No,No,2,Yes,No,DSL,Yes,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.1,113.35,Yes
+6286-SUUWT,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,19.95,No
+4819-HJPIW,Male,0,No,No,18,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,25.15,476.8,No
+8654-DHAOW,Female,0,No,No,2,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Mailed check,54.15,101.65,No
+5438-QMDDL,Female,0,Yes,No,19,Yes,No,DSL,No,Yes,No,No,No,Yes,Month-to-month,Yes,Mailed check,59.8,1130.85,No
+9812-GHVRI,Female,0,No,No,40,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),83.85,3532.25,No
+5553-AOINX,Female,1,Yes,Yes,66,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.9,6891.45,No
+1228-ZLNBX,Male,0,No,No,21,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,75.3,1570.7,No
+4226-KKDON,Male,0,No,No,8,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,66.65,520.95,No
+3831-YCPUO,Female,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.5,7854.9,No
+7445-WMRBW,Female,0,No,No,48,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,One year,Yes,Bank transfer (automatic),73.85,3581.4,No
+3308-DGHKL,Male,0,No,No,69,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.3,1447.9,No
+9924-JPRMC,Male,0,No,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,118.2,8547.15,No
+0080-EMYVY,Female,0,No,No,14,Yes,No,DSL,No,Yes,No,No,No,No,One year,No,Credit card (automatic),51.45,727.85,No
+6218-KNUBD,Male,0,No,No,6,Yes,No,DSL,No,Yes,No,No,Yes,No,Month-to-month,No,Electronic check,59.45,357.6,No
+7337-CINUD,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.5,159.35,No
+7609-NRNCA,Female,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.55,280.85,No
+0623-EJQEG,Male,0,No,No,65,Yes,Yes,Fiber optic,Yes,No,No,Yes,No,Yes,One year,No,Electronic check,93.55,6069.25,No
+7153-CHRBV,Female,0,Yes,Yes,57,Yes,No,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Mailed check,59.3,3274.35,No
+0871-URUWO,Male,0,Yes,No,13,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.25,1359,Yes
+9190-MFJLN,Male,1,No,No,19,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),95.9,1777.9,Yes
+6198-PNNSZ,Female,0,Yes,No,56,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),109.8,6109.65,No
+3317-VLGQT,Female,0,Yes,No,14,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,78.1,1122.4,No
+4132-POCZS,Male,0,Yes,No,52,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,39.9,2020.9,No
+5614-DNZCE,Female,0,No,No,58,Yes,No,DSL,Yes,Yes,Yes,Yes,No,No,Two year,Yes,Credit card (automatic),64.9,3795.45,No
+1095-JUDTC,Female,1,No,No,47,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,95.05,4504.55,Yes
+3896-RCYYE,Female,0,No,No,67,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),53.4,3579.15,No
+9638-JIQYA,Male,0,No,No,2,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),24.9,49.7,No
+3258-SANFR,Male,1,No,No,6,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),44.7,276.5,No
+3726-TBHQT,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),114,8175.9,No
+3190-ITQXP,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.25,890.35,No
+0870-VEMYL,Female,0,No,No,5,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Credit card (automatic),53.85,259.8,Yes
+4833-QTJNO,Male,1,Yes,No,67,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Bank transfer (automatic),83.85,5588.8,No
+3039-MJSLN,Male,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.2,50.6,No
+0178-CIIKR,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.95,58,No
+5385-SUIRI,Male,1,Yes,No,52,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.2,5568.35,Yes
+3982-XWFZQ,Female,0,Yes,No,42,Yes,Yes,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.25,2203.65,Yes
+5774-QPLTF,Male,0,Yes,Yes,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,20.35,938.95,No
+2322-VCZHZ,Male,1,Yes,No,23,Yes,No,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,90,2024.1,No
+5010-IPEAQ,Female,0,Yes,Yes,67,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Credit card (automatic),54.2,3623.95,No
+4009-ALQFH,Female,0,No,No,25,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.5,2369.05,Yes
+6383-ZTSIW,Female,1,Yes,No,39,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Mailed check,99.1,3877.95,No
+8990-YOZLV,Female,0,No,No,69,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Mailed check,66.9,4577.9,No
+3069-SSVSN,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,One year,No,Mailed check,25.85,25.85,No
+5222-IMUKT,Male,0,No,No,32,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,91.05,2871.5,No
+3627-FCRDW,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,71,672.55,Yes
+7562-GSUHK,Female,0,No,No,16,Yes,No,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),93.2,1573.7,Yes
+6685-XSHHU,Male,0,Yes,Yes,60,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.95,1270.55,No
+8901-UPRHR,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.2,7711.45,No
+4903-UYAVB,Male,0,Yes,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.35,126.05,Yes
+0118-JPNOY,Female,1,No,No,26,Yes,No,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.8,2193.65,No
+6776-TLWOI,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.85,64.55,Yes
+3845-FXCYS,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.65,31.2,No
+5857-XRECV,Female,0,No,Yes,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.5,38.25,No
+0725-CXOTM,Female,0,No,No,36,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),89.65,3348.1,No
+4343-EJVQB,Male,0,No,No,7,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,74.35,533.6,No
+9000-PLFUZ,Female,1,Yes,No,60,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Two year,Yes,Credit card (automatic),49.45,2907.55,No
+3427-GGZZI,Female,0,Yes,No,19,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,89.1,1620.8,No
+7779-ORAEL,Male,1,Yes,No,45,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),75.15,3480.35,No
+0644-OQMDK,Male,1,No,No,4,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.65,293.85,No
+4077-CROMM,Female,0,Yes,Yes,31,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,104.2,3243.45,Yes
+3154-CFSZG,Male,0,Yes,Yes,47,Yes,No,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,No,Electronic check,90.05,4137.2,No
+2868-MZAGQ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.25,79.25,Yes
+4847-QNOKA,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.9,44.9,Yes
+2220-IAHLS,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.4,19.4,No
+1658-XUHBX,Female,1,Yes,Yes,59,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),88.75,5348.65,No
+6379-RXJRQ,Male,0,Yes,No,10,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.1,659.65,Yes
+2378-HTWFW,Male,1,No,No,35,Yes,Yes,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Credit card (automatic),91,3180.5,No
+8650-RHRKE,Male,0,No,No,4,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,29.65,118.5,Yes
+4377-VDHYI,Male,0,Yes,Yes,32,Yes,No,Fiber optic,Yes,Yes,No,No,No,Yes,One year,Yes,Electronic check,90.8,3023.85,No
+0475-RIJEP,Male,0,No,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,77.85,3365.85,Yes
+1260-TTRXI,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,54.3,195.3,Yes
+3719-TDVQB,Female,1,Yes,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,18.95,1031.1,No
+6328-ZPBGN,Female,1,No,No,11,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),95.15,997.65,Yes
+0201-MIBOL,Female,1,No,No,66,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),102.4,6471.85,No
+4937-QPZPO,Male,0,Yes,Yes,61,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,99.9,6241.35,No
+2925-VDZHY,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),88.7,6501.35,No
+6981-TDRFT,Male,0,Yes,Yes,44,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,54.3,2317.1,No
+3413-CSSTH,Male,0,No,No,41,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Bank transfer (automatic),55.7,2237.55,No
+8033-VCZGH,Male,0,Yes,No,50,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,103.95,5231.3,No
+4789-KWMXN,Male,0,Yes,No,47,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),110.85,5275.8,Yes
+0224-NIJLP,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,165.5,No
+7542-CYDDM,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,358.5,No
+4718-WXBGI,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),91.95,6614.9,No
+2867-UIMSS,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,No,Electronic check,80.5,80.5,Yes
+8495-PRWFH,Female,1,No,No,42,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,55.65,2421.75,No
+0439-IFYUN,Female,1,No,No,18,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,74.7,1294.6,No
+5716-LIBJC,Female,0,No,Yes,13,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),104.15,1299.1,No
+2868-LLSKM,Female,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),83.65,5733.4,No
+8111-RKSPX,Male,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),72.2,305.55,Yes
+2988-QRAJY,Male,0,No,No,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),110.05,7430.75,No
+1585-MQSSU,Male,0,No,No,17,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Mailed check,51.5,900.5,Yes
+0071-NDAFP,Male,0,Yes,Yes,25,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.5,630.6,No
+2856-NNASM,Male,1,No,No,43,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Mailed check,89.55,3856.75,Yes
+7328-ZJAJO,Female,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.5,1222.65,No
+6458-CYIDZ,Female,1,No,No,5,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,No,Electronic check,80.7,374.8,No
+8559-CIZFV,Male,0,Yes,Yes,21,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,One year,No,Mailed check,77.5,1625,Yes
+1090-PYKCI,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,One year,Yes,Credit card (automatic),105.1,7234.8,No
+3058-WQDRE,Male,0,No,No,13,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),25.15,331.85,No
+7547-EKNFS,Male,0,Yes,No,42,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),95.25,3959.35,Yes
+2279-AXJJK,Male,0,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Credit card (automatic),95.65,5088.4,No
+6769-DYBQN,Male,1,No,No,46,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Electronic check,85,3969.4,Yes
+0909-SELIE,Male,0,Yes,No,61,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),80.8,4860.85,No
+3417-TSCIC,Male,0,No,No,29,No,No phone service,DSL,No,No,No,No,No,No,One year,Yes,Mailed check,24.85,788.05,No
+1042-HFUCW,Female,0,No,Yes,25,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),54.75,1266.35,No
+4439-JMPMT,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,85.75,470.95,Yes
+6537-OTKMY,Male,0,No,No,15,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,One year,No,Electronic check,50.75,688.2,No
+8999-EXMNO,Female,0,Yes,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,387.7,No
+6725-TPKJO,Male,0,No,No,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.05,845.25,No
+2446-BEGGB,Female,1,No,No,6,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.25,560.6,Yes
+7162-WPHPM,Male,0,Yes,Yes,58,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Credit card (automatic),71.6,4230.25,No
+1599-MMYRQ,Male,0,Yes,Yes,62,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),81.45,4983.05,No
+8821-KVZKQ,Female,0,Yes,Yes,70,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),58.4,4113.15,No
+1496-GGSUK,Female,1,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,25.7,25.7,Yes
+6434-TTGJP,Male,0,Yes,Yes,10,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),53.7,521,No
+0042-JVWOJ,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),19.6,471.85,No
+0130-SXOUN,Male,0,No,No,66,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,No,Credit card (automatic),89.4,5976.9,No
+2575-GFSOE,Female,0,Yes,Yes,7,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69,506.9,Yes
+2330-PQGDQ,Male,0,Yes,Yes,51,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),84.2,4299.75,No
+1452-UZOSF,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),106.1,7548.6,No
+2097-YVPKN,Male,0,No,No,65,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.75,1654.75,No
+2842-JTCCU,Male,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),46.05,80.35,Yes
+8597-CTXVJ,Male,0,No,Yes,70,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),64.95,4551.5,No
+6631-HMANX,Male,0,Yes,No,72,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),85.45,6227.5,No
+9962-BFPDU,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,20.05,No
+3296-SILRA,Female,1,Yes,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,76.4,76.4,Yes
+9681-OXGVC,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.5,514,Yes
+6394-HHHZM,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.7,57.5,No
+5995-LFTLE,Male,0,No,No,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.3,1474.35,No
+5180-UCIIQ,Male,1,Yes,Yes,22,No,No phone service,DSL,Yes,No,No,No,No,Yes,Month-to-month,No,Mailed check,40.05,880.2,Yes
+1932-UEDCX,Male,1,Yes,No,33,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),100.6,3270.25,No
+7153-OANIO,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.95,69.95,Yes
+5188-HGMLP,Male,1,Yes,No,54,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),74,3919.15,No
+0665-XHDJU,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,Yes,Electronic check,99.4,7285.7,No
+6521-YYTYI,Male,0,No,Yes,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.3,93.3,Yes
+8878-HMWBV,Male,0,No,No,3,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,49.15,169.05,Yes
+8265-HKSOW,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),107.45,7658.3,No
+3544-FBCAS,Female,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),83.6,5959.3,No
+5331-RGMTT,Male,1,Yes,No,54,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99.05,5295.7,No
+4759-PXTAN,Female,0,Yes,No,59,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Electronic check,80.1,4693.2,No
+7054-DMVAS,Male,0,No,No,54,Yes,No,DSL,Yes,Yes,No,No,Yes,No,Two year,Yes,Bank transfer (automatic),65.3,3512.9,No
+3428-MMGUB,Male,0,No,No,60,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Two year,Yes,Electronic check,89.55,5231.2,No
+6549-BTYPG,Female,0,Yes,No,60,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),60.8,3603.45,No
+7823-JSOAG,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,74.5,217.45,No
+3705-RHRFR,Female,0,Yes,No,69,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Two year,No,Bank transfer (automatic),99.15,6875.35,No
+9374-YOLBJ,Female,0,Yes,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Electronic check,19.25,19.25,No
+9074-KGVOX,Male,0,Yes,Yes,50,No,No phone service,DSL,Yes,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),39.45,2021.35,No
+6128-DAFVY,Female,0,No,No,56,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),44.85,2564.95,No
+9933-QRGTX,Female,0,Yes,No,60,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,Yes,Two year,Yes,Electronic check,97.2,5611.75,No
+4476-OSWTN,Male,1,Yes,No,69,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,110.55,7610.1,No
+3751-KTZEL,Female,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,No,Mailed check,35.05,35.05,Yes
+1977-STDKI,Female,1,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,73,73,Yes
+5066-GFJMM,Female,0,Yes,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.9,45.75,No
+2661-GKBTK,Male,0,Yes,Yes,60,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),76.95,4543.95,No
+0458-HEUZG,Female,0,No,No,13,No,No phone service,DSL,No,No,Yes,Yes,No,No,Two year,No,Mailed check,35.4,450.4,No
+7268-WNTCP,Male,0,Yes,Yes,62,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.45,1297.35,No
+0824-VWDPO,Female,0,No,No,45,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,One year,No,Bank transfer (automatic),96.75,4442.75,No
+8409-WQJUX,Female,0,No,No,25,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,54.2,1423.15,No
+2578-JQPHZ,Male,0,No,No,44,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),100.1,4378.35,No
+3278-FSIXX,Female,0,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.25,74.2,No
+5813-UECBU,Male,1,No,No,33,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),83.85,2716.3,Yes
+0328-GRPMV,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.1,70.1,Yes
+9746-UGFAC,Female,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.85,450.65,No
+2946-KIQSP,Female,0,No,No,35,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,33.45,1175.85,No
+0625-AFOHS,Female,0,Yes,Yes,29,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.2,558.8,No
+6726-WEXXK,Male,1,Yes,No,27,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,One year,Yes,Electronic check,85.9,2220.1,No
+4636-OLWOE,Male,0,No,Yes,54,Yes,No,DSL,No,Yes,Yes,Yes,No,No,One year,Yes,Electronic check,61,3283.05,No
+4342-HENTK,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.65,142.35,Yes
+4685-ERGHK,Male,0,No,No,57,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,One year,Yes,Electronic check,86.9,4939.25,No
+0885-HMGPY,Male,0,No,No,62,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),69.4,4237.5,No
+5003-OKNNK,Female,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),20.35,335.95,No
+4195-SMMNX,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,33.2,Yes
+7208-PSIHR,Female,0,Yes,No,70,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.3,7188.5,No
+4012-ZTHBR,Female,0,Yes,Yes,21,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.95,926.25,No
+0489-WMEMG,Female,0,No,Yes,23,Yes,No,DSL,No,No,No,Yes,No,No,One year,Yes,Electronic check,49.45,1119.35,No
+7435-ZNUYY,Male,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.6,116.6,No
+1354-YZFNB,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.55,68.8,No
+6956-SMUCM,Female,0,No,No,3,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),99,287.4,Yes
+2985-FMWYF,Female,0,No,No,23,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.5,2341.55,No
+2812-ENYMO,Male,0,No,No,26,No,No phone service,DSL,Yes,No,No,Yes,Yes,Yes,One year,No,Credit card (automatic),54.55,1362.85,No
+2717-HVIZY,Female,0,No,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,163.6,No
+3223-WZWJM,Male,0,No,No,26,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,83.95,2254.2,Yes
+3422-GALYP,Male,0,No,No,2,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.45,145.15,No
+9053-JZFKV,Male,0,No,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),116.2,7752.3,Yes
+2530-ENDWQ,Female,0,Yes,No,71,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),93.7,6585.35,Yes
+2890-WFBHU,Female,0,No,No,59,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,Yes,One year,No,Credit card (automatic),79.85,4786.1,No
+0327-WFZSY,Male,0,Yes,Yes,39,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,No,Electronic check,100,3835.55,No
+7977-HXJKU,Male,0,No,Yes,21,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.6,397,No
+6615-ZGEDR,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.7,19.7,Yes
+7033-CLAMM,Female,0,Yes,Yes,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.2,1027.25,No
+4023-RTIQM,Female,1,Yes,No,31,No,No phone service,DSL,Yes,Yes,Yes,No,No,Yes,One year,Yes,Credit card (automatic),50.4,1580.1,No
+0864-FVJNJ,Female,0,Yes,Yes,64,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,113.35,7222.75,No
+7356-IWLFW,Male,0,Yes,Yes,46,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80,3769.7,No
+9541-PWTWO,Female,0,No,No,52,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.95,4233.95,No
+3967-VQOGC,Female,0,Yes,Yes,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,24.9,1680.25,No
+7872-RDDLZ,Female,1,No,No,67,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,54.9,3725.5,No
+9250-WYPLL,Female,0,No,No,5,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.55,413.65,Yes
+6308-CQRBU,Female,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,109.25,7707.7,No
+2754-XBHTB,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),77.65,714.15,Yes
+1597-LHYNC,Female,1,No,No,26,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,95,2497.2,Yes
+0186-CAERR,Male,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),116.3,8309.55,No
+3043-SUDUA,Female,0,No,No,32,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.9,601.55,No
+5442-BHQNG,Female,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.35,139.25,No
+2169-RRLFW,Female,0,Yes,No,71,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.6,1888.25,No
+5872-OEQNH,Female,0,No,No,60,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,One year,Yes,Electronic check,44.45,2773.9,No
+3162-KKZXO,Female,1,No,No,55,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,100.15,5409.75,No
+7055-VKGDA,Male,0,No,No,54,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.4,5643.4,Yes
+0754-UKWQP,Male,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.85,197.7,Yes
+8873-GLDMH,Female,0,No,No,6,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,73.85,401.3,No
+5696-JVVQY,Female,0,Yes,Yes,48,Yes,No,DSL,Yes,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),70.1,3238.4,No
+8946-BFWSG,Male,0,Yes,Yes,63,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.25,1573.05,No
+7493-TPUWZ,Male,0,No,No,1,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,79.15,79.15,Yes
+0547-HURJB,Male,0,No,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),21.05,235.8,No
+8904-OPDCK,Male,1,Yes,No,54,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),24.95,1364.75,No
+8845-LWKGE,Female,0,Yes,Yes,30,Yes,No,DSL,No,No,Yes,Yes,Yes,No,One year,Yes,Electronic check,64.5,1985.15,No
+1577-HKTFG,Female,0,Yes,No,30,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.65,655.85,Yes
+8752-STIVR,Female,0,No,No,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79,303.15,Yes
+0691-NIKRI,Female,0,No,No,40,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),105.95,4335.2,No
+5022-JNQEQ,Female,0,Yes,Yes,9,Yes,No,Fiber optic,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,75.85,647.5,No
+4558-FANTW,Female,0,Yes,Yes,17,Yes,No,Fiber optic,Yes,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,91.85,1574.45,Yes
+3249-VHRIP,Female,0,No,No,62,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Two year,Yes,Credit card (automatic),43.6,2748.7,No
+3736-BLEPA,Male,0,No,No,28,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,One year,Yes,Bank transfer (automatic),91.25,2483.65,No
+4701-LKOZD,Female,0,No,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),89.75,6367.2,No
+6603-QWSPR,Female,0,No,No,46,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),104.4,4904.2,No
+9921-QFQUL,Female,0,Yes,No,23,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Mailed check,90.15,2044.95,No
+1929-ZCBHE,Male,0,Yes,Yes,47,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,One year,No,Electronic check,40.3,1794.8,No
+4378-MYPGO,Male,0,Yes,Yes,68,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.25,7173.15,No
+8651-ENBZX,Female,1,No,No,60,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,106,6441.4,Yes
+4129-LYCOI,Female,0,No,Yes,67,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,104,7039.05,No
+7798-JVXYM,Female,0,No,No,14,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,69.65,921.4,No
+8647-SDTWQ,Male,0,Yes,Yes,57,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.3,4018.35,No
+2696-ECXKC,Female,0,Yes,Yes,55,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,No,Mailed check,100.9,5448.6,No
+2081-KJSQF,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.25,20.25,No
+7623-TRNQN,Male,0,No,Yes,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,49.9,49.9,Yes
+3323-CPBWR,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),96.9,2085.45,No
+9330-IJWIO,Female,0,No,No,13,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.35,1358.85,Yes
+3384-CTMSF,Male,0,Yes,No,47,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.1,5135.15,No
+0902-RFHOF,Male,0,No,No,38,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,730.1,No
+9286-DOJGF,Female,1,Yes,No,38,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.95,2869.85,Yes
+6048-QBXKL,Female,1,No,No,2,Yes,Yes,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),56.55,118.25,No
+9661-MHUMO,Male,1,Yes,Yes,1,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),49.25,49.25,Yes
+7718-RXDGG,Male,0,Yes,No,15,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),68.6,1108.6,No
+1025-FALIX,Female,0,No,No,26,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.05,1815.65,No
+3451-VAWLI,Female,0,Yes,Yes,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,730.4,No
+3407-QGWLG,Male,0,No,Yes,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.05,75.45,No
+1842-EZJMK,Male,0,Yes,Yes,50,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,103.7,5071.05,Yes
+2347-WKKAE,Male,0,Yes,No,42,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,94.4,4014.6,No
+8735-DCXNF,Male,0,Yes,No,10,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),54.95,568.85,No
+3214-IYUUQ,Female,0,Yes,No,61,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,One year,No,Bank transfer (automatic),93.7,5860.7,No
+1936-UAFEH,Female,0,No,No,68,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),110.25,7279.35,No
+3587-PMCOY,Male,0,No,No,10,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,98.9,1064.95,No
+8079-XRJRS,Male,0,Yes,No,65,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,One year,No,Electronic check,89.75,5769.6,Yes
+1027-LKKQQ,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),80.45,5886.85,No
+7148-XZPHA,Male,0,Yes,Yes,55,Yes,No,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,79.4,4238.45,No
+8073-IJDCM,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.3,20.3,Yes
+5309-TAIKL,Female,0,No,No,7,Yes,No,DSL,No,No,Yes,Yes,No,Yes,Month-to-month,No,Bank transfer (automatic),62.8,418.3,No
+6856-RAURS,Female,0,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.9,136.05,No
+8778-LMWTJ,Female,0,No,No,9,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.85,708.2,No
+9717-IOAAF,Male,0,No,Yes,27,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.85,788.55,No
+8884-ADFVN,Male,1,Yes,No,7,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.95,700.85,Yes
+2845-KDHVX,Female,0,Yes,No,64,Yes,No,DSL,No,Yes,Yes,Yes,No,Yes,Two year,Yes,Mailed check,68.3,4378.8,No
+0848-SOMKO,Male,0,No,No,70,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Bank transfer (automatic),48.4,3442.8,No
+2720-WGKHP,Male,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94,181.7,Yes
+6734-FQAJX,Male,1,No,No,67,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,105.05,7171.7,No
+2869-ADAWR,Female,0,No,No,45,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),89.3,4016.85,Yes
+1535-VTJOQ,Female,0,No,No,24,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.15,553,No
+6368-TZZDT,Male,0,Yes,Yes,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.5,96.85,No
+0943-ZQPXH,Male,0,Yes,Yes,44,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),92.95,4122.9,No
+1293-BSEUN,Female,0,Yes,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.7,1482.3,No
+9894-EZEWG,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.3,74.3,Yes
+1302-TPUBN,Male,0,No,No,66,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.35,1240.8,No
+7851-FLGGQ,Male,0,No,No,1,No,No phone service,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Mailed check,44.65,44.65,Yes
+0637-UBJRP,Male,0,Yes,Yes,13,Yes,No,Fiber optic,Yes,No,No,No,No,Yes,Month-to-month,No,Electronic check,84.05,1095.3,Yes
+0940-OUQEC,Male,0,No,No,10,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),80.7,788.8,Yes
+2378-VTKDH,Male,1,Yes,No,65,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,104.35,6578.55,No
+0927-CNGRH,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,No
+8608-OZTLB,Male,0,Yes,Yes,38,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Electronic check,74.05,2802.3,No
+2335-GSODA,Male,0,No,Yes,23,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,Two year,No,Mailed check,40.1,857.75,No
+1952-DVVSW,Female,0,Yes,No,10,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,184.4,No
+0702-PGIBZ,Male,0,No,Yes,4,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.7,364.55,Yes
+6656-GULJQ,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),83.55,6093.3,No
+9853-JFZDU,Female,0,Yes,No,35,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,56.85,1861.1,No
+1963-VAUKV,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,20.4,Yes
+3777-XROBG,Female,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.55,1079.65,No
+6994-FGRHH,Male,0,Yes,Yes,70,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,106.15,7475.1,No
+4000-VGMQP,Male,0,Yes,Yes,38,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),78.95,2862.55,No
+3999-WRNGR,Female,0,Yes,Yes,60,No,No phone service,DSL,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,49.75,3069.45,No
+3466-RITXD,Male,0,No,No,26,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),92.4,2349.8,No
+2680-XKKNJ,Female,0,No,No,8,Yes,No,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),58.2,469.25,No
+3058-HJCUY,Male,0,Yes,Yes,41,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,102.6,4213.35,Yes
+2974-GGUXS,Female,1,Yes,No,36,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),91.95,3301.05,No
+7994-XIRTR,Male,1,No,No,54,Yes,No,DSL,No,No,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),65.25,3529.95,No
+3259-FDWOY,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),106,7723.7,Yes
+2101-RANCD,Female,0,No,No,55,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,73.1,4144.9,No
+7921-BEPCI,Female,0,No,No,72,Yes,Yes,DSL,Yes,Yes,No,No,No,No,Two year,No,Bank transfer (automatic),59.75,4265,No
+0807-ZABDG,Female,0,No,Yes,3,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,55.1,154.65,Yes
+8510-TMWYB,Female,0,Yes,Yes,54,Yes,Yes,DSL,No,Yes,Yes,No,No,No,Two year,Yes,Bank transfer (automatic),59.8,3246.45,No
+3258-ZKPAI,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),116.6,8337.45,No
+1428-IEDPR,Male,0,No,No,52,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,109.3,5731.4,No
+5298-GSTLM,Female,1,No,No,60,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),101.4,6176.6,No
+4450-YOOHP,Female,0,No,No,39,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),50.65,1905.4,No
+6728-CZFEI,Female,0,No,No,15,Yes,No,DSL,No,No,No,No,Yes,No,One year,No,Mailed check,56.15,931.9,No
+5748-RNCJT,Male,0,No,No,69,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),106.5,7348.8,Yes
+3653-NCRDJ,Male,0,Yes,Yes,43,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.2,776.25,No
+6121-TNHBO,Female,1,No,No,63,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),83,5243.05,No
+5119-NZPTV,Male,1,Yes,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.1,141.65,No
+6519-ZHPXP,Female,0,Yes,Yes,72,Yes,No,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),108.3,7679.65,No
+7739-LAXOG,Female,0,Yes,Yes,32,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),91.05,2954.5,Yes
+3472-QPRCH,Male,0,Yes,Yes,40,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.25,1006.9,No
+8224-UAXBZ,Female,0,Yes,No,58,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,One year,Yes,Electronic check,45.35,2540.1,No
+9610-WCESF,Male,0,No,No,67,No,No phone service,DSL,Yes,No,No,Yes,Yes,No,Two year,No,Electronic check,43.9,3097.2,No
+9776-CLUJA,Female,1,Yes,No,51,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,77.5,3807.35,Yes
+2486-WYVVE,Male,0,Yes,No,31,Yes,Yes,DSL,No,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,79.3,2484,No
+3865-ZYKAD,Male,1,Yes,No,69,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),84.9,5785.65,No
+1422-DGUBX,Male,0,Yes,No,32,Yes,Yes,Fiber optic,No,No,No,Yes,No,No,One year,Yes,Electronic check,79.25,2619.15,No
+2999-AANRQ,Female,0,No,No,21,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,No,Credit card (automatic),71.05,1524.85,No
+8668-KNZTI,Male,0,No,No,52,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,No,Electronic check,53.75,2790.65,No
+0480-KYJVA,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),24.25,1784.5,No
+6034-ZRYCV,Female,0,Yes,No,72,No,No phone service,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Electronic check,54.2,3937.45,Yes
+9746-YKGXB,Male,0,Yes,Yes,52,No,No phone service,DSL,Yes,No,Yes,No,No,Yes,One year,No,Bank transfer (automatic),44.25,2276.1,No
+3926-YZVVX,Female,0,No,No,41,Yes,No,DSL,No,No,No,Yes,No,No,One year,No,Bank transfer (automatic),50.05,2029.05,No
+6000-UKLWI,Male,0,No,No,41,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,802.35,No
+2079-FBMZK,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.25,418.4,Yes
+6332-FBZRI,Male,0,Yes,Yes,67,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),69.35,4653.25,No
+2096-XOTMO,Female,0,Yes,Yes,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.35,275.9,No
+3266-FTKHB,Male,0,No,No,17,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.15,343.45,No
+8221-EQDGL,Male,0,Yes,No,35,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Month-to-month,No,Mailed check,61,2130.45,No
+2346-LOCWC,Female,0,Yes,Yes,58,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.5,1191.4,No
+6608-QQLVK,Male,0,No,No,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,50.5,50.5,Yes
+0298-XACET,Male,0,Yes,Yes,52,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,50.2,2554,No
+7560-QJAVJ,Female,0,No,No,70,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),79.6,5589.45,No
+2995-YWTCD,Female,0,Yes,Yes,19,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),24.9,467.7,No
+3551-HUAZH,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.4,74.4,Yes
+9137-UIYPG,Female,0,Yes,Yes,35,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,106.9,3756.45,No
+2809-ZMYOQ,Female,0,No,No,32,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.35,3334.9,No
+5084-OOVCJ,Female,0,Yes,Yes,17,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Credit card (automatic),55.35,920.5,No
+0254-WWRKD,Female,0,Yes,Yes,67,Yes,No,DSL,No,Yes,No,No,No,No,One year,No,Credit card (automatic),50.55,3431.75,No
+7727-SHVZV,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.5,150.35,No
+6302-JGYRJ,Male,0,No,Yes,31,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Mailed check,79.45,2587.7,Yes
+5339-PXDVH,Male,0,No,No,4,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,No,Electronic check,90.65,367.95,No
+2205-LPVGL,Male,1,Yes,Yes,58,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,No,No,One year,Yes,Bank transfer (automatic),89.85,5125.75,No
+8782-NUUOL,Male,0,No,No,60,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,No,Mailed check,79,4801.1,No
+3685-YLCMQ,Male,0,No,No,58,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,104.65,6219.6,Yes
+7601-WFVZV,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.55,19.55,No
+4609-KNNWG,Female,0,Yes,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.9,550.1,No
+4868-AADLV,Male,1,Yes,Yes,66,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,116.25,7862.25,No
+9529-OFXHY,Male,0,No,No,15,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),87.75,1242.2,No
+8634-MPHTR,Male,1,Yes,No,47,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.05,4871.05,Yes
+2669-QVCRG,Female,0,No,No,41,Yes,Yes,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),81.3,3190.65,No
+2478-EEWWM,Male,0,Yes,No,59,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.3,2666.75,No
+9174-IHETN,Female,0,No,Yes,50,Yes,No,Fiber optic,No,No,No,No,No,No,Two year,No,Credit card (automatic),70.35,3533.6,No
+2233-TXSIU,Male,0,Yes,Yes,17,No,No phone service,DSL,Yes,No,Yes,No,Yes,No,One year,No,Credit card (automatic),44.45,792.15,No
+9644-UMGQA,Male,0,Yes,Yes,6,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.15,295.65,No
+3256-EZDBI,Male,1,Yes,No,51,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),29.45,1459.35,No
+8761-NSOBC,Male,0,No,No,44,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,100.55,4398.15,Yes
+0419-YAAPX,Male,0,Yes,No,49,Yes,No,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.3,4297.95,No
+8413-VONUO,Male,0,No,No,2,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,No,Electronic check,95.65,167.3,Yes
+0872-CASZJ,Male,0,Yes,No,59,Yes,Yes,DSL,Yes,No,No,Yes,No,Yes,One year,Yes,Mailed check,69.1,4096.9,No
+4726-DLWQN,Male,1,No,No,50,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),70.35,3454.6,No
+8164-OCKUJ,Female,0,Yes,Yes,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.6,1286,No
+3094-JOJAI,Male,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,74.15,1387,No
+9494-MRNYX,Male,0,No,No,10,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,75.05,786.3,No
+2599-CZABP,Male,0,Yes,No,14,Yes,No,DSL,No,No,No,No,No,No,One year,No,Electronic check,44.6,641.25,No
+7945-PRBVF,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),21.45,705.45,No
+4544-RXFMG,Male,0,Yes,Yes,8,Yes,No,DSL,No,No,No,No,No,No,One year,Yes,Mailed check,43.45,345.5,No
+0859-YGKFW,Male,0,Yes,Yes,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.05,345.9,No
+1150-FTQGN,Female,0,Yes,Yes,60,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,One year,No,Bank transfer (automatic),94.15,5811.8,No
+9223-UCPVT,Female,0,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,94.4,94.4,Yes
+7196-LIWRH,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,124.45,No
+5448-VWNAM,Female,0,No,Yes,19,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,No,Month-to-month,Yes,Mailed check,75.9,1375.6,No
+1177-XZBJL,Male,0,Yes,Yes,53,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),64.15,3491.55,No
+3518-FSTWG,Male,1,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.55,7920.7,No
+9330-VOFSZ,Female,0,Yes,No,60,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.8,6640.7,No
+4351-QLCSU,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Credit card (automatic),55,55,Yes
+0939-EREMR,Female,0,No,Yes,13,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,53.45,718.1,No
+9389-ACWBI,Female,0,Yes,Yes,5,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),69.95,320.4,No
+5419-JPRRN,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.45,101.45,Yes
+3644-QXEHN,Male,0,Yes,Yes,13,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,97,1334.45,No
+2911-IJORQ,Male,0,No,No,37,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),90.6,3358.65,No
+5921-NGYRH,Male,0,Yes,No,64,Yes,No,Fiber optic,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,73.55,4764,No
+2100-BDNSN,Female,0,Yes,No,5,Yes,Yes,DSL,No,No,Yes,Yes,Yes,No,Month-to-month,No,Bank transfer (automatic),67.95,350.3,Yes
+5998-DZLYR,Female,0,Yes,No,61,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),94.35,5703,No
+0488-GSLFR,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.5,69.5,Yes
+9318-NKNFC,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,18.85,18.85,Yes
+3985-HOYPM,Male,0,No,No,26,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.4,525.55,No
+9728-FTTVZ,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.2,69.2,Yes
+9548-LERKT,Male,0,Yes,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.75,483.15,No
+6576-FBXOJ,Male,0,Yes,No,17,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,One year,No,Electronic check,54.6,934.8,No
+4310-KEDTB,Female,0,No,Yes,26,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,29.8,786.5,No
+7254-IQWOZ,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.65,69.65,Yes
+2474-BRUCM,Male,1,Yes,No,40,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,101.85,4086.3,Yes
+4062-HBMOS,Male,0,No,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.05,5364.8,No
+0742-NXBGR,Female,0,No,No,1,Yes,No,Fiber optic,No,Yes,Yes,No,No,No,Month-to-month,Yes,Electronic check,82.3,82.3,Yes
+2676-ISHSF,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,20.3,No
+9236-NDUCW,Female,0,No,No,21,No,No phone service,DSL,Yes,No,No,Yes,No,No,Two year,No,Mailed check,35.1,770.4,No
+4753-PADAS,Female,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),105.7,6816.95,No
+6103-QCKFX,Female,0,Yes,Yes,44,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),56.25,2419.55,No
+7781-EWARA,Male,0,Yes,Yes,70,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,60.35,4138.7,No
+6110-OHIHY,Male,0,No,No,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,79.25,267.6,Yes
+7018-FPXHH,Male,0,Yes,Yes,56,Yes,No,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Bank transfer (automatic),59.8,3457.45,No
+5889-LFOLL,Female,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.6,1115.2,Yes
+5708-EVONK,Female,0,Yes,Yes,58,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),93.4,5435.6,Yes
+8708-XPXHZ,Female,0,Yes,Yes,42,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,94.2,4186.3,Yes
+0616-ATFGB,Male,1,Yes,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.05,25.05,Yes
+0572-ZJKLT,Female,0,Yes,Yes,46,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,Yes,Two year,No,Mailed check,99.65,4630.2,No
+5135-RDDQL,Female,0,Yes,Yes,63,Yes,No,DSL,Yes,No,No,No,No,No,Two year,Yes,Bank transfer (automatic),50.65,3221.25,No
+1353-LJWEM,Male,0,No,No,11,Yes,No,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,60.9,688.5,No
+1794-SWWKL,Male,0,Yes,Yes,15,Yes,Yes,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),59.65,867.1,No
+6166-YIPFO,Male,0,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,64.7,4746.05,No
+7016-BPGEU,Female,0,No,No,29,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),25.1,712.85,Yes
+6876-ADESB,Male,0,No,Yes,1,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,48.95,48.95,Yes
+9332-GYWLO,Female,0,Yes,Yes,6,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,54.85,355.1,No
+1963-SVUCV,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,45.3,45.3,Yes
+4184-VODJZ,Male,0,Yes,Yes,63,Yes,Yes,Fiber optic,No,Yes,No,No,No,Yes,One year,Yes,Electronic check,91.35,5764.7,No
+0220-EBGCE,Male,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,85.85,167.3,Yes
+1092-WPIVQ,Female,0,Yes,Yes,18,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,25.1,428.45,No
+7233-IOQNP,Female,0,Yes,No,43,No,No phone service,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),34,1505.35,No
+2834-JKOOW,Female,0,No,No,15,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,One year,No,Mailed check,45.9,693.45,No
+2754-VDLTR,Male,0,No,Yes,10,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,95.2,930.4,Yes
+2400-FEQME,Male,0,Yes,Yes,55,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.5,1177.95,No
+3190-XFANI,Male,0,No,Yes,49,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),100.6,5069.65,Yes
+7551-DACSP,Male,0,Yes,Yes,6,Yes,No,DSL,No,Yes,Yes,No,No,No,Month-to-month,Yes,Mailed check,55.3,324.25,Yes
+4957-SREEC,Male,0,Yes,Yes,70,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.35,1458.1,No
+7235-NXZCP,Male,1,No,No,2,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.85,156.4,Yes
+0230-UBYPQ,Male,1,Yes,No,63,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,No,Bank transfer (automatic),36.1,2298.9,No
+4923-ADWXJ,Female,0,No,No,25,Yes,No,DSL,No,Yes,No,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),65.8,1679.65,No
+1763-KUAAW,Female,1,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.35,369.6,No
+1104-TNLZA,Male,1,Yes,No,28,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105.8,2998,No
+5195-KPUNQ,Female,1,No,No,53,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,96.75,5206.55,No
+0520-FDVVT,Male,0,No,No,35,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),102.35,3626.1,Yes
+3439-GVUSX,Male,0,No,No,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.4,24.4,No
+1444-VVSGW,Male,0,Yes,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),115.65,7968.85,Yes
+5712-PTIWW,Male,0,No,No,2,Yes,No,Fiber optic,No,No,Yes,Yes,No,No,Month-to-month,Yes,Electronic check,79.85,152.45,Yes
+5949-HGVJL,Female,0,Yes,No,26,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,73.05,1959.5,No
+5203-XEHAX,Female,0,No,No,34,Yes,No,DSL,Yes,Yes,No,No,No,Yes,One year,No,Electronic check,64.35,2053.05,No
+1635-HDGFT,Female,0,No,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.5,398.55,No
+3315-TOTBP,Male,0,No,No,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,76,1130.85,Yes
+8050-XGRVL,Female,0,Yes,Yes,62,Yes,No,DSL,Yes,No,Yes,No,No,No,One year,No,Credit card (automatic),54.75,3425.35,No
+6661-HBGWL,Female,1,No,No,42,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),104.75,4323.45,Yes
+1518-VOWAV,Female,0,No,No,9,Yes,No,DSL,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,74.65,703.55,Yes
+9828-QHFBK,Male,0,No,No,24,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,51.15,1275.7,No
+3908-MKIMJ,Male,1,Yes,No,68,No,No phone service,DSL,Yes,Yes,Yes,No,No,No,Two year,Yes,Electronic check,41.95,2965.75,No
+5012-YSPJJ,Male,0,Yes,Yes,31,Yes,No,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Mailed check,54.35,1647,No
+5909-ECHUI,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,56.25,56.25,Yes
+1309-BXVOQ,Male,0,Yes,No,21,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,106.1,2249.95,Yes
+6728-VOIFY,Female,0,Yes,No,63,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Electronic check,96,6109.75,No
+9317-WZPGV,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),79.75,159.4,Yes
+4891-NLUBA,Female,0,Yes,Yes,61,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),61.45,3751.15,No
+2856-HYAPG,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,68.65,68.65,Yes
+8969-PRHFK,Male,0,No,No,18,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,411.25,No
+0661-KQHNK,Female,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19,105.5,No
+8709-KRDVL,Female,0,No,No,33,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100,3320.6,No
+4488-PSYCG,Male,0,No,No,16,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.25,327.45,Yes
+1427-VERSM,Female,0,Yes,No,56,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,98.7,5669.5,No
+9801-NOSHQ,Male,0,No,Yes,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.8,465.45,No
+6339-YPSAH,Male,0,No,No,9,Yes,No,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,73.8,704.3,No
+3621-CEOVK,Female,1,Yes,No,14,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.2,1369.8,Yes
+6906-ANDWJ,Male,0,Yes,Yes,15,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.9,1107.25,Yes
+2159-TURXX,Male,0,No,No,5,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.05,95.55,No
+1548-FEHVL,Male,0,Yes,No,61,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,106.2,6375.2,No
+3795-CAWEX,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),116.55,8152.3,No
+6215-NQCPY,Male,0,No,No,15,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.7,1566.75,No
+5647-URDKA,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.7,130.25,No
+2229-DPMBI,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.5,162.15,No
+9626-WEQRM,Female,0,No,No,4,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,29.15,110.05,No
+2188-SXWVT,Female,0,No,No,34,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,No,Mailed check,55,1885.15,No
+6258-PVZWJ,Male,0,Yes,No,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),90.8,6302.85,No
+1094-BKOSX,Female,0,Yes,Yes,45,No,No phone service,DSL,No,Yes,Yes,Yes,No,Yes,One year,No,Bank transfer (automatic),51,2264.5,No
+6969-MVBAI,Female,1,No,No,9,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,90.1,816.8,No
+2978-XXSOG,Female,0,No,No,22,Yes,Yes,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,59.05,1253.5,No
+2342-CKIAO,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.3,41.2,No
+6953-PBDIN,Male,0,Yes,No,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Bank transfer (automatic),72.95,5265.55,No
+3898-BSJYF,Female,0,No,Yes,10,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,One year,No,Credit card (automatic),73.55,693.3,No
+3938-YFPXD,Male,0,No,No,72,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),84.3,5997.1,No
+7696-AMHOD,Female,0,Yes,Yes,49,Yes,No,DSL,No,Yes,Yes,No,Yes,Yes,One year,No,Credit card (automatic),78,3824.2,No
+2453-SAFNS,Female,1,No,No,54,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,No,Mailed check,72.1,3886.05,No
+9558-IHEZX,Female,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),106.75,7283.25,No
+9617-UDPEU,Female,0,No,No,22,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.25,412.55,No
+6340-DACFT,Female,0,Yes,Yes,50,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.55,1070.25,No
+4955-VCWBI,Female,0,Yes,Yes,43,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20,817.95,No
+6813-GZQCG,Female,0,Yes,Yes,45,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),24.65,1171.3,No
+7426-GSWPO,Male,1,No,No,64,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),103.5,6548.65,No
+3389-KTRXV,Female,0,Yes,Yes,23,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,23.85,625.65,No
+3118-UHVVQ,Female,0,Yes,No,68,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.8,1911.5,No
+1031-IIDEO,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.85,70.85,Yes
+5832-XKAES,Male,0,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.8,134.7,Yes
+3205-MXZRA,Male,0,No,No,26,Yes,No,DSL,No,No,No,Yes,Yes,No,One year,No,Credit card (automatic),59.45,1507,No
+4701-AHWMW,Male,0,Yes,No,55,No,No phone service,DSL,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),54.55,2978.3,Yes
+8317-BVKSO,Male,0,No,No,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.05,299.3,No
+5702-KVQRD,Male,0,Yes,No,71,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,82.55,5832.65,No
+0083-PIVIK,Male,0,No,No,64,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Electronic check,81.25,5567.55,No
+9210-IAHGH,Female,0,No,No,7,Yes,No,DSL,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),70.75,450.8,Yes
+6169-PGNCD,Female,0,No,No,57,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,Yes,Credit card (automatic),74.3,4166.35,No
+0023-XUOPT,Female,0,Yes,No,13,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Electronic check,94.1,1215.6,Yes
+4592-IWTJI,Female,0,Yes,Yes,3,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),29.7,91.7,Yes
+4909-JOUPP,Male,1,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),109.7,7898.45,No
+4657-FWVFY,Female,0,Yes,Yes,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),96.35,3915.4,No
+2257-BOVXD,Male,0,Yes,No,14,Yes,Yes,DSL,No,No,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),66.6,979.5,No
+7729-JTEEC,Male,0,Yes,Yes,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,44.5,90.05,No
+7632-MNYOY,Male,1,No,No,66,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,No,Credit card (automatic),110.9,7432.05,Yes
+6518-LGAOV,Female,0,Yes,No,38,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,105,4026.4,Yes
+7242-QZLXF,Male,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,25.3,25.3,Yes
+4576-CSAJH,Male,0,No,No,22,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),55.15,1193.05,Yes
+1000-AJSLD,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.1,20.1,Yes
+9696-RMYBA,Male,0,No,No,5,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,80.1,398.55,No
+8690-UPCZI,Male,0,Yes,Yes,29,Yes,Yes,DSL,Yes,No,No,Yes,Yes,No,One year,No,Bank transfer (automatic),69.05,1958.45,No
+1062-LHZOD,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.9,69.9,Yes
+9770-LXDBK,Female,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.4,63.15,No
+4086-WITJG,Male,0,Yes,Yes,71,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,1301.1,No
+8344-WFMFH,Male,0,No,Yes,9,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,50.1,484.05,No
+9600-NAXZN,Male,0,No,No,43,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.4,4528,Yes
+8822-KNBHV,Female,0,No,No,48,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),83.45,3887.85,No
+8404-FYDIB,Male,0,No,No,26,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),86.65,2208.75,No
+2791-SFVEW,Female,0,No,No,9,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),20.15,238.15,No
+6457-USBER,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.8,80.8,Yes
+6762-NSODU,Female,0,Yes,Yes,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.4,958.15,No
+4662-EKDPQ,Male,0,No,No,2,Yes,Yes,DSL,No,No,No,No,No,Yes,Month-to-month,No,Bank transfer (automatic),62.05,118.3,Yes
+9248-OJYKK,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,76.45,76.45,Yes
+9888-ZCUMM,Male,0,Yes,Yes,64,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),60.05,3845.45,No
+3398-FSHON,Female,1,No,No,12,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,91.3,1094.5,Yes
+3319-DWOEP,Male,1,Yes,No,6,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.75,573.75,Yes
+1074-WVEVG,Female,0,Yes,No,59,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.35,1267,No
+9979-RGMZT,Female,0,No,No,7,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Mailed check,94.05,633.45,No
+6437-UDQJM,Female,1,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),84.1,6129.65,No
+1226-IENZN,Male,1,No,No,16,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,78.75,1218.25,No
+2108-GLPQB,Male,0,Yes,No,25,Yes,Yes,DSL,No,No,Yes,No,No,No,Month-to-month,No,Credit card (automatic),55.55,1405.3,No
+9259-PACGQ,Female,0,Yes,No,34,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Electronic check,62.65,2274.9,Yes
+4415-IJZTP,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.5,74.5,Yes
+1465-VINDH,Female,0,Yes,Yes,10,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.1,1068.85,Yes
+5474-LAMUQ,Male,0,Yes,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,20.1,533.9,No
+3468-DRVQJ,Female,0,Yes,Yes,10,Yes,Yes,DSL,Yes,Yes,No,No,Yes,No,One year,No,Electronic check,70.3,676.15,No
+1122-YJBCS,Male,0,Yes,No,69,Yes,No,DSL,Yes,Yes,No,No,No,No,One year,Yes,Credit card (automatic),53.65,3804.4,No
+4990-ALDGW,Male,0,No,No,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.75,1118.8,No
+4816-OKWNX,Male,0,Yes,Yes,50,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),103.4,5236.4,No
+3315-IKYZQ,Male,0,Yes,Yes,28,No,No phone service,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Mailed check,50.8,1386.8,No
+2810-FTLEM,Female,0,No,No,16,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,50.15,762.25,Yes
+4747-LCAQL,Male,0,No,No,25,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,79,1902,No
+3411-WLRSQ,Female,1,Yes,No,3,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.6,239.05,No
+0898-XCGTF,Male,0,Yes,No,61,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Bank transfer (automatic),96.5,5673.7,No
+8006-PYCSW,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.1,39.8,No
+8249-THVEC,Male,0,Yes,Yes,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.4,997.75,No
+8722-PRFDV,Female,0,Yes,Yes,71,Yes,No,DSL,Yes,No,Yes,No,Yes,Yes,Two year,No,Credit card (automatic),77.55,5574.35,No
+2164-SOQXL,Female,0,Yes,Yes,20,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.05,406.05,No
+4020-KIUDI,Male,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),19.85,138.85,No
+5850-BDWCY,Female,0,No,No,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.2,123.65,No
+2038-YSEZE,Female,0,No,No,29,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,67.45,1801.1,No
+0827-ITJPH,Male,0,No,No,36,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),18.55,689,No
+8799-OXZMD,Female,0,No,No,28,No,No phone service,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,29.75,790.7,No
+1925-TIBLE,Female,0,Yes,No,7,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,86.5,582.5,Yes
+1705-GUHPV,Female,0,No,No,63,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,24.2,1618.2,No
+4779-ZGICK,Male,0,Yes,Yes,48,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,23.55,1173.35,No
+0048-PIHNL,Female,0,Yes,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Bank transfer (automatic),20.45,900.9,No
+0818-OCPZO,Male,1,No,No,27,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,81.45,2122.05,Yes
+3967-KXAPS,Male,0,Yes,No,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),92.3,6719.9,No
+1447-GIQMR,Male,0,Yes,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.15,69.15,Yes
+3704-IEAXF,Female,0,Yes,Yes,72,No,No phone service,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Credit card (automatic),53.65,3784,No
+6810-VCAEX,Female,0,No,No,47,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Credit card (automatic),39.65,1798.65,No
+0674-EYYZV,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,54.65,54.65,No
+4480-MBMLB,Female,0,No,No,36,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),104.8,3886.45,No
+1395-OFUWC,Male,0,Yes,Yes,43,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),29.3,1224.05,No
+0822-QGCXA,Female,1,Yes,No,27,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,83.85,2310.2,No
+0872-NXJYS,Female,0,No,No,9,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,79.55,723.4,Yes
+2832-KJCRD,Female,0,No,No,38,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,103.65,3988.5,No
+3588-WSTTJ,Female,1,No,No,35,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,99.05,3554.6,No
+4075-WKNIU,Female,0,Yes,Yes,0,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,73.35, ,No
+5090-EMGTC,Female,0,Yes,No,59,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Mailed check,100.05,6034.85,No
+2346-CZYIL,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.35,531.6,No
+3798-EPWRR,Female,1,No,No,2,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,43.95,85.1,No
+6258-NGCNG,Male,0,No,No,7,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,23.5,173,No
+4186-ZBUEW,Female,0,No,No,36,Yes,Yes,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,70.7,2511.95,No
+5274-XHAKY,Female,0,Yes,Yes,41,Yes,Yes,Fiber optic,Yes,Yes,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),94.3,3893.6,No
+1442-BQPVU,Female,0,No,No,13,No,No phone service,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,29.15,357.15,No
+3978-YNKDD,Male,0,No,Yes,19,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),20.85,467.5,No
+4940-KHCWD,Female,0,Yes,No,60,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),37.7,2288.7,No
+9412-ARGBX,Female,0,No,Yes,48,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,Two year,Yes,Mailed check,95.5,4627.85,Yes
+1389-CXMLU,Male,1,No,No,3,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,91.05,289.1,Yes
+9589-ABEPT,Male,0,Yes,No,69,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Two year,Yes,Mailed check,92.45,6460.55,No
+9388-ZEYVT,Male,0,No,No,43,No,No phone service,DSL,No,No,Yes,Yes,No,Yes,One year,No,Electronic check,44.15,1931.3,No
+0305-SQECB,Female,0,No,Yes,11,No,No phone service,DSL,Yes,No,No,Yes,No,No,One year,Yes,Mailed check,36.05,402.6,No
+7605-SNLQG,Female,0,Yes,No,45,Yes,No,DSL,No,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),50.25,2221.55,No
+4670-TABXH,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),109.75,7758.9,No
+5561-NWEVX,Female,1,Yes,No,2,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,79.2,172.85,Yes
+0064-SUDOG,Female,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.3,224.5,No
+8561-NMTBD,Female,0,Yes,Yes,67,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Credit card (automatic),112.35,7388.45,No
+5161-XEUVX,Male,0,Yes,No,37,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Two year,No,Mailed check,94.3,3460.95,No
+8292-FRFZQ,Female,0,No,No,39,No,No phone service,DSL,Yes,Yes,No,Yes,No,No,One year,No,Bank transfer (automatic),41.15,1700.9,No
+8591-NXRCV,Female,0,No,No,41,Yes,Yes,DSL,Yes,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),74.65,3090.65,No
+7895-VONWT,Female,0,No,No,25,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,48.25,1293.8,No
+4526-RMTLL,Male,0,Yes,Yes,8,Yes,Yes,DSL,Yes,Yes,No,Yes,No,Yes,Two year,Yes,Credit card (automatic),76.15,645.8,No
+6253-GNHWH,Female,0,Yes,Yes,71,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Mailed check,71.1,5224.95,No
+4566-NECEV,Male,0,No,No,5,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Electronic check,96.55,500.1,No
+8080-POTJR,Female,0,No,No,30,Yes,Yes,DSL,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),79.3,2427.1,No
+6906-MPARY,Male,0,No,No,40,Yes,No,Fiber optic,No,Yes,No,Yes,No,Yes,One year,Yes,Credit card (automatic),89.6,3488.15,No
+3662-FXJFO,Female,0,No,No,54,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.5,1035.7,No
+8107-RZLNV,Male,0,Yes,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),106.3,7565.35,No
+0516-OOHAR,Male,0,Yes,Yes,28,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,No,Bank transfer (automatic),100.35,2799,No
+2027-CWDNU,Male,0,Yes,Yes,18,Yes,No,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,85.6,1601.5,No
+2737-WFVYW,Female,0,No,No,2,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,45.25,85.5,Yes
+9912-OMZDS,Female,0,Yes,Yes,59,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,106.15,6256.2,No
+3733-ZEECP,Male,0,Yes,Yes,22,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,51.1,1232.9,No
+7878-RTCZG,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.9,19.9,No
+2452-MRMZF,Female,1,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),25.7,1937.4,No
+2272-QAGFO,Female,1,No,No,14,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.3,1096.25,Yes
+7103-IPXPJ,Male,0,Yes,No,50,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,One year,No,Electronic check,99.4,5059.75,No
+4342-HFXWS,Female,0,Yes,Yes,48,Yes,No,DSL,Yes,No,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),69.7,3023.65,No
+3545-CNWRG,Female,0,Yes,Yes,49,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.35,4889.2,No
+1785-BPHTP,Male,0,Yes,Yes,28,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,No,Electronic check,85.45,2289.9,No
+4989-LIXVT,Male,1,No,No,68,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,95.9,6503.2,No
+7315-WYOAW,Male,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Electronic check,100.75,1313.25,No
+1173-XZPYF,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,89.2,990.3,No
+9850-OWRHQ,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),74.1,228,Yes
+3768-NLUBH,Male,1,Yes,No,57,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,100.6,5746.15,Yes
+8676-TRMJS,Male,0,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Mailed check,75,209.1,Yes
+2509-TFPJU,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.75,1864.2,No
+9530-GRMJG,Male,0,Yes,Yes,70,Yes,Yes,DSL,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,84.1,5979.7,No
+6898-RBTLU,Female,0,Yes,Yes,49,Yes,Yes,DSL,No,Yes,No,Yes,Yes,Yes,Two year,No,Bank transfer (automatic),79.3,3902.45,No
+5481-NTDOH,Female,1,Yes,No,67,Yes,Yes,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),107.05,7142.5,No
+2068-WWXQZ,Male,0,No,No,46,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.05,902,No
+7359-SSBJK,Female,1,No,No,64,Yes,No,DSL,Yes,Yes,No,Yes,Yes,No,Two year,Yes,Credit card (automatic),70.2,4481,Yes
+2061-VVFST,Female,0,Yes,No,37,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),19.5,805.2,No
+0685-MLYYM,Female,1,No,No,2,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,70.75,154.85,Yes
+0749-IRGQE,Female,1,Yes,No,13,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,No,Electronic check,45.3,528.45,No
+2380-DAMQP,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Electronic check,115.15,8349.7,No
+2256-YLYLP,Male,0,Yes,Yes,68,Yes,No,DSL,No,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),72.95,4953.25,No
+1272-ILHFG,Male,0,Yes,Yes,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.65,332.65,No
+0164-XAIRP,Female,0,No,No,24,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.55,470.2,No
+5666-CYCYZ,Female,0,No,No,24,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,Month-to-month,Yes,Electronic check,89.55,2259.35,No
+4011-ARPHK,Male,0,No,No,27,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,50.35,1411.35,No
+8182-PNAGI,Male,0,No,No,12,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,No,Bank transfer (automatic),50.25,593.75,Yes
+6365-HITVU,Female,0,Yes,Yes,71,Yes,No,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),87.25,6328.7,No
+7558-IMLMT,Male,0,Yes,Yes,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.8,1411.9,No
+9488-FYQAU,Female,0,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),109.25,6841.4,No
+3590-TCXTB,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.35,20.35,No
+6994-KERXL,Male,0,No,No,4,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,55.9,238.5,No
+7957-RYHQD,Female,1,No,No,40,Yes,No,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),79.2,3233.85,Yes
+2180-DXNEG,Female,0,No,No,12,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,96,1062.1,Yes
+1960-UOTYM,Male,0,Yes,Yes,52,Yes,No,DSL,Yes,Yes,Yes,No,Yes,Yes,Two year,No,Electronic check,79.2,4016.3,No
+2740-JFBOK,Male,0,No,No,10,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,24,226.55,No
+6500-JVEGC,Male,0,No,No,68,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,One year,No,Bank transfer (automatic),101.35,7110.75,No
+5515-AKOAJ,Female,0,No,No,54,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,100.1,5440.9,Yes
+8976-OQHGT,Female,0,Yes,Yes,4,Yes,No,DSL,No,Yes,No,Yes,No,No,Month-to-month,No,Mailed check,56.5,235.1,Yes
+5245-VDBUR,Female,0,Yes,No,52,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,No,Mailed check,35.45,1958.95,No
+6230-BSUXY,Female,1,No,No,1,Yes,No,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,85,85,Yes
+8469-SNFFH,Male,0,Yes,No,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,One year,No,Bank transfer (automatic),79.4,5528.9,No
+2144-ESWKO,Male,0,No,No,43,No,No phone service,DSL,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),35.2,1463.7,No
+1241-FPMOF,Male,0,No,No,52,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.65,1025.05,No
+9928-BZVLZ,Female,0,No,No,12,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Mailed check,49.85,552.1,No
+4114-QMKVN,Female,0,Yes,Yes,56,Yes,Yes,DSL,Yes,Yes,No,No,No,Yes,One year,No,Bank transfer (automatic),68.75,3815.4,No
+2775-SEFEE,Male,0,No,Yes,0,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),61.9, ,No
+9003-CPATH,Male,0,No,No,42,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,No,Electronic check,79.9,3313.4,No
+1754-GKYPY,Male,1,Yes,No,22,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),89.75,1938.9,No
+5294-CDGWY,Male,0,Yes,Yes,51,No,No phone service,DSL,Yes,Yes,Yes,No,Yes,Yes,One year,No,Electronic check,59.3,3014.65,Yes
+7956-XQWGU,Male,0,No,No,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),19.4,460.25,No
+1591-MQJTP,Male,1,Yes,No,51,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,Yes,One year,Yes,Bank transfer (automatic),93.65,4839.15,No
+5295-PCJOO,Male,0,No,Yes,4,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,49.4,184.4,Yes
+2369-FEVNO,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.9,19.9,No
+9863-JZAIC,Male,0,No,No,35,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,55,2010.55,Yes
+7471-MQPOS,Male,1,Yes,Yes,71,Yes,Yes,DSL,No,Yes,Yes,Yes,No,Yes,One year,Yes,Bank transfer (automatic),72.9,5139.65,No
+7660-HDPJV,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.2,69.2,Yes
+5115-SQAAU,Female,0,Yes,Yes,69,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.6,1673.4,No
+9845-QOMAD,Male,0,Yes,Yes,14,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,19.75,309.35,No
+7672-VFMXZ,Male,0,Yes,No,57,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,No,Electronic check,55.7,3171.6,No
+9739-JLPQJ,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Credit card (automatic),117.5,8670.1,No
+9916-AYHTC,Male,0,No,No,48,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.85,916,No
+3855-ONCAR,Female,0,Yes,Yes,4,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Mailed check,78.9,299.75,No
+1488-SYSFC,Male,0,Yes,Yes,31,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,20.65,702.05,No
+7931-PXHFC,Male,0,No,No,38,Yes,No,DSL,No,No,Yes,Yes,No,Yes,One year,Yes,Mailed check,62.3,2354.8,Yes
+3990-QYKBE,Male,1,Yes,No,37,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.5,3473.4,Yes
+0970-QXPXW,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,19.65,19.65,No
+3259-KNMRR,Male,1,No,No,57,Yes,Yes,DSL,No,Yes,Yes,No,Yes,Yes,Two year,Yes,Bank transfer (automatic),79.75,4438.2,No
+6120-RJKLU,Female,1,Yes,No,62,Yes,No,DSL,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),79.95,4819.75,No
+9572-WUKSB,Male,0,Yes,No,3,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,29.9,92.25,No
+5893-KCLGT,Female,0,No,Yes,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,19.75,1567,No
+0076-LVEPS,Male,0,No,Yes,29,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,45,1242.45,No
+3640-JQGJG,Male,0,Yes,Yes,13,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),44.8,559.2,No
+4919-MOAVT,Male,0,No,Yes,3,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,69.65,220.1,Yes
+1596-OQSPS,Female,0,No,No,11,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,51.1,531.15,No
+9867-XOBQA,Female,0,No,Yes,21,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Mailed check,53.15,1183.2,No
+9546-KDTRB,Female,0,No,No,19,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Bank transfer (automatic),24.7,465.85,No
+3090-HAWSU,Male,0,No,No,61,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),111.6,6876.05,Yes
+9820-RMCQV,Female,0,No,No,11,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,No,Credit card (automatic),48.55,501,Yes
+3174-RKMOW,Male,0,Yes,Yes,35,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Electronic check,109.95,3782.4,No
+1760-CAZHT,Male,0,No,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.8,460.2,No
+7839-QRKXN,Female,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.2,20.2,Yes
+8441-SHIPE,Female,0,No,No,67,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),25.6,1790.35,No
+5204-QZXPU,Male,0,No,No,19,No,No phone service,DSL,No,No,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,39.65,733.35,Yes
+4597-ELFTS,Male,0,No,No,56,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,24.9,1334,Yes
+1320-GVNHT,Male,0,Yes,Yes,72,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Credit card (automatic),108.4,7767.25,No
+1047-RNXZV,Male,0,No,No,43,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.55,876.15,No
+3541-ZNUHK,Female,0,Yes,Yes,55,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,85.1,4600.95,No
+8450-JOVAH,Male,0,Yes,Yes,2,Yes,Yes,DSL,Yes,No,No,No,No,No,Month-to-month,No,Mailed check,56.7,113.55,Yes
+8725-JEDFD,Male,0,No,No,27,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,69.05,1793.25,No
+7562-UXTPG,Female,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.15,886.7,No
+8071-SBTRN,Female,0,No,No,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Two year,Yes,Mailed check,111.15,7737.55,No
+1113-IUJYX,Female,0,Yes,No,14,Yes,No,Fiber optic,Yes,Yes,No,Yes,Yes,Yes,One year,No,Mailed check,105.95,1348.9,Yes
+6668-CNMFP,Female,0,Yes,Yes,19,Yes,Yes,Fiber optic,No,No,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),89.35,1686.85,No
+5626-MGTUK,Female,0,No,No,20,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),89.1,1879.25,No
+5681-LLOEI,Male,0,Yes,Yes,43,Yes,Yes,Fiber optic,Yes,No,Yes,Yes,No,No,One year,Yes,Credit card (automatic),91.25,4013.8,No
+8212-DJRCH,Male,0,Yes,Yes,5,Yes,No,Fiber optic,Yes,No,Yes,No,No,Yes,Month-to-month,Yes,Mailed check,90.35,434.5,No
+4323-OHFOW,Female,1,Yes,No,70,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),105.55,7195.35,No
+4933-BSAIP,Female,0,Yes,No,40,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.1,780.1,No
+2030-BTZRO,Male,0,Yes,Yes,6,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20.4,107.6,No
+1116-DXXDF,Male,0,No,No,39,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,100.45,3801.7,No
+9274-CNFMO,Male,1,Yes,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Electronic check,74.95,308.7,Yes
+7758-XKCBS,Male,0,No,No,15,No,No phone service,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Electronic check,29.7,438.25,Yes
+8992-JQYUN,Male,0,Yes,No,1,No,No phone service,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,50.35,50.35,Yes
+6563-VNPMN,Female,1,No,No,45,Yes,No,Fiber optic,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),85.7,3778.1,No
+0617-AQNWT,Female,0,Yes,No,64,No,No phone service,DSL,Yes,No,Yes,Yes,No,Yes,Two year,No,Electronic check,47.85,3147.5,Yes
+2267-WTPYD,Female,1,Yes,No,57,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),94,5438.95,No
+0270-THENM,Male,0,Yes,Yes,72,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,Two year,No,Bank transfer (automatic),69.85,5102.35,No
+2446-PLQVO,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,70.3,70.3,Yes
+4707-MAXGU,Male,0,Yes,No,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.85,1872.2,No
+2710-WYVXG,Female,0,No,No,3,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,Two year,No,Mailed check,71.1,213.35,No
+3005-NFMTA,Male,1,No,No,55,Yes,Yes,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,98.8,5617.75,Yes
+6300-BWMJX,Female,0,Yes,No,59,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,No,Electronic check,93.35,5386.5,No
+8784-CGILN,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),99.85,1776.95,Yes
+5389-FFVKB,Male,1,Yes,No,32,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Credit card (automatic),80.3,2483.05,Yes
+7009-LGECI,Female,0,No,No,4,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),50.55,235.65,No
+8444-WRIDW,Female,1,No,No,66,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Credit card (automatic),80.45,5224.35,Yes
+5022-KVDQT,Male,0,No,No,27,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,81.3,2272.8,No
+7976-CICYS,Male,0,No,No,4,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Credit card (automatic),20.7,83.75,No
+1036-GUDCL,Male,0,Yes,Yes,60,Yes,No,DSL,Yes,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),79.05,4663.4,No
+2005-DWQZJ,Female,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,19.05,201.7,No
+8148-WOCMK,Male,0,Yes,Yes,8,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.6,125,No
+7815-PDTHL,Male,0,No,No,35,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,20.2,684.4,No
+9451-LPGOO,Male,0,No,No,7,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,86.8,620.35,Yes
+9173-IVZVP,Female,0,Yes,Yes,53,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.9,1146.05,No
+9129-UXERG,Female,1,No,No,18,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Month-to-month,Yes,Credit card (automatic),103.6,1806.35,No
+3635-JBPSG,Female,0,No,No,15,No,No phone service,DSL,No,Yes,No,No,No,Yes,Two year,Yes,Mailed check,38.8,603,No
+7964-ZRKKG,Male,0,Yes,No,67,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),88.4,5798.3,No
+5868-YWPDW,Male,1,Yes,No,6,Yes,No,Fiber optic,No,Yes,No,No,No,Yes,Month-to-month,Yes,Electronic check,84.2,519.15,Yes
+6229-LSCKB,Male,1,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,79.7,497.6,No
+2025-JKFWI,Male,0,No,No,13,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,Yes,Mailed check,99,1301.7,Yes
+4078-SAYYN,Female,0,No,No,11,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,100.75,1129.75,Yes
+1724-IQWNM,Male,0,No,Yes,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.3,19.3,No
+8217-QYOHV,Male,0,No,No,5,Yes,No,DSL,Yes,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,55.75,266.95,No
+5546-QUERU,Male,0,No,No,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Mailed check,19.95,257,No
+9940-HPQPG,Female,0,Yes,No,9,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,No,No,Month-to-month,No,Bank transfer (automatic),91.75,865.8,Yes
+6897-UUBNU,Male,0,No,No,29,Yes,No,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,No,Mailed check,89.65,2623.65,No
+6127-IYJOZ,Male,1,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,45.85,45.85,No
+6618-RYATB,Female,0,No,No,1,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Mailed check,79.55,79.55,Yes
+8930-XOTDP,Female,0,Yes,Yes,18,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Mailed check,55.95,1082.8,No
+5916-QEWPT,Female,0,Yes,No,2,Yes,No,DSL,No,No,Yes,No,Yes,Yes,Month-to-month,No,Credit card (automatic),69,147.8,No
+5060-TQUQN,Male,0,Yes,Yes,30,Yes,Yes,Fiber optic,No,Yes,No,Yes,No,No,Month-to-month,Yes,Bank transfer (automatic),83.55,2570.2,No
+0531-XBKMM,Male,0,No,Yes,66,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,No,Bank transfer (automatic),65.7,4378.9,No
+8465-SBRXP,Male,0,Yes,Yes,38,Yes,Yes,Fiber optic,No,No,Yes,Yes,Yes,No,Two year,Yes,Bank transfer (automatic),94.9,3616.25,No
+2408-PSJVE,Male,0,Yes,Yes,44,Yes,No,DSL,No,Yes,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,61.9,2924.05,No
+9079-YEXQJ,Female,0,No,No,54,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,No,Electronic check,111.1,6014.85,Yes
+9700-ZCLOT,Male,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20,32.7,No
+8738-JOKAR,Female,0,No,No,42,Yes,No,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Mailed check,67.7,2882.25,No
+4534-WGCIR,Female,0,Yes,Yes,58,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),25.15,1509.9,No
+1930-WNXSB,Male,0,Yes,Yes,58,Yes,No,Fiber optic,No,Yes,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,92.85,5305.05,No
+2685-SREOM,Female,0,Yes,Yes,25,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,89.1,2368.4,Yes
+3508-CFVZL,Female,0,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,111.3,7985.9,No
+0402-CQAJN,Female,0,No,No,37,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),101.9,3545.35,Yes
+6692-UDPJC,Female,0,Yes,Yes,14,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,91.65,1301,Yes
+1273-MTETI,Female,1,No,No,4,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,Yes,Electronic check,88.85,372.45,Yes
+0657-DOGUM,Female,0,Yes,No,48,Yes,Yes,DSL,Yes,No,No,Yes,No,No,One year,Yes,Bank transfer (automatic),60.6,2985.25,No
+5480-HPRRX,Female,1,No,No,3,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Electronic check,25.3,77.75,Yes
+8792-AOROI,Female,0,Yes,No,8,Yes,No,DSL,Yes,No,No,Yes,No,Yes,Two year,No,Mailed check,65.5,564.35,No
+0295-PPHDO,Male,0,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,95.45,95.45,Yes
+5146-YYFRZ,Male,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.95,1311.75,No
+1195-OIYEJ,Male,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,91.1,1135.7,Yes
+5906-CVLHP,Female,0,Yes,Yes,45,Yes,No,DSL,No,Yes,No,Yes,No,No,One year,Yes,Credit card (automatic),54.15,2319.8,Yes
+3452-SRFEG,Male,0,No,No,49,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.6,3720.35,No
+4070-OKWVH,Female,0,Yes,No,52,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,No,Bank transfer (automatic),94.6,5025.8,No
+5297-MDOIR,Female,0,Yes,No,63,Yes,No,Fiber optic,Yes,No,Yes,No,No,No,One year,Yes,Credit card (automatic),81.15,5224.5,No
+6369-MCAKO,Female,0,Yes,Yes,68,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),89.05,6185.8,No
+4926-UMJZD,Female,0,Yes,No,31,Yes,No,DSL,No,No,No,Yes,No,No,Month-to-month,Yes,Mailed check,49.2,1498.55,No
+6848-HJTXY,Female,0,Yes,No,64,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.45,1208.6,No
+6341-AEVKX,Female,0,Yes,No,62,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,104.3,6613.65,No
+4501-EQDRN,Female,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),69.7,69.7,Yes
+2990-HWIML,Female,0,No,No,6,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,89.5,573.3,Yes
+0682-USIXD,Female,0,Yes,No,21,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,86.05,1818.9,No
+6976-BWGLQ,Female,0,Yes,Yes,72,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),25.2,1787.35,No
+7078-NVFAM,Female,0,Yes,Yes,32,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,35.15,1051.05,No
+8065-QBYTO,Female,1,No,No,71,Yes,Yes,Fiber optic,Yes,Yes,No,Yes,Yes,No,One year,Yes,Credit card (automatic),99.65,7181.25,No
+2133-TSRRM,Female,0,No,Yes,34,Yes,No,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),105.35,3688.6,No
+7384-GHBPI,Male,0,Yes,No,3,No,No phone service,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,35.15,99.75,Yes
+7603-USHJS,Male,0,No,Yes,12,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),73.75,871.4,Yes
+7234-FECYN,Female,1,No,No,8,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Month-to-month,No,Electronic check,101.35,780.5,Yes
+2511-ALLCS,Female,0,Yes,Yes,35,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),24.3,821.6,No
+3191-CSNMG,Female,0,Yes,Yes,3,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,80.7,239.45,No
+7952-OBOYL,Male,0,No,No,3,Yes,No,Fiber optic,No,No,Yes,Yes,Yes,No,Month-to-month,Yes,Mailed check,89.85,244.45,No
+7470-DYNOE,Male,0,No,No,53,Yes,No,DSL,No,No,Yes,No,No,Yes,One year,Yes,Electronic check,61.1,3357.9,No
+7853-OETYL,Female,0,Yes,No,4,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,29.05,129.6,No
+1545-ACTAS,Female,0,Yes,Yes,48,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),99.7,4977.2,No
+5876-HZVZM,Female,0,Yes,Yes,6,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),55.9,365.35,Yes
+1400-MMYXY,Male,1,Yes,No,3,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,105.9,334.65,Yes
+5126-RCXYW,Male,0,Yes,Yes,54,No,No phone service,DSL,No,No,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),46,2424.05,No
+3085-QUOZK,Female,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,43.95,43.95,Yes
+2363-BJLSL,Male,0,No,No,62,Yes,No,DSL,No,Yes,Yes,Yes,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),80.4,4981.15,No
+1956-YIFGE,Male,0,Yes,Yes,22,Yes,No,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Mailed check,100.05,2090.25,No
+3926-CUQZX,Male,0,No,No,1,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),45.1,45.1,Yes
+7921-LMDFQ,Male,1,No,No,51,Yes,No,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Bank transfer (automatic),94,4905.75,No
+2925-MXLSX,Female,0,No,No,30,Yes,Yes,DSL,No,Yes,Yes,No,Yes,No,One year,Yes,Credit card (automatic),68.95,2038.7,No
+5820-PTRYM,Female,1,Yes,No,56,Yes,Yes,DSL,Yes,Yes,Yes,Yes,No,No,One year,Yes,Credit card (automatic),68.45,4014,No
+9609-BENEA,Male,0,Yes,No,35,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,One year,Yes,Electronic check,69,2441.7,No
+6211-WHMYA,Female,1,No,No,64,No,No phone service,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,43.85,2751,No
+4459-BBGHE,Male,0,No,Yes,30,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.5,1307.8,No
+9945-PSVIP,Female,0,Yes,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Mailed check,18.7,383.65,No
+1041-RXHRA,Female,0,No,No,41,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),70.25,2868.05,Yes
+1750-CSKKM,Male,0,No,Yes,9,Yes,No,DSL,No,No,No,No,Yes,No,Month-to-month,No,Electronic check,55.35,449.75,Yes
+9108-EJFJP,Female,0,Yes,No,1,Yes,No,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,53.55,53.55,No
+0530-IJVDB,Male,0,No,Yes,70,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Electronic check,114.6,7882.5,No
+0508-SQWPL,Female,0,Yes,Yes,57,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),20.1,1087.7,No
+2215-ZAFGX,Male,0,No,No,9,Yes,Yes,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Electronic check,85.5,791.7,No
+8213-TAZPM,Female,0,Yes,Yes,69,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Bank transfer (automatic),108.75,7493.05,No
+7142-HVGBG,Male,1,Yes,No,43,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103,4414.3,Yes
+1304-SEGFY,Female,0,Yes,No,72,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,Two year,Yes,Electronic check,97.85,6841.3,No
+7242-EDTYC,Male,0,No,Yes,44,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),19.55,819.95,No
+2904-GGUAZ,Female,0,Yes,No,72,Yes,Yes,Fiber optic,Yes,Yes,No,No,No,No,Two year,Yes,Bank transfer (automatic),84.05,6052.25,No
+8267-ZNYVZ,Female,0,Yes,No,33,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,103.75,3361.05,Yes
+5136-GFPMB,Male,0,No,No,54,Yes,Yes,Fiber optic,No,No,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),89.4,4869.5,No
+2595-KIWPV,Male,0,No,Yes,27,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Credit card (automatic),19.7,509.3,No
+5243-SAOTC,Male,0,No,No,54,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),79.85,4308.25,No
+9398-MMQTO,Male,0,No,No,3,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,No,Credit card (automatic),74.45,221.1,No
+7619-PLRLP,Female,0,Yes,No,53,Yes,Yes,DSL,Yes,Yes,Yes,No,No,Yes,One year,No,Bank transfer (automatic),74.1,3833.95,No
+6457-GIRWB,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.35,69.35,Yes
+6508-NJYRO,Male,0,Yes,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Mailed check,18.8,294.95,No
+1450-SKCVI,Female,0,No,No,56,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,73.85,4092.85,Yes
+4710-NKCAW,Male,0,Yes,Yes,5,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,No,Credit card (automatic),64.4,316.9,No
+5306-BVTKJ,Male,0,Yes,Yes,48,Yes,No,DSL,No,Yes,Yes,No,No,No,One year,Yes,Credit card (automatic),55.8,2651.2,No
+0357-NVCRI,Female,0,No,Yes,25,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Credit card (automatic),20.05,471.7,No
+5570-PTWEH,Female,0,Yes,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.15,216.75,Yes
+2371-KFUOG,Male,0,No,No,58,Yes,No,Fiber optic,Yes,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),99.15,5720.95,No
+0463-ZSDNT,Male,0,No,No,10,Yes,No,DSL,No,No,No,No,No,Yes,Month-to-month,Yes,Bank transfer (automatic),56.75,503.25,No
+6502-MJQAE,Male,0,No,No,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,69.6,69.6,Yes
+6257-DTAYD,Male,0,Yes,No,71,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),104.15,7365.3,No
+4616-ULAOA,Female,0,Yes,Yes,65,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Credit card (automatic),110.8,7245.9,No
+7693-LCKZL,Male,0,Yes,Yes,5,Yes,Yes,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,80.15,385,Yes
+7746-AWNQW,Female,0,No,No,28,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,Yes,Mailed check,35.75,961.4,No
+5996-EBTKM,Female,0,Yes,Yes,67,Yes,Yes,DSL,No,No,Yes,Yes,No,Yes,Two year,Yes,Bank transfer (automatic),69.9,4615.9,No
+2758-RNWXS,Male,0,No,No,35,Yes,No,Fiber optic,Yes,No,No,Yes,No,Yes,One year,Yes,Electronic check,89.2,3251.3,No
+2314-TNDJQ,Female,0,Yes,Yes,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,No,Credit card (automatic),55.65,3880.05,No
+2405-LBMUW,Female,0,Yes,Yes,61,No,No phone service,DSL,Yes,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),50.7,3088.75,No
+3454-JFUBC,Male,1,No,No,68,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),20,1396,No
+0032-PGELS,Female,0,Yes,Yes,1,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,No,Bank transfer (automatic),30.5,30.5,Yes
+9039-ZVJDC,Male,0,No,No,3,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.1,53.05,No
+6797-LNAQX,Male,0,Yes,Yes,70,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,Two year,No,Bank transfer (automatic),98.3,6859.5,Yes
+9013-AQORL,Female,0,No,Yes,48,No,No phone service,DSL,No,Yes,Yes,No,No,Yes,Month-to-month,No,Credit card (automatic),45.55,2108.35,No
+2898-MRKPI,Male,0,Yes,Yes,68,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,Yes,Credit card (automatic),101.05,6770.5,No
+2750-BJLSB,Female,0,No,No,47,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,103.7,4730.6,No
+3648-GZPHF,Male,0,Yes,Yes,32,No,No phone service,DSL,No,No,Yes,Yes,No,No,One year,Yes,Mailed check,36.25,1151.05,No
+2075-RMJIK,Female,0,Yes,Yes,5,Yes,No,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Mailed check,49.4,232.55,No
+9588-YRFHY,Male,0,No,No,49,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Credit card (automatic),19.9,1022.6,No
+6394-MFYNG,Female,0,No,No,48,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Bank transfer (automatic),107.4,5121.3,Yes
+1564-NTYXF,Female,1,No,No,13,Yes,Yes,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,82,1127.2,Yes
+9364-YKUVW,Male,0,No,No,15,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,19.8,309.4,No
+5392-AKEMH,Female,0,No,No,12,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,45.05,523.1,No
+2451-YMUXS,Male,1,No,No,67,Yes,Yes,DSL,Yes,Yes,No,Yes,No,No,Two year,Yes,Bank transfer (automatic),64.55,4250.1,No
+3914-FDRHP,Male,0,No,No,9,Yes,No,Fiber optic,No,No,No,Yes,Yes,No,Month-to-month,No,Electronic check,86.25,770.5,No
+3078-ZKNTS,Female,0,Yes,Yes,13,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),19.75,246.7,No
+1024-KPRBB,Female,0,No,No,38,Yes,No,Fiber optic,No,No,No,No,Yes,Yes,One year,Yes,Mailed check,89.1,3342,No
+4690-PKDQG,Female,1,Yes,No,42,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Electronic check,95.55,3930.6,No
+8155-IBNHG,Female,0,Yes,No,24,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.4,1747.85,Yes
+0886-QGENL,Female,1,Yes,No,27,Yes,No,Fiber optic,Yes,No,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,101.25,2754.45,Yes
+9547-ITEFG,Male,0,Yes,Yes,9,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),102.6,897.75,No
+1264-FUHCX,Female,0,Yes,No,49,No,No phone service,DSL,No,Yes,No,Yes,Yes,Yes,One year,Yes,Credit card (automatic),56.3,2780.6,No
+7789-CRUVC,Female,1,Yes,No,61,Yes,Yes,Fiber optic,Yes,Yes,Yes,Yes,No,No,Month-to-month,Yes,Credit card (automatic),94.2,5895.45,No
+6598-KELSS,Male,0,No,Yes,50,No,No phone service,DSL,Yes,Yes,No,No,Yes,No,One year,Yes,Bank transfer (automatic),43.05,2208.05,No
+8739-WWKDU,Male,1,No,No,25,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.5,2196.15,Yes
+8685-WHQPW,Female,1,No,No,22,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),74.4,1692.6,Yes
+4745-LSPLO,Male,0,No,No,1,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,No,Mailed check,20.5,20.5,Yes
+8083-YTZES,Male,0,No,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.35,265.35,Yes
+7240-FQLHE,Female,1,Yes,No,18,Yes,Yes,Fiber optic,No,No,No,Yes,Yes,Yes,Month-to-month,No,Bank transfer (automatic),99.75,1836.25,Yes
+6664-FPDAC,Female,1,No,No,56,Yes,Yes,Fiber optic,No,Yes,Yes,Yes,Yes,Yes,One year,Yes,Electronic check,111.95,6418.9,Yes
+9972-VAFJJ,Female,1,Yes,No,53,Yes,No,Fiber optic,No,Yes,Yes,Yes,Yes,No,One year,Yes,Electronic check,94,4871.45,No
+0422-UXFAP,Female,0,Yes,No,51,Yes,Yes,Fiber optic,No,No,Yes,No,Yes,Yes,One year,Yes,Electronic check,98.85,4947.55,No
+1904-WAJAA,Female,0,Yes,Yes,24,Yes,Yes,DSL,Yes,Yes,Yes,No,No,No,Two year,No,Electronic check,64.35,1558.65,No
+5130-YPIRV,Female,0,Yes,No,62,Yes,No,DSL,Yes,Yes,Yes,No,Yes,No,Two year,Yes,Credit card (automatic),72,4284.2,No
+2843-CQMEG,Male,0,No,No,24,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,49.7,1218.25,No
+6439-PKTRR,Female,0,Yes,Yes,70,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,No,Two year,No,Electronic check,80.7,5617.95,No
+5351-QESIO,Male,0,No,Yes,1,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Mailed check,24.2,24.2,No
+0786-VSSUD,Male,1,No,No,16,No,No phone service,DSL,No,Yes,No,No,Yes,No,Month-to-month,Yes,Mailed check,39,679.85,Yes
+5568-DMXZS,Female,0,No,No,8,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Electronic check,65.45,554.45,No
+8468-FZTOE,Female,0,Yes,Yes,72,Yes,No,DSL,Yes,Yes,Yes,Yes,No,Yes,Two year,Yes,Electronic check,74.35,5237.4,No
+6633-SYEUS,Female,0,No,No,23,Yes,No,Fiber optic,No,Yes,Yes,Yes,No,No,Month-to-month,No,Bank transfer (automatic),83.2,2032.3,No
+6447-GORXK,Male,0,No,Yes,31,No,No phone service,DSL,No,No,No,No,No,No,Month-to-month,No,Credit card (automatic),25,789.2,No
+6967-PEJLL,Male,0,Yes,Yes,37,No,No phone service,DSL,Yes,No,Yes,Yes,No,No,One year,Yes,Electronic check,40.2,1525.35,No
+3976-BWUCK,Female,0,Yes,No,30,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),94.1,2804.45,Yes
+5981-ZVXOT,Female,1,No,No,35,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,108.35,3726.15,No
+1684-FLBGS,Female,0,Yes,Yes,23,Yes,Yes,DSL,No,Yes,No,Yes,Yes,No,Month-to-month,Yes,Credit card (automatic),69.5,1652.1,No
+1389-WNUIB,Female,0,Yes,Yes,20,Yes,Yes,DSL,Yes,No,No,No,Yes,Yes,One year,No,Bank transfer (automatic),76,1588.75,No
+0376-OIWME,Male,0,Yes,No,36,Yes,No,Fiber optic,Yes,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,93.6,3366.05,No
+3585-ISXZP,Female,0,No,No,8,Yes,Yes,Fiber optic,No,No,No,No,Yes,Yes,Month-to-month,No,Bank transfer (automatic),95.65,778.1,Yes
+0218-QNVAS,Male,0,Yes,Yes,71,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,Yes,One year,No,Bank transfer (automatic),100.55,7113.75,No
+6583-QGCSI,Female,1,Yes,No,50,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,88.05,4367.35,Yes
+0804-YGEQV,Female,0,Yes,Yes,43,Yes,Yes,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,Yes,Bank transfer (automatic),24.45,993.15,No
+7164-BPTUT,Male,0,No,Yes,57,Yes,Yes,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,No,Mailed check,89.55,5012.35,No
+4174-LPGTI,Female,0,Yes,Yes,41,Yes,No,DSL,No,Yes,No,Yes,No,Yes,One year,Yes,Bank transfer (automatic),66.5,2728.6,Yes
+2523-EWWZL,Female,0,Yes,No,27,Yes,No,Fiber optic,No,Yes,No,No,No,No,Month-to-month,Yes,Electronic check,76.1,2093.4,No
+0928-XUTSN,Female,0,No,No,13,Yes,No,Fiber optic,No,No,No,No,No,Yes,Month-to-month,Yes,Electronic check,80.5,1011.8,No
+2108-XWMPY,Male,0,No,No,3,No,No phone service,DSL,Yes,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,35.45,106.85,Yes
+0052-YNYOT,Female,0,No,No,67,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Electronic check,20.55,1343.4,No
+6304-IJFSQ,Male,0,No,No,3,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,Yes,Mailed check,49.9,130.1,Yes
+9586-JGQKH,Female,0,Yes,No,64,Yes,Yes,Fiber optic,No,Yes,No,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.4,6794.75,No
+4501-VCPFK,Male,0,No,No,26,No,No phone service,DSL,No,No,Yes,Yes,No,No,Month-to-month,No,Electronic check,35.75,1022.5,No
+6075-SLNIL,Male,0,No,No,38,Yes,Yes,Fiber optic,No,Yes,Yes,No,No,Yes,Month-to-month,Yes,Credit card (automatic),95.1,3691.2,No
+9347-AERRL,Male,0,Yes,No,23,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,One year,No,Credit card (automatic),19.3,486.2,No
+0093-XWZFY,Male,0,No,No,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Credit card (automatic),104.5,4036.85,Yes
+2274-XUATA,Male,1,Yes,No,72,No,No phone service,DSL,Yes,Yes,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),63.1,4685.55,No
+1980-KXVPM,Female,1,No,No,3,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),75.05,256.25,Yes
+7703-ZEKEF,Male,0,No,No,23,Yes,Yes,Fiber optic,No,No,Yes,No,No,No,Month-to-month,Yes,Electronic check,81,1917.1,Yes
+0723-DRCLG,Female,1,Yes,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,74.45,74.45,Yes
+5482-NUPNA,Female,0,No,No,4,Yes,No,DSL,Yes,Yes,No,Yes,No,No,Month-to-month,Yes,Mailed check,60.4,272.15,Yes
+6691-CCIHA,Female,0,Yes,No,62,Yes,Yes,DSL,Yes,Yes,No,Yes,Yes,Yes,Two year,Yes,Electronic check,84.95,5150.55,No
+1685-BQULA,Female,0,No,No,40,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),93.4,3756.4,No
+9053-EJUNL,Male,0,No,No,41,Yes,Yes,Fiber optic,No,Yes,No,No,Yes,No,Month-to-month,Yes,Electronic check,89.2,3645.75,No
+0666-UXTJO,Male,1,Yes,No,34,Yes,No,Fiber optic,No,No,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),85.2,2874.45,No
+1471-GIQKQ,Female,0,No,No,1,Yes,No,DSL,No,Yes,No,No,No,No,Month-to-month,No,Electronic check,49.95,49.95,No
+4807-IZYOZ,Female,0,No,No,51,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Bank transfer (automatic),20.65,1020.75,No
+1122-JWTJW,Male,0,Yes,Yes,1,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,70.65,70.65,Yes
+9710-NJERN,Female,0,No,No,39,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,No,Mailed check,20.15,826,No
+9837-FWLCH,Male,0,Yes,Yes,12,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Electronic check,19.2,239,No
+1699-HPSBG,Male,0,No,No,12,Yes,No,DSL,No,No,No,Yes,Yes,No,One year,Yes,Electronic check,59.8,727.8,Yes
+7203-OYKCT,Male,0,No,No,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Electronic check,104.95,7544.3,No
+1035-IPQPU,Female,1,Yes,No,63,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,Month-to-month,Yes,Electronic check,103.5,6479.4,No
+7398-LXGYX,Male,0,Yes,No,44,Yes,Yes,Fiber optic,Yes,No,Yes,No,No,No,Month-to-month,Yes,Credit card (automatic),84.8,3626.35,No
+2823-LKABH,Female,0,No,No,18,Yes,Yes,Fiber optic,No,No,Yes,Yes,No,Yes,Month-to-month,Yes,Bank transfer (automatic),95.05,1679.4,No
+8775-CEBBJ,Female,0,No,No,9,Yes,No,DSL,No,No,No,No,No,No,Month-to-month,Yes,Bank transfer (automatic),44.2,403.35,Yes
+0550-DCXLH,Male,0,No,No,13,Yes,No,DSL,No,Yes,No,Yes,Yes,Yes,Month-to-month,No,Mailed check,73.35,931.55,No
+9281-CEDRU,Female,0,Yes,No,68,Yes,No,DSL,No,Yes,No,Yes,Yes,No,Two year,No,Bank transfer (automatic),64.1,4326.25,No
+2235-DWLJU,Female,1,No,No,6,No,No phone service,DSL,No,No,No,No,Yes,Yes,Month-to-month,Yes,Electronic check,44.4,263.05,No
+0871-OPBXW,Female,0,No,No,2,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Month-to-month,Yes,Mailed check,20.05,39.25,No
+3605-JISKB,Male,1,Yes,No,55,Yes,Yes,DSL,Yes,Yes,No,No,No,No,One year,No,Credit card (automatic),60,3316.1,No
+6894-LFHLY,Male,1,No,No,1,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Electronic check,75.75,75.75,Yes
+9767-FFLEM,Male,0,No,No,38,Yes,No,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Credit card (automatic),69.5,2625.25,No
+0639-TSIQW,Female,0,No,No,67,Yes,Yes,Fiber optic,Yes,Yes,Yes,No,Yes,No,Month-to-month,Yes,Credit card (automatic),102.95,6886.25,Yes
+8456-QDAVC,Male,0,No,No,19,Yes,No,Fiber optic,No,No,No,No,Yes,No,Month-to-month,Yes,Bank transfer (automatic),78.7,1495.1,No
+7750-EYXWZ,Female,0,No,No,12,No,No phone service,DSL,No,Yes,Yes,Yes,Yes,Yes,One year,No,Electronic check,60.65,743.3,No
+2569-WGERO,Female,0,No,No,72,Yes,No,No,No internet service,No internet service,No internet service,No internet service,No internet service,No internet service,Two year,Yes,Bank transfer (automatic),21.15,1419.4,No
+6840-RESVB,Male,0,Yes,Yes,24,Yes,Yes,DSL,Yes,No,Yes,Yes,Yes,Yes,One year,Yes,Mailed check,84.8,1990.5,No
+2234-XADUH,Female,0,Yes,Yes,72,Yes,Yes,Fiber optic,No,Yes,Yes,No,Yes,Yes,One year,Yes,Credit card (automatic),103.2,7362.9,No
+4801-JZAZL,Female,0,Yes,Yes,11,No,No phone service,DSL,Yes,No,No,No,No,No,Month-to-month,Yes,Electronic check,29.6,346.45,No
+8361-LTMKD,Male,1,Yes,No,4,Yes,Yes,Fiber optic,No,No,No,No,No,No,Month-to-month,Yes,Mailed check,74.4,306.6,Yes
+3186-AJIEK,Male,0,No,No,66,Yes,No,Fiber optic,Yes,No,Yes,Yes,Yes,Yes,Two year,Yes,Bank transfer (automatic),105.65,6844.5,No
diff --git a/a0.1/Assignments/a1.11/a1.11.py b/a0.1/Assignments/a1.11/a1.11.py
new file mode 100644
index 0000000..87b5f13
--- /dev/null
+++ b/a0.1/Assignments/a1.11/a1.11.py
@@ -0,0 +1,41 @@
+# Install dependencies as needed:
+# pip install kagglehub[pandas-datasets]
+import pandas as pd
+import kagglehub
+from kagglehub import KaggleDatasetAdapter
+
+# Set the path to the file you'd like to load
+file_path = "WA_Fn-UseC_-Telco-Customer-Churn.csv"
+
+# Load the latest version
+df = kagglehub.load_dataset(
+  KaggleDatasetAdapter.PANDAS,
+  "blastchar/telco-customer-churn",
+  file_path,
+  # Provide any additional arguments like 
+  # sql_query or pandas_kwargs. See the 
+  # documenation for more information:
+  # https://github.com/Kaggle/kagglehub/blob/main/README.md#kaggledatasetadapterpandas
+)
+
+pd.set_option("display.max_columns", None)
+# print("First 5 records:", df.head())
+# print(df.shape)
+# print(df.columns)
+# print(df.tail)
+# print(df.sample(10))
+# print(df.info())
+# print(df.describe())
+# print(df.describe(include="object"))
+for col in df.columns:
+    print(f"{col}: {df[col].uniqe()}")
+print(df.nunique())
+
+# print(df.isnull())
+# print(df.isnull().sum())
+# print(df.isnull().sum().sum())
+# df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')
+# print(df.info())
+# print(df.isnull().sum().sum())
+# df['TotalCharges'].fillna(['TotalCarges'].median, inplace = True)
+# df['TotalCharges'] = df['TotalCharges'].fillna(['TotalCarges'].median())
diff --git a/a0.1/Copy_of_Assignment_01_Python__Nexus__RezaShokrzad.ipynb b/a0.1/Copy_of_Assignment_01_Python__Nexus__RezaShokrzad.ipynb
index b63ec79..67a4e1c 100644
--- a/a0.1/Copy_of_Assignment_01_Python__Nexus__RezaShokrzad.ipynb
+++ b/a0.1/Copy_of_Assignment_01_Python__Nexus__RezaShokrzad.ipynb
@@ -22,7 +22,11 @@
         "colab_type": "text"
       },
       "source": [
+<<<<<<< HEAD
+        "<a href=\"https://colab.research.google.com/github/gitSajjad/Nexus_Assignments/blob/main/Copy_of_Assignment_01_Python_%7C_Nexus_%7C_RezaShokrzad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+=======
         "<a href=\"https://colab.research.google.com/github/gitSajjad/Nexus_Assignments/blob/main/a0.1/Copy_of_Assignment_01_Python_%7C_Nexus_%7C_RezaShokrzad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+>>>>>>> 763e05f (first commit)
       ]
     },
     {
@@ -90,6 +94,29 @@
     },
     {
       "cell_type": "code",
+<<<<<<< HEAD
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 377
+        },
+        "outputId": "ec95dd5a-ac91-4cab-e431-6151fdcd145a"
+      },
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "Interrupted by user",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-635499762.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# TODO:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mcelsius_text\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter temperature in Celsius: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mcelsius\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcelsius_text\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mfahrenheit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcelsius\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m9\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m5\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m32\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m   1175\u001b[0m                 \u001b[0;34m\"raw_input was called, but this frontend does not support input requests.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1176\u001b[0m             )\n\u001b[0;32m-> 1177\u001b[0;31m         return self._input_request(\n\u001b[0m\u001b[1;32m   1178\u001b[0m             \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"shell\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m   1217\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1218\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1219\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1220\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1221\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid Message:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
+=======
       "execution_count": 2,
       "metadata": {
         "id": "lmaiboJe8E15",
@@ -105,6 +132,7 @@
           "text": [
             "Enter temperature in Celsius: 20\n",
             "20.00°C is 68.00°F\n"
+>>>>>>> 763e05f (first commit)
           ]
         }
       ],
@@ -112,7 +140,10 @@
         "# 👉 a Celsius temperature (as text), convert it to float,\n",
         "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
         "# TODO:\n",
+<<<<<<< HEAD
+=======
         "\n",
+>>>>>>> 763e05f (first commit)
         "celsius_text = input(\"Enter temperature in Celsius: \")\n",
         "celsius = float(celsius_text)\n",
         "fahrenheit = celsius * 9 / 5 + 32\n",
diff --git a/a0.1/Descriptive Statistics_SoroorParsafar.py b/a0.1/Descriptive Statistics_SoroorParsafar.py
new file mode 100644
index 0000000..bb58e6c
--- /dev/null
+++ b/a0.1/Descriptive Statistics_SoroorParsafar.py	
@@ -0,0 +1,91 @@
+# -*- coding: utf-8 -*-
+"""Copy of Assignment 06. Descriptive Stats | Nexus | RezaShokrzad.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Tu1UYX3Ky4UWDUSYgneKM8om-y2oUrdy
+
+# 📚 Assignment 06 — Descriptive Statistics
+
+
+<p align="center">📢⚠️📂  </p>
+
+<p align="center"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>
+
+<p align="center"> 🚨📝🧠 </p>
+
+------------------------------------------------
+
+
+
+## 📘 Exploring Descriptive Statistics with NumPy & SciPy
+This assignment guides you through generating random data, analyzing its statistical properties, and visualizing it using plots.
+"""
+
+# 📦 Import Required Libraries
+import numpy as np
+from scipy import stats
+import matplotlib.pyplot as plt
+
+# 🎲 Step 1: Generate Random Integer List
+np.random.seed(42)  # For reproducibility
+data = np.random.randint(20, 40, size=30)
+print("Generated Data:", data)
+
+"""### 📊 Step 2: Central Tendency and Quantiles
+Let's compute basic statistics — mean, median, mode, and quantiles.
+"""
+
+# 📐 Central Tendency
+# mean
+mean_val = np.mean(data)
+# median
+median_val = np.median(data)
+# mode
+mode_val = stats.mode(data, keepdims=True).mode[0]
+
+# 📏 Quantiles
+q1 = np.quantile(data, 0.25)
+q3 = np.quantile(data, 0.75)
+
+print(f"Mean: {mean_val}")
+print(f"Median: {median_val}")
+print(f"Mode: {mode_val}")
+print(f"Q1 (25%): {q1}")
+print(f"Q3 (75%): {q3}")
+
+"""### 🧪 Step 3: Skewness and Kurtosis
+Skewness shows asymmetry, and kurtosis indicates the "tailedness" of the distribution.
+"""
+
+# 📉 Skewness and Kurtosis
+skew_val = stats.skew(data)
+kurtosis_val = stats.kurtosis(data)
+
+print(f"Skewness: {skew_val:.2f}")
+print(f"Kurtosis: {kurtosis_val:.2f}")
+
+"""### 📈 Step 4: Visualization - Bar Chart and Boxplot
+Visualize the data to better understand its distribution and spread.
+"""
+
+# 📊 Bar Plot
+plt.figure(figsize=(12, 4))
+
+plt.subplot(1, 2, 1)
+# make a bar chart
+plt.bar(range(len(data)), data)
+plt.title("Bar Plot of Random Data")
+plt.xlabel("Index")
+plt.ylabel("Value")
+
+# 📦 Boxplot
+plt.subplot(1, 2, 2)
+# make a box plot
+plt.boxplot(data, vert=False)
+plt.title("Boxplot of Random Data")
+plt.xlabel("Value")
+
+plt.tight_layout()
+plt.show()
\ No newline at end of file
diff --git a/a0.1/Exercises/01-readcsvfile.py b/a0.1/Exercises/01-readcsvfile.py
new file mode 100644
index 0000000..a26641b
--- /dev/null
+++ b/a0.1/Exercises/01-readcsvfile.py
@@ -0,0 +1,22 @@
+import pandas as pd
+df = pd.read_csv('E:/Nexus/Nexus_Assignments/Exercises/access.csv')
+print(df.head(4))
+print(df.tail(2))
+print(df.shape)
+print(df.columns)
+print(df.columns.tolist())
+print(df.dtypes)
+print(df.info())
+df.to_feather('access.feather')
+df2 = pd.read_feather('access.feather')
+print(df2.head())
+df3 = pd.read_parquet('titanic.parquet')
+print(df3.head())
+df4 = pd.read_json('fruits.json')
+print(df4.head())
+df4.to_excel('fruits.xlsx')
+df5 = pd.read_excel('fruits.xlsx')
+print(df5.head())
+# import nltk
+# nltk.download('punkt')
+# nltk.download('punkt_tab')
\ No newline at end of file
diff --git a/a0.1/Exercises/02-hugginface.py b/a0.1/Exercises/02-hugginface.py
new file mode 100644
index 0000000..943c5ed
--- /dev/null
+++ b/a0.1/Exercises/02-hugginface.py
@@ -0,0 +1,28 @@
+# If needed:
+# !pip install datasets
+
+import matplotlib.pyplot as plt
+from datasets import load_dataset, load_dataset_builder
+from collections import Counter
+
+ds = load_dataset("imdb")
+
+# Basic structure
+print(ds)
+print("Train rows:", ds["train"].num_rows, "Test rows:", ds["test"].num_rows)
+print("Features:", load_dataset_builder("imdb").info.features)
+
+# Label balance (train)
+label_counts = Counter(ds["train"]["label"])
+print("Label counts:", label_counts)
+
+# Bar chart
+plt.bar(["Negative","Positive"], [label_counts[0], label_counts[1]])
+plt.title("IMDB Sentiment Distribution (Train)")
+plt.ylabel("Count")
+plt.tight_layout()
+plt.show()
+
+# Sample row
+ds["train"][0]
+
diff --git a/a0.1/Exercises/02-kaggle.py b/a0.1/Exercises/02-kaggle.py
new file mode 100644
index 0000000..68c4274
--- /dev/null
+++ b/a0.1/Exercises/02-kaggle.py
@@ -0,0 +1,20 @@
+# If needed:
+# !pip install "kagglehub[pandas-datasets]"
+
+import kagglehub
+from kagglehub import KaggleDatasetAdapter
+
+# kagglehub.login()  # uncomment if required on your environment
+
+handle = "kunshbhatia/delhi-air-quality-dataset"
+file_in_dataset = "delhi_air_quality.csv"  # adjust if the filename differs
+
+df_kaggle = kagglehub.dataset_load(
+    KaggleDatasetAdapter.PANDAS,
+    handle,
+    file_in_dataset,
+)
+
+print("Kaggle (Delhi Air) shape:", df_kaggle.shape)
+print(df_kaggle.head())
+print(df_kaggle.isna().sum().sort_values(ascending=False).head(10))
diff --git a/a0.1/Exercises/02-openml.py b/a0.1/Exercises/02-openml.py
new file mode 100644
index 0000000..9c106ae
--- /dev/null
+++ b/a0.1/Exercises/02-openml.py
@@ -0,0 +1,20 @@
+# If needed:
+# !pip install openml
+
+import openml
+
+d_irish = openml.datasets.get_dataset(451)  # "irish"
+target_col = d_irish.default_target_attribute
+X_irish, y_irish, cat_ind, names = d_irish.get_data(dataset_format="dataframe", target=target_col)
+
+df_irish = X_irish.copy()
+df_irish[target_col] = y_irish
+
+print("OpenML 'irish' shape:", df_irish.shape)
+print(df_irish.head())
+print("Target:", target_col)
+print("Categorical flags per feature:", dict(zip(names, cat_ind)))
+print(df_irish.isna().sum().sort_values(ascending=False))
+
+# Simple target distribution
+df_irish[target_col].value_counts(dropna=False)
diff --git a/a0.1/Exercises/02-sklearn.py b/a0.1/Exercises/02-sklearn.py
new file mode 100644
index 0000000..ae7f792
--- /dev/null
+++ b/a0.1/Exercises/02-sklearn.py
@@ -0,0 +1,64 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+# from sklearn.datasets import fetch_california_housing
+# calDB = fetch_california_housing()
+
+# from sklearn.datasets import load_iris
+# irisDB = load_iris()
+# print(irisDB.items())
+# print(irisDB.keys())
+# print(irisDB.values())
+# print(irisDB['data'][:5])
+# print(irisDB['feature_names'])
+# print(irisDB.target)
+# print(irisDB.data.shape)
+# print(irisDB.target_names)
+# print(irisDB.target_names.tolist())
+# print(irisDB.DESCR)
+# print(irisDB.filename)
+# X = irisDB.data
+# y = irisDB.target
+# print(X)
+# print(y)
+# df = pd.DataFrame(X, columns=irisDB.feature_names)
+# df['target'] = irisDB.target
+# print(df.shape)
+# print(df.corr())
+
+# from sklearn.datasets import make_moons
+from sklearn.datasets import load_iris, fetch_california_housing, make_moons
+# --- Iris (toy, tabular) ---
+iris = load_iris()
+X_iris = iris.data
+y_iris = iris.target
+df_iris = pd.DataFrame(X_iris, columns=iris.feature_names)
+df_iris["target"] = y_iris
+
+print("Iris shape:", df_iris.shape)
+print(df_iris.head())
+print(df_iris.isna().sum().to_dict())
+
+# --- California Housing (realistic, tabular) ---
+cal = fetch_california_housing()
+df_cal = pd.DataFrame(cal.data, columns=cal.feature_names)
+df_cal["MedHouseVal"] = cal.target
+
+print("\nCalifornia shape:", df_cal.shape)
+print(df_cal.head())
+print(df_cal.isna().sum().to_dict())
+
+# --- Synthetic Moons (controlled patterns) ---
+X_moon, y_moon = make_moons(n_samples=400, noise=0.15)
+df_moon = pd.DataFrame(X_moon, columns=["x1","x2"])
+df_moon["label"] = y_moon
+print("\nMoons shape:", df_moon.shape)
+print(df_moon.head())
+
+# Quick visuals (optional)
+plt.figure(figsize=(4,3))
+plt.scatter(df_moon["x1"], df_moon["x2"], c=df_moon["label"])
+plt.title("make_moons() scatter")
+plt.tight_layout()
+plt.show()
diff --git a/a0.1/Exercises/02-uci.py b/a0.1/Exercises/02-uci.py
new file mode 100644
index 0000000..a0afa8e
--- /dev/null
+++ b/a0.1/Exercises/02-uci.py
@@ -0,0 +1,17 @@
+# If needed:
+# !pip install ucimlrepo
+
+from ucimlrepo import fetch_ucirepo
+
+heart = fetch_ucirepo(id=45)  # Heart Disease
+X_uci = heart.data.features
+y_uci = heart.data.targets
+
+df_uci = X_uci.copy()
+for c in y_uci.columns:
+    df_uci[c] = y_uci[c]
+
+print("UCI Heart shape:", df_uci.shape)
+print(df_uci.head())
+print("Targets:", list(y_uci.columns))
+print(df_uci.isna().sum().sort_values(ascending=False).head(10))
diff --git a/a0.1/Exercises/access.csv b/a0.1/Exercises/access.csv
new file mode 100644
index 0000000..3016328
--- /dev/null
+++ b/a0.1/Exercises/access.csv
@@ -0,0 +1,6 @@
+Identifier,First name,Last name
+901242,Rachel,Booker
+207074,Laura,Grey
+408129,Craig,Johnson
+934600,Mary,Jenkins
+507916,Jamie,Smith
\ No newline at end of file
diff --git a/a0.1/Exercises/access.feather b/a0.1/Exercises/access.feather
new file mode 100644
index 0000000..db4506d
Binary files /dev/null and b/a0.1/Exercises/access.feather differ
diff --git a/a0.1/Exercises/fruits.json b/a0.1/Exercises/fruits.json
new file mode 100644
index 0000000..6550253
--- /dev/null
+++ b/a0.1/Exercises/fruits.json
@@ -0,0 +1,22 @@
+[
+  {
+    "fruit": "Apple",
+    "size": "Large",
+    "color": "Red"
+  },
+  {
+    "fruit": "Banana",
+    "size": "Medium",
+    "color": "Yellow"
+  },
+  {
+    "fruit": "Grapes",
+    "size": "Small",
+    "color": "Green"
+  },
+  {
+    "fruit": "Orange",
+    "size": "Medium",
+    "color": "Orange"
+  }
+]
diff --git a/a0.1/Exercises/fruits.xlsx b/a0.1/Exercises/fruits.xlsx
new file mode 100644
index 0000000..45cd570
Binary files /dev/null and b/a0.1/Exercises/fruits.xlsx differ
diff --git a/a0.1/Exercises/titanic.parquet b/a0.1/Exercises/titanic.parquet
new file mode 100644
index 0000000..a6bd1ab
Binary files /dev/null and b/a0.1/Exercises/titanic.parquet differ
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_10_Discriptive_Statistics.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_10_Discriptive_Statistics.ipynb"
new file mode 100644
index 0000000..39947ca
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_10_Discriptive_Statistics.ipynb"
@@ -0,0 +1,452 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8YulQRADcq-e"
+      },
+      "source": [
+        "#Discriptive Statistics"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BANXK4VMcqLd"
+      },
+      "source": [
+        "import numpy as np"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Bd_nvQnVcpcd",
+        "outputId": "a22e1b7e-03c3-4387-d9b5-efe54f238956"
+      },
+      "source": [
+        "#list of integers\n",
+        "import random; ex = random.choices(range(10, 36), k=20)\n",
+        "type(ex)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "list"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Nlow2Qxqe642",
+        "outputId": "667717b5-8924-45f3-f46c-316786d464ec"
+      },
+      "source": [
+        "np.min(ex), np.max(ex)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(10, 35)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5_ZJZI33fHDI",
+        "outputId": "796115e8-e419-4168-f028-17abba7b7e34"
+      },
+      "source": [
+        "#طول لیست = تعداد عناصر آرایه\n",
+        "len(ex)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "20"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pOS8K0HStMeo",
+        "outputId": "529dfabe-058c-4688-fa79-e31593f31be8"
+      },
+      "source": [
+        "#\n",
+        "sum(ex)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "428"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tt7dBshKtY_m",
+        "outputId": "b53b9ecd-217e-4797-acb6-6334084c7f76"
+      },
+      "source": [
+        "#میانگین دستی داده‌ها\n",
+        "ex_ = sum(ex) / len(ex)\n",
+        "ex_"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "21.4"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FFaXp8nGfKpt",
+        "outputId": "e3122f7c-8eaf-442b-9b49-e0044c06592b"
+      },
+      "source": [
+        "#میانگین داده‌ها\n",
+        "np.mean(ex)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "21.4"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8lM6_sZvfckl",
+        "outputId": "81051515-81bf-4cf7-e5c2-9dbce84fe107"
+      },
+      "source": [
+        "#مرتب‌سازی لیست\n",
+        "np.sort(ex)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([10, 11, 12, 13, 14, 15, 17, 18, 19, 23, 23, 23, 23, 23, 24, 30, 31,\n",
+              "       32, 32, 35])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AQjwNGuGfS8x",
+        "outputId": "7b5ae1df-1fd8-42be-ffe3-523186a74992"
+      },
+      "source": [
+        "#میانه\n",
+        "np.median(ex)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "23.0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BL18XIxuff5y",
+        "outputId": "5f542e66-5d8c-4392-ced2-bd234569ef37"
+      },
+      "source": [
+        "#مد\n",
+        "from scipy import stats\n",
+        "stats.mode(ex)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "ModeResult(mode=array([23]), count=array([5]))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "t5lHn9Yif3HS",
+        "outputId": "42d84387-2dbf-4f96-ebde-1d1d3a13804a"
+      },
+      "source": [
+        "#انحراف معیار\n",
+        "np.std(ex)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "7.4993333037010705"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lj6DwG6Hg30s",
+        "outputId": "3e9a57ae-75eb-41ed-bbcb-57737f5862fa"
+      },
+      "source": [
+        "#واریانس\n",
+        "np.var(ex)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "56.24000000000001"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OWzqfdsXg7lN",
+        "outputId": "ce1a4183-b97f-44c1-a0cc-cc5c9f27f799"
+      },
+      "source": [
+        "#صدک\n",
+        "np.percentile(ex, 75)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "25.5"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cc5BK1pUhIEy",
+        "outputId": "27e58f13-4452-4258-e6ad-ce8bbfe9696a"
+      },
+      "source": [
+        "#چارک\n",
+        "np.quantile(ex, q=0.5)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "23.0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_lwJ7aRPhTAj",
+        "outputId": "e5dd4e35-ce26-4355-a3f7-546143877aaa"
+      },
+      "source": [
+        "#لیست حاوی مقدار نامعلوم\n",
+        "#missing values!\n",
+        "ex_nan = [23, 12, 24, 32, 30, None, 10, 15, 18, 23, 23, 14, 13, 31, 23, 19, 23, 32, 17, 35]\n",
+        "type(ex_nan)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "list"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jLzVlqPTibCu",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 346
+        },
+        "outputId": "d819199e-8844-47ca-9835-336058ae2e6b"
+      },
+      "source": [
+        "np.mean(ex_nan)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "TypeError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-29-1a4a7cfcf40e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mex_nan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mmean\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36mmean\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m   3371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3372\u001b[0m     return _methods._mean(a, axis=axis, dtype=dtype,\n\u001b[0;32m-> 3373\u001b[0;31m                           out=out, **kwargs)\n\u001b[0m\u001b[1;32m   3374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_mean\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m    158\u001b[0m             \u001b[0mis_float16_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m     \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m         ret = um.true_divide(\n",
+            "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_15_Types_of_ML.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_15_Types_of_ML.ipynb"
new file mode 100644
index 0000000..a4f46d6
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_15_Types_of_ML.ipynb"
@@ -0,0 +1,215 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lIYdn1woOS1n"
+      },
+      "outputs": [],
+      "source": [
+        "# import supervised models\n",
+        "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
+        "from sklearn.svm import SVC, SVR\n",
+        "from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\n",
+        "from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor\n",
+        "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
+        "from sklearn.naive_bayes import GaussianNB\n",
+        "from sklearn.neural_network import MLPClassifier, MLPRegressor\n",
+        "\n",
+        "# import unsupervised models\n",
+        "from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering\n",
+        "from sklearn.decomposition import PCA\n",
+        "from sklearn.manifold import TSNE\n",
+        "from sklearn.ensemble import IsolationForest\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# training and inference phase\n",
+        "model = LinearRegression()\n",
+        "model.fit(X_train, y_train)\n",
+        "y_pred = model.predict(X_test)\n",
+        "\n",
+        "\n",
+        "\n",
+        "unsupervised_model = KMeans(n_clusters=3)\n",
+        "unsupervised_model.fit(X_train)\n",
+        "y_pred = unsupervised_model.predict(X_test)"
+      ],
+      "metadata": {
+        "id": "A2Wu5zE79HQv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "models = {\n",
+        "    \"Supervised\": [\n",
+        "        LinearRegression(),\n",
+        "        LogisticRegression(),\n",
+        "        SVC(),\n",
+        "        SVR(),\n",
+        "        KNeighborsClassifier(),\n",
+        "        KNeighborsRegressor(),\n",
+        "        DecisionTreeClassifier(),\n",
+        "        DecisionTreeRegressor(),\n",
+        "        RandomForestClassifier(),\n",
+        "        RandomForestRegressor(),\n",
+        "        GaussianNB(),\n",
+        "        ],\n",
+        "    \"Unsupervised\": [\n",
+        "        KMeans(),\n",
+        "        DBSCAN(),\n",
+        "        AgglomerativeClustering(),\n",
+        "        PCA(),\n",
+        "        TSNE(),\n",
+        "        IsolationForest(),\n",
+        "    ]\n",
+        "\n",
+        "}"
+      ],
+      "metadata": {
+        "id": "bFb5yuVC-751"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "models.keys()"
+      ],
+      "metadata": {
+        "id": "dymlXN8L_mri",
+        "outputId": "fbb2485d-486c-47ca-e9d9-8c073f7c886f",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "dict_keys(['Supervised', 'Unsupervised'])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "models.values()"
+      ],
+      "metadata": {
+        "id": "tIOQHstl_pnC",
+        "outputId": "2f112528-6bdc-4feb-ee01-2ce9b9ea3b2b",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "dict_values([[LinearRegression(), LogisticRegression(), SVC(), SVR(), KNeighborsClassifier(), KNeighborsRegressor(), DecisionTreeClassifier(), DecisionTreeRegressor(), RandomForestClassifier(), RandomForestRegressor(), GaussianNB()], [KMeans(), DBSCAN(), AgglomerativeClustering(), PCA(), TSNE(), IsolationForest()]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "models.items()"
+      ],
+      "metadata": {
+        "id": "N1a5oJc9_v-4",
+        "outputId": "116e687e-cc17-4a04-9dad-5f79ef720fb0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "dict_items([('Supervised', [LinearRegression(), LogisticRegression(), SVC(), SVR(), KNeighborsClassifier(), KNeighborsRegressor(), DecisionTreeClassifier(), DecisionTreeRegressor(), RandomForestClassifier(), RandomForestRegressor(), GaussianNB()]), ('Unsupervised', [KMeans(), DBSCAN(), AgglomerativeClustering(), PCA(), TSNE(), IsolationForest()])])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for category, model_list in models.items():\n",
+        "    print(f\"{category} Models:\")\n",
+        "    for model in model_list:\n",
+        "        print(f\"- {model.__class__.__name__}\")"
+      ],
+      "metadata": {
+        "id": "txeNEwiC_zcA",
+        "outputId": "dab81b12-f38e-49ce-f3f1-b72ade6ed895",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Supervised Models:\n",
+            "- LinearRegression\n",
+            "- LogisticRegression\n",
+            "- SVC\n",
+            "- SVR\n",
+            "- KNeighborsClassifier\n",
+            "- KNeighborsRegressor\n",
+            "- DecisionTreeClassifier\n",
+            "- DecisionTreeRegressor\n",
+            "- RandomForestClassifier\n",
+            "- RandomForestRegressor\n",
+            "- GaussianNB\n",
+            "Unsupervised Models:\n",
+            "- KMeans\n",
+            "- DBSCAN\n",
+            "- AgglomerativeClustering\n",
+            "- PCA\n",
+            "- TSNE\n",
+            "- IsolationForest\n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_16_PyTorch_Introduction.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_16_PyTorch_Introduction.ipynb"
new file mode 100644
index 0000000..1b9cb0d
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_16_PyTorch_Introduction.ipynb"
@@ -0,0 +1,3292 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SBCeIdK_4-_x"
+      },
+      "source": [
+        "#Introduction to PyTorch"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lgcv7U5Q4-_6"
+      },
+      "source": [
+        "There are many great tutorials online, including the [\"60-min blitz\"](https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html) on the official [PyTorch website](https://pytorch.org/tutorials/)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sq9YyfNJ4-_9"
+      },
+      "outputs": [],
+      "source": [
+        "## Standard libraries\n",
+        "import os\n",
+        "import math\n",
+        "import numpy as np\n",
+        "import time\n",
+        "\n",
+        "## Imports for plotting\n",
+        "import matplotlib.pyplot as plt\n",
+        "#%matplotlib inline #jupyter notebook\n",
+        "from IPython.display import set_matplotlib_formats\n",
+        "set_matplotlib_formats('svg', 'pdf') # For export\n",
+        "from matplotlib.colors import to_rgba\n",
+        "import seaborn as sns\n",
+        "sns.set()\n",
+        "\n",
+        "## Progress bar\n",
+        "from tqdm.notebook import tqdm"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aMIjdx9p4-__"
+      },
+      "source": [
+        "## PyTorch Basics\n",
+        "\n",
+        "As a prerequisite, we recommend to be familiar with the `numpy` package as most machine learning frameworks are based on very similar concepts.\n",
+        "Let's start with **importing** PyTorch. The package is called `torch`, based on its original framework [Torch](http://torch.ch/). As a first step, we can check its **version**:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9BGoNVhl4_AA",
+        "outputId": "98a42fb1-3369-4dd2-a802-ffac173ef459",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Using torch 1.11.0+cu113\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "print(\"Using torch\", torch.__version__)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VRpk9FvY4_AB"
+      },
+      "source": [
+        "As in every machine learning framework, PyTorch provides functions that are stochastic like generating random numbers. However, a very good practice is to setup your code to be reproducible with the exact same random numbers. This is why we set a seed below."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-_j8lErf4_AD",
+        "outputId": "7566a328-8de2-451a-c452-3ad99f4df849",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<torch._C.Generator at 0x7f873f004710>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ],
+      "source": [
+        "torch.manual_seed(42) # Setting the seed"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3XJKRXLd4_AE"
+      },
+      "source": [
+        "## Tensors\n",
+        "\n",
+        "Tensors are the PyTorch equivalent to Numpy arrays, with the addition to also have support for GPU acceleration (more on that later).\n",
+        "The name \"tensor\" is a generalization of concepts you already know. For instance, a vector is a 1-D tensor, and a matrix a 2-D tensor. When working with neural networks, we will use tensors of various shapes and number of dimensions.\n",
+        "\n",
+        "Most common functions you know from numpy can be used on tensors as well. Actually, since numpy arrays are so similar to tensors, we can convert most tensors to numpy arrays (and back) but we don't need it too often.\n",
+        "\n",
+        "### Initialization\n",
+        "\n",
+        "Let's first start by looking at different ways of creating a tensor. There are many possible options, the most simple one is to call `torch.Tensor` passing the desired shape as input argument:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QJpjh3A24_AF",
+        "outputId": "fe255a3b-2810-4e68-ff7c-c06a735a3ff7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([[[3.8032e-35, 0.0000e+00, 7.0065e-44, 7.0065e-44],\n",
+            "         [6.3058e-44, 6.7262e-44, 7.4269e-44, 6.3058e-44],\n",
+            "         [7.0065e-44, 7.2868e-44, 1.1771e-43, 7.0065e-44]],\n",
+            "\n",
+            "        [[6.7262e-44, 8.1275e-44, 7.1466e-44, 6.8664e-44],\n",
+            "         [8.1275e-44, 7.1466e-44, 7.0065e-44, 6.4460e-44],\n",
+            "         [6.7262e-44, 7.9874e-44, 7.2868e-44, 7.0065e-44]]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "x = torch.Tensor(2, 3, 4)\n",
+        "print(x)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eil6nlPY4_AG"
+      },
+      "source": [
+        "The function `torch.Tensor` allocates memory for the desired tensor, but reuses any values that have already been in the memory. To directly assign values to the tensor during initialization, there are many alternatives including:\n",
+        "\n",
+        "* `torch.zeros`: Creates a tensor filled with zeros\n",
+        "* `torch.ones`: Creates a tensor filled with ones\n",
+        "* `torch.rand`: Creates a tensor with random values uniformly sampled between 0 and 1\n",
+        "* `torch.randn`: Creates a tensor with random values sampled from a normal distribution with mean 0 and variance 1\n",
+        "* `torch.arange`: Creates a tensor containing the values $N,N+1,N+2,...,M$\n",
+        "* `torch.Tensor` (input list): Creates a tensor from the list elements you provide"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Pm_q-lIT4_AH",
+        "outputId": "6e364821-e97d-439f-e7db-e02c6fca42a4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([[1., 2.],\n",
+            "        [3., 4.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Create a tensor from a (nested) list\n",
+        "x = torch.Tensor([[1, 2], [3, 4]])\n",
+        "print(x)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "INfjoqC54_AH",
+        "outputId": "d037b9b4-0cce-4c36-97a6-59dc08a6fa5e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([[[0.8823, 0.9150, 0.3829, 0.9593],\n",
+            "         [0.3904, 0.6009, 0.2566, 0.7936],\n",
+            "         [0.9408, 0.1332, 0.9346, 0.5936]],\n",
+            "\n",
+            "        [[0.8694, 0.5677, 0.7411, 0.4294],\n",
+            "         [0.8854, 0.5739, 0.2666, 0.6274],\n",
+            "         [0.2696, 0.4414, 0.2969, 0.8317]]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Create a tensor with random values between 0 and 1 with the shape [2, 3, 4]\n",
+        "x = torch.rand(2, 3, 4)\n",
+        "print(x)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3svWUXeU4_AI"
+      },
+      "source": [
+        "You can obtain the shape of a tensor in the same way as in numpy (`x.shape`), or using the `.size` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Jadryf-_4_AI",
+        "outputId": "d041203a-d897-462d-e1a4-09086d17e845",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape: torch.Size([2, 3, 4])\n",
+            "Size: torch.Size([2, 3, 4])\n",
+            "Size: 2 3 4\n"
+          ]
+        }
+      ],
+      "source": [
+        "shape = x.shape\n",
+        "print(\"Shape:\", x.shape)\n",
+        "\n",
+        "size = x.size()\n",
+        "print(\"Size:\", size)\n",
+        "\n",
+        "dim1, dim2, dim3 = x.size()\n",
+        "print(\"Size:\", dim1, dim2, dim3)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FszMCTCt4_AJ"
+      },
+      "source": [
+        "### Tensor to Numpy, and Numpy to Tensor\n",
+        "\n",
+        "Tensors can be converted to numpy arrays, and numpy arrays back to tensors. To transform a numpy array into a tensor, we can use the function `torch.from_numpy`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pusiqk2t4_AJ",
+        "outputId": "87c76652-be3c-4b34-8625-e0191546f050",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Numpy array: [[1 2]\n",
+            " [3 4]]\n",
+            "PyTorch tensor: tensor([[1, 2],\n",
+            "        [3, 4]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "np_arr = np.array([[1, 2], [3, 4]])\n",
+        "tensor = torch.from_numpy(np_arr)\n",
+        "\n",
+        "print(\"Numpy array:\", np_arr)\n",
+        "print(\"PyTorch tensor:\", tensor)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bEPAswnY4_AK"
+      },
+      "source": [
+        "To transform a PyTorch tensor back to a numpy array, we can use the function `.numpy()` on tensors:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VwexWTjL4_AK",
+        "outputId": "b88529b8-89a0-4215-fd5a-f56bd3084f10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "PyTorch tensor: tensor([0, 1, 2, 3])\n",
+            "Numpy array: [0 1 2 3]\n"
+          ]
+        }
+      ],
+      "source": [
+        "tensor = torch.arange(4)\n",
+        "np_arr = tensor.numpy()\n",
+        "\n",
+        "print(\"PyTorch tensor:\", tensor)\n",
+        "print(\"Numpy array:\", np_arr)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0wLzStLw4_AL"
+      },
+      "source": [
+        "The conversion of tensors to numpy require the tensor to be on the CPU, and not the GPU (more on GPU support in a later section). In case you have a tensor on GPU, you need to call `.cpu()` on the tensor beforehand. Hence, you get a line like `np_arr = tensor.cpu().numpy()`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k087mepQ4_AL"
+      },
+      "source": [
+        "#### Operations\n",
+        "\n",
+        "Most operations that exist in numpy, also exist in PyTorch. A full list of operations can be found in the [PyTorch documentation](https://pytorch.org/docs/stable/tensors.html#), but we will review the most important ones here.\n",
+        "\n",
+        "The simplest operation is to add two tensors:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tfzhIUMQ4_AL",
+        "outputId": "e5546709-8a02-45b4-a548-9c4a228008a1",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X1 tensor([[0.1053, 0.2695, 0.3588],\n",
+            "        [0.1994, 0.5472, 0.0062]])\n",
+            "X2 tensor([[0.9516, 0.0753, 0.8860],\n",
+            "        [0.5832, 0.3376, 0.8090]])\n",
+            "Y tensor([[1.0569, 0.3448, 1.2448],\n",
+            "        [0.7826, 0.8848, 0.8151]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "x1 = torch.rand(2, 3)\n",
+        "x2 = torch.rand(2, 3)\n",
+        "y = x1 + x2\n",
+        "\n",
+        "print(\"X1\", x1)\n",
+        "print(\"X2\", x2)\n",
+        "print(\"Y\", y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MNAErIv84_AM"
+      },
+      "source": [
+        "Calling `x1 + x2` creates a new tensor containing the sum of the two inputs. However, we can also use in-place operations that are applied directly on the memory of a tensor. We therefore change the values of `x2` without the chance to re-accessing the values of `x2` before the operation. An example is shown below:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QVQHLJxZ4_AM",
+        "outputId": "59daa707-d5e9-4c7f-ce2f-322c828d5025",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X1 (before) tensor([[0.5779, 0.9040, 0.5547],\n",
+            "        [0.3423, 0.6343, 0.3644]])\n",
+            "X2 (before) tensor([[0.7104, 0.9464, 0.7890],\n",
+            "        [0.2814, 0.7886, 0.5895]])\n",
+            "X1 (after) tensor([[0.5779, 0.9040, 0.5547],\n",
+            "        [0.3423, 0.6343, 0.3644]])\n",
+            "X2 (after) tensor([[1.2884, 1.8504, 1.3437],\n",
+            "        [0.6237, 1.4230, 0.9539]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "x1 = torch.rand(2, 3)\n",
+        "x2 = torch.rand(2, 3)\n",
+        "print(\"X1 (before)\", x1)\n",
+        "print(\"X2 (before)\", x2)\n",
+        "\n",
+        "x2.add_(x1)\n",
+        "print(\"X1 (after)\", x1)\n",
+        "print(\"X2 (after)\", x2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "79IrjMom4_AN"
+      },
+      "source": [
+        "In-place operations are usually marked with a underscore postfix (e.g. \"add_\" instead of \"add\").\n",
+        "\n",
+        "Another common operation aims at changing the shape of a tensor. A tensor of size (2,3) can be re-organized to any other shape with the same number of elements (e.g. a tensor of size (6), or (3,2), ...). In PyTorch, this operation is called `view`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "riZMRgTp4_AO",
+        "outputId": "3122d342-696a-463f-a46c-4bb918d056aa",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X tensor([0, 1, 2, 3, 4, 5])\n"
+          ]
+        }
+      ],
+      "source": [
+        "x = torch.arange(6)\n",
+        "print(\"X\", x)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rtuXEHW-4_AO",
+        "outputId": "1199e662-02e8-4a9f-834f-3cb151665dc8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X tensor([[0, 1, 2],\n",
+            "        [3, 4, 5]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "x = x.view(2, 3)\n",
+        "print(\"X\", x)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rxE4W-Fk4_AO",
+        "outputId": "af2d8eb1-1dec-45c0-9324-71f0fb8df79a",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X tensor([[0, 3],\n",
+            "        [1, 4],\n",
+            "        [2, 5]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "x = x.permute(1, 0) # Swapping dimension 0 and 1\n",
+        "print(\"X\", x)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vhjInNrb4_AP"
+      },
+      "source": [
+        "Other commonly used operations include matrix multiplications, which are essential for neural networks. Quite often, we have an input vector $\\mathbf{x}$, which is transformed using a learned weight matrix $\\mathbf{W}$. There are multiple ways and functions to perform matrix multiplication, some of which we list below:\n",
+        "\n",
+        "* `torch.matmul`: Performs the matrix product over two tensors, where the specific behavior depends on the dimensions. If both inputs are matrices (2-dimensional tensors), it performs the standard matrix product. For higher dimensional inputs, the function supports broadcasting (for details see the [documentation](https://pytorch.org/docs/stable/generated/torch.matmul.html?highlight=matmul#torch.matmul)). Can also be written as `a @ b`, similar to numpy.\n",
+        "* `torch.mm`: Performs the matrix product over two matrices, but doesn't support broadcasting (see [documentation](https://pytorch.org/docs/stable/generated/torch.mm.html?highlight=torch%20mm#torch.mm))\n",
+        "* `torch.bmm`: Performs the matrix product with a support batch dimension. If the first tensor $T$ is of shape ($b\\times n\\times m$), and the second tensor $R$ ($b\\times m\\times p$), the output $O$ is of shape ($b\\times n\\times p$), and has been calculated by performing $b$ matrix multiplications of the submatrices of $T$ and $R$: $O_i = T_i @ R_i$\n",
+        "* `torch.einsum`: Performs matrix multiplications and more (i.e. sums of products) using the Einstein summation convention. Explanation of the Einstein sum can be found in assignment 1.\n",
+        "\n",
+        "Usually, we use `torch.matmul` or `torch.bmm`. We can try a matrix multiplication with `torch.matmul` below."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5JimY3bx4_AP",
+        "outputId": "3125fe4a-cd08-4836-dc93-0935aabbfa53",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X tensor([[0, 1, 2],\n",
+            "        [3, 4, 5]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "x = torch.arange(6)\n",
+        "x = x.view(2, 3)\n",
+        "print(\"X\", x)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "w8adgV7_4_AP",
+        "outputId": "1e909af3-8f6c-48b3-c74a-60b8d87efbb0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "W tensor([[0, 1, 2],\n",
+            "        [3, 4, 5],\n",
+            "        [6, 7, 8]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "W = torch.arange(9).view(3, 3) # We can also stack multiple operations in a single line\n",
+        "print(\"W\", W)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_NYcdt5N4_AQ",
+        "outputId": "9c7ec039-d5b7-4a9a-c02b-d6a662149daf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "h tensor([[15, 18, 21],\n",
+            "        [42, 54, 66]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "h = torch.matmul(x, W) # Verify the result by calculating it by hand too!\n",
+        "print(\"h\", h)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5y40NNwT4_AQ"
+      },
+      "source": [
+        "#### Indexing\n",
+        "\n",
+        "We often have the situation where we need to select a part of a tensor. Indexing works just like in numpy, so let's try it:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xyH-MRJy4_AQ",
+        "outputId": "03f118d5-59ad-47a1-8d53-34eff4149ea0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X tensor([[ 0,  1,  2,  3],\n",
+            "        [ 4,  5,  6,  7],\n",
+            "        [ 8,  9, 10, 11]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "x = torch.arange(12).view(3, 4)\n",
+        "print(\"X\", x)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6dvFXO_h4_AR",
+        "outputId": "94a03e65-1596-4d93-f815-a2b8b01525cd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([1, 5, 9])\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(x[:, 1])   # Second column"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rKm-mkzg4_AR",
+        "outputId": "adf3dbf9-69e6-4cdc-997a-172312c7a96d",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([0, 1, 2, 3])\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(x[0])      # First row"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "m4UWS0qr4_AR",
+        "outputId": "dc474f52-e26a-4846-dff1-8498b5f246be",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([3, 7])\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(x[:2, -1]) # First two rows, last column"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NgPH1LTM4_AS",
+        "outputId": "18613c3a-f405-4860-9c6d-7ea57c225994",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([[ 4,  5,  6,  7],\n",
+            "        [ 8,  9, 10, 11]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(x[1:3, :]) # Middle two rows"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qZ_kjjpD4_AS"
+      },
+      "source": [
+        "### Dynamic Computation Graph and Backpropagation\n",
+        "\n",
+        "One of the main reasons for using PyTorch in Deep Learning projects is that we can automatically get **gradients/derivatives** of functions that we define. We will mainly use PyTorch for implementing neural networks, and they are just fancy functions. If we use weight matrices in our function that we want to learn, then those are called the **parameters** or simply the **weights**.\n",
+        "\n",
+        "If our neural network would output a single scalar value, we would talk about taking the **derivative**, but you will see that quite often we will have **multiple** output variables (\"values\"); in that case we talk about **gradients**. It's a more general term.\n",
+        "\n",
+        "Given an input $\\mathbf{x}$, we define our function by **manipulating** that input, usually by matrix-multiplications with weight matrices and additions with so-called bias vectors. As we manipulate our input, we are automatically creating a **computational graph**. This graph shows how to arrive at our output from our input.\n",
+        "PyTorch is a **define-by-run** framework; this means that we can just do our manipulations, and PyTorch will keep track of that graph for us. Thus, we create a dynamic computation graph along the way.\n",
+        "\n",
+        "So, to recap: the only thing we have to do is to compute the **output**, and then we can ask PyTorch to automatically get the **gradients**.\n",
+        "\n",
+        "> **Note:  Why do we want gradients?** Consider that we have defined a function, a neural net, that is supposed to compute a certain output $y$ for an input vector $\\mathbf{x}$. We then define an **error measure** that tells us how wrong our network is; how bad it is in predicting output $y$ from input $\\mathbf{x}$. Based on this error measure, we can use the gradients to **update** the weights $\\mathbf{W}$ that were responsible for the output, so that the next time we present input $\\mathbf{x}$ to our network, the output will be closer to what we want.\n",
+        "\n",
+        "The first thing we have to do is to specify which tensors require gradients. By default, when we create a tensor, it does not require gradients."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "o3oka9d34_AS",
+        "outputId": "69640215-a57f-4d2e-95ae-ab629aff429c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "False\n"
+          ]
+        }
+      ],
+      "source": [
+        "x = torch.ones((3,))\n",
+        "print(x.requires_grad)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KDZr8NdS4_AT"
+      },
+      "source": [
+        "We can change this for an existing tensor using the function `requires_grad_()` (underscore indicating that this is a in-place operation). Alternatively, when creating a tensor, you can pass the argument `requires_grad=True` to most initializers we have seen above."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "C-t-LwIN4_AT",
+        "outputId": "c0a18e2d-6846-42d5-eac4-01882467e913",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "True\n"
+          ]
+        }
+      ],
+      "source": [
+        "x.requires_grad_(True)\n",
+        "print(x.requires_grad)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "REIpPRwQ4_AT"
+      },
+      "source": [
+        "In order to get familiar with the concept of a computation graph, we will create one for the following function:\n",
+        "\n",
+        "$$y = \\frac{1}{|x|}\\sum_i \\left[(x_i + 2)^2 + 3\\right]$$\n",
+        "\n",
+        "You could imagine that $x$ are our parameters, and we want to optimize (either maximize or minimize) the output $y$. For this, we want to obtain the gradients $\\partial y / \\partial \\mathbf{x}$. For our example, we'll use $\\mathbf{x}=[0,1,2]$ as our input."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hjmPc1g_4_AT",
+        "outputId": "150c5a66-d02a-4070-9a42-b70b90dd4286",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X tensor([0., 1., 2.], requires_grad=True)\n"
+          ]
+        }
+      ],
+      "source": [
+        "x = torch.arange(3, dtype=torch.float32, requires_grad=True) # Only float tensors can have gradients\n",
+        "print(\"X\", x)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Rnf2jy_f4_AU"
+      },
+      "source": [
+        "Now let's build the computation graph step by step. You can combine multiple operations in a single line, but we will separate them here to get a better understanding of how each operation is added to the computation graph."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UzmmV2DD4_AU",
+        "outputId": "0c2850bc-04a5-40f7-c39c-ad518aa33fd3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Y tensor(12.6667, grad_fn=<MeanBackward0>)\n"
+          ]
+        }
+      ],
+      "source": [
+        "a = x + 2\n",
+        "b = a ** 2\n",
+        "c = b + 3\n",
+        "y = c.mean()\n",
+        "print(\"Y\", y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nKpRCH2P4_AU"
+      },
+      "source": [
+        "Using the statements above, we have created a computation graph that looks similar to the figure below:\n",
+        "\n",
+        "<center style=\"width: 100%\"><img src=\"https://github.com/phlippe/uvadlc_notebooks/blob/master/docs/tutorial_notebooks/tutorial2/pytorch_computation_graph.svg?raw=1\" width=\"200px\"></center>\n",
+        "\n",
+        "We calculate $a$ based on the inputs $x$ and the constant $2$, $b$ is $a$ squared, and so on. The visualization is an abstraction of the dependencies between inputs and outputs of the operations we have applied.\n",
+        "Each node of the computation graph has automatically defined a function for calculating the gradients with respect to its inputs, `grad_fn`. You can see this when we printed the output tensor $y$. This is why the computation graph is usually visualized in the reverse direction (arrows point from the result to the inputs). We can perform backpropagation on the computation graph by calling the function `backward()` on the last output, which effectively calculates the gradients for each tensor that has the property `requires_grad=True`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1pa0M64X4_AV"
+      },
+      "outputs": [],
+      "source": [
+        "y.backward()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lWRyGXto4_AV"
+      },
+      "source": [
+        "`x.grad` will now contain the gradient $\\partial y/ \\partial \\mathcal{x}$, and this gradient indicates how a change in $\\mathbf{x}$ will affect output $y$ given the current input $\\mathbf{x}=[0,1,2]$:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5hfdF58G4_AV",
+        "outputId": "952c6a45-fe88-4eec-d47d-a3288baf2181",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([1.3333, 2.0000, 2.6667])\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(x.grad)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y7uU3PfK4_AV"
+      },
+      "source": [
+        "We can also verify these gradients by hand. We will calculate the gradients using the chain rule, in the same way as PyTorch did it:\n",
+        "\n",
+        "$$\\frac{\\partial y}{\\partial x_i} = \\frac{\\partial y}{\\partial c_i}\\frac{\\partial c_i}{\\partial b_i}\\frac{\\partial b_i}{\\partial a_i}\\frac{\\partial a_i}{\\partial x_i}$$\n",
+        "\n",
+        "Note that we have simplified this equation to index notation, and by using the fact that all operation besides the mean do not combine the elements in the tensor. The partial derivatives are:\n",
+        "\n",
+        "$$\n",
+        "\\frac{\\partial a_i}{\\partial x_i} = 1,\\hspace{1cm}\n",
+        "\\frac{\\partial b_i}{\\partial a_i} = 2\\cdot a_i\\hspace{1cm}\n",
+        "\\frac{\\partial c_i}{\\partial b_i} = 1\\hspace{1cm}\n",
+        "\\frac{\\partial y}{\\partial c_i} = \\frac{1}{3}\n",
+        "$$\n",
+        "\n",
+        "Hence, with the input being $\\mathbf{x}=[0,1,2]$, our gradients are $\\partial y/\\partial \\mathbf{x}=[4/3,2,8/3]$. The previous code cell should have printed the same result."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uiWRfK-I4_AV"
+      },
+      "source": [
+        "### GPU support\n",
+        "\n",
+        "A crucial feature of PyTorch is the support of GPUs, short for Graphics Processing Unit. A GPU can perform many thousands of small operations in parallel, making it very well suitable for performing large matrix operations in neural networks. When comparing GPUs to CPUs, we can list the following main differences (credit: [Kevin Krewell, 2009](https://blogs.nvidia.com/blog/2009/12/16/whats-the-difference-between-a-cpu-and-a-gpu/))\n",
+        "\n",
+        "<center style=\"width: 100%\"><img src=\"https://github.com/phlippe/uvadlc_notebooks/blob/master/docs/tutorial_notebooks/tutorial2/comparison_CPU_GPU.png?raw=1\" width=\"700px\"></center>\n",
+        "\n",
+        "CPUs and GPUs have both different advantages and disadvantages, which is why many computers contain both components and use them for different tasks. In case you are not familiar with GPUs, you can read up more details in this [NVIDIA blog post](https://blogs.nvidia.com/blog/2009/12/16/whats-the-difference-between-a-cpu-and-a-gpu/) or [here](https://www.intel.com/content/www/us/en/products/docs/processors/what-is-a-gpu.html).\n",
+        "\n",
+        "GPUs can accelerate the training of your network up to a factor of $100$ which is essential for large neural networks. PyTorch implements a lot of functionality for supporting GPUs (mostly those of NVIDIA due to the libraries [CUDA](https://developer.nvidia.com/cuda-zone) and [cuDNN](https://developer.nvidia.com/cudnn)). First, let's check whether you have a GPU available:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vljVD9wR4_AW",
+        "outputId": "2cf97c4f-a355-4be6-8265-43e2e2f8b03e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Is the GPU available? True\n"
+          ]
+        }
+      ],
+      "source": [
+        "gpu_avail = torch.cuda.is_available()\n",
+        "print(f\"Is the GPU available? {gpu_avail}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6HkDP0M14_AW"
+      },
+      "source": [
+        "If you have a GPU on your computer but the command above returns False, make sure you have the correct CUDA-version installed. The `dl2021` environment comes with the CUDA-toolkit 11.3, which is selected for the Lisa supercomputer. Please change it if necessary (CUDA 11.1 is currently common on Colab). On Google Colab, make sure that you have selected a GPU in your runtime setup (in the menu, check under `Runtime -> Change runtime type`).\n",
+        "\n",
+        "By default, all tensors you create are stored on the CPU. We can push a tensor to the GPU by using the function `.to(...)`, or `.cuda()`. However, it is often a good practice to define a `device` object in your code which points to the GPU if you have one, and otherwise to the CPU. Then, you can write your code with respect to this device object, and it allows you to run the same code on both a CPU-only system, and one with a GPU. Let's try it below. We can specify the device as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "CXmayMT-4_AW",
+        "outputId": "72797216-f2cb-4428-9bcd-5a169ebd6835",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Device cuda\n"
+          ]
+        }
+      ],
+      "source": [
+        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
+        "print(\"Device\", device)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OjPSom6A4_AW"
+      },
+      "source": [
+        "Now let's create a tensor and push it to the device:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_fJzFn2Q4_AX",
+        "outputId": "c6f2317a-7f4a-4909-ff64-7103b789c801",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "X tensor([[0., 0., 0.],\n",
+            "        [0., 0., 0.]], device='cuda:0')\n"
+          ]
+        }
+      ],
+      "source": [
+        "x = torch.zeros(2, 3)\n",
+        "x = x.to(device)\n",
+        "print(\"X\", x)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0XRa_i-54_AX"
+      },
+      "source": [
+        "In case you have a GPU, you should now see the attribute `device='cuda:0'` being printed next to your tensor. The zero next to cuda indicates that this is the zero-th GPU device on your computer. PyTorch also supports multi-GPU systems, but this you will only need once you have very big networks to train (if interested, see the [PyTorch documentation](https://pytorch.org/docs/stable/distributed.html#distributed-basics)). We can also compare the runtime of a large matrix multiplication on the CPU with a operation on the GPU:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8NBSla0G4_AX",
+        "outputId": "725663c2-86a6-4c6b-969c-50bbe868b0c8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "CPU time: 3.47703s\n",
+            "GPU time: 0.14844s\n"
+          ]
+        }
+      ],
+      "source": [
+        "x = torch.randn(5000, 5000)\n",
+        "\n",
+        "## CPU version\n",
+        "start_time = time.time()\n",
+        "_ = torch.matmul(x, x)\n",
+        "end_time = time.time()\n",
+        "print(f\"CPU time: {(end_time - start_time):6.5f}s\")\n",
+        "\n",
+        "## GPU version\n",
+        "x = x.to(device)\n",
+        "# CUDA is asynchronous, so we need to use different timing functions\n",
+        "start = torch.cuda.Event(enable_timing=True)\n",
+        "end = torch.cuda.Event(enable_timing=True)\n",
+        "start.record()\n",
+        "_ = torch.matmul(x, x)\n",
+        "end.record()\n",
+        "torch.cuda.synchronize()  # Waits for everything to finish running on the GPU\n",
+        "print(f\"GPU time: {0.001 * start.elapsed_time(end):6.5f}s\")  # Milliseconds to seconds"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JW5Y0SPx4_AY"
+      },
+      "source": [
+        "Depending on the size of the operation and the CPU/GPU in your system, the speedup of this operation can be >50x. As `matmul` operations are very common in neural networks, we can already see the great benefit of training a NN on a GPU. The time estimate can be relatively noisy here because we haven't run it for multiple times. Feel free to extend this, but it also takes longer to run.\n",
+        "\n",
+        "When generating random numbers, the seed between CPU and GPU is not synchronized. Hence, we need to set the seed on the GPU separately to ensure a reproducible code. Note that due to different GPU architectures, running the same code on different GPUs does not guarantee the same random numbers. Still, we don't want that our code gives us a different output every time we run it on the exact same hardware. Hence, we also set the seed on the GPU:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1PqR7iLw4_AY"
+      },
+      "outputs": [],
+      "source": [
+        "# GPU operations have a separate seed we also want to set\n",
+        "if torch.cuda.is_available():\n",
+        "    torch.cuda.manual_seed(42)\n",
+        "    torch.cuda.manual_seed_all(42)\n",
+        "\n",
+        "# Additionally, some operations on a GPU are implemented stochastic for efficiency\n",
+        "# We want to ensure that all operations are deterministic on GPU (if used) for reproducibility\n",
+        "torch.backends.cudnn.determinstic = True\n",
+        "torch.backends.cudnn.benchmark = False"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xuhcGLH64_AY"
+      },
+      "source": [
+        "## Learning by example: Continuous XOR\n",
+        "\n",
+        "If we want to build a neural network in PyTorch, we could specify all our parameters (weight matrices, bias vectors) using `Tensors` (with `requires_grad=True`), ask PyTorch to calculate the gradients and then adjust the parameters. But things can quickly get cumbersome if we have a lot of parameters. In PyTorch, there is a package called `torch.nn` that makes building neural networks more convenient.\n",
+        "\n",
+        "We will introduce the libraries and all additional parts you might need to train a neural network in PyTorch, using a simple example classifier on a simple yet well known example: XOR. Given two binary inputs $x_1$ and $x_2$, the label to predict is $1$ if either $x_1$ or $x_2$ is $1$ while the other is $0$, or the label is $0$ in all other cases. The example became famous by the fact that a single neuron, i.e. a linear classifier, cannot learn this simple function.\n",
+        "Hence, we will learn how to build a small neural network that can learn this function.\n",
+        "To make it a little bit more interesting, we move the XOR into continuous space and introduce some gaussian noise on the binary inputs. Our desired separation of an XOR dataset could look as follows:\n",
+        "\n",
+        "<center style=\"width: 100%\"><img src=\"https://github.com/phlippe/uvadlc_notebooks/blob/master/docs/tutorial_notebooks/tutorial2/continuous_xor.svg?raw=1\" width=\"350px\"></center>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IlWsS5lH4_AY"
+      },
+      "source": [
+        "### The model\n",
+        "\n",
+        "The package `torch.nn` defines a series of useful classes like linear networks layers, activation functions, loss functions etc. A full list can be found [here](https://pytorch.org/docs/stable/nn.html). In case you need a certain network layer, check the documentation of the package first before writing the layer yourself as the package likely contains the code for it already. We import it below:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_wbcHfpQ4_AZ"
+      },
+      "outputs": [],
+      "source": [
+        "import torch.nn as nn"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5hKxRVNn4_AZ"
+      },
+      "source": [
+        "Additionally to `torch.nn`, there is also `torch.nn.functional`. It contains functions that are used in network layers. This is in contrast to `torch.nn` which defines them as `nn.Modules` (more on it below), and `torch.nn` actually uses a lot of functionalities from `torch.nn.functional`. Hence, the functional package is useful in many situations, and so we import it as well here."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qDBxzGiN4_AZ"
+      },
+      "outputs": [],
+      "source": [
+        "import torch.nn.functional as F"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "guUzu5C-4_AZ"
+      },
+      "source": [
+        "#### nn.Module\n",
+        "\n",
+        "In PyTorch, a neural network is built up out of modules. Modules can contain other modules, and a neural network is considered to be a module itself as well. The basic template of a module is as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "C5bbwtJo4_AZ"
+      },
+      "outputs": [],
+      "source": [
+        "class MyModule(nn.Module):\n",
+        "\n",
+        "    def __init__(self):\n",
+        "        super().__init__()\n",
+        "        # Some init for my module\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        # Function for performing the calculation of the module.\n",
+        "        pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ee1wVCxp4_Aa"
+      },
+      "source": [
+        "The forward function is where the computation of the module is taken place, and is executed when you call the module (`nn = MyModule(); nn(x)`). In the init function, we usually create the parameters of the module, using `nn.Parameter`, or defining other modules that are used in the forward function. The backward calculation is done automatically, but could be overwritten as well if wanted.\n",
+        "\n",
+        "#### Simple classifier\n",
+        "We can now make use of the pre-defined modules in the `torch.nn` package, and define our own small neural network. We will use a minimal network with a input layer, one hidden layer with tanh as activation function, and a output layer. In other words, our networks should look something like this:\n",
+        "\n",
+        "<center width=\"100%\"><img src=\"https://github.com/phlippe/uvadlc_notebooks/blob/master/docs/tutorial_notebooks/tutorial2/small_neural_network.svg?raw=1\" width=\"300px\"></center>\n",
+        "\n",
+        "The input neurons are shown in blue, which represent the coordinates $x_1$ and $x_2$ of a data point. The hidden neurons including a tanh activation are shown in white, and the output neuron in red.\n",
+        "In PyTorch, we can define this as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OIcu3_t-4_Aa"
+      },
+      "outputs": [],
+      "source": [
+        "class SimpleClassifier(nn.Module):\n",
+        "\n",
+        "    def __init__(self, num_inputs, num_hidden, num_outputs):\n",
+        "        super().__init__()\n",
+        "        # Initialize the modules we need to build the network\n",
+        "        self.linear1 = nn.Linear(num_inputs, num_hidden)\n",
+        "        self.act_fn = nn.Tanh()\n",
+        "        self.linear2 = nn.Linear(num_hidden, num_outputs)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        # Perform the calculation of the model to determine the prediction\n",
+        "        x = self.linear1(x)\n",
+        "        x = self.act_fn(x)\n",
+        "        x = self.linear2(x)\n",
+        "        return x"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QQrh4xCJ4_Aa"
+      },
+      "source": [
+        "For the examples in this notebook, we will use a tiny neural network with two input neurons and four hidden neurons. As we perform binary classification, we will use a single output neuron. Note that we do not apply a sigmoid on the output yet. This is because other functions, especially the loss, are more efficient and precise to calculate on the original outputs instead of the sigmoid output. We will discuss the detailed reason later."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aLVfo2n44_Aa",
+        "outputId": "e731b902-81b3-4f72-b815-8aaf57846dcc",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "SimpleClassifier(\n",
+            "  (linear1): Linear(in_features=2, out_features=4, bias=True)\n",
+            "  (act_fn): Tanh()\n",
+            "  (linear2): Linear(in_features=4, out_features=1, bias=True)\n",
+            ")\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = SimpleClassifier(num_inputs=2, num_hidden=4, num_outputs=1)\n",
+        "# Printing a module shows all its submodules\n",
+        "print(model)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wPx3XDh34_Ab"
+      },
+      "source": [
+        "Printing the model lists all submodules it contains. The parameters of a module can be obtained by using its `parameters()` functions, or `named_parameters()` to get a name to each parameter object. For our small neural network, we have the following parameters:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xwoSrSwE4_Ab",
+        "outputId": "7804d5ac-5bbd-4cca-c064-e1d7f9059794",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Parameter linear1.weight, shape torch.Size([4, 2])\n",
+            "Parameter linear1.bias, shape torch.Size([4])\n",
+            "Parameter linear2.weight, shape torch.Size([1, 4])\n",
+            "Parameter linear2.bias, shape torch.Size([1])\n"
+          ]
+        }
+      ],
+      "source": [
+        "for name, param in model.named_parameters():\n",
+        "    print(f\"Parameter {name}, shape {param.shape}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PLsLdRBP4_Ab"
+      },
+      "source": [
+        "Each linear layer has a weight matrix of the shape `[output, input]`, and a bias of the shape `[output]`. The tanh activation function does not have any parameters. Note that parameters are only registered for `nn.Module` objects that are direct object attributes, i.e. `self.a = ...`. If you define a list of modules, the parameters of those are not registered for the outer module and can cause some issues when you try to optimize your module. There are alternatives, like `nn.ModuleList`, `nn.ModuleDict` and `nn.Sequential`, that allow you to have different data structures of modules. We will use them in a few later tutorials and explain them there."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IzMdNLPK4_Ab"
+      },
+      "source": [
+        "### The data\n",
+        "\n",
+        "PyTorch also provides a few functionalities to load the training and test data efficiently, summarized in the package `torch.utils.data`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "elkMXPTS4_Ab"
+      },
+      "outputs": [],
+      "source": [
+        "import torch.utils.data as data"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h8ayIK-M4_Ac"
+      },
+      "source": [
+        "The data package defines two classes which are the standard interface for handling data in PyTorch: `data.Dataset`, and `data.DataLoader`. The dataset class provides an uniform interface to access the training/test data, while the data loader makes sure to efficiently load and stack the data points from the dataset into batches during training."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uUFvUDLe4_Ac"
+      },
+      "source": [
+        "#### The dataset class\n",
+        "\n",
+        "The dataset class summarizes the basic functionality of a dataset in a natural way. To define a dataset in PyTorch, we simply specify two functions: `__getitem__`, and `__len__`. The get-item function has to return the $i$-th data point in the dataset, while the len function returns the size of the dataset. For the XOR dataset, we can define the dataset class as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "U0TJHTld4_Ac"
+      },
+      "outputs": [],
+      "source": [
+        "class XORDataset(data.Dataset):\n",
+        "\n",
+        "    def __init__(self, size, std=0.1):\n",
+        "        \"\"\"\n",
+        "        Inputs:\n",
+        "            size - Number of data points we want to generate\n",
+        "            std - Standard deviation of the noise (see generate_continuous_xor function)\n",
+        "        \"\"\"\n",
+        "        super().__init__()\n",
+        "        self.size = size\n",
+        "        self.std = std\n",
+        "        self.generate_continuous_xor()\n",
+        "\n",
+        "    def generate_continuous_xor(self):\n",
+        "        # Each data point in the XOR dataset has two variables, x and y, that can be either 0 or 1\n",
+        "        # The label is their XOR combination, i.e. 1 if only x or only y is 1 while the other is 0.\n",
+        "        # If x=y, the label is 0.\n",
+        "        data = torch.randint(low=0, high=2, size=(self.size, 2), dtype=torch.float32)\n",
+        "        label = (data.sum(dim=1) == 1).to(torch.long)\n",
+        "        # To make it slightly more challenging, we add a bit of gaussian noise to the data points.\n",
+        "        data += self.std * torch.randn(data.shape)\n",
+        "\n",
+        "        self.data = data\n",
+        "        self.label = label\n",
+        "\n",
+        "    def __len__(self):\n",
+        "        # Number of data point we have. Alternatively self.data.shape[0], or self.label.shape[0]\n",
+        "        return self.size\n",
+        "\n",
+        "    def __getitem__(self, idx):\n",
+        "        # Return the idx-th data point of the dataset\n",
+        "        # If we have multiple things to return (data point and label), we can return them as tuple\n",
+        "        data_point = self.data[idx]\n",
+        "        data_label = self.label[idx]\n",
+        "        return data_point, data_label"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rcbiFhvX4_Ac"
+      },
+      "source": [
+        "Let's try to create such a dataset and inspect it:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dccdlHVs4_Ad",
+        "outputId": "e6c273a3-c1dd-4bd0-e091-1e9626007942",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Size of dataset: 200\n",
+            "Data point 0: (tensor([0.9632, 0.1117]), tensor(1))\n"
+          ]
+        }
+      ],
+      "source": [
+        "dataset = XORDataset(size=200)\n",
+        "print(\"Size of dataset:\", len(dataset))\n",
+        "print(\"Data point 0:\", dataset[0])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VtQXFXid4_Ad"
+      },
+      "source": [
+        "To better relate to the dataset, we visualize the samples below."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cWiMG3J-4_Ad"
+      },
+      "outputs": [],
+      "source": [
+        "def visualize_samples(data, label):\n",
+        "    if isinstance(data, torch.Tensor):\n",
+        "        data = data.cpu().numpy()\n",
+        "    if isinstance(label, torch.Tensor):\n",
+        "        label = label.cpu().numpy()\n",
+        "    data_0 = data[label == 0]\n",
+        "    data_1 = data[label == 1]\n",
+        "\n",
+        "    plt.figure(figsize=(4,4))\n",
+        "    plt.scatter(data_0[:,0], data_0[:,1], edgecolor=\"#333\", label=\"Class 0\")\n",
+        "    plt.scatter(data_1[:,0], data_1[:,1], edgecolor=\"#333\", label=\"Class 1\")\n",
+        "    plt.title(\"Dataset samples\")\n",
+        "    plt.ylabel(r\"$x_2$\")\n",
+        "    plt.xlabel(r\"$x_1$\")\n",
+        "    plt.legend()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NrY9XZUn4_Ad",
+        "outputId": "ba502c22-c0b8-4dd1-f530-fd74d04525b0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 288x288 with 1 Axes>"
+            ],
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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "visualize_samples(dataset.data, dataset.label)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0ATLBiM04_Ae"
+      },
+      "source": [
+        "#### The data loader class\n",
+        "\n",
+        "The class `torch.utils.data.DataLoader` represents a Python iterable over a dataset with support for automatic batching, multi-process data loading and many more features. The data loader communicates with the dataset using the function `__getitem__`, and stacks its outputs as tensors over the first dimension to form a batch.\n",
+        "In contrast to the dataset class, we usually don't have to define our own data loader class, but can just create an object of it with the dataset as input. Additionally, we can configure our data loader with the following input arguments (only a selection, see full list [here](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader)):\n",
+        "\n",
+        "* `batch_size`: Number of samples to stack per batch\n",
+        "* `shuffle`: If True, the data is returned in a random order. This is important during training for introducing stochasticity.\n",
+        "* `num_workers`: Number of subprocesses to use for data loading. The default, 0, means that the data will be loaded in the main process which can slow down training for datasets where loading a data point takes a considerable amount of time (e.g. large images). More workers are recommended for those, but can cause issues on Windows computers. For tiny datasets as ours, 0 workers are usually faster.\n",
+        "* `pin_memory`: If True, the data loader will copy Tensors into CUDA pinned memory before returning them. This can save some time for large data points on GPUs. Usually a good practice to use for a training set, but not necessarily for validation and test to save memory on the GPU.\n",
+        "* `drop_last`: If True, the last batch is dropped in case it is smaller than the specified batch size. This occurs when the dataset size is not a multiple of the batch size. Only potentially helpful during training to keep a consistent batch size.\n",
+        "\n",
+        "Let's create a simple data loader below:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oJrGpx5E4_Ae"
+      },
+      "outputs": [],
+      "source": [
+        "data_loader = data.DataLoader(dataset, batch_size=8, shuffle=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pGt3q_lO4_Ae",
+        "outputId": "de950d8a-2ac5-4f55-a1cb-2f7e3d3a88ad",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Data inputs torch.Size([8, 2]) \n",
+            " tensor([[-0.0890,  0.8608],\n",
+            "        [ 1.0905, -0.0128],\n",
+            "        [ 0.7967,  0.2268],\n",
+            "        [-0.0688,  0.0371],\n",
+            "        [ 0.8732, -0.2240],\n",
+            "        [-0.0559, -0.0282],\n",
+            "        [ 0.9277,  0.0978],\n",
+            "        [ 1.0150,  0.9689]])\n",
+            "Data labels torch.Size([8]) \n",
+            " tensor([1, 1, 1, 0, 1, 0, 1, 0])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# next(iter(...)) catches the first batch of the data loader\n",
+        "# If shuffle is True, this will return a different batch every time we run this cell\n",
+        "# For iterating over the whole dataset, we can simple use \"for batch in data_loader: ...\"\n",
+        "data_inputs, data_labels = next(iter(data_loader))\n",
+        "\n",
+        "# The shape of the outputs are [batch_size, d_1,...,d_N] where d_1,...,d_N are the\n",
+        "# dimensions of the data point returned from the dataset class\n",
+        "print(\"Data inputs\", data_inputs.shape, \"\\n\", data_inputs)\n",
+        "print(\"Data labels\", data_labels.shape, \"\\n\", data_labels)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fIGXRmKV4_Ae"
+      },
+      "source": [
+        "### Optimization\n",
+        "\n",
+        "After defining the model and the dataset, it is time to prepare the optimization of the model. During training, we will perform the following steps:\n",
+        "\n",
+        "1. Get a batch from the data loader\n",
+        "2. Obtain the predictions from the model for the batch\n",
+        "3. Calculate the loss based on the difference between predictions and labels\n",
+        "4. Backpropagation: calculate the gradients for every parameter with respect to the loss\n",
+        "5. Update the parameters of the model in the direction of the gradients\n",
+        "\n",
+        "We have seen how we can do step 1, 2 and 4 in PyTorch. Now, we will look at step 3 and 5."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_hIrKg4s4_Af"
+      },
+      "source": [
+        "#### Loss modules\n",
+        "\n",
+        "We can calculate the loss for a batch by simply performing a few tensor operations as those are automatically added to the computation graph. For instance, for binary classification, we can use Binary Cross Entropy (BCE) which is defined as follows:\n",
+        "\n",
+        "$$\\mathcal{L}_{BCE} = -\\sum_i \\left[ y_i \\log x_i + (1 - y_i) \\log (1 - x_i) \\right]$$\n",
+        "\n",
+        "where $y$ are our labels, and $x$ our predictions, both in the range of $[0,1]$. However, PyTorch already provides a list of predefined loss functions which we can use (see [here](https://pytorch.org/docs/stable/nn.html#loss-functions) for a full list). For instance, for BCE, PyTorch has two modules: `nn.BCELoss()`, `nn.BCEWithLogitsLoss()`. While `nn.BCELoss` expects the inputs $x$ to be in the range $[0,1]$, i.e. the output of a sigmoid, `nn.BCEWithLogitsLoss` combines a sigmoid layer and the BCE loss in a single class. This version is numerically more stable than using a plain Sigmoid followed by a BCE loss because of the logarithms applied in the loss function. Hence, it is adviced to use loss functions applied on \"logits\" where possible (remember to not apply a sigmoid on the output of the model in this case!). For our model defined above, we therefore use the module `nn.BCEWithLogitsLoss`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AlGyML444_Af"
+      },
+      "outputs": [],
+      "source": [
+        "loss_module = nn.BCEWithLogitsLoss()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BziBXsfV4_Af"
+      },
+      "source": [
+        "#### Stochastic Gradient Descent\n",
+        "\n",
+        "For updating the parameters, PyTorch provides the package `torch.optim` that has most popular optimizers implemented. We will discuss the specific optimizers and their differences later in the course, but will for now use the simplest of them: `torch.optim.SGD`. Stochastic Gradient Descent updates parameters by multiplying the gradients with a small constant, called learning rate, and subtracting those from the parameters (hence minimizing the loss). Therefore, we slowly move towards the direction of minimizing the loss. A good default value of the learning rate for a small network as ours is 0.1."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sd9z_B4r4_Af"
+      },
+      "outputs": [],
+      "source": [
+        "# Input to the optimizer are the parameters of the model: model.parameters()\n",
+        "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kcCTgWm64_Af"
+      },
+      "source": [
+        "The optimizer provides two useful functions: `optimizer.step()`, and `optimizer.zero_grad()`. The step function updates the parameters based on the gradients as explained above. The function `optimizer.zero_grad()` sets the gradients of all parameters to zero. While this function seems less relevant at first, it is a crucial pre-step before performing backpropagation. If we would call the `backward` function on the loss while the parameter gradients are non-zero from the previous batch, the new gradients would actually be added to the previous ones instead of overwriting them. This is done because a parameter might occur multiple times in a computation graph, and we need to sum the gradients in this case instead of replacing them. Hence, remember to call `optimizer.zero_grad()` before calculating the gradients of a batch."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5JBJ_NFR4_Ag"
+      },
+      "source": [
+        "### Training\n",
+        "\n",
+        "Finally, we are ready to train our model. As a first step, we create a slightly larger dataset and specify a data loader with a larger batch size."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "54j0b31K4_Ag"
+      },
+      "outputs": [],
+      "source": [
+        "train_dataset = XORDataset(size=2500)\n",
+        "train_data_loader = data.DataLoader(train_dataset, batch_size=128, shuffle=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jc2dtGGJ4_Ag"
+      },
+      "source": [
+        "Now, we can write a small training function. Remember our five steps: load a batch, obtain the predictions, calculate the loss, backpropagate, and update. Additionally, we have to push all data and model parameters to the device of our choice (GPU if available). For the tiny neural network we have, communicating the data to the GPU actually takes much more time than we could save from running the operation on GPU. For large networks, the communication time is significantly smaller than the actual runtime making a GPU crucial in these cases. Still, to practice, we will push the data to GPU here."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "I2YxS4Wu4_Ag",
+        "outputId": "c94d2f18-b56e-49a7-d5b1-9057f63d57f2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SimpleClassifier(\n",
+              "  (linear1): Linear(in_features=2, out_features=4, bias=True)\n",
+              "  (act_fn): Tanh()\n",
+              "  (linear2): Linear(in_features=4, out_features=1, bias=True)\n",
+              ")"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 79
+        }
+      ],
+      "source": [
+        "# Push model to device. Has to be only done once\n",
+        "model.to(device)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qDKclQnC4_Ag"
+      },
+      "source": [
+        "In addition, we set our model to training mode. This is done by calling `model.train()`. There exist certain modules that need to perform a different forward step during training than during testing (e.g. BatchNorm and Dropout), and we can switch between them using `model.train()` and `model.eval()`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XfMrXzgS4_Ah"
+      },
+      "outputs": [],
+      "source": [
+        "def train_model(model, optimizer, data_loader, loss_module, num_epochs=100):\n",
+        "    # Set model to train mode\n",
+        "    model.train()\n",
+        "\n",
+        "    # Training loop\n",
+        "    for epoch in tqdm(range(num_epochs)):\n",
+        "        for data_inputs, data_labels in data_loader:\n",
+        "\n",
+        "            ## Step 1: Move input data to device (only strictly necessary if we use GPU)\n",
+        "            data_inputs = data_inputs.to(device)\n",
+        "            data_labels = data_labels.to(device)\n",
+        "\n",
+        "            ## Step 2: Run the model on the input data\n",
+        "            preds = model(data_inputs)\n",
+        "            preds = preds.squeeze(dim=1) # Output is [Batch size, 1], but we want [Batch size]\n",
+        "\n",
+        "            ## Step 3: Calculate the loss\n",
+        "            loss = loss_module(preds, data_labels.float())\n",
+        "\n",
+        "            ## Step 4: Perform backpropagation\n",
+        "            # Before calculating the gradients, we need to ensure that they are all zero.\n",
+        "            # The gradients would not be overwritten, but actually added to the existing ones.\n",
+        "            optimizer.zero_grad()\n",
+        "            # Perform backpropagation\n",
+        "            loss.backward()\n",
+        "\n",
+        "            ## Step 5: Update the parameters\n",
+        "            optimizer.step()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rc0u6Eja4_Ah",
+        "outputId": "c9ce7b06-9f0a-4bb0-fffe-0f1423b34144",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "referenced_widgets": [
+            "8f77981965c240ef808b7cdd8d352cce",
+            "b705d6268387481cb08c28445dadd8d4",
+            "078591503cac4eaf85cf3c6f81b5c84f",
+            "a3c21412aa684dcca539a444ef52f1a6",
+            "ee0d63e9f9a84ef4ad83604a0c5a5e98",
+            "95a96a23d0da41edb8a95955dfc452cc",
+            "98c647013e7a4910a77c0c869c0a75e2",
+            "2413e05b5232490bb29c2bd1ece03eca",
+            "154abd17ecb64a86a99af945e18fb4a2",
+            "2e96a26957474f86baa86893900c9769",
+            "246be9746fed47249129775fc7e5d9be"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/100 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "8f77981965c240ef808b7cdd8d352cce"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "train_model(model, optimizer, train_data_loader, loss_module)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S0ipSUTV4_Ah"
+      },
+      "source": [
+        "#### Saving a model\n",
+        "\n",
+        "After finish training a model, we save the model to disk so that we can load the same weights at a later time. For this, we extract the so-called `state_dict` from the model which contains all learnable parameters. For our simple model, the state dict contains the following entries:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MMfK2iSX4_Ai",
+        "outputId": "bc01852c-41ce-4a5a-a639-e520432342ca",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "OrderedDict([('linear1.weight', tensor([[-2.6034, -3.3292],\n",
+            "        [ 1.9774, -2.4076],\n",
+            "        [-2.5968, -1.5908],\n",
+            "        [-0.5717, -0.8101]], device='cuda:0')), ('linear1.bias', tensor([ 1.4459, -1.3992,  2.9882, -0.1375], device='cuda:0')), ('linear2.weight', tensor([[-4.4623,  3.0885,  4.4030, -0.1377]], device='cuda:0')), ('linear2.bias', tensor([-1.6853], device='cuda:0'))])\n"
+          ]
+        }
+      ],
+      "source": [
+        "state_dict = model.state_dict()\n",
+        "print(state_dict)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qPgfZN5o4_Ai"
+      },
+      "source": [
+        "To save the state dictionary, we can use `torch.save`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Scoflp_e4_Ai"
+      },
+      "outputs": [],
+      "source": [
+        "# torch.save(object, filename). For the filename, any extension can be used\n",
+        "torch.save(state_dict, \"our_model.tar\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uRd_48Zs4_Ai"
+      },
+      "source": [
+        "To load a model from a state dict, we use the function `torch.load` to load the state dict from the disk, and the module function `load_state_dict` to overwrite our parameters with the new values:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PBrwG1b-4_Aj",
+        "outputId": "bce4be5e-04cf-4743-abaa-d117e67faee3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Original model\n",
+            " OrderedDict([('linear1.weight', tensor([[-2.6034, -3.3292],\n",
+            "        [ 1.9774, -2.4076],\n",
+            "        [-2.5968, -1.5908],\n",
+            "        [-0.5717, -0.8101]], device='cuda:0')), ('linear1.bias', tensor([ 1.4459, -1.3992,  2.9882, -0.1375], device='cuda:0')), ('linear2.weight', tensor([[-4.4623,  3.0885,  4.4030, -0.1377]], device='cuda:0')), ('linear2.bias', tensor([-1.6853], device='cuda:0'))])\n",
+            "\n",
+            "Loaded model\n",
+            " OrderedDict([('linear1.weight', tensor([[-2.6034, -3.3292],\n",
+            "        [ 1.9774, -2.4076],\n",
+            "        [-2.5968, -1.5908],\n",
+            "        [-0.5717, -0.8101]])), ('linear1.bias', tensor([ 1.4459, -1.3992,  2.9882, -0.1375])), ('linear2.weight', tensor([[-4.4623,  3.0885,  4.4030, -0.1377]])), ('linear2.bias', tensor([-1.6853]))])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load state dict from the disk (make sure it is the same name as above)\n",
+        "state_dict = torch.load(\"our_model.tar\")\n",
+        "\n",
+        "# Create a new model and load the state\n",
+        "new_model = SimpleClassifier(num_inputs=2, num_hidden=4, num_outputs=1)\n",
+        "new_model.load_state_dict(state_dict)\n",
+        "\n",
+        "# Verify that the parameters are the same\n",
+        "print(\"Original model\\n\", model.state_dict())\n",
+        "print(\"\\nLoaded model\\n\", new_model.state_dict())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lfOm3MQA4_Aj"
+      },
+      "source": [
+        "A detailed tutorial on saving and loading models in PyTorch can be found [here](https://pytorch.org/tutorials/beginner/saving_loading_models.html)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bIA8RtMg4_Aj"
+      },
+      "source": [
+        "### Evaluation\n",
+        "\n",
+        "Once we have trained a model, it is time to evaluate it on a held-out test set. As our dataset consist of randomly generated data points, we need to first create a test set with a corresponding data loader."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "UVAseIyz4_Aj"
+      },
+      "outputs": [],
+      "source": [
+        "test_dataset = XORDataset(size=500)\n",
+        "# drop_last -> Don't drop the last batch although it is smaller than 128\n",
+        "test_data_loader = data.DataLoader(test_dataset, batch_size=128, shuffle=False, drop_last=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RGGQ5NT_4_Aj"
+      },
+      "source": [
+        "As metric, we will use accuracy which is calculated as follows:\n",
+        "\n",
+        "$$acc = \\frac{\\#\\text{correct predictions}}{\\#\\text{all predictions}} = \\frac{TP+TN}{TP+TN+FP+FN}$$\n",
+        "\n",
+        "where TP are the true positives, TN true negatives, FP false positives, and FN the fale negatives.\n",
+        "\n",
+        "When evaluating the model, we don't need to keep track of the computation graph as we don't intend to calculate the gradients. This reduces the required memory and speed up the model. In PyTorch, we can deactivate the computation graph using `with torch.no_grad(): ...`. Remember to additionally set the model to eval mode."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "je9T70mQ4_Ak"
+      },
+      "outputs": [],
+      "source": [
+        "def eval_model(model, data_loader):\n",
+        "    model.eval() # Set model to eval mode\n",
+        "    true_preds, num_preds = 0., 0.\n",
+        "\n",
+        "    with torch.no_grad(): # Deactivate gradients for the following code\n",
+        "        for data_inputs, data_labels in data_loader:\n",
+        "\n",
+        "            # Determine prediction of model on dev set\n",
+        "            data_inputs, data_labels = data_inputs.to(device), data_labels.to(device)\n",
+        "            preds = model(data_inputs)\n",
+        "            preds = preds.squeeze(dim=1)\n",
+        "            preds = torch.sigmoid(preds) # Sigmoid to map predictions between 0 and 1\n",
+        "            pred_labels = (preds >= 0.5).long() # Binarize predictions to 0 and 1\n",
+        "\n",
+        "            # Keep records of predictions for the accuracy metric (true_preds=TP+TN, num_preds=TP+TN+FP+FN)\n",
+        "            true_preds += (pred_labels == data_labels).sum()\n",
+        "            num_preds += data_labels.shape[0]\n",
+        "\n",
+        "    acc = true_preds / num_preds\n",
+        "    print(f\"Accuracy of the model: {100.0*acc:4.2f}%\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6dWeedbD4_Ak",
+        "outputId": "589da4ff-a81e-44dd-e1d3-4925a4af9a1c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy of the model: 100.00%\n"
+          ]
+        }
+      ],
+      "source": [
+        "eval_model(model, test_data_loader)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gYYsFGV94_Ak"
+      },
+      "source": [
+        "If we trained our model correctly, we should see a score close to 100% accuracy. However, this is only possible because of our simple task, and unfortunately, we usually don't get such high scores on test sets of more complex tasks."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ukfSq4qw4_Ak"
+      },
+      "source": [
+        "#### Visualizing classification boundaries\n",
+        "\n",
+        "To visualize what our model has learned, we can perform a prediction for every data point in a range of $[-0.5, 1.5]$, and visualize the predicted class as in the sample figure at the beginning of this section. This shows where the model has created decision boundaries, and which points would be classified as $0$, and which as $1$. We therefore get a background image out of blue (class 0) and orange (class 1). The spots where the model is uncertain we will see a blurry overlap. The specific code is less relevant compared to the output figure which should hopefully show us a clear separation of classes:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5UXwA8tr4_Al",
+        "outputId": "98848b69-3a7d-4e9f-90aa-da59e290565e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2228.)\n",
+            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x2000 with 1 Axes>"
+            ],
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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "@torch.no_grad() # Decorator, same effect as \"with torch.no_grad(): ...\" over the whole function.\n",
+        "def visualize_classification(model, data, label):\n",
+        "    if isinstance(data, torch.Tensor):\n",
+        "        data = data.cpu().numpy()\n",
+        "    if isinstance(label, torch.Tensor):\n",
+        "        label = label.cpu().numpy()\n",
+        "    data_0 = data[label == 0]\n",
+        "    data_1 = data[label == 1]\n",
+        "\n",
+        "    fig = plt.figure(figsize=(4,4), dpi=500)\n",
+        "    plt.scatter(data_0[:,0], data_0[:,1], edgecolor=\"#333\", label=\"Class 0\")\n",
+        "    plt.scatter(data_1[:,0], data_1[:,1], edgecolor=\"#333\", label=\"Class 1\")\n",
+        "    plt.title(\"Dataset samples\")\n",
+        "    plt.ylabel(r\"$x_2$\")\n",
+        "    plt.xlabel(r\"$x_1$\")\n",
+        "    plt.legend()\n",
+        "\n",
+        "    # Let's make use of a lot of operations we have learned above\n",
+        "    model.to(device)\n",
+        "    c0 = torch.Tensor(to_rgba(\"C0\")).to(device)\n",
+        "    c1 = torch.Tensor(to_rgba(\"C1\")).to(device)\n",
+        "    x1 = torch.arange(-0.5, 1.5, step=0.01, device=device)\n",
+        "    x2 = torch.arange(-0.5, 1.5, step=0.01, device=device)\n",
+        "    xx1, xx2 = torch.meshgrid(x1, x2)  # Meshgrid function as in numpy\n",
+        "    model_inputs = torch.stack([xx1, xx2], dim=-1)\n",
+        "    preds = model(model_inputs)\n",
+        "    preds = torch.sigmoid(preds)\n",
+        "    output_image = (1 - preds) * c0[None,None] + preds * c1[None,None]  # Specifying \"None\" in a dimension creates a new one\n",
+        "    output_image = output_image.cpu().numpy()  # Convert to numpy array. This only works for tensors on CPU, hence first push to CPU\n",
+        "    plt.imshow(output_image, origin='lower', extent=(-0.5, 1.5, -0.5, 1.5))\n",
+        "    plt.grid(False)\n",
+        "    return fig\n",
+        "\n",
+        "_ = visualize_classification(model, dataset.data, dataset.label)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7Nut9X6H4_Al"
+      },
+      "source": [
+        "The decision boundaries might not look exactly as in the figure in the preamble of this section which can be caused by running it on CPU or a different GPU architecture. Nevertheless, the result on the accuracy metric should be the approximately the same."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VAw6dPw34_Al"
+      },
+      "source": [
+        "## Additional features we didn't get to discuss yet\n",
+        "\n",
+        "Finally, you are all set to start with your own PyTorch project! In summary, we have looked at how we can build neural networks in PyTorch, and train and test them on data. However, there is still much more to PyTorch we haven't discussed yet. In the comming series of Jupyter notebooks, we will discover more and more functionalities of PyTorch, so that you also get familiar to PyTorch concepts beyond the basics. If you are already interested in learning more of PyTorch, we recommend the official [tutorial website](https://pytorch.org/tutorials/) that contains many tutorials on various topics. Especially logging with Tensorboard ([official tutorial here](https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html)) is a good practice that we will explore further from Tutorial 5 on in combination with PyTorch Lightning.\n",
+        "Nonetheless, let's check it shortly out how we could use TensorBoard in our small example."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "VHalnDEp4_Am"
+      },
+      "source": [
+        "### TensorBoard logging\n",
+        "\n",
+        "TensorBoard is a logging and visualization tool that is a popular choice for training deep learning models. Although initially published for TensorFlow, TensorBoard is also integrated in PyTorch allowing us to easily use it. First, let's import it below."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "r-lDoypZ4_Am"
+      },
+      "outputs": [],
+      "source": [
+        "# Import tensorboard logger from PyTorch\n",
+        "from torch.utils.tensorboard import SummaryWriter\n",
+        "\n",
+        "# Load tensorboard extension for Jupyter Notebook, only need to start TB in the notebook\n",
+        "%load_ext tensorboard"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9fqMZ4W14_Am"
+      },
+      "source": [
+        "The last line is required if you want to run TensorBoard directly in the Jupyter Notebook. Otherwise, you can start TensorBoard from the terminal.\n",
+        "\n",
+        "PyTorch's TensorBoard API is simple to use. We start the logging process by creating a new object, `writer = SummaryWriter(...)`, where we specify the directory in which the logging file should be saved. With this object, we can log different aspects of our model by calling functions of the style `writer.add_...`. For example, we can visualize the computation graph with the function `writer.add_graph`, or add a scalar value like the loss with `writer.add_scalar`. Let's adapt our initial training function with adding a TensorBoard logger below."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6U7B7fg54_Am"
+      },
+      "outputs": [],
+      "source": [
+        "def train_model_with_logger(model, optimizer, data_loader, loss_module, val_dataset, num_epochs=100, logging_dir='runs/our_experiment'):\n",
+        "    # Create TensorBoard logger\n",
+        "    writer = SummaryWriter(logging_dir)\n",
+        "    model_plotted = False\n",
+        "\n",
+        "    # Set model to train mode\n",
+        "    model.train()\n",
+        "\n",
+        "    # Training loop\n",
+        "    for epoch in tqdm(range(num_epochs)):\n",
+        "        epoch_loss = 0.0\n",
+        "        for data_inputs, data_labels in data_loader:\n",
+        "\n",
+        "            ## Step 1: Move input data to device (only strictly necessary if we use GPU)\n",
+        "            data_inputs = data_inputs.to(device)\n",
+        "            data_labels = data_labels.to(device)\n",
+        "\n",
+        "            # For the very first batch, we visualize the computation graph in TensorBoard\n",
+        "            if not model_plotted:\n",
+        "                writer.add_graph(model, data_inputs)\n",
+        "                model_plotted = True\n",
+        "\n",
+        "            ## Step 2: Run the model on the input data\n",
+        "            preds = model(data_inputs)\n",
+        "            preds = preds.squeeze(dim=1) # Output is [Batch size, 1], but we want [Batch size]\n",
+        "\n",
+        "            ## Step 3: Calculate the loss\n",
+        "            loss = loss_module(preds, data_labels.float())\n",
+        "\n",
+        "            ## Step 4: Perform backpropagation\n",
+        "            # Before calculating the gradients, we need to ensure that they are all zero.\n",
+        "            # The gradients would not be overwritten, but actually added to the existing ones.\n",
+        "            optimizer.zero_grad()\n",
+        "            # Perform backpropagation\n",
+        "            loss.backward()\n",
+        "\n",
+        "            ## Step 5: Update the parameters\n",
+        "            optimizer.step()\n",
+        "\n",
+        "            ## Step 6: Take the running average of the loss\n",
+        "            epoch_loss += loss.item()\n",
+        "\n",
+        "        # Add average loss to TensorBoard\n",
+        "        epoch_loss /= len(data_loader)\n",
+        "        writer.add_scalar('training_loss',\n",
+        "                          epoch_loss,\n",
+        "                          global_step = epoch + 1)\n",
+        "\n",
+        "        # Visualize prediction and add figure to TensorBoard\n",
+        "        # Since matplotlib figures can be slow in rendering, we only do it every 10th epoch\n",
+        "        if (epoch + 1) % 10 == 0:\n",
+        "            fig = visualize_classification(model, val_dataset.data, val_dataset.label)\n",
+        "            writer.add_figure('predictions',\n",
+        "                              fig,\n",
+        "                              global_step = epoch + 1)\n",
+        "\n",
+        "    writer.close()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yyHvMYw_4_An"
+      },
+      "source": [
+        "Let's use this method to train a model as before, with a new model and optimizer."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BIs7kUMW4_An",
+        "outputId": "daa9cdc9-2172-4b2e-db91-9c845c072eec",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "45fb190bbdba4521bd5cdcb33bd2f443",
+            "ee311357933f4fa9b6d06ec99e995543",
+            "23e4aa3da17c4162a6c56544fd58fac2",
+            "60db3a0d56b74bd095f401628a002248",
+            "c6b2d14531294383a355b1f2fd5215d0",
+            "812c66d181ed4041b327c6e4ccccc8fe",
+            "ba817b44ebf045a5b6d17a45058e5bc5",
+            "a34e75d232054a4da965965940424888",
+            "da308fa0dd284890a4184db770dc9c70",
+            "9754daa9bb7c4e6fb027135b2056085f",
+            "93f473640a7e499893e12d4719720549"
+          ]
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/100 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "45fb190bbdba4521bd5cdcb33bd2f443"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "model = SimpleClassifier(num_inputs=2, num_hidden=4, num_outputs=1).to(device)\n",
+        "optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n",
+        "train_model_with_logger(model, optimizer, train_data_loader, loss_module, val_dataset=dataset)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SJitBm654_An"
+      },
+      "source": [
+        "The TensorBoard file in the folder `runs/our_experiment` now contains a loss curve, the computation graph of our network, and a visualization of the learned predictions over number of epochs. To start the TensorBoard visualizer, simply run the following statement:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zHLCFsGV4_Ao",
+        "colab": {
+          "resources": {
+            "https://localhost:6006/?tensorboardColab=true": {
+              "data": "<!doctype html><meta name="tb-relative-root" content="./"><!doctype html><!--
@license
Copyright 2019 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
--><html><head><meta charset="utf-8">
<title>TensorBoard</title>
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xnLgWr/rygoBdZnUxmJA5tOQQaGr6ctWyoAZwJeyVW84F122UY3w3h4/F4XWIoBZJr2Js9iHF4B7iKIZ7mIUW3gDpZEsmgFCaghZwidAhbtM/mjJmCy2EHoTX9VmVmLxKEQvZETv5Hi9i8cRfg0cHoSG0J5vKk2s1mmMRhjlPrMPgtIlC4bQBfTDYhrCdGAvv+vzGr2H/emiDgl+PSS0hgDA4nqUs4D+aZcRvDl86zKpIiziYoSFEL2rinU5RRRzHV3hWQ8JZZcpiTTxDkK9dwV6VlLq+DTtqgs5kUU8Cawmima4kxEU8RJKC4TDDJBlQ6hNobp9LPvSArzouZjsmGMTyqteFqhNHKoLWY7wR+BUL8vOBnoHQ/R2HnDi2r4ZtJ6d8d0QOpk+OoURWkErn/AuU7jKTX6x6US5msSCoD/4YQ7lAyzOkhre8KzIhVxEAX+G6IVlA+jtjAdeQ9yHZWcLX75UnUJfncIoncJKCvkQeBgoAw5CaNYqBropT+bzHHCzH1p3rcyTUt6mgO9INS97UtoXHEO0Y72GEfJxq2fitJwBKOcijEL5EdC/l4drH7q4BVzOHikzEH4E7syUEemZ4zUK+b5MZ6PHagxZIKMWQiexv8YYqzHaUD4kMcAbw55nhUZqBT92U5fM51Mk9LvPHqeA040ZootrQ+hkBmoZV2uMNgp5n8Rq6EjcxskIS7XS3SqqNHI/yq9c1eMVe24t1lLCeVLNv7OgxuAT7i5uL+ciLN5BWE7CBJnsoDuAOM2ucymTIOCHbmdzKPfyZS6SCu9usjEEg9srtfrh5ZyMcLlO5YeussxnI8oszzRkisUSurhSJoYjzNuQGcFP3Qk3azX7usrTlyXAH/0RlDIK1MsMysQmHrAWg0cEbwg4hE6a3GQQmzgWEyGw/5W7UK6RWuyA6jf4RBgMATBBp/E9NxlkHq8AS3zSszvaES6TOlYEULfBZ8JiCCHOcrVdBvH1oRZ42x9JPbIZYZTMZE0W6zRkkbAYAoTD2cxcV1lstjghw9ngYyxGSC2/y1J9hgAIjyESTNbpnO4mg8zjQeAXPulJ8g7CqTIz8IG8wWfCZgiLOLer7XKTSyeTgX/5I4n1FHC61LLBp/INISJshgAYwmZq3WRwDkSb6YOW5yjiTJnJ39MtQBs4yktBBn8JoyEAqrSaE1zlKOYW4CkPNTzGXpwtNfwj3QL0RqYjVHqoyeAzYTVEIXHu0KtTDw0RmzgFXIM3axO/Ai6Q6XyabgHawGzn1AhDhAirIQCOYQBVbjLIHF5F0oiP2pG7gUvEZls6mVURbaAFcdftM4SDMBsCoFanuzyDdAs28D9p1SY0ic04selMJ7uupoB53A7E0qrfEDhhN0Qf4A69JPWjSaSZrU5EbOoIilIpNtVuBSbR1RTwZ+4CxqdbhiF4wm4IUE7i60x2k0Xm8RDCz1P8eBcwQWZzk3txCdSmmD+zBmFMumUYwkH4DQEgNGg1R7rKE6cc2LSHT31OYrxwZ7rS1KaEQtqAH6VbhiE8RMMQUIJym5sjbGQeH8Juu0D/wmKEzOb+dEXpAvo5ZnAVmGgIL1ExBMB3qeanrnI0cBvwZA/vJI6IsXt8LyXUZgDbWAecnW4ZhvARJUOAskBn8NVUPy6JTTwTSHSNkiSOiLF5KW0ZNgdSwH8DJ6dbhiGcRMsQsDdd3Oomg8xlA7DQ+evrxPmO2LyVrgC1OZgCHiGCp2sb9kzUDAFwgVZzhaschcxFuZsCTpeGFO6d6AWdy2FYPAEmPilXCfUparuhVWtY5wyc94iz6jwukwrVZjBx1kHqXTZD9IhiCwGwP8ribFWmcxiGxWMYM+Q8UTUEKKV6Axf5Xo3N8cR5HDjU77oMwRNdQyRYpjb7+VW4zuZEhIeBA/yqwxAuom6Ig9mWfsjF7tDZnInyCPhnOEP4iLohQLhKqznPyyK1nvOJ8yBZvgHTEDzRNwSAsFyrvHl41eYHKPcDfb0ozxAtcsMQ8DUKuTHTQrSOy4Bfkgg7N+QhuWIIgGt1Bmekm1ltrka4l+iuzRg8IJcMYaHcplPcd3W0jknAreTW92FIg1x7AAZT4u4AYq1jOsJSNAQ3WhsCJ9cMAcpUreXElD46i3owJ2PkMG+g/NJNBneG6OJxJPRnmxYQ5w61e7/iS0F0Fq0oddkUZsgam4BqCjlORrs7ftTVAFKW8Tfg+zqFEcRpAZcnYmSP4bRzA1C/8xtqY9HJCpSfBKDL4C9x4D6KmCql6R0wl1aXSZpZx1aOQykn6PveekOo0VqGd39JL6GATu5EjBlyDuFRLI6TKxibrhkggzGErKBDFtNKJ0cAi0mcXhEmiolzj9qJVlBtihnKaoQrgxZm8JS3EErlCs6Ry3kl08IyHlTLMj6SVmIIw4GHMi3PY46jg3KdTB86WQP+R8cassZmoJ5OhssV3l1g49kilLSwHjhPY1yCMB843KuyM0KYzT5cBJwStBSDJyhwL51UyTg+8Lpwz6ddpZU1DGAIQjnwidflp0FfjBlyhWdRTpOxjPXDDODTOoTYtEszrRQxDFgB5tpaQ0a8h3IlYzhJruRpPyvydWFO5rNRmpkInAT8wc+6DDnJVqCJvgyVq1gpgvpdYVYC2aSZ5xS+QwUXAwuAw7JRryHSrEWZLFfx12xWmrXQDQGVRazhM76JUE/C/QbDzryAcoZcxSgZl10zQACxTLKCLbIQmwIGA6vA/2bQEAk+Asrpx7dlHE8EJSKw4D6Zz7tyE2OBUxBz3W0e0wEspp0jZBytUhrsAm/g0a6ykGfox2nOCnJKB48ZcoZ1wLEynphMDEcIUOCGgMSFibKAlShHOOOLz/eYyRBlNmBxoYznXBnPG0GL6U4oDJFEFrJZ5mNTwFGId8vxhtDwMcpk/s5RMo7fBC2mJ0K5f1gaeQso1WrOdsLMh+8pjyH8yITMD4Lwm1C1EDsjjTxKCccjTIT0Q3oNhlQJtSEAxKZTGlmBMASlCWgPWpMhdwm9IZJII5tkPtXEORrC2f80RJ/IGCKJzGeDNHIhcC6Ea4bCEH0iZ4gk0sg6PuJYJ8w8FHPYhugTWUOAs411Hq3AEc4FKmHbxmqIGJE2RBK5kY/kRmLEORF4PGg9huiSE4ZIIo28KPM4E/gB8HbQegzRI6cMkUQaaGML30KpBj4NWo8hOuSkIQCkma0yjyYKGYqabayG1MhZQyQRm43SwESUkxCeClpPxPgv4tsvvc8Lct4QSaSB55jN6QilwDtB6wk5bwLnyyT+Uybn11gsbwwBzjbW2ayhgGEkzn3dFrSmkPExSjn/ZLhM4rdBiwmCvDJEErHZIrOxsRgErCILpzmEnE5gBUUMkcm0ik1n0IKCIi8NkURs3pXZjKWLs4CXgtYTCMojxDlOJjNRJvLPoOUETV4bIonM4fcIJ+TZNta/IJRKGSMkxmtBiwkLxhAOYhMXm5XAkZDT21g/Q6kHhstksytxZ4whdkJsPhMbmwKGQ049MHFgFcqREsOWspw1fEaEcgtpGJBa/gKUaj3nAM1EexvrMygxKeeZoIWEHdNC7AGZxSMox0Mkt7G+C1xJjFOMGVLDGCIFxKZT6lhBnKEkbksK+7TkFqCeOIOlPDuHBOcKxhAuEJuPpY4YBQxHeTBoPT2gwBq6+KZMwZYKc36uW8wYIg2khjeBC3QOI0i0GMMClgTC80BMpphrBzLBtBAZILWs48scE/BtrO8jTOTffNuYIXOMITJEJtIhtbRicQSS1W2s7cBiihgqU1ghtglv9wJjCI+QGj6SGmLAt8H349zXYjFMphKTslDc45czmDGEx8gMXgDO0LmMwqLV4+LfRDhbpvKYx+UaDP6jNiXayBlB6zCkzv8DQd7QrMbLR1AAAAAASUVORK5CYII=">
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">

<style>
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/uYECMKoHcO9x1wdmbyHIm3-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/sTdaA6j0Psb920Vjv-mrzH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/_VYFx-s824kXq_Ul2BHqYH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/tnj4SB6DNbdaQnsM8CFqBX-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/oMMgfZMQthOryQo9n22dcuvvDin1pK8aKteLpeZ5c0A.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/Ks_cVxiCiwUWVsFWFA3Bjn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/NJ4vxlgWwWbEsv18dAhqnn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/isZ-wbCXNKAbnjo6_TwHToX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/77FXFjRbGzN4aCrSFhlh3oX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/jSN2CGVDbcVyCnfJfjSdfIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/UX6i4JxQDm3fVTc1CPuwqoX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
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@font-face {
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}
</style>



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0UTVEIniStK2KkQNSWnadTS44MC0ZcShqn/tNbjSpzhJPWqmVtdIahqMy4JIurfISwFqdDknlN/ZkJWaJOfPmu0hCIViLEiCXeDo1fx31S8zs39RXB2funyM/h/4qJvzk1FE1AlLSBoRckNzDUC2Vie0A3dVydpP2+nlQlXWLmsM6kpxf2qNAnzxXE9I8zbaSdNBbUmBp9qN4v7SdxKEPSQw7OPKYqmGDHU1G25UrXOzQT8UESQ8gg7ZI7KplJNU1l8Ei8eqUd9TGu/Hq7MDh3qFLtVYpmTYB1RVu8jGGn+HIKixbp7pl08klbTB60qEpikY9sjLo9EaglvU96Bz3EvfEcLRjtkf+1rba2RopIJY4orU7uObY3Y9SlGnOR8UZRdWYYRWrEpkS6FgkfdvN4lxqew4RQe5mP6W+wnEkhvXYbXxIZd2qOGV+DxkBE9TT6D3p7Zq2VzJUAseOmluQLiocOOkAKDVWaXR015dYYhhgYrCcD6ml8QGDF/GOWXf4pN3DFIOvh2olo92vyup7stXa0MvaZcUB90LXMpfiqT0HdGwvKlR7KYetkJMa2zJ/eArTc+pPLwuQVoRD7jxII8nWDhElbFkfTa6MUhEyZ1g5yvyhBBlM5pzxUDZLT/37WD/EMrpTwTbt4IhlG7ZotGO0+OI4aZ8Go5MwWJcz21FJpnoSIFY/ccFOPVCPGOEsSkbF/PQIj5jGOrVbVnfwRTqNq6nNasQEQSYH7oLXLiW31UgXpk8J44vqKc0GKe+BUadt3wUGAkJHB7zFtQeGQxccdSieReslSPWf21E+PxPTi3gAyczhLgUOemFaBT28Nb0szb2eFfH0QV74/FTUWTUSxGVUnmvtGSxEF+eUJztVfeYpr5KlNEbYbqQ/w8UBwJAH0HamgKFli1D0/cDgt1aKRsyIiBkajPOqYfpIH2OmzjUXl2DjQBcnYNPzP23nDgeJTLHZjvA3F6eZS+qL+hBHp2CFSjh9r5hEHI5LWf8yYeu+fiXuj2dEpxTy4BSscCAS7mcaFpIjOC3TN15a8XSiiv1d5nw1J3XvVVdSdCXBLyTQ7X6UP791na9WoVPtyKsOfHdGN4V1cnOIkpCaGROZGQLYjJzIqtrI6Io6olJUVVV4RG1VQhxWw8BWVUnfSk9FQ88uQ89O+1zrjciqGmx1dYKSoDTFEzCaH0DKetZ6qpr6NipKhta6Pz/vyl71JjjIgm2Mo3dThbeljyxJqar/UU1W/8NoQ8MMt3tOGvIR8uHF1YdJ9djPRUuCiuyQgv0MsjKrmh7gbl1DWxUY4ZuThDq3YelD8tGWvyGgLy1vY6imbeEGLDOpHO7hsJgJURqr/sUfdMIUeQNS3akvcKdANyLxpG6ttwWvR5SKf+OeDogqLHnnfDSKVObzyPWhfkSuc08aOBScttLk84hUfq/nmw8c5a+vGV3LuuaOM8ilM73oei1b0HiQOciMcqb1WLAxXFpjymrKekl9Wf4AqqVC9FkHJbMIdHx1e6qd9XNg5NrO3gaSJT+J0MR/vZGQBOTgYj7stSKQKXNVXJDY31kSRgv+kF24ebC3cBUl1sW4FzlLSg6fQRYu7hOXE+KiplBr8IY64U2sYRs6DULU6B833EN1i/QIcale8TkFiZTnqB9vXuxNN79muxhd9Q6O2QgmRCCZ0QQ4jJKJnpTGv+v9oztv+iwCmx2v+PFGND0YOBw/gIUOqldmXGhMUkLkg/zYNCQBblntjGSPaFO1s10v9n8avPBleHDEAF69UZnZrVe/kugkTRzewS6vfEa97f0yw/q4iTWYFOobntaUn1ZEjgpQEKYF4W/NhTyv0Yd/jpcJkcNPVxmmV74yjU6gZf4ppenXaNNMkCyKPbn0FNpK3wWEJHdmZqV0gZyeMm+7ch9v5jGOnDmQHhB2JdRa3fDkj3rk9+T/0vjk3+XHvmFPPPktb7jXunTtve7t2X9N52URmJf7Oyv7Z1pSZER6MpvtCdeYGG/3KOJiemfEJYDQapiYQomOZSg1yG6ZmOa28yKIOPdT7qhorWaMNs35ggvr6G+bbL/MCjzP6ah2FK4FBKZTczOTe4A20vPvgNDZoDTV5MABkCq/LlcqJyNXIgcCLfEZqKciVoFpdFmDesA2F/EViFPTiYjRMXd1dPV1vB/kbEJBSleHOp5MjjQ3lda6OCtr5fRIdt1PHpfS7wyfXh9tfyRcLaIbr62anLqFf8lc4Y96mu5478FlTWmhS/jNdM+EJUwrPupOtsbzm0OtfvTfGH6QXPh6JozbrrScJ8y+FHD8bb/cUsVwggG/0BH0/sMvm6XGSpkVD6YkrWxDSTe7c+a4a9+DZXxyDkByItiaGDTEM0iu4ZkhD7Yc//CjBmoBQ54/eN/fc9+rwOayDzexK2zg39L2rRpGqZi8+EBartu5PKFZIS7+CS6kU6AePnpik+v3e5Sop2jiixQhuoqm7aMozvXeY41V+Vld4yx/kg4Gj7aU1KWcbDU7WvOfnqsuEoFnZqNFInyV/QiTCN5nZPE7+yH+3MbPICCNKvWjWSgsBAw5vWhq2g8k+yJtInsAQ44pGvpsA+o93SqOOAdyC3tVlhAy8kBF0JFz/cg210yPCDdP7xAzQPhtibvbpfSGstK9AlbQCoPe0sXS0v49hEVCL/h99hL428m9tBO/9ybefFp41hNCTIjwS8uEGDhR0dy1v7/a8ZixbckIvvJrm+BY0FLoZabrhRNHyKcHKnr6S5R8k83m9Hp++unpHLzlzRK4Kve1aWvrXsVreu2rvX03aKXVzpKkaavJDXQ1abeQV46TqbL0dQ1N9DUkmZmnCHqM41HHOAfqm/OfSS4K9QUz6i84iIkjPTGOf2D+8nwat/5Dv8AorzwlLqI8PxllC1r42qlofn899IsIf8EvSmxaRteT6WtFppluQF5yPm0H5ff4GW+Z9FRNX92p9/Tqd9zSLDHteYSAniPjaOrOBVJNenBsCSkb+ECP+P3O8mhOA355PmvXK+xFQG9UdVZuclGwu1FWoJPdy0Drcvdm9umtLDQhcfhBb1RfTnlOadBspjeo+OCnAYPvBgx+qug+qFaSF+EVYN6xnP1wEkFWlp++LWp8WFre0Fj0DuL/n6Bpnjw7SK47206egNWRk90XG/Don2Vs6T8vNHH1M59b7+U8Qm7CD/ByunG6sXvzNnHWWfcvykeYXlt9MTG7sG530/7m2uuFlxMrYDsd9PbtGZvUMz1npmGdh1sFsoXnUwTWI9iKfiQBd/t2N2CJ3TkBQNsVAKhoMpkPe40M8E5YoCEH5vQGfmL1womKR09WGi7PPrg0Ww9iYdkZ2RlwuuJtMcK7BBcPd0eHRbeWvGxLqAXqTpuelRfB0ybaRol6j5A1YOD7aIqKLv+cyWvA+jXRIN+IR2kxVXfYwMsK5FBsP9eu9rxyT8EF1wEWKFLu8fHLl9BdNE9SX8V1atZO69a2Rae/AqkIm3Q/WqMmGlOf6gEbtwUaPjhT5PVtLjaj4+Mb2sYmWofkGMekvJGec49yn4M576+9691P3cKFKYCFOpyXW+zQ432hn13YruyxGLJW4SftnAZi9gSosc+zc3/mKQ38O58cPrVzBK2+gC5uGYi3jtUUlPUwiB3He414ZJexpXEx0ybG5sFWfT2p3v0h5UmdzDJd5raIsZWDNWGLtFUPFrz/di73Drkl4PxquSd/fvrZZ8lGXN/CzTetITYrO97M7UHP010ByufOzPHYpntF73Xz6mKzJ4BDpzMwa9AAFfGC8hK8HdzdjWMteNXRFMACBfQo4pRhrmuuLiHGQS7OwNEm3nnrpJRAxg9haRk5nUd4rqtuIqKZvwlQvnr5B2uUXmrtq2WzUtE/4/MG9JI3MrBXiNP4+SaMrN1rFFFvsPC1xfjPUEa8HQkh9rGB9Paj1MH9rQ65PE6KOLC3iWNig/KfOSOIf67GfMXB/xbjSAusAv+Zivx/LyekzbMK/KXg2qreJCZVzg+31MwnxlW/AdlVth8r13oHPYLpKIB5W4vz80h43r/Ki+1Cd+ZPEWt0MjDa6fXRBZNgznu4PjDY4xZG61IHmLcUGnc/6sA2/98uWmrBVHSlfireILkspnAaLnllZAl0yOQBRbwNFWycUi36BPjPnrnroX1MokJHw0srermzqWv9SQZVLczV2s7TQEneUZ5Dql5P298gdeshJWLYxKPCkkJjGu9CcAzVhWM+MtgR0kixrIiMLHdQljK7YnBIZnzxIGkQC4Jhzs6hE41tz2Cgh/Lo+7NpxUJ2ijgZPv5e6b/95JiCeNKxKPEnoFBy6JYEhb1NPIvVS5gVLZzFXiHexaRzmaX/ciZ8f/ghfs6K04Vn9lfGr4zXLjxWnLNwwMzSf2XopPCKLdM8jwJjFru5w92zthddj52/PgJiWQHZJkReneNqucKR4OdKFmhZfzz8ZTp1+vM40vFwuUWQlzAud2JkrcqwymhjFIV/JQvyTJmAfZoJ+N8jMli6+a02nbZ7Oy9OjMvxEsgCzcvQNflhOnX6y8jjtpWHAqDxha6IOhen/EC4Jl3l3GUNHSfzEqSk81WCWsLtppJfvWrmXhZQW18D0xx9jJlqUfd4qETYpvvTGjZhQgafkY7vUHZbzg6fnD83/fwUmXuVspTJ2T2FyWx/XN5I63GlfZcnZbwABbhCdX+BCMQCRztfWViCQw9/PXj2dGGquds5NMjdMTw3BIe+XQgKHz2KIifGisKjY8Nj4hPt80dG3cvSssJj0zIL/foHivwzsiOJWbmuZS8G7IqSSZGx4qigIMJUf8Bqf6DHZNCww4C/h2e/370LcLb1uFDWjNsYJkShUiFYQa5XTjpYulI6hJZG+RykMR2lHn1G/U4FpdSmJxRdBX6lWNsa9NC6x7ObirfiPV5RkIjWrAJipbelUZLDuFT7jW430zx3aruBLP+dWNsq+uHFbfTKK0pIdV5CcgnBWvGmZowMl2aNkoelWBhXndFR1ROaeFVbxTsimlGyJ++R1dl1bBUN410Aq2cyj4Fgrmu8HNb/FBQ/6+U2lBQVPp7scCM/ncwvqS/PTLMx1zUxd44L8vR3jjM3NdWx+x/ZUHweXynDVll8vqGu8Cy+qrSEvaLoHKF2hGqkOhSmMkQ1GhlsNVAdHxpUHW03AMeeZr27emF+4UH27lV+DS7CscGKGiJCJQz29eFx0bnJMZJWx7VMAq8YXKAme2iDobSQOUis+FFx0aW3wWgO0UEYcqC8klL2moKzDfUF3Ozpp1v3V+2kexy5NREhQQ9kAgQgV0ofw5o6ReFmsSCgmx4UKCEGsqIq6pK28SC4wVlCKgHc+R6HjHwnh7Q8R6e0TAfHzMz/R0RV7oiJq6iK3lTVcFFVgYvp+i7ZLkXVReHZ4YXVHBxf5HPkoS7PwOXKcRc7RYG/knfv3BUrJFSl43JD3RycnHQ0mlJIwJpxX1dVSZL/2s4dMVXBS4XWZYHBcfn+DjcVjWWPZVwkOdUGE6MqQ5yLrmlIyN9RlhDyzEcMgX0Ilhl4M+GIsHgAlN3P9x6WyuamilfjCKYxdelayjMUwLyFGJzuJu7f7Azd/00mwJAhbe73yGi98ZCQDKdbc06kWRV7kLBOmvpCN67+ssKdiYb3eYVGr2+FWXvjgbbCpuhRX17+o54q1WJqr/CT3soyjK2lp66OOcFOw8bSU+eehStiCXerwfFiqIVPZw+9NYerF8HVNtjDhIKULL/oHJh51tpY/NDFQ1Y9rUcyfuzXrqbD6OMBXsyWwi8tV29iyJ05reyzGtnKR/C9emz+z7jmwVBZRVNJwzdQWUN5mq+koaJpVdXq8iy6yqyPuGeZl0zafNLtoDNDavVsevD9iqSi/0QqwislY/PVKvGcPNHOrWoRVqaAFlPHzQgeHU/j2wGzsc9NTgyZ92j6Owff3tA+2s01vBgVMGTKY54jycVZ/kHFWbEAz1AMxkd3qW0DLcUhD0YUhVTNH5H0YtWKR5o77rVdMbmcxxJzRcTZw0xbWMXicZp+3D2j+FFdc3snw7e8oLHMgYltldFbMUuqftSQXbjauLCk5+wa6OCY4HqvE0/61PbIq0lbQFtao1HfcXILdrzmsE2NTzMz/O++vx1b6H/bI0u5UOXIwbo3kuCqo+5oKoIW9sqoY40/cSYqOg2EFI/8pM01K/L++5oUE/suzH8pJt19ezQ8vNzf80ZfYuKbdGXr7ohI4w7+W0XvRR7GPw5TtsFGMh1ljFc2TS19Wp1Y1R8WkrPSBXKThptdmxZiMZHcSEhC/WAVfhwwcr7SgX+X40luZpeU04PtXvblyhQF/Vsipjjo8ahkBNuMvMzoEu2zdNNvX1KOr35j19ah76bT5CCXUPWWAnKE0fTiaUsooh6RfbKHelRxKnikgFYzB6HsX2YCM9SJNMSz/ssqC65lv/Nj4Uuu0I9mQUnmpKcdU7ILs0tj7zoyKjbVtwuWfCX+/94Sk+OeeUvayNj1EzzGdv7teY9ia8sHYh77OauEECO/xb50l7aRsh7yiJmnvfG/C9dZ/Dohrnh2tKNkLj62dEZufNYms+xIHPOJEGIm8Nu+p8/NrM2ozWX8xuIBPD5hBkHeJqaBPg2BAV2BAap6N+TkBfjlZa3lFAQEZRVBaGPeJTDa9FKrpVTDqUtFAkFYZ/8Es3NeFmJnz1y5JX8NWDfZHZVMQ1FpxC7v+5bDQJfB6aBkGo4ipXRGgI3lECSecFA2DkWl+lJs7ayfQ+JVByVjIiqHQqlwsZ6cv50bQmqFs+U44MppDNzcTU08Xcx4DVxdzc3c3AzhNC+SmMQXyc4XlQTMXu2zkGVMRpKQZChztf9XC2d/bUXwp8PQfa0Tld52Z9mL/U77/zx1HsPQEtwS1xLSQoc5xfXFKYaLvdSFx62CPIJ59CAkLC4MpFsS5LPlYWTu2FLT9tHnTzzqJLnygwU8/RycnM0XSAv1Ko2WUQhjE8Su9mzSeT5JKQc+HL7Q0N3ZbHdZEjBzlLlngRRjHMHL0cV8kbT46iml1KdRxEuEMJrPJpQ6HKgfYi0ferUX0wUKZdmNSQn59VCcXJ7XmJCUXY8UFGB74qPRra19CVHoNsCNDMfvAs3LzhHOtYW1fhg7f0xp0M7SWQZwjxBIXYwyO7JVVStzSjUsmRgQXJwSE1AVgrF9mM76elklVQVpdTVVlTrguZFrzMYI54dKhxF8Y1L8vItyoxKqKrOFflz46uUOuVPolSssPMdp696WUtcIvnMXKp40pVaZpg1GBBC/j6+vaLz42KnYn7Uv2klOxfL31Tv42mf5hISqKoiDwupi0rdVxYRF7oqF4k2b8OHmOv/1LY/vTvvDtz33k7HzTYwLDSgrDo/Lr0Cl7/5RIvvag6xIq8BC80t3JHU0NCUl7urIy6qow/BDX2R/8eCH/sKLD/7dvqB0/Tw9bdbyF/8BbA7/B8ACfv5RXi4s/FISFuuq3TPy/xld+aEw/7/MKFgNXAXhBB65quZ3mSNchXEMzjWjNmffEVy77YdFatbn0cri6o3s6doSBL/VEMh8d5R8AGqv7OXj7Ca4f4GMTaYvg+n0kAfMWAUnGXGbq5WubLETFC6/WIYgF1AL8uFFVasHdK2u0/W6QTfiIbHA5gvygmuF4A/RnOkT3Way54spBkO81cmv3dOk0ApZXJgOiMn5By5dK/gTcmD6RLyZLGPEyQCumQpztelDxEyeZUacZg8r6CL+WCHfqB23tFDzijCT7c2n2ArJAFtxcrcVQzi4EqKJ6RNUM9nAiJOvVkoJxLqiODYDRAJrtvBsknxFNpJyCU2M1VykPk3yDYNVohi3tQoHgVZp6OZCZDnjOkFDskq4nijcegh9PuvE5JKvQ50g+jd1bdeqZ1sdixjTOjCv6diLvkJ1nJdYR4410sawNoJ+zUjOShINpFgzFZyEmXM6HtqB8mpjAeduDMj/r/PZkuf+D7hl/haureDS0u+otzR8X3bZVuu5csq/2umBcedopEW8gHvF349HyjOhk4pHKvnj+bNyaIF8NLGTbM6MmQDumb/1qAmimZ+PBplWJmXh6LqSU9bIxFQDfVc/Ooi7qETTFD9xKpmamfyj8OaZZEJ1OAXGegvRs0KesdMf8OOk8lvar+fGgFrQByzkYE7awz8Q118VeWAeDX/e4QBmRp+xoE2+m7GMlofvvD/oaG9B4fka2H8Se6HrFqJLTH+wgH5KKNfdqN0JIDb2+w0mUSZMl/ar/dZgRnxZN8SfnmUeAnHhOHQfyB3h9xrb8lTF+40BiaA+G/tMsBstPAFki2qMQUS/5tcNCQScSy8XOQVfxRiEALnJ2ldCUowtWCR/bx1fBoCnf+puD8A7of3bP+v8X7X5l3kBy6EAAiwuW1oFsPzgUpZe2gHEfVPfVyJbQ+Pfi3eaa0BJkhD7gF+HchvzHPsXu7Bjn49F5zN8RXCq42hEmtMpgjdPSik1+KfGC9Rhhjki5BGFOh7P2jY+iolml5qNctvK6etiatjdxQ1O45ZT/UftEOE3bqvtRDIu1wZeAkdbRF7T2U5EBo1LadSY/qpN8LQwvoPx7dm69oGa7qbVQwQGv8Rx1KnFcVKDBsR6FEuP0M8z0krZRXo2dGVIpgrQZACxNubaIRKbn2lHsmgpZjlM0jyXZTtIjE+SYmfuBjMu9EnBAg0J7SSxU5jouEQW2Q64r7UQXTsi1rKzExqu1A+X6jlOatkw28nEF02OfXiKPKIS2pJjVzEZbbh3ISyxJ723GZchu0mFSaVLr173282DSdX1rOok5z8RfYdVfiCXH0hnhKAmyqsd8skpPf7wplGBVT7cT4/7Y57eJEJrEuNezyksAufPrcCIunfeVFXdJ2HqpdFkoZ1KqYlMukBm/Ja/KOWwUGBVA2qzUUANCRoyyqbbUiKXJc7FH9nO5zSHfEcye4+oFvHDWEbe54KHMapyEvmBaIey6DQ17eZMJ5Ccps8yPphAoIU0opigjziaqITS2h0IHAZOJg0ztFwK+wTcwrzSCwDrvIzxyBPmHfMMp0JDJH6JwCfgTFgBHAL+5BPz43dD42VNaMz8iDuhIZIjIvBGKEtHgB+QcNKFyZXG6iChNoVd1bfMfWhI0wE4A1wvQzAyX4bCzEmN3qWmgWUYfPyXYZ1S1F1muqjkufMQgMCRPQfeuAniJ9At5KbEXeAZLuxWVeHGxnWjrMG7WLQ1mFfOaLe47BD4atnWdUN2Z13y3lDgbc+HC6sgr7+IwIsjc7PHX98u5CYJ6wVv6hZ2bQUWvb15EHej0/q5rrPikXjbcGDnOhsJmr8hO7LRvBu4V/sNalTIU6RBh6Jrts38i4GTzfts3QMAAAA=",
+              "ok": true,
+              "headers": [
+                [
+                  "content-type",
+                  "font/woff2"
+                ]
+              ],
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/index.js?_file_hash=4a366cbe": {
+              "data": "var CLOSURE_NO_DEPS = true;
window.polymerSkipLoadingFontRoboto = true;
/** vim: et:ts=4:sw=4:sts=4
 * @license RequireJS 2.3.6 Copyright jQuery Foundation and other contributors.
 * Released under MIT license, https://github.com/requirejs/requirejs/blob/master/LICENSE
 */
//Not using strict: uneven strict support in browsers, #392, and causes
//problems with requirejs.exec()/transpiler plugins that may not be strict.
/*jslint regexp: true, nomen: true, sloppy: true */
/*global window, navigator, document, importScripts, setTimeout, opera */

var requirejs, require, define;
(function (global, setTimeout) {
    var req, s, head, baseElement, dataMain, src,
        interactiveScript, currentlyAddingScript, mainScript, subPath,
        version = '2.3.6',
        commentRegExp = /\/\*[\s\S]*?\*\/|([^:"'=]|^)\/\/.*$/mg,
        cjsRequireRegExp = /[^.]\s*require\s*\(\s*["']([^'"\s]+)["']\s*\)/g,
        jsSuffixRegExp = /\.js$/,
        currDirRegExp = /^\.\//,
        op = Object.prototype,
        ostring = op.toString,
        hasOwn = op.hasOwnProperty,
        isBrowser = !!(typeof window !== 'undefined' && typeof navigator !== 'undefined' && window.document),
        isWebWorker = !isBrowser && typeof importScripts !== 'undefined',
        //PS3 indicates loaded and complete, but need to wait for complete
        //specifically. Sequence is 'loading', 'loaded', execution,
        // then 'complete'. The UA check is unfortunate, but not sure how
        //to feature test w/o causing perf issues.
        readyRegExp = isBrowser && navigator.platform === 'PLAYSTATION 3' ?
                      /^complete$/ : /^(complete|loaded)$/,
        defContextName = '_',
        //Oh the tragedy, detecting opera. See the usage of isOpera for reason.
        isOpera = typeof opera !== 'undefined' && opera.toString() === '[object Opera]',
        contexts = {},
        cfg = {},
        globalDefQueue = [],
        useInteractive = false;

    //Could match something like ')//comment', do not lose the prefix to comment.
    function commentReplace(match, singlePrefix) {
        return singlePrefix || '';
    }

    function isFunction(it) {
        return ostring.call(it) === '[object Function]';
    }

    function isArray(it) {
        return ostring.call(it) === '[object Array]';
    }

    /**
     * Helper function for iterating over an array. If the func returns
     * a true value, it will break out of the loop.
     */
    function each(ary, func) {
        if (ary) {
            var i;
            for (i = 0; i < ary.length; i += 1) {
                if (ary[i] && func(ary[i], i, ary)) {
                    break;
                }
            }
        }
    }

    /**
     * Helper function for iterating over an array backwards. If the func
     * returns a true value, it will break out of the loop.
     */
    function eachReverse(ary, func) {
        if (ary) {
            var i;
            for (i = ary.length - 1; i > -1; i -= 1) {
                if (ary[i] && func(ary[i], i, ary)) {
                    break;
                }
            }
        }
    }

    function hasProp(obj, prop) {
        return hasOwn.call(obj, prop);
    }

    function getOwn(obj, prop) {
        return hasProp(obj, prop) && obj[prop];
    }

    /**
     * Cycles over properties in an object and calls a function for each
     * property value. If the function returns a truthy value, then the
     * iteration is stopped.
     */
    function eachProp(obj, func) {
        var prop;
        for (prop in obj) {
            if (hasProp(obj, prop)) {
                if (func(obj[prop], prop)) {
                    break;
                }
            }
        }
    }

    /**
     * Simple function to mix in properties from source into target,
     * but only if target does not already have a property of the same name.
     */
    function mixin(target, source, force, deepStringMixin) {
        if (source) {
            eachProp(source, function (value, prop) {
                if (force || !hasProp(target, prop)) {
                    if (deepStringMixin && typeof value === 'object' && value &&
                        !isArray(value) && !isFunction(value) &&
                        !(value instanceof RegExp)) {

                        if (!target[prop]) {
                            target[prop] = {};
                        }
                        mixin(target[prop], value, force, deepStringMixin);
                    } else {
                        target[prop] = value;
                    }
                }
            });
        }
        return target;
    }

    //Similar to Function.prototype.bind, but the 'this' object is specified
    //first, since it is easier to read/figure out what 'this' will be.
    function bind(obj, fn) {
        return function () {
            return fn.apply(obj, arguments);
        };
    }

    function scripts() {
        return document.getElementsByTagName('script');
    }

    function defaultOnError(err) {
        throw err;
    }

    //Allow getting a global that is expressed in
    //dot notation, like 'a.b.c'.
    function getGlobal(value) {
        if (!value) {
            return value;
        }
        var g = global;
        each(value.split('.'), function (part) {
            g = g[part];
        });
        return g;
    }

    /**
     * Constructs an error with a pointer to an URL with more information.
     * @param {String} id the error ID that maps to an ID on a web page.
     * @param {String} message human readable error.
     * @param {Error} [err] the original error, if there is one.
     *
     * @returns {Error}
     */
    function makeError(id, msg, err, requireModules) {
        var e = new Error(msg + '\nhttps://requirejs.org/docs/errors.html#' + id);
        e.requireType = id;
        e.requireModules = requireModules;
        if (err) {
            e.originalError = err;
        }
        return e;
    }

    if (typeof define !== 'undefined') {
        //If a define is already in play via another AMD loader,
        //do not overwrite.
        return;
    }

    if (typeof requirejs !== 'undefined') {
        if (isFunction(requirejs)) {
            //Do not overwrite an existing requirejs instance.
            return;
        }
        cfg = requirejs;
        requirejs = undefined;
    }

    //Allow for a require config object
    if (typeof require !== 'undefined' && !isFunction(require)) {
        //assume it is a config object.
        cfg = require;
        require = undefined;
    }

    function newContext(contextName) {
        var inCheckLoaded, Module, context, handlers,
            checkLoadedTimeoutId,
            config = {
                //Defaults. Do not set a default for map
                //config to speed up normalize(), which
                //will run faster if there is no default.
                waitSeconds: 7,
                baseUrl: './',
                paths: {},
                bundles: {},
                pkgs: {},
                shim: {},
                config: {}
            },
            registry = {},
            //registry of just enabled modules, to speed
            //cycle breaking code when lots of modules
            //are registered, but not activated.
            enabledRegistry = {},
            undefEvents = {},
            defQueue = [],
            defined = {},
            urlFetched = {},
            bundlesMap = {},
            requireCounter = 1,
            unnormalizedCounter = 1;

        /**
         * Trims the . and .. from an array of path segments.
         * It will keep a leading path segment if a .. will become
         * the first path segment, to help with module name lookups,
         * which act like paths, but can be remapped. But the end result,
         * all paths that use this function should look normalized.
         * NOTE: this method MODIFIES the input array.
         * @param {Array} ary the array of path segments.
         */
        function trimDots(ary) {
            var i, part;
            for (i = 0; i < ary.length; i++) {
                part = ary[i];
                if (part === '.') {
                    ary.splice(i, 1);
                    i -= 1;
                } else if (part === '..') {
                    // If at the start, or previous value is still ..,
                    // keep them so that when converted to a path it may
                    // still work when converted to a path, even though
                    // as an ID it is less than ideal. In larger point
                    // releases, may be better to just kick out an error.
                    if (i === 0 || (i === 1 && ary[2] === '..') || ary[i - 1] === '..') {
                        continue;
                    } else if (i > 0) {
                        ary.splice(i - 1, 2);
                        i -= 2;
                    }
                }
            }
        }

        /**
         * Given a relative module name, like ./something, normalize it to
         * a real name that can be mapped to a path.
         * @param {String} name the relative name
         * @param {String} baseName a real name that the name arg is relative
         * to.
         * @param {Boolean} applyMap apply the map config to the value. Should
         * only be done if this normalization is for a dependency ID.
         * @returns {String} normalized name
         */
        function normalize(name, baseName, applyMap) {
            var pkgMain, mapValue, nameParts, i, j, nameSegment, lastIndex,
                foundMap, foundI, foundStarMap, starI, normalizedBaseParts,
                baseParts = (baseName && baseName.split('/')),
                map = config.map,
                starMap = map && map['*'];

            //Adjust any relative paths.
            if (name) {
                name = name.split('/');
                lastIndex = name.length - 1;

                // If wanting node ID compatibility, strip .js from end
                // of IDs. Have to do this here, and not in nameToUrl
                // because node allows either .js or non .js to map
                // to same file.
                if (config.nodeIdCompat && jsSuffixRegExp.test(name[lastIndex])) {
                    name[lastIndex] = name[lastIndex].replace(jsSuffixRegExp, '');
                }

                // Starts with a '.' so need the baseName
                if (name[0].charAt(0) === '.' && baseParts) {
                    //Convert baseName to array, and lop off the last part,
                    //so that . matches that 'directory' and not name of the baseName's
                    //module. For instance, baseName of 'one/two/three', maps to
                    //'one/two/three.js', but we want the directory, 'one/two' for
                    //this normalization.
                    normalizedBaseParts = baseParts.slice(0, baseParts.length - 1);
                    name = normalizedBaseParts.concat(name);
                }

                trimDots(name);
                name = name.join('/');
            }

            //Apply map config if available.
            if (applyMap && map && (baseParts || starMap)) {
                nameParts = name.split('/');

                outerLoop: for (i = nameParts.length; i > 0; i -= 1) {
                    nameSegment = nameParts.slice(0, i).join('/');

                    if (baseParts) {
                        //Find the longest baseName segment match in the config.
                        //So, do joins on the biggest to smallest lengths of baseParts.
                        for (j = baseParts.length; j > 0; j -= 1) {
                            mapValue = getOwn(map, baseParts.slice(0, j).join('/'));

                            //baseName segment has config, find if it has one for
                            //this name.
                            if (mapValue) {
                                mapValue = getOwn(mapValue, nameSegment);
                                if (mapValue) {
                                    //Match, update name to the new value.
                                    foundMap = mapValue;
                                    foundI = i;
                                    break outerLoop;
                                }
                            }
                        }
                    }

                    //Check for a star map match, but just hold on to it,
                    //if there is a shorter segment match later in a matching
                    //config, then favor over this star map.
                    if (!foundStarMap && starMap && getOwn(starMap, nameSegment)) {
                        foundStarMap = getOwn(starMap, nameSegment);
                        starI = i;
                    }
                }

                if (!foundMap && foundStarMap) {
                    foundMap = foundStarMap;
                    foundI = starI;
                }

                if (foundMap) {
                    nameParts.splice(0, foundI, foundMap);
                    name = nameParts.join('/');
                }
            }

            // If the name points to a package's name, use
            // the package main instead.
            pkgMain = getOwn(config.pkgs, name);

            return pkgMain ? pkgMain : name;
        }

        function removeScript(name) {
            if (isBrowser) {
                each(scripts(), function (scriptNode) {
                    if (scriptNode.getAttribute('data-requiremodule') === name &&
                            scriptNode.getAttribute('data-requirecontext') === context.contextName) {
                        scriptNode.parentNode.removeChild(scriptNode);
                        return true;
                    }
                });
            }
        }

        function hasPathFallback(id) {
            var pathConfig = getOwn(config.paths, id);
            if (pathConfig && isArray(pathConfig) && pathConfig.length > 1) {
                //Pop off the first array value, since it failed, and
                //retry
                pathConfig.shift();
                context.require.undef(id);

                //Custom require that does not do map translation, since
                //ID is "absolute", already mapped/resolved.
                context.makeRequire(null, {
                    skipMap: true
                })([id]);

                return true;
            }
        }

        //Turns a plugin!resource to [plugin, resource]
        //with the plugin being undefined if the name
        //did not have a plugin prefix.
        function splitPrefix(name) {
            var prefix,
                index = name ? name.indexOf('!') : -1;
            if (index > -1) {
                prefix = name.substring(0, index);
                name = name.substring(index + 1, name.length);
            }
            return [prefix, name];
        }

        /**
         * Creates a module mapping that includes plugin prefix, module
         * name, and path. If parentModuleMap is provided it will
         * also normalize the name via require.normalize()
         *
         * @param {String} name the module name
         * @param {String} [parentModuleMap] parent module map
         * for the module name, used to resolve relative names.
         * @param {Boolean} isNormalized: is the ID already normalized.
         * This is true if this call is done for a define() module ID.
         * @param {Boolean} applyMap: apply the map config to the ID.
         * Should only be true if this map is for a dependency.
         *
         * @returns {Object}
         */
        function makeModuleMap(name, parentModuleMap, isNormalized, applyMap) {
            var url, pluginModule, suffix, nameParts,
                prefix = null,
                parentName = parentModuleMap ? parentModuleMap.name : null,
                originalName = name,
                isDefine = true,
                normalizedName = '';

            //If no name, then it means it is a require call, generate an
            //internal name.
            if (!name) {
                isDefine = false;
                name = '_@r' + (requireCounter += 1);
            }

            nameParts = splitPrefix(name);
            prefix = nameParts[0];
            name = nameParts[1];

            if (prefix) {
                prefix = normalize(prefix, parentName, applyMap);
                pluginModule = getOwn(defined, prefix);
            }

            //Account for relative paths if there is a base name.
            if (name) {
                if (prefix) {
                    if (isNormalized) {
                        normalizedName = name;
                    } else if (pluginModule && pluginModule.normalize) {
                        //Plugin is loaded, use its normalize method.
                        normalizedName = pluginModule.normalize(name, function (name) {
                            return normalize(name, parentName, applyMap);
                        });
                    } else {
                        // If nested plugin references, then do not try to
                        // normalize, as it will not normalize correctly. This
                        // places a restriction on resourceIds, and the longer
                        // term solution is not to normalize until plugins are
                        // loaded and all normalizations to allow for async
                        // loading of a loader plugin. But for now, fixes the
                        // common uses. Details in #1131
                        normalizedName = name.indexOf('!') === -1 ?
                                         normalize(name, parentName, applyMap) :
                                         name;
                    }
                } else {
                    //A regular module.
                    normalizedName = normalize(name, parentName, applyMap);

                    //Normalized name may be a plugin ID due to map config
                    //application in normalize. The map config values must
                    //already be normalized, so do not need to redo that part.
                    nameParts = splitPrefix(normalizedName);
                    prefix = nameParts[0];
                    normalizedName = nameParts[1];
                    isNormalized = true;

                    url = context.nameToUrl(normalizedName);
                }
            }

            //If the id is a plugin id that cannot be determined if it needs
            //normalization, stamp it with a unique ID so two matching relative
            //ids that may conflict can be separate.
            suffix = prefix && !pluginModule && !isNormalized ?
                     '_unnormalized' + (unnormalizedCounter += 1) :
                     '';

            return {
                prefix: prefix,
                name: normalizedName,
                parentMap: parentModuleMap,
                unnormalized: !!suffix,
                url: url,
                originalName: originalName,
                isDefine: isDefine,
                id: (prefix ?
                        prefix + '!' + normalizedName :
                        normalizedName) + suffix
            };
        }

        function getModule(depMap) {
            var id = depMap.id,
                mod = getOwn(registry, id);

            if (!mod) {
                mod = registry[id] = new context.Module(depMap);
            }

            return mod;
        }

        function on(depMap, name, fn) {
            var id = depMap.id,
                mod = getOwn(registry, id);

            if (hasProp(defined, id) &&
                    (!mod || mod.defineEmitComplete)) {
                if (name === 'defined') {
                    fn(defined[id]);
                }
            } else {
                mod = getModule(depMap);
                if (mod.error && name === 'error') {
                    fn(mod.error);
                } else {
                    mod.on(name, fn);
                }
            }
        }

        function onError(err, errback) {
            var ids = err.requireModules,
                notified = false;

            if (errback) {
                errback(err);
            } else {
                each(ids, function (id) {
                    var mod = getOwn(registry, id);
                    if (mod) {
                        //Set error on module, so it skips timeout checks.
                        mod.error = err;
                        if (mod.events.error) {
                            notified = true;
                            mod.emit('error', err);
                        }
                    }
                });

                if (!notified) {
                    req.onError(err);
                }
            }
        }

        /**
         * Internal method to transfer globalQueue items to this context's
         * defQueue.
         */
        function takeGlobalQueue() {
            //Push all the globalDefQueue items into the context's defQueue
            if (globalDefQueue.length) {
                each(globalDefQueue, function(queueItem) {
                    var id = queueItem[0];
                    if (typeof id === 'string') {
                        context.defQueueMap[id] = true;
                    }
                    defQueue.push(queueItem);
                });
                globalDefQueue = [];
            }
        }

        handlers = {
            'require': function (mod) {
                if (mod.require) {
                    return mod.require;
                } else {
                    return (mod.require = context.makeRequire(mod.map));
                }
            },
            'exports': function (mod) {
                mod.usingExports = true;
                if (mod.map.isDefine) {
                    if (mod.exports) {
                        return (defined[mod.map.id] = mod.exports);
                    } else {
                        return (mod.exports = defined[mod.map.id] = {});
                    }
                }
            },
            'module': function (mod) {
                if (mod.module) {
                    return mod.module;
                } else {
                    return (mod.module = {
                        id: mod.map.id,
                        uri: mod.map.url,
                        config: function () {
                            return getOwn(config.config, mod.map.id) || {};
                        },
                        exports: mod.exports || (mod.exports = {})
                    });
                }
            }
        };

        function cleanRegistry(id) {
            //Clean up machinery used for waiting modules.
            delete registry[id];
            delete enabledRegistry[id];
        }

        function breakCycle(mod, traced, processed) {
            var id = mod.map.id;

            if (mod.error) {
                mod.emit('error', mod.error);
            } else {
                traced[id] = true;
                each(mod.depMaps, function (depMap, i) {
                    var depId = depMap.id,
                        dep = getOwn(registry, depId);

                    //Only force things that have not completed
                    //being defined, so still in the registry,
                    //and only if it has not been matched up
                    //in the module already.
                    if (dep && !mod.depMatched[i] && !processed[depId]) {
                        if (getOwn(traced, depId)) {
                            mod.defineDep(i, defined[depId]);
                            mod.check(); //pass false?
                        } else {
                            breakCycle(dep, traced, processed);
                        }
                    }
                });
                processed[id] = true;
            }
        }

        function checkLoaded() {
            var err, usingPathFallback,
                waitInterval = config.waitSeconds * 1000,
                //It is possible to disable the wait interval by using waitSeconds of 0.
                expired = waitInterval && (context.startTime + waitInterval) < new Date().getTime(),
                noLoads = [],
                reqCalls = [],
                stillLoading = false,
                needCycleCheck = true;

            //Do not bother if this call was a result of a cycle break.
            if (inCheckLoaded) {
                return;
            }

            inCheckLoaded = true;

            //Figure out the state of all the modules.
            eachProp(enabledRegistry, function (mod) {
                var map = mod.map,
                    modId = map.id;

                //Skip things that are not enabled or in error state.
                if (!mod.enabled) {
                    return;
                }

                if (!map.isDefine) {
                    reqCalls.push(mod);
                }

                if (!mod.error) {
                    //If the module should be executed, and it has not
                    //been inited and time is up, remember it.
                    if (!mod.inited && expired) {
                        if (hasPathFallback(modId)) {
                            usingPathFallback = true;
                            stillLoading = true;
                        } else {
                            noLoads.push(modId);
                            removeScript(modId);
                        }
                    } else if (!mod.inited && mod.fetched && map.isDefine) {
                        stillLoading = true;
                        if (!map.prefix) {
                            //No reason to keep looking for unfinished
                            //loading. If the only stillLoading is a
                            //plugin resource though, keep going,
                            //because it may be that a plugin resource
                            //is waiting on a non-plugin cycle.
                            return (needCycleCheck = false);
                        }
                    }
                }
            });

            if (expired && noLoads.length) {
                //If wait time expired, throw error of unloaded modules.
                err = makeError('timeout', 'Load timeout for modules: ' + noLoads, null, noLoads);
                err.contextName = context.contextName;
                return onError(err);
            }

            //Not expired, check for a cycle.
            if (needCycleCheck) {
                each(reqCalls, function (mod) {
                    breakCycle(mod, {}, {});
                });
            }

            //If still waiting on loads, and the waiting load is something
            //other than a plugin resource, or there are still outstanding
            //scripts, then just try back later.
            if ((!expired || usingPathFallback) && stillLoading) {
                //Something is still waiting to load. Wait for it, but only
                //if a timeout is not already in effect.
                if ((isBrowser || isWebWorker) && !checkLoadedTimeoutId) {
                    checkLoadedTimeoutId = setTimeout(function () {
                        checkLoadedTimeoutId = 0;
                        checkLoaded();
                    }, 50);
                }
            }

            inCheckLoaded = false;
        }

        Module = function (map) {
            this.events = getOwn(undefEvents, map.id) || {};
            this.map = map;
            this.shim = getOwn(config.shim, map.id);
            this.depExports = [];
            this.depMaps = [];
            this.depMatched = [];
            this.pluginMaps = {};
            this.depCount = 0;

            /* this.exports this.factory
               this.depMaps = [],
               this.enabled, this.fetched
            */
        };

        Module.prototype = {
            init: function (depMaps, factory, errback, options) {
                options = options || {};

                //Do not do more inits if already done. Can happen if there
                //are multiple define calls for the same module. That is not
                //a normal, common case, but it is also not unexpected.
                if (this.inited) {
                    return;
                }

                this.factory = factory;

                if (errback) {
                    //Register for errors on this module.
                    this.on('error', errback);
                } else if (this.events.error) {
                    //If no errback already, but there are error listeners
                    //on this module, set up an errback to pass to the deps.
                    errback = bind(this, function (err) {
                        this.emit('error', err);
                    });
                }

                //Do a copy of the dependency array, so that
                //source inputs are not modified. For example
                //"shim" deps are passed in here directly, and
                //doing a direct modification of the depMaps array
                //would affect that config.
                this.depMaps = depMaps && depMaps.slice(0);

                this.errback = errback;

                //Indicate this module has be initialized
                this.inited = true;

                this.ignore = options.ignore;

                //Could have option to init this module in enabled mode,
                //or could have been previously marked as enabled. However,
                //the dependencies are not known until init is called. So
                //if enabled previously, now trigger dependencies as enabled.
                if (options.enabled || this.enabled) {
                    //Enable this module and dependencies.
                    //Will call this.check()
                    this.enable();
                } else {
                    this.check();
                }
            },

            defineDep: function (i, depExports) {
                //Because of cycles, defined callback for a given
                //export can be called more than once.
                if (!this.depMatched[i]) {
                    this.depMatched[i] = true;
                    this.depCount -= 1;
                    this.depExports[i] = depExports;
                }
            },

            fetch: function () {
                if (this.fetched) {
                    return;
                }
                this.fetched = true;

                context.startTime = (new Date()).getTime();

                var map = this.map;

                //If the manager is for a plugin managed resource,
                //ask the plugin to load it now.
                if (this.shim) {
                    context.makeRequire(this.map, {
                        enableBuildCallback: true
                    })(this.shim.deps || [], bind(this, function () {
                        return map.prefix ? this.callPlugin() : this.load();
                    }));
                } else {
                    //Regular dependency.
                    return map.prefix ? this.callPlugin() : this.load();
                }
            },

            load: function () {
                var url = this.map.url;

                //Regular dependency.
                if (!urlFetched[url]) {
                    urlFetched[url] = true;
                    context.load(this.map.id, url);
                }
            },

            /**
             * Checks if the module is ready to define itself, and if so,
             * define it.
             */
            check: function () {
                if (!this.enabled || this.enabling) {
                    return;
                }

                var err, cjsModule,
                    id = this.map.id,
                    depExports = this.depExports,
                    exports = this.exports,
                    factory = this.factory;

                if (!this.inited) {
                    // Only fetch if not already in the defQueue.
                    if (!hasProp(context.defQueueMap, id)) {
                        this.fetch();
                    }
                } else if (this.error) {
                    this.emit('error', this.error);
                } else if (!this.defining) {
                    //The factory could trigger another require call
                    //that would result in checking this module to
                    //define itself again. If already in the process
                    //of doing that, skip this work.
                    this.defining = true;

                    if (this.depCount < 1 && !this.defined) {
                        if (isFunction(factory)) {
                            //If there is an error listener, favor passing
                            //to that instead of throwing an error. However,
                            //only do it for define()'d  modules. require
                            //errbacks should not be called for failures in
                            //their callbacks (#699). However if a global
                            //onError is set, use that.
                            if ((this.events.error && this.map.isDefine) ||
                                req.onError !== defaultOnError) {
                                try {
                                    exports = context.execCb(id, factory, depExports, exports);
                                } catch (e) {
                                    err = e;
                                }
                            } else {
                                exports = context.execCb(id, factory, depExports, exports);
                            }

                            // Favor return value over exports. If node/cjs in play,
                            // then will not have a return value anyway. Favor
                            // module.exports assignment over exports object.
                            if (this.map.isDefine && exports === undefined) {
                                cjsModule = this.module;
                                if (cjsModule) {
                                    exports = cjsModule.exports;
                                } else if (this.usingExports) {
                                    //exports already set the defined value.
                                    exports = this.exports;
                                }
                            }

                            if (err) {
                                err.requireMap = this.map;
                                err.requireModules = this.map.isDefine ? [this.map.id] : null;
                                err.requireType = this.map.isDefine ? 'define' : 'require';
                                return onError((this.error = err));
                            }

                        } else {
                            //Just a literal value
                            exports = factory;
                        }

                        this.exports = exports;

                        if (this.map.isDefine && !this.ignore) {
                            defined[id] = exports;

                            if (req.onResourceLoad) {
                                var resLoadMaps = [];
                                each(this.depMaps, function (depMap) {
                                    resLoadMaps.push(depMap.normalizedMap || depMap);
                                });
                                req.onResourceLoad(context, this.map, resLoadMaps);
                            }
                        }

                        //Clean up
                        cleanRegistry(id);

                        this.defined = true;
                    }

                    //Finished the define stage. Allow calling check again
                    //to allow define notifications below in the case of a
                    //cycle.
                    this.defining = false;

                    if (this.defined && !this.defineEmitted) {
                        this.defineEmitted = true;
                        this.emit('defined', this.exports);
                        this.defineEmitComplete = true;
                    }

                }
            },

            callPlugin: function () {
                var map = this.map,
                    id = map.id,
                    //Map already normalized the prefix.
                    pluginMap = makeModuleMap(map.prefix);

                //Mark this as a dependency for this plugin, so it
                //can be traced for cycles.
                this.depMaps.push(pluginMap);

                on(pluginMap, 'defined', bind(this, function (plugin) {
                    var load, normalizedMap, normalizedMod,
                        bundleId = getOwn(bundlesMap, this.map.id),
                        name = this.map.name,
                        parentName = this.map.parentMap ? this.map.parentMap.name : null,
                        localRequire = context.makeRequire(map.parentMap, {
                            enableBuildCallback: true
                        });

                    //If current map is not normalized, wait for that
                    //normalized name to load instead of continuing.
                    if (this.map.unnormalized) {
                        //Normalize the ID if the plugin allows it.
                        if (plugin.normalize) {
                            name = plugin.normalize(name, function (name) {
                                return normalize(name, parentName, true);
                            }) || '';
                        }

                        //prefix and name should already be normalized, no need
                        //for applying map config again either.
                        normalizedMap = makeModuleMap(map.prefix + '!' + name,
                                                      this.map.parentMap,
                                                      true);
                        on(normalizedMap,
                            'defined', bind(this, function (value) {
                                this.map.normalizedMap = normalizedMap;
                                this.init([], function () { return value; }, null, {
                                    enabled: true,
                                    ignore: true
                                });
                            }));

                        normalizedMod = getOwn(registry, normalizedMap.id);
                        if (normalizedMod) {
                            //Mark this as a dependency for this plugin, so it
                            //can be traced for cycles.
                            this.depMaps.push(normalizedMap);

                            if (this.events.error) {
                                normalizedMod.on('error', bind(this, function (err) {
                                    this.emit('error', err);
                                }));
                            }
                            normalizedMod.enable();
                        }

                        return;
                    }

                    //If a paths config, then just load that file instead to
                    //resolve the plugin, as it is built into that paths layer.
                    if (bundleId) {
                        this.map.url = context.nameToUrl(bundleId);
                        this.load();
                        return;
                    }

                    load = bind(this, function (value) {
                        this.init([], function () { return value; }, null, {
                            enabled: true
                        });
                    });

                    load.error = bind(this, function (err) {
                        this.inited = true;
                        this.error = err;
                        err.requireModules = [id];

                        //Remove temp unnormalized modules for this module,
                        //since they will never be resolved otherwise now.
                        eachProp(registry, function (mod) {
                            if (mod.map.id.indexOf(id + '_unnormalized') === 0) {
                                cleanRegistry(mod.map.id);
                            }
                        });

                        onError(err);
                    });

                    //Allow plugins to load other code without having to know the
                    //context or how to 'complete' the load.
                    load.fromText = bind(this, function (text, textAlt) {
                        /*jslint evil: true */
                        var moduleName = map.name,
                            moduleMap = makeModuleMap(moduleName),
                            hasInteractive = useInteractive;

                        //As of 2.1.0, support just passing the text, to reinforce
                        //fromText only being called once per resource. Still
                        //support old style of passing moduleName but discard
                        //that moduleName in favor of the internal ref.
                        if (textAlt) {
                            text = textAlt;
                        }

                        //Turn off interactive script matching for IE for any define
                        //calls in the text, then turn it back on at the end.
                        if (hasInteractive) {
                            useInteractive = false;
                        }

                        //Prime the system by creating a module instance for
                        //it.
                        getModule(moduleMap);

                        //Transfer any config to this other module.
                        if (hasProp(config.config, id)) {
                            config.config[moduleName] = config.config[id];
                        }

                        try {
                            req.exec(text);
                        } catch (e) {
                            return onError(makeError('fromtexteval',
                                             'fromText eval for ' + id +
                                            ' failed: ' + e,
                                             e,
                                             [id]));
                        }

                        if (hasInteractive) {
                            useInteractive = true;
                        }

                        //Mark this as a dependency for the plugin
                        //resource
                        this.depMaps.push(moduleMap);

                        //Support anonymous modules.
                        context.completeLoad(moduleName);

                        //Bind the value of that module to the value for this
                        //resource ID.
                        localRequire([moduleName], load);
                    });

                    //Use parentName here since the plugin's name is not reliable,
                    //could be some weird string with no path that actually wants to
                    //reference the parentName's path.
                    plugin.load(map.name, localRequire, load, config);
                }));

                context.enable(pluginMap, this);
                this.pluginMaps[pluginMap.id] = pluginMap;
            },

            enable: function () {
                enabledRegistry[this.map.id] = this;
                this.enabled = true;

                //Set flag mentioning that the module is enabling,
                //so that immediate calls to the defined callbacks
                //for dependencies do not trigger inadvertent load
                //with the depCount still being zero.
                this.enabling = true;

                //Enable each dependency
                each(this.depMaps, bind(this, function (depMap, i) {
                    var id, mod, handler;

                    if (typeof depMap === 'string') {
                        //Dependency needs to be converted to a depMap
                        //and wired up to this module.
                        depMap = makeModuleMap(depMap,
                                               (this.map.isDefine ? this.map : this.map.parentMap),
                                               false,
                                               !this.skipMap);
                        this.depMaps[i] = depMap;

                        handler = getOwn(handlers, depMap.id);

                        if (handler) {
                            this.depExports[i] = handler(this);
                            return;
                        }

                        this.depCount += 1;

                        on(depMap, 'defined', bind(this, function (depExports) {
                            if (this.undefed) {
                                return;
                            }
                            this.defineDep(i, depExports);
                            this.check();
                        }));

                        if (this.errback) {
                            on(depMap, 'error', bind(this, this.errback));
                        } else if (this.events.error) {
                            // No direct errback on this module, but something
                            // else is listening for errors, so be sure to
                            // propagate the error correctly.
                            on(depMap, 'error', bind(this, function(err) {
                                this.emit('error', err);
                            }));
                        }
                    }

                    id = depMap.id;
                    mod = registry[id];

                    //Skip special modules like 'require', 'exports', 'module'
                    //Also, don't call enable if it is already enabled,
                    //important in circular dependency cases.
                    if (!hasProp(handlers, id) && mod && !mod.enabled) {
                        context.enable(depMap, this);
                    }
                }));

                //Enable each plugin that is used in
                //a dependency
                eachProp(this.pluginMaps, bind(this, function (pluginMap) {
                    var mod = getOwn(registry, pluginMap.id);
                    if (mod && !mod.enabled) {
                        context.enable(pluginMap, this);
                    }
                }));

                this.enabling = false;

                this.check();
            },

            on: function (name, cb) {
                var cbs = this.events[name];
                if (!cbs) {
                    cbs = this.events[name] = [];
                }
                cbs.push(cb);
            },

            emit: function (name, evt) {
                each(this.events[name], function (cb) {
                    cb(evt);
                });
                if (name === 'error') {
                    //Now that the error handler was triggered, remove
                    //the listeners, since this broken Module instance
                    //can stay around for a while in the registry.
                    delete this.events[name];
                }
            }
        };

        function callGetModule(args) {
            //Skip modules already defined.
            if (!hasProp(defined, args[0])) {
                getModule(makeModuleMap(args[0], null, true)).init(args[1], args[2]);
            }
        }

        function removeListener(node, func, name, ieName) {
            //Favor detachEvent because of IE9
            //issue, see attachEvent/addEventListener comment elsewhere
            //in this file.
            if (node.detachEvent && !isOpera) {
                //Probably IE. If not it will throw an error, which will be
                //useful to know.
                if (ieName) {
                    node.detachEvent(ieName, func);
                }
            } else {
                node.removeEventListener(name, func, false);
            }
        }

        /**
         * Given an event from a script node, get the requirejs info from it,
         * and then removes the event listeners on the node.
         * @param {Event} evt
         * @returns {Object}
         */
        function getScriptData(evt) {
            //Using currentTarget instead of target for Firefox 2.0's sake. Not
            //all old browsers will be supported, but this one was easy enough
            //to support and still makes sense.
            var node = evt.currentTarget || evt.srcElement;

            //Remove the listeners once here.
            removeListener(node, context.onScriptLoad, 'load', 'onreadystatechange');
            removeListener(node, context.onScriptError, 'error');

            return {
                node: node,
                id: node && node.getAttribute('data-requiremodule')
            };
        }

        function intakeDefines() {
            var args;

            //Any defined modules in the global queue, intake them now.
            takeGlobalQueue();

            //Make sure any remaining defQueue items get properly processed.
            while (defQueue.length) {
                args = defQueue.shift();
                if (args[0] === null) {
                    return onError(makeError('mismatch', 'Mismatched anonymous define() module: ' +
                        args[args.length - 1]));
                } else {
                    //args are id, deps, factory. Should be normalized by the
                    //define() function.
                    callGetModule(args);
                }
            }
            context.defQueueMap = {};
        }

        context = {
            config: config,
            contextName: contextName,
            registry: registry,
            defined: defined,
            urlFetched: urlFetched,
            defQueue: defQueue,
            defQueueMap: {},
            Module: Module,
            makeModuleMap: makeModuleMap,
            nextTick: req.nextTick,
            onError: onError,

            /**
             * Set a configuration for the context.
             * @param {Object} cfg config object to integrate.
             */
            configure: function (cfg) {
                //Make sure the baseUrl ends in a slash.
                if (cfg.baseUrl) {
                    if (cfg.baseUrl.charAt(cfg.baseUrl.length - 1) !== '/') {
                        cfg.baseUrl += '/';
                    }
                }

                // Convert old style urlArgs string to a function.
                if (typeof cfg.urlArgs === 'string') {
                    var urlArgs = cfg.urlArgs;
                    cfg.urlArgs = function(id, url) {
                        return (url.indexOf('?') === -1 ? '?' : '&') + urlArgs;
                    };
                }

                //Save off the paths since they require special processing,
                //they are additive.
                var shim = config.shim,
                    objs = {
                        paths: true,
                        bundles: true,
                        config: true,
                        map: true
                    };

                eachProp(cfg, function (value, prop) {
                    if (objs[prop]) {
                        if (!config[prop]) {
                            config[prop] = {};
                        }
                        mixin(config[prop], value, true, true);
                    } else {
                        config[prop] = value;
                    }
                });

                //Reverse map the bundles
                if (cfg.bundles) {
                    eachProp(cfg.bundles, function (value, prop) {
                        each(value, function (v) {
                            if (v !== prop) {
                                bundlesMap[v] = prop;
                            }
                        });
                    });
                }

                //Merge shim
                if (cfg.shim) {
                    eachProp(cfg.shim, function (value, id) {
                        //Normalize the structure
                        if (isArray(value)) {
                            value = {
                                deps: value
                            };
                        }
                        if ((value.exports || value.init) && !value.exportsFn) {
                            value.exportsFn = context.makeShimExports(value);
                        }
                        shim[id] = value;
                    });
                    config.shim = shim;
                }

                //Adjust packages if necessary.
                if (cfg.packages) {
                    each(cfg.packages, function (pkgObj) {
                        var location, name;

                        pkgObj = typeof pkgObj === 'string' ? {name: pkgObj} : pkgObj;

                        name = pkgObj.name;
                        location = pkgObj.location;
                        if (location) {
                            config.paths[name] = pkgObj.location;
                        }

                        //Save pointer to main module ID for pkg name.
                        //Remove leading dot in main, so main paths are normalized,
                        //and remove any trailing .js, since different package
                        //envs have different conventions: some use a module name,
                        //some use a file name.
                        config.pkgs[name] = pkgObj.name + '/' + (pkgObj.main || 'main')
                                     .replace(currDirRegExp, '')
                                     .replace(jsSuffixRegExp, '');
                    });
                }

                //If there are any "waiting to execute" modules in the registry,
                //update the maps for them, since their info, like URLs to load,
                //may have changed.
                eachProp(registry, function (mod, id) {
                    //If module already has init called, since it is too
                    //late to modify them, and ignore unnormalized ones
                    //since they are transient.
                    if (!mod.inited && !mod.map.unnormalized) {
                        mod.map = makeModuleMap(id, null, true);
                    }
                });

                //If a deps array or a config callback is specified, then call
                //require with those args. This is useful when require is defined as a
                //config object before require.js is loaded.
                if (cfg.deps || cfg.callback) {
                    context.require(cfg.deps || [], cfg.callback);
                }
            },

            makeShimExports: function (value) {
                function fn() {
                    var ret;
                    if (value.init) {
                        ret = value.init.apply(global, arguments);
                    }
                    return ret || (value.exports && getGlobal(value.exports));
                }
                return fn;
            },

            makeRequire: function (relMap, options) {
                options = options || {};

                function localRequire(deps, callback, errback) {
                    var id, map, requireMod;

                    if (options.enableBuildCallback && callback && isFunction(callback)) {
                        callback.__requireJsBuild = true;
                    }

                    if (typeof deps === 'string') {
                        if (isFunction(callback)) {
                            //Invalid call
                            return onError(makeError('requireargs', 'Invalid require call'), errback);
                        }

                        //If require|exports|module are requested, get the
                        //value for them from the special handlers. Caveat:
                        //this only works while module is being defined.
                        if (relMap && hasProp(handlers, deps)) {
                            return handlers[deps](registry[relMap.id]);
                        }

                        //Synchronous access to one module. If require.get is
                        //available (as in the Node adapter), prefer that.
                        if (req.get) {
                            return req.get(context, deps, relMap, localRequire);
                        }

                        //Normalize module name, if it contains . or ..
                        map = makeModuleMap(deps, relMap, false, true);
                        id = map.id;

                        if (!hasProp(defined, id)) {
                            return onError(makeError('notloaded', 'Module name "' +
                                        id +
                                        '" has not been loaded yet for context: ' +
                                        contextName +
                                        (relMap ? '' : '. Use require([])')));
                        }
                        return defined[id];
                    }

                    //Grab defines waiting in the global queue.
                    intakeDefines();

                    //Mark all the dependencies as needing to be loaded.
                    context.nextTick(function () {
                        //Some defines could have been added since the
                        //require call, collect them.
                        intakeDefines();

                        requireMod = getModule(makeModuleMap(null, relMap));

                        //Store if map config should be applied to this require
                        //call for dependencies.
                        requireMod.skipMap = options.skipMap;

                        requireMod.init(deps, callback, errback, {
                            enabled: true
                        });

                        checkLoaded();
                    });

                    return localRequire;
                }

                mixin(localRequire, {
                    isBrowser: isBrowser,

                    /**
                     * Converts a module name + .extension into an URL path.
                     * *Requires* the use of a module name. It does not support using
                     * plain URLs like nameToUrl.
                     */
                    toUrl: function (moduleNamePlusExt) {
                        var ext,
                            index = moduleNamePlusExt.lastIndexOf('.'),
                            segment = moduleNamePlusExt.split('/')[0],
                            isRelative = segment === '.' || segment === '..';

                        //Have a file extension alias, and it is not the
                        //dots from a relative path.
                        if (index !== -1 && (!isRelative || index > 1)) {
                            ext = moduleNamePlusExt.substring(index, moduleNamePlusExt.length);
                            moduleNamePlusExt = moduleNamePlusExt.substring(0, index);
                        }

                        return context.nameToUrl(normalize(moduleNamePlusExt,
                                                relMap && relMap.id, true), ext,  true);
                    },

                    defined: function (id) {
                        return hasProp(defined, makeModuleMap(id, relMap, false, true).id);
                    },

                    specified: function (id) {
                        id = makeModuleMap(id, relMap, false, true).id;
                        return hasProp(defined, id) || hasProp(registry, id);
                    }
                });

                //Only allow undef on top level require calls
                if (!relMap) {
                    localRequire.undef = function (id) {
                        //Bind any waiting define() calls to this context,
                        //fix for #408
                        takeGlobalQueue();

                        var map = makeModuleMap(id, relMap, true),
                            mod = getOwn(registry, id);

                        mod.undefed = true;
                        removeScript(id);

                        delete defined[id];
                        delete urlFetched[map.url];
                        delete undefEvents[id];

                        //Clean queued defines too. Go backwards
                        //in array so that the splices do not
                        //mess up the iteration.
                        eachReverse(defQueue, function(args, i) {
                            if (args[0] === id) {
                                defQueue.splice(i, 1);
                            }
                        });
                        delete context.defQueueMap[id];

                        if (mod) {
                            //Hold on to listeners in case the
                            //module will be attempted to be reloaded
                            //using a different config.
                            if (mod.events.defined) {
                                undefEvents[id] = mod.events;
                            }

                            cleanRegistry(id);
                        }
                    };
                }

                return localRequire;
            },

            /**
             * Called to enable a module if it is still in the registry
             * awaiting enablement. A second arg, parent, the parent module,
             * is passed in for context, when this method is overridden by
             * the optimizer. Not shown here to keep code compact.
             */
            enable: function (depMap) {
                var mod = getOwn(registry, depMap.id);
                if (mod) {
                    getModule(depMap).enable();
                }
            },

            /**
             * Internal method used by environment adapters to complete a load event.
             * A load event could be a script load or just a load pass from a synchronous
             * load call.
             * @param {String} moduleName the name of the module to potentially complete.
             */
            completeLoad: function (moduleName) {
                var found, args, mod,
                    shim = getOwn(config.shim, moduleName) || {},
                    shExports = shim.exports;

                takeGlobalQueue();

                while (defQueue.length) {
                    args = defQueue.shift();
                    if (args[0] === null) {
                        args[0] = moduleName;
                        //If already found an anonymous module and bound it
                        //to this name, then this is some other anon module
                        //waiting for its completeLoad to fire.
                        if (found) {
                            break;
                        }
                        found = true;
                    } else if (args[0] === moduleName) {
                        //Found matching define call for this script!
                        found = true;
                    }

                    callGetModule(args);
                }
                context.defQueueMap = {};

                //Do this after the cycle of callGetModule in case the result
                //of those calls/init calls changes the registry.
                mod = getOwn(registry, moduleName);

                if (!found && !hasProp(defined, moduleName) && mod && !mod.inited) {
                    if (config.enforceDefine && (!shExports || !getGlobal(shExports))) {
                        if (hasPathFallback(moduleName)) {
                            return;
                        } else {
                            return onError(makeError('nodefine',
                                             'No define call for ' + moduleName,
                                             null,
                                             [moduleName]));
                        }
                    } else {
                        //A script that does not call define(), so just simulate
                        //the call for it.
                        callGetModule([moduleName, (shim.deps || []), shim.exportsFn]);
                    }
                }

                checkLoaded();
            },

            /**
             * Converts a module name to a file path. Supports cases where
             * moduleName may actually be just an URL.
             * Note that it **does not** call normalize on the moduleName,
             * it is assumed to have already been normalized. This is an
             * internal API, not a public one. Use toUrl for the public API.
             */
            nameToUrl: function (moduleName, ext, skipExt) {
                var paths, syms, i, parentModule, url,
                    parentPath, bundleId,
                    pkgMain = getOwn(config.pkgs, moduleName);

                if (pkgMain) {
                    moduleName = pkgMain;
                }

                bundleId = getOwn(bundlesMap, moduleName);

                if (bundleId) {
                    return context.nameToUrl(bundleId, ext, skipExt);
                }

                //If a colon is in the URL, it indicates a protocol is used and it is just
                //an URL to a file, or if it starts with a slash, contains a query arg (i.e. ?)
                //or ends with .js, then assume the user meant to use an url and not a module id.
                //The slash is important for protocol-less URLs as well as full paths.
                if (req.jsExtRegExp.test(moduleName)) {
                    //Just a plain path, not module name lookup, so just return it.
                    //Add extension if it is included. This is a bit wonky, only non-.js things pass
                    //an extension, this method probably needs to be reworked.
                    url = moduleName + (ext || '');
                } else {
                    //A module that needs to be converted to a path.
                    paths = config.paths;

                    syms = moduleName.split('/');
                    //For each module name segment, see if there is a path
                    //registered for it. Start with most specific name
                    //and work up from it.
                    for (i = syms.length; i > 0; i -= 1) {
                        parentModule = syms.slice(0, i).join('/');

                        parentPath = getOwn(paths, parentModule);
                        if (parentPath) {
                            //If an array, it means there are a few choices,
                            //Choose the one that is desired
                            if (isArray(parentPath)) {
                                parentPath = parentPath[0];
                            }
                            syms.splice(0, i, parentPath);
                            break;
                        }
                    }

                    //Join the path parts together, then figure out if baseUrl is needed.
                    url = syms.join('/');
                    url += (ext || (/^data\:|^blob\:|\?/.test(url) || skipExt ? '' : '.js'));
                    url = (url.charAt(0) === '/' || url.match(/^[\w\+\.\-]+:/) ? '' : config.baseUrl) + url;
                }

                return config.urlArgs && !/^blob\:/.test(url) ?
                       url + config.urlArgs(moduleName, url) : url;
            },

            //Delegates to req.load. Broken out as a separate function to
            //allow overriding in the optimizer.
            load: function (id, url) {
                req.load(context, id, url);
            },

            /**
             * Executes a module callback function. Broken out as a separate function
             * solely to allow the build system to sequence the files in the built
             * layer in the right sequence.
             *
             * @private
             */
            execCb: function (name, callback, args, exports) {
                return callback.apply(exports, args);
            },

            /**
             * callback for script loads, used to check status of loading.
             *
             * @param {Event} evt the event from the browser for the script
             * that was loaded.
             */
            onScriptLoad: function (evt) {
                //Using currentTarget instead of target for Firefox 2.0's sake. Not
                //all old browsers will be supported, but this one was easy enough
                //to support and still makes sense.
                if (evt.type === 'load' ||
                        (readyRegExp.test((evt.currentTarget || evt.srcElement).readyState))) {
                    //Reset interactive script so a script node is not held onto for
                    //to long.
                    interactiveScript = null;

                    //Pull out the name of the module and the context.
                    var data = getScriptData(evt);
                    context.completeLoad(data.id);
                }
            },

            /**
             * Callback for script errors.
             */
            onScriptError: function (evt) {
                var data = getScriptData(evt);
                if (!hasPathFallback(data.id)) {
                    var parents = [];
                    eachProp(registry, function(value, key) {
                        if (key.indexOf('_@r') !== 0) {
                            each(value.depMaps, function(depMap) {
                                if (depMap.id === data.id) {
                                    parents.push(key);
                                    return true;
                                }
                            });
                        }
                    });
                    return onError(makeError('scripterror', 'Script error for "' + data.id +
                                             (parents.length ?
                                             '", needed by: ' + parents.join(', ') :
                                             '"'), evt, [data.id]));
                }
            }
        };

        context.require = context.makeRequire();
        return context;
    }

    /**
     * Main entry point.
     *
     * If the only argument to require is a string, then the module that
     * is represented by that string is fetched for the appropriate context.
     *
     * If the first argument is an array, then it will be treated as an array
     * of dependency string names to fetch. An optional function callback can
     * be specified to execute when all of those dependencies are available.
     *
     * Make a local req variable to help Caja compliance (it assumes things
     * on a require that are not standardized), and to give a short
     * name for minification/local scope use.
     */
    req = requirejs = function (deps, callback, errback, optional) {

        //Find the right context, use default
        var context, config,
            contextName = defContextName;

        // Determine if have config object in the call.
        if (!isArray(deps) && typeof deps !== 'string') {
            // deps is a config object
            config = deps;
            if (isArray(callback)) {
                // Adjust args if there are dependencies
                deps = callback;
                callback = errback;
                errback = optional;
            } else {
                deps = [];
            }
        }

        if (config && config.context) {
            contextName = config.context;
        }

        context = getOwn(contexts, contextName);
        if (!context) {
            context = contexts[contextName] = req.s.newContext(contextName);
        }

        if (config) {
            context.configure(config);
        }

        return context.require(deps, callback, errback);
    };

    /**
     * Support require.config() to make it easier to cooperate with other
     * AMD loaders on globally agreed names.
     */
    req.config = function (config) {
        return req(config);
    };

    /**
     * Execute something after the current tick
     * of the event loop. Override for other envs
     * that have a better solution than setTimeout.
     * @param  {Function} fn function to execute later.
     */
    req.nextTick = typeof setTimeout !== 'undefined' ? function (fn) {
        setTimeout(fn, 4);
    } : function (fn) { fn(); };

    /**
     * Export require as a global, but only if it does not already exist.
     */
    if (!require) {
        require = req;
    }

    req.version = version;

    //Used to filter out dependencies that are already paths.
    req.jsExtRegExp = /^\/|:|\?|\.js$/;
    req.isBrowser = isBrowser;
    s = req.s = {
        contexts: contexts,
        newContext: newContext
    };

    //Create default context.
    req({});

    //Exports some context-sensitive methods on global require.
    each([
        'toUrl',
        'undef',
        'defined',
        'specified'
    ], function (prop) {
        //Reference from contexts instead of early binding to default context,
        //so that during builds, the latest instance of the default context
        //with its config gets used.
        req[prop] = function () {
            var ctx = contexts[defContextName];
            return ctx.require[prop].apply(ctx, arguments);
        };
    });

    if (isBrowser) {
        head = s.head = document.getElementsByTagName('head')[0];
        //If BASE tag is in play, using appendChild is a problem for IE6.
        //When that browser dies, this can be removed. Details in this jQuery bug:
        //http://dev.jquery.com/ticket/2709
        baseElement = document.getElementsByTagName('base')[0];
        if (baseElement) {
            head = s.head = baseElement.parentNode;
        }
    }

    /**
     * Any errors that require explicitly generates will be passed to this
     * function. Intercept/override it if you want custom error handling.
     * @param {Error} err the error object.
     */
    req.onError = defaultOnError;

    /**
     * Creates the node for the load command. Only used in browser envs.
     */
    req.createNode = function (config, moduleName, url) {
        var node = config.xhtml ?
                document.createElementNS('http://www.w3.org/1999/xhtml', 'html:script') :
                document.createElement('script');
        node.type = config.scriptType || 'text/javascript';
        node.charset = 'utf-8';
        node.async = true;
        return node;
    };

    /**
     * Does the request to load a module for the browser case.
     * Make this a separate function to allow other environments
     * to override it.
     *
     * @param {Object} context the require context to find state.
     * @param {String} moduleName the name of the module.
     * @param {Object} url the URL to the module.
     */
    req.load = function (context, moduleName, url) {
        var config = (context && context.config) || {},
            node;
        if (isBrowser) {
            //In the browser so use a script tag
            node = req.createNode(config, moduleName, url);

            node.setAttribute('data-requirecontext', context.contextName);
            node.setAttribute('data-requiremodule', moduleName);

            //Set up load listener. Test attachEvent first because IE9 has
            //a subtle issue in its addEventListener and script onload firings
            //that do not match the behavior of all other browsers with
            //addEventListener support, which fire the onload event for a
            //script right after the script execution. See:
            //https://connect.microsoft.com/IE/feedback/details/648057/script-onload-event-is-not-fired-immediately-after-script-execution
            //UNFORTUNATELY Opera implements attachEvent but does not follow the script
            //script execution mode.
            if (node.attachEvent &&
                    //Check if node.attachEvent is artificially added by custom script or
                    //natively supported by browser
                    //read https://github.com/requirejs/requirejs/issues/187
                    //if we can NOT find [native code] then it must NOT natively supported.
                    //in IE8, node.attachEvent does not have toString()
                    //Note the test for "[native code" with no closing brace, see:
                    //https://github.com/requirejs/requirejs/issues/273
                    !(node.attachEvent.toString && node.attachEvent.toString().indexOf('[native code') < 0) &&
                    !isOpera) {
                //Probably IE. IE (at least 6-8) do not fire
                //script onload right after executing the script, so
                //we cannot tie the anonymous define call to a name.
                //However, IE reports the script as being in 'interactive'
                //readyState at the time of the define call.
                useInteractive = true;

                node.attachEvent('onreadystatechange', context.onScriptLoad);
                //It would be great to add an error handler here to catch
                //404s in IE9+. However, onreadystatechange will fire before
                //the error handler, so that does not help. If addEventListener
                //is used, then IE will fire error before load, but we cannot
                //use that pathway given the connect.microsoft.com issue
                //mentioned above about not doing the 'script execute,
                //then fire the script load event listener before execute
                //next script' that other browsers do.
                //Best hope: IE10 fixes the issues,
                //and then destroys all installs of IE 6-9.
                //node.attachEvent('onerror', context.onScriptError);
            } else {
                node.addEventListener('load', context.onScriptLoad, false);
                node.addEventListener('error', context.onScriptError, false);
            }
            node.src = url;

            //Calling onNodeCreated after all properties on the node have been
            //set, but before it is placed in the DOM.
            if (config.onNodeCreated) {
                config.onNodeCreated(node, config, moduleName, url);
            }

            //For some cache cases in IE 6-8, the script executes before the end
            //of the appendChild execution, so to tie an anonymous define
            //call to the module name (which is stored on the node), hold on
            //to a reference to this node, but clear after the DOM insertion.
            currentlyAddingScript = node;
            if (baseElement) {
                head.insertBefore(node, baseElement);
            } else {
                head.appendChild(node);
            }
            currentlyAddingScript = null;

            return node;
        } else if (isWebWorker) {
            try {
                //In a web worker, use importScripts. This is not a very
                //efficient use of importScripts, importScripts will block until
                //its script is downloaded and evaluated. However, if web workers
                //are in play, the expectation is that a build has been done so
                //that only one script needs to be loaded anyway. This may need
                //to be reevaluated if other use cases become common.

                // Post a task to the event loop to work around a bug in WebKit
                // where the worker gets garbage-collected after calling
                // importScripts(): https://webkit.org/b/153317
                setTimeout(function() {}, 0);
                importScripts(url);

                //Account for anonymous modules
                context.completeLoad(moduleName);
            } catch (e) {
                context.onError(makeError('importscripts',
                                'importScripts failed for ' +
                                    moduleName + ' at ' + url,
                                e,
                                [moduleName]));
            }
        }
    };

    function getInteractiveScript() {
        if (interactiveScript && interactiveScript.readyState === 'interactive') {
            return interactiveScript;
        }

        eachReverse(scripts(), function (script) {
            if (script.readyState === 'interactive') {
                return (interactiveScript = script);
            }
        });
        return interactiveScript;
    }

    //Look for a data-main script attribute, which could also adjust the baseUrl.
    if (isBrowser && !cfg.skipDataMain) {
        //Figure out baseUrl. Get it from the script tag with require.js in it.
        eachReverse(scripts(), function (script) {
            //Set the 'head' where we can append children by
            //using the script's parent.
            if (!head) {
                head = script.parentNode;
            }

            //Look for a data-main attribute to set main script for the page
            //to load. If it is there, the path to data main becomes the
            //baseUrl, if it is not already set.
            dataMain = script.getAttribute('data-main');
            if (dataMain) {
                //Preserve dataMain in case it is a path (i.e. contains '?')
                mainScript = dataMain;

                //Set final baseUrl if there is not already an explicit one,
                //but only do so if the data-main value is not a loader plugin
                //module ID.
                if (!cfg.baseUrl && mainScript.indexOf('!') === -1) {
                    //Pull off the directory of data-main for use as the
                    //baseUrl.
                    src = mainScript.split('/');
                    mainScript = src.pop();
                    subPath = src.length ? src.join('/')  + '/' : './';

                    cfg.baseUrl = subPath;
                }

                //Strip off any trailing .js since mainScript is now
                //like a module name.
                mainScript = mainScript.replace(jsSuffixRegExp, '');

                //If mainScript is still a path, fall back to dataMain
                if (req.jsExtRegExp.test(mainScript)) {
                    mainScript = dataMain;
                }

                //Put the data-main script in the files to load.
                cfg.deps = cfg.deps ? cfg.deps.concat(mainScript) : [mainScript];

                return true;
            }
        });
    }

    /**
     * The function that handles definitions of modules. Differs from
     * require() in that a string for the module should be the first argument,
     * and the function to execute after dependencies are loaded should
     * return a value to define the module corresponding to the first argument's
     * name.
     */
    define = function (name, deps, callback) {
        var node, context;

        //Allow for anonymous modules
        if (typeof name !== 'string') {
            //Adjust args appropriately
            callback = deps;
            deps = name;
            name = null;
        }

        //This module may not have dependencies
        if (!isArray(deps)) {
            callback = deps;
            deps = null;
        }

        //If no name, and callback is a function, then figure out if it a
        //CommonJS thing with dependencies.
        if (!deps && isFunction(callback)) {
            deps = [];
            //Remove comments from the callback string,
            //look for require calls, and pull them into the dependencies,
            //but only if there are function args.
            if (callback.length) {
                callback
                    .toString()
                    .replace(commentRegExp, commentReplace)
                    .replace(cjsRequireRegExp, function (match, dep) {
                        deps.push(dep);
                    });

                //May be a CommonJS thing even without require calls, but still
                //could use exports, and module. Avoid doing exports and module
                //work though if it just needs require.
                //REQUIRES the function to expect the CommonJS variables in the
                //order listed below.
                deps = (callback.length === 1 ? ['require'] : ['require', 'exports', 'module']).concat(deps);
            }
        }

        //If in IE 6-8 and hit an anonymous define() call, do the interactive
        //work.
        if (useInteractive) {
            node = currentlyAddingScript || getInteractiveScript();
            if (node) {
                if (!name) {
                    name = node.getAttribute('data-requiremodule');
                }
                context = contexts[node.getAttribute('data-requirecontext')];
            }
        }

        //Always save off evaluating the def call until the script onload handler.
        //This allows multiple modules to be in a file without prematurely
        //tracing dependencies, and allows for anonymous module support,
        //where the module name is not known until the script onload event
        //occurs. If no context, use the global queue, and get it processed
        //in the onscript load callback.
        if (context) {
            context.defQueue.push([name, deps, callback]);
            context.defQueueMap[name] = true;
        } else {
            globalDefQueue.push([name, deps, callback]);
        }
    };

    define.amd = {
        jQuery: true
    };

    /**
     * Executes the text. Normally just uses eval, but can be modified
     * to use a better, environment-specific call. Only used for transpiling
     * loader plugins, not for plain JS modules.
     * @param {String} text the text to execute/evaluate.
     */
    req.exec = function (text) {
        /*jslint evil: true */
        return eval(text);
    };

    //Set up with config info.
    req(cfg);
}(this, (typeof setTimeout === 'undefined' ? undefined : setTimeout)));

// Copyright 2014 Google Inc. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
//     You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
//     See the License for the specific language governing permissions and
// limitations under the License.

!function(){var a={},b={},c={};!function(a,b){function c(a){if("number"==typeof a)return a;var b={};for(var c in a)b[c]=a[c];return b}function d(){this._delay=0,this._endDelay=0,this._fill="none",this._iterationStart=0,this._iterations=1,this._duration=0,this._playbackRate=1,this._direction="normal",this._easing="linear",this._easingFunction=x}function e(){return a.isDeprecated("Invalid timing inputs","2016-03-02","TypeError exceptions will be thrown instead.",!0)}function f(b,c,e){var f=new d;return c&&(f.fill="both",f.duration="auto"),"number"!=typeof b||isNaN(b)?void 0!==b&&Object.getOwnPropertyNames(b).forEach(function(c){if("auto"!=b[c]){if(("number"==typeof f[c]||"duration"==c)&&("number"!=typeof b[c]||isNaN(b[c])))return;if("fill"==c&&-1==v.indexOf(b[c]))return;if("direction"==c&&-1==w.indexOf(b[c]))return;if("playbackRate"==c&&1!==b[c]&&a.isDeprecated("AnimationEffectTiming.playbackRate","2014-11-28","Use Animation.playbackRate 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n=super._stampTemplate(t,e);if(me.pop(),e.nodeList=n.nodeList,!e.wasPreBound){let t=e.childNodes=[];for(let e=n.firstChild;e;e=e.nextSibling)t.push(e)}return n.templateInfo=e,(function i(t,e){let{nodeList:n,nodeInfoList:i}=e;if(i.length)for(let e=0;e<i.length;e++){let r=n[e],o=i[e].bindings;if(o)for(let e=0;e<o.length;e++){let n=o[e];ne(r,n),ie(r,t,n)}r.__dataHost=t}})(this,e),this.__dataClientsReady&&(this._runEffectsForTemplate(e,this.__data,null,!1),this._flushClients()),n}_removeBoundDom(t){const e=t.templateInfo,{previousSibling:n,nextSibling:i,parent:r}=e;n?n.nextSibling=i:r&&(r.firstChild=i),i?i.previousSibling=n:r&&(r.lastChild=n),e.nextSibling=e.previousSibling=null;let o=e.childNodes;for(let t=0;t<o.length;t++){let e=o[t];Y(Y(e).parentNode).removeChild(e)}}static _parseTemplateNode(t,n,i){let r=e._parseTemplateNode.call(this,t,n,i);if(t.nodeType===Node.TEXT_NODE){let e=this._parseBindings(t.textContent,n);e&&(t.textContent=le(e)||" 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/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
function e(t){const e={};for(let n in t){const i=t[n];e[n]="function"==typeof i?{type:i}:i}return e})(n))}t.__ownProperties=e}return t.__ownProperties}class r extends e{static get observedAttributes(){if(!this.hasOwnProperty(JSCompiler_renameProperty("__observedAttributes",this))){const t=this._properties;this.__observedAttributes=t?Object.keys(t).map((t=>this.prototype._addPropertyToAttributeMap(t))):[]}return this.__observedAttributes}static finalize(){if(!this.hasOwnProperty(JSCompiler_renameProperty("__finalized",this))){const t=n(this);t&&t.finalize(),this.__finalized=!0,this._finalizeClass()}}static _finalizeClass(){const t=i(this);t&&this.createProperties(t)}static get _properties(){if(!this.hasOwnProperty(JSCompiler_renameProperty("__properties",this))){const t=n(this);this.__properties=Object.assign({},t&&t._properties,i(this))}return this.__properties}static typeForProperty(t){const e=this._properties[t];return e&&e.type}_initializeProperties(){this.constructor.finalize(),super._initializeProperties()}connectedCallback(){super.connectedCallback&&super.connectedCallback(),this._enableProperties()}disconnectedCallback(){super.disconnectedCallback&&super.disconnectedCallback()}}return r})),_e=window.ShadyCSS&&window.ShadyCSS.cssBuild,ye=I((t=>{const e=ge(fe(t));function n(t,e,n,i){n.computed&&(n.readOnly=!0),n.computed&&(t._hasReadOnlyEffect(e)?console.warn(`Cannot redefine computed property '${e}'.`):t._createComputedProperty(e,n.computed,i)),n.readOnly&&!t._hasReadOnlyEffect(e)?t._createReadOnlyProperty(e,!n.computed):!1===n.readOnly&&t._hasReadOnlyEffect(e)&&console.warn(`Cannot make readOnly property '${e}' non-readOnly.`),n.reflectToAttribute&&!t._hasReflectEffect(e)?t._createReflectedProperty(e):!1===n.reflectToAttribute&&t._hasReflectEffect(e)&&console.warn(`Cannot make reflected property '${e}' non-reflected.`),n.notify&&!t._hasNotifyEffect(e)?t._createNotifyingProperty(e):!1===n.notify&&t._hasNotifyEffect(e)&&console.warn(`Cannot make notify property '${e}' non-notify.`),n.observer&&t._createPropertyObserver(e,n.observer,i[n.observer]),t._addPropertyToAttributeMap(e)}function i(t,e,n,i){if(!_e){const r=e.content.querySelectorAll("style"),o=G(e),a=(function r(t){let e=F(t);return e?W(e):[]})(n),s=e.content.firstElementChild;for(let n=0;n<a.length;n++){let r=a[n];r.textContent=t._processStyleText(r.textContent,i),e.content.insertBefore(r,s)}let l=0;for(let e=0;e<o.length;e++){let n=o[e],a=r[l];a!==n?(n=n.cloneNode(!0),a.parentNode.insertBefore(n,a)):l++,n.textContent=t._processStyleText(n.textContent,i)}}if(window.ShadyCSS&&window.ShadyCSS.prepareTemplate(e,n),P&&_e&&g){const n=e.content.querySelectorAll("style");if(n){let e="";Array.from(n).forEach((t=>{e+=t.textContent,t.parentNode.removeChild(t)})),t._styleSheet=new CSSStyleSheet,t._styleSheet.replaceSync(e)}}}return class extends e{static get polymerElementVersion(){return"3.4.1"}static _finalizeClass(){e._finalizeClass.call(this);const t=(function n(t){return t.hasOwnProperty(JSCompiler_renameProperty("__ownObservers",t))||(t.__ownObservers=t.hasOwnProperty(JSCompiler_renameProperty("observers",t))?t.observers:null),t.__ownObservers})(this);t&&this.createObservers(t,this._properties),this._prepareTemplate()}static _prepareTemplate(){let t=this.template;t&&("string"==typeof t?(console.error("template getter must return HTMLTemplateElement"),t=null):w||(t=t.cloneNode(!0))),this.prototype._template=t}static createProperties(t){for(let e in t)n(this.prototype,e,t[e],t)}static createObservers(t,e){const n=this.prototype;for(let i=0;i<t.length;i++)n._createMethodObserver(t[i],e)}static get template(){if(!this.hasOwnProperty(JSCompiler_renameProperty("_template",this))){const t=this.prototype.hasOwnProperty(JSCompiler_renameProperty("_template",this.prototype))?this.prototype._template:void 0;this._template=void 0!==t?t:this.hasOwnProperty(JSCompiler_renameProperty("is",this))&&(function e(t){let e=null;if(t&&(!b||x)&&(e=B.import(t,"template"),b&&!e))throw new Error(`strictTemplatePolicy: expecting dom-module or null template for ${t}`);return e})(this.is)||Object.getPrototypeOf(this.prototype).constructor.template}return this._template}static set template(t){this._template=t}static get importPath(){if(!this.hasOwnProperty(JSCompiler_renameProperty("_importPath",this))){const t=this.importMeta;if(t)this._importPath=f(t.url);else{const t=B.import(this.is);this._importPath=t&&t.assetpath||Object.getPrototypeOf(this.prototype).constructor.importPath}}return this._importPath}constructor(){super()}_initializeProperties(){this.constructor.finalize(),this.constructor._finalizeTemplate(this.localName),super._initializeProperties(),this.rootPath=_,this.importPath=this.constructor.importPath;let t=(function e(t){if(!t.hasOwnProperty(JSCompiler_renameProperty("__propertyDefaults",t))){t.__propertyDefaults=null;let e=t._properties;for(let n in e){let i=e[n];"value"in i&&(t.__propertyDefaults=t.__propertyDefaults||{},t.__propertyDefaults[n]=i)}}return t.__propertyDefaults})(this.constructor);if(t)for(let e in t){let n=t[e];if(this._canApplyPropertyDefault(e)){let t="function"==typeof n.value?n.value.call(this):n.value;this._hasAccessor(e)?this._setPendingProperty(e,t,!0):this[e]=t}}}_canApplyPropertyDefault(t){return!this.hasOwnProperty(t)}static _processStyleText(t,e){return p(t,e)}static _finalizeTemplate(t){const e=this.prototype._template;if(e&&!e.__polymerFinalized){e.__polymerFinalized=!0;const n=this.importPath;i(this,e,t,n?d(n):""),this.prototype._bindTemplate(e)}}connectedCallback(){window.ShadyCSS&&this._template&&window.ShadyCSS.styleElement(this),super.connectedCallback()}ready(){this._template&&(this.root=this._stampTemplate(this._template),this.$=this.root.$),super.ready()}_readyClients(){this._template&&(this.root=this._attachDom(this.root)),super._readyClients()}_attachDom(t){const e=Y(this);if(e.attachShadow)return t?(e.shadowRoot||(e.attachShadow({mode:"open",shadyUpgradeFragment:t}),e.shadowRoot.appendChild(t),this.constructor._styleSheet&&(e.shadowRoot.adoptedStyleSheets=[this.constructor._styleSheet])),M&&window.ShadyDOM&&window.ShadyDOM.flushInitial(e.shadowRoot),e.shadowRoot):null;throw new Error("ShadowDOM not available. PolymerElement can create dom as children instead of in ShadowDOM by setting `this.root = this;` before `ready`.")}updateStyles(t){window.ShadyCSS&&window.ShadyCSS.styleSubtree(this,t)}resolveUrl(t,e){return!e&&this.importPath&&(e=d(this.importPath)),d(t,e)}static _parseTemplateContent(t,n,i){return n.dynamicFns=n.dynamicFns||this._properties,e._parseTemplateContent.call(this,t,n,i)}static _addTemplatePropertyEffect(t,n,i){return!S||n in this._properties||i.info.part.signature&&i.info.part.signature.static||i.info.part.hostProp||t.nestedTemplate||console.warn(`Property '${n}' used in template but not declared in 'properties'; attribute will not be observed.`),e._addTemplatePropertyEffect.call(this,t,n,i)}}}));
/**
     * @fileoverview
     * @suppress {checkPrototypalTypes}
     * @license Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt The complete set of authors may be found
     * at http://polymer.github.io/AUTHORS.txt The complete set of contributors may
     * be found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by
     * Google as part of the polymer project is also subject to an additional IP
     * rights grant found at http://polymer.github.io/PATENTS.txt
     */
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
class ve{constructor(t){this.value=t.toString()}toString(){return this.value}}function be(t){if(t instanceof HTMLTemplateElement)return t.innerHTML;if(t instanceof ve)return(function e(t){if(t instanceof ve)return t.value;throw new Error(`non-literal value passed to Polymer's htmlLiteral function: ${t}`)})(t);throw new Error(`non-template value passed to Polymer's html function: ${t}`)}const xe=function t(e,...n){const i=document.createElement("template");return i.innerHTML=n.reduce(((t,n,i)=>t+be(n)+e[i+1]),e[0]),i},we=ye(HTMLElement);
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */var Se="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{};function Me(t){if(t.__esModule)return t;var e=Object.defineProperty({},"__esModule",{value:!0});return Object.keys(t).forEach((function(n){var i=Object.getOwnPropertyDescriptor(t,n);Object.defineProperty(e,n,i.get?i:{enumerable:!0,get:function(){return t[n]}})})),e}var Ee,Te,Ce={exports:{}};
/**
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     * Lodash <https://lodash.com/>
     * Copyright OpenJS Foundation and other contributors <https://openjsf.org/>
     * Released under MIT license <https://lodash.com/license>
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     * Copyright Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
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/**
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/**
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/**
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
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/**
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    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
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    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
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/**
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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    */
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
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    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
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    */
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observers(){return["__itemsChanged(items.*)"]}constructor(){super(),this.__instances=[],this.__renderDebouncer=null,this.__itemsIdxToInstIdx={},this.__chunkCount=null,this.__renderStartTime=null,this.__itemsArrayChanged=!1,this.__shouldMeasureChunk=!1,this.__shouldContinueChunking=!1,this.__chunkingId=0,this.__sortFn=null,this.__filterFn=null,this.__observePaths=null,this.__ctor=null,this.__isDetached=!0,this.template=null}disconnectedCallback(){super.disconnectedCallback(),this.__isDetached=!0;for(let t=0;t<this.__instances.length;t++)this.__detachInstance(t)}connectedCallback(){if(super.connectedCallback(),uo()||(this.style.display="none"),this.__isDetached){this.__isDetached=!1;let t=Y(Y(this).parentNode);for(let e=0;e<this.__instances.length;e++)this.__attachInstance(e,t)}}__ensureTemplatized(){if(!this.__ctor){const t=this;let e=this.template=t._templateInfo?t:this.querySelector("template");if(!e){let t=new MutationObserver((()=>{if(!this.querySelector("template"))throw new Error("dom-repeat requires a <template> child");t.disconnect(),this.__render()}));return t.observe(this,{childList:!0}),!1}let n={};n[this.as]=!0,n[this.indexAs]=!0,n[this.itemsIndexAs]=!0,this.__ctor=ao(e,this,{mutableData:this.mutableData,parentModel:!0,instanceProps:n,forwardHostProp:function(t,e){let n=this.__instances;for(let i,r=0;r<n.length&&(i=n[r]);r++)i.forwardHostProp(t,e)},notifyInstanceProp:function(t,e,n){if(Q(this.as,e)){let i=t[this.itemsIndexAs];e==this.as&&(this.items[i]=n);let r=J(this.as,`${JSCompiler_renameProperty("items",this)}.${i}`,e);this.notifyPath(r,n)}}})}return!0}__getMethodHost(){return this.__dataHost._methodHost||this.__dataHost}__functionFromPropertyValue(t){if("string"==typeof t){let e=t,n=this.__getMethodHost();return function(){return n[e].apply(n,arguments)}}return t}__sortChanged(t){this.__sortFn=this.__functionFromPropertyValue(t),this.items&&this.__debounceRender(this.__render)}__filterChanged(t){this.__filterFn=this.__functionFromPropertyValue(t),this.items&&this.__debounceRender(this.__render)}__computeFrameTime(t){return Math.ceil(1e3/t)}__observeChanged(){this.__observePaths=this.observe&&this.observe.replace(".*",".").split(" ")}__handleObservedPaths(t){if(this.__sortFn||this.__filterFn)if(t){if(this.__observePaths){let e=this.__observePaths;for(let n=0;n<e.length;n++)0===t.indexOf(e[n])&&this.__debounceRender(this.__render,this.delay)}}else this.__debounceRender(this.__render,this.delay)}__itemsChanged(t){this.items&&!Array.isArray(this.items)&&console.warn("dom-repeat expected array for `items`, 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this.__filterFn&&(e=e.filter(((e,n,i)=>this.__filterFn(t[e],n,i)))),this.__sortFn&&e.sort(((e,n)=>this.__sortFn(t[e],t[n]))),e}__calculateLimit(t){let e=t;const n=this.__instances.length;if(this.initialCount){let i;!this.__chunkCount||this.__itemsArrayChanged&&!this.reuseChunkedInstances?(e=Math.min(t,this.initialCount),i=Math.max(e-n,0),this.__chunkCount=i||1):(i=Math.min(Math.max(t-n,0),this.__chunkCount),e=Math.min(n+i,t)),this.__shouldMeasureChunk=i===this.__chunkCount,this.__shouldContinueChunking=e<t,this.__renderStartTime=performance.now()}return this.__itemsArrayChanged=!1,e}__continueChunking(){if(this.__shouldMeasureChunk){const t=performance.now()-this.__renderStartTime;this.__chunkCount=Math.round(this.__chunkCount*(this._targetFrameTime/t))||1}this.__shouldContinueChunking&&this.__debounceRender(this.__render)}__updateInstances(t,e,n){const i=this.__itemsIdxToInstIdx={};let r;for(r=0;r<e;r++){let e=this.__instances[r],o=n[r],a=t[o];i[o]=r,e?(e._setPendingProperty(this.as,a),e._setPendingProperty(this.indexAs,r),e._setPendingProperty(this.itemsIndexAs,o),e._flushProperties()):this.__insertInstance(a,r,o)}for(let t=this.__instances.length-1;t>=r;t--)this.__detachAndRemoveInstance(t)}__detachInstance(t){let e=this.__instances[t];const n=Y(e.root);for(let t=0;t<e.children.length;t++){n.appendChild(e.children[t])}return e}__attachInstance(t,e){e.insertBefore(this.__instances[t].root,this)}__detachAndRemoveInstance(t){this.__detachInstance(t),this.__instances.splice(t,1)}__stampInstance(t,e,n){let i={};return i[this.as]=t,i[this.indexAs]=e,i[this.itemsIndexAs]=n,new this.__ctor(i)}__insertInstance(t,e,n){const i=this.__stampInstance(t,e,n);let r=this.__instances[e+1],o=r?r.children[0]:this;return Y(Y(this).parentNode).insertBefore(i.root,o),this.__instances[e]=i,i}_showHideChildren(t){for(let e=0;e<this.__instances.length;e++)this.__instances[e]._showHideChildren(t)}__handleItemPath(t,e){let n=t.slice(6),i=n.indexOf("."),r=i<0?n:n.substring(0,i);if(r==parseInt(r,10)){let t=i<0?"":n.substring(i+1);this.__handleObservedPaths(t);let o=this.__instances[this.__itemsIdxToInstIdx[r]];if(o){o._setPendingPropertyOrPath(this.as+(t?"."+t:""),e,!1,!0),o._flushProperties()}return!0}}itemForElement(t){let e=this.modelForElement(t);return e&&e[this.as]}indexForElement(t){let e=this.modelForElement(t);return e&&e[this.indexAs]}modelForElement(t){return so(this.template,t)}}customElements.define(fo.is,fo);
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
class mo extends we{static get is(){return"dom-if"}static get template(){return null}static get properties(){return{if:{type:Boolean,observer:"__debounceRender"},restamp:{type:Boolean,observer:"__debounceRender"},notifyDomChange:{type:Boolean}}}constructor(){super(),this.__renderDebouncer=null,this._lastIf=!1,this.__hideTemplateChildren__=!1}__debounceRender(){this.__renderDebouncer=Nn.debounce(this.__renderDebouncer,vt,(()=>this.__render())),Rn(this.__renderDebouncer)}disconnectedCallback(){super.disconnectedCallback();const t=Y(this).parentNode;t&&(t.nodeType!=Node.DOCUMENT_FRAGMENT_NODE||Y(t).host)||this.__teardownInstance()}connectedCallback(){super.connectedCallback(),uo()||(this.style.display="none"),this.if&&this.__debounceRender()}__ensureTemplate(){if(!this.__template){const t=this;let e=t._templateInfo?t:Y(t).querySelector("template");if(!e){let t=new MutationObserver((()=>{if(!Y(this).querySelector("template"))throw new Error("dom-if requires a <template> child");t.disconnect(),this.__render()}));return t.observe(this,{childList:!0}),!1}this.__template=e}return!0}__ensureInstance(){let t=Y(this).parentNode;if(this.__hasInstance()){let e=this.__getInstanceNodes();if(e&&e.length){if(Y(this).previousSibling!==e[e.length-1])for(let n,i=0;i<e.length&&(n=e[i]);i++)Y(t).insertBefore(n,this)}}else{if(!t)return!1;if(!this.__ensureTemplate())return!1;this.__createAndInsertInstance(t)}return!0}render(){Wi()}__render(){if(this.if){if(!this.__ensureInstance())return}else this.restamp&&this.__teardownInstance();this._showHideChildren(),k&&!this.notifyDomChange||this.if==this._lastIf||(this.dispatchEvent(new CustomEvent("dom-change",{bubbles:!0,composed:!0})),this._lastIf=this.if)}__hasInstance(){}__getInstanceNodes(){}__createAndInsertInstance(t){}__teardownInstance(){}_showHideChildren(){}}const go=A?class extends mo{constructor(){super(),this.__instance=null,this.__syncInfo=null}__hasInstance(){return Boolean(this.__instance)}__getInstanceNodes(){return this.__instance.templateInfo.childNodes}__createAndInsertInstance(t){const e=this.__dataHost||this;if(b&&!this.__dataHost)throw new Error("strictTemplatePolicy: template owner not trusted");const n=e._bindTemplate(this.__template,!0);n.runEffects=(t,e,n)=>{let i=this.__syncInfo;if(this.if)i&&(this.__syncInfo=null,this._showHideChildren(),e=Object.assign(i.changedProps,e)),t(e,n);else if(this.__instance)if(i||(i=this.__syncInfo={runEffects:t,changedProps:{}}),n)for(const t in e){const e=$(t);i.changedProps[e]=this.__dataHost[e]}else Object.assign(i.changedProps,e)},this.__instance=e._stampTemplate(this.__template,n),Y(t).insertBefore(this.__instance,this)}__syncHostProperties(){const t=this.__syncInfo;t&&(this.__syncInfo=null,t.runEffects(t.changedProps,!1))}__teardownInstance(){this.__instance&&((this.__dataHost||this)._removeBoundDom(this.__instance),this.__instance=null,this.__syncInfo=null)}_showHideChildren(){const t=this.__hideTemplateChildren__||!this.if;this.__instance&&Boolean(this.__instance.__hidden)!==t&&(this.__instance.__hidden=t,Jr(t,this.__instance.templateInfo.childNodes)),t||this.__syncHostProperties()}}:class extends mo{constructor(){super(),this.__ctor=null,this.__instance=null,this.__invalidProps=null}__hasInstance(){return Boolean(this.__instance)}__getInstanceNodes(){return this.__instance.children}__createAndInsertInstance(t){this.__ctor||(this.__ctor=ao(this.__template,this,{mutableData:!0,forwardHostProp:function(t,e){this.__instance&&(this.if?this.__instance.forwardHostProp(t,e):(this.__invalidProps=this.__invalidProps||Object.create(null),this.__invalidProps[$(t)]=!0))}})),this.__instance=new this.__ctor,Y(t).insertBefore(this.__instance.root,this)}__teardownInstance(){if(this.__instance){let t=this.__instance.children;if(t&&t.length){let e=Y(t[0]).parentNode;if(e){e=Y(e);for(let n,i=0;i<t.length&&(n=t[i]);i++)e.removeChild(n)}}this.__invalidProps=null,this.__instance=null}}__syncHostProperties(){let t=this.__invalidProps;if(t){this.__invalidProps=null;for(let e in t)this.__instance._setPendingProperty(e,this.__dataHost[e]);this.__instance._flushProperties()}}_showHideChildren(){const t=this.__hideTemplateChildren__||!this.if;this.__instance&&Boolean(this.__instance.__hidden)!==t&&(this.__instance.__hidden=t,this.__instance._showHideChildren(t)),t||this.__syncHostProperties()}};customElements.define(go.is,go);
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
let _o=I((t=>{let e=ye(t);return class extends e{static get properties(){return{items:{type:Array},multi:{type:Boolean,value:!1},selected:{type:Object,notify:!0},selectedItem:{type:Object,notify:!0},toggle:{type:Boolean,value:!1}}}static get observers(){return["__updateSelection(multi, items.*)"]}constructor(){super(),this.__lastItems=null,this.__lastMulti=null,this.__selectedMap=null}__updateSelection(t,e){let n=e.path;if(n==JSCompiler_renameProperty("items",this)){let n=e.base||[],i=this.__lastItems;if(t!==this.__lastMulti&&this.clearSelection(),i){let t=Vi(n,i);this.__applySplices(t)}this.__lastItems=n,this.__lastMulti=t}else if(e.path==`${JSCompiler_renameProperty("items",this)}.splices`)this.__applySplices(e.value.indexSplices);else{let t=n.slice(`${JSCompiler_renameProperty("items",this)}.`.length),e=parseInt(t,10);t.indexOf(".")<0&&t==e&&this.__deselectChangedIdx(e)}}__applySplices(t){let e=this.__selectedMap;for(let n=0;n<t.length;n++){let i=t[n];e.forEach(((t,n)=>{t<i.index||e.set(n,t>=i.index+i.removed.length?t+i.addedCount-i.removed.length:-1)}));for(let t=0;t<i.addedCount;t++){let n=i.index+t;e.has(this.items[n])&&e.set(this.items[n],n)}}this.__updateLinks();let n=0;e.forEach(((t,i)=>{t<0?(this.multi?this.splice(JSCompiler_renameProperty("selected",this),n,1):this.selected=this.selectedItem=null,e.delete(i)):n++}))}__updateLinks(){if(this.__dataLinkedPaths={},this.multi){let t=0;this.__selectedMap.forEach((e=>{e>=0&&this.linkPaths(`${JSCompiler_renameProperty("items",this)}.${e}`,`${JSCompiler_renameProperty("selected",this)}.${t++}`)}))}else this.__selectedMap.forEach((t=>{this.linkPaths(JSCompiler_renameProperty("selected",this),`${JSCompiler_renameProperty("items",this)}.${t}`),this.linkPaths(JSCompiler_renameProperty("selectedItem",this),`${JSCompiler_renameProperty("items",this)}.${t}`)}))}clearSelection(){this.__dataLinkedPaths={},this.__selectedMap=new Map,this.selected=this.multi?[]:null,this.selectedItem=null}isSelected(t){return this.__selectedMap.has(t)}isIndexSelected(t){return this.isSelected(this.items[t])}__deselectChangedIdx(t){let e=this.__selectedIndexForItemIndex(t);if(e>=0){let t=0;this.__selectedMap.forEach(((n,i)=>{e==t++&&this.deselect(i)}))}}__selectedIndexForItemIndex(t){let e=this.__dataLinkedPaths[`${JSCompiler_renameProperty("items",this)}.${t}`];if(e)return parseInt(e.slice(`${JSCompiler_renameProperty("selected",this)}.`.length),10)}deselect(t){let e=this.__selectedMap.get(t);if(e>=0){let n;this.__selectedMap.delete(t),this.multi&&(n=this.__selectedIndexForItemIndex(e)),this.__updateLinks(),this.multi?this.splice(JSCompiler_renameProperty("selected",this),n,1):this.selected=this.selectedItem=null}}deselectIndex(t){this.deselect(this.items[t])}select(t){this.selectIndex(this.items.indexOf(t))}selectIndex(t){let e=this.items[t];this.isSelected(e)?this.toggle&&this.deselectIndex(t):(this.multi||this.__selectedMap.clear(),this.__selectedMap.set(e,t),this.__updateLinks(),this.multi?this.push(JSCompiler_renameProperty("selected",this),e):this.selected=this.selectedItem=e)}}}))(we);class yo extends _o{static get is(){return"array-selector"}static get template(){return null}}customElements.define(yo.is,yo);
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const vo=new kn;window.ShadyCSS||(window.ShadyCSS={prepareTemplate(t,e,n){},prepareTemplateDom(t,e){},prepareTemplateStyles(t,e,n){},styleSubtree(t,e){vo.processStyles(),on(t,e)},styleElement(t){vo.processStyles()},styleDocument(t){vo.processStyles(),on(document.body,t)},getComputedStyleValue:(t,e)=>an(t,e),flushCustomStyles(){},nativeCss:Ie,nativeShadow:Ae,cssBuild:Le,disableRuntime:Ne}),window.ShadyCSS.CustomStyleInterface=vo;
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const bo="include",xo=window.ShadyCSS.CustomStyleInterface;class wo extends HTMLElement{constructor(){super(),this._style=null,xo.addCustomStyle(this)}getStyle(){if(this._style)return this._style;const t=this.querySelector("style");if(!t)return null;this._style=t;const e=t.getAttribute(bo);return e&&(t.removeAttribute(bo),t.textContent=(function n(t){let e=t.trim().split(/\s+/),n="";for(let t=0;t<e.length;t++)n+=q(e[t]);return n})(e)+t.textContent),this.ownerDocument!==window.document&&window.document.head.appendChild(this),this._style}}
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
let So;window.customElements.define("custom-style",wo),So=Wr._mutablePropertyChange;const Mo={properties:{mutableData:Boolean},_shouldPropertyChange(t,e,n){return So(this,t,e,n,this.mutableData)}},Eo=cr(HTMLElement).prototype;
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var To=new Set;const Co={properties:{_parentResizable:{type:Object,observer:"_parentResizableChanged"},_notifyingDescendant:{type:Boolean,value:!1}},listeners:{"iron-request-resize-notifications":"_onIronRequestResizeNotifications"},created:function(){this._interestedResizables=[],this._boundNotifyResize=this.notifyResize.bind(this),this._boundOnDescendantIronResize=this._onDescendantIronResize.bind(this)},attached:function(){this._requestResizeNotifications()},detached:function(){this._parentResizable?this._parentResizable.stopResizeNotificationsFor(this):(To.delete(this),window.removeEventListener("resize",this._boundNotifyResize)),this._parentResizable=null},notifyResize:function(){this.isAttached&&(this._interestedResizables.forEach((function(t){this.resizerShouldNotify(t)&&this._notifyDescendant(t)}),this),this._fireResize())},assignParentResizable:function(t){this._parentResizable&&this._parentResizable.stopResizeNotificationsFor(this),this._parentResizable=t,t&&-1===t._interestedResizables.indexOf(this)&&(t._interestedResizables.push(this),t._subscribeIronResize(this))},stopResizeNotificationsFor:function(t){var e=this._interestedResizables.indexOf(t);e>-1&&(this._interestedResizables.splice(e,1),this._unsubscribeIronResize(t))},_subscribeIronResize:function(t){t.addEventListener("iron-resize",this._boundOnDescendantIronResize)},_unsubscribeIronResize:function(t){t.removeEventListener("iron-resize",this._boundOnDescendantIronResize)},resizerShouldNotify:function(t){return!0},_onDescendantIronResize:function(t){this._notifyingDescendant?t.stopPropagation():m||this._fireResize()},_fireResize:function(){this.fire("iron-resize",null,{node:this,bubbles:!1})},_onIronRequestResizeNotifications:function(t){var e=er(t).rootTarget;e!==this&&(e.assignParentResizable(this),this._notifyDescendant(e),t.stopPropagation())},_parentResizableChanged:function(t){t&&window.removeEventListener("resize",this._boundNotifyResize)},_notifyDescendant:function(t){this.isAttached&&(this._notifyingDescendant=!0,t.notifyResize(),this._notifyingDescendant=!1)},_requestResizeNotifications:function(){if(this.isAttached)if("loading"===document.readyState){var t=this._requestResizeNotifications.bind(this);document.addEventListener("readystatechange",(function e(){document.removeEventListener("readystatechange",e),t()}))}else this._findParent(),this._parentResizable?this._parentResizable._interestedResizables.forEach((function(t){t!==this&&t._findParent()}),this):(To.forEach((function(t){t!==this&&t._findParent()}),this),window.addEventListener("resize",this._boundNotifyResize),this.notifyResize())},_findParent:function(){this.assignParentResizable(null),this.fire("iron-request-resize-notifications",null,{node:this,bubbles:!0,cancelable:!0}),this._parentResizable?To.delete(this):To.add(this)}};var Ao=Object.freeze({__proto__:null,IronResizableBehavior:Co});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */jr({_template:xe`
    <style>
      :host {
        display: block;
        transition-duration: var(--iron-collapse-transition-duration, 300ms);
        /* Safari 10 needs this property prefixed to correctly apply the custom property */
        -webkit-transition-duration: var(--iron-collapse-transition-duration, 300ms);
        overflow: visible;
      }

      :host(.iron-collapse-closed) {
        display: none;
      }

      :host(:not(.iron-collapse-opened)) {
        overflow: hidden;
      }
    </style>

    <slot></slot>
`,is:"iron-collapse",behaviors:[Co],properties:{horizontal:{type:Boolean,value:!1,observer:"_horizontalChanged"},opened:{type:Boolean,value:!1,notify:!0,observer:"_openedChanged"},transitioning:{type:Boolean,notify:!0,readOnly:!0},noAnimation:{type:Boolean},_desiredSize:{type:String,value:""}},get dimension(){return this.horizontal?"width":"height"},get _dimensionMax(){return this.horizontal?"maxWidth":"maxHeight"},get _dimensionMaxCss(){return this.horizontal?"max-width":"max-height"},hostAttributes:{role:"group","aria-hidden":"true"},listeners:{transitionend:"_onTransitionEnd"},toggle:function(){this.opened=!this.opened},show:function(){this.opened=!0},hide:function(){this.opened=!1},updateSize:function(t,e){t="auto"===t?"":t;var n=e&&!this.noAnimation&&this.isAttached&&this._desiredSize!==t;if(this._desiredSize=t,this._updateTransition(!1),n){var i=this._calcSize();""===t&&(this.style[this._dimensionMax]="",t=this._calcSize()),this.style[this._dimensionMax]=i,this.scrollTop=this.scrollTop,this._updateTransition(!0),n=t!==i}this.style[this._dimensionMax]=t,n||this._transitionEnd()},enableTransition:function(t){Eo._warn("`enableTransition()` is deprecated, use `noAnimation` instead."),this.noAnimation=!t},_updateTransition:function(t){this.style.transitionDuration=t&&!this.noAnimation?"":"0s"},_horizontalChanged:function(){this.style.transitionProperty=this._dimensionMaxCss,this.style["maxWidth"===this._dimensionMax?"maxHeight":"maxWidth"]="",this.updateSize(this.opened?"auto":"0px",!1)},_openedChanged:function(){this.setAttribute("aria-hidden",!this.opened),this._setTransitioning(!0),this.toggleClass("iron-collapse-closed",!1),this.toggleClass("iron-collapse-opened",!1),this.updateSize(this.opened?"auto":"0px",!0),this.opened&&this.focus()},_transitionEnd:function(){this.style[this._dimensionMax]=this._desiredSize,this.toggleClass("iron-collapse-closed",!this.opened),this.toggleClass("iron-collapse-opened",this.opened),this._updateTransition(!1),this.notifyResize(),this._setTransitioning(!1)},_onTransitionEnd:function(t){er(t).rootTarget===this&&this._transitionEnd()},_calcSize:function(){return this.getBoundingClientRect()[this.dimension]+"px"}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ko=xe`
/* Most common used flex styles*/
<dom-module id="iron-flex">
  <template>
    <style>
      .layout.horizontal,
      .layout.vertical {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      }

      .layout.inline {
        display: -ms-inline-flexbox;
        display: -webkit-inline-flex;
        display: inline-flex;
      }

      .layout.horizontal {
        -ms-flex-direction: row;
        -webkit-flex-direction: row;
        flex-direction: row;
      }

      .layout.vertical {
        -ms-flex-direction: column;
        -webkit-flex-direction: column;
        flex-direction: column;
      }

      .layout.wrap {
        -ms-flex-wrap: wrap;
        -webkit-flex-wrap: wrap;
        flex-wrap: wrap;
      }

      .layout.no-wrap {
        -ms-flex-wrap: nowrap;
        -webkit-flex-wrap: nowrap;
        flex-wrap: nowrap;
      }

      .layout.center,
      .layout.center-center {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      }

      .layout.center-justified,
      .layout.center-center {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      }

      .flex {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      }

      .flex-auto {
        -ms-flex: 1 1 auto;
        -webkit-flex: 1 1 auto;
        flex: 1 1 auto;
      }

      .flex-none {
        -ms-flex: none;
        -webkit-flex: none;
        flex: none;
      }
    </style>
  </template>
</dom-module>
/* Basic flexbox reverse styles */
<dom-module id="iron-flex-reverse">
  <template>
    <style>
      .layout.horizontal-reverse,
      .layout.vertical-reverse {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      }

      .layout.horizontal-reverse {
        -ms-flex-direction: row-reverse;
        -webkit-flex-direction: row-reverse;
        flex-direction: row-reverse;
      }

      .layout.vertical-reverse {
        -ms-flex-direction: column-reverse;
        -webkit-flex-direction: column-reverse;
        flex-direction: column-reverse;
      }

      .layout.wrap-reverse {
        -ms-flex-wrap: wrap-reverse;
        -webkit-flex-wrap: wrap-reverse;
        flex-wrap: wrap-reverse;
      }
    </style>
  </template>
</dom-module>
/* Flexbox alignment */
<dom-module id="iron-flex-alignment">
  <template>
    <style>
      /**
       * Alignment in cross axis.
       */
      .layout.start {
        -ms-flex-align: start;
        -webkit-align-items: flex-start;
        align-items: flex-start;
      }

      .layout.center,
      .layout.center-center {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      }

      .layout.end {
        -ms-flex-align: end;
        -webkit-align-items: flex-end;
        align-items: flex-end;
      }

      .layout.baseline {
        -ms-flex-align: baseline;
        -webkit-align-items: baseline;
        align-items: baseline;
      }

      /**
       * Alignment in main axis.
       */
      .layout.start-justified {
        -ms-flex-pack: start;
        -webkit-justify-content: flex-start;
        justify-content: flex-start;
      }

      .layout.center-justified,
      .layout.center-center {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      }

      .layout.end-justified {
        -ms-flex-pack: end;
        -webkit-justify-content: flex-end;
        justify-content: flex-end;
      }

      .layout.around-justified {
        -ms-flex-pack: distribute;
        -webkit-justify-content: space-around;
        justify-content: space-around;
      }

      .layout.justified {
        -ms-flex-pack: justify;
        -webkit-justify-content: space-between;
        justify-content: space-between;
      }

      /**
       * Self alignment.
       */
      .self-start {
        -ms-align-self: flex-start;
        -webkit-align-self: flex-start;
        align-self: flex-start;
      }

      .self-center {
        -ms-align-self: center;
        -webkit-align-self: center;
        align-self: center;
      }

      .self-end {
        -ms-align-self: flex-end;
        -webkit-align-self: flex-end;
        align-self: flex-end;
      }

      .self-stretch {
        -ms-align-self: stretch;
        -webkit-align-self: stretch;
        align-self: stretch;
      }

      .self-baseline {
        -ms-align-self: baseline;
        -webkit-align-self: baseline;
        align-self: baseline;
      }

      /**
       * multi-line alignment in main axis.
       */
      .layout.start-aligned {
        -ms-flex-line-pack: start;  /* IE10 */
        -ms-align-content: flex-start;
        -webkit-align-content: flex-start;
        align-content: flex-start;
      }

      .layout.end-aligned {
        -ms-flex-line-pack: end;  /* IE10 */
        -ms-align-content: flex-end;
        -webkit-align-content: flex-end;
        align-content: flex-end;
      }

      .layout.center-aligned {
        -ms-flex-line-pack: center;  /* IE10 */
        -ms-align-content: center;
        -webkit-align-content: center;
        align-content: center;
      }

      .layout.between-aligned {
        -ms-flex-line-pack: justify;  /* IE10 */
        -ms-align-content: space-between;
        -webkit-align-content: space-between;
        align-content: space-between;
      }

      .layout.around-aligned {
        -ms-flex-line-pack: distribute;  /* IE10 */
        -ms-align-content: space-around;
        -webkit-align-content: space-around;
        align-content: space-around;
      }
    </style>
  </template>
</dom-module>
/* Non-flexbox positioning helper styles */
<dom-module id="iron-flex-factors">
  <template>
    <style>
      .flex,
      .flex-1 {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      }

      .flex-2 {
        -ms-flex: 2;
        -webkit-flex: 2;
        flex: 2;
      }

      .flex-3 {
        -ms-flex: 3;
        -webkit-flex: 3;
        flex: 3;
      }

      .flex-4 {
        -ms-flex: 4;
        -webkit-flex: 4;
        flex: 4;
      }

      .flex-5 {
        -ms-flex: 5;
        -webkit-flex: 5;
        flex: 5;
      }

      .flex-6 {
        -ms-flex: 6;
        -webkit-flex: 6;
        flex: 6;
      }

      .flex-7 {
        -ms-flex: 7;
        -webkit-flex: 7;
        flex: 7;
      }

      .flex-8 {
        -ms-flex: 8;
        -webkit-flex: 8;
        flex: 8;
      }

      .flex-9 {
        -ms-flex: 9;
        -webkit-flex: 9;
        flex: 9;
      }

      .flex-10 {
        -ms-flex: 10;
        -webkit-flex: 10;
        flex: 10;
      }

      .flex-11 {
        -ms-flex: 11;
        -webkit-flex: 11;
        flex: 11;
      }

      .flex-12 {
        -ms-flex: 12;
        -webkit-flex: 12;
        flex: 12;
      }
    </style>
  </template>
</dom-module>
<dom-module id="iron-positioning">
  <template>
    <style>
      .block {
        display: block;
      }

      [hidden] {
        display: none !important;
      }

      .invisible {
        visibility: hidden !important;
      }

      .relative {
        position: relative;
      }

      .fit {
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      }

      body.fullbleed {
        margin: 0;
        height: 100vh;
      }

      .scroll {
        -webkit-overflow-scrolling: touch;
        overflow: auto;
      }

      /* fixed position */
      .fixed-bottom,
      .fixed-left,
      .fixed-right,
      .fixed-top {
        position: fixed;
      }

      .fixed-top {
        top: 0;
        left: 0;
        right: 0;
      }

      .fixed-right {
        top: 0;
        right: 0;
        bottom: 0;
      }

      .fixed-bottom {
        right: 0;
        bottom: 0;
        left: 0;
      }

      .fixed-left {
        top: 0;
        bottom: 0;
        left: 0;
      }
    </style>
  </template>
</dom-module>
`;ko.setAttribute("style","display: none;"),document.head.appendChild(ko.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Lo=xe`
<custom-style>
  <style is="custom-style">
    [hidden] {
      display: none !important;
    }
  </style>
</custom-style>
<custom-style>
  <style is="custom-style">
    html {

      --layout: {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      };

      --layout-inline: {
        display: -ms-inline-flexbox;
        display: -webkit-inline-flex;
        display: inline-flex;
      };

      --layout-horizontal: {
        @apply --layout;

        -ms-flex-direction: row;
        -webkit-flex-direction: row;
        flex-direction: row;
      };

      --layout-horizontal-reverse: {
        @apply --layout;

        -ms-flex-direction: row-reverse;
        -webkit-flex-direction: row-reverse;
        flex-direction: row-reverse;
      };

      --layout-vertical: {
        @apply --layout;

        -ms-flex-direction: column;
        -webkit-flex-direction: column;
        flex-direction: column;
      };

      --layout-vertical-reverse: {
        @apply --layout;

        -ms-flex-direction: column-reverse;
        -webkit-flex-direction: column-reverse;
        flex-direction: column-reverse;
      };

      --layout-wrap: {
        -ms-flex-wrap: wrap;
        -webkit-flex-wrap: wrap;
        flex-wrap: wrap;
      };

      --layout-wrap-reverse: {
        -ms-flex-wrap: wrap-reverse;
        -webkit-flex-wrap: wrap-reverse;
        flex-wrap: wrap-reverse;
      };

      --layout-flex-auto: {
        -ms-flex: 1 1 auto;
        -webkit-flex: 1 1 auto;
        flex: 1 1 auto;
      };

      --layout-flex-none: {
        -ms-flex: none;
        -webkit-flex: none;
        flex: none;
      };

      --layout-flex: {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      };

      --layout-flex-2: {
        -ms-flex: 2;
        -webkit-flex: 2;
        flex: 2;
      };

      --layout-flex-3: {
        -ms-flex: 3;
        -webkit-flex: 3;
        flex: 3;
      };

      --layout-flex-4: {
        -ms-flex: 4;
        -webkit-flex: 4;
        flex: 4;
      };

      --layout-flex-5: {
        -ms-flex: 5;
        -webkit-flex: 5;
        flex: 5;
      };

      --layout-flex-6: {
        -ms-flex: 6;
        -webkit-flex: 6;
        flex: 6;
      };

      --layout-flex-7: {
        -ms-flex: 7;
        -webkit-flex: 7;
        flex: 7;
      };

      --layout-flex-8: {
        -ms-flex: 8;
        -webkit-flex: 8;
        flex: 8;
      };

      --layout-flex-9: {
        -ms-flex: 9;
        -webkit-flex: 9;
        flex: 9;
      };

      --layout-flex-10: {
        -ms-flex: 10;
        -webkit-flex: 10;
        flex: 10;
      };

      --layout-flex-11: {
        -ms-flex: 11;
        -webkit-flex: 11;
        flex: 11;
      };

      --layout-flex-12: {
        -ms-flex: 12;
        -webkit-flex: 12;
        flex: 12;
      };

      /* alignment in cross axis */

      --layout-start: {
        -ms-flex-align: start;
        -webkit-align-items: flex-start;
        align-items: flex-start;
      };

      --layout-center: {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      };

      --layout-end: {
        -ms-flex-align: end;
        -webkit-align-items: flex-end;
        align-items: flex-end;
      };

      --layout-baseline: {
        -ms-flex-align: baseline;
        -webkit-align-items: baseline;
        align-items: baseline;
      };

      /* alignment in main axis */

      --layout-start-justified: {
        -ms-flex-pack: start;
        -webkit-justify-content: flex-start;
        justify-content: flex-start;
      };

      --layout-center-justified: {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      };

      --layout-end-justified: {
        -ms-flex-pack: end;
        -webkit-justify-content: flex-end;
        justify-content: flex-end;
      };

      --layout-around-justified: {
        -ms-flex-pack: distribute;
        -webkit-justify-content: space-around;
        justify-content: space-around;
      };

      --layout-justified: {
        -ms-flex-pack: justify;
        -webkit-justify-content: space-between;
        justify-content: space-between;
      };

      --layout-center-center: {
        @apply --layout-center;
        @apply --layout-center-justified;
      };

      /* self alignment */

      --layout-self-start: {
        -ms-align-self: flex-start;
        -webkit-align-self: flex-start;
        align-self: flex-start;
      };

      --layout-self-center: {
        -ms-align-self: center;
        -webkit-align-self: center;
        align-self: center;
      };

      --layout-self-end: {
        -ms-align-self: flex-end;
        -webkit-align-self: flex-end;
        align-self: flex-end;
      };

      --layout-self-stretch: {
        -ms-align-self: stretch;
        -webkit-align-self: stretch;
        align-self: stretch;
      };

      --layout-self-baseline: {
        -ms-align-self: baseline;
        -webkit-align-self: baseline;
        align-self: baseline;
      };

      /* multi-line alignment in main axis */

      --layout-start-aligned: {
        -ms-flex-line-pack: start;  /* IE10 */
        -ms-align-content: flex-start;
        -webkit-align-content: flex-start;
        align-content: flex-start;
      };

      --layout-end-aligned: {
        -ms-flex-line-pack: end;  /* IE10 */
        -ms-align-content: flex-end;
        -webkit-align-content: flex-end;
        align-content: flex-end;
      };

      --layout-center-aligned: {
        -ms-flex-line-pack: center;  /* IE10 */
        -ms-align-content: center;
        -webkit-align-content: center;
        align-content: center;
      };

      --layout-between-aligned: {
        -ms-flex-line-pack: justify;  /* IE10 */
        -ms-align-content: space-between;
        -webkit-align-content: space-between;
        align-content: space-between;
      };

      --layout-around-aligned: {
        -ms-flex-line-pack: distribute;  /* IE10 */
        -ms-align-content: space-around;
        -webkit-align-content: space-around;
        align-content: space-around;
      };

      /*******************************
                Other Layout
      *******************************/

      --layout-block: {
        display: block;
      };

      --layout-invisible: {
        visibility: hidden !important;
      };

      --layout-relative: {
        position: relative;
      };

      --layout-fit: {
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      };

      --layout-scroll: {
        -webkit-overflow-scrolling: touch;
        overflow: auto;
      };

      --layout-fullbleed: {
        margin: 0;
        height: 100vh;
      };

      /* fixed position */

      --layout-fixed-top: {
        position: fixed;
        top: 0;
        left: 0;
        right: 0;
      };

      --layout-fixed-right: {
        position: fixed;
        top: 0;
        right: 0;
        bottom: 0;
      };

      --layout-fixed-bottom: {
        position: fixed;
        right: 0;
        bottom: 0;
        left: 0;
      };

      --layout-fixed-left: {
        position: fixed;
        top: 0;
        bottom: 0;
        left: 0;
      };

    }
  </style>
</custom-style>`;Lo.setAttribute("style","display: none;"),document.head.appendChild(Lo.content);var Po=document.createElement("style");Po.textContent="[hidden] { display: none !important; }",document.head.appendChild(Po);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
class No{constructor(t){No[" "](t),this.type=t&&t.type||"default",this.key=t&&t.key,t&&"value"in t&&(this.value=t.value)}get value(){var t=this.type,e=this.key;if(t&&e)return No.types[t]&&No.types[t][e]}set value(t){var e=this.type,n=this.key;e&&n&&(e=No.types[e]=No.types[e]||{},null==t?delete e[n]:e[n]=t)}get list(){if(this.type){var t=No.types[this.type];return t?Object.keys(t).map((function(t){return Io[this.type][t]}),this):[]}}byKey(t){return this.key=t,this.value}}No[" "]=function(){},No.types={};var Io=No.types;jr({is:"iron-meta",properties:{type:{type:String,value:"default"},key:{type:String},value:{type:String,notify:!0},self:{type:Boolean,observer:"_selfChanged"},__meta:{type:Boolean,computed:"__computeMeta(type, key, value)"}},hostAttributes:{hidden:!0},__computeMeta:function(t,e,n){var i=new No({type:t,key:e});return void 0!==n&&n!==i.value?i.value=n:this.value!==i.value&&(this.value=i.value),i},get list(){return this.__meta&&this.__meta.list},_selfChanged:function(t){t&&(this.value=this)},byKey:function(t){return new No({type:this.type,key:t}).value}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style>
      :host {
        @apply --layout-inline;
        @apply --layout-center-center;
        position: relative;

        vertical-align: middle;

        fill: var(--iron-icon-fill-color, currentcolor);
        stroke: var(--iron-icon-stroke-color, none);

        width: var(--iron-icon-width, 24px);
        height: var(--iron-icon-height, 24px);
        @apply --iron-icon;
      }

      :host([hidden]) {
        display: none;
      }
    </style>
`,is:"iron-icon",properties:{icon:{type:String},theme:{type:String},src:{type:String},_meta:{value:Eo.create("iron-meta",{type:"iconset"})}},observers:["_updateIcon(_meta, isAttached)","_updateIcon(theme, isAttached)","_srcChanged(src, isAttached)","_iconChanged(icon, isAttached)"],_DEFAULT_ICONSET:"icons",_iconChanged:function(t){var e=(t||"").split(":");this._iconName=e.pop(),this._iconsetName=e.pop()||this._DEFAULT_ICONSET,this._updateIcon()},_srcChanged:function(t){this._updateIcon()},_usesIconset:function(){return this.icon||!this.src},_updateIcon:function(){this._usesIconset()?(this._img&&this._img.parentNode&&er(this.root).removeChild(this._img),""===this._iconName?this._iconset&&this._iconset.removeIcon(this):this._iconsetName&&this._meta&&(this._iconset=this._meta.byKey(this._iconsetName),this._iconset?(this._iconset.applyIcon(this,this._iconName,this.theme),this.unlisten(window,"iron-iconset-added","_updateIcon")):this.listen(window,"iron-iconset-added","_updateIcon"))):(this._iconset&&this._iconset.removeIcon(this),this._img||(this._img=document.createElement("img"),this._img.style.width="100%",this._img.style.height="100%",this._img.draggable=!1),this._img.src=this.src,er(this.root).appendChild(this._img))}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({is:"iron-iconset-svg",properties:{name:{type:String,observer:"_nameChanged"},size:{type:Number,value:24},rtlMirroring:{type:Boolean,value:!1},useGlobalRtlAttribute:{type:Boolean,value:!1}},created:function(){this._meta=new No({type:"iconset",key:null,value:null})},attached:function(){this.style.display="none"},getIconNames:function(){return this._icons=this._createIconMap(),Object.keys(this._icons).map((function(t){return this.name+":"+t}),this)},applyIcon:function(t,e){this.removeIcon(t);var n=this._cloneIcon(e,this.rtlMirroring&&this._targetIsRTL(t));if(n){var i=er(t.root||t);return i.insertBefore(n,i.childNodes[0]),t._svgIcon=n}return null},removeIcon:function(t){t._svgIcon&&(er(t.root||t).removeChild(t._svgIcon),t._svgIcon=null)},_targetIsRTL:function(t){if(null==this.__targetIsRTL)if(this.useGlobalRtlAttribute){var e=document.body&&document.body.hasAttribute("dir")?document.body:document.documentElement;this.__targetIsRTL="rtl"===e.getAttribute("dir")}else t&&t.nodeType!==Node.ELEMENT_NODE&&(t=t.host),this.__targetIsRTL=t&&"rtl"===window.getComputedStyle(t).direction;return this.__targetIsRTL},_nameChanged:function(){this._meta.value=null,this._meta.key=this.name,this._meta.value=this,this.async((function(){this.fire("iron-iconset-added",this,{node:window})}))},_createIconMap:function(){var t=Object.create(null);return er(this).querySelectorAll("[id]").forEach((function(e){t[e.id]=e})),t},_cloneIcon:function(t,e){return this._icons=this._icons||this._createIconMap(),this._prepareSvgClone(this._icons[t],this.size,e)},_prepareSvgClone:function(t,e,n){if(t){var i=t.cloneNode(!0),r=document.createElementNS("http://www.w3.org/2000/svg","svg"),o=i.getAttribute("viewBox")||"0 0 "+e+" "+e,a="pointer-events: none; display: block; width: 100%; height: 100%;";return n&&i.hasAttribute("mirror-in-rtl")&&(a+="-webkit-transform:scale(-1,1);transform:scale(-1,1);transform-origin:center;"),r.setAttribute("viewBox",o),r.setAttribute("preserveAspectRatio","xMidYMid meet"),r.setAttribute("focusable","false"),r.style.cssText=a,r.appendChild(i).removeAttribute("id"),r}return null}});
/**
    @license
    Copyright (c) 2014 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ro=xe`<iron-iconset-svg name="image" size="24">
<svg><defs>
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/**
    @license
    Copyright (c) 2014 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Oo=xe`<iron-iconset-svg name="icons" size="24">
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/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
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/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
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        margin: 0;
        position: absolute;
        top: 0;
        will-change: transform;
      }
    </style>

    <array-selector id="selector" items="{{items}}" selected="{{selectedItems}}" selected-item="{{selectedItem}}"></array-selector>

    <div id="items">
      <slot></slot>
    </div>
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this._estScrollHeight-this._viewportHeight+this._scrollOffset},get _maxVirtualStart(){var t=this._convertIndexToCompleteRow(this._virtualCount);return Math.max(0,t-this._physicalCount)},set _virtualStart(t){t=this._clamp(t,0,this._maxVirtualStart),this.grid&&(t-=t%this._itemsPerRow),this._virtualStartVal=t},get _virtualStart(){return this._virtualStartVal||0},set _physicalStart(t){(t%=this._physicalCount)<0&&(t=this._physicalCount+t),this.grid&&(t-=t%this._itemsPerRow),this._physicalStartVal=t},get _physicalStart(){return this._physicalStartVal||0},get _physicalEnd(){return(this._physicalStart+this._physicalCount-1)%this._physicalCount},set _physicalCount(t){this._physicalCountVal=t},get _physicalCount(){return this._physicalCountVal||0},get _optPhysicalSize(){return 0===this._viewportHeight?1/0:this._viewportHeight*this._maxPages},get _isVisible(){return Boolean(this.offsetWidth||this.offsetHeight)},get firstVisibleIndex(){var t=this._firstVisibleIndexVal;if(null==t){var e=this._physicalTop+this._scrollOffset;t=this._iterateItems((function(t,n){return(e+=this._getPhysicalSizeIncrement(t))>this._scrollPosition?this.grid?n-n%this._itemsPerRow:n:this.grid&&this._virtualCount-1===n?n-n%this._itemsPerRow:void 0}))||0,this._firstVisibleIndexVal=t}return t},get lastVisibleIndex(){var t=this._lastVisibleIndexVal;if(null==t){if(this.grid)t=Math.min(this._virtualCount,this.firstVisibleIndex+this._estRowsInView*this._itemsPerRow-1);else{var e=this._physicalTop+this._scrollOffset;this._iterateItems((function(n,i){e<this._scrollBottom&&(t=i),e+=this._getPhysicalSizeIncrement(n)}))}this._lastVisibleIndexVal=t}return t},get _defaultScrollTarget(){return this},get _virtualRowCount(){return Math.ceil(this._virtualCount/this._itemsPerRow)},get _estRowsInView(){return Math.ceil(this._viewportHeight/this._rowHeight)},get _physicalRows(){return Math.ceil(this._physicalCount/this._itemsPerRow)},get _scrollOffset(){return this._scrollerPaddingTop+this.scrollOffset},ready:function(){this.addEventListener("focus",this._didFocus.bind(this),!0)},attached:function(){this._debounce("_render",this._render,_t),this.listen(this,"iron-resize","_resizeHandler"),this.listen(this,"keydown","_keydownHandler")},detached:function(){this.unlisten(this,"iron-resize","_resizeHandler"),this.unlisten(this,"keydown","_keydownHandler")},_setOverflow:function(t){this.style.webkitOverflowScrolling=t===this?"touch":"",this.style.overflowY=t===this?"auto":"",this._lastVisibleIndexVal=null,this._firstVisibleIndexVal=null,this._debounce("_render",this._render,_t)},updateViewportBoundaries:function(){var t=window.getComputedStyle(this);this._scrollerPaddingTop=this.scrollTarget===this?0:parseInt(t["padding-top"],10),this._isRTL=Boolean("rtl"===t.direction),this._viewportWidth=this.$.items.offsetWidth,this._viewportHeight=this._scrollTargetHeight,this.grid&&this._updateGridMetrics()},_scrollHandler:function(){var t=Math.max(0,Math.min(this._maxScrollTop,this._scrollTop)),e=t-this._scrollPosition,n=e>=0;if(this._scrollPosition=t,this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,Math.abs(e)>this._physicalSize&&this._physicalSize>0){e-=this._scrollOffset;var i=Math.round(e/this._physicalAverage)*this._itemsPerRow;this._virtualStart=this._virtualStart+i,this._physicalStart=this._physicalStart+i,this._physicalTop=Math.min(Math.floor(this._virtualStart/this._itemsPerRow)*this._physicalAverage,this._scrollPosition),this._update()}else if(this._physicalCount>0){var r=this._getReusables(n);n?(this._physicalTop=r.physicalTop,this._virtualStart=this._virtualStart+r.indexes.length,this._physicalStart=this._physicalStart+r.indexes.length):(this._virtualStart=this._virtualStart-r.indexes.length,this._physicalStart=this._physicalStart-r.indexes.length),this._update(r.indexes,n?null:r.indexes),this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,0),vt)}},_getReusables:function(t){var e,n,i,r=[],o=this._hiddenContentSize*this._ratio,a=this._virtualStart,s=this._virtualEnd,l=this._physicalCount,c=this._physicalTop+this._scrollOffset,u=this._scrollPosition,h=this._scrollBottom;for(t?(e=this._physicalStart,n=u-c):(e=this._physicalEnd,n=this._physicalBottom+this._scrollOffset-h);n-=i=this._getPhysicalSizeIncrement(e),!(r.length>=l||n<=o);)if(t){if(s+r.length+1>=this._virtualCount)break;if(c+i>=u-this._scrollOffset)break;r.push(e),c+=i,e=(e+1)%l}else{if(a-r.length<=0)break;if(c+this._physicalSize-i<=h)break;r.push(e),c-=i,e=0===e?l-1:e-1}return{indexes:r,physicalTop:c-this._scrollOffset}},_update:function(t,e){if(!(t&&0===t.length||0===this._physicalCount)){if(this._manageFocus(),this._assignModels(t),this._updateMetrics(t),e)for(;e.length;){var n=e.pop();this._physicalTop-=this._getPhysicalSizeIncrement(n)}this._positionItems(),this._updateScrollerSize()}},_createPool:function(t){var e,n;this._ensureTemplatized();var i=new Array(t);for(e=0;e<t;e++)n=this.stamp(null),i[e]=n.root.querySelector("*"),this._itemsParent.appendChild(n.root);return i},_isClientFull:function(){return 0!=this._scrollBottom&&this._physicalBottom-1>=this._scrollBottom&&this._physicalTop<=this._scrollPosition},_increasePoolIfNeeded:function(t){var e=this._clamp(this._physicalCount+t,3,this._virtualCount-this._virtualStart);if(e=this._convertIndexToCompleteRow(e),this.grid){var n=e%this._itemsPerRow;n&&e-n<=this._physicalCount&&(e+=this._itemsPerRow),e-=n}var i=e-this._physicalCount,r=Math.round(.5*this._physicalCount);if(!(i<0)){if(i>0){var o=window.performance.now();[].push.apply(this._physicalItems,this._createPool(i));for(var a=0;a<i;a++)this._physicalSizes.push(0);this._physicalCount=this._physicalCount+i,this._physicalStart>this._physicalEnd&&this._isIndexRendered(this._focusedVirtualIndex)&&this._getPhysicalIndex(this._focusedVirtualIndex)<this._physicalEnd&&(this._physicalStart=this._physicalStart+i),this._update(),this._templateCost=(window.performance.now()-o)/i,r=Math.round(.5*this._physicalCount)}this._virtualEnd>=this._virtualCount-1||0===r||(this._isClientFull()?this._physicalSize<this._optPhysicalSize&&this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,this._clamp(Math.round(50/this._templateCost),1,r)),yt):this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,r),vt))}},_render:function(){if(this.isAttached&&this._isVisible)if(0!==this._physicalCount){var t=this._getReusables(!0);this._physicalTop=t.physicalTop,this._virtualStart=this._virtualStart+t.indexes.length,this._physicalStart=this._physicalStart+t.indexes.length,this._update(t.indexes),this._update(),this._increasePoolIfNeeded(0)}else this._virtualCount>0&&(this.updateViewportBoundaries(),this._increasePoolIfNeeded(3))},_ensureTemplatized:function(){if(!this.ctor){this._userTemplate=this.queryEffectiveChildren("template"),this._userTemplate||console.warn("iron-list requires a template to be provided in light-dom");var t={__key__:!0};t[this.as]=!0,t[this.indexAs]=!0,t[this.selectedAs]=!0,t.tabIndex=!0,this._instanceProps=t,this.templatize(this._userTemplate,this.mutableData)}},_gridChanged:function(t,e){void 0!==e&&(this.notifyResize(),Wi(),t&&this._updateGridMetrics())},_itemsChanged:function(t){if("items"===t.path)this._virtualStart=0,this._physicalTop=0,this._virtualCount=this.items?this.items.length:0,this._physicalIndexForKey={},this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,this._physicalCount=this._physicalCount||0,this._physicalItems=this._physicalItems||[],this._physicalSizes=this._physicalSizes||[],this._physicalStart=0,this._scrollTop>this._scrollOffset&&this._resetScrollPosition(0),this._removeFocusedItem(),this._debounce("_render",this._render,_t);else if("items.splices"===t.path){if(this._adjustVirtualIndex(t.value.indexSplices),this._virtualCount=this.items?this.items.length:0,t.value.indexSplices.some((function(t){return t.addedCount>0||t.removed.length>0}))){var e=this._getActiveElement();this.contains(e)&&e.blur()}var n=t.value.indexSplices.some((function(t){return t.index+t.addedCount>=this._virtualStart&&t.index<=this._virtualEnd}),this);this._isClientFull()&&!n||this._debounce("_render",this._render,_t)}else"items.length"!==t.path&&this._forwardItemPath(t.path,t.value)},_forwardItemPath:function(t,e){var n,i,r,o=(t=t.slice(6)).indexOf(".");-1===o&&(o=t.length);var a=this.modelForElement(this._offscreenFocusedItem),s=parseInt(t.substring(0,o),10);(n=this._isIndexRendered(s))?(i=this._getPhysicalIndex(s),r=this.modelForElement(this._physicalItems[i])):a&&(r=a),r&&r[this.indexAs]===s&&(t=t.substring(o+1),r._setPendingPropertyOrPath(t=this.as+(t?"."+t:""),e,!1,!0),r._flushProperties&&r._flushProperties(),n&&(this._updateMetrics([i]),this._positionItems(),this._updateScrollerSize()))},_adjustVirtualIndex:function(t){t.forEach((function(t){if(t.removed.forEach(this._removeItem,this),t.index<this._virtualStart){var e=Math.max(t.addedCount-t.removed.length,t.index-this._virtualStart);this._virtualStart=this._virtualStart+e,this._focusedVirtualIndex>=0&&(this._focusedVirtualIndex=this._focusedVirtualIndex+e)}}),this)},_removeItem:function(t){this.$.selector.deselect(t),this._focusedItem&&this.modelForElement(this._focusedItem)[this.as]===t&&this._removeFocusedItem()},_iterateItems:function(t,e){var n,i,r,o;if(2===arguments.length&&e){for(o=0;o<e.length;o++)if(i=this._computeVidx(n=e[o]),null!=(r=t.call(this,n,i)))return r}else{for(n=this._physicalStart,i=this._virtualStart;n<this._physicalCount;n++,i++)if(null!=(r=t.call(this,n,i)))return r;for(n=0;n<this._physicalStart;n++,i++)if(null!=(r=t.call(this,n,i)))return r}},_computeVidx:function(t){return t>=this._physicalStart?this._virtualStart+(t-this._physicalStart):this._virtualStart+(this._physicalCount-this._physicalStart)+t},_assignModels:function(t){this._iterateItems((function(t,e){var n=this._physicalItems[t],i=this.items&&this.items[e];if(null!=i){var r=this.modelForElement(n);r.__key__=null,this._forwardProperty(r,this.as,i),this._forwardProperty(r,this.selectedAs,this.$.selector.isSelected(i)),this._forwardProperty(r,this.indexAs,e),this._forwardProperty(r,"tabIndex",this._focusedVirtualIndex===e?0:-1),this._physicalIndexForKey[r.__key__]=t,r._flushProperties&&r._flushProperties(!0),n.removeAttribute("hidden")}else n.setAttribute("hidden","")}),t)},_updateMetrics:function(t){Wi();var e=0,n=0,i=this._physicalAverageCount,r=this._physicalAverage;this._iterateItems((function(t,i){n+=this._physicalSizes[t],this._physicalSizes[t]=this._physicalItems[t].offsetHeight,e+=this._physicalSizes[t],this._physicalAverageCount+=this._physicalSizes[t]?1:0}),t),this.grid?(this._updateGridMetrics(),this._physicalSize=Math.ceil(this._physicalCount/this._itemsPerRow)*this._rowHeight):(n=1===this._itemsPerRow?n:Math.ceil(this._physicalCount/this._itemsPerRow)*this._rowHeight,this._physicalSize=this._physicalSize+e-n,this._itemsPerRow=1),this._physicalAverageCount!==i&&(this._physicalAverage=Math.round((r*i+e)/this._physicalAverageCount))},_updateGridMetrics:function(){this._itemWidth=this._physicalCount>0?this._physicalItems[0].getBoundingClientRect().width:200,this._rowHeight=this._physicalCount>0?this._physicalItems[0].offsetHeight:200,this._itemsPerRow=this._itemWidth?Math.floor(this._viewportWidth/this._itemWidth):this._itemsPerRow},_positionItems:function(){this._adjustScrollPosition();var t=this._physicalTop;if(this.grid){var e=(this._viewportWidth-this._itemsPerRow*this._itemWidth)/2;this._iterateItems((function(n,i){var r=Math.floor(i%this._itemsPerRow*this._itemWidth+e);this._isRTL&&(r*=-1),this.translate3d(r+"px",t+"px",0,this._physicalItems[n]),this._shouldRenderNextRow(i)&&(t+=this._rowHeight)}))}else{const e=[];this._iterateItems((function(n,i){const r=this._physicalItems[n];this.translate3d(0,t+"px",0,r),t+=this._physicalSizes[n];const o=r.id;o&&e.push(o)})),e.length&&this.setAttribute("aria-owns",e.join(" "))}},_getPhysicalSizeIncrement:function(t){return this.grid?this._computeVidx(t)%this._itemsPerRow!=this._itemsPerRow-1?0:this._rowHeight:this._physicalSizes[t]},_shouldRenderNextRow:function(t){return t%this._itemsPerRow==this._itemsPerRow-1},_adjustScrollPosition:function(){var t=0===this._virtualStart?this._physicalTop:Math.min(this._scrollPosition+this._physicalTop,0);if(0!==t){this._physicalTop=this._physicalTop-t;var e=this._scrollPosition;!Zo&&e>0&&this._resetScrollPosition(e-t)}},_resetScrollPosition:function(t){this.scrollTarget&&t>=0&&(this._scrollTop=t,this._scrollPosition=this._scrollTop)},_updateScrollerSize:function(t){this._estScrollHeight=this.grid?this._virtualRowCount*this._rowHeight:this._physicalBottom+Math.max(this._virtualCount-this._physicalCount-this._virtualStart,0)*this._physicalAverage,((t=(t=(t=t||0===this._scrollHeight)||this._scrollPosition>=this._estScrollHeight-this._physicalSize)||this.grid&&this.$.items.style.height<this._estScrollHeight)||Math.abs(this._estScrollHeight-this._scrollHeight)>=this._viewportHeight)&&(this.$.items.style.height=this._estScrollHeight+"px",this._scrollHeight=this._estScrollHeight)},scrollToItem:function(t){return this.scrollToIndex(this.items.indexOf(t))},scrollToIndex:function(t){if(!("number"!=typeof t||t<0||t>this.items.length-1)&&(Wi(),0!==this._physicalCount)){t=this._clamp(t,0,this._virtualCount-1),(!this._isIndexRendered(t)||t>=this._maxVirtualStart)&&(this._virtualStart=this.grid?t-2*this._itemsPerRow:t-1),this._manageFocus(),this._assignModels(),this._updateMetrics(),this._physicalTop=Math.floor(this._virtualStart/this._itemsPerRow)*this._physicalAverage;for(var e=this._physicalStart,n=this._virtualStart,i=0,r=this._hiddenContentSize;n<t&&i<=r;)i+=this._getPhysicalSizeIncrement(e),e=(e+1)%this._physicalCount,n++;this._updateScrollerSize(!0),this._positionItems(),this._resetScrollPosition(this._physicalTop+this._scrollOffset+i),this._increasePoolIfNeeded(0),this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null}},_resetAverage:function(){this._physicalAverage=0,this._physicalAverageCount=0},_resizeHandler:function(){this._debounce("_render",(function(){this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,this._isVisible?(this.updateViewportBoundaries(),this.toggleScrollListener(!0),this._resetAverage(),this._render()):this.toggleScrollListener(!1)}),_t)},selectItem:function(t){return this.selectIndex(this.items.indexOf(t))},selectIndex:function(t){if(!(t<0||t>=this._virtualCount)){if(!this.multiSelection&&this.selectedItem&&this.clearSelection(),this._isIndexRendered(t)){var e=this.modelForElement(this._physicalItems[this._getPhysicalIndex(t)]);e&&(e[this.selectedAs]=!0),this.updateSizeForIndex(t)}this.$.selector.selectIndex(t)}},deselectItem:function(t){return this.deselectIndex(this.items.indexOf(t))},deselectIndex:function(t){if(!(t<0||t>=this._virtualCount)){if(this._isIndexRendered(t))this.modelForElement(this._physicalItems[this._getPhysicalIndex(t)])[this.selectedAs]=!1,this.updateSizeForIndex(t);this.$.selector.deselectIndex(t)}},toggleSelectionForItem:function(t){return this.toggleSelectionForIndex(this.items.indexOf(t))},toggleSelectionForIndex:function(t){(this.$.selector.isIndexSelected?this.$.selector.isIndexSelected(t):this.$.selector.isSelected(this.items[t]))?this.deselectIndex(t):this.selectIndex(t)},clearSelection:function(){this._iterateItems((function(t,e){this.modelForElement(this._physicalItems[t])[this.selectedAs]=!1})),this.$.selector.clearSelection()},_selectionEnabledChanged:function(t){(t?this.listen:this.unlisten).call(this,this,"tap","_selectionHandler")},_selectionHandler:function(t){var e=this.modelForElement(t.target);if(e){var n,i,r=er(t).path[0],o=this._getActiveElement(),a=this._physicalItems[this._getPhysicalIndex(e[this.indexAs])];"input"!==r.localName&&"button"!==r.localName&&"select"!==r.localName&&(n=e.tabIndex,e.tabIndex=Qo,i=o?o.tabIndex:-1,e.tabIndex=n,o&&a!==o&&a.contains(o)&&i!==Qo||this.toggleSelectionForItem(e[this.as]))}},_multiSelectionChanged:function(t){this.clearSelection(),this.$.selector.multi=t},updateSizeForItem:function(t){return this.updateSizeForIndex(this.items.indexOf(t))},updateSizeForIndex:function(t){return this._isIndexRendered(t)?(this._updateMetrics([this._getPhysicalIndex(t)]),this._positionItems(),null):null},_manageFocus:function(){var t=this._focusedVirtualIndex;t>=0&&t<this._virtualCount?this._isIndexRendered(t)?this._restoreFocusedItem():this._createFocusBackfillItem():this._virtualCount>0&&this._physicalCount>0&&(this._focusedPhysicalIndex=this._physicalStart,this._focusedVirtualIndex=this._virtualStart,this._focusedItem=this._physicalItems[this._physicalStart])},_convertIndexToCompleteRow:function(t){return this._itemsPerRow=this._itemsPerRow||1,this.grid?Math.ceil(t/this._itemsPerRow)*this._itemsPerRow:t},_isIndexRendered:function(t){return t>=this._virtualStart&&t<=this._virtualEnd},_isIndexVisible:function(t){return t>=this.firstVisibleIndex&&t<=this.lastVisibleIndex},_getPhysicalIndex:function(t){return(this._physicalStart+(t-this._virtualStart))%this._physicalCount},focusItem:function(t){this._focusPhysicalItem(t)},_focusPhysicalItem:function(t){if(!(t<0||t>=this._virtualCount)){this._restoreFocusedItem(),this._isIndexRendered(t)||this.scrollToIndex(t);var e,n=this._physicalItems[this._getPhysicalIndex(t)],i=this.modelForElement(n);i.tabIndex=Qo,n.tabIndex===Qo&&(e=n),e||(e=er(n).querySelector('[tabindex="-100"]')),i.tabIndex=0,this._focusedVirtualIndex=t,e&&e.focus()}},_removeFocusedItem:function(){this._offscreenFocusedItem&&this._itemsParent.removeChild(this._offscreenFocusedItem),this._offscreenFocusedItem=null,this._focusBackfillItem=null,this._focusedItem=null,this._focusedVirtualIndex=-1,this._focusedPhysicalIndex=-1},_createFocusBackfillItem:function(){var t=this._focusedPhysicalIndex;if(!(this._offscreenFocusedItem||this._focusedVirtualIndex<0)){if(!this._focusBackfillItem){var e=this.stamp(null);this._focusBackfillItem=e.root.querySelector("*"),this._itemsParent.appendChild(e.root)}this._offscreenFocusedItem=this._physicalItems[t],this.modelForElement(this._offscreenFocusedItem).tabIndex=0,this._physicalItems[t]=this._focusBackfillItem,this._focusedPhysicalIndex=t,this.translate3d(0,Jo,0,this._offscreenFocusedItem)}},_restoreFocusedItem:function(){if(this._offscreenFocusedItem&&!(this._focusedVirtualIndex<0)){this._assignModels();var t=this._focusedPhysicalIndex=this._getPhysicalIndex(this._focusedVirtualIndex),e=this._physicalItems[t];if(e){var n=this.modelForElement(e),i=this.modelForElement(this._offscreenFocusedItem);n[this.as]===i[this.as]?(this._focusBackfillItem=e,n.tabIndex=-1,this._physicalItems[t]=this._offscreenFocusedItem,this.translate3d(0,Jo,0,this._focusBackfillItem)):(this._removeFocusedItem(),this._focusBackfillItem=null),this._offscreenFocusedItem=null}}},_didFocus:function(t){var e=this.modelForElement(t.target),n=this.modelForElement(this._focusedItem),i=null!==this._offscreenFocusedItem,r=this._focusedVirtualIndex;e&&(n===e?this._isIndexVisible(r)||this.scrollToIndex(r):(this._restoreFocusedItem(),n&&(n.tabIndex=-1),e.tabIndex=0,this._focusedVirtualIndex=r=e[this.indexAs],this._focusedPhysicalIndex=this._getPhysicalIndex(r),this._focusedItem=this._physicalItems[this._focusedPhysicalIndex],i&&!this._offscreenFocusedItem&&this._update()))},_keydownHandler:function(t){switch(t.keyCode){case 40:this._focusedVirtualIndex<this._virtualCount-1&&t.preventDefault(),this._focusPhysicalItem(this._focusedVirtualIndex+(this.grid?this._itemsPerRow:1));break;case 39:this.grid&&this._focusPhysicalItem(this._focusedVirtualIndex+(this._isRTL?-1:1));break;case 38:this._focusedVirtualIndex>0&&t.preventDefault(),this._focusPhysicalItem(this._focusedVirtualIndex-(this.grid?this._itemsPerRow:1));break;case 37:this.grid&&this._focusPhysicalItem(this._focusedVirtualIndex+(this._isRTL?1:-1));break;case 13:this._focusPhysicalItem(this._focusedVirtualIndex),this.selectionEnabled&&this._selectionHandler(t)}},_clamp:function(t,e,n){return Math.min(n,Math.max(e,t))},_debounce:function(t,e,n){this._debouncers=this._debouncers||{},this._debouncers[t]=Nn.debounce(this._debouncers[t],n,e.bind(this)),Rn(this._debouncers[t])},_forwardProperty:function(t,e,n){t._setPendingProperty(e,n)},_forwardHostPropV2:function(t,e){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach((function(n){n&&this.modelForElement(n).forwardHostProp(t,e)}),this)},_notifyInstancePropV2:function(t,e,n){if(Q(this.as,e)){var i=t[this.indexAs];e==this.as&&(this.items[i]=n),this.notifyPath(J(this.as,"items."+i,e),n)}},_getStampedChildren:function(){return this._physicalItems},_forwardInstancePath:function(t,e,n){0===e.indexOf(this.as+".")&&this.notifyPath("items."+t.__key__+"."+e.slice(this.as.length+1),n)},_forwardParentPath:function(t,e){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach((function(n){n&&this.modelForElement(n).notifyPath(t,e)}),this)},_forwardParentProp:function(t,e){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach((function(n){n&&(this.modelForElement(n)[t]=e)}),this)},_getActiveElement:function(){var t=this._itemsParent.node.domHost;return er(t?t.root:document).activeElement}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
class ta{constructor(t){this.selection=[],this.selectCallback=t}get(){return this.multi?this.selection.slice():this.selection[0]}clear(t){this.selection.slice().forEach((function(e){(!t||t.indexOf(e)<0)&&this.setItemSelected(e,!1)}),this)}isSelected(t){return this.selection.indexOf(t)>=0}setItemSelected(t,e){if(null!=t&&e!==this.isSelected(t)){if(e)this.selection.push(t);else{var n=this.selection.indexOf(t);n>=0&&this.selection.splice(n,1)}this.selectCallback&&this.selectCallback(t,e)}}select(t){this.multi?this.toggle(t):this.get()!==t&&(this.setItemSelected(this.get(),!1),this.setItemSelected(t,!0))}toggle(t){this.setItemSelected(t,!this.isSelected(t))}}
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */const ea={properties:{attrForSelected:{type:String,value:null},selected:{type:String,notify:!0},selectedItem:{type:Object,readOnly:!0,notify:!0},activateEvent:{type:String,value:"tap",observer:"_activateEventChanged"},selectable:String,selectedClass:{type:String,value:"iron-selected"},selectedAttribute:{type:String,value:null},fallbackSelection:{type:String,value:null},items:{type:Array,readOnly:!0,notify:!0,value:function(){return[]}},_excludedLocalNames:{type:Object,value:function(){return{template:1,"dom-bind":1,"dom-if":1,"dom-repeat":1}}}},observers:["_updateAttrForSelected(attrForSelected)","_updateSelected(selected)","_checkFallback(fallbackSelection)"],created:function(){this._bindFilterItem=this._filterItem.bind(this),this._selection=new ta(this._applySelection.bind(this))},attached:function(){this._observer=this._observeItems(this),this._addListener(this.activateEvent)},detached:function(){this._observer&&er(this).unobserveNodes(this._observer),this._removeListener(this.activateEvent)},indexOf:function(t){return this.items?this.items.indexOf(t):-1},select:function(t){this.selected=t},selectPrevious:function(){var t=this.items.length,e=t-1;void 0!==this.selected&&(e=(Number(this._valueToIndex(this.selected))-1+t)%t),this.selected=this._indexToValue(e)},selectNext:function(){var t=0;void 0!==this.selected&&(t=(Number(this._valueToIndex(this.selected))+1)%this.items.length),this.selected=this._indexToValue(t)},selectIndex:function(t){this.select(this._indexToValue(t))},forceSynchronousItemUpdate:function(){this._observer&&"function"==typeof this._observer.flush?this._observer.flush():this._updateItems()},get _shouldUpdateSelection(){return null!=this.selected},_checkFallback:function(){this._updateSelected()},_addListener:function(t){this.listen(this,t,"_activateHandler")},_removeListener:function(t){this.unlisten(this,t,"_activateHandler")},_activateEventChanged:function(t,e){this._removeListener(e),this._addListener(t)},_updateItems:function(){var t=er(this).queryDistributedElements(this.selectable||"*");t=Array.prototype.filter.call(t,this._bindFilterItem),this._setItems(t)},_updateAttrForSelected:function(){this.selectedItem&&(this.selected=this._valueForItem(this.selectedItem))},_updateSelected:function(){this._selectSelected(this.selected)},_selectSelected:function(t){if(this.items){var e=this._valueToItem(this.selected);e?this._selection.select(e):this._selection.clear(),this.fallbackSelection&&this.items.length&&void 0===this._selection.get()&&(this.selected=this.fallbackSelection)}},_filterItem:function(t){return!this._excludedLocalNames[t.localName]},_valueToItem:function(t){return null==t?null:this.items[this._valueToIndex(t)]},_valueToIndex:function(t){if(!this.attrForSelected)return Number(t);for(var e,n=0;e=this.items[n];n++)if(this._valueForItem(e)==t)return n},_indexToValue:function(t){if(!this.attrForSelected)return t;var e=this.items[t];return e?this._valueForItem(e):void 0},_valueForItem:function(t){if(!t)return null;if(!this.attrForSelected){var e=this.indexOf(t);return-1===e?null:e}var n=t[st(this.attrForSelected)];return null!=n?n:t.getAttribute(this.attrForSelected)},_applySelection:function(t,e){this.selectedClass&&this.toggleClass(this.selectedClass,e,t),this.selectedAttribute&&this.toggleAttribute(this.selectedAttribute,e,t),this._selectionChange(),this.fire("iron-"+(e?"select":"deselect"),{item:t})},_selectionChange:function(){this._setSelectedItem(this._selection.get())},_observeItems:function(t){return er(t).observeNodes((function(t){this._updateItems(),this._updateSelected(),this.fire("iron-items-changed",t,{bubbles:!1,cancelable:!1})}))},_activateHandler:function(t){for(var e=t.target,n=this.items;e&&e!=this;){var i=n.indexOf(e);if(i>=0){var r=this._indexToValue(i);return void this._itemActivate(r,e)}e=e.parentNode}},_itemActivate:function(t,e){this.fire("iron-activate",{selected:t,item:e},{cancelable:!0}).defaultPrevented||this.select(t)}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */jr({_template:xe`
    <style>
      :host {
        display: block;
      }

      :host > ::slotted(:not(slot):not(.iron-selected)) {
        display: none !important;
      }
    </style>

    <slot></slot>
`,is:"iron-pages",behaviors:[Co,ea],properties:{activateEvent:{type:String,value:null}},observers:["_selectedPageChanged(selected)"],_selectedPageChanged:function(t,e){this.async(this.notifyResize)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const na=xe`
<custom-style>
  <style is="custom-style">
    html {

      --shadow-transition: {
        transition: box-shadow 0.28s cubic-bezier(0.4, 0, 0.2, 1);
      };

      --shadow-none: {
        box-shadow: none;
      };

      /* from http://codepen.io/shyndman/pen/c5394ddf2e8b2a5c9185904b57421cdb */

      --shadow-elevation-2dp: {
        box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.14),
                    0 1px 5px 0 rgba(0, 0, 0, 0.12),
                    0 3px 1px -2px rgba(0, 0, 0, 0.2);
      };

      --shadow-elevation-3dp: {
        box-shadow: 0 3px 4px 0 rgba(0, 0, 0, 0.14),
                    0 1px 8px 0 rgba(0, 0, 0, 0.12),
                    0 3px 3px -2px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-4dp: {
        box-shadow: 0 4px 5px 0 rgba(0, 0, 0, 0.14),
                    0 1px 10px 0 rgba(0, 0, 0, 0.12),
                    0 2px 4px -1px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-6dp: {
        box-shadow: 0 6px 10px 0 rgba(0, 0, 0, 0.14),
                    0 1px 18px 0 rgba(0, 0, 0, 0.12),
                    0 3px 5px -1px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-8dp: {
        box-shadow: 0 8px 10px 1px rgba(0, 0, 0, 0.14),
                    0 3px 14px 2px rgba(0, 0, 0, 0.12),
                    0 5px 5px -3px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-12dp: {
        box-shadow: 0 12px 16px 1px rgba(0, 0, 0, 0.14),
                    0 4px 22px 3px rgba(0, 0, 0, 0.12),
                    0 6px 7px -4px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-16dp: {
        box-shadow: 0 16px 24px 2px rgba(0, 0, 0, 0.14),
                    0  6px 30px 5px rgba(0, 0, 0, 0.12),
                    0  8px 10px -5px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-24dp: {
        box-shadow: 0 24px 38px 3px rgba(0, 0, 0, 0.14),
                    0 9px 46px 8px rgba(0, 0, 0, 0.12),
                    0 11px 15px -7px rgba(0, 0, 0, 0.4);
      };
    }
  </style>
</custom-style>`;na.setAttribute("style","display: none;"),document.head.appendChild(na.content);
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ia=xe`
<dom-module id="paper-material-styles">
  <template>
    <style>
      html {
        --paper-material: {
          display: block;
          position: relative;
        };
        --paper-material-elevation-1: {
          @apply --shadow-elevation-2dp;
        };
        --paper-material-elevation-2: {
          @apply --shadow-elevation-4dp;
        };
        --paper-material-elevation-3: {
          @apply --shadow-elevation-6dp;
        };
        --paper-material-elevation-4: {
          @apply --shadow-elevation-8dp;
        };
        --paper-material-elevation-5: {
          @apply --shadow-elevation-16dp;
        };
      }
      .paper-material {
        @apply --paper-material;
      }
      .paper-material[elevation="1"] {
        @apply --paper-material-elevation-1;
      }
      .paper-material[elevation="2"] {
        @apply --paper-material-elevation-2;
      }
      .paper-material[elevation="3"] {
        @apply --paper-material-elevation-3;
      }
      .paper-material[elevation="4"] {
        @apply --paper-material-elevation-4;
      }
      .paper-material[elevation="5"] {
        @apply --paper-material-elevation-5;
      }

      /* Duplicate the styles because of https://github.com/webcomponents/shadycss/issues/193 */
      :host {
        --paper-material: {
          display: block;
          position: relative;
        };
        --paper-material-elevation-1: {
          @apply --shadow-elevation-2dp;
        };
        --paper-material-elevation-2: {
          @apply --shadow-elevation-4dp;
        };
        --paper-material-elevation-3: {
          @apply --shadow-elevation-6dp;
        };
        --paper-material-elevation-4: {
          @apply --shadow-elevation-8dp;
        };
        --paper-material-elevation-5: {
          @apply --shadow-elevation-16dp;
        };
      }
      :host(.paper-material) {
        @apply --paper-material;
      }
      :host(.paper-material[elevation="1"]) {
        @apply --paper-material-elevation-1;
      }
      :host(.paper-material[elevation="2"]) {
        @apply --paper-material-elevation-2;
      }
      :host(.paper-material[elevation="3"]) {
        @apply --paper-material-elevation-3;
      }
      :host(.paper-material[elevation="4"]) {
        @apply --paper-material-elevation-4;
      }
      :host(.paper-material[elevation="5"]) {
        @apply --paper-material-elevation-5;
      }
    </style>
  </template>
</dom-module>`;ia.setAttribute("style","display: none;"),document.head.appendChild(ia.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ra={properties:{focused:{type:Boolean,value:!1,notify:!0,readOnly:!0,reflectToAttribute:!0},disabled:{type:Boolean,value:!1,notify:!0,observer:"_disabledChanged",reflectToAttribute:!0},_oldTabIndex:{type:String},_boundFocusBlurHandler:{type:Function,value:function(){return this._focusBlurHandler.bind(this)}}},observers:["_changedControlState(focused, disabled)"],ready:function(){this.addEventListener("focus",this._boundFocusBlurHandler,!0),this.addEventListener("blur",this._boundFocusBlurHandler,!0)},_focusBlurHandler:function(t){this._setFocused("focus"===t.type)},_disabledChanged:function(t,e){this.setAttribute("aria-disabled",t?"true":"false"),this.style.pointerEvents=t?"none":"",t?(this._oldTabIndex=this.getAttribute("tabindex"),this._setFocused(!1),this.tabIndex=-1,this.blur()):void 0!==this._oldTabIndex&&(null===this._oldTabIndex?this.removeAttribute("tabindex"):this.setAttribute("tabindex",this._oldTabIndex))},_changedControlState:function(){this._controlStateChanged&&this._controlStateChanged()}},oa={properties:{pressed:{type:Boolean,readOnly:!0,value:!1,reflectToAttribute:!0,observer:"_pressedChanged"},toggles:{type:Boolean,value:!1,reflectToAttribute:!0},active:{type:Boolean,value:!1,notify:!0,reflectToAttribute:!0},pointerDown:{type:Boolean,readOnly:!0,value:!1},receivedFocusFromKeyboard:{type:Boolean,readOnly:!0},ariaActiveAttribute:{type:String,value:"aria-pressed",observer:"_ariaActiveAttributeChanged"}},listeners:{down:"_downHandler",up:"_upHandler",tap:"_tapHandler"},observers:["_focusChanged(focused)","_activeChanged(active, ariaActiveAttribute)"],keyBindings:{"enter:keydown":"_asyncClick","space:keydown":"_spaceKeyDownHandler","space:keyup":"_spaceKeyUpHandler"},_mouseEventRe:/^mouse/,_tapHandler:function(){this.toggles?this._userActivate(!this.active):this.active=!1},_focusChanged:function(t){this._detectKeyboardFocus(t),t||this._setPressed(!1)},_detectKeyboardFocus:function(t){this._setReceivedFocusFromKeyboard(!this.pointerDown&&t)},_userActivate:function(t){this.active!==t&&(this.active=t,this.fire("change"))},_downHandler:function(t){this._setPointerDown(!0),this._setPressed(!0),this._setReceivedFocusFromKeyboard(!1)},_upHandler:function(){this._setPointerDown(!1),this._setPressed(!1)},_spaceKeyDownHandler:function(t){var e=t.detail.keyboardEvent,n=er(e).localTarget;this.isLightDescendant(n)||(e.preventDefault(),e.stopImmediatePropagation(),this._setPressed(!0))},_spaceKeyUpHandler:function(t){var e=er(t.detail.keyboardEvent).localTarget;this.isLightDescendant(e)||(this.pressed&&this._asyncClick(),this._setPressed(!1))},_asyncClick:function(){this.async((function(){this.click()}),1)},_pressedChanged:function(t){this._changedButtonState()},_ariaActiveAttributeChanged:function(t,e){e&&e!=t&&this.hasAttribute(e)&&this.removeAttribute(e)},_activeChanged:function(t,e){this.toggles?this.setAttribute(this.ariaActiveAttribute,t?"true":"false"):this.removeAttribute(this.ariaActiveAttribute),this._changedButtonState()},_controlStateChanged:function(){this.disabled?this._setPressed(!1):this._changedButtonState()},_changedButtonState:function(){this._buttonStateChanged&&this._buttonStateChanged()}},aa=[Xo,oa];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2014 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var sa={distance:function(t,e,n,i){var r=t-n,o=e-i;return Math.sqrt(r*r+o*o)},now:window.performance&&window.performance.now?window.performance.now.bind(window.performance):Date.now};function la(t){this.element=t,this.width=this.boundingRect.width,this.height=this.boundingRect.height,this.size=Math.max(this.width,this.height)}function ca(t){this.element=t,this.color=window.getComputedStyle(t).color,this.wave=document.createElement("div"),this.waveContainer=document.createElement("div"),this.wave.style.backgroundColor=this.color,this.wave.classList.add("wave"),this.waveContainer.classList.add("wave-container"),er(this.waveContainer).appendChild(this.wave),this.resetInteractionState()}la.prototype={get boundingRect(){return this.element.getBoundingClientRect()},furthestCornerDistanceFrom:function(t,e){var n=sa.distance(t,e,0,0),i=sa.distance(t,e,this.width,0),r=sa.distance(t,e,0,this.height),o=sa.distance(t,e,this.width,this.height);return Math.max(n,i,r,o)}},ca.MAX_RADIUS=300,ca.prototype={get recenters(){return this.element.recenters},get center(){return this.element.center},get mouseDownElapsed(){var t;return this.mouseDownStart?(t=sa.now()-this.mouseDownStart,this.mouseUpStart&&(t-=this.mouseUpElapsed),t):0},get mouseUpElapsed(){return this.mouseUpStart?sa.now()-this.mouseUpStart:0},get mouseDownElapsedSeconds(){return this.mouseDownElapsed/1e3},get mouseUpElapsedSeconds(){return this.mouseUpElapsed/1e3},get mouseInteractionSeconds(){return this.mouseDownElapsedSeconds+this.mouseUpElapsedSeconds},get initialOpacity(){return this.element.initialOpacity},get opacityDecayVelocity(){return this.element.opacityDecayVelocity},get radius(){var t=1.1*Math.min(Math.sqrt(this.containerMetrics.width*this.containerMetrics.width+this.containerMetrics.height*this.containerMetrics.height),ca.MAX_RADIUS)+5,e=t*(1-Math.pow(80,-(this.mouseInteractionSeconds/(1.1-t/ca.MAX_RADIUS*.2))));return Math.abs(e)},get opacity(){return this.mouseUpStart?Math.max(0,this.initialOpacity-this.mouseUpElapsedSeconds*this.opacityDecayVelocity):this.initialOpacity},get outerOpacity(){return Math.max(0,Math.min(.3*this.mouseUpElapsedSeconds,this.opacity))},get isOpacityFullyDecayed(){return this.opacity<.01&&this.radius>=Math.min(this.maxRadius,ca.MAX_RADIUS)},get isRestingAtMaxRadius(){return this.opacity>=this.initialOpacity&&this.radius>=Math.min(this.maxRadius,ca.MAX_RADIUS)},get isAnimationComplete(){return this.mouseUpStart?this.isOpacityFullyDecayed:this.isRestingAtMaxRadius},get translationFraction(){return Math.min(1,this.radius/this.containerMetrics.size*2/Math.sqrt(2))},get xNow(){return this.xEnd?this.xStart+this.translationFraction*(this.xEnd-this.xStart):this.xStart},get yNow(){return this.yEnd?this.yStart+this.translationFraction*(this.yEnd-this.yStart):this.yStart},get isMouseDown(){return this.mouseDownStart&&!this.mouseUpStart},resetInteractionState:function(){this.maxRadius=0,this.mouseDownStart=0,this.mouseUpStart=0,this.xStart=0,this.yStart=0,this.xEnd=0,this.yEnd=0,this.slideDistance=0,this.containerMetrics=new la(this.element)},draw:function(){var t,e,n;this.wave.style.opacity=this.opacity,t=this.radius/(this.containerMetrics.size/2),this.waveContainer.style.webkitTransform="translate("+(e=this.xNow-this.containerMetrics.width/2)+"px, "+(n=this.yNow-this.containerMetrics.height/2)+"px)",this.waveContainer.style.transform="translate3d("+e+"px, "+n+"px, 0)",this.wave.style.webkitTransform="scale("+t+","+t+")",this.wave.style.transform="scale3d("+t+","+t+",1)"},downAction:function(t){var e=this.containerMetrics.width/2,n=this.containerMetrics.height/2;this.resetInteractionState(),this.mouseDownStart=sa.now(),this.center?(this.xStart=e,this.yStart=n,this.slideDistance=sa.distance(this.xStart,this.yStart,this.xEnd,this.yEnd)):(this.xStart=t?t.detail.x-this.containerMetrics.boundingRect.left:this.containerMetrics.width/2,this.yStart=t?t.detail.y-this.containerMetrics.boundingRect.top:this.containerMetrics.height/2),this.recenters&&(this.xEnd=e,this.yEnd=n,this.slideDistance=sa.distance(this.xStart,this.yStart,this.xEnd,this.yEnd)),this.maxRadius=this.containerMetrics.furthestCornerDistanceFrom(this.xStart,this.yStart),this.waveContainer.style.top=(this.containerMetrics.height-this.containerMetrics.size)/2+"px",this.waveContainer.style.left=(this.containerMetrics.width-this.containerMetrics.size)/2+"px",this.waveContainer.style.width=this.containerMetrics.size+"px",this.waveContainer.style.height=this.containerMetrics.size+"px"},upAction:function(t){this.isMouseDown&&(this.mouseUpStart=sa.now())},remove:function(){er(er(this.waveContainer).parentNode).removeChild(this.waveContainer)}},jr({_template:xe`
    <style>
      :host {
        display: block;
        position: absolute;
        border-radius: inherit;
        overflow: hidden;
        top: 0;
        left: 0;
        right: 0;
        bottom: 0;

        /* See PolymerElements/paper-behaviors/issues/34. On non-Chrome browsers,
         * creating a node (with a position:absolute) in the middle of an event
         * handler "interrupts" that event handler (which happens when the
         * ripple is created on demand) */
        pointer-events: none;
      }

      :host([animating]) {
        /* This resolves a rendering issue in Chrome (as of 40) where the
           ripple is not properly clipped by its parent (which may have
           rounded corners). See: http://jsbin.com/temexa/4

           Note: We only apply this style conditionally. Otherwise, the browser
           will create a new compositing layer for every ripple element on the
           page, and that would be bad. */
        -webkit-transform: translate(0, 0);
        transform: translate3d(0, 0, 0);
      }

      #background,
      #waves,
      .wave-container,
      .wave {
        pointer-events: none;
        position: absolute;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
      }

      #background,
      .wave {
        opacity: 0;
      }

      #waves,
      .wave {
        overflow: hidden;
      }

      .wave-container,
      .wave {
        border-radius: 50%;
      }

      :host(.circle) #background,
      :host(.circle) #waves {
        border-radius: 50%;
      }

      :host(.circle) .wave-container {
        overflow: hidden;
      }
    </style>

    <div id="background"></div>
    <div id="waves"></div>
`,is:"paper-ripple",behaviors:[Xo],properties:{initialOpacity:{type:Number,value:.25},opacityDecayVelocity:{type:Number,value:.8},recenters:{type:Boolean,value:!1},center:{type:Boolean,value:!1},ripples:{type:Array,value:function(){return[]}},animating:{type:Boolean,readOnly:!0,reflectToAttribute:!0,value:!1},holdDown:{type:Boolean,value:!1,observer:"_holdDownChanged"},noink:{type:Boolean,value:!1},_animating:{type:Boolean},_boundAnimate:{type:Function,value:function(){return this.animate.bind(this)}}},get target(){return this.keyEventTarget},keyBindings:{"enter:keydown":"_onEnterKeydown","space:keydown":"_onSpaceKeydown","space:keyup":"_onSpaceKeyup"},attached:function(){this.keyEventTarget=11==er(this).parentNode.nodeType?er(this).getOwnerRoot().host:er(this).parentNode;var t=this.keyEventTarget;this.listen(t,"up","uiUpAction"),this.listen(t,"down","uiDownAction")},detached:function(){this.unlisten(this.keyEventTarget,"up","uiUpAction"),this.unlisten(this.keyEventTarget,"down","uiDownAction"),this.keyEventTarget=null},get shouldKeepAnimating(){for(var t=0;t<this.ripples.length;++t)if(!this.ripples[t].isAnimationComplete)return!0;return!1},simulatedRipple:function(){this.downAction(null),this.async((function(){this.upAction()}),1)},uiDownAction:function(t){this.noink||this.downAction(t)},downAction:function(t){this.holdDown&&this.ripples.length>0||(this.addRipple().downAction(t),this._animating||(this._animating=!0,this.animate()))},uiUpAction:function(t){this.noink||this.upAction(t)},upAction:function(t){this.holdDown||(this.ripples.forEach((function(e){e.upAction(t)})),this._animating=!0,this.animate())},onAnimationComplete:function(){this._animating=!1,this.$.background.style.backgroundColor="",this.fire("transitionend")},addRipple:function(){var t=new ca(this);return er(this.$.waves).appendChild(t.waveContainer),this.$.background.style.backgroundColor=t.color,this.ripples.push(t),this._setAnimating(!0),t},removeRipple:function(t){var e=this.ripples.indexOf(t);e<0||(this.ripples.splice(e,1),t.remove(),this.ripples.length||this._setAnimating(!1))},animate:function(){if(this._animating){var t,e;for(t=0;t<this.ripples.length;++t)(e=this.ripples[t]).draw(),this.$.background.style.opacity=e.outerOpacity,e.isOpacityFullyDecayed&&!e.isRestingAtMaxRadius&&this.removeRipple(e);this.shouldKeepAnimating||0!==this.ripples.length?window.requestAnimationFrame(this._boundAnimate):this.onAnimationComplete()}},animateRipple:function(){return this.animate()},_onEnterKeydown:function(){this.uiDownAction(),this.async(this.uiUpAction,1)},_onSpaceKeydown:function(){this.uiDownAction()},_onSpaceKeyup:function(){this.uiUpAction()},_holdDownChanged:function(t,e){void 0!==e&&(t?this.downAction():this.upAction())}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ua={properties:{noink:{type:Boolean,observer:"_noinkChanged"},_rippleContainer:{type:Object}},_buttonStateChanged:function(){this.focused&&this.ensureRipple()},_downHandler:function(t){oa._downHandler.call(this,t),this.pressed&&this.ensureRipple(t)},ensureRipple:function(t){if(!this.hasRipple()){this._ripple=this._createRipple(),this._ripple.noink=this.noink;var e=this._rippleContainer||this.root;if(e&&er(e).appendChild(this._ripple),t){var n=er(this._rippleContainer||this),i=er(t).rootTarget;n.deepContains(i)&&this._ripple.uiDownAction(t)}}},getRipple:function(){return this.ensureRipple(),this._ripple},hasRipple:function(){return Boolean(this._ripple)},_createRipple:function(){return document.createElement("paper-ripple")},_noinkChanged:function(t){this.hasRipple()&&(this._ripple.noink=t)}},ha={properties:{elevation:{type:Number,reflectToAttribute:!0,readOnly:!0}},observers:["_calculateElevation(focused, disabled, active, pressed, receivedFocusFromKeyboard)","_computeKeyboardClass(receivedFocusFromKeyboard)"],hostAttributes:{role:"button",tabindex:"0",animated:!0},_calculateElevation:function(){var t=1;this.disabled?t=0:this.active||this.pressed?t=4:this.receivedFocusFromKeyboard&&(t=3),this._setElevation(t)},_computeKeyboardClass:function(t){this.toggleClass("keyboard-focus",t)},_spaceKeyDownHandler:function(t){oa._spaceKeyDownHandler.call(this,t),this.hasRipple()&&this.getRipple().ripples.length<1&&this._ripple.uiDownAction()},_spaceKeyUpHandler:function(t){oa._spaceKeyUpHandler.call(this,t),this.hasRipple()&&this._ripple.uiUpAction()}},da=[aa,ra,ua,ha],pa=xe`
  <style include="paper-material-styles">
    /* Need to specify the same specificity as the styles imported from paper-material. */
    :host {
      @apply --layout-inline;
      @apply --layout-center-center;
      position: relative;
      box-sizing: border-box;
      min-width: 5.14em;
      margin: 0 0.29em;
      background: transparent;
      -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
      -webkit-tap-highlight-color: transparent;
      font: inherit;
      text-transform: uppercase;
      outline-width: 0;
      border-radius: 3px;
      -moz-user-select: none;
      -ms-user-select: none;
      -webkit-user-select: none;
      user-select: none;
      cursor: pointer;
      z-index: 0;
      padding: 0.7em 0.57em;

      @apply --paper-font-common-base;
      @apply --paper-button;
    }

    :host([elevation="1"]) {
      @apply --paper-material-elevation-1;
    }

    :host([elevation="2"]) {
      @apply --paper-material-elevation-2;
    }

    :host([elevation="3"]) {
      @apply --paper-material-elevation-3;
    }

    :host([elevation="4"]) {
      @apply --paper-material-elevation-4;
    }

    :host([elevation="5"]) {
      @apply --paper-material-elevation-5;
    }

    :host([hidden]) {
      display: none !important;
    }

    :host([raised].keyboard-focus) {
      font-weight: bold;
      @apply --paper-button-raised-keyboard-focus;
    }

    :host(:not([raised]).keyboard-focus) {
      font-weight: bold;
      @apply --paper-button-flat-keyboard-focus;
    }

    :host([disabled]) {
      background: none;
      color: #a8a8a8;
      cursor: auto;
      pointer-events: none;

      @apply --paper-button-disabled;
    }

    :host([disabled][raised]) {
      background: #eaeaea;
    }


    :host([animated]) {
      @apply --shadow-transition;
    }

    paper-ripple {
      color: var(--paper-button-ink-color);
    }
  </style>

  <slot></slot>`;
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */pa.setAttribute("strip-whitespace",""),jr({_template:pa,is:"paper-button",behaviors:[da],properties:{raised:{type:Boolean,reflectToAttribute:!0,value:!1,observer:"_calculateElevation"}},_calculateElevation:function(){this.raised?ha._calculateElevation.apply(this):this._setElevation(0)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const fa=xe`
<custom-style>
  <style is="custom-style">
    html {

      /* Material Design color palette for Google products */

      --google-red-100: #f4c7c3;
      --google-red-300: #e67c73;
      --google-red-500: #db4437;
      --google-red-700: #c53929;

      --google-blue-100: #c6dafc;
      --google-blue-300: #7baaf7;
      --google-blue-500: #4285f4;
      --google-blue-700: #3367d6;

      --google-green-100: #b7e1cd;
      --google-green-300: #57bb8a;
      --google-green-500: #0f9d58;
      --google-green-700: #0b8043;

      --google-yellow-100: #fce8b2;
      --google-yellow-300: #f7cb4d;
      --google-yellow-500: #f4b400;
      --google-yellow-700: #f09300;

      --google-grey-100: #f5f5f5;
      --google-grey-300: #e0e0e0;
      --google-grey-500: #9e9e9e;
      --google-grey-700: #616161;

      /* Material Design color palette from online spec document */

      --paper-red-50: #ffebee;
      --paper-red-100: #ffcdd2;
      --paper-red-200: #ef9a9a;
      --paper-red-300: #e57373;
      --paper-red-400: #ef5350;
      --paper-red-500: #f44336;
      --paper-red-600: #e53935;
      --paper-red-700: #d32f2f;
      --paper-red-800: #c62828;
      --paper-red-900: #b71c1c;
      --paper-red-a100: #ff8a80;
      --paper-red-a200: #ff5252;
      --paper-red-a400: #ff1744;
      --paper-red-a700: #d50000;

      --paper-pink-50: #fce4ec;
      --paper-pink-100: #f8bbd0;
      --paper-pink-200: #f48fb1;
      --paper-pink-300: #f06292;
      --paper-pink-400: #ec407a;
      --paper-pink-500: #e91e63;
      --paper-pink-600: #d81b60;
      --paper-pink-700: #c2185b;
      --paper-pink-800: #ad1457;
      --paper-pink-900: #880e4f;
      --paper-pink-a100: #ff80ab;
      --paper-pink-a200: #ff4081;
      --paper-pink-a400: #f50057;
      --paper-pink-a700: #c51162;

      --paper-purple-50: #f3e5f5;
      --paper-purple-100: #e1bee7;
      --paper-purple-200: #ce93d8;
      --paper-purple-300: #ba68c8;
      --paper-purple-400: #ab47bc;
      --paper-purple-500: #9c27b0;
      --paper-purple-600: #8e24aa;
      --paper-purple-700: #7b1fa2;
      --paper-purple-800: #6a1b9a;
      --paper-purple-900: #4a148c;
      --paper-purple-a100: #ea80fc;
      --paper-purple-a200: #e040fb;
      --paper-purple-a400: #d500f9;
      --paper-purple-a700: #aa00ff;

      --paper-deep-purple-50: #ede7f6;
      --paper-deep-purple-100: #d1c4e9;
      --paper-deep-purple-200: #b39ddb;
      --paper-deep-purple-300: #9575cd;
      --paper-deep-purple-400: #7e57c2;
      --paper-deep-purple-500: #673ab7;
      --paper-deep-purple-600: #5e35b1;
      --paper-deep-purple-700: #512da8;
      --paper-deep-purple-800: #4527a0;
      --paper-deep-purple-900: #311b92;
      --paper-deep-purple-a100: #b388ff;
      --paper-deep-purple-a200: #7c4dff;
      --paper-deep-purple-a400: #651fff;
      --paper-deep-purple-a700: #6200ea;

      --paper-indigo-50: #e8eaf6;
      --paper-indigo-100: #c5cae9;
      --paper-indigo-200: #9fa8da;
      --paper-indigo-300: #7986cb;
      --paper-indigo-400: #5c6bc0;
      --paper-indigo-500: #3f51b5;
      --paper-indigo-600: #3949ab;
      --paper-indigo-700: #303f9f;
      --paper-indigo-800: #283593;
      --paper-indigo-900: #1a237e;
      --paper-indigo-a100: #8c9eff;
      --paper-indigo-a200: #536dfe;
      --paper-indigo-a400: #3d5afe;
      --paper-indigo-a700: #304ffe;

      --paper-blue-50: #e3f2fd;
      --paper-blue-100: #bbdefb;
      --paper-blue-200: #90caf9;
      --paper-blue-300: #64b5f6;
      --paper-blue-400: #42a5f5;
      --paper-blue-500: #2196f3;
      --paper-blue-600: #1e88e5;
      --paper-blue-700: #1976d2;
      --paper-blue-800: #1565c0;
      --paper-blue-900: #0d47a1;
      --paper-blue-a100: #82b1ff;
      --paper-blue-a200: #448aff;
      --paper-blue-a400: #2979ff;
      --paper-blue-a700: #2962ff;

      --paper-light-blue-50: #e1f5fe;
      --paper-light-blue-100: #b3e5fc;
      --paper-light-blue-200: #81d4fa;
      --paper-light-blue-300: #4fc3f7;
      --paper-light-blue-400: #29b6f6;
      --paper-light-blue-500: #03a9f4;
      --paper-light-blue-600: #039be5;
      --paper-light-blue-700: #0288d1;
      --paper-light-blue-800: #0277bd;
      --paper-light-blue-900: #01579b;
      --paper-light-blue-a100: #80d8ff;
      --paper-light-blue-a200: #40c4ff;
      --paper-light-blue-a400: #00b0ff;
      --paper-light-blue-a700: #0091ea;

      --paper-cyan-50: #e0f7fa;
      --paper-cyan-100: #b2ebf2;
      --paper-cyan-200: #80deea;
      --paper-cyan-300: #4dd0e1;
      --paper-cyan-400: #26c6da;
      --paper-cyan-500: #00bcd4;
      --paper-cyan-600: #00acc1;
      --paper-cyan-700: #0097a7;
      --paper-cyan-800: #00838f;
      --paper-cyan-900: #006064;
      --paper-cyan-a100: #84ffff;
      --paper-cyan-a200: #18ffff;
      --paper-cyan-a400: #00e5ff;
      --paper-cyan-a700: #00b8d4;

      --paper-teal-50: #e0f2f1;
      --paper-teal-100: #b2dfdb;
      --paper-teal-200: #80cbc4;
      --paper-teal-300: #4db6ac;
      --paper-teal-400: #26a69a;
      --paper-teal-500: #009688;
      --paper-teal-600: #00897b;
      --paper-teal-700: #00796b;
      --paper-teal-800: #00695c;
      --paper-teal-900: #004d40;
      --paper-teal-a100: #a7ffeb;
      --paper-teal-a200: #64ffda;
      --paper-teal-a400: #1de9b6;
      --paper-teal-a700: #00bfa5;

      --paper-green-50: #e8f5e9;
      --paper-green-100: #c8e6c9;
      --paper-green-200: #a5d6a7;
      --paper-green-300: #81c784;
      --paper-green-400: #66bb6a;
      --paper-green-500: #4caf50;
      --paper-green-600: #43a047;
      --paper-green-700: #388e3c;
      --paper-green-800: #2e7d32;
      --paper-green-900: #1b5e20;
      --paper-green-a100: #b9f6ca;
      --paper-green-a200: #69f0ae;
      --paper-green-a400: #00e676;
      --paper-green-a700: #00c853;

      --paper-light-green-50: #f1f8e9;
      --paper-light-green-100: #dcedc8;
      --paper-light-green-200: #c5e1a5;
      --paper-light-green-300: #aed581;
      --paper-light-green-400: #9ccc65;
      --paper-light-green-500: #8bc34a;
      --paper-light-green-600: #7cb342;
      --paper-light-green-700: #689f38;
      --paper-light-green-800: #558b2f;
      --paper-light-green-900: #33691e;
      --paper-light-green-a100: #ccff90;
      --paper-light-green-a200: #b2ff59;
      --paper-light-green-a400: #76ff03;
      --paper-light-green-a700: #64dd17;

      --paper-lime-50: #f9fbe7;
      --paper-lime-100: #f0f4c3;
      --paper-lime-200: #e6ee9c;
      --paper-lime-300: #dce775;
      --paper-lime-400: #d4e157;
      --paper-lime-500: #cddc39;
      --paper-lime-600: #c0ca33;
      --paper-lime-700: #afb42b;
      --paper-lime-800: #9e9d24;
      --paper-lime-900: #827717;
      --paper-lime-a100: #f4ff81;
      --paper-lime-a200: #eeff41;
      --paper-lime-a400: #c6ff00;
      --paper-lime-a700: #aeea00;

      --paper-yellow-50: #fffde7;
      --paper-yellow-100: #fff9c4;
      --paper-yellow-200: #fff59d;
      --paper-yellow-300: #fff176;
      --paper-yellow-400: #ffee58;
      --paper-yellow-500: #ffeb3b;
      --paper-yellow-600: #fdd835;
      --paper-yellow-700: #fbc02d;
      --paper-yellow-800: #f9a825;
      --paper-yellow-900: #f57f17;
      --paper-yellow-a100: #ffff8d;
      --paper-yellow-a200: #ffff00;
      --paper-yellow-a400: #ffea00;
      --paper-yellow-a700: #ffd600;

      --paper-amber-50: #fff8e1;
      --paper-amber-100: #ffecb3;
      --paper-amber-200: #ffe082;
      --paper-amber-300: #ffd54f;
      --paper-amber-400: #ffca28;
      --paper-amber-500: #ffc107;
      --paper-amber-600: #ffb300;
      --paper-amber-700: #ffa000;
      --paper-amber-800: #ff8f00;
      --paper-amber-900: #ff6f00;
      --paper-amber-a100: #ffe57f;
      --paper-amber-a200: #ffd740;
      --paper-amber-a400: #ffc400;
      --paper-amber-a700: #ffab00;

      --paper-orange-50: #fff3e0;
      --paper-orange-100: #ffe0b2;
      --paper-orange-200: #ffcc80;
      --paper-orange-300: #ffb74d;
      --paper-orange-400: #ffa726;
      --paper-orange-500: #ff9800;
      --paper-orange-600: #fb8c00;
      --paper-orange-700: #f57c00;
      --paper-orange-800: #ef6c00;
      --paper-orange-900: #e65100;
      --paper-orange-a100: #ffd180;
      --paper-orange-a200: #ffab40;
      --paper-orange-a400: #ff9100;
      --paper-orange-a700: #ff6500;

      --paper-deep-orange-50: #fbe9e7;
      --paper-deep-orange-100: #ffccbc;
      --paper-deep-orange-200: #ffab91;
      --paper-deep-orange-300: #ff8a65;
      --paper-deep-orange-400: #ff7043;
      --paper-deep-orange-500: #ff5722;
      --paper-deep-orange-600: #f4511e;
      --paper-deep-orange-700: #e64a19;
      --paper-deep-orange-800: #d84315;
      --paper-deep-orange-900: #bf360c;
      --paper-deep-orange-a100: #ff9e80;
      --paper-deep-orange-a200: #ff6e40;
      --paper-deep-orange-a400: #ff3d00;
      --paper-deep-orange-a700: #dd2c00;

      --paper-brown-50: #efebe9;
      --paper-brown-100: #d7ccc8;
      --paper-brown-200: #bcaaa4;
      --paper-brown-300: #a1887f;
      --paper-brown-400: #8d6e63;
      --paper-brown-500: #795548;
      --paper-brown-600: #6d4c41;
      --paper-brown-700: #5d4037;
      --paper-brown-800: #4e342e;
      --paper-brown-900: #3e2723;

      --paper-grey-50: #fafafa;
      --paper-grey-100: #f5f5f5;
      --paper-grey-200: #eeeeee;
      --paper-grey-300: #e0e0e0;
      --paper-grey-400: #bdbdbd;
      --paper-grey-500: #9e9e9e;
      --paper-grey-600: #757575;
      --paper-grey-700: #616161;
      --paper-grey-800: #424242;
      --paper-grey-900: #212121;

      --paper-blue-grey-50: #eceff1;
      --paper-blue-grey-100: #cfd8dc;
      --paper-blue-grey-200: #b0bec5;
      --paper-blue-grey-300: #90a4ae;
      --paper-blue-grey-400: #78909c;
      --paper-blue-grey-500: #607d8b;
      --paper-blue-grey-600: #546e7a;
      --paper-blue-grey-700: #455a64;
      --paper-blue-grey-800: #37474f;
      --paper-blue-grey-900: #263238;

      /* opacity for dark text on a light background */
      --dark-divider-opacity: 0.12;
      --dark-disabled-opacity: 0.38; /* or hint text or icon */
      --dark-secondary-opacity: 0.54;
      --dark-primary-opacity: 0.87;

      /* opacity for light text on a dark background */
      --light-divider-opacity: 0.12;
      --light-disabled-opacity: 0.3; /* or hint text or icon */
      --light-secondary-opacity: 0.7;
      --light-primary-opacity: 1.0;

    }

  </style>
</custom-style>
`;fa.setAttribute("style","display: none;"),document.head.appendChild(fa.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ma=xe`
<custom-style>
  <style is="custom-style">
    html {
      /*
       * You can use these generic variables in your elements for easy theming.
       * For example, if all your elements use \`--primary-text-color\` as its main
       * color, then switching from a light to a dark theme is just a matter of
       * changing the value of \`--primary-text-color\` in your application.
       */
      --primary-text-color: var(--light-theme-text-color);
      --primary-background-color: var(--light-theme-background-color);
      --secondary-text-color: var(--light-theme-secondary-color);
      --disabled-text-color: var(--light-theme-disabled-color);
      --divider-color: var(--light-theme-divider-color);
      --error-color: var(--paper-deep-orange-a700);

      /*
       * Primary and accent colors. Also see color.js for more colors.
       */
      --primary-color: var(--paper-indigo-500);
      --light-primary-color: var(--paper-indigo-100);
      --dark-primary-color: var(--paper-indigo-700);

      --accent-color: var(--paper-pink-a200);
      --light-accent-color: var(--paper-pink-a100);
      --dark-accent-color: var(--paper-pink-a400);


      /*
       * Material Design Light background theme
       */
      --light-theme-background-color: #ffffff;
      --light-theme-base-color: #000000;
      --light-theme-text-color: var(--paper-grey-900);
      --light-theme-secondary-color: #737373;  /* for secondary text and icons */
      --light-theme-disabled-color: #9b9b9b;  /* disabled/hint text */
      --light-theme-divider-color: #dbdbdb;

      /*
       * Material Design Dark background theme
       */
      --dark-theme-background-color: var(--paper-grey-900);
      --dark-theme-base-color: #ffffff;
      --dark-theme-text-color: #ffffff;
      --dark-theme-secondary-color: #bcbcbc;  /* for secondary text and icons */
      --dark-theme-disabled-color: #646464;  /* disabled/hint text */
      --dark-theme-divider-color: #3c3c3c;

      /*
       * Deprecated values because of their confusing names.
       */
      --text-primary-color: var(--dark-theme-text-color);
      --default-primary-color: var(--primary-color);
    }
  </style>
</custom-style>`;ma.setAttribute("style","display: none;"),document.head.appendChild(ma.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ga={properties:{name:{type:String},value:{notify:!0,type:String},required:{type:Boolean,value:!1}},attached:function(){},detached:function(){}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */let _a=null;const ya={properties:{validator:{type:String},invalid:{notify:!0,reflectToAttribute:!0,type:Boolean,value:!1,observer:"_invalidChanged"}},registered:function(){_a=new No({type:"validator"})},_invalidChanged:function(){this.invalid?this.setAttribute("aria-invalid","true"):this.removeAttribute("aria-invalid")},get _validator(){return _a&&_a.byKey(this.validator)},hasValidator:function(){return null!=this._validator},validate:function(t){return this.invalid=void 0===t&&void 0!==this.value?!this._getValidity(this.value):!this._getValidity(t),!this.invalid},_getValidity:function(t){return!this.hasValidator()||this._validator.validate(t)}},va={properties:{checked:{type:Boolean,value:!1,reflectToAttribute:!0,notify:!0,observer:"_checkedChanged"},toggles:{type:Boolean,value:!0,reflectToAttribute:!0},value:{type:String,value:"on",observer:"_valueChanged"}},observers:["_requiredChanged(required)"],created:function(){this._hasIronCheckedElementBehavior=!0},_getValidity:function(t){return this.disabled||!this.required||this.checked},_requiredChanged:function(){this.required?this.setAttribute("aria-required","true"):this.removeAttribute("aria-required")},_checkedChanged:function(){this.active=this.checked,this.fire("iron-change")},_valueChanged:function(){null==this.value&&(this.value="on")}},ba={observers:["_focusedChanged(receivedFocusFromKeyboard)"],_focusedChanged:function(t){t&&this.ensureRipple(),this.hasRipple()&&(this._ripple.holdDown=t)},_createRipple:function(){var t=ua._createRipple();return t.id="ink",t.setAttribute("center",""),t.classList.add("circle"),t}},xa=[aa,ra,ua,ba],wa=[xa,[ga,ya,va],{_checkedChanged:function(){va._checkedChanged.call(this),this.hasRipple()&&(this.checked?this._ripple.setAttribute("checked",""):this._ripple.removeAttribute("checked"))},_buttonStateChanged:function(){ua._buttonStateChanged.call(this),this.disabled||this.isAttached&&(this.checked=this.active)}}],Sa=xe`<style>
  :host {
    display: inline-block;
    white-space: nowrap;
    cursor: pointer;
    --calculated-paper-checkbox-size: var(--paper-checkbox-size, 18px);
    /* -1px is a sentinel for the default and is replaced in \`attached\`. */
    --calculated-paper-checkbox-ink-size: var(--paper-checkbox-ink-size, -1px);
    @apply --paper-font-common-base;
    line-height: 0;
    -webkit-tap-highlight-color: transparent;
  }

  :host([hidden]) {
    display: none !important;
  }

  :host(:focus) {
    outline: none;
  }

  .hidden {
    display: none;
  }

  #checkboxContainer {
    display: inline-block;
    position: relative;
    width: var(--calculated-paper-checkbox-size);
    height: var(--calculated-paper-checkbox-size);
    min-width: var(--calculated-paper-checkbox-size);
    margin: var(--paper-checkbox-margin, initial);
    vertical-align: var(--paper-checkbox-vertical-align, middle);
    background-color: var(--paper-checkbox-unchecked-background-color, transparent);
  }

  #ink {
    position: absolute;

    /* Center the ripple in the checkbox by negative offsetting it by
     * (inkWidth - rippleWidth) / 2 */
    top: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
    left: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
    width: var(--calculated-paper-checkbox-ink-size);
    height: var(--calculated-paper-checkbox-ink-size);
    color: var(--paper-checkbox-unchecked-ink-color, var(--primary-text-color));
    opacity: 0.6;
    pointer-events: none;
  }

  #ink:dir(rtl) {
    right: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
    left: auto;
  }

  #ink[checked] {
    color: var(--paper-checkbox-checked-ink-color, var(--primary-color));
  }

  #checkbox {
    position: relative;
    box-sizing: border-box;
    height: 100%;
    border: solid 2px;
    border-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
    border-radius: 2px;
    pointer-events: none;
    -webkit-transition: background-color 140ms, border-color 140ms;
    transition: background-color 140ms, border-color 140ms;

    -webkit-transition-duration: var(--paper-checkbox-animation-duration, 140ms);
    transition-duration: var(--paper-checkbox-animation-duration, 140ms);
  }

  /* checkbox checked animations */
  #checkbox.checked #checkmark {
    -webkit-animation: checkmark-expand 140ms ease-out forwards;
    animation: checkmark-expand 140ms ease-out forwards;

    -webkit-animation-duration: var(--paper-checkbox-animation-duration, 140ms);
    animation-duration: var(--paper-checkbox-animation-duration, 140ms);
  }

  @-webkit-keyframes checkmark-expand {
    0% {
      -webkit-transform: scale(0, 0) rotate(45deg);
    }
    100% {
      -webkit-transform: scale(1, 1) rotate(45deg);
    }
  }

  @keyframes checkmark-expand {
    0% {
      transform: scale(0, 0) rotate(45deg);
    }
    100% {
      transform: scale(1, 1) rotate(45deg);
    }
  }

  #checkbox.checked {
    background-color: var(--paper-checkbox-checked-color, var(--primary-color));
    border-color: var(--paper-checkbox-checked-color, var(--primary-color));
  }

  #checkmark {
    position: absolute;
    width: 36%;
    height: 70%;
    border-style: solid;
    border-top: none;
    border-left: none;
    border-right-width: calc(2/15 * var(--calculated-paper-checkbox-size));
    border-bottom-width: calc(2/15 * var(--calculated-paper-checkbox-size));
    border-color: var(--paper-checkbox-checkmark-color, white);
    -webkit-transform-origin: 97% 86%;
    transform-origin: 97% 86%;
    box-sizing: content-box; /* protect against page-level box-sizing */
  }

  #checkmark:dir(rtl) {
    -webkit-transform-origin: 50% 14%;
    transform-origin: 50% 14%;
  }

  /* label */
  #checkboxLabel {
    position: relative;
    display: inline-block;
    vertical-align: middle;
    padding-left: var(--paper-checkbox-label-spacing, 8px);
    white-space: normal;
    line-height: normal;
    color: var(--paper-checkbox-label-color, var(--primary-text-color));
    @apply --paper-checkbox-label;
  }

  :host([checked]) #checkboxLabel {
    color: var(--paper-checkbox-label-checked-color, var(--paper-checkbox-label-color, var(--primary-text-color)));
    @apply --paper-checkbox-label-checked;
  }

  #checkboxLabel:dir(rtl) {
    padding-right: var(--paper-checkbox-label-spacing, 8px);
    padding-left: 0;
  }

  #checkboxLabel[hidden] {
    display: none;
  }

  /* disabled state */

  :host([disabled]) #checkbox {
    opacity: 0.5;
    border-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
  }

  :host([disabled][checked]) #checkbox {
    background-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
    opacity: 0.5;
  }

  :host([disabled]) #checkboxLabel  {
    opacity: 0.65;
  }

  /* invalid state */
  #checkbox.invalid:not(.checked) {
    border-color: var(--paper-checkbox-error-color, var(--error-color));
  }
</style>

<div id="checkboxContainer">
  <div id="checkbox" class$="[[_computeCheckboxClass(checked, invalid)]]">
    <div id="checkmark" class$="[[_computeCheckmarkClass(checked)]]"></div>
  </div>
</div>

<div id="checkboxLabel"><slot></slot></div>`;
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
if(Sa.setAttribute("strip-whitespace",""),jr({_template:Sa,is:"paper-checkbox",behaviors:[wa],hostAttributes:{role:"checkbox","aria-checked":!1,tabindex:0},properties:{ariaActiveAttribute:{type:String,value:"aria-checked"}},attached:function(){Di(this,(function(){if("-1px"===this.getComputedStyleValue("--calculated-paper-checkbox-ink-size").trim()){var t=this.getComputedStyleValue("--calculated-paper-checkbox-size").trim(),e="px",n=t.match(/[A-Za-z]+$/);null!==n&&(e=n[0]);var i=parseFloat(t),r=8/3*i;"px"===e&&(r=Math.floor(r))%2!=i%2&&r++,this.updateStyles({"--paper-checkbox-ink-size":r+e})}}))},_computeCheckboxClass:function(t,e){var n="";return t&&(n+="checked "),e&&(n+="invalid"),n},_computeCheckmarkClass:function(t){return t?"":"hidden"},_createRipple:function(){return this._rippleContainer=this.$.checkboxContainer,ba._createRipple.call(this)}}),!window.polymerSkipLoadingFontRoboto){const t=document.createElement("link");t.rel="stylesheet",t.type="text/css",t.crossOrigin="anonymous",t.href="https://fonts.googleapis.com/css?family=Roboto+Mono:400,700|Roboto:400,300,300italic,400italic,500,500italic,700,700italic",document.head.appendChild(t)}
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */const Ma=xe`<custom-style>
  <style is="custom-style">
    html {

      /* Shared Styles */
      --paper-font-common-base: {
        font-family: 'Roboto', 'Noto', sans-serif;
        -webkit-font-smoothing: antialiased;
      };

      --paper-font-common-code: {
        font-family: 'Roboto Mono', 'Consolas', 'Menlo', monospace;
        -webkit-font-smoothing: antialiased;
      };

      --paper-font-common-expensive-kerning: {
        text-rendering: optimizeLegibility;
      };

      --paper-font-common-nowrap: {
        white-space: nowrap;
        overflow: hidden;
        text-overflow: ellipsis;
      };

      /* Material Font Styles */

      --paper-font-display4: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 112px;
        font-weight: 300;
        letter-spacing: -.044em;
        line-height: 120px;
      };

      --paper-font-display3: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 56px;
        font-weight: 400;
        letter-spacing: -.026em;
        line-height: 60px;
      };

      --paper-font-display2: {
        @apply --paper-font-common-base;

        font-size: 45px;
        font-weight: 400;
        letter-spacing: -.018em;
        line-height: 48px;
      };

      --paper-font-display1: {
        @apply --paper-font-common-base;

        font-size: 34px;
        font-weight: 400;
        letter-spacing: -.01em;
        line-height: 40px;
      };

      --paper-font-headline: {
        @apply --paper-font-common-base;

        font-size: 24px;
        font-weight: 400;
        letter-spacing: -.012em;
        line-height: 32px;
      };

      --paper-font-title: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 20px;
        font-weight: 500;
        line-height: 28px;
      };

      --paper-font-subhead: {
        @apply --paper-font-common-base;

        font-size: 16px;
        font-weight: 400;
        line-height: 24px;
      };

      --paper-font-body2: {
        @apply --paper-font-common-base;

        font-size: 14px;
        font-weight: 500;
        line-height: 24px;
      };

      --paper-font-body1: {
        @apply --paper-font-common-base;

        font-size: 14px;
        font-weight: 400;
        line-height: 20px;
      };

      --paper-font-caption: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 12px;
        font-weight: 400;
        letter-spacing: 0.011em;
        line-height: 20px;
      };

      --paper-font-menu: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 13px;
        font-weight: 500;
        line-height: 24px;
      };

      --paper-font-button: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 14px;
        font-weight: 500;
        letter-spacing: 0.018em;
        line-height: 24px;
        text-transform: uppercase;
      };

      --paper-font-code2: {
        @apply --paper-font-common-code;

        font-size: 14px;
        font-weight: 700;
        line-height: 20px;
      };

      --paper-font-code1: {
        @apply --paper-font-common-code;

        font-size: 14px;
        font-weight: 500;
        line-height: 20px;
      };

    }

  </style>
</custom-style>`;Ma.setAttribute("style","display: none;"),document.head.appendChild(Ma.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ea=document.createElement("template");Ea.setAttribute("style","display: none;"),Ea.innerHTML='<dom-module id="paper-dialog-shared-styles">\n  <template>\n    <style>\n      :host {\n        display: block;\n        margin: 24px 40px;\n\n        background: var(--paper-dialog-background-color, var(--primary-background-color));\n        color: var(--paper-dialog-color, var(--primary-text-color));\n\n        @apply --paper-font-body1;\n        @apply --shadow-elevation-16dp;\n        @apply --paper-dialog;\n      }\n\n      :host > ::slotted(*) {\n        margin-top: 20px;\n        padding: 0 24px;\n      }\n\n      :host > ::slotted(.no-padding) {\n        padding: 0;\n      }\n\n      \n      :host > ::slotted(*:first-child) {\n        margin-top: 24px;\n      }\n\n      :host > ::slotted(*:last-child) {\n        margin-bottom: 24px;\n      }\n\n      /* In 1.x, this selector was `:host > ::content h2`. In 2.x <slot> allows\n      to select direct children only, which increases the weight of this\n      selector, so we have to re-define first-child/last-child margins below. */\n      :host > ::slotted(h2) {\n        position: relative;\n        margin: 0;\n\n        @apply --paper-font-title;\n        @apply --paper-dialog-title;\n      }\n\n      /* Apply mixin again, in case it sets margin-top. */\n      :host > ::slotted(h2:first-child) {\n        margin-top: 24px;\n        @apply --paper-dialog-title;\n      }\n\n      /* Apply mixin again, in case it sets margin-bottom. */\n      :host > ::slotted(h2:last-child) {\n        margin-bottom: 24px;\n        @apply --paper-dialog-title;\n      }\n\n      :host > ::slotted(.paper-dialog-buttons),\n      :host > ::slotted(.buttons) {\n        position: relative;\n        padding: 8px 8px 8px 24px;\n        margin: 0;\n\n        color: var(--paper-dialog-button-color, var(--primary-color));\n\n        @apply --layout-horizontal;\n        @apply --layout-end-justified;\n      }\n    </style>\n  </template>\n</dom-module>',document.head.appendChild(Ea.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ta=[{properties:{animationConfig:{type:Object},entryAnimation:{observer:"_entryAnimationChanged",type:String},exitAnimation:{observer:"_exitAnimationChanged",type:String}},_entryAnimationChanged:function(){this.animationConfig=this.animationConfig||{},this.animationConfig.entry=[{name:this.entryAnimation,node:this}]},_exitAnimationChanged:function(){this.animationConfig=this.animationConfig||{},this.animationConfig.exit=[{name:this.exitAnimation,node:this}]},_copyProperties:function(t,e){for(var n in e)t[n]=e[n]},_cloneConfig:function(t){var e={isClone:!0};return this._copyProperties(e,t),e},_getAnimationConfigRecursive:function(t,e,n){var i;if(this.animationConfig)if(this.animationConfig.value&&"function"==typeof this.animationConfig.value)this._warn(this._logf("playAnimation","Please put 'animationConfig' inside of your components 'properties' object instead of outside of it."));else if(i=t?this.animationConfig[t]:this.animationConfig,Array.isArray(i)||(i=[i]),i)for(var r,o=0;r=i[o];o++)if(r.animatable)r.animatable._getAnimationConfigRecursive(r.type||t,e,n);else if(r.id){var a=e[r.id];a?(a.isClone||(e[r.id]=this._cloneConfig(a),a=e[r.id]),this._copyProperties(a,r)):e[r.id]=r}else n.push(r)},getAnimationConfig:function(t){var e={},n=[];for(var i in this._getAnimationConfigRecursive(t,e,n),e)n.push(e[i]);return n}},{_configureAnimations:function(t){var e=[],n=[];if(t.length>0)for(let e,i=0;e=t[i];i++){let t=document.createElement(e.name);if(t.isNeonAnimation){let i=null;t.configure||(t.configure=function(t){return null}),i=t.configure(e),n.push({result:i,config:e,neonAnimation:t})}else console.warn(this.is+":",e.name,"not found!")}for(var i=0;i<n.length;i++){let t=n[i].result,r=n[i].config,o=n[i].neonAnimation;try{"function"!=typeof t.cancel&&(t=document.timeline.play(t))}catch(e){t=null,console.warn("Couldnt play","(",r.name,").",e)}t&&e.push({neonAnimation:o,config:r,animation:t})}return e},_shouldComplete:function(t){for(var e=!0,n=0;n<t.length;n++)if("finished"!=t[n].animation.playState){e=!1;break}return e},_complete:function(t){for(var e=0;e<t.length;e++)t[e].neonAnimation.complete(t[e].config);for(e=0;e<t.length;e++)t[e].animation.cancel()},playAnimation:function(t,e){var n=this.getAnimationConfig(t);if(n){this._active=this._active||{},this._active[t]&&(this._complete(this._active[t]),delete this._active[t]);var i=this._configureAnimations(n);if(0!=i.length){this._active[t]=i;for(var r=0;r<i.length;r++)i[r].animation.onfinish=function(){this._shouldComplete(i)&&(this._complete(i),delete this._active[t],this.fire("neon-animation-finish",e,{bubbles:!1}))}.bind(this)}else this.fire("neon-animation-finish",e,{bubbles:!1})}},cancelAnimation:function(){for(var t in this._active){var e=this._active[t];for(var n in e)e[n].animation.cancel()}this._active={}}}];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
let Ca;const Aa={properties:{sizingTarget:{type:Object,value:function(){return this}},fitInto:{type:Object,value:window},noOverlap:{type:Boolean},positionTarget:{type:Element},horizontalAlign:{type:String},verticalAlign:{type:String},dynamicAlign:{type:Boolean},horizontalOffset:{type:Number,value:0,notify:!0},verticalOffset:{type:Number,value:0,notify:!0},autoFitOnAttach:{type:Boolean,value:!1},expandSizingTargetForScrollbars:{type:Boolean,value:!1},_fitInfo:{type:Object}},get _fitWidth(){return this.fitInto===window?this.fitInto.innerWidth:this.fitInto.getBoundingClientRect().width},get _fitHeight(){return this.fitInto===window?this.fitInto.innerHeight:this.fitInto.getBoundingClientRect().height},get _fitLeft(){return this.fitInto===window?0:this.fitInto.getBoundingClientRect().left},get _fitTop(){return this.fitInto===window?0:this.fitInto.getBoundingClientRect().top},get _defaultPositionTarget(){var t=er(this).parentNode;return t&&t.nodeType===Node.DOCUMENT_FRAGMENT_NODE&&(t=t.host),t},get _localeHorizontalAlign(){if(this._isRTL){if("right"===this.horizontalAlign)return"left";if("left"===this.horizontalAlign)return"right"}return this.horizontalAlign},get __shouldPosition(){return(this.horizontalAlign||this.verticalAlign)&&this.positionTarget},get _isRTL(){return void 0===this._memoizedIsRTL&&(this._memoizedIsRTL="rtl"==window.getComputedStyle(this).direction),this._memoizedIsRTL},attached:function(){this.positionTarget=this.positionTarget||this._defaultPositionTarget,this.autoFitOnAttach&&("none"===window.getComputedStyle(this).display?setTimeout(function(){this.fit()}.bind(this)):(window.ShadyDOM&&ShadyDOM.flush(),this.fit()))},detached:function(){this.__deferredFit&&(clearTimeout(this.__deferredFit),this.__deferredFit=null)},fit:function(){this.position(),this.constrain(),this.center()},_discoverInfo:function(){if(!this._fitInfo){var t=window.getComputedStyle(this),e=window.getComputedStyle(this.sizingTarget);this._fitInfo={inlineStyle:{top:this.style.top||"",left:this.style.left||"",position:this.style.position||""},sizerInlineStyle:{maxWidth:this.sizingTarget.style.maxWidth||"",maxHeight:this.sizingTarget.style.maxHeight||"",boxSizing:this.sizingTarget.style.boxSizing||""},positionedBy:{vertically:"auto"!==t.top?"top":"auto"!==t.bottom?"bottom":null,horizontally:"auto"!==t.left?"left":"auto"!==t.right?"right":null},sizedBy:{height:"none"!==e.maxHeight,width:"none"!==e.maxWidth,minWidth:parseInt(e.minWidth,10)||0,minHeight:parseInt(e.minHeight,10)||0},margin:{top:parseInt(t.marginTop,10)||0,right:parseInt(t.marginRight,10)||0,bottom:parseInt(t.marginBottom,10)||0,left:parseInt(t.marginLeft,10)||0}}}},resetFit:function(){var t=this._fitInfo||{};for(var e in t.sizerInlineStyle)this.sizingTarget.style[e]=t.sizerInlineStyle[e];for(var e in t.inlineStyle)this.style[e]=t.inlineStyle[e];this._fitInfo=null},refit:function(){var t=this.sizingTarget.scrollLeft,e=this.sizingTarget.scrollTop;this.resetFit(),this.fit(),this.sizingTarget.scrollLeft=t,this.sizingTarget.scrollTop=e},position:function(){if(!this.__shouldPosition)return;this._discoverInfo(),window.ShadyDOM&&window.ShadyDOM.flush(),this.style.position="fixed",this.sizingTarget.style.boxSizing="border-box",this.style.left="0px",this.style.top="0px";var t=this.getBoundingClientRect(),e=this.__getNormalizedRect(this.positionTarget),n=this.__getNormalizedRect(this.fitInto);let i,r,o,a;this.expandSizingTargetForScrollbars&&(i=this.sizingTarget.offsetWidth,r=this.sizingTarget.offsetHeight,o=this.sizingTarget.clientWidth,a=this.sizingTarget.clientHeight);var s=this._fitInfo.margin,l=this.__getPosition(this._localeHorizontalAlign,this.verticalAlign,{width:t.width+s.left+s.right,height:t.height+s.top+s.bottom},t,e,n),c=l.left+s.left,u=l.top+s.top,h=Math.min(n.right-s.right,c+t.width),d=Math.min(n.bottom-s.bottom,u+t.height);c=Math.max(n.left+s.left,Math.min(c,h-this._fitInfo.sizedBy.minWidth)),u=Math.max(n.top+s.top,Math.min(u,d-this._fitInfo.sizedBy.minHeight));const p=Math.max(h-c,this._fitInfo.sizedBy.minWidth),f=Math.max(d-u,this._fitInfo.sizedBy.minHeight);this.sizingTarget.style.maxWidth=p+"px",this.sizingTarget.style.maxHeight=f+"px";const m=c-t.left,g=u-t.top;if(this.style.left=`${m}px`,this.style.top=`${g}px`,this.expandSizingTargetForScrollbars){const t=this.sizingTarget.offsetHeight,e=t-this.sizingTarget.clientHeight-(r-a);if(e>0){const i=Math.min(n.height-s.top-s.bottom,f+e);this.sizingTarget.style.maxHeight=`${i}px`;const r=this.sizingTarget.offsetHeight,o=r-t;let a;"top"===l.verticalAlign?a=g:"middle"===l.verticalAlign?a=g-o/2:"bottom"===l.verticalAlign&&(a=g-o),a=Math.max(n.top+s.top,Math.min(a,n.bottom-s.bottom-r)),this.style.top=`${a}px`}const c=this.sizingTarget.offsetWidth,u=c-this.sizingTarget.clientWidth-(i-o);if(u>0){const t=(()=>{if(void 0!==Ca)return Ca;const t=document.createElement("div");Object.assign(t.style,{overflow:"auto",position:"fixed",left:"0px",top:"0px",maxWidth:"100px",maxHeight:"100px"});const e=document.createElement("div");return e.style.width="200px",e.style.height="200px",t.appendChild(e),document.body.appendChild(t),Ca=Math.abs(t.offsetWidth-100)>1?t.offsetWidth-t.clientWidth:0,document.body.removeChild(t),Ca})(),e=Math.min(n.width-s.left-s.right,p+u-t);this.sizingTarget.style.maxWidth=`${e}px`;const i=this.sizingTarget.offsetWidth+t,r=i-c;let o;"left"===l.horizontalAlign?o=m:"center"===l.horizontalAlign?o=m-r/2:"right"===l.horizontalAlign&&(o=m-r),o=Math.max(n.left+s.left,Math.min(o,n.right-s.right-i)),this.style.left=`${o}px`}}},constrain:function(){if(!this.__shouldPosition){this._discoverInfo();var t=this._fitInfo;t.positionedBy.vertically||(this.style.position="fixed",this.style.top="0px"),t.positionedBy.horizontally||(this.style.position="fixed",this.style.left="0px"),this.sizingTarget.style.boxSizing="border-box";var e=this.getBoundingClientRect();t.sizedBy.height||this.__sizeDimension(e,t.positionedBy.vertically,"top","bottom","Height"),t.sizedBy.width||this.__sizeDimension(e,t.positionedBy.horizontally,"left","right","Width")}},_sizeDimension:function(t,e,n,i,r){this.__sizeDimension(t,e,n,i,r)},__sizeDimension:function(t,e,n,i,r){var o=this._fitInfo,a=this.__getNormalizedRect(this.fitInto),s="Width"===r?a.width:a.height,l=e===i,c="offset"+r;this.sizingTarget.style["max"+r]=s-o.margin[l?n:i]-(l?s-t[i]:t[n])-(this[c]-this.sizingTarget[c])+"px"},center:function(){if(!this.__shouldPosition){this._discoverInfo();var t=this._fitInfo.positionedBy;if(!t.vertically||!t.horizontally){this.style.position="fixed",t.vertically||(this.style.top="0px"),t.horizontally||(this.style.left="0px");var e=this.getBoundingClientRect(),n=this.__getNormalizedRect(this.fitInto);if(!t.vertically)this.style.top=n.top-e.top+(n.height-e.height)/2+"px";if(!t.horizontally)this.style.left=n.left-e.left+(n.width-e.width)/2+"px"}}},__getNormalizedRect:function(t){return t===document.documentElement||t===window?{top:0,left:0,width:window.innerWidth,height:window.innerHeight,right:window.innerWidth,bottom:window.innerHeight}:t.getBoundingClientRect()},__getOffscreenArea:function(t,e,n){var i=Math.min(0,t.top)+Math.min(0,n.bottom-(t.top+e.height)),r=Math.min(0,t.left)+Math.min(0,n.right-(t.left+e.width));return Math.abs(i)*e.width+Math.abs(r)*e.height},__getPosition:function(t,e,n,i,r,o){var a,s=[{verticalAlign:"top",horizontalAlign:"left",top:r.top+this.verticalOffset,left:r.left+this.horizontalOffset},{verticalAlign:"top",horizontalAlign:"right",top:r.top+this.verticalOffset,left:r.right-n.width-this.horizontalOffset},{verticalAlign:"bottom",horizontalAlign:"left",top:r.bottom-n.height-this.verticalOffset,left:r.left+this.horizontalOffset},{verticalAlign:"bottom",horizontalAlign:"right",top:r.bottom-n.height-this.verticalOffset,left:r.right-n.width-this.horizontalOffset}];if(this.noOverlap){for(var l=0,c=s.length;l<c;l++){var u={};for(var h in s[l])u[h]=s[l][h];s.push(u)}s[0].top=s[1].top+=r.height,s[2].top=s[3].top-=r.height,s[4].left=s[6].left+=r.width,s[5].left=s[7].left-=r.width}e="auto"===e?null:e,(t="auto"===t?null:t)&&"center"!==t||(s.push({verticalAlign:"top",horizontalAlign:"center",top:r.top+this.verticalOffset+(this.noOverlap?r.height:0),left:r.left-i.width/2+r.width/2+this.horizontalOffset}),s.push({verticalAlign:"bottom",horizontalAlign:"center",top:r.bottom-n.height-this.verticalOffset-(this.noOverlap?r.height:0),left:r.left-i.width/2+r.width/2+this.horizontalOffset})),e&&"middle"!==e||(s.push({verticalAlign:"middle",horizontalAlign:"left",top:r.top-i.height/2+r.height/2+this.verticalOffset,left:r.left+this.horizontalOffset+(this.noOverlap?r.width:0)}),s.push({verticalAlign:"middle",horizontalAlign:"right",top:r.top-i.height/2+r.height/2+this.verticalOffset,left:r.right-n.width-this.horizontalOffset-(this.noOverlap?r.width:0)})),"middle"===e&&"center"===t&&s.push({verticalAlign:"middle",horizontalAlign:"center",top:r.top-i.height/2+r.height/2+this.verticalOffset,left:r.left-i.width/2+r.width/2+this.horizontalOffset});for(l=0;l<s.length;l++){var d=s[l],p=d.verticalAlign===e,f=d.horizontalAlign===t;if(!this.dynamicAlign&&!this.noOverlap&&p&&f){a=d;break}var m=(!e||p)&&(!t||f);if(this.dynamicAlign||m){if(d.offscreenArea=this.__getOffscreenArea(d,n,o),0===d.offscreenArea&&m){a=d;break}var g=d.offscreenArea-(a=a||d).offscreenArea;(g<0||0===g&&(p||f))&&(a=d)}}return a}};
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var ka=Element.prototype,La=ka.matches||ka.matchesSelector||ka.mozMatchesSelector||ka.msMatchesSelector||ka.oMatchesSelector||ka.webkitMatchesSelector;const Pa=new class{getTabbableNodes(t){var e=[];return this._collectTabbableNodes(t,e)?this._sortByTabIndex(e):e}isFocusable(t){return La.call(t,"input, select, textarea, button, object")?La.call(t,":not([disabled])"):La.call(t,"a[href], area[href], iframe, [tabindex], [contentEditable]")}isTabbable(t){return this.isFocusable(t)&&La.call(t,':not([tabindex="-1"])')&&this._isVisible(t)}_normalizedTabIndex(t){if(this.isFocusable(t)){var e=t.getAttribute("tabindex")||0;return Number(e)}return-1}_collectTabbableNodes(t,e){if(t.nodeType!==Node.ELEMENT_NODE)return!1;var n=t;if(!this._isVisible(n))return!1;var i,r=this._normalizedTabIndex(n),o=r>0;r>=0&&e.push(n),i="content"===n.localName||"slot"===n.localName?er(n).getDistributedNodes():er(n.root||n).children;for(var a=0;a<i.length;a++)o=this._collectTabbableNodes(i[a],e)||o;return o}_isVisible(t){var e=t.style;return"hidden"!==e.visibility&&"none"!==e.display&&("hidden"!==(e=window.getComputedStyle(t)).visibility&&"none"!==e.display)}_sortByTabIndex(t){var e=t.length;if(e<2)return t;var n=Math.ceil(e/2),i=this._sortByTabIndex(t.slice(0,n)),r=this._sortByTabIndex(t.slice(n));return this._mergeSortByTabIndex(i,r)}_mergeSortByTabIndex(t,e){for(var n=[];t.length>0&&e.length>0;)this._hasLowerTabOrder(t[0],e[0])?n.push(e.shift()):n.push(t.shift());return n.concat(t,e)}_hasLowerTabOrder(t,e){var n=Math.max(t.tabIndex,0),i=Math.max(e.tabIndex,0);return 0===n||0===i?i>n:n>i}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */jr({_template:xe`
    <style>
      :host {
        position: fixed;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
        background-color: var(--iron-overlay-backdrop-background-color, #000);
        opacity: 0;
        transition: opacity 0.2s;
        pointer-events: none;
        @apply --iron-overlay-backdrop;
      }

      :host(.opened) {
        opacity: var(--iron-overlay-backdrop-opacity, 0.6);
        pointer-events: auto;
        @apply --iron-overlay-backdrop-opened;
      }
    </style>

    <slot></slot>
`,is:"iron-overlay-backdrop",properties:{opened:{reflectToAttribute:!0,type:Boolean,value:!1,observer:"_openedChanged"}},listeners:{transitionend:"_onTransitionend"},created:function(){this.__openedRaf=null},attached:function(){this.opened&&this._openedChanged(this.opened)},prepare:function(){this.opened&&!this.parentNode&&er(document.body).appendChild(this)},open:function(){this.opened=!0},close:function(){this.opened=!1},complete:function(){this.opened||this.parentNode!==document.body||er(this.parentNode).removeChild(this)},_onTransitionend:function(t){t&&t.target===this&&this.complete()},_openedChanged:function(t){if(t)this.prepare();else{var e=window.getComputedStyle(this);"0s"!==e.transitionDuration&&0!=e.opacity||this.complete()}this.isAttached&&(this.__openedRaf&&(window.cancelAnimationFrame(this.__openedRaf),this.__openedRaf=null),this.scrollTop=this.scrollTop,this.__openedRaf=window.requestAnimationFrame(function(){this.__openedRaf=null,this.toggleClass("opened",this.opened)}.bind(this)))}});const Na=new
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
class{constructor(){this._overlays=[],this._minimumZ=101,this._backdropElement=null,ci(document.documentElement,"tap",(function(){})),document.addEventListener("tap",this._onCaptureClick.bind(this),!0),document.addEventListener("focus",this._onCaptureFocus.bind(this),!0),document.addEventListener("keydown",this._onCaptureKeyDown.bind(this),!0)}get backdropElement(){return this._backdropElement||(this._backdropElement=document.createElement("iron-overlay-backdrop")),this._backdropElement}get deepActiveElement(){var t=document.activeElement;for(t&&t instanceof Element!=!1||(t=document.body);t.root&&er(t.root).activeElement;)t=er(t.root).activeElement;return t}_bringOverlayAtIndexToFront(t){var e=this._overlays[t];if(e){var n=this._overlays.length-1,i=this._overlays[n];if(i&&this._shouldBeBehindOverlay(e,i)&&n--,!(t>=n)){var r=Math.max(this.currentOverlayZ(),this._minimumZ);for(this._getZ(e)<=r&&this._applyOverlayZ(e,r);t<n;)this._overlays[t]=this._overlays[t+1],t++;this._overlays[n]=e}}}addOrRemoveOverlay(t){t.opened?this.addOverlay(t):this.removeOverlay(t)}addOverlay(t){var e=this._overlays.indexOf(t);if(e>=0)return this._bringOverlayAtIndexToFront(e),void this.trackBackdrop();var n=this._overlays.length,i=this._overlays[n-1],r=Math.max(this._getZ(i),this._minimumZ),o=this._getZ(t);i&&this._shouldBeBehindOverlay(t,i)&&(this._applyOverlayZ(i,r),n--,r=Math.max(this._getZ(this._overlays[n-1]),this._minimumZ));o<=r&&this._applyOverlayZ(t,r),this._overlays.splice(n,0,t),this.trackBackdrop()}removeOverlay(t){var e=this._overlays.indexOf(t);-1!==e&&(this._overlays.splice(e,1),this.trackBackdrop())}currentOverlay(){return this._overlays[this._overlays.length-1]}currentOverlayZ(){return this._getZ(this.currentOverlay())}ensureMinimumZ(t){this._minimumZ=Math.max(this._minimumZ,t)}focusOverlay(){var t=this.currentOverlay();t&&t._applyFocus()}trackBackdrop(){var t=this._overlayWithBackdrop();(t||this._backdropElement)&&(this.backdropElement.style.zIndex=this._getZ(t)-1,this.backdropElement.opened=!!t,this.backdropElement.prepare())}getBackdrops(){for(var t=[],e=0;e<this._overlays.length;e++)this._overlays[e].withBackdrop&&t.push(this._overlays[e]);return t}backdropZ(){return this._getZ(this._overlayWithBackdrop())-1}_overlayWithBackdrop(){for(var t=this._overlays.length-1;t>=0;t--)if(this._overlays[t].withBackdrop)return this._overlays[t]}_getZ(t){var e=this._minimumZ;if(t){var n=Number(t.style.zIndex||window.getComputedStyle(t).zIndex);n==n&&(e=n)}return e}_setZ(t,e){t.style.zIndex=e}_applyOverlayZ(t,e){this._setZ(t,e+2)}_overlayInPath(t){t=t||[];for(var e=0;e<t.length;e++)if(t[e]._manager===this)return t[e]}_onCaptureClick(t){var e=this._overlays.length-1;if(-1!==e)for(var n,i=er(t).path;(n=this._overlays[e])&&this._overlayInPath(i)!==n&&(n._onCaptureClick(t),n.allowClickThrough);)e--}_onCaptureFocus(t){var e=this.currentOverlay();e&&e._onCaptureFocus(t)}_onCaptureKeyDown(t){var e=this.currentOverlay();e&&(Xo.keyboardEventMatchesKeys(t,"esc")?e._onCaptureEsc(t):Xo.keyboardEventMatchesKeys(t,"tab")&&e._onCaptureTab(t))}_shouldBeBehindOverlay(t,e){return!t.alwaysOnTop&&e.alwaysOnTop}};
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */var Ia,Ra,Oa={pageX:0,pageY:0},za=null,Da=[],Ba=["wheel","mousewheel","DOMMouseScroll","touchstart","touchmove"];function Ha(t){Va.indexOf(t)>=0||(0===Va.length&&(function e(){Ia=Ia||Ua.bind(void 0);for(var t=0,e=Ba.length;t<e;t++)document.addEventListener(Ba[t],Ia,{capture:!0,passive:!1})})(),Va.push(t),Ra=Va[Va.length-1])}function Fa(t){var e=Va.indexOf(t);-1!==e&&(Va.splice(e,1),Ra=Va[Va.length-1],0===Va.length&&(function n(){for(var t=0,e=Ba.length;t<e;t++)document.removeEventListener(Ba[t],Ia,{capture:!0,passive:!1})})())}const Va=[];function Ua(t){if(t.cancelable&&(function e(t){var e=er(t).rootTarget;"touchmove"!==t.type&&za!==e&&(za=e,Da=(function n(t){for(var e=[],n=t.indexOf(Ra),i=0;i<=n;i++)if(t[i].nodeType===Node.ELEMENT_NODE){var r=t[i],o=r.style;"scroll"!==o.overflow&&"auto"!==o.overflow&&(o=window.getComputedStyle(r)),"scroll"!==o.overflow&&"auto"!==o.overflow||e.push(r)}return e})(er(t).path));if(!Da.length)return!0;if("touchstart"===t.type)return!1;var i=(function r(t){var e={deltaX:t.deltaX,deltaY:t.deltaY};if("deltaX"in t);else if("wheelDeltaX"in t&&"wheelDeltaY"in t)e.deltaX=-t.wheelDeltaX,e.deltaY=-t.wheelDeltaY;else if("wheelDelta"in t)e.deltaX=0,e.deltaY=-t.wheelDelta;else if("axis"in t)e.deltaX=1===t.axis?t.detail:0,e.deltaY=2===t.axis?t.detail:0;else if(t.targetTouches){var n=t.targetTouches[0];e.deltaX=Oa.pageX-n.pageX,e.deltaY=Oa.pageY-n.pageY}return e}
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */)(t);return!(function o(t,e,n){if(!e&&!n)return;for(var i=Math.abs(n)>=Math.abs(e),r=0;r<t.length;r++){var o=t[r];if(i?n<0?o.scrollTop>0:o.scrollTop<o.scrollHeight-o.clientHeight:e<0?o.scrollLeft>0:o.scrollLeft<o.scrollWidth-o.clientWidth)return o}})(Da,i.deltaX,i.deltaY)})(t)&&t.preventDefault(),t.targetTouches){var n=t.targetTouches[0];Oa.pageX=n.pageX,Oa.pageY=n.pageY}}const ja={properties:{opened:{observer:"_openedChanged",type:Boolean,value:!1,notify:!0},canceled:{observer:"_canceledChanged",readOnly:!0,type:Boolean,value:!1},withBackdrop:{observer:"_withBackdropChanged",type:Boolean},noAutoFocus:{type:Boolean,value:!1},noCancelOnEscKey:{type:Boolean,value:!1},noCancelOnOutsideClick:{type:Boolean,value:!1},closingReason:{type:Object},restoreFocusOnClose:{type:Boolean,value:!1},allowClickThrough:{type:Boolean},alwaysOnTop:{type:Boolean},scrollAction:{type:String},_manager:{type:Object,value:Na},_focusedChild:{type:Object}},listeners:{"iron-resize":"_onIronResize"},observers:["__updateScrollObservers(isAttached, opened, scrollAction)"],get backdropElement(){return this._manager.backdropElement},get _focusNode(){return this._focusedChild||er(this).querySelector("[autofocus]")||this},get _focusableNodes(){return Pa.getTabbableNodes(this)},ready:function(){this.__isAnimating=!1,this.__shouldRemoveTabIndex=!1,this.__firstFocusableNode=this.__lastFocusableNode=null,this.__rafs={},this.__restoreFocusNode=null,this.__scrollTop=this.__scrollLeft=null,this.__onCaptureScroll=this.__onCaptureScroll.bind(this),this.__rootNodes=null,this._ensureSetup()},attached:function(){this.opened&&this._openedChanged(this.opened),this._observer=er(this).observeNodes(this._onNodesChange)},detached:function(){for(var t in this._observer&&er(this).unobserveNodes(this._observer),this._observer=null,this.__rafs)null!==this.__rafs[t]&&cancelAnimationFrame(this.__rafs[t]);this.__rafs={},this._manager.removeOverlay(this),this.__isAnimating&&(this.opened?this._finishRenderOpened():(this._applyFocus(),this._finishRenderClosed()))},toggle:function(){this._setCanceled(!1),this.opened=!this.opened},open:function(){this._setCanceled(!1),this.opened=!0},close:function(){this._setCanceled(!1),this.opened=!1},cancel:function(t){this.fire("iron-overlay-canceled",t,{cancelable:!0}).defaultPrevented||(this._setCanceled(!0),this.opened=!1)},invalidateTabbables:function(){this.__firstFocusableNode=this.__lastFocusableNode=null},_ensureSetup:function(){this._overlaySetup||(this._overlaySetup=!0,this.style.outline="none",this.style.display="none")},_openedChanged:function(t){t?this.removeAttribute("aria-hidden"):this.setAttribute("aria-hidden","true"),this.isAttached&&(this.__isAnimating=!0,this.__deraf("__openedChanged",this.__openedChanged))},_canceledChanged:function(){this.closingReason=this.closingReason||{},this.closingReason.canceled=this.canceled},_withBackdropChanged:function(){this.withBackdrop&&!this.hasAttribute("tabindex")?(this.setAttribute("tabindex","-1"),this.__shouldRemoveTabIndex=!0):this.__shouldRemoveTabIndex&&(this.removeAttribute("tabindex"),this.__shouldRemoveTabIndex=!1),this.opened&&this.isAttached&&this._manager.trackBackdrop()},_prepareRenderOpened:function(){this.__restoreFocusNode=this._manager.deepActiveElement,this._preparePositioning(),this.refit(),this._finishPositioning(),this.noAutoFocus&&document.activeElement===this._focusNode&&(this._focusNode.blur(),this.__restoreFocusNode.focus())},_renderOpened:function(){this._finishRenderOpened()},_renderClosed:function(){this._finishRenderClosed()},_finishRenderOpened:function(){this.notifyResize(),this.__isAnimating=!1,this.fire("iron-overlay-opened")},_finishRenderClosed:function(){this.style.display="none",this.style.zIndex="",this.notifyResize(),this.__isAnimating=!1,this.fire("iron-overlay-closed",this.closingReason)},_preparePositioning:function(){this.style.transition=this.style.webkitTransition="none",this.style.transform=this.style.webkitTransform="none",this.style.display=""},_finishPositioning:function(){this.style.display="none",this.scrollTop=this.scrollTop,this.style.transition=this.style.webkitTransition="",this.style.transform=this.style.webkitTransform="",this.style.display="",this.scrollTop=this.scrollTop},_applyFocus:function(){if(this.opened)this.noAutoFocus||this._focusNode.focus();else{if(this.restoreFocusOnClose&&this.__restoreFocusNode){var t=this._manager.deepActiveElement;(t===document.body||Ga(this,t))&&this.__restoreFocusNode.focus()}this.__restoreFocusNode=null,this._focusNode.blur(),this._focusedChild=null}},_onCaptureClick:function(t){this.noCancelOnOutsideClick||this.cancel(t)},_onCaptureFocus:function(t){if(this.withBackdrop){var e=er(t).path;-1===e.indexOf(this)?(t.stopPropagation(),this._applyFocus()):this._focusedChild=e[0]}},_onCaptureEsc:function(t){this.noCancelOnEscKey||this.cancel(t)},_onCaptureTab:function(t){if(this.withBackdrop){this.__ensureFirstLastFocusables();var e=t.shiftKey,n=e?this.__firstFocusableNode:this.__lastFocusableNode,i=e?this.__lastFocusableNode:this.__firstFocusableNode,r=!1;if(n===i)r=!0;else{var o=this._manager.deepActiveElement;r=o===n||o===this}r&&(t.preventDefault(),this._focusedChild=i,this._applyFocus())}},_onIronResize:function(){this.opened&&!this.__isAnimating&&this.__deraf("refit",this.refit)},_onNodesChange:function(){this.opened&&!this.__isAnimating&&(this.invalidateTabbables(),this.notifyResize())},__ensureFirstLastFocusables:function(){var t=this._focusableNodes;this.__firstFocusableNode=t[0],this.__lastFocusableNode=t[t.length-1]},__openedChanged:function(){this.opened?(this._prepareRenderOpened(),this._manager.addOverlay(this),this._applyFocus(),this._renderOpened()):(this._manager.removeOverlay(this),this._applyFocus(),this._renderClosed())},__deraf:function(t,e){var n=this.__rafs;null!==n[t]&&cancelAnimationFrame(n[t]),n[t]=requestAnimationFrame(function i(){n[t]=null,e.call(this)}.bind(this))},__updateScrollObservers:function(t,e,n){t&&e&&this.__isValidScrollAction(n)?("lock"===n&&(this.__saveScrollPosition(),Ha(this)),this.__addScrollListeners()):(Fa(this),this.__removeScrollListeners())},__addScrollListeners:function(){if(!this.__rootNodes){if(this.__rootNodes=[],m)for(var t=this;t;)t.nodeType===Node.DOCUMENT_FRAGMENT_NODE&&t.host&&this.__rootNodes.push(t),t=t.host||t.assignedSlot||t.parentNode;this.__rootNodes.push(document)}this.__rootNodes.forEach((function(t){t.addEventListener("scroll",this.__onCaptureScroll,{capture:!0,passive:!0})}),this)},__removeScrollListeners:function(){this.__rootNodes&&this.__rootNodes.forEach((function(t){t.removeEventListener("scroll",this.__onCaptureScroll,{capture:!0,passive:!0})}),this),this.isAttached||(this.__rootNodes=null)},__isValidScrollAction:function(t){return"lock"===t||"refit"===t||"cancel"===t},__onCaptureScroll:function(t){if(!(this.__isAnimating||er(t).path.indexOf(this)>=0))switch(this.scrollAction){case"lock":this.__restoreScrollPosition();break;case"refit":this.__deraf("refit",this.refit);break;case"cancel":this.cancel(t)}},__saveScrollPosition:function(){document.scrollingElement?(this.__scrollTop=document.scrollingElement.scrollTop,this.__scrollLeft=document.scrollingElement.scrollLeft):(this.__scrollTop=Math.max(document.documentElement.scrollTop,document.body.scrollTop),this.__scrollLeft=Math.max(document.documentElement.scrollLeft,document.body.scrollLeft))},__restoreScrollPosition:function(){document.scrollingElement?(document.scrollingElement.scrollTop=this.__scrollTop,document.scrollingElement.scrollLeft=this.__scrollLeft):(document.documentElement.scrollTop=document.body.scrollTop=this.__scrollTop,document.documentElement.scrollLeft=document.body.scrollLeft=this.__scrollLeft)}},Ga=(t,e)=>{for(let i=e;i;i=(n=i).assignedSlot||n.parentNode||n.host)if(i===t)return!0;var n;return!1},Wa=[Aa,Co,ja],qa={hostAttributes:{role:"dialog",tabindex:"-1"},properties:{modal:{type:Boolean,value:!1},__readied:{type:Boolean,value:!1}},observers:["_modalChanged(modal, __readied)"],listeners:{tap:"_onDialogClick"},ready:function(){this.__prevNoCancelOnOutsideClick=this.noCancelOnOutsideClick,this.__prevNoCancelOnEscKey=this.noCancelOnEscKey,this.__prevWithBackdrop=this.withBackdrop,this.__readied=!0},_modalChanged:function(t,e){e&&(t?(this.__prevNoCancelOnOutsideClick=this.noCancelOnOutsideClick,this.__prevNoCancelOnEscKey=this.noCancelOnEscKey,this.__prevWithBackdrop=this.withBackdrop,this.noCancelOnOutsideClick=!0,this.noCancelOnEscKey=!0,this.withBackdrop=!0):(this.noCancelOnOutsideClick=this.noCancelOnOutsideClick&&this.__prevNoCancelOnOutsideClick,this.noCancelOnEscKey=this.noCancelOnEscKey&&this.__prevNoCancelOnEscKey,this.withBackdrop=this.withBackdrop&&this.__prevWithBackdrop))},_updateClosingReasonConfirmed:function(t){this.closingReason=this.closingReason||{},this.closingReason.confirmed=t},_onDialogClick:function(t){for(var e=er(t).path,n=0,i=e.indexOf(this);n<i;n++){var r=e[n];if(r.hasAttribute&&(r.hasAttribute("dialog-dismiss")||r.hasAttribute("dialog-confirm"))){this._updateClosingReasonConfirmed(r.hasAttribute("dialog-confirm")),this.close(),t.stopPropagation();break}}}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style include="paper-dialog-shared-styles"></style>
    <slot></slot>
`,is:"paper-dialog",behaviors:[[Wa,qa],Ta],listeners:{"neon-animation-finish":"_onNeonAnimationFinish"},_renderOpened:function(){this.cancelAnimation(),this.playAnimation("entry")},_renderClosed:function(){this.cancelAnimation(),this.playAnimation("exit")},_onNeonAnimationFinish:function(){this.opened?this._finishRenderOpened():this._finishRenderClosed()}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style>

      :host {
        display: block;
        @apply --layout-relative;
      }

      :host(.is-scrolled:not(:first-child))::before {
        content: '';
        position: absolute;
        top: 0;
        left: 0;
        right: 0;
        height: 1px;
        background: var(--divider-color);
      }

      :host(.can-scroll:not(.scrolled-to-bottom):not(:last-child))::after {
        content: '';
        position: absolute;
        bottom: 0;
        left: 0;
        right: 0;
        height: 1px;
        background: var(--divider-color);
      }

      .scrollable {
        padding: 0 24px;

        @apply --layout-scroll;
        @apply --paper-dialog-scrollable;
      }

      .fit {
        @apply --layout-fit;
      }
    </style>

    <div id="scrollable" class="scrollable" on-scroll="updateScrollState">
      <slot></slot>
    </div>
`,is:"paper-dialog-scrollable",properties:{dialogElement:{type:Object}},get scrollTarget(){return this.$.scrollable},ready:function(){this._ensureTarget(),this.classList.add("no-padding")},attached:function(){this._ensureTarget(),requestAnimationFrame(this.updateScrollState.bind(this))},updateScrollState:function(){this.toggleClass("is-scrolled",this.scrollTarget.scrollTop>0),this.toggleClass("can-scroll",this.scrollTarget.offsetHeight<this.scrollTarget.scrollHeight),this.toggleClass("scrolled-to-bottom",this.scrollTarget.scrollTop+this.scrollTarget.offsetHeight>=this.scrollTarget.scrollHeight)},_ensureTarget:function(){this.dialogElement=this.dialogElement||this.parentElement,this.dialogElement&&this.dialogElement.behaviors&&this.dialogElement.behaviors.indexOf(qa)>=0?(this.dialogElement.sizingTarget=this.scrollTarget,this.scrollTarget.classList.remove("fit")):this.dialogElement&&this.scrollTarget.classList.add("fit")}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ya=jr({_template:xe`
    <style>
      :host {
        display: inline-block;
        position: fixed;
        clip: rect(0px,0px,0px,0px);
      }
    </style>
    <div aria-live$="[[mode]]">[[_text]]</div>
`,is:"iron-a11y-announcer",properties:{mode:{type:String,value:"polite"},timeout:{type:Number,value:150},_text:{type:String,value:""}},created:function(){Ya.instance||(Ya.instance=this),document.addEventListener("iron-announce",this._onIronAnnounce.bind(this))},announce:function(t){this._text="",this.async((function(){this._text=t}),this.timeout)},_onIronAnnounce:function(t){t.detail&&t.detail.text&&this.announce(t.detail.text)}});Ya.instance=null,Ya.requestAvailability=function(){Ya.instance||(Ya.instance=document.createElement("iron-a11y-announcer")),document.body?document.body.appendChild(Ya.instance):document.addEventListener("load",(function(){document.body.appendChild(Ya.instance)}))},
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style>
      :host {
        display: inline-block;
      }
    </style>
    <slot id="content"></slot>
`,is:"iron-input",behaviors:[ya],properties:{bindValue:{type:String,value:""},value:{type:String,computed:"_computeValue(bindValue)"},allowedPattern:{type:String},autoValidate:{type:Boolean,value:!1},_inputElement:Object},observers:["_bindValueChanged(bindValue, _inputElement)"],listeners:{input:"_onInput",keypress:"_onKeypress"},created:function(){Ya.requestAvailability(),this._previousValidInput="",this._patternAlreadyChecked=!1},attached:function(){this._observer=er(this).observeNodes(function(t){this._initSlottedInput()}.bind(this))},detached:function(){this._observer&&(er(this).unobserveNodes(this._observer),this._observer=null)},get inputElement(){return this._inputElement},_initSlottedInput:function(){this._inputElement=this.getEffectiveChildren()[0],this.inputElement&&this.inputElement.value&&(this.bindValue=this.inputElement.value),this.fire("iron-input-ready")},get _patternRegExp(){var t;if(this.allowedPattern)t=new RegExp(this.allowedPattern);else switch(this.inputElement.type){case"number":t=/[0-9.,e-]/}return t},_bindValueChanged:function(t,e){e&&(void 0===t?e.value=null:t!==e.value&&(this.inputElement.value=t),this.autoValidate&&this.validate(),this.fire("bind-value-changed",{value:t}))},_onInput:function(){this.allowedPattern&&!this._patternAlreadyChecked&&(this._checkPatternValidity()||(this._announceInvalidCharacter("Invalid string of characters not entered."),this.inputElement.value=this._previousValidInput));this.bindValue=this._previousValidInput=this.inputElement.value,this._patternAlreadyChecked=!1},_isPrintable:function(t){return!(8==t.keyCode||9==t.keyCode||13==t.keyCode||27==t.keyCode||0==t.charCode&&(19==t.keyCode||20==t.keyCode||45==t.keyCode||46==t.keyCode||144==t.keyCode||145==t.keyCode||t.keyCode>32&&t.keyCode<41||t.keyCode>111&&t.keyCode<124))},_onKeypress:function(t){if(this.allowedPattern||"number"===this.inputElement.type){var e=this._patternRegExp;if(e&&!(t.metaKey||t.ctrlKey||t.altKey)){this._patternAlreadyChecked=!0;var n=String.fromCharCode(t.charCode);this._isPrintable(t)&&!e.test(n)&&(t.preventDefault(),this._announceInvalidCharacter("Invalid character "+n+" not entered."))}}},_checkPatternValidity:function(){var t=this._patternRegExp;if(!t)return!0;for(var e=0;e<this.inputElement.value.length;e++)if(!t.test(this.inputElement.value[e]))return!1;return!0},validate:function(){if(!this.inputElement)return this.invalid=!1,!0;var t=this.inputElement.checkValidity();return t&&(this.required&&""===this.bindValue?t=!1:this.hasValidator()&&(t=ya.validate.call(this,this.bindValue))),this.invalid=!t,this.fire("iron-input-validate"),t},_announceInvalidCharacter:function(t){this.fire("iron-announce",{text:t})},_computeValue:function(t){return t}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Xa={attached:function(){this.fire("addon-attached")},update:function(t){}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */jr({_template:xe`
    <style>
      :host {
        display: inline-block;
        float: right;

        @apply --paper-font-caption;
        @apply --paper-input-char-counter;
      }

      :host([hidden]) {
        display: none !important;
      }

      :host(:dir(rtl)) {
        float: left;
      }
    </style>

    <span>[[_charCounterStr]]</span>
`,is:"paper-input-char-counter",behaviors:[Xa],properties:{_charCounterStr:{type:String,value:"0"}},update:function(t){if(t.inputElement){t.value=t.value||"";var e=t.value.toString().length.toString();t.inputElement.hasAttribute("maxlength")&&(e+="/"+t.inputElement.getAttribute("maxlength")),this._charCounterStr=e}}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const $a=xe`
<custom-style>
  <style is="custom-style">
    html {
      --paper-input-container-shared-input-style: {
        position: relative; /* to make a stacking context */
        outline: none;
        box-shadow: none;
        padding: 0;
        margin: 0;
        width: 100%;
        max-width: 100%;
        background: transparent;
        border: none;
        color: var(--paper-input-container-input-color, var(--primary-text-color));
        -webkit-appearance: none;
        text-align: inherit;
        vertical-align: var(--paper-input-container-input-align, bottom);

        @apply --paper-font-subhead;
      };
    }
  </style>
</custom-style>
`;$a.setAttribute("style","display: none;"),document.head.appendChild($a.content),jr({_template:xe`
    <style>
      :host {
        display: block;
        padding: 8px 0;
        @apply --paper-input-container;
      }

      :host([inline]) {
        display: inline-block;
      }

      :host([disabled]) {
        pointer-events: none;
        opacity: 0.33;

        @apply --paper-input-container-disabled;
      }

      :host([hidden]) {
        display: none !important;
      }

      [hidden] {
        display: none !important;
      }

      .floated-label-placeholder {
        @apply --paper-font-caption;
      }

      .underline {
        height: 2px;
        position: relative;
      }

      .focused-line {
        @apply --layout-fit;
        border-bottom: 2px solid var(--paper-input-container-focus-color, var(--primary-color));

        -webkit-transform-origin: center center;
        transform-origin: center center;
        -webkit-transform: scale3d(0,1,1);
        transform: scale3d(0,1,1);

        @apply --paper-input-container-underline-focus;
      }

      .underline.is-highlighted .focused-line {
        -webkit-transform: none;
        transform: none;
        -webkit-transition: -webkit-transform 0.25s;
        transition: transform 0.25s;

        @apply --paper-transition-easing;
      }

      .underline.is-invalid .focused-line {
        border-color: var(--paper-input-container-invalid-color, var(--error-color));
        -webkit-transform: none;
        transform: none;
        -webkit-transition: -webkit-transform 0.25s;
        transition: transform 0.25s;

        @apply --paper-transition-easing;
      }

      .unfocused-line {
        @apply --layout-fit;
        border-bottom: 1px solid var(--paper-input-container-color, var(--secondary-text-color));
        @apply --paper-input-container-underline;
      }

      :host([disabled]) .unfocused-line {
        border-bottom: 1px dashed;
        border-color: var(--paper-input-container-color, var(--secondary-text-color));
        @apply --paper-input-container-underline-disabled;
      }

      .input-wrapper {
        @apply --layout-horizontal;
        @apply --layout-center;
        position: relative;
      }

      .input-content {
        @apply --layout-flex-auto;
        @apply --layout-relative;
        max-width: 100%;
      }

      .input-content ::slotted(label),
      .input-content ::slotted(.paper-input-label) {
        position: absolute;
        top: 0;
        left: 0;
        width: 100%;
        font: inherit;
        color: var(--paper-input-container-color, var(--secondary-text-color));
        -webkit-transition: -webkit-transform 0.25s, width 0.25s;
        transition: transform 0.25s, width 0.25s;
        -webkit-transform-origin: left top;
        transform-origin: left top;
        /* Fix for safari not focusing 0-height date/time inputs with -webkit-apperance: none; */
        min-height: 1px;

        @apply --paper-font-common-nowrap;
        @apply --paper-font-subhead;
        @apply --paper-input-container-label;
        @apply --paper-transition-easing;
      }


      .input-content ::slotted(label):before,
      .input-content ::slotted(.paper-input-label):before {
        @apply --paper-input-container-label-before;
      }

      .input-content ::slotted(label):after,
      .input-content ::slotted(.paper-input-label):after {
        @apply --paper-input-container-label-after;
      }

      .input-content.label-is-floating ::slotted(label),
      .input-content.label-is-floating ::slotted(.paper-input-label) {
        -webkit-transform: translateY(-75%) scale(0.75);
        transform: translateY(-75%) scale(0.75);

        /* Since we scale to 75/100 of the size, we actually have 100/75 of the
        original space now available */
        width: 133%;

        @apply --paper-input-container-label-floating;
      }

      :host(:dir(rtl)) .input-content.label-is-floating ::slotted(label),
      :host(:dir(rtl)) .input-content.label-is-floating ::slotted(.paper-input-label) {
        right: 0;
        left: auto;
        -webkit-transform-origin: right top;
        transform-origin: right top;
      }

      .input-content.label-is-highlighted ::slotted(label),
      .input-content.label-is-highlighted ::slotted(.paper-input-label) {
        color: var(--paper-input-container-focus-color, var(--primary-color));

        @apply --paper-input-container-label-focus;
      }

      .input-content.is-invalid ::slotted(label),
      .input-content.is-invalid ::slotted(.paper-input-label) {
        color: var(--paper-input-container-invalid-color, var(--error-color));
      }

      .input-content.label-is-hidden ::slotted(label),
      .input-content.label-is-hidden ::slotted(.paper-input-label) {
        visibility: hidden;
      }

      .input-content ::slotted(input),
      .input-content ::slotted(iron-input),
      .input-content ::slotted(textarea),
      .input-content ::slotted(iron-autogrow-textarea),
      .input-content ::slotted(.paper-input-input) {
        @apply --paper-input-container-shared-input-style;
        /* The apply shim doesn't apply the nested color custom property,
          so we have to re-apply it here. */
        color: var(--paper-input-container-input-color, var(--primary-text-color));
        @apply --paper-input-container-input;
      }

      .input-content ::slotted(input)::-webkit-outer-spin-button,
      .input-content ::slotted(input)::-webkit-inner-spin-button {
        @apply --paper-input-container-input-webkit-spinner;
      }

      .input-content.focused ::slotted(input),
      .input-content.focused ::slotted(iron-input),
      .input-content.focused ::slotted(textarea),
      .input-content.focused ::slotted(iron-autogrow-textarea),
      .input-content.focused ::slotted(.paper-input-input) {
        @apply --paper-input-container-input-focus;
      }

      .input-content.is-invalid ::slotted(input),
      .input-content.is-invalid ::slotted(iron-input),
      .input-content.is-invalid ::slotted(textarea),
      .input-content.is-invalid ::slotted(iron-autogrow-textarea),
      .input-content.is-invalid ::slotted(.paper-input-input) {
        @apply --paper-input-container-input-invalid;
      }

      .prefix ::slotted(*) {
        display: inline-block;
        @apply --paper-font-subhead;
        @apply --layout-flex-none;
        @apply --paper-input-prefix;
      }

      .suffix ::slotted(*) {
        display: inline-block;
        @apply --paper-font-subhead;
        @apply --layout-flex-none;

        @apply --paper-input-suffix;
      }

      /* Firefox sets a min-width on the input, which can cause layout issues */
      .input-content ::slotted(input) {
        min-width: 0;
      }

      .input-content ::slotted(textarea) {
        resize: none;
      }

      .add-on-content {
        position: relative;
      }

      .add-on-content.is-invalid ::slotted(*) {
        color: var(--paper-input-container-invalid-color, var(--error-color));
      }

      .add-on-content.is-highlighted ::slotted(*) {
        color: var(--paper-input-container-focus-color, var(--primary-color));
      }
    </style>

    <div class="floated-label-placeholder" aria-hidden="true" hidden="[[noLabelFloat]]">&nbsp;</div>

    <div class="input-wrapper">
      <span class="prefix"><slot name="prefix"></slot></span>

      <div class$="[[_computeInputContentClass(noLabelFloat,alwaysFloatLabel,focused,invalid,_inputHasContent)]]" id="labelAndInputContainer">
        <slot name="label"></slot>
        <slot name="input"></slot>
      </div>

      <span class="suffix"><slot name="suffix"></slot></span>
    </div>

    <div class$="[[_computeUnderlineClass(focused,invalid)]]">
      <div class="unfocused-line"></div>
      <div class="focused-line"></div>
    </div>

    <div class$="[[_computeAddOnContentClass(focused,invalid)]]">
      <slot name="add-on"></slot>
    </div>
`,is:"paper-input-container",properties:{noLabelFloat:{type:Boolean,value:!1},alwaysFloatLabel:{type:Boolean,value:!1},attrForValue:{type:String,value:"bind-value"},autoValidate:{type:Boolean,value:!1},invalid:{observer:"_invalidChanged",type:Boolean,value:!1},focused:{readOnly:!0,type:Boolean,value:!1,notify:!0},_addons:{type:Array},_inputHasContent:{type:Boolean,value:!1},_inputSelector:{type:String,value:"input,iron-input,textarea,.paper-input-input"},_boundOnFocus:{type:Function,value:function(){return this._onFocus.bind(this)}},_boundOnBlur:{type:Function,value:function(){return this._onBlur.bind(this)}},_boundOnInput:{type:Function,value:function(){return this._onInput.bind(this)}},_boundValueChanged:{type:Function,value:function(){return this._onValueChanged.bind(this)}}},listeners:{"addon-attached":"_onAddonAttached","iron-input-validate":"_onIronInputValidate"},get _valueChangedEvent(){return this.attrForValue+"-changed"},get _propertyForValue(){return st(this.attrForValue)},get _inputElement(){return er(this).querySelector(this._inputSelector)},get _inputElementValue(){return this._inputElement[this._propertyForValue]||this._inputElement.value},ready:function(){this.__isFirstValueUpdate=!0,this._addons||(this._addons=[]),this.addEventListener("focus",this._boundOnFocus,!0),this.addEventListener("blur",this._boundOnBlur,!0)},attached:function(){this.attrForValue?this._inputElement.addEventListener(this._valueChangedEvent,this._boundValueChanged):this.addEventListener("input",this._onInput),this._inputElementValue&&""!=this._inputElementValue?this._handleValueAndAutoValidate(this._inputElement):this._handleValue(this._inputElement)},_onAddonAttached:function(t){this._addons||(this._addons=[]);var e=t.target;-1===this._addons.indexOf(e)&&(this._addons.push(e),this.isAttached&&this._handleValue(this._inputElement))},_onFocus:function(){this._setFocused(!0)},_onBlur:function(){this._setFocused(!1),this._handleValueAndAutoValidate(this._inputElement)},_onInput:function(t){this._handleValueAndAutoValidate(t.target)},_onValueChanged:function(t){var e=t.target;this.__isFirstValueUpdate&&(this.__isFirstValueUpdate=!1,void 0===e.value||""===e.value)||this._handleValueAndAutoValidate(t.target)},_handleValue:function(t){var e=this._inputElementValue;this._inputHasContent=!(!e&&0!==e&&("number"!==t.type||t.checkValidity())),this.updateAddons({inputElement:t,value:e,invalid:this.invalid})},_handleValueAndAutoValidate:function(t){var e;this.autoValidate&&t&&(e=t.validate?t.validate(this._inputElementValue):t.checkValidity(),this.invalid=!e);this._handleValue(t)},_onIronInputValidate:function(t){this.invalid=this._inputElement.invalid},_invalidChanged:function(){this._addons&&this.updateAddons({invalid:this.invalid})},updateAddons:function(t){for(var e,n=0;e=this._addons[n];n++)e.update(t)},_computeInputContentClass:function(t,e,n,i,r){var o="input-content";if(t)r&&(o+=" label-is-hidden"),i&&(o+=" is-invalid");else{var a=this.querySelector("label");e||r?(o+=" label-is-floating",this.$.labelAndInputContainer.style.position="static",i?o+=" is-invalid":n&&(o+=" label-is-highlighted")):(a&&(this.$.labelAndInputContainer.style.position="relative"),i&&(o+=" is-invalid"))}return n&&(o+=" focused"),o},_computeUnderlineClass:function(t,e){var n="underline";return e?n+=" is-invalid":t&&(n+=" is-highlighted"),n},_computeAddOnContentClass:function(t,e){var n="add-on-content";return e?n+=" is-invalid":t&&(n+=" is-highlighted"),n}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style>
      :host {
        display: inline-block;
        visibility: hidden;

        color: var(--paper-input-container-invalid-color, var(--error-color));

        @apply --paper-font-caption;
        @apply --paper-input-error;
        position: absolute;
        left:0;
        right:0;
      }

      :host([invalid]) {
        visibility: visible;
      }

      #a11yWrapper {
        visibility: hidden;
      }

      :host([invalid]) #a11yWrapper {
        visibility: visible;
      }
    </style>

    <!--
    If the paper-input-error element is directly referenced by an
    \`aria-describedby\` attribute, such as when used as a paper-input add-on,
    then applying \`visibility: hidden;\` to the paper-input-error element itself
    does not hide the error.

    For more information, see:
    https://www.w3.org/TR/accname-1.1/#mapping_additional_nd_description
    -->
    <div id="a11yWrapper">
      <slot></slot>
    </div>
`,is:"paper-input-error",behaviors:[Xa],properties:{invalid:{readOnly:!0,reflectToAttribute:!0,type:Boolean}},update:function(t){this._setInvalid(t.invalid)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ka={NextLabelID:1,NextAddonID:1,NextInputID:1},Za={properties:{label:{type:String},value:{notify:!0,type:String},disabled:{type:Boolean,value:!1},invalid:{type:Boolean,value:!1,notify:!0},allowedPattern:{type:String},type:{type:String},list:{type:String},pattern:{type:String},required:{type:Boolean,value:!1},errorMessage:{type:String},charCounter:{type:Boolean,value:!1},noLabelFloat:{type:Boolean,value:!1},alwaysFloatLabel:{type:Boolean,value:!1},autoValidate:{type:Boolean,value:!1},validator:{type:String},autocomplete:{type:String,value:"off"},autofocus:{type:Boolean,observer:"_autofocusChanged"},inputmode:{type:String},minlength:{type:Number},maxlength:{type:Number},min:{type:String},max:{type:String},step:{type:String},name:{type:String},placeholder:{type:String,value:""},readonly:{type:Boolean,value:!1},size:{type:Number},autocapitalize:{type:String,value:"none"},autocorrect:{type:String,value:"off"},autosave:{type:String},results:{type:Number},accept:{type:String},multiple:{type:Boolean},_ariaDescribedBy:{type:String,value:""},_ariaLabelledBy:{type:String,value:""},_inputId:{type:String,value:""}},listeners:{"addon-attached":"_onAddonAttached"},keyBindings:{"shift+tab:keydown":"_onShiftTabDown"},hostAttributes:{tabindex:0},get inputElement(){return this.$||(this.$={}),this.$.input||(this._generateInputId(),this.$.input=this.$$("#"+this._inputId)),this.$.input},get _focusableElement(){return this.inputElement},created:function(){this._typesThatHaveText=["date","datetime","datetime-local","month","time","week","file"]},attached:function(){this._updateAriaLabelledBy(),!we&&this.inputElement&&-1!==this._typesThatHaveText.indexOf(this.inputElement.type)&&(this.alwaysFloatLabel=!0)},_appendStringWithSpace:function(t,e){return t=t?t+" "+e:e},_onAddonAttached:function(t){var e=er(t).rootTarget;if(e.id)this._ariaDescribedBy=this._appendStringWithSpace(this._ariaDescribedBy,e.id);else{var n="paper-input-add-on-"+Ka.NextAddonID++;e.id=n,this._ariaDescribedBy=this._appendStringWithSpace(this._ariaDescribedBy,n)}},validate:function(){return this.inputElement.validate()},_focusBlurHandler:function(t){ra._focusBlurHandler.call(this,t),this.focused&&!this._shiftTabPressed&&this._focusableElement&&this._focusableElement.focus()},_onShiftTabDown:function(t){var e=this.getAttribute("tabindex");this._shiftTabPressed=!0,this.setAttribute("tabindex","-1"),this.async((function(){this.setAttribute("tabindex",e),this._shiftTabPressed=!1}),1)},_handleAutoValidate:function(){this.autoValidate&&this.validate()},updateValueAndPreserveCaret:function(t){try{var e=this.inputElement.selectionStart;this.value=t,this.inputElement.selectionStart=e,this.inputElement.selectionEnd=e}catch(e){this.value=t}},_computeAlwaysFloatLabel:function(t,e){return e||t},_updateAriaLabelledBy:function(){var t,e=er(this.root).querySelector("label");e?(e.id?t=e.id:(t="paper-input-label-"+Ka.NextLabelID++,e.id=t),this._ariaLabelledBy=t):this._ariaLabelledBy=""},_generateInputId:function(){this._inputId&&""!==this._inputId||(this._inputId="input-"+Ka.NextInputID++)},_onChange:function(t){this.shadowRoot&&this.fire(t.type,{sourceEvent:t},{node:this,bubbles:t.bubbles,cancelable:t.cancelable})},_autofocusChanged:function(){if(this.autofocus&&this._focusableElement){var t=document.activeElement;t instanceof HTMLElement&&t!==document.body&&t!==document.documentElement||this._focusableElement.focus()}}},Ja=[ra,Xo,Za];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({is:"paper-input",_template:xe`
    <style>
      :host {
        display: block;
      }

      :host([focused]) {
        outline: none;
      }

      :host([hidden]) {
        display: none !important;
      }

      input {
        /* Firefox sets a min-width on the input, which can cause layout issues */
        min-width: 0;
      }

      /* In 1.x, the <input> is distributed to paper-input-container, which styles it.
      In 2.x the <iron-input> is distributed to paper-input-container, which styles
      it, but in order for this to work correctly, we need to reset some
      of the native input's properties to inherit (from the iron-input) */
      iron-input > input {
        @apply --paper-input-container-shared-input-style;
        font-family: inherit;
        font-weight: inherit;
        font-size: inherit;
        letter-spacing: inherit;
        word-spacing: inherit;
        line-height: inherit;
        text-shadow: inherit;
        color: inherit;
        cursor: inherit;
      }

      input:disabled {
        @apply --paper-input-container-input-disabled;
      }

      input::-webkit-outer-spin-button,
      input::-webkit-inner-spin-button {
        @apply --paper-input-container-input-webkit-spinner;
      }

      input::-webkit-clear-button {
        @apply --paper-input-container-input-webkit-clear;
      }

      input::-webkit-calendar-picker-indicator {
        @apply --paper-input-container-input-webkit-calendar-picker-indicator;
      }

      input::-webkit-input-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input:-moz-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input::-moz-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input::-ms-clear {
        @apply --paper-input-container-ms-clear;
      }

      input::-ms-reveal {
        @apply --paper-input-container-ms-reveal;
      }

      input:-ms-input-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      label {
        pointer-events: none;
      }
    </style>

    <paper-input-container id="container" no-label-float="[[noLabelFloat]]" always-float-label="[[_computeAlwaysFloatLabel(alwaysFloatLabel,placeholder)]]" auto-validate$="[[autoValidate]]" disabled$="[[disabled]]" invalid="[[invalid]]">

      <slot name="prefix" slot="prefix"></slot>

      <label hidden$="[[!label]]" aria-hidden="true" for$="[[_inputId]]" slot="label">[[label]]</label>

      <!-- Need to bind maxlength so that the paper-input-char-counter works correctly -->
      <iron-input bind-value="{{value}}" slot="input" class="input-element" id$="[[_inputId]]" maxlength$="[[maxlength]]" allowed-pattern="[[allowedPattern]]" invalid="{{invalid}}" validator="[[validator]]">
        <input aria-labelledby$="[[_ariaLabelledBy]]" aria-describedby$="[[_ariaDescribedBy]]" disabled$="[[disabled]]" title$="[[title]]" type$="[[type]]" pattern$="[[pattern]]" required$="[[required]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" inputmode$="[[inputmode]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]" min$="[[min]]" max$="[[max]]" step$="[[step]]" name$="[[name]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" list$="[[list]]" size$="[[size]]" autocapitalize$="[[autocapitalize]]" autocorrect$="[[autocorrect]]" on-change="_onChange" tabindex$="[[tabIndex]]" autosave$="[[autosave]]" results$="[[results]]" accept$="[[accept]]" multiple$="[[multiple]]" role$="[[inputRole]]" aria-haspopup$="[[inputAriaHaspopup]]">
      </iron-input>

      <slot name="suffix" slot="suffix"></slot>

      <template is="dom-if" if="[[errorMessage]]">
        <paper-input-error aria-live="assertive" slot="add-on">[[errorMessage]]</paper-input-error>
      </template>

      <template is="dom-if" if="[[charCounter]]">
        <paper-input-char-counter slot="add-on"></paper-input-char-counter>
      </template>

    </paper-input-container>
  `,behaviors:[Ja,ga],properties:{value:{type:String},inputRole:{type:String,value:void 0},inputAriaHaspopup:{type:String,value:void 0}},get _focusableElement(){return this.inputElement._inputElement},listeners:{"iron-input-ready":"_onIronInputReady"},_onIronInputReady:function(){this.$.nativeInput||(this.$.nativeInput=this.$$("input")),this.inputElement&&-1!==this._typesThatHaveText.indexOf(this.$.nativeInput.type)&&(this.alwaysFloatLabel=!0),this.inputElement.bindValue&&this.$.container._handleValueAndAutoValidate(this.inputElement)}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style>
      :host {
        position: fixed;
      }

      #contentWrapper ::slotted(*) {
        overflow: auto;
      }

      #contentWrapper.animating ::slotted(*) {
        overflow: hidden;
        pointer-events: none;
      }
    </style>

    <div id="contentWrapper">
      <slot id="content" name="dropdown-content"></slot>
    </div>
`,is:"iron-dropdown",behaviors:[ra,Xo,Wa,Ta],properties:{horizontalAlign:{type:String,value:"left",reflectToAttribute:!0},verticalAlign:{type:String,value:"top",reflectToAttribute:!0},openAnimationConfig:{type:Object},closeAnimationConfig:{type:Object},focusTarget:{type:Object},noAnimations:{type:Boolean,value:!1},allowOutsideScroll:{type:Boolean,value:!1,observer:"_allowOutsideScrollChanged"}},listeners:{"neon-animation-finish":"_onNeonAnimationFinish"},observers:["_updateOverlayPosition(positionTarget, verticalAlign, horizontalAlign, verticalOffset, horizontalOffset)"],get containedElement(){for(var t=er(this.$.content).getDistributedNodes(),e=0,n=t.length;e<n;e++)if(t[e].nodeType===Node.ELEMENT_NODE)return t[e]},ready:function(){this.scrollAction||(this.scrollAction=this.allowOutsideScroll?"refit":"lock"),this._readied=!0},attached:function(){this.sizingTarget&&this.sizingTarget!==this||(this.sizingTarget=this.containedElement||this)},detached:function(){this.cancelAnimation()},_openedChanged:function(){this.opened&&this.disabled?this.cancel():(this.cancelAnimation(),this._updateAnimationConfig(),ja._openedChanged.apply(this,arguments))},_renderOpened:function(){!this.noAnimations&&this.animationConfig.open?(this.$.contentWrapper.classList.add("animating"),this.playAnimation("open")):ja._renderOpened.apply(this,arguments)},_renderClosed:function(){!this.noAnimations&&this.animationConfig.close?(this.$.contentWrapper.classList.add("animating"),this.playAnimation("close")):ja._renderClosed.apply(this,arguments)},_onNeonAnimationFinish:function(){this.$.contentWrapper.classList.remove("animating"),this.opened?this._finishRenderOpened():this._finishRenderClosed()},_updateAnimationConfig:function(){for(var t=this.containedElement,e=[].concat(this.openAnimationConfig||[]).concat(this.closeAnimationConfig||[]),n=0;n<e.length;n++)e[n].node=t;this.animationConfig={open:this.openAnimationConfig,close:this.closeAnimationConfig}},_updateOverlayPosition:function(){this.isAttached&&this.notifyResize()},_allowOutsideScrollChanged:function(t){this._readied&&(t?this.scrollAction&&"lock"!==this.scrollAction||(this.scrollAction="refit"):this.scrollAction="lock")},_applyFocus:function(){var t=this.focusTarget||this.containedElement;t&&this.opened&&!this.noAutoFocus?t.focus():ja._applyFocus.apply(this,arguments)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Qa={properties:{animationTiming:{type:Object,value:function(){return{duration:500,easing:"cubic-bezier(0.4, 0, 0.2, 1)",fill:"both"}}}},isNeonAnimation:!0,created:function(){document.body.animate||console.warn("No web animations detected. This element will not function without a web animations polyfill.")},timingFromConfig:function(t){if(t.timing)for(var e in t.timing)this.animationTiming[e]=t.timing[e];return this.animationTiming},setPrefixedProperty:function(t,e,n){for(var i,r={transform:["webkitTransform"],transformOrigin:["mozTransformOrigin","webkitTransformOrigin"]}[e],o=0;i=r[o];o++)t.style[i]=n;t.style[e]=n},complete:function(t){}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */jr({is:"fade-in-animation",behaviors:[Qa],configure:function(t){return this._effect=new KeyframeEffect(t.node,[{opacity:"0"},{opacity:"1"}],this.timingFromConfig(t)),this._effect}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({is:"fade-out-animation",behaviors:[Qa],configure:function(t){return this._effect=new KeyframeEffect(t.node,[{opacity:"1"},{opacity:"0"}],this.timingFromConfig(t)),this._effect}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({is:"paper-menu-grow-height-animation",_template:null,behaviors:[Qa],configure:function(t){var e=t.node,n=e.getBoundingClientRect().height;return this._effect=new KeyframeEffect(e,[{height:n/2+"px"},{height:n+"px"}],this.timingFromConfig(t)),this._effect}}),jr({is:"paper-menu-grow-width-animation",_template:null,behaviors:[Qa],configure:function(t){var e=t.node,n=e.getBoundingClientRect().width;return this._effect=new KeyframeEffect(e,[{width:n/2+"px"},{width:n+"px"}],this.timingFromConfig(t)),this._effect}}),jr({is:"paper-menu-shrink-width-animation",_template:null,behaviors:[Qa],configure:function(t){var e=t.node,n=e.getBoundingClientRect().width;return this._effect=new KeyframeEffect(e,[{width:n+"px"},{width:n-n/20+"px"}],this.timingFromConfig(t)),this._effect}}),jr({is:"paper-menu-shrink-height-animation",_template:null,behaviors:[Qa],configure:function(t){var e=t.node,n=e.getBoundingClientRect().height;return this.setPrefixedProperty(e,"transformOrigin","0 0"),this._effect=new KeyframeEffect(e,[{height:n+"px",transform:"translateY(0)"},{height:n/2+"px",transform:"translateY(-20px)"}],this.timingFromConfig(t)),this._effect}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var ts={ANIMATION_CUBIC_BEZIER:"cubic-bezier(.3,.95,.5,1)",MAX_ANIMATION_TIME_MS:400};const es=jr({_template:xe`
    <style>
      :host {
        display: inline-block;
        position: relative;
        padding: 8px;
        outline: none;

        @apply --paper-menu-button;
      }

      :host([disabled]) {
        cursor: auto;
        color: var(--disabled-text-color);

        @apply --paper-menu-button-disabled;
      }

      iron-dropdown {
        @apply --paper-menu-button-dropdown;
      }

      .dropdown-content {
        @apply --shadow-elevation-2dp;

        position: relative;
        border-radius: 2px;
        background-color: var(--paper-menu-button-dropdown-background, var(--primary-background-color));

        @apply --paper-menu-button-content;
      }

      :host([vertical-align="top"]) .dropdown-content {
        margin-bottom: 20px;
        margin-top: -10px;
        top: 10px;
      }

      :host([vertical-align="bottom"]) .dropdown-content {
        bottom: 10px;
        margin-bottom: -10px;
        margin-top: 20px;
      }

      #trigger {
        cursor: pointer;
      }
    </style>

    <div id="trigger" on-tap="toggle">
      <slot name="dropdown-trigger"></slot>
    </div>

    <iron-dropdown id="dropdown" opened="{{opened}}" horizontal-align="[[horizontalAlign]]" vertical-align="[[verticalAlign]]" dynamic-align="[[dynamicAlign]]" horizontal-offset="[[horizontalOffset]]" vertical-offset="[[verticalOffset]]" no-overlap="[[noOverlap]]" open-animation-config="[[openAnimationConfig]]" close-animation-config="[[closeAnimationConfig]]" no-animations="[[noAnimations]]" focus-target="[[_dropdownContent]]" allow-outside-scroll="[[allowOutsideScroll]]" restore-focus-on-close="[[restoreFocusOnClose]]" on-iron-overlay-canceled="__onIronOverlayCanceled" expand-sizing-target-for-scrollbars="[[expandSizingTargetForScrollbars]]">
      <div slot="dropdown-content" class="dropdown-content">
        <slot id="content" name="dropdown-content"></slot>
      </div>
    </iron-dropdown>
`,is:"paper-menu-button",behaviors:[Xo,ra],properties:{opened:{type:Boolean,value:!1,notify:!0,observer:"_openedChanged"},horizontalAlign:{type:String,value:"left",reflectToAttribute:!0},verticalAlign:{type:String,value:"top",reflectToAttribute:!0},dynamicAlign:{type:Boolean},horizontalOffset:{type:Number,value:0,notify:!0},verticalOffset:{type:Number,value:0,notify:!0},noOverlap:{type:Boolean},noAnimations:{type:Boolean,value:!1},ignoreSelect:{type:Boolean,value:!1},closeOnActivate:{type:Boolean,value:!1},openAnimationConfig:{type:Object,value:function(){return[{name:"fade-in-animation",timing:{delay:100,duration:200}},{name:"paper-menu-grow-width-animation",timing:{delay:100,duration:150,easing:ts.ANIMATION_CUBIC_BEZIER}},{name:"paper-menu-grow-height-animation",timing:{delay:100,duration:275,easing:ts.ANIMATION_CUBIC_BEZIER}}]}},closeAnimationConfig:{type:Object,value:function(){return[{name:"fade-out-animation",timing:{duration:150}},{name:"paper-menu-shrink-width-animation",timing:{delay:100,duration:50,easing:ts.ANIMATION_CUBIC_BEZIER}},{name:"paper-menu-shrink-height-animation",timing:{duration:200,easing:"ease-in"}}]}},allowOutsideScroll:{type:Boolean,value:!1},restoreFocusOnClose:{type:Boolean,value:!0},expandSizingTargetForScrollbars:{type:Boolean,value:!1},_dropdownContent:{type:Object}},hostAttributes:{role:"group","aria-haspopup":"true"},listeners:{"iron-activate":"_onIronActivate","iron-select":"_onIronSelect"},get contentElement(){for(var t=er(this.$.content).getDistributedNodes(),e=0,n=t.length;e<n;e++)if(t[e].nodeType===Node.ELEMENT_NODE)return t[e]},toggle:function(){this.opened?this.close():this.open()},open:function(){this.disabled||this.$.dropdown.open()},close:function(){this.$.dropdown.close()},_onIronSelect:function(t){this.ignoreSelect||this.close()},_onIronActivate:function(t){this.closeOnActivate&&this.close()},_openedChanged:function(t,e){t?(this._dropdownContent=this.contentElement,this.fire("paper-dropdown-open")):null!=e&&this.fire("paper-dropdown-close")},_disabledChanged:function(t){ra._disabledChanged.apply(this,arguments),t&&this.opened&&this.close()},__onIronOverlayCanceled:function(t){var e=this.$.trigger;er(t.detail).path.indexOf(e)>-1&&t.preventDefault()}});Object.keys(ts).forEach((function(t){es[t]=ts[t]}));
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ns=document.createElement("template");ns.setAttribute("style","display: none;"),ns.innerHTML='<iron-iconset-svg name="paper-dropdown-menu" size="24">\n<svg><defs>\n<g id="arrow-drop-down"><path d="M7 10l5 5 5-5z"></path></g>\n</defs></svg>\n</iron-iconset-svg>',document.head.appendChild(ns.content);
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const is=document.createElement("template");is.setAttribute("style","display: none;"),is.innerHTML='<dom-module id="paper-dropdown-menu-shared-styles">\n  <template>\n    <style>\n      :host {\n        display: inline-block;\n        position: relative;\n        text-align: left;\n\n        /* NOTE(cdata): Both values are needed, since some phones require the\n         * value to be `transparent`.\n         */\n        -webkit-tap-highlight-color: rgba(0,0,0,0);\n        -webkit-tap-highlight-color: transparent;\n\n        --paper-input-container-input: {\n          overflow: hidden;\n          white-space: nowrap;\n          text-overflow: ellipsis;\n          max-width: 100%;\n          box-sizing: border-box;\n          cursor: pointer;\n        };\n\n        @apply --paper-dropdown-menu;\n      }\n\n      /* paper-dropdown-menu and paper-dropdown-menu-light both delegate focus\n       * to other internal elements which manage focus styling. */\n      :host(:focus) {\n        outline: none;\n      }\n\n      :host(:dir(rtl)) {\n        text-align: right;\n\n        @apply(--paper-dropdown-menu);\n      }\n\n      :host([disabled]) {\n        @apply --paper-dropdown-menu-disabled;\n      }\n\n      :host([noink]) paper-ripple {\n        display: none;\n      }\n\n      :host([no-label-float]) paper-ripple {\n        top: 8px;\n      }\n\n      paper-ripple {\n        top: 12px;\n        left: 0px;\n        bottom: 8px;\n        right: 0px;\n\n        @apply --paper-dropdown-menu-ripple;\n      }\n\n      paper-menu-button {\n        display: block;\n        padding: 0;\n\n        @apply --paper-dropdown-menu-button;\n      }\n\n      paper-input {\n        @apply --paper-dropdown-menu-input;\n      }\n\n      iron-icon {\n        color: var(--disabled-text-color);\n\n        @apply --paper-dropdown-menu-icon;\n      }\n    </style>\n  </template>\n</dom-module>',document.head.appendChild(is.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const rs=cr(HTMLElement);jr({_template:xe`
    <style include="paper-dropdown-menu-shared-styles"></style>

    <paper-menu-button id="menuButton" vertical-align="[[verticalAlign]]" horizontal-align="[[horizontalAlign]]" dynamic-align="[[dynamicAlign]]" vertical-offset="[[_computeMenuVerticalOffset(noLabelFloat, verticalOffset)]]" disabled="[[disabled]]" no-animations="[[noAnimations]]" on-iron-select="_onIronSelect" on-iron-deselect="_onIronDeselect" opened="{{opened}}" close-on-activate allow-outside-scroll="[[allowOutsideScroll]]" restore-focus-on-close="[[restoreFocusOnClose]]" expand-sizing-target-for-scrollbars="[[expandSizingTargetForScrollbars]]">
      <!-- support hybrid mode: user might be using paper-menu-button 1.x which distributes via <content> -->
      <div class="dropdown-trigger" slot="dropdown-trigger">
        <paper-ripple></paper-ripple>
        <!-- paper-input has type="text" for a11y, do not remove -->
        <paper-input id="input" type="text" invalid="[[invalid]]" readonly disabled="[[disabled]]" value="[[value]]" placeholder="[[placeholder]]" error-message="[[errorMessage]]" always-float-label="[[alwaysFloatLabel]]" no-label-float="[[noLabelFloat]]" label="[[label]]" input-role="button" input-aria-haspopup="listbox" autocomplete="off">
          <!-- support hybrid mode: user might be using paper-input 1.x which distributes via <content> -->
          <iron-icon icon="paper-dropdown-menu:arrow-drop-down" suffix slot="suffix"></iron-icon>
        </paper-input>
      </div>
      <slot id="content" name="dropdown-content" slot="dropdown-content"></slot>
    </paper-menu-button>
`,is:"paper-dropdown-menu",behaviors:[aa,ra,ga,ya],properties:{selectedItemLabel:{type:String,notify:!0,readOnly:!0},selectedItem:{type:Object,notify:!0,readOnly:!0},value:{type:String,notify:!0},label:{type:String},placeholder:{type:String},errorMessage:{type:String},opened:{type:Boolean,notify:!0,value:!1,observer:"_openedChanged"},allowOutsideScroll:{type:Boolean,value:!1},noLabelFloat:{type:Boolean,value:!1,reflectToAttribute:!0},alwaysFloatLabel:{type:Boolean,value:!1},noAnimations:{type:Boolean,value:!1},horizontalAlign:{type:String,value:"right"},verticalAlign:{type:String,value:"top"},verticalOffset:Number,dynamicAlign:{type:Boolean},restoreFocusOnClose:{type:Boolean,value:!0},expandSizingTargetForScrollbars:{type:Boolean,value:!1}},listeners:{tap:"_onTap"},keyBindings:{"up down":"open",esc:"close"},observers:["_selectedItemChanged(selectedItem)"],_attachDom(t){const e=Y(this);return e.attachShadow({mode:"open",delegatesFocus:!0,shadyUpgradeFragment:t}),e.shadowRoot.appendChild(t),rs.prototype._attachDom.call(this,t)},focus(){this.$.input._focusableElement.focus()},attached:function(){var t=this.contentElement;t&&t.selectedItem&&this._setSelectedItem(t.selectedItem)},get contentElement(){for(var t=er(this.$.content).getDistributedNodes(),e=0,n=t.length;e<n;e++)if(t[e].nodeType===Node.ELEMENT_NODE)return t[e]},open:function(){this.$.menuButton.open()},close:function(){this.$.menuButton.close()},_onIronSelect:function(t){this._setSelectedItem(t.detail.item)},_onIronDeselect:function(t){this._setSelectedItem(null)},_onTap:function(t){vi(t)===this&&this.open()},_selectedItemChanged:function(t){var e="";e=t?t.label||t.getAttribute("label")||t.textContent.trim():"",this.value=e,this._setSelectedItemLabel(e)},_computeMenuVerticalOffset:function(t,e){return e||(t?-4:8)},_getValidity:function(t){return this.disabled||!this.required||this.required&&!!this.value},_openedChanged:function(){var t=this.contentElement;t&&t.setAttribute("aria-expanded",this.opened?"true":"false")}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var os={outerScroll:{scroll:!0},shadowMode:{standard:2,waterfall:1,"waterfall-tall":1},tallMode:{"waterfall-tall":!0}};jr({_template:xe`
    <style>
      :host {
        @apply --layout-vertical;
        position: relative;
        height: 100%;
        @apply --paper-header-panel;
      }

      #mainContainer {
        @apply --layout-flex;
        position: relative;
        overflow-y: auto;
        overflow-x: hidden;
        -webkit-overflow-scrolling: touch;
      }

      #mainPanel {
        @apply --layout-vertical;
        @apply --layout-flex;
        position: relative;
        min-height: 0;
        @apply --paper-header-panel-body;
      }

      #mainContainer {
        @apply --paper-header-panel-container;
      }

      /*
       * mode: scroll
       */
      :host([mode=scroll]) #mainContainer {
        @apply --paper-header-panel-scroll-container;
        overflow: visible;
      }

      :host([mode=scroll]) {
        overflow-y: auto;
        overflow-x: hidden;
        -webkit-overflow-scrolling: touch;
      }

      /*
       * mode: cover
       */
      :host([mode=cover]) #mainContainer {
        @apply --paper-header-panel-cover-container;
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      }

      :host([mode=cover]) #mainPanel {
        position: static;
      }

      /*
       * mode: standard
       */
      :host([mode=standard]) #mainContainer {
        @apply --paper-header-panel-standard-container;
      }

      /*
       * mode: seamed
       */
      :host([mode=seamed]) #mainContainer {
        @apply --paper-header-panel-seamed-container;
      }


      /*
       * mode: waterfall
       */
      :host([mode=waterfall]) #mainContainer {
        @apply --paper-header-panel-waterfall-container;
      }

      /*
       * mode: waterfall-tall
       */
      :host([mode=waterfall-tall]) #mainContainer {
        @apply --paper-header-panel-waterfall-tall-container;
      }

      #dropShadow {
        transition: opacity 0.5s;
        height: 6px;
        box-shadow: inset 0px 5px 6px -3px rgba(0, 0, 0, 0.4);
        @apply --paper-header-panel-shadow;
        position: absolute;
        top: 0;
        left: 0;
        right: 0;
        opacity: 0;
        pointer-events: none;
      }

      #dropShadow.has-shadow {
        opacity: 1;
      }

      #mainContainer > ::slotted(.fit) {
        @apply --layout-fit;
      }

    </style>

    <slot id="headerSlot" name="header"></slot>

    <div id="mainPanel">
      <div id="mainContainer" class\$="[[_computeMainContainerClass(mode)]]">
        <slot></slot>
      </div>
      <div id="dropShadow"></div>
    </div>
`,is:"paper-header-panel",properties:{mode:{type:String,value:"standard",observer:"_modeChanged",reflectToAttribute:!0},shadow:{type:Boolean,value:!1},tallClass:{type:String,value:"tall"},atTop:{type:Boolean,value:!0,notify:!0,readOnly:!0,reflectToAttribute:!0}},observers:["_computeDropShadowHidden(atTop, mode, shadow)"],attached:function(){this._addListener(),this._keepScrollingState()},detached:function(){this._removeListener()},ready:function(){this.scrollHandler=this._scroll.bind(this),console.warn(this.is,"is deprecated. Please use app-layout instead!")},get header(){return er(this.$.headerSlot).getDistributedNodes()[0]},get scroller(){return this._getScrollerForMode(this.mode)},get visibleShadow(){return this.$.dropShadow.classList.contains("has-shadow")},_computeDropShadowHidden:function(t,e,n){var i=os.shadowMode[e];this.toggleClass("has-shadow",!!this.shadow||(2===i||1===i&&!t),this.$.dropShadow)},_computeMainContainerClass:function(t){var e={};return e.flex="cover"!==t,Object.keys(e).filter((function(t){return e[t]})).join(" ")},_addListener:function(){this.scroller.addEventListener("scroll",this.scrollHandler)},_removeListener:function(){this.scroller.removeEventListener("scroll",this.scrollHandler)},_modeChanged:function(t,e){var n=os,i=this.header;i&&(n.tallMode[e]&&!n.tallMode[t]?(i.classList.remove(this.tallClass),this.async((function(){i.classList.remove("animate")}),200)):this.toggleClass("animate",n.tallMode[t],i)),this._keepScrollingState()},_keepScrollingState:function(){var t=this.scroller,e=this.header;this._setAtTop(0===t.scrollTop),e&&this.tallClass&&os.tallMode[this.mode]&&this.toggleClass(this.tallClass,this.atTop||e.classList.contains(this.tallClass)&&t.scrollHeight<this.offsetHeight,e)},_scroll:function(){this._keepScrollingState(),this.fire("content-scroll",{target:this.scroller},{bubbles:!1})},_getScrollerForMode:function(t){return os.outerScroll[t]?this:this.$.mainContainer}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({is:"paper-icon-button",_template:xe`
    <style>
      :host {
        display: inline-block;
        position: relative;
        padding: 8px;
        outline: none;
        -webkit-user-select: none;
        -moz-user-select: none;
        -ms-user-select: none;
        user-select: none;
        cursor: pointer;
        z-index: 0;
        line-height: 1;

        width: 40px;
        height: 40px;

        /*
          NOTE: Both values are needed, since some phones require the value to
          be \`transparent\`.
        */
        -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
        -webkit-tap-highlight-color: transparent;

        /* Because of polymer/2558, this style has lower specificity than * */
        box-sizing: border-box !important;

        @apply --paper-icon-button;
      }

      :host #ink {
        color: var(--paper-icon-button-ink-color, var(--primary-text-color));
        opacity: 0.6;
      }

      :host([disabled]) {
        color: var(--paper-icon-button-disabled-text, var(--disabled-text-color));
        pointer-events: none;
        cursor: auto;

        @apply --paper-icon-button-disabled;
      }

      :host([hidden]) {
        display: none !important;
      }

      :host(:hover) {
        @apply --paper-icon-button-hover;
      }

      iron-icon {
        --iron-icon-width: 100%;
        --iron-icon-height: 100%;
      }
    </style>

    <iron-icon id="icon" src="[[src]]" icon="[[icon]]"
               alt$="[[alt]]"></iron-icon>
  `,hostAttributes:{role:"button",tabindex:"0"},behaviors:[xa],registered:function(){this._template.setAttribute("strip-whitespace","")},properties:{src:{type:String},icon:{type:String},alt:{type:String,observer:"_altChanged"}},_altChanged:function(t,e){var n=this.getAttribute("aria-label");n&&e!=n||this.setAttribute("aria-label",t)}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style>
      :host {
        display: inline-block;
        position: relative;
        width: 400px;
        border: 1px solid;
        padding: 2px;
        -moz-appearance: textarea;
        -webkit-appearance: textarea;
        overflow: hidden;
      }

      .mirror-text {
        visibility: hidden;
        word-wrap: break-word;
        @apply --iron-autogrow-textarea;
      }

      .fit {
        @apply --layout-fit;
      }

      textarea {
        position: relative;
        outline: none;
        border: none;
        resize: none;
        background: inherit;
        color: inherit;
        /* see comments in template */
        width: 100%;
        height: 100%;
        font-size: inherit;
        font-family: inherit;
        line-height: inherit;
        text-align: inherit;
        @apply --iron-autogrow-textarea;
      }

      textarea::-webkit-input-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea:-moz-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea::-moz-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea:-ms-input-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }
    </style>

    <!-- the mirror sizes the input/textarea so it grows with typing -->
    <!-- use &#160; instead &nbsp; of to allow this element to be used in XHTML -->
    <div id="mirror" class="mirror-text" aria-hidden="true">&nbsp;</div>

    <!-- size the input/textarea with a div, because the textarea has intrinsic size in ff -->
    <div class="textarea-container fit">
      <textarea id="textarea" name$="[[name]]" aria-label$="[[label]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" autocapitalize$="[[autocapitalize]]" inputmode$="[[inputmode]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" required$="[[required]]" disabled$="[[disabled]]" rows$="[[rows]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]"></textarea>
    </div>
`,is:"iron-autogrow-textarea",behaviors:[ya,ra],properties:{value:{observer:"_valueChanged",type:String,notify:!0},bindValue:{observer:"_bindValueChanged",type:String,notify:!0},rows:{type:Number,value:1,observer:"_updateCached"},maxRows:{type:Number,value:0,observer:"_updateCached"},autocomplete:{type:String,value:"off"},autofocus:{type:Boolean,value:!1},autocapitalize:{type:String,value:"none"},inputmode:{type:String},placeholder:{type:String},readonly:{type:String},required:{type:Boolean},minlength:{type:Number},maxlength:{type:Number},label:{type:String}},listeners:{input:"_onInput"},get textarea(){return this.$.textarea},get selectionStart(){return this.$.textarea.selectionStart},get selectionEnd(){return this.$.textarea.selectionEnd},set selectionStart(t){this.$.textarea.selectionStart=t},set selectionEnd(t){this.$.textarea.selectionEnd=t},attached:function(){navigator.userAgent.match(/iP(?:[oa]d|hone)/)&&!navigator.userAgent.match(/OS 1[3456789]/)&&(this.$.textarea.style.marginLeft="-3px")},validate:function(){var t=this.$.textarea.validity.valid;return t&&(this.required&&""===this.value?t=!1:this.hasValidator()&&(t=ya.validate.call(this,this.value))),this.invalid=!t,this.fire("iron-input-validate"),t},_bindValueChanged:function(t){this.value=t},_valueChanged:function(t){var e=this.textarea;e&&(e.value!==t&&(e.value=t||0===t?t:""),this.bindValue=t,this.$.mirror.innerHTML=this._valueForMirror(),this.fire("bind-value-changed",{value:this.bindValue}))},_onInput:function(t){var e=er(t).path;this.value=e?e[0].value:t.target.value},_constrain:function(t){var e;for(t=t||[""],e=this.maxRows>0&&t.length>this.maxRows?t.slice(0,this.maxRows):t.slice(0);this.rows>0&&e.length<this.rows;)e.push("");return e.join("<br/>")+"&#160;"},_valueForMirror:function(){var t=this.textarea;if(t)return this.tokens=t&&t.value?t.value.replace(/&/gm,"&amp;").replace(/"/gm,"&quot;").replace(/'/gm,"&#39;").replace(/</gm,"&lt;").replace(/>/gm,"&gt;").split("\n"):[""],this._constrain(this.tokens)},_updateCached:function(){this.$.mirror.innerHTML=this._constrain(this.tokens)}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style>
      :host {
        display: block;
      }

      :host([hidden]) {
        display: none !important;
      }

      label {
        pointer-events: none;
      }
    </style>

    <paper-input-container no-label-float$="[[noLabelFloat]]" always-float-label="[[_computeAlwaysFloatLabel(alwaysFloatLabel,placeholder)]]" auto-validate$="[[autoValidate]]" disabled$="[[disabled]]" invalid="[[invalid]]">

      <label hidden$="[[!label]]" aria-hidden="true" for$="[[_inputId]]" slot="label">[[label]]</label>

      <iron-autogrow-textarea class="paper-input-input" slot="input" id$="[[_inputId]]" aria-labelledby$="[[_ariaLabelledBy]]" aria-describedby$="[[_ariaDescribedBy]]" bind-value="{{value}}" invalid="{{invalid}}" validator$="[[validator]]" disabled$="[[disabled]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" inputmode$="[[inputmode]]" name$="[[name]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" required$="[[required]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]" autocapitalize$="[[autocapitalize]]" rows$="[[rows]]" max-rows$="[[maxRows]]" on-change="_onChange"></iron-autogrow-textarea>

      <template is="dom-if" if="[[errorMessage]]">
        <paper-input-error aria-live="assertive" slot="add-on">[[errorMessage]]</paper-input-error>
      </template>

      <template is="dom-if" if="[[charCounter]]">
        <paper-input-char-counter slot="add-on"></paper-input-char-counter>
      </template>

    </paper-input-container>
`,is:"paper-textarea",behaviors:[Ja,ga],properties:{_ariaLabelledBy:{observer:"_ariaLabelledByChanged",type:String},_ariaDescribedBy:{observer:"_ariaDescribedByChanged",type:String},value:{type:String},rows:{type:Number,value:1},maxRows:{type:Number,value:0}},get selectionStart(){return this.$.input.textarea.selectionStart},set selectionStart(t){this.$.input.textarea.selectionStart=t},get selectionEnd(){return this.$.input.textarea.selectionEnd},set selectionEnd(t){this.$.input.textarea.selectionEnd=t},_ariaLabelledByChanged:function(t){this._focusableElement.setAttribute("aria-labelledby",t)},_ariaDescribedByChanged:function(t){this._focusableElement.setAttribute("aria-describedby",t)},get _focusableElement(){return this.inputElement.textarea}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const as=document.createElement("template");as.setAttribute("style","display: none;"),as.innerHTML="<dom-module id=\"paper-item-shared-styles\">\n  <template>\n    <style>\n      :host, .paper-item {\n        display: block;\n        position: relative;\n        min-height: var(--paper-item-min-height, 48px);\n        padding: 0px 16px;\n      }\n\n      .paper-item {\n        @apply --paper-font-subhead;\n        border:none;\n        outline: none;\n        background: white;\n        width: 100%;\n        text-align: left;\n      }\n\n      :host([hidden]), .paper-item[hidden] {\n        display: none !important;\n      }\n\n      :host(.iron-selected), .paper-item.iron-selected {\n        font-weight: var(--paper-item-selected-weight, bold);\n\n        @apply --paper-item-selected;\n      }\n\n      :host([disabled]), .paper-item[disabled] {\n        color: var(--paper-item-disabled-color, var(--disabled-text-color));\n\n        @apply --paper-item-disabled;\n      }\n\n      :host(:focus), .paper-item:focus {\n        position: relative;\n        outline: 0;\n\n        @apply --paper-item-focused;\n      }\n\n      :host(:focus):before, .paper-item:focus:before {\n        @apply --layout-fit;\n\n        background: currentColor;\n        content: '';\n        opacity: var(--dark-divider-opacity);\n        pointer-events: none;\n\n        @apply --paper-item-focused-before;\n      }\n    </style>\n  </template>\n</dom-module>",document.head.appendChild(as.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ss=[aa,ra,{hostAttributes:{role:"option",tabindex:"0"}}];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style include="paper-item-shared-styles">
      :host {
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-font-subhead;

        @apply --paper-item;
      }
    </style>
    <slot></slot>
`,is:"paper-item",behaviors:[ss]}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style>
      :host {
        overflow: hidden; /* needed for text-overflow: ellipsis to work on ff */
        @apply --layout-vertical;
        @apply --layout-center-justified;
        @apply --layout-flex;
      }

      :host([two-line]) {
        min-height: var(--paper-item-body-two-line-min-height, 72px);
      }

      :host([three-line]) {
        min-height: var(--paper-item-body-three-line-min-height, 88px);
      }

      :host > ::slotted(*) {
        overflow: hidden;
        text-overflow: ellipsis;
        white-space: nowrap;
      }

      :host > ::slotted([secondary]) {
        @apply --paper-font-body1;

        color: var(--paper-item-body-secondary-color, var(--secondary-text-color));

        @apply --paper-item-body-secondary;
      }
    </style>

    <slot></slot>
`,is:"paper-item-body"}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style include="paper-item-shared-styles"></style>
    <style>
      :host {
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-font-subhead;

        @apply --paper-item;
        @apply --paper-icon-item;
      }

      .content-icon {
        @apply --layout-horizontal;
        @apply --layout-center;

        width: var(--paper-item-icon-width, 56px);
        @apply --paper-item-icon;
      }
    </style>

    <div id="contentIcon" class="content-icon">
      <slot name="item-icon"></slot>
    </div>
    <slot></slot>
`,is:"paper-icon-item",behaviors:[ss]});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ls={properties:{multi:{type:Boolean,value:!1,observer:"multiChanged"},selectedValues:{type:Array,notify:!0,value:function(){return[]}},selectedItems:{type:Array,readOnly:!0,notify:!0,value:function(){return[]}}},observers:["_updateSelected(selectedValues.splices)"],select:function(t){this.multi?this._toggleSelected(t):this.selected=t},multiChanged:function(t){this._selection.multi=t,this._updateSelected()},get _shouldUpdateSelection(){return null!=this.selected||null!=this.selectedValues&&this.selectedValues.length},_updateAttrForSelected:function(){this.multi?this.selectedItems&&this.selectedItems.length>0&&(this.selectedValues=this.selectedItems.map((function(t){return this._indexToValue(this.indexOf(t))}),this).filter((function(t){return null!=t}),this)):ea._updateAttrForSelected.apply(this)},_updateSelected:function(){this.multi?this._selectMulti(this.selectedValues):this._selectSelected(this.selected)},_selectMulti:function(t){var e=(this._valuesToItems(t=t||[])||[]).filter((function(t){return null!=t}));this._selection.clear(e);for(var n=0;n<e.length;n++)this._selection.setItemSelected(e[n],!0);this.fallbackSelection&&!this._selection.get().length&&(this._valueToItem(this.fallbackSelection)&&this.select(this.fallbackSelection))},_selectionChange:function(){var t=this._selection.get();this.multi?(this._setSelectedItems(t),this._setSelectedItem(t.length?t[0]:null)):null!=t?(this._setSelectedItems([t]),this._setSelectedItem(t)):(this._setSelectedItems([]),this._setSelectedItem(null))},_toggleSelected:function(t){var e=this.selectedValues.indexOf(t);e<0?this.push("selectedValues",t):this.splice("selectedValues",e,1)},_valuesToItems:function(t){return null==t?null:t.map((function(t){return this._valueToItem(t)}),this)}},cs={properties:{focusedItem:{observer:"_focusedItemChanged",readOnly:!0,type:Object},attrForItemTitle:{type:String},disabled:{type:Boolean,value:!1,observer:"_disabledChanged"}},_MODIFIER_KEYS:["Alt","AltGraph","CapsLock","Control","Fn","FnLock","Hyper","Meta","NumLock","OS","ScrollLock","Shift","Super","Symbol","SymbolLock"],_SEARCH_RESET_TIMEOUT_MS:1e3,_previousTabIndex:0,hostAttributes:{role:"menu"},observers:["_updateMultiselectable(multi)"],listeners:{focus:"_onFocus",keydown:"_onKeydown","iron-items-changed":"_onIronItemsChanged"},keyBindings:{up:"_onUpKey",down:"_onDownKey",esc:"_onEscKey","shift+tab:keydown":"_onShiftTabDown"},attached:function(){this._resetTabindices()},select:function(t){this._defaultFocusAsync&&(this.cancelAsync(this._defaultFocusAsync),this._defaultFocusAsync=null);var e=this._valueToItem(t);e&&e.hasAttribute("disabled")||(this._setFocusedItem(e),ls.select.apply(this,arguments))},_resetTabindices:function(){var t=this.multi?this.selectedItems&&this.selectedItems[0]:this.selectedItem;this.items.forEach((function(e){e.setAttribute("tabindex",e===t?"0":"-1"),e.setAttribute("aria-selected",this._selection.isSelected(e))}),this)},_updateMultiselectable:function(t){t?this.setAttribute("aria-multiselectable","true"):this.removeAttribute("aria-multiselectable")},_focusWithKeyboardEvent:function(t){if(-1===this._MODIFIER_KEYS.indexOf(t.key)){this.cancelDebouncer("_clearSearchText");for(var e,n=this._searchText||"",i=(n+=(t.key&&1==t.key.length?t.key:String.fromCharCode(t.keyCode)).toLocaleLowerCase()).length,r=0;e=this.items[r];r++)if(!e.hasAttribute("disabled")){var o=this.attrForItemTitle||"textContent",a=(e[o]||e.getAttribute(o)||"").trim();if(!(a.length<i)&&a.slice(0,i).toLocaleLowerCase()==n){this._setFocusedItem(e);break}}this._searchText=n,this.debounce("_clearSearchText",this._clearSearchText,this._SEARCH_RESET_TIMEOUT_MS)}},_clearSearchText:function(){this._searchText=""},_focusPrevious:function(){for(var t=this.items.length,e=Number(this.indexOf(this.focusedItem)),n=1;n<t+1;n++){var i=this.items[(e-n+t)%t];if(!i.hasAttribute("disabled")){var r=er(i).getOwnerRoot()||document;if(this._setFocusedItem(i),er(r).activeElement==i)return}}},_focusNext:function(){for(var t=this.items.length,e=Number(this.indexOf(this.focusedItem)),n=1;n<t+1;n++){var i=this.items[(e+n)%t];if(!i.hasAttribute("disabled")){var r=er(i).getOwnerRoot()||document;if(this._setFocusedItem(i),er(r).activeElement==i)return}}},_applySelection:function(t,e){t.setAttribute("aria-selected",e?"true":"false"),ea._applySelection.apply(this,arguments)},_focusedItemChanged:function(t,e){e&&e.setAttribute("tabindex","-1"),!t||t.hasAttribute("disabled")||this.disabled||(t.setAttribute("tabindex","0"),t.focus())},_onIronItemsChanged:function(t){t.detail.addedNodes.length&&this._resetTabindices()},_onShiftTabDown:function(t){var e=this.getAttribute("tabindex");cs._shiftTabPressed=!0,this._setFocusedItem(null),this.setAttribute("tabindex","-1"),this.async((function(){this.setAttribute("tabindex",e),cs._shiftTabPressed=!1}),1)},_onFocus:function(t){if(!cs._shiftTabPressed){var e=er(t).rootTarget;(e===this||void 0===e.tabIndex||this.isLightDescendant(e))&&(this._defaultFocusAsync=this.async((function(){var t=this.multi?this.selectedItems&&this.selectedItems[0]:this.selectedItem;this._setFocusedItem(null),t?this._setFocusedItem(t):this.items[0]&&this._focusNext()})))}},_onUpKey:function(t){this._focusPrevious(),t.detail.keyboardEvent.preventDefault()},_onDownKey:function(t){this._focusNext(),t.detail.keyboardEvent.preventDefault()},_onEscKey:function(t){var e=this.focusedItem;e&&e.blur()},_onKeydown:function(t){this.keyboardEventMatchesKeys(t,"up down esc")||this._focusWithKeyboardEvent(t),t.stopPropagation()},_activateHandler:function(t){ea._activateHandler.call(this,t),t.stopPropagation()},_disabledChanged:function(t){t?(this._previousTabIndex=this.hasAttribute("tabindex")?this.tabIndex:0,this.removeAttribute("tabindex")):this.hasAttribute("tabindex")||this.setAttribute("tabindex",this._previousTabIndex)},_shiftTabPressed:!1},us=[[ea,ls],Xo,cs];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style>
      :host {
        display: block;
        padding: 8px 0;

        background: var(--paper-listbox-background-color, var(--primary-background-color));
        color: var(--paper-listbox-color, var(--primary-text-color));

        @apply --paper-listbox;
      }
    </style>

    <slot></slot>
`,is:"paper-listbox",behaviors:[us],hostAttributes:{role:"listbox"}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const hs=xe`
<dom-module id="paper-material-shared-styles">
  <template>
    <style>
      :host {
        display: block;
        position: relative;
      }

      :host([elevation="1"]) {
        @apply --shadow-elevation-2dp;
      }

      :host([elevation="2"]) {
        @apply --shadow-elevation-4dp;
      }

      :host([elevation="3"]) {
        @apply --shadow-elevation-6dp;
      }

      :host([elevation="4"]) {
        @apply --shadow-elevation-8dp;
      }

      :host([elevation="5"]) {
        @apply --shadow-elevation-16dp;
      }
    </style>
  </template>
</dom-module>
`;hs.setAttribute("style","display: none;"),document.body.appendChild(hs.content),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style include="paper-material-shared-styles"></style>
    <style>
      :host([animated]) {
        @apply --shadow-transition;
      }
      :host {
        @apply --paper-material;
      }
    </style>

    <slot></slot>
`,is:"paper-material",properties:{elevation:{type:Number,reflectToAttribute:!0,value:1},animated:{type:Boolean,reflectToAttribute:!0,value:!1}}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ds={properties:{value:{type:Number,value:0,notify:!0,reflectToAttribute:!0},min:{type:Number,value:0,notify:!0},max:{type:Number,value:100,notify:!0},step:{type:Number,value:1,notify:!0},ratio:{type:Number,value:0,readOnly:!0,notify:!0}},observers:["_update(value, min, max, step)"],_calcRatio:function(t){return(this._clampValue(t)-this.min)/(this.max-this.min)},_clampValue:function(t){return Math.min(this.max,Math.max(this.min,this._calcStep(t)))},_calcStep:function(t){if(t=parseFloat(t),!this.step)return t;var e=Math.round((t-this.min)/this.step);return this.step<1?e/(1/this.step)+this.min:e*this.step+this.min},_validateValue:function(){var t=this._clampValue(this.value);return this.value=this.oldValue=isNaN(t)?this.oldValue:t,this.value!==t},_update:function(){this._validateValue(),this._setRatio(100*this._calcRatio(this.value))}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */jr({_template:xe`
    <style>
      :host {
        display: block;
        width: 200px;
        position: relative;
        overflow: hidden;
      }

      :host([hidden]), [hidden] {
        display: none !important;
      }

      #progressContainer {
        @apply --paper-progress-container;
        position: relative;
      }

      #progressContainer,
      /* the stripe for the indeterminate animation*/
      .indeterminate::after {
        height: var(--paper-progress-height, 4px);
      }

      #primaryProgress,
      #secondaryProgress,
      .indeterminate::after {
        @apply --layout-fit;
      }

      #progressContainer,
      .indeterminate::after {
        background: var(--paper-progress-container-color, var(--google-grey-300));
      }

      :host(.transiting) #primaryProgress,
      :host(.transiting) #secondaryProgress {
        -webkit-transition-property: -webkit-transform;
        transition-property: transform;

        /* Duration */
        -webkit-transition-duration: var(--paper-progress-transition-duration, 0.08s);
        transition-duration: var(--paper-progress-transition-duration, 0.08s);

        /* Timing function */
        -webkit-transition-timing-function: var(--paper-progress-transition-timing-function, ease);
        transition-timing-function: var(--paper-progress-transition-timing-function, ease);

        /* Delay */
        -webkit-transition-delay: var(--paper-progress-transition-delay, 0s);
        transition-delay: var(--paper-progress-transition-delay, 0s);
      }

      #primaryProgress,
      #secondaryProgress {
        @apply --layout-fit;
        -webkit-transform-origin: left center;
        transform-origin: left center;
        -webkit-transform: scaleX(0);
        transform: scaleX(0);
        will-change: transform;
      }

      #primaryProgress {
        background: var(--paper-progress-active-color, var(--google-green-500));
      }

      #secondaryProgress {
        background: var(--paper-progress-secondary-color, var(--google-green-100));
      }

      :host([disabled]) #primaryProgress {
        background: var(--paper-progress-disabled-active-color, var(--google-grey-500));
      }

      :host([disabled]) #secondaryProgress {
        background: var(--paper-progress-disabled-secondary-color, var(--google-grey-300));
      }

      :host(:not([disabled])) #primaryProgress.indeterminate {
        -webkit-transform-origin: right center;
        transform-origin: right center;
        -webkit-animation: indeterminate-bar var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
        animation: indeterminate-bar var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
      }

      :host(:not([disabled])) #primaryProgress.indeterminate::after {
        content: "";
        -webkit-transform-origin: center center;
        transform-origin: center center;

        -webkit-animation: indeterminate-splitter var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
        animation: indeterminate-splitter var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
      }

      @-webkit-keyframes indeterminate-bar {
        0% {
          -webkit-transform: scaleX(1) translateX(-100%);
        }
        50% {
          -webkit-transform: scaleX(1) translateX(0%);
        }
        75% {
          -webkit-transform: scaleX(1) translateX(0%);
          -webkit-animation-timing-function: cubic-bezier(.28,.62,.37,.91);
        }
        100% {
          -webkit-transform: scaleX(0) translateX(0%);
        }
      }

      @-webkit-keyframes indeterminate-splitter {
        0% {
          -webkit-transform: scaleX(.75) translateX(-125%);
        }
        30% {
          -webkit-transform: scaleX(.75) translateX(-125%);
          -webkit-animation-timing-function: cubic-bezier(.42,0,.6,.8);
        }
        90% {
          -webkit-transform: scaleX(.75) translateX(125%);
        }
        100% {
          -webkit-transform: scaleX(.75) translateX(125%);
        }
      }

      @keyframes indeterminate-bar {
        0% {
          transform: scaleX(1) translateX(-100%);
        }
        50% {
          transform: scaleX(1) translateX(0%);
        }
        75% {
          transform: scaleX(1) translateX(0%);
          animation-timing-function: cubic-bezier(.28,.62,.37,.91);
        }
        100% {
          transform: scaleX(0) translateX(0%);
        }
      }

      @keyframes indeterminate-splitter {
        0% {
          transform: scaleX(.75) translateX(-125%);
        }
        30% {
          transform: scaleX(.75) translateX(-125%);
          animation-timing-function: cubic-bezier(.42,0,.6,.8);
        }
        90% {
          transform: scaleX(.75) translateX(125%);
        }
        100% {
          transform: scaleX(.75) translateX(125%);
        }
      }
    </style>

    <div id="progressContainer">
      <div id="secondaryProgress" hidden\$="[[_hideSecondaryProgress(secondaryRatio)]]"></div>
      <div id="primaryProgress"></div>
    </div>
`,is:"paper-progress",behaviors:[ds],properties:{secondaryProgress:{type:Number,value:0},secondaryRatio:{type:Number,value:0,readOnly:!0},indeterminate:{type:Boolean,value:!1,observer:"_toggleIndeterminate"},disabled:{type:Boolean,value:!1,reflectToAttribute:!0,observer:"_disabledChanged"}},observers:["_progressChanged(secondaryProgress, value, min, max, indeterminate)"],hostAttributes:{role:"progressbar"},_toggleIndeterminate:function(t){this.toggleClass("indeterminate",t,this.$.primaryProgress)},_transformProgress:function(t,e){t.style.transform=t.style.webkitTransform="scaleX("+e/100+")"},_mainRatioChanged:function(t){this._transformProgress(this.$.primaryProgress,t)},_progressChanged:function(t,e,n,i,r){t=this._clampValue(t),e=this._clampValue(e);var o=100*this._calcRatio(t),a=100*this._calcRatio(e);this._setSecondaryRatio(o),this._transformProgress(this.$.secondaryProgress,o),this._transformProgress(this.$.primaryProgress,a),this.secondaryProgress=t,r?this.removeAttribute("aria-valuenow"):this.setAttribute("aria-valuenow",e),this.setAttribute("aria-valuemin",n),this.setAttribute("aria-valuemax",i)},_disabledChanged:function(t){this.setAttribute("aria-disabled",t?"true":"false")},_hideSecondaryProgress:function(t){return 0===t}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const ps=xe`
<style>
  :host {
    display: inline-block;
    line-height: 0;
    white-space: nowrap;
    cursor: pointer;
    @apply --paper-font-common-base;
    --calculated-paper-radio-button-size: var(--paper-radio-button-size, 16px);
    /* -1px is a sentinel for the default and is replace in \`attached\`. */
    --calculated-paper-radio-button-ink-size: var(--paper-radio-button-ink-size, -1px);
  }

  :host(:focus) {
    outline: none;
  }

  #radioContainer {
    @apply --layout-inline;
    @apply --layout-center-center;
    position: relative;
    width: var(--calculated-paper-radio-button-size);
    height: var(--calculated-paper-radio-button-size);
    vertical-align: middle;

    @apply --paper-radio-button-radio-container;
  }

  #ink {
    position: absolute;
    top: 50%;
    left: 50%;
    right: auto;
    width: var(--calculated-paper-radio-button-ink-size);
    height: var(--calculated-paper-radio-button-ink-size);
    color: var(--paper-radio-button-unchecked-ink-color, var(--primary-text-color));
    opacity: 0.6;
    pointer-events: none;
    -webkit-transform: translate(-50%, -50%);
    transform: translate(-50%, -50%);
  }

  #ink[checked] {
    color: var(--paper-radio-button-checked-ink-color, var(--primary-color));
  }

  #offRadio, #onRadio {
    position: absolute;
    box-sizing: border-box;
    top: 0;
    left: 0;
    width: 100%;
    height: 100%;
    border-radius: 50%;
  }

  #offRadio {
    border: 2px solid var(--paper-radio-button-unchecked-color, var(--primary-text-color));
    background-color: var(--paper-radio-button-unchecked-background-color, transparent);
    transition: border-color 0.28s;
  }

  #onRadio {
    background-color: var(--paper-radio-button-checked-color, var(--primary-color));
    -webkit-transform: scale(0);
    transform: scale(0);
    transition: -webkit-transform ease 0.28s;
    transition: transform ease 0.28s;
    will-change: transform;
  }

  :host([checked]) #offRadio {
    border-color: var(--paper-radio-button-checked-color, var(--primary-color));
  }

  :host([checked]) #onRadio {
    -webkit-transform: scale(0.5);
    transform: scale(0.5);
  }

  #radioLabel {
    line-height: normal;
    position: relative;
    display: inline-block;
    vertical-align: middle;
    margin-left: var(--paper-radio-button-label-spacing, 10px);
    white-space: normal;
    color: var(--paper-radio-button-label-color, var(--primary-text-color));

    @apply --paper-radio-button-label;
  }

  :host([checked]) #radioLabel {
    @apply --paper-radio-button-label-checked;
  }

  #radioLabel:dir(rtl) {
    margin-left: 0;
    margin-right: var(--paper-radio-button-label-spacing, 10px);
  }

  #radioLabel[hidden] {
    display: none;
  }

  /* disabled state */

  :host([disabled]) #offRadio {
    border-color: var(--paper-radio-button-unchecked-color, var(--primary-text-color));
    opacity: 0.5;
  }

  :host([disabled][checked]) #onRadio {
    background-color: var(--paper-radio-button-unchecked-color, var(--primary-text-color));
    opacity: 0.5;
  }

  :host([disabled]) #radioLabel {
    /* slightly darker than the button, so that it's readable */
    opacity: 0.65;
  }
</style>

<div id="radioContainer">
  <div id="offRadio"></div>
  <div id="onRadio"></div>
</div>

<div id="radioLabel"><slot></slot></div>`;ps.setAttribute("strip-whitespace",""),jr({_template:ps,is:"paper-radio-button",behaviors:[wa],hostAttributes:{role:"radio","aria-checked":!1,tabindex:0},properties:{ariaActiveAttribute:{type:String,value:"aria-checked"}},ready:function(){this._rippleContainer=this.$.radioContainer},attached:function(){Di(this,(function(){if("-1px"===this.getComputedStyleValue("--calculated-paper-radio-button-ink-size").trim()){var t=parseFloat(this.getComputedStyleValue("--calculated-paper-radio-button-size").trim()),e=Math.floor(3*t);e%2!=t%2&&e++,this.updateStyles({"--paper-radio-button-ink-size":e+"px"})}}))}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const fs={hostAttributes:{role:"menubar"},keyBindings:{left:"_onLeftKey",right:"_onRightKey"},_onUpKey:function(t){this.focusedItem.click(),t.detail.keyboardEvent.preventDefault()},_onDownKey:function(t){this.focusedItem.click(),t.detail.keyboardEvent.preventDefault()},get _isRTL(){return"rtl"===window.getComputedStyle(this).direction},_onLeftKey:function(t){this._isRTL?this._focusNext():this._focusPrevious(),t.detail.keyboardEvent.preventDefault()},_onRightKey:function(t){this._isRTL?this._focusPrevious():this._focusNext(),t.detail.keyboardEvent.preventDefault()},_onKeydown:function(t){this.keyboardEventMatchesKeys(t,"up down left right esc")||this._focusWithKeyboardEvent(t)}},ms=[us,fs];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style>
      :host {
        display: inline-block;
      }

      :host ::slotted(*) {
        padding: var(--paper-radio-group-item-padding, 12px);
      }
    </style>

    <slot></slot>
`,is:"paper-radio-group",behaviors:[ms],hostAttributes:{role:"radiogroup"},properties:{attrForSelected:{type:String,value:"name"},selectedAttribute:{type:String,value:"checked"},selectable:{type:String,value:"paper-radio-button"},allowEmptySelection:{type:Boolean,value:!1}},select:function(t){var e=this._valueToItem(t);if(!e||!e.hasAttribute("disabled")){if(this.selected){var n=this._valueToItem(this.selected);if(this.selected==t){if(!this.allowEmptySelection)return void(n&&(n.checked=!0));t=""}n&&(n.checked=!1)}ea.select.apply(this,[t]),this.fire("paper-radio-group-changed")}},_activateFocusedItem:function(){this._itemActivate(this._valueForItem(this.focusedItem),this.focusedItem)},_onUpKey:function(t){this._focusPrevious(),t.preventDefault(),this._activateFocusedItem()},_onDownKey:function(t){this._focusNext(),t.preventDefault(),this._activateFocusedItem()},_onLeftKey:function(t){fs._onLeftKey.apply(this,arguments),this._activateFocusedItem()},_onRightKey:function(t){fs._onRightKey.apply(this,arguments),this._activateFocusedItem()}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const gs=xe`
  <style>
    :host {
      @apply --layout;
      @apply --layout-justified;
      @apply --layout-center;
      width: 200px;
      cursor: default;
      -webkit-user-select: none;
      -moz-user-select: none;
      -ms-user-select: none;
      user-select: none;
      -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
      --paper-progress-active-color: var(--paper-slider-active-color, var(--google-blue-700));
      --paper-progress-secondary-color: var(--paper-slider-secondary-color, var(--google-blue-300));
      --paper-progress-disabled-active-color: var(--paper-slider-disabled-active-color, var(--paper-grey-400));
      --paper-progress-disabled-secondary-color: var(--paper-slider-disabled-secondary-color, var(--paper-grey-400));
      --calculated-paper-slider-height: var(--paper-slider-height, 2px);
    }

    /* focus shows the ripple */
    :host(:focus) {
      outline: none;
    }

    /**
      * NOTE(keanulee): Though :host-context is not universally supported, some pages
      * still rely on paper-slider being flipped when dir="rtl" is set on body. For full
      * compatibility, dir="rtl" must be explicitly set on paper-slider.
      */
    :dir(rtl) #sliderContainer {
      -webkit-transform: scaleX(-1);
      transform: scaleX(-1);
    }

    /**
      * NOTE(keanulee): This is separate from the rule above because :host-context may
      * not be recognized.
      */
    :host([dir="rtl"]) #sliderContainer {
      -webkit-transform: scaleX(-1);
      transform: scaleX(-1);
    }

    /**
      * NOTE(keanulee): Needed to override the :host-context rule (where supported)
      * to support LTR sliders in RTL pages.
      */
    :host([dir="ltr"]) #sliderContainer {
      -webkit-transform: scaleX(1);
      transform: scaleX(1);
    }

    #sliderContainer {
      position: relative;
      width: 100%;
      height: calc(30px + var(--calculated-paper-slider-height));
      margin-left: calc(15px + var(--calculated-paper-slider-height)/2);
      margin-right: calc(15px + var(--calculated-paper-slider-height)/2);
    }

    #sliderContainer:focus {
      outline: 0;
    }

    #sliderContainer.editable {
      margin-top: 12px;
      margin-bottom: 12px;
    }

    .bar-container {
      position: absolute;
      top: 0;
      bottom: 0;
      left: 0;
      right: 0;
      overflow: hidden;
    }

    .ring > .bar-container {
      left: calc(5px + var(--calculated-paper-slider-height)/2);
      transition: left 0.18s ease;
    }

    .ring.expand.dragging > .bar-container {
      transition: none;
    }

    .ring.expand:not(.pin) > .bar-container {
      left: calc(8px + var(--calculated-paper-slider-height)/2);
    }

    #sliderBar {
      padding: 15px 0;
      width: 100%;
      background-color: var(--paper-slider-bar-color, transparent);
      --paper-progress-container-color: var(--paper-slider-container-color, var(--paper-grey-400));
      --paper-progress-height: var(--calculated-paper-slider-height);
    }

    .slider-markers {
      position: absolute;
      /* slider-knob is 30px + the slider-height so that the markers should start at a offset of 15px*/
      top: 15px;
      height: var(--calculated-paper-slider-height);
      left: 0;
      right: -1px;
      box-sizing: border-box;
      pointer-events: none;
      @apply --layout-horizontal;
    }

    .slider-marker {
      @apply --layout-flex;
    }
    .slider-markers::after,
    .slider-marker::after {
      content: "";
      display: block;
      margin-left: -1px;
      width: 2px;
      height: var(--calculated-paper-slider-height);
      border-radius: 50%;
      background-color: var(--paper-slider-markers-color, #000);
    }

    .slider-knob {
      position: absolute;
      left: 0;
      top: 0;
      margin-left: calc(-15px - var(--calculated-paper-slider-height)/2);
      width: calc(30px + var(--calculated-paper-slider-height));
      height: calc(30px + var(--calculated-paper-slider-height));
    }

    .transiting > .slider-knob {
      transition: left 0.08s ease;
    }

    .slider-knob:focus {
      outline: none;
    }

    .slider-knob.dragging {
      transition: none;
    }

    .snaps > .slider-knob.dragging {
      transition: -webkit-transform 0.08s ease;
      transition: transform 0.08s ease;
    }

    .slider-knob-inner {
      margin: 10px;
      width: calc(100% - 20px);
      height: calc(100% - 20px);
      background-color: var(--paper-slider-knob-color, var(--google-blue-700));
      border: 2px solid var(--paper-slider-knob-color, var(--google-blue-700));
      border-radius: 50%;

      -moz-box-sizing: border-box;
      box-sizing: border-box;

      transition-property: -webkit-transform, background-color, border;
      transition-property: transform, background-color, border;
      transition-duration: 0.18s;
      transition-timing-function: ease;
    }

    .expand:not(.pin) > .slider-knob > .slider-knob-inner {
      -webkit-transform: scale(1.5);
      transform: scale(1.5);
    }

    .ring > .slider-knob > .slider-knob-inner {
      background-color: var(--paper-slider-knob-start-color, transparent);
      border: 2px solid var(--paper-slider-knob-start-border-color, var(--paper-grey-400));
    }

    .slider-knob-inner::before {
      background-color: var(--paper-slider-pin-color, var(--google-blue-700));
    }

    .pin > .slider-knob > .slider-knob-inner::before {
      content: "";
      position: absolute;
      top: 0;
      left: 50%;
      margin-left: -13px;
      width: 26px;
      height: 26px;
      border-radius: 50% 50% 50% 0;

      -webkit-transform: rotate(-45deg) scale(0) translate(0);
      transform: rotate(-45deg) scale(0) translate(0);
    }

    .slider-knob-inner::before,
    .slider-knob-inner::after {
      transition: -webkit-transform .18s ease, background-color .18s ease;
      transition: transform .18s ease, background-color .18s ease;
    }

    .pin.ring > .slider-knob > .slider-knob-inner::before {
      background-color: var(--paper-slider-pin-start-color, var(--paper-grey-400));
    }

    .pin.expand > .slider-knob > .slider-knob-inner::before {
      -webkit-transform: rotate(-45deg) scale(1) translate(17px, -17px);
      transform: rotate(-45deg) scale(1) translate(17px, -17px);
    }

    .pin > .slider-knob > .slider-knob-inner::after {
      content: attr(value);
      position: absolute;
      top: 0;
      left: 50%;
      margin-left: -16px;
      width: 32px;
      height: 26px;
      text-align: center;
      color: var(--paper-slider-font-color, #fff);
      font-size: 10px;

      -webkit-transform: scale(0) translate(0);
      transform: scale(0) translate(0);
    }

    .pin.expand > .slider-knob > .slider-knob-inner::after {
      -webkit-transform: scale(1) translate(0, -17px);
      transform: scale(1) translate(0, -17px);
    }

    /* paper-input */
    .slider-input {
      width: 50px;
      overflow: hidden;
      --paper-input-container-input: {
        text-align: center;
        @apply --paper-slider-input-container-input;
      };
      @apply --paper-slider-input;
    }

    /* disabled state */
    #sliderContainer.disabled {
      pointer-events: none;
    }

    .disabled > .slider-knob > .slider-knob-inner {
      background-color: var(--paper-slider-disabled-knob-color, var(--paper-grey-400));
      border: 2px solid var(--paper-slider-disabled-knob-color, var(--paper-grey-400));
      -webkit-transform: scale3d(0.75, 0.75, 1);
      transform: scale3d(0.75, 0.75, 1);
    }

    .disabled.ring > .slider-knob > .slider-knob-inner {
      background-color: var(--paper-slider-knob-start-color, transparent);
      border: 2px solid var(--paper-slider-knob-start-border-color, var(--paper-grey-400));
    }

    paper-ripple {
      color: var(--paper-slider-knob-color, var(--google-blue-700));
    }
  </style>

  <div id="sliderContainer" class\$="[[_getClassNames(disabled, pin, snaps, immediateValue, min, expand, dragging, transiting, editable)]]">
    <div class="bar-container">
      <paper-progress disabled\$="[[disabled]]" id="sliderBar" aria-hidden="true" min="[[min]]" max="[[max]]" step="[[step]]" value="[[immediateValue]]" secondary-progress="[[secondaryProgress]]" on-down="_bardown" on-up="_resetKnob" on-track="_bartrack" on-tap="_barclick">
      </paper-progress>
    </div>

    <template is="dom-if" if="[[snaps]]">
      <div class="slider-markers">
        <template is="dom-repeat" items="[[markers]]">
          <div class="slider-marker"></div>
        </template>
      </div>
    </template>

    <div id="sliderKnob" class="slider-knob" on-down="_knobdown" on-up="_resetKnob" on-track="_onTrack" on-transitionend="_knobTransitionEnd">
        <div class="slider-knob-inner" value\$="[[immediateValue]]"></div>
    </div>
  </div>

  <template is="dom-if" if="[[editable]]">
    <paper-input id="input" type="number" step="[[step]]" min="[[min]]" max="[[max]]" class="slider-input" disabled\$="[[disabled]]" value="[[immediateValue]]" on-change="_changeValue" on-keydown="_inputKeyDown" no-label-float>
    </paper-input>
  </template>
`;gs.setAttribute("strip-whitespace",""),jr({_template:gs,is:"paper-slider",behaviors:[Xo,ga,xa,ds],properties:{value:{type:Number,value:0},snaps:{type:Boolean,value:!1,notify:!0},pin:{type:Boolean,value:!1,notify:!0},secondaryProgress:{type:Number,value:0,notify:!0,observer:"_secondaryProgressChanged"},editable:{type:Boolean,value:!1},immediateValue:{type:Number,value:0,readOnly:!0,notify:!0},maxMarkers:{type:Number,value:0,notify:!0},expand:{type:Boolean,value:!1,readOnly:!0},ignoreBarTouch:{type:Boolean,value:!1},dragging:{type:Boolean,value:!1,readOnly:!0,notify:!0},transiting:{type:Boolean,value:!1,readOnly:!0},markers:{type:Array,readOnly:!0,value:function(){return[]}}},observers:["_updateKnob(value, min, max, snaps, step)","_valueChanged(value)","_immediateValueChanged(immediateValue)","_updateMarkers(maxMarkers, min, max, snaps)"],hostAttributes:{role:"slider",tabindex:0},keyBindings:{left:"_leftKey",right:"_rightKey","down pagedown home":"_decrementKey","up pageup end":"_incrementKey"},ready:function(){this.ignoreBarTouch&&di(this.$.sliderBar,"auto")},increment:function(){this.value=this._clampValue(this.value+this.step)},decrement:function(){this.value=this._clampValue(this.value-this.step)},_updateKnob:function(t,e,n,i,r){this.setAttribute("aria-valuemin",e),this.setAttribute("aria-valuemax",n),this.setAttribute("aria-valuenow",t),this._positionKnob(100*this._calcRatio(t))},_valueChanged:function(){this.fire("value-change",{composed:!0})},_immediateValueChanged:function(){this.dragging?this.fire("immediate-value-change",{composed:!0}):this.value=this.immediateValue},_secondaryProgressChanged:function(){this.secondaryProgress=this._clampValue(this.secondaryProgress)},_expandKnob:function(){this._setExpand(!0)},_resetKnob:function(){this.cancelDebouncer("expandKnob"),this._setExpand(!1)},_positionKnob:function(t){this._setImmediateValue(this._calcStep(this._calcKnobPosition(t))),this._setRatio(100*this._calcRatio(this.immediateValue)),this.$.sliderKnob.style.left=this.ratio+"%",this.dragging&&(this._knobstartx=this.ratio*this._w/100,this.translate3d(0,0,0,this.$.sliderKnob))},_calcKnobPosition:function(t){return(this.max-this.min)*t/100+this.min},_onTrack:function(t){switch(t.stopPropagation(),t.detail.state){case"start":this._trackStart(t);break;case"track":this._trackX(t);break;case"end":this._trackEnd()}},_trackStart:function(t){this._setTransiting(!1),this._w=this.$.sliderBar.offsetWidth,this._x=this.ratio*this._w/100,this._startx=this._x,this._knobstartx=this._startx,this._minx=-this._startx,this._maxx=this._w-this._startx,this.$.sliderKnob.classList.add("dragging"),this._setDragging(!0)},_trackX:function(t){this.dragging||this._trackStart(t);var e=Math.min(this._maxx,Math.max(this._minx,t.detail.dx*(this._isRTL?-1:1)));this._x=this._startx+e;var n=this._calcStep(this._calcKnobPosition(this._x/this._w*100));this._setImmediateValue(n);var i=this._calcRatio(this.immediateValue)*this._w-this._knobstartx;this.translate3d(i+"px",0,0,this.$.sliderKnob)},_trackEnd:function(){var t=this.$.sliderKnob.style;this.$.sliderKnob.classList.remove("dragging"),this._setDragging(!1),this._resetKnob(),this.value=this.immediateValue,t.transform=t.webkitTransform="",this.fire("change",{composed:!0})},_knobdown:function(t){this._expandKnob(),t.preventDefault(),this.focus()},_bartrack:function(t){this._allowBarEvent(t)&&this._onTrack(t)},_barclick:function(t){this._w=this.$.sliderBar.offsetWidth;var e=this.$.sliderBar.getBoundingClientRect(),n=(t.detail.x-e.left)/this._w*100;this._isRTL&&(n=100-n);var i=this.ratio;this._setTransiting(!0),this._positionKnob(n),i===this.ratio&&this._setTransiting(!1),this.async((function(){this.fire("change",{composed:!0})})),t.preventDefault(),this.focus()},_bardown:function(t){this._allowBarEvent(t)&&(this.debounce("expandKnob",this._expandKnob,60),this._barclick(t))},_knobTransitionEnd:function(t){t.target===this.$.sliderKnob&&this._setTransiting(!1)},_updateMarkers:function(t,e,n,i){i||this._setMarkers([]);var r=Math.round((n-e)/this.step);r>t&&(r=t),(r<0||!isFinite(r))&&(r=0),this._setMarkers(new Array(r))},_mergeClasses:function(t){return Object.keys(t).filter((function(e){return t[e]})).join(" ")},_getClassNames:function(){return this._mergeClasses({disabled:this.disabled,pin:this.pin,snaps:this.snaps,ring:this.immediateValue<=this.min,expand:this.expand,dragging:this.dragging,transiting:this.transiting,editable:this.editable})},_allowBarEvent:function(t){return!this.ignoreBarTouch||t.detail.sourceEvent instanceof MouseEvent},get _isRTL(){return void 0===this.__isRTL&&(this.__isRTL="rtl"===window.getComputedStyle(this).direction),this.__isRTL},_leftKey:function(t){this._isRTL?this._incrementKey(t):this._decrementKey(t)},_rightKey:function(t){this._isRTL?this._decrementKey(t):this._incrementKey(t)},_incrementKey:function(t){this.disabled||("end"===t.detail.key?this.value=this.max:this.increment(),this.fire("change"),t.preventDefault())},_decrementKey:function(t){this.disabled||("home"===t.detail.key?this.value=this.min:this.decrement(),this.fire("change"),t.preventDefault())},_changeValue:function(t){this.value=t.target.value,this.fire("change",{composed:!0})},_inputKeyDown:function(t){t.stopPropagation()},_createRipple:function(){return this._rippleContainer=this.$.sliderKnob,ba._createRipple.call(this)},_focusedChanged:function(t){t&&this.ensureRipple(),this.hasRipple()&&(this._ripple.style.display=t?"":"none",this._ripple.holdDown=t)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const _s=document.createElement("template");_s.setAttribute("style","display: none;"),_s.innerHTML="<dom-module id=\"paper-spinner-styles\">\n  <template>\n    <style>\n      /*\n      /**************************/\n      /* STYLES FOR THE SPINNER */\n      /**************************/\n\n      /*\n       * Constants:\n       *      ARCSIZE     = 270 degrees (amount of circle the arc takes up)\n       *      ARCTIME     = 1333ms (time it takes to expand and contract arc)\n       *      ARCSTARTROT = 216 degrees (how much the start location of the arc\n       *                                should rotate each time, 216 gives us a\n       *                                5 pointed star shape (it's 360/5 * 3).\n       *                                For a 7 pointed star, we might do\n       *                                360/7 * 3 = 154.286)\n       *      SHRINK_TIME = 400ms\n       */\n\n      :host {\n        display: inline-block;\n        position: relative;\n        width: 28px;\n        height: 28px;\n\n        /* 360 * ARCTIME / (ARCSTARTROT + (360-ARCSIZE)) */\n        --paper-spinner-container-rotation-duration: 1568ms;\n\n        /* ARCTIME */\n        --paper-spinner-expand-contract-duration: 1333ms;\n\n        /* 4 * ARCTIME */\n        --paper-spinner-full-cycle-duration: 5332ms;\n\n        /* SHRINK_TIME */\n        --paper-spinner-cooldown-duration: 400ms;\n      }\n\n      #spinnerContainer {\n        width: 100%;\n        height: 100%;\n\n        /* The spinner does not have any contents that would have to be\n         * flipped if the direction changes. Always use ltr so that the\n         * style works out correctly in both cases. */\n        direction: ltr;\n      }\n\n      #spinnerContainer.active {\n        -webkit-animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite;\n        animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite;\n      }\n\n      @-webkit-keyframes container-rotate {\n        to { -webkit-transform: rotate(360deg) }\n      }\n\n      @keyframes container-rotate {\n        to { transform: rotate(360deg) }\n      }\n\n      .spinner-layer {\n        position: absolute;\n        width: 100%;\n        height: 100%;\n        opacity: 0;\n        white-space: nowrap;\n        color: var(--paper-spinner-color, var(--google-blue-500));\n      }\n\n      .layer-1 {\n        color: var(--paper-spinner-layer-1-color, var(--google-blue-500));\n      }\n\n      .layer-2 {\n        color: var(--paper-spinner-layer-2-color, var(--google-red-500));\n      }\n\n      .layer-3 {\n        color: var(--paper-spinner-layer-3-color, var(--google-yellow-500));\n      }\n\n      .layer-4 {\n        color: var(--paper-spinner-layer-4-color, var(--google-green-500));\n      }\n\n      /**\n       * IMPORTANT NOTE ABOUT CSS ANIMATION PROPERTIES (keanulee):\n       *\n       * iOS Safari (tested on iOS 8.1) does not handle animation-delay very well - it doesn't\n       * guarantee that the animation will start _exactly_ after that value. So we avoid using\n       * animation-delay and instead set custom keyframes for each color (as layer-2undant as it\n       * seems).\n       */\n      .active .spinner-layer {\n        -webkit-animation-name: fill-unfill-rotate;\n        -webkit-animation-duration: var(--paper-spinner-full-cycle-duration);\n        -webkit-animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);\n        -webkit-animation-iteration-count: infinite;\n        animation-name: fill-unfill-rotate;\n        animation-duration: var(--paper-spinner-full-cycle-duration);\n        animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);\n        animation-iteration-count: infinite;\n        opacity: 1;\n      }\n\n      .active .spinner-layer.layer-1 {\n        -webkit-animation-name: fill-unfill-rotate, layer-1-fade-in-out;\n        animation-name: fill-unfill-rotate, layer-1-fade-in-out;\n      }\n\n      .active .spinner-layer.layer-2 {\n        -webkit-animation-name: fill-unfill-rotate, layer-2-fade-in-out;\n        animation-name: fill-unfill-rotate, layer-2-fade-in-out;\n      }\n\n      .active .spinner-layer.layer-3 {\n        -webkit-animation-name: fill-unfill-rotate, layer-3-fade-in-out;\n        animation-name: fill-unfill-rotate, layer-3-fade-in-out;\n      }\n\n      .active .spinner-layer.layer-4 {\n        -webkit-animation-name: fill-unfill-rotate, layer-4-fade-in-out;\n        animation-name: fill-unfill-rotate, layer-4-fade-in-out;\n      }\n\n      @-webkit-keyframes fill-unfill-rotate {\n        12.5% { -webkit-transform: rotate(135deg) } /* 0.5 * ARCSIZE */\n        25%   { -webkit-transform: rotate(270deg) } /* 1   * ARCSIZE */\n        37.5% { -webkit-transform: rotate(405deg) } /* 1.5 * ARCSIZE */\n        50%   { -webkit-transform: rotate(540deg) } /* 2   * ARCSIZE */\n        62.5% { -webkit-transform: rotate(675deg) } /* 2.5 * ARCSIZE */\n        75%   { -webkit-transform: rotate(810deg) } /* 3   * ARCSIZE */\n        87.5% { -webkit-transform: rotate(945deg) } /* 3.5 * ARCSIZE */\n        to    { -webkit-transform: rotate(1080deg) } /* 4   * ARCSIZE */\n      }\n\n      @keyframes fill-unfill-rotate {\n        12.5% { transform: rotate(135deg) } /* 0.5 * ARCSIZE */\n        25%   { transform: rotate(270deg) } /* 1   * ARCSIZE */\n        37.5% { transform: rotate(405deg) } /* 1.5 * ARCSIZE */\n        50%   { transform: rotate(540deg) } /* 2   * ARCSIZE */\n        62.5% { transform: rotate(675deg) } /* 2.5 * ARCSIZE */\n        75%   { transform: rotate(810deg) } /* 3   * ARCSIZE */\n        87.5% { transform: rotate(945deg) } /* 3.5 * ARCSIZE */\n        to    { transform: rotate(1080deg) } /* 4   * ARCSIZE */\n      }\n\n      @-webkit-keyframes layer-1-fade-in-out {\n        0% { opacity: 1 }\n        25% { opacity: 1 }\n        26% { opacity: 0 }\n        89% { opacity: 0 }\n        90% { opacity: 1 }\n        to { opacity: 1 }\n      }\n\n      @keyframes layer-1-fade-in-out {\n        0% { opacity: 1 }\n        25% { opacity: 1 }\n        26% { opacity: 0 }\n        89% { opacity: 0 }\n        90% { opacity: 1 }\n        to { opacity: 1 }\n      }\n\n      @-webkit-keyframes layer-2-fade-in-out {\n        0% { opacity: 0 }\n        15% { opacity: 0 }\n        25% { opacity: 1 }\n        50% { opacity: 1 }\n        51% { opacity: 0 }\n        to { opacity: 0 }\n      }\n\n      @keyframes layer-2-fade-in-out {\n        0% { opacity: 0 }\n        15% { opacity: 0 }\n        25% { opacity: 1 }\n        50% { opacity: 1 }\n        51% { opacity: 0 }\n        to { opacity: 0 }\n      }\n\n      @-webkit-keyframes layer-3-fade-in-out {\n        0% { opacity: 0 }\n        40% { opacity: 0 }\n        50% { opacity: 1 }\n        75% { opacity: 1 }\n        76% { opacity: 0 }\n        to { opacity: 0 }\n      }\n\n      @keyframes layer-3-fade-in-out {\n        0% { opacity: 0 }\n        40% { opacity: 0 }\n        50% { opacity: 1 }\n        75% { opacity: 1 }\n        76% { opacity: 0 }\n        to { opacity: 0 }\n      }\n\n      @-webkit-keyframes layer-4-fade-in-out {\n        0% { opacity: 0 }\n        65% { opacity: 0 }\n        75% { opacity: 1 }\n        90% { opacity: 1 }\n        to { opacity: 0 }\n      }\n\n      @keyframes layer-4-fade-in-out {\n        0% { opacity: 0 }\n        65% { opacity: 0 }\n        75% { opacity: 1 }\n        90% { opacity: 1 }\n        to { opacity: 0 }\n      }\n\n      .circle-clipper {\n        display: inline-block;\n        position: relative;\n        width: 50%;\n        height: 100%;\n        overflow: hidden;\n      }\n\n      /**\n       * Patch the gap that appear between the two adjacent div.circle-clipper while the\n       * spinner is rotating (appears on Chrome 50, Safari 9.1.1, and Edge).\n       */\n      .spinner-layer::after {\n        content: '';\n        left: 45%;\n        width: 10%;\n        border-top-style: solid;\n      }\n\n      .spinner-layer::after,\n      .circle-clipper .circle {\n        box-sizing: border-box;\n        position: absolute;\n        top: 0;\n        border-width: var(--paper-spinner-stroke-width, 3px);\n        border-radius: 50%;\n      }\n\n      .circle-clipper .circle {\n        bottom: 0;\n        width: 200%;\n        border-style: solid;\n        border-bottom-color: transparent !important;\n      }\n\n      .circle-clipper.left .circle {\n        left: 0;\n        border-right-color: transparent !important;\n        -webkit-transform: rotate(129deg);\n        transform: rotate(129deg);\n      }\n\n      .circle-clipper.right .circle {\n        left: -100%;\n        border-left-color: transparent !important;\n        -webkit-transform: rotate(-129deg);\n        transform: rotate(-129deg);\n      }\n\n      .active .gap-patch::after,\n      .active .circle-clipper .circle {\n        -webkit-animation-duration: var(--paper-spinner-expand-contract-duration);\n        -webkit-animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);\n        -webkit-animation-iteration-count: infinite;\n        animation-duration: var(--paper-spinner-expand-contract-duration);\n        animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);\n        animation-iteration-count: infinite;\n      }\n\n      .active .circle-clipper.left .circle {\n        -webkit-animation-name: left-spin;\n        animation-name: left-spin;\n      }\n\n      .active .circle-clipper.right .circle {\n        -webkit-animation-name: right-spin;\n        animation-name: right-spin;\n      }\n\n      @-webkit-keyframes left-spin {\n        0% { -webkit-transform: rotate(130deg) }\n        50% { -webkit-transform: rotate(-5deg) }\n        to { -webkit-transform: rotate(130deg) }\n      }\n\n      @keyframes left-spin {\n        0% { transform: rotate(130deg) }\n        50% { transform: rotate(-5deg) }\n        to { transform: rotate(130deg) }\n      }\n\n      @-webkit-keyframes right-spin {\n        0% { -webkit-transform: rotate(-130deg) }\n        50% { -webkit-transform: rotate(5deg) }\n        to { -webkit-transform: rotate(-130deg) }\n      }\n\n      @keyframes right-spin {\n        0% { transform: rotate(-130deg) }\n        50% { transform: rotate(5deg) }\n        to { transform: rotate(-130deg) }\n      }\n\n      #spinnerContainer.cooldown {\n        -webkit-animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite, fade-out var(--paper-spinner-cooldown-duration) cubic-bezier(0.4, 0.0, 0.2, 1);\n        animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite, fade-out var(--paper-spinner-cooldown-duration) cubic-bezier(0.4, 0.0, 0.2, 1);\n      }\n\n      @-webkit-keyframes fade-out {\n        0% { opacity: 1 }\n        to { opacity: 0 }\n      }\n\n      @keyframes fade-out {\n        0% { opacity: 1 }\n        to { opacity: 0 }\n      }\n    </style>\n  </template>\n</dom-module>",document.head.appendChild(_s.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ys={properties:{active:{type:Boolean,value:!1,reflectToAttribute:!0,observer:"__activeChanged"},alt:{type:String,value:"loading",observer:"__altChanged"},__coolingDown:{type:Boolean,value:!1}},__computeContainerClasses:function(t,e){return[t||e?"active":"",e?"cooldown":""].join(" ")},__activeChanged:function(t,e){this.__setAriaHidden(!t),this.__coolingDown=!t&&e},__altChanged:function(t){"loading"===t?this.alt=this.getAttribute("aria-label")||t:(this.__setAriaHidden(""===t),this.setAttribute("aria-label",t))},__setAriaHidden:function(t){var e="aria-hidden";t?this.setAttribute(e,"true"):this.removeAttribute(e)},__reset:function(){this.active=!1,this.__coolingDown=!1}},vs=xe`
  <style include="paper-spinner-styles"></style>

  <div id="spinnerContainer" class-name="[[__computeContainerClasses(active, __coolingDown)]]" on-animationend="__reset" on-webkit-animation-end="__reset">
    <div class="spinner-layer layer-1">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>

    <div class="spinner-layer layer-2">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>

    <div class="spinner-layer layer-3">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>

    <div class="spinner-layer layer-4">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>
  </div>
`;
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */vs.setAttribute("strip-whitespace",""),jr({_template:vs,is:"paper-spinner",behaviors:[ys]});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const bs=xe`
  <style include="paper-spinner-styles"></style>

  <div id="spinnerContainer" class-name="[[__computeContainerClasses(active, __coolingDown)]]" on-animationend="__reset" on-webkit-animation-end="__reset">
    <div class="spinner-layer">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>
  </div>
`;bs.setAttribute("strip-whitespace",""),jr({_template:bs,is:"paper-spinner-lite",behaviors:[ys]});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const xs=xe`<iron-iconset-svg name="paper-tabs" size="24">
<svg><defs>
<g id="chevron-left"><path d="M15.41 7.41L14 6l-6 6 6 6 1.41-1.41L10.83 12z"></path></g>
<g id="chevron-right"><path d="M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z"></path></g>
</defs></svg>
</iron-iconset-svg>`;document.head.appendChild(xs.content),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style>
      :host {
        @apply --layout-inline;
        @apply --layout-center;
        @apply --layout-center-justified;
        @apply --layout-flex-auto;

        position: relative;
        padding: 0 12px;
        overflow: hidden;
        cursor: pointer;
        vertical-align: middle;

        @apply --paper-font-common-base;
        @apply --paper-tab;
      }

      :host(:focus) {
        outline: none;
      }

      :host([link]) {
        padding: 0;
      }

      .tab-content {
        height: 100%;
        transform: translateZ(0);
          -webkit-transform: translateZ(0);
        transition: opacity 0.1s cubic-bezier(0.4, 0.0, 1, 1);
        @apply --layout-horizontal;
        @apply --layout-center-center;
        @apply --layout-flex-auto;
        @apply --paper-tab-content;
      }

      :host(:not(.iron-selected)) > .tab-content {
        opacity: 0.8;

        @apply --paper-tab-content-unselected;
      }

      :host(:focus) .tab-content {
        opacity: 1;
        font-weight: 700;

        @apply --paper-tab-content-focused;
      }

      paper-ripple {
        color: var(--paper-tab-ink, var(--paper-yellow-a100));
      }

      .tab-content > ::slotted(a) {
        @apply --layout-flex-auto;

        height: 100%;
      }
    </style>

    <div class="tab-content">
      <slot></slot>
    </div>
`,is:"paper-tab",behaviors:[ra,aa,ua],properties:{link:{type:Boolean,value:!1,reflectToAttribute:!0}},hostAttributes:{role:"tab"},listeners:{down:"_updateNoink",tap:"_onTap"},attached:function(){this._updateNoink()},get _parentNoink(){var t=er(this).parentNode;return!!t&&!!t.noink},_updateNoink:function(){this.noink=!!this.noink||!!this._parentNoink},_onTap:function(t){if(this.link){var e=this.queryEffectiveChildren("a");if(!e)return;if(t.target===e)return;e.click()}}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style>
      :host {
        @apply --layout;
        @apply --layout-center;

        height: 48px;
        font-size: 14px;
        font-weight: 500;
        overflow: hidden;
        -moz-user-select: none;
        -ms-user-select: none;
        -webkit-user-select: none;
        user-select: none;

        /* NOTE: Both values are needed, since some phones require the value to be \`transparent\`. */
        -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
        -webkit-tap-highlight-color: transparent;

        @apply --paper-tabs;
      }

      :host(:dir(rtl)) {
        @apply --layout-horizontal-reverse;
      }

      #tabsContainer {
        position: relative;
        height: 100%;
        white-space: nowrap;
        overflow: hidden;
        @apply --layout-flex-auto;
        @apply --paper-tabs-container;
      }

      #tabsContent {
        height: 100%;
        -moz-flex-basis: auto;
        -ms-flex-basis: auto;
        flex-basis: auto;
        @apply --paper-tabs-content;
      }

      #tabsContent.scrollable {
        position: absolute;
        white-space: nowrap;
      }

      #tabsContent:not(.scrollable),
      #tabsContent.scrollable.fit-container {
        @apply --layout-horizontal;
      }

      #tabsContent.scrollable.fit-container {
        min-width: 100%;
      }

      #tabsContent.scrollable.fit-container > ::slotted(*) {
        /* IE - prevent tabs from compressing when they should scroll. */
        -ms-flex: 1 0 auto;
        -webkit-flex: 1 0 auto;
        flex: 1 0 auto;
      }

      .hidden {
        display: none;
      }

      .not-visible {
        opacity: 0;
        cursor: default;
      }

      paper-icon-button {
        width: 48px;
        height: 48px;
        padding: 12px;
        margin: 0 4px;
      }

      #selectionBar {
        position: absolute;
        height: 0;
        bottom: 0;
        left: 0;
        right: 0;
        border-bottom: 2px solid var(--paper-tabs-selection-bar-color, var(--paper-yellow-a100));
          -webkit-transform: scale(0);
        transform: scale(0);
          -webkit-transform-origin: left center;
        transform-origin: left center;
          transition: -webkit-transform;
        transition: transform;

        @apply --paper-tabs-selection-bar;
      }

      #selectionBar.align-bottom {
        top: 0;
        bottom: auto;
      }

      #selectionBar.expand {
        transition-duration: 0.15s;
        transition-timing-function: cubic-bezier(0.4, 0.0, 1, 1);
      }

      #selectionBar.contract {
        transition-duration: 0.18s;
        transition-timing-function: cubic-bezier(0.0, 0.0, 0.2, 1);
      }

      #tabsContent > ::slotted(:not(#selectionBar)) {
        height: 100%;
      }
    </style>

    <paper-icon-button icon="paper-tabs:chevron-left" class$="[[_computeScrollButtonClass(_leftHidden, scrollable, hideScrollButtons)]]" on-up="_onScrollButtonUp" on-down="_onLeftScrollButtonDown" tabindex="-1"></paper-icon-button>

    <div id="tabsContainer" on-track="_scroll" on-down="_down">
      <div id="tabsContent" class$="[[_computeTabsContentClass(scrollable, fitContainer)]]">
        <div id="selectionBar" class$="[[_computeSelectionBarClass(noBar, alignBottom)]]" on-transitionend="_onBarTransitionEnd"></div>
        <slot></slot>
      </div>
    </div>

    <paper-icon-button icon="paper-tabs:chevron-right" class$="[[_computeScrollButtonClass(_rightHidden, scrollable, hideScrollButtons)]]" on-up="_onScrollButtonUp" on-down="_onRightScrollButtonDown" tabindex="-1"></paper-icon-button>
`,is:"paper-tabs",behaviors:[Co,ms],properties:{noink:{type:Boolean,value:!1,observer:"_noinkChanged"},noBar:{type:Boolean,value:!1},noSlide:{type:Boolean,value:!1},scrollable:{type:Boolean,value:!1},fitContainer:{type:Boolean,value:!1},disableDrag:{type:Boolean,value:!1},hideScrollButtons:{type:Boolean,value:!1},alignBottom:{type:Boolean,value:!1},selectable:{type:String,value:"paper-tab"},autoselect:{type:Boolean,value:!1},autoselectDelay:{type:Number,value:0},_step:{type:Number,value:10},_holdDelay:{type:Number,value:1},_leftHidden:{type:Boolean,value:!1},_rightHidden:{type:Boolean,value:!1},_previousTab:{type:Object}},hostAttributes:{role:"tablist"},listeners:{"iron-resize":"_onTabSizingChanged","iron-items-changed":"_onTabSizingChanged","iron-select":"_onIronSelect","iron-deselect":"_onIronDeselect"},keyBindings:{"left:keyup right:keyup":"_onArrowKeyup"},created:function(){this._holdJob=null,this._pendingActivationItem=void 0,this._pendingActivationTimeout=void 0,this._bindDelayedActivationHandler=this._delayedActivationHandler.bind(this),this.addEventListener("blur",this._onBlurCapture.bind(this),!0)},ready:function(){this.setScrollDirection("y",this.$.tabsContainer)},detached:function(){this._cancelPendingActivation()},_noinkChanged:function(t){er(this).querySelectorAll("paper-tab").forEach(t?this._setNoinkAttribute:this._removeNoinkAttribute)},_setNoinkAttribute:function(t){t.setAttribute("noink","")},_removeNoinkAttribute:function(t){t.removeAttribute("noink")},_computeScrollButtonClass:function(t,e,n){return!e||n?"hidden":t?"not-visible":""},_computeTabsContentClass:function(t,e){return t?"scrollable"+(e?" fit-container":""):" fit-container"},_computeSelectionBarClass:function(t,e){return t?"hidden":e?"align-bottom":""},_onTabSizingChanged:function(){this.debounce("_onTabSizingChanged",(function(){this._scroll(),this._tabChanged(this.selectedItem)}),10)},_onIronSelect:function(t){this._tabChanged(t.detail.item,this._previousTab),this._previousTab=t.detail.item,this.cancelDebouncer("tab-changed")},_onIronDeselect:function(t){this.debounce("tab-changed",(function(){this._tabChanged(null,this._previousTab),this._previousTab=null}),1)},_activateHandler:function(){this._cancelPendingActivation(),cs._activateHandler.apply(this,arguments)},_scheduleActivation:function(t,e){this._pendingActivationItem=t,this._pendingActivationTimeout=this.async(this._bindDelayedActivationHandler,e)},_delayedActivationHandler:function(){var t=this._pendingActivationItem;this._pendingActivationItem=void 0,this._pendingActivationTimeout=void 0,t.fire(this.activateEvent,null,{bubbles:!0,cancelable:!0})},_cancelPendingActivation:function(){void 0!==this._pendingActivationTimeout&&(this.cancelAsync(this._pendingActivationTimeout),this._pendingActivationItem=void 0,this._pendingActivationTimeout=void 0)},_onArrowKeyup:function(t){this.autoselect&&this._scheduleActivation(this.focusedItem,this.autoselectDelay)},_onBlurCapture:function(t){t.target===this._pendingActivationItem&&this._cancelPendingActivation()},get _tabContainerScrollSize(){return Math.max(0,this.$.tabsContainer.scrollWidth-this.$.tabsContainer.offsetWidth)},_scroll:function(t,e){this.scrollable&&this._affectScroll(e&&-e.ddx||0)},_down:function(t){this.async((function(){this._defaultFocusAsync&&(this.cancelAsync(this._defaultFocusAsync),this._defaultFocusAsync=null)}),1)},_affectScroll:function(t){this.$.tabsContainer.scrollLeft+=t;var e=this.$.tabsContainer.scrollLeft;this._leftHidden=0===e,this._rightHidden=e===this._tabContainerScrollSize},_onLeftScrollButtonDown:function(){this._scrollToLeft(),this._holdJob=setInterval(this._scrollToLeft.bind(this),this._holdDelay)},_onRightScrollButtonDown:function(){this._scrollToRight(),this._holdJob=setInterval(this._scrollToRight.bind(this),this._holdDelay)},_onScrollButtonUp:function(){clearInterval(this._holdJob),this._holdJob=null},_scrollToLeft:function(){this._affectScroll(-this._step)},_scrollToRight:function(){this._affectScroll(this._step)},_tabChanged:function(t,e){if(!t)return this.$.selectionBar.classList.remove("expand"),this.$.selectionBar.classList.remove("contract"),void this._positionBar(0,0);var n=this.$.tabsContent.getBoundingClientRect(),i=n.width,r=t.getBoundingClientRect(),o=r.left-n.left;if(this._pos={width:this._calcPercent(r.width,i),left:this._calcPercent(o,i)},this.noSlide||null==e)return this.$.selectionBar.classList.remove("expand"),this.$.selectionBar.classList.remove("contract"),void this._positionBar(this._pos.width,this._pos.left);var a=e.getBoundingClientRect(),s=this.items.indexOf(e),l=this.items.indexOf(t);this.$.selectionBar.classList.add("expand");var c=s<l;this._isRTL&&(c=!c),c?this._positionBar(this._calcPercent(r.left+r.width-a.left,i)-5,this._left):this._positionBar(this._calcPercent(a.left+a.width-r.left,i)-5,this._calcPercent(o,i)+5),this.scrollable&&this._scrollToSelectedIfNeeded(r.width,o)},_scrollToSelectedIfNeeded:function(t,e){var n=e-this.$.tabsContainer.scrollLeft;(n<0||(n+=t-this.$.tabsContainer.offsetWidth)>0)&&(this.$.tabsContainer.scrollLeft+=n)},_calcPercent:function(t,e){return 100*t/e},_positionBar:function(t,e){e=e||0,this._width=t=t||0,this._left=e,this.transform("translateX("+e+"%) scaleX("+t/100+")",this.$.selectionBar)},_onBarTransitionEnd:function(t){var e=this.$.selectionBar.classList;e.contains("expand")?(e.remove("expand"),e.add("contract"),this._positionBar(this._pos.width,this._pos.left)):e.contains("contract")&&e.remove("contract")}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var ws=null;jr({_template:xe`
    <style>
      :host {
        display: block;
        position: fixed;
        background-color: var(--paper-toast-background-color, #323232);
        color: var(--paper-toast-color, #f1f1f1);
        min-height: 48px;
        min-width: 288px;
        padding: 16px 24px;
        box-sizing: border-box;
        box-shadow: 0 2px 5px 0 rgba(0, 0, 0, 0.26);
        border-radius: 2px;
        margin: 12px;
        font-size: 14px;
        cursor: default;
        -webkit-transition: -webkit-transform 0.3s, opacity 0.3s;
        transition: transform 0.3s, opacity 0.3s;
        opacity: 0;
        -webkit-transform: translateY(100px);
        transform: translateY(100px);
        @apply --paper-font-common-base;
      }

      :host(.capsule) {
        border-radius: 24px;
      }

      :host(.fit-bottom) {
        width: 100%;
        min-width: 0;
        border-radius: 0;
        margin: 0;
      }

      :host(.paper-toast-open) {
        opacity: 1;
        -webkit-transform: translateY(0px);
        transform: translateY(0px);
      }
    </style>

    <span id="label">{{text}}</span>
    <slot></slot>
`,is:"paper-toast",behaviors:[Wa],properties:{fitInto:{type:Object,value:window,observer:"_onFitIntoChanged"},horizontalAlign:{type:String,value:"left"},verticalAlign:{type:String,value:"bottom"},duration:{type:Number,value:3e3},text:{type:String,value:""},noCancelOnOutsideClick:{type:Boolean,value:!0},noAutoFocus:{type:Boolean,value:!0}},listeners:{transitionend:"__onTransitionEnd"},get visible(){return Eo._warn("`visible` is deprecated, use `opened` instead"),this.opened},get _canAutoClose(){return this.duration>0&&this.duration!==1/0},created:function(){this._autoClose=null,Ya.requestAvailability()},show:function(t){for(var e in"string"==typeof t&&(t={text:t}),t)0===e.indexOf("_")?Eo._warn('The property "'+e+'" is private and was not set.'):e in this?this[e]=t[e]:Eo._warn('The property "'+e+'" is not valid.');this.open()},hide:function(){this.close()},__onTransitionEnd:function(t){t&&t.target===this&&"opacity"===t.propertyName&&(this.opened?this._finishRenderOpened():this._finishRenderClosed())},_openedChanged:function(){null!==this._autoClose&&(this.cancelAsync(this._autoClose),this._autoClose=null),this.opened?(ws&&ws!==this&&ws.close(),ws=this,this.fire("iron-announce",{text:this.text}),this._canAutoClose&&(this._autoClose=this.async(this.close,this.duration))):ws===this&&(ws=null),ja._openedChanged.apply(this,arguments)},_renderOpened:function(){this.classList.add("paper-toast-open")},_renderClosed:function(){this.classList.remove("paper-toast-open")},_onFitIntoChanged:function(t){this.positionTarget=t}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const Ss=xe`

    <style>
      :host {
        display: inline-block;
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-font-common-base;
      }

      :host([disabled]) {
        pointer-events: none;
      }

      :host(:focus) {
        outline:none;
      }

      .toggle-bar {
        position: absolute;
        height: 100%;
        width: 100%;
        border-radius: 8px;
        pointer-events: none;
        opacity: 0.4;
        transition: background-color linear .08s;
        background-color: var(--paper-toggle-button-unchecked-bar-color, #000000);

        @apply --paper-toggle-button-unchecked-bar;
      }

      .toggle-button {
        position: absolute;
        top: -3px;
        left: 0;
        height: 20px;
        width: 20px;
        border-radius: 50%;
        box-shadow: 0 1px 5px 0 rgba(0, 0, 0, 0.6);
        transition: -webkit-transform linear .08s, background-color linear .08s;
        transition: transform linear .08s, background-color linear .08s;
        will-change: transform;
        background-color: var(--paper-toggle-button-unchecked-button-color, var(--paper-grey-50));

        @apply --paper-toggle-button-unchecked-button;
      }

      .toggle-button.dragging {
        -webkit-transition: none;
        transition: none;
      }

      :host([checked]:not([disabled])) .toggle-bar {
        opacity: 0.5;
        background-color: var(--paper-toggle-button-checked-bar-color, var(--primary-color));

        @apply --paper-toggle-button-checked-bar;
      }

      :host([disabled]) .toggle-bar {
        background-color: #000;
        opacity: 0.12;
      }

      :host([checked]) .toggle-button {
        -webkit-transform: translate(16px, 0);
        transform: translate(16px, 0);
      }

      :host([checked]:not([disabled])) .toggle-button {
        background-color: var(--paper-toggle-button-checked-button-color, var(--primary-color));

        @apply --paper-toggle-button-checked-button;
      }

      :host([disabled]) .toggle-button {
        background-color: #bdbdbd;
        opacity: 1;
      }

      .toggle-ink {
        position: absolute;
        top: -14px;
        left: -14px;
        right: auto;
        bottom: auto;
        width: 48px;
        height: 48px;
        opacity: 0.5;
        pointer-events: none;
        color: var(--paper-toggle-button-unchecked-ink-color, var(--primary-text-color));

        @apply --paper-toggle-button-unchecked-ink;
      }

      :host([checked]) .toggle-ink {
        color: var(--paper-toggle-button-checked-ink-color, var(--primary-color));

        @apply --paper-toggle-button-checked-ink;
      }

      .toggle-container {
        display: inline-block;
        position: relative;
        width: 36px;
        height: 14px;
        /* The toggle button has an absolute position of -3px; The extra 1px
        /* accounts for the toggle button shadow box. */
        margin: 4px 1px;
      }

      .toggle-label {
        position: relative;
        display: inline-block;
        vertical-align: middle;
        padding-left: var(--paper-toggle-button-label-spacing, 8px);
        pointer-events: none;
        color: var(--paper-toggle-button-label-color, var(--primary-text-color));
      }

      /* invalid state */
      :host([invalid]) .toggle-bar {
        background-color: var(--paper-toggle-button-invalid-bar-color, var(--error-color));
      }

      :host([invalid]) .toggle-button {
        background-color: var(--paper-toggle-button-invalid-button-color, var(--error-color));
      }

      :host([invalid]) .toggle-ink {
        color: var(--paper-toggle-button-invalid-ink-color, var(--error-color));
      }
    </style>

    <div class="toggle-container">
      <div id="toggleBar" class="toggle-bar"></div>
      <div id="toggleButton" class="toggle-button"></div>
    </div>

    <div class="toggle-label"><slot></slot></div>

  `;Ss.setAttribute("strip-whitespace",""),jr({_template:Ss,is:"paper-toggle-button",behaviors:[wa],hostAttributes:{role:"button","aria-pressed":"false",tabindex:0},properties:{},listeners:{track:"_ontrack"},attached:function(){Di(this,(function(){di(this,"pan-y")}))},_ontrack:function(t){var e=t.detail;"start"===e.state?this._trackStart(e):"track"===e.state?this._trackMove(e):"end"===e.state&&this._trackEnd(e)},_trackStart:function(t){this._width=this.$.toggleBar.offsetWidth/2,this._trackChecked=this.checked,this.$.toggleButton.classList.add("dragging")},_trackMove:function(t){var e=t.dx;this._x=Math.min(this._width,Math.max(0,this._trackChecked?this._width+e:e)),this.translate3d(this._x+"px",0,0,this.$.toggleButton),this._userActivate(this._x>this._width/2)},_trackEnd:function(t){this.$.toggleButton.classList.remove("dragging"),this.transform("",this.$.toggleButton)},_createRipple:function(){this._rippleContainer=this.$.toggleButton;var t=ua._createRipple();return t.id="ink",t.setAttribute("recenters",""),t.classList.add("circle","toggle-ink"),t}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style>
      :host {
        --calculated-paper-toolbar-height: var(--paper-toolbar-height, 64px);
        --calculated-paper-toolbar-sm-height: var(--paper-toolbar-sm-height, 56px);
        display: block;
        position: relative;
        box-sizing: border-box;
        -moz-box-sizing: border-box;
        height: var(--calculated-paper-toolbar-height);
        background: var(--paper-toolbar-background, var(--primary-color));
        color: var(--paper-toolbar-color, var(--dark-theme-text-color));
        @apply --paper-toolbar;
      }

      :host(.animate) {
        transition: var(--paper-toolbar-transition, height 0.18s ease-in);
      }

      :host(.medium-tall) {
        height: calc(var(--calculated-paper-toolbar-height) * 2);
        @apply --paper-toolbar-medium;
      }

      :host(.tall) {
        height: calc(var(--calculated-paper-toolbar-height) * 3);
        @apply --paper-toolbar-tall;
      }

      .toolbar-tools {
        position: relative;
        height: var(--calculated-paper-toolbar-height);
        padding: 0 16px;
        pointer-events: none;
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-toolbar-content;
      }

      /*
       * TODO: Where should media query breakpoints live so they can be shared between elements?
       */

      @media (max-width: 600px) {
        :host {
          height: var(--calculated-paper-toolbar-sm-height);
        }

        :host(.medium-tall) {
          height: calc(var(--calculated-paper-toolbar-sm-height) * 2);
        }

        :host(.tall) {
          height: calc(var(--calculated-paper-toolbar-sm-height) * 3);
        }

        .toolbar-tools {
          height: var(--calculated-paper-toolbar-sm-height);
        }
      }

      #topBar {
        position: relative;
      }

      /* middle bar */
      #middleBar {
        position: absolute;
        top: 0;
        right: 0;
        left: 0;
      }

      :host(.tall) #middleBar,
      :host(.medium-tall) #middleBar {
        -webkit-transform: translateY(100%);
        transform: translateY(100%);
      }

      /* bottom bar */
      #bottomBar {
        position: absolute;
        right: 0;
        bottom: 0;
        left: 0;
      }

      /*
       * make elements (e.g. buttons) respond to mouse/touch events
       *
       * \`.toolbar-tools\` disables touch events so multiple toolbars can stack and not
       * absorb events. All children must have pointer events re-enabled to work as
       * expected.
       */
      .toolbar-tools > ::slotted(*:not([disabled])) {
        pointer-events: auto;
      }

      .toolbar-tools > ::slotted(.title) {
        @apply --paper-font-common-base;
        white-space: nowrap;
        overflow: hidden;
        text-overflow: ellipsis;
        font-size: 20px;
        font-weight: 400;
        line-height: 1;
        pointer-events: none;
        @apply --layout-flex;
      }

      .toolbar-tools > ::slotted(.title) {
        margin-left: 56px;
      }

      .toolbar-tools > ::slotted(paper-icon-button + .title) {
        margin-left: 0;
      }

      /**
       * The --paper-toolbar-title mixin is applied here instead of above to
       * fix the issue with margin-left being ignored due to css ordering.
       */
      .toolbar-tools > ::slotted(.title) {
        @apply --paper-toolbar-title;
      }

      .toolbar-tools > ::slotted(paper-icon-button[icon=menu]) {
        margin-right: 24px;
      }

      .toolbar-tools > ::slotted(.fit) {
        position: absolute;
        top: auto;
        right: 0;
        bottom: 0;
        left: 0;
        width: auto;
        margin: 0;
      }

      /* TODO(noms): Until we have a better solution for classes that don't use
       * /deep/ create our own.
       */
      .start-justified {
        @apply --layout-start-justified;
      }

      .center-justified {
        @apply --layout-center-justified;
      }

      .end-justified {
        @apply --layout-end-justified;
      }

      .around-justified {
        @apply --layout-around-justified;
      }

      .justified {
        @apply --layout-justified;
      }
    </style>

    <div id="topBar" class\$="toolbar-tools [[_computeBarExtraClasses(justify)]]">
      <slot name="top"></slot>
    </div>

    <div id="middleBar" class\$="toolbar-tools [[_computeBarExtraClasses(middleJustify)]]">
      <slot name="middle"></slot>
    </div>

    <div id="bottomBar" class\$="toolbar-tools [[_computeBarExtraClasses(bottomJustify)]]">
      <slot name="bottom"></slot>
    </div>
`,is:"paper-toolbar",hostAttributes:{role:"toolbar"},properties:{bottomJustify:{type:String,value:""},justify:{type:String,value:""},middleJustify:{type:String,value:""}},ready:function(){console.warn(this.is,"is deprecated. Please use app-layout instead!")},attached:function(){this._observer=this._observe(this),this._updateAriaLabelledBy()},detached:function(){this._observer&&this._observer.disconnect()},_observe:function(t){var e=new MutationObserver(function(){this._updateAriaLabelledBy()}.bind(this));return e.observe(t,{childList:!0,subtree:!0}),e},_updateAriaLabelledBy:function(){Wi();for(var t,e=[],n=Array.prototype.slice.call(er(this.root).querySelectorAll("slot")).concat(Array.prototype.slice.call(er(this.root).querySelectorAll("content"))),i=0;t=n[i];i++)for(var r,o=er(t).getDistributedNodes(),a=0;r=o[a];a++)if(r.classList&&r.classList.contains("title"))if(r.id)e.push(r.id);else{var s="paper-toolbar-label-"+Math.floor(1e4*Math.random());r.id=s,e.push(s)}e.length>0&&this.setAttribute("aria-labelledby",e.join(" "))},_computeBarExtraClasses:function(t){return t?t+("justified"===t?"":"-justified"):""}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
jr({_template:xe`
    <style>
      :host {
        display: block;
        position: absolute;
        outline: none;
        z-index: 1002;
        -moz-user-select: none;
        -ms-user-select: none;
        -webkit-user-select: none;
        user-select: none;
        cursor: default;
      }

      #tooltip {
        display: block;
        outline: none;
        @apply --paper-font-common-base;
        font-size: 10px;
        line-height: 1;
        background-color: var(--paper-tooltip-background, #616161);
        color: var(--paper-tooltip-text-color, white);
        padding: 8px;
        border-radius: 2px;
        @apply --paper-tooltip;
      }

      @keyframes keyFrameScaleUp {
        0% {
          transform: scale(0.0);
        }
        100% {
          transform: scale(1.0);
        }
      }

      @keyframes keyFrameScaleDown {
        0% {
          transform: scale(1.0);
        }
        100% {
          transform: scale(0.0);
        }
      }

      @keyframes keyFrameFadeInOpacity {
        0% {
          opacity: 0;
        }
        100% {
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
      }

      @keyframes keyFrameFadeOutOpacity {
        0% {
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
        100% {
          opacity: 0;
        }
      }

      @keyframes keyFrameSlideDownIn {
        0% {
          transform: translateY(-2000px);
          opacity: 0;
        }
        10% {
          opacity: 0.2;
        }
        100% {
          transform: translateY(0);
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
      }

      @keyframes keyFrameSlideDownOut {
        0% {
          transform: translateY(0);
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
        10% {
          opacity: 0.2;
        }
        100% {
          transform: translateY(-2000px);
          opacity: 0;
        }
      }

      .fade-in-animation {
        opacity: 0;
        animation-delay: var(--paper-tooltip-delay-in, 500ms);
        animation-name: keyFrameFadeInOpacity;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-in, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .fade-out-animation {
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 0ms);
        animation-name: keyFrameFadeOutOpacity;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .scale-up-animation {
        transform: scale(0);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-in, 500ms);
        animation-name: keyFrameScaleUp;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-in, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .scale-down-animation {
        transform: scale(1);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameScaleDown;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .slide-down-animation {
        transform: translateY(-2000px);
        opacity: 0;
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameSlideDownIn;
        animation-iteration-count: 1;
        animation-timing-function: cubic-bezier(0.0, 0.0, 0.2, 1);
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .slide-down-animation-out {
        transform: translateY(0);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameSlideDownOut;
        animation-iteration-count: 1;
        animation-timing-function: cubic-bezier(0.4, 0.0, 1, 1);
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .cancel-animation {
        animation-delay: -30s !important;
      }

      /* Thanks IE 10. */

      .hidden {
        display: none !important;
      }
    </style>

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e=(this.category.items||[]).length-1;t>e&&(t=e),t<0&&(t=0),this.set("_activeIndex",t)}_clampActiveIndex(){this._setActiveIndex(this._activeIndex)}_performPreviousPage(){this._setActiveIndex(this._activeIndex-this._limit)}_performNextPage(){this._setActiveIndex(this._activeIndex+this._limit)}_computePageInputValue(t,e,n){return t?e:n.toString()}_handlePageInputEvent(t){this.set("_pageInputRawValue",t.target.value);const e=Number(t.target.value||NaN);if(isNaN(e))return;const n=Math.max(1,Math.min(e,this._pageCount))-1;this._setActiveIndex(this._limit*n)}_handlePageChangeEvent(){this.set("_pageInputRawValue",this._currentPage.toString())}_handlePageFocusEvent(){this.set("_pageInputRawValue",this._pageInputValue),this.set("_pageInputFocused",!0)}_handlePageBlurEvent(){this.set("_pageInputFocused",!1)}_updatePageInputValue(t){const e=this.shadowRoot.querySelector("#page-input input");e&&(e.value=t)}_updateInputWidth(){this.updateStyles({"--tf-category-paginated-view-page-input-width":this._inputWidth})}};Cl.template=xe`
    <template is="dom-if" if="[[_paneRendered]]" id="ifRendered">
      <button class="heading" on-tap="_togglePane" open-button$="[[opened]]">
        <span class="name">
          <template is="dom-if" if="[[_isSearchResults]]">
            <template is="dom-if" if="[[_isCompositeSearch(category)]]">
              <span>Tags matching multiple experiments</span>
              <template is="dom-if" if="[[_isInvalidSearchResults]]">
                <span
                  >&nbsp;<strong>(malformed regular expression)</strong></span
                >
              </template>
            </template>
            <template is="dom-if" if="[[!_isCompositeSearch(category)]]">
              <span class="light">Tags matching /</span>
              <span class="category-name" title$="[[category.name]]"
                >[[category.name]]</span
              >
              <span class="light">/</span>
              <template is="dom-if" if="[[_isUniversalSearchQuery]]">
                <span> (all tags)</span>
              </template>
              <template is="dom-if" if="[[_isInvalidSearchResults]]">
                <span> <strong>(malformed regular expression)</strong></span>
              </template>
            </template>
          </template>
          <template is="dom-if" if="[[!_isSearchResults]]">
            <span class="category-name" title$="[[category.name]]"
              >[[category.name]]</span
            >
          </template>
        </span>
        <span class="count">
          <template is="dom-if" if="[[_hasMultiple]]">
            <span>[[_count]]</span>
          </template>
          <iron-icon icon="expand-more" class="expand-arrow"></iron-icon>
        </span>
      </button>
      <!-- TODO(stephanwlee): investigate further. For some reason,
        transitionend that the iron-collapse relies on sometimes does not
        trigger when rendering a chart with a spinner. A toy example cannot
        reproduce this bug. -->
      <iron-collapse opened="[[opened]]" no-animation="">
        <div class="content">
          <span id="top-of-container"></span>
          <template is="dom-if" if="[[_multiplePagesExist]]">
            <div class="big-page-buttons" style="margin-bottom: 10px;">
              <paper-button
                on-tap="_performPreviousPage"
                disabled$="[[!_hasPreviousPage]]"
                >Previous page</paper-button
              >
              <paper-button
                on-tap="_performNextPage"
                disabled$="[[!_hasNextPage]]"
                >Next page</paper-button
              >
            </div>
          </template>

          <div id="items">
            <slot name="items"></slot>
          </div>
          <template is="dom-if" if="[[_multiplePagesExist]]">
            <div id="controls-container">
              <div style="display: inline-block; padding: 0 5px">
                Page
                <paper-input
                  id="page-input"
                  type="number"
                  no-label-float=""
                  min="1"
                  max="[[_pageCount]]"
                  value="[[_pageInputValue]]"
                  on-input="_handlePageInputEvent"
                  on-change="_handlePageChangeEvent"
                  on-focus="_handlePageFocusEvent"
                  on-blur="_handlePageBlurEvent"
                ></paper-input>
                of [[_pageCount]]
              </div>
            </div>

            <div class="big-page-buttons" style="margin-top: 10px;">
              <paper-button
                on-tap="_performPreviousPage"
                disabled$="[[!_hasPreviousPage]]"
                >Previous page</paper-button
              >
              <paper-button
                on-tap="_performNextPage"
                disabled$="[[!_hasNextPage]]"
                >Next page</paper-button
              >
            </div>
          </template>
        </div>
      </iron-collapse>
    </template>
    <style>
      :host {
        display: block;
        margin: 0 5px 1px 10px;
      }

      :host(:first-of-type) {
        margin-top: 10px;
      }

      :host(:last-of-type) {
        margin-bottom: 20px;
      }

      .heading {
        background-color: var(--primary-background-color);
        border: none;
        color: inherit;
        cursor: pointer;
        width: 100%;
        font-size: 15px;
        line-height: 1;
        box-shadow: 0 1px 5px var(--tb-raised-button-shadow-color);
        padding: 10px 15px;
        display: flex;
        align-items: center;
        justify-content: space-between;
      }

      .heading::-moz-focus-inner {
        padding: 10px 15px;
      }

      [open-button] {
        border-bottom-left-radius: 0 !important;
        border-bottom-right-radius: 0 !important;
      }

      [open-button] .expand-arrow {
        transform: rotateZ(180deg);
      }

      .name {
        display: inline-flex;
        overflow: hidden;
      }

      .light {
        color: var(--paper-grey-500);
      }

      .category-name {
        white-space: pre;
        overflow: hidden;
        text-overflow: ellipsis;
        padding: 2px 0;
      }

      .count {
        margin: 0 5px;
        font-size: 12px;
        color: var(--paper-grey-500);
        display: flex;
        align-items: center;
        flex: none;
      }

      .heading::-moz-focus-inner {
        padding: 10px 15px;
      }

      .content {
        display: flex;
        flex-direction: column;
        background-color: var(--primary-background-color);
        border-bottom-left-radius: 2px;
        border-bottom-right-radius: 2px;
        border-top: none;
        border: 1px solid #dedede;
        padding: 15px;
      }

      .light {
        color: var(--paper-grey-500);
      }

      #controls-container {
        justify-content: center;
        display: flex;
        flex-direction: row;
        flex-grow: 0;
        flex-shrink: 0;
        width: 100%;
      }

      #controls-container paper-button {
        display: inline-block;
      }

      .big-page-buttons {
        display: flex;
      }

      .big-page-buttons paper-button {
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
        display: inline-block;
        flex-basis: 0;
        flex-grow: 1;
        flex-shrink: 1;
        font-size: 13px;
      }

      .big-page-buttons paper-button[disabled] {
        background: none;
      }

      slot {
        display: flex;
        flex-direction: row;
        flex-wrap: wrap;
      }

      ::slotted([slot='items']) {
        /* Tooltip for descriptions and others break with more strict ones. */
        contain: style;
      }

      #page-input {
        display: inline-block;
        width: var(--tf-category-paginated-view-page-input-width, 100%);
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],Cl.prototype,"category",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Cl.prototype,"initialOpened",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],Cl.prototype,"opened",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Cl.prototype,"disablePagination",void 0),t([o({type:Number,computed:"_computeCount(category.items.*)"}),e("design:type",Number)],Cl.prototype,"_count",void 0),t([o({type:Boolean,computed:"_computePaneRendered(category)",observer:"_onPaneRenderedChanged"}),e("design:type",Boolean)],Cl.prototype,"_paneRendered",void 0),t([o({type:Boolean,computed:"_computeIsSearchResults(category.metadata.type)"}),e("design:type",Boolean)],Cl.prototype,"_isSearchResults",void 0),t([o({type:Boolean,computed:"_computeIsInvalidSearchResults(category.metadata)"}),e("design:type",Boolean)],Cl.prototype,"_isInvalidSearchResults",void 0),t([o({type:Boolean,computed:"_computeIsUniversalSearchQuery(category.metadata)"}),e("design:type",Boolean)],Cl.prototype,"_isUniversalSearchQuery",void 0),t([o({type:Object,observer:"_getCategoryItemKeyChanged"}),e("design:type",Object)],Cl.prototype,"getCategoryItemKey",void 0),t([o({type:Number,observer:"_limitChanged"}),e("design:type",Number)],Cl.prototype,"_limit",void 0),t([o({type:Number}),e("design:type",Number)],Cl.prototype,"_activeIndex",void 0),t([o({type:Number,computed:"_computePageCount(category.items.*, _limit)"}),e("design:type",Number)],Cl.prototype,"_pageCount",void 0),t([o({type:String,computed:"_computeInputWidth(_pageCount)",observer:"_updateInputWidth"}),e("design:type",String)],Cl.prototype,"_inputWidth",void 0),t([o({type:String,computed:"_computePageInputValue(_pageInputFocused, _pageInputRawValue, _currentPage)",observer:"_updatePageInputValue"}),e("design:type",String)],Cl.prototype,"_pageInputValue",void 0),t([o({type:String}),e("design:type",String)],Cl.prototype,"_pageInputRawValue",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Cl.prototype,"_pageInputFocused",void 0),t([s("_count"),e("design:type",Boolean),e("design:paramtypes",[])],Cl.prototype,"_hasMultiple",null),t([a("opened"),e("design:type",Function),e("design:paramtypes",[Boolean]),e("design:returntype",void 0)],Cl.prototype,"_changeContentActive",null),t([s("opened","_paneRendered"),e("design:type",Boolean),e("design:paramtypes",[])],Cl.prototype,"_itemsRendered",null),t([a("_itemsRendered","category.items.*","_limit","_activeIndex","_pageCount","disablePagination"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Cl.prototype,"_updateRenderedItems",null),t([s("_limit","_activeIndex"),e("design:type",Number),e("design:paramtypes",[])],Cl.prototype,"_currentPage",null),t([s("_pageCount","disablePagination"),e("design:type",Boolean),e("design:paramtypes",[])],Cl.prototype,"_multiplePagesExist",null),t([s("_currentPage"),e("design:type",Boolean),e("design:paramtypes",[])],Cl.prototype,"_hasPreviousPage",null),t([s("_currentPage","_pageCount"),e("design:type",Boolean),e("design:paramtypes",[])],Cl.prototype,"_hasNextPage",null),t([a("category.items.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Cl.prototype,"_clampActiveIndex",null),Cl=t([i("tf-category-paginated-view")],Cl);class Al{constructor(t){this.listener=t}}class kl{constructor(){this.requestManager=new br(1),this._listeners=new Set,this.initialized=!1}refresh(){return this.load().then((()=>{this.initialized=!0}))}addListener(t){const e=new Al(t);return this._listeners.add(e),e}removeListenerByKey(t){this._listeners.delete(t)}emitChange(){this._listeners.forEach((t=>{try{t.listener()}catch(t){}}))}}class Ll extends kl{load(){const t=Er().environment();return this.requestManager.request(t).then((t=>{const e={dataLocation:t.data_location,windowTitle:t.window_title};void 0!==t.experiment_name&&(e.experimentName=t.experiment_name),void 0!==t.experiment_description&&(e.experimentDescription=t.experiment_description),void 0!==t.creation_time&&(e.creationTime=t.creation_time),Ce.exports.isEqual(this.environment,e)||(this.environment=e,this.emitChange())}))}getDataLocation(){return this.environment?this.environment.dataLocation:""}getWindowTitle(){return this.environment?this.environment.windowTitle:""}getExperimentName(){return this.environment?this.environment.experimentName:""}getExperimentDescription(){return this.environment?this.environment.experimentDescription:""}getCreationTime(){return this.environment?this.environment.creationTime:null}}const Pl=new Ll;class Nl extends kl{constructor(){super(...arguments),this._runs=[]}load(){const t=Er().runs();return this.requestManager.request(t).then((t=>{Ce.exports.isEqual(this._runs,t)||(this._runs=t,this.emitChange())}))}getRuns(){return this._runs.slice()}}const Il=new Nl;function Rl(t,e){return t<e?-1:t>e?1:t>=e?0:NaN}function Ol(t){return 1===t.length&&(t=(function e(t){return function(e,n){return Rl(t(e),n)}})(t)),{left:function(e,n,i,r){for(null==i&&(i=0),null==r&&(r=e.length);i<r;){var o=i+r>>>1;t(e[o],n)<0?i=o+1:r=o}return i},right:function(e,n,i,r){for(null==i&&(i=0),null==r&&(r=e.length);i<r;){var o=i+r>>>1;t(e[o],n)>0?r=o:i=o+1}return i}}}var zl=Ol(Rl),Dl=zl.right,Bl=zl.left;function Hl(t,e){return[t,e]}function Fl(t,e,n){var i,r,o,a,s=t.length,l=e.length,c=new Array(s*l);for(null==n&&(n=Hl),i=o=0;i<s;++i)for(a=t[i],r=0;r<l;++r,++o)c[o]=n(a,e[r]);return c}function Vl(t){return null===t?NaN:+t}function Ul(t,e){var 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xP(t){this._context=t}function wP(t){return new xP(t)}function SP(t){this._context=t}function MP(t){this._context=t}function EP(t,e){this._basis=new xP(t),this._beta=e}xP.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._y0=this._y1=NaN,this._point=0},lineEnd:function(){switch(this._point){case 3:bP(this,this._x1,this._y1);case 2:this._context.lineTo(this._x1,this._y1)}(this._line||0!==this._line&&1===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,e){switch(t=+t,e=+e,this._point){case 0:this._point=1,this._line?this._context.lineTo(t,e):this._context.moveTo(t,e);break;case 1:this._point=2;break;case 2:this._point=3,this._context.lineTo((5*this._x0+this._x1)/6,(5*this._y0+this._y1)/6);default:bP(this,t,e)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=e}},SP.prototype={areaStart:vP,areaEnd:vP,lineStart:function(){this._x0=this._x1=this._x2=this._x3=this._x4=this._y0=this._y1=this._y2=this._y3=this._y4=NaN,this._point=0},lineEnd:function(){switch(this._point){case 1:this._context.moveTo(this._x2,this._y2),this._context.closePath();break;case 2:this._context.moveTo((this._x2+2*this._x3)/3,(this._y2+2*this._y3)/3),this._context.lineTo((this._x3+2*this._x2)/3,(this._y3+2*this._y2)/3),this._context.closePath();break;case 3:this.point(this._x2,this._y2),this.point(this._x3,this._y3),this.point(this._x4,this._y4)}},point:function(t,e){switch(t=+t,e=+e,this._point){case 0:this._point=1,this._x2=t,this._y2=e;break;case 1:this._point=2,this._x3=t,this._y3=e;break;case 2:this._point=3,this._x4=t,this._y4=e,this._context.moveTo((this._x0+4*this._x1+t)/6,(this._y0+4*this._y1+e)/6);break;default:bP(this,t,e)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=e}},MP.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._y0=this._y1=NaN,this._point=0},lineEnd:function(){(this._line||0!==this._line&&3===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,e){switch(t=+t,e=+e,this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3;var n=(this._x0+4*this._x1+t)/6,i=(this._y0+4*this._y1+e)/6;this._line?this._context.lineTo(n,i):this._context.moveTo(n,i);break;case 3:this._point=4;default:bP(this,t,e)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=e}},EP.prototype={lineStart:function(){this._x=[],this._y=[],this._basis.lineStart()},lineEnd:function(){var t=this._x,e=this._y,n=t.length-1;if(n>0)for(var 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OP(t,e){this._context=t,this._alpha=e}OP.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._l01_a=this._l12_a=this._l23_a=this._l01_2a=this._l12_2a=this._l23_2a=this._point=0},lineEnd:function(){switch(this._point){case 2:this._context.lineTo(this._x2,this._y2);break;case 3:this.point(this._x2,this._y2)}(this._line||0!==this._line&&1===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,e){if(t=+t,e=+e,this._point){var n=this._x2-t,i=this._y2-e;this._l23_a=Math.sqrt(this._l23_2a=Math.pow(n*n+i*i,this._alpha))}switch(this._point){case 0:this._point=1,this._line?this._context.lineTo(t,e):this._context.moveTo(t,e);break;case 1:this._point=2;break;case 2:this._point=3;default:RP(this,t,e)}this._l01_a=this._l12_a,this._l12_a=this._l23_a,this._l01_2a=this._l12_2a,this._l12_2a=this._l23_2a,this._x0=this._x1,this._x1=this._x2,this._x2=t,this._y0=this._y1,this._y1=this._y2,this._y2=e}};var zP=(function t(e){function n(t){return e?new OP(t,e):new AP(t,0)}return n.alpha=function(e){return t(+e)},n})(.5);function DP(t,e){this._context=t,this._alpha=e}DP.prototype={areaStart:vP,areaEnd:vP,lineStart:function(){this._x0=this._x1=this._x2=this._x3=this._x4=this._x5=this._y0=this._y1=this._y2=this._y3=this._y4=this._y5=NaN,this._l01_a=this._l12_a=this._l23_a=this._l01_2a=this._l12_2a=this._l23_2a=this._point=0},lineEnd:function(){switch(this._point){case 1:this._context.moveTo(this._x3,this._y3),this._context.closePath();break;case 2:this._context.lineTo(this._x3,this._y3),this._context.closePath();break;case 3:this.point(this._x3,this._y3),this.point(this._x4,this._y4),this.point(this._x5,this._y5)}},point:function(t,e){if(t=+t,e=+e,this._point){var n=this._x2-t,i=this._y2-e;this._l23_a=Math.sqrt(this._l23_2a=Math.pow(n*n+i*i,this._alpha))}switch(this._point){case 0:this._point=1,this._x3=t,this._y3=e;break;case 1:this._point=2,this._context.moveTo(this._x4=t,this._y4=e);break;case 2:this._point=3,this._x5=t,this._y5=e;break;default:RP(this,t,e)}this._l01_a=this._l12_a,this._l12_a=this._l23_a,this._l01_2a=this._l12_2a,this._l12_2a=this._l23_2a,this._x0=this._x1,this._x1=this._x2,this._x2=t,this._y0=this._y1,this._y1=this._y2,this._y2=e}};var BP=(function t(e){function n(t){return e?new DP(t,e):new LP(t,0)}return n.alpha=function(e){return t(+e)},n})(.5);function HP(t,e){this._context=t,this._alpha=e}HP.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._x2=this._y0=this._y1=this._y2=NaN,this._l01_a=this._l12_a=this._l23_a=this._l01_2a=this._l12_2a=this._l23_2a=this._point=0},lineEnd:function(){(this._line||0!==this._line&&3===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,e){if(t=+t,e=+e,this._point){var n=this._x2-t,i=this._y2-e;this._l23_a=Math.sqrt(this._l23_2a=Math.pow(n*n+i*i,this._alpha))}switch(this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3,this._line?this._context.lineTo(this._x2,this._y2):this._context.moveTo(this._x2,this._y2);break;case 3:this._point=4;default:RP(this,t,e)}this._l01_a=this._l12_a,this._l12_a=this._l23_a,this._l01_2a=this._l12_2a,this._l12_2a=this._l23_2a,this._x0=this._x1,this._x1=this._x2,this._x2=t,this._y0=this._y1,this._y1=this._y2,this._y2=e}};var FP=(function 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gO(t,e){const n=new mO;function i(){n.setDomain(e())}return t.addListener(i),i(),t=>n.getColor(t)}const _O=gO(Il,(()=>Il.getRuns()));gO(pO,(()=>pO.getExperiments().map((({name:t})=>t)))),ml({moduleName:"run-color-style",styleContent:"\n    [color-class='light-blue'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-light-blue-500);\n      --paper-checkbox-checked-ink-color: var(--paper-light-blue-500);\n      --paper-checkbox-unchecked-color: var(--paper-light-blue-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-light-blue-900);\n    }\n    [color-class='red'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-red-500);\n      --paper-checkbox-checked-ink-color: var(--paper-red-500);\n      --paper-checkbox-unchecked-color: var(--paper-red-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-red-900);\n    }\n    [color-class='green'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-green-500);\n      --paper-checkbox-checked-ink-color: var(--paper-green-500);\n      --paper-checkbox-unchecked-color: var(--paper-green-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-green-900);\n    }\n    [color-class='purple'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-purple-500);\n      --paper-checkbox-checked-ink-color: var(--paper-purple-500);\n      --paper-checkbox-unchecked-color: var(--paper-purple-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-purple-900);\n    }\n    [color-class='teal'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-teal-500);\n      --paper-checkbox-checked-ink-color: var(--paper-teal-500);\n      --paper-checkbox-unchecked-color: var(--paper-teal-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-teal-900);\n    }\n    [color-class='pink'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-pink-500);\n      --paper-checkbox-checked-ink-color: var(--paper-pink-500);\n      --paper-checkbox-unchecked-color: var(--paper-pink-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-pink-900);\n    }\n    [color-class='orange'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-orange-500);\n      --paper-checkbox-checked-ink-color: var(--paper-orange-500);\n      --paper-checkbox-unchecked-color: var(--paper-orange-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-orange-900);\n    }\n    [color-class='brown'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-brown-500);\n      --paper-checkbox-checked-ink-color: var(--paper-brown-500);\n      --paper-checkbox-unchecked-color: var(--paper-brown-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-brown-900);\n    }\n    [color-class='indigo'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-indigo-500);\n      --paper-checkbox-checked-ink-color: var(--paper-indigo-500);\n      --paper-checkbox-unchecked-color: var(--paper-indigo-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-indigo-900);\n    }\n  "});let yO=class extends(cr(we)){constructor(){super(...arguments),this.names=[],this.coloring={getColor:()=>""},this.regex="",this.selectionState={},this.maxNamesToEnableByDefault=40,this._debouncedRegexChange=this._debouncedRegexChangeImpl()}_debouncedRegexChangeImpl(){var t=Ce.exports.debounce((t=>{this.regex=t}),150,{leading:!1});return function(){var e=this.$$("#names-regex").value;""==e?this.async((()=>{this.regex=e}),30):t(e)}}get _regex(){var t=this.regex;try{return new RegExp(t)}catch(t){return null}}_setIsolatorIcon(){var t=this.selectionState,e=Ce.exports.filter(Ce.exports.values(t)).length;Array.prototype.slice.call(this.root.querySelectorAll(".isolator")).forEach((function(n){n.icon=1===e&&t[n.name]?"radio-button-checked":"radio-button-unchecked"}))}computeNamesMatchingRegex(t,e){const n=this._regex;return n?this.names.filter((t=>n.test(t))):this.names}computeOutSelected(t,e){var n=this.selectionState,i=this.namesMatchingRegex.length<=this.maxNamesToEnableByDefault;return this.namesMatchingRegex.filter((t=>null==n[t]?i:n[t]))}synchronizeColors(t){this._setIsolatorIcon();this.root.querySelectorAll("paper-checkbox").forEach((t=>{const e=this.coloring.getColor(t.name);t.updateStyles({"--paper-checkbox-checked-color":e,"--paper-checkbox-checked-ink-color":e,"--paper-checkbox-unchecked-color":e,"--paper-checkbox-unchecked-ink-color":e})}));this.root.querySelectorAll(".isolator").forEach((t=>{const e=this.coloring.getColor(t.name);t.style.color=e})),window.requestAnimationFrame((()=>{this.updateStyles()}))}_isolateName(t){var e=t.target.name,n={};this.names.forEach((function(t){n[t]=t==e})),this.selectionState=n}_checkboxChange(t){var e=t.target;const n=Ce.exports.clone(this.selectionState);n[e.name]=e.checked,this.selectionState=n}_isChecked(t,e){return-1!=this.outSelected.indexOf(t)}toggleAll(){const t=this.namesMatchingRegex.some((t=>this.outSelected.includes(t))),e={};this.names.forEach((n=>{e[n]=!t})),this.selectionState=e}};yO.template=xe`
    <style include="scrollbar-style"></style>
    <style include="run-color-style"></style>

    <paper-input
      id="names-regex"
      no-label-float=""
      label="Write a regex to filter runs"
      value="[[regex]]"
      on-bind-value-changed="_debouncedRegexChange"
    ></paper-input>
    <div id="outer-container" class="scrollbar">
      <template
        is="dom-repeat"
        items="[[namesMatchingRegex]]"
        on-dom-change="synchronizeColors"
      >
        <div class="name-row">
          <div
            class="icon-container checkbox-container vertical-align-container"
          >
            <paper-checkbox
              class="checkbox vertical-align-center"
              id$="checkbox-[[item]]"
              name="[[item]]"
              checked$="[[_isChecked(item, selectionState.*)]]"
              on-change="_checkboxChange"
            ></paper-checkbox>
          </div>
          <div
            class="icon-container isolator-container vertical-align-container"
          >
            <paper-icon-button
              icon="radio-button-unchecked"
              class="isolator vertical-align-center"
              on-tap="_isolateName"
              name="[[item]]"
            ></paper-icon-button>
          </div>
          <div class="item-label-container">
            <span>[[item]]</span>
          </div>
        </div>
      </template>
    </div>
    <style>
      paper-input {
        --paper-input-container-focus-color: var(--tb-orange-strong);
        --paper-input-container-input: {
          font-size: 14px;
        }
        --paper-input-container-label: {
          font-size: 14px;
        }
      }
      :host {
        display: flex;
        flex-direction: column;
        height: 100%;
        overflow: hidden;
      }
      #outer-container {
        contain: content;
        flex-grow: 1;
        flex-shrink: 1;
        overflow-x: hidden;
        overflow-y: auto;
        width: 100%;
        will-change: transform;
        word-wrap: break-word;
      }
      .name-row {
        contain: content;
        padding-top: 5px;
        padding-bottom: 5px;
        display: flex;
        flex-direction: row;
        font-size: 13px;
        word-break: break-all; /* makes wrapping of hyperparam strings better */
      }
      .icon-container {
        flex-grow: 0;
        flex-shrink: 0;
        padding-left: 2px;
      }
      .checkbox {
        padding-left: 2px;
        width: 18px;
        height: 18px;
      }
      .isolator {
        width: 18px;
        height: 18px;
        padding: 0px;
      }
      .isolator-container {
        padding-left: 6px;
        padding-right: 3px;
      }
      .checkbox-container {
        padding-left: 2px;
      }
      .item-label-container {
        padding-left: 5px;
        flex-grow: 1;
        flex-shrink: 1;
        width: 0px; /* hack to get the flex-grow to work properly */
      }
      .tooltip-value-container {
        display: flex;
        justify-content: center;
        flex-grow: 0;
        flex-shrink: 0;
        text-align: right;
        padding-left: 2px;
      }
      .vertical-align-container {
        display: flex;
        justify-content: center;
      }
      .vertical-align-container .vertical-align-center {
        align-self: center;
      }
      .vertical-align-container .vertical-align-top {
        align-self: start;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],yO.prototype,"names",void 0),t([o({type:Object}),e("design:type",Object)],yO.prototype,"coloring",void 0),t([o({type:String,notify:!0}),e("design:type",String)],yO.prototype,"regex",void 0),t([o({type:Array,computed:"computeNamesMatchingRegex(names.*, _regex)"}),e("design:type",Array)],yO.prototype,"namesMatchingRegex",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],yO.prototype,"selectionState",void 0),t([o({type:Array,notify:!0,computed:"computeOutSelected(namesMatchingRegex.*, selectionState.*)"}),e("design:type",Array)],yO.prototype,"outSelected",void 0),t([o({type:Number}),e("design:type",Number)],yO.prototype,"maxNamesToEnableByDefault",void 0),t([o({type:Object}),e("design:type",Object)],yO.prototype,"_debouncedRegexChange",void 0),t([s("regex"),e("design:type",RegExp),e("design:paramtypes",[])],yO.prototype,"_regex",null),t([a("selectionState","names"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],yO.prototype,"_setIsolatorIcon",null),yO=t([i("tf-multi-checkbox")],yO);let vO=class extends we{get _parts(){var t=this.value,e=this.delimiterPattern;const n=[];for(;;){const i=new RegExp(e,"g");if(i.test(t),0===i.lastIndex){n.push(t);break}n.push(t.slice(0,i.lastIndex)),t=t.slice(i.lastIndex)}return n}};vO.template=xe`
    <!--
      This ugly formatting is required to prevent spaces from slipping
      into the HTML.
    -->
    <template is="dom-repeat" items="[[_parts]]" as="part"
      >[[part]]<wbr
    /></template>
  `,t([o({type:String}),e("design:type",String)],vO.prototype,"value",void 0),t([o({type:String}),e("design:type",String)],vO.prototype,"delimiterPattern",void 0),t([s("value","delimiterPattern"),e("design:type",Array),e("design:paramtypes",[])],vO.prototype,"_parts",null),vO=t([i("tf-wbr-string")],vO);let bO=class extends(cr(we)){constructor(){super(...arguments),this.runSelectionState=cl("runSelectionState",{defaultValue:{}}).call(this),this.regexInput=Xs("regexInput",{defaultValue:""}).call(this),this._dataLocationClipLength=250,this._dataLocationDelimiterPattern="[/=_,-]",this.coloring={getColor:_O},this._storeRunSelectionState=ul("runSelectionState",{defaultValue:{}}),this._regexObserver=$s("regexInput",{defaultValue:""})}attached(){this._runStoreListener=Il.addListener((()=>{this.set("runs",Il.getRuns())})),this.set("runs",Il.getRuns()),this._envStoreListener=Pl.addListener((()=>{this.set("dataLocation",Pl.getDataLocation())})),this.set("dataLocation",Pl.getDataLocation())}detached(){Il.removeListenerByKey(this._runStoreListener),Pl.removeListenerByKey(this._envStoreListener)}_toggleAll(){this.$.multiCheckbox.toggleAll()}get _clippedDataLocation(){var t=this.dataLocation,e=this._dataLocationClipLength;if(void 0!==t)return t.length>e?t.substring(0,e):t}_openDataLocationDialog(t){t.preventDefault(),this.$$("#data-location-dialog").open()}_shouldShowExpandDataLocationButton(t,e){return t&&t.length>e}};bO.template=xe`
    <paper-dialog with-backdrop="" id="data-location-dialog">
      <h2>Data Location</h2>
      <tf-wbr-string
        value="[[dataLocation]]"
        delimiter-pattern="[[_dataLocationDelimiterPattern]]"
      >
      </tf-wbr-string
    ></paper-dialog>
    <div id="top-text">
      <h3 id="tooltip-help" class="tooltip-container">Runs</h3>
    </div>
    <tf-multi-checkbox
      id="multiCheckbox"
      names="[[runs]]"
      selection-state="{{runSelectionState}}"
      out-selected="{{selectedRuns}}"
      regex="{{regexInput}}"
      coloring="[[coloring]]"
    ></tf-multi-checkbox>
    <paper-button class="x-button" id="toggle-all" on-tap="_toggleAll">
      Toggle All Runs
    </paper-button>
    <template is="dom-if" if="[[dataLocation]]">
      <div id="data-location">
        <tf-wbr-string
          value="[[_clippedDataLocation]]"
          delimiter-pattern="[[_dataLocationDelimiterPattern]]"
        ></tf-wbr-string
        ><!--
          We use HTML comments to remove spaces before the ellipsis.
        --><template
          is="dom-if"
          if="[[_shouldShowExpandDataLocationButton(dataLocation, _dataLocationClipLength)]]"
          ><!--
          --><a href="" on-click="_openDataLocationDialog">…</a>
        </template>
      </div>
    </template>
    <style>
      :host {
        box-sizing: border-box;
        display: flex;
        flex-direction: column;
        padding-bottom: 10px;
      }
      #top-text {
        color: var(--tb-secondary-text-color);
        width: 100%;
        flex-grow: 0;
        flex-shrink: 0;
        padding-right: 16px;
        box-sizing: border-box;
      }
      tf-wbr-string {
        overflow-wrap: break-word;
      }
      tf-multi-checkbox {
        display: flex;
        flex-grow: 1;
        flex-shrink: 1;
        overflow: hidden;
      }
      .x-button {
        font-size: 13px;
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
      }
      #tooltip-help {
        color: var(--tb-secondary-text-color);
        margin: 0;
        font-weight: normal;
        font-size: 14px;
        margin-bottom: 5px;
      }
      paper-button {
        margin-left: 0;
      }
      #data-location {
        color: var(--tb-ui-dark-accent);
        font-size: 13px;
        margin: 5px 0 0 0;
        max-width: 288px;
      }
    </style>
  `,t([o({type:Object,observer:"_storeRunSelectionState"}),e("design:type",Object)],bO.prototype,"runSelectionState",void 0),t([o({type:String,observer:"_regexObserver"}),e("design:type",String)],bO.prototype,"regexInput",void 0),t([o({type:Array,notify:!0}),e("design:type",Array)],bO.prototype,"selectedRuns",void 0),t([o({type:Array}),e("design:type",Array)],bO.prototype,"runs",void 0),t([o({type:String,notify:!0}),e("design:type",String)],bO.prototype,"dataLocation",void 0),t([o({type:Number}),e("design:type",Number)],bO.prototype,"_dataLocationClipLength",void 0),t([o({type:String}),e("design:type",String)],bO.prototype,"_dataLocationDelimiterPattern",void 0),t([o({type:Object}),e("design:type",Object)],bO.prototype,"coloring",void 0),t([s("dataLocation","_dataLocationClipLength"),e("design:type",String),e("design:paramtypes",[])],bO.prototype,"_clippedDataLocation",null),bO=t([i("tf-runs-selector")],bO);class xO{constructor(){this.cancellationCount=0}cancellable(t){const e=this.cancellationCount;return n=>t({value:n,cancelled:this.cancellationCount!==e})}cancelAll(){this.cancellationCount++}}let wO=class extends(cr(we)){constructor(){super(...arguments),this.html=""}get sanitizedHtml(){return this.html}attached(){window.requestAnimationFrame((()=>{this.scopeSubtree(this.$.markdown,!0)}))}};function SO(t){return t?t.toString().replace(/GMT-\d+ \(([^)]+)\)/,"$1"):""}function MO(t){const e=(function n(t){if(!t)return null;let e=t.match(/^#([0-9a-f]{1,2})([0-9a-f]{1,2})([0-9a-f]{1,2})$/);if(!e)return null;if(4==t.length)for(var n=1;n<=3;n++)e[n]=e[n]+e[n];return[parseInt(e[1],16),parseInt(e[2],16),parseInt(e[3],16)]})(t);if(!e)return"inherit";return Math.round((299*e[0]+587*e[1]+114*e[2])/1e3)>125?"inherit":"#eee"}wO.template=xe`
    <div id="markdown" inner-h-t-m-l="[[sanitizedHtml]]"></div>
    <style>
      /*
       * Reduce topmost and bottommost margins from 16px to 0.3em (renders
       * at about 4.8px) to keep the layout compact. This improves the
       * appearance when there is only one line of text; standard Markdown
       * renderers will still include a \`<p>\` element.
       *
       * By targeting only the top-level, extremal elements, we preserve any
       * actual paragraph breaks and only change the padding against the
       * component edges.
       */
      #markdown > p:first-child {
        margin-top: 0.3em;
      }
      #markdown > p:last-child {
        margin-bottom: 0.3em;
      }

      /* Pleasant styles for Markdown tables. */
      #markdown table {
        border-collapse: collapse;
      }
      #markdown table th {
        font-weight: 600;
      }
      #markdown table th,
      #markdown table td {
        padding: 6px 13px;
        border: 1px solid var(--tb-ui-border, #dfe2e5);
      }
      #markdown table tr {
        background-color: inherit;
        border-top: 1px solid var(--tb-ui-border, #c6cbd1);
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],wO.prototype,"html",void 0),t([s("html"),e("design:type",Object),e("design:paramtypes",[])],wO.prototype,"sanitizedHtml",null),wO=t([i("tf-markdown-view")],wO),ml({moduleName:"tf-card-heading-style",styleContent:"\n    figcaption {\n      width: 100%;\n    }\n\n    /** Horizontal line of labels. */\n    .heading-row {\n      margin-top: -4px;\n      display: flex;\n      flex-direction: row;\n      flex-wrap: wrap;\n    }\n\n    /** Piece of text in the figure caption. */\n    .heading-label {\n      flex-grow: 1;\n      margin-top: 4px;\n      max-width: 100%;\n      word-wrap: break-word;\n    }\n\n    /** Makes label show on the right. */\n    .heading-right {\n      flex-grow: 0;\n    }\n  "});let EO=class extends we{constructor(){super(...arguments),this.displayName=null,this.tag=null,this.run=null,this.description=null,this.color=null}_updateHeadingStyle(){this.updateStyles({"--tf-card-heading-background-color":this._runBackground,"--tf-card-heading-color":this._runColor})}_computeRunBackground(t){return t||"none"}_computeRunColor(t){return MO(t)}get _nameLabel(){return this.displayName||this.tag||""}get _tagLabel(){var t=this.tag;return t&&t!==this.displayName?t:""}_toggleDescriptionDialog(t){const e=this.$.descriptionDialog;e.positionTarget=t.target,e.toggle()}};EO.template=xe`
    <div class="container">
      <figcaption class="content">
        <div class="heading-row">
          <template is="dom-if" if="[[_nameLabel]]">
            <div itemprop="name" class="heading-label name">[[_nameLabel]]</div>
          </template>
          <template is="dom-if" if="[[run]]">
            <!-- Extra wrapping span needed to avoid flexbox blockification. -->
            <!-- (see flexbox spec, section 4 "Flex Items") -->
            <span>
              <span
                itemprop="run"
                id="heading-run"
                class="heading-label heading-right run"
                >[[run]]</span
              >
            </span>
          </template>
        </div>
        <template is="dom-if" if="[[_tagLabel]]">
          <div class="heading-row">
            <div class="heading-label">
              tag: <span itemprop="tag">[[_tagLabel]]</span>
            </div>
          </div>
        </template>
        <slot></slot>
      </figcaption>
      <template is="dom-if" if="[[description]]">
        <paper-icon-button
          icon="info"
          on-tap="_toggleDescriptionDialog"
          title="Show summary description"
        ></paper-icon-button>
      </template>
      <paper-dialog
        id="descriptionDialog"
        no-overlap=""
        horizontal-align="auto"
        vertical-align="auto"
      >
        <paper-dialog-scrollable>
          <tf-markdown-view html="[[description]]"></tf-markdown-view>
        </paper-dialog-scrollable>
      </paper-dialog>
    </div>
    <style include="tf-card-heading-style">
      .container {
        display: flex;
      }
      .content {
        font-size: 12px;
        flex-grow: 1;
      }
      .name {
        font-size: 14px;
      }
      .run {
        font-size: 11px;
        width: auto;
        border-radius: 3px;
        font-weight: bold;
        padding: 1px 4px 2px;
      }
      paper-icon-button {
        flex-grow: 0;
      }
      paper-dialog-scrollable {
        max-width: 640px;
      }
      #heading-run {
        background: var(--tf-card-heading-background-color);
        color: var(--tf-card-heading-color);
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],EO.prototype,"displayName",void 0),t([o({type:String}),e("design:type",String)],EO.prototype,"tag",void 0),t([o({type:String}),e("design:type",String)],EO.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],EO.prototype,"description",void 0),t([o({type:String}),e("design:type",String)],EO.prototype,"color",void 0),t([o({type:String,computed:"_computeRunBackground(color)",readOnly:!0,observer:"_updateHeadingStyle"}),e("design:type",String)],EO.prototype,"_runBackground",void 0),t([o({type:String,computed:"_computeRunColor(color)",readOnly:!0,observer:"_updateHeadingStyle"}),e("design:type",String)],EO.prototype,"_runColor",void 0),t([s("displayName","tag"),e("design:type",String),e("design:paramtypes",[])],EO.prototype,"_nameLabel",null),t([s("displayName","tag"),e("design:type",String),e("design:paramtypes",[])],EO.prototype,"_tagLabel",null),EO=t([i("tf-card-heading")],EO);let TO=class extends(cr(we)){constructor(){super(...arguments),this._metadataCanceller=new xO,this._steps=[],this._attached=!1}get _runColor(){return _O(this.run)}get _hasAtLeastOneStep(){var t=this._steps;return!!t&&t.length>0}get _hasMultipleSteps(){var t=this._steps;return!!t&&t.length>1}get _maxStepIndex(){return this._steps.length-1}get _currentDatum(){return this._steps[this._stepIndex]}get _sampleText(){return`${this.sample+1}`}get _hasMultipleSamples(){return this.totalSamples>1}attached(){this._attached=!0,this.reload()}_reloadOnRunTagChange(){this.reload()}reload(){if(!this._attached)return;this._metadataCanceller.cancelAll();const t=Er().pluginRoute("audio","/audio",new URLSearchParams({tag:this.tag,run:this.run,sample:String(this.sample)})),e=this._metadataCanceller.cancellable((t=>{if(t.cancelled)return;const e=t.value.map(this._createStepDatum.bind(this));this.set("_steps",e),this.set("_stepIndex",e.length-1)}));this.requestManager.request(t).then(e)}_createStepDatum(t){const e=new URLSearchParams(t.query);e.append("ts",String(t.wall_time));const n=Er().pluginRoute("audio","/individualAudio",e);return{wall_time:SO(new Date(1e3*t.wall_time)),step:t.step,label:t.label,contentType:t.contentType,url:n}}};TO.template=xe`
    <tf-card-heading
      tag="[[tag]]"
      run="[[run]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
      color="[[_runColor]]"
    >
      <template is="dom-if" if="[[_hasMultipleSamples]]">
        <div class="heading-row">
          <div class="heading-label">
            sample: [[_sampleText]] of [[totalSamples]]
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep]]">
        <div class="heading-row">
          <div class="heading-label">
            step <strong>[[_currentDatum.step]]</strong>
          </div>
          <template is="dom-if" if="[[_currentDatum.wall_time]]">
            <div class="heading-label heading-right">
              [[_currentDatum.wall_time]]
            </div>
          </template>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps]]">
        <div class="heading-row">
          <paper-slider
            id="steps"
            immediate-value="{{_stepIndex}}"
            max="[[_maxStepIndex]]"
            max-markers="[[_maxStepIndex]]"
            snaps=""
            step="1"
            value="{{_stepIndex}}"
          ></paper-slider>
        </div>
      </template>
    </tf-card-heading>
    <template is="dom-if" if="[[_hasAtLeastOneStep]]">
      <audio
        controls=""
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            </p>
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/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     *
     * @fileoverview manually add d3-selection-multi to d3 default bundle. Most of this code is
     * copied from d3-selection-multi@1.0.0.
     * See https://github.com/d3/d3-selection-multi/issues/11 for why we have to do this
     */
Object.defineProperty({},"__esModule",{value:!0});var Zmt=wpt,Jmt=wpt;function Qmt(t,e){return t.each((function(){var t=e.apply(this,arguments),n=Zmt.select(this);for(var i in t)n.attr(i,t[i])}))}function tgt(t,e){for(var n in e)t.attr(n,e[n]);return t}function egt(t,e,n){return t.each((function(){var t=e.apply(this,arguments),i=Zmt.select(this);for(var r in t)i.style(r,t[r],n)}))}function ngt(t,e,n){for(var i in e)t.style(i,e[i],n);return t}function igt(t,e){return t.each((function(){var t=e.apply(this,arguments),n=Zmt.select(this);for(var i in t)n.property(i,t[i])}))}function rgt(t,e){for(var n in e)t.property(n,e[n]);return t}function ogt(t,e){return t.each((function(){var n=e.apply(this,arguments),i=Zmt.select(this).transition(t);for(var r in n)i.attr(r,n[r])}))}function agt(t,e){for(var n in e)t.attr(n,e[n]);return t}function sgt(t,e,n){return t.each((function(){var i=e.apply(this,arguments),r=Zmt.select(this).transition(t);for(var o in i)r.style(o,i[o],n)}))}function lgt(t,e,n){for(var i in e)t.style(i,e[i],n);return t}Zmt.selection.prototype.attrs=function cgt(t){return("function"==typeof t?Qmt:tgt)(this,t)},Zmt.selection.prototype.styles=function ugt(t,e){return("function"==typeof t?egt:ngt)(this,t,null==e?"":e)},Zmt.selection.prototype.properties=function hgt(t){return("function"==typeof t?igt:rgt)(this,t)},Jmt.transition.prototype.attrs=function dgt(t){return("function"==typeof t?ogt:agt)(this,t)},Jmt.transition.prototype.styles=function pgt(t,e){return("function"==typeof t?sgt:lgt)(this,t,null==e?"":e)};var fgt={},mgt={},ggt=Me(Fm),_gt={};Object.defineProperty(_gt,"__esModule",{value:!0});var ygt=wpt;_gt.coerceExternalD3=function vgt(t){if(null==t.attrs){if(null==t.nodes){var e=[];return t.each((function(){e.push(this)})),ygt.selectAll(e)}return ygt.selectAll(t.nodes())}return t};var bgt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(bgt,"__esModule",{value:!0}),bgt.makeEnum=function xgt(t){return t.reduce((function(t,e){return t[e]=e,t}),{})},
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
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/**
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     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
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/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
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     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Tyt,"__esModule",{value:!0});var Ayt=Cyt;function kyt(t){Ayt.SHOW_WARNINGS&&console.warn(t)}function Lyt(t,e){for(var n=[],i=2;i<arguments.length;i++)n[i-2]=arguments[i];return 0===e?(t(n),-1):window.setTimeout(t,e,n)}Tyt.warn=kyt,Tyt.setTimeout=Lyt,Tyt.debounce=function Pyt(t,e,n){var i=null,r=[],o=function(){e.apply(n,r)};return function(){r=Array.prototype.slice.call(arguments),clearTimeout(i),i=Lyt(o,t)}},Tyt.deprecated=function Nyt(t,e,n){void 0===n&&(n=""),kyt("Method "+t+" has been deprecated in version "+e+". Please refer to the release notes. "+n)};var Iyt={};
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     * Copyright 2017-present Palantir Technologies
     * @license MIT
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/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(zyt,"__esModule",{value:!0});var Dyt=(function(){function t(){"function"==typeof window.Set?this._es6Set=new window.Set:this._values=[],this.size=0}return t.prototype.add=function(t){return null!=this._es6Set?(this._es6Set.add(t),this.size=this._es6Set.size,this):(this.has(t)||(this._values.push(t),this.size=this._values.length),this)},t.prototype.delete=function(t){if(null!=this._es6Set){var e=this._es6Set.delete(t);return this.size=this._es6Set.size,e}var n=this._values.indexOf(t);return-1!==n&&(this._values.splice(n,1),this.size=this._values.length,!0)},t.prototype.has=function(t){return null!=this._es6Set?this._es6Set.has(t):-1!==this._values.indexOf(t)},t.prototype.forEach=function(t,e){var n=this;if(null==this._es6Set)this._values.forEach((function(i){t.call(e,i,i,n)}));else{this._es6Set.forEach((function(i,r){return t.call(e,i,r,n)}),e)}},t})();zyt.Set=Dyt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Oyt,"__esModule",{value:!0});var Byt=BO,Hyt=(function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return Byt.__extends(e,t),e.prototype.callCallbacks=function(){for(var t=this,e=[],n=0;n<arguments.length;n++)e[n]=arguments[n];return this.forEach((function(n){n.apply(t,e)})),this},e})(zyt.Set);Oyt.CallbackSet=Hyt;var Fyt={};
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     * Copyright 2014-present Palantir Technologies
     * @license MIT
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     * Copyright 2014-present Palantir Technologies
     * @license MIT
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     * Copyright 2017-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(qyt,"__esModule",{value:!0}),qyt.assign=function Yyt(){for(var t=[],e=0;e<arguments.length;e++)t[e]=arguments[e];for(var n={},i=0,r=t;i<r.length;i++)for(var o=r[i],a=Object.keys(o),s=0,l=a;s<l.length;s++){var c=l[s];n[c]=o[c]}return n};var Xyt={};
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     * Copyright 2017-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Xyt,"__esModule",{value:!0});var $yt=D_t,Kyt="__Plottable_ClientTranslator";Xyt.getTranslator=function Zyt(t){var e=t.root().rootElement().node(),n=e[Kyt];return null==n&&(n=new Jyt(e),e[Kyt]=n),n};var Jyt=(function(){function t(t){this._rootElement=t}return t.prototype.computePosition=function(t,e){var n={x:t,y:e},i=$yt.Math.getCumulativeTransform(this._rootElement);return null==i?n:$yt.Math.applyTransform(i,n)},t.isEventInside=function(t,e){return $yt.DOM.contains(t.root().rootElement().node(),e.target)},t})();Xyt.Translator=Jyt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=BO;t.Array=B_t,t.Color=W_t,t.DOM=J_t,t.Math=Q_t,t.RTree=xyt,t.Stacking=Eyt,t.Window=Tyt,e.__exportStar(Iyt,t),e.__exportStar(Oyt,t),e.__exportStar(_gt,t),e.__exportStar(Fyt,t),e.__exportStar(jyt,t),e.__exportStar(qyt,t),e.__exportStar(zyt,t),e.__exportStar(Xyt,t)})(D_t);var Qyt={};
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     * Copyright 2014-present Palantir Technologies
     * @license MIT
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/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=D_t,n=bgt,i=Qyt,r=new e.Set,o=new e.Set,a=!1,s=!1;t.Policy=n.makeEnum(["immediate","animationFrame","timeout"]);var l=new i.AnimationFrame;function c(t){o.add(t),r.add(t),u()}function u(){a||(a=!0,l.render())}t.renderPolicy=function h(n){if(null==n)return l;switch(n){case t.Policy.immediate:l=new i.Immediate;break;case t.Policy.animationFrame:l=new i.AnimationFrame;break;case t.Policy.timeout:l=new i.Timeout;break;default:e.Window.warn("Unrecognized renderPolicy: "+n)}},t.registerToRender=function d(t){s&&e.Window.warn("Registered to render while other components are flushing: request may be ignored"),r.add(t),u()},t.registerToComputeLayoutAndRender=c,t.registerToComputeLayout=function p(t){c(t)},t.flush=function f(){if(a){o.forEach((function(t){return t.computeLayout()})),r.forEach((function(t){return t.render()})),s=!0;var t=new e.Set;r.forEach((function(e){try{e.renderImmediately()}catch(n){window.setTimeout((function(){throw n}),0),t.add(e)}})),o=new e.Set,r=t,a=!1,s=!1}}})(z_t),
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(O_t,"__esModule",{value:!0});var ovt=wpt,avt=z_t,svt=D_t,lvt=Ce.exports,cvt=_gt,uvt=bgt;O_t.XAlignment=uvt.makeEnum(["left","center","right"]),O_t.YAlignment=uvt.makeEnum(["top","center","bottom"]);var hvt=(function(){function t(){this._overflowHidden=!1,this._origin={x:0,y:0},this._xAlignment="left",this._yAlignment="top",this._isSetup=!1,this._isAnchored=!1,this._cssClasses=new svt.Set,this._destroyed=!1,this._onAnchorCallbacks=new svt.CallbackSet,this._onDetachCallbacks=new svt.CallbackSet,this._cssClasses.add("component")}return t.prototype.anchor=function(t){if(t=cvt.coerceExternalD3(t),this._destroyed)throw new Error("Can't reuse destroy()-ed Components!");return this.isRoot()&&(this._rootElement=t,this._rootElement.classed("plottable",!0)),null!=this._element?t.node().appendChild(this._element.node()):(this._element=t.append("div"),this._setup()),this._isAnchored=!0,this._onAnchorCallbacks.callCallbacks(this),this},t.prototype.onAnchor=function(t){return this._isAnchored&&t(this),this._onAnchorCallbacks.add(t),this},t.prototype.offAnchor=function(t){return this._onAnchorCallbacks.delete(t),this},t.prototype._setup=function(){var t=this;this._isSetup||(this._cssClasses.forEach((function(e){t._element.classed(e,!0)})),this._cssClasses=new svt.Set,this._backgroundContainer=this._element.append("svg").classed("background-container",!0),this._content=this._element.append("svg").classed("content",!0),this._foregroundContainer=this._element.append("svg").classed("foreground-container",!0),this._content.classed(this._overflowHidden?"component-overflow-hidden":"component-overflow-visible",!0),this._isSetup=!0)},t.prototype.requestedSpace=function(t,e){return{minWidth:0,minHeight:0}},t.prototype.computeLayout=function(e,n,i){if(null==e||null==n||null==i){if(null==this._element)throw new Error("anchor() must be called before computeLayout()");if(null==this._rootElement)throw new Error("null arguments cannot be passed to computeLayout() on a non-root, unanchored node");e={x:0,y:0};var r=this._rootElement.node();n=svt.DOM.elementWidth(r),i=svt.DOM.elementHeight(r)}var o=this._sizeFromOffer(n,i),a=o.height,s=o.width;return this.setBounds(s,a,e.x+(n-s)*t._xAlignToProportion[this._xAlignment],e.y+(i-a)*t._yAlignToProportion[this._yAlignment]),this},t.prototype.setBounds=function(t,e,n,i){return void 0===n&&(n=0),void 0===i&&(i=0),this._width=t,this._height=e,this._origin={x:n,y:i},null!=this._element&&this._element.styles({left:n+"px",height:e+"px",top:i+"px",width:t+"px"}),null!=this._resizeHandler&&this._resizeHandler({width:t,height:e}),this},t.prototype._sizeFromOffer=function(t,e){var n=this.requestedSpace(t,e);return{width:this.fixedWidth()?Math.min(t,n.minWidth):t,height:this.fixedHeight()?Math.min(e,n.minHeight):e}},t.prototype.render=function(){return this._isAnchored&&this._isSetup&&this.width()>=0&&this.height()>=0&&avt.registerToRender(this),this},t.prototype.renderLowPriority=function(){return this.render()},t.prototype._scheduleComputeLayout=function(){this._isAnchored&&this._isSetup&&avt.registerToComputeLayoutAndRender(this)},t.prototype.onResize=function(t){return this._resizeHandler=t,this},t.prototype.renderImmediately=function(){return this},t.prototype.redraw=function(){return this._isAnchored&&this._isSetup&&(this.isRoot()?this._scheduleComputeLayout():this.parent().redraw()),this},t.prototype.invalidateCache=function(){},t.prototype.renderTo=function(t){if(this.detach(),null!=t){var e=void 0;if(!(e="string"==typeof t||lvt.isElement(t)?ovt.select(t):cvt.coerceExternalD3(t)).node()||null==e.node().nodeName)throw new Error("Plottable requires a valid Element to renderTo");if("svg"===e.node().nodeName)throw new Error("Plottable 3.x and later can only renderTo an HTML component; pass a div instead!");this.anchor(e)}if(null==this._element)throw new Error("If a Component has never been rendered before, then renderTo must be given a node to render to, or a d3.Selection, or a selector string");return avt.registerToComputeLayoutAndRender(this),avt.flush(),this},t.prototype.xAlignment=function(e){if(null==e)return this._xAlignment;if(e=e.toLowerCase(),null==t._xAlignToProportion[e])throw new Error("Unsupported alignment: "+e);return this._xAlignment=e,this.redraw(),this},t.prototype.yAlignment=function(e){if(null==e)return this._yAlignment;if(e=e.toLowerCase(),null==t._yAlignToProportion[e])throw new Error("Unsupported alignment: "+e);return this._yAlignment=e,this.redraw(),this},t.prototype.hasClass=function(t){return null!=t&&(null==this._element?this._cssClasses.has(t):this._element.classed(t))},t.prototype.addClass=function(t){return null==t||(null==this._element?this._cssClasses.add(t):this._element.classed(t,!0)),this},t.prototype.removeClass=function(t){return null==t||(null==this._element?this._cssClasses.delete(t):this._element.classed(t,!1)),this},t.prototype.fixedWidth=function(){return!1},t.prototype.fixedHeight=function(){return!1},t.prototype.detach=function(){return this.parent(null),this._isAnchored&&this._element.remove(),this._isAnchored=!1,this._onDetachCallbacks.callCallbacks(this),this},t.prototype.onDetach=function(t){return this._onDetachCallbacks.add(t),this},t.prototype.offDetach=function(t){return this._onDetachCallbacks.delete(t),this},t.prototype.parent=function(t){if(void 0===t)return this._parent;if(null!==t&&!t.has(this))throw new Error("Passed invalid parent");return this._parent=t,this},t.prototype.bounds=function(){var t=this.origin();return{topLeft:t,bottomRight:{x:t.x+this.width(),y:t.y+this.height()}}},t.prototype.destroy=function(){this._destroyed=!0,this.detach()},t.prototype.width=function(){return this._width},t.prototype.height=function(){return this._height},t.prototype.origin=function(){return{x:this._origin.x,y:this._origin.y}},t.prototype.originToRoot=function(){for(var t=this.origin(),e=this.parent();null!=e;){var n=e.origin();t.x+=n.x,t.y+=n.y,e=e.parent()}return t},t.prototype.root=function(){for(var t=this;!t.isRoot();)t=t.parent();return t},t.prototype.isRoot=function(){return null==this.parent()},t.prototype.foreground=function(){return this._foregroundContainer},t.prototype.content=function(){return this._content},t.prototype.element=function(){return this._element},t.prototype.rootElement=function(){return this.root()._rootElement},t.prototype.background=function(){return this._backgroundContainer},t._xAlignToProportion={left:0,center:.5,right:1},t._yAlignToProportion={top:0,center:.5,bottom:1},t})();O_t.Component=hvt;var dvt={},pvt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(pvt,"__esModule",{value:!0});var fvt=wpt;function mvt(t){return void 0===t&&(t=3),gvt(t),function(e){return e.toFixed(t)}}function gvt(t){if(t<0||t>20)throw new RangeError("Formatter precision must be between 0 and 20");if(t!==Math.floor(t))throw new RangeError("Formatter precision must be an integer")}
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */pvt.currency=function _vt(t,e,n){void 0===t&&(t=2),void 0===e&&(e="$"),void 0===n&&(n=!0);var i=mvt(t);return function(t){var r=i(Math.abs(t));return""!==r&&(n?r=e+r:r+=e,t<0&&(r="-"+r)),r}},pvt.fixed=mvt,pvt.general=function yvt(t){return void 0===t&&(t=3),gvt(t),function(e){if("number"==typeof e){var n=Math.pow(10,t);return String(Math.round(e*n)/n)}return String(e)}},pvt.identity=function vvt(){return function(t){return String(t)}},pvt.percentage=function bvt(t){void 0===t&&(t=0);var e=mvt(t);return function(t){var n=100*t,i=t.toString(),r=Math.pow(10,i.length-(i.indexOf(".")+1));return n=parseInt((n*r).toString(),10)/r,e(n)+"%"}},pvt.siSuffix=function xvt(t){return void 0===t&&(t=3),gvt(t),function(e){return fvt.format("."+t+"s")(e)}},pvt.shortScale=function wvt(t){void 0===t&&(t=3),gvt(t);var e="KMBTQ",n=fvt.format("."+t+"e"),i=fvt.format("."+t+"f"),r=Math.pow(10,3*(e.length+1)),o=Math.pow(10,-t);return function(t){var a=Math.abs(t);if((a<o||a>=r)&&0!==a)return n(t);for(var s=-1;a>=Math.pow(1e3,s+2)&&s<e.length-1;)s++;var l="";return l=-1===s?i(t):i(t/Math.pow(1e3,s+1))+e[s],(t>0&&"1000"===l.substr(0,4)||t<0&&"-1000"===l.substr(0,5))&&(s<e.length-1?(s++,l=i(t/Math.pow(1e3,s+1))+e[s]):l=n(t)),l}},pvt.multiTime=function Svt(){var t=[{specifier:".%L",predicate:function(t){return 0!==t.getMilliseconds()}},{specifier:":%S",predicate:function(t){return 0!==t.getSeconds()}},{specifier:"%I:%M",predicate:function(t){return 0!==t.getMinutes()}},{specifier:"%I %p",predicate:function(t){return 0!==t.getHours()}},{specifier:"%a %d",predicate:function(t){return 0!==t.getDay()&&1!==t.getDate()}},{specifier:"%b %d",predicate:function(t){return 1!==t.getDate()}},{specifier:"%b",predicate:function(t){return 0!==t.getMonth()}}];return function(e){var n=t.filter((function(t){return t.predicate(e)}));return fvt.timeFormat(n.length>0?n[0].specifier:"%Y")(e)}},pvt.time=function Mvt(t,e){return void 0===e&&(e=false),e?fvt.utcFormat(t):fvt.timeFormat(t)},Object.defineProperty(dvt,"__esModule",{value:!0});var Evt=BO,Tvt=wpt,Cvt=Lgt,Avt=O_t,kvt=pvt,Lvt=D_t;dvt.AxisOrientation=bgt.makeEnum(["bottom","left","right","top"]);var Pvt=(function(t){function e(e,n){var i=t.call(this)||this;if(i._endTickLength=5,i._innerTickLength=5,i._tickLabelPadding=10,i._margin=15,i._showEndTickLabels=!1,i._annotationsEnabled=!1,i._annotationTierCount=1,null==e||null==n)throw new Error("Axis requires a scale and orientation");return i._scale=e,i.orientation(n),i._setDefaultAlignment(),i.addClass("axis"),i.isHorizontal()?i.addClass("x-axis"):i.addClass("y-axis"),i.formatter(kvt.identity()),i._rescaleCallback=function(t){return i._rescale()},i._scale.onUpdate(i._rescaleCallback),i._annotatedTicks=[],i._annotationFormatter=kvt.identity(),i}return Evt.__extends(e,t),e.prototype.destroy=function(){t.prototype.destroy.call(this),this._scale.offUpdate(this._rescaleCallback)},e.prototype.tickLabelDataOnElement=function(t){if(null!=t){for(var n;null!=t&&t.classList&&void 0===n;)t.classList.contains(e.TICK_LABEL_CLASS)?n=t:t=t.parentNode;return void 0===t?void 0:Tvt.select(t).datum()}},e.prototype._computeWidth=function(){return this._maxLabelTickLength()},e.prototype._computeHeight=function(){return this._maxLabelTickLength()},e.prototype.requestedSpace=function(t,n){var i=0,r=0;this.isHorizontal()?(r=this._computeHeight()+this._margin,this.annotationsEnabled()&&(r+=(this._annotationMeasurer.measure().height+2*e._ANNOTATION_LABEL_PADDING)*this.annotationTierCount())):(i=this._computeWidth()+this._margin,this.annotationsEnabled()&&(i+=(this._annotationMeasurer.measure().height+2*e._ANNOTATION_LABEL_PADDING)*this.annotationTierCount()));return{minWidth:i,minHeight:r}},e.prototype.fixedHeight=function(){return this.isHorizontal()},e.prototype.fixedWidth=function(){return!this.isHorizontal()},e.prototype._rescale=function(){this.render()},e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),this.isHorizontal()?this._scale.range([0,this.width()]):this._scale.range([this.height(),0]),this},e.prototype._sizeFromOffer=function(t,e){var n=this.requestedSpace(t,e);return this.isHorizontal()?{width:t,height:n.minHeight}:{height:e,width:n.minWidth}},e.prototype._setup=function(){t.prototype._setup.call(this),this._tickMarkContainer=this.content().append("g").classed(e.TICK_MARK_CLASS+"-container",!0),this._tickLabelContainer=this.content().append("g").classed(e.TICK_LABEL_CLASS+"-container",!0),this._baseline=this.content().append("line").classed("baseline",!0),this._annotationContainer=this.content().append("g").classed("annotation-container",!0),this._annotationContainer.append("g").classed("annotation-line-container",!0),this._annotationContainer.append("g").classed("annotation-circle-container",!0),this._annotationContainer.append("g").classed("annotation-rect-container",!0);var n=this._annotationContainer.append("g").classed("annotation-label-container",!0),i=new Cvt.SvgContext(n.node());this._annotationMeasurer=new Cvt.CacheMeasurer(i),this._annotationWriter=new Cvt.Writer(this._annotationMeasurer,i)},e.prototype._getTickValues=function(){return[]},e.prototype.renderImmediately=function(){var t=this._getTickValues(),n=this._tickMarkContainer.selectAll("."+e.TICK_MARK_CLASS).data(t),i=n.enter().append("line").classed(e.TICK_MARK_CLASS,!0).merge(n);return i.attrs(this._generateTickMarkAttrHash()),Tvt.select(i.nodes()[0]).classed(e.END_TICK_MARK_CLASS,!0).attrs(this._generateTickMarkAttrHash(!0)),Tvt.select(i.nodes()[t.length-1]).classed(e.END_TICK_MARK_CLASS,!0).attrs(this._generateTickMarkAttrHash(!0)),n.exit().remove(),this._baseline.attrs(this._generateBaselineAttrHash()),this.annotationsEnabled()?this._drawAnnotations():this._removeAnnotations(),this},e.prototype.annotatedTicks=function(t){return null==t?this._annotatedTicks:(this._annotatedTicks=t,this.render(),this)},e.prototype.annotationFormatter=function(t){return null==t?this._annotationFormatter:(this._annotationFormatter=t,this.render(),this)},e.prototype.annotationsEnabled=function(t){return null==t?this._annotationsEnabled:(this._annotationsEnabled=t,this.redraw(),this)},e.prototype.annotationTierCount=function(t){if(null==t)return this._annotationTierCount;if(t<0)throw new Error("annotationTierCount cannot be negative");return this._annotationTierCount=t,this.redraw(),this},e.prototype._drawAnnotations=function(){var t=this,n=e._ANNOTATION_LABEL_PADDING,i=new Lvt.Map,r=this._annotatedTicksToRender();r.forEach((function(e){var r=t._annotationMeasurer.measure(t.annotationFormatter()(e));i.set(e,{width:r.width+2*n,height:r.height+2*n})}));var o=this._annotationMeasurer.measure().height+2*n,a=this._annotationToTier(i),s=new Lvt.Set,l=this.isHorizontal()?this.height():this.width(),c=this._coreSize(),u=Math.min(this.annotationTierCount(),Math.floor((l-c)/o));a.forEach((function(t,e){(-1===t||t>=u)&&s.add(e)}));var h,d=function(t,e,n){var i=t.selectAll("."+n).data(r),o=i.enter().append(e).classed(n,!0).merge(i);return i.exit().remove(),o},p=function(e){switch(t.orientation()){case"bottom":case"right":return a.get(e)*o+c;case"top":case"left":return l-c-a.get(e)*o}},f=function(e){return t._scale.scale(e)},m=function(t){return s.has(t)?"hidden":"visible"};switch(this.orientation()){case"bottom":case"right":h=0;break;case"top":h=this.height();break;case"left":h=this.width()}var g=this.isHorizontal();d(this._annotationContainer.select(".annotation-line-container"),"line",e.ANNOTATION_LINE_CLASS).attrs({x1:g?f:h,x2:g?f:p,y1:g?h:f,y2:g?p:f,visibility:m}),d(this._annotationContainer.select(".annotation-circle-container"),"circle",e.ANNOTATION_CIRCLE_CLASS).attrs({cx:g?f:h,cy:g?h:f,r:3});var _=function(e){switch(t.orientation()){case"bottom":case"right":return p(e);case"top":case"left":return p(e)-i.get(e).height}};d(this._annotationContainer.select(".annotation-rect-container"),"rect",e.ANNOTATION_RECT_CLASS).attrs({x:g?f:_,y:g?_:f,width:g?function(t){return i.get(t).width}:function(t){return i.get(t).height},height:g?function(t){return i.get(t).height}:function(t){return i.get(t).width},visibility:m});var y=this._annotationWriter,v=this.annotationFormatter(),b=d(this._annotationContainer.select(".annotation-label-container"),"g",e.ANNOTATION_LABEL_CLASS);b.selectAll(".text-container").remove(),b.attrs({transform:function(t){return"translate("+(g?f(t):_(t))+","+(g?_(t):f(t))+")"},visibility:m}).each((function(t){y.write(v(t),g?i.get(t).width:i.get(t).height,g?i.get(t).height:i.get(t).width,{xAlign:"center",yAlign:"center",textRotation:g?0:90},Tvt.select(this).node())}))},e.prototype._annotatedTicksToRender=function(){var t=this,e=this._scale.range();return Lvt.Array.uniq(this.annotatedTicks().filter((function(n){return null!=n&&Lvt.Math.inRange(t._scale.scale(n),e[0],e[1])})))},e.prototype._coreSize=function(){var t=this.isHorizontal()?this.height():this.width(),e=this.isHorizontal()?this._computeHeight():this._computeWidth();return Math.min(e,t)},e.prototype._annotationTierHeight=function(){return this._annotationMeasurer.measure().height+2*e._ANNOTATION_LABEL_PADDING},e.prototype._annotationToTier=function(t){var e=this,n=[[]],i=new Lvt.Map,r=this.isHorizontal()?this.width():this.height();return this._annotatedTicksToRender().forEach((function(o){var a=e._scale.scale(o),s=t.get(o).width;if(a<0||a+s>r)i.set(o,-1);else{for(var l=0;n[l].some((function(n){var i=e._scale.scale(n),r=t.get(n).width;return a+s>=i&&a<=i+r}));)l++,n.length===l&&n.push([]);n[l].push(o),i.set(o,l)}})),i},e.prototype._removeAnnotations=function(){this._annotationContainer.selectAll(".annotation-line").remove(),this._annotationContainer.selectAll(".annotation-circle").remove(),this._annotationContainer.selectAll(".annotation-rect").remove(),this._annotationContainer.selectAll(".annotation-label").remove()},e.prototype._generateBaselineAttrHash=function(){var t={x1:0,y1:0,x2:0,y2:0};switch(this._orientation){case"bottom":t.x2=this.width();break;case"top":t.x2=this.width(),t.y1=this.height(),t.y2=this.height();break;case"left":t.x1=this.width(),t.x2=this.width(),t.y2=this.height();break;case"right":t.y2=this.height()}return t},e.prototype._generateTickMarkAttrHash=function(t){var e=this;void 0===t&&(t=!1);var n={x1:0,y1:0,x2:0,y2:0},i=function(t){return e._scale.scale(t)};this.isHorizontal()?(n.x1=i,n.x2=i):(n.y1=i,n.y2=i);var r=t?this._endTickLength:this._innerTickLength;switch(this._orientation){case"bottom":n.y2=r;break;case"top":n.y1=this.height(),n.y2=this.height()-r;break;case"left":n.x1=this.width(),n.x2=this.width()-r;break;case"right":n.x2=r}return n},e.prototype._setDefaultAlignment=function(){switch(this._orientation){case"bottom":this.yAlignment("top");break;case"top":this.yAlignment("bottom");break;case"left":this.xAlignment("right");break;case"right":this.xAlignment("left")}},e.prototype.isHorizontal=function(){return"top"===this._orientation||"bottom"===this._orientation},e.prototype.getScale=function(){return this._scale},e.prototype.formatter=function(t){return null==t?this._formatter:(this._formatter=t,this.redraw(),this)},e.prototype.innerTickLength=function(t){if(null==t)return this._innerTickLength;if(t<0)throw new Error("inner tick length must be positive");return this._innerTickLength=t,this.redraw(),this},e.prototype.endTickLength=function(t){if(null==t)return this._endTickLength;if(t<0)throw new Error("end tick length must be positive");return this._endTickLength=t,this.redraw(),this},e.prototype._maxLabelTickLength=function(){return this.showEndTickLabels()?Math.max(this.innerTickLength(),this.endTickLength()):this.innerTickLength()},e.prototype.tickLabelPadding=function(t){if(null==t)return this._tickLabelPadding;if(t<0)throw new Error("tick label padding must be positive");return this._tickLabelPadding=t,this.redraw(),this},e.prototype.margin=function(t){if(null==t)return this._margin;if(t<0)throw new Error("margin size must be positive");return this._margin=t,this.redraw(),this},e.prototype.orientation=function(t){if(null==t)return this._orientation;var e=t.toLowerCase();if("top"!==e&&"bottom"!==e&&"left"!==e&&"right"!==e)throw new Error("unsupported orientation");return this._orientation=e,this.redraw(),this},e.prototype.showEndTickLabels=function(t){return null==t?this._showEndTickLabels:(this._showEndTickLabels=t,this.render(),this)},e.prototype._showAllTickMarks=function(){this._tickMarkContainer.selectAll("."+e.TICK_MARK_CLASS).each((function(){Tvt.select(this).style("visibility","inherit")}))},e.prototype._showAllTickLabels=function(){this._tickLabelContainer.selectAll("."+e.TICK_LABEL_CLASS).each((function(){Tvt.select(this).style("visibility","inherit")}))},e.prototype._hideOverflowingTickLabels=function(){var t=this.element().node().getBoundingClientRect(),n=this._tickLabelContainer.selectAll("."+e.TICK_LABEL_CLASS);n.empty()||n.each((function(e,n){Lvt.DOM.clientRectInside(this.getBoundingClientRect(),t)||Tvt.select(this).style("visibility","hidden")}))},e.prototype._hideTickMarksWithoutLabel=function(){var t=this._tickMarkContainer.selectAll("."+e.TICK_MARK_CLASS),n=this._tickLabelContainer.selectAll("."+e.TICK_LABEL_CLASS).filter((function(t,e){var n=Tvt.select(this).style("visibility");return"inherit"===n||"visible"===n})).data();t.each((function(t,e){-1===n.indexOf(t)&&Tvt.select(this).style("visibility","hidden")}))},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._annotationMeasurer.reset()},e.END_TICK_MARK_CLASS="end-tick-mark",e.TICK_MARK_CLASS="tick-mark",e.TICK_LABEL_CLASS="tick-label",e.ANNOTATION_LINE_CLASS="annotation-line",e.ANNOTATION_RECT_CLASS="annotation-rect",e.ANNOTATION_CIRCLE_CLASS="annotation-circle",e.ANNOTATION_LABEL_CLASS="annotation-label",e._ANNOTATION_LABEL_PADDING=4,e})(Avt.Component);dvt.Axis=Pvt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(kgt,"__esModule",{value:!0});var Nvt=BO,Ivt=wpt,Rvt=Lgt,Ovt=O_t,zvt=D_t,Dvt=dvt,Bvt=(function(t){function e(e,n){void 0===n&&(n="bottom");var i=t.call(this,e,n)||this;return i._tickLabelAngle=0,i._tickLabelShearAngle=0,i.addClass("category-axis"),i}return Nvt.__extends(e,t),Object.defineProperty(e.prototype,"_wrapper",{get:function(){var t=new Rvt.Wrapper;return null!=this._tickLabelMaxLines&&t.maxLines(this._tickLabelMaxLines),t},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"_writer",{get:function(){return new Rvt.Writer(this._measurer,this._typesetterContext,this._wrapper)},enumerable:!0,configurable:!0}),e.prototype._setup=function(){t.prototype._setup.call(this),this._typesetterContext=new Rvt.SvgContext(this._tickLabelContainer.node()),this._measurer=new Rvt.CacheMeasurer(this._typesetterContext)},e.prototype._rescale=function(){return this.redraw()},e.prototype.requestedSpace=function(t,e){var n=this.isHorizontal()?0:this._tickSpaceRequired()+this.margin(),i=this.isHorizontal()?this._tickSpaceRequired()+this.margin():0;if(0===this._scale.domain().length)return{minWidth:0,minHeight:0};if(this.annotationsEnabled()){var r=this._annotationTierHeight()*this.annotationTierCount();this.isHorizontal()?i+=r:n+=r}var o=this._measureTickLabels(t,e);return{minWidth:o.usedWidth+n,minHeight:o.usedHeight+i}},e.prototype._coreSize=function(){var t=this.isHorizontal()?this.height():this.width(),e=this.isHorizontal()?this.requestedSpace(this.width(),this.height()).minHeight:this.requestedSpace(this.width(),this.height()).minWidth,n=this.margin()+this._annotationTierHeight();return Math.min(e-n,t)},e.prototype._getTickValues=function(){return this.getDownsampleInfo().domain},e.prototype._sizeFromOffer=function(t,e){return Ovt.Component.prototype._sizeFromOffer.call(this,t,e)},e.prototype.getDownsampleInfo=function(t,n){void 0===t&&(t=this._scale),void 0===n&&(n=t.invertRange());var i=0===this._tickLabelAngle?1:1/Math.cos(this._tickLabelShearAngle/180*Math.PI),r=Math.ceil(e._MINIMUM_WIDTH_PER_LABEL_PX*i/t.stepWidth());return{domain:n.filter((function(t,e){return e%r==0})),stepWidth:r*t.stepWidth()}},e.prototype.tickLabelAngle=function(t){if(null==t)return this._tickLabelAngle;if(0!==t&&90!==t&&-90!==t)throw new Error("Angle "+t+" not supported; only 0, 90, and -90 are valid values");return this._tickLabelAngle=t,this.redraw(),this},e.prototype.tickLabelShearAngle=function(t){if(null==t)return this._tickLabelShearAngle;if(t<-80||t>80)throw new Error("Angle "+t+" not supported; Must be between [-80, 80]");return this._tickLabelShearAngle=t,this.redraw(),this},e.prototype.tickLabelMaxWidth=function(t){return 0===arguments.length?this._tickLabelMaxWidth:(this._tickLabelMaxWidth=t,this.redraw(),this)},e.prototype.tickLabelMaxLines=function(t){return 0===arguments.length?this._tickLabelMaxLines:(this._tickLabelMaxLines=t,this.redraw(),this)},e.prototype._tickSpaceRequired=function(){return this._maxLabelTickLength()+this.tickLabelPadding()},e.prototype._drawTicks=function(t,e){var n,i,r=this;switch(this.tickLabelAngle()){case 0:n={left:"right",right:"left",top:"center",bottom:"center"},i={left:"center",right:"center",top:"bottom",bottom:"top"};break;case 90:n={left:"center",right:"center",top:"right",bottom:"left"},i={left:"top",right:"bottom",top:"center",bottom:"center"};break;case-90:n={left:"center",right:"center",top:"left",bottom:"right"},i={left:"bottom",right:"top",top:"center",bottom:"center"}}e.each((function(e){var o=Ivt.select(this),a=r.isHorizontal()?t:r.width()-r._tickSpaceRequired(),s=r.isHorizontal()?r.height()-r._tickSpaceRequired():t,l={xAlign:n[r.orientation()],yAlign:i[r.orientation()],textRotation:r.tickLabelAngle(),textShear:r.tickLabelShearAngle()};if(null!=r._tickLabelMaxWidth){if("left"===r.orientation()&&a>r._tickLabelMaxWidth){var c=a-r._tickLabelMaxWidth,u=o.attr("transform")+" translate("+c+", 0)";o.attr("transform",u)}a=Math.min(a,r._tickLabelMaxWidth)}r._writer.write(r.formatter()(e),a,s,l,o.node())}))},e.prototype._measureTickLabels=function(t,e){var n=this,i=this._scale.cloneWithoutProviders().range([0,this.isHorizontal()?t:e]),r=this.getDownsampleInfo(i),o=r.domain,a=r.stepWidth,s=t-this._tickSpaceRequired();this.isHorizontal()&&(s=a,0!==this._tickLabelAngle&&(s=e-this._tickSpaceRequired()),s=Math.max(s,0));var l=a;this.isHorizontal()&&(l=e-this._tickSpaceRequired(),0!==this._tickLabelAngle&&(l=t-this._tickSpaceRequired()),l=Math.max(l,0)),null!=this._tickLabelMaxWidth&&(s=Math.min(s,this._tickLabelMaxWidth));var c,u=o.map((function(t){return n._wrapper.wrap(n.formatter()(t),n._measurer,s,l)})),h=this.isHorizontal()&&0===this._tickLabelAngle?Ivt.sum:zvt.Math.max,d=this.isHorizontal()&&0===this._tickLabelAngle?zvt.Math.max:Ivt.sum,p=h(u,(function(t){return n._measurer.measure(t.wrappedText).width}),0),f=d(u,(function(t){return n._measurer.measure(t.wrappedText).height}),0);return 0!==this._tickLabelAngle&&(p=(c=[f,p])[0],f=c[1]),{usedWidth:p,usedHeight:f}},e.prototype.renderImmediately=function(){var e=this;t.prototype.renderImmediately.call(this);var n=this._scale,i=this.getDownsampleInfo(n),r=i.domain,o=i.stepWidth,a=o;this.isHorizontal()&&null!=this._tickLabelMaxWidth&&(a=Math.min(a,this._tickLabelMaxWidth));var s=this._tickLabelContainer.selectAll("."+Dvt.Axis.TICK_LABEL_CLASS).data(r),l=s.enter().append("g").classed(Dvt.Axis.TICK_LABEL_CLASS,!0).merge(s);s.exit().remove(),l.attr("transform",(function(t,i){var r=n.scale(t)-a/2;return"translate("+(e.isHorizontal()?r:0)+","+(e.isHorizontal()?0:r)+")"})),l.text(""),this._drawTicks(o,l);var c="right"===this.orientation()?this._tickSpaceRequired():0,u="bottom"===this.orientation()?this._tickSpaceRequired():0;return this._tickLabelContainer.attr("transform","translate("+c+","+u+")"),this._showAllTickMarks(),this._showAllTickLabels(),this._hideTickMarksWithoutLabel(),this},e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),this.isHorizontal()||this._scale.range([0,this.height()]),this},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e._MINIMUM_WIDTH_PER_LABEL_PX=15,e})(Dvt.Axis);kgt.Category=Bvt;var Hvt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Hvt,"__esModule",{value:!0});var Fvt=BO,Vvt=wpt,Uvt=Lgt,jvt=pvt,Gvt=D_t,Wvt=dvt,qvt=(function(t){function e(e,n){var i=t.call(this,e,n)||this;return i._tickLabelPositioning="center",i._usesTextWidthApproximation=!1,i.formatter(jvt.general()),i}return Fvt.__extends(e,t),e.prototype._setup=function(){t.prototype._setup.call(this);var e=new Uvt.SvgContext(this._tickLabelContainer.node(),Wvt.Axis.TICK_LABEL_CLASS);this._measurer=new Uvt.CacheMeasurer(e),this._wrapper=(new Uvt.Wrapper).maxLines(1)},e.prototype._computeWidth=function(){var t=this._usesTextWidthApproximation?this._computeApproximateTextWidth():this._computeExactTextWidth();return"center"===this._tickLabelPositioning?this._maxLabelTickLength()+this.tickLabelPadding()+t:Math.max(this._maxLabelTickLength(),this.tickLabelPadding()+t)},e.prototype._computeExactTextWidth=function(){var t=this,e=this._getTickValues().map((function(e){var n=t.formatter()(e);return t._measurer.measure(n).width}));return Gvt.Math.max(e,0)},e.prototype._computeApproximateTextWidth=function(){var t=this,e=this._getTickValues(),n=this._measurer.measure("M").width,i=e.map((function(e){return t.formatter()(e).length*n}));return Gvt.Math.max(i,0)},e.prototype._computeHeight=function(){var t=this._measurer.measure().height;return"center"===this._tickLabelPositioning?this._maxLabelTickLength()+this.tickLabelPadding()+t:Math.max(this._maxLabelTickLength(),this.tickLabelPadding()+t)},e.prototype._getTickValues=function(){var t=this._scale,e=t.domain(),n=e[0]<=e[1]?e[0]:e[1],i=e[0]>=e[1]?e[0]:e[1];return t.ticks().filter((function(t){return t>=n&&t<=i}))},e.prototype._rescale=function(){if(this._isSetup){if(!this.isHorizontal()){var t=this._computeWidth();if(t>this.width()||t<this.width()-this.margin())return void this.redraw()}this.render()}},e.prototype.renderImmediately=function(){var e=this;t.prototype.renderImmediately.call(this);var n={x:0,y:0,dx:"0em",dy:"0.3em"},i=this._maxLabelTickLength(),r=this.tickLabelPadding(),o="middle",a=0,s=0,l=0,c=0;if(this.isHorizontal())switch(this._tickLabelPositioning){case"left":o="end",a=-r,c=r;break;case"center":c=i+r;break;case"right":o="start",a=r,c=r}else switch(this._tickLabelPositioning){case"top":n.dy="-0.3em",l=r,s=-r;break;case"center":l=i+r;break;case"bottom":n.dy="1em",l=r,s=r}var u=this._generateTickMarkAttrHash();switch(this.orientation()){case"bottom":n.x=u.x1,n.dy="0.95em",s=u.y1+c;break;case"top":n.x=u.x1,n.dy="-.25em",s=u.y1-c;break;case"left":o="end",a=u.x1-l,n.y=u.y1;break;case"right":o="start",a=u.x1+l,n.y=u.y1}var h=this._getTickValues(),d=this._tickLabelContainer.selectAll("."+Wvt.Axis.TICK_LABEL_CLASS).data(h);return d.exit().remove(),d.enter().append("text").classed(Wvt.Axis.TICK_LABEL_CLASS,!0).merge(d).style("text-anchor",o).style("visibility","inherit").attrs(n).text((function(t){return e.formatter()(t)})),this._tickLabelContainer.attr("transform","translate("+a+", "+s+")"),this._showAllTickMarks(),this.showEndTickLabels()||this._hideEndTickLabels(),this._hideOverflowingTickLabels(),this._hideOverlappingTickLabels(),"center"!==this._tickLabelPositioning&&this._hideTickMarksWithoutLabel(),this},e.prototype.tickLabelPosition=function(t){if(null==t)return this._tickLabelPositioning;var e=t.toLowerCase();if(this.isHorizontal()){if("left"!==e&&"center"!==e&&"right"!==e)throw new Error(e+" is not a valid tick label position for a horizontal NumericAxis")}else if("top"!==e&&"center"!==e&&"bottom"!==e)throw new Error(e+" is not a valid tick label position for a vertical NumericAxis");return this._tickLabelPositioning=e,this.redraw(),this},e.prototype.usesTextWidthApproximation=function(t){return null==t?this._usesTextWidthApproximation:(this._usesTextWidthApproximation=t,this)},e.prototype._hideEndTickLabels=function(){var t=this.element().node().getBoundingClientRect(),e=this._tickLabelContainer.selectAll("."+Wvt.Axis.TICK_LABEL_CLASS);if(0!==e.size()){var n=e.nodes()[0];Gvt.DOM.clientRectInside(n.getBoundingClientRect(),t)||Vvt.select(n).style("visibility","hidden");var i=e.nodes()[e.size()-1];Gvt.DOM.clientRectInside(i.getBoundingClientRect(),t)||Vvt.select(i).style("visibility","hidden")}},e.prototype._hideOverlappingTickLabels=function(){for(var t=this._tickLabelContainer.selectAll("."+Wvt.Axis.TICK_LABEL_CLASS).filter((function(t,e){var n=Vvt.select(this).style("visibility");return"inherit"===n||"visible"===n})),e=t.nodes().map((function(t){return t.getBoundingClientRect()})),n=1;!this._hasOverlapWithInterval(n,e)&&n<e.length;)n+=1;t.each((function(t,e){var i=Vvt.select(this);e%n!=0&&i.style("visibility","hidden")}))},e.prototype._hasOverlapWithInterval=function(t,e){for(var n="center"===this._tickLabelPositioning?this.tickLabelPadding():3*this.tickLabelPadding(),i=e.map((function(t){return Gvt.DOM.expandRect(t,n)})),r=0;r<i.length-t;r+=t){if(Gvt.DOM.clientRectsOverlap(i[r],i[r+t]))return!1}return!0},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e})(Wvt.Axis);Hvt.Numeric=qvt;var Yvt={},Xvt={},$vt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty($vt,"__esModule",{value:!0});var Kvt=D_t;$vt.intervalTickGenerator=function Zvt(t){if(t<=0)throw new Error("interval must be positive number");return function(e){var n=e.domain(),i=Math.min(n[0],n[1]),r=Math.max(n[0],n[1]),o=Math.ceil(i/t)*t,a=Math.floor((r-o)/t)+1,s=i%t==0?[]:[i],l=Kvt.Math.range(0,a).map((function(e){return o+e*t})),c=r%t==0?[]:[r];return s.concat(l).concat(c)}},$vt.integerTickGenerator=function Jvt(){return function(t){var e=t.defaultTicks();return e.filter((function(t,n){return t%1==0||0===n||n===e.length-1}))}};var Qvt={},tbt={};function ebt(t,e,n){return n-(n-t)*e}function nbt(t,e,n){return(t*e-n)/(e-1)}function ibt(t,e,n,i){var r=e>1,o=r?i:n;if(null==o)return e;var a=t.getTransformationDomain(),s=Math.abs(a[1]-a[0]);return(r?Math.min:Math.max)(e,o/s)}function rbt(t,e,n,i,r){if(e<=1)return{centerPoint:n,zoomAmount:e};if(null==i&&null==r)return{centerPoint:n,zoomAmount:e};var o=obt(t),a=(function s(t){var e=t.getTransformationDomain();return e[1]<e[0]})(t);i=null==i?a?1/0:-1/0:i,r=null==r?a?-1/0:1/0:r;var l=t.getTransformationDomain(),c=l[0],u=l[1],h=t.scaleTransformation(r),d=t.scaleTransformation(u),p=ebt(d,e,n),f=t.scaleTransformation(i),m=t.scaleTransformation(c),g=ebt(m,e,n),_=Math.abs(h-f);if(Math.abs(p-g)>_){var y=(h-f)/(d-m);return 1!==y?{centerPoint:nbt(d,y,h),zoomAmount:y}:{centerPoint:n,zoomAmount:y}}return p>h!=o?{centerPoint:nbt(d,e,h),zoomAmount:e}:g<f!=o?{centerPoint:nbt(m,e,f),zoomAmount:e}:{centerPoint:n,zoomAmount:e}}function obt(t){var e=t.range();return e[1]<e[0]}
/**
     * Copyright 2017-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(tbt,"__esModule",{value:!0}),tbt.zoomOut=ebt,tbt.constrainedZoom=function abt(t,e,n,i,r,o,a){return rbt(t,e=ibt(t,e,i,r),n,o,a)},tbt.constrainZoomExtents=ibt,tbt.constrainZoomValues=rbt,tbt.constrainedTranslation=function sbt(t,e,n,i){var r,o=t.getTransformationDomain(),a=o[0],s=o[1],l=obt(t);if(e>0!==l){if(null!=(r=i)){var c=t.scaleTransformation(s),u=t.scaleTransformation(r);e=(l?Math.max:Math.min)(c+e,u)-c}}else if(null!=(r=n)){var h=t.scaleTransformation(a),d=t.scaleTransformation(r);e=(l?Math.min:Math.max)(h+e,d)-h}return e};var lbt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(lbt,"__esModule",{value:!0});var cbt=D_t,ubt=(function(){function t(){this._autoDomainAutomatically=!0,this._domainModificationInProgress=!1,this._updateId=0,this._callbacks=new cbt.CallbackSet,this._includedValuesProviders=new cbt.Set}return t.prototype.extentOfValues=function(t){return[]},t.prototype._getAllIncludedValues=function(t){var e=this;void 0===t&&(t=!1);var n=[];return this._includedValuesProviders.forEach((function(i){var r=i(e,t);n=n.concat(r)})),n},t.prototype._getExtent=function(){return[]},t.prototype.onUpdate=function(t){return this._callbacks.add(t),this},t.prototype.offUpdate=function(t){return this._callbacks.delete(t),this},t.prototype._dispatchUpdate=function(){this._updateId++,this._callbacks.callCallbacks(this)},t.prototype.autoDomain=function(){return this._autoDomainAutomatically=!0,this._setDomain(this._getExtent()),this},t.prototype.autoDomainIfAutomaticMode=function(){this._autoDomainAutomatically&&this.autoDomain()},t.prototype.scale=function(t){throw new Error("Subclasses should override scale")},t.prototype.ticks=function(){return this.domain()},t.prototype.domain=function(t){return null==t?this._getDomain():(this._autoDomainAutomatically=!1,this._setDomain(t),this)},t.prototype._getDomain=function(){throw new Error("Subclasses should override _getDomain")},t.prototype._setDomain=function(t){this._domainModificationInProgress||(this._domainModificationInProgress=!0,this._backingScaleDomain(t),this._dispatchUpdate(),this._domainModificationInProgress=!1)},t.prototype._backingScaleDomain=function(t){throw new Error("Subclasses should override _backingDomain")},t.prototype.range=function(t){return null==t?this._getRange():(this._setRange(t),this)},t.prototype._getRange=function(){throw new Error("Subclasses should override _getRange")},t.prototype._setRange=function(t){throw new Error("Subclasses should override _setRange")},t.prototype.addIncludedValuesProvider=function(t){return this._includedValuesProviders.add(t),this.autoDomainIfAutomaticMode(),this},t.prototype.removeIncludedValuesProvider=function(t){return this._includedValuesProviders.delete(t),this.autoDomainIfAutomaticMode(),this},t.prototype.updateId=function(){return this._updateId},t})();lbt.Scale=ubt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Qvt,"__esModule",{value:!0});var hbt=BO,dbt=wpt,pbt=tbt,fbt=D_t,mbt=[0,1],gbt=(function(t){function e(){var n=t.call(this)||this;n._range=[0,1],n._d3Scale=dbt.scaleBand(),n._d3Scale.range(mbt),n._d3TransformationScale=dbt.scaleLinear(),n._d3TransformationScale.domain(mbt);return n._innerPadding=e._convertToPlottableInnerPadding(.3),n._outerPadding=e._convertToPlottableOuterPadding(.5,.3),n}return hbt.__extends(e,t),e.prototype.cloneWithoutProviders=function(){var t=(new e).domain(this.domain()).range(this.range()).innerPadding(this.innerPadding()).outerPadding(this.outerPadding());return t._d3TransformationScale.domain(this._d3TransformationScale.domain()),t},e.prototype.extentOfValues=function(t){return fbt.Array.uniq(t)},e.prototype._getExtent=function(){return fbt.Array.uniq(this._getAllIncludedValues())},e.prototype.domain=function(e){return t.prototype.domain.call(this,e)},e.prototype.invertRange=function(t){var e=this;void 0===t&&(t=this.range());var n=this._d3Scale.bandwidth(),i=this.invertedTransformation(t[0]),r=this.invertedTransformation(t[1]),o=this._d3Scale.domain(),a=o.map((function(t){return e._d3Scale(t)+n/2})),s=dbt.bisect(a,i),l=dbt.bisect(a,r);return o.slice(s,l)},e.prototype.range=function(e){return t.prototype.range.call(this,e)},e._convertToPlottableInnerPadding=function(t){return 1/(1-t)-1},e._convertToPlottableOuterPadding=function(t,e){return t/(1-e)},e.prototype._setBands=function(){var t=1-1/(1+this.innerPadding()),e=this.outerPadding()/(1+this.innerPadding());this._d3Scale.paddingInner(t),this._d3Scale.paddingOuter(e)},e.prototype.rangeBand=function(){return this._rescaleBand(this._d3Scale.bandwidth())},e.prototype.stepWidth=function(){return this._rescaleBand(this._d3Scale.bandwidth()*(1+this.innerPadding()))},e.prototype.ticks=function(){return this.domain()},e.prototype.innerPadding=function(t){return null==t?this._innerPadding:(this._innerPadding=t,this.range(this.range()),this._dispatchUpdate(),this)},e.prototype.outerPadding=function(t){return null==t?this._outerPadding:(this._outerPadding=t,this.range(this.range()),this._dispatchUpdate(),this)},e.prototype.scale=function(t){var e=this._d3Scale(t)+this._d3Scale.bandwidth()/2;return this._d3TransformationScale(e)},e.prototype.zoom=function(t,e){var n=this;this._d3TransformationScale.domain(this._d3TransformationScale.range().map((function(i){return n._d3TransformationScale.invert(pbt.zoomOut(i,t,e))}))),this._dispatchUpdate()},e.prototype.pan=function(t){var e=this;this._d3TransformationScale.domain(this._d3TransformationScale.range().map((function(n){return e._d3TransformationScale.invert(n+t)}))),this._dispatchUpdate()},e.prototype.scaleTransformation=function(t){return this._d3TransformationScale(t)},e.prototype.invertedTransformation=function(t){return this._d3TransformationScale.invert(t)},e.prototype.getTransformationExtent=function(){return mbt},e.prototype.getTransformationDomain=function(){return this._d3TransformationScale.domain()},e.prototype.setTransformationDomain=function(t){this._d3TransformationScale.domain(t),this._dispatchUpdate()},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this._setBands(),this)},e.prototype._getRange=function(){return this._range},e.prototype._setRange=function(t){this._range=t,this._d3TransformationScale.range(t),this._setBands()},e.prototype._rescaleBand=function(t){return Math.abs(this._d3TransformationScale(t)-this._d3TransformationScale(0))},e})(lbt.Scale);Qvt.Category=gbt;var _bt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(_bt,"__esModule",{value:!0});var ybt=BO,vbt=wpt,bbt=D_t,xbt=lbt,wbt=(function(){function t(){this.count=0,this.tracker={}}return t.prototype.getIndex=function(t){if(null!=this.tracker[t])return this.tracker[t];var e=this.count;return this.tracker[t]=e,this.count+=1,e},t.prototype.clear=function(){this.count=0,this.tracker={}},t})(),Sbt=(function(t){function e(n){var i,r=t.call(this)||this;switch(r._rangeLength=1,r._tracker=new wbt,n){case null:case void 0:null==e._plottableColorCache&&(e._plottableColorCache=e._getPlottableColors()),i=vbt.scaleOrdinal().range(e._plottableColorCache);break;case"Category10":case"category10":case"10":i=vbt.scaleOrdinal(vbt.schemeCategory10);break;case"Category20":case"category20":case"20":i=vbt.scaleOrdinal(vbt.schemeCategory20);break;case"Category20b":case"category20b":case"20b":i=vbt.scaleOrdinal(vbt.schemeCategory20b);break;case"Category20c":case"category20c":case"20c":i=vbt.scaleOrdinal(vbt.schemeCategory20c);break;default:throw new Error("Unsupported ColorScale type")}return r._d3Scale=i,r._rangeLength=r._d3Scale.range().length,r}return ybt.__extends(e,t),e.prototype.extentOfValues=function(t){return bbt.Array.uniq(t)},e.prototype._getExtent=function(){return bbt.Array.uniq(this._getAllIncludedValues())},e.invalidateColorCache=function(){e._plottableColorCache=null},e._getPlottableColors=function(){for(var t=[],e=vbt.select("body").append("plottable-color-tester"),n=bbt.Color.colorTest(e,""),i=0,r=bbt.Color.colorTest(e,"plottable-colors-0");null!=r&&i<this._MAXIMUM_COLORS_FROM_CSS&&(r!==n||r!==t[t.length-1]);)t.push(r),i++,r=bbt.Color.colorTest(e,"plottable-colors-"+i);return e.remove(),t},e.prototype.scale=function(t){var n=this._d3Scale(t),i=this._tracker.getIndex(t),r=Math.floor(i/this._rangeLength);if(0===r)return n;var o=Math.log(r*e._LOOP_LIGHTEN_FACTOR+1);return bbt.Color.lightenColor(n,o)},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this._tracker.clear(),this)},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t),this._rangeLength=t.length},e._LOOP_LIGHTEN_FACTOR=1.6,e._MAXIMUM_COLORS_FROM_CSS=256,e})(xbt.Scale);_bt.Color=Sbt;var Mbt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Mbt,"__esModule",{value:!0});var Ebt=BO,Tbt=wpt,Cbt=D_t,Abt=(function(t){function e(n){void 0===n&&(n="linear");var i=t.call(this)||this;switch(n){case"linear":i._colorScale=Tbt.scaleLinear();break;case"log":i._colorScale=Tbt.scaleLog();break;case"sqrt":i._colorScale=Tbt.scaleSqrt();break;case"pow":i._colorScale=Tbt.scalePow()}if(null==i._colorScale)throw new Error("unknown QuantitativeScale scale type "+n);return i.range(e.REDS),i}return Ebt.__extends(e,t),e.prototype.extentOfValues=function(t){var e=Tbt.extent(t);return null==e[0]||null==e[1]?[]:e},e.prototype._d3InterpolatedScale=function(){return this._colorScale.range([0,1]).interpolate(this._interpolateColors())},e.prototype._interpolateColors=function(){var t=this._colorRange;if(t.length<2)throw new Error("Color scale arrays must have at least two elements.");return function(e,n){return function(e){var n=(e=Math.max(0,Math.min(1,e)))*(t.length-1),i=Math.floor(n),r=Math.ceil(n),o=n-i;return Tbt.interpolateLab(t[i],t[r])(o)}}},e.prototype._resetScale=function(){this._d3Scale=this._d3InterpolatedScale(),this.autoDomainIfAutomaticMode(),this._dispatchUpdate()},e.prototype.autoDomain=function(){var t=this._getAllIncludedValues();return t.length>0&&this._setDomain([Cbt.Math.min(t,0),Cbt.Math.max(t,0)]),this},e.prototype.scale=function(t){return this._d3Scale(t)},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._getRange=function(){return this._colorRange},e.prototype._setRange=function(t){this._colorRange=t,this._resetScale()},e.REDS=["#FFFFFF","#FFF6E1","#FEF4C0","#FED976","#FEB24C","#FD8D3C","#FC4E2A","#E31A1C","#B10026"],e.BLUES=["#FFFFFF","#CCFFFF","#A5FFFD","#85F7FB","#6ED3EF","#55A7E0","#417FD0","#2545D3","#0B02E1"],e.POSNEG=["#0B02E1","#2545D3","#417FD0","#55A7E0","#6ED3EF","#85F7FB","#A5FFFD","#CCFFFF","#FFFFFF","#FFF6E1","#FEF4C0","#FED976","#FEB24C","#FD8D3C","#FC4E2A","#E31A1C","#B10026"],e})(lbt.Scale);Mbt.InterpolatedColor=Abt;var kbt={},Lbt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Lbt,"__esModule",{value:!0});var Pbt=BO,Nbt=wpt,Ibt=tbt,Rbt=D_t,Obt=(function(t){function e(){var e=t.call(this)||this;return e._tickGenerator=function(t){return t.defaultTicks()},e._padProportion=.05,e._snappingDomainEnabled=!0,e._paddingExceptionsProviders=new Rbt.Set,e}return Pbt.__extends(e,t),e.prototype.autoDomain=function(){return this._domainMin=null,this._domainMax=null,t.prototype.autoDomain.call(this),this},e.prototype.autoDomainIfAutomaticMode=function(){if(null==this._domainMin||null==this._domainMax){var e=this._getExtent();if(null!=this._domainMin){var n=e[1];return this._domainMin>=n&&(n=this._expandSingleValueDomain([this._domainMin,this._domainMin])[1]),void this._setDomain([this._domainMin,n])}if(null!=this._domainMax){var i=e[0];return this._domainMax<=i&&(i=this._expandSingleValueDomain([this._domainMax,this._domainMax])[0]),void this._setDomain([i,this._domainMax])}t.prototype.autoDomainIfAutomaticMode.call(this)}else this._setDomain([this._domainMin,this._domainMax])},e.prototype._getUnboundedExtent=function(t){void 0===t&&(t=!1);var e=this._getAllIncludedValues(t),n=this._defaultExtent();if(0!==e.length){var i=[Rbt.Math.min(e,n[0]),Rbt.Math.max(e,n[1])];n=this._padDomain(i)}return n},e.prototype._getExtent=function(){var t=this._getUnboundedExtent();return null!=this._domainMin&&(t[0]=this._domainMin),null!=this._domainMax&&(t[1]=this._domainMax),t},e.prototype.addPaddingExceptionsProvider=function(t){return this._paddingExceptionsProviders.add(t),this.autoDomainIfAutomaticMode(),this},e.prototype.removePaddingExceptionsProvider=function(t){return this._paddingExceptionsProviders.delete(t),this.autoDomainIfAutomaticMode(),this},e.prototype.padProportion=function(t){if(null==t)return this._padProportion;if(t<0)throw new Error("padProportion must be non-negative");return this._padProportion=t,this.autoDomainIfAutomaticMode(),this},e.prototype._padDomain=function(t){var e=this;if(t[0].valueOf()===t[1].valueOf())return this._expandSingleValueDomain(t);if(0===this._padProportion)return t;var n=this._padProportion/2,i=t[0],r=t[1],o=!1,a=!1;this._paddingExceptionsProviders.forEach((function(t){t(e).forEach((function(t){t.valueOf()===i.valueOf()&&(o=!0),t.valueOf()===r.valueOf()&&(a=!0)}))}));var s=this._backingScaleDomain();this._backingScaleDomain(t);var l=o?i:this.invert(this.scale(i)-(this.scale(r)-this.scale(i))*n),c=a?r:this.invert(this.scale(r)+(this.scale(r)-this.scale(i))*n);return this._backingScaleDomain(s),this._snappingDomainEnabled?this._niceDomain([l,c]):[l,c]},e.prototype.snappingDomainEnabled=function(t){return null==t?this._snappingDomainEnabled:(this._snappingDomainEnabled=t,this.autoDomainIfAutomaticMode(),this)},e.prototype._expandSingleValueDomain=function(t){return t},e.prototype.invert=function(t){throw new Error("Subclasses should override invert")},e.prototype.domain=function(e){return null!=e&&(this._domainMin=e[0],this._domainMax=e[1]),t.prototype.domain.call(this,e)},e.prototype.domainMin=function(t){return null==t?this.domain()[0]:(this._domainMin=t,this.autoDomainIfAutomaticMode(),this)},e.prototype.domainMax=function(t){return null==t?this.domain()[1]:(this._domainMax=t,this.autoDomainIfAutomaticMode(),this)},e.prototype.extentOfValues=function(t){var e=Nbt.extent(t.filter((function(t){return Rbt.Math.isValidNumber(+t)})));return null==e[0]||null==e[1]?[]:e},e.prototype.zoom=function(t,e){var n=this;this.domain(this.range().map((function(i){return n.invert(Ibt.zoomOut(i,t,e))})))},e.prototype.pan=function(t){var e=this;this.domain(this.range().map((function(n){return e.invert(n+t)})))},e.prototype.scaleTransformation=function(t){throw new Error("Subclasses should override scaleTransformation")},e.prototype.invertedTransformation=function(t){throw new Error("Subclasses should override invertedTransformation")},e.prototype.getTransformationExtent=function(){throw new Error("Subclasses should override getTransformationExtent")},e.prototype.getTransformationDomain=function(){throw new Error("Subclasses should override getTransformationDomain")},e.prototype.setTransformationDomain=function(t){throw new Error("Subclasses should override setTransformationDomain")},e.prototype._setDomain=function(e){var n=function(t){return Rbt.Math.isNaN(t)||t===1/0||t===-1/0};n(e[0])||n(e[1])?Rbt.Window.warn("Warning: QuantitativeScales cannot take NaN or Infinity as a domain value. Ignoring."):t.prototype._setDomain.call(this,e)},e.prototype.defaultTicks=function(){throw new Error("Subclasses should override _getDefaultTicks")},e.prototype.ticks=function(){return this._tickGenerator(this)},e.prototype._niceDomain=function(t,e){throw new Error("Subclasses should override _niceDomain")},e.prototype._defaultExtent=function(){throw new Error("Subclasses should override _defaultExtent")},e.prototype.tickGenerator=function(t){return null==t?this._tickGenerator:(this._tickGenerator=t,this)},e._DEFAULT_NUM_TICKS=10,e})(lbt.Scale);Lbt.QuantitativeScale=Obt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(kbt,"__esModule",{value:!0});var zbt=BO,Dbt=wpt,Bbt=(function(t){function e(){var e=t.call(this)||this;return e._d3Scale=Dbt.scaleLinear(),e}return zbt.__extends(e,t),e.prototype._defaultExtent=function(){return[0,1]},e.prototype._expandSingleValueDomain=function(t){return t[0]===t[1]?[t[0]-1,t[1]+1]:t},e.prototype.scale=function(t){return this._d3Scale(t)},e.prototype.scaleTransformation=function(t){return this.scale(t)},e.prototype.invertedTransformation=function(t){return this.invert(t)},e.prototype.getTransformationExtent=function(){return this._getUnboundedExtent(!0)},e.prototype.getTransformationDomain=function(){return this.domain()},e.prototype.setTransformationDomain=function(t){this.domain(t)},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t)},e.prototype.invert=function(t){return this._d3Scale.invert(t)},e.prototype.defaultTicks=function(){return this._d3Scale.ticks(e._DEFAULT_NUM_TICKS)},e.prototype._niceDomain=function(t,e){return this._d3Scale.copy().domain(t).nice(e).domain()},e})(Lbt.QuantitativeScale);kbt.Linear=Bbt;var Hbt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Hbt,"__esModule",{value:!0});var Fbt=BO,Vbt=wpt,Ubt=(function(t){function e(e){void 0===e&&(e=10);var n=t.call(this)||this;return n._d3Scale=Vbt.scaleLog().base(e),n._setDomain(n._defaultExtent()),n}return Fbt.__extends(e,t),e.prototype._defaultExtent=function(){return[1,this._d3Scale.base()]},e.prototype._expandSingleValueDomain=function(t){return t[0]===t[1]?[t[0]/this._d3Scale.base(),t[1]*this._d3Scale.base()]:t},e.prototype.scale=function(t){return this._d3Scale(t)},e.prototype.scaleTransformation=function(t){return this.scale(t)},e.prototype.invertedTransformation=function(t){return this.invert(t)},e.prototype.getTransformationExtent=function(){return this._getUnboundedExtent(!0)},e.prototype.getTransformationDomain=function(){return this.domain()},e.prototype.setTransformationDomain=function(t){this.domain(t)},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t)},e.prototype.invert=function(t){return this._d3Scale.invert(t)},e.prototype.defaultTicks=function(){return this._d3Scale.ticks(e._DEFAULT_NUM_TICKS)},e.prototype._niceDomain=function(t,e){return this._d3Scale.copy().domain(t).nice().domain()},e})(Lbt.QuantitativeScale);Hbt.Log=Ubt;var jbt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(jbt,"__esModule",{value:!0});var Gbt=BO,Wbt=wpt,qbt=D_t,Ybt=Xvt,Xbt=(function(t){function e(e){void 0===e&&(e=10);var n=t.call(this)||this;if(n._logTickGenerator=function(t){var e=function(t,e,n){return[t,e,n].sort((function(t,e){return t-e}))[1]},i=qbt.Math.min(n._untransformedDomain,0),r=qbt.Math.max(n._untransformedDomain,0),o=i,a=e(i,r,-n._pivot),s=e(i,r,n._pivot),l=r,c=n._logTicks(-a,-o).map((function(t){return-t})).reverse(),u=n._logTicks(s,l),h=Math.max(i,-n._pivot),d=Math.min(r,n._pivot),p=Wbt.scaleLinear().domain([h,d]).ticks(n._howManyTicks(h,d)),f=c.concat(p).concat(u);return f.length<=1&&(f=Wbt.scaleLinear().domain([i,r]).ticks(Ybt.ModifiedLog._DEFAULT_NUM_TICKS)),f},n._d3Scale=Wbt.scaleLinear(),n._base=e,n._pivot=n._base,n._setDomain(n._defaultExtent()),n.tickGenerator(n._logTickGenerator),e<=1)throw new Error("ModifiedLogScale: The base must be > 1");return n}return Gbt.__extends(e,t),e.prototype._adjustedLog=function(t){var e=t<0?-1:1;return(t*=e)<this._pivot&&(t+=(this._pivot-t)/this._pivot),t=Math.log(t)/Math.log(this._base),t*=e},e.prototype._invertedAdjustedLog=function(t){var e=t<0?-1:1;return t*=e,(t=Math.pow(this._base,t))<this._pivot&&(t=this._pivot*(t-1)/(this._pivot-1)),t*=e},e.prototype.scale=function(t){return this._d3Scale(this._adjustedLog(t))},e.prototype.invert=function(t){return this._invertedAdjustedLog(this._d3Scale.invert(t))},e.prototype.scaleTransformation=function(t){return this.scale(t)},e.prototype.invertedTransformation=function(t){return this.invert(t)},e.prototype.getTransformationExtent=function(){return this._getUnboundedExtent(!0)},e.prototype.getTransformationDomain=function(){return this.domain()},e.prototype.setTransformationDomain=function(t){this.domain(t)},e.prototype._getDomain=function(){return this._untransformedDomain},e.prototype._setDomain=function(e){this._untransformedDomain=e;var n=[this._adjustedLog(e[0]),this._adjustedLog(e[1])];t.prototype._setDomain.call(this,n)},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._logTicks=function(t,e){var n=this,i=this._howManyTicks(t,e);if(0===i)return[];var r=Math.floor(Math.log(t)/Math.log(this._base)),o=Math.ceil(Math.log(e)/Math.log(this._base)),a=Wbt.range(o,r,-Math.ceil((o-r)/i)),s=Wbt.range(this._base,1,-(this._base-1)).map(Math.floor),l=qbt.Array.uniq(s),c=a.map((function(t){return l.map((function(e){return Math.pow(n._base,t-1)*e}))}));return qbt.Array.flatten(c).filter((function(n){return t<=n&&n<=e})).sort((function(t,e){return t-e}))},e.prototype._howManyTicks=function(t,e){var n=this._adjustedLog(qbt.Math.min(this._untransformedDomain,0)),i=this._adjustedLog(qbt.Math.max(this._untransformedDomain,0)),r=this._adjustedLog(t),o=this._adjustedLog(e);return Math.ceil((o-r)/(i-n)*Ybt.ModifiedLog._DEFAULT_NUM_TICKS)},e.prototype._niceDomain=function(t,e){return t},e.prototype._defaultExtent=function(){return[0,this._base]},e.prototype._expandSingleValueDomain=function(t){if(t[0]===t[1]){var e=t[0];return e>0?[e/this._base,e*this._base]:0===e?[-this._base,this._base]:[e*this._base,e/this._base]}return t},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t)},e.prototype.defaultTicks=function(){return this._d3Scale.ticks(Ybt.ModifiedLog._DEFAULT_NUM_TICKS)},e})(Lbt.QuantitativeScale);jbt.ModifiedLog=Xbt;var $bt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty($bt,"__esModule",{value:!0});var Kbt=BO,Zbt=wpt,Jbt=Yvt,Qbt=(function(t){function e(){var e=t.call(this)||this;return e._d3Scale=Zbt.scaleTime(),e.autoDomain(),e}return Kbt.__extends(e,t),e.prototype.tickInterval=function(t,n,i){void 0===n&&(n=1),void 0===i&&(i=!1);var r=Zbt.scaleTime(),o=e.timeIntervalToD3Time(t,i).every(n);return r.domain(this.domain()),r.range(this.range()),r.ticks(o)},e.prototype._setDomain=function(e){if(e[1]<e[0])throw new Error("Scale.Time domain values must be in chronological order");return t.prototype._setDomain.call(this,e)},e.prototype._defaultExtent=function(){return[new Date("1970-01-01"),new Date("1970-01-02")]},e.prototype._expandSingleValueDomain=function(t){var e=t[0].getTime(),n=t[1].getTime();if(e===n){var i=new Date(e);i.setDate(i.getDate()-1);var r=new Date(n);return r.setDate(r.getDate()+1),[i,r]}return t},e.prototype.scale=function(t){return this._d3Scale(t)},e.prototype.scaleTransformation=function(t){return this.scale(new Date(t))},e.prototype.invertedTransformation=function(t){return this.invert(t).getTime()},e.prototype.getTransformationExtent=function(){var t=this._getUnboundedExtent(!0);return[t[0].valueOf(),t[1].valueOf()]},e.prototype.getTransformationDomain=function(){var t=this.domain();return[t[0].valueOf(),t[1].valueOf()]},e.prototype.setTransformationDomain=function(t){var e=t[1];this.domain([new Date(t[0]),new Date(e)])},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t)},e.prototype.invert=function(t){return this._d3Scale.invert(t)},e.prototype.defaultTicks=function(){return this._d3Scale.ticks(e._DEFAULT_NUM_TICKS)},e.prototype._niceDomain=function(t){return this._d3Scale.copy().domain(t).nice().domain()},e.timeIntervalToD3Time=function(t,e){switch(t){case Jbt.TimeInterval.second:return e?Zbt.utcSecond:Zbt.timeSecond;case Jbt.TimeInterval.minute:return e?Zbt.utcMinute:Zbt.timeMinute;case Jbt.TimeInterval.hour:return e?Zbt.utcHour:Zbt.timeHour;case Jbt.TimeInterval.day:return e?Zbt.utcDay:Zbt.timeDay;case Jbt.TimeInterval.week:return e?Zbt.utcWeek:Zbt.timeWeek;case Jbt.TimeInterval.month:return e?Zbt.utcMonth:Zbt.timeMonth;case Jbt.TimeInterval.year:return e?Zbt.utcYear:Zbt.timeYear;default:throw Error("TimeInterval specified does not exist: "+t)}},e})(Lbt.QuantitativeScale);$bt.Time=Qbt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=BO;t.TickGenerators=$vt,e.__exportStar(Qvt,t),e.__exportStar(_bt,t),e.__exportStar(Mbt,t),e.__exportStar(kbt,t),e.__exportStar(Hbt,t),e.__exportStar(jbt,t),e.__exportStar($bt,t);var n=Qvt,i=Lbt;t.isTransformable=function r(t){return t instanceof i.QuantitativeScale||t instanceof n.Category}})(Xvt),(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=BO,n=wpt,i=Lgt,r=pvt,o=Xvt,a=D_t,s=bgt,l=dvt;t.TimeInterval=s.makeEnum(["second","minute","hour","day","week","month","year"]),t.TimeAxisOrientation=s.makeEnum(["top","bottom"]),t.TierLabelPosition=s.makeEnum(["between","center"]);var c,u=(function(s){function u(t,e,n){var i=s.call(this,t,e)||this;return i._maxTimeIntervalPrecision=null,i._tierLabelPositions=[],i._useUTC=n,i.addClass("time-axis"),i.tickLabelPadding(5),i.axisConfigurations(u._DEFAULT_TIME_AXIS_CONFIGURATIONS(i._useUTC)),i.annotationFormatter(r.time("%a %b %d, %Y",i._useUTC)),i}return e.__extends(u,s),u.prototype.tierLabelPositions=function(t){if(null==t)return this._tierLabelPositions;if(!t.every((function(t){return"between"===t.toLowerCase()||"center"===t.toLowerCase()})))throw new Error("Unsupported position for tier labels");return this._tierLabelPositions=t,this.redraw(),this},u.prototype.maxTimeIntervalPrecision=function(t){return null==t?this._maxTimeIntervalPrecision:(this._maxTimeIntervalPrecision=t,this.redraw(),this)},u.prototype.currentAxisConfiguration=function(){return this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex]},u.prototype.axisConfigurations=function(t){if(null==t)return this._possibleTimeAxisConfigurations;this._possibleTimeAxisConfigurations=t,this._numTiers=a.Math.max(this._possibleTimeAxisConfigurations.map((function(t){return t.length})),0),this._isAnchored&&this._setupDomElements();for(var e=this.tierLabelPositions(),n=[],i=0;i<this._numTiers;i++)n.push(e[i]||"between");return this.tierLabelPositions(n),this.redraw(),this},u.prototype._getMostPreciseConfigurationIndex=function(){var t=this,e=this._possibleTimeAxisConfigurations.length;return this._possibleTimeAxisConfigurations.forEach((function(n,i){i<e&&n.every((function(e){return t._checkTimeAxisTierConfiguration(e)}))&&(e=i)})),e===this._possibleTimeAxisConfigurations.length&&(a.Window.warn("zoomed out too far: could not find suitable interval to display labels"),--e),e},u.prototype.orientation=function(t){if(t&&("right"===t.toLowerCase()||"left"===t.toLowerCase()))throw new Error(t+" is not a supported orientation for TimeAxis - only horizontal orientations are supported");return s.prototype.orientation.call(this,t)},u.prototype._computeHeight=function(){var t=this._measurer.measure().height;this._tierHeights=[];for(var e=0;e<this._numTiers;e++)this._tierHeights.push(t+this.tickLabelPadding()+("between"===this._tierLabelPositions[e]?0:this._maxLabelTickLength()));return n.sum(this._tierHeights)},u.prototype._getIntervalLength=function(t){var e=this._scale.domain()[0],n=o.Time.timeIntervalToD3Time(t.interval,this._useUTC).offset(e,t.step);return n>this._scale.domain()[1]?this.width():Math.abs(this._scale.scale(n)-this._scale.scale(e))},u.prototype._maxWidthForInterval=function(t){return this._measurer.measure(t.formatter(u._LONG_DATE)).width},u.prototype._checkTimeAxisTierConfiguration=function(t){if(null!=this._maxTimeIntervalPrecision){var e=u._SORTED_TIME_INTERVAL_INDEX[this._maxTimeIntervalPrecision],n=u._SORTED_TIME_INTERVAL_INDEX[t.interval];if(null!=e&&null!=n&&n<e)return!1}var i=this._maxWidthForInterval(t)+2*this.tickLabelPadding();return Math.min(this._getIntervalLength(t),this.width())>=i},u.prototype._sizeFromOffer=function(t,e){var n=s.prototype._sizeFromOffer.call(this,t,e),i=this._tierHeights.reduce((function(t,e,i,r){return t+e>n.height?t:t+e})),r=this.margin()+(this.annotationsEnabled()?this.annotationTierCount()*this._annotationTierHeight():0);return n.height=Math.min(n.height,i+r),n},u.prototype._setup=function(){s.prototype._setup.call(this),this._setupDomElements()},u.prototype._setupDomElements=function(){this.content().selectAll("."+u.TIME_AXIS_TIER_CLASS).remove(),this._tierLabelContainers=[],this._tierMarkContainers=[],this._tierBaselines=[],this._tickLabelContainer.remove(),this._baseline.remove();for(var t=0;t<this._numTiers;++t){var e=this.content().append("g").classed(u.TIME_AXIS_TIER_CLASS,!0);this._tierLabelContainers.push(e.append("g").classed(l.Axis.TICK_LABEL_CLASS+"-container",!0)),this._tierMarkContainers.push(e.append("g").classed(l.Axis.TICK_MARK_CLASS+"-container",!0)),this._tierBaselines.push(e.append("line").classed("baseline",!0))}var n=new i.SvgContext(this._tierLabelContainers[0].node());this._measurer=new i.CacheMeasurer(n)},u.prototype._getTickIntervalValues=function(t){return this._scale.tickInterval(t.interval,t.step,this._useUTC)},u.prototype._getTickValues=function(){var t=this;return this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex].reduce((function(e,n){return e.concat(t._getTickIntervalValues(n))}),[])},u.prototype._cleanTiers=function(){for(var t=0;t<this._tierLabelContainers.length;t++)this._tierLabelContainers[t].selectAll("."+l.Axis.TICK_LABEL_CLASS).remove(),this._tierMarkContainers[t].selectAll("."+l.Axis.TICK_MARK_CLASS).remove(),this._tierBaselines[t].style("visibility","hidden")},u.prototype._getTickValuesForConfiguration=function(t){var e=this._scale.tickInterval(t.interval,t.step,this._useUTC),n=this._scale.domain(),i=e.map((function(t){return t.valueOf()}));return-1===i.indexOf(n[0].valueOf())&&e.unshift(n[0]),-1===i.indexOf(n[1].valueOf())&&e.push(n[1]),e},u.prototype._renderTierLabels=function(t,e,i){var r=this,o=this._getTickValuesForConfiguration(e),a=[];"between"===this._tierLabelPositions[i]&&1===e.step?o.map((function(t,e){e+1>=o.length||a.push(new Date((o[e+1].valueOf()-o[e].valueOf())/2+o[e].valueOf()))})):a=o;var s=t.selectAll("."+l.Axis.TICK_LABEL_CLASS).data(a,(function(t){return String(t.valueOf())})),c=s.enter().append("g").classed(l.Axis.TICK_LABEL_CLASS,!0);c.append("text");var u,h="center"===this._tierLabelPositions[i]||1===e.step?0:this.tickLabelPadding();u="bottom"===this.orientation()?n.sum(this._tierHeights.slice(0,i+1))-this.tickLabelPadding():"center"===this._tierLabelPositions[i]?this.height()-n.sum(this._tierHeights.slice(0,i))-this.tickLabelPadding()-this._maxLabelTickLength():this.height()-n.sum(this._tierHeights.slice(0,i))-this.tickLabelPadding();var d=s.merge(c),p=d.selectAll("text");p.size()>0&&p.attr("transform","translate("+h+","+u+")"),s.exit().remove(),d.attr("transform",(function(t){return"translate("+r._scale.scale(t)+",0)"}));var f="center"===this._tierLabelPositions[i]||1===e.step?"middle":"start";d.selectAll("text").text(e.formatter).style("text-anchor",f)},u.prototype._renderTickMarks=function(t,e){var i=this._tierMarkContainers[e].selectAll("."+l.Axis.TICK_MARK_CLASS).data(t),r=i.enter().append("line").classed(l.Axis.TICK_MARK_CLASS,!0).merge(i),o=this._generateTickMarkAttrHash(),a=this._tierHeights.slice(0,e).reduce((function(t,e){return t+e}),0);"bottom"===this.orientation()?(o.y1=a,o.y2=a+("center"===this._tierLabelPositions[e]?this.innerTickLength():this._tierHeights[e])):(o.y1=this.height()-a,o.y2=this.height()-(a+("center"===this._tierLabelPositions[e]?this.innerTickLength():this._tierHeights[e]))),r.attrs(o),"bottom"===this.orientation()?(o.y1=a,o.y2=a+("center"===this._tierLabelPositions[e]?this.endTickLength():this._tierHeights[e])):(o.y1=this.height()-a,o.y2=this.height()-(a+("center"===this._tierLabelPositions[e]?this.endTickLength():this._tierHeights[e]))),n.select(r.nodes()[0]).attrs(o),n.select(r.nodes()[r.size()-1]).attrs(o),n.select(r.nodes()[0]).classed(l.Axis.END_TICK_MARK_CLASS,!0),n.select(r.nodes()[r.size()-1]).classed(l.Axis.END_TICK_MARK_CLASS,!0),i.exit().remove()},u.prototype._renderLabellessTickMarks=function(t){var e=this._tickMarkContainer.selectAll("."+l.Axis.TICK_MARK_CLASS).data(t),n=e.enter().append("line").classed(l.Axis.TICK_MARK_CLASS,!0).merge(e),i=this._generateTickMarkAttrHash();i.y2="bottom"===this.orientation()?this.tickLabelPadding():this.height()-this.tickLabelPadding(),n.attrs(i),e.exit().remove()},u.prototype._generateLabellessTicks=function(){return this._mostPreciseConfigIndex<1?[]:this._getTickIntervalValues(this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex-1][0])},u.prototype.renderImmediately=function(){var t=this;this._mostPreciseConfigIndex=this._getMostPreciseConfigurationIndex();var e=this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex];this._cleanTiers(),e.forEach((function(e,n){return t._renderTierLabels(t._tierLabelContainers[n],e,n)}));for(var n=e.map((function(e,n){return t._getTickValuesForConfiguration(e)})),i=0,r=0;r<Math.max(e.length,1);++r){var o=this._generateBaselineAttrHash();o.y1+="bottom"===this.orientation()?i:-i,o.y2=o.y1,this._tierBaselines[r].attrs(o).style("visibility","inherit"),i+=this._tierHeights[r]}var a=[],s=this._scale.domain(),l=this._scale.scale(s[1])-this._scale.scale(s[0]);1.5*this._getIntervalLength(e[0])>=l&&(a=this._generateLabellessTicks()),this._renderLabellessTickMarks(a),this._hideOverflowingTiers();for(r=0;r<e.length;++r)this._renderTickMarks(n[r],r),this._hideOverlappingAndCutOffLabels(r);return this.annotationsEnabled()?this._drawAnnotations():this._removeAnnotations(),this},u.prototype._hideOverflowingTiers=function(){var t=this,e=this.height(),n=0;this.content().selectAll("."+u.TIME_AXIS_TIER_CLASS).attr("visibility",(function(i,r){return(n+=t._tierHeights[r])<=e?"inherit":"hidden"}))},u.prototype._hideOverlappingAndCutOffLabels=function(t){var e,i=this,r=this.element().node().getBoundingClientRect(),o=this._tierMarkContainers[t].selectAll("."+l.Axis.TICK_MARK_CLASS).filter((function(t,e){var i=n.select(this).style("visibility");return"visible"===i||"inherit"===i})).nodes().map((function(t){return t.getBoundingClientRect()}));this._tierLabelContainers[t].selectAll("."+l.Axis.TICK_LABEL_CLASS).filter((function(t,e){var i=n.select(this).style("visibility");return"visible"===i||"inherit"===i})).each((function(t,s){var l,c=this.getBoundingClientRect(),u=n.select(this),h=o[s],d=o[s+1],p=null!=e&&a.DOM.clientRectsOverlap(c,e),f=null!=h&&a.DOM.clientRectsOverlap(c,h),m=null!=d&&a.DOM.clientRectsOverlap(c,d);l=c,!(Math.floor(r.left)<=Math.ceil(l.left)&&Math.floor(r.top)<=Math.ceil(l.top)&&Math.floor(l.right)<=Math.ceil(r.left+i.width())&&Math.floor(l.bottom)<=Math.ceil(r.top+i.height()))||p||f||m?u.style("visibility","hidden"):(e=c,u.style("visibility","inherit"))}))},u.prototype.invalidateCache=function(){s.prototype.invalidateCache.call(this),this._measurer.reset()},u.TIME_AXIS_TIER_CLASS="time-axis-tier",u._SORTED_TIME_INTERVAL_INDEX=((c={})[t.TimeInterval.second]=0,c[t.TimeInterval.minute]=1,c[t.TimeInterval.hour]=2,c[t.TimeInterval.day]=3,c[t.TimeInterval.week]=4,c[t.TimeInterval.month]=5,c[t.TimeInterval.year]=6,c),u._DEFAULT_TIME_AXIS_CONFIGURATIONS=function(e){var n=function(t){return r.time(t,e)};return[[{interval:t.TimeInterval.second,step:1,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.second,step:5,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.second,step:10,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.second,step:15,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.second,step:30,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:1,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:5,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:10,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:15,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:30,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.hour,step:1,formatter:n("%I %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.hour,step:3,formatter:n("%I %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.hour,step:6,formatter:n("%I %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.hour,step:12,formatter:n("%I %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.day,step:1,formatter:n("%a %e")},{interval:t.TimeInterval.month,step:1,formatter:n("%B %Y")}],[{interval:t.TimeInterval.day,step:1,formatter:n("%e")},{interval:t.TimeInterval.month,step:1,formatter:n("%B %Y")}],[{interval:t.TimeInterval.month,step:1,formatter:n("%B")},{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.month,step:1,formatter:n("%b")},{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.month,step:3,formatter:n("%b")},{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.month,step:6,formatter:n("%b")},{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:1,formatter:n("%y")}],[{interval:t.TimeInterval.year,step:5,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:25,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:50,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:100,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:200,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:500,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:1e3,formatter:n("%Y")}]]},u._LONG_DATE=new Date(9999,8,29,12,59,9999),u})(l.Axis);t.Time=u})(Yvt),(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=BO;e.__exportStar(kgt,t),e.__exportStar(Hvt,t),e.__exportStar(Yvt,t)})(Agt);var txt={},ext={},nxt={},ixt={},rxt={},oxt={},axt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(axt,"__esModule",{value:!0});var sxt=D_t,lxt=(function(){function t(){this._eventToProcessingFunction={},this._eventTarget=document,this._eventNameToCallbackSet={},this._connected=!1}return t.prototype._hasNoCallbacks=function(){for(var t=Object.keys(this._eventNameToCallbackSet),e=0;e<t.length;e++)if(0!==this._eventNameToCallbackSet[t[e]].size)return!1;return!0},t.prototype._connect=function(){var t=this;this._connected||(Object.keys(this._eventToProcessingFunction).forEach((function(e){t._eventTarget.addEventListener(e,t._eventToProcessingFunction[e],"wheel"===e?{passive:!1}:void 0)})),this._connected=!0)},t.prototype._disconnect=function(){var t=this;this._connected&&this._hasNoCallbacks()&&(Object.keys(this._eventToProcessingFunction).forEach((function(e){t._eventTarget.removeEventListener(e,t._eventToProcessingFunction[e])})),this._connected=!1)},t.prototype._addCallbackForEvent=function(t,e){null==this._eventNameToCallbackSet[t]&&(this._eventNameToCallbackSet[t]=new sxt.CallbackSet),this._eventNameToCallbackSet[t].add(e),this._connect()},t.prototype._removeCallbackForEvent=function(t,e){null!=this._eventNameToCallbackSet[t]&&this._eventNameToCallbackSet[t].delete(e),this._disconnect()},t.prototype._callCallbacksForEvent=function(t){for(var e=[],n=1;n<arguments.length;n++)e[n-1]=arguments[n];var i=this._eventNameToCallbackSet[t];null!=i&&i.callCallbacks.apply(i,e)},t})();axt.Dispatcher=lxt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(oxt,"__esModule",{value:!0});var cxt=BO,uxt=(function(t){function e(){var n=t.call(this)||this;return n._eventToProcessingFunction[e._KEYDOWN_EVENT_NAME]=function(t){return n._processKeydown(t)},n._eventToProcessingFunction[e._KEYUP_EVENT_NAME]=function(t){return n._processKeyup(t)},n}return cxt.__extends(e,t),e.getDispatcher=function(){var t=document[e._DISPATCHER_KEY];return null==t&&(t=new e,document[e._DISPATCHER_KEY]=t),t},e.prototype._processKeydown=function(t){this._callCallbacksForEvent(e._KEYDOWN_EVENT_NAME,t.keyCode,t)},e.prototype._processKeyup=function(t){this._callCallbacksForEvent(e._KEYUP_EVENT_NAME,t.keyCode,t)},e.prototype.onKeyDown=function(t){return this._addCallbackForEvent(e._KEYDOWN_EVENT_NAME,t),this},e.prototype.offKeyDown=function(t){return this._removeCallbackForEvent(e._KEYDOWN_EVENT_NAME,t),this},e.prototype.onKeyUp=function(t){return this._addCallbackForEvent(e._KEYUP_EVENT_NAME,t),this},e.prototype.offKeyUp=function(t){return this._removeCallbackForEvent(e._KEYUP_EVENT_NAME,t),this},e._DISPATCHER_KEY="__Plottable_Dispatcher_Key",e._KEYDOWN_EVENT_NAME="keydown",e._KEYUP_EVENT_NAME="keyup",e})(axt.Dispatcher);oxt.Key=uxt;var hxt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(hxt,"__esModule",{value:!0});var dxt=BO,pxt=D_t,fxt=(function(t){function e(n){var i=t.call(this)||this;i._lastMousePosition={x:-1,y:-1},i._translator=pxt.getTranslator(n);var r=function(t){return i._measureAndDispatch(n,t,e._MOUSEMOVE_EVENT_NAME,"page")};return i._eventToProcessingFunction[e._MOUSEOVER_EVENT_NAME]=r,i._eventToProcessingFunction[e._MOUSEMOVE_EVENT_NAME]=r,i._eventToProcessingFunction[e._MOUSEOUT_EVENT_NAME]=r,i._eventToProcessingFunction[e._MOUSEDOWN_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._MOUSEDOWN_EVENT_NAME)},i._eventToProcessingFunction[e._MOUSEUP_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._MOUSEUP_EVENT_NAME,"page")},i._eventToProcessingFunction[e._WHEEL_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._WHEEL_EVENT_NAME)},i._eventToProcessingFunction[e._DBLCLICK_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._DBLCLICK_EVENT_NAME)},i}return dxt.__extends(e,t),e.getDispatcher=function(t){var n=t.root().rootElement(),i=n[e._DISPATCHER_KEY];return null==i&&(i=new e(t),n[e._DISPATCHER_KEY]=i),i},e.prototype.onMouseMove=function(t){return this._addCallbackForEvent(e._MOUSEMOVE_EVENT_NAME,t),this},e.prototype.offMouseMove=function(t){return this._removeCallbackForEvent(e._MOUSEMOVE_EVENT_NAME,t),this},e.prototype.onMouseDown=function(t){return this._addCallbackForEvent(e._MOUSEDOWN_EVENT_NAME,t),this},e.prototype.offMouseDown=function(t){return this._removeCallbackForEvent(e._MOUSEDOWN_EVENT_NAME,t),this},e.prototype.onMouseUp=function(t){return this._addCallbackForEvent(e._MOUSEUP_EVENT_NAME,t),this},e.prototype.offMouseUp=function(t){return this._removeCallbackForEvent(e._MOUSEUP_EVENT_NAME,t),this},e.prototype.onWheel=function(t){return this._addCallbackForEvent(e._WHEEL_EVENT_NAME,t),this},e.prototype.offWheel=function(t){return this._removeCallbackForEvent(e._WHEEL_EVENT_NAME,t),this},e.prototype.onDblClick=function(t){return this._addCallbackForEvent(e._DBLCLICK_EVENT_NAME,t),this},e.prototype.offDblClick=function(t){return this._removeCallbackForEvent(e._DBLCLICK_EVENT_NAME,t),this},e.prototype._measureAndDispatch=function(t,e,n,i){if(void 0===i&&(i="element"),"page"!==i&&"element"!==i)throw new Error("Invalid scope '"+i+"', must be 'element' or 'page'");if("page"===i||this.eventInside(t,e)){var r=this._translator.computePosition(e.clientX,e.clientY);this._lastMousePosition=r,this._callCallbacksForEvent(n,this.lastMousePosition(),e)}},e.prototype.eventInside=function(t,e){return pxt.Translator.isEventInside(t,e)},e.prototype.lastMousePosition=function(){return this._lastMousePosition},e._DISPATCHER_KEY="__Plottable_Dispatcher_Mouse",e._MOUSEOVER_EVENT_NAME="mouseover",e._MOUSEMOVE_EVENT_NAME="mousemove",e._MOUSEOUT_EVENT_NAME="mouseout",e._MOUSEDOWN_EVENT_NAME="mousedown",e._MOUSEUP_EVENT_NAME="mouseup",e._WHEEL_EVENT_NAME="wheel",e._DBLCLICK_EVENT_NAME="dblclick",e})(axt.Dispatcher);hxt.Mouse=fxt;var mxt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(mxt,"__esModule",{value:!0});var gxt=BO,_xt=D_t,yxt=(function(t){function e(n){var i=t.call(this)||this;return i._translator=_xt.getTranslator(n),i._eventToProcessingFunction[e._TOUCHSTART_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._TOUCHSTART_EVENT_NAME,"page")},i._eventToProcessingFunction[e._TOUCHMOVE_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._TOUCHMOVE_EVENT_NAME,"page")},i._eventToProcessingFunction[e._TOUCHEND_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._TOUCHEND_EVENT_NAME,"page")},i._eventToProcessingFunction[e._TOUCHCANCEL_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._TOUCHCANCEL_EVENT_NAME,"page")},i}return gxt.__extends(e,t),e.getDispatcher=function(t){var n=t.root().rootElement(),i=n[e._DISPATCHER_KEY];return null==i&&(i=new e(t),n[e._DISPATCHER_KEY]=i),i},e.prototype.onTouchStart=function(t){return this._addCallbackForEvent(e._TOUCHSTART_EVENT_NAME,t),this},e.prototype.offTouchStart=function(t){return this._removeCallbackForEvent(e._TOUCHSTART_EVENT_NAME,t),this},e.prototype.onTouchMove=function(t){return this._addCallbackForEvent(e._TOUCHMOVE_EVENT_NAME,t),this},e.prototype.offTouchMove=function(t){return this._removeCallbackForEvent(e._TOUCHMOVE_EVENT_NAME,t),this},e.prototype.onTouchEnd=function(t){return this._addCallbackForEvent(e._TOUCHEND_EVENT_NAME,t),this},e.prototype.offTouchEnd=function(t){return this._removeCallbackForEvent(e._TOUCHEND_EVENT_NAME,t),this},e.prototype.onTouchCancel=function(t){return this._addCallbackForEvent(e._TOUCHCANCEL_EVENT_NAME,t),this},e.prototype.offTouchCancel=function(t){return this._removeCallbackForEvent(e._TOUCHCANCEL_EVENT_NAME,t),this},e.prototype._measureAndDispatch=function(t,e,n,i){if(void 0===i&&(i="element"),"page"!==i&&"element"!==i)throw new Error("Invalid scope '"+i+"', must be 'element' or 'page'");if("element"!==i||this.eventInside(t,e)){for(var r=e.changedTouches,o={},a=[],s=0;s<r.length;s++){var l=r[s],c=l.identifier,u=this._translator.computePosition(l.clientX,l.clientY);null!=u&&(o[c]=u,a.push(c))}a.length>0&&this._callCallbacksForEvent(n,a,o,e)}},e.prototype.eventInside=function(t,e){return _xt.Translator.isEventInside(t,e)},e._DISPATCHER_KEY="__Plottable_Dispatcher_Touch",e._TOUCHSTART_EVENT_NAME="touchstart",e._TOUCHMOVE_EVENT_NAME="touchmove",e._TOUCHEND_EVENT_NAME="touchend",e._TOUCHCANCEL_EVENT_NAME="touchcancel",e})(axt.Dispatcher);mxt.Touch=yxt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=BO;e.__exportStar(oxt,t),e.__exportStar(hxt,t),e.__exportStar(mxt,t)})(rxt);var vxt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(vxt,"__esModule",{value:!0});var bxt=(function(){function t(){var t=this;this._anchorCallback=function(e){return t._anchor(e)},this._enabled=!0}return t.prototype.attachTo=function(t){return this._disconnect(),this._componentAttachedTo=t,this._connect(),this},t.prototype.detachFrom=function(t){return this.detach()},t.prototype.detach=function(){return this._disconnect(),this._componentAttachedTo=null,this},t.prototype.enabled=function(t){return null==t?this._enabled:(this._enabled=t,this._enabled?this._connect():this._disconnect(),this)},t.prototype._anchor=function(t){this._isAnchored=!0},t.prototype._unanchor=function(){this._isAnchored=!1},t.prototype._translateToComponentSpace=function(t){var e=this._componentAttachedTo.originToRoot();return{x:t.x-e.x,y:t.y-e.y}},t.prototype._isInsideComponent=function(t){return 0<=t.x&&0<=t.y&&t.x<=this._componentAttachedTo.width()&&t.y<=this._componentAttachedTo.height()},t.prototype._connect=function(){this.enabled()&&null!=this._componentAttachedTo&&!this._isAnchored&&this._componentAttachedTo.onAnchor(this._anchorCallback)},t.prototype._disconnect=function(){this._isAnchored&&this._unanchor(),null!=this._componentAttachedTo&&this._componentAttachedTo.offAnchor(this._anchorCallback)},t})();vxt.Interaction=bxt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(ixt,"__esModule",{value:!0});var xxt=BO,wxt=rxt,Sxt=D_t,Mxt=(function(t){function e(){var e=null!==t&&t.apply(this,arguments)||this;return e._clickedDown=!1,e._doubleClicking=!1,e._onClickCallbacks=new Sxt.CallbackSet,e._onDoubleClickCallbacks=new Sxt.CallbackSet,e._mouseDownCallback=function(t,n){return e._handleClickDown(t,n)},e._mouseUpCallback=function(t,n){return e._handleClickUp(t,n)},e._dblClickCallback=function(t,n){return e._handleDblClick(t,n)},e._touchStartCallback=function(t,n,i){return e._handleClickDown(n[t[0]],i)},e._touchEndCallback=function(t,n,i){return e._handleClickUp(n[t[0]],i)},e._touchCancelCallback=function(t,n){return e._clickedDown=!1},e}return xxt.__extends(e,t),e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._mouseDispatcher=wxt.Mouse.getDispatcher(e),this._mouseDispatcher.onMouseDown(this._mouseDownCallback),this._mouseDispatcher.onMouseUp(this._mouseUpCallback),this._mouseDispatcher.onDblClick(this._dblClickCallback),this._touchDispatcher=wxt.Touch.getDispatcher(e),this._touchDispatcher.onTouchStart(this._touchStartCallback),this._touchDispatcher.onTouchEnd(this._touchEndCallback),this._touchDispatcher.onTouchCancel(this._touchCancelCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._mouseDispatcher.offMouseDown(this._mouseDownCallback),this._mouseDispatcher.offMouseUp(this._mouseUpCallback),this._mouseDispatcher.offDblClick(this._dblClickCallback),this._mouseDispatcher=null,this._touchDispatcher.offTouchStart(this._touchStartCallback),this._touchDispatcher.offTouchEnd(this._touchEndCallback),this._touchDispatcher.offTouchCancel(this._touchCancelCallback),this._touchDispatcher=null},e.prototype._handleClickDown=function(t,e){var n=this._translateToComponentSpace(t);this._isInsideComponent(n)&&(this._clickedDown=!0,this._clickedPoint=n)},e.prototype._handleClickUp=function(t,n){var i=this,r=this._translateToComponentSpace(t);this._clickedDown&&e._pointsEqual(r,this._clickedPoint)&&setTimeout((function(){i._doubleClicking||i._onClickCallbacks.callCallbacks(r,n)}),0),this._clickedDown=!1},e.prototype._handleDblClick=function(t,e){var n=this,i=this._translateToComponentSpace(t);this._doubleClicking=!0,this._onDoubleClickCallbacks.callCallbacks(i,e),setTimeout((function(){return n._doubleClicking=!1}),0)},e._pointsEqual=function(t,e){return t.x===e.x&&t.y===e.y},e.prototype.onClick=function(t){return this._onClickCallbacks.add(t),this},e.prototype.offClick=function(t){return this._onClickCallbacks.delete(t),this},e.prototype.onDoubleClick=function(t){return this._onDoubleClickCallbacks.add(t),this},e.prototype.offDoubleClick=function(t){return this._onDoubleClickCallbacks.delete(t),this},e})(vxt.Interaction);ixt.Click=Mxt;var Ext={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Ext,"__esModule",{value:!0});var Txt=BO,Cxt=rxt,Axt=D_t,kxt=(function(t){function e(n){var i=t.call(this)||this;return i._dragging=!1,i._constrainedToComponent=!0,i._mouseFilter=e._DEFAULT_MOUSE_FILTER,i._dragStartCallbacks=new Axt.CallbackSet,i._dragCallbacks=new Axt.CallbackSet,i._dragEndCallbacks=new Axt.CallbackSet,i._mouseDownCallback=function(t,e){return i._startDrag(t,e)},i._mouseMoveCallback=function(t,e){return i._doDrag(t,e)},i._mouseUpCallback=function(t,e){return i._endDrag(t,e)},i._touchStartCallback=function(t,e,n){return i._startDrag(e[t[0]],n)},i._touchMoveCallback=function(t,e,n){return i._doDrag(e[t[0]],n)},i._touchEndCallback=function(t,e,n){return i._endDrag(e[t[0]],n)},i._mouseButton=void 0!==n?n:0,i}return Txt.__extends(e,t),e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._mouseDispatcher=Cxt.Mouse.getDispatcher(this._componentAttachedTo),this._mouseDispatcher.onMouseDown(this._mouseDownCallback),this._mouseDispatcher.onMouseMove(this._mouseMoveCallback),this._mouseDispatcher.onMouseUp(this._mouseUpCallback),this._touchDispatcher=Cxt.Touch.getDispatcher(this._componentAttachedTo),this._touchDispatcher.onTouchStart(this._touchStartCallback),this._touchDispatcher.onTouchMove(this._touchMoveCallback),this._touchDispatcher.onTouchEnd(this._touchEndCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._mouseDispatcher.offMouseDown(this._mouseDownCallback),this._mouseDispatcher.offMouseMove(this._mouseMoveCallback),this._mouseDispatcher.offMouseUp(this._mouseUpCallback),this._mouseDispatcher=null,this._touchDispatcher.offTouchStart(this._touchStartCallback),this._touchDispatcher.offTouchMove(this._touchMoveCallback),this._touchDispatcher.offTouchEnd(this._touchEndCallback),this._touchDispatcher=null},e.prototype._translateAndConstrain=function(t){var e=this._translateToComponentSpace(t);return this._constrainedToComponent?{x:Axt.Math.clamp(e.x,0,this._componentAttachedTo.width()),y:Axt.Math.clamp(e.y,0,this._componentAttachedTo.height())}:e},e.prototype._startDrag=function(t,e){if(!(e instanceof MouseEvent)||this._mouseFilter(e)){var n=this._translateToComponentSpace(t);this._isInsideComponent(n)&&(e.preventDefault(),this._dragging=!0,this._dragOrigin=n,this._dragStartCallbacks.callCallbacks(this._dragOrigin))}},e.prototype._doDrag=function(t,e){this._dragging&&this._dragCallbacks.callCallbacks(this._dragOrigin,this._translateAndConstrain(t))},e.prototype._endDrag=function(t,e){e instanceof MouseEvent&&e.button!==this._mouseButton||this._dragging&&(this._dragging=!1,this._dragEndCallbacks.callCallbacks(this._dragOrigin,this._translateAndConstrain(t)))},e.prototype.constrainedToComponent=function(t){return null==t?this._constrainedToComponent:(this._constrainedToComponent=t,this)},e.prototype.mouseFilter=function(t){return 0===arguments.length?this._mouseFilter:(this._mouseFilter=t,this)},e.prototype.onDragStart=function(t){return this._dragStartCallbacks.add(t),this},e.prototype.offDragStart=function(t){return this._dragStartCallbacks.delete(t),this},e.prototype.onDrag=function(t){return this._dragCallbacks.add(t),this},e.prototype.offDrag=function(t){return this._dragCallbacks.delete(t),this},e.prototype.onDragEnd=function(t){return this._dragEndCallbacks.add(t),this},e.prototype.offDragEnd=function(t){return this._dragEndCallbacks.delete(t),this},e._DEFAULT_MOUSE_FILTER=function(t){return 0===t.button},e})(vxt.Interaction);Ext.Drag=kxt;var Lxt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Lxt,"__esModule",{value:!0});var Pxt=BO,Nxt=rxt,Ixt=D_t,Rxt=(function(t){function e(){var e=null!==t&&t.apply(this,arguments)||this;return e._keyPressCallbacks={},e._keyReleaseCallbacks={},e._mouseMoveCallback=function(t){return!1},e._downedKeys=new Ixt.Set,e._keyDownCallback=function(t,n){return e._handleKeyDownEvent(t,n)},e._keyUpCallback=function(t){return e._handleKeyUpEvent(t)},e}return Pxt.__extends(e,t),e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._positionDispatcher=Nxt.Mouse.getDispatcher(this._componentAttachedTo),this._positionDispatcher.onMouseMove(this._mouseMoveCallback),this._keyDispatcher=Nxt.Key.getDispatcher(),this._keyDispatcher.onKeyDown(this._keyDownCallback),this._keyDispatcher.onKeyUp(this._keyUpCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._positionDispatcher.offMouseMove(this._mouseMoveCallback),this._positionDispatcher=null,this._keyDispatcher.offKeyDown(this._keyDownCallback),this._keyDispatcher.offKeyUp(this._keyUpCallback),this._keyDispatcher=null},e.prototype._handleKeyDownEvent=function(t,e){var n=this._translateToComponentSpace(this._positionDispatcher.lastMousePosition());this._isInsideComponent(n)&&!e.repeat&&(this._keyPressCallbacks[t]&&this._keyPressCallbacks[t].callCallbacks(t),this._downedKeys.add(t))},e.prototype._handleKeyUpEvent=function(t){this._downedKeys.has(t)&&this._keyReleaseCallbacks[t]&&this._keyReleaseCallbacks[t].callCallbacks(t),this._downedKeys.delete(t)},e.prototype.onKeyPress=function(t,e){return this._keyPressCallbacks[t]||(this._keyPressCallbacks[t]=new Ixt.CallbackSet),this._keyPressCallbacks[t].add(e),this},e.prototype.offKeyPress=function(t,e){return this._keyPressCallbacks[t].delete(e),0===this._keyPressCallbacks[t].size&&delete this._keyPressCallbacks[t],this},e.prototype.onKeyRelease=function(t,e){return this._keyReleaseCallbacks[t]||(this._keyReleaseCallbacks[t]=new Ixt.CallbackSet),this._keyReleaseCallbacks[t].add(e),this},e.prototype.offKeyRelease=function(t,e){return this._keyReleaseCallbacks[t].delete(e),0===this._keyReleaseCallbacks[t].size&&delete this._keyReleaseCallbacks[t],this},e})(vxt.Interaction);Lxt.Key=Rxt;var Oxt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Oxt,"__esModule",{value:!0});var zxt=BO,Dxt=wpt,Bxt=rxt,Hxt=Xvt,Fxt=D_t,Vxt=nxt,Uxt=tbt,jxt=(function(t){function e(e,n){var i=t.call(this)||this;return i._wheelFilter=function(t){return!0},i._wheelCallback=function(t,e){return i._handleWheelEvent(t,e)},i._touchStartCallback=function(t,e,n){return i._handleTouchStart(t,e,n)},i._touchMoveCallback=function(t,e,n){return i._handlePinch(t,e,n)},i._touchEndCallback=function(t,e,n){return i._handleTouchEnd(t,e,n)},i._touchCancelCallback=function(t,e,n){return i._handleTouchEnd(t,e,n)},i._panEndCallbacks=new Fxt.CallbackSet,i._zoomEndCallbacks=new Fxt.CallbackSet,i._panZoomUpdateCallbacks=new Fxt.CallbackSet,i._xScales=new Fxt.Set,i._yScales=new Fxt.Set,i._dragInteraction=new Vxt.Drag,i._setupDragInteraction(),i._touchIds=Dxt.map(),i._minDomainExtents=new Fxt.Map,i._maxDomainExtents=new Fxt.Map,i._minDomainValues=new Fxt.Map,i._maxDomainValues=new Fxt.Map,null!=e&&i.addXScale(e),null!=n&&i.addYScale(n),i}return zxt.__extends(e,t),e.prototype.dragInteraction=function(){return this._dragInteraction},e.prototype.wheelFilter=function(t){return 0===arguments.length?this._wheelFilter:(this._wheelFilter=t,this)},e.prototype.pan=function(t){var e=this;this.xScales().forEach((function(n){n.pan(e._constrainedTranslation(n,t.x))})),this.yScales().forEach((function(n){n.pan(e._constrainedTranslation(n,t.y))})),this._panZoomUpdateCallbacks.callCallbacks()},e.prototype.zoom=function(t,e,n){var i,r,o=this;return void 0===n&&(n=!0),null!=e&&(i=e.x,r=e.y,n&&(this.xScales().forEach((function(e){var n=o._constrainedZoom(e,t,i);i=n.centerPoint,t=n.zoomAmount})),this.yScales().forEach((function(e){var n=o._constrainedZoom(e,t,r);r=n.centerPoint,t=n.zoomAmount})))),this.xScales().forEach((function(e){var n=e.range();e.zoom(t,null==i?(n[1]+n[0])/2:i)})),this.yScales().forEach((function(e){var n=e.range();e.zoom(t,null==r?(n[1]+n[0])/2:r)})),this._panZoomUpdateCallbacks.callCallbacks(),{zoomAmount:t,centerValue:{centerX:i,centerY:r}}},e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._dragInteraction.attachTo(e),this._mouseDispatcher=Bxt.Mouse.getDispatcher(this._componentAttachedTo),this._mouseDispatcher.onWheel(this._wheelCallback),this._touchDispatcher=Bxt.Touch.getDispatcher(this._componentAttachedTo),this._touchDispatcher.onTouchStart(this._touchStartCallback),this._touchDispatcher.onTouchMove(this._touchMoveCallback),this._touchDispatcher.onTouchEnd(this._touchEndCallback),this._touchDispatcher.onTouchCancel(this._touchCancelCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._mouseDispatcher.offWheel(this._wheelCallback),this._mouseDispatcher=null,this._touchDispatcher.offTouchStart(this._touchStartCallback),this._touchDispatcher.offTouchMove(this._touchMoveCallback),this._touchDispatcher.offTouchEnd(this._touchEndCallback),this._touchDispatcher.offTouchCancel(this._touchCancelCallback),this._touchDispatcher=null,this._dragInteraction.detach()},e.prototype._handleTouchStart=function(t,e,n){for(var i=0;i<t.length&&this._touchIds.size()<2;i++){var r=t[i];this._touchIds.set(r.toString(),this._translateToComponentSpace(e[r]))}},e.prototype._handlePinch=function(t,n,i){var r=this;if(!(this._touchIds.size()<2)){var o=this._touchIds.values();if(this._isInsideComponent(this._translateToComponentSpace(o[0]))&&this._isInsideComponent(this._translateToComponentSpace(o[1]))){var a=e._pointDistance(o[0],o[1]);if(0!==a){t.forEach((function(t){r._touchIds.has(t.toString())&&r._touchIds.set(t.toString(),r._translateToComponentSpace(n[t]))}));var s=this._touchIds.values(),l=e._pointDistance(s[0],s[1]);if(0!==l){var c=a/l,u=s.map((function(t,e){return{x:(t.x-o[e].x)/c,y:(t.y-o[e].y)/c}})),h=e.centerPoint(o[0],o[1]),d=this.zoom(c,h),p=d.centerValue,f=d.zoomAmount,m=p.centerX,g=p.centerY,_=o.map((function(t,e){return{x:u[e].x*f+t.x,y:u[e].y*f+t.y}}));this.pan({x:m-(_[0].x+_[1].x)/2,y:g-(_[0].y+_[1].y)/2})}}}}},e.centerPoint=function(t,e){var n=Math.min(t.x,e.x),i=Math.max(t.x,e.x),r=Math.min(t.y,e.y);return{x:(n+i)/2,y:(Math.max(t.y,e.y)+r)/2}},e._pointDistance=function(t,e){var n=Math.min(t.x,e.x),i=Math.max(t.x,e.x),r=Math.min(t.y,e.y),o=Math.max(t.y,e.y);return Math.sqrt(Math.pow(i-n,2)+Math.pow(o-r,2))},e.prototype._handleTouchEnd=function(t,e,n){var i=this;t.forEach((function(t){i._touchIds.remove(t.toString())})),this._touchIds.size()>0&&this._zoomEndCallbacks.callCallbacks()},e.prototype._handleWheelEvent=function(t,n){if(this._wheelFilter(n)){var i=this._translateToComponentSpace(t);if(this._isInsideComponent(i)){n.preventDefault();var r=Math.pow(2,.002*((0!==n.deltaY?n.deltaY:n.deltaX)*(n.deltaMode?e._PIXELS_PER_LINE:1)));this.zoom(r,i),this._zoomEndCallbacks.callCallbacks()}}},e.prototype._constrainedZoom=function(t,e,n){return Uxt.constrainedZoom(t,e,n,this.minDomainExtent(t),this.maxDomainExtent(t),this.minDomainValue(t),this.maxDomainValue(t))},e.prototype._constrainedTranslation=function(t,e){return Uxt.constrainedTranslation(t,e,this.minDomainValue(t),this.maxDomainValue(t))},e.prototype._setupDragInteraction=function(){var t,e=this;this._dragInteraction.constrainedToComponent(!1),this._dragInteraction.onDragStart((function(){return t=null})),this._dragInteraction.onDrag((function(n,i){e._touchIds.size()>=2||(e.pan({x:(null==t?n.x:t.x)-i.x,y:(null==t?n.y:t.y)-i.y}),t=i)})),this._dragInteraction.onDragEnd((function(){return e._panEndCallbacks.callCallbacks()}))},e.prototype._nonLinearScaleWithExtents=function(t){return!(null==this.minDomainExtent(t)||null==this.maxDomainExtent(t)||t instanceof Hxt.Linear||t instanceof Hxt.Time)},e.prototype.xScales=function(t){var e=this;if(null==t){var n=[];return this._xScales.forEach((function(t){n.push(t)})),n}return this._xScales=new Fxt.Set,t.forEach((function(t){e.addXScale(t)})),this},e.prototype.yScales=function(t){var e=this;if(null==t){var n=[];return this._yScales.forEach((function(t){n.push(t)})),n}return this._yScales=new Fxt.Set,t.forEach((function(t){e.addYScale(t)})),this},e.prototype.addXScale=function(t){return this._xScales.add(t),this},e.prototype.removeXScale=function(t){return this._xScales.delete(t),this._minDomainExtents.delete(t),this._maxDomainExtents.delete(t),this._minDomainValues.delete(t),this._maxDomainValues.delete(t),this},e.prototype.addYScale=function(t){return this._yScales.add(t),this},e.prototype.removeYScale=function(t){return this._yScales.delete(t),this._minDomainExtents.delete(t),this._maxDomainExtents.delete(t),this._minDomainValues.delete(t),this._maxDomainValues.delete(t),this},e.prototype.minDomainExtent=function(t,e){if(null==e)return this._minDomainExtents.get(t);if(e.valueOf()<0)throw new Error("extent must be non-negative");var n=this.maxDomainExtent(t);if(null!=n&&n.valueOf()<e.valueOf())throw new Error("minDomainExtent must be smaller than maxDomainExtent for the same Scale");return this._nonLinearScaleWithExtents(t)&&Fxt.Window.warn("Panning and zooming with extents on a nonlinear scale may have unintended behavior."),this._minDomainExtents.set(t,e),this},e.prototype.maxDomainExtent=function(t,e){if(null==e)return this._maxDomainExtents.get(t);if(e.valueOf()<=0)throw new Error("extent must be positive");var n=this.minDomainExtent(t);if(null!=n&&e.valueOf()<n.valueOf())throw new Error("maxDomainExtent must be larger than minDomainExtent for the same Scale");return this._nonLinearScaleWithExtents(t)&&Fxt.Window.warn("Panning and zooming with extents on a nonlinear scale may have unintended behavior."),this._maxDomainExtents.set(t,e),this},e.prototype.minDomainValue=function(t,e){return null==e?this._minDomainValues.get(t):(this._minDomainValues.set(t,e),this)},e.prototype.maxDomainValue=function(t,e){return null==e?this._maxDomainValues.get(t):(this._maxDomainValues.set(t,e),this)},e.prototype.setMinMaxDomainValuesTo=function(t){this._minDomainValues.delete(t),this._maxDomainValues.delete(t);var e=t.getTransformationDomain(),n=e[1];return this.minDomainValue(t,e[0]),this.maxDomainValue(t,n),this},e.prototype.onPanEnd=function(t){return this._panEndCallbacks.add(t),this},e.prototype.offPanEnd=function(t){return this._panEndCallbacks.delete(t),this},e.prototype.onZoomEnd=function(t){return this._zoomEndCallbacks.add(t),this},e.prototype.offZoomEnd=function(t){return this._zoomEndCallbacks.delete(t),this},e.prototype.onPanZoomUpdate=function(t){return this._panZoomUpdateCallbacks.add(t),this},e.prototype.offPanZoomUpdate=function(t){return this._panZoomUpdateCallbacks.delete(t),this},e._PIXELS_PER_LINE=120,e})(vxt.Interaction);Oxt.PanZoom=jxt;var Gxt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Gxt,"__esModule",{value:!0});var Wxt=BO,qxt=rxt,Yxt=D_t,Xxt=(function(t){function e(){var e=null!==t&&t.apply(this,arguments)||this;return e._overComponent=!1,e._pointerEnterCallbacks=new Yxt.CallbackSet,e._pointerMoveCallbacks=new Yxt.CallbackSet,e._pointerExitCallbacks=new Yxt.CallbackSet,e._mouseMoveCallback=function(t,n){return e._handleMouseEvent(t,n)},e._touchStartCallback=function(t,n,i){return e._handleTouchEvent(n[t[0]],i)},e}return Wxt.__extends(e,t),e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._mouseDispatcher=qxt.Mouse.getDispatcher(this._componentAttachedTo),this._mouseDispatcher.onMouseMove(this._mouseMoveCallback),this._touchDispatcher=qxt.Touch.getDispatcher(this._componentAttachedTo),this._touchDispatcher.onTouchStart(this._touchStartCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._mouseDispatcher.offMouseMove(this._mouseMoveCallback),this._mouseDispatcher=null,this._touchDispatcher.offTouchStart(this._touchStartCallback),this._touchDispatcher=null},e.prototype._handleMouseEvent=function(t,e){var n=this._mouseDispatcher.eventInside(this._componentAttachedTo,e);this._handlePointerEvent(t,n)},e.prototype._handleTouchEvent=function(t,e){var n=this._touchDispatcher.eventInside(this._componentAttachedTo,e);this._handlePointerEvent(t,n)},e.prototype._handlePointerEvent=function(t,e){var n=this._translateToComponentSpace(t),i=this._isInsideComponent(n);i&&e?(this._overComponent||this._pointerEnterCallbacks.callCallbacks(n),this._pointerMoveCallbacks.callCallbacks(n)):this._overComponent&&this._pointerExitCallbacks.callCallbacks(n),this._overComponent=i&&e},e.prototype.onPointerEnter=function(t){return this._pointerEnterCallbacks.add(t),this},e.prototype.offPointerEnter=function(t){return this._pointerEnterCallbacks.delete(t),this},e.prototype.onPointerMove=function(t){return this._pointerMoveCallbacks.add(t),this},e.prototype.offPointerMove=function(t){return this._pointerMoveCallbacks.delete(t),this},e.prototype.onPointerExit=function(t){return this._pointerExitCallbacks.add(t),this},e.prototype.offPointerExit=function(t){return this._pointerExitCallbacks.delete(t),this},e})(vxt.Interaction);Gxt.Pointer=Xxt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=BO;e.__exportStar(ixt,t),e.__exportStar(Ext,t),e.__exportStar(Lxt,t),e.__exportStar(Oxt,t),e.__exportStar(Gxt,t),t.zoomOut=tbt.zoomOut})(nxt);var $xt={};!(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e,n=BO,i=D_t,r=O_t;!(function(t){t[t.VALUE=0]="VALUE",t[t.PIXEL=1]="PIXEL"})(e=t.PropertyMode||(t.PropertyMode={}));var o=(function(t){function r(){var n=t.call(this)||this;return n._boxVisible=!1,n._boxBounds={topLeft:{x:0,y:0},bottomRight:{x:0,y:0}},n._xBoundsMode=e.PIXEL,n._yBoundsMode=e.PIXEL,n.addClass("selection-box-layer"),n._adjustBoundsCallback=function(){n.render()},n._overflowHidden=!0,n._xExtent=[void 0,void 0],n._yExtent=[void 0,void 0],n}return n.__extends(r,t),r.prototype._setup=function(){t.prototype._setup.call(this),this._box=this.content().append("g").classed("selection-box",!0).remove(),this._boxArea=this._box.append("rect").classed("selection-area",!0)},r.prototype._sizeFromOffer=function(t,e){return{width:t,height:e}},r.prototype.bounds=function(t){return null==t?this._getBounds():(this._setBounds(t),this._xBoundsMode=e.PIXEL,this._yBoundsMode=e.PIXEL,this.render(),this)},r.prototype._setBounds=function(t){var e={x:Math.min(t.topLeft.x,t.bottomRight.x),y:Math.min(t.topLeft.y,t.bottomRight.y)},n={x:Math.max(t.topLeft.x,t.bottomRight.x),y:Math.max(t.topLeft.y,t.bottomRight.y)};this._boxBounds={topLeft:e,bottomRight:n}},r.prototype._getBounds=function(){return{topLeft:{x:this._xBoundsMode===e.PIXEL?this._boxBounds.topLeft.x:null==this._xScale?0:Math.min(this.xScale().scale(this.xExtent()[0]),this.xScale().scale(this.xExtent()[1])),y:this._yBoundsMode===e.PIXEL?this._boxBounds.topLeft.y:null==this._yScale?0:Math.min(this.yScale().scale(this.yExtent()[0]),this.yScale().scale(this.yExtent()[1]))},bottomRight:{x:this._xBoundsMode===e.PIXEL?this._boxBounds.bottomRight.x:null==this._xScale?0:Math.max(this.xScale().scale(this.xExtent()[0]),this.xScale().scale(this.xExtent()[1])),y:this._yBoundsMode===e.PIXEL?this._boxBounds.bottomRight.y:null==this._yScale?0:Math.max(this.yScale().scale(this.yExtent()[0]),this.yScale().scale(this.yExtent()[1]))}}},r.prototype.renderImmediately=function(){if(t.prototype.renderImmediately.call(this),this._boxVisible){var e=this.bounds(),n=e.topLeft.y,r=e.bottomRight.y,o=e.topLeft.x,a=e.bottomRight.x;if(!(i.Math.isValidNumber(n)&&i.Math.isValidNumber(r)&&i.Math.isValidNumber(o)&&i.Math.isValidNumber(a)))throw new Error("bounds have not been properly set");this._boxArea.attrs({x:o,y:n,width:a-o,height:r-n}),this.content().node().appendChild(this._box.node())}else this._box.remove();return this},r.prototype.boxVisible=function(t){return null==t?this._boxVisible:(this._boxVisible=t,this.render(),this)},r.prototype.fixedWidth=function(){return!0},r.prototype.fixedHeight=function(){return!0},r.prototype.xScale=function(t){return null==t?this._xScale:(null!=this._xScale&&this._xScale.offUpdate(this._adjustBoundsCallback),this._xScale=t,this._xBoundsMode=e.VALUE,this._xScale.onUpdate(this._adjustBoundsCallback),this.render(),this)},r.prototype.yScale=function(t){return null==t?this._yScale:(null!=this._yScale&&this._yScale.offUpdate(this._adjustBoundsCallback),this._yScale=t,this._yBoundsMode=e.VALUE,this._yScale.onUpdate(this._adjustBoundsCallback),this.render(),this)},r.prototype.xExtent=function(t){return null==t?this._getXExtent():(this._setXExtent(t),this._xBoundsMode=e.VALUE,this.render(),this)},r.prototype._getXExtent=function(){return this._xBoundsMode===e.VALUE?this._xExtent:null==this._xScale?[void 0,void 0]:[this._xScale.invert(this._boxBounds.topLeft.x),this._xScale.invert(this._boxBounds.bottomRight.x)]},r.prototype._setXExtent=function(t){this._xExtent=t},r.prototype.yExtent=function(t){return null==t?this._getYExtent():(this._setYExtent(t),this._yBoundsMode=e.VALUE,this.render(),this)},r.prototype._getYExtent=function(){return this._yBoundsMode===e.VALUE?this._yExtent:null==this._yScale?[void 0,void 0]:[this._yScale.invert(this._boxBounds.topLeft.y),this._yScale.invert(this._boxBounds.bottomRight.y)]},r.prototype._setYExtent=function(t){this._yExtent=t},r.prototype.destroy=function(){t.prototype.destroy.call(this),null!=this._xScale&&this.xScale().offUpdate(this._adjustBoundsCallback),null!=this._yScale&&this.yScale().offUpdate(this._adjustBoundsCallback)},r})(r.Component);t.SelectionBoxLayer=o})($xt),
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(ext,"__esModule",{value:!0});var Kxt=BO,Zxt=nxt,Jxt=D_t,Qxt=_gt,twt=txt,ewt=(function(t){function e(){var e=t.call(this)||this;return e._detectionRadius=3,e._resizable=!1,e._movable=!1,e._hasCorners=!0,e.addClass("drag-box-layer"),e._dragInteraction=new Zxt.Drag,e._setUpCallbacks(),e._dragInteraction.attachTo(e),e._dragStartCallbacks=new Jxt.CallbackSet,e._dragCallbacks=new Jxt.CallbackSet,e._dragEndCallbacks=new Jxt.CallbackSet,e}return Kxt.__extends(e,t),e.prototype._setUpCallbacks=function(){var t,e,n,i,r=this,o=0,a=1,s=2,l=o,c=function(c){t=r._getResizingEdges(c);var u=r.bounds(),h=u.topLeft.x<=c.x&&c.x<=u.bottomRight.x&&u.topLeft.y<=c.y&&c.y<=u.bottomRight.y;r.boxVisible()&&(t.top||t.bottom||t.left||t.right)?l=a:r.boxVisible()&&r.movable()&&h?l=s:(l=o,r._setBounds({topLeft:c,bottomRight:c}),r._xBoundsMode===twt.PropertyMode.VALUE&&null!=r.xScale()&&r._setXExtent([r.xScale().invert(c.x),r.xScale().invert(c.x)]),r._yBoundsMode===twt.PropertyMode.VALUE&&null!=r.yScale()&&r._setYExtent([r.yScale().invert(c.y),r.yScale().invert(c.y)]),r.render()),r.boxVisible(!0),u=r.bounds(),e={x:u.topLeft.x,y:u.topLeft.y},n={x:u.bottomRight.x,y:u.bottomRight.y},i=c,r._dragStartCallbacks.callCallbacks(u)},u=function(c,u){switch(l){case o:n.x=u.x,n.y=u.y;break;case a:t.bottom?n.y=u.y:t.top&&(e.y=u.y),t.right?n.x=u.x:t.left&&(e.x=u.x);break;case s:var h=u.x-i.x,d=u.y-i.y;e.x+=h,e.y+=d,n.x+=h,n.y+=d,i=u}r._setBounds({topLeft:e,bottomRight:n}),r._xBoundsMode===twt.PropertyMode.VALUE&&null!=r.xScale()&&r._setXExtent([r.xScale().invert(e.x),r.xScale().invert(n.x)]),r._yBoundsMode===twt.PropertyMode.VALUE&&null!=r.yScale()&&r._setYExtent([r.yScale().invert(e.y),r.yScale().invert(n.y)]),r.render(),r._dragCallbacks.callCallbacks(r.bounds())},h=function(t,e){l===o&&t.x===e.x&&t.y===e.y&&r.boxVisible(!1),r._dragEndCallbacks.callCallbacks(r.bounds())};this._dragInteraction.onDragStart(c),this._dragInteraction.onDrag(u),this._dragInteraction.onDragEnd(h),this._disconnectInteraction=function(){r._dragInteraction.offDragStart(c),r._dragInteraction.offDrag(u),r._dragInteraction.offDragEnd(h),r._dragInteraction.detach()}},e.prototype._setup=function(){var e=this;t.prototype._setup.call(this);var n=function(){return e._box.append("line").styles({opacity:0,stroke:"pink","pointer-events":"visibleStroke"})};if(this._detectionEdgeT=n().classed("drag-edge-tb",!0),this._detectionEdgeB=n().classed("drag-edge-tb",!0),this._detectionEdgeL=n().classed("drag-edge-lr",!0),this._detectionEdgeR=n().classed("drag-edge-lr",!0),this._hasCorners){var i=function(){return e._box.append("circle").styles({opacity:0,fill:"pink","pointer-events":"visibleFill"})};this._detectionCornerTL=i().classed("drag-corner-tl",!0),this._detectionCornerTR=i().classed("drag-corner-tr",!0),this._detectionCornerBL=i().classed("drag-corner-bl",!0),this._detectionCornerBR=i().classed("drag-corner-br",!0)}},e.prototype._getResizingEdges=function(t){var e={top:!1,bottom:!1,left:!1,right:!1};if(!this.resizable())return e;var n=this.bounds(),i=n.topLeft.y,r=n.bottomRight.y,o=n.topLeft.x,a=n.bottomRight.x,s=this._detectionRadius;return o-s<=t.x&&t.x<=a+s&&(e.top=i-s<=t.y&&t.y<=i+s,e.bottom=r-s<=t.y&&t.y<=r+s),i-s<=t.y&&t.y<=r+s&&(e.left=o-s<=t.x&&t.x<=o+s,e.right=a-s<=t.x&&t.x<=a+s),e},e.prototype.renderImmediately=function(){if(t.prototype.renderImmediately.call(this),this.boxVisible()){var e=this.bounds(),n=e.topLeft.y,i=e.bottomRight.y,r=e.topLeft.x,o=e.bottomRight.x;this._detectionEdgeT.attrs({x1:r,y1:n,x2:o,y2:n,"stroke-width":2*this._detectionRadius}),this._detectionEdgeB.attrs({x1:r,y1:i,x2:o,y2:i,"stroke-width":2*this._detectionRadius}),this._detectionEdgeL.attrs({x1:r,y1:n,x2:r,y2:i,"stroke-width":2*this._detectionRadius}),this._detectionEdgeR.attrs({x1:o,y1:n,x2:o,y2:i,"stroke-width":2*this._detectionRadius}),this._hasCorners&&(this._detectionCornerTL.attrs({cx:r,cy:n,r:this._detectionRadius}),this._detectionCornerTR.attrs({cx:o,cy:n,r:this._detectionRadius}),this._detectionCornerBL.attrs({cx:r,cy:i,r:this._detectionRadius}),this._detectionCornerBR.attrs({cx:o,cy:i,r:this._detectionRadius}))}return this},e.prototype.detectionRadius=function(t){if(null==t)return this._detectionRadius;if(t<0)throw new Error("detection radius cannot be negative.");return this._detectionRadius=t,this.render(),this},e.prototype.resizable=function(t){return null==t?this._resizable:(this._resizable=t,this._setResizableClasses(t),this)},e.prototype._setResizableClasses=function(t){t&&this.enabled()?(this.addClass("x-resizable"),this.addClass("y-resizable")):(this.removeClass("x-resizable"),this.removeClass("y-resizable"))},e.prototype.movable=function(t){return null==t?this._movable:(this._movable=t,this._setMovableClass(),this)},e.prototype._setMovableClass=function(){this.movable()&&this.enabled()?this.addClass("movable"):this.removeClass("movable")},e.prototype.onDragStart=function(t){return this._dragStartCallbacks.add(t),this},e.prototype.offDragStart=function(t){return this._dragStartCallbacks.delete(t),this},e.prototype.onDrag=function(t){return this._dragCallbacks.add(t),this},e.prototype.offDrag=function(t){return this._dragCallbacks.delete(t),this},e.prototype.onDragEnd=function(t){return this._dragEndCallbacks.add(t),this},e.prototype.offDragEnd=function(t){return this._dragEndCallbacks.delete(t),this},e.prototype.dragInteraction=function(){return this._dragInteraction},e.prototype.enabled=function(t){return null==t?this._dragInteraction.enabled():(this._dragInteraction.enabled(t),this._setResizableClasses(this.resizable()),this._setMovableClass(),this)},e.prototype.destroy=function(){var e=this;t.prototype.destroy.call(this),this._dragStartCallbacks.forEach((function(t){return e._dragCallbacks.delete(t)})),this._dragCallbacks.forEach((function(t){return e._dragCallbacks.delete(t)})),this._dragEndCallbacks.forEach((function(t){return e._dragEndCallbacks.delete(t)})),this._disconnectInteraction()},e.prototype.detach=function(){return this._resetState(),this._dragInteraction.detach(),t.prototype.detach.call(this),this},e.prototype.anchor=function(e){return e=Qxt.coerceExternalD3(e),this._dragInteraction.attachTo(this),t.prototype.anchor.call(this,e),this},e.prototype._resetState=function(){this.bounds({topLeft:{x:0,y:0},bottomRight:{x:0,y:0}})},e})($xt.SelectionBoxLayer);ext.DragBoxLayer=ewt;var nwt={},iwt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(iwt,"__esModule",{value:!0});var rwt,owt=BO,awt=D_t,swt=O_t;!(function(t){t[t.VALUE=0]="VALUE",t[t.PIXEL=1]="PIXEL"})(rwt||(rwt={}));var lwt=(function(t){function e(n){var i=t.call(this)||this;if(i._mode=rwt.VALUE,n!==e.ORIENTATION_VERTICAL&&n!==e.ORIENTATION_HORIZONTAL)throw new Error(n+" is not a valid orientation for GuideLineLayer");return i._orientation=n,i._overflowHidden=!0,i.addClass("guide-line-layer"),i._isVertical()?i.addClass("vertical"):i.addClass("horizontal"),i._scaleUpdateCallback=function(){i._syncPixelPositionAndValue(),i.render()},i}return owt.__extends(e,t),e.prototype._setup=function(){t.prototype._setup.call(this),this._guideLine=this.content().append("line").classed("guide-line",!0)},e.prototype._sizeFromOffer=function(t,e){return{width:t,height:e}},e.prototype._isVertical=function(){return this._orientation===e.ORIENTATION_VERTICAL},e.prototype.fixedWidth=function(){return!0},e.prototype.fixedHeight=function(){return!0},e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),null!=this.scale()&&(this._isVertical()?this.scale().range([0,this.width()]):this.scale().range([this.height(),0])),this},e.prototype.renderImmediately=function(){return t.prototype.renderImmediately.call(this),this._syncPixelPositionAndValue(),this._guideLine.attrs({x1:this._isVertical()?this.pixelPosition():0,y1:this._isVertical()?0:this.pixelPosition(),x2:this._isVertical()?this.pixelPosition():this.width(),y2:this._isVertical()?this.height():this.pixelPosition()}),this},e.prototype._syncPixelPositionAndValue=function(){null!=this.scale()&&(this._mode===rwt.VALUE&&null!=this.value()?this._pixelPosition=this.scale().scale(this.value()):this._mode===rwt.PIXEL&&null!=this.pixelPosition()&&(this._value=this.scale().invert(this.pixelPosition())))},e.prototype._setPixelPositionWithoutChangingMode=function(t){this._pixelPosition=t,null!=this.scale()&&(this._value=this.scale().invert(this.pixelPosition())),this.render()},e.prototype.scale=function(t){if(null==t)return this._scale;var e=this._scale;return null!=e&&e.offUpdate(this._scaleUpdateCallback),this._scale=t,this._scale.onUpdate(this._scaleUpdateCallback),this._syncPixelPositionAndValue(),this.redraw(),this},e.prototype.value=function(t){return null==t?this._value:(this._value=t,this._mode=rwt.VALUE,this._syncPixelPositionAndValue(),this.render(),this)},e.prototype.pixelPosition=function(t){if(null==t)return this._pixelPosition;if(!awt.Math.isValidNumber(t))throw new Error("pixelPosition must be a finite number");return this._pixelPosition=t,this._mode=rwt.PIXEL,this._syncPixelPositionAndValue(),this.render(),this},e.prototype.destroy=function(){t.prototype.destroy.call(this),null!=this.scale()&&this.scale().offUpdate(this._scaleUpdateCallback)},e.ORIENTATION_VERTICAL="vertical",e.ORIENTATION_HORIZONTAL="horizontal",e})(swt.Component);iwt.GuideLineLayer=lwt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(nwt,"__esModule",{value:!0});var cwt=BO,uwt=nxt,hwt=D_t,dwt=(function(t){function e(e){var n=t.call(this,e)||this;n._detectionRadius=3,n._enabled=!0,n.addClass("drag-line-layer"),n.addClass("enabled"),n._dragInteraction=new uwt.Drag,n._dragInteraction.attachTo(n);var i=!1,r=function(t){(function(t){return n._isVertical()&&n.pixelPosition()-n.detectionRadius()<=t.x&&t.x<=n.pixelPosition()+n.detectionRadius()||!n._isVertical()&&n.pixelPosition()-n.detectionRadius()<=t.y&&t.y<=n.pixelPosition()+n.detectionRadius()})(t)&&(i=!0,n._dragStartCallbacks.callCallbacks(n))};n._dragInteraction.onDragStart(r);var o=function(t,e){i&&(n._setPixelPositionWithoutChangingMode(n._isVertical()?e.x:e.y),n._dragCallbacks.callCallbacks(n))};n._dragInteraction.onDrag(o);var a=function(t,e){i&&(i=!1,n._dragEndCallbacks.callCallbacks(n))};return n._dragInteraction.onDragEnd(a),n._disconnectInteraction=function(){n._dragInteraction.offDragStart(r),n._dragInteraction.offDrag(o),n._dragInteraction.offDragEnd(a),n._dragInteraction.detach()},n._dragStartCallbacks=new hwt.CallbackSet,n._dragCallbacks=new hwt.CallbackSet,n._dragEndCallbacks=new hwt.CallbackSet,n}return cwt.__extends(e,t),e.prototype._setup=function(){t.prototype._setup.call(this),this._detectionEdge=this.content().append("line").styles({opacity:0,stroke:"pink","pointer-events":"visibleStroke"}).classed("drag-edge",!0)},e.prototype.renderImmediately=function(){return t.prototype.renderImmediately.call(this),this._detectionEdge.attrs({x1:this._isVertical()?this.pixelPosition():0,y1:this._isVertical()?0:this.pixelPosition(),x2:this._isVertical()?this.pixelPosition():this.width(),y2:this._isVertical()?this.height():this.pixelPosition(),"stroke-width":2*this._detectionRadius}),this},e.prototype.detectionRadius=function(t){if(null==t)return this._detectionRadius;if(t<0)throw new Error("detection radius cannot be negative.");return this._detectionRadius=t,this.render(),this},e.prototype.enabled=function(t){return null==t?this._enabled:(this._enabled=t,t?this.addClass("enabled"):this.removeClass("enabled"),this._dragInteraction.enabled(t),this)},e.prototype.onDragStart=function(t){return this._dragStartCallbacks.add(t),this},e.prototype.offDragStart=function(t){return this._dragStartCallbacks.delete(t),this},e.prototype.onDrag=function(t){return this._dragCallbacks.add(t),this},e.prototype.offDrag=function(t){return this._dragCallbacks.delete(t),this},e.prototype.onDragEnd=function(t){return this._dragEndCallbacks.add(t),this},e.prototype.offDragEnd=function(t){return this._dragEndCallbacks.delete(t),this},e.prototype.destroy=function(){var e=this;t.prototype.destroy.call(this),this._dragStartCallbacks.forEach((function(t){return e._dragStartCallbacks.delete(t)})),this._dragCallbacks.forEach((function(t){return e._dragCallbacks.delete(t)})),this._dragEndCallbacks.forEach((function(t){return e._dragEndCallbacks.delete(t)})),this._disconnectInteraction()},e})(iwt.GuideLineLayer);nwt.DragLineLayer=dwt;var pwt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(pwt,"__esModule",{value:!0});var fwt=BO;function mwt(t,e,n){var i={};if(void 0!==n)for(var r=0;r<n.length;r++){i[n[r]]=n[r-1]}return function(n){var r,o=t.scale(n);if(!e)return o;var a=void 0===i[n]?void 0:t.scale(i[n]);return void 0!==a&&(r=a+(o-a)/2),r}}var gwt=(function(t){function e(e,n){var i=t.call(this)||this;return i.addClass("gridlines"),i._xScale=e,i._yScale=n,i._renderCallback=function(t){return i.render()},i._xScale&&i._xScale.onUpdate(i._renderCallback),i._yScale&&i._yScale.onUpdate(i._renderCallback),i}return fwt.__extends(e,t),e.prototype.betweenX=function(t){return void 0===t?this._betweenX:(t!==this._betweenX&&(this._betweenX=t,this.render()),this)},e.prototype.betweenY=function(t){return void 0===t?this._betweenY:(t!==this._betweenY&&(this._betweenY=t,this.render()),this)},e.prototype.destroy=function(){return t.prototype.destroy.call(this),this._xScale&&this._xScale.offUpdate(this._renderCallback),this._yScale&&this._yScale.offUpdate(this._renderCallback),this},e.prototype._setup=function(){t.prototype._setup.call(this),this._xLinesContainer=this.content().append("g").classed("x-gridlines",!0),this._yLinesContainer=this.content().append("g").classed("y-gridlines",!0)},e.prototype.renderImmediately=function(){return t.prototype.renderImmediately.call(this),this._redrawXLines(),this._redrawYLines(),this},e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),null!=this._xScale&&this._xScale.range([0,this.width()]),null!=this._yScale&&this._yScale.range([this.height(),0]),this},e.prototype._redrawXLines=function(){if(this._xScale){var t=this.betweenX(),e=this._xScale.ticks().slice(t?1:0),n=this._xLinesContainer.selectAll("line").data(e);n.enter().append("line").merge(n).attr("x1",mwt(this._xScale,t,this._xScale.ticks())).attr("y1",0).attr("x2",mwt(this._xScale,t,this._xScale.ticks())).attr("y2",this.height()).classed("betweenline",t).classed("zeroline",(function(t){return 0===t})),n.exit().remove()}},e.prototype._redrawYLines=function(){if(this._yScale){var t=this.betweenY(),e=this._yScale.ticks().slice(t?1:0),n=this._yLinesContainer.selectAll("line").data(e);n.enter().append("line").merge(n).attr("x1",0).attr("y1",mwt(this._yScale,t,this._yScale.ticks())).attr("x2",this.width()).attr("y2",mwt(this._yScale,t,this._yScale.ticks())).classed("betweenline",t).classed("zeroline",(function(t){return 0===t})),n.exit().remove()}},e})(O_t.Component);pwt.Gridlines=gwt;var _wt={},ywt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(ywt,"__esModule",{value:!0});var vwt=BO,bwt=_gt,xwt=(function(t){function e(){var e=t.call(this)||this;return e._detachCallback=function(t){return e.remove(t)},e}return vwt.__extends(e,t),e.prototype.anchor=function(e){var n=this;return e=bwt.coerceExternalD3(e),t.prototype.anchor.call(this,e),this._forEach((function(t){return t.anchor(n.element())})),this},e.prototype.render=function(){return this._forEach((function(t){return t.render()})),this},e.prototype.has=function(t){throw new Error("has() is not implemented on ComponentContainer")},e.prototype._adoptAndAnchor=function(t){t.parent(this),t.onDetach(this._detachCallback),this._isAnchored&&t.anchor(this.element())},e.prototype.remove=function(t){return this.has(t)&&(t.offDetach(this._detachCallback),this._remove(t),t.detach(),this.redraw()),this},e.prototype._remove=function(t){return!1},e.prototype._forEach=function(t){throw new Error("_forEach() is not implemented on ComponentContainer")},e.prototype.destroy=function(){t.prototype.destroy.call(this),this._forEach((function(t){return t.destroy()}))},e.prototype.invalidateCache=function(){this._forEach((function(t){return t.invalidateCache()}))},e})(O_t.Component);ywt.ComponentContainer=xwt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(_wt,"__esModule",{value:!0});var wwt=BO,Swt=D_t,Mwt=(function(t){function e(e){void 0===e&&(e=[]);var n=t.call(this)||this;return n._components=[],n.addClass("component-group"),e.forEach((function(t){return n.append(t)})),n}return wwt.__extends(e,t),e.prototype._forEach=function(t){this.components().forEach(t)},e.prototype.has=function(t){return this._components.indexOf(t)>=0},e.prototype.requestedSpace=function(t,e){var n=this._components.map((function(n){return n.requestedSpace(t,e)}));return{minWidth:Swt.Math.max(n,(function(t){return t.minWidth}),0),minHeight:Swt.Math.max(n,(function(t){return t.minHeight}),0)}},e.prototype.computeLayout=function(e,n,i){var r=this;return t.prototype.computeLayout.call(this,e,n,i),this._forEach((function(t){t.computeLayout({x:0,y:0},r.width(),r.height())})),this},e.prototype._sizeFromOffer=function(t,e){return{width:t,height:e}},e.prototype.fixedWidth=function(){return this._components.every((function(t){return t.fixedWidth()}))},e.prototype.fixedHeight=function(){return this._components.every((function(t){return t.fixedHeight()}))},e.prototype.components=function(){return this._components.slice()},e.prototype.append=function(t){return null==t||this.has(t)||(t.detach(),this._components.push(t),this._adoptAndAnchor(t),this.redraw()),this},e.prototype._remove=function(t){var e=this._components.indexOf(t);return e>=0&&(this._components.splice(e,1),!0)},e})(ywt.ComponentContainer);_wt.Group=Mwt;var Ewt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Ewt,"__esModule",{value:!0});var Twt=BO,Cwt=Lgt,Awt=Cyt,kwt=pvt,Lwt=D_t,Pwt=(function(t){function e(e){var n=t.call(this)||this;if(n._textPadding=5,null==e)throw new Error("InterpolatedColorLegend requires a interpolatedColorScale");return n._scale=e,n._redrawCallback=function(t){return n.redraw()},n._scale.onUpdate(n._redrawCallback),n._formatter=kwt.general(),n._orientation="horizontal",n._expands=!1,n.addClass("legend"),n.addClass("interpolated-color-legend"),n}return Twt.__extends(e,t),e.prototype.destroy=function(){t.prototype.destroy.call(this),this._scale.offUpdate(this._redrawCallback)},e.prototype.formatter=function(t){return void 0===t?this._formatter:(this._formatter=t,this.redraw(),this)},e.prototype.expands=function(t){return null==t?this._expands:(this._expands=t,this.redraw(),this)},e._ensureOrientation=function(t){if("horizontal"===(t=t.toLowerCase())||"left"===t||"right"===t)return t;throw new Error('"'+t+'" is not a valid orientation for InterpolatedColorLegend')},e.prototype.orientation=function(t){return null==t?this._orientation:(this._orientation=e._ensureOrientation(t),this.redraw(),this)},e.prototype.fixedWidth=function(){return!this.expands()||this._isVertical()},e.prototype.fixedHeight=function(){return!this.expands()||!this._isVertical()},e.prototype._generateTicks=function(t){void 0===t&&(t=e._DEFAULT_NUM_SWATCHES);var n=this._scale.domain();if(1===t)return[n[0]];for(var i=(n[1]-n[0])/(t-1),r=[],o=0;o<t;o++)r.push(n[0]+i*o);return r},e.prototype._setup=function(){t.prototype._setup.call(this),this._swatchContainer=this.content().append("g").classed("swatch-container",!0),this._swatchBoundingBox=this.content().append("rect").classed("swatch-bounding-box",!0),this._lowerLabel=this.content().append("g").classed(e.LEGEND_LABEL_CLASS,!0),this._upperLabel=this.content().append("g").classed(e.LEGEND_LABEL_CLASS,!0);var n=new Cwt.SvgContext(this.content().node());this._measurer=new Cwt.Measurer(n),this._wrapper=new Cwt.Wrapper,this._writer=new Cwt.Writer(this._measurer,n,this._wrapper)},e.prototype.requestedSpace=function(t,n){var i,r,o=this,a=this._measurer.measure().height,s=a,l=this._scale.domain().map((function(t){return o._measurer.measure(o._formatter(t)).width})),c=e._DEFAULT_NUM_SWATCHES;if(this._isVertical()){var u=Lwt.Math.max(l,0);r=s+a+this._textPadding+u+this._textPadding,i=c*a}else i=s+a+s,r=this._textPadding+l[0]+c*a+l[1]+this._textPadding;return{minWidth:r,minHeight:i}},e.prototype._isVertical=function(){return"horizontal"!==this._orientation},e.prototype.renderImmediately=function(){var e=this;t.prototype.renderImmediately.call(this);var n,i,r,o,a,s,l=this._scale.domain(),c=this._formatter(l[0]),u=this._measurer.measure(c).width,h=this._formatter(l[1]),d=this._measurer.measure(h).width,p=this._measurer.measure().height,f=this._textPadding,m={x:0,y:0},g={x:0,y:0},_={xAlign:"center",yAlign:"center",textRotation:0},y={xAlign:"center",yAlign:"center",textRotation:0},v={x:0,y:0,width:0,height:0};if(this._isVertical()){s=Math.floor(this.height());var b=Math.max(u,d);a=(this.width()-b-2*this._textPadding)/2,n=Math.max(this.width()-a-2*f-b,0),i=1,o=function(t,n){return e.height()-(n+1)},y.yAlign="top",m.y=0,_.yAlign="bottom",g.y=0,"left"===this._orientation?(r=function(t,e){return f+b+f},y.xAlign="right",m.x=-(a+n+f),_.xAlign="right",g.x=-(a+n+f)):(r=function(t,e){return a},y.xAlign="left",m.x=a+n+f,_.xAlign="left",g.x=a+n+f),v.width=n,v.height=s*i}else a=Math.max(f,(this.height()-p)/2),s=Math.max(Math.floor(this.width()-4*f-u-d),0),n=1,i=Math.max(this.height()-2*a,0),r=function(t,e){return Math.floor(u+2*f)+e},o=function(t,e){return a},y.xAlign="right",m.x=-f,_.xAlign="left",g.x=f,v.y=a,v.width=s*n,v.height=i;v.x=r(null,0),this._upperLabel.text(""),this._writer.write(h,this.width(),this.height(),y,this._upperLabel.node()),this._upperLabel.attr("transform","translate("+m.x+", "+m.y+")"),this._lowerLabel.text(""),this._writer.write(c,this.width(),this.height(),_,this._lowerLabel.node()),this._lowerLabel.attr("transform","translate("+g.x+", "+g.y+")"),this._swatchBoundingBox.attrs(v);var x=this._generateTicks(s),w=this._swatchContainer.selectAll("rect.swatch").data(x),S=w.enter().append("rect").classed("swatch",!0),M=w.merge(S);return w.exit().remove(),M.attrs({fill:function(t,n){return e._scale.scale(t)},width:n,height:i,x:r,y:o,"shape-rendering":"crispEdges"}),Awt.ADD_TITLE_ELEMENTS&&S.append("title").text((function(t){return e._formatter(t)})),this},e._DEFAULT_NUM_SWATCHES=11,e.LEGEND_LABEL_CLASS="legend-label",e})(O_t.Component);Ewt.InterpolatedColorLegend=Pwt;var Nwt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Nwt,"__esModule",{value:!0});var Iwt=BO,Rwt=Lgt,Owt=(function(t){function e(e,n){void 0===e&&(e=""),void 0===n&&(n=0);var i=t.call(this)||this;return i.addClass("label"),i.text(e),i.angle(n),i.xAlignment("center").yAlignment("center"),i._padding=0,i}return Iwt.__extends(e,t),e.prototype.requestedSpace=function(t,e){var n=this._measurer.measure(this._text);return{minWidth:(0===this.angle()?n.width:n.height)+2*this.padding(),minHeight:(0===this.angle()?n.height:n.width)+2*this.padding()}},e.prototype._setup=function(){t.prototype._setup.call(this),this._textContainer=this.content().append("g");var e=new Rwt.SvgContext(this._textContainer.node());this._measurer=new Rwt.CacheMeasurer(e),this._wrapper=new Rwt.Wrapper,this._writer=new Rwt.Writer(this._measurer,e,this._wrapper),this.text(this._text)},e.prototype.text=function(t){if(null==t)return this._text;if("string"!=typeof t)throw new Error("Label.text() only takes strings as input");return this._text=t,this.redraw(),this},e.prototype.angle=function(t){if(null==t)return this._angle;if((t%=360)>180?t-=360:t<-180&&(t+=360),-90!==t&&0!==t&&90!==t)throw new Error(t+" is not a valid angle for Label");return this._angle=t,this.redraw(),this},e.prototype.padding=function(t){if(null==t)return this._padding;if((t=+t)<0)throw new Error(t+" is not a valid padding value. Cannot be less than 0.");return this._padding=t,this.redraw(),this},e.prototype.fixedWidth=function(){return!0},e.prototype.fixedHeight=function(){return!0},e.prototype.renderImmediately=function(){t.prototype.renderImmediately.call(this),this._textContainer.selectAll("g").remove();var e=this._measurer.measure(this._text),n=Math.max(Math.min((this.height()-e.height)/2,this.padding()),0),i=Math.max(Math.min((this.width()-e.width)/2,this.padding()),0);this._textContainer.attr("transform","translate("+i+","+n+")");var r=this.width()-2*i,o=this.height()-2*n,a={xAlign:this.xAlignment(),yAlign:this.yAlignment(),textRotation:this.angle()};return this._writer.write(this._text,r,o,a),this},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e})(O_t.Component);Nwt.Label=Owt;var zwt=(function(t){function e(n,i){var r=t.call(this,n,i)||this;return r.addClass(e.TITLE_LABEL_CLASS),r}return Iwt.__extends(e,t),e.TITLE_LABEL_CLASS="title-label",e})(Owt);Nwt.TitleLabel=zwt;var Dwt=(function(t){function e(n,i){var r=t.call(this,n,i)||this;return r.addClass(e.AXIS_LABEL_CLASS),r}return Iwt.__extends(e,t),e.AXIS_LABEL_CLASS="axis-label",e})(Owt);Nwt.AxisLabel=Dwt;var Bwt={},Hwt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Hwt,"__esModule",{value:!0});var Fwt=wpt;Hwt.circle=function Vwt(){return function(t){return Fwt.symbol().type(Fwt.symbolCircle).size(Math.PI*Math.pow(t/2,2))}},Hwt.square=function Uwt(){return function(t){return Fwt.symbol().type(Fwt.symbolSquare).size(Math.pow(t,2))}},Hwt.cross=function jwt(){return function(t){return Fwt.symbol().type(Fwt.symbolCross).size(5/9*Math.pow(t,2))}},Hwt.diamond=function Gwt(){return function(t){return Fwt.symbol().type(Fwt.symbolDiamond).size(Math.tan(Math.PI/6)*Math.pow(t,2)/2)}},Hwt.triangle=function Wwt(){return function(t){return Fwt.symbol().type(Fwt.symbolTriangle).size(Math.sqrt(3)*Math.pow(t/2,2))}};Hwt.star=function qwt(){return function(t){return Fwt.symbol().type(Fwt.symbolStar).size(.8908130915292852*Math.pow(t/2,2))}};var Ywt=3*(1/Math.sqrt(12)/2+1);Hwt.wye=function Xwt(){return function(t){return Fwt.symbol().type(Fwt.symbolWye).size(Ywt*Math.pow(t/2.4,2))}},
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Bwt,"__esModule",{value:!0});var $wt=BO,Kwt=wpt,Zwt=Lgt,Jwt=Cyt,Qwt=pvt,tSt=Hwt,eSt=D_t,nSt=O_t,iSt=(function(){function t(t,e,n){void 0===t&&(t=[]),void 0===e&&(e=0),void 0===n&&(n=1/0),this.columns=t,this.bottomPadding=e,this.maxWidth=n}return t.prototype.addColumn=function(t){var e=t.width,n=this.getWidthAvailable();t.width=Math.min(n,e),this.columns.push(t)},t.prototype.getBounds=function(t){for(var e=this.columns[t],n=0,i=0;i<t;i++)n+=this.columns[i].width;return{topLeft:{x:n,y:0},bottomRight:{x:n+e.width,y:e.height}}},t.prototype.getHeight=function(){return eSt.Math.max(this.columns.map((function(t){return t.height})),0)+this.bottomPadding},t.prototype.getWidth=function(){return Math.min(this.columns.reduce((function(t,e){return t+e.width}),0),this.maxWidth)},t.prototype.getWidthAvailable=function(){var t=this.getWidth();return Math.max(this.maxWidth-t,0)},t})(),rSt=(function(){function t(t,e,n,i){void 0===t&&(t=1/0),void 0===e&&(e=1/0),void 0===n&&(n=0),void 0===i&&(i=[]),this.maxWidth=t,this.maxHeight=e,this.padding=n,this.rows=i}return t.prototype.addRow=function(t){t.maxWidth=this.maxWidth-2*this.padding,this.rows.push(t)},t.prototype.getColumnBounds=function(t,e){var n=this.getRowBounds(t),i=this.rows[t].getBounds(e);return i.topLeft.x+=n.topLeft.x,i.bottomRight.x+=n.topLeft.x,i.topLeft.y+=n.topLeft.y,i.bottomRight.y+=n.topLeft.y,i},t.prototype.getRowBounds=function(t){for(var e=this.padding,n=this.padding,i=0;i<t;i++)n+=this.rows[i].getHeight();return{topLeft:{x:e,y:n},bottomRight:{x:e+this.rows[t].getWidth(),y:n+this.rows[t].getHeight()}}},t.prototype.getHeight=function(){return Math.min(this.rows.reduce((function(t,e){return t+e.getHeight()}),0)+2*this.padding,this.maxHeight)},t.prototype.getWidth=function(){return Math.min(eSt.Math.max(this.rows.map((function(t){return t.getWidth()})),0)+2*this.padding,this.maxWidth)},t})(),oSt=(function(t){function e(e){var n=t.call(this)||this;if(n._padding=5,n._rowBottomPadding=3,n.addClass("legend"),n.maxEntriesPerRow(1),null==e)throw new Error("Legend requires a colorScale");return n._colorScale=e,n._redrawCallback=function(t){return n.redraw()},n._colorScale.onUpdate(n._redrawCallback),n._formatter=Qwt.identity(),n.maxLinesPerEntry(1),n.xAlignment("right").yAlignment("top"),n.comparator((function(t,e){var i=n._colorScale.domain().slice().map((function(t){return n._formatter(t)}));return i.indexOf(t)-i.indexOf(e)})),n._symbolFactoryAccessor=function(){return tSt.circle()},n._symbolOpacityAccessor=function(){return 1},n}return $wt.__extends(e,t),e.prototype._setup=function(){t.prototype._setup.call(this);var n=this.content().append("g").classed(e.LEGEND_ROW_CLASS,!0);n.append("g").classed(e.LEGEND_ENTRY_CLASS,!0).append("text");var i=new Zwt.SvgContext(n.node(),null,Jwt.ADD_TITLE_ELEMENTS);this._measurer=new Zwt.CacheMeasurer(i),this._wrapper=(new Zwt.Wrapper).maxLines(this.maxLinesPerEntry()),this._writer=new Zwt.Writer(this._measurer,i,this._wrapper)},e.prototype.formatter=function(t){return null==t?this._formatter:(this._formatter=t,this.redraw(),this)},e.prototype.maxEntriesPerRow=function(t){return null==t?this._maxEntriesPerRow:(this._maxEntriesPerRow=t,this.redraw(),this)},e.prototype.maxLinesPerEntry=function(t){return null==t?this._maxLinesPerEntry:(this._maxLinesPerEntry=t,this.redraw(),this)},e.prototype.maxWidth=function(t){return null==t?this._maxWidth:(this._maxWidth=t,this.redraw(),this)},e.prototype.comparator=function(t){return null==t?this._comparator:(this._comparator=t,this.redraw(),this)},e.prototype.colorScale=function(t){return null!=t?(this._colorScale.offUpdate(this._redrawCallback),this._colorScale=t,this._colorScale.onUpdate(this._redrawCallback),this.redraw(),this):this._colorScale},e.prototype.destroy=function(){t.prototype.destroy.call(this),this._colorScale.offUpdate(this._redrawCallback)},e.prototype._buildLegendTable=function(t,e){var n=this,i=this._measurer.measure().height,r=new rSt(t,e,this._padding),o=this._colorScale.domain().slice().sort((function(t,e){return n._comparator(n._formatter(t),n._formatter(e))})),a=new iSt;return r.addRow(a),a.bottomPadding=this._rowBottomPadding,o.forEach((function(t,e){a.columns.length/2===n.maxEntriesPerRow()&&((a=new iSt).bottomPadding=n._rowBottomPadding,r.addRow(a));var o=a.getWidthAvailable(),s=n._formatter(t),l=n._measurer.measure(s).width;o-i-l<0&&a.columns.length>1&&((a=new iSt).bottomPadding=n._rowBottomPadding,r.addRow(a)),a.addColumn({width:i,height:i,data:{name:t,type:"symbol"}}),o=a.getWidthAvailable();var c=Math.min(o,l);n._wrapper.maxLines(n.maxLinesPerEntry());var u=n._wrapper.wrap(s,n._measurer,c).noLines;a.addColumn({width:c,height:u*i,data:{name:t,type:"text"}})})),r},e.prototype.requestedSpace=function(t,e){var n=this._buildLegendTable(eSt.Math.min([this.maxWidth(),t],t),e);return{minHeight:n.getHeight(),minWidth:n.getWidth()}},e.prototype.entitiesAt=function(t){var n=this;if(!this._isSetup)return[];var i=this._buildLegendTable(this.width(),this.height());return i.rows.reduce((function(r,o,a){if(0!==r.length)return r;var s=i.getRowBounds(a);return eSt.Math.within(t,s)?o.columns.reduce((function(r,o,s){var l=i.getColumnBounds(a,s);if(eSt.Math.within(t,l)){var c=n.content().selectAll("."+e.LEGEND_ROW_CLASS).nodes()[a],u=Kwt.select(c).selectAll("."+e.LEGEND_ENTRY_CLASS).nodes()[Math.floor(s/2)],h=Kwt.select(u).select("."+e.LEGEND_SYMBOL_CLASS),d=eSt.DOM.getTranslateValues(Kwt.select(c)),p=eSt.DOM.getTranslateValues(h);return[{bounds:eSt.DOM.elementBBox(Kwt.select(c)),datum:o.data.name,position:{x:d[0]+p[0],y:d[1]+p[1]},selection:Kwt.select(u),component:n}]}return r}),r):r}),[])},e.prototype.renderImmediately=function(){t.prototype.renderImmediately.call(this);var n=this._buildLegendTable(this.width(),this.height());this.content().selectAll("*").remove();var i=this.content().selectAll("g."+e.LEGEND_ROW_CLASS).data(n.rows),r=i.enter().append("g").classed(e.LEGEND_ROW_CLASS,!0).merge(i);i.exit().remove(),r.attr("transform",(function(t,e){var i=n.getRowBounds(e);return"translate("+i.topLeft.x+", "+i.topLeft.y+")"}));var o=this;return r.each((function(t,i){for(var r=[],a=0;a<t.columns.length;a+=2)r.push([t.columns[a],t.columns[a+1]]);var s=Kwt.select(this).selectAll("g."+e.LEGEND_ENTRY_CLASS).data(r),l=s.enter().append("g").classed(e.LEGEND_ENTRY_CLASS,!0).merge(s);l.append("path").attr("d",(function(t,e){var n=t[0];return o.symbol()(n.data.name,i)(.6*n.height)(null)})).attr("transform",(function(t,e){var r=t[0],o=n.rows[i].columns.indexOf(r);return"translate("+(n.getColumnBounds(i,o).topLeft.x+r.width/2)+", "+r.height/2+")"})).attr("fill",(function(t){return o._colorScale.scale(t[0].data.name)})).attr("opacity",(function(t,e){return o.symbolOpacity()(t[0].data.name,i)})).classed(e.LEGEND_SYMBOL_CLASS,!0),l.append("g").classed("text-container",!0).attr("transform",(function(t,e){var r=n.rows[i].columns.indexOf(t[1]);return"translate("+n.getColumnBounds(i,r).topLeft.x+", 0)"})).each((function(t,e,n){var i=Kwt.select(this),r=t[1];o._writer.write(o._formatter(r.data.name),r.width,o.height(),{xAlign:"left",yAlign:"top",textRotation:0},i.node())})),s.exit().remove()})),this},e.prototype.symbol=function(t){return null==t?this._symbolFactoryAccessor:(this._symbolFactoryAccessor=t,this.render(),this)},e.prototype.symbolOpacity=function(t){return null==t?this._symbolOpacityAccessor:(this._symbolOpacityAccessor="number"==typeof t?function(){return t}:t,this.render(),this)},e.prototype.fixedWidth=function(){return!0},e.prototype.fixedHeight=function(){return!0},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e.LEGEND_ROW_CLASS="legend-row",e.LEGEND_ENTRY_CLASS="legend-entry",e.LEGEND_SYMBOL_CLASS="legend-symbol",e})(nSt.Component);Bwt.Legend=oSt;var aSt={},sSt={},lSt={};!(function(t){var e;
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0}),(e=t.Animator||(t.Animator={})).MAIN="main",e.RESET="reset"})(lSt);var cSt={},uSt={},hSt={},dSt=function t(e){return null!=e&&"object"==typeof e&&!1===Array.isArray(e)};function pSt(t){return!0===dSt(t)&&"[object Object]"===Object.prototype.toString.call(t)}var fSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(fSt,"__esModule",{value:!0});var mSt=D_t,gSt=0,_St=(function(){function t(t,e){void 0===t&&(t=[]),void 0===e&&(e={}),this._updateId=gSt++,this._data=t,this._metadata=e,this._callbacks=new mSt.CallbackSet}return t.prototype.onUpdate=function(t){return this._callbacks.add(t),this},t.prototype.offUpdate=function(t){return this._callbacks.delete(t),this},t.prototype.data=function(t){return null==t?this._data:(this._data=t,this._dispatchUpdate(),this)},t.prototype.metadata=function(t){return null==t?this._metadata:(this._metadata=t,this._dispatchUpdate(),this)},t.prototype.updateId=function(){return this._updateId},t.prototype._dispatchUpdate=function(){this._updateId=gSt++,this._callbacks.callCallbacks(this)},t})();fSt.Dataset=_St,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     * @fileoverview Implements the Signature API to help in comparing when two
     * Plottable objects have "changed".
     *
     * Memoization in Plottable is complicated by mutable scales and datasets. We cannot simply
     * reference compare two e.g. scales since it may have internally mutated. To resolve this,
     * we write a recursive Signature interface that holds an immutable snapshot of whatever
     * state the scale/data was in at the time. Then on memoized function invocation we sign the
     * new inputs and compare the signatures to decide if we should recompute.
     *
     * We must hand-write a signature for each custom class we wish to support.
     */
Object.defineProperty(hSt,"__esModule",{value:!0});var ySt=BO,vSt=function t(e){var n,i;return!1!==pSt(e)&&("function"==typeof(n=e.constructor)&&(!1!==pSt(i=n.prototype)&&!1!==i.hasOwnProperty("isPrototypeOf")))},bSt=fSt,xSt=lbt;function wSt(t){return t instanceof ASt?t:t instanceof Date?ESt(t.valueOf()):t instanceof xSt.Scale?SSt(t):t instanceof bSt.Dataset?MSt(t):vSt(t)?CSt(t):Array.isArray(t)?TSt(t):ESt(t)}function SSt(t){return CSt({domain:t.domain(),range:t.range(),updateId:t.updateId(),ref:ESt(t)})}function MSt(t){return CSt({ref:ESt(t),updateId:t.updateId()})}function ESt(t){return new LSt(t)}function TSt(t){return new kSt(t.map((function(t){return wSt(t)})))}function CSt(t){var e={};for(var n in t)t.hasOwnProperty(n)&&(e[n]=wSt(t[n]));return new PSt(e)}hSt.sign=wSt,hSt.signScale=SSt,hSt.signDataset=MSt,hSt.signRef=ESt,hSt.signArray=TSt,hSt.signObj=CSt;var ASt=(function(){function t(){}return t.prototype.isDifferent=function(t){return!(t instanceof this.constructor)||this.isSignatureDifferent(t)},t})();hSt.Signature=ASt;var kSt=(function(t){function e(e){var n=t.call(this)||this;return n.array=e,n}return ySt.__extends(e,t),e.prototype.isSignatureDifferent=function(t){if(t.array.length!==this.array.length)return!0;for(var e=0;e<this.array.length;e++)if(this.array[e].isDifferent(t.array[e]))return!0;return!1},e})(ASt);hSt.ArraySignature=kSt;var LSt=(function(t){function e(e){var n=t.call(this)||this;return n.ref=e,n}return ySt.__extends(e,t),e.prototype.isSignatureDifferent=function(t){return this.ref!==t.ref},e})(ASt);hSt.ReferenceSignature=LSt;var PSt=(function(t){function e(e){var n=t.call(this)||this;return n.obj=e,n}return ySt.__extends(e,t),e.prototype.isSignatureDifferent=function(t){var e=Object.keys(this.obj),n=Object.keys(t.obj);if(e.length!==n.length)return!0;for(var i=0,r=e;i<r.length;i++){var o=r[i];if(!t.obj.hasOwnProperty(o))return!0;if(this.obj[o].isDifferent(t.obj[o]))return!0}return!1},e})(ASt);hSt.ObjectSignature=PSt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     * @fileoverview Implements a function memoizer using the Signature API.
     */
Object.defineProperty(uSt,"__esModule",{value:!0});var NSt=hSt;uSt.memoize=function ISt(t){var e,n=void 0,i=!1,r=!1,o=function(){for(var o=[],a=0;a<arguments.length;a++)o[a]=arguments[a];if(i)return e;var s=NSt.signArray(o);return void 0===n||n.isDifferent(s)?(r&&console.warn("cache miss! computing"),n=s,e=t.apply(this,o)):r&&console.warn("cache hit!"),e};return o.doLocked=function(t){if(i)throw new Error("Locking an already locked memoize function!");i=!0;var e=t.apply(this);return i=!1,e},o.logPerformance=function(t){return void 0===t&&(t=!0),r=t,this},o};var RSt={};Object.defineProperty(RSt,"__esModule",{value:!0});var OSt=Ce.exports,zSt=(function(){function t(){this.map=Object.create(null),this.exists=Object.create(null)}return t.prototype.delete=function(t){return delete this.map[t],delete this.exists[t],!0},t.prototype.get=function(t){return this.map[t]},t.prototype.has=function(t){return!!this.exists[t]},t.prototype.set=function(t,e){return this.map[t]=e,this.exists[t]=!0,this},t})(),DSt=(function(){function t(){this.map=new zSt}return t.prototype.get=function(t){return this.map.get(t[0]).get(t[1])},t.prototype.has=function(t){return this.map.has(t[0])&&this.map.get(t[0]).has(t[1])},t.prototype.set=function(t,e){return this.map.has(t[0])||this.map.set(t[0],new zSt),this.map.get(t[0]).set(t[1],e),this},t.prototype.delete=function(t){return this.map.has(t[0])&&this.map.get(t[0]).delete(t[1]),!0},t.prototype.clear=function(){this.map=new zSt},t.resolver=function(t,e,n){return[n.updateId(),e]},t})();function BSt(t){var e=OSt.memoize(t,DSt.resolver);return e.cache=new DSt,e}RSt.memoizeProjector=BSt,RSt.memoizeProjectors=function HSt(t){return Object.keys(t).forEach((function(e){t[e]=BSt(t[e])})),t};var FSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     * @fileoverview Implements a convenient thunk function to handle the common case
     * of creating a memoized function that takes its inputs from mutable class properties.
     */Object.defineProperty(FSt,"__esModule",{value:!0});var VSt=cSt;FSt.memThunk=function USt(){for(var t=[],e=0;e<arguments.length;e++)t[e]=arguments[e];var n=t.slice(0,-1),i=t[t.length-1],r=VSt.memoize(i),o=function(){var t=this,e=n.map((function(e){return e.apply(t)}));return r.apply(void 0,e)};return o},(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=BO;e.__exportStar(uSt,t),e.__exportStar(RSt,t),e.__exportStar(FSt,t),t.sign=hSt.sign})(cSt);var jSt={};!(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=wpt,n=(function(){function t(t,e){this._context=t,this._drawStep=e}return t.prototype.getDrawStep=function(){return this._drawStep},t.prototype.draw=function(t,e){var n=e[e.length-1].attrToAppliedProjector;this._context.save(),this._drawStep(this._context,t,n),this._context.restore()},t.prototype.getVisualPrimitives=function(){return[]},t.prototype.getVisualPrimitiveAtIndex=function(t){return null},t.prototype.remove=function(){},t})();function i(t,e,n,i){for(var r={},o=0,a=e;o<a.length;o++){var s=a[o];t.hasOwnProperty(s)&&(r[s]=t[s](n,i))}return r}function r(t){return null!=t["stroke-width"]?parseFloat(t["stroke-width"]):1}function o(t){var e=t["stroke-dasharray"];if(null!=e)try{return e.split(/[ ,]+/).map((function(t){return parseInt(t,10)}))}catch(t){return console.error("getStrokeDashArray failed with: "+t),[]}return[]}function a(t,n){if(n.stroke){t.lineWidth=r(n);var i=e.color(n.stroke),a=o(n);t.setLineDash(a),i.opacity*=(function s(t){var e=null!=t.opacity?parseFloat(t.opacity):1;return(null!=t["stroke-opacity"]?parseFloat(t["stroke-opacity"]):1)*e})(n),t.strokeStyle=i.toString(),t.stroke()}if(n.fill){var l=e.color(n.fill);l.opacity*=(function c(t){var e=null!=t.opacity?parseFloat(t.opacity):1;return(null!=t["fill-opacity"]?parseFloat(t["fill-opacity"]):1)*e})(n),t.fillStyle=l.toString(),t.fill()}}t.CanvasDrawer=n,t.ContextStyleAttrs=["fill-opacity","fill","opacity","stroke-opacity","stroke-width","stroke","stroke-dasharray"],t.resolveAttributesSubsetWithStyles=function s(e,n,r,o){return i(e,t.ContextStyleAttrs.concat(n),r,o)},t.resolveAttributes=i,t.getStrokeWidth=r,t.getStrokeDashArray=o,t.renderArea=function l(t,e,n,i){t.save(),t.beginPath(),e.context(t),e(n),t.lineJoin="round",a(t,i),t.restore()},t.renderLine=function c(t,e,n,i){t.save(),t.beginPath(),e.context(t),e(n),t.lineJoin="round",a(t,i),t.restore()},t.renderPathWithStyle=a})(jSt);var GSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(GSt,"__esModule",{value:!0});var WSt=(function(){function t(t,e){this._svgDrawerFactory=t,this._canvasDrawerFactory=e}return t.prototype.useSVG=function(t){null!=this._currentDrawer&&this._currentDrawer.remove();var e=this._svgDrawerFactory();e.attachTo(t),this._currentDrawer=e},t.prototype.useCanvas=function(t){null!=this._currentDrawer&&this._currentDrawer.remove(),this._currentDrawer=this._canvasDrawerFactory(t.node().getContext("2d"))},t.prototype.getDrawer=function(){return this._currentDrawer},t.prototype.remove=function(){null!=this._currentDrawer&&this._currentDrawer.remove()},t.prototype.draw=function(t,e){this._currentDrawer.draw(t,e)},t.prototype.getVisualPrimitives=function(){return this._currentDrawer.getVisualPrimitives()},t.prototype.getVisualPrimitiveAtIndex=function(t){return this._currentDrawer.getVisualPrimitiveAtIndex(t)},t})();GSt.ProxyDrawer=WSt;var qSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(qSt,"__esModule",{value:!0});var YSt=wpt,XSt=D_t,$St=(function(){function t(t,e){this._root=YSt.select(document.createElementNS("http://www.w3.org/2000/svg","g")),this._className=e,this._svgElementName=t}return t.prototype.draw=function(t,e){var n=this;this._createAndDestroyDOMElements(t);for(var i=0,r=e.length,o=function(r){var o=e[r];XSt.Window.setTimeout((function(){return n._drawStep(o)}),i),i+=o.animator.totalTime(t.length)},a=0;a<r;a++)o(a)},t.prototype.getVisualPrimitives=function(){return null==this._cachedVisualPrimitivesNodes&&(this._cachedVisualPrimitivesNodes=this._selection.nodes()),this._cachedVisualPrimitivesNodes},t.prototype.getVisualPrimitiveAtIndex=function(t){return null==this._cachedVisualPrimitivesNodeMap?null:this._cachedVisualPrimitivesNodeMap.get(t)},t.prototype.remove=function(){this._root.remove()},t.prototype.attachTo=function(t){t.node().appendChild(this._root.node())},t.prototype.getRoot=function(){return this._root},t.prototype.selector=function(){return this._svgElementName},t.prototype._applyDefaultAttributes=function(t){},t.prototype._createAndDestroyDOMElements=function(t){var e=t.map((function(t,e){return null!=t?{d:t,i:e}:null})).filter((function(t){return null!=t})),n=this._root.selectAll(this.selector()).data(e);this._selection=n.enter().append(this._svgElementName).merge(n),n.exit().remove();var i=new XSt.Map;this._selection.each((function(t){i.set(t.i,this)})),this._cachedVisualPrimitivesNodeMap=i,this._cachedVisualPrimitivesNodes=null,this._selection.data(this._selection.data().map((function(t){return t.d}))),null!=this._className&&this._selection.classed(this._className,!0),this._applyDefaultAttributes(this._selection)},t.prototype._drawStep=function(t){var e=this;["fill","stroke"].forEach((function(n){null!=t.attrToAppliedProjector[n]&&e._selection.attr(n,t.attrToAppliedProjector[n])})),t.animator.animate(this._selection,t.attrToAppliedProjector),null!=this._className&&this._selection.classed(this._className,!0)},t})();qSt.SVGDrawer=$St;var KSt={};
/**
     * Copyright 2017-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(KSt,"__esModule",{value:!0});var ZSt=(function(){function t(){var t=this;this.scale=0,this.translate=0,this.cachedDomain=[null,null],this.lastSeenDomain=[null,null],this.updateDomain=function(e){t.lastSeenDomain=e.getTransformationDomain();var n=e.scaleTransformation(t.cachedDomain[1])-e.scaleTransformation(t.cachedDomain[0]),i=e.scaleTransformation(t.lastSeenDomain[1])-e.scaleTransformation(t.lastSeenDomain[0]);t.scale=n/i||1,t.translate=e.scaleTransformation(t.cachedDomain[0])-e.scaleTransformation(t.lastSeenDomain[0])*t.scale||0}}return t.prototype.reset=function(){this.scale=1,this.translate=0,this.cachedDomain=this.lastSeenDomain},t.prototype.setDomain=function(t){this.cachedDomain=t.getTransformationDomain()},t})(),JSt=(function(){function t(e,n){var i=this;this.renderCallback=e,this.applyTransformCallback=n,this.domainTransformX=new ZSt,this.domainTransformY=new ZSt,this.renderDeferred=function(){i.applyTransform(),clearTimeout(i.timeoutToken),i.timeoutToken=setTimeout((function(){i.renderCallback()}),t.DEFERRED_RENDERING_DELAY)}}return t.prototype.setDomains=function(t,e){t&&this.domainTransformX.setDomain(t),e&&this.domainTransformY.setDomain(e),this.renderDeferred()},t.prototype.updateDomains=function(t,e){t&&this.domainTransformX.updateDomain(t),e&&this.domainTransformY.updateDomain(e),this.renderDeferred()},t.prototype.resetTransforms=function(){this.domainTransformX.reset(),this.domainTransformY.reset(),this.applyTransform()},t.prototype.applyTransform=function(){this.applyTransformCallback(this.domainTransformX.translate,this.domainTransformY.translate,this.domainTransformX.scale,this.domainTransformY.scale)},t.DEFERRED_RENDERING_DELAY=200,t})();KSt.DeferredRenderer=JSt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(sSt,"__esModule",{value:!0});var QSt=BO,tMt=wpt,eMt=fgt,nMt=D_t,iMt=lSt,rMt=cSt,oMt=O_t,aMt=jSt,sMt=GSt,lMt=qSt,cMt=_gt,uMt=KSt;sSt.Renderer=bgt.makeEnum(["svg","canvas"]);var hMt=(function(t){function e(){var n=t.call(this)||this;n._dataChanged=!1,n._attrExtents={},n._animate=!1,n._animators={},n._propertyExtents={},n._resetEntityStore=function(){n._cachedEntityStore=void 0},n._overflowHidden=!0,n.addClass("plot"),n._datasetToDrawer=new nMt.Map,n._attrBindings=tMt.map(),n._includedValuesProvider=function(t,e){return n._includedValuesForScale(t,e)},n._renderCallback=function(){return n.render()},n._onDatasetUpdateCallback=function(){return n._onDatasetUpdate()},n._propertyBindings=tMt.map();var i=(new eMt.Easing).maxTotalDuration(e._ANIMATION_MAX_DURATION);return n.animator(iMt.Animator.MAIN,i),n.animator(iMt.Animator.RESET,new eMt.Null),n._deferredResetEntityStore=nMt.Window.debounce(uMt.DeferredRenderer.DEFERRED_RENDERING_DELAY,n._resetEntityStore),n}return 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c=this._bufferCanvas.node();c.width>0&&c.height>0?l.canvas.width>0&&l.canvas.height>0&&l.drawImage(c,0,0,e,n):console.warn("Failed to fill canvas with 0x0 buffer canvas")}}return this},e.prototype.destroy=function(){var e=this;t.prototype.destroy.call(this),this._scales().forEach((function(t){return t.offUpdate(e._renderCallback)})),this.datasets([])},e.prototype._createNodesForDataset=function(t){var e=this._datasetToDrawer.get(t);return"svg"===this.renderer()?e.useSVG(this._renderArea):e.useCanvas(this._canvas),e},e.prototype._createDrawer=function(t){return new sMt.ProxyDrawer((function(){return new lMt.SVGDrawer("path","")}),(function(t){return new aMt.CanvasDrawer(t,(function(){}))}))},e.prototype._getAnimator=function(t){return this._animateOnNextRender()&&this._animators[t]||new eMt.Null},e.prototype._onDatasetUpdate=function(){this._updateExtents(),this._dataChanged=!0,this._resetEntityStore(),this.renderLowPriority()},e.prototype.attr=function(t,e,n){return null==e?this._attrBindings.get(t):(this._bindAttr(t,e,n),this.render(),this)},e.prototype._bindProperty=function(t,e,n,i){var r=this._propertyBindings.get(t),o=null!=r?r.scale:null;this._propertyBindings.set(t,{accessor:"function"==typeof e?e:function(){return e},scale:n,postScale:i}),null!=o&&this._uninstallScaleForKey(o,t),null!=n&&this._installScaleForKey(n,t),this._clearAttrToProjectorCache()},e.prototype._bindAttr=function(t,e,n){var i=this._attrBindings.get(t),r=null!=i?i.scale:null;this._attrBindings.set(t,{accessor:"function"==typeof e?e:function(){return e},scale:n}),null!=r&&this._uninstallScaleForKey(r,t),null!=n&&this._installScaleForKey(n,t),this._clearAttrToProjectorCache()},e.prototype._clearAttrToProjectorCache=function(){delete this._cachedAttrToProjector},e.prototype._getAttrToProjector=function(){if(null==this._cachedAttrToProjector){var t=this._generateAttrToProjector();e.OPTIMIZE_MEMOIZE_PROJECTORS&&(t=rMt.memoizeProjectors(t)),this._cachedAttrToProjector=t}return nMt.assign({},this._cachedAttrToProjector)},e.prototype._generateAttrToProjector=function(){var t={};this._attrBindings.each((function(n,i){t[i]=e._scaledAccessor(n)}));var n=this._propertyProjectors();return Object.keys(n).forEach((function(e){null==t[e]&&(t[e]=n[e])})),t},e.prototype.renderImmediately=function(){return t.prototype.renderImmediately.call(this),this._isAnchored&&(this._paint(),this._dataChanged=!1),this},e.prototype.renderLowPriority=function(){return this._renderCallback(),this},e.prototype.animated=function(t){return null==t?this._animate:(this._animate=t,this)},e.prototype.detach=function(){return t.prototype.detach.call(this),this._updateExtents(),this},e.prototype._scales=function(){var t=[];return this._attrBindings.each((function(e,n){var i=e.scale;null!=i&&-1===t.indexOf(i)&&t.push(i)})),this._propertyBindings.each((function(e,n){var i=e.scale;null!=i&&-1===t.indexOf(i)&&t.push(i)})),t},e.prototype._updateExtents=function(){var t=this;this._resetEntityStore(),this._scales().forEach((function(e){return e.addIncludedValuesProvider(t._includedValuesProvider)}))},e.prototype._filterForProperty=function(t){return null},e.prototype.getExtentsForAttr=function(t){var e=this;if(null==this._attrExtents[t]){var n=rMt.memThunk((function(){return e.datasets()}),(function(){return e._attrBindings.get(t)}),(function(t,e){return null==e||null==e.accessor?null:t.map((function(t){return dMt(t,e,null)}))}));this._attrExtents[t]=n}return this._attrExtents[t]()},e.prototype.getExtentsForProperty=function(t){var e=this;if(null==this._propertyExtents[t]){var n=rMt.memThunk((function(){return e.datasets()}),(function(){return e._propertyBindings.get(t)}),(function(){return e._filterForProperty(t)}),(function(t,e,n){return null==e||null==e.accessor?null:t.map((function(t){return dMt(t,e,n)}))}));this._propertyExtents[t]=n}return this._propertyExtents[t]()},e.prototype._includedValuesForScale=function(t,e){var n=this;if(!this._isAnchored&&!e)return[];var i=[];return this._attrBindings.each((function(e,r){if(e.scale===t){var o=n.getExtentsForAttr(r);null!=o&&(i=i.concat(tMt.merge(o)))}})),this._propertyBindings.each((function(e,r){if(e.scale===t){var o=n.getExtentsForProperty(r);null!=o&&(i=i.concat(tMt.merge(o)))}})),i},e.prototype.animator=function(t,e){return void 0===e?this._animators[t]:(this._animators[t]=e,this)},e.prototype.renderer=function(t){var e=this;return void 0===t?null==this._canvas?"svg":"canvas":(null==this._canvas&&"canvas"===t?(this._canvas=tMt.select(document.createElement("canvas")).classed("plot-canvas",!0),this._bufferCanvas=tMt.select(document.createElement("canvas")),null!=this.element()&&this._appendCanvasNode(),this._datasetToDrawer.forEach((function(t){t.useCanvas(e._canvas)})),this.render()):null!=this._canvas&&"svg"==t&&(this._canvas.remove(),this._canvas=null,this._bufferCanvas=null,this._datasetToDrawer.forEach((function(t){t.useSVG(e._renderArea)})),this.render()),this)},e.prototype.addDataset=function(t){return this._addDataset(t),this._onDatasetUpdate(),this},e.prototype._addDataset=function(t){this._removeDataset(t);var e=this._createDrawer(t);return this._datasetToDrawer.set(t,e),this._isSetup&&this._createNodesForDataset(t),t.onUpdate(this._onDatasetUpdateCallback),this},e.prototype.removeDataset=function(t){return this._removeDataset(t),this._onDatasetUpdate(),this},e.prototype._removeDataset=function(t){return-1===this.datasets().indexOf(t)||(this._removeDatasetNodes(t),t.offUpdate(this._onDatasetUpdateCallback),this._datasetToDrawer.delete(t)),this},e.prototype._removeDatasetNodes=function(t){this._datasetToDrawer.get(t).remove()},e.prototype.datasets=function(t){var e=this,n=[];return this._datasetToDrawer.forEach((function(t,e){return n.push(e)})),null==t?n:(n.forEach((function(t){return e._removeDataset(t)})),t.forEach((function(t){return e._addDataset(t)})),this._onDatasetUpdate(),this)},e.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new eMt.Null}]},e.prototype._additionalPaint=function(t){},e.prototype._buildLightweightPlotEntities=function(t){var e=this,n=[];return t.forEach((function(t,i){for(var r=e._datasetToDrawer.get(t),o=0,a=t.data(),s=a.length,l=function(s){var 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e.getTotalDrawTime(i.get(t),n)})),s=nMt.Math.max(a,0);this._additionalPaint(s)},e.prototype.selections=function(t){var e=this;if(void 0===t&&(t=this.datasets()),"canvas"===this.renderer())return tMt.selectAll();var n=[];return t.forEach((function(t){var i=e._datasetToDrawer.get(t);if(null!=i){var r=i.getVisualPrimitives();n.push.apply(n,r)}})),tMt.selectAll(n)},e.prototype.entities=function(t){var e=this;return this._getEntityStore(t).entities().map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype.filterEntities=function(t){var e=this;return this._getEntityStore().entities().filter(t).map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype._getEntityStore=function(t){var e,n=this,i=function(t){return n._entityBounds(t)};return void 0!==t?((e=new nMt.EntityStore).addAll(this._buildLightweightPlotEntities(t),i,this._localOriginBounds()),e):(void 0===this._cachedEntityStore&&((e=new nMt.EntityStore).addAll(this._buildLightweightPlotEntities(this.datasets()),i,this._localOriginBounds()),this._cachedEntityStore=e),this._cachedEntityStore)},e.prototype._localOriginBounds=function(){return{topLeft:{x:0,y:0},bottomRight:{x:this.width(),y:this.height()}}},e.prototype._entityBounds=function(t){var e=this._pixelPoint(t.datum,t.index,t.dataset);return{x:e.x,y:e.y,width:0,height:0}},e.prototype._lightweightPlotEntityToPlotEntity=function(t){return{bounds:this._entityBounds(t),component:t.component,dataset:t.dataset,datasetIndex:t.datasetIndex,datum:t.datum,index:t.index,position:t.position,selection:tMt.select(t.drawer.getVisualPrimitiveAtIndex(t.validDatumIndex))}},e.prototype.entitiesAt=function(t){throw new Error("plots must implement entitiesAt")},e.prototype.entityNearest=function(t){var e=this._getEntityStore().entityNearest(t);return void 0===e?void 0:this._lightweightPlotEntityToPlotEntity(e)},e.prototype.entitiesIn=function(t,e){var n;if(null==e){n={x:t.topLeft.x,y:t.topLeft.y,width:t.bottomRight.x-t.topLeft.x,height:t.bottomRight.y-t.topLeft.y}}else{n={x:t.min,y:e.min,width:t.max-t.min,height:e.max-e.min}}return this.entitiesInBounds(n)},e.prototype.entitiesInBounds=function(t){var e=this,n=this._getEntityStore().entitiesInBounds(t);if(n)return n.map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype.entitiesInXBounds=function(t){var e=this,n=this._getEntityStore().entitiesInXBounds(t);if(n)return n.map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype.entitiesInYBounds=function(t){var e=this,n=this._getEntityStore().entitiesInYBounds(t);if(n)return n.map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype._uninstallScaleForKey=function(t,e){t.offUpdate(this._renderCallback),t.offUpdate(this._deferredResetEntityStore),t.removeIncludedValuesProvider(this._includedValuesProvider)},e.prototype._installScaleForKey=function(t,e){t.onUpdate(this._renderCallback),t.onUpdate(this._deferredResetEntityStore),t.addIncludedValuesProvider(this._includedValuesProvider)},e.prototype._propertyProjectors=function(){return{}},e._scaledAccessor=function(t){var e=t.scale,n=t.accessor,i=t.postScale,r=null==e?n:function(t,i,r){return e.scale(n(t,i,r))};return null==i?r:function(t,e,n){return i(r(t,e,n),t,e,n)}},e.prototype._pixelPoint=function(t,e,n){return{x:0,y:0}},e.prototype._animateOnNextRender=function(){return this._animate&&this._dataChanged},e.OPTIMIZE_MEMOIZE_PROJECTORS=!1,e._ANIMATION_MAX_DURATION=600,e})(oMt.Component);function dMt(t,e,n){var i=e.accessor,r=e.scale;if(null==r)return[];var o=t.data();null!=n&&(o=o.filter((function(e,i){return n(e,i,t)})));var a=o.map((function(e,n){return i(e,n,t)}));return r.extentOfValues(a)}
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */sSt.Plot=hMt,Object.defineProperty(aSt,"__esModule",{value:!0});var pMt=BO,fMt=sSt,mMt=D_t,gMt=(function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return pMt.__extends(e,t),e.prototype.entityNearest=function(t){var e,n=1/0;return this.components().forEach((function(i){var r=i.entityNearest(t);if(null!=r){var o=mMt.Math.distanceSquared(r.position,t);o<=n&&(n=o,e=r)}})),e},e.prototype.append=function(e){if(null!=e&&!(e instanceof fMt.Plot))throw new Error("Plot Group only accepts plots");return t.prototype.append.call(this,e),this},e})(_wt.Group);aSt.PlotGroup=gMt;var _Mt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(_Mt,"__esModule",{value:!0});var yMt=BO,vMt=wpt,bMt=D_t,xMt=(function(t){function e(e){void 0===e&&(e=[]);var n=t.call(this)||this;return n._rowPadding=0,n._columnPadding=0,n._rows=[],n._rowWeights=[],n._columnWeights=[],n._nRows=0,n._nCols=0,n._calculatedLayout=null,n.addClass("table"),e.forEach((function(t,e){t.forEach((function(t,i){null!=t&&n.add(t,e,i)}))})),n}return yMt.__extends(e,t),e.prototype._forEach=function(t){for(var e=0;e<this._nRows;e++)for(var n=0;n<this._nCols;n++)null!=this._rows[e][n]&&t(this._rows[e][n])},e.prototype.has=function(t){for(var e=0;e<this._nRows;e++)for(var n=0;n<this._nCols;n++)if(this._rows[e][n]===t)return!0;return!1},e.prototype.componentAt=function(t,e){return t<0||t>=this._nRows||e<0||e>=this._nCols?null:this._rows[t][e]},e.prototype.add=function(t,e,n){if(null==t)throw Error("Cannot add null to a table cell");if(!this.has(t)){if(null!=(this._rows[e]&&this._rows[e][n]))throw new Error("cell is occupied");t.detach(),this._nRows=Math.max(e+1,this._nRows),this._nCols=Math.max(n+1,this._nCols),this._padTableToSize(this._nRows,this._nCols),this._rows[e][n]=t,this._adoptAndAnchor(t),this.redraw()}return this},e.prototype._remove=function(t){for(var e=0;e<this._nRows;e++)for(var n=0;n<this._nCols;n++)if(this._rows[e][n]===t)return this._rows[e][n]=null,!0;return!1},e.prototype._iterateLayout=function(t,n,i){void 0===i&&(i=!1);for(var r,o,a,s,l,c=this._rows,u=vMt.transpose(this._rows),h=t-this._columnPadding*(this._nCols-1),d=n-this._rowPadding*(this._nRows-1),p=e._calcComponentWeights(this._rowWeights,c,(function(t){return null==t||t.fixedHeight()})),f=e._calcComponentWeights(this._columnWeights,u,(function(t){return null==t||t.fixedWidth()})),m=f.map((function(t){return 0===t?.5:t})),g=p.map((function(t){return 0===t?.5:t})),_=e._calcProportionalSpace(m,h),y=e._calcProportionalSpace(g,d),v=bMt.Array.createFilledArray(0,this._nCols),b=bMt.Array.createFilledArray(0,this._nRows),x=0;;){var w=bMt.Array.add(b,y),S=bMt.Array.add(v,_);v=(a=this._determineGuarantees(S,w,i)).guaranteedWidths,b=a.guaranteedHeights,s=a.wantsWidthArr.some((function(t){return t})),l=a.wantsHeightArr.some((function(t){return t}));var M=r,E=o;r=h-vMt.sum(a.guaranteedWidths),o=d-vMt.sum(a.guaranteedHeights);var T=void 0;s?(T=a.wantsWidthArr.map((function(t){return t?.1:0})),T=bMt.Array.add(T,f)):T=f;var C=void 0;if(l?(C=a.wantsHeightArr.map((function(t){return t?.1:0})),C=bMt.Array.add(C,p)):C=p,_=e._calcProportionalSpace(T,r),y=e._calcProportionalSpace(C,o),x++,!(r>0&&r!==M)&&!(o>0&&o!==E))break;if(x>5)break}return r=h-vMt.sum(a.guaranteedWidths),o=d-vMt.sum(a.guaranteedHeights),{colProportionalSpace:_=e._calcProportionalSpace(f,r),rowProportionalSpace:y=e._calcProportionalSpace(p,o),guaranteedWidths:a.guaranteedWidths,guaranteedHeights:a.guaranteedHeights,wantsWidth:s,wantsHeight:l}},e.prototype._determineGuarantees=function(t,e,n){void 0===n&&(n=!1);var i=bMt.Array.createFilledArray(0,this._nCols),r=bMt.Array.createFilledArray(0,this._nRows),o=bMt.Array.createFilledArray(!1,this._nCols),a=bMt.Array.createFilledArray(!1,this._nRows);return this._rows.forEach((function(s,l){s.forEach((function(s,c){var u;u=null!=s?s.requestedSpace(t[c],e[l]):{minWidth:0,minHeight:0};var h=n?Math.min(u.minWidth,t[c]):u.minWidth;i[c]=Math.max(i[c],h);var d=n?Math.min(u.minHeight,e[l]):u.minHeight;r[l]=Math.max(r[l],d),o[c]=o[c]||u.minWidth>t[c],a[l]=a[l]||u.minHeight>e[l]}))})),{guaranteedWidths:i,guaranteedHeights:r,wantsWidthArr:o,wantsHeightArr:a}},e.prototype.requestedSpace=function(t,e){return this._calculatedLayout=this._iterateLayout(t,e),{minWidth:vMt.sum(this._calculatedLayout.guaranteedWidths),minHeight:vMt.sum(this._calculatedLayout.guaranteedHeights)}},e.prototype.computeLayout=function(e,n,i){var r=this;t.prototype.computeLayout.call(this,e,n,i);var o=vMt.sum(this._calculatedLayout.guaranteedWidths),a=vMt.sum(this._calculatedLayout.guaranteedHeights),s=this._calculatedLayout;(o>this.width()||a>this.height())&&(s=this._iterateLayout(this.width(),this.height(),!0));var l=0,c=bMt.Array.add(s.rowProportionalSpace,s.guaranteedHeights),u=bMt.Array.add(s.colProportionalSpace,s.guaranteedWidths);return this._rows.forEach((function(t,e){var n=0;t.forEach((function(t,i){null!=t&&t.computeLayout({x:n,y:l},u[i],c[e]),n+=u[i]+r._columnPadding})),l+=c[e]+r._rowPadding})),this},e.prototype.rowPadding=function(t){if(null==t)return this._rowPadding;if(!bMt.Math.isValidNumber(t)||t<0)throw Error("rowPadding must be a non-negative finite value");return this._rowPadding=t,this.redraw(),this},e.prototype.columnPadding=function(t){if(null==t)return this._columnPadding;if(!bMt.Math.isValidNumber(t)||t<0)throw Error("columnPadding must be a non-negative finite value");return this._columnPadding=t,this.redraw(),this},e.prototype.rowWeight=function(t,e){if(null==e)return this._rowWeights[t];if(!bMt.Math.isValidNumber(e)||e<0)throw Error("rowWeight must be a non-negative finite value");return this._rowWeights[t]=e,this.redraw(),this},e.prototype.columnWeight=function(t,e){if(null==e)return this._columnWeights[t];if(!bMt.Math.isValidNumber(e)||e<0)throw Error("columnWeight must be a non-negative finite value");return this._columnWeights[t]=e,this.redraw(),this},e.prototype.fixedWidth=function(){var t=vMt.transpose(this._rows);return e._fixedSpace(t,(function(t){return null==t||t.fixedWidth()}))},e.prototype.fixedHeight=function(){return e._fixedSpace(this._rows,(function(t){return null==t||t.fixedHeight()}))},e.prototype._padTableToSize=function(t,e){for(var n=0;n<t;n++){void 0===this._rows[n]&&(this._rows[n]=[],this._rowWeights[n]=null);for(var i=0;i<e;i++)void 0===this._rows[n][i]&&(this._rows[n][i]=null)}for(i=0;i<e;i++)void 0===this._columnWeights[i]&&(this._columnWeights[i]=null)},e._calcComponentWeights=function(t,e,n){return t.map((function(t,i){return null!=t?t:e[i].map(n).reduce((function(t,e){return t&&e}),!0)?0:1}))},e._calcProportionalSpace=function(t,e){var n=vMt.sum(t);return 0===n?bMt.Array.createFilledArray(0,t.length):t.map((function(t){return e*t/n}))},e._fixedSpace=function(t,e){var n=function(t){return t.reduce((function(t,e){return t&&e}),!0)};return n(t.map((function(t){return n(t.map(e))})))},e})(ywt.ComponentContainer);_Mt.Table=xMt;var wMt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(wMt,"__esModule",{value:!0});var SMt=BO,MMt=(function(t){function e(){var e=null!==t&&t.apply(this,arguments)||this;return e._maxLines=2,e}return SMt.__extends(e,t),e.prototype.requestedSpace=function(t,e){this._wrapper.maxLines(this._maxLines);var n=0===this.angle()?t:e;0===n&&(n=1/0);var i=this._wrapper.wrap(this._text,this._measurer,n),r=this._measurer.measure(i.wrappedText);return{minWidth:(0===this.angle()?r.width:r.height)+2*this.padding(),minHeight:(0===this.angle()?r.height:r.width)+2*this.padding()}},e.prototype.maxLines=function(t){return 0===arguments.length?this._maxLines:(this._maxLines=t,this.redraw(),this)},e})(Nwt.Label);wMt.WrappedLabel=MMt;var EMt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(EMt,"__esModule",{value:!0});var TMt=BO,CMt=(function(t){function e(){var e=t.call(this)||this;return e.addClass("x-drag-box-layer"),e._hasCorners=!1,e}return TMt.__extends(e,t),e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),this._setBounds(this.bounds()),this},e.prototype._setBounds=function(e){t.prototype._setBounds.call(this,{topLeft:{x:e.topLeft.x,y:0},bottomRight:{x:e.bottomRight.x,y:this.height()}})},e.prototype._setResizableClasses=function(t){t&&this.enabled()?this.addClass("x-resizable"):this.removeClass("x-resizable")},e.prototype.yScale=function(e){if(null==e)return t.prototype.yScale.call(this);throw new Error("yScales cannot be set on an XDragBoxLayer")},e.prototype.yExtent=function(e){if(null==e)return t.prototype.yExtent.call(this);throw new Error("XDragBoxLayer has no yExtent")},e})(ext.DragBoxLayer);EMt.XDragBoxLayer=CMt;var AMt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(AMt,"__esModule",{value:!0});var kMt=BO,LMt=(function(t){function e(){var e=t.call(this)||this;return e.addClass("y-drag-box-layer"),e._hasCorners=!1,e}return kMt.__extends(e,t),e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),this._setBounds(this.bounds()),this},e.prototype._setBounds=function(e){t.prototype._setBounds.call(this,{topLeft:{x:0,y:e.topLeft.y},bottomRight:{x:this.width(),y:e.bottomRight.y}})},e.prototype._setResizableClasses=function(t){t&&this.enabled()?this.addClass("y-resizable"):this.removeClass("y-resizable")},e.prototype.xScale=function(e){if(null==e)return t.prototype.xScale.call(this);throw new Error("xScales cannot be set on an YDragBoxLayer")},e.prototype.xExtent=function(e){if(null==e)return t.prototype.xExtent.call(this);throw new Error("YDragBoxLayer has no xExtent")},e})(ext.DragBoxLayer);AMt.YDragBoxLayer=LMt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=BO;e.__exportStar(ext,t),e.__exportStar(nwt,t),e.__exportStar(pwt,t),e.__exportStar(_wt,t),e.__exportStar(iwt,t),e.__exportStar(Ewt,t),e.__exportStar(Nwt,t),e.__exportStar(Bwt,t),e.__exportStar(aSt,t),e.__exportStar($xt,t),e.__exportStar(_Mt,t),e.__exportStar(wMt,t),e.__exportStar(EMt,t),e.__exportStar(AMt,t)})(txt);var PMt={},NMt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(NMt,"__esModule",{value:!0});var IMt=BO,RMt=(function(t){function e(){return t.call(this,"path","arc fill")||this}return IMt.__extends(e,t),e.prototype._applyDefaultAttributes=function(t){t.style("stroke","none")},e})(qSt.SVGDrawer);NMt.ArcSVGDrawer=RMt;var OMt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(OMt,"__esModule",{value:!0});var zMt=BO,DMt=(function(t){function e(){return t.call(this,"path","arc outline")||this}return zMt.__extends(e,t),e.prototype._applyDefaultAttributes=function(t){t.style("fill","none")},e})(qSt.SVGDrawer);OMt.ArcOutlineSVGDrawer=DMt;var BMt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(BMt,"__esModule",{value:!0});var HMt=BO,FMt=jSt,VMt=(function(t){function e(){return t.call(this,"path","area")||this}return HMt.__extends(e,t),e.prototype._applyDefaultAttributes=function(t){t.style("stroke","none")},e.prototype.getVisualPrimitiveAtIndex=function(e){return t.prototype.getVisualPrimitiveAtIndex.call(this,0)},e})(qSt.SVGDrawer);BMt.AreaSVGDrawer=VMt;var UMt=["opacity","fill","fill-opacity"],jMt=["opacity","stroke","stroke-width"];BMt.makeAreaCanvasDrawStep=function GMt(t,e){return function(n,i,r){var o=FMt.resolveAttributes(r,UMt,i[0],0);FMt.renderArea(n,t(),i[0],o);var a=FMt.resolveAttributes(r,jMt,i[0],0);FMt.renderLine(n,e(),i[0],a)}};var WMt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(WMt,"__esModule",{value:!0});var qMt=BO,YMt=jSt,XMt=(function(t){function e(){return t.call(this,"path","line")||this}return qMt.__extends(e,t),e.prototype._applyDefaultAttributes=function(t){t.style("fill","none")},e.prototype.getVisualPrimitiveAtIndex=function(e){return t.prototype.getVisualPrimitiveAtIndex.call(this,0)},e})(qSt.SVGDrawer);WMt.LineSVGDrawer=XMt;var $Mt=["opacity","stroke-opacity","stroke-width","stroke","stroke-dasharray"];WMt.makeLineCanvasDrawStep=function KMt(t){return function(e,n,i){var r=YMt.resolveAttributes(i,$Mt,n[0],0);YMt.renderLine(e,t(),n[0],r)}};var ZMt={};!(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=BO,n=jSt,i=(function(t){function n(e){void 0===e&&(e="");var n=t.call(this,"rect","")||this;return n._rootClassName=e,n._root.classed(n._rootClassName,!0),n}return e.__extends(n,t),n})(qSt.SVGDrawer);t.RectangleSVGDrawer=i;var r=n.ContextStyleAttrs.concat(["x","y","width","height"]);t.RectangleCanvasDrawStep=function(t,e,i){t.save();for(var o=e.length,a=0;a<o;a++){var s=e[a];if(null!=s){var l=n.resolveAttributes(i,r,s,a);t.beginPath(),t.rect(l.x,l.y,l.width,l.height),n.renderPathWithStyle(t,l)}}t.restore()};var o=(function(n){function i(e){return n.call(this,e,t.RectangleCanvasDrawStep)||this}return e.__extends(i,n),i})(n.CanvasDrawer);t.RectangleCanvasDrawer=o})(ZMt);var JMt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(JMt,"__esModule",{value:!0});var QMt=BO,tEt=(function(t){function e(){return t.call(this,"line","")||this}return QMt.__extends(e,t),e})(qSt.SVGDrawer);JMt.SegmentSVGDrawer=tEt;var eEt={},nEt={};
/**
     * Copyright 2017-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(nEt,"__esModule",{value:!0});var iEt=(function(){function t(e,n,i){void 0===i&&(i=window.devicePixelRatio),this.screenWidth=e,this.screenHeight=n,this.devicePixelRatio=i,this.pixelWidth=e*i,this.pixelHeight=n*i,this.canvas=document.createElement("canvas"),this.ctx=this.canvas.getContext("2d"),t.sizePixels(this.ctx,e,n,i)}return t.sizePixels=function(t,e,n,i){var r=t.canvas;r.width=e*i,r.height=n*i,r.style.width=e+"px",r.style.height=n+"px",t.setTransform(1,0,0,1,0,0),t.scale(i,i)},t.prototype.blit=function(t,e,n){void 0===e&&(e=0),void 0===n&&(n=0),t.drawImage(this.canvas,e,n,this.screenWidth,this.screenHeight)},t.prototype.blitCenter=function(t,e,n){void 0===e&&(e=0),void 0===n&&(n=0),this.blit(t,Math.floor(e-this.screenWidth/2),Math.floor(n-this.screenHeight/2))},t.prototype.resize=function(e,n,i){void 0===i&&(i=!1);var r=this.devicePixelRatio;return this.screenWidth=e,this.screenHeight=n,this.pixelWidth=e*r,this.pixelHeight=n*r,t.sizePixels(this.ctx,e,n,r),i&&this.ctx.translate(e/2,e/2),this},t.prototype.clear=function(t){var e=this,n=e.pixelWidth,i=e.pixelHeight,r=e.ctx;return r.save(),r.setTransform(1,0,0,1,0,0),null==t?r.clearRect(0,0,n,i):(r.fillStyle=t,r.fillRect(0,0,n,i)),r.restore(),this},t.prototype.getImageData=function(){return this.ctx.getImageData(0,0,this.pixelWidth,this.pixelHeight)},t})();nEt.CanvasBuffer=iEt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(eEt,"__esModule",{value:!0});var rEt=BO,oEt=jSt,aEt=nEt,sEt=(function(t){function e(){return t.call(this,"path","symbol")||this}return rEt.__extends(e,t),e})(qSt.SVGDrawer);eEt.SymbolSVGDrawer=sEt;var lEt=oEt.ContextStyleAttrs.concat(["x","y"]);function cEt(t,e,n,i,r){return n+r>=0&&n-r<=t&&i+r>=0&&i-r<=e}function uEt(t,e,n){if(null==t)return!1;for(var i=0;i<n.length;i++){var r=n[i];if(t[r]!=e[r])return!1}return!0}eEt.makeSymbolCanvasDrawStep=function hEt(t,e,n,i){var r=this;return function(o,a,s){for(var l=o.canvas,c=l.clientWidth,u=l.clientHeight,h=void 0===i?new aEt.CanvasBuffer(0,0):i,d=e(),p=n(),f=null,m=null,g=null,_=0;_<a.length;_++){var y=a[_];if(null!=y){var v=oEt.resolveAttributes(s,lEt,y,_),b=p(y,_,t);if(cEt(c,u,v.x,v.y,b)){var x=uEt(f,v,oEt.ContextStyleAttrs),w=d(y,_,r._dataset);if(x&&g==b&&m==w);else{var S=b+oEt.getStrokeWidth(v)+1;(S>h.screenWidth||S>h.screenHeight)&&h.resize(S,S,!0),h.clear();var M=h.ctx;M.beginPath(),w(b).context(M)(null),M.closePath(),oEt.renderPathWithStyle(M,v),m=w,g=b,f=v}h.blitCenter(o,v.x,v.y)}}}}},(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=BO;e.__exportStar(NMt,t),e.__exportStar(OMt,t),e.__exportStar(BMt,t),e.__exportStar(jSt,t),e.__exportStar(GSt,t),e.__exportStar(WMt,t),e.__exportStar(ZMt,t),e.__exportStar(JMt,t),e.__exportStar(qSt,t),e.__exportStar(eEt,t)})(PMt);var dEt={},pEt={},fEt={},mEt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(mEt,"__esModule",{value:!0});var gEt=BO,_Et=Xvt,yEt=D_t,vEt=KSt,bEt=sSt,xEt=(function(t){function e(){var e=t.call(this)||this;return e._autoAdjustXScaleDomain=!1,e._autoAdjustYScaleDomain=!1,e._deferredRendering=!1,e._applyDeferredRenderingTransform=function(t,n,i,r){e._isAnchored&&(null!=e._renderArea&&e._renderArea.attr("transform","translate("+t+", "+n+") scale("+i+", "+r+")"),null!=e._canvas&&e._canvas.style("transform","translate("+t+"px, "+n+"px) scale("+i+", "+r+")"))},e.addClass("xy-plot"),e._adjustYDomainOnChangeFromXCallback=function(t){return e._adjustYDomainOnChangeFromX()},e._adjustXDomainOnChangeFromYCallback=function(t){return e._adjustXDomainOnChangeFromY()},e._renderCallback=function(){if(e.deferredRendering()){var t=e.x()&&e.x().scale,n=e.y()&&e.y().scale;e._deferredRenderer.updateDomains(t,n)}else e.render()},e._deferredRenderer=new vEt.DeferredRenderer((function(){return e.render()}),e._applyDeferredRenderingTransform),e}return gEt.__extends(e,t),e.prototype.render=function(){return this.deferredRendering()&&this._deferredRenderer.resetTransforms(),t.prototype.render.call(this)},e.prototype.deferredRendering=function(t){if(null==t)return this._deferredRendering;if(t){var e=this.x()&&this.x().scale,n=this.y()&&this.y().scale;this._deferredRenderer.setDomains(e,n)}return this._deferredRendering=t,this},e.prototype.x=function(t,n,i){if(null==t)return this._propertyBindings.get(e._X_KEY);this._bindProperty(e._X_KEY,t,n,i);var r=this.width();return null!=n&&null!=r&&n.range([0,r]),this._autoAdjustYScaleDomain&&this._updateYExtentsAndAutodomain(),this.render(),this},e.prototype.y=function(t,n,i){if(null==t)return this._propertyBindings.get(e._Y_KEY);this._bindProperty(e._Y_KEY,t,n,i);var r=this.height();return null!=n&&null!=r&&n.range(n instanceof _Et.Category?[0,r]:[r,0]),this._autoAdjustXScaleDomain&&this._updateXExtentsAndAutodomain(),this.render(),this},e.prototype._filterForProperty=function(t){return"x"===t&&this._autoAdjustXScaleDomain?this._makeFilterByProperty("y"):"y"!==t&&"y0"!==t||!this._autoAdjustYScaleDomain?null:this._makeFilterByProperty("x")},e.prototype._makeFilterByProperty=function(t){var e=this._propertyBindings.get(t);if(null!=e){var n=e.accessor,i=e.scale;if(null!=i)return function(t,e,r){var o=i.range();return yEt.Math.inRange(i.scale(n(t,e,r)),o[0],o[1])}}return null},e.prototype._uninstallScaleForKey=function(n,i){t.prototype._uninstallScaleForKey.call(this,n,i),n.offUpdate(i===e._X_KEY?this._adjustYDomainOnChangeFromXCallback:this._adjustXDomainOnChangeFromYCallback)},e.prototype._installScaleForKey=function(n,i){t.prototype._installScaleForKey.call(this,n,i),n.onUpdate(i===e._X_KEY?this._adjustYDomainOnChangeFromXCallback:this._adjustXDomainOnChangeFromYCallback)},e.prototype.destroy=function(){return t.prototype.destroy.call(this),this.x().scale&&this.x().scale.offUpdate(this._adjustYDomainOnChangeFromXCallback),this.y().scale&&this.y().scale.offUpdate(this._adjustXDomainOnChangeFromYCallback),this},e.prototype.autorangeMode=function(t){if(null==t)return this._autoAdjustXScaleDomain?"x":this._autoAdjustYScaleDomain?"y":"none";switch(t){case"x":this._autoAdjustXScaleDomain=!0,this._autoAdjustYScaleDomain=!1,this._adjustXDomainOnChangeFromY();break;case"y":this._autoAdjustXScaleDomain=!1,this._autoAdjustYScaleDomain=!0,this._adjustYDomainOnChangeFromX();break;case"none":this._autoAdjustXScaleDomain=!1,this._autoAdjustYScaleDomain=!1;break;default:throw new Error("Invalid scale name '"+t+"', must be 'x', 'y' or 'none'")}return this},e.prototype.computeLayout=function(e,n,i){t.prototype.computeLayout.call(this,e,n,i);var r=this.x(),o=r&&r.scale;null!=o&&o.range([0,this.width()]);var a=this.y(),s=a&&a.scale;return null!=s&&s.range(s instanceof _Et.Category?[0,this.height()]:[this.height(),0]),this},e.prototype._updateXExtentsAndAutodomain=function(){var t=this.x().scale;null!=t&&t.autoDomain()},e.prototype._updateYExtentsAndAutodomain=function(){var t=this.y().scale;null!=t&&t.autoDomain()},e.prototype.showAllData=function(){return this._updateXExtentsAndAutodomain(),this._updateYExtentsAndAutodomain(),this},e.prototype._adjustYDomainOnChangeFromX=function(){this._projectorsReady()&&this._autoAdjustYScaleDomain&&this._updateYExtentsAndAutodomain()},e.prototype._adjustXDomainOnChangeFromY=function(){this._projectorsReady()&&this._autoAdjustXScaleDomain&&this._updateXExtentsAndAutodomain()},e.prototype._projectorsReady=function(){var t=this.x(),e=this.y();return null!=t&&null!=t.accessor&&null!=e&&null!=e.accessor},e.prototype._pixelPoint=function(t,e,n){var i=bEt.Plot._scaledAccessor(this.x()),r=bEt.Plot._scaledAccessor(this.y());return{x:i(t,e,n),y:r(t,e,n)}},e.prototype._getDataToDraw=function(){var e=this,n=t.prototype._getDataToDraw.call(this),i=this.attr("defined");return this.datasets().forEach((function(t){n.set(t,n.get(t).filter((function(n,r){return(function(t,n,r){var o=bEt.Plot._scaledAccessor(e.x())(t,n,r),a=bEt.Plot._scaledAccessor(e.y())(t,n,r);return(!i||!1!==i.accessor(t,n,r))&&yEt.Math.isValidNumber(o)&&yEt.Math.isValidNumber(a)})(n,r,t)})))})),n},e._X_KEY="x",e._Y_KEY="y",e})(bEt.Plot);mEt.XYPlot=xEt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(fEt,"__esModule",{value:!0});var wEt=BO,SEt=wpt,MEt=fgt,EEt=PMt,TEt=GSt,CEt=WMt,AEt=Xvt,kEt=Lbt,LEt=D_t,PEt=dEt,NEt=sSt,IEt=mEt,REt={linear:SEt.curveLinear,linearClosed:SEt.curveLinearClosed,step:SEt.curveStep,stepBefore:SEt.curveStepBefore,stepAfter:SEt.curveStepAfter,basis:SEt.curveBasis,basisOpen:SEt.curveBasisOpen,basisClosed:SEt.curveBasisClosed,bundle:SEt.curveBundle,cardinal:SEt.curveCardinal,cardinalOpen:SEt.curveCardinalOpen,cardinalClosed:SEt.curveCardinalClosed,monotone:SEt.curveMonotoneX};fEt.CurveName=bgt.makeEnum(["linear","linearClosed","step","stepBefore","stepAfter","basis","basisOpen","basisClosed","bundle","cardinal","cardinalOpen","cardinalClosed","monotone"]);var OEt=(function(t){function e(){var e=t.call(this)||this;e._curve="linear",e._autorangeSmooth=!1,e._croppedRenderingEnabled=!0,e._collapseDenseVerticalLinesEnabled=!1,e._downsamplingEnabled=!1,e.addClass("line-plot");var n=new MEt.Easing;return n.stepDuration(NEt.Plot._ANIMATION_MAX_DURATION),n.easingMode("expInOut"),n.maxTotalDuration(NEt.Plot._ANIMATION_MAX_DURATION),e.animator(PEt.Animator.MAIN,n),e.attr("stroke",(new AEt.Color).range()[0]),e.attr("stroke-width","2px"),e}return wEt.__extends(e,t),e.prototype.x=function(e,n,i){return null==e?t.prototype.x.call(this):(t.prototype.x.call(this,e,n,i),this._setScaleSnapping(),this)},e.prototype.y=function(e,n,i){return null==e?t.prototype.y.call(this):(t.prototype.y.call(this,e,n,i),this._setScaleSnapping(),this)},e.prototype.autorangeMode=function(e){return null==e?t.prototype.autorangeMode.call(this):(t.prototype.autorangeMode.call(this,e),this._setScaleSnapping(),this)},e.prototype.autorangeSmooth=function(t){return null==t?this._autorangeSmooth:(this._autorangeSmooth=t,this._setScaleSnapping(),this)},e.prototype._setScaleSnapping=function(){"x"===this.autorangeMode()&&this.x()&&this.x().scale&&this.x().scale instanceof kEt.QuantitativeScale&&this.x().scale.snappingDomainEnabled(!this.autorangeSmooth()),"y"===this.autorangeMode()&&this.y()&&this.y().scale&&this.y().scale instanceof kEt.QuantitativeScale&&this.y().scale.snappingDomainEnabled(!this.autorangeSmooth())},e.prototype.curve=function(t){return null==t?this._curve:(this._curve=t,this.render(),this)},e.prototype.downsamplingEnabled=function(t){return null==t?this._downsamplingEnabled:(this._downsamplingEnabled=t,this)},e.prototype.croppedRenderingEnabled=function(t){return null==t?this._croppedRenderingEnabled:(this._croppedRenderingEnabled=t,this.render(),this)},e.prototype.collapseDenseLinesEnabled=function(t){return null==t?this._collapseDenseVerticalLinesEnabled:(this._collapseDenseVerticalLinesEnabled=t,this.render(),this)},e.prototype._createDrawer=function(t){var e=this;return new TEt.ProxyDrawer((function(){return new CEt.LineSVGDrawer}),(function(n){return new EEt.CanvasDrawer(n,CEt.makeLineCanvasDrawStep((function(){return e._d3LineFactory(t)})))}))},e.prototype.getExtentsForProperty=function(e){var n=t.prototype.getExtentsForProperty.call(this,e);if(!this._autorangeSmooth)return n;if(this.autorangeMode()!==e)return n;if("x"!==this.autorangeMode()&&"y"!==this.autorangeMode())return n;var i,r=this._getEdgeIntersectionPoints();return i="y"===this.autorangeMode()?r.left.concat(r.right).map((function(t){return t.y})):r.top.concat(r.bottom).map((function(t){return t.x})),n.map((function(t){return SEt.extent(SEt.merge([t,i]))}))},e.prototype._getEdgeIntersectionPoints=function(){var t=this;if(!(this.y().scale instanceof kEt.QuantitativeScale&&this.x().scale instanceof kEt.QuantitativeScale))return{left:[],right:[],top:[],bottom:[]};var e=this.y().scale,n=this.x().scale,i={left:[],right:[],top:[],bottom:[]},r=n.scale(n.domain()[0]),o=n.scale(n.domain()[1]),a=e.scale(e.domain()[0]),s=e.scale(e.domain()[1]);return this.datasets().forEach((function(l){for(var c,u,h,d,p=l.data(),f=1;f<p.length;f++)c=h||n.scale(t.x().accessor(p[f-1],f-1,l)),u=d||e.scale(t.y().accessor(p[f-1],f-1,l)),h=n.scale(t.x().accessor(p[f],f,l)),d=e.scale(t.y().accessor(p[f],f,l)),c<r==r<=h&&i.left.push({x:r,y:e.invert(u+(r-c)*(d-u)/(h-c))}),c<o==o<=h&&i.right.push({x:o,y:e.invert(u+(o-c)*(d-u)/(h-c))}),u<s==s<=d&&i.top.push({x:n.invert(c+(s-u)*(h-c)/(d-u)),y:s}),u<a==a<=d&&i.bottom.push({x:n.invert(c+(a-u)*(h-c)/(d-u)),y:a})})),i},e.prototype._getResetYFunction=function(){var t=this.y().scale.domain(),e=Math.max(t[0],t[1]),n=Math.min(t[0],t[1]),i=e<0&&e||n>0&&n||0,r=this.y().scale.scale(i);return function(t,e,n){return r}},e.prototype._generateDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._getAttrToProjector();e.d=this._constructLineProjector(NEt.Plot._scaledAccessor(this.x()),this._getResetYFunction()),t.push({attrToProjector:e,animator:this._getAnimator(PEt.Animator.RESET)})}return t.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(PEt.Animator.MAIN)}),t},e.prototype._generateAttrToProjector=function(){var e=t.prototype._generateAttrToProjector.call(this);return Object.keys(e).forEach((function(t){if("d"!==t){var n=e[t];e[t]=function(t,e,i){return t.length>0?n(t[0],e,i):null}}})),e},e.prototype.entitiesAt=function(t){var e=this.entityNearestByXThenY(t);return null!=e?[e]:[]},e.prototype.entityNearestByXThenY=function(t){for(var e,n=1/0,i=1/0,r=this.bounds(),o=this.entities(),a=o.length,s=0;s<a;s++){var l=o[s];if(LEt.Math.within(l.position,r)){var c=Math.abs(t.x-l.position.x),u=Math.abs(t.y-l.position.y);(c<n||c===n&&u<i)&&(e=l,n=c,i=u)}}return e},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this);return e.d=this._constructLineProjector(NEt.Plot._scaledAccessor(this.x()),NEt.Plot._scaledAccessor(this.y())),e},e.prototype._constructLineProjector=function(t,e){var n=this;return function(i,r,o){return n._d3LineFactory(o,t,e)(i)}},e.prototype._d3LineFactory=function(t,e,n){void 0===e&&(e=NEt.Plot._scaledAccessor(this.x())),void 0===n&&(n=NEt.Plot._scaledAccessor(this.y()));return SEt.line().x((function(n,i){return e(n,i,t)})).y((function(e,i){return n(e,i,t)})).curve(this._getCurveFactory()).defined((function(i,r){return(function(t,i,r){var o=e(t,i,r),a=n(t,i,r);return LEt.Math.isValidNumber(o)&&LEt.Math.isValidNumber(a)})(i,r,t)}))},e.prototype._getCurveFactory=function(){var t=this.curve();if("string"==typeof t){var e=REt[t];return null==e?REt.linear:e}return t},e.prototype._getDataToDraw=function(){var t=this,e=new LEt.Map;return this.datasets().forEach((function(n){var i=n.data();if(t._croppedRenderingEnabled||t._downsamplingEnabled){for(var r=[],o=i.length,a=0;a<o;a++)r[a]=a;t._croppedRenderingEnabled&&(r=t._filterCroppedRendering(n,r)),t._downsamplingEnabled&&(r=t._filterDownsampling(n,r)),t._collapseDenseVerticalLinesEnabled&&(r=t._filterDenseLines(n,r));var s=[],l=r.length;for(a=0;a<l;a++){s[a]=i[r[a]]}e.set(n,[s])}else e.set(n,[i])})),e},e.prototype._filterCroppedRendering=function(t,e){for(var n=this,i=NEt.Plot._scaledAccessor(this.x()),r=NEt.Plot._scaledAccessor(this.y()),o=t.data(),a=[],s=function(t,e){return LEt.Math.inRange(t,0,n.width())&&LEt.Math.inRange(e,0,n.height())},l=0;l<e.length;l++){var c=s(i(o[e[l]],e[l],t),r(o[e[l]],e[l],t));if(!c&&null!=e[l-1]&&null!=o[e[l-1]]){var u=i(o[e[l-1]],e[l-1],t),h=r(o[e[l-1]],e[l-1],t);c=c||s(u,h)}if(!c&&null!=e[l+1]&&null!=o[e[l+1]]){var d=i(o[e[l+1]],e[l+1],t),p=r(o[e[l+1]],e[l+1],t);c=c||s(d,p)}c&&a.push(e[l])}return a},e.prototype._filterDownsampling=function(t,e){if(0===e.length)return[];for(var n=t.data(),i=NEt.Plot._scaledAccessor(this.x()),r=NEt.Plot._scaledAccessor(this.y()),o=[e[0]],a=function(o,a){var s=i(n[e[o]],e[o],t),l=r(n[e[o]],e[o],t),c=i(n[e[o+1]],e[o+1],t),u=r(n[e[o+1]],e[o+1],t);if(a===1/0)return Math.floor(s)===Math.floor(c);var h=l+(c-s)*a;return Math.floor(u)===Math.floor(h)},s=0;s<e.length-1;){for(var l=e[s],c=i(n[e[s]],e[s],t),u=r(n[e[s]],e[s],t),h=i(n[e[s+1]],e[s+1],t),d=r(n[e[s+1]],e[s+1],t),p=Math.floor(c)===Math.floor(h)?1/0:(d-u)/(h-c),f=e[s],m=p===1/0?u:c,g=f,_=m,y=!0;s<e.length-1&&(y||a(s,p));){s++,y=!1;var v=p===1/0?r(n[e[s]],e[s],t):i(n[e[s]],e[s],t);v>_&&(_=v,g=e[s]),v<m&&(m=v,f=e[s])}var b=e[s];f!==l&&o.push(f),g!==f&&g!==l&&o.push(g),b!==l&&b!==f&&b!==g&&o.push(b)}return o},e.prototype._filterDenseLines=function(t,e){if(0===e.length)return[];var n=t.data(),i=NEt.Plot._scaledAccessor(this.x()),r=NEt.Plot._scaledAccessor(this.y());return this._bucketByX(t,e,(function(e){return i(n[e],e,t)}),(function(e){return r(n[e],e,t)}))},e.prototype._bucketByX=function(t,e,n,i){for(var r=[],o=t.data(),a=null,s=e.length,l=0;l<=s;++l){var c=e[l];if(null!=o[c]){var u=Math.floor(n(c)),h=i(c);null==a?a=new LEt.Bucket(c,u,h):a.isInBucket(u)?a.addToBucket(h,c):(r.push.apply(r,a.getUniqueIndices()),a=new LEt.Bucket(c,u,h))}}return null!=a&&r.push.apply(r,a.getUniqueIndices()),r},e})(IEt.XYPlot);fEt.Line=OEt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(pEt,"__esModule",{value:!0});var zEt=BO,DEt=wpt,BEt=Xvt,HEt=D_t,FEt=PMt,VEt=BMt,UEt=GSt,jEt=WMt,GEt=dEt,WEt=sSt,qEt=(function(t){function e(){var e=t.call(this)||this;return e.addClass("area-plot"),e.y0(0),e.attr("fill-opacity",.25),e.attr("fill",(new BEt.Color).range()[0]),e._lineDrawers=new HEt.Map,e}return zEt.__extends(e,t),e.prototype.y=function(n,i){if(null==n)return t.prototype.y.call(this);if(null==i?t.prototype.y.call(this,n):t.prototype.y.call(this,n,i),null!=i){var r=this.y0().accessor;null!=r&&this._bindProperty(e._Y0_KEY,r,i),this._updateYScale()}return this},e.prototype.y0=function(t){if(null==t)return this._propertyBindings.get(e._Y0_KEY);var n=this.y();return this._bindProperty(e._Y0_KEY,t,n&&n.scale),this._updateYScale(),this.render(),this},e.prototype._onDatasetUpdate=function(){t.prototype._onDatasetUpdate.call(this),this._updateYScale()},e.prototype._addDataset=function(e){var n=this;return this._lineDrawers.set(e,new FEt.ProxyDrawer((function(){return new jEt.LineSVGDrawer}),(function(t){return new FEt.CanvasDrawer(t,jEt.makeLineCanvasDrawStep((function(){var t=WEt.Plot._scaledAccessor(n.x()),i=WEt.Plot._scaledAccessor(n.y());return n._d3LineFactory(e,t,i)})))}))),t.prototype._addDataset.call(this,e),this},e.prototype._createNodesForDataset=function(e){t.prototype._createNodesForDataset.call(this,e);var n=this._lineDrawers.get(e);return"svg"===this.renderer()?n.useSVG(this._renderArea):n.useCanvas(this._canvas),n},e.prototype._removeDatasetNodes=function(e){t.prototype._removeDatasetNodes.call(this,e),this._lineDrawers.get(e).remove()},e.prototype._additionalPaint=function(){var t=this,e=this._generateLineDrawSteps(),n=this._getDataToDraw();this.datasets().forEach((function(i){var r=WEt.Plot.applyDrawSteps(e,i);t._lineDrawers.get(i).draw(n.get(i),r)}))},e.prototype._generateLineDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._generateLineAttrToProjector();e.d=this._constructLineProjector(WEt.Plot._scaledAccessor(this.x()),this._getResetYFunction()),t.push({attrToProjector:e,animator:this._getAnimator(GEt.Animator.RESET)})}return t.push({attrToProjector:this._generateLineAttrToProjector(),animator:this._getAnimator(GEt.Animator.MAIN)}),t},e.prototype._generateLineAttrToProjector=function(){var t=this._getAttrToProjector();return t.d=this._constructLineProjector(WEt.Plot._scaledAccessor(this.x()),WEt.Plot._scaledAccessor(this.y())),t},e.prototype._createDrawer=function(t){var e=this;return new UEt.ProxyDrawer((function(){return new VEt.AreaSVGDrawer}),(function(n){return new FEt.CanvasDrawer(n,VEt.makeAreaCanvasDrawStep((function(){var n=e._coordinateProjectors(),i=n[0],r=n[1],o=n[2],a=e._createDefinedProjector(i,r);return e._createAreaGenerator(i,r,o,a,t)}),(function(){var n=e._coordinateProjectors(),i=n[0],r=n[1],o=e._createDefinedProjector(i,r);return e._createTopLineGenerator(i,r,o,t)})))}))},e.prototype._generateDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._getAttrToProjector();e.d=this._constructAreaProjector(WEt.Plot._scaledAccessor(this.x()),this._getResetYFunction(),WEt.Plot._scaledAccessor(this.y0())),t.push({attrToProjector:e,animator:this._getAnimator(GEt.Animator.RESET)})}return t.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(GEt.Animator.MAIN)}),t},e.prototype._updateYScale=function(){var t=this.getExtentsForProperty("y0"),e=HEt.Array.flatten(t),n=HEt.Array.uniq(e),i=1===n.length?n[0]:null,r=this.y(),o=r&&r.scale;null!=o&&(null!=this._constantBaselineValueProvider&&(o.removePaddingExceptionsProvider(this._constantBaselineValueProvider),this._constantBaselineValueProvider=null),null!=i&&(this._constantBaselineValueProvider=function(){return[i]},o.addPaddingExceptionsProvider(this._constantBaselineValueProvider)))},e.prototype._getResetYFunction=function(){return WEt.Plot._scaledAccessor(this.y0())},e.prototype._coordinateProjectors=function(){return[WEt.Plot._scaledAccessor(this.x()),WEt.Plot._scaledAccessor(this.y()),WEt.Plot._scaledAccessor(this.y0())]},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this),n=this._coordinateProjectors();return e.d=this._constructAreaProjector(n[0],n[1],n[2]),e},e.prototype.selections=function(e){var n=this;if(void 0===e&&(e=this.datasets()),"canvas"===this.renderer())return DEt.selectAll();var i=t.prototype.selections.call(this,e).nodes();return e.map((function(t){return n._lineDrawers.get(t)})).filter((function(t){return null!=t})).forEach((function(t){return i.push.apply(i,t.getVisualPrimitives())})),DEt.selectAll(i)},e.prototype._constructAreaProjector=function(t,e,n){var i=this,r=this._createDefinedProjector(WEt.Plot._scaledAccessor(this.x()),WEt.Plot._scaledAccessor(this.y()));return function(o,a,s){return i._createAreaGenerator(t,e,n,r,s)(o)}},e.prototype._createDefinedProjector=function(t,e){return function(n,i,r){var o=t(n,i,r),a=e(n,i,r);return HEt.Math.isValidNumber(o)&&HEt.Math.isValidNumber(a)}},e.prototype._createAreaGenerator=function(t,e,n,i,r){var o=this._getCurveFactory();return DEt.area().x((function(e,n){return t(e,n,r)})).y1((function(t,n){return e(t,n,r)})).y0((function(t,e){return n(t,e,r)})).curve(o).defined((function(t,e){return i(t,e,r)}))},e.prototype._createTopLineGenerator=function(t,e,n,i){var r=this._getCurveFactory();return DEt.line().x((function(e,n){return t(e,n,i)})).y((function(t,n){return e(t,n,i)})).curve(r).defined((function(t,e){return n(t,e,i)}))},e._Y0_KEY="y0",e})(fEt.Line);pEt.Area=qEt;var YEt={};!(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=BO,n=wpt,i=Lgt,r=fgt,o=pvt,a=PMt,s=GSt,l=ZMt,c=cSt,u=Xvt,h=Lbt,d=D_t,p=bgt,f=dEt,m=sSt,g=mEt;t.BarOrientation=p.makeEnum(["vertical","horizontal"]),t.LabelsPosition=p.makeEnum(["start","middle","end","outside"]),t.BarAlignment=p.makeEnum(["start","middle","end"]);var _=(function(p){function g(e){void 0===e&&(e="vertical");var n=p.call(this)||this;if(n._labelFormatter=o.identity(),n._labelsEnabled=!1,n._labelsPosition=t.LabelsPosition.end,n._hideBarsIfAnyAreTooWide=!0,n._barAlignment="middle",n._computeBarPixelThickness=c.memoize(y),n._fixedBarPixelThickness=!0,n.addClass("bar-plot"),"vertical"!==e&&"horizontal"!==e)throw new Error(e+" is not a valid orientation for Plots.Bar");return n._isVertical="vertical"===e,n.animator("baseline",new r.Null),n.attr("fill",(new u.Color).range()[0]),n.attr(g._BAR_THICKNESS_KEY,(function(){return n._barPixelThickness()})),n._labelConfig=new d.Map,n._baselineValueProvider=function(){return[n.baselineValue()]},n}return e.__extends(g,p),g.prototype.computeLayout=function(t,e,n){return p.prototype.computeLayout.call(this,t,e,n),this._updateExtents(),this},g.prototype.x=function(t,e){return null==t?p.prototype.x.call(this):(null==e?p.prototype.x.call(this,t):p.prototype.x.call(this,t,e),this._updateThicknessAttr(),this._updateLengthScale(),this)},g.prototype.y=function(t,e){return null==t?p.prototype.y.call(this):(null==e?p.prototype.y.call(this,t):p.prototype.y.call(this,t,e),this._updateLengthScale(),this)},g.prototype.length=function(){return this._isVertical?this.y():this.x()},g.prototype.position=function(){return this._isVertical?this.x():this.y()},g.prototype.barEnd=function(t){if(null==t)return this._propertyBindings.get(g._BAR_END_KEY);var e=this.position();return this._bindProperty(g._BAR_END_KEY,t,e&&e.scale),this._updateThicknessAttr(),this._updateLengthScale(),this.render(),this},g.prototype.barAlignment=function(t){return null==t?this._barAlignment:(this._barAlignment=t,this._clearAttrToProjectorCache(),this.render(),this)},g.prototype.orientation=function(){return this._isVertical?"vertical":"horizontal"},g.prototype._createDrawer=function(){return new s.ProxyDrawer((function(){return new l.RectangleSVGDrawer(g._BAR_AREA_CLASS)}),(function(t){return new a.RectangleCanvasDrawer(t)}))},g.prototype._setup=function(){p.prototype._setup.call(this),this._baseline=this._renderArea.append("line").classed("baseline",!0)},g.prototype.baselineValue=function(t){if(null==t){if(null!=this._baselineValue)return this._baselineValue;if(!this._projectorsReady())return 0;var e=this.length().scale;return e&&e instanceof u.Time?new Date(0):0}return this._baselineValue=t,this._updateLengthScale(),this._clearAttrToProjectorCache(),this.render(),this},g.prototype.addDataset=function(t){return p.prototype.addDataset.call(this,t),this},g.prototype._addDataset=function(t){return p.prototype._addDataset.call(this,t),this},g.prototype.removeDataset=function(t){return p.prototype.removeDataset.call(this,t),this},g.prototype._removeDataset=function(t){return p.prototype._removeDataset.call(this,t),this},g.prototype.datasets=function(t){return null==t?p.prototype.datasets.call(this):(p.prototype.datasets.call(this,t),this)},g.prototype.labelsEnabled=function(t,e){return null==t?this._labelsEnabled:(this._labelsEnabled=t,null!=e&&(this._labelsPosition=e),this._clearAttrToProjectorCache(),this.render(),this)},g.prototype.labelFormatter=function(t){return null==t?this._labelFormatter:(this._labelFormatter=t,this._clearAttrToProjectorCache(),this.render(),this)},g.prototype._createNodesForDataset=function(t){var e=p.prototype._createNodesForDataset.call(this,t),n=this._renderArea.append("g").classed(g._LABEL_AREA_CLASS,!0),r=new i.SvgContext(n.node()),o=new i.CacheMeasurer(r),a=new i.Writer(o,r);return this._labelConfig.set(t,{labelArea:n,measurer:o,writer:a}),e},g.prototype._removeDatasetNodes=function(t){p.prototype._removeDatasetNodes.call(this,t);var e=this._labelConfig.get(t);null!=e&&(e.labelArea.remove(),this._labelConfig.delete(t))},g.prototype.entityNearest=function(t){var e=this,n=function(){var n=e._isVertical?e._getEntityStore().entityNearestX(t):e._getEntityStore().entityNearestY(t);return void 0===n?void 0:e._lightweightPlotEntityToPlotEntity(n)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype.entitiesAt=function(t){var e=this,n=function(){return e._entitiesIntersecting(t.x,t.y)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype.entitiesInBounds=function(t){var e=this,n=function(){return p.prototype.entitiesInBounds.call(e,t)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype.entitiesInXBounds=function(t){var e=this,n=function(){return p.prototype.entitiesInXBounds.call(e,t)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype.entitiesInYBounds=function(t){var e=this,n=function(){return p.prototype.entitiesInYBounds.call(e,t)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype._entitiesIntersecting=function(t,e){for(var n=[],i=this._getEntityStore().entities(),r=i.length,o=0;o<r;o++){var a=i[o];d.DOM.intersectsBBox(t,e,this._entityBounds(a))&&n.push(this._lightweightPlotEntityToPlotEntity(a))}return n},g.prototype._updateLengthScale=function(){if(this._projectorsReady()){var t=this.length().scale;t instanceof h.QuantitativeScale&&(t.addPaddingExceptionsProvider(this._baselineValueProvider),t.addIncludedValuesProvider(this._baselineValueProvider))}},g.prototype.renderImmediately=function(){var t=this;return this._barPixelThickness(),this._computeBarPixelThickness.doLocked((function(){return p.prototype.renderImmediately.call(t)}))},g.prototype._additionalPaint=function(t){var e=this,n=this.length().scale.scale(this.baselineValue()),i={x1:this._isVertical?0:n,y1:this._isVertical?n:0,x2:this._isVertical?this.width():n,y2:this._isVertical?n:this.height()};this._getAnimator("baseline").animate(this._baseline,i),this.datasets().forEach((function(t){return e._labelConfig.get(t).labelArea.selectAll("g").remove()})),this._labelsEnabled&&d.Window.setTimeout((function(){return e._drawLabels()}),t)},g.prototype.getExtentsForProperty=function(t){var e,i=this,r=p.prototype.getExtentsForProperty.call(this,t);if("x"===t&&this._isVertical)e=this.x();else{if("y"!==t||this._isVertical)return r;e=this.y()}if(!(e&&e.scale&&e.scale instanceof h.QuantitativeScale))return r;var o=e.scale,a=this._barPixelThickness();return r=r.map((function(t){return n.extent([o.invert(i._getPositionAttr(o.scale(t[0]),a)),o.invert(i._getPositionAttr(o.scale(t[0]),a)+a),o.invert(i._getPositionAttr(o.scale(t[1]),a)),o.invert(i._getPositionAttr(o.scale(t[1]),a)+a)])}))},g.prototype._getPositionAttr=function(t,e){switch(this._isVertical||(t-=e,e*=-1),this._barAlignment){case"start":return t;case"end":return t-e;case"middle":default:return t-e/2}},g.prototype._drawLabels=function(){var t=this,e=this._getDataToDraw(),n=this._getAttrToProjector(),i=this.datasets().some((function(i){return e.get(i).some((function(e,r){return null!=e&&t._drawLabel(e,r,i,n)}))}));this._hideBarsIfAnyAreTooWide&&i&&this.datasets().forEach((function(e){return t._labelConfig.get(e).labelArea.selectAll("g").remove()}))},g.prototype._drawLabel=function(t,e,n,i){var r=this._labelConfig.get(n),o=r.labelArea,a=r.measurer,s=r.writer,l=(0,this.length().accessor)(t,e,n),c=this.length().scale,u=null!=c?c.scale(l):l,h=null!=c?c.scale(this.baselineValue()):this.baselineValue(),d={x:i.x(t,e,n),y:i.y(t,e,n)},p={width:i.width(t,e,n),height:i.height(t,e,n)},f=this._labelFormatter(l,t,e,n),m=a.measure(f),g=this._shouldShowLabelOnBar(d,p,m),_=this._calculateLabelProperties(d,p,m,g,this._isVertical?u<=h:u<h),y=_.containerDimensions,v=_.labelContainerOrigin,b=_.labelOrigin,x=_.alignment,w=i.fill(t,e,n),S=this._createLabelContainer(o,v,b,m,g,w);return s.write(f,y.width,y.height,{xAlign:x.x,yAlign:x.y},S.node()),this._isVertical?p.width<m.width:p.height<m.height},g.prototype._shouldShowLabelOnBar=function(e,n,i){if(this._labelsPosition===t.LabelsPosition.outside)return!1;var r=this._isVertical?e.y:e.x,o=this._isVertical?n.height:n.width,a=this._isVertical?this.height():this.width(),s=r+o,l=o;return s>a?l=a-r:r<0&&(l=s),(this._isVertical?i.height:i.width)+g._LABEL_MARGIN_INSIDE_BAR<=l},g.prototype._calculateLabelProperties=function(e,n,i,r,o){var a=this,s=this._isVertical?e.y:e.x,l=this._isVertical?n.height:n.width,c=this._isVertical?i.height:i.width,u="center",h=l,d=s,p=s,f=function(t){switch(t){case"topLeft":return u=a._isVertical?"top":"left",d+=g._LABEL_MARGIN_INSIDE_BAR,void(p+=g._LABEL_MARGIN_INSIDE_BAR);case"center":return void(p+=(l+c)/2);case"bottomRight":return u=a._isVertical?"bottom":"right",d-=g._LABEL_MARGIN_INSIDE_BAR,void(p+=h-g._LABEL_MARGIN_INSIDE_BAR-c)}};if(r)switch(this._labelsPosition){case t.LabelsPosition.start:f(o?"bottomRight":"topLeft");break;case t.LabelsPosition.middle:f("center");break;case t.LabelsPosition.end:f(o?"topLeft":"bottomRight")}else o?(u=this._isVertical?"top":"left",h=l+g._LABEL_MARGIN_INSIDE_BAR+c,d-=g._LABEL_MARGIN_INSIDE_BAR+c,p-=g._LABEL_MARGIN_INSIDE_BAR+c):(u=this._isVertical?"bottom":"right",h=l+g._LABEL_MARGIN_INSIDE_BAR+c,p+=l+g._LABEL_MARGIN_INSIDE_BAR);return{containerDimensions:{width:this._isVertical?n.width:h,height:this._isVertical?h:n.height},labelContainerOrigin:{x:this._isVertical?e.x:d,y:this._isVertical?d:e.y},labelOrigin:{x:this._isVertical?e.x+n.width/2-i.width/2:p,y:this._isVertical?p:e.y+n.height/2-i.height/2},alignment:{x:this._isVertical?"center":u,y:this._isVertical?u:"center"}}},g.prototype._createLabelContainer=function(t,e,n,i,r,o){var a=t.append("g").attr("transform","translate("+e.x+", "+e.y+")");if(r){a.classed("on-bar-label",!0);var s=1.6*d.Color.contrast("white",o)<d.Color.contrast("black",o);a.classed(s?"dark-label":"light-label",!0)}else a.classed("off-bar-label",!0);return a},g.prototype._generateDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._getAttrToProjector(),n=this.length().scale.scale(this.baselineValue()),i=this._isVertical?"height":"width";e[this._isVertical?"y":"x"]=function(){return n},e[i]=function(){return 0},t.push({attrToProjector:e,animator:this._getAnimator(f.Animator.RESET)})}return t.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(f.Animator.MAIN)}),t},g.prototype._generateAttrToProjector=function(){var t=this,e=p.prototype._generateAttrToProjector.call(this),n=this.length().scale.scale(this.baselineValue()),i=this._isVertical?"y":"x",r=this._isVertical?"x":"y",o=m.Plot._scaledAccessor(this.position()),a=m.Plot._scaledAccessor(this.length()),s=function(t,e,i){return Math.abs(n-a(t,e,i))},l=e[g._BAR_THICKNESS_KEY],c=e.gap,u=null==c?l:function(t,e,n){var i=l(t,e,n);return i<g._BAR_GAPLESS_THRESHOLD_PX?i:i-c(t,e,n)};return e.width=this._isVertical?u:s,e.height=this._isVertical?s:u,e[i]=function(t,e,i){var r=a(t,e,i);return r>n?n:r},e[r]=function(e,n,i){return t._getPositionAttr(o(e,n,i),l(e,n,i))},e},g.prototype._updateThicknessAttr=function(){var t=this,e=this.position(),n=this.barEnd();null!=e&&null!=n?(this._fixedBarPixelThickness=!1,this.attr(g._BAR_THICKNESS_KEY,(function(t,i,r){var o=e.accessor(t,i,r),a=n.accessor(t,i,r);return o=e.scale?e.scale.scale(o):o,a=n.scale?n.scale.scale(a):a,Math.abs(a-o)}))):(this._fixedBarPixelThickness=!0,this.attr(g._BAR_THICKNESS_KEY,(function(){return t._barPixelThickness()})))},g.prototype._barPixelThickness=function(){return this._fixedBarPixelThickness&&this._projectorsReady()?this._computeBarPixelThickness(this.position(),this.datasets(),this._isVertical?this.width():this.height()):0},g.prototype.entities=function(t){return void 0===t&&(t=this.datasets()),this._projectorsReady()?p.prototype.entities.call(this,t):[]},g.prototype._entityBounds=function(t){return this._pixelBounds(t.datum,t.index,t.dataset)},g.prototype._pixelBounds=function(t,e,n){var i=this._getAttrToProjector();return{x:i.x(t,e,n),y:i.y(t,e,n),width:i.width(t,e,n),height:i.height(t,e,n)}},g.prototype._pixelPoint=function(t,e,n){var i=this._pixelBounds(t,e,n),r=m.Plot._scaledAccessor(this._isVertical?this.y():this.x())(t,e,n),o=(this._isVertical?this.y().scale:this.x().scale).scale(this.baselineValue());return this._pixelPointBar(r,o,i)},g.prototype._pixelPointBar=function(t,e,n){var i,r;return this._isVertical?(i=n.x+n.width/2,r=t<=e?n.y:n.y+n.height):(i=t>=e?n.x+n.width:n.x,r=n.y+n.height/2),{x:i,y:r}},g.prototype._uninstallScaleForKey=function(t,e){p.prototype._uninstallScaleForKey.call(this,t,e)},g.prototype._getDataToDraw=function(){var t=this,e=new d.Map,n=this._getAttrToProjector(),i=this.width(),r=this.height();return this.datasets().forEach((function(o){var a=o.data().map((function(e,a){return t._isDatumOnScreen(n,i,r,e,a,o)?e:null}));e.set(o,a)})),e},g.prototype._isDatumOnScreen=function(t,e,n,i,r,o){var a=t.x(i,r,o),s=t.y(i,r,o),l=t.width(i,r,o),c=t.height(i,r,o);return!!(d.Math.isValidNumber(a)&&d.Math.isValidNumber(s)&&d.Math.isValidNumber(l)&&d.Math.isValidNumber(c))&&d.Math.boundsIntersects(a,s,l,c,0,0,e,n)},g.prototype.invalidateCache=function(){var t=this;p.prototype.invalidateCache.call(this),this.datasets().forEach((function(e){return t._labelConfig.get(e).measurer.reset()}))},g._BAR_THICKNESS_RATIO=.95,g._BAR_GAPLESS_THRESHOLD_PX=3,g._SINGLE_BAR_DIMENSION_RATIO=.4,g._BAR_AREA_CLASS="bar-area",g._BAR_END_KEY="barEnd",g._BAR_THICKNESS_KEY="width",g._LABEL_AREA_CLASS="bar-label-text-area",g._LABEL_MARGIN_INSIDE_BAR=10,g})(g.XYPlot);function y(t,e,i){var r,o=t.scale;if(o instanceof u.Category)r=o.rangeBand();else{var a=t.accessor,s=n.set(d.Array.flatten(e.map((function(t){return t.data().map((function(e,n){return a(e,n,t)})).filter((function(t){return null!=t})).map((function(t){return t.valueOf()}))})))).values().map((function(t){return+t}));s.sort((function(t,e){return t-e}));var l=s.map((function(t){return o.scale(t)})),c=n.pairs(l);r=d.Math.min(c,(function(t,e){return Math.abs(t[1]-t[0])}),i*_._SINGLE_BAR_DIMENSION_RATIO),r*=_._BAR_THICKNESS_RATIO}return r}t.Bar=_})(YEt);var XEt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(XEt,"__esModule",{value:!0});var $Et=BO,KEt=Xvt,ZEt=D_t,JEt=YEt,QEt=sSt,tTt=(function(t){function e(e){void 0===e&&(e="vertical");var n=t.call(this,e)||this;return n._clusterOffsets=new ZEt.Map,n}return $Et.__extends(e,t),e.prototype._generateAttrToProjector=function(){var e=this,n=t.prototype._generateAttrToProjector.call(this),i=this._makeInnerScale(),r=function(t,e){return i.rangeBand()};n.width=this._isVertical?r:n.width,n.height=this._isVertical?n.height:r;var o=n.x,a=n.y;return n.x=this._isVertical?function(t,n,i){return o(t,n,i)+e._clusterOffsets.get(i)}:function(t,e,n){return o(t,e,n)},n.y=this._isVertical?function(t,e,n){return a(t,e,n)}:function(t,n,i){return a(t,n,i)+e._clusterOffsets.get(i)},n},e.prototype._updateClusterPosition=function(){var t=this,e=this._makeInnerScale();this.datasets().forEach((function(n,i){return t._clusterOffsets.set(n,e.scale(String(i))-e.rangeBand()/2)}))},e.prototype._makeInnerScale=function(){var t=new KEt.Category;t.domain(this.datasets().map((function(t,e){return String(e)})));var e=QEt.Plot._scaledAccessor(this.attr(JEt.Bar._BAR_THICKNESS_KEY));return t.range([0,e(null,0,null)]),t},e.prototype._getDataToDraw=function(){return this._updateClusterPosition(),t.prototype._getDataToDraw.call(this)},e})(JEt.Bar);XEt.ClusteredBar=tTt;var eTt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(eTt,"__esModule",{value:!0});var nTt=BO,iTt=wpt,rTt=Lgt,oTt=fgt,aTt=pvt,sTt=Xvt,lTt=D_t,cTt=NMt,uTt=OMt,hTt=GSt,dTt=Tyt,pTt=sSt,fTt=(function(t){function e(){var e=t.call(this)||this;return e._startAngle=0,e._endAngle=2*Math.PI,e._labelFormatter=aTt.identity(),e._labelsEnabled=!1,e.innerRadius(0),e.outerRadius((function(){var t=e._pieCenter();return Math.min(Math.max(e.width()-t.x,t.x),Math.max(e.height()-t.y,t.y))})),e.addClass("pie-plot"),e.attr("fill",(function(t,e){return String(e)}),new sTt.Color),e._strokeDrawers=new lTt.Map,e}return nTt.__extends(e,t),e.prototype._setup=function(){var e=this;t.prototype._setup.call(this),this._strokeDrawers.forEach((function(t){return t.attachTo(e._renderArea)}))},e.prototype.computeLayout=function(e,n,i){t.prototype.computeLayout.call(this,e,n,i);var r=this._pieCenter();this._renderArea.attr("transform","translate("+r.x+","+r.y+")");var o=Math.min(Math.max(this.width()-r.x,r.x),Math.max(this.height()-r.y,r.y));return null!=this.innerRadius().scale&&this.innerRadius().scale.range([0,o]),null!=this.outerRadius().scale&&this.outerRadius().scale.range([0,o]),this},e.prototype.addDataset=function(e){return t.prototype.addDataset.call(this,e),this},e.prototype._addDataset=function(e){if(1===this.datasets().length)return lTt.Window.warn("Only one dataset is supported in Pie plots"),this;this._updatePieAngles(),t.prototype._addDataset.call(this,e);var n=new uTt.ArcOutlineSVGDrawer;return this._isSetup&&n.attachTo(this._renderArea),this._strokeDrawers.set(e,n),this},e.prototype.removeDataset=function(e){return t.prototype.removeDataset.call(this,e),this},e.prototype._removeDatasetNodes=function(e){t.prototype._removeDatasetNodes.call(this,e),this._strokeDrawers.get(e).remove()},e.prototype._removeDataset=function(e){return t.prototype._removeDataset.call(this,e),this._strokeDrawers.delete(e),this._startAngles=[],this._endAngles=[],this},e.prototype.selections=function(e){var n=this;void 0===e&&(e=this.datasets());var i=t.prototype.selections.call(this,e).nodes();return e.forEach((function(t){var e=n._strokeDrawers.get(t);null!=e&&i.push.apply(i,e.getVisualPrimitives())})),iTt.selectAll(i)},e.prototype._onDatasetUpdate=function(){t.prototype._onDatasetUpdate.call(this),this._updatePieAngles(),this.render()},e.prototype._createDrawer=function(){return new hTt.ProxyDrawer((function(){return new cTt.ArcSVGDrawer}),(function(){return dTt.warn("canvas renderer is not supported on Pie Plot!"),null}))},e.prototype.entities=function(e){var n=this;return void 0===e&&(e=this.datasets()),t.prototype.entities.call(this,e).map((function(t){t.position.x+=n.width()/2,t.position.y+=n.height()/2;var e=iTt.select(n._strokeDrawers.get(t.dataset).getVisualPrimitiveAtIndex(t.index)),i=t;return i.strokeSelection=e,i}))},e.prototype.sectorValue=function(t,n){return null==t?this._propertyBindings.get(e._SECTOR_VALUE_KEY):(this._bindProperty(e._SECTOR_VALUE_KEY,t,n),this._updatePieAngles(),this.render(),this)},e.prototype.innerRadius=function(t,n){return null==t?this._propertyBindings.get(e._INNER_RADIUS_KEY):(this._bindProperty(e._INNER_RADIUS_KEY,t,n),this.render(),this)},e.prototype.outerRadius=function(t,n){return null==t?this._propertyBindings.get(e._OUTER_RADIUS_KEY):(this._bindProperty(e._OUTER_RADIUS_KEY,t,n),this.render(),this)},e.prototype.startAngle=function(t){return null==t?this._startAngle:(this._startAngle=t,this._updatePieAngles(),this.render(),this)},e.prototype.endAngle=function(t){return null==t?this._endAngle:(this._endAngle=t,this._updatePieAngles(),this.render(),this)},e.prototype.labelsEnabled=function(t){return null==t?this._labelsEnabled:(this._labelsEnabled=t,this.render(),this)},e.prototype.labelFormatter=function(t){return null==t?this._labelFormatter:(this._labelFormatter=t,this.render(),this)},e.prototype.entitiesAt=function(t){var e=this.width()/2,n=this.height()/2,i=this._sliceIndexForPoint({x:t.x-e,y:t.y-n});return null==i?[]:[this.entities()[i]]},e.prototype._propertyProjectors=function(){var e=this,n=t.prototype._propertyProjectors.call(this),i=pTt.Plot._scaledAccessor(this.innerRadius()),r=pTt.Plot._scaledAccessor(this.outerRadius());return n.d=function(t,n,o){return iTt.arc().innerRadius(i(t,n,o)).outerRadius(r(t,n,o)).startAngle(e._startAngles[n]).endAngle(e._endAngles[n])(t,n)},n},e.prototype._updatePieAngles=function(){if(null!=this.sectorValue()&&0!==this.datasets().length){var t=pTt.Plot._scaledAccessor(this.sectorValue()),e=this.datasets()[0],n=this._getDataToDraw().get(e),i=iTt.pie().sort(null).startAngle(this._startAngle).endAngle(this._endAngle).value((function(n,i){return t(n,i,e)}))(n);this._startAngles=i.map((function(t){return t.startAngle})),this._endAngles=i.map((function(t){return t.endAngle}))}},e.prototype._pieCenter=function(){var t,e,n,i,r=this._startAngle<this._endAngle?this._startAngle:this._endAngle,o=this._startAngle<this._endAngle?this._endAngle:this._startAngle,a=Math.sin(r),s=Math.cos(r),l=Math.sin(o),c=Math.cos(o);return a>=0&&l>=0?s>=0&&c>=0?(t=s,e=0,i=0,n=l):s<0&&c<0?(t=0,e=-c,i=0,n=a):s>=0&&c<0?(t=s,e=-c,i=0,n=a):s<0&&c>=0&&(t=1,e=1,i=1,n=Math.max(a,l)):a>=0&&l<0?s>=0&&c>=0?(t=Math.max(s,c),e=1,i=1,n=1):s<0&&c<0?(t=0,e=1,i=-l,n=a):s>=0&&c<0?(t=s,e=1,i=-l,n=1):s<0&&c>=0&&(t=c,e=1,i=1,n=a):a<0&&l>=0?s>=0&&c>=0?(t=1,e=0,i=-a,n=l):s<0&&c<0?(t=1,e=Math.max(-s,-c),i=1,n=1):s>=0&&c<0?(t=1,e=-c,i=-a,n=1):s<0&&c>=0&&(t=1,e=-s,i=1,n=l):a<0&&l<0&&(s>=0&&c>=0?(t=c,e=0,i=-a,n=0):s<0&&c<0?(t=0,e=-s,i=-l,n=0):s>=0&&c<0?(t=1,e=1,i=Math.max(s,-c),n=1):s<0&&c>=0&&(t=c,e=-s,i=1,n=0)),{x:i+n==0?0:i/(i+n)*this.width(),y:t+e==0?0:t/(t+e)*this.height()}},e.prototype._getDataToDraw=function(){var n=t.prototype._getDataToDraw.call(this);if(0===this.datasets().length)return n;var i=pTt.Plot._scaledAccessor(this.sectorValue()),r=this.datasets()[0],o=n.get(r).filter((function(t,n){return e._isValidData(i(t,n,r))}));return n.set(r,o),n},e._isValidData=function(t){return lTt.Math.isValidNumber(t)&&t>=0},e.prototype._pixelPoint=function(t,n,i){var r=pTt.Plot._scaledAccessor(this.sectorValue());if(!e._isValidData(r(t,n,i)))return{x:NaN,y:NaN};var o=(pTt.Plot._scaledAccessor(this.innerRadius())(t,n,i)+pTt.Plot._scaledAccessor(this.outerRadius())(t,n,i))/2,a=iTt.pie().sort(null).value((function(t,n){var o=r(t,n,i);return e._isValidData(o)?o:0})).startAngle(this._startAngle).endAngle(this._endAngle)(i.data()),s=(a[n].startAngle+a[n].endAngle)/2;return{x:o*Math.sin(s),y:-o*Math.cos(s)}},e.prototype._additionalPaint=function(t){var e=this;this._renderArea.select(".label-area").remove(),this._labelsEnabled&&lTt.Window.setTimeout((function(){return e._drawLabels()}),t);var n=this._generateStrokeDrawSteps(),i=this._getDataToDraw();this.datasets().forEach((function(t){var r=pTt.Plot.applyDrawSteps(n,t);e._strokeDrawers.get(t).draw(i.get(t),r)}))},e.prototype._generateStrokeDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new oTt.Null}]},e.prototype._sliceIndexForPoint=function(t){var e,n=Math.sqrt(Math.pow(t.x,2)+Math.pow(t.y,2)),i=Math.acos(-t.y/n);t.x<0&&(i=2*Math.PI-i);for(var r=0;r<this._startAngles.length;r++)if(this._startAngles[r]<i&&this._endAngles[r]>i){e=r;break}if(void 0!==e){var o=this.datasets()[0],a=o.data()[e],s=this.innerRadius().accessor(a,e,o),l=this.outerRadius().accessor(a,e,o);if(n>s&&n<l)return e}return null},e.prototype._drawLabels=function(){for(var t=this,e=this._getAttrToProjector(),n=this._renderArea.append("g").classed("label-area",!0),i=new rTt.SvgContext(n.node()),r=new rTt.CacheMeasurer(i),o=new rTt.Writer(r,i),a=this.datasets()[0],s=this._getDataToDraw().get(a),l=s.length,c=function(i){var l=s[i],c=u.sectorValue().accessor(l,i,a);if(!lTt.Math.isValidNumber(c))return"continue";c=u._labelFormatter(c,l,i,a);var h=r.measure(c),d=(u._endAngles[i]+u._startAngles[i])/2,p=u.outerRadius().accessor(l,i,a);u.outerRadius().scale&&(p=u.outerRadius().scale.scale(p));var f=u.innerRadius().accessor(l,i,a);u.innerRadius().scale&&(f=u.innerRadius().scale.scale(f));var m=(p+f)/2,g=Math.sin(d)*m-h.width/2,_=-Math.cos(d)*m-h.height/2,y=[{x:g,y:_},{x:g,y:_+h.height},{x:g+h.width,y:_},{x:g+h.width,y:_+h.height}],v=y.every((function(e){return Math.abs(e.x)<=t.width()/2&&Math.abs(e.y)<=t.height()/2}));v&&(v=y.map((function(e){return t._sliceIndexForPoint(e)})).every((function(t){return t===i})));var b=e.fill(l,i,a),x=1.6*lTt.Color.contrast("white",b)<lTt.Color.contrast("black",b),w=n.append("g").attr("transform","translate("+g+","+_+")");w.classed(x?"dark-label":"light-label",!0),w.style("visibility",v?"inherit":"hidden"),o.write(c,h.width,h.height,{xAlign:"center",yAlign:"center"},w.node())},u=this,h=0;h<l;h++)c(h)},e._INNER_RADIUS_KEY="inner-radius",e._OUTER_RADIUS_KEY="outer-radius",e._SECTOR_VALUE_KEY="sector-value",e})(pTt.Plot);eTt.Pie=fTt;var mTt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(mTt,"__esModule",{value:!0});var gTt=BO,_Tt=wpt,yTt=Lgt,vTt=fgt,bTt=PMt,xTt=GSt,wTt=ZMt,STt=Xvt,MTt=D_t,ETt=sSt,TTt=(function(t){function e(){var e=t.call(this)||this;return e._labelsEnabled=!1,e._label=null,e.animator("rectangles",new vTt.Null),e.addClass("rectangle-plot"),e.attr("fill",(new STt.Color).range()[0]),e}return gTt.__extends(e,t),e.prototype._createDrawer=function(){return new xTt.ProxyDrawer((function(){return new wTt.RectangleSVGDrawer}),(function(t){return new bTt.RectangleCanvasDrawer(t)}))},e.prototype._generateAttrToProjector=function(){var n=this,i=t.prototype._generateAttrToProjector.call(this),r=ETt.Plot._scaledAccessor(this.x()),o=i[e._X2_KEY],a=ETt.Plot._scaledAccessor(this.y()),s=i[e._Y2_KEY],l=this.x().scale,c=this.y().scale;return null!=o?(i.width=function(t,e,n){return Math.abs(o(t,e,n)-r(t,e,n))},i.x=function(t,e,n){return Math.min(o(t,e,n),r(t,e,n))}):(i.width=function(t,e,i){return n._rectangleWidth(l)},i.x=function(t,e,n){return r(t,e,n)-.5*i.width(t,e,n)}),null!=s?(i.height=function(t,e,n){return Math.abs(s(t,e,n)-a(t,e,n))},i.y=function(t,e,n){return Math.max(s(t,e,n),a(t,e,n))-i.height(t,e,n)}):(i.height=function(t,e,i){return n._rectangleWidth(c)},i.y=function(t,e,n){return a(t,e,n)-.5*i.height(t,e,n)}),delete i[e._X2_KEY],delete i[e._Y2_KEY],i},e.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:this._getAnimator("rectangles")}]},e.prototype._filterForProperty=function(e){return t.prototype._filterForProperty.call(this,"x2"===e?"x":"y2"===e?"y":e)},e.prototype.x=function(n,i,r){if(null==n)return t.prototype.x.call(this);if(null==i?t.prototype.x.call(this,n):t.prototype.x.call(this,n,i,r),null!=i){var o=this.x2(),a=o&&o.accessor;null!=a&&this._bindProperty(e._X2_KEY,a,i,o.postScale)}return i instanceof STt.Category&&i.innerPadding(0).outerPadding(0),this},e.prototype.x2=function(t,n){if(null==t)return this._propertyBindings.get(e._X2_KEY);var i=this.x();return this._bindProperty(e._X2_KEY,t,i&&i.scale,n),this.render(),this},e.prototype.y=function(n,i,r){if(null==n)return t.prototype.y.call(this);if(null==i?t.prototype.y.call(this,n):t.prototype.y.call(this,n,i,r),null!=i){var o=this.y2(),a=o&&o.accessor;null!=a&&this._bindProperty(e._Y2_KEY,a,i,o.postScale)}return i instanceof STt.Category&&i.innerPadding(0).outerPadding(0),this},e.prototype.y2=function(t,n){if(null==t)return this._propertyBindings.get(e._Y2_KEY);var i=this.y();return this._bindProperty(e._Y2_KEY,t,i&&i.scale,n),this.render(),this},e.prototype.entitiesAt=function(t){var e=this._getAttrToProjector();return this.entities().filter((function(n){var i=n.datum,r=n.index,o=n.dataset,a=e.x(i,r,o),s=e.y(i,r,o),l=e.width(i,r,o),c=e.height(i,r,o);return a<=t.x&&t.x<=a+l&&s<=t.y&&t.y<=s+c}))},e.prototype._entityBounds=function(t){return this._entityBBox(t.datum,t.index,t.dataset,this._getAttrToProjector())},e.prototype._entityBBox=function(t,e,n,i){return{x:i.x(t,e,n),y:i.y(t,e,n),width:i.width(t,e,n),height:i.height(t,e,n)}},e.prototype.label=function(t){return null==t?this._label:(this._label=t,this.render(),this)},e.prototype.labelsEnabled=function(t){return null==t?this._labelsEnabled:(this._labelsEnabled=t,this.render(),this)},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this);return null!=this.x2()&&(e.x2=ETt.Plot._scaledAccessor(this.x2())),null!=this.y2()&&(e.y2=ETt.Plot._scaledAccessor(this.y2())),e},e.prototype._pixelPoint=function(t,e,n){var i=this._getAttrToProjector(),r=i.x(t,e,n),o=i.y(t,e,n);return{x:r+i.width(t,e,n)/2,y:o+i.height(t,e,n)/2}},e.prototype._rectangleWidth=function(t){if(t instanceof STt.Category)return t.rangeBand();var e=t===this.x().scale?this.x().accessor:this.y().accessor,n=_Tt.set(MTt.Array.flatten(this.datasets().map((function(t){return t.data().map((function(n,i){return e(n,i,t).valueOf()}))})))).values().map((function(t){return+t})),i=MTt.Math.min(n,0),r=MTt.Math.max(n,0),o=t.scale(i);return(t.scale(r)-o)/Math.abs(r-i)},e.prototype._getDataToDraw=function(){var t=new MTt.Map,e=this._getAttrToProjector();return this.datasets().forEach((function(n){var i=n.data().map((function(t,i){return MTt.Math.isValidNumber(e.x(t,i,n))&&MTt.Math.isValidNumber(e.y(t,i,n))&&MTt.Math.isValidNumber(e.width(t,i,n))&&MTt.Math.isValidNumber(e.height(t,i,n))?t:null}));t.set(n,i)})),t},e.prototype._additionalPaint=function(t){var e=this;this._renderArea.selectAll(".label-area").remove(),this._labelsEnabled&&null!=this.label()&&MTt.Window.setTimeout((function(){return e._drawLabels()}),t)},e.prototype._drawLabels=function(){var t=this,e=this._getDataToDraw();this.datasets().forEach((function(n,i){return t._drawLabel(e,n,i)}))},e.prototype._drawLabel=function(t,e,n){for(var i=this._getAttrToProjector(),r=this._renderArea.append("g").classed("label-area",!0),o=new yTt.SvgContext(r.node()),a=new yTt.CacheMeasurer(o),s=new yTt.Writer(a,o),l=this.x().scale.range(),c=this.y().scale.range(),u=Math.min.apply(null,l),h=Math.max.apply(null,l),d=Math.min.apply(null,c),p=Math.max.apply(null,c),f=t.get(e),m=f.length,g=0;g<m;g++){var _=f[g];if(null!=_){var y=""+this.label()(_,g,e),v=a.measure(y),b=i.x(_,g,e),x=i.y(_,g,e),w=i.width(_,g,e),S=i.height(_,g,e);if(v.height<=S&&v.width<=w){var M={min:b+=(w-v.width)/2,max:b+v.width},E={min:x+=(S-v.height)/2,max:x+v.height};if(M.min<u||M.max>h||E.min<d||E.max>p)continue;if(this._overlayLabel(M,E,g,n,t))continue;var T=i.fill(_,g,e),C=1.6*MTt.Color.contrast("white",T)<MTt.Color.contrast("black",T),A=r.append("g").attr("transform","translate("+b+","+x+")");A.classed(C?"dark-label":"light-label",!0),s.write(y,v.width,v.height,{xAlign:"center",yAlign:"center"},A.node())}}}},e.prototype._overlayLabel=function(t,e,n,i,r){for(var o=this._getAttrToProjector(),a=this.datasets(),s=i;s<a.length;s++)for(var l=a[s],c=r.get(l),u=c.length,h=s===i?n+1:0;h<u;h++)if(MTt.DOM.intersectsBBox(t,e,this._entityBBox(c[h],h,l,o)))return!0;return!1},e._X2_KEY="x2",e._Y2_KEY="y2",e})(mEt.XYPlot);mTt.Rectangle=TTt;var CTt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(CTt,"__esModule",{value:!0});var ATt=BO,kTt=Lgt,LTt=pvt,PTt=Hwt,NTt=GSt,ITt=eEt,RTt=fgt,OTt=PMt,zTt=Xvt,DTt=D_t,BTt=dEt,HTt=sSt,FTt=(function(t){function e(){var e=t.call(this)||this;e._labelFormatter=LTt.identity(),e._labelsEnabled=!1,e.addClass("scatter-plot");var n=new RTt.Easing;n.startDelay(5),n.stepDuration(250),n.maxTotalDuration(HTt.Plot._ANIMATION_MAX_DURATION),e.animator(BTt.Animator.MAIN,n),e.attr("opacity",.6),e.attr("fill",(new zTt.Color).range()[0]),e.size(6);var i=PTt.circle();return e.symbol((function(){return i})),e._labelConfig=new DTt.Map,e}return ATt.__extends(e,t),e.prototype._buildLightweightPlotEntities=function(e){var n=this;return t.prototype._buildLightweightPlotEntities.call(this,e).map((function(t){var e=HTt.Plot._scaledAccessor(n.size())(t.datum,t.index,t.dataset);return t.diameter=e,t}))},e.prototype._createDrawer=function(t){var e=this;return new NTt.ProxyDrawer((function(){return new ITt.SymbolSVGDrawer}),(function(n){return new OTt.CanvasDrawer(n,ITt.makeSymbolCanvasDrawStep(t,(function(){return HTt.Plot._scaledAccessor(e.symbol())}),(function(){return HTt.Plot._scaledAccessor(e.size())})))}))},e.prototype.size=function(t,n){return null==t?this._propertyBindings.get(e._SIZE_KEY):(this._bindProperty(e._SIZE_KEY,t,n),this.render(),this)},e.prototype.symbol=function(t){return null==t?this._propertyBindings.get(e._SYMBOL_KEY):(this._propertyBindings.set(e._SYMBOL_KEY,{accessor:t}),this.render(),this)},e.prototype._generateDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._getAttrToProjector(),n=HTt.Plot._scaledAccessor(this.symbol());e.d=function(t,e,i){return n(t,e,i)(0)(null)},t.push({attrToProjector:e,animator:this._getAnimator(BTt.Animator.RESET)})}return t.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(BTt.Animator.MAIN)}),t},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this),n=HTt.Plot._scaledAccessor(this.x()),i=HTt.Plot._scaledAccessor(this.y());return e.x=n,e.y=i,e.transform=function(t,e,r){return"translate("+n(t,e,r)+","+i(t,e,r)+")"},e.d=this._constructSymbolGenerator(),e},e.prototype._constructSymbolGenerator=function(){var t=HTt.Plot._scaledAccessor(this.symbol()),e=HTt.Plot._scaledAccessor(this.size());return function(n,i,r){return t(n,i,r)(e(n,i,r))(null)}},e.prototype._entityBounds=function(t){return{x:t.position.x-t.diameter/2,y:t.position.y-t.diameter/2,width:t.diameter,height:t.diameter}},e.prototype._entityVisibleOnPlot=function(t,e){var n={min:e.topLeft.x,max:e.bottomRight.x},i={min:e.topLeft.y,max:e.bottomRight.y},r=this._entityBounds(t);return DTt.DOM.intersectsBBox(n,i,r)},e.prototype.entitiesAt=function(t){var e=HTt.Plot._scaledAccessor(this.x()),n=HTt.Plot._scaledAccessor(this.y()),i=HTt.Plot._scaledAccessor(this.size());return this.entities().filter((function(r){var o=r.datum,a=r.index,s=r.dataset,l=e(o,a,s),c=n(o,a,s),u=i(o,a,s);return l-u/2<=t.x&&t.x<=l+u/2&&c-u/2<=t.y&&t.y<=c+u/2}))},e.prototype.labelsEnabled=function(t){return null==t?this._labelsEnabled:(this._labelsEnabled=t,this._clearAttrToProjectorCache(),this.render(),this)},e.prototype._createNodesForDataset=function(n){var i=t.prototype._createNodesForDataset.call(this,n),r=this._renderArea.append("g").classed(e._LABEL_AREA_CLASS,!0),o=new kTt.SvgContext(r.node()),a=new kTt.CacheMeasurer(o),s=new kTt.Writer(a,o);return this._labelConfig.set(n,{labelArea:r,measurer:a,writer:s}),i},e.prototype._removeDatasetNodes=function(e){t.prototype._removeDatasetNodes.call(this,e);var n=this._labelConfig.get(e);null!=n&&(n.labelArea.remove(),this._labelConfig.delete(e))},e.prototype._additionalPaint=function(t){var e=this;this.datasets().forEach((function(t){return e._labelConfig.get(t).labelArea.selectAll("g").remove()})),this._labelsEnabled&&DTt.Window.setTimeout((function(){return e._drawLabels()}),t)},e.prototype._drawLabels=function(){var t=this,e=this._getDataToDraw(),n=this._getAttrToProjector();this.datasets().forEach((function(i){for(var r=e.get(i),o=r.length,a=0;a<o;a++){var s=r[a];null!=s&&t._drawLabel(s,a,i,n)}}))},e.prototype._drawLabel=function(t,e,n,i){if(null!=t.label){var r=this._labelConfig.get(n),o=r.labelArea,a=r.measurer,s=r.writer,l={x:i.x(t,e,n),y:i.y(t,e,n)},c=HTt.Plot._scaledAccessor(this.size())(t,e,n),u=this._labelFormatter(t.label,t,e,n),h=a.measure(u),d=this._calculateLabelProperties(l,c,h),p=d.containerDimensions,f=d.alignment,m=this._createLabelContainer(o,d.labelContainerOrigin,d.labelOrigin,h);s.write(u,p.width,p.height,{xAlign:f.x,yAlign:f.y},m.node())}},e.prototype._calculateLabelProperties=function(t,n,i){return{containerDimensions:{width:i.width,height:i.height},labelContainerOrigin:{x:t.x-i.width/2,y:t.y-i.height/2+(n<i.height?n/2+e._LABEL_MARGIN_FROM_BUBBLE:0)},labelOrigin:{x:t.x,y:t.y},alignment:{x:"center",y:"center"}}},e.prototype._createLabelContainer=function(t,e,n,i){var r=t.append("g").attr("transform","translate("+e.x+", "+e.y+")");return r.classed("on-bar-label",!0),r},e._SIZE_KEY="size",e._SYMBOL_KEY="symbol",e._LABEL_AREA_CLASS="scatter-label-text-area",e._LABEL_MARGIN_FROM_BUBBLE=15,e})(mEt.XYPlot);CTt.Scatter=FTt;var VTt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(VTt,"__esModule",{value:!0});var UTt=BO,jTt=fgt,GTt=GSt,WTt=JMt,qTt=Xvt,YTt=Tyt,XTt=sSt,$Tt=(function(t){function e(){var e=t.call(this)||this;return e.addClass("segment-plot"),e.attr("stroke",(new qTt.Color).range()[0]),e.attr("stroke-width","2px"),e}return UTt.__extends(e,t),e.prototype._createDrawer=function(){return new GTt.ProxyDrawer((function(){return new WTt.SegmentSVGDrawer}),(function(){return YTt.warn("canvas renderer is not supported on Segment Plot!"),null}))},e.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new jTt.Null}]},e.prototype._filterForProperty=function(e){return t.prototype._filterForProperty.call(this,"x2"===e?"x":"y2"===e?"y":e)},e.prototype.x=function(n,i){if(null==n)return t.prototype.x.call(this);if(null==i)t.prototype.x.call(this,n);else{t.prototype.x.call(this,n,i);var r=this.x2(),o=r&&r.accessor;null!=o&&this._bindProperty(e._X2_KEY,o,i)}return this},e.prototype.x2=function(t){if(null==t)return this._propertyBindings.get(e._X2_KEY);var n=this.x();return this._bindProperty(e._X2_KEY,t,n&&n.scale),this.render(),this},e.prototype.y=function(n,i){if(null==n)return t.prototype.y.call(this);if(null==i)t.prototype.y.call(this,n);else{t.prototype.y.call(this,n,i);var r=this.y2(),o=r&&r.accessor;null!=o&&this._bindProperty(e._Y2_KEY,o,i)}return this},e.prototype.y2=function(t){if(null==t)return this._propertyBindings.get(e._Y2_KEY);var n=this.y();return this._bindProperty(e._Y2_KEY,t,n&&n.scale),this.render(),this},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this);return e.x1=XTt.Plot._scaledAccessor(this.x()),e.x2=null==this.x2()?XTt.Plot._scaledAccessor(this.x()):XTt.Plot._scaledAccessor(this.x2()),e.y1=XTt.Plot._scaledAccessor(this.y()),e.y2=null==this.y2()?XTt.Plot._scaledAccessor(this.y()):XTt.Plot._scaledAccessor(this.y2()),e},e.prototype.entitiesAt=function(t){var e=this.entityNearest(t);return null!=e?[e]:[]},e.prototype.entitiesIn=function(t,e){var n,i;if(null==e){n={min:t.topLeft.x,max:t.bottomRight.x},i={min:t.topLeft.y,max:t.bottomRight.y}}else n=t,i=e;return this._entitiesIntersecting(n,i)},e.prototype._entitiesIntersecting=function(t,e){for(var n=[],i=this._getAttrToProjector(),r=this.entities(),o=r.length,a=0;a<o;a++){var s=r[a];this._lineIntersectsBox(s,t,e,i)&&n.push(s)}return n},e.prototype._lineIntersectsBox=function(t,e,n,i){var r=this,o=i.x1(t.datum,t.index,t.dataset),a=i.x2(t.datum,t.index,t.dataset),s=i.y1(t.datum,t.index,t.dataset),l=i.y2(t.datum,t.index,t.dataset);if(e.min<=o&&o<=e.max&&n.min<=s&&s<=n.max||e.min<=a&&a<=e.max&&n.min<=l&&l<=n.max)return!0;var c={x:o,y:s},u={x:a,y:l},h=[{x:e.min,y:n.min},{x:e.min,y:n.max},{x:e.max,y:n.max},{x:e.max,y:n.min}];return h.filter((function(t,e){return 0!==e&&(r._lineIntersectsSegment(c,u,t,h[e-1])&&r._lineIntersectsSegment(t,h[e-1],c,u))})).length>0},e.prototype._lineIntersectsSegment=function(t,e,n,i){var r=function(t,e,n){return(e.x-t.x)*(n.y-e.y)-(e.y-t.y)*(n.x-e.x)};return r(t,e,n)*r(t,e,i)<0},e._X2_KEY="x2",e._Y2_KEY="y2",e})(mEt.XYPlot);VTt.Segment=$Tt;var KTt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(KTt,"__esModule",{value:!0});var ZTt=BO,JTt=wpt,QTt=fgt,tCt=cSt,eCt=D_t,nCt=sSt,iCt=(function(t){function e(){var e=t.call(this)||this;return e._stackingResult=tCt.memThunk((function(){return e.datasets()}),(function(){return e.x().accessor}),(function(){return e.y().accessor}),(function(){return e._stackingOrder}),(function(t,e,n,i){return eCt.Stacking.stack(t,e,n,i)})),e._stackedExtent=tCt.memThunk(e._stackingResult,(function(){return e.x().accessor}),(function(){return e._filterForProperty("y")}),(function(t,e,n){return eCt.Stacking.stackedExtent(t,e,n)})),e._baselineValue=0,e._stackingOrder="bottomup",e.addClass("stacked-area-plot"),e._baselineValueProvider=function(){return[e._baselineValue]},e.croppedRenderingEnabled(!1),e}return ZTt.__extends(e,t),e.prototype.croppedRenderingEnabled=function(e){return null==e?t.prototype.croppedRenderingEnabled.call(this):e?(eCt.Window.warn("Warning: Stacked Area Plot does not support cropped rendering."),this):t.prototype.croppedRenderingEnabled.call(this,e)},e.prototype._getAnimator=function(t){return new QTt.Null},e.prototype._setup=function(){t.prototype._setup.call(this),this._baseline=this._renderArea.append("line").classed("baseline",!0)},e.prototype.x=function(e,n){return null==e?t.prototype.x.call(this):(null==n?t.prototype.x.call(this,e):t.prototype.x.call(this,e,n),this._checkSameDomain(),this)},e.prototype.y=function(e,n){return null==e?t.prototype.y.call(this):(null==n?t.prototype.y.call(this,e):t.prototype.y.call(this,e,n),this._checkSameDomain(),this)},e.prototype.yOffset=function(t,e){var n=this._stackingResult();if(null!=n){var i=n.get(t);if(null!=i){var r=i.get(String(e));if(null!=r)return r.offset}}},e.prototype.stackingOrder=function(t){return null==t?this._stackingOrder:(this._stackingOrder=t,this._onDatasetUpdate(),this)},e.prototype.downsamplingEnabled=function(e){return null==e?t.prototype.downsamplingEnabled.call(this):(eCt.Window.warn("Warning: Stacked Area Plot does not support downsampling"),this)},e.prototype._additionalPaint=function(){var t=this.y().scale.scale(this._baselineValue),e={x1:0,y1:t,x2:this.width(),y2:t};this._getAnimator("baseline").animate(this._baseline,e)},e.prototype._updateYScale=function(){var t=this.y(),e=t&&t.scale;null!=e&&(e.addPaddingExceptionsProvider(this._baselineValueProvider),e.addIncludedValuesProvider(this._baselineValueProvider))},e.prototype._onDatasetUpdate=function(){return this._checkSameDomain(),t.prototype._onDatasetUpdate.call(this),this},e.prototype.getExtentsForProperty=function(e){return"y"===e?[this._stackedExtent()]:t.prototype.getExtentsForProperty.call(this,e)},e.prototype._checkSameDomain=function(){if(this._projectorsReady()){var t=this.datasets(),n=this.x().accessor,i=t.map((function(t){return JTt.set(t.data().map((function(e,i){return eCt.Stacking.normalizeKey(n(e,i,t))}))).values()})),r=e._domainKeys(t,n);i.some((function(t){return t.length!==r.length}))&&eCt.Window.warn("the domains across the datasets are not the same. Plot may produce unintended behavior.")}},e._domainKeys=function(t,e){var n=JTt.set();return t.forEach((function(t){for(var i=t.data(),r=i.length,o=0;o<r;o++){n.add(e(i[o],o,t))}})),n.values()},e.prototype._coordinateProjectors=function(){var t=this,e=nCt.Plot._scaledAccessor(this.x()),n=this.y().accessor,i=this.x().accessor,r=function(t,e,n){return eCt.Stacking.normalizeKey(i(t,e,n))},o=this._stackingResult();return[e,function(e,i,a){var s=+n(e,i,a),l=o.get(a).get(r(e,i,a)).offset;return t.y().scale.scale(s+l)},function(e,n,i){var a=o.get(i).get(r(e,n,i)).offset;return t.y().scale.scale(a)}]},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this),n=this._coordinateProjectors();return e.d=this._constructAreaProjector(n[0],n[1],n[2]),e},e.prototype._pixelPoint=function(e,n,i){var r=t.prototype._pixelPoint.call(this,e,n,i),o=this.x().accessor(e,n,i),a=this.y().accessor(e,n,i),s=this.y().scale.scale(+a+this._stackingResult().get(i).get(eCt.Stacking.normalizeKey(o)).offset);return{x:r.x,y:s}},e})(pEt.Area);KTt.StackedArea=iCt;var rCt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(rCt,"__esModule",{value:!0});var oCt=BO,aCt=Lgt,sCt=pvt,lCt=cSt,cCt=D_t,uCt=YEt,hCt=sSt,dCt=(function(t){function e(e){void 0===e&&(e="vertical");var n=t.call(this,e)||this;return n._extremaFormatter=sCt.identity(),n._stackingResult=lCt.memThunk((function(){return n.datasets()}),(function(){return n.position().accessor}),(function(){return n.length().accessor}),(function(){return n._stackingOrder}),(function(t,e,n,i){return cCt.Stacking.stack(t,e,n,i)})),n._stackedExtent=lCt.memThunk(n._stackingResult,(function(){return n.position().accessor}),(function(){return n._filterForProperty(n._isVertical?"y":"x")}),(function(t,e,n){return cCt.Stacking.stackedExtent(t,e,n)})),n.addClass("stacked-bar-plot"),n._stackingOrder="bottomup",n}return oCt.__extends(e,t),e.prototype.stackingOrder=function(t){return null==t?this._stackingOrder:(this._stackingOrder=t,this._onDatasetUpdate(),this)},e.prototype.extremaFormatter=function(t){return 0===arguments.length?this._extremaFormatter:(this._extremaFormatter=t,this.render(),this)},e.prototype._setup=function(){t.prototype._setup.call(this),this._labelArea=this._renderArea.append("g").classed(uCt.Bar._LABEL_AREA_CLASS,!0);var e=new aCt.SvgContext(this._labelArea.node());this._measurer=new aCt.CacheMeasurer(e),this._writer=new aCt.Writer(this._measurer,e)},e.prototype._drawLabels=function(){var n=this;t.prototype._drawLabels.call(this),this._labelArea.selectAll("g").remove();var i=+this.baselineValue(),r=this.position().scale,o=this.length().scale,a=cCt.Stacking.stackedExtents(this._stackingResult()),s=a.minimumExtents,l=[],c=function(t,e){var a=n._generateAttrToProjector(),s=n.width(),c=n.height();t.forEach((function(t){if(t.extent!==i){var u=n.extremaFormatter()(t.extent),h=n._measurer.measure(u),d=t.stackedDatum,p=d.originalDatum,f=d.originalIndex,m=d.originalDataset;if(!n._isDatumOnScreen(a,s,c,p,f,m))return;var g=hCt.Plot._scaledAccessor(n.attr(uCt.Bar._BAR_THICKNESS_KEY))(p,f,m),_=o.scale(t.extent),y=n._getPositionAttr(r.scale(t.axisValue),g)+g/2,v=e(n._isVertical?{x:y,y:_}:{x:_,y:y},h,g),b=(function(t,e,i){var r=e.topLeft,o=r.x,a=r.y,s=e.bottomRight.x-e.topLeft.x,l=e.bottomRight.y-e.topLeft.y,c=n._isVertical?s>i:l>i;if(!c){var u=n._labelArea.append("g").attr("transform","translate("+o+", "+a+")");u.classed("stacked-bar-label",!0),n._writer.write(t,s,l,{xAlign:"center",yAlign:"center"},u.node())}return c})(u,{topLeft:v,bottomRight:{x:v.x+h.width,y:v.y+h.height}},g);l.push(b)}}))};c(a.maximumExtents,(function(t,i,r){var o=n._isVertical?i.width:i.height;return{x:n._isVertical?t.x-o/2:t.x+e._EXTREMA_LABEL_MARGIN_FROM_BAR,y:n._isVertical?t.y-(n._isVertical?i.height:i.width):t.y-o/2}})),c(s,(function(t,i,r){var o=n._isVertical?i.width:i.height;return{x:n._isVertical?t.x-o/2:t.x-(n._isVertical?i.height:i.width),y:n._isVertical?t.y+e._EXTREMA_LABEL_MARGIN_FROM_BAR:t.y-o/2}})),l.some((function(t){return t}))&&this._labelArea.selectAll("g").remove()},e.prototype._generateAttrToProjector=function(){var e=this,n=t.prototype._generateAttrToProjector.call(this),i=this._isVertical?"y":"x",r=this.length().scale,o=this.length().accessor,a=this.position().accessor,s=function(t,e,n){return cCt.Stacking.normalizeKey(a(t,e,n))},l=this._stackingResult(),c=function(t,e,n){return r.scale(l.get(n).get(s(t,e,n)).offset)},u=function(t,e,n){return r.scale(+o(t,e,n)+l.get(n).get(s(t,e,n)).offset)},h=function(t,e,n){return Math.abs(u(t,e,n)-c(t,e,n))};n[this._isVertical?"height":"width"]=h;var d=function(t,e,n){return+o(t,e,n)<0?c(t,e,n):u(t,e,n)};return n[i]=function(t,n,i){return e._isVertical?d(t,n,i):d(t,n,i)-h(t,n,i)},n},e.prototype.getExtentsForProperty=function(e){return e===(this._isVertical?"y":"x")?[this._stackedExtent()]:t.prototype.getExtentsForProperty.call(this,e)},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e._EXTREMA_LABEL_MARGIN_FROM_BAR=5,e})(uCt.Bar);rCt.StackedBar=dCt;var pCt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(pCt,"__esModule",{value:!0});var fCt=BO,mCt=D_t,gCt=sSt,_Ct=(function(t){function e(){var e=t.call(this)||this;return e._connectorsEnabled=!1,e.addClass("waterfall-plot"),e}return fCt.__extends(e,t),e.prototype.connectorsEnabled=function(t){return null==t?this._connectorsEnabled:(this._connectorsEnabled=t,this)},e.prototype.total=function(t){return null==t?this._propertyBindings.get(e._TOTAL_KEY):(this._bindProperty(e._TOTAL_KEY,t,null),this)},e.prototype._additionalPaint=function(t){var e=this;this._connectorArea.selectAll("line").remove(),this._connectorsEnabled&&mCt.Window.setTimeout((function(){return e._drawConnectors()}),t)},e.prototype._createNodesForDataset=function(n){var i=t.prototype._createNodesForDataset.call(this,n);return this._connectorArea=this._renderArea.append("g").classed(e._CONNECTOR_AREA_CLASS,!0),i},e.prototype.getExtentsForProperty=function(e){return"y"===e?[this._extent]:t.prototype.getExtentsForProperty.call(this,e)},e.prototype._generateAttrToProjector=function(){var n=this,i=t.prototype._generateAttrToProjector.call(this),r=this.y().scale,o=gCt.Plot._scaledAccessor(this.total());return null==this.attr("y")&&(i.y=function(t,e,i){var a=n.y().accessor(t,e,i);if(o(t,e,i))return Math.min(r.scale(a),r.scale(0));var s=n._subtotals[e];if(0===e)return r.scale(a<0?s-a:s);var l=n._subtotals[e-1];return r.scale(s>l?s:l)}),null==this.attr("height")&&(i.height=function(t,e,i){var a=o(t,e,i),s=n.y().accessor(t,e,i);if(a)return Math.abs(r.scale(s)-r.scale(0));var l=n._subtotals[e];if(0===e)return Math.abs(r.scale(l)-r.scale(l-s));var c=n._subtotals[e-1];return Math.abs(r.scale(l)-r.scale(c))}),i.class=function(t,i,r){var a="";return null!=n.attr("class")&&(a=n.attr("class").accessor(t,i,r)+" "),o(t,i,r)?a+e._BAR_TOTAL_CLASS:a+(n.y().accessor(t,i,r)>0?e._BAR_GROWTH_CLASS:e._BAR_DECLINE_CLASS)},i},e.prototype._onDatasetUpdate=function(){return this._updateSubtotals(),t.prototype._onDatasetUpdate.call(this),this},e.prototype._calculateSubtotalsAndExtent=function(t){for(var e=Number.MAX_VALUE,n=Number.MIN_VALUE,i=0,r=!1,o=t.data(),a=o.length,s=0;s<a;s++){var l=o[s],c=this.y().accessor(l,s,t),u=this.total().accessor(l,s,t);if(u&&0!==s||(i+=c),this._subtotals.push(i),i<e&&(e=i),i>n&&(n=i),u&&(c<e&&(e=c),c>n&&(n=c)),!r&&u){for(var h=c-i,d=0;d<this._subtotals.length;d++)this._subtotals[d]+=h;r=!0,i+=h,e+=h,n+=h}}this._extent=[e,n]},e.prototype._drawConnectors=function(){for(var t=this._getAttrToProjector(),n=this.datasets()[0],i=1;i<n.data().length;i++){var r=i-1,o=n.data()[i],a=n.data()[r],s=t.x(a,r,n),l=t.x(o,i,n)+t.width(o,i,n),c=t.y(o,i,n);(this._subtotals[i]>0&&this._subtotals[i]>this._subtotals[r]||this._subtotals[i]<0&&this._subtotals[i]>=this._subtotals[r])&&(c=t.y(o,i,n)+t.height(o,i,n)),this._connectorArea.append("line").classed(e._CONNECTOR_CLASS,!0).attr("x1",s).attr("x2",l).attr("y1",c).attr("y2",c)}},e.prototype._updateSubtotals=function(){var t=this.datasets();if(t.length>0){var e=t[t.length-1];this._subtotals=new Array,this._calculateSubtotalsAndExtent(e)}},e._BAR_DECLINE_CLASS="waterfall-decline",e._BAR_GROWTH_CLASS="waterfall-growth",e._BAR_TOTAL_CLASS="waterfall-total",e._CONNECTOR_CLASS="connector",e._CONNECTOR_AREA_CLASS="connector-area",e._TOTAL_KEY="total",e})(YEt.Bar);pCt.Waterfall=_Ct,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=BO;e.__exportStar(pEt,t),e.__exportStar(YEt,t),e.__exportStar(lSt,t),e.__exportStar(XEt,t),e.__exportStar(fEt,t),e.__exportStar(eTt,t),e.__exportStar(mTt,t),e.__exportStar(CTt,t),e.__exportStar(VTt,t),e.__exportStar(KTt,t),e.__exportStar(rCt,t),e.__exportStar(pCt,t)})(dEt);var yCt,vCt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */function bCt(t){return class extends t{constructor(){super(...arguments),this.loadKey="",this.dataToLoad=[],this.getDataLoadName=t=>String(t),this.dataLoading=!1,this.dataLoadedAtLeastOnce=!1,this._isConnected=!1,this._dataLoadState=new Map,this._canceller=new xO,this._loadDataAsync=null,this._loadData=Ce.exports.throttle(this._loadDataImpl,100,{leading:!0,trailing:!0})}connectedCallback(){super.connectedCallback(),this._isConnected=!0}disconnectedCallback(){super.disconnectedCallback(),this._isConnected=!1}static get properties(){return{active:{type:Boolean,observer:"_loadDataIfActive"},_isConnected:{type:Boolean},loadKey:{type:String},dataToLoad:{type:Array},getDataLoadName:{type:Object},loadDataCallback:{type:Object},requestData:{type:Object}}}static get observers(){return["_dataToLoadChanged(_isConnected, 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e=BO;t.Animators=fgt,t.Axes=Agt,t.Components=txt,t.Configs=Cyt,t.Formatters=pvt,t.RenderController=z_t,t.RenderPolicies=Qyt,t.SymbolFactories=Hwt,t.Dispatchers=rxt,t.Drawers=PMt,t.Interactions=nxt,t.Plots=dEt,t.Scales=Xvt,t.Utils=D_t,e.__exportStar(dvt,t),t.TimeInterval=Yvt.TimeInterval,e.__exportStar(O_t,t),e.__exportStar(ywt,t),e.__exportStar(fSt,t),t.version=vCt.version,e.__exportStar(axt,t),e.__exportStar(GSt,t),e.__exportStar(vxt,t),e.__exportStar(Lxt,t),e.__exportStar(mEt,t),e.__exportStar(sSt,t),e.__exportStar(Lbt,t),e.__exportStar(lbt,t)})(NO),(function(t){t[t.LOADING=0]="LOADING",t[t.LOADED=1]="LOADED"})(yCt||(yCt={}));const xCt=[{character:"◼",method:NO.SymbolFactories.square},{character:"◆",method:NO.SymbolFactories.diamond},{character:"▲",method:NO.SymbolFactories.triangle},{character:"★",method:NO.SymbolFactories.star},{character:"✚",method:NO.SymbolFactories.cross}];var wCt;!(function(t){t.STEP="step",t.RELATIVE="relative",t.WALL_TIME="wall_time"})(wCt||(wCt={}));function SCt(t){return e=>{let n,i=Math.abs(e);return i<1e-15&&(i=0),n=Dy(i>=1e4||i>0&&i<.01?"."+t+"~e":"."+t+"~g"),n(e)}}const MCt=Dy(".4~s");function ECt(){let t=new NO.Scales.Linear;t.tickGenerator(NO.Scales.TickGenerators.integerTickGenerator());let e=new NO.Axes.Numeric(t,"bottom");return e.formatter(MCt),{scale:t,axis:e,accessor:t=>t.step}}let TCt=NO.Formatters.time("%a %b %e, %H:%M:%S");let CCt=(t,e,n)=>{if(null!=t.relative)return t.relative;let i=n.data();return(+t.wall_time-(i.length>0?+i[0].wall_time:0))/36e5},ACt=t=>{let e="",n=Math.floor(t/24);t-=24*n,n&&(e+=n+"d ");let i=Math.floor(t);t-=i,t*=60,(i||n)&&(e+=i+"h ");let r=Math.floor(t);return t-=r,t*=60,(r||i||n)&&(e+=r+"m "),e+Math.floor(t)+"s"};function kCt(t){switch(t){case wCt.STEP:return ECt();case wCt.WALL_TIME:return(function e(){let t=new NO.Scales.Time;return{scale:t,axis:new NO.Axes.Time(t,"bottom"),accessor:t=>t.wall_time}})();case wCt.RELATIVE:return(function n(){let t=new NO.Scales.Linear;return{scale:t,axis:new NO.Axes.Numeric(t,"bottom"),accessor:CCt}})();default:throw new Error("invalid xType: "+t)}}const LCt=[1,0,0,1,0,0];function PCt(t){const e=(function n(t){const e=[];for(;t&&t instanceof HTMLElement;)if(e.push(t),t.assignedSlot)t=t.assignedSlot;else if(t.parentElement)t=t.parentElement;else{const e=t.parentNode;t=e instanceof DocumentFragment?e.host:e!==t?e:null}return e})(t);let i=LCt,r=null;for(const t of e){const e=NO.Utils.DOM.getElementTransform(t);if(null!=e){const n=t.clientWidth/2,r=t.clientHeight/2;i=NO.Utils.Math.multiplyTranslate(i,[n,r]),i=NO.Utils.Math.multiplyMatrix(i,NO.Utils.Math.invertMatrix(e)),i=NO.Utils.Math.multiplyTranslate(i,[-n,-r])}let n=t.scrollLeft,o=t.scrollTop;null!==r&&t!==r||(n-=t.offsetLeft+t.clientLeft,o-=t.offsetTop+t.clientTop,r=t.offsetParent),i=NO.Utils.Math.multiplyTranslate(i,[n,o])}return i}class NCt extends NO.Utils.Translator{computePosition(t,e){const n={x:t,y:e},i=PCt(this._rootElement);if(null==i)return n;return NO.Utils.Math.applyTransform(i,n)}}class ICt extends NO.Dispatchers.Mouse{constructor(t){super(t),this._eventTarget=t.root().rootElement().node(),this._translator=new NCt(t.root().rootElement().node())}static getDispatcher(t){const e=t.root().rootElement();let n=e[ICt._DISPATCHER_KEY];return n||(n=new ICt(t),e[ICt._DISPATCHER_KEY]=n),n}}class RCt extends NO.Dispatchers.Touch{constructor(t){super(t),this._eventTarget=t.root().rootElement().node(),this._translator=new NCt(t.root().rootElement().node())}static getDispatcher(t){const e=t.root().rootElement();let n=e[RCt._DISPATCHER_KEY];return n||(n=new RCt(t),e[RCt._DISPATCHER_KEY]=n),n}}NO.Interaction.prototype._isInsideComponent=function(t){return 0<=t.x&&0<=t.y&&t.x<this._componentAttachedTo.width()&&t.y<this._componentAttachedTo.height()};class OCt extends NO.Interactions.Pointer{_anchor(t){const e=this;e._isAnchored=!0,e._mouseDispatcher=ICt.getDispatcher(e._componentAttachedTo),e._mouseDispatcher.onMouseMove(e._mouseMoveCallback),e._touchDispatcher=RCt.getDispatcher(e._componentAttachedTo),e._touchDispatcher.onTouchStart(e._touchStartCallback)}}var zCt;!(function(t){t.AUTO="auto",t.BOTTOM="bottom",t.RIGHT="right"})(zCt||(zCt={}));const DCt={boxShadow:"0 1px 4px rgba(0, 0, 0, .3)",opacity:0,position:"fixed",willChange:"transform",zIndex:5};let BCt=class extends(cr(we)){constructor(){super(...arguments),this.position=zCt.AUTO,this.minDistFromEdge=15,this._styleCache=null,this._raf=null,this._tunnel=null}ready(){this._styleCache=null,this._raf=null,this._tunnel=null}attached(){this._tunnel=this._createTunnel(),this._hideOnBlur=()=>{document.hidden&&this.hide()},window.addEventListener("visibilitychange",this._hideOnBlur)}detached(){this.hide(),this._removeTunnel(this._tunnel),this._tunnel=null,window.removeEventListener("visibilitychange",this._hideOnBlur)}content(){return this._tunnel.shadowRoot}hide(){window.cancelAnimationFrame(this._raf),this._styleCache=null,this._tunnel.style.opacity=0}updateAndPosition(t){window.cancelAnimationFrame(this._raf),this._raf=window.requestAnimationFrame((()=>{this.isAttached&&this._repositionImpl(t)}))}_repositionImpl(t){const e=this._tunnel,n=t.getBoundingClientRect(),i=e.getBoundingClientRect(),r=window.innerHeight,o=document.body.clientWidth,a=n.top,s=a+n.height,l=i.height+20;let c=null,u=Math.max(this.minDistFromEdge,n.left),h=null,d=a;this.position==zCt.RIGHT?u=n.right:(d=s+20,o<u+i.width+this.minDistFromEdge&&(u=null,h=this.minDistFromEdge)),this.position==zCt.AUTO&&n.top-l>0&&r<n.top+n.height+l&&(d=null,c=r-a+20);const p={contain:"content",opacity:1,left:u?`${u}px`:null,right:h?`${h}px`:null,top:d?`${d}px`:null,bottom:c?`${c}px`:null};Ce.exports.isEqual(this._styleCache,p)||(Object.assign(e.style,p),this._styleCache=p)}_createTunnel(){if(!this.contentComponentName)throw new RangeError("Require `contentComponentName` to be a name of a Polymer component");const t=document.createElement(this.contentComponentName);return Object.assign(t.style,DCt),document.body.appendChild(t),t}_removeTunnel(t){document.body.removeChild(t)}};t([o({type:String}),e("design:type",String)],BCt.prototype,"contentComponentName",void 0),t([o({type:String}),e("design:type",String)],BCt.prototype,"position",void 0),t([o({type:Number}),e("design:type",Number)],BCt.prototype,"minDistFromEdge",void 0),BCt=t([i("vz-chart-tooltip")],BCt);const HCt=Dy(".2~e"),FCt=Dy(".4~r"),VCt=Dy(",~");function UCt(t){if(0===t)return"0";const e=Math.abs(t);return e>=1e4||e<.001?HCt(t):FCt(t)}const jCt={formatTick:UCt,formatShort:UCt,formatReadable(t){const e=Math.abs(t);return e>=1e4||e<.001?HCt(t):VCt(t)},formatLong:VCt};Dy("0.3~s"),Dy(",.3~f"),Dy(".4~");const GCt=zA().tickFormat();let WCt;const qCt={formatTick:t=>GCt(new Date(t)),formatShort:t=>new Date(t).toLocaleString(WCt,{year:"numeric",month:"short",day:"numeric",hour:"numeric",minute:"numeric",second:"numeric"}),formatReadable:t=>new Date(t).toLocaleString(WCt,{year:"numeric",month:"short",day:"numeric",hour:"numeric",minute:"numeric",second:"numeric",timeZoneName:"short"}),formatLong:t=>new Date(t).toLocaleString(WCt,{year:"numeric",month:"long",day:"numeric",hour:"numeric",minute:"numeric",second:"numeric",timeZoneName:"short",fractionalSecondDigits:3})};var YCt;!(function(t){t[t.LINEAR=0]="LINEAR",t[t.LOG10=1]="LOG10",t[t.TIME=2]="TIME"})(YCt||(YCt={}));class XCt{constructor(){this.defaultFormatter=jCt}transform(t,e,n){const[i,r]=t,o=r-i,[a,s]=e;return 0===o?a:(s-a)/o*(n-i)+a}forward(t,e,n){return this.transform(t,e,n)}reverse(t,e,n){return this.transform(e,t,n)}niceDomain(t){let[e,n]=t;if(n<e)throw new Error("Unexpected input: min is larger than max");if(n===e)return 0===e?[-1,1]:e<0?[2*e,0]:[0,2*e];const i=cE(),r=.05*(n-e+Number.EPSILON),[o,a]=i.domain([e-r,n+r]).nice().domain();return[o,a]}ticks(t,e){return cE().domain(t).ticks(e)}isSafeNumber(t){return Number.isFinite(t)}}class $Ct{constructor(){this.defaultFormatter=jCt}transform(t){return Math.log10(t>0?t:Number.MIN_VALUE)}untransform(t){return Math.exp(t/Math.LOG10E)}forward(t,e,n){if(n<=0)return e[0];const[i,r]=t,[o,a]=e,s=this.transform(i),l=this.transform(r)-s,c=a-o;return n=this.transform(n),c/(l+Number.EPSILON)*(n-s)+o}reverse(t,e,n){const[i,r]=t,[o,a]=e,s=this.transform(i),l=this.transform(r);return this.untransform((l-s)/(a-o+Number.EPSILON)*(n-o)+s)}niceDomain(t){const[e,n]=t;if(e>n)throw new Error("Unexpected input: min is larger than max");const i=Math.max(e,Number.MIN_VALUE),r=Math.max(n,Number.MIN_VALUE);return n<=0?[Number.MIN_VALUE,1]:[Math.max(Number.MIN_VALUE,.5*i),2*r]}ticks(t,e){const n=t[0]<=0?Number.MIN_VALUE:t[0],i=t[1]<=0?Number.MIN_VALUE:t[1],r=yE().domain([n,i]).ticks(e);return r.length?r:t}isSafeNumber(t){return Number.isFinite(t)&&t>0}}class KCt{constructor(){this.scale=zA(),this.defaultFormatter=qCt}forward(t,e,n){return this.scale.domain(t).range(e)(n)}reverse(t,e,n){return this.scale.domain(t).range(e).invert(n).getTime()}niceDomain(t){const[e,n]=this.scale.domain(t).nice().domain();return[e.getTime(),n.getTime()]}ticks(t,e){return this.scale.domain(t).ticks(e).map((t=>t.getTime()))}isSafeNumber(t){return Number.isFinite(t)}}class ZCt extends NO.Scales.Linear{constructor(){super(),this._ignoreOutlier=!1,this.padProportion(.2)}setValueProviderForDomain(t){return this._valueProviderForDomain=t,this}_niceDomain(t,e){const[n,i]=t;return(function r(t){switch(t){case YCt.LINEAR:return new XCt;case YCt.LOG10:return new $Ct;case YCt.TIME:return new KCt;default:throw new RangeError(`ScaleType ${t} not supported.`)}})(YCt.LINEAR).niceDomain([n,i])}_getUnboundedExtent(t){const e=this._getAllIncludedValues(t);let n=this._defaultExtent();if(0!==e.length){const t=[NO.Utils.Math.min(e,n[0]),NO.Utils.Math.max(e,n[1])];n=this._niceDomain(t)}return n}_getAllIncludedValues(t=!1){const e=this._valueProviderForDomain?this._valueProviderForDomain():[];return this.extentOfValues(e)}extentOfValues(t){const e=t.filter((t=>NO.Utils.Math.isValidNumber(t)));let n=e;if(this.ignoreOutlier()){const t=e.sort(((t,e)=>t-e)),i=rc(t,.05),r=rc(t,.95);n=e.filter((t=>t>=i&&t<=r))}const i=Gl(n);return null==i[0]||null==i[1]?[]:i}ignoreOutlier(t){return"boolean"==typeof t?(this._ignoreOutlier=t,this):this._ignoreOutlier}}class JCt extends NO.QuantitativeScale{constructor(){super(...arguments),this._ignoreOutlier=!1}setValueProviderForDomain(t){return this._valueProviderForDomain=t,this}ignoreOutlier(t){return"boolean"==typeof t?(this._ignoreOutlier=t,this):this._ignoreOutlier}_getAllIncludedValues(t=!1){const e=this._valueProviderForDomain?this._valueProviderForDomain():[];return this.extentOfValues(e)}}const QCt=Math.pow(2,-1074);function tAt(t){return Math.log10(t)}function eAt(t){return Math.pow(10,t)}class nAt extends JCt{constructor(){super(),this._d3LogScale=yE(),this.padProportion(.2)}scale(t){return t<=0?NaN:this._d3LogScale(t)}invert(t){return this._d3LogScale.invert(t)}scaleTransformation(t){return this.scale(t)}invertedTransformation(t){return this.invert(t)}getTransformationDomain(){return this.domain()}setTransformationDomain(t){this.domain(t)}getTransformationExtent(){return this._getUnboundedExtent(!0)}_getDomain(){return this._untransformedDomain}_setDomain(t){this._untransformedDomain=t;const[e,n]=t;super._setDomain([Math.max(QCt,e),n])}_niceDomain(t,e){const[n,i]=t,r=Math.max(tAt(QCt),tAt(n)),o=tAt(i),a=o-r,s=a?a*this.padProportion():1;return[eAt(Math.max(tAt(QCt),r-s)),eAt(o+s)]}_getUnboundedExtent(t){const e=this._getAllIncludedValues(t);let n=this._defaultExtent();if(0!==e.length){const t=[NO.Utils.Math.min(e,n[0]),NO.Utils.Math.max(e,n[1])];n=this._niceDomain(t)}return n}_getAllIncludedValues(t=!1){return super._getAllIncludedValues().map((t=>t>0?t:QCt))}_defaultExtent(){return[1,10]}_backingScaleDomain(t){return null==t?this._d3LogScale.domain():(this._d3LogScale.domain(t),this)}_getRange(){return this._d3LogScale.range()}_setRange(t){this._d3LogScale.range(t)}defaultTicks(){return this._d3LogScale.ticks(1)}ticks(){return this._d3LogScale.ticks()}extentOfValues(t){const e=t.filter((t=>NO.Utils.Math.isValidNumber(t)&&t>0));let n=e;if(this.ignoreOutlier()){const t=e.map(tAt).sort(((t,e)=>t-e)),i=rc(t,.05),r=rc(t,.95);n=t.filter((t=>t>=i&&t<=r)).map(eAt)}const i=Gl(n);return null==i[0]||null==i[1]?[]:i}}class iAt extends NO.Components.SelectionBoxLayer{constructor(t,e,n){super(),this.easeFn=qf,this._animationTime=750,this.xScale(t),this.yScale(e),this._dragInteraction=new NO.Interactions.Drag,this._doubleClickInteraction=new NO.Interactions.Click,this.setupCallbacks(),this.unzoomMethod=n,this.onDetach((()=>{this._doubleClickInteraction.detachFrom(this),this._dragInteraction.detachFrom(this)})),this.onAnchor((()=>{this._doubleClickInteraction.attachTo(this),this._dragInteraction.attachTo(this)}))}interactionStart(t){this.onStart=t}interactionEnd(t){this.onEnd=t}dragInteraction(){return this._dragInteraction}setupCallbacks(){let t=!1;this._dragInteraction.onDragStart((t=>{this.bounds({topLeft:t,bottomRight:t}),this.onStart()})),this._dragInteraction.onDrag(((e,n)=>{this.bounds({topLeft:e,bottomRight:n}),this.boxVisible(!0),t=!0})),this._dragInteraction.onDragEnd(((e,n)=>{this.boxVisible(!1),this.bounds({topLeft:e,bottomRight:n}),t?this.zoom():this.onEnd(),t=!1})),this._doubleClickInteraction.onDoubleClick(this.unzoom.bind(this))}animationTime(t){if(null==t)return this._animationTime;if(t<0)throw new Error("animationTime cannot be negative");return this._animationTime=t,this}ease(t){if("function"!=typeof t)throw new Error("ease function must be a function");return 0===t(0)&&1===t(1)||NO.Utils.Window.warn("Easing function does not maintain invariant f(0)==0 && f(1)==1. Bad behavior may result."),this.easeFn=t,this}zoom(){let t=this.xExtent()[0].valueOf(),e=this.xExtent()[1].valueOf(),n=this.yExtent()[1].valueOf(),i=this.yExtent()[0].valueOf();t!==e&&n!==i&&this.interpolateZoom(t,e,n,i)}unzoom(){let t=this.xScale();t._domainMin=null,t._domainMax=null;let e=t._getExtent();this.xScale().domain(e),this.unzoomMethod()}isZooming(t){this._dragInteraction.enabled(!t),this._doubleClickInteraction.enabled(!t)}interpolateZoom(t,e,n,i){let r=this.xScale().domain()[0].valueOf(),o=this.xScale().domain()[1].valueOf(),a=this.yScale().domain()[0].valueOf(),s=this.yScale().domain()[1].valueOf(),l=this.easeFn,c=(t,e,n)=>rp(t,e)(l(n));this.isZooming(!0);let u=Date.now(),h=()=>{let l=Date.now(),d=0===this._animationTime?1:Math.min(1,(l-u)/this._animationTime),p=c(r,t,d),f=c(o,e,d),m=c(a,n,d),g=c(s,i,d);this.xScale().domain([p,f]),this.yScale().domain([m,g]),d<1?NO.Utils.DOM.requestAnimationFramePolyfill(h):(this.onEnd(),this.isZooming(!1))};h()}}var rAt,oAt,aAt;!(function(t){t[t.NONE=0]="NONE",t[t.DRAG_ZOOMING=1]="DRAG_ZOOMING",t[t.PANNING=2]="PANNING"})(rAt||(rAt={}));class sAt extends NO.Components.Group{constructor(t,e,n){super(),this.state=rAt.NONE,this.panStartCallback=new NO.Utils.CallbackSet,this.panEndCallback=new NO.Utils.CallbackSet,this.panZoom=new NO.Interactions.PanZoom(t,e),this.panZoom.dragInteraction().mouseFilter((t=>sAt.isPanKey(t)&&0===t.button)),this.panZoom.wheelFilter(this.canScrollZoom),this.dragZoomLayer=new iAt(t,e,n),this.dragZoomLayer.dragInteraction().mouseFilter((t=>!sAt.isPanKey(t)&&0===t.button)),this.append(this.dragZoomLayer);const i=this.onWheel.bind(this);this.onAnchor((()=>{this._mouseDispatcher=NO.Dispatchers.Mouse.getDispatcher(this),this._mouseDispatcher.onWheel(i),this.panZoom.attachTo(this)})),this.onDetach((()=>{this.panZoom.detachFrom(this),this._mouseDispatcher&&(this._mouseDispatcher.offWheel(i),this._mouseDispatcher=null)})),this.panZoom.dragInteraction().onDragStart((()=>{this.state==rAt.NONE&&this.setState(rAt.PANNING)})),this.panZoom.dragInteraction().onDragEnd((()=>{this.state==rAt.PANNING&&this.setState(rAt.NONE)})),this.dragZoomLayer.dragInteraction().onDragStart((()=>{this.state==rAt.NONE&&this.setState(rAt.DRAG_ZOOMING)})),this.dragZoomLayer.dragInteraction().onDragEnd((()=>{this.state==rAt.DRAG_ZOOMING&&this.setState(rAt.NONE)}))}onWheel(t,e){if(this.canScrollZoom(e))return;const n=this.element();if(!n.select(".help").empty())return;const i=n.append("div").classed("help",!0);i.append("span").text("Alt + Scroll to Zoom"),i.on("animationend",(()=>{i.remove()}))}static isPanKey(t){return Boolean(t.altKey)||Boolean(t.shiftKey)}canScrollZoom(t){return t.altKey}setState(t){if(this.state==t)return;const e=this.state;this.state=t,this.root().removeClass(this.stateClassName(e)),this.root().addClass(this.stateClassName(t)),e==rAt.PANNING&&this.panEndCallback.callCallbacks(),t==rAt.PANNING&&this.panStartCallback.callCallbacks()}stateClassName(t){switch(t){case rAt.PANNING:return"panning";case rAt.DRAG_ZOOMING:return"drag-zooming";case rAt.NONE:default:return""}}onPanStart(t){this.panStartCallback.add(t)}onPanEnd(t){this.panEndCallback.add(t)}onScrollZoom(t){this.panZoom.onZoomEnd(t)}onDragZoomStart(t){this.dragZoomLayer.interactionStart(t)}onDragZoomEnd(t){this.dragZoomLayer.interactionEnd(t)}}!(function(t){t[t.TEXT=0]="TEXT",t[t.DOM=1]="DOM"})(oAt||(oAt={})),(function(t){t.LOG="log",t.LINEAR="linear"})(aAt||(aAt={}));class lAt{constructor(t,e,n,i,r,o,a,s,l,c,u){this.dirtyDatasets=new Set,this.seriesNames=[],this.name2datasets={},this.colorScale=i,this.tooltip=r,this.datasets=[],this._ignoreYOutliers=!1,this.lastPointsDataset=new NO.Dataset,this.nanDataset=new NO.Dataset,this.yValueAccessor=e,this.symbolFunction=c,this._defaultXRange=s,this._defaultYRange=l,this.tooltipColumns=o,this.buildChart(t,e,n,a,u)}buildChart(t,e,n,i,r){this.destroy();const o=t();this.xAccessor=o.accessor,this.xScale=o.scale,this.xAxis=o.axis,this.xAxis.margin(1).tickLabelPadding(3),r&&this.xAxis.formatter(r),this.yScale=lAt.getYScaleFromType(n),this.yScale.setValueProviderForDomain((()=>this.getValuesForYAxisDomainCompute())),this.yAxis=new NO.Axes.Numeric(this.yScale,"left");let a=SCt(3);this.yAxis.margin(0).tickLabelPadding(5).formatter(a),this.yAxis.usesTextWidthApproximation(!0),this.fillArea=i;const s=new sAt(this.xScale,this.yScale,(()=>this.resetDomain()));this.tooltipInteraction=this.createTooltipInteraction(s),this.tooltipPointsComponent=new NO.Component;const l=this.buildPlot(this.xScale,this.yScale,i);this.gridlines=new NO.Components.Gridlines(this.xScale,this.yScale);let c=null;n!==aAt.LOG&&(c=new NO.Components.GuideLineLayer("horizontal"),c.scale(this.yScale).value(0));let u=new NO.Components.GuideLineLayer("vertical");u.scale(this.xScale).value(0),this.center=new NO.Components.Group([this.gridlines,c,u,l,this.tooltipPointsComponent,s]),this.center.addClass("main"),this.outer=new NO.Components.Table([[this.yAxis,this.center],[null,this.xAxis]])}buildPlot(t,e,n){n&&(this.marginAreaPlot=new NO.Plots.Area,this.marginAreaPlot.x(this.xAccessor,t),this.marginAreaPlot.y(n.higherAccessor,e),this.marginAreaPlot.y0(n.lowerAccessor),this.marginAreaPlot.attr("fill",((t,e,n)=>this.colorScale.scale(n.metadata().name))),this.marginAreaPlot.attr("fill-opacity",.3),this.marginAreaPlot.attr("stroke-width",0)),this.smoothedAccessor=t=>t.smoothed;let i=new NO.Plots.Line;i.x(this.xAccessor,t),i.y(this.yValueAccessor,e),i.attr("stroke",((t,e,n)=>this.colorScale.scale(n.metadata().name))),this.linePlot=i,this.setupTooltips(i);let r=new NO.Plots.Line;if(r.x(this.xAccessor,t),r.y(this.smoothedAccessor,e),r.attr("stroke",((t,e,n)=>this.colorScale.scale(n.metadata().name))),this.smoothLinePlot=r,this.symbolFunction){const n=new NO.Plots.Scatter;n.x(this.xAccessor,t),n.y(this.yValueAccessor,e),n.attr("fill",((t,e,n)=>this.colorScale.scale(n.metadata().name))),n.attr("opacity",1),n.size(8),n.symbol(((t,e,n)=>this.symbolFunction(n.metadata().name))),this.markersScatterPlot=n}let o=new NO.Plots.Scatter;o.x(this.xAccessor,t),o.y(this.yValueAccessor,e),o.attr("fill",(t=>this.colorScale.scale(t.name))),o.attr("opacity",1),o.size(8),o.datasets([this.lastPointsDataset]),this.scatterPlot=o;let a=new NO.Plots.Scatter;a.x(this.xAccessor,t),a.y((t=>t.displayY),e),a.attr("fill",(t=>this.colorScale.scale(t.name))),a.attr("opacity",1),a.size(12),a.datasets([this.nanDataset]),a.symbol(NO.SymbolFactories.triangle),this.nanDisplay=a;const s=[a,o,r,i];return this.marginAreaPlot&&s.push(this.marginAreaPlot),this.markersScatterPlot&&s.push(this.markersScatterPlot),new NO.Components.Group(s)}ignoreYOutliers(t){t!==this._ignoreYOutliers&&(this._ignoreYOutliers=t,this.updateSpecialDatasets(),this.yScale.ignoreOutlier(t),this.resetYDomain())}getValuesForYAxisDomainCompute(){const t=this.getAccessorsForComputingYRange();return Ce.exports.flattenDeep(this.datasets.map((e=>t.map((t=>e.data().map((n=>t(n,-1,e)))))))).filter(isFinite)}updateSpecialDatasets(){const t=this.getYAxisAccessor();let e=this.datasets.map((e=>{let n=null,i=e.data().filter((n=>!isNaN(t(n,-1,e))));if(i.length>0){n=i[i.length-1],n.name=e.metadata().name,n.relative=CCt(n,0,e)}return n})).filter((t=>null!=t));this.lastPointsDataset.data(e),this.markersScatterPlot&&this.markersScatterPlot.datasets(this.datasets.map(this.createSampledDatasetForMarkers));let n=Ce.exports.flatten(this.datasets.map((e=>{let n=null,i=e.data(),r=0;for(;r<i.length&&null==n;)isNaN(t(i[r],-1,e))||(n=t(i[r],-1,e)),r++;null==n&&(n=0);let o=[];for(r=0;r<i.length;r++)isNaN(t(i[r],-1,e))?(i[r].name=e.metadata().name,i[r].displayY=n,i[r].relative=CCt(i[r],0,e),o.push(i[r])):n=t(i[r],-1,e);return o})));this.nanDataset.data(n)}resetDomain(){this.resetXDomain(),this.resetYDomain()}resetXDomain(){let t;if(null!=this._defaultXRange)t=this._defaultXRange;else{const e=this.xScale;e._domainMin=null,e._domainMax=null,t=e._getExtent()}this.xScale.domain(t)}resetYDomain(){null!=this._defaultYRange?this.yScale.domain(this._defaultYRange):(this.yScale.autoDomain(),this.yScale.domain(this.yScale.domain()))}getAccessorsForComputingYRange(){const t=[this.getYAxisAccessor()];return this.fillArea&&t.push(this.fillArea.lowerAccessor,this.fillArea.higherAccessor),t}getYAxisAccessor(){return this.smoothingEnabled?this.smoothedAccessor:this.yValueAccessor}createTooltipInteraction(t){const e=new OCt,n=()=>{e.enabled(!1),this.hideTooltips()},i=()=>e.enabled(!0);return t.onPanStart(n),t.onDragZoomStart(n),t.onPanEnd(i),t.onDragZoomEnd(i),t.onScrollZoom((()=>this.updateTooltipContent(this._lastMousePosition))),e.onPointerMove((t=>{this._lastMousePosition=t,this.updateTooltipContent(t)})),e.onPointerExit((()=>this.hideTooltips())),e}updateTooltipContent(t){this.linePlot&&(window.cancelAnimationFrame(this._tooltipUpdateAnimationFrame),this._tooltipUpdateAnimationFrame=window.requestAnimationFrame((()=>{let e={x:t.x,y:t.y,datum:null,dataset:null},n=this.gridlines.content().node().getBBox(),i=this.linePlot.datasets().map((t=>this.findClosestPoint(e,t))).filter(Boolean),r=NO.Utils.DOM.intersectsBBox,o=i.filter((t=>r(t.x,t.y,n)||isNaN(this.yValueAccessor(t.datum,0,t.dataset)))),a=o.filter((t=>!isNaN(this.yValueAccessor(t.datum,0,t.dataset))));if(0!==i.length){this.scatterPlot.attr("display","none");const t=this.tooltipPointsComponent.content().selectAll(".point").data(a,(t=>t.dataset.metadata().name));t.enter().append("circle").classed("point",!0),t.attr("r",4).attr("cx",(t=>t.x)).attr("cy",(t=>t.y)).style("stroke","none").attr("fill",(t=>this.colorScale.scale(t.dataset.metadata().name))),t.exit().remove(),this.drawTooltips(o,e,this.tooltipColumns)}else this.hideTooltips()})))}hideTooltips(){window.cancelAnimationFrame(this._tooltipUpdateAnimationFrame),this.tooltip.hide(),this.scatterPlot.attr("display","block"),this.tooltipPointsComponent.content().selectAll(".point").remove()}setupTooltips(t){t.onDetach((()=>{this.tooltipInteraction.detachFrom(t),this.tooltipInteraction.enabled(!1)})),t.onAnchor((()=>{this.tooltipInteraction.attachTo(t),this.tooltipInteraction.enabled(!0)}))}drawTooltips(t,e,n){if(!t.length)return void this.tooltip.hide();const{colorScale:i}=this;n=[{title:"",static:!1,evalType:oAt.DOM,evaluate(t){return Du(this).select("span").style("background-color",(()=>i.scale(t.dataset.metadata().name))),""},enter(t){Du(this).append("span").classed("swatch",!0).style("background-color",(()=>i.scale(t.dataset.metadata().name)))}},...n];const r=t=>Math.pow(t.x-e.x,2)+Math.pow(t.y-e.y,2),o=Ce.exports.min(t.map(r)),a=this.smoothingEnabled?this.smoothedAccessor:this.yValueAccessor;t="ascending"===this.tooltipSortingMethod?Ce.exports.sortBy(t,(t=>a(t.datum,-1,t.dataset))):"descending"===this.tooltipSortingMethod?Ce.exports.sortBy(t,(t=>a(t.datum,-1,t.dataset))).reverse():"nearest"===this.tooltipSortingMethod?Ce.exports.sortBy(t,r):t.slice(0).reverse();const s=this,l=Du(this.tooltip.content()).select("table"),c=l.select("thead").selectAll("th").data(n,((t,e,n)=>t.title));c.enter().append("th").text((t=>t.title)).nodes(),c.exit().remove();const u=l.select("tbody").selectAll("tr").data(t,((t,e,n)=>t.dataset.metadata().name));u.classed("distant",(t=>{let n=t.dataset.data()[0],i=Ce.exports.last(t.dataset.data()),r=this.xScale.scale(this.xAccessor(n,0,t.dataset)),o=this.xScale.scale(this.xAccessor(i,0,t.dataset)),a=this.smoothingEnabled?t.datum.smoothed:this.yValueAccessor(t.datum,0,t.dataset);return e.x<r||e.x>o||isNaN(a)})).classed("closest",(t=>r(t)===o)).each((function(t){s.drawTooltipRow(this,n,t)})).order(),u.exit().remove(),u.enter().append("tr").each((function(t){s.drawTooltipRow(this,n,t)})).nodes(),this.tooltip.updateAndPosition(this.targetSVG.node())}drawTooltipRow(t,e,n){const i=this,r=Du(t).selectAll("td").data(e);r.each((function(t){t.static||i.drawTooltipColumn.call(i,this,t,n)})),r.exit().remove(),r.enter().append("td").each((function(t){if("enter"in t&&t.enter){t.enter.call(this,n)}i.drawTooltipColumn.call(i,this,t,n)}))}drawTooltipColumn(t,e,n){const{smoothingEnabled:i}=this;"evalType"in e&&e.evalType==oAt.DOM?e.evaluate.call(t,n,{smoothingEnabled:i}):Du(t).text(e.evaluate.call(t,n,{smoothingEnabled:i}))}findClosestPoint(t,e){const n=e.data().map(((t,n)=>this.xScale.scale(this.xAccessor(t,n,e))));let i=Ce.exports.sortedIndex(n,t.x);if(0==n.length)return null;if(i===n.length)i-=1;else if(0!==i){i=Math.abs(n[i-1]-t.x)<Math.abs(n[i]-t.x)?i-1:i}const r=e.data()[i],o=this.smoothingEnabled?this.smoothedAccessor(r,i,e):this.yValueAccessor(r,i,e);return{x:n[i],y:this.yScale.scale(o),datum:r,dataset:e}}resmoothDataset(t){let e=t.data();const n=this.smoothingWeight;let i=e.length>0?0:NaN,r=0;const o=e.map(((e,n)=>this.yValueAccessor(e,n,t))),a=o.every((t=>t==o[0]));e.forEach(((t,e)=>{const s=o[e];if(a||!Number.isFinite(s))t.smoothed=s;else{i=i*n+(1-n)*s,r++;let e=1;1!==n&&(e=1-Math.pow(n,r)),t.smoothed=i/e}}))}getDataset(t){return void 0===this.name2datasets[t]&&(this.name2datasets[t]=new NO.Dataset([],{name:t,meta:null})),this.name2datasets[t]}static getYScaleFromType(t){if(t===aAt.LOG)return new nAt;if(t===aAt.LINEAR)return new ZCt;throw new Error("Unrecognized yScale type "+t)}setVisibleSeries(t){this.disableChanges(),(t=t.sort()).reverse(),this.seriesNames=t}disableChanges(){this.dirtyDatasets.size||(this.linePlot.datasets([]),this.smoothLinePlot&&this.smoothLinePlot.datasets([]),this.marginAreaPlot&&this.marginAreaPlot.datasets([]))}commitChanges(){this.datasets=this.seriesNames.map((t=>this.getDataset(t))),[...this.dirtyDatasets].forEach((t=>{this.smoothingEnabled&&this.resmoothDataset(this.getDataset(t))})),this.updateSpecialDatasets(),this.linePlot.datasets(this.datasets),this.smoothingEnabled&&this.smoothLinePlot.datasets(this.datasets),this.marginAreaPlot&&this.marginAreaPlot.datasets(this.datasets),this.measureBBoxAndMaybeInvalidateLayoutInRaf(),this.dirtyDatasets.clear()}createSampledDatasetForMarkers(t){const e=t.data();if(e.length<=20)return t;const n=Math.ceil(e.length/20),i=new Array(Math.floor(e.length/n));for(let t=0,r=0;t<i.length;t++,r+=n)i[t]=e[r];return new NO.Dataset(i,t.metadata())}setSeriesData(t,e){this.disableChanges(),this.getDataset(t).data(e),this.dirtyDatasets.add(t)}setSeriesMetadata(t,e){this.disableChanges(),this.getDataset(t).metadata(Object.assign(Object.assign({},this.getDataset(t).metadata()),{meta:e})),this.dirtyDatasets.add(t)}smoothingUpdate(t){this.smoothingWeight=t,this.datasets.forEach((t=>this.resmoothDataset(t))),this.smoothingEnabled||(this.linePlot.addClass("ghost"),this.scatterPlot.y(this.smoothedAccessor,this.yScale),this.smoothingEnabled=!0,this.smoothLinePlot.datasets(this.datasets)),this.markersScatterPlot&&this.markersScatterPlot.y(this.getYAxisAccessor(),this.yScale),this.updateSpecialDatasets()}smoothingDisable(){this.smoothingEnabled&&(this.linePlot.removeClass("ghost"),this.scatterPlot.y(this.yValueAccessor,this.yScale),this.smoothLinePlot.datasets([]),this.smoothingEnabled=!1,this.updateSpecialDatasets()),this.markersScatterPlot&&this.markersScatterPlot.y(this.getYAxisAccessor(),this.yScale)}setColorScale(t){this.colorScale=t}setTooltipColumns(t){this.tooltipColumns=t}setTooltipSortingMethod(t){this.tooltipSortingMethod=t}renderTo(t){this.targetSVG=t,this.outer.renderTo(t),null!=this._defaultXRange&&this.resetXDomain(),null!=this._defaultYRange&&this.resetYDomain(),this.measureBBoxAndMaybeInvalidateLayoutInRaf()}redraw(){window.cancelAnimationFrame(this._redrawRaf),this._redrawRaf=window.requestAnimationFrame((()=>{this.measureBBoxAndMaybeInvalidateLayout(),this.outer.redraw()}))}measureBBoxAndMaybeInvalidateLayoutInRaf(){window.cancelAnimationFrame(this._invalidateLayoutRaf),this._invalidateLayoutRaf=window.requestAnimationFrame((()=>{this.measureBBoxAndMaybeInvalidateLayout()}))}measureBBoxAndMaybeInvalidateLayout(){if(this._lastDrawBBox){const{width:t}=this._lastDrawBBox,{width:e}=this.targetSVG.node().getBoundingClientRect();0==t&&t<e&&this.outer.invalidateCache()}this._lastDrawBBox=this.targetSVG.node().getBoundingClientRect()}destroy(){window.cancelAnimationFrame(this._redrawRaf),window.cancelAnimationFrame(this._invalidateLayoutRaf),this.outer&&this.outer.destroy()}onAnchor(t){this.outer&&this.outer.onAnchor(t)}isDataFitToDomain(){return t(this.xAxis.getScale())&&t(this.yAxis.getScale());function t(t){const e=t.getTransformationDomain(),n=t.getTransformationExtent();return n[0]===e[0]&&n[1]===e[1]}}}var cAt;ml({moduleName:"plottable-style",styleContent:"\n    \n.plottable-colors-0 {\n  background-color: #5279c7; /* INDIGO */\n}\n\n.plottable-colors-1 {\n  background-color: #fd373e; /* CORAL_RED */\n}\n\n.plottable-colors-2 {\n  background-color: #63c261; /* FERN */\n}\n\n.plottable-colors-3 {\n  background-color: #fad419; /* BRIGHT_SUN */\n}\n\n.plottable-colors-4 {\n  background-color: #2c2b6f; /* JACARTA */\n}\n\n.plottable-colors-5 {\n  background-color: #ff7939; /* BURNING_ORANGE */\n}\n\n.plottable-colors-6 {\n  background-color: #db2e65; /* CERISE_RED */\n}\n\n.plottable-colors-7 {\n  background-color: #99ce50; /* CONIFER */\n}\n\n.plottable-colors-8 {\n  background-color: #962565; /* ROYAL_HEATH */\n}\n\n.plottable-colors-9 {\n  background-color: #06cccc; /* ROBINS_EGG_BLUE */\n}\n\n/**\n * User-supplied 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NO.Scales.Color).range(jA.slice(0)),this.smoothingEnabled=!1,this.smoothingWeight=.6,this.xType=null,this.xComponentsCreationMethod=null,this.yValueAccessor=t=>t.scalar,this.tooltipColumns=pAt,this.yScaleType=aAt.LINEAR,this.ignoreYOutliers=!1,this.tooltipSortingMethod="default",this.tooltipPosition=zCt.BOTTOM,this._visibleSeriesCache=[],this._seriesDataCache={},this._seriesMetadataCache={},this._makeChartAsyncCallbackId=null}ready(){super.ready(),this.scopeSubtree(this.$.chartdiv,!0)}attached(){const t={capture:!0,passive:!0};this._listen(this,"mousedown",this._onMouseDown.bind(this),t),this._listen(this,"mouseup",this._onMouseUp.bind(this),t),this._listen(window,"keydown",this._onKeyDown.bind(this),t),this._listen(window,"keyup",this._onKeyUp.bind(this),t)}detached(){this.cancelAsync(this._makeChartAsyncCallbackId),this._chart&&(this._chart.destroy(),this._chart=void 0),this._listeners&&(this._listeners.forEach((({node:t,eventName:e,func:n,option:i})=>{t.removeEventListener(e,n,i)})),this._listeners.clear())}_listen(t,e,n,i={}){this._listeners||(this._listeners=new Set),this._listeners.add({node:t,eventName:e,func:n,option:i}),t.addEventListener(e,n,i)}_onKeyDown(t){this.toggleClass("pankey",sAt.isPanKey(t))}_onKeyUp(t){this.toggleClass("pankey",sAt.isPanKey(t))}_onMouseDown(t){this.toggleClass("mousedown",!0)}_onMouseUp(t){this.toggleClass("mousedown",!1)}isDataFitToDomain(){return!this._chart||this._chart.isDataFitToDomain()}setVisibleSeries(t){Ce.exports.isEqual(this._visibleSeriesCache,t)||(this._visibleSeriesCache=t)}setSeriesData(t,e){this._seriesDataCache[t]=e,this._chart&&this._chart.setSeriesData(t,e)}setSeriesMetadata(t,e){this._seriesMetadataCache[t]=e,this._chart&&this._chart.setSeriesMetadata(t,e)}commitChanges(){this._chart&&this._chart.commitChanges()}resetDomain(){this._chart&&this._chart.resetDomain()}redraw(){this._chart&&this._chart.redraw()}_makeChart(){null!==this._makeChartAsyncCallbackId&&(this.cancelAsync(this._makeChartAsyncCallbackId),this._makeChartAsyncCallbackId=null),this._makeChartAsyncCallbackId=this.async((function(){this._makeChartAsyncCallbackId=null;let t=this.xComponentsCreationMethod;if(this.xType||t?this.xType&&(t=()=>kCt(this.xType)):t=ECt,t&&this.yValueAccessor&&this.tooltipColumns){var e=new lAt(t,this.yValueAccessor,this.yScaleType,this.colorScale,this.$.tooltip,this.tooltipColumns,this.fillArea,this.defaultXRange,this.defaultYRange,this.symbolFunction,this.xAxisFormatter),n=Du(this.$.chartdiv);e.renderTo(n),this._chart&&this._chart.destroy(),this._chart=e,this._chart.onAnchor((()=>this.fire("chart-attached")))}}),350)}_reloadFromCache(){this._chart&&(this._visibleSeriesCache.forEach((t=>{this._chart.setSeriesData(t,this._seriesDataCache[t]||[])})),this._visibleSeriesCache.filter((t=>this._seriesMetadataCache[t])).forEach((t=>{this._chart.setSeriesMetadata(t,this._seriesMetadataCache[t])})),this._chart.setVisibleSeries(this._visibleSeriesCache),this._chart.commitChanges())}_smoothingChanged(){this._chart&&(this.smoothingEnabled?this._chart.smoothingUpdate(this.smoothingWeight):this._chart.smoothingDisable())}_outliersChanged(){this._chart&&this._chart.ignoreYOutliers(this.ignoreYOutliers)}_colorScaleChanged(){this._chart&&(this._chart.setColorScale(this.colorScale),this._chart.redraw())}_tooltipColumnsChanged(){this._chart&&this._chart.setTooltipColumns(this.tooltipColumns)}_tooltipSortingMethodChanged(){this._chart&&this._chart.setTooltipSortingMethod(this.tooltipSortingMethod)}getExporter(){return new uAt(this.$.chartdiv)}};fAt.template=xe`
    <div id="chartdiv"></div>
    <vz-chart-tooltip
      id="tooltip"
      position="[[tooltipPosition]]"
      content-component-name="vz-line-chart-tooltip"
    ></vz-chart-tooltip>
    <style include="plottable-style"></style>
    <style include="vz-pan-zoom-style"></style>
    <style>
      :host {
        -moz-user-select: none;
        -webkit-user-select: none;
        display: flex;
        flex-direction: column;
        flex-grow: 1;
        flex-shrink: 1;
        outline: none;
        position: relative;
        white-space: nowrap;
      }
      div {
        -webkit-user-select: none;
        -moz-user-select: none;
        flex-grow: 1;
        flex-shrink: 1;
      }

      #chartdiv .main {
        contain: strict;
        cursor: crosshair;
      }

      :host(.pankey) #chartdiv :not(.drag-zooming) .main {
        cursor: -webkit-grab;
        cursor: grab;
      }

      :host(.mousedown) #chartdiv .panning .main {
        cursor: -webkit-grabbing;
        cursor: grabbing;
      }

      #chartdiv {
        contain: strict;
      }

      #chartdiv line.guide-line {
        stroke: #999;
        stroke-width: 1.5px;
      }
      #chartdiv:hover .main {
        will-change: transform;
      }

      .ghost {
        opacity: 0.2;
        stroke-width: 1px;
      }

      .plottable .axis text {
        fill: currentColor;
      }

      .plottable .gridlines line {
        stroke: var(--tb-secondary-text-color);
      }
    </style>
  `,t([o({type:Object}),e("design:type",NO.Scales.Color)],fAt.prototype,"colorScale",void 0),t([o({type:Object}),e("design:type",Function)],fAt.prototype,"symbolFunction",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],fAt.prototype,"smoothingEnabled",void 0),t([o({type:Number}),e("design:type",Number)],fAt.prototype,"smoothingWeight",void 0),t([o({type:String}),e("design:type",String)],fAt.prototype,"xType",void 0),t([o({type:Object}),e("design:type",Function)],fAt.prototype,"xComponentsCreationMethod",void 0),t([o({type:Object}),e("design:type",Function)],fAt.prototype,"xAxisFormatter",void 0),t([o({type:Object}),e("design:type",Function)],fAt.prototype,"yValueAccessor",void 0),t([o({type:Array}),e("design:type",Array)],fAt.prototype,"tooltipColumns",void 0),t([o({type:Object}),e("design:type",Object)],fAt.prototype,"fillArea",void 0),t([o({type:Array}),e("design:type",Array)],fAt.prototype,"defaultXRange",void 0),t([o({type:Array}),e("design:type",Array)],fAt.prototype,"defaultYRange",void 0),t([o({type:String}),e("design:type",String)],fAt.prototype,"yScaleType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],fAt.prototype,"ignoreYOutliers",void 0),t([o({type:String}),e("design:type",String)],fAt.prototype,"tooltipSortingMethod",void 0),t([o({type:String}),e("design:type",String)],fAt.prototype,"tooltipPosition",void 0),t([o({type:Object}),e("design:type",Object)],fAt.prototype,"_chart",void 0),t([o({type:Array}),e("design:type",Array)],fAt.prototype,"_visibleSeriesCache",void 0),t([o({type:Object}),e("design:type",Object)],fAt.prototype,"_seriesDataCache",void 0),t([o({type:Object}),e("design:type",Object)],fAt.prototype,"_seriesMetadataCache",void 0),t([o({type:Number}),e("design:type",Number)],fAt.prototype,"_makeChartAsyncCallbackId",void 0),t([a("xComponentsCreationMethod","xType","yValueAccessor","yScaleType","isAttached"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fAt.prototype,"_makeChart",null),t([a("_chart","_visibleSeriesCache"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fAt.prototype,"_reloadFromCache",null),t([a("smoothingEnabled","smoothingWeight","_chart"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fAt.prototype,"_smoothingChanged",null),t([a("ignoreYOutliers","_chart"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fAt.prototype,"_outliersChanged",null),t([a("colorScale"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fAt.prototype,"_colorScaleChanged",null),t([a("tooltipColumns"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fAt.prototype,"_tooltipColumnsChanged",null),t([a("tooltipSortingMethod","_chart"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fAt.prototype,"_tooltipSortingMethodChanged",null),fAt=t([i("vz-line-chart2")],fAt);let mAt=class extends we{};mAt.template=xe`
    <div class="content">
      <table>
        <thead></thead>
        <tbody></tbody>
      </table>
    </div>
    <style>
      :host {
        pointer-events: none;
      }

      .content {
        background: rgba(0, 0, 0, 0.8);
        border-radius: 4px;
        color: #fff;
        overflow: hidden;
        pointer-events: none;
      }

      table {
        font-size: 13px;
        line-height: 1.4em;
        margin-top: 10px;
        padding: 8px;
      }

      thead {
        font-size: 14px;
      }

      tbody {
        font-size: 13px;
        line-height: 21px;
        white-space: nowrap;
      }

      td {
        padding: 0 5px;
      }

      .swatch {
        border-radius: 50%;
        display: block;
        height: 18px;
        width: 18px;
      }

      .closest .swatch {
        box-shadow: inset 0 0 0 2px #fff;
      }

      th {
        padding: 0 5px;
        text-align: left;
      }

      .distant td:not(.swatch) {
        opacity: 0.8;
      }

      .ghost {
        opacity: 0.2;
        stroke-width: 1px;
      }
    </style>
  `,mAt=t([i("vz-line-chart-tooltip")],mAt);const gAt=[];const _At=Ce.exports.throttle((function t(){if(0==gAt.length)return;const e=gAt.shift();e&&e.active&&(e.redraw(),e._maybeRenderedInBadState=!1),window.cancelAnimationFrame(0),window.requestAnimationFrame(t)}),100);let yAt=class extends(bCt(cr(we))){constructor(){super(...arguments),this._redrawRaf=null,this.active=!1,this.logScaleActive=!1,this.colorScale={scale:_O},this._resetDomainOnNextLoad=!0,this._maybeRenderedInBadState=!1}onLoadFinish(){this.commitChanges(),this.dataToLoad.length>0&&this._resetDomainOnNextLoad&&(this._resetDomainOnNextLoad=!1,this.getChart().resetDomain()),this.redraw()}disconnectedCallback(){super.disconnectedCallback(),null!==this._redrawRaf&&cancelAnimationFrame(this._redrawRaf)}exportAsSvgString(){return this.getChart().getExporter().exportAsString()}getChart(){return this.$.chart}resetDomain(){this.getChart().resetDomain()}setSeriesData(t,e){this.getChart().setSeriesData(t,e)}setSeriesMetadata(t,e){this.getChart().setSeriesMetadata(t,e)}commitChanges(){this.getChart().commitChanges()}redraw(){null!==this._redrawRaf&&cancelAnimationFrame(this._redrawRaf),this._redrawRaf=window.requestAnimationFrame((()=>{this.active?this.getChart().redraw():this._maybeRenderedInBadState=!0}))}_loadKeyChanged(){this.reset(),this._resetDomainOnNextLoad=!0}_dataSeriesChanged(){this.getChart().setVisibleSeries(this.dataSeries)}_logScaleChanged(t){this.getChart().yScaleType=t?aAt.LOG:aAt.LINEAR,this.redraw()}_fixBadStateWhenActive(){this.active&&this._maybeRenderedInBadState&&(gAt.push(this),_At())}_onChartAttached(){this.active||(this._maybeRenderedInBadState=!0)}};yAt.template=xe`
    <div id="chart-and-spinner-container">
      <vz-line-chart2
        id="chart"
        data-loading$="[[dataLoading]]"
        data-loaded-once$="[[dataLoadedAtLeastOnce]]"
        color-scale="[[colorScale]]"
        default-x-range="[[defaultXRange]]"
        default-y-range="[[defaultYRange]]"
        fill-area="[[fillArea]]"
        ignore-y-outliers="[[ignoreYOutliers]]"
        on-chart-attached="_onChartAttached"
        smoothing-enabled="[[smoothingEnabled]]"
        smoothing-weight="[[smoothingWeight]]"
        symbol-function="[[symbolFunction]]"
        tooltip-columns="[[tooltipColumns]]"
        tooltip-position="[[tooltipPosition]]"
        tooltip-sorting-method="[[tooltipSortingMethod]]"
        x-components-creation-method="[[xComponentsCreationMethod]]"
        x-type="[[xType]]"
        y-value-accessor="[[yValueAccessor]]"
      ></vz-line-chart2>
      <template is="dom-if" if="[[dataLoading]]">
        <div id="loading-spinner-container">
          <paper-spinner-lite active=""></paper-spinner-lite>
        </div>
      </template>
    </div>
    <style>
      :host {
        height: 100%;
        width: 100%;
        display: flex;
        flex-direction: column;
      }

      :host([_maybe-rendered-in-bad-state]) vz-line-chart {
        visibility: hidden;
      }

      #chart-and-spinner-container {
        display: flex;
        flex-grow: 1;
        position: relative;
      }

      #loading-spinner-container {
        align-items: center;
        bottom: 0;
        display: flex;
        display: flex;
        justify-content: center;
        left: 0;
        pointer-events: none;
        position: absolute;
        right: 0;
        top: 0;
      }

      vz-line-chart2 {
        -webkit-user-select: none;
        -moz-user-select: none;
      }

      vz-line-chart2[data-loading] {
        opacity: 0.3;
      }
    </style>
  `,t([o({type:Boolean,observer:"_fixBadStateWhenActive"}),e("design:type",Boolean)],yAt.prototype,"active",void 0),t([o({type:Array}),e("design:type",Array)],yAt.prototype,"dataSeries",void 0),t([o({type:Object}),e("design:type",br)],yAt.prototype,"requestManager",void 0),t([o({type:Boolean,observer:"_logScaleChanged"}),e("design:type",Boolean)],yAt.prototype,"logScaleActive",void 0),t([o({type:Object}),e("design:type",Object)],yAt.prototype,"xComponentsCreationMethod",void 0),t([o({type:String}),e("design:type",String)],yAt.prototype,"xType",void 0),t([o({type:Object}),e("design:type",Function)],yAt.prototype,"yValueAccessor",void 0),t([o({type:Object}),e("design:type",Object)],yAt.prototype,"fillArea",void 0),t([o({type:Boolean}),e("design:type",Boolean)],yAt.prototype,"smoothingEnabled",void 0),t([o({type:Number}),e("design:type",Number)],yAt.prototype,"smoothingWeight",void 0),t([o({type:Array}),e("design:type",Array)],yAt.prototype,"tooltipColumns",void 0),t([o({type:String}),e("design:type",Object)],yAt.prototype,"tooltipSortingMethod",void 0),t([o({type:String}),e("design:type",String)],yAt.prototype,"tooltipPosition",void 0),t([o({type:Boolean}),e("design:type",Boolean)],yAt.prototype,"ignoreYOutliers",void 0),t([o({type:Array}),e("design:type",Array)],yAt.prototype,"defaultXRange",void 0),t([o({type:Array}),e("design:type",Array)],yAt.prototype,"defaultYRange",void 0),t([o({type:Object}),e("design:type",Function)],yAt.prototype,"symbolFunction",void 0),t([o({type:Object}),e("design:type",Object)],yAt.prototype,"colorScale",void 0),t([o({type:Boolean}),e("design:type",Boolean)],yAt.prototype,"_resetDomainOnNextLoad",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],yAt.prototype,"_maybeRenderedInBadState",void 0),t([a("loadKey"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],yAt.prototype,"_loadKeyChanged",null),t([a("dataSeries.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],yAt.prototype,"_dataSeriesChanged",null),yAt=t([i("tf-line-chart-data-loader")],yAt),ml({moduleName:"tf-custom-scalar-card-style",styleContent:"\n    :host {\n      margin: 5px 10px;\n      display: inline-block;\n      width: 330px;\n      vertical-align: text-top;\n    }\n\n    :host([_expanded]) {\n      width: 100%;\n    }\n\n    :host([_expanded]) #tf-line-chart-data-loader-container {\n      height: 400px;\n    }\n\n    h1 {\n      font-size: 19px;\n      font-weight: normal;\n    }\n\n    #tf-line-chart-data-loader-container {\n      height: 200px;\n      width: 100%;\n    }\n\n    #buttons {\n      display: flex;\n      flex-direction: row;\n    }\n\n    paper-icon-button {\n      color: #2196f3;\n      border-radius: 100%;\n      width: 32px;\n      height: 32px;\n      padding: 4px;\n    }\n\n    paper-icon-button[selected] {\n      background: var(--tb-ui-light-accent);\n    }\n\n    .download-links {\n      display: flex;\n      height: 32px;\n    }\n\n    .download-links a {\n      font-size: 10px;\n      align-self: center;\n      margin: 2px;\n    }\n\n    .download-links paper-dropdown-menu {\n      width: 100px;\n      --paper-input-container-label: {\n        font-size: 10px;\n      }\n      --paper-input-container-input: {\n        font-size: 10px;\n      }\n    }\n  "});class vAt{constructor(t,e,n,i,r){this.run=t,this.tag=e,this.name=n,this.scalarData=i,this.symbol=r}getName(){return this.name}setData(t){this.scalarData=t}getData(){return this.scalarData}getRun(){return this.run}getTag(){return this.tag}getSymbol(){return this.symbol}}function bAt(t,e){return`${e} (${t})`}class xAt{constructor(t){this.runBasedColorScale=t}scale(t){return this.runBasedColorScale.scale(this.parseRunName(t))}parseRunName(t){const e=t.match(/\((.*)\)$/);return e?e[1]:""}}let wAt=class extends(cr(we)){constructor(){super(...arguments),this.active=!0,this._colorScale=new xAt({scale:_O}),this._nameToDataSeries={},this._expanded=!1,this._requestData=(t,e,n)=>{const i=Er().pluginRoute("custom_scalars","/scalars");Promise.all(t.map((t=>{const n=PO(i,{tag:this._tagFilter,run:t});return this.requestManager.request(n).then((n=>{e({item:t,data:n})}))}))).finally((()=>{n()}))},this._runToNextAvailableSymbolIndex={},this._matchesListOpened=!1,this._fillArea={lowerAccessor:t=>t.lower,higherAccessor:t=>t.upper},this._tooltipColumns=(()=>{const t=SCt(4),e=e=>isNaN(e)?"NaN":t(e);return[{title:"Name",evaluate:t=>t.dataset.metadata().name},{title:"Value",evaluate:t=>e(t.datum.scalar)},{title:"Lower Margin",evaluate:t=>e(t.datum.lower)},{title:"Upper Margin",evaluate:t=>e(t.datum.upper)},{title:"Step",evaluate:t=>MCt(t.datum.step)},{title:"Time",evaluate:t=>TCt(t.datum.wall_time)},{title:"Relative",evaluate:t=>ACt(CCt(t.datum,0,t.dataset))}]})(),this._missingTags=[],this._missingTagsCollapsibleOpened=!1}reload(){this.$.loader.reload()}redraw(){this.$.loader.redraw()}_toggleExpanded(t){this.set("_expanded",!this._expanded),this.redraw()}_toggleLogScale(){this.set("_logScaleActive",!this._logScaleActive)}_resetDomain(){const t=this.$.loader;t&&t.resetDomain()}_csvUrl(t,e){if(!e)return"";return PO(this._downloadDataUrl(t,e),{format:"csv"})}_jsonUrl(t,e){if(!e)return"";return PO(this._downloadDataUrl(t,e),{format:"json"})}_downloadDataUrl(t,e){const n=t[e],i={tag:n.getTag(),run:n.getRun()};return PO(Er().pluginRoute("custom_scalars","/download_data"),i)}_createProcessDataFunction(t){return(e,n,i)=>{if(!i.regex_valid)return void this.set("_tagFilterInvalid",!0);const r=Ce.exports.clone(this._nameToDataSeries),o=[];Ce.exports.forEach(t,(t=>{let e=!1;const a=i.tag_to_events[t.value],s=i.tag_to_events[t.lower],l=i.tag_to_events[t.upper];if(Ce.exports.isUndefined(a)&&(o.push(t.value),e=!0),Ce.exports.isUndefined(s)&&(o.push(t.lower),e=!0),Ce.exports.isUndefined(l)&&(o.push(t.upper),e=!0),e)return;const c=t=>t[1],u=this._findStepMismatch(t,a.map(c),s.map(c),l.map(c));if(u)return void this.set("_stepsMismatch",u);const h=t=>t[2],d=a.map(((t,e)=>({wall_time:new Date(1e3*t[0]),step:c(t),scalar:h(t),lower:h(s[e]),upper:h(l[e])}))),p=bAt(n,t.value),f=r[p];if(f)f.setData(d);else{const e=this._createNewDataSeries(n,t.value,p,d);r[p]=e}})),this.set("_nameToDataSeries",r);const a=Ce.exports.findIndex(this._missingTags,(t=>t.run===n));if(o.length&&3!=o.length){const t={run:n,tags:o};a>=0?this.splice("_missingTags",a,1,t):this.push("_missingTags",t)}else a>=0&&this.splice("_missingTags",a,1)}}_findStepMismatch(t,e,n,i){return Ce.exports.isEqual(n,e)&&Ce.exports.isEqual(i,e)?null:{seriesObject:t,valueSteps:e,lowerSteps:n,upperSteps:i}}_createNewDataSeries(t,e,n,i){this._runToNextAvailableSymbolIndex[t]|=0;const r=new vAt(t,e,n,i,xCt[this._runToNextAvailableSymbolIndex[t]]);return this._runToNextAvailableSymbolIndex[t]=(this._runToNextAvailableSymbolIndex[t]+1)%xCt.length,r}_updateChart(){Ce.exports.forOwn(this._nameToDataSeries,(t=>{this.$.loader.setSeriesData(t.getName(),t.getData())})),this.$.loader.commitChanges()}get _seriesNames(){const t=new Set(this.runs);return Object.entries(this._nameToDataSeries).filter((([e,n])=>t.has(n.run))).map((([t])=>t))}_determineColor(t,e){return t.scale(e)}_refreshDataSeries(){this.set("_nameToDataSeries",{})}_createSymbolFunction(){return t=>this._nameToDataSeries[t].getSymbol().method()}_determineSymbol(t,e){return t[e].getSymbol().character}get _tagFilter(){return Ce.exports.flatten(this.marginChartSeries.map((t=>[t.value,t.lower,t.upper]))).map((t=>"("+this._escapeRegexCharacters(t)+")")).join("|")}_escapeRegexCharacters(t){return t.replace(/[.*+?^${}()|[\]\\]/g,"\\$&")}_getToggleCollapsibleIcon(t){return t?"expand-less":"expand-more"}_toggleMatchesOpen(){this.set("_matchesListOpened",!this._matchesListOpened)}get _titleDisplayString(){return this.title||"untitled"}_separateWithCommas(t){return t.join(", ")}_toggleMissingTagsCollapsibleOpen(){this.set("_missingTagsCollapsibleOpened",!this._missingTagsCollapsibleOpened)}_matchListEntryColorUpdated(){const t=this.$$("#match-list-repeat");t&&this.root.querySelectorAll(".match-list-entry").forEach((e=>{const n=t.itemForElement(e);e.style.color=this._determineColor(this._colorScale,n)}))}};wAt.template=xe`
    <tf-card-heading display-name="[[_titleDisplayString]]"></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader
        id="loader"
        active="[[active]]"
        color-scale="[[_colorScale]]"
        data-series="[[_seriesNames]]"
        fill-area="[[_fillArea]]"
        ignore-y-outliers="[[ignoreYOutliers]]"
        load-key="[[_tagFilter]]"
        data-to-load="[[runs]]"
        request-data="[[_requestData]]"
        log-scale-active="[[_logScaleActive]]"
        load-data-callback="[[_createProcessDataFunction(marginChartSeries)]]"
        request-manager="[[requestManager]]"
        symbol-function="[[_createSymbolFunction()]]"
        tooltip-columns="[[_tooltipColumns]]"
        tooltip-sorting-method="[[tooltipSortingMethod]]"
        x-type="[[xType]]"
      >
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
      <paper-icon-button
        selected$="[[_logScaleActive]]"
        icon="line-weight"
        on-tap="_toggleLogScale"
        title="Toggle y-axis log scale"
      ></paper-icon-button>
      <paper-icon-button
        icon="settings-overscan"
        on-tap="_resetDomain"
        title="Fit domain to data"
      ></paper-icon-button>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <paper-dropdown-menu
            no-label-float="true"
            label="series to download"
            selected-item-label="{{_dataSeriesNameToDownload}}"
          >
            <paper-listbox class="dropdown-content" slot="dropdown-content">
              <template
                is="dom-repeat"
                items="[[_seriesNames]]"
                as="dataSeriesName"
              >
                <paper-item no-label-float="true"
                  >[[dataSeriesName]]</paper-item
                >
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
          <a
            download="[[_dataSeriesNameToDownload]].csv"
            href="[[_csvUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]"
            >CSV</a
          >
          <a
            download="[[_dataSeriesNameToDownload]].json"
            href="[[_jsonUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]"
            >JSON</a
          >
        </div>
      </template>
    </div>

    <!-- here -->
    <template is="dom-if" if="[[_missingTags.length]]">
      <div class="collapsible-list-title">
        <paper-icon-button
          icon="[[_getToggleCollapsibleIcon(_missingTagsCollapsibleOpened)]]"
          on-click="_toggleMissingTagsCollapsibleOpen"
          class="toggle-collapsible-button"
        >
        </paper-icon-button>
        <span class="collapsible-title-text">
          <iron-icon icon="icons:error"></iron-icon> Missing Tags
        </span>
      </div>
      <iron-collapse opened="[[_missingTagsCollapsibleOpened]]">
        <div class="error-content">
          <iron-icon class="error-icon" icon="icons:error"></iron-icon>
          <template is="dom-repeat" items="[[_missingTags]]" as="missingEntry">
            <div class="missing-tags-for-run-container">
              Run "[[missingEntry.run]]" lacks data for tags
              <ul>
                <template
                  is="dom-repeat"
                  items="[[missingEntry.tags]]"
                  as="tag"
                >
                  <li>[[tag]]</li>
                </template>
              </ul>
            </div>
          </template>
        </div>
      </iron-collapse>
    </template>

    <template is="dom-if" if="[[_tagFilterInvalid]]">
      <div class="error-content">
        <iron-icon class="error-icon" icon="icons:error"></iron-icon>
        This regular expresion is invalid:<br />
        <span class="invalid-regex">[[_tagFilter]]</span>
      </div>
    </template>

    <template is="dom-if" if="[[_stepsMismatch]]">
      <div class="error-content">
        <iron-icon class="error-icon" icon="icons:error"></iron-icon>
        The steps for value, lower, and upper tags do not match:
        <ul>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.value]]</span>:
            [[_separateWithCommas(_stepsMismatch.valueSteps)]]
          </li>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.lower]]</span>:
            [[_separateWithCommas(_stepsMismatch.lowerSteps)]]
          </li>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.upper]]</span>:
            [[_separateWithCommas(_stepsMismatch.upperSteps)]]
          </li>
        </ul>
      </div>
    </template>

    <div id="matches-container">
      <div class="collapsible-list-title">
        <template is="dom-if" if="[[_seriesNames.length]]">
          <paper-icon-button
            icon="[[_getToggleCollapsibleIcon(_matchesListOpened)]]"
            on-click="_toggleMatchesOpen"
            class="toggle-matches-button"
          >
          </paper-icon-button>
        </template>

        <span class="collapsible-title-text">
          Matches ([[_seriesNames.length]])
        </span>
      </div>
      <template is="dom-if" if="[[_seriesNames.length]]">
        <iron-collapse opened="[[_matchesListOpened]]">
          <div id="matches-list">
            <template
              is="dom-repeat"
              items="[[_seriesNames]]"
              as="seriesName"
              id="match-list-repeat"
              on-dom-change="_matchListEntryColorUpdated"
            >
              <div class="match-list-entry">
                <span class="match-entry-symbol">
                  [[_determineSymbol(_nameToDataSeries, seriesName)]]
                </span>
                [[seriesName]]
              </div>
            </template>
          </div>
        </iron-collapse>
      </template>
    </div>

    <style include="tf-custom-scalar-card-style"></style>
    <style>
      .error-content {
        background: #f00;
        border-radius: 5px;
        color: #fff;
        margin: 10px 0 0 0;
        padding: 10px;
      }

      .error-icon {
        display: block;
        fill: #fff;
        margin: 0 auto 5px auto;
      }

      .invalid-regex {
        font-weight: bold;
      }

      .error-content ul {
        margin: 1px 0 0 0;
        padding: 0 0 0 19px;
      }

      .tag-name {
        font-weight: bold;
      }

      .collapsible-list-title {
        margin: 10px 0 5px 0;
      }

      .collapsible-title-text {
        vertical-align: middle;
      }

      #matches-list {
        max-height: 200px;
        overflow-y: auto;
      }

      .match-list-entry {
        margin: 0 0 5px 0;
      }

      .match-entry-symbol {
        font-family: arial, sans-serif;
        display: inline-block;
        width: 10px;
      }

      .missing-tags-for-run-container {
        margin: 8px 0 0 0;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],wAt.prototype,"runs",void 0),t([o({type:String}),e("design:type",String)],wAt.prototype,"xType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wAt.prototype,"active",void 0),t([o({type:String}),e("design:type",String)],wAt.prototype,"title",void 0),t([o({type:Array}),e("design:type",Array)],wAt.prototype,"marginChartSeries",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wAt.prototype,"ignoreYOutliers",void 0),t([o({type:Object}),e("design:type",br)],wAt.prototype,"requestManager",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wAt.prototype,"showDownloadLinks",void 0),t([o({type:Object}),e("design:type",Object)],wAt.prototype,"tagMetadata",void 0),t([o({type:String}),e("design:type",String)],wAt.prototype,"tooltipSortingMethod",void 0),t([o({type:Object}),e("design:type",Object)],wAt.prototype,"_colorScale",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wAt.prototype,"_tagFilterInvalid",void 0),t([o({type:Object}),e("design:type",Object)],wAt.prototype,"_nameToDataSeries",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],wAt.prototype,"_expanded",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wAt.prototype,"_logScaleActive",void 0),t([o({type:Object}),e("design:type",Function)],wAt.prototype,"_requestData",void 0),t([o({type:Object}),e("design:type",Object)],wAt.prototype,"_runToNextAvailableSymbolIndex",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wAt.prototype,"_matchesListOpened",void 0),t([o({type:Object}),e("design:type",Object)],wAt.prototype,"_fillArea",void 0),t([o({type:Array}),e("design:type",Array)],wAt.prototype,"_tooltipColumns",void 0),t([o({type:Array}),e("design:type",Array)],wAt.prototype,"_missingTags",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wAt.prototype,"_missingTagsCollapsibleOpened",void 0),t([o({type:Object}),e("design:type",Object)],wAt.prototype,"_stepsMismatch",void 0),t([a("_nameToDataSeries"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],wAt.prototype,"_updateChart",null),t([s("_nameToDataSeries","runs"),e("design:type",Object),e("design:paramtypes",[])],wAt.prototype,"_seriesNames",null),t([a("_tagFilter"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],wAt.prototype,"_refreshDataSeries",null),t([s("marginChartSeries"),e("design:type",String),e("design:paramtypes",[])],wAt.prototype,"_tagFilter",null),t([s("title"),e("design:type",String),e("design:paramtypes",[])],wAt.prototype,"_titleDisplayString",null),wAt=t([i("tf-custom-scalar-margin-chart-card")],wAt);var SAt=Object.freeze({__proto__:null,TYPES:[],getRunsNamed:function MAt(t){return Ce.exports.keys(t).sort(ur)},getTags:fr,filterTags:function EAt(t,e){let n=[];return e.forEach((e=>n=n.concat(t[e]))),Ce.exports.uniq(n).sort(ur)},ListenKey:Al,BaseStore:kl,Canceller:xO,EnvironmentStore:Ll,environmentStore:Pl,ExperimentsStore:dO,experimentsStore:pO,RequestCancellationError:mr,InvalidRequestOptionsError:gr,RequestNetworkError:_r,get HttpMethodType(){return yr},RequestOptions:vr,RequestManager:br,createRouter:Mr,getRouter:Er,setRouter:function TAt(t){if(null==t)throw new Error("Router required, but got: "+t);Sr=t},createSearchParam:Cr,RunsStore:Nl,runsStore:Il,addParams:PO});let CAt=class extends(cr(we)){constructor(){super(...arguments),this.active=!0,this._colorScale=new xAt({scale:_O}),this._nameToDataSeries={},this._expanded=!1,this._requestData=(t,e,n)=>{const i=Er().pluginRoute("custom_scalars","/scalars");Promise.all(t.map((t=>{const n=PO(i,{tag:this._tagFilter,run:t});return this.requestManager.request(n).then((n=>{e({item:t,data:n})}))}))).finally((()=>{n()}))},this._runToNextAvailableSymbolIndex={},this._matchesListOpened=!1}reload(){this.$.loader.reload()}redraw(){this.$.loader.redraw()}_toggleExpanded(t){this.set("_expanded",!this._expanded),this.redraw()}_toggleLogScale(){this.set("_logScaleActive",!this._logScaleActive)}_resetDomain(){const t=this.$.loader;t&&t.resetDomain()}_csvUrl(t,e){if(!e)return"";return PO(this._downloadDataUrl(t,e),{format:"csv"})}_jsonUrl(t,e){if(!e)return"";return PO(this._downloadDataUrl(t,e),{format:"json"})}_downloadDataUrl(t,e){const n=t[e],i={tag:n.getTag(),run:n.getRun()};return PO(Er().pluginRoute("custom_scalars","/download_data"),i)}_createProcessDataFunction(){return(t,e,n)=>{if(n.regex_valid){const t=Ce.exports.clone(this._nameToDataSeries);Ce.exports.forOwn(n.tag_to_events,((n,i)=>{const r=n.map((t=>({wall_time:new Date(1e3*t[0]),step:t[1],scalar:t[2]}))),o=bAt(e,i),a=t[o];if(a)a.setData(r);else{Ce.exports.isUndefined(this._runToNextAvailableSymbolIndex[e])&&(this._runToNextAvailableSymbolIndex[e]=0);const n=new vAt(e,i,o,r,xCt[this._runToNextAvailableSymbolIndex[e]]);t[o]=n;this._runToNextAvailableSymbolIndex[e]=(this._runToNextAvailableSymbolIndex[e]+1)%xCt.length}})),this.set("_nameToDataSeries",t)}}}_updateChart(){Object.entries(this._nameToDataSeries).forEach((([t,e])=>{this.$.loader.setSeriesData(t,e.getData())})),this.$.loader.commitChanges()}_computeSelectedRunsSet(t){const e={};return Ce.exports.forEach(t,(t=>{e[t]=1})),e}get _seriesNames(){const t=new Set(this.runs);return Object.entries(this._nameToDataSeries).filter((([e,n])=>t.has(n.run))).map((([t])=>t))}_determineColor(t,e){return t.scale(e)}_refreshDataSeries(){this.set("_nameToDataSeries",{})}_createSymbolFunction(){return t=>this._nameToDataSeries[t].getSymbol().method()}_determineSymbol(t,e){return t[e].getSymbol().character}get _tagFilter(){var t=this.tagRegexes;return 1===t.length?t[0]:t.map((t=>"("+t+")")).join("|")}_getToggleMatchesIcon(t){return t?"expand-less":"expand-more"}_toggleMatchesOpen(){this.set("_matchesListOpened",!this._matchesListOpened)}get _titleDisplayString(){return this.title||"untitled"}_matchListEntryColorUpdated(t){const e=this.$$("#match-list-repeat");e&&this.root.querySelectorAll(".match-list-entry").forEach((t=>{const n=e.itemForElement(t);t.style.color=this._determineColor(this._colorScale,n)}))}};CAt.template=xe`
    <tf-card-heading display-name="[[_titleDisplayString]]"></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader
        id="loader"
        active="[[active]]"
        color-scale="[[_colorScale]]"
        data-series="[[_seriesNames]]"
        ignore-y-outliers="[[ignoreYOutliers]]"
        load-key="[[_tagFilter]]"
        data-to-load="[[runs]]"
        request-data="[[_requestData]]"
        log-scale-active="[[_logScaleActive]]"
        load-data-callback="[[_createProcessDataFunction()]]"
        request-manager="[[requestManager]]"
        smoothing-enabled="[[smoothingEnabled]]"
        smoothing-weight="[[smoothingWeight]]"
        symbol-function="[[_createSymbolFunction()]]"
        tooltip-sorting-method="[[tooltipSortingMethod]]"
        x-type="[[xType]]"
      >
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
      <paper-icon-button
        selected$="[[_logScaleActive]]"
        icon="line-weight"
        on-tap="_toggleLogScale"
        title="Toggle y-axis log scale"
      ></paper-icon-button>
      <paper-icon-button
        icon="settings-overscan"
        on-tap="_resetDomain"
        title="Fit domain to data"
      ></paper-icon-button>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <paper-dropdown-menu
            no-label-float="true"
            label="series to download"
            selected-item-label="{{_dataSeriesNameToDownload}}"
          >
            <paper-listbox class="dropdown-content" slot="dropdown-content">
              <template
                is="dom-repeat"
                items="[[_seriesNames]]"
                as="dataSeriesName"
              >
                <paper-item no-label-float="true"
                  >[[dataSeriesName]]</paper-item
                >
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
          <a
            download="[[_dataSeriesNameToDownload]].csv"
            href="[[_csvUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]"
            >CSV</a
          >
          <a
            download="[[_dataSeriesNameToDownload]].json"
            href="[[_jsonUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]"
            >JSON</a
          >
        </div>
      </template>
    </div>
    <div id="matches-container">
      <div id="matches-list-title">
        <template is="dom-if" if="[[_seriesNames.length]]">
          <paper-icon-button
            icon="[[_getToggleMatchesIcon(_matchesListOpened)]]"
            on-click="_toggleMatchesOpen"
            class="toggle-matches-button"
          >
          </paper-icon-button>
        </template>

        <span class="matches-text"> Matches ([[_seriesNames.length]]) </span>
      </div>
      <template is="dom-if" if="[[_seriesNames.length]]">
        <iron-collapse opened="[[_matchesListOpened]]">
          <div id="matches-list">
            <template
              is="dom-repeat"
              items="[[_seriesNames]]"
              as="seriesName"
              id="match-list-repeat"
              on-dom-change="_matchListEntryColorUpdated"
            >
              <div class="match-list-entry">
                <span class="match-entry-symbol">
                  [[_determineSymbol(_nameToDataSeries, seriesName)]]
                </span>
                [[seriesName]]
              </div>
            </template>
          </div>
        </iron-collapse>
      </template>
    </div>

    <style include="tf-custom-scalar-card-style"></style>
    <style>
      #matches-list-title {
        margin: 10px 0 5px 0;
      }

      #matches-list {
        max-height: 200px;
        overflow-y: auto;
      }

      .match-list-entry {
        margin: 0 0 5px 0;
      }

      .match-entry-symbol {
        font-family: arial, sans-serif;
        display: inline-block;
        width: 10px;
      }

      .matches-text {
        vertical-align: middle;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],CAt.prototype,"runs",void 0),t([o({type:String}),e("design:type",String)],CAt.prototype,"xType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CAt.prototype,"active",void 0),t([o({type:String}),e("design:type",String)],CAt.prototype,"title",void 0),t([o({type:Array}),e("design:type",Array)],CAt.prototype,"tagRegexes",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CAt.prototype,"ignoreYOutliers",void 0),t([o({type:Object}),e("design:type",br)],CAt.prototype,"requestManager",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CAt.prototype,"showDownloadLinks",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CAt.prototype,"smoothingEnabled",void 0),t([o({type:Number}),e("design:type",Number)],CAt.prototype,"smoothingWeight",void 0),t([o({type:Object}),e("design:type",Object)],CAt.prototype,"tagMetadata",void 0),t([o({type:String}),e("design:type",String)],CAt.prototype,"tooltipSortingMethod",void 0),t([o({type:Object}),e("design:type",xAt)],CAt.prototype,"_colorScale",void 0),t([o({type:Object}),e("design:type",Object)],CAt.prototype,"_nameToDataSeries",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],CAt.prototype,"_expanded",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CAt.prototype,"_logScaleActive",void 0),t([o({type:Object}),e("design:type",Function)],CAt.prototype,"_requestData",void 0),t([o({type:Object}),e("design:type",Object)],CAt.prototype,"_runToNextAvailableSymbolIndex",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CAt.prototype,"_matchesListOpened",void 0),t([a("_nameToDataSeries"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],CAt.prototype,"_updateChart",null),t([s("_nameToDataSeries","runs"),e("design:type",Object),e("design:paramtypes",[])],CAt.prototype,"_seriesNames",null),t([a("_tagFilter"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],CAt.prototype,"_refreshDataSeries",null),t([s("tagRegexes"),e("design:type",String),e("design:paramtypes",[])],CAt.prototype,"_tagFilter",null),t([s("title"),e("design:type",String),e("design:paramtypes",[])],CAt.prototype,"_titleDisplayString",null),CAt=t([i("tf-custom-scalar-multi-line-chart-card")],CAt);let AAt=class extends we{constructor(){super(...arguments),this._requestManager=new br(50),this._canceller=new xO,this._showDownloadLinks=Qs("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}).call(this),this._smoothingWeight=rl("_smoothingWeight",{defaultValue:.6}).call(this),this._ignoreYOutliers=Qs("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0}).call(this),this._xType="step",this._active=!0,this.reloadOnReady=!0,this._showDownloadLinksObserver=tl("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}),this._smoothingWeightObserver=ol("_smoothingWeight",{defaultValue:.6}),this._ignoreYOutliersObserver=tl("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0})}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){const t=Er().pluginsListing(),e=this._canceller.cancellable((t=>{t.cancelled||(this.set("_dataNotFound",!t.value.custom_scalars),this._dataNotFound||this._retrieveLayoutAndData())}));this._requestManager.request(t).then(e)}_reloadCharts(){this.root.querySelectorAll("tf-custom-scalar-margin-chart-card, tf-custom-scalar-multi-line-chart-card").forEach((t=>{t.reload()}))}_retrieveLayoutAndData(){const t=Er().pluginRoute("custom_scalars","/layout"),e=this._canceller.cancellable((t=>{t.cancelled||(this.set("_layout",t.value),this._dataNotFound||this._reloadCharts())}));this._requestManager.request(t).then(e)}get _smoothingEnabled(){return this._smoothingWeight>0}get _categories(){var t=this._layout;if(!t.category)return[];let e=!1;this._openedCategories||(e=!0,this._openedCategories={});return t.category.map((t=>(e&&!t.closed&&(this._openedCategories[t.title]=!0),{name:t.title,items:t.chart,metadata:{type:Ar.PREFIX_GROUP,opened:!!this._openedCategories[t.title]}})))}_categoryOpenedToggled(t){const e=t.target;e.opened?this._openedCategories[e.category.name]=!0:delete this._openedCategories[e.category.name]}};AAt.template=xe`
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="settings">
          <div class="sidebar-section">
            <div class="line-item">
              <paper-checkbox checked="{{_showDownloadLinks}}"
                >Show data download links</paper-checkbox
              >
            </div>
            <div class="line-item">
              <paper-checkbox checked="{{_ignoreYOutliers}}"
                >Ignore outliers in chart scaling</paper-checkbox
              >
            </div>
            <div id="tooltip-sorting">
              <div id="tooltip-sorting-label">Tooltip sorting method:</div>
              <paper-dropdown-menu
                no-label-float=""
                selected-item-label="{{_tooltipSortingMethod}}"
              >
                <paper-listbox
                  class="dropdown-content"
                  selected="0"
                  slot="dropdown-content"
                >
                  <paper-item>default</paper-item>
                  <paper-item>descending</paper-item>
                  <paper-item>ascending</paper-item>
                  <paper-item>nearest</paper-item>
                </paper-listbox>
              </paper-dropdown-menu>
            </div>
          </div>
          <div class="sidebar-section">
            <tf-smoothing-input
              weight="{{_smoothingWeight}}"
              step="0.001"
              min="0"
              max="1"
            ></tf-smoothing-input>
          </div>
          <div class="sidebar-section">
            <tf-option-selector
              id="x-type-selector"
              name="Horizontal Axis"
              selected-id="{{_xType}}"
            >
              <paper-button id="step">step</paper-button
              ><!--
            --><paper-button id="relative">relative</paper-button
              ><!--
            --><paper-button id="wall_time">wall</paper-button>
            </tf-option-selector>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center" id="categories-container">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>The custom scalars dashboard is inactive.</h3>
            <p>Probable causes:</p>
            <ol>
              <li>You haven't laid out the dashboard.</li>
              <li>You haven’t written any scalar data to your event files.</li>
            </ol>

            <p>
              To lay out the dashboard, pass a <code>Layout</code> protobuffer
              to the <code>set_layout</code> method. For example,
            </p>
            <pre>
from tensorboard import summary
from tensorboard.plugins.custom_scalar import layout_pb2
...
# This action does not have to be performed at every step, so the action is not
# taken care of by an op in the graph. We only need to specify the layout once
# (instead of per step).
layout_summary = summary_lib.custom_scalar_pb(layout_pb2.Layout(
  category=[
    layout_pb2.Category(
      title='losses',
      chart=[
          layout_pb2.Chart(
              title='losses',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'loss.*'],
              )),
          layout_pb2.Chart(
              title='baz',
              margin=layout_pb2.MarginChartContent(
                series=[
                  layout_pb2.MarginChartContent.Series(
                    value='loss/baz/scalar_summary',
                    lower='baz_lower/baz/scalar_summary',
                    upper='baz_upper/baz/scalar_summary'),
                ],
              )),
      ]),
    layout_pb2.Category(
      title='trig functions',
      chart=[
          layout_pb2.Chart(
              title='wave trig functions',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'trigFunctions/cosine', r'trigFunctions/sine'],
              )),
          # The range of tangent is different. Let's give it its own chart.
          layout_pb2.Chart(
              title='tan',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'trigFunctions/tangent'],
              )),
      ],
      # This category we care less about. Let's make it initially closed.
      closed=True),
  ]))
writer.add_summary(layout_summary)
</pre
            >
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              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
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this.setNode(t),this._removeFromParentsChildList(t),this._parent[t]=e,this._children[e][t]=!0,this},fVt.prototype._removeFromParentsChildList=function(t){delete this._children[this._parent[t]][t]},fVt.prototype.parent=function(t){if(this._isCompound){var e=this._parent[t];if(e!==pVt)return e}},fVt.prototype.children=function(t){if(hVt.isUndefined(t)&&(t=pVt),this._isCompound){var e=this._children[t];if(e)return hVt.keys(e)}else{if(t===pVt)return this.nodes();if(this.hasNode(t))return[]}},fVt.prototype.predecessors=function(t){var e=this._preds[t];if(e)return hVt.keys(e)},fVt.prototype.successors=function(t){var e=this._sucs[t];if(e)return hVt.keys(e)},fVt.prototype.neighbors=function(t){var e=this.predecessors(t);if(e)return hVt.union(e,this.successors(t))},fVt.prototype.isLeaf=function(t){return 0===(this.isDirected()?this.successors(t):this.neighbors(t)).length},fVt.prototype.filterNodes=function(t){var e=new this.constructor({directed:this._isDirected,multigraph:this._isMultigraph,compound:this._isCompound});e.setGraph(this.graph());var n=this;hVt.each(this._nodes,(function(n,i){t(i)&&e.setNode(i,n)})),hVt.each(this._edgeObjs,(function(t){e.hasNode(t.v)&&e.hasNode(t.w)&&e.setEdge(t,n.edge(t))}));var i={};function r(t){var o=n.parent(t);return void 0===o||e.hasNode(o)?(i[t]=o,o):o in i?i[o]:r(o)}return this._isCompound&&hVt.each(e.nodes(),(function(t){e.setParent(t,r(t))})),e},fVt.prototype.setDefaultEdgeLabel=function(t){return hVt.isFunction(t)||(t=hVt.constant(t)),this._defaultEdgeLabelFn=t,this},fVt.prototype.edgeCount=function(){return this._edgeCount},fVt.prototype.edges=function(){return hVt.values(this._edgeObjs)},fVt.prototype.setPath=function(t,e){var n=this,i=arguments;return hVt.reduce(t,(function(t,r){return i.length>1?n.setEdge(t,r,e):n.setEdge(t,r),r})),this},fVt.prototype.setEdge=function(){var t,e,n,i,r=!1,o=arguments[0];"object"==typeof o&&null!==o&&"v"in 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i=a.node(e);t.metagraph.setNode(e,i),t.parentNode=i.parentNode,t.cardinality++,null!=i.device&&(t.deviceHistogram[i.device]=(t.deviceHistogram[i.device]||0)+1),null!=i.xlaCluster&&(t.xlaClusterHistogram[i.xlaCluster]=(t.xlaClusterHistogram[i.xlaCluster]||0)+1),i.compatible?t.compatibilityHistogram.compatible=(t.compatibilityHistogram.compatible||0)+1:t.compatibilityHistogram.incompatible=(t.compatibilityHistogram.incompatible||0)+1,Ce.exports.each(i.inEmbeddings,(e=>{e.compatible?t.compatibilityHistogram.compatible=(t.compatibilityHistogram.compatible||0)+1:t.compatibilityHistogram.incompatible=(t.compatibilityHistogram.incompatible||0)+1})),Ce.exports.each(i.outEmbeddings,(e=>{e.compatible?t.compatibilityHistogram.compatible=(t.compatibilityHistogram.compatible||0)+1:t.compatibilityHistogram.incompatible=(t.compatibilityHistogram.incompatible||0)+1})),i.parentNode=t,n[e]=r,a.removeNode(e)})))}))}function cKt(t,e,n){let i={};return Ce.exports.each(t,(function(t,r){if(t.length<=1)return;let o={};Ce.exports.each(t,(function(t){const e="*"===t.charAt(t.length-1),i=t.split("/"),r=i[i.length-1],a=i.slice(0,i.length-1).join("/"),s=r.match(/^(\D*)(\d+)$/);let l,c,u="";s?(l=s[1],c=s[2]):(l=e?r.substr(0,r.length-1):r,c=0,u=e?"*":"");const h=V$t(l,u,a);o[h]=o[h]||[];const d=F$t(l,u,a,+c,t,n);o[h].push(d)})),Ce.exports.each(o,(function(t,o){if(t.length<2)return;t.sort((function(t,e){return+t.clusterId-+e.clusterId}));let a=[t[0]];for(let o=1;o<t.length;o++){let s=t[o];s.clusterId!==a[a.length-1].clusterId+1?(hKt(a,i,+r,e,n),a=[s]):a.push(s)}hKt(a,i,+r,e,n)}))})),i}function uKt(t,e,n){let i={};return Ce.exports.each(t,(function(t,r){if(t.length<=1)return;let o={},a={};Ce.exports.each(t,(function(t){let e="*"===t.charAt(t.length-1),i=t.split("/"),r=i[i.length-1],s=i.slice(0,i.length-1).join("/");const l=/(\d+)/g;let c,u,h,d,p,f=0;for(;c=l.exec(r);)++f,u=r.slice(0,c.index),h=c[0],d=r.slice(c.index+c[0].length),p=V$t(u,d,s),o[p]=o[p],o[p]||(o[p]=F$t(u,d,s,+h,t,n)),o[p].ids.push(h),a[t]=a[t]||[],a[t].push([p,h]);f<1&&(u=e?r.substr(0,r.length-1):r,h=0,d=e?"*":"",p=V$t(u,d,s),o[p]=o[p],o[p]||(o[p]=F$t(u,d,s,+h,t,n)),o[p].ids.push(h),a[t]=a[t]||[],a[t].push([p,h]))}));var s={};Ce.exports.each(a,(function(t,e){t.sort((function(t,e){return o[e[0]].ids.length-o[t[0]].ids.length}));var i=t[0][0],r=t[0][1];s[i]=s[i]||[];const a=e.split("/"),l=a.slice(0,a.length-1).join("/");var c=F$t(o[i].prefix,o[i].suffix,l,+r,e,n);s[i].push(c)})),Ce.exports.each(s,(function(t,o){if(t.length<2)return;t.sort((function(t,e){return+t.clusterId-+e.clusterId}));let a=[t[0]];for(let o=1;o<t.length;o++){let s=t[o];s.clusterId!==a[a.length-1].clusterId+1?(hKt(a,i,+r,e,n),a=[s]):a.push(s)}hKt(a,i,+r,e,n)}))})),i}function hKt(t,e,n,i,r){if(t.length>1){let o=V$t(t[0].prefix,t[0].suffix,t[0].parent,t[0].clusterId,t[t.length-1].clusterId),a=F$t(t[0].prefix,t[0].suffix,t[0].parent,n,o,r);Ce.exports.each(t,(function(t){a.ids.push(t.clusterId),a.metagraph.setNode(t.name,i.node(t.name))})),e[o]=a}}let dKt="#ffffff",pKt="#b2b2b2",fKt="#0f9d58",mKt="#db4437",gKt={DEFAULT_FILL:"#d9d9d9",DEFAULT_STROKE:"#a6a6a6",SATURATION:.6,LIGHTNESS:.85,EXPANDED_COLOR:"#f0f0f0",HUES:[220,100,180,40,20,340,260,300,140,60],STRUCTURE_PALETTE(t,e){let n=gKt.HUES,i=n[t%n.length],r=Math.sin(i*Math.PI/360);return cd(i,.01*(e?30:90-60*r),.01*(e?95:80)).toString()},DEVICE_PALETTE:t=>gKt.STRUCTURE_PALETTE(t),XLA_CLUSTER_PALETTE:t=>gKt.STRUCTURE_PALETTE(t),UNKNOWN:"#eee",GRADIENT_OUTLINE:"#888"},_Kt="white",yKt="#b2b2b2";const vKt=15,bKt=5,xKt=4,wKt=4,SKt=["NoOp"],MKt=[],EKt=["#fff5f0","#fb6a4a"],TKt=5,CKt=new RegExp("^(?:__function_library__)?(\\w+)_[a-z0-9]{8}(?:_\\d+)?$");class AKt{constructor(t,e,n){this.hierarchy=t,this.displayingStats=e,this.autoExtractNodes=n,this.index={},this.renderedOpNames=[],this.computeScales(),this.hasSubhierarchy={},this.root=new zKt(t.root,t.graphOptions),this.index[t.root.name]=this.root,this.renderedOpNames.push(t.root.name),this.buildSubhierarchy(t.root.name),this.root.expanded=!0,this.traceInputs=!1}computeScales(){this.deviceColorMap=YM().domain(this.hierarchy.devices).range(Ce.exports.map(Kl(this.hierarchy.devices.length),gKt.DEVICE_PALETTE)),this.xlaClusterColorMap=YM().domain(this.hierarchy.xlaClusters).range(Ce.exports.map(Kl(this.hierarchy.xlaClusters.length),gKt.XLA_CLUSTER_PALETTE));let t=this.hierarchy.root.metagraph,e=oc(t.nodes(),((e,n)=>{let i=t.node(e);if(null!=i.stats)return i.stats.totalBytes}));this.memoryUsageScale=cE().domain([0,e]).range(EKt);let n=oc(t.nodes(),((e,n)=>{let i=t.node(e);if(null!=i.stats)return i.stats.getTotalMicros()}));this.computeTimeScale=cE().domain([0,n]).range(EKt),this.edgeWidthSizedBasedScale=this.hierarchy.hasShapeInfo?u$t:cE().domain([1,this.hierarchy.maxMetaEdgeSize]).range([c$t,12])}getRenderNodeByName(t){return this.index[t]}getNodeByName(t){return this.hierarchy.node(t)}colorHistogram(t,e){if(Object.keys(t).length>0){const n=Ce.exports.sum(Object.keys(t).map((e=>t[e])));return Object.keys(t).map((i=>({color:e(i),proportion:t[i]/n})))}return null}getOrCreateRenderNodeByName(t){if(!t)return null;if(t in this.index)return this.index[t];let e=this.hierarchy.node(t);if(!e)return null;let n=e.isGroupNode?new zKt(e,this.hierarchy.graphOptions):new NKt(e);this.index[t]=n,this.renderedOpNames.push(t),e.stats&&(n.memoryColor=this.memoryUsageScale(e.stats.totalBytes),n.computeTimeColor=this.computeTimeScale(e.stats.getTotalMicros())),n.isFadedOut=this.displayingStats&&!b$t(e.stats);var i=null,r=null,o=null;if(e.isGroupNode){i=e.deviceHistogram,r=e.xlaClusterHistogram;let t=e.compatibilityHistogram.compatible,n=e.compatibilityHistogram.incompatible;0==t&&0==n||(o=t/(t+n))}else{let t=n.node.device;t&&(i={[t]:1});let e=n.node.xlaCluster;e&&(r={[e]:1}),n.node.type===A$t.OP&&(o=n.node.compatible?1:0)}return i&&(n.deviceColors=this.colorHistogram(i,this.deviceColorMap)),r&&(n.xlaClusterColors=this.colorHistogram(r,this.xlaClusterColorMap)),null!=o&&(n.compatibilityColors=[{color:fKt,proportion:o},{color:mKt,proportion:1-o}]),this.index[t]}getNearestVisibleAncestor(t){let e=Z$t(t),n=0,i=null,r=t;for(;n<e.length&&(r=e[n],i=this.getRenderNodeByName(r),i.expanded);n++);if(n==e.length-2){let t=e[n+1];if(i.inAnnotations.nodeNames[t])return t;if(i.outAnnotations.nodeNames[t])return t}return r}setDepth(t){DKt(this.root,+t)}isNodeAuxiliary(t){let e=this.getRenderNodeByName(t.node.parentNode.name),n=Ce.exports.find(e.isolatedInExtract,(e=>e.node.name===t.node.name));return!!n||(n=Ce.exports.find(e.isolatedOutExtract,(e=>e.node.name===t.node.name)),!!n)}getNamesOfRenderedOps(){return this.renderedOpNames}cloneAndAddFunctionOpNode(t,e,n,i){const r=n.name.replace(e,i);let o=t.metagraph.node(r);if(o)return o;o=new O$t({name:r,input:[],device:n.device,op:n.op,attr:Ce.exports.cloneDeep(n.attr)}),o.cardinality=n.cardinality,o.include=n.include,o.outputShapes=Ce.exports.cloneDeep(n.outputShapes),o.xlaCluster=n.xlaCluster,o.functionInputIndex=n.functionInputIndex,o.functionOutputIndex=n.functionOutputIndex,o.inputs=n.inputs.map((t=>{const n=Ce.exports.clone(t);return n.name=t.name.replace(e,i),n})),o.parentNode=t,t.metagraph.setNode(o.name,o),this.hierarchy.setNode(o.name,o);const a=n=>this.cloneAndAddFunctionOpNode(t,e,n,i);return o.inEmbeddings=n.inEmbeddings.map(a),o.outEmbeddings=n.outEmbeddings.map(a),o}cloneFunctionLibraryMetanode(t,e,n,i,r){const o={},a=this.cloneFunctionLibraryMetanodeHelper(t,e,n,i,r,o);return Ce.exports.isEmpty(o)||this.patchEdgesFromFunctionOutputs(e,o),a}cloneFunctionLibraryMetanodeHelper(t,e,n,i,r,o){const a=z$t(n.name.replace(i,r));return a.depth=n.depth,a.cardinality=n.cardinality,a.templateId=n.templateId,a.opHistogram=Ce.exports.clone(n.opHistogram),a.deviceHistogram=Ce.exports.clone(n.deviceHistogram),a.xlaClusterHistogram=Ce.exports.clone(n.xlaClusterHistogram),a.hasNonControlEdges=n.hasNonControlEdges,a.include=n.include,a.nodeAttributes=Ce.exports.clone(n.nodeAttributes),a.associatedFunction=n.associatedFunction,Ce.exports.each(n.metagraph.nodes(),(s=>{const l=n.metagraph.node(s);switch(l.type){case A$t.META:const n=this.cloneFunctionLibraryMetanodeHelper(t,e,l,i,r,o);n.parentNode=a,a.metagraph.setNode(n.name,n),this.hierarchy.setNode(n.name,n);break;case A$t.OP:const s=this.cloneAndAddFunctionOpNode(a,i,l,r);Ce.exports.isNumber(s.functionInputIndex)&&this.patchEdgesIntoFunctionInputs(e,s),Ce.exports.isNumber(s.functionOutputIndex)&&(o[s.functionOutputIndex]=s);break;default:console.warn(l.name+" is oddly neither a metanode nor an opnode.")}})),this.cloneLibraryMetanodeEdges(n,a,i,r),a}cloneLibraryMetanodeEdges(t,e,n,i){Ce.exports.each(t.metagraph.edges(),(r=>{const o=t.metagraph.edge(r),a=o.v.replace(n,i),s=o.w.replace(n,i),l=new H$t(a,s);l.inbound=o.inbound,l.numRegularEdges=o.numRegularEdges,l.numControlEdges=o.numControlEdges,l.numRefEdges=o.numRefEdges,l.totalSize=o.totalSize,o.baseEdgeList&&(l.baseEdgeList=o.baseEdgeList.map((t=>{const e=Ce.exports.clone(t);return e.v=t.v.replace(n,i),e.w=t.w.replace(n,i),e}))),e.metagraph.node(s)?e.metagraph.setEdge(a,s,l):e.metagraph.setEdge(s,a,l)}))}patchEdgesIntoFunctionInputs(t,e){let n=Math.min(e.functionInputIndex,t.inputs.length-1),i=Ce.exports.clone(t.inputs[n]);for(;i.isControlDependency;)n++,i=t.inputs[n];e.inputs.push(i);const r=this.hierarchy.getPredecessors(t.name);let o,a=0;Ce.exports.each(r.regular,(t=>{if(a+=t.numRegularEdges,a>n)return o=t,!1})),Ce.exports.each(o.baseEdgeList,(n=>{n.w===t.name&&(n.w=e.name),n.v===t.name&&(n.v=e.name)}))}patchEdgesFromFunctionOutputs(t,e){const n=this.hierarchy.getSuccessors(t.name);Ce.exports.each(n.regular,(n=>{Ce.exports.each(n.baseEdgeList,(n=>{const i=this.hierarchy.node(n.w);Ce.exports.each(i.inputs,(i=>{if(i.name===t.name){i.name=e[i.outputTensorKey].name,i.outputTensorKey=n.outputTensorKey}}))})),Ce.exports.each(n.baseEdgeList,(t=>{t.v=e[t.outputTensorKey].name,t.outputTensorKey="0"}))}))}buildSubhierarchy(t){if(t in this.hasSubhierarchy)return;this.hasSubhierarchy[t]=!0;let e=this.index[t];if(e.node.type!==A$t.META&&e.node.type!==A$t.SERIES)return;let n=e,i=n.node.metagraph,r=n.coreGraph;const o=[],a=[];Ce.exports.isEmpty(this.hierarchy.libraryFunctions)||(Ce.exports.each(i.nodes(),(t=>{const e=i.node(t),n=this.hierarchy.libraryFunctions[e.op];if(!n)return;if(0===t.indexOf(T$t))return;const r=this.cloneFunctionLibraryMetanode(i,e,n.node,n.node.name,e.name);o.push(e),a.push(r)})),Ce.exports.each(a,((t,e)=>{const n=o[e];t.parentNode=n.parentNode,i.setNode(n.name,t),this.hierarchy.setNode(n.name,t)}))),Ce.exports.each(i.nodes(),(t=>{let e=this.getOrCreateRenderNodeByName(t),n=e.node;r.setNode(t,e),n.isGroupNode||(Ce.exports.each(n.inEmbeddings,(t=>{let n=new IKt(null),i=new NKt(t);RKt(e,t,i,n,LKt.CONSTANT),this.index[t.name]=i})),Ce.exports.each(n.outEmbeddings,(t=>{let n=new IKt(null),i=new NKt(t);OKt(e,t,i,n,LKt.SUMMARY),this.index[t.name]=i})))})),Ce.exports.each(i.edges(),(t=>{let e=i.edge(t),n=new IKt(e);n.isFadedOut=this.index[t.v].isFadedOut||this.index[t.w].isFadedOut,r.setEdge(t.v,t.w,n)})),n.node.type===A$t.META&&(function s(t,e){(function n(t){let e=t.coreGraph;Ce.exports.each(e.nodes(),(n=>{e.node(n).node.include!==k$t.EXCLUDE||n.startsWith(T$t)||(t.coreGraph.outEdges(n).length>t.coreGraph.inEdges(n).length?HKt(t,n):FKt(t,n))}))})(t),SKt.length&&(function i(t){let e=t.coreGraph;Ce.exports.each(e.nodes(),(n=>{let i=e.node(n);i.node.include===k$t.UNSPECIFIED&&VKt(i.node,SKt)&&HKt(t,n)}))})(t);MKt.length&&(function r(t){let e=t.coreGraph;Ce.exports.each(e.nodes(),(n=>{let i=e.node(n);i.node.include===k$t.UNSPECIFIED&&VKt(i.node,MKt)&&FKt(t,n)}))})(t);e&&(function o(t){let e=t.coreGraph,n={},i={},r=0;if(Ce.exports.each(e.nodes(),(t=>{if(e.node(t).node.include!==k$t.UNSPECIFIED)return;let o=Ce.exports.reduce(e.predecessors(t),((n,i)=>n+(e.edge(i,t).metaedge.numRegularEdges?1:0)),0);0===o&&e.predecessors(t).length>0&&(o=e.predecessors(t).length);let a=Ce.exports.reduce(e.successors(t),((n,i)=>n+(e.edge(t,i).metaedge.numRegularEdges?1:0)),0);0===a&&e.successors(t).length>0&&(a=e.successors(t).length),n[t]=o,i[t]=a,r++})),r<vKt)return;let o=bKt-1,a=Math.round(.75*r),s=Math.round(.25*r),l=Object.keys(n).sort(((t,e)=>n[t]-n[e])),c=n[l[a]],u=c+c-n[l[s]];u=Math.max(u,o);for(let e=r-1;n[l[e]]>u;e--)FKt(t,l[e]);let h=Object.keys(i).sort(((t,e)=>i[t]-i[e])),d=i[h[a]],p=d+4*(d-i[h[s]]);p=Math.max(p,o);for(let n=r-1;i[h[n]]>p;n--){let i=e.node(h[n]);i&&!i.isInExtract&&HKt(t,h[n])}})(t);!(function a(t){let e=t.coreGraph,n={};Ce.exports.each(e.edges(),(t=>{e.edge(t).metaedge.numRegularEdges||((n[t.v]=n[t.v]||[]).push(t),(n[t.w]=n[t.w]||[]).push(t))})),Ce.exports.each(n,((t,n)=>{t.length>xKt&&Ce.exports.each(t,(t=>BKt(e,t.v,t.w)))}))})(t);let s=t.coreGraph;Ce.exports.each(s.nodes(),(e=>{let n=s.node(e),i=s.neighbors(e).length;if(n.node.include===k$t.UNSPECIFIED&&0===i){let i=n.outAnnotations.list.length>0,r=n.inAnnotations.list.length>0;n.isInExtract?(t.isolatedInExtract.push(n),n.node.include=k$t.EXCLUDE,s.removeNode(e)):n.isOutExtract?(t.isolatedOutExtract.push(n),n.node.include=k$t.EXCLUDE,s.removeNode(e)):i&&!r?(n.isInExtract=!0,t.isolatedInExtract.push(n),n.node.include=k$t.EXCLUDE,s.removeNode(e)):r&&!i&&(n.isOutExtract=!0,t.isolatedOutExtract.push(n),n.node.include=k$t.EXCLUDE,s.removeNode(e))}}))})(n,this.autoExtractNodes),Ce.exports.isEmpty(this.hierarchy.libraryFunctions)||this.buildSubhierarchiesForNeededFunctions(i),t===E$t&&Ce.exports.forOwn(this.hierarchy.libraryFunctions,((t,e)=>{const i=t.node,o=this.getOrCreateRenderNodeByName(i.name);n.libraryFunctionsExtract.push(o),o.node.include=k$t.EXCLUDE,r.removeNode(i.name)}));let l=n.node.parentNode;if(!l)return;let c=this.index[l.name],u=(t,...e)=>e.concat([t?"IN":"OUT"]).join("~~"),h=this.hierarchy.getBridgegraph(t),d={in:{},out:{},control:{}};Ce.exports.each(h.edges(),(t=>{let e=!!i.node(t.w),n=e?t.v:t.w;h.edge(t).numRegularEdges?e?d.out[n]=(d.out[n]||0)+1:d.in[n]=(d.in[n]||0)+1:d.control[n]=(d.control[n]||0)+1}));let p=this.hierarchy.getNodeMap();Ce.exports.each(h.edges(),(n=>{let o=h.edge(n),a=!!i.node(n.w),[s,f]=a?[n.w,n.v]:[n.v,n.w],m=this.index[s],g=this.index[f],_=g?g.node:p[f],y=!o.numRegularEdges&&d.control[f]>xKt,[,v]=a?[e.inAnnotations,m.inAnnotations]:[e.outAnnotations,m.outAnnotations],b=null,x=!1;if(!((a?d.out:d.in)[f]>wKt)&&!y&&m.isInCore()){let e=e=>c.coreGraph.edge(a?{v:e,w:t}:{v:t,w:e});b=e(f),b||(b=e(u(a,f,l.name))),x=!!b}let w=!1;if(b&&!o.numRegularEdges){let t=b,e=c.node;for(;t.adjoiningMetaedge;)t=t.adjoiningMetaedge,e=e.parentNode;let n=this.hierarchy.getTopologicalOrdering(e.name),i=t.metaedge;w=n[i.v]>n[i.w]}if(x=x&&!w,!x)return void v.push(new kKt(_,g,new IKt(o),LKt.SHORTCUT,a));let S=u(a,t),M=u(a,f,t),E=r.node(M);if(!E){let t=r.node(S);if(!t){t=new NKt({name:S,type:A$t.BRIDGE,isGroupNode:!1,cardinality:0,parentNode:null,stats:null,include:k$t.UNSPECIFIED,inbound:a,nodeAttributes:{}}),this.index[S]=t,r.setNode(S,t)}E=new NKt({name:M,type:A$t.BRIDGE,isGroupNode:!1,cardinality:1,parentNode:null,stats:null,include:k$t.UNSPECIFIED,inbound:a,nodeAttributes:{}}),this.index[M]=E,r.setNode(M,E),r.setParent(M,S),t.node.cardinality++}let T=new IKt(o);T.adjoiningMetaedge=b,a?r.setEdge(M,s,T):r.setEdge(s,M,T)})),Ce.exports.each([!0,!1],(e=>{let n=u(e,t),i=r.node(n);i&&Ce.exports.each(r.nodes(),(o=>{if(r.node(o).node.type===A$t.BRIDGE)return;if(!(e?!r.predecessors(o).length:!r.successors(o).length))return;let a=u(e,t,"STRUCTURAL_TARGET"),s=r.node(a);if(!s){s=new NKt({name:a,type:A$t.BRIDGE,isGroupNode:!1,cardinality:1,parentNode:null,stats:null,include:k$t.UNSPECIFIED,inbound:e,nodeAttributes:{}}),s.structural=!0,this.index[a]=s,r.setNode(a,s),i.node.cardinality++,r.setParent(a,n)}let l=new IKt(null);l.structural=!0,l.weight--,e?r.setEdge(a,o,l):r.setEdge(o,a,l)}))}))}buildSubhierarchiesForNeededFunctions(t){Ce.exports.each(t.edges(),(e=>{let n=t.edge(e),i=new IKt(n);Ce.exports.forEach(i.metaedge.baseEdgeList,(t=>{const e=t.v.split(M$t);for(let t=e.length;t>=0;t--){const n=e.slice(0,t),i=this.hierarchy.node(n.join(M$t));if(i){if(i.type===A$t.OP&&this.hierarchy.libraryFunctions[i.op])for(let t=1;t<n.length;t++){const e=n.slice(0,t).join(M$t);e&&this.buildSubhierarchy(e)}break}}}))}))}}class kKt{constructor(t,e,n,i,r){this.node=t,this.renderNodeInfo=e,this.renderMetaedgeInfo=n,this.annotationType=i,this.dx=0,this.dy=0,this.width=0,this.height=0,n&&n.metaedge&&(this.v=n.metaedge.v,this.w=n.metaedge.w),this.isIn=r,this.points=[]}}var LKt;!(function(t){t[t.SHORTCUT=0]="SHORTCUT",t[t.CONSTANT=1]="CONSTANT",t[t.SUMMARY=2]="SUMMARY",t[t.ELLIPSIS=3]="ELLIPSIS"})(LKt||(LKt={}));class PKt{constructor(){this.list=[],this.nodeNames={}}push(t){if(t.node.name in this.nodeNames)return;if(this.nodeNames[t.node.name]=!0,this.list.length<TKt)return void this.list.push(t);let e=this.list[this.list.length-1];if(e.annotationType===LKt.ELLIPSIS){let t=e.node;return void t.setNumMoreNodes(++t.numMoreNodes)}let n=new R$t(1);this.list.push(new kKt(n,new NKt(n),null,LKt.ELLIPSIS,t.isIn))}}class NKt{constructor(t){if(this.node=t,this.expanded=!1,this.inAnnotations=new PKt,this.outAnnotations=new PKt,this.x=0,this.y=0,this.width=0,this.height=0,this.inboxWidth=0,this.outboxWidth=0,this.excluded=!1,this.structural=!1,this.labelOffset=0,this.radius=0,this.labelHeight=0,this.paddingTop=0,this.paddingLeft=0,this.paddingRight=0,this.paddingBottom=0,this.isInExtract=!1,this.isOutExtract=!1,this.coreBox={width:0,height:0},this.isFadedOut=!1,this.displayName=t.name.substring(t.name.lastIndexOf(M$t)+1),t.type===A$t.META&&t.associatedFunction){const t=this.displayName.match(CKt);t?this.displayName=t[1]:Ce.exports.startsWith(this.displayName,T$t)&&(this.displayName=this.displayName.substring(T$t.length))}}isInCore(){return!this.isInExtract&&!this.isOutExtract&&!this.isLibraryFunction}}class IKt{constructor(t){this.metaedge=t,this.adjoiningMetaedge=null,this.structural=!1,this.weight=1,this.isFadedOut=!1}}function RKt(t,e,n,i,r){let o=new kKt(e,n,i,r,!0);t.inAnnotations.push(o)}function OKt(t,e,n,i,r){let o=new kKt(e,n,i,r,!1);t.outAnnotations.push(o)}class zKt extends NKt{constructor(t,e){super(t);let n=t.metagraph.graph();this.coreGraph=Y$t(n.name,C$t.CORE,e),this.inExtractBox={width:0,height:0},this.outExtractBox={width:0,height:0},this.libraryFunctionsBox={width:0,height:0},this.isolatedInExtract=[],this.isolatedOutExtract=[],this.libraryFunctionsExtract=[]}}function DKt(t,e){t.coreGraph&&(function n(t,e){Ce.exports.each(t.nodes(),(n=>{let i=t.node(n);if(i.expanded=e>1,e>0)switch(i.node.type){case A$t.META:case A$t.SERIES:DKt(i,e-1)}}))})(t.coreGraph,e)}function BKt(t,e,n){let i=t.node(e),r=t.node(n),o=t.edge(e,n);(i.node.include!==k$t.INCLUDE&&r.node.include!==k$t.INCLUDE||i.node.include===k$t.EXCLUDE||r.node.include===k$t.EXCLUDE)&&(OKt(i,r.node,r,o,LKt.SHORTCUT),RKt(r,i.node,i,o,LKt.SHORTCUT),t.removeEdge(e,n))}function HKt(t,e,n){let i=t.coreGraph,r=i.node(e);r.isOutExtract=!0,Ce.exports.each(i.predecessors(e),((t,n)=>{BKt(i,t,e)})),Ce.exports.each(i.successors(e),((t,n)=>{BKt(i,e,t)})),0===i.neighbors(e).length&&(r.node.include=k$t.EXCLUDE,t.isolatedOutExtract.push(r),i.removeNode(e))}function FKt(t,e,n){let i=t.coreGraph,r=i.node(e);r.isInExtract=!0,Ce.exports.each(i.successors(e),((t,n)=>{BKt(i,e,t)})),Ce.exports.each(i.predecessors(e),((t,n)=>{BKt(i,t,e)})),0===i.neighbors(e).length&&(r.node.include=k$t.EXCLUDE,t.isolatedInExtract.push(r),i.removeNode(e))}function VKt(t,e){if(t.type===A$t.OP){for(let n=0;n<e.length;n++)if(t.op===e[n])return!0}else if(t.type===A$t.META){let n=t.getRootOp();if(n)for(let t=0;t<e.length;t++)if(n.op===e[t])return!0}return!1}const UKt={animation:{duration:250},graph:{meta:{nodeSep:5,rankSep:25,edgeSep:5},series:{nodeSep:5,rankSep:25,edgeSep:5},padding:{paddingTop:40,paddingLeft:20}},subscene:{meta:{paddingTop:10,paddingBottom:10,paddingLeft:10,paddingRight:10,labelHeight:20,extractXOffset:15,extractYOffset:20},series:{paddingTop:10,paddingBottom:10,paddingLeft:10,paddingRight:10,labelHeight:10}},nodeSize:{meta:{radius:5,width:60,maxLabelWidth:52,height:cE().domain([1,200]).range([15,60]).clamp(!0),expandButtonRadius:3},op:{width:15,height:6,radius:3,labelOffset:-8,maxLabelWidth:30},series:{expanded:{radius:10,labelOffset:0},vertical:{width:16,height:13,labelOffset:-13},horizontal:{width:24,height:8,radius:10,labelOffset:-10}},bridge:{width:20,height:20,radius:2,labelOffset:0}},shortcutSize:{op:{width:10,height:4},meta:{width:12,height:4,radius:1},series:{width:14,height:4}},annotations:{inboxWidth:50,outboxWidth:50,xOffset:10,yOffset:3,labelOffset:2,maxLabelWidth:40},constant:{size:{width:4,height:4}},series:{maxStackCount:3,parallelStackOffsetRatio:.2,towerStackOffsetRatio:.5},minimap:{size:150}},jKt=140;function GKt(t){t.node.isGroupNode&&(function e(t){let e=t.coreGraph.nodes().map((e=>t.coreGraph.node(e))).concat(t.isolatedInExtract,t.isolatedOutExtract,t.libraryFunctionsExtract);Ce.exports.each(e,(t=>{switch(t.node.type){case A$t.OP:Ce.exports.extend(t,UKt.nodeSize.op);break;case A$t.BRIDGE:Ce.exports.extend(t,UKt.nodeSize.bridge);break;case A$t.META:if(t.expanded){GKt(t)}else Ce.exports.extend(t,UKt.nodeSize.meta),t.height=UKt.nodeSize.meta.height(t.node.cardinality);break;case A$t.SERIES:if(t.expanded){Ce.exports.extend(t,UKt.nodeSize.series.expanded),GKt(t)}else{Ce.exports.extend(t,t.node.hasNonControlEdges?UKt.nodeSize.series.vertical:UKt.nodeSize.series.horizontal)}break;default:throw Error("Unrecognized node type: "+t.node.type)}t.expanded||(function e(t){t.inboxWidth=t.inAnnotations.list.length>0?UKt.annotations.inboxWidth:0,t.outboxWidth=t.outAnnotations.list.length>0?UKt.annotations.outboxWidth:0,t.coreBox.width=t.width,t.coreBox.height=t.height,t.width=Math.max(t.coreBox.width+t.inboxWidth+t.outboxWidth,3*t.displayName.length)})(t),(function n(t){if(t.expanded)return;let e=t.inAnnotations.list,n=t.outAnnotations.list;Ce.exports.each(e,(t=>qKt(t))),Ce.exports.each(n,(t=>qKt(t)));let i=UKt.annotations,r=Ce.exports.reduce(e,((e,n,r)=>{let o=r>0?i.yOffset:0;return n.dx=-(t.coreBox.width+n.width)/2-i.xOffset,n.dy=e+o+n.height/2,e+o+n.height}),0);Ce.exports.each(e,(t=>{t.dy-=r/2,t.labelOffset=i.labelOffset}));let o=Ce.exports.reduce(n,((e,n,r)=>{let o=r>0?i.yOffset:0;return n.dx=(t.coreBox.width+n.width)/2+i.xOffset,n.dy=e+o+n.height/2,e+o+n.height}),0);Ce.exports.each(n,(t=>{t.dy-=o/2,t.labelOffset=i.labelOffset}));let a=Math.min(t.height/2-t.radius,r/2);a=a<0?0:a;let s=cE().domain([0,e.length-1]).range([-a,a]);Ce.exports.each(e,((n,i)=>{n.points=[{dx:n.dx+n.width/2,dy:n.dy},{dx:-t.coreBox.width/2,dy:e.length>1?s(i):0}]}));let l=Math.min(t.height/2-t.radius,o/2);l=l<0?0:l;let c=cE().domain([0,n.length-1]).range([-l,l]);Ce.exports.each(n,((e,i)=>{e.points=[{dx:t.coreBox.width/2,dy:n.length>1?c(i):0},{dx:e.dx-e.width/2,dy:e.dy}]})),t.height=Math.max(t.height,r,o)})(t)}))})(t),t.node.type===A$t.META?(function n(t){let e=UKt.subscene.meta;Ce.exports.extend(t,e),Ce.exports.extend(t.coreBox,WKt(t.coreGraph,UKt.graph.meta));let n=t.isolatedInExtract.length?Ce.exports.maxBy(t.isolatedInExtract,(t=>t.width)).width:null;t.inExtractBox.width=null!=n?n:0,t.inExtractBox.height=Ce.exports.reduce(t.isolatedInExtract,((t,n,i)=>{let r=i>0?e.extractYOffset:0;return n.x=0,n.y=t+r+n.height/2,t+r+n.height}),0);let i=t.isolatedOutExtract.length?Ce.exports.maxBy(t.isolatedOutExtract,(t=>t.width)).width:null;t.outExtractBox.width=null!=i?i:0,t.outExtractBox.height=Ce.exports.reduce(t.isolatedOutExtract,((t,n,i)=>{let r=i>0?e.extractYOffset:0;return n.x=0,n.y=t+r+n.height/2,t+r+n.height}),0);let r=t.libraryFunctionsExtract.length?Ce.exports.maxBy(t.libraryFunctionsExtract,(t=>t.width)).width:null;t.libraryFunctionsBox.width=null!=r?r:0,t.libraryFunctionsBox.height=Ce.exports.reduce(t.libraryFunctionsExtract,((t,n,i)=>{let r=i>0?e.extractYOffset:0;return n.x=0,n.y=t+r+n.height/2,t+r+n.height}),0);let o=0;t.isolatedInExtract.length>0&&o++;t.isolatedOutExtract.length>0&&o++;t.libraryFunctionsExtract.length>0&&o++;t.coreGraph.nodeCount()>0&&o++;let a=UKt.subscene.meta.extractXOffset,s=o<=1?0:o*a;const l=Math.max(jKt,t.inExtractBox.width+t.outExtractBox.width);t.coreBox.width+=l+s+t.libraryFunctionsBox.width+s,t.coreBox.height=e.labelHeight+Math.max(t.inExtractBox.height,t.coreBox.height,t.libraryFunctionsBox.height,t.outExtractBox.height),t.width=t.coreBox.width+e.paddingLeft+e.paddingRight,t.height=t.paddingTop+t.coreBox.height+t.paddingBottom})(t):t.node.type===A$t.SERIES&&(function i(t){let e=t.coreGraph,n=UKt.subscene.series;Ce.exports.extend(t,n),Ce.exports.extend(t.coreBox,WKt(t.coreGraph,UKt.graph.series)),Ce.exports.each(e.nodes(),(t=>{e.node(t).excluded=!1})),t.width=t.coreBox.width+n.paddingLeft+n.paddingRight,t.height=t.coreBox.height+n.paddingTop+n.paddingBottom})(t)}function WKt(t,e){Ce.exports.extend(t.graph(),{nodesep:e.nodeSep,ranksep:e.rankSep,edgesep:e.edgeSep});let n=[];if(Ce.exports.each(t.nodes(),(e=>{t.node(e).node.type===A$t.BRIDGE||n.push(e)})),!n.length)return{width:0,height:0};t$t.layout(t);let i=1/0,r=1/0,o=-1/0,a=-1/0;return Ce.exports.each(n,(e=>{let n=t.node(e),s=.5*n.width,l=n.x-s,c=n.x+s;i=l<i?l:i,o=c>o?c:o;let u=.5*n.height,h=n.y-u,d=n.y+u;r=h<r?h:r,a=d>a?d:a})),Ce.exports.each(t.edges(),(e=>{let n=t.edge(e);if(n.structural)return;let s=t.node(n.metaedge.v),l=t.node(n.metaedge.w);if(3===n.points.length&&(function c(t){let e=XKt(t[0],t[1]);for(let n=1;n<t.length-1;n++){let i=XKt(t[n],t[n+1]);if(Math.abs(i-e)>1)return!1;e=i}return!0})(n.points)){if(null!=s){let t=s.expanded?s.x:YKt(s);n.points[0].x=t}if(null!=l){let t=l.expanded?l.x:YKt(l);n.points[2].x=t}n.points=[n.points[0],n.points[1]]}null!=l&&(n.points[n.points.length-1]=$Kt(n.points[n.points.length-2],l)),null!=s&&(n.points[0]=$Kt(n.points[1],s)),Ce.exports.each(n.points,(t=>{i=t.x<i?t.x:i,o=t.x>o?t.x:o,r=t.y<r?t.y:r,a=t.y>a?t.y:a}))})),Ce.exports.each(t.nodes(),(e=>{let n=t.node(e);n.x-=i,n.y-=r})),Ce.exports.each(t.edges(),(e=>{Ce.exports.each(t.edge(e).points,(t=>{t.x-=i,t.y-=r}))})),{width:o-i,height:a-r}}function qKt(t){switch(t.annotationType){case LKt.CONSTANT:Ce.exports.extend(t,UKt.constant.size);break;case LKt.SHORTCUT:if(t.node.type===A$t.OP)Ce.exports.extend(t,UKt.shortcutSize.op);else if(t.node.type===A$t.META)Ce.exports.extend(t,UKt.shortcutSize.meta);else{if(t.node.type!==A$t.SERIES)throw Error("Invalid node type: "+t.node.type);Ce.exports.extend(t,UKt.shortcutSize.series)}break;case LKt.SUMMARY:Ce.exports.extend(t,UKt.constant.size)}}function YKt(t){if(t.expanded)return t.x;return t.x-t.width/2+(t.inAnnotations.list.length?t.inboxWidth:0)+t.coreBox.width/2}function XKt(t,e){return 180*Math.atan((e.y-t.y)/(e.x-t.x))/Math.PI}function $Kt(t,e){let n,i,r=e.expanded?e.x:YKt(e),o=e.y,a=t.x-r,s=t.y-o,l=e.expanded?e.width:e.coreBox.width,c=e.expanded?e.height:e.coreBox.height;return Math.abs(s)*l/2>Math.abs(a)*c/2?(s<0&&(c=-c),n=0===s?0:c/2*a/s,i=c/2):(a<0&&(l=-l),n=l/2,i=0===a?0:l/2*s/a),{x:r+n,y:o+i}}const KKt=a$t,ZKt=n$t;let 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i=OZt(e,n),r=a[i.name];r?r.opNodes.push(n):a[i.name]={visibleParent:i,opNodes:[n]}}));let s={},l=[o];s[o.name]={traced:!1,index:0,connectionEndpoints:[]};let c=o;for(let t=1;c.name!==E$t;t++)c=c.parentNode,s[c.name]={traced:!1,index:t,connectionEndpoints:[]},l[t]=c;return Ce.exports.forOwn(a,(function(n,r){let a=n.visibleParent;Ce.exports.each(n.opNodes,(function(n){i=RZt(t,e,n,i)})),a.name!==o.name&&(function c(t,e,n,i){let r=e,o=e,a=[];for(;!n[r.name];)o.name!==r.name&&a.push([o,r]),o=r,r=r.parentNode;let s=n[r.name].index,l=i[Math.max(s-1,0)].name,c=l,u=o.name,h=o.name;const d=Du(t);d.selectAll(`[data-edge="${h}--${l}"]`).classed("input-edge-highlight",!0),Ce.exports.each(a,(function(t){d.selectAll(`[data-edge="${t[0].name}--${c}~~${t[1].name}~~OUT"]`).classed("input-edge-highlight",!0)}));for(let t=1;t<s;t++){d.selectAll(`[data-edge="${u}~~${i[t].name}~~IN--${i[t-1].name}"]`).classed("input-edge-highlight",!0)}})(t,a,s,l)})),i}function OZt(t,e){let n=!1,i=e;for(;!n;)if(i=(e=i).parentNode,void 0===i)n=!0;else{let e=t.getRenderNodeByName(i.name);e&&(e.expanded||i instanceof O$t)&&(n=!0)}return e}function zZt(t,e,n,i){let r=t.selectAll((function(){return this.childNodes})).data(e.list,(t=>t.node.name));return r.enter().append("g").attr("data-name",(t=>t.node.name)).each((function(t){let e=Du(this);i.addAnnotationGroup(t,n,e);let r=n$t.Annotation.EDGE,o=t.renderMetaedgeInfo&&t.renderMetaedgeInfo.metaedge;o&&!o.numRegularEdges&&(r+=" "+n$t.Annotation.CONTROL_EDGE),o&&o.numRefEdges&&(r+=" "+n$t.Edge.REF_LINE),pZt(e,t,i,r),t.annotationType!==LKt.ELLIPSIS?(!(function a(t,e){let n=e.node.name.split("/");return DZt(t,n[n.length-1],e,null)})(e,t),(function s(t,e){if(e.annotationType===LKt.SUMMARY){s$t(t,"use").attr("class","summary").attr("xlink:href","#summary-icon").attr("cursor","pointer")}else{s$t(EZt(t,e,n$t.Annotation.NODE),"title").text(e.node.name)}})(e,t)):DZt(e,t.node.name,t,n$t.Annotation.ELLIPSIS)})).merge(r).attr("class",(t=>n$t.Annotation.GROUP+" "+(function e(t){return(LKt[t]||"").toLowerCase()||null})(t.annotationType)+" "+TZt(t))).each((function(t){let e=Du(this);!(function r(t,e,n,i){let r=YKt(e);n.renderNodeInfo&&n.annotationType!==LKt.ELLIPSIS&&LZt(t,n.renderNodeInfo,i,n$t.Annotation.NODE);n.annotationType===LKt.SUMMARY&&(n.width+=10);t.select("text."+n$t.Annotation.LABEL).transition().attr("x",r+n.dx+(n.isIn?-1:1)*(n.width/2+n.labelOffset)).attr("y",e.y+n.dy),t.select("use.summary").transition().attr("x",r+n.dx-3).attr("y",e.y+n.dy-6),eZt(t.select("."+n$t.Annotation.NODE+" ellipse"),r+n.dx,e.y+n.dy,n.width,n.height),tZt(t.select("."+n$t.Annotation.NODE+" rect"),r+n.dx,e.y+n.dy,n.width,n.height),tZt(t.select("."+n$t.Annotation.NODE+" use"),r+n.dx,e.y+n.dy,n.width,n.height),t.select("path."+n$t.Annotation.EDGE).transition().attr("d",(t=>{let n=t.points.map((t=>({x:t.dx+r,y:t.dy+e.y})));return fZt(n)}))})(e,n,t,i),t.annotationType!==LKt.ELLIPSIS&&(function o(t,e,n,i){t.on("mouseover",(t=>{i.fire("annotation-highlight",{name:t.node.name,hostName:e.node.name})})).on("mouseout",(t=>{i.fire("annotation-unhighlight",{name:t.node.name,hostName:e.node.name})})).on("click",(t=>{Mu.stopPropagation(),i.fire("annotation-select",{name:t.node.name,hostName:e.node.name})})),n.annotationType!==LKt.SUMMARY&&n.annotationType!==LKt.CONSTANT&&t.on("contextmenu",oZt(i,yZt(n.node,i)))})(e,n,t,i)})),r.exit().each((function(t){i.removeAnnotationGroup(t,n)})).remove(),r}function DZt(t,e,n,i){let r=n$t.Annotation.LABEL;return i&&(r+=" "+i),wZt(t.append("text").attr("class",r).attr("dy",".35em").attr("text-anchor",n.isIn?"end":"start").text(e),-1,i$t.LABEL)}function BZt(t,e,n,i){let r=a$t(t,"g",i=i||n$t.Scene.GROUP).empty(),o=s$t(t,"g",i),a=s$t(o,"g",n$t.Scene.CORE),s=Ce.exports.reduce(e.coreGraph.nodes(),((t,n)=>{let i=e.coreGraph.node(n);return i.excluded||t.push(i),t}),[]);if(e.node.type===A$t.SERIES&&s.reverse(),lZt(a,e.coreGraph,n),mZt(a,s,n),e.isolatedInExtract.length>0){mZt(s$t(o,"g",n$t.Scene.INEXTRACT),e.isolatedInExtract,n)}else a$t(o,"g",n$t.Scene.INEXTRACT).remove();if(e.isolatedOutExtract.length>0){mZt(s$t(o,"g",n$t.Scene.OUTEXTRACT),e.isolatedOutExtract,n)}else a$t(o,"g",n$t.Scene.OUTEXTRACT).remove();if(e.libraryFunctionsExtract.length>0){mZt(s$t(o,"g",n$t.Scene.FUNCTION_LIBRARY),e.libraryFunctionsExtract,n)}else a$t(o,"g",n$t.Scene.FUNCTION_LIBRARY).remove();return(function l(t,e){let n=e.node.type===A$t.SERIES?0:UKt.subscene.meta.labelHeight;QKt(KKt(t,"g",ZKt.Scene.CORE),0,n);let i=e.isolatedInExtract.length>0,r=e.isolatedOutExtract.length>0,o=e.libraryFunctionsExtract.length>0,a=UKt.subscene.meta.extractXOffset,s=0;if(i&&(s+=e.outExtractBox.width),r&&(s+=e.outExtractBox.width),i){let i=e.coreBox.width;i=s<jKt?i-jKt+e.inExtractBox.width/2:i-e.inExtractBox.width/2-e.outExtractBox.width-(r?a:0),i=i-e.libraryFunctionsBox.width-(o?a:0),QKt(KKt(t,"g",ZKt.Scene.INEXTRACT),i,n)}if(r){let i=e.coreBox.width;s<jKt?i=i-jKt+e.outExtractBox.width/2:i-=e.outExtractBox.width/2,i=i-e.libraryFunctionsBox.width-(o?a:0),QKt(KKt(t,"g",ZKt.Scene.OUTEXTRACT),i,n)}if(o){let i=e.coreBox.width-e.libraryFunctionsBox.width/2;QKt(KKt(t,"g",ZKt.Scene.FUNCTION_LIBRARY),i,n)}})(o,e),r&&o.attr("opacity",0).transition().attr("opacity",1),o}class HZt{constructor(t,e,n,i,r,o){this.svg=t,this.labelPadding=o,this.zoomG=e,this.mainZoom=n,this.maxWandH=r;let a=Du(i.shadowRoot),s=a.select("svg"),l=s.select("rect");this.viewpointCoord={x:0,y:0};let c=Ih().subject(Object).on("drag",(t=>{this.viewpointCoord.x=Mu.x,this.viewpointCoord.y=Mu.y,this.updateViewpoint()}));l.datum(this.viewpointCoord).call(c),s.on("click",(()=>{if(Mu.defaultPrevented)return;let t=Number(l.attr("width")),e=Number(l.attr("height")),n=bh(s.node());this.viewpointCoord.x=n[0]-t/2,this.viewpointCoord.y=n[1]-e/2,this.updateViewpoint()})),this.viewpoint=l.node(),this.minimapSvg=s.node(),this.minimap=i,this.canvas=a.select("canvas.first").node(),this.canvasBuffer=a.select("canvas.second").node(),this.downloadCanvas=a.select("canvas.download").node(),Du(this.downloadCanvas).style("display","none"),this.update()}updateViewpoint(){Du(this.viewpoint).attr("x",this.viewpointCoord.x).attr("y",this.viewpointCoord.y);let t=-this.viewpointCoord.x*this.scaleMain/this.scaleMinimap,e=-this.viewpointCoord.y*this.scaleMain/this.scaleMinimap;Du(this.svg).call(this.mainZoom.transform,qN.translate(t,e).scale(this.scaleMain))}getImageBlob(){return new Promise((t=>{this.downloadCanvas.toBlob((e=>{t(e)}),"image/png")}))}update(){let t=null;try{if(t=this.zoomG.getBBox(),0===t.width)return}catch(t){return}let e=Du(this.svg),n="";const i=this.svg,r=(i.getRootNode?i.getRootNode():this.svg.parentNode).styleSheets;for(let t=0;t<r.length;t++)try{let e=r[t].cssRules||r[t].rules;if(null==e)continue;for(let t=0;t<e.length;t++)n+=e[t].cssText.replace(/ ?tf-[\w-]+ ?/g,"")+"\n"}catch(t){if("SecurityError"!==t.name)throw t}let o=e.append("style");o.text(n);let a=Du(this.zoomG),s=a.attr("transform");a.attr("transform",null),t.height+=t.y,t.width+=t.x,t.height+=2*this.labelPadding,t.width+=2*this.labelPadding,e.attr("width",t.width).attr("height",t.height),this.scaleMinimap=this.maxWandH/Math.max(t.width,t.height),this.minimapSize={width:t.width*this.scaleMinimap,height:t.height*this.scaleMinimap},Du(this.minimapSvg).attr(this.minimapSize),Du(this.canvasBuffer).attr(this.minimapSize);const l=Du(this.downloadCanvas);l.style("width",t.width),l.style("height",t.height),l.attr("width",3*t.width),l.attr("height",3*t.height),null!=this.translate&&null!=this.zoom&&requestAnimationFrame((()=>this.zoom()));let c=(new XMLSerializer).serializeToString(this.svg);o.remove(),e.attr("width",null).attr("height",null),a.attr("transform",s);let u=new Image;u.onload=()=>{let t=this.canvasBuffer.getContext("2d");t.clearRect(0,0,this.canvasBuffer.width,this.canvasBuffer.height),t.drawImage(u,0,0,this.minimapSize.width,this.minimapSize.height),requestAnimationFrame((()=>{Du(this.canvasBuffer).style("display",null),Du(this.canvas).style("display","none"),[this.canvas,this.canvasBuffer]=[this.canvasBuffer,this.canvas]}));let e=this.downloadCanvas.getContext("2d");e.clearRect(0,0,this.downloadCanvas.width,this.downloadCanvas.height),e.drawImage(u,0,0,this.downloadCanvas.width,this.downloadCanvas.height)},u.onerror=()=>{let t=new Blob([c],{type:"image/svg+xml;charset=utf-8"});u.src=URL.createObjectURL(t)},u.src="data:image/svg+xml;charset=utf-8,"+encodeURIComponent(c)}zoom(t){if(null==this.scaleMinimap)return;t&&(this.translate=[t.x,t.y],this.scaleMain=t.k);let e=this.svg.getBoundingClientRect(),n=Du(this.viewpoint);this.viewpointCoord.x=-this.translate[0]*this.scaleMinimap/this.scaleMain,this.viewpointCoord.y=-this.translate[1]*this.scaleMinimap/this.scaleMain;let i=e.width*this.scaleMinimap/this.scaleMain,r=e.height*this.scaleMinimap/this.scaleMain;n.attr("x",this.viewpointCoord.x).attr("y",this.viewpointCoord.y).attr("width",i).attr("height",r);let o=this.minimapSize.width,a=this.minimapSize.height,s=this.viewpointCoord.x,l=this.viewpointCoord.y;(Math.min(Math.max(0,s+i),o)-Math.min(Math.max(0,s),o))*(Math.min(Math.max(0,l+r),a)-Math.min(Math.max(0,l),a))/(o*a)<.8?this.minimap.classList.remove("hidden"):this.minimap.classList.add("hidden")}}let FZt=class extends we{init(t,e,n,i,r){return new HZt(t,e,n,this,i,r)}};FZt.template=xe`
    <style>
      :host {
        background-color: white;
        transition: opacity 0.3s linear;
        pointer-events: auto;
      }

      :host(.hidden) {
        opacity: 0;
        pointer-events: none;
      }

      canvas {
        border: 1px solid #999;
      }

      rect {
        fill: white;
        stroke: #111111;
        stroke-width: 1px;
        fill-opacity: 0;
        filter: url(#minimapDropShadow);
        cursor: move;
      }

      svg {
        position: absolute;
      }
    </style>
    <svg>
      <defs>
        <filter
          id="minimapDropShadow"
          x="-20%"
          y="-20%"
          width="150%"
          height="150%"
        >
          <feOffset result="offOut" in="SourceGraphic" dx="1" dy="1"></feOffset>
          <feColorMatrix
            result="matrixOut"
            in="offOut"
            type="matrix"
            values="0.1 0 0 0 0 0 0.1 0 0 0 0 0 0.1 0 0 0 0 0 0.5 0"
          ></feColorMatrix>
          <feGaussianBlur
            result="blurOut"
            in="matrixOut"
            stdDeviation="2"
          ></feGaussianBlur>
          <feBlend in="SourceGraphic" in2="blurOut" mode="normal"></feBlend>
        </filter>
      </defs>
      <rect></rect>
    </svg>
    <canvas class="first"></canvas>
    <!-- Additional canvas to use as buffer to avoid flickering between updates -->
    <canvas class="second"></canvas>
    <canvas class="download"></canvas>
  `,FZt=t([i("tf-graph-minimap")],FZt);const VZt=xe`
  <style>
    :host(.dark-mode) {
      filter: invert(1);
    }

    :host {
      display: flex;
      font-size: 20px;
      height: 100%;
      width: 100%;
    }

    #svg {
      flex: 1;
      font-family: Roboto, sans-serif;
      height: 100%;
      overflow: hidden;
      width: 100%;
    }

    #hidden {
      position: fixed;
      top: 0px;
      visibility: hidden;
    }

    text {
      user-select: none;
    }

    /* --- Node and annotation-node for Metanode --- */

    .meta > .nodeshape > rect,
    .meta > .annotation-node > rect {
      cursor: pointer;
      fill: hsl(0, 0%, 70%);
    }
    .node.meta.highlighted > .nodeshape > rect,
    .node.meta.highlighted > .annotation-node > rect {
      stroke-width: 2;
    }
    .annotation.meta.highlighted > .nodeshape > rect,
    .annotation.meta.highlighted > .annotation-node > rect {
      stroke-width: 1;
    }
    .meta.selected > .nodeshape > rect,
    .meta.selected > .annotation-node > rect {
      stroke: red;
      stroke-width: 2;
    }
    .node.meta.selected.expanded > .nodeshape > rect,
    .node.meta.selected.expanded > .annotation-node > rect {
      stroke: red;
      stroke-width: 3;
    }
    .annotation.meta.selected > .nodeshape > rect,
    .annotation.meta.selected > .annotation-node > rect {
      stroke: red;
      stroke-width: 2;
    }
    .node.meta.selected.expanded.highlighted > .nodeshape > rect,
    .node.meta.selected.expanded.highlighted > .annotation-node > rect {
      stroke: red;
      stroke-width: 4;
    }

    .faded,
    .faded rect,
    .faded ellipse,
    .faded path,
    .faded use,
    #rectHatch line,
    #ellipseHatch line {
      color: #e0d4b3 !important;
      fill: white;
      stroke: #e0d4b3 !important;
    }

    .faded path {
      stroke-width: 1px !important;
    }

    .faded rect {
      fill: url(#rectHatch) !important;
    }

    .faded ellipse,
    .faded use {
      fill: url(#ellipseHatch) !important;
    }

    .faded text {
      opacity: 0;
    }

    /* Rules used for input-tracing. */
    .input-highlight > * > rect,
    .input-highlight > * > ellipse,
    .input-highlight > * > use {
      fill: white;
      stroke: #ff9800 !important;
    }

    /*  - Faded non-input styling */
    .non-input > * > rect,
.non-input > * > ellipse,
.non-input > * > use,
/* For Const nodes. */
.non-input > * > .constant:not([class*="input-highlight"]) >
  .annotation-node > ellipse,
/* For styling of annotation nodes of non-input nodes. */
.non-input > g > .annotation > .annotation-node > rect {
      stroke: #e0d4b3 !important;
      stroke-width: inherit;
      stroke-dasharray: inherit;
    }

    .non-input path {
      visibility: hidden;
    }

    .non-input > .nodeshape > rect,
.non-input > .annotation-node > rect,
/* For styling of annotation nodes of non-input nodes. */
.non-input > g > .annotation > .annotation-node > rect {
      fill: url(#rectHatch) !important;
    }

    .non-input ellipse,
    .non-input use {
      fill: url(#ellipseHatch) !important;
    }

    .non-input > text {
      opacity: 0;
    }

    .non-input .annotation > .annotation-edge {
      marker-end: url(#annotation-arrowhead-faded);
    }

    .non-input .annotation > .annotation-edge.refline {
      marker-start: url(#ref-annotation-arrowhead-faded);
    }

    /* Input edges. */
    .input-edge-highlight > text {
      fill: black !important;
    }
    .input-highlight > .in-annotations > .annotation > .annotation-edge,
    .input-highlight-selected
      > .in-annotations
      > .annotation
      > .annotation-edge {
      stroke: #999 !important;
    }

    /* Non-input edges. */
    .non-input-edge-highlight,
.non-input > g > .annotation > path,
/* Annotation styles (label and edges respectively). */
.non-input > g >
.annotation:not(.input-highlight):not(.input-highlight-selected) >
.annotation-label
/*.annotation-edge*/ {
      visibility: hidden;
    }

    /* --- Op Node --- */

    .op > .nodeshape > .nodecolortarget,
    .op > .annotation-node > .nodecolortarget {
      cursor: pointer;
      fill: #fff;
      stroke: #ccc;
    }

    .op.selected > .nodeshape > .nodecolortarget,
    .op.selected > .annotation-node > .nodecolortarget {
      stroke: red;
      stroke-width: 2;
    }

    .op.highlighted > .nodeshape > .nodecolortarget,
    .op.highlighted > .annotation-node > .nodecolortarget {
      stroke-width: 2;
    }

    /* --- Series Node --- */

    /* By default, don't show the series background <rect>. */
    .series > .nodeshape > rect {
      fill: hsl(0, 0%, 70%);
      fill-opacity: 0;
      stroke-dasharray: 5, 5;
      stroke-opacity: 0;
      cursor: pointer;
    }

    /* Once expanded, show the series background <rect> and hide the <use>. */
    .series.expanded > .nodeshape > rect {
      fill-opacity: 0.15;
      stroke: hsl(0, 0%, 70%);
      stroke-opacity: 1;
    }
    .series.expanded > .nodeshape > use {
      visibility: hidden;
    }

    /**
 * TODO: Simplify this by applying a stable class name to all <g>
 * elements that currently have either the nodeshape or annotation-node classes.
 */
    .series > .nodeshape > use,
    .series > .annotation-node > use {
      stroke: #ccc;
    }
    .series.highlighted > .nodeshape > use,
    .series.highlighted > .annotation-node > use {
      stroke-width: 2;
    }
    .series.selected > .nodeshape > use,
    .series.selected > .annotation-node > use {
      stroke: red;
      stroke-width: 2;
    }

    .series.selected > .nodeshape > rect {
      stroke: red;
      stroke-width: 2;
    }

    .annotation.series.selected > .annotation-node > use {
      stroke: red;
      stroke-width: 2;
    }

    /* --- Bridge Node --- */
    .bridge > .nodeshape > rect {
      stroke: #f0f;
      opacity: 0.2;
      display: none;
    }

    /* --- Structural Elements --- */
    .edge > path.edgeline.structural {
      stroke: #f0f;
      opacity: 0.2;
      display: none;
    }

    /* Reference Edge */
    .edge > path.edgeline.referenceedge {
      stroke: #ffb74d;
      opacity: 1;
    }

    /* --- Series Nodes --- */

    /* Hide the rect for a series' annotation. */
    .series > .annotation-node > rect {
      display: none;
    }

    /* --- Node label --- */

    .node {
      /* Provide a hint to browsers to avoid using their static rasterization
      at initial scale, which looks very pixelated on Chromium when zoomed in.
      Note that we intentionally do *not* use 'will-change: transform' and
      'translateZ(0) here, which introduce blurriness on Firefox.
      See https://github.com/tensorflow/tensorboard/issues/4744 */
      transform: translateZ(1px);
    }

    .node > text.nodelabel {
      cursor: pointer;
      fill: #444;
    }

    .meta.expanded > text.nodelabel {
      font-size: 9px;
    }

    .series > text.nodelabel {
      font-size: 8px;
    }

    .op > text.nodelabel {
      font-size: 6px;
    }

    .bridge > text.nodelabel {
      display: none;
    }

    .node.meta.expanded > text.nodelabel {
      cursor: normal;
    }

    .annotation.meta.highlighted > text.annotation-label {
      fill: #50a3f7;
    }

    .annotation.meta.selected > text.annotation-label {
      fill: #4285f4;
    }

    /* --- Annotation --- */

    /* only applied for annotations that are not summary or constant.
(.summary, .constant gets overridden below) */
    .annotation > .annotation-node > * {
      stroke-width: 0.5;
      stroke-dasharray: 1, 1;
    }

    .annotation.summary > .annotation-node > *,
    .annotation.constant > .annotation-node > * {
      stroke-width: 1;
      stroke-dasharray: none;
    }

    .annotation > .annotation-edge {
      fill: none;
      stroke: #aaa;
      stroke-width: 0.5;
      marker-end: url(#annotation-arrowhead);
    }

    .faded .annotation > .annotation-edge {
      marker-end: url(#annotation-arrowhead-faded);
    }

    .annotation > .annotation-edge.refline {
      marker-start: url(#ref-annotation-arrowhead);
    }

    .faded .annotation > .annotation-edge.refline {
      marker-start: url(#ref-annotation-arrowhead-faded);
    }

    .annotation > .annotation-control-edge {
      stroke-dasharray: 1, 1;
    }

    #annotation-arrowhead {
      fill: #aaa;
    }

    #annotation-arrowhead-faded {
      fill: #e0d4b3;
    }

    #ref-annotation-arrowhead {
      fill: #aaa;
    }

    #ref-annotation-arrowhead-faded {
      fill: #e0d4b3;
    }

    .annotation > .annotation-label {
      font-size: 5px;
      cursor: pointer;
    }
    .annotation > .annotation-label.annotation-ellipsis {
      cursor: default;
    }

    /* Hide annotations on expanded meta nodes since they're redundant. */
    .expanded > .in-annotations,
    .expanded > .out-annotations {
      display: none;
    }

    /* --- Annotation: Constant --- */

    .constant > .annotation-node > ellipse {
      cursor: pointer;
      fill: white;
      stroke: #848484;
    }

    .constant.selected > .annotation-node > ellipse {
      fill: white;
      stroke: red;
    }

    .constant.highlighted > .annotation-node > ellipse {
      stroke-width: 1.5;
    }

    /* --- Annotation: Summary --- */

    .summary > .annotation-node > ellipse {
      cursor: pointer;
      fill: #db4437;
      stroke: #db4437;
    }

    .summary.selected > .annotation-node > ellipse {
      fill: #a52714;
      stroke: #a52714;
    }

    .summary.highlighted > .annotation-node > ellipse {
      stroke-width: 1.5;
    }

    /* --- Edge --- */

    .edge > path.edgeline {
      fill: none;
      stroke: #bbb;
      stroke-linecap: round;
      stroke-width: 0.75;
    }

    .edge .selectableedge {
      cursor: pointer;
    }

    .selectededge > path.edgeline {
      cursor: default;
      stroke: #f00;
    }

    .edge.selectededge text {
      fill: #000;
    }

    /* Labels showing tensor shapes on edges */
    .edge > text {
      font-size: 3.5px;
      fill: #666;
    }

    .dataflow-arrowhead {
      fill: #bbb;
    }

    .reference-arrowhead {
      fill: #ffb74d;
    }

    .selected-arrowhead {
      fill: #f00;
    }

    .edge .control-dep {
      stroke-dasharray: 2, 2;
    }

    /* --- Group node expand/collapse button --- */

    /* Hides expand/collapse buttons when a node isn't expanded or highlighted. Using
   incredibly small opacity so that the bounding box of the <g> parent still takes
   this container into account even when it isn't visible */
    .node:not(.highlighted):not(.expanded) > .nodeshape > .buttoncontainer {
      opacity: 0.01;
    }
    .node.highlighted > .nodeshape > .buttoncontainer {
      cursor: pointer;
    }
    .buttoncircle {
      fill: #e7811d;
    }
    .buttoncircle:hover {
      fill: #b96717;
    }
    .expandbutton,
    .collapsebutton {
      stroke: white;
    }
    /* Do not let the path elements in the button take pointer focus */
    .node > .nodeshape > .buttoncontainer > .expandbutton,
    .node > .nodeshape > .buttoncontainer > .collapsebutton {
      pointer-events: none;
    }
    /* Only show the expand button when a node is collapsed and only show the
   collapse button when a node is expanded. */
    .node.expanded > .nodeshape > .buttoncontainer > .expandbutton {
      display: none;
    }
    .node:not(.expanded) > .nodeshape > .buttoncontainer > .collapsebutton {
      display: none;
    }

    .health-pill-stats {
      font-size: 4px;
      text-anchor: middle;
    }

    .health-pill rect {
      filter: url(#health-pill-shadow);
      rx: 3;
      ry: 3;
    }

    .titleContainer {
      position: relative;
      top: 20px;
    }

    .title,
    .auxTitle,
    .functionLibraryTitle {
      position: absolute;
    }

    #minimap {
      position: absolute;
      right: 20px;
      bottom: 20px;
    }

    .context-menu {
      position: absolute;
      display: none;
      background-color: #e2e2e2;
      border-radius: 2px;
      font-size: 14px;
      min-width: 150px;
      border: 1px solid #d4d4d4;
    }

    .context-menu ul {
      list-style-type: none;
      margin: 0;
      padding: 0;
      cursor: default;
    }

    .context-menu ul li {
      padding: 4px 16px;
    }

    .context-menu ul li:hover {
      background-color: #f3913e;
      color: white;
    }
  </style>
  <div class="titleContainer">
    <div id="title" class="title">Main Graph</div>
    <div id="auxTitle" class="auxTitle">Auxiliary Nodes</div>
    <div id="functionLibraryTitle" class="functionLibraryTitle">Functions</div>
  </div>
  <svg id="svg">
    <defs>
      <!-- Arrow heads for reference edge paths of different predefined sizes per color. -->
      <path
        id="reference-arrowhead-path"
        d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0"
      ></path>
      <marker
        class="reference-arrowhead"
        id="reference-arrowhead-small"
        viewBox="0 0 10 10"
        markerWidth="5"
        markerHeight="5"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#reference-arrowhead-path"></use>
      </marker>
      <marker
        class="reference-arrowhead"
        id="reference-arrowhead-medium"
        viewBox="0 0 10 10"
        markerWidth="13"
        markerHeight="13"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#reference-arrowhead-path"></use>
      </marker>
      <marker
        class="reference-arrowhead"
        id="reference-arrowhead-large"
        viewBox="0 0 10 10"
        markerWidth="16"
        markerHeight="16"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#reference-arrowhead-path"></use>
      </marker>
      <marker
        class="reference-arrowhead"
        id="reference-arrowhead-xlarge"
        viewBox="0 0 10 10"
        markerWidth="20"
        markerHeight="20"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#reference-arrowhead-path"></use>
      </marker>

      <!-- Arrow heads for dataflow edge paths of different predefined sizes per color. -->
      <path
        id="dataflow-arrowhead-path"
        d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0"
      ></path>
      <marker
        class="dataflow-arrowhead"
        id="dataflow-arrowhead-small"
        viewBox="0 0 10 10"
        markerWidth="5"
        markerHeight="5"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#dataflow-arrowhead-path"></use>
      </marker>
      <marker
        class="dataflow-arrowhead"
        id="dataflow-arrowhead-medium"
        viewBox="0 0 10 10"
        markerWidth="13"
        markerHeight="13"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#dataflow-arrowhead-path"></use>
      </marker>
      <marker
        class="dataflow-arrowhead"
        id="dataflow-arrowhead-large"
        viewBox="0 0 10 10"
        markerWidth="16"
        markerHeight="16"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#dataflow-arrowhead-path"></use>
      </marker>
      <marker
        class="dataflow-arrowhead"
        id="dataflow-arrowhead-xlarge"
        viewBox="0 0 10 10"
        markerWidth="20"
        markerHeight="20"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#dataflow-arrowhead-path"></use>
      </marker>

      <!-- Arrow head for annotation edge paths. -->
      <marker
        id="annotation-arrowhead"
        markerWidth="5"
        markerHeight="5"
        refX="5"
        refY="2.5"
        orient="auto"
      >
        <path d="M 0,0 L 5,2.5 L 0,5 L 0,0"></path>
      </marker>
      <marker
        id="annotation-arrowhead-faded"
        markerWidth="5"
        markerHeight="5"
        refX="5"
        refY="2.5"
        orient="auto"
      >
        <path d="M 0,0 L 5,2.5 L 0,5 L 0,0"></path>
      </marker>
      <marker
        id="ref-annotation-arrowhead"
        markerWidth="5"
        markerHeight="5"
        refX="0"
        refY="2.5"
        orient="auto"
      >
        <path d="M 5,0 L 0,2.5 L 5,5 L 5,0"></path>
      </marker>
      <marker
        id="ref-annotation-arrowhead-faded"
        markerWidth="5"
        markerHeight="5"
        refX="0"
        refY="2.5"
        orient="auto"
      >
        <path d="M 5,0 L 0,2.5 L 5,5 L 5,0"></path>
      </marker>
      <!-- Template for an Op node ellipse. -->
      <ellipse
        id="op-node-stamp"
        rx="7.5"
        ry="3"
        stroke="inherit"
        fill="inherit"
      ></ellipse>
      <!-- Template for an Op node annotation ellipse (smaller). -->
      <ellipse
        id="op-node-annotation-stamp"
        rx="5"
        ry="2"
        stroke="inherit"
        fill="inherit"
      ></ellipse>
      <!-- Vertically stacked series of Op nodes when unexpanded. -->
      <g id="op-series-vertical-stamp">
        <use xlink:href="#op-node-stamp" x="8" y="9"></use>
        <use xlink:href="#op-node-stamp" x="8" y="6"></use>
        <use xlink:href="#op-node-stamp" x="8" y="3"></use>
      </g>
      <!-- Horizontally stacked series of Op nodes when unexpanded. -->
      <g id="op-series-horizontal-stamp">
        <use xlink:href="#op-node-stamp" x="16" y="4"></use>
        <use xlink:href="#op-node-stamp" x="12" y="4"></use>
        <use xlink:href="#op-node-stamp" x="8" y="4"></use>
      </g>
      <!-- Horizontally stacked series of Op nodes for annotation. -->
      <g id="op-series-annotation-stamp">
        <use xlink:href="#op-node-annotation-stamp" x="9" y="2"></use>
        <use xlink:href="#op-node-annotation-stamp" x="7" y="2"></use>
        <use xlink:href="#op-node-annotation-stamp" x="5" y="2"></use>
      </g>
      <svg
        id="summary-icon"
        fill="#848484"
        height="12"
        viewBox="0 0 24 24"
        width="12"
      >
        <path
          d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z"
        ></path>
      </svg>

      <!-- Hatch patterns for faded out nodes. -->
      <pattern
        id="rectHatch"
        patternTransform="rotate(45 0 0)"
        width="5"
        height="5"
        patternUnits="userSpaceOnUse"
      >
        <line x1="0" y1="0" x2="0" y2="5" style="stroke-width: 1"></line>
      </pattern>
      <pattern
        id="ellipseHatch"
        patternTransform="rotate(45 0 0)"
        width="2"
        height="2"
        patternUnits="userSpaceOnUse"
      >
        <line x1="0" y1="0" x2="0" y2="2" style="stroke-width: 1"></line>
      </pattern>

      <!-- A shadow for health pills. -->
      <filter
        id="health-pill-shadow"
        x="-40%"
        y="-40%"
        width="180%"
        height="180%"
      >
        <feGaussianBlur in="SourceAlpha" stdDeviation="0.8"></feGaussianBlur>
        <feOffset dx="0" dy="0" result="offsetblur"></feOffset>
        <feFlood flood-color="#000000"></feFlood>
        <feComposite in2="offsetblur" operator="in"></feComposite>
        <feMerge>
          <feMergeNode></feMergeNode>
          <feMergeNode in="SourceGraphic"></feMergeNode>
        </feMerge>
      </filter>
    </defs>
    <!-- Make a large rectangle that fills the svg space so that
  zoom events get captured on safari -->
    <rect fill="white" width="10000" height="10000"></rect>
    <g id="root"></g>
  </svg>
  <tf-graph-minimap id="minimap"></tf-graph-minimap>
  <div id="contextMenu" class="context-menu"></div>
`;let UZt=class extends(cr(gl(we))){constructor(){super(...arguments),this._zoomed=!1,this._zoomStartCoords=null,this._zoomTransform=null,this._maxZoomDistanceForClick=20,this._nodeGroupIndex={},this._annotationGroupIndex={},this._edgeGroupIndex={},this.maxMetanodeLabelLengthFontSize=9,this.minMetanodeLabelLengthFontSize=6,this.maxMetanodeLabelLengthLargeFont=11,this.maxMetanodeLabelLength=18}getNode(t){return this.renderHierarchy.getRenderNodeByName(t)}isNodeExpanded(t){return t.expanded}setNodeExpanded(t){this._build(this.renderHierarchy),this._updateLabels(!this._zoomed)}panToNode(t){(function e(t,n,i,r){const o=Du(n).select(`[data-name="${t}"]`).node();if(!o)return console.warn(`panToNode() failed for node name "${t}"`),!1;let a=o.getBBox(),s=o.getScreenCTM(),l=n.createSVGPoint(),c=n.createSVGPoint();l.x=a.x,l.y=a.y,c.x=a.x+a.width,c.y=a.y+a.height,l=l.matrixTransform(s),c=c.matrixTransform(s);let u=(t,e,n,i)=>!(t>n&&e<i),h=n.getBoundingClientRect();const d=h.top+h.height-150;if(u(l.x,c.x,h.left,h.left+h.width-320)||u(l.y,c.y,h.top,d)){let t=h.left+h.width/2-(l.x+c.x)/2,e=h.top+h.height/2-(l.y+c.y)/2;const i=YN(n);return Du(n).transition().duration(500).call(r.translateBy,t/i.k,e/i.k),!0}return!1})(t,this.$.svg,0,this._zoom)&&(this._zoomed=!0)}getGraphSvgRoot(){return this.$.svg}getContextMenu(){return this.$.contextMenu}_resetState(){this._nodeGroupIndex={},this._annotationGroupIndex={},this._edgeGroupIndex={},this._updateLabels(!1),Du(this.$.svg).select("#root").selectAll("*").remove(),AZt(this.$.svg)}_build(t){this.templateIndex=t.hierarchy.getTemplateIndex(),d$t("tf-graph-scene (layout):",function(){GKt(t.root)}.bind(this),BAt.RENDER_SCENE_LAYOUT),d$t("tf-graph-scene (build scene):",function(){BZt(Du(this.$.root),t.root,this),(function e(t,n){Du(t).on("click",(()=>{n.fire("graph-select")}))})(this.$.svg,this),this._updateInputTrace()}.bind(this),BAt.RENDER_SCENE_BUILD_SCENE),setTimeout(function(){this._updateHealthPills(this.nodeNamesToHealthPills,this.healthPillStepIndex),this.minimap.update()}.bind(this),UKt.animation.duration)}ready(){super.ready(),this._zoom=nI().on("end",function(){this._zoomStartCoords&&(Math.sqrt(Math.pow(this._zoomStartCoords.x-this._zoomTransform.x,2)+Math.pow(this._zoomStartCoords.y-this._zoomTransform.y,2))<this._maxZoomDistanceForClick?this._fireEnableClick():setTimeout(this._fireEnableClick.bind(this),50));this._zoomStartCoords=null}.bind(this)).on("zoom",function(){this._zoomTransform=Mu.transform,this._zoomStartCoords||(this._zoomStartCoords=this._zoomTransform,this.fire("disable-click")),this._zoomed=!0,Du(this.$.root).attr("transform",Mu.transform),this.minimap.zoom(Mu.transform)}.bind(this)),Du(this.$.svg).call(this._zoom).on("dblclick.zoom",null),Du(window).on("resize",function(){this.minimap.zoom()}.bind(this)),this.minimap=this.$.minimap.init(this.$.svg,this.$.root,this._zoom,UKt.minimap.size,UKt.subscene.meta.labelHeight)}attached(){this.set("_isAttached",!0)}detached(){this.set("_isAttached",!1)}_renderHierarchyChanged(){var t=this.renderHierarchy;this._hasRenderHierarchyBeenFitOnce=!1,this._resetState(),this._build(t)}_animateAndFit(){!this._hasRenderHierarchyBeenFitOnce&&this._isAttached&&setTimeout(this.fit.bind(this),UKt.animation.duration)}_updateLabels(t){var e=this.$$(".title"),n=e.style,i=this.$$(".auxTitle"),r=i.style,o=this.$$(".functionLibraryTitle").style;const a=Du(this.$.svg);var s=a.select("."+ZKt.Scene.GROUP+">."+ZKt.Scene.CORE).node();if(t&&s&&this.progress&&100===this.progress.value){var l=a.select("."+ZKt.Scene.GROUP+">."+ZKt.Scene.INEXTRACT).node()||a.select("."+ZKt.Scene.GROUP+">."+ZKt.Scene.OUTEXTRACT).node(),c=s.getCTM().e,u=l?l.getCTM().e:null;n.display="inline",n.left=c+"px",null!==u&&u!==c?(r.display="inline",u=Math.max(c+e.getBoundingClientRect().width,u),r.left=u+"px"):r.display="none";let t=a.select("."+ZKt.Scene.GROUP+">."+ZKt.Scene.FUNCTION_LIBRARY).node(),h=t?t.getCTM().e:null;null!==h&&h!==u?(o.display="inline",h=Math.max(u+i.getBoundingClientRect().width,h),o.left=h+"px"):o.display="none"}else n.display="none",r.display="none",o.display="none"}nodeColorsChanged(){null!=this.renderHierarchy&&(this.templateIndex=this.renderHierarchy.hierarchy.getTemplateIndex(),Ce.exports.each(this._nodeGroupIndex,((t,e)=>{this._updateNodeState(e)})),this.minimap.update())}fit(){this._hasRenderHierarchyBeenFitOnce=!0,(function t(e,n,i,r){let o=e.getBoundingClientRect(),a=null;try{if(a=n.getBBox(),0===a.width)return}catch(t){return}let s=.9*Math.min(o.width/a.width,o.height/a.height,2),l=UKt.graph;const c=qN.scale(s).translate(l.padding.paddingLeft,l.padding.paddingTop);Du(e).transition().duration(500).call(i.transform,c).on("end.fitted",(()=>{i.on("end.fitted",null),r()}))})(this.$.svg,this.$.root,this._zoom,function(){this._zoomed=!1}.bind(this))}getImageBlob(){return this.minimap.getImageBlob()}isNodeSelected(t){return t===this.selectedNode}isNodeHighlighted(t){return t===this.highlightedNode}addAnnotationGroup(t,e,n){var i=t.node.name;this._annotationGroupIndex[i]=this._annotationGroupIndex[i]||{},this._annotationGroupIndex[i][e.node.name]=n}getAnnotationGroupsIndex(t){return this._annotationGroupIndex[t]}removeAnnotationGroup(t,e){delete this._annotationGroupIndex[t.node.name][e.node.name]}addNodeGroup(t,e){this._nodeGroupIndex[t]=e}getNodeGroup(t){return this._nodeGroupIndex[t]}removeNodeGroup(t){delete this._nodeGroupIndex[t]}addEdgeGroup(t,e){this._edgeGroupIndex[t]=e}getEdgeGroup(t){return this._edgeGroupIndex[t]}_updateHealthPills(){!(function t(e,n,i){if(!n)return;let r=1;Du(e).selectAll("g.nodeshape").each((function(t){const e=n[t.node.name];iZt(this,e?e[i]:null,t,r++)}))})(this.$.svg,this.nodeNamesToHealthPills,this.healthPillStepIndex)}_updateNodeState(t){var e=this.getNode(t),n=this.getNodeGroup(t);(n&&LZt(n,e,this),e.node.type===A$t.META&&e.node.associatedFunction&&!e.isLibraryFunction)&&LZt(Du("."+ZKt.Scene.GROUP+">."+ZKt.Scene.FUNCTION_LIBRARY+' g[data-name="'+(T$t+e.node.associatedFunction)+'"]'),e,this);var i=this.getAnnotationGroupsIndex(t);Ce.exports.each(i,((t,n)=>{LZt(t,e,this,ZKt.Annotation.NODE)}))}_selectedNodeChanged(t,e){if(t!==e&&(e&&this._updateNodeState(e),t)){this.minimap.update();for(var n,i=this.renderHierarchy.hierarchy.node(t),r=[];null!=i.parentNode&&i.parentNode.name!=E$t;)r.push((i=i.parentNode).name);Ce.exports.forEachRight(r,(t=>{this.renderHierarchy.buildSubhierarchy(t);var e=this.renderHierarchy.getRenderNodeByName(t);e.node.isGroupNode&&!e.expanded&&(e.expanded=!0,n||(n=e))})),n&&(this.setNodeExpanded(n),this._zoomed=!0),t&&this._updateNodeState(t),setTimeout((()=>{this.panToNode(t)}),UKt.animation.duration)}}_highlightedNodeChanged(t,e){t!==e&&(t&&this._updateNodeState(t),e&&this._updateNodeState(e))}_onZoomChanged(){this._updateLabels(!this._zoomed)}_fireEnableClick(){this.fire("enable-click")}_updateInputTrace(){NZt(this.getGraphSvgRoot(),this.renderHierarchy,this.selectedNode,this.traceInputs)}};UZt.template=VZt,t([o({type:Object}),e("design:type",AKt)],UZt.prototype,"renderHierarchy",void 0),t([o({type:String}),e("design:type",String)],UZt.prototype,"name",void 0),t([o({type:String}),e("design:type",String)],UZt.prototype,"colorBy",void 0),t([o({type:Boolean}),e("design:type",Boolean)],UZt.prototype,"traceInputs",void 0),t([o({type:Boolean}),e("design:type",Boolean)],UZt.prototype,"_hasRenderHierarchyBeenFitOnce",void 0),t([o({type:Boolean}),e("design:type",Boolean)],UZt.prototype,"_isAttached",void 0),t([o({type:Object}),e("design:type",Object)],UZt.prototype,"_zoom",void 0),t([o({type:String,observer:"_highlightedNodeChanged"}),e("design:type",String)],UZt.prototype,"highlightedNode",void 0),t([o({type:String,observer:"_selectedNodeChanged"}),e("design:type",String)],UZt.prototype,"selectedNode",void 0),t([o({type:Object}),e("design:type",Object)],UZt.prototype,"handleEdgeSelected",void 0),t([o({type:Boolean,observer:"_onZoomChanged"}),e("design:type",Boolean)],UZt.prototype,"_zoomed",void 0),t([o({type:Object}),e("design:type",Object)],UZt.prototype,"_zoomStartCoords",void 0),t([o({type:Object}),e("design:type",Object)],UZt.prototype,"_zoomTransform",void 0),t([o({type:Number}),e("design:type",Number)],UZt.prototype,"_maxZoomDistanceForClick",void 0),t([o({type:Object}),e("design:type",Function)],UZt.prototype,"templateIndex",void 0),t([o({type:Object}),e("design:type",Object)],UZt.prototype,"_nodeGroupIndex",void 0),t([o({type:Object}),e("design:type",Object)],UZt.prototype,"_annotationGroupIndex",void 0),t([o({type:Object}),e("design:type",Object)],UZt.prototype,"_edgeGroupIndex",void 0),t([o({type:Number}),e("design:type",Number)],UZt.prototype,"maxMetanodeLabelLengthFontSize",void 0),t([o({type:Number}),e("design:type",Number)],UZt.prototype,"minMetanodeLabelLengthFontSize",void 0),t([o({type:Number}),e("design:type",Number)],UZt.prototype,"maxMetanodeLabelLengthLargeFont",void 0),t([o({type:Number}),e("design:type",Number)],UZt.prototype,"maxMetanodeLabelLength",void 0),t([o({type:Object}),e("design:type",Object)],UZt.prototype,"progress",void 0),t([o({type:Array}),e("design:type",Array)],UZt.prototype,"nodeContextMenuItems",void 0),t([o({type:Object}),e("design:type",Object)],UZt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Number}),e("design:type",Number)],UZt.prototype,"healthPillStepIndex",void 0),t([a("renderHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],UZt.prototype,"_renderHierarchyChanged",null),t([a("_isAttached","renderHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],UZt.prototype,"_animateAndFit",null),t([a("colorBy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],UZt.prototype,"nodeColorsChanged",null),t([a("nodeNamesToHealthPills","healthPillStepIndex"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],UZt.prototype,"_updateHealthPills",null),t([a("traceInputs","selectedNode"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],UZt.prototype,"_updateInputTrace",null),UZt=t([i("tf-graph-scene")],UZt);let jZt=class extends(cr(we)){constructor(){super(...arguments),this._renderDepth=1,this._allowGraphSelect=!0,this.edgeWidthFunction="",this.handleNodeSelected="",this.edgeLabelFunction="",this.handleEdgeSelected=""}panToNode(t){this.$$("tf-graph-scene").panToNode(t)}_autoExtractNodesChanged(){var t=this.graphHierarchy;if(t){for(const e of Object.values(t.getNodeMap()))e.include=k$t.UNSPECIFIED;this._buildRenderHierarchy(t)}}_buildNewRenderHierarchy(){var t=this.graphHierarchy;t&&this._buildRenderHierarchy(t)}_statsChanged(){var t=this.stats,e=this.devicesForStats;this.graphHierarchy&&(t&&e&&(!(function n(t,e,i){Ce.exports.each(t.nodes,(t=>{t.stats=null})),Ce.exports.each(e.dev_stats,(e=>{i&&!i[e.device]||Ce.exports.each(e.node_stats,(n=>{let i=n.node_name in t.nodes?n.node_name:$$t(n.node_name);if(!(i in t.nodes))return;let r=0;n.memory&&Ce.exports.each(n.memory,(t=>{t.total_bytes&&(t.total_bytes>0?r+=Number(t.total_bytes):console.log("ignoring negative memory allocation for "+i))}));let o=null;n.output&&(o=Ce.exports.map(n.output,(t=>Ce.exports.map(t.tensor_description.shape.dim,(t=>Number(t.size)))))),t.nodes[i].device=e.device,null==t.nodes[i].stats&&(t.nodes[i].stats=new l$t(o)),t.nodes[i].stats.addBytesAllocation(r),n.all_end_rel_micros&&(n.all_end_rel_micros>0?t.nodes[i].stats.addExecutionTime(n.all_start_micros,n.all_start_micros+n.all_end_rel_micros):console.log("ignoring negative runtime for "+i))}))}))})(this.basicGraph,t,e),(function i(t,e){let n={},i={};Ce.exports.each(t.root.leaves(),(e=>{let r=t.node(e);null!=r.device&&(n[r.device]=!0),null!=r.xlaCluster&&(i[r.xlaCluster]=!0)})),t.devices=Ce.exports.keys(n),t.xlaClusters=Ce.exports.keys(i),Ce.exports.each(t.getNodeMap(),((t,e)=>{t.isGroupNode&&(t.stats=new l$t(null),t.deviceHistogram={})})),Ce.exports.each(t.root.leaves(),(e=>{let n=t.node(e),i=n;for(;null!=i.parentNode;){if(null!=n.device){let t=i.parentNode.deviceHistogram;t[n.device]=(t[n.device]||0)+1}if(null!=n.xlaCluster){let t=i.parentNode.xlaClusterHistogram;t[n.xlaCluster]=(t[n.xlaCluster]||0)+1}null!=n.stats&&i.parentNode.stats.combine(n.stats),i=i.parentNode}}))})(this.graphHierarchy)),this._buildRenderHierarchy(this.graphHierarchy))}ready(){super.ready(),this.addEventListener("graph-select",this._graphSelected.bind(this)),this.addEventListener("disable-click",this._disableClick.bind(this)),this.addEventListener("enable-click",this._enableClick.bind(this)),this.addEventListener("node-toggle-expand",this._nodeToggleExpand.bind(this)),this.addEventListener("node-select",this._nodeSelected.bind(this)),this.addEventListener("node-highlight",this._nodeHighlighted.bind(this)),this.addEventListener("node-unhighlight",this._nodeUnhighlighted.bind(this)),this.addEventListener("node-toggle-extract",this._nodeToggleExtract.bind(this)),this.addEventListener("node-toggle-seriesgroup",this._nodeToggleSeriesGroup.bind(this)),this.addEventListener("edge-select",this._edgeSelected.bind(this)),this.addEventListener("annotation-select",this._nodeSelected.bind(this)),this.addEventListener("annotation-highlight",this._nodeHighlighted.bind(this)),this.addEventListener("annotation-unhighlight",this._nodeUnhighlighted.bind(this))}_buildRenderHierarchy(t){if(t.root.type!==A$t.META)return;const e=this,n=d$t("new tf_graph_render.Hierarchy",(()=>{const n=new AKt(t,!!this.stats,this.autoExtractNodes);function i(t){return{minValue:t.domain()[0],maxValue:t.domain()[1],startColor:t.range()[0],endColor:t.range()[1]}}return n.edgeLabelFunction=this.edgeLabelFunction,n.edgeWidthFunction=this.edgeWidthFunction,e._setColorByParams({compute_time:i(n.computeTimeScale),memory:i(n.memoryUsageScale),device:Ce.exports.map(n.deviceColorMap.domain(),(function(t){return{device:t,color:n.deviceColorMap(t)}})),xla_cluster:Ce.exports.map(n.xlaClusterColorMap.domain(),(function(t){return{xla_cluster:t,color:n.xlaClusterColorMap(t)}}))}),n}),BAt.RENDER_BUILD_HIERARCHY);e._setRenderHierarchy(n)}_getVisible(t){return t?this.renderHierarchy.getNearestVisibleAncestor(t):t}fit(){this.$.scene.fit()}getImageBlob(){return this.$.scene.getImageBlob()}_graphChanged(){this.graphHierarchy&&(this.graphHierarchy.addListener(iKt.TEMPLATES_UPDATED,(()=>{this.$.scene.nodeColorsChanged()})),this.fire("graph-select"))}_graphSelected(t){this._allowGraphSelect&&(this.set("selectedNode",null),this.set("selectedEdge",null)),this._allowGraphSelect=!0}_disableClick(t){this._allowGraphSelect=!1}_enableClick(t){this._allowGraphSelect=!0}_selectedNodeChanged(){this.handleNodeSelected&&this.handleNodeSelected(this.selectedNode)}_selectedEdgeChanged(){var t=this.selectedEdge;this._deselectPreviousEdge(),t&&(this._lastSelectedEdgeGroup.classed(ZKt.Edge.SELECTED,!0),this._updateMarkerOfSelectedEdge(t)),this.handleEdgeSelected&&this.handleEdgeSelected(t)}_nodeSelected(t){this._allowGraphSelect&&this.set("selectedNode",t.detail.name),this._allowGraphSelect=!0}_edgeSelected(t){this._allowGraphSelect&&(this.set("_lastSelectedEdgeGroup",t.detail.edgeGroup),this.set("selectedEdge",t.detail.edgeData)),this._allowGraphSelect=!0}_nodeHighlighted(t){this.set("highlightedNode",t.detail.name)}_nodeUnhighlighted(t){this.set("highlightedNode",null)}_nodeToggleExpand(t){this._nodeSelected(t);var e=t.detail.name,n=this.renderHierarchy.getRenderNodeByName(e);n.node.type!==A$t.OP&&(this.renderHierarchy.buildSubhierarchy(e),n.expanded=!n.expanded,this.async((function(){this.$.scene.setNodeExpanded(n)}),75),h$t({actionId:BAt.NODE_EXPANSION_TOGGLED,eventLabel:n.expanded?"expanded":"collapsed"}))}_nodeToggleExtract(t){this.nodeToggleExtract(t.detail.name)}nodeToggleExtract(t){const e=this.renderHierarchy.getRenderNodeByName(t);e.node.include=e.node.include==k$t.INCLUDE?k$t.EXCLUDE:e.node.include==k$t.EXCLUDE||this.renderHierarchy.isNodeAuxiliary(e)?k$t.INCLUDE:k$t.EXCLUDE,this._buildRenderHierarchy(this.graphHierarchy),h$t({actionId:BAt.NODE_AUXILIARY_EXTRACTION_CHANGED,eventLabel:e.node.include===k$t.INCLUDE?"Auxiliary to Main":"Main to Auxiliary"})}_nodeToggleSeriesGroup(t){this.nodeToggleSeriesGroup(t.detail.name)}nodeToggleSeriesGroup(t){this.set("progress",{value:0,msg:""});var e=f$t(p$t(this),100,"Namespace hierarchy");const n=Object.assign(Object.assign({},this.hierarchyParams),{seriesMap:this.graphHierarchy.buildSeriesGroupMapToggled(t)});sKt(this.basicGraph,n,e).then(function(t){this.set("graphHierarchy",t),this._buildRenderHierarchy(this.graphHierarchy)}.bind(this))}_deselectPreviousEdge(){Du("."+ZKt.Edge.SELECTED).classed(ZKt.Edge.SELECTED,!1).each(((t,e)=>{if(t.label){const e=Du(this).selectAll("path.edgeline");t.label.startMarkerId&&e.style("marker-start",`url(#${t.label.startMarkerId})`),t.label.endMarkerId&&e.style("marker-end",`url(#${t.label.endMarkerId})`)}}))}_updateMarkerOfSelectedEdge(t){if(t.label){const e=t.label.startMarkerId||t.label.endMarkerId;if(e){const n=e.replace("dataflow-","selected-");let i=this.$$("#"+n);if(!i){const t=this.$.scene.querySelector("#"+e);i=t.cloneNode(!0),i.setAttribute("id",n),i.classList.add("selected-arrowhead"),t.parentNode.appendChild(i)}const r=t.label.startMarkerId?"marker-start":"marker-end";this._lastSelectedEdgeGroup.selectAll("path.edgeline").style(r,`url(#${n})`)}}}not(t){return!t}};jZt.template=xe`
    <style>
      .container {
        width: 100%;
        height: 100%;
        background: white;
        box-shadow: 0 1px 5px rgba(0, 0, 0, 0.2);
      }

      .vertical {
        width: 100%;
        height: 100%;
        @apply --layout-vertical;
      }

      .auto {
        @apply --layout-flex-auto;
        @apply --layout-vertical;
      }

      h2 {
        text-align: center;
      }

      paper-button {
        text-transform: none;
      }
    </style>
    <div class="container">
      <div class="vertical">
        <template is="dom-if" if="[[title]]">
          <h2>[[title]]</h2>
        </template>
        <tf-graph-scene
          id="scene"
          class="auto"
          render-hierarchy="[[renderHierarchy]]"
          highlighted-node="[[_getVisible(highlightedNode)]]"
          selected-node="{{selectedNode}}"
          selected-edge="{{selectedEdge}}"
          color-by="[[colorBy]]"
          progress="[[progress]]"
          node-context-menu-items="[[nodeContextMenuItems]]"
          node-names-to-health-pills="[[nodeNamesToHealthPills]]"
          health-pill-step-index="{{healthPillStepIndex}}"
          handle-edge-selected="[[handleEdgeSelected]]"
          trace-inputs="[[traceInputs]]"
        ></tf-graph-scene>
      </div>
    </div>
  `,t([o({type:Object,notify:!0,observer:"_graphChanged"}),e("design:type",rKt)],jZt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",I$t)],jZt.prototype,"basicGraph",void 0),t([o({type:Object}),e("design:type",Object)],jZt.prototype,"stats",void 0),t([o({type:Object}),e("design:type",Object)],jZt.prototype,"devicesForStats",void 0),t([o({type:Object}),e("design:type",Object)],jZt.prototype,"hierarchyParams",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],jZt.prototype,"progress",void 0),t([o({type:String}),e("design:type",String)],jZt.prototype,"title",void 0),t([o({type:String,notify:!0}),e("design:type",String)],jZt.prototype,"selectedNode",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],jZt.prototype,"selectedEdge",void 0),t([o({type:Object}),e("design:type",Object)],jZt.prototype,"_lastSelectedEdgeGroup",void 0),t([o({type:String,notify:!0}),e("design:type",String)],jZt.prototype,"highlightedNode",void 0),t([o({type:String}),e("design:type",String)],jZt.prototype,"colorBy",void 0),t([o({type:Object,notify:!0,readOnly:!0}),e("design:type",Object)],jZt.prototype,"colorByParams",void 0),t([o({type:Object,readOnly:!0,notify:!0}),e("design:type",AKt)],jZt.prototype,"renderHierarchy",void 0),t([o({type:Boolean}),e("design:type",Boolean)],jZt.prototype,"traceInputs",void 0),t([o({type:Boolean}),e("design:type",Boolean)],jZt.prototype,"autoExtractNodes",void 0),t([o({type:Array}),e("design:type",Array)],jZt.prototype,"nodeContextMenuItems",void 0),t([o({type:Number}),e("design:type",Number)],jZt.prototype,"_renderDepth",void 0),t([o({type:Boolean}),e("design:type",Boolean)],jZt.prototype,"_allowGraphSelect",void 0),t([o({type:Object}),e("design:type",Object)],jZt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Number}),e("design:type",Number)],jZt.prototype,"healthPillStepIndex",void 0),t([o({type:Object}),e("design:type",Object)],jZt.prototype,"edgeWidthFunction",void 0),t([o({type:Object}),e("design:type",Object)],jZt.prototype,"handleNodeSelected",void 0),t([o({type:Object}),e("design:type",Object)],jZt.prototype,"edgeLabelFunction",void 0),t([o({type:Object}),e("design:type",Object)],jZt.prototype,"handleEdgeSelected",void 0),t([a("autoExtractNodes"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],jZt.prototype,"_autoExtractNodesChanged",null),t([a("graphHierarchy","edgeWidthFunction","handleNodeSelected","edgeLabelFunction","handleEdgeSelected"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],jZt.prototype,"_buildNewRenderHierarchy",null),t([a("stats","devicesForStats"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],jZt.prototype,"_statsChanged",null),t([a("selectedNode"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],jZt.prototype,"_selectedNodeChanged",null),t([a("selectedEdge"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],jZt.prototype,"_selectedEdgeChanged",null),jZt=t([i("tf-graph")],jZt);let GZt=class extends(cr(we)){constructor(){super(...arguments),this.specificHealthPillStep=0,this.healthPillEntries=JKt}ready(){super.ready();var t=document.getElementById("mainContainer"),e=document.querySelector("tf-dashboard-layout .scrollbar");t&&e&&(t.style.overflow="hidden",e.style.overflow="hidden")}_healthPillsAvailable(t,e){return t&&e}_computeTensorCountString(t,e){return t?t[e].toFixed(0):""}get healthPillValuesForSelectedNode(){var t=this.selectedNode;if(this.areHealthPillsLoading)return null;if(!t)return null;const e=this.nodeNamesToHealthPills[t];if(!e)return null;const n=e[this.allStepsModeEnabled?0:this.healthPillStepIndex];return n?n.value.slice(2,8):null}get _currentStepDisplayValue(){var t=this.nodeNamesToHealthPills,e=this.healthPillStepIndex,n=this.areHealthPillsLoading;if(this.allStepsModeEnabled)return this.specificHealthPillStep.toFixed(0);if(n)return 0;for(let n in t)return t[n][e].step.toFixed(0);return 0}get _biggestStepEverSeen(){var t=this.nodeNamesToHealthPills;for(let n in t){var e=t[n];return Math.max(this._biggestStepEverSeen,e[e.length-1].step)}return this._biggestStepEverSeen||0}get _maxStepIndex(){var t=this.nodeNamesToHealthPills;for(let e in t)return t[e].length-1;return 0}_hasDebuggerNumericAlerts(t){return t&&t.length}_updateAlertsList(){var t=this.debuggerNumericAlerts,e=this.$$("#numeric-alerts-body");if(e){e.innerText="";for(var n=0;n<t.length;n++){var i=t[n],r=document.createElement("tr"),o=document.createElement("td");o.innerText=(f=i.first_timestamp,m=void 0,(m=+new Date-+new Date(f/1e3))<3e4?"just now":m<6e4?Math.floor(m/1e3)+" seconds ago":m<12e4?"a minute ago":m<36e5?Math.floor(m/6e4)+" minutes ago":1==Math.floor(m/36e5)?"an hour ago":m<864e5?Math.floor(m/36e5)+" hours ago":m<1728e5?"yesterday":Math.floor(m/864e5)+" days ago"),o.classList.add("first-offense-td"),r.appendChild(o);var a=document.createElement("td");a.classList.add("tensor-device-td");var s=document.createElement("div");s.classList.add("tensor-section-within-table"),s.innerText=i.tensor_name,this._addOpExpansionListener(s,i.tensor_name),a.appendChild(s);var l=document.createElement("div");l.classList.add("device-section-within-table"),l.innerText="("+i.device_name+")",a.appendChild(l),r.appendChild(a);var c=document.createElement("div");c.classList.add("mini-health-pill");var u=document.createElement("td");if(u.classList.add("mini-health-pill-td"),u.appendChild(c),r.appendChild(u),i.neg_inf_event_count){var h=document.createElement("div");h.classList.add("negative-inf-mini-health-pill-section"),h.innerText=i.neg_inf_event_count,h.setAttribute("title",i.neg_inf_event_count+" events with -∞"),c.appendChild(h)}if(i.pos_inf_event_count){var d=document.createElement("div");d.classList.add("positive-inf-mini-health-pill-section"),d.innerText=i.pos_inf_event_count,d.setAttribute("title",i.pos_inf_event_count+" events with +∞"),c.appendChild(d)}if(i.nan_event_count){var p=document.createElement("div");p.classList.add("nan-mini-health-pill-section"),p.innerText=i.nan_event_count,p.setAttribute("title",i.nan_event_count+" events with NaN"),c.appendChild(p)}er(e).appendChild(r)}var f,m}}_addOpExpansionListener(t,e){t.addEventListener("click",(()=>{var t,n=(function i(t,e,n){const i=n.split("/"),r=i[i.length-1].match(/(.*):\w+/);2===r.length&&(i[i.length-1]=r[1]);let o=i[0],a=e.getRenderNodeByName(o);for(let n=1;n<i.length&&a.node.type!==A$t.OP;n++)e.buildSubhierarchy(o),a.expanded=!0,t.setNodeExpanded(a),o+="/"+i[n],a=e.getRenderNodeByName(o);return a.node.name})(document.getElementById("scene"),this.renderHierarchy,e),r=document.querySelector("tf-graph-info#graph-info");r&&(t=r.scrollHeight-r.scrollTop);var o=this.selectedNode;this.set("selectedNode",n);var a=()=>{r.scrollTop=r.scrollHeight-t};r&&(o?a():window.setTimeout(a,20))}))}};var WZt;GZt.template=xe`
    <style>
      :host {
        font-size: 12px;
        margin: 0;
        padding: 0;
        display: block;
      }

      h2 {
        padding: 0;
        text-align: center;
        margin: 0;
      }

      .health-pill-legend {
        padding: 15px;
      }

      .health-pill-legend h2 {
        text-align: left;
      }

      .health-pill-entry {
        margin: 10px 10px 10px 0;
      }

      .health-pill-entry .color-preview {
        width: 26px;
        height: 26px;
        border-radius: 3px;
        display: inline-block;
        margin: 0 10px 0 0;
      }

      .health-pill-entry .color-label,
      .health-pill-entry .tensor-count {
        color: #777;
        display: inline-block;
        height: 26px;
        font-size: 22px;
        line-height: 26px;
        vertical-align: top;
      }

      .health-pill-entry .tensor-count {
        float: right;
      }

      #health-pill-step-slider {
        width: 100%;
        margin: 0 0 0 -15px;
        /* 31 comes from adding a padding of 15px from both sides of the paper-slider, subtracting
   * 1px so that the slider width aligns with the image (the last slider marker takes up 1px),
   * and adding 2px to account for a border of 1px on both sides of the image. 30 - 1 + 2.
   * Apparently, the paper-slider lacks a mixin for those padding values. */
        width: calc(100% + 31px);
      }

      #health-pills-loading-spinner {
        width: 20px;
        height: 20px;
        vertical-align: top;
      }

      #health-pill-step-number-input {
        text-align: center;
        vertical-align: top;
      }

      #numeric-alerts-table-container {
        max-height: 400px;
        overflow-x: hidden;
        overflow-y: auto;
      }

      #numeric-alerts-table {
        text-align: left;
      }

      #numeric-alerts-table td {
        vertical-align: top;
      }

      #numeric-alerts-table .first-offense-td {
        display: inline-block;
      }

      .first-offense-td {
        width: 80px;
      }

      .tensor-device-td {
        max-width: 140px;
        word-wrap: break-word;
      }

      .tensor-section-within-table {
        color: #266236;
        cursor: pointer;
        opacity: 0.8;
        text-decoration: underline;
      }

      .tensor-section-within-table:hover {
        opacity: 1;
      }

      .device-section-within-table {
        color: #666;
      }

      .mini-health-pill {
        width: 130px;
      }

      .mini-health-pill > div {
        height: 100%;
        width: 60px;
        border-radius: 3px;
      }

      #event-counts-th {
        padding: 0 0 0 10px;
      }

      .negative-inf-mini-health-pill-section {
        background: rgb(255, 141, 0);
        width: 20px;
      }

      .positive-inf-mini-health-pill-section {
        background: rgb(0, 62, 212);
        width: 20px;
      }

      .nan-mini-health-pill-section {
        background: rgb(204, 47, 44);
        width: 20px;
      }

      .negative-inf-mini-health-pill-section,
      .positive-inf-mini-health-pill-section,
      .nan-mini-health-pill-section {
        color: #fff;
        display: inline-block;
        height: 100%;
        line-height: 20px;
        margin: 0 0 0 10px;
        text-align: center;
      }

      .no-numeric-alerts-notification {
        margin: 0;
      }
    </style>
    <paper-material elevation="1" class="card health-pill-legend">
      <div class="title">
        Enable all (not just sampled) steps. Requires slow disk read.
      </div>
      <paper-toggle-button
        id="enableAllStepsModeToggle"
        checked="{{allStepsModeEnabled}}"
      >
      </paper-toggle-button>
      <h2>
        Step of Health Pills:
        <template is="dom-if" if="[[allStepsModeEnabled]]">
          <input
            type="number"
            id="health-pill-step-number-input"
            min="0"
            max="[[_biggestStepEverSeen]]"
            value="{{specificHealthPillStep::input}}"
          />
        </template>
        <template is="dom-if" if="[[!allStepsModeEnabled]]">
          [[_currentStepDisplayValue]]
        </template>
        <paper-spinner-lite
          active
          hidden$="[[!areHealthPillsLoading]]"
          id="health-pills-loading-spinner"
        ></paper-spinner-lite>
      </h2>
      <template is="dom-if" if="[[allStepsModeEnabled]]">
        <paper-slider
          id="health-pill-step-slider"
          immediate-value="{{specificHealthPillStep}}"
          max="[[_biggestStepEverSeen]]"
          snaps
          step="1"
          value="{{specificHealthPillStep}}"
        ></paper-slider>
      </template>
      <template is="dom-if" if="[[!allStepsModeEnabled]]">
        <template is="dom-if" if="[[_maxStepIndex]]">
          <paper-slider
            id="health-pill-step-slider"
            immediate-value="{{healthPillStepIndex}}"
            max="[[_maxStepIndex]]"
            snaps
            step="1"
            value="{{healthPillStepIndex}}"
          ></paper-slider>
        </template>
      </template>
      <h2>
        Health Pill
        <template is="dom-if" if="[[healthPillValuesForSelectedNode]]">
          Counts for Selected Node
        </template>
        <template is="dom-if" if="[[!healthPillValuesForSelectedNode]]">
          Legend
        </template>
      </h2>
      <template is="dom-repeat" items="[[healthPillEntries]]">
        <div class="health-pill-entry">
          <div
            class="color-preview"
            style="background:[[item.background_color]]"
          ></div>
          <div class="color-label">[[item.label]]</div>
          <div class="tensor-count">
            [[_computeTensorCountString(healthPillValuesForSelectedNode,
            index)]]
          </div>
        </div>
      </template>
      <div hidden$="[[!_hasDebuggerNumericAlerts(debuggerNumericAlerts)]]">
        <h2 id="numeric-alerts-header">Numeric Alerts</h2>
        <p>Alerts are sorted from top to bottom by increasing timestamp.</p>
        <div id="numeric-alerts-table-container">
          <table id="numeric-alerts-table">
            <thead>
              <tr>
                <th>First Offense</th>
                <th>Tensor (Device)</th>
                <th id="event-counts-th">Event Counts</th>
              </tr>
            </thead>
            <tbody id="numeric-alerts-body"></tbody>
          </table>
        </div>
      </div>
      <template
        is="dom-if"
        if="[[!_hasDebuggerNumericAlerts(debuggerNumericAlerts)]]"
      >
        <p class="no-numeric-alerts-notification">
          No numeric alerts so far. That is likely good. Alerts indicate the
          presence of NaN or (+/-) Infinity values, which may be concerning.
        </p>
      </template>
    </paper-material>
  `,t([o({type:Object}),e("design:type",AKt)],GZt.prototype,"renderHierarchy",void 0),t([o({type:Array,notify:!0}),e("design:type",Object)],GZt.prototype,"debuggerNumericAlerts",void 0),t([o({type:Object}),e("design:type",Object)],GZt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Number,notify:!0}),e("design:type",Object)],GZt.prototype,"healthPillStepIndex",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],GZt.prototype,"specificHealthPillStep",void 0),t([o({type:String,notify:!0}),e("design:type",Object)],GZt.prototype,"selectedNode",void 0),t([o({type:String,notify:!0}),e("design:type",Object)],GZt.prototype,"highlightedNode",void 0),t([o({type:Number,notify:!0}),e("design:type",Object)],GZt.prototype,"selectedNodeInclude",void 0),t([o({type:Boolean}),e("design:type",Object)],GZt.prototype,"areHealthPillsLoading",void 0),t([o({type:Array}),e("design:type",Array)],GZt.prototype,"healthPillEntries",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Object)],GZt.prototype,"allStepsModeEnabled",void 0),t([s("nodeNamesToHealthPills","healthPillStepIndex","selectedNode","allStepsModeEnabled","areHealthPillsLoading"),e("design:type",Array),e("design:paramtypes",[])],GZt.prototype,"healthPillValuesForSelectedNode",null),t([s("nodeNamesToHealthPills","healthPillStepIndex","allStepsModeEnabled","specificHealthPillStep","areHealthPillsLoading"),e("design:type",Object),e("design:paramtypes",[])],GZt.prototype,"_currentStepDisplayValue",null),t([s("nodeNamesToHealthPills"),e("design:type",Number),e("design:paramtypes",[])],GZt.prototype,"_biggestStepEverSeen",null),t([s("nodeNamesToHealthPills"),e("design:type",Number),e("design:paramtypes",[])],GZt.prototype,"_maxStepIndex",null),t([a("debuggerNumericAlerts"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],GZt.prototype,"_updateAlertsList",null),GZt=t([i("tf-graph-debugger-data-card")],GZt),(function(t){t.CONST="CONST",t.META="META",t.OP="OP",t.SERIES="SERIES",t.SUMMARY="SUMMARY"})(WZt||(WZt={}));let qZt=class extends(cr(gl(we))){constructor(){super(...arguments),this.vertical=!1,this.fillOverride=null,this.strokeOverride=null,this.height=20,this.faded=!1}getSvgDefinableElement(){return this.$.svgDefs}get _fill(){var t=this.fillOverride;if(null!=t)return t;switch(this.type){case WZt.META:return gKt.DEFAULT_FILL;case WZt.SERIES:return _Kt;default:return dKt}}get _stroke(){var t=this.strokeOverride;if(null!=t)return t;switch(this.type){case WZt.META:return gKt.DEFAULT_STROKE;case WZt.SERIES:return yKt;default:return pKt}}_isType(t,e){return t===e}_fadedClass(t,e){return t?"faded-"+e:""}};qZt.template=xe`
    <style>
      :host {
        font-size: 0;
      }

      :host(.dark-mode) svg {
        filter: invert(1);
      }

      .faded-rect {
        fill: url(#rectHatch);
      }

      .faded-ellipse {
        fill: url(#ellipseHatch);
      }

      .faded-rect,
      .faded-ellipse,
      .faded-series {
        stroke: var(--tb-graph-faded) !important;
      }
      #rectHatch line,
      #ellipseHatch line {
        color: #e0d4b3 !important;
        fill: white;
        stroke: #e0d4b3 !important;
      }
    </style>
    <!-- SVG for definitions -->
    <svg height="0" width="0" id="svgDefs">
      <defs>
        <!-- Hatch patterns for faded out nodes. -->
        <pattern
          id="rectHatch"
          patternTransform="rotate(45 0 0)"
          width="5"
          height="5"
          patternUnits="userSpaceOnUse"
        >
          <line x1="0" y1="0" x2="0" y2="5" style="stroke-width: 1"></line>
        </pattern>
        <pattern
          id="ellipseHatch"
          patternTransform="rotate(45 0 0)"
          width="2"
          height="2"
          patternUnits="userSpaceOnUse"
        >
          <line x1="0" y1="0" x2="0" y2="2" style="stroke-width: 1"></line>
        </pattern>
        <!-- Template for an Op node ellipse. -->
        <ellipse
          id="op-node-stamp"
          rx="7.5"
          ry="3"
          stroke="inherit"
          fill="inherit"
        ></ellipse>
        <!-- Template for an Op node annotation ellipse (smaller). -->
        <ellipse
          id="op-node-annotation-stamp"
          rx="5"
          ry="2"
          stroke="inherit"
          fill="inherit"
        ></ellipse>
        <!-- Vertically stacked series of Op nodes when unexpanded. -->
        <g id="op-series-vertical-stamp">
          <use xlink:href="#op-node-stamp" x="8" y="9"></use>
          <use xlink:href="#op-node-stamp" x="8" y="6"></use>
          <use xlink:href="#op-node-stamp" x="8" y="3"></use>
        </g>
        <g id="op-series-horizontal-stamp">
          <use xlink:href="#op-node-stamp" x="16" y="4"></use>
          <use xlink:href="#op-node-stamp" x="12" y="4"></use>
          <use xlink:href="#op-node-stamp" x="8" y="4"></use>
        </g>
        <g
          id="summary-icon"
          fill="#848484"
          height="12"
          viewBox="0 0 24 24"
          width="12"
        >
          <path
            d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z"
          ></path>
        </g>
      </defs>
    </svg>
    <template is="dom-if" if="[[_isType(type, 'CONST')]]">
      <svg
        height$="[[height]]"
        preserveAspectRatio="xMinYMid meet"
        viewBox="0 0 10 10"
      >
        <circle
          cx="5"
          cy="5"
          r="3"
          fill$="[[_fill]]"
          stroke$="[[_stroke]]"
        ></circle>
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'SUMMARY')]]">
      <svg
        width$="[[height]]"
        height$="[[height]]"
        viewBox="0 0 24 24"
        fill="#848484"
      >
        <path
          d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z"
        ></path>
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'OP')]]">
      <svg
        height$="[[height]]"
        preserveAspectRatio="xMinYMid meet"
        viewBox="0 0 16 8"
      >
        <use
          xmlns:xlink="http://www.w3.org/1999/xlink"
          xlink:href="#op-node-stamp"
          fill$="[[_fill]]"
          stroke$="[[_stroke]]"
          class$="{{_fadedClass(faded, 'ellipse')}}"
          x="8"
          y="4"
        ></use>
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'META')]]">
      <svg
        height$="[[height]]"
        preserveAspectRatio="xMinYMid meet"
        viewBox="0 0 37 16"
      >
        <rect
          x="1"
          y="1"
          fill$="[[_fill]]"
          stroke$="[[_stroke]]"
          class$="{{_fadedClass(faded, 'rect')}}"
          stroke-width="2px"
          height="14"
          width="35"
          rx="5"
          ry="5"
        ></rect>
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'SERIES')]]">
      <template is="dom-if" if="[[vertical]]">
        <svg
          height$="[[height]]"
          preserveAspectRatio="xMinYMid meet"
          viewBox="0 0 16 15"
        >
          <use
            xmlns:xlink="http://www.w3.org/1999/xlink"
            xlink:href="#op-series-vertical-stamp"
            fill$="[[_fill]]"
            stroke$="[[_stroke]]"
            class$="{{_fadedClass(faded, 'series')}}"
            x="0"
            y="2"
          ></use>
        </svg>
      </template>
      <template is="dom-if" if="[[!vertical]]">
        <svg
          height$="[[height]]"
          preserveAspectRatio="xMinYMid meet"
          viewBox="0 0 24 10"
        >
          <use
            xmlns:xlink="http://www.w3.org/1999/xlink"
            xlink:href="#op-series-horizontal-stamp"
            fill$="[[_fill]]"
            stroke$="[[_stroke]]"
            class$="{{_fadedClass(faded, 'series')}}"
            x="0"
            y="1"
          ></use>
        </svg>
      </template>
    </template>
  `,t([o({type:String}),e("design:type",String)],qZt.prototype,"type",void 0),t([o({type:Boolean}),e("design:type",Boolean)],qZt.prototype,"vertical",void 0),t([o({type:String}),e("design:type",String)],qZt.prototype,"fillOverride",void 0),t([o({type:String}),e("design:type",String)],qZt.prototype,"strokeOverride",void 0),t([o({type:Number}),e("design:type",Number)],qZt.prototype,"height",void 0),t([o({type:Boolean}),e("design:type",Boolean)],qZt.prototype,"faded",void 0),t([s("type","fillOverride"),e("design:type",String),e("design:paramtypes",[])],qZt.prototype,"_fill",null),t([s("type","strokeOverride"),e("design:type",String),e("design:paramtypes",[])],qZt.prototype,"_stroke",null),qZt=t([i("tf-graph-icon")],qZt);var YZt=Object.freeze({__proto__:null,get GraphIconType(){return WZt}});let XZt=class extends(cr(we)){constructor(){super(...arguments),this.node=null,this.renderInfo=null,this.colorBy=rZt.STRUCTURE,this.templateIndex=null,this.type=null,this.vertical=!1,this.const=!1,this.summary=!1,this.fill=null,this.height=20}_computeFillOverride(t,e,n,i,r){return t&&e&&i?kZt(i,n,e,!1):r}_getStrokeOverride(t){return t?PZt(t):null}_getType(t,e,n,i){const{GraphIconType:r}=YZt;if(t)switch(t.type){case A$t.OP:{const i=t.op;return"string"!=typeof i?r.OP:"Const"===i||n?r.CONST:i.endsWith("Summary")||e?r.SUMMARY:r.OP}case A$t.META:return r.META;case A$t.SERIES:return r.SERIES}return i}_isVertical(t,e){return t?t.hasNonControlEdges:!!e}_getFaded(t){return t&&t.isFadedOut}_onFillOverrideChanged(t,e){const{node:n,renderInfo:i,colorBy:r,templateIndex:o}=this;t!==e&&AZt(this.$.icon.getSvgDefinableElement()),n&&i&&o&&kZt(o,r,i,!1,this.$.icon.getSvgDefinableElement())}};XZt.template=xe`
    <style>
      tf-graph-icon {
        --tb-graph-faded: var(--tb-graph-faded);
      }
    </style>
    <tf-graph-icon
      id="icon"
      type="[[_getType(node, summary, const, type)]]"
      height="[[height]]"
      fill-override="[[_fillOverride]]"
      stroke-override="[[_getStrokeOverride(_fillOverride)]]"
      faded="[[_getFaded(renderInfo)]]"
      vertical="[[_isVertical(node, vertical)]]"
    ></tf-graph-icon>
  `,t([o({type:Object}),e("design:type",Object)],XZt.prototype,"node",void 0),t([o({type:Object}),e("design:type",Object)],XZt.prototype,"renderInfo",void 0),t([o({type:Object}),e("design:type",String)],XZt.prototype,"colorBy",void 0),t([o({type:Object}),e("design:type",Function)],XZt.prototype,"templateIndex",void 0),t([o({type:String}),e("design:type",String)],XZt.prototype,"type",void 0),t([o({type:Boolean}),e("design:type",Boolean)],XZt.prototype,"vertical",void 0),t([o({type:Boolean}),e("design:type",Boolean)],XZt.prototype,"const",void 0),t([o({type:Boolean}),e("design:type",Boolean)],XZt.prototype,"summary",void 0),t([o({type:String}),e("design:type",String)],XZt.prototype,"fill",void 0),t([o({type:Number}),e("design:type",Number)],XZt.prototype,"height",void 0),t([o({type:String,computed:"_computeFillOverride(node, renderInfo, colorBy, templateIndex, fill)",observer:"_onFillOverrideChanged"}),e("design:type",String)],XZt.prototype,"_fillOverride",void 0),XZt=t([i("tf-node-icon")],XZt);let $Zt=class extends(cr(we)){_itemTypeChanged(){"subnode"!==this.itemType?this.$["list-item"].classList.add("clickable"):this.$["list-item"].classList.remove("clickable")}_nodeListener(t){this.fire("node-list-item-"+t.type,{nodeName:this.name,type:this.itemType})}_fadedClass(t){return t&&t.isFadedOut?"faded":""}};$Zt.template=xe`
    <style>
      #list-item {
        width: 100%;
        color: var(--secondary-text-color);
        font-size: 11pt;
        font-weight: 400;
        position: relative;
        display: inline-block;
      }

      #list-item:hover {
        background-color: var(--google-yellow-100);
      }

      .clickable {
        cursor: pointer;
      }

      #list-item span {
        margin-left: 40px;
      }

      #list-item.excluded span {
        color: #999;
      }

      #list-item span.edge-label {
        float: right;
        font-size: 10px;
        margin-left: 3px;
        margin-right: 5px;
      }

      .node-icon {
        position: absolute;
        top: 1px;
        left: 2px;
      }

      .faded span {
        color: var(--tb-graph-faded);
      }
    </style>

    <div
      id="list-item"
      on-mouseover="_nodeListener"
      on-mouseout="_nodeListener"
      on-click="_nodeListener"
    >
      <div class$="{{_fadedClass(itemRenderInfo)}}">
        <tf-node-icon
          class="node-icon"
          height="12"
          color-by="[[colorBy]]"
          color-by-params="[[colorByParams]]"
          node="[[itemNode]]"
          render-info="[[itemRenderInfo]]"
          template-index="[[templateIndex]]"
        >
        </tf-node-icon>
        <span title$="[[name]]">[[name]]</span>
      </div>
    </div>
  `,t([o({type:Object}),e("design:type",Object)],$Zt.prototype,"cardNode",void 0),t([o({type:Object}),e("design:type",Object)],$Zt.prototype,"itemNode",void 0),t([o({type:String}),e("design:type",String)],$Zt.prototype,"edgeLabel",void 0),t([o({type:Object}),e("design:type",Object)],$Zt.prototype,"itemRenderInfo",void 0),t([o({type:String}),e("design:type",String)],$Zt.prototype,"name",void 0),t([o({type:String,observer:"_itemTypeChanged"}),e("design:type",String)],$Zt.prototype,"itemType",void 0),t([o({type:String}),e("design:type",String)],$Zt.prototype,"colorBy",void 0),t([o({type:Object}),e("design:type",Object)],$Zt.prototype,"colorByParams",void 0),t([o({type:Object}),e("design:type",Function)],$Zt.prototype,"templateIndex",void 0),$Zt=t([i("tf-graph-op-compat-list-item")],$Zt);let KZt=class extends(cr(gl(we))){constructor(){super(...arguments),this._expanded=!0,this._opCompatColor=fKt,this._opIncompatColor=mKt,this._templateIndex=null}_getNode(t,e){return e.node(t)}_getRenderInfo(t,e){return this.renderHierarchy.getOrCreateRenderNodeByName(t)}_toggleExpanded(){this._expanded=!this._expanded}_getToggleIcon(t){return t?"expand-less":"expand-more"}_resizeList(t){var e=document.querySelector(t);e&&e.fire("iron-resize")}get _incompatibleOpNodes(){const t=this.graphHierarchy;return t&&t.root?(this.async(this._resizeList.bind(this,"#incompatibleOpsList")),(function e(t){const e=[],n={};return Ce.exports.each(t.root.leaves(),(i=>{let r=t.node(i);if(r.type==A$t.OP){let i=r;if(!i.compatible)if(i.owningSeries){if(t.getSeriesGroupType(i.owningSeries)===L$t.UNGROUP)e.push(i);else if(!n[i.owningSeries]){let r=t.node(i.owningSeries);r&&(n[i.owningSeries]=r,e.push(r))}}else e.push(i);Ce.exports.each(i.inEmbeddings,(t=>{t.compatible||e.push(t)})),Ce.exports.each(i.outEmbeddings,(t=>{t.compatible||e.push(t)}))}})),e})(t)):[]}get _opCompatScore(){var t=this.graphHierarchy;if(t&&t.root){var e=t.root,n=e.compatibilityHistogram.compatible,i=e.compatibilityHistogram.incompatible;return 0==n&&0==i?0:Math.floor(100*n/(n+i))/100}return 0}get _opCompatScoreLabel(){var t=this._opCompatScore;return Dy(".0%")(t)}get _totalIncompatOps(){var t=this.graphHierarchy;return t&&t.root?t.root.compatibilityHistogram.incompatible:0}_graphHierarchyChanged(){this._templateIndex=this.graphHierarchy.getTemplateIndex(),this.graphHierarchy.addListener(iKt.TEMPLATES_UPDATED,(()=>{this._templateIndex=this.graphHierarchy.getTemplateIndex()}))}};KZt.template=xe`
    <style>
      :host {
        max-height: 500px;
      }

      .incompatible-ops-list {
        height: 350px;
        max-height: 400px;
        overflow-y: scroll;
        display: flex;
        flex-direction: column;
      }

      iron-list {
        flex: 1 1 auto;
      }

      paper-item {
        padding: 0;
        background: var(--secondary-background-color);
      }

      paper-item-body[two-line] {
        min-height: 0;
        padding: 8px 12px 4px;
      }

      .expandedInfo {
        padding: 8px 12px;
        font-weight: 500;
        font-size: 12pt;
        width: 100%;
      }

      .node-name {
        white-space: normal;
        word-wrap: break-word;
        font-size: 14pt;
        font-weight: 500;
      }

      .subtitle {
        color: var(--secondary-text-color);
        font-size: 12pt;
      }

      .toggle-button {
        float: right;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .non-control-list-item {
        padding-left: 10px;
      }

      div.op-compat-display {
        margin-top: 10px;
        display: inline-block;
      }

      /**
       * Sadly, because the whole body is inverted in color, legends also need
       * to be inverted.
       **/
      :host(.dark-mode) div.op-compat-display {
        filter: invert(1);
      }

      svg.op-compat {
        width: 250px;
        height: 25px;
        float: left;
      }

      div.op-compat-value {
        float: right;
        height: 100%;
        font-size: 14px;
        color: black;
        margin-left: 10px;
      }
    </style>

    <paper-item>
      <paper-item-body two-line>
        <div>
          <paper-icon-button
            icon="{{_getToggleIcon(_expanded)}}"
            on-click="_toggleExpanded"
            class="toggle-button"
          >
          </paper-icon-button>
          <div class="node-name" id="nodetitle">[[nodeTitle]]</div>
        </div>
        <div secondary>
          <div class="subtitle">
            <div class="op-compat-display">
              <svg
                class="op-compat"
                preserveAspectRatio="xMinYMid meet"
                viewBox="0 0 250 25"
              >
                <defs>
                  <linearGradient id="op-compat-fill">
                    <stop offset="0" stop-color$="[[_opCompatColor]]"></stop>
                    <stop
                      offset$="[[_opCompatScore]]"
                      stop-color$="[[_opCompatColor]]"
                    ></stop>
                    <stop
                      offset$="[[_opCompatScore]]"
                      stop-color$="[[_opIncompatColor]]"
                    ></stop>
                    <stop offset="1" stop-color$="[[_opIncompatColor ]]"></stop>
                  </linearGradient>
                </defs>
                <rect
                  height="25"
                  width="250"
                  rx="5"
                  ry="5"
                  style="fill: url('#op-compat-fill');"
                ></rect>
              </svg>
              <div class="op-compat-value">[[_opCompatScoreLabel]]</div>
            </div>
          </div>
        </div>
      </paper-item-body>
    </paper-item>

    <iron-collapse opened="{{_expanded}}">
      <template is="dom-if" if="{{_expanded}}" restamp="true">
        <div class="expandedInfo">
          Incompatible Operations: (<span>[[_totalIncompatOps]]</span>)
          <iron-list
            class="incompatible-ops-list"
            id="incompatibleOpsList"
            items="[[_incompatibleOpNodes]]"
          >
            <template>
              <tf-graph-op-compat-list-item
                class="non-control-list-item"
                item-node="[[item]]"
                item-render-info="[[_getRenderInfo(item.name, renderHierarchy)]]"
                name="[[item.name]]"
                template-index="[[_templateIndex]]"
                color-by="[[colorBy]]"
                item-type="incompatible-ops"
              >
              </tf-graph-op-compat-list-item>
            </template>
          </iron-list>
        </div>
      </template>
    </iron-collapse>
  `,t([o({type:Object}),e("design:type",rKt)],KZt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",AKt)],KZt.prototype,"renderHierarchy",void 0),t([o({type:String}),e("design:type",String)],KZt.prototype,"nodeTitle",void 0),t([o({type:Boolean}),e("design:type",Boolean)],KZt.prototype,"_expanded",void 0),t([o({type:String}),e("design:type",String)],KZt.prototype,"_opCompatColor",void 0),t([o({type:String}),e("design:type",String)],KZt.prototype,"_opIncompatColor",void 0),t([o({type:Object}),e("design:type",Function)],KZt.prototype,"_templateIndex",void 0),t([s("graphHierarchy"),e("design:type",Array),e("design:paramtypes",[])],KZt.prototype,"_incompatibleOpNodes",null),t([s("graphHierarchy"),e("design:type",Number),e("design:paramtypes",[])],KZt.prototype,"_opCompatScore",null),t([s("_opCompatScore"),e("design:type",String),e("design:paramtypes",[])],KZt.prototype,"_opCompatScoreLabel",null),t([s("graphHierarchy"),e("design:type",Number),e("design:paramtypes",[])],KZt.prototype,"_totalIncompatOps",null),t([a("graphHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],KZt.prototype,"_graphHierarchyChanged",null),KZt=t([i("tf-graph-op-compat-card")],KZt);let ZZt=class extends(cr(gl(we))){_itemTypeChanged(){"subnode"!==this.itemType?this.$["list-item"].classList.add("clickable"):this.$["list-item"].classList.remove("clickable")}_nodeListener(t){this.fire("node-list-item-"+t.type,{cardNode:this.cardNode.name,nodeName:this.name,type:this.itemType})}_fadedClass(t){return t&&t.isFadedOut?"faded":""}};ZZt.template=xe`
    <style>
      #list-item {
        width: 100%;
        color: var(--secondary-text-color);
        font-size: 11pt;
        font-weight: 400;
        position: relative;
        display: inline-block;
      }

      #list-item:hover {
        background-color: var(--google-yellow-100);
      }

      :host(.dark-mode) #list-item:hover {
        background-color: var(--paper-yellow-900);
        color: #fff;
      }

      .clickable {
        cursor: pointer;
      }

      #list-item span {
        margin-left: 40px;
      }

      #list-item.excluded span {
        color: #999;
      }

      #list-item span.edge-label {
        float: right;
        font-size: 10px;
        margin-left: 3px;
        margin-right: 5px;
      }

      .node-icon {
        position: absolute;
        top: 1px;
        left: 2px;
      }

      .faded span {
        color: var(--tb-graph-faded);
      }
    </style>
    <div
      id="list-item"
      on-mouseover="_nodeListener"
      on-mouseout="_nodeListener"
      on-click="_nodeListener"
    >
      <div class$="{{_fadedClass(itemRenderInfo)}}">
        <tf-node-icon
          class="node-icon"
          height="12"
          color-by="[[colorBy]]"
          color-by-params="[[colorByParams]]"
          node="[[itemNode]]"
          render-info="[[itemRenderInfo]]"
          template-index="[[templateIndex]]"
        ></tf-node-icon>
        <span title$="[[name]]">[[name]]</span>
        <span class="edge-label">[[edgeLabel]]</span>
      </div>
    </div>
  `,t([o({type:Object}),e("design:type",Object)],ZZt.prototype,"cardNode",void 0),t([o({type:Object}),e("design:type",Object)],ZZt.prototype,"itemNode",void 0),t([o({type:String}),e("design:type",String)],ZZt.prototype,"edgeLabel",void 0),t([o({type:Object}),e("design:type",Object)],ZZt.prototype,"itemRenderInfo",void 0),t([o({type:String}),e("design:type",String)],ZZt.prototype,"name",void 0),t([o({type:String,observer:"_itemTypeChanged"}),e("design:type",String)],ZZt.prototype,"itemType",void 0),t([o({type:String}),e("design:type",String)],ZZt.prototype,"colorBy",void 0),t([o({type:Object}),e("design:type",Object)],ZZt.prototype,"colorByParams",void 0),t([o({type:Object}),e("design:type",Object)],ZZt.prototype,"templateIndex",void 0),ZZt=t([i("tf-node-list-item")],ZZt);let JZt=class extends(cr(we)){constructor(){super(...arguments),this._expanded=!0,this._openedControlPred=!1,this._openedControlSucc=!1,this._templateIndex=null}expandNode(){this.fire("_node.expand",this.node)}_getNode(t,e){return e.node(t)}_getNodeStats(t,e){var n=this._getNode(t,e);return n?n.stats:null}_getTotalMicros(t){return t?t.getTotalMicros():0}get _hasDisplayableNodeStats(){return b$t(this._nodeStats)}get _nodeStatsFormattedBytes(){var t=this._nodeStats;if(t&&t.totalBytes)return v$t(t.totalBytes,_$t)}get _nodeStatsFormattedComputeTime(){var t=this._nodeStats;if(t&&t.getTotalMicros())return v$t(t.getTotalMicros(),y$t)}get _nodeStatsFormattedOutputSizes(){var t=this._nodeStats;if(t&&t.outputSize&&t.outputSize.length)return Ce.exports.map(t.outputSize,(function(t){return 0===t.length?"scalar":"["+t.join(", ")+"]"}))}_getRenderInfo(t,e){return this.renderHierarchy.getOrCreateRenderNodeByName(t)}get _attributes(){var t=this._node;if(this.async(this._resizeList.bind(this,"#attributesList")),!t||!t.attr)return[];var e=[];return Ce.exports.each(t.attr,(function(t){"_too_large_attrs"===t.key?e=e.concat(t.value.list.s.map((function(t){return{key:t,value:"Too large to show..."}}))):e.push({key:t.key,value:JSON.stringify(t.value)})})),e}get _device(){var t=this._node;return t?t.device:null}get _successors(){var t=this._node,e=this.graphHierarchy;return this._refreshNodeItemList("inputsList"),t?this._convertEdgeListToEdgeInfoList(e.getSuccessors(t.name),!1,t.isGroupNode):{regular:[],control:[]}}get _predecessors(){var t=this._node,e=this.graphHierarchy;return this._refreshNodeItemList("outputsList"),t?this._convertEdgeListToEdgeInfoList(e.getPredecessors(t.name),!0,t.isGroupNode):{regular:[],control:[]}}get _functionUsages(){var t=this._node,e=this.graphHierarchy;if(this._refreshNodeItemList("functionUsagesList"),!t||t.type!==A$t.META)return[];const n=e.libraryFunctions[t.associatedFunction];return n?n.usages:[]}_refreshNodeItemList(t){this.async(this._resizeList.bind(this,`#${t}`))}_convertEdgeListToEdgeInfoList(t,e,n){var i=t=>Ce.exports.map(t.baseEdgeList,(t=>{var n=e?t.v:t.w;return{name:n,node:this._getNode(n,this.graphHierarchy),edgeLabel:cZt(t,this.renderHierarchy),renderInfo:this._getRenderInfo(n,this.renderHierarchy)}})),r=function(t){var r=[];return Ce.exports.each(t,(t=>{var o=e?t.v:t.w;n&&1!=t.baseEdgeList.length?r.push({name:o,node:this._getNode(o,this.graphHierarchy),edgeLabel:uZt(t,this.renderHierarchy),renderInfo:this._getRenderInfo(o,this.renderHierarchy)}):r=r.concat(i(t))})),r}.bind(this);return{regular:r(t.regular),control:r(t.control)}}get _subnodes(){var t=this._node;return t&&t.metagraph?t.metagraph.nodes():null}get _totalPredecessors(){var t=this._predecessors;return t.regular.length+t.control.length}get _totalSuccessors(){var t=this._successors;return t.regular.length+t.control.length}_toggleControlPred(){this._openedControlPred=!this._openedControlPred}_toggleControlSucc(){this._openedControlSucc=!this._openedControlSucc}_toggleExpanded(){this._expanded=!this._expanded}_getToggleIcon(t){return t?"expand-less":"expand-more"}_resetState(){this._openedControlPred=!1,this._openedControlSucc=!1,this.set("_groupButtonText",xZt(this._node))}_resizeList(t){var e=document.querySelector(t);e&&e.fire("iron-resize")}_toggleInclude(){this.fire("node-toggle-inclusion",{name:this.graphNodeName})}_nodeIncludeStateChanged(t,e){this.set("_auxButtonText",J$t(t))}_toggleGroup(){var t=bZt(this._node);this.fire("node-toggle-seriesgroup",{name:t})}_isLibraryFunction(t){return t&&t.name.startsWith(T$t)}_isInSeries(t){return vZt(t)}_graphHierarchyChanged(){this._templateIndex=this.graphHierarchy.getTemplateIndex(),this.graphHierarchy.addListener(iKt.TEMPLATES_UPDATED,(()=>{this._templateIndex=this.graphHierarchy.getTemplateIndex()}))}};JZt.template=xe`
    <style>
      .sub-list-group {
        font-weight: 500;
        font-size: 12pt;
        padding-bottom: 8px;
        width: 100%;
      }

      .sub-list {
        max-height: 300px;
        overflow-y: scroll;
      }

      .attr-left {
        float: left;
        width: 30%;
        word-wrap: break-word;
        color: var(--secondary-text-color);
        font-size: 11pt;
        font-weight: 400;
      }

      .attr-right {
        margin-left: 30%;
        word-wrap: break-word;
        color: var(--secondary-text-color);
        font-weight: 400;
      }

      .sub-list-table {
        display: table;
        width: 100%;
      }

      .sub-list-table-row {
        display: table-row;
      }

      .sub-list-table-row .sub-list-table-cell:last-child {
        text-align: right;
      }

      .sub-list-table-cell {
        color: var(--secondary-text-color);
        display: table-cell;
        font-size: 11pt;
        font-weight: 400;
        max-width: 200px;
        padding: 0 4px;
      }

      paper-item {
        padding: 0;
        background: var(--primary-background-color);
      }

      paper-item-body[two-line] {
        min-height: 0;
        padding: 8px 12px 4px;
      }

      .expandedInfo {
        padding: 8px 12px;
      }

      .controlDeps {
        padding: 0 0 0 8px;
      }

      .node-name {
        white-space: normal;
        word-wrap: break-word;
        font-size: 14pt;
        font-weight: 500;
      }

      .node-icon {
        float: right;
      }

      .subtitle {
        color: var(--secondary-text-color);
        font-size: 12pt;
      }

      .controlLine {
        font-size: 11pt;
        font-weight: 400;
      }

      .toggle-button {
        float: right;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .control-toggle-button {
        float: left;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .toggle-include-group {
        padding-top: 4px;
      }

      .toggle-include {
        margin: 5px 6px;
        text-transform: none;
        padding: 4px 6px;
        font-size: 10pt;
        background-color: #fafafa;
        color: #666;
      }

      .toggle-include:hover {
        background-color: var(--google-yellow-100);
      }

      .non-control-list-item {
        padding-left: 10px;
      }
    </style>
    <paper-item>
      <paper-item-body two-line>
        <div>
          <paper-icon-button
            icon="{{_getToggleIcon(_expanded)}}"
            on-click="_toggleExpanded"
            class="toggle-button"
          >
          </paper-icon-button>
          <div class="node-name">
            <tf-wbr-string value="[[_node.name]]" delimiter-pattern="/">
            </tf-wbr-string>
          </div>
        </div>
        <div secondary>
          <tf-node-icon
            class="node-icon"
            node="[[_node]]"
            render-info="[[_getRenderInfo(graphNodeName, renderHierarchy)]]"
            color-by="[[colorBy]]"
            template-index="[[_templateIndex]]"
          ></tf-node-icon>
          <template is="dom-if" if="{{_node.op}}">
            <div class="subtitle">
              Operation:
              <span>[[_node.op]]</span>
            </div>
          </template>
          <template is="dom-if" if="{{_node.metagraph}}">
            <div class="subtitle">
              Subgraph:
              <span>[[_node.cardinality]]</span> nodes
            </div>
          </template>
        </div>
      </paper-item-body>
    </paper-item>
    <iron-collapse opened="{{_expanded}}">
      <template is="dom-if" if="{{_expanded}}" restamp="true">
        <div class="expandedInfo">
          <div class="sub-list-group attributes">
            Attributes (<span>[[_attributes.length]]</span>)
            <iron-list
              class="sub-list"
              id="attributesList"
              items="[[_attributes]]"
            >
              <template>
                <div>
                  <div class="attr-left">[[item.key]]</div>
                  <div class="attr-right">[[item.value]]</div>
                </div>
              </template>
            </iron-list>
          </div>

          <template is="dom-if" if="{{_device}}">
            <div class="sub-list-group device">
              <div class="attr-left">Device</div>
              <div class="attr-right">[[_device]]</div>
            </div>
          </template>

          <div class="sub-list-group predecessors">
            Inputs (<span>[[_totalPredecessors]]</span>)
            <iron-list
              class="sub-list"
              id="inputsList"
              items="[[_predecessors.regular]]"
            >
              <template>
                <tf-node-list-item
                  class="non-control-list-item"
                  card-node="[[_node]]"
                  item-node="[[item.node]]"
                  edge-label="[[item.edgeLabel]]"
                  item-render-info="[[item.renderInfo]]"
                  name="[[item.name]]"
                  item-type="predecessors"
                  color-by="[[colorBy]]"
                  template-index="[[_templateIndex]]"
                >
                </tf-node-list-item>
              </template>
            </iron-list>
            <template is="dom-if" if="[[_predecessors.control.length]]">
              <div class="controlDeps">
                <div class="controlLine">
                  <paper-icon-button
                    icon="{{_getToggleIcon(_openedControlPred)}}"
                    on-click="_toggleControlPred"
                    class="control-toggle-button"
                  >
                  </paper-icon-button>
                  Control dependencies
                </div>
                <iron-collapse opened="{{_openedControlPred}}" no-animation>
                  <template
                    is="dom-if"
                    if="{{_openedControlPred}}"
                    restamp="true"
                  >
                    <iron-list
                      class="sub-list"
                      items="[[_predecessors.control]]"
                    >
                      <template>
                        <tf-node-list-item
                          card-node="[[_node]]"
                          item-node="[[item.node]]"
                          item-render-info="[[item.renderInfo]]"
                          name="[[item.name]]"
                          item-type="predecessors"
                          color-by="[[colorBy]]"
                          template-index="[[_templateIndex]]"
                        >
                        </tf-node-list-item>
                      </template>
                    </iron-list>
                  </template>
                </iron-collapse>
              </div>
            </template>
          </div>

          <div class="sub-list-group successors">
            Outputs (<span>[[_totalSuccessors]]</span>)
            <iron-list
              class="sub-list"
              id="outputsList"
              items="[[_successors.regular]]"
            >
              <template>
                <tf-node-list-item
                  class="non-control-list-item"
                  card-node="[[_node]]"
                  item-node="[[item.node]]"
                  edge-label="[[item.edgeLabel]]"
                  item-render-info="[[item.renderInfo]]"
                  name="[[item.name]]"
                  item-type="successor"
                  color-by="[[colorBy]]"
                  template-index="[[_templateIndex]]"
                >
                </tf-node-list-item>
              </template>
            </iron-list>
            <template is="dom-if" if="[[_successors.control.length]]">
              <div class="controlDeps">
                <div class="controlLine">
                  <paper-icon-button
                    icon="{{_getToggleIcon(_openedControlSucc)}}"
                    on-click="_toggleControlSucc"
                    class="control-toggle-button"
                  >
                  </paper-icon-button>
                  Control dependencies
                </div>
                <iron-collapse opened="{{_openedControlSucc}}" no-animation>
                  <template
                    is="dom-if"
                    if="{{_openedControlSucc}}"
                    restamp="true"
                  >
                    <iron-list class="sub-list" items="[[_successors.control]]">
                      <template>
                        <tf-node-list-item
                          card-node="[[_node]]"
                          item-node="[[item.node]]"
                          item-render-info="[[item.renderInfo]]"
                          name="[[item.name]]"
                          item-type="successors"
                          color-by="[[colorBy]]"
                          template-index="[[_templateIndex]]"
                        >
                        </tf-node-list-item>
                      </template>
                    </iron-list>
                  </template>
                </iron-collapse>
              </div>
            </template>
          </div>
          <template is="dom-if" if="{{_hasDisplayableNodeStats}}">
            <div class="sub-list-group node-stats">
              Node Stats
              <div class="sub-list-table">
                <template is="dom-if" if="{{_nodeStats.totalBytes}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Memory</div>
                    <div class="sub-list-table-cell">
                      [[_nodeStatsFormattedBytes]]
                    </div>
                  </div>
                </template>
                <template is="dom-if" if="{{_getTotalMicros(_nodeStats)}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Compute Time</div>
                    <div class="sub-list-table-cell">
                      [[_nodeStatsFormattedComputeTime]]
                    </div>
                  </div>
                </template>
                <template is="dom-if" if="{{_nodeStats.outputSize}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Tensor Output Sizes</div>
                    <div class="sub-list-table-cell">
                      <template
                        is="dom-repeat"
                        items="{{_nodeStatsFormattedOutputSizes}}"
                      >
                        [[item]] <br />
                      </template>
                    </div>
                  </div>
                </template>
              </div>
            </div>
          </template>

          <template is="dom-if" if="[[_functionUsages.length]]">
            <div class="sub-list-group predecessors">
              Usages of the Function (<span>[[_functionUsages.length]]</span>)
              <iron-list
                class="sub-list"
                id="functionUsagesList"
                items="[[_functionUsages]]"
              >
                <template>
                  <tf-node-list-item
                    class="non-control-list-item"
                    card-node="[[_node]]"
                    item-node="[[item]]"
                    name="[[item.name]]"
                    item-type="functionUsages"
                    color-by="[[colorBy]]"
                    template-index="[[_templateIndex]]"
                  >
                  </tf-node-list-item>
                </template>
              </iron-list>
            </div>
          </template>

          <template is="dom-if" if="[[!_isLibraryFunction(_node)]]">
            <div class="toggle-include-group">
              <paper-button
                raised
                class="toggle-include"
                on-click="_toggleInclude"
              >
                <span>[[_auxButtonText]]</span>
              </paper-button>
            </div>
          </template>

          <template is="dom-if" if="{{_isInSeries(_node)}}">
            <div class="toggle-include-group">
              <paper-button
                raised
                class="toggle-include"
                on-click="_toggleGroup"
              >
                <span>[[_groupButtonText]]</span>
              </paper-button>
            </div>
          </template>
        </div>
      </template>
    </iron-collapse>
  `,t([o({type:String}),e("design:type",String)],JZt.prototype,"graphNodeName",void 0),t([o({type:Object}),e("design:type",rKt)],JZt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",Object)],JZt.prototype,"renderHierarchy",void 0),t([o({type:String}),e("design:type",String)],JZt.prototype,"colorBy",void 0),t([o({type:Object,computed:"_getNode(graphNodeName, graphHierarchy)",observer:"_resetState"}),e("design:type",Object)],JZt.prototype,"_node",void 0),t([o({type:Object,computed:"_getNodeStats(graphNodeName, graphHierarchy)",observer:"_resetState"}),e("design:type",Object)],JZt.prototype,"_nodeStats",void 0),t([o({type:Number,observer:"_nodeIncludeStateChanged"}),e("design:type",Number)],JZt.prototype,"nodeInclude",void 0),t([o({type:Boolean}),e("design:type",Boolean)],JZt.prototype,"_expanded",void 0),t([o({type:Boolean}),e("design:type",Boolean)],JZt.prototype,"_openedControlPred",void 0),t([o({type:Boolean}),e("design:type",Boolean)],JZt.prototype,"_openedControlSucc",void 0),t([o({type:String}),e("design:type",String)],JZt.prototype,"_auxButtonText",void 0),t([o({type:String}),e("design:type",String)],JZt.prototype,"_groupButtonText",void 0),t([o({type:Object}),e("design:type",Function)],JZt.prototype,"_templateIndex",void 0),t([s("_nodeStats"),e("design:type",Boolean),e("design:paramtypes",[])],JZt.prototype,"_hasDisplayableNodeStats",null),t([s("_nodeStats"),e("design:type",String),e("design:paramtypes",[])],JZt.prototype,"_nodeStatsFormattedBytes",null),t([s("_nodeStats"),e("design:type",String),e("design:paramtypes",[])],JZt.prototype,"_nodeStatsFormattedComputeTime",null),t([s("_nodeStats"),e("design:type",Array),e("design:paramtypes",[])],JZt.prototype,"_nodeStatsFormattedOutputSizes",null),t([s("_node"),e("design:type",Array),e("design:paramtypes",[])],JZt.prototype,"_attributes",null),t([s("_node"),e("design:type",String),e("design:paramtypes",[])],JZt.prototype,"_device",null),t([s("_node","graphHierarchy"),e("design:type",Object),e("design:paramtypes",[])],JZt.prototype,"_successors",null),t([s("_node","graphHierarchy"),e("design:type",Object),e("design:paramtypes",[])],JZt.prototype,"_predecessors",null),t([s("_node","graphHierarchy"),e("design:type",Array),e("design:paramtypes",[])],JZt.prototype,"_functionUsages",null),t([s("_node"),e("design:type",Array),e("design:paramtypes",[])],JZt.prototype,"_subnodes",null),t([s("_predecessors"),e("design:type",Number),e("design:paramtypes",[])],JZt.prototype,"_totalPredecessors",null),t([s("_successors"),e("design:type",Number),e("design:paramtypes",[])],JZt.prototype,"_totalSuccessors",null),t([a("graphHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],JZt.prototype,"_graphHierarchyChanged",null),JZt=t([i("tf-node-info")],JZt);let QZt=class extends(cr(we)){ready(){super.ready(),this.addEventListener("node-list-item-click",this._nodeListItemClicked.bind(this)),this.addEventListener("node-list-item-mouseover",this._nodeListItemMouseover.bind(this)),this.addEventListener("node-list-item-mouseout",this._nodeListItemMouseout.bind(this))}_nodeListItemClicked(t){this.selectedNode=t.detail.nodeName}_nodeListItemMouseover(t){this.highlightedNode=t.detail.nodeName}_nodeListItemMouseout(){this.highlightedNode=null}_healthPillsAvailable(t,e){return t&&e&&Object.keys(e).length>0}_equals(t,e){return t===e}};QZt.template=xe`
    <style>
      :host {
        background: var(--secondary-background-color);
        font-size: 12px;
        margin: 0;
        padding: 0;
        display: block;
        max-height: 650px;
        overflow-x: hidden;
        overflow-y: auto;
      }

      h2 {
        padding: 0;
        text-align: center;
        margin: 0;
      }
    </style>
    <template is="dom-if" if="{{selectedNode}}">
      <paper-material elevation="1" class="card">
        <tf-node-info
          graph-hierarchy="[[graphHierarchy]]"
          render-hierarchy="[[renderHierarchy]]"
          flat-graph="[[graph]]"
          graph-node-name="[[selectedNode]]"
          node-include="[[selectedNodeInclude]]"
          highlighted-node="{{highlightedNode}}"
          color-by="[[colorBy]]"
        >
        </tf-node-info>
      </paper-material>
    </template>
    <template is="dom-if" if="[[_equals(colorBy, 'op_compatibility')]]">
      <tf-graph-op-compat-card
        graph-hierarchy="[[graphHierarchy]]"
        render-hierarchy="[[renderHierarchy]]"
        color-by="[[colorBy]]"
        node-title="[[compatNodeTitle]]"
      >
      </tf-graph-op-compat-card>
    </template>
    <template
      is="dom-if"
      if="[[_healthPillsAvailable(debuggerDataEnabled, nodeNamesToHealthPills)]]"
    >
      <tf-graph-debugger-data-card
        render-hierarchy="[[renderHierarchy]]"
        debugger-numeric-alerts="[[debuggerNumericAlerts]]"
        node-names-to-health-pills="[[nodeNamesToHealthPills]]"
        selected-node="{{selectedNode}}"
        highlighted-node="{{highlightedNode}}"
        are-health-pills-loading="[[areHealthPillsLoading]]"
        all-steps-mode-enabled="{{allStepsModeEnabled}}"
        specific-health-pill-step="{{specificHealthPillStep}}"
        health-pill-step-index="{{healthPillStepIndex}}"
      >
      </tf-graph-debugger-data-card>
    </template>
  `,t([o({type:String}),e("design:type",String)],QZt.prototype,"title",void 0),t([o({type:Object}),e("design:type",rKt)],QZt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",I$t)],QZt.prototype,"graph",void 0),t([o({type:Object}),e("design:type",AKt)],QZt.prototype,"renderHierarchy",void 0),t([o({type:Object}),e("design:type",Object)],QZt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],QZt.prototype,"healthPillStepIndex",void 0),t([o({type:String}),e("design:type",String)],QZt.prototype,"colorBy",void 0),t([o({type:String}),e("design:type",String)],QZt.prototype,"compatNodeTitle",void 0),t([o({type:String,notify:!0}),e("design:type",String)],QZt.prototype,"selectedNode",void 0),t([o({type:String,notify:!0}),e("design:type",String)],QZt.prototype,"highlightedNode",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],QZt.prototype,"selectedNodeInclude",void 0),t([o({type:Boolean}),e("design:type",Boolean)],QZt.prototype,"debuggerDataEnabled",void 0),QZt=t([i("tf-graph-info")],QZt);const tJt={MAX_NODE_COUNT:1e4,MAX_EDGE_COUNT:1e4};let eJt=class extends(cr(we)){constructor(){super(...arguments),this.hierarchyParams=aKt,this.allStepsModeEnabled=!1,this.specificHealthPillStep=0,this.compatNodeTitle="TPU Compatibility"}fit(){this.$.graph.fit()}downloadAsImage(t){return n(this,void 0,void 0,(function*(){const e=yield this.$.graph.getImageBlob(),n=document.createElement("a");n.href=URL.createObjectURL(e),n.download=t,n.click(),URL.revokeObjectURL(n.href)}))}_isNotComplete(t){return t.value<100}_getContainerClass(t){var e="container";return t.error&&(e+=" error"),this._isNotComplete(t)&&(e+=" loading"),e}_onNodeInclusionToggled(t){this.$.graph.nodeToggleExtract(t.detail.name)}_onNodeSeriesGroupToggled(t){this.$.graph.nodeToggleSeriesGroup(t.detail.name)}_updateNodeInclude(){const t=this.renderHierarchy?this.renderHierarchy.getNodeByName(this.selectedNode):null;this._selectedNodeInclude=t?t.include:k$t.UNSPECIFIED}_slimGraphChanged(){if(!this.graph)return;const{MAX_NODE_COUNT:t,MAX_EDGE_COUNT:e}=tJt;Object.keys(this.graph.nodes).length>t&&this.graph.edges.length>e&&this.colorBy===rZt.STRUCTURE&&(this.colorBy=rZt.NONE)}_ensureTemplates(){this.graphHierarchy&&this.colorBy===rZt.STRUCTURE&&(this.graphHierarchy.getTemplateIndex()||this.graphHierarchy.updateTemplates())}};eJt.template=xe`
    <style>
      ::host {
        display: block;
      }

      /deep/ .close {
        position: absolute;
        cursor: pointer;
        left: 15px;
        bottom: 15px;
      }

      .container {
        width: 100%;
        height: 100%;
        opacity: 1;
      }

      .container.loading {
        cursor: progress;
        opacity: 0.1;
      }

      .container.loading.error {
        cursor: auto;
      }

      #info {
        position: absolute;
        right: 5px;
        top: 5px;
        padding: 0px;
        max-width: 380px;
        min-width: 320px;
        background-color: rgba(255, 255, 255, 0.9);
        @apply --shadow-elevation-2dp;
      }

      #main {
        width: 100%;
        height: 100%;
      }

      #progress-bar {
        display: flex;
        flex-direction: column;
        align-items: center;
        justify-content: center;
        width: 100%;
        position: absolute;
        top: 40px;
        left: 0;
        font-size: 13px;
      }

      #progress-msg {
        margin-bottom: 5px;
        white-space: pre-wrap;
        width: 400px;
      }

      paper-progress {
        width: 400px;
        --paper-progress-height: 6px;
        --paper-progress-active-color: #f3913e;
      }

      .context-menu {
        position: absolute;
        display: none;
        background-color: #e2e2e2;
        border-radius: 2px;
        font-size: 14px;
        min-width: 150px;
        border: 1px solid #d4d4d4;
      }

      /deep/ .context-menu ul {
        list-style-type: none;
        margin: 0;
        padding: 0;
        cursor: default;
      }

      /deep/ .context-menu ul li {
        padding: 4px 16px;
      }

      /deep/ .context-menu ul li:hover {
        background-color: #f3913e;
        color: white;
      }
    </style>
    <template is="dom-if" if="[[_isNotComplete(progress)]]">
      <div id="progress-bar">
        <div id="progress-msg">[[progress.msg]]</div>
        <paper-progress value="[[progress.value]]"></paper-progress>
      </div>
    </template>
    <div class$="[[_getContainerClass(progress)]]">
      <div id="main">
        <tf-graph
          id="graph"
          graph-hierarchy="{{graphHierarchy}}"
          basic-graph="[[graph]]"
          hierarchy-params="[[hierarchyParams]]"
          render-hierarchy="{{renderHierarchy}}"
          devices-for-stats="[[devicesForStats]]"
          stats="[[stats]]"
          selected-node="{{selectedNode}}"
          highlighted-node="{{_highlightedNode}}"
          color-by="[[colorBy]]"
          color-by-params="{{colorByParams}}"
          progress="{{progress}}"
          edge-label-function="[[edgeLabelFunction]]"
          edge-width-function="[[edgeWidthFunction]]"
          node-names-to-health-pills="[[nodeNamesToHealthPills]]"
          health-pill-step-index="[[healthPillStepIndex]]"
          handle-node-selected="[[handleNodeSelected]]"
          handle-edge-selected="[[handleEdgeSelected]]"
          trace-inputs="[[traceInputs]]"
          auto-extract-nodes="[[autoExtractNodes]]"
        ></tf-graph>
      </div>
      <div id="info">
        <tf-graph-info
          id="graph-info"
          title="selected"
          graph-hierarchy="[[graphHierarchy]]"
          render-hierarchy="[[renderHierarchy]]"
          graph="[[graph]]"
          selected-node="{{selectedNode}}"
          selected-node-include="{{_selectedNodeInclude}}"
          highlighted-node="{{_highlightedNode}}"
          color-by="[[colorBy]]"
          color-by-params="[[colorByParams]]"
          debugger-data-enabled="[[debuggerDataEnabled]]"
          are-health-pills-loading="[[areHealthPillsLoading]]"
          debugger-numeric-alerts="[[debuggerNumericAlerts]]"
          node-names-to-health-pills="[[nodeNamesToHealthPills]]"
          all-steps-mode-enabled="{{allStepsModeEnabled}}"
          specific-health-pill-step="{{specificHealthPillStep}}"
          health-pill-step-index="{{healthPillStepIndex}}"
          compat-node-title="[[compatNodeTitle]]"
          on-node-toggle-inclusion="_onNodeInclusionToggled"
          on-node-toggle-seriesgroup="_onNodeSeriesGroupToggled"
        ></tf-graph-info>
      </div>
    </div>
  `,t([o({type:Object}),e("design:type",rKt)],eJt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",I$t)],eJt.prototype,"graph",void 0),t([o({type:Object}),e("design:type",Object)],eJt.prototype,"hierarchyParams",void 0),t([o({type:Object}),e("design:type",Object)],eJt.prototype,"stats",void 0),t([o({type:Object}),e("design:type",Object)],eJt.prototype,"progress",void 0),t([o({type:Boolean}),e("design:type",Boolean)],eJt.prototype,"traceInputs",void 0),t([o({type:Boolean}),e("design:type",Boolean)],eJt.prototype,"autoExtractNodes",void 0),t([o({type:String,notify:!0}),e("design:type",String)],eJt.prototype,"colorBy",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],eJt.prototype,"colorByParams",void 0),t([o({type:Object,notify:!0}),e("design:type",AKt)],eJt.prototype,"renderHierarchy",void 0),t([o({type:Boolean}),e("design:type",Boolean)],eJt.prototype,"debuggerDataEnabled",void 0),t([o({type:Boolean}),e("design:type",Boolean)],eJt.prototype,"areHealthPillsLoading",void 0),t([o({type:Array,notify:!0}),e("design:type",Array)],eJt.prototype,"debuggerNumericAlerts",void 0),t([o({type:Object}),e("design:type",Object)],eJt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],eJt.prototype,"allStepsModeEnabled",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],eJt.prototype,"specificHealthPillStep",void 0),t([o({type:Number}),e("design:type",Number)],eJt.prototype,"healthPillStepIndex",void 0),t([o({type:String,notify:!0}),e("design:type",String)],eJt.prototype,"selectedNode",void 0),t([o({type:String}),e("design:type",String)],eJt.prototype,"compatNodeTitle",void 0),t([o({type:Object}),e("design:type",Object)],eJt.prototype,"edgeWidthFunction",void 0),t([o({type:Number}),e("design:type",Number)],eJt.prototype,"_selectedNodeInclude",void 0),t([o({type:String}),e("design:type",String)],eJt.prototype,"_highlightedNode",void 0),t([o({type:Object}),e("design:type",Object)],eJt.prototype,"handleNodeSelected",void 0),t([o({type:Object}),e("design:type",Object)],eJt.prototype,"edgeLabelFunction",void 0),t([o({type:Object}),e("design:type",Object)],eJt.prototype,"handleEdgeSelected",void 0),t([a("selectedNode","renderHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],eJt.prototype,"_updateNodeInclude",null),t([a("graph"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],eJt.prototype,"_slimGraphChanged",null),t([a("colorBy","graphHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],eJt.prototype,"_ensureTemplates",null),eJt=t([i("tf-graph-board")],eJt);class nJt{isNotTpuOp(t){return-1!=t.toLowerCase().search("cpu:")||(-1!=t.toLowerCase().search("gpu:")||-1==t.toLowerCase().search("tpu"))}opValid(t){return 0==t.name.search(T$t)||(!t.op||(!(!t.device||!this.isNotTpuOp(t.device))||(!(!t.device||-1==t.device.search("TPU_SYSTEM"))||Ce.exports.includes(nJt.WHITELIST,t.op))))}}nJt.WHITELIST=["Abs","Acos","Acosh","Add","AddN","AddV2","AdjustContrastv2","AdjustHue","AdjustSaturation","All","AllToAll","Angle","Any","ApproximateEqual","ArgMax","ArgMin","Asin","Asinh","Assert","AssignAddVariableOp","AssignSubVariableOp","AssignVariableOp","Atan","Atan2","Atanh","AvgPool","AvgPool3D","AvgPool3DGrad","AvgPoolGrad","BatchMatMul","BatchMatMulV2","BatchToSpace","BatchToSpaceND","BesselI0e","BesselI1e","Betainc","BiasAdd","BiasAddGrad","BiasAddV1","Bitcast","BitwiseAnd","BitwiseOr","BitwiseXor","BroadcastArgs","BroadcastGradientArgs","BroadcastTo","Bucketize","Case","Cast","Ceil","CheckNumerics","Cholesky","ClipByValue","CollectivePermute","CollectiveReduceV2","Complex","ComplexAbs","Concat","ConcatOffset","ConcatV2","Conj","ConjugateTranspose","Const","ControlTrigger","Conv2D","Conv2DBackpropFilter","Conv2DBackpropInput","Conv3D","Conv3DBackpropFilterV2","Conv3DBackpropInputV2","Cos","Cosh","Cross","CrossReplicaSum","Cumprod","Cumsum","DataFormatDimMap","DataFormatVecPermute","DepthToSpace","DepthwiseConv2dNative","DepthwiseConv2dNativeBackpropFilter","DepthwiseConv2dNativeBackpropInput","Dequantize","DeviceIndex","Diag","DiagPart","Digamma","Div","DivNoNan","DynamicStitch","Einsum","Elu","EluGrad","Empty","EmptyTensorList","EnsureShape","Equal","Erf","Erfc","Erfinv","Exp","ExpandDims","Expm1","ExtractImagePatches","FFT","FFT2D","FFT3D","FakeParam","FakeQuantWithMinMaxArgs","FakeQuantWithMinMaxArgsGradient","FakeQuantWithMinMaxVars","FakeQuantWithMinMaxVarsGradient","Fill","Floor","FloorDiv","FloorMod","FusedBatchNorm","FusedBatchNormGrad","FusedBatchNormGradV2","FusedBatchNormGradV3","FusedBatchNormV2","FusedBatchNormV3","Gather","GatherNd","GatherV2","GetItem","Greater","GreaterEqual","HSVToRGB","IFFT","IFFT2D","IFFT3D","IRFFT","IRFFT2D","IRFFT3D","Identity","IdentityN","If","Igamma","IgammaGradA","Igammac","Imag","InTopKV2","InfeedDequeue","InfeedDequeueTuple","InplaceAdd","InplaceUpdate","Inv","Invert","InvertPermutation","IsFinite","IsInf","IsNan","KthOrderStatistic","L2Loss","LRN","LRNGrad","LeakyRelu","LeakyReluGrad","LeftShift","Less","LessEqual","Lgamma","LinSpace","ListDiff","Log","Log1p","LogSoftmax","LogicalAnd","LogicalNot","LogicalOr","LowerBound","MakeUnique","MatMul","MatrixBandPart","MatrixDiag","MatrixDiagPart","MatrixDiagPartV2","MatrixDiagPartV3","MatrixDiagV2","MatrixDiagV3","MatrixInverse","MatrixSetDiag","MatrixSetDiagV2","MatrixSetDiagV3","MatrixSolve","MatrixTriangularSolve","Max","MaxPool","MaxPool3D","MaxPool3DGrad","MaxPool3DGradGrad","MaxPoolGrad","MaxPoolGradGrad","MaxPoolGradGradV2","MaxPoolGradV2","MaxPoolV2","Maximum","Mean","Min","Minimum","MirrorPad","MirrorPadGrad","Mod","Mul","MulNoNan","Multinomial","Ndtri","Neg","NextAfter","NoOp","NonMaxSuppressionV4","NotEqual","OneHot","OnesLike","OutfeedEnqueue","OutfeedEnqueueTuple","Pack","Pad","PadV2","ParallelDynamicStitch","ParameterizedTruncatedNormal","PartitionedCall","PlaceholderWithDefault","Polygamma","PopulationCount","Pow","PreventGradient","Prod","Qr","QuantizeAndDequantizeV2","QuantizeAndDequantizeV3","RFFT","RFFT2D","RFFT3D","RGBToHSV","RandomGammaGrad","RandomShuffle","RandomStandardNormal","RandomUniform","RandomUniformInt","Range","Rank","ReadVariableOp","Real","RealDiv","Reciprocal","ReciprocalGrad","Relu","Relu6","Relu6Grad","ReluGrad","Reshape","ResizeBilinear","ResizeBilinearGrad","ResizeNearestNeighbor","ResizeNearestNeighborGrad","ResourceApplyAdaMax","ResourceApplyAdadelta","ResourceApplyAdagrad","ResourceApplyAdagradDA","ResourceApplyAdagradV2","ResourceApplyAdam","ResourceApplyAddSign","ResourceApplyCenteredRMSProp","ResourceApplyFtrl","ResourceApplyFtrlV2","ResourceApplyGradientDescent","ResourceApplyKerasMomentum","ResourceApplyMomentum","ResourceApplyPowerSign","ResourceApplyProximalAdagrad","ResourceApplyProximalGradientDescent","ResourceApplyRMSProp","ResourceGather","ResourceScatterAdd","ResourceScatterDiv","ResourceScatterMax","ResourceScatterMin","ResourceScatterMul","ResourceScatterNdAdd","ResourceScatterNdSub","ResourceScatterNdUpdate","ResourceScatterSub","ResourceScatterUpdate","ResourceStridedSliceAssign","Reverse","ReverseSequence","ReverseV2","RightShift","Rint","RngReadAndSkip","RngSkip","Roll","Round","Rsqrt","RsqrtGrad","ScatterNd","Select","SelectV2","SelfAdjointEigV2","Selu","SeluGrad","Shape","ShapeN","Sigmoid","SigmoidGrad","Sign","Sin","Sinh","Size","Slice","Snapshot","Softmax","SoftmaxCrossEntropyWithLogits","Softplus","SoftplusGrad","Softsign","SoftsignGrad","SpaceToBatch","SpaceToBatchND","SpaceToDepth","SparseMatMul","SparseSoftmaxCrossEntropyWithLogits","SparseToDense","Split","SplitV","Sqrt","SqrtGrad","Square","SquaredDifference","Squeeze","StackCloseV2","StackPopV2","StackPushV2","StackV2","StatefulPartitionedCall","StatefulStandardNormalV2","StatefulTruncatedNormal","StatefulUniform","StatefulUniformFullInt","StatefulUniformInt","StatelessCase","StatelessIf","StatelessMultinomial","StatelessRandomGetAlg","StatelessRandomGetKeyCounter","StatelessRandomGetKeyCounterAlg","StatelessRandomNormal","StatelessRandomNormalV2","StatelessRandomUniform","StatelessRandomUniformFullInt","StatelessRandomUniformFullIntV2","StatelessRandomUniformInt","StatelessRandomUniformIntV2","StatelessRandomUniformV2","StatelessTruncatedNormal","StatelessTruncatedNormalV2","StatelessWhile","StopGradient","StridedSlice","StridedSliceGrad","Sub","Sum","Svd","SymbolicGradient","TPUEmbeddingActivations","Tan","Tanh","TanhGrad","TensorArrayCloseV3","TensorArrayConcatV3","TensorArrayGatherV3","TensorArrayGradV3","TensorArrayReadV3","TensorArrayScatterV3","TensorArraySizeV3","TensorArraySplitV3","TensorArrayV3","TensorArrayWriteV3","TensorListConcatV2","TensorListElementShape","TensorListFromTensor","TensorListGather","TensorListGetItem","TensorListLength","TensorListPopBack","TensorListPushBack","TensorListReserve","TensorListSetItem","TensorListSplit","TensorListStack","TensorScatterAdd","TensorScatterMax","TensorScatterMin","TensorScatterSub","TensorScatterUpdate","TensorStridedSliceUpdate","Tile","TopKUnique","TopKV2","TopKWithUnique","Transpose","TridiagonalSolve","TruncateDiv","TruncateMod","TruncatedNormal","Unique","Unpack","UnsortedSegmentMax","UnsortedSegmentMin","UnsortedSegmentProd","UnsortedSegmentSum","UpperBound","VarIsInitializedOp","VariableShape","Where","While","Xdivy","XlaBroadcastHelper","XlaConv","XlaConvV2","XlaDequantize","XlaDot","XlaDotV2","XlaDynamicSlice","XlaDynamicUpdateSlice","XlaEinsum","XlaGather","XlaHostCompute","XlaIf","XlaKeyValueSort","XlaPad","XlaRecv","XlaRecvFromHost","XlaReduce","XlaReduceWindow","XlaReplicaId","XlaScatter","XlaSelectAndScatter","XlaSelfAdjointEig","XlaSend","XlaSendToHost","XlaSetBound","XlaSetDynamicDimensionSize","XlaSharding","XlaSort","XlaSpmdFullToShardShape","XlaSpmdShardToFullShape","XlaSvd","XlaVariadicReduce","XlaVariadicSort","XlaWhile","Xlog1py","Xlogy","ZerosLike","Zeta","Enter","Exit","LoopCond","Merge","NextIteration","Switch","_Arg","_ArrayToList","_FusedBatchNormEx","_ListToArray","_ParallelConcatUpdate","_RecvTPUEmbeddingActivations","_RecvTPUEmbeddingDeduplicationData","_Retval","_SendTPUEmbeddingGradients","_TPUCompile","_TPUExecute","_UnaryOpsComposition","TPUCompilationResult","TPUReplicatedInput","TPUReplicatedOutput","TPUReplicateMetadata","MergeV2Checkpoints","RestoreV2","SaveV2","Abort","Assert","Assign","Placeholder","PlaceholderV2","ShardedFilename","StringJoin","Variable","VariableV2","VarHandleOp","AudioSummary","AudioSummaryV2","DebugNumericSummary","HistogramSummary","ImageSummary","MergeSummary","ScalarSummary","StatsAggregatorSummary"];let iJt=class extends(cr(we)){constructor(){super(...arguments),this._rawRegexInput="",this._previousRegexInput="",this._searchTimeoutDelay=150,this._maxRegexResults=42}get _regexInput(){return this._rawRegexInput.trim()}_regexInputChanged(){this._requestSearch()}_clearSearchResults(){this.set("_regexMatches",[])}_requestSearch(){this._searchPending||(this._regexInput!==this._previousRegexInput?(this._searchPending=!0,this._executeSearch(),this.async((()=>{this._searchPending=!1,this._requestSearch()}),this._searchTimeoutDelay)):this._searchPending=!1)}_executeSearch(){if(this._previousRegexInput=this._regexInput,!this._regexInput)return void this._clearSearchResults();try{var t=new RegExp(this._regexInput)}catch(t){return void this._clearSearchResults()}const e=[],n=this.renderHierarchy.hierarchy.getNodeMap();Ce.exports.each(n,((n,i)=>{if(e.length>=this._maxRegexResults)return!1;t.test(i)&&e.push(i)})),this.set("_regexMatches",e)}_matchClicked(t){this.set("selectedNode",t.model.item),h$t({actionId:BAt.NODE_SEARCH_RESULT_FOCUSED})}};iJt.template=xe`
    <div id="search-container">
      <paper-input
        id="runs-regex"
        label="Search nodes (regex)"
        value="{{_rawRegexInput}}"
      >
      </paper-input>
      <div id="search-results-anchor">
        <div id="search-results">
          <template is="dom-repeat" items="[[_regexMatches]]">
            <div id="search-match" on-click="_matchClicked">[[item]]</div>
          </template>
        </div>
      </div>
    </div>
    <style>
      #search-container {
        width: 100%;
        overflow: visible;
      }

      #runs-regex {
        width: 100%;
      }

      #search-results-anchor {
        position: relative;
      }

      #search-results {
        color: #fff;
        position: absolute;
        max-height: 200px;
        overflow-x: hidden;
        overflow-y: auto;
        text-align: right;
        max-width: 100%;
        box-sizing: border-box;
      }

      #search-match {
        background: var(--tb-orange-strong);
        padding: 3px;
        float: right;
        width: 100%;
        box-sizing: border-box;
        direction: rtl;
      }

      #search-match:hover {
        background: var(--tb-orange-weak);
        cursor: pointer;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],iJt.prototype,"renderHierarchy",void 0),t([o({type:String,notify:!0}),e("design:type",String)],iJt.prototype,"selectedNode",void 0),t([o({type:String}),e("design:type",String)],iJt.prototype,"_rawRegexInput",void 0),t([o({type:String}),e("design:type",String)],iJt.prototype,"_previousRegexInput",void 0),t([o({type:Number}),e("design:type",Number)],iJt.prototype,"_searchTimeoutDelay",void 0),t([o({type:Boolean}),e("design:type",Boolean)],iJt.prototype,"_searchPending",void 0),t([o({type:Number}),e("design:type",Number)],iJt.prototype,"_maxRegexResults",void 0),t([o({type:Array}),e("design:type",Array)],iJt.prototype,"_regexMatches",void 0),t([s("renderHierarchy","_rawRegexInput"),e("design:type",String),e("design:paramtypes",[])],iJt.prototype,"_regexInput",null),t([a("_regexInput"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],iJt.prototype,"_regexInputChanged",null),iJt=t([i("tf-graph-node-search")],iJt);const rJt=/device:([^:]+:[0-9]+)$/,oJt=[{regex:rJt}],aJt=[],sJt=new Set([rZt.COMPUTE_TIME,rZt.MEMORY]);let lJt=class extends(cr(gl(we))){constructor(){super(...arguments),this.ColorBy=rZt,this.stats=null,this.devicesForStats=null,this.colorBy=rZt.STRUCTURE,this.datasets=[],this._selectedRunIndex=0,this.traceInputs=!1,this.autoExtractNodes=!0,this._selectedTagIndex=0,this._selectedGraphType=QXt.OP_GRAPH,this.showSessionRunsDropdown=!0,this.showUploadButton=!0,this._legendOpened=!0,this._downloadFilename="graph.png"}_onGraphTypeChangedByUserGesture(){h$t({actionId:BAt.GRAPH_TYPE_CHANGED,eventLabel:this._selectedGraphType})}_onColorByChangedByUserGesture(){h$t({actionId:BAt.NODE_COLOR_MODE_CHANGED,eventLabel:this.colorBy})}_onTraceInputsChangedByUserGesture(){h$t({actionId:BAt.TRACE_INPUT_MODE_TOGGLED})}_xlaClustersProvided(t){return t&&t.hierarchy&&t.hierarchy.xlaClusters.length>0}_statsChanged(t){if(null!=t){var e={};Ce.exports.each(t.dev_stats,(function(t){var n=Ce.exports.some(oJt,(function(e){return e.regex.test(t.device)})),i=Ce.exports.some(aJt,(function(e){return e.regex.test(t.device)}));n&&!i&&(e[t.device]=!0)})),this.set("devicesForStats",e)}}get _currentDevices(){var t=this.devicesForStats;const e=this.stats,n=(e?e.dev_stats:[]).map((t=>t.device)).filter((t=>oJt.some((e=>e.regex.test(t))))),i=x$t(n);if(1==i.length){const t=i[0].match(rJt);t&&(i[0]=t[1])}return n.map(((e,n)=>{let r=null;return aJt.forEach((t=>{t.regex.test(e)&&(r=t.msg)})),{device:e,suffix:i[n],used:t[e],ignoredMsg:r}}))}_deviceCheckboxClicked(t){const e=t.target,n=Object.assign({},this.devicesForStats),i=e.value;e.checked?n[i]=!0:delete n[i],this.set("devicesForStats",n)}_numTags(t,e){return this._getTags(t,e).length}_getTags(t,e){return t&&t[e]?t[e].tags:[]}_fit(){this.fire("fit-tap")}_isGradientColoring(t,e){return sJt.has(e)&&null!=t}_equals(t,e){return t===e}get _currentDeviceParams(){const t=this.colorByParams.device.filter((t=>oJt.some((e=>e.regex.test(t.device))))),e=x$t(t.map((t=>t.device)));if(1==e.length){var n=e[0].match(rJt);n&&(e[0]=n[1])}return t.map(((t,n)=>({device:e[n],color:t.color})))}get _currentXlaClusterParams(){return this.colorByParams.xla_cluster}get _currentGradientParams(){var t=this.colorByParams,e=this.colorBy;if(!this._isGradientColoring(this.stats,e))return;const n=t[e];let i=n.minValue,r=n.maxValue;return e===rZt.MEMORY?(i=v$t(i,_$t),r=v$t(r,_$t)):e===rZt.COMPUTE_TIME&&(i=v$t(i,y$t),r=v$t(r,y$t)),{minValue:i,maxValue:r,startColor:n.startColor,endColor:n.endColor}}download(){this.fire("download-image-requested",this._downloadFilename)}_updateFileInput(t){const e=t.target.files[0];if(!e)return;let n=e.name;const i=n.lastIndexOf(".");i>=0&&(n=n.substring(0,i));const r=n.lastIndexOf("/");r>=0&&(n=n.substring(r+1)),this._setDownloadFilename(n),this.set("selectedFile",t),h$t({actionId:BAt.UPLOADED_GRAPH_FROM_FILESYSTEM})}_datasetsChanged(t,e){var n;null!=e&&(this._selectedRunIndex=0),this._setDownloadFilename(null===(n=this.datasets[this._selectedRunIndex])||void 0===n?void 0:n.name)}_computeSelection(t,e,n,i){return t[e]&&t[e].tags[n]?{run:t[e].name,tag:t[e].tags[n].tag,type:i}:null}_selectedRunIndexChanged(t){var e;this.datasets&&(this.colorBy=rZt.STRUCTURE,this._selectedTagIndex=0,this._selectedGraphType=this._getDefaultSelectionType(),this.traceInputs=!1,this._setDownloadFilename(null===(e=this.datasets[t])||void 0===e?void 0:e.name))}_selectedTagIndexChanged(){this._selectedGraphType=this._getDefaultSelectionType()}_getDefaultSelectionType(){const{datasets:t,_selectedRunIndex:e,_selectedTagIndex:n}=this;if(!t||!t[e]||!t[e].tags[n]||t[e].tags[n].opGraph)return QXt.OP_GRAPH;const i=t[e];return i.tags[n].profile?QXt.PROFILE:i.tags[n].conceptualGraph?QXt.CONCEPTUAL_GRAPH:QXt.OP_GRAPH}_getFile(){this.$$("#file").click()}_setDownloadFilename(t){this._downloadFilename=(t||"graph")+".png"}_statsNotNull(t){return null!==t}_toggleLegendOpen(){this.set("_legendOpened",!this._legendOpened)}_getToggleLegendIcon(t){return t?"expand-more":"expand-less"}_getSelectionOpGraphDisabled(t,e,n){return!t[e]||!t[e].tags[n]||!t[e].tags[n].opGraph}_getSelectionProfileDisabled(t,e,n){return!t[e]||!t[e].tags[n]||!t[e].tags[n].profile}_getSelectionConceptualGraphDisabled(t,e,n){return!t[e]||!t[e].tags[n]||!t[e].tags[n].conceptualGraph}};function cJt(t){return new Promise(((e,n)=>{fetch(t).then((t=>{t.ok?t.arrayBuffer().then(e,n):t.text().then(n,n)}))}))}function uJt(t,e){return(function n(t,e,i,r,o){r.setMessage(t);try{let n=d$t(t,i,o);return r.updateProgress(e),n}catch(e){r.reportError("Failed "+t,e)}})("Reading metadata pbtxt",40,(()=>null==t?Promise.resolve(null):cJt(t)),e,BAt.FETCH_METADATA_PBTXT_BYTES).then((t=>g$t("Parsing metadata.pbtxt",60,(()=>null!=t?(function e(t){return fJt(t,pJt).then((t=>t.step_stats))})(t):Promise.resolve(null)),e,BAt.PARSE_METADATA_PBTXT_INTO_OBJECT)))}function hJt(t,e,i){return g$t("Reading graph pbtxt",40,(()=>n(this,void 0,void 0,(function*(){const n=Date.now();if(e){const t=yield new Promise((function(t,n){let i=new FileReader;i.onload=()=>t(i.result),i.onerror=()=>n(i.error),i.readAsArrayBuffer(e)}));return h$t({timingId:BAt.FETCH_PBTXT_BYTES_FROM_FILESYSTEM,eventValue:Date.now()-n}),t}const i=yield cJt(t);return h$t({timingId:BAt.FETCH_PBTXT_BYTES_FROM_SERVER,eventValue:Date.now()-n}),i}))),i,BAt.FETCH_PBTXT_BYTES).then((t=>g$t("Parsing graph.pbtxt",60,(()=>(function e(t){return fJt(t,dJt)})(t)),i,BAt.PARSE_PBTXT_INTO_OBJECT)))}lJt.template=xe`
    <style>
      :host {
        color: #555;
        display: flex;
        flex-direction: column;
        font-size: 12px;
        width: 100%;
        --tb-graph-controls-title-color: #000;
        --tb-graph-controls-legend-text-color: #000;
        --tb-graph-controls-text-color: #555;
        --tb-graph-controls-title-font-size: 14px;
        --tb-graph-controls-subtitle-font-size: 14px;
        --paper-input-container-shared-input-style_-_font-size: 14px;
        --paper-font-subhead_-_font-size: 14px;
      }

      :host(.dark-mode) {
        --tb-graph-controls-title-color: #fff;
        --tb-graph-controls-legend-text-color: #f3f3f3;
        --tb-graph-controls-text-color: #eee;
      }

      paper-dropdown-menu {
        --paper-dropdown-menu-input: {
          padding: 0;
          color: gray;
        }
        --iron-icon-width: 15px;
        --iron-icon-height: 15px;
        --primary-text-color: gray;
        --paper-item-min-height: 30px;
      }

      paper-button[raised].keyboard-focus {
        font-weight: normal;
      }

      .run-dropdown {
        --paper-input-container: {
          padding: 5px 0 5px 5px;
        }
      }

      table {
        border-collapse: collapse;
        border-spacing: 0;
      }

      table tr {
        height: 20px;
      }

      table td {
        padding: 0;
        margin: 0;
      }

      .allcontrols {
        padding: 0 20px 20px;
        flex-grow: 1;
        overflow-y: auto;
      }

      .legend-holder {
        background: var(--secondary-background-color);
        box-sizing: border-box;
        color: var(--tb-graph-controls-text-color);
        width: 100%;
      }

      .legend-toolbar {
        appearance: none;
        background-color: inherit;
        border-top: 1px solid #ccc;
        border-bottom: 1px solid #ccc;
        border-right: none;
        border-left: none;
        cursor: pointer;
        color: var(--tb-graph-controls-legend-text-color);
        font: inherit;
        display: flex;
        align-items: center;
        justify-content: space-between;
        width: 100%;
      }

      .legend-toolbar,
      .legend-content {
        padding: 8px 20px;
      }

      .toggle-legend-button {
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .toggle-legend-text {
        font-size: var(--tb-graph-controls-subtitle-font-size);
      }

      paper-radio-button {
        display: block;
        padding: 5px;
      }
      svg.icon,
      tf-graph-icon {
        width: 60px;
        height: 18px;
      }
      .domainValues {
        margin-bottom: 10px;
        width: 165px;
      }
      .domainStart {
        float: left;
      }
      .domainEnd {
        float: right;
      }
      .colorBox {
        width: 20px;
      }

      .image-icon {
        width: 24px;
        height: 24px;
      }

      .help-icon {
        height: 15px;
        margin: 0;
        padding: 0;
      }

      .gray {
        color: #666;
      }

      .title {
        font-size: var(--tb-graph-controls-title-font-size);
        margin: 8px 5px 8px 0;
        color: var(--tb-graph-controls-title-color);
      }
      .title small {
        font-weight: normal;
      }
      .deviceList,
      .xlaClusterList {
        max-height: 200px;
        overflow-y: auto;
      }

      #file {
        padding: 8px 0;
      }

      .color-legend-row {
        align-items: center;
        clear: both;
        display: flex;
        height: 20px;
        margin-top: 5px;
      }

      .color-legend-row .label,
      .color-legend-row svg,
      .color-legend-row tf-graph-icon {
        flex: 0 0 40px;
        margin-right: 20px;
      }

      .devices-checkbox input {
        text-align: left;
        vertical-align: middle;
      }

      .control-holder .icon-button {
        font-size: var(--tb-graph-controls-subtitle-font-size);
        margin: 0 -5px;
        padding: 5px;
        display: flex;
        justify-content: flex-start;
        color: var(--tb-graph-controls-text-color);
      }

      .button-text {
        padding-left: 20px;
        text-transform: none;
      }

      .upload-button {
        width: 165px;
        height: 25px;
        text-transform: none;
        margin-top: 4px;
      }

      .button-icon {
        width: 26px;
        height: 26px;
        color: var(--paper-orange-500);
      }

      .hidden-input {
        display: none;
      }

      .allcontrols .control-holder {
        clear: both;
        display: flex;
        justify-content: space-between;
      }

      .allcontrols .control-holder.control-options {
        padding: 0 0 15px 15px;
        flex-direction: column;
      }

      .allcontrols .control-holder paper-toggle-button {
        margin-bottom: 5px;
      }

      span.counter {
        font-size: var(--tb-graph-controls-subtitle-font-size);
        color: gray;
        margin-left: 4px;
      }

      .runs-row .title,
      .tags-row .title {
        display: flex;
        align-items: baseline;
      }

      .runs-row paper-item,
      .tags-row paper-item {
        --paper-item: {
          white-space: nowrap;
        }
      }

      table.control-holder {
        border: 0;
        border-collapse: collapse;
      }

      table.tf-graph-controls td.input-element-table-data {
        padding: 0 0 0 20px;
      }

      .spacer {
        flex-grow: 1;
      }

      .color-text {
        overflow: hidden;
      }

      .color-text.gradient-container {
        margin: 0 5px;
      }

      /** Override inline styles that suppress pointer events for disabled buttons. Otherwise, the */
      /*  tooltips do not appear. */
      paper-radio-group paper-radio-button {
        pointer-events: auto !important;
      }

      .legend-clarifier {
        color: #266236;
        cursor: help;
        display: inline-block;
        text-decoration: underline;
      }

      .legend-clarifier paper-tooltip {
        width: 150px;
      }

      /** Otherwise, polymer UI controls appear atop node search. */
      tf-graph-node-search {
        z-index: 1;
        width: 100%;
      }

      paper-dropdown-menu {
        flex-grow: 1;
      }
    </style>

    <div class="allcontrols">
      <div class="control-holder">
        <tf-graph-node-search
          selected-node="{{selectedNode}}"
          render-hierarchy="[[renderHierarchy]]"
        ></tf-graph-node-search>
      </div>
      <div class="control-holder">
        <paper-button class="icon-button" on-tap="_fit" alt="Fit to screen">
          <iron-icon icon="aspect-ratio" class="button-icon"></iron-icon>
          <span class="button-text">Fit to screen</span>
        </paper-button>
      </div>
      <div class="control-holder">
        <paper-button
          class="icon-button"
          on-click="download"
          alt="Download PNG"
        >
          <iron-icon icon="file-download" class="button-icon"></iron-icon>
          <span class="button-text">Download PNG</span>
        </paper-button>
      </div>
      <template is="dom-if" if="[[showUploadButton]]">
        <div class="control-holder">
          <paper-button
            class="icon-button"
            on-click="_getFile"
            alt="Upload file"
            title="Upload a pbtxt file to view a graph from the local filesystem"
          >
            <iron-icon icon="file-upload" class="button-icon"></iron-icon>
            <span class="button-text">Upload file</span>
          </paper-button>

          <div class="hidden-input">
            <input
              type="file"
              id="file"
              name="file"
              on-change="_updateFileInput"
              accept=".pbtxt"
            />
          </div>
        </div>
      </template>
      <div class="control-holder runs-row">
        <div class="title">
          Run <span class="counter">([[datasets.length]])</span>
        </div>
        <paper-dropdown-menu
          no-label-float
          no-animations
          noink
          horizontal-align="left"
          class="run-dropdown"
        >
          <paper-listbox
            class="dropdown-content"
            selected="{{_selectedRunIndex}}"
            slot="dropdown-content"
          >
            <template is="dom-repeat" items="[[datasets]]">
              <paper-item>[[item.name]]</paper-item>
            </template>
          </paper-listbox>
        </paper-dropdown-menu>
      </div>
      <template is="dom-if" if="[[showSessionRunsDropdown]]">
        <div class="control-holder tags-row">
          <div class="title">
            Tag
            <span class="counter"
              >([[_numTags(datasets, _selectedRunIndex)]])</span
            >
          </div>
          <paper-dropdown-menu
            no-label-float
            no-animations
            horizontal-align="left"
            noink
            class="run-dropdown"
          >
            <paper-listbox
              class="dropdown-content"
              selected="{{_selectedTagIndex}}"
              slot="dropdown-content"
            >
              <template
                is="dom-repeat"
                items="[[_getTags(datasets, _selectedRunIndex)]]"
              >
                <paper-item>[[item.displayName]]</paper-item>
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
        </div>
      </template>
      <div class="title">Graph type</div>
      <div class="control-holder control-options">
        <paper-radio-group
          selected="{{_selectedGraphType}}"
          on-paper-radio-group-changed="_onGraphTypeChangedByUserGesture"
        >
          <!-- Note that the name has to match that of tf_graph_common.SelectionType. -->
          <paper-radio-button
            name="op_graph"
            disabled="[[_getSelectionOpGraphDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]"
            >Op graph</paper-radio-button
          >
          <paper-radio-button
            name="conceptual_graph"
            disabled="[[_getSelectionConceptualGraphDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]"
            >Conceptual graph</paper-radio-button
          >
          <paper-radio-button
            name="profile"
            disabled="[[_getSelectionProfileDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]"
            >Profile</paper-radio-button
          >
        </paper-radio-group>
      </div>
      <div class="title">Node options</div>
      <div class="control-holder control-options">
        <paper-toggle-button
          checked="{{traceInputs}}"
          on-change="_onTraceInputsChangedByUserGesture"
        >
          Trace inputs
        </paper-toggle-button>
        <paper-toggle-button checked="{{autoExtractNodes}}">
          Auto-extract high-degree nodes
        </paper-toggle-button>
      </div>
      <template is="dom-if" if="[[healthPillsFeatureEnabled]]">
        <div class="control-holder">
          <paper-toggle-button checked="{{healthPillsToggledOn}}"
            >Show health pills</paper-toggle-button
          >
        </div>
      </template>
      <div class="title">Color by</div>
      <div class="control-holder control-options">
        <paper-radio-group
          selected="{{colorBy}}"
          on-paper-radio-group-changed="_onColorByChangedByUserGesture"
        >
          <paper-radio-button name="[[ColorBy.NONE]]">None</paper-radio-button>

          <paper-radio-button name="[[ColorBy.STRUCTURE]]"
            >Structure</paper-radio-button
          >

          <paper-radio-button name="[[ColorBy.DEVICE]]"
            >Device</paper-radio-button
          >

          <paper-radio-button
            id="xla-cluster-radio-button"
            name="[[ColorBy.XLA_CLUSTER]]"
            disabled="[[!_xlaClustersProvided(renderHierarchy)]]"
          >
            XLA cluster
          </paper-radio-button>
          <paper-tooltip
            animation-delay="0"
            for="xla-cluster-radio-button"
            position="right"
            offset="0"
          >
            Coloring by XLA cluster is only enabled if at least 1 op specifies
            an XLA cluster.
          </paper-tooltip>

          <paper-radio-button
            id="compute-time-radio-button"
            name="[[ColorBy.COMPUTE_TIME]]"
            disabled="[[!stats]]"
          >
            Compute time
          </paper-radio-button>
          <paper-tooltip
            animation-delay="0"
            for="compute-time-radio-button"
            position="right"
            offset="0"
          >
            Coloring by compute time is only enabled if the RunMetadata proto is
            passed to the FileWriter when a specific session is run.
          </paper-tooltip>

          <paper-radio-button
            id="memory-radio-button"
            name="[[ColorBy.MEMORY]]"
            disabled="[[!stats]]"
          >
            Memory
          </paper-radio-button>
          <paper-tooltip
            animation-delay="0"
            for="memory-radio-button"
            position="right"
            offset="0"
          >
            Coloring by memory is only enabled if the RunMetadata proto is
            passed to the FileWriter when a specific session is run.
          </paper-tooltip>

          <paper-radio-button
            id="tpu-compatibility-radio-button"
            name="[[ColorBy.OP_COMPATIBILITY]]"
          >
            TPU compatibility
          </paper-radio-button>
          <paper-tooltip
            animation-delay="0"
            for="tpu-compatibility-radio-button"
            position="right"
            offset="0"
          >
            Coloring by whether an operation is compatible for the TPU device.
          </paper-tooltip>
        </paper-radio-group>
        <span class="spacer"></span>
      </div>
    </div>
    <div class="legend-holder">
      <button class="legend-toolbar" on-click="_toggleLegendOpen">
        <span class="toggle-legend-text">Legend</span>
        <iron-icon
          icon="[[_getToggleLegendIcon(_legendOpened)]]"
          class="toggle-legend-button"
        >
        </iron-icon>
      </button>
      <iron-collapse opened="[[_legendOpened]]" class="legend-content">
        <!-- Color-mode-specific legend items -->
        <div>
          <template is="dom-if" if="[[_isGradientColoring(stats, colorBy)]]">
            <svg width="140" height="20" class="color-text gradient-container">
              <defs>
                <linearGradient
                  id="linearGradient"
                  x1="0%"
                  y1="0%"
                  x2="100%"
                  y2="0%"
                >
                  <stop
                    class="start"
                    offset="0%"
                    stop-color$="[[_currentGradientParams.startColor]]"
                  ></stop>
                  <stop
                    class="end"
                    offset="100%"
                    stop-color$="[[_currentGradientParams.endColor]]"
                  ></stop>
                </linearGradient>
              </defs>
              <rect
                x="0"
                y="0"
                width="135"
                height="20"
                fill="url(#linearGradient)"
                stroke="black"
              ></rect>
            </svg>
            <div class="domainValues color-text">
              <div class="domainStart">[[_currentGradientParams.minValue]]</div>
              <div class="domainEnd">[[_currentGradientParams.maxValue]]</div>
            </div>
            <br style="clear: both" />
            <div>Devices included in stats:</div>
            <div class="deviceList">
              <template is="dom-repeat" items="[[_currentDevices]]">
                <div class="color-legend-row devices-checkbox">
                  <span
                    ><input
                      type="checkbox"
                      value$="[[item.device]]"
                      checked$="[[item.used]]"
                      on-click="_deviceCheckboxClicked"
                  /></span>
                  <span>[[item.suffix]]</span>
                  <template is="dom-if" if="[[item.ignoredMsg]]">
                    <paper-icon-button
                      icon="help"
                      class="help-icon"
                    ></paper-icon-button>
                    <paper-tooltip
                      position="right"
                      offset="0"
                      animation-delay="0"
                      >[[item.ignoredMsg]]</paper-tooltip
                    >
                  </template>
                </div>
              </template>
            </div>
          </template>
          <template is="dom-if" if="[[_equals(colorBy, 'structure')]]">
            <div class="color-text">
              <div class="color-legend-row">
                <span class="label"> colors </span>
                <span class="color-legend-value">same substructure</span>
              </div>
              <div class="color-legend-row">
                <tf-graph-icon
                  type="META"
                  height="16"
                  fill-override="#eee"
                  stroke-override="#a6a6a6"
                ></tf-graph-icon>
                <span class="color-legend-value">unique substructure</span>
              </div>
            </div>
          </template>
          <template is="dom-if" if="[[_equals(colorBy, 'device')]]">
            <div>
              <template is="dom-repeat" items="[[_currentDeviceParams]]">
                <div class="color-legend-row">
                  <tf-graph-icon
                    type="META"
                    height="16"
                    fill-override="[[item.color]]"
                    stroke-override="#a6a6a6"
                  ></tf-graph-icon>
                  <span class="color-legend-value">[[item.device]]</span>
                </div>
              </template>
              <div class="color-legend-row">
                <tf-graph-icon
                  type="META"
                  height="16"
                  fill-override="#eee"
                  stroke-override="#a6a6a6"
                ></tf-graph-icon>
                <span class="color-legend-value">unknown device</span>
              </div>
            </div>
          </template>
          <template is="dom-if" if="[[_equals(colorBy, 'xla_cluster')]]">
            <div>
              <template is="dom-repeat" items="[[_currentXlaClusterParams]]">
                <div class="color-legend-row">
                  <svg>
                    <use
                      xmlns:xlink="http://www.w3.org/1999/xlink"
                      xlink:href="#unfilled-rect"
                      x="0"
                      y="0"
                      style="fill:[[item.color]]"
                    ></use>
                  </svg>
                  <span class="color-legend-value">[[item.xla_cluster]]</span>
                </div>
              </template>
              <div class="color-legend-row">
                <svg>
                  <use
                    xmlns:xlink="http://www.w3.org/1999/xlink"
                    xlink:href="#grey-rect"
                    x="0"
                    y="0"
                  ></use>
                </svg>
                <span class="color-legend-value">unknown XLA cluster</span>
              </div>
            </div>
          </template>
          <template is="dom-if" if="[[_equals(colorBy, 'op_compatibility')]]">
            <div class="color-text">
              <div class="color-legend-row">
                <tf-graph-icon
                  type="OP"
                  height="16"
                  fill-override="#0f9d58"
                  stroke-override="#ccc"
                ></tf-graph-icon>
                <span class="color-legend-value">Valid Op</span>
              </div>
              <div class="color-legend-row">
                <tf-graph-icon
                  type="OP"
                  height="16"
                  fill-override="#db4437"
                  stroke-override="#ccc"
                ></tf-graph-icon>
                <span class="color-legend-value">Invalid Op</span>
              </div>
            </div>
          </template>
          <template is="dom-if" if="[[_statsNotNull(stats)]]">
            <div class="color-legend-row">
              <tf-graph-icon type="META" height="16" faded></tf-graph-icon>
              <span class="color-legend-value">unused substructure</span>
            </div>
          </template>
        </div>

        <!-- Common legend items -->
        <div>
          <table>
            <tbody>
              <tr>
                <td></td>
                <td>(* = expandable)</td>
              </tr>
              <tr>
                <td>
                  <tf-graph-icon
                    type="META"
                    height="16"
                    fill-override="#d9d9d9"
                    stroke-override="#ccc"
                  ></tf-graph-icon>
                </td>
                <td>
                  Namespace<span class="gray">*</span>
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Encapsulates a set of nodes. Namespace is hierarchical and
                      based on scope.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <tf-graph-icon type="OP" height="16"></tf-graph-icon>
                </td>
                <td>
                  OpNode
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Node that performs an operation. These nodes cannot
                      expand.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <tf-graph-icon type="SERIES" height="16"></tf-graph-icon>
                </td>
                <td>
                  Unconnected series<span class="gray">*</span>
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Sequence of numbered nodes that are not connected to each
                      other.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <tf-graph-icon
                    type="SERIES"
                    height="16"
                    vertical
                  ></tf-graph-icon>
                </td>
                <td>
                  Connected series<span class="gray">*</span>
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
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            >.
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          <p>
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              >README</a
            >
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      .graphboard {
        height: 100%;
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      a {
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      a:visited {
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    <div id="tooltip"><span></span></div>
    <svg id="svg">
      <g>
        <g class="axis x"></g>
        <g class="axis y"></g>
        <g class="axis y slice"></g>
        <g class="stage">
          <rect class="background"></rect>
        </g>
        <g class="x-axis-hover"></g>
        <g class="y-axis-hover"></g>
        <g class="y-slice-axis-hover"></g>
      </g>
    </svg>

    <style>
      :host {
        color: #aaa;
        display: flex;
        flex-direction: column;
        flex-grow: 1;
        flex-shrink: 1;
        position: relative;
        --vz-histogram-timeseries-hover-bg-color: #fff;
        --vz-histogram-timeseries-outline-color: #fff;
        --vz-histogram-timeseries-hover-outline-color: #000;
      }

      :host(.dark-mode) {
        --vz-histogram-timeseries-hover-bg-color: var(
          --primary-background-color
        );
        --vz-histogram-timeseries-outline-color: var(--paper-grey-600);
        --vz-histogram-timeseries-hover-outline-color: #fff;
      }

      svg {
        font-family: roboto, sans-serif;
        overflow: visible;
        display: block;
        width: 100%;
        flex-grow: 1;
        flex-shrink: 1;
      }

      text {
        fill: currentColor;
      }

      #tooltip {
        position: absolute;
        display: block;
        opacity: 0;
        font-weight: bold;
        font-size: 11px;
      }

      .background {
        fill-opacity: 0;
        fill: red;
      }

      .histogram {
        pointer-events: none;
      }

      .hover {
        font-size: 9px;
        dominant-baseline: middle;
        opacity: 0;
      }

      .hover circle {
        stroke: white;
        stroke-opacity: 0.5;
        stroke-width: 1px;
      }

      .hover text {
        fill: black;
        opacity: 0;
      }

      .hover.hover-closest circle {
        fill: var(--vz-histogram-timeseries-hover-outline-color) !important;
      }

      .hover.hover-closest text {
        opacity: 1;
      }

      .baseline {
        stroke: black;
        stroke-opacity: 0.1;
      }

      .outline {
        fill: none;
        stroke: var(--vz-histogram-timeseries-outline-color);
        stroke-opacity: 0.5;
      }

      .outline.outline-hover {
        stroke: var(--vz-histogram-timeseries-hover-outline-color) !important;
        stroke-opacity: 1;
      }

      .x-axis-hover,
      .y-axis-hover,
      .y-slice-axis-hover {
        pointer-events: none;
      }

      .x-axis-hover .label,
      .y-axis-hover .label,
      .y-slice-axis-hover .label {
        opacity: 0;
        font-weight: bold;
        font-size: 11px;
        text-anchor: end;
      }

      .x-axis-hover text {
        text-anchor: middle;
      }

      .y-axis-hover text,
      .y-slice-axis-hover text {
        text-anchor: start;
      }

      .x-axis-hover line,
      .y-axis-hover line,
      .y-slice-axis-hover line {
        stroke: currentColor;
      }

      .x-axis-hover rect,
      .y-axis-hover rect,
      .y-slice-axis-hover rect {
        fill: var(--vz-histogram-timeseries-hover-bg-color);
      }

      #tooltip,
      .x-axis-hover text,
      .y-axis-hover text,
      .y-slice-axis-hover text {
        color: var(--vz-histogram-timeseries-hover-outline-color);
      }

      .axis {
        font-size: 11px;
      }

      .axis path.domain {
        fill: none;
      }

      .axis .tick line {
        stroke: #ddd;
      }

      .axis.slice {
        opacity: 0;
      }

      .axis.slice .tick line {
        stroke-dasharray: 2;
      }

      .small .axis text {
        display: none;
      }
      .small .axis .tick:first-of-type text {
        display: block;
      }
      .small .axis .tick:last-of-type text {
        display: block;
      }
      .medium .axis text {
        display: none;
      }
      .medium .axis .tick:nth-child(2n + 1) text {
        display: block;
      }
      .large .axis text {
        display: none;
      }
      .large .axis .tick:nth-child(2n + 1) text {
        display: block;
      }
    </style>
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    <tf-card-heading
      tag="[[tag]]"
      run="[[run]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
      color="[[_runColor]]"
    ></tf-card-heading>
    <!--
      The main histogram that we render. Data is set directly with
      \`setSeriesData\`, not with a bound property.
    -->
    <vz-histogram-timeseries
      id="chart"
      time-property="[[timeProperty]]"
      mode="[[histogramMode]]"
      color-scale="[[_colorScaleFunction]]"
    ></vz-histogram-timeseries>
    <div style="display: flex; flex-direction: row;">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
    </div>
    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 330px;
        height: 235px;
        margin-right: 10px;
        margin-bottom: 15px;
      }
      :host([_expanded]) {
        width: 700px;
        height: 500px;
      }

      vz-histogram-timeseries {
        -moz-user-select: none;
        -webkit-user-select: none;
        will-change: transform;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }

      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      tf-card-heading {
        margin-bottom: 10px;
        width: 90%;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],wJt.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],wJt.prototype,"tag",void 0),t([o({type:Object}),e("design:type",Object)],wJt.prototype,"getDataLoadName",void 0),t([o({type:Object}),e("design:type",br)],wJt.prototype,"requestManager",void 0),t([o({type:Object}),e("design:type",Object)],wJt.prototype,"loadDataCallback",void 0),t([o({type:Object}),e("design:type",Object)],wJt.prototype,"tagMetadata",void 0),t([o({type:String}),e("design:type",String)],wJt.prototype,"timeProperty",void 0),t([o({type:String}),e("design:type",String)],wJt.prototype,"histogramMode",void 0),t([o({type:Object}),e("design:type",Function)],wJt.prototype,"_colorScaleFunction",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],wJt.prototype,"_expanded",void 0),t([a("run","tag","requestManager"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],wJt.prototype,"_reloadOnRunTagRequestManagerChange",null),t([a("run","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],wJt.prototype,"_updateDataToLoad",null),t([s("run"),e("design:type",String),e("design:paramtypes",[])],wJt.prototype,"_runColor",null),wJt=t([i("tf-histogram-loader")],wJt);let SJt=class extends(cr(we)){constructor(){super(...arguments),this.reloadOnReady=!0,this._histogramMode="offset",this._timeProperty="step",this._restamp=!1,this._requestManager=new br}_redrawCategoryPane(t,e){e&&t.target.querySelectorAll("tf-histogram-loader").forEach((t=>t.redraw()))}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){this._fetchTags().then((()=>{this._reloadHistograms()}))}_fetchTags(){const t=Er().pluginRoute("histograms","/tags");return this._requestManager.request(t).then((t=>{if(Ce.exports.isEqual(t,this._runToTagInfo))return;const e=Ce.exports.mapValues(t,(t=>Object.keys(t))),n=fr(e);this.set("_dataNotFound",0===n.length),this.set("_runToTag",e),this.set("_runToTagInfo",t),this.async((()=>{this.set("_categoriesDomReady",!0)}))}))}_reloadHistograms(){this.root.querySelectorAll("tf-histogram-loader").forEach((t=>{t.reload()}))}_shouldOpen(t){return t<=2}get _categories(){return Ir(this._runToTag,this._selectedRuns,this._tagFilter)}_tagMetadata(t,e,n){return t[e][n]}};SJt.template=xe`
    <tf-dashboard-layout>
      <div slot="sidebar">
        <div class="settings">
          <div class="sidebar-section">
            <tf-option-selector
              id="histogramModeSelector"
              name="Histogram mode"
              selected-id="{{_histogramMode}}"
            >
              <paper-button id="overlay">overlay</paper-button>
              <paper-button id="offset">offset</paper-button>
            </tf-option-selector>
          </div>
          <div class="sidebar-section">
            <tf-option-selector
              id="timePropertySelector"
              name="Offset time axis"
              selected-id="{{_timeProperty}}"
            >
              <paper-button id="step">step</paper-button>
              <paper-button id="relative">relative</paper-button>
              <paper-button id="wall_time">wall</paper-button>
            </tf-option-selector>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No histogram data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any histogram data to your event files.
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
            >
              <template>
                <tf-histogram-loader
                  run="[[item.run]]"
                  tag="[[item.tag]]"
                  active="[[active]]"
                  tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]"
                  time-property="[[_timeProperty]]"
                  histogram-mode="[[_histogramMode]]"
                  request-manager="[[_requestManager]]"
                ></tf-histogram-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  `,t([o({type:Boolean}),e("design:type",Boolean)],SJt.prototype,"reloadOnReady",void 0),t([o({type:String}),e("design:type",String)],SJt.prototype,"_histogramMode",void 0),t([o({type:String}),e("design:type",String)],SJt.prototype,"_timeProperty",void 0),t([o({type:Array}),e("design:type",Array)],SJt.prototype,"_selectedRuns",void 0),t([o({type:Object}),e("design:type",Object)],SJt.prototype,"_runToTag",void 0),t([o({type:Object}),e("design:type",Object)],SJt.prototype,"_runToTagInfo",void 0),t([o({type:Boolean}),e("design:type",Boolean)],SJt.prototype,"_dataNotFound",void 0),t([o({type:String}),e("design:type",String)],SJt.prototype,"_tagFilter",void 0),t([o({type:Boolean}),e("design:type",Boolean)],SJt.prototype,"_restamp",void 0),t([o({type:Boolean}),e("design:type",Boolean)],SJt.prototype,"_categoriesDomReady",void 0),t([o({type:Object}),e("design:type",br)],SJt.prototype,"_requestManager",void 0),t([s("_runToTag","_selectedRuns","_tagFilter","_categoriesDomReady"),e("design:type",Array),e("design:paramtypes",[])],SJt.prototype,"_categories",null),SJt=t([i("tf-histogram-dashboard")],SJt);class MJt{constructor(t,e,n=!0){this._apiUrl=t,this._requestManager=e,this._useHttpGet=n}getExperiment(t){return this._sendRequest("experiment",t)}getDownloadUrl(t,e,n){return this._apiUrl+"/download_data?"+new URLSearchParams({format:t,columnsVisibility:JSON.stringify(n),request:JSON.stringify(e)})}listSessionGroups(t){return this._sendRequest("session_groups",t)}listMetricEvals(t){return this._sendRequest("metric_evals",t)}_sendRequest(t,e){if(this._useHttpGet){const n=encodeURIComponent(JSON.stringify(e));return this._requestManager.request(this._apiUrl+"/"+t+"?request="+n)}const n=new vr;n.withCredentials=!0,n.methodType=yr.POST,n.contentType="text/plain",n.body=JSON.stringify(e);return this._requestManager.requestWithOptions(this._apiUrl+"/"+t,n)}}let EJt=class extends we{constructor(){super(...arguments),this.orientation="horizontal"}};function TJt(t){return""!==t.displayName&&void 0!==t.displayName?t.displayName:t.name}function CJt(t){if(""!==t.displayName&&void 0!==t.displayName)return t.displayName;let e=t.name.group,n=t.name.tag;return void 0===e&&(e=""),void 0===n&&(n=""),""===e?n:e+"."+n}function AJt(t,e){if(e<t.hparamColumns.length)return TJt(t.hparamColumns[e].hparamInfo);return CJt(t.metricColumns[e-t.hparamColumns.length].metricInfo)}function kJt(t){return t.hparamColumns.length}function LJt(t){return t.metricColumns.length}function PJt(t){return kJt(t)+LJt(t)}function NJt(t,e){return t[e]}function IJt(t,e){return t.find((t=>Ce.exports.isEqual(t.name,e)))}function RJt(t,e,n){return e.hparams[t.hparamColumns[n].hparamInfo.name]}function OJt(t,e,n){const i=IJt(e.metricValues,t.metricColumns[n].metricInfo.name);return void 0===i?void 0:i.value}function zJt(t,e,n){return n<t.hparamColumns.length?RJt(t,e,n):OJt(t,e,n-t.hparamColumns.length)}function DJt(t,e,n){return Gl(e,(e=>zJt(t,e,n)))}function BJt(t,e,n){let i;if(n<e.hparamInfos.length)i=t.hparamColumns.findIndex((t=>t.hparamInfo.name===e.hparamInfos[n].name));else{const r=e.metricInfos[n-e.hparamInfos.length].name;i=t.hparamColumns.length+t.metricColumns.findIndex((t=>t.metricInfo.name===r))}return console.assert(-1!==i),i}function HJt(t){return t.hparamInfos.length}function FJt(t){return t.metricInfos.length}function VJt(t,e,n){return Gl(e,(e=>WJt(t,e,n)))}function UJt(t,e){return t.find((t=>t.name===e))}function jJt(t,e,n){return e.hparams[t.hparamInfos[n].name]}function GJt(t,e,n){const i=IJt(e.metricValues,t.metricInfos[n].name);return void 0===i?void 0:i.value}function WJt(t,e,n){return n<t.hparamInfos.length?jJt(t,e,n):GJt(t,e,n-t.hparamInfos.length)}function qJt(t){return Ce.exports.isNumber(t)?t.toPrecision(5):void 0===t?"":t.toString()}function YJt(t,e){return t*t+e*e}function XJt(t,e,n,i){return Math.sqrt(YJt(t-n,e-i))}function $Jt(t,e,n,i,r,o){if(t<n&&e<i)return XJt(t,e,n,i);if(n<=t&&t<r&&e<i)return i-e;if(r<=t&&e<i)return XJt(t,e,r,i);if(t<n&&i<=e&&e<o)return n-t;if(n<=t&&t<r&&i<=e&&e<o)return 0;if(r<=t&&i<=e&&e<o)return t-r;if(t<n&&o<=e)return XJt(t,e,n,o);if(n<=t&&t<r&&o<=e)return e-o;if(r<=t&&o<=e)return XJt(t,e,r,o);throw"Point (x,y) must be in one of the regions defined above."}function KJt(t,e){return void 0===e?"translate("+t+")":"translate("+t+","+e+")"}function ZJt(t,e,n){const i=t.get(e,t);Array.isArray(i)?t.splice.apply(t,[e,0,i.length].concat(n)):t.set(e,n)}function JJt(t){let e=0;for(let n=0;n<t.length;++n)e=31*e+t.charCodeAt(n)&4294967295;return e+Math.pow(2,31)}EJt.template=xe`
    <slot name="content"></slot>

    <style>
      :host {
        display: block;
      }

      :host slot {
        display: flex;
        height: 100%;
        width: 100%;
      }

      :host ::slotted(*) {
        flex: 0 0 auto;
      }

      :host([orientation='horizontal']) slot {
        flex-direction: row;
        overflow-x: auto;
      }

      :host([orientation='vertical']) slot {
        flex-direction: column;
        overflow-y: auto;
      }

      :host ::slotted(*:not(:last-child)) {
        border: 0 solid var(--divider-color, #ccc);
      }

      :host([orientation='vertical']) ::slotted(*:not(:last-child)) {
        border-bottom-width: 5px;
      }

      :host([orientation='horizontal']) ::slotted(*:not(:last-child)) {
        border-right-width: 5px;
      }
    </style>
  `,t([o({type:String,reflectToAttribute:!0}),e("design:type",String)],EJt.prototype,"orientation",void 0),EJt=t([i("hparams-split-layout")],EJt);var QJt=Object.freeze({__proto__:null,hparamName:TJt,metricName:CJt,schemaColumnName:AJt,numHParams:kJt,numMetrics:LJt,numColumns:PJt,hparamValueByName:NJt,metricValueByName:IJt,hparamValueByIndex:RJt,metricValueByIndex:OJt,columnValueByIndex:zJt,numericColumnExtent:DJt,getAbsoluteColumnIndex:BJt,schemaVisibleColumnName:function tQt(t,e){return e<t.hparamInfos.length?TJt(t.hparamInfos[e]):CJt(t.metricInfos[e-t.hparamInfos.length])},numVisibleHParams:HJt,numVisibleMetrics:FJt,numVisibleColumns:function eQt(t){return HJt(t)+FJt(t)},visibleNumericColumnExtent:VJt,prettyPrintHParamValueByName:function nQt(t,e){return qJt(NJt(t,e))},prettyPrintMetricValueByName:function iQt(t,e){return qJt(IJt(t,e))},sessionGroupWithName:UJt,hparamValueByVisibleIndex:jJt,metricValueByVisibleIndex:GJt,columnValueByVisibleIndex:WJt,prettyPrint:qJt,l2NormSquared:YJt,euclideanDist:XJt,pointToRectangleDist:$Jt,translateStr:KJt,rotateStr:function rQt(t,e,n){let i="rotate("+t;return void 0!==e&&void 0!==n&&(i=i+","+e+","+n),i+=")",i},isNullOrUndefined:function oQt(t){return null==t},quadTreeVisitPointsInRect:function aQt(t,e,n,i,r,o){t.visit(((a,s,l,c,u)=>{if(void 0===a.length){do{const s=t.x()(a.data),l=t.y()(a.data);e<=s&&s<i&&n<=l&&l<r&&o(a.data)}while(a=a.next);return!0}return s>=i||c<=e||l>=r||u<=n}))},quadTreeVisitPointsInDisk:function sQt(t,e,n,i,r){t.visit(((o,a,s,l,c)=>{if(void 0===o.length){do{const a=t.x()(o.data),s=t.y()(o.data),l=XJt(e,n,a,s);l<=i&&r(o.data,l)}while(o=o.next);return!0}return $Jt(e,n,a,s,l,c)>i}))},filterSet:function lQt(t,e){const n=new Set;return t.forEach((t=>{e(t)&&n.add(t)})),n},setArrayObservably:ZJt,hashOfString:JJt});let cQt=class extends(cr(we)){constructor(){super(...arguments),this.configuration={schema:{hparamColumns:[],metricColumns:[]},columnsVisibility:[],visibleSchema:{hparamInfos:[],metricInfos:[]}},this.sessionGroups=[],this.dataLoadedWithNonEmptyHparams=!1,this.dataLoadedWithEmptyHparams=!1,this._statuses=[{value:"STATUS_UNKNOWN",displayName:"Unknown",allowed:!0},{value:"STATUS_SUCCESS",displayName:"Success",allowed:!0},{value:"STATUS_FAILURE",displayName:"Failure",allowed:!0},{value:"STATUS_RUNNING",displayName:"Running",allowed:!0}],this._getExperimentResolved=new Promise((t=>{this._resolveGetExperiment=t})),this._listSessionGroupsCanceller=new xO,this._pageSizeInput={value:"100",invalid:!1},this._pageNumberInput={value:"1",invalid:!1},this._pageCountStr="?",this._hparamName=TJt,this._metricName=CJt,this._prettyPrint=qJt}reload(){this._queryServer()}_csvUrl(t,e){return this._downloadDataUrl(t,e,"csv")}_jsonUrl(t,e){return this._downloadDataUrl(t,e,"json")}_latexUrl(t,e){return this._downloadDataUrl(t,e,"latex")}_downloadDataUrl(t,e,n){return this.backend.getDownloadUrl(n,t,e.columnsVisibility)}_computeExperimentAndRelatedProps(){const t=QJt;if(t.isNullOrUndefined(this.backend)||t.isNullOrUndefined(this.experimentName))return;this.backend.getExperiment({experimentName:this.experimentName}).then((t=>{Ce.exports.isEqual(t,this._experiment)||(this.set("_experiment",t),this._computeHParams(),this._computeMetrics(),this._queryServer(),this._resolveGetExperiment())})).finally((()=>{this._computeDataFound()}))}_computeDataFound(){const t=Boolean(this._experiment&&this._experiment.hparamInfos&&this._experiment.hparamInfos.length>0&&this._experiment.metricInfos&&this._experiment.metricInfos.length>0);this.set("dataLoadedWithNonEmptyHparams",t),this.set("dataLoadedWithEmptyHparams",!t)}_computeHParams(){const t=[];this._experiment.hparamInfos.forEach(((e,n)=>{const i={info:e,displayed:n<5,filter:{}};i.info.hasOwnProperty("domainDiscrete")?(i.filter.domainDiscrete=[],i.info.domainDiscrete.forEach((t=>{i.filter.domainDiscrete.push({value:t,checked:!0})}))):"DATA_TYPE_BOOL"===i.info.type?i.filter.domainDiscrete=[{value:!1,checked:!0},{value:!0,checked:!0}]:"DATA_TYPE_FLOAT64"===i.info.type?i.filter.interval={min:{value:"",invalid:!1},max:{value:"",invalid:!1}}:"DATA_TYPE_STRING"===i.info.type?i.filter.regexp="":console.warn("unknown hparam.info.type: %s",i.info.type),t.push(i)})),this.set("_hparams",t)}_computeMetrics(){const t=[];this._experiment.metricInfos.forEach(((e,n)=>{t.push({info:e,filter:{interval:{min:{value:"",invalid:!1},max:{value:"",invalid:!1}}},displayed:n<5})})),this.set("_metrics",t)}_computeSchema(){return this._hparams&&this._metrics?{hparamColumns:this._hparams.map((t=>({hparamInfo:t.info}))),metricColumns:this._metrics.map((t=>({metricInfo:t.info})))}:{hparamColumns:[],metricColumns:[]}}_updateConfiguration(){this.debounce("_updateConfiguration",(()=>{this.configuration={schema:this._computeSchema(),columnsVisibility:this._computeColumnsVisibility(),visibleSchema:this._computeVisibleSchema()}}))}_computeColumnsVisibility(){return this._hparams&&this._metrics?this._hparams.map((t=>t.displayed)).concat(this._metrics.map((t=>t.displayed))):[]}_computeVisibleSchema(){if(!this._hparams||!this._metrics)return{hparamInfos:[],metricInfos:[]};return{hparamInfos:this._hparams.filter((t=>t.displayed)).map((t=>t.info)),metricInfos:this._metrics.filter((t=>t.displayed)).map((t=>t.info))}}_queryServer(){this.debounce("queryServer",(()=>this._queryServerNoDebounce()),100)}_queryServerNoDebounce(){if(this._hparams&&this._metrics)return this._sendListSessionGroupsRequest().then(this._listSessionGroupsCanceller.cancellable((({value:t,cancelled:e})=>{if(!e){if(t.totalSize>=0){this.set("_pageCountStr",String(Math.ceil(t.totalSize/+this._pageSizeInput.value))),this.set("_totalSessionGroupsCountStr",t.totalSize)}else this.set("_pageCountStr","?"),this.set("_totalSessionGroupsCountStr","Unknown");ZJt(this,"sessionGroups",t.sessionGroups)}})))}_sendListSessionGroupsRequest(){const t=this._buildListSessionGroupsRequest();if(null!==t)return this.set("_sessionGroupsRequest",t),this._listSessionGroupsCanceller.cancelAll(),this.backend.listSessionGroups(t)}_buildListSessionGroupsRequest(){const t=this;let e=!0;function n(n){const i=t.get(n+".min.value");console.assert(void 0!==i);const r=""===i?"-Infinity":+i;t.set(n+".min.invalid",isNaN(r)),e=e&&!isNaN(r);const o=t.get(n+".max.value");console.assert(void 0!==o);const a=""===o?"Infinity":+o;return t.set(n+".max.invalid",isNaN(a)),e=e&&!isNaN(a),isNaN(r)||isNaN(a)?null:{minValue:r,maxValue:a}}function i(n){const i=t.get(n+".value");console.assert(void 0!==i);const r=+i,o=Number.isInteger(r)&&r>0;return t.set(n+".invalid",!o),e=e&&o,o?r:null}const r=this._statuses.filter((t=>t.allowed)).map((t=>t.value));let o=[];if(this._hparams.forEach(((t,e)=>{let i={hparam:t.info.name};if(t.filter.domainDiscrete)i.filterDiscrete=[],t.filter.domainDiscrete.forEach((t=>{t.checked&&i.filterDiscrete.push(t.value)}));else if(t.filter.interval)i.filterInterval=n("_hparams."+e+".filter.interval");else{if(!t.filter.regexp)return console.error("hparam.filter with no domainDiscrete, interval or regexp properties set: %s",t),null;i.filterRegexp=t.filter.regexp}o.push(i)})),this._metrics.forEach(((t,e)=>{let i={metric:t.info.name,filterInterval:n("_metrics."+e+".filter.interval")};o.push(i)})),void 0!==this._sortByIndex&&void 0!==this._sortDirection){if(!(this._sortByIndex in o))return console.error("No column in colParams with index sortByIndex: %s",this._sortByIndex),null;o[this._sortByIndex].order=0===this._sortDirection?"ORDER_ASC":"ORDER_DESC"}const a=i("_pageNumberInput"),s=i("_pageSizeInput");if(!e)return null;return{experimentName:this.experimentName,allowedStatuses:r,colParams:o,startIndex:s*(a-1),sliceSize:s}}_metricSortByIndex(t){return t+this._hparams.length}};cQt.template=xe`
    <hparams-split-layout orientation="vertical">
      <div slot="content" class="section hyperparameters">
        <div class="section-title">Hyperparameters</div>
        <template is="dom-repeat" items="{{_hparams}}" as="hparam">
          <div class="hparam">
            <paper-checkbox
              checked="{{hparam.displayed}}"
              class="hparam-checkbox"
            >
              [[_hparamName(hparam.info)]]
            </paper-checkbox>
            <!-- Precisely one of the templates below will be stamped.-->
            <!-- 1. A list of checkboxes -->
            <template is="dom-if" if="[[hparam.filter.domainDiscrete]]">
              <template
                is="dom-repeat"
                items="[[hparam.filter.domainDiscrete]]"
              >
                <paper-checkbox
                  checked="{{item.checked}}"
                  class="discrete-value-checkbox"
                  on-change="_queryServer"
                >
                  [[_prettyPrint(item.value)]]
                </paper-checkbox>
              </template>
            </template>
            <!-- 2. A numeric interval -->
            <template is="dom-if" if="[[hparam.filter.interval]]">
              <paper-input
                label="Min"
                value="{{hparam.filter.interval.min.value}}"
                allowed_pattern="[0-9.e\\-]"
                on-value-changed="_queryServer"
                error-message="Invalid input"
                invalid="[[hparam.filter.interval.min.invalid]]"
                placeholder="-infinity"
              >
              </paper-input>
              <paper-input
                label="Max"
                value="{{hparam.filter.interval.max.value}}"
                allowed_pattern="[0-9.e\\-]"
                on-value-changed="_queryServer"
                error-message="Invalid input"
                invalid="[[hparam.filter.interval.max.invalid]]"
                placeholder="+infinity"
              >
              </paper-input>
            </template>
            <!-- 3. A regexp -->
            <template is="dom-if" if="[[hparam.filter.regexp]]">
              <paper-input
                label="Regular expression"
                value="{{hparam.filter.regexp}}"
                on-value-changed="_queryServer"
              >
              </paper-input>
            </template>
          </div>
        </template>
      </div>
      <div slot="content" class="section metrics">
        <div class="section-title">Metrics</div>
        <template is="dom-repeat" items="{{_metrics}}" as="metric">
          <div class="metric">
            <!-- TODO(erez): Make it easier to handle a large number of
                  metrics:
                  1. Add an 'isolator' radio-button to select just one
                  metric and
                  hide all the rest
                  2. Add a 'toggle-all' button that will hide/unhide
                    all the
                  metrics.
                  Use similar logic/appearance to the run-selector of
                  scalars.-->
            <paper-checkbox
              checked="{{metric.displayed}}"
              class="metric-checkbox"
            >
              [[_metricName(metric.info)]]
            </paper-checkbox>
            <div class="inline-element">
              <paper-input
                label="Min"
                value="{{metric.filter.interval.min.value}}"
                allowed-pattern="[0-9.e\\-]"
                on-value-changed="_queryServer"
                error-message="Invalid input"
                invalid="{{metric.filter.interval.min.invalid}}"
                placeholder="-infinity"
              >
              </paper-input>
            </div>
            <div class="inline-element">
              <paper-input
                label="Max"
                allowed-pattern="[0-9.e\\-]"
                value="{{metric.filter.interval.max.value}}"
                on-value-changed="_queryServer"
                error-message="Invalid input"
                invalid="{{metric.filter.interval.max.invalid}}"
                placeholder="+infinity"
              >
              </paper-input>
            </div>
          </div>
        </template>
      </div>
      <div slot="content" class="section status">
        <div class="section-title">Status</div>
        <template is="dom-repeat" items="[[_statuses]]" as="status">
          <paper-checkbox checked="{{status.allowed}}" on-change="_queryServer">
            [[status.displayName]]
          </paper-checkbox>
        </template>
      </div>
      <div slot="content" class="section sorting">
        <div class="section-title">Sorting</div>
        <paper-dropdown-menu
          label="Sort by"
          on-selected-item-changed="_queryServer"
          horizontal-align="left"
        >
          <paper-listbox
            class="dropdown-content"
            slot="dropdown-content"
            selected="{{_sortByIndex}}"
            on-selected-item-changed="_queryServer"
          >
            <template is="dom-repeat" items="[[_hparams]]" as="hparam">
              <paper-item> [[_hparamName(hparam.info)]] </paper-item>
            </template>
            <template is="dom-repeat" items="[[_metrics]]" as="metric">
              <paper-item> [[_metricName(metric.info)]] </paper-item>
            </template>
          </paper-listbox>
        </paper-dropdown-menu>
        <paper-dropdown-menu
          label="Direction"
          on-selected-item-changed="_queryServer"
          horizontal-align="left"
        >
          <paper-listbox
            class="dropdown-content"
            slot="dropdown-content"
            selected="{{_sortDirection}}"
          >
            <paper-item>Ascending</paper-item>
            <paper-item>Descending</paper-item>
          </paper-listbox>
        </paper-dropdown-menu>
      </div>
      <div slot="content" class="section paging">
        <div class="section-title">Paging</div>
        <div>
          Number of matching session groups: [[_totalSessionGroupsCountStr]]
        </div>
        <div class="inline-element page-number-input">
          <paper-input
            label="Page #"
            value="{{_pageNumberInput.value}}"
            allowed-pattern="[0-9]"
            error-message="Invalid input"
            invalid="[[_pageNumberInput.invalid]]"
            on-value-changed="_queryServer"
          >
            <div slot="suffix" class="page-suffix">/ [[_pageCountStr]]</div>
          </paper-input>
        </div>
        <div class="inline-element page-size-input">
          <paper-input
            label="Max # of session groups per page:"
            value="{{_pageSizeInput.value}}"
            allowed-pattern="[0-9]"
            error-message="Invalid input"
            invalid="[[_pageSizeInput.invalid]]"
            on-value-changed="_queryServer"
          >
          </paper-input>
        </div>
      </div>
      <div slot="content" class="section download">
        <template is="dom-if" if="[[_sessionGroupsRequest]]">
          Download data as
          <span>
            <a
              id="csvLink"
              download="hparams_table.csv"
              href="[[_csvUrl(_sessionGroupsRequest, configuration)]]"
              >CSV</a
            >
            <a
              id="jsonLink"
              download="hparams_table.json"
              href="[[_jsonUrl(_sessionGroupsRequest, configuration)]]"
              >JSON</a
            >
            <a
              id="latexLink"
              download="hparams_table.tex"
              href="[[_latexUrl(_sessionGroupsRequest, configuration)]]"
              >LaTeX</a
            >
          </span>
        </template>
      </div>
    </hparams-split-layout>
    <style>
      .section {
        padding: 10px;
      }
      .section-title {
        display: block;
        font-weight: bold;
        text-decoration: underline;
        margin-bottom: 7px;
      }
      .discrete-value-checkbox,
      .metric-checkbox,
      .hparam-checkbox {
        display: block;
      }
      .discrete-value-checkbox {
        margin-left: 20px;
      }
      .hparam,
      .metric {
        display: block;
      }
      .inline-element {
        display: inline-block;
        width: 40%;
        margin-left: 10px;
      }
      .page-number-input {
        width: 20%;
      }
      .page-size-input {
        width: 60%;
      }
      vaadin-split-layout {
        height: 100%;
      }
      paper-listbox {
        max-height: 15em;
      }
      .page-suffix {
        white-space: nowrap;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],cQt.prototype,"experimentName",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],cQt.prototype,"configuration",void 0),t([o({type:Array,notify:!0}),e("design:type",Object)],cQt.prototype,"sessionGroups",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],cQt.prototype,"dataLoadedWithNonEmptyHparams",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],cQt.prototype,"dataLoadedWithEmptyHparams",void 0),t([o({type:Object}),e("design:type",Object)],cQt.prototype,"_experiment",void 0),t([o({type:Array}),e("design:type",Array)],cQt.prototype,"_hparams",void 0),t([o({type:Array}),e("design:type",Array)],cQt.prototype,"_metrics",void 0),t([o({type:Array}),e("design:type",Object)],cQt.prototype,"_statuses",void 0),t([o({type:Object}),e("design:type",Object)],cQt.prototype,"_getExperimentResolved",void 0),t([o({type:Object}),e("design:type",Function)],cQt.prototype,"_resolveGetExperiment",void 0),t([o({type:Object}),e("design:type",Object)],cQt.prototype,"_listSessionGroupsCanceller",void 0),t([o({type:Number}),e("design:type",Number)],cQt.prototype,"_sortByIndex",void 0),t([o({type:Number}),e("design:type",Number)],cQt.prototype,"_sortDirection",void 0),t([o({type:Object}),e("design:type",Object)],cQt.prototype,"_pageSizeInput",void 0),t([o({type:Object}),e("design:type",Object)],cQt.prototype,"_pageNumberInput",void 0),t([o({type:String}),e("design:type",String)],cQt.prototype,"_pageCountStr",void 0),t([o({type:String}),e("design:type",String)],cQt.prototype,"_totalSessionGroupsCountStr",void 0),t([o({type:Object}),e("design:type",Object)],cQt.prototype,"_sessionGroupsRequest",void 0),t([a("backend","experimentName"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],cQt.prototype,"_computeExperimentAndRelatedProps",null),t([a("_hparams.*","_metrics.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],cQt.prototype,"_updateConfiguration",null),cQt=t([i("tf-hparams-query-pane")],cQt);
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
const uQt="undefined"!=typeof window&&null!=window.customElements&&void 0!==window.customElements.polyfillWrapFlushCallback,hQt=(t,e,n=null)=>{for(;e!==n;){const n=e.nextSibling;t.removeChild(e),e=n}},dQt=`{{lit-${String(Math.random()).slice(2)}}}`,pQt=`\x3c!--${dQt}--\x3e`,fQt=new RegExp(`${dQt}|${pQt}`),mQt="$lit$";class gQt{constructor(t,e){this.parts=[],this.element=e;const n=[],i=[],r=document.createTreeWalker(e.content,133,null,!1);let o=0,a=-1,s=0;const{strings:l,values:{length:c}}=t;for(;s<c;){const t=r.nextNode();if(null!==t){if(a++,1===t.nodeType){if(t.hasAttributes()){const e=t.attributes,{length:n}=e;let i=0;for(let t=0;t<n;t++)_Qt(e[t].name,mQt)&&i++;for(;i-- >0;){const e=bQt.exec(l[s])[2],n=e.toLowerCase()+mQt,i=t.getAttribute(n);t.removeAttribute(n);const r=i.split(fQt);this.parts.push({type:"attribute",index:a,name:e,strings:r}),s+=r.length-1}}"TEMPLATE"===t.tagName&&(i.push(t),r.currentNode=t.content)}else if(3===t.nodeType){const e=t.data;if(e.indexOf(dQt)>=0){const i=t.parentNode,r=e.split(fQt),o=r.length-1;for(let e=0;e<o;e++){let n,o=r[e];if(""===o)n=vQt();else{const t=bQt.exec(o);null!==t&&_Qt(t[2],mQt)&&(o=o.slice(0,t.index)+t[1]+t[2].slice(0,-mQt.length)+t[3]),n=document.createTextNode(o)}i.insertBefore(n,t),this.parts.push({type:"node",index:++a})}""===r[o]?(i.insertBefore(vQt(),t),n.push(t)):t.data=r[o],s+=o}}else if(8===t.nodeType)if(t.data===dQt){const e=t.parentNode;null!==t.previousSibling&&a!==o||(a++,e.insertBefore(vQt(),t)),o=a,this.parts.push({type:"node",index:a}),null===t.nextSibling?t.data="":(n.push(t),a--),s++}else{let e=-1;for(;-1!==(e=t.data.indexOf(dQt,e+1));)this.parts.push({type:"node",index:-1}),s++}}else r.currentNode=i.pop()}for(const t of n)t.parentNode.removeChild(t)}}const _Qt=(t,e)=>{const n=t.length-e.length;return n>=0&&t.slice(n)===e},yQt=t=>-1!==t.index,vQt=()=>document.createComment(""),bQt=/([ \x09\x0a\x0c\x0d])([^\0-\x1F\x7F-\x9F "'>=/]+)([ \x09\x0a\x0c\x0d]*=[ \x09\x0a\x0c\x0d]*(?:[^ \x09\x0a\x0c\x0d"'`<>=]*|"[^"]*|'[^']*))$/;function xQt(t,e){const{element:{content:n},parts:i}=t,r=document.createTreeWalker(n,133,null,!1);let o=SQt(i),a=i[o],s=-1,l=0;const c=[];let u=null;for(;r.nextNode();){s++;const t=r.currentNode;for(t.previousSibling===u&&(u=null),e.has(t)&&(c.push(t),null===u&&(u=t)),null!==u&&l++;void 0!==a&&a.index===s;)a.index=null!==u?-1:a.index-l,o=SQt(i,o),a=i[o]}c.forEach((t=>t.parentNode.removeChild(t)))}const wQt=t=>{let e=11===t.nodeType?0:1;const n=document.createTreeWalker(t,133,null,!1);for(;n.nextNode();)e++;return e},SQt=(t,e=-1)=>{for(let n=e+1;n<t.length;n++){if(yQt(t[n]))return n}return-1};
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
const MQt=new WeakMap,EQt={},TQt={};
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
class CQt{constructor(t,e,n){this.__parts=[],this.template=t,this.processor=e,this.options=n}update(t){let e=0;for(const n of this.__parts)void 0!==n&&n.setValue(t[e]),e++;for(const t of this.__parts)void 0!==t&&t.commit()}_clone(){const t=uQt?this.template.element.content.cloneNode(!0):document.importNode(this.template.element.content,!0),e=[],n=this.template.parts,i=document.createTreeWalker(t,133,null,!1);let r,o=0,a=0,s=i.nextNode();for(;o<n.length;)if(r=n[o],yQt(r)){for(;a<r.index;)a++,"TEMPLATE"===s.nodeName&&(e.push(s),i.currentNode=s.content),null===(s=i.nextNode())&&(i.currentNode=e.pop(),s=i.nextNode());if("node"===r.type){const t=this.processor.handleTextExpression(this.options);t.insertAfterNode(s.previousSibling),this.__parts.push(t)}else this.__parts.push(...this.processor.handleAttributeExpressions(s,r.name,r.strings,this.options));o++}else this.__parts.push(void 0),o++;return uQt&&(document.adoptNode(t),customElements.upgrade(t)),t}}
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */const AQt=window.trustedTypes&&trustedTypes.createPolicy("lit-html",{createHTML:t=>t}),kQt=` ${dQt} `;class LQt{constructor(t){this.value=void 0,this.__pendingValue=void 0,this.options=t}appendInto(t){this.startNode=t.appendChild(vQt()),this.endNode=t.appendChild(vQt())}insertAfterNode(t){this.startNode=t,this.endNode=t.nextSibling}appendIntoPart(t){t.__insert(this.startNode=vQt()),t.__insert(this.endNode=vQt())}insertAfterPart(t){t.__insert(this.startNode=vQt()),this.endNode=t.endNode,t.endNode=this.startNode}setValue(t){this.__pendingValue=t}commit(){if(null===this.startNode.parentNode)return;for(;"function"==typeof(t=this.__pendingValue)&&MQt.has(t);){const t=this.__pendingValue;this.__pendingValue=EQt,t(this)}var t;const e=this.__pendingValue;e!==EQt&&((t=>null===t||!("object"==typeof t||"function"==typeof t))(e)?e!==this.value&&this.__commitText(e):e instanceof class{constructor(t,e,n,i){this.strings=t,this.values=e,this.type=n,this.processor=i}getHTML(){const t=this.strings.length-1;let e="",n=!1;for(let i=0;i<t;i++){const t=this.strings[i],r=t.lastIndexOf("\x3c!--");n=(r>-1||n)&&-1===t.indexOf("--\x3e",r+1);const o=bQt.exec(t);e+=null===o?t+(n?kQt:pQt):t.substr(0,o.index)+o[1]+o[2]+mQt+o[3]+dQt}return e+=this.strings[t],e}getTemplateElement(){const t=document.createElement("template");let e=this.getHTML();return void 0!==AQt&&(e=AQt.createHTML(e)),t.innerHTML=e,t}}
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */?this.__commitTemplateResult(e):e instanceof Node?this.__commitNode(e):(t=>Array.isArray(t)||!(!t||!t[Symbol.iterator]))(e)?this.__commitIterable(e):e===TQt?(this.value=TQt,this.clear()):this.__commitText(e))}__insert(t){this.endNode.parentNode.insertBefore(t,this.endNode)}__commitNode(t){this.value!==t&&(this.clear(),this.__insert(t),this.value=t)}__commitText(t){const e=this.startNode.nextSibling,n="string"==typeof(t=null==t?"":t)?t:String(t);e===this.endNode.previousSibling&&3===e.nodeType?e.data=n:this.__commitNode(document.createTextNode(n)),this.value=t}__commitTemplateResult(t){const e=this.options.templateFactory(t);if(this.value instanceof CQt&&this.value.template===e)this.value.update(t.values);else{const n=new CQt(e,t.processor,this.options),i=n._clone();n.update(t.values),this.__commitNode(i),this.value=n}}__commitIterable(t){Array.isArray(this.value)||(this.value=[],this.clear());const e=this.value;let n,i=0;for(const r of t)n=e[i],void 0===n&&(n=new LQt(this.options),e.push(n),0===i?n.appendIntoPart(this):n.insertAfterPart(e[i-1])),n.setValue(r),n.commit(),i++;i<e.length&&(e.length=i,this.clear(n&&n.endNode))}clear(t=this.startNode){hQt(this.startNode.parentNode,t.nextSibling,this.endNode)}}let PQt=!1;
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
function NQt(t){let e=IQt.get(t.type);void 0===e&&(e={stringsArray:new WeakMap,keyString:new Map},IQt.set(t.type,e));let n=e.stringsArray.get(t.strings);if(void 0!==n)return n;const i=t.strings.join(dQt);return n=e.keyString.get(i),void 0===n&&(n=new gQt(t,t.getTemplateElement()),e.keyString.set(i,n)),e.stringsArray.set(t.strings,n),n}(()=>{try{const t={get capture(){return PQt=!0,!1}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){}})();const IQt=new Map,RQt=new WeakMap;
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
"undefined"!=typeof window&&(window.litHtmlVersions||(window.litHtmlVersions=[])).push("1.4.1");
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */const OQt=(t,e)=>`${t}--${e}`;let zQt=!0;void 0===window.ShadyCSS?zQt=!1:void 0===window.ShadyCSS.prepareTemplateDom&&(console.warn("Incompatible ShadyCSS version detected. Please update to at least @webcomponents/webcomponentsjs@2.0.2 and @webcomponents/shadycss@1.3.1."),zQt=!1);const DQt=t=>e=>{const n=OQt(e.type,t);let i=IQt.get(n);void 0===i&&(i={stringsArray:new WeakMap,keyString:new Map},IQt.set(n,i));let r=i.stringsArray.get(e.strings);if(void 0!==r)return r;const o=e.strings.join(dQt);if(r=i.keyString.get(o),void 0===r){const n=e.getTemplateElement();zQt&&window.ShadyCSS.prepareTemplateDom(n,t),r=new gQt(e,n),i.keyString.set(o,r)}return i.stringsArray.set(e.strings,r),r},BQt=["html","svg"],HQt=new Set,FQt=(t,e,n)=>{HQt.add(t);const i=n?n.element:document.createElement("template"),r=e.querySelectorAll("style"),{length:o}=r;if(0===o)return void window.ShadyCSS.prepareTemplateStyles(i,t);const a=document.createElement("style");for(let t=0;t<o;t++){const e=r[t];e.parentNode.removeChild(e),a.textContent+=e.textContent}(t=>{BQt.forEach((e=>{const n=IQt.get(OQt(e,t));void 0!==n&&n.keyString.forEach((t=>{const{element:{content:e}}=t,n=new Set;Array.from(e.querySelectorAll("style")).forEach((t=>{n.add(t)})),xQt(t,n)}))}))})(t);const s=i.content;n?(function l(t,e,n=null){const{element:{content:i},parts:r}=t;if(null==n)return void i.appendChild(e);const o=document.createTreeWalker(i,133,null,!1);let a=SQt(r),s=0,l=-1;for(;o.nextNode();)for(l++,o.currentNode===n&&(s=wQt(e),n.parentNode.insertBefore(e,n));-1!==a&&r[a].index===l;){if(s>0){for(;-1!==a;)r[a].index+=s,a=SQt(r,a);return}a=SQt(r,a)}})(n,a,s.firstChild):s.insertBefore(a,s.firstChild),window.ShadyCSS.prepareTemplateStyles(i,t);const c=s.querySelector("style");if(window.ShadyCSS.nativeShadow&&null!==c)e.insertBefore(c.cloneNode(!0),e.firstChild);else if(n){s.insertBefore(a,s.firstChild);const t=new Set;t.add(a),xQt(n,t)}};window.JSCompiler_renameProperty=(t,e)=>t;const VQt={toAttribute(t,e){switch(e){case Boolean:return t?"":null;case Object:case Array:return null==t?t:JSON.stringify(t)}return t},fromAttribute(t,e){switch(e){case Boolean:return null!==t;case Number:return null===t?null:Number(t);case Object:case Array:return JSON.parse(t)}return t}},UQt=(t,e)=>e!==t&&(e==e||t==t),jQt={attribute:!0,type:String,converter:VQt,reflect:!1,hasChanged:UQt},GQt="finalized";class WQt extends HTMLElement{constructor(){super(),this.initialize()}static get observedAttributes(){this.finalize();const t=[];return this._classProperties.forEach(((e,n)=>{const i=this._attributeNameForProperty(n,e);void 0!==i&&(this._attributeToPropertyMap.set(i,n),t.push(i))})),t}static _ensureClassProperties(){if(!this.hasOwnProperty(JSCompiler_renameProperty("_classProperties",this))){this._classProperties=new Map;const t=Object.getPrototypeOf(this)._classProperties;void 0!==t&&t.forEach(((t,e)=>this._classProperties.set(e,t)))}}static createProperty(t,e=jQt){if(this._ensureClassProperties(),this._classProperties.set(t,e),e.noAccessor||this.prototype.hasOwnProperty(t))return;const n="symbol"==typeof t?Symbol():`__${t}`,i=this.getPropertyDescriptor(t,n,e);void 0!==i&&Object.defineProperty(this.prototype,t,i)}static getPropertyDescriptor(t,e,n){return{get(){return this[e]},set(i){const r=this[t];this[e]=i,this.requestUpdateInternal(t,r,n)},configurable:!0,enumerable:!0}}static getPropertyOptions(t){return this._classProperties&&this._classProperties.get(t)||jQt}static finalize(){const t=Object.getPrototypeOf(this);if(t.hasOwnProperty(GQt)||t.finalize(),this.finalized=!0,this._ensureClassProperties(),this._attributeToPropertyMap=new Map,this.hasOwnProperty(JSCompiler_renameProperty("properties",this))){const t=this.properties,e=[...Object.getOwnPropertyNames(t),..."function"==typeof Object.getOwnPropertySymbols?Object.getOwnPropertySymbols(t):[]];for(const n of e)this.createProperty(n,t[n])}}static _attributeNameForProperty(t,e){const n=e.attribute;return!1===n?void 0:"string"==typeof n?n:"string"==typeof t?t.toLowerCase():void 0}static _valueHasChanged(t,e,n=UQt){return n(t,e)}static _propertyValueFromAttribute(t,e){const n=e.converter||VQt,i="function"==typeof n?n:n.fromAttribute;return i?i(t,e.type):t}static _propertyValueToAttribute(t,e){if(void 0===e.reflect)return;const n=e.converter;return(n&&n.toAttribute||VQt.toAttribute)(t,e.type)}initialize(){this._updateState=0,this._updatePromise=new Promise((t=>this._enableUpdatingResolver=t)),this._changedProperties=new Map,this._saveInstanceProperties(),this.requestUpdateInternal()}_saveInstanceProperties(){this.constructor._classProperties.forEach(((t,e)=>{if(this.hasOwnProperty(e)){const t=this[e];delete this[e],this._instanceProperties||(this._instanceProperties=new Map),this._instanceProperties.set(e,t)}}))}_applyInstanceProperties(){this._instanceProperties.forEach(((t,e)=>this[e]=t)),this._instanceProperties=void 0}connectedCallback(){this.enableUpdating()}enableUpdating(){void 0!==this._enableUpdatingResolver&&(this._enableUpdatingResolver(),this._enableUpdatingResolver=void 0)}disconnectedCallback(){}attributeChangedCallback(t,e,n){e!==n&&this._attributeToProperty(t,n)}_propertyToAttribute(t,e,n=jQt){const i=this.constructor,r=i._attributeNameForProperty(t,n);if(void 0!==r){const t=i._propertyValueToAttribute(e,n);if(void 0===t)return;this._updateState=8|this._updateState,null==t?this.removeAttribute(r):this.setAttribute(r,t),this._updateState=-9&this._updateState}}_attributeToProperty(t,e){if(8&this._updateState)return;const n=this.constructor,i=n._attributeToPropertyMap.get(t);if(void 0!==i){const t=n.getPropertyOptions(i);this._updateState=16|this._updateState,this[i]=n._propertyValueFromAttribute(e,t),this._updateState=-17&this._updateState}}requestUpdateInternal(t,e,n){let i=!0;if(void 0!==t){const r=this.constructor;n=n||r.getPropertyOptions(t),r._valueHasChanged(this[t],e,n.hasChanged)?(this._changedProperties.has(t)||this._changedProperties.set(t,e),!0!==n.reflect||16&this._updateState||(void 0===this._reflectingProperties&&(this._reflectingProperties=new Map),this._reflectingProperties.set(t,n))):i=!1}!this._hasRequestedUpdate&&i&&(this._updatePromise=this._enqueueUpdate())}requestUpdate(t,e){return this.requestUpdateInternal(t,e),this.updateComplete}async _enqueueUpdate(){this._updateState=4|this._updateState;try{await this._updatePromise}catch(t){}const t=this.performUpdate();return null!=t&&await t,!this._hasRequestedUpdate}get _hasRequestedUpdate(){return 4&this._updateState}get hasUpdated(){return 1&this._updateState}performUpdate(){if(!this._hasRequestedUpdate)return;this._instanceProperties&&this._applyInstanceProperties();let t=!1;const e=this._changedProperties;try{t=this.shouldUpdate(e),t?this.update(e):this._markUpdated()}catch(e){throw t=!1,this._markUpdated(),e}t&&(1&this._updateState||(this._updateState=1|this._updateState,this.firstUpdated(e)),this.updated(e))}_markUpdated(){this._changedProperties=new Map,this._updateState=-5&this._updateState}get updateComplete(){return this._getUpdateComplete()}_getUpdateComplete(){return this.getUpdateComplete()}getUpdateComplete(){return this._updatePromise}shouldUpdate(t){return!0}update(t){void 0!==this._reflectingProperties&&this._reflectingProperties.size>0&&(this._reflectingProperties.forEach(((t,e)=>this._propertyToAttribute(e,this[e],t))),this._reflectingProperties=void 0),this._markUpdated()}updated(t){}firstUpdated(t){}}WQt.finalized=!0;
/**
    @license
    Copyright (c) 2019 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const qQt=window.ShadowRoot&&(void 0===window.ShadyCSS||window.ShadyCSS.nativeShadow)&&"adoptedStyleSheets"in Document.prototype&&"replace"in CSSStyleSheet.prototype,YQt=Symbol();class XQt{constructor(t,e){if(e!==YQt)throw new Error("CSSResult is not constructable. Use `unsafeCSS` or `css` instead.");this.cssText=t}get styleSheet(){return void 0===this._styleSheet&&(qQt?(this._styleSheet=new CSSStyleSheet,this._styleSheet.replaceSync(this.cssText)):this._styleSheet=null),this._styleSheet}toString(){return this.cssText}}const $Qt=(t,...e)=>{const n=e.reduce(((e,n,i)=>e+(t=>{if(t instanceof XQt)return t.cssText;if("number"==typeof t)return t;throw new Error(`Value passed to 'css' function must be a 'css' function result: ${t}. Use 'unsafeCSS' to pass non-literal values, but\n            take care to ensure page security.`)})(n)+t[i+1]),t[0]);return new XQt(n,YQt)};
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
(window.litElementVersions||(window.litElementVersions=[])).push("2.5.1");const KQt={};class ZQt extends WQt{static getStyles(){return this.styles}static _getUniqueStyles(){if(this.hasOwnProperty(JSCompiler_renameProperty("_styles",this)))return;const t=this.getStyles();if(Array.isArray(t)){const e=(t,n)=>t.reduceRight(((t,n)=>Array.isArray(n)?e(n,t):(t.add(n),t)),n),n=e(t,new Set),i=[];n.forEach((t=>i.unshift(t))),this._styles=i}else this._styles=void 0===t?[]:[t];this._styles=this._styles.map((t=>{if(t instanceof CSSStyleSheet&&!qQt){return(t=>new XQt(String(t),YQt))(Array.prototype.slice.call(t.cssRules).reduce(((t,e)=>t+e.cssText),""))}return t}))}initialize(){super.initialize(),this.constructor._getUniqueStyles(),this.renderRoot=this.createRenderRoot(),window.ShadowRoot&&this.renderRoot instanceof window.ShadowRoot&&this.adoptStyles()}createRenderRoot(){return this.attachShadow(this.constructor.shadowRootOptions)}adoptStyles(){const t=this.constructor._styles;0!==t.length&&(void 0===window.ShadyCSS||window.ShadyCSS.nativeShadow?qQt?this.renderRoot.adoptedStyleSheets=t.map((t=>t instanceof CSSStyleSheet?t:t.styleSheet)):this._needsShimAdoptedStyleSheets=!0:window.ShadyCSS.ScopingShim.prepareAdoptedCssText(t.map((t=>t.cssText)),this.localName))}connectedCallback(){super.connectedCallback(),this.hasUpdated&&void 0!==window.ShadyCSS&&window.ShadyCSS.styleElement(this)}update(t){const e=this.render();super.update(t),e!==KQt&&this.constructor.render(e,this.renderRoot,{scopeName:this.localName,eventContext:this}),this._needsShimAdoptedStyleSheets&&(this._needsShimAdoptedStyleSheets=!1,this.constructor._styles.forEach((t=>{const e=document.createElement("style");e.textContent=t.cssText,this.renderRoot.appendChild(e)})))}render(){return KQt}}ZQt.finalized=!0,ZQt.render=(t,e,n)=>{if(!n||"object"!=typeof n||!n.scopeName)throw new Error("The `scopeName` option is required.");const i=n.scopeName,r=RQt.has(e),o=zQt&&11===e.nodeType&&!!e.host,a=o&&!HQt.has(i),s=a?document.createDocumentFragment():e;if(((t,e,n)=>{let i=RQt.get(e);void 0===i&&(hQt(e,e.firstChild),RQt.set(e,i=new LQt(Object.assign({templateFactory:NQt},n))),i.appendInto(e)),i.setValue(t),i.commit()})(t,s,Object.assign({templateFactory:DQt(i)},n)),a){const t=RQt.get(s);RQt.delete(s);FQt(i,s,t.value instanceof CQt?t.value.template:void 0),hQt(e,e.firstChild),e.appendChild(s),RQt.set(e,t)}!r&&o&&window.ShadyCSS.styleElement(e.host)},ZQt.shadowRootOptions={mode:"open"};let JQt=0;const QQt={},t1t=(t,e,n)=>{const i=n&&n.moduleId||"custom-style-module-"+JQt++;Array.isArray(e)||(e=e?[e]:[]),e.forEach((t=>{if(!(t instanceof XQt))throw new Error("An item in styles is not of type CSSResult. Use `unsafeCSS` or `css`.");if(!QQt[t]){const e=document.createElement("dom-module");e.innerHTML=`\n        <template>\n          <style>${t.toString()}</style>\n        </template>\n      `;const n="custom-style-module-"+JQt++;e.register(n),QQt[t]=n}}));const r=document.createElement("dom-module");if(t){const e=customElements.get(t);e&&Object.prototype.hasOwnProperty.call(e,"__finalized")&&console.warn(`The custom element definition for "${t}"\n      was finalized before a style module was registered.\n      Make sure to add component specific style modules before\n      importing the corresponding custom element.`),r.setAttribute("theme-for",t)}r.innerHTML=`\n    <template>\n      ${(n&&n.include||[]).map((t=>`<style include=${t}></style>`))}\n      ${e.map((t=>`<style include=${QQt[t]}></style>`))}\n    </template>\n  `,r.register(i)};
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
class e1t extends HTMLElement{static get version(){return"20.0.2"}}customElements.define("vaadin-lumo-styles",e1t);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const n1t=$Qt`
  :host {
    /* Base (background) */
    --lumo-base-color: #fff;

    /* Tint */
    --lumo-tint-5pct: hsla(0, 0%, 100%, 0.3);
    --lumo-tint-10pct: hsla(0, 0%, 100%, 0.37);
    --lumo-tint-20pct: hsla(0, 0%, 100%, 0.44);
    --lumo-tint-30pct: hsla(0, 0%, 100%, 0.5);
    --lumo-tint-40pct: hsla(0, 0%, 100%, 0.57);
    --lumo-tint-50pct: hsla(0, 0%, 100%, 0.64);
    --lumo-tint-60pct: hsla(0, 0%, 100%, 0.7);
    --lumo-tint-70pct: hsla(0, 0%, 100%, 0.77);
    --lumo-tint-80pct: hsla(0, 0%, 100%, 0.84);
    --lumo-tint-90pct: hsla(0, 0%, 100%, 0.9);
    --lumo-tint: #fff;

    /* Shade */
    --lumo-shade-5pct: hsla(214, 61%, 25%, 0.05);
    --lumo-shade-10pct: hsla(214, 57%, 24%, 0.1);
    --lumo-shade-20pct: hsla(214, 53%, 23%, 0.16);
    --lumo-shade-30pct: hsla(214, 50%, 22%, 0.26);
    --lumo-shade-40pct: hsla(214, 47%, 21%, 0.38);
    --lumo-shade-50pct: hsla(214, 45%, 20%, 0.5);
    --lumo-shade-60pct: hsla(214, 43%, 19%, 0.61);
    --lumo-shade-70pct: hsla(214, 42%, 18%, 0.72);
    --lumo-shade-80pct: hsla(214, 41%, 17%, 0.83);
    --lumo-shade-90pct: hsla(214, 40%, 16%, 0.94);
    --lumo-shade: hsl(214, 35%, 15%);

    /* Contrast */
    --lumo-contrast-5pct: var(--lumo-shade-5pct);
    --lumo-contrast-10pct: var(--lumo-shade-10pct);
    --lumo-contrast-20pct: var(--lumo-shade-20pct);
    --lumo-contrast-30pct: var(--lumo-shade-30pct);
    --lumo-contrast-40pct: var(--lumo-shade-40pct);
    --lumo-contrast-50pct: var(--lumo-shade-50pct);
    --lumo-contrast-60pct: var(--lumo-shade-60pct);
    --lumo-contrast-70pct: var(--lumo-shade-70pct);
    --lumo-contrast-80pct: var(--lumo-shade-80pct);
    --lumo-contrast-90pct: var(--lumo-shade-90pct);
    --lumo-contrast: var(--lumo-shade);

    /* Text */
    --lumo-header-text-color: var(--lumo-contrast);
    --lumo-body-text-color: var(--lumo-contrast-90pct);
    --lumo-secondary-text-color: var(--lumo-contrast-70pct);
    --lumo-tertiary-text-color: var(--lumo-contrast-50pct);
    --lumo-disabled-text-color: var(--lumo-contrast-30pct);

    /* Primary */
    --lumo-primary-color: hsl(214, 90%, 52%);
    --lumo-primary-color-50pct: hsla(214, 90%, 52%, 0.5);
    --lumo-primary-color-10pct: hsla(214, 90%, 52%, 0.1);
    --lumo-primary-text-color: var(--lumo-primary-color);
    --lumo-primary-contrast-color: #fff;

    /* Error */
    --lumo-error-color: hsl(3, 100%, 61%);
    --lumo-error-color-50pct: hsla(3, 100%, 60%, 0.5);
    --lumo-error-color-10pct: hsla(3, 100%, 60%, 0.1);
    --lumo-error-text-color: hsl(3, 92%, 53%);
    --lumo-error-contrast-color: #fff;

    /* Success */
    --lumo-success-color: hsl(145, 80%, 42%); /* hsl(144,82%,37%); */
    --lumo-success-color-50pct: hsla(145, 76%, 44%, 0.55);
    --lumo-success-color-10pct: hsla(145, 76%, 44%, 0.12);
    --lumo-success-text-color: hsl(145, 100%, 32%);
    --lumo-success-contrast-color: #fff;
  }
`,i1t=document.createElement("template");i1t.innerHTML=`<style>${n1t.toString().replace(":host","html")}</style>`,document.head.appendChild(i1t.content);t1t("",$Qt`
  [theme~='dark'] {
    /* Base (background) */
    --lumo-base-color: hsl(214, 35%, 21%);

    /* Tint */
    --lumo-tint-5pct: hsla(214, 65%, 85%, 0.06);
    --lumo-tint-10pct: hsla(214, 60%, 80%, 0.14);
    --lumo-tint-20pct: hsla(214, 64%, 82%, 0.23);
    --lumo-tint-30pct: hsla(214, 69%, 84%, 0.32);
    --lumo-tint-40pct: hsla(214, 73%, 86%, 0.41);
    --lumo-tint-50pct: hsla(214, 78%, 88%, 0.5);
    --lumo-tint-60pct: hsla(214, 82%, 90%, 0.6);
    --lumo-tint-70pct: hsla(214, 87%, 92%, 0.7);
    --lumo-tint-80pct: hsla(214, 91%, 94%, 0.8);
    --lumo-tint-90pct: hsla(214, 96%, 96%, 0.9);
    --lumo-tint: hsl(214, 100%, 98%);

    /* Shade */
    --lumo-shade-5pct: hsla(214, 0%, 0%, 0.07);
    --lumo-shade-10pct: hsla(214, 4%, 2%, 0.15);
    --lumo-shade-20pct: hsla(214, 8%, 4%, 0.23);
    --lumo-shade-30pct: hsla(214, 12%, 6%, 0.32);
    --lumo-shade-40pct: hsla(214, 16%, 8%, 0.41);
    --lumo-shade-50pct: hsla(214, 20%, 10%, 0.5);
    --lumo-shade-60pct: hsla(214, 24%, 12%, 0.6);
    --lumo-shade-70pct: hsla(214, 28%, 13%, 0.7);
    --lumo-shade-80pct: hsla(214, 32%, 13%, 0.8);
    --lumo-shade-90pct: hsla(214, 33%, 13%, 0.9);
    --lumo-shade: hsl(214, 33%, 13%);

    /* Contrast */
    --lumo-contrast-5pct: var(--lumo-tint-5pct);
    --lumo-contrast-10pct: var(--lumo-tint-10pct);
    --lumo-contrast-20pct: var(--lumo-tint-20pct);
    --lumo-contrast-30pct: var(--lumo-tint-30pct);
    --lumo-contrast-40pct: var(--lumo-tint-40pct);
    --lumo-contrast-50pct: var(--lumo-tint-50pct);
    --lumo-contrast-60pct: var(--lumo-tint-60pct);
    --lumo-contrast-70pct: var(--lumo-tint-70pct);
    --lumo-contrast-80pct: var(--lumo-tint-80pct);
    --lumo-contrast-90pct: var(--lumo-tint-90pct);
    --lumo-contrast: var(--lumo-tint);

    /* Text */
    --lumo-header-text-color: var(--lumo-contrast);
    --lumo-body-text-color: var(--lumo-contrast-90pct);
    --lumo-secondary-text-color: var(--lumo-contrast-70pct);
    --lumo-tertiary-text-color: var(--lumo-contrast-50pct);
    --lumo-disabled-text-color: var(--lumo-contrast-30pct);

    /* Primary */
    --lumo-primary-color: hsl(214, 86%, 55%);
    --lumo-primary-color-50pct: hsla(214, 86%, 55%, 0.5);
    --lumo-primary-color-10pct: hsla(214, 90%, 63%, 0.1);
    --lumo-primary-text-color: hsl(214, 100%, 70%);
    --lumo-primary-contrast-color: #fff;

    /* Error */
    --lumo-error-color: hsl(3, 90%, 63%);
    --lumo-error-color-50pct: hsla(3, 90%, 63%, 0.5);
    --lumo-error-color-10pct: hsla(3, 90%, 63%, 0.1);
    --lumo-error-text-color: hsl(3, 100%, 67%);

    /* Success */
    --lumo-success-color: hsl(145, 65%, 42%);
    --lumo-success-color-50pct: hsla(145, 65%, 42%, 0.5);
    --lumo-success-color-10pct: hsla(145, 65%, 42%, 0.1);
    --lumo-success-text-color: hsl(145, 85%, 47%);
  }

  html {
    color: var(--lumo-body-text-color);
    background-color: var(--lumo-base-color);
  }

  [theme~='dark'] {
    color: var(--lumo-body-text-color);
    background-color: var(--lumo-base-color);
  }

  h1,
  h2,
  h3,
  h4,
  h5,
  h6 {
    color: var(--lumo-header-text-color);
  }

  a {
    color: var(--lumo-primary-text-color);
  }

  blockquote {
    color: var(--lumo-secondary-text-color);
  }

  code,
  pre {
    background-color: var(--lumo-contrast-10pct);
    border-radius: var(--lumo-border-radius-m);
  }
`,{moduleId:"lumo-color"});t1t("",$Qt`
  :host {
    color: var(--lumo-body-text-color) !important;
    background-color: var(--lumo-base-color) !important;
  }
`,{moduleId:"lumo-color-legacy",include:["lumo-color"]});
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const r1t=document.createElement("template");r1t.innerHTML='\n  <style>\n    @font-face {\n      font-family: \'lumo-icons\';\n      src: url(data:application/font-woff;charset=utf-8;base64,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) format(\'woff\');\n      font-weight: normal;\n      font-style: normal;\n    }\n\n    html {\n      --lumo-icons-align-center: "\\ea01";\n      --lumo-icons-align-left: "\\ea02";\n      --lumo-icons-align-right: "\\ea03";\n      --lumo-icons-angle-down: "\\ea04";\n      --lumo-icons-angle-left: "\\ea05";\n      --lumo-icons-angle-right: "\\ea06";\n      --lumo-icons-angle-up: "\\ea07";\n      --lumo-icons-arrow-down: "\\ea08";\n      --lumo-icons-arrow-left: "\\ea09";\n      --lumo-icons-arrow-right: "\\ea0a";\n      --lumo-icons-arrow-up: "\\ea0b";\n      --lumo-icons-bar-chart: "\\ea0c";\n      --lumo-icons-bell: "\\ea0d";\n      --lumo-icons-calendar: "\\ea0e";\n      --lumo-icons-checkmark: "\\ea0f";\n      --lumo-icons-chevron-down: "\\ea10";\n      --lumo-icons-chevron-left: "\\ea11";\n      --lumo-icons-chevron-right: "\\ea12";\n      --lumo-icons-chevron-up: "\\ea13";\n      --lumo-icons-clock: "\\ea14";\n      --lumo-icons-cog: "\\ea15";\n      --lumo-icons-cross: "\\ea16";\n      --lumo-icons-download: "\\ea17";\n      --lumo-icons-dropdown: "\\ea18";\n      --lumo-icons-edit: "\\ea19";\n      --lumo-icons-error: "\\ea1a";\n      --lumo-icons-eye: "\\ea1b";\n      --lumo-icons-eye-disabled: "\\ea1c";\n      --lumo-icons-menu: "\\ea1d";\n      --lumo-icons-minus: "\\ea1e";\n      --lumo-icons-ordered-list: "\\ea1f";\n      --lumo-icons-phone: "\\ea20";\n      --lumo-icons-photo: "\\ea21";\n      --lumo-icons-play: "\\ea22";\n      --lumo-icons-plus: "\\ea23";\n      --lumo-icons-redo: "\\ea24";\n      --lumo-icons-reload: "\\ea25";\n      --lumo-icons-search: "\\ea26";\n      --lumo-icons-undo: "\\ea27";\n      --lumo-icons-unordered-list: "\\ea28";\n      --lumo-icons-upload: "\\ea29";\n      --lumo-icons-user: "\\ea2a";\n    }\n  </style>\n',document.head.appendChild(r1t.content);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const o1t=$Qt`
  :host {
    --lumo-size-xs: 1.625rem;
    --lumo-size-s: 1.875rem;
    --lumo-size-m: 2.25rem;
    --lumo-size-l: 2.75rem;
    --lumo-size-xl: 3.5rem;

    /* Icons */
    --lumo-icon-size-s: 1.25em;
    --lumo-icon-size-m: 1.5em;
    --lumo-icon-size-l: 2.25em;
    /* For backwards compatibility */
    --lumo-icon-size: var(--lumo-icon-size-m);
  }
`,a1t=document.createElement("template");a1t.innerHTML=`<style>${o1t.toString().replace(":host","html")}</style>`,document.head.appendChild(a1t.content);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const s1t=$Qt`
  :host {
    /* Square */
    --lumo-space-xs: 0.25rem;
    --lumo-space-s: 0.5rem;
    --lumo-space-m: 1rem;
    --lumo-space-l: 1.5rem;
    --lumo-space-xl: 2.5rem;

    /* Wide */
    --lumo-space-wide-xs: calc(var(--lumo-space-xs) / 2) var(--lumo-space-xs);
    --lumo-space-wide-s: calc(var(--lumo-space-s) / 2) var(--lumo-space-s);
    --lumo-space-wide-m: calc(var(--lumo-space-m) / 2) var(--lumo-space-m);
    --lumo-space-wide-l: calc(var(--lumo-space-l) / 2) var(--lumo-space-l);
    --lumo-space-wide-xl: calc(var(--lumo-space-xl) / 2) var(--lumo-space-xl);

    /* Tall */
    --lumo-space-tall-xs: var(--lumo-space-xs) calc(var(--lumo-space-xs) / 2);
    --lumo-space-tall-s: var(--lumo-space-s) calc(var(--lumo-space-s) / 2);
    --lumo-space-tall-m: var(--lumo-space-m) calc(var(--lumo-space-m) / 2);
    --lumo-space-tall-l: var(--lumo-space-l) calc(var(--lumo-space-l) / 2);
    --lumo-space-tall-xl: var(--lumo-space-xl) calc(var(--lumo-space-xl) / 2);
  }
`,l1t=document.createElement("template");l1t.innerHTML=`<style>${s1t.toString().replace(":host","html")}</style>`,document.head.appendChild(l1t.content);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const c1t=$Qt`
  :host {
    /* Border radius */
    --lumo-border-radius-s: 0.25em; /* Checkbox, badge, date-picker year indicator, etc */
    --lumo-border-radius-m: var(--lumo-border-radius, 0.25em); /* Button, text field, menu overlay, etc */
    --lumo-border-radius-l: 0.5em; /* Dialog, notification, etc */
    --lumo-border-radius: 0.25em; /* Deprecated */

    /* Shadow */
    --lumo-box-shadow-xs: 0 1px 4px -1px var(--lumo-shade-50pct);
    --lumo-box-shadow-s: 0 2px 4px -1px var(--lumo-shade-20pct), 0 3px 12px -1px var(--lumo-shade-30pct);
    --lumo-box-shadow-m: 0 2px 6px -1px var(--lumo-shade-20pct), 0 8px 24px -4px var(--lumo-shade-40pct);
    --lumo-box-shadow-l: 0 3px 18px -2px var(--lumo-shade-20pct), 0 12px 48px -6px var(--lumo-shade-40pct);
    --lumo-box-shadow-xl: 0 4px 24px -3px var(--lumo-shade-20pct), 0 18px 64px -8px var(--lumo-shade-40pct);

    /* Clickable element cursor */
    --lumo-clickable-cursor: default;
  }
`,u1t=document.createElement("template");u1t.innerHTML=`<style>${c1t.toString().replace(":host","html")}</style>`,document.head.appendChild(u1t.content);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const h1t=$Qt`
  :host {
    /* prettier-ignore */
    --lumo-font-family: -apple-system, BlinkMacSystemFont, 'Roboto', 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';

    /* Font sizes */
    --lumo-font-size-xxs: 0.75rem;
    --lumo-font-size-xs: 0.8125rem;
    --lumo-font-size-s: 0.875rem;
    --lumo-font-size-m: 1rem;
    --lumo-font-size-l: 1.125rem;
    --lumo-font-size-xl: 1.375rem;
    --lumo-font-size-xxl: 1.75rem;
    --lumo-font-size-xxxl: 2.5rem;

    /* Line heights */
    --lumo-line-height-xs: 1.25;
    --lumo-line-height-s: 1.375;
    --lumo-line-height-m: 1.625;
  }
`,d1t=document.createElement("template");d1t.innerHTML=`<style>${h1t.toString().replace(":host","html")}</style>`,document.head.appendChild(d1t.content);t1t("",$Qt`
  html {
    font-family: var(--lumo-font-family);
    font-size: var(--lumo-font-size, var(--lumo-font-size-m));
    line-height: var(--lumo-line-height-m);
    -webkit-text-size-adjust: 100%;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
  }

  /* Can’t combine with the above selector because that doesn’t work in browsers without native shadow dom */
  :host {
    font-family: var(--lumo-font-family);
    font-size: var(--lumo-font-size, var(--lumo-font-size-m));
    line-height: var(--lumo-line-height-m);
    -webkit-text-size-adjust: 100%;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
  }

  small,
  [theme~='font-size-s'] {
    font-size: var(--lumo-font-size-s);
    line-height: var(--lumo-line-height-s);
  }

  [theme~='font-size-xs'] {
    font-size: var(--lumo-font-size-xs);
    line-height: var(--lumo-line-height-xs);
  }

  h1,
  h2,
  h3,
  h4,
  h5,
  h6 {
    font-weight: 600;
    line-height: var(--lumo-line-height-xs);
    margin-top: 1.25em;
  }

  h1 {
    font-size: var(--lumo-font-size-xxxl);
    margin-bottom: 0.75em;
  }

  h2 {
    font-size: var(--lumo-font-size-xxl);
    margin-bottom: 0.5em;
  }

  h3 {
    font-size: var(--lumo-font-size-xl);
    margin-bottom: 0.5em;
  }

  h4 {
    font-size: var(--lumo-font-size-l);
    margin-bottom: 0.5em;
  }

  h5 {
    font-size: var(--lumo-font-size-m);
    margin-bottom: 0.25em;
  }

  h6 {
    font-size: var(--lumo-font-size-xs);
    margin-bottom: 0;
    text-transform: uppercase;
    letter-spacing: 0.03em;
  }

  p,
  blockquote {
    margin-top: 0.5em;
    margin-bottom: 0.75em;
  }

  a {
    text-decoration: none;
  }

  a:hover {
    text-decoration: underline;
  }

  hr {
    display: block;
    align-self: stretch;
    height: 1px;
    border: 0;
    padding: 0;
    margin: var(--lumo-space-s) calc(var(--lumo-border-radius-m) / 2);
    background-color: var(--lumo-contrast-10pct);
  }

  blockquote {
    border-left: 2px solid var(--lumo-contrast-30pct);
  }

  b,
  strong {
    font-weight: 600;
  }

  /* RTL specific styles */

  blockquote[dir='rtl'] {
    border-left: none;
    border-right: 2px solid var(--lumo-contrast-30pct);
  }
`,{moduleId:"lumo-typography"}),t1t("vaadin-checkbox",$Qt`
    :host {
      -webkit-tap-highlight-color: transparent;
      -webkit-user-select: none;
      -moz-user-select: none;
      user-select: none;
      cursor: default;
      outline: none;
    }

    [part='label']:not([empty]) {
      margin: 0.1875em 0.875em 0.1875em 0.375em;
    }

    [part='checkbox'] {
      width: calc(1em + 2px);
      height: calc(1em + 2px);
      margin: 0.1875em;
      position: relative;
      border-radius: var(--lumo-border-radius-s);
      background-color: var(--lumo-contrast-20pct);
      transition: transform 0.2s cubic-bezier(0.12, 0.32, 0.54, 2), background-color 0.15s;
      pointer-events: none;
      line-height: 1.2;
    }

    :host([indeterminate]) [part='checkbox'],
    :host([checked]) [part='checkbox'] {
      background-color: var(--lumo-primary-color);
    }

    /* Needed to align the checkbox nicely on the baseline */
    [part='checkbox']::before {
      content: '\\2003';
    }

    /* Checkmark */
    [part='checkbox']::after {
      content: '';
      display: inline-block;
      width: 0;
      height: 0;
      border: 0 solid var(--lumo-primary-contrast-color);
      border-width: 0.1875em 0 0 0.1875em;
      box-sizing: border-box;
      transform-origin: 0 0;
      position: absolute;
      top: 0.8125em;
      left: 0.5em;
      transform: scale(0.55) rotate(-135deg);
      opacity: 0;
    }

    :host([checked]) [part='checkbox']::after {
      opacity: 1;
      width: 0.625em;
      height: 1.0625em;
    }

    /* Indeterminate checkmark */
    :host([indeterminate]) [part='checkbox']::after {
      transform: none;
      opacity: 1;
      top: 45%;
      height: 10%;
      left: 22%;
      right: 22%;
      width: auto;
      border: 0;
      background-color: var(--lumo-primary-contrast-color);
      transition: opacity 0.25s;
    }

    /* Focus ring */
    :host([focus-ring]) [part='checkbox'] {
      box-shadow: 0 0 0 3px var(--lumo-primary-color-50pct);
    }

    /* Disabled */
    :host([disabled]) {
      pointer-events: none;
      color: var(--lumo-disabled-text-color);
    }

    :host([disabled]) [part='label'] ::slotted(*) {
      color: inherit;
    }

    :host([disabled]) [part='checkbox'] {
      background-color: var(--lumo-contrast-10pct);
    }

    :host([disabled]) [part='checkbox']::after {
      border-color: var(--lumo-contrast-30pct);
    }

    :host([indeterminate][disabled]) [part='checkbox']::after {
      background-color: var(--lumo-contrast-30pct);
    }

    /* RTL specific styles */
    :host([dir='rtl']) [part='label']:not([empty]) {
      margin: 0.1875em 0.375em 0.1875em 0.875em;
    }

    /* Transition the checkmark if activated with the mouse (disabled for grid select-all this way) */
    :host(:hover) [part='checkbox']::after {
      transition: width 0.1s, height 0.25s;
    }

    /* Used for activation "halo" */
    [part='checkbox']::before {
      color: transparent;
      display: inline-block;
      width: 100%;
      height: 100%;
      border-radius: inherit;
      background-color: inherit;
      transform: scale(1.4);
      opacity: 0;
      transition: transform 0.1s, opacity 0.8s;
    }

    /* Hover */
    :host(:not([checked]):not([indeterminate]):not([disabled]):hover) [part='checkbox'] {
      background-color: var(--lumo-contrast-30pct);
    }

    /* Disable hover for touch devices */
    @media (pointer: coarse) {
      :host(:not([checked]):not([indeterminate]):not([disabled]):hover) [part='checkbox'] {
        background-color: var(--lumo-contrast-20pct);
      }
    }

    /* Active */
    :host([active]) [part='checkbox'] {
      transform: scale(0.9);
      transition-duration: 0.05s;
    }

    :host([active][checked]) [part='checkbox'] {
      transform: scale(1.1);
    }

    :host([active]:not([checked])) [part='checkbox']::before {
      transition-duration: 0.01s, 0.01s;
      transform: scale(0);
      opacity: 0.4;
    }
  `,{moduleId:"lumo-checkbox"});const p1t=t=>class extends t{static get properties(){return{theme:{type:String,readOnly:!0}}}attributeChangedCallback(t,e,n){super.attributeChangedCallback(t,e,n),"theme"===t&&this._setTheme(n)}},f1t=t=>class extends(p1t(t)){static finalize(){super.finalize();const t=this.prototype._template,e=Object.getPrototypeOf(this.prototype)._template;e&&Array.from(e.content.querySelectorAll("style[include]")).forEach((e=>{this._includeStyle(e.getAttribute("include"),t)})),this._includeMatchingThemes(t)}static _includeMatchingThemes(t){const e=B.prototype.modules;let n=!1;const i=this.is+"-default-theme";Object.keys(e).sort(((t,e)=>{const n=0===t.indexOf("vaadin-"),i=0===e.indexOf("vaadin-"),r=["lumo-","material-"],o=r.filter((e=>0===t.indexOf(e))).length>0,a=r.filter((t=>0===e.indexOf(t))).length>0;return n!==i?n?-1:1:o!==a?o?-1:1:0})).forEach((r=>{if(r!==i){const i=e[r].getAttribute("theme-for");i&&i.split(" ").forEach((e=>{new RegExp("^"+e.split("*").join(".*")+"$").test(this.is)&&(n=!0,this._includeStyle(r,t))}))}})),!n&&e[i]&&this._includeStyle(i,t)}static _includeStyle(t,e){if(e&&!e.content.querySelector(`style[include="${t}"]`)){const n=document.createElement("style");n.setAttribute("include",t),e.content.appendChild(n)}}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */;let m1t=!1;window.addEventListener("keydown",(()=>{m1t=!0}),{capture:!0}),window.addEventListener("mousedown",(()=>{m1t=!1}),{capture:!0});const g1t=t=>class extends((t=>class extends t{static get properties(){return{tabindex:{type:Number,value:0,reflectToAttribute:!0,observer:"_tabindexChanged"}}}})(t)){static get properties(){return{autofocus:{type:Boolean},_previousTabIndex:{type:Number},disabled:{type:Boolean,observer:"_disabledChanged",reflectToAttribute:!0},_isShiftTabbing:{type:Boolean}}}ready(){this.addEventListener("focusin",(t=>{t.composedPath()[0]===this?this.contains(t.relatedTarget)||this._focus():-1===t.composedPath().indexOf(this.focusElement)||this.disabled||this._setFocused(!0)})),this.addEventListener("focusout",(()=>this._setFocused(!1))),super.ready(),this.addEventListener("keydown",(t=>{!t.defaultPrevented&&9===t.keyCode&&t.shiftKey&&(this._isShiftTabbing=!0,HTMLElement.prototype.focus.apply(this),this._setFocused(!1),setTimeout((()=>this._isShiftTabbing=!1),0))})),this.autofocus&&!this.disabled&&window.requestAnimationFrame((()=>{this._focus(),this._setFocused(!0),this.setAttribute("focus-ring","")}))}disconnectedCallback(){super.disconnectedCallback(),this.hasAttribute("focused")&&this._setFocused(!1)}_setFocused(t){t?this.setAttribute("focused",""):this.removeAttribute("focused"),t&&m1t?this.setAttribute("focus-ring",""):this.removeAttribute("focus-ring")}get focusElement(){return window.console.warn(`Please implement the 'focusElement' property in <${this.localName}>`),this}_focus(){this.focusElement&&!this._isShiftTabbing&&(this.focusElement.focus(),this._setFocused(!0))}focus(){this.focusElement&&!this.disabled&&(this.focusElement.focus(),this._setFocused(!0))}blur(){this.focusElement&&(this.focusElement.blur(),this._setFocused(!1))}_disabledChanged(t){this.focusElement.disabled=t,t?(this.blur(),this._previousTabIndex=this.tabindex,this.tabindex=-1,this.setAttribute("aria-disabled","true")):(void 0!==this._previousTabIndex&&(this.tabindex=this._previousTabIndex),this.removeAttribute("aria-disabled"))}_tabindexChanged(t){void 0!==t&&(this.focusElement.tabIndex=t),this.disabled&&this.tabindex&&(-1!==this.tabindex&&(this._previousTabIndex=this.tabindex),this.tabindex=t=void 0)}click(){this.disabled||super.click()}},_1t=/\/\*\*\s+vaadin-dev-mode:start([\s\S]*)vaadin-dev-mode:end\s+\*\*\//i,y1t=window.Vaadin&&window.Vaadin.Flow&&window.Vaadin.Flow.clients;function v1t(t,e){if("function"!=typeof t)return;const n=_1t.exec(t.toString());if(n)try{t=new Function(n[1])}catch(t){console.log("vaadin-development-mode-detector: uncommentAndRun() failed",t)}return t(e)}window.Vaadin=window.Vaadin||{};const b1t=function(t,e){if(window.Vaadin.developmentMode)return v1t(t,e)};function x1t(){}void 0===window.Vaadin.developmentMode&&(window.Vaadin.developmentMode=(function w1t(){try{return!!(function t(){return localStorage.getItem("vaadin.developmentmode.force")})()||!!(function e(){return["localhost","127.0.0.1"].indexOf(window.location.hostname)>=0})()&&(y1t?!(function n(){return!!(y1t&&Object.keys(y1t).map((t=>y1t[t])).filter((t=>t.productionMode)).length>0)})():!(function i(){return v1t((function t(){return!0}))})())}catch(t){return!1}})());
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
class S1t{static detectScrollType(){const t=document.createElement("div");t.textContent="ABCD",t.dir="rtl",t.style.fontSize="14px",t.style.width="4px",t.style.height="1px",t.style.position="absolute",t.style.top="-1000px",t.style.overflow="scroll",document.body.appendChild(t);let e="reverse";return t.scrollLeft>0?e="default":(t.scrollLeft=2,t.scrollLeft<2&&(e="negative")),document.body.removeChild(t),e}static getNormalizedScrollLeft(t,e,n){const{scrollLeft:i}=n;if("rtl"!==e||!t)return i;switch(t){case"negative":return n.scrollWidth-n.clientWidth+i;case"reverse":return n.scrollWidth-n.clientWidth-i}return i}static setNormalizedScrollLeft(t,e,n,i){if("rtl"===e&&t)switch(t){case"negative":n.scrollLeft=n.clientWidth-n.scrollWidth+i;break;case"reverse":n.scrollLeft=n.scrollWidth-n.clientWidth-i;break;default:n.scrollLeft=i}else n.scrollLeft=i}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */const M1t=[];let E1t;new MutationObserver((function(){const t=C1t();M1t.forEach((e=>{T1t(e,t)}))})).observe(document.documentElement,{attributes:!0,attributeFilter:["dir"]});const T1t=function(t,e,n=t.getAttribute("dir")){e?t.setAttribute("dir",e):null!=n&&t.removeAttribute("dir")},C1t=function(){return document.documentElement.getAttribute("dir")},A1t=t=>class extends t{static get properties(){return{dir:{type:String,value:"",reflectToAttribute:!0}}}static finalize(){super.finalize(),E1t||(E1t=S1t.detectScrollType())}connectedCallback(){super.connectedCallback(),this.hasAttribute("dir")||(this.__subscribe(),T1t(this,C1t(),null))}attributeChangedCallback(t,e,n){if(super.attributeChangedCallback(t,e,n),"dir"!==t)return;const i=C1t(),r=n===i&&-1===M1t.indexOf(this),o=!n&&e&&-1===M1t.indexOf(this),a=n!==i&&e===i;r||o?(this.__subscribe(),T1t(this,i,n)):a&&this.__subscribe(!1)}disconnectedCallback(){super.disconnectedCallback(),this.__subscribe(!1),this.removeAttribute("dir")}_valueToNodeAttribute(t,e,n){("dir"!==n||""!==e||t.hasAttribute("dir"))&&super._valueToNodeAttribute(t,e,n)}_attributeToProperty(t,e,n){"dir"!==t||e?super._attributeToProperty(t,e,n):this.dir=""}__subscribe(t=!0){t?-1===M1t.indexOf(this)&&M1t.push(this):M1t.indexOf(this)>-1&&M1t.splice(M1t.indexOf(this),1)}__getNormalizedScrollLeft(t){return S1t.getNormalizedScrollLeft(E1t,this.getAttribute("dir")||"ltr",t)}__setNormalizedScrollLeft(t,e){return S1t.setNormalizedScrollLeft(E1t,this.getAttribute("dir")||"ltr",t,e)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */;let k1t;window.Vaadin=window.Vaadin||{},window.Vaadin.registrations=window.Vaadin.registrations||[],window.Vaadin.developmentModeCallback=window.Vaadin.developmentModeCallback||{},window.Vaadin.developmentModeCallback["vaadin-usage-statistics"]=function(){b1t(x1t)};const L1t=new Set,P1t=t=>class extends(A1t(t)){static finalize(){super.finalize();const{is:t}=this;t&&!L1t.has(t)&&(window.Vaadin.registrations.push(this),L1t.add(t),window.Vaadin.developmentModeCallback&&(k1t=Nn.debounce(k1t,yt,(()=>{window.Vaadin.developmentModeCallback["vaadin-usage-statistics"]()})),Rn(k1t)))}constructor(){super(),null===document.doctype&&console.warn('Vaadin components require the "standards mode" declaration. Please add <!DOCTYPE html> to the HTML document.')}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */;class N1t extends(P1t(g1t(f1t(bi(we))))){static get template(){return xe`
      <style>
        :host {
          display: inline-block;
        }

        :host([hidden]) {
          display: none !important;
        }

        label {
          display: inline-flex;
          align-items: baseline;
          outline: none;
        }

        [part='checkbox'] {
          position: relative;
          display: inline-block;
          flex: none;
        }

        input[type='checkbox'] {
          position: absolute;
          top: 0;
          left: 0;
          right: 0;
          width: 100%;
          height: 100%;
          opacity: 0;
          cursor: inherit;
          margin: 0;
        }

        :host([disabled]) {
          -webkit-tap-highlight-color: transparent;
        }
      </style>

      <label>
        <span part="checkbox">
          <input
            type="checkbox"
            checked="{{checked::change}}"
            disabled$="[[disabled]]"
            indeterminate="{{indeterminate::change}}"
            role="presentation"
            tabindex="-1"
          />
        </span>

        <span part="label">
          <slot></slot>
        </span>
      </label>
    `}static get is(){return"vaadin-checkbox"}static get version(){return"20.0.2"}static get properties(){return{checked:{type:Boolean,value:!1,notify:!0,observer:"_checkedChanged",reflectToAttribute:!0},indeterminate:{type:Boolean,notify:!0,observer:"_indeterminateChanged",reflectToAttribute:!0,value:!1},value:{type:String,value:"on"},_nativeCheckbox:{type:Object}}}constructor(){super()}get name(){return this.checked?this._storedName:""}set name(t){this._storedName=t}ready(){super.ready(),this.setAttribute("role","checkbox"),this._nativeCheckbox=this.shadowRoot.querySelector('input[type="checkbox"]'),this.addEventListener("click",this._handleClick.bind(this)),this._addActiveListeners();const t=this.getAttribute("name");t&&(this.name=t),this.shadowRoot.querySelector('[part~="label"]').querySelector("slot").addEventListener("slotchange",this._updateLabelAttribute.bind(this)),this._updateLabelAttribute()}_updateLabelAttribute(){const t=this.shadowRoot.querySelector('[part~="label"]'),e=t.firstElementChild.assignedNodes();this._isAssignedNodesEmpty(e)?t.setAttribute("empty",""):t.removeAttribute("empty")}_isAssignedNodesEmpty(t){return 0===t.length||1==t.length&&t[0].nodeType==Node.TEXT_NODE&&""===t[0].textContent.trim()}_checkedChanged(t){this.setAttribute("aria-checked",this.indeterminate?"mixed":Boolean(t))}_indeterminateChanged(t){this.setAttribute("aria-checked",t?"mixed":this.checked)}_addActiveListeners(){this._addEventListenerToNode(this,"down",(t=>{this.__interactionsAllowed(t)&&this.setAttribute("active","")})),this._addEventListenerToNode(this,"up",(()=>this.removeAttribute("active"))),this.addEventListener("keydown",(t=>{this.__interactionsAllowed(t)&&32===t.keyCode&&(t.preventDefault(),this.setAttribute("active",""))})),this.addEventListener("keyup",(t=>{this.__interactionsAllowed(t)&&32===t.keyCode&&(t.preventDefault(),this._toggleChecked(),this.removeAttribute("active"),this.indeterminate&&(this.indeterminate=!1))}))}get focusElement(){return this.shadowRoot.querySelector("input")}__interactionsAllowed(t){return!this.disabled&&"a"!==t.target.localName}_handleClick(t){this.__interactionsAllowed(t)&&(this.indeterminate?(this.indeterminate=!1,t.preventDefault(),this._toggleChecked()):t.composedPath()[0]!==this._nativeCheckbox&&(t.preventDefault(),this._toggleChecked()))}_toggleChecked(){this.checked=!this.checked,this.dispatchEvent(new CustomEvent("change",{composed:!1,bubbles:!0}))}}customElements.define(N1t.is,N1t),t1t("vaadin-grid",$Qt`
    :host {
      font-family: var(--lumo-font-family);
      font-size: var(--lumo-font-size-m);
      line-height: var(--lumo-line-height-s);
      color: var(--lumo-body-text-color);
      background-color: var(--lumo-base-color);
      box-sizing: border-box;
      -webkit-text-size-adjust: 100%;
      -webkit-tap-highlight-color: transparent;
      -webkit-font-smoothing: antialiased;
      -moz-osx-font-smoothing: grayscale;

      /* For internal use only */
      --_lumo-grid-border-color: var(--lumo-contrast-20pct);
      --_lumo-grid-secondary-border-color: var(--lumo-contrast-10pct);
      --_lumo-grid-border-width: 1px;
      --_lumo-grid-selected-row-color: var(--lumo-primary-color-10pct);
    }

    /* No (outer) border */

    :host(:not([theme~='no-border'])) {
      border: var(--_lumo-grid-border-width) solid var(--_lumo-grid-border-color);
    }

    /* Cell styles */

    [part~='cell'] {
      min-height: var(--lumo-size-m);
      background-color: var(--lumo-base-color);
    }

    [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      cursor: default;
      padding: var(--lumo-space-xs) var(--lumo-space-m);
    }

    /* Apply row borders by default and introduce the "no-row-borders" variant */
    :host(:not([theme~='no-row-borders'])) [part~='cell']:not([part~='details-cell']) {
      border-top: var(--_lumo-grid-border-width) solid var(--_lumo-grid-secondary-border-color);
    }

    /* Hide first body row top border */
    :host(:not([theme~='no-row-borders'])) [part='row'][first] [part~='cell']:not([part~='details-cell']) {
      border-top: 0;
      min-height: calc(var(--lumo-size-m) - var(--_lumo-grid-border-width));
    }

    /* Focus-ring */

    [part~='cell']:focus {
      outline: none;
    }

    :host([navigating]) [part~='cell']:focus::before {
      content: '';
      position: absolute;
      top: 0;
      right: 0;
      bottom: 0;
      left: 0;
      pointer-events: none;
      box-shadow: inset 0 0 0 2px var(--lumo-primary-color-50pct);
    }

    /* Drag and Drop styles */
    :host([dragover])::after {
      content: '';
      position: absolute;
      z-index: 100;
      top: 0;
      right: 0;
      bottom: 0;
      left: 0;
      pointer-events: none;
      box-shadow: inset 0 0 0 2px var(--lumo-primary-color-50pct);
    }

    [part~='row'][dragover] {
      z-index: 100 !important;
    }

    [part~='row'][dragover] [part~='cell'] {
      overflow: visible;
    }

    [part~='row'][dragover] [part~='cell']::after {
      content: '';
      position: absolute;
      top: 0;
      right: 0;
      bottom: 0;
      left: 0;
      height: calc(var(--_lumo-grid-border-width) + 2px);
      pointer-events: none;
      background: var(--lumo-primary-color-50pct);
    }

    :host([theme~='no-row-borders']) [dragover] [part~='cell']::after {
      height: 2px;
    }

    [part~='row'][dragover='below'] [part~='cell']::after {
      top: 100%;
      bottom: auto;
      margin-top: -1px;
    }

    [part~='row'][dragover='above'] [part~='cell']::after {
      top: auto;
      bottom: 100%;
      margin-bottom: -1px;
    }

    [part~='row'][details-opened][dragover='below'] [part~='cell']:not([part~='details-cell'])::after,
    [part~='row'][details-opened][dragover='above'] [part~='details-cell']::after {
      display: none;
    }

    [part~='row'][dragover][dragover='on-top'] [part~='cell']::after {
      height: 100%;
    }

    [part~='row'][dragstart] {
      /* Add bottom-space to the row so the drag number doesn't get clipped. Needed for IE/Edge */
      border-bottom: 100px solid transparent;
      z-index: 100 !important;
      opacity: 0.9;
    }

    [part~='row'][dragstart] [part~='cell'] {
      border: none !important;
      box-shadow: none !important;
    }

    [part~='row'][dragstart] [part~='cell'][last-column] {
      border-radius: 0 var(--lumo-border-radius-s) var(--lumo-border-radius-s) 0;
    }

    [part~='row'][dragstart] [part~='cell'][first-column] {
      border-radius: var(--lumo-border-radius-s) 0 0 var(--lumo-border-radius-s);
    }

    [ios] [part~='row'][dragstart] [part~='cell'] {
      background: var(--lumo-primary-color-50pct);
    }

    #scroller:not([ios]) [part~='row'][dragstart]:not([dragstart=''])::after {
      display: block;
      position: absolute;
      left: var(--_grid-drag-start-x);
      top: var(--_grid-drag-start-y);
      z-index: 100;
      content: attr(dragstart);
      align-items: center;
      justify-content: center;
      box-sizing: border-box;
      padding: calc(var(--lumo-space-xs) * 0.8);
      color: var(--lumo-error-contrast-color);
      background-color: var(--lumo-error-color);
      border-radius: var(--lumo-border-radius-m);
      font-family: var(--lumo-font-family);
      font-size: var(--lumo-font-size-xxs);
      line-height: 1;
      font-weight: 500;
      text-transform: initial;
      letter-spacing: initial;
      min-width: calc(var(--lumo-size-s) * 0.7);
      text-align: center;
    }

    /* Headers and footers */

    [part~='header-cell'] ::slotted(vaadin-grid-cell-content),
    [part~='footer-cell'] ::slotted(vaadin-grid-cell-content),
    [part~='reorder-ghost'] {
      font-size: var(--lumo-font-size-s);
      font-weight: 500;
    }

    [part~='footer-cell'] ::slotted(vaadin-grid-cell-content) {
      font-weight: 400;
    }

    [part='row']:only-child [part~='header-cell'] {
      min-height: var(--lumo-size-xl);
    }

    /* Header borders */

    /* Hide first header row top border */
    :host(:not([theme~='no-row-borders'])) [part='row']:first-child [part~='header-cell'] {
      border-top: 0;
    }

    [part='row']:last-child [part~='header-cell'] {
      border-bottom: var(--_lumo-grid-border-width) solid transparent;
    }

    :host(:not([theme~='no-row-borders'])) [part='row']:last-child [part~='header-cell'] {
      border-bottom-color: var(--_lumo-grid-secondary-border-color);
    }

    /* Overflow uses a stronger border color */
    :host([overflow~='top']) [part='row']:last-child [part~='header-cell'] {
      border-bottom-color: var(--_lumo-grid-border-color);
    }

    /* Footer borders */

    [part='row']:first-child [part~='footer-cell'] {
      border-top: var(--_lumo-grid-border-width) solid transparent;
    }

    :host(:not([theme~='no-row-borders'])) [part='row']:first-child [part~='footer-cell'] {
      border-top-color: var(--_lumo-grid-secondary-border-color);
    }

    /* Overflow uses a stronger border color */
    :host([overflow~='bottom']) [part='row']:first-child [part~='footer-cell'] {
      border-top-color: var(--_lumo-grid-border-color);
    }

    /* Column reordering */

    :host([reordering]) [part~='cell'] {
      background: linear-gradient(var(--lumo-shade-20pct), var(--lumo-shade-20pct)) var(--lumo-base-color);
    }

    :host([reordering]) [part~='cell'][reorder-status='allowed'] {
      background: var(--lumo-base-color);
    }

    :host([reordering]) [part~='cell'][reorder-status='dragging'] {
      background: linear-gradient(var(--lumo-contrast-5pct), var(--lumo-contrast-5pct)) var(--lumo-base-color);
    }

    [part~='reorder-ghost'] {
      opacity: 0.85;
      box-shadow: var(--lumo-box-shadow-s);
      /* TODO Use the same styles as for the cell element (reorder-ghost copies styles from the cell element) */
      padding: var(--lumo-space-s) var(--lumo-space-m) !important;
    }

    /* Column resizing */

    [part='resize-handle'] {
      width: 3px;
      background-color: var(--lumo-primary-color-50pct);
      opacity: 0;
      transition: opacity 0.2s;
    }

    :host(:not([reordering])) *:not([column-resizing]) [part~='cell']:hover [part='resize-handle'],
    [part='resize-handle']:active {
      opacity: 1;
      transition-delay: 0.15s;
    }

    /* Column borders */

    :host([theme~='column-borders']) [part~='cell']:not([last-column]):not([part~='details-cell']) {
      border-right: var(--_lumo-grid-border-width) solid var(--_lumo-grid-secondary-border-color);
    }

    /* Frozen columns */

    [last-frozen] {
      border-right: var(--_lumo-grid-border-width) solid transparent;
      overflow: hidden;
    }

    :host([overflow~='left']) [part~='cell'][last-frozen]:not([part~='details-cell']) {
      border-right-color: var(--_lumo-grid-border-color);
    }

    /* Row stripes */

    :host([theme~='row-stripes']) [part~='row']:not([odd]) [part~='body-cell'],
    :host([theme~='row-stripes']) [part~='row']:not([odd]) [part~='details-cell'] {
      background-image: linear-gradient(var(--lumo-contrast-5pct), var(--lumo-contrast-5pct));
      background-repeat: repeat-x;
    }

    /* Selected row */

    /* Raise the selected rows above unselected rows (so that box-shadow can cover unselected rows) */
    :host(:not([reordering])) [part~='row'][selected] {
      z-index: 1;
    }

    :host(:not([reordering])) [part~='row'][selected] [part~='body-cell']:not([part~='details-cell']) {
      background-image: linear-gradient(var(--_lumo-grid-selected-row-color), var(--_lumo-grid-selected-row-color));
      background-repeat: repeat;
    }

    /* Cover the border of an unselected row */
    :host(:not([theme~='no-row-borders'])) [part~='row'][selected] [part~='cell']:not([part~='details-cell']) {
      box-shadow: 0 var(--_lumo-grid-border-width) 0 0 var(--_lumo-grid-selected-row-color);
    }

    /* Compact */

    :host([theme~='compact']) [part='row']:only-child [part~='header-cell'] {
      min-height: var(--lumo-size-m);
    }

    :host([theme~='compact']) [part~='cell'] {
      min-height: var(--lumo-size-s);
    }

    :host([theme~='compact']) [part='row'][first] [part~='cell']:not([part~='details-cell']) {
      min-height: calc(var(--lumo-size-s) - var(--_lumo-grid-border-width));
    }

    :host([theme~='compact']) [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      padding: var(--lumo-space-xs) var(--lumo-space-s);
    }

    /* Wrap cell contents */

    :host([theme~='wrap-cell-content']) [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      white-space: normal;
    }

    /* RTL specific styles */

    :host([dir='rtl']) [part~='row'][dragstart] [part~='cell'][last-column] {
      border-radius: var(--lumo-border-radius-s) 0 0 var(--lumo-border-radius-s);
    }

    :host([dir='rtl']) [part~='row'][dragstart] [part~='cell'][first-column] {
      border-radius: 0 var(--lumo-border-radius-s) var(--lumo-border-radius-s) 0;
    }

    :host([dir='rtl'][theme~='column-borders']) [part~='cell']:not([last-column]):not([part~='details-cell']) {
      border-right: none;
      border-left: var(--_lumo-grid-border-width) solid var(--_lumo-grid-secondary-border-color);
    }

    :host([dir='rtl']) [last-frozen] {
      border-right: none;
      border-left: var(--_lumo-grid-border-width) solid transparent;
    }

    :host([dir='rtl'][overflow~='right']) [part~='cell'][last-frozen]:not([part~='details-cell']) {
      border-left-color: var(--_lumo-grid-border-color);
    }
  `,{moduleId:"lumo-grid"}),t1t("vaadin-checkbox",$Qt`
    :host(.vaadin-grid-select-all-checkbox) {
      font-size: var(--lumo-font-size-m);
    }
  `,{moduleId:"vaadin-grid-select-all-checkbox-lumo"});
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const I1t=navigator.userAgent.match(/iP(?:hone|ad;(?: U;)? CPU) OS (\d+)/),R1t=I1t&&I1t[1]>=8,O1t=Ur({behaviors:[Co,$o],_ratio:.5,_scrollerPaddingTop:0,_scrollPosition:0,_physicalSize:0,_physicalAverage:0,_physicalAverageCount:0,_physicalTop:0,_virtualCount:0,_estScrollHeight:0,_scrollHeight:0,_viewportHeight:0,_viewportWidth:0,_physicalItems:null,_physicalSizes:null,_firstVisibleIndexVal:null,_lastVisibleIndexVal:null,_maxPages:2,_focusedVirtualIndex:-1,_templateCost:0,get _physicalBottom(){return this._physicalTop+this._physicalSize},get _scrollBottom(){return this._scrollPosition+this._viewportHeight},get _virtualEnd(){return this._virtualStart+this._physicalCount-1},get _hiddenContentSize(){return this._physicalSize-this._viewportHeight},get _maxScrollTop(){return this._estScrollHeight-this._viewportHeight+this._scrollOffset},get _maxVirtualStart(){return Math.max(0,this._virtualCount-this._physicalCount)},set _virtualStart(t){t=this._clamp(t,0,this._maxVirtualStart),this._virtualStartVal=t},get _virtualStart(){return this._virtualStartVal||0},set _physicalStart(t){(t%=this._physicalCount)<0&&(t=this._physicalCount+t),this._physicalStartVal=t},get _physicalStart(){return this._physicalStartVal||0},get _physicalEnd(){return(this._physicalStart+this._physicalCount-1)%this._physicalCount},set _physicalCount(t){this._physicalCountVal=t},get _physicalCount(){return this._physicalCountVal||0},get _optPhysicalSize(){return 0===this._viewportHeight?1/0:this._viewportHeight*this._maxPages},get _isVisible(){return Boolean(this.offsetWidth||this.offsetHeight)},get firstVisibleIndex(){let t=this._firstVisibleIndexVal;if(null==t){let e=this._physicalTop+this._scrollOffset;t=this._iterateItems((function(t,n){if(e+=this._physicalSizes[t],e>this._scrollPosition)return n}))||0,this._firstVisibleIndexVal=t}return t},get lastVisibleIndex(){let t=this._lastVisibleIndexVal;if(null==t){let e=this._physicalTop+this._scrollOffset;this._iterateItems((function(n,i){e<this._scrollBottom&&(t=i),e+=this._physicalSizes[n]})),this._lastVisibleIndexVal=t}return t},get _scrollOffset(){return this._scrollerPaddingTop},attached:function(){this._debounce("_render",this._render,_t),this.listen(this,"iron-resize","_resizeHandler")},detached:function(){this.unlisten(this,"iron-resize","_resizeHandler")},updateViewportBoundaries:function(){const t=window.getComputedStyle(this);this._scrollerPaddingTop=this.scrollTarget===this?0:parseInt(t["padding-top"],10),this._isRTL=Boolean("rtl"===t.direction),this._viewportWidth=this.$.items.offsetWidth,this._viewportHeight=this._scrollTargetHeight},_scrollHandler:function(){const t=Math.max(0,Math.min(this._maxScrollTop,this._scrollTop));let e=t-this._scrollPosition;const n=e>=0;if(this._scrollPosition=t,this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,Math.abs(e)>this._physicalSize&&this._physicalSize>0){e-=this._scrollOffset;const t=Math.round(e/this._physicalAverage);this._virtualStart=this._virtualStart+t,this._physicalStart=this._physicalStart+t,this._physicalTop=Math.floor(this._virtualStart)*this._physicalAverage,this._update()}else if(this._physicalCount>0){const{physicalTop:t,indexes:e}=this._getReusables(n);n?(this._physicalTop=t,this._virtualStart=this._virtualStart+e.length,this._physicalStart=this._physicalStart+e.length):(this._virtualStart=this._virtualStart-e.length,this._physicalStart=this._physicalStart-e.length),this._update(e,n?null:e),this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,0),vt)}},_getReusables:function(t){let e,n,i;const r=[],o=this._hiddenContentSize*this._ratio,a=this._virtualStart,s=this._virtualEnd,l=this._physicalCount;let c=this._physicalTop+this._scrollOffset;const u=this._scrollTop,h=this._scrollBottom;for(t?(e=this._physicalStart,n=u-c):(e=this._physicalEnd,n=this._physicalBottom+this._scrollOffset-h);i=this._physicalSizes[e],n-=i,!(r.length>=l||n<=o);)if(t){if(s+r.length+1>=this._virtualCount)break;if(c+i>=u-this._scrollOffset)break;r.push(e),c+=i,e=(e+1)%l}else{if(a-r.length<=0)break;if(c+this._physicalSize-i<=h)break;r.push(e),c-=i,e=0===e?l-1:e-1}return{indexes:r,physicalTop:c-this._scrollOffset}},_update:function(t,e){if(!(t&&0===t.length||0===this._physicalCount)){if(this._assignModels(t),this._updateMetrics(t),e)for(;e.length;){const t=e.pop();this._physicalTop-=this._physicalSizes[t]}this._positionItems(),this._updateScrollerSize()}},_isClientFull:function(){return 0!=this._scrollBottom&&this._physicalBottom-1>=this._scrollBottom&&this._physicalTop<=this._scrollPosition},_increasePoolIfNeeded:function(t){const e=this._clamp(this._physicalCount+t,3,this._virtualCount-this._virtualStart)-this._physicalCount;let n=Math.round(.5*this._physicalCount);if(!(e<0)){if(e>0){const t=window.performance.now();[].push.apply(this._physicalItems,this._createPool(e));for(let t=0;t<e;t++)this._physicalSizes.push(0);this._physicalCount=this._physicalCount+e,this._physicalStart>this._physicalEnd&&this._isIndexRendered(this._focusedVirtualIndex)&&this._getPhysicalIndex(this._focusedVirtualIndex)<this._physicalEnd&&(this._physicalStart=this._physicalStart+e),this._update(),this._templateCost=(window.performance.now()-t)/e,n=Math.round(.5*this._physicalCount)}this._virtualEnd>=this._virtualCount-1||0===n||(this._isClientFull()?this._physicalSize<this._optPhysicalSize&&this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,this._clamp(Math.round(50/this._templateCost),1,n)),yt):this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,n),vt))}},_render:function(){if(this.isAttached&&this._isVisible)if(0!==this._physicalCount){const{physicalTop:t,indexes:e}=this._getReusables(!0);this._physicalTop=t,this._virtualStart=this._virtualStart+e.length,this._physicalStart=this._physicalStart+e.length,this._update(e),this._update(),this._increasePoolIfNeeded(0)}else this._virtualCount>0&&(this.updateViewportBoundaries(),this._increasePoolIfNeeded(3))},_itemsChanged:function(t){"items"===t.path&&(this._virtualStart=0,this._physicalTop=0,this._virtualCount=this.items?this.items.length:0,this._physicalIndexForKey={},this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,this._physicalCount=this._physicalCount||0,this._physicalItems=this._physicalItems||[],this._physicalSizes=this._physicalSizes||[],this._physicalStart=0,this._scrollTop>this._scrollOffset&&this._resetScrollPosition(0),this._debounce("_render",this._render,_t))},_iterateItems:function(t,e){let n,i,r,o;if(2===arguments.length&&e){for(o=0;o<e.length;o++)if(n=e[o],i=this._computeVidx(n),null!=(r=t.call(this,n,i)))return r}else{for(n=this._physicalStart,i=this._virtualStart;n<this._physicalCount;n++,i++)if(null!=(r=t.call(this,n,i)))return r;for(n=0;n<this._physicalStart;n++,i++)if(null!=(r=t.call(this,n,i)))return r}},_computeVidx:function(t){return t>=this._physicalStart?this._virtualStart+(t-this._physicalStart):this._virtualStart+(this._physicalCount-this._physicalStart)+t},_updateMetrics:function(t){if(!this._isVisible)return;Wi();let e=0,n=0;const i=this._physicalAverageCount,r=this._physicalAverage;this._iterateItems((function(t){n+=this._physicalSizes[t],this._physicalSizes[t]=this._physicalItems[t].offsetHeight,e+=this._physicalSizes[t],this._physicalAverageCount+=this._physicalSizes[t]?1:0}),t),this._physicalSize=this._physicalSize+e-n,this._physicalAverageCount!==i&&(this._physicalAverage=Math.round((r*i+e)/this._physicalAverageCount))},_positionItems:function(){this._adjustScrollPosition();let t=this._physicalTop;this._iterateItems((function(e){this.translate3d(0,t+"px",0,this._physicalItems[e]),t+=this._physicalSizes[e]}))},_adjustScrollPosition:function(){const t=0===this._virtualStart?this._physicalTop:Math.min(this._scrollPosition+this._physicalTop,0);if(0!==t){this._physicalTop=this._physicalTop-t;const e=this._scrollTop;!R1t&&e>0&&this._resetScrollPosition(e-t)}},_resetScrollPosition:function(t){this.scrollTarget&&t>=0&&(this._scrollTop=t,this._scrollPosition=this._scrollTop)},_updateScrollerSize:function(t){this._estScrollHeight=this._physicalBottom+Math.max(this._virtualCount-this._physicalCount-this._virtualStart,0)*this._physicalAverage,((t=(t=t||0===this._scrollHeight)||this._scrollPosition>=this._estScrollHeight-this._physicalSize)||Math.abs(this._estScrollHeight-this._scrollHeight)>=this._viewportHeight)&&(this.$.items.style.height=this._estScrollHeight+"px",this._scrollHeight=this._estScrollHeight)},scrollToIndex:function(t){if("number"!=typeof t||t<0||t>this.items.length-1)return;if(Wi(),0===this._physicalCount)return;t=this._clamp(t,0,this._virtualCount-1),(!this._isIndexRendered(t)||t>=this._maxVirtualStart)&&(this._virtualStart=t-1),this._assignModels(),this._updateMetrics(),this._physicalTop=Math.floor(this._virtualStart)*this._physicalAverage;let e=this._physicalStart,n=this._virtualStart,i=0;const r=this._hiddenContentSize;for(;n<t&&i<=r;)i+=this._physicalSizes[e],e=(e+1)%this._physicalCount,n++;this._updateScrollerSize(!0),this._positionItems(),this._resetScrollPosition(this._physicalTop+this._scrollOffset+i),this._increasePoolIfNeeded(0),this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null},_resetAverage:function(){this._physicalAverage=0,this._physicalAverageCount=0},_resizeHandler:function(){this._debounce("_render",(function(){this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,this.updateViewportBoundaries(),this._isVisible?(this.toggleScrollListener(!0),this._resetAverage(),this._render()):this.toggleScrollListener(!1)}),_t)},_isIndexRendered:function(t){return t>=this._virtualStart&&t<=this._virtualEnd},_getPhysicalIndex:function(t){return(this._physicalStart+(t-this._virtualStart))%this._physicalCount},_clamp:function(t,e,n){return Math.min(n,Math.max(e,t))},_debounce:function(t,e,n){this._debouncers=this._debouncers||{},this._debouncers[t]=Nn.debounce(this._debouncers[t],n,e.bind(this)),Rn(this._debouncers[t])}});
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
class z1t extends O1t{static get properties(){return{size:{type:Number,notify:!0},_vidxOffset:{type:Number,value:0}}}static get observers(){return["_effectiveSizeChanged(_effectiveSize)"]}connectedCallback(){super.connectedCallback(),this._scrollHandler()}_updateScrollerItem(){}_afterScroll(){}_getRowTarget(){}_createScrollerRows(){}_canPopulate(){}scrollToIndex(t){this._warnPrivateAPIAccess("scrollToIndex"),this._scrollingToIndex=!0,t=Math.min(Math.max(t,0),this._effectiveSize-1),this.$.table.scrollTop=t/this._effectiveSize*(this.$.table.scrollHeight-this.$.table.offsetHeight),this._scrollHandler(),this._accessIronListAPI((()=>this._maxScrollTop))&&this._virtualCount<this._effectiveSize&&this._adjustVirtualIndexOffset(1e6),this._accessIronListAPI((()=>super.scrollToIndex(t-this._vidxOffset))),this._scrollHandler();const e=Array.from(this.$.items.children).filter((e=>e.index===t))[0];if(e){const t=e.getBoundingClientRect().top-this.$.header.getBoundingClientRect().bottom;Math.abs(t)>1&&(this.$.table.scrollTop+=t,this._scrollHandler())}this._scrollingToIndex=!1}_effectiveSizeChanged(t){let e,n=0;if(this._iterateItems(((t,i)=>{if(i===this._firstVisibleIndex){const i=this._physicalItems[t];e=i.index,n=i.getBoundingClientRect().top}})),this.items&&t<this.items.length&&(this._scrollTop=0),!Array.isArray(this.items)){this.items={length:Math.min(t,1e5)}}this._accessIronListAPI((()=>super._itemsChanged({path:"items"}))),this._virtualCount=Math.min(this.items.length,t)||0,0===this._scrollTop&&(this._accessIronListAPI((()=>this._scrollToIndex(Math.min(t-1,e)))),this._iterateItems((t=>{const i=this._physicalItems[t];if(i.index===e&&(this.$.table.scrollTop+=Math.round(i.getBoundingClientRect().top-n)),i.index===this._focusedItemIndex&&this._itemsFocusable&&this.$.items.contains(this.shadowRoot.activeElement)){const t=Array.from(this._itemsFocusable.parentElement.children).indexOf(this._itemsFocusable);i.children[t].focus()}}))),this._assignModels(),requestAnimationFrame((()=>this._update())),this.__updateFooterPositioning()}_positionItems(){let t;this._adjustScrollPosition(),isNaN(this._physicalTop)&&(t=!0,this._physicalTop=0);let e=this._physicalTop;this._iterateItems((t=>{this._physicalItems[t].style.transform=`translateY(${e}px)`,e+=this._physicalSizes[t]})),t&&this._scrollToIndex(0)}_increasePoolIfNeeded(t){0===t&&this._scrollingToIndex||!this._canPopulate()||!this._effectiveSize||(this._initialPoolCreated?this._optPhysicalSize!==1/0&&(this._debounceIncreasePool=Nn.debounce(this._debounceIncreasePool,_t,(()=>{this._updateMetrics();let t=Math.ceil((this._optPhysicalSize-this._physicalSize)/this._physicalAverage);this._physicalCount+t>this._effectiveSize&&(t=Math.max(0,this._effectiveSize-this._physicalCount)),this._physicalSize&&t>0&&this._optPhysicalSize!==1/0&&(super._increasePoolIfNeeded(t),this.__reorderChildNodes())}))):(this._initialPoolCreated=!0,super._increasePoolIfNeeded(25)))}__reorderChildNodes(){const t=Array.from(this.$.items.childNodes);!!t.reduce(((t,e,n,i)=>{if(0===n||i[n-1].index===e.index-1)return t}),!0)||t.sort(((t,e)=>t.index-e.index)).forEach((t=>this.$.items.appendChild(t)))}_createPool(t){const e=document.createDocumentFragment(),n=this._createScrollerRows(t);n.forEach((t=>e.appendChild(t))),this._getRowTarget().appendChild(e);const i=this.querySelector("[slot]");if(i){const t=i.getAttribute("slot");i.setAttribute("slot","foo-bar"),i.setAttribute("slot",t)}return Di(this,(()=>this.notifyResize())),n}_assignModels(t){this._iterateItems(((t,e)=>{const n=this._physicalItems[t];this._toggleAttribute("hidden",e>=this._effectiveSize,n),this._updateScrollerItem(n,e+(this._vidxOffset||0))}),t)}_scrollHandler(){const t=this.$.table.scrollTop-this._scrollPosition;this._accessIronListAPI(super._scrollHandler);const e=this._vidxOffset;this._accessIronListAPI((()=>this._maxScrollTop))&&this._virtualCount<this._effectiveSize?this._adjustVirtualIndexOffset(t):this._vidxOffset=0,this._vidxOffset!==e&&this._update(),this._afterScroll()}_adjustVirtualIndexOffset(t){if(Math.abs(t)>1e4){if(this._noScale)return void(this._noScale=!1);const t=this.$.table.scrollTop/(this.$.table.scrollHeight-this.$.table.offsetHeight);this._vidxOffset=Math.round(t*this._effectiveSize-t*this._virtualCount)}else{const t=this._vidxOffset||0,e=1e3,n=100;0===this._scrollTop?(this._vidxOffset=0,t!==this._vidxOffset&&super.scrollToIndex(0)):this.firstVisibleIndex<e&&this._vidxOffset>0&&(this._vidxOffset-=Math.min(this._vidxOffset,n),t!==this._vidxOffset&&super.scrollToIndex(this.firstVisibleIndex+(t-this._vidxOffset)),this._noScale=!0);const i=this._effectiveSize-this._virtualCount;this._scrollTop>=this._maxScrollTop&&this._maxScrollTop>0?(this._vidxOffset=i,t!==this._vidxOffset&&super.scrollToIndex(this._virtualCount)):this.firstVisibleIndex>this._virtualCount-e&&this._vidxOffset<i&&(this._vidxOffset+=Math.min(i-this._vidxOffset,n),t!==this._vidxOffset&&super.scrollToIndex(this.firstVisibleIndex-(this._vidxOffset-t)),this._noScale=!0)}}_accessIronListAPI(t){this._warnPrivateAPIAccessAsyncEnabled=!1;const e=t.apply(this);return this._debouncerWarnPrivateAPIAccess=Nn.debounce(this._debouncerWarnPrivateAPIAccess,_t,(()=>this._warnPrivateAPIAccessAsyncEnabled=!0)),e}_debounceRender(t,e){super._debounceRender((()=>this._accessIronListAPI(t)),e)}_warnPrivateAPIAccess(t){this._warnPrivateAPIAccessAsyncEnabled&&console.warn(`Accessing private API (${t})!`)}_render(){this._accessIronListAPI(super._render)}_itemsChanged(){}get _firstVisibleIndex(){return this._accessIronListAPI((()=>super.firstVisibleIndex))}get _lastVisibleIndex(){return this._accessIronListAPI((()=>super.lastVisibleIndex))}_scrollToIndex(t){this._accessIronListAPI((()=>this.scrollToIndex(t)))}get firstVisibleIndex(){return this._warnPrivateAPIAccess("firstVisibleIndex"),super.firstVisibleIndex}set firstVisibleIndex(t){this._warnPrivateAPIAccess("firstVisibleIndex"),super.firstVisibleIndex=t}get lastVisibleIndex(){return this._warnPrivateAPIAccess("lastVisibleIndex"),super.lastVisibleIndex}set lastVisibleIndex(t){this._warnPrivateAPIAccess("lastVisibleIndex"),super.lastVisibleIndex=t}updateViewportBoundaries(){this._warnPrivateAPIAccess("updateViewportBoundaries"),super.updateViewportBoundaries.apply(this,arguments)}_resizeHandler(){super._resizeHandler(),Wi()}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */const D1t=t=>class extends t{static get observers(){return["_a11yUpdateGridSize(size, _columnTree, _columnTree.*)"]}_a11yGetHeaderRowCount(t){return t.filter((t=>t.some((t=>t._headerTemplate||t.headerRenderer||t.path||t.header)))).length}_a11yGetFooterRowCount(t){return t.filter((t=>t.some((t=>t._headerTemplate||t.headerRenderer)))).length}_a11yUpdateGridSize(t,e){if(void 0===t||void 0===e)return;const n=e[e.length-1];this.$.table.setAttribute("aria-rowcount",t+this._a11yGetHeaderRowCount(e)+this._a11yGetFooterRowCount(e)),this.$.table.setAttribute("aria-colcount",n&&n.length||0),this._a11yUpdateHeaderRows(),this._a11yUpdateFooterRows()}_a11yUpdateHeaderRows(){Array.from(this.$.header.children).forEach(((t,e)=>t.setAttribute("aria-rowindex",e+1)))}_a11yUpdateFooterRows(){Array.from(this.$.footer.children).forEach(((t,e)=>t.setAttribute("aria-rowindex",this._a11yGetHeaderRowCount(this._columnTree)+this.size+e+1)))}_a11yUpdateRowRowindex(t,e){t.setAttribute("aria-rowindex",e+this._a11yGetHeaderRowCount(this._columnTree)+1)}_a11yUpdateRowSelected(t,e){t.setAttribute("aria-selected",Boolean(e)),Array.from(t.children).forEach((t=>t.setAttribute("aria-selected",Boolean(e))))}_a11yUpdateRowLevel(t,e){t.setAttribute("aria-level",e+1)}_a11yUpdateRowDetailsOpened(t,e){Array.from(t.children).forEach((t=>{"boolean"==typeof e?t.setAttribute("aria-expanded",e):t.hasAttribute("aria-expanded")&&t.removeAttribute("aria-expanded")}))}_a11ySetRowDetailsCell(t,e){Array.from(t.children).forEach((t=>{t!==e&&t.setAttribute("aria-controls",e.id)}))}_a11yUpdateCellColspan(t,e){t.setAttribute("aria-colspan",Number(e))}_a11yUpdateSorters(){Array.from(this.querySelectorAll("vaadin-grid-sorter")).forEach((t=>{let e=t.parentNode;for(;e&&"vaadin-grid-cell-content"!==e.localName;)e=e.parentNode;if(e&&e.assignedSlot){e.assignedSlot.parentNode.setAttribute("aria-sort",{asc:"ascending",desc:"descending"}[String(t.direction)]||"none")}}))}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,B1t=t=>class extends t{static get properties(){return{activeItem:{type:Object,notify:!0,value:null}}}ready(){super.ready(),this.$.scroller.addEventListener("click",this._onClick.bind(this)),this.addEventListener("cell-activate",this._activateItem.bind(this))}_activateItem(t){const e=t.detail.model,n=e?e.item:null;n&&(this.activeItem=this._itemsEqual(this.activeItem,n)?null:n)}_onClick(t){if(t.defaultPrevented)return;const e=t.composedPath(),n=e[e.indexOf(this.$.table)-3];if(!n||n.getAttribute("part").indexOf("details-cell")>-1)return;const i=n._content,r=this.getRootNode().activeElement;i.contains(r)||this._isFocusable(t.target)||this.dispatchEvent(new CustomEvent("cell-activate",{detail:{model:this.__getRowModel(n.parentElement)}}))}_isFocusable(t){return H1t(t)}},H1t=t=>{if(!t.parentNode)return!1;const e=-1!==Array.from(t.parentNode.querySelectorAll("[tabindex], button, input, select, textarea, object, iframe, label, a[href], area[href]")).filter((t=>"cell body-cell"!==t.getAttribute("part"))).indexOf(t);return!t.disabled&&e},F1t=t=>class extends t{static get properties(){return{items:Array}}static get observers(){return["_itemsChanged(items, items.*, isAttached)"]}_itemsChanged(t,e,n){if(n){if(!Array.isArray(t))return null==t&&(this.size=0),void(this.dataProvider===this._arrayDataProvider&&(this.dataProvider=void 0));this.size=t.length,this.dataProvider=this.dataProvider||this._arrayDataProvider,this.clearCache(),this._ensureFirstPageLoaded()}}_arrayDataProvider(t,e){let n=(Array.isArray(this.items)?this.items:[]).slice(0);this._filters&&this._checkPaths(this._filters,"filtering",n)&&(n=this._filter(n)),this.size=n.length,t.sortOrders.length&&this._checkPaths(this._sorters,"sorting",n)&&(n=n.sort(this._multiSort.bind(this)));const i=t.page*t.pageSize;e(n.slice(i,i+t.pageSize),n.length)}_checkPaths(t,e,n){if(!n.length)return!1;let i=!0;for(let r in t){const o=t[r].path;if(!o||-1===o.indexOf("."))continue;const a=o.replace(/\.[^.]*$/,"");void 0===Eo.get(a,n[0])&&(console.warn(`Path "${o}" used for ${e} does not exist in all of the items, ${e} is disabled.`),i=!1)}return i}_multiSort(t,e){return this._sorters.map((n=>"asc"===n.direction?this._compare(Eo.get(n.path,t),Eo.get(n.path,e)):"desc"===n.direction?this._compare(Eo.get(n.path,e),Eo.get(n.path,t)):0)).reduce(((t,e)=>t||e),0)}_normalizeEmptyValue(t){return[void 0,null].indexOf(t)>=0?"":isNaN(t)?t.toString():t}_compare(t,e){return(t=this._normalizeEmptyValue(t))<(e=this._normalizeEmptyValue(e))?-1:t>e?1:0}_filter(t){return t.filter((t=>0===this._filters.filter((e=>{const n=this._normalizeEmptyValue(Eo.get(e.path,t)),i=this._normalizeEmptyValue(e.value).toString().toLowerCase();return-1===n.toString().toLowerCase().indexOf(i)})).length))}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,V1t=t=>class extends(bi(t)){ready(){super.ready();const t=this.$.scroller;ci(t,"track",this._onHeaderTrack.bind(this)),t.addEventListener("touchmove",(e=>t.hasAttribute("column-resizing")&&e.preventDefault())),t.addEventListener("contextmenu",(t=>"resize-handle"==t.target.getAttribute("part")&&t.preventDefault())),t.addEventListener("mousedown",(t=>"resize-handle"===t.target.getAttribute("part")&&t.preventDefault()))}_onHeaderTrack(t){const e=t.target;if("resize-handle"===e.getAttribute("part")){let n=e.parentElement._column;for(this._toggleAttribute("column-resizing",!0,this.$.scroller);"vaadin-grid-column-group"===n.localName;)n=Array.prototype.slice.call(n._childColumns,0).sort((function(t,e){return t._order-e._order})).filter((function(t){return!t.hidden})).pop();const i=Array.from(this.$.header.querySelectorAll('[part~="row"]:last-child [part~="cell"]')),r=i.filter((t=>t._column===n))[0];if(r.offsetWidth){const e=window.getComputedStyle(r),i=10+parseInt(e.paddingLeft)+parseInt(e.paddingRight)+parseInt(e.borderLeftWidth)+parseInt(e.borderRightWidth)+parseInt(e.marginLeft)+parseInt(e.marginRight),o=r.offsetWidth+(this.__isRTL?r.getBoundingClientRect().left-t.detail.x:t.detail.x-r.getBoundingClientRect().right);n.width=Math.max(i,o)+"px",n.flexGrow=0}i.sort((function(t,e){return t._column._order-e._column._order})).forEach((function(t,e,n){e<n.indexOf(r)&&(t._column.width=t.offsetWidth+"px",t._column.flexGrow=0)})),"end"===t.detail.state&&(this._toggleAttribute("column-resizing",!1,this.$.scroller),this.dispatchEvent(new CustomEvent("column-resize",{detail:{resizedColumn:n}}))),this._resizeHandler()}}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,U1t=class t{constructor(t,e,n){this.grid=t,this.parentCache=e,this.parentItem=n,this.itemCaches={},this.items={},this.effectiveSize=0,this.size=0,this.pendingRequests={}}isLoading(){return Boolean(Object.keys(this.pendingRequests).length||Object.keys(this.itemCaches).filter((t=>this.itemCaches[t].isLoading()))[0])}getItemForIndex(t){const{cache:e,scaledIndex:n}=this.getCacheAndIndex(t);return e.items[n]}updateSize(){this.effectiveSize=!this.parentItem||this.grid._isExpanded(this.parentItem)?this.size+Object.keys(this.itemCaches).reduce(((t,e)=>{const n=this.itemCaches[e];return n.updateSize(),t+n.effectiveSize}),0):0}ensureSubCacheForScaledIndex(e){if(!this.itemCaches[e]){const n=new t(this.grid,this,this.items[e]);this.itemCaches[e]=n,this.grid._loadPage(0,n)}}getCacheAndIndex(t){let e=t;const n=Object.keys(this.itemCaches);for(let t=0;t<n.length;t++){const i=Number(n[t]),r=this.itemCaches[i];if(e<=i)return{cache:this,scaledIndex:e};if(e<=i+r.effectiveSize)return r.getCacheAndIndex(e-i-1);e-=r.effectiveSize}return{cache:this,scaledIndex:e}}},j1t=t=>class extends t{static get properties(){return{pageSize:{type:Number,value:50,observer:"_pageSizeChanged"},dataProvider:{type:Object,notify:!0,observer:"_dataProviderChanged"},loading:{type:Boolean,notify:!0,readOnly:!0,reflectToAttribute:!0},_cache:{type:Object,value:function(){return new U1t(this)}},itemIdPath:{type:String,value:null},expandedItems:{type:Object,notify:!0,value:()=>[]}}}static get observers(){return["_sizeChanged(size)","_itemIdPathChanged(itemIdPath)","_expandedItemsChanged(expandedItems.*)"]}_sizeChanged(t){const e=t-this._cache.size;this._cache.size+=e,this._cache.effectiveSize+=e,this._effectiveSize=this._cache.effectiveSize,this._increasePoolIfNeeded(0),this._debounceIncreasePool&&this._debounceIncreasePool.flush()}_getItem(t,e){if(t>=this._effectiveSize)return;e.index=t;const{cache:n,scaledIndex:i}=this._cache.getCacheAndIndex(t),r=n.items[i];r?(this._toggleAttribute("loading",!1,e),this._updateItem(e,r),this._isExpanded(r)&&n.ensureSubCacheForScaledIndex(i)):(this._toggleAttribute("loading",!0,e),this._loadPage(this._getPageForIndex(i),n))}_expandedInstanceChangedCallback(t,e){void 0!==t.item&&(e?this.expandItem(t.item):this.collapseItem(t.item))}getItemId(t){return this.itemIdPath?this.get(this.itemIdPath,t):t}_isExpanded(t){return this.__expandedKeys.has(this.getItemId(t))}_expandedItemsChanged(){this.__cacheExpandedKeys(),this._cache.updateSize(),this._effectiveSize=this._cache.effectiveSize,this._assignModels()}_itemIdPathChanged(){this.__cacheExpandedKeys()}__cacheExpandedKeys(){this.expandedItems&&(this.__expandedKeys=new Set,this.expandedItems.forEach((t=>{this.__expandedKeys.add(this.getItemId(t))})))}expandItem(t){this._isExpanded(t)||(this.expandedItems=[...this.expandedItems,t])}collapseItem(t){this._isExpanded(t)&&(this.expandedItems=this.expandedItems.filter((e=>!this._itemsEqual(e,t))))}_getIndexLevel(t){let{cache:e}=this._cache.getCacheAndIndex(t),n=0;for(;e.parentCache;)e=e.parentCache,n++;return n}_canPopulate(){return Boolean(this._hasData&&this._columnTree)}_loadPage(t,e){if(!e.pendingRequests[t]&&this.dataProvider){this._setLoading(!0),e.pendingRequests[t]=!0;const n={page:t,pageSize:this.pageSize,sortOrders:this._mapSorters(),filters:this._mapFilters(),parentItem:e.parentItem};this._debounceIncreasePool&&this._debounceIncreasePool.flush(),this.dataProvider(n,((i,r)=>{void 0!==r?e.size=r:n.parentItem&&(e.size=i.length);const o=Array.from(this.$.items.children).map((t=>t._item));i.forEach(((n,i)=>{const r=t*this.pageSize+i;e.items[r]=n,this._isExpanded(n)&&o.indexOf(n)>-1&&e.ensureSubCacheForScaledIndex(r)})),this._hasData=!0,delete e.pendingRequests[t],this._debouncerApplyCachedData=Nn.debounce(this._debouncerApplyCachedData,gt.after(0),(()=>{this._setLoading(!1),this._cache.updateSize(),this._effectiveSize=this._cache.effectiveSize,Array.from(this.$.items.children).filter((t=>!t.hidden)).forEach((t=>{this._cache.getItemForIndex(t.index)&&this._getItem(t.index,t)})),this._increasePoolIfNeeded(0),this.__scrollToPendingIndex()})),this._cache.isLoading()||this._debouncerApplyCachedData.flush(),this.__itemsReceived()}))}}_getPageForIndex(t){return Math.floor(t/this.pageSize)}clearCache(){this._cache=new U1t(this),Array.from(this.$.items.children).forEach((t=>{Array.from(t.children).forEach((t=>{t._instance&&t._instance._setPendingProperty("item",{},!1)}))})),this._cache.size=this.size||0,this._cache.updateSize(),this._hasData=!1,this._assignModels(),this._effectiveSize&&this._initialPoolCreated||this._loadPage(0,this._cache)}_pageSizeChanged(t,e){void 0!==e&&t!==e&&this.clearCache()}_checkSize(){void 0===this.size&&0===this._effectiveSize&&console.warn("The <vaadin-grid> needs the total number of items in order to display rows. Set the total number of items to the `size` property, or provide the total number of items in the second argument of the `dataProvider`’s `callback` call.")}_dataProviderChanged(t,e){void 0!==e&&this.clearCache(),t&&this.items&&this.items.length&&this._scrollToIndex(this._firstVisibleIndex),this._ensureFirstPageLoaded(),this._debouncerCheckSize=Nn.debounce(this._debouncerCheckSize,gt.after(2e3),this._checkSize.bind(this)),this._scrollHandler()}_ensureFirstPageLoaded(){this._hasData||this._loadPage(0,this._cache)}_itemsEqual(t,e){return this.getItemId(t)===this.getItemId(e)}_getItemIndexInArray(t,e){let n=-1;return e.forEach(((e,i)=>{this._itemsEqual(e,t)&&(n=i)})),n}scrollToIndex(t){super.scrollToIndex(t),isNaN(t)||!this._cache.isLoading()&&this.clientHeight||(this.__pendingScrollToIndex=t)}__scrollToPendingIndex(){if(this.__pendingScrollToIndex&&this.$.items.children.length){const t=this.__pendingScrollToIndex;delete this.__pendingScrollToIndex,this._debounceIncreasePool&&this._debounceIncreasePool.flush(),this.scrollToIndex(t)}}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,G1t=t=>class extends t{ready(){super.ready(),this._addNodeObserver()}_hasColumnGroups(t){for(let e=0;e<t.length;e++)if("vaadin-grid-column-group"===t[e].localName)return!0;return!1}_getChildColumns(t){return Gi.getFlattenedNodes(t).filter(this._isColumnElement)}_flattenColumnGroups(t){return t.map((t=>"vaadin-grid-column-group"===t.localName?this._getChildColumns(t):[t])).reduce(((t,e)=>t.concat(e)),[])}_getColumnTree(){const t=[];for(let e=Gi.getFlattenedNodes(this).filter(this._isColumnElement);t.push(e),this._hasColumnGroups(e);)e=this._flattenColumnGroups(e);return t}_updateColumnTree(){const t=this._getColumnTree();this._arrayEquals(t,this._columnTree)||(this._columnTree=t)}_addNodeObserver(){this._observer=new Gi(this,(t=>{const e=t.addedNodes.filter((t=>"template"===t.localName&&t.classList.contains("row-details")))[0];e&&this._rowDetailsTemplate!==e&&(this._rowDetailsTemplate=e);const n=t=>t.filter(this._isColumnElement).length>0;if(n(t.addedNodes)||n(t.removedNodes)){const e=t.removedNodes.flatMap((t=>t._allCells)),n=t=>e.filter((e=>e._content.contains(t))).length;this.__removeSorters(this._sorters.filter(n)),this.__removeFilters(this._filters.filter(n)),this._updateColumnTree()}this._debouncerCheckImports=Nn.debounce(this._debouncerCheckImports,gt.after(2e3),this._checkImports.bind(this)),this._ensureFirstPageLoaded()}))}_arrayEquals(t,e){if(!t||!e||t.length!=e.length)return!1;for(let n=0,i=t.length;n<i;n++)if(t[n]instanceof Array&&e[n]instanceof Array){if(!this._arrayEquals(t[n],e[n]))return!1}else if(t[n]!=e[n])return!1;return!0}_checkImports(){["vaadin-grid-column-group","vaadin-grid-filter","vaadin-grid-filter-column","vaadin-grid-tree-toggle","vaadin-grid-selection-column","vaadin-grid-sort-column","vaadin-grid-sorter"].forEach((t=>{const e=this.querySelector(t);!e||e instanceof we||console.warn(`Make sure you have imported the required module for <${t}> element.`)}))}_updateFirstAndLastColumn(){Array.from(this.shadowRoot.querySelectorAll("tr")).forEach((t=>this._updateFirstAndLastColumnForRow(t)))}_updateFirstAndLastColumnForRow(t){Array.from(t.querySelectorAll('[part~="cell"]:not([part~="details-cell"])')).sort(((t,e)=>t._column._order-e._column._order)).forEach(((t,e,n)=>{this._toggleAttribute("first-column",0===e,t),this._toggleAttribute("last-column",e===n.length-1,t)}))}_isColumnElement(t){return t.nodeType===Node.ELEMENT_NODE&&/\bcolumn\b/.test(t.localName)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,W1t=t=>class extends t{getEventContext(t){const e={},n=t.composedPath(),i=n[n.indexOf(this.$.table)-3];return i?(e.section=["body","header","footer","details"].filter((t=>i.getAttribute("part").indexOf(t)>-1))[0],i._column&&(e.column=i._column),"body"!==e.section&&"details"!==e.section||Object.assign(e,this.__getRowModel(i.parentElement)),e):e}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,q1t=t=>class extends t{static get properties(){return{_filters:{type:Array,value:function(){return[]}}}}ready(){super.ready(),this.addEventListener("filter-changed",this._filterChanged.bind(this))}_filterChanged(t){t.stopPropagation(),this.__addFilter(t.target),this.__applyFilters()}__removeFilters(t){0!=t.length&&(this._filters=this._filters.filter((e=>t.indexOf(e)<0)),this.__applyFilters())}__addFilter(t){-1===this._filters.indexOf(t)&&this._filters.push(t)}__applyFilters(){this.dataProvider&&this.isAttached&&this.clearCache()}_mapFilters(){return this._filters.map((t=>({path:t.path,value:t.value})))}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */;class Y1t extends we{static get is(){return"vaadin-grid-templatizer"}static get properties(){return{dataHost:Object,template:Object,_templateInstances:{type:Array,value:function(){return[]}},_parentPathValues:{value:function(){return{}}},_grid:Object}}static get observers(){return["_templateInstancesChanged(_templateInstances.*, _parentPathValues.*)"]}constructor(){super(),this._instanceProps={detailsOpened:!0,index:!0,item:!0,selected:!0,expanded:!0,level:!0}}createInstance(){this._ensureTemplatized();const t=new this._TemplateClass({});return this.addInstance(t),t}addInstance(t){-1===this._templateInstances.indexOf(t)&&(this._templateInstances.push(t),requestAnimationFrame((()=>this.notifyPath("_templateInstances.*",this._templateInstances))))}removeInstance(t){const e=this._templateInstances.indexOf(t);this.splice("_templateInstances",e,1)}_ensureTemplatized(){this._TemplateClass||(this._TemplateClass=ao(this.template,this,{instanceProps:this._instanceProps,parentModel:!0,forwardHostProp:function(t,e){this._forwardParentProp(t,e),this._templateInstances&&this._templateInstances.forEach((n=>n.notifyPath(t,e)))},notifyInstanceProp:function(t,e,n){if("index"===e||"item"===e)return;const i=`__${e}__`;if(t[i]===n)return;t[i]=n;const r=Array.from(this._grid.$.items.children).filter((e=>this._grid._itemsEqual(e._item,t.item)))[0];r&&Array.from(r.children).forEach((t=>{t._instance&&(t._instance[i]=n,t._instance.notifyPath(e,n))}));const o="item.";if(Array.isArray(this._grid.items)&&0===e.indexOf(o)){const i=this._grid.items.indexOf(t.item),r=e.slice(o.length);this._grid.notifyPath(`items.${i}.${r}`,n)}const a=`_${e}InstanceChangedCallback`;this._grid&&this._grid[a]&&this._grid[a](t,n)}}))}_forwardParentProp(t,e){this._parentPathValues[t]=e,this._templateInstances.forEach((n=>n.notifyPath(t,e)))}_templateInstancesChanged(t){let e,n;if("_templateInstances"===t.path)e=0,n=this._templateInstances.length;else{if("_templateInstances.splices"!==t.path)return;e=t.value.index,n=t.value.addedCount}Object.keys(this._parentPathValues||{}).forEach((t=>{for(let i=e;i<e+n;i++)this._templateInstances[i].set(t,this._parentPathValues[t])}))}}customElements.define(Y1t.is,Y1t);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const X1t=t=>class extends t{static get properties(){return{detailsOpenedItems:{type:Array,value:function(){return[]}},_rowDetailsTemplate:Object,rowDetailsRenderer:Function,_detailsCells:{type:Array}}}static get observers(){return["_detailsOpenedItemsChanged(detailsOpenedItems.*, _rowDetailsTemplate, rowDetailsRenderer)","_rowDetailsTemplateOrRendererChanged(_rowDetailsTemplate, rowDetailsRenderer)"]}_rowDetailsTemplateOrRendererChanged(t,e){if(t&&e)throw new Error("You should only use either a renderer or a template for row details");if(t||e){if(t&&!t.templatizer){const e=new Y1t;e._grid=this,e.dataHost=this.dataHost,e.template=t,t.templatizer=e}this._columnTree&&Array.from(this.$.items.children).forEach((t=>{t.querySelector("[part~=details-cell]")||(this._updateRow(t,this._columnTree[this._columnTree.length-1]),this._a11yUpdateRowDetailsOpened(t,!1)),delete t.querySelector("[part~=details-cell]")._instance})),this.detailsOpenedItems.length&&(Array.from(this.$.items.children).forEach(this._toggleDetailsCell,this),this._update())}}_detailsOpenedItemsChanged(t){"detailsOpenedItems.length"!==t.path&&t.value&&Array.from(this.$.items.children).forEach((t=>{this._toggleDetailsCell(t,t._item),this._a11yUpdateRowDetailsOpened(t,this._isDetailsOpened(t._item)),this._toggleAttribute("details-opened",this._isDetailsOpened(t._item),t)}))}_configureDetailsCell(t){t.setAttribute("part","cell details-cell"),this._toggleAttribute("frozen",!0,t)}_toggleDetailsCell(t,e){const n=t.querySelector('[part~="details-cell"]');if(!n)return;const i=!this._isDetailsOpened(e),r=!!n.hidden!==i;(n._instance||n._renderer)&&n.hidden===i||(n.hidden=i,i?t.style.removeProperty("padding-bottom"):(this.rowDetailsRenderer?(n._renderer=this.rowDetailsRenderer,n._renderer.call(this,n._content,this,{index:t.index,item:e})):this._rowDetailsTemplate&&!n._instance&&(n._instance=this._rowDetailsTemplate.templatizer.createInstance(),n._content.innerHTML="",n._content.appendChild(n._instance.root),this._updateItem(t,e)),Wi(),t.style.setProperty("padding-bottom",`${n.offsetHeight}px`),requestAnimationFrame((()=>this.notifyResize())))),r&&(this._updateMetrics(),this._positionItems())}_updateDetailsCellHeights(){Array.from(this.$.items.querySelectorAll('[part~="details-cell"]:not([hidden])')).forEach((t=>{t.parentElement.style.setProperty("padding-bottom",`${t.offsetHeight}px`)}))}_isDetailsOpened(t){return this.detailsOpenedItems&&-1!==this._getItemIndexInArray(t,this.detailsOpenedItems)}openItemDetails(t){this._isDetailsOpened(t)||(this.detailsOpenedItems=[...this.detailsOpenedItems,t])}closeItemDetails(t){this._isDetailsOpened(t)&&(this.detailsOpenedItems=this.detailsOpenedItems.filter((e=>!this._itemsEqual(e,t))))}_detailsOpenedInstanceChangedCallback(t,e){e?this.openItemDetails(t.item):this.closeItemDetails(t.item)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,$1t=500,K1t=500,Z1t=t=>class extends t{static get properties(){return{_frozenCells:{type:Array,value:()=>[]},_rowWithFocusedElement:Element,_deltaYAcc:{type:Number,value:0},_useSticky:{type:Boolean,value:window.CSS&&window.CSS.supports&&(window.CSS.supports("position","sticky")||window.CSS.supports("position","-webkit-sticky"))}}}static get observers(){return["_scrollViewportHeightUpdated(_viewportHeight)"]}set _scrollTop(t){this.$.table.scrollTop=t}get _scrollTop(){return this.$.table.scrollTop}constructor(){super(),this._scrollLineHeight=this._getScrollLineHeight()}_getScrollLineHeight(){const t=document.createElement("div");t.style.fontSize="initial",t.style.display="none",document.body.appendChild(t);const e=window.getComputedStyle(t).fontSize;return document.body.removeChild(t),e?window.parseInt(e):void 0}_scrollViewportHeightUpdated(t){this._scrollPageHeight=t-this.$.header.clientHeight-this.$.footer.clientHeight-this._scrollLineHeight}ready(){super.ready(),this.$.outerscroller=document.createElement("div"),this.scrollTarget=this.$.table,this.addEventListener("wheel",this._onWheel),this.$.items.addEventListener("focusin",(t=>{const e=t.composedPath().indexOf(this.$.items);this._rowWithFocusedElement=t.composedPath()[e-1]})),this.$.items.addEventListener("focusout",(()=>this._rowWithFocusedElement=void 0)),this.scrollTarget.addEventListener("mousedown",(()=>this.__mouseDown=!0)),this.scrollTarget.addEventListener("mouseup",(()=>{this.__mouseDown=!1,this.__pendingReorder&&(this.__pendingReorder=!1,setTimeout((()=>this._reorderRows()),$1t))}))}scrollToIndex(t){this._accessIronListAPI((()=>super.scrollToIndex(t)))}_onWheel(t){if(t.ctrlKey||this._hasScrolledAncestor(t.target,t.deltaX,t.deltaY))return;const e=this.$.table;let n=t.deltaY;if(t.deltaMode===WheelEvent.DOM_DELTA_LINE?n*=this._scrollLineHeight:t.deltaMode===WheelEvent.DOM_DELTA_PAGE&&(n*=this._scrollPageHeight),this._wheelAnimationFrame)return this._deltaYAcc+=n,void t.preventDefault();n+=this._deltaYAcc,this._deltaYAcc=0,this._wheelAnimationFrame=!0,this._debouncerWheelAnimationFrame=Nn.debounce(this._debouncerWheelAnimationFrame,_t,(()=>this._wheelAnimationFrame=!1));const i=Math.abs(t.deltaX)+Math.abs(n);this._canScroll(e,t.deltaX,n)?(t.preventDefault(),e.scrollTop+=n,e.scrollLeft+=t.deltaX,this._scrollHandler(),this._hasResidualMomentum=!0,this._ignoreNewWheel=!0,this._debouncerIgnoreNewWheel=Nn.debounce(this._debouncerIgnoreNewWheel,gt.after(K1t),(()=>this._ignoreNewWheel=!1))):this._hasResidualMomentum&&i<=this._previousMomentum||this._ignoreNewWheel?t.preventDefault():i>this._previousMomentum&&(this._hasResidualMomentum=!1),this._previousMomentum=i}_hasScrolledAncestor(t,e,n){return"vaadin-grid-cell-content"!==t.localName&&(!(!this._canScroll(t,e,n)||-1===["auto","scroll"].indexOf(getComputedStyle(t).overflow))||(t!==this&&t.parentElement?this._hasScrolledAncestor(t.parentElement,e,n):void 0))}_canScroll(t,e,n){return n>0&&t.scrollTop<t.scrollHeight-t.offsetHeight||n<0&&t.scrollTop>0||e>0&&t.scrollLeft<t.scrollWidth-t.offsetWidth||e<0&&t.scrollLeft>0}_scheduleScrolling(){this._scrollingFrame||(this._scrollingFrame=requestAnimationFrame((()=>this._toggleAttribute("scrolling",!0,this.$.scroller)))),this._debounceScrolling=Nn.debounce(this._debounceScrolling,gt.after($1t),(()=>{cancelAnimationFrame(this._scrollingFrame),delete this._scrollingFrame,this._toggleAttribute("scrolling",!1,this.$.scroller),this._reorderRows()}))}_afterScroll(){this._translateStationaryElements(),this.hasAttribute("reordering")||this._scheduleScrolling(),this._updateOverflow()}_updateOverflow(){let t="";const e=this.$.table;e.scrollTop<e.scrollHeight-e.clientHeight&&(t+=" bottom"),e.scrollTop>0&&(t+=" top"),e.scrollLeft<e.scrollWidth-e.clientWidth&&(t+=" right"),e.scrollLeft>0&&(t+=" left"),this._debounceOverflow=Nn.debounce(this._debounceOverflow,_t,(()=>{const e=t.trim();e.length>0&&this.getAttribute("overflow")!==e?this.setAttribute("overflow",e):0==e.length&&this.hasAttribute("overflow")&&this.removeAttribute("overflow")}))}_reorderRows(){if(this.__mouseDown)return void(this.__pendingReorder=!0);const t=this.$.items,e=t.querySelectorAll("tr");if(!e.length)return;const n=this._virtualStart+this._vidxOffset,i=this._rowWithFocusedElement||Array.from(e).filter((t=>!t.hidden))[0];if(!i)return;const r=i.index-n,o=Array.from(e).indexOf(i)-r;if(o>0)for(let n=0;n<o;n++)t.appendChild(e[n]);else if(o<0)for(let n=e.length+o;n<e.length;n++)t.insertBefore(e[n],e[0]);if(this._safari){const{transform:t}=this.$.header.style;this.$.header.style.transform="",setTimeout((()=>this.$.header.style.transform=t))}}_frozenCellsChanged(){this._debouncerCacheElements=Nn.debounce(this._debouncerCacheElements,vt,(()=>{Array.from(this.shadowRoot.querySelectorAll('[part~="cell"]')).forEach((function(t){t.style.transform=""})),this._frozenCells=Array.prototype.slice.call(this.$.table.querySelectorAll("[frozen]")),this._updateScrollerMeasurements(),this._translateStationaryElements()})),this._updateLastFrozen()}_updateScrollerMeasurements(){this._frozenCells.length>0&&this.__isRTL&&(this.__scrollerMetrics={scrollWidth:this.$.table.scrollWidth,clientWidth:this.$.table.clientWidth})}_updateLastFrozen(){if(!this._columnTree)return;const t=this._columnTree[this._columnTree.length-1].slice(0);t.sort(((t,e)=>t._order-e._order));const e=t.reduce(((t,e,n)=>(e._lastFrozen=!1,e.frozen&&!e.hidden?n:t)),void 0);void 0!==e&&(t[e]._lastFrozen=!0)}_translateStationaryElements(){const t=Math.max(0,this._scrollLeft),e=Math.max(0,this._scrollTop);let n=0,i=0,r=0;if(this._useSticky||(n=t,i=e,r=this.$.table.clientHeight-this.$.footer.offsetHeight-this.$.footer.offsetTop),this.$.header.style.transform=this._getTranslate(-t+n,i),this.$.footer.style.transform=this._getTranslate(-t+n,i+r),this.$.items.style.transform=this._getTranslate(-t+n,0),this._frozenCells.length>0){const t=this.__isRTL?this.__getNormalizedScrollLeft(this.$.table)+this.__scrollerMetrics.clientWidth-this.__scrollerMetrics.scrollWidth:this._scrollLeft,e=this._getTranslate(t,0);for(let t=0;t<this._frozenCells.length;t++)this._frozenCells[t].style.transform=e}}_getTranslate(t,e){return`translate(${t}px, ${e}px)`}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,J1t=t=>class extends t{static get properties(){return{selectedItems:{type:Object,notify:!0,value:()=>[]}}}static get observers(){return["_selectedItemsChanged(selectedItems.*)"]}_isSelected(t){return this.selectedItems&&this._getItemIndexInArray(t,this.selectedItems)>-1}selectItem(t){this._isSelected(t)||(this.selectedItems=[...this.selectedItems,t])}deselectItem(t){this._isSelected(t)&&(this.selectedItems=this.selectedItems.filter((e=>!this._itemsEqual(e,t))))}_toggleItem(t){-1===this._getItemIndexInArray(t,this.selectedItems)?this.selectItem(t):this.deselectItem(t)}_selectedItemsChanged(t){!this.$.items.children.length||"selectedItems"!==t.path&&"selectedItems.splices"!==t.path||Array.from(this.$.items.children).forEach((t=>{this._updateItem(t,t._item)}))}_selectedInstanceChangedCallback(t,e){e?this.selectItem(t.item):this.deselectItem(t.item)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,Q1t=t=>class extends t{static get properties(){return{multiSort:{type:Boolean,value:!1},_sorters:{type:Array,value:function(){return[]}},_previousSorters:{type:Array,value:function(){return[]}}}}ready(){super.ready(),this.addEventListener("sorter-changed",this._onSorterChanged)}_onSorterChanged(t){const e=t.target;t.stopPropagation(),this.__updateSorter(e),this.__applySorters()}__removeSorters(t){0!=t.length&&(this._sorters=this._sorters.filter((e=>t.indexOf(e)<0)),this.multiSort&&this.__updateSortOrders(),this.__applySorters())}__updateSortOrders(){this._sorters.forEach(((t,e)=>t._order=this._sorters.length>1?e:null),this)}__updateSorter(t){if(t.direction||-1!==this._sorters.indexOf(t))if(t._order=null,this.multiSort)this._removeArrayItem(this._sorters,t),t.direction&&this._sorters.unshift(t),this.__updateSortOrders();else if(t.direction){const e=this._sorters.filter((e=>e!=t));this._sorters=[t],e.forEach((t=>{t._order=null,t.direction=null}))}}__applySorters(){this.dataProvider&&this.isAttached&&JSON.stringify(this._previousSorters)!==JSON.stringify(this._mapSorters())&&this.clearCache(),this._a11yUpdateSorters(),this._previousSorters=this._mapSorters()}_mapSorters(){return this._sorters.map((t=>({path:t.path,direction:t.direction})))}_removeArrayItem(t,e){const n=t.indexOf(e);n>-1&&t.splice(n,1)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,t0t=t=>class extends t{static get properties(){return{cellClassNameGenerator:Function}}static get observers(){return["__cellClassNameGeneratorChanged(cellClassNameGenerator)"]}__cellClassNameGeneratorChanged(){this.generateCellClassNames()}generateCellClassNames(){Array.from(this.$.items.children).filter((t=>!t.hidden)).forEach((t=>this._generateCellClassNames(t,this.__getRowModel(t))))}_generateCellClassNames(t,e){Array.from(t.children).forEach((t=>{if(t.__generatedClasses&&t.__generatedClasses.forEach((e=>t.classList.remove(e))),this.cellClassNameGenerator){const n=this.cellClassNameGenerator(t._column,e);t.__generatedClasses=n&&n.split(" ").filter((t=>t.length>0)),t.__generatedClasses&&t.__generatedClasses.forEach((e=>t.classList.add(e)))}}))}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,e0t="between",n0t="on-top-or-between",i0t="on-grid",r0t="on-top",o0t="above",a0t="below",s0t="empty",l0t=t=>class extends t{static get properties(){return{dropMode:String,rowsDraggable:Boolean,dragFilter:Function,dropFilter:Function,__dndAutoScrollThreshold:{value:50}}}static get observers(){return["_dragDropAccessChanged(rowsDraggable, dropMode, dragFilter, dropFilter)"]}ready(){super.ready(),this.$.table.addEventListener("dragstart",this._onDragStart.bind(this)),this.$.table.addEventListener("dragend",this._onDragEnd.bind(this)),this.$.table.addEventListener("dragover",this._onDragOver.bind(this)),this.$.table.addEventListener("dragleave",this._onDragLeave.bind(this)),this.$.table.addEventListener("drop",this._onDrop.bind(this)),this.$.table.addEventListener("dragenter",(t=>{this.dropMode&&(t.preventDefault(),t.stopPropagation())}))}_onDragStart(t){if(this.rowsDraggable){let e=t.target;if("vaadin-grid-cell-content"===e.localName&&(e=e.assignedSlot.parentNode.parentNode),e.parentNode!==this.$.items)return;if(t.stopPropagation(),this._toggleAttribute("dragging-rows",!0,this),this._safari){const t=e.style.transform;e.style.top=/translateY\((.*)\)/.exec(t)[1],e.style.transform="none",requestAnimationFrame((()=>{e.style.top="",e.style.transform=t}))}const n=e.getBoundingClientRect();this._ios?t.dataTransfer.setDragImage(e):t.dataTransfer.setDragImage(e,t.clientX-n.left,t.clientY-n.top);let i=[e];this._isSelected(e._item)&&(i=this.__getViewportRows().filter((t=>this._isSelected(t._item))).filter((t=>!this.dragFilter||this.dragFilter(this.__getRowModel(t))))),t.dataTransfer.setData("text",this.__formatDefaultTransferData(i)),e.setAttribute("dragstart",i.length>1?i.length:""),this.updateStyles({"--_grid-drag-start-x":t.clientX-n.left+20+"px","--_grid-drag-start-y":t.clientY-n.top+10+"px"}),requestAnimationFrame((()=>{e.removeAttribute("dragstart"),this.updateStyles({"--_grid-drag-start-x":"","--_grid-drag-start-y":""})}));const r=new CustomEvent("grid-dragstart",{detail:{draggedItems:i.map((t=>t._item)),setDragData:(e,n)=>t.dataTransfer.setData(e,n),setDraggedItemsCount:t=>e.setAttribute("dragstart",t)}});r.originalEvent=t,this.dispatchEvent(r)}}_onDragEnd(t){this._toggleAttribute("dragging-rows",!1,this),t.stopPropagation();const e=new CustomEvent("grid-dragend");e.originalEvent=t,this.dispatchEvent(e)}_onDragLeave(t){t.stopPropagation(),this._clearDragStyles()}_onDragOver(t){if(this.dropMode){if(this._dropLocation=void 0,this._dragOverItem=void 0,this.__dndAutoScroll(t.clientY))return void this._clearDragStyles();let e=t.composedPath().filter((t=>"tr"===t.localName))[0];if(this._effectiveSize&&this.dropMode!==i0t)if(e&&e.parentNode===this.$.items){const n=e.getBoundingClientRect();if(this._dropLocation=r0t,this.dropMode===e0t){this._dropLocation=t.clientY-n.top<n.bottom-t.clientY?o0t:a0t}else this.dropMode===n0t&&(t.clientY-n.top<n.height/3?this._dropLocation=o0t:t.clientY-n.top>n.height/3*2&&(this._dropLocation=a0t))}else{if(e)return;if(this.dropMode!==e0t&&this.dropMode!==n0t)return;e=Array.from(this.$.items.children).filter((t=>!t.hidden)).pop(),this._dropLocation=a0t}else this._dropLocation=s0t;if(e&&e.hasAttribute("drop-disabled"))return void(this._dropLocation=void 0);t.stopPropagation(),t.preventDefault(),this._dropLocation===s0t?this._toggleAttribute("dragover",!0,this):e?(this._dragOverItem=e._item,e.getAttribute("dragover")!==this._dropLocation&&e.setAttribute("dragover",this._dropLocation)):this._clearDragStyles()}}__dndAutoScroll(t){if(this.__dndAutoScrolling)return!0;const e=this.$.header.getBoundingClientRect().bottom,n=this.$.footer.getBoundingClientRect().top,i=e-t+this.__dndAutoScrollThreshold,r=t-n+this.__dndAutoScrollThreshold;let o=0;if(r>0?o=2*r:i>0&&(o=2*-i),o){const t=this.$.table.scrollTop;this.$.table.scrollTop+=o;if(t!==this.$.table.scrollTop)return this.__dndAutoScrolling=!0,setTimeout((()=>this.__dndAutoScrolling=!1),20),this._scrollHandler(),!0}}__getViewportRows(){const t=this.$.header.getBoundingClientRect().bottom,e=this.$.footer.getBoundingClientRect().top;return Array.from(this.$.items.children).filter((n=>{const i=n.getBoundingClientRect();return i.bottom>t&&i.top<e}))}_clearDragStyles(){this.removeAttribute("dragover"),Array.from(this.$.items.children).forEach((t=>t.removeAttribute("dragover")))}_onDrop(t){if(this.dropMode){t.stopPropagation(),t.preventDefault();const e=t.dataTransfer.types&&Array.from(t.dataTransfer.types).map((e=>({type:e,data:t.dataTransfer.getData(e)})));this._clearDragStyles();const n=new CustomEvent("grid-drop",{bubbles:t.bubbles,cancelable:t.cancelable,detail:{dropTargetItem:this._dragOverItem,dropLocation:this._dropLocation,dragData:e}});n.originalEvent=t,this.dispatchEvent(n)}}__formatDefaultTransferData(t){return t.map((t=>Array.from(t.children).filter((t=>!t.hidden&&-1===t.getAttribute("part").indexOf("details-cell"))).sort(((t,e)=>t._column._order>e._column._order?1:-1)).map((t=>t._content.textContent.trim())).filter((t=>t)).join("\t"))).join("\n")}_dragDropAccessChanged(){this.filterDragAndDrop()}filterDragAndDrop(){Array.from(this.$.items.children).filter((t=>!t.hidden)).forEach((t=>{this._filterDragAndDrop(t,this.__getRowModel(t))}))}_filterDragAndDrop(t,e){const n=!this.rowsDraggable||this.dragFilter&&!this.dragFilter(e),i=!this.dropMode||this.dropFilter&&!this.dropFilter(e);Array.from(t.children).map((t=>t._content)).forEach((t=>{n?t.removeAttribute("draggable"):t.setAttribute("draggable",!0)})),this._toggleAttribute("drag-disabled",n,t),this._toggleAttribute("drop-disabled",i,t)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,c0t=t=>class extends t{static get properties(){return{_headerFocusable:{type:Object,observer:"_focusableChanged"},_itemsFocusable:{type:Object,observer:"_focusableChanged"},_footerFocusable:{type:Object,observer:"_focusableChanged"},_navigatingIsHidden:Boolean,_focusedItemIndex:{type:Number,value:0},_focusedColumnOrder:Number,interacting:{type:Boolean,value:!1,reflectToAttribute:!0,readOnly:!0,observer:"_interactingChanged"}}}ready(){super.ready(),this._ios||this._android||(this.addEventListener("keydown",this._onKeyDown),this.addEventListener("keyup",this._onKeyUp),this.addEventListener("focusin",this._onFocusIn),this.addEventListener("focusout",this._onFocusOut),this.$.table.addEventListener("focusin",this._onCellFocusIn.bind(this)),this.$.table.addEventListener("focusout",this._onCellFocusOut.bind(this)),this.addEventListener("mousedown",(()=>{this._toggleAttribute("navigating",!1,this),this._isMousedown=!0})),this.addEventListener("mouseup",(()=>this._isMousedown=!1)))}_focusableChanged(t,e){e&&e.setAttribute("tabindex","-1"),t&&this._updateGridSectionFocusTarget(t)}_interactingChanged(){this._updateGridSectionFocusTarget(this._headerFocusable),this._updateGridSectionFocusTarget(this._itemsFocusable),this._updateGridSectionFocusTarget(this._footerFocusable)}_onKeyDown(t){const e=t.key;let n;switch(e){case"ArrowUp":case"ArrowDown":case"ArrowLeft":case"ArrowRight":case"PageUp":case"PageDown":case"Home":case"End":n="Navigation";break;case"Enter":case"Escape":case"F2":n="Interaction";break;case"Tab":n="Tab";break;case" ":n="Space"}this._detectInteracting(t),this.interacting&&"Interaction"!==n&&(n=void 0),n&&this[`_on${n}KeyDown`](t,e)}_ensureScrolledToIndex(t){Array.from(this.$.items.children).filter((e=>e.index===t))[0]||this._scrollToIndex(t)}_onNavigationKeyDown(t,e){function n(t){return Array.prototype.indexOf.call(t.parentNode.children,t)}this._scrollHandler(),t.preventDefault();const i=this._lastVisibleIndex-this._firstVisibleIndex-1;let r=0,o=0;switch(e){case"ArrowRight":r=this.__isRTL?-1:1;break;case"ArrowLeft":r=this.__isRTL?1:-1;break;case"Home":r=-1/0,t.ctrlKey&&(o=-1/0);break;case"End":r=1/0,t.ctrlKey&&(o=1/0);break;case"ArrowDown":o=1;break;case"ArrowUp":o=-1;break;case"PageDown":o=i;break;case"PageUp":o=-i}const a=t.composedPath()[0],s=n(a),l=this._elementMatches(a,'[part~="details-cell"]'),c=a.parentNode,u=c.parentNode,h=(u===this.$.items?this._effectiveSize:u.children.length)-1,d=u===this.$.items?void 0!==this._focusedItemIndex?this._focusedItemIndex:c.index:n(c);let p=Math.max(0,Math.min(d+o,h)),f=!1;if(u===this.$.items){const t=c._item,e=this._cache.getItemForIndex(p);f=l?0===o:1===o&&this._isDetailsOpened(t)||-1===o&&p!==d&&this._isDetailsOpened(e),f!==l&&(1===o&&f||-1===o&&!f)&&(p=d)}if(u!==this.$.items)if(p>d)for(;p<h&&u.children[p].hidden;)p++;else if(p<d)for(;p>0&&u.children[p].hidden;)p--;void 0===this._focusedColumnOrder&&(this._focusedColumnOrder=l?0:this._getColumns(u,d).filter((t=>!t.hidden))[s]._order);const m=this._getColumns(u,p).filter((t=>!t.hidden)),g=m.map((t=>t._order)).sort(((t,e)=>t-e)),_=g.length-1,y=g.indexOf(g.slice(0).sort(((t,e)=>Math.abs(t-this._focusedColumnOrder)-Math.abs(e-this._focusedColumnOrder)))[0]),v=0===o&&l?y:Math.max(0,Math.min(y+r,_));v!==y&&(this._focusedColumnOrder=void 0),u===this.$.items&&this._ensureScrolledToIndex(p),this._toggleAttribute("navigating",!0,this);const b=m.reduce(((t,e,n)=>(t[e._order]=n,t)),{})[g[v]],x=u===this.$.items?Array.from(u.children).filter((t=>t.index===p))[0]:u.children[p];if(!x)return;const w=f?Array.from(x.children).filter((t=>this._elementMatches(t,'[part~="details-cell"]')))[0]:x.children[b];if(this._scrollHorizontallyToCell(w),u===this.$.items&&(this._focusedItemIndex=p),u===this.$.items){const t=w.getBoundingClientRect(),e=this.$.footer.getBoundingClientRect().top,n=this.$.header.getBoundingClientRect().bottom;t.bottom>e?(this.$.table.scrollTop+=t.bottom-e,this._scrollHandler()):t.top<n&&(this.$.table.scrollTop-=n-t.top,this._scrollHandler())}w.focus()}_onInteractionKeyDown(t,e){const n=t.composedPath()[0],i="input"===n.localName&&!/^(button|checkbox|color|file|image|radio|range|reset|submit)$/i.test(n.type);let r;switch(e){case"Enter":r=!this.interacting||!i;break;case"Escape":r=!1;break;case"F2":r=!this.interacting}const{cell:o}=this._getGridEventLocation(t);if(this.interacting!==r&&null!==o)if(r){const e=o._content.querySelector("[focus-target]")||o._content.firstElementChild;e&&(t.preventDefault(),e.focus(),this._setInteracting(!0),this._toggleAttribute("navigating",!1,this))}else t.preventDefault(),this._focusedColumnOrder=void 0,o.focus(),this._setInteracting(!1),this._toggleAttribute("navigating",!0,this)}_predictFocusStepTarget(t,e){const n=[this.$.table,this._headerFocusable,this._itemsFocusable,this._footerFocusable,this.$.focusexit];let i=n.indexOf(t);for(i+=e;i>=0&&i<=n.length-1&&(!n[i]||n[i].parentNode.hidden);)i+=e;return n[i]}_onTabKeyDown(t){const e=this._predictFocusStepTarget(t.composedPath()[0],t.shiftKey?-1:1);if(e===this.$.table)this.$.table.focus();else if(e===this.$.focusexit)this.$.focusexit.focus();else if(e===this._itemsFocusable){let n=e;const i=this._itemsFocusable.parentNode;if(this._ensureScrolledToIndex(this._focusedItemIndex),i.index!==this._focusedItemIndex){const t=Array.from(i.children).indexOf(this._itemsFocusable),e=Array.from(this.$.items.children).filter((t=>t.index===this._focusedItemIndex))[0];e&&(n=e.children[t])}t.preventDefault(),n.focus()}else t.preventDefault(),e.focus();this._toggleAttribute("navigating",!0,this)}_onSpaceKeyDown(t){t.preventDefault();const e=t.composedPath()[0];e._content&&e._content.firstElementChild||this.dispatchEvent(new CustomEvent("cell-activate",{detail:{model:this.__getRowModel(e.parentElement)}}))}_onKeyUp(t){if(!/^( |SpaceBar)$/.test(t.key))return;t.preventDefault();const e=t.composedPath()[0];if(e._content&&e._content.firstElementChild){const t=this.hasAttribute("navigating");e._content.firstElementChild.click(),this._toggleAttribute("navigating",t,this)}}_onFocusIn(t){this._isMousedown||this._toggleAttribute("navigating",!0,this);const e=t.composedPath()[0];e===this.$.table||e===this.$.focusexit?(this._predictFocusStepTarget(e,e===this.$.table?1:-1).focus(),this._setInteracting(!1)):this._detectInteracting(t)}_onFocusOut(t){this._toggleAttribute("navigating",!1,this),this._detectInteracting(t)}_onCellFocusIn(t){const{section:e,cell:n}=this._getGridEventLocation(t);this._detectInteracting(t),e&&n&&(this._activeRowGroup=e,this.$.header===e?this._headerFocusable=n:this.$.items===e?this._itemsFocusable=n:this.$.footer===e&&(this._footerFocusable=n),n._content.dispatchEvent(new CustomEvent("cell-focusin",{bubbles:!1})),n.dispatchEvent(new CustomEvent("cell-focus",{bubbles:!0,composed:!0}))),this._detectFocusedItemIndex(t)}_onCellFocusOut(t){if(3===t.composedPath().indexOf(this.$.table)){t.composedPath()[0]._content.dispatchEvent(new CustomEvent("cell-focusout",{bubbles:!1}))}}_detectInteracting(t){const e=t.composedPath().some((t=>"vaadin-grid-cell-content"===t.localName));this._setInteracting(e)}_detectFocusedItemIndex(t){const{section:e,row:n}=this._getGridEventLocation(t);e===this.$.items&&(this._focusedItemIndex=n.index)}_updateGridSectionFocusTarget(t){if(!t)return;const e=this._getGridSectionFromFocusTarget(t);t.tabIndex=this.interacting&&e===this._activeRowGroup?-1:0}_preventScrollerRotatingCellFocus(t,e){t.index===this._focusedItemIndex&&this.hasAttribute("navigating")&&this._activeRowGroup===this.$.items&&(this._navigatingIsHidden=!0,this._toggleAttribute("navigating",!1,this)),e===this._focusedItemIndex&&this._navigatingIsHidden&&(this._navigatingIsHidden=!1,this._toggleAttribute("navigating",!0,this))}_getColumns(t,e){let n=this._columnTree.length-1;return t===this.$.header?n=e:t===this.$.footer&&(n=this._columnTree.length-1-e),this._columnTree[n]}_resetKeyboardNavigation(){if(this.$.header.firstElementChild&&(this._headerFocusable=Array.from(this.$.header.firstElementChild.children).filter((t=>!t.hidden))[0]),this.$.items.firstElementChild){const t=this._iterateItems(((t,e)=>{if(this._firstVisibleIndex===e)return this.$.items.children[t]}));t&&(this._itemsFocusable=Array.from(t.children).filter((t=>!t.hidden))[0])}this.$.footer.firstElementChild&&(this._footerFocusable=Array.from(this.$.footer.firstElementChild.children).filter((t=>!t.hidden))[0])}_scrollHorizontallyToCell(t){if(t.hasAttribute("frozen")||this._elementMatches(t,'[part~="details-cell"]'))return;const e=t.getBoundingClientRect(),n=t.parentNode,i=Array.from(n.children).indexOf(t),r=this.$.table.getBoundingClientRect();let o=r.left,a=r.right;for(let t=i-1;t>=0;t--){const e=n.children[t];if(!e.hasAttribute("hidden")&&!this._elementMatches(e,'[part~="details-cell"]')&&e.hasAttribute("frozen")){o=e.getBoundingClientRect().right;break}}for(let t=i+1;t<n.children.length;t++){const e=n.children[t];if(!e.hasAttribute("hidden")&&!this._elementMatches(e,'[part~="details-cell"]')&&e.hasAttribute("frozen")){a=e.getBoundingClientRect().left;break}}e.left<o&&(this.$.table.scrollLeft+=Math.round(e.left-o)),e.right>a&&(this.$.table.scrollLeft+=Math.round(e.right-a))}_elementMatches(t,e){return t.matches?t.matches(e):-1!==Array.from(t.parentNode.querySelectorAll(e)).indexOf(t)}_getGridEventLocation(t){const e=t.composedPath(),n=e.indexOf(this.$.table);return{section:n>=1?e[n-1]:null,row:n>=2?e[n-2]:null,cell:n>=3?e[n-3]:null}}_getGridSectionFromFocusTarget(t){return t===this._headerFocusable?this.$.header:t===this._itemsFocusable?this.$.items:t===this._footerFocusable?this.$.footer:null}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */;
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const u0t=t=>class extends(bi(t)){static get properties(){return{columnReorderingAllowed:{type:Boolean,value:!1},_orderBaseScope:{type:Number,value:1e7}}}static get observers(){return["_updateOrders(_columnTree, _columnTree.*)"]}ready(){super.ready(),ci(this,"track",this._onTrackEvent),this._reorderGhost=this.shadowRoot.querySelector('[part="reorder-ghost"]'),this.addEventListener("touchstart",this._onTouchStart.bind(this)),this.addEventListener("touchmove",this._onTouchMove.bind(this)),this.addEventListener("touchend",this._onTouchEnd.bind(this)),this.addEventListener("contextmenu",this._onContextMenu.bind(this))}_onContextMenu(t){this.hasAttribute("reordering")&&t.preventDefault()}_onTouchStart(t){this._startTouchReorderTimeout=setTimeout((()=>{this._onTrackStart({detail:{x:t.touches[0].clientX,y:t.touches[0].clientY}})}),100)}_onTouchMove(t){this._draggedColumn&&t.preventDefault(),clearTimeout(this._startTouchReorderTimeout)}_onTouchEnd(){clearTimeout(this._startTouchReorderTimeout),this._onTrackEnd()}_onTrackEvent(t){if("start"===t.detail.state){const e=t.composedPath(),n=e[e.indexOf(this.$.header)-2];if(!n||!n._content)return;if(n._content.contains(this.getRootNode().activeElement))return;if(this.$.scroller.hasAttribute("column-resizing"))return;this._touchDevice||this._onTrackStart(t)}else"track"===t.detail.state?this._onTrack(t):"end"===t.detail.state&&this._onTrackEnd(t)}_onTrackStart(t){if(!this.columnReorderingAllowed)return;const e=t.composedPath&&t.composedPath();if(e&&e.filter((t=>t.hasAttribute&&t.hasAttribute("draggable")))[0])return;const n=this._cellFromPoint(t.detail.x,t.detail.y);if(n&&-1!==n.getAttribute("part").indexOf("header-cell")){for(this._toggleAttribute("reordering",!0,this),this._draggedColumn=n._column;1===this._draggedColumn.parentElement.childElementCount;)this._draggedColumn=this._draggedColumn.parentElement;this._setSiblingsReorderStatus(this._draggedColumn,"allowed"),this._draggedColumn._reorderStatus="dragging",this._updateGhost(n),this._reorderGhost.style.visibility="visible",this._updateGhostPosition(t.detail.x,this._touchDevice?t.detail.y-50:t.detail.y),this._autoScroller()}}_onTrack(t){if(!this._draggedColumn)return;const e=this._cellFromPoint(t.detail.x,t.detail.y);if(!e)return;const n=this._getTargetColumn(e,this._draggedColumn);this._isSwapAllowed(this._draggedColumn,n)&&this._isSwappableByPosition(n,t.detail.x)&&this._swapColumnOrders(this._draggedColumn,n),this._updateGhostPosition(t.detail.x,this._touchDevice?t.detail.y-50:t.detail.y),this._lastDragClientX=t.detail.x}_onTrackEnd(){this._draggedColumn&&(this._toggleAttribute("reordering",!1,this),this._draggedColumn._reorderStatus="",this._setSiblingsReorderStatus(this._draggedColumn,""),this._draggedColumn=null,this._lastDragClientX=null,this._reorderGhost.style.visibility="hidden",this.dispatchEvent(new CustomEvent("column-reorder",{detail:{columns:this._getColumnsInOrder()}})))}_getColumnsInOrder(){return this._columnTree.slice(0).pop().filter((t=>!t.hidden)).sort(((t,e)=>t._order-e._order))}_cellFromPoint(t,e){t=t||0,e=e||0,this._draggedColumn||this._toggleAttribute("no-content-pointer-events",!0,this.$.scroller);const n=this.shadowRoot.elementFromPoint(t,e);if(this._toggleAttribute("no-content-pointer-events",!1,this.$.scroller),n&&n._column)return n}_updateGhostPosition(t,e){const n=this._reorderGhost.getBoundingClientRect(),i=t-n.width/2,r=e-n.height/2,o=parseInt(this._reorderGhost._left||0),a=parseInt(this._reorderGhost._top||0);this._reorderGhost._left=o-(n.left-i),this._reorderGhost._top=a-(n.top-r),this._reorderGhost.style.transform=`translate(${this._reorderGhost._left}px, ${this._reorderGhost._top}px)`}_updateGhost(t){const e=this._reorderGhost;e.textContent=t._content.innerText;const n=window.getComputedStyle(t);return["boxSizing","display","width","height","background","alignItems","padding","border","flex-direction","overflow"].forEach((t=>e.style[t]=n[t])),e}_updateOrders(t,e){void 0!==t&&void 0!==e&&(t[0].forEach((t=>t._order=0)),(function n(t,e,i){let r=1;t.forEach((t=>{r%10==0&&r++,t._order=i+r*e,r++}))})(t[0],this._orderBaseScope,0))}_setSiblingsReorderStatus(t,e){Array.from(t.parentNode.children).filter((e=>/column/.test(e.localName)&&this._isSwapAllowed(e,t))).forEach((t=>t._reorderStatus=e))}_autoScroller(){if(this._lastDragClientX){const t=this._lastDragClientX-this.getBoundingClientRect().right+50,e=this.getBoundingClientRect().left-this._lastDragClientX+50;t>0?this.$.table.scrollLeft+=t/10:e>0&&(this.$.table.scrollLeft-=e/10),this._scrollHandler()}this._draggedColumn&&this.async(this._autoScroller,10)}_isSwapAllowed(t,e){if(t&&e){const n=t.parentElement===e.parentElement,i=t.frozen===e.frozen;return t!==e&&n&&i}}_isSwappableByPosition(t,e){const n=Array.from(this.$.header.querySelectorAll('tr:not([hidden]) [part~="cell"]')).filter((e=>t.contains(e._column)))[0],i=this.$.header.querySelector("tr:not([hidden]) [reorder-status=dragging]").getBoundingClientRect(),r=n.getBoundingClientRect();return r.left>i.left?e>r.right-i.width:e<r.left+i.width}_swapColumnOrders(t,e){const n=t._order;t._order=e._order,e._order=n,this._updateLastFrozen(),this._updateFirstAndLastColumn()}_getTargetColumn(t,e){if(t&&e){let n=t._column;for(;n.parentElement!==e.parentElement&&n!==this;)n=n.parentElement;return n.parentElement===e.parentElement?n:t._column}}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,h0t=t=>class extends t{static get properties(){return{resizable:{type:Boolean,value:function(){if("vaadin-grid-column-group"===this.localName)return;const t=this.parentNode;return t&&"vaadin-grid-column-group"===t.localName&&t.resizable||!1}},_headerTemplate:{type:Object},_footerTemplate:{type:Object},frozen:{type:Boolean,value:!1},hidden:{type:Boolean},header:{type:String},textAlign:{type:String},_lastFrozen:{type:Boolean,value:!1},_order:Number,_reorderStatus:Boolean,_emptyCells:Array,_headerCell:Object,_footerCell:Object,_grid:Object,headerRenderer:Function,footerRenderer:Function}}static get observers(){return["_widthChanged(width, _headerCell, _footerCell, _cells.*)","_frozenChanged(frozen, _headerCell, _footerCell, _cells.*)","_flexGrowChanged(flexGrow, _headerCell, _footerCell, _cells.*)","_pathOrHeaderChanged(path, header, _headerCell, _footerCell, _cells.*, renderer, headerRenderer, _bodyTemplate, _headerTemplate)","_textAlignChanged(textAlign, _cells.*, _headerCell, _footerCell)","_orderChanged(_order, _headerCell, _footerCell, _cells.*)","_lastFrozenChanged(_lastFrozen)","_setBodyTemplateOrRenderer(_bodyTemplate, renderer, _cells, _cells.*)","_setHeaderTemplateOrRenderer(_headerTemplate, headerRenderer, _headerCell)","_setFooterTemplateOrRenderer(_footerTemplate, footerRenderer, _footerCell)","_resizableChanged(resizable, _headerCell)","_reorderStatusChanged(_reorderStatus, _headerCell, _footerCell, _cells.*)","_hiddenChanged(hidden, _headerCell, _footerCell, _cells.*)"]}connectedCallback(){super.connectedCallback(),this._bodyTemplate&&(this._bodyTemplate.templatizer._grid=this._grid),this._headerTemplate&&(this._headerTemplate.templatizer._grid=this._grid),this._footerTemplate&&(this._footerTemplate.templatizer._grid=this._grid),this._templateObserver.flush(),this._bodyTemplate||this._templateObserver.callback(),requestAnimationFrame((()=>{this._allCells.forEach((t=>{t._content.parentNode||this._grid&&this._grid.appendChild(t._content)}))}))}disconnectedCallback(){super.disconnectedCallback(),requestAnimationFrame((()=>{this._findHostGrid()||this._allCells.forEach((t=>{t._content.parentNode&&t._content.parentNode.removeChild(t._content)}))})),this._gridValue=void 0}_findHostGrid(){let t=this;for(;t&&!/^vaadin.*grid(-pro)?$/.test(t.localName);)t=t.assignedSlot?t.assignedSlot.parentNode:t.parentNode;return t||void 0}get _grid(){return this._gridValue||(this._gridValue=this._findHostGrid()),this._gridValue}get _allCells(){return[].concat(this._cells||[]).concat(this._emptyCells||[]).concat(this._headerCell).concat(this._footerCell).filter((t=>t))}constructor(){super(),this._templateObserver=new Gi(this,(()=>{this._headerTemplate=this._prepareHeaderTemplate(),this._footerTemplate=this._prepareFooterTemplate(),this._bodyTemplate=this._prepareBodyTemplate()}))}_prepareHeaderTemplate(){return this._prepareTemplatizer(this._findTemplate(!0)||null,{})}_prepareFooterTemplate(){return this._prepareTemplatizer(this._findTemplate(!1,!0)||null,{})}_prepareBodyTemplate(){return this._prepareTemplatizer(this._findTemplate()||null)}_prepareTemplatizer(t,e){if(t&&!t.templatizer){const n=new Y1t;n._grid=this._grid,n.dataHost=this.dataHost,n._instanceProps=e||n._instanceProps,n.template=t,t.templatizer=n}return t}_renderHeaderAndFooter(){this.headerRenderer&&this._headerCell&&this.__runRenderer(this.headerRenderer,this._headerCell),this.footerRenderer&&this._footerCell&&this.__runRenderer(this.footerRenderer,this._footerCell)}__runRenderer(t,e,n){const i=[e._content,this];n&&n.item&&i.push(n),t.apply(this,i)}__setColumnTemplateOrRenderer(t,e,n){if(!this.hidden){if(t&&e)throw new Error("You should only use either a renderer or a template");n.forEach((n=>{const i=this._grid.__getRowModel(n.parentElement);if(e)n._renderer=e,(i.item||e===this.headerRenderer||e===this.footerRenderer)&&this.__runRenderer(e,n,i);else if(n._template!==t){n._template=t,n._content.innerHTML="",t.templatizer._grid=t.templatizer._grid||this._grid;const e=t.templatizer.createInstance();n._content.appendChild(e.root),n._instance=e,i.item&&n._instance.setProperties(i)}}))}}_setBodyTemplateOrRenderer(t,e,n){(t||e)&&n&&this.__setColumnTemplateOrRenderer(t,e,n)}_setHeaderTemplateOrRenderer(t,e,n){(t||e)&&n&&this.__setColumnTemplateOrRenderer(t,e,[n])}_setFooterTemplateOrRenderer(t,e,n){(t||e)&&n&&(this.__setColumnTemplateOrRenderer(t,e,[n]),this._grid.__updateHeaderFooterRowVisibility(n.parentElement))}_selectFirstTemplate(t=!1,e=!1){return Gi.getFlattenedNodes(this).filter((n=>"template"===n.localName&&n.classList.contains("header")===t&&n.classList.contains("footer")===e))[0]}_findTemplate(t,e){const n=this._selectFirstTemplate(t,e);return n&&this.dataHost&&(n._rootDataHost=this.dataHost._rootDataHost||this.dataHost),n}_flexGrowChanged(t){this.parentElement&&this.parentElement._columnPropChanged&&this.parentElement._columnPropChanged("flexGrow"),this._allCells.forEach((e=>e.style.flexGrow=t))}_orderChanged(t){this._allCells.forEach((e=>e.style.order=t))}_widthChanged(t){this.parentElement&&this.parentElement._columnPropChanged&&this.parentElement._columnPropChanged("width"),this._allCells.forEach((e=>e.style.width=t)),this._grid&&this._grid.__forceReflow&&this._grid.__forceReflow()}_frozenChanged(t){this.parentElement&&this.parentElement._columnPropChanged&&this.parentElement._columnPropChanged("frozen",t),this._allCells.forEach((e=>this._toggleAttribute("frozen",t,e))),this._grid&&this._grid._frozenCellsChanged&&this._grid._frozenCellsChanged()}_lastFrozenChanged(t){this._allCells.forEach((e=>this._toggleAttribute("last-frozen",t,e))),this.parentElement&&this.parentElement._columnPropChanged&&(this.parentElement._lastFrozen=t)}_pathOrHeaderChanged(t,e,n,i,r,o,a,s,l){const c=void 0!==e;if(!a&&!l&&c&&n&&this.__setTextContent(n._content,e),t&&r.value){if(!o&&!s){this.__setColumnTemplateOrRenderer(void 0,((e,n,{item:i})=>this.__setTextContent(e,this.get(t,i))),r.value)}a||l||c||!n||null===e||this.__setTextContent(n._content,this._generateHeader(t))}n&&this._grid.__updateHeaderFooterRowVisibility(n.parentElement)}__setTextContent(t,e){t.textContent!==e&&(t.textContent=e)}_generateHeader(t){return t.substr(t.lastIndexOf(".")+1).replace(/([A-Z])/g,"-$1").toLowerCase().replace(/-/g," ").replace(/^./,(t=>t.toUpperCase()))}_toggleAttribute(t,e,n){n.hasAttribute(t)===!e&&(e?n.setAttribute(t,""):n.removeAttribute(t))}_reorderStatusChanged(t){this._allCells.forEach((e=>e.setAttribute("reorder-status",t)))}_resizableChanged(t,e){void 0!==t&&void 0!==e&&e&&[e].concat(this._emptyCells).forEach((e=>{if(e){const n=e.querySelector('[part~="resize-handle"]');if(n&&e.removeChild(n),t){const t=document.createElement("div");t.setAttribute("part","resize-handle"),e.appendChild(t)}}}))}_textAlignChanged(t){if(void 0===t)return;if(-1===["start","end","center"].indexOf(t))return void console.warn('textAlign can only be set as "start", "end" or "center"');let e;"ltr"===getComputedStyle(this._grid).direction?"start"===t?e="left":"end"===t&&(e="right"):"start"===t?e="right":"end"===t&&(e="left"),this._allCells.forEach((n=>{n._content.style.textAlign=t,getComputedStyle(n._content).textAlign!==t&&(n._content.style.textAlign=e)}))}_hiddenChanged(t){this.parentElement&&this.parentElement._columnPropChanged&&this.parentElement._columnPropChanged("hidden",t),!!t!=!!this._previousHidden&&this._grid&&(!0===t&&this._allCells.forEach((t=>{t._content.parentNode&&t._content.parentNode.removeChild(t._content)})),this._grid._debouncerHiddenChanged=Nn.debounce(this._grid._debouncerHiddenChanged,_t,(()=>{this._grid&&this._grid._renderColumnTree&&this._grid._renderColumnTree(this._grid._columnTree)})),this._grid._updateLastFrozen&&this._grid._updateLastFrozen(),this._grid.notifyResize&&this._grid.notifyResize(),this._grid._resetKeyboardNavigation&&this._grid._resetKeyboardNavigation()),this._previousHidden=t}};class d0t extends(h0t(A1t(we))){static get is(){return"vaadin-grid-column"}static get properties(){return{width:{type:String,value:"100px"},flexGrow:{type:Number,value:1},renderer:Function,path:{type:String},autoWidth:{type:Boolean,value:!1},_bodyTemplate:{type:Object},_cells:Array}}}customElements.define(d0t.is,d0t),
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
t1t("vaadin-grid",$Qt`
    @keyframes vaadin-grid-appear {
      to {
        opacity: 1;
      }
    }

    :host {
      display: block;
      animation: 1ms vaadin-grid-appear;
      height: 400px;
      flex: 1 1 auto;
      align-self: stretch;
      position: relative;
    }

    :host([hidden]) {
      display: none !important;
    }

    #scroller {
      display: block;
      transform: translateY(0);
      width: auto;
      height: auto;
      position: absolute;
      top: 0;
      right: 0;
      bottom: 0;
      left: 0;
    }

    :host([height-by-rows]) {
      height: auto;
      align-self: flex-start;
      flex-grow: 0;
      width: 100%;
    }

    :host([height-by-rows]) #scroller {
      width: 100%;
      height: 100%;
      position: relative;
    }

    #table {
      display: flex;
      flex-direction: column;
      width: 100%;
      height: 100%;
      overflow: auto;
      position: relative;
      outline: none;
      /* Workaround for a Desktop Safari bug: new stacking context here prevents the scrollbar from getting hidden */
      z-index: 0;
    }

    #header,
    #footer {
      display: block;
      position: -webkit-sticky;
      position: sticky;
      left: 0;
      overflow: visible;
      width: 100%;
      z-index: 1;
    }

    #header {
      top: 0;
    }

    th {
      text-align: inherit;
    }

    /* Safari doesn't work with "inherit" */
    [safari] th {
      text-align: initial;
    }

    #footer {
      bottom: 0;
    }

    #items {
      flex-grow: 1;
      flex-shrink: 0;
      display: block;
      position: -webkit-sticky;
      position: sticky;
      width: 100%;
      left: 0;
      overflow: visible;
    }

    [part~='row'] {
      display: flex;
      width: 100%;
      box-sizing: border-box;
      margin: 0;
    }

    [part~='row'][loading] [part~='body-cell'] ::slotted(vaadin-grid-cell-content) {
      opacity: 0;
    }

    #items [part~='row'] {
      position: absolute;
    }

    #items [part~='row']:empty {
      height: 1em;
    }

    [part~='cell']:not([part~='details-cell']) {
      flex-shrink: 0;
      flex-grow: 1;
      box-sizing: border-box;
      display: flex;
      width: 100%;
      position: relative;
      align-items: center;
      padding: 0;
      white-space: nowrap;
    }

    [part~='details-cell'] {
      position: absolute;
      bottom: 0;
      width: 100%;
      box-sizing: border-box;
      padding: 0;
    }

    [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      display: block;
      width: 100%;
      box-sizing: border-box;
      overflow: hidden;
      text-overflow: ellipsis;
    }

    [hidden] {
      display: none !important;
    }

    [frozen] {
      z-index: 2;
      will-change: transform;
    }

    [no-scrollbars][safari] #table,
    [no-scrollbars][firefox] #table {
      overflow: hidden;
    }

    /* Reordering styles */
    :host([reordering]) [part~='cell'] ::slotted(vaadin-grid-cell-content),
    :host([reordering]) [part~='resize-handle'],
    #scroller[no-content-pointer-events] [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      pointer-events: none;
    }

    [part~='reorder-ghost'] {
      visibility: hidden;
      position: fixed;
      pointer-events: none;
      opacity: 0.5;

      /* Prevent overflowing the grid in Firefox */
      top: 0;
      left: 0;
    }

    :host([reordering]) {
      -moz-user-select: none;
      -webkit-user-select: none;
      user-select: none;
    }

    /* Resizing styles */
    [part~='resize-handle'] {
      position: absolute;
      top: 0;
      right: 0;
      height: 100%;
      cursor: col-resize;
      z-index: 1;
    }

    [part~='resize-handle']::before {
      position: absolute;
      content: '';
      height: 100%;
      width: 35px;
      transform: translateX(-50%);
    }

    [last-column] [part~='resize-handle']::before,
    [last-frozen] [part~='resize-handle']::before {
      width: 18px;
      transform: none;
      right: 0;
    }

    #scroller[column-resizing] {
      -ms-user-select: none;
      -moz-user-select: none;
      -webkit-user-select: none;
      user-select: none;
    }

    /* Sizer styles */
    #sizer {
      display: flex;
      position: absolute;
      visibility: hidden;
    }

    #sizer [part~='details-cell'] {
      display: none !important;
    }

    #sizer [part~='cell'][hidden] {
      display: none !important;
    }

    #sizer [part~='cell'] {
      display: block;
      flex-shrink: 0;
      line-height: 0;
      height: 0 !important;
      min-height: 0 !important;
      max-height: 0 !important;
      padding: 0 !important;
      border: none !important;
    }

    #sizer [part~='cell']::before {
      content: '-';
    }

    #sizer [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      display: none !important;
    }

    /* RTL specific styles */

    :host([dir='rtl']) #items,
    :host([dir='rtl']) #header,
    :host([dir='rtl']) #footer {
      left: auto;
    }

    :host([dir='rtl']) [part~='reorder-ghost'] {
      left: auto;
      right: 0;
    }

    :host([dir='rtl']) [part~='resize-handle'] {
      left: 0;
      right: auto;
    }

    :host([dir='rtl']) [part~='resize-handle']::before {
      transform: translateX(50%);
    }

    :host([dir='rtl']) [last-column] [part~='resize-handle']::before,
    :host([dir='rtl']) [last-frozen] [part~='resize-handle']::before {
      left: 0;
      right: auto;
    }
  `,{moduleId:"vaadin-grid-styles"});
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const p0t=(()=>{try{return document.createEvent("TouchEvent"),!0}catch(t){return!1}})();class f0t extends(P1t(f1t(j1t(F1t(G1t(B1t(Z1t(J1t(Q1t(X1t(c0t(D1t(q1t(u0t(V1t(W1t(l0t(t0t(z1t))))))))))))))))))){static get template(){return xe`
      <div
        id="scroller"
        safari$="[[_safari]]"
        ios$="[[_ios]]"
        loading$="[[loading]]"
        column-reordering-allowed$="[[columnReorderingAllowed]]"
      >
        <table id="table" role="grid" aria-multiselectable="true" tabindex="0">
          <caption id="sizer" part="row"></caption>
          <thead id="header" role="rowgroup"></thead>
          <tbody id="items" role="rowgroup"></tbody>
          <tfoot id="footer" role="rowgroup"></tfoot>
        </table>

        <div part="reorder-ghost"></div>
      </div>

      <div id="focusexit" tabindex="0"></div>
    `}static get is(){return"vaadin-grid"}static get version(){return"20.0.2"}static get observers(){return["_columnTreeChanged(_columnTree, _columnTree.*)"]}static get properties(){return{_safari:{type:Boolean,value:/^((?!chrome|android).)*safari/i.test(navigator.userAgent)},_ios:{type:Boolean,value:/iPad|iPhone|iPod/.test(navigator.userAgent)&&!window.MSStream||"MacIntel"===navigator.platform&&navigator.maxTouchPoints>1},_firefox:{type:Boolean,value:navigator.userAgent.toLowerCase().indexOf("firefox")>-1},_android:{type:Boolean,value:/android/i.test(navigator.userAgent)},_touchDevice:{type:Boolean,value:p0t},heightByRows:{type:Boolean,value:!1,reflectToAttribute:!0,observer:"_heightByRowsChanged"},_recalculateColumnWidthOnceLoadingFinished:{type:Boolean,value:!0}}}constructor(){super(),this.addEventListener("animationend",this._onAnimationEnd)}connectedCallback(){super.connectedCallback(),this.recalculateColumnWidths()}attributeChangedCallback(t,e,n){super.attributeChangedCallback(t,e,n),"dir"===t&&(this.__isRTL="rtl"===n,this._updateScrollerMeasurements())}__hasRowsWithClientHeight(){return!!Array.from(this.$.items.children).filter((t=>t.clientHeight)).length}__itemsReceived(){this._recalculateColumnWidthOnceLoadingFinished&&!this._cache.isLoading()&&this.__hasRowsWithClientHeight()&&(this._recalculateColumnWidthOnceLoadingFinished=!1,this.recalculateColumnWidths())}_recalculateColumnWidths(t){t.forEach((t=>{t.width="auto",t._origFlexGrow=t.flexGrow,t.flexGrow=0})),t.forEach((t=>{t._currentWidth=0,t._allCells.forEach((e=>{t._currentWidth=Math.max(t._currentWidth,e.offsetWidth+1)}))})),t.forEach((t=>{t.width=`${t._currentWidth}px`,t.flexGrow=t._origFlexGrow,t._currentWidth=void 0,t._origFlexGrow=void 0}))}recalculateColumnWidths(){if(this._columnTree)if(this._cache.isLoading())this._recalculateColumnWidthOnceLoadingFinished=!0;else{const t=this._getColumns().filter((t=>!t.hidden&&t.autoWidth));this._recalculateColumnWidths(t)}}_createScrollerRows(t){const e=[];for(let n=0;n<t;n++){const t=document.createElement("tr");t.setAttribute("part","row"),t.setAttribute("role","row"),this._columnTree&&this._updateRow(t,this._columnTree[this._columnTree.length-1],"body",!1,!0),e.push(t)}return this._columnTree&&this._columnTree[this._columnTree.length-1].forEach((t=>t.isConnected&&t.notifyPath&&t.notifyPath("_cells.*",t._cells))),(function n(t,e,i){Ni||Oi(),Ii.push([t,e,i])})(this,(()=>{this._updateFirstAndLastColumn(),this._resetKeyboardNavigation()})),e}_getRowTarget(){return this.$.items}_createCell(t){const e="vaadin-grid-cell-content-"+(this._contentIndex=this._contentIndex+1||0),n=document.createElement("vaadin-grid-cell-content");n.setAttribute("slot",e);const i=document.createElement(t);i.id=e.replace("-content-","-"),i.setAttribute("tabindex","-1"),i.setAttribute("role","td"===t?"gridcell":"columnheader");const r=document.createElement("slot");return r.setAttribute("name",e),i.appendChild(r),i._content=n,n.addEventListener("mousedown",(()=>{if(window.chrome){const t=()=>{n.contains(this.getRootNode().activeElement)||i.focus(),document.removeEventListener("mouseup",t,!0)};document.addEventListener("mouseup",t,!0)}else setTimeout((()=>{n.contains(this.getRootNode().activeElement)||i.focus()}))})),i}_updateRow(t,e,n,i,r){n=n||"body";const o=document.createDocumentFragment();Array.from(t.children).forEach((t=>t._vacant=!0)),t.innerHTML="","sizer"!==t.id&&(t.hidden=!0),e.filter((t=>!t.hidden)).forEach(((e,a,s)=>{let l;if("body"===n){if(e._cells=e._cells||[],l=e._cells.filter((t=>t._vacant))[0],l||(l=this._createCell("td"),e._cells.push(l)),l.setAttribute("part","cell body-cell"),t.appendChild(l),a===s.length-1&&(this._rowDetailsTemplate||this.rowDetailsRenderer)){this._detailsCells=this._detailsCells||[];const e=this._detailsCells.filter((t=>t._vacant))[0]||this._createCell("td");-1===this._detailsCells.indexOf(e)&&this._detailsCells.push(e),e._content.parentElement||o.appendChild(e._content),this._configureDetailsCell(e),t.appendChild(e),this._a11ySetRowDetailsCell(t,e),e._vacant=!1}e.notifyPath&&!r&&e.notifyPath("_cells.*",e._cells)}else{const r="header"===n?"th":"td";i||"vaadin-grid-column-group"===e.localName?(l=e[`_${n}Cell`]||this._createCell(r),l._column=e,t.appendChild(l),e[`_${n}Cell`]=l):(e._emptyCells=e._emptyCells||[],l=e._emptyCells.filter((t=>t._vacant))[0]||this._createCell(r),l._column=e,t.appendChild(l),-1===e._emptyCells.indexOf(l)&&e._emptyCells.push(l)),l.setAttribute("part",`cell ${n}-cell`),this.__updateHeaderFooterRowVisibility(t)}l._content.parentElement||o.appendChild(l._content),l._vacant=!1,l._column=e})),this.appendChild(o),this._frozenCellsChanged(),this._updateFirstAndLastColumnForRow(t)}__updateHeaderFooterRowVisibility(t){if(!t)return;const e=Array.from(t.children).filter((e=>{const n=e._column;if(n._emptyCells&&n._emptyCells.indexOf(e)>-1)return!1;if(t.parentElement===this.$.header){if(n.headerRenderer||n._headerTemplate)return!0;if(null===n.header)return!1;if(n.path||void 0!==n.header)return!0}else if(n.footerRenderer||n._footerTemplate)return!0}));t.hidden!==!e.length&&(t.hidden=!e.length,this.notifyResize())}_updateScrollerItem(t,e){this._preventScrollerRotatingCellFocus(t,e),this._columnTree&&(this._toggleAttribute("first",0===e,t),this._toggleAttribute("odd",e%2,t),this._a11yUpdateRowRowindex(t,e),this._getItem(e,t))}_columnTreeChanged(t){this._renderColumnTree(t),this.recalculateColumnWidths()}_renderColumnTree(t){for(Array.from(this.$.items.children).forEach((e=>this._updateRow(e,t[t.length-1],null,!1,!0)));this.$.header.children.length<t.length;){const t=document.createElement("tr");t.setAttribute("part","row"),t.setAttribute("role","row"),this.$.header.appendChild(t);const e=document.createElement("tr");e.setAttribute("part","row"),e.setAttribute("role","row"),this.$.footer.appendChild(e)}for(;this.$.header.children.length>t.length;)this.$.header.removeChild(this.$.header.firstElementChild),this.$.footer.removeChild(this.$.footer.firstElementChild);Array.from(this.$.header.children).forEach(((e,n)=>this._updateRow(e,t[n],"header",n===t.length-1))),Array.from(this.$.footer.children).forEach(((e,n)=>this._updateRow(e,t[t.length-1-n],"footer",0===n))),this._updateRow(this.$.sizer,t[t.length-1]),this._resizeHandler(),this._frozenCellsChanged(),this._updateFirstAndLastColumn(),this._resetKeyboardNavigation(),this._a11yUpdateHeaderRows(),this._a11yUpdateFooterRows(),this.__updateFooterPositioning()}__updateFooterPositioning(){if(this._firefox&&(this.$.items.style.paddingBottom=0,this.heightByRows||(this.$.items.style.paddingBottom=`${this.$.footer.offsetHeight}px`)),this._ios){!window.CSS.supports("position","sticky")&&(this.$.table.style.height="",this.$.table.style.minHeight="100%",this.$.table.style.maxHeight="100%",setTimeout((()=>this.$.table.style.height=`${this.$.scroller.offsetHeight}px`)))}}_updateItem(t,e){t._item=e;const n=this.__getRowModel(t);this._toggleAttribute("selected",n.selected,t),this._a11yUpdateRowSelected(t,n.selected),this._a11yUpdateRowLevel(t,n.level),this._toggleAttribute("expanded",n.expanded,t),this._toggleAttribute("details-opened",this._isDetailsOpened(e),t),(this._rowDetailsTemplate||this.rowDetailsRenderer)&&this._toggleDetailsCell(t,e),this._generateCellClassNames(t,n),this._filterDragAndDrop(t,n),Array.from(t.children).forEach((t=>{if(t._renderer){const e=t._column||this;t._renderer.call(e,t._content,e,n)}else t._instance&&(t._instance.__detailsOpened__=n.detailsOpened,t._instance.__selected__=n.selected,t._instance.__level__=n.level,t._instance.__expanded__=n.expanded,t._instance.setProperties(n))})),this._debouncerUpdateHeights=Nn.debounce(this._debouncerUpdateHeights,gt.after(1),(()=>{this._updateMetrics(),this._positionItems(),this._updateScrollerSize()}))}_resizeHandler(){this._updateDetailsCellHeights(),this._accessIronListAPI(super._resizeHandler,!0),this._updateScrollerMeasurements(),this.__updateFooterPositioning()}_onAnimationEnd(t){0===t.animationName.indexOf("vaadin-grid-appear")&&(this._render(),t.stopPropagation(),this.notifyResize(),this.__itemsReceived(),requestAnimationFrame((()=>{this.__scrollToPendingIndex(),this.$.table.style.webkitOverflowScrolling="touch"})))}_toggleAttribute(t,e,n){n.hasAttribute(t)===!e&&(e?n.setAttribute(t,""):n.removeAttribute(t))}__getRowModel(t){return{index:t.index,item:t._item,level:this._getIndexLevel(t.index),expanded:this._isExpanded(t._item),selected:this._isSelected(t._item),detailsOpened:!(!this._rowDetailsTemplate&&!this.rowDetailsRenderer)&&this._isDetailsOpened(t._item)}}render(){this._columnTree&&(this._columnTree.forEach((t=>{t.forEach((t=>t._renderHeaderAndFooter()))})),this._update())}notifyResize(){super.notifyResize()}_heightByRowsChanged(t,e){(t||e)&&this.notifyResize()}__forceReflow(){this._debouncerForceReflow=Nn.debounce(this._debouncerForceReflow,_t,(()=>{this.$.scroller.style.overflow="hidden",setTimeout((()=>this.$.scroller.style.overflow=""))}))}}function m0t(t,e){return(function n(t,e){return Vr({},cr(e),t)})(t,e)}customElements.define(f0t.is,f0t);let g0t=class extends we{constructor(){super(...arguments),this._run=""}_csvUrl(t,e,n){return e?PO(n(t,e),{format:"csv"}):""}_jsonUrl(t,e,n){return e?n(t,e):""}_csvName(t,e){return e?`run-${e}-tag-${t}.csv`:""}_jsonName(t,e){return e?`run-${e}-tag-${t}.json`:""}};g0t.template=xe`
    <paper-dropdown-menu
      no-label-float="true"
      label="run to download"
      selected-item-label="{{_run}}"
    >
      <paper-listbox slot="dropdown-content">
        <template is="dom-repeat" items="[[runs]]">
          <paper-item no-label-float="true">[[item]]</paper-item>
        </template>
      </paper-listbox>
    </paper-dropdown-menu>
    <template is="dom-if" if="[[_run]]">
      <a download="[[_csvName(tag, _run)]]" href="[[_csvUrl(tag, _run, urlFn)]]"
        >CSV</a
      ><!--
      --><a
        download="[[_jsonName(tag, _run)]]"
        href="[[_jsonUrl(tag, _run, urlFn)]]"
        >JSON</a
      >
    </template>
    <style>
      :host {
        display: flex;
        align-items: center;
        height: 32px;
      }
      paper-dropdown-menu {
        width: 100px;
        --paper-input-container-label: {
          font-size: 10px;
        }
        --paper-input-container-input: {
          font-size: 10px;
        }
      }
      a {
        font-size: 10px;
        margin: 0 0.2em;
      }
      paper-input {
        font-size: 22px;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],g0t.prototype,"_run",void 0),t([o({type:Array}),e("design:type",Array)],g0t.prototype,"runs",void 0),t([o({type:String}),e("design:type",String)],g0t.prototype,"tag",void 0),t([o({type:Object}),e("design:type",Object)],g0t.prototype,"urlFn",void 0),g0t=t([i("tf-downloader")],g0t);new URLSearchParams(window.location.search);let _0t=class extends we{constructor(){super(...arguments),this.colorScale=null,this._loadDataCallback=(t,e,n)=>{if(null==n)return void console.error("Failed to load data for:",e);const i=n.map((t=>({wall_time:new Date(1e3*t[0]),step:t[1],scalar:t[2]}))),r=this._getSeriesNameFromDatum(e);t.setSeriesMetadata(r,e),t.setSeriesData(r,i)},this.getDataLoadUrl=({tag:t,run:e})=>Er().pluginRoute("scalars","/scalars",new URLSearchParams({tag:t,run:e})),this._downloadUrlFn=(t,e)=>this.getDataLoadUrl({tag:t,run:e}),this.requestData=(t,e,n)=>this.inColab?this._requestDataGet(t,e,n):this._requestDataPost(t,e,n),this._requestDataGet=(t,e,n)=>{const i=Er().pluginRoute("scalars","/scalars");Promise.all(t.map((t=>{const n=PO(i,{tag:t.tag,run:t.run});return this.requestManager.request(n).then((n=>{e({item:t,data:n})}))}))).finally((()=>{n()}))},this._requestDataPost=(t,e,n)=>{var i;const r=Er().pluginRoute("scalars","/scalars_multirun"),o=new Map;for(const{tag:e,run:n}of t){let t=o.get(e);null==t&&o.set(e,t=[]),t.push(n)}const a=null!==(i=this.batchSize)&&void 0!==i?i:64,s=[];for(const[t,e]of o)for(let n=0;n<e.length;n+=a)s.push({tag:t,runs:e.slice(n,n+a)});Promise.all(s.map((({tag:t,runs:n})=>this.requestManager.request(r,{tag:t,runs:n}).then((i=>{for(const r of n){const n={tag:t,run:r};Object.prototype.hasOwnProperty.call(i,r)?e({item:n,data:i[r]}):e({item:n,data:null})}}))))).finally((()=>{n()}))},this._getDataLoadName=t=>this._getSeriesNameFromDatum(t),this._expanded=!1,this._tooltipColumns=(()=>{const t=pAt.slice(),e=t.findIndex((t=>"Name"==t.title));return t.splice(e,1,{title:"Name",evaluate:t=>{const e=t.dataset.metadata().meta;return this._getSeriesDisplayNameFromDatum(e)}}),t})()}_getChartDataLoader(){return this.shadowRoot.querySelector("tf-line-chart-data-loader")}reload(){this._getChartDataLoader().reload()}redraw(){this._getChartDataLoader().redraw()}_toggleExpanded(t){this.set("_expanded",!this._expanded),this.redraw()}_toggleLogScale(){this.set("_logScaleActive",!this._logScaleActive)}_resetDomain(){const t=this._getChartDataLoader();t&&t.resetDomain()}_updateDownloadLink(){const t=this._getChartDataLoader().exportAsSvgString();this.shadowRoot.querySelector("#svgLink").href=`data:image/svg+xml;base64,${btoa(t)}`}_runsFromData(t){return t.map((t=>t.run))}_getDataSeries(){return this.dataToLoad.map((t=>this._getSeriesNameFromDatum(t)))}_getSeriesNameFromDatum({run:t,experiment:e={name:"_default"}}){return JSON.stringify([e.name,t])}_getSeriesDisplayNameFromDatum(t){return t.run}_getColorScale(){return null!==this.colorScale?this.colorScale:{scale:t=>{const[,e]=JSON.parse(t);return _O(e)}}}};_0t.template=xe`
    <tf-card-heading
      tag="[[tag]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
    ></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader
        active="[[active]]"
        color-scale="[[_getColorScale(colorScale)]]"
        data-series="[[_getDataSeries(dataToLoad.*)]]"
        data-to-load="[[dataToLoad]]"
        get-data-load-name="[[_getDataLoadName]]"
        get-data-load-url="[[getDataLoadUrl]]"
        request-data="[[requestData]]"
        ignore-y-outliers="[[ignoreYOutliers]]"
        load-data-callback="[[_loadDataCallback]]"
        load-key="[[tag]]"
        log-scale-active="[[_logScaleActive]]"
        request-manager="[[requestManager]]"
        smoothing-enabled="[[smoothingEnabled]]"
        smoothing-weight="[[smoothingWeight]]"
        tag-metadata="[[tagMetadata]]"
        tooltip-columns="[[_tooltipColumns]]"
        tooltip-position="auto"
        tooltip-sorting-method="[[tooltipSortingMethod]]"
        x-type="[[xType]]"
      >
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
      <paper-icon-button
        selected$="[[_logScaleActive]]"
        icon="line-weight"
        on-tap="_toggleLogScale"
        title="Toggle y-axis log scale"
      ></paper-icon-button>
      <paper-icon-button
        icon="settings-overscan"
        on-tap="_resetDomain"
        title="Fit domain to data"
      ></paper-icon-button>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <paper-menu-button on-paper-dropdown-open="_updateDownloadLink">
          <paper-icon-button
            class="dropdown-trigger"
            slot="dropdown-trigger"
            icon="file-download"
          ></paper-icon-button>
          <paper-listbox class="dropdown-content" slot="dropdown-content">
            <paper-item>
              <a id="svgLink" download="[[tag]].svg">
                Download Current Chart as SVG
              </a>
            </paper-item>
          </paper-listbox>
        </paper-menu-button>
      </template>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <tf-downloader
            runs="[[_runsFromData(dataToLoad)]]"
            tag="[[tag]]"
            url-fn="[[_downloadUrlFn]]"
          ></tf-downloader>
        </div>
      </template>
    </div>
    <style>
      :host {
        margin: 5px;
        display: block;
        width: 330px;
      }

      :host([_expanded]) {
        width: 100%;
      }

      :host([_expanded]) #tf-line-chart-data-loader-container {
        height: 400px;
      }

      #tf-line-chart-data-loader-container {
        height: 200px;
        width: 100%;
      }

      tf-card-heading {
        display: block;
        margin-bottom: 10px;
      }

      #buttons {
        display: flex;
        flex-direction: row;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }

      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      .download-links {
        display: flex;
        height: 32px;
      }

      .download-links a {
        align-self: center;
        font-size: 10px;
        margin: 2px;
      }

      .download-links paper-dropdown-menu {
        width: 100px;
        --paper-input-container-label: {
          font-size: 10px;
        }
        --paper-input-container-input: {
          font-size: 10px;
        }
      }

      paper-menu-button {
        padding: 0;
      }
      paper-item a {
        color: inherit;
        text-decoration: none;
        white-space: nowrap;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],_0t.prototype,"tag",void 0),t([o({type:Array}),e("design:type",Array)],_0t.prototype,"dataToLoad",void 0),t([o({type:String}),e("design:type",String)],_0t.prototype,"xType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],_0t.prototype,"active",void 0),t([o({type:Boolean}),e("design:type",Boolean)],_0t.prototype,"ignoreYOutliers",void 0),t([o({type:Object}),e("design:type",br)],_0t.prototype,"requestManager",void 0),t([o({type:Boolean}),e("design:type",Boolean)],_0t.prototype,"showDownLinks",void 0),t([o({type:Boolean}),e("design:type",Boolean)],_0t.prototype,"smoothingEnabled",void 0),t([o({type:Number}),e("design:type",Number)],_0t.prototype,"smoothingWeight",void 0),t([o({type:Object}),e("design:type",Object)],_0t.prototype,"tagMetadata",void 0),t([o({type:Object}),e("design:type",Object)],_0t.prototype,"colorScale",void 0),t([o({type:String}),e("design:type",String)],_0t.prototype,"tooltipSortingMethod",void 0),t([o({type:Number}),e("design:type",Number)],_0t.prototype,"batchSize",void 0),t([o({type:Boolean}),e("design:type",Number)],_0t.prototype,"inColab",void 0),t([o({type:Object}),e("design:type",Object)],_0t.prototype,"_loadDataCallback",void 0),t([o({type:Object}),e("design:type",Function)],_0t.prototype,"getDataLoadUrl",void 0),t([o({type:Object}),e("design:type",Object)],_0t.prototype,"_downloadUrlFn",void 0),t([o({type:Object}),e("design:type",Function)],_0t.prototype,"requestData",void 0),t([o({type:Object}),e("design:type",Object)],_0t.prototype,"_getDataLoadName",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],_0t.prototype,"_expanded",void 0),t([o({type:Boolean}),e("design:type",Boolean)],_0t.prototype,"_logScaleActive",void 0),t([o({type:Array}),e("design:type",Array)],_0t.prototype,"_tooltipColumns",void 0),_0t=t([i("tf-scalar-card")],_0t);let y0t=class extends(m0t([Ao],we)){constructor(){super(...arguments),this.sessionGroup=null,this._xType=wCt.STEP,this._noMultiExperiments=!1,this._requestData=(t,e,n)=>{Promise.all(t.map((t=>this.backend.listMetricEvals({experimentName:this.experimentName,sessionName:t.run,metricName:t.tag}).then((n=>{e({item:t,data:n})}))))).finally((()=>{n()}))},this._colorScale={scale:t=>{const e=JSON.parse(t)[1],n=this._indexOfSession.get(e);return fO[(this._sessionGroupNameHash+n)%fO.length]}}}connectedCallback(){super.connectedCallback(),this.addEventListener("iron-resize",this.redraw.bind(this))}redraw(){this.shadowRoot.querySelectorAll("tf-scalar-card").forEach((t=>t.redraw()))}_sessionGroupChanged(){this.sessionGroup&&0!=Object.keys(this.sessionGroup).length?(this._indexOfSession=new Map(this.sessionGroup.sessions.map(((t,e)=>[t.name,e]))),this._sessionGroupNameHash=JJt(this.sessionGroup.name)):(this._indexOfSession=new Map,this._sessionGroupNameHash=0),this.shadowRoot.querySelectorAll("tf-scalar-card").forEach((t=>{const e=t,n=e.get("tag");e.set("tag",""),e.set("tag",n)}))}_haveMetrics(){return this.visibleSchema&&Array.isArray(this.visibleSchema.metricInfos)&&this.visibleSchema.metricInfos.length>0}_haveMetricsAndSessionGroup(){return this.sessionGroup&&this._haveMetrics()}_computeSeriesForSessionGroupMetric(t,e){return null===t||0==Object.keys(t).length||null===e?[]:t.sessions.filter((t=>void 0!==IJt(t.metricValues,e.name))).map((t=>({tag:e.name,run:t.name})))}_computeTagMetadata(t){return{displayName:CJt(t),description:t.description||""}}};y0t.template=xe`
    <template is="dom-if" if="[[!sessionGroup]]">
      <div>
        <h3>No session group selected</h3>
        <p>Please select a session group to see its metric-graphs here.</p>
      </div>
    </template>
    <template is="dom-if" if="[[!_haveMetrics(visibleSchema.*)]]">
      <div>
        <h3>No metrics are enabled</h3>
        <p>Please enable some metrics to see content here.</p>
      </div>
    </template>
    <div class="layout horizontal wrap session-group-details">
      <template
        is="dom-if"
        if="[[_haveMetricsAndSessionGroup(visibleSchema.*, sessionGroup)]]"
      >
        <template
          is="dom-repeat"
          items="[[visibleSchema.metricInfos]]"
          as="metricInfo"
        >
          <!-- Note that we do not provide a request-manager attribute since
               we provide a function in request-data for calling the backend
               to get the metrics data.
            -->
          <tf-scalar-card
            class="scalar-card"
            color-scale="[[_colorScale]]"
            data-to-load="[[_computeSeriesForSessionGroupMetric(sessionGroup, metricInfo)]]"
            tag="[[metricInfo.name.tag]]"
            tag-metadata="[[_computeTagMetadata(metricInfo)]]"
            x-type="[[_xType]]"
            multi-experiments="[[_noMultiExperiments]]"
            request-data="[[_requestData]]"
            active
          >
          </tf-scalar-card>
        </template>
      </template>
    </div>
    <!-- "iron-flex" is needed to use the layout classes in the div above -->
    <style include="iron-flex">
      :host {
        display: block;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],y0t.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],y0t.prototype,"experimentName",void 0),t([o({type:Object}),e("design:type",Object)],y0t.prototype,"visibleSchema",void 0),t([o({type:Object}),e("design:type",Object)],y0t.prototype,"sessionGroup",void 0),t([o({type:String}),e("design:type",String)],y0t.prototype,"_xType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],y0t.prototype,"_noMultiExperiments",void 0),t([o({type:Object}),e("design:type",Object)],y0t.prototype,"_indexOfSession",void 0),t([o({type:Number}),e("design:type",Number)],y0t.prototype,"_sessionGroupNameHash",void 0),t([o({type:Object}),e("design:type",Function)],y0t.prototype,"_requestData",void 0),t([o({type:Object}),e("design:type",Object)],y0t.prototype,"_colorScale",void 0),t([a("sessionGroup.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],y0t.prototype,"_sessionGroupChanged",null),y0t=t([i("tf-hparams-session-group-details")],y0t);let v0t=class extends(cr(gl(we))){constructor(){super(...arguments),this._hparamName=TJt,this._metricName=CJt}_visibleSchemaOrSessionGroupsChanged(){const t=this.$.sessionGroupsTable.get("detailsOpenedItems");this.$.sessionGroupsTable.set("detailsOpenedItems",[]),Wi();const e=new Map;this.sessionGroups.forEach((t=>{e.set(t.name,t)})),this.$.sessionGroupsTable.set("detailsOpenedItems",t.map((t=>e.get(t.name))).filter(Boolean))}_sessionGroupHParam(t,e){return null!=t&&0!=Object.keys(t).length&&Object.prototype.hasOwnProperty.call(t.hparams,e)?qJt(t.hparams[e]):""}_sessionGroupMetric(t,e){if(null==t||0==Object.keys(t).length)return"";for(let n=0;n<t.metricValues.length;++n){let i=t.metricValues[n];if(i.name.group===e.group&&i.name.tag==e.tag)return qJt(i.value)}return""}_rowNumber(t){return t+1}};v0t.template=xe`
    <vaadin-grid
      class="session-group-table"
      id="sessionGroupsTable"
      column-reordering-allowed=""
      items="[[sessionGroups]]"
    >
      <vaadin-grid-column flex-grow="0" width="10em" resizable="">
        <template class="header">
          <div class="table-header table-cell">Trial ID</div>
        </template>
        <template>
          <div class="table-cell">[[item.name]]</div>
        </template>
      </vaadin-grid-column>
      <template is="dom-if" if="[[enableShowMetrics]]">
        <vaadin-grid-column flex-grow="0" autoWidth="" resizable="">
          <template class="header">
            <div class="table-header table-cell">Show Metrics</div>
          </template>
          <template>
            <paper-checkbox class="table-cell" checked="{{detailsOpened}}">
            </paper-checkbox>
          </template>
        </vaadin-grid-column>
      </template>
      <template
        is="dom-repeat"
        items="[[visibleSchema.hparamInfos]]"
        as="hparamInfo"
        index-as="hparamIndex"
      >
        <vaadin-grid-column flex-grow="2" width="10em" resizable="">
          <template class="header">
            <div class="table-header table-cell">
              [[_hparamName(hparamInfo)]]
            </div>
          </template>
          <template>
            <div class="table-cell">
              [[_sessionGroupHParam(item, hparamInfo.name)]]
            </div>
          </template>
        </vaadin-grid-column>
      </template>
      <template
        is="dom-repeat"
        items="{{visibleSchema.metricInfos}}"
        as="metricInfo"
        index-as="metricIndex"
      >
        <vaadin-grid-column flex-grow="2" width="10em" resizable="">
          <template class="header">
            <div class="table-header table-cell">
              [[_metricName(metricInfo)]]
            </div>
          </template>
          <template>
            <div class="table-cell">
              [[_sessionGroupMetric(item, metricInfo.name)]]
            </div>
          </template>
        </vaadin-grid-column>
      </template>
      <template class="row-details">
        <tf-hparams-session-group-details
          backend="[[backend]]"
          experiment-name="[[experimentName]]"
          session-group="[[item]]"
          visible-schema="[[visibleSchema]]"
          class="session-group-details"
        >
        </tf-hparams-session-group-details>
      </template>
    </vaadin-grid>

    <style>
      :host {
        display: inline;
      }

      :host(.dark-mode) {
        --lumo-base-color: #303030;
        --lumo-body-text-color: #fff;
      }

      :host(.dark-mode) vaadin-grid {
        --_lumo-grid-secondary-border-color: #505050;
      }

      .table-cell {
        white-space: nowrap;
        text-overflow: ellipsis;
        overflow: hidden;
      }
      .table-header {
        /* line-break overflowing column headers */
        white-space: normal;
        overflow-wrap: break-word;
      }
      .session-group-table {
        height: 100%;
      }
      .session-group-details {
        height: 360px;
        overflow-y: auto;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],v0t.prototype,"visibleSchema",void 0),t([o({type:Array}),e("design:type",Array)],v0t.prototype,"sessionGroups",void 0),t([o({type:Boolean}),e("design:type",Boolean)],v0t.prototype,"enableShowMetrics",void 0),t([o({type:Object}),e("design:type",Object)],v0t.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],v0t.prototype,"experimentName",void 0),t([a("visibleSchema.*","sessionGroups.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],v0t.prototype,"_visibleSchemaOrSessionGroupsChanged",null),v0t=t([i("tf-hparams-table-view")],v0t);let b0t=class extends we{constructor(){super(...arguments),this.sessionGroup=null,this.visibleSchema=null}_propertiesArePopulated(t,e){return null!=t&&null!=e}_singletonSessionGroups(t){return null==t?[]:[t]}};function x0t(t,e,n,i){if(e.length<2)return console.error("Less than two axes in parallel coordinates plot."),null;const r=n[0],o=n[1];if(r<=e[0]||r>=e[e.length-1])return null;const a=Ce.exports.sortedIndex(e,r);console.assert(a>0),console.assert(a<e.length);const s=a-1;let l=null,c=null;return t.forEach((t=>{const e=(function n(t,e,i,a){const s=t-i,l=e-a,c=r-i,u=o-a,h=(s*c+l*u)/(s*s+l*l);if(h<=0)return YJt(c,u);if(h>=1)return YJt(t-r,e-o);return YJt(c-h*s,u-h*l)})(t.controlPoints[s][0],t.controlPoints[s][1],t.controlPoints[a][0],t.controlPoints[a][1]);e>i||(null===l||e<l)&&(l=e,c=t)})),c}function w0t(t,e,n){function i(){if(0===t.length)return[1,2];const[e,n]=Gl(t);return e!==n?[e,n]:e>0?[.5*e,1.5*e]:e<0?[1.5*e,.5*e]:[-1,1]}if("LINEAR"===n)return cE().domain(i()).range([e,0]);if("LOG"===n){const n=i();return n[0]<=0&&n[1]>=0?w0t(t,e,"LINEAR"):yE().domain(n).range([e,0])}if("QUANTILE"===n){const n=20,i=Kl(n).map((t=>e-t*e/(n-1)));return 0===t.length&&(t=[1]),CE().domain(Ce.exports.uniq(t)).range(i)}if("NON_NUMERIC"===n)return KM().domain(Ce.exports.uniq(t.sort())).range([e,0]).padding(.1);throw RangeError("Unknown scale: "+n)}var S0t,M0t;b0t.template=xe`
    <!-- If sessionGroup or visibleSchema are not populated, do not display
         anything.
      -->
    <template
      is="dom-if"
      if="[[_propertiesArePopulated(visibleSchema, sessionGroup)]]"
    >
      <!-- Display one row without a "show-metrics" column -->
      <tf-hparams-table-view
        visible-schema="[[visibleSchema]]"
        session-groups="[[_singletonSessionGroups(sessionGroup)]]"
      >
      </tf-hparams-table-view>
    </template>
    <template
      is="dom-if"
      if="[[!_propertiesArePopulated(visibleSchema, sessionGroup)]]"
    >
      <div>Click or hover over a session group to display its values here.</div>
    </template>

    <style>
      :host {
        display: block;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],b0t.prototype,"sessionGroup",void 0),t([o({type:Object}),e("design:type",Object)],b0t.prototype,"visibleSchema",void 0),b0t=t([i("tf-hparams-session-group-values")],b0t),(function(t){t.LINEAR="LINEAR",t.LOG="LOG",t.QUANTILE="QUANTILE",t.NON_NUMERIC="NON_NUMERIC"})(S0t||(S0t={}));class E0t{isPassing(t){return!0}}class T0t{constructor(t,e,n,i){this._lower=t,this._upper=e,this._lowerOpen=n,this._upperOpen=i}isPassing(t){const e=t;return this._before(this._lower,e,!this._lowerOpen)&&this._before(e,this._upper,!this._upperOpen)}_before(t,e,n){return n?t<=e:t<e}}class C0t{constructor(t){this._domainSet=t}isPassing(t){return-1!==this._domainSet.findIndex((e=>e===t))}}class A0t{constructor(t,e,n,i){this._svgProps=t,this._schema=e,this._interactionManager=n,this._colIndex=i,this._isDisplayed=!1,this._yScale=null,this._scaleType=null,this.setBrushSelection(null)}colIndex(){return this._colIndex}yScale(){return this._yScale}scaleType(){return this._scaleType}brushSelection(){return this._brushSelection}isDisplayed(){return this._isDisplayed}setBrushSelection(t){this._brushSelection=t,this._brushFilter=this._buildBrushFilter(this.brushSelection(),this.scaleType(),this.yScale())}setDomainAndScale(t,e){this._scaleType=e,this._yScale=w0t(t.slice(),this._svgProps.height,this.scaleType()),this._brushFilter=this._buildBrushFilter(this.brushSelection(),this.scaleType(),this.yScale())}brushFilter(){return this._brushFilter}updateDOM(t){let e=bc(this.yScale());this.scaleType()===S0t.QUANTILE&&(e=e.tickValues(this.yScale().quantiles()).tickFormat(Dy("-.6g")));const n=Du(t);n.selectAll("g").remove(),n.append("g").classed("axis",!0).call(e).append("text").classed("axis-title",!0).style("cursor","move").style("text-anchor","middle").attr("y",-9).text((t=>AJt(this._schema,t))),n.call(Ih().on("start",(()=>{t.setAttribute("is-dragging",""),this._interactionManager.onDragStart(this.colIndex())})).on("drag",(()=>this._interactionManager.onDrag(Mu.x))).on("end",(()=>{this._interactionManager.onDragEnd(),t.removeAttribute("is-dragging")})));const i=yg().extent([[-8,0],[8,this._svgProps.height+1]]).on("start",(()=>{L0t(Mu)&&(t.setAttribute("is-brushing",""),this._interactionManager.onBrushChanged(this.colIndex(),Mu.selection))})).on("brush",(()=>{L0t(Mu)&&this._interactionManager.onBrushChanged(this.colIndex(),Mu.selection)})).on("end",(()=>{L0t(Mu)&&(this._interactionManager.onBrushChanged(this.colIndex(),Mu.selection),t.removeAttribute("is-brushing"))})),r=Du(t).append("g").classed("brush",!0);r.call(i),i.move(r,this.brushSelection())}setDisplayed(t){this._isDisplayed=t}_buildBrushFilter(t,e,n){if(null===t)return new E0t;if(null===e)return console.error("Scale type is null, but brushSelection isn't: ",t),new E0t;switch(e){case S0t.LINEAR:case S0t.LOG:{const[e,i]=(function r(t,e,n){return[t.invert(e),t.invert(n)].sort(((t,e)=>t-e))})(n,t[0],t[1]);return new T0t(e,i,!1,!1)}case S0t.QUANTILE:{const[e,i]=(function r(t,e,n){const i=t.range(),r=i.filter((t=>e<=t&&t<=n)).map((e=>{const n=t.invertExtent(e);return e===i[i.length-1]?[n[0],n[1]+1]:n}));return 0==r.length?[0,0]:Gl(ac(r))})(n,t[0],t[1]);return new T0t(e,i,!1,!0)}case S0t.NON_NUMERIC:return new C0t((function e(t,n,i){return t.domain().filter((e=>{const r=t(e);return n<=r&&r<=i}))})(n,t[0],t[1]))}return console.error("Unknown scale type: ",e),new E0t}}class k0t{constructor(t,e,n){this._svgProps=t,this._schema=e,this._axes=this._createAxes(n),this._stationaryAxesPositions=KM().range([1,this._svgProps.width-1]).padding(.5),this._draggedAxis=null,this._svgProps.svgG.selectAll("g.axis-parent").remove(),this._parentsSel=this._svgProps.svgG.selectAll(".axis-parent")}updateAxes(t,e){console.assert(!this.isAxisDragging());const n=new Set;t.columns.forEach((t=>{const i=t.absoluteIndex;let r=this._axes[i];r.setDisplayed(!0);const o=e.map((t=>zJt(this._schema,t,i)));r.setDomainAndScale(o,t.scale),n.add(i)})),this._axes.forEach((t=>{n.has(t.colIndex())||t.setDisplayed(!1)})),this._updateStationaryAxesPositions(n),this._parentsSel=this._parentsSel.data(Array.from(n),(t=>t)),this._parentsSel.exit().remove(),this._parentsSel=this._parentsSel.enter().append("g").classed("axis-parent",!0).merge(this._parentsSel);const i=this;this._parentsSel.call((t=>this._updateAxesPositionsInDOM(t))).each((function(t){i._axes[t].updateDOM(this)}))}mapVisibleAxes(t){return this._stationaryAxesPositions.domain().map((e=>t(this.getAxisPosition(e),this._axes[e])))}allVisibleAxesSatisfy(t){return this._stationaryAxesPositions.domain().every((e=>t(this.getAxisPosition(e),this._axes[e])))}getAxisForColIndex(t){return this._axes[t]}dragStart(t){console.assert(!this.isAxisDragging()),console.assert(this._axes[t].isDisplayed()),this._draggedAxis=this._axes[t],this._draggedAxisPosition=this._stationaryAxesPositions(t)}drag(t){t=Math.min(Math.max(t,0),this._svgProps.width),this._draggedAxisPosition=t;let e=this._stationaryAxesPositions.domain();e.sort(((t,e)=>this.getAxisPosition(t)-this.getAxisPosition(e))),this._stationaryAxesPositions.domain(e),this._updateAxesPositionsInDOM(this._parentsSel)}dragEnd(t){console.assert(this.isAxisDragging()),this._draggedAxisPosition=null,this._draggedAxis=null,this._updateAxesPositionsInDOM(this._parentsSel.transition().duration(t))}isAxisDragging(){return null!==this._draggedAxis}getAxisPosition(t){return null!==this._draggedAxis&&this._draggedAxis.colIndex()===t?this._draggedAxisPosition:this._stationaryAxesPositions(t)}_updateStationaryAxesPositions(t){const e=this._stationaryAxesPositions.domain().filter((e=>t.has(e))),n=Array.from(new Set([...e,...Array.from(t)]));this._stationaryAxesPositions.domain(n)}_updateAxesPositionsInDOM(t){t.attr("transform",(t=>KJt(this.getAxisPosition(t))))}_createAxes(t){return Kl(PJt(this._schema)).map((e=>new A0t(this._svgProps,this._schema,t,e)))}}function L0t(t){return null!==t.sourceEvent}!(function(t){t[t.FOREGROUND=0]="FOREGROUND",t[t.BACKGROUND=1]="BACKGROUND"})(M0t||(M0t={}));class P0t{constructor(t){void 0===t&&(t=xh(null)),console.assert(t.size()<=1),this._sessionGroupSel=t}sessionGroup(){return 1===this._sessionGroupSel.size()?this._sessionGroupSel.datum():null}isNull(){return null===this.sessionGroup()}selection(){return this._sessionGroupSel}equalsTo(t){return this.isNull()?t.isNull():!t.isNull()&&t.sessionGroup().name==this.sessionGroup().name}}class N0t{constructor(t,e,n){this._svgProps=t,this._schema=e,this._axesCollection=n,this._sessionGroups=[],this._svgProps.svgG.selectAll("g.background").remove(),this._svgProps.svgG.selectAll("g.foreground").remove(),this._bgPathsSel=this._svgProps.svgG.append("g").classed("background",!0).selectAll("path"),this._fgPathsSel=this._svgProps.svgG.append("g").classed("foreground",!0).selectAll("path"),this._updateVisibleFgPathsSel(),this._peakedSessionGroupHandle=new P0t,this._selectedSessionGroupHandle=new P0t,this._d3line=DL().curve(RL)}getSessionGroupHandle(t){return null==t?new P0t:new P0t(this._fgPathsSel.filter((e=>e.name===t.name)))}hideBackgroundLines(){this._bgPathsSel.attr("visibility","hidden")}showBackgroundLines(){this._bgPathsSel.attr("visibility",null)}peakedSessionGroupHandle(){return this._peakedSessionGroupHandle}selectedSessionGroupHandle(){return this._selectedSessionGroupHandle}recomputeControlPoints(t,e=0){(t===M0t.FOREGROUND?this._fgPathsSel:this._bgPathsSel).transition().duration(e).attr("d",(t=>this._pathDAttribute(t))),t===M0t.FOREGROUND&&window.setTimeout((()=>{const t=this;this._fgPathsSel.each((function(e){t._setControlPointsProperty(this,e)}))}))}recomputeForegroundLinesVisibility(){this._fgPathsSel.classed("invisible-path",(t=>!this._axesCollection.allVisibleAxesSatisfy(((e,n)=>n.brushFilter().isPassing(zJt(this._schema,t,n.colIndex())))))),this._updateVisibleFgPathsSel()}setForegroundLinesColor(t,e,n){const i=this._createLineColorFunction(t,e,n);this._fgPathsSel.attr("stroke",i)}redraw(t,e,n,i){const r=this._peakedSessionGroupHandle.sessionGroup(),o=this._selectedSessionGroupHandle.sessionGroup();this._sessionGroups=t,this._fgPathsSel=this._recomputePathSelection(this._fgPathsSel),this._bgPathsSel=this._recomputePathSelection(this._bgPathsSel),this._peakedSessionGroupHandle=this.getSessionGroupHandle(r),this._selectedSessionGroupHandle=this.getSessionGroupHandle(o),this.recomputeControlPoints(M0t.FOREGROUND),this.recomputeControlPoints(M0t.BACKGROUND),this.recomputeForegroundLinesVisibility(),this.setForegroundLinesColor(e,n,i)}updatePeakedSessionGroup(t){this._peakedSessionGroupHandle.selection().classed("peaked-path",!1),this._peakedSessionGroupHandle=t,this._peakedSessionGroupHandle.selection().classed("peaked-path",!0)}clearPeakedSessionGroup(){this.updatePeakedSessionGroup(new P0t)}updateSelectedSessionGroup(t){this._selectedSessionGroupHandle.selection().classed("selected-path",!1),this._selectedSessionGroupHandle=t,this._selectedSessionGroupHandle.selection().classed("selected-path",!0)}findClosestSessionGroup(t,e){const n=this._axesCollection.mapVisibleAxes(((t,e)=>t)),i=x0t(this._visibleFgPathsSel.nodes(),n,[t,e],100);return null===i?new P0t:new P0t(Du(i))}_createLineColorFunction(t,e,n){if(null===t)return()=>"red";const i=cE().domain(DJt(this._schema,this._sessionGroups,t)).range([e,n]).interpolate(kp);return e=>i(zJt(this._schema,e,t))}_recomputePathSelection(t){return(t=t.data(this._sessionGroups,(t=>t.name))).exit().remove(),t.enter().append("path").merge(t)}_setControlPointsProperty(t,e){t.controlPoints=this._computeControlPoints(e)}_computeControlPoints(t){return this._axesCollection.mapVisibleAxes(((e,n)=>[e,n.yScale()(zJt(this._schema,t,n.colIndex()))]))}_pathDAttribute(t){return this._d3line(this._computeControlPoints(t))}_updateVisibleFgPathsSel(){this._visibleFgPathsSel=this._fgPathsSel.filter(":not(.invisible-path)")}}class I0t{constructor(t,e){this.svg=Du(t);const n=30,i=10,r=10,o=10,a=100*e+o+i,s=200+n+r;this.svg.attr("viewBox",`0 0 ${a} ${s}`),this.svg.attr("preserveAspectRatio","xMidYMid"),this.svg.style("min-width",a+"px"),this.svg.style("min-height",s+"px"),this.width=a-o-i,this.height=s-n-r,this.svgG=this.svg.append("g").attr("transform",KJt(o,n))}}class R0t{constructor(t,e,n,i){this._svgProps=t,this._schema=e,this._peakedSessionGroupChangedCB=n,this._selectedSessionGroupChangedCB=i,this._axesCollection=new k0t(t,e,this),this._linesCollection=new N0t(t,e,this._axesCollection),this._svgProps.svg.on("click",(()=>this.onClick())).on("mousemove mouseenter",(()=>{const[t,e]=bh(this._svgProps.svgG.node());this.onMouseMoved(t,e)})).on("mouseleave",(()=>this.onMouseLeave()))}onDragStart(t){this._axesCollection.dragStart(t),this._linesCollection.hideBackgroundLines()}onDrag(t){this._axesCollection.drag(t),this._linesCollection.recomputeControlPoints(M0t.FOREGROUND)}onDragEnd(){this._axesCollection.dragEnd(500),this._linesCollection.recomputeControlPoints(M0t.FOREGROUND,500),window.setTimeout((()=>{this._linesCollection.recomputeControlPoints(M0t.BACKGROUND),this._linesCollection.showBackgroundLines()}),500)}onBrushChanged(t,e){this._axesCollection.getAxisForColIndex(t).setBrushSelection(e),this._linesCollection.recomputeForegroundLinesVisibility()}onMouseMoved(t,e){this._linesCollection.updatePeakedSessionGroup(this._linesCollection.findClosestSessionGroup(t,e)),this._peakedSessionGroupChangedCB(this._linesCollection.peakedSessionGroupHandle().sessionGroup())}onMouseLeave(){this._linesCollection.peakedSessionGroupHandle().isNull()||(this._linesCollection.clearPeakedSessionGroup(),this._peakedSessionGroupChangedCB(null))}onClick(){this._linesCollection.peakedSessionGroupHandle().sessionGroup()===this._linesCollection.selectedSessionGroupHandle().sessionGroup()?this._linesCollection.updateSelectedSessionGroup(new P0t):this._linesCollection.updateSelectedSessionGroup(this._linesCollection.peakedSessionGroupHandle()),this._selectedSessionGroupChangedCB(this._linesCollection.selectedSessionGroupHandle().sessionGroup())}onOptionsOrSessionGroupsChanged(t,e){this._axesCollection.updateAxes(t,e);const n=this._linesCollection.peakedSessionGroupHandle(),i=this._linesCollection.selectedSessionGroupHandle();this._linesCollection.redraw(e,void 0!==t.colorByColumnIndex?t.columns[t.colorByColumnIndex].absoluteIndex:null,t.minColor,t.maxColor),n.equalsTo(this._linesCollection.peakedSessionGroupHandle())||this._peakedSessionGroupChangedCB(this._linesCollection.peakedSessionGroupHandle().sessionGroup()),i.equalsTo(this._linesCollection.selectedSessionGroupHandle())||this._selectedSessionGroupChangedCB(this._linesCollection.selectedSessionGroupHandle().sessionGroup())}schema(){return this._schema}}let O0t=class extends(cr(gl(we))){constructor(){super(...arguments),this.selectedSessionGroup=null,this.closestSessionGroup=null,this.redrawCount=0}_optionsOrSessionGroupsChanged(){var t;if(!this.options)return;const{configuration:e}=null!==(t=this._prevOptions)&&void 0!==t?t:{},{configuration:n}=this.options;if(void 0===this._interactionManager||!Ce.exports.isEqual(e.schema,n.schema)||!Ce.exports.isEqual(e.columnsVisibility,n.columnsVisibility)){Du(this.$.svg).selectAll("*").remove();const t=new I0t(this.$.svg,n.columnsVisibility.filter(Boolean).length);this.scopeSubtree(this.$.svg,!0),this._interactionManager=new R0t(t,n.schema,(t=>this.closestSessionGroupChanged(t)),(t=>this.selectedSessionGroupChanged(t)))}this._computeValidSessionGroups(),this._interactionManager.onOptionsOrSessionGroupsChanged(this.options,this._validSessionGroups),this.redrawCount++,this._prevOptions=this.options}closestSessionGroupChanged(t){this.closestSessionGroup=t}selectedSessionGroupChanged(t){this.selectedSessionGroup=t}_computeValidSessionGroups(){const t=QJt;if(void 0===this.sessionGroups)return void(this._validSessionGroups=void 0);const e=this.options.configuration.schema;this._validSessionGroups=this.sessionGroups.filter((n=>{for(let i=0;i<t.numColumns(e);++i)if(this.options.configuration.columnsVisibility[i]&&void 0===t.columnValueByIndex(e,n,i))return!1;return!0}))}};O0t.template=xe`
    <div id="container">
      <svg id="svg"></svg>
    </div>
    <style>
      :host {
        display: block;
        --tf-hparams-parallel-coords-plot-axis-shadow: 0 1px 0 #fff,
          1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;
      }
      :host(.dark-mode) {
        --tf-hparams-parallel-coords-plot-axis-shadow: 0 1px 0 #000,
          1px 0 0 #000, 0 -1px 0 #000, -1px 0 0 #000;
      }
      svg {
        font: 10px sans-serif;
      }

      .background path {
        fill: none;
        stroke: #ddd;
        shape-rendering: crispEdges;
      }

      .foreground path {
        fill: none;
        stroke-opacity: 0.7;
        stroke-width: 1;
      }

      /* Will be set on foreground paths that are not "contained" in the current
         axes brushes. If no brushes are set, no path will have this class. */
      .foreground .invisible-path {
        display: none;
      }

      /* Style for the path closest to the mouse pointer (typically will become
      the selected path when the user clicks). */
      .foreground .peaked-path {
        stroke-width: 3;
      }

      /* The currently selected path class. We use !important to override the
         inline style that sets the regular color of a path. */
      .foreground .selected-path {
        stroke-width: 3 !important;
        stroke: #0f0 !important;
      }

      #container {
        height: 100%;
        width: 100%;
      }

      svg {
        width: 100%;
        height: 100%;
      }

      .axis text {
        text-shadow: var(--tf-hparams-parallel-coords-plot-axis-shadow);
        fill: currentColor;
        cursor: move;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],O0t.prototype,"sessionGroups",void 0),t([o({type:Object}),e("design:type",Object)],O0t.prototype,"options",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],O0t.prototype,"selectedSessionGroup",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],O0t.prototype,"closestSessionGroup",void 0),t([o({type:Number}),e("design:type",Number)],O0t.prototype,"redrawCount",void 0),t([o({type:Array}),e("design:type",Array)],O0t.prototype,"_validSessionGroups",void 0),t([o({type:Object}),e("design:type",Object)],O0t.prototype,"_interactionManager",void 0),t([a("options.*","sessionGroups.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],O0t.prototype,"_optionsOrSessionGroupsChanged",null),O0t=t([i("tf-hparams-parallel-coords-plot")],O0t);let z0t=class extends we{constructor(){super(...arguments),this.options=null}_configurationChanged(){const t=this.configuration.visibleSchema,e=this.configuration.schema,n={columns:t.hparamInfos.map(((n,i)=>({name:TJt(n),index:i,absoluteIndex:BJt(e,t,i),scale:this._isNumericColumn(i)?"LINEAR":"NON_NUMERIC"}))).concat(t.metricInfos.map(((n,i)=>{const r=i+t.hparamInfos.length;return{scale:"LINEAR",name:CJt(n),index:r,absoluteIndex:BJt(e,t,r)}}))),minColor:"#0000FF",maxColor:"#FF0000",configuration:this.configuration};this.set("options",n),Wi(),this.set("options.colorByColumnIndex",this._defaultColorByColumnIndex())}_unselectDisabledLogScales(){null!==this.options&&this.options.columns.forEach((t=>{const e="options.columns."+t.index;this._allowLogScale(t)||"LOG"!==t.scale||this.set(e+".scale","LINEAR")}))}_allowLogScale(t){if(!this._isNumericColumn(t.index)||!this.sessionGroups)return!1;const[e,n]=VJt(this.configuration.visibleSchema,this.sessionGroups,t.index);return e>0||n<0}_isNumericColumn(t){return t>=this.configuration.visibleSchema.hparamInfos.length||"DATA_TYPE_FLOAT64"===this.configuration.visibleSchema.hparamInfos[t].type}_defaultColorByColumnIndex(){if(this.configuration.visibleSchema.metricInfos.length>0)return this.configuration.visibleSchema.hparamInfos.length;const t=this.configuration.visibleSchema.hparamInfos.findIndex((t=>"DATA_TYPE_FLOAT64"===t.type));return-1!==t?t:void 0}};z0t.template=xe`
    <div class="control-panel">
      <!-- 'Color by' drop down menu -->
      <paper-dropdown-menu
        label="Color by"
        id="colorByDropDownMenu"
        horizontal-align="left"
      >
        <paper-listbox
          class="dropdown-content"
          slot="dropdown-content"
          selected="{{options.colorByColumnIndex}}"
          id="colorByListBox"
        >
          <template
            is="dom-repeat"
            items="[[options.columns]]"
            as="column"
            id="colorByColumnTemplate"
          >
            <paper-item disabled="[[!_isNumericColumn(column.index)]]">
              [[column.name]]
            </paper-item>
          </template>
        </paper-listbox>
      </paper-dropdown-menu>

      <!-- Columns scales -->
      <div class="columns-container">
        <!-- Scale options for each numeric feature -->
        <template is="dom-repeat" items="{{options.columns}}" as="column">
          <template is="dom-if" if="[[_isNumericColumn(column.index)]]">
            <div class="column">
              <div class="column-title">[[column.name]]</div>
              <div>
                <paper-radio-group
                  class="scale-radio-group"
                  selected="{{column.scale}}"
                >
                  <paper-radio-button name="LINEAR">
                    Linear
                  </paper-radio-button>
                  <!-- The id here is used to access this button in unit
                       tests.-->
                  <paper-radio-button
                    id="logScaleButton_[[column.name]]"
                    name="LOG"
                    disabled="[[!_allowLogScale(column, sessionGroups.*)]]"
                  >
                    Logarithmic
                  </paper-radio-button>
                  <paper-radio-button name="QUANTILE">
                    Quantile
                  </paper-radio-button>
                </paper-radio-group>
              </div>
            </div>
          </template>
        </template>
      </div>
    </div>

    <style>
      :host {
        display: block;
      }
      .control-panel {
        overflow: auto;
      }
      .column {
        flex-grow: 1;
        flex-shrink: 1;
        margin-right: 5px;
        border: solid 1px darkgray;
        padding: 3px;
      }
      .column-title {
        /* Fit every title in one line so the radio boxes align vertically. */
        white-space: nowrap;
        text-decoration: underline;
      }
      .columns-container {
        display: flex;
        flex-direction: row;
      }
      .scale-radio-group paper-radio-button {
        padding: 2px;
        display: block;
      }
      paper-listbox {
        max-height: 15em;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],z0t.prototype,"configuration",void 0),t([o({type:Array}),e("design:type",Array)],z0t.prototype,"sessionGroups",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],z0t.prototype,"options",void 0),t([a("configuration.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],z0t.prototype,"_configurationChanged",null),t([a("sessionGroups.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],z0t.prototype,"_unselectDisabledLogScales",null),z0t=t([i("tf-hparams-scale-and-color-controls")],z0t);let D0t=class extends we{_closestOrSelected(t,e){return null!==t?t:e}};D0t.template=xe`
    <!-- Controls behavior of parallel coordinates plot
         outputs set options to the _options property.
      -->
    <hparams-split-layout orientation="vertical">
      <!-- The scale and color controls. -->
      <tf-hparams-scale-and-color-controls
        id="controls"
        slot="content"
        class="section"
        configuration="[[configuration]]"
        session-groups="[[sessionGroups]]"
        options="{{_options}}"
      >
      </tf-hparams-scale-and-color-controls>
      <!-- The actual parallel coordinates plot -->
      <tf-hparams-parallel-coords-plot
        id="plot"
        slot="content"
        class="section"
        session-groups="[[sessionGroups]]"
        selected-session-group="{{_selectedGroup}}"
        closest-session-group="{{_closestGroup}}"
        options="[[_options]]"
      >
      </tf-hparams-parallel-coords-plot>
      <tf-hparams-session-group-values
        id="values"
        slot="content"
        class="section"
        visible-schema="[[configuration.visibleSchema]]"
        session-group="[[_closestOrSelected(
                             _closestGroup, _selectedGroup)]]"
      >
      </tf-hparams-session-group-values>
      <tf-hparams-session-group-details
        id="details"
        slot="content"
        class="section"
        backend="[[backend]]"
        experiment-name="[[experimentName]]"
        session-group="[[_selectedGroup]]"
        visible-schema="[[configuration.visibleSchema]]"
      >
      </tf-hparams-session-group-details>
    </hparams-split-layout>

    <style>
      .section {
        padding: 10px;
      }
      #values {
        height: 115px;
      }
      #details {
        flex-grow: 1;
        max-height: fit-content;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],D0t.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],D0t.prototype,"experimentName",void 0),t([o({type:Object}),e("design:type",Object)],D0t.prototype,"configuration",void 0),t([o({type:Array}),e("design:type",Array)],D0t.prototype,"sessionGroups",void 0),D0t=t([i("tf-hparams-parallel-coords-view")],D0t);let B0t=class extends(cr(we)){constructor(){super(...arguments),this.selectedSessionGroup=null,this.closestSessionGroup=null,this._container=null,this._svg=null,this.width=0,this.height=0,this._brushedCellIndex=null,this._brushSelection=null}ready(){super.ready(),this._container=this.$.container,this._svg=Du(this.$.svg),this._redraw()}_sessionGroupsChanged(){null!==this.selectedSessionGroup&&(this.selectedSessionGroup=UJt(this.sessionGroups,this.selectedSessionGroup.name)||null),this._redraw()}_visibleSchemaChanged(){this._brushedCellIndex=null,this._brushSelection=null,this._redraw()}_redraw(){this.debounce("_redraw",(()=>{const t=QJt;this.width=Math.max(150*t.numVisibleColumns(this.visibleSchema),1200),this.height=Math.max(112.5*t.numVisibleMetrics(this.visibleSchema),480),this._container.style.width=this.width+"px",this._container.style.height=this.height+"px",this._svg.attr("width",this.width).attr("height",this.height),this._svg.selectAll("g").remove(),this._draw()}),100)}_draw(){const t=QJt,e=this;if(!this.sessionGroups||0==this.sessionGroups.length||!this.visibleSchema||0==this.visibleSchema.metricInfos.length)return;const n=Kl(t.numVisibleColumns(e.visibleSchema)),i=Kl(t.numVisibleMetrics(e.visibleSchema)),r=80,o=50,a=XM().domain(n).range([85,this.width-1-5]).paddingInner(.1),s=XM().domain(i).range([this.height-1-5-o,5]).paddingInner(.1),l=a.bandwidth(),c=s.bandwidth(),u=n.map((t=>e._cellScale(t,[0,l-1]))),h=i.map((n=>e._cellScale(n+t.numVisibleHParams(e.visibleSchema),[c-1,0]))),d=this._svg.selectAll(".x-axis").data(n).enter().append("g").classed("x-axis",!0).attr("transform",(e=>t.translateStr(a(e),0)));function p(t){return"x-axis-clip-path-"+t}function f(t){return"x-label-clip-path-"+t}d.append("clipPath").attr("id",p).append("rect").attr("x",-5).attr("y",0).attr("width",l+10).attr("height",e.height-25),d.append("clipPath").attr("id",f).append("rect").attr("x",0).attr("y",e.height-25).attr("width",l).attr("height",25),d.append("g").attr("clip-path",(t=>"url(#"+p(t)+")")).each((function(t){Du(this).call(y,vc(u[t]).tickSize(e.height-o),l,40,e.options.columns[t].scale)})),d.append("g").classed("x-axis-label",!0).attr("clip-path",(t=>"url(#"+f(t)+")")).append("text").attr("text-anchor","middle").attr("x",l/2).attr("y",e.height-1-12.5).text((n=>t.schemaVisibleColumnName(e.visibleSchema,n))).append("title").text((n=>t.schemaVisibleColumnName(e.visibleSchema,n)));const m=this._svg.selectAll(".y-axis").data(i).enter().append("g").classed("y-axis",!0).attr("transform",(n=>t.translateStr(e.width-1,s(n))));function g(t){return"y-axis-clip-path-"+t}function _(t){return"y-label-clip-path-"+t}function y(t,e,n,i,r){const o=Math.floor(n/i),a=e.scale();if("QUANTILE"===r){let t=a.quantiles();const n=Math.ceil(t.length/o);t=Kl(0,t.length,n).map((e=>t[e])),e.tickValues(t).tickFormat(Dy("-.2g"))}"LINEAR"!==r&&"LOG"!==r||e.ticks(o),t.call(e),t.selectAll(".domain").remove(),t.selectAll(".tick line").attr("stroke","#ddd")}m.append("clipPath").attr("id",g).append("rect").attr("x",-(e.width-40-1)).attr("y",-5).attr("width",e.width-40).attr("height",c+10),m.append("clipPath").attr("id",_).append("rect").attr("x",-(e.width-1)).attr("y",0).attr("width",40).attr("height",c),m.append("g").attr("clip-path",(t=>"url(#"+g(t)+")")).each((function(n){Du(this).call(y,bc(h[n]).tickSize(e.width-r),c,20,e.options.columns[n+t.numVisibleHParams(e.visibleSchema)].scale)})),m.append("g").classed("y-axis-label",!0).attr("clip-path",(t=>"url(#"+_(t)+")")).append("text").attr("text-anchor","middle").attr("x",-(e.width-20-1)).attr("y",c/2).attr("transform",t.rotateStr(90,-(e.width-20-1),c/2)).text((n=>t.metricName(e.visibleSchema.metricInfos[n]))).append("title").text((n=>t.metricName(e.visibleSchema.metricInfos[n])));const v=this._svg.selectAll(".cell").data(Fl(n,i)).enter().append("g").classed("cell",!0).attr("transform",(([e,n])=>t.translateStr(a(e),s(n))));v.append("g").classed("frame",!0).append("rect").attr("x",-5).attr("y",-5).attr("width",l+10).attr("height",c+10).attr("stroke","#000").attr("fill","none").attr("shape-rendering","crispEdges");let b=null;void 0!==e.options.colorByColumnIndex&&(b=cE().domain(this._colExtent(this.options.colorByColumnIndex)).range([this.options.minColor,this.options.maxColor]).interpolate(kp));const x=void 0===e.options.colorByColumnIndex?()=>"red":({sessionGroup:t})=>b(this._colValue(t,e.options.colorByColumnIndex));function w(t,n){return u[n](e._colValue(t,n))}function S(t,n){return h[n](e._metricValue(t,n))}const[M,E,T]=(function C(t,r){const o=t.selectAll(".data-marker").data((([t,n])=>e.sessionGroups.filter((i=>void 0!==e._colValue(i,t)&&void 0!==e._metricValue(i,n))).map((e=>({col:t,metric:n,sessionGroup:e,x:w(e,t),y:S(e,n),sessionGroupMarkers:null}))))).enter().append("circle").classed("data-marker",!0).attr("cx",(({x:t})=>t)).attr("cy",(({y:t})=>t)).attr("r",2).attr("fill",r),a=new Map;e.sessionGroups.forEach((t=>{a.set(t,[])})),o.each((function(t){a.get(t.sessionGroup).push(this)})),o.each((t=>{const e=a.get(t.sessionGroup);t.sessionGroupMarkers=new Set(e)}));const s=n.map((t=>i.map((e=>o.filter((n=>n.col==t&&n.metric==e))))));return[o,s,a]})(v.append("g"),x);const A=n.map((t=>i.map((e=>(function n(t,e){const n=[];return E[t][e].each((function(){n.push(this)})),ty().x((t=>Du(t).datum().x)).y((t=>Du(t).datum().y)).addAll(n)})(t,e)))));let k=null;R()&&(k=v.filter((t=>Ce.exports.isEqual(t,e._brushedCellIndex))),console.assert(1==k.size(),k));let L=new Set(M.nodes());function P(){let n=new Set(M.nodes());(function i(){return!R()||e._brushSelection[0][0]===e._brushSelection[1][0]||e._brushSelection[0][1]===e._brushSelection[1][1]})()||(n=(function r(e,n){console.assert(null!==e),console.assert(null!==n);const[i,r]=e,o=new Set;return t.quadTreeVisitPointsInRect(A[i][r],n[0][0],n[0][1],n[1][0],n[1][1],(t=>{Du(t).datum().sessionGroupMarkers.forEach((t=>{o.add(t)}))})),o})(e._brushedCellIndex,e._brushSelection)),xh(Array.from(t.filterSet(n,(t=>!L.has(t))))).attr("fill",x),xh(Array.from(t.filterSet(L,(t=>!n.has(t))))).attr("fill","#ddd"),L=n}P();const N=vg().extent([[-4,-4],[l-1+5-1,c-1+5-1]]).on("start",(function(){R()&&k.node()!=this&&N.move(k,null),I(this)})).on("brush",(function(){I(this)})).on("end",(function(){I(this)}));function I(t){const n=_g(t);!R()&&null===n||R()&&t===k.node()&&Ce.exports.isEqual(n,e._brushSelection)||(e._brushSelection=n,null!==n?(k=Du(t),e._brushedCellIndex=k.datum()):(k=null,e._brushedCellIndex=null),P())}function R(){return null!==e._brushedCellIndex&&null!==e._brushSelection}v.call(N),R()&&N.move(k,e._brushSelection);let O=null,z=null;null!==this.selectedSessionGroup&&(z=xh(T.get(this.selectedSessionGroup)).classed("selected-marker",!0)),v.on("click",(function(){const t=O===z?null:O;if(t===z)return;null!==z&&z.classed("selected-marker",!1),z=t,null!==z&&z.classed("selected-marker",!0);const n=null===z?null:z.datum().sessionGroup;e.selectedSessionGroup=n})).on("mousemove mouseenter",(function([n,i]){const[r,o]=bh(this),a=(function s(e,n,i,r,o){let a=1/0,s=null;if(t.quadTreeVisitPointsInDisk(A[e][n],i,r,o,((t,e)=>{if(L.has(t)&&e<a){const n=Du(t).datum();a=e,s=n.sessionGroup}})),null===s)return null;return xh(T.get(s))})(n,i,r,o,20);O!==a&&(null!==O&&O.classed("closest-marker",!1),O=a,null!==O?(O.classed("closest-marker",!0),e.closestSessionGroup=O.datum().sessionGroup):e.closestSessionGroup=null)})).on("mouseleave",(function([t,n]){null!==O&&(O.classed("closest-marker",!1),O=null,e.closestSessionGroup=null)})),this._svg.selectAll("*").classed("tf-hparams-scatter-plot-matrix-plot",!0)}_cellScale(t,e){const n=this._colExtent(t),i=cE().domain(n).range(e);if("LINEAR"===this.options.columns[t].scale)return i;if("LOG"===this.options.columns[t].scale)return n[0]<=0&&n[1]>=0?i:yE().domain(n).range(e);if("QUANTILE"===this.options.columns[t].scale){const n=(e[1]-e[0])/19,i=Kl(20).map((t=>e[0]+n*t));return CE().domain(Ce.exports.uniq(this.sessionGroups.map((e=>this._colValue(e,t))))).range(i)}if("NON_NUMERIC"===this.options.columns[t].scale)return KM().domain(Ce.exports.uniq(this.sessionGroups.map((e=>this._colValue(e,t))).sort())).range(e).padding(.1);throw"Unknown scale for column: "+t+". options: "+this.options}_colValue(t,e){return WJt(this.visibleSchema,t,e)}_metricValue(t,e){return GJt(this.visibleSchema,t,e)}_colExtent(t){return VJt(this.visibleSchema,this.sessionGroups,t)}};B0t.template=xe`
    <div id="container">
      <svg id="svg"></svg>
    </div>

    <style>
      :host {
        display: block;
      }
      svg {
        font: 10px sans-serif;
      }

      text {
        fill: currentColor;
      }

      .frame rect {
        stroke: currentColor;
      }

      /* The closest data point marker to the mouse pointer. We use !important
         to override the inline style that sets the regular style of a marker.
      */
      .closest-marker {
        r: 6 !important;
      }

      /* The currently selected data point marker. We use !important to
         override the inline style that sets the regular style of a marker. */
      .selected-marker {
        r: 6 !important;
        fill: #0f0 !important;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],B0t.prototype,"visibleSchema",void 0),t([o({type:Array}),e("design:type",Array)],B0t.prototype,"sessionGroups",void 0),t([o({type:Object}),e("design:type",Object)],B0t.prototype,"options",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],B0t.prototype,"selectedSessionGroup",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],B0t.prototype,"closestSessionGroup",void 0),t([o({type:Object}),e("design:type",HTMLElement)],B0t.prototype,"_container",void 0),t([o({type:Object}),e("design:type",Object)],B0t.prototype,"_svg",void 0),t([o({type:Number}),e("design:type",Number)],B0t.prototype,"width",void 0),t([o({type:Number}),e("design:type",Number)],B0t.prototype,"height",void 0),t([o({type:Object}),e("design:type",Object)],B0t.prototype,"_brushedCellIndex",void 0),t([o({type:Object}),e("design:type",Object)],B0t.prototype,"_brushSelection",void 0),t([a("sessionGroups.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],B0t.prototype,"_sessionGroupsChanged",null),t([a("visibleSchema.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],B0t.prototype,"_visibleSchemaChanged",null),t([a("options.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],B0t.prototype,"_redraw",null),B0t=t([i("tf-hparams-scatter-plot-matrix-plot")],B0t);let H0t=class extends we{_closestOrSelected(t,e){return null!==t?t:e}};H0t.template=xe`
    <hparams-split-layout orientation="vertical">
      <!-- Controls behavior of the scatter plot matrix
             outputs the configured options to the _options property. -->
      <tf-hparams-scale-and-color-controls
        slot="content"
        class="section"
        id="controls"
        configuration="[[configuration]]"
        session-groups="[[sessionGroups]]"
        options="{{_options}}"
      >
      </tf-hparams-scale-and-color-controls>
      <!-- The actual scatter plot matrix -->
      <tf-hparams-scatter-plot-matrix-plot
        slot="content"
        class="section"
        id="plot"
        visible-schema="[[configuration.visibleSchema]]"
        session-groups="[[sessionGroups]]"
        selected-session-group="{{_selectedGroup}}"
        closest-session-group="{{_closestGroup}}"
        options="[[_options]]"
      >
      </tf-hparams-scatter-plot-matrix-plot>
      <tf-hparams-session-group-values
        slot="content"
        class="section"
        id="values"
        visible-schema="[[configuration.visibleSchema]]"
        session-group="[[_closestOrSelected(
                                 _closestGroup, _selectedGroup)]]"
      >
      </tf-hparams-session-group-values>
      <!-- Shows session group details for the clicked marker. -->
      <tf-hparams-session-group-details
        slot="content"
        class="section"
        id="details"
        backend="[[backend]]"
        experiment-name="[[experimentName]]"
        session-group="[[_selectedGroup]]"
        visible-schema="[[configuration.visibleSchema]]"
      >
      </tf-hparams-session-group-details>
    </hparams-split-layout>
    <style>
      .section {
        padding: 10px;
      }
      #controls {
        flex-grow: 0;
        flex-shrink: 0;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      #plot {
        flex-grow: 1;
        flex-shrink: 1;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      #values {
        flex-grow: 0;
        flex-shrink: 0;
        flex-basis: auto;
        height: 115px;
        overflow-y: auto;
        max-height: fit-content;
      }
      #details {
        flex-grow: 0;
        flex-shrink: 1;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      vaadin-split-layout {
        height: 100%;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],H0t.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],H0t.prototype,"experimentName",void 0),t([o({type:Object}),e("design:type",Object)],H0t.prototype,"configuration",void 0),t([o({type:Array}),e("design:type",Array)],H0t.prototype,"sessionGroups",void 0),H0t=t([i("tf-hparams-scatter-plot-matrix-view")],H0t);let F0t=class extends we{constructor(){super(...arguments),this._selectedTab=0}};F0t.template=xe`
    <paper-header-panel>
      <paper-toolbar slot="header" class="tab-bar">
        <paper-tabs selected="{{_selectedTab}}" slot="top">
          <!-- view-id can be used by integration tests to locate a tab.
               It should be the name of the root element implementing the view
               without the 'tf-hparams-' prefix. -->
          <paper-tab view-id="table-view"> TABLE VIEW </paper-tab>
          <paper-tab view-id="parallel-coords-view">
            PARALLEL COORDINATES VIEW
          </paper-tab>
          <paper-tab view-id="scatter-plot-matrix-view">
            SCATTER PLOT MATRIX VIEW
          </paper-tab>
          <div class="help-and-feedback">
            <template is="dom-if" if="[[bugReportUrl]]">
              <a
                href$="[[bugReportUrl]]"
                target="_blank"
                rel="noopener noreferrer"
              >
                <paper-button
                  id="bug-report"
                  raised
                  title="Send a bug report or feature request"
                >
                  Bug Report / Feature Request
                </paper-button>
              </a>
            </template>
            <template is="dom-if" if="[[helpUrl]]">
              <a href$="[[helpUrl]]" target="_blank" rel="noopener noreferrer">
                <paper-icon-button
                  icon="help-outline"
                  title="View documentation"
                >
                </paper-icon-button>
              </a>
            </template>
          </div>
        </paper-tabs>
      </paper-toolbar>
      <iron-pages selected="[[_selectedTab]]" class="fit tab-view">
        <div id="0" class="tab">
          <tf-hparams-table-view
            backend="[[backend]]"
            experiment-name="[[experimentName]]"
            visible-schema="[[configuration.visibleSchema]]"
            session-groups="[[sessionGroups]]"
            enable-show-metrics
          >
          </tf-hparams-table-view>
        </div>
        <div id="1" class="tab">
          <tf-hparams-parallel-coords-view
            backend="[[backend]]"
            experiment-name="[[experimentName]]"
            configuration="[[configuration]]"
            session-groups="[[sessionGroups]]"
          >
          </tf-hparams-parallel-coords-view>
        </div>
        <div id="2" class="tab">
          <tf-hparams-scatter-plot-matrix-view
            backend="[[backend]]"
            experiment-name="[[experimentName]]"
            configuration="[[configuration]]"
            session-groups="[[sessionGroups]]"
          >
          </tf-hparams-scatter-plot-matrix-view>
        </div>
      </iron-pages>
    </paper-header-panel>

    <style>
      .tab-view {
        height: 100%;
      }
      .tab-bar {
        overflow-y: auto;
        color: white;
        background-color: var(
          --tb-toolbar-background-color,
          var(--tb-orange-strong)
        );
      }
      .tab {
        height: 100%;
      }
      paper-tabs {
        flex-grow: 1;
        width: 100%;
        height: 100%;
        --paper-tabs-selection-bar-color: white;
        --paper-tabs-content: {
          -webkit-font-smoothing: antialiased;
        }
      }
      tf-hparams-table-view {
        width: 100%;
        height: 100%;
      }
      .help-and-feedback {
        display: inline-flex; /* Ensure that icons stay aligned */
        justify-content: flex-end;
        align-items: center;
        text-align: right;
        color: white;
      }
      #bug-report {
        border: solid black;
        background: red;
        white-space: normal;
        word-break: break-words;
        font-size: 12px;
        max-width: 150px;
        text-align: left;
      }
      .help-and-feedback a {
        color: white;
        text-decoration: none;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],F0t.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],F0t.prototype,"helpUrl",void 0),t([o({type:String}),e("design:type",String)],F0t.prototype,"bugReportUrl",void 0),t([o({type:String}),e("design:type",String)],F0t.prototype,"experimentName",void 0),t([o({type:Object}),e("design:type",Object)],F0t.prototype,"configuration",void 0),t([o({type:Array}),e("design:type",Array)],F0t.prototype,"sessionGroups",void 0),t([o({type:Number}),e("design:type",Number)],F0t.prototype,"_selectedTab",void 0),F0t=t([i("tf-hparams-sessions-pane")],F0t);let V0t=class extends(cr(we)){reload(){this.$["query-pane"].reload()}};V0t.template=xe`
    <hparams-split-layout>
      <div slot="content" class="sidebar">
        <tf-hparams-query-pane
          id="query-pane"
          backend="[[backend]]"
          experiment-name="[[experimentName]]"
          configuration="{{_configuration}}"
          session-groups="{{_sessionGroups}}"
          data-loaded-with-non-empty-hparams="{{_dataLoadedWithNonEmptyHparams}}"
          data-loaded-with-empty-hparams="{{_dataLoadedWithEmptyHparams}}"
        >
        </tf-hparams-query-pane>
      </div>
      <div slot="content" class="center">
        <template is="dom-if" if="[[_dataLoadedWithEmptyHparams]]">
          <div class="no-data-warning">
            <h3>No hparams data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>You haven’t written any hparams data to your event files.</li>
              <li>
                Event files are still being loaded (try reloading this page).
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>

        <template is="dom-if" if="[[_dataLoadedWithNonEmptyHparams]]">
          <tf-hparams-sessions-pane
            id="sessions-pane"
            backend="[[backend]]"
            help-url="[[helpUrl]]"
            bug-report-url="[[bugReportUrl]]"
            experiment-name="[[experimentName]]"
            configuration="[[_configuration]]"
            session-groups="[[_sessionGroups]]"
          >
          </tf-hparams-sessions-pane>
        </template>
      </div>
    </hparams-split-layout>
    <style>
      hparams-split-layout {
        width: 100%;
      }

      .sidebar {
        width: 20%;
        height: 100%;
        overflow: auto;
        flex-grow: 0;
        flex-shrink: 0;
        min-width: 10%;
      }

      .center {
        height: 100%;
        overflow-y: auto;
        flex-grow: 1;
        flex-shrink: 1;
        width: 80%;
      }

      :host {
        display: flex;
        flex-direction: row;
        height: 100%;
        width: 100%;
      }

      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }

      a {
        color: var(--tb-link);
      }

      a:visited {
        color: var(--tb-link-visited);
      }
    </style>
  `,t([o({type:Object}),e("design:type",MJt)],V0t.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],V0t.prototype,"experimentName",void 0),t([o({type:String}),e("design:type",String)],V0t.prototype,"helpUrl",void 0),t([o({type:String}),e("design:type",String)],V0t.prototype,"bugReportUrl",void 0),t([o({type:Object}),e("design:type",Object)],V0t.prototype,"_configuration",void 0),t([o({type:Array}),e("design:type",Array)],V0t.prototype,"_sessionGroups",void 0),t([o({type:Boolean}),e("design:type",Boolean)],V0t.prototype,"_dataLoadedWithNonEmptyHparams",void 0),t([o({type:Boolean}),e("design:type",Boolean)],V0t.prototype,"_dataLoadedWithEmptyHparams",void 0),V0t=t([i("tf-hparams-main")],V0t);const U0t="true"===new URLSearchParams(window.location.search).get("tensorboardColab");let j0t=class extends(cr(we)){constructor(){super(...arguments),this._backend=new MJt(Er().pluginRoute("hparams",""),new br,U0t)}reload(){this.$["hparams-main"].reload()}};j0t.template=xe`
    <!-- TensorBoard does not specify an experimentName. Currently it only
         supports one experiment per invocation. -->
    <tf-hparams-main
      id="hparams-main"
      backend="[[_backend]]"
      experiment-name=""
    >
    </tf-hparams-main>
  `,t([o({type:Object}),e("design:type",Object)],j0t.prototype,"_backend",void 0),j0t=t([i("tf-hparams-dashboard")],j0t);let G0t=class extends(cr(we)){constructor(){super(...arguments),this.actualSize=!1,this.brightnessAdjustment=.5,this.contrastPercentage=0,this._metadataCanceller=new xO,this._imageCanceller=new xO,this._steps=[],this._isImageLoading=!1}get _runColor(){return _O(this.run)}get _hasAtLeastOneStep(){var t=this._steps;return!!t&&t.length>0}get _hasMultipleSteps(){var t=this._steps;return!!t&&t.length>1}get _currentStep(){return this._steps[this._stepIndex]||null}get _stepValue(){var t=this._currentStep;return t?t.step:0}get _currentWallTime(){var t=this._currentStep;return t?SO(t.wall_time):""}get _maxStepIndex(){return this._steps.length-1}get _sampleText(){return`${this.sample+1}`}get _hasMultipleSamples(){return this.ofSamples>1}_getAriaExpanded(){return this.actualSize?"true":"false"}attached(){this.reload()}reload(){if(!this.isAttached)return;this._metadataCanceller.cancelAll();const t=PO(Er().pluginRoute("images","/images"),{tag:this.tag,run:this.run,sample:this.sample}),e=this._metadataCanceller.cancellable((t=>{if(t.cancelled)return;const e=t.value.map(this._createStepDatum.bind(this));this.set("_steps",e),this.set("_stepIndex",e.length-1)}));this.requestManager.request(t).then(e)}_createStepDatum(t){let e=Er().pluginRoute("images","/individualImage");return e=PO(e,{ts:t.wall_time}),e+="&"+t.query,{wall_time:new Date(1e3*t.wall_time),step:t.step,url:e}}_updateImageUrl(){var t=this._currentStep,e=this.brightnessAdjustment,n=this.contrastPercentage;if(!t)return;const i=new Image;this._imageCanceller.cancelAll(),i.onload=i.onerror=this._imageCanceller.cancellable((t=>{if(t.cancelled)return;const e=this.$$("#main-image-container");e.textContent="",er(e).appendChild(i),this.set("_isImageLoading",!1)})).bind(this),i.style.filter=`contrast(${n}%) `,i.style.filter+=`brightness(${e})`,this.set("_isImageLoading",!0),i.src=t.url}_handleTap(t){this.set("actualSize",!this.actualSize)}_toLocaleString(t){return t.toLocaleString()}};G0t.template=xe`
    <tf-card-heading
      tag="[[tag]]"
      run="[[run]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
      color="[[_runColor]]"
    >
      <template is="dom-if" if="[[_hasMultipleSamples]]">
        <div>sample: [[_sampleText]] of [[ofSamples]]</div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep]]">
        <div class="heading-row">
          <div class="heading-label">
            step
            <span style="font-weight: bold"
              >[[_toLocaleString(_stepValue)]]</span
            >
          </div>
          <div class="heading-label heading-right datetime">
            <template is="dom-if" if="[[_currentWallTime]]">
              [[_currentWallTime]]
            </template>
          </div>
          <div class="label right">
            <paper-spinner-lite active hidden$="[[!_isImageLoading]]">
            </paper-spinner-lite>
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps]]">
        <div>
          <paper-slider
            id="steps"
            immediate-value="{{_stepIndex}}"
            max="[[_maxStepIndex]]"
            max-markers="[[_maxStepIndex]]"
            snaps
            step="1"
            value="{{_stepIndex}}"
          ></paper-slider>
        </div>
      </template>
    </tf-card-heading>

    <!-- Semantically a button but <img> inside a <button> disallows user to do
    an interesting operation like "Copy Image" in non-Chromium browsers. -->
    <a
      id="main-image-container"
      role="button"
      aria-label="Toggle actual size"
      aria-expanded$="[[_getAriaExpanded(actualSize)]]"
      on-tap="_handleTap"
    ></a>

    <style include="tf-card-heading-style">
      /** Make button a div. */
      button {
        width: 100%;
        display: block;
        background: none;
        border: 0;
        padding: 0;
      }

      /** Firefox: Get rid of dotted line inside button. */
      button::-moz-focus-inner {
        border: 0;
        padding: 0;
      }

      /** Firefox: Simulate Chrome's outer glow on button when focused. */
      button:-moz-focusring {
        outline: none;
        box-shadow: 0px 0px 1px 2px Highlight;
      }

      :host {
        display: block;
        width: 350px;
        height: auto;
        position: relative;
        margin: 0 15px 40px 0;
        overflow-x: auto;
      }

      /** When actual size shown is on, use the actual image width. */
      :host([actual-size]) {
        max-width: 100%;
        width: auto;
      }

      :host([actual-size]) #main-image-container {
        max-height: none;
        width: auto;
      }

      :host([actual-size]) #main-image-container img {
        width: auto;
      }

      paper-spinner-lite {
        width: 14px;
        height: 14px;
        vertical-align: text-bottom;
        --paper-spinner-color: var(--tb-orange-strong);
      }

      #steps {
        height: 15px;
        margin: 0 0 0 -15px;
        /*
         * 31 comes from adding a padding of 15px from both sides of the
         * paper-slider, subtracting 1px so that the slider width aligns
         * with the image (the last slider marker takes up 1px), and
         * adding 2px to account for a border of 1px on both sides of
         * the image. 30 - 1 + 2.
         */
        width: calc(100% + 31px);
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
      }

      #main-image-container {
        max-height: 1024px;
        overflow: auto;
      }

      #main-image-container img {
        cursor: pointer;
        display: block;
        image-rendering: -moz-crisp-edges;
        image-rendering: pixelated;
        width: 100%;
        height: auto;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }
      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }
      [hidden] {
        display: none;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],G0t.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],G0t.prototype,"tag",void 0),t([o({type:Number}),e("design:type",Number)],G0t.prototype,"sample",void 0),t([o({type:Number}),e("design:type",Number)],G0t.prototype,"ofSamples",void 0),t([o({type:Object}),e("design:type",Object)],G0t.prototype,"tagMetadata",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],G0t.prototype,"actualSize",void 0),t([o({type:Number}),e("design:type",Number)],G0t.prototype,"brightnessAdjustment",void 0),t([o({type:Number}),e("design:type",Number)],G0t.prototype,"contrastPercentage",void 0),t([o({type:Object}),e("design:type",br)],G0t.prototype,"requestManager",void 0),t([o({type:Object}),e("design:type",Object)],G0t.prototype,"_metadataCanceller",void 0),t([o({type:Object}),e("design:type",Object)],G0t.prototype,"_imageCanceller",void 0),t([o({type:Array,notify:!0}),e("design:type",Array)],G0t.prototype,"_steps",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],G0t.prototype,"_stepIndex",void 0),t([o({type:Boolean}),e("design:type",Boolean)],G0t.prototype,"_isImageLoading",void 0),t([s("run"),e("design:type",String),e("design:paramtypes",[])],G0t.prototype,"_runColor",null),t([s("_steps"),e("design:type",Boolean),e("design:paramtypes",[])],G0t.prototype,"_hasAtLeastOneStep",null),t([s("_steps"),e("design:type",Boolean),e("design:paramtypes",[])],G0t.prototype,"_hasMultipleSteps",null),t([s("_steps","_stepIndex"),e("design:type",Object),e("design:paramtypes",[])],G0t.prototype,"_currentStep",null),t([s("_currentStep"),e("design:type",Number),e("design:paramtypes",[])],G0t.prototype,"_stepValue",null),t([s("_currentStep"),e("design:type",String),e("design:paramtypes",[])],G0t.prototype,"_currentWallTime",null),t([s("_steps"),e("design:type",Number),e("design:paramtypes",[])],G0t.prototype,"_maxStepIndex",null),t([s("sample"),e("design:type",String),e("design:paramtypes",[])],G0t.prototype,"_sampleText",null),t([s("ofSamples"),e("design:type",Boolean),e("design:paramtypes",[])],G0t.prototype,"_hasMultipleSamples",null),t([a("run","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],G0t.prototype,"reload",null),t([a("_currentStep","brightnessAdjustment","contrastPercentage"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],G0t.prototype,"_updateImageUrl",null),G0t=t([i("tf-image-loader")],G0t);let W0t=class extends(cr(we)){constructor(){super(...arguments),this.reloadOnReady=!0,this._defaultBrightnessAdjustment=1,this._defaultContrastPercentage=100,this._brightnessAdjustment=1,this._contrastPercentage=100,this._requestManager=new br}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){this._fetchTags().then((()=>{this._reloadImages()}))}_fetchTags(){const t=Er().pluginRoute("images","/tags");return this._requestManager.request(t).then((t=>{if(Ce.exports.isEqual(t,this._runToTagInfo))return;const e=fr(Ce.exports.mapValues(t,(t=>Object.keys(t))));this.set("_dataNotFound",0===e.length),this.set("_runToTagInfo",t),this.async((()=>{this.set("_categoriesDomReady",!0)}))}))}_reloadImages(){this.root.querySelectorAll("tf-image-loader").forEach((t=>{t.reload()}))}_shouldOpen(t){return t<=2}_resetBrightness(){this._brightnessAdjustment=this._defaultBrightnessAdjustment}_resetContrast(){this._contrastPercentage=this._defaultContrastPercentage}get _brightnessIsDefault(){return this._brightnessAdjustment===this._defaultBrightnessAdjustment}get _contrastIsDefault(){return this._contrastPercentage===this._defaultContrastPercentage}get _categories(){var t=this._runToTagInfo,e=this._selectedRuns,n=this._tagFilter;function i(e){const n=t[e.run][e.tag].samples;return Ce.exports.range(n).map((t=>Object.assign({},e,{sample:t,ofSamples:n})))}return Ir(Ce.exports.mapValues(t,(t=>Object.keys(t))),e,n).map((t=>Object.assign({},t,{items:[].concat.apply([],t.items.map(i))})))}_tagMetadata(t,e,n){return t[e][n]}};var q0t,Y0t,X0t;W0t.template=xe`
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="settings">
          <div class="sidebar-section">
            <div class="line-item">
              <paper-checkbox checked="{{_actualSize}}"
                >Show actual image size</paper-checkbox
              >
            </div>
          </div>
          <div class="sidebar-section">
            <h3 class="tooltip-container">Brightness adjustment</h3>
            <div class="resettable-slider-container">
              <paper-slider
                min="0"
                max="2"
                snaps
                pin
                step="0.01"
                value="{{_brightnessAdjustment}}"
                immediate-value="{{_brightnessAdjustment}}"
              ></paper-slider>
              <paper-button
                class="x-button"
                on-tap="_resetBrightness"
                disabled="[[_brightnessIsDefault]]"
                >Reset</paper-button
              >
            </div>
          </div>
          <div class="sidebar-section">
            <h3 class="tooltip-container">Contrast adjustment</h3>
            <div class="resettable-slider-container">
              <paper-slider
                min="0"
                max="500"
                snaps
                pin
                step="1"
                value="{{_contrastPercentage}}"
                immediate-value="{{_contrastPercentage}}"
              ></paper-slider>
              <paper-button
                class="x-button"
                on-tap="_resetContrast"
                disabled="[[_contrastIsDefault]]"
                >Reset</paper-button
              >
            </div>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector
            id="runs-selector"
            selected-runs="{{_selectedRuns}}"
          ></tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No image data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>You haven’t written any image data to your event files.</li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
            >
              <template>
                <tf-image-loader
                  active="[[active]]"
                  run="[[item.run]]"
                  tag="[[item.tag]]"
                  sample="[[item.sample]]"
                  of-samples="[[item.ofSamples]]"
                  tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]"
                  request-manager="[[_requestManager]]"
                  actual-size="[[_actualSize]]"
                  brightness-adjustment="[[_brightnessAdjustment]]"
                  contrast-percentage="[[_contrastPercentage]]"
                ></tf-image-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>
    <style include="dashboard-style"></style>
    <style>
      .resettable-slider-container {
        display: flex;
      }
      .resettable-slider-container paper-slider {
        flex-grow: 1;
      }
      .resettable-slider-container paper-button {
        flex-grow: 0;
      }
      .resettable-slider-container paper-button[disabled] {
        background-color: unset;
      }
      .x-button {
        font-size: 13px;
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
      }
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      paper-slider {
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
      }
    </style>
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/**
     * @license
     * Copyright 2010-2021 Three.js Authors
     * SPDX-License-Identifier: MIT
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C5t(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new A5t,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.encoding=c,this.version=0,this.onUpdate=null,this.isRenderTargetTexture=!1}updateMatrix(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}clone(){return(new this.constructor).copy(this)}copy(t){return this.name=t.name,this.image=t.image,this.mipmaps=t.mipmaps.slice(0),this.mapping=t.mapping,this.wrapS=t.wrapS,this.wrapT=t.wrapT,this.magFilter=t.magFilter,this.minFilter=t.minFilter,this.anisotropy=t.anisotropy,this.format=t.format,this.internalFormat=t.internalFormat,this.type=t.type,this.offset.copy(t.offset),this.repeat.copy(t.repeat),this.center.copy(t.center),this.rotation=t.rotation,this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrix.copy(t.matrix),this.generateMipmaps=t.generateMipmaps,this.premultiplyAlpha=t.premultiplyAlpha,this.flipY=t.flipY,this.unpackAlignment=t.unpackAlignment,this.encoding=t.encoding,this}toJSON(t){const e=void 0===t||"string"==typeof t;if(!e&&void 0!==t.textures[this.uuid])return t.textures[this.uuid];const n={metadata:{version:4.5,type:"Texture",generator:"Texture.toJSON"},uuid:this.uuid,name:this.name,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){const i=this.image;if(void 0===i.uuid&&(i.uuid=a5t()),!e&&void 0===t.images[i.uuid]){let e;if(Array.isArray(i)){e=[];for(let t=0,n=i.length;t<n;t++)e.push(I5t(i[t].isDataTexture?i[t].image:i[t]))}else e=I5t(i);t.images[i.uuid]={uuid:i.uuid,url:e}}n.image=i.uuid}return e||(t.textures[this.uuid]=n),n}dispose(){this.dispatchEvent({type:"dispose"})}transformUv(t){if(this.mapping!==t2t)return t;if(t.applyMatrix3(this.matrix),t.x<0||t.x>1)switch(this.wrapS){case s2t:t.x=t.x-Math.floor(t.x);break;case l2t:t.x=t.x<0?0:1;break;case c2t:t.x=1===Math.abs(Math.floor(t.x)%2)?Math.ceil(t.x)-t.x:t.x-Math.floor(t.x)}if(t.y<0||t.y>1)switch(this.wrapT){case s2t:t.y=t.y-Math.floor(t.y);break;case l2t:t.y=t.y<0?0:1;break;case c2t:t.y=1===Math.abs(Math.floor(t.y)%2)?Math.ceil(t.y)-t.y:t.y-Math.floor(t.y)}return this.flipY&&(t.y=1-t.y),t}set needsUpdate(t){!0===t&&this.version++}}function I5t(t){return"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof ImageBitmap&&t instanceof ImageBitmap?L5t.getDataURL(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn("THREE.Texture: Unable to serialize Texture."),{})}N5t.DEFAULT_IMAGE=void 0,N5t.DEFAULT_MAPPING=t2t,N5t.prototype.isTexture=!0;class R5t{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn("THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=this.w,o=t.elements;return this.x=o[0]*e+o[4]*n+o[8]*i+o[12]*r,this.y=o[1]*e+o[5]*n+o[9]*i+o[13]*r,this.z=o[2]*e+o[6]*n+o[10]*i+o[14]*r,this.w=o[3]*e+o[7]*n+o[11]*i+o[15]*r,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,r;const o=.01,a=.1,s=t.elements,l=s[0],c=s[4],u=s[8],h=s[1],d=s[5],p=s[9],f=s[2],m=s[6],g=s[10];if(Math.abs(c-h)<o&&Math.abs(u-f)<o&&Math.abs(p-m)<o){if(Math.abs(c+h)<a&&Math.abs(u+f)<a&&Math.abs(p+m)<a&&Math.abs(l+d+g-3)<a)return this.set(1,0,0,0),this;e=Math.PI;const t=(l+1)/2,s=(d+1)/2,_=(g+1)/2,y=(c+h)/4,v=(u+f)/4,b=(p+m)/4;return t>s&&t>_?t<o?(n=0,i=.707106781,r=.707106781):(n=Math.sqrt(t),i=y/n,r=v/n):s>_?s<o?(n=.707106781,i=0,r=.707106781):(i=Math.sqrt(s),n=y/i,r=b/i):_<o?(n=.707106781,i=.707106781,r=0):(r=Math.sqrt(_),n=v/r,i=b/r),this.set(n,i,r,e),this}let _=Math.sqrt((m-p)*(m-p)+(u-f)*(u-f)+(h-c)*(h-c));return Math.abs(_)<.001&&(_=1),this.x=(m-p)/_,this.y=(u-f)/_,this.z=(h-c)/_,this.w=Math.acos((l+d+g-1)/2),this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this.w=Math.min(this.w,t.w),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this.w=Math.max(this.w,t.w),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this.w=Math.max(t.w,Math.min(e.w,this.w)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this.w=Math.max(t,Math.min(e,this.w)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this.w=Math.floor(this.w),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this.w=Math.ceil(this.w),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this.w=Math.round(this.w),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this.w=this.w<0?Math.ceil(this.w):Math.floor(this.w),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this.w=-this.w,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z+this.w*t.w}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)+Math.abs(this.w)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this.w+=(t.w-this.w)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this.w=t.w+(e.w-t.w)*n,this}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z&&t.w===this.w}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this.w=t[e+3],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t[e+3]=this.w,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector4: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this.w=t.getW(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this.w=Math.random(),this}}R5t.prototype.isVector4=!0;class O5t extends e5t{constructor(t,e,n={}){super(),this.width=t,this.height=e,this.depth=1,this.scissor=new R5t(0,0,t,e),this.scissorTest=!1,this.viewport=new R5t(0,0,t,e),this.texture=new N5t(void 0,n.mapping,n.wrapS,n.wrapT,n.magFilter,n.minFilter,n.format,n.type,n.anisotropy,n.encoding),this.texture.isRenderTargetTexture=!0,this.texture.image={width:t,height:e,depth:1},this.texture.generateMipmaps=void 0!==n.generateMipmaps&&n.generateMipmaps,this.texture.minFilter=void 0!==n.minFilter?n.minFilter:p2t,this.depthBuffer=void 0===n.depthBuffer||n.depthBuffer,this.stencilBuffer=void 0!==n.stencilBuffer&&n.stencilBuffer,this.depthTexture=void 0!==n.depthTexture?n.depthTexture:null}setTexture(t){t.image={width:this.width,height:this.height,depth:this.depth},this.texture=t}setSize(t,e,n=1){this.width===t&&this.height===e&&this.depth===n||(this.width=t,this.height=e,this.depth=n,this.texture.image.width=t,this.texture.image.height=e,this.texture.image.depth=n,this.dispose()),this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e)}clone(){return(new this.constructor).copy(this)}copy(t){return this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.copy(t.viewport),this.texture=t.texture.clone(),this.texture.image={...this.texture.image},this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this}dispose(){this.dispatchEvent({type:"dispose"})}}O5t.prototype.isWebGLRenderTarget=!0;class z5t extends O5t{constructor(t,e,n){super(t,e);const i=this.texture;this.texture=[];for(let t=0;t<n;t++)this.texture[t]=i.clone()}setSize(t,e,n=1){if(this.width!==t||this.height!==e||this.depth!==n){this.width=t,this.height=e,this.depth=n;for(let i=0,r=this.texture.length;i<r;i++)this.texture[i].image.width=t,this.texture[i].image.height=e,this.texture[i].image.depth=n;this.dispose()}return this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e),this}copy(t){this.dispose(),this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.set(0,0,this.width,this.height),this.scissor.set(0,0,this.width,this.height),this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this.texture.length=0;for(let e=0,n=t.texture.length;e<n;e++)this.texture[e]=t.texture[e].clone();return this}}z5t.prototype.isWebGLMultipleRenderTargets=!0;class D5t extends O5t{constructor(t,e,n){super(t,e,n),this.samples=4}copy(t){return super.copy.call(this,t),this.samples=t.samples,this}}D5t.prototype.isWebGLMultisampleRenderTarget=!0;class B5t{constructor(t=0,e=0,n=0,i=1){this._x=t,this._y=e,this._z=n,this._w=i}static slerp(t,e,n,i){return console.warn("THREE.Quaternion: Static .slerp() has been deprecated. Use qm.slerpQuaternions( qa, qb, t ) instead."),n.slerpQuaternions(t,e,i)}static slerpFlat(t,e,n,i,r,o,a){let s=n[i+0],l=n[i+1],c=n[i+2],u=n[i+3];const h=r[o+0],d=r[o+1],p=r[o+2],f=r[o+3];if(0===a)return t[e+0]=s,t[e+1]=l,t[e+2]=c,void(t[e+3]=u);if(1===a)return t[e+0]=h,t[e+1]=d,t[e+2]=p,void(t[e+3]=f);if(u!==f||s!==h||l!==d||c!==p){let t=1-a;const e=s*h+l*d+c*p+u*f,n=e>=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const r=Math.sqrt(i),o=Math.atan2(r,e*n);t=Math.sin(t*o)/r,a=Math.sin(a*o)/r}const r=a*n;if(s=s*t+h*r,l=l*t+d*r,c=c*t+p*r,u=u*t+f*r,t===1-a){const t=1/Math.sqrt(s*s+l*l+c*c+u*u);s*=t,l*=t,c*=t,u*=t}}t[e]=s,t[e+1]=l,t[e+2]=c,t[e+3]=u}static multiplyQuaternionsFlat(t,e,n,i,r,o){const a=n[i],s=n[i+1],l=n[i+2],c=n[i+3],u=r[o],h=r[o+1],d=r[o+2],p=r[o+3];return t[e]=a*p+c*u+s*d-l*h,t[e+1]=s*p+c*h+l*u-a*d,t[e+2]=l*p+c*d+a*h-s*u,t[e+3]=c*p-a*u-s*h-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error("THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.");const n=t._x,i=t._y,r=t._z,o=t._order,a=Math.cos,s=Math.sin,l=a(n/2),c=a(i/2),u=a(r/2),h=s(n/2),d=s(i/2),p=s(r/2);switch(o){case"XYZ":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case"YXZ":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case"ZXY":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case"ZYX":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case"YZX":this._x=h*c*u+l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u-h*d*p;break;case"XZY":this._x=h*c*u-l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u+h*d*p;break;default:console.warn("THREE.Quaternion: .setFromEuler() encountered an unknown order: "+o)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],r=e[8],o=e[1],a=e[5],s=e[9],l=e[2],c=e[6],u=e[10],h=n+a+u;if(h>0){const t=.5/Math.sqrt(h+1);this._w=.25/t,this._x=(c-s)*t,this._y=(r-l)*t,this._z=(o-i)*t}else if(n>a&&n>u){const t=2*Math.sqrt(1+n-a-u);this._w=(c-s)/t,this._x=.25*t,this._y=(i+o)/t,this._z=(r+l)/t}else if(a>u){const t=2*Math.sqrt(1+a-n-u);this._w=(r-l)/t,this._x=(i+o)/t,this._y=.25*t,this._z=(s+c)/t}else{const t=2*Math.sqrt(1+u-n-a);this._w=(o-i)/t,this._x=(r+l)/t,this._y=(s+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return n<Number.EPSILON?(n=0,Math.abs(t.x)>Math.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(s5t(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn("THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead."),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,r=t._z,o=t._w,a=e._x,s=e._y,l=e._z,c=e._w;return this._x=n*c+o*a+i*l-r*s,this._y=i*c+o*s+r*a-n*l,this._z=r*c+o*l+n*s-i*a,this._w=o*c-n*a-i*s-r*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,r=this._z,o=this._w;let a=o*t._w+n*t._x+i*t._y+r*t._z;if(a<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,a=-a):this.copy(t),a>=1)return this._w=o,this._x=n,this._y=i,this._z=r,this;const s=1-a*a;if(s<=Number.EPSILON){const t=1-e;return this._w=t*o+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*r+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(s),c=Math.atan2(l,a),u=Math.sin((1-e)*c)/l,h=Math.sin(e*c)/l;return this._w=o*u+this._w*h,this._x=n*u+this._x*h,this._y=i*u+this._y*h,this._z=r*u+this._z*h,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}B5t.prototype.isQuaternion=!0;class H5t{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}set(t,e,n){return void 0===n&&(n=this.z),this.x=t,this.y=e,this.z=n,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this}sub(t,e){return void 0!==e?(console.warn("THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiply(t,e){return void 0!==e?(console.warn("THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead."),this.multiplyVectors(t,e)):(this.x*=t.x,this.y*=t.y,this.z*=t.z,this)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}applyEuler(t){return t&&t.isEuler||console.error("THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order."),this.applyQuaternion(V5t.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(V5t.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[3]*n+r[6]*i,this.y=r[1]*e+r[4]*n+r[7]*i,this.z=r[2]*e+r[5]*n+r[8]*i,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=t.elements,o=1/(r[3]*e+r[7]*n+r[11]*i+r[15]);return this.x=(r[0]*e+r[4]*n+r[8]*i+r[12])*o,this.y=(r[1]*e+r[5]*n+r[9]*i+r[13])*o,this.z=(r[2]*e+r[6]*n+r[10]*i+r[14])*o,this}applyQuaternion(t){const e=this.x,n=this.y,i=this.z,r=t.x,o=t.y,a=t.z,s=t.w,l=s*e+o*i-a*n,c=s*n+a*e-r*i,u=s*i+r*n-o*e,h=-r*e-o*n-a*i;return this.x=l*s+h*-r+c*-a-u*-o,this.y=c*s+h*-o+u*-r-l*-a,this.z=u*s+h*-a+l*-o-c*-r,this}project(t){return this.applyMatrix4(t.matrixWorldInverse).applyMatrix4(t.projectionMatrix)}unproject(t){return this.applyMatrix4(t.projectionMatrixInverse).applyMatrix4(t.matrixWorld)}transformDirection(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i,this.y=r[1]*e+r[5]*n+r[9]*i,this.z=r[2]*e+r[6]*n+r[10]*i,this.normalize()}divide(t){return this.x/=t.x,this.y/=t.y,this.z/=t.z,this}divideScalar(t){return this.multiplyScalar(1/t)}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this}cross(t,e){return void 0!==e?(console.warn("THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead."),this.crossVectors(t,e)):this.crossVectors(this,t)}crossVectors(t,e){const n=t.x,i=t.y,r=t.z,o=e.x,a=e.y,s=e.z;return this.x=i*s-r*a,this.y=r*o-n*s,this.z=n*a-i*o,this}projectOnVector(t){const e=t.lengthSq();if(0===e)return this.set(0,0,0);const n=t.dot(this)/e;return this.copy(t).multiplyScalar(n)}projectOnPlane(t){return F5t.copy(this).projectOnVector(t),this.sub(F5t)}reflect(t){return this.sub(F5t.copy(t).multiplyScalar(2*this.dot(t)))}angleTo(t){const e=Math.sqrt(this.lengthSq()*t.lengthSq());if(0===e)return Math.PI/2;const n=this.dot(t)/e;return Math.acos(s5t(n,-1,1))}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y,i=this.z-t.z;return e*e+n*n+i*i}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)+Math.abs(this.z-t.z)}setFromSpherical(t){return this.setFromSphericalCoords(t.radius,t.phi,t.theta)}setFromSphericalCoords(t,e,n){const i=Math.sin(e)*t;return this.x=i*Math.sin(n),this.y=Math.cos(e)*t,this.z=i*Math.cos(n),this}setFromCylindrical(t){return this.setFromCylindricalCoords(t.radius,t.theta,t.y)}setFromCylindricalCoords(t,e,n){return this.x=t*Math.sin(e),this.y=n,this.z=t*Math.cos(e),this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}setFromMatrixScale(t){const e=this.setFromMatrixColumn(t,0).length(),n=this.setFromMatrixColumn(t,1).length(),i=this.setFromMatrixColumn(t,2).length();return this.x=e,this.y=n,this.z=i,this}setFromMatrixColumn(t,e){return this.fromArray(t.elements,4*e)}setFromMatrix3Column(t,e){return this.fromArray(t.elements,3*e)}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector3: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}}H5t.prototype.isVector3=!0;const F5t=new H5t,V5t=new B5t;class U5t{constructor(t=new H5t(1/0,1/0,1/0),e=new H5t(-1/0,-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromArray(t){let e=1/0,n=1/0,i=1/0,r=-1/0,o=-1/0,a=-1/0;for(let s=0,l=t.length;s<l;s+=3){const l=t[s],c=t[s+1],u=t[s+2];l<e&&(e=l),c<n&&(n=c),u<i&&(i=u),l>r&&(r=l),c>o&&(o=c),u>a&&(a=u)}return this.min.set(e,n,i),this.max.set(r,o,a),this}setFromBufferAttribute(t){let e=1/0,n=1/0,i=1/0,r=-1/0,o=-1/0,a=-1/0;for(let s=0,l=t.count;s<l;s++){const l=t.getX(s),c=t.getY(s),u=t.getZ(s);l<e&&(e=l),c<n&&(n=c),u<i&&(i=u),l>r&&(r=l),c>o&&(o=c),u>a&&(a=u)}return this.min.set(e,n,i),this.max.set(r,o,a),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;e<n;e++)this.expandByPoint(t[e]);return this}setFromCenterAndSize(t,e){const n=G5t.copy(e).multiplyScalar(.5);return this.min.copy(t).sub(n),this.max.copy(t).add(n),this}setFromObject(t){return this.makeEmpty(),this.expandByObject(t)}clone(){return(new this.constructor).copy(this)}copy(t){return this.min.copy(t.min),this.max.copy(t.max),this}makeEmpty(){return this.min.x=this.min.y=this.min.z=1/0,this.max.x=this.max.y=this.max.z=-1/0,this}isEmpty(){return this.max.x<this.min.x||this.max.y<this.min.y||this.max.z<this.min.z}getCenter(t){return this.isEmpty()?t.set(0,0,0):t.addVectors(this.min,this.max).multiplyScalar(.5)}getSize(t){return this.isEmpty()?t.set(0,0,0):t.subVectors(this.max,this.min)}expandByPoint(t){return this.min.min(t),this.max.max(t),this}expandByVector(t){return this.min.sub(t),this.max.add(t),this}expandByScalar(t){return this.min.addScalar(-t),this.max.addScalar(t),this}expandByObject(t){t.updateWorldMatrix(!1,!1);const e=t.geometry;void 0!==e&&(null===e.boundingBox&&e.computeBoundingBox(),W5t.copy(e.boundingBox),W5t.applyMatrix4(t.matrixWorld),this.union(W5t));const n=t.children;for(let t=0,e=n.length;t<e;t++)this.expandByObject(n[t]);return this}containsPoint(t){return!(t.x<this.min.x||t.x>this.max.x||t.y<this.min.y||t.y>this.max.y||t.z<this.min.z||t.z>this.max.z)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y&&this.min.z<=t.min.z&&t.max.z<=this.max.z}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y),(t.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(t){return!(t.max.x<this.min.x||t.min.x>this.max.x||t.max.y<this.min.y||t.min.y>this.max.y||t.max.z<this.min.z||t.min.z>this.max.z)}intersectsSphere(t){return this.clampPoint(t.center,G5t),G5t.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,n;return t.normal.x>0?(e=t.normal.x*this.min.x,n=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,n=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,n+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,n+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,n+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,n+=t.normal.z*this.min.z),e<=-t.constant&&n>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(J5t),Q5t.subVectors(this.max,J5t),q5t.subVectors(t.a,J5t),Y5t.subVectors(t.b,J5t),X5t.subVectors(t.c,J5t),$5t.subVectors(Y5t,q5t),K5t.subVectors(X5t,Y5t),Z5t.subVectors(q5t,X5t);let e=[0,-$5t.z,$5t.y,0,-K5t.z,K5t.y,0,-Z5t.z,Z5t.y,$5t.z,0,-$5t.x,K5t.z,0,-K5t.x,Z5t.z,0,-Z5t.x,-$5t.y,$5t.x,0,-K5t.y,K5t.x,0,-Z5t.y,Z5t.x,0];return!!n3t(e,q5t,Y5t,X5t,Q5t)&&(e=[1,0,0,0,1,0,0,0,1],!!n3t(e,q5t,Y5t,X5t,Q5t)&&(t3t.crossVectors($5t,K5t),e=[t3t.x,t3t.y,t3t.z],n3t(e,q5t,Y5t,X5t,Q5t)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return G5t.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(G5t).length(),t}intersect(t){return this.min.max(t.min),this.max.min(t.max),this.isEmpty()&&this.makeEmpty(),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}applyMatrix4(t){return this.isEmpty()||(j5t[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),j5t[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),j5t[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),j5t[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),j5t[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),j5t[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),j5t[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),j5t[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(j5t)),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}U5t.prototype.isBox3=!0;const j5t=[new H5t,new H5t,new H5t,new H5t,new H5t,new H5t,new H5t,new H5t],G5t=new H5t,W5t=new U5t,q5t=new H5t,Y5t=new H5t,X5t=new H5t,$5t=new H5t,K5t=new H5t,Z5t=new H5t,J5t=new H5t,Q5t=new H5t,t3t=new H5t,e3t=new H5t;function n3t(t,e,n,i,r){for(let o=0,a=t.length-3;o<=a;o+=3){e3t.fromArray(t,o);const a=r.x*Math.abs(e3t.x)+r.y*Math.abs(e3t.y)+r.z*Math.abs(e3t.z),s=e.dot(e3t),l=n.dot(e3t),c=i.dot(e3t);if(Math.max(-Math.max(s,l,c),Math.min(s,l,c))>a)return!1}return!0}const i3t=new U5t,r3t=new H5t,o3t=new H5t,a3t=new H5t;class s3t{constructor(t=new H5t,e=-1){this.center=t,this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}setFromPoints(t,e){const n=this.center;void 0!==e?n.copy(e):i3t.setFromPoints(t).getCenter(n);let i=0;for(let e=0,r=t.length;e<r;e++)i=Math.max(i,n.distanceToSquared(t[e]));return this.radius=Math.sqrt(i),this}copy(t){return this.center.copy(t.center),this.radius=t.radius,this}isEmpty(){return this.radius<0}makeEmpty(){return this.center.set(0,0,0),this.radius=-1,this}containsPoint(t){return t.distanceToSquared(this.center)<=this.radius*this.radius}distanceToPoint(t){return t.distanceTo(this.center)-this.radius}intersectsSphere(t){const e=this.radius+t.radius;return t.center.distanceToSquared(this.center)<=e*e}intersectsBox(t){return t.intersectsSphere(this)}intersectsPlane(t){return Math.abs(t.distanceToPoint(this.center))<=this.radius}clampPoint(t,e){const n=this.center.distanceToSquared(t);return e.copy(t),n>this.radius*this.radius&&(e.sub(this.center).normalize(),e.multiplyScalar(this.radius).add(this.center)),e}getBoundingBox(t){return this.isEmpty()?(t.makeEmpty(),t):(t.set(this.center,this.center),t.expandByScalar(this.radius),t)}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}expandByPoint(t){a3t.subVectors(t,this.center);const e=a3t.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(a3t.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return o3t.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(r3t.copy(t.center).add(o3t)),this.expandByPoint(r3t.copy(t.center).sub(o3t)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const l3t=new H5t,c3t=new H5t,u3t=new H5t,h3t=new H5t,d3t=new H5t,p3t=new H5t,f3t=new H5t;class m3t{constructor(t=new H5t,e=new H5t(0,0,-1)){this.origin=t,this.direction=e}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return e.copy(this.direction).multiplyScalar(t).add(this.origin)}lookAt(t){return this.direction.copy(t).sub(this.origin).normalize(),this}recast(t){return this.origin.copy(this.at(t,l3t)),this}closestPointToPoint(t,e){e.subVectors(t,this.origin);const n=e.dot(this.direction);return n<0?e.copy(this.origin):e.copy(this.direction).multiplyScalar(n).add(this.origin)}distanceToPoint(t){return Math.sqrt(this.distanceSqToPoint(t))}distanceSqToPoint(t){const e=l3t.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(l3t.copy(this.direction).multiplyScalar(e).add(this.origin),l3t.distanceToSquared(t))}distanceSqToSegment(t,e,n,i){c3t.copy(t).add(e).multiplyScalar(.5),u3t.copy(e).sub(t).normalize(),h3t.copy(this.origin).sub(c3t);const r=.5*t.distanceTo(e),o=-this.direction.dot(u3t),a=h3t.dot(this.direction),s=-h3t.dot(u3t),l=h3t.lengthSq(),c=Math.abs(1-o*o);let u,h,d,p;if(c>0)if(u=o*s-a,h=o*a-s,p=r*c,u>=0)if(h>=-p)if(h<=p){const t=1/c;u*=t,h*=t,d=u*(u+o*h+2*a)+h*(o*u+h+2*s)+l}else h=r,u=Math.max(0,-(o*h+a)),d=-u*u+h*(h+2*s)+l;else h=-r,u=Math.max(0,-(o*h+a)),d=-u*u+h*(h+2*s)+l;else h<=-p?(u=Math.max(0,-(-o*r+a)),h=u>0?-r:Math.min(Math.max(-r,-s),r),d=-u*u+h*(h+2*s)+l):h<=p?(u=0,h=Math.min(Math.max(-r,-s),r),d=h*(h+2*s)+l):(u=Math.max(0,-(o*r+a)),h=u>0?r:Math.min(Math.max(-r,-s),r),d=-u*u+h*(h+2*s)+l);else h=o>0?-r:r,u=Math.max(0,-(o*h+a)),d=-u*u+h*(h+2*s)+l;return n&&n.copy(this.direction).multiplyScalar(u).add(this.origin),i&&i.copy(u3t).multiplyScalar(h).add(c3t),d}intersectSphere(t,e){l3t.subVectors(t.center,this.origin);const n=l3t.dot(this.direction),i=l3t.dot(l3t)-n*n,r=t.radius*t.radius;if(i>r)return null;const o=Math.sqrt(r-i),a=n-o,s=n+o;return a<0&&s<0?null:this.at(a<0?s:a,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,r,o,a,s;const l=1/this.direction.x,c=1/this.direction.y,u=1/this.direction.z,h=this.origin;return l>=0?(n=(t.min.x-h.x)*l,i=(t.max.x-h.x)*l):(n=(t.max.x-h.x)*l,i=(t.min.x-h.x)*l),c>=0?(r=(t.min.y-h.y)*c,o=(t.max.y-h.y)*c):(r=(t.max.y-h.y)*c,o=(t.min.y-h.y)*c),n>o||r>i?null:((r>n||n!=n)&&(n=r),(o<i||i!=i)&&(i=o),u>=0?(a=(t.min.z-h.z)*u,s=(t.max.z-h.z)*u):(a=(t.max.z-h.z)*u,s=(t.min.z-h.z)*u),n>s||a>i?null:((a>n||n!=n)&&(n=a),(s<i||i!=i)&&(i=s),i<0?null:this.at(n>=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,l3t)}intersectTriangle(t,e,n,i,r){d3t.subVectors(e,t),p3t.subVectors(n,t),f3t.crossVectors(d3t,p3t);let o,a=this.direction.dot(f3t);if(a>0){if(i)return null;o=1}else{if(!(a<0))return null;o=-1,a=-a}h3t.subVectors(this.origin,t);const s=o*this.direction.dot(p3t.crossVectors(h3t,p3t));if(s<0)return null;const l=o*this.direction.dot(d3t.cross(h3t));if(l<0)return null;if(s+l>a)return null;const c=-o*h3t.dot(f3t);return c<0?null:this.at(c/a,r)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}class g3t{constructor(){this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],arguments.length>0&&console.error("THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.")}set(t,e,n,i,r,o,a,s,l,c,u,h,d,p,f,m){const g=this.elements;return g[0]=t,g[4]=e,g[8]=n,g[12]=i,g[1]=r,g[5]=o,g[9]=a,g[13]=s,g[2]=l,g[6]=c,g[10]=u,g[14]=h,g[3]=d,g[7]=p,g[11]=f,g[15]=m,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new g3t).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return this.set(e[0],e[3],e[6],0,e[1],e[4],e[7],0,e[2],e[5],e[8],0,0,0,0,1),this}extractBasis(t,e,n){return t.setFromMatrixColumn(this,0),e.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(t,e,n){return this.set(t.x,e.x,n.x,0,t.y,e.y,n.y,0,t.z,e.z,n.z,0,0,0,0,1),this}extractRotation(t){const e=this.elements,n=t.elements,i=1/_3t.setFromMatrixColumn(t,0).length(),r=1/_3t.setFromMatrixColumn(t,1).length(),o=1/_3t.setFromMatrixColumn(t,2).length();return e[0]=n[0]*i,e[1]=n[1]*i,e[2]=n[2]*i,e[3]=0,e[4]=n[4]*r,e[5]=n[5]*r,e[6]=n[6]*r,e[7]=0,e[8]=n[8]*o,e[9]=n[9]*o,e[10]=n[10]*o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error("THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.");const e=this.elements,n=t.x,i=t.y,r=t.z,o=Math.cos(n),a=Math.sin(n),s=Math.cos(i),l=Math.sin(i),c=Math.cos(r),u=Math.sin(r);if("XYZ"===t.order){const t=o*c,n=o*u,i=a*c,r=a*u;e[0]=s*c,e[4]=-s*u,e[8]=l,e[1]=n+i*l,e[5]=t-r*l,e[9]=-a*s,e[2]=r-t*l,e[6]=i+n*l,e[10]=o*s}else if("YXZ"===t.order){const t=s*c,n=s*u,i=l*c,r=l*u;e[0]=t+r*a,e[4]=i*a-n,e[8]=o*l,e[1]=o*u,e[5]=o*c,e[9]=-a,e[2]=n*a-i,e[6]=r+t*a,e[10]=o*s}else if("ZXY"===t.order){const t=s*c,n=s*u,i=l*c,r=l*u;e[0]=t-r*a,e[4]=-o*u,e[8]=i+n*a,e[1]=n+i*a,e[5]=o*c,e[9]=r-t*a,e[2]=-o*l,e[6]=a,e[10]=o*s}else if("ZYX"===t.order){const t=o*c,n=o*u,i=a*c,r=a*u;e[0]=s*c,e[4]=i*l-n,e[8]=t*l+r,e[1]=s*u,e[5]=r*l+t,e[9]=n*l-i,e[2]=-l,e[6]=a*s,e[10]=o*s}else if("YZX"===t.order){const t=o*s,n=o*l,i=a*s,r=a*l;e[0]=s*c,e[4]=r-t*u,e[8]=i*u+n,e[1]=u,e[5]=o*c,e[9]=-a*c,e[2]=-l*c,e[6]=n*u+i,e[10]=t-r*u}else if("XZY"===t.order){const t=o*s,n=o*l,i=a*s,r=a*l;e[0]=s*c,e[4]=-u,e[8]=l*c,e[1]=t*u+r,e[5]=o*c,e[9]=n*u-i,e[2]=i*u-n,e[6]=a*c,e[10]=r*u+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(v3t,t,b3t)}lookAt(t,e,n){const i=this.elements;return S3t.subVectors(t,e),0===S3t.lengthSq()&&(S3t.z=1),S3t.normalize(),x3t.crossVectors(n,S3t),0===x3t.lengthSq()&&(1===Math.abs(n.z)?S3t.x+=1e-4:S3t.z+=1e-4,S3t.normalize(),x3t.crossVectors(n,S3t)),x3t.normalize(),w3t.crossVectors(S3t,x3t),i[0]=x3t.x,i[4]=w3t.x,i[8]=S3t.x,i[1]=x3t.y,i[5]=w3t.y,i[9]=S3t.y,i[2]=x3t.z,i[6]=w3t.z,i[10]=S3t.z,this}multiply(t,e){return void 0!==e?(console.warn("THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead."),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,o=n[0],a=n[4],s=n[8],l=n[12],c=n[1],u=n[5],h=n[9],d=n[13],p=n[2],f=n[6],m=n[10],g=n[14],_=n[3],y=n[7],v=n[11],b=n[15],x=i[0],w=i[4],S=i[8],M=i[12],E=i[1],T=i[5],C=i[9],A=i[13],k=i[2],L=i[6],P=i[10],N=i[14],I=i[3],R=i[7],O=i[11],z=i[15];return r[0]=o*x+a*E+s*k+l*I,r[4]=o*w+a*T+s*L+l*R,r[8]=o*S+a*C+s*P+l*O,r[12]=o*M+a*A+s*N+l*z,r[1]=c*x+u*E+h*k+d*I,r[5]=c*w+u*T+h*L+d*R,r[9]=c*S+u*C+h*P+d*O,r[13]=c*M+u*A+h*N+d*z,r[2]=p*x+f*E+m*k+g*I,r[6]=p*w+f*T+m*L+g*R,r[10]=p*S+f*C+m*P+g*O,r[14]=p*M+f*A+m*N+g*z,r[3]=_*x+y*E+v*k+b*I,r[7]=_*w+y*T+v*L+b*R,r[11]=_*S+y*C+v*P+b*O,r[15]=_*M+y*A+v*N+b*z,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],r=t[12],o=t[1],a=t[5],s=t[9],l=t[13],c=t[2],u=t[6],h=t[10],d=t[14];return t[3]*(+r*s*u-i*l*u-r*a*h+n*l*h+i*a*d-n*s*d)+t[7]*(+e*s*d-e*l*h+r*o*h-i*o*d+i*l*c-r*s*c)+t[11]*(+e*l*u-e*a*d-r*o*u+n*o*d+r*a*c-n*l*c)+t[15]*(-i*a*c-e*s*u+e*a*h+i*o*u-n*o*h+n*s*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],o=t[4],a=t[5],s=t[6],l=t[7],c=t[8],u=t[9],h=t[10],d=t[11],p=t[12],f=t[13],m=t[14],g=t[15],_=u*m*l-f*h*l+f*s*d-a*m*d-u*s*g+a*h*g,y=p*h*l-c*m*l-p*s*d+o*m*d+c*s*g-o*h*g,v=c*f*l-p*u*l+p*a*d-o*f*d-c*a*g+o*u*g,b=p*u*s-c*f*s-p*a*h+o*f*h+c*a*m-o*u*m,x=e*_+n*y+i*v+r*b;if(0===x)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/x;return t[0]=_*w,t[1]=(f*h*r-u*m*r-f*i*d+n*m*d+u*i*g-n*h*g)*w,t[2]=(a*m*r-f*s*r+f*i*l-n*m*l-a*i*g+n*s*g)*w,t[3]=(u*s*r-a*h*r-u*i*l+n*h*l+a*i*d-n*s*d)*w,t[4]=y*w,t[5]=(c*m*r-p*h*r+p*i*d-e*m*d-c*i*g+e*h*g)*w,t[6]=(p*s*r-o*m*r-p*i*l+e*m*l+o*i*g-e*s*g)*w,t[7]=(o*h*r-c*s*r+c*i*l-e*h*l-o*i*d+e*s*d)*w,t[8]=v*w,t[9]=(p*u*r-c*f*r-p*n*d+e*f*d+c*n*g-e*u*g)*w,t[10]=(o*f*r-p*a*r+p*n*l-e*f*l-o*n*g+e*a*g)*w,t[11]=(c*a*r-o*u*r-c*n*l+e*u*l+o*n*d-e*a*d)*w,t[12]=b*w,t[13]=(c*f*i-p*u*i+p*n*h-e*f*h-c*n*m+e*u*m)*w,t[14]=(p*a*i-o*f*i-p*n*s+e*f*s+o*n*m-e*a*m)*w,t[15]=(o*u*i-c*a*i+c*n*s-e*u*s-o*n*h+e*a*h)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,r=t.z;return e[0]*=n,e[4]*=i,e[8]*=r,e[1]*=n,e[5]*=i,e[9]*=r,e[2]*=n,e[6]*=i,e[10]*=r,e[3]*=n,e[7]*=i,e[11]*=r,this}getMaxScaleOnAxis(){const t=this.elements;return Math.sqrt(Math.max(t[0]*t[0]+t[1]*t[1]+t[2]*t[2],t[4]*t[4]+t[5]*t[5]+t[6]*t[6],t[8]*t[8]+t[9]*t[9]+t[10]*t[10]))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),r=1-n,o=t.x,a=t.y,s=t.z,l=r*o,c=r*a;return this.set(l*o+n,l*a-i*s,l*s+i*a,0,l*a+i*s,c*a+n,c*s-i*o,0,l*s-i*a,c*s+i*o,r*s*s+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,r,o){return this.set(1,n,r,0,t,1,o,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,r=e._x,o=e._y,a=e._z,s=e._w,l=r+r,c=o+o,u=a+a,h=r*l,d=r*c,p=r*u,f=o*c,m=o*u,g=a*u,_=s*l,y=s*c,v=s*u,b=n.x,x=n.y,w=n.z;return i[0]=(1-(f+g))*b,i[1]=(d+v)*b,i[2]=(p-y)*b,i[3]=0,i[4]=(d-v)*x,i[5]=(1-(h+g))*x,i[6]=(m+_)*x,i[7]=0,i[8]=(p+y)*w,i[9]=(m-_)*w,i[10]=(1-(h+f))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let r=_3t.set(i[0],i[1],i[2]).length();const o=_3t.set(i[4],i[5],i[6]).length(),a=_3t.set(i[8],i[9],i[10]).length();this.determinant()<0&&(r=-r),t.x=i[12],t.y=i[13],t.z=i[14],y3t.copy(this);const s=1/r,l=1/o,c=1/a;return y3t.elements[0]*=s,y3t.elements[1]*=s,y3t.elements[2]*=s,y3t.elements[4]*=l,y3t.elements[5]*=l,y3t.elements[6]*=l,y3t.elements[8]*=c,y3t.elements[9]*=c,y3t.elements[10]*=c,e.setFromRotationMatrix(y3t),n.x=r,n.y=o,n.z=a,this}makePerspective(t,e,n,i,r,o){void 0===o&&console.warn("THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.");const a=this.elements,s=2*r/(n-i),l=(e+t)/(e-t),c=(n+i)/(n-i),u=-(o+r)/(o-r),h=-2*o*r/(o-r);return a[0]=2*r/(e-t),a[4]=0,a[8]=l,a[12]=0,a[1]=0,a[5]=s,a[9]=c,a[13]=0,a[2]=0,a[6]=0,a[10]=u,a[14]=h,a[3]=0,a[7]=0,a[11]=-1,a[15]=0,this}makeOrthographic(t,e,n,i,r,o){const a=this.elements,s=1/(e-t),l=1/(n-i),c=1/(o-r),u=(e+t)*s,h=(n+i)*l,d=(o+r)*c;return a[0]=2*s,a[4]=0,a[8]=0,a[12]=-u,a[1]=0,a[5]=2*l,a[9]=0,a[13]=-h,a[2]=0,a[6]=0,a[10]=-2*c,a[14]=-d,a[3]=0,a[7]=0,a[11]=0,a[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}g3t.prototype.isMatrix4=!0;const _3t=new H5t,y3t=new g3t,v3t=new H5t(0,0,0),b3t=new H5t(1,1,1),x3t=new H5t,w3t=new H5t,S3t=new H5t,M3t=new g3t,E3t=new B5t;class T3t{constructor(t=0,e=0,n=0,i=T3t.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=i}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,i=this._order){return this._x=t,this._y=e,this._z=n,this._order=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const i=t.elements,r=i[0],o=i[4],a=i[8],s=i[1],l=i[5],c=i[9],u=i[2],h=i[6],d=i[10];switch(e){case"XYZ":this._y=Math.asin(s5t(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-c,d),this._z=Math.atan2(-o,r)):(this._x=Math.atan2(h,l),this._z=0);break;case"YXZ":this._x=Math.asin(-s5t(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(a,d),this._z=Math.atan2(s,l)):(this._y=Math.atan2(-u,r),this._z=0);break;case"ZXY":this._x=Math.asin(s5t(h,-1,1)),Math.abs(h)<.9999999?(this._y=Math.atan2(-u,d),this._z=Math.atan2(-o,l)):(this._y=0,this._z=Math.atan2(s,r));break;case"ZYX":this._y=Math.asin(-s5t(u,-1,1)),Math.abs(u)<.9999999?(this._x=Math.atan2(h,d),this._z=Math.atan2(s,r)):(this._x=0,this._z=Math.atan2(-o,l));break;case"YZX":this._z=Math.asin(s5t(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-u,r)):(this._x=0,this._y=Math.atan2(a,d));break;case"XZY":this._z=Math.asin(-s5t(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(h,l),this._y=Math.atan2(a,r)):(this._x=Math.atan2(-c,d),this._y=0);break;default:console.warn("THREE.Euler: .setFromRotationMatrix() encountered an unknown order: "+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return M3t.makeRotationFromQuaternion(t),this.setFromRotationMatrix(M3t,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return E3t.setFromEuler(this),this.setFromQuaternion(E3t,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new H5t(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}T3t.prototype.isEuler=!0,T3t.DefaultOrder="XYZ",T3t.RotationOrders=["XYZ","YZX","ZXY","XZY","YXZ","ZYX"];class C3t{constructor(){this.mask=1}set(t){this.mask=1<<t|0}enable(t){this.mask|=1<<t|0}enableAll(){this.mask=-1}toggle(t){this.mask^=1<<t|0}disable(t){this.mask&=~(1<<t|0)}disableAll(){this.mask=0}test(t){return 0!=(this.mask&t.mask)}}let A3t=0;const k3t=new H5t,L3t=new B5t,P3t=new g3t,N3t=new H5t,I3t=new H5t,R3t=new H5t,O3t=new B5t,z3t=new H5t(1,0,0),D3t=new H5t(0,1,0),B3t=new H5t(0,0,1),H3t={type:"added"},F3t={type:"removed"};class V3t extends e5t{constructor(){super(),Object.defineProperty(this,"id",{value:A3t++}),this.uuid=a5t(),this.name="",this.type="Object3D",this.parent=null,this.children=[],this.up=V3t.DefaultUp.clone();const t=new H5t,e=new T3t,n=new B5t,i=new H5t(1,1,1);e._onChange((function r(){n.setFromEuler(e,!1)})),n._onChange((function o(){e.setFromQuaternion(n,void 0,!1)})),Object.defineProperties(this,{position:{configurable:!0,enumerable:!0,value:t},rotation:{configurable:!0,enumerable:!0,value:e},quaternion:{configurable:!0,enumerable:!0,value:n},scale:{configurable:!0,enumerable:!0,value:i},modelViewMatrix:{value:new g3t},normalMatrix:{value:new A5t}}),this.matrix=new g3t,this.matrixWorld=new g3t,this.matrixAutoUpdate=V3t.DefaultMatrixAutoUpdate,this.matrixWorldNeedsUpdate=!1,this.layers=new C3t,this.visible=!0,this.castShadow=!1,this.receiveShadow=!1,this.frustumCulled=!0,this.renderOrder=0,this.animations=[],this.userData={}}onBeforeRender(){}onAfterRender(){}applyMatrix4(t){this.matrixAutoUpdate&&this.updateMatrix(),this.matrix.premultiply(t),this.matrix.decompose(this.position,this.quaternion,this.scale)}applyQuaternion(t){return this.quaternion.premultiply(t),this}setRotationFromAxisAngle(t,e){this.quaternion.setFromAxisAngle(t,e)}setRotationFromEuler(t){this.quaternion.setFromEuler(t,!0)}setRotationFromMatrix(t){this.quaternion.setFromRotationMatrix(t)}setRotationFromQuaternion(t){this.quaternion.copy(t)}rotateOnAxis(t,e){return L3t.setFromAxisAngle(t,e),this.quaternion.multiply(L3t),this}rotateOnWorldAxis(t,e){return L3t.setFromAxisAngle(t,e),this.quaternion.premultiply(L3t),this}rotateX(t){return this.rotateOnAxis(z3t,t)}rotateY(t){return this.rotateOnAxis(D3t,t)}rotateZ(t){return this.rotateOnAxis(B3t,t)}translateOnAxis(t,e){return k3t.copy(t).applyQuaternion(this.quaternion),this.position.add(k3t.multiplyScalar(e)),this}translateX(t){return this.translateOnAxis(z3t,t)}translateY(t){return this.translateOnAxis(D3t,t)}translateZ(t){return this.translateOnAxis(B3t,t)}localToWorld(t){return t.applyMatrix4(this.matrixWorld)}worldToLocal(t){return t.applyMatrix4(P3t.copy(this.matrixWorld).invert())}lookAt(t,e,n){t.isVector3?N3t.copy(t):N3t.set(t,e,n);const i=this.parent;this.updateWorldMatrix(!0,!1),I3t.setFromMatrixPosition(this.matrixWorld),this.isCamera||this.isLight?P3t.lookAt(I3t,N3t,this.up):P3t.lookAt(N3t,I3t,this.up),this.quaternion.setFromRotationMatrix(P3t),i&&(P3t.extractRotation(i.matrixWorld),L3t.setFromRotationMatrix(P3t),this.quaternion.premultiply(L3t.invert()))}add(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.add(arguments[t]);return this}return t===this?(console.error("THREE.Object3D.add: object can't be added as a child of itself.",t),this):(t&&t.isObject3D?(null!==t.parent&&t.parent.remove(t),t.parent=this,this.children.push(t),t.dispatchEvent(H3t)):console.error("THREE.Object3D.add: object not an instance of THREE.Object3D.",t),this)}remove(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.remove(arguments[t]);return this}const e=this.children.indexOf(t);return-1!==e&&(t.parent=null,this.children.splice(e,1),t.dispatchEvent(F3t)),this}removeFromParent(){const t=this.parent;return null!==t&&t.remove(this),this}clear(){for(let t=0;t<this.children.length;t++){const e=this.children[t];e.parent=null,e.dispatchEvent(F3t)}return this.children.length=0,this}attach(t){return this.updateWorldMatrix(!0,!1),P3t.copy(this.matrixWorld).invert(),null!==t.parent&&(t.parent.updateWorldMatrix(!0,!1),P3t.multiply(t.parent.matrixWorld)),t.applyMatrix4(P3t),this.add(t),t.updateWorldMatrix(!1,!0),this}getObjectById(t){return this.getObjectByProperty("id",t)}getObjectByName(t){return this.getObjectByProperty("name",t)}getObjectByProperty(t,e){if(this[t]===e)return this;for(let n=0,i=this.children.length;n<i;n++){const i=this.children[n].getObjectByProperty(t,e);if(void 0!==i)return i}}getWorldPosition(t){return this.updateWorldMatrix(!0,!1),t.setFromMatrixPosition(this.matrixWorld)}getWorldQuaternion(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(I3t,t,R3t),t}getWorldScale(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(I3t,O3t,t),t}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(e[8],e[9],e[10]).normalize()}raycast(){}traverse(t){t(this);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].traverse(t)}traverseVisible(t){if(!1===this.visible)return;t(this);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].traverseVisible(t)}traverseAncestors(t){const e=this.parent;null!==e&&(t(e),e.traverseAncestors(t))}updateMatrix(){this.matrix.compose(this.position,this.quaternion,this.scale),this.matrixWorldNeedsUpdate=!0}updateMatrixWorld(t){this.matrixAutoUpdate&&this.updateMatrix(),(this.matrixWorldNeedsUpdate||t)&&(null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),this.matrixWorldNeedsUpdate=!1,t=!0);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].updateMatrixWorld(t)}updateWorldMatrix(t,e){const n=this.parent;if(!0===t&&null!==n&&n.updateWorldMatrix(!0,!1),this.matrixAutoUpdate&&this.updateMatrix(),null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),!0===e){const t=this.children;for(let e=0,n=t.length;e<n;e++)t[e].updateWorldMatrix(!1,!0)}}toJSON(t){const e=void 0===t||"string"==typeof t,n={};e&&(t={geometries:{},materials:{},textures:{},images:{},shapes:{},skeletons:{},animations:{}},n.metadata={version:4.5,type:"Object",generator:"Object3D.toJSON"});const i={};function r(e,n){return void 0===e[n.uuid]&&(e[n.uuid]=n.toJSON(t)),n.uuid}if(i.uuid=this.uuid,i.type=this.type,""!==this.name&&(i.name=this.name),!0===this.castShadow&&(i.castShadow=!0),!0===this.receiveShadow&&(i.receiveShadow=!0),!1===this.visible&&(i.visible=!1),!1===this.frustumCulled&&(i.frustumCulled=!1),0!==this.renderOrder&&(i.renderOrder=this.renderOrder),"{}"!==JSON.stringify(this.userData)&&(i.userData=this.userData),i.layers=this.layers.mask,i.matrix=this.matrix.toArray(),!1===this.matrixAutoUpdate&&(i.matrixAutoUpdate=!1),this.isInstancedMesh&&(i.type="InstancedMesh",i.count=this.count,i.instanceMatrix=this.instanceMatrix.toJSON(),null!==this.instanceColor&&(i.instanceColor=this.instanceColor.toJSON())),this.isScene)this.background&&(this.background.isColor?i.background=this.background.toJSON():this.background.isTexture&&(i.background=this.background.toJSON(t).uuid)),this.environment&&this.environment.isTexture&&(i.environment=this.environment.toJSON(t).uuid);else if(this.isMesh||this.isLine||this.isPoints){i.geometry=r(t.geometries,this.geometry);const e=this.geometry.parameters;if(void 0!==e&&void 0!==e.shapes){const n=e.shapes;if(Array.isArray(n))for(let e=0,i=n.length;e<i;e++){r(t.shapes,n[e])}else r(t.shapes,n)}}if(this.isSkinnedMesh&&(i.bindMode=this.bindMode,i.bindMatrix=this.bindMatrix.toArray(),void 0!==this.skeleton&&(r(t.skeletons,this.skeleton),i.skeleton=this.skeleton.uuid)),void 0!==this.material)if(Array.isArray(this.material)){const e=[];for(let n=0,i=this.material.length;n<i;n++)e.push(r(t.materials,this.material[n]));i.material=e}else i.material=r(t.materials,this.material);if(this.children.length>0){i.children=[];for(let e=0;e<this.children.length;e++)i.children.push(this.children[e].toJSON(t).object)}if(this.animations.length>0){i.animations=[];for(let e=0;e<this.animations.length;e++){i.animations.push(r(t.animations,this.animations[e]))}}if(e){const e=o(t.geometries),i=o(t.materials),r=o(t.textures),a=o(t.images),s=o(t.shapes),l=o(t.skeletons),c=o(t.animations);e.length>0&&(n.geometries=e),i.length>0&&(n.materials=i),r.length>0&&(n.textures=r),a.length>0&&(n.images=a),s.length>0&&(n.shapes=s),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function o(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e<t.children.length;e++){this.add(t.children[e].clone())}return this}}V3t.DefaultUp=new H5t(0,1,0),V3t.DefaultMatrixAutoUpdate=!0,V3t.prototype.isObject3D=!0;const U3t=new H5t,j3t=new H5t,G3t=new H5t,W3t=new H5t,q3t=new H5t,Y3t=new H5t,X3t=new H5t,$3t=new H5t,K3t=new H5t,Z3t=new H5t;class J3t{constructor(t=new H5t,e=new H5t,n=new H5t){this.a=t,this.b=e,this.c=n}static getNormal(t,e,n,i){i.subVectors(n,e),U3t.subVectors(t,e),i.cross(U3t);const r=i.lengthSq();return r>0?i.multiplyScalar(1/Math.sqrt(r)):i.set(0,0,0)}static getBarycoord(t,e,n,i,r){U3t.subVectors(i,e),j3t.subVectors(n,e),G3t.subVectors(t,e);const o=U3t.dot(U3t),a=U3t.dot(j3t),s=U3t.dot(G3t),l=j3t.dot(j3t),c=j3t.dot(G3t),u=o*l-a*a;if(0===u)return r.set(-2,-1,-1);const h=1/u,d=(l*s-a*c)*h,p=(o*c-a*s)*h;return r.set(1-d-p,p,d)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,W3t),W3t.x>=0&&W3t.y>=0&&W3t.x+W3t.y<=1}static getUV(t,e,n,i,r,o,a,s){return this.getBarycoord(t,e,n,i,W3t),s.set(0,0),s.addScaledVector(r,W3t.x),s.addScaledVector(o,W3t.y),s.addScaledVector(a,W3t.z),s}static isFrontFacing(t,e,n,i){return U3t.subVectors(n,e),j3t.subVectors(t,e),U3t.cross(j3t).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return U3t.subVectors(this.c,this.b),j3t.subVectors(this.a,this.b),.5*U3t.cross(j3t).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return J3t.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return J3t.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,r){return J3t.getUV(t,this.a,this.b,this.c,e,n,i,r)}containsPoint(t){return J3t.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return J3t.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,r=this.c;let o,a;q3t.subVectors(i,n),Y3t.subVectors(r,n),$3t.subVectors(t,n);const s=q3t.dot($3t),l=Y3t.dot($3t);if(s<=0&&l<=0)return e.copy(n);K3t.subVectors(t,i);const c=q3t.dot(K3t),u=Y3t.dot(K3t);if(c>=0&&u<=c)return e.copy(i);const h=s*u-c*l;if(h<=0&&s>=0&&c<=0)return o=s/(s-c),e.copy(n).addScaledVector(q3t,o);Z3t.subVectors(t,r);const d=q3t.dot(Z3t),p=Y3t.dot(Z3t);if(p>=0&&d<=p)return e.copy(r);const f=d*l-s*p;if(f<=0&&l>=0&&p<=0)return a=l/(l-p),e.copy(n).addScaledVector(Y3t,a);const m=c*p-d*u;if(m<=0&&u-c>=0&&d-p>=0)return X3t.subVectors(r,i),a=(u-c)/(u-c+(d-p)),e.copy(i).addScaledVector(X3t,a);const g=1/(m+f+h);return o=f*g,a=h*g,e.copy(n).addScaledVector(q3t,o).addScaledVector(Y3t,a)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let Q3t=0;class t4t extends e5t{constructor(){super(),Object.defineProperty(this,"id",{value:Q3t++}),this.uuid=a5t(),this.name="",this.type="Material",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=Q0t,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=3,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=519,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=Z2t,this.stencilZFail=Z2t,this.stencilZPass=Z2t,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaTest=0,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0}onBuild(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn("THREE.Material: '"+e+"' parameter is undefined.");continue}if("shading"===e){console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead."),this.flatShading=1===n;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn("THREE."+this.type+": '"+e+"' is not a property of this material.")}}toJSON(t){const e=void 0===t||"string"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:"Material",generator:"Material.toJSON"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,""!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),this.sheen&&this.sheen.isColor&&(n.sheen=this.sheen.getHex()),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),1!==this.blending&&(n.blending=this.blending),0!==this.side&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),"round"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),"round"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),"{}"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),r=i(t.images);e.length>0&&(n.textures=e),r.length>0&&(n.images=r)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:"dispose"})}set needsUpdate(t){!0===t&&this.version++}}t4t.prototype.isMaterial=!0;const e4t={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},n4t={h:0,s:0,l:0},i4t={h:0,s:0,l:0};function r4t(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function o4t(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function a4t(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class s4t{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):"number"==typeof t?this.setHex(t):"string"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=l5t(t,1),e=s5t(e,0,1),n=s5t(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,r=2*n-i;this.r=r4t(r,i,t+1/3),this.g=r4t(r,i,t),this.b=r4t(r,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn("THREE.Color: Alpha component of "+t+" will be ignored.")}let n;if(n=/^((?:rgb|hsl)a?)\(([^\)]*)\)/.exec(t)){let t;const i=n[2];switch(n[1]){case"rgb":case"rgba":if(t=/^\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(i))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\s*(\d+)\%\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(i))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case"hsl":case"hsla":if(t=/^\s*(\d*\.?\d+)\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(i)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,r=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,r)}}}else if(n=/^\#([A-Fa-f\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=e4t[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn("THREE.Color: Unknown color "+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=o4t(t.r),this.g=o4t(t.g),this.b=o4t(t.b),this}copyLinearToSRGB(t){return this.r=a4t(t.r),this.g=a4t(t.g),this.b=a4t(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return("000000"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,r=Math.max(e,n,i),o=Math.min(e,n,i);let a,s;const l=(o+r)/2;if(o===r)a=0,s=0;else{const t=r-o;switch(s=l<=.5?t/(r+o):t/(2-r-o),r){case e:a=(n-i)/t+(n<i?6:0);break;case n:a=(i-e)/t+2;break;case i:a=(e-n)/t+4}a/=6}return t.h=a,t.s=s,t.l=l,t}getStyle(){return"rgb("+(255*this.r|0)+","+(255*this.g|0)+","+(255*this.b|0)+")"}offsetHSL(t,e,n){return this.getHSL(n4t),n4t.h+=t,n4t.s+=e,n4t.l+=n,this.setHSL(n4t.h,n4t.s,n4t.l),this}add(t){return this.r+=t.r,this.g+=t.g,this.b+=t.b,this}addColors(t,e){return this.r=t.r+e.r,this.g=t.g+e.g,this.b=t.b+e.b,this}addScalar(t){return this.r+=t,this.g+=t,this.b+=t,this}sub(t){return this.r=Math.max(0,this.r-t.r),this.g=Math.max(0,this.g-t.g),this.b=Math.max(0,this.b-t.b),this}multiply(t){return this.r*=t.r,this.g*=t.g,this.b*=t.b,this}multiplyScalar(t){return this.r*=t,this.g*=t,this.b*=t,this}lerp(t,e){return this.r+=(t.r-this.r)*e,this.g+=(t.g-this.g)*e,this.b+=(t.b-this.b)*e,this}lerpColors(t,e,n){return this.r=t.r+(e.r-t.r)*n,this.g=t.g+(e.g-t.g)*n,this.b=t.b+(e.b-t.b)*n,this}lerpHSL(t,e){this.getHSL(n4t),t.getHSL(i4t);const n=c5t(n4t.h,i4t.h,e),i=c5t(n4t.s,i4t.s,e),r=c5t(n4t.l,i4t.l,e);return this.setHSL(n,i,r),this}equals(t){return t.r===this.r&&t.g===this.g&&t.b===this.b}fromArray(t,e=0){return this.r=t[e],this.g=t[e+1],this.b=t[e+2],this}toArray(t=[],e=0){return t[e]=this.r,t[e+1]=this.g,t[e+2]=this.b,t}fromBufferAttribute(t,e){return this.r=t.getX(e),this.g=t.getY(e),this.b=t.getZ(e),!0===t.normalized&&(this.r/=255,this.g/=255,this.b/=255),this}toJSON(){return this.getHex()}}s4t.NAMES=e4t,s4t.prototype.isColor=!0,s4t.prototype.r=1,s4t.prototype.g=1,s4t.prototype.b=1;class l4t extends t4t{constructor(t){super(),this.type="MeshBasicMaterial",this.color=new s4t(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}l4t.prototype.isMeshBasicMaterial=!0;const c4t=new H5t,u4t=new C5t;class h4t{constructor(t,e,n){if(Array.isArray(t))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");this.name="",this.array=t,this.itemSize=e,this.count=void 0!==t?t.length/e:0,this.normalized=!0===n,this.usage=J2t,this.updateRange={offset:0,count:-1},this.version=0}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.name=t.name,this.array=new t.array.constructor(t.array),this.itemSize=t.itemSize,this.count=t.count,this.normalized=t.normalized,this.usage=t.usage,this}copyAt(t,e,n){t*=this.itemSize,n*=e.itemSize;for(let i=0,r=this.itemSize;i<r;i++)this.array[t+i]=e.array[n+i];return this}copyArray(t){return this.array.set(t),this}copyColorsArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn("THREE.BufferAttribute.copyColorsArray(): color is undefined",i),r=new s4t),e[n++]=r.r,e[n++]=r.g,e[n++]=r.b}return this}copyVector2sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn("THREE.BufferAttribute.copyVector2sArray(): vector is undefined",i),r=new C5t),e[n++]=r.x,e[n++]=r.y}return this}copyVector3sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn("THREE.BufferAttribute.copyVector3sArray(): vector is undefined",i),r=new H5t),e[n++]=r.x,e[n++]=r.y,e[n++]=r.z}return this}copyVector4sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn("THREE.BufferAttribute.copyVector4sArray(): vector is undefined",i),r=new R5t),e[n++]=r.x,e[n++]=r.y,e[n++]=r.z,e[n++]=r.w}return this}applyMatrix3(t){if(2===this.itemSize)for(let e=0,n=this.count;e<n;e++)u4t.fromBufferAttribute(this,e),u4t.applyMatrix3(t),this.setXY(e,u4t.x,u4t.y);else if(3===this.itemSize)for(let e=0,n=this.count;e<n;e++)c4t.fromBufferAttribute(this,e),c4t.applyMatrix3(t),this.setXYZ(e,c4t.x,c4t.y,c4t.z);return this}applyMatrix4(t){for(let e=0,n=this.count;e<n;e++)c4t.x=this.getX(e),c4t.y=this.getY(e),c4t.z=this.getZ(e),c4t.applyMatrix4(t),this.setXYZ(e,c4t.x,c4t.y,c4t.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)c4t.x=this.getX(e),c4t.y=this.getY(e),c4t.z=this.getZ(e),c4t.applyNormalMatrix(t),this.setXYZ(e,c4t.x,c4t.y,c4t.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)c4t.x=this.getX(e),c4t.y=this.getY(e),c4t.z=this.getZ(e),c4t.transformDirection(t),this.setXYZ(e,c4t.x,c4t.y,c4t.z);return this}set(t,e=0){return this.array.set(t,e),this}getX(t){return this.array[t*this.itemSize]}setX(t,e){return this.array[t*this.itemSize]=e,this}getY(t){return this.array[t*this.itemSize+1]}setY(t,e){return this.array[t*this.itemSize+1]=e,this}getZ(t){return this.array[t*this.itemSize+2]}setZ(t,e){return this.array[t*this.itemSize+2]=e,this}getW(t){return this.array[t*this.itemSize+3]}setW(t,e){return this.array[t*this.itemSize+3]=e,this}setXY(t,e,n){return this.array[(t*=this.itemSize)+0]=e,this.array[t+1]=n,this}setXYZ(t,e,n,i){return this.array[(t*=this.itemSize)+0]=e,this.array[t+1]=n,this.array[t+2]=i,this}setXYZW(t,e,n,i,r){return this.array[(t*=this.itemSize)+0]=e,this.array[t+1]=n,this.array[t+2]=i,this.array[t+3]=r,this}onUpload(t){return this.onUploadCallback=t,this}clone(){return new this.constructor(this.array,this.itemSize).copy(this)}toJSON(){const t={itemSize:this.itemSize,type:this.array.constructor.name,array:Array.prototype.slice.call(this.array),normalized:this.normalized};return""!==this.name&&(t.name=this.name),this.usage!==J2t&&(t.usage=this.usage),0===this.updateRange.offset&&-1===this.updateRange.count||(t.updateRange=this.updateRange),t}}h4t.prototype.isBufferAttribute=!0;class d4t extends h4t{constructor(t,e,n){super(new Int8Array(t),e,n)}}class p4t extends h4t{constructor(t,e,n){super(new Uint8Array(t),e,n)}}class f4t extends h4t{constructor(t,e,n){super(new Uint8ClampedArray(t),e,n)}}class m4t extends h4t{constructor(t,e,n){super(new Int16Array(t),e,n)}}class g4t extends h4t{constructor(t,e,n){super(new Uint16Array(t),e,n)}}class _4t extends h4t{constructor(t,e,n){super(new Int32Array(t),e,n)}}class y4t extends h4t{constructor(t,e,n){super(new Uint32Array(t),e,n)}}class v4t extends h4t{constructor(t,e,n){super(new Uint16Array(t),e,n)}}v4t.prototype.isFloat16BufferAttribute=!0;class b4t extends h4t{constructor(t,e,n){super(new Float32Array(t),e,n)}}class x4t extends h4t{constructor(t,e,n){super(new Float64Array(t),e,n)}}function w4t(t){if(0===t.length)return-1/0;let e=t[0];for(let n=1,i=t.length;n<i;++n)t[n]>e&&(e=t[n]);return e}const S4t={Int8Array:Int8Array,Uint8Array:Uint8Array,Uint8ClampedArray:Uint8ClampedArray,Int16Array:Int16Array,Uint16Array:Uint16Array,Int32Array:Int32Array,Uint32Array:Uint32Array,Float32Array:Float32Array,Float64Array:Float64Array};function M4t(t,e){return new S4t[t](e)}let E4t=0;const T4t=new g3t,C4t=new V3t,A4t=new H5t,k4t=new U5t,L4t=new U5t,P4t=new H5t;class N4t extends e5t{constructor(){super(),Object.defineProperty(this,"id",{value:E4t++}),this.uuid=a5t(),this.name="",this.type="BufferGeometry",this.index=null,this.attributes={},this.morphAttributes={},this.morphTargetsRelative=!1,this.groups=[],this.boundingBox=null,this.boundingSphere=null,this.drawRange={start:0,count:1/0},this.userData={}}getIndex(){return this.index}setIndex(t){return this.index=Array.isArray(t)?new(w4t(t)>65535?y4t:g4t)(t,1):t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new A5t).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return T4t.makeRotationFromQuaternion(t),this.applyMatrix4(T4t),this}rotateX(t){return T4t.makeRotationX(t),this.applyMatrix4(T4t),this}rotateY(t){return T4t.makeRotationY(t),this.applyMatrix4(T4t),this}rotateZ(t){return T4t.makeRotationZ(t),this.applyMatrix4(T4t),this}translate(t,e,n){return T4t.makeTranslation(t,e,n),this.applyMatrix4(T4t),this}scale(t,e,n){return T4t.makeScale(t,e,n),this.applyMatrix4(T4t),this}lookAt(t){return C4t.lookAt(t),C4t.updateMatrix(),this.applyMatrix4(C4t.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(A4t).negate(),this.translate(A4t.x,A4t.y,A4t.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n<i;n++){const i=t[n];e.push(i.x,i.y,i.z||0)}return this.setAttribute("position",new b4t(e,3)),this}computeBoundingBox(){null===this.boundingBox&&(this.boundingBox=new U5t);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingBox.set(new H5t(-1/0,-1/0,-1/0),new H5t(1/0,1/0,1/0));if(void 0!==t){if(this.boundingBox.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++){k4t.setFromBufferAttribute(e[t]),this.morphTargetsRelative?(P4t.addVectors(this.boundingBox.min,k4t.min),this.boundingBox.expandByPoint(P4t),P4t.addVectors(this.boundingBox.max,k4t.max),this.boundingBox.expandByPoint(P4t)):(this.boundingBox.expandByPoint(k4t.min),this.boundingBox.expandByPoint(k4t.max))}}else this.boundingBox.makeEmpty();(isNaN(this.boundingBox.min.x)||isNaN(this.boundingBox.min.y)||isNaN(this.boundingBox.min.z))&&console.error('THREE.BufferGeometry.computeBoundingBox(): Computed min/max have NaN values. The "position" attribute is likely to have NaN values.',this)}computeBoundingSphere(){null===this.boundingSphere&&(this.boundingSphere=new s3t);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingSphere(): GLBufferAttribute requires a manual bounding sphere. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingSphere.set(new H5t,1/0);if(t){const n=this.boundingSphere.center;if(k4t.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++){L4t.setFromBufferAttribute(e[t]),this.morphTargetsRelative?(P4t.addVectors(k4t.min,L4t.min),k4t.expandByPoint(P4t),P4t.addVectors(k4t.max,L4t.max),k4t.expandByPoint(P4t)):(k4t.expandByPoint(L4t.min),k4t.expandByPoint(L4t.max))}k4t.getCenter(n);let i=0;for(let e=0,r=t.count;e<r;e++)P4t.fromBufferAttribute(t,e),i=Math.max(i,n.distanceToSquared(P4t));if(e)for(let r=0,o=e.length;r<o;r++){const o=e[r],a=this.morphTargetsRelative;for(let e=0,r=o.count;e<r;e++)P4t.fromBufferAttribute(o,e),a&&(A4t.fromBufferAttribute(t,e),P4t.add(A4t)),i=Math.max(i,n.distanceToSquared(P4t))}this.boundingSphere.radius=Math.sqrt(i),isNaN(this.boundingSphere.radius)&&console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The "position" attribute is likely to have NaN values.',this)}}computeFaceNormals(){}computeTangents(){const t=this.index,e=this.attributes;if(null===t||void 0===e.position||void 0===e.normal||void 0===e.uv)return void console.error("THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)");const n=t.array,i=e.position.array,r=e.normal.array,o=e.uv.array,a=i.length/3;void 0===e.tangent&&this.setAttribute("tangent",new h4t(new Float32Array(4*a),4));const s=e.tangent.array,l=[],c=[];for(let t=0;t<a;t++)l[t]=new H5t,c[t]=new H5t;const u=new H5t,h=new H5t,d=new H5t,p=new C5t,f=new C5t,m=new C5t,g=new H5t,_=new H5t;function y(t,e,n){u.fromArray(i,3*t),h.fromArray(i,3*e),d.fromArray(i,3*n),p.fromArray(o,2*t),f.fromArray(o,2*e),m.fromArray(o,2*n),h.sub(u),d.sub(u),f.sub(p),m.sub(p);const r=1/(f.x*m.y-m.x*f.y);isFinite(r)&&(g.copy(h).multiplyScalar(m.y).addScaledVector(d,-f.y).multiplyScalar(r),_.copy(d).multiplyScalar(f.x).addScaledVector(h,-m.x).multiplyScalar(r),l[t].add(g),l[e].add(g),l[n].add(g),c[t].add(_),c[e].add(_),c[n].add(_))}let v=this.groups;0===v.length&&(v=[{start:0,count:n.length}]);for(let t=0,e=v.length;t<e;++t){const e=v[t],i=e.start;for(let t=i,r=i+e.count;t<r;t+=3)y(n[t+0],n[t+1],n[t+2])}const b=new H5t,x=new H5t,w=new H5t,S=new H5t;function M(t){w.fromArray(r,3*t),S.copy(w);const e=l[t];b.copy(e),b.sub(w.multiplyScalar(w.dot(e))).normalize(),x.crossVectors(S,e);const n=x.dot(c[t])<0?-1:1;s[4*t]=b.x,s[4*t+1]=b.y,s[4*t+2]=b.z,s[4*t+3]=n}for(let t=0,e=v.length;t<e;++t){const e=v[t],i=e.start;for(let t=i,r=i+e.count;t<r;t+=3)M(n[t+0]),M(n[t+1]),M(n[t+2])}}computeVertexNormals(){const t=this.index,e=this.getAttribute("position");if(void 0!==e){let n=this.getAttribute("normal");if(void 0===n)n=new h4t(new Float32Array(3*e.count),3),this.setAttribute("normal",n);else for(let t=0,e=n.count;t<e;t++)n.setXYZ(t,0,0,0);const i=new H5t,r=new H5t,o=new H5t,a=new H5t,s=new H5t,l=new H5t,c=new H5t,u=new H5t;if(t)for(let h=0,d=t.count;h<d;h+=3){const d=t.getX(h+0),p=t.getX(h+1),f=t.getX(h+2);i.fromBufferAttribute(e,d),r.fromBufferAttribute(e,p),o.fromBufferAttribute(e,f),c.subVectors(o,r),u.subVectors(i,r),c.cross(u),a.fromBufferAttribute(n,d),s.fromBufferAttribute(n,p),l.fromBufferAttribute(n,f),a.add(c),s.add(c),l.add(c),n.setXYZ(d,a.x,a.y,a.z),n.setXYZ(p,s.x,s.y,s.z),n.setXYZ(f,l.x,l.y,l.z)}else for(let t=0,a=e.count;t<a;t+=3)i.fromBufferAttribute(e,t+0),r.fromBufferAttribute(e,t+1),o.fromBufferAttribute(e,t+2),c.subVectors(o,r),u.subVectors(i,r),c.cross(u),n.setXYZ(t+0,c.x,c.y,c.z),n.setXYZ(t+1,c.x,c.y,c.z),n.setXYZ(t+2,c.x,c.y,c.z);this.normalizeNormals(),n.needsUpdate=!0}}merge(t,e){if(!t||!t.isBufferGeometry)return void console.error("THREE.BufferGeometry.merge(): geometry not an instance of THREE.BufferGeometry.",t);void 0===e&&(e=0,console.warn("THREE.BufferGeometry.merge(): Overwriting original geometry, starting at offset=0. Use BufferGeometryUtils.mergeBufferGeometries() for lossless merge."));const n=this.attributes;for(const i in n){if(void 0===t.attributes[i])continue;const r=n[i].array,o=t.attributes[i],a=o.array,s=o.itemSize*e,l=Math.min(a.length,r.length-s);for(let t=0,e=s;t<l;t++,e++)r[e]=a[t]}return this}normalizeNormals(){const t=this.attributes.normal;for(let e=0,n=t.count;e<n;e++)P4t.fromBufferAttribute(t,e),P4t.normalize(),t.setXYZ(e,P4t.x,P4t.y,P4t.z)}toNonIndexed(){function t(t,e){const n=t.array,i=t.itemSize,r=t.normalized,o=new n.constructor(e.length*i);let a=0,s=0;for(let r=0,l=e.length;r<l;r++){a=t.isInterleavedBufferAttribute?e[r]*t.data.stride+t.offset:e[r]*i;for(let t=0;t<i;t++)o[s++]=n[a++]}return new h4t(o,i,r)}if(null===this.index)return console.warn("THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed."),this;const e=new N4t,n=this.index.array,i=this.attributes;for(const r in i){const o=t(i[r],n);e.setAttribute(r,o)}const r=this.morphAttributes;for(const i in r){const o=[],a=r[i];for(let e=0,i=a.length;e<i;e++){const i=t(a[e],n);o.push(i)}e.morphAttributes[i]=o}e.morphTargetsRelative=this.morphTargetsRelative;const o=this.groups;for(let t=0,n=o.length;t<n;t++){const n=o[t];e.addGroup(n.start,n.count,n.materialIndex)}return e}toJSON(){const t={metadata:{version:4.5,type:"BufferGeometry",generator:"BufferGeometry.toJSON"}};if(t.uuid=this.uuid,t.type=this.type,""!==this.name&&(t.name=this.name),Object.keys(this.userData).length>0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){t.data.attributes[e]=n[e].toJSON(t.data)}const i={};let r=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],o=[];for(let e=0,i=n.length;e<i;e++){o.push(n[e].toJSON(t.data))}o.length>0&&(i[e]=o,r=!0)}r&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const o=this.groups;o.length>0&&(t.data.groups=JSON.parse(JSON.stringify(o)));const a=this.boundingSphere;return null!==a&&(t.data.boundingSphere={center:a.center.toArray(),radius:a.radius}),t}clone(){return(new N4t).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i){this.setAttribute(t,i[t].clone(e))}const r=t.morphAttributes;for(const t in r){const n=[],i=r[t];for(let t=0,r=i.length;t<r;t++)n.push(i[t].clone(e));this.morphAttributes[t]=n}this.morphTargetsRelative=t.morphTargetsRelative;const o=t.groups;for(let t=0,e=o.length;t<e;t++){const e=o[t];this.addGroup(e.start,e.count,e.materialIndex)}const a=t.boundingBox;null!==a&&(this.boundingBox=a.clone());const s=t.boundingSphere;return null!==s&&(this.boundingSphere=s.clone()),this.drawRange.start=t.drawRange.start,this.drawRange.count=t.drawRange.count,this.userData=t.userData,this}dispose(){this.dispatchEvent({type:"dispose"})}}N4t.prototype.isBufferGeometry=!0;const I4t=new g3t,R4t=new m3t,O4t=new s3t,z4t=new H5t,D4t=new H5t,B4t=new H5t,H4t=new H5t,F4t=new H5t,V4t=new H5t,U4t=new H5t,j4t=new H5t,G4t=new H5t,W4t=new C5t,q4t=new C5t,Y4t=new C5t,X4t=new H5t,$4t=new H5t;class K4t extends V3t{constructor(t=new N4t,e=new l4t){super(),this.type="Mesh",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),void 0!==t.morphTargetInfluences&&(this.morphTargetInfluences=t.morphTargetInfluences.slice()),void 0!==t.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},t.morphTargetDictionary)),this.material=t.material,this.geometry=t.geometry,this}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}}raycast(t,e){const n=this.geometry,i=this.material,r=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),O4t.copy(n.boundingSphere),O4t.applyMatrix4(r),!1===t.ray.intersectsSphere(O4t))return;if(I4t.copy(r).invert(),R4t.copy(t.ray).applyMatrix4(I4t),null!==n.boundingBox&&!1===R4t.intersectsBox(n.boundingBox))return;let o;if(n.isBufferGeometry){const r=n.index,a=n.attributes.position,s=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,u=n.attributes.uv2,h=n.groups,d=n.drawRange;if(null!==r)if(Array.isArray(i))for(let n=0,p=h.length;n<p;n++){const p=h[n],f=i[p.materialIndex];for(let n=Math.max(p.start,d.start),i=Math.min(p.start+p.count,d.start+d.count);n<i;n+=3){const i=r.getX(n),h=r.getX(n+1),d=r.getX(n+2);o=Z4t(this,f,t,R4t,a,s,l,c,u,i,h,d),o&&(o.faceIndex=Math.floor(n/3),o.face.materialIndex=p.materialIndex,e.push(o))}}else{for(let n=Math.max(0,d.start),h=Math.min(r.count,d.start+d.count);n<h;n+=3){const h=r.getX(n),d=r.getX(n+1),p=r.getX(n+2);o=Z4t(this,i,t,R4t,a,s,l,c,u,h,d,p),o&&(o.faceIndex=Math.floor(n/3),e.push(o))}}else if(void 0!==a)if(Array.isArray(i))for(let n=0,r=h.length;n<r;n++){const r=h[n],p=i[r.materialIndex];for(let n=Math.max(r.start,d.start),i=Math.min(r.start+r.count,d.start+d.count);n<i;n+=3){o=Z4t(this,p,t,R4t,a,s,l,c,u,n,n+1,n+2),o&&(o.faceIndex=Math.floor(n/3),o.face.materialIndex=r.materialIndex,e.push(o))}}else{for(let n=Math.max(0,d.start),r=Math.min(a.count,d.start+d.count);n<r;n+=3){o=Z4t(this,i,t,R4t,a,s,l,c,u,n,n+1,n+2),o&&(o.faceIndex=Math.floor(n/3),e.push(o))}}}else n.isGeometry&&console.error("THREE.Mesh.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}}function Z4t(t,e,n,i,r,o,a,s,l,c,u,h){z4t.fromBufferAttribute(r,c),D4t.fromBufferAttribute(r,u),B4t.fromBufferAttribute(r,h);const d=t.morphTargetInfluences;if(o&&d){U4t.set(0,0,0),j4t.set(0,0,0),G4t.set(0,0,0);for(let t=0,e=o.length;t<e;t++){const e=d[t],n=o[t];0!==e&&(H4t.fromBufferAttribute(n,c),F4t.fromBufferAttribute(n,u),V4t.fromBufferAttribute(n,h),a?(U4t.addScaledVector(H4t,e),j4t.addScaledVector(F4t,e),G4t.addScaledVector(V4t,e)):(U4t.addScaledVector(H4t.sub(z4t),e),j4t.addScaledVector(F4t.sub(D4t),e),G4t.addScaledVector(V4t.sub(B4t),e)))}z4t.add(U4t),D4t.add(j4t),B4t.add(G4t)}t.isSkinnedMesh&&(t.boneTransform(c,z4t),t.boneTransform(u,D4t),t.boneTransform(h,B4t));const p=(function f(t,e,n,i,r,o,a,s){let l;if(l=1===e.side?i.intersectTriangle(a,o,r,!0,s):i.intersectTriangle(r,o,a,2!==e.side,s),null===l)return null;$4t.copy(s),$4t.applyMatrix4(t.matrixWorld);const c=n.ray.origin.distanceTo($4t);return c<n.near||c>n.far?null:{distance:c,point:$4t.clone(),object:t}})(t,e,n,i,z4t,D4t,B4t,X4t);if(p){s&&(W4t.fromBufferAttribute(s,c),q4t.fromBufferAttribute(s,u),Y4t.fromBufferAttribute(s,h),p.uv=J3t.getUV(X4t,z4t,D4t,B4t,W4t,q4t,Y4t,new C5t)),l&&(W4t.fromBufferAttribute(l,c),q4t.fromBufferAttribute(l,u),Y4t.fromBufferAttribute(l,h),p.uv2=J3t.getUV(X4t,z4t,D4t,B4t,W4t,q4t,Y4t,new C5t));const t={a:c,b:u,c:h,normal:new H5t,materialIndex:0};J3t.getNormal(z4t,D4t,B4t,t.normal),p.face=t}return p}K4t.prototype.isMesh=!0;class J4t extends N4t{constructor(t=1,e=1,n=1,i=1,r=1,o=1){super(),this.type="BoxGeometry",this.parameters={width:t,height:e,depth:n,widthSegments:i,heightSegments:r,depthSegments:o};const a=this;i=Math.floor(i),r=Math.floor(r),o=Math.floor(o);const s=[],l=[],c=[],u=[];let h=0,d=0;function p(t,e,n,i,r,o,p,f,m,g,_){const y=o/m,v=p/g,b=o/2,x=p/2,w=f/2,S=m+1,M=g+1;let E=0,T=0;const C=new H5t;for(let o=0;o<M;o++){const a=o*v-x;for(let s=0;s<S;s++){C[t]=(s*y-b)*i,C[e]=a*r,C[n]=w,l.push(C.x,C.y,C.z),C[t]=0,C[e]=0,C[n]=f>0?1:-1,c.push(C.x,C.y,C.z),u.push(s/m),u.push(1-o/g),E+=1}}for(let t=0;t<g;t++)for(let e=0;e<m;e++){const n=h+e+S*(t+1),i=h+(e+1)+S*(t+1),r=h+(e+1)+S*t;s.push(h+e+S*t,n,r),s.push(n,i,r),T+=6}a.addGroup(d,T,_),d+=T,h+=E}p("z","y","x",-1,-1,n,e,t,o,r,0),p("z","y","x",1,-1,n,e,-t,o,r,1),p("x","z","y",1,1,t,n,e,i,o,2),p("x","z","y",1,-1,t,n,-e,i,o,3),p("x","y","z",1,-1,t,e,n,i,r,4),p("x","y","z",-1,-1,t,e,-n,i,r,5),this.setIndex(s),this.setAttribute("position",new b4t(l,3)),this.setAttribute("normal",new b4t(c,3)),this.setAttribute("uv",new b4t(u,2))}static fromJSON(t){return new J4t(t.width,t.height,t.depth,t.widthSegments,t.heightSegments,t.depthSegments)}}function Q4t(t){const e={};for(const n in t){e[n]={};for(const i in t[n]){const r=t[n][i];e[n][i]=r&&(r.isColor||r.isMatrix3||r.isMatrix4||r.isVector2||r.isVector3||r.isVector4||r.isTexture||r.isQuaternion)?r.clone():Array.isArray(r)?r.slice():r}}return e}function t6t(t){const e={};for(let n=0;n<t.length;n++){const i=Q4t(t[n]);for(const t in i)e[t]=i[t]}return e}const e6t={clone:Q4t,merge:t6t};class n6t extends t4t{constructor(t){super(),this.type="ShaderMaterial",this.defines={},this.uniforms={},this.vertexShader="void main() {\n\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n}",this.fragmentShader="void main() {\n\tgl_FragColor = vec4( 1.0, 0.0, 0.0, 1.0 );\n}",this.linewidth=1,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.lights=!1,this.clipping=!1,this.extensions={derivatives:!1,fragDepth:!1,drawBuffers:!1,shaderTextureLOD:!1},this.defaultAttributeValues={color:[1,1,1],uv:[0,0],uv2:[0,0]},this.index0AttributeName=void 0,this.uniformsNeedUpdate=!1,this.glslVersion=null,void 0!==t&&(void 0!==t.attributes&&console.error("THREE.ShaderMaterial: attributes should now be defined in THREE.BufferGeometry instead."),this.setValues(t))}copy(t){return super.copy(t),this.fragmentShader=t.fragmentShader,this.vertexShader=t.vertexShader,this.uniforms=Q4t(t.uniforms),this.defines=Object.assign({},t.defines),this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.lights=t.lights,this.clipping=t.clipping,this.extensions=Object.assign({},t.extensions),this.glslVersion=t.glslVersion,this}toJSON(t){const e=super.toJSON(t);e.glslVersion=this.glslVersion,e.uniforms={};for(const n in this.uniforms){const i=this.uniforms[n].value;e.uniforms[n]=i&&i.isTexture?{type:"t",value:i.toJSON(t).uuid}:i&&i.isColor?{type:"c",value:i.getHex()}:i&&i.isVector2?{type:"v2",value:i.toArray()}:i&&i.isVector3?{type:"v3",value:i.toArray()}:i&&i.isVector4?{type:"v4",value:i.toArray()}:i&&i.isMatrix3?{type:"m3",value:i.toArray()}:i&&i.isMatrix4?{type:"m4",value:i.toArray()}:{value:i}}Object.keys(this.defines).length>0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}n6t.prototype.isShaderMaterial=!0;class i6t extends V3t{constructor(){super(),this.type="Camera",this.matrixWorldInverse=new g3t,this.projectionMatrix=new g3t,this.projectionMatrixInverse=new g3t}copy(t,e){return super.copy(t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()}updateMatrixWorld(t){super.updateMatrixWorld(t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(t,e){super.updateWorldMatrix(t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return(new this.constructor).copy(this)}}i6t.prototype.isCamera=!0;class r6t extends i6t{constructor(t=50,e=1,n=.1,i=2e3){super(),this.type="PerspectiveCamera",this.fov=t,this.zoom=1,this.near=n,this.far=i,this.focus=10,this.aspect=e,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.fov=t.fov,this.zoom=t.zoom,this.near=t.near,this.far=t.far,this.focus=t.focus,this.aspect=t.aspect,this.view=null===t.view?null:Object.assign({},t.view),this.filmGauge=t.filmGauge,this.filmOffset=t.filmOffset,this}setFocalLength(t){const e=.5*this.getFilmHeight()/t;this.fov=2*o5t*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*r5t*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*o5t*Math.atan(Math.tan(.5*r5t*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}setViewOffset(t,e,n,i,r,o){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=o,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*r5t*this.fov)/this.zoom,n=2*e,i=this.aspect*n,r=-.5*i;const o=this.view;if(null!==this.view&&this.view.enabled){const t=o.fullWidth,a=o.fullHeight;r+=o.offsetX*i/t,e-=o.offsetY*n/a,i*=o.width/t,n*=o.height/a}const a=this.filmOffset;0!==a&&(r+=t*a/this.getFilmWidth()),this.projectionMatrix.makePerspective(r,r+i,e,e-n,t,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.fov=this.fov,e.object.zoom=this.zoom,e.object.near=this.near,e.object.far=this.far,e.object.focus=this.focus,e.object.aspect=this.aspect,null!==this.view&&(e.object.view=Object.assign({},this.view)),e.object.filmGauge=this.filmGauge,e.object.filmOffset=this.filmOffset,e}}r6t.prototype.isPerspectiveCamera=!0;const o6t=90;class a6t extends V3t{constructor(t,e,n){if(super(),this.type="CubeCamera",!0!==n.isWebGLCubeRenderTarget)return void console.error("THREE.CubeCamera: The constructor now expects an instance of WebGLCubeRenderTarget as third parameter.");this.renderTarget=n;const i=new r6t(o6t,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new H5t(1,0,0)),this.add(i);const r=new r6t(o6t,1,t,e);r.layers=this.layers,r.up.set(0,-1,0),r.lookAt(new H5t(-1,0,0)),this.add(r);const o=new r6t(o6t,1,t,e);o.layers=this.layers,o.up.set(0,0,1),o.lookAt(new H5t(0,1,0)),this.add(o);const a=new r6t(o6t,1,t,e);a.layers=this.layers,a.up.set(0,0,-1),a.lookAt(new H5t(0,-1,0)),this.add(a);const s=new r6t(o6t,1,t,e);s.layers=this.layers,s.up.set(0,-1,0),s.lookAt(new H5t(0,0,1)),this.add(s);const l=new r6t(o6t,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new H5t(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[i,r,o,a,s,l]=this.children,c=t.xr.enabled,u=t.getRenderTarget();t.xr.enabled=!1;const h=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,i),t.setRenderTarget(n,1),t.render(e,r),t.setRenderTarget(n,2),t.render(e,o),t.setRenderTarget(n,3),t.render(e,a),t.setRenderTarget(n,4),t.render(e,s),n.texture.generateMipmaps=h,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(u),t.xr.enabled=c}}class s6t extends N5t{constructor(t,e,n,i,r,o,a,s,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:e2t,n,i,r,o,a=void 0!==a?a:w2t,s,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}s6t.prototype.isCubeTexture=!0;class l6t extends O5t{constructor(t,e,n){Number.isInteger(e)&&(console.warn("THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )"),e=n),super(t,t,e),this.texture=new s6t(void 0,(e=e||{}).mapping,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.format,e.type,e.anisotropy,e.encoding),this.texture.isRenderTargetTexture=!0,this.texture.generateMipmaps=void 0!==e.generateMipmaps&&e.generateMipmaps,this.texture.minFilter=void 0!==e.minFilter?e.minFilter:p2t,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=S2t,this.texture.encoding=e.encoding,this.texture.generateMipmaps=e.generateMipmaps,this.texture.minFilter=e.minFilter,this.texture.magFilter=e.magFilter;const n={uniforms:{tEquirect:{value:null}},vertexShader:"\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\n\t\t\t\t\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n\n\t\t\t\t}\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvWorldDirection = transformDirection( position, modelMatrix );\n\n\t\t\t\t\t#include <begin_vertex>\n\t\t\t\t\t#include <project_vertex>\n\n\t\t\t\t}\n\t\t\t",fragmentShader:"\n\n\t\t\t\tuniform sampler2D tEquirect;\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\t#include <common>\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvec3 direction = normalize( vWorldDirection );\n\n\t\t\t\t\tvec2 sampleUV = equirectUv( direction );\n\n\t\t\t\t\tgl_FragColor = texture2D( tEquirect, sampleUV );\n\n\t\t\t\t}\n\t\t\t"},i=new J4t(5,5,5),r=new n6t({name:"CubemapFromEquirect",uniforms:Q4t(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:1,blending:0});r.uniforms.tEquirect.value=e;const o=new K4t(i,r),a=e.minFilter;e.minFilter===m2t&&(e.minFilter=p2t);return new a6t(1,10,this).update(t,o),e.minFilter=a,o.geometry.dispose(),o.material.dispose(),this}clear(t,e,n,i){const r=t.getRenderTarget();for(let r=0;r<6;r++)t.setRenderTarget(this,r),t.clear(e,n,i);t.setRenderTarget(r)}}l6t.prototype.isWebGLCubeRenderTarget=!0;const c6t=new H5t,u6t=new H5t,h6t=new A5t;class d6t{constructor(t=new H5t(1,0,0),e=0){this.normal=t,this.constant=e}set(t,e){return this.normal.copy(t),this.constant=e,this}setComponents(t,e,n,i){return this.normal.set(t,e,n),this.constant=i,this}setFromNormalAndCoplanarPoint(t,e){return this.normal.copy(t),this.constant=-e.dot(this.normal),this}setFromCoplanarPoints(t,e,n){const i=c6t.subVectors(n,e).cross(u6t.subVectors(t,e)).normalize();return this.setFromNormalAndCoplanarPoint(i,t),this}copy(t){return this.normal.copy(t.normal),this.constant=t.constant,this}normalize(){const t=1/this.normal.length();return this.normal.multiplyScalar(t),this.constant*=t,this}negate(){return this.constant*=-1,this.normal.negate(),this}distanceToPoint(t){return this.normal.dot(t)+this.constant}distanceToSphere(t){return this.distanceToPoint(t.center)-t.radius}projectPoint(t,e){return e.copy(this.normal).multiplyScalar(-this.distanceToPoint(t)).add(t)}intersectLine(t,e){const n=t.delta(c6t),i=this.normal.dot(n);if(0===i)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const r=-(t.start.dot(this.normal)+this.constant)/i;return r<0||r>1?null:e.copy(n).multiplyScalar(r).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||h6t.getNormalMatrix(t),i=this.coplanarPoint(c6t).applyMatrix4(t),r=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(r),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}d6t.prototype.isPlane=!0;const p6t=new s3t,f6t=new H5t;class m6t{constructor(t=new d6t,e=new d6t,n=new d6t,i=new d6t,r=new d6t,o=new d6t){this.planes=[t,e,n,i,r,o]}set(t,e,n,i,r,o){const a=this.planes;return a[0].copy(t),a[1].copy(e),a[2].copy(n),a[3].copy(i),a[4].copy(r),a[5].copy(o),this}copy(t){const e=this.planes;for(let n=0;n<6;n++)e[n].copy(t.planes[n]);return this}setFromProjectionMatrix(t){const e=this.planes,n=t.elements,i=n[0],r=n[1],o=n[2],a=n[3],s=n[4],l=n[5],c=n[6],u=n[7],h=n[8],d=n[9],p=n[10],f=n[11],m=n[12],g=n[13],_=n[14],y=n[15];return e[0].setComponents(a-i,u-s,f-h,y-m).normalize(),e[1].setComponents(a+i,u+s,f+h,y+m).normalize(),e[2].setComponents(a+r,u+l,f+d,y+g).normalize(),e[3].setComponents(a-r,u-l,f-d,y-g).normalize(),e[4].setComponents(a-o,u-c,f-p,y-_).normalize(),e[5].setComponents(a+o,u+c,f+p,y+_).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),p6t.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(p6t)}intersectsSprite(t){return p6t.center.set(0,0,0),p6t.radius=.7071067811865476,p6t.applyMatrix4(t.matrixWorld),this.intersectsSphere(p6t)}intersectsSphere(t){const e=this.planes,n=t.center,i=-t.radius;for(let t=0;t<6;t++){if(e[t].distanceToPoint(n)<i)return!1}return!0}intersectsBox(t){const e=this.planes;for(let n=0;n<6;n++){const i=e[n];if(f6t.x=i.normal.x>0?t.max.x:t.min.x,f6t.y=i.normal.y>0?t.max.y:t.min.y,f6t.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(f6t)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let n=0;n<6;n++)if(e[n].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}function g6t(){let t=null,e=!1,n=null,i=null;function r(e,o){n(e,o),i=t.requestAnimationFrame(r)}return{start:function(){!0!==e&&null!==n&&(i=t.requestAnimationFrame(r),e=!0)},stop:function(){t.cancelAnimationFrame(i),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function _6t(t,e){const n=e.isWebGL2,i=new WeakMap;return{get:function r(t){return t.isInterleavedBufferAttribute&&(t=t.data),i.get(t)},remove:function o(e){e.isInterleavedBufferAttribute&&(e=e.data);const n=i.get(e);n&&(t.deleteBuffer(n.buffer),i.delete(e))},update:function a(e,r){if(e.isGLBufferAttribute){const t=i.get(e);return void((!t||t.version<e.version)&&i.set(e,{buffer:e.buffer,type:e.type,bytesPerElement:e.elementSize,version:e.version}))}e.isInterleavedBufferAttribute&&(e=e.data);const o=i.get(e);void 0===o?i.set(e,(function a(e,i){const r=e.array,o=e.usage,a=t.createBuffer();t.bindBuffer(i,a),t.bufferData(i,r,o),e.onUploadCallback();let s=5126;return r instanceof Float32Array?s=5126:r instanceof Float64Array?console.warn("THREE.WebGLAttributes: Unsupported data buffer format: Float64Array."):r instanceof Uint16Array?e.isFloat16BufferAttribute?n?s=5131:console.warn("THREE.WebGLAttributes: Usage of Float16BufferAttribute requires WebGL2."):s=5123:r instanceof Int16Array?s=5122:r instanceof Uint32Array?s=5125:r instanceof Int32Array?s=5124:r instanceof Int8Array?s=5120:(r instanceof Uint8Array||r instanceof Uint8ClampedArray)&&(s=5121),{buffer:a,type:s,bytesPerElement:r.BYTES_PER_ELEMENT,version:e.version}})(e,r)):o.version<e.version&&(!(function s(e,i,r){const o=i.array,a=i.updateRange;t.bindBuffer(r,e),-1===a.count?t.bufferSubData(r,0,o):(n?t.bufferSubData(r,a.offset*o.BYTES_PER_ELEMENT,o,a.offset,a.count):t.bufferSubData(r,a.offset*o.BYTES_PER_ELEMENT,o.subarray(a.offset,a.offset+a.count)),a.count=-1)})(o.buffer,e,r),o.version=e.version)}}}class y6t extends N4t{constructor(t=1,e=1,n=1,i=1){super(),this.type="PlaneGeometry",this.parameters={width:t,height:e,widthSegments:n,heightSegments:i};const r=t/2,o=e/2,a=Math.floor(n),s=Math.floor(i),l=a+1,c=s+1,u=t/a,h=e/s,d=[],p=[],f=[],m=[];for(let t=0;t<c;t++){const e=t*h-o;for(let n=0;n<l;n++){p.push(n*u-r,-e,0),f.push(0,0,1),m.push(n/a),m.push(1-t/s)}}for(let t=0;t<s;t++)for(let e=0;e<a;e++){const n=e+l*(t+1),i=e+1+l*(t+1),r=e+1+l*t;d.push(e+l*t,n,r),d.push(n,i,r)}this.setIndex(d),this.setAttribute("position",new b4t(p,3)),this.setAttribute("normal",new b4t(f,3)),this.setAttribute("uv",new b4t(m,2))}static fromJSON(t){return new y6t(t.width,t.height,t.widthSegments,t.heightSegments)}}const v6t={alphamap_fragment:"#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\n#endif",alphamap_pars_fragment:"#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",alphatest_fragment:"#ifdef ALPHATEST\n\tif ( diffuseColor.a < ALPHATEST ) discard;\n#endif",aomap_fragment:"#ifdef USE_AOMAP\n\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\n\treflectedLight.indirectDiffuse *= ambientOcclusion;\n\t#if defined( USE_ENVMAP ) && defined( STANDARD )\n\t\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.specularRoughness );\n\t#endif\n#endif",aomap_pars_fragment:"#ifdef USE_AOMAP\n\tuniform sampler2D aoMap;\n\tuniform float aoMapIntensity;\n#endif",begin_vertex:"vec3 transformed = vec3( position );",beginnormal_vertex:"vec3 objectNormal = vec3( normal );\n#ifdef USE_TANGENT\n\tvec3 objectTangent = vec3( tangent.xyz );\n#endif",bsdfs:"vec2 integrateSpecularBRDF( const in float dotNV, const in float roughness ) {\n\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\n\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\n\tvec4 r = roughness * c0 + c1;\n\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\n\treturn vec2( -1.04, 1.04 ) * a004 + r.zw;\n}\nfloat punctualLightIntensityToIrradianceFactor( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\n#if defined ( PHYSICALLY_CORRECT_LIGHTS )\n\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\n\tif( cutoffDistance > 0.0 ) {\n\t\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\n\t}\n\treturn distanceFalloff;\n#else\n\tif( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\n\t\treturn pow( saturate( -lightDistance / cutoffDistance + 1.0 ), decayExponent );\n\t}\n\treturn 1.0;\n#endif\n}\nvec3 BRDF_Diffuse_Lambert( const in vec3 diffuseColor ) {\n\treturn RECIPROCAL_PI * diffuseColor;\n}\nvec3 F_Schlick( const in vec3 f0, const in vec3 f90, const in float dotVH ) {\n\tfloat fresnel = exp2( ( -5.55473 * dotVH - 6.98316 ) * dotVH );\n\treturn ( f90 - f0 ) * fresnel + f0;\n}\nvec3 F_Schlick_RoughnessDependent( const in vec3 F0, const in float dotNV, const in float roughness ) {\n\tfloat fresnel = exp2( ( -5.55473 * dotNV - 6.98316 ) * dotNV );\n\tvec3 Fr = max( vec3( 1.0 - roughness ), F0 ) - F0;\n\treturn Fr * fresnel + F0;\n}\nfloat G_GGX_Smith( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 = pow2( alpha );\n\tfloat gl = dotNL + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\tfloat gv = dotNV + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\treturn 1.0 / ( gl * gv );\n}\nfloat G_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 = pow2( alpha );\n\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\treturn 0.5 / max( gv + gl, EPSILON );\n}\nfloat D_GGX( const in float alpha, const in float dotNH ) {\n\tfloat a2 = pow2( alpha );\n\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;\n\treturn RECIPROCAL_PI * a2 / pow2( denom );\n}\nvec3 BRDF_Specular_GGX( const in IncidentLight incidentLight, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in vec3 f90, const in float roughness ) {\n\tfloat alpha = pow2( roughness );\n\tvec3 halfDir = normalize( incidentLight.direction + viewDir );\n\tfloat dotNL = saturate( dot( normal, incidentLight.direction ) );\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F = F_Schlick( f0, f90, dotLH );\n\tfloat G = G_GGX_SmithCorrelated( alpha, dotNL, dotNV );\n\tfloat D = D_GGX( alpha, dotNH );\n\treturn F * ( G * D );\n}\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\n\tconst float LUT_SIZE = 64.0;\n\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\n\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\n\tfloat dotNV = saturate( dot( N, V ) );\n\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\n\tuv = uv * LUT_SCALE + LUT_BIAS;\n\treturn uv;\n}\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\n\tfloat l = length( f );\n\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\n}\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\n\tfloat x = dot( v1, v2 );\n\tfloat y = abs( x );\n\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\n\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\n\tfloat v = a / b;\n\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\n\treturn cross( v1, v2 ) * theta_sintheta;\n}\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\n\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\n\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\n\tvec3 lightNormal = cross( v1, v2 );\n\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\n\tvec3 T1, T2;\n\tT1 = normalize( V - N * dot( V, N ) );\n\tT2 = - cross( N, T1 );\n\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\n\tvec3 coords[ 4 ];\n\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\n\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\n\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\n\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\n\tcoords[ 0 ] = normalize( coords[ 0 ] );\n\tcoords[ 1 ] = normalize( coords[ 1 ] );\n\tcoords[ 2 ] = normalize( coords[ 2 ] );\n\tcoords[ 3 ] = normalize( coords[ 3 ] );\n\tvec3 vectorFormFactor = vec3( 0.0 );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\n\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\n\treturn vec3( result );\n}\nvec3 BRDF_Specular_GGX_Environment( const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float roughness ) {\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\n\treturn specularColor * brdf.x + brdf.y;\n}\nvoid BRDF_Specular_Multiscattering_Environment( const in GeometricContext geometry, const in vec3 specularColor, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\n\tvec3 F = F_Schlick_RoughnessDependent( specularColor, dotNV, roughness );\n\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\n\tvec3 FssEss = F * brdf.x + brdf.y;\n\tfloat Ess = brdf.x + brdf.y;\n\tfloat Ems = 1.0 - Ess;\n\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619;\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\n\tsingleScatter += FssEss;\n\tmultiScatter += Fms * Ems;\n}\nfloat G_BlinnPhong_Implicit( ) {\n\treturn 0.25;\n}\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\n\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\n}\nvec3 BRDF_Specular_BlinnPhong( const in IncidentLight incidentLight, const in GeometricContext geometry, const in vec3 specularColor, const in float shininess ) {\n\tvec3 halfDir = normalize( incidentLight.direction + geometry.viewDir );\n\tfloat dotNH = saturate( dot( geometry.normal, halfDir ) );\n\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F = F_Schlick( specularColor, vec3( 1.0 ), dotLH );\n\tfloat G = G_BlinnPhong_Implicit( );\n\tfloat D = D_BlinnPhong( shininess, dotNH );\n\treturn F * ( G * D );\n}\nfloat GGXRoughnessToBlinnExponent( const in float ggxRoughness ) {\n\treturn ( 2.0 / pow2( ggxRoughness + 0.0001 ) - 2.0 );\n}\nfloat BlinnExponentToGGXRoughness( const in float blinnExponent ) {\n\treturn sqrt( 2.0 / ( blinnExponent + 2.0 ) );\n}\n#if defined( USE_SHEEN )\nfloat D_Charlie(float roughness, float NoH) {\n\tfloat invAlpha = 1.0 / roughness;\n\tfloat cos2h = NoH * NoH;\n\tfloat sin2h = max(1.0 - cos2h, 0.0078125);\treturn (2.0 + invAlpha) * pow(sin2h, invAlpha * 0.5) / (2.0 * PI);\n}\nfloat V_Neubelt(float NoV, float NoL) {\n\treturn saturate(1.0 / (4.0 * (NoL + NoV - NoL * NoV)));\n}\nvec3 BRDF_Specular_Sheen( const in float roughness, const in vec3 L, const in GeometricContext geometry, vec3 specularColor ) {\n\tvec3 N = geometry.normal;\n\tvec3 V = geometry.viewDir;\n\tvec3 H = normalize( V + L );\n\tfloat dotNH = saturate( dot( N, H ) );\n\treturn specularColor * D_Charlie( roughness, dotNH ) * V_Neubelt( dot(N, V), dot(N, L) );\n}\n#endif",bumpmap_pars_fragment:"#ifdef USE_BUMPMAP\n\tuniform sampler2D bumpMap;\n\tuniform float bumpScale;\n\tvec2 dHdxy_fwd() {\n\t\tvec2 dSTdx = dFdx( vUv );\n\t\tvec2 dSTdy = dFdy( vUv );\n\t\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\n\t\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\n\t\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\n\t\treturn vec2( dBx, dBy );\n\t}\n\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\n\t\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\n\t\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\n\t\tvec3 vN = surf_norm;\n\t\tvec3 R1 = cross( vSigmaY, vN );\n\t\tvec3 R2 = cross( vN, vSigmaX );\n\t\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\n\t\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\n\t\treturn normalize( abs( fDet ) * surf_norm - vGrad );\n\t}\n#endif",clipping_planes_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvec4 plane;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\n\t\tplane = clippingPlanes[ i ];\n\t\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\n\t}\n\t#pragma unroll_loop_end\n\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\n\t\tbool clipped = true;\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\n\t\t\tplane = clippingPlanes[ i ];\n\t\t\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t\tif ( clipped ) discard;\n\t#endif\n#endif",clipping_planes_pars_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\n#endif",clipping_planes_pars_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n#endif",clipping_planes_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvClipPosition = - mvPosition.xyz;\n#endif",color_fragment:"#if defined( USE_COLOR_ALPHA )\n\tdiffuseColor *= vColor;\n#elif defined( USE_COLOR )\n\tdiffuseColor.rgb *= vColor;\n#endif",color_pars_fragment:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR )\n\tvarying vec3 vColor;\n#endif",color_pars_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvarying vec3 vColor;\n#endif",color_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvColor = vec4( 1.0 );\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvColor = vec3( 1.0 );\n#endif\n#ifdef USE_COLOR\n\tvColor *= color;\n#endif\n#ifdef USE_INSTANCING_COLOR\n\tvColor.xyz *= instanceColor.xyz;\n#endif",common:"#define PI 3.141592653589793\n#define PI2 6.283185307179586\n#define PI_HALF 1.5707963267948966\n#define RECIPROCAL_PI 0.3183098861837907\n#define RECIPROCAL_PI2 0.15915494309189535\n#define EPSILON 1e-6\n#ifndef saturate\n#define saturate(a) clamp( a, 0.0, 1.0 )\n#endif\n#define whiteComplement(a) ( 1.0 - saturate( a ) )\nfloat pow2( const in float x ) { return x*x; }\nfloat pow3( const in float x ) { return x*x*x; }\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\nhighp float rand( const in vec2 uv ) {\n\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\n\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\n\treturn fract(sin(sn) * c);\n}\n#ifdef HIGH_PRECISION\n\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\n#else\n\tfloat max3( vec3 v ) { return max( max( v.x, v.y ), v.z ); }\n\tfloat precisionSafeLength( vec3 v ) {\n\t\tfloat maxComponent = max3( abs( v ) );\n\t\treturn length( v / maxComponent ) * maxComponent;\n\t}\n#endif\nstruct IncidentLight {\n\tvec3 color;\n\tvec3 direction;\n\tbool visible;\n};\nstruct ReflectedLight {\n\tvec3 directDiffuse;\n\tvec3 directSpecular;\n\tvec3 indirectDiffuse;\n\tvec3 indirectSpecular;\n};\nstruct GeometricContext {\n\tvec3 position;\n\tvec3 normal;\n\tvec3 viewDir;\n#ifdef CLEARCOAT\n\tvec3 clearcoatNormal;\n#endif\n};\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n}\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\n}\nvec3 projectOnPlane(in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\tfloat distance = dot( planeNormal, point - pointOnPlane );\n\treturn - distance * planeNormal + point;\n}\nfloat sideOfPlane( in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn sign( dot( point - pointOnPlane, planeNormal ) );\n}\nvec3 linePlaneIntersect( in vec3 pointOnLine, in vec3 lineDirection, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn lineDirection * ( dot( planeNormal, pointOnPlane - pointOnLine ) / dot( planeNormal, lineDirection ) ) + pointOnLine;\n}\nmat3 transposeMat3( const in mat3 m ) {\n\tmat3 tmp;\n\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\n\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\n\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\n\treturn tmp;\n}\nfloat linearToRelativeLuminance( const in vec3 color ) {\n\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\n\treturn dot( weights, color.rgb );\n}\nbool isPerspectiveMatrix( mat4 m ) {\n\treturn m[ 2 ][ 3 ] == - 1.0;\n}\nvec2 equirectUv( in vec3 dir ) {\n\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\n\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\treturn vec2( u, v );\n}",cube_uv_reflection_fragment:"#ifdef ENVMAP_TYPE_CUBE_UV\n\t#define cubeUV_maxMipLevel 8.0\n\t#define cubeUV_minMipLevel 4.0\n\t#define cubeUV_maxTileSize 256.0\n\t#define cubeUV_minTileSize 16.0\n\tfloat getFace( vec3 direction ) {\n\t\tvec3 absDirection = abs( direction );\n\t\tfloat face = - 1.0;\n\t\tif ( absDirection.x > absDirection.z ) {\n\t\t\tif ( absDirection.x > absDirection.y )\n\t\t\t\tface = direction.x > 0.0 ? 0.0 : 3.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t} else {\n\t\t\tif ( absDirection.z > absDirection.y )\n\t\t\t\tface = direction.z > 0.0 ? 2.0 : 5.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t}\n\t\treturn face;\n\t}\n\tvec2 getUV( vec3 direction, float face ) {\n\t\tvec2 uv;\n\t\tif ( face == 0.0 ) {\n\t\t\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 1.0 ) {\n\t\t\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\n\t\t} else if ( face == 2.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\n\t\t} else if ( face == 3.0 ) {\n\t\t\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 4.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\n\t\t} else {\n\t\t\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\n\t\t}\n\t\treturn 0.5 * ( uv + 1.0 );\n\t}\n\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\n\t\tfloat face = getFace( direction );\n\t\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\n\t\tmipInt = max( mipInt, cubeUV_minMipLevel );\n\t\tfloat faceSize = exp2( mipInt );\n\t\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\n\t\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\n\t\tvec2 f = fract( uv );\n\t\tuv += 0.5 - f;\n\t\tif ( face > 2.0 ) {\n\t\t\tuv.y += faceSize;\n\t\t\tface -= 3.0;\n\t\t}\n\t\tuv.x += face * faceSize;\n\t\tif ( mipInt < cubeUV_maxMipLevel ) {\n\t\t\tuv.y += 2.0 * cubeUV_maxTileSize;\n\t\t}\n\t\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\n\t\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\n\t\tuv *= texelSize;\n\t\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.x += texelSize;\n\t\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.y += texelSize;\n\t\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.x -= texelSize;\n\t\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tvec3 tm = mix( tl, tr, f.x );\n\t\tvec3 bm = mix( bl, br, f.x );\n\t\treturn mix( tm, bm, f.y );\n\t}\n\t#define r0 1.0\n\t#define v0 0.339\n\t#define m0 - 2.0\n\t#define r1 0.8\n\t#define v1 0.276\n\t#define m1 - 1.0\n\t#define r4 0.4\n\t#define v4 0.046\n\t#define m4 2.0\n\t#define r5 0.305\n\t#define v5 0.016\n\t#define m5 3.0\n\t#define r6 0.21\n\t#define v6 0.0038\n\t#define m6 4.0\n\tfloat roughnessToMip( float roughness ) {\n\t\tfloat mip = 0.0;\n\t\tif ( roughness >= r1 ) {\n\t\t\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\n\t\t} else if ( roughness >= r4 ) {\n\t\t\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\n\t\t} else if ( roughness >= r5 ) {\n\t\t\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\n\t\t} else if ( roughness >= r6 ) {\n\t\t\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\n\t\t} else {\n\t\t\tmip = - 2.0 * log2( 1.16 * roughness );\t\t}\n\t\treturn mip;\n\t}\n\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\n\t\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\n\t\tfloat mipF = fract( mip );\n\t\tfloat mipInt = floor( mip );\n\t\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\n\t\tif ( mipF == 0.0 ) {\n\t\t\treturn vec4( color0, 1.0 );\n\t\t} else {\n\t\t\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\n\t\t\treturn vec4( mix( color0, color1, mipF ), 1.0 );\n\t\t}\n\t}\n#endif",defaultnormal_vertex:"vec3 transformedNormal = objectNormal;\n#ifdef USE_INSTANCING\n\tmat3 m = mat3( instanceMatrix );\n\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\n\ttransformedNormal = m * transformedNormal;\n#endif\ntransformedNormal = normalMatrix * transformedNormal;\n#ifdef FLIP_SIDED\n\ttransformedNormal = - transformedNormal;\n#endif\n#ifdef USE_TANGENT\n\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#ifdef FLIP_SIDED\n\t\ttransformedTangent = - transformedTangent;\n\t#endif\n#endif",displacementmap_pars_vertex:"#ifdef USE_DISPLACEMENTMAP\n\tuniform sampler2D displacementMap;\n\tuniform float displacementScale;\n\tuniform float displacementBias;\n#endif",displacementmap_vertex:"#ifdef USE_DISPLACEMENTMAP\n\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\n#endif",emissivemap_fragment:"#ifdef USE_EMISSIVEMAP\n\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\n\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\n\ttotalEmissiveRadiance *= emissiveColor.rgb;\n#endif",emissivemap_pars_fragment:"#ifdef USE_EMISSIVEMAP\n\tuniform sampler2D emissiveMap;\n#endif",encodings_fragment:"gl_FragColor = linearToOutputTexel( gl_FragColor );",encodings_pars_fragment:"\nvec4 LinearToLinear( in vec4 value ) {\n\treturn value;\n}\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\n}\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\n}\nvec4 sRGBToLinear( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\n}\nvec4 LinearTosRGB( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\n}\nvec4 RGBEToLinear( in vec4 value ) {\n\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\n}\nvec4 LinearToRGBE( in vec4 value ) {\n\tfloat maxComponent = max( max( value.r, value.g ), value.b );\n\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\n\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\n}\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\n}\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\n\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\n\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\n\tM = ceil( M * 255.0 ) / 255.0;\n\treturn vec4( value.rgb / ( M * maxRange ), M );\n}\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\n}\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\n\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\n\tfloat D = max( maxRange / maxRGB, 1.0 );\n\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\n\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\n}\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\nvec4 LinearToLogLuv( in vec4 value ) {\n\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\n\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\n\tvec4 vResult;\n\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\n\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\n\tvResult.w = fract( Le );\n\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\n\treturn vResult;\n}\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\nvec4 LogLuvToLinear( in vec4 value ) {\n\tfloat Le = value.z * 255.0 + value.w;\n\tvec3 Xp_Y_XYZp;\n\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\n\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\n\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\n\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\n\treturn vec4( max( vRGB, 0.0 ), 1.0 );\n}",envmap_fragment:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvec3 cameraToFrag;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\n\t\t#endif\n\t#else\n\t\tvec3 reflectVec = vReflect;\n\t#endif\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\n\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\n\t#else\n\t\tvec4 envColor = vec4( 0.0 );\n\t#endif\n\t#ifndef ENVMAP_TYPE_CUBE_UV\n\t\tenvColor = envMapTexelToLinear( envColor );\n\t#endif\n\t#ifdef ENVMAP_BLENDING_MULTIPLY\n\t\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_MIX )\n\t\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_ADD )\n\t\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\n\t#endif\n#endif",envmap_common_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float envMapIntensity;\n\tuniform float flipEnvMap;\n\tuniform int maxMipLevel;\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tuniform samplerCube envMap;\n\t#else\n\t\tuniform sampler2D envMap;\n\t#endif\n\t\n#endif",envmap_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float reflectivity;\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\tvarying vec3 vWorldPosition;\n\t\tuniform float refractionRatio;\n\t#else\n\t\tvarying vec3 vReflect;\n\t#endif\n#endif",envmap_pars_vertex:"#ifdef USE_ENVMAP\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\t\n\t\tvarying vec3 vWorldPosition;\n\t#else\n\t\tvarying vec3 vReflect;\n\t\tuniform float refractionRatio;\n\t#endif\n#endif",envmap_physical_pars_fragment:"#if defined( USE_ENVMAP )\n\t#ifdef ENVMAP_MODE_REFRACTION\n\t\tuniform float refractionRatio;\n\t#endif\n\tvec3 getLightProbeIndirectIrradiance( const in GeometricContext geometry, const in int maxMIPLevel ) {\n\t\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryVec = vec3( flipEnvMap * worldNormal.x, worldNormal.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor = textureCubeLodEXT( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor = textureCube( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\n\t\t#else\n\t\t\tvec4 envMapColor = vec4( 0.0 );\n\t\t#endif\n\t\treturn PI * envMapColor.rgb * envMapIntensity;\n\t}\n\tfloat getSpecularMIPLevel( const in float roughness, const in int maxMIPLevel ) {\n\t\tfloat maxMIPLevelScalar = float( maxMIPLevel );\n\t\tfloat sigma = PI * roughness * roughness / ( 1.0 + roughness );\n\t\tfloat desiredMIPLevel = maxMIPLevelScalar + log2( sigma );\n\t\treturn clamp( desiredMIPLevel, 0.0, maxMIPLevelScalar );\n\t}\n\tvec3 getLightProbeIndirectRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness, const in int maxMIPLevel ) {\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( -viewDir, normal );\n\t\t\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( -viewDir, normal, refractionRatio );\n\t\t#endif\n\t\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\n\t\tfloat specularMIPLevel = getSpecularMIPLevel( roughness, maxMIPLevel );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryReflectVec = vec3( flipEnvMap * reflectVec.x, reflectVec.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor = textureCubeLodEXT( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor = textureCube( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\n\t\t#endif\n\t\treturn envMapColor.rgb * envMapIntensity;\n\t}\n#endif",envmap_vertex:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvWorldPosition = worldPosition.xyz;\n\t#else\n\t\tvec3 cameraToVertex;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvReflect = reflect( cameraToVertex, worldNormal );\n\t\t#else\n\t\t\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\n\t\t#endif\n\t#endif\n#endif",fog_vertex:"#ifdef USE_FOG\n\tfogDepth = - mvPosition.z;\n#endif",fog_pars_vertex:"#ifdef USE_FOG\n\tvarying float fogDepth;\n#endif",fog_fragment:"#ifdef USE_FOG\n\t#ifdef FOG_EXP2\n\t\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * fogDepth * fogDepth );\n\t#else\n\t\tfloat fogFactor = smoothstep( fogNear, fogFar, fogDepth );\n\t#endif\n\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\n#endif",fog_pars_fragment:"#ifdef USE_FOG\n\tuniform vec3 fogColor;\n\tvarying float fogDepth;\n\t#ifdef FOG_EXP2\n\t\tuniform float fogDensity;\n\t#else\n\t\tuniform float fogNear;\n\t\tuniform float fogFar;\n\t#endif\n#endif",gradientmap_pars_fragment:"#ifdef USE_GRADIENTMAP\n\tuniform sampler2D gradientMap;\n#endif\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\n\tfloat dotNL = dot( normal, lightDirection );\n\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\n\t#ifdef USE_GRADIENTMAP\n\t\treturn texture2D( gradientMap, coord ).rgb;\n\t#else\n\t\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\n\t#endif\n}",lightmap_fragment:"#ifdef USE_LIGHTMAP\n\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\treflectedLight.indirectDiffuse += PI * lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n#endif",lightmap_pars_fragment:"#ifdef USE_LIGHTMAP\n\tuniform sampler2D lightMap;\n\tuniform float lightMapIntensity;\n#endif",lights_lambert_vertex:"vec3 diffuse = vec3( 1.0 );\nGeometricContext geometry;\ngeometry.position = mvPosition.xyz;\ngeometry.normal = normalize( transformedNormal );\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\nGeometricContext backGeometry;\nbackGeometry.position = geometry.position;\nbackGeometry.normal = -geometry.normal;\nbackGeometry.viewDir = geometry.viewDir;\nvLightFront = vec3( 0.0 );\nvIndirectFront = vec3( 0.0 );\n#ifdef DOUBLE_SIDED\n\tvLightBack = vec3( 0.0 );\n\tvIndirectBack = vec3( 0.0 );\n#endif\nIncidentLight directLight;\nfloat dotNL;\nvec3 directLightColor_Diffuse;\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry );\n#ifdef DOUBLE_SIDED\n\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\n\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry );\n#endif\n#if NUM_POINT_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tgetPointDirectLightIrradiance( pointLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tgetSpotDirectLightIrradiance( spotLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_DIR_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tgetDirectionalDirectLightIrradiance( directionalLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry );\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif",lights_pars_begin:"uniform bool receiveShadow;\nuniform vec3 ambientLightColor;\nuniform vec3 lightProbe[ 9 ];\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\n\tfloat x = normal.x, y = normal.y, z = normal.z;\n\tvec3 result = shCoefficients[ 0 ] * 0.886227;\n\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\n\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\n\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\n\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\n\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\n\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\n\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\n\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\n\treturn result;\n}\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in GeometricContext geometry ) {\n\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\n\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\n\treturn irradiance;\n}\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\n\tvec3 irradiance = ambientLightColor;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treturn irradiance;\n}\n#if NUM_DIR_LIGHTS > 0\n\tstruct DirectionalLight {\n\t\tvec3 direction;\n\t\tvec3 color;\n\t};\n\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n\tvoid getDirectionalDirectLightIrradiance( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tdirectLight.color = directionalLight.color;\n\t\tdirectLight.direction = directionalLight.direction;\n\t\tdirectLight.visible = true;\n\t}\n#endif\n#if NUM_POINT_LIGHTS > 0\n\tstruct PointLight {\n\t\tvec3 position;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t};\n\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n\tvoid getPointDirectLightIrradiance( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tvec3 lVector = pointLight.position - geometry.position;\n\t\tdirectLight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tdirectLight.color = pointLight.color;\n\t\tdirectLight.color *= punctualLightIntensityToIrradianceFactor( lightDistance, pointLight.distance, pointLight.decay );\n\t\tdirectLight.visible = ( directLight.color != vec3( 0.0 ) );\n\t}\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\tstruct SpotLight {\n\t\tvec3 position;\n\t\tvec3 direction;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t\tfloat coneCos;\n\t\tfloat penumbraCos;\n\t};\n\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\n\tvoid getSpotDirectLightIrradiance( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tvec3 lVector = spotLight.position - geometry.position;\n\t\tdirectLight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tfloat angleCos = dot( directLight.direction, spotLight.direction );\n\t\tif ( angleCos > spotLight.coneCos ) {\n\t\t\tfloat spotEffect = smoothstep( spotLight.coneCos, spotLight.penumbraCos, angleCos );\n\t\t\tdirectLight.color = spotLight.color;\n\t\t\tdirectLight.color *= spotEffect * punctualLightIntensityToIrradianceFactor( lightDistance, spotLight.distance, spotLight.decay );\n\t\t\tdirectLight.visible = true;\n\t\t} else {\n\t\t\tdirectLight.color = vec3( 0.0 );\n\t\t\tdirectLight.visible = false;\n\t\t}\n\t}\n#endif\n#if NUM_RECT_AREA_LIGHTS > 0\n\tstruct RectAreaLight {\n\t\tvec3 color;\n\t\tvec3 position;\n\t\tvec3 halfWidth;\n\t\tvec3 halfHeight;\n\t};\n\tuniform sampler2D ltc_1;\tuniform sampler2D ltc_2;\n\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\tstruct HemisphereLight {\n\t\tvec3 direction;\n\t\tvec3 skyColor;\n\t\tvec3 groundColor;\n\t};\n\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\n\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in GeometricContext geometry ) {\n\t\tfloat dotNL = dot( geometry.normal, hemiLight.direction );\n\t\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\n\t\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tirradiance *= PI;\n\t\t#endif\n\t\treturn irradiance;\n\t}\n#endif",lights_toon_fragment:"ToonMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;",lights_toon_pars_fragment:"varying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\nstruct ToonMaterial {\n\tvec3 diffuseColor;\n};\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_Toon\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Toon\n#define Material_LightProbeLOD( material )\t(0)",lights_phong_fragment:"BlinnPhongMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;\nmaterial.specularColor = specular;\nmaterial.specularShininess = shininess;\nmaterial.specularStrength = specularStrength;",lights_phong_pars_fragment:"varying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\nstruct BlinnPhongMaterial {\n\tvec3 diffuseColor;\n\tvec3 specularColor;\n\tfloat specularShininess;\n\tfloat specularStrength;\n};\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n\treflectedLight.directSpecular += irradiance * BRDF_Specular_BlinnPhong( directLight, geometry, material.specularColor, material.specularShininess ) * material.specularStrength;\n}\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_BlinnPhong\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_BlinnPhong\n#define Material_LightProbeLOD( material )\t(0)",lights_physical_fragment:"PhysicalMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\nmaterial.specularRoughness = max( roughnessFactor, 0.0525 );material.specularRoughness += geometryRoughness;\nmaterial.specularRoughness = min( material.specularRoughness, 1.0 );\n#ifdef REFLECTIVITY\n\t#ifdef SPECULAR\n\t\tvec3 specularIntensityFactor = vec3( specularIntensity );\n\t\tvec3 specularTintFactor = specularTint;\n\t\t#ifdef USE_SPECULARINTENSITYMAP\n\t\t\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\n\t\t#endif\n\t\t#ifdef USE_SPECULARTINTMAP\n\t\t\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\n\t\t#endif\n\t\tmaterial.specularColorF90 = mix( specularIntensityFactor, vec3( 1.0 ), metalnessFactor );\n\t#else\n\t\tvec3 specularIntensityFactor = vec3( 1.0 );\n\t\tvec3 specularTintFactor = vec3( 1.0 );\n\t\tmaterial.specularColorF90 = vec3( 1.0 );\n\t#endif\n\tmaterial.specularColor = mix( min( vec3( MAXIMUM_SPECULAR_COEFFICIENT * pow2( reflectivity ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\n#else\n\tmaterial.specularColor = mix( vec3( DEFAULT_SPECULAR_COEFFICIENT ), diffuseColor.rgb, metalnessFactor );\n\tmaterial.specularColorF90 = vec3( 1.0 );\n#endif\n#ifdef CLEARCOAT\n\tmaterial.clearcoat = clearcoat;\n\tmaterial.clearcoatRoughness = clearcoatRoughness;\n\t#ifdef USE_CLEARCOATMAP\n\t\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\n\t#endif\n\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\t\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\n\t#endif\n\tmaterial.clearcoat = saturate( material.clearcoat );\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\n\tmaterial.clearcoatRoughness += geometryRoughness;\n\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\n#endif\n#ifdef USE_SHEEN\n\tmaterial.sheenColor = sheen;\n#endif",lights_physical_pars_fragment:"struct PhysicalMaterial {\n\tvec3 diffuseColor;\n\tfloat specularRoughness;\n\tvec3 specularColor;\n\tvec3 specularColorF90;\n#ifdef CLEARCOAT\n\tfloat clearcoat;\n\tfloat clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tvec3 sheenColor;\n#endif\n};\n#define MAXIMUM_SPECULAR_COEFFICIENT 0.16\n#define DEFAULT_SPECULAR_COEFFICIENT 0.04\nfloat clearcoatDHRApprox( const in float roughness, const in float dotNL ) {\n\treturn DEFAULT_SPECULAR_COEFFICIENT + ( 1.0 - DEFAULT_SPECULAR_COEFFICIENT ) * ( pow( 1.0 - dotNL, 5.0 ) * pow( 1.0 - roughness, 2.0 ) );\n}\n#if NUM_RECT_AREA_LIGHTS > 0\n\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\t\tvec3 normal = geometry.normal;\n\t\tvec3 viewDir = geometry.viewDir;\n\t\tvec3 position = geometry.position;\n\t\tvec3 lightPos = rectAreaLight.position;\n\t\tvec3 halfWidth = rectAreaLight.halfWidth;\n\t\tvec3 halfHeight = rectAreaLight.halfHeight;\n\t\tvec3 lightColor = rectAreaLight.color;\n\t\tfloat roughness = material.specularRoughness;\n\t\tvec3 rectCoords[ 4 ];\n\t\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\t\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\n\t\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\n\t\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\n\t\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\n\t\tvec4 t1 = texture2D( ltc_1, uv );\n\t\tvec4 t2 = texture2D( ltc_2, uv );\n\t\tmat3 mInv = mat3(\n\t\t\tvec3( t1.x, 0, t1.y ),\n\t\t\tvec3(    0, 1,    0 ),\n\t\t\tvec3( t1.z, 0, t1.w )\n\t\t);\n\t\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\n\t\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\n\t\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\n\t}\n#endif\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNL = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\n\t\tvec3 ccIrradiance = ccDotNL * directLight.color;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tccIrradiance *= PI;\n\t\t#endif\n\t\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t\treflectedLight.directSpecular += ccIrradiance * material.clearcoat * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), vec3( 1.0 ), material.clearcoatRoughness );\n\t#else\n\t\tfloat clearcoatDHR = 0.0;\n\t#endif\n\t#ifdef USE_SHEEN\n\t\treflectedLight.directSpecular += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_Sheen(\n\t\t\tmaterial.specularRoughness,\n\t\t\tdirectLight.direction,\n\t\t\tgeometry,\n\t\t\tmaterial.sheenColor\n\t\t);\n\t#else\n\t\treflectedLight.directSpecular += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.normal, material.specularColor, material.specularColorF90, material.specularRoughness);\n\t#endif\n\treflectedLight.directDiffuse += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNV = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular += clearcoatRadiance * material.clearcoat * BRDF_Specular_GGX_Environment( geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearcoatRoughness );\n\t\tfloat ccDotNL = ccDotNV;\n\t\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t#else\n\t\tfloat clearcoatDHR = 0.0;\n\t#endif\n\tfloat clearcoatInv = 1.0 - clearcoatDHR;\n\tvec3 singleScattering = vec3( 0.0 );\n\tvec3 multiScattering = vec3( 0.0 );\n\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\n\tBRDF_Specular_Multiscattering_Environment( geometry, material.specularColor, material.specularRoughness, singleScattering, multiScattering );\n\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\n\treflectedLight.indirectSpecular += clearcoatInv * radiance * singleScattering;\n\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\n\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\n}\n#define RE_Direct\t\t\t\tRE_Direct_Physical\n#define RE_Direct_RectArea\t\tRE_Direct_RectArea_Physical\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Physical\n#define RE_IndirectSpecular\t\tRE_IndirectSpecular_Physical\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\n\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\n}",lights_fragment_begin:"\nGeometricContext geometry;\ngeometry.position = - vViewPosition;\ngeometry.normal = normal;\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\n#ifdef CLEARCOAT\n\tgeometry.clearcoatNormal = clearcoatNormal;\n#endif\nIncidentLight directLight;\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\n\tPointLight pointLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tpointLight = pointLights[ i ];\n\t\tgetPointDirectLightIrradiance( pointLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\n\t\tpointLightShadow = pointLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\n\tSpotLight spotLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tspotLight = spotLights[ i ];\n\t\tgetSpotDirectLightIrradiance( spotLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\tspotLightShadow = spotLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\n\tDirectionalLight directionalLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tdirectionalLight = directionalLights[ i ];\n\t\tgetDirectionalDirectLightIrradiance( directionalLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\n\t\tdirectionalLightShadow = directionalLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\n\tRectAreaLight rectAreaLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\n\t\trectAreaLight = rectAreaLights[ i ];\n\t\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if defined( RE_IndirectDiffuse )\n\tvec3 iblIrradiance = vec3( 0.0 );\n\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\n\tirradiance += getLightProbeIrradiance( lightProbe, geometry );\n\t#if ( NUM_HEMI_LIGHTS > 0 )\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\t\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n#endif\n#if defined( RE_IndirectSpecular )\n\tvec3 radiance = vec3( 0.0 );\n\tvec3 clearcoatRadiance = vec3( 0.0 );\n#endif",lights_fragment_maps:"#if defined( RE_IndirectDiffuse )\n\t#ifdef USE_LIGHTMAP\n\t\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\t\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tlightMapIrradiance *= PI;\n\t\t#endif\n\t\tirradiance += lightMapIrradiance;\n\t#endif\n\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\n\t\tiblIrradiance += getLightProbeIndirectIrradiance( geometry, maxMipLevel );\n\t#endif\n#endif\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\n\tradiance += getLightProbeIndirectRadiance( geometry.viewDir, geometry.normal, material.specularRoughness, maxMipLevel );\n\t#ifdef CLEARCOAT\n\t\tclearcoatRadiance += getLightProbeIndirectRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness, maxMipLevel );\n\t#endif\n#endif",lights_fragment_end:"#if defined( RE_IndirectDiffuse )\n\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\n#endif\n#if defined( RE_IndirectSpecular )\n\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\n#endif",logdepthbuf_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\n#endif",logdepthbuf_pars_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tuniform float logDepthBufFC;\n\tvarying float vFragDepth;\n\tvarying float vIsPerspective;\n#endif",logdepthbuf_pars_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvarying float vFragDepth;\n\t\tvarying float vIsPerspective;\n\t#else\n\t\tuniform float logDepthBufFC;\n\t#endif\n#endif",logdepthbuf_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvFragDepth = 1.0 + gl_Position.w;\n\t\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\n\t#else\n\t\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\n\t\t\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\n\t\t\tgl_Position.z *= gl_Position.w;\n\t\t}\n\t#endif\n#endif",map_fragment:"#ifdef USE_MAP\n\tvec4 texelColor = texture2D( map, vUv );\n\ttexelColor = mapTexelToLinear( texelColor );\n\tdiffuseColor *= texelColor;\n#endif",map_pars_fragment:"#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif",map_particle_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\n#endif\n#ifdef USE_MAP\n\tvec4 mapTexel = texture2D( map, uv );\n\tdiffuseColor *= mapTexelToLinear( mapTexel );\n#endif\n#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\n#endif",map_particle_pars_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tuniform mat3 uvTransform;\n#endif\n#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif\n#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",metalnessmap_fragment:"float metalnessFactor = metalness;\n#ifdef USE_METALNESSMAP\n\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\n\tmetalnessFactor *= texelMetalness.b;\n#endif",metalnessmap_pars_fragment:"#ifdef USE_METALNESSMAP\n\tuniform sampler2D metalnessMap;\n#endif",morphnormal_vertex:"#ifdef USE_MORPHNORMALS\n\tobjectNormal *= morphTargetBaseInfluence;\n\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\n\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\n\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\n\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\n#endif",morphtarget_pars_vertex:"#ifdef USE_MORPHTARGETS\n\tuniform float morphTargetBaseInfluence;\n\t#ifndef USE_MORPHNORMALS\n\t\tuniform float morphTargetInfluences[ 8 ];\n\t#else\n\t\tuniform float morphTargetInfluences[ 4 ];\n\t#endif\n#endif",morphtarget_vertex:"#ifdef USE_MORPHTARGETS\n\ttransformed *= morphTargetBaseInfluence;\n\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\n\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\n\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\n\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\n\t#ifndef USE_MORPHNORMALS\n\t\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\n\t\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\n\t\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\n\t\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\n\t#endif\n#endif",normal_fragment_begin:"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\n#ifdef FLAT_SHADED\n\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\n\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\n\tvec3 normal = normalize( cross( fdx, fdy ) );\n#else\n\tvec3 normal = normalize( vNormal );\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\t#ifdef USE_TANGENT\n\t\tvec3 tangent = normalize( vTangent );\n\t\tvec3 bitangent = normalize( vBitangent );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\ttangent = tangent * faceDirection;\n\t\t\tbitangent = bitangent * faceDirection;\n\t\t#endif\n\t\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\n\t\t\tmat3 vTBN = mat3( tangent, bitangent, normal );\n\t\t#endif\n\t#endif\n#endif\nvec3 geometryNormal = normal;",normal_fragment_maps:"#ifdef OBJECTSPACE_NORMALMAP\n\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t#ifdef FLIP_SIDED\n\t\tnormal = - normal;\n\t#endif\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\tnormal = normalize( normalMatrix * normal );\n#elif defined( TANGENTSPACE_NORMALMAP )\n\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\tmapN.xy *= normalScale;\n\t#ifdef USE_TANGENT\n\t\tnormal = normalize( vTBN * mapN );\n\t#else\n\t\tnormal = perturbNormal2Arb( -vViewPosition, normal, mapN, faceDirection );\n\t#endif\n#elif defined( USE_BUMPMAP )\n\tnormal = perturbNormalArb( -vViewPosition, normal, dHdxy_fwd(), faceDirection );\n#endif",normalmap_pars_fragment:"#ifdef USE_NORMALMAP\n\tuniform sampler2D normalMap;\n\tuniform vec2 normalScale;\n#endif\n#ifdef OBJECTSPACE_NORMALMAP\n\tuniform mat3 normalMatrix;\n#endif\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\n\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\n\t\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\n\t\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\n\t\tvec2 st0 = dFdx( vUv.st );\n\t\tvec2 st1 = dFdy( vUv.st );\n\t\tvec3 N = surf_norm;\n\t\tvec3 q1perp = cross( q1, N );\n\t\tvec3 q0perp = cross( N, q0 );\n\t\tvec3 T = q1perp * st0.x + q0perp * st1.x;\n\t\tvec3 B = q1perp * st0.y + q0perp * st1.y;\n\t\tfloat det = max( dot( T, T ), dot( B, B ) );\n\t\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\n\t\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\n\t}\n#endif",clearcoat_normal_fragment_begin:"#ifdef CLEARCOAT\n\tvec3 clearcoatNormal = geometryNormal;\n#endif",clearcoat_normal_fragment_maps:"#ifdef USE_CLEARCOAT_NORMALMAP\n\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\n\tclearcoatMapN.xy *= clearcoatNormalScale;\n\t#ifdef USE_TANGENT\n\t\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\n\t#else\n\t\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\n\t#endif\n#endif",clearcoat_pars_fragment:"#ifdef USE_CLEARCOATMAP\n\tuniform sampler2D clearcoatMap;\n#endif\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\tuniform sampler2D clearcoatRoughnessMap;\n#endif\n#ifdef USE_CLEARCOAT_NORMALMAP\n\tuniform sampler2D clearcoatNormalMap;\n\tuniform vec2 clearcoatNormalScale;\n#endif",packing:"vec3 packNormalToRGB( const in vec3 normal ) {\n\treturn normalize( normal ) * 0.5 + 0.5;\n}\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\n\treturn 2.0 * rgb.xyz - 1.0;\n}\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\nconst float ShiftRight8 = 1. / 256.;\nvec4 packDepthToRGBA( const in float v ) {\n\tvec4 r = vec4( fract( v * PackFactors ), v );\n\tr.yzw -= r.xyz * ShiftRight8;\treturn r * PackUpscale;\n}\nfloat unpackRGBAToDepth( const in vec4 v ) {\n\treturn dot( v, UnpackFactors );\n}\nvec4 pack2HalfToRGBA( vec2 v ) {\n\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ));\n\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w);\n}\nvec2 unpackRGBATo2Half( vec4 v ) {\n\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\n}\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn ( viewZ + near ) / ( near - far );\n}\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\n\treturn linearClipZ * ( near - far ) - near;\n}\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn (( near + viewZ ) * far ) / (( far - near ) * viewZ );\n}\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\n\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\n}",premultiplied_alpha_fragment:"#ifdef PREMULTIPLIED_ALPHA\n\tgl_FragColor.rgb *= gl_FragColor.a;\n#endif",project_vertex:"vec4 mvPosition = vec4( transformed, 1.0 );\n#ifdef USE_INSTANCING\n\tmvPosition = instanceMatrix * mvPosition;\n#endif\nmvPosition = modelViewMatrix * mvPosition;\ngl_Position = projectionMatrix * mvPosition;",dithering_fragment:"#ifdef DITHERING\n\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\n#endif",dithering_pars_fragment:"#ifdef DITHERING\n\tvec3 dithering( vec3 color ) {\n\t\tfloat grid_position = rand( gl_FragCoord.xy );\n\t\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\n\t\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\n\t\treturn color + dither_shift_RGB;\n\t}\n#endif",roughnessmap_fragment:"float roughnessFactor = roughness;\n#ifdef USE_ROUGHNESSMAP\n\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\n\troughnessFactor *= texelRoughness.g;\n#endif",roughnessmap_pars_fragment:"#ifdef USE_ROUGHNESSMAP\n\tuniform sampler2D roughnessMap;\n#endif",shadowmap_pars_fragment:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\n\t\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\n\t}\n\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\n\t\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\n\t}\n\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\n\t\tfloat occlusion = 1.0;\n\t\tvec2 distribution = texture2DDistribution( shadow, uv );\n\t\tfloat hard_shadow = step( compare , distribution.x );\n\t\tif (hard_shadow != 1.0 ) {\n\t\t\tfloat distance = compare - distribution.x ;\n\t\t\tfloat variance = max( 0.00000, distribution.y * distribution.y );\n\t\t\tfloat softness_probability = variance / (variance + distance * distance );\t\t\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\t\t\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\n\t\t}\n\t\treturn occlusion;\n\t}\n\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\n\t\tfloat shadow = 1.0;\n\t\tshadowCoord.xyz /= shadowCoord.w;\n\t\tshadowCoord.z += shadowBias;\n\t\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\n\t\tbool inFrustum = all( inFrustumVec );\n\t\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\n\t\tbool frustumTest = all( frustumTestVec );\n\t\tif ( frustumTest ) {\n\t\t#if defined( SHADOWMAP_TYPE_PCF )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx0 = - texelSize.x * shadowRadius;\n\t\t\tfloat dy0 = - texelSize.y * shadowRadius;\n\t\t\tfloat dx1 = + texelSize.x * shadowRadius;\n\t\t\tfloat dy1 = + texelSize.y * shadowRadius;\n\t\t\tfloat dx2 = dx0 / 2.0;\n\t\t\tfloat dy2 = dy0 / 2.0;\n\t\t\tfloat dx3 = dx1 / 2.0;\n\t\t\tfloat dy3 = dy1 / 2.0;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\n\t\t\t) * ( 1.0 / 17.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx = texelSize.x;\n\t\t\tfloat dy = texelSize.y;\n\t\t\tvec2 uv = shadowCoord.xy;\n\t\t\tvec2 f = fract( uv * shadowMapSize + 0.5 );\n\t\t\tuv -= f * texelSize;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t f.y )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_VSM )\n\t\t\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#else\n\t\t\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#endif\n\t\t}\n\t\treturn shadow;\n\t}\n\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\n\t\tvec3 absV = abs( v );\n\t\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\n\t\tabsV *= scaleToCube;\n\t\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\n\t\tvec2 planar = v.xy;\n\t\tfloat almostATexel = 1.5 * texelSizeY;\n\t\tfloat almostOne = 1.0 - almostATexel;\n\t\tif ( absV.z >= almostOne ) {\n\t\t\tif ( v.z > 0.0 )\n\t\t\t\tplanar.x = 4.0 - v.x;\n\t\t} else if ( absV.x >= almostOne ) {\n\t\t\tfloat signX = sign( v.x );\n\t\t\tplanar.x = v.z * signX + 2.0 * signX;\n\t\t} else if ( absV.y >= almostOne ) {\n\t\t\tfloat signY = sign( v.y );\n\t\t\tplanar.x = v.x + 2.0 * signY + 2.0;\n\t\t\tplanar.y = v.z * signY - 2.0;\n\t\t}\n\t\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\n\t}\n\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\n\t\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\n\t\tvec3 lightToPosition = shadowCoord.xyz;\n\t\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\t\tdp += shadowBias;\n\t\tvec3 bd3D = normalize( lightToPosition );\n\t\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\n\t\t\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\n\t\t\treturn (\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#else\n\t\t\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\n\t\t#endif\n\t}\n#endif",shadowmap_pars_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n#endif",shadowmap_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\n\t\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\tvec4 shadowWorldPosition;\n\t#endif\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n#endif",shadowmask_pars_fragment:"float getShadowMask() {\n\tfloat shadow = 1.0;\n\t#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tdirectionalLight = directionalLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tspotLight = spotLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tpointLight = pointLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#endif\n\treturn shadow;\n}",skinbase_vertex:"#ifdef USE_SKINNING\n\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\n\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\n\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\n\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\n#endif",skinning_pars_vertex:"#ifdef USE_SKINNING\n\tuniform mat4 bindMatrix;\n\tuniform mat4 bindMatrixInverse;\n\t#ifdef BONE_TEXTURE\n\t\tuniform highp sampler2D boneTexture;\n\t\tuniform int boneTextureSize;\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tfloat j = i * 4.0;\n\t\t\tfloat x = mod( j, float( boneTextureSize ) );\n\t\t\tfloat y = floor( j / float( boneTextureSize ) );\n\t\t\tfloat dx = 1.0 / float( boneTextureSize );\n\t\t\tfloat dy = 1.0 / float( boneTextureSize );\n\t\t\ty = dy * ( y + 0.5 );\n\t\t\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\n\t\t\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\n\t\t\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\n\t\t\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\n\t\t\tmat4 bone = mat4( v1, v2, v3, v4 );\n\t\t\treturn bone;\n\t\t}\n\t#else\n\t\tuniform mat4 boneMatrices[ MAX_BONES ];\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tmat4 bone = boneMatrices[ int(i) ];\n\t\t\treturn bone;\n\t\t}\n\t#endif\n#endif",skinning_vertex:"#ifdef USE_SKINNING\n\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\n\tvec4 skinned = vec4( 0.0 );\n\tskinned += boneMatX * skinVertex * skinWeight.x;\n\tskinned += boneMatY * skinVertex * skinWeight.y;\n\tskinned += boneMatZ * skinVertex * skinWeight.z;\n\tskinned += boneMatW * skinVertex * skinWeight.w;\n\ttransformed = ( bindMatrixInverse * skinned ).xyz;\n#endif",skinnormal_vertex:"#ifdef USE_SKINNING\n\tmat4 skinMatrix = mat4( 0.0 );\n\tskinMatrix += skinWeight.x * boneMatX;\n\tskinMatrix += skinWeight.y * boneMatY;\n\tskinMatrix += skinWeight.z * boneMatZ;\n\tskinMatrix += skinWeight.w * boneMatW;\n\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\n\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\n\t#ifdef USE_TANGENT\n\t\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#endif\n#endif",specularmap_fragment:"float specularStrength;\n#ifdef USE_SPECULARMAP\n\tvec4 texelSpecular = texture2D( specularMap, vUv );\n\tspecularStrength = texelSpecular.r;\n#else\n\tspecularStrength = 1.0;\n#endif",specularmap_pars_fragment:"#ifdef USE_SPECULARMAP\n\tuniform sampler2D specularMap;\n#endif",tonemapping_fragment:"#if defined( TONE_MAPPING )\n\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\n#endif",tonemapping_pars_fragment:"#ifndef saturate\n#define saturate(a) clamp( a, 0.0, 1.0 )\n#endif\nuniform float toneMappingExposure;\nvec3 LinearToneMapping( vec3 color ) {\n\treturn toneMappingExposure * color;\n}\nvec3 ReinhardToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\treturn saturate( color / ( vec3( 1.0 ) + color ) );\n}\nvec3 OptimizedCineonToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\tcolor = max( vec3( 0.0 ), color - 0.004 );\n\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\n}\nvec3 RRTAndODTFit( vec3 v ) {\n\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\n\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\n\treturn a / b;\n}\nvec3 ACESFilmicToneMapping( vec3 color ) {\n\tconst mat3 ACESInputMat = mat3(\n\t\tvec3( 0.59719, 0.07600, 0.02840 ),\t\tvec3( 0.35458, 0.90834, 0.13383 ),\n\t\tvec3( 0.04823, 0.01566, 0.83777 )\n\t);\n\tconst mat3 ACESOutputMat = mat3(\n\t\tvec3(  1.60475, -0.10208, -0.00327 ),\t\tvec3( -0.53108,  1.10813, -0.07276 ),\n\t\tvec3( -0.07367, -0.00605,  1.07602 )\n\t);\n\tcolor *= toneMappingExposure / 0.6;\n\tcolor = ACESInputMat * color;\n\tcolor = RRTAndODTFit( color );\n\tcolor = ACESOutputMat * color;\n\treturn saturate( color );\n}\nvec3 CustomToneMapping( vec3 color ) { return color; }",transmission_fragment:"#ifdef USE_TRANSMISSION\n\tfloat transmissionFactor = transmission;\n\tfloat thicknessFactor = thickness;\n\t#ifdef USE_TRANSMISSIONMAP\n\t\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\n\t#endif\n\tvec3 pos = vWorldPosition.xyz / vWorldPosition.w;\n\tvec3 v = normalize( cameraPosition - pos );\n\tvec3 n = inverseTransformDirection( normal, viewMatrix );\n\tfloat ior = ( 1.0 + 0.4 * reflectivity ) / ( 1.0 - 0.4 * reflectivity );\n\tvec3 transmission = transmissionFactor * getIBLVolumeRefraction(\n\t\tn, v, roughnessFactor, material.diffuseColor, material.specularColor,\n\t\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\n\t\tattenuationTint, attenuationDistance );\n\ttotalDiffuse = mix( totalDiffuse, transmission, transmissionFactor );\n#endif",transmission_pars_fragment:"#ifdef USE_TRANSMISSION\n\t#ifdef USE_TRANSMISSIONMAP\n\t\tuniform sampler2D transmissionMap;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tuniform sampler2D thicknessMap;\n\t#endif\n\tuniform vec2 transmissionSamplerSize;\n\tuniform sampler2D transmissionSamplerMap;\n\tuniform mat4 modelMatrix;\n\tuniform mat4 projectionMatrix;\n\tvarying vec4 vWorldPosition;\n\tvec3 getVolumeTransmissionRay(vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix) {\n\t\tvec3 refractionVector = refract(-v, normalize(n), 1.0 / ior);\n\t\tvec3 modelScale;\n\t\tmodelScale.x = length(vec3(modelMatrix[0].xyz));\n\t\tmodelScale.y = length(vec3(modelMatrix[1].xyz));\n\t\tmodelScale.z = length(vec3(modelMatrix[2].xyz));\n\t\treturn normalize(refractionVector) * thickness * modelScale;\n\t}\n\tfloat applyIorToRoughness(float roughness, float ior) {\n\t\treturn roughness * clamp(ior * 2.0 - 2.0, 0.0, 1.0);\n\t}\n\tvec3 getTransmissionSample(vec2 fragCoord, float roughness, float ior) {\n\t\tfloat framebufferLod = log2(transmissionSamplerSize.x) * applyIorToRoughness(roughness, ior);\n\t\treturn texture2DLodEXT(transmissionSamplerMap, fragCoord.xy, framebufferLod).rgb;\n\t}\n\tvec3 applyVolumeAttenuation(vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance) {\n\t\tif (attenuationDistance == 0.0) {\n\t\t\treturn radiance;\n\t\t} else {\n\t\t\tvec3 attenuationCoefficient = -log(attenuationColor) / attenuationDistance;\n\t\t\tvec3 transmittance = exp(-attenuationCoefficient * transmissionDistance);\t\t\treturn transmittance * radiance;\n\t\t}\n\t}\n\tvec3 getIBLVolumeRefraction(vec3 n, vec3 v, float perceptualRoughness, vec3 baseColor, vec3 specularColor,\n\t\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\n\t\tvec3 attenuationColor, float attenuationDistance) {\n\t\tvec3 transmissionRay = getVolumeTransmissionRay(n, v, thickness, ior, modelMatrix);\n\t\tvec3 refractedRayExit = position + transmissionRay;\n\t\tvec4 ndcPos = projMatrix * viewMatrix * vec4(refractedRayExit, 1.0);\n\t\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\n\t\trefractionCoords += 1.0;\n\t\trefractionCoords /= 2.0;\n\t\tvec3 transmittedLight = getTransmissionSample(refractionCoords, perceptualRoughness, ior);\n\t\tvec3 attenuatedColor = applyVolumeAttenuation(transmittedLight, length(transmissionRay), attenuationColor, attenuationDistance);\n\t\treturn (1.0 - specularColor) * attenuatedColor * baseColor;\n\t}\n#endif",uv_pars_fragment:"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\n\tvarying vec2 vUv;\n#endif",uv_pars_vertex:"#ifdef USE_UV\n\t#ifdef UVS_VERTEX_ONLY\n\t\tvec2 vUv;\n\t#else\n\t\tvarying vec2 vUv;\n\t#endif\n\tuniform mat3 uvTransform;\n#endif",uv_vertex:"#ifdef USE_UV\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n#endif",uv2_pars_fragment:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvarying vec2 vUv2;\n#endif",uv2_pars_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tattribute vec2 uv2;\n\tvarying vec2 vUv2;\n\tuniform mat3 uv2Transform;\n#endif",uv2_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\n#endif",worldpos_vertex:"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\n\tvec4 worldPosition = vec4( transformed, 1.0 );\n\t#ifdef USE_INSTANCING\n\t\tworldPosition = instanceMatrix * worldPosition;\n\t#endif\n\tworldPosition = modelMatrix * worldPosition;\n#endif",background_frag:"uniform sampler2D t2D;\nvarying vec2 vUv;\nvoid main() {\n\tvec4 texColor = texture2D( t2D, vUv );\n\tgl_FragColor = mapTexelToLinear( texColor );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",background_vert:"varying vec2 vUv;\nuniform mat3 uvTransform;\nvoid main() {\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n\tgl_Position = vec4( position.xy, 1.0, 1.0 );\n}",cube_frag:"#include <envmap_common_pars_fragment>\nuniform float opacity;\nvarying vec3 vWorldDirection;\n#include <cube_uv_reflection_fragment>\nvoid main() {\n\tvec3 vReflect = vWorldDirection;\n\t#include <envmap_fragment>\n\tgl_FragColor = envColor;\n\tgl_FragColor.a *= opacity;\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",cube_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\tgl_Position.z = gl_Position.w;\n}",depth_frag:"#if DEPTH_PACKING == 3200\n\tuniform float opacity;\n#endif\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#if DEPTH_PACKING == 3200\n\t\tdiffuseColor.a = opacity;\n\t#endif\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <logdepthbuf_fragment>\n\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\n\t#if DEPTH_PACKING == 3200\n\t\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\n\t#elif DEPTH_PACKING == 3201\n\t\tgl_FragColor = packDepthToRGBA( fragCoordZ );\n\t#endif\n}",depth_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvHighPrecisionZW = gl_Position.zw;\n}",distanceRGBA_frag:"#define DISTANCE\nuniform vec3 referencePosition;\nuniform float nearDistance;\nuniform float farDistance;\nvarying vec3 vWorldPosition;\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main () {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\tfloat dist = length( vWorldPosition - referencePosition );\n\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\n\tdist = saturate( dist );\n\tgl_FragColor = packDepthToRGBA( dist );\n}",distanceRGBA_vert:"#define DISTANCE\nvarying vec3 vWorldPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <clipping_planes_vertex>\n\tvWorldPosition = worldPosition.xyz;\n}",equirect_frag:"uniform sampler2D tEquirect;\nvarying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvec3 direction = normalize( vWorldDirection );\n\tvec2 sampleUV = equirectUv( direction );\n\tvec4 texColor = texture2D( tEquirect, sampleUV );\n\tgl_FragColor = mapTexelToLinear( texColor );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",equirect_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n}",linedashed_frag:"uniform vec3 diffuse;\nuniform float opacity;\nuniform float dashSize;\nuniform float totalSize;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\n\t\tdiscard;\n\t}\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <color_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",linedashed_vert:"uniform float scale;\nattribute float lineDistance;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\tvLineDistance = scale * lineDistance;\n\t#include <color_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n}",meshbasic_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <fog_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\t#ifdef USE_LIGHTMAP\n\t\n\t\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\t\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n\t#else\n\t\treflectedLight.indirectDiffuse += vec3( 1.0 );\n\t#endif\n\t#include <aomap_fragment>\n\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\n\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshbasic_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinbase_vertex>\n\t\t#include <skinnormal_vertex>\n\t\t#include <defaultnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <fog_vertex>\n}",meshlambert_frag:"uniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <fog_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <shadowmask_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <emissivemap_fragment>\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\n\t#else\n\t\treflectedLight.indirectDiffuse += vIndirectFront;\n\t#endif\n\t#include <lightmap_fragment>\n\treflectedLight.indirectDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb );\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\n\t#else\n\t\treflectedLight.directDiffuse = vLightFront;\n\t#endif\n\treflectedLight.directDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb ) * getShadowMask();\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshlambert_vert:"#define LAMBERT\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <envmap_pars_vertex>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <lights_lambert_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshmatcap_frag:"#define MATCAP\nuniform vec3 diffuse;\nuniform float opacity;\nuniform sampler2D matcap;\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tvec3 viewDir = normalize( vViewPosition );\n\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\n\tvec3 y = cross( viewDir, x );\n\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\n\t#ifdef USE_MATCAP\n\t\tvec4 matcapColor = texture2D( matcap, uv );\n\t\tmatcapColor = matcapTexelToLinear( matcapColor );\n\t#else\n\t\tvec4 matcapColor = vec4( 1.0 );\n\t#endif\n\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshmatcap_vert:"#define MATCAP\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <color_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#ifndef FLAT_SHADED\n\t\tvNormal = normalize( transformedNormal );\n\t\t#ifdef USE_TANGENT\n\t\t\tvTangent = normalize( transformedTangent );\n\t\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t\t#endif\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n\tvViewPosition = - mvPosition.xyz;\n}",meshtoon_frag:"#define TOON\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <gradientmap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <lights_toon_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_toon_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshtoon_vert:"#define TOON\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshphong_frag:"#define PHONG\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform vec3 specular;\nuniform float shininess;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <lights_phong_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_phong_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshphong_vert:"#define PHONG\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshphysical_frag:"#define STANDARD\n#ifdef PHYSICAL\n\t#define REFLECTIVITY\n\t#define CLEARCOAT\n\t#define SPECULAR\n#endif\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float roughness;\nuniform float metalness;\nuniform float opacity;\n#ifdef USE_TRANSMISSION\n\tuniform float transmission;\n\tuniform float thickness;\n\tuniform float attenuationDistance;\n\tuniform vec3 attenuationTint;\n#endif\n#ifdef REFLECTIVITY\n\tuniform float reflectivity;\n#endif\n#ifdef SPECULAR\n\tuniform float specularIntensity;\n\tuniform vec3 specularTint;\n\t#ifdef USE_SPECULARINTENSITYMAP\n\t\tuniform sampler2D specularIntensityMap;\n\t#endif\n\t#ifdef USE_SPECULARTINTMAP\n\t\tuniform sampler2D specularTintMap;\n\t#endif\n#endif\n#ifdef CLEARCOAT\n\tuniform float clearcoat;\n\tuniform float clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tuniform vec3 sheen;\n#endif\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <bsdfs>\n#include <transmission_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_physical_pars_fragment>\n#include <fog_pars_fragment>\n#include <lights_pars_begin>\n#include <lights_physical_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <clearcoat_pars_fragment>\n#include <roughnessmap_pars_fragment>\n#include <metalnessmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <roughnessmap_fragment>\n\t#include <metalnessmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <clearcoat_normal_fragment_begin>\n\t#include <clearcoat_normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_physical_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\n\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\n\t#include <transmission_fragment>\n\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshphysical_vert:"#define STANDARD\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#ifdef USE_TRANSMISSION\n\tvarying vec4 vWorldPosition;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n#ifdef USE_TRANSMISSION\n\tvWorldPosition = worldPosition;\n#endif\n}",normal_frag:"#define NORMAL\nuniform float opacity;\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <packing>\n#include <uv_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\t#include <logdepthbuf_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\n}",normal_vert:"#define NORMAL\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvViewPosition = - mvPosition.xyz;\n#endif\n}",points_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include <common>\n#include <color_pars_fragment>\n#include <map_particle_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_particle_fragment>\n\t#include <color_fragment>\n\t#include <alphatest_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",points_vert:"uniform float size;\nuniform float scale;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <color_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\tgl_PointSize = size;\n\t#ifdef USE_SIZEATTENUATION\n\t\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\n\t\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\n\t#endif\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <fog_vertex>\n}",shadow_frag:"uniform vec3 color;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <shadowmap_pars_fragment>\n#include <shadowmask_pars_fragment>\nvoid main() {\n\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n}",shadow_vert:"#include <common>\n#include <fog_pars_vertex>\n#include <shadowmap_pars_vertex>\nvoid main() {\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",sprite_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include <common>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n}",sprite_vert:"uniform float rotation;\nuniform vec2 center;\n#include <common>\n#include <uv_pars_vertex>\n#include <fog_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\n\tvec2 scale;\n\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\n\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\n\t#ifndef USE_SIZEATTENUATION\n\t\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\n\t\tif ( isPerspective ) scale *= - mvPosition.z;\n\t#endif\n\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\n\tvec2 rotatedPosition;\n\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\n\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\n\tmvPosition.xy += rotatedPosition;\n\tgl_Position = projectionMatrix * mvPosition;\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n}"},b6t={common:{diffuse:{value:new s4t(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new A5t},uv2Transform:{value:new A5t},alphaMap:{value:null}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new C5t(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new s4t(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new 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super.copy(t,e),this.left=t.left,this.right=t.right,this.top=t.top,this.bottom=t.bottom,this.near=t.near,this.far=t.far,this.zoom=t.zoom,this.view=null===t.view?null:Object.assign({},t.view),this}setViewOffset(t,e,n,i,r,o){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=o,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=(this.right-this.left)/(2*this.zoom),e=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,i=(this.top+this.bottom)/2;let r=n-t,o=n+t,a=i+e,s=i-e;if(null!==this.view&&this.view.enabled){const t=(this.right-this.left)/this.view.fullWidth/this.zoom,e=(this.top-this.bottom)/this.view.fullHeight/this.zoom;r+=t*this.view.offsetX,o=r+t*this.view.width,a-=e*this.view.offsetY,s=a-e*this.view.height}this.projectionMatrix.makeOrthographic(r,o,a,s,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.zoom=this.zoom,e.object.left=this.left,e.object.right=this.right,e.object.top=this.top,e.object.bottom=this.bottom,e.object.near=this.near,e.object.far=this.far,null!==this.view&&(e.object.view=Object.assign({},this.view)),e}}A6t.prototype.isOrthographicCamera=!0;class k6t extends n6t{constructor(t){super(t),this.type="RawShaderMaterial"}}k6t.prototype.isRawShaderMaterial=!0;const L6t=Math.pow(2,8),P6t=[.125,.215,.35,.446,.526,.582],N6t=5+P6t.length,I6t=20,R6t={[G2t]:0,[W2t]:1,[Y2t]:2,[X2t]:3,[$2t]:4,[K2t]:5,[q2t]:6},O6t=new l4t({side:1,depthWrite:!1,depthTest:!1}),z6t=new K4t(new J4t,O6t),D6t=new A6t,{_lodPlanes:B6t,_sizeLods:H6t,_sigmas:F6t}=$6t(),V6t=new s4t;let U6t=null;const j6t=(1+Math.sqrt(5))/2,G6t=1/j6t,W6t=[new H5t(1,1,1),new H5t(-1,1,1),new H5t(1,1,-1),new H5t(-1,1,-1),new H5t(0,j6t,G6t),new H5t(0,j6t,-G6t),new H5t(G6t,0,j6t),new H5t(-G6t,0,j6t),new H5t(j6t,G6t,0),new H5t(-j6t,G6t,0)];function q6t(t){const e=Math.max(t.r,t.g,t.b),n=Math.min(Math.max(Math.ceil(Math.log2(e)),-128),127);t.multiplyScalar(Math.pow(2,-n));return(n+128)/255}class Y6t{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=(function e(t){const e=new Float32Array(t),n=new H5t(0,1,0);return new k6t({name:"SphericalGaussianBlur",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:R6t[3e3]},outputEncoding:{value:R6t[3e3]}},vertexShader:t9t(),fragmentShader:`\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\t\t\tuniform int samples;\n\t\t\tuniform float weights[ n ];\n\t\t\tuniform bool latitudinal;\n\t\t\tuniform float dTheta;\n\t\t\tuniform float mipInt;\n\t\t\tuniform vec3 poleAxis;\n\n\t\t\t${e9t()}\n\n\t\t\t#define ENVMAP_TYPE_CUBE_UV\n\t\t\t#include <cube_uv_reflection_fragment>\n\n\t\t\tvec3 getSample( float theta, vec3 axis ) {\n\n\t\t\t\tfloat cosTheta = cos( theta );\n\t\t\t\t// Rodrigues' axis-angle rotation\n\t\t\t\tvec3 sampleDirection = vOutputDirection * cosTheta\n\t\t\t\t\t+ cross( axis, vOutputDirection ) * sin( theta )\n\t\t\t\t\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\n\n\t\t\t\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\n\n\t\t\t}\n\n\t\t\tvoid main() {\n\n\t\t\t\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\n\n\t\t\t\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\n\n\t\t\t\t\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\n\n\t\t\t\t}\n\n\t\t\t\taxis = normalize( axis );\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t\t\t\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\n\n\t\t\t\tfor ( int i = 1; i < n; i++ ) {\n\n\t\t\t\t\tif ( i >= samples ) {\n\n\t\t\t\t\t\tbreak;\n\n\t\t\t\t\t}\n\n\t\t\t\t\tfloat theta = dTheta * float( i );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\n\n\t\t\t\t}\n\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t`,blending:0,depthTest:!1,depthWrite:!1})})(I6t),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){U6t=this._renderer.getRenderTarget();const r=this._allocateTargets();return this._sceneToCubeUV(t,n,i,r),e>0&&this._blur(r,0,0,e),this._applyPMREM(r),this._cleanup(r),r}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=Q6t(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=J6t(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t<B6t.length;t++)B6t[t].dispose()}_cleanup(t){this._pingPongRenderTarget.dispose(),this._renderer.setRenderTarget(U6t),t.scissorTest=!1,Z6t(t,0,0,t.width,t.height)}_fromTexture(t){U6t=this._renderer.getRenderTarget();const e=this._allocateTargets(t);return this._textureToCubeUV(t,e),this._applyPMREM(e),this._cleanup(e),e}_allocateTargets(t){const e={magFilter:u2t,minFilter:u2t,generateMipmaps:!1,type:g2t,format:1023,encoding:X6t(t)?t.encoding:Y2t,depthBuffer:!1},n=K6t(e);return n.depthBuffer=!t,this._pingPongRenderTarget=K6t(e),n}_compileMaterial(t){const e=new K4t(B6t[0],t);this._renderer.compile(e,D6t)}_sceneToCubeUV(t,e,n,i){const r=new r6t(90,1,e,n),o=[1,-1,1,1,1,1],a=[1,1,1,-1,-1,-1],s=this._renderer,l=s.autoClear,c=s.outputEncoding,u=s.toneMapping;s.getClearColor(V6t),s.toneMapping=0,s.outputEncoding=G2t,s.autoClear=!1;let h=!1;const d=t.background;if(d){if(d.isColor){O6t.color.copy(d).convertSRGBToLinear(),t.background=null;const e=q6t(O6t.color);O6t.opacity=e,h=!0}}else{O6t.color.copy(V6t).convertSRGBToLinear();const t=q6t(O6t.color);O6t.opacity=t,h=!0}for(let e=0;e<6;e++){const n=e%3;0==n?(r.up.set(0,o[e],0),r.lookAt(a[e],0,0)):1==n?(r.up.set(0,0,o[e]),r.lookAt(0,a[e],0)):(r.up.set(0,o[e],0),r.lookAt(0,0,a[e])),Z6t(i,n*L6t,e>2?L6t:0,L6t,L6t),s.setRenderTarget(i),h&&s.render(z6t,r),s.render(t,r)}s.toneMapping=u,s.outputEncoding=c,s.autoClear=l}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=Q6t()):null==this._equirectShader&&(this._equirectShader=J6t());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,r=new K4t(B6t[0],i),o=i.uniforms;o.envMap.value=t,t.isCubeTexture||o.texelSize.value.set(1/t.image.width,1/t.image.height),o.inputEncoding.value=R6t[t.encoding],o.outputEncoding.value=R6t[e.texture.encoding],Z6t(e,0,0,3*L6t,2*L6t),n.setRenderTarget(e),n.render(r,D6t)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;e<N6t;e++){const n=Math.sqrt(F6t[e]*F6t[e]-F6t[e-1]*F6t[e-1]);this._blur(t,e-1,e,n,W6t[(e-1)%W6t.length])}e.autoClear=n}_blur(t,e,n,i,r){const o=this._pingPongRenderTarget;this._halfBlur(t,o,e,n,i,"latitudinal",r),this._halfBlur(o,t,n,n,i,"longitudinal",r)}_halfBlur(t,e,n,i,r,o,a){const s=this._renderer,l=this._blurMaterial;"latitudinal"!==o&&"longitudinal"!==o&&console.error("blur direction must be either latitudinal or longitudinal!");const c=new K4t(B6t[i],l),u=l.uniforms,h=H6t[n]-1,d=isFinite(r)?Math.PI/(2*h):2*Math.PI/39,p=r/d,f=isFinite(r)?1+Math.floor(3*p):I6t;f>I6t&&console.warn(`sigmaRadians, ${r}, is too large and will clip, as it requested ${f} samples when the maximum is set to 20`);const m=[];let g=0;for(let t=0;t<I6t;++t){const e=t/p,n=Math.exp(-e*e/2);m.push(n),0==t?g+=n:t<f&&(g+=2*n)}for(let t=0;t<m.length;t++)m[t]=m[t]/g;u.envMap.value=t.texture,u.samples.value=f,u.weights.value=m,u.latitudinal.value="latitudinal"===o,a&&(u.poleAxis.value=a),u.dTheta.value=d,u.mipInt.value=8-n,u.inputEncoding.value=R6t[t.texture.encoding],u.outputEncoding.value=R6t[t.texture.encoding];const _=H6t[i];Z6t(e,3*Math.max(0,L6t-2*_),(0===i?0:2*L6t)+2*_*(i>4?i-8+4:0),3*_,2*_),s.setRenderTarget(e),s.render(c,D6t)}}function X6t(t){return void 0!==t&&t.type===g2t&&(t.encoding===G2t||t.encoding===W2t||t.encoding===q2t)}function $6t(){const t=[],e=[],n=[];let i=8;for(let r=0;r<N6t;r++){const o=Math.pow(2,i);e.push(o);let a=1/o;r>4?a=P6t[r-8+4-1]:0==r&&(a=0),n.push(a);const s=1/(o-1),l=-s/2,c=1+s/2,u=[l,l,c,l,c,c,l,l,c,c,l,c],h=6,d=6,p=3,f=2,m=1,g=new Float32Array(p*d*h),_=new Float32Array(f*d*h),y=new Float32Array(m*d*h);for(let t=0;t<h;t++){const e=t%3*2/3-1,n=t>2?0:-1;g.set([e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0],p*d*t),_.set(u,f*d*t);y.set([t,t,t,t,t,t],m*d*t)}const v=new N4t;v.setAttribute("position",new h4t(g,p)),v.setAttribute("uv",new h4t(_,f)),v.setAttribute("faceIndex",new h4t(y,m)),t.push(v),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function K6t(t){const e=new O5t(3*L6t,3*L6t,t);return e.texture.mapping=o2t,e.texture.name="PMREM.cubeUv",e.scissorTest=!0,e}function Z6t(t,e,n,i,r){t.viewport.set(e,n,i,r),t.scissor.set(e,n,i,r)}function J6t(){const t=new C5t(1,1);return new k6t({name:"EquirectangularToCubeUV",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:R6t[3e3]},outputEncoding:{value:R6t[3e3]}},vertexShader:t9t(),fragmentShader:`\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\t\t\tuniform vec2 texelSize;\n\n\t\t\t${e9t()}\n\n\t\t\t#include <common>\n\n\t\t\tvoid main() {\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\n\t\t\t\tvec3 outputDirection = normalize( vOutputDirection );\n\t\t\t\tvec2 uv = equirectUv( outputDirection );\n\n\t\t\t\tvec2 f = fract( uv / texelSize - 0.5 );\n\t\t\t\tuv -= f * texelSize;\n\t\t\t\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.x += texelSize.x;\n\t\t\t\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.y += texelSize.y;\n\t\t\t\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.x -= texelSize.x;\n\t\t\t\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\n\t\t\t\tvec3 tm = mix( tl, tr, f.x );\n\t\t\t\tvec3 bm = mix( bl, br, f.x );\n\t\t\t\tgl_FragColor.rgb = mix( tm, bm, f.y );\n\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t`,blending:0,depthTest:!1,depthWrite:!1})}function Q6t(){return new k6t({name:"CubemapToCubeUV",uniforms:{envMap:{value:null},inputEncoding:{value:R6t[3e3]},outputEncoding:{value:R6t[3e3]}},vertexShader:t9t(),fragmentShader:`\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform samplerCube envMap;\n\n\t\t\t${e9t()}\n\n\t\t\tvoid main() {\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t\t\t\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t`,blending:0,depthTest:!1,depthWrite:!1})}function t9t(){return"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute vec3 position;\n\t\tattribute vec2 uv;\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t"}function e9t(){return"\n\n\t\tuniform int inputEncoding;\n\t\tuniform int outputEncoding;\n\n\t\t#include <encodings_pars_fragment>\n\n\t\tvec4 inputTexelToLinear( vec4 value ) {\n\n\t\t\tif ( inputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( inputEncoding == 1 ) {\n\n\t\t\t\treturn sRGBToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 2 ) {\n\n\t\t\t\treturn RGBEToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 3 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 7.0 );\n\n\t\t\t} else if ( inputEncoding == 4 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 16.0 );\n\n\t\t\t} else if ( inputEncoding == 5 ) {\n\n\t\t\t\treturn RGBDToLinear( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn GammaToLinear( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 linearToOutputTexel( vec4 value ) {\n\n\t\t\tif ( outputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( outputEncoding == 1 ) {\n\n\t\t\t\treturn LinearTosRGB( value );\n\n\t\t\t} else if ( outputEncoding == 2 ) {\n\n\t\t\t\treturn LinearToRGBE( value );\n\n\t\t\t} else if ( outputEncoding == 3 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 7.0 );\n\n\t\t\t} else if ( outputEncoding == 4 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 16.0 );\n\n\t\t\t} else if ( outputEncoding == 5 ) {\n\n\t\t\t\treturn LinearToRGBD( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn LinearToGamma( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 envMapTexelToLinear( vec4 color ) {\n\n\t\t\treturn inputTexelToLinear( color );\n\n\t\t}\n\t"}function n9t(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener("dispose",i);const r=e.get(n);void 0!==r&&(r.delete(n),r.dispose())}return{get:function r(o){if(o&&o.isTexture&&!1===o.isRenderTargetTexture){const r=o.mapping,a=r===i2t||r===r2t,s=r===e2t||r===n2t;if(a||s){if(e.has(o))return e.get(o).texture;{const r=o.image;if(a&&r&&r.height>0||s&&r&&(function a(t){let e=0;const n=6;for(let i=0;i<n;i++)void 0!==t[i]&&e++;return e===n})(r)){const r=t.getRenderTarget();null===n&&(n=new Y6t(t));const s=a?n.fromEquirectangular(o):n.fromCubemap(o);return e.set(o,s),t.setRenderTarget(r),o.addEventListener("dispose",i),s.texture}return null}}}return o},dispose:function o(){e=new WeakMap,null!==n&&(n.dispose(),n=null)}}}function i9t(t){const e={};function n(n){if(void 0!==e[n])return e[n];let i;switch(n){case"WEBGL_depth_texture":i=t.getExtension("WEBGL_depth_texture")||t.getExtension("MOZ_WEBGL_depth_texture")||t.getExtension("WEBKIT_WEBGL_depth_texture");break;case"EXT_texture_filter_anisotropic":i=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic");break;case"WEBGL_compressed_texture_s3tc":i=t.getExtension("WEBGL_compressed_texture_s3tc")||t.getExtension("MOZ_WEBGL_compressed_texture_s3tc")||t.getExtension("WEBKIT_WEBGL_compressed_texture_s3tc");break;case"WEBGL_compressed_texture_pvrtc":i=t.getExtension("WEBGL_compressed_texture_pvrtc")||t.getExtension("WEBKIT_WEBGL_compressed_texture_pvrtc");break;default:i=t.getExtension(n)}return e[n]=i,i}return{has:function(t){return null!==n(t)},init:function(t){t.isWebGL2?n("EXT_color_buffer_float"):(n("WEBGL_depth_texture"),n("OES_texture_float"),n("OES_texture_half_float"),n("OES_texture_half_float_linear"),n("OES_standard_derivatives"),n("OES_element_index_uint"),n("OES_vertex_array_object"),n("ANGLE_instanced_arrays")),n("OES_texture_float_linear"),n("EXT_color_buffer_half_float")},get:function(t){const e=n(t);return null===e&&console.warn("THREE.WebGLRenderer: "+t+" extension not supported."),e}}}function r9t(t,e,n,i){const r={},o=new WeakMap;function a(t){const s=t.target;null!==s.index&&e.remove(s.index);for(const t in s.attributes)e.remove(s.attributes[t]);s.removeEventListener("dispose",a),delete r[s.id];const l=o.get(s);l&&(e.remove(l),o.delete(s)),i.releaseStatesOfGeometry(s),!0===s.isInstancedBufferGeometry&&delete s._maxInstanceCount,n.memory.geometries--}function s(t){const n=[],i=t.index,r=t.attributes.position;let a=0;if(null!==i){const t=i.array;a=i.version;for(let e=0,i=t.length;e<i;e+=3){const i=t[e+0],r=t[e+1],o=t[e+2];n.push(i,r,r,o,o,i)}}else{a=r.version;for(let t=0,e=r.array.length/3-1;t<e;t+=3){const e=t+0,i=t+1,r=t+2;n.push(e,i,i,r,r,e)}}const s=new(w4t(n)>65535?y4t:g4t)(n,1);s.version=a;const l=o.get(t);l&&e.remove(l),o.set(t,s)}return{get:function l(t,e){return!0===r[e.id]||(e.addEventListener("dispose",a),r[e.id]=!0,n.memory.geometries++),e},update:function c(t){const n=t.attributes;for(const t in n)e.update(n[t],34962);const i=t.morphAttributes;for(const t in i){const n=i[t];for(let t=0,i=n.length;t<i;t++)e.update(n[t],34962)}},getWireframeAttribute:function u(t){const e=o.get(t);if(e){const n=t.index;null!==n&&e.version<n.version&&s(t)}else s(t);return o.get(t)}}}function o9t(t,e,n,i){const r=i.isWebGL2;let o,a,s;this.setMode=function l(t){o=t},this.setIndex=function c(t){a=t.type,s=t.bytesPerElement},this.render=function u(e,i){t.drawElements(o,i,a,e*s),n.update(i,o,1)},this.renderInstances=function h(i,l,c){if(0===c)return;let 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t[e.id]=n,n}}}let U8t=0;function j8t(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function G8t(t,e){const n=new V8t,i=(function r(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case"DirectionalLight":case"SpotLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new C5t};break;case"PointLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new C5t,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}})(),o={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)o.probe.push(new H5t);const a=new H5t,s=new g3t,l=new g3t;return{setup:function c(r){let a=0,s=0,l=0;for(let t=0;t<9;t++)o.probe[t].set(0,0,0);let c=0,u=0,h=0,d=0,p=0,f=0,m=0,g=0;r.sort(j8t);for(let t=0,e=r.length;t<e;t++){const e=r[t],_=e.color,y=e.intensity,v=e.distance,b=e.shadow&&e.shadow.map?e.shadow.map.texture:null;if(e.isAmbientLight)a+=_.r*y,s+=_.g*y,l+=_.b*y;else if(e.isLightProbe)for(let t=0;t<9;t++)o.probe[t].addScaledVector(e.sh.coefficients[t],y);else if(e.isDirectionalLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity),e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,o.directionalShadow[c]=n,o.directionalShadowMap[c]=b,o.directionalShadowMatrix[c]=e.shadow.matrix,f++}o.directional[c]=t,c++}else if(e.isSpotLight){const t=n.get(e);if(t.position.setFromMatrixPosition(e.matrixWorld),t.color.copy(_).multiplyScalar(y),t.distance=v,t.coneCos=Math.cos(e.angle),t.penumbraCos=Math.cos(e.angle*(1-e.penumbra)),t.decay=e.decay,e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,o.spotShadow[h]=n,o.spotShadowMap[h]=b,o.spotShadowMatrix[h]=e.shadow.matrix,g++}o.spot[h]=t,h++}else if(e.isRectAreaLight){const t=n.get(e);t.color.copy(_).multiplyScalar(y),t.halfWidth.set(.5*e.width,0,0),t.halfHeight.set(0,.5*e.height,0),o.rectArea[d]=t,d++}else if(e.isPointLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity),t.distance=e.distance,t.decay=e.decay,e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,n.shadowCameraNear=t.camera.near,n.shadowCameraFar=t.camera.far,o.pointShadow[u]=n,o.pointShadowMap[u]=b,o.pointShadowMatrix[u]=e.shadow.matrix,m++}o.point[u]=t,u++}else if(e.isHemisphereLight){const t=n.get(e);t.skyColor.copy(e.color).multiplyScalar(y),t.groundColor.copy(e.groundColor).multiplyScalar(y),o.hemi[p]=t,p++}}d>0&&(e.isWebGL2||!0===t.has("OES_texture_float_linear")?(o.rectAreaLTC1=b6t.LTC_FLOAT_1,o.rectAreaLTC2=b6t.LTC_FLOAT_2):!0===t.has("OES_texture_half_float_linear")?(o.rectAreaLTC1=b6t.LTC_HALF_1,o.rectAreaLTC2=b6t.LTC_HALF_2):console.error("THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.")),o.ambient[0]=a,o.ambient[1]=s,o.ambient[2]=l;const _=o.hash;_.directionalLength===c&&_.pointLength===u&&_.spotLength===h&&_.rectAreaLength===d&&_.hemiLength===p&&_.numDirectionalShadows===f&&_.numPointShadows===m&&_.numSpotShadows===g||(o.directional.length=c,o.spot.length=h,o.rectArea.length=d,o.point.length=u,o.hemi.length=p,o.directionalShadow.length=f,o.directionalShadowMap.length=f,o.pointShadow.length=m,o.pointShadowMap.length=m,o.spotShadow.length=g,o.spotShadowMap.length=g,o.directionalShadowMatrix.length=f,o.pointShadowMatrix.length=m,o.spotShadowMatrix.length=g,_.directionalLength=c,_.pointLength=u,_.spotLength=h,_.rectAreaLength=d,_.hemiLength=p,_.numDirectionalShadows=f,_.numPointShadows=m,_.numSpotShadows=g,o.version=U8t++)},setupView:function u(t,e){let n=0,i=0,r=0,c=0,u=0;const h=e.matrixWorldInverse;for(let e=0,d=t.length;e<d;e++){const d=t[e];if(d.isDirectionalLight){const t=o.directional[n];t.direction.setFromMatrixPosition(d.matrixWorld),a.setFromMatrixPosition(d.target.matrixWorld),t.direction.sub(a),t.direction.transformDirection(h),n++}else if(d.isSpotLight){const t=o.spot[r];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),t.direction.setFromMatrixPosition(d.matrixWorld),a.setFromMatrixPosition(d.target.matrixWorld),t.direction.sub(a),t.direction.transformDirection(h),r++}else if(d.isRectAreaLight){const t=o.rectArea[c];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),l.identity(),s.copy(d.matrixWorld),s.premultiply(h),l.extractRotation(s),t.halfWidth.set(.5*d.width,0,0),t.halfHeight.set(0,.5*d.height,0),t.halfWidth.applyMatrix4(l),t.halfHeight.applyMatrix4(l),c++}else if(d.isPointLight){const t=o.point[i];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),i++}else if(d.isHemisphereLight){const t=o.hemi[u];t.direction.setFromMatrixPosition(d.matrixWorld),t.direction.transformDirection(h),t.direction.normalize(),u++}}},state:o}}function W8t(t,e){const n=new G8t(t,e),i=[],r=[];return{init:function o(){i.length=0,r.length=0},state:{lightsArray:i,shadowsArray:r,lights:n},setupLights:function a(){n.setup(i)},setupLightsView:function s(t){n.setupView(i,t)},pushLight:function l(t){i.push(t)},pushShadow:function c(t){r.push(t)}}}function q8t(t,e){let n=new WeakMap;return{get:function i(r,o=0){let a;return!1===n.has(r)?(a=new W8t(t,e),n.set(r,[a])):o>=n.get(r).length?(a=new W8t(t,e),n.get(r).push(a)):a=n.get(r)[o],a},dispose:function r(){n=new WeakMap}}}class Y8t extends t4t{constructor(t){super(),this.type="MeshDepthMaterial",this.depthPacking=3200,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}Y8t.prototype.isMeshDepthMaterial=!0;class X8t extends t4t{constructor(t){super(),this.type="MeshDistanceMaterial",this.referencePosition=new H5t,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}X8t.prototype.isMeshDistanceMaterial=!0;function $8t(t,e,n){let i=new m6t;const r=new C5t,o=new C5t,a=new R5t,s=new Y8t({depthPacking:3201}),l=new X8t,c={},u=n.maxTextureSize,h={0:1,1:0,2:2},d=new n6t({defines:{SAMPLE_RATE:2/8,HALF_SAMPLE_RATE:1/8},uniforms:{shadow_pass:{value:null},resolution:{value:new C5t},radius:{value:4}},vertexShader:"void main() {\n\tgl_Position = vec4( position, 1.0 );\n}",fragmentShader:"uniform sampler2D shadow_pass;\nuniform vec2 resolution;\nuniform float radius;\n#include <packing>\nvoid main() {\n\tfloat mean = 0.0;\n\tfloat squared_mean = 0.0;\n\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\n\tfor ( float i = -1.0; i < 1.0 ; i += SAMPLE_RATE) {\n\t\t#ifdef HORIZONTAL_PASS\n\t\t\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( i, 0.0 ) * radius ) / resolution ) );\n\t\t\tmean += distribution.x;\n\t\t\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\n\t\t#else\n\t\t\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, i ) * radius ) / resolution ) );\n\t\t\tmean += depth;\n\t\t\tsquared_mean += depth * depth;\n\t\t#endif\n\t}\n\tmean = mean * HALF_SAMPLE_RATE;\n\tsquared_mean = squared_mean * HALF_SAMPLE_RATE;\n\tfloat std_dev = sqrt( squared_mean - mean * mean );\n\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\n}"}),p=d.clone();p.defines.HORIZONTAL_PASS=1;const f=new N4t;f.setAttribute("position",new h4t(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const m=new K4t(f,d),g=this;function _(n,i){const r=e.update(m);d.uniforms.shadow_pass.value=n.map.texture,d.uniforms.resolution.value=n.mapSize,d.uniforms.radius.value=n.radius,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,r,d,m,null),p.uniforms.shadow_pass.value=n.mapPass.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,r,p,m,null)}function y(e,n,i,r,o,a,u){let d=null;const p=!0===r.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===r.isPointLight?l:s,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let r=n[e];void 0===r&&(r=d.clone(),n[e]=r),d=r}return d.visible=i.visible,d.wireframe=i.wireframe,d.side=3===u?null!==i.shadowSide?i.shadowSide:i.side:null!==i.shadowSide?i.shadowSide:h[i.side],d.clipShadows=i.clipShadows,d.clippingPlanes=i.clippingPlanes,d.clipIntersection=i.clipIntersection,d.wireframeLinewidth=i.wireframeLinewidth,d.linewidth=i.linewidth,!0===r.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(r.matrixWorld),d.nearDistance=o,d.farDistance=a),d}function v(n,r,o,a,s){if(!1===n.visible)return;if(n.layers.test(r.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&3===s)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(o.matrixWorldInverse,n.matrixWorld);const i=e.update(n),r=n.material;if(Array.isArray(r)){const e=i.groups;for(let l=0,c=e.length;l<c;l++){const c=e[l],u=r[c.materialIndex];if(u&&u.visible){const e=y(n,0,u,a,o.near,o.far,s);t.renderBufferDirect(o,null,i,e,n,c)}}}else if(r.visible){const e=y(n,0,r,a,o.near,o.far,s);t.renderBufferDirect(o,null,i,e,n,null)}}const l=n.children;for(let t=0,e=l.length;t<e;t++)v(l[t],r,o,a,s)}this.enabled=!1,this.autoUpdate=!0,this.needsUpdate=!1,this.type=1,this.render=function(e,n,s){if(!1===g.enabled)return;if(!1===g.autoUpdate&&!1===g.needsUpdate)return;if(0===e.length)return;const l=t.getRenderTarget(),c=t.getActiveCubeFace(),h=t.getActiveMipmapLevel(),d=t.state;d.setBlending(0),d.buffers.color.setClear(1,1,1,1),d.buffers.depth.setTest(!0),d.setScissorTest(!1);for(let l=0,c=e.length;l<c;l++){const c=e[l],h=c.shadow;if(void 0===h){console.warn("THREE.WebGLShadowMap:",c,"has no shadow.");continue}if(!1===h.autoUpdate&&!1===h.needsUpdate)continue;r.copy(h.mapSize);const p=h.getFrameExtents();if(r.multiply(p),o.copy(h.mapSize),(r.x>u||r.y>u)&&(r.x>u&&(o.x=Math.floor(u/p.x),r.x=o.x*p.x,h.mapSize.x=o.x),r.y>u&&(o.y=Math.floor(u/p.y),r.y=o.y*p.y,h.mapSize.y=o.y)),null===h.map&&!h.isPointLightShadow&&3===this.type){const t={minFilter:p2t,magFilter:p2t,format:S2t};h.map=new O5t(r.x,r.y,t),h.map.texture.name=c.name+".shadowMap",h.mapPass=new O5t(r.x,r.y,t),h.camera.updateProjectionMatrix()}if(null===h.map){h.map=new O5t(r.x,r.y,{minFilter:u2t,magFilter:u2t,format:S2t}),h.map.texture.name=c.name+".shadowMap",h.camera.updateProjectionMatrix()}t.setRenderTarget(h.map),t.clear();const f=h.getViewportCount();for(let t=0;t<f;t++){const e=h.getViewport(t);a.set(o.x*e.x,o.y*e.y,o.x*e.z,o.y*e.w),d.viewport(a),h.updateMatrices(c,t),i=h.getFrustum(),v(n,s,h.camera,c,this.type)}h.isPointLightShadow||3!==this.type||_(h,s),h.needsUpdate=!1}g.needsUpdate=!1,t.setRenderTarget(l,c,h)}}function K8t(t,e,n){const i=n.isWebGL2;const r=new(function o(){let e=!1;const n=new R5t;let i=null;const r=new R5t(0,0,0,0);return{setMask:function(n){i===n||e||(t.colorMask(n,n,n,n),i=n)},setLocked:function(t){e=t},setClear:function(e,i,o,a,s){!0===s&&(e*=a,i*=a,o*=a),n.set(e,i,o,a),!1===r.equals(n)&&(t.clearColor(e,i,o,a),r.copy(n))},reset:function(){e=!1,i=null,r.set(-1,0,0,0)}}}),a=new(function s(){let e=!1,n=null,i=null,r=null;return{setTest:function(t){t?F(2929):V(2929)},setMask:function(i){n===i||e||(t.depthMask(i),n=i)},setFunc:function(e){if(i!==e){if(e)switch(e){case 0:t.depthFunc(512);break;case 1:t.depthFunc(519);break;case 2:t.depthFunc(513);break;case 3:t.depthFunc(515);break;case 4:t.depthFunc(514);break;case 5:t.depthFunc(518);break;case 6:t.depthFunc(516);break;case 7:t.depthFunc(517);break;default:t.depthFunc(515)}else t.depthFunc(515);i=e}},setLocked:function(t){e=t},setClear:function(e){r!==e&&(t.clearDepth(e),r=e)},reset:function(){e=!1,n=null,i=null,r=null}}}),l=new(function c(){let e=!1,n=null,i=null,r=null,o=null,a=null,s=null,l=null,c=null;return{setTest:function(t){e||(t?F(2960):V(2960))},setMask:function(i){n===i||e||(t.stencilMask(i),n=i)},setFunc:function(e,n,a){i===e&&r===n&&o===a||(t.stencilFunc(e,n,a),i=e,r=n,o=a)},setOp:function(e,n,i){a===e&&s===n&&l===i||(t.stencilOp(e,n,i),a=e,s=n,l=i)},setLocked:function(t){e=t},setClear:function(e){c!==e&&(t.clearStencil(e),c=e)},reset:function(){e=!1,n=null,i=null,r=null,o=null,a=null,s=null,l=null,c=null}}});let u={},h=null,d={},p=null,f=!1,m=null,g=null,_=null,y=null,v=null,b=null,x=null,w=!1,S=null,M=null,E=null,T=null,C=null;const A=t.getParameter(35661);let k=!1,L=0;const P=t.getParameter(7938);-1!==P.indexOf("WebGL")?(L=parseFloat(/^WebGL (\d)/.exec(P)[1]),k=L>=1):-1!==P.indexOf("OpenGL ES")&&(L=parseFloat(/^OpenGL ES (\d)/.exec(P)[1]),k=L>=2);let N=null,I={};const R=t.getParameter(3088),O=t.getParameter(2978),z=(new R5t).fromArray(R),D=(new R5t).fromArray(O);function B(e,n,i){const r=new 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z=!1,D=!1;this.allocateTextureUnit=function B(){const t=M;return t>=l&&console.warn("THREE.WebGLTextures: Trying to use "+t+" texture units while this GPU supports only "+l),M+=1,t},this.resetTextureUnits=function H(){M=0},this.setTexture2D=E,this.setTexture2DArray=function F(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?P(r,t,e):(n.activeTexture(33984+e),n.bindTexture(35866,r.__webglTexture))},this.setTexture3D=function V(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?P(r,t,e):(n.activeTexture(33984+e),n.bindTexture(32879,r.__webglTexture))},this.setTextureCube=T,this.setupRenderTarget=function U(e){const l=e.texture,c=i.get(e),u=i.get(l);e.addEventListener("dispose",S),!0!==e.isWebGLMultipleRenderTargets&&(u.__webglTexture=t.createTexture(),u.__version=l.version,a.memory.textures++);const h=!0===e.isWebGLCubeRenderTarget,d=!0===e.isWebGLMultipleRenderTargets,p=!0===e.isWebGLMultisampleRenderTarget,f=l.isDataTexture3D||l.isDataTexture2DArray,m=_(e)||s;if(!s||l.format!==w2t||l.type!==v2t&&l.type!==b2t||(l.format=S2t,console.warn("THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.")),h){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),d)if(r.drawBuffers){const n=e.texture;for(let e=0,r=n.length;e<r;e++){const r=i.get(n[e]);void 0===r.__webglTexture&&(r.__webglTexture=t.createTexture(),a.memory.textures++)}}else console.warn("THREE.WebGLRenderer: WebGLMultipleRenderTargets can only be used with WebGL2 or WEBGL_draw_buffers extension.");else if(p)if(s){c.__webglMultisampledFramebuffer=t.createFramebuffer(),c.__webglColorRenderbuffer=t.createRenderbuffer(),t.bindRenderbuffer(36161,c.__webglColorRenderbuffer);const i=o.convert(l.format),r=o.convert(l.type),a=b(l.internalFormat,i,r),s=O(e);t.renderbufferStorageMultisample(36161,s,a,e.width,e.height),n.bindFramebuffer(36160,c.__webglMultisampledFramebuffer),t.framebufferRenderbuffer(36160,36064,36161,c.__webglColorRenderbuffer),t.bindRenderbuffer(36161,null),e.depthBuffer&&(c.__webglDepthRenderbuffer=t.createRenderbuffer(),I(c.__webglDepthRenderbuffer,e,!0)),n.bindFramebuffer(36160,null)}else console.warn("THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.");if(h){n.bindTexture(34067,u.__webglTexture),k(34067,l,m);for(let t=0;t<6;t++)N(c.__webglFramebuffer[t],e,l,36064,34069+t);y(l,m)&&v(34067,l,e.width,e.height),n.bindTexture(34067,null)}else if(d){const t=e.texture;for(let r=0,o=t.length;r<o;r++){const o=t[r],a=i.get(o);n.bindTexture(3553,a.__webglTexture),k(3553,o,m),N(c.__webglFramebuffer,e,o,36064+r,3553),y(o,m)&&v(3553,o,e.width,e.height)}n.bindTexture(3553,null)}else{let t=3553;if(f)if(s){t=l.isDataTexture3D?32879:35866}else console.warn("THREE.DataTexture3D and THREE.DataTexture2DArray only supported with WebGL2.");n.bindTexture(t,u.__webglTexture),k(t,l,m),N(c.__webglFramebuffer,e,l,36064,t),y(l,m)&&v(t,l,e.width,e.height,e.depth),n.bindTexture(t,null)}e.depthBuffer&&R(e)},this.updateRenderTargetMipmap=function j(t){const e=_(t)||s,r=!0===t.isWebGLMultipleRenderTargets?t.texture:[t.texture];for(let o=0,a=r.length;o<a;o++){const a=r[o];if(y(a,e)){const e=t.isWebGLCubeRenderTarget?34067:3553,r=i.get(a).__webglTexture;n.bindTexture(e,r),v(e,a,t.width,t.height),n.bindTexture(e,null)}}},this.updateMultisampleRenderTarget=function G(e){if(e.isWebGLMultisampleRenderTarget)if(s){const r=e.width,o=e.height;let a=16384;e.depthBuffer&&(a|=256),e.stencilBuffer&&(a|=1024);const s=i.get(e);n.bindFramebuffer(36008,s.__webglMultisampledFramebuffer),n.bindFramebuffer(36009,s.__webglFramebuffer),t.blitFramebuffer(0,0,r,o,0,0,r,o,a,9728),n.bindFramebuffer(36008,null),n.bindFramebuffer(36009,s.__webglMultisampledFramebuffer)}else console.warn("THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.")},this.safeSetTexture2D=function W(t,e){t&&t.isWebGLRenderTarget&&(!1===z&&(console.warn("THREE.WebGLTextures.safeSetTexture2D: don't use render targets as textures. Use their .texture property instead."),z=!0),t=t.texture),E(t,e)},this.safeSetTextureCube=function q(t,e){t&&t.isWebGLCubeRenderTarget&&(!1===D&&(console.warn("THREE.WebGLTextures.safeSetTextureCube: don't use cube render targets as textures. Use their .texture property instead."),D=!0),t=t.texture),T(t,e)}}function J8t(t,e,n){const i=n.isWebGL2;return{convert:function r(t){let n;if(t===g2t)return 5121;if(1017===t)return 32819;if(1018===t)return 32820;if(1019===t)return 33635;if(1010===t)return 5120;if(1011===t)return 5122;if(t===_2t)return 5123;if(1013===t)return 5124;if(t===y2t)return 5125;if(t===v2t)return 5126;if(t===b2t)return i?5131:(n=e.get("OES_texture_half_float"),null!==n?n.HALF_FLOAT_OES:null);if(1021===t)return 6406;if(t===w2t)return 6407;if(t===S2t)return 6408;if(1024===t)return 6409;if(1025===t)return 6410;if(t===M2t)return 6402;if(t===E2t)return 34041;if(1028===t)return 6403;if(1029===t)return 36244;if(1030===t)return 33319;if(1031===t)return 33320;if(1032===t)return 36248;if(1033===t)return 36249;if(t===T2t||t===C2t||t===A2t||t===k2t){if(n=e.get("WEBGL_compressed_texture_s3tc"),null===n)return null;if(t===T2t)return n.COMPRESSED_RGB_S3TC_DXT1_EXT;if(t===C2t)return n.COMPRESSED_RGBA_S3TC_DXT1_EXT;if(t===A2t)return n.COMPRESSED_RGBA_S3TC_DXT3_EXT;if(t===k2t)return n.COMPRESSED_RGBA_S3TC_DXT5_EXT}if(t===L2t||t===P2t||t===N2t||t===I2t){if(n=e.get("WEBGL_compressed_texture_pvrtc"),null===n)return null;if(t===L2t)return n.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(t===P2t)return n.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(t===N2t)return n.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(t===I2t)return n.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}if(36196===t)return n=e.get("WEBGL_compressed_texture_etc1"),null!==n?n.COMPRESSED_RGB_ETC1_WEBGL:null;if((t===R2t||t===O2t)&&(n=e.get("WEBGL_compressed_texture_etc"),null!==n)){if(t===R2t)return n.COMPRESSED_RGB8_ETC2;if(t===O2t)return n.COMPRESSED_RGBA8_ETC2_EAC}return 37808===t||37809===t||37810===t||37811===t||37812===t||37813===t||37814===t||37815===t||37816===t||37817===t||37818===t||37819===t||37820===t||37821===t||37840===t||37841===t||37842===t||37843===t||37844===t||37845===t||37846===t||37847===t||37848===t||37849===t||37850===t||37851===t||37852===t||37853===t?(n=e.get("WEBGL_compressed_texture_astc"),null!==n?t:null):36492===t?(n=e.get("EXT_texture_compression_bptc"),null!==n?t:null):t===x2t?i?34042:(n=e.get("WEBGL_depth_texture"),null!==n?n.UNSIGNED_INT_24_8_WEBGL:null):void 0}}}class Q8t extends r6t{constructor(t=[]){super(),this.cameras=t}}Q8t.prototype.isArrayCamera=!0;class t7t extends V3t{constructor(){super(),this.type="Group"}}t7t.prototype.isGroup=!0;const e7t={type:"move"};class n7t{constructor(){this._targetRay=null,this._grip=null,this._hand=null}getHandSpace(){return null===this._hand&&(this._hand=new t7t,this._hand.matrixAutoUpdate=!1,this._hand.visible=!1,this._hand.joints={},this._hand.inputState={pinching:!1}),this._hand}getTargetRaySpace(){return null===this._targetRay&&(this._targetRay=new t7t,this._targetRay.matrixAutoUpdate=!1,this._targetRay.visible=!1,this._targetRay.hasLinearVelocity=!1,this._targetRay.linearVelocity=new H5t,this._targetRay.hasAngularVelocity=!1,this._targetRay.angularVelocity=new H5t),this._targetRay}getGripSpace(){return null===this._grip&&(this._grip=new t7t,this._grip.matrixAutoUpdate=!1,this._grip.visible=!1,this._grip.hasLinearVelocity=!1,this._grip.linearVelocity=new H5t,this._grip.hasAngularVelocity=!1,this._grip.angularVelocity=new H5t),this._grip}dispatchEvent(t){return null!==this._targetRay&&this._targetRay.dispatchEvent(t),null!==this._grip&&this._grip.dispatchEvent(t),null!==this._hand&&this._hand.dispatchEvent(t),this}disconnect(t){return this.dispatchEvent({type:"disconnected",data:t}),null!==this._targetRay&&(this._targetRay.visible=!1),null!==this._grip&&(this._grip.visible=!1),null!==this._hand&&(this._hand.visible=!1),this}update(t,e,n){let i=null,r=null,o=null;const a=this._targetRay,s=this._grip,l=this._hand;if(t&&"visible-blurred"!==e.session.visibilityState)if(null!==a&&(i=e.getPose(t.targetRaySpace,n),null!==i&&(a.matrix.fromArray(i.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),i.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(i.linearVelocity)):a.hasLinearVelocity=!1,i.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(i.angularVelocity)):a.hasAngularVelocity=!1,this.dispatchEvent(e7t))),l&&t.hand){o=!0;for(const i of t.hand.values()){const t=e.getJointPose(i,n);if(void 0===l.joints[i.jointName]){const t=new t7t;t.matrixAutoUpdate=!1,t.visible=!1,l.joints[i.jointName]=t,l.add(t)}const r=l.joints[i.jointName];null!==t&&(r.matrix.fromArray(t.transform.matrix),r.matrix.decompose(r.position,r.rotation,r.scale),r.jointRadius=t.radius),r.visible=null!==t}const i=l.joints["index-finger-tip"].position.distanceTo(l.joints["thumb-tip"].position),r=.02,a=.005;l.inputState.pinching&&i>r+a?(l.inputState.pinching=!1,this.dispatchEvent({type:"pinchend",handedness:t.handedness,target:this})):!l.inputState.pinching&&i<=r-a&&(l.inputState.pinching=!0,this.dispatchEvent({type:"pinchstart",handedness:t.handedness,target:this}))}else null!==s&&t.gripSpace&&(r=e.getPose(t.gripSpace,n),null!==r&&(s.matrix.fromArray(r.transform.matrix),s.matrix.decompose(s.position,s.rotation,s.scale),r.linearVelocity?(s.hasLinearVelocity=!0,s.linearVelocity.copy(r.linearVelocity)):s.hasLinearVelocity=!1,r.angularVelocity?(s.hasAngularVelocity=!0,s.angularVelocity.copy(r.angularVelocity)):s.hasAngularVelocity=!1));return null!==a&&(a.visible=null!==i),null!==s&&(s.visible=null!==r),null!==l&&(l.visible=null!==o),this}}class i7t extends e5t{constructor(t,e){super();const n=this,i=t.state;let r=null,o=1,a=null,s="local-floor",l=null,c=null,u=null,h=null,d=null;const p=[],f=new Map,m=new r6t;m.layers.enable(1),m.viewport=new R5t;const g=new r6t;g.layers.enable(2),g.viewport=new R5t;const _=[m,g],y=new Q8t;y.layers.enable(1),y.layers.enable(2);let v=null,b=null;function x(t){const e=f.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function w(){f.forEach((function(t,e){t.disconnect(e)})),f.clear(),v=null,b=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),A.stop(),n.isPresenting=!1,n.dispatchEvent({type:"sessionend"})}function S(t){const e=r.inputSources;for(let t=0;t<p.length;t++)f.set(e[t],p[t]);for(let e=0;e<t.removed.length;e++){const n=t.removed[e],i=f.get(n);i&&(i.dispatchEvent({type:"disconnected",data:n}),f.delete(n))}for(let e=0;e<t.added.length;e++){const n=t.added[e],i=f.get(n);i&&i.dispatchEvent({type:"connected",data:n})}}this.cameraAutoUpdate=!0,this.enabled=!1,this.isPresenting=!1,this.getController=function(t){let e=p[t];return void 0===e&&(e=new n7t,p[t]=e),e.getTargetRaySpace()},this.getControllerGrip=function(t){let e=p[t];return void 0===e&&(e=new n7t,p[t]=e),e.getGripSpace()},this.getHand=function(t){let e=p[t];return void 0===e&&(e=new n7t,p[t]=e),e.getHandSpace()},this.setFramebufferScaleFactor=function(t){o=t,!0===n.isPresenting&&console.warn("THREE.WebXRManager: Cannot change framebuffer scale while presenting.")},this.setReferenceSpaceType=function(t){s=t,!0===n.isPresenting&&console.warn("THREE.WebXRManager: Cannot change reference space type while presenting.")},this.getReferenceSpace=function(){return a},this.getSession=function(){return r},this.setSession=async function(t){if(r=t,null!==r){r.addEventListener("select",x),r.addEventListener("selectstart",x),r.addEventListener("selectend",x),r.addEventListener("squeeze",x),r.addEventListener("squeezestart",x),r.addEventListener("squeezeend",x),r.addEventListener("end",w),r.addEventListener("inputsourceschange",S);const t=e.getContextAttributes();if(!0!==t.xrCompatible&&await e.makeXRCompatible(),void 0===r.renderState.layers){d=new XRWebGLLayer(r,e,{antialias:t.antialias,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:o}),r.updateRenderState({baseLayer:d})}else{let n=0;if(t.antialias){d=new XRWebGLLayer(r,e,{antialias:!0,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:o}),r.updateRenderState({layers:[d]})}else{t.depth&&(n=t.stencil?34041:6402);const i={colorFormat:t.alpha?6408:6407,depthFormat:n,scaleFactor:o};c=new XRWebGLBinding(r,e),h=c.createProjectionLayer(i),u=e.createFramebuffer(),r.updateRenderState({layers:[h]})}}a=await r.requestReferenceSpace(s),A.setContext(r),A.start(),n.isPresenting=!0,n.dispatchEvent({type:"sessionstart"})}};const M=new H5t,E=new H5t;function T(t,e){null===e?t.matrixWorld.copy(t.matrix):t.matrixWorld.multiplyMatrices(e.matrixWorld,t.matrix),t.matrixWorldInverse.copy(t.matrixWorld).invert()}this.updateCamera=function(t){if(null===r)return;y.near=g.near=m.near=t.near,y.far=g.far=m.far=t.far,v===y.near&&b===y.far||(r.updateRenderState({depthNear:y.near,depthFar:y.far}),v=y.near,b=y.far);const e=t.parent,n=y.cameras;T(y,e);for(let t=0;t<n.length;t++)T(n[t],e);y.matrixWorld.decompose(y.position,y.quaternion,y.scale),t.position.copy(y.position),t.quaternion.copy(y.quaternion),t.scale.copy(y.scale),t.matrix.copy(y.matrix),t.matrixWorld.copy(y.matrixWorld);const i=t.children;for(let t=0,e=i.length;t<e;t++)i[t].updateMatrixWorld(!0);2===n.length?(function o(t,e,n){M.setFromMatrixPosition(e.matrixWorld),E.setFromMatrixPosition(n.matrixWorld);const i=M.distanceTo(E),r=e.projectionMatrix.elements,o=n.projectionMatrix.elements,a=r[14]/(r[10]-1),s=r[14]/(r[10]+1),l=(r[9]+1)/r[5],c=(r[9]-1)/r[5],u=(r[8]-1)/r[0],h=(o[8]+1)/o[0],d=a*u,p=a*h,f=i/(-u+h),m=f*-u;e.matrixWorld.decompose(t.position,t.quaternion,t.scale),t.translateX(m),t.translateZ(f),t.matrixWorld.compose(t.position,t.quaternion,t.scale),t.matrixWorldInverse.copy(t.matrixWorld).invert();const g=a+f,_=s+f;t.projectionMatrix.makePerspective(d-m,p+(i-m),l*s/_*g,c*s/_*g,g,_)})(y,m,g):y.projectionMatrix.copy(m.projectionMatrix)},this.getCamera=function(){return y},this.getFoveation=function(){return null!==h?h.fixedFoveation:null!==d?d.fixedFoveation:void 0},this.setFoveation=function(t){null!==h&&(h.fixedFoveation=t),null!==d&&void 0!==d.fixedFoveation&&(d.fixedFoveation=t)};let C=null;const A=new g6t;A.setAnimationLoop((function k(t,n){if(l=n.getViewerPose(a),null!==l){const t=l.views;null!==d&&i.bindXRFramebuffer(d.framebuffer);let n=!1;t.length!==y.cameras.length&&(y.cameras.length=0,n=!0);for(let r=0;r<t.length;r++){const o=t[r];let a=null;if(null!==d)a=d.getViewport(o);else{const t=c.getViewSubImage(h,o);i.bindXRFramebuffer(u),void 0!==t.depthStencilTexture&&e.framebufferTexture2D(36160,36096,3553,t.depthStencilTexture,0),e.framebufferTexture2D(36160,36064,3553,t.colorTexture,0),a=t.viewport}const s=_[r];s.matrix.fromArray(o.transform.matrix),s.projectionMatrix.fromArray(o.projectionMatrix),s.viewport.set(a.x,a.y,a.width,a.height),0===r&&y.matrix.copy(s.matrix),!0===n&&y.cameras.push(s)}}const o=r.inputSources;for(let t=0;t<p.length;t++){p[t].update(o[t],n,a)}C&&C(t,n)})),this.setAnimationLoop=function(t){C=t},this.dispose=function(){}}}function r7t(t){function e(e,n){e.opacity.value=n.opacity,n.color&&e.diffuse.value.copy(n.color),n.emissive&&e.emissive.value.copy(n.emissive).multiplyScalar(n.emissiveIntensity),n.map&&(e.map.value=n.map),n.alphaMap&&(e.alphaMap.value=n.alphaMap),n.specularMap&&(e.specularMap.value=n.specularMap);const i=t.get(n).envMap;if(i){e.envMap.value=i,e.flipEnvMap.value=i.isCubeTexture&&!1===i.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.refractionRatio.value=n.refractionRatio;const r=t.get(i).__maxMipLevel;void 0!==r&&(e.maxMipLevel.value=r)}let r,o;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?r=n.map:n.specularMap?r=n.specularMap:n.displacementMap?r=n.displacementMap:n.normalMap?r=n.normalMap:n.bumpMap?r=n.bumpMap:n.roughnessMap?r=n.roughnessMap:n.metalnessMap?r=n.metalnessMap:n.alphaMap?r=n.alphaMap:n.emissiveMap?r=n.emissiveMap:n.clearcoatMap?r=n.clearcoatMap:n.clearcoatNormalMap?r=n.clearcoatNormalMap:n.clearcoatRoughnessMap?r=n.clearcoatRoughnessMap:n.specularIntensityMap?r=n.specularIntensityMap:n.specularTintMap&&(r=n.specularTintMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uvTransform.value.copy(r.matrix)),n.aoMap?o=n.aoMap:n.lightMap&&(o=n.lightMap),void 0!==o&&(o.isWebGLRenderTarget&&(o=o.texture),!0===o.matrixAutoUpdate&&o.updateMatrix(),e.uv2Transform.value.copy(o.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,1===n.side&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),1===n.side&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function i(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function r(t,i,o,a,s){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),(function l(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)})(t,i)):i.isMeshToonMaterial?(e(t,i),(function c(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,i)):i.isMeshPhongMaterial?(e(t,i),(function u(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?(function 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e=S.map.cameraPosition;void 0!==e&&e.setValue(dt,F.setFromMatrixPosition(t.matrixWorld))}(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial)&&S.setValue(dt,"isOrthographic",!0===t.isOrthographicCamera),(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial||n.isShadowMaterial||i.isSkinnedMesh)&&S.setValue(dt,"viewMatrix",t.matrixWorldInverse)}if(i.isSkinnedMesh){S.setOptional(dt,i,"bindMatrix"),S.setOptional(dt,i,"bindMatrixInverse");const t=i.skeleton;t&&(G.floatVertexTextures?(null===t.boneTexture&&t.computeBoneTexture(),S.setValue(dt,"boneTexture",t.boneTexture,X),S.setValue(dt,"boneTextureSize",t.boneTextureSize)):S.setOptional(dt,t,"boneMatrices"))}return(y||h.receiveShadow!==i.receiveShadow)&&(h.receiveShadow=i.receiveShadow,S.setValue(dt,"receiveShadow",i.receiveShadow)),y&&(S.setValue(dt,"toneMappingExposure",g.toneMappingExposure),h.needsLights&&(function E(t,e){t.ambientLightColor.needsUpdate=e,t.lightProbe.needsUpdate=e,t.directionalLights.needsUpdate=e,t.directionalLightShadows.needsUpdate=e,t.pointLights.needsUpdate=e,t.pointLightShadows.needsUpdate=e,t.spotLights.needsUpdate=e,t.spotLightShadows.needsUpdate=e,t.rectAreaLights.needsUpdate=e,t.hemisphereLights.needsUpdate=e})(M,v),r&&n.fog&&et.refreshFogUniforms(M,r),et.refreshMaterialUniforms(M,n,A,C,B),f8t.upload(dt,h.uniformsList,M,X)),n.isShaderMaterial&&!0===n.uniformsNeedUpdate&&(f8t.upload(dt,h.uniformsList,M,X),n.uniformsNeedUpdate=!1),n.isSpriteMaterial&&S.setValue(dt,"center",i.center),S.setValue(dt,"modelViewMatrix",i.modelViewMatrix),S.setValue(dt,"normalMatrix",i.normalMatrix),S.setValue(dt,"modelMatrix",i.matrixWorld),m}wt.setAnimationLoop((function kt(t){vt&&vt(t)})),"undefined"!=typeof window&&wt.setContext(window),this.setAnimationLoop=function(t){vt=t,mt.setAnimationLoop(t),null===t?wt.stop():wt.start()},mt.addEventListener("sessionstart",bt),mt.addEventListener("sessionend",xt),this.render=function(t,e){if(void 0!==e&&!0!==e.isCamera)return void console.error("THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera.");if(!0===_)return;!0===t.autoUpdate&&t.updateMatrixWorld(),null===e.parent&&e.updateMatrixWorld(),!0===mt.enabled&&!0===mt.isPresenting&&(!0===mt.cameraAutoUpdate&&mt.updateCamera(e),e=mt.getCamera()),!0===t.isScene&&t.onBeforeRender(g,t,e,b),p=it.get(t,m.length),p.init(),m.push(p),H.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),O.setFromProjectionMatrix(H),D=this.localClippingEnabled,z=rt.init(this.clippingPlanes,D,e),d=nt.get(t,f.length),d.init(),f.push(d),St(t,e,0,g.sortObjects),d.finish(),!0===g.sortObjects&&d.sort(k,L),!0===z&&rt.beginShadows();ot.render(p.state.shadowsArray,t,e),p.setupLights(),p.setupLightsView(e),!0===z&&rt.endShadows(),!0===this.info.autoReset&&this.info.reset(),at.render(d,t);const n=d.opaque,i=d.transmissive,r=d.transparent;n.length>0&&Mt(n,t,e),i.length>0&&(function o(t,e,n,i){if(null===B){B=new(!0===s&&!0===G.isWebGL2?D5t:O5t)(1024,1024,{generateMipmaps:!0,type:null!==ut.convert(b2t)?b2t:g2t,minFilter:m2t,magFilter:u2t,wrapS:l2t,wrapT:l2t})}const r=g.getRenderTarget();g.setRenderTarget(B),g.clear();const o=g.toneMapping;g.toneMapping=0,Mt(t,n,i),g.toneMapping=o,X.updateMultisampleRenderTarget(B),X.updateRenderTargetMipmap(B),g.setRenderTarget(r),Mt(e,n,i)})(n,i,t,e),r.length>0&&Mt(r,t,e),null!==b&&(X.updateMultisampleRenderTarget(b),X.updateRenderTargetMipmap(b)),!0===t.isScene&&t.onAfterRender(g,t,e),W.buffers.depth.setTest(!0),W.buffers.depth.setMask(!0),W.buffers.color.setMask(!0),W.setPolygonOffset(!1),ht.resetDefaultState(),x=-1,w=null,m.pop(),p=m.length>0?m[m.length-1]:null,f.pop(),d=f.length>0?f[f.length-1]:null},this.getActiveCubeFace=function(){return y},this.getActiveMipmapLevel=function(){return v},this.getRenderTarget=function(){return b},this.setRenderTarget=function(t,e=0,n=0){b=t,y=e,v=n,t&&void 0===Y.get(t).__webglFramebuffer&&X.setupRenderTarget(t);let i=null,r=!1,o=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(o=!0);const a=Y.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=a[e],r=!0):i=t.isWebGLMultisampleRenderTarget?Y.get(t).__webglMultisampledFramebuffer:a,S.copy(t.viewport),M.copy(t.scissor),E=t.scissorTest}else S.copy(P).multiplyScalar(A).floor(),M.copy(N).multiplyScalar(A).floor(),E=I;if(W.bindFramebuffer(36160,i)&&G.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(R.length!==n.length||36064!==R[0]){for(let t=0,e=n.length;t<e;t++)R[t]=36064+t;R.length=n.length,e=!0}}else 1===R.length&&36064===R[0]||(R[0]=36064,R.length=1,e=!0);else 1===R.length&&1029===R[0]||(R[0]=1029,R.length=1,e=!0);e&&(G.isWebGL2?dt.drawBuffers(R):j.get("WEBGL_draw_buffers").drawBuffersWEBGL(R))}if(W.viewport(S),W.scissor(M),W.setScissorTest(E),r){const i=Y.get(t.texture);dt.framebufferTexture2D(36160,36064,34069+e,i.__webglTexture,n)}else if(o){const i=Y.get(t.texture);dt.framebufferTextureLayer(36160,36064,i.__webglTexture,n||0,e||0)}},this.readRenderTargetPixels=function(t,e,n,i,r,o,a){if(!t||!t.isWebGLRenderTarget)return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.");let s=Y.get(t).__webglFramebuffer;if(t.isWebGLCubeRenderTarget&&void 0!==a&&(s=s[a]),s){W.bindFramebuffer(36160,s);try{const a=t.texture,s=a.format,l=a.type;if(s!==S2t&&ut.convert(s)!==dt.getParameter(35739))return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format.");const c=l===b2t&&(j.has("EXT_color_buffer_half_float")||G.isWebGL2&&j.has("EXT_color_buffer_float"));if(!(l===g2t||ut.convert(l)===dt.getParameter(35738)||l===v2t&&(G.isWebGL2||j.has("OES_texture_float")||j.has("WEBGL_color_buffer_float"))||c))return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.");36053===dt.checkFramebufferStatus(36160)?e>=0&&e<=t.width-i&&n>=0&&n<=t.height-r&&dt.readPixels(e,n,i,r,ut.convert(s),ut.convert(l),o):console.error("THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.")}finally{const t=null!==b?Y.get(b).__webglFramebuffer:null;W.bindFramebuffer(36160,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),r=Math.floor(e.image.width*i),o=Math.floor(e.image.height*i);let a=ut.convert(e.format);G.isWebGL2&&(6407===a&&(a=32849),6408===a&&(a=32856)),X.setTexture2D(e,0),dt.copyTexImage2D(3553,n,a,t.x,t.y,r,o,0),W.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const r=e.image.width,o=e.image.height,a=ut.convert(n.format),s=ut.convert(n.type);X.setTexture2D(n,0),dt.pixelStorei(37440,n.flipY),dt.pixelStorei(37441,n.premultiplyAlpha),dt.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?dt.texSubImage2D(3553,i,t.x,t.y,r,o,a,s,e.image.data):e.isCompressedTexture?dt.compressedTexSubImage2D(3553,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,a,e.mipmaps[0].data):dt.texSubImage2D(3553,i,t.x,t.y,a,s,e.image),0===i&&n.generateMipmaps&&dt.generateMipmap(3553),W.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,r=0){if(g.isWebGL1Renderer)return void console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.");const o=t.max.x-t.min.x+1,a=t.max.y-t.min.y+1,s=t.max.z-t.min.z+1,l=ut.convert(i.format),c=ut.convert(i.type);let u;if(i.isDataTexture3D)X.setTexture3D(i,0),u=32879;else{if(!i.isDataTexture2DArray)return void console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.");X.setTexture2DArray(i,0),u=35866}dt.pixelStorei(37440,i.flipY),dt.pixelStorei(37441,i.premultiplyAlpha),dt.pixelStorei(3317,i.unpackAlignment);const h=dt.getParameter(3314),d=dt.getParameter(32878),p=dt.getParameter(3316),f=dt.getParameter(3315),m=dt.getParameter(32877),_=n.isCompressedTexture?n.mipmaps[0]:n.image;dt.pixelStorei(3314,_.width),dt.pixelStorei(32878,_.height),dt.pixelStorei(3316,t.min.x),dt.pixelStorei(3315,t.min.y),dt.pixelStorei(32877,t.min.z),n.isDataTexture||n.isDataTexture3D?dt.texSubImage3D(u,r,e.x,e.y,e.z,o,a,s,l,c,_.data):n.isCompressedTexture?(console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture."),dt.compressedTexSubImage3D(u,r,e.x,e.y,e.z,o,a,s,l,_.data)):dt.texSubImage3D(u,r,e.x,e.y,e.z,o,a,s,l,c,_),dt.pixelStorei(3314,h),dt.pixelStorei(32878,d),dt.pixelStorei(3316,p),dt.pixelStorei(3315,f),dt.pixelStorei(32877,m),0===r&&i.generateMipmaps&&dt.generateMipmap(u),W.unbindTexture()},this.initTexture=function(t){X.setTexture2D(t,0),W.unbindTexture()},this.resetState=function(){y=0,v=0,b=null,W.reset(),ht.reset()},"undefined"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}class a7t extends o7t{}a7t.prototype.isWebGL1Renderer=!0;class s7t{constructor(t,e=25e-5){this.name="",this.color=new s4t(t),this.density=e}clone(){return new s7t(this.color,this.density)}toJSON(){return{type:"FogExp2",color:this.color.getHex(),density:this.density}}}s7t.prototype.isFogExp2=!0;class l7t{constructor(t,e=1,n=1e3){this.name="",this.color=new s4t(t),this.near=e,this.far=n}clone(){return new l7t(this.color,this.near,this.far)}toJSON(){return{type:"Fog",color:this.color.getHex(),near:this.near,far:this.far}}}l7t.prototype.isFog=!0;class c7t extends V3t{constructor(){super(),this.type="Scene",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,"undefined"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}c7t.prototype.isScene=!0;class u7t{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=J2t,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=a5t()}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.array=new t.array.constructor(t.array),this.count=t.count,this.stride=t.stride,this.usage=t.usage,this}copyAt(t,e,n){t*=this.stride,n*=e.stride;for(let i=0,r=this.stride;i<r;i++)this.array[t+i]=e.array[n+i];return this}set(t,e=0){return this.array.set(t,e),this}clone(t){void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=a5t()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=this.array.slice(0).buffer);const e=new this.array.constructor(t.arrayBuffers[this.array.buffer._uuid]),n=new this.constructor(e,this.stride);return n.setUsage(this.usage),n}onUpload(t){return this.onUploadCallback=t,this}toJSON(t){return void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=a5t()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=Array.prototype.slice.call(new Uint32Array(this.array.buffer))),{uuid:this.uuid,buffer:this.array.buffer._uuid,type:this.array.constructor.name,stride:this.stride}}}u7t.prototype.isInterleavedBuffer=!0;const h7t=new H5t;class d7t{constructor(t,e,n,i=!1){this.name="",this.data=t,this.itemSize=e,this.offset=n,this.normalized=!0===i}get count(){return this.data.count}get array(){return this.data.array}set needsUpdate(t){this.data.needsUpdate=t}applyMatrix4(t){for(let e=0,n=this.data.count;e<n;e++)h7t.x=this.getX(e),h7t.y=this.getY(e),h7t.z=this.getZ(e),h7t.applyMatrix4(t),this.setXYZ(e,h7t.x,h7t.y,h7t.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)h7t.x=this.getX(e),h7t.y=this.getY(e),h7t.z=this.getZ(e),h7t.applyNormalMatrix(t),this.setXYZ(e,h7t.x,h7t.y,h7t.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)h7t.x=this.getX(e),h7t.y=this.getY(e),h7t.z=this.getZ(e),h7t.transformDirection(t),this.setXYZ(e,h7t.x,h7t.y,h7t.z);return this}setX(t,e){return this.data.array[t*this.data.stride+this.offset]=e,this}setY(t,e){return this.data.array[t*this.data.stride+this.offset+1]=e,this}setZ(t,e){return this.data.array[t*this.data.stride+this.offset+2]=e,this}setW(t,e){return this.data.array[t*this.data.stride+this.offset+3]=e,this}getX(t){return this.data.array[t*this.data.stride+this.offset]}getY(t){return this.data.array[t*this.data.stride+this.offset+1]}getZ(t){return this.data.array[t*this.data.stride+this.offset+2]}getW(t){return this.data.array[t*this.data.stride+this.offset+3]}setXY(t,e,n){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this}setXYZ(t,e,n,i){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=i,this}setXYZW(t,e,n,i,r){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=i,this.data.array[t+3]=r,this}clone(t){if(void 0===t){console.log("THREE.InterleavedBufferAttribute.clone(): Cloning an interlaved buffer attribute will deinterleave buffer data.");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return new h4t(new this.array.constructor(t),this.itemSize,this.normalized)}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.clone(t)),new d7t(t.interleavedBuffers[this.data.uuid],this.itemSize,this.offset,this.normalized)}toJSON(t){if(void 0===t){console.log("THREE.InterleavedBufferAttribute.toJSON(): Serializing an interlaved buffer attribute will deinterleave buffer data.");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return{itemSize:this.itemSize,type:this.array.constructor.name,array:t,normalized:this.normalized}}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.toJSON(t)),{isInterleavedBufferAttribute:!0,itemSize:this.itemSize,data:this.data.uuid,offset:this.offset,normalized:this.normalized}}}d7t.prototype.isInterleavedBufferAttribute=!0;class p7t extends t4t{constructor(t){super(),this.type="SpriteMaterial",this.color=new s4t(16777215),this.map=null,this.alphaMap=null,this.rotation=0,this.sizeAttenuation=!0,this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.rotation=t.rotation,this.sizeAttenuation=t.sizeAttenuation,this}}let f7t;p7t.prototype.isSpriteMaterial=!0;const m7t=new H5t,g7t=new H5t,_7t=new H5t,y7t=new C5t,v7t=new C5t,b7t=new g3t,x7t=new H5t,w7t=new H5t,S7t=new H5t,M7t=new C5t,E7t=new C5t,T7t=new C5t;class C7t extends V3t{constructor(t){if(super(),this.type="Sprite",void 0===f7t){f7t=new N4t;const t=new Float32Array([-.5,-.5,0,0,0,.5,-.5,0,1,0,.5,.5,0,1,1,-.5,.5,0,0,1]),e=new u7t(t,5);f7t.setIndex([0,1,2,0,2,3]),f7t.setAttribute("position",new d7t(e,3,0,!1)),f7t.setAttribute("uv",new d7t(e,2,3,!1))}this.geometry=f7t,this.material=void 0!==t?t:new p7t,this.center=new C5t(.5,.5)}raycast(t,e){null===t.camera&&console.error('THREE.Sprite: "Raycaster.camera" needs to be set in order to raycast against sprites.'),g7t.setFromMatrixScale(this.matrixWorld),b7t.copy(t.camera.matrixWorld),this.modelViewMatrix.multiplyMatrices(t.camera.matrixWorldInverse,this.matrixWorld),_7t.setFromMatrixPosition(this.modelViewMatrix),t.camera.isPerspectiveCamera&&!1===this.material.sizeAttenuation&&g7t.multiplyScalar(-_7t.z);const n=this.material.rotation;let i,r;0!==n&&(r=Math.cos(n),i=Math.sin(n));const o=this.center;A7t(x7t.set(-.5,-.5,0),_7t,o,g7t,i,r),A7t(w7t.set(.5,-.5,0),_7t,o,g7t,i,r),A7t(S7t.set(.5,.5,0),_7t,o,g7t,i,r),M7t.set(0,0),E7t.set(1,0),T7t.set(1,1);let a=t.ray.intersectTriangle(x7t,w7t,S7t,!1,m7t);if(null===a&&(A7t(w7t.set(-.5,.5,0),_7t,o,g7t,i,r),E7t.set(0,1),a=t.ray.intersectTriangle(x7t,S7t,w7t,!1,m7t),null===a))return;const s=t.ray.origin.distanceTo(m7t);s<t.near||s>t.far||e.push({distance:s,point:m7t.clone(),uv:J3t.getUV(m7t,x7t,w7t,S7t,M7t,E7t,T7t,new C5t),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function A7t(t,e,n,i,r,o){y7t.subVectors(t,n).addScalar(.5).multiply(i),void 0!==r?(v7t.x=o*y7t.x-r*y7t.y,v7t.y=r*y7t.x+o*y7t.y):v7t.copy(y7t),t.copy(e),t.x+=v7t.x,t.y+=v7t.y,t.applyMatrix4(b7t)}C7t.prototype.isSprite=!0;const k7t=new H5t,L7t=new H5t;class P7t extends V3t{constructor(){super(),this._currentLevel=0,this.type="LOD",Object.defineProperties(this,{levels:{enumerable:!0,value:[]},isLOD:{value:!0}}),this.autoUpdate=!0}copy(t){super.copy(t,!1);const e=t.levels;for(let t=0,n=e.length;t<n;t++){const n=e[t];this.addLevel(n.object.clone(),n.distance)}return this.autoUpdate=t.autoUpdate,this}addLevel(t,e=0){e=Math.abs(e);const n=this.levels;let i;for(i=0;i<n.length&&!(e<n[i].distance);i++);return n.splice(i,0,{distance:e,object:t}),this.add(t),this}getCurrentLevel(){return this._currentLevel}getObjectForDistance(t){const e=this.levels;if(e.length>0){let n,i;for(n=1,i=e.length;n<i&&!(t<e[n].distance);n++);return e[n-1].object}return null}raycast(t,e){if(this.levels.length>0){k7t.setFromMatrixPosition(this.matrixWorld);const n=t.ray.origin.distanceTo(k7t);this.getObjectForDistance(n).raycast(t,e)}}update(t){const e=this.levels;if(e.length>1){k7t.setFromMatrixPosition(t.matrixWorld),L7t.setFromMatrixPosition(this.matrixWorld);const n=k7t.distanceTo(L7t)/t.zoom;let i,r;for(e[0].object.visible=!0,i=1,r=e.length;i<r&&n>=e[i].distance;i++)e[i-1].object.visible=!1,e[i].object.visible=!0;for(this._currentLevel=i-1;i<r;i++)e[i].object.visible=!1}}toJSON(t){const e=super.toJSON(t);!1===this.autoUpdate&&(e.object.autoUpdate=!1),e.object.levels=[];const n=this.levels;for(let t=0,i=n.length;t<i;t++){const i=n[t];e.object.levels.push({object:i.object.uuid,distance:i.distance})}return e}}const N7t=new H5t,I7t=new R5t,R7t=new R5t,O7t=new H5t,z7t=new g3t;class D7t extends K4t{constructor(t,e){super(t,e),this.type="SkinnedMesh",this.bindMode="attached",this.bindMatrix=new g3t,this.bindMatrixInverse=new g3t}copy(t){return super.copy(t),this.bindMode=t.bindMode,this.bindMatrix.copy(t.bindMatrix),this.bindMatrixInverse.copy(t.bindMatrixInverse),this.skeleton=t.skeleton,this}bind(t,e){this.skeleton=t,void 0===e&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),e=this.matrixWorld),this.bindMatrix.copy(e),this.bindMatrixInverse.copy(e).invert()}pose(){this.skeleton.pose()}normalizeSkinWeights(){const t=new R5t,e=this.geometry.attributes.skinWeight;for(let n=0,i=e.count;n<i;n++){t.x=e.getX(n),t.y=e.getY(n),t.z=e.getZ(n),t.w=e.getW(n);const i=1/t.manhattanLength();i!==1/0?t.multiplyScalar(i):t.set(1,0,0,0),e.setXYZW(n,t.x,t.y,t.z,t.w)}}updateMatrixWorld(t){super.updateMatrixWorld(t),"attached"===this.bindMode?this.bindMatrixInverse.copy(this.matrixWorld).invert():"detached"===this.bindMode?this.bindMatrixInverse.copy(this.bindMatrix).invert():console.warn("THREE.SkinnedMesh: Unrecognized bindMode: "+this.bindMode)}boneTransform(t,e){const n=this.skeleton,i=this.geometry;I7t.fromBufferAttribute(i.attributes.skinIndex,t),R7t.fromBufferAttribute(i.attributes.skinWeight,t),N7t.fromBufferAttribute(i.attributes.position,t).applyMatrix4(this.bindMatrix),e.set(0,0,0);for(let t=0;t<4;t++){const i=R7t.getComponent(t);if(0!==i){const r=I7t.getComponent(t);z7t.multiplyMatrices(n.bones[r].matrixWorld,n.boneInverses[r]),e.addScaledVector(O7t.copy(N7t).applyMatrix4(z7t),i)}}return e.applyMatrix4(this.bindMatrixInverse)}}D7t.prototype.isSkinnedMesh=!0;class B7t extends V3t{constructor(){super(),this.type="Bone"}}B7t.prototype.isBone=!0;class H7t extends N5t{constructor(t=null,e=1,n=1,i,r,o,a,s,l=1003,c=1003,u,h){super(null,o,a,s,l,c,i,r,u,h),this.image={data:t,width:e,height:n},this.magFilter=l,this.minFilter=c,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}H7t.prototype.isDataTexture=!0;const F7t=new g3t,V7t=new g3t;class U7t{constructor(t=[],e=[]){this.uuid=a5t(),this.bones=t.slice(0),this.boneInverses=e,this.boneMatrices=null,this.boneTexture=null,this.boneTextureSize=0,this.frame=-1,this.init()}init(){const t=this.bones,e=this.boneInverses;if(this.boneMatrices=new Float32Array(16*t.length),0===e.length)this.calculateInverses();else if(t.length!==e.length){console.warn("THREE.Skeleton: Number of inverse bone matrices does not match amount of bones."),this.boneInverses=[];for(let t=0,e=this.bones.length;t<e;t++)this.boneInverses.push(new g3t)}}calculateInverses(){this.boneInverses.length=0;for(let t=0,e=this.bones.length;t<e;t++){const e=new g3t;this.bones[t]&&e.copy(this.bones[t].matrixWorld).invert(),this.boneInverses.push(e)}}pose(){for(let t=0,e=this.bones.length;t<e;t++){const e=this.bones[t];e&&e.matrixWorld.copy(this.boneInverses[t]).invert()}for(let t=0,e=this.bones.length;t<e;t++){const e=this.bones[t];e&&(e.parent&&e.parent.isBone?(e.matrix.copy(e.parent.matrixWorld).invert(),e.matrix.multiply(e.matrixWorld)):e.matrix.copy(e.matrixWorld),e.matrix.decompose(e.position,e.quaternion,e.scale))}}update(){const t=this.bones,e=this.boneInverses,n=this.boneMatrices,i=this.boneTexture;for(let i=0,r=t.length;i<r;i++){F7t.multiplyMatrices(t[i]?t[i].matrixWorld:V7t,e[i]),F7t.toArray(n,16*i)}null!==i&&(i.needsUpdate=!0)}clone(){return new U7t(this.bones,this.boneInverses)}computeBoneTexture(){let t=Math.sqrt(4*this.bones.length);t=h5t(t),t=Math.max(t,4);const e=new Float32Array(t*t*4);e.set(this.boneMatrices);const n=new H7t(e,t,t,S2t,v2t);return this.boneMatrices=e,this.boneTexture=n,this.boneTextureSize=t,this}getBoneByName(t){for(let e=0,n=this.bones.length;e<n;e++){const n=this.bones[e];if(n.name===t)return n}}dispose(){null!==this.boneTexture&&(this.boneTexture.dispose(),this.boneTexture=null)}fromJSON(t,e){this.uuid=t.uuid;for(let n=0,i=t.bones.length;n<i;n++){const i=t.bones[n];let r=e[i];void 0===r&&(console.warn("THREE.Skeleton: No bone found with UUID:",i),r=new B7t),this.bones.push(r),this.boneInverses.push((new g3t).fromArray(t.boneInverses[n]))}return this.init(),this}toJSON(){const t={metadata:{version:4.5,type:"Skeleton",generator:"Skeleton.toJSON"},bones:[],boneInverses:[]};t.uuid=this.uuid;const e=this.bones,n=this.boneInverses;for(let i=0,r=e.length;i<r;i++){t.bones.push(e[i].uuid);t.boneInverses.push(n[i].toArray())}return t}}const j7t=new g3t,G7t=new g3t,W7t=[],q7t=new K4t;class Y7t extends K4t{constructor(t,e,n){super(t,e),this.instanceMatrix=new h4t(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}copy(t){return super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,i=this.count;if(q7t.geometry=this.geometry,q7t.material=this.material,void 0!==q7t.material)for(let r=0;r<i;r++){this.getMatrixAt(r,j7t),G7t.multiplyMatrices(n,j7t),q7t.matrixWorld=G7t,q7t.raycast(t,W7t);for(let t=0,n=W7t.length;t<n;t++){const n=W7t[t];n.instanceId=r,n.object=this,e.push(n)}W7t.length=0}}setColorAt(t,e){null===this.instanceColor&&(this.instanceColor=new h4t(new Float32Array(3*this.instanceMatrix.count),3)),e.toArray(this.instanceColor.array,3*t)}setMatrixAt(t,e){e.toArray(this.instanceMatrix.array,16*t)}updateMorphTargets(){}dispose(){this.dispatchEvent({type:"dispose"})}}Y7t.prototype.isInstancedMesh=!0;class X7t extends t4t{constructor(t){super(),this.type="LineBasicMaterial",this.color=new s4t(16777215),this.linewidth=1,this.linecap="round",this.linejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.linewidth=t.linewidth,this.linecap=t.linecap,this.linejoin=t.linejoin,this}}X7t.prototype.isLineBasicMaterial=!0;const $7t=new H5t,K7t=new H5t,Z7t=new g3t,J7t=new m3t,Q7t=new s3t;class tte extends V3t{constructor(t=new N4t,e=new X7t){super(),this.type="Line",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[0];for(let t=1,i=e.count;t<i;t++)$7t.fromBufferAttribute(e,t-1),K7t.fromBufferAttribute(e,t),n[t]=n[t-1],n[t]+=$7t.distanceTo(K7t);t.setAttribute("lineDistance",new b4t(n,1))}else console.warn("THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else t.isGeometry&&console.error("THREE.Line.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");return this}raycast(t,e){const n=this.geometry,i=this.matrixWorld,r=t.params.Line.threshold,o=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),Q7t.copy(n.boundingSphere),Q7t.applyMatrix4(i),Q7t.radius+=r,!1===t.ray.intersectsSphere(Q7t))return;Z7t.copy(i).invert(),J7t.copy(t.ray).applyMatrix4(Z7t);const a=r/((this.scale.x+this.scale.y+this.scale.z)/3),s=a*a,l=new H5t,c=new H5t,u=new H5t,h=new H5t,d=this.isLineSegments?2:1;if(n.isBufferGeometry){const i=n.index,r=n.attributes.position;if(null!==i){for(let n=Math.max(0,o.start),a=Math.min(i.count,o.start+o.count)-1;n<a;n+=d){const o=i.getX(n),a=i.getX(n+1);l.fromBufferAttribute(r,o),c.fromBufferAttribute(r,a);if(J7t.distanceSqToSegment(l,c,h,u)>s)continue;h.applyMatrix4(this.matrixWorld);const d=t.ray.origin.distanceTo(h);d<t.near||d>t.far||e.push({distance:d,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,o.start),i=Math.min(r.count,o.start+o.count)-1;n<i;n+=d){l.fromBufferAttribute(r,n),c.fromBufferAttribute(r,n+1);if(J7t.distanceSqToSegment(l,c,h,u)>s)continue;h.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(h);i<t.near||i>t.far||e.push({distance:i,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}}else n.isGeometry&&console.error("THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.")}}}tte.prototype.isLine=!0;const ete=new H5t,nte=new H5t;class ite extends tte{constructor(t,e){super(t,e),this.type="LineSegments"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t<i;t+=2)ete.fromBufferAttribute(e,t),nte.fromBufferAttribute(e,t+1),n[t]=0===t?0:n[t-1],n[t+1]=n[t]+ete.distanceTo(nte);t.setAttribute("lineDistance",new b4t(n,1))}else console.warn("THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else t.isGeometry&&console.error("THREE.LineSegments.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");return this}}ite.prototype.isLineSegments=!0;class rte extends tte{constructor(t,e){super(t,e),this.type="LineLoop"}}rte.prototype.isLineLoop=!0;class ote extends t4t{constructor(t){super(),this.type="PointsMaterial",this.color=new s4t(16777215),this.map=null,this.alphaMap=null,this.size=1,this.sizeAttenuation=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.size=t.size,this.sizeAttenuation=t.sizeAttenuation,this}}ote.prototype.isPointsMaterial=!0;const ate=new g3t,ste=new m3t,lte=new s3t,cte=new H5t;class ute extends V3t{constructor(t=new N4t,e=new ote){super(),this.type="Points",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}raycast(t,e){const n=this.geometry,i=this.matrixWorld,r=t.params.Points.threshold,o=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),lte.copy(n.boundingSphere),lte.applyMatrix4(i),lte.radius+=r,!1===t.ray.intersectsSphere(lte))return;ate.copy(i).invert(),ste.copy(t.ray).applyMatrix4(ate);const a=r/((this.scale.x+this.scale.y+this.scale.z)/3),s=a*a;if(n.isBufferGeometry){const r=n.index,a=n.attributes.position;if(null!==r){for(let n=Math.max(0,o.start),l=Math.min(r.count,o.start+o.count);n<l;n++){const o=r.getX(n);cte.fromBufferAttribute(a,o),hte(cte,o,s,i,t,e,this)}}else{for(let n=Math.max(0,o.start),r=Math.min(a.count,o.start+o.count);n<r;n++)cte.fromBufferAttribute(a,n),hte(cte,n,s,i,t,e,this)}}else console.error("THREE.Points.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.")}}}function hte(t,e,n,i,r,o,a){const s=ste.distanceSqToPoint(t);if(s<n){const n=new H5t;ste.closestPointToPoint(t,n),n.applyMatrix4(i);const l=r.ray.origin.distanceTo(n);if(l<r.near||l>r.far)return;o.push({distance:l,distanceToRay:Math.sqrt(s),point:n,index:e,face:null,object:a})}}ute.prototype.isPoints=!0;class dte extends N5t{constructor(t,e,n,i,r,o,a,s,l){super(t,e,n,i,r,o,a,s,l),this.format=void 0!==a?a:w2t,this.minFilter=void 0!==o?o:p2t,this.magFilter=void 0!==r?r:p2t,this.generateMipmaps=!1;const c=this;"requestVideoFrameCallback"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1==="requestVideoFrameCallback"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}dte.prototype.isVideoTexture=!0;class pte extends N5t{constructor(t,e,n,i,r,o,a,s,l,c,u,h){super(null,o,a,s,l,c,i,r,u,h),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}pte.prototype.isCompressedTexture=!0;class fte extends N5t{constructor(t,e,n,i,r,o,a,s,l){super(t,e,n,i,r,o,a,s,l),this.needsUpdate=!0}}fte.prototype.isCanvasTexture=!0;class mte extends N5t{constructor(t,e,n,i,r,o,a,s,l,c){if((c=void 0!==c?c:M2t)!==M2t&&c!==E2t)throw new Error("DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat");void 0===n&&c===M2t&&(n=_2t),void 0===n&&c===E2t&&(n=x2t),super(null,i,r,o,a,s,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==a?a:u2t,this.minFilter=void 0!==s?s:u2t,this.flipY=!1,this.generateMipmaps=!1}}mte.prototype.isDepthTexture=!0;class gte extends N4t{constructor(t=1,e=8,n=0,i=2*Math.PI){super(),this.type="CircleGeometry",this.parameters={radius:t,segments:e,thetaStart:n,thetaLength:i},e=Math.max(3,e);const r=[],o=[],a=[],s=[],l=new H5t,c=new C5t;o.push(0,0,0),a.push(0,0,1),s.push(.5,.5);for(let r=0,u=3;r<=e;r++,u+=3){const h=n+r/e*i;l.x=t*Math.cos(h),l.y=t*Math.sin(h),o.push(l.x,l.y,l.z),a.push(0,0,1),c.x=(o[u]/t+1)/2,c.y=(o[u+1]/t+1)/2,s.push(c.x,c.y)}for(let t=1;t<=e;t++)r.push(t,t+1,0);this.setIndex(r),this.setAttribute("position",new b4t(o,3)),this.setAttribute("normal",new b4t(a,3)),this.setAttribute("uv",new b4t(s,2))}static fromJSON(t){return new gte(t.radius,t.segments,t.thetaStart,t.thetaLength)}}class _te extends N4t{constructor(t=1,e=1,n=1,i=8,r=1,o=!1,a=0,s=2*Math.PI){super(),this.type="CylinderGeometry",this.parameters={radiusTop:t,radiusBottom:e,height:n,radialSegments:i,heightSegments:r,openEnded:o,thetaStart:a,thetaLength:s};const l=this;i=Math.floor(i),r=Math.floor(r);const c=[],u=[],h=[],d=[];let p=0;const f=[],m=n/2;let g=0;function _(n){const r=p,o=new C5t,f=new H5t;let _=0;const y=!0===n?t:e,v=!0===n?1:-1;for(let t=1;t<=i;t++)u.push(0,m*v,0),h.push(0,v,0),d.push(.5,.5),p++;const b=p;for(let t=0;t<=i;t++){const e=t/i*s+a,n=Math.cos(e),r=Math.sin(e);f.x=y*r,f.y=m*v,f.z=y*n,u.push(f.x,f.y,f.z),h.push(0,v,0),o.x=.5*n+.5,o.y=.5*r*v+.5,d.push(o.x,o.y),p++}for(let t=0;t<i;t++){const e=r+t,i=b+t;!0===n?c.push(i,i+1,e):c.push(i+1,i,e),_+=3}l.addGroup(g,_,!0===n?1:2),g+=_}!(function y(){const o=new H5t,_=new H5t;let y=0;const v=(e-t)/n;for(let l=0;l<=r;l++){const c=[],g=l/r,y=g*(e-t)+t;for(let t=0;t<=i;t++){const e=t/i,r=e*s+a,l=Math.sin(r),f=Math.cos(r);_.x=y*l,_.y=-g*n+m,_.z=y*f,u.push(_.x,_.y,_.z),o.set(l,v,f).normalize(),h.push(o.x,o.y,o.z),d.push(e,1-g),c.push(p++)}f.push(c)}for(let t=0;t<i;t++)for(let e=0;e<r;e++){const n=f[e+1][t],i=f[e+1][t+1],r=f[e][t+1];c.push(f[e][t],n,r),c.push(n,i,r),y+=6}l.addGroup(g,y,0),g+=y})(),!1===o&&(t>0&&_(!0),e>0&&_(!1)),this.setIndex(c),this.setAttribute("position",new b4t(u,3)),this.setAttribute("normal",new b4t(h,3)),this.setAttribute("uv",new b4t(d,2))}static fromJSON(t){return new _te(t.radiusTop,t.radiusBottom,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class yte extends _te{constructor(t=1,e=1,n=8,i=1,r=!1,o=0,a=2*Math.PI){super(0,t,e,n,i,r,o,a),this.type="ConeGeometry",this.parameters={radius:t,height:e,radialSegments:n,heightSegments:i,openEnded:r,thetaStart:o,thetaLength:a}}static fromJSON(t){return new yte(t.radius,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class vte extends N4t{constructor(t,e,n=1,i=0){super(),this.type="PolyhedronGeometry",this.parameters={vertices:t,indices:e,radius:n,detail:i};const r=[],o=[];function a(t,e,n,i){const r=i+1,o=[];for(let i=0;i<=r;i++){o[i]=[];const a=t.clone().lerp(n,i/r),s=e.clone().lerp(n,i/r),l=r-i;for(let t=0;t<=l;t++)o[i][t]=0===t&&i===r?a:a.clone().lerp(s,t/l)}for(let t=0;t<r;t++)for(let e=0;e<2*(r-t)-1;e++){const n=Math.floor(e/2);e%2==0?(s(o[t][n+1]),s(o[t+1][n]),s(o[t][n])):(s(o[t][n+1]),s(o[t+1][n+1]),s(o[t+1][n]))}}function s(t){r.push(t.x,t.y,t.z)}function l(e,n){const i=3*e;n.x=t[i+0],n.y=t[i+1],n.z=t[i+2]}function c(t,e,n,i){i<0&&1===t.x&&(o[e]=t.x-1),0===n.x&&0===n.z&&(o[e]=i/2/Math.PI+.5)}function u(t){return Math.atan2(t.z,-t.x)}!(function h(t){const n=new H5t,i=new H5t,r=new H5t;for(let o=0;o<e.length;o+=3)l(e[o+0],n),l(e[o+1],i),l(e[o+2],r),a(n,i,r,t)})(i),(function d(t){const e=new H5t;for(let n=0;n<r.length;n+=3)e.x=r[n+0],e.y=r[n+1],e.z=r[n+2],e.normalize().multiplyScalar(t),r[n+0]=e.x,r[n+1]=e.y,r[n+2]=e.z})(n),(function p(){const t=new H5t;for(let n=0;n<r.length;n+=3){t.x=r[n+0],t.y=r[n+1],t.z=r[n+2];const i=u(t)/2/Math.PI+.5,a=(e=t,Math.atan2(-e.y,Math.sqrt(e.x*e.x+e.z*e.z))/Math.PI+.5);o.push(i,1-a)}var e;(function n(){const t=new H5t,e=new H5t,n=new H5t,i=new H5t,a=new C5t,s=new C5t,l=new C5t;for(let h=0,d=0;h<r.length;h+=9,d+=6){t.set(r[h+0],r[h+1],r[h+2]),e.set(r[h+3],r[h+4],r[h+5]),n.set(r[h+6],r[h+7],r[h+8]),a.set(o[d+0],o[d+1]),s.set(o[d+2],o[d+3]),l.set(o[d+4],o[d+5]),i.copy(t).add(e).add(n).divideScalar(3);const p=u(i);c(a,d+0,t,p),c(s,d+2,e,p),c(l,d+4,n,p)}})(),(function i(){for(let t=0;t<o.length;t+=6){const e=o[t+0],n=o[t+2],i=o[t+4],r=Math.max(e,n,i),a=Math.min(e,n,i);r>.9&&a<.1&&(e<.2&&(o[t+0]+=1),n<.2&&(o[t+2]+=1),i<.2&&(o[t+4]+=1))}})()})(),this.setAttribute("position",new b4t(r,3)),this.setAttribute("normal",new b4t(r.slice(),3)),this.setAttribute("uv",new b4t(o,2)),0===i?this.computeVertexNormals():this.normalizeNormals()}static fromJSON(t){return new vte(t.vertices,t.indices,t.radius,t.details)}}class bte extends vte{constructor(t=1,e=0){const n=(1+Math.sqrt(5))/2,i=1/n;super([-1,-1,-1,-1,-1,1,-1,1,-1,-1,1,1,1,-1,-1,1,-1,1,1,1,-1,1,1,1,0,-i,-n,0,-i,n,0,i,-n,0,i,n,-i,-n,0,-i,n,0,i,-n,0,i,n,0,-n,0,-i,n,0,-i,-n,0,i,n,0,i],[3,11,7,3,7,15,3,15,13,7,19,17,7,17,6,7,6,15,17,4,8,17,8,10,17,10,6,8,0,16,8,16,2,8,2,10,0,12,1,0,1,18,0,18,16,6,10,2,6,2,13,6,13,15,2,16,18,2,18,3,2,3,13,18,1,9,18,9,11,18,11,3,4,14,12,4,12,0,4,0,8,11,9,5,11,5,19,11,19,7,19,5,14,19,14,4,19,4,17,1,12,14,1,14,5,1,5,9],t,e),this.type="DodecahedronGeometry",this.parameters={radius:t,detail:e}}static fromJSON(t){return new bte(t.radius,t.detail)}}const xte=new H5t,wte=new H5t,Ste=new H5t,Mte=new J3t;class Ete extends N4t{constructor(t,e){if(super(),this.type="EdgesGeometry",this.parameters={thresholdAngle:e},e=void 0!==e?e:1,!0===t.isGeometry)return void console.error("THREE.EdgesGeometry no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");const n=Math.cos(r5t*e),i=t.getIndex(),r=t.getAttribute("position"),o=i?i.count:r.count,a=[0,0,0],s=["a","b","c"],l=new Array(3),c={},u=[];for(let t=0;t<o;t+=3){i?(a[0]=i.getX(t),a[1]=i.getX(t+1),a[2]=i.getX(t+2)):(a[0]=t,a[1]=t+1,a[2]=t+2);const{a:e,b:o,c:h}=Mte;if(e.fromBufferAttribute(r,a[0]),o.fromBufferAttribute(r,a[1]),h.fromBufferAttribute(r,a[2]),Mte.getNormal(Ste),l[0]=`${e.x},${e.y},${e.z}`,l[1]=`${o.x},${o.y},${o.z}`,l[2]=`${h.x},${h.y},${h.z}`,l[0]!==l[1]&&l[1]!==l[2]&&l[2]!==l[0])for(let t=0;t<3;t++){const e=(t+1)%3,i=l[t],r=l[e],o=Mte[s[t]],h=Mte[s[e]],d=`${i}_${r}`,p=`${r}_${i}`;p in c&&c[p]?(Ste.dot(c[p].normal)<=n&&(u.push(o.x,o.y,o.z),u.push(h.x,h.y,h.z)),c[p]=null):d in c||(c[d]={index0:a[t],index1:a[e],normal:Ste.clone()})}}for(const t in c)if(c[t]){const{index0:e,index1:n}=c[t];xte.fromBufferAttribute(r,e),wte.fromBufferAttribute(r,n),u.push(xte.x,xte.y,xte.z),u.push(wte.x,wte.y,wte.z)}this.setAttribute("position",new b4t(u,3))}}class Tte{constructor(){this.type="Curve",this.arcLengthDivisions=200}getPoint(){return console.warn("THREE.Curve: .getPoint() not implemented."),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,i=this.getPoint(0),r=0;e.push(0);for(let o=1;o<=t;o++)n=this.getPoint(o/t),r+=n.distanceTo(i),e.push(r),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const r=n.length;let o;o=e||t*n[r-1];let a,s=0,l=r-1;for(;s<=l;)if(i=Math.floor(s+(l-s)/2),a=n[i]-o,a<0)s=i+1;else{if(!(a>0)){l=i;break}l=i-1}if(i=l,n[i]===o)return i/(r-1);const c=n[i];return(i+(o-c)/(n[i+1]-c))/(r-1)}getTangent(t,e){const n=1e-4;let i=t-n,r=t+n;i<0&&(i=0),r>1&&(r=1);const o=this.getPoint(i),a=this.getPoint(r),s=e||(o.isVector2?new C5t:new H5t);return s.copy(a).sub(o).normalize(),s}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new H5t,i=[],r=[],o=[],a=new H5t,s=new g3t;for(let e=0;e<=t;e++){i[e]=this.getTangentAt(e/t,new H5t),i[e].normalize()}r[0]=new H5t,o[0]=new H5t;let l=Number.MAX_VALUE;const c=Math.abs(i[0].x),u=Math.abs(i[0].y),h=Math.abs(i[0].z);c<=l&&(l=c,n.set(1,0,0)),u<=l&&(l=u,n.set(0,1,0)),h<=l&&n.set(0,0,1),a.crossVectors(i[0],n).normalize(),r[0].crossVectors(i[0],a),o[0].crossVectors(i[0],r[0]);for(let e=1;e<=t;e++){if(r[e]=r[e-1].clone(),o[e]=o[e-1].clone(),a.crossVectors(i[e-1],i[e]),a.length()>Number.EPSILON){a.normalize();const t=Math.acos(s5t(i[e-1].dot(i[e]),-1,1));r[e].applyMatrix4(s.makeRotationAxis(a,t))}o[e].crossVectors(i[e],r[e])}if(!0===e){let e=Math.acos(s5t(r[0].dot(r[t]),-1,1));e/=t,i[0].dot(a.crossVectors(r[0],r[t]))>0&&(e=-e);for(let n=1;n<=t;n++)r[n].applyMatrix4(s.makeRotationAxis(i[n],e*n)),o[n].crossVectors(i[n],r[n])}return{tangents:i,normals:r,binormals:o}}clone(){return(new this.constructor).copy(this)}copy(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}toJSON(){const t={metadata:{version:4.5,type:"Curve",generator:"Curve.toJSON"}};return t.arcLengthDivisions=this.arcLengthDivisions,t.type=this.type,t}fromJSON(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}}class Cte extends Tte{constructor(t=0,e=0,n=1,i=1,r=0,o=2*Math.PI,a=!1,s=0){super(),this.type="EllipseCurve",this.aX=t,this.aY=e,this.xRadius=n,this.yRadius=i,this.aStartAngle=r,this.aEndAngle=o,this.aClockwise=a,this.aRotation=s}getPoint(t,e){const n=e||new C5t,i=2*Math.PI;let r=this.aEndAngle-this.aStartAngle;const o=Math.abs(r)<Number.EPSILON;for(;r<0;)r+=i;for(;r>i;)r-=i;r<Number.EPSILON&&(r=o?0:i),!0!==this.aClockwise||o||(r===i?r=-i:r-=i);const a=this.aStartAngle+t*r;let s=this.aX+this.xRadius*Math.cos(a),l=this.aY+this.yRadius*Math.sin(a);if(0!==this.aRotation){const t=Math.cos(this.aRotation),e=Math.sin(this.aRotation),n=s-this.aX,i=l-this.aY;s=n*t-i*e+this.aX,l=n*e+i*t+this.aY}return n.set(s,l)}copy(t){return super.copy(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}toJSON(){const t=super.toJSON();return t.aX=this.aX,t.aY=this.aY,t.xRadius=this.xRadius,t.yRadius=this.yRadius,t.aStartAngle=this.aStartAngle,t.aEndAngle=this.aEndAngle,t.aClockwise=this.aClockwise,t.aRotation=this.aRotation,t}fromJSON(t){return super.fromJSON(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}}Cte.prototype.isEllipseCurve=!0;class Ate extends Cte{constructor(t,e,n,i,r,o){super(t,e,n,n,i,r,o),this.type="ArcCurve"}}function kte(){let t=0,e=0,n=0,i=0;function r(r,o,a,s){t=r,e=a,n=-3*r+3*o-2*a-s,i=2*r-2*o+a+s}return{initCatmullRom:function(t,e,n,i,o){r(e,n,o*(n-t),o*(i-e))},initNonuniformCatmullRom:function(t,e,n,i,o,a,s){let l=(e-t)/o-(n-t)/(o+a)+(n-e)/a,c=(n-e)/a-(i-e)/(a+s)+(i-n)/s;l*=a,c*=a,r(e,n,l,c)},calc:function(r){const o=r*r;return t+e*r+n*o+i*(o*r)}}}Ate.prototype.isArcCurve=!0;const Lte=new H5t,Pte=new kte,Nte=new kte,Ite=new kte;class Rte extends Tte{constructor(t=[],e=!1,n="centripetal",i=.5){super(),this.type="CatmullRomCurve3",this.points=t,this.closed=e,this.curveType=n,this.tension=i}getPoint(t,e=new H5t){const n=e,i=this.points,r=i.length,o=(r-(this.closed?0:1))*t;let a,s,l=Math.floor(o),c=o-l;this.closed?l+=l>0?0:(Math.floor(Math.abs(l)/r)+1)*r:0===c&&l===r-1&&(l=r-2,c=1),this.closed||l>0?a=i[(l-1)%r]:(Lte.subVectors(i[0],i[1]).add(i[0]),a=Lte);const u=i[l%r],h=i[(l+1)%r];if(this.closed||l+2<r?s=i[(l+2)%r]:(Lte.subVectors(i[r-1],i[r-2]).add(i[r-1]),s=Lte),"centripetal"===this.curveType||"chordal"===this.curveType){const t="chordal"===this.curveType?.5:.25;let e=Math.pow(a.distanceToSquared(u),t),n=Math.pow(u.distanceToSquared(h),t),i=Math.pow(h.distanceToSquared(s),t);n<1e-4&&(n=1),e<1e-4&&(e=n),i<1e-4&&(i=n),Pte.initNonuniformCatmullRom(a.x,u.x,h.x,s.x,e,n,i),Nte.initNonuniformCatmullRom(a.y,u.y,h.y,s.y,e,n,i),Ite.initNonuniformCatmullRom(a.z,u.z,h.z,s.z,e,n,i)}else"catmullrom"===this.curveType&&(Pte.initCatmullRom(a.x,u.x,h.x,s.x,this.tension),Nte.initCatmullRom(a.y,u.y,h.y,s.y,this.tension),Ite.initCatmullRom(a.z,u.z,h.z,s.z,this.tension));return n.set(Pte.calc(c),Nte.calc(c),Ite.calc(c)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){this.points.push(t.points[e].clone())}return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++){t.points.push(this.points[e].toArray())}return t.closed=this.closed,t.curveType=this.curveType,t.tension=this.tension,t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new H5t).fromArray(n))}return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}}function Ote(t,e,n,i,r){const o=.5*(i-e),a=.5*(r-n),s=t*t;return(2*n-2*i+o+a)*(t*s)+(-3*n+3*i-2*o-a)*s+o*t+n}function zte(t,e,n,i){return(function r(t,e){const n=1-t;return n*n*e})(t,e)+(function o(t,e){return 2*(1-t)*t*e})(t,n)+(function a(t,e){return t*t*e})(t,i)}function Dte(t,e,n,i,r){return(function o(t,e){const n=1-t;return n*n*n*e})(t,e)+(function a(t,e){const n=1-t;return 3*n*n*t*e})(t,n)+(function s(t,e){return 3*(1-t)*t*t*e})(t,i)+(function l(t,e){return t*t*t*e})(t,r)}Rte.prototype.isCatmullRomCurve3=!0;class Bte extends Tte{constructor(t=new C5t,e=new C5t,n=new C5t,i=new C5t){super(),this.type="CubicBezierCurve",this.v0=t,this.v1=e,this.v2=n,this.v3=i}getPoint(t,e=new C5t){const n=e,i=this.v0,r=this.v1,o=this.v2,a=this.v3;return n.set(Dte(t,i.x,r.x,o.x,a.x),Dte(t,i.y,r.y,o.y,a.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}Bte.prototype.isCubicBezierCurve=!0;class Hte extends Tte{constructor(t=new H5t,e=new H5t,n=new H5t,i=new H5t){super(),this.type="CubicBezierCurve3",this.v0=t,this.v1=e,this.v2=n,this.v3=i}getPoint(t,e=new H5t){const n=e,i=this.v0,r=this.v1,o=this.v2,a=this.v3;return n.set(Dte(t,i.x,r.x,o.x,a.x),Dte(t,i.y,r.y,o.y,a.y),Dte(t,i.z,r.z,o.z,a.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}Hte.prototype.isCubicBezierCurve3=!0;class Fte extends Tte{constructor(t=new C5t,e=new C5t){super(),this.type="LineCurve",this.v1=t,this.v2=e}getPoint(t,e=new C5t){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}getTangent(t,e){const n=e||new C5t;return n.copy(this.v2).sub(this.v1).normalize(),n}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}Fte.prototype.isLineCurve=!0;class Vte extends Tte{constructor(t=new H5t,e=new H5t){super(),this.type="LineCurve3",this.isLineCurve3=!0,this.v1=t,this.v2=e}getPoint(t,e=new H5t){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}class Ute extends Tte{constructor(t=new C5t,e=new C5t,n=new C5t){super(),this.type="QuadraticBezierCurve",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new C5t){const n=e,i=this.v0,r=this.v1,o=this.v2;return n.set(zte(t,i.x,r.x,o.x),zte(t,i.y,r.y,o.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}Ute.prototype.isQuadraticBezierCurve=!0;class jte extends Tte{constructor(t=new H5t,e=new H5t,n=new H5t){super(),this.type="QuadraticBezierCurve3",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new H5t){const n=e,i=this.v0,r=this.v1,o=this.v2;return n.set(zte(t,i.x,r.x,o.x),zte(t,i.y,r.y,o.y),zte(t,i.z,r.z,o.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}jte.prototype.isQuadraticBezierCurve3=!0;class Gte extends Tte{constructor(t=[]){super(),this.type="SplineCurve",this.points=t}getPoint(t,e=new C5t){const n=e,i=this.points,r=(i.length-1)*t,o=Math.floor(r),a=r-o,s=i[0===o?o:o-1],l=i[o],c=i[o>i.length-2?i.length-1:o+1],u=i[o>i.length-3?i.length-1:o+2];return n.set(Ote(a,s.x,l.x,c.x,u.x),Ote(a,s.y,l.y,c.y,u.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){this.points.push(t.points[e].clone())}return this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++){t.points.push(this.points[e].toArray())}return t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new C5t).fromArray(n))}return this}}Gte.prototype.isSplineCurve=!0;var Wte=Object.freeze({__proto__:null,ArcCurve:Ate,CatmullRomCurve3:Rte,CubicBezierCurve:Bte,CubicBezierCurve3:Hte,EllipseCurve:Cte,LineCurve:Fte,LineCurve3:Vte,QuadraticBezierCurve:Ute,QuadraticBezierCurve3:jte,SplineCurve:Gte});const qte=function(t,e,n=2){const i=e&&e.length,r=i?e[0]*n:t.length;let o=Yte(t,0,r,n,!0);const a=[];if(!o||o.next===o.prev)return a;let s,l,c,u,h,d,p;if(i&&(o=(function f(t,e,n,i){const r=[];let o,a,s,l,c;for(o=0,a=e.length;o<a;o++)s=e[o]*i,l=o<a-1?e[o+1]*i:t.length,c=Yte(t,s,l,i,!1),c===c.next&&(c.steiner=!0),r.push(ree(c));for(r.sort(tee),o=0;o<r.length;o++)eee(r[o],n),n=Xte(n,n.next);return n})(t,e,o,n)),t.length>80*n){s=c=t[0],l=u=t[1];for(let e=n;e<r;e+=n)h=t[e],d=t[e+1],h<s&&(s=h),d<l&&(l=d),h>c&&(c=h),d>u&&(u=d);p=Math.max(c-s,u-l),p=0!==p?1/p:0}return $te(o,a,n,s,l,p),a};function Yte(t,e,n,i,r){let o,a;if(r===(function s(t,e,n,i){let r=0;for(let o=e,a=n-i;o<n;o+=i)r+=(t[a]-t[o])*(t[o+1]+t[a+1]),a=o;return r})(t,e,n,i)>0)for(o=e;o<n;o+=i)a=fee(o,t[o],t[o+1],a);else for(o=n-i;o>=e;o-=i)a=fee(o,t[o],t[o+1],a);return a&&lee(a,a.next)&&(mee(a),a=a.next),a}function Xte(t,e){if(!t)return t;e||(e=t);let n,i=t;do{if(n=!1,i.steiner||!lee(i,i.next)&&0!==see(i.prev,i,i.next))i=i.next;else{if(mee(i),i=e=i.prev,i===i.next)break;n=!0}}while(n||i!==e);return e}function $te(t,e,n,i,r,o,a){if(!t)return;!a&&o&&(function s(t,e,n,i){let r=t;do{null===r.z&&(r.z=iee(r.x,r.y,e,n,i)),r.prevZ=r.prev,r.nextZ=r.next,r=r.next}while(r!==t);r.prevZ.nextZ=null,r.prevZ=null,(function o(t){let e,n,i,r,o,a,s,l,c=1;do{for(n=t,t=null,o=null,a=0;n;){for(a++,i=n,s=0,e=0;e<c&&(s++,i=i.nextZ,i);e++);for(l=c;s>0||l>0&&i;)0!==s&&(0===l||!i||n.z<=i.z)?(r=n,n=n.nextZ,s--):(r=i,i=i.nextZ,l--),o?o.nextZ=r:t=r,r.prevZ=o,o=r;n=i}o.nextZ=null,c*=2}while(a>1);return t})(r)})(t,i,r,o);let l,c,u=t;for(;t.prev!==t.next;)if(l=t.prev,c=t.next,o?Zte(t,i,r,o):Kte(t))e.push(l.i/n),e.push(t.i/n),e.push(c.i/n),mee(t),t=c.next,u=c.next;else if((t=c)===u){a?1===a?$te(t=Jte(Xte(t),e,n),e,n,i,r,o,2):2===a&&Qte(t,e,n,i,r,o):$te(Xte(t),e,n,i,r,o,1);break}}function Kte(t){const e=t.prev,n=t,i=t.next;if(see(e,n,i)>=0)return!1;let r=t.next.next;for(;r!==t.prev;){if(oee(e.x,e.y,n.x,n.y,i.x,i.y,r.x,r.y)&&see(r.prev,r,r.next)>=0)return!1;r=r.next}return!0}function Zte(t,e,n,i){const r=t.prev,o=t,a=t.next;if(see(r,o,a)>=0)return!1;const s=r.x>o.x?r.x>a.x?r.x:a.x:o.x>a.x?o.x:a.x,l=r.y>o.y?r.y>a.y?r.y:a.y:o.y>a.y?o.y:a.y,c=iee(r.x<o.x?r.x<a.x?r.x:a.x:o.x<a.x?o.x:a.x,r.y<o.y?r.y<a.y?r.y:a.y:o.y<a.y?o.y:a.y,e,n,i),u=iee(s,l,e,n,i);let h=t.prevZ,d=t.nextZ;for(;h&&h.z>=c&&d&&d.z<=u;){if(h!==t.prev&&h!==t.next&&oee(r.x,r.y,o.x,o.y,a.x,a.y,h.x,h.y)&&see(h.prev,h,h.next)>=0)return!1;if(h=h.prevZ,d!==t.prev&&d!==t.next&&oee(r.x,r.y,o.x,o.y,a.x,a.y,d.x,d.y)&&see(d.prev,d,d.next)>=0)return!1;d=d.nextZ}for(;h&&h.z>=c;){if(h!==t.prev&&h!==t.next&&oee(r.x,r.y,o.x,o.y,a.x,a.y,h.x,h.y)&&see(h.prev,h,h.next)>=0)return!1;h=h.prevZ}for(;d&&d.z<=u;){if(d!==t.prev&&d!==t.next&&oee(r.x,r.y,o.x,o.y,a.x,a.y,d.x,d.y)&&see(d.prev,d,d.next)>=0)return!1;d=d.nextZ}return!0}function Jte(t,e,n){let i=t;do{const r=i.prev,o=i.next.next;!lee(r,o)&&cee(r,i,i.next,o)&&dee(r,o)&&dee(o,r)&&(e.push(r.i/n),e.push(i.i/n),e.push(o.i/n),mee(i),mee(i.next),i=t=o),i=i.next}while(i!==t);return Xte(i)}function Qte(t,e,n,i,r,o){let a=t;do{let t=a.next.next;for(;t!==a.prev;){if(a.i!==t.i&&aee(a,t)){let s=pee(a,t);return a=Xte(a,a.next),s=Xte(s,s.next),$te(a,e,n,i,r,o),void $te(s,e,n,i,r,o)}t=t.next}a=a.next}while(a!==t)}function tee(t,e){return t.x-e.x}function eee(t,e){if(e=(function n(t,e){let n=e;const i=t.x,r=t.y;let o,a=-1/0;do{if(r<=n.y&&r>=n.next.y&&n.next.y!==n.y){const t=n.x+(r-n.y)*(n.next.x-n.x)/(n.next.y-n.y);if(t<=i&&t>a){if(a=t,t===i){if(r===n.y)return n;if(r===n.next.y)return n.next}o=n.x<n.next.x?n:n.next}}n=n.next}while(n!==e);if(!o)return null;if(i===a)return o;const s=o,l=o.x,c=o.y;let u,h=1/0;n=o;do{i>=n.x&&n.x>=l&&i!==n.x&&oee(r<c?i:a,r,l,c,r<c?a:i,r,n.x,n.y)&&(u=Math.abs(r-n.y)/(i-n.x),dee(n,t)&&(u<h||u===h&&(n.x>o.x||n.x===o.x&&nee(o,n)))&&(o=n,h=u)),n=n.next}while(n!==s);return o})(t,e)){const n=pee(e,t);Xte(e,e.next),Xte(n,n.next)}}function nee(t,e){return see(t.prev,t,e.prev)<0&&see(e.next,t,t.next)<0}function iee(t,e,n,i,r){return(t=1431655765&((t=858993459&((t=252645135&((t=16711935&((t=32767*(t-n)*r)|t<<8))|t<<4))|t<<2))|t<<1))|(e=1431655765&((e=858993459&((e=252645135&((e=16711935&((e=32767*(e-i)*r)|e<<8))|e<<4))|e<<2))|e<<1))<<1}function ree(t){let e=t,n=t;do{(e.x<n.x||e.x===n.x&&e.y<n.y)&&(n=e),e=e.next}while(e!==t);return n}function oee(t,e,n,i,r,o,a,s){return(r-a)*(e-s)-(t-a)*(o-s)>=0&&(t-a)*(i-s)-(n-a)*(e-s)>=0&&(n-a)*(o-s)-(r-a)*(i-s)>=0}function aee(t,e){return t.next.i!==e.i&&t.prev.i!==e.i&&!(function n(t,e){let n=t;do{if(n.i!==t.i&&n.next.i!==t.i&&n.i!==e.i&&n.next.i!==e.i&&cee(n,n.next,t,e))return!0;n=n.next}while(n!==t);return!1})(t,e)&&(dee(t,e)&&dee(e,t)&&(function i(t,e){let n=t,i=!1;const r=(t.x+e.x)/2,o=(t.y+e.y)/2;do{n.y>o!=n.next.y>o&&n.next.y!==n.y&&r<(n.next.x-n.x)*(o-n.y)/(n.next.y-n.y)+n.x&&(i=!i),n=n.next}while(n!==t);return i})(t,e)&&(see(t.prev,t,e.prev)||see(t,e.prev,e))||lee(t,e)&&see(t.prev,t,t.next)>0&&see(e.prev,e,e.next)>0)}function see(t,e,n){return(e.y-t.y)*(n.x-e.x)-(e.x-t.x)*(n.y-e.y)}function lee(t,e){return t.x===e.x&&t.y===e.y}function cee(t,e,n,i){const r=hee(see(t,e,n)),o=hee(see(t,e,i)),a=hee(see(n,i,t)),s=hee(see(n,i,e));return r!==o&&a!==s||(!(0!==r||!uee(t,n,e))||(!(0!==o||!uee(t,i,e))||(!(0!==a||!uee(n,t,i))||!(0!==s||!uee(n,e,i)))))}function uee(t,e,n){return e.x<=Math.max(t.x,n.x)&&e.x>=Math.min(t.x,n.x)&&e.y<=Math.max(t.y,n.y)&&e.y>=Math.min(t.y,n.y)}function hee(t){return t>0?1:t<0?-1:0}function dee(t,e){return see(t.prev,t,t.next)<0?see(t,e,t.next)>=0&&see(t,t.prev,e)>=0:see(t,e,t.prev)<0||see(t,t.next,e)<0}function pee(t,e){const n=new gee(t.i,t.x,t.y),i=new gee(e.i,e.x,e.y),r=t.next,o=e.prev;return t.next=e,e.prev=t,n.next=r,r.prev=n,i.next=n,n.prev=i,o.next=i,i.prev=o,i}function fee(t,e,n,i){const r=new gee(t,e,n);return i?(r.next=i.next,r.prev=i,i.next.prev=r,i.next=r):(r.prev=r,r.next=r),r}function mee(t){t.next.prev=t.prev,t.prev.next=t.next,t.prevZ&&(t.prevZ.nextZ=t.nextZ),t.nextZ&&(t.nextZ.prevZ=t.prevZ)}function gee(t,e,n){this.i=t,this.x=e,this.y=n,this.prev=null,this.next=null,this.z=null,this.prevZ=null,this.nextZ=null,this.steiner=!1}class _ee{static area(t){const e=t.length;let n=0;for(let i=e-1,r=0;r<e;i=r++)n+=t[i].x*t[r].y-t[r].x*t[i].y;return.5*n}static isClockWise(t){return _ee.area(t)<0}static triangulateShape(t,e){const n=[],i=[],r=[];yee(t),vee(n,t);let o=t.length;e.forEach(yee);for(let t=0;t<e.length;t++)i.push(o),o+=e[t].length,vee(n,e[t]);const a=qte(n,i);for(let t=0;t<a.length;t+=3)r.push(a.slice(t,t+3));return r}}function yee(t){const e=t.length;e>2&&t[e-1].equals(t[0])&&t.pop()}function vee(t,e){for(let n=0;n<e.length;n++)t.push(e[n].x),t.push(e[n].y)}class bee extends N4t{constructor(t,e){super(),this.type="ExtrudeGeometry",this.parameters={shapes:t,options:e},t=Array.isArray(t)?t:[t];const n=this,i=[],r=[];for(let e=0,n=t.length;e<n;e++){o(t[e])}function o(t){const o=[],a=void 0!==e.curveSegments?e.curveSegments:12,s=void 0!==e.steps?e.steps:1;let l=void 0!==e.depth?e.depth:100,c=void 0===e.bevelEnabled||e.bevelEnabled,u=void 0!==e.bevelThickness?e.bevelThickness:6,h=void 0!==e.bevelSize?e.bevelSize:u-2,d=void 0!==e.bevelOffset?e.bevelOffset:0,p=void 0!==e.bevelSegments?e.bevelSegments:3;const f=e.extrudePath,m=void 0!==e.UVGenerator?e.UVGenerator:xee;void 0!==e.amount&&(console.warn("THREE.ExtrudeBufferGeometry: amount has been renamed to depth."),l=e.amount);let g,_,y,v,b,x=!1;f&&(g=f.getSpacedPoints(s),x=!0,c=!1,_=f.computeFrenetFrames(s,!1),y=new H5t,v=new H5t,b=new H5t),c||(p=0,u=0,h=0,d=0);const w=t.extractPoints(a);let S=w.shape;const M=w.holes;if(!_ee.isClockWise(S)){S=S.reverse();for(let t=0,e=M.length;t<e;t++){const e=M[t];_ee.isClockWise(e)&&(M[t]=e.reverse())}}const E=_ee.triangulateShape(S,M),T=S;for(let t=0,e=M.length;t<e;t++){S=S.concat(M[t])}function C(t,e,n){return e||console.error("THREE.ExtrudeGeometry: vec does not exist"),e.clone().multiplyScalar(n).add(t)}const A=S.length,k=E.length;function L(t,e,n){let i,r,o;const a=t.x-e.x,s=t.y-e.y,l=n.x-t.x,c=n.y-t.y,u=a*a+s*s;if(Math.abs(a*c-s*l)>Number.EPSILON){const h=Math.sqrt(u),d=Math.sqrt(l*l+c*c),p=e.x-s/h,f=e.y+a/h,m=((n.x-c/d-p)*c-(n.y+l/d-f)*l)/(a*c-s*l);i=p+a*m-t.x,r=f+s*m-t.y;const g=i*i+r*r;if(g<=2)return new C5t(i,r);o=Math.sqrt(g/2)}else{let t=!1;a>Number.EPSILON?l>Number.EPSILON&&(t=!0):a<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(s)===Math.sign(c)&&(t=!0),t?(i=-s,r=a,o=Math.sqrt(u)):(i=a,r=s,o=Math.sqrt(u/2))}return new C5t(i/o,r/o)}const P=[];for(let t=0,e=T.length,n=e-1,i=t+1;t<e;t++,n++,i++)n===e&&(n=0),i===e&&(i=0),P[t]=L(T[t],T[n],T[i]);const N=[];let I,R=P.concat();for(let t=0,e=M.length;t<e;t++){const e=M[t];I=[];for(let t=0,n=e.length,i=n-1,r=t+1;t<n;t++,i++,r++)i===n&&(i=0),r===n&&(r=0),I[t]=L(e[t],e[i],e[r]);N.push(I),R=R.concat(I)}for(let t=0;t<p;t++){const e=t/p,n=u*Math.cos(e*Math.PI/2),i=h*Math.sin(e*Math.PI/2)+d;for(let t=0,e=T.length;t<e;t++){const e=C(T[t],P[t],i);D(e.x,e.y,-n)}for(let t=0,e=M.length;t<e;t++){const e=M[t];I=N[t];for(let t=0,r=e.length;t<r;t++){const r=C(e[t],I[t],i);D(r.x,r.y,-n)}}}const O=h+d;for(let t=0;t<A;t++){const e=c?C(S[t],R[t],O):S[t];x?(v.copy(_.normals[0]).multiplyScalar(e.x),y.copy(_.binormals[0]).multiplyScalar(e.y),b.copy(g[0]).add(v).add(y),D(b.x,b.y,b.z)):D(e.x,e.y,0)}for(let t=1;t<=s;t++)for(let e=0;e<A;e++){const n=c?C(S[e],R[e],O):S[e];x?(v.copy(_.normals[t]).multiplyScalar(n.x),y.copy(_.binormals[t]).multiplyScalar(n.y),b.copy(g[t]).add(v).add(y),D(b.x,b.y,b.z)):D(n.x,n.y,l/s*t)}for(let t=p-1;t>=0;t--){const e=t/p,n=u*Math.cos(e*Math.PI/2),i=h*Math.sin(e*Math.PI/2)+d;for(let t=0,e=T.length;t<e;t++){const e=C(T[t],P[t],i);D(e.x,e.y,l+n)}for(let t=0,e=M.length;t<e;t++){const e=M[t];I=N[t];for(let t=0,r=e.length;t<r;t++){const r=C(e[t],I[t],i);x?D(r.x,r.y+g[s-1].y,g[s-1].x+n):D(r.x,r.y,l+n)}}}function z(t,e){let n=t.length;for(;--n>=0;){const i=n;let r=n-1;r<0&&(r=t.length-1);for(let t=0,n=s+2*p;t<n;t++){const n=A*t,o=A*(t+1);H(e+i+n,e+r+n,e+r+o,e+i+o)}}}function D(t,e,n){o.push(t),o.push(e),o.push(n)}function B(t,e,r){F(t),F(e),F(r);const o=i.length/3,a=m.generateTopUV(n,i,o-3,o-2,o-1);V(a[0]),V(a[1]),V(a[2])}function H(t,e,r,o){F(t),F(e),F(o),F(e),F(r),F(o);const a=i.length/3,s=m.generateSideWallUV(n,i,a-6,a-3,a-2,a-1);V(s[0]),V(s[1]),V(s[3]),V(s[1]),V(s[2]),V(s[3])}function F(t){i.push(o[3*t+0]),i.push(o[3*t+1]),i.push(o[3*t+2])}function V(t){r.push(t.x),r.push(t.y)}!(function U(){const t=i.length/3;if(c){let t=0,e=A*t;for(let t=0;t<k;t++){const n=E[t];B(n[2]+e,n[1]+e,n[0]+e)}t=s+2*p,e=A*t;for(let t=0;t<k;t++){const n=E[t];B(n[0]+e,n[1]+e,n[2]+e)}}else{for(let t=0;t<k;t++){const e=E[t];B(e[2],e[1],e[0])}for(let t=0;t<k;t++){const e=E[t];B(e[0]+A*s,e[1]+A*s,e[2]+A*s)}}n.addGroup(t,i.length/3-t,0)})(),(function j(){const t=i.length/3;let e=0;z(T,e),e+=T.length;for(let t=0,n=M.length;t<n;t++){const n=M[t];z(n,e),e+=n.length}n.addGroup(t,i.length/3-t,1)})()}this.setAttribute("position",new b4t(i,3)),this.setAttribute("uv",new b4t(r,2)),this.computeVertexNormals()}toJSON(){const t=super.toJSON();return(function e(t,n,i){if(i.shapes=[],Array.isArray(t))for(let e=0,n=t.length;e<n;e++){i.shapes.push(t[e].uuid)}else i.shapes.push(t.uuid);void 0!==n.extrudePath&&(i.options.extrudePath=n.extrudePath.toJSON());return i})(this.parameters.shapes,this.parameters.options,t)}static fromJSON(t,e){const n=[];for(let i=0,r=t.shapes.length;i<r;i++){n.push(e[t.shapes[i]])}const i=t.options.extrudePath;return void 0!==i&&(t.options.extrudePath=(new Wte[i.type]).fromJSON(i)),new bee(n,t.options)}}const xee={generateTopUV:function(t,e,n,i,r){const o=e[3*i],a=e[3*i+1],s=e[3*r],l=e[3*r+1];return[new C5t(e[3*n],e[3*n+1]),new C5t(o,a),new C5t(s,l)]},generateSideWallUV:function(t,e,n,i,r,o){const a=e[3*n],s=e[3*n+1],l=e[3*n+2],c=e[3*i],u=e[3*i+1],h=e[3*i+2],d=e[3*r],p=e[3*r+1],f=e[3*r+2],m=e[3*o],g=e[3*o+1],_=e[3*o+2];return Math.abs(s-u)<Math.abs(a-c)?[new C5t(a,1-l),new C5t(c,1-h),new C5t(d,1-f),new C5t(m,1-_)]:[new C5t(s,1-l),new C5t(u,1-h),new C5t(p,1-f),new C5t(g,1-_)]}};class wee extends vte{constructor(t=1,e=0){const n=(1+Math.sqrt(5))/2;super([-1,n,0,1,n,0,-1,-n,0,1,-n,0,0,-1,n,0,1,n,0,-1,-n,0,1,-n,n,0,-1,n,0,1,-n,0,-1,-n,0,1],[0,11,5,0,5,1,0,1,7,0,7,10,0,10,11,1,5,9,5,11,4,11,10,2,10,7,6,7,1,8,3,9,4,3,4,2,3,2,6,3,6,8,3,8,9,4,9,5,2,4,11,6,2,10,8,6,7,9,8,1],t,e),this.type="IcosahedronGeometry",this.parameters={radius:t,detail:e}}static fromJSON(t){return new wee(t.radius,t.detail)}}class See extends N4t{constructor(t,e=12,n=0,i=2*Math.PI){super(),this.type="LatheGeometry",this.parameters={points:t,segments:e,phiStart:n,phiLength:i},e=Math.floor(e),i=s5t(i,0,2*Math.PI);const r=[],o=[],a=[],s=1/e,l=new H5t,c=new C5t;for(let r=0;r<=e;r++){const u=n+r*s*i,h=Math.sin(u),d=Math.cos(u);for(let n=0;n<=t.length-1;n++)l.x=t[n].x*h,l.y=t[n].y,l.z=t[n].x*d,o.push(l.x,l.y,l.z),c.x=r/e,c.y=n/(t.length-1),a.push(c.x,c.y)}for(let n=0;n<e;n++)for(let e=0;e<t.length-1;e++){const i=e+n*t.length,o=i+t.length,a=i+t.length+1,s=i+1;r.push(i,o,s),r.push(o,a,s)}if(this.setIndex(r),this.setAttribute("position",new b4t(o,3)),this.setAttribute("uv",new b4t(a,2)),this.computeVertexNormals(),i===2*Math.PI){const n=this.attributes.normal.array,i=new H5t,r=new H5t,o=new H5t,a=e*t.length*3;for(let e=0,s=0;e<t.length;e++,s+=3)i.x=n[s+0],i.y=n[s+1],i.z=n[s+2],r.x=n[a+s+0],r.y=n[a+s+1],r.z=n[a+s+2],o.addVectors(i,r).normalize(),n[s+0]=n[a+s+0]=o.x,n[s+1]=n[a+s+1]=o.y,n[s+2]=n[a+s+2]=o.z}}static fromJSON(t){return new See(t.points,t.segments,t.phiStart,t.phiLength)}}class Mee extends vte{constructor(t=1,e=0){super([1,0,0,-1,0,0,0,1,0,0,-1,0,0,0,1,0,0,-1],[0,2,4,0,4,3,0,3,5,0,5,2,1,2,5,1,5,3,1,3,4,1,4,2],t,e),this.type="OctahedronGeometry",this.parameters={radius:t,detail:e}}static fromJSON(t){return new Mee(t.radius,t.detail)}}class Eee extends N4t{constructor(t,e,n){super(),this.type="ParametricGeometry",this.parameters={func:t,slices:e,stacks:n};const i=[],r=[],o=[],a=[],s=1e-5,l=new H5t,c=new H5t,u=new H5t,h=new H5t,d=new H5t;t.length<3&&console.error("THREE.ParametricGeometry: Function must now modify a Vector3 as third parameter.");const p=e+1;for(let i=0;i<=n;i++){const p=i/n;for(let n=0;n<=e;n++){const i=n/e;t(i,p,c),r.push(c.x,c.y,c.z),i-s>=0?(t(i-s,p,u),h.subVectors(c,u)):(t(i+s,p,u),h.subVectors(u,c)),p-s>=0?(t(i,p-s,u),d.subVectors(c,u)):(t(i,p+s,u),d.subVectors(u,c)),l.crossVectors(h,d).normalize(),o.push(l.x,l.y,l.z),a.push(i,p)}}for(let t=0;t<n;t++)for(let n=0;n<e;n++){const e=t*p+n+1,r=(t+1)*p+n+1,o=(t+1)*p+n;i.push(t*p+n,e,o),i.push(e,r,o)}this.setIndex(i),this.setAttribute("position",new b4t(r,3)),this.setAttribute("normal",new b4t(o,3)),this.setAttribute("uv",new b4t(a,2))}}class Tee extends N4t{constructor(t=.5,e=1,n=8,i=1,r=0,o=2*Math.PI){super(),this.type="RingGeometry",this.parameters={innerRadius:t,outerRadius:e,thetaSegments:n,phiSegments:i,thetaStart:r,thetaLength:o},n=Math.max(3,n);const a=[],s=[],l=[],c=[];let u=t;const h=(e-t)/(i=Math.max(1,i)),d=new H5t,p=new C5t;for(let t=0;t<=i;t++){for(let t=0;t<=n;t++){const i=r+t/n*o;d.x=u*Math.cos(i),d.y=u*Math.sin(i),s.push(d.x,d.y,d.z),l.push(0,0,1),p.x=(d.x/e+1)/2,p.y=(d.y/e+1)/2,c.push(p.x,p.y)}u+=h}for(let t=0;t<i;t++){const e=t*(n+1);for(let t=0;t<n;t++){const i=t+e,r=i+n+1,o=i+n+2,s=i+1;a.push(i,r,s),a.push(r,o,s)}}this.setIndex(a),this.setAttribute("position",new b4t(s,3)),this.setAttribute("normal",new b4t(l,3)),this.setAttribute("uv",new b4t(c,2))}static fromJSON(t){return new Tee(t.innerRadius,t.outerRadius,t.thetaSegments,t.phiSegments,t.thetaStart,t.thetaLength)}}class Cee extends N4t{constructor(t,e=12){super(),this.type="ShapeGeometry",this.parameters={shapes:t,curveSegments:e};const n=[],i=[],r=[],o=[];let a=0,s=0;if(!1===Array.isArray(t))l(t);else for(let e=0;e<t.length;e++)l(t[e]),this.addGroup(a,s,e),a+=s,s=0;function l(t){const a=i.length/3,l=t.extractPoints(e);let c=l.shape;const u=l.holes;!1===_ee.isClockWise(c)&&(c=c.reverse());for(let t=0,e=u.length;t<e;t++){const e=u[t];!0===_ee.isClockWise(e)&&(u[t]=e.reverse())}const h=_ee.triangulateShape(c,u);for(let t=0,e=u.length;t<e;t++){c=c.concat(u[t])}for(let t=0,e=c.length;t<e;t++){const e=c[t];i.push(e.x,e.y,0),r.push(0,0,1),o.push(e.x,e.y)}for(let t=0,e=h.length;t<e;t++){const e=h[t];n.push(e[0]+a,e[1]+a,e[2]+a),s+=3}}this.setIndex(n),this.setAttribute("position",new b4t(i,3)),this.setAttribute("normal",new b4t(r,3)),this.setAttribute("uv",new b4t(o,2))}toJSON(){const t=super.toJSON();return(function e(t,n){if(n.shapes=[],Array.isArray(t))for(let e=0,i=t.length;e<i;e++){n.shapes.push(t[e].uuid)}else n.shapes.push(t.uuid);return n})(this.parameters.shapes,t)}static fromJSON(t,e){const n=[];for(let i=0,r=t.shapes.length;i<r;i++){n.push(e[t.shapes[i]])}return new Cee(n,t.curveSegments)}}class Aee extends N4t{constructor(t=1,e=32,n=16,i=0,r=2*Math.PI,o=0,a=Math.PI){super(),this.type="SphereGeometry",this.parameters={radius:t,widthSegments:e,heightSegments:n,phiStart:i,phiLength:r,thetaStart:o,thetaLength:a},e=Math.max(3,Math.floor(e)),n=Math.max(2,Math.floor(n));const s=Math.min(o+a,Math.PI);let l=0;const c=[],u=new H5t,h=new H5t,d=[],p=[],f=[],m=[];for(let d=0;d<=n;d++){const g=[],_=d/n;let y=0;0==d&&0==o?y=.5/e:d==n&&s==Math.PI&&(y=-.5/e);for(let n=0;n<=e;n++){const s=n/e;u.x=-t*Math.cos(i+s*r)*Math.sin(o+_*a),u.y=t*Math.cos(o+_*a),u.z=t*Math.sin(i+s*r)*Math.sin(o+_*a),p.push(u.x,u.y,u.z),h.copy(u).normalize(),f.push(h.x,h.y,h.z),m.push(s+y,1-_),g.push(l++)}c.push(g)}for(let t=0;t<n;t++)for(let i=0;i<e;i++){const e=c[t][i+1],r=c[t][i],a=c[t+1][i],l=c[t+1][i+1];(0!==t||o>0)&&d.push(e,r,l),(t!==n-1||s<Math.PI)&&d.push(r,a,l)}this.setIndex(d),this.setAttribute("position",new b4t(p,3)),this.setAttribute("normal",new b4t(f,3)),this.setAttribute("uv",new b4t(m,2))}static fromJSON(t){return new Aee(t.radius,t.widthSegments,t.heightSegments,t.phiStart,t.phiLength,t.thetaStart,t.thetaLength)}}class kee extends vte{constructor(t=1,e=0){super([1,1,1,-1,-1,1,-1,1,-1,1,-1,-1],[2,1,0,0,3,2,1,3,0,2,3,1],t,e),this.type="TetrahedronGeometry",this.parameters={radius:t,detail:e}}static fromJSON(t){return new kee(t.radius,t.detail)}}class Lee extends bee{constructor(t,e={}){const n=e.font;if(!n||!n.isFont)return console.error("THREE.TextGeometry: font parameter is not an instance of THREE.Font."),new N4t;const i=n.generateShapes(t,e.size);e.depth=void 0!==e.height?e.height:50,void 0===e.bevelThickness&&(e.bevelThickness=10),void 0===e.bevelSize&&(e.bevelSize=8),void 0===e.bevelEnabled&&(e.bevelEnabled=!1),super(i,e),this.type="TextGeometry"}}class Pee extends N4t{constructor(t=1,e=.4,n=8,i=6,r=2*Math.PI){super(),this.type="TorusGeometry",this.parameters={radius:t,tube:e,radialSegments:n,tubularSegments:i,arc:r},n=Math.floor(n),i=Math.floor(i);const o=[],a=[],s=[],l=[],c=new H5t,u=new H5t,h=new H5t;for(let o=0;o<=n;o++)for(let d=0;d<=i;d++){const p=d/i*r,f=o/n*Math.PI*2;u.x=(t+e*Math.cos(f))*Math.cos(p),u.y=(t+e*Math.cos(f))*Math.sin(p),u.z=e*Math.sin(f),a.push(u.x,u.y,u.z),c.x=t*Math.cos(p),c.y=t*Math.sin(p),h.subVectors(u,c).normalize(),s.push(h.x,h.y,h.z),l.push(d/i),l.push(o/n)}for(let t=1;t<=n;t++)for(let e=1;e<=i;e++){const n=(i+1)*(t-1)+e-1,r=(i+1)*(t-1)+e,a=(i+1)*t+e;o.push((i+1)*t+e-1,n,a),o.push(n,r,a)}this.setIndex(o),this.setAttribute("position",new b4t(a,3)),this.setAttribute("normal",new b4t(s,3)),this.setAttribute("uv",new b4t(l,2))}static fromJSON(t){return new Pee(t.radius,t.tube,t.radialSegments,t.tubularSegments,t.arc)}}class Nee extends N4t{constructor(t=1,e=.4,n=64,i=8,r=2,o=3){super(),this.type="TorusKnotGeometry",this.parameters={radius:t,tube:e,tubularSegments:n,radialSegments:i,p:r,q:o},n=Math.floor(n),i=Math.floor(i);const a=[],s=[],l=[],c=[],u=new H5t,h=new H5t,d=new H5t,p=new H5t,f=new H5t,m=new H5t,g=new H5t;for(let a=0;a<=n;++a){const y=a/n*r*Math.PI*2;_(y,r,o,t,d),_(y+.01,r,o,t,p),m.subVectors(p,d),g.addVectors(p,d),f.crossVectors(m,g),g.crossVectors(f,m),f.normalize(),g.normalize();for(let t=0;t<=i;++t){const r=t/i*Math.PI*2,o=-e*Math.cos(r),p=e*Math.sin(r);u.x=d.x+(o*g.x+p*f.x),u.y=d.y+(o*g.y+p*f.y),u.z=d.z+(o*g.z+p*f.z),s.push(u.x,u.y,u.z),h.subVectors(u,d).normalize(),l.push(h.x,h.y,h.z),c.push(a/n),c.push(t/i)}}for(let t=1;t<=n;t++)for(let e=1;e<=i;e++){const n=(i+1)*t+(e-1),r=(i+1)*t+e,o=(i+1)*(t-1)+e;a.push((i+1)*(t-1)+(e-1),n,o),a.push(n,r,o)}function _(t,e,n,i,r){const o=Math.cos(t),a=Math.sin(t),s=n/e*t,l=Math.cos(s);r.x=i*(2+l)*.5*o,r.y=i*(2+l)*a*.5,r.z=i*Math.sin(s)*.5}this.setIndex(a),this.setAttribute("position",new b4t(s,3)),this.setAttribute("normal",new b4t(l,3)),this.setAttribute("uv",new b4t(c,2))}static fromJSON(t){return new Nee(t.radius,t.tube,t.tubularSegments,t.radialSegments,t.p,t.q)}}class Iee extends N4t{constructor(t,e=64,n=1,i=8,r=!1){super(),this.type="TubeGeometry",this.parameters={path:t,tubularSegments:e,radius:n,radialSegments:i,closed:r};const o=t.computeFrenetFrames(e,r);this.tangents=o.tangents,this.normals=o.normals,this.binormals=o.binormals;const a=new H5t,s=new H5t,l=new C5t;let c=new H5t;const u=[],h=[],d=[],p=[];function f(r){c=t.getPointAt(r/e,c);const l=o.normals[r],d=o.binormals[r];for(let t=0;t<=i;t++){const e=t/i*Math.PI*2,r=Math.sin(e),o=-Math.cos(e);s.x=o*l.x+r*d.x,s.y=o*l.y+r*d.y,s.z=o*l.z+r*d.z,s.normalize(),h.push(s.x,s.y,s.z),a.x=c.x+n*s.x,a.y=c.y+n*s.y,a.z=c.z+n*s.z,u.push(a.x,a.y,a.z)}}!(function m(){for(let t=0;t<e;t++)f(t);f(!1===r?e:0),(function t(){for(let t=0;t<=e;t++)for(let n=0;n<=i;n++)l.x=t/e,l.y=n/i,d.push(l.x,l.y)})(),(function n(){for(let t=1;t<=e;t++)for(let e=1;e<=i;e++){const n=(i+1)*t+(e-1),r=(i+1)*t+e,o=(i+1)*(t-1)+e;p.push((i+1)*(t-1)+(e-1),n,o),p.push(n,r,o)}})()})(),this.setIndex(p),this.setAttribute("position",new b4t(u,3)),this.setAttribute("normal",new b4t(h,3)),this.setAttribute("uv",new b4t(d,2))}toJSON(){const t=super.toJSON();return t.path=this.parameters.path.toJSON(),t}static fromJSON(t){return new Iee((new Wte[t.path.type]).fromJSON(t.path),t.tubularSegments,t.radius,t.radialSegments,t.closed)}}class Ree extends N4t{constructor(t){if(super(),this.type="WireframeGeometry",!0===t.isGeometry)return void console.error("THREE.WireframeGeometry no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");const e=[],n=new Set,i=new H5t,r=new H5t;if(null!==t.index){const o=t.attributes.position,a=t.index;let s=t.groups;0===s.length&&(s=[{start:0,count:a.count,materialIndex:0}]);for(let t=0,l=s.length;t<l;++t){const l=s[t],c=l.start;for(let t=c,s=c+l.count;t<s;t+=3)for(let s=0;s<3;s++){const l=a.getX(t+s),c=a.getX(t+(s+1)%3);i.fromBufferAttribute(o,l),r.fromBufferAttribute(o,c),!0===Oee(i,r,n)&&(e.push(i.x,i.y,i.z),e.push(r.x,r.y,r.z))}}}else{const o=t.attributes.position;for(let t=0,a=o.count/3;t<a;t++)for(let a=0;a<3;a++){const s=3*t+(a+1)%3;i.fromBufferAttribute(o,3*t+a),r.fromBufferAttribute(o,s),!0===Oee(i,r,n)&&(e.push(i.x,i.y,i.z),e.push(r.x,r.y,r.z))}}this.setAttribute("position",new b4t(e,3))}}function Oee(t,e,n){const i=`${t.x},${t.y},${t.z}-${e.x},${e.y},${e.z}`,r=`${e.x},${e.y},${e.z}-${t.x},${t.y},${t.z}`;return!0!==n.has(i)&&!0!==n.has(r)&&(n.add(i,r),!0)}var zee=Object.freeze({__proto__:null,BoxGeometry:J4t,BoxBufferGeometry:J4t,CircleGeometry:gte,CircleBufferGeometry:gte,ConeGeometry:yte,ConeBufferGeometry:yte,CylinderGeometry:_te,CylinderBufferGeometry:_te,DodecahedronGeometry:bte,DodecahedronBufferGeometry:bte,EdgesGeometry:Ete,ExtrudeGeometry:bee,ExtrudeBufferGeometry:bee,IcosahedronGeometry:wee,IcosahedronBufferGeometry:wee,LatheGeometry:See,LatheBufferGeometry:See,OctahedronGeometry:Mee,OctahedronBufferGeometry:Mee,ParametricGeometry:Eee,ParametricBufferGeometry:Eee,PlaneGeometry:y6t,PlaneBufferGeometry:y6t,PolyhedronGeometry:vte,PolyhedronBufferGeometry:vte,RingGeometry:Tee,RingBufferGeometry:Tee,ShapeGeometry:Cee,ShapeBufferGeometry:Cee,SphereGeometry:Aee,SphereBufferGeometry:Aee,TetrahedronGeometry:kee,TetrahedronBufferGeometry:kee,TextGeometry:Lee,TextBufferGeometry:Lee,TorusGeometry:Pee,TorusBufferGeometry:Pee,TorusKnotGeometry:Nee,TorusKnotBufferGeometry:Nee,TubeGeometry:Iee,TubeBufferGeometry:Iee,WireframeGeometry:Ree});class Dee extends t4t{constructor(t){super(),this.type="ShadowMaterial",this.color=new s4t(0),this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this}}Dee.prototype.isShadowMaterial=!0;class Bee extends t4t{constructor(t){super(),this.defines={STANDARD:""},this.type="MeshStandardMaterial",this.color=new s4t(16777215),this.roughness=1,this.metalness=0,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new s4t(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new C5t(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.roughnessMap=null,this.metalnessMap=null,this.alphaMap=null,this.envMap=null,this.envMapIntensity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:""},this.color.copy(t.color),this.roughness=t.roughness,this.metalness=t.metalness,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.roughnessMap=t.roughnessMap,this.metalnessMap=t.metalnessMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.envMapIntensity=t.envMapIntensity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}Bee.prototype.isMeshStandardMaterial=!0;class Hee extends Bee{constructor(t){super(),this.defines={STANDARD:"",PHYSICAL:""},this.type="MeshPhysicalMaterial",this.clearcoat=0,this.clearcoatMap=null,this.clearcoatRoughness=0,this.clearcoatRoughnessMap=null,this.clearcoatNormalScale=new C5t(1,1),this.clearcoatNormalMap=null,this.reflectivity=.5,Object.defineProperty(this,"ior",{get:function(){return(1+.4*this.reflectivity)/(1-.4*this.reflectivity)},set:function(t){this.reflectivity=s5t(2.5*(t-1)/(t+1),0,1)}}),this.sheen=null,this.transmission=0,this.transmissionMap=null,this.thickness=.01,this.thicknessMap=null,this.attenuationDistance=0,this.attenuationTint=new s4t(1,1,1),this.specularIntensity=1,this.specularIntensityMap=null,this.specularTint=new s4t(1,1,1),this.specularTintMap=null,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:"",PHYSICAL:""},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.reflectivity=t.reflectivity,this.sheen=t.sheen?(this.sheen||new s4t).copy(t.sheen):null,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}Hee.prototype.isMeshPhysicalMaterial=!0;class Fee extends t4t{constructor(t){super(),this.type="MeshPhongMaterial",this.color=new s4t(16777215),this.specular=new s4t(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new s4t(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new C5t(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.specular.copy(t.specular),this.shininess=t.shininess,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}Fee.prototype.isMeshPhongMaterial=!0;class Vee extends t4t{constructor(t){super(),this.defines={TOON:""},this.type="MeshToonMaterial",this.color=new s4t(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new s4t(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new C5t(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.gradientMap=t.gradientMap,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}Vee.prototype.isMeshToonMaterial=!0;class Uee extends t4t{constructor(t){super(),this.type="MeshNormalMaterial",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new C5t(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.flatShading=t.flatShading,this}}Uee.prototype.isMeshNormalMaterial=!0;class jee extends t4t{constructor(t){super(),this.type="MeshLambertMaterial",this.color=new s4t(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new s4t(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}jee.prototype.isMeshLambertMaterial=!0;class Gee extends t4t{constructor(t){super(),this.defines={MATCAP:""},this.type="MeshMatcapMaterial",this.color=new s4t(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new C5t(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={MATCAP:""},this.color.copy(t.color),this.matcap=t.matcap,this.map=t.map,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.flatShading=t.flatShading,this}}Gee.prototype.isMeshMatcapMaterial=!0;class Wee extends X7t{constructor(t){super(),this.type="LineDashedMaterial",this.scale=1,this.dashSize=3,this.gapSize=1,this.setValues(t)}copy(t){return super.copy(t),this.scale=t.scale,this.dashSize=t.dashSize,this.gapSize=t.gapSize,this}}Wee.prototype.isLineDashedMaterial=!0;var qee=Object.freeze({__proto__:null,ShadowMaterial:Dee,SpriteMaterial:p7t,RawShaderMaterial:k6t,ShaderMaterial:n6t,PointsMaterial:ote,MeshPhysicalMaterial:Hee,MeshStandardMaterial:Bee,MeshPhongMaterial:Fee,MeshToonMaterial:Vee,MeshNormalMaterial:Uee,MeshLambertMaterial:jee,MeshDepthMaterial:Y8t,MeshDistanceMaterial:X8t,MeshBasicMaterial:l4t,MeshMatcapMaterial:Gee,LineDashedMaterial:Wee,LineBasicMaterial:X7t,Material:t4t});const Yee={arraySlice:function(t,e,n){return Yee.isTypedArray(t)?new t.constructor(t.subarray(e,void 0!==n?n:t.length)):t.slice(e,n)},convertArray:function(t,e,n){return!t||!n&&t.constructor===e?t:"number"==typeof e.BYTES_PER_ELEMENT?new e(t):Array.prototype.slice.call(t)},isTypedArray:function(t){return ArrayBuffer.isView(t)&&!(t instanceof DataView)},getKeyframeOrder:function(t){const e=t.length,n=new Array(e);for(let t=0;t!==e;++t)n[t]=t;return n.sort((function i(e,n){return t[e]-t[n]})),n},sortedArray:function(t,e,n){const i=t.length,r=new t.constructor(i);for(let o=0,a=0;a!==i;++o){const i=n[o]*e;for(let n=0;n!==e;++n)r[a++]=t[i+n]}return r},flattenJSON:function(t,e,n,i){let r=1,o=t[0];for(;void 0!==o&&void 0===o[i];)o=t[r++];if(void 0===o)return;let a=o[i];if(void 0!==a)if(Array.isArray(a))do{a=o[i],void 0!==a&&(e.push(o.time),n.push.apply(n,a)),o=t[r++]}while(void 0!==o);else if(void 0!==a.toArray)do{a=o[i],void 0!==a&&(e.push(o.time),a.toArray(n,n.length)),o=t[r++]}while(void 0!==o);else do{a=o[i],void 0!==a&&(e.push(o.time),n.push(a)),o=t[r++]}while(void 0!==o)},subclip:function(t,e,n,i,r=30){const o=t.clone();o.name=e;const a=[];for(let t=0;t<o.tracks.length;++t){const e=o.tracks[t],s=e.getValueSize(),l=[],c=[];for(let t=0;t<e.times.length;++t){const o=e.times[t]*r;if(!(o<n||o>=i)){l.push(e.times[t]);for(let n=0;n<s;++n)c.push(e.values[t*s+n])}}0!==l.length&&(e.times=Yee.convertArray(l,e.times.constructor),e.values=Yee.convertArray(c,e.values.constructor),a.push(e))}o.tracks=a;let s=1/0;for(let t=0;t<o.tracks.length;++t)s>o.tracks[t].times[0]&&(s=o.tracks[t].times[0]);for(let t=0;t<o.tracks.length;++t)o.tracks[t].shift(-1*s);return o.resetDuration(),o},makeClipAdditive:function(t,e=0,n=t,i=30){i<=0&&(i=30);const r=n.tracks.length,o=e/i;for(let e=0;e<r;++e){const i=n.tracks[e],r=i.ValueTypeName;if("bool"===r||"string"===r)continue;const a=t.tracks.find((function(t){return t.name===i.name&&t.ValueTypeName===r}));if(void 0===a)continue;let s=0;const l=i.getValueSize();i.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(s=l/3);let c=0;const u=a.getValueSize();a.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(c=u/3);const h=i.times.length-1;let d;if(o<=i.times[0]){d=Yee.arraySlice(i.values,s,l-s)}else if(o>=i.times[h]){const t=h*l+s;d=Yee.arraySlice(i.values,t,t+l-s)}else{const t=i.createInterpolant(),e=s,n=l-s;t.evaluate(o),d=Yee.arraySlice(t.resultBuffer,e,n)}if("quaternion"===r){(new B5t).fromArray(d).normalize().conjugate().toArray(d)}const p=a.times.length;for(let t=0;t<p;++t){const e=t*u+c;if("quaternion"===r)B5t.multiplyQuaternionsFlat(a.values,e,d,0,a.values,e);else{const t=u-2*c;for(let n=0;n<t;++n)a.values[e+n]-=d[n]}}}return t.blendMode=j2t,t}};class Xee{constructor(t,e,n,i){this.parameterPositions=t,this._cachedIndex=0,this.resultBuffer=void 0!==i?i:new e.constructor(n),this.sampleValues=e,this.valueSize=n,this.settings=null,this.DefaultSettings_={}}evaluate(t){const e=this.parameterPositions;let n=this._cachedIndex,i=e[n],r=e[n-1];t:{e:{let o;n:{i:if(!(t<i)){for(let o=n+2;;){if(void 0===i){if(t<r)break i;return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,t,r)}if(n===o)break;if(r=i,i=e[++n],t<i)break e}o=e.length;break n}if(t>=r)break t;{const a=e[1];t<a&&(n=2,r=a);for(let o=n-2;;){if(void 0===r)return this._cachedIndex=0,this.beforeStart_(0,t,i);if(n===o)break;if(i=r,r=e[--n-1],t>=r)break e}o=n,n=0}}for(;n<o;){const i=n+o>>>1;t<e[i]?o=i:n=i+1}if(i=e[n],r=e[n-1],void 0===r)return this._cachedIndex=0,this.beforeStart_(0,t,i);if(void 0===i)return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,r,t)}this._cachedIndex=n,this.intervalChanged_(n,r,i)}return this.interpolate_(n,r,t,i)}getSettings_(){return this.settings||this.DefaultSettings_}copySampleValue_(t){const e=this.resultBuffer,n=this.sampleValues,i=this.valueSize,r=t*i;for(let t=0;t!==i;++t)e[t]=n[r+t];return e}interpolate_(){throw new Error("call to abstract method")}intervalChanged_(){}}Xee.prototype.beforeStart_=Xee.prototype.copySampleValue_,Xee.prototype.afterEnd_=Xee.prototype.copySampleValue_;class $ee extends Xee{constructor(t,e,n,i){super(t,e,n,i),this._weightPrev=-0,this._offsetPrev=-0,this._weightNext=-0,this._offsetNext=-0,this.DefaultSettings_={endingStart:H2t,endingEnd:H2t}}intervalChanged_(t,e,n){const i=this.parameterPositions;let r=t-2,o=t+1,a=i[r],s=i[o];if(void 0===a)switch(this.getSettings_().endingStart){case F2t:r=t,a=2*e-n;break;case V2t:r=i.length-2,a=e+i[r]-i[r+1];break;default:r=t,a=n}if(void 0===s)switch(this.getSettings_().endingEnd){case F2t:o=t,s=2*n-e;break;case V2t:o=1,s=n+i[1]-i[0];break;default:o=t-1,s=e}const l=.5*(n-e),c=this.valueSize;this._weightPrev=l/(e-a),this._weightNext=l/(s-n),this._offsetPrev=r*c,this._offsetNext=o*c}interpolate_(t,e,n,i){const r=this.resultBuffer,o=this.sampleValues,a=this.valueSize,s=t*a,l=s-a,c=this._offsetPrev,u=this._offsetNext,h=this._weightPrev,d=this._weightNext,p=(n-e)/(i-e),f=p*p,m=f*p,g=-h*m+2*h*f-h*p,_=(1+h)*m+(-1.5-2*h)*f+(-.5+h)*p+1,y=(-1-d)*m+(1.5+d)*f+.5*p,v=d*m-d*f;for(let t=0;t!==a;++t)r[t]=g*o[c+t]+_*o[l+t]+y*o[s+t]+v*o[u+t];return r}}class Kee extends Xee{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const r=this.resultBuffer,o=this.sampleValues,a=this.valueSize,s=t*a,l=s-a,c=(n-e)/(i-e),u=1-c;for(let t=0;t!==a;++t)r[t]=o[l+t]*u+o[s+t]*c;return r}}class Zee extends Xee{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t){return this.copySampleValue_(t-1)}}class Jee{constructor(t,e,n,i){if(void 0===t)throw new Error("THREE.KeyframeTrack: track name is undefined");if(void 0===e||0===e.length)throw new Error("THREE.KeyframeTrack: no keyframes in track named "+t);this.name=t,this.times=Yee.convertArray(e,this.TimeBufferType),this.values=Yee.convertArray(n,this.ValueBufferType),this.setInterpolation(i||this.DefaultInterpolation)}static toJSON(t){const e=t.constructor;let n;if(e.toJSON!==this.toJSON)n=e.toJSON(t);else{n={name:t.name,times:Yee.convertArray(t.times,Array),values:Yee.convertArray(t.values,Array)};const e=t.getInterpolation();e!==t.DefaultInterpolation&&(n.interpolation=e)}return n.type=t.ValueTypeName,n}InterpolantFactoryMethodDiscrete(t){return new Zee(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodLinear(t){return new Kee(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodSmooth(t){return new $ee(this.times,this.values,this.getValueSize(),t)}setInterpolation(t){let e;switch(t){case z2t:e=this.InterpolantFactoryMethodDiscrete;break;case D2t:e=this.InterpolantFactoryMethodLinear;break;case B2t:e=this.InterpolantFactoryMethodSmooth}if(void 0===e){const e="unsupported interpolation for "+this.ValueTypeName+" keyframe track named "+this.name;if(void 0===this.createInterpolant){if(t===this.DefaultInterpolation)throw new Error(e);this.setInterpolation(this.DefaultInterpolation)}return console.warn("THREE.KeyframeTrack:",e),this}return this.createInterpolant=e,this}getInterpolation(){switch(this.createInterpolant){case this.InterpolantFactoryMethodDiscrete:return z2t;case this.InterpolantFactoryMethodLinear:return D2t;case this.InterpolantFactoryMethodSmooth:return B2t}}getValueSize(){return this.values.length/this.times.length}shift(t){if(0!==t){const e=this.times;for(let n=0,i=e.length;n!==i;++n)e[n]+=t}return this}scale(t){if(1!==t){const e=this.times;for(let n=0,i=e.length;n!==i;++n)e[n]*=t}return this}trim(t,e){const n=this.times,i=n.length;let r=0,o=i-1;for(;r!==i&&n[r]<t;)++r;for(;-1!==o&&n[o]>e;)--o;if(++o,0!==r||o!==i){r>=o&&(o=Math.max(o,1),r=o-1);const t=this.getValueSize();this.times=Yee.arraySlice(n,r,o),this.values=Yee.arraySlice(this.values,r*t,o*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error("THREE.KeyframeTrack: Invalid value size in track.",this),t=!1);const n=this.times,i=this.values,r=n.length;0===r&&(console.error("THREE.KeyframeTrack: Track is empty.",this),t=!1);let o=null;for(let e=0;e!==r;e++){const i=n[e];if("number"==typeof i&&isNaN(i)){console.error("THREE.KeyframeTrack: Time is not a valid number.",this,e,i),t=!1;break}if(null!==o&&o>i){console.error("THREE.KeyframeTrack: Out of order keys.",this,e,i,o),t=!1;break}o=i}if(void 0!==i&&Yee.isTypedArray(i))for(let e=0,n=i.length;e!==n;++e){const n=i[e];if(isNaN(n)){console.error("THREE.KeyframeTrack: Value is not a valid number.",this,e,n),t=!1;break}}return t}optimize(){const t=Yee.arraySlice(this.times),e=Yee.arraySlice(this.values),n=this.getValueSize(),i=this.getInterpolation()===B2t,r=t.length-1;let o=1;for(let a=1;a<r;++a){let r=!1;const s=t[a];if(s!==t[a+1]&&(1!==a||s!==t[0]))if(i)r=!0;else{const t=a*n,i=t-n,o=t+n;for(let a=0;a!==n;++a){const n=e[t+a];if(n!==e[i+a]||n!==e[o+a]){r=!0;break}}}if(r){if(a!==o){t[o]=t[a];const i=a*n,r=o*n;for(let t=0;t!==n;++t)e[r+t]=e[i+t]}++o}}if(r>0){t[o]=t[r];for(let t=r*n,i=o*n,a=0;a!==n;++a)e[i+a]=e[t+a];++o}return o!==t.length?(this.times=Yee.arraySlice(t,0,o),this.values=Yee.arraySlice(e,0,o*n)):(this.times=t,this.values=e),this}clone(){const t=Yee.arraySlice(this.times,0),e=Yee.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}Jee.prototype.TimeBufferType=Float32Array,Jee.prototype.ValueBufferType=Float32Array,Jee.prototype.DefaultInterpolation=D2t;class Qee extends Jee{}Qee.prototype.ValueTypeName="bool",Qee.prototype.ValueBufferType=Array,Qee.prototype.DefaultInterpolation=z2t,Qee.prototype.InterpolantFactoryMethodLinear=void 0,Qee.prototype.InterpolantFactoryMethodSmooth=void 0;class tne extends Jee{}tne.prototype.ValueTypeName="color";class ene extends Jee{}ene.prototype.ValueTypeName="number";class nne extends Xee{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const r=this.resultBuffer,o=this.sampleValues,a=this.valueSize,s=(n-e)/(i-e);let l=t*a;for(let t=l+a;l!==t;l+=4)B5t.slerpFlat(r,0,o,l-a,o,l,s);return r}}class ine extends Jee{InterpolantFactoryMethodLinear(t){return new nne(this.times,this.values,this.getValueSize(),t)}}ine.prototype.ValueTypeName="quaternion",ine.prototype.DefaultInterpolation=D2t,ine.prototype.InterpolantFactoryMethodSmooth=void 0;class rne extends Jee{}rne.prototype.ValueTypeName="string",rne.prototype.ValueBufferType=Array,rne.prototype.DefaultInterpolation=z2t,rne.prototype.InterpolantFactoryMethodLinear=void 0,rne.prototype.InterpolantFactoryMethodSmooth=void 0;class one extends Jee{}one.prototype.ValueTypeName="vector";class ane{constructor(t,e=-1,n,i=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=a5t(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,i=1/(t.fps||1);for(let t=0,r=n.length;t!==r;++t)e.push(sne(n[t]).scale(i));const r=new this(t.name,t.duration,e,t.blendMode);return r.uuid=t.uuid,r}static toJSON(t){const e=[],n=t.tracks,i={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,i=n.length;t!==i;++t)e.push(Jee.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,i){const r=e.length,o=[];for(let t=0;t<r;t++){let a=[],s=[];a.push((t+r-1)%r,t,(t+1)%r),s.push(0,1,0);const l=Yee.getKeyframeOrder(a);a=Yee.sortedArray(a,1,l),s=Yee.sortedArray(s,1,l),i||0!==a[0]||(a.push(r),s.push(s[0])),o.push(new ene(".morphTargetInfluences["+e[t].name+"]",a,s).scale(1/n))}return new this(t,-1,o)}static findByName(t,e){let n=t;if(!Array.isArray(t)){const e=t;n=e.geometry&&e.geometry.animations||e.animations}for(let t=0;t<n.length;t++)if(n[t].name===e)return n[t];return null}static CreateClipsFromMorphTargetSequences(t,e,n){const i={},r=/^([\w-]*?)([\d]+)$/;for(let e=0,n=t.length;e<n;e++){const n=t[e],o=n.name.match(r);if(o&&o.length>1){const t=o[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const o=[];for(const t in i)o.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return o}static parseAnimation(t,e){if(!t)return console.error("THREE.AnimationClip: No animation in JSONLoader data."),null;const n=function(t,e,n,i,r){if(0!==n.length){const o=[],a=[];Yee.flattenJSON(n,o,a,i),0!==o.length&&r.push(new t(e,o,a))}},i=[],r=t.name||"default",o=t.fps||30,a=t.blendMode;let s=t.length||-1;const l=t.hierarchy||[];for(let t=0;t<l.length;t++){const r=l[t].keys;if(r&&0!==r.length)if(r[0].morphTargets){const t={};let e;for(e=0;e<r.length;e++)if(r[e].morphTargets)for(let n=0;n<r[e].morphTargets.length;n++)t[r[e].morphTargets[n]]=-1;for(const n in t){const t=[],o=[];for(let i=0;i!==r[e].morphTargets.length;++i){const i=r[e];t.push(i.time),o.push(i.morphTarget===n?1:0)}i.push(new ene(".morphTargetInfluence["+n+"]",t,o))}s=t.length*(o||1)}else{const o=".bones["+e[t].name+"]";n(one,o+".position",r,"pos",i),n(ine,o+".quaternion",r,"rot",i),n(one,o+".scale",r,"scl",i)}}if(0===i.length)return null;return new this(r,s,i,a)}resetDuration(){let t=0;for(let e=0,n=this.tracks.length;e!==n;++e){const n=this.tracks[e];t=Math.max(t,n.times[n.times.length-1])}return this.duration=t,this}trim(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].trim(0,this.duration);return this}validate(){let t=!0;for(let e=0;e<this.tracks.length;e++)t=t&&this.tracks[e].validate();return t}optimize(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].optimize();return this}clone(){const t=[];for(let e=0;e<this.tracks.length;e++)t.push(this.tracks[e].clone());return new this.constructor(this.name,this.duration,t,this.blendMode)}toJSON(){return this.constructor.toJSON(this)}}function sne(t){if(void 0===t.type)throw new Error("THREE.KeyframeTrack: track type undefined, can not parse");const e=(function n(t){switch(t.toLowerCase()){case"scalar":case"double":case"float":case"number":case"integer":return ene;case"vector":case"vector2":case"vector3":case"vector4":return one;case"color":return tne;case"quaternion":return ine;case"bool":case"boolean":return Qee;case"string":return rne}throw new Error("THREE.KeyframeTrack: Unsupported typeName: "+t)})(t.type);if(void 0===t.times){const e=[],n=[];Yee.flattenJSON(t.keys,e,n,"value"),t.times=e,t.values=n}return void 0!==e.parse?e.parse(t):new e(t.name,t.times,t.values,t.interpolation)}const lne={enabled:!1,files:{},add:function(t,e){!1!==this.enabled&&(this.files[t]=e)},get:function(t){if(!1!==this.enabled)return this.files[t]},remove:function(t){delete this.files[t]},clear:function(){this.files={}}};class cne{constructor(t,e,n){const i=this;let r,o=!1,a=0,s=0;const l=[];this.onStart=void 0,this.onLoad=t,this.onProgress=e,this.onError=n,this.itemStart=function(t){s++,!1===o&&void 0!==i.onStart&&i.onStart(t,a,s),o=!0},this.itemEnd=function(t){a++,void 0!==i.onProgress&&i.onProgress(t,a,s),a===s&&(o=!1,void 0!==i.onLoad&&i.onLoad())},this.itemError=function(t){void 0!==i.onError&&i.onError(t)},this.resolveURL=function(t){return r?r(t):t},this.setURLModifier=function(t){return r=t,this},this.addHandler=function(t,e){return l.push(t,e),this},this.removeHandler=function(t){const e=l.indexOf(t);return-1!==e&&l.splice(e,2),this},this.getHandler=function(t){for(let e=0,n=l.length;e<n;e+=2){const n=l[e],i=l[e+1];if(n.global&&(n.lastIndex=0),n.test(t))return i}return null}}}const une=new cne;class hne{constructor(t){this.manager=void 0!==t?t:une,this.crossOrigin="anonymous",this.withCredentials=!1,this.path="",this.resourcePath="",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(i,r){n.load(t,i,e,r)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}const dne={};class pne extends hne{constructor(t){super(t)}load(t,e,n,i){void 0===t&&(t=""),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,o=lne.get(t);if(void 0!==o)return r.manager.itemStart(t),setTimeout((function(){e&&e(o),r.manager.itemEnd(t)}),0),o;if(void 0!==dne[t])return void dne[t].push({onLoad:e,onProgress:n,onError:i});const a=t.match(/^data:(.*?)(;base64)?,(.*)$/);let s;if(a){const n=a[1],o=!!a[2];let s=a[3];s=decodeURIComponent(s),o&&(s=atob(s));try{let i;const o=(this.responseType||"").toLowerCase();switch(o){case"arraybuffer":case"blob":const t=new Uint8Array(s.length);for(let e=0;e<s.length;e++)t[e]=s.charCodeAt(e);i="blob"===o?new Blob([t.buffer],{type:n}):t.buffer;break;case"document":const e=new DOMParser;i=e.parseFromString(s,n);break;case"json":i=JSON.parse(s);break;default:i=s}setTimeout((function(){e&&e(i),r.manager.itemEnd(t)}),0)}catch(e){setTimeout((function(){i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)}),0)}}else{dne[t]=[],dne[t].push({onLoad:e,onProgress:n,onError:i}),s=new XMLHttpRequest,s.open("GET",t,!0),s.addEventListener("load",(function(e){const n=this.response,i=dne[t];if(delete dne[t],200===this.status||0===this.status){0===this.status&&console.warn("THREE.FileLoader: HTTP Status 0 received."),lne.add(t,n);for(let t=0,e=i.length;t<e;t++){const e=i[t];e.onLoad&&e.onLoad(n)}r.manager.itemEnd(t)}else{for(let t=0,n=i.length;t<n;t++){const n=i[t];n.onError&&n.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}}),!1),s.addEventListener("progress",(function(e){const n=dne[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onProgress&&i.onProgress(e)}}),!1),s.addEventListener("error",(function(e){const n=dne[t];delete dne[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onError&&i.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}),!1),s.addEventListener("abort",(function(e){const n=dne[t];delete dne[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onError&&i.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}),!1),void 0!==this.responseType&&(s.responseType=this.responseType),void 0!==this.withCredentials&&(s.withCredentials=this.withCredentials),s.overrideMimeType&&s.overrideMimeType(void 0!==this.mimeType?this.mimeType:"text/plain");for(const t in this.requestHeader)s.setRequestHeader(t,this.requestHeader[t]);s.send(null)}return r.manager.itemStart(t),s}setResponseType(t){return this.responseType=t,this}setMimeType(t){return this.mimeType=t,this}}class fne extends hne{constructor(t){super(t)}load(t,e,n,i){void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,o=lne.get(t);if(void 0!==o)return r.manager.itemStart(t),setTimeout((function(){e&&e(o),r.manager.itemEnd(t)}),0),o;const a=document.createElementNS("http://www.w3.org/1999/xhtml","img");function s(){a.removeEventListener("load",s,!1),a.removeEventListener("error",l,!1),lne.add(t,this),e&&e(this),r.manager.itemEnd(t)}function l(e){a.removeEventListener("load",s,!1),a.removeEventListener("error",l,!1),i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)}return a.addEventListener("load",s,!1),a.addEventListener("error",l,!1),"data:"!==t.substr(0,5)&&void 0!==this.crossOrigin&&(a.crossOrigin=this.crossOrigin),r.manager.itemStart(t),a.src=t,a}}class mne extends hne{constructor(t){super(t)}load(t,e,n,i){const r=new s6t,o=new fne(this.manager);o.setCrossOrigin(this.crossOrigin),o.setPath(this.path);let a=0;function s(n){o.load(t[n],(function(t){r.images[n]=t,a++,6===a&&(r.needsUpdate=!0,e&&e(r))}),void 0,i)}for(let e=0;e<t.length;++e)s(e);return r}}class gne extends hne{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new H7t,a=new pne(this.manager);return a.setResponseType("arraybuffer"),a.setRequestHeader(this.requestHeader),a.setPath(this.path),a.setWithCredentials(r.withCredentials),a.load(t,(function(t){const n=r.parse(t);n&&(void 0!==n.image?o.image=n.image:void 0!==n.data&&(o.image.width=n.width,o.image.height=n.height,o.image.data=n.data),o.wrapS=void 0!==n.wrapS?n.wrapS:l2t,o.wrapT=void 0!==n.wrapT?n.wrapT:l2t,o.magFilter=void 0!==n.magFilter?n.magFilter:p2t,o.minFilter=void 0!==n.minFilter?n.minFilter:p2t,o.anisotropy=void 0!==n.anisotropy?n.anisotropy:1,void 0!==n.encoding&&(o.encoding=n.encoding),void 0!==n.flipY&&(o.flipY=n.flipY),void 0!==n.format&&(o.format=n.format),void 0!==n.type&&(o.type=n.type),void 0!==n.mipmaps&&(o.mipmaps=n.mipmaps,o.minFilter=m2t),1===n.mipmapCount&&(o.minFilter=p2t),void 0!==n.generateMipmaps&&(o.generateMipmaps=n.generateMipmaps),o.needsUpdate=!0,e&&e(o,n))}),n,i),o}}class _ne extends hne{constructor(t){super(t)}load(t,e,n,i){const r=new N5t,o=new fne(this.manager);return o.setCrossOrigin(this.crossOrigin),o.setPath(this.path),o.load(t,(function(n){r.image=n;const i=t.search(/\.jpe?g($|\?)/i)>0||0===t.search(/^data\:image\/jpeg/);r.format=i?w2t:S2t,r.needsUpdate=!0,void 0!==e&&e(r)}),n,i),r}}class yne extends Tte{constructor(){super(),this.type="CurvePath",this.curves=[],this.autoClose=!1}add(t){this.curves.push(t)}closePath(){const t=this.curves[0].getPoint(0),e=this.curves[this.curves.length-1].getPoint(1);t.equals(e)||this.curves.push(new Fte(e,t))}getPoint(t){const e=t*this.getLength(),n=this.getCurveLengths();let i=0;for(;i<n.length;){if(n[i]>=e){const t=n[i]-e,r=this.curves[i],o=r.getLength();return r.getPointAt(0===o?0:1-t/o)}i++}return null}getLength(){const t=this.getCurveLengths();return t[t.length-1]}updateArcLengths(){this.needsUpdate=!0,this.cacheLengths=null,this.getCurveLengths()}getCurveLengths(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;const t=[];let e=0;for(let n=0,i=this.curves.length;n<i;n++)e+=this.curves[n].getLength(),t.push(e);return this.cacheLengths=t,t}getSpacedPoints(t=40){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return this.autoClose&&e.push(e[0]),e}getPoints(t=12){const e=[];let n;for(let i=0,r=this.curves;i<r.length;i++){const o=r[i],a=o.getPoints(o&&o.isEllipseCurve?2*t:o&&(o.isLineCurve||o.isLineCurve3)?1:o&&o.isSplineCurve?t*o.points.length:t);for(let t=0;t<a.length;t++){const i=a[t];n&&n.equals(i)||(e.push(i),n=i)}}return this.autoClose&&e.length>1&&!e[e.length-1].equals(e[0])&&e.push(e[0]),e}copy(t){super.copy(t),this.curves=[];for(let e=0,n=t.curves.length;e<n;e++){this.curves.push(t.curves[e].clone())}return this.autoClose=t.autoClose,this}toJSON(){const t=super.toJSON();t.autoClose=this.autoClose,t.curves=[];for(let e=0,n=this.curves.length;e<n;e++){t.curves.push(this.curves[e].toJSON())}return t}fromJSON(t){super.fromJSON(t),this.autoClose=t.autoClose,this.curves=[];for(let e=0,n=t.curves.length;e<n;e++){const n=t.curves[e];this.curves.push((new Wte[n.type]).fromJSON(n))}return this}}class vne extends yne{constructor(t){super(),this.type="Path",this.currentPoint=new C5t,t&&this.setFromPoints(t)}setFromPoints(t){this.moveTo(t[0].x,t[0].y);for(let e=1,n=t.length;e<n;e++)this.lineTo(t[e].x,t[e].y);return this}moveTo(t,e){return this.currentPoint.set(t,e),this}lineTo(t,e){const n=new Fte(this.currentPoint.clone(),new C5t(t,e));return this.curves.push(n),this.currentPoint.set(t,e),this}quadraticCurveTo(t,e,n,i){const r=new Ute(this.currentPoint.clone(),new C5t(t,e),new C5t(n,i));return this.curves.push(r),this.currentPoint.set(n,i),this}bezierCurveTo(t,e,n,i,r,o){const a=new Bte(this.currentPoint.clone(),new C5t(t,e),new C5t(n,i),new C5t(r,o));return this.curves.push(a),this.currentPoint.set(r,o),this}splineThru(t){const e=[this.currentPoint.clone()].concat(t),n=new Gte(e);return this.curves.push(n),this.currentPoint.copy(t[t.length-1]),this}arc(t,e,n,i,r,o){return this.absarc(t+this.currentPoint.x,e+this.currentPoint.y,n,i,r,o),this}absarc(t,e,n,i,r,o){return this.absellipse(t,e,n,n,i,r,o),this}ellipse(t,e,n,i,r,o,a,s){return this.absellipse(t+this.currentPoint.x,e+this.currentPoint.y,n,i,r,o,a,s),this}absellipse(t,e,n,i,r,o,a,s){const l=new Cte(t,e,n,i,r,o,a,s);if(this.curves.length>0){const t=l.getPoint(0);t.equals(this.currentPoint)||this.lineTo(t.x,t.y)}this.curves.push(l);const c=l.getPoint(1);return this.currentPoint.copy(c),this}copy(t){return super.copy(t),this.currentPoint.copy(t.currentPoint),this}toJSON(){const t=super.toJSON();return t.currentPoint=this.currentPoint.toArray(),t}fromJSON(t){return super.fromJSON(t),this.currentPoint.fromArray(t.currentPoint),this}}class bne extends vne{constructor(t){super(t),this.uuid=a5t(),this.type="Shape",this.holes=[]}getPointsHoles(t){const e=[];for(let n=0,i=this.holes.length;n<i;n++)e[n]=this.holes[n].getPoints(t);return e}extractPoints(t){return{shape:this.getPoints(t),holes:this.getPointsHoles(t)}}copy(t){super.copy(t),this.holes=[];for(let e=0,n=t.holes.length;e<n;e++){this.holes.push(t.holes[e].clone())}return this}toJSON(){const t=super.toJSON();t.uuid=this.uuid,t.holes=[];for(let e=0,n=this.holes.length;e<n;e++){t.holes.push(this.holes[e].toJSON())}return t}fromJSON(t){super.fromJSON(t),this.uuid=t.uuid,this.holes=[];for(let e=0,n=t.holes.length;e<n;e++){const n=t.holes[e];this.holes.push((new vne).fromJSON(n))}return this}}class xne extends V3t{constructor(t,e=1){super(),this.type="Light",this.color=new s4t(t),this.intensity=e}dispose(){}copy(t){return super.copy(t),this.color.copy(t.color),this.intensity=t.intensity,this}toJSON(t){const e=super.toJSON(t);return e.object.color=this.color.getHex(),e.object.intensity=this.intensity,void 0!==this.groundColor&&(e.object.groundColor=this.groundColor.getHex()),void 0!==this.distance&&(e.object.distance=this.distance),void 0!==this.angle&&(e.object.angle=this.angle),void 0!==this.decay&&(e.object.decay=this.decay),void 0!==this.penumbra&&(e.object.penumbra=this.penumbra),void 0!==this.shadow&&(e.object.shadow=this.shadow.toJSON()),e}}xne.prototype.isLight=!0;class wne extends xne{constructor(t,e,n){super(t,n),this.type="HemisphereLight",this.position.copy(V3t.DefaultUp),this.updateMatrix(),this.groundColor=new s4t(e)}copy(t){return xne.prototype.copy.call(this,t),this.groundColor.copy(t.groundColor),this}}wne.prototype.isHemisphereLight=!0;const Sne=new g3t,Mne=new H5t,Ene=new H5t;class Tne{constructor(t){this.camera=t,this.bias=0,this.normalBias=0,this.radius=1,this.mapSize=new C5t(512,512),this.map=null,this.mapPass=null,this.matrix=new g3t,this.autoUpdate=!0,this.needsUpdate=!1,this._frustum=new m6t,this._frameExtents=new C5t(1,1),this._viewportCount=1,this._viewports=[new R5t(0,0,1,1)]}getViewportCount(){return this._viewportCount}getFrustum(){return this._frustum}updateMatrices(t){const e=this.camera,n=this.matrix;Mne.setFromMatrixPosition(t.matrixWorld),e.position.copy(Mne),Ene.setFromMatrixPosition(t.target.matrixWorld),e.lookAt(Ene),e.updateMatrixWorld(),Sne.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),this._frustum.setFromProjectionMatrix(Sne),n.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),n.multiply(e.projectionMatrix),n.multiply(e.matrixWorldInverse)}getViewport(t){return this._viewports[t]}getFrameExtents(){return this._frameExtents}dispose(){this.map&&this.map.dispose(),this.mapPass&&this.mapPass.dispose()}copy(t){return this.camera=t.camera.clone(),this.bias=t.bias,this.radius=t.radius,this.mapSize.copy(t.mapSize),this}clone(){return(new this.constructor).copy(this)}toJSON(){const t={};return 0!==this.bias&&(t.bias=this.bias),0!==this.normalBias&&(t.normalBias=this.normalBias),1!==this.radius&&(t.radius=this.radius),512===this.mapSize.x&&512===this.mapSize.y||(t.mapSize=this.mapSize.toArray()),t.camera=this.camera.toJSON(!1).object,delete t.camera.matrix,t}}class Cne extends Tne{constructor(){super(new r6t(50,1,.5,500)),this.focus=1}updateMatrices(t){const e=this.camera,n=2*o5t*t.angle*this.focus,i=this.mapSize.width/this.mapSize.height,r=t.distance||e.far;n===e.fov&&i===e.aspect&&r===e.far||(e.fov=n,e.aspect=i,e.far=r,e.updateProjectionMatrix()),super.updateMatrices(t)}copy(t){return super.copy(t),this.focus=t.focus,this}}Cne.prototype.isSpotLightShadow=!0;class Ane extends xne{constructor(t,e,n=0,i=Math.PI/3,r=0,o=1){super(t,e),this.type="SpotLight",this.position.copy(V3t.DefaultUp),this.updateMatrix(),this.target=new V3t,this.distance=n,this.angle=i,this.penumbra=r,this.decay=o,this.shadow=new Cne}get power(){return this.intensity*Math.PI}set power(t){this.intensity=t/Math.PI}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.angle=t.angle,this.penumbra=t.penumbra,this.decay=t.decay,this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}Ane.prototype.isSpotLight=!0;const kne=new g3t,Lne=new H5t,Pne=new H5t;class Nne extends Tne{constructor(){super(new r6t(90,1,.5,500)),this._frameExtents=new C5t(4,2),this._viewportCount=6,this._viewports=[new R5t(2,1,1,1),new R5t(0,1,1,1),new R5t(3,1,1,1),new R5t(1,1,1,1),new R5t(3,0,1,1),new R5t(1,0,1,1)],this._cubeDirections=[new H5t(1,0,0),new H5t(-1,0,0),new H5t(0,0,1),new H5t(0,0,-1),new H5t(0,1,0),new H5t(0,-1,0)],this._cubeUps=[new H5t(0,1,0),new H5t(0,1,0),new H5t(0,1,0),new H5t(0,1,0),new H5t(0,0,1),new H5t(0,0,-1)]}updateMatrices(t,e=0){const n=this.camera,i=this.matrix,r=t.distance||n.far;r!==n.far&&(n.far=r,n.updateProjectionMatrix()),Lne.setFromMatrixPosition(t.matrixWorld),n.position.copy(Lne),Pne.copy(n.position),Pne.add(this._cubeDirections[e]),n.up.copy(this._cubeUps[e]),n.lookAt(Pne),n.updateMatrixWorld(),i.makeTranslation(-Lne.x,-Lne.y,-Lne.z),kne.multiplyMatrices(n.projectionMatrix,n.matrixWorldInverse),this._frustum.setFromProjectionMatrix(kne)}}Nne.prototype.isPointLightShadow=!0;class Ine extends xne{constructor(t,e,n=0,i=1){super(t,e),this.type="PointLight",this.distance=n,this.decay=i,this.shadow=new Nne}get power(){return 4*this.intensity*Math.PI}set power(t){this.intensity=t/(4*Math.PI)}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.decay=t.decay,this.shadow=t.shadow.clone(),this}}Ine.prototype.isPointLight=!0;class Rne extends Tne{constructor(){super(new A6t(-5,5,5,-5,.5,500))}}Rne.prototype.isDirectionalLightShadow=!0;class One extends xne{constructor(t,e){super(t,e),this.type="DirectionalLight",this.position.copy(V3t.DefaultUp),this.updateMatrix(),this.target=new V3t,this.shadow=new Rne}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}One.prototype.isDirectionalLight=!0;class zne extends xne{constructor(t,e){super(t,e),this.type="AmbientLight"}}zne.prototype.isAmbientLight=!0;class Dne extends xne{constructor(t,e,n=10,i=10){super(t,e),this.type="RectAreaLight",this.width=n,this.height=i}copy(t){return super.copy(t),this.width=t.width,this.height=t.height,this}toJSON(t){const e=super.toJSON(t);return e.object.width=this.width,e.object.height=this.height,e}}Dne.prototype.isRectAreaLight=!0;class Bne{constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new H5t)}set(t){for(let e=0;e<9;e++)this.coefficients[e].copy(t[e]);return this}zero(){for(let t=0;t<9;t++)this.coefficients[t].set(0,0,0);return this}getAt(t,e){const n=t.x,i=t.y,r=t.z,o=this.coefficients;return e.copy(o[0]).multiplyScalar(.282095),e.addScaledVector(o[1],.488603*i),e.addScaledVector(o[2],.488603*r),e.addScaledVector(o[3],.488603*n),e.addScaledVector(o[4],n*i*1.092548),e.addScaledVector(o[5],i*r*1.092548),e.addScaledVector(o[6],.315392*(3*r*r-1)),e.addScaledVector(o[7],n*r*1.092548),e.addScaledVector(o[8],.546274*(n*n-i*i)),e}getIrradianceAt(t,e){const n=t.x,i=t.y,r=t.z,o=this.coefficients;return e.copy(o[0]).multiplyScalar(.886227),e.addScaledVector(o[1],1.023328*i),e.addScaledVector(o[2],1.023328*r),e.addScaledVector(o[3],1.023328*n),e.addScaledVector(o[4],.858086*n*i),e.addScaledVector(o[5],.858086*i*r),e.addScaledVector(o[6],.743125*r*r-.247708),e.addScaledVector(o[7],.858086*n*r),e.addScaledVector(o[8],.429043*(n*n-i*i)),e}add(t){for(let e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].fromArray(t,e+3*i);return this}toArray(t=[],e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].toArray(t,e+3*i);return t}static getBasisAt(t,e){const n=t.x,i=t.y,r=t.z;e[0]=.282095,e[1]=.488603*i,e[2]=.488603*r,e[3]=.488603*n,e[4]=1.092548*n*i,e[5]=1.092548*i*r,e[6]=.315392*(3*r*r-1),e[7]=1.092548*n*r,e[8]=.546274*(n*n-i*i)}}Bne.prototype.isSphericalHarmonics3=!0;class Hne extends xne{constructor(t=new Bne,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}Hne.prototype.isLightProbe=!0;class Fne extends hne{constructor(t){super(t),this.textures={}}load(t,e,n,i){const r=this,o=new pne(r.manager);o.setPath(r.path),o.setRequestHeader(r.requestHeader),o.setWithCredentials(r.withCredentials),o.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e=this.textures;function n(t){return void 0===e[t]&&console.warn("THREE.MaterialLoader: Undefined texture",t),e[t]}const i=new qee[t.type];if(void 0!==t.uuid&&(i.uuid=t.uuid),void 0!==t.name&&(i.name=t.name),void 0!==t.color&&void 0!==i.color&&i.color.setHex(t.color),void 0!==t.roughness&&(i.roughness=t.roughness),void 0!==t.metalness&&(i.metalness=t.metalness),void 0!==t.sheen&&(i.sheen=(new s4t).setHex(t.sheen)),void 0!==t.emissive&&void 0!==i.emissive&&i.emissive.setHex(t.emissive),void 0!==t.specular&&void 0!==i.specular&&i.specular.setHex(t.specular),void 0!==t.specularIntensity&&(i.specularIntensity=t.specularIntensity),void 0!==t.specularTint&&void 0!==i.specularTint&&i.specularTint.setHex(t.specularTint),void 0!==t.shininess&&(i.shininess=t.shininess),void 0!==t.clearcoat&&(i.clearcoat=t.clearcoat),void 0!==t.clearcoatRoughness&&(i.clearcoatRoughness=t.clearcoatRoughness),void 0!==t.transmission&&(i.transmission=t.transmission),void 0!==t.thickness&&(i.thickness=t.thickness),void 0!==t.attenuationDistance&&(i.attenuationDistance=t.attenuationDistance),void 0!==t.attenuationTint&&void 0!==i.attenuationTint&&i.attenuationTint.setHex(t.attenuationTint),void 0!==t.fog&&(i.fog=t.fog),void 0!==t.flatShading&&(i.flatShading=t.flatShading),void 0!==t.blending&&(i.blending=t.blending),void 0!==t.combine&&(i.combine=t.combine),void 0!==t.side&&(i.side=t.side),void 0!==t.shadowSide&&(i.shadowSide=t.shadowSide),void 0!==t.opacity&&(i.opacity=t.opacity),void 0!==t.transparent&&(i.transparent=t.transparent),void 0!==t.alphaTest&&(i.alphaTest=t.alphaTest),void 0!==t.depthTest&&(i.depthTest=t.depthTest),void 0!==t.depthWrite&&(i.depthWrite=t.depthWrite),void 0!==t.colorWrite&&(i.colorWrite=t.colorWrite),void 0!==t.stencilWrite&&(i.stencilWrite=t.stencilWrite),void 0!==t.stencilWriteMask&&(i.stencilWriteMask=t.stencilWriteMask),void 0!==t.stencilFunc&&(i.stencilFunc=t.stencilFunc),void 0!==t.stencilRef&&(i.stencilRef=t.stencilRef),void 0!==t.stencilFuncMask&&(i.stencilFuncMask=t.stencilFuncMask),void 0!==t.stencilFail&&(i.stencilFail=t.stencilFail),void 0!==t.stencilZFail&&(i.stencilZFail=t.stencilZFail),void 0!==t.stencilZPass&&(i.stencilZPass=t.stencilZPass),void 0!==t.wireframe&&(i.wireframe=t.wireframe),void 0!==t.wireframeLinewidth&&(i.wireframeLinewidth=t.wireframeLinewidth),void 0!==t.wireframeLinecap&&(i.wireframeLinecap=t.wireframeLinecap),void 0!==t.wireframeLinejoin&&(i.wireframeLinejoin=t.wireframeLinejoin),void 0!==t.rotation&&(i.rotation=t.rotation),1!==t.linewidth&&(i.linewidth=t.linewidth),void 0!==t.dashSize&&(i.dashSize=t.dashSize),void 0!==t.gapSize&&(i.gapSize=t.gapSize),void 0!==t.scale&&(i.scale=t.scale),void 0!==t.polygonOffset&&(i.polygonOffset=t.polygonOffset),void 0!==t.polygonOffsetFactor&&(i.polygonOffsetFactor=t.polygonOffsetFactor),void 0!==t.polygonOffsetUnits&&(i.polygonOffsetUnits=t.polygonOffsetUnits),void 0!==t.dithering&&(i.dithering=t.dithering),void 0!==t.alphaToCoverage&&(i.alphaToCoverage=t.alphaToCoverage),void 0!==t.premultipliedAlpha&&(i.premultipliedAlpha=t.premultipliedAlpha),void 0!==t.visible&&(i.visible=t.visible),void 0!==t.toneMapped&&(i.toneMapped=t.toneMapped),void 0!==t.userData&&(i.userData=t.userData),void 0!==t.vertexColors&&(i.vertexColors="number"==typeof t.vertexColors?t.vertexColors>0:t.vertexColors),void 0!==t.uniforms)for(const e in t.uniforms){const r=t.uniforms[e];switch(i.uniforms[e]={},r.type){case"t":i.uniforms[e].value=n(r.value);break;case"c":i.uniforms[e].value=(new s4t).setHex(r.value);break;case"v2":i.uniforms[e].value=(new C5t).fromArray(r.value);break;case"v3":i.uniforms[e].value=(new H5t).fromArray(r.value);break;case"v4":i.uniforms[e].value=(new R5t).fromArray(r.value);break;case"m3":i.uniforms[e].value=(new A5t).fromArray(r.value);break;case"m4":i.uniforms[e].value=(new g3t).fromArray(r.value);break;default:i.uniforms[e].value=r.value}}if(void 0!==t.defines&&(i.defines=t.defines),void 0!==t.vertexShader&&(i.vertexShader=t.vertexShader),void 0!==t.fragmentShader&&(i.fragmentShader=t.fragmentShader),void 0!==t.extensions)for(const e in t.extensions)i.extensions[e]=t.extensions[e];if(void 0!==t.shading&&(i.flatShading=1===t.shading),void 0!==t.size&&(i.size=t.size),void 0!==t.sizeAttenuation&&(i.sizeAttenuation=t.sizeAttenuation),void 0!==t.map&&(i.map=n(t.map)),void 0!==t.matcap&&(i.matcap=n(t.matcap)),void 0!==t.alphaMap&&(i.alphaMap=n(t.alphaMap)),void 0!==t.bumpMap&&(i.bumpMap=n(t.bumpMap)),void 0!==t.bumpScale&&(i.bumpScale=t.bumpScale),void 0!==t.normalMap&&(i.normalMap=n(t.normalMap)),void 0!==t.normalMapType&&(i.normalMapType=t.normalMapType),void 0!==t.normalScale){let e=t.normalScale;!1===Array.isArray(e)&&(e=[e,e]),i.normalScale=(new C5t).fromArray(e)}return void 0!==t.displacementMap&&(i.displacementMap=n(t.displacementMap)),void 0!==t.displacementScale&&(i.displacementScale=t.displacementScale),void 0!==t.displacementBias&&(i.displacementBias=t.displacementBias),void 0!==t.roughnessMap&&(i.roughnessMap=n(t.roughnessMap)),void 0!==t.metalnessMap&&(i.metalnessMap=n(t.metalnessMap)),void 0!==t.emissiveMap&&(i.emissiveMap=n(t.emissiveMap)),void 0!==t.emissiveIntensity&&(i.emissiveIntensity=t.emissiveIntensity),void 0!==t.specularMap&&(i.specularMap=n(t.specularMap)),void 0!==t.specularIntensityMap&&(i.specularIntensityMap=n(t.specularIntensityMap)),void 0!==t.specularTintMap&&(i.specularTintMap=n(t.specularTintMap)),void 0!==t.envMap&&(i.envMap=n(t.envMap)),void 0!==t.envMapIntensity&&(i.envMapIntensity=t.envMapIntensity),void 0!==t.reflectivity&&(i.reflectivity=t.reflectivity),void 0!==t.refractionRatio&&(i.refractionRatio=t.refractionRatio),void 0!==t.lightMap&&(i.lightMap=n(t.lightMap)),void 0!==t.lightMapIntensity&&(i.lightMapIntensity=t.lightMapIntensity),void 0!==t.aoMap&&(i.aoMap=n(t.aoMap)),void 0!==t.aoMapIntensity&&(i.aoMapIntensity=t.aoMapIntensity),void 0!==t.gradientMap&&(i.gradientMap=n(t.gradientMap)),void 0!==t.clearcoatMap&&(i.clearcoatMap=n(t.clearcoatMap)),void 0!==t.clearcoatRoughnessMap&&(i.clearcoatRoughnessMap=n(t.clearcoatRoughnessMap)),void 0!==t.clearcoatNormalMap&&(i.clearcoatNormalMap=n(t.clearcoatNormalMap)),void 0!==t.clearcoatNormalScale&&(i.clearcoatNormalScale=(new C5t).fromArray(t.clearcoatNormalScale)),void 0!==t.transmissionMap&&(i.transmissionMap=n(t.transmissionMap)),void 0!==t.thicknessMap&&(i.thicknessMap=n(t.thicknessMap)),i}setTextures(t){return this.textures=t,this}}class Vne{static decodeText(t){if("undefined"!=typeof TextDecoder)return(new TextDecoder).decode(t);let e="";for(let n=0,i=t.length;n<i;n++)e+=String.fromCharCode(t[n]);try{return decodeURIComponent(escape(e))}catch(t){return e}}static extractUrlBase(t){const e=t.lastIndexOf("/");return-1===e?"./":t.substr(0,e+1)}}class Une extends N4t{constructor(){super(),this.type="InstancedBufferGeometry",this.instanceCount=1/0}copy(t){return super.copy(t),this.instanceCount=t.instanceCount,this}clone(){return(new this.constructor).copy(this)}toJSON(){const t=super.toJSON(this);return t.instanceCount=this.instanceCount,t.isInstancedBufferGeometry=!0,t}}Une.prototype.isInstancedBufferGeometry=!0;class jne extends h4t{constructor(t,e,n,i=1){"number"==typeof n&&(i=n,n=!1,console.error("THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument.")),super(t,e,n),this.meshPerAttribute=i}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}toJSON(){const t=super.toJSON();return t.meshPerAttribute=this.meshPerAttribute,t.isInstancedBufferAttribute=!0,t}}jne.prototype.isInstancedBufferAttribute=!0;class Gne extends hne{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new pne(r.manager);o.setPath(r.path),o.setRequestHeader(r.requestHeader),o.setWithCredentials(r.withCredentials),o.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e={},n={};function i(t,i){if(void 0!==e[i])return e[i];const r=t.interleavedBuffers[i],o=(function a(t,e){if(void 0!==n[e])return n[e];const i=new Uint32Array(t.arrayBuffers[e]).buffer;return n[e]=i,i})(t,r.buffer),s=M4t(r.type,o),l=new u7t(s,r.stride);return l.uuid=r.uuid,e[i]=l,l}const r=t.isInstancedBufferGeometry?new Une:new N4t,o=t.data.index;if(void 0!==o){const t=M4t(o.type,o.array);r.setIndex(new h4t(t,1))}const a=t.data.attributes;for(const e in a){const n=a[e];let o;if(n.isInterleavedBufferAttribute){const e=i(t.data,n.data);o=new d7t(e,n.itemSize,n.offset,n.normalized)}else{const t=M4t(n.type,n.array);o=new(n.isInstancedBufferAttribute?jne:h4t)(t,n.itemSize,n.normalized)}void 0!==n.name&&(o.name=n.name),void 0!==n.usage&&o.setUsage(n.usage),void 0!==n.updateRange&&(o.updateRange.offset=n.updateRange.offset,o.updateRange.count=n.updateRange.count),r.setAttribute(e,o)}const s=t.data.morphAttributes;if(s)for(const e in s){const n=s[e],o=[];for(let e=0,r=n.length;e<r;e++){const r=n[e];let a;if(r.isInterleavedBufferAttribute){const e=i(t.data,r.data);a=new d7t(e,r.itemSize,r.offset,r.normalized)}else{const t=M4t(r.type,r.array);a=new h4t(t,r.itemSize,r.normalized)}void 0!==r.name&&(a.name=r.name),o.push(a)}r.morphAttributes[e]=o}t.data.morphTargetsRelative&&(r.morphTargetsRelative=!0);const l=t.data.groups||t.data.drawcalls||t.data.offsets;if(void 0!==l)for(let t=0,e=l.length;t!==e;++t){const e=l[t];r.addGroup(e.start,e.count,e.materialIndex)}const c=t.data.boundingSphere;if(void 0!==c){const t=new H5t;void 0!==c.center&&t.fromArray(c.center),r.boundingSphere=new s3t(t,c.radius)}return t.name&&(r.name=t.name),t.userData&&(r.userData=t.userData),r}}const Wne={UVMapping:t2t,CubeReflectionMapping:e2t,CubeRefractionMapping:n2t,EquirectangularReflectionMapping:i2t,EquirectangularRefractionMapping:r2t,CubeUVReflectionMapping:o2t,CubeUVRefractionMapping:a2t},qne={RepeatWrapping:s2t,ClampToEdgeWrapping:l2t,MirroredRepeatWrapping:c2t},Yne={NearestFilter:u2t,NearestMipmapNearestFilter:h2t,NearestMipmapLinearFilter:d2t,LinearFilter:p2t,LinearMipmapNearestFilter:f2t,LinearMipmapLinearFilter:m2t};class Xne extends hne{constructor(t){super(t),"undefined"==typeof createImageBitmap&&console.warn("THREE.ImageBitmapLoader: createImageBitmap() not supported."),"undefined"==typeof fetch&&console.warn("THREE.ImageBitmapLoader: fetch() not supported."),this.options={premultiplyAlpha:"none"}}setOptions(t){return this.options=t,this}load(t,e,n,i){void 0===t&&(t=""),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,o=lne.get(t);if(void 0!==o)return r.manager.itemStart(t),setTimeout((function(){e&&e(o),r.manager.itemEnd(t)}),0),o;const a={};a.credentials="anonymous"===this.crossOrigin?"same-origin":"include",a.headers=this.requestHeader,fetch(t,a).then((function(t){return t.blob()})).then((function(t){return createImageBitmap(t,Object.assign(r.options,{colorSpaceConversion:"none"}))})).then((function(n){lne.add(t,n),e&&e(n),r.manager.itemEnd(t)})).catch((function(e){i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)})),r.manager.itemStart(t)}}Xne.prototype.isImageBitmapLoader=!0;class $ne{constructor(){this.type="ShapePath",this.color=new s4t,this.subPaths=[],this.currentPath=null}moveTo(t,e){return this.currentPath=new vne,this.subPaths.push(this.currentPath),this.currentPath.moveTo(t,e),this}lineTo(t,e){return this.currentPath.lineTo(t,e),this}quadraticCurveTo(t,e,n,i){return this.currentPath.quadraticCurveTo(t,e,n,i),this}bezierCurveTo(t,e,n,i,r,o){return this.currentPath.bezierCurveTo(t,e,n,i,r,o),this}splineThru(t){return this.currentPath.splineThru(t),this}toShapes(t,e){function n(t){const e=[];for(let n=0,i=t.length;n<i;n++){const i=t[n],r=new bne;r.curves=i.curves,e.push(r)}return e}function i(t,e){const n=e.length;let i=!1;for(let r=n-1,o=0;o<n;r=o++){let n=e[r],a=e[o],s=a.x-n.x,l=a.y-n.y;if(Math.abs(l)>Number.EPSILON){if(l<0&&(n=e[o],s=-s,a=e[r],l=-l),t.y<n.y||t.y>a.y)continue;if(t.y===n.y){if(t.x===n.x)return!0}else{const e=l*(t.x-n.x)-s*(t.y-n.y);if(0===e)return!0;if(e<0)continue;i=!i}}else{if(t.y!==n.y)continue;if(a.x<=t.x&&t.x<=n.x||n.x<=t.x&&t.x<=a.x)return!0}}return i}const r=_ee.isClockWise,o=this.subPaths;if(0===o.length)return[];if(!0===e)return n(o);let a,s,l;const c=[];if(1===o.length)return s=o[0],l=new bne,l.curves=s.curves,c.push(l),c;let u=!r(o[0].getPoints());u=t?!u:u;const h=[],d=[];let p,f,m=[],g=0;d[g]=void 0,m[g]=[];for(let e=0,n=o.length;e<n;e++)s=o[e],p=s.getPoints(),a=r(p),a=t?!a:a,a?(!u&&d[g]&&g++,d[g]={s:new bne,p:p},d[g].s.curves=s.curves,u&&g++,m[g]=[]):m[g].push({h:s,p:p[0]});if(!d[0])return n(o);if(d.length>1){let t=!1;const e=[];for(let t=0,e=d.length;t<e;t++)h[t]=[];for(let n=0,r=d.length;n<r;n++){const r=m[n];for(let o=0;o<r.length;o++){const a=r[o];let s=!0;for(let r=0;r<d.length;r++)i(a.p,d[r].p)&&(n!==r&&e.push({froms:n,tos:r,hole:o}),s?(s=!1,h[r].push(a)):t=!0);s&&h[n].push(a)}}e.length>0&&(t||(m=h))}for(let t=0,e=d.length;t<e;t++){l=d[t].s,c.push(l),f=m[t];for(let t=0,e=f.length;t<e;t++)l.holes.push(f[t].h)}return c}}class Kne{constructor(t){this.type="Font",this.data=t}generateShapes(t,e=100){const n=[],i=(function r(t,e,n){const i=Array.from(t),r=e/n.resolution,o=(n.boundingBox.yMax-n.boundingBox.yMin+n.underlineThickness)*r,a=[];let s=0,l=0;for(let t=0;t<i.length;t++){const e=i[t];if("\n"===e)s=0,l-=o;else{const t=Zne(e,r,s,l,n);s+=t.offsetX,a.push(t.path)}}return a})(t,e,this.data);for(let t=0,e=i.length;t<e;t++)Array.prototype.push.apply(n,i[t].toShapes());return n}}function Zne(t,e,n,i,r){const o=r.glyphs[t]||r.glyphs["?"];if(!o)return void console.error('THREE.Font: character "'+t+'" does not exists in font family '+r.familyName+".");const a=new $ne;let s,l,c,u,h,d,p,f;if(o.o){const t=o._cachedOutline||(o._cachedOutline=o.o.split(" "));for(let r=0,o=t.length;r<o;){switch(t[r++]){case"m":s=t[r++]*e+n,l=t[r++]*e+i,a.moveTo(s,l);break;case"l":s=t[r++]*e+n,l=t[r++]*e+i,a.lineTo(s,l);break;case"q":c=t[r++]*e+n,u=t[r++]*e+i,h=t[r++]*e+n,d=t[r++]*e+i,a.quadraticCurveTo(h,d,c,u);break;case"b":c=t[r++]*e+n,u=t[r++]*e+i,h=t[r++]*e+n,d=t[r++]*e+i,p=t[r++]*e+n,f=t[r++]*e+i,a.bezierCurveTo(h,d,p,f,c,u)}}}return{offsetX:o.ha*e,path:a}}Kne.prototype.isFont=!0;let Jne;const Qne={getContext:function(){return void 0===Jne&&(Jne=new(window.AudioContext||window.webkitAudioContext)),Jne},setContext:function(t){Jne=t}};class tie extends hne{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new pne(this.manager);o.setResponseType("arraybuffer"),o.setPath(this.path),o.setRequestHeader(this.requestHeader),o.setWithCredentials(this.withCredentials),o.load(t,(function(n){try{const t=n.slice(0);Qne.getContext().decodeAudioData(t,(function(t){e(t)}))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}}class eie extends Hne{constructor(t,e,n=1){super(void 0,n);const i=(new s4t).set(t),r=(new s4t).set(e),o=new H5t(i.r,i.g,i.b),a=new H5t(r.r,r.g,r.b),s=Math.sqrt(Math.PI),l=s*Math.sqrt(.75);this.sh.coefficients[0].copy(o).add(a).multiplyScalar(s),this.sh.coefficients[1].copy(o).sub(a).multiplyScalar(l)}}eie.prototype.isHemisphereLightProbe=!0;class nie extends Hne{constructor(t,e=1){super(void 0,e);const n=(new s4t).set(t);this.sh.coefficients[0].set(n.r,n.g,n.b).multiplyScalar(2*Math.sqrt(Math.PI))}}nie.prototype.isAmbientLightProbe=!0;const iie=new g3t,rie=new g3t;class oie{constructor(t=!0){this.autoStart=t,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}start(){this.startTime=aie(),this.oldTime=this.startTime,this.elapsedTime=0,this.running=!0}stop(){this.getElapsedTime(),this.running=!1,this.autoStart=!1}getElapsedTime(){return this.getDelta(),this.elapsedTime}getDelta(){let t=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){const e=aie();t=(e-this.oldTime)/1e3,this.oldTime=e,this.elapsedTime+=t}return t}}function aie(){return("undefined"==typeof performance?Date:performance).now()}const sie=new H5t,lie=new B5t,cie=new H5t,uie=new H5t;class hie extends V3t{constructor(t){super(),this.type="Audio",this.listener=t,this.context=t.context,this.gain=this.context.createGain(),this.gain.connect(t.getInput()),this.autoplay=!1,this.buffer=null,this.detune=0,this.loop=!1,this.loopStart=0,this.loopEnd=0,this.offset=0,this.duration=void 0,this.playbackRate=1,this.isPlaying=!1,this.hasPlaybackControl=!0,this.source=null,this.sourceType="empty",this._startedAt=0,this._progress=0,this._connected=!1,this.filters=[]}getOutput(){return this.gain}setNodeSource(t){return this.hasPlaybackControl=!1,this.sourceType="audioNode",this.source=t,this.connect(),this}setMediaElementSource(t){return this.hasPlaybackControl=!1,this.sourceType="mediaNode",this.source=this.context.createMediaElementSource(t),this.connect(),this}setMediaStreamSource(t){return this.hasPlaybackControl=!1,this.sourceType="mediaStreamNode",this.source=this.context.createMediaStreamSource(t),this.connect(),this}setBuffer(t){return this.buffer=t,this.sourceType="buffer",this.autoplay&&this.play(),this}play(t=0){if(!0===this.isPlaying)return void console.warn("THREE.Audio: Audio is already playing.");if(!1===this.hasPlaybackControl)return void console.warn("THREE.Audio: this Audio has no playback control.");this._startedAt=this.context.currentTime+t;const e=this.context.createBufferSource();return e.buffer=this.buffer,e.loop=this.loop,e.loopStart=this.loopStart,e.loopEnd=this.loopEnd,e.onended=this.onEnded.bind(this),e.start(this._startedAt,this._progress+this.offset,this.duration),this.isPlaying=!0,this.source=e,this.setDetune(this.detune),this.setPlaybackRate(this.playbackRate),this.connect()}pause(){if(!1!==this.hasPlaybackControl)return!0===this.isPlaying&&(this._progress+=Math.max(this.context.currentTime-this._startedAt,0)*this.playbackRate,!0===this.loop&&(this._progress=this._progress%(this.duration||this.buffer.duration)),this.source.stop(),this.source.onended=null,this.isPlaying=!1),this;console.warn("THREE.Audio: this Audio has no playback control.")}stop(){if(!1!==this.hasPlaybackControl)return this._progress=0,this.source.stop(),this.source.onended=null,this.isPlaying=!1,this;console.warn("THREE.Audio: this Audio has no playback control.")}connect(){if(this.filters.length>0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].connect(this.filters[t]);this.filters[this.filters.length-1].connect(this.getOutput())}else this.source.connect(this.getOutput());return this._connected=!0,this}disconnect(){if(this.filters.length>0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].disconnect(this.filters[t]);this.filters[this.filters.length-1].disconnect(this.getOutput())}else this.source.disconnect(this.getOutput());return this._connected=!1,this}getFilters(){return this.filters}setFilters(t){return t||(t=[]),!0===this._connected?(this.disconnect(),this.filters=t.slice(),this.connect()):this.filters=t.slice(),this}setDetune(t){if(this.detune=t,void 0!==this.source.detune)return!0===this.isPlaying&&this.source.detune.setTargetAtTime(this.detune,this.context.currentTime,.01),this}getDetune(){return this.detune}getFilter(){return this.getFilters()[0]}setFilter(t){return this.setFilters(t?[t]:[])}setPlaybackRate(t){if(!1!==this.hasPlaybackControl)return this.playbackRate=t,!0===this.isPlaying&&this.source.playbackRate.setTargetAtTime(this.playbackRate,this.context.currentTime,.01),this;console.warn("THREE.Audio: this Audio has no playback control.")}getPlaybackRate(){return this.playbackRate}onEnded(){this.isPlaying=!1}getLoop(){return!1===this.hasPlaybackControl?(console.warn("THREE.Audio: this Audio has no playback control."),!1):this.loop}setLoop(t){if(!1!==this.hasPlaybackControl)return this.loop=t,!0===this.isPlaying&&(this.source.loop=this.loop),this;console.warn("THREE.Audio: this Audio has no playback control.")}setLoopStart(t){return this.loopStart=t,this}setLoopEnd(t){return this.loopEnd=t,this}getVolume(){return this.gain.gain.value}setVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}}const die=new H5t,pie=new B5t,fie=new H5t,mie=new H5t;class gie{constructor(t,e=2048){this.analyser=t.context.createAnalyser(),this.analyser.fftSize=e,this.data=new Uint8Array(this.analyser.frequencyBinCount),t.getOutput().connect(this.analyser)}getFrequencyData(){return this.analyser.getByteFrequencyData(this.data),this.data}getAverageFrequency(){let t=0;const e=this.getFrequencyData();for(let n=0;n<e.length;n++)t+=e[n];return t/e.length}}class _ie{constructor(t,e,n){let i,r,o;switch(this.binding=t,this.valueSize=n,e){case"quaternion":i=this._slerp,r=this._slerpAdditive,o=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case"string":case"bool":i=this._select,r=this._select,o=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:i=this._lerp,r=this._lerpAdditive,o=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=i,this._mixBufferRegionAdditive=r,this._setIdentity=o,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(t,e){const n=this.buffer,i=this.valueSize,r=t*i+i;let o=this.cumulativeWeight;if(0===o){for(let t=0;t!==i;++t)n[r+t]=n[t];o=e}else{o+=e;this._mixBufferRegion(n,r,0,e/o,i)}this.cumulativeWeight=o}accumulateAdditive(t){const e=this.buffer,n=this.valueSize,i=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(e,i,0,t,n),this.cumulativeWeightAdditive+=t}apply(t){const e=this.valueSize,n=this.buffer,i=t*e+e,r=this.cumulativeWeight,o=this.cumulativeWeightAdditive,a=this.binding;if(this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,r<1){this._mixBufferRegion(n,i,e*this._origIndex,1-r,e)}o>0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,r=e+e;t!==r;++t)if(n[t]!==n[t+e]){a.setValue(n,i);break}}saveOriginalState(){const t=this.buffer,e=this.valueSize,n=e*this._origIndex;this.binding.getValue(t,n);for(let i=e,r=n;i!==r;++i)t[i]=t[n+i%e];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){this.binding.setValue(this.buffer,3*this.valueSize)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n<e;n++)this.buffer[n]=0}_setAdditiveIdentityQuaternion(){this._setAdditiveIdentityNumeric(),this.buffer[this._addIndex*this.valueSize+3]=1}_setAdditiveIdentityOther(){const t=this._origIndex*this.valueSize,e=this._addIndex*this.valueSize;for(let n=0;n<this.valueSize;n++)this.buffer[e+n]=this.buffer[t+n]}_select(t,e,n,i,r){if(i>=.5)for(let i=0;i!==r;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){B5t.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,r){const o=this._workIndex*r;B5t.multiplyQuaternionsFlat(t,o,t,e,t,n),B5t.slerpFlat(t,e,t,e,t,o,i)}_lerp(t,e,n,i,r){const o=1-i;for(let a=0;a!==r;++a){const r=e+a;t[r]=t[r]*o+t[n+a]*i}}_lerpAdditive(t,e,n,i,r){for(let o=0;o!==r;++o){const r=e+o;t[r]=t[r]+t[n+o]*i}}}const yie="\\[\\]\\.:\\/",vie=new RegExp("[\\[\\]\\.:\\/]","g"),bie="[^\\[\\]\\.:\\/]",xie="[^"+yie.replace("\\.","")+"]",wie=/((?:WC+[\/:])*)/.source.replace("WC",bie),Sie=/(WCOD+)?/.source.replace("WCOD",xie),Mie=/(?:\.(WC+)(?:\[(.+)\])?)?/.source.replace("WC",bie),Eie=/\.(WC+)(?:\[(.+)\])?/.source.replace("WC",bie),Tie=new RegExp("^"+wie+Sie+Mie+Eie+"$"),Cie=["material","materials","bones"];class Aie{constructor(t,e,n){this.path=e,this.parsedPath=n||Aie.parseTrackName(e),this.node=Aie.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new Aie.Composite(t,e,n):new Aie(t,e,n)}static sanitizeNodeName(t){return t.replace(/\s/g,"_").replace(vie,"")}static parseTrackName(t){const e=Tie.exec(t);if(!e)throw new Error("PropertyBinding: Cannot parse trackName: "+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},i=n.nodeName&&n.nodeName.lastIndexOf(".");if(void 0!==i&&-1!==i){const t=n.nodeName.substring(i+1);-1!==Cie.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,i),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error("PropertyBinding: can not parse propertyName from trackName: "+t);return n}static findNode(t,e){if(!e||""===e||"."===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let i=0;i<t.length;i++){const r=t[i];if(r.name===e||r.uuid===e)return r;const o=n(r.children);if(o)return o}return null},i=n(t.children);if(i)return i}return null}_getValue_unavailable(){}_setValue_unavailable(){}_getValue_direct(t,e){t[e]=this.node[this.propertyName]}_getValue_array(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)t[e++]=n[i]}_getValue_arrayElement(t,e){t[e]=this.resolvedProperty[this.propertyIndex]}_getValue_toArray(t,e){this.resolvedProperty.toArray(t,e)}_setValue_direct(t,e){this.targetObject[this.propertyName]=t[e]}_setValue_direct_setNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.needsUpdate=!0}_setValue_direct_setMatrixWorldNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_array(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++]}_setValue_array_setNeedsUpdate(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++];this.targetObject.needsUpdate=!0}_setValue_array_setMatrixWorldNeedsUpdate(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++];this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_arrayElement(t,e){this.resolvedProperty[this.propertyIndex]=t[e]}_setValue_arrayElement_setNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.needsUpdate=!0}_setValue_arrayElement_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_fromArray(t,e){this.resolvedProperty.fromArray(t,e)}_setValue_fromArray_setNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.needsUpdate=!0}_setValue_fromArray_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.matrixWorldNeedsUpdate=!0}_getValue_unbound(t,e){this.bind(),this.getValue(t,e)}_setValue_unbound(t,e){this.bind(),this.setValue(t,e)}bind(){let t=this.node;const e=this.parsedPath,n=e.objectName,i=e.propertyName;let r=e.propertyIndex;if(t||(t=Aie.findNode(this.rootNode,e.nodeName)||this.rootNode,this.node=t),this.getValue=this._getValue_unavailable,this.setValue=this._setValue_unavailable,!t)return void console.error("THREE.PropertyBinding: Trying to update node for track: "+this.path+" but it wasn't found.");if(n){let i=e.objectIndex;switch(n){case"materials":if(!t.material)return void console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.",this);if(!t.material.materials)return void console.error("THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.",this);t=t.material.materials;break;case"bones":if(!t.skeleton)return void console.error("THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.",this);t=t.skeleton.bones;for(let e=0;e<t.length;e++)if(t[e].name===i){i=e;break}break;default:if(void 0===t[n])return void console.error("THREE.PropertyBinding: Can not bind to objectName of node undefined.",this);t=t[n]}if(void 0!==i){if(void 0===t[i])return void console.error("THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.",this,t);t=t[i]}}const o=t[i];if(void 0===o){return void console.error("THREE.PropertyBinding: Trying to update property for track: "+e.nodeName+"."+i+" but it wasn't found.",t)}let a=this.Versioning.None;this.targetObject=t,void 0!==t.needsUpdate?a=this.Versioning.NeedsUpdate:void 0!==t.matrixWorldNeedsUpdate&&(a=this.Versioning.MatrixWorldNeedsUpdate);let s=this.BindingType.Direct;if(void 0!==r){if("morphTargetInfluences"===i){if(!t.geometry)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.",this);if(!t.geometry.isBufferGeometry)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences on THREE.Geometry. Use THREE.BufferGeometry instead.",this);if(!t.geometry.morphAttributes)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.",this);void 0!==t.morphTargetDictionary[r]&&(r=t.morphTargetDictionary[r])}s=this.BindingType.ArrayElement,this.resolvedProperty=o,this.propertyIndex=r}else void 0!==o.fromArray&&void 0!==o.toArray?(s=this.BindingType.HasFromToArray,this.resolvedProperty=o):Array.isArray(o)?(s=this.BindingType.EntireArray,this.resolvedProperty=o):this.propertyName=i;this.getValue=this.GetterByBindingType[s],this.setValue=this.SetterByBindingTypeAndVersioning[s][a]}unbind(){this.node=null,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}}Aie.Composite=class{constructor(t,e,n){const i=n||Aie.parseTrackName(e);this._targetGroup=t,this._bindings=t.subscribe_(e,i)}getValue(t,e){this.bind();const n=this._bindings[this._targetGroup.nCachedObjects_];void 0!==n&&n.getValue(t,e)}setValue(t,e){const n=this._bindings;for(let i=this._targetGroup.nCachedObjects_,r=n.length;i!==r;++i)n[i].setValue(t,e)}bind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].bind()}unbind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].unbind()}},Aie.prototype.BindingType={Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3},Aie.prototype.Versioning={None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2},Aie.prototype.GetterByBindingType=[Aie.prototype._getValue_direct,Aie.prototype._getValue_array,Aie.prototype._getValue_arrayElement,Aie.prototype._getValue_toArray],Aie.prototype.SetterByBindingTypeAndVersioning=[[Aie.prototype._setValue_direct,Aie.prototype._setValue_direct_setNeedsUpdate,Aie.prototype._setValue_direct_setMatrixWorldNeedsUpdate],[Aie.prototype._setValue_array,Aie.prototype._setValue_array_setNeedsUpdate,Aie.prototype._setValue_array_setMatrixWorldNeedsUpdate],[Aie.prototype._setValue_arrayElement,Aie.prototype._setValue_arrayElement_setNeedsUpdate,Aie.prototype._setValue_arrayElement_setMatrixWorldNeedsUpdate],[Aie.prototype._setValue_fromArray,Aie.prototype._setValue_fromArray_setNeedsUpdate,Aie.prototype._setValue_fromArray_setMatrixWorldNeedsUpdate]];class kie{constructor(){this.uuid=a5t(),this._objects=Array.prototype.slice.call(arguments),this.nCachedObjects_=0;const t={};this._indicesByUUID=t;for(let e=0,n=arguments.length;e!==n;++e)t[arguments[e].uuid]=e;this._paths=[],this._parsedPaths=[],this._bindings=[],this._bindingsIndicesByPath={};const e=this;this.stats={objects:{get total(){return e._objects.length},get inUse(){return this.total-e.nCachedObjects_}},get bindingsPerObject(){return e._bindings.length}}}add(){const t=this._objects,e=this._indicesByUUID,n=this._paths,i=this._parsedPaths,r=this._bindings,o=r.length;let a,s=t.length,l=this.nCachedObjects_;for(let c=0,u=arguments.length;c!==u;++c){const u=arguments[c],h=u.uuid;let d=e[h];if(void 0===d){d=s++,e[h]=d,t.push(u);for(let t=0,e=o;t!==e;++t)r[t].push(new Aie(u,n[t],i[t]))}else if(d<l){a=t[d];const s=--l,c=t[s];e[c.uuid]=d,t[d]=c,e[h]=s,t[s]=u;for(let t=0,e=o;t!==e;++t){const e=r[t];let o=e[d];e[d]=e[s],void 0===o&&(o=new Aie(u,n[t],i[t])),e[s]=o}}else t[d]!==a&&console.error("THREE.AnimationObjectGroup: Different objects with the same UUID detected. Clean the caches or recreate your infrastructure when reloading scenes.")}this.nCachedObjects_=l}remove(){const t=this._objects,e=this._indicesByUUID,n=this._bindings,i=n.length;let r=this.nCachedObjects_;for(let o=0,a=arguments.length;o!==a;++o){const a=arguments[o],s=a.uuid,l=e[s];if(void 0!==l&&l>=r){const o=r++,c=t[o];e[c.uuid]=l,t[l]=c,e[s]=o,t[o]=a;for(let t=0,e=i;t!==e;++t){const e=n[t],i=e[l];e[l]=e[o],e[o]=i}}}this.nCachedObjects_=r}uncache(){const t=this._objects,e=this._indicesByUUID,n=this._bindings,i=n.length;let r=this.nCachedObjects_,o=t.length;for(let a=0,s=arguments.length;a!==s;++a){const s=arguments[a].uuid,l=e[s];if(void 0!==l)if(delete e[s],l<r){const a=--r,s=t[a],c=--o,u=t[c];e[s.uuid]=l,t[l]=s,e[u.uuid]=a,t[a]=u,t.pop();for(let t=0,e=i;t!==e;++t){const e=n[t],i=e[c];e[l]=e[a],e[a]=i,e.pop()}}else{const r=--o,a=t[r];r>0&&(e[a.uuid]=l),t[l]=a,t.pop();for(let t=0,e=i;t!==e;++t){const e=n[t];e[l]=e[r],e.pop()}}}this.nCachedObjects_=r}subscribe_(t,e){const n=this._bindingsIndicesByPath;let i=n[t];const r=this._bindings;if(void 0!==i)return r[i];const o=this._paths,a=this._parsedPaths,s=this._objects,l=this.nCachedObjects_,c=new Array(s.length);i=r.length,n[t]=i,o.push(t),a.push(e),r.push(c);for(let n=l,i=s.length;n!==i;++n){c[n]=new Aie(s[n],t,e)}return c}unsubscribe_(t){const e=this._bindingsIndicesByPath,n=e[t];if(void 0!==n){const i=this._paths,r=this._parsedPaths,o=this._bindings,a=o.length-1,s=o[a];e[t[a]]=n,o[n]=s,o.pop(),r[n]=r[a],r.pop(),i[n]=i[a],i.pop()}}}kie.prototype.isAnimationObjectGroup=!0;class Lie{constructor(t,e,n=null,i=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=i;const r=e.tracks,o=r.length,a=new Array(o),s={endingStart:H2t,endingEnd:H2t};for(let t=0;t!==o;++t){const e=r[t].createInterpolant(null);a[t]=e,e.settings=s}this._interpolantSettings=s,this._interpolants=a,this._propertyBindings=new Array(o),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=2201,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(t){return this._startTime=t,this}setLoop(t,e){return this.loop=t,this.repetitions=e,this}setEffectiveWeight(t){return this.weight=t,this._effectiveWeight=this.enabled?t:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(t){return this._scheduleFading(t,0,1)}fadeOut(t){return this._scheduleFading(t,1,0)}crossFadeFrom(t,e,n){if(t.fadeOut(e),this.fadeIn(e),n){const n=this._clip.duration,i=t._clip.duration,r=n/i;t.warp(1,i/n,e),this.warp(r,1,e)}return this}crossFadeTo(t,e,n){return t.crossFadeFrom(this,e,n)}stopFading(){const t=this._weightInterpolant;return null!==t&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}setEffectiveTimeScale(t){return this.timeScale=t,this._effectiveTimeScale=this.paused?0:t,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(t){return this.timeScale=this._clip.duration/t,this.stopWarping()}syncWith(t){return this.time=t.time,this.timeScale=t.timeScale,this.stopWarping()}halt(t){return this.warp(this._effectiveTimeScale,0,t)}warp(t,e,n){const i=this._mixer,r=i.time,o=this.timeScale;let a=this._timeScaleInterpolant;null===a&&(a=i._lendControlInterpolant(),this._timeScaleInterpolant=a);const s=a.parameterPositions,l=a.sampleValues;return s[0]=r,s[1]=r+n,l[0]=t/o,l[1]=e/o,this}stopWarping(){const t=this._timeScaleInterpolant;return null!==t&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(t,e,n,i){if(!this.enabled)return void this._updateWeight(t);const r=this._startTime;if(null!==r){const i=(t-r)*n;if(i<0||0===n)return;this._startTime=null,e=n*i}e*=this._updateTimeScale(t);const o=this._updateTime(e),a=this._updateWeight(t);if(a>0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case j2t:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(o),e[n].accumulateAdditive(a);break;case U2t:default:for(let n=0,r=t.length;n!==r;++n)t[n].evaluate(o),e[n].accumulate(i,a)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,r=this._loopCount;const o=2202===n;if(0===t)return-1===r?i:o&&1==(1&r)?e-i:i;if(2200===n){-1===r&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:"finished",action:this,direction:t<0?-1:1})}}else{if(-1===r&&(t>=0?(r=0,this._setEndings(!0,0===this.repetitions,o)):this._setEndings(0===this.repetitions,!0,o)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,r+=Math.abs(n);const a=this.repetitions-r;if(a<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:"finished",action:this,direction:t>0?1:-1});else{if(1===a){const e=t<0;this._setEndings(e,!e,o)}else this._setEndings(!1,!1,o);this._loopCount=r,this.time=i,this._mixer.dispatchEvent({type:"loop",action:this,loopDelta:n})}}else this.time=i;if(o&&1==(1&r))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=F2t,i.endingEnd=F2t):(i.endingStart=t?this.zeroSlopeAtStart?F2t:H2t:V2t,i.endingEnd=e?this.zeroSlopeAtEnd?F2t:H2t:V2t)}_scheduleFading(t,e,n){const i=this._mixer,r=i.time;let o=this._weightInterpolant;null===o&&(o=i._lendControlInterpolant(),this._weightInterpolant=o);const a=o.parameterPositions,s=o.sampleValues;return a[0]=r,s[0]=e,a[1]=r+t,s[1]=n,this}}class Pie extends e5t{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,r=i.length,o=t._propertyBindings,a=t._interpolants,s=n.uuid,l=this._bindingsByRootAndName;let c=l[s];void 0===c&&(c={},l[s]=c);for(let t=0;t!==r;++t){const r=i[t],l=r.name;let u=c[l];if(void 0!==u)o[t]=u;else{if(u=o[t],void 0!==u){null===u._cacheIndex&&(++u.referenceCount,this._addInactiveBinding(u,s,l));continue}u=new _ie(Aie.create(n,l,e&&e._propertyBindings[t].binding.parsedPath),r.ValueTypeName,r.getValueSize()),++u.referenceCount,this._addInactiveBinding(u,s,l),o[t]=u}a[t].resultBuffer=u.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e<this._nActiveActions}_addInactiveAction(t,e,n){const i=this._actions,r=this._actionsByClip;let o=r[e];if(void 0===o)o={knownActions:[t],actionByRoot:{}},t._byClipCacheIndex=0,r[e]=o;else{const e=o.knownActions;t._byClipCacheIndex=e.length,e.push(t)}t._cacheIndex=i.length,i.push(t),o.actionByRoot[n]=t}_removeInactiveAction(t){const e=this._actions,n=e[e.length-1],i=t._cacheIndex;n._cacheIndex=i,e[i]=n,e.pop(),t._cacheIndex=null;const r=t._clip.uuid,o=this._actionsByClip,a=o[r],s=a.knownActions,l=s[s.length-1],c=t._byClipCacheIndex;l._byClipCacheIndex=c,s[c]=l,s.pop(),t._byClipCacheIndex=null;delete a.actionByRoot[(t._localRoot||this._root).uuid],0===s.length&&delete o[r],this._removeInactiveBindingsForAction(t)}_removeInactiveBindingsForAction(t){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.referenceCount&&this._removeInactiveBinding(n)}}_lendAction(t){const e=this._actions,n=t._cacheIndex,i=this._nActiveActions++,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_takeBackAction(t){const e=this._actions,n=t._cacheIndex,i=--this._nActiveActions,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_addInactiveBinding(t,e,n){const i=this._bindingsByRootAndName,r=this._bindings;let o=i[e];void 0===o&&(o={},i[e]=o),o[n]=t,t._cacheIndex=r.length,r.push(t)}_removeInactiveBinding(t){const e=this._bindings,n=t.binding,i=n.rootNode.uuid,r=n.path,o=this._bindingsByRootAndName,a=o[i],s=e[e.length-1],l=t._cacheIndex;s._cacheIndex=l,e[l]=s,e.pop(),delete a[r],0===Object.keys(a).length&&delete o[i]}_lendBinding(t){const e=this._bindings,n=t._cacheIndex,i=this._nActiveBindings++,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_takeBackBinding(t){const e=this._bindings,n=t._cacheIndex,i=--this._nActiveBindings,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_lendControlInterpolant(){const t=this._controlInterpolants,e=this._nActiveControlInterpolants++;let n=t[e];return void 0===n&&(n=new Kee(new Float32Array(2),new Float32Array(2),1,this._controlInterpolantsResultBuffer),n.__cacheIndex=e,t[e]=n),n}_takeBackControlInterpolant(t){const e=this._controlInterpolants,n=t.__cacheIndex,i=--this._nActiveControlInterpolants,r=e[i];t.__cacheIndex=i,e[i]=t,r.__cacheIndex=n,e[n]=r}clipAction(t,e,n){const i=e||this._root,r=i.uuid;let o="string"==typeof t?ane.findByName(i,t):t;const a=null!==o?o.uuid:t,s=this._actionsByClip[a];let l=null;if(void 0===n&&(n=null!==o?o.blendMode:U2t),void 0!==s){const t=s.actionByRoot[r];if(void 0!==t&&t.blendMode===n)return t;l=s.knownActions[0],null===o&&(o=l._clip)}if(null===o)return null;const c=new Lie(this,o,e,n);return this._bindAction(c,l),this._addInactiveAction(c,a,r),c}existingAction(t,e){const n=e||this._root,i=n.uuid,r="string"==typeof t?ane.findByName(n,t):t,o=this._actionsByClip[r?r.uuid:t];return void 0!==o&&o.actionByRoot[i]||null}stopAllAction(){const t=this._actions;for(let e=this._nActiveActions-1;e>=0;--e)t[e].stop();return this}update(t){const e=this._actions,n=this._nActiveActions,i=this.time+=t*=this.timeScale,r=Math.sign(t),o=this._accuIndex^=1;for(let a=0;a!==n;++a){e[a]._update(i,t,r,o)}const a=this._bindings,s=this._nActiveBindings;for(let t=0;t!==s;++t)a[t].apply(o);return this}setTime(t){this.time=0;for(let t=0;t<this._actions.length;t++)this._actions[t].time=0;return this.update(t)}getRoot(){return this._root}uncacheClip(t){const e=this._actions,n=t.uuid,i=this._actionsByClip,r=i[n];if(void 0!==r){const t=r.knownActions;for(let n=0,i=t.length;n!==i;++n){const i=t[n];this._deactivateAction(i);const r=i._cacheIndex,o=e[e.length-1];i._cacheIndex=null,i._byClipCacheIndex=null,o._cacheIndex=r,e[r]=o,e.pop(),this._removeInactiveBindingsForAction(i)}delete i[n]}}uncacheRoot(t){const e=t.uuid,n=this._actionsByClip;for(const t in n){const i=n[t].actionByRoot[e];void 0!==i&&(this._deactivateAction(i),this._removeInactiveAction(i))}const i=this._bindingsByRootAndName[e];if(void 0!==i)for(const t in i){const e=i[t];e.restoreOriginalState(),this._removeInactiveBinding(e)}}uncacheAction(t,e){const n=this.existingAction(t,e);null!==n&&(this._deactivateAction(n),this._removeInactiveAction(n))}}Pie.prototype._controlInterpolantsResultBuffer=new Float32Array(1);class Nie{constructor(t){"string"==typeof t&&(console.warn("THREE.Uniform: Type parameter is no longer needed."),t=arguments[1]),this.value=t}clone(){return new Nie(void 0===this.value.clone?this.value:this.value.clone())}}class Iie extends u7t{constructor(t,e,n=1){super(t,e),this.meshPerAttribute=n}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}clone(t){const e=super.clone(t);return e.meshPerAttribute=this.meshPerAttribute,e}toJSON(t){const e=super.toJSON(t);return e.isInstancedInterleavedBuffer=!0,e.meshPerAttribute=this.meshPerAttribute,e}}Iie.prototype.isInstancedInterleavedBuffer=!0;class Rie{constructor(t,e,n,i,r){this.buffer=t,this.type=e,this.itemSize=n,this.elementSize=i,this.count=r,this.version=0}set needsUpdate(t){!0===t&&this.version++}setBuffer(t){return this.buffer=t,this}setType(t,e){return this.type=t,this.elementSize=e,this}setItemSize(t){return this.itemSize=t,this}setCount(t){return this.count=t,this}}Rie.prototype.isGLBufferAttribute=!0;function Oie(t,e){return t.distance-e.distance}function zie(t,e,n,i){if(t.layers.test(e.layers)&&t.raycast(e,n),!0===i){const i=t.children;for(let t=0,r=i.length;t<r;t++)zie(i[t],e,n,!0)}}class Die{constructor(t=1,e=0,n=0){return this.radius=t,this.phi=e,this.theta=n,this}set(t,e,n){return this.radius=t,this.phi=e,this.theta=n,this}copy(t){return this.radius=t.radius,this.phi=t.phi,this.theta=t.theta,this}makeSafe(){const t=1e-6;return this.phi=Math.max(t,Math.min(Math.PI-t,this.phi)),this}setFromVector3(t){return this.setFromCartesianCoords(t.x,t.y,t.z)}setFromCartesianCoords(t,e,n){return this.radius=Math.sqrt(t*t+e*e+n*n),0===this.radius?(this.theta=0,this.phi=0):(this.theta=Math.atan2(t,n),this.phi=Math.acos(s5t(e/this.radius,-1,1))),this}clone(){return(new this.constructor).copy(this)}}const Bie=new C5t;class Hie{constructor(t=new C5t(1/0,1/0),e=new C5t(-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;e<n;e++)this.expandByPoint(t[e]);return this}setFromCenterAndSize(t,e){const n=Bie.copy(e).multiplyScalar(.5);return this.min.copy(t).sub(n),this.max.copy(t).add(n),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.min.copy(t.min),this.max.copy(t.max),this}makeEmpty(){return this.min.x=this.min.y=1/0,this.max.x=this.max.y=-1/0,this}isEmpty(){return this.max.x<this.min.x||this.max.y<this.min.y}getCenter(t){return this.isEmpty()?t.set(0,0):t.addVectors(this.min,this.max).multiplyScalar(.5)}getSize(t){return this.isEmpty()?t.set(0,0):t.subVectors(this.max,this.min)}expandByPoint(t){return this.min.min(t),this.max.max(t),this}expandByVector(t){return this.min.sub(t),this.max.add(t),this}expandByScalar(t){return this.min.addScalar(-t),this.max.addScalar(t),this}containsPoint(t){return!(t.x<this.min.x||t.x>this.max.x||t.y<this.min.y||t.y>this.max.y)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y))}intersectsBox(t){return!(t.max.x<this.min.x||t.min.x>this.max.x||t.max.y<this.min.y||t.min.y>this.max.y)}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return Bie.copy(t).clamp(this.min,this.max).sub(t).length()}intersect(t){return this.min.max(t.min),this.max.min(t.max),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}Hie.prototype.isBox2=!0;const Fie=new H5t,Vie=new H5t;class Uie{constructor(t=new H5t,e=new H5t){this.start=t,this.end=e}set(t,e){return this.start.copy(t),this.end.copy(e),this}copy(t){return this.start.copy(t.start),this.end.copy(t.end),this}getCenter(t){return t.addVectors(this.start,this.end).multiplyScalar(.5)}delta(t){return t.subVectors(this.end,this.start)}distanceSq(){return this.start.distanceToSquared(this.end)}distance(){return this.start.distanceTo(this.end)}at(t,e){return this.delta(e).multiplyScalar(t).add(this.start)}closestPointToPointParameter(t,e){Fie.subVectors(t,this.start),Vie.subVectors(this.end,this.start);const n=Vie.dot(Vie);let i=Vie.dot(Fie)/n;return e&&(i=s5t(i,0,1)),i}closestPointToPoint(t,e,n){const i=this.closestPointToPointParameter(t,e);return this.delta(n).multiplyScalar(i).add(this.start)}applyMatrix4(t){return this.start.applyMatrix4(t),this.end.applyMatrix4(t),this}equals(t){return t.start.equals(this.start)&&t.end.equals(this.end)}clone(){return(new this.constructor).copy(this)}}class jie extends V3t{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}jie.prototype.isImmediateRenderObject=!0;const Gie=new H5t;const Wie=new H5t,qie=new g3t,Yie=new g3t;class Xie extends ite{constructor(t){const e=$ie(t),n=new N4t,i=[],r=[],o=new s4t(0,0,1),a=new s4t(0,1,0);for(let t=0;t<e.length;t++){const n=e[t];n.parent&&n.parent.isBone&&(i.push(0,0,0),i.push(0,0,0),r.push(o.r,o.g,o.b),r.push(a.r,a.g,a.b))}n.setAttribute("position",new b4t(i,3)),n.setAttribute("color",new b4t(r,3));super(n,new X7t({vertexColors:!0,depthTest:!1,depthWrite:!1,toneMapped:!1,transparent:!0})),this.type="SkeletonHelper",this.isSkeletonHelper=!0,this.root=t,this.bones=e,this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1}updateMatrixWorld(t){const e=this.bones,n=this.geometry,i=n.getAttribute("position");Yie.copy(this.root.matrixWorld).invert();for(let t=0,n=0;t<e.length;t++){const r=e[t];r.parent&&r.parent.isBone&&(qie.multiplyMatrices(Yie,r.matrixWorld),Wie.setFromMatrixPosition(qie),i.setXYZ(n,Wie.x,Wie.y,Wie.z),qie.multiplyMatrices(Yie,r.parent.matrixWorld),Wie.setFromMatrixPosition(qie),i.setXYZ(n+1,Wie.x,Wie.y,Wie.z),n+=2)}n.getAttribute("position").needsUpdate=!0,super.updateMatrixWorld(t)}}function $ie(t){const e=[];t&&t.isBone&&e.push(t);for(let n=0;n<t.children.length;n++)e.push.apply(e,$ie(t.children[n]));return e}const Kie=new H5t,Zie=new s4t,Jie=new s4t;class Qie extends ite{constructor(t=10,e=10,n=4473924,i=8947848){n=new s4t(n),i=new s4t(i);const r=e/2,o=t/e,a=t/2,s=[],l=[];for(let t=0,c=0,u=-a;t<=e;t++,u+=o){s.push(-a,0,u,a,0,u),s.push(u,0,-a,u,0,a);const e=t===r?n:i;e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3}const c=new N4t;c.setAttribute("position",new b4t(s,3)),c.setAttribute("color",new b4t(l,3));super(c,new X7t({vertexColors:!0,toneMapped:!1})),this.type="GridHelper"}}const tre=new H5t,ere=new H5t,nre=new H5t;const ire=new H5t,rre=new i6t;function ore(t,e,n,i,r,o,a){ire.set(r,o,a).unproject(i);const s=e[t];if(void 0!==s){const t=n.getAttribute("position");for(let e=0,n=s.length;e<n;e++)t.setXYZ(s[e],ire.x,ire.y,ire.z)}}const are=new U5t;class sre extends ite{constructor(t,e=16776960){const n=new Uint16Array([0,1,1,2,2,3,3,0,4,5,5,6,6,7,7,4,0,4,1,5,2,6,3,7]),i=new Float32Array(24),r=new N4t;r.setIndex(new h4t(n,1)),r.setAttribute("position",new h4t(i,3)),super(r,new X7t({color:e,toneMapped:!1})),this.object=t,this.type="BoxHelper",this.matrixAutoUpdate=!1,this.update()}update(t){if(void 0!==t&&console.warn("THREE.BoxHelper: .update() has no longer arguments."),void 0!==this.object&&are.setFromObject(this.object),are.isEmpty())return;const e=are.min,n=are.max,i=this.geometry.attributes.position,r=i.array;r[0]=n.x,r[1]=n.y,r[2]=n.z,r[3]=e.x,r[4]=n.y,r[5]=n.z,r[6]=e.x,r[7]=e.y,r[8]=n.z,r[9]=n.x,r[10]=e.y,r[11]=n.z,r[12]=n.x,r[13]=n.y,r[14]=e.z,r[15]=e.x,r[16]=n.y,r[17]=e.z,r[18]=e.x,r[19]=e.y,r[20]=e.z,r[21]=n.x,r[22]=e.y,r[23]=e.z,i.needsUpdate=!0,this.geometry.computeBoundingSphere()}setFromObject(t){return this.object=t,this.update(),this}copy(t){return ite.prototype.copy.call(this,t),this.object=t.object,this}}const lre=new H5t;let cre,ure;class hre extends ite{constructor(t=1){const e=[0,0,0,t,0,0,0,0,0,0,t,0,0,0,0,0,0,t],n=new N4t;n.setAttribute("position",new b4t(e,3)),n.setAttribute("color",new b4t([1,0,0,1,.6,0,0,1,0,.6,1,0,0,0,1,0,.6,1],3));super(n,new X7t({vertexColors:!0,toneMapped:!1})),this.type="AxesHelper"}setColors(t,e,n){const i=new s4t,r=this.geometry.attributes.color.array;return i.set(t),i.toArray(r,0),i.toArray(r,3),i.set(e),i.toArray(r,6),i.toArray(r,9),i.set(n),i.toArray(r,12),i.toArray(r,15),this.geometry.attributes.color.needsUpdate=!0,this}dispose(){this.geometry.dispose(),this.material.dispose()}}const dre=new Float32Array(1),pre=new Int32Array(dre.buffer);Tte.create=function(t,e){return console.log("THREE.Curve.create() has been deprecated"),t.prototype=Object.create(Tte.prototype),t.prototype.constructor=t,t.prototype.getPoint=e,t},vne.prototype.fromPoints=function(t){return console.warn("THREE.Path: .fromPoints() has been renamed to .setFromPoints()."),this.setFromPoints(t)},Qie.prototype.setColors=function(){console.error("THREE.GridHelper: setColors() has been deprecated, pass them in the constructor instead.")},Xie.prototype.update=function(){console.error("THREE.SkeletonHelper: update() no longer needs to be called.")},hne.prototype.extractUrlBase=function(t){return console.warn("THREE.Loader: .extractUrlBase() has been deprecated. Use THREE.LoaderUtils.extractUrlBase() instead."),Vne.extractUrlBase(t)},hne.Handlers={add:function(){console.error("THREE.Loader: Handlers.add() has been removed. Use LoadingManager.addHandler() instead.")},get:function(){console.error("THREE.Loader: Handlers.get() has been removed. Use LoadingManager.getHandler() instead.")}},Hie.prototype.center=function(t){return console.warn("THREE.Box2: .center() has been renamed to .getCenter()."),this.getCenter(t)},Hie.prototype.empty=function(){return console.warn("THREE.Box2: .empty() has been renamed to .isEmpty()."),this.isEmpty()},Hie.prototype.isIntersectionBox=function(t){return console.warn("THREE.Box2: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},Hie.prototype.size=function(t){return console.warn("THREE.Box2: .size() has been renamed to .getSize()."),this.getSize(t)},U5t.prototype.center=function(t){return console.warn("THREE.Box3: .center() has been renamed to .getCenter()."),this.getCenter(t)},U5t.prototype.empty=function(){return console.warn("THREE.Box3: .empty() has been renamed to .isEmpty()."),this.isEmpty()},U5t.prototype.isIntersectionBox=function(t){return console.warn("THREE.Box3: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},U5t.prototype.isIntersectionSphere=function(t){return console.warn("THREE.Box3: .isIntersectionSphere() has been renamed to .intersectsSphere()."),this.intersectsSphere(t)},U5t.prototype.size=function(t){return console.warn("THREE.Box3: .size() has been renamed to .getSize()."),this.getSize(t)},s3t.prototype.empty=function(){return console.warn("THREE.Sphere: .empty() has been renamed to .isEmpty()."),this.isEmpty()},m6t.prototype.setFromMatrix=function(t){return console.warn("THREE.Frustum: .setFromMatrix() has been renamed to .setFromProjectionMatrix()."),this.setFromProjectionMatrix(t)},Uie.prototype.center=function(t){return console.warn("THREE.Line3: .center() has been renamed to .getCenter()."),this.getCenter(t)},A5t.prototype.flattenToArrayOffset=function(t,e){return console.warn("THREE.Matrix3: .flattenToArrayOffset() has been deprecated. Use .toArray() instead."),this.toArray(t,e)},A5t.prototype.multiplyVector3=function(t){return console.warn("THREE.Matrix3: .multiplyVector3() has been removed. Use vector.applyMatrix3( matrix ) instead."),t.applyMatrix3(this)},A5t.prototype.multiplyVector3Array=function(){console.error("THREE.Matrix3: .multiplyVector3Array() has been removed.")},A5t.prototype.applyToBufferAttribute=function(t){return console.warn("THREE.Matrix3: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix3( matrix ) instead."),t.applyMatrix3(this)},A5t.prototype.applyToVector3Array=function(){console.error("THREE.Matrix3: .applyToVector3Array() has been removed.")},A5t.prototype.getInverse=function(t){return console.warn("THREE.Matrix3: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead."),this.copy(t).invert()},g3t.prototype.extractPosition=function(t){return console.warn("THREE.Matrix4: .extractPosition() has been renamed to .copyPosition()."),this.copyPosition(t)},g3t.prototype.flattenToArrayOffset=function(t,e){return console.warn("THREE.Matrix4: .flattenToArrayOffset() has been deprecated. Use .toArray() instead."),this.toArray(t,e)},g3t.prototype.getPosition=function(){return console.warn("THREE.Matrix4: .getPosition() has been removed. Use Vector3.setFromMatrixPosition( matrix ) instead."),(new H5t).setFromMatrixColumn(this,3)},g3t.prototype.setRotationFromQuaternion=function(t){return console.warn("THREE.Matrix4: .setRotationFromQuaternion() has been renamed to .makeRotationFromQuaternion()."),this.makeRotationFromQuaternion(t)},g3t.prototype.multiplyToArray=function(){console.warn("THREE.Matrix4: .multiplyToArray() has been removed.")},g3t.prototype.multiplyVector3=function(t){return console.warn("THREE.Matrix4: .multiplyVector3() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},g3t.prototype.multiplyVector4=function(t){return console.warn("THREE.Matrix4: .multiplyVector4() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},g3t.prototype.multiplyVector3Array=function(){console.error("THREE.Matrix4: .multiplyVector3Array() has been removed.")},g3t.prototype.rotateAxis=function(t){console.warn("THREE.Matrix4: .rotateAxis() has been removed. Use Vector3.transformDirection( matrix ) instead."),t.transformDirection(this)},g3t.prototype.crossVector=function(t){return console.warn("THREE.Matrix4: .crossVector() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},g3t.prototype.translate=function(){console.error("THREE.Matrix4: .translate() has been removed.")},g3t.prototype.rotateX=function(){console.error("THREE.Matrix4: .rotateX() has been removed.")},g3t.prototype.rotateY=function(){console.error("THREE.Matrix4: .rotateY() has been removed.")},g3t.prototype.rotateZ=function(){console.error("THREE.Matrix4: .rotateZ() has been removed.")},g3t.prototype.rotateByAxis=function(){console.error("THREE.Matrix4: .rotateByAxis() has been removed.")},g3t.prototype.applyToBufferAttribute=function(t){return console.warn("THREE.Matrix4: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},g3t.prototype.applyToVector3Array=function(){console.error("THREE.Matrix4: .applyToVector3Array() has been removed.")},g3t.prototype.makeFrustum=function(t,e,n,i,r,o){return console.warn("THREE.Matrix4: .makeFrustum() has been removed. Use .makePerspective( left, right, top, bottom, near, far ) instead."),this.makePerspective(t,e,i,n,r,o)},g3t.prototype.getInverse=function(t){return console.warn("THREE.Matrix4: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead."),this.copy(t).invert()},d6t.prototype.isIntersectionLine=function(t){return console.warn("THREE.Plane: .isIntersectionLine() has been renamed to .intersectsLine()."),this.intersectsLine(t)},B5t.prototype.multiplyVector3=function(t){return console.warn("THREE.Quaternion: .multiplyVector3() has been removed. Use is now vector.applyQuaternion( quaternion ) instead."),t.applyQuaternion(this)},B5t.prototype.inverse=function(){return console.warn("THREE.Quaternion: .inverse() has been renamed to invert()."),this.invert()},m3t.prototype.isIntersectionBox=function(t){return console.warn("THREE.Ray: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},m3t.prototype.isIntersectionPlane=function(t){return console.warn("THREE.Ray: .isIntersectionPlane() has been renamed to .intersectsPlane()."),this.intersectsPlane(t)},m3t.prototype.isIntersectionSphere=function(t){return console.warn("THREE.Ray: .isIntersectionSphere() has been renamed to .intersectsSphere()."),this.intersectsSphere(t)},J3t.prototype.area=function(){return console.warn("THREE.Triangle: .area() has been renamed to .getArea()."),this.getArea()},J3t.prototype.barycoordFromPoint=function(t,e){return console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord()."),this.getBarycoord(t,e)},J3t.prototype.midpoint=function(t){return console.warn("THREE.Triangle: .midpoint() has been renamed to .getMidpoint()."),this.getMidpoint(t)},J3t.prototypenormal=function(t){return console.warn("THREE.Triangle: .normal() has been renamed to .getNormal()."),this.getNormal(t)},J3t.prototype.plane=function(t){return console.warn("THREE.Triangle: .plane() has been renamed to .getPlane()."),this.getPlane(t)},J3t.barycoordFromPoint=function(t,e,n,i,r){return console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord()."),J3t.getBarycoord(t,e,n,i,r)},J3t.normal=function(t,e,n,i){return console.warn("THREE.Triangle: .normal() has been renamed to .getNormal()."),J3t.getNormal(t,e,n,i)},bne.prototype.extractAllPoints=function(t){return console.warn("THREE.Shape: .extractAllPoints() has been removed. Use .extractPoints() instead."),this.extractPoints(t)},bne.prototype.extrude=function(t){return console.warn("THREE.Shape: .extrude() has been removed. Use ExtrudeGeometry() instead."),new bee(this,t)},bne.prototype.makeGeometry=function(t){return console.warn("THREE.Shape: .makeGeometry() has been removed. Use ShapeGeometry() instead."),new Cee(this,t)},C5t.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector2: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},C5t.prototype.distanceToManhattan=function(t){return console.warn("THREE.Vector2: .distanceToManhattan() has been renamed to .manhattanDistanceTo()."),this.manhattanDistanceTo(t)},C5t.prototype.lengthManhattan=function(){return console.warn("THREE.Vector2: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},H5t.prototype.setEulerFromRotationMatrix=function(){console.error("THREE.Vector3: .setEulerFromRotationMatrix() has been removed. Use Euler.setFromRotationMatrix() instead.")},H5t.prototype.setEulerFromQuaternion=function(){console.error("THREE.Vector3: .setEulerFromQuaternion() has been removed. Use Euler.setFromQuaternion() instead.")},H5t.prototype.getPositionFromMatrix=function(t){return console.warn("THREE.Vector3: .getPositionFromMatrix() has been renamed to .setFromMatrixPosition()."),this.setFromMatrixPosition(t)},H5t.prototype.getScaleFromMatrix=function(t){return console.warn("THREE.Vector3: .getScaleFromMatrix() has been renamed to .setFromMatrixScale()."),this.setFromMatrixScale(t)},H5t.prototype.getColumnFromMatrix=function(t,e){return console.warn("THREE.Vector3: .getColumnFromMatrix() has been renamed to .setFromMatrixColumn()."),this.setFromMatrixColumn(e,t)},H5t.prototype.applyProjection=function(t){return console.warn("THREE.Vector3: .applyProjection() has been removed. Use .applyMatrix4( m ) instead."),this.applyMatrix4(t)},H5t.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector3: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},H5t.prototype.distanceToManhattan=function(t){return console.warn("THREE.Vector3: .distanceToManhattan() has been renamed to .manhattanDistanceTo()."),this.manhattanDistanceTo(t)},H5t.prototype.lengthManhattan=function(){return console.warn("THREE.Vector3: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},R5t.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector4: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},R5t.prototype.lengthManhattan=function(){return console.warn("THREE.Vector4: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},V3t.prototype.getChildByName=function(t){return console.warn("THREE.Object3D: .getChildByName() has been renamed to .getObjectByName()."),this.getObjectByName(t)},V3t.prototype.renderDepth=function(){console.warn("THREE.Object3D: .renderDepth has been removed. Use .renderOrder, instead.")},V3t.prototype.translate=function(t,e){return console.warn("THREE.Object3D: .translate() has been removed. Use .translateOnAxis( axis, distance ) instead."),this.translateOnAxis(e,t)},V3t.prototype.getWorldRotation=function(){console.error("THREE.Object3D: .getWorldRotation() has been removed. Use THREE.Object3D.getWorldQuaternion( target ) instead.")},V3t.prototype.applyMatrix=function(t){return console.warn("THREE.Object3D: .applyMatrix() has been renamed to .applyMatrix4()."),this.applyMatrix4(t)},Object.defineProperties(V3t.prototype,{eulerOrder:{get:function(){return console.warn("THREE.Object3D: .eulerOrder is now .rotation.order."),this.rotation.order},set:function(t){console.warn("THREE.Object3D: .eulerOrder is now .rotation.order."),this.rotation.order=t}},useQuaternion:{get:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")},set:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")}}}),K4t.prototype.setDrawMode=function(){console.error("THREE.Mesh: .setDrawMode() has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.")},Object.defineProperties(K4t.prototype,{drawMode:{get:function(){return console.error("THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode."),0},set:function(){console.error("THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.")}}}),D7t.prototype.initBones=function(){console.error("THREE.SkinnedMesh: initBones() has been removed.")},r6t.prototype.setLens=function(t,e){console.warn("THREE.PerspectiveCamera.setLens is deprecated. Use .setFocalLength and .filmGauge for a photographic setup."),void 0!==e&&(this.filmGauge=e),this.setFocalLength(t)},Object.defineProperties(xne.prototype,{onlyShadow:{set:function(){console.warn("THREE.Light: .onlyShadow has been removed.")}},shadowCameraFov:{set:function(t){console.warn("THREE.Light: .shadowCameraFov is now .shadow.camera.fov."),this.shadow.camera.fov=t}},shadowCameraLeft:{set:function(t){console.warn("THREE.Light: .shadowCameraLeft is now .shadow.camera.left."),this.shadow.camera.left=t}},shadowCameraRight:{set:function(t){console.warn("THREE.Light: .shadowCameraRight is now .shadow.camera.right."),this.shadow.camera.right=t}},shadowCameraTop:{set:function(t){console.warn("THREE.Light: .shadowCameraTop is now .shadow.camera.top."),this.shadow.camera.top=t}},shadowCameraBottom:{set:function(t){console.warn("THREE.Light: .shadowCameraBottom is now .shadow.camera.bottom."),this.shadow.camera.bottom=t}},shadowCameraNear:{set:function(t){console.warn("THREE.Light: .shadowCameraNear is now .shadow.camera.near."),this.shadow.camera.near=t}},shadowCameraFar:{set:function(t){console.warn("THREE.Light: .shadowCameraFar is now .shadow.camera.far."),this.shadow.camera.far=t}},shadowCameraVisible:{set:function(){console.warn("THREE.Light: .shadowCameraVisible has been removed. Use new THREE.CameraHelper( light.shadow.camera ) instead.")}},shadowBias:{set:function(t){console.warn("THREE.Light: .shadowBias is now .shadow.bias."),this.shadow.bias=t}},shadowDarkness:{set:function(){console.warn("THREE.Light: .shadowDarkness has been removed.")}},shadowMapWidth:{set:function(t){console.warn("THREE.Light: .shadowMapWidth is now .shadow.mapSize.width."),this.shadow.mapSize.width=t}},shadowMapHeight:{set:function(t){console.warn("THREE.Light: .shadowMapHeight is now .shadow.mapSize.height."),this.shadow.mapSize.height=t}}}),Object.defineProperties(h4t.prototype,{length:{get:function(){return console.warn("THREE.BufferAttribute: .length has been deprecated. Use .count instead."),this.array.length}},dynamic:{get:function(){return console.warn("THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead."),this.usage===Q2t},set:function(){console.warn("THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead."),this.setUsage(Q2t)}}}),h4t.prototype.setDynamic=function(t){return console.warn("THREE.BufferAttribute: .setDynamic() has been deprecated. Use .setUsage() instead."),this.setUsage(!0===t?Q2t:J2t),this},h4t.prototype.copyIndicesArray=function(){console.error("THREE.BufferAttribute: .copyIndicesArray() has been removed.")},h4t.prototype.setArray=function(){console.error("THREE.BufferAttribute: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers")},N4t.prototype.addIndex=function(t){console.warn("THREE.BufferGeometry: .addIndex() has been renamed to .setIndex()."),this.setIndex(t)},N4t.prototype.addAttribute=function(t,e){return console.warn("THREE.BufferGeometry: .addAttribute() has been renamed to .setAttribute()."),e&&e.isBufferAttribute||e&&e.isInterleavedBufferAttribute?"index"===t?(console.warn("THREE.BufferGeometry.addAttribute: Use .setIndex() for index attribute."),this.setIndex(e),this):this.setAttribute(t,e):(console.warn("THREE.BufferGeometry: .addAttribute() now expects ( name, attribute )."),this.setAttribute(t,new h4t(arguments[1],arguments[2])))},N4t.prototype.addDrawCall=function(t,e,n){void 0!==n&&console.warn("THREE.BufferGeometry: .addDrawCall() no longer supports indexOffset."),console.warn("THREE.BufferGeometry: .addDrawCall() is now .addGroup()."),this.addGroup(t,e)},N4t.prototype.clearDrawCalls=function(){console.warn("THREE.BufferGeometry: .clearDrawCalls() is now .clearGroups()."),this.clearGroups()},N4t.prototype.computeOffsets=function(){console.warn("THREE.BufferGeometry: .computeOffsets() has been removed.")},N4t.prototype.removeAttribute=function(t){return console.warn("THREE.BufferGeometry: .removeAttribute() has been renamed to .deleteAttribute()."),this.deleteAttribute(t)},N4t.prototype.applyMatrix=function(t){return console.warn("THREE.BufferGeometry: .applyMatrix() has been renamed to .applyMatrix4()."),this.applyMatrix4(t)},Object.defineProperties(N4t.prototype,{drawcalls:{get:function(){return console.error("THREE.BufferGeometry: .drawcalls has been renamed to .groups."),this.groups}},offsets:{get:function(){return console.warn("THREE.BufferGeometry: .offsets has been renamed to .groups."),this.groups}}}),u7t.prototype.setDynamic=function(t){return console.warn("THREE.InterleavedBuffer: .setDynamic() has been deprecated. Use .setUsage() instead."),this.setUsage(!0===t?Q2t:J2t),this},u7t.prototype.setArray=function(){console.error("THREE.InterleavedBuffer: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers")},bee.prototype.getArrays=function(){console.error("THREE.ExtrudeGeometry: .getArrays() has been removed.")},bee.prototype.addShapeList=function(){console.error("THREE.ExtrudeGeometry: .addShapeList() has been removed.")},bee.prototype.addShape=function(){console.error("THREE.ExtrudeGeometry: .addShape() has been removed.")},c7t.prototype.dispose=function(){console.error("THREE.Scene: .dispose() has been removed.")},Nie.prototype.onUpdate=function(){return console.warn("THREE.Uniform: .onUpdate() has been removed. Use object.onBeforeRender() instead."),this},Object.defineProperties(t4t.prototype,{wrapAround:{get:function(){console.warn("THREE.Material: .wrapAround has been removed.")},set:function(){console.warn("THREE.Material: .wrapAround has been removed.")}},overdraw:{get:function(){console.warn("THREE.Material: .overdraw has been removed.")},set:function(){console.warn("THREE.Material: .overdraw has been removed.")}},wrapRGB:{get:function(){return console.warn("THREE.Material: .wrapRGB has been removed."),new s4t}},shading:{get:function(){console.error("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead.")},set:function(t){console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead."),this.flatShading=1===t}},stencilMask:{get:function(){return console.warn("THREE."+this.type+": .stencilMask has been removed. Use .stencilFuncMask instead."),this.stencilFuncMask},set:function(t){console.warn("THREE."+this.type+": .stencilMask has been removed. Use .stencilFuncMask instead."),this.stencilFuncMask=t}},vertexTangents:{get:function(){console.warn("THREE."+this.type+": .vertexTangents has been removed.")},set:function(){console.warn("THREE."+this.type+": .vertexTangents has been removed.")}}}),Object.defineProperties(n6t.prototype,{derivatives:{get:function(){return console.warn("THREE.ShaderMaterial: .derivatives has been moved to .extensions.derivatives."),this.extensions.derivatives},set:function(t){console.warn("THREE. ShaderMaterial: .derivatives has been moved to .extensions.derivatives."),this.extensions.derivatives=t}}}),o7t.prototype.clearTarget=function(t,e,n,i){console.warn("THREE.WebGLRenderer: .clearTarget() has been deprecated. Use .setRenderTarget() and .clear() instead."),this.setRenderTarget(t),this.clear(e,n,i)},o7t.prototype.animate=function(t){console.warn("THREE.WebGLRenderer: .animate() is now .setAnimationLoop()."),this.setAnimationLoop(t)},o7t.prototype.getCurrentRenderTarget=function(){return console.warn("THREE.WebGLRenderer: .getCurrentRenderTarget() is now .getRenderTarget()."),this.getRenderTarget()},o7t.prototype.getMaxAnisotropy=function(){return console.warn("THREE.WebGLRenderer: .getMaxAnisotropy() is now .capabilities.getMaxAnisotropy()."),this.capabilities.getMaxAnisotropy()},o7t.prototype.getPrecision=function(){return console.warn("THREE.WebGLRenderer: .getPrecision() is now .capabilities.precision."),this.capabilities.precision},o7t.prototype.resetGLState=function(){return console.warn("THREE.WebGLRenderer: .resetGLState() is now .state.reset()."),this.state.reset()},o7t.prototype.supportsFloatTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsFloatTextures() is now .extensions.get( 'OES_texture_float' )."),this.extensions.get("OES_texture_float")},o7t.prototype.supportsHalfFloatTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsHalfFloatTextures() is now .extensions.get( 'OES_texture_half_float' )."),this.extensions.get("OES_texture_half_float")},o7t.prototype.supportsStandardDerivatives=function(){return console.warn("THREE.WebGLRenderer: .supportsStandardDerivatives() is now .extensions.get( 'OES_standard_derivatives' )."),this.extensions.get("OES_standard_derivatives")},o7t.prototype.supportsCompressedTextureS3TC=function(){return console.warn("THREE.WebGLRenderer: .supportsCompressedTextureS3TC() is now .extensions.get( 'WEBGL_compressed_texture_s3tc' )."),this.extensions.get("WEBGL_compressed_texture_s3tc")},o7t.prototype.supportsCompressedTexturePVRTC=function(){return console.warn("THREE.WebGLRenderer: .supportsCompressedTexturePVRTC() is now .extensions.get( 'WEBGL_compressed_texture_pvrtc' )."),this.extensions.get("WEBGL_compressed_texture_pvrtc")},o7t.prototype.supportsBlendMinMax=function(){return console.warn("THREE.WebGLRenderer: .supportsBlendMinMax() is now .extensions.get( 'EXT_blend_minmax' )."),this.extensions.get("EXT_blend_minmax")},o7t.prototype.supportsVertexTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsVertexTextures() is now .capabilities.vertexTextures."),this.capabilities.vertexTextures},o7t.prototype.supportsInstancedArrays=function(){return console.warn("THREE.WebGLRenderer: .supportsInstancedArrays() is now .extensions.get( 'ANGLE_instanced_arrays' )."),this.extensions.get("ANGLE_instanced_arrays")},o7t.prototype.enableScissorTest=function(t){console.warn("THREE.WebGLRenderer: .enableScissorTest() is now .setScissorTest()."),this.setScissorTest(t)},o7t.prototype.initMaterial=function(){console.warn("THREE.WebGLRenderer: .initMaterial() has been removed.")},o7t.prototype.addPrePlugin=function(){console.warn("THREE.WebGLRenderer: .addPrePlugin() has been removed.")},o7t.prototype.addPostPlugin=function(){console.warn("THREE.WebGLRenderer: .addPostPlugin() has been removed.")},o7t.prototype.updateShadowMap=function(){console.warn("THREE.WebGLRenderer: .updateShadowMap() has been removed.")},o7t.prototype.setFaceCulling=function(){console.warn("THREE.WebGLRenderer: .setFaceCulling() has been removed.")},o7t.prototype.allocTextureUnit=function(){console.warn("THREE.WebGLRenderer: .allocTextureUnit() has been removed.")},o7t.prototype.setTexture=function(){console.warn("THREE.WebGLRenderer: .setTexture() has been removed.")},o7t.prototype.setTexture2D=function(){console.warn("THREE.WebGLRenderer: .setTexture2D() has been removed.")},o7t.prototype.setTextureCube=function(){console.warn("THREE.WebGLRenderer: .setTextureCube() has been removed.")},o7t.prototype.getActiveMipMapLevel=function(){return console.warn("THREE.WebGLRenderer: .getActiveMipMapLevel() is now .getActiveMipmapLevel()."),this.getActiveMipmapLevel()},Object.defineProperties(o7t.prototype,{shadowMapEnabled:{get:function(){return this.shadowMap.enabled},set:function(t){console.warn("THREE.WebGLRenderer: .shadowMapEnabled is now .shadowMap.enabled."),this.shadowMap.enabled=t}},shadowMapType:{get:function(){return this.shadowMap.type},set:function(t){console.warn("THREE.WebGLRenderer: .shadowMapType is now .shadowMap.type."),this.shadowMap.type=t}},shadowMapCullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")}},context:{get:function(){return console.warn("THREE.WebGLRenderer: .context has been removed. Use .getContext() instead."),this.getContext()}},vr:{get:function(){return console.warn("THREE.WebGLRenderer: .vr has been renamed to .xr"),this.xr}},gammaInput:{get:function(){return console.warn("THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead."),!1},set:function(){console.warn("THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead.")}},gammaOutput:{get:function(){return console.warn("THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead."),!1},set:function(t){console.warn("THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead."),this.outputEncoding=!0===t?W2t:G2t}},toneMappingWhitePoint:{get:function(){return console.warn("THREE.WebGLRenderer: .toneMappingWhitePoint has been removed."),1},set:function(){console.warn("THREE.WebGLRenderer: .toneMappingWhitePoint has been removed.")}}}),Object.defineProperties($8t.prototype,{cullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")}},renderReverseSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")}},renderSingleSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")}}}),Object.defineProperties(O5t.prototype,{wrapS:{get:function(){return console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS."),this.texture.wrapS},set:function(t){console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS."),this.texture.wrapS=t}},wrapT:{get:function(){return console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT."),this.texture.wrapT},set:function(t){console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT."),this.texture.wrapT=t}},magFilter:{get:function(){return console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter."),this.texture.magFilter},set:function(t){console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter."),this.texture.magFilter=t}},minFilter:{get:function(){return console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter."),this.texture.minFilter},set:function(t){console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter."),this.texture.minFilter=t}},anisotropy:{get:function(){return console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy."),this.texture.anisotropy},set:function(t){console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy."),this.texture.anisotropy=t}},offset:{get:function(){return console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset."),this.texture.offset},set:function(t){console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset."),this.texture.offset=t}},repeat:{get:function(){return console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat."),this.texture.repeat},set:function(t){console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat."),this.texture.repeat=t}},format:{get:function(){return console.warn("THREE.WebGLRenderTarget: .format is now .texture.format."),this.texture.format},set:function(t){console.warn("THREE.WebGLRenderTarget: .format is now .texture.format."),this.texture.format=t}},type:{get:function(){return console.warn("THREE.WebGLRenderTarget: .type is now .texture.type."),this.texture.type},set:function(t){console.warn("THREE.WebGLRenderTarget: .type is now .texture.type."),this.texture.type=t}},generateMipmaps:{get:function(){return console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps."),this.texture.generateMipmaps},set:function(t){console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps."),this.texture.generateMipmaps=t}}}),hie.prototype.load=function(t){console.warn("THREE.Audio: .load has been deprecated. Use THREE.AudioLoader instead.");const e=this;return(new tie).load(t,(function(t){e.setBuffer(t)})),this},gie.prototype.getData=function(){return console.warn("THREE.AudioAnalyser: .getData() is now .getFrequencyData()."),this.getFrequencyData()},a6t.prototype.updateCubeMap=function(t,e){return console.warn("THREE.CubeCamera: .updateCubeMap() is now .update()."),this.update(t,e)},a6t.prototype.clear=function(t,e,n,i){return console.warn("THREE.CubeCamera: .clear() is now .renderTarget.clear()."),this.renderTarget.clear(t,e,n,i)},L5t.crossOrigin=void 0,L5t.loadTexture=function(t,e,n,i){console.warn("THREE.ImageUtils.loadTexture has been deprecated. Use THREE.TextureLoader() instead.");const r=new _ne;r.setCrossOrigin(this.crossOrigin);const o=r.load(t,n,void 0,i);return e&&(o.mapping=e),o},L5t.loadTextureCube=function(t,e,n,i){console.warn("THREE.ImageUtils.loadTextureCube has been deprecated. Use THREE.CubeTextureLoader() instead.");const r=new mne;r.setCrossOrigin(this.crossOrigin);const o=r.load(t,n,void 0,i);return e&&(o.mapping=e),o},L5t.loadCompressedTexture=function(){console.error("THREE.ImageUtils.loadCompressedTexture has been removed. Use THREE.DDSLoader instead.")},L5t.loadCompressedTextureCube=function(){console.error("THREE.ImageUtils.loadCompressedTextureCube has been removed. Use THREE.DDSLoader instead.")};const fre={createMultiMaterialObject:function(){console.error("THREE.SceneUtils has been moved to /examples/jsm/utils/SceneUtils.js")},detach:function(){console.error("THREE.SceneUtils has been moved to /examples/jsm/utils/SceneUtils.js")},attach:function(){console.error("THREE.SceneUtils has been moved to /examples/jsm/utils/SceneUtils.js")}};"undefined"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("register",{detail:{revision:K0t}})),"undefined"!=typeof window&&(window.__THREE__?console.warn("WARNING: Multiple instances of Three.js being imported."):window.__THREE__=K0t);var mre=Object.freeze({__proto__:null,ACESFilmicToneMapping:4,AddEquation:Q0t,AddOperation:2,AdditiveAnimationBlendMode:j2t,AdditiveBlending:2,AlphaFormat:1021,AlwaysDepth:1,AlwaysStencilFunc:519,AmbientLight:zne,AmbientLightProbe:nie,AnimationClip:ane,AnimationLoader:class extends hne{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new pne(this.manager);o.setPath(this.path),o.setRequestHeader(this.requestHeader),o.setWithCredentials(this.withCredentials),o.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e=[];for(let n=0;n<t.length;n++){const i=ane.parse(t[n]);e.push(i)}return e}},AnimationMixer:Pie,AnimationObjectGroup:kie,AnimationUtils:Yee,ArcCurve:Ate,ArrayCamera:Q8t,ArrowHelper:class extends V3t{constructor(t=new H5t(0,0,1),e=new H5t(0,0,0),n=1,i=16776960,r=.2*n,o=.2*r){super(),this.type="ArrowHelper",void 0===cre&&(cre=new N4t,cre.setAttribute("position",new b4t([0,0,0,0,1,0],3)),ure=new _te(0,.5,1,5,1),ure.translate(0,-.5,0)),this.position.copy(e),this.line=new tte(cre,new X7t({color:i,toneMapped:!1})),this.line.matrixAutoUpdate=!1,this.add(this.line),this.cone=new K4t(ure,new l4t({color:i,toneMapped:!1})),this.cone.matrixAutoUpdate=!1,this.add(this.cone),this.setDirection(t),this.setLength(n,r,o)}setDirection(t){if(t.y>.99999)this.quaternion.set(0,0,0,1);else if(t.y<-.99999)this.quaternion.set(1,0,0,0);else{lre.set(t.z,0,-t.x).normalize();const e=Math.acos(t.y);this.quaternion.setFromAxisAngle(lre,e)}}setLength(t,e=.2*t,n=.2*e){this.line.scale.set(1,Math.max(1e-4,t-e),1),this.line.updateMatrix(),this.cone.scale.set(n,e,n),this.cone.position.y=t,this.cone.updateMatrix()}setColor(t){this.line.material.color.set(t),this.cone.material.color.set(t)}copy(t){return super.copy(t,!1),this.line.copy(t.line),this.cone.copy(t.cone),this}},Audio:hie,AudioAnalyser:gie,AudioContext:Qne,AudioListener:class extends V3t{constructor(){super(),this.type="AudioListener",this.context=Qne.getContext(),this.gain=this.context.createGain(),this.gain.connect(this.context.destination),this.filter=null,this.timeDelta=0,this._clock=new oie}getInput(){return this.gain}removeFilter(){return null!==this.filter&&(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination),this.gain.connect(this.context.destination),this.filter=null),this}getFilter(){return this.filter}setFilter(t){return null!==this.filter?(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination)):this.gain.disconnect(this.context.destination),this.filter=t,this.gain.connect(this.filter),this.filter.connect(this.context.destination),this}getMasterVolume(){return this.gain.gain.value}setMasterVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}updateMatrixWorld(t){super.updateMatrixWorld(t);const e=this.context.listener,n=this.up;if(this.timeDelta=this._clock.getDelta(),this.matrixWorld.decompose(sie,lie,cie),uie.set(0,0,-1).applyQuaternion(lie),e.positionX){const t=this.context.currentTime+this.timeDelta;e.positionX.linearRampToValueAtTime(sie.x,t),e.positionY.linearRampToValueAtTime(sie.y,t),e.positionZ.linearRampToValueAtTime(sie.z,t),e.forwardX.linearRampToValueAtTime(uie.x,t),e.forwardY.linearRampToValueAtTime(uie.y,t),e.forwardZ.linearRampToValueAtTime(uie.z,t),e.upX.linearRampToValueAtTime(n.x,t),e.upY.linearRampToValueAtTime(n.y,t),e.upZ.linearRampToValueAtTime(n.z,t)}else e.setPosition(sie.x,sie.y,sie.z),e.setOrientation(uie.x,uie.y,uie.z,n.x,n.y,n.z)}},AudioLoader:tie,AxesHelper:hre,AxisHelper:function gre(t){return console.warn("THREE.AxisHelper has been renamed to THREE.AxesHelper."),new hre(t)},BackSide:1,BasicDepthPacking:3200,BasicShadowMap:0,BinaryTextureLoader:function _re(t){return console.warn("THREE.BinaryTextureLoader has been renamed to THREE.DataTextureLoader."),new gne(t)},Bone:B7t,BooleanKeyframeTrack:Qee,BoundingBoxHelper:function yre(t,e){return console.warn("THREE.BoundingBoxHelper has been deprecated. Creating a THREE.BoxHelper instead."),new sre(t,e)},Box2:Hie,Box3:U5t,Box3Helper:class extends ite{constructor(t,e=16776960){const n=new Uint16Array([0,1,1,2,2,3,3,0,4,5,5,6,6,7,7,4,0,4,1,5,2,6,3,7]),i=new N4t;i.setIndex(new h4t(n,1)),i.setAttribute("position",new b4t([1,1,1,-1,1,1,-1,-1,1,1,-1,1,1,1,-1,-1,1,-1,-1,-1,-1,1,-1,-1],3)),super(i,new X7t({color:e,toneMapped:!1})),this.box=t,this.type="Box3Helper",this.geometry.computeBoundingSphere()}updateMatrixWorld(t){const e=this.box;e.isEmpty()||(e.getCenter(this.position),e.getSize(this.scale),this.scale.multiplyScalar(.5),super.updateMatrixWorld(t))}},BoxBufferGeometry:J4t,BoxGeometry:J4t,BoxHelper:sre,BufferAttribute:h4t,BufferGeometry:N4t,BufferGeometryLoader:Gne,ByteType:1010,Cache:lne,Camera:i6t,CameraHelper:class extends ite{constructor(t){const e=new N4t,n=new X7t({color:16777215,vertexColors:!0,toneMapped:!1}),i=[],r=[],o={},a=new s4t(16755200),s=new s4t(16711680),l=new s4t(43775),c=new s4t(16777215),u=new s4t(3355443);function h(t,e,n){d(t,n),d(e,n)}function d(t,e){i.push(0,0,0),r.push(e.r,e.g,e.b),void 0===o[t]&&(o[t]=[]),o[t].push(i.length/3-1)}h("n1","n2",a),h("n2","n4",a),h("n4","n3",a),h("n3","n1",a),h("f1","f2",a),h("f2","f4",a),h("f4","f3",a),h("f3","f1",a),h("n1","f1",a),h("n2","f2",a),h("n3","f3",a),h("n4","f4",a),h("p","n1",s),h("p","n2",s),h("p","n3",s),h("p","n4",s),h("u1","u2",l),h("u2","u3",l),h("u3","u1",l),h("c","t",c),h("p","c",u),h("cn1","cn2",u),h("cn3","cn4",u),h("cf1","cf2",u),h("cf3","cf4",u),e.setAttribute("position",new b4t(i,3)),e.setAttribute("color",new b4t(r,3)),super(e,n),this.type="CameraHelper",this.camera=t,this.camera.updateProjectionMatrix&&this.camera.updateProjectionMatrix(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.pointMap=o,this.update()}update(){const t=this.geometry,e=this.pointMap;rre.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse),ore("c",e,t,rre,0,0,-1),ore("t",e,t,rre,0,0,1),ore("n1",e,t,rre,-1,-1,-1),ore("n2",e,t,rre,1,-1,-1),ore("n3",e,t,rre,-1,1,-1),ore("n4",e,t,rre,1,1,-1),ore("f1",e,t,rre,-1,-1,1),ore("f2",e,t,rre,1,-1,1),ore("f3",e,t,rre,-1,1,1),ore("f4",e,t,rre,1,1,1),ore("u1",e,t,rre,.7,1.1,-1),ore("u2",e,t,rre,-.7,1.1,-1),ore("u3",e,t,rre,0,2,-1),ore("cf1",e,t,rre,-1,0,1),ore("cf2",e,t,rre,1,0,1),ore("cf3",e,t,rre,0,-1,1),ore("cf4",e,t,rre,0,1,1),ore("cn1",e,t,rre,-1,0,-1),ore("cn2",e,t,rre,1,0,-1),ore("cn3",e,t,rre,0,-1,-1),ore("cn4",e,t,rre,0,1,-1),t.getAttribute("position").needsUpdate=!0}dispose(){this.geometry.dispose(),this.material.dispose()}},CanvasRenderer:function vre(){console.error("THREE.CanvasRenderer has been removed")},CanvasTexture:fte,CatmullRomCurve3:Rte,CineonToneMapping:3,CircleBufferGeometry:gte,CircleGeometry:gte,ClampToEdgeWrapping:l2t,Clock:oie,Color:s4t,ColorKeyframeTrack:tne,CompressedTexture:pte,CompressedTextureLoader:class extends hne{constructor(t){super(t)}load(t,e,n,i){const r=this,o=[],a=new pte,s=new pne(this.manager);s.setPath(this.path),s.setResponseType("arraybuffer"),s.setRequestHeader(this.requestHeader),s.setWithCredentials(r.withCredentials);let l=0;function c(c){s.load(t[c],(function(t){const n=r.parse(t,!0);o[c]={width:n.width,height:n.height,format:n.format,mipmaps:n.mipmaps},l+=1,6===l&&(1===n.mipmapCount&&(a.minFilter=p2t),a.image=o,a.format=n.format,a.needsUpdate=!0,e&&e(a))}),n,i)}if(Array.isArray(t))for(let e=0,n=t.length;e<n;++e)c(e);else s.load(t,(function(t){const n=r.parse(t,!0);if(n.isCubemap){const t=n.mipmaps.length/n.mipmapCount;for(let e=0;e<t;e++){o[e]={mipmaps:[]};for(let t=0;t<n.mipmapCount;t++)o[e].mipmaps.push(n.mipmaps[e*n.mipmapCount+t]),o[e].format=n.format,o[e].width=n.width,o[e].height=n.height}a.image=o}else a.image.width=n.width,a.image.height=n.height,a.mipmaps=n.mipmaps;1===n.mipmapCount&&(a.minFilter=p2t),a.format=n.format,a.needsUpdate=!0,e&&e(a)}),n,i);return a}},ConeBufferGeometry:yte,ConeGeometry:yte,CubeCamera:a6t,CubeReflectionMapping:e2t,CubeRefractionMapping:n2t,CubeTexture:s6t,CubeTextureLoader:mne,CubeUVReflectionMapping:o2t,CubeUVRefractionMapping:a2t,CubicBezierCurve:Bte,CubicBezierCurve3:Hte,CubicInterpolant:$ee,CullFaceBack:1,CullFaceFront:2,CullFaceFrontBack:3,CullFaceNone:0,Curve:Tte,CurvePath:yne,CustomBlending:5,CustomToneMapping:5,CylinderBufferGeometry:_te,CylinderGeometry:_te,Cylindrical:class{constructor(t=1,e=0,n=0){return this.radius=t,this.theta=e,this.y=n,this}set(t,e,n){return this.radius=t,this.theta=e,this.y=n,this}copy(t){return this.radius=t.radius,this.theta=t.theta,this.y=t.y,this}setFromVector3(t){return this.setFromCartesianCoords(t.x,t.y,t.z)}setFromCartesianCoords(t,e,n){return this.radius=Math.sqrt(t*t+n*n),this.theta=Math.atan2(t,n),this.y=e,this}clone(){return(new this.constructor).copy(this)}},DataTexture:H7t,DataTexture2DArray:h9t,DataTexture3D:d9t,DataTextureLoader:gne,DataUtils:class{static toHalfFloat(t){dre[0]=t;const e=pre[0];let n=e>>16&32768,i=e>>12&2047;const r=e>>23&255;return r<103?n:r>142?(n|=31744,n|=(255==r?0:1)&&8388607&e,n):r<113?(i|=2048,n|=(i>>114-r)+(i>>113-r&1),n):(n|=r-112<<10|i>>1,n+=1&i,n)}},DecrementStencilOp:7683,DecrementWrapStencilOp:34056,DefaultLoadingManager:une,DepthFormat:M2t,DepthStencilFormat:E2t,DepthTexture:mte,DirectionalLight:One,DirectionalLightHelper:class extends V3t{constructor(t,e,n){super(),this.light=t,this.light.updateMatrixWorld(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.color=n,void 0===e&&(e=1);let i=new N4t;i.setAttribute("position",new b4t([-e,e,0,e,e,0,e,-e,0,-e,-e,0,-e,e,0],3));const r=new X7t({fog:!1,toneMapped:!1});this.lightPlane=new tte(i,r),this.add(this.lightPlane),i=new N4t,i.setAttribute("position",new b4t([0,0,0,0,0,1],3)),this.targetLine=new tte(i,r),this.add(this.targetLine),this.update()}dispose(){this.lightPlane.geometry.dispose(),this.lightPlane.material.dispose(),this.targetLine.geometry.dispose(),this.targetLine.material.dispose()}update(){tre.setFromMatrixPosition(this.light.matrixWorld),ere.setFromMatrixPosition(this.light.target.matrixWorld),nre.subVectors(ere,tre),this.lightPlane.lookAt(ere),void 0!==this.color?(this.lightPlane.material.color.set(this.color),this.targetLine.material.color.set(this.color)):(this.lightPlane.material.color.copy(this.light.color),this.targetLine.material.color.copy(this.light.color)),this.targetLine.lookAt(ere),this.targetLine.scale.z=nre.length()}},DiscreteInterpolant:Zee,DodecahedronBufferGeometry:bte,DodecahedronGeometry:bte,DoubleSide:2,DstAlphaFactor:206,DstColorFactor:208,DynamicBufferAttribute:function bre(t,e){return console.warn("THREE.DynamicBufferAttribute has been removed. Use new THREE.BufferAttribute().setUsage( THREE.DynamicDrawUsage ) instead."),new h4t(t,e).setUsage(Q2t)},DynamicCopyUsage:35050,DynamicDrawUsage:Q2t,DynamicReadUsage:35049,EdgesGeometry:Ete,EdgesHelper:function xre(t,e){return console.warn("THREE.EdgesHelper has been removed. Use THREE.EdgesGeometry instead."),new ite(new Ete(t.geometry),new X7t({color:void 0!==e?e:16777215}))},EllipseCurve:Cte,EqualDepth:4,EqualStencilFunc:514,EquirectangularReflectionMapping:i2t,EquirectangularRefractionMapping:r2t,Euler:T3t,EventDispatcher:e5t,ExtrudeBufferGeometry:bee,ExtrudeGeometry:bee,FaceColors:1,FileLoader:pne,FlatShading:1,Float16BufferAttribute:v4t,Float32Attribute:function wre(t,e){return console.warn("THREE.Float32Attribute has been removed. Use new THREE.Float32BufferAttribute() instead."),new b4t(t,e)},Float32BufferAttribute:b4t,Float64Attribute:function Sre(t,e){return console.warn("THREE.Float64Attribute has been removed. Use new THREE.Float64BufferAttribute() instead."),new x4t(t,e)},Float64BufferAttribute:x4t,FloatType:v2t,Fog:l7t,FogExp2:s7t,Font:Kne,FontLoader:class extends hne{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new pne(this.manager);o.setPath(this.path),o.setRequestHeader(this.requestHeader),o.setWithCredentials(r.withCredentials),o.load(t,(function(t){let n;try{n=JSON.parse(t)}catch(e){console.warn("THREE.FontLoader: typeface.js support is being deprecated. Use typeface.json instead."),n=JSON.parse(t.substring(65,t.length-2))}const i=r.parse(n);e&&e(i)}),n,i)}parse(t){return new Kne(t)}},FrontSide:0,Frustum:m6t,GLBufferAttribute:Rie,GLSL1:"100",GLSL3:t5t,GammaEncoding:q2t,GreaterDepth:6,GreaterEqualDepth:5,GreaterEqualStencilFunc:518,GreaterStencilFunc:516,GridHelper:Qie,Group:t7t,HalfFloatType:b2t,HemisphereLight:wne,HemisphereLightHelper:class extends V3t{constructor(t,e,n){super(),this.light=t,this.light.updateMatrixWorld(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.color=n;const i=new Mee(e);i.rotateY(.5*Math.PI),this.material=new l4t({wireframe:!0,fog:!1,toneMapped:!1}),void 0===this.color&&(this.material.vertexColors=!0);const r=i.getAttribute("position"),o=new Float32Array(3*r.count);i.setAttribute("color",new h4t(o,3)),this.add(new K4t(i,this.material)),this.update()}dispose(){this.children[0].geometry.dispose(),this.children[0].material.dispose()}update(){const t=this.children[0];if(void 0!==this.color)this.material.color.set(this.color);else{const e=t.geometry.getAttribute("color");Zie.copy(this.light.color),Jie.copy(this.light.groundColor);for(let t=0,n=e.count;t<n;t++){const i=t<n/2?Zie:Jie;e.setXYZ(t,i.r,i.g,i.b)}e.needsUpdate=!0}t.lookAt(Kie.setFromMatrixPosition(this.light.matrixWorld).negate())}},HemisphereLightProbe:eie,IcosahedronBufferGeometry:wee,IcosahedronGeometry:wee,ImageBitmapLoader:Xne,ImageLoader:fne,ImageUtils:L5t,ImmediateRenderObject:jie,IncrementStencilOp:7682,IncrementWrapStencilOp:34055,InstancedBufferAttribute:jne,InstancedBufferGeometry:Une,InstancedInterleavedBuffer:Iie,InstancedMesh:Y7t,Int16Attribute:function Mre(t,e){return console.warn("THREE.Int16Attribute has been removed. Use new THREE.Int16BufferAttribute() instead."),new m4t(t,e)},Int16BufferAttribute:m4t,Int32Attribute:function Ere(t,e){return console.warn("THREE.Int32Attribute has been removed. Use new THREE.Int32BufferAttribute() instead."),new _4t(t,e)},Int32BufferAttribute:_4t,Int8Attribute:function Tre(t,e){return console.warn("THREE.Int8Attribute has been removed. Use new THREE.Int8BufferAttribute() instead."),new d4t(t,e)},Int8BufferAttribute:d4t,IntType:1013,InterleavedBuffer:u7t,InterleavedBufferAttribute:d7t,Interpolant:Xee,InterpolateDiscrete:z2t,InterpolateLinear:D2t,InterpolateSmooth:B2t,InvertStencilOp:5386,JSONLoader:function Cre(){console.error("THREE.JSONLoader has been removed.")},KeepStencilOp:Z2t,KeyframeTrack:Jee,LOD:P7t,LatheBufferGeometry:See,LatheGeometry:See,Layers:C3t,LensFlare:function Are(){console.error("THREE.LensFlare has been moved to /examples/jsm/objects/Lensflare.js")},LessDepth:2,LessEqualDepth:3,LessEqualStencilFunc:515,LessStencilFunc:513,Light:xne,LightProbe:Hne,Line:tte,Line3:Uie,LineBasicMaterial:X7t,LineCurve:Fte,LineCurve3:Vte,LineDashedMaterial:Wee,LineLoop:rte,LinePieces:1,LineSegments:ite,LineStrip:0,LinearEncoding:G2t,LinearFilter:p2t,LinearInterpolant:Kee,LinearMipMapLinearFilter:1008,LinearMipMapNearestFilter:1007,LinearMipmapLinearFilter:m2t,LinearMipmapNearestFilter:f2t,LinearToneMapping:1,Loader:hne,LoaderUtils:Vne,LoadingManager:cne,LogLuvEncoding:3003,LoopOnce:2200,LoopPingPong:2202,LoopRepeat:2201,LuminanceAlphaFormat:1025,LuminanceFormat:1024,MOUSE:Z0t,Material:t4t,MaterialLoader:Fne,Math:p5t,MathUtils:p5t,Matrix3:A5t,Matrix4:g3t,MaxEquation:104,Mesh:K4t,MeshBasicMaterial:l4t,MeshDepthMaterial:Y8t,MeshDistanceMaterial:X8t,MeshFaceMaterial:function kre(t){return console.warn("THREE.MeshFaceMaterial has been removed. Use an Array instead."),t},MeshLambertMaterial:jee,MeshMatcapMaterial:Gee,MeshNormalMaterial:Uee,MeshPhongMaterial:Fee,MeshPhysicalMaterial:Hee,MeshStandardMaterial:Bee,MeshToonMaterial:Vee,MinEquation:103,MirroredRepeatWrapping:c2t,MixOperation:1,MultiMaterial:function Lre(t=[]){return console.warn("THREE.MultiMaterial has been removed. Use an Array instead."),t.isMultiMaterial=!0,t.materials=t,t.clone=function(){return t.slice()},t},MultiplyBlending:4,MultiplyOperation:0,NearestFilter:u2t,NearestMipMapLinearFilter:1005,NearestMipMapNearestFilter:1004,NearestMipmapLinearFilter:d2t,NearestMipmapNearestFilter:h2t,NeverDepth:0,NeverStencilFunc:512,NoBlending:0,NoColors:0,NoToneMapping:0,NormalAnimationBlendMode:U2t,NormalBlending:1,NotEqualDepth:7,NotEqualStencilFunc:517,NumberKeyframeTrack:ene,Object3D:V3t,ObjectLoader:class extends hne{constructor(t){super(t)}load(t,e,n,i){const r=this,o=""===this.path?Vne.extractUrlBase(t):this.path;this.resourcePath=this.resourcePath||o;const a=new pne(this.manager);a.setPath(this.path),a.setRequestHeader(this.requestHeader),a.setWithCredentials(this.withCredentials),a.load(t,(function(n){let o=null;try{o=JSON.parse(n)}catch(e){return void 0!==i&&i(e),void console.error("THREE:ObjectLoader: Can't parse "+t+".",e.message)}const a=o.metadata;void 0!==a&&void 0!==a.type&&"geometry"!==a.type.toLowerCase()?r.parse(o,e):console.error("THREE.ObjectLoader: Can't load "+t)}),n,i)}async loadAsync(t,e){const n=""===this.path?Vne.extractUrlBase(t):this.path;this.resourcePath=this.resourcePath||n;const i=new pne(this.manager);i.setPath(this.path),i.setRequestHeader(this.requestHeader),i.setWithCredentials(this.withCredentials);const r=await i.loadAsync(t,e),o=JSON.parse(r),a=o.metadata;if(void 0===a||void 0===a.type||"geometry"===a.type.toLowerCase())throw new Error("THREE.ObjectLoader: Can't load "+t);return await this.parseAsync(o)}parse(t,e){const n=this.parseAnimations(t.animations),i=this.parseShapes(t.shapes),r=this.parseGeometries(t.geometries,i),o=this.parseImages(t.images,(function(){void 0!==e&&e(l)})),a=this.parseTextures(t.textures,o),s=this.parseMaterials(t.materials,a),l=this.parseObject(t.object,r,s,a,n),c=this.parseSkeletons(t.skeletons,l);if(this.bindSkeletons(l,c),void 0!==e){let t=!1;for(const e in o)if(o[e]instanceof HTMLImageElement){t=!0;break}!1===t&&e(l)}return l}async parseAsync(t){const e=this.parseAnimations(t.animations),n=this.parseShapes(t.shapes),i=this.parseGeometries(t.geometries,n),r=await this.parseImagesAsync(t.images),o=this.parseTextures(t.textures,r),a=this.parseMaterials(t.materials,o),s=this.parseObject(t.object,i,a,o,e),l=this.parseSkeletons(t.skeletons,s);return this.bindSkeletons(s,l),s}parseShapes(t){const e={};if(void 0!==t)for(let n=0,i=t.length;n<i;n++){const i=(new bne).fromJSON(t[n]);e[i.uuid]=i}return e}parseSkeletons(t,e){const n={},i={};if(e.traverse((function(t){t.isBone&&(i[t.uuid]=t)})),void 0!==t)for(let e=0,r=t.length;e<r;e++){const r=(new U7t).fromJSON(t[e],i);n[r.uuid]=r}return n}parseGeometries(t,e){const n={};if(void 0!==t){const i=new Gne;for(let r=0,o=t.length;r<o;r++){let o;const a=t[r];switch(a.type){case"BufferGeometry":case"InstancedBufferGeometry":o=i.parse(a);break;case"Geometry":console.error("THREE.ObjectLoader: The legacy Geometry type is no longer supported.");break;default:a.type in zee?o=zee[a.type].fromJSON(a,e):console.warn(`THREE.ObjectLoader: Unsupported geometry type "${a.type}"`)}o.uuid=a.uuid,void 0!==a.name&&(o.name=a.name),!0===o.isBufferGeometry&&void 0!==a.userData&&(o.userData=a.userData),n[a.uuid]=o}}return n}parseMaterials(t,e){const n={},i={};if(void 0!==t){const r=new Fne;r.setTextures(e);for(let e=0,o=t.length;e<o;e++){const o=t[e];if("MultiMaterial"===o.type){const t=[];for(let e=0;e<o.materials.length;e++){const i=o.materials[e];void 0===n[i.uuid]&&(n[i.uuid]=r.parse(i)),t.push(n[i.uuid])}i[o.uuid]=t}else void 0===n[o.uuid]&&(n[o.uuid]=r.parse(o)),i[o.uuid]=n[o.uuid]}}return i}parseAnimations(t){const e={};if(void 0!==t)for(let n=0;n<t.length;n++){const i=ane.parse(t[n]);e[i.uuid]=i}return e}parseImages(t,e){const n=this,i={};let r;function o(t){if("string"==typeof t){const e=t;return(function i(t){return n.manager.itemStart(t),r.load(t,(function(){n.manager.itemEnd(t)}),void 0,(function(){n.manager.itemError(t),n.manager.itemEnd(t)}))})(/^(\/\/)|([a-z]+:(\/\/)?)/i.test(e)?e:n.resourcePath+e)}return t.data?{data:M4t(t.type,t.data),width:t.width,height:t.height}:null}if(void 0!==t&&t.length>0){const n=new cne(e);r=new fne(n),r.setCrossOrigin(this.crossOrigin);for(let e=0,n=t.length;e<n;e++){const n=t[e],r=n.url;if(Array.isArray(r)){i[n.uuid]=[];for(let t=0,e=r.length;t<e;t++){const e=o(r[t]);null!==e&&(e instanceof HTMLImageElement?i[n.uuid].push(e):i[n.uuid].push(new H7t(e.data,e.width,e.height)))}}else{const t=o(n.url);null!==t&&(i[n.uuid]=t)}}}return i}async parseImagesAsync(t){const e=this,n={};let i;async function r(t){if("string"==typeof t){const n=t,r=/^(\/\/)|([a-z]+:(\/\/)?)/i.test(n)?n:e.resourcePath+n;return await i.loadAsync(r)}return t.data?{data:M4t(t.type,t.data),width:t.width,height:t.height}:null}if(void 0!==t&&t.length>0){i=new fne(this.manager),i.setCrossOrigin(this.crossOrigin);for(let e=0,i=t.length;e<i;e++){const i=t[e],o=i.url;if(Array.isArray(o)){n[i.uuid]=[];for(let t=0,e=o.length;t<e;t++){const e=o[t],a=await r(e);null!==a&&(a instanceof HTMLImageElement?n[i.uuid].push(a):n[i.uuid].push(new H7t(a.data,a.width,a.height)))}}else{const t=await r(i.url);null!==t&&(n[i.uuid]=t)}}}return n}parseTextures(t,e){function n(t,e){return"number"==typeof t?t:(console.warn("THREE.ObjectLoader.parseTexture: Constant should be in numeric form.",t),e[t])}const i={};if(void 0!==t)for(let r=0,o=t.length;r<o;r++){const o=t[r];let a;void 0===o.image&&console.warn('THREE.ObjectLoader: No "image" specified for',o.uuid),void 0===e[o.image]&&console.warn("THREE.ObjectLoader: Undefined image",o.image);const s=e[o.image];Array.isArray(s)?(a=new s6t(s),6===s.length&&(a.needsUpdate=!0)):(a=s&&s.data?new H7t(s.data,s.width,s.height):new N5t(s),s&&(a.needsUpdate=!0)),a.uuid=o.uuid,void 0!==o.name&&(a.name=o.name),void 0!==o.mapping&&(a.mapping=n(o.mapping,Wne)),void 0!==o.offset&&a.offset.fromArray(o.offset),void 0!==o.repeat&&a.repeat.fromArray(o.repeat),void 0!==o.center&&a.center.fromArray(o.center),void 0!==o.rotation&&(a.rotation=o.rotation),void 0!==o.wrap&&(a.wrapS=n(o.wrap[0],qne),a.wrapT=n(o.wrap[1],qne)),void 0!==o.format&&(a.format=o.format),void 0!==o.type&&(a.type=o.type),void 0!==o.encoding&&(a.encoding=o.encoding),void 0!==o.minFilter&&(a.minFilter=n(o.minFilter,Yne)),void 0!==o.magFilter&&(a.magFilter=n(o.magFilter,Yne)),void 0!==o.anisotropy&&(a.anisotropy=o.anisotropy),void 0!==o.flipY&&(a.flipY=o.flipY),void 0!==o.premultiplyAlpha&&(a.premultiplyAlpha=o.premultiplyAlpha),void 0!==o.unpackAlignment&&(a.unpackAlignment=o.unpackAlignment),i[o.uuid]=a}return i}parseObject(t,e,n,i,r){let o,a,s;function l(t){return void 0===e[t]&&console.warn("THREE.ObjectLoader: Undefined geometry",t),e[t]}function c(t){if(void 0!==t){if(Array.isArray(t)){const e=[];for(let i=0,r=t.length;i<r;i++){const r=t[i];void 0===n[r]&&console.warn("THREE.ObjectLoader: Undefined material",r),e.push(n[r])}return e}return void 0===n[t]&&console.warn("THREE.ObjectLoader: Undefined material",t),n[t]}}function u(t){return void 0===i[t]&&console.warn("THREE.ObjectLoader: Undefined texture",t),i[t]}switch(t.type){case"Scene":o=new c7t,void 0!==t.background&&(o.background=Number.isInteger(t.background)?new s4t(t.background):u(t.background)),void 0!==t.environment&&(o.environment=u(t.environment)),void 0!==t.fog&&("Fog"===t.fog.type?o.fog=new l7t(t.fog.color,t.fog.near,t.fog.far):"FogExp2"===t.fog.type&&(o.fog=new s7t(t.fog.color,t.fog.density)));break;case"PerspectiveCamera":o=new r6t(t.fov,t.aspect,t.near,t.far),void 0!==t.focus&&(o.focus=t.focus),void 0!==t.zoom&&(o.zoom=t.zoom),void 0!==t.filmGauge&&(o.filmGauge=t.filmGauge),void 0!==t.filmOffset&&(o.filmOffset=t.filmOffset),void 0!==t.view&&(o.view=Object.assign({},t.view));break;case"OrthographicCamera":o=new A6t(t.left,t.right,t.top,t.bottom,t.near,t.far),void 0!==t.zoom&&(o.zoom=t.zoom),void 0!==t.view&&(o.view=Object.assign({},t.view));break;case"AmbientLight":o=new zne(t.color,t.intensity);break;case"DirectionalLight":o=new One(t.color,t.intensity);break;case"PointLight":o=new Ine(t.color,t.intensity,t.distance,t.decay);break;case"RectAreaLight":o=new Dne(t.color,t.intensity,t.width,t.height);break;case"SpotLight":o=new Ane(t.color,t.intensity,t.distance,t.angle,t.penumbra,t.decay);break;case"HemisphereLight":o=new wne(t.color,t.groundColor,t.intensity);break;case"LightProbe":o=(new Hne).fromJSON(t);break;case"SkinnedMesh":a=l(t.geometry),s=c(t.material),o=new D7t(a,s),void 0!==t.bindMode&&(o.bindMode=t.bindMode),void 0!==t.bindMatrix&&o.bindMatrix.fromArray(t.bindMatrix),void 0!==t.skeleton&&(o.skeleton=t.skeleton);break;case"Mesh":a=l(t.geometry),s=c(t.material),o=new K4t(a,s);break;case"InstancedMesh":a=l(t.geometry),s=c(t.material);const e=t.instanceMatrix,n=t.instanceColor;o=new Y7t(a,s,t.count),o.instanceMatrix=new h4t(new Float32Array(e.array),16),void 0!==n&&(o.instanceColor=new h4t(new Float32Array(n.array),n.itemSize));break;case"LOD":o=new P7t;break;case"Line":o=new tte(l(t.geometry),c(t.material));break;case"LineLoop":o=new rte(l(t.geometry),c(t.material));break;case"LineSegments":o=new ite(l(t.geometry),c(t.material));break;case"PointCloud":case"Points":o=new ute(l(t.geometry),c(t.material));break;case"Sprite":o=new C7t(c(t.material));break;case"Group":o=new t7t;break;case"Bone":o=new B7t;break;default:o=new V3t}if(o.uuid=t.uuid,void 0!==t.name&&(o.name=t.name),void 0!==t.matrix?(o.matrix.fromArray(t.matrix),void 0!==t.matrixAutoUpdate&&(o.matrixAutoUpdate=t.matrixAutoUpdate),o.matrixAutoUpdate&&o.matrix.decompose(o.position,o.quaternion,o.scale)):(void 0!==t.position&&o.position.fromArray(t.position),void 0!==t.rotation&&o.rotation.fromArray(t.rotation),void 0!==t.quaternion&&o.quaternion.fromArray(t.quaternion),void 0!==t.scale&&o.scale.fromArray(t.scale)),void 0!==t.castShadow&&(o.castShadow=t.castShadow),void 0!==t.receiveShadow&&(o.receiveShadow=t.receiveShadow),t.shadow&&(void 0!==t.shadow.bias&&(o.shadow.bias=t.shadow.bias),void 0!==t.shadow.normalBias&&(o.shadow.normalBias=t.shadow.normalBias),void 0!==t.shadow.radius&&(o.shadow.radius=t.shadow.radius),void 0!==t.shadow.mapSize&&o.shadow.mapSize.fromArray(t.shadow.mapSize),void 0!==t.shadow.camera&&(o.shadow.camera=this.parseObject(t.shadow.camera))),void 0!==t.visible&&(o.visible=t.visible),void 0!==t.frustumCulled&&(o.frustumCulled=t.frustumCulled),void 0!==t.renderOrder&&(o.renderOrder=t.renderOrder),void 0!==t.userData&&(o.userData=t.userData),void 0!==t.layers&&(o.layers.mask=t.layers),void 0!==t.children){const a=t.children;for(let t=0;t<a.length;t++)o.add(this.parseObject(a[t],e,n,i,r))}if(void 0!==t.animations){const e=t.animations;for(let t=0;t<e.length;t++){o.animations.push(r[e[t]])}}if("LOD"===t.type){void 0!==t.autoUpdate&&(o.autoUpdate=t.autoUpdate);const e=t.levels;for(let t=0;t<e.length;t++){const n=e[t],i=o.getObjectByProperty("uuid",n.object);void 0!==i&&o.addLevel(i,n.distance)}}return o}bindSkeletons(t,e){0!==Object.keys(e).length&&t.traverse((function(t){if(!0===t.isSkinnedMesh&&void 0!==t.skeleton){const n=e[t.skeleton];void 0===n?console.warn("THREE.ObjectLoader: No skeleton found with UUID:",t.skeleton):t.bind(n,t.bindMatrix)}}))}setTexturePath(t){return console.warn("THREE.ObjectLoader: .setTexturePath() has been renamed to .setResourcePath()."),this.setResourcePath(t)}},ObjectSpaceNormalMap:1,OctahedronBufferGeometry:Mee,OctahedronGeometry:Mee,OneFactor:201,OneMinusDstAlphaFactor:207,OneMinusDstColorFactor:209,OneMinusSrcAlphaFactor:205,OneMinusSrcColorFactor:203,OrthographicCamera:A6t,PCFShadowMap:1,PCFSoftShadowMap:2,PMREMGenerator:Y6t,ParametricBufferGeometry:Eee,ParametricGeometry:Eee,Particle:function Pre(t){return console.warn("THREE.Particle has been renamed to THREE.Sprite."),new C7t(t)},ParticleBasicMaterial:function Nre(t){return console.warn("THREE.ParticleBasicMaterial has been renamed to THREE.PointsMaterial."),new ote(t)},ParticleSystem:function Ire(t,e){return console.warn("THREE.ParticleSystem has been renamed to THREE.Points."),new ute(t,e)},ParticleSystemMaterial:function Rre(t){return console.warn("THREE.ParticleSystemMaterial has been renamed to THREE.PointsMaterial."),new ote(t)},Path:vne,PerspectiveCamera:r6t,Plane:d6t,PlaneBufferGeometry:y6t,PlaneGeometry:y6t,PlaneHelper:class extends tte{constructor(t,e=1,n=16776960){const i=n,r=new N4t;r.setAttribute("position",new b4t([1,-1,1,-1,1,1,-1,-1,1,1,1,1,-1,1,1,-1,-1,1,1,-1,1,1,1,1,0,0,1,0,0,0],3)),r.computeBoundingSphere(),super(r,new X7t({color:i,toneMapped:!1})),this.type="PlaneHelper",this.plane=t,this.size=e;const o=new N4t;o.setAttribute("position",new b4t([1,1,1,-1,1,1,-1,-1,1,1,1,1,-1,-1,1,1,-1,1],3)),o.computeBoundingSphere(),this.add(new K4t(o,new l4t({color:i,opacity:.2,transparent:!0,depthWrite:!1,toneMapped:!1})))}updateMatrixWorld(t){let e=-this.plane.constant;Math.abs(e)<1e-8&&(e=1e-8),this.scale.set(.5*this.size,.5*this.size,e),this.children[0].material.side=e<0?1:0,this.lookAt(this.plane.normal),super.updateMatrixWorld(t)}},PointCloud:function Ore(t,e){return console.warn("THREE.PointCloud has been renamed to THREE.Points."),new ute(t,e)},PointCloudMaterial:function zre(t){return console.warn("THREE.PointCloudMaterial has been renamed to THREE.PointsMaterial."),new ote(t)},PointLight:Ine,PointLightHelper:class extends K4t{constructor(t,e,n){super(new Aee(e,4,2),new l4t({wireframe:!0,fog:!1,toneMapped:!1})),this.light=t,this.light.updateMatrixWorld(),this.color=n,this.type="PointLightHelper",this.matrix=this.light.matrixWorld,this.matrixAutoUpdate=!1,this.update()}dispose(){this.geometry.dispose(),this.material.dispose()}update(){void 0!==this.color?this.material.color.set(this.color):this.material.color.copy(this.light.color)}},Points:ute,PointsMaterial:ote,PolarGridHelper:class extends ite{constructor(t=10,e=16,n=8,i=64,r=4473924,o=8947848){r=new s4t(r),o=new s4t(o);const a=[],s=[];for(let n=0;n<=e;n++){const i=n/e*(2*Math.PI),l=Math.sin(i)*t,c=Math.cos(i)*t;a.push(0,0,0),a.push(l,0,c);const u=1&n?r:o;s.push(u.r,u.g,u.b),s.push(u.r,u.g,u.b)}for(let e=0;e<=n;e++){const l=1&e?r:o,c=t-t/n*e;for(let t=0;t<i;t++){let e=t/i*(2*Math.PI),n=Math.sin(e)*c,r=Math.cos(e)*c;a.push(n,0,r),s.push(l.r,l.g,l.b),e=(t+1)/i*(2*Math.PI),n=Math.sin(e)*c,r=Math.cos(e)*c,a.push(n,0,r),s.push(l.r,l.g,l.b)}}const l=new N4t;l.setAttribute("position",new b4t(a,3)),l.setAttribute("color",new b4t(s,3));super(l,new X7t({vertexColors:!0,toneMapped:!1})),this.type="PolarGridHelper"}},PolyhedronBufferGeometry:vte,PolyhedronGeometry:vte,PositionalAudio:class extends hie{constructor(t){super(t),this.panner=this.context.createPanner(),this.panner.panningModel="HRTF",this.panner.connect(this.gain)}getOutput(){return this.panner}getRefDistance(){return this.panner.refDistance}setRefDistance(t){return this.panner.refDistance=t,this}getRolloffFactor(){return this.panner.rolloffFactor}setRolloffFactor(t){return this.panner.rolloffFactor=t,this}getDistanceModel(){return this.panner.distanceModel}setDistanceModel(t){return this.panner.distanceModel=t,this}getMaxDistance(){return this.panner.maxDistance}setMaxDistance(t){return this.panner.maxDistance=t,this}setDirectionalCone(t,e,n){return this.panner.coneInnerAngle=t,this.panner.coneOuterAngle=e,this.panner.coneOuterGain=n,this}updateMatrixWorld(t){if(super.updateMatrixWorld(t),!0===this.hasPlaybackControl&&!1===this.isPlaying)return;this.matrixWorld.decompose(die,pie,fie),mie.set(0,0,1).applyQuaternion(pie);const e=this.panner;if(e.positionX){const t=this.context.currentTime+this.listener.timeDelta;e.positionX.linearRampToValueAtTime(die.x,t),e.positionY.linearRampToValueAtTime(die.y,t),e.positionZ.linearRampToValueAtTime(die.z,t),e.orientationX.linearRampToValueAtTime(mie.x,t),e.orientationY.linearRampToValueAtTime(mie.y,t),e.orientationZ.linearRampToValueAtTime(mie.z,t)}else e.setPosition(die.x,die.y,die.z),e.setOrientation(mie.x,mie.y,mie.z)}},PropertyBinding:Aie,PropertyMixer:_ie,QuadraticBezierCurve:Ute,QuadraticBezierCurve3:jte,Quaternion:B5t,QuaternionKeyframeTrack:ine,QuaternionLinearInterpolant:nne,REVISION:K0t,RGBADepthPacking:3201,RGBAFormat:S2t,RGBAIntegerFormat:1033,RGBA_ASTC_10x10_Format:37819,RGBA_ASTC_10x5_Format:37816,RGBA_ASTC_10x6_Format:37817,RGBA_ASTC_10x8_Format:37818,RGBA_ASTC_12x10_Format:37820,RGBA_ASTC_12x12_Format:37821,RGBA_ASTC_4x4_Format:37808,RGBA_ASTC_5x4_Format:37809,RGBA_ASTC_5x5_Format:37810,RGBA_ASTC_6x5_Format:37811,RGBA_ASTC_6x6_Format:37812,RGBA_ASTC_8x5_Format:37813,RGBA_ASTC_8x6_Format:37814,RGBA_ASTC_8x8_Format:37815,RGBA_BPTC_Format:36492,RGBA_ETC2_EAC_Format:O2t,RGBA_PVRTC_2BPPV1_Format:I2t,RGBA_PVRTC_4BPPV1_Format:N2t,RGBA_S3TC_DXT1_Format:C2t,RGBA_S3TC_DXT3_Format:A2t,RGBA_S3TC_DXT5_Format:k2t,RGBDEncoding:K2t,RGBEEncoding:Y2t,RGBEFormat:1023,RGBFormat:w2t,RGBIntegerFormat:1032,RGBM16Encoding:$2t,RGBM7Encoding:X2t,RGB_ETC1_Format:36196,RGB_ETC2_Format:R2t,RGB_PVRTC_2BPPV1_Format:P2t,RGB_PVRTC_4BPPV1_Format:L2t,RGB_S3TC_DXT1_Format:T2t,RGFormat:1030,RGIntegerFormat:1031,RawShaderMaterial:k6t,Ray:m3t,Raycaster:class{constructor(t,e,n=0,i=1/0){this.ray=new m3t(t,e),this.near=n,this.far=i,this.camera=null,this.layers=new C3t,this.params={Mesh:{},Line:{threshold:1},LOD:{},Points:{threshold:1},Sprite:{}}}set(t,e){this.ray.set(t,e)}setFromCamera(t,e){e&&e.isPerspectiveCamera?(this.ray.origin.setFromMatrixPosition(e.matrixWorld),this.ray.direction.set(t.x,t.y,.5).unproject(e).sub(this.ray.origin).normalize(),this.camera=e):e&&e.isOrthographicCamera?(this.ray.origin.set(t.x,t.y,(e.near+e.far)/(e.near-e.far)).unproject(e),this.ray.direction.set(0,0,-1).transformDirection(e.matrixWorld),this.camera=e):console.error("THREE.Raycaster: Unsupported camera type: "+e.type)}intersectObject(t,e=!1,n=[]){return zie(t,this,n,e),n.sort(Oie),n}intersectObjects(t,e=!1,n=[]){for(let i=0,r=t.length;i<r;i++)zie(t[i],this,n,e);return n.sort(Oie),n}},RectAreaLight:Dne,RedFormat:1028,RedIntegerFormat:1029,ReinhardToneMapping:2,RepeatWrapping:s2t,ReplaceStencilOp:7681,ReverseSubtractEquation:102,RingBufferGeometry:Tee,RingGeometry:Tee,SRGB8_ALPHA8_ASTC_10x10_Format:37851,SRGB8_ALPHA8_ASTC_10x5_Format:37848,SRGB8_ALPHA8_ASTC_10x6_Format:37849,SRGB8_ALPHA8_ASTC_10x8_Format:37850,SRGB8_ALPHA8_ASTC_12x10_Format:37852,SRGB8_ALPHA8_ASTC_12x12_Format:37853,SRGB8_ALPHA8_ASTC_4x4_Format:37840,SRGB8_ALPHA8_ASTC_5x4_Format:37841,SRGB8_ALPHA8_ASTC_5x5_Format:37842,SRGB8_ALPHA8_ASTC_6x5_Format:37843,SRGB8_ALPHA8_ASTC_6x6_Format:37844,SRGB8_ALPHA8_ASTC_8x5_Format:37845,SRGB8_ALPHA8_ASTC_8x6_Format:37846,SRGB8_ALPHA8_ASTC_8x8_Format:37847,Scene:c7t,SceneUtils:fre,ShaderChunk:v6t,ShaderLib:x6t,ShaderMaterial:n6t,ShadowMaterial:Dee,Shape:bne,ShapeBufferGeometry:Cee,ShapeGeometry:Cee,ShapePath:$ne,ShapeUtils:_ee,ShortType:1011,Skeleton:U7t,SkeletonHelper:Xie,SkinnedMesh:D7t,SmoothShading:2,Sphere:s3t,SphereBufferGeometry:Aee,SphereGeometry:Aee,Spherical:Die,SphericalHarmonics3:Bne,SplineCurve:Gte,SpotLight:Ane,SpotLightHelper:class extends V3t{constructor(t,e){super(),this.light=t,this.light.updateMatrixWorld(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.color=e;const n=new N4t,i=[0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,-1,0,1,0,0,0,0,1,1,0,0,0,0,-1,1];for(let t=0,e=1,n=32;t<n;t++,e++){const r=t/n*Math.PI*2,o=e/n*Math.PI*2;i.push(Math.cos(r),Math.sin(r),1,Math.cos(o),Math.sin(o),1)}n.setAttribute("position",new b4t(i,3));const r=new X7t({fog:!1,toneMapped:!1});this.cone=new ite(n,r),this.add(this.cone),this.update()}dispose(){this.cone.geometry.dispose(),this.cone.material.dispose()}update(){this.light.updateMatrixWorld();const t=this.light.distance?this.light.distance:1e3,e=t*Math.tan(this.light.angle);this.cone.scale.set(e,e,t),Gie.setFromMatrixPosition(this.light.target.matrixWorld),this.cone.lookAt(Gie),void 0!==this.color?this.cone.material.color.set(this.color):this.cone.material.color.copy(this.light.color)}},Sprite:C7t,SpriteMaterial:p7t,SrcAlphaFactor:204,SrcAlphaSaturateFactor:210,SrcColorFactor:202,StaticCopyUsage:35046,StaticDrawUsage:J2t,StaticReadUsage:35045,StereoCamera:class{constructor(){this.type="StereoCamera",this.aspect=1,this.eyeSep=.064,this.cameraL=new r6t,this.cameraL.layers.enable(1),this.cameraL.matrixAutoUpdate=!1,this.cameraR=new r6t,this.cameraR.layers.enable(2),this.cameraR.matrixAutoUpdate=!1,this._cache={focus:null,fov:null,aspect:null,near:null,far:null,zoom:null,eyeSep:null}}update(t){const e=this._cache;if(e.focus!==t.focus||e.fov!==t.fov||e.aspect!==t.aspect*this.aspect||e.near!==t.near||e.far!==t.far||e.zoom!==t.zoom||e.eyeSep!==this.eyeSep){e.focus=t.focus,e.fov=t.fov,e.aspect=t.aspect*this.aspect,e.near=t.near,e.far=t.far,e.zoom=t.zoom,e.eyeSep=this.eyeSep;const n=t.projectionMatrix.clone(),i=e.eyeSep/2,r=i*e.near/e.focus,o=e.near*Math.tan(r5t*e.fov*.5)/e.zoom;let a,s;rie.elements[12]=-i,iie.elements[12]=i,a=-o*e.aspect+r,s=o*e.aspect+r,n.elements[0]=2*e.near/(s-a),n.elements[8]=(s+a)/(s-a),this.cameraL.projectionMatrix.copy(n),a=-o*e.aspect-r,s=o*e.aspect-r,n.elements[0]=2*e.near/(s-a),n.elements[8]=(s+a)/(s-a),this.cameraR.projectionMatrix.copy(n)}this.cameraL.matrixWorld.copy(t.matrixWorld).multiply(rie),this.cameraR.matrixWorld.copy(t.matrixWorld).multiply(iie)}},StreamCopyUsage:35042,StreamDrawUsage:35040,StreamReadUsage:35041,StringKeyframeTrack:rne,SubtractEquation:101,SubtractiveBlending:3,TOUCH:J0t,TangentSpaceNormalMap:0,TetrahedronBufferGeometry:kee,TetrahedronGeometry:kee,TextBufferGeometry:Lee,TextGeometry:Lee,Texture:N5t,TextureLoader:_ne,TorusBufferGeometry:Pee,TorusGeometry:Pee,TorusKnotBufferGeometry:Nee,TorusKnotGeometry:Nee,Triangle:J3t,TriangleFanDrawMode:2,TriangleStripDrawMode:1,TrianglesDrawMode:0,TubeBufferGeometry:Iee,TubeGeometry:Iee,UVMapping:t2t,Uint16Attribute:function Dre(t,e){return console.warn("THREE.Uint16Attribute has been removed. Use new THREE.Uint16BufferAttribute() instead."),new g4t(t,e)},Uint16BufferAttribute:g4t,Uint32Attribute:function Bre(t,e){return console.warn("THREE.Uint32Attribute has been removed. Use new THREE.Uint32BufferAttribute() instead."),new y4t(t,e)},Uint32BufferAttribute:y4t,Uint8Attribute:function Hre(t,e){return console.warn("THREE.Uint8Attribute has been removed. Use new THREE.Uint8BufferAttribute() instead."),new p4t(t,e)},Uint8BufferAttribute:p4t,Uint8ClampedAttribute:function Fre(t,e){return console.warn("THREE.Uint8ClampedAttribute has been removed. Use new THREE.Uint8ClampedBufferAttribute() instead."),new f4t(t,e)},Uint8ClampedBufferAttribute:f4t,Uniform:Nie,UniformsLib:b6t,UniformsUtils:e6t,UnsignedByteType:g2t,UnsignedInt248Type:x2t,UnsignedIntType:y2t,UnsignedShort4444Type:1017,UnsignedShort5551Type:1018,UnsignedShort565Type:1019,UnsignedShortType:_2t,VSMShadowMap:3,Vector2:C5t,Vector3:H5t,Vector4:R5t,VectorKeyframeTrack:one,Vertex:function Vre(t,e,n){return console.warn("THREE.Vertex has been removed. Use THREE.Vector3 instead."),new H5t(t,e,n)},VertexColors:2,VideoTexture:dte,WebGL1Renderer:a7t,WebGLCubeRenderTarget:l6t,WebGLMultipleRenderTargets:z5t,WebGLMultisampleRenderTarget:D5t,WebGLRenderTarget:O5t,WebGLRenderTargetCube:function Ure(t,e,n){return console.warn("THREE.WebGLRenderTargetCube( width, height, options ) is now WebGLCubeRenderTarget( size, options )."),new l6t(t,n)},WebGLRenderer:o7t,WebGLUtils:J8t,WireframeGeometry:Ree,WireframeHelper:function jre(t,e){return console.warn("THREE.WireframeHelper has been removed. Use THREE.WireframeGeometry instead."),new ite(new Ree(t.geometry),new X7t({color:void 0!==e?e:16777215}))},WrapAroundEnding:V2t,XHRLoader:function Gre(t){return console.warn("THREE.XHRLoader has been renamed to THREE.FileLoader."),new pne(t)},ZeroCurvatureEnding:H2t,ZeroFactor:200,ZeroSlopeEnding:F2t,ZeroStencilOp:0,sRGBEncoding:W2t});const Wre={type:"change"},qre={type:"start"},Yre={type:"end"};class Xre extends e5t{constructor(t,e){super(),void 0===e&&console.warn('THREE.OrbitControls: The second parameter "domElement" is now mandatory.'),e===document&&console.error('THREE.OrbitControls: "document" should not be used as the target "domElement". Please use "renderer.domElement" instead.'),this.object=t,this.domElement=e,this.domElement.style.touchAction="none",this.enabled=!0,this.target=new H5t,this.minDistance=0,this.maxDistance=1/0,this.minZoom=0,this.maxZoom=1/0,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.minAzimuthAngle=-1/0,this.maxAzimuthAngle=1/0,this.enableDamping=!1,this.dampingFactor=.05,this.enableZoom=!0,this.zoomSpeed=1,this.enableRotate=!0,this.rotateSpeed=1,this.enablePan=!0,this.panSpeed=1,this.screenSpacePanning=!0,this.keyPanSpeed=7,this.autoRotate=!1,this.autoRotateSpeed=2,this.keys={LEFT:"ArrowLeft",UP:"ArrowUp",RIGHT:"ArrowRight",BOTTOM:"ArrowDown"},this.mouseButtons={LEFT:Z0t.ROTATE,MIDDLE:Z0t.DOLLY,RIGHT:Z0t.PAN},this.touches={ONE:J0t.ROTATE,TWO:J0t.DOLLY_PAN},this.target0=this.target.clone(),this.position0=this.object.position.clone(),this.zoom0=this.object.zoom,this._domElementKeyEvents=null,this.getPolarAngle=function(){return a.phi},this.getAzimuthalAngle=function(){return a.theta},this.getDistance=function(){return this.object.position.distanceTo(this.target)},this.listenToKeyEvents=function(t){t.addEventListener("keydown",j),this._domElementKeyEvents=t},this.saveState=function(){n.target0.copy(n.target),n.position0.copy(n.object.position),n.zoom0=n.object.zoom},this.reset=function(){n.target.copy(n.target0),n.object.position.copy(n.position0),n.object.zoom=n.zoom0,n.object.updateProjectionMatrix(),n.dispatchEvent(Wre),n.update(),r=i.NONE},this.update=(function(){const e=new H5t,h=(new B5t).setFromUnitVectors(t.up,new H5t(0,1,0)),d=h.clone().invert(),p=new H5t,f=new B5t,m=2*Math.PI;return function t(){const g=n.object.position;e.copy(g).sub(n.target),e.applyQuaternion(h),a.setFromVector3(e),n.autoRotate&&r===i.NONE&&S((function _(){return 2*Math.PI/60/60*n.autoRotateSpeed})()),n.enableDamping?(a.theta+=s.theta*n.dampingFactor,a.phi+=s.phi*n.dampingFactor):(a.theta+=s.theta,a.phi+=s.phi);let y=n.minAzimuthAngle,v=n.maxAzimuthAngle;return isFinite(y)&&isFinite(v)&&(y<-Math.PI?y+=m:y>Math.PI&&(y-=m),v<-Math.PI?v+=m:v>Math.PI&&(v-=m),a.theta=y<=v?Math.max(y,Math.min(v,a.theta)):a.theta>(y+v)/2?Math.max(y,a.theta):Math.min(v,a.theta)),a.phi=Math.max(n.minPolarAngle,Math.min(n.maxPolarAngle,a.phi)),a.makeSafe(),a.radius*=l,a.radius=Math.max(n.minDistance,Math.min(n.maxDistance,a.radius)),!0===n.enableDamping?n.target.addScaledVector(c,n.dampingFactor):n.target.add(c),e.setFromSpherical(a),e.applyQuaternion(d),g.copy(n.target).add(e),n.object.lookAt(n.target),!0===n.enableDamping?(s.theta*=1-n.dampingFactor,s.phi*=1-n.dampingFactor,c.multiplyScalar(1-n.dampingFactor)):(s.set(0,0,0),c.set(0,0,0)),l=1,!!(u||p.distanceToSquared(n.object.position)>o||8*(1-f.dot(n.object.quaternion))>o)&&(n.dispatchEvent(Wre),p.copy(n.object.position),f.copy(n.object.quaternion),u=!1,!0)}})(),this.dispose=function(){n.domElement.removeEventListener("contextmenu",G),n.domElement.removeEventListener("pointerdown",B),n.domElement.removeEventListener("pointercancel",V),n.domElement.removeEventListener("wheel",U),n.domElement.removeEventListener("pointermove",H),n.domElement.removeEventListener("pointerup",F),null!==n._domElementKeyEvents&&n._domElementKeyEvents.removeEventListener("keydown",j)};const n=this,i={NONE:-1,ROTATE:0,DOLLY:1,PAN:2,TOUCH_ROTATE:3,TOUCH_PAN:4,TOUCH_DOLLY_PAN:5,TOUCH_DOLLY_ROTATE:6};let r=i.NONE;const o=1e-6,a=new Die,s=new Die;let l=1;const c=new H5t;let u=!1;const h=new C5t,d=new C5t,p=new C5t,f=new C5t,m=new C5t,g=new C5t,_=new C5t,y=new C5t,v=new C5t,b=[],x={};function w(){return Math.pow(.95,n.zoomSpeed)}function S(t){s.theta-=t}function M(t){s.phi-=t}const E=(function(){const t=new H5t;return function e(n,i){t.setFromMatrixColumn(i,0),t.multiplyScalar(-n),c.add(t)}})(),T=(function(){const t=new H5t;return function e(i,r){!0===n.screenSpacePanning?t.setFromMatrixColumn(r,1):(t.setFromMatrixColumn(r,0),t.crossVectors(n.object.up,t)),t.multiplyScalar(i),c.add(t)}})(),C=(function(){const t=new H5t;return function e(i,r){const o=n.domElement;if(n.object.isPerspectiveCamera){t.copy(n.object.position).sub(n.target);let e=t.length();e*=Math.tan(n.object.fov/2*Math.PI/180),E(2*i*e/o.clientHeight,n.object.matrix),T(2*r*e/o.clientHeight,n.object.matrix)}else n.object.isOrthographicCamera?(E(i*(n.object.right-n.object.left)/n.object.zoom/o.clientWidth,n.object.matrix),T(r*(n.object.top-n.object.bottom)/n.object.zoom/o.clientHeight,n.object.matrix)):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - pan disabled."),n.enablePan=!1)}})();function A(t){n.object.isPerspectiveCamera?l/=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom*t)),n.object.updateProjectionMatrix(),u=!0):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled."),n.enableZoom=!1)}function k(t){n.object.isPerspectiveCamera?l*=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom/t)),n.object.updateProjectionMatrix(),u=!0):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled."),n.enableZoom=!1)}function L(t){h.set(t.clientX,t.clientY)}function P(t){f.set(t.clientX,t.clientY)}function N(){if(1===b.length)h.set(b[0].pageX,b[0].pageY);else{h.set(.5*(b[0].pageX+b[1].pageX),.5*(b[0].pageY+b[1].pageY))}}function I(){if(1===b.length)f.set(b[0].pageX,b[0].pageY);else{f.set(.5*(b[0].pageX+b[1].pageX),.5*(b[0].pageY+b[1].pageY))}}function R(){const t=b[0].pageX-b[1].pageX,e=b[0].pageY-b[1].pageY,n=Math.sqrt(t*t+e*e);_.set(0,n)}function O(t){if(1==b.length)d.set(t.pageX,t.pageY);else{const e=Y(t);d.set(.5*(t.pageX+e.x),.5*(t.pageY+e.y))}p.subVectors(d,h).multiplyScalar(n.rotateSpeed);const e=n.domElement;S(2*Math.PI*p.x/e.clientHeight),M(2*Math.PI*p.y/e.clientHeight),h.copy(d)}function z(t){if(1===b.length)m.set(t.pageX,t.pageY);else{const e=Y(t);m.set(.5*(t.pageX+e.x),.5*(t.pageY+e.y))}g.subVectors(m,f).multiplyScalar(n.panSpeed),C(g.x,g.y),f.copy(m)}function D(t){const e=Y(t),i=t.pageX-e.x,r=t.pageY-e.y,o=Math.sqrt(i*i+r*r);y.set(0,o),v.set(0,Math.pow(y.y/_.y,n.zoomSpeed)),A(v.y),_.copy(y)}function B(t){!1!==n.enabled&&(0===b.length&&(n.domElement.setPointerCapture(t.pointerId),n.domElement.addEventListener("pointermove",H),n.domElement.addEventListener("pointerup",F)),(function e(t){b.push(t)})(t),"touch"===t.pointerType?(function o(t){switch(q(t),b.length){case 1:switch(n.touches.ONE){case J0t.ROTATE:if(!1===n.enableRotate)return;N(),r=i.TOUCH_ROTATE;break;case J0t.PAN:if(!1===n.enablePan)return;I(),r=i.TOUCH_PAN;break;default:r=i.NONE}break;case 2:switch(n.touches.TWO){case J0t.DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;!(function t(){n.enableZoom&&R(),n.enablePan&&I()})(),r=i.TOUCH_DOLLY_PAN;break;case J0t.DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;!(function e(){n.enableZoom&&R(),n.enableRotate&&N()})(),r=i.TOUCH_DOLLY_ROTATE;break;default:r=i.NONE}break;default:r=i.NONE}r!==i.NONE&&n.dispatchEvent(qre)})(t):(function a(t){let e;switch(t.button){case 0:e=n.mouseButtons.LEFT;break;case 1:e=n.mouseButtons.MIDDLE;break;case 2:e=n.mouseButtons.RIGHT;break;default:e=-1}switch(e){case Z0t.DOLLY:if(!1===n.enableZoom)return;!(function e(t){_.set(t.clientX,t.clientY)})(t),r=i.DOLLY;break;case Z0t.ROTATE:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enablePan)return;P(t),r=i.PAN}else{if(!1===n.enableRotate)return;L(t),r=i.ROTATE}break;case Z0t.PAN:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enableRotate)return;L(t),r=i.ROTATE}else{if(!1===n.enablePan)return;P(t),r=i.PAN}break;default:r=i.NONE}r!==i.NONE&&n.dispatchEvent(qre)})(t))}function H(t){!1!==n.enabled&&("touch"===t.pointerType?(function e(t){switch(q(t),r){case i.TOUCH_ROTATE:if(!1===n.enableRotate)return;O(t),n.update();break;case i.TOUCH_PAN:if(!1===n.enablePan)return;z(t),n.update();break;case i.TOUCH_DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;!(function e(t){n.enableZoom&&D(t),n.enablePan&&z(t)})(t),n.update();break;case i.TOUCH_DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;!(function o(t){n.enableZoom&&D(t),n.enableRotate&&O(t)})(t),n.update();break;default:r=i.NONE}})(t):(function o(t){if(!1===n.enabled)return;switch(r){case i.ROTATE:if(!1===n.enableRotate)return;!(function e(t){d.set(t.clientX,t.clientY),p.subVectors(d,h).multiplyScalar(n.rotateSpeed);const e=n.domElement;S(2*Math.PI*p.x/e.clientHeight),M(2*Math.PI*p.y/e.clientHeight),h.copy(d),n.update()})(t);break;case i.DOLLY:if(!1===n.enableZoom)return;!(function r(t){y.set(t.clientX,t.clientY),v.subVectors(y,_),v.y>0?A(w()):v.y<0&&k(w()),_.copy(y),n.update()})(t);break;case i.PAN:if(!1===n.enablePan)return;!(function o(t){m.set(t.clientX,t.clientY),g.subVectors(m,f).multiplyScalar(n.panSpeed),C(g.x,g.y),f.copy(m),n.update()})(t)}})(t))}function F(t){!1!==n.enabled&&("touch"===t.pointerType?(function e(t){n.dispatchEvent(Yre),r=i.NONE})():(function o(t){n.dispatchEvent(Yre),r=i.NONE})(),W(t),0===b.length&&(n.domElement.releasePointerCapture(t.pointerId),n.domElement.removeEventListener("pointermove",H),n.domElement.removeEventListener("pointerup",F)))}function V(t){W(t)}function U(t){!1===n.enabled||!1===n.enableZoom||r!==i.NONE&&r!==i.ROTATE||(t.preventDefault(),n.dispatchEvent(qre),(function e(t){t.deltaY<0?k(w()):t.deltaY>0&&A(w()),n.update()})(t),n.dispatchEvent(Yre))}function j(t){!1!==n.enabled&&!1!==n.enablePan&&(function e(t){let e=!1;switch(t.code){case n.keys.UP:C(0,n.keyPanSpeed),e=!0;break;case n.keys.BOTTOM:C(0,-n.keyPanSpeed),e=!0;break;case n.keys.LEFT:C(n.keyPanSpeed,0),e=!0;break;case n.keys.RIGHT:C(-n.keyPanSpeed,0),e=!0}e&&(t.preventDefault(),n.update())})(t)}function G(t){!1!==n.enabled&&t.preventDefault()}function W(t){delete x[t.pointerId];for(let e=0;e<b.length;e++)if(b[e].pointerId==t.pointerId)return void b.splice(e,1)}function q(t){let e=x[t.pointerId];void 0===e&&(e=new C5t,x[t.pointerId]=e),e.set(t.pageX,t.pageY)}function Y(t){return x[(t.pointerId===b[0].pointerId?b[1]:b[0]).pointerId]}n.domElement.addEventListener("contextmenu",G),n.domElement.addEventListener("pointerdown",B),n.domElement.addEventListener("pointercancel",V),n.domElement.addEventListener("wheel",U,{passive:!1}),this.update()}}class $re extends e5t{constructor(t){super(),this._lastMesh=null,this._clock=new oie,this._canvasSize=null,this._layersConfig=null,this._runColor=t}_isObject(t){return"object"==typeof t&&null!=t&&!Array.isArray(t)}_applyDefaults(t,e){let n={};const i=[t,e];for(let t=0;t<i.length;t++){const e=i[t];for(let t in e){const i=t in n;this._isObject(e[t])?n[t]=this._applyDefaults(n[t]||{},e[t]):i||(n[t]=e[t])}}return n}_createLayers(){if(this._layersConfig&&this._scene&&this._lastMesh){if(this._layersConfig.showBoundingBox){var t=new sre(this._lastMesh,new s4t("rgb(0, 0, 255)"));this._scene.add(t)}if(this._layersConfig.showAxes){var e=new hre(5);this._scene.add(e)}}}setLayersConfig(t){this._layersConfig=this._applyDefaults(t,this._layersConfig||{})}_createWorld(t,e){if(this.isReady())return;this._scene=new c7t;var n=new mre[t.camera.cls](t.camera.fov,this._canvasSize.width/this._canvasSize.height,t.camera.near,t.camera.far);this._camera=n,this.initCameraPosition=null,t.camera.position&&(this.initCameraPosition=(new H5t).fromArray(t.camera.position)),this.initCameraLookAt=null,t.camera.lookAt&&(this.initCameraLookAt=(new H5t).fromArray(t.camera.lookAt));var i=new Xre(n,e);const r=i;r.lookSpeed=.4,r.movementSpeed=20,r.noFly=!0,r.lookVertical=!0,r.constrainVertical=!0,r.verticalMin=1,r.verticalMax=2,r.addEventListener("change",this._onCameraPositionChange.bind(this)),this._cameraControls=i,this._renderer=new o7t({antialias:!0}),this._renderer.setPixelRatio(window.devicePixelRatio),this._renderer.setSize(this._canvasSize.width,this._canvasSize.height),this._renderer.setClearColor(16777215,1)}_clearScene(){for(;this._scene.children.length>0;)this._scene.remove(this._scene.children[0])}getRenderer(){return this._renderer}getCameraControls(){return this._cameraControls}isReady(){return!!this._camera&&!!this._cameraControls}getCameraPosition(){return{far:this._camera.far,position:this._camera.position.clone(),target:this._cameraControls.target.clone()}}setCanvasSize(t){this._canvasSize=t}draw(){this._animationFrameIndex&&cancelAnimationFrame(this._animationFrameIndex),this._camera.aspect=this._canvasSize.width/this._canvasSize.height,this._camera.updateProjectionMatrix(),this._renderer.setSize(this._canvasSize.width,this._canvasSize.height);const t=function(){var e=this._clock.getDelta();this._cameraControls.update(e),this._animationFrameIndex=requestAnimationFrame(t),this._renderer.render(this._scene,this._camera)}.bind(this);t()}updateScene(t,e){let n={};"config"in t&&t.config&&(n=JSON.parse(t.config)),this.dispatchEvent({type:"beforeUpdateScene"});n=this._applyDefaults(n,{camera:{cls:"PerspectiveCamera",fov:75,near:.1,far:1e3},lights:[{cls:"AmbientLight",color:"#ffffff",intensity:.75},{cls:"DirectionalLight",color:"#ffffff",intensity:.75,position:[0,-1,2]}]}),this._createWorld(n,e),this._clearScene(),this._createLights(this._scene,n),this._createGeometry(t,n),this._createLayers(),this.draw()}resetView(t){if(!this.isReady())return;let e;this._cameraControls.reset(),!t&&this._lastMesh&&(e=this._lastMesh),e&&(this._fitObjectToViewport(e),this._lastMesh=e),this._cameraControls.update()}_createGeometry(t,e){const n=t.mesh;n.vertices&&n.faces&&n.faces.length?this._createMesh(n,e):this._createPointCloud(n,e)}_createPointCloud(t,e){const n=t.vertices,i=t.colors;let r={material:{cls:"PointsMaterial",size:.005}};i&&i.length==n.length?r.material.vertexColors=!0:r.material.color=this._runColor;const o=this._applyDefaults(e,r),a=new N4t,s=new Float32Array(n.flat());if(a.setAttribute("position",new h4t(s,3)),i&&i.length==n.length){const t=new Float32Array(i.flat());for(let e=0;e<t.length;e++)t[e]=t[e]/255;a.setAttribute("color",new h4t(t,3))}var l=new mre[o.material.cls](o.material),c=new ute(a,l);this._scene.add(c),this._lastMesh=c}setCameraViewpoint(t,e,n){this._silent=!0,this._camera.far=e,this._camera.position.set(t.x,t.y,t.z),this._camera.lookAt(n.clone()),this._camera.updateProjectionMatrix(),this._cameraControls.target=n.clone(),this._cameraControls.update(),this._silent=!1}_onCameraPositionChange(t){this._silent||this.dispatchEvent({type:"cameraPositionChange",event:t})}_fitObjectToViewport(t){var e,n;const i=new U5t,r=new H5t,o=new H5t;i.setFromObject(t),i.getCenter(r),i.getSize(o);const a=Math.max(o.x,o.y,o.z),s=this._camera.fov*(Math.PI/180);let l=1.25*Math.abs(a/(2*Math.tan(s/2)));const c=i.min.z,u=c<0?-c+l:l-c,h=null!==(e=this.initCameraPosition)&&void 0!==e?e:new H5t(r.x,r.y,l),d=null!==(n=this.initCameraLookAt)&&void 0!==n?n:r;this.setCameraViewpoint(h,3*u,d)}_createMesh(t,e){const n=t.vertices,i=t.faces,r=t.colors,o=this._applyDefaults(e,{material:{cls:"MeshStandardMaterial",color:"#a0a0a0",roughness:1,metalness:0}}),a=new N4t,s=new Float32Array(n.flat());a.setAttribute("position",new h4t(s,3));const l=new Uint16Array(i.flat());if(r&&r.length){const t=r.flat();for(let e=0;e<t.length;e++)t[e]=t[e]/255;a.setAttribute("color",new h4t(new Float32Array(t),3)),o.material=o.material||{},o.material.vertexColors=!0}a.center(),a.computeBoundingSphere(),a.setIndex(new h4t(l,1)),a.computeVertexNormals();let c=new mre[o.material.cls](o.material),u=new K4t(a,c);u.castShadow=!0,u.receiveShadow=!0,this._scene.add(u),this._lastMesh=u}_createLights(t,e){for(let n=0;n<e.lights.length;n++){const i=e.lights[n];let r=new mre[i.cls](i.color,i.intensity);i.position&&r.position.set(i.position[0],i.position[1],i.position[2]),t.add(r)}}}let Kre=class extends(cr(we)){constructor(){super(...arguments),this.selectedView="all",this.active=!1,this._colorScaleFunction=_O,this._steps=[],this._meshViewerAttached=!1,this._cameraPositionInitialized=!1,this._isMeshLoading=!1}get _runColor(){return this._colorScaleFunction(this.run)}connectedCallback(){super.connectedCallback(),this._dataProvider=new $0t(this.requestManager);const t=new $re(this._runColor);t.addEventListener("beforeUpdateScene",this._updateCanvasSize.bind(this)),t.addEventListener("cameraPositionChange",this._onCameraPositionChange.bind(this)),this._meshViewer=t}reload(){this.active&&this._dataProvider&&(this._isMeshLoading=!0,this._dataProvider.reload(this.run,this.tag,this.sample).then((t=>{t&&(this._steps=t,this._stepIndex=t.length-1)})).catch((t=>{if(!t||!t.code||t.code!=q0t.CANCELLED)throw t=t||"Response processing failed.",new Error(t)})))}_updateScene(){const t=this._currentStep;t&&t.mesh&&(this._meshViewer.updateScene(t,this),this._cameraPositionInitialized||(this._meshViewer.resetView(),this._cameraPositionInitialized=!0),this._meshViewerAttached||(this.shadowRoot.appendChild(this._meshViewer.getRenderer().domElement),this._meshViewerAttached=!0))}_debouncedFetchMesh(){this.debounce("fetchMesh",(()=>this._maybeFetchMesh()),100)}_maybeFetchMesh(){return n(this,void 0,void 0,(function*(){const t=this._currentStep;if(t&&!t.mesh&&!t.meshFetching){t.meshFetching=!0,this._isMeshLoading=!0;try{const e=yield this._dataProvider.fetchData(t,this.run,this.tag,this.sample);t.mesh=e[0],this.notifyPath("_currentStep.mesh")}catch(t){if(!t||!t.code||t.code!=q0t.CANCELLED)throw t=t||"Response processing failed.",new Error(t)}finally{this._isMeshLoading=!1,t.meshFetching=!1}}}))}_onCameraPositionChange(){if(!this._meshViewer.isReady())return;const t=new CustomEvent("camera-position-change",{detail:this._meshViewer.getCameraPosition()});this.dispatchEvent(t)}setCameraViewpoint(t,e,n){this._meshViewer.setCameraViewpoint(t,e,n)}_updateCanvasSize(){const t=this.offsetWidth,e=t,n=this.$$(".tf-mesh-loader-header").offsetHeight;this._meshViewer.setCanvasSize({width:t,height:e-n})}redraw(){this._updateCanvasSize(),this.isConnected&&this._meshViewer.draw()}_hasAtLeastOneStep(t){return!!t&&t.length>0}_hasMultipleSteps(t){return!!t&&t.length>1}get _currentStep(){return this._steps[this._stepIndex]||null}get _stepValue(){const t=this._currentStep;return t?t.step:0}get _currentWallTime(){const t=this._currentStep;return t?SO(t.wall_time):""}_getMaxStepIndex(t){return t.length-1}_getSampleText(t){return String(t+1)}_hasMultipleSamples(t){return t>1}_updateView(){this._meshViewer&&"all"==this.selectedView&&this._meshViewer.resetView()}toLocaleString_(t){return t.toLocaleString()}};Kre.template=xe`
    <tf-card-heading color="[[_runColor]]" class="tf-mesh-loader-header">
      <template is="dom-if" if="[[_hasMultipleSamples(ofSamples)]]">
        <div>sample: [[_getSampleText(sample)]] of [[ofSamples]]</div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep(_steps)]]">
        <div class="heading-row">
          <div class="heading-label">
            step
            <span style="font-weight: bold"
              >[[toLocaleString_(_stepValue)]]</span
            >
          </div>
          <div class="heading-label heading-right">
            <template is="dom-if" if="[[_currentWallTime]]">
              [[_currentWallTime]]
            </template>
          </div>
          <div class="label right">
            <paper-spinner-lite active hidden$="[[!_isMeshLoading]]">
            </paper-spinner-lite>
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps(_steps)]]">
        <div>
          <paper-slider
            id="steps"
            immediate-value="{{_stepIndex}}"
            max="[[_getMaxStepIndex(_steps)]]"
            max-markers="[[_getMaxStepIndex(_steps)]]"
            snaps
            step="1"
            value="{{_stepIndex}}"
          ></paper-slider>
        </div>
      </template>
    </tf-card-heading>
    <style>
      paper-slider {
        width: 100%;
        margin-left: 1px;
        margin-right: 1px;
      }
      .tf-mesh-loader-header {
        display: block;
        height: 105px;
      }
      [hidden] {
        display: none;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],Kre.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],Kre.prototype,"tag",void 0),t([o({type:Number}),e("design:type",Number)],Kre.prototype,"sample",void 0),t([o({type:Number}),e("design:type",Number)],Kre.prototype,"ofSamples",void 0),t([o({type:String}),e("design:type",String)],Kre.prototype,"selectedView",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Kre.prototype,"active",void 0),t([o({type:Object}),e("design:type",br)],Kre.prototype,"requestManager",void 0),t([o({type:Object}),e("design:type",$re)],Kre.prototype,"_meshViewer",void 0),t([o({type:Object}),e("design:type",$0t)],Kre.prototype,"_dataProvider",void 0),t([o({type:Object}),e("design:type",Object)],Kre.prototype,"_colorScaleFunction",void 0),t([o({type:Array,notify:!0}),e("design:type",Array)],Kre.prototype,"_steps",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],Kre.prototype,"_stepIndex",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Kre.prototype,"_meshViewerAttached",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Kre.prototype,"_cameraPositionInitialized",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Kre.prototype,"_isMeshLoading",void 0),t([s("run"),e("design:type",String),e("design:paramtypes",[])],Kre.prototype,"_runColor",null),t([a("run","tag","active","_dataProvider","_meshViewer"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Kre.prototype,"reload",null),t([a("_currentStep.*","_meshViewer"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Kre.prototype,"_updateScene",null),t([a("_currentStep"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Kre.prototype,"_debouncedFetchMesh",null),t([s("_steps","_stepIndex"),e("design:type",Object),e("design:paramtypes",[])],Kre.prototype,"_currentStep",null),t([s("_currentStep"),e("design:type",Number),e("design:paramtypes",[])],Kre.prototype,"_stepValue",null),t([s("_currentStep"),e("design:type",String),e("design:paramtypes",[])],Kre.prototype,"_currentWallTime",null),t([a("selectedView"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Kre.prototype,"_updateView",null),Kre=t([i("tf-mesh-loader")],Kre);let Zre=class extends we{constructor(){super(),this.reloadOnReady=!0,this._tagFilter=".*",this._selectedView="all",this._requestManager=new br,window.addEventListener("resize",(()=>{this._handleWindowResize()}),!1),this.reloadOnReady&&this.reload()}_getAllChildren(){return Array.from(this.shadowRoot.querySelectorAll("tf-mesh-loader"))}_onCameraPositionChanged(t){"share"==this._selectedView&&this._getAllChildren().forEach((e=>{t.target!=e&&e.setCameraViewpoint(t.detail.position,t.detail.far,t.detail.target)}))}_shouldOpen(t){return t<=2}reload(){this._fetchTags().then(this._reloadMeshes.bind(this))}_handleWindowResize(){this._getAllChildren().forEach((t=>{t.redraw()}))}_fetchTags(){const t=Er().pluginRoute("mesh","/tags");return this._requestManager.request(t).then((t=>{if(Ce.exports.isEqual(t,this._runToTagInfo))return;const e=fr(Ce.exports.mapValues(t,(t=>Object.keys(t))));this._dataNotFound=0===e.length,this._runToTagInfo=t}))}_reloadMeshes(){this._getAllChildren().forEach((t=>{t.reload()}))}get _categories(){var t=this._runToTagInfo,e=this._selectedRuns,n=this._tagFilter;function i(e){const n=t[e.run][e.tag].samples;return Ce.exports.range(n).map((t=>Object.assign({},e,{sample:t,ofSamples:n})))}return Ir(Ce.exports.mapValues(t,(t=>Object.keys(t))),e,n).map((t=>Object.assign({},t,{items:[].concat.apply([],t.items.map(i))})))}};Zre.template=xe`
    <tf-dashboard-layout>
      <div slot="sidebar" class="all-controls">
        <div class="settings">
          <div class="sidebar-section view-control">
            <h3 class="title">Point of view</h3>
            <div>
              <paper-radio-group
                id="view-radio-group"
                selected="{{_selectedView}}"
              >
                <paper-radio-button id="all-radio-button" name="all">
                  Display all points
                </paper-radio-button>
                <paper-tooltip
                  animation-delay="0"
                  for="all-radio-button"
                  position="right"
                  offset="0"
                >
                  Zoom and center camera to display all points at once. Note,
                  that some points could be too far (i.e. too small) to be
                  visible.
                </paper-tooltip>
                <paper-radio-button id="user-radio-button" name="user">
                  Current view
                </paper-radio-button>
                <paper-tooltip
                  animation-delay="0"
                  for="user-radio-button"
                  position="right"
                  offset="0"
                >
                  Keep current camera position and zoom level.
                </paper-tooltip>
                <paper-radio-button id="share-radio-button" name="share">
                  Share viewpoint
                </paper-radio-button>
                <paper-tooltip
                  animation-delay="0"
                  for="share-radio-button"
                  position="right"
                  offset="0"
                >
                  Share viewpoint among all cameras.
                </paper-tooltip>
              </paper-radio-group>
            </div>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No point cloud data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any point cloud data to your event files.
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
            >
              <template>
                <tf-mesh-loader
                  active="[[active]]"
                  selected-view="[[_selectedView]]"
                  run="[[item.run]]"
                  tag="[[item.tag]]"
                  sample="[[item.sample]]"
                  of-samples="[[item.ofSamples]]"
                  request-manager="[[_requestManager]]"
                  class="tf-mesh-loader-container"
                  on-camera-position-change="_onCameraPositionChanged"
                >
                </tf-mesh-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      paper-radio-button {
        display: block;
        padding: 5px;
      }
      .sidebar-section h3 {
        margin: 0;
        font-weight: normal;
        font-size: 14px;
        margin-bottom: 5px;
      }

      .runs-selector {
        flex-grow: 1;
      }

      tf-runs-selector {
        display: flex;
      }

      .view-control {
        display: block !important;
      }

      .view-control h3.title {
        padding-top: 16px;
        padding-bottom: 16px;
      }

      .allcontrols .view-control paper-radio-group {
        margin-top: 5px;
      }
      /* Layout must be horizontal, i.e. items arranged in a row. If items cannot fit in a row,
       * they should be moved to next line. All items must be square at all times. Minimum size of
       * the item is 480px. This means that maximum size of the item must be 480px + 479px = 959px.
       * */
      .horizontal {
        display: flex;
        flex-direction: row;
        flex-wrap: wrap;
      }
      tf-mesh-loader {
        width: 480px;
        flex-basis: 480px;
        flex-grow: 1;
        display: block;
      }
    </style>
  `,t([o({type:Boolean}),e("design:type",Boolean)],Zre.prototype,"reloadOnReady",void 0),t([o({type:Array}),e("design:type",Array)],Zre.prototype,"_selectedRuns",void 0),t([o({type:Object}),e("design:type",Object)],Zre.prototype,"_runToTagInfo",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Zre.prototype,"_dataNotFound",void 0),t([o({type:String}),e("design:type",String)],Zre.prototype,"_tagFilter",void 0),t([o({type:String,notify:!0}),e("design:type",String)],Zre.prototype,"_selectedView",void 0),t([o({type:Object}),e("design:type",Object)],Zre.prototype,"_requestManager",void 0),t([s("_runToTagInfo","_selectedRuns","_tagFilter"),e("design:type",Array),e("design:paramtypes",[])],Zre.prototype,"_categories",null),Zre=t([i("mesh-dashboard"),e("design:paramtypes",[])],Zre);let Jre=class extends(cr(we)){constructor(){super(...arguments),this._installCommand="pip install -U tensorboard-plugin-profile"}_copyInstallCommand(){return n(this,void 0,void 0,(function*(){const t=()=>n(this,void 0,void 0,(function*(){this.$.commandTextarea.select();try{yield navigator.clipboard.writeText(this._installCommand)}catch(t){if(!document.execCommand("copy"))return Promise.reject()}})),e=this.$.copiedMessage;try{yield t(),e.innerText="Copied."}catch(t){e.innerText="Failed to copy to clipboard."}}))}_removeCopiedMessage(){this.$.copiedMessage.innerText=""}};Jre.template=xe`
    <div class="message">
      <h3>The profile plugin has moved.</h3>
      <p>
        Please install the new version of the profile plugin from PyPI by
        running the following command from the machine running TensorBoard:
      </p>
      <textarea
        id="commandTextarea"
        readonly=""
        rows="1"
        on-blur="_removeCopiedMessage"
      >
[[_installCommand]]</textarea
      >
      <div id="copyContainer">
        <span id="copiedMessage"></span>
        <paper-button raised="" on-tap="_copyInstallCommand"
          >Copy to clipboard</paper-button
        >
      </div>
    </div>

    <style>
      :host {
        display: flex;
      }

      .message {
        margin: 80px auto 0 auto;
        max-width: 540px;
      }
      #commandTextarea {
        margin-top: 1ex;
        padding: 1ex 1em;
        resize: vertical;
        width: 100%;
      }
      #copyContainer {
        display: flex;
      }
      #copiedMessage {
        align-self: center;
        flex-grow: 1;
        font-style: italic;
        padding-right: 1em;
        text-align: right;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],Jre.prototype,"_installCommand",void 0),Jre=t([i("tf-profile-redirect-dashboard")],Jre);let Qre=class extends we{constructor(){super(...arguments),this._expanded=!1,this._runToPrCurveEntry={},this._previousRunToPrCurveEntry={},this._colorScaleFunction={scale:_O},this._canceller=new xO,this._xComponentsCreationMethod=()=>{const t=new NO.Scales.Linear;return{scale:t,axis:new NO.Axes.Numeric(t,"bottom"),accessor:t=>t.recall}},this._yValueAccessor=t=>t.precision,this._tooltipColumns=(()=>{const t=SCt(4),e=e=>isNaN(e)?"NaN":t(e);return[{title:"Run",evaluate:t=>t.dataset.metadata().name},{title:"Threshold",evaluate:t=>e(t.datum.thresholds)},{title:"Precision",evaluate:t=>e(t.datum.precision)},{title:"Recall",evaluate:t=>e(t.datum.recall)},{title:"TP",evaluate:t=>t.datum.true_positives},{title:"FP",evaluate:t=>t.datum.false_positives},{title:"TN",evaluate:t=>t.datum.true_negatives},{title:"FN",evaluate:t=>t.datum.false_negatives}]})(),this._seriesDataFields=["thresholds","precision","recall","true_positives","false_positives","true_negatives","false_negatives"],this._defaultXRange=[-.05,1.05],this._defaultYRange=[-.05,1.05],this._requestData=(t,e,n)=>{const i=Er().pluginRoute("pr_curves","/pr_curves");Promise.all(t.map((t=>{const n=PO(i,{tag:this.tag,run:t});return this.requestManager.request(n).then((n=>{e({item:t,data:n})}))}))).finally((()=>{n()}))},this._smoothingEnabled=!1}_createProcessDataFunction(){return(t,e,n)=>{this.set("_runToDataOverTime",Object.assign({},this._runToDataOverTime,n))}}_computeRunColor(t){return _O(t)}connectedCallback(){super.connectedCallback(),this._attached=!0,this.reload()}_getChartDataLoader(){return this.shadowRoot.querySelector("tf-line-chart-data-loader")}reload(){this._attached&&(0!==this.runs.length?this._getChartDataLoader().reload():this.set("_runToDataOverTime",{}))}_setChartData(){var t=this._runToPrCurveEntry,e=this._previousRunToPrCurveEntry,n=this._setOfRelevantRuns;Ce.exports.forOwn(t,((i,r)=>{const o=e[r];o&&t[r].step===o.step||(n[r]?this._updateSeriesDataForRun(r,i):this._clearSeriesData(r))}))}_updateSeriesDataForRun(t,e){const n=Ce.exports.reduce(this._seriesDataFields,((t,n)=>(t[n]=e[n].slice().reverse(),t)),{}),i=new Array(n[this._seriesDataFields[0]].length);for(let t=0;t<i.length;t++)i[t]=Ce.exports.mapValues(n,(e=>e[t]));const r=this._getChartDataLoader();r.setSeriesData(t,i),r.commitChanges()}_clearSeriesData(t){const e=this._getChartDataLoader();e.setSeriesData(t,[]),e.commitChanges()}_updateRunToPrCurveEntry(){var t=this.runToStepCap;const e={};Ce.exports.forOwn(this._runToDataOverTime,((n,i)=>{n&&n.length&&(e[i]=this._computeEntryClosestOrEqualToStepCap(t[i],n))})),this.set("_previousRunToPrCurveEntry",this._runToPrCurveEntry),this.set("_runToPrCurveEntry",e)}_notifyDataChange(){this.onDataChange&&this.onDataChange(this._runToDataOverTime)}_computeEntryClosestOrEqualToStepCap(t,e){const n=Math.min(Ce.exports.sortedIndex(e.map((t=>t.step)),t),e.length-1);return e[n]}get _runsWithStepAvailable(){var t=this._runToPrCurveEntry;return Ce.exports.filter(this.runs,(e=>t[e])).sort()}get _setOfRelevantRuns(){const t={};return Ce.exports.forEach(this._runsWithStepAvailable,(e=>{t[e]=!0})),t}_computeCurrentStepForRun(t,e){const n=t[e];return n?n.step:null}_computeCurrentWallTimeForRun(t,e){const n=t[e];return n?new Date(1e3*n.wall_time).toString():null}_toggleExpanded(t){this.set("_expanded",!this._expanded),this.redraw()}_resetDomain(){this._getChartDataLoader().resetDomain()}redraw(){this._getChartDataLoader().redraw()}};Qre.template=xe`
    <tf-card-heading
      tag="[[tag]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
    ></tf-card-heading>

    <tf-line-chart-data-loader
      x-components-creation-method="[[_xComponentsCreationMethod]]"
      y-value-accessor="[[_yValueAccessor]]"
      tooltip-columns="[[_tooltipColumns]]"
      color-scale="[[_colorScaleFunction]]"
      default-x-range="[[_defaultXRange]]"
      default-y-range="[[_defaultYRange]]"
      smoothing-enabled="[[_smoothingEnabled]]"
      request-manager="[[requestManager]]"
      data-to-load="[[runs]]"
      data-series="[[runs]]"
      load-key="[[tag]]"
      request-data="[[_requestData]]"
      load-data-callback="[[_createProcessDataFunction()]]"
      active="[[active]]"
    ></tf-line-chart-data-loader>

    <div id="buttons-row">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
      <paper-icon-button
        icon="settings-overscan"
        on-tap="_resetDomain"
        title="Reset axes to [0, 1]."
      ></paper-icon-button>
    </div>

    <div id="step-legend">
      <template is="dom-repeat" items="[[_runsWithStepAvailable]]" as="run">
        <div class="legend-row">
          <div
            class="color-box"
            style="background: [[_computeRunColor(run)]];"
          ></div>
          [[run]] is at
          <span class="step-label-text">
            step [[_computeCurrentStepForRun(_runToPrCurveEntry, run)]] </span
          ><br />
          <span class="wall-time-label-text">
            ([[_computeCurrentWallTimeForRun(_runToPrCurveEntry, run)]])
          </span>
        </div>
      </template>
    </div>

    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 500px;
        margin-right: 10px;
        margin-bottom: 25px;
      }
      :host([_expanded]) {
        width: 100%;
      }
      tf-line-chart-data-loader {
        height: 300px;
        position: relative;
      }
      :host([_expanded]) tf-line-chart-data-loader {
        height: 600px;
      }
      #buttons-row {
        display: flex;
        flex-direction: row;
      }
      #buttons-row paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }
      #buttons-row paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }
      #step-legend {
        box-sizing: border-box;
        font-size: 0.8em;
        max-height: 200px;
        overflow-y: auto;
        padding: 0 0 0 10px;
        width: 100%;
      }
      .legend-row {
        margin: 5px 0 5px 0;
        width: 100%;
      }
      .color-box {
        display: inline-block;
        border-radius: 1px;
        width: 10px;
        height: 10px;
      }
      .step-label-text {
        font-weight: bold;
      }
      .wall-time-label-text {
        color: #888;
        font-size: 0.8em;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],Qre.prototype,"runs",void 0),t([o({type:String}),e("design:type",String)],Qre.prototype,"tag",void 0),t([o({type:Object}),e("design:type",Object)],Qre.prototype,"tagMetadata",void 0),t([o({type:Object}),e("design:type",Object)],Qre.prototype,"runToStepCap",void 0),t([o({type:Object}),e("design:type",br)],Qre.prototype,"requestManager",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Qre.prototype,"active",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],Qre.prototype,"_expanded",void 0),t([o({type:Object}),e("design:type",Object)],Qre.prototype,"_runToPrCurveEntry",void 0),t([o({type:Object}),e("design:type",Object)],Qre.prototype,"_previousRunToPrCurveEntry",void 0),t([o({type:Object}),e("design:type",Object)],Qre.prototype,"_runToDataOverTime",void 0),t([o({type:Object}),e("design:type",Function)],Qre.prototype,"onDataChange",void 0),t([o({type:Object}),e("design:type",Object)],Qre.prototype,"_colorScaleFunction",void 0),t([o({type:Object}),e("design:type",xO)],Qre.prototype,"_canceller",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Qre.prototype,"_attached",void 0),t([o({type:Object}),e("design:type",Object)],Qre.prototype,"_xComponentsCreationMethod",void 0),t([o({type:Object}),e("design:type",Object)],Qre.prototype,"_yValueAccessor",void 0),t([o({type:Array}),e("design:type",Array)],Qre.prototype,"_tooltipColumns",void 0),t([o({type:Array}),e("design:type",Array)],Qre.prototype,"_seriesDataFields",void 0),t([o({type:Array}),e("design:type",Array)],Qre.prototype,"_defaultXRange",void 0),t([o({type:Array}),e("design:type",Array)],Qre.prototype,"_defaultYRange",void 0),t([o({type:Object}),e("design:type",Function)],Qre.prototype,"_requestData",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Qre.prototype,"_smoothingEnabled",void 0),t([a("runs","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Qre.prototype,"reload",null),t([a("_runToPrCurveEntry","_previousRunToPrCurveEntry","_setOfRelevantRuns"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Qre.prototype,"_setChartData",null),t([a("_runToDataOverTime","runToStepCap"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Qre.prototype,"_updateRunToPrCurveEntry",null),t([a("_runToDataOverTime"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Qre.prototype,"_notifyDataChange",null),t([s("runs","_runToPrCurveEntry"),e("design:type",Array),e("design:paramtypes",[])],Qre.prototype,"_runsWithStepAvailable",null),t([s("_runsWithStepAvailable"),e("design:type",Object),e("design:paramtypes",[])],Qre.prototype,"_setOfRelevantRuns",null),Qre=t([i("tf-pr-curve-card")],Qre);let toe=class extends we{constructor(){super(...arguments),this._runToStepIndex={}}_computeColorForRun(t){return _O(t)}_computeTimeTextForRun(t,e,n,i){const r=e[n];if(!Ce.exports.isNumber(r))return"";const o=t[n];if(!o)return"";const a=o[r][i];if("step"===i)return`step ${a}`;if("relative"===i)return a<1?`${(1e3*a).toFixed(2)} ms`:`${a.toFixed(2)} s`;if("wall_time"===i)return new Date(1e3*a).toString();throw new Error(`The display type of ${i} is not recognized.`)}_sliderValueChanged(t){const e=t.target.dataset.run,n=t.target.immediateValue,i=Object.assign({},this._runToStepIndex);isNaN(n)?delete i[e]:i[e]=t.target.immediateValue,this._runToStepIndex=i}_computeMaxStepIndexForRun(t,e){const n=t[e];return n&&n.length?n.length-1:0}_updateStepsForNewRuns(){var t=this.runToAvailableTimeEntries;const e=Object.assign({},this._runToStepIndex);Ce.exports.forOwn(t,((t,n)=>{Ce.exports.isNumber(e[n])||(e[n]=t.length-1)})),this._runToStepIndex=e}_getStep(t,e){return this._runToStepIndex?this._runToStepIndex[e]:0}_computeRunToStep(t,e){const n={};return Ce.exports.forOwn(e,((e,i)=>{const r=t[i];r&&(n[i]=r[e].step)})),n}get _runsWithSliders(){var t=this.runToAvailableTimeEntries;return this.runs.filter((e=>t[e]))}};toe.template=xe`
    <template is="dom-repeat" items="[[_runsWithSliders]]" as="run">
      <div class="run-widget">
        <div class="run-display-container">
          <div
            class="run-color-box"
            style="background:[[_computeColorForRun(run)]];"
          ></div>
          <div class="run-text">[[run]]</div>
        </div>
        <div class="step-display-container">
          [[_computeTimeTextForRun(runToAvailableTimeEntries, _runToStepIndex,
          run, timeDisplayType)]]
        </div>
        <paper-slider
          data-run$="[[run]]"
          step="1"
          type="number"
          min="0"
          max="[[_computeMaxStepIndexForRun(runToAvailableTimeEntries, run)]]"
          value="[[_getStep(_runToStepIndex, run)]]"
          on-immediate-value-changed="_sliderValueChanged"
        ></paper-slider>
      </div>
    </template>
    <style>
      .run-widget {
        margin: 10px 0 0 0;
      }
      paper-slider {
        margin: -8px 0 0 -15px;
        width: 100%;
      }
      .step-display-container {
        font-size: 0.9em;
        margin: 0 15px 0 0;
      }
      .run-text {
        display: inline-block;
      }
      .run-color-box {
        width: 12px;
        height: 12px;
        border-radius: 3px;
        display: inline-block;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],toe.prototype,"runs",void 0),t([o({type:Object}),e("design:type",Object)],toe.prototype,"runToAvailableTimeEntries",void 0),t([o({type:Object,notify:!0,computed:"_computeRunToStep(runToAvailableTimeEntries, _runToStepIndex)"}),e("design:type",Object)],toe.prototype,"runToStep",void 0),t([o({type:String}),e("design:type",String)],toe.prototype,"timeDisplayType",void 0),t([o({type:Object}),e("design:type",Object)],toe.prototype,"_runToStepIndex",void 0),t([a("runToAvailableTimeEntries"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],toe.prototype,"_updateStepsForNewRuns",null),t([s("runs","runToAvailableTimeEntries"),e("design:type",Array),e("design:paramtypes",[])],toe.prototype,"_runsWithSliders",null),toe=t([i("tf-pr-curve-steps-selector")],toe);let eoe=class extends(cr(we)){constructor(){super(...arguments),this.reloadOnReady=!0,this._timeDisplayType="step",this._selectedRuns=[],this._runToTagInfo={},this._tagToRunToData={},this._getCategoryItemKey=t=>t.tag,this._requestManager=new br,this._step=0}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){Promise.all([this._fetchTags()]).then((()=>{this._reloadCards()}))}_shouldOpen(t){return t<=2}_fetchTags(){const t=Er().pluginRoute("pr_curves","/tags");return this._requestManager.request(t).then((t=>{if(Ce.exports.isEqual(t,this._runToTagInfo))return;const e=fr(Ce.exports.mapValues(t,(t=>Ce.exports.keys(t))));this.set("_dataNotFound",0===e.length),this.set("_runToTagInfo",t),this.async((()=>{this.set("_categoriesDomReady",!0)}))}))}_reloadCards(){Ce.exports.forEach(this.root.querySelectorAll("tf-pr-curve-card"),(t=>{t.reload()}))}get _categories(){var t=this._selectedRuns,e=this._tagFilter;return Pr(Ce.exports.mapValues(this._runToTagInfo,(t=>Object.keys(t))),t,e)}get _relevantSelectedRuns(){var t=this._runToTagInfo;return this._selectedRuns.filter((e=>t[e]))}_tagMetadata(t,e,n){const i={};e.forEach((e=>{i[e]=t[e][n]}));const r=n.replace(/\/pr_curves$/,"");return kO(i,r)}_createDataChangeCallback(t){return e=>{this.set("_tagToRunToData",Object.assign(Object.assign({},this._tagToRunToData),{[t]:e}))}}get _runToAvailableTimeEntries(){var t=this._tagToRunToData;const e={};for(const[n,i]of Object.entries(t))for(const[t]of Object.entries(i))(null==e[t]||n<e[t])&&(e[t]=n);const n={};for(const[i,r]of Object.entries(e)){const e=t[r][i];n[i]=e.map((t=>({step:t.step,wall_time:t.wall_time,relative:t.wall_time-e[0].wall_time})))}return n}};eoe.template=xe`
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="settings">
          <div class="sidebar-section">
            <tf-option-selector
              id="time-type-selector"
              name="Time Display Type"
              selected-id="{{_timeDisplayType}}"
            >
              <paper-button id="step">step</paper-button>
              <!--
            -->
              <paper-button id="relative">relative</paper-button>
              <!--
            -->
              <paper-button id="wall_time">wall</paper-button>
            </tf-option-selector>
          </div>
          <template is="dom-if" if="[[_runToAvailableTimeEntries]]">
            <div class="sidebar-section" id="steps-selector-container">
              <tf-pr-curve-steps-selector
                runs="[[_relevantSelectedRuns]]"
                run-to-step="{{_runToStep}}"
                run-to-available-time-entries="[[_runToAvailableTimeEntries]]"
                time-display-type="[[_timeDisplayType]]"
              >
              </tf-pr-curve-steps-selector>
            </div>
          </template>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No precision–recall curve data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any precision–recall data to your event
                files.
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>
            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
              get-category-item-key="[[_getCategoryItemKey]]"
            >
              <template>
                <tf-pr-curve-card
                  active="[[active]]"
                  runs="[[item.runs]]"
                  tag="[[item.tag]]"
                  tag-metadata="[[_tagMetadata(_runToTagInfo, item.runs, item.tag)]]"
                  request-manager="[[_requestManager]]"
                  run-to-step-cap="[[_runToStep]]"
                  on-data-change="[[_createDataChangeCallback(item.tag)]]"
                ></tf-pr-curve-card>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }

      /** Do not let the steps selector occlude the run selector. */
      #steps-selector-container {
        max-height: 60%;
        overflow-y: auto;
      }
    </style>
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                  slot="dropdown-content"
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              </paper-dropdown-menu>
            </div>
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          <div class="sidebar-section">
            <tf-smoothing-input
              weight="{{_smoothingWeight}}"
              step="0.001"
              min="0"
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              selected-id="{{_xType}}"
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              ><!--
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        </div>
        <div class="sidebar-section runs-selector">
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            <h3>No scalar data was found.</h3>
            <p>Probable causes:</p>
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            <p>
              If you’re new to using TensorBoard, and want to find out how to
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                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
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              initial-opened="[[_shouldOpen(index)]]"
              get-category-item-key="[[_getCategoryItemKey]]"
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                  data-to-load="[[item.series]]"
                  ignore-y-outliers="[[_ignoreYOutliers]]"
                  multi-experiments="[[_getMultiExperiments(dataSelection)]]"
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                  show-download-links="[[_showDownloadLinks]]"
                  smoothing-enabled="[[_smoothingEnabled]]"
                  smoothing-weight="[[_smoothingWeight]]"
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                  tag="[[item.tag]]"
                  tooltip-sorting-method="[[_tooltipSortingMethod]]"
                  x-type="[[_xType]]"
                  batch-size="[[featureFlags.scalarsBatchSize]]"
                  in-colab="[[featureFlags.inColab]]"
                ></tf-scalar-card>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      #tooltip-sorting {
        align-items: center;
        display: flex;
        font-size: 14px;
        margin-top: 15px;
      }

      #tooltip-sorting paper-dropdown-menu {
        margin-left: 10px;
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        width: 105px;
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      .line-item {
        display: block;
        padding-top: 5px;
      }
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        max-width: 540px;
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        overflow-x: hidden;
      }
    </style>
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      id="steps-container"
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        <paper-material elevation="1" class="text">
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      :host {
        display: flex;
        flex-direction: column;
        width: 100%;
        height: auto;
        margin-right: 10px;
        margin-bottom: 15px;
      }
      .scrollbar {
        will-change: transform;
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      #steps-container {
        border-radius: 3px;
        border: 2px solid /* color computed and set as inline style */;
        display: block;
        max-height: 500px;
        overflow: auto;
        padding: 10px;
        border-color: var(--tb-text-loader-outline);
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      .text {
        background-color: inherit;
        border-radius: 0 3px 3px 3px;
        padding: 5px;
        word-break: break-word;
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      .step-container {
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        border-bottom: none;
        border-radius: 3px 3px 0 0;
        border: 1px solid var(--tb-ui-border);
        display: inline-block;
        font-size: 12px;
        font-style: italic;
        margin-left: -1px; /* to correct for border */
        padding: 3px;
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        margin-top: 15px;
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      tf-card-heading {
        margin-bottom: 10px;
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          <div class="no-data-warning">
            <h3>No text data was found.</h3>
            <p>Probable causes:</p>
            <ul>
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                  run="[[item.run]]"
                  request-manager="[[_requestManager]]"
                  markdown-enabled="[[_markdownEnabled]]"
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/*! *****************************************************************************
    Copyright (c) Microsoft Corporation.

    Permission to use, copy, modify, and/or distribute this software for any
    purpose with or without fee is hereby granted.

    THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
    REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
    AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
    INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
    LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
    OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
    PERFORMANCE OF THIS SOFTWARE.
    ***************************************************************************** */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let oi;function ii(){return void 0!==oi?oi:"undefined"!=typeof document?document:void 0}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */var ai;function ri(t){return!!t.listen}!(function(t){t[t.Important=1]="Important",t[t.DashCase=2]="DashCase"})(ai||(ai={}));const si={createRenderer:(t,e)=>ii()};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function li(t){for(;Array.isArray(t);)t=t[0];return t}function ci(t,e){return ngDevMode&&wn(e,t),ngDevMode&&vn(t,vo,"Expected to be past HEADER_OFFSET"),li(e[t])}function di(t,e){ngDevMode&&Eo(t,e),ngDevMode&&wn(e,t.index);const n=li(e[t.index]);return ngDevMode&&!ri(e[11])&&Pn(n),n}function pi(t,e){ngDevMode&&Mn(e,-1,"wrong index for TNode"),ngDevMode&&_n(e,t.data.length,"wrong index for TNode");const n=t.data[e];return ngDevMode&&null!==n&&To(n),n}function mi(t,e){return ngDevMode&&wn(t,e),t[e]}function fi(t,e){ngDevMode&&wn(e,t);const n=e[t];return Po(n)?n:n[0]}function ui(t){return 4==(4&t[2])}function gi(t){return 128==(128&t[2])}function hi(t,e){return null==e?null:(ngDevMode&&wn(t,e),t[e])}function bi(t){t[18]=0}function yi(t,e){t[5]+=e;let n=t,o=t[3];for(;null!==o&&(1===e&&1===n[5]||-1===e&&0===n[5]);)o[5]+=e,n=o,o=o[3]}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const ma=-1;class fa{constructor(t,e,n){this.factory=t,this.resolving=!1,ngDevMode&&xn(t,"Factory not specified"),ngDevMode&&gn(typeof t,"function","Expected factory function."),this.canSeeViewProviders=e,this.injectImpl=n}}function ua(t){let e="";return 1&t&&(e+="|Text"),2&t&&(e+="|Element"),4&t&&(e+="|Container"),8&t&&(e+="|ElementContainer"),16&t&&(e+="|Projection"),32&t&&(e+="|IcuContainer"),64&t&&(e+="|Placeholder"),e.length>0?e.substring(1):e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Ma(t){return t!==ma}function va(t){ngDevMode&&pn(t,"Number expected"),ngDevMode&&hn(t,-1,"Not a valid state.");return ngDevMode&&Mn(32767&t,vo,"Parent injector must be pointing past HEADER_OFFSET."),32767&t}function xa(t,e){let n=(function o(t){return t>>16})(t),i=e;for(;n>0;)i=i[15],n--;return i}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function l(t,e,n){ngDevMode&&Lo(n);const{ngOnChanges:o,ngOnInit:i,ngDoCheck:a}=e.type.prototype;if(o){const o=Zo(e);(n.preOrderHooks||(n.preOrderHooks=[])).push(t,o),(n.preOrderCheckHooks||(n.preOrderCheckHooks=[])).push(t,o)}i&&(n.preOrderHooks||(n.preOrderHooks=[])).push(0-t,i),a&&((n.preOrderHooks||(n.preOrderHooks=[])).push(t,a),(n.preOrderCheckHooks||(n.preOrderCheckHooks=[])).push(t,a))})(n,a[n],e))}finally{null!==l&&Ln(l),Pa(s),r.resolving=!1,Ji()}}return i}function Ba(t,e,n){return!!(n[e+(t>>5)]&1<<t)}function Va(t,e){return!(t&Hn.Self||t&Hn.Host&&e)}class ja{constructor(t,e){this._tNode=t,this._lView=e}get(t,e){return Na(this._tNode,this._lView,t,void 0,e)}}function Ua(t){return Vn((()=>{const e=t.prototype.constructor,n=e[io]||Wa(e),o=Object.prototype;let i=Object.getPrototypeOf(t.prototype).constructor;for(;i&&i!==o;){const t=i[io]||Wa(i);if(t&&t!==n)return t;i=Object.getPrototypeOf(i)}return t=>new t}))}function Wa(t){return en(t)?()=>{const e=Wa(tn(t));return e&&e()}:Go(t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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     * found in the LICENSE file at https://angular.io/license
     */function Ga(t){return(function e(t,n){if(ngDevMode&&ga(t,15),ngDevMode&&xn(t,"expecting tNode"),"class"===n)return t.classes;if("style"===n)return t.styles;const o=t.attrs;if(o){const t=o.length;let e=0;for(;e<t;){const i=o[e];if(ba(i))break;if(0===i)e+=2;else if("number"==typeof i)for(e++;e<t&&"string"==typeof o[e];)e++;else{if(i===n)return o[e+1];e+=2}}}return null})(wi(),t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class er{constructor(t,e){this._desc=t,this.ngMetadataName="InjectionToken",this.ɵprov=void 0,"number"==typeof e?(("undefined"==typeof ngDevMode||ngDevMode)&&_n(e,0,"Only negative numbers are supported here"),this.__NG_ELEMENT_ID__=e):void 0!==e&&(this.ɵprov=Sn({token:this,providedIn:e.providedIn||"root",factory:e.factory}))}toString(){return`InjectionToken ${this._desc}`}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */new er("AnalyzeForEntryComponents");class nr{}const or=Qa("ContentChildren",((t,e={})=>Object.assign({selector:t,first:!1,isViewQuery:!1,descendants:!1,emitDistinctChangesOnly:true},e)),nr),ir=Qa("ContentChild",((t,e={})=>Object.assign({selector:t,first:!0,isViewQuery:!1,descendants:!0},e)),nr);Qa("ViewChildren",((t,e={})=>Object.assign({selector:t,first:!1,isViewQuery:!0,descendants:!0,emitDistinctChangesOnly:true},e)),nr);const ar=Qa("ViewChild",((t,e)=>Object.assign({selector:t,first:!0,isViewQuery:!0,descendants:!0},e)),nr);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */!(function(t){t[t.Directive=0]="Directive",t[t.Component=1]="Component",t[t.Injectable=2]="Injectable",t[t.Pipe=3]="Pipe",t[t.NgModule=4]="NgModule"})(rr||(rr={})),(function(t){t[t.Emulated=0]="Emulated",t[t.None=2]="None",t[t.ShadowDom=3]="ShadowDom"})(sr||(sr={}));const cr=Function;function dr(t){return"function"==typeof t}function pr(t,e){void 0===e&&(e=t);for(let n=0;n<t.length;n++){let o=t[n];Array.isArray(o)?(e===t&&(e=t.slice(0,n)),pr(o,e)):e!==t&&e.push(o)}return e}function mr(t,e){t.forEach((t=>Array.isArray(t)?mr(t,e):e(t)))}function fr(t,e,n){e>=t.length?t.push(n):t.splice(e,0,n)}function ur(t,e){return e>=t.length-1?t.pop():t.splice(e,1)[0]}function gr(t,e){const n=[];for(let o=0;o<t;o++)n.push(e);return n}function hr(t,e,n){let o=yr(t,e);return o>=0?t[1|o]=n:(o=~o,(function i(t,e,n,o){ngDevMode&&Cn(e,t.length,"Can't insert past array end.");let i=t.length;if(i==e)t.push(n,o);else if(1===i)t.push(o,t[0]),t[0]=n;else{for(i--,t.push(t[i-1],t[i]);i>e;)t[i]=t[i-2],i--;t[e]=n,t[e+1]=o}})(t,o,e,n)),o}function br(t,e){const n=yr(t,e);if(n>=0)return t[1|n]}function yr(t,e){return(function n(t,e,o){ngDevMode&&gn(Array.isArray(t),!0,"Expecting an array");let i=0,a=t.length>>o;for(;a!==i;){const n=i+(a-i>>1),r=t[n<<o];if(e===r)return n<<o;r>e?a=n:i=n+1}return~(a<<o)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t,e,1)}const _r=/^function\s+\S+\(\)\s*{[\s\S]+\.apply\(this,\s*(arguments|(?:[^()]+\(\[\],)?[^()]+\(arguments\))\)/,Cr=/^class\s+[A-Za-z\d$_]*\s*extends\s+[^{]+{/,Mr=/^class\s+[A-Za-z\d$_]*\s*extends\s+[^{]+{[\s\S]*constructor\s*\(/,vr=/^class\s+[A-Za-z\d$_]*\s*extends\s+[^{]+{[\s\S]*constructor\s*\(\)\s*{\s*super\(\.\.\.arguments\)/;class xr{constructor(t){this._reflect=t||Xn.Reflect}isReflectionEnabled(){return!0}factory(t){return(...e)=>new t(...e)}_zipTypesAndAnnotations(t,e){let n;n=gr(void 0===t?e.length:t.length);for(let o=0;o<n.length;o++)n[o]=void 0===t?[]:t[o]&&t[o]!=Object?[t[o]]:[],e&&null!=e[o]&&(n[o]=n[o].concat(e[o]));return n}_ownParameters(t,e){if((function n(t){return _r.test(t)||vr.test(t)||Cr.test(t)&&!Mr.test(t)})(t.toString()))return null;if(t.parameters&&t.parameters!==e.parameters)return t.parameters;const o=t.ctorParameters;if(o&&o!==e.ctorParameters){const t="function"==typeof o?o():o,e=t.map((t=>t&&t.type)),n=t.map((t=>t&&Or(t.decorators)));return this._zipTypesAndAnnotations(e,n)}const i=t.hasOwnProperty(qa)&&t[qa],a=this._reflect&&this._reflect.getOwnMetadata&&this._reflect.getOwnMetadata("design:paramtypes",t);return a||i?this._zipTypesAndAnnotations(a,i):gr(t.length)}parameters(t){if(!dr(t))return[];const e=Pr(t);let n=this._ownParameters(t,e);return n||e===Object||(n=this.parameters(e)),n||[]}_ownAnnotations(t,e){if(t.annotations&&t.annotations!==e.annotations){let e=t.annotations;return"function"==typeof e&&e.annotations&&(e=e.annotations),e}return t.decorators&&t.decorators!==e.decorators?Or(t.decorators):t.hasOwnProperty(Ya)?t[Ya]:null}annotations(t){if(!dr(t))return[];const e=Pr(t),n=this._ownAnnotations(t,e)||[];return(e!==Object?this.annotations(e):[]).concat(n)}_ownPropMetadata(t,e){if(t.propMetadata&&t.propMetadata!==e.propMetadata){let e=t.propMetadata;return"function"==typeof e&&e.propMetadata&&(e=e.propMetadata),e}if(t.propDecorators&&t.propDecorators!==e.propDecorators){const e=t.propDecorators,n={};return Object.keys(e).forEach((t=>{n[t]=Or(e[t])})),n}return t.hasOwnProperty(Za)?t[Za]:null}propMetadata(t){if(!dr(t))return{};const e=Pr(t),n={};if(e!==Object){const t=this.propMetadata(e);Object.keys(t).forEach((e=>{n[e]=t[e]}))}const o=this._ownPropMetadata(t,e);return o&&Object.keys(o).forEach((t=>{const e=[];n.hasOwnProperty(t)&&e.push(...n[t]),e.push(...o[t]),n[t]=e})),n}ownPropMetadata(t){return dr(t)&&this._ownPropMetadata(t,Pr(t))||{}}hasLifecycleHook(t,e){return t instanceof cr&&e in t.prototype}guards(t){return{}}getter(t){return new Function("o","return o."+t+";")}setter(t){return new Function("o","v","return o."+t+" = v;")}method(t){return new Function("o","args",`if (!o.${t}) throw new Error('"${t}" is undefined');\n        return o.${t}.apply(o, args);`)}importUri(t){return"object"==typeof t&&t.filePath?t.filePath:`./${Ke(t)}`}resourceUri(t){return`./${Ke(t)}`}resolveIdentifier(t,e,n,o){return o}resolveEnum(t,e){return t[e]}}function Or(t){return t?t.map((t=>new(0,t.type.annotationCls)(...t.args?t.args:[]))):[]}function Pr(t){const e=t.prototype?Object.getPrototypeOf(t.prototype):null;return(e?e.constructor:null)||Object}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function is(){if(void 0===ts&&(ts=null,Xn.trustedTypes))try{ts=Xn.trustedTypes.createPolicy("angular#unsafe-bypass",{createHTML:t=>t,createScript:t=>t,createScriptURL:t=>t})}catch(t){}return ts}function as(t){var e;return(null===(e=is())||void 0===e?void 0:e.createHTML(t))||t}function rs(t){var e;return(null===(e=is())||void 0===e?void 0:e.createScript(t))||t}function ss(t){var e;return(null===(e=is())||void 0===e?void 0:e.createScriptURL(t))||t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class ls{constructor(t){this.changingThisBreaksApplicationSecurity=t}toString(){return`SafeValue must use [property]=binding: ${this.changingThisBreaksApplicationSecurity} (see https://g.co/ng/security#xss)`}}class cs extends ls{getTypeName(){return"HTML"}}class ds extends ls{getTypeName(){return"Style"}}class ps extends ls{getTypeName(){return"Script"}}class ms extends ls{getTypeName(){return"URL"}}class fs extends ls{getTypeName(){return"ResourceURL"}}function us(t){return t instanceof ls?t.changingThisBreaksApplicationSecurity:t}function gs(t,e){const n=hs(t);if(null!=n&&n!==e){if("ResourceURL"===n&&"URL"===e)return!0;throw new Error(`Required a safe ${e}, got a ${n} (see https://g.co/ng/security#xss)`)}return n===e}function hs(t){return t instanceof ls&&t.getTypeName()||null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function bs(t){const e=new _s(t);return(function n(){try{return!!(new window.DOMParser).parseFromString(ns(""),"text/html")}catch(t){return!1}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)()?new ys(e):e}class ys{constructor(t){this.inertDocumentHelper=t}getInertBodyElement(t){t="<body><remove></remove>"+t;try{const e=(new window.DOMParser).parseFromString(ns(t),"text/html").body;return null===e?this.inertDocumentHelper.getInertBodyElement(t):(e.removeChild(e.firstChild),e)}catch(t){return null}}}class _s{constructor(t){if(this.defaultDoc=t,this.inertDocument=this.defaultDoc.implementation.createHTMLDocument("sanitization-inert"),null==this.inertDocument.body){const t=this.inertDocument.createElement("html");this.inertDocument.appendChild(t);const e=this.inertDocument.createElement("body");t.appendChild(e)}}getInertBodyElement(t){const e=this.inertDocument.createElement("template");if("content"in e)return e.innerHTML=ns(t),e;const n=this.inertDocument.createElement("body");return n.innerHTML=ns(t),this.defaultDoc.documentMode&&this.stripCustomNsAttrs(n),n}stripCustomNsAttrs(t){const e=t.attributes;for(let n=e.length-1;0<n;n--){const o=e.item(n).name;"xmlns:ns1"!==o&&0!==o.indexOf("ns1:")||t.removeAttribute(o)}let n=t.firstChild;for(;n;)n.nodeType===Node.ELEMENT_NODE&&this.stripCustomNsAttrs(n),n=n.nextSibling}}const Cs=/^(?:(?:https?|mailto|ftp|tel|file|sms):|[^&:/?#]*(?:[/?#]|$))/gi,Ms=/^data:(?:image\/(?:bmp|gif|jpeg|jpg|png|tiff|webp)|video\/(?:mpeg|mp4|ogg|webm)|audio\/(?:mp3|oga|ogg|opus));base64,[a-z0-9+\/]+=*$/i;function vs(t){return(t=String(t)).match(Cs)||t.match(Ms)?t:(("undefined"==typeof ngDevMode||ngDevMode)&&console.warn(`WARNING: sanitizing unsafe URL value ${t} (see https://g.co/ng/security#xss)`),"unsafe:"+t)}function xs(t){return(t=String(t)).split(",").map((t=>vs(t.trim()))).join(", ")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Os(t){const e={};for(const n of t.split(","))e[n]=!0;return e}function Ps(...t){const e={};for(const n of t)for(const t in n)n.hasOwnProperty(t)&&(e[t]=!0);return e}const ws=Os("area,br,col,hr,img,wbr"),ks=Os("colgroup,dd,dt,li,p,tbody,td,tfoot,th,thead,tr"),Ss=Os("rp,rt"),Ds=Ps(Ss,ks),Rs=Ps(ks,Os("address,article,aside,blockquote,caption,center,del,details,dialog,dir,div,dl,figure,figcaption,footer,h1,h2,h3,h4,h5,h6,header,hgroup,hr,ins,main,map,menu,nav,ol,pre,section,summary,table,ul")),Es=Ps(Ss,Os("a,abbr,acronym,audio,b,bdi,bdo,big,br,cite,code,del,dfn,em,font,i,img,ins,kbd,label,map,mark,picture,q,ruby,rp,rt,s,samp,small,source,span,strike,strong,sub,sup,time,track,tt,u,var,video")),As=Ps(ws,Rs,Es,Ds),Ts=Os("background,cite,href,itemtype,longdesc,poster,src,xlink:href"),Ns=Os("srcset"),zs=Os("abbr,accesskey,align,alt,autoplay,axis,bgcolor,border,cellpadding,cellspacing,class,clear,color,cols,colspan,compact,controls,coords,datetime,default,dir,download,face,headers,height,hidden,hreflang,hspace,ismap,itemscope,itemprop,kind,label,lang,language,loop,media,muted,nohref,nowrap,open,preload,rel,rev,role,rows,rowspan,rules,scope,scrolling,shape,size,sizes,span,srclang,start,summary,tabindex,target,title,translate,type,usemap,valign,value,vspace,width"),Is=Os("aria-activedescendant,aria-atomic,aria-autocomplete,aria-busy,aria-checked,aria-colcount,aria-colindex,aria-colspan,aria-controls,aria-current,aria-describedby,aria-details,aria-disabled,aria-dropeffect,aria-errormessage,aria-expanded,aria-flowto,aria-grabbed,aria-haspopup,aria-hidden,aria-invalid,aria-keyshortcuts,aria-label,aria-labelledby,aria-level,aria-live,aria-modal,aria-multiline,aria-multiselectable,aria-orientation,aria-owns,aria-placeholder,aria-posinset,aria-pressed,aria-readonly,aria-relevant,aria-required,aria-roledescription,aria-rowcount,aria-rowindex,aria-rowspan,aria-selected,aria-setsize,aria-sort,aria-valuemax,aria-valuemin,aria-valuenow,aria-valuetext"),Hs=Ps(Ts,Ns,zs,Is),Fs=Os("script,style,template");class Ls{constructor(){this.sanitizedSomething=!1,this.buf=[]}sanitizeChildren(t){let e=t.firstChild,n=!0;for(;e;)if(e.nodeType===Node.ELEMENT_NODE?n=this.startElement(e):e.nodeType===Node.TEXT_NODE?this.chars(e.nodeValue):this.sanitizedSomething=!0,n&&e.firstChild)e=e.firstChild;else for(;e;){e.nodeType===Node.ELEMENT_NODE&&this.endElement(e);let t=this.checkClobberedElement(e,e.nextSibling);if(t){e=t;break}e=this.checkClobberedElement(e,e.parentNode)}return this.buf.join("")}startElement(t){const e=t.nodeName.toLowerCase();if(!As.hasOwnProperty(e))return this.sanitizedSomething=!0,!Fs.hasOwnProperty(e);this.buf.push("<"),this.buf.push(e);const n=t.attributes;for(let t=0;t<n.length;t++){const e=n.item(t),o=e.name,i=o.toLowerCase();if(!Hs.hasOwnProperty(i)){this.sanitizedSomething=!0;continue}let a=e.value;Ts[i]&&(a=vs(a)),Ns[i]&&(a=xs(a)),this.buf.push(" ",o,'="',js(a),'"')}return this.buf.push(">"),!0}endElement(t){const e=t.nodeName.toLowerCase();As.hasOwnProperty(e)&&!ws.hasOwnProperty(e)&&(this.buf.push("</"),this.buf.push(e),this.buf.push(">"))}chars(t){this.buf.push(js(t))}checkClobberedElement(t,e){if(e&&(t.compareDocumentPosition(e)&Node.DOCUMENT_POSITION_CONTAINED_BY)===Node.DOCUMENT_POSITION_CONTAINED_BY)throw new Error(`Failed to sanitize html because the element is clobbered: ${t.outerHTML}`);return e}}const Bs=/[\uD800-\uDBFF][\uDC00-\uDFFF]/g,Vs=/([^\#-~ |!])/g;function js(t){return t.replace(/&/g,"&amp;").replace(Bs,(function(t){return"&#"+(1024*(t.charCodeAt(0)-55296)+(t.charCodeAt(1)-56320)+65536)+";"})).replace(Vs,(function(t){return"&#"+t.charCodeAt(0)+";"})).replace(/</g,"&lt;").replace(/>/g,"&gt;")}let Us;function Ws(t,e){let n=null;try{Us=Us||bs(t);let o=e?String(e):"";n=Us.getInertBodyElement(o);let i=5,a=o;do{if(0===i)throw new Error("Failed to sanitize html because the input is unstable");i--,o=a,a=n.innerHTML,n=Us.getInertBodyElement(o)}while(o!==a);const r=new Ls,s=r.sanitizeChildren(Gs(n)||n);return("undefined"==typeof ngDevMode||ngDevMode)&&r.sanitizedSomething&&console.warn("WARNING: sanitizing HTML stripped some content, see https://g.co/ng/security#xss"),ns(s)}finally{if(n){const t=Gs(n)||n;for(;t.firstChild;)t.removeChild(t.firstChild)}}}function Gs(t){return"content"in t&&(function e(t){return t.nodeType===Node.ELEMENT_NODE&&"TEMPLATE"===t.nodeName}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t)?t.content:null}var Ys;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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function qs(t){const e=el();return e?as(e.sanitize(Ys.HTML,t)||""):gs(t,"HTML")?as(us(t)):Ws(ii(),rn(t))}function Zs(t){const e=el();return e?e.sanitize(Ys.STYLE,t)||"":gs(t,"Style")?us(t):rn(t)}function Xs(t){const e=el();return e?e.sanitize(Ys.URL,t)||"":gs(t,"URL")?us(t):vs(rn(t))}function Ks(t){const e=el();if(e)return ss(e.sanitize(Ys.RESOURCE_URL,t)||"");if(gs(t,"ResourceURL"))return ss(us(t));throw new Error("unsafe value used in a resource URL context (see https://g.co/ng/security#xss)")}function Js(t){const e=el();if(e)return rs(e.sanitize(Ys.SCRIPT,t)||"");if(gs(t,"Script"))return rs(us(t));throw new Error("unsafe value used in a script context")}function Qs(t){if(ngDevMode&&(!Array.isArray(t)||!Array.isArray(t.raw)||1!==t.length))throw new Error(`Unexpected interpolation in trusted HTML constant: ${t.join("?")}`);return ns(t[0])}function $s(t){if(ngDevMode&&(!Array.isArray(t)||!Array.isArray(t.raw)||1!==t.length))throw new Error(`Unexpected interpolation in trusted URL constant: ${t.join("?")}`);return(function e(t){var e;return(null===(e=es())||void 0===e?void 0:e.createScriptURL(t))||t})(t[0])}function tl(t,e,n){return(function o(t,e){return"src"===e&&("embed"===t||"frame"===t||"iframe"===t||"media"===t||"script"===t)||"href"===e&&("base"===t||"link"===t)?Ks:Xs})(e,n)(t)}function el(){const t=xi();return t&&t[12]}
/**
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     * Copyright Google LLC All Rights Reserved.
     *
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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     * found in the LICENSE file at https://angular.io/license
     */function Ml(t){return`ng-reflect-${t=(function e(t){return t.replace(vl,((...t)=>"-"+t[1].toLowerCase()))})(t.replace(/[$@]/g,"_"))}`}const vl=/([A-Z])/g;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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const xl=("undefined"!=typeof requestAnimationFrame&&requestAnimationFrame||setTimeout).bind(Xn);function Ol(t){return t.ownerDocument.defaultView}function Pl(t){return t.ownerDocument}function wl(t){return t.ownerDocument.body}const kl="�";function Sl(t){return t instanceof Function?t():t}
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     * Copyright Google LLC All Rights Reserved.
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function hc(t,e,n){ngDevMode&&hn(e,"",'can not look for "" string.');let o=t.length;for(;;){const i=t.indexOf(e,n);if(-1===i)return i;if(0===i||t.charCodeAt(i-1)<=32){const n=e.length;if(i+n===o||t.charCodeAt(i+n)<=32)return i}n=i+1}}const bc="ng-template";function yc(t,e,n){ngDevMode&&gn(e,e.toLowerCase(),"Class name expected to be lowercase.");let o=0;for(;o<t.length;){let i=t[o++];if(n&&"class"===i){if(i=t[o],-1!==hc(i.toLowerCase(),e,0))return!0}else if(1===i){for(;o<t.length&&"string"==typeof(i=t[o++]);)if(i.toLowerCase()===e)return!0;return!1}}return!1}function _c(t){return 4===t.type&&t.value!==bc}function Cc(t,e,n){return e===(4!==t.type||n?t.value:bc)}function Mc(t,e,n){ngDevMode&&xn(e[0],"Selector should have a tag name");let o=4;const i=t.attrs||[],a=(function r(t){for(let e=0;e<t.length;e++){if(ba(t[e]))return e}return t.length})(i);let s=!1;for(let r=0;r<e.length;r++){const l=e[r];if("number"!=typeof l){if(!s)if(4&o){if(o=2|1&o,""!==l&&!Cc(t,l,n)||""===l&&1===e.length){if(vc(o))return!1;s=!0}}else{const c=8&o?l:e[++r];if(8&o&&null!==t.attrs){if(!yc(t.attrs,c,n)){if(vc(o))return!1;s=!0}continue}const d=xc(8&o?"class":l,i,_c(t),n);if(-1===d){if(vc(o))return!1;s=!0;continue}if(""!==c){let t;d>a?t="":(ngDevMode&&hn(i[d],0,"We do not match directives on namespaced attributes"),t=i[d+1].toLowerCase());const e=8&o?t:null;if(e&&-1!==hc(e,c,0)||2&o&&c!==t){if(vc(o))return!1;s=!0}}}}else{if(!s&&!vc(o)&&!vc(l))return!1;if(s&&vc(l))continue;s=!1,o=l|1&o}}return vc(o)||s}function vc(t){return 0==(1&t)}function xc(t,e,n,o){if(null===e)return-1;let i=0;if(o||!n){let n=!1;for(;i<e.length;){const o=e[i];if(o===t)return i;if(3===o||6===o)n=!0;else{if(1===o||2===o){let t=e[++i];for(;"string"==typeof t;)t=e[++i];continue}if(4===o)break;if(0===o){i+=4;continue}}i+=n?1:2}return-1}return(function a(t,e){let n=t.indexOf(4);if(n>-1)for(n++;n<t.length;){const o=t[n];if("number"==typeof o)return-1;if(o===e)return n;n++}return-1})(e,t)}function Oc(t,e,n=!1){for(let o=0;o<e.length;o++)if(Mc(t,e[o],n))return!0;return!1}function Pc(t,e){t:for(let n=0;n<e.length;n++){const o=e[n];if(t.length===o.length){for(let e=0;e<t.length;e++)if(t[e]!==o[e])continue t;return!0}}return!1}function wc(t,e){return t?":not("+e.trim()+")":e}function kc(t){let e=t[0],n=1,o=2,i="",a=!1;for(;n<t.length;){let r=t[n];if("string"==typeof r)if(2&o){const e=t[++n];i+="["+r+(e.length>0?'="'+e+'"':"")+"]"}else 8&o?i+="."+r:4&o&&(i+=" "+r);else""===i||vc(r)||(e+=wc(a,i),i=""),o=r,a=a||!vc(o);n++}return""!==i&&(e+=wc(a,i)),e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Sc="undefined"==typeof ngDevMode||ngDevMode?{__brand__:"NO_CHANGE"}:{};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Ec(t,e){return ngDevMode&&mn(t,0,32767),ngDevMode&&mn(e,0,32767),t<<17|e<<2}function Ac(t){return ngDevMode&&pn(t,"expected number"),t>>17&32767}function Tc(t){return ngDevMode&&pn(t,"expected number"),2==(2&t)}function Nc(t){return ngDevMode&&pn(t,"expected number"),2|t}function zc(t){return ngDevMode&&pn(t,"expected number"),(131068&t)>>2}function Ic(t,e){return ngDevMode&&pn(t,"expected number"),ngDevMode&&mn(e,0,32767),-131069&t|e<<2}function Hc(t){return ngDevMode&&pn(t,"expected number"),1==(1&t)}function Fc(t){return ngDevMode&&pn(t,"expected number"),1|t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Lc(t,e){if(!ngDevMode)throw new Error("This method should be guarded with `ngDevMode` so that it can be tree shaken in production!");Object.defineProperty(t,"debug",{value:e,enumerable:!1})}function Bc(t,e){if(!ngDevMode)throw new Error("This method should be guarded with `ngDevMode` so that it can be tree shaken in production!");Object.defineProperty(t,"debug",{get:e,enumerable:!1})}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Vc=("undefined"==typeof ngDevMode||!!ngDevMode)&&Kn();let jc,Uc,Wc;function Gc(t){return(function e(t,n){switch(t){case 0:return void 0===Wc&&(Wc=new(Cl("LRootView"))),Wc;case 1:void 0===jc&&(jc=new Map);let t=jc.get(n);return void 0===t&&(t=new(Cl("LComponentView"+Yc(n))),jc.set(n,t)),t;case 2:void 0===Uc&&(Uc=new Map);let e=Uc.get(n);return void 0===e&&(e=new(Cl("LEmbeddedView"+Yc(n))),Uc.set(n,e)),e}})(t.type,t.template&&t.template.name).concat(t.blueprint)}function Yc(t){if(null==t)return"";const e=t.lastIndexOf("_Template");return"_"+(-1===e?t:t.substr(0,e))}const qc=class{constructor(t,e,n,o,i,a,r,s,l,c,d,p,m,f,u,g,h,b,y,_,C,M,v,x,O,P,w,k,S,D,R,E){this.tView_=t,this.type=e,this.index=n,this.insertBeforeIndex=o,this.injectorIndex=i,this.directiveStart=a,this.directiveEnd=r,this.directiveStylingLast=s,this.propertyBindings=l,this.flags=c,this.providerIndexes=d,this.value=p,this.attrs=m,this.mergedAttrs=f,this.localNames=u,this.initialInputs=g,this.inputs=h,this.outputs=b,this.tViews=y,this.next=_,this.projectionNext=C,this.child=M,this.parent=v,this.projection=x,this.styles=O,this.stylesWithoutHost=P,this.residualStyles=w,this.classes=k,this.classesWithoutHost=S,this.residualClasses=D,this.classBindings=R,this.styleBindings=E}debugNodeInjectorPath(t){const e=[];let n=Da(this,t);if(-1===n){const e=Ra(this,t);e!==ma&&(n=va(e),t=xa(e,t))}for(;-1!==n;){ngDevMode&&Wo(t,n);e.push(pd(t[1].data[n+8],t));const o=t[n+8];o===ma?n=-1:(n=va(o),t=xa(o,t))}return e}get type_(){return ua(this.type)||`TNodeType.?${this.type}?`}get flags_(){const t=[];return 16&this.flags&&t.push("TNodeFlags.hasClassInput"),8&this.flags&&t.push("TNodeFlags.hasContentQuery"),32&this.flags&&t.push("TNodeFlags.hasStyleInput"),128&this.flags&&t.push("TNodeFlags.hasHostBindings"),2&this.flags&&t.push("TNodeFlags.isComponentHost"),1&this.flags&&t.push("TNodeFlags.isDirectiveHost"),64&this.flags&&t.push("TNodeFlags.isDetached"),4&this.flags&&t.push("TNodeFlags.isProjected"),t.join("|")}get template_(){if(1&this.type)return this.value;const t=[],e="string"==typeof this.value&&this.value||this.type_;if(t.push("<",e),this.flags&&t.push(" ",this.flags_),this.attrs)for(let e=0;e<this.attrs.length;){const n=this.attrs[e++];if("number"==typeof n)break;const o=this.attrs[e++];t.push(" ",n,'="',o,'"')}return t.push(">"),Xc(this.child,t),t.push("</",e,">"),t.join("")}get styleBindings_(){return Zc(this,!1)}get classBindings_(){return Zc(this,!0)}get providerIndexStart_(){return 1048575&this.providerIndexes}get providerIndexEnd_(){return this.providerIndexStart_+(this.providerIndexes>>>20)}};function Zc(t,e){const n=t.tView_.data,o=[],i=e?t.classBindings:t.styleBindings,a=Ac(i),r=zc(i);let s=0!==r,l=s?r:a;for(;0!==l;){const t=n[l+1];o.unshift({key:n[l],index:l,isTemplate:s,prevDuplicate:Tc(t),nextDuplicate:Hc(t),nextIndex:zc(t),prevIndex:Ac(t)}),l===a&&(s=!1),l=Ac(t)}return o.push((e?t.residualClasses:t.residualStyles)||null),o}function Xc(t,e){for(;t;)e.push(t.template_),t=t.next}const Kc=Vc&&Cl("TViewData")||null;let Jc;const Qc=Vc&&Cl("LViewBlueprint")||null,$c=Vc&&Cl("MatchesArray")||null,td=Vc&&Cl("TViewComponents")||null,ed=Vc&&Cl("TNodeLocalNames")||null,nd=Vc&&Cl("TNodeInitialInputs")||null;Vc&&Cl("TNodeInitialData");const od=Vc&&Cl("LCleanup")||null,id=Vc&&Cl("TCleanup")||null;function ad(t){if(t){const e=t.debug;return xn(e,"Object does not have a debug representation."),e}return t}function rd(t,e=!1){const n=li(t);if(n)switch(n.nodeType){case Node.TEXT_NODE:return n.textContent;case Node.COMMENT_NODE:return`\x3c!--${n.textContent}--\x3e`;case Node.ELEMENT_NODE:const t=n.outerHTML;if(e)return t;return t.split(">"+n.innerHTML+"<")[0]+">"}return null}class sd{constructor(t){this._raw_lView=t}get flags(){const t=this._raw_lView[2];return{__raw__flags__:t,initPhaseState:3&t,creationMode:!!(4&t),firstViewPass:!!(8&t),checkAlways:!!(16&t),dirty:!!(64&t),attached:!!(128&t),destroyed:!!(256&t),isRoot:!!(512&t),indexWithinInitPhase:t>>11}}get parent(){return ad(this._raw_lView[3])}get hostHTML(){return rd(this._raw_lView[0],!0)}get html(){return(this.nodes||[]).map(ld).join("")}get context(){return this._raw_lView[8]}get nodes(){const t=this._raw_lView;return dd(t[1].firstChild,t)}get template(){return this.tView.template_}get tView(){return this._raw_lView[1]}get cleanup(){return this._raw_lView[7]}get injector(){return this._raw_lView[9]}get rendererFactory(){return this._raw_lView[10]}get renderer(){return this._raw_lView[11]}get sanitizer(){return this._raw_lView[12]}get childHead(){return ad(this._raw_lView[13])}get next(){return ad(this._raw_lView[4])}get childTail(){return ad(this._raw_lView[14])}get declarationView(){return ad(this._raw_lView[15])}get queries(){return this._raw_lView[19]}get tHost(){return this._raw_lView[6]}get decls(){return cd(this.tView,this._raw_lView,vo,this.tView.bindingStartIndex)}get vars(){return cd(this.tView,this._raw_lView,this.tView.bindingStartIndex,this.tView.expandoStartIndex)}get expando(){return cd(this.tView,this._raw_lView,this.tView.expandoStartIndex,this._raw_lView.length)}get childViews(){const t=[];let e=this.childHead;for(;e;)t.push(e),e=e.next;return t}}function ld(t){if("ElementContainer"===t.type)return(t.children||[]).map(ld).join("");if("IcuContainer"===t.type)throw new Error("Not implemented");return rd(t.native,!0)||""}function cd(t,e,n,o){let i=[];for(let a=n;a<o;a++)i.push({index:a,t:t.data[a],l:e[a]});return{start:n,end:o,length:o-n,content:i}}function dd(t,e){if(t){const n=[];let o=t;for(;o;)n.push(pd(o,e)),o=o.next;return n}return[]}function pd(t,e){const n=li(e[t.index]),o=[],i=[],a=e[1];for(let n=t.directiveStart;n<t.directiveEnd;n++){o.push(a.data[n].type),i.push(e[n])}return{html:rd(n),type:ua(t.type),tNode:t,native:n,children:dd(t.child,e),factories:o,instances:i,injector:md(t,a,e),get injectorResolutionPath(){return t.debugNodeInjectorPath(e)}}}function md(t,e,n){const o=[];for(let n=t.providerIndexStart_;n<t.providerIndexEnd_;n++)o.push(e.data[n]);const i=[];for(let n=t.providerIndexEnd_;n<t.directiveEnd;n++)i.push(e.data[n]);return{bloom:ud(n,t.injectorIndex),cumulativeBloom:ud(e.data,t.injectorIndex),providers:i,viewProviders:o,parentInjectorIndex:n[t.providerIndexStart_-1]}}function fd(t,e){const n=t[e];if("number"!=typeof n)return"????????";const o="00000000"+n.toString(2);return o.substring(o.length-8)}function ud(t,e){return e<0?"NO_NODE_INJECTOR":`${fd(t,e+7)}_${fd(t,e+6)}_${fd(t,e+5)}_${fd(t,e+4)}_${fd(t,e+3)}_${fd(t,e+2)}_${fd(t,e+1)}_${fd(t,e+0)}`}class gd{constructor(t){this._raw_lContainer=t}get hasTransplantedViews(){return this._raw_lContainer[2]}get views(){return this._raw_lContainer.slice(Oo).map(ad)}get parent(){return ad(this._raw_lContainer[3])}get movedViews(){return this._raw_lContainer[9]}get host(){return this._raw_lContainer[0]}get native(){return this._raw_lContainer[7]}get next(){return ad(this._raw_lContainer[4])}}const hd=Promise.resolve(null);function bd(t,e){const n=t.contentQueries;if(null!==n)for(let o=0;o<n.length;o+=2){const i=n[o],a=n[o+1];if(-1!==a){const n=t.data[a];ngDevMode&&xn(n,"DirectiveDef not found."),ngDevMode&&xn(n.contentQueries,"contentQueries function should be defined"),Wi(i),n.contentQueries(2,e[a],a)}}}function yd(t,e,n,o,i,a,r,s,l,c){const d=ngDevMode?Gc(e):e.blueprint.slice();return d[0]=i,d[2]=140|o,bi(d),ngDevMode&&e.declTNode&&t&&Eo(e.declTNode,t),d[3]=d[15]=t,d[8]=n,d[10]=r||t&&t[10],ngDevMode&&xn(d[10],"RendererFactory is required"),d[11]=s||t&&t[11],ngDevMode&&xn(d[11],"Renderer is required"),d[12]=l||t&&t[12]||null,d[9]=c||t&&t[9]||null,d[6]=a,ngDevMode&&gn(2!=e.type||null!==t,!0,"Embedded views must have parentLView"),d[16]=2==e.type?t[16]:d,ngDevMode&&(function p(t){Lc(t,new sd(t))})(d),d}function _d(t,e,n,o,i){ngDevMode&&0!==e&&vn(e,vo,"TNodes can't be in the LView header."),ngDevMode&&(function a(t){2!==t&&1!==t&&4!==t&&8!==t&&32!==t&&16!==t&&64!==t&&On(`Expected TNodeType to have only a single type selected, but got ${ua(t)}.`)})(n);let r=t.data[e];if(null===r)r=Cd(t,e,n,o,i),(function s(){return _i.lFrame.inI18n})()&&(r.flags|=64);else if(64&r.type){r.type=n,r.value=o,r.attrs=i;const a=Si();r.injectorIndex=null===a?-1:a.injectorIndex,ngDevMode&&Ao(r,t),ngDevMode&&gn(e,r.index,"Expecting same index")}return Di(r,!0),r}function Cd(t,e,n,o,i){const a=ki(),r=Ri(),s=t.data[e]=(function l(t,e,n,o,i,a){ngDevMode&&0!==o&&vn(o,vo,"TNodes can't be in the LView header."),ngDevMode&&yn(a,void 0,"'undefined' is not valid value for 'attrs'"),ngDevMode&&ngDevMode.tNode++,ngDevMode&&e&&Ao(e,t);let r=e?e.injectorIndex:-1;const s=ngDevMode?new qc(t,n,o,null,r,-1,-1,-1,null,0,0,i,a,null,null,void 0,null,null,null,null,null,null,e,null,null,null,void 0,null,null,void 0,0,0):{type:n,index:o,insertBeforeIndex:null,injectorIndex:r,directiveStart:-1,directiveEnd:-1,directiveStylingLast:-1,propertyBindings:null,flags:0,providerIndexes:0,value:i,attrs:a,mergedAttrs:null,localNames:null,initialInputs:void 0,inputs:null,outputs:null,tViews:null,next:null,projectionNext:null,child:null,parent:e,projection:null,styles:null,stylesWithoutHost:null,residualStyles:void 0,classes:null,classesWithoutHost:null,residualClasses:void 0,classBindings:0,styleBindings:0};ngDevMode&&Object.seal(s);return s})(t,r?a:a&&a.parent,n,e,o,i);return null===t.firstChild&&(t.firstChild=s),null!==a&&(r?null==a.child&&null!==s.parent&&(a.child=s):null===a.next&&(a.next=s)),s}function Md(t,e,n,o){if(0===n)return-1;ngDevMode&&(Lo(t),bn(t,e[1],"`LView` must be associated with `TView`!"),gn(t.data.length,e.length,"Expecting LView to be same size as TView"),gn(t.data.length,t.blueprint.length,"Expecting Blueprint to be same size as TView"),Bo(t));const i=e.length;for(let i=0;i<n;i++)e.push(o),t.blueprint.push(o),t.data.push(null);return i}function vd(t,e,n){ngDevMode&&gn(ui(e),!0,"Should be run in creation mode"),qi(e);try{const o=t.viewQuery;null!==o&&sp(1,o,n);const i=t.template;null!==i&&Pd(t,e,i,1,n),t.firstCreatePass&&(t.firstCreatePass=!1),t.staticContentQueries&&bd(t,e),t.staticViewQueries&&sp(2,t.viewQuery,n);const a=t.components;null!==a&&(function o(t,e){for(let n=0;n<e.length;n++)ep(t,e[n])})(e,a)}catch(e){throw t.firstCreatePass&&(t.incompleteFirstPass=!0,t.firstCreatePass=!1),e}finally{e[2]&=-5,Qi()}}function xd(t,e,n,o){ngDevMode&&gn(ui(e),!1,"Should be run in update mode");const i=e[2];if(256==(256&i))return;qi(e);const a=Ai();try{bi(e),Ii(t.bindingStartIndex),null!==n&&Pd(t,e,n,2,o);const r=3==(3&i);if(!a)if(r){const n=t.preOrderCheckHooks;null!==n&&sa(e,n,null)}else{const n=t.preOrderHooks;null!==n&&la(e,n,0,null),ca(e,0)}if((function r(t){for(let e=Il(t);null!==e;e=Hl(e)){if(!e[2])continue;const t=e[9];ngDevMode&&xn(t,"Transplanted View flags set but missing MOVED_VIEWS");for(let e=0;e<t.length;e++){const n=t[e],o=n[3];ngDevMode&&Io(o),0==(1024&n[2])&&yi(o,1),n[2]|=1024}}})(e),(function s(t){for(let e=Il(t);null!==e;e=Hl(e))for(let t=Oo;t<e.length;t++){const n=e[t],o=n[1];ngDevMode&&xn(o,"TView must be allocated"),gi(n)&&xd(o,n,o.template,n[8])}})(e),null!==t.contentQueries&&bd(t,e),!a)if(r){const n=t.contentCheckHooks;null!==n&&sa(e,n)}else{const n=t.contentHooks;null!==n&&la(e,n,1),ca(e,1)}!(function l(t,e){const n=t.hostBindingOpCodes;if(null!==n)try{for(let t=0;t<n.length;t++){const o=n[t];if(o<0)ea(~o);else{const i=o,a=n[++t],r=n[++t];Bi(a,i),r(2,e[i])}}}finally{ea(-1)}})(t,e);const s=t.components;null!==s&&(function c(t,e){for(let n=0;n<e.length;n++)$d(t,e[n])})(e,s);const l=t.viewQuery;if(null!==l&&sp(2,l,o),!a)if(r){const n=t.viewCheckHooks;null!==n&&sa(e,n)}else{const n=t.viewHooks;null!==n&&la(e,n,2),ca(e,2)}!0===t.firstUpdatePass&&(t.firstUpdatePass=!1),a||(e[2]&=-73),1024&e[2]&&(e[2]&=-1025,yi(e[3],-1))}finally{Qi()}}function Od(t,e,n,o){const i=e[10],a=!Ai(),r=ui(e);try{a&&!r&&i.begin&&i.begin(),r&&vd(t,e,o),xd(t,e,n,o)}finally{a&&!r&&i.end&&i.end()}}function Pd(t,e,n,o,i){const a=ta(),r=2&o;try{ea(-1),r&&e.length>vo&&Rc(t,e,vo,Ai());ti(r?2:0,i),n(o,i)}finally{ea(a);ti(r?3:1,i)}}function wd(t,e,n){if(ko(e)){const o=e.directiveEnd;for(let i=e.directiveStart;i<o;i++){const e=t.data[i];e.contentQueries&&e.contentQueries(1,n[i],i)}}}function kd(t,e,n){Ci()&&(!(function o(t,e,n,i){const a=n.directiveStart,r=n.directiveEnd;t.firstCreatePass||ka(n,e);al(i,e);const s=n.initialInputs;for(let o=a;o<r;o++){const i=t.data[o],r=Ro(i);r&&(ngDevMode&&ga(n,3),Yd(e,n,i));const l=La(e,t,o,n);if(al(l,e),null!==s&&Xd(e,o-a,l,i,n,s),r){fi(n.index,e)[8]=l}}})(t,e,n,di(n,e)),128==(128&n.flags)&&(function i(t,e,n){const o=n.directiveStart,i=n.directiveEnd;const a=n.index,r=(function s(){return _i.lFrame.currentDirectiveIndex})();try{ea(a);for(let n=o;n<i;n++){const o=t.data[n],i=e[n];Vi(n),null===o.hostBindings&&0===o.hostVars&&null===o.hostAttrs||Vd(o,i)}}finally{ea(-1),Vi(r)}})(t,e,n))}function Sd(t,e,n=di){const o=e.localNames;if(null!==o){let i=e.index+1;for(let a=0;a<o.length;a+=2){const r=o[a+1],s=-1===r?n(e,t):t[r];t[i++]=s}}}function Dd(t){const e=t.tView;if(null===e||e.incompleteFirstPass){return t.tView=Rd(1,null,t.template,t.decls,t.vars,t.directiveDefs,t.pipeDefs,t.viewQuery,t.schemas,t.consts)}return e}function Rd(t,e,n,o,i,a,r,s,l,c){ngDevMode&&ngDevMode.tView++;const d=vo+o,p=d+i,m=(function f(t,e){const n=ngDevMode?new Qc:[];for(let o=0;o<e;o++)n.push(o<t?null:Sc);return n})(d,p),u="function"==typeof c?c():c,g=m[1]=ngDevMode?new class{constructor(t,e,n,o,i,a,r,s,l,c,d,p,m,f,u,g,h,b,y,_,C,M,v,x,O,P,w,k,S,D,R,E){this.type=t,this.blueprint=e,this.template=n,this.queries=o,this.viewQuery=i,this.declTNode=a,this.data=r,this.bindingStartIndex=s,this.expandoStartIndex=l,this.hostBindingOpCodes=c,this.firstCreatePass=d,this.firstUpdatePass=p,this.staticViewQueries=m,this.staticContentQueries=f,this.preOrderHooks=u,this.preOrderCheckHooks=g,this.contentHooks=h,this.contentCheckHooks=b,this.viewHooks=y,this.viewCheckHooks=_,this.destroyHooks=C,this.cleanup=M,this.contentQueries=v,this.components=x,this.directiveRegistry=O,this.pipeRegistry=P,this.firstChild=w,this.schemas=k,this.consts=S,this.incompleteFirstPass=D,this._decls=R,this._vars=E}get template_(){const t=[];return Xc(this.firstChild,t),t.join("")}get type_(){return xo[this.type]||`TViewType.?${this.type}?`}}(t,m,n,null,s,e,(function h(t){return void 0===Jc&&(Jc=new Kc),Jc.concat(t)})(m).fill(null,d),d,p,null,!0,!0,!1,!1,null,null,null,null,null,null,null,null,null,null,"function"==typeof a?a():a,"function"==typeof r?r():r,null,l,u,!1,o,i):{type:t,blueprint:m,template:n,queries:null,viewQuery:s,declTNode:e,data:m.slice().fill(null,d),bindingStartIndex:d,expandoStartIndex:p,hostBindingOpCodes:null,firstCreatePass:!0,firstUpdatePass:!0,staticViewQueries:!1,staticContentQueries:!1,preOrderHooks:null,preOrderCheckHooks:null,contentHooks:null,contentCheckHooks:null,viewHooks:null,viewCheckHooks:null,destroyHooks:null,cleanup:null,contentQueries:null,components:null,directiveRegistry:"function"==typeof a?a():a,pipeRegistry:"function"==typeof r?r():r,firstChild:null,schemas:l,consts:u,incompleteFirstPass:!1};return ngDevMode&&Object.seal(g),g}function Ed(t,e){return new Error(`Renderer: ${t} [${sn(e)}]`)}function Ad(t,e,n){if(ri(t)){return 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function hp(t,e,n){ngDevMode&&Lo(Oi(),"Expecting to be called in first template pass only");let o=n?t.styles:null,i=n?t.classes:null,a=0;if(null!==e)for(let t=0;t<e.length;t++){const n=e[t];if("number"==typeof n)a=n;else if(1==a)i=Je(i,n);else if(2==a){o=Je(o,n+": "+e[++t]+";")}}n?t.styles=o:t.stylesWithoutHost=o,n?t.classes=i:t.classesWithoutHost=i}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function bp(t){ngDevMode&&xn(t,"component");const e=op(il(t));ngDevMode&&xn(e[8],"rootContext should be defined"),(function n(t,e){if(0===t.flags&&t.clean==hd){let n;t.flags|=e,t.clean=new Promise((t=>n=t)),t.scheduler((()=>{if(1&t.flags&&(t.flags&=-2,ip(t)),2&t.flags){t.flags&=-3;const e=t.playerHandler;e&&e.flushPlayers()}t.clean=hd,n(null)}))}})(e[8],1)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const yp=new er("INJECTOR",-1);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class _p{get(t,e=wr){if(e===wr){const e=new Error(`NullInjectorError: No provider for ${Ke(t)}!`);throw e.name="NullInjectorError",e}return e}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Cp=new er("Set Injector scope."),Mp={},vp={};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let xp;function Op(){return void 0===xp&&(xp=new _p),xp}function Pp(t,e=null,n=null,o){return new wp(t,n,e||Op(),o)}class wp{constructor(t,e,n,o=null){this.parent=n,this.records=new Map,this.injectorDefTypes=new Set,this.onDestroy=new Set,this._destroyed=!1;const i=[];e&&mr(e,(n=>this.processProvider(n,t,e))),mr([t],(t=>this.processInjectorType(t,[],i))),this.records.set(yp,Dp(void 0,this));const a=this.records.get(Cp);this.scope=null!=a?a.value:null,this.source=o||("object"==typeof t?null:Ke(t))}get destroyed(){return this._destroyed}destroy(){this.assertNotDestroyed(),this._destroyed=!0;try{this.onDestroy.forEach((t=>t.ngOnDestroy()))}finally{this.records.clear(),this.onDestroy.clear(),this.injectorDefTypes.clear()}}get(t,e=wr,n=Hn.Default){this.assertNotDestroyed();const o=Er(this),i=Ln(void 0);try{if(!(n&Hn.SkipSelf)){let e=this.records.get(t);if(void 0===e){const n=(function o(t){return"function"==typeof t||"object"==typeof t&&t instanceof er})(t)&&Rn(t);e=n&&this.injectableDefInScope(n)?Dp(kp(t),Mp):null,this.records.set(t,e)}if(null!=e)return this.hydrate(t,e)}return(n&Hn.Self?Op():this.parent).get(t,e=n&Hn.Optional&&e===wr?null:e)}catch(e){if("NullInjectorError"===e.name){if((e.ngTempTokenPath=e.ngTempTokenPath||[]).unshift(Ke(t)),o)throw e;return Fr(e,t,"R3InjectorError",this.source)}throw e}finally{Ln(i),Er(o)}}_resolveInjectorDefTypes(){this.injectorDefTypes.forEach((t=>this.get(t)))}toString(){const t=[];return this.records.forEach(((e,n)=>t.push(Ke(n)))),`R3Injector[${t.join(", ")}]`}assertNotDestroyed(){if(this._destroyed)throw new Error("Injector has already been destroyed.")}processInjectorType(t,e,n){if(!(t=tn(t)))return!1;let o=An(t);const i=null==o&&t.ngModule||void 0,a=void 0===i?t:i;if(ngDevMode&&-1!==e.indexOf(a)){ln(Ke(a),e.map(Ke))}const r=-1!==n.indexOf(a);if(void 0!==i&&(o=An(i)),null==o)return!1;if(null!=o.imports&&!r){let t;ngDevMode&&e.push(a),n.push(a);try{mr(o.imports,(o=>{this.processInjectorType(o,e,n)&&(void 0===t&&(t=[]),t.push(o))}))}finally{ngDevMode&&e.pop()}if(void 0!==t)for(let e=0;e<t.length;e++){const{ngModule:n,providers:o}=t[e];mr(o,(t=>this.processProvider(t,n,o||Qn)))}}this.injectorDefTypes.add(a);const s=Go(a)||(()=>new a);this.records.set(a,Dp(s,Mp));const l=o.providers;if(null!=l&&!r){const e=t;mr(l,(t=>this.processProvider(t,e,l)))}return void 0!==i&&void 0!==t.providers}processProvider(t,e,n){let o=Ep(t=tn(t))?t:tn(t&&t.provide);const i=(function a(t,e,n){if(Rp(t))return Dp(void 0,t.useValue);return Dp(Sp(t,e,n),Mp)})(t,e,n);if(Ep(t)||!0!==t.multi){const t=this.records.get(o);ngDevMode&&t&&void 0!==t.multi&&cn()}else{let e=this.records.get(o);e?ngDevMode&&void 0===e.multi&&cn():(e=Dp(void 0,Mp,!0),e.factory=()=>Ir(e.multi),this.records.set(o,e)),o=t,e.multi.push(t)}this.records.set(o,i)}hydrate(t,e){return ngDevMode&&e.value===vp?ln(Ke(t)):e.value===Mp&&(e.value=vp,e.value=e.factory()),"object"==typeof e.value&&e.value&&(function n(t){return null!==t&&"object"==typeof t&&"function"==typeof t.ngOnDestroy})(e.value)&&this.onDestroy.add(e.value),e.value}injectableDefInScope(t){if(!t.providedIn)return!1;const e=tn(t.providedIn);return"string"==typeof e?"any"===e||e===this.scope:this.injectorDefTypes.has(e)}}function kp(t){const e=Rn(t),n=null!==e?e.factory:Go(t);if(null!==n)return n;if(t instanceof er)throw new Error(`Token ${Ke(t)} is missing a ɵprov definition.`);if(t instanceof Function)return(function o(t){const e=t.length;if(e>0){const n=gr(e,"?");throw new Error(`Can't resolve all parameters for ${Ke(t)}: (${n.join(", ")}).`)}const n=(function o(t){const e=t&&(t[Tn]||t[zn]);if(e){const n=(function n(t){if(t.hasOwnProperty("name"))return t.name;const e=(""+t).match(/^function\s*([^\s(]+)/);return null===e?"":e[1]})(t);return console.warn(`DEPRECATED: DI is instantiating a token "${n}" that inherits its @Injectable decorator but does not provide one itself.\nThis will become an error in a future version of Angular. Please add @Injectable() to the "${n}" class.`),e}return null})(t);return null!==n?()=>n.factory(t):()=>new t})(t);throw new Error("unreachable")}function Sp(t,e,n){let o;if(Ep(t)){const e=tn(t);return Go(e)||kp(e)}if(Rp(t))o=()=>tn(t.useValue);else if((function i(t){return!(!t||!t.useFactory)})(t))o=()=>t.useFactory(...Ir(t.deps||[]));else if((function a(t){return!(!t||!t.useExisting)})(t))o=()=>Tr(tn(t.useExisting));else{const i=tn(t&&(t.useClass||t.provide));if(ngDevMode&&!i&&(function r(t,e,n){let o="";t&&e&&(o=` - only instances of Provider and Type are allowed, got: [${e.map((t=>t==n?"?"+n+"?":"...")).join(", ")}]`);throw new Error(`Invalid provider for the NgModule '${Ke(t)}'`+o)})(e,n,t),!(function s(t){return!!t.deps})(t))return Go(i)||kp(i);o=()=>new i(...Ir(t.deps))}return o}function Dp(t,e,n=!1){return{factory:t,value:e,multi:n?[]:void 0}}function Rp(t){return null!==t&&"object"==typeof t&&Dr in t}function Ep(t){return"function"==typeof t}const Ap=function Tp(t,e,n){return(function o(t,e=null,n=null,i){const a=Pp(t,e,n,i);return a._resolveInjectorDefTypes(),a})({name:n},e,t,n)};class Np{static create(t,e){return Array.isArray(t)?Ap(t,e,""):Ap(t.providers,t.parent,t.name||"")}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function zp(t){Gp(t);const e=nl(t);return null===e?null:(void 0===e.component&&(e.component=(function n(t,e){const n=e[1].data[t];return 2&n.flags?e[n.directiveStart]:null})(e.nodeIndex,e.lView)),e.component)}function Ip(t){Gp(t);const e=nl(t);return null===e?null:e.lView[8]}function Hp(t){const e=nl(t);if(null===e)return null;let n,o=e.lView;for(ngDevMode&&Fo(o);2===o[1].type&&(n=Nl(o));)o=n;return 512&o[2]?null:o[8]}function Fp(t){return[...zl(t).components]}function Lp(t){const e=nl(t);if(null===e)return Np.NULL;return new ja(e.lView[1].data[e.nodeIndex],e.lView)}function Bp(t){if(t instanceof Text)return[];const e=nl(t);if(null===e)return[];const n=e.lView,o=n[1],i=e.nodeIndex;return(null==o?void 0:o.data[i])?(void 0===e.directives&&(e.directives=pl(i,n,!1)),null===e.directives?[]:[...e.directives]):[]}function Vp(t){const{constructor:e}=t;if(!e)throw new Error("Unable to find the instance constructor");const n=yo(e);if(n)return{inputs:n.inputs,outputs:n.outputs,encapsulation:n.encapsulation,changeDetection:n.onPush?jn.OnPush:jn.Default};const o=_o(e);return o?{inputs:o.inputs,outputs:o.outputs}:null}function jp(t){return nl(t).native}function Up(t){Gp(t);const e=nl(t);if(null===e)return[];const n=e.lView,o=n[7],i=n[1].cleanup,a=[];if(i&&o)for(let e=0;e<i.length;){const r=i[e++],s=i[e++];if("string"==typeof r){const l=r,c=li(n[s]),d=o[i[e++]],p=i[e++],m="boolean"==typeof p||p>=0?"dom":"output",f="boolean"==typeof p&&p;t==c&&a.push({element:t,name:l,callback:d,useCapture:f,type:m})}}return a.sort(Wp),a}function Wp(t,e){return t.name==e.name?0:t.name<e.name?-1:1}function Gp(t){if("undefined"!=typeof Element&&!(t instanceof Element))throw new Error("Expecting instance of DOM Element")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Yp(t){bp(t),Fp(t).forEach((t=>(function e(t){const e=il(t);ap(e[1],e,t)})(t)))}
/**
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Hm(t,e,n,o,i,a,r,s){const l=xi(),c=Oi(),d=t+vo,p=c.firstCreatePass?(function m(t,e,n,o,i,a,r,s,l){ngDevMode&&Lo(e),ngDevMode&&ngDevMode.firstCreatePass++;const c=e.consts,d=_d(e,t,4,r||null,hi(c,s));Ld(e,n,d,hi(c,l)),ra(e,d);const p=d.tViews=Rd(2,d,o,i,a,e.directiveRegistry,e.pipeRegistry,null,e.schemas,c);return null!==e.queries&&(e.queries.template(e,d),p.queries=e.queries.embeddedTView(d)),d})(d,c,l,e,n,o,i,a,r):c.data[d];Di(p,!1);const f=l[11].createComment(ngDevMode?"container":"");rc(c,l,f,p),al(f,l),np(l,l[d]=Qd(f,l,f,p)),Do(p)&&kd(c,l,p),null!=r&&Sd(l,p,s)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Fm(t){return mi((function e(){return _i.lFrame.contextLView})(),vo+t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Lm={"ɵɵdefineInjectable":Sn,"ɵɵdefineInjector":Dn,"ɵɵinject":Tr,"ɵɵinvalidFactoryDep":Nr,resolveForwardRef:tn};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Bm=Ze({provide:String,useValue:Ze});function Vm(t){return void 0!==t.useClass}function jm(t){return void 0!==t.useFactory}Ze({provide:String,useValue:Ze});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Um=Xa("Injectable",void 0,void 0,void 0,((t,e)=>Wm(t,e))),Wm=function Gm(t,e){let n=null,o=null;t.hasOwnProperty(Tn)||Object.defineProperty(t,Tn,{get:()=>{if(null===n){const o=lr({usage:0,kind:"injectable",type:t});n=o.compileInjectable(Lm,`ng:///${t.name}/ɵprov.js`,(function o(t,e){const n=e||{providedIn:null},o={name:t.name,type:t,typeArgumentCount:0,providedIn:n.providedIn};(Vm(n)||jm(n))&&void 0!==n.deps&&(o.deps=qr(n.deps));Vm(n)?o.useClass=n.useClass:!(function i(t){return Bm in t})(n)?jm(n)?o.useFactory=n.useFactory:(function a(t){return void 0!==t.useExisting})(n)&&(o.useExisting=n.useExisting):o.useValue=n.useValue;return o}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function e(t){const e=[];for(let n=0;n<t.length;++n){if(e.indexOf(t[n])>-1)return e.push(t[n]),e;e.push(t[n])}return e})(t.slice().reverse()).map((t=>Ke(t.token))).join(" -> ")+")"}return""}function qm(t,e,n,o){const i=[e],a=n(i),r=o?(function s(t,e){const n=`${t} caused by: ${e instanceof Error?e.message:e}`,o=Error(n);return o.ngOriginalError=e,o})(a,o):Error(a);return r.addKey=Zm,r.keys=i,r.injectors=[t],r.constructResolvingMessage=n,r.ngOriginalError=o,r}function Zm(t,e){this.injectors.push(t),this.keys.push(e),this.message=this.constructResolvingMessage(this.keys)}function Xm(t,e){const n=[];for(let t=0,o=e.length;t<o;t++){const o=e[t];n.push(o&&0!=o.length?o.map(Ke).join(" "):"?")}return Error("Cannot resolve all parameters for '"+Ke(t)+"'("+n.join(", ")+"). Make sure that all the parameters are decorated with Inject or have valid type annotations and that '"+Ke(t)+"' is decorated with Injectable.")}function Km(t,e){return Error(`Cannot mix multi providers and regular providers, got: ${t} ${e}`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Jm{constructor(t,e){if(this.token=t,this.id=e,!t)throw new Error("Token must be defined!");this.displayName=Ke(this.token)}static get(t){return Qm.get(tn(t))}static get numberOfKeys(){return Qm.numberOfKeys}}const Qm=new class{constructor(){this._allKeys=new Map}get(t){if(t instanceof Jm)return t;if(this._allKeys.has(t))return this._allKeys.get(t);const e=new Jm(t,Jm.numberOfKeys);return this._allKeys.set(t,e),e}get numberOfKeys(){return this._allKeys.size}};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const $m=new class{constructor(t){this.reflectionCapabilities=t}updateCapabilities(t){this.reflectionCapabilities=t}factory(t){return this.reflectionCapabilities.factory(t)}parameters(t){return this.reflectionCapabilities.parameters(t)}annotations(t){return this.reflectionCapabilities.annotations(t)}propMetadata(t){return this.reflectionCapabilities.propMetadata(t)}hasLifecycleHook(t,e){return this.reflectionCapabilities.hasLifecycleHook(t,e)}getter(t){return this.reflectionCapabilities.getter(t)}setter(t){return this.reflectionCapabilities.setter(t)}method(t){return this.reflectionCapabilities.method(t)}importUri(t){return this.reflectionCapabilities.importUri(t)}resourceUri(t){return this.reflectionCapabilities.resourceUri(t)}resolveIdentifier(t,e,n,o){return this.reflectionCapabilities.resolveIdentifier(t,e,n,o)}resolveEnum(t,e){return this.reflectionCapabilities.resolveEnum(t,e)}}(new xr);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function kf(t){return!!t&&"function"==typeof t.then}function Sf(t){return!!t&&"function"==typeof t.subscribe}uf.INJECTOR_KEY=Jm.get(Np);const Df=Sf;
/**
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     * Copyright Google LLC All Rights Reserved.
     *
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     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function zf(t=1){return $i(t)}
/**
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     * Copyright Google LLC All Rights Reserved.
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     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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     * Copyright Google LLC All Rights Reserved.
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let Og=Cg;function Pg(t){xn(t,"Expected localeId to be defined"),"string"==typeof t&&(Og=t.toLowerCase().replace(/_/g,"-"))}
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     * Copyright Google LLC All Rights Reserved.
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     * Copyright Google LLC All Rights Reserved.
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     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Tg(t,e){const n=t.data[e];if(null===n||"string"==typeof n)return null;!ngDevMode||n.hasOwnProperty("tViews")||n.hasOwnProperty("currentCaseLViewIndex")||On("We expect to get 'null'|'TIcu'|'TIcuContainer', but got: "+n);const o=n.hasOwnProperty("currentCaseLViewIndex")?n:n.value;return ngDevMode&&No(o),o}function Ng(t,e,n){const o=Cd(t,n,64,null,null);return Sg(e,o),o}function zg(t,e){const n=e[t.currentCaseLViewIndex];return null===n?n:n<0?~n:n}function Ig(t){return t>>>17}function Hg(t){return(131070&t)>>>1}function Fg(t){return 1&t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let Lg=0,Bg=0;function Vg(t,e,n,o){ngDevMode&&Pn(o);const i=n[11];let a,r=null;for(let s=0;s<e.length;s++){const l=e[s];if("string"==typeof l){const t=e[++s];null===n[t]&&(ngDevMode&&ngDevMode.rendererCreateTextNode++,ngDevMode&&wn(n,t),n[t]=Bl(i,l))}else if("number"==typeof l)switch(1&l){case 0:const c=Ig(l);let d,p;if(null===r&&(r=c,a=tc(i,o)),c===r?(d=o,p=a):(d=null,p=li(n[c])),null!==p){ngDevMode&&Pn(p);const e=Hg(l);ngDevMode&&Mn(e,vo,"Missing ref");const o=n[e];ngDevMode&&Pn(o),Jl(i,p,o,d,!1);const a=Tg(t,e);if(null!==a&&"object"==typeof a){ngDevMode&&No(a);const e=zg(a,n);null!==e&&Vg(t,a.create[e],n,n[a.anchorIdx])}}break;case 1:const m=e[++s],f=e[++s];Zd(i,ci(l>>>1,n),null,null,m,f,null);break;default:throw new Error(`Unable to determine the type of mutate operation for "${l}"`)}else switch(l){case vg:const t=e[++s],o=e[++s];if(null===n[o]){ngDevMode&&gn(typeof t,"string",`Expected "${t}" to be a comment node value`),ngDevMode&&ngDevMode.rendererCreateComment++,ngDevMode&&Vo(n,o);al(n[o]=jl(i,t),n)}break;case Mg:const a=e[++s],r=e[++s];if(null===n[r]){ngDevMode&&gn(typeof a,"string",`Expected "${a}" to be an element node tag name`),ngDevMode&&ngDevMode.rendererCreateElement++,ngDevMode&&Vo(n,r);al(n[r]=Ul(i,a,null),n)}break;default:ngDevMode&&On(`Unable to determine the type of mutate operation for "${l}"`)}}}function jg(t,e,n,o,i){for(let a=0;a<n.length;a++){const r=n[a],s=n[++a];if(r&i){let i="";for(let r=a+1;r<=a+s;r++){const a=n[r];if("string"==typeof a)i+=a;else if("number"==typeof a)if(a<0)i+=rn(e[o-a]);else{const s=a>>>2;switch(3&a){case 1:const a=n[++r],l=n[++r],c=t.data[s];ngDevMode&&xn(c,"Experting TNode or string"),"string"==typeof c?Zd(e[11],e[s],null,c,a,i,l):zd(t,c,e,a,i,e[11],l,!1);break;case 0:const d=e[s];null!==d&&Vl(e[11],d,i);break;case 2:Wg(t,Tg(t,s),e,i);break;case 3:Ug(t,Tg(t,s),o,e)}}}}else{const i=n[a+1];if(i>0&&3==(3&i)){const n=Tg(t,i>>>2);e[n.currentCaseLViewIndex]<0&&Ug(t,n,o,e)}}a+=s}}function Ug(t,e,n,o){ngDevMode&&wn(o,e.currentCaseLViewIndex);let i=o[e.currentCaseLViewIndex];if(null!==i){let a=Lg;i<0&&(i=o[e.currentCaseLViewIndex]=~i,a=-1),jg(t,o,e.update[i],n,a)}}function Wg(t,e,n,o){const i=(function a(t,e){let n=t.cases.indexOf(e);if(-1===n)switch(t.type){case 1:{const o=(function i(t,e){const n=hg(e)(parseInt(t,10)),o=_g[n];return void 0!==o?o:"other"})(e,(function o(){return Og})());n=t.cases.indexOf(o),-1===n&&"other"!==o&&(n=t.cases.indexOf("other"));break}case 0:n=t.cases.indexOf("other")}return-1===n?null:n}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(e,o);if(zg(e,n)!==i&&(Gg(t,e,n),n[e.currentCaseLViewIndex]=null===i?null:~i,null!==i)){const o=n[e.anchorIdx];o&&(ngDevMode&&Pn(o),Vg(t,e.create[i],n,o))}}function Gg(t,e,n){let o=zg(e,n);if(null!==o){const i=e.remove[o];for(let e=0;e<i.length;e++){const o=i[e];if(o>0){const t=ci(o,n);null!==t&&dc(n[11],t)}else Gg(t,Tg(t,~o),n)}}}function Yg(){const t=[];let e,n,o=-1;function i(t,e){o=0;const i=zg(t,e);null!==i?(ngDevMode&&mn(i,0,t.cases.length-1),n=t.remove[i]):n=Qn}function a(){if(o<n.length){const r=n[o++];if(ngDevMode&&pn(r,"Expecting OpCode number"),r>0){const t=e[r];return ngDevMode&&Pn(t),t}{t.push(o,n);const s=e[1].data[~r];return ngDevMode&&No(s),i(s,e),a()}}return 0===t.length?null:(n=t.pop(),o=t.pop(),a())}return function r(n,o){for(e=o;t.length;)t.pop();return ngDevMode&&Eo(n,o),i(n.value,o),a}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function qg(t){const e=t||(Array.isArray(this)?this:[]);let n=[];for(let t=0;t<e.length;t++){const o=e[t++],i=(o&xg.APPEND_EAGERLY)===xg.APPEND_EAGERLY,a=o>>>xg.SHIFT;n.push(`lView[${a}] = document.${(o&xg.COMMENT)===xg.COMMENT?"createComment":"createText"}(${JSON.stringify(e[t])});`),i&&n.push(`parent.appendChild(lView[${a}]);`)}return n}function Zg(t){const e=new Jg(t||(Array.isArray(this)?this:[]));let n=[];function o(t){const n=t>>>2;switch(3&t){case 0:return`(lView[${n}] as Text).textContent = $$$`;case 1:const t=e.consumeString(),o=e.consumeFunction();return`(lView[${n}] as Element).setAttribute('${t}', ${o?`(${o})($$$)`:"$$$"})`;case 2:return`icuSwitchCase(${n}, $$$)`;case 3:return`icuUpdateCase(${n})`}throw new Error("unexpected OpCode")}for(;e.hasMore();){let t=e.consumeNumber(),i=e.consumeNumber();const a=e.i+i,r=[];let s="";for(;e.i<a;){let t=e.consumeNumberOrString();if("string"==typeof t)s+=t;else if(t<0)s+="${lView[i"+t+"]}";else{const e=o(t);r.push(e.replace("$$$","`"+s+"`")+";"),s=""}}n.push(`if (mask & 0b${t.toString(2)}) { ${r.join(" ")} }`)}return n}function Xg(t){const e=new Jg(t||(Array.isArray(this)?this:[]));let n=[];function o(t){const n=Ig(t),o=Hg(t);switch(Fg(t)){case 0:return`(lView[${n}] as Element).appendChild(lView[${i}])`;case 1:return`(lView[${o}] as Element).setAttribute("${e.consumeString()}", "${e.consumeString()}")`}throw new Error("Unexpected OpCode: "+Fg(t))}let i=-1;for(;e.hasMore();){let t=e.consumeNumberStringOrMarker();if(t===vg){const t=e.consumeString();i=e.consumeNumber(),n.push(`lView[${i}] = document.createComment("${t}")`)}else if(t===Mg){const t=e.consumeString();i=e.consumeNumber(),n.push(`lView[${i}] = document.createElement("${t}")`)}else if("string"==typeof t)i=e.consumeNumber(),n.push(`lView[${i}] = document.createTextNode("${t}")`);else{if("number"!=typeof t)throw new Error("Unexpected value");{const e=o(t);e&&n.push(e)}}}return n}function Kg(t){const e=t||(Array.isArray(this)?this:[]);let n=[];for(let t=0;t<e.length;t++){const o=e[t];n.push(o>0?`remove(lView[${o}])`:`removeNestedICU(${~o})`)}return n}class Jg{constructor(t){this.i=0,this.codes=t}hasMore(){return this.i<this.codes.length}consumeNumber(){let t=this.codes[this.i++];return pn(t,"expecting number in OpCode"),t}consumeString(){let t=this.codes[this.i++];return fn(t,"expecting string in OpCode"),t}consumeFunction(){let t=this.codes[this.i++];if(null===t||"function"==typeof t)return t;throw new Error("expecting function in OpCode")}consumeNumberOrString(){let t=this.codes[this.i++];return"string"==typeof t||pn(t,"expecting number or string in OpCode"),t}consumeNumberStringOrMarker(){let t=this.codes[this.i++];return"string"==typeof t||"number"==typeof t||t==vg||t==Mg||pn(t,"expecting number, string, ICU_MARKER or ELEMENT_MARKER in OpCode"),t}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Qg=/�(\d+):?\d*�/gi,$g=/({\s*�\d+:?\d*�\s*,\s*\S{6}\s*,[\s\S]*})/gi,th=/�(\d+)�/,eh=/^\s*(�\d+:?\d*�)\s*,\s*(select|plural)\s*,/,nh=/�\/?\*(\d+:\d+)�/gi,oh=/�(\/?[#*]\d+):?\d*�/gi,ih=/\uE500/g;function ah(t,e,n,o,i,a){const r=Si(),s=[],l=[],c=[[]];ngDevMode&&(Bc(s,qg),Bc(l,Zg));const d=(function m(t){return t.replace(ih," ")})(i=(function p(t,e){if((function n(t){return-1===t})(e))return ph(t);{const n=t.indexOf(`:${e}�`)+2+e.toString().length,o=t.search(new RegExp(`�\\/\\*\\d+:${e}�`));return ph(t.substring(n,o))}})(i,a)).split(oh);for(let a=0;a<d.length;a++){let p=d[a];if(0==(1&a)){const a=uh(p);for(let d=0;d<a.length;d++){let p=a[d];if(0==(1&d)){const e=p;ngDevMode&&fn(e,"Parsed ICU part should be string"),""!==e&&sh(t,r,c[0],s,l,n,e)}else{const a=p;if("object"!=typeof a)throw new Error(`Unable to parse ICU expression in "${i}" message.`);const d=rh(t,r,c[0],n,s,ngDevMode?`ICU ${o}:${a.mainBinding}`:"",!0).index;ngDevMode&&vn(d,vo,"Index must be in absolute LView offset"),mh(t,n,l,e,a,d)}}}else{const e=47===p.charCodeAt(0),n=p.charCodeAt(e?1:0);ngDevMode&&kn(n,42,35);const o=vo+Number.parseInt(p.substring(e?2:1));if(e)c.shift(),Di(Si(),!1);else{const e=Ng(t,c[0],o);c.unshift([]),Di(e,!0)}}}t.data[o]={create:s,update:l}}function rh(t,e,n,o,i,a,r){const s=Md(t,o,1,null);let l=s<<xg.SHIFT,c=Si();e===c&&(c=null),null===c&&(l|=xg.APPEND_EAGERLY),r&&(l|=xg.COMMENT,(function d(t){void 0===Al&&(Al=t())})(Yg)),i.push(l,null===a?"":a);const p=Cd(t,s,r?32:1,null===a?ngDevMode?"{{?}}":"":a,null);Sg(n,p);const m=p.index;return Di(p,!1),null!==c&&e!==c&&(function f(t,e){ngDevMode&&To(t);let n=t.insertBeforeIndex;null===n?(ac(wg,kg),n=t.insertBeforeIndex=[null,e]):(gn(Array.isArray(n),!0,"Expecting array here"),n.push(e))})(c,m),p}function sh(t,e,n,o,i,a,r){const s=r.match(Qg),l=rh(t,e,n,a,o,s?null:r,!1);s&&lh(i,r,l.index,null,0,null)}function lh(t,e,n,o,i,a){ngDevMode&&vn(n,vo,"Index must be in absolute LView offset");const r=t.length,s=r+1;t.push(null,null);const l=r+2;ngDevMode&&Bc(t,Zg);const c=e.split(Qg);let d=0;for(let e=0;e<c.length;e++){const n=c[e];if(1&e){const e=i+parseInt(n,10);t.push(-1-e),d|=dh(e)}else""!==n&&t.push(n)}return t.push(n<<2|(o?1:0)),o&&t.push(o,a),t[r]=d,t[s]=t.length-l,d}function ch(t){let e=0;for(let n=0;n<t.length;n++){const o=t[n];"number"==typeof o&&o<0&&e++}return e}function dh(t){return 1<<Math.min(t,31)}function ph(t){let e,n,o="",i=0,a=!1;for(;null!==(e=nh.exec(t));)a?e[0]===`�/*${n}�`&&(i=e.index,a=!1):(o+=t.substring(i,e.index+e[0].length),n=e[1],a=!0);return ngDevMode&&gn(a,!1,`Tag mismatch: unable to find the end of the sub-template in the translation "${t}"`),o+=t.substr(i),o}function mh(t,e,n,o,i,a){ngDevMode&&xn(i,"ICU expression must be defined");let r=0;const s={type:i.type,currentCaseLViewIndex:Md(t,e,1,null),anchorIdx:a,cases:[],create:[],remove:[],update:[]};!(function l(t,e,n){t.push(dh(e.mainBinding),2,-1-e.mainBinding,n<<2|2)})(n,i,a),(function c(t,e,n){const o=t.data[e];ngDevMode&&gn(null===o||o.hasOwnProperty("tViews"),!0,"We expect to get 'null'|'TIcuContainer'"),null===o?t.data[e]=n:(ngDevMode&&ga(o,32),o.value=n)})(t,a,s);const d=i.values;for(let a=0;a<d.length;a++){const l=d[a],c=[];for(let t=0;t<l.length;t++){const e=l[t];if("string"!=typeof e){const n=c.push(e)-1;l[t]=`\x3c!--�${n}�--\x3e`}}r=gh(t,s,e,n,o,i.cases[a],l.join(""),c)|r}r&&(function p(t,e,n){t.push(e,1,n<<2|3)})(n,r,a)}function fh(t){const e=[],n=[];let o=1,i=0;const a=uh(t=t.replace(eh,(function(t,e,n){return o="select"===n?0:1,i=parseInt(e.substr(1),10),""})));for(let t=0;t<a.length;){let i=a[t++].trim();1===o&&(i=i.replace(/\s*(?:=)?(\w+)\s*/,"$1")),i.length&&e.push(i);const r=uh(a[t++]);e.length>n.length&&n.push(r)}return{type:o,mainBinding:i,cases:e,values:n}}function uh(t){if(!t)return[];let e=0;const n=[],o=[],i=/[{}]/g;let a;for(i.lastIndex=0;a=i.exec(t);){const i=a.index;if("}"==a[0]){if(n.pop(),0==n.length){const n=t.substring(e,i);eh.test(n)?o.push(fh(n)):o.push(n),e=i+1}}else{if(0==n.length){const n=t.substring(e,i);o.push(n),e=i+1}n.push("{")}}const r=t.substring(e);return o.push(r),o}function gh(t,e,n,o,i,a,r,s){const l=[],c=[],d=[];ngDevMode&&(Bc(l,Xg),Bc(c,Kg),Bc(d,Zg)),e.cases.push(a),e.create.push(l),e.remove.push(c),e.update.push(d);const p=bs(ii()).getInertBodyElement(r);ngDevMode&&xn(p,"Unable to generate inert body element");const m=Gs(p)||p;return m?hh(t,e,n,o,l,c,d,m,i,s,0):0}function hh(t,e,n,o,i,a,r,s,l,c,d){let p=0,m=s.firstChild;for(;m;){const s=Md(t,n,1,null);switch(m.nodeType){case Node.ELEMENT_NODE:const f=m,u=f.tagName.toLowerCase();if(As.hasOwnProperty(u)){_h(i,Mg,u,l,s),t.data[s]=u;const g=f.attributes;for(let t=0;t<g.length;t++){const e=g.item(t),n=e.name.toLowerCase();!!e.value.match(Qg)?Hs.hasOwnProperty(n)?lh(r,e.value,s,e.name,0,Ts[n]?vs:Ns[n]?xs:null):ngDevMode&&console.warn(`WARNING: ignoring unsafe attribute value ${n} on element ${u} (see https://g.co/ng/security#xss)`):Ch(i,s,e)}p=hh(t,e,n,o,i,a,r,m,s,c,d+1)|p,bh(a,s,d)}break;case Node.TEXT_NODE:const g=m.textContent||"",h=g.match(Qg);_h(i,null,h?"":g,l,s),bh(a,s,d),h&&(p=lh(r,g,s,null,0,null)|p);break;case Node.COMMENT_NODE:const b=th.exec(m.textContent||"");if(b){const e=parseInt(b[1],10),r=c[e];_h(i,vg,ngDevMode?`nested ICU ${e}`:"",l,s),mh(t,n,o,l,r,s),yh(a,s,d)}}m=m.nextSibling}return p}function bh(t,e,n){0===n&&t.push(e)}function yh(t,e,n){0===n&&(t.push(~e),t.push(e))}function _h(t,e,n,o,i){null!==e&&t.push(e),t.push(n,i,(function a(t,e,n){return ngDevMode&&vn(e,0,"Missing parent index"),ngDevMode&&Mn(n,0,"Missing ref index"),t|e<<17|n<<1})(0,o,i))}function Ch(t,e,n){t.push(e<<1|1,n.name,n.value)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Mh=/\[(�.+?�?)\]/,vh=/\[(�.+?�?)\]|(�\/?\*\d+:\d+�)/g,xh=/({\s*)(VAR_(PLURAL|SELECT)(_\d+)?)(\s*,)/g,Oh=/{([A-Z0-9_]+)}/g,Ph=/�I18N_EXP_(ICU(_\d+)?)�/g,wh=/\/\*/,kh=/\d+\:(\d+)/;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function Sh(t,e,n=-1){const o=Oi(),i=xi(),a=vo+t;ngDevMode&&xn(o,"tView should be defined");const r=hi(o.consts,e),s=Si();o.firstCreatePass&&ah(o,null===s?0:s.index,i,a,r,n);const l=o.data[a],c=Kl(o,s===i[6]?null:s,i);!(function d(t,e,n,o){const i=t[11];for(let a=0;a<e.length;a++){const r=e[a++],s=e[a],l=(r&xg.COMMENT)===xg.COMMENT,c=(r&xg.APPEND_EAGERLY)===xg.APPEND_EAGERLY,d=r>>>xg.SHIFT;let p=t[d];null===p&&(p=t[d]=l?i.createComment(s):Bl(i,s)),c&&null!==n&&Jl(i,n,p,o,!1)}})(i,l.create,c,s&&8&s.type?i[s.index]:null),Li(!0)}function Dh(){Li(!1)}function Rh(t,e,n){Sh(t,e,n),Dh()}function Eh(t,e){const n=Oi();ngDevMode&&xn(n,"tView should be defined");const o=hi(n.consts,e);!(function i(t,e,n){const o=wi().index,i=[];if(ngDevMode&&Bc(i,Zg),t.firstCreatePass&&null===t.data[e]){for(let t=0;t<n.length;t+=2){const e=n[t],a=n[t+1];if(""!==a){if($g.test(a))throw new Error(`ICU expressions are not supported in attributes. Message: "${a}".`);lh(i,a,o,e,ch(i),null)}}t.data[e]=i}})(n,t+vo,o)}function Ah(t){return(function e(t){t&&(Lg|=1<<Math.min(Bg,31)),Bg++})(um(xi(),Hi(),t)),Ah}function Th(t){!(function e(t,n,o){if(Bg>0){ngDevMode&&xn(t,"tView should be defined");const e=t.data[o];jg(t,n,Array.isArray(e)?e:e.update,zi()-Bg-1,Lg)}Lg=0,Bg=0})(Oi(),xi(),t+vo)}function Nh(t,e={}){return(function n(t,e={}){let n=t;if(Mh.test(t)){const t={},e=[0];n=n.replace(vh,((n,o,i)=>{const a=o||i,r=t[a]||[];if(r.length||(a.split("|").forEach((t=>{const e=t.match(kh),n=e?parseInt(e[1],10):0,o=wh.test(t);r.push([n,o,t])})),t[a]=r),!r.length)throw new Error(`i18n postprocess: unmatched placeholder - ${a}`);const s=e[e.length-1];let l=0;for(let t=0;t<r.length;t++)if(r[t][0]===s){l=t;break}const[c,d,p]=r[l];return d?e.pop():s!==c&&e.push(c),r.splice(l,1),p}))}return Object.keys(e).length?(n=n.replace(xh,((t,n,o,i,a,r)=>e.hasOwnProperty(o)?`${n}${e[o]}${r}`:t)),n=n.replace(Oh,((t,n)=>e.hasOwnProperty(n)?e[n]:t)),n=n.replace(Ph,((t,n)=>{if(e.hasOwnProperty(n)){const o=e[n];if(!o.length)throw new Error(`i18n postprocess: unmatched ICU - ${t} with key: ${n}`);return o.shift()}return t})),n):n})(t,e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function zh(t,e,n,o,i){if(t=tn(t),Array.isArray(t))for(let a=0;a<t.length;a++)zh(t[a],e,n,o,i);else{const a=Oi(),r=xi();let s=Ep(t)?t:tn(t.provide),l=Sp(t);const c=wi(),d=1048575&c.providerIndexes,p=c.directiveStart,m=c.providerIndexes>>20;if(Ep(t)||!t.multi){const o=new fa(l,i,gf),f=Fh(s,e,i?d:d+m,p);-1===f?(Ea(ka(c,r),a,s),Ih(a,t,e.length),e.push(s),c.directiveStart++,c.directiveEnd++,i&&(c.providerIndexes+=1048576),n.push(o),r.push(o)):(n[f]=o,r[f]=o)}else{const f=Fh(s,e,d+m,p),u=Fh(s,e,d,d+m),g=f>=0&&n[f],h=u>=0&&n[u];if(i&&!h||!i&&!g){Ea(ka(c,r),a,s);const d=(function a(t,e,n,o,i){const a=new fa(t,n,gf);return a.multi=[],a.index=e,a.componentProviders=0,Hh(a,i,o&&!n),a})(i?Bh:Lh,n.length,i,o,l);!i&&h&&(n[u].providerFactory=d),Ih(a,t,e.length,0),e.push(s),c.directiveStart++,c.directiveEnd++,i&&(c.providerIndexes+=1048576),n.push(d),r.push(d)}else{Ih(a,t,f>-1?f:u,Hh(n[i?u:f],l,!i&&o))}!i&&o&&h&&n[u].componentProviders++}}}function Ih(t,e,n,o){const i=Ep(e);if(i||(function a(t){return!!t.useClass})(e)){const a=(e.useClass||e).prototype.ngOnDestroy;if(a){const r=t.destroyHooks||(t.destroyHooks=[]);if(!i&&e.multi){ngDevMode&&xn(o,"indexInFactory when registering multi factory destroy hook");const t=r.indexOf(n);-1===t?r.push(n,[o,a]):r[t+1].push(o,a)}else r.push(n,a)}}}function Hh(t,e,n){return n&&t.componentProviders++,t.multi.push(e)-1}function Fh(t,e,n,o){for(let i=n;i<o;i++)if(e[i]===t)return i;return-1}function Lh(t,e,n,o){return Vh(this.multi,[])}function Bh(t,e,n,o){const i=this.multi;let a;if(this.providerFactory){const t=this.providerFactory.componentProviders,e=La(n,n[1],this.providerFactory.index,o);a=e.slice(0,t),Vh(i,a);for(let n=t;n<e.length;n++)a.push(e[n])}else a=[],Vh(i,a);return a}function Vh(t,e){for(let n=0;n<t.length;n++){e.push((0,t[n])())}return e}function jh(t,e=[]){return n=>{n.providersResolver=(n,o)=>(function i(t,e,n){const o=Oi();if(o.firstCreatePass){const i=Ro(t);zh(n,o.data,o.blueprint,i,!0),zh(e,o.data,o.blueprint,i,!1)}})(n,o?o(t):t,e)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Uh{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Wh="ngComponent";class Gh{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function Yh(...t){}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function qh(t,e){return new Kh(di(t,e))}Gh.NULL=new class{resolveComponentFactory(t){throw(function e(t){const e=Error(`No component factory found for ${Ke(t)}. Did you add it to @NgModule.entryComponents?`);return e[Wh]=t,e})(t)}};const Zh=function Xh(){return qh(wi(),xi())};class Kh{constructor(t){this.nativeElement=t}}function Jh(t){return t instanceof Kh?t.nativeElement:t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */Kh.__NG_ELEMENT_ID__=Zh,new er("Renderer2Interceptor");class Qh{}class $h{}$h.__NG_ELEMENT_ID__=()=>tb();const tb=function eb(){const t=xi(),e=fi(wi().index,t);return(function n(t){const e=t[11];if(ngDevMode&&!ri(e))throw new Error("Cannot inject Renderer2 when the application uses Renderer3!");return e})(Po(e)?e:t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */;class nb{}nb.ɵprov=Sn({token:nb,providedIn:"root",factory:()=>null});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class ob{constructor(t){this.full=t,this.major=t.split(".")[0],this.minor=t.split(".")[1],this.patch=t.split(".").slice(2).join(".")}}const ib=new ob("12.2.1");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class ab{constructor(){}supports(t){return dm(t)}create(t){return new sb(t)}}const rb=(t,e)=>e;class sb{constructor(t){this.length=0,this._linkedRecords=null,this._unlinkedRecords=null,this._previousItHead=null,this._itHead=null,this._itTail=null,this._additionsHead=null,this._additionsTail=null,this._movesHead=null,this._movesTail=null,this._removalsHead=null,this._removalsTail=null,this._identityChangesHead=null,this._identityChangesTail=null,this._trackByFn=t||rb}forEachItem(t){let e;for(e=this._itHead;null!==e;e=e._next)t(e)}forEachOperation(t){let e=this._itHead,n=this._removalsHead,o=0,i=null;for(;e||n;){const a=!n||e&&e.currentIndex<pb(n,o,i)?e:n,r=pb(a,o,i),s=a.currentIndex;if(a===n)o--,n=n._nextRemoved;else if(e=e._next,null==a.previousIndex)o++;else{i||(i=[]);const t=r-o,e=s-o;if(t!=e){for(let n=0;n<t;n++){const o=n<i.length?i[n]:i[n]=0,a=o+n;e<=a&&a<t&&(i[n]=o+1)}i[a.previousIndex]=e-t}}r!==s&&t(a,r,s)}}forEachPreviousItem(t){let e;for(e=this._previousItHead;null!==e;e=e._nextPrevious)t(e)}forEachAddedItem(t){let e;for(e=this._additionsHead;null!==e;e=e._nextAdded)t(e)}forEachMovedItem(t){let e;for(e=this._movesHead;null!==e;e=e._nextMoved)t(e)}forEachRemovedItem(t){let e;for(e=this._removalsHead;null!==e;e=e._nextRemoved)t(e)}forEachIdentityChange(t){let e;for(e=this._identityChangesHead;null!==e;e=e._nextIdentityChange)t(e)}diff(t){if(null==t&&(t=[]),!dm(t))throw new Error(`Error trying to diff '${Ke(t)}'. Only arrays and iterables are allowed`);return this.check(t)?this:null}onDestroy(){}check(t){this._reset();let e,n,o,i=this._itHead,a=!1;if(Array.isArray(t)){this.length=t.length;for(let e=0;e<this.length;e++)n=t[e],o=this._trackByFn(e,n),null!==i&&Object.is(i.trackById,o)?(a&&(i=this._verifyReinsertion(i,n,o,e)),Object.is(i.item,n)||this._addIdentityChange(i,n)):(i=this._mismatch(i,n,o,e),a=!0),i=i._next}else e=0,(function r(t,e){if(Array.isArray(t))for(let n=0;n<t.length;n++)e(t[n]);else{const n=t[sm()]();let o;for(;!(o=n.next()).done;)e(o.value)}})(t,(t=>{o=this._trackByFn(e,t),null!==i&&Object.is(i.trackById,o)?(a&&(i=this._verifyReinsertion(i,t,o,e)),Object.is(i.item,t)||this._addIdentityChange(i,t)):(i=this._mismatch(i,t,o,e),a=!0),i=i._next,e++})),this.length=e;return this._truncate(i),this.collection=t,this.isDirty}get isDirty(){return null!==this._additionsHead||null!==this._movesHead||null!==this._removalsHead||null!==this._identityChangesHead}_reset(){if(this.isDirty){let t;for(t=this._previousItHead=this._itHead;null!==t;t=t._next)t._nextPrevious=t._next;for(t=this._additionsHead;null!==t;t=t._nextAdded)t.previousIndex=t.currentIndex;for(this._additionsHead=this._additionsTail=null,t=this._movesHead;null!==t;t=t._nextMoved)t.previousIndex=t.currentIndex;this._movesHead=this._movesTail=null,this._removalsHead=this._removalsTail=null,this._identityChangesHead=this._identityChangesTail=null}}_mismatch(t,e,n,o){let i;return null===t?i=this._itTail:(i=t._prev,this._remove(t)),null!==(t=null===this._unlinkedRecords?null:this._unlinkedRecords.get(n,null))?(Object.is(t.item,e)||this._addIdentityChange(t,e),this._reinsertAfter(t,i,o)):null!==(t=null===this._linkedRecords?null:this._linkedRecords.get(n,o))?(Object.is(t.item,e)||this._addIdentityChange(t,e),this._moveAfter(t,i,o)):t=this._addAfter(new lb(e,n),i,o),t}_verifyReinsertion(t,e,n,o){let i=null===this._unlinkedRecords?null:this._unlinkedRecords.get(n,null);return null!==i?t=this._reinsertAfter(i,t._prev,o):t.currentIndex!=o&&(t.currentIndex=o,this._addToMoves(t,o)),t}_truncate(t){for(;null!==t;){const e=t._next;this._addToRemovals(this._unlink(t)),t=e}null!==this._unlinkedRecords&&this._unlinkedRecords.clear(),null!==this._additionsTail&&(this._additionsTail._nextAdded=null),null!==this._movesTail&&(this._movesTail._nextMoved=null),null!==this._itTail&&(this._itTail._next=null),null!==this._removalsTail&&(this._removalsTail._nextRemoved=null),null!==this._identityChangesTail&&(this._identityChangesTail._nextIdentityChange=null)}_reinsertAfter(t,e,n){null!==this._unlinkedRecords&&this._unlinkedRecords.remove(t);const o=t._prevRemoved,i=t._nextRemoved;return null===o?this._removalsHead=i:o._nextRemoved=i,null===i?this._removalsTail=o:i._prevRemoved=o,this._insertAfter(t,e,n),this._addToMoves(t,n),t}_moveAfter(t,e,n){return this._unlink(t),this._insertAfter(t,e,n),this._addToMoves(t,n),t}_addAfter(t,e,n){return this._insertAfter(t,e,n),this._additionsTail=null===this._additionsTail?this._additionsHead=t:this._additionsTail._nextAdded=t,t}_insertAfter(t,e,n){const o=null===e?this._itHead:e._next;return t._next=o,t._prev=e,null===o?this._itTail=t:o._prev=t,null===e?this._itHead=t:e._next=t,null===this._linkedRecords&&(this._linkedRecords=new db),this._linkedRecords.put(t),t.currentIndex=n,t}_remove(t){return this._addToRemovals(this._unlink(t))}_unlink(t){null!==this._linkedRecords&&this._linkedRecords.remove(t);const e=t._prev,n=t._next;return null===e?this._itHead=n:e._next=n,null===n?this._itTail=e:n._prev=e,t}_addToMoves(t,e){return t.previousIndex===e||(this._movesTail=null===this._movesTail?this._movesHead=t:this._movesTail._nextMoved=t),t}_addToRemovals(t){return null===this._unlinkedRecords&&(this._unlinkedRecords=new db),this._unlinkedRecords.put(t),t.currentIndex=null,t._nextRemoved=null,null===this._removalsTail?(this._removalsTail=this._removalsHead=t,t._prevRemoved=null):(t._prevRemoved=this._removalsTail,this._removalsTail=this._removalsTail._nextRemoved=t),t}_addIdentityChange(t,e){return t.item=e,this._identityChangesTail=null===this._identityChangesTail?this._identityChangesHead=t:this._identityChangesTail._nextIdentityChange=t,t}}class lb{constructor(t,e){this.item=t,this.trackById=e,this.currentIndex=null,this.previousIndex=null,this._nextPrevious=null,this._prev=null,this._next=null,this._prevDup=null,this._nextDup=null,this._prevRemoved=null,this._nextRemoved=null,this._nextAdded=null,this._nextMoved=null,this._nextIdentityChange=null}}class cb{constructor(){this._head=null,this._tail=null}add(t){null===this._head?(this._head=this._tail=t,t._nextDup=null,t._prevDup=null):(this._tail._nextDup=t,t._prevDup=this._tail,t._nextDup=null,this._tail=t)}get(t,e){let n;for(n=this._head;null!==n;n=n._nextDup)if((null===e||e<=n.currentIndex)&&Object.is(n.trackById,t))return n;return null}remove(t){const e=t._prevDup,n=t._nextDup;return null===e?this._head=n:e._nextDup=n,null===n?this._tail=e:n._prevDup=e,null===this._head}}class db{constructor(){this.map=new Map}put(t){const e=t.trackById;let n=this.map.get(e);n||(n=new cb,this.map.set(e,n)),n.add(t)}get(t,e){const n=this.map.get(t);return n?n.get(t,e):null}remove(t){const e=t.trackById;return this.map.get(e).remove(t)&&this.map.delete(e),t}get isEmpty(){return 0===this.map.size}clear(){this.map.clear()}}function pb(t,e,n){const o=t.previousIndex;if(null===o)return o;let i=0;return n&&o<n.length&&(i=n[o]),o+e+i}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class mb{constructor(){}supports(t){return t instanceof Map||pm(t)}create(){return new fb}}class fb{constructor(){this._records=new Map,this._mapHead=null,this._appendAfter=null,this._previousMapHead=null,this._changesHead=null,this._changesTail=null,this._additionsHead=null,this._additionsTail=null,this._removalsHead=null,this._removalsTail=null}get isDirty(){return null!==this._additionsHead||null!==this._changesHead||null!==this._removalsHead}forEachItem(t){let e;for(e=this._mapHead;null!==e;e=e._next)t(e)}forEachPreviousItem(t){let e;for(e=this._previousMapHead;null!==e;e=e._nextPrevious)t(e)}forEachChangedItem(t){let e;for(e=this._changesHead;null!==e;e=e._nextChanged)t(e)}forEachAddedItem(t){let e;for(e=this._additionsHead;null!==e;e=e._nextAdded)t(e)}forEachRemovedItem(t){let e;for(e=this._removalsHead;null!==e;e=e._nextRemoved)t(e)}diff(t){if(t){if(!(t instanceof Map||pm(t)))throw new Error(`Error trying to diff '${Ke(t)}'. Only maps and objects are allowed`)}else t=new Map;return this.check(t)?this:null}onDestroy(){}check(t){this._reset();let e=this._mapHead;if(this._appendAfter=null,this._forEach(t,((t,n)=>{if(e&&e.key===n)this._maybeAddToChanges(e,t),this._appendAfter=e,e=e._next;else{const o=this._getOrCreateRecordForKey(n,t);e=this._insertBeforeOrAppend(e,o)}})),e){e._prev&&(e._prev._next=null),this._removalsHead=e;for(let t=e;null!==t;t=t._nextRemoved)t===this._mapHead&&(this._mapHead=null),this._records.delete(t.key),t._nextRemoved=t._next,t.previousValue=t.currentValue,t.currentValue=null,t._prev=null,t._next=null}return this._changesTail&&(this._changesTail._nextChanged=null),this._additionsTail&&(this._additionsTail._nextAdded=null),this.isDirty}_insertBeforeOrAppend(t,e){if(t){const n=t._prev;return e._next=t,e._prev=n,t._prev=e,n&&(n._next=e),t===this._mapHead&&(this._mapHead=e),this._appendAfter=t,t}return this._appendAfter?(this._appendAfter._next=e,e._prev=this._appendAfter):this._mapHead=e,this._appendAfter=e,null}_getOrCreateRecordForKey(t,e){if(this._records.has(t)){const n=this._records.get(t);this._maybeAddToChanges(n,e);const o=n._prev,i=n._next;return o&&(o._next=i),i&&(i._prev=o),n._next=null,n._prev=null,n}const n=new ub(t);return this._records.set(t,n),n.currentValue=e,this._addToAdditions(n),n}_reset(){if(this.isDirty){let t;for(this._previousMapHead=this._mapHead,t=this._previousMapHead;null!==t;t=t._next)t._nextPrevious=t._next;for(t=this._changesHead;null!==t;t=t._nextChanged)t.previousValue=t.currentValue;for(t=this._additionsHead;null!=t;t=t._nextAdded)t.previousValue=t.currentValue;this._changesHead=this._changesTail=null,this._additionsHead=this._additionsTail=null,this._removalsHead=null}}_maybeAddToChanges(t,e){Object.is(e,t.currentValue)||(t.previousValue=t.currentValue,t.currentValue=e,this._addToChanges(t))}_addToAdditions(t){null===this._additionsHead?this._additionsHead=this._additionsTail=t:(this._additionsTail._nextAdded=t,this._additionsTail=t)}_addToChanges(t){null===this._changesHead?this._changesHead=this._changesTail=t:(this._changesTail._nextChanged=t,this._changesTail=t)}_forEach(t,e){t instanceof Map?t.forEach(e):Object.keys(t).forEach((n=>e(t[n],n)))}}class ub{constructor(t){this.key=t,this.previousValue=null,this.currentValue=null,this._nextPrevious=null,this._next=null,this._prev=null,this._nextAdded=null,this._nextRemoved=null,this._nextChanged=null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function gb(){return new hb([new ab])}class hb{constructor(t){this.factories=t}static create(t,e){if(null!=e){const n=e.factories.slice();t=t.concat(n)}return new hb(t)}static extend(t){return{provide:hb,useFactory:e=>hb.create(t,e||gb()),deps:[[hb,new jr,new Br]]}}find(t){const e=this.factories.find((e=>e.supports(t)));if(null!=e)return e;throw new Error(`Cannot find a differ supporting object '${t}' of type '${(function n(t){return t.name||typeof t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t)}'`)}}function bb(){return new yb([new mb])}hb.ɵprov=Sn({token:hb,providedIn:"root",factory:gb});class yb{constructor(t){this.factories=t}static create(t,e){if(e){const n=e.factories.slice();t=t.concat(n)}return new yb(t)}static extend(t){return{provide:yb,useFactory:e=>yb.create(t,e||bb()),deps:[[yb,new jr,new Br]]}}find(t){const e=this.factories.find((e=>e.supports(t)));if(e)return e;throw new Error(`Cannot find a differ supporting object '${t}'`)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function _b(t,e,n,o,i=!1){for(;null!==n;){ngDevMode&&ga(n,63);const a=e[n.index];if(null!==a&&o.push(li(a)),wo(a))for(let t=Oo;t<a.length;t++){const e=a[t],n=e[1].firstChild;null!==n&&_b(e[1],e,n,o)}const r=n.type;if(8&r)_b(t,e,n.child,o);else if(32&r){const t=Tl(n,e);let i;for(;i=t();)o.push(i)}else if(16&r){const t=lc(e,n);if(Array.isArray(t))o.push(...t);else{const n=Nl(e[16]);ngDevMode&&Uo(n),_b(n[1],n,t,o,!0)}}n=i?n.projectionNext:n.next}return o}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */yb.ɵprov=Sn({token:yb,providedIn:"root",factory:bb});class Cb{constructor(t,e){this._lView=t,this._cdRefInjectingView=e,this._appRef=null,this._attachedToViewContainer=!1}get rootNodes(){const t=this._lView,e=t[1];return _b(e,t,e.firstChild,[])}get context(){return this._lView[8]}set context(t){this._lView[8]=t}get destroyed(){return 256==(256&this._lView[2])}destroy(){if(this._appRef)this._appRef.detachView(this);else if(this._attachedToViewContainer){const t=this._lView[3];if(wo(t)){const e=t[8],n=e?e.indexOf(this):-1;n>-1&&(ngDevMode&&gn(n,t.indexOf(this._lView)-Oo,"An attached view should be in the same position within its container as its ViewRef in the VIEW_REFS array."),Yl(t,n),ur(e,n))}this._attachedToViewContainer=!1}ql(this._lView[1],this._lView)}onDestroy(t){Td(this._lView[1],this._lView,null,t)}markForCheck(){op(this._cdRefInjectingView||this._lView)}detach(){this._lView[2]&=-129}reattach(){this._lView[2]|=128}detectChanges(){ap(this._lView[1],this._lView,this.context)}checkNoChanges(){!(function t(e,n,o){Ti(!0);try{ap(e,n,o)}finally{Ti(!1)}})(this._lView[1],this._lView,this.context)}attachToViewContainerRef(){if(this._appRef)throw new Error("This view is already attached directly to the ApplicationRef!");this._attachedToViewContainer=!0}detachFromAppRef(){this._appRef=null,(function t(e,n){mc(e,n,n[11],2,null,null)})(this._lView[1],this._lView)}attachToAppRef(t){if(this._attachedToViewContainer)throw new Error("This view is already attached to a ViewContainer!");this._appRef=t}}class Mb extends Cb{constructor(t){super(t),this._view=t}detectChanges(){rp(this._view)}checkNoChanges(){!(function t(e){Ti(!0);try{rp(e)}finally{Ti(!1)}})(this._view)}get context(){return null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const vb=function xb(t){return(function e(t,n,o){if(So(t)&&!o){const e=fi(t.index,n);return new Cb(e,e)}if(47&t.type){return new Cb(n[16],n)}return null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(wi(),xi(),16==(16&t))};class Ob{}Ob.__NG_ELEMENT_ID__=vb;const Pb=[new mb],wb=[new ab],kb=new hb(wb),Sb=new yb(Pb),Db=function Rb(){return Nb(wi(),xi())};class Eb{}Eb.__NG_ELEMENT_ID__=Db;const Ab=Eb,Tb=class extends Ab{constructor(t,e,n){super(),this._declarationLView=t,this._declarationTContainer=e,this.elementRef=n}createEmbeddedView(t){const e=this._declarationTContainer.tViews,n=yd(this._declarationLView,e,t,16,null,e.declTNode,null,null,null,null),o=this._declarationLView[this._declarationTContainer.index];ngDevMode&&Io(o),n[17]=o;const i=this._declarationLView[19];return null!==i&&(n[19]=i.createEmbeddedView(e)),vd(e,n,t),new Cb(n)}};function Nb(t,e){return 4&t.type?(ngDevMode&&xn(t.tViews,"TView must be allocated"),new Tb(e,t,qh(t,e))):null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class zb{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Ib=function Hb(){return Ub(wi(),xi())};class Fb{}Fb.__NG_ELEMENT_ID__=Ib;const Lb=Fb,Bb=class extends Lb{constructor(t,e,n){super(),this._lContainer=t,this._hostTNode=e,this._hostLView=n}get element(){return qh(this._hostTNode,this._hostLView)}get injector(){return new ja(this._hostTNode,this._hostLView)}get parentInjector(){const t=Ra(this._hostTNode,this._hostLView);if(Ma(t)){const e=xa(t,this._hostLView),n=va(t);ngDevMode&&Wo(e,n);return new ja(e[1].data[n+8],e)}return new ja(null,this._hostLView)}clear(){for(;this.length>0;)this.remove(this.length-1)}get(t){const e=Vb(this._lContainer);return null!==e&&e[t]||null}get length(){return this._lContainer.length-Oo}createEmbeddedView(t,e,n){const o=t.createEmbeddedView(e||{});return this.insert(o,n),o}createComponent(t,e,n,o,i){const a=n||this.parentInjector;if(!i&&null==t.ngModule&&a){const t=a.get(zb,null);t&&(i=t)}const r=t.create(a,o,void 0,i);return this.insert(r.hostView,e),r}insert(t,e){const n=t._lView,o=n[1];if(ngDevMode&&t.destroyed)throw new Error("Cannot insert a destroyed View in a ViewContainer!");if((function i(t){return wo(t[3])})(n)){const e=this.indexOf(t);if(-1!==e)this.detach(e);else{const e=n[3];ngDevMode&&gn(wo(e),!0,"An attached view should have its PARENT point to a container.");const o=new Bb(e,e[6],e[3]);o.detach(o.indexOf(t))}}const a=this._adjustIndex(e),r=this._lContainer;Wl(o,n,r,a);const s=cc(a,r),l=n[11],c=tc(l,r[7]);return null!==c&&(function d(t,e,n,o,i,a){o[0]=i,o[6]=e,mc(t,o,n,1,i,a)})(o,r[6],l,n,c,s),t.attachToViewContainerRef(),fr(jb(r),a,t),t}move(t,e){if(ngDevMode&&t.destroyed)throw new Error("Cannot move a destroyed View in a ViewContainer!");return this.insert(t,e)}indexOf(t){const e=Vb(this._lContainer);return null!==e?e.indexOf(t):-1}remove(t){const e=this._adjustIndex(t,-1),n=Yl(this._lContainer,e);n&&(ur(jb(this._lContainer),e),ql(n[1],n))}detach(t){const e=this._adjustIndex(t,-1),n=Yl(this._lContainer,e);return n&&null!=ur(jb(this._lContainer),e)?new Cb(n):null}_adjustIndex(t,e=0){return null==t?this.length+e:(ngDevMode&&(Mn(t,-1,`ViewRef index must be positive, got ${t}`),_n(t,this.length+1+e,"index")),t)}};function Vb(t){return t[8]}function jb(t){return t[8]||(t[8]=[])}function Ub(t,e){let n;ngDevMode&&ga(t,15);const o=e[t.index];if(wo(o))n=o;else{let i;if(8&t.type)i=li(o);else{const n=e[11];ngDevMode&&ngDevMode.rendererCreateComment++,i=n.createComment(ngDevMode?"container":"");const o=di(t,e);Jl(n,tc(n,o),i,(function i(t,e){return ri(t)?t.nextSibling(e):e.nextSibling})(n,o),!1)}e[t.index]=n=Qd(o,e,i,t),np(e,n)}return new Bb(n,t,e)}const Wb=new Map;function Gb(t){let e=Wb.get(t);return e||(e=Ke(t)+"_"+Wb.size,Wb.set(t,e)),e}Gb(Np),Gb(yp),Gb(zb),
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
Gb($h),Gb(Kh),Gb(Fb),Gb(Eb),Gb(Ob),Gb(Np),Gb(yp);const Yb={};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class qb extends Gh{constructor(t){super(),this.ngModule=t}resolveComponentFactory(t){ngDevMode&&(function e(t,n="Type passed in is not ComponentType, it does not have 'ɵcmp' property."){yo(t)||On(n)})(t);const n=yo(t);return new Kb(n,this.ngModule)}}function Zb(t){const e=[];for(let n in t)if(t.hasOwnProperty(n)){e.push({propName:t[n],templateName:n})}return e}const Xb=new er("SCHEDULER_TOKEN",{providedIn:"root",factory:()=>xl});class Kb extends Uh{constructor(t,e){super(),this.componentDef=t,this.ngModule=e,this.componentType=t.type,this.selector=(function n(t){return t.map(kc).join(",")})(t.selectors),this.ngContentSelectors=t.ngContentSelectors?t.ngContentSelectors:[],this.isBoundToModule=!!e}get inputs(){return Zb(this.componentDef.inputs)}get outputs(){return Zb(this.componentDef.outputs)}create(t,e,n,o){const i=(o=o||this.ngModule)?(function a(t,e){return{get:(n,o,i)=>{const a=t.get(n,Yb,i);return a!==Yb||o===Yb?a:e.get(n,o,i)}}})(t,o.injector):t,r=i.get(Qh,si),s=i.get(nb,null),l=r.createRenderer(null,this.componentDef),c=this.componentDef.selectors[0][0]||"div",d=n?Ad(l,n,this.componentDef.encapsulation):Ul(r.createRenderer(null,this.componentDef),c,(function p(t){const e=t.toLowerCase();return"svg"===e?ei:"math"===e?ni:null})(c)),m=this.componentDef.onPush?576:528,f=(function u(t,e){return{components:[],scheduler:t||xl,clean:cp,playerHandler:e||null,flags:0}})(),g=Rd(0,null,null,1,0,null,null,null,null,null),h=yd(null,g,f,m,null,null,r,l,s,i);let b,y;qi(h);try{const t=(function o(t,e,n,i,a,r){const s=n[1];ngDevMode&&wn(n,20),n[20]=t;const l=_d(s,20,2,"#host",null),c=l.mergedAttrs=e.hostAttrs;null!==c&&(hp(l,c,!0),null!==t&&(ha(a,t,c),null!==l.classes&&gc(a,t,l.classes),null!==l.styles&&uc(a,t,l.styles)));const d=i.createRenderer(t,e),p=yd(n,Dd(e),null,e.onPush?64:16,n[20],l,i,d,r||null,null);return s.firstCreatePass&&(Ea(ka(l,n),s,e.type),jd(s,l),Wd(l,n.length,1)),np(n,p),n[20]=p})(d,this.componentDef,h,r,l);if(d)if(n)ha(l,d,["ng-version",ib.full]);else{const{attrs:t,classes:e}=(function n(t){const e=[],n=[];let o=1,i=2;for(;o<t.length;){let a=t[o];if("string"==typeof a)2===i?""!==a&&e.push(a,t[++o]):8===i&&n.push(a);else{if(!vc(i))break;i=a}o++}return{attrs:e,classes:n}})(this.componentDef.selectors[0]);t&&ha(l,d,t),e&&e.length>0&&gc(l,d,e.join(" "))}if(y=pi(g,vo),void 0!==e){const t=y.projection=[];for(let n=0;n<this.ngContentSelectors.length;n++){const o=e[n];t.push(null!=o?Array.from(o):null)}}b=Xp(t,this.componentDef,h,f,[Kp]),vd(g,h,null)}finally{Qi()}return new Jb(this.componentType,b,qh(y,h),h,y)}}new qb;class Jb extends class{}{constructor(t,e,n,o,i){super(),this.location=n,this._rootLView=o,this._tNode=i,this.instance=e,this.hostView=this.changeDetectorRef=new Mb(o),this.componentType=t}get injector(){return new ja(this._tNode,this._rootLView)}destroy(){this.hostView.destroy()}onDestroy(t){this.hostView.onDestroy(t)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Qb(t,e,n,o){return Vn((()=>{const i=t;null!==e&&(i.hasOwnProperty("decorators")&&void 0!==i.decorators?i.decorators.push(...e):i.decorators=e),null!==n&&(i.ctorParameters=n),null!==o&&(i.propDecorators=i.hasOwnProperty("propDecorators")&&void 0!==i.propDecorators?Object.assign(Object.assign({},i.propDecorators),o):o)}))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const $b=new Map;function ty(t){const e=new Set;!(function t(n){const o=Mo(n,!0),i=o.id;if(null!==i){const t=$b.get(i);!(function a(t,e,n){if(e&&e!==n)throw new Error(`Duplicate module registered for ${t} - ${Ke(e)} vs ${Ke(e.name)}`)})(i,t,n),$b.set(i,n)}const r=Sl(o.imports);for(const n of r)e.has(n)||(e.add(n),t(n))})(t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class ey extends zb{constructor(t,e){super(),this._parent=e,this._bootstrapComponents=[],this.injector=this,this.destroyCbs=[],this.componentFactoryResolver=new qb(this);const n=Mo(t);ngDevMode&&xn(n,`NgModule '${Ke(t)}' is not a subtype of 'NgModuleType'.`);const o=(function i(t){return t[oo]||null})(t);o&&Pg(o),this._bootstrapComponents=Sl(n.bootstrap),this._r3Injector=Pp(t,e,[{provide:zb,useValue:this},{provide:Gh,useValue:this.componentFactoryResolver}],Ke(t)),this._r3Injector._resolveInjectorDefTypes(),this.instance=this.get(t)}get(t,e=Np.THROW_IF_NOT_FOUND,n=Hn.Default){return t===Np||t===zb||t===yp?this:this._r3Injector.get(t,e,n)}destroy(){ngDevMode&&xn(this.destroyCbs,"NgModule already destroyed");const t=this._r3Injector;!t.destroyed&&t.destroy(),this.destroyCbs.forEach((t=>t())),this.destroyCbs=null}onDestroy(t){ngDevMode&&xn(this.destroyCbs,"NgModule already destroyed"),this.destroyCbs.push(t)}}class ny extends class{}{constructor(t){super(),this.moduleType=t;null!==Mo(t)&&ty(t)}create(t){return new ey(this.moduleType,t)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function oy(t,e,n){const o=Ni()+t,i=xi();return i[o]===Sc?mm(i,o,n?e.call(n):e()):fm(i,o)}function iy(t,e,n,o){return uy(xi(),Ni(),t,e,n,o)}function ay(t,e,n,o,i){return gy(xi(),Ni(),t,e,n,o,i)}function ry(t,e,n,o,i,a){return hy(xi(),Ni(),t,e,n,o,i,a)}function sy(t,e,n,o,i,a,r){return by(xi(),Ni(),t,e,n,o,i,a,r)}function ly(t,e,n,o,i,a,r,s){const l=Ni()+t,c=xi(),d=bm(c,l,n,o,i,a);return um(c,l+4,r)||d?mm(c,l+5,s?e.call(s,n,o,i,a,r):e(n,o,i,a,r)):fm(c,l+5)}function cy(t,e,n,o,i,a,r,s,l){const c=Ni()+t,d=xi(),p=bm(d,c,n,o,i,a);return gm(d,c+4,r,s)||p?mm(d,c+6,l?e.call(l,n,o,i,a,r,s):e(n,o,i,a,r,s)):fm(d,c+6)}function dy(t,e,n,o,i,a,r,s,l,c){const d=Ni()+t,p=xi();let m=bm(p,d,n,o,i,a);return hm(p,d+4,r,s,l)||m?mm(p,d+7,c?e.call(c,n,o,i,a,r,s,l):e(n,o,i,a,r,s,l)):fm(p,d+7)}function py(t,e,n,o,i,a,r,s,l,c,d){const p=Ni()+t,m=xi(),f=bm(m,p,n,o,i,a);return bm(m,p+4,r,s,l,c)||f?mm(m,p+8,d?e.call(d,n,o,i,a,r,s,l,c):e(n,o,i,a,r,s,l,c)):fm(m,p+8)}function my(t,e,n,o){return yy(xi(),Ni(),t,e,n,o)}function fy(t,e){ngDevMode&&wn(t,e);const n=t[e];return n===Sc?void 0:n}function uy(t,e,n,o,i,a){const r=e+n;return um(t,r,i)?mm(t,r+1,a?o.call(a,i):o(i)):fy(t,r+1)}function gy(t,e,n,o,i,a,r){const s=e+n;return gm(t,s,i,a)?mm(t,s+2,r?o.call(r,i,a):o(i,a)):fy(t,s+2)}function hy(t,e,n,o,i,a,r,s){const l=e+n;return hm(t,l,i,a,r)?mm(t,l+3,s?o.call(s,i,a,r):o(i,a,r)):fy(t,l+3)}function by(t,e,n,o,i,a,r,s,l){const c=e+n;return bm(t,c,i,a,r,s)?mm(t,c+4,l?o.call(l,i,a,r,s):o(i,a,r,s)):fy(t,c+4)}function yy(t,e,n,o,i,a){let r=e+n,s=!1;for(let e=0;e<i.length;e++)um(t,r++,i[e])&&(s=!0);return s?mm(t,r,o.apply(a,i)):fy(t,r)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function _y(t,e){const n=Oi();let o;const i=t+vo;n.firstCreatePass?(o=(function a(t,e){if(e)for(let n=e.length-1;n>=0;n--){const o=e[n];if(t===o.name)return o}throw new nn("302",`The pipe '${t}' could not be found!`)})(e,n.pipeRegistry),n.data[i]=o,o.onDestroy&&(n.destroyHooks||(n.destroyHooks=[])).push(i,o.onDestroy)):o=n.data[i];const r=o.factory||(o.factory=Go(o.type,!0)),s=Ln(gf);try{const t=Pa(!1),e=r();return Pa(t),(function l(t,e,n,o){n>=t.data.length&&(t.data[n]=null,t.blueprint[n]=null),e[n]=o})(n,xi(),i,e),e}finally{Ln(s)}}function Cy(t,e,n){const o=t+vo,i=xi(),a=mi(i,o);return wy(i,Py(i,o)?uy(i,Ni(),e,a.transform,n,a):a.transform(n))}function My(t,e,n,o){const i=t+vo,a=xi(),r=mi(a,i);return wy(a,Py(a,i)?gy(a,Ni(),e,r.transform,n,o,r):r.transform(n,o))}function vy(t,e,n,o,i){const a=t+vo,r=xi(),s=mi(r,a);return wy(r,Py(r,a)?hy(r,Ni(),e,s.transform,n,o,i,s):s.transform(n,o,i))}function xy(t,e,n,o,i,a){const r=t+vo,s=xi(),l=mi(s,r);return wy(s,Py(s,r)?by(s,Ni(),e,l.transform,n,o,i,a,l):l.transform(n,o,i,a))}function Oy(t,e,n){const o=t+vo,i=xi(),a=mi(i,o);return wy(i,Py(i,o)?yy(i,Ni(),e,a.transform,n,a):a.transform.apply(a,n))}function Py(t,e){return t[1].data[e].pure}function wy(t,e){if(cm.isWrapped(e)){e=cm.unwrap(e);t[zi()]=Sc}return e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function ky(t){return e=>{setTimeout(t,void 0,e)}}const Sy=class extends B{constructor(t=!1){super(),this.__isAsync=t}emit(t){super.next(t)}subscribe(t,e,n){var o,i,a;let r=t,s=e||(()=>null),l=n;if(t&&"object"==typeof t){const e=t;r=null===(o=e.next)||void 0===o?void 0:o.bind(e),s=null===(i=e.error)||void 0===i?void 0:i.bind(e),l=null===(a=e.complete)||void 0===a?void 0:a.bind(e)}this.__isAsync&&(s=ky(s),r&&(r=ky(r)),l&&(l=ky(l)));const c=super.subscribe({next:r,error:s,complete:l});return t instanceof u&&t.add(c),c}};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Dy(){return this._results[sm()]()}class Ry{constructor(t=!1){this._emitDistinctChangesOnly=t,this.dirty=!0,this._results=[],this._changesDetected=!1,this._changes=null,this.length=0,this.first=void 0,this.last=void 0;const e=sm(),n=Ry.prototype;n[e]||(n[e]=Dy)}get changes(){return this._changes||(this._changes=new Sy)}get(t){return this._results[t]}map(t){return this._results.map(t)}filter(t){return this._results.filter(t)}find(t){return this._results.find(t)}reduce(t,e){return this._results.reduce(t,e)}forEach(t){this._results.forEach(t)}some(t){return this._results.some(t)}toArray(){return this._results.slice()}toString(){return this._results.toString()}reset(t,e){const n=this;n.dirty=!1;const o=pr(t);(this._changesDetected=!(function i(t,e,n){if(t.length!==e.length)return!1;for(let o=0;o<t.length;o++){let 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Ty{constructor(t,e,n=null){this.predicate=t,this.flags=e,this.read=n}}class Ny{constructor(t=[]){this.queries=t}elementStart(t,e){ngDevMode&&Lo(t,"Queries should collect results on the first template pass only");for(let n=0;n<this.queries.length;n++)this.queries[n].elementStart(t,e)}elementEnd(t){for(let e=0;e<this.queries.length;e++)this.queries[e].elementEnd(t)}embeddedTView(t){let e=null;for(let n=0;n<this.length;n++){const o=null!==e?e.length:0,i=this.getByIndex(n).embeddedTView(t,o);i&&(i.indexInDeclarationView=n,null!==e?e.push(i):e=[i])}return null!==e?new Ny(e):null}template(t,e){ngDevMode&&Lo(t,"Queries should collect results on the first template pass only");for(let n=0;n<this.queries.length;n++)this.queries[n].template(t,e)}getByIndex(t){return ngDevMode&&wn(this.queries,t),this.queries[t]}get length(){return this.queries.length}track(t){this.queries.push(t)}}class 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o=t.data,a=n.matches,r=[];for(let t=0;t<a.length;t+=2){const i=a[t];if(i<0)r.push(null);else{ngDevMode&&wn(o,i);r.push(Hy(e,o[i],a[t+1],n.metadata.read))}}i.matches=r}return i.matches}function Ly(t,e,n,o){const i=t.queries.getByIndex(n),a=i.matches;if(null!==a){const r=Fy(t,e,i,n);for(let t=0;t<a.length;t+=2){const n=a[t];if(n>0)o.push(r[t/2]);else{const i=a[t+1],r=e[-n];ngDevMode&&Io(r);for(let t=Oo;t<r.length;t++){const e=r[t];e[17]===e[3]&&Ly(e[1],e,i,o)}if(null!==r[9]){const t=r[9];for(let e=0;e<t.length;e++){const n=t[e];Ly(n[1],n,i,o)}}}}}return o}function By(t){const e=xi(),n=Oi(),o=Ui();Wi(o+1);const i=Yy(n,o);if(t.dirty&&ui(e)===(2==(2&i.metadata.flags))){if(null===i.matches)t.reset([]);else{const a=i.crossesNgTemplate?Ly(n,e,o,[]):Fy(n,e,i,o);t.reset(a,Jh),t.notifyOnChanges()}return!0}return!1}function Vy(t,e,n){ngDevMode&&pn(e,"Expecting flags");const o=Oi();o.firstCreatePass&&(Gy(o,new Ty(t,e,n),-1),2==(2&e)&&(o.staticViewQueries=!0)),Wy(o,xi(),e)}function jy(t,e,n,o){ngDevMode&&pn(n,"Expecting flags");const i=Oi();if(i.firstCreatePass){const a=wi();Gy(i,new Ty(e,n,o),a.index),(function a(t,e){const n=t.contentQueries||(t.contentQueries=[]);e!==(n.length?n[n.length-1]:-1)&&n.push(t.queries.length-1,e)})(i,t),2==(2&n)&&(i.staticContentQueries=!0)}Wy(i,xi(),n)}function Uy(){return(function t(e,n){return ngDevMode&&xn(e[19],"LQueries should be defined when trying to load a query"),ngDevMode&&wn(e[19].queries,n),e[19].queries[n].queryList})(xi(),Ui())}function Wy(t,e,n){const o=new Ry(4==(4&n));Td(t,e,o,o.destroy),null===e[19]&&(e[19]=new Ay),e[19].queries.push(new Ey(o))}function Gy(t,e,n){null===t.queries&&(t.queries=new Ny),t.queries.track(new zy(e,n))}function Yy(t,e){return ngDevMode&&xn(t.queries,"TQueries must be defined to retrieve a TQuery"),t.queries.getByIndex(e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function qy(t,e){return Nb(t,e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Zy={"ɵɵattribute":ym,"ɵɵattributeInterpolate1":Sm,"ɵɵattributeInterpolate2":Dm,"ɵɵattributeInterpolate3":Rm,"ɵɵattributeInterpolate4":Em,"ɵɵattributeInterpolate5":Am,"ɵɵattributeInterpolate6":Tm,"ɵɵattributeInterpolate7":Nm,"ɵɵattributeInterpolate8":zm,"ɵɵattributeInterpolateV":Im,"ɵɵdefineComponent":so,"ɵɵdefineDirective":ho,"ɵɵdefineInjectable":Sn,"ɵɵdefineInjector":Dn,"ɵɵdefineNgModule":fo,"ɵɵdefinePipe":bo,"ɵɵdirectiveInject":gf,"ɵɵgetInheritedFactory":Ua,"ɵɵinject":Tr,"ɵɵinjectAttribute":Ga,"ɵɵinvalidFactory":hf,"ɵɵinvalidFactoryDep":Nr,"ɵɵtemplateRefExtractor":qy,"ɵɵNgOnChangesFeature":qo,"ɵɵProvidersFeature":jh,"ɵɵCopyDefinitionFeature":am,"ɵɵInheritDefinitionFeature":Qp,"ɵɵnextContext":zf,"ɵɵnamespaceHTML":aa,"ɵɵnamespaceMathML":ia,"ɵɵnamespaceSVG":oa,"ɵɵenableBindings":Mi,"ɵɵdisableBindings":vi,"ɵɵelementStart":Cf,"ɵɵelementEnd":Mf,"ɵɵelement":vf,"ɵɵelementContainerStart":xf,"ɵɵelementContainerEnd":Of,"ɵɵelementContainer":Pf,"ɵɵpureFunction0":oy,"ɵɵpureFunction1":iy,"ɵɵpureFunction2":ay,"ɵɵpureFunction3":ry,"ɵɵpureFunction4":sy,"ɵɵpureFunction5":ly,"ɵɵpureFunction6":cy,"ɵɵpureFunction7":dy,"ɵɵpureFunction8":py,"ɵɵpureFunctionV":my,"ɵɵgetCurrentView":wf,"ɵɵrestoreView":Pi,"ɵɵlistener":Rf,"ɵɵprojection":Ff,"ɵɵsyntheticHostProperty":dg,"ɵɵsyntheticHostListener":Ef,"ɵɵpipeBind1":Cy,"ɵɵpipeBind2":My,"ɵɵpipeBind3":vy,"ɵɵpipeBind4":xy,"ɵɵpipeBindV":Oy,"ɵɵprojectionDef":Hf,"ɵɵhostProperty":cg,"ɵɵproperty":bf,"ɵɵpropertyInterpolate":Lf,"ɵɵpropertyInterpolate1":Bf,"ɵɵpropertyInterpolate2":Vf,"ɵɵpropertyInterpolate3":jf,"ɵɵpropertyInterpolate4":Uf,"ɵɵpropertyInterpolate5":Wf,"ɵɵpropertyInterpolate6":Gf,"ɵɵpropertyInterpolate7":Yf,"ɵɵpropertyInterpolate8":qf,"ɵɵpropertyInterpolateV":Zf,"ɵɵpipe":_y,"ɵɵqueryRefresh":By,"ɵɵviewQuery":Vy,"ɵɵloadQuery":Uy,"ɵɵcontentQuery":jy,"ɵɵreference":Fm,"ɵɵclassMap":mu,"ɵɵclassMapInterpolate1":Iu,"ɵɵclassMapInterpolate2":Hu,"ɵɵclassMapInterpolate3":Fu,"ɵɵclassMapInterpolate4":Lu,"ɵɵclassMapInterpolate5":Bu,"ɵɵclassMapInterpolate6":Vu,"ɵɵclassMapInterpolate7":ju,"ɵɵclassMapInterpolate8":Uu,"ɵɵclassMapInterpolateV":Wu,"ɵɵstyleMap":du,"ɵɵstyleMapInterpolate1":Gu,"ɵɵstyleMapInterpolate2":Yu,"ɵɵstyleMapInterpolate3":qu,"ɵɵstyleMapInterpolate4":Zu,"ɵɵstyleMapInterpolate5":Xu,"ɵɵstyleMapInterpolate6":Ku,"ɵɵstyleMapInterpolate7":Ju,"ɵɵstyleMapInterpolate8":Qu,"ɵɵstyleMapInterpolateV":$u,"ɵɵstyleProp":lu,"ɵɵstylePropInterpolate1":tg,"ɵɵstylePropInterpolate2":eg,"ɵɵstylePropInterpolate3":ng,"ɵɵstylePropInterpolate4":og,"ɵɵstylePropInterpolate5":ig,"ɵɵstylePropInterpolate6":ag,"ɵɵstylePropInterpolate7":rg,"ɵɵstylePropInterpolate8":sg,"ɵɵstylePropInterpolateV":lg,"ɵɵclassProp":cu,"ɵɵadvance":Dc,"ɵɵtemplate":Hm,"ɵɵtext":Pu,"ɵɵtextInterpolate":wu,"ɵɵtextInterpolate1":ku,"ɵɵtextInterpolate2":Su,"ɵɵtextInterpolate3":Du,"ɵɵtextInterpolate4":Ru,"ɵɵtextInterpolate5":Eu,"ɵɵtextInterpolate6":Au,"ɵɵtextInterpolate7":Tu,"ɵɵtextInterpolate8":Nu,"ɵɵtextInterpolateV":zu,"ɵɵi18n":Rh,"ɵɵi18nAttributes":Eh,"ɵɵi18nExp":Ah,"ɵɵi18nStart":Sh,"ɵɵi18nEnd":Dh,"ɵɵi18nApply":Th,"ɵɵi18nPostprocess":Nh,"ɵɵresolveWindow":Ol,"ɵɵresolveDocument":Pl,"ɵɵresolveBody":wl,"ɵɵsetComponentScope":lo,"ɵɵsetNgModuleScope":uo,"ɵɵsanitizeHtml":qs,"ɵɵsanitizeStyle":Zs,"ɵɵsanitizeResourceUrl":Ks,"ɵɵsanitizeScript":Js,"ɵɵsanitizeUrl":Xs,"ɵɵsanitizeUrlOrResourceUrl":tl,"ɵɵtrustConstantHtml":Qs,"ɵɵtrustConstantResourceUrl":$s,forwardRef:$e,resolveForwardRef:tn};let Xy=null;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Ky=[];let Jy=!1;function Qy(t){return Array.isArray(t)?t.every(Qy):!!tn(t)}function $y(t,e,n){if(o_.get(t))return;let o;if(o_.set(t,!0),t=tn(t),n){if(o=Mo(t),!o)throw new Error(`Unexpected value '${t.name}' imported by the module '${n.name}'. Please add an @NgModule annotation.`)}else o=Mo(t,!0);const i=[],a=Sl(o.declarations),r=Sl(o.imports);pr(r).map(t_).forEach((e=>{h(e,t),$y(e,!1,t)}));const s=Sl(o.exports);a.forEach((function l(e){e=tn(e);yo(e)||_o(e)||Co(e)||i.push(`Unexpected value '${sn(e)}' declared by the module '${sn(t)}'. Please add a @Pipe/@Directive/@Component annotation.`)})),a.forEach((function c(t){const e=_o(t=tn(t));!yo(t)&&e&&0==e.selectors.length&&i.push(`Directive ${sn(t)} has no selector, please add it!`)}));const d=[...a.map(tn),...pr(r.map(i_)).map(tn)];s.forEach((function p(e){const n=(yo(e=tn(e))?"component":_o(e)&&"directive")||Co(e)&&"pipe";n&&-1===d.lastIndexOf(e)&&i.push(`Can't export ${n} ${sn(e)} from ${sn(t)} as it was neither declared nor imported!`)})),a.forEach((n=>(function o(e,n){e=tn(e);const o=n_.get(e);if(o&&o!==t){if(!n){const n=[o,t].map(sn).sort();i.push(`Type ${sn(e)} is part of the declarations of 2 modules: ${n[0]} and ${n[1]}! Please consider moving ${sn(e)} to a higher module that imports ${n[0]} and ${n[1]}. You can also create a new NgModule that exports and includes ${sn(e)} then import that NgModule in ${n[0]} and ${n[1]}.`)}}else n_.set(e,t)})(n,e))),a.forEach((function m(t){if(yo(t=tn(t))){const e=e_(t,"Component");e&&e.entryComponents&&mr(e.entryComponents,g)}}));const f=e_(t,"NgModule");if(f&&(f.imports&&pr(f.imports).map(t_).forEach((e=>{h(e,t),$y(e,!1,t)})),f.bootstrap&&mr(f.bootstrap,(function u(t){yo(t=tn(t))||i.push(`${sn(t)} cannot be used as an entry component.`)})),f.bootstrap&&mr(f.bootstrap,g),f.entryComponents&&mr(f.entryComponents,g)),i.length)throw new Error(i.join("\n"));function g(t){t=tn(t);n_.get(t)||i.push(`Component ${sn(t)} is not part of any NgModule or the module has not been imported into your module.`)}function h(t,e){if(yo(t=tn(t))||_o(t))throw new Error(`Unexpected directive '${t.name}' imported by the module '${e.name}'. Please add an @NgModule annotation.`);if(Co(t))throw new Error(`Unexpected pipe '${t.name}' imported by the module '${e.name}'. Please add an @NgModule annotation.`)}}function t_(t){return(t=tn(t)).ngModule||t}function e_(t,e){let n=null;return o(t.__annotations__),o(t.decorators),n;function o(t){t&&t.forEach(i)}function i(t){if(!n){if(Object.getPrototypeOf(t).ngMetadataName==e)n=t;else if(t.type){Object.getPrototypeOf(t.type).ngMetadataName==e&&(n=t.args[0])}}}}let n_=new WeakMap,o_=new WeakMap;function i_(t){return[...pr(Sl(Mo(t=tn(t),!0).exports).map((t=>Mo(t)?($y(t,!1),i_(t)):t)))]}function a_(t,e){const n=pr(e.declarations||Qn),o=s_(t);n.forEach((e=>{if(e.hasOwnProperty($n)){r_(yo(e),o)}else e.hasOwnProperty(to)||e.hasOwnProperty(eo)||(e.ngSelectorScope=t)}))}function r_(t,e){t.directiveDefs=()=>Array.from(e.compilation.directives).map((t=>t.hasOwnProperty($n)?yo(t):_o(t))).filter((t=>!!t)),t.pipeDefs=()=>Array.from(e.compilation.pipes).map((t=>Co(t))),t.schemas=e.schemas,t.tView=null}function s_(t){if(!c_(t))throw new Error(`${t.name} does not have a module def (ɵmod property)`);const e=Mo(t);if(null!==e.transitiveCompileScopes)return e.transitiveCompileScopes;const n={schemas:e.schemas||null,compilation:{directives:new Set,pipes:new Set},exported:{directives:new Set,pipes:new Set}};return Sl(e.imports).forEach((t=>{const e=t;if(!c_(e))throw new Error(`Importing ${e.name} which does not have a ɵmod property`);const o=s_(e);o.exported.directives.forEach((t=>n.compilation.directives.add(t))),o.exported.pipes.forEach((t=>n.compilation.pipes.add(t)))})),Sl(e.declarations).forEach((t=>{Co(t)?n.compilation.pipes.add(t):n.compilation.directives.add(t)})),Sl(e.exports).forEach((t=>{const e=t;if(c_(e)){const t=s_(e);t.exported.directives.forEach((t=>{n.compilation.directives.add(t),n.exported.directives.add(t)})),t.exported.pipes.forEach((t=>{n.compilation.pipes.add(t),n.exported.pipes.add(t)}))}else Co(e)?n.exported.pipes.add(e):n.exported.directives.add(e)})),e.transitiveCompileScopes=n,n}function l_(t){return(function e(t){return void 0!==t.ngModule})(t)?t.ngModule:t}function c_(t){return!!Mo(t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let d_=0;function p_(t,e){let n=null;f_(t,e||{}),Object.defineProperty(t,to,{get:()=>{if(null===n){const o=m_(t,e||{}),i=lr({usage:0,kind:"directive",type:t});n=i.compileDirective(Zy,o.sourceMapUrl,o.metadata)}return n},configurable:!!ngDevMode})}function m_(t,e){const n=t&&t.name,o=`ng:///${n}/ɵdir.js`,i=lr({usage:0,kind:"directive",type:t}),a=g_(t,e);return a.typeSourceSpan=i.createParseSourceSpan("Directive",n,o),a.usesInheritance&&h_(t),{metadata:a,sourceMapUrl:o}}function f_(t,e){let n=null;Object.defineProperty(t,io,{get:()=>{if(null===n){const o=m_(t,e),i=lr({usage:0,kind:"directive",type:t});n=i.compileFactory(Zy,`ng:///${t.name}/ɵfac.js`,{name:o.metadata.name,type:o.metadata.type,typeArgumentCount:0,deps:Yr(t),target:i.FactoryTarget.Directive})}return n},configurable:!!ngDevMode})}function u_(t){return Object.getPrototypeOf(t.prototype)===Object.prototype}function g_(t,e){const n=Gr(),o=n.ownPropMetadata(t);return{name:t.name,type:t,selector:void 0!==e.selector?e.selector:null,host:e.host||Jn,propMetadata:o,inputs:e.inputs||Qn,outputs:e.outputs||Qn,queries:__(t,o,C_),lifecycle:{usesOnChanges:n.hasLifecycleHook(t,"ngOnChanges")},typeSourceSpan:null,usesInheritance:!u_(t),exportAs:(i=e.exportAs,void 0===i?null:x_(i)),providers:e.providers||null,viewQueries:__(t,o,M_)};var i}function h_(t){const e=Object.prototype;let n=Object.getPrototypeOf(t.prototype).constructor;for(;n&&n!==e;)_o(n)||yo(n)||!P_(n)||p_(n,null),n=Object.getPrototypeOf(n)}function b_(t){return"string"==typeof t?x_(t):tn(t)}function y_(t,e){return{propertyName:t,predicate:b_(e.selector),descendants:e.descendants,first:e.first,read:e.read?e.read:null,static:!!e.static,emitDistinctChangesOnly:!!e.emitDistinctChangesOnly}}function __(t,e,n){const o=[];for(const i in e)if(e.hasOwnProperty(i)){const a=e[i];a.forEach((e=>{if(n(e)){if(!e.selector)throw new Error(`Can't construct a query for the property "${i}" of "${sn(t)}" since the query selector wasn't defined.`);if(a.some(v_))throw new Error("Cannot combine @Input decorators with query decorators");o.push(y_(i,e))}}))}return o}function C_(t){const e=t.ngMetadataName;return"ContentChild"===e||"ContentChildren"===e}function M_(t){const e=t.ngMetadataName;return"ViewChild"===e||"ViewChildren"===e}function v_(t){return"Input"===t.ngMetadataName}function x_(t){return t.split(",").map((t=>t.trim()))}const O_=["ngOnChanges","ngOnInit","ngOnDestroy","ngDoCheck","ngAfterViewInit","ngAfterViewChecked","ngAfterContentInit","ngAfterContentChecked"];function P_(t){const e=Gr();if(O_.some((n=>e.hasLifecycleHook(t,n))))return!0;const n=e.propMetadata(t);for(const t in n){const e=n[t];for(let t=0;t<e.length;t++){const n=e[t],o=n.ngMetadataName;if(v_(n)||C_(n)||M_(n)||"Output"===o||"HostBinding"===o||"HostListener"===o)return!0}}return!1}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function w_(t,e){return{type:t,name:t.name,pipeName:e.name,pure:void 0===e.pure||e.pure}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const k_=Xa("Directive",((t={})=>t),void 0,void 0,((t,e)=>I_(t,e))),S_=Xa("Component",((t={})=>Object.assign({changeDetection:jn.Default},t)),k_,void 0,((t,e)=>N_(t,e))),D_=Xa("Pipe",(t=>Object.assign({pure:!0},t)),void 0,void 0,((t,e)=>H_(t,e))),R_=Qa("Input",(t=>({bindingPropertyName:t}))),E_=Qa("Output",(t=>({bindingPropertyName:t}))),A_=Qa("HostBinding",(t=>({hostPropertyName:t}))),T_=Qa("HostListener",((t,e)=>({eventName:t,args:e}))),N_=function z_(t,e){("undefined"==typeof ngDevMode||ngDevMode)&&Kn();let n=null;!(function o(t,e){Jr(e)&&(Xr.set(t,e),Kr.add(t))})(t,e),f_(t,e),Object.defineProperty(t,$n,{get:()=>{if(null===n){const o=lr({usage:0,kind:"component",type:t});if(Jr(e)){const n=[`Component '${t.name}' is not resolved:`];throw e.templateUrl&&n.push(` - templateUrl: ${e.templateUrl}`),e.styleUrls&&e.styleUrls.length&&n.push(` - styleUrls: ${JSON.stringify(e.styleUrls)}`),n.push("Did you run and wait for 'resolveComponentResources()'?"),new Error(n.join("\n"))}const i=(function o(){return Xy})();let a=e.preserveWhitespaces;void 0===a&&(a=null!==i&&void 0!==i.preserveWhitespaces&&i.preserveWhitespaces);let r=e.encapsulation;void 0===r&&(r=null!==i&&void 0!==i.defaultEncapsulation?i.defaultEncapsulation:Wn.Emulated);const s=e.templateUrl||`ng:///${t.name}/template.html`,l=Object.assign(Object.assign({},g_(t,e)),{typeSourceSpan:o.createParseSourceSpan("Component",t.name,s),template:e.template||"",preserveWhitespaces:a,styles:e.styles||Qn,animations:e.animations,directives:[],changeDetection:e.changeDetection,pipes:new Map,encapsulation:r,interpolation:e.interpolation,viewProviders:e.viewProviders||null});d_++;try{l.usesInheritance&&h_(t),n=o.compileComponent(Zy,s,l)}finally{d_--}if(0===d_&&(function i(){if(!Jy){Jy=!0;try{for(let t=Ky.length-1;t>=0;t--){const{moduleType:e,ngModule:n}=Ky[t];n.declarations&&n.declarations.every(Qy)&&(Ky.splice(t,1),a_(e,n))}}finally{Jy=!1}}})(),(function a(t){return void 0!==t.ngSelectorScope})(t)){const e=s_(t.ngSelectorScope);r_(n,e)}}return n},configurable:!!ngDevMode})},I_=p_,H_=function F_(t,e){let n=null,o=null;Object.defineProperty(t,io,{get:()=>{if(null===o){const n=w_(t,e),i=lr({usage:0,kind:"pipe",type:n.type});o=i.compileFactory(Zy,`ng:///${n.name}/ɵfac.js`,{name:n.name,type:n.type,typeArgumentCount:0,deps:Yr(t),target:i.FactoryTarget.Pipe})}return o},configurable:!!ngDevMode}),Object.defineProperty(t,eo,{get:()=>{if(null===n){const o=w_(t,e),i=lr({usage:0,kind:"pipe",type:o.type});n=i.compilePipe(Zy,`ng:///${o.name}/ɵpipe.js`,o)}return n},configurable:!!ngDevMode})},L_=Xa("NgModule",(t=>t),void 0,void 0,((t,e)=>B_(t,e))),B_=function V_(t,e={}){!(function n(t,e,o=!1){ngDevMode&&xn(t,"Required value moduleType"),ngDevMode&&xn(e,"Required value ngModule");const i=pr(e.declarations||Qn);let a=null;Object.defineProperty(t,no,{configurable:!0,get:()=>{if(null===a){if(ngDevMode&&e.imports&&e.imports.indexOf(t)>-1)throw new Error(`'${sn(t)}' module can't import itself`);const n=lr({usage:0,kind:"NgModule",type:t});a=n.compileNgModule(Zy,`ng:///${t.name}/ɵmod.js`,{type:t,bootstrap:pr(e.bootstrap||Qn).map(tn),declarations:i.map(tn),imports:pr(e.imports||Qn).map(tn).map(l_),exports:pr(e.exports||Qn).map(tn).map(l_),schemas:e.schemas?pr(e.schemas):null,id:e.id||null}),a.schemas||(a.schemas=[])}return a}});let r=null;Object.defineProperty(t,io,{get:()=>{if(null===r){const e=lr({usage:0,kind:"NgModule",type:t});r=e.compileFactory(Zy,`ng:///${t.name}/ɵfac.js`,{name:t.name,type:t,deps:Yr(t),target:e.FactoryTarget.NgModule,typeArgumentCount:0})}return r},configurable:!!ngDevMode});let s=null;Object.defineProperty(t,Nn,{get:()=>{if(null===s){ngDevMode&&$y(t,o);const n={name:t.name,type:t,providers:e.providers||Qn,imports:[(e.imports||Qn).map(tn),(e.exports||Qn).map(tn)]},i=lr({usage:0,kind:"NgModule",type:t});s=i.compileInjector(Zy,`ng:///${t.name}/ɵinj.js`,n)}return s},configurable:!!ngDevMode})})(t,e),(function o(t,e){Ky.push({moduleType:t,ngModule:e})})(t,e)},j_=new er("Application Initializer");class U_{constructor(t){this.appInits=t,this.resolve=Yh,this.reject=Yh,this.initialized=!1,this.done=!1,this.donePromise=new Promise(((t,e)=>{this.resolve=t,this.reject=e}))}runInitializers(){if(this.initialized)return;const t=[],e=()=>{this.done=!0,this.resolve()};if(this.appInits)for(let e=0;e<this.appInits.length;e++){const n=this.appInits[e]();if(kf(n))t.push(n);else if(Df(n)){const e=new Promise(((t,e)=>{n.subscribe({complete:t,error:e})}));t.push(e)}}Promise.all(t).then((()=>{e()})).catch((t=>{this.reject(t)})),0===t.length&&e(),this.initialized=!0}}U_.ɵfac=function t(e){return new(e||U_)(Tr(j_,8))},U_.ɵprov=Sn({token:U_,factory:U_.ɵfac}),U_.ctorParameters=()=>[{type:void 0,decorators:[{type:Lr,args:[j_]},{type:Br}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(U_,[{type:Um}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[j_]},{type:Br}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const W_=new er("AppId");const G_={provide:W_,useFactory:function Y_(){return`${q_()}${q_()}${q_()}`},deps:[]};function q_(){return String.fromCharCode(97+Math.floor(25*Math.random()))}const Z_=new er("Platform Initializer"),X_=new er("Platform ID"),K_=new er("appBootstrapListener");new er("Application Packages Root URL");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class J_{log(t){console.log(t)}warn(t){console.warn(t)}}J_.ɵfac=function t(e){return new(e||J_)},J_.ɵprov=Sn({token:J_,factory:J_.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(J_,[{type:Um}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Q_=new er("LocaleId"),$_=new er("DefaultCurrencyCode");var tC;new er("Translations"),new er("TranslationsFormat"),(function(t){t[t.Error=0]="Error",t[t.Warning=1]="Warning",t[t.Ignore=2]="Ignore"})(tC||(tC={}));
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class eC{constructor(t,e){this.ngModuleFactory=t,this.componentFactories=e}}const nC=function(t){return new ny(t)},oC=nC,iC=function(t){return Promise.resolve(nC(t))},aC=function(t){const e=nC(t),n=Sl(Mo(t).declarations).reduce(((t,e)=>{const n=yo(e);return n&&t.push(new Kb(n)),t}),[]);return new eC(e,n)},rC=aC,sC=function(t){return Promise.resolve(aC(t))};class lC{constructor(){this.compileModuleSync=oC,this.compileModuleAsync=iC,this.compileModuleAndAllComponentsSync=rC,this.compileModuleAndAllComponentsAsync=sC}clearCache(){}clearCacheFor(t){}getModuleId(t){}}lC.ɵfac=function t(e){return new(e||lC)},lC.ɵprov=Sn({token:lC,factory:lC.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(lC,[{type:Um}],(function(){return[]}),null);const cC=new er("compilerOptions"),dC=Promise.resolve(0);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function pC(t){"undefined"==typeof Zone?dC.then((()=>{t&&t.apply(null,null)})):Zone.current.scheduleMicroTask("scheduleMicrotask",t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class mC{constructor({enableLongStackTrace:t=!1,shouldCoalesceEventChangeDetection:e=!1,shouldCoalesceRunChangeDetection:n=!1}){if(this.hasPendingMacrotasks=!1,this.hasPendingMicrotasks=!1,this.isStable=!0,this.onUnstable=new Sy(!1),this.onMicrotaskEmpty=new Sy(!1),this.onStable=new Sy(!1),this.onError=new Sy(!1),"undefined"==typeof Zone)throw new Error("In this configuration Angular requires Zone.js");Zone.assertZonePatched();const o=this;o._nesting=0,o._outer=o._inner=Zone.current,Zone.TaskTrackingZoneSpec&&(o._inner=o._inner.fork(new Zone.TaskTrackingZoneSpec)),t&&Zone.longStackTraceZoneSpec&&(o._inner=o._inner.fork(Zone.longStackTraceZoneSpec)),o.shouldCoalesceEventChangeDetection=!n&&e,o.shouldCoalesceRunChangeDetection=n,o.lastRequestAnimationFrameId=-1,o.nativeRequestAnimationFrame=(function i(){let t=Xn.requestAnimationFrame,e=Xn.cancelAnimationFrame;if("undefined"!=typeof Zone&&t&&e){const n=t[Zone.__symbol__("OriginalDelegate")];n&&(t=n);const o=e[Zone.__symbol__("OriginalDelegate")];o&&(e=o)}return{nativeRequestAnimationFrame:t,nativeCancelAnimationFrame:e}})().nativeRequestAnimationFrame,(function a(t){const e=()=>{!(function e(t){if(t.isCheckStableRunning||-1!==t.lastRequestAnimationFrameId)return;t.lastRequestAnimationFrameId=t.nativeRequestAnimationFrame.call(Xn,(()=>{t.fakeTopEventTask||(t.fakeTopEventTask=Zone.root.scheduleEventTask("fakeTopEventTask",(()=>{t.lastRequestAnimationFrameId=-1,gC(t),t.isCheckStableRunning=!0,uC(t),t.isCheckStableRunning=!1}),void 0,(()=>{}),(()=>{}))),t.fakeTopEventTask.invoke()})),gC(t)})(t)};t._inner=t._inner.fork({name:"angular",properties:{isAngularZone:!0},onInvokeTask:(n,o,i,a,r,s)=>{try{return hC(t),n.invokeTask(i,a,r,s)}finally{(t.shouldCoalesceEventChangeDetection&&"eventTask"===a.type||t.shouldCoalesceRunChangeDetection)&&e(),bC(t)}},onInvoke:(n,o,i,a,r,s,l)=>{try{return hC(t),n.invoke(i,a,r,s,l)}finally{t.shouldCoalesceRunChangeDetection&&e(),bC(t)}},onHasTask:(e,n,o,i)=>{e.hasTask(o,i),n===o&&("microTask"==i.change?(t._hasPendingMicrotasks=i.microTask,gC(t),uC(t)):"macroTask"==i.change&&(t.hasPendingMacrotasks=i.macroTask))},onHandleError:(e,n,o,i)=>(e.handleError(o,i),t.runOutsideAngular((()=>t.onError.emit(i))),!1)})})(o)}static isInAngularZone(){return!0===Zone.current.get("isAngularZone")}static assertInAngularZone(){if(!mC.isInAngularZone())throw new Error("Expected to be in Angular Zone, but it is not!")}static assertNotInAngularZone(){if(mC.isInAngularZone())throw new Error("Expected to not be in Angular Zone, but it is!")}run(t,e,n){return this._inner.run(t,e,n)}runTask(t,e,n,o){const i=this._inner,a=i.scheduleEventTask("NgZoneEvent: "+o,t,fC,Yh,Yh);try{return i.runTask(a,e,n)}finally{i.cancelTask(a)}}runGuarded(t,e,n){return this._inner.runGuarded(t,e,n)}runOutsideAngular(t){return this._outer.run(t)}}const fC={};function uC(t){if(0==t._nesting&&!t.hasPendingMicrotasks&&!t.isStable)try{t._nesting++,t.onMicrotaskEmpty.emit(null)}finally{if(t._nesting--,!t.hasPendingMicrotasks)try{t.runOutsideAngular((()=>t.onStable.emit(null)))}finally{t.isStable=!0}}}function gC(t){t.hasPendingMicrotasks=!!(t._hasPendingMicrotasks||(t.shouldCoalesceEventChangeDetection||t.shouldCoalesceRunChangeDetection)&&-1!==t.lastRequestAnimationFrameId)}function hC(t){t._nesting++,t.isStable&&(t.isStable=!1,t.onUnstable.emit(null))}function bC(t){t._nesting--,uC(t)}class yC{constructor(){this.hasPendingMicrotasks=!1,this.hasPendingMacrotasks=!1,this.isStable=!0,this.onUnstable=new Sy,this.onMicrotaskEmpty=new Sy,this.onStable=new Sy,this.onError=new Sy}run(t,e,n){return t.apply(e,n)}runGuarded(t,e,n){return t.apply(e,n)}runOutsideAngular(t){return t()}runTask(t,e,n,o){return t.apply(e,n)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class _C{constructor(t){this._ngZone=t,this._pendingCount=0,this._isZoneStable=!0,this._didWork=!1,this._callbacks=[],this.taskTrackingZone=null,this._watchAngularEvents(),t.run((()=>{this.taskTrackingZone="undefined"==typeof Zone?null:Zone.current.get("TaskTrackingZone")}))}_watchAngularEvents(){this._ngZone.onUnstable.subscribe({next:()=>{this._didWork=!0,this._isZoneStable=!1}}),this._ngZone.runOutsideAngular((()=>{this._ngZone.onStable.subscribe({next:()=>{mC.assertNotInAngularZone(),pC((()=>{this._isZoneStable=!0,this._runCallbacksIfReady()}))}})}))}increasePendingRequestCount(){return this._pendingCount+=1,this._didWork=!0,this._pendingCount}decreasePendingRequestCount(){if(this._pendingCount-=1,this._pendingCount<0)throw new Error("pending async requests below zero");return this._runCallbacksIfReady(),this._pendingCount}isStable(){return this._isZoneStable&&0===this._pendingCount&&!this._ngZone.hasPendingMacrotasks}_runCallbacksIfReady(){if(this.isStable())pC((()=>{for(;0!==this._callbacks.length;){let t=this._callbacks.pop();clearTimeout(t.timeoutId),t.doneCb(this._didWork)}this._didWork=!1}));else{let t=this.getPendingTasks();this._callbacks=this._callbacks.filter((e=>!e.updateCb||!e.updateCb(t)||(clearTimeout(e.timeoutId),!1))),this._didWork=!0}}getPendingTasks(){return this.taskTrackingZone?this.taskTrackingZone.macroTasks.map((t=>({source:t.source,creationLocation:t.creationLocation,data:t.data}))):[]}addCallback(t,e,n){let o=-1;e&&e>0&&(o=setTimeout((()=>{this._callbacks=this._callbacks.filter((t=>t.timeoutId!==o)),t(this._didWork,this.getPendingTasks())}),e)),this._callbacks.push({doneCb:t,timeoutId:o,updateCb:n})}whenStable(t,e,n){if(n&&!this.taskTrackingZone)throw new Error('Task tracking zone is required when passing an update callback to whenStable(). Is "zone.js/plugins/task-tracking" loaded?');this.addCallback(t,e,n),this._runCallbacksIfReady()}getPendingRequestCount(){return this._pendingCount}findProviders(t,e,n){return[]}}_C.ɵfac=function t(e){return new(e||_C)(Tr(mC))},_C.ɵprov=Sn({token:_C,factory:_C.ɵfac}),_C.ctorParameters=()=>[{type:mC}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(_C,[{type:Um}],(function(){return[{type:mC}]}),null);class CC{constructor(){this._applications=new Map,vC.addToWindow(this)}registerApplication(t,e){this._applications.set(t,e)}unregisterApplication(t){this._applications.delete(t)}unregisterAllApplications(){this._applications.clear()}getTestability(t){return this._applications.get(t)||null}getAllTestabilities(){return Array.from(this._applications.values())}getAllRootElements(){return Array.from(this._applications.keys())}findTestabilityInTree(t,e=!0){return vC.findTestabilityInTree(this,t,e)}}CC.ɵfac=function t(e){return new(e||CC)},CC.ɵprov=Sn({token:CC,factory:CC.ɵfac}),CC.ctorParameters=()=>[],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(CC,[{type:Um}],(function(){return[]}),null);let MC,vC=new class{addToWindow(t){}findTestabilityInTree(t,e,n){return null}},xC=!0,OC=!1;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function PC(){return OC=!0,xC}let wC=function kC(t,e,n){ngDevMode&&(function o(t,e="Type passed in is not NgModuleType, it does not have 'ɵmod' property."){Mo(t)||On(e)})(n);const i=new ny(n);if("undefined"!=typeof ngJitMode&&!ngJitMode)return Promise.resolve(i);const a=t.get(cC,[]).concat(e);if((function r(t){if(null!==Xy){if(t.defaultEncapsulation!==Xy.defaultEncapsulation)return void(ngDevMode&&console.error("Provided value for `defaultEncapsulation` can not be changed once it has been set."));if(t.preserveWhitespaces!==Xy.preserveWhitespaces)return void(ngDevMode&&console.error("Provided value for `preserveWhitespaces` can not be changed once it has been set."))}Xy=t})({defaultEncapsulation:LC(a.map((t=>t.defaultEncapsulation))),preserveWhitespaces:LC(a.map((t=>t.preserveWhitespaces)))}),(function s(){return 0===Xr.size})())return Promise.resolve(i);const l=(function c(t){const e=[];return t.forEach((t=>t&&e.push(...t))),e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(a.map((t=>t.providers)));if(0===l.length)return Promise.resolve(i);const d=lr({usage:0,kind:"NgModule",type:n}),p=Np.create({providers:l}).get(d.ResourceLoader);return Zr((t=>Promise.resolve(p.get(t)))).then((()=>i))};let SC=function DC(){ngDevMode&&(function t(){qp||(qp=!0,Zp("ɵsetProfiler",$o),Zp("getDirectiveMetadata",Vp),Zp("getComponent",zp),Zp("getContext",Ip),Zp("getListeners",Up),Zp("getOwningComponent",Hp),Zp("getHostElement",jp),Zp("getInjector",Lp),Zp("getRootComponents",Fp),Zp("getDirectives",Bp),Zp("applyChanges",Yp))})()},RC=function EC(t){return t.isBoundToModule};const AC=new er("AllowMultipleToken");function TC(t,e,n=[]){const o=`Platform: ${e}`,i=new er(o);return(e=[])=>{let a=NC();if(!a||a.injector.get(AC,!1))if(t)t(n.concat(e).concat({provide:i,useValue:!0}));else{const t=n.concat(e).concat({provide:i,useValue:!0},{provide:Cp,useValue:"platform"});!(function r(t){if(MC&&!MC.destroyed&&!MC.injector.get(AC,!1))throw new Error("There can be only one platform. 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Please define one of these.`);t.instance.ngDoBootstrap(e)}this._modules.push(t)}onDestroy(t){this._destroyListeners.push(t)}get injector(){return this._injector}destroy(){if(this._destroyed)throw new Error("The platform has already been destroyed!");this._modules.slice().forEach((t=>t.destroy())),this._destroyListeners.forEach((t=>t())),this._destroyed=!0}get destroyed(){return this._destroyed}}function IC(t,e){return t=Array.isArray(e)?e.reduce(IC,t):Object.assign(Object.assign({},t),e)}zC.ɵfac=function t(e){return new(e||zC)(Tr(Np))},zC.ɵprov=Sn({token:zC,factory:zC.ɵfac}),zC.ctorParameters=()=>[{type:Np}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(zC,[{type:Um}],(function(){return[{type:Np}]}),null);class HC{constructor(t,e,n,o,i){this._zone=t,this._injector=e,this._exceptionHandler=n,this._componentFactoryResolver=o,this._initStatus=i,this._bootstrapListeners=[],this._views=[],this._runningTick=!1,this._stable=!0,this.componentTypes=[],this.components=[],this._onMicrotaskEmptySubscription=this._zone.onMicrotaskEmpty.subscribe({next:()=>{this._zone.run((()=>{this.tick()}))}});const a=new T((t=>{this._stable=this._zone.isStable&&!this._zone.hasPendingMacrotasks&&!this._zone.hasPendingMicrotasks,this._zone.runOutsideAngular((()=>{t.next(this._stable),t.complete()}))})),r=new T((t=>{let e;this._zone.runOutsideAngular((()=>{e=this._zone.onStable.subscribe((()=>{mC.assertNotInAngularZone(),pC((()=>{this._stable||this._zone.hasPendingMacrotasks||this._zone.hasPendingMicrotasks||(this._stable=!0,t.next(!0))}))}))}));const n=this._zone.onUnstable.subscribe((()=>{mC.assertInAngularZone(),this._stable&&(this._stable=!1,this._zone.runOutsideAngular((()=>{t.next(!1)})))}));return()=>{e.unsubscribe(),n.unsubscribe()}}));this.isStable=pe(a,r.pipe(Ie()))}bootstrap(t,e){if(!this._initStatus.done)throw new Error("Cannot bootstrap as there are still asynchronous initializers running. Bootstrap components in the `ngDoBootstrap` method of the root module.");let n;n=t instanceof Uh?t:this._componentFactoryResolver.resolveComponentFactory(t),this.componentTypes.push(n.componentType);const o=RC(n)?void 0:this._injector.get(zb),i=n.create(Np.NULL,[],e||n.selector,o),a=i.location.nativeElement,r=i.injector.get(_C,null),s=r&&i.injector.get(CC);if(r&&s&&s.registerApplication(a,r),i.onDestroy((()=>{this.detachView(i.hostView),FC(this.components,i),s&&s.unregisterApplication(a)})),this._loadComponent(i),("undefined"==typeof ngDevMode||ngDevMode)&&PC()){this._injector.get(J_).log("Angular is running in development mode. Call enableProdMode() to enable production mode.")}return i}tick(){if(this._runningTick)throw new Error("ApplicationRef.tick is called recursively");try{this._runningTick=!0;for(let t of this._views)t.detectChanges();if(("undefined"==typeof ngDevMode||ngDevMode)&&PC())for(let t of this._views)t.checkNoChanges()}catch(t){this._zone.runOutsideAngular((()=>this._exceptionHandler.handleError(t)))}finally{this._runningTick=!1}}attachView(t){const e=t;this._views.push(e),e.attachToAppRef(this)}detachView(t){const e=t;FC(this._views,e),e.detachFromAppRef()}_loadComponent(t){this.attachView(t.hostView),this.tick(),this.components.push(t);this._injector.get(K_,[]).concat(this._bootstrapListeners).forEach((e=>e(t)))}ngOnDestroy(){this._views.slice().forEach((t=>t.destroy())),this._onMicrotaskEmptySubscription.unsubscribe()}get viewCount(){return this._views.length}}function FC(t,e){const n=t.indexOf(e);n>-1&&t.splice(n,1)}function LC(t){for(let e=t.length-1;e>=0;e--)if(void 0!==t[e])return t[e]}HC.ɵfac=function t(e){return new(e||HC)(Tr(mC),Tr(Np),Tr(ul),Tr(Gh),Tr(U_))},HC.ɵprov=Sn({token:HC,factory:HC.ɵfac}),HC.ctorParameters=()=>[{type:mC},{type:Np},{type:ul},{type:Gh},{type:U_}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(HC,[{type:Um}],(function(){return[{type:mC},{type:Np},{type:ul},{type:Gh},{type:U_}]}),null);class BC{}const VC={factoryPathPrefix:"",factoryPathSuffix:".ngfactory"};class jC{constructor(t,e){this._compiler=t,this._config=e||VC}load(t){return this.loadAndCompile(t)}loadAndCompile(t){let[e,n]=t.split("#");return void 0===n&&(n="default"),System.import(e).then((t=>t[n])).then((t=>UC(t,e,n))).then((t=>this._compiler.compileModuleAsync(t)))}loadFactory(t){let[e,n]=t.split("#"),o="NgFactory";return void 0===n&&(n="default",o=""),System.import(this._config.factoryPathPrefix+e+this._config.factoryPathSuffix).then((t=>t[n+o])).then((t=>UC(t,e,n)))}}function UC(t,e,n){if(!t)throw new Error(`Cannot find '${n}' in '${e}'`);return t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */jC.ɵfac=function t(e){return new(e||jC)(Tr(lC),Tr(BC,8))},jC.ɵprov=Sn({token:jC,factory:jC.ɵfac}),jC.ctorParameters=()=>[{type:lC},{type:BC,decorators:[{type:Br}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(jC,[{type:Um}],(function(){return[{type:lC},{type:BC,decorators:[{type:Br}]}]}),null);const WC=TC(null,"core",[{provide:X_,useValue:"unknown"},{provide:zC,deps:[Np]},{provide:CC,deps:[]},{provide:J_,deps:[]}]);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const GC=[{provide:HC,useClass:HC,deps:[mC,Np,ul,Gh,U_]},{provide:Xb,deps:[mC],useFactory:function YC(t){let e=[];return t.onStable.subscribe((()=>{for(;e.length;)e.pop()()})),function(t){e.push(t)}}},{provide:U_,useClass:U_,deps:[[new Br,j_]]},{provide:lC,useClass:lC,deps:[]},G_,{provide:hb,useFactory:
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function qC(){return kb},deps:[]},{provide:yb,useFactory:function ZC(){return Sb},deps:[]},{provide:Q_,useFactory:function XC(t){return Pg(t=t||(function e(){return"undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode&&"undefined"!=typeof goog&&"en"!==goog.getLocale()?goog.getLocale():"undefined"!=typeof $localize&&$localize.locale||Cg})()),t},deps:[[new Lr(Q_),new Br,new jr]]},{provide:$_,useValue:"USD"}];class KC{constructor(t){}}var JC,QC;KC.ɵfac=function t(e){return new(e||KC)(Tr(HC))},KC.ɵmod=fo({type:KC}),KC.ɵinj=Dn({providers:GC}),KC.ctorParameters=()=>[{type:HC}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(KC,[{type:L_,args:[{providers:GC}]}],(function(){return[{type:HC}]}),null),(function(t){t[t.CreateViewNodes=0]="CreateViewNodes",t[t.CheckNoChanges=1]="CheckNoChanges",t[t.CheckNoChangesProjectedViews=2]="CheckNoChangesProjectedViews",t[t.CheckAndUpdate=3]="CheckAndUpdate",t[t.CheckAndUpdateProjectedViews=4]="CheckAndUpdateProjectedViews",t[t.Destroy=5]="Destroy"})(JC||(JC={})),(function(t){t[t.create=0]="create",t[t.detectChanges=1]="detectChanges",t[t.checkNoChanges=2]="checkNoChanges",t[t.destroy=3]="destroy",t[t.handleEvent=4]="handleEvent"})(QC||(QC={})),
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
"undefined"!=typeof ngDevMode&&ngDevMode&&(Xn.$localize=Xn.$localize||function(){throw new Error("It looks like your application or one of its dependencies is using i18n.\nAngular 9 introduced a global `$localize()` function that needs to be loaded.\nPlease run `ng add @angular/localize` from the Angular CLI.\n(For non-CLI projects, add `import '@angular/localize/init';` to your `polyfills.ts` file.\nFor server-side rendering applications add the import to your `main.server.ts` file.)")})
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */;const $C="undefined"!=typeof globalThis&&globalThis,tM="undefined"!=typeof window&&window,eM="undefined"!=typeof self&&"undefined"!=typeof WorkerGlobalScope&&self instanceof WorkerGlobalScope&&self,nM="undefined"!=typeof global&&global,oM=function(t,...e){if(oM.translate){const n=oM.translate(t,e);t=n[0],e=n[1]}let n=iM(t[0],t.raw[0]);for(let o=1;o<t.length;o++)n+=e[o-1]+iM(t[o],t.raw[o]);return n};function iM(t,e){return":"===e.charAt(0)?t.substring((function n(t,e){for(let n=1,o=1;n<t.length;n++,o++)if("\\"===e[o])o++;else if(":"===t[n])return n;throw new Error(`Unterminated $localize metadata block in "${e}".`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t,e)+1):t}($C||nM||tM||eM).$localize=oM;
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
let aM=null;function rM(){return aM}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const sM=new er("DocumentToken");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class lM{historyGo(t){throw new Error("Not implemented")}}function cM(){return Tr(dM)}lM.ɵfac=function t(e){return new(e||lM)},lM.ɵprov=Sn({factory:cM,token:lM,providedIn:"platform"}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(lM,[{type:Um,args:[{providedIn:"platform",useFactory:cM}]}],null,null),new er("Location Initialized");class dM extends lM{constructor(t){super(),this._doc=t,this._init()}_init(){this.location=window.location,this._history=window.history}getBaseHrefFromDOM(){return rM().getBaseHref(this._doc)}onPopState(t){const e=rM().getGlobalEventTarget(this._doc,"window");return e.addEventListener("popstate",t,!1),()=>e.removeEventListener("popstate",t)}onHashChange(t){const e=rM().getGlobalEventTarget(this._doc,"window");return e.addEventListener("hashchange",t,!1),()=>e.removeEventListener("hashchange",t)}get href(){return this.location.href}get protocol(){return this.location.protocol}get hostname(){return this.location.hostname}get port(){return this.location.port}get pathname(){return this.location.pathname}get search(){return this.location.search}get hash(){return this.location.hash}set pathname(t){this.location.pathname=t}pushState(t,e,n){pM()?this._history.pushState(t,e,n):this.location.hash=n}replaceState(t,e,n){pM()?this._history.replaceState(t,e,n):this.location.hash=n}forward(){this._history.forward()}back(){this._history.back()}historyGo(t=0){this._history.go(t)}getState(){return this._history.state}}function pM(){return!!window.history.pushState}function mM(){return new dM(Tr(sM))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function fM(t,e){if(0==t.length)return e;if(0==e.length)return t;let n=0;return t.endsWith("/")&&n++,e.startsWith("/")&&n++,2==n?t+e.substring(1):1==n?t+e:t+"/"+e}function uM(t){const e=t.match(/#|\?|$/),n=e&&e.index||t.length;return t.slice(0,n-("/"===t[n-1]?1:0))+t.slice(n)}function gM(t){return t&&"?"!==t[0]?"?"+t:t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */dM.ɵfac=function t(e){return new(e||dM)(Tr(sM))},dM.ɵprov=Sn({factory:mM,token:dM,providedIn:"platform"}),dM.ctorParameters=()=>[{type:void 0,decorators:[{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(dM,[{type:Um,args:[{providedIn:"platform",useFactory:mM}]}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),null);class hM{historyGo(t){throw new Error("Not implemented")}}function bM(t){const e=Tr(sM).location;return new _M(Tr(lM),e&&e.origin||"")}hM.ɵfac=function t(e){return new(e||hM)},hM.ɵprov=Sn({factory:bM,token:hM,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(hM,[{type:Um,args:[{providedIn:"root",useFactory:bM}]}],null,null);const yM=new er("appBaseHref");class _M extends hM{constructor(t,e){if(super(),this._platformLocation=t,this._removeListenerFns=[],null==e&&(e=this._platformLocation.getBaseHrefFromDOM()),null==e)throw new Error("No base href set. Please provide a value for the APP_BASE_HREF token or add a base element to the document.");this._baseHref=e}ngOnDestroy(){for(;this._removeListenerFns.length;)this._removeListenerFns.pop()()}onPopState(t){this._removeListenerFns.push(this._platformLocation.onPopState(t),this._platformLocation.onHashChange(t))}getBaseHref(){return this._baseHref}prepareExternalUrl(t){return fM(this._baseHref,t)}path(t=!1){const e=this._platformLocation.pathname+gM(this._platformLocation.search),n=this._platformLocation.hash;return n&&t?`${e}${n}`:e}pushState(t,e,n,o){const i=this.prepareExternalUrl(n+gM(o));this._platformLocation.pushState(t,e,i)}replaceState(t,e,n,o){const i=this.prepareExternalUrl(n+gM(o));this._platformLocation.replaceState(t,e,i)}forward(){this._platformLocation.forward()}back(){this._platformLocation.back()}historyGo(t=0){var e,n;null===(n=(e=this._platformLocation).historyGo)||void 0===n||n.call(e,t)}}_M.ɵfac=function t(e){return new(e||_M)(Tr(lM),Tr(yM,8))},_M.ɵprov=Sn({token:_M,factory:_M.ɵfac}),_M.ctorParameters=()=>[{type:lM},{type:String,decorators:[{type:Br},{type:Lr,args:[yM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(_M,[{type:Um}],(function(){return[{type:lM},{type:String,decorators:[{type:Br},{type:Lr,args:[yM]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class CM extends hM{constructor(t,e){super(),this._platformLocation=t,this._baseHref="",this._removeListenerFns=[],null!=e&&(this._baseHref=e)}ngOnDestroy(){for(;this._removeListenerFns.length;)this._removeListenerFns.pop()()}onPopState(t){this._removeListenerFns.push(this._platformLocation.onPopState(t),this._platformLocation.onHashChange(t))}getBaseHref(){return this._baseHref}path(t=!1){let e=this._platformLocation.hash;return null==e&&(e="#"),e.length>0?e.substring(1):e}prepareExternalUrl(t){const e=fM(this._baseHref,t);return e.length>0?"#"+e:e}pushState(t,e,n,o){let i=this.prepareExternalUrl(n+gM(o));0==i.length&&(i=this._platformLocation.pathname),this._platformLocation.pushState(t,e,i)}replaceState(t,e,n,o){let i=this.prepareExternalUrl(n+gM(o));0==i.length&&(i=this._platformLocation.pathname),this._platformLocation.replaceState(t,e,i)}forward(){this._platformLocation.forward()}back(){this._platformLocation.back()}historyGo(t=0){var e,n;null===(n=(e=this._platformLocation).historyGo)||void 0===n||n.call(e,t)}}CM.ɵfac=function t(e){return new(e||CM)(Tr(lM),Tr(yM,8))},CM.ɵprov=Sn({token:CM,factory:CM.ɵfac}),CM.ctorParameters=()=>[{type:lM},{type:String,decorators:[{type:Br},{type:Lr,args:[yM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(CM,[{type:Um}],(function(){return[{type:lM},{type:String,decorators:[{type:Br},{type:Lr,args:[yM]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class MM{constructor(t,e){this._subject=new Sy,this._urlChangeListeners=[],this._platformStrategy=t;const n=this._platformStrategy.getBaseHref();this._platformLocation=e,this._baseHref=uM(xM(n)),this._platformStrategy.onPopState((t=>{this._subject.emit({url:this.path(!0),pop:!0,state:t.state,type:t.type})}))}path(t=!1){return this.normalize(this._platformStrategy.path(t))}getState(){return this._platformLocation.getState()}isCurrentPathEqualTo(t,e=""){return this.path()==this.normalize(t+gM(e))}normalize(t){return MM.stripTrailingSlash((function e(t,n){return t&&n.startsWith(t)?n.substring(t.length):n})(this._baseHref,xM(t)))}prepareExternalUrl(t){return t&&"/"!==t[0]&&(t="/"+t),this._platformStrategy.prepareExternalUrl(t)}go(t,e="",n=null){this._platformStrategy.pushState(n,"",t,e),this._notifyUrlChangeListeners(this.prepareExternalUrl(t+gM(e)),n)}replaceState(t,e="",n=null){this._platformStrategy.replaceState(n,"",t,e),this._notifyUrlChangeListeners(this.prepareExternalUrl(t+gM(e)),n)}forward(){this._platformStrategy.forward()}back(){this._platformStrategy.back()}historyGo(t=0){var e,n;null===(n=(e=this._platformStrategy).historyGo)||void 0===n||n.call(e,t)}onUrlChange(t){this._urlChangeListeners.push(t),this._urlChangeSubscription||(this._urlChangeSubscription=this.subscribe((t=>{this._notifyUrlChangeListeners(t.url,t.state)})))}_notifyUrlChangeListeners(t="",e){this._urlChangeListeners.forEach((n=>n(t,e)))}subscribe(t,e,n){return this._subject.subscribe({next:t,error:e,complete:n})}}function vM(){return new MM(Tr(hM),Tr(lM))}function xM(t){return t.replace(/\/index.html$/,"")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */MM.ɵfac=function t(e){return new(e||MM)(Tr(hM),Tr(lM))},MM.normalizeQueryParams=gM,MM.joinWithSlash=fM,MM.stripTrailingSlash=uM,MM.ɵprov=Sn({factory:vM,token:MM,providedIn:"root"}),MM.ctorParameters=()=>[{type:hM},{type:lM}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(MM,[{type:Um,args:[{providedIn:"root",useFactory:vM}]}],(function(){return[{type:hM},{type:lM}]}),null);const OM={ADP:[void 0,void 0,0],AFN:[void 0,void 0,0],ALL:[void 0,void 0,0],AMD:[void 0,void 0,2],AOA:[void 0,"Kz"],ARS:[void 0,"$"],AUD:["A$","$"],BAM:[void 0,"KM"],BBD:[void 0,"$"],BDT:[void 0,"৳"],BHD:[void 0,void 0,3],BIF:[void 0,void 0,0],BMD:[void 0,"$"],BND:[void 0,"$"],BOB:[void 0,"Bs"],BRL:["R$"],BSD:[void 0,"$"],BWP:[void 0,"P"],BYN:[void 0,"р.",2],BYR:[void 0,void 0,0],BZD:[void 0,"$"],CAD:["CA$","$",2],CHF:[void 0,void 0,2],CLF:[void 0,void 0,4],CLP:[void 0,"$",0],CNY:["CN¥","¥"],COP:[void 0,"$",2],CRC:[void 0,"₡",2],CUC:[void 0,"$"],CUP:[void 0,"$"],CZK:[void 0,"Kč",2],DJF:[void 0,void 0,0],DKK:[void 0,"kr",2],DOP:[void 0,"$"],EGP:[void 0,"E£"],ESP:[void 0,"₧",0],EUR:["€"],FJD:[void 0,"$"],FKP:[void 0,"£"],GBP:["£"],GEL:[void 0,"₾"],GIP:[void 0,"£"],GNF:[void 0,"FG",0],GTQ:[void 0,"Q"],GYD:[void 0,"$",2],HKD:["HK$","$"],HNL:[void 0,"L"],HRK:[void 0,"kn"],HUF:[void 0,"Ft",2],IDR:[void 0,"Rp",2],ILS:["₪"],INR:["₹"],IQD:[void 0,void 0,0],IRR:[void 0,void 0,0],ISK:[void 0,"kr",0],ITL:[void 0,void 0,0],JMD:[void 0,"$"],JOD:[void 0,void 0,3],JPY:["¥",void 0,0],KHR:[void 0,"៛"],KMF:[void 0,"CF",0],KPW:[void 0,"₩",0],KRW:["₩",void 0,0],KWD:[void 0,void 0,3],KYD:[void 0,"$"],KZT:[void 0,"₸"],LAK:[void 0,"₭",0],LBP:[void 0,"L£",0],LKR:[void 0,"Rs"],LRD:[void 0,"$"],LTL:[void 0,"Lt"],LUF:[void 0,void 0,0],LVL:[void 0,"Ls"],LYD:[void 0,void 0,3],MGA:[void 0,"Ar",0],MGF:[void 0,void 0,0],MMK:[void 0,"K",0],MNT:[void 0,"₮",2],MRO:[void 0,void 0,0],MUR:[void 0,"Rs",2],MXN:["MX$","$"],MYR:[void 0,"RM"],NAD:[void 0,"$"],NGN:[void 0,"₦"],NIO:[void 0,"C$"],NOK:[void 0,"kr",2],NPR:[void 0,"Rs"],NZD:["NZ$","$"],OMR:[void 0,void 0,3],PHP:[void 0,"₱"],PKR:[void 0,"Rs",2],PLN:[void 0,"zł"],PYG:[void 0,"₲",0],RON:[void 0,"lei"],RSD:[void 0,void 0,0],RUB:[void 0,"₽"],RUR:[void 0,"р."],RWF:[void 0,"RF",0],SBD:[void 0,"$"],SEK:[void 0,"kr",2],SGD:[void 0,"$"],SHP:[void 0,"£"],SLL:[void 0,void 0,0],SOS:[void 0,void 0,0],SRD:[void 0,"$"],SSP:[void 0,"£"],STD:[void 0,void 0,0],STN:[void 0,"Db"],SYP:[void 0,"£",0],THB:[void 0,"฿"],TMM:[void 0,void 0,0],TND:[void 0,void 0,3],TOP:[void 0,"T$"],TRL:[void 0,void 0,0],TRY:[void 0,"₺"],TTD:[void 0,"$"],TWD:["NT$","$",2],TZS:[void 0,void 0,2],UAH:[void 0,"₴"],UGX:[void 0,void 0,0],USD:["$"],UYI:[void 0,void 0,0],UYU:[void 0,"$"],UYW:[void 0,void 0,4],UZS:[void 0,void 0,2],VEF:[void 0,"Bs",2],VND:["₫",void 0,0],VUV:[void 0,void 0,0],XAF:["FCFA",void 0,0],XCD:["EC$","$"],XOF:["CFA",void 0,0],XPF:["CFPF",void 0,0],XXX:["¤"],YER:[void 0,void 0,0],ZAR:[void 0,"R"],ZMK:[void 0,void 0,0],ZMW:[void 0,"ZK"],ZWD:[void 0,void 0,0]};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */var PM,wM,kM,SM,DM,RM,EM;function AM(t,e){return LM(gg(t)[yg.DateFormat],e)}function TM(t,e){return LM(gg(t)[yg.TimeFormat],e)}function NM(t,e){return LM(gg(t)[yg.DateTimeFormat],e)}function zM(t,e){const n=gg(t),o=n[yg.NumberSymbols][e];if(void 0===o){if(e===RM.CurrencyDecimal)return n[yg.NumberSymbols][RM.Decimal];if(e===RM.CurrencyGroup)return n[yg.NumberSymbols][RM.Group]}return o}function IM(t,e){return gg(t)[yg.NumberFormats][e]}!(function(t){t[t.Decimal=0]="Decimal",t[t.Percent=1]="Percent",t[t.Currency=2]="Currency",t[t.Scientific=3]="Scientific"})(PM||(PM={})),(function(t){t[t.Zero=0]="Zero",t[t.One=1]="One",t[t.Two=2]="Two",t[t.Few=3]="Few",t[t.Many=4]="Many",t[t.Other=5]="Other"})(wM||(wM={})),(function(t){t[t.Format=0]="Format",t[t.Standalone=1]="Standalone"})(kM||(kM={})),(function(t){t[t.Narrow=0]="Narrow",t[t.Abbreviated=1]="Abbreviated",t[t.Wide=2]="Wide",t[t.Short=3]="Short"})(SM||(SM={})),(function(t){t[t.Short=0]="Short",t[t.Medium=1]="Medium",t[t.Long=2]="Long",t[t.Full=3]="Full"})(DM||(DM={})),(function(t){t[t.Decimal=0]="Decimal",t[t.Group=1]="Group",t[t.List=2]="List",t[t.PercentSign=3]="PercentSign",t[t.PlusSign=4]="PlusSign",t[t.MinusSign=5]="MinusSign",t[t.Exponential=6]="Exponential",t[t.SuperscriptingExponent=7]="SuperscriptingExponent",t[t.PerMille=8]="PerMille",t[t[1/0]=9]="Infinity",t[t.NaN=10]="NaN",t[t.TimeSeparator=11]="TimeSeparator",t[t.CurrencyDecimal=12]="CurrencyDecimal",t[t.CurrencyGroup=13]="CurrencyGroup"})(RM||(RM={})),(function(t){t[t.Sunday=0]="Sunday",t[t.Monday=1]="Monday",t[t.Tuesday=2]="Tuesday",t[t.Wednesday=3]="Wednesday",t[t.Thursday=4]="Thursday",t[t.Friday=5]="Friday",t[t.Saturday=6]="Saturday"})(EM||(EM={}));const HM=hg;function FM(t){if(!t[yg.ExtraData])throw new Error(`Missing extra locale data for the locale "${t[yg.LocaleId]}". Use "registerLocaleData" to load new data. See the "I18n guide" on angular.io to know more.`)}function LM(t,e){for(let n=e;n>-1;n--)if(void 0!==t[n])return t[n];throw new Error("Locale data API: locale data undefined")}function BM(t){const[e,n]=t.split(":");return{hours:+e,minutes:+n}}function VM(t,e,n="en"){const o=(function i(t){return gg(t)[yg.Currencies]})(n)[t]||OM[t]||[],a=o[1];return"narrow"===e&&"string"==typeof a?a:o[0]||t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const jM=/^(\d{4})-?(\d\d)-?(\d\d)(?:T(\d\d)(?::?(\d\d)(?::?(\d\d)(?:\.(\d+))?)?)?(Z|([+-])(\d\d):?(\d\d))?)?$/,UM={},WM=/((?:[^BEGHLMOSWYZabcdhmswyz']+)|(?:'(?:[^']|'')*')|(?:G{1,5}|y{1,4}|Y{1,4}|M{1,5}|L{1,5}|w{1,2}|W{1}|d{1,2}|E{1,6}|c{1,6}|a{1,5}|b{1,5}|B{1,5}|h{1,2}|H{1,2}|m{1,2}|s{1,2}|S{1,3}|z{1,4}|Z{1,5}|O{1,4}))([\s\S]*)/;var GM,YM,qM;function ZM(t,e,n,o){let i=(function a(t){if(sv(t))return t;if("number"==typeof t&&!isNaN(t))return new Date(t);if("string"==typeof t){if(t=t.trim(),/^(\d{4}(-\d{1,2}(-\d{1,2})?)?)$/.test(t)){const[e,n=1,o=1]=t.split("-").map((t=>+t));return XM(e,n-1,o)}const e=parseFloat(t);if(!isNaN(t-e))return new Date(e);let n;if(n=t.match(jM))return(function e(t){const e=new Date(0);let n=0,o=0;const i=t[8]?e.setUTCFullYear:e.setFullYear,a=t[8]?e.setUTCHours:e.setHours;t[9]&&(n=Number(t[9]+t[10]),o=Number(t[9]+t[11]));i.call(e,Number(t[1]),Number(t[2])-1,Number(t[3]));const r=Number(t[4]||0)-n,s=Number(t[5]||0)-o,l=Number(t[6]||0),c=Math.floor(1e3*parseFloat("0."+(t[7]||0)));return a.call(e,r,s,l,c),e})(n)}const n=new Date(t);if(!sv(n))throw new Error(`Unable to convert "${t}" into a date`);return n})(t);e=KM(n,e)||e;let r,s=[];for(;e;){if(r=WM.exec(e),!r){s.push(e);break}{s=s.concat(r.slice(1));const t=s.pop();if(!t)break;e=t}}let l=i.getTimezoneOffset();o&&(l=rv(o,l),i=(function c(t,e,n){const o=n?-1:1,i=t.getTimezoneOffset(),a=rv(e,i);return(function r(t,e){return(t=new Date(t.getTime())).setMinutes(t.getMinutes()+e),t})(t,o*(a-i))})(i,o,!0));let d="";return s.forEach((t=>{const e=(function o(t){if(av[t])return av[t];let e;switch(t){case"G":case"GG":case"GGG":e=tv(qM.Eras,SM.Abbreviated);break;case"GGGG":e=tv(qM.Eras,SM.Wide);break;case"GGGGG":e=tv(qM.Eras,SM.Narrow);break;case"y":e=$M(YM.FullYear,1,0,!1,!0);break;case"yy":e=$M(YM.FullYear,2,0,!0,!0);break;case"yyy":e=$M(YM.FullYear,3,0,!1,!0);break;case"yyyy":e=$M(YM.FullYear,4,0,!1,!0);break;case"Y":e=iv(1);break;case"YY":e=iv(2,!0);break;case"YYY":e=iv(3);break;case"YYYY":e=iv(4);break;case"M":case"L":e=$M(YM.Month,1,1);break;case"MM":case"LL":e=$M(YM.Month,2,1);break;case"MMM":e=tv(qM.Months,SM.Abbreviated);break;case"MMMM":e=tv(qM.Months,SM.Wide);break;case"MMMMM":e=tv(qM.Months,SM.Narrow);break;case"LLL":e=tv(qM.Months,SM.Abbreviated,kM.Standalone);break;case"LLLL":e=tv(qM.Months,SM.Wide,kM.Standalone);break;case"LLLLL":e=tv(qM.Months,SM.Narrow,kM.Standalone);break;case"w":e=ov(1);break;case"ww":e=ov(2);break;case"W":e=ov(1,!0);break;case"d":e=$M(YM.Date,1);break;case"dd":e=$M(YM.Date,2);break;case"c":case"cc":e=$M(YM.Day,1);break;case"ccc":e=tv(qM.Days,SM.Abbreviated,kM.Standalone);break;case"cccc":e=tv(qM.Days,SM.Wide,kM.Standalone);break;case"ccccc":e=tv(qM.Days,SM.Narrow,kM.Standalone);break;case"cccccc":e=tv(qM.Days,SM.Short,kM.Standalone);break;case"E":case"EE":case"EEE":e=tv(qM.Days,SM.Abbreviated);break;case"EEEE":e=tv(qM.Days,SM.Wide);break;case"EEEEE":e=tv(qM.Days,SM.Narrow);break;case"EEEEEE":e=tv(qM.Days,SM.Short);break;case"a":case"aa":case"aaa":e=tv(qM.DayPeriods,SM.Abbreviated);break;case"aaaa":e=tv(qM.DayPeriods,SM.Wide);break;case"aaaaa":e=tv(qM.DayPeriods,SM.Narrow);break;case"b":case"bb":case"bbb":e=tv(qM.DayPeriods,SM.Abbreviated,kM.Standalone,!0);break;case"bbbb":e=tv(qM.DayPeriods,SM.Wide,kM.Standalone,!0);break;case"bbbbb":e=tv(qM.DayPeriods,SM.Narrow,kM.Standalone,!0);break;case"B":case"BB":case"BBB":e=tv(qM.DayPeriods,SM.Abbreviated,kM.Format,!0);break;case"BBBB":e=tv(qM.DayPeriods,SM.Wide,kM.Format,!0);break;case"BBBBB":e=tv(qM.DayPeriods,SM.Narrow,kM.Format,!0);break;case"h":e=$M(YM.Hours,1,-12);break;case"hh":e=$M(YM.Hours,2,-12);break;case"H":e=$M(YM.Hours,1);break;case"HH":e=$M(YM.Hours,2);break;case"m":e=$M(YM.Minutes,1);break;case"mm":e=$M(YM.Minutes,2);break;case"s":e=$M(YM.Seconds,1);break;case"ss":e=$M(YM.Seconds,2);break;case"S":e=$M(YM.FractionalSeconds,1);break;case"SS":e=$M(YM.FractionalSeconds,2);break;case"SSS":e=$M(YM.FractionalSeconds,3);break;case"Z":case"ZZ":case"ZZZ":e=ev(GM.Short);break;case"ZZZZZ":e=ev(GM.Extended);break;case"O":case"OO":case"OOO":case"z":case"zz":case"zzz":e=ev(GM.ShortGMT);break;case"OOOO":case"ZZZZ":case"zzzz":e=ev(GM.Long);break;default:return null}return av[t]=e,e})(t);d+=e?e(i,n,l):"''"===t?"'":t.replace(/(^'|'$)/g,"").replace(/''/g,"'")})),d}function XM(t,e,n){const o=new Date(0);return o.setFullYear(t,e,n),o.setHours(0,0,0),o}function KM(t,e){const n=(function o(t){return gg(t)[yg.LocaleId]})(t);if(UM[n]=UM[n]||{},UM[n][e])return UM[n][e];let i="";switch(e){case"shortDate":i=AM(t,DM.Short);break;case"mediumDate":i=AM(t,DM.Medium);break;case"longDate":i=AM(t,DM.Long);break;case"fullDate":i=AM(t,DM.Full);break;case"shortTime":i=TM(t,DM.Short);break;case"mediumTime":i=TM(t,DM.Medium);break;case"longTime":i=TM(t,DM.Long);break;case"fullTime":i=TM(t,DM.Full);break;case"short":const e=KM(t,"shortTime"),n=KM(t,"shortDate");i=JM(NM(t,DM.Short),[e,n]);break;case"medium":const o=KM(t,"mediumTime"),a=KM(t,"mediumDate");i=JM(NM(t,DM.Medium),[o,a]);break;case"long":const r=KM(t,"longTime"),s=KM(t,"longDate");i=JM(NM(t,DM.Long),[r,s]);break;case"full":const l=KM(t,"fullTime"),c=KM(t,"fullDate");i=JM(NM(t,DM.Full),[l,c])}return i&&(UM[n][e]=i),i}function JM(t,e){return e&&(t=t.replace(/\{([^}]+)}/g,(function(t,n){return null!=e&&n in e?e[n]:t}))),t}function QM(t,e,n="-",o,i){let a="";(t<0||i&&t<=0)&&(i?t=1-t:(t=-t,a=n));let r=String(t);for(;r.length<e;)r="0"+r;return o&&(r=r.substr(r.length-e)),a+r}function $M(t,e,n=0,o=!1,i=!1){return function(a,r){let s=(function l(t,e){switch(t){case YM.FullYear:return e.getFullYear();case YM.Month:return e.getMonth();case YM.Date:return e.getDate();case YM.Hours:return e.getHours();case YM.Minutes:return e.getMinutes();case YM.Seconds:return e.getSeconds();case YM.FractionalSeconds:return e.getMilliseconds();case YM.Day:return e.getDay();default:throw new Error(`Unknown DateType value "${t}".`)}})(t,a);if((n>0||s>-n)&&(s+=n),t===YM.Hours)0===s&&-12===n&&(s=12);else if(t===YM.FractionalSeconds)return(function c(t,e){return QM(t,3).substr(0,e)})(s,e);const d=zM(r,RM.MinusSign);return QM(s,e,d,o,i)}}function tv(t,e,n=kM.Format,o=!1){return function(i,a){return(function r(t,e,n,o,i,a){switch(n){case qM.Months:return(function r(t,e,n){const o=gg(t),i=LM([o[yg.MonthsFormat],o[yg.MonthsStandalone]],e);return LM(i,n)})(e,i,o)[t.getMonth()];case qM.Days:return(function s(t,e,n){const o=gg(t),i=LM([o[yg.DaysFormat],o[yg.DaysStandalone]],e);return LM(i,n)})(e,i,o)[t.getDay()];case qM.DayPeriods:const r=t.getHours(),s=t.getMinutes();if(a){const t=(function l(t){const e=gg(t);return FM(e),(e[yg.ExtraData][2]||[]).map((t=>"string"==typeof t?BM(t):[BM(t[0]),BM(t[1])]))})(e),n=(function c(t,e,n){const o=gg(t);FM(o);const i=LM([o[yg.ExtraData][0],o[yg.ExtraData][1]],e)||[];return LM(i,n)||[]})(e,i,o),a=t.findIndex((t=>{if(Array.isArray(t)){const[e,n]=t,o=r>=e.hours&&s>=e.minutes,i=r<n.hours||r===n.hours&&s<n.minutes;if(e.hours<n.hours){if(o&&i)return!0}else if(o||i)return!0}else if(t.hours===r&&t.minutes===s)return!0;return!1}));if(-1!==a)return n[a]}return(function d(t,e,n){const o=gg(t),i=LM([o[yg.DayPeriodsFormat],o[yg.DayPeriodsStandalone]],e);return LM(i,n)})(e,i,o)[r<12?0:1];case qM.Eras:return(function p(t,e){return LM(gg(t)[yg.Eras],e)})(e,o)[t.getFullYear()<=0?0:1];default:throw new Error(`unexpected translation type ${n}`)}})(i,a,t,e,n,o)}}function ev(t){return function(e,n,o){const i=-1*o,a=zM(n,RM.MinusSign),r=i>0?Math.floor(i/60):Math.ceil(i/60);switch(t){case GM.Short:return(i>=0?"+":"")+QM(r,2,a)+QM(Math.abs(i%60),2,a);case GM.ShortGMT:return"GMT"+(i>=0?"+":"")+QM(r,1,a);case GM.Long:return"GMT"+(i>=0?"+":"")+QM(r,2,a)+":"+QM(Math.abs(i%60),2,a);case GM.Extended:return 0===o?"Z":(i>=0?"+":"")+QM(r,2,a)+":"+QM(Math.abs(i%60),2,a);default:throw new Error(`Unknown zone width "${t}"`)}}}!(function(t){t[t.Short=0]="Short",t[t.ShortGMT=1]="ShortGMT",t[t.Long=2]="Long",t[t.Extended=3]="Extended"})(GM||(GM={})),(function(t){t[t.FullYear=0]="FullYear",t[t.Month=1]="Month",t[t.Date=2]="Date",t[t.Hours=3]="Hours",t[t.Minutes=4]="Minutes",t[t.Seconds=5]="Seconds",t[t.FractionalSeconds=6]="FractionalSeconds",t[t.Day=7]="Day"})(YM||(YM={})),(function(t){t[t.DayPeriods=0]="DayPeriods",t[t.Days=1]="Days",t[t.Months=2]="Months",t[t.Eras=3]="Eras"})(qM||(qM={}));function nv(t){return XM(t.getFullYear(),t.getMonth(),t.getDate()+(4-t.getDay()))}function ov(t,e=!1){return function(n,o){let i;if(e){const t=new Date(n.getFullYear(),n.getMonth(),1).getDay()-1,e=n.getDate();i=1+Math.floor((e+t)/7)}else{const t=nv(n),e=(function a(t){const e=XM(t,0,1).getDay();return XM(t,0,1+(e<=4?4:11)-e)})(t.getFullYear()),o=t.getTime()-e.getTime();i=1+Math.round(o/6048e5)}return QM(i,t,zM(o,RM.MinusSign))}}function iv(t,e=!1){return function(n,o){return QM(nv(n).getFullYear(),t,zM(o,RM.MinusSign),e)}}const av={};function rv(t,e){t=t.replace(/:/g,"");const n=Date.parse("Jan 01, 1970 00:00:00 "+t)/6e4;return isNaN(n)?e:n}function sv(t){return t instanceof Date&&!isNaN(t.valueOf())}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const lv=/^(\d+)?\.((\d+)(-(\d+))?)?$/,cv=".",dv="0";function pv(t,e,n,o,i,a,r=!1){let s="",l=!1;if(isFinite(t)){let c=(function c(t){let e,n,o,i,a,r=Math.abs(t)+"",s=0;(n=r.indexOf(cv))>-1&&(r=r.replace(cv,""));(o=r.search(/e/i))>0?(n<0&&(n=o),n+=+r.slice(o+1),r=r.substring(0,o)):n<0&&(n=r.length);for(o=0;r.charAt(o)===dv;o++);if(o===(a=r.length))e=[0],n=1;else{for(a--;r.charAt(a)===dv;)a--;for(n-=o,e=[],i=0;o<=a;o++,i++)e[i]=Number(r.charAt(o))}n>22&&(e=e.splice(0,21),s=n-1,n=1);return{digits:e,exponent:s,integerLen:n}})(t);r&&(c=(function d(t){if(0===t.digits[0])return t;const e=t.digits.length-t.integerLen;t.exponent?t.exponent+=2:(0===e?t.digits.push(0,0):1===e&&t.digits.push(0),t.integerLen+=2);return t})(c));let d=e.minInt,p=e.minFrac,m=e.maxFrac;if(a){const t=a.match(lv);if(null===t)throw new Error(`${a} is not a valid digit info`);const e=t[1],n=t[3],o=t[5];null!=e&&(d=gv(e)),null!=n&&(p=gv(n)),null!=o?m=gv(o):null!=n&&p>m&&(m=p)}!(function p(t,e,n){if(e>n)throw new Error(`The minimum number of digits after fraction (${e}) is higher than the maximum (${n}).`);let o=t.digits,i=o.length-t.integerLen;const a=Math.min(Math.max(e,i),n);let r=a+t.integerLen,s=o[r];if(r>0){o.splice(Math.max(t.integerLen,r));for(let t=r;t<o.length;t++)o[t]=0}else{i=Math.max(0,i),t.integerLen=1,o.length=Math.max(1,r=a+1),o[0]=0;for(let t=1;t<r;t++)o[t]=0}if(s>=5)if(r-1<0){for(let e=0;e>r;e--)o.unshift(0),t.integerLen++;o.unshift(1),t.integerLen++}else o[r-1]++;for(;i<Math.max(0,a);i++)o.push(0);let l=0!==a;const c=e+t.integerLen,d=o.reduceRight((function(t,e,n,o){return o[n]=(e+=t)<10?e:e-10,l&&(0===o[n]&&n>=c?o.pop():l=!1),e>=10?1:0}),0);d&&(o.unshift(d),t.integerLen++)})(c,p,m);let f=c.digits,u=c.integerLen;const g=c.exponent;let h=[];for(l=f.every((t=>!t));u<d;u++)f.unshift(0);for(;u<0;u++)f.unshift(0);u>0?h=f.splice(u,f.length):(h=f,f=[0]);const b=[];for(f.length>=e.lgSize&&b.unshift(f.splice(-e.lgSize,f.length).join(""));f.length>e.gSize;)b.unshift(f.splice(-e.gSize,f.length).join(""));f.length&&b.unshift(f.join("")),s=b.join(zM(n,o)),h.length&&(s+=zM(n,i)+h.join("")),g&&(s+=zM(n,RM.Exponential)+"+"+g)}else s=zM(n,RM.Infinity);return s=t<0&&!l?e.negPre+s+e.negSuf:e.posPre+s+e.posSuf,s}function mv(t,e,n,o,i){const a=uv(IM(e,PM.Currency),zM(e,RM.MinusSign));a.minFrac=(function r(t){let e;const n=OM[t];return n&&(e=n[2]),"number"==typeof e?e:2})(o),a.maxFrac=a.minFrac;return pv(t,a,e,RM.CurrencyGroup,RM.CurrencyDecimal,i).replace("¤",n).replace("¤","").trim()}function fv(t,e,n){return pv(t,uv(IM(e,PM.Decimal),zM(e,RM.MinusSign)),e,RM.Group,RM.Decimal,n)}function uv(t,e="-"){const n={minInt:1,minFrac:0,maxFrac:0,posPre:"",posSuf:"",negPre:"",negSuf:"",gSize:0,lgSize:0},o=t.split(";"),i=o[0],a=o[1],r=-1!==i.indexOf(cv)?i.split(cv):[i.substring(0,i.lastIndexOf(dv)+1),i.substring(i.lastIndexOf(dv)+1)],s=r[0],l=r[1]||"";n.posPre=s.substr(0,s.indexOf("#"));for(let t=0;t<l.length;t++){const e=l.charAt(t);e===dv?n.minFrac=n.maxFrac=t+1:"#"===e?n.maxFrac=t+1:n.posSuf+=e}const c=s.split(",");if(n.gSize=c[1]?c[1].length:0,n.lgSize=c[2]||c[1]?(c[2]||c[1]).length:0,a){const t=i.length-n.posPre.length-n.posSuf.length,e=a.indexOf("#");n.negPre=a.substr(0,e).replace(/'/g,""),n.negSuf=a.substr(e+t).replace(/'/g,"")}else n.negPre=e+n.posPre,n.negSuf=n.posSuf;return n}function gv(t){const e=parseInt(t);if(isNaN(e))throw new Error("Invalid integer literal when parsing "+t);return e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class hv{}function bv(t,e,n,o){let i=`=${t}`;if(e.indexOf(i)>-1)return i;if(i=n.getPluralCategory(t,o),e.indexOf(i)>-1)return i;if(e.indexOf("other")>-1)return"other";throw new Error(`No plural message found for value "${t}"`)}class yv extends hv{constructor(t){super(),this.locale=t}getPluralCategory(t,e){switch(HM(e||this.locale)(t)){case wM.Zero:return"zero";case wM.One:return"one";case wM.Two:return"two";case wM.Few:return"few";case wM.Many:return"many";default:return"other"}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function _v(t,e){e=encodeURIComponent(e);for(const n of t.split(";")){const t=n.indexOf("="),[o,i]=-1==t?[n,""]:[n.slice(0,t),n.slice(t+1)];if(o.trim()===e)return decodeURIComponent(i)}return null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */yv.ɵfac=function t(e){return new(e||yv)(Tr(Q_))},yv.ɵprov=Sn({token:yv,factory:yv.ɵfac}),yv.ctorParameters=()=>[{type:String,decorators:[{type:Lr,args:[Q_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(yv,[{type:Um}],(function(){return[{type:String,decorators:[{type:Lr,args:[Q_]}]}]}),null);class Cv{constructor(t,e,n,o){this._iterableDiffers=t,this._keyValueDiffers=e,this._ngEl=n,this._renderer=o,this._iterableDiffer=null,this._keyValueDiffer=null,this._initialClasses=[],this._rawClass=null}set klass(t){this._removeClasses(this._initialClasses),this._initialClasses="string"==typeof t?t.split(/\s+/):[],this._applyClasses(this._initialClasses),this._applyClasses(this._rawClass)}set ngClass(t){this._removeClasses(this._rawClass),this._applyClasses(this._initialClasses),this._iterableDiffer=null,this._keyValueDiffer=null,this._rawClass="string"==typeof t?t.split(/\s+/):t,this._rawClass&&(dm(this._rawClass)?this._iterableDiffer=this._iterableDiffers.find(this._rawClass).create():this._keyValueDiffer=this._keyValueDiffers.find(this._rawClass).create())}ngDoCheck(){if(this._iterableDiffer){const t=this._iterableDiffer.diff(this._rawClass);t&&this._applyIterableChanges(t)}else if(this._keyValueDiffer){const t=this._keyValueDiffer.diff(this._rawClass);t&&this._applyKeyValueChanges(t)}}_applyKeyValueChanges(t){t.forEachAddedItem((t=>this._toggleClass(t.key,t.currentValue))),t.forEachChangedItem((t=>this._toggleClass(t.key,t.currentValue))),t.forEachRemovedItem((t=>{t.previousValue&&this._toggleClass(t.key,!1)}))}_applyIterableChanges(t){t.forEachAddedItem((t=>{if("string"!=typeof t.item)throw new Error(`NgClass can only toggle CSS classes expressed as strings, got ${Ke(t.item)}`);this._toggleClass(t.item,!0)})),t.forEachRemovedItem((t=>this._toggleClass(t.item,!1)))}_applyClasses(t){t&&(Array.isArray(t)||t instanceof Set?t.forEach((t=>this._toggleClass(t,!0))):Object.keys(t).forEach((e=>this._toggleClass(e,!!t[e]))))}_removeClasses(t){t&&(Array.isArray(t)||t instanceof Set?t.forEach((t=>this._toggleClass(t,!1))):Object.keys(t).forEach((t=>this._toggleClass(t,!1))))}_toggleClass(t,e){(t=t.trim())&&t.split(/\s+/g).forEach((t=>{e?this._renderer.addClass(this._ngEl.nativeElement,t):this._renderer.removeClass(this._ngEl.nativeElement,t)}))}}Cv.ɵfac=function t(e){return new(e||Cv)(gf(hb),gf(yb),gf(Kh),gf($h))},Cv.ɵdir=ho({type:Cv,selectors:[["","ngClass",""]],inputs:{klass:["class","klass"],ngClass:"ngClass"}}),Cv.ctorParameters=()=>[{type:hb},{type:yb},{type:Kh},{type:$h}],Cv.propDecorators={klass:[{type:R_,args:["class"]}],ngClass:[{type:R_,args:["ngClass"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Cv,[{type:k_,args:[{selector:"[ngClass]"}]}],(function(){return[{type:hb},{type:yb},{type:Kh},{type:$h}]}),{klass:[{type:R_,args:["class"]}],ngClass:[{type:R_,args:["ngClass"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Mv{constructor(t){this._viewContainerRef=t,this._componentRef=null,this._moduleRef=null}ngOnChanges(t){if(this._viewContainerRef.clear(),this._componentRef=null,this.ngComponentOutlet){const e=this.ngComponentOutletInjector||this._viewContainerRef.parentInjector;if(t.ngComponentOutletNgModuleFactory)if(this._moduleRef&&this._moduleRef.destroy(),this.ngComponentOutletNgModuleFactory){const t=e.get(zb);this._moduleRef=this.ngComponentOutletNgModuleFactory.create(t.injector)}else this._moduleRef=null;const n=(this._moduleRef?this._moduleRef.componentFactoryResolver:e.get(Gh)).resolveComponentFactory(this.ngComponentOutlet);this._componentRef=this._viewContainerRef.createComponent(n,this._viewContainerRef.length,e,this.ngComponentOutletContent)}}ngOnDestroy(){this._moduleRef&&this._moduleRef.destroy()}}Mv.ɵfac=function t(e){return new(e||Mv)(gf(Fb))},Mv.ɵdir=ho({type:Mv,selectors:[["","ngComponentOutlet",""]],inputs:{ngComponentOutlet:"ngComponentOutlet",ngComponentOutletInjector:"ngComponentOutletInjector",ngComponentOutletContent:"ngComponentOutletContent",ngComponentOutletNgModuleFactory:"ngComponentOutletNgModuleFactory"},features:[qo]}),Mv.ctorParameters=()=>[{type:Fb}],Mv.propDecorators={ngComponentOutlet:[{type:R_}],ngComponentOutletInjector:[{type:R_}],ngComponentOutletContent:[{type:R_}],ngComponentOutletNgModuleFactory:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Mv,[{type:k_,args:[{selector:"[ngComponentOutlet]"}]}],(function(){return[{type:Fb}]}),{ngComponentOutlet:[{type:R_}],ngComponentOutletInjector:[{type:R_}],ngComponentOutletContent:[{type:R_}],ngComponentOutletNgModuleFactory:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class vv{constructor(t,e,n,o){this.$implicit=t,this.ngForOf=e,this.index=n,this.count=o}get first(){return 0===this.index}get last(){return this.index===this.count-1}get even(){return this.index%2==0}get odd(){return!this.even}}class xv{constructor(t,e,n){this._viewContainer=t,this._template=e,this._differs=n,this._ngForOf=null,this._ngForOfDirty=!0,this._differ=null}set ngForOf(t){this._ngForOf=t,this._ngForOfDirty=!0}set ngForTrackBy(t){"undefined"!=typeof ngDevMode&&!ngDevMode||null==t||"function"==typeof t||console&&console.warn&&console.warn(`trackBy must be a function, but received ${JSON.stringify(t)}. See https://angular.io/api/common/NgForOf#change-propagation for more information.`),this._trackByFn=t}get ngForTrackBy(){return this._trackByFn}set ngForTemplate(t){t&&(this._template=t)}ngDoCheck(){if(this._ngForOfDirty){this._ngForOfDirty=!1;const t=this._ngForOf;if(!this._differ&&t)try{this._differ=this._differs.find(t).create(this.ngForTrackBy)}catch(e){throw new Error(`Cannot find a differ supporting object '${t}' of type '${(function n(t){return t.name||typeof t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t)}'. NgFor only supports binding to Iterables such as Arrays.`)}}if(this._differ){const t=this._differ.diff(this._ngForOf);t&&this._applyChanges(t)}}_applyChanges(t){const e=[];t.forEachOperation(((t,n,o)=>{if(null==t.previousIndex){const n=this._viewContainer.createEmbeddedView(this._template,new vv(null,this._ngForOf,-1,-1),null===o?void 0:o),i=new Ov(t,n);e.push(i)}else if(null==o)this._viewContainer.remove(null===n?void 0:n);else if(null!==n){const i=this._viewContainer.get(n);this._viewContainer.move(i,o);const a=new Ov(t,i);e.push(a)}}));for(let t=0;t<e.length;t++)this._perViewChange(e[t].view,e[t].record);for(let t=0,e=this._viewContainer.length;t<e;t++){const n=this._viewContainer.get(t);n.context.index=t,n.context.count=e,n.context.ngForOf=this._ngForOf}t.forEachIdentityChange((t=>{this._viewContainer.get(t.currentIndex).context.$implicit=t.item}))}_perViewChange(t,e){t.context.$implicit=e.item}static ngTemplateContextGuard(t,e){return!0}}xv.ɵfac=function t(e){return new(e||xv)(gf(Fb),gf(Eb),gf(hb))},xv.ɵdir=ho({type:xv,selectors:[["","ngFor","","ngForOf",""]],inputs:{ngForOf:"ngForOf",ngForTrackBy:"ngForTrackBy",ngForTemplate:"ngForTemplate"}}),xv.ctorParameters=()=>[{type:Fb},{type:Eb},{type:hb}],xv.propDecorators={ngForOf:[{type:R_}],ngForTrackBy:[{type:R_}],ngForTemplate:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(xv,[{type:k_,args:[{selector:"[ngFor][ngForOf]"}]}],(function(){return[{type:Fb},{type:Eb},{type:hb}]}),{ngForOf:[{type:R_}],ngForTrackBy:[{type:R_}],ngForTemplate:[{type:R_}]});class Ov{constructor(t,e){this.record=t,this.view=e}}class Pv{constructor(t,e){this._viewContainer=t,this._context=new wv,this._thenTemplateRef=null,this._elseTemplateRef=null,this._thenViewRef=null,this._elseViewRef=null,this._thenTemplateRef=e}set ngIf(t){this._context.$implicit=this._context.ngIf=t,this._updateView()}set ngIfThen(t){kv("ngIfThen",t),this._thenTemplateRef=t,this._thenViewRef=null,this._updateView()}set ngIfElse(t){kv("ngIfElse",t),this._elseTemplateRef=t,this._elseViewRef=null,this._updateView()}_updateView(){this._context.$implicit?this._thenViewRef||(this._viewContainer.clear(),this._elseViewRef=null,this._thenTemplateRef&&(this._thenViewRef=this._viewContainer.createEmbeddedView(this._thenTemplateRef,this._context))):this._elseViewRef||(this._viewContainer.clear(),this._thenViewRef=null,this._elseTemplateRef&&(this._elseViewRef=this._viewContainer.createEmbeddedView(this._elseTemplateRef,this._context)))}static ngTemplateContextGuard(t,e){return!0}}Pv.ɵfac=function t(e){return new(e||Pv)(gf(Fb),gf(Eb))},Pv.ɵdir=ho({type:Pv,selectors:[["","ngIf",""]],inputs:{ngIf:"ngIf",ngIfThen:"ngIfThen",ngIfElse:"ngIfElse"}}),Pv.ctorParameters=()=>[{type:Fb},{type:Eb}],Pv.propDecorators={ngIf:[{type:R_}],ngIfThen:[{type:R_}],ngIfElse:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Pv,[{type:k_,args:[{selector:"[ngIf]"}]}],(function(){return[{type:Fb},{type:Eb}]}),{ngIf:[{type:R_}],ngIfThen:[{type:R_}],ngIfElse:[{type:R_}]});class wv{constructor(){this.$implicit=null,this.ngIf=null}}function kv(t,e){if(!!(e&&!e.createEmbeddedView))throw new Error(`${t} must be a TemplateRef, but received '${Ke(e)}'.`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Sv{constructor(t,e){this._viewContainerRef=t,this._templateRef=e,this._created=!1}create(){this._created=!0,this._viewContainerRef.createEmbeddedView(this._templateRef)}destroy(){this._created=!1,this._viewContainerRef.clear()}enforceState(t){t&&!this._created?this.create():!t&&this._created&&this.destroy()}}class Dv{constructor(){this._defaultUsed=!1,this._caseCount=0,this._lastCaseCheckIndex=0,this._lastCasesMatched=!1}set ngSwitch(t){this._ngSwitch=t,0===this._caseCount&&this._updateDefaultCases(!0)}_addCase(){return this._caseCount++}_addDefault(t){this._defaultViews||(this._defaultViews=[]),this._defaultViews.push(t)}_matchCase(t){const e=t==this._ngSwitch;return this._lastCasesMatched=this._lastCasesMatched||e,this._lastCaseCheckIndex++,this._lastCaseCheckIndex===this._caseCount&&(this._updateDefaultCases(!this._lastCasesMatched),this._lastCaseCheckIndex=0,this._lastCasesMatched=!1),e}_updateDefaultCases(t){if(this._defaultViews&&t!==this._defaultUsed){this._defaultUsed=t;for(let e=0;e<this._defaultViews.length;e++){this._defaultViews[e].enforceState(t)}}}}Dv.ɵfac=function t(e){return new(e||Dv)},Dv.ɵdir=ho({type:Dv,selectors:[["","ngSwitch",""]],inputs:{ngSwitch:"ngSwitch"}}),Dv.propDecorators={ngSwitch:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Dv,[{type:k_,args:[{selector:"[ngSwitch]"}]}],(function(){return[]}),{ngSwitch:[{type:R_}]});class Rv{constructor(t,e,n){this.ngSwitch=n,"undefined"!=typeof ngDevMode&&!ngDevMode||n||Av("ngSwitchCase","NgSwitchCase"),n._addCase(),this._view=new Sv(t,e)}ngDoCheck(){this._view.enforceState(this.ngSwitch._matchCase(this.ngSwitchCase))}}Rv.ɵfac=function t(e){return new(e||Rv)(gf(Fb),gf(Eb),gf(Dv,9))},Rv.ɵdir=ho({type:Rv,selectors:[["","ngSwitchCase",""]],inputs:{ngSwitchCase:"ngSwitchCase"}}),Rv.ctorParameters=()=>[{type:Fb},{type:Eb},{type:Dv,decorators:[{type:Br},{type:Ur}]}],Rv.propDecorators={ngSwitchCase:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Rv,[{type:k_,args:[{selector:"[ngSwitchCase]"}]}],(function(){return[{type:Fb},{type:Eb},{type:Dv,decorators:[{type:Br},{type:Ur}]}]}),{ngSwitchCase:[{type:R_}]});class Ev{constructor(t,e,n){"undefined"!=typeof ngDevMode&&!ngDevMode||n||Av("ngSwitchDefault","NgSwitchDefault"),n._addDefault(new Sv(t,e))}}function Av(t,e){throw new nn("305",`An element with the "${t}" attribute (matching the "${e}" directive) must be located inside an element with the "ngSwitch" attribute (matching "NgSwitch" directive)`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */Ev.ɵfac=function t(e){return new(e||Ev)(gf(Fb),gf(Eb),gf(Dv,9))},Ev.ɵdir=ho({type:Ev,selectors:[["","ngSwitchDefault",""]]}),Ev.ctorParameters=()=>[{type:Fb},{type:Eb},{type:Dv,decorators:[{type:Br},{type:Ur}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Ev,[{type:k_,args:[{selector:"[ngSwitchDefault]"}]}],(function(){return[{type:Fb},{type:Eb},{type:Dv,decorators:[{type:Br},{type:Ur}]}]}),null);class Tv{constructor(t){this._localization=t,this._caseViews={}}set ngPlural(t){this._switchValue=t,this._updateView()}addCase(t,e){this._caseViews[t]=e}_updateView(){this._clearViews();const t=Object.keys(this._caseViews),e=bv(this._switchValue,t,this._localization);this._activateView(this._caseViews[e])}_clearViews(){this._activeView&&this._activeView.destroy()}_activateView(t){t&&(this._activeView=t,this._activeView.create())}}Tv.ɵfac=function t(e){return new(e||Tv)(gf(hv))},Tv.ɵdir=ho({type:Tv,selectors:[["","ngPlural",""]],inputs:{ngPlural:"ngPlural"}}),Tv.ctorParameters=()=>[{type:hv}],Tv.propDecorators={ngPlural:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Tv,[{type:k_,args:[{selector:"[ngPlural]"}]}],(function(){return[{type:hv}]}),{ngPlural:[{type:R_}]});class Nv{constructor(t,e,n,o){this.value=t;const i=!isNaN(Number(t));o.addCase(i?`=${t}`:t,new Sv(n,e))}}Nv.ɵfac=function t(e){return new(e||Nv)(Ga("ngPluralCase"),gf(Eb),gf(Fb),gf(Tv,1))},Nv.ɵdir=ho({type:Nv,selectors:[["","ngPluralCase",""]]}),Nv.ctorParameters=()=>[{type:String,decorators:[{type:$a,args:["ngPluralCase"]}]},{type:Eb},{type:Fb},{type:Tv,decorators:[{type:Ur}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Nv,[{type:k_,args:[{selector:"[ngPluralCase]"}]}],(function(){return[{type:String,decorators:[{type:$a,args:["ngPluralCase"]}]},{type:Eb},{type:Fb},{type:Tv,decorators:[{type:Ur}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class zv{constructor(t,e,n){this._ngEl=t,this._differs=e,this._renderer=n,this._ngStyle=null,this._differ=null}set ngStyle(t){this._ngStyle=t,!this._differ&&t&&(this._differ=this._differs.find(t).create())}ngDoCheck(){if(this._differ){const t=this._differ.diff(this._ngStyle);t&&this._applyChanges(t)}}_setStyle(t,e){const[n,o]=t.split(".");null!=(e=null!=e&&o?`${e}${o}`:e)?this._renderer.setStyle(this._ngEl.nativeElement,n,e):this._renderer.removeStyle(this._ngEl.nativeElement,n)}_applyChanges(t){t.forEachRemovedItem((t=>this._setStyle(t.key,null))),t.forEachAddedItem((t=>this._setStyle(t.key,t.currentValue))),t.forEachChangedItem((t=>this._setStyle(t.key,t.currentValue)))}}zv.ɵfac=function t(e){return new(e||zv)(gf(Kh),gf(yb),gf($h))},zv.ɵdir=ho({type:zv,selectors:[["","ngStyle",""]],inputs:{ngStyle:"ngStyle"}}),zv.ctorParameters=()=>[{type:Kh},{type:yb},{type:$h}],zv.propDecorators={ngStyle:[{type:R_,args:["ngStyle"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(zv,[{type:k_,args:[{selector:"[ngStyle]"}]}],(function(){return[{type:Kh},{type:yb},{type:$h}]}),{ngStyle:[{type:R_,args:["ngStyle"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Iv{constructor(t){this._viewContainerRef=t,this._viewRef=null,this.ngTemplateOutletContext=null,this.ngTemplateOutlet=null}ngOnChanges(t){if(t.ngTemplateOutlet){const t=this._viewContainerRef;this._viewRef&&t.remove(t.indexOf(this._viewRef)),this._viewRef=this.ngTemplateOutlet?t.createEmbeddedView(this.ngTemplateOutlet,this.ngTemplateOutletContext):null}else this._viewRef&&t.ngTemplateOutletContext&&this.ngTemplateOutletContext&&(this._viewRef.context=this.ngTemplateOutletContext)}}Iv.ɵfac=function t(e){return new(e||Iv)(gf(Fb))},Iv.ɵdir=ho({type:Iv,selectors:[["","ngTemplateOutlet",""]],inputs:{ngTemplateOutletContext:"ngTemplateOutletContext",ngTemplateOutlet:"ngTemplateOutlet"},features:[qo]}),Iv.ctorParameters=()=>[{type:Fb}],Iv.propDecorators={ngTemplateOutletContext:[{type:R_}],ngTemplateOutlet:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Iv,[{type:k_,args:[{selector:"[ngTemplateOutlet]"}]}],(function(){return[{type:Fb}]}),{ngTemplateOutletContext:[{type:R_}],ngTemplateOutlet:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Hv=[Cv,Mv,xv,Pv,Iv,zv,Dv,Rv,Ev,Tv,Nv];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Fv(t,e){return Error(`InvalidPipeArgument: '${e}' for pipe '${Ke(t)}'`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Lv=new class{createSubscription(t,e){return t.then(e,(t=>{throw t}))}dispose(t){}onDestroy(t){}},Bv=new class{createSubscription(t,e){return t.subscribe({next:e,error:t=>{throw t}})}dispose(t){t.unsubscribe()}onDestroy(t){t.unsubscribe()}};class Vv{constructor(t){this._ref=t,this._latestValue=null,this._subscription=null,this._obj=null,this._strategy=null}ngOnDestroy(){this._subscription&&this._dispose()}transform(t){return this._obj?t!==this._obj?(this._dispose(),this.transform(t)):this._latestValue:(t&&this._subscribe(t),this._latestValue)}_subscribe(t){this._obj=t,this._strategy=this._selectStrategy(t),this._subscription=this._strategy.createSubscription(t,(e=>this._updateLatestValue(t,e)))}_selectStrategy(t){if(kf(t))return Lv;if(Sf(t))return Bv;throw Fv(Vv,t)}_dispose(){this._strategy.dispose(this._subscription),this._latestValue=null,this._subscription=null,this._obj=null}_updateLatestValue(t,e){t===this._obj&&(this._latestValue=e,this._ref.markForCheck())}}Vv.ɵfac=function t(e){return new(e||Vv)(gf(Ob,16))},Vv.ɵpipe=bo({name:"async",type:Vv,pure:!1}),Vv.ctorParameters=()=>[{type:Ob}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Vv,[{type:D_,args:[{name:"async",pure:!1}]}],(function(){return[{type:Ob}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class jv{transform(t){if(null==t)return null;if("string"!=typeof t)throw Fv(jv,t);return t.toLowerCase()}}jv.ɵfac=function t(e){return new(e||jv)},jv.ɵpipe=bo({name:"lowercase",type:jv,pure:!0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(jv,[{type:D_,args:[{name:"lowercase"}]}],null,null);const 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Wv{transform(t){if(null==t)return null;if("string"!=typeof t)throw Fv(Wv,t);return t.replace(Uv,(t=>t[0].toUpperCase()+t.substr(1).toLowerCase()))}}Wv.ɵfac=function t(e){return new(e||Wv)},Wv.ɵpipe=bo({name:"titlecase",type:Wv,pure:!0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Wv,[{type:D_,args:[{name:"titlecase"}]}],null,null);class Gv{transform(t){if(null==t)return null;if("string"!=typeof t)throw Fv(Gv,t);return t.toUpperCase()}}Gv.ɵfac=function t(e){return new(e||Gv)},Gv.ɵpipe=bo({name:"uppercase",type:Gv,pure:!0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Gv,[{type:D_,args:[{name:"uppercase"}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Yv{constructor(t){this.locale=t}transform(t,e="mediumDate",n,o){if(null==t||""===t||t!=t)return null;try{return ZM(t,e,o||this.locale,n)}catch(t){throw Fv(Yv,t.message)}}}Yv.ɵfac=function t(e){return new(e||Yv)(gf(Q_,16))},Yv.ɵpipe=bo({name:"date",type:Yv,pure:!0}),Yv.ctorParameters=()=>[{type:String,decorators:[{type:Lr,args:[Q_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Yv,[{type:D_,args:[{name:"date",pure:!0}]}],(function(){return[{type:String,decorators:[{type:Lr,args:[Q_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const qv=/#/g;class Zv{constructor(t){this._localization=t}transform(t,e,n){if(null==t)return"";if("object"!=typeof e||null===e)throw Fv(Zv,e);return e[bv(t,Object.keys(e),this._localization,n)].replace(qv,t.toString())}}Zv.ɵfac=function t(e){return new(e||Zv)(gf(hv,16))},Zv.ɵpipe=bo({name:"i18nPlural",type:Zv,pure:!0}),Zv.ctorParameters=()=>[{type:hv}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Zv,[{type:D_,args:[{name:"i18nPlural",pure:!0}]}],(function(){return[{type:hv}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Xv{transform(t,e){if(null==t)return"";if("object"!=typeof e||"string"!=typeof t)throw Fv(Xv,e);return e.hasOwnProperty(t)?e[t]:e.hasOwnProperty("other")?e.other:""}}Xv.ɵfac=function t(e){return new(e||Xv)},Xv.ɵpipe=bo({name:"i18nSelect",type:Xv,pure:!0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Xv,[{type:D_,args:[{name:"i18nSelect",pure:!0}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Kv{transform(t){return JSON.stringify(t,null,2)}}Kv.ɵfac=function t(e){return new(e||Kv)},Kv.ɵpipe=bo({name:"json",type:Kv,pure:!1}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Kv,[{type:D_,args:[{name:"json",pure:!1}]}],null,null);class Jv{constructor(t){this.differs=t,this.keyValues=[],this.compareFn=Qv}transform(t,e=Qv){if(!t||!(t instanceof Map)&&"object"!=typeof t)return null;this.differ||(this.differ=this.differs.find(t).create());const n=this.differ.diff(t),o=e!==this.compareFn;return n&&(this.keyValues=[],n.forEachItem((t=>{this.keyValues.push((
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function e(t,n){return{key:t,value:n}})(t.key,t.currentValue))}))),(n||o)&&(this.keyValues.sort(e),this.compareFn=e),this.keyValues}}function Qv(t,e){const n=t.key,o=e.key;if(n===o)return 0;if(void 0===n)return 1;if(void 0===o)return-1;if(null===n)return 1;if(null===o)return-1;if("string"==typeof n&&"string"==typeof o)return n<o?-1:1;if("number"==typeof n&&"number"==typeof o)return n-o;if("boolean"==typeof n&&"boolean"==typeof o)return n<o?-1:1;const i=String(n),a=String(o);return i==a?0:i<a?-1:1}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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     *
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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const sx="browser";
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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new ob("12.2.1");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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     * Copyright Google LLC All Rights Reserved.
     *
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     * found in the LICENSE file at https://angular.io/license
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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     * Copyright Google LLC All Rights Reserved.
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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function pO(){return new mO(Tr(sM))}dO.ɵfac=function t(e){return new(e||dO)(Tr(dO,12))},dO.ɵmod=fo({type:dO}),dO.ɵinj=Dn({providers:lO,imports:[rx,KC]}),dO.ctorParameters=()=>[{type:dO,decorators:[{type:Br},{type:jr},{type:Lr,args:[dO]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(dO,[{type:L_,args:[{providers:lO,exports:[rx,KC]}]}],(function(){return[{type:dO,decorators:[{type:Br},{type:jr},{type:Lr,args:[dO]}]}]}),null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(dO,{exports:function(){return[rx,KC]}});class mO{constructor(t){this._doc=t,this._dom=rM()}addTag(t,e=!1){return t?this._getOrCreateElement(t,e):null}addTags(t,e=!1){return t?t.reduce(((t,n)=>(n&&t.push(this._getOrCreateElement(n,e)),t)),[]):[]}getTag(t){return t&&this._doc.querySelector(`meta[${t}]`)||null}getTags(t){if(!t)return[];const e=this._doc.querySelectorAll(`meta[${t}]`);return e?[].slice.call(e):[]}updateTag(t,e){if(!t)return null;e=e||this._parseSelector(t);const n=this.getTag(e);return n?this._setMetaElementAttributes(t,n):this._getOrCreateElement(t,!0)}removeTag(t){this.removeTagElement(this.getTag(t))}removeTagElement(t){t&&this._dom.remove(t)}_getOrCreateElement(t,e=!1){if(!e){const e=this._parseSelector(t),n=this.getTags(e).filter((e=>this._containsAttributes(t,e)))[0];if(void 0!==n)return n}const n=this._dom.createElement("meta");this._setMetaElementAttributes(t,n);return this._doc.getElementsByTagName("head")[0].appendChild(n),n}_setMetaElementAttributes(t,e){return Object.keys(t).forEach((n=>e.setAttribute(this._getMetaKeyMap(n),t[n]))),e}_parseSelector(t){const e=t.name?"name":"property";return`${e}="${t[e]}"`}_containsAttributes(t,e){return Object.keys(t).every((n=>e.getAttribute(this._getMetaKeyMap(n))===t[n]))}_getMetaKeyMap(t){return fO[t]||t}}mO.ɵfac=function t(e){return new(e||mO)(Tr(sM))},mO.ɵprov=Sn({factory:pO,token:mO,providedIn:"root"}),mO.ctorParameters=()=>[{type:void 0,decorators:[{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(mO,[{type:Um,args:[{providedIn:"root",useFactory:pO,deps:[]}]}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),null);const fO={httpEquiv:"http-equiv"};
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     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license Angular v12.0.0-next.0
     * (c) 2010-2020 Google LLC. https://angular.io/
     * License: MIT
     */!(function(){
/**
         * @license
         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
         * found in the LICENSE file at https://angular.io/license
         */
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o=n&&n.onHasTask;(o||e&&e._hasTaskZS)&&(this._hasTaskZS=o?n:c,this._hasTaskDlgt=e,this._hasTaskDlgtOwner=this,this._hasTaskCurrZone=t,n.onScheduleTask||(this._scheduleTaskZS=c,this._scheduleTaskDlgt=e,this._scheduleTaskCurrZone=this.zone),n.onInvokeTask||(this._invokeTaskZS=c,this._invokeTaskDlgt=e,this._invokeTaskCurrZone=this.zone),n.onCancelTask||(this._cancelTaskZS=c,this._cancelTaskDlgt=e,this._cancelTaskCurrZone=this.zone))}return t.prototype.fork=function(t,e){return this._forkZS?this._forkZS.onFork(this._forkDlgt,this.zone,t,e):new s(t,e)},t.prototype.intercept=function(t,e,n){return this._interceptZS?this._interceptZS.onIntercept(this._interceptDlgt,this._interceptCurrZone,t,e,n):e},t.prototype.invoke=function(t,e,n,o,i){return this._invokeZS?this._invokeZS.onInvoke(this._invokeDlgt,this._invokeCurrZone,t,e,n,o,i):e.apply(n,o)},t.prototype.handleError=function(t,e){return!this._handleErrorZS||this._handleErrorZS.onHandleError(this._handleErrorDlgt,this._handleErrorCurrZone,t,e)},t.prototype.scheduleTask=function(t,e){var n=e;if(this._scheduleTaskZS)this._hasTaskZS&&n._zoneDelegates.push(this._hasTaskDlgtOwner),(n=this._scheduleTaskZS.onScheduleTask(this._scheduleTaskDlgt,this._scheduleTaskCurrZone,t,e))||(n=e);else if(e.scheduleFn)e.scheduleFn(e);else{if(e.type!=w)throw new Error("Task is missing scheduleFn.");b(e)}return n},t.prototype.invokeTask=function(t,e,n,o){return this._invokeTaskZS?this._invokeTaskZS.onInvokeTask(this._invokeTaskDlgt,this._invokeTaskCurrZone,t,e,n,o):e.callback.apply(n,o)},t.prototype.cancelTask=function(t,e){var n;if(this._cancelTaskZS)n=this._cancelTaskZS.onCancelTask(this._cancelTaskDlgt,this._cancelTaskCurrZone,t,e);else{if(!e.cancelFn)throw Error("Task is not 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N},ObjectGetOwnPropertyDescriptor:function(){},ObjectCreate:function(){},ArraySlice:function(){return[]},patchClass:function(){return N},wrapWithCurrentZone:function(){return N},filterProperties:function(){return[]},attachOriginToPatched:function(){return N},_redefineProperty:function(){return N},patchCallbacks:function(){return N}},E={parent:null,zone:new s(null,null)},A=null,T=0;function N(){}o("Zone","Zone"),t.Zone=s})("undefined"!=typeof window&&window||"undefined"!=typeof self&&self||_O);
/**
         * @license
         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
         * found in the LICENSE file at https://angular.io/license
         */
var t=Object.getOwnPropertyDescriptor,e=Object.defineProperty,n=Object.getPrototypeOf,o=Object.create,i=Array.prototype.slice,a="addEventListener",r="removeEventListener",s=Zone.__symbol__(a),l=Zone.__symbol__(r),c="true",d="false",p=Zone.__symbol__("");function m(t,e){return Zone.current.wrap(t,e)}function f(t,e,n,o,i){return Zone.current.scheduleMacroTask(t,e,n,o,i)}var u=Zone.__symbol__,g="undefined"!=typeof window,h=g?window:void 0,b=g&&h||"object"==typeof self&&self||_O,y="removeAttribute",_=[null];function C(t,e){for(var n=t.length-1;n>=0;n--)"function"==typeof t[n]&&(t[n]=m(t[n],e+"_"+n));return t}function M(e,n){for(var o=e.constructor.name,i=function(i){var a=n[i],r=e[a];if(r){if(!v(t(e,a)))return"continue";e[a]=(function(t){var e=function(){return t.apply(this,C(arguments,o+"."+a))};return z(e,t),e})(r)}},a=0;a<n.length;a++)i(a)}function v(t){return!t||!1!==t.writable&&!("function"==typeof t.get&&void 0===t.set)}var x="undefined"!=typeof WorkerGlobalScope&&self instanceof WorkerGlobalScope,O=!("nw"in b)&&void 0!==b.process&&"[object process]"==={}.toString.call(b.process),P=!O&&!x&&!(!g||!h.HTMLElement),w=void 0!==b.process&&"[object process]"==={}.toString.call(b.process)&&!x&&!(!g||!h.HTMLElement),k={},S=function(t){if(t=t||b.event){var e=k[t.type];e||(e=k[t.type]=u("ON_PROPERTY"+t.type));var n,o=this||t.target||b,i=o[e];return P&&o===h&&"error"===t.type?!0===(n=i&&i.call(this,t.message,t.filename,t.lineno,t.colno,t.error))&&t.preventDefault():null==(n=i&&i.apply(this,arguments))||n||t.preventDefault(),n}};function D(n,o,i){var a=t(n,o);if(!a&&i&&t(i,o)&&(a={enumerable:!0,configurable:!0}),a&&a.configurable){var r=u("on"+o+"patched");if(!n.hasOwnProperty(r)||!n[r]){delete a.writable,delete a.value;var s=a.get,l=a.set,c=o.substr(2),d=k[c];d||(d=k[c]=u("ON_PROPERTY"+c)),a.set=function(t){var e=this;e||n!==b||(e=b),e&&(e[d]&&e.removeEventListener(c,S),l&&l.apply(e,_),"function"==typeof t?(e[d]=t,e.addEventListener(c,S,!1)):e[d]=null)},a.get=function(){var t=this;if(t||n!==b||(t=b),!t)return null;var e=t[d];if(e)return e;if(s){var i=s&&s.call(this);if(i)return a.set.call(this,i),"function"==typeof t[y]&&t.removeAttribute(o),i}return null},e(n,o,a),n[r]=!0}}}function R(t,e,n){if(e)for(var o=0;o<e.length;o++)D(t,"on"+e[o],n);else{var i=[];for(var a in t)"on"==a.substr(0,2)&&i.push(a);for(var r=0;r<i.length;r++)D(t,i[r],n)}}var E=u("originalInstance");function A(t){var n=b[t];if(n){b[u(t)]=n,b[t]=function(){var e=C(arguments,t);switch(e.length){case 0:this[E]=new n;break;case 1:this[E]=new n(e[0]);break;case 2:this[E]=new n(e[0],e[1]);break;case 3:this[E]=new n(e[0],e[1],e[2]);break;case 4:this[E]=new n(e[0],e[1],e[2],e[3]);break;default:throw new Error("Arg list too long.")}},z(b[t],n);var o,i=new n((function(){}));for(o in i)"XMLHttpRequest"===t&&"responseBlob"===o||(function(n){"function"==typeof i[n]?b[t].prototype[n]=function(){return this[E][n].apply(this[E],arguments)}:e(b[t].prototype,n,{set:function(e){"function"==typeof e?(this[E][n]=m(e,t+"."+n),z(this[E][n],e)):this[E][n]=e},get:function(){return this[E][n]}})})(o);for(o in n)"prototype"!==o&&n.hasOwnProperty(o)&&(b[t][o]=n[o])}}function T(e,o,i){for(var a=e;a&&!a.hasOwnProperty(o);)a=n(a);!a&&e[o]&&(a=e);var r=u(o),s=null;if(a&&(!(s=a[r])||!a.hasOwnProperty(r))&&(s=a[r]=a[o],v(a&&t(a,o)))){var l=i(s,r,o);a[o]=function(){return l(this,arguments)},z(a[o],s)}return s}function N(t,e,n){var o=null;function i(t){var e=t.data;return e.args[e.cbIdx]=function(){t.invoke.apply(this,arguments)},o.apply(e.target,e.args),t}o=T(t,e,(function(t){return function(e,o){var a=n(e,o);return a.cbIdx>=0&&"function"==typeof o[a.cbIdx]?f(a.name,o[a.cbIdx],a,i):t.apply(e,o)}}))}function z(t,e){t[u("OriginalDelegate")]=e}var I=!1,H=!1;function F(){try{var t=h.navigator.userAgent;if(-1!==t.indexOf("MSIE ")||-1!==t.indexOf("Trident/"))return!0}catch(t){}return!1}function L(){if(I)return H;I=!0;try{var t=h.navigator.userAgent;-1===t.indexOf("MSIE ")&&-1===t.indexOf("Trident/")&&-1===t.indexOf("Edge/")||(H=!0)}catch(t){}return H}
/**
         * @license
         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
         * found in the LICENSE file at https://angular.io/license
         */Zone.__load_patch("ZoneAwarePromise",(function(t,e,n){var o=Object.getOwnPropertyDescriptor,i=Object.defineProperty;function a(t){if(t&&t.toString===Object.prototype.toString){var e=t.constructor&&t.constructor.name;return(e||"")+": "+JSON.stringify(t)}return t?t.toString():Object.prototype.toString.call(t)}var r=n.symbol,s=[],l=!0===t[r("DISABLE_WRAPPING_UNCAUGHT_PROMISE_REJECTION")],c=r("Promise"),d=r("then"),p="__creationTrace__";n.onUnhandledError=function(t){if(n.showUncaughtError()){var e=t&&t.rejection;e?console.error("Unhandled Promise rejection:",e instanceof Error?e.message:e,"; Zone:",t.zone.name,"; Task:",t.task&&t.task.source,"; Value:",e,e instanceof Error?e.stack:void 0):console.error(t)}},n.microtaskDrainDone=function(){for(var t=function(){var t=s.shift();try{t.zone.runGuarded((function(){if(t.throwOriginal)throw t.rejection;throw t}))}catch(t){f(t)}};s.length;)t()};var m=r("unhandledPromiseRejectionHandler");function f(t){n.onUnhandledError(t);try{var o=e[m];"function"==typeof o&&o.call(this,t)}catch(t){}}function u(t){return t&&t.then}function g(t){return t}function h(t){return F.reject(t)}var b=r("state"),y=r("value"),_=r("finally"),C=r("parentPromiseValue"),M=r("parentPromiseState"),v="Promise.then",x=null,O=!0,P=!1,w=0;function k(t,e){return function(n){try{E(t,e,n)}catch(e){E(t,!1,e)}}}var S=function(){var t=!1;return function e(n){return function(){t||(t=!0,n.apply(null,arguments))}}},D="Promise resolved with itself",R=r("currentTaskTrace");function E(t,o,r){var c=S();if(t===r)throw new TypeError(D);if(t[b]===x){var d=null;try{"object"!=typeof r&&"function"!=typeof r||(d=r&&r.then)}catch(e){return c((function(){E(t,!1,e)}))(),t}if(o!==P&&r instanceof F&&r.hasOwnProperty(b)&&r.hasOwnProperty(y)&&r[b]!==x)N(r),E(t,r[b],r[y]);else if(o!==P&&"function"==typeof d)try{d.call(r,c(k(t,o)),c(k(t,!1)))}catch(e){c((function(){E(t,!1,e)}))()}else{t[b]=o;var m=t[y];if(t[y]=r,t[_]===_&&o===O&&(t[b]=t[M],t[y]=t[C]),o===P&&r instanceof Error){var f=e.currentTask&&e.currentTask.data&&e.currentTask.data[p];f&&i(r,R,{configurable:!0,enumerable:!1,writable:!0,value:f})}for(var u=0;u<m.length;)z(t,m[u++],m[u++],m[u++],m[u++]);if(0==m.length&&o==P){t[b]=w;var g=r;try{throw new Error("Uncaught (in promise): "+a(r)+(r&&r.stack?"\n"+r.stack:""))}catch(t){g=t}l&&(g.throwOriginal=!0),g.rejection=r,g.promise=t,g.zone=e.current,g.task=e.currentTask,s.push(g),n.scheduleMicroTask()}}}return t}var A=r("rejectionHandledHandler");function N(t){if(t[b]===w){try{var n=e[A];n&&"function"==typeof n&&n.call(this,{rejection:t[y],promise:t})}catch(t){}t[b]=P;for(var o=0;o<s.length;o++)t===s[o].promise&&s.splice(o,1)}}function z(t,e,n,o,i){N(t);var a=t[b],r=a?"function"==typeof o?o:g:"function"==typeof i?i:h;e.scheduleMicroTask(v,(function(){try{var o=t[y],i=!!n&&_===n[_];i&&(n[C]=o,n[M]=a);var s=e.run(r,void 0,i&&r!==h&&r!==g?[]:[o]);E(n,!0,s)}catch(t){E(n,!1,t)}}),n)}var I="function ZoneAwarePromise() { [native code] 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i=r;try{t.then((function(t){s[i]=e?e.thenCallback(t):t,0==--a&&n(s)}),(function(t){e?(s[i]=e.errorCallback(t),0==--a&&n(s)):o(t)}))}catch(t){o(t)}a++,r++},c=this,d=0,p=t;d<p.length;d++)l(p[d]);return 0==(a-=2)&&n(s),i},Object.defineProperty(t.prototype,Symbol.toStringTag,{get:function(){return"Promise"},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,Symbol.species,{get:function(){return t},enumerable:!1,configurable:!0}),t.prototype.then=function(n,o){var i=this.constructor[Symbol.species];i&&"function"==typeof i||(i=this.constructor||t);var a=new i(H),r=e.current;return this[b]==x?this[y].push(r,a,n,o):z(this,r,a,n,o),a},t.prototype.catch=function(t){return this.then(null,t)},t.prototype.finally=function(n){var o=this.constructor[Symbol.species];o&&"function"==typeof o||(o=t);var i=new o(H);i[_]=_;var a=e.current;return this[b]==x?this[y].push(a,i,n,n):z(this,a,i,n,n),i},t})();F.resolve=F.resolve,F.reject=F.reject,F.race=F.race,F.all=F.all;var L=t[c]=t.Promise;t.Promise=F;var B=r("thenPatched");function V(t){var e=t.prototype,n=o(e,"then");if(!n||!1!==n.writable&&n.configurable){var i=e.then;e[d]=i,t.prototype.then=function(t,e){var n=this;return new F((function(t,e){i.call(n,t,e)})).then(t,e)},t[B]=!0}}function j(t){return function(e,n){var o=t.apply(e,n);if(o instanceof F)return o;var i=o.constructor;return i[B]||V(i),o}}return n.patchThen=V,L&&(V(L),T(t,"fetch",(function(t){return j(t)}))),Promise[e.__symbol__("uncaughtPromiseErrors")]=s,F})),
/**
         * @license
         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
         * found in the LICENSE file at https://angular.io/license
         */
Zone.__load_patch("toString",(function(t){var e=Function.prototype.toString,n=u("OriginalDelegate"),o=u("Promise"),i=u("Error"),a=function a(){if("function"==typeof this){var r=this[n];if(r)return"function"==typeof r?e.call(r):Object.prototype.toString.call(r);if(this===Promise){var s=t[o];if(s)return e.call(s)}if(this===Error){var l=t[i];if(l)return e.call(l)}}return e.call(this)};a[n]=e,Function.prototype.toString=a;var r=Object.prototype.toString,s="[object Promise]";Object.prototype.toString=function(){return"function"==typeof Promise&&this instanceof Promise?s:r.call(this)}}));
/**
         * @license
         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
         * found in the LICENSE file at https://angular.io/license
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var kt=u("zoneTask");function St(t,e,n,o){var i=null,a=null;n+=o;var r={};function s(e){var n=e.data;return n.args[0]=function(){return e.invoke.apply(this,arguments)},n.handleId=i.apply(t,n.args),e}function l(e){return a.call(t,e.data.handleId)}i=T(t,e+=o,(function(n){return function(i,a){if("function"==typeof a[0]){var c={isPeriodic:"Interval"===o,delay:"Timeout"===o||"Interval"===o?a[1]||0:void 0,args:a},d=a[0];a[0]=function t(){try{return d.apply(this,arguments)}finally{c.isPeriodic||("number"==typeof c.handleId?delete r[c.handleId]:c.handleId&&(c.handleId[kt]=null))}};var p=f(e,a[0],c,s,l);if(!p)return p;var m=p.data.handleId;return"number"==typeof m?r[m]=p:m&&(m[kt]=p),m&&m.ref&&m.unref&&"function"==typeof m.ref&&"function"==typeof m.unref&&(p.ref=m.ref.bind(m),p.unref=m.unref.bind(m)),"number"==typeof m||m?m:p}return n.apply(t,a)}})),a=T(t,n,(function(e){return function(n,o){var i,a=o[0];"number"==typeof a?i=r[a]:(i=a&&a[kt])||(i=a),i&&"string"==typeof i.type?"notScheduled"!==i.state&&(i.cancelFn&&i.data.isPeriodic||0===i.runCount)&&("number"==typeof a?delete r[a]:a&&(a[kt]=null),i.zone.cancelTask(i)):e.apply(t,o)}}))}
/**
         * @license
         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
         * found in the LICENSE file at https://angular.io/license
         */function Dt(t,e){var n=e.getGlobalObjects();(n.isBrowser||n.isMix)&&t.customElements&&"customElements"in t&&e.patchCallbacks(e,t.customElements,"customElements","define",["connectedCallback","disconnectedCallback","adoptedCallback","attributeChangedCallback"])}
/**
         * @license
         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
         * found in the LICENSE file at https://angular.io/license
         */function Rt(t,e){if(!Zone[e.symbol("patchEventTarget")]){for(var n=e.getGlobalObjects(),o=n.eventNames,i=n.zoneSymbolEventNames,a=n.TRUE_STR,r=n.FALSE_STR,s=n.ZONE_SYMBOL_PREFIX,l=0;l<o.length;l++){var c=o[l],d=s+(c+r),p=s+(c+a);i[c]={},i[c][r]=d,i[c][a]=p}var m=t.EventTarget;if(m&&m.prototype)return e.patchEventTarget(t,[m&&m.prototype]),!0}}function Et(t,e){e.patchEventPrototype(t,e)}
/**
         * @license
         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
         * found in the LICENSE file at https://angular.io/license
         */Zone.__load_patch("legacy",(function(t){var e=t[Zone.__symbol__("legacyPatch")];e&&e()})),Zone.__load_patch("queueMicrotask",(function(t,e,n){n.patchMethod(t,"queueMicrotask",(function(t){return function(t,n){e.current.scheduleMicroTask("queueMicrotask",n[0])}}))})),Zone.__load_patch("timers",(function(t){var e="set",n="clear";St(t,e,n,"Timeout"),St(t,e,n,"Interval"),St(t,e,n,"Immediate")})),Zone.__load_patch("requestAnimationFrame",(function(t){St(t,"request","cancel","AnimationFrame"),St(t,"mozRequest","mozCancel","AnimationFrame"),St(t,"webkitRequest","webkitCancel","AnimationFrame")})),Zone.__load_patch("blocking",(function(t,e){for(var n=["alert","prompt","confirm"],o=0;o<n.length;o++)T(t,n[o],(function(n,o,i){return function(o,a){return e.current.run(n,t,a,i)}}))})),Zone.__load_patch("EventTarget",(function(t,e,n){Et(t,n),Rt(t,n);var o=t.XMLHttpRequestEventTarget;o&&o.prototype&&n.patchEventTarget(t,[o.prototype])})),Zone.__load_patch("MutationObserver",(function(t,e,n){A("MutationObserver"),A("WebKitMutationObserver")})),Zone.__load_patch("IntersectionObserver",(function(t,e,n){A("IntersectionObserver")})),Zone.__load_patch("FileReader",(function(t,e,n){A("FileReader")})),Zone.__load_patch("on_property",(function(t,e,n){gt(n,t)})),Zone.__load_patch("customElements",(function(t,e,n){Dt(t,n)})),Zone.__load_patch("XHR",(function(t,e){d(t);var n=u("xhrTask"),o=u("xhrSync"),i=u("xhrListener"),a=u("xhrScheduled"),r=u("xhrURL"),c=u("xhrErrorBeforeScheduled");function d(t){var d=t.XMLHttpRequest;if(d){var p=d.prototype,m=p[s],g=p[l];if(!m){var h=t.XMLHttpRequestEventTarget;if(h){var b=h.prototype;m=b[s],g=b[l]}}var y="readystatechange",_="scheduled",C=T(p,"open",(function(){return function(t,e){return t[o]=0==e[2],t[r]=e[1],C.apply(t,e)}})),M="XMLHttpRequest.send",v=u("fetchTaskAborting"),x=u("fetchTaskScheduling"),O=T(p,"send",(function(){return function(t,n){if(!0===e.current[x])return O.apply(t,n);if(t[o])return O.apply(t,n);var i={target:t,url:t[r],isPeriodic:!1,args:n,aborted:!1},a=f(M,S,i,k,D);t&&!0===t[c]&&!i.aborted&&a.state===_&&a.invoke()}})),P=T(p,"abort",(function(){return function(t,n){var o=w(t);if(o&&"string"==typeof o.type){if(null==o.cancelFn||o.data&&o.data.aborted)return;o.zone.cancelTask(o)}else if(!0===e.current[v])return P.apply(t,n)}}))}function w(t){return t[n]}function k(t){var o=t.data,r=o.target;r[a]=!1,r[c]=!1;var d=r[i];m||(m=r[s],g=r[l]),d&&g.call(r,y,d);var p=r[i]=function(){if(r.readyState===r.DONE)if(!o.aborted&&r[a]&&t.state===_){var n=r[e.__symbol__("loadfalse")];if(0!==r.status&&n&&n.length>0){var i=t.invoke;t.invoke=function(){for(var n=r[e.__symbol__("loadfalse")],a=0;a<n.length;a++)n[a]===t&&n.splice(a,1);o.aborted||t.state!==_||i.call(t)},n.push(t)}else t.invoke()}else o.aborted||!1!==r[a]||(r[c]=!0)};return m.call(r,y,p),r[n]||(r[n]=t),O.apply(r,o.args),r[a]=!0,t}function S(){}function D(t){var e=t.data;return e.aborted=!0,P.apply(e.target,e.args)}}})),Zone.__load_patch("geolocation",(function(t){t.navigator&&t.navigator.geolocation&&M(t.navigator.geolocation,["getCurrentPosition","watchPosition"])})),Zone.__load_patch("PromiseRejectionEvent",(function(t,e){function n(e){return function(n){X(t,e).forEach((function(o){var i=t.PromiseRejectionEvent;if(i){var a=new i(e,{promise:n.promise,reason:n.rejection});o.invoke(a)}}))}}t.PromiseRejectionEvent&&(e[u("unhandledPromiseRejectionHandler")]=n("unhandledrejection"),e[u("rejectionHandledHandler")]=n("rejectionhandled"))}))})();
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
class MO{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const vO="*";function xO(t,e){return{type:7,name:t,definitions:e,options:{}}}function OO(t,e=null){return{type:4,styles:e,timings:t}}function PO(t,e=null){return{type:2,steps:t,options:e}}function wO(t){return{type:6,styles:t,offset:null}}function kO(t,e,n){return{type:0,name:t,styles:e,options:n}}function SO(t){return{type:5,steps:t}}function DO(t,e,n=null){return{type:1,expr:t,animation:e,options:n}}function RO(t=null){return{type:9,options:t}}function EO(t,e,n=null){return{type:11,selector:t,animation:e,options:n}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function AO(t){Promise.resolve(null).then(t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class TO{constructor(t=0,e=0){this._onDoneFns=[],this._onStartFns=[],this._onDestroyFns=[],this._started=!1,this._destroyed=!1,this._finished=!1,this._position=0,this.parentPlayer=null,this.totalTime=t+e}_onFinish(){this._finished||(this._finished=!0,this._onDoneFns.forEach((t=>t())),this._onDoneFns=[])}onStart(t){this._onStartFns.push(t)}onDone(t){this._onDoneFns.push(t)}onDestroy(t){this._onDestroyFns.push(t)}hasStarted(){return this._started}init(){}play(){this.hasStarted()||(this._onStart(),this.triggerMicrotask()),this._started=!0}triggerMicrotask(){AO((()=>this._onFinish()))}_onStart(){this._onStartFns.forEach((t=>t())),this._onStartFns=[]}pause(){}restart(){}finish(){this._onFinish()}destroy(){this._destroyed||(this._destroyed=!0,this.hasStarted()||this._onStart(),this.finish(),this._onDestroyFns.forEach((t=>t())),this._onDestroyFns=[])}reset(){this._started=!1}setPosition(t){this._position=this.totalTime?t*this.totalTime:1}getPosition(){return this.totalTime?this._position/this.totalTime:1}triggerCallback(t){const e="start"==t?this._onStartFns:this._onDoneFns;e.forEach((t=>t())),e.length=0}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class NO{constructor(t){this._onDoneFns=[],this._onStartFns=[],this._finished=!1,this._started=!1,this._destroyed=!1,this._onDestroyFns=[],this.parentPlayer=null,this.totalTime=0,this.players=t;let e=0,n=0,o=0;const i=this.players.length;0==i?AO((()=>this._onFinish())):this.players.forEach((t=>{t.onDone((()=>{++e==i&&this._onFinish()})),t.onDestroy((()=>{++n==i&&this._onDestroy()})),t.onStart((()=>{++o==i&&this._onStart()}))})),this.totalTime=this.players.reduce(((t,e)=>Math.max(t,e.totalTime)),0)}_onFinish(){this._finished||(this._finished=!0,this._onDoneFns.forEach((t=>t())),this._onDoneFns=[])}init(){this.players.forEach((t=>t.init()))}onStart(t){this._onStartFns.push(t)}_onStart(){this.hasStarted()||(this._started=!0,this._onStartFns.forEach((t=>t())),this._onStartFns=[])}onDone(t){this._onDoneFns.push(t)}onDestroy(t){this._onDestroyFns.push(t)}hasStarted(){return this._started}play(){this.parentPlayer||this.init(),this._onStart(),this.players.forEach((t=>t.play()))}pause(){this.players.forEach((t=>t.pause()))}restart(){this.players.forEach((t=>t.restart()))}finish(){this._onFinish(),this.players.forEach((t=>t.finish()))}destroy(){this._onDestroy()}_onDestroy(){this._destroyed||(this._destroyed=!0,this._onFinish(),this.players.forEach((t=>t.destroy())),this._onDestroyFns.forEach((t=>t())),this._onDestroyFns=[])}reset(){this.players.forEach((t=>t.reset())),this._destroyed=!1,this._finished=!1,this._started=!1}setPosition(t){const e=t*this.totalTime;this.players.forEach((t=>{const n=t.totalTime?Math.min(1,e/t.totalTime):1;t.setPosition(n)}))}getPosition(){const t=this.players.reduce(((t,e)=>null===t||e.totalTime>t.totalTime?e:t),null);return null!=t?t.getPosition():0}beforeDestroy(){this.players.forEach((t=>{t.beforeDestroy&&t.beforeDestroy()}))}triggerCallback(t){const e="start"==t?this._onStartFns:this._onDoneFns;e.forEach((t=>t())),e.length=0}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
function zO(){return"undefined"!=typeof window&&void 0!==window.document}function IO(){return"undefined"!=typeof process&&"[object process]"==={}.toString.call(process)}function HO(t){switch(t.length){case 0:return new TO;case 1:return t[0];default:return new NO(t)}}function FO(t,e,n,o,i={},a={}){const r=[],s=[];let l=-1,c=null;if(o.forEach((t=>{const n=t.offset,o=n==l,d=o&&c||{};Object.keys(t).forEach((n=>{let o=n,s=t[n];if("offset"!==n)switch(o=e.normalizePropertyName(o,r),s){case"!":s=i[n];break;case vO:s=a[n];break;default:s=e.normalizeStyleValue(n,o,s,r)}d[o]=s})),o||s.push(d),c=d,l=n})),r.length){const t="\n - ";throw new Error(`Unable to animate due to the following errors:${t}${r.join(t)}`)}return s}function LO(t,e,n,o){switch(e){case"start":t.onStart((()=>o(n&&BO(n,"start",t))));break;case"done":t.onDone((()=>o(n&&BO(n,"done",t))));break;case"destroy":t.onDestroy((()=>o(n&&BO(n,"destroy",t))))}}function BO(t,e,n){const o=n.totalTime,i=VO(t.element,t.triggerName,t.fromState,t.toState,e||t.phaseName,null==o?t.totalTime:o,!!n.disabled),a=t._data;return null!=a&&(i._data=a),i}function VO(t,e,n,o,i="",a=0,r){return{element:t,triggerName:e,fromState:n,toState:o,phaseName:i,totalTime:a,disabled:!!r}}function jO(t,e,n){let o;return t instanceof Map?(o=t.get(e),o||t.set(e,o=n)):(o=t[e],o||(o=t[e]=n)),o}function UO(t){const e=t.indexOf(":");return[t.substring(1,e),t.substr(e+1)]}let WO=(t,e)=>!1,GO=(t,e)=>!1,YO=(t,e,n)=>[];const qO=IO();(qO||"undefined"!=typeof Element)&&(WO=zO()?(t,e)=>{for(;e&&e!==document.documentElement;){if(e===t)return!0;e=e.parentNode||e.host}return!1}:(t,e)=>t.contains(e),GO=(()=>{if(qO||Element.prototype.matches)return(t,e)=>t.matches(e);{const t=Element.prototype,e=t.matchesSelector||t.mozMatchesSelector||t.msMatchesSelector||t.oMatchesSelector||t.webkitMatchesSelector;return e?(t,n)=>e.apply(t,[n]):GO}})(),YO=(t,e,n)=>{let o=[];if(n){const n=t.querySelectorAll(e);for(let t=0;t<n.length;t++)o.push(n[t])}else{const n=t.querySelector(e);n&&o.push(n)}return o});let ZO=null,XO=!1;function KO(t){ZO||(ZO=(function e(){if("undefined"!=typeof document)return document.body;return null})()||{},XO=!!ZO.style&&"WebkitAppearance"in ZO.style);let n=!0;if(ZO.style&&!(function o(t){return"ebkit"==t.substring(1,6)})(t)&&(n=t in ZO.style,!n&&XO)){n="Webkit"+t.charAt(0).toUpperCase()+t.substr(1)in ZO.style}return n}const JO=GO,QO=WO,$O=YO;function tP(t){const e={};return Object.keys(t).forEach((n=>{const o=n.replace(/([a-z])([A-Z])/g,"$1-$2");e[o]=t[n]})),e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class eP{validateStyleProperty(t){return KO(t)}matchesElement(t,e){return JO(t,e)}containsElement(t,e){return QO(t,e)}query(t,e,n){return $O(t,e,n)}computeStyle(t,e,n){return n||""}animate(t,e,n,o,i,a=[],r){return new TO(n,o)}}eP.ɵfac=function t(e){return new(e||eP)},eP.ɵprov=Sn({token:eP,factory:eP.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(eP,[{type:Um}],null,null);class nP{}nP.NOOP=new eP;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const oP="ng-enter",iP="ng-leave",aP="ng-trigger",rP=".ng-trigger",sP="ng-animating",lP=".ng-animating";function cP(t){if("number"==typeof t)return t;const e=t.match(/^(-?[\.\d]+)(m?s)/);return!e||e.length<2?0:dP(parseFloat(e[1]),e[2])}function dP(t,e){switch(e){case"s":return 1e3*t;default:return t}}function pP(t,e,n){return t.hasOwnProperty("duration")?t:(function o(t,e,n){const o=/^(-?[\.\d]+)(m?s)(?:\s+(-?[\.\d]+)(m?s))?(?:\s+([-a-z]+(?:\(.+?\))?))?$/i;let i,a=0,r="";if("string"==typeof t){const n=t.match(o);if(null===n)return e.push(`The provided timing value "${t}" is invalid.`),{duration:0,delay:0,easing:""};i=dP(parseFloat(n[1]),n[2]);const s=n[3];null!=s&&(a=dP(parseFloat(s),n[4]));const l=n[5];l&&(r=l)}else i=t;if(!n){let n=!1,o=e.length;i<0&&(e.push("Duration values below 0 are not allowed for this animation step."),n=!0),a<0&&(e.push("Delay values below 0 are not allowed for this animation step."),n=!0),n&&e.splice(o,0,`The provided timing value "${t}" is invalid.`)}return{duration:i,delay:a,easing:r}})(t,e,n)}function mP(t,e={}){return Object.keys(t).forEach((n=>{e[n]=t[n]})),e}function fP(t,e,n={}){if(e)for(let e in t)n[e]=t[e];else mP(t,n);return n}function uP(t,e,n){return n?e+":"+n+";":""}function gP(t){let e="";for(let n=0;n<t.style.length;n++){const o=t.style.item(n);e+=uP(0,o,t.style.getPropertyValue(o))}for(const n in t.style){if(!t.style.hasOwnProperty(n)||n.startsWith("_"))continue;e+=uP(0,n.replace(/([a-z])([A-Z])/g,"$1-$2").toLowerCase(),t.style[n])}t.setAttribute("style",e)}function hP(t,e,n){t.style&&(Object.keys(e).forEach((o=>{const i=OP(o);n&&!n.hasOwnProperty(o)&&(n[o]=t.style[i]),t.style[i]=e[o]})),IO()&&gP(t))}function bP(t,e){t.style&&(Object.keys(e).forEach((e=>{const n=OP(e);t.style[n]=""})),IO()&&gP(t))}function yP(t){return Array.isArray(t)?1==t.length?t[0]:PO(t):t}const _P=new RegExp("{{\\s*(.+?)\\s*}}","g");function CP(t){let e=[];if("string"==typeof t){let n;for(;n=_P.exec(t);)e.push(n[1]);_P.lastIndex=0}return e}function MP(t,e,n){const o=t.toString(),i=o.replace(_P,((t,o)=>{let i=e[o];return e.hasOwnProperty(o)||(n.push(`Please provide a value for the animation param ${o}`),i=""),i.toString()}));return i==o?t:i}function vP(t){const e=[];let n=t.next();for(;!n.done;)e.push(n.value),n=t.next();return e}const xP=/-+([a-z0-9])/g;function OP(t){return t.replace(xP,((...t)=>t[1].toUpperCase()))}function PP(t,e){return 0===t||0===e}function wP(t,e,n){const o=Object.keys(n);if(o.length&&e.length){let a=e[0],r=[];if(o.forEach((t=>{a.hasOwnProperty(t)||r.push(t),a[t]=n[t]})),r.length)for(var i=1;i<e.length;i++){let n=e[i];r.forEach((function(e){n[e]=SP(t,e)}))}}return e}function kP(t,e,n){switch(e.type){case 7:return t.visitTrigger(e,n);case 0:return t.visitState(e,n);case 1:return t.visitTransition(e,n);case 2:return t.visitSequence(e,n);case 3:return t.visitGroup(e,n);case 4:return t.visitAnimate(e,n);case 5:return t.visitKeyframes(e,n);case 6:return t.visitStyle(e,n);case 8:return t.visitReference(e,n);case 9:return t.visitAnimateChild(e,n);case 10:return t.visitAnimateRef(e,n);case 11:return t.visitQuery(e,n);case 12:return t.visitStagger(e,n);default:throw new Error(`Unable to resolve animation metadata node #${e.type}`)}}function SP(t,e){return window.getComputedStyle(t)[e]}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const DP="*";function RP(t,e){const n=[];return"string"==typeof t?t.split(/\s*,\s*/).forEach((t=>(function o(t,e,n){if(":"==t[0]){const o=(function o(t,e){switch(t){case":enter":return"void => *";case":leave":return"* => void";case":increment":return(t,e)=>parseFloat(e)>parseFloat(t);case":decrement":return(t,e)=>parseFloat(e)<parseFloat(t);default:return e.push(`The transition alias value "${t}" is not supported`),"* => *"}})(t,n);if("function"==typeof o)return void e.push(o);t=o}const i=t.match(/^(\*|[-\w]+)\s*(<?[=-]>)\s*(\*|[-\w]+)$/);if(null==i||i.length<4)return n.push(`The provided transition expression "${t}" is not supported`),e;const a=i[1],r=i[2],s=i[3];e.push(TP(a,s));const l=a==DP&&s==DP;"<"!=r[0]||l||e.push(TP(s,a))})(t,n,e))):n.push(t),n}const EP=new Set(["true","1"]),AP=new Set(["false","0"]);function TP(t,e){const n=EP.has(t)||AP.has(t),o=EP.has(e)||AP.has(e);return(i,a)=>{let r=t==DP||t==i,s=e==DP||e==a;return!r&&n&&"boolean"==typeof i&&(r=i?EP.has(t):AP.has(t)),!s&&o&&"boolean"==typeof a&&(s=a?EP.has(e):AP.has(e)),r&&s}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const NP=":self",zP=new RegExp("s*:selfs*,?","g");function IP(t,e,n){return new HP(t).build(e,n)}class HP{constructor(t){this._driver=t}build(t,e){const n=new FP(e);return this._resetContextStyleTimingState(n),kP(this,yP(t),n)}_resetContextStyleTimingState(t){t.currentQuerySelector="",t.collectedStyles={},t.collectedStyles[""]={},t.currentTime=0}visitTrigger(t,e){let n=e.queryCount=0,o=e.depCount=0;const i=[],a=[];return"@"==t.name.charAt(0)&&e.errors.push("animation triggers cannot be prefixed with an `@` sign (e.g. trigger('@foo', [...]))"),t.definitions.forEach((t=>{if(this._resetContextStyleTimingState(e),0==t.type){const n=t,o=n.name;o.toString().split(/\s*,\s*/).forEach((t=>{n.name=t,i.push(this.visitState(n,e))})),n.name=o}else if(1==t.type){const i=this.visitTransition(t,e);n+=i.queryCount,o+=i.depCount,a.push(i)}else e.errors.push("only state() and transition() definitions can sit inside of a trigger()")})),{type:7,name:t.name,states:i,transitions:a,queryCount:n,depCount:o,options:null}}visitState(t,e){const n=this.visitStyle(t.styles,e),o=t.options&&t.options.params||null;if(n.containsDynamicStyles){const i=new Set,a=o||{};if(n.styles.forEach((t=>{if(LP(t)){const e=t;Object.keys(e).forEach((t=>{CP(e[t]).forEach((t=>{a.hasOwnProperty(t)||i.add(t)}))}))}})),i.size){const n=vP(i.values());e.errors.push(`state("${t.name}", ...) must define default values for all the following style substitutions: ${n.join(", ")}`)}}return{type:0,name:t.name,style:n,options:o?{params:o}:null}}visitTransition(t,e){e.queryCount=0,e.depCount=0;const n=kP(this,yP(t.animation),e);return{type:1,matchers:RP(t.expr,e.errors),animation:n,queryCount:e.queryCount,depCount:e.depCount,options:BP(t.options)}}visitSequence(t,e){return{type:2,steps:t.steps.map((t=>kP(this,t,e))),options:BP(t.options)}}visitGroup(t,e){const n=e.currentTime;let o=0;const i=t.steps.map((t=>{e.currentTime=n;const i=kP(this,t,e);return o=Math.max(o,e.currentTime),i}));return e.currentTime=o,{type:3,steps:i,options:BP(t.options)}}visitAnimate(t,e){const n=(function o(t,e){let n=null;if(t.hasOwnProperty("duration"))n=t;else if("number"==typeof t){return VP(pP(t,e).duration,0,"")}const o=t;if(o.split(/\s+/).some((t=>"{"==t.charAt(0)&&"{"==t.charAt(1)))){const t=VP(0,0,"");return t.dynamic=!0,t.strValue=o,t}return n=n||pP(o,e),VP(n.duration,n.delay,n.easing)})(t.timings,e.errors);let i;e.currentAnimateTimings=n;let a=t.styles?t.styles:wO({});if(5==a.type)i=this.visitKeyframes(a,e);else{let o=t.styles,a=!1;if(!o){a=!0;const t={};n.easing&&(t.easing=n.easing),o=wO(t)}e.currentTime+=n.duration+n.delay;const r=this.visitStyle(o,e);r.isEmptyStep=a,i=r}return e.currentAnimateTimings=null,{type:4,timings:n,style:i,options:null}}visitStyle(t,e){const n=this._makeStyleAst(t,e);return this._validateStyleAst(n,e),n}_makeStyleAst(t,e){const n=[];Array.isArray(t.styles)?t.styles.forEach((t=>{"string"==typeof t?t==vO?n.push(t):e.errors.push(`The provided style string value ${t} is not allowed.`):n.push(t)})):n.push(t.styles);let o=!1,i=null;return n.forEach((t=>{if(LP(t)){const e=t,n=e.easing;if(n&&(i=n,delete e.easing),!o)for(let t in e){if(e[t].toString().indexOf("{{")>=0){o=!0;break}}}})),{type:6,styles:n,easing:i,offset:t.offset,containsDynamicStyles:o,options:null}}_validateStyleAst(t,e){const n=e.currentAnimateTimings;let o=e.currentTime,i=e.currentTime;n&&i>0&&(i-=n.duration+n.delay),t.styles.forEach((t=>{"string"!=typeof t&&Object.keys(t).forEach((n=>{if(!this._driver.validateStyleProperty(n))return void e.errors.push(`The provided animation property "${n}" is not a supported CSS property for animations`);const a=e.collectedStyles[e.currentQuerySelector],r=a[n];let s=!0;r&&(i!=o&&i>=r.startTime&&o<=r.endTime&&(e.errors.push(`The CSS property "${n}" that exists between the times of "${r.startTime}ms" and "${r.endTime}ms" is also being animated in a parallel animation between the times of "${i}ms" and "${o}ms"`),s=!1),i=r.startTime),s&&(a[n]={startTime:i,endTime:o}),e.options&&(function l(t,e,n){const o=e.params||{},i=CP(t);i.length&&i.forEach((t=>{o.hasOwnProperty(t)||n.push(`Unable to resolve the local animation param ${t} in the given list of values`)}))})(t[n],e.options,e.errors)}))}))}visitKeyframes(t,e){const n={type:5,styles:[],options:null};if(!e.currentAnimateTimings)return e.errors.push("keyframes() must be placed inside of a call to animate()"),n;let o=0;const i=[];let a=!1,r=!1,s=0;const l=t.steps.map((t=>{const n=this._makeStyleAst(t,e);let l=null!=n.offset?n.offset:(function c(t){if("string"==typeof t)return null;let e=null;if(Array.isArray(t))t.forEach((t=>{if(LP(t)&&t.hasOwnProperty("offset")){const n=t;e=parseFloat(n.offset),delete n.offset}}));else if(LP(t)&&t.hasOwnProperty("offset")){const n=t;e=parseFloat(n.offset),delete n.offset}return e})(n.styles),d=0;return null!=l&&(o++,d=n.offset=l),r=r||d<0||d>1,a=a||d<s,s=d,i.push(d),n}));r&&e.errors.push("Please ensure that all keyframe offsets are between 0 and 1"),a&&e.errors.push("Please ensure that all keyframe offsets are in order");const c=t.steps.length;let d=0;o>0&&o<c?e.errors.push("Not all style() steps within the declared keyframes() contain offsets"):0==o&&(d=1/(c-1));const p=c-1,m=e.currentTime,f=e.currentAnimateTimings,u=f.duration;return l.forEach(((t,o)=>{const a=d>0?o==p?1:d*o:i[o],r=a*u;e.currentTime=m+f.delay+r,f.duration=r,this._validateStyleAst(t,e),t.offset=a,n.styles.push(t)})),n}visitReference(t,e){return{type:8,animation:kP(this,yP(t.animation),e),options:BP(t.options)}}visitAnimateChild(t,e){return e.depCount++,{type:9,options:BP(t.options)}}visitAnimateRef(t,e){return{type:10,animation:this.visitReference(t.animation,e),options:BP(t.options)}}visitQuery(t,e){const n=e.currentQuerySelector,o=t.options||{};e.queryCount++,e.currentQuery=t;const[i,a]=(function r(t){const e=!!t.split(/\s*,\s*/).find((t=>t==NP));e&&(t=t.replace(zP,""));return[t=t.replace(/@\*/g,rP).replace(/@\w+/g,(t=>".ng-trigger-"+t.substr(1))).replace(/:animating/g,lP),e]})(t.selector);e.currentQuerySelector=n.length?n+" "+i:i,jO(e.collectedStyles,e.currentQuerySelector,{});const s=kP(this,yP(t.animation),e);return e.currentQuery=null,e.currentQuerySelector=n,{type:11,selector:i,limit:o.limit||0,optional:!!o.optional,includeSelf:a,animation:s,originalSelector:t.selector,options:BP(t.options)}}visitStagger(t,e){e.currentQuery||e.errors.push("stagger() can only be used inside of query()");const n="full"===t.timings?{duration:0,delay:0,easing:"full"}:pP(t.timings,e.errors,!0);return{type:12,animation:kP(this,yP(t.animation),e),timings:n,options:null}}}class FP{constructor(t){this.errors=t,this.queryCount=0,this.depCount=0,this.currentTransition=null,this.currentQuery=null,this.currentQuerySelector=null,this.currentAnimateTimings=null,this.currentTime=0,this.collectedStyles={},this.options=null}}function LP(t){return!Array.isArray(t)&&"object"==typeof t}function BP(t){return t?(t=mP(t)).params&&(t.params=(function e(t){return t?mP(t):null})(t.params)):t={},t}function VP(t,e,n){return{duration:t,delay:e,easing:n}}function jP(t,e,n,o,i,a,r=null,s=!1){return{type:1,element:t,keyframes:e,preStyleProps:n,postStyleProps:o,duration:i,delay:a,totalTime:i+a,easing:r,subTimeline:s}}class UP{constructor(){this._map=new Map}consume(t){let e=this._map.get(t);return e?this._map.delete(t):e=[],e}append(t,e){let n=this._map.get(t);n||this._map.set(t,n=[]),n.push(...e)}has(t){return this._map.has(t)}clear(){this._map.clear()}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const WP=new RegExp(":enter","g"),GP=new RegExp(":leave","g");function YP(t,e,n,o,i,a={},r={},s,l,c=[]){return(new qP).buildKeyframes(t,e,n,o,i,a,r,s,l,c)}class qP{buildKeyframes(t,e,n,o,i,a,r,s,l,c=[]){l=l||new UP;const d=new XP(t,e,l,o,i,c,[]);d.options=s,d.currentTimeline.setStyles([a],null,d.errors,s),kP(this,n,d);const p=d.timelines.filter((t=>t.containsAnimation()));if(p.length&&Object.keys(r).length){const t=p[p.length-1];t.allowOnlyTimelineStyles()||t.setStyles([r],null,d.errors,s)}return p.length?p.map((t=>t.buildKeyframes())):[jP(e,[],[],[],0,0,"",!1)]}visitTrigger(t,e){}visitState(t,e){}visitTransition(t,e){}visitAnimateChild(t,e){const n=e.subInstructions.consume(e.element);if(n){const o=e.createSubContext(t.options),i=e.currentTimeline.currentTime,a=this._visitSubInstructions(n,o,o.options);i!=a&&e.transformIntoNewTimeline(a)}e.previousNode=t}visitAnimateRef(t,e){const n=e.createSubContext(t.options);n.transformIntoNewTimeline(),this.visitReference(t.animation,n),e.transformIntoNewTimeline(n.currentTimeline.currentTime),e.previousNode=t}_visitSubInstructions(t,e,n){let o=e.currentTimeline.currentTime;const i=null!=n.duration?cP(n.duration):null,a=null!=n.delay?cP(n.delay):null;return 0!==i&&t.forEach((t=>{const n=e.appendInstructionToTimeline(t,i,a);o=Math.max(o,n.duration+n.delay)})),o}visitReference(t,e){e.updateOptions(t.options,!0),kP(this,t.animation,e),e.previousNode=t}visitSequence(t,e){const n=e.subContextCount;let o=e;const i=t.options;if(i&&(i.params||i.delay)&&(o=e.createSubContext(i),o.transformIntoNewTimeline(),null!=i.delay)){6==o.previousNode.type&&(o.currentTimeline.snapshotCurrentStyles(),o.previousNode=ZP);const t=cP(i.delay);o.delayNextStep(t)}t.steps.length&&(t.steps.forEach((t=>kP(this,t,o))),o.currentTimeline.applyStylesToKeyframe(),o.subContextCount>n&&o.transformIntoNewTimeline()),e.previousNode=t}visitGroup(t,e){const n=[];let o=e.currentTimeline.currentTime;const i=t.options&&t.options.delay?cP(t.options.delay):0;t.steps.forEach((a=>{const r=e.createSubContext(t.options);i&&r.delayNextStep(i),kP(this,a,r),o=Math.max(o,r.currentTimeline.currentTime),n.push(r.currentTimeline)})),n.forEach((t=>e.currentTimeline.mergeTimelineCollectedStyles(t))),e.transformIntoNewTimeline(o),e.previousNode=t}_visitTiming(t,e){if(t.dynamic){const n=t.strValue;return pP(e.params?MP(n,e.params,e.errors):n,e.errors)}return{duration:t.duration,delay:t.delay,easing:t.easing}}visitAnimate(t,e){const n=e.currentAnimateTimings=this._visitTiming(t.timings,e),o=e.currentTimeline;n.delay&&(e.incrementTime(n.delay),o.snapshotCurrentStyles());const i=t.style;5==i.type?this.visitKeyframes(i,e):(e.incrementTime(n.duration),this.visitStyle(i,e),o.applyStylesToKeyframe()),e.currentAnimateTimings=null,e.previousNode=t}visitStyle(t,e){const n=e.currentTimeline,o=e.currentAnimateTimings;!o&&n.getCurrentStyleProperties().length&&n.forwardFrame();const i=o&&o.easing||t.easing;t.isEmptyStep?n.applyEmptyStep(i):n.setStyles(t.styles,i,e.errors,e.options),e.previousNode=t}visitKeyframes(t,e){const n=e.currentAnimateTimings,o=e.currentTimeline.duration,i=n.duration,a=e.createSubContext().currentTimeline;a.easing=n.easing,t.styles.forEach((t=>{a.forwardTime((t.offset||0)*i),a.setStyles(t.styles,t.easing,e.errors,e.options),a.applyStylesToKeyframe()})),e.currentTimeline.mergeTimelineCollectedStyles(a),e.transformIntoNewTimeline(o+i),e.previousNode=t}visitQuery(t,e){const n=e.currentTimeline.currentTime,o=t.options||{},i=o.delay?cP(o.delay):0;i&&(6===e.previousNode.type||0==n&&e.currentTimeline.getCurrentStyleProperties().length)&&(e.currentTimeline.snapshotCurrentStyles(),e.previousNode=ZP);let a=n;const r=e.invokeQuery(t.selector,t.originalSelector,t.limit,t.includeSelf,!!o.optional,e.errors);e.currentQueryTotal=r.length;let s=null;r.forEach(((n,o)=>{e.currentQueryIndex=o;const r=e.createSubContext(t.options,n);i&&r.delayNextStep(i),n===e.element&&(s=r.currentTimeline),kP(this,t.animation,r),r.currentTimeline.applyStylesToKeyframe();a=Math.max(a,r.currentTimeline.currentTime)})),e.currentQueryIndex=0,e.currentQueryTotal=0,e.transformIntoNewTimeline(a),s&&(e.currentTimeline.mergeTimelineCollectedStyles(s),e.currentTimeline.snapshotCurrentStyles()),e.previousNode=t}visitStagger(t,e){const n=e.parentContext,o=e.currentTimeline,i=t.timings,a=Math.abs(i.duration),r=a*(e.currentQueryTotal-1);let s=a*e.currentQueryIndex;switch(i.duration<0?"reverse":i.easing){case"reverse":s=r-s;break;case"full":s=n.currentStaggerTime}const l=e.currentTimeline;s&&l.delayNextStep(s);const c=l.currentTime;kP(this,t.animation,e),e.previousNode=t,n.currentStaggerTime=o.currentTime-c+(o.startTime-n.currentTimeline.startTime)}}const ZP={};class XP{constructor(t,e,n,o,i,a,r,s){this._driver=t,this.element=e,this.subInstructions=n,this._enterClassName=o,this._leaveClassName=i,this.errors=a,this.timelines=r,this.parentContext=null,this.currentAnimateTimings=null,this.previousNode=ZP,this.subContextCount=0,this.options={},this.currentQueryIndex=0,this.currentQueryTotal=0,this.currentStaggerTime=0,this.currentTimeline=s||new KP(this._driver,e,0),r.push(this.currentTimeline)}get params(){return this.options.params}updateOptions(t,e){if(!t)return;const n=t;let o=this.options;null!=n.duration&&(o.duration=cP(n.duration)),null!=n.delay&&(o.delay=cP(n.delay));const i=n.params;if(i){let t=o.params;t||(t=this.options.params={}),Object.keys(i).forEach((n=>{e&&t.hasOwnProperty(n)||(t[n]=MP(i[n],t,this.errors))}))}}_copyOptions(){const t={};if(this.options){const e=this.options.params;if(e){const n=t.params={};Object.keys(e).forEach((t=>{n[t]=e[t]}))}}return t}createSubContext(t=null,e,n){const o=e||this.element,i=new XP(this._driver,o,this.subInstructions,this._enterClassName,this._leaveClassName,this.errors,this.timelines,this.currentTimeline.fork(o,n||0));return i.previousNode=this.previousNode,i.currentAnimateTimings=this.currentAnimateTimings,i.options=this._copyOptions(),i.updateOptions(t),i.currentQueryIndex=this.currentQueryIndex,i.currentQueryTotal=this.currentQueryTotal,i.parentContext=this,this.subContextCount++,i}transformIntoNewTimeline(t){return this.previousNode=ZP,this.currentTimeline=this.currentTimeline.fork(this.element,t),this.timelines.push(this.currentTimeline),this.currentTimeline}appendInstructionToTimeline(t,e,n){const o={duration:null!=e?e:t.duration,delay:this.currentTimeline.currentTime+(null!=n?n:0)+t.delay,easing:""},i=new JP(this._driver,t.element,t.keyframes,t.preStyleProps,t.postStyleProps,o,t.stretchStartingKeyframe);return this.timelines.push(i),o}incrementTime(t){this.currentTimeline.forwardTime(this.currentTimeline.duration+t)}delayNextStep(t){t>0&&this.currentTimeline.delayNextStep(t)}invokeQuery(t,e,n,o,i,a){let r=[];if(o&&r.push(this.element),t.length>0){t=(t=t.replace(WP,"."+this._enterClassName)).replace(GP,"."+this._leaveClassName);let e=this._driver.query(this.element,t,1!=n);0!==n&&(e=n<0?e.slice(e.length+n,e.length):e.slice(0,n)),r.push(...e)}return i||0!=r.length||a.push(`\`query("${e}")\` returned zero elements. (Use \`query("${e}", { optional: true })\` if you wish to allow this.)`),r}}class KP{constructor(t,e,n,o){this._driver=t,this.element=e,this.startTime=n,this._elementTimelineStylesLookup=o,this.duration=0,this._previousKeyframe={},this._currentKeyframe={},this._keyframes=new Map,this._styleSummary={},this._pendingStyles={},this._backFill={},this._currentEmptyStepKeyframe=null,this._elementTimelineStylesLookup||(this._elementTimelineStylesLookup=new Map),this._localTimelineStyles=Object.create(this._backFill,{}),this._globalTimelineStyles=this._elementTimelineStylesLookup.get(e),this._globalTimelineStyles||(this._globalTimelineStyles=this._localTimelineStyles,this._elementTimelineStylesLookup.set(e,this._localTimelineStyles)),this._loadKeyframe()}containsAnimation(){switch(this._keyframes.size){case 0:return!1;case 1:return this.getCurrentStyleProperties().length>0;default:return!0}}getCurrentStyleProperties(){return Object.keys(this._currentKeyframe)}get currentTime(){return this.startTime+this.duration}delayNextStep(t){const e=1==this._keyframes.size&&Object.keys(this._pendingStyles).length;this.duration||e?(this.forwardTime(this.currentTime+t),e&&this.snapshotCurrentStyles()):this.startTime+=t}fork(t,e){return this.applyStylesToKeyframe(),new KP(this._driver,t,e||this.currentTime,this._elementTimelineStylesLookup)}_loadKeyframe(){this._currentKeyframe&&(this._previousKeyframe=this._currentKeyframe),this._currentKeyframe=this._keyframes.get(this.duration),this._currentKeyframe||(this._currentKeyframe=Object.create(this._backFill,{}),this._keyframes.set(this.duration,this._currentKeyframe))}forwardFrame(){this.duration+=1,this._loadKeyframe()}forwardTime(t){this.applyStylesToKeyframe(),this.duration=t,this._loadKeyframe()}_updateStyle(t,e){this._localTimelineStyles[t]=e,this._globalTimelineStyles[t]=e,this._styleSummary[t]={time:this.currentTime,value:e}}allowOnlyTimelineStyles(){return this._currentEmptyStepKeyframe!==this._currentKeyframe}applyEmptyStep(t){t&&(this._previousKeyframe.easing=t),Object.keys(this._globalTimelineStyles).forEach((t=>{this._backFill[t]=this._globalTimelineStyles[t]||vO,this._currentKeyframe[t]=vO})),this._currentEmptyStepKeyframe=this._currentKeyframe}setStyles(t,e,n,o){e&&(this._previousKeyframe.easing=e);const i=o&&o.params||{},a=(function r(t,e){const n={};let o;return t.forEach((t=>{"*"===t?(o=o||Object.keys(e),o.forEach((t=>{n[t]=vO}))):fP(t,!1,n)})),n}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t,this._globalTimelineStyles);Object.keys(a).forEach((t=>{const e=MP(a[t],i,n);this._pendingStyles[t]=e,this._localTimelineStyles.hasOwnProperty(t)||(this._backFill[t]=this._globalTimelineStyles.hasOwnProperty(t)?this._globalTimelineStyles[t]:vO),this._updateStyle(t,e)}))}applyStylesToKeyframe(){const t=this._pendingStyles,e=Object.keys(t);0!=e.length&&(this._pendingStyles={},e.forEach((e=>{this._currentKeyframe[e]=t[e]})),Object.keys(this._localTimelineStyles).forEach((t=>{this._currentKeyframe.hasOwnProperty(t)||(this._currentKeyframe[t]=this._localTimelineStyles[t])})))}snapshotCurrentStyles(){Object.keys(this._localTimelineStyles).forEach((t=>{const e=this._localTimelineStyles[t];this._pendingStyles[t]=e,this._updateStyle(t,e)}))}getFinalKeyframe(){return this._keyframes.get(this.duration)}get properties(){const t=[];for(let e in this._currentKeyframe)t.push(e);return t}mergeTimelineCollectedStyles(t){Object.keys(t._styleSummary).forEach((e=>{const n=this._styleSummary[e],o=t._styleSummary[e];(!n||o.time>n.time)&&this._updateStyle(e,o.value)}))}buildKeyframes(){this.applyStylesToKeyframe();const t=new Set,e=new Set,n=1===this._keyframes.size&&0===this.duration;let o=[];this._keyframes.forEach(((i,a)=>{const r=fP(i,!0);Object.keys(r).forEach((n=>{const o=r[n];"!"==o?t.add(n):o==vO&&e.add(n)})),n||(r.offset=a/this.duration),o.push(r)}));const i=t.size?vP(t.values()):[],a=e.size?vP(e.values()):[];if(n){const t=o[0],e=mP(t);t.offset=0,e.offset=1,o=[t,e]}return jP(this.element,o,i,a,this.duration,this.startTime,this.easing,!1)}}class JP extends KP{constructor(t,e,n,o,i,a,r=!1){super(t,e,a.delay),this.keyframes=n,this.preStyleProps=o,this.postStyleProps=i,this._stretchStartingKeyframe=r,this.timings={duration:a.duration,delay:a.delay,easing:a.easing}}containsAnimation(){return this.keyframes.length>1}buildKeyframes(){let t=this.keyframes,{delay:e,duration:n,easing:o}=this.timings;if(this._stretchStartingKeyframe&&e){const i=[],a=n+e,r=e/a,s=fP(t[0],!1);s.offset=0,i.push(s);const l=fP(t[0],!1);l.offset=QP(r),i.push(l);const c=t.length-1;for(let o=1;o<=c;o++){let r=fP(t[o],!1);r.offset=QP((e+r.offset*n)/a),i.push(r)}n=a,e=0,o="",t=i}return jP(this.element,t,this.preStyleProps,this.postStyleProps,n,e,o,!0)}}function QP(t,e=3){const n=Math.pow(10,e-1);return Math.round(t*n)/n}class $P{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class tw extends $P{normalizePropertyName(t,e){return OP(t)}normalizeStyleValue(t,e,n,o){let i="";const a=n.toString().trim();if(ew[e]&&0!==n&&"0"!==n)if("number"==typeof n)i="px";else{const e=n.match(/^[+-]?[\d\.]+([a-z]*)$/);e&&0==e[1].length&&o.push(`Please provide a CSS unit value for ${t}:${n}`)}return a+i}}const ew=(function nw(t){const e={};return t.forEach((t=>e[t]=!0)),e})("width,height,minWidth,minHeight,maxWidth,maxHeight,left,top,bottom,right,fontSize,outlineWidth,outlineOffset,paddingTop,paddingLeft,paddingBottom,paddingRight,marginTop,marginLeft,marginBottom,marginRight,borderRadius,borderWidth,borderTopWidth,borderLeftWidth,borderRightWidth,borderBottomWidth,textIndent,perspective".split(","));function ow(t,e,n,o,i,a,r,s,l,c,d,p,m){return{type:0,element:t,triggerName:e,isRemovalTransition:i,fromState:n,fromStyles:a,toState:o,toStyles:r,timelines:s,queriedElements:l,preStyleProps:c,postStyleProps:d,totalTime:p,errors:m}}const iw={};class aw{constructor(t,e,n){this._triggerName=t,this.ast=e,this._stateStyles=n}match(t,e,n,o){return(function i(t,e,n,o,a){return t.some((t=>t(e,n,o,a)))})(this.ast.matchers,t,e,n,o)}buildStyles(t,e,n){const o=this._stateStyles["*"],i=this._stateStyles[t],a=o?o.buildStyles(e,n):{};return i?i.buildStyles(e,n):a}build(t,e,n,o,i,a,r,s,l,c){const d=[],p=this.ast.options&&this.ast.options.params||iw,m=this.buildStyles(n,r&&r.params||iw,d),f=s&&s.params||iw,u=this.buildStyles(o,f,d),g=new Set,h=new Map,b=new Map,y="void"===o,_={params:Object.assign(Object.assign({},p),f)},C=c?[]:YP(t,e,this.ast.animation,i,a,m,u,_,l,d);let M=0;if(C.forEach((t=>{M=Math.max(t.duration+t.delay,M)})),d.length)return ow(e,this._triggerName,n,o,y,m,u,[],[],h,b,M,d);C.forEach((t=>{const n=t.element,o=jO(h,n,{});t.preStyleProps.forEach((t=>o[t]=!0));const i=jO(b,n,{});t.postStyleProps.forEach((t=>i[t]=!0)),n!==e&&g.add(n)}));const v=vP(g.values());return ow(e,this._triggerName,n,o,y,m,u,C,v,h,b,M)}}class rw{constructor(t,e,n){this.styles=t,this.defaultParams=e,this.normalizer=n}buildStyles(t,e){const n={},o=mP(this.defaultParams);return Object.keys(t).forEach((e=>{const n=t[e];null!=n&&(o[e]=n)})),this.styles.styles.forEach((t=>{if("string"!=typeof t){const i=t;Object.keys(i).forEach((t=>{let a=i[t];a.length>1&&(a=MP(a,o,e));const r=this.normalizer.normalizePropertyName(t,e);a=this.normalizer.normalizeStyleValue(t,r,a,e),n[r]=a}))}})),n}}class sw{constructor(t,e,n){this.name=t,this.ast=e,this._normalizer=n,this.transitionFactories=[],this.states={},e.states.forEach((t=>{this.states[t.name]=new rw(t.style,t.options&&t.options.params||{},n)})),lw(this.states,"true","1"),lw(this.states,"false","0"),e.transitions.forEach((e=>{this.transitionFactories.push(new aw(t,e,this.states))})),this.fallbackTransition=(function o(t,e,n){return new aw(t,{type:1,animation:{type:2,steps:[],options:null},matchers:[(t,e)=>!0],options:null,queryCount:0,depCount:0},e)})(t,this.states)}get containsQueries(){return this.ast.queryCount>0}matchTransition(t,e,n,o){return this.transitionFactories.find((i=>i.match(t,e,n,o)))||null}matchStyles(t,e,n){return this.fallbackTransition.buildStyles(t,e,n)}}function lw(t,e,n){t.hasOwnProperty(e)?t.hasOwnProperty(n)||(t[n]=t[e]):t.hasOwnProperty(n)&&(t[e]=t[n])}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const cw=new UP;class dw{constructor(t,e,n){this.bodyNode=t,this._driver=e,this._normalizer=n,this._animations={},this._playersById={},this.players=[]}register(t,e){const n=[],o=IP(this._driver,e,n);if(n.length)throw new Error(`Unable to build the animation due to the following errors: ${n.join("\n")}`);this._animations[t]=o}_buildPlayer(t,e,n){const o=t.element,i=FO(0,this._normalizer,0,t.keyframes,e,n);return this._driver.animate(o,i,t.duration,t.delay,t.easing,[],!0)}create(t,e,n={}){const o=[],i=this._animations[t];let a;const r=new Map;if(i?(a=YP(this._driver,e,i,oP,iP,{},{},n,cw,o),a.forEach((t=>{const e=jO(r,t.element,{});t.postStyleProps.forEach((t=>e[t]=null))}))):(o.push("The requested animation doesn't exist or has already been destroyed"),a=[]),o.length)throw new Error(`Unable to create the animation due to the following errors: ${o.join("\n")}`);r.forEach(((t,e)=>{Object.keys(t).forEach((n=>{t[n]=this._driver.computeStyle(e,n,vO)}))}));const s=HO(a.map((t=>{const e=r.get(t.element);return this._buildPlayer(t,{},e)})));return this._playersById[t]=s,s.onDestroy((()=>this.destroy(t))),this.players.push(s),s}destroy(t){const e=this._getPlayer(t);e.destroy(),delete this._playersById[t];const n=this.players.indexOf(e);n>=0&&this.players.splice(n,1)}_getPlayer(t){const e=this._playersById[t];if(!e)throw new Error(`Unable to find the timeline player referenced by ${t}`);return e}listen(t,e,n,o){const i=VO(e,"","","");return LO(this._getPlayer(t),n,i,o),()=>{}}command(t,e,n,o){if("register"==n)return void this.register(t,o[0]);if("create"==n){return void this.create(t,e,o[0]||{})}const i=this._getPlayer(t);switch(n){case"play":i.play();break;case"pause":i.pause();break;case"reset":i.reset();break;case"restart":i.restart();break;case"finish":i.finish();break;case"init":i.init();break;case"setPosition":i.setPosition(parseFloat(o[0]));break;case"destroy":this.destroy(t)}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const pw="ng-animate-queued",mw="ng-animate-disabled",fw=".ng-animate-disabled",uw=[],gw={namespaceId:"",setForRemoval:!1,setForMove:!1,hasAnimation:!1,removedBeforeQueried:!1},hw={namespaceId:"",setForMove:!1,setForRemoval:!1,hasAnimation:!1,removedBeforeQueried:!0};class bw{constructor(t,e=""){this.namespaceId=e;const n=t&&t.hasOwnProperty("value");if(this.value=(function o(t){return null!=t?t:null})(n?t.value:t),n){const e=mP(t);delete e.value,this.options=e}else this.options={};this.options.params||(this.options.params={})}get params(){return this.options.params}absorbOptions(t){const e=t.params;if(e){const t=this.options.params;Object.keys(e).forEach((n=>{null==t[n]&&(t[n]=e[n])}))}}}const yw="void",_w=new bw(yw);class Cw{constructor(t,e,n){this.id=t,this.hostElement=e,this._engine=n,this.players=[],this._triggers={},this._queue=[],this._elementListeners=new Map,this._hostClassName="ng-tns-"+t,kw(e,this._hostClassName)}listen(t,e,n,o){if(!this._triggers.hasOwnProperty(e))throw new Error(`Unable to listen on the animation trigger event "${n}" because the animation trigger "${e}" doesn't exist!`);if(null==n||0==n.length)throw new Error(`Unable to listen on the animation trigger "${e}" because the provided event is undefined!`);if(!(function i(t){return"start"==t||"done"==t})(n))throw new Error(`The provided animation trigger event "${n}" for the animation trigger "${e}" is not supported!`);const a=jO(this._elementListeners,t,[]),r={name:e,phase:n,callback:o};a.push(r);const s=jO(this._engine.statesByElement,t,{});return s.hasOwnProperty(e)||(kw(t,aP),kw(t,"ng-trigger-"+e),s[e]=_w),()=>{this._engine.afterFlush((()=>{const t=a.indexOf(r);t>=0&&a.splice(t,1),this._triggers[e]||delete s[e]}))}}register(t,e){return!this._triggers[t]&&(this._triggers[t]=e,!0)}_getTrigger(t){const e=this._triggers[t];if(!e)throw new Error(`The provided animation trigger "${t}" has not been registered!`);return e}trigger(t,e,n,o=!0){const i=this._getTrigger(e),a=new vw(this.id,e,t);let r=this._engine.statesByElement.get(t);r||(kw(t,aP),kw(t,"ng-trigger-"+e),this._engine.statesByElement.set(t,r={}));let s=r[e];const l=new bw(n,this.id);!(n&&n.hasOwnProperty("value"))&&s&&l.absorbOptions(s.options),r[e]=l,s||(s=_w);if(!(l.value===yw)&&s.value===l.value){if(!(function e(t,n){const o=Object.keys(t),i=Object.keys(n);if(o.length!=i.length)return!1;for(let e=0;e<o.length;e++){const i=o[e];if(!n.hasOwnProperty(i)||t[i]!==n[i])return!1}return!0})(s.params,l.params)){const e=[],n=i.matchStyles(s.value,s.params,e),o=i.matchStyles(l.value,l.params,e);e.length?this._engine.reportError(e):this._engine.afterFlush((()=>{bP(t,n),hP(t,o)}))}return}const c=jO(this._engine.playersByElement,t,[]);c.forEach((t=>{t.namespaceId==this.id&&t.triggerName==e&&t.queued&&t.destroy()}));let d=i.matchTransition(s.value,l.value,t,l.params),p=!1;if(!d){if(!o)return;d=i.fallbackTransition,p=!0}return this._engine.totalQueuedPlayers++,this._queue.push({element:t,triggerName:e,transition:d,fromState:s,toState:l,player:a,isFallbackTransition:p}),p||(kw(t,pw),a.onStart((()=>{Sw(t,pw)}))),a.onDone((()=>{let e=this.players.indexOf(a);e>=0&&this.players.splice(e,1);const n=this._engine.playersByElement.get(t);if(n){let t=n.indexOf(a);t>=0&&n.splice(t,1)}})),this.players.push(a),c.push(a),a}deregister(t){delete this._triggers[t],this._engine.statesByElement.forEach(((e,n)=>{delete e[t]})),this._elementListeners.forEach(((e,n)=>{this._elementListeners.set(n,e.filter((e=>e.name!=t)))}))}clearElementCache(t){this._engine.statesByElement.delete(t),this._elementListeners.delete(t);const e=this._engine.playersByElement.get(t);e&&(e.forEach((t=>t.destroy())),this._engine.playersByElement.delete(t))}_signalRemovalForInnerTriggers(t,e){const n=this._engine.driver.query(t,rP,!0);n.forEach((t=>{if(t.__ng_removed)return;const n=this._engine.fetchNamespacesByElement(t);n.size?n.forEach((n=>n.triggerLeaveAnimation(t,e,!1,!0))):this.clearElementCache(t)})),this._engine.afterFlushAnimationsDone((()=>n.forEach((t=>this.clearElementCache(t)))))}triggerLeaveAnimation(t,e,n,o){const i=this._engine.statesByElement.get(t);if(i){const a=[];if(Object.keys(i).forEach((e=>{if(this._triggers[e]){const n=this.trigger(t,e,yw,o);n&&a.push(n)}})),a.length)return this._engine.markElementAsRemoved(this.id,t,!0,e),n&&HO(a).onDone((()=>this._engine.processLeaveNode(t))),!0}return!1}prepareLeaveAnimationListeners(t){const e=this._elementListeners.get(t),n=this._engine.statesByElement.get(t);if(e&&n){const o=new Set;e.forEach((e=>{const i=e.name;if(o.has(i))return;o.add(i);const a=this._triggers[i].fallbackTransition,r=n[i]||_w,s=new bw(yw),l=new vw(this.id,i,t);this._engine.totalQueuedPlayers++,this._queue.push({element:t,triggerName:i,transition:a,fromState:r,toState:s,player:l,isFallbackTransition:!0})}))}}removeNode(t,e){const n=this._engine;if(t.childElementCount&&this._signalRemovalForInnerTriggers(t,e),this.triggerLeaveAnimation(t,e,!0))return;let o=!1;if(n.totalAnimations){const e=n.players.length?n.playersByQueriedElement.get(t):[];if(e&&e.length)o=!0;else{let e=t;for(;e=e.parentNode;){if(n.statesByElement.get(e)){o=!0;break}}}}if(this.prepareLeaveAnimationListeners(t),o)n.markElementAsRemoved(this.id,t,!1,e);else{const o=t.__ng_removed;o&&o!==gw||(n.afterFlush((()=>this.clearElementCache(t))),n.destroyInnerAnimations(t),n._onRemovalComplete(t,e))}}insertNode(t,e){kw(t,this._hostClassName)}drainQueuedTransitions(t){const e=[];return this._queue.forEach((n=>{const o=n.player;if(o.destroyed)return;const i=n.element,a=this._elementListeners.get(i);a&&a.forEach((e=>{if(e.name==n.triggerName){const o=VO(i,n.triggerName,n.fromState.value,n.toState.value);o._data=t,LO(n.player,e.phase,o,e.callback)}})),o.markedForDestroy?this._engine.afterFlush((()=>{o.destroy()})):e.push(n)})),this._queue=[],e.sort(((t,e)=>{const n=t.transition.ast.depCount,o=e.transition.ast.depCount;return 0==n||0==o?n-o:this._engine.driver.containsElement(t.element,e.element)?1:-1}))}destroy(t){this.players.forEach((t=>t.destroy())),this._signalRemovalForInnerTriggers(this.hostElement,t)}elementContainsData(t){let e=!1;return this._elementListeners.has(t)&&(e=!0),e=!!this._queue.find((e=>e.element===t))||e,e}}class Mw{constructor(t,e,n){this.bodyNode=t,this.driver=e,this._normalizer=n,this.players=[],this.newHostElements=new Map,this.playersByElement=new Map,this.playersByQueriedElement=new Map,this.statesByElement=new Map,this.disabledNodes=new Set,this.totalAnimations=0,this.totalQueuedPlayers=0,this._namespaceLookup={},this._namespaceList=[],this._flushFns=[],this._whenQuietFns=[],this.namespacesByHostElement=new Map,this.collectedEnterElements=[],this.collectedLeaveElements=[],this.onRemovalComplete=(t,e)=>{}}_onRemovalComplete(t,e){this.onRemovalComplete(t,e)}get queuedPlayers(){const t=[];return this._namespaceList.forEach((e=>{e.players.forEach((e=>{e.queued&&t.push(e)}))})),t}createNamespace(t,e){const n=new Cw(t,e,this);return this.bodyNode&&this.driver.containsElement(this.bodyNode,e)?this._balanceNamespaceList(n,e):(this.newHostElements.set(e,n),this.collectEnterElement(e)),this._namespaceLookup[t]=n}_balanceNamespaceList(t,e){const n=this._namespaceList.length-1;if(n>=0){let o=!1;for(let i=n;i>=0;i--){if(this.driver.containsElement(this._namespaceList[i].hostElement,e)){this._namespaceList.splice(i+1,0,t),o=!0;break}}o||this._namespaceList.splice(0,0,t)}else this._namespaceList.push(t);return this.namespacesByHostElement.set(e,t),t}register(t,e){let n=this._namespaceLookup[t];return n||(n=this.createNamespace(t,e)),n}registerTrigger(t,e,n){let o=this._namespaceLookup[t];o&&o.register(e,n)&&this.totalAnimations++}destroy(t,e){if(!t)return;const n=this._fetchNamespace(t);this.afterFlush((()=>{this.namespacesByHostElement.delete(n.hostElement),delete this._namespaceLookup[t];const e=this._namespaceList.indexOf(n);e>=0&&this._namespaceList.splice(e,1)})),this.afterFlushAnimationsDone((()=>n.destroy(e)))}_fetchNamespace(t){return this._namespaceLookup[t]}fetchNamespacesByElement(t){const e=new Set,n=this.statesByElement.get(t);if(n){const t=Object.keys(n);for(let o=0;o<t.length;o++){const i=n[t[o]].namespaceId;if(i){const t=this._fetchNamespace(i);t&&e.add(t)}}}return e}trigger(t,e,n,o){if(xw(e)){const i=this._fetchNamespace(t);if(i)return i.trigger(e,n,o),!0}return!1}insertNode(t,e,n,o){if(!xw(e))return;const i=e.__ng_removed;if(i&&i.setForRemoval){i.setForRemoval=!1,i.setForMove=!0;const t=this.collectedLeaveElements.indexOf(e);t>=0&&this.collectedLeaveElements.splice(t,1)}if(t){const o=this._fetchNamespace(t);o&&o.insertNode(e,n)}o&&this.collectEnterElement(e)}collectEnterElement(t){this.collectedEnterElements.push(t)}markElementAsDisabled(t,e){e?this.disabledNodes.has(t)||(this.disabledNodes.add(t),kw(t,mw)):this.disabledNodes.has(t)&&(this.disabledNodes.delete(t),Sw(t,mw))}removeNode(t,e,n,o){if(xw(e)){const i=t?this._fetchNamespace(t):null;if(i?i.removeNode(e,o):this.markElementAsRemoved(t,e,!1,o),n){const n=this.namespacesByHostElement.get(e);n&&n.id!==t&&n.removeNode(e,o)}}else this._onRemovalComplete(e,o)}markElementAsRemoved(t,e,n,o){this.collectedLeaveElements.push(e),e.__ng_removed={namespaceId:t,setForRemoval:o,hasAnimation:n,removedBeforeQueried:!1}}listen(t,e,n,o,i){return xw(e)?this._fetchNamespace(t).listen(e,n,o,i):()=>{}}_buildInstruction(t,e,n,o,i){return t.transition.build(this.driver,t.element,t.fromState.value,t.toState.value,n,o,t.fromState.options,t.toState.options,e,i)}destroyInnerAnimations(t){let e=this.driver.query(t,rP,!0);e.forEach((t=>this.destroyActiveAnimationsForElement(t))),0!=this.playersByQueriedElement.size&&(e=this.driver.query(t,lP,!0),e.forEach((t=>this.finishActiveQueriedAnimationOnElement(t))))}destroyActiveAnimationsForElement(t){const e=this.playersByElement.get(t);e&&e.forEach((t=>{t.queued?t.markedForDestroy=!0:t.destroy()}))}finishActiveQueriedAnimationOnElement(t){const e=this.playersByQueriedElement.get(t);e&&e.forEach((t=>t.finish()))}whenRenderingDone(){return new Promise((t=>{if(this.players.length)return HO(this.players).onDone((()=>t()));t()}))}processLeaveNode(t){const e=t.__ng_removed;if(e&&e.setForRemoval){if(t.__ng_removed=gw,e.namespaceId){this.destroyInnerAnimations(t);const n=this._fetchNamespace(e.namespaceId);n&&n.clearElementCache(t)}this._onRemovalComplete(t,e.setForRemoval)}this.driver.matchesElement(t,fw)&&this.markElementAsDisabled(t,!1),this.driver.query(t,fw,!0).forEach((t=>{this.markElementAsDisabled(t,!1)}))}flush(t=-1){let e=[];if(this.newHostElements.size&&(this.newHostElements.forEach(((t,e)=>this._balanceNamespaceList(t,e))),this.newHostElements.clear()),this.totalAnimations&&this.collectedEnterElements.length)for(let t=0;t<this.collectedEnterElements.length;t++){kw(this.collectedEnterElements[t],"ng-star-inserted")}if(this._namespaceList.length&&(this.totalQueuedPlayers||this.collectedLeaveElements.length)){const n=[];try{e=this._flushAnimations(n,t)}finally{for(let t=0;t<n.length;t++)n[t]()}}else for(let t=0;t<this.collectedLeaveElements.length;t++){this.processLeaveNode(this.collectedLeaveElements[t])}if(this.totalQueuedPlayers=0,this.collectedEnterElements.length=0,this.collectedLeaveElements.length=0,this._flushFns.forEach((t=>t())),this._flushFns=[],this._whenQuietFns.length){const t=this._whenQuietFns;this._whenQuietFns=[],e.length?HO(e).onDone((()=>{t.forEach((t=>t()))})):t.forEach((t=>t()))}}reportError(t){throw new Error(`Unable to process animations due to the following failed trigger transitions\n ${t.join("\n")}`)}_flushAnimations(t,e){const n=new UP,o=[],i=new Map,a=[],r=new Map,s=new Map,l=new Map,c=new Set;this.disabledNodes.forEach((t=>{c.add(t);const e=this.driver.query(t,".ng-animate-queued",!0);for(let t=0;t<e.length;t++)c.add(e[t])}));const d=this.bodyNode,p=Array.from(this.statesByElement.keys()),m=ww(p,this.collectedEnterElements),f=new Map;let u=0;m.forEach(((t,e)=>{const n=oP+u++;f.set(e,n),t.forEach((t=>kw(t,n)))}));const g=[],h=new Set,b=new Set;for(let t=0;t<this.collectedLeaveElements.length;t++){const e=this.collectedLeaveElements[t],n=e.__ng_removed;n&&n.setForRemoval&&(g.push(e),h.add(e),n.hasAnimation?this.driver.query(e,".ng-star-inserted",!0).forEach((t=>h.add(t))):b.add(e))}const y=new Map,_=ww(p,Array.from(h));_.forEach(((t,e)=>{const n=iP+u++;y.set(e,n),t.forEach((t=>kw(t,n)))})),t.push((()=>{m.forEach(((t,e)=>{const n=f.get(e);t.forEach((t=>Sw(t,n)))})),_.forEach(((t,e)=>{const n=y.get(e);t.forEach((t=>Sw(t,n)))})),g.forEach((t=>{this.processLeaveNode(t)}))}));const C=[],M=[];for(let t=this._namespaceList.length-1;t>=0;t--){this._namespaceList[t].drainQueuedTransitions(e).forEach((t=>{const e=t.player,i=t.element;if(C.push(e),this.collectedEnterElements.length){const t=i.__ng_removed;if(t&&t.setForMove)return void e.destroy()}const c=!d||!this.driver.containsElement(d,i),p=y.get(i),m=f.get(i),u=this._buildInstruction(t,n,m,p,c);if(u.errors&&u.errors.length)return void M.push(u);if(c)return e.onStart((()=>bP(i,u.fromStyles))),e.onDestroy((()=>hP(i,u.toStyles))),void o.push(e);if(t.isFallbackTransition)return e.onStart((()=>bP(i,u.fromStyles))),e.onDestroy((()=>hP(i,u.toStyles))),void o.push(e);u.timelines.forEach((t=>t.stretchStartingKeyframe=!0)),n.append(i,u.timelines);a.push({instruction:u,player:e,element:i}),u.queriedElements.forEach((t=>jO(r,t,[]).push(e))),u.preStyleProps.forEach(((t,e)=>{const n=Object.keys(t);if(n.length){let t=s.get(e);t||s.set(e,t=new Set),n.forEach((e=>t.add(e)))}})),u.postStyleProps.forEach(((t,e)=>{const n=Object.keys(t);let o=l.get(e);o||l.set(e,o=new Set),n.forEach((t=>o.add(t)))}))}))}if(M.length){const t=[];M.forEach((e=>{t.push(`@${e.triggerName} has failed due to:\n`),e.errors.forEach((e=>t.push(`- ${e}\n`)))})),C.forEach((t=>t.destroy())),this.reportError(t)}const v=new Map,x=new Map;a.forEach((t=>{const e=t.element;n.has(e)&&(x.set(e,e),this._beforeAnimationBuild(t.player.namespaceId,t.instruction,v))})),o.forEach((t=>{const e=t.element;this._getPreviousPlayers(e,!1,t.namespaceId,t.triggerName,null).forEach((t=>{jO(v,e,[]).push(t),t.destroy()}))}));const O=g.filter((t=>Ew(t,s,l))),P=new Map;Pw(P,this.driver,b,l,vO).forEach((t=>{Ew(t,s,l)&&O.push(t)}));const w=new Map;m.forEach(((t,e)=>{Pw(w,this.driver,new Set(t),s,"!")})),O.forEach((t=>{const e=P.get(t),n=w.get(t);P.set(t,Object.assign(Object.assign({},e),n))}));const k=[],S=[],D={};a.forEach((t=>{const{element:e,player:a,instruction:r}=t;if(n.has(e)){if(c.has(e))return a.onDestroy((()=>hP(e,r.toStyles))),a.disabled=!0,a.overrideTotalTime(r.totalTime),void o.push(a);let t=D;if(x.size>1){let n=e;const o=[];for(;n=n.parentNode;){const e=x.get(n);if(e){t=e;break}o.push(n)}o.forEach((e=>x.set(e,t)))}const n=this._buildAnimation(a.namespaceId,r,v,i,w,P);if(a.setRealPlayer(n),t===D)k.push(a);else{const e=this.playersByElement.get(t);e&&e.length&&(a.parentPlayer=HO(e)),o.push(a)}}else bP(e,r.fromStyles),a.onDestroy((()=>hP(e,r.toStyles))),S.push(a),c.has(e)&&o.push(a)})),S.forEach((t=>{const e=i.get(t.element);if(e&&e.length){const n=HO(e);t.setRealPlayer(n)}})),o.forEach((t=>{t.parentPlayer?t.syncPlayerEvents(t.parentPlayer):t.destroy()}));for(let t=0;t<g.length;t++){const e=g[t],n=e.__ng_removed;if(Sw(e,iP),n&&n.hasAnimation)continue;let o=[];if(r.size){let t=r.get(e);t&&t.length&&o.push(...t);let n=this.driver.query(e,lP,!0);for(let t=0;t<n.length;t++){let e=r.get(n[t]);e&&e.length&&o.push(...e)}}const i=o.filter((t=>!t.destroyed));i.length?Dw(this,e,i):this.processLeaveNode(e)}return g.length=0,k.forEach((t=>{this.players.push(t),t.onDone((()=>{t.destroy();const e=this.players.indexOf(t);this.players.splice(e,1)})),t.play()})),k}elementContainsData(t,e){let n=!1;const o=e.__ng_removed;return o&&o.setForRemoval&&(n=!0),this.playersByElement.has(e)&&(n=!0),this.playersByQueriedElement.has(e)&&(n=!0),this.statesByElement.has(e)&&(n=!0),this._fetchNamespace(t).elementContainsData(e)||n}afterFlush(t){this._flushFns.push(t)}afterFlushAnimationsDone(t){this._whenQuietFns.push(t)}_getPreviousPlayers(t,e,n,o,i){let a=[];if(e){const e=this.playersByQueriedElement.get(t);e&&(a=e)}else{const e=this.playersByElement.get(t);if(e){const t=!i||i==yw;e.forEach((e=>{e.queued||(t||e.triggerName==o)&&a.push(e)}))}}return(n||o)&&(a=a.filter((t=>(!n||n==t.namespaceId)&&(!o||o==t.triggerName)))),a}_beforeAnimationBuild(t,e,n){const o=e.element,i=e.isRemovalTransition?void 0:t,a=e.isRemovalTransition?void 0:e.triggerName;for(const t of e.timelines){const r=t.element,s=r!==o,l=jO(n,r,[]);this._getPreviousPlayers(r,s,i,a,e.toState).forEach((t=>{const e=t.getRealPlayer();e.beforeDestroy&&e.beforeDestroy(),t.destroy(),l.push(t)}))}bP(o,e.fromStyles)}_buildAnimation(t,e,n,o,i,a){const r=e.triggerName,s=e.element,l=[],c=new Set,d=new Set,p=e.timelines.map((e=>{const p=e.element;c.add(p);const m=p.__ng_removed;if(m&&m.removedBeforeQueried)return new TO(e.duration,e.delay);const f=p!==s,u=(function g(t){const e=[];return Rw(t,e),e})((n.get(p)||uw).map((t=>t.getRealPlayer()))).filter((t=>!!t.element&&t.element===p)),h=i.get(p),b=a.get(p),y=FO(0,this._normalizer,0,e.keyframes,h,b),_=this._buildPlayer(e,y,u);if(e.subTimeline&&o&&d.add(p),f){const e=new vw(t,r,p);e.setRealPlayer(_),l.push(e)}return _}));l.forEach((t=>{jO(this.playersByQueriedElement,t.element,[]).push(t),t.onDone((()=>(function e(t,n,o){let i;if(t instanceof Map){if(i=t.get(n),i){if(i.length){const t=i.indexOf(o);i.splice(t,1)}0==i.length&&t.delete(n)}}else if(i=t[n],i){if(i.length){const t=i.indexOf(o);i.splice(t,1)}0==i.length&&delete t[n]}return i})(this.playersByQueriedElement,t.element,t)))})),c.forEach((t=>kw(t,sP)));const m=HO(p);return m.onDestroy((()=>{c.forEach((t=>Sw(t,sP))),hP(s,e.toStyles)})),d.forEach((t=>{jO(o,t,[]).push(m)})),m}_buildPlayer(t,e,n){return e.length>0?this.driver.animate(t.element,e,t.duration,t.delay,t.easing,n):new TO(t.duration,t.delay)}}class vw{constructor(t,e,n){this.namespaceId=t,this.triggerName=e,this.element=n,this._player=new TO,this._containsRealPlayer=!1,this._queuedCallbacks={},this.destroyed=!1,this.markedForDestroy=!1,this.disabled=!1,this.queued=!0,this.totalTime=0}setRealPlayer(t){this._containsRealPlayer||(this._player=t,Object.keys(this._queuedCallbacks).forEach((e=>{this._queuedCallbacks[e].forEach((n=>LO(t,e,void 0,n)))})),this._queuedCallbacks={},this._containsRealPlayer=!0,this.overrideTotalTime(t.totalTime),this.queued=!1)}getRealPlayer(){return this._player}overrideTotalTime(t){this.totalTime=t}syncPlayerEvents(t){const e=this._player;e.triggerCallback&&t.onStart((()=>e.triggerCallback("start"))),t.onDone((()=>this.finish())),t.onDestroy((()=>this.destroy()))}_queueEvent(t,e){jO(this._queuedCallbacks,t,[]).push(e)}onDone(t){this.queued&&this._queueEvent("done",t),this._player.onDone(t)}onStart(t){this.queued&&this._queueEvent("start",t),this._player.onStart(t)}onDestroy(t){this.queued&&this._queueEvent("destroy",t),this._player.onDestroy(t)}init(){this._player.init()}hasStarted(){return!this.queued&&this._player.hasStarted()}play(){!this.queued&&this._player.play()}pause(){!this.queued&&this._player.pause()}restart(){!this.queued&&this._player.restart()}finish(){this._player.finish()}destroy(){this.destroyed=!0,this._player.destroy()}reset(){!this.queued&&this._player.reset()}setPosition(t){this.queued||this._player.setPosition(t)}getPosition(){return this.queued?0:this._player.getPosition()}triggerCallback(t){const e=this._player;e.triggerCallback&&e.triggerCallback(t)}}function xw(t){return t&&1===t.nodeType}function Ow(t,e){const n=t.style.display;return t.style.display=null!=e?e:"none",n}function Pw(t,e,n,o,i){const a=[];n.forEach((t=>a.push(Ow(t))));const r=[];o.forEach(((n,o)=>{const a={};n.forEach((t=>{const n=a[t]=e.computeStyle(o,t,i);n&&0!=n.length||(o.__ng_removed=hw,r.push(o))})),t.set(o,a)}));let s=0;return n.forEach((t=>Ow(t,a[s++]))),r}function ww(t,e){const n=new Map;if(t.forEach((t=>n.set(t,[]))),0==e.length)return n;const o=new Set(e),i=new Map;function a(t){if(!t)return 1;let e=i.get(t);if(e)return e;const r=t.parentNode;return e=n.has(r)?r:o.has(r)?1:a(r),i.set(t,e),e}return e.forEach((t=>{const e=a(t);1!==e&&n.get(e).push(t)})),n}function kw(t,e){if(t.classList)t.classList.add(e);else{let n=t.$$classes;n||(n=t.$$classes={}),n[e]=!0}}function Sw(t,e){if(t.classList)t.classList.remove(e);else{let n=t.$$classes;n&&delete n[e]}}function Dw(t,e,n){HO(n).onDone((()=>t.processLeaveNode(e)))}function Rw(t,e){for(let n=0;n<t.length;n++){const o=t[n];o instanceof NO?Rw(o.players,e):e.push(o)}}function Ew(t,e,n){const o=n.get(t);if(!o)return!1;let i=e.get(t);return i?o.forEach((t=>i.add(t))):e.set(t,o),n.delete(t),!0}class Aw{constructor(t,e,n){this.bodyNode=t,this._driver=e,this._normalizer=n,this._triggerCache={},this.onRemovalComplete=(t,e)=>{},this._transitionEngine=new Mw(t,e,n),this._timelineEngine=new dw(t,e,n),this._transitionEngine.onRemovalComplete=(t,e)=>this.onRemovalComplete(t,e)}registerTrigger(t,e,n,o,i){const a=t+"-"+o;let r=this._triggerCache[a];if(!r){const t=[],e=IP(this._driver,i,t);if(t.length)throw new Error(`The animation trigger "${o}" has failed to build due to the following errors:\n - ${t.join("\n - ")}`);r=(function n(t,e,o){return new sw(t,e,o)})(o,e,this._normalizer),this._triggerCache[a]=r}this._transitionEngine.registerTrigger(e,o,r)}register(t,e){this._transitionEngine.register(t,e)}destroy(t,e){this._transitionEngine.destroy(t,e)}onInsert(t,e,n,o){this._transitionEngine.insertNode(t,e,n,o)}onRemove(t,e,n,o){this._transitionEngine.removeNode(t,e,o||!1,n)}disableAnimations(t,e){this._transitionEngine.markElementAsDisabled(t,e)}process(t,e,n,o){if("@"==n.charAt(0)){const[t,i]=UO(n);this._timelineEngine.command(t,e,i,o)}else this._transitionEngine.trigger(t,e,n,o)}listen(t,e,n,o,i){if("@"==n.charAt(0)){const[t,o]=UO(n);return this._timelineEngine.listen(t,e,o,i)}return this._transitionEngine.listen(t,e,n,o,i)}flush(t=-1){this._transitionEngine.flush(t)}get players(){return this._transitionEngine.players.concat(this._timelineEngine.players)}whenRenderingDone(){return this._transitionEngine.whenRenderingDone()}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Tw(t,e){let n=null,o=null;return Array.isArray(e)&&e.length?(n=zw(e[0]),e.length>1&&(o=zw(e[e.length-1]))):e&&(n=zw(e)),n||o?new Nw(t,n,o):null}class Nw{constructor(t,e,n){this._element=t,this._startStyles=e,this._endStyles=n,this._state=0;let o=Nw.initialStylesByElement.get(t);o||Nw.initialStylesByElement.set(t,o={}),this._initialStyles=o}start(){this._state<1&&(this._startStyles&&hP(this._element,this._startStyles,this._initialStyles),this._state=1)}finish(){this.start(),this._state<2&&(hP(this._element,this._initialStyles),this._endStyles&&(hP(this._element,this._endStyles),this._endStyles=null),this._state=1)}destroy(){this.finish(),this._state<3&&(Nw.initialStylesByElement.delete(this._element),this._startStyles&&(bP(this._element,this._startStyles),this._endStyles=null),this._endStyles&&(bP(this._element,this._endStyles),this._endStyles=null),hP(this._element,this._initialStyles),this._state=3)}}function zw(t){let e=null;const n=Object.keys(t);for(let o=0;o<n.length;o++){const i=n[o];Iw(i)&&(e=e||{},e[i]=t[i])}return e}function Iw(t){return"display"===t||"position"===t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */Nw.initialStylesByElement=new WeakMap;const Hw="animation",Fw="animationend";class Lw{constructor(t,e,n,o,i,a,r){this._element=t,this._name=e,this._duration=n,this._delay=o,this._easing=i,this._fillMode=a,this._onDoneFn=r,this._finished=!1,this._destroyed=!1,this._startTime=0,this._position=0,this._eventFn=t=>this._handleCallback(t)}apply(){!(function t(e,n){const o=Gw(e,"").trim();let i=0;o.length&&(i=(function a(t,e){let n=0;for(let o=0;o<t.length;o++){t.charAt(o)===e&&n++}return n})(o,",")+1,n=`${o}, ${n}`);return Ww(e,"",n),i})(this._element,`${this._duration}ms ${this._easing} ${this._delay}ms 1 normal ${this._fillMode} ${this._name}`),Uw(this._element,this._eventFn,!1),this._startTime=Date.now()}pause(){Bw(this._element,this._name,"paused")}resume(){Bw(this._element,this._name,"running")}setPosition(t){const e=Vw(this._element,this._name);this._position=t*this._duration,Ww(this._element,"Delay",`-${this._position}ms`,e)}getPosition(){return this._position}_handleCallback(t){const e=t._ngTestManualTimestamp||Date.now(),n=1e3*parseFloat(t.elapsedTime.toFixed(3));t.animationName==this._name&&Math.max(e-this._startTime,0)>=this._delay&&n>=this._duration&&this.finish()}finish(){this._finished||(this._finished=!0,this._onDoneFn(),Uw(this._element,this._eventFn,!0))}destroy(){this._destroyed||(this._destroyed=!0,this.finish(),(function t(e,n){const o=Gw(e,"").split(","),i=jw(o,n);if(i>=0){o.splice(i,1);Ww(e,"",o.join(","))}})(this._element,this._name))}}function Bw(t,e,n){Ww(t,"PlayState",n,Vw(t,e))}function Vw(t,e){const n=Gw(t,"");if(n.indexOf(",")>0){return jw(n.split(","),e)}return jw([n],e)}function jw(t,e){for(let n=0;n<t.length;n++)if(t[n].indexOf(e)>=0)return n;return-1}function Uw(t,e,n){n?t.removeEventListener(Fw,e):t.addEventListener(Fw,e)}function Ww(t,e,n,o){const i=Hw+e;if(null!=o){const e=t.style[i];if(e.length){const t=e.split(",");t[o]=n,n=t.join(",")}}t.style[i]=n}function Gw(t,e){return t.style[Hw+e]||""}class Yw{constructor(t,e,n,o,i,a,r,s){this.element=t,this.keyframes=e,this.animationName=n,this._duration=o,this._delay=i,this._finalStyles=r,this._specialStyles=s,this._onDoneFns=[],this._onStartFns=[],this._onDestroyFns=[],this.currentSnapshot={},this._state=0,this.easing=a||"linear",this.totalTime=o+i,this._buildStyler()}onStart(t){this._onStartFns.push(t)}onDone(t){this._onDoneFns.push(t)}onDestroy(t){this._onDestroyFns.push(t)}destroy(){this.init(),this._state>=4||(this._state=4,this._styler.destroy(),this._flushStartFns(),this._flushDoneFns(),this._specialStyles&&this._specialStyles.destroy(),this._onDestroyFns.forEach((t=>t())),this._onDestroyFns=[])}_flushDoneFns(){this._onDoneFns.forEach((t=>t())),this._onDoneFns=[]}_flushStartFns(){this._onStartFns.forEach((t=>t())),this._onStartFns=[]}finish(){this.init(),this._state>=3||(this._state=3,this._styler.finish(),this._flushStartFns(),this._specialStyles&&this._specialStyles.finish(),this._flushDoneFns())}setPosition(t){this._styler.setPosition(t)}getPosition(){return this._styler.getPosition()}hasStarted(){return this._state>=2}init(){this._state>=1||(this._state=1,this._styler.apply(),this._delay&&this._styler.pause())}play(){this.init(),this.hasStarted()||(this._flushStartFns(),this._state=2,this._specialStyles&&this._specialStyles.start()),this._styler.resume()}pause(){this.init(),this._styler.pause()}restart(){this.reset(),this.play()}reset(){this._state=0,this._styler.destroy(),this._buildStyler(),this._styler.apply()}_buildStyler(){this._styler=new Lw(this.element,this.animationName,this._duration,this._delay,this.easing,"forwards",(()=>this.finish()))}triggerCallback(t){const e="start"==t?this._onStartFns:this._onDoneFns;e.forEach((t=>t())),e.length=0}beforeDestroy(){this.init();const t={};if(this.hasStarted()){const e=this._state>=3;Object.keys(this._finalStyles).forEach((n=>{"offset"!=n&&(t[n]=e?this._finalStyles[n]:SP(this.element,n))}))}this.currentSnapshot=t}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class qw extends TO{constructor(t,e){super(),this.element=t,this._startingStyles={},this.__initialized=!1,this._styles=tP(e)}init(){!this.__initialized&&this._startingStyles&&(this.__initialized=!0,Object.keys(this._styles).forEach((t=>{this._startingStyles[t]=this.element.style[t]})),super.init())}play(){this._startingStyles&&(this.init(),Object.keys(this._styles).forEach((t=>this.element.style.setProperty(t,this._styles[t]))),super.play())}destroy(){this._startingStyles&&(Object.keys(this._startingStyles).forEach((t=>{const e=this._startingStyles[t];e?this.element.style.setProperty(t,e):this.element.style.removeProperty(t)})),this._startingStyles=null,super.destroy())}}class Zw{constructor(){this._count=0}validateStyleProperty(t){return KO(t)}matchesElement(t,e){return JO(t,e)}containsElement(t,e){return QO(t,e)}query(t,e,n){return $O(t,e,n)}computeStyle(t,e,n){return window.getComputedStyle(t)[e]}buildKeyframeElement(t,e,n){n=n.map((t=>tP(t)));let o=`@keyframes ${e} {\n`,i="";n.forEach((t=>{i=" ";const e=parseFloat(t.offset);o+=`${i}${100*e}% {\n`,i+=" ",Object.keys(t).forEach((e=>{const n=t[e];switch(e){case"offset":return;case"easing":return void(n&&(o+=`${i}animation-timing-function: ${n};\n`));default:return void(o+=`${i}${e}: ${n};\n`)}})),o+=`${i}}\n`})),o+="}\n";const a=document.createElement("style");return a.textContent=o,a}animate(t,e,n,o,i,a=[],r){("undefined"==typeof ngDevMode||ngDevMode)&&r&&(function s(){if(Xw)return;console.warn("@angular/animations: please load the web-animations.js polyfill to allow programmatic access...\n","  visit https://bit.ly/IWukam to learn more about using the web-animation-js polyfill."),Xw=!0})();const l=a.filter((t=>t instanceof Yw)),c={};PP(n,o)&&l.forEach((t=>{let e=t.currentSnapshot;Object.keys(e).forEach((t=>c[t]=e[t]))}));const d=(function p(t){let e={};if(t){(Array.isArray(t)?t:[t]).forEach((t=>{Object.keys(t).forEach((n=>{"offset"!=n&&"easing"!=n&&(e[n]=t[n])}))}))}return e})(e=wP(t,e,c));if(0==n)return new qw(t,d);const m="gen_css_kf_"+this._count++,f=this.buildKeyframeElement(t,m,e);(function u(t){var e;const n=null===(e=t.getRootNode)||void 0===e?void 0:e.call(t);if("undefined"!=typeof ShadowRoot&&n instanceof ShadowRoot)return n;return document.head})(t).appendChild(f);const g=Tw(t,e),h=new Yw(t,e,m,n,o,i,d,g);return h.onDestroy((()=>(function t(e){e.parentNode.removeChild(e)})(f))),h}}let Xw=!1;class Kw{constructor(t,e,n,o){this.element=t,this.keyframes=e,this.options=n,this._specialStyles=o,this._onDoneFns=[],this._onStartFns=[],this._onDestroyFns=[],this._initialized=!1,this._finished=!1,this._started=!1,this._destroyed=!1,this.time=0,this.parentPlayer=null,this.currentSnapshot={},this._duration=n.duration,this._delay=n.delay||0,this.time=this._duration+this._delay}_onFinish(){this._finished||(this._finished=!0,this._onDoneFns.forEach((t=>t())),this._onDoneFns=[])}init(){this._buildPlayer(),this._preparePlayerBeforeStart()}_buildPlayer(){if(this._initialized)return;this._initialized=!0;const t=this.keyframes;this.domPlayer=this._triggerWebAnimation(this.element,t,this.options),this._finalKeyframe=t.length?t[t.length-1]:{},this.domPlayer.addEventListener("finish",(()=>this._onFinish()))}_preparePlayerBeforeStart(){this._delay?this._resetDomPlayerState():this.domPlayer.pause()}_triggerWebAnimation(t,e,n){return t.animate(e,n)}onStart(t){this._onStartFns.push(t)}onDone(t){this._onDoneFns.push(t)}onDestroy(t){this._onDestroyFns.push(t)}play(){this._buildPlayer(),this.hasStarted()||(this._onStartFns.forEach((t=>t())),this._onStartFns=[],this._started=!0,this._specialStyles&&this._specialStyles.start()),this.domPlayer.play()}pause(){this.init(),this.domPlayer.pause()}finish(){this.init(),this._specialStyles&&this._specialStyles.finish(),this._onFinish(),this.domPlayer.finish()}reset(){this._resetDomPlayerState(),this._destroyed=!1,this._finished=!1,this._started=!1}_resetDomPlayerState(){this.domPlayer&&this.domPlayer.cancel()}restart(){this.reset(),this.play()}hasStarted(){return this._started}destroy(){this._destroyed||(this._destroyed=!0,this._resetDomPlayerState(),this._onFinish(),this._specialStyles&&this._specialStyles.destroy(),this._onDestroyFns.forEach((t=>t())),this._onDestroyFns=[])}setPosition(t){void 0===this.domPlayer&&this.init(),this.domPlayer.currentTime=t*this.time}getPosition(){return this.domPlayer.currentTime/this.time}get totalTime(){return this._delay+this._duration}beforeDestroy(){const t={};this.hasStarted()&&Object.keys(this._finalKeyframe).forEach((e=>{"offset"!=e&&(t[e]=this._finished?this._finalKeyframe[e]:SP(this.element,e))})),this.currentSnapshot=t}triggerCallback(t){const e="start"==t?this._onStartFns:this._onDoneFns;e.forEach((t=>t())),e.length=0}}class Jw{constructor(){this._isNativeImpl=/\{\s*\[native\s+code\]\s*\}/.test(Qw().toString()),this._cssKeyframesDriver=new Zw}validateStyleProperty(t){return KO(t)}matchesElement(t,e){return JO(t,e)}containsElement(t,e){return QO(t,e)}query(t,e,n){return $O(t,e,n)}computeStyle(t,e,n){return window.getComputedStyle(t)[e]}overrideWebAnimationsSupport(t){this._isNativeImpl=t}animate(t,e,n,o,i,a=[],r){if(!r&&!this._isNativeImpl)return this._cssKeyframesDriver.animate(t,e,n,o,i,a);const s={duration:n,delay:o,fill:0==o?"both":"forwards"};i&&(s.easing=i);const l={},c=a.filter((t=>t instanceof Kw));PP(n,o)&&c.forEach((t=>{let e=t.currentSnapshot;Object.keys(e).forEach((t=>l[t]=e[t]))}));const d=Tw(t,e=wP(t,e=e.map((t=>fP(t,!1))),l));return new Kw(t,e,s,d)}}function Qw(){return zO()&&Element.prototype.animate||{}}
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */class $w extends MO{constructor(t,e){super(),this._nextAnimationId=0;this._renderer=t.createRenderer(e.body,{id:"0",encapsulation:Wn.None,styles:[],data:{animation:[]}})}build(t){const e=this._nextAnimationId.toString();this._nextAnimationId++;const n=Array.isArray(t)?PO(t):t;return nk(this._renderer,null,e,"register",[n]),new tk(e,this._renderer)}}$w.ɵfac=function t(e){return new(e||$w)(Tr(Qh),Tr(sM))},$w.ɵprov=Sn({token:$w,factory:$w.ɵfac}),$w.ctorParameters=()=>[{type:Qh},{type:void 0,decorators:[{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb($w,[{type:Um}],(function(){return[{type:Qh},{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),null);class tk extends class{}{constructor(t,e){super(),this._id=t,this._renderer=e}create(t,e){return new ek(this._id,t,e||{},this._renderer)}}class ek{constructor(t,e,n,o){this.id=t,this.element=e,this._renderer=o,this.parentPlayer=null,this._started=!1,this.totalTime=0,this._command("create",n)}_listen(t,e){return this._renderer.listen(this.element,`@@${this.id}:${t}`,e)}_command(t,...e){return nk(this._renderer,this.element,this.id,t,e)}onDone(t){this._listen("done",t)}onStart(t){this._listen("start",t)}onDestroy(t){this._listen("destroy",t)}init(){this._command("init")}hasStarted(){return this._started}play(){this._command("play"),this._started=!0}pause(){this._command("pause")}restart(){this._command("restart")}finish(){this._command("finish")}destroy(){this._command("destroy")}reset(){this._command("reset"),this._started=!1}setPosition(t){this._command("setPosition",t)}getPosition(){var t,e;return null!==(e=null===(t=this._renderer.engine.players[+this.id])||void 0===t?void 0:t.getPosition())&&void 0!==e?e:0}}function nk(t,e,n,o,i){return t.setProperty(e,`@@${n}:${o}`,i)}const ok="@",ik="@.disabled";class ak{constructor(t,e,n){this.delegate=t,this.engine=e,this._zone=n,this._currentId=0,this._microtaskId=1,this._animationCallbacksBuffer=[],this._rendererCache=new Map,this._cdRecurDepth=0,this.promise=Promise.resolve(0),e.onRemovalComplete=(t,e)=>{e&&e.parentNode(t)&&e.removeChild(t.parentNode,t)}}createRenderer(t,e){const n=this.delegate.createRenderer(t,e);if(!(t&&e&&e.data&&e.data.animation)){let t=this._rendererCache.get(n);return t||(t=new rk("",n,this.engine),this._rendererCache.set(n,t)),t}const o=e.id,i=e.id+"-"+this._currentId;this._currentId++,this.engine.register(i,t);const a=e=>{Array.isArray(e)?e.forEach(a):this.engine.registerTrigger(o,i,t,e.name,e)};return e.data.animation.forEach(a),new sk(this,i,n,this.engine)}begin(){this._cdRecurDepth++,this.delegate.begin&&this.delegate.begin()}_scheduleCountTask(){this.promise.then((()=>{this._microtaskId++}))}scheduleListenerCallback(t,e,n){t>=0&&t<this._microtaskId?this._zone.run((()=>e(n))):(0==this._animationCallbacksBuffer.length&&Promise.resolve(null).then((()=>{this._zone.run((()=>{this._animationCallbacksBuffer.forEach((t=>{const[e,n]=t;e(n)})),this._animationCallbacksBuffer=[]}))})),this._animationCallbacksBuffer.push([e,n]))}end(){this._cdRecurDepth--,0==this._cdRecurDepth&&this._zone.runOutsideAngular((()=>{this._scheduleCountTask(),this.engine.flush(this._microtaskId)})),this.delegate.end&&this.delegate.end()}whenRenderingDone(){return this.engine.whenRenderingDone()}}ak.ɵfac=function t(e){return new(e||ak)(Tr(Qh),Tr(Aw),Tr(mC))},ak.ɵprov=Sn({token:ak,factory:ak.ɵfac}),ak.ctorParameters=()=>[{type:Qh},{type:Aw},{type:mC}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(ak,[{type:Um}],(function(){return[{type:Qh},{type:Aw},{type:mC}]}),null);class rk{constructor(t,e,n){this.namespaceId=t,this.delegate=e,this.engine=n,this.destroyNode=this.delegate.destroyNode?t=>e.destroyNode(t):null}get data(){return this.delegate.data}destroy(){this.engine.destroy(this.namespaceId,this.delegate),this.delegate.destroy()}createElement(t,e){return this.delegate.createElement(t,e)}createComment(t){return this.delegate.createComment(t)}createText(t){return this.delegate.createText(t)}appendChild(t,e){this.delegate.appendChild(t,e),this.engine.onInsert(this.namespaceId,e,t,!1)}insertBefore(t,e,n,o=!0){this.delegate.insertBefore(t,e,n),this.engine.onInsert(this.namespaceId,e,t,o)}removeChild(t,e,n){this.engine.onRemove(this.namespaceId,e,this.delegate,n)}selectRootElement(t,e){return this.delegate.selectRootElement(t,e)}parentNode(t){return this.delegate.parentNode(t)}nextSibling(t){return this.delegate.nextSibling(t)}setAttribute(t,e,n,o){this.delegate.setAttribute(t,e,n,o)}removeAttribute(t,e,n){this.delegate.removeAttribute(t,e,n)}addClass(t,e){this.delegate.addClass(t,e)}removeClass(t,e){this.delegate.removeClass(t,e)}setStyle(t,e,n,o){this.delegate.setStyle(t,e,n,o)}removeStyle(t,e,n){this.delegate.removeStyle(t,e,n)}setProperty(t,e,n){e.charAt(0)==ok&&e==ik?this.disableAnimations(t,!!n):this.delegate.setProperty(t,e,n)}setValue(t,e){this.delegate.setValue(t,e)}listen(t,e,n){return this.delegate.listen(t,e,n)}disableAnimations(t,e){this.engine.disableAnimations(t,e)}}class sk extends rk{constructor(t,e,n,o){super(e,n,o),this.factory=t,this.namespaceId=e}setProperty(t,e,n){e.charAt(0)==ok?"."==e.charAt(1)&&e==ik?this.disableAnimations(t,n=void 0===n||!!n):this.engine.process(this.namespaceId,t,e.substr(1),n):this.delegate.setProperty(t,e,n)}listen(t,e,n){if(e.charAt(0)==ok){const o=(function i(t){switch(t){case"body":return document.body;case"document":return document;case"window":return window;default:return t}})(t);let a=e.substr(1),r="";return a.charAt(0)!=ok&&([a,r]=(function s(t){const e=t.indexOf("."),n=t.substring(0,e),o=t.substr(e+1);return[n,o]}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(a)),this.engine.listen(this.namespaceId,o,a,r,(t=>{this.factory.scheduleListenerCallback(t._data||-1,n,t)}))}return this.delegate.listen(t,e,n)}}class lk extends Aw{constructor(t,e,n){super(t.body,e,n)}ngOnDestroy(){this.flush()}}lk.ɵfac=function t(e){return new(e||lk)(Tr(sM),Tr(nP),Tr($P))},lk.ɵprov=Sn({token:lk,factory:lk.ɵfac}),lk.ctorParameters=()=>[{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:nP},{type:$P}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(lk,[{type:Um}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:nP},{type:$P}]}),null);const ck=new er("AnimationModuleType"),dk=[{provide:MO,useClass:$w},{provide:$P,useFactory:function pk(){return new tw}},{provide:Aw,useClass:lk},{provide:Qh,useFactory:function mk(t,e,n){return new ak(t,e,n)},deps:[zx,Aw,mC]}],fk=[{provide:nP,useFactory:function uk(){return(function t(){return"function"==typeof Qw()})()?new Jw:new Zw}},{provide:ck,useValue:"BrowserAnimations"},...dk],gk=[{provide:nP,useClass:eP},{provide:ck,useValue:"NoopAnimations"},...dk];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class hk{static withConfig(t){return{ngModule:hk,providers:t.disableAnimations?gk:fk}}}hk.ɵfac=function t(e){return new(e||hk)},hk.ɵmod=fo({type:hk}),hk.ɵinj=Dn({providers:fk,imports:[dO]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(hk,[{type:L_,args:[{exports:[dO],providers:fk}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(hk,{exports:function(){return[dO]}});class bk{}bk.ɵfac=function t(e){return new(e||bk)},bk.ɵmod=fo({type:bk}),bk.ɵinj=Dn({providers:gk,imports:[dO]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(bk,[{type:L_,args:[{exports:[dO],providers:gk}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(bk,{exports:function(){return[dO]}});const yk={};function _k(t,e){if(yk[t]=(yk[t]||0)+1,"function"==typeof e)return Ck(t,((...n)=>Object.assign(Object.assign({},e(...n)),{type:t})));switch(e?e._as:"empty"){case"empty":return Ck(t,(()=>({type:t})));case"props":return Ck(t,(e=>Object.assign(Object.assign({},e),{type:t})));default:throw new Error("Unexpected config.")}}function Ck(t,e){return Object.defineProperty(e,"type",{value:t,writable:!1})}const Mk="@ngrx/store/init";class vk extends j{constructor(){super({type:Mk})}next(t){if("function"==typeof t)throw new TypeError("\n        Dispatch expected an object, instead it received a function.\n        If you're using the createAction function, make sure to invoke the function\n        before dispatching the action. 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Internal Store Features"),Ik=new er("@ngrx/store Internal Feature Reducers Token"),Hk=new er("@ngrx/store Feature Reducers"),Fk=new er("@ngrx/store User Provided Meta Reducers"),Lk=new er("@ngrx/store Meta Reducers"),Bk=new er("@ngrx/store Internal Resolved Meta Reducers"),Vk=new er("@ngrx/store User Runtime Checks Config"),jk=new er("@ngrx/store Internal User Runtime Checks Config"),Uk=new er("@ngrx/store Internal Runtime Checks"),Wk=new er("@ngrx/store Check if Action types are unique");function Gk(t,e={}){const n=Object.keys(t),o={};for(let e=0;e<n.length;e++){const i=n[e];"function"==typeof t[i]&&(o[i]=t[i])}const i=Object.keys(o);return function t(n,a){n=void 0===n?e:n;let r=!1;const s={};for(let t=0;t<i.length;t++){const e=i[t],l=n[e],c=(0,o[e])(l,a);s[e]=c,r=r||c!==l}return r?s:n}}function Yk(...t){return function(e){if(0===t.length)return e;const n=t[t.length-1];return t.slice(0,-1).reduceRight(((t,e)=>e(t)),n(e))}}function qk(t,e){return Array.isArray(e)&&e.length>0&&(t=Yk.apply(null,[...e,t])),(e,n)=>{const o=t(e);return(t,e)=>o(t=void 0===t?n:t,e)}}class Zk extends T{}class Xk extends vk{}class Kk extends j{constructor(t,e,n,o){super(o(n,e)),this.dispatcher=t,this.initialState=e,this.reducers=n,this.reducerFactory=o}get currentReducers(){return this.reducers}addFeature(t){this.addFeatures([t])}addFeatures(t){const e=t.reduce(((t,{reducers:e,reducerFactory:n,metaReducers:o,initialState:i,key:a})=>{const r="function"==typeof e?(function s(t){const e=Array.isArray(t)&&t.length>0?Yk(...t):t=>t;return(t,n)=>(t=e(t),(e,o)=>t(e=void 0===e?n:e,o))})(o)(e,i):qk(n,o)(e,i);return t[a]=r,t}),{});this.addReducers(e)}removeFeature(t){this.removeFeatures([t])}removeFeatures(t){this.removeReducers(t.map((t=>t.key)))}addReducer(t,e){this.addReducers({[t]:e})}addReducers(t){this.reducers=Object.assign(Object.assign({},this.reducers),t),this.updateReducers(Object.keys(t))}removeReducer(t){this.removeReducers([t])}removeReducers(t){t.forEach((t=>{this.reducers=(function e(t,n){return Object.keys(t).filter((t=>t!==n)).reduce(((e,n)=>Object.assign(e,{[n]:t[n]})),{})})(this.reducers,t)})),this.updateReducers(t)}updateReducers(t){this.next(this.reducerFactory(this.reducers,this.initialState)),this.dispatcher.next({type:"@ngrx/store/update-reducers",features:t})}ngOnDestroy(){this.complete()}}Kk.ɵfac=function t(e){return new(e||Kk)(Tr(Xk),Tr(wk),Tr(Dk),Tr(kk))},Kk.ɵprov=Sn({token:Kk,factory:Kk.ɵfac}),Kk.ctorParameters=()=>[{type:Xk},{type:void 0,decorators:[{type:Lr,args:[wk]}]},{type:void 0,decorators:[{type:Lr,args:[Dk]}]},{type:void 0,decorators:[{type:Lr,args:[kk]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Kk,[{type:Um}],(function(){return[{type:Xk},{type:void 0,decorators:[{type:Lr,args:[wk]}]},{type:void 0,decorators:[{type:Lr,args:[Dk]}]},{type:void 0,decorators:[{type:Lr,args:[kk]}]}]}),null);const Jk=[Kk,{provide:Zk,useExisting:Kk},{provide:Xk,useExisting:vk}];class Qk extends B{ngOnDestroy(){this.complete()}}Qk.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(Qk)))(n||Qk)}})(),Qk.ɵprov=Sn({token:Qk,factory:Qk.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Qk,[{type:Um}],null,null);const $k=[Qk];class tS extends T{}class eS extends j{constructor(t,e,n,o){super(o);const i=t.pipe((function r(t,e){return void 0===e&&(e=0),z((function(n,o){n.subscribe(new H(o,(function(n){return o.add(t.schedule((function(){return o.next(n)}),e))}),(function(){return o.add(t.schedule((function(){return o.complete()}),e))}),(function(n){return o.add(t.schedule((function(){return o.error(n)}),e))})))}))})(st)).pipe(qe(e)).pipe((function a(t,e){return z(be(t,e,arguments.length>=2,!0))})(nS,{state:o}));this.stateSubscription=i.subscribe((({state:t,action:e})=>{this.next(t),n.next(e)}))}ngOnDestroy(){this.stateSubscription.unsubscribe(),this.complete()}}function nS(t={state:void 0},[e,n]){const{state:o}=t;return{state:n(o,e),action:e}}eS.ɵfac=function t(e){return new(e||eS)(Tr(vk),Tr(Zk),Tr(Qk),Tr(wk))},eS.ɵprov=Sn({token:eS,factory:eS.ɵfac}),eS.INIT=Mk,eS.ctorParameters=()=>[{type:vk},{type:Zk},{type:Qk},{type:void 0,decorators:[{type:Lr,args:[wk]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(eS,[{type:Um}],(function(){return[{type:vk},{type:Zk},{type:Qk},{type:void 0,decorators:[{type:Lr,args:[wk]}]}]}),null);const oS=[eS,{provide:tS,useExisting:eS}];class iS extends T{constructor(t,e,n){super(),this.actionsObserver=e,this.reducerManager=n,this.source=t}select(t,...e){return rS.call(null,t,...e)(this)}lift(t){const e=new iS(this,this.actionsObserver,this.reducerManager);return e.operator=t,e}dispatch(t){this.actionsObserver.next(t)}next(t){this.actionsObserver.next(t)}error(t){this.actionsObserver.error(t)}complete(){this.actionsObserver.complete()}addReducer(t,e){this.reducerManager.addReducer(t,e)}removeReducer(t){this.reducerManager.removeReducer(t)}}iS.ɵfac=function t(e){return new(e||iS)(Tr(tS),Tr(vk),Tr(Kk))},iS.ɵprov=Sn({token:iS,factory:iS.ɵfac}),iS.ctorParameters=()=>[{type:tS},{type:vk},{type:Kk}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(iS,[{type:Um}],(function(){return[{type:tS},{type:vk},{type:Kk}]}),null);const aS=[iS];function rS(t,e,...n){return function o(i){let a;if("string"==typeof t){const o=[e,...n].filter(Boolean);a=i.pipe((function r(){for(var t=[],e=0;e<arguments.length;e++)t[e]=arguments[e];var n=t.length;if(0===n)throw new Error("list of properties cannot be empty.");return Vt((function(e){for(var o=e,i=0;i<n;i++){var a=null==o?void 0:o[t[i]];if(void 0===a)return;o=a}return o}))})(t,...o))}else{if("function"!=typeof t)throw new TypeError(`Unexpected type '${typeof t}' in select operator, expected 'string' or 'function'`);a=i.pipe(Vt((n=>t(n,e))))}return a.pipe(we())}}const sS="https://ngrx.io/guide/store/configuration/runtime-checks";function lS(t){return void 0===t}function cS(t){return null===t}function dS(t){return Array.isArray(t)}function pS(t){return"object"==typeof t&&null!==t}function mS(t){if(!(function e(t){return pS(t)&&!dS(t)})(t))return!1;const n=Object.getPrototypeOf(t);return n===Object.prototype||null===n}function fS(t){return"function"==typeof t}function uS(t,e){return t===e}function gS(t,e,n){for(let o=0;o<t.length;o++)if(!n(t[o],e[o]))return!0;return!1}function hS(t,e=uS,n=uS){let o,i=null,a=null;return{memoized:function r(){if(void 0!==o)return o.result;if(!i)return a=t.apply(null,arguments),i=arguments,a;if(!gS(arguments,i,e))return a;const r=t.apply(null,arguments);return i=arguments,n(a,r)?a:(a=r,r)},reset:function s(){i=null,a=null},setResult:function l(t){o={result:t}},clearResult:function c(){o=void 0}}}function bS(...t){return(function e(t,n={stateFn:yS}){return function(...e){let o=e;if(Array.isArray(o[0])){const[t,...e]=o;o=[...t,...e]}const i=o.slice(0,o.length-1),a=o[o.length-1],r=i.filter((t=>t.release&&"function"==typeof t.release)),s=t((function(...t){return a.apply(null,t)})),l=hS((function(t,e){return n.stateFn.apply(null,[t,i,e,s])}));function c(){l.reset(),s.reset(),r.forEach((t=>t.release()))}return Object.assign(l.memoized,{release:c,projector:s.memoized,setResult:l.setResult,clearResult:l.clearResult})}})(hS)(...t)}function yS(t,e,n,o){if(void 0===n){const n=e.map((e=>e(t)));return o.memoized.apply(null,n)}const i=e.map((e=>e(t,n)));return o.memoized.apply(null,[...i,n])}function _S(t){return bS((e=>{const n=e[t];return PC()&&!(t in e)&&console.warn(`@ngrx/store: The feature name "${t}" does not exist in the state, therefore 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AD="feature",TD=_S(AD),ND=bS(TD,(t=>t.isFeatureFlagsLoaded)),zD=bS(TD,(t=>Object.assign(Object.assign({},t.defaultFlags),t.flagOverrides))),ID=bS(TD,(t=>t.flagOverrides||{})),HD=bS(zD,(t=>t.isAutoDarkModeAllowed)),FD=bS(zD,(t=>null!==t.enableDarkModeOverride?t.enableDarkModeOverride:t.defaultEnableDarkMode)),LD=bS(zD,(t=>t.enableDarkModeOverride)),BD=bS(zD,(t=>t.enabledExperimentalPlugins)),VD=bS(zD,(t=>t.inColab)),jD=bS(zD,(t=>t.enabledColorGroup)),UD=bS(zD,(t=>t.enabledColorGroupByRegex)),WD=bS(zD,(t=>t.metricsImageSupportEnabled)),GD=bS(zD,(t=>t.enabledLinkedTime)),YD=bS(zD,(t=>t.enableTimeSeriesPromotion)),qD=bS(zD,(t=>t.enabledCardWidthSetting)),ZD=bS(zD,(t=>t.enabledTimeNamespacedState)),XD=_k("[App Routing] Discarding Unsaved Updates"),KD=_k("[App Routing] State Rehydrated From Url",{_as:"props",_p:void 0}),JD=_k("[App Routing] Route Config Loaded",{_as:"props",_p:void 0}),QD=_k("[App Routing] In App Navigation Requested",{_as:"props",_p:void 0}),$D=_k("[App Routing] In App Navigating",{_as:"props",_p:void 0}),tR=_k("[App Routing] In App Navigated",{_as:"props",_p:void 0}),eR=new er("[App Routing] Programmatical Navigation Provider");class nR{constructor(t){this.providers=new Map;for(const e of t||[]){if(this.providers.has(e.actionCreator.type))throw new RangeError(`"${e.actionCreator.type}" is already registered for nav. Multiple navigations on same kick is not allowed.`);this.providers.set(e.actionCreator.type,e.lambda)}}getNavigation(t){const e=this.providers.get(t.type);return e?e(t):null}static registerProgrammaticalNavigation(t){return{ngModule:nR,providers:[{provide:eR,multi:!0,useFactory:t}]}}}function oR(t){return null!=t.routeKind}function iR(t){return dR(t).map((t=>{const e=t.startsWith(":");return e?{pathPart:t,isParam:!0,paramName:t.slice(1)}:{pathPart:t,isParam:e}}))}nR.ɵfac=function t(e){return new(e||nR)(Tr(eR,8))},nR.ɵmod=fo({type:nR}),nR.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(nR,[{type:L_,args:[{}]}],(function(){return[{type:void 0,decorators:[{type:Br},{type:Lr,args:[eR]}]}]}),null);class aR{constructor(t){this.validateConfig(t),this.pathFragments=iR(t.path),this.pathMatchers=this.getPathMatchers(this.pathFragments)}static getMatcher(t){return oR(t)?new rR(t):(function e(t){return void 0!==t.redirectionPath})(t)?new sR(t):new lR(t)}validateConfig({path:t}){if(!t.startsWith("/"))throw new RangeError(`config.path should start with '/'. ${t}`);let e=0;for(;(e=t.indexOf(":",e+1))>=0;){if("/"!==t[e-1])throw new RangeError(`config.path parameter should come after '/'. ${t}`);if(void 0===t[e+1]||"/"===t[e+1])throw new RangeError(`config.path parameter should have non-empty name. ${t}`)}}getPathMatchers(t){return t.map((t=>{const{pathPart:e}=t;return t.isParam?e=>({isParamPathPart:!0,partMatched:!0,paramName:t.paramName,paramValue:e}):t=>({isParamPathPart:!1,partMatched:t===e})}))}match(t){let e={};if(this.pathMatchers.length!==t.length)return{result:!1};let n=0;for(const o of this.pathMatchers){const i=o(t[n++]);if(!i.partMatched)return{result:!1};i.isParamPathPart&&(e=Object.assign(Object.assign({},e),{[i.paramName]:i.paramValue}))}return{result:!0,params:e,pathParts:t,isRedirection:!1,redirectionQueryParams:void 0}}matchByParams(t){return{result:!0,params:t,pathParts:this.reprojectPathByParams(this.pathFragments,t),isRedirection:!1,redirectionQueryParams:void 0}}reprojectPathByParams(t,e){const n=[];for(const o of t)if(o.isParam){const{paramName:t}=o;if(!e.hasOwnProperty(t))throw new RangeError(`Failed to reproject parameter. "${t}" parameter should be present.`);n.push(e[t])}else n.push(o.pathPart);return n}}class rR extends aR{constructor(t){super(t),this.definition=t}}class sR extends aR{constructor(t){super(t),this.definition=t,this.redirectionFragments=iR(t.redirectionPath)}match(t){const e=super.match(t);if(!e.result)return e;const n=this.reprojectPathByParams(this.redirectionFragments,e.params);return{result:!0,params:e.params,pathParts:n,isRedirection:!0,redirectionQueryParams:void 0}}}class lR extends aR{constructor(t){super(t),this.definition=t}match(t){const e=super.match(t);if(!e.result)return e;const{pathParts:n,queryParams:o}=this.definition.redirector(t);return{result:!0,params:e.params,pathParts:n,isRedirection:!0,redirectionQueryParams:o}}}class cR{constructor(t,e=3){if(this.maxRedirection=e,e<0)throw new RangeError("maxRedirection has to be non-negative number");this.validateRouteConfigs(t),this.defaultRouteConfig=null,this.routeKindToConcreteConfigMatchers=new Map,this.configMatchers=[];for(const e of t){const t=aR.getMatcher(e);this.configMatchers.push(t),t instanceof rR&&(this.routeKindToConcreteConfigMatchers.set(t.definition.routeKind,t),t.definition.defaultRoute&&(this.defaultRouteConfig=t))}}validateRouteConfigs(t){const e=t.filter(oR),n=e.filter((t=>t.defaultRoute));if(n.length>1){const t=n.map((({path:t})=>t)).join(", ");throw new RangeError(`There are more than one defaultRoutes. ${t}`)}if(1===n.length){const{path:t}=n[0];if(Boolean(iR(t).find((({isParam:t})=>t))))throw new RangeError(`A defaultRoute cannot have any params. ${t}`)}const o=new Set;for(const{routeKind:t}of e){if(o.has(t))throw new RangeError(`Multiple route configuration for kind: ${t}. Configurations should have unique routeKinds`);o.add(t)}}match(t){var e;if(!t.pathname.startsWith("/"))throw new RangeError('Navigation has to made with pathname that starts with "/"');let n,o=dR(t.pathname),i=0,a=!1;for(;;){let t=!1;for(const e of this.configMatchers){const i=e.match(o);if(i.result){t=!0;const{params:r,pathParts:s,isRedirection:l}=i;if(l){o=s,a=!0,n=i.redirectionQueryParams;break}if(!(e instanceof rR))throw new RangeError("No concrete route definition `match` return redirection");const{definition:c}=e,d={routeKind:c.routeKind,params:r,pathname:pR(s),deepLinkProvider:c.deepLinkProvider||null};return Object.assign(Object.assign({},d),a?{originateFromRedirection:!0,redirectionOnlyQueryParams:n}:{originateFromRedirection:!1})}}if(a&&i++,!t||i>this.maxRedirection)break}if(i>this.maxRedirection)throw new Error(`Potential redirection loop (redirecting more than ${this.maxRedirection} times. Please do not have cycles in the routes.`);if(this.defaultRouteConfig){const{definition:t}=this.defaultRouteConfig;return{routeKind:t.routeKind,deepLinkProvider:null!==(e=t.deepLinkProvider)&&void 0!==e?e:null,pathname:t.path,params:{},originateFromRedirection:!0,redirectionOnlyQueryParams:void 0}}return null}matchByRouteKind(t,e){const n=this.routeKindToConcreteConfigMatchers.get(t);if(!n)throw new RangeError(`Requires configuration for routeKind: ${t}`);return{routeKind:t,params:e,pathname:pR(n.matchByParams(e).pathParts),deepLinkProvider:n.definition.deepLinkProvider||null,originateFromRedirection:!1}}}function dR(t){return t.split("/").slice(1)}function pR(t){return"/"+t.join("/")}const mR=new er("[App Routing] Route Config");class fR{constructor(t){if(this.routeKindToNgComponent=new Map,!t)return void(this.routeConfigs=new cR([]));const e=[];for(const n of t)for(const t of n)e.push(t);this.routeConfigs=new cR(e),e.forEach((t=>{oR(t)&&this.routeKindToNgComponent.set(t.routeKind,t.ngComponent)}))}getRegisteredRouteKinds(){return this.routeKindToNgComponent.keys()}getRouteConfigs(){return this.routeConfigs}getNgComponentByRouteKind(t){return this.routeKindToNgComponent.get(t)||null}static registerRoutes(t){return{ngModule:fR,providers:[{provide:mR,multi:!0,useFactory:t}]}}}fR.ɵfac=function t(e){return new(e||fR)(Tr(mR,8))},fR.ɵmod=fo({type:fR}),fR.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(fR,[{type:L_,args:[{}]}],(function(){return[{type:void 0,decorators:[{type:Br},{type:Lr,args:[mR]}]}]}),null);const uR="app_routing",gR=_S(uR),hR=bS(gR,(t=>t.activeRoute)),bR=bS(gR,(t=>t.nextRoute)),yR=bS(gR,(t=>t.activeNamespaceId)),_R=bS(gR,(t=>t.knownNamespaceIds)),CR=bS(gR,(t=>t.registeredRouteKeys)),MR=bS(hR,(t=>t?t.routeKind:_D.NOT_SET)),vR=bS(hR,(t=>t?t.params:{})),xR=bS(MR,vR,((t,e)=>vD(t,e)));bS(MR,vR,((t,e)=>{if(t!==_D.COMPARE_EXPERIMENT)return{};const n=(function o(t){const e=new Map,n=MD(t.experimentIds);for(const{id:t,name:o}of n)o&&e.set(t,o);return e})(e);return Object.fromEntries(n.entries())}));const OR=bS(MR,vR,((t,e)=>{if(t!==_D.COMPARE_EXPERIMENT)return{};const n=(function o(t){const e=new Map,n=MD(t.experimentIds);let o=0;for(const{id:t,name:i}of n)o++,!e.has(t)&&i&&e.set(t,{aliasText:i,aliasNumber:o});return e})(e);return Object.fromEntries(n.entries())})),PR=_k("[App Routing] Effects Init");var wR;!(function(t){t[t.UNCHANGED=0]="UNCHANGED",t[t.NEW=1]="NEW",t[t.FROM_HISTORY=2]="FROM_HISTORY"})(wR||(wR={}));class kR{constructor(t,e,n,o,i,a,r){this.actions$=t,this.store=e,this.location=n,this.dirtyUpdatesRegistry=o,this.registry=i,this.programmaticalNavModule=a,this.appRootProvider=r,this.onNavigationRequested$=this.actions$.pipe($S(QD),Vt((t=>{var e;return{pathname:t.pathname.startsWith("/")?this.appRootProvider.getAbsPathnameWithAppRoot(t.pathname):this.location.getResolvedPath(t.pathname),options:{browserInitiated:!1,replaceState:null!==(e=t.replaceState)&&void 0!==e&&e,namespaceUpdate:{option:t.resetNamespacedState?wR.NEW:wR.UNCHANGED}}}}))),this.bootstrapReducers$=WS((()=>this.actions$.pipe($S(PR),Vt((()=>JD({routeKinds:new Set(this.registry.getRegisteredRouteKinds())})))))),this.onInit$=this.actions$.pipe($S(PR)).pipe(Pe(0),Vt((()=>{var t;const e=null===(t=this.location.getHistoryState())||void 0===t?void 0:t.namespaceId,n=void 0===e?{option:wR.NEW}:{option:wR.FROM_HISTORY,namespaceId:e};return{pathname:this.location.getPath(),options:{browserInitiated:!0,replaceState:!0,namespaceUpdate:n}}}))),this.onPopState$=this.location.onPopState().pipe(Vt((t=>{var e;return{pathname:t.pathname,options:{browserInitiated:!0,replaceState:!0,namespaceUpdate:{option:wR.FROM_HISTORY,namespaceId:null===(e=t.state)||void 0===e?void 0:e.namespaceId}}}}))),this.userInitNavRoute$=pe(this.onNavigationRequested$,this.onInit$,this.onPopState$).pipe(Vt((t=>{if(!t.pathname.startsWith("/"))throw new Error(`[App routing] pathname must start with '/'. Got: ${t.pathname}`);return Object.assign(Object.assign({},t),{pathname:this.appRootProvider.getAppRootlessPathname(t.pathname)})})),Vt((t=>({routeMatch:this.routeConfigs.match(t),options:t.options})))),this.programmaticalNavRoute$=this.actions$.pipe(Vt((t=>this.programmaticalNavModule.getNavigation(t))),ue((t=>null!==t)),Vt((t=>{const e=t,n=e.routeKind;let o;switch(e.routeKind){case _D.COMPARE_EXPERIMENT:o={experimentIds:(i=e.routeParams.aliasAndExperimentIds,i.map((({alias:t,id:e})=>`${t}:${e}`)).join(","))};break;default:o=e.routeParams}var i;return{routeKind:n,routeParams:o}})),Vt((({routeKind:t,routeParams:e})=>({routeMatch:this.routeConfigs?this.routeConfigs.matchByRouteKind(t,e):null,options:{replaceState:!1,browserInitiated:!1,namespaceUpdate:{option:wR.UNCHANGED}}})))),this.validatedRouteMatch$=pe(this.userInitNavRoute$,this.programmaticalNavRoute$).pipe(ue((({routeMatch:t})=>Boolean(t))),Vt((({routeMatch:t,options:e})=>({routeMatch:t,options:e})))),this.navigate$=WS((()=>this.validatedRouteMatch$.pipe(qe(this.store.select(hR)),$t((([t,e])=>{const n=null!==e&&xD(e,t.routeMatch),o=this.dirtyUpdatesRegistry.getDirtyUpdatesSelectors();return n||!o.length?zt(t):ie(this.dirtyUpdatesRegistry.getDirtyUpdatesSelectors().map((t=>this.store.select(t).pipe(ve(1))))).pipe(Vt((t=>void 0!==t[0].experimentIds&&t[0].experimentIds.length>0)),ue((t=>{if(t){const t=window.confirm("You have unsaved edits, are you sure you want to discard them?");return t&&this.store.dispatch(XD()),t}return!0})),Vt((()=>t)))})),qe(this.store.select(_R)),We((([{routeMatch:t,options:e},n])=>{const o=e.namespaceUpdate.option===wR.FROM_HISTORY&&n.has(e.namespaceUpdate.namespaceId);if(!e.browserInitiated||o||!t.deepLinkProvider)return;const i=t.originateFromRedirection&&t.redirectionOnlyQueryParams?t.redirectionOnlyQueryParams:this.location.getSearch(),a=t.deepLinkProvider.deserializeQueryParams(i);this.store.dispatch(KD({routeKind:t.routeKind,partialState:a}))})),Ve((([{routeMatch:t,options:e}])=>null===t.deepLinkProvider?zt({route:{routeKind:t.routeKind,params:t.params,pathname:t.pathname,queryParams:[]},options:e}):t.deepLinkProvider.serializeStateToQueryParams(this.store).pipe(Vt(((n,o)=>({route:{routeKind:t.routeKind,params:t.params,pathname:t.pathname,queryParams:n},options:0===o?e:Object.assign(Object.assign({},e),{replaceState:!0,resetNamespacedState:!1})})))))),We((({route:t})=>{this.store.dispatch($D({after:t}))})),Ce(0)).pipe(qe(this.store.select(hR)),Vt((([{route:t,options:e},n])=>({preserveHash:null===n||null===t||xD(n,t),route:t,options:e}))),We((({preserveHash:t,route:e,options:n})=>{!(function o(t,e){return t.pathname===e.pathname&&t.queryParams.length===e.queryParams.length&&t.queryParams.every(((t,n)=>{const o=e.queryParams[n];return t.key===o.key&&t.value===o.value}))})(e,{pathname:this.appRootProvider.getAppRootlessPathname(this.location.getPath()),queryParams:this.location.getSearch()})&&(n.replaceState?this.location.replaceStateUrl(this.appRootProvider.getAbsPathnameWithAppRoot(this.location.getFullPathFromRoute(e,t))):this.location.pushStateUrl(this.appRootProvider.getAbsPathnameWithAppRoot(this.location.getFullPathFromRoute(e,t))))}))).pipe(qe(this.store.select(hR),this.store.select(yR),this.store.select(ZD)),Vt((([{route:t,options:e},n,o,i])=>{const a=(function r(t,e,n,o){return t?n.namespaceUpdate.option===wR.FROM_HISTORY?n.namespaceUpdate.namespaceId:null==o||n.namespaceUpdate.option===wR.NEW?Date.now().toString():o:(function i(t,e){var n;switch(t){case _D.COMPARE_EXPERIMENT:case _D.EXPERIMENT:{const o=null!==(n=vD(t,e))&&void 0!==n?n:[];return o.sort(),`${t}/${o.join(",")}`}case _D.EXPERIMENTS:return String(t);case _D.NOT_SET:return"__not_set";default:return""}})(e.routeKind,e.params)})(i,t,e,o);return this.location.replaceStateData(Object.assign(Object.assign({},this.location.getHistoryState()),{namespaceId:a})),tR({before:n,after:t,beforeNamespaceId:o,afterNamespaceId:a})}))))),this.routeConfigs=i.getRouteConfigs()}ngrxOnInitEffects(){return PR()}}kR.ɵfac=function t(e){return new(e||kR)(Tr(QS),Tr(iS),Tr(PD),Tr(ED),Tr(fR),Tr(nR),Tr(wD))},kR.ɵprov=Sn({token:kR,factory:kR.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(kR,[{type:Um}],(function(){return[{type:QS},{type:iS},{type:PD},{type:ED},{type:fR},{type:nR},{type:wD}]}),null);const SR=VS({activeRoute:null,nextRoute:null,activeNamespaceId:null,knownNamespaceIds:new Set,registeredRouteKeys:new Set},BS($D,((t,{after:e})=>Object.assign(Object.assign({},t),{nextRoute:e}))),BS(tR,((t,{after:e,afterNamespaceId:n})=>{let o=t.knownNamespaceIds;return t.knownNamespaceIds.has(n)||(o=new Set(t.knownNamespaceIds),o.add(n)),Object.assign(Object.assign({},t),{activeRoute:e,nextRoute:null,activeNamespaceId:n,knownNamespaceIds:o})})),BS(JD,((t,{routeKinds:e})=>Object.assign(Object.assign({},t),{registeredRouteKeys:e}))));function DR(t,e){return SR(t,e)}class RR{}RR.ɵfac=function t(e){return new(e||RR)},RR.ɵmod=fo({type:RR}),RR.ɵinj=Dn({providers:[ED,nR],imports:[[fR,TS.forFeature(uR,DR),hD.forFeature([kR]),DD,SD]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(RR,[{type:L_,args:[{imports:[fR,TS.forFeature(uR,DR),hD.forFeature([kR]),DD,SD],providers:[ED,nR]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(RR,{imports:[fR,AS,gD,DD,SD]});const ER="__tab__";class AR{constructor(){this.tfStorage=document.createElement("tf-storage");document.createElement("tf-globals").tf_globals.setUseHash(!0),this.tfStorage.tf_storage.migrateLegacyURLScheme()}getString(t){return this.tfStorage.tf_storage.getString(t)}setString(t,e,n){this.tfStorage.tf_storage.setString(t,e,n)}getPluginId(){return this.getString(ER)}setPluginId(t,e){this.setString(ER,t,e)}}AR.ɵfac=function t(e){return new(e||AR)},AR.ɵprov=Sn({token:AR,factory:AR.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(AR,[{type:Um}],(function(){return[]}),null);class TR{}const NR=new er("[Persistent Settings] Global Settings");class zR{constructor(t){this.globalSettingSelectors=[],t&&(this.globalSettingSelectors=t.map((t=>t())))}getGlobalSettingSelectors(){var t;return null!==(t=this.globalSettingSelectors)&&void 0!==t?t:[]}static defineGlobalSetting(t){return{ngModule:zR,providers:[{provide:NR,multi:!0,useValue:t}]}}}zR.ɵfac=function t(e){return new(e||zR)(Tr(NR,8))},zR.ɵmod=fo({type:zR}),zR.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(zR,[{type:L_}],(function(){return[{type:Array,decorators:[{type:Br},{type:Lr,args:[NR]}]}]}),null);class IR{setItem(t,e){localStorage.setItem(t,e)}getItem(t){return localStorage.getItem(t)}removeItem(t){localStorage.removeItem(t)}}IR.ɵfac=function t(e){return new(e||IR)},IR.ɵprov=Sn({token:IR,factory:IR.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(IR,[{type:Um}],null,null);class HR{}var FR;HR.ɵfac=function t(e){return new(e||HR)},HR.ɵmod=fo({type:HR}),HR.ɵinj=Dn({providers:[IR]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(HR,[{type:L_,args:[{providers:[IR]}]}],null,null),(function(t){t.BROWSER_DEFAULT="browser_default",t.LIGHT="light",t.DARK="dark"})(FR||(FR={}));const LR="_tb_global_settings.timeseries",BR="_tb_global_settings",VR="notificationLastReadTimestamp";class jR{}jR.ɵfac=function t(e){return new(e||jR)},jR.ɵprov=Sn({token:jR,factory:jR.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(jR,[{type:Um}],null,null);class UR{}UR.ɵfac=function t(e){return new(e||UR)},UR.ɵprov=Sn({token:UR,factory:UR.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(UR,[{type:Um}],null,null);class WR extends UR{uiToBackend(t){const e={};return void 0!==t.ignoreOutliers&&(e.ignoreOutliers=t.ignoreOutliers),void 0!==t.scalarSmoothing&&(e.scalarSmoothing=t.scalarSmoothing),void 0!==t.tooltipSortString&&(e.tooltipSort=t.tooltipSortString),void 0!==t.autoReload&&(e.autoReload=t.autoReload),void 0!==t.autoReloadPeriodInMs&&(e.autoReloadPeriodInMs=t.autoReloadPeriodInMs),void 0!==t.pageSize&&(e.paginationSize=t.pageSize),void 0!==t.themeOverride&&(e.theme=t.themeOverride),void 0!==t.notificationLastReadTimeInMs&&(e.notificationLastReadTimeInMs=t.notificationLastReadTimeInMs),void 0!==t.sideBarWidthInPercent&&(e.sideBarWidthInPercent=t.sideBarWidthInPercent),void 0!==t.timeSeriesPromotionDismissed&&(e.timeSeriesPromotionDismissed=t.timeSeriesPromotionDismissed),void 0!==t.timeSeriesSettingsPaneOpened&&(e.timeSeriesSettingsPaneOpened=t.timeSeriesSettingsPaneOpened),void 0!==t.timeSeriesCardMinWidth&&(e.timeSeriesCardMinWidth=t.timeSeriesCardMinWidth),e}backendToUi(t){const e={};return t.hasOwnProperty("scalarSmoothing")&&"number"==typeof t.scalarSmoothing&&(e.scalarSmoothing=t.scalarSmoothing),t.hasOwnProperty("ignoreOutliers")&&"boolean"==typeof t.ignoreOutliers&&(e.ignoreOutliers=t.ignoreOutliers),t.hasOwnProperty("tooltipSort")&&"string"==typeof t.tooltipSort&&(e.tooltipSortString=t.tooltipSort),t.hasOwnProperty("autoReload")&&"boolean"==typeof t.autoReload&&(e.autoReload=t.autoReload),t.hasOwnProperty("autoReloadPeriodInMs")&&"number"==typeof t.autoReloadPeriodInMs&&(e.autoReloadPeriodInMs=t.autoReloadPeriodInMs),t.hasOwnProperty("paginationSize")&&"number"==typeof t.paginationSize&&(e.pageSize=t.paginationSize),t.hasOwnProperty("theme")&&"string"==typeof t.theme&&new Set(Object.values(FR)).has(t.theme)&&(e.themeOverride=t.theme),t.hasOwnProperty("notificationLastReadTimeInMs")&&"number"==typeof t.notificationLastReadTimeInMs&&(e.notificationLastReadTimeInMs=t.notificationLastReadTimeInMs),t.hasOwnProperty("sideBarWidthInPercent")&&"number"==typeof t.sideBarWidthInPercent&&(e.sideBarWidthInPercent=t.sideBarWidthInPercent),t.hasOwnProperty("timeSeriesPromotionDismissed")&&"boolean"==typeof t.timeSeriesPromotionDismissed&&(e.timeSeriesPromotionDismissed=t.timeSeriesPromotionDismissed),t.hasOwnProperty("timeSeriesSettingsPaneOpened")&&"boolean"==typeof t.timeSeriesSettingsPaneOpened&&(e.timeSeriesSettingsPaneOpened=t.timeSeriesSettingsPaneOpened),t.hasOwnProperty("timeSeriesCardMinWidth")&&"number"==typeof t.timeSeriesCardMinWidth&&(e.timeSeriesCardMinWidth=t.timeSeriesCardMinWidth),e}}WR.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(WR)))(n||WR)}})(),WR.ɵprov=Sn({token:WR,factory:WR.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(WR,[{type:Um}],null,null);class GR{constructor(t,e){this.localStorage=t,this.converter=e}setSettings(t){return Object.keys(t)?this.getSettings().pipe(We((e=>{this.localStorage.setItem(BR,JSON.stringify(this.converter.uiToBackend(Object.assign(Object.assign({},e),t)))),this.localStorage.removeItem(LR),this.localStorage.removeItem(VR)})),Vt((()=>{}))):dt}deserialize(t){try{return JSON.parse(t)}catch(t){return{}}}getSettings(){var t,e;const n=this.localStorage.getItem(VR),o=this.converter.backendToUi(this.deserialize(n?JSON.stringify({notificationLastReadTimeInMs:Number(n)}):"{}")),i=this.converter.backendToUi(this.deserialize(null!==(t=this.localStorage.getItem(LR))&&void 0!==t?t:"{}")),a=this.converter.backendToUi(this.deserialize(null!==(e=this.localStorage.getItem(BR))&&void 0!==e?e:"{}"));return zt(Object.assign(Object.assign(Object.assign({},o),i),a))}}GR.ɵfac=function t(e){return new(e||GR)(Tr(IR),Tr(UR))},GR.ɵprov=Sn({token:GR,factory:GR.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(GR,[{type:Um}],(function(){return[{type:IR},{type:UR}]}),null);class YR{}YR.ɵfac=function t(e){return new(e||YR)},YR.ɵmod=fo({type:YR}),YR.ɵinj=Dn({providers:[{provide:jR,useClass:GR},WR,{provide:UR,useExisting:WR}],imports:[[HR]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(YR,[{type:L_,args:[{imports:[HR],providers:[{provide:jR,useClass:GR},WR,{provide:UR,useExisting:WR}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(YR,{imports:[HR]});const qR=_k("[Persistent Settings] Global Settings Loaded",{_as:"props",_p:void 0}),ZR=_k("[Persistent Settings] Effects Init");class XR{constructor(t,e,n,o){this.actions$=t,this.store=e,this.configModule=n,this.dataSource=o,this.initializeAndUpdateSettings$=WS((()=>{const t=this.actions$.pipe($S(ZR),$t((()=>this.dataSource.getSettings())),We((t=>{this.store.dispatch(qR({partialSettings:t}))})),Pe(0),$t((()=>pe(...this.configModule.getGlobalSettingSelectors().map((t=>this.store.select(t).pipe(we(((t,e)=>{const n=Object.values(t),o=Object.values(e);return n.length===o.length&&n.every(((t,e)=>t===o[e]))})),Le(1))))))),Ie());return t.pipe((function e(t){return z((function(e,n){var o=[];return e.subscribe(new H(n,(function(t){return o.push(t)}),(function(){n.next(o),n.complete()}))),t.subscribe(new H(n,(function(){var t=o;o=[],n.next(t)}),M)),function(){o=null}}))})(t.pipe(Ce(500))),$t((t=>{const e={};for(const n of t)Object.assign(e,n);return this.dataSource.setSettings(e)})))}),{dispatch:!1})}ngrxOnInitEffects(){return ZR()}}XR.ɵfac=function t(e){return new(e||XR)(Tr(QS),Tr(iS),Tr(zR),Tr(jR))},XR.ɵprov=Sn({token:XR,factory:XR.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(XR,[{type:Um}],(function(){return[{type:QS},{type:iS},{type:zR},{type:jR}]}),null);class KR{}KR.ɵfac=function t(e){return new(e||KR)},KR.ɵmod=fo({type:KR}),KR.ɵinj=Dn({providers:[zR],imports:[[hD.forFeature([XR]),YR]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(KR,[{type:L_,args:[{imports:[hD.forFeature([XR]),YR],providers:[zR]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(KR,{imports:[gD,YR]});
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
class JR{}class QR{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class $R{constructor(t){this.normalizedNames=new Map,this.lazyUpdate=null,t?this.lazyInit="string"==typeof t?()=>{this.headers=new Map,t.split("\n").forEach((t=>{const e=t.indexOf(":");if(e>0){const n=t.slice(0,e),o=n.toLowerCase(),i=t.slice(e+1).trim();this.maybeSetNormalizedName(n,o),this.headers.has(o)?this.headers.get(o).push(i):this.headers.set(o,[i])}}))}:()=>{this.headers=new Map,Object.keys(t).forEach((e=>{let n=t[e];const o=e.toLowerCase();"string"==typeof n&&(n=[n]),n.length>0&&(this.headers.set(o,n),this.maybeSetNormalizedName(e,o))}))}:this.headers=new Map}has(t){return this.init(),this.headers.has(t.toLowerCase())}get(t){this.init();const e=this.headers.get(t.toLowerCase());return e&&e.length>0?e[0]:null}keys(){return this.init(),Array.from(this.normalizedNames.values())}getAll(t){return this.init(),this.headers.get(t.toLowerCase())||null}append(t,e){return this.clone({name:t,value:e,op:"a"})}set(t,e){return this.clone({name:t,value:e,op:"s"})}delete(t,e){return this.clone({name:t,value:e,op:"d"})}maybeSetNormalizedName(t,e){this.normalizedNames.has(e)||this.normalizedNames.set(e,t)}init(){this.lazyInit&&(this.lazyInit instanceof $R?this.copyFrom(this.lazyInit):this.lazyInit(),this.lazyInit=null,this.lazyUpdate&&(this.lazyUpdate.forEach((t=>this.applyUpdate(t))),this.lazyUpdate=null))}copyFrom(t){t.init(),Array.from(t.headers.keys()).forEach((e=>{this.headers.set(e,t.headers.get(e)),this.normalizedNames.set(e,t.normalizedNames.get(e))}))}clone(t){const e=new $R;return e.lazyInit=this.lazyInit&&this.lazyInit instanceof $R?this.lazyInit:this,e.lazyUpdate=(this.lazyUpdate||[]).concat([t]),e}applyUpdate(t){const e=t.name.toLowerCase();switch(t.op){case"a":case"s":let n=t.value;if("string"==typeof n&&(n=[n]),0===n.length)return;this.maybeSetNormalizedName(t.name,e);const o=("a"===t.op?this.headers.get(e):void 0)||[];o.push(...n),this.headers.set(e,o);break;case"d":const i=t.value;if(i){let t=this.headers.get(e);if(!t)return;t=t.filter((t=>-1===i.indexOf(t))),0===t.length?(this.headers.delete(e),this.normalizedNames.delete(e)):this.headers.set(e,t)}else this.headers.delete(e),this.normalizedNames.delete(e)}}forEach(t){this.init(),Array.from(this.normalizedNames.keys()).forEach((e=>t(this.normalizedNames.get(e),this.headers.get(e))))}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class tE{encodeKey(t){return eE(t)}encodeValue(t){return eE(t)}decodeKey(t){return decodeURIComponent(t)}decodeValue(t){return decodeURIComponent(t)}}function eE(t){return encodeURIComponent(t).replace(/%40/gi,"@").replace(/%3A/gi,":").replace(/%24/gi,"$").replace(/%2C/gi,",").replace(/%3B/gi,";").replace(/%2B/gi,"+").replace(/%3D/gi,"=").replace(/%3F/gi,"?").replace(/%2F/gi,"/")}function nE(t){return`${t}`}class oE{constructor(t={}){if(this.updates=null,this.cloneFrom=null,this.encoder=t.encoder||new tE,t.fromString){if(t.fromObject)throw new Error("Cannot specify both fromString and fromObject.");this.map=(function e(t,n){const o=new Map;t.length>0&&t.replace(/^\?/,"").split("&").forEach((t=>{const e=t.indexOf("="),[i,a]=-1==e?[n.decodeKey(t),""]:[n.decodeKey(t.slice(0,e)),n.decodeValue(t.slice(e+1))],r=o.get(i)||[];r.push(a),o.set(i,r)}));return o})(t.fromString,this.encoder)}else t.fromObject?(this.map=new Map,Object.keys(t.fromObject).forEach((e=>{const n=t.fromObject[e];this.map.set(e,Array.isArray(n)?n:[n])}))):this.map=null}has(t){return this.init(),this.map.has(t)}get(t){this.init();const e=this.map.get(t);return e?e[0]:null}getAll(t){return this.init(),this.map.get(t)||null}keys(){return this.init(),Array.from(this.map.keys())}append(t,e){return this.clone({param:t,value:e,op:"a"})}appendAll(t){const e=[];return Object.keys(t).forEach((n=>{const o=t[n];Array.isArray(o)?o.forEach((t=>{e.push({param:n,value:t,op:"a"})})):e.push({param:n,value:o,op:"a"})})),this.clone(e)}set(t,e){return this.clone({param:t,value:e,op:"s"})}delete(t,e){return this.clone({param:t,value:e,op:"d"})}toString(){return this.init(),this.keys().map((t=>{const e=this.encoder.encodeKey(t);return this.map.get(t).map((t=>e+"="+this.encoder.encodeValue(t))).join("&")})).filter((t=>""!==t)).join("&")}clone(t){const e=new oE({encoder:this.encoder});return e.cloneFrom=this.cloneFrom||this,e.updates=(this.updates||[]).concat(t),e}init(){null===this.map&&(this.map=new Map),null!==this.cloneFrom&&(this.cloneFrom.init(),this.cloneFrom.keys().forEach((t=>this.map.set(t,this.cloneFrom.map.get(t)))),this.updates.forEach((t=>{switch(t.op){case"a":case"s":const e=("a"===t.op?this.map.get(t.param):void 0)||[];e.push(nE(t.value)),this.map.set(t.param,e);break;case"d":if(void 0===t.value){this.map.delete(t.param);break}{let e=this.map.get(t.param)||[];const n=e.indexOf(nE(t.value));-1!==n&&e.splice(n,1),e.length>0?this.map.set(t.param,e):this.map.delete(t.param)}}})),this.cloneFrom=this.updates=null)}}class iE{constructor(){this.map=new Map}set(t,e){return this.map.set(t,e),this}get(t){return this.map.has(t)||this.map.set(t,t.defaultValue()),this.map.get(t)}delete(t){return this.map.delete(t),this}keys(){return this.map.keys()}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function aE(t){return"undefined"!=typeof ArrayBuffer&&t instanceof ArrayBuffer}function rE(t){return"undefined"!=typeof Blob&&t instanceof Blob}function sE(t){return"undefined"!=typeof FormData&&t instanceof FormData}class lE{constructor(t,e,n,o){let i;if(this.url=e,this.body=null,this.reportProgress=!1,this.withCredentials=!1,this.responseType="json",this.method=t.toUpperCase(),(function a(t){switch(t){case"DELETE":case"GET":case"HEAD":case"OPTIONS":case"JSONP":return!1;default:return!0}})(this.method)||o?(this.body=void 0!==n?n:null,i=o):i=n,i&&(this.reportProgress=!!i.reportProgress,this.withCredentials=!!i.withCredentials,i.responseType&&(this.responseType=i.responseType),i.headers&&(this.headers=i.headers),i.context&&(this.context=i.context),i.params&&(this.params=i.params)),this.headers||(this.headers=new $R),this.context||(this.context=new iE),this.params){const t=this.params.toString();if(0===t.length)this.urlWithParams=e;else{const n=e.indexOf("?");this.urlWithParams=e+(-1===n?"?":n<e.length-1?"&":"")+t}}else this.params=new oE,this.urlWithParams=e}serializeBody(){return null===this.body?null:aE(this.body)||rE(this.body)||sE(this.body)||(function t(e){return"undefined"!=typeof URLSearchParams&&e instanceof URLSearchParams})(this.body)||"string"==typeof this.body?this.body:this.body instanceof oE?this.body.toString():"object"==typeof this.body||"boolean"==typeof this.body||Array.isArray(this.body)?JSON.stringify(this.body):this.body.toString()}detectContentTypeHeader(){return null===this.body||sE(this.body)?null:rE(this.body)?this.body.type||null:aE(this.body)?null:"string"==typeof this.body?"text/plain":this.body instanceof oE?"application/x-www-form-urlencoded;charset=UTF-8":"object"==typeof this.body||"number"==typeof this.body||"boolean"==typeof this.body?"application/json":null}clone(t={}){var e;const n=t.method||this.method,o=t.url||this.url,i=t.responseType||this.responseType,a=void 0!==t.body?t.body:this.body,r=void 0!==t.withCredentials?t.withCredentials:this.withCredentials,s=void 0!==t.reportProgress?t.reportProgress:this.reportProgress;let l=t.headers||this.headers,c=t.params||this.params;const d=null!==(e=t.context)&&void 0!==e?e:this.context;return void 0!==t.setHeaders&&(l=Object.keys(t.setHeaders).reduce(((e,n)=>e.set(n,t.setHeaders[n])),l)),t.setParams&&(c=Object.keys(t.setParams).reduce(((e,n)=>e.set(n,t.setParams[n])),c)),new lE(n,o,a,{params:c,headers:l,context:d,reportProgress:s,responseType:i,withCredentials:r})}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */var cE;!(function(t){t[t.Sent=0]="Sent",t[t.UploadProgress=1]="UploadProgress",t[t.ResponseHeader=2]="ResponseHeader",t[t.DownloadProgress=3]="DownloadProgress",t[t.Response=4]="Response",t[t.User=5]="User"})(cE||(cE={}));class dE{constructor(t,e=200,n="OK"){this.headers=t.headers||new $R,this.status=void 0!==t.status?t.status:e,this.statusText=t.statusText||n,this.url=t.url||null,this.ok=this.status>=200&&this.status<300}}class pE extends dE{constructor(t={}){super(t),this.type=cE.ResponseHeader}clone(t={}){return new pE({headers:t.headers||this.headers,status:void 0!==t.status?t.status:this.status,statusText:t.statusText||this.statusText,url:t.url||this.url||void 0})}}class mE extends dE{constructor(t={}){super(t),this.type=cE.Response,this.body=void 0!==t.body?t.body:null}clone(t={}){return new mE({body:void 0!==t.body?t.body:this.body,headers:t.headers||this.headers,status:void 0!==t.status?t.status:this.status,statusText:t.statusText||this.statusText,url:t.url||this.url||void 0})}}class fE extends dE{constructor(t){super(t,0,"Unknown Error"),this.name="HttpErrorResponse",this.ok=!1,this.message=this.status>=200&&this.status<300?`Http failure during parsing for ${t.url||"(unknown url)"}`:`Http failure response for ${t.url||"(unknown url)"}: ${t.status} ${t.statusText}`,this.error=t.error||null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function uE(t,e){return{body:e,headers:t.headers,context:t.context,observe:t.observe,params:t.params,reportProgress:t.reportProgress,responseType:t.responseType,withCredentials:t.withCredentials}}class gE{constructor(t){this.handler=t}request(t,e,n={}){let o;if(t instanceof lE)o=t;else{let i,a;i=n.headers instanceof $R?n.headers:new $R(n.headers),n.params&&(a=n.params instanceof oE?n.params:new oE({fromObject:n.params})),o=new lE(t,e,void 0!==n.body?n.body:null,{headers:i,context:n.context,params:a,reportProgress:n.reportProgress,responseType:n.responseType||"json",withCredentials:n.withCredentials})}const i=zt(o).pipe((function a(t,e){return d(e)?$t(t,e,1):$t(t,1)})((t=>this.handler.handle(t))));if(t instanceof lE||"events"===n.observe)return i;const r=i.pipe(ue((t=>t instanceof mE)));switch(n.observe||"body"){case"body":switch(o.responseType){case"arraybuffer":return r.pipe(Vt((t=>{if(null!==t.body&&!(t.body instanceof ArrayBuffer))throw new Error("Response is not an ArrayBuffer.");return t.body})));case"blob":return r.pipe(Vt((t=>{if(null!==t.body&&!(t.body instanceof Blob))throw new Error("Response is not a Blob.");return t.body})));case"text":return r.pipe(Vt((t=>{if(null!==t.body&&"string"!=typeof t.body)throw new Error("Response is not a string.");return t.body})));case"json":default:return r.pipe(Vt((t=>t.body)))}case"response":return r;default:throw new Error(`Unreachable: unhandled observe type ${n.observe}}`)}}delete(t,e={}){return this.request("DELETE",t,e)}get(t,e={}){return this.request("GET",t,e)}head(t,e={}){return this.request("HEAD",t,e)}jsonp(t,e){return this.request("JSONP",t,{params:(new oE).append(e,"JSONP_CALLBACK"),observe:"body",responseType:"json"})}options(t,e={}){return this.request("OPTIONS",t,e)}patch(t,e,n={}){return this.request("PATCH",t,uE(n,e))}post(t,e,n={}){return this.request("POST",t,uE(n,e))}put(t,e,n={}){return this.request("PUT",t,uE(n,e))}}gE.ɵfac=function t(e){return new(e||gE)(Tr(JR))},gE.ɵprov=Sn({token:gE,factory:gE.ɵfac}),gE.ctorParameters=()=>[{type:JR}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(gE,[{type:Um}],(function(){return[{type:JR}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class hE{constructor(t,e){this.next=t,this.interceptor=e}handle(t){return this.interceptor.intercept(t,this.next)}}const bE=new er("HTTP_INTERCEPTORS");class yE{intercept(t,e){return e.handle(t)}}yE.ɵfac=function t(e){return new(e||yE)},yE.ɵprov=Sn({token:yE,factory:yE.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(yE,[{type:Um}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let _E=0;class CE{}class ME{constructor(t,e){this.callbackMap=t,this.document=e,this.resolvedPromise=Promise.resolve()}nextCallback(){return"ng_jsonp_callback_"+_E++}handle(t){if("JSONP"!==t.method)throw new Error("JSONP requests must use JSONP request method.");if("json"!==t.responseType)throw new Error("JSONP requests must use Json response type.");return new T((e=>{const n=this.nextCallback(),o=t.urlWithParams.replace(/=JSONP_CALLBACK(&|$)/,`=${n}$1`),i=this.document.createElement("script");i.src=o;let a=null,r=!1,s=!1;this.callbackMap[n]=t=>{delete this.callbackMap[n],s||(a=t,r=!0)};const l=()=>{i.parentNode&&i.parentNode.removeChild(i),delete this.callbackMap[n]},c=t=>{s||this.resolvedPromise.then((()=>{l(),r?(e.next(new mE({body:a,status:200,statusText:"OK",url:o})),e.complete()):e.error(new fE({url:o,status:0,statusText:"JSONP Error",error:new Error("JSONP injected script did not invoke callback.")}))}))},d=t=>{s||(l(),e.error(new fE({error:t,status:0,statusText:"JSONP Error",url:o})))};return i.addEventListener("load",c),i.addEventListener("error",d),this.document.body.appendChild(i),e.next({type:cE.Sent}),()=>{s=!0,i.removeEventListener("load",c),i.removeEventListener("error",d),l()}}))}}ME.ɵfac=function t(e){return new(e||ME)(Tr(CE),Tr(sM))},ME.ɵprov=Sn({token:ME,factory:ME.ɵfac}),ME.ctorParameters=()=>[{type:CE},{type:void 0,decorators:[{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(ME,[{type:Um}],(function(){return[{type:CE},{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),null);class vE{constructor(t){this.jsonp=t}intercept(t,e){return"JSONP"===t.method?this.jsonp.handle(t):e.handle(t)}}vE.ɵfac=function t(e){return new(e||vE)(Tr(ME))},vE.ɵprov=Sn({token:vE,factory:vE.ɵfac}),vE.ctorParameters=()=>[{type:ME}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(vE,[{type:Um}],(function(){return[{type:ME}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const PE=new er("XSRF_COOKIE_NAME"),wE=new er("XSRF_HEADER_NAME");class kE{}class SE{constructor(t,e,n){this.doc=t,this.platform=e,this.cookieName=n,this.lastCookieString="",this.lastToken=null,this.parseCount=0}getToken(){if("server"===this.platform)return null;const t=this.doc.cookie||"";return t!==this.lastCookieString&&(this.parseCount++,this.lastToken=_v(t,this.cookieName),this.lastCookieString=t),this.lastToken}}SE.ɵfac=function t(e){return new(e||SE)(Tr(sM),Tr(X_),Tr(PE))},SE.ɵprov=Sn({token:SE,factory:SE.ɵfac}),SE.ctorParameters=()=>[{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:String,decorators:[{type:Lr,args:[X_]}]},{type:String,decorators:[{type:Lr,args:[PE]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(SE,[{type:Um}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:String,decorators:[{type:Lr,args:[X_]}]},{type:String,decorators:[{type:Lr,args:[PE]}]}]}),null);class DE{constructor(t,e){this.tokenService=t,this.headerName=e}intercept(t,e){const n=t.url.toLowerCase();if("GET"===t.method||"HEAD"===t.method||n.startsWith("http://")||n.startsWith("https://"))return e.handle(t);const o=this.tokenService.getToken();return null===o||t.headers.has(this.headerName)||(t=t.clone({headers:t.headers.set(this.headerName,o)})),e.handle(t)}}DE.ɵfac=function t(e){return new(e||DE)(Tr(kE),Tr(wE))},DE.ɵprov=Sn({token:DE,factory:DE.ɵfac}),DE.ctorParameters=()=>[{type:kE},{type:String,decorators:[{type:Lr,args:[wE]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(DE,[{type:Um}],(function(){return[{type:kE},{type:String,decorators:[{type:Lr,args:[wE]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class RE{constructor(t,e){this.backend=t,this.injector=e,this.chain=null}handle(t){if(null===this.chain){const t=this.injector.get(bE,[]);this.chain=t.reduceRight(((t,e)=>new hE(t,e)),this.backend)}return this.chain.handle(t)}}function EE(){return"object"==typeof window?window:{}}RE.ɵfac=function t(e){return new(e||RE)(Tr(QR),Tr(Np))},RE.ɵprov=Sn({token:RE,factory:RE.ɵfac}),RE.ctorParameters=()=>[{type:QR},{type:Np}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(RE,[{type:Um}],(function(){return[{type:QR},{type:Np}]}),null);class AE{static disable(){return{ngModule:AE,providers:[{provide:DE,useClass:yE}]}}static withOptions(t={}){return{ngModule:AE,providers:[t.cookieName?{provide:PE,useValue:t.cookieName}:[],t.headerName?{provide:wE,useValue:t.headerName}:[]]}}}AE.ɵfac=function t(e){return new(e||AE)},AE.ɵmod=fo({type:AE}),AE.ɵinj=Dn({providers:[DE,{provide:bE,useExisting:DE,multi:!0},{provide:kE,useClass:SE},{provide:PE,useValue:"XSRF-TOKEN"},{provide:wE,useValue:"X-XSRF-TOKEN"}]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(AE,[{type:L_,args:[{providers:[DE,{provide:bE,useExisting:DE,multi:!0},{provide:kE,useClass:SE},{provide:PE,useValue:"XSRF-TOKEN"},{provide:wE,useValue:"X-XSRF-TOKEN"}]}]}],null,null);class TE{}TE.ɵfac=function t(e){return new(e||TE)},TE.ɵmod=fo({type:TE}),TE.ɵinj=Dn({providers:[gE,{provide:JR,useClass:RE},OE,{provide:QR,useExisting:OE}],imports:[[AE.withOptions({cookieName:"XSRF-TOKEN",headerName:"X-XSRF-TOKEN"})]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(TE,[{type:L_,args:[{imports:[AE.withOptions({cookieName:"XSRF-TOKEN",headerName:"X-XSRF-TOKEN"})],providers:[gE,{provide:JR,useClass:RE},OE,{provide:QR,useExisting:OE}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(TE,{imports:[AE]});class NE{}function zE(t){const e={};for(const[n,o]of t.entries())e[n]=o;return e}NE.ɵfac=function t(e){return new(e||NE)},NE.ɵmod=fo({type:NE}),NE.ɵinj=Dn({providers:[ME,{provide:CE,useFactory:EE},{provide:bE,useClass:vE,multi:!0}]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(NE,[{type:L_,args:[{providers:[ME,{provide:CE,useFactory:EE},{provide:bE,useClass:vE,multi:!0}]}]}],null,null);function IE(t){let e=t.headers||new $R;return e=e.append("X-XSRF-Protected","1"),Object.assign(Object.assign({},t),{headers:e})}class HE{constructor(t,e,n){this.appRootProvider=t,this.http=e,this.store=n}resolveAppRoot(t){return t.startsWith("/")?this.appRootProvider.getAbsPathnameWithAppRoot(t):t}get(t,e={}){return this.http.get(this.resolveAppRoot(t),e)}post(t,e,n={}){return n=IE(n),this.store.select(ND).pipe(ue((t=>Boolean(t))),ve(1),qe(this.store.select(VD)),$t((([,o])=>{var i;const a=this.resolveAppRoot(t);return o?this.http.get(a,{headers:null!==(i=n.headers)&&void 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Set,sideBarWidthInPercent:20},qA=VS(YA,BS(ZE,XE,((t,{plugin:e})=>Object.assign(Object.assign({},t),{activePlugin:e}))),BS($E,(t=>Object.assign(Object.assign({},t),{coreDataLoadState:Object.assign(Object.assign({},t.coreDataLoadState),{state:UE.LOADING}),pluginsListLoaded:Object.assign(Object.assign({},t.pluginsListLoaded),{state:UE.LOADING})}))),BS(eA,((t,{failureCode:e})=>Object.assign(Object.assign({},t),{coreDataLoadState:Object.assign(Object.assign({},t.coreDataLoadState),{state:UE.FAILED}),pluginsListLoaded:Object.assign(Object.assign({},t.pluginsListLoaded),{state:UE.FAILED,failureCode:e})}))),BS(tA,((t,{plugins:e})=>{const n=Object.keys(e).find((t=>e[t].enabled))||null,o=t.activePlugin||n,i=Date.now();let a=t.coreDataLoadState;return t.polymerRunsLoadState.state===UE.LOADED&&(a={state:UE.LOADED,lastLoadedTimeInMs:i}),Object.assign(Object.assign({},t),{activePlugin:o,coreDataLoadState:a,plugins:e,pluginsListLoaded:{state:UE.LOADED,lastLoadedTimeInMs:i,failureCode:null}})})),BS(nA,(t=>Object.assign(Object.assign({},t),{coreDataLoadState:Object.assign(Object.assign({},t.coreDataLoadState),{state:UE.LOADING}),polymerRunsLoadState:Object.assign(Object.assign({},t.polymerRunsLoadState),{state:UE.LOADING})}))),BS(oA,(t=>{const e=Date.now();let n=t.coreDataLoadState;return 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o&&o>=0&&o<=100&&(n.sideBarWidthInPercent=o),n})),BS(UA,(t=>Object.assign(Object.assign({},t),{activePlugin:"scalars"})))),{reducers:ZA}=cA(YA,{});function XA(t,e){return WA(qA,ZA)(t,e)}const KA=_S(GA),JA=bS(KA,(t=>t.pluginsListLoaded)),QA=bS(KA,(t=>t.polymerRunsLoadState)),$A=bS(KA,(t=>t.coreDataLoadState.state)),tT=bS(KA,(t=>t.coreDataLoadState.lastLoadedTimeInMs)),eT=bS(KA,(t=>t.activePlugin)),nT=bS(KA,(t=>t.plugins)),oT=bS(KA,(t=>t.environment)),iT=bS(KA,(t=>t.sideBarWidthInPercent)),aT=new Set([_D.COMPARE_EXPERIMENT,_D.EXPERIMENT,_D.NOT_SET]);class rT{constructor(t,e,n){this.actions$=t,this.store=e,this.webappDataSource=n,this.tfBackend={ref:document.createElement("tf-backend").tf_backend},this.onDashboardLoad$=pe(this.actions$.pipe($S(KE,tR),qe(this.store.select(hR)),we((([,t],[,e])=>xD(t,e)))),this.actions$.pipe($S(QE,JE))).pipe(qe(this.store.select(MR)),ue((([,t])=>aT.has(t))),Ye(1,void 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GE?{failureCode:t.failureCode}:{failureCode:LE.UNKNOWN})),dt))))))),this.onDashboardLoad$.pipe(Vt((([,t])=>t)),Ve((t=>t!==_D.COMPARE_EXPERIMENT?zt([]):this.store.select(OR).pipe(we(((t,e)=>{const n=Object.entries(t),o=new Map(Object.entries(e));if(n.length!==o.size)return!1;for(const[t,e]of n){if(!o.get(t))return!1;if(o.get(t).aliasText!==e.aliasText||o.get(t).aliasNumber!==e.aliasNumber)return!1}return!0})),Pe(0),Ye(500,void 0,{leading:!0,trailing:!0})))),qe(this.store.select(MR),this.store.select(QA)),ue((([,t,e])=>aT.has(t)&&e.state!==UE.LOADING)),We((()=>{this.store.dispatch(nA())})),Ve((()=>this.refreshPolymerRuns())),We((()=>{this.store.dispatch(oA())})),he((()=>(this.store.dispatch(iA()),dt)))))),{dispatch:!1}),this.dispatchChangePlugin$=WS((()=>pe(this.onDashboardLoad$,this.actions$.pipe($S(tA))).pipe(qe(this.store.select(eT)),Vt((([,t])=>t)),we(),ue((t=>null!==t)),ve(1),We((t=>{this.store.dispatch(ZE({plugin:t}))})))),{dispatch:!1})}refreshPolymerRuns(){return Pt(this.tfBackend.ref.runsStore.refresh())}fetchEnvironment(){return this.webappDataSource.fetchEnvironment().pipe(We((t=>{this.store.dispatch(aA({environment:t}))})))}}rT.ɵfac=function t(e){return new(e||rT)(Tr(QS),Tr(iS),Tr(YE))},rT.ɵprov=Sn({token:rT,factory:rT.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(rT,[{type:Um}],(function(){return[{type:QS},{type:iS},{type:YE}]}),null);const sT=new er("Core Feature Config");function lT(t){return{initialState:Object.assign(Object.assign({},YA),{activePlugin:t.getPluginId()||null})}}function cT(){return bS(iT,(t=>({sideBarWidthInPercent:t})))}class dT{}dT.ɵfac=function t(e){return new(e||dT)},dT.ɵmod=fo({type:dT}),dT.ɵinj=Dn({providers:[{provide:sT,deps:[TR],useFactory:lT}],imports:[[hD.forFeature([rT]),TS.forFeature(GA,XA,sT),qE,zR.defineGlobalSetting(cT)]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(dT,[{type:L_,args:[{imports:[hD.forFeature([rT]),TS.forFeature(GA,XA,sT),qE,zR.defineGlobalSetting(cT)],providers:[{provide:sT,deps:[TR],useFactory:lT}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(dT,{imports:[gD,AS,qE,zR]});const pT=new er("[Alert] Action-To-Alert Provider");class mT{constructor(t){this.providers=new Map;for(const e of t||[])for(const t of e){if(this.providers.has(t.actionCreator.type))throw new RangeError(`"${t.actionCreator.type}" is already registered for alerts. Multiple alerts for the same action is not allowed.`);this.providers.set(t.actionCreator.type,t.alertFromAction)}}getAlertFromAction(t){const e=this.providers.get(t.type);return e?e(t):null}static registerAlertActions(t){return{ngModule:mT,providers:[{provide:pT,multi:!0,useFactory:t}]}}}mT.ɵfac=function t(e){return new(e||mT)(Tr(pT,8))},mT.ɵmod=fo({type:mT}),mT.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(mT,[{type:L_,args:[{}]}],(function(){return[{type:void 0,decorators:[{type:Br},{type:Lr,args:[pT]}]}]}),null);const fT=_k("[Runs] Fetch Runs Requested",{_as:"props",_p:void 0}),uT=_k("[Runs] Fetch Runs Succeeded",{_as:"props",_p:void 0}),gT=_k("[Runs] Fetch Runs Failed",{_as:"props",_p:void 0}),hT=_k("[Runs] Run Selection Toggled",{_as:"props",_p:void 0}),bT=_k("[Runs] Run Page Selection Toggled",{_as:"props",_p:void 0}),yT=_k("[Runs] Run Selector Pagination Option Changed",{_as:"props",_p:void 0}),_T=_k("[Runs] Run Selector Sort Changed",{_as:"props",_p:void 0}),CT=_k("[Runs] Run Selector Regex Filter Changed",{_as:"props",_p:void 0}),MT=_k("[Runs] Run Color Changed",{_as:"props",_p:void 0}),vT=_k("[Runs] Run Table Shown",{_as:"props",_p:void 0}),xT=_k("[Runs] Run Group By Changed",{_as:"props",_p:void 0});var OT,PT,wT,kT;!(function(t){t.DATASET_UNKNOWN="DATASET_UNKNOWN",t.DATASET_TRAINING="DATASET_TRAINING",t.DATASET_VALIDATION="DATASET_VALIDATION"})(OT||(OT={})),(function(t){t.STATUS_UNKNOWN="STATUS_UNKNOWN",t.STATUS_SUCCESS="STATUS_SUCCESS",t.STATUS_FAILURE="STATUS_FAILURE",t.STATUS_RUNNING="STATUS_RUNNING"})(PT||(PT={})),(function(t){t.DATA_TYPE_UNSET="DATA_TYPE_UNSET",t.DATA_TYPE_STRING="DATA_TYPE_STRING",t.DATA_TYPE_BOOL="DATA_TYPE_BOOL",t.DATA_TYPE_FLOAT64="DATA_TYPE_FLOAT64"})(wT||(wT={})),(function(t){t[t.DISCRETE=0]="DISCRETE",t[t.INTERVAL=1]="INTERVAL"})(kT||(kT={}));class ST{}ST.ɵfac=function t(e){return new(e||ST)},ST.ɵprov=Sn({token:ST,factory:ST.ɵfac,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(ST,[{type:Um,args:[{providedIn:"root"}]}],null,null);const DT=_k("[Hparams] Hparams Discrete Hparam Filter Changed",{_as:"props",_p:void 0}),RT=_k("[Hparams] Hparams Interval Hparam Filter Changed",{_as:"props",_p:void 0}),ET=_k("[Hparams] Hparams Metric Filter Changed",{_as:"props",_p:void 0});function AT(t){return JSON.stringify([...t].sort())}function TT(t){var e,n,o,i;const a=new Map,r=new Map,s=new Map;for(const a of t)for(const[t,l]of a)if(l.type===kT.DISCRETE){const{possibleValues:e,values:n}=r.get(t)||{possibleValues:new Set,values:new Set};for(const t of l.filterValues)n.add(t);for(const t of l.possibleValues)e.add(t);r.set(t,{possibleValues:e,values:n})}else{const a=s.get(t);s.set(t,{filterLowerValue:Math.min(l.filterLowerValue,null!==(e=null==a?void 0:a.filterLowerValue)&&void 0!==e?e:1/0),filterUpperValue:Math.max(l.filterUpperValue,null!==(n=null==a?void 0:a.filterUpperValue)&&void 0!==n?n:-1/0),minValue:Math.min(l.minValue,null!==(o=null==a?void 0:a.minValue)&&void 0!==o?o:1/0),maxValue:Math.max(l.maxValue,null!==(i=null==a?void 0:a.maxValue)&&void 0!==i?i:-1/0)})}for(const[t,{values:e,possibleValues:n}]of r)a.set(t,{type:kT.DISCRETE,includeUndefined:!0,possibleValues:[...n],filterValues:[...e]});for(const[t,{minValue:e,maxValue:n,filterLowerValue:o,filterUpperValue:i}]of s){if(a.has(t)){const e=a.get(t);if(e.type===kT.DISCRETE&&e.possibleValues.some((t=>t)))throw new RangeError(`Cannot combine hparam, ${t}, as it is of mixed types.`)}a.set(t,{type:kT.INTERVAL,includeUndefined:!0,minValue:e,maxValue:n,filterLowerValue:o,filterUpperValue:i})}return a}function NT(t){var e,n,o,i;const a=new Map;for(const r of t)for(const[t,s]of r){const r=a.get(t);a.set(t,Object.assign(Object.assign({type:kT.INTERVAL,includeUndefined:!0},r),{minValue:Math.min(s.minValue,null!==(e=null==r?void 0:r.minValue)&&void 0!==e?e:1/0),maxValue:Math.max(s.maxValue,null!==(n=null==r?void 0:r.maxValue)&&void 0!==n?n:-1/0),filterLowerValue:Math.min(s.filterLowerValue,null!==(o=null==r?void 0:r.filterLowerValue)&&void 0!==o?o:1/0),filterUpperValue:Math.max(s.filterUpperValue,null!==(i=null==r?void 0:r.filterUpperValue)&&void 0!==i?i:-1/0)}))}return a}const zT=VS({specs:{},filters:{}},BS(DT,((t,e)=>{var n;const{experimentIds:o,hparamName:i,filterValues:a,includeUndefined:r}=e,s=AT(o),l=null!==(n=t.filters[s])&&void 0!==n?n:{hparams:new Map},c=l.hparams.get(i);if(c&&c.type!==kT.DISCRETE)throw new RangeError(`New discrete filter of ${i} conflicts existing filter of `+kT[c.type]);const d=TT(o.filter((e=>Boolean(t.specs[e]))).map((e=>t.specs[e].hparam.defaultFilters))).get(i);if(!d)throw new Error(`Cannot set hparam, ${i}, when it is not known for experimentIds: ${o.join(", ")}`);if(d.type!==kT.DISCRETE)throw new Error(`Cannot set ${i} when default filter is not of discrete type.`);const p=new Set(d.possibleValues),m=[...a].filter((t=>!p.has(t)));if(m.length)throw new Error(`New filter for ${i} has more than one value that is not present in the spec. Bad values: ${m.join(", ")}`);const f=new Map(l.hparams);return f.set(i,Object.assign(Object.assign({},c),{type:kT.DISCRETE,includeUndefined:r,possibleValues:[...p],filterValues:a})),Object.assign(Object.assign({},t),{filters:Object.assign(Object.assign({},t.filters),{[s]:Object.assign(Object.assign({},l),{hparams:f})})})})),BS(RT,((t,e)=>{var n;const{experimentIds:o,hparamName:i,filterLowerValue:a,filterUpperValue:r,includeUndefined:s}=e,l=AT(o),c=null!==(n=t.filters[l])&&void 0!==n?n:{metrics:new Map,hparams:new Map},d=c.hparams.get(i);if(d&&d.type!==kT.INTERVAL)throw new RangeError(`New interval filter of ${i} conflicts existing filter of `+kT[d.type]);const p=TT(o.filter((e=>Boolean(t.specs[e]))).map((e=>t.specs[e].hparam.defaultFilters))).get(i);if(!p)throw new Error(`Cannot set hpara, ${i}, when it is not known for experimentIds: ${o.join(", ")}`);if(p.type!==kT.INTERVAL)throw new Error(`Cannot set ${i} when default filter is not of interval type.`);const m=new Map(c.hparams);return m.set(i,Object.assign(Object.assign({},d),{type:kT.INTERVAL,includeUndefined:s,minValue:p.minValue,maxValue:p.maxValue,filterLowerValue:a,filterUpperValue:r})),Object.assign(Object.assign({},t),{filters:Object.assign(Object.assign({},t.filters),{[l]:Object.assign(Object.assign({},c),{hparams:m})})})})),BS(ET,((t,e)=>{var n;const{experimentIds:o,metricTag:i,filterLowerValue:a,filterUpperValue:r,includeUndefined:s}=e,l=AT(o),c=null!==(n=t.filters[l])&&void 0!==n?n:{metrics:new Map,hparams:new Map},d=NT(o.filter((e=>Boolean(t.specs[e]))).map((e=>t.specs[e].metric.defaultFilters))).get(i);if(!d)throw new Error(`Cannot set metric, ${i}, when it is not known for experimentIds: ${o.join(", ")}`);const p=c.metrics.get(i),m=new Map(c.metrics);return m.set(i,Object.assign(Object.assign({},p),{type:kT.INTERVAL,includeUndefined:s,minValue:d.minValue,maxValue:d.maxValue,filterLowerValue:a,filterUpperValue:r})),Object.assign(Object.assign({},t),{filters:Object.assign(Object.assign({},t.filters),{[l]:Object.assign(Object.assign({},c),{metrics:m})})})})),BS(uT,((t,e)=>{var n,o,i,a;if(0===Object.keys(e.newRunsAndMetadata).length)return t;const r=Object.assign({},t.specs),s=new Map,l=new Set;for(const t of Object.keys(e.newRunsAndMetadata)){const c=new Map,d=new Map,p=new Map,m=new Map,{runs:f,metadata:u}=e.newRunsAndMetadata[t];for(const t of f){const e=u.runToHparamsAndMetrics[t.id];if(e)for(const t of e.metrics){const e=s.get(t.tag);s.set(t.tag,{min:e?Math.min(e.min,t.value):t.value,max:e?Math.max(e.max,t.value):t.value})}}for(const{name:t,domain:e}of u.hparamSpecs)if(e.type===kT.DISCRETE){const n=p.get(t)||new Set;for(const t of e.values)n.add(t);p.set(t,n)}else{const n=m.get(t);m.set(t,{minValue:n?Math.min(e.minValue,n.minValue):e.minValue,maxValue:n?Math.max(e.maxValue,n.maxValue):e.maxValue})}for(const t of u.metricSpecs)l.add(t.tag);for(const[t,e]of p)c.set(t,{type:kT.DISCRETE,includeUndefined:!0,possibleValues:[...e],filterValues:[...e]});for(const[t,{minValue:e,maxValue:n}]of m)c.set(t,{type:kT.INTERVAL,includeUndefined:!0,minValue:e,maxValue:n,filterLowerValue:e,filterUpperValue:n});for(const t of l){const e=s.get(t),i=null!==(n=null==e?void 0:e.min)&&void 0!==n?n:0,a=null!==(o=null==e?void 0:e.max)&&void 0!==o?o:0;d.set(t,{type:kT.INTERVAL,includeUndefined:!0,minValue:i,maxValue:a,filterLowerValue:i,filterUpperValue:a})}r[t]={hparam:Object.assign(Object.assign({},null===(i=r[t])||void 0===i?void 0:i.hparam),{specs:u.hparamSpecs,defaultFilters:c}),metric:Object.assign(Object.assign({},null===(a=r[t])||void 0===a?void 0:a.metric),{specs:u.metricSpecs,defaultFilters:d})}}return Object.assign(Object.assign({},t),{specs:r})})));function IT(t,e){return zT(t,e)}const HT="hparams";class FT{}FT.ɵfac=function t(e){return new(e||FT)},FT.ɵmod=fo({type:FT}),FT.ɵinj=Dn({imports:[[TS.forFeature(HT,IT)]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(FT,[{type:L_,args:[{imports:[TS.forFeature(HT,IT)]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(FT,{imports:[AS]});class LT{}function BT(t,e){return`${e}/${t}`}LT.ɵfac=function t(e){return new(e||LT)},LT.ɵmod=fo({type:LT}),LT.ɵinj=Dn({imports:[[FT]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(LT,[{type:L_,args:[{imports:[FT]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(LT,{imports:[FT]});class VT{constructor(t){this.http=t}fetchRuns(t){return this.http.get("data/runs").pipe(Vt((e=>e.map((e=>({id:BT(e,t),name:e,startTime:0}))))))}fetchHparamsMetadata(t){return zt({hparamSpecs:[],metricSpecs:[],runToHparamsAndMetrics:{}})}}VT.ɵfac=function t(e){return new(e||VT)(Tr(HE))},VT.ɵprov=Sn({token:VT,factory:VT.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(VT,[{type:Um}],(function(){return[{type:HE}]}),null);class jT{}jT.ɵfac=function t(e){return new(e||jT)},jT.ɵmod=fo({type:jT}),jT.ɵinj=Dn({providers:[{provide:ST,useClass:VT}]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(jT,[{type:L_,args:[{providers:[{provide:ST,useClass:VT}]}]}],null,null);const UT="alerts",WT=bS(_S(UT),(t=>t.latestAlert)),GT="experiments",YT=bS(_S(GT),(t=>t.data)),qT=bS(YT,((t,e)=>{const{experimentId:n}=e;return t.experimentMap[n]||null}));
/*! *****************************************************************************
    Copyright (c) Microsoft Corporation.

    Permission to use, copy, modify, and/or distribute this software for any
    purpose with or without fee is hereby granted.

    THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
    REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
    AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
    INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
    LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
    OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
    PERFORMANCE OF THIS SOFTWARE.
    ***************************************************************************** */
function ZT(t,e){var n={};for(var o in t)Object.prototype.hasOwnProperty.call(t,o)&&e.indexOf(o)<0&&(n[o]=t[o]);if(null!=t&&"function"==typeof Object.getOwnPropertySymbols){var i=0;for(o=Object.getOwnPropertySymbols(t);i<o.length;i++)e.indexOf(o[i])<0&&Object.prototype.propertyIsEnumerable.call(t,o[i])&&(n[o[i]]=t[o[i]])}return n}function XT(t,e,n,o){return new(n||(n=Promise))((function(i,a){function r(t){try{l(o.next(t))}catch(t){a(t)}}function s(t){try{l(o.throw(t))}catch(t){a(t)}}function l(t){t.done?i(t.value):(function e(t){return t instanceof n?t:new n((function(e){e(t)}))})(t.value).then(r,s)}l((o=o.apply(t,e||[])).next())}))}var KT,JT,QT;!(function(t){t.SCALARS="scalars",t.HISTOGRAMS="histograms",t.IMAGES="images"})(KT||(KT={})),(function(t){t.DEFAULT="default",t.ALPHABETICAL="alphabetical",t.ASCENDING="ascending",t.DESCENDING="descending",t.NEAREST="nearest"})(JT||(JT={})),(function(t){t[t.STEP=0]="STEP",t[t.RELATIVE=1]="RELATIVE",t[t.WALL_TIME=2]="WALL_TIME"})(QT||(QT={}));const $T="timeseries";const tN=[KT.IMAGES];function eN(t){return tN.includes(t)}const nN=[KT.HISTOGRAMS,KT.IMAGES];function oN(t){return nN.includes(t)}function iN(t){return oN(t.plugin)}class aN{}function rN(t){return t.hasOwnProperty("error")}const sN="data/plugin/timeseries";function lN(t){const e=t.indexOf("/");return{run:t.substring(e+1),experimentId:t.substring(0,e)}}function cN(t,e){return`${e}/${t}`}function dN(t,e){const{runToSeries:n,run:o}=t,i=ZT(t,["runToSeries","run"]),a=Object.assign({},i);return n&&(a.runToSeries=pN(n,e)),o&&(a.runId=cN(o,e)),a}function pN(t,e){const n={};for(const o in t)if(t.hasOwnProperty(o)){n[cN(o,e)]=t[o]}return n}class mN{constructor(t,e){this.http=t,this.store=e}fetchTagMetadata(t){const e=t.map((t=>this.http.get(`/experiment/${t}/${sN}/tags`).pipe(Vt((e=>(function n(t,e){const n={};for(const o of Object.keys(t)){const i=o;if(eN(i)){const o=t[i],{tagRunSampledInfo:a}=o,r=ZT(o,["tagRunSampledInfo"]),s={};for(const t in a)a.hasOwnProperty(t)&&(s[t]=pN(a[t],e));n[i]=Object.assign(Object.assign({},r),{tagRunSampledInfo:s})}else{const o=t[i],{runTagInfo:a}=o,r=ZT(o,["runTagInfo"]);n[i]=Object.assign(Object.assign({},r),{runTagInfo:pN(a,e)})}}return n})(e,t)))))),n=this.store.select(ND).pipe(ue(Boolean),ve(1),qe(this.store.select(WD)),Vt((([,t])=>t)));return ie(e).pipe(qe(n),Vt((([t,e])=>{const n=(function o(t){const e={};for(const n of t)for(const t of Object.values(KT))if(eN(t)){e[t]=e[t]||{tagDescriptions:{},tagRunSampledInfo:{}};const{tagDescriptions:o,tagRunSampledInfo:i}=n[t];e[t].tagDescriptions=Object.assign(Object.assign({},e[t].tagDescriptions),o);const a=e[t].tagRunSampledInfo;for(const t of Object.keys(i)){a[t]=a[t]||{};for(const e of Object.keys(i[t]))a[t][e]=i[t][e]}}else{e[t]=e[t]||{tagDescriptions:{},runTagInfo:{}};const{tagDescriptions:o,runTagInfo:i}=n[t];e[t].tagDescriptions=Object.assign(Object.assign({},e[t].tagDescriptions),o),e[t].runTagInfo=Object.assign(Object.assign({},e[t].runTagInfo),i)}return e})(t);return e||(n[KT.IMAGES]={tagDescriptions:{},tagRunSampledInfo:{}}),n})))}fetchTimeSeries(t){const e=t.map((t=>{if(oN(t.plugin)){const e=t,{runId:n}=e,o=ZT(e,["runId"]),{run:i,experimentId:a}=lN(n),r=Object.assign(Object.assign({},o),{run:i});return this.fetchTimeSeriesBackendRequest(r,a).pipe(Vt((({response:t,experimentId:e})=>dN(t,e))))}const e=t,{experimentIds:n}=e,o=ZT(e,["experimentIds"]);return ie(n.map((t=>this.fetchTimeSeriesBackendRequest(o,t)))).pipe(Vt((t=>{const e=ZT(t[0].response,["runToSeries","error"]);for(const{response:n,experimentId:o}of t){const t=dN(n,o);if(e.error)continue;const{runToSeries:i,error:a}=t;if(a)e.error=a,e.runToSeries=void 0;else{e.runToSeries=e.runToSeries||{};for(const t of Object.keys(i))e.runToSeries[t]=i[t]}}return e})))}));return ie(e)}fetchTimeSeriesBackendRequest(t,e){const n=new FormData;return n.append("requests",JSON.stringify([t])),this.http.post(`/experiment/${e}/${sN}/timeSeries`,n).pipe(Vt((t=>({response:t[0],experimentId:e}))))}imageUrl(t){return`${sN}/imageData?imageId=${t}`}downloadUrl(t,e,n,o){const{run:i,experimentId:a}=lN(n);let r;switch(t){case KT.SCALARS:r="scalars/scalars";break;default:throw new Error(`Not implemented: downloadUrl for ${t} is not implemented yet`)}if(!a)throw new Error("experimentId is empty; it is required to form downloadUrl.");return`/experiment/${a}/data/plugin/${r}?${new URLSearchParams({tag:e,run:i,format:o})}`}}mN.ɵfac=function t(e){return new(e||mN)(Tr(HE),Tr(iS))},mN.ɵprov=Sn({token:mN,factory:mN.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(mN,[{type:Um}],(function(){return[{type:HE},{type:iS}]}),null);const fN=new URLSearchParams(window.location.search);class uN{getParams(){return fN}}uN.ɵfac=function t(e){return new(e||uN)},uN.ɵprov=Sn({token:uN,factory:uN.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(uN,[{type:Um}],null,null);class gN{}gN.ɵfac=function t(e){return new(e||gN)},gN.ɵprov=Sn({token:gN,factory:gN.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(gN,[{type:Um}],null,null);const hN="experimentalPlugin",bN="scalarsBatchSize",yN="enableCardWidthSetting",_N="enableColorGroup",CN="enableColorGroupByRegex",MN="darkMode",vN="enableLinkTime",xN="enableTimeNamespacedState";class ON{constructor(t){this.queryParams=t}getFeatures(t=!1){const e=this.queryParams.getParams(),n=t?this.getPartialFeaturesFromMediaQuery():{};return e.has(hN)&&(n.enabledExperimentalPlugins=e.getAll(hN)),e.has("tensorboardColab")&&(n.inColab="true"===e.get("tensorboardColab")),e.has(bN)&&(n.scalarsBatchSize=Number(e.get(bN))),e.has(_N)&&(n.enabledColorGroup="false"!==e.get(_N)),e.has(CN)&&(n.enabledColorGroupByRegex="false"!==e.get(CN)),e.has(MN)&&(n.defaultEnableDarkMode="false"!==e.get(MN)),e.has(vN)&&(n.enabledLinkedTime="false"!==e.get(vN)),e.has(yN)&&(n.enabledCardWidthSetting="false"!==e.get(yN)),e.has(xN)&&(n.enabledTimeNamespacedState="false"!==e.get(xN)),n}getPartialFeaturesFromMediaQuery(){const t={};return window.matchMedia("(prefers-color-scheme: dark)").matches&&(t.defaultEnableDarkMode=!0),t}}ON.ɵfac=function t(e){return new(e||ON)(Tr(uN))},ON.ɵprov=Sn({token:ON,factory:ON.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(ON,[{type:Um}],(function(){return[{type:uN}]}),null);class PN{}PN.ɵfac=function t(e){return new(e||PN)},PN.ɵmod=fo({type:PN}),PN.ɵinj=Dn({providers:[ON,uN,{provide:gN,useClass:ON}]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(PN,[{type:L_,args:[{providers:[ON,uN,{provide:gN,useClass:ON}]}]}],null,null);const wN=_k("[FEATURE FLAG] Partial Feature Flags Loaded",{_as:"props",_p:void 0}),kN=_k("[FEATURE FLAG] Enable Dark Mode Override Changed",{_as:"props",_p:void 0}),SN=_k("[FEATURE FLAG] Effects Init");class DN{constructor(t,e,n){this.actions$=t,this.store=e,this.dataSource=n,this.getFeatureFlags$=WS((()=>this.actions$.pipe($S(SN),_e(this.store.select(HD)),Vt((([,t])=>{const e=this.dataSource.getFeatures(t);return wN({features:e})})))))}ngrxOnInitEffects(){return SN()}}DN.ɵfac=function t(e){return new(e||DN)(Tr(QS),Tr(iS),Tr(gN))},DN.ɵprov=Sn({token:DN,factory:DN.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(DN,[{type:Um}],(function(){return[{type:QS},{type:iS},{type:gN}]}),null);const RN={isFeatureFlagsLoaded:!1,defaultFlags:{isAutoDarkModeAllowed:!0,defaultEnableDarkMode:!1,enableDarkModeOverride:null,enabledColorGroup:!0,enabledColorGroupByRegex:!0,enabledExperimentalPlugins:[],inColab:!1,scalarsBatchSize:void 0,metricsImageSupportEnabled:!0,enabledLinkedTime:!1,enableTimeSeriesPromotion:!1,enabledCardWidthSetting:!0,enabledTimeNamespacedState:!1},flagOverrides:{}},EN=new er("[Feature Flag] Store Config");function AN(){return{initialState:RN}}const TN=VS(RN,BS(wN,((t,{features:e})=>Object.assign(Object.assign({},t),{isFeatureFlagsLoaded:!0,flagOverrides:Object.assign(Object.assign({},t.flagOverrides),e)}))),BS(kN,((t,{enableDarkMode:e})=>Object.assign(Object.assign({},t),{flagOverrides:Object.assign(Object.assign({},t.flagOverrides),{enableDarkModeOverride:e})}))),BS(qR,((t,{partialSettings:e})=>{if(!e.themeOverride)return t;let n;switch(e.themeOverride){case FR.BROWSER_DEFAULT:n=null;break;case FR.DARK:n=!0;break;case FR.LIGHT:n=!1}return 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n;return t.selectedTime?Object.assign(Object.assign({},t.selectedTime),{end:null!==(n=t.selectedTime.end)&&void 0!==n?n:{step:e.max}}):{start:{step:e.min},end:{step:e.max}}})),Ez=bS(KN,Rz,((t,e)=>t.selectTimeEnabled?t.useRangeSelectTime?e:Object.assign(Object.assign({},e),{end:null}):null)),Az=bS(KN,(t=>t.filteredPluginTypes)),Tz=bS(KN,(t=>t.promoteTimeSeries)),Nz=bS(KN,(t=>t.isSettingsPaneOpen));var zz;!(function(t){t[t.NONE=0]="NONE",t[t.WHATS_NEW=1]="WHATS_NEW"})(zz||(zz={}));const Iz=_S("notification");bS(Iz,(t=>t.notifications)),bS(Iz,(t=>{var e;return null!==(e=t.lastReadTimestampInMs)&&void 0!==e?e:-1}));const Hz="runs";var Fz,Lz;function Bz(t){return JSON.stringify(t.slice().sort())}function Vz(t,e,n){const o={},i=[],a={matches:o,nonMatches:i};switch(t.key){case Lz.RUN:for(const t of e)o[t.id]=[t];break;case Lz.EXPERIMENT:for(const t of e){const e=n[t.id],i=o[e]||[];i.push(t),o[e]=i}break;case Lz.REGEX:if(!t.regexString)break;let a;try{a=new RegExp(t.regexString)}catch(t){break}for(const t of e){const e=t.name.match(a);if(e){const n=e.length>1?JSON.stringify(e.slice(1)):"pseudo_group",i=o[n]||[];i.push(t),o[n]=i}else i.push(t)}}return a}function jz(t,e){switch(t){case Lz.REGEX:return{key:t,regexString:null!=e?e:""};case Lz.RUN:case Lz.EXPERIMENT:default:return{key:t}}}!(function(t){t[t.EXPERIMENT_NAME=0]="EXPERIMENT_NAME",t[t.HPARAM=1]="HPARAM",t[t.METRIC=2]="METRIC",t[t.RUN_NAME=3]="RUN_NAME"})(Fz||(Fz={})),(function(t){t[t.RUN=0]="RUN",t[t.EXPERIMENT=1]="EXPERIMENT",t[t.REGEX=2]="REGEX"})(Lz||(Lz={}));const Uz=_S(Hz),Wz=bS(Uz,(t=>t.data)),Gz=bS(Wz,((t,e)=>{var n;return null!==(n=t.runIdToExpId[e.runId])&&void 0!==n?n:null})),Yz=bS(Wz,((t,e)=>{var n;return null!==(n=t.runMetadata[e.runId])&&void 0!==n?n:null})),qz=bS(Wz,((t,e)=>(t.runIds[e.experimentId]||[]).filter((e=>Boolean(t.runMetadata[e]))).map((e=>t.runMetadata[e])))),Zz=bS(Wz,((t,e)=>{var n;return null!==(n=t.runIds[e.experimentId])&&void 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MI{constructor(t,e,n){this.actions$=t,this.store=e,this.runsDataSource=n,this.loadRunsOnRunTableShown$=WS((()=>this.actions$.pipe($S(vT),$t((({experimentIds:t})=>this.getExperimentsWithLoadState(t,(t=>t===UE.FAILED||t===UE.NOT_LOADED)).pipe(ue((t=>!!t.length)),$t((e=>this.fetchAllRunsList(t,e)))))))),{dispatch:!1}),this.experimentsWithStaleRunsOnRouteChange$=this.actions$.pipe($S(tR),qe(this.store.select(hR)),we((([,t],[,e])=>xD(t,e))),qe(this.store.select(xR)),ue((([,t])=>!!t)),Vt((([,t])=>t)),$t((t=>this.getExperimentsWithLoadState(t,(t=>t===UE.FAILED||t===UE.NOT_LOADED)).pipe(Vt((e=>({experimentIds:t,experimentIdsToBeFetched:e}))))))),this.experimentsWithStaleRunsOnReload$=this.actions$.pipe($S(QE,JE),qe(this.store.select(xR)),ue((([,t])=>!!t)),Vt((([,t])=>t)),$t((t=>this.getExperimentsWithLoadState(t,(t=>t!==UE.LOADING)).pipe(Vt((e=>({experimentIds:t,experimentIdsToBeFetched:e}))))))),this.loadRunsOnNavigationOrReload$=WS((()=>pe(this.experimentsWithStaleRunsOnRouteChange$,this.experimentsWithStaleRunsOnReload$).pipe($t((({experimentIds:t,experimentIdsToBeFetched:e})=>this.fetchAllRunsList(t,e))))),{dispatch:!1})}getRunsListLoadState(t){return this.store.select(Kz,{experimentId:t}).pipe(ve(1))}getExperimentsWithLoadState(t,e){return ie(t.map((t=>this.getRunsListLoadState(t)))).pipe(Vt((n=>t.filter(((t,o)=>e(n[o].state))))))}fetchAllRunsList(t,e){return zt({experimentIds:t,experimentIdsToBeFetched:e}).pipe(We((()=>{this.store.dispatch(fT({experimentIds:t,requestedExperimentIds:e}))})),$t((()=>{const n=new Set(e);return ie(t.map((t=>n.has(t)?this.fetchRunsForExperiment(t):this.maybeWaitForRunsAndGetRuns(t))))})),Vt((t=>{const e={},n=[];for(const o of t)n.push(...o.runs),o.fromRemote&&(e[o.experimentId]={runs:o.runs,metadata:o.metadata});return{newRunsAndMetadata:e,runsForAllExperiments:n}})),We((({newRunsAndMetadata:e,runsForAllExperiments:n})=>{this.store.dispatch(uT({experimentIds:t,newRunsAndMetadata:e,runsForAllExperiments:n}))})),he((n=>(this.store.dispatch(gT({experimentIds:t,requestedExperimentIds:e})),zt(null)))),Vt((()=>null)))}maybeWaitForRunsAndGetRuns(t){return this.store.select(Kz,{experimentId:t}).pipe(ue((t=>t.state!==UE.LOADING)),ve(1),$t((t=>t.state===UE.FAILED?It(new Error("Pending request failed")):zt(t))),qe(this.store.select(qz,{experimentId:t})),Vt((([,e])=>({fromRemote:!1,experimentId:t,runs:e}))))}fetchRunsForExperiment(t){return ie([this.runsDataSource.fetchRuns(t),this.runsDataSource.fetchHparamsMetadata(t)]).pipe(Vt((([e,n])=>({fromRemote:!0,experimentId:t,runs:e,metadata:n}))))}}var vI,xI;MI.ɵfac=function t(e){return new(e||MI)(Tr(QS),Tr(iS),Tr(ST))},MI.ɵprov=Sn({token:MI,factory:MI.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(MI,[{type:Um}],(function(){return[{type:QS},{type:iS},{type:ST}]}),null),(function(t){t.ASC="asc",t.DESC="desc",t.UNSET=""})(vI||(vI={})),(function(t){t.EXPERIMENT_ID="id",t.USER="user",t.BEFORE="before",t.AFTER="after",t.REGEX="regex"})(xI||(xI={}));const{initialState:OI,reducers:PI}=cA({runColorOverrideForGroupBy:new Map,defaultRunColorIdForGroupBy:new Map,groupKeyToColorId:new 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const XI=13,KI=27,JI=32,QI=38,$I=40;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function nH(t,e=0){return oH(t)?Number(t):e}function oH(t){return!isNaN(parseFloat(t))&&!isNaN(Number(t))}
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     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function iH(t){return Array.isArray(t)?t:[t]}
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     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function aH(t){return null==t?"":"string"==typeof t?t:`${t}px`}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function rH(t){return t instanceof Kh?t.nativeElement:t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let sH,lH;try{sH="undefined"!=typeof Intl&&Intl.v8BreakIterator}catch(mCt){sH=!1}class cH{constructor(t){this._platformId=t,this.isBrowser=this._platformId?(function e(t){return t===sx})(this._platformId):"object"==typeof document&&!!document,this.EDGE=this.isBrowser&&/(edge)/i.test(navigator.userAgent),this.TRIDENT=this.isBrowser&&/(msie|trident)/i.test(navigator.userAgent),this.BLINK=this.isBrowser&&!(!window.chrome&&!sH)&&"undefined"!=typeof CSS&&!this.EDGE&&!this.TRIDENT,this.WEBKIT=this.isBrowser&&/AppleWebKit/i.test(navigator.userAgent)&&!this.BLINK&&!this.EDGE&&!this.TRIDENT,this.IOS=this.isBrowser&&/iPad|iPhone|iPod/.test(navigator.userAgent)&&!("MSStream"in window),this.FIREFOX=this.isBrowser&&/(firefox|minefield)/i.test(navigator.userAgent),this.ANDROID=this.isBrowser&&/android/i.test(navigator.userAgent)&&!this.TRIDENT,this.SAFARI=this.isBrowser&&/safari/i.test(navigator.userAgent)&&this.WEBKIT}}cH.ɵfac=function t(e){return new(e||cH)(Tr(X_))},cH.ɵprov=Sn({factory:function t(){return new cH(Tr(X_))},token:cH,providedIn:"root"}),cH.ctorParameters=()=>[{type:Object,decorators:[{type:Lr,args:[X_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(cH,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:Object,decorators:[{type:Lr,args:[X_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class dH{}dH.ɵfac=function t(e){return new(e||dH)},dH.ɵmod=fo({type:dH}),dH.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(dH,[{type:L_,args:[{}]}],null,null);const pH=["color","button","checkbox","date","datetime-local","email","file","hidden","image","month","number","password","radio","range","reset","search","submit","tel","text","time","url","week"];function mH(){if(lH)return lH;if("object"!=typeof document||!document)return lH=new Set(pH),lH;let t=document.createElement("input");return lH=new Set(pH.filter((e=>(t.setAttribute("type",e),t.type===e)))),lH}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let fH,uH,gH,hH;function bH(t){return(function e(){if(null==fH&&"undefined"!=typeof window)try{window.addEventListener("test",null,Object.defineProperty({},"passive",{get:()=>fH=!0}))}finally{fH=fH||!1}return fH})()?t:!!t.capture}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function yH(){if(null==gH){if("object"!=typeof document||!document||"function"!=typeof Element||!Element)return gH=!1,gH;if("scrollBehavior"in document.documentElement.style)gH=!0;else{const t=Element.prototype.scrollTo;gH=!!t&&!/\{\s*\[native code\]\s*\}/.test(t.toString())}}return gH}function _H(){if("object"!=typeof document||!document)return 0;if(null==uH){const t=document.createElement("div"),e=t.style;t.dir="rtl",e.width="1px",e.overflow="auto",e.visibility="hidden",e.pointerEvents="none",e.position="absolute";const n=document.createElement("div"),o=n.style;o.width="2px",o.height="1px",t.appendChild(n),document.body.appendChild(t),uH=0,0===t.scrollLeft&&(t.scrollLeft=1,uH=0===t.scrollLeft?1:2),t.parentNode.removeChild(t)}return uH}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function CH(t){if((function e(){if(null==hH){const t="undefined"!=typeof document?document.head:null;hH=!(!t||!t.createShadowRoot&&!t.attachShadow)}return hH})()){const e=t.getRootNode?t.getRootNode():null;if("undefined"!=typeof ShadowRoot&&ShadowRoot&&e instanceof ShadowRoot)return e}return null}function MH(){let t="undefined"!=typeof document&&document?document.activeElement:null;for(;t&&t.shadowRoot;){const e=t.shadowRoot.activeElement;if(e===t)break;t=e}return t}function vH(t){return t.composedPath?t.composedPath()[0]:t.target}class xH{create(t){return"undefined"==typeof MutationObserver?null:new MutationObserver(t)}}xH.ɵfac=function t(e){return new(e||xH)},xH.ɵprov=Sn({factory:function t(){return new xH},token:xH,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(xH,[{type:Um,args:[{providedIn:"root"}]}],null,null);class OH{constructor(t){this._mutationObserverFactory=t,this._observedElements=new Map}ngOnDestroy(){this._observedElements.forEach(((t,e)=>this._cleanupObserver(e)))}observe(t){const e=rH(t);return new T((t=>{const n=this._observeElement(e).subscribe(t);return()=>{n.unsubscribe(),this._unobserveElement(e)}}))}_observeElement(t){if(this._observedElements.has(t))this._observedElements.get(t).count++;else{const e=new B,n=this._mutationObserverFactory.create((t=>e.next(t)));n&&n.observe(t,{characterData:!0,childList:!0,subtree:!0}),this._observedElements.set(t,{observer:n,stream:e,count:1})}return this._observedElements.get(t).stream}_unobserveElement(t){this._observedElements.has(t)&&(this._observedElements.get(t).count--,this._observedElements.get(t).count||this._cleanupObserver(t))}_cleanupObserver(t){if(this._observedElements.has(t)){const{observer:e,stream:n}=this._observedElements.get(t);e&&e.disconnect(),n.complete(),this._observedElements.delete(t)}}}OH.ɵfac=function t(e){return new(e||OH)(Tr(xH))},OH.ɵprov=Sn({factory:function t(){return new OH(Tr(xH))},token:OH,providedIn:"root"}),OH.ctorParameters=()=>[{type:xH}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(OH,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:xH}]}),null);class PH{constructor(t,e,n){this._contentObserver=t,this._elementRef=e,this._ngZone=n,this.event=new Sy,this._disabled=!1,this._currentSubscription=null}get disabled(){return this._disabled}set disabled(t){this._disabled=eH(t),this._disabled?this._unsubscribe():this._subscribe()}get debounce(){return this._debounce}set debounce(t){this._debounce=nH(t),this._subscribe()}ngAfterContentInit(){this._currentSubscription||this.disabled||this._subscribe()}ngOnDestroy(){this._unsubscribe()}_subscribe(){this._unsubscribe();const t=this._contentObserver.observe(this._elementRef);this._ngZone.runOutsideAngular((()=>{this._currentSubscription=(this.debounce?t.pipe(Ce(this.debounce)):t).subscribe(this.event)}))}_unsubscribe(){var t;null===(t=this._currentSubscription)||void 0===t||t.unsubscribe()}}PH.ɵfac=function t(e){return new(e||PH)(gf(OH),gf(Kh),gf(mC))},PH.ɵdir=ho({type:PH,selectors:[["","cdkObserveContent",""]],inputs:{disabled:["cdkObserveContentDisabled","disabled"],debounce:"debounce"},outputs:{event:"cdkObserveContent"},exportAs:["cdkObserveContent"]}),PH.ctorParameters=()=>[{type:OH},{type:Kh},{type:mC}],PH.propDecorators={event:[{type:E_,args:["cdkObserveContent"]}],disabled:[{type:R_,args:["cdkObserveContentDisabled"]}],debounce:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(PH,[{type:k_,args:[{selector:"[cdkObserveContent]",exportAs:"cdkObserveContent"}]}],(function(){return[{type:OH},{type:Kh},{type:mC}]}),{event:[{type:E_,args:["cdkObserveContent"]}],disabled:[{type:R_,args:["cdkObserveContentDisabled"]}],debounce:[{type:R_}]});class wH{}wH.ɵfac=function t(e){return new(e||wH)},wH.ɵmod=fo({type:wH}),wH.ɵinj=Dn({providers:[xH]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(wH,[{type:L_,args:[{exports:[PH],declarations:[PH],providers:[xH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(wH,{declarations:[PH],exports:[PH]});function kH(t,e){return(t.getAttribute(e)||"").match(/\S+/g)||[]}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const SH="cdk-describedby-message-container",DH="cdk-describedby-message",RH="cdk-describedby-host";let EH=0;const AH=new Map;let TH=null;class NH{constructor(t){this._document=t}describe(t,e,n){if(!this._canBeDescribed(t,e))return;const o=zH(e,n);"string"!=typeof e?(IH(e),AH.set(o,{messageElement:e,referenceCount:0})):AH.has(o)||this._createMessageElement(e,n),this._isElementDescribedByMessage(t,o)||this._addMessageReference(t,o)}removeDescription(t,e,n){if(!e||!this._isElementNode(t))return;const o=zH(e,n);if(this._isElementDescribedByMessage(t,o)&&this._removeMessageReference(t,o),"string"==typeof e){const t=AH.get(o);t&&0===t.referenceCount&&this._deleteMessageElement(o)}TH&&0===TH.childNodes.length&&this._deleteMessagesContainer()}ngOnDestroy(){const t=this._document.querySelectorAll("[cdk-describedby-host]");for(let e=0;e<t.length;e++)this._removeCdkDescribedByReferenceIds(t[e]),t[e].removeAttribute(RH);TH&&this._deleteMessagesContainer(),AH.clear()}_createMessageElement(t,e){const n=this._document.createElement("div");IH(n),n.textContent=t,e&&n.setAttribute("role",e),this._createMessagesContainer(),TH.appendChild(n),AH.set(zH(t,e),{messageElement:n,referenceCount:0})}_deleteMessageElement(t){const e=AH.get(t),n=e&&e.messageElement;TH&&n&&TH.removeChild(n),AH.delete(t)}_createMessagesContainer(){if(!TH){const t=this._document.getElementById(SH);t&&t.parentNode&&t.parentNode.removeChild(t),TH=this._document.createElement("div"),TH.id=SH,TH.style.visibility="hidden",TH.classList.add("cdk-visually-hidden"),this._document.body.appendChild(TH)}}_deleteMessagesContainer(){TH&&TH.parentNode&&(TH.parentNode.removeChild(TH),TH=null)}_removeCdkDescribedByReferenceIds(t){const e=kH(t,"aria-describedby").filter((t=>0!=t.indexOf(DH)));t.setAttribute("aria-describedby",e.join(" "))}_addMessageReference(t,e){const n=AH.get(e);!(function o(t,e,n){const o=kH(t,e);o.some((t=>t.trim()==n.trim()))||(o.push(n.trim()),t.setAttribute(e,o.join(" ")))})(t,"aria-describedby",n.messageElement.id),t.setAttribute(RH,""),n.referenceCount++}_removeMessageReference(t,e){const n=AH.get(e);n.referenceCount--,(function o(t,e,n){const o=kH(t,e).filter((t=>t!=n.trim()));o.length?t.setAttribute(e,o.join(" ")):t.removeAttribute(e)})(t,"aria-describedby",n.messageElement.id),t.removeAttribute(RH)}_isElementDescribedByMessage(t,e){const n=kH(t,"aria-describedby"),o=AH.get(e),i=o&&o.messageElement.id;return!!i&&-1!=n.indexOf(i)}_canBeDescribed(t,e){if(!this._isElementNode(t))return!1;if(e&&"object"==typeof e)return!0;const n=null==e?"":`${e}`.trim(),o=t.getAttribute("aria-label");return!!n&&(!o||o.trim()!==n)}_isElementNode(t){return t.nodeType===this._document.ELEMENT_NODE}}function zH(t,e){return"string"==typeof t?`${e||""}/${t}`:t}function IH(t){t.id||(t.id="cdk-describedby-message-"+EH++)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */NH.ɵfac=function t(e){return new(e||NH)(Tr(sM))},NH.ɵprov=Sn({factory:function t(){return new NH(Tr(sM))},token:NH,providedIn:"root"}),NH.ctorParameters=()=>[{type:void 0,decorators:[{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(NH,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),null);class HH{constructor(t){this._items=t,this._activeItemIndex=-1,this._activeItem=null,this._wrap=!1,this._letterKeyStream=new B,this._typeaheadSubscription=u.EMPTY,this._vertical=!0,this._allowedModifierKeys=[],this._homeAndEnd=!1,this._skipPredicateFn=t=>t.disabled,this._pressedLetters=[],this.tabOut=new B,this.change=new B,t instanceof Ry&&t.changes.subscribe((t=>{if(this._activeItem){const e=t.toArray().indexOf(this._activeItem);e>-1&&e!==this._activeItemIndex&&(this._activeItemIndex=e)}}))}skipPredicate(t){return this._skipPredicateFn=t,this}withWrap(t=!0){return this._wrap=t,this}withVerticalOrientation(t=!0){return this._vertical=t,this}withHorizontalOrientation(t){return this._horizontal=t,this}withAllowedModifierKeys(t){return this._allowedModifierKeys=t,this}withTypeAhead(t=200){if(("undefined"==typeof ngDevMode||ngDevMode)&&this._items.length&&this._items.some((t=>"function"!=typeof t.getLabel)))throw Error("ListKeyManager items in typeahead mode must implement the `getLabel` method.");return this._typeaheadSubscription.unsubscribe(),this._typeaheadSubscription=this._letterKeyStream.pipe(We((t=>this._pressedLetters.push(t))),Ce(t),ue((()=>this._pressedLetters.length>0)),Vt((()=>this._pressedLetters.join("")))).subscribe((t=>{const e=this._getItemsArray();for(let n=1;n<e.length+1;n++){const o=(this._activeItemIndex+n)%e.length,i=e[o];if(!this._skipPredicateFn(i)&&0===i.getLabel().toUpperCase().trim().indexOf(t)){this.setActiveItem(o);break}}this._pressedLetters=[]})),this}withHomeAndEnd(t=!0){return this._homeAndEnd=t,this}setActiveItem(t){const e=this._activeItem;this.updateActiveItem(t),this._activeItem!==e&&this.change.next(this._activeItemIndex)}onKeydown(t){const e=t.keyCode,n=["altKey","ctrlKey","metaKey","shiftKey"].every((e=>!t[e]||this._allowedModifierKeys.indexOf(e)>-1));switch(e){case 9:return void this.tabOut.next();case $I:if(this._vertical&&n){this.setNextItemActive();break}return;case QI:if(this._vertical&&n){this.setPreviousItemActive();break}return;case 39:if(this._horizontal&&n){"rtl"===this._horizontal?this.setPreviousItemActive():this.setNextItemActive();break}return;case 37:if(this._horizontal&&n){"rtl"===this._horizontal?this.setNextItemActive():this.setPreviousItemActive();break}return;case 36:if(this._homeAndEnd&&n){this.setFirstItemActive();break}return;case 35:if(this._homeAndEnd&&n){this.setLastItemActive();break}return;default:return void((n||tH(t,"shiftKey"))&&(t.key&&1===t.key.length?this._letterKeyStream.next(t.key.toLocaleUpperCase()):(e>=65&&e<=90||e>=48&&e<=57)&&this._letterKeyStream.next(String.fromCharCode(e))))}this._pressedLetters=[],t.preventDefault()}get activeItemIndex(){return this._activeItemIndex}get activeItem(){return this._activeItem}isTyping(){return this._pressedLetters.length>0}setFirstItemActive(){this._setActiveItemByIndex(0,1)}setLastItemActive(){this._setActiveItemByIndex(this._items.length-1,-1)}setNextItemActive(){this._activeItemIndex<0?this.setFirstItemActive():this._setActiveItemByDelta(1)}setPreviousItemActive(){this._activeItemIndex<0&&this._wrap?this.setLastItemActive():this._setActiveItemByDelta(-1)}updateActiveItem(t){const e=this._getItemsArray(),n="number"==typeof t?t:e.indexOf(t),o=e[n];this._activeItem=null==o?null:o,this._activeItemIndex=n}_setActiveItemByDelta(t){this._wrap?this._setActiveInWrapMode(t):this._setActiveInDefaultMode(t)}_setActiveInWrapMode(t){const e=this._getItemsArray();for(let n=1;n<=e.length;n++){const o=(this._activeItemIndex+t*n+e.length)%e.length;if(!this._skipPredicateFn(e[o]))return void this.setActiveItem(o)}}_setActiveInDefaultMode(t){this._setActiveItemByIndex(this._activeItemIndex+t,t)}_setActiveItemByIndex(t,e){const n=this._getItemsArray();if(n[t]){for(;this._skipPredicateFn(n[t]);)if(!n[t+=e])return;this.setActiveItem(t)}}_getItemsArray(){return this._items instanceof Ry?this._items.toArray():this._items}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class FH extends HH{setActiveItem(t){this.activeItem&&this.activeItem.setInactiveStyles(),super.setActiveItem(t),this.activeItem&&this.activeItem.setActiveStyles()}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class LH extends HH{constructor(){super(...arguments),this._origin="program"}setFocusOrigin(t){return this._origin=t,this}setActiveItem(t){super.setActiveItem(t),this.activeItem&&this.activeItem.focus(this._origin)}}class BH{constructor(t){this._platform=t}isDisabled(t){return t.hasAttribute("disabled")}isVisible(t){return(function e(t){return!!(t.offsetWidth||t.offsetHeight||"function"==typeof t.getClientRects&&t.getClientRects().length)})(t)&&"visible"===getComputedStyle(t).visibility}isTabbable(t){if(!this._platform.isBrowser)return!1;const e=(function o(t){try{return t.frameElement}catch(t){return null}})((function n(t){return t.ownerDocument&&t.ownerDocument.defaultView||window}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t));if(e){if(-1===jH(e))return!1;if(!this.isVisible(e))return!1}let i=t.nodeName.toLowerCase(),a=jH(t);return t.hasAttribute("contenteditable")?-1!==a:"iframe"!==i&&"object"!==i&&(!(this._platform.WEBKIT&&this._platform.IOS&&!(function r(t){let e=t.nodeName.toLowerCase(),n="input"===e&&t.type;return"text"===n||"password"===n||"select"===e||"textarea"===e})(t))&&("audio"===i?!!t.hasAttribute("controls")&&-1!==a:"video"===i?-1!==a&&(null!==a||(this._platform.FIREFOX||t.hasAttribute("controls"))):t.tabIndex>=0))}isFocusable(t,e){return(function n(t){if((function e(t){return(function e(t){return"input"==t.nodeName.toLowerCase()})(t)&&"hidden"==t.type})(t))return!1;return(function n(t){let e=t.nodeName.toLowerCase();return"input"===e||"select"===e||"button"===e||"textarea"===e})(t)||(function o(t){return(function e(t){return"a"==t.nodeName.toLowerCase()})(t)&&t.hasAttribute("href")})(t)||t.hasAttribute("contenteditable")||VH(t)})(t)&&!this.isDisabled(t)&&((null==e?void 0:e.ignoreVisibility)||this.isVisible(t))}}function VH(t){if(!t.hasAttribute("tabindex")||void 0===t.tabIndex)return!1;let e=t.getAttribute("tabindex");return"-32768"!=e&&!(!e||isNaN(parseInt(e,10)))}function jH(t){if(!VH(t))return null;const e=parseInt(t.getAttribute("tabindex")||"",10);return isNaN(e)?-1:e}BH.ɵfac=function t(e){return new(e||BH)(Tr(cH))},BH.ɵprov=Sn({factory:function t(){return new BH(Tr(cH))},token:BH,providedIn:"root"}),BH.ctorParameters=()=>[{type:cH}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(BH,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:cH}]}),null);class UH{constructor(t,e,n,o,i=!1){this._element=t,this._checker=e,this._ngZone=n,this._document=o,this._hasAttached=!1,this.startAnchorListener=()=>this.focusLastTabbableElement(),this.endAnchorListener=()=>this.focusFirstTabbableElement(),this._enabled=!0,i||this.attachAnchors()}get enabled(){return this._enabled}set enabled(t){this._enabled=t,this._startAnchor&&this._endAnchor&&(this._toggleAnchorTabIndex(t,this._startAnchor),this._toggleAnchorTabIndex(t,this._endAnchor))}destroy(){const t=this._startAnchor,e=this._endAnchor;t&&(t.removeEventListener("focus",this.startAnchorListener),t.parentNode&&t.parentNode.removeChild(t)),e&&(e.removeEventListener("focus",this.endAnchorListener),e.parentNode&&e.parentNode.removeChild(e)),this._startAnchor=this._endAnchor=null,this._hasAttached=!1}attachAnchors(){return!!this._hasAttached||(this._ngZone.runOutsideAngular((()=>{this._startAnchor||(this._startAnchor=this._createAnchor(),this._startAnchor.addEventListener("focus",this.startAnchorListener)),this._endAnchor||(this._endAnchor=this._createAnchor(),this._endAnchor.addEventListener("focus",this.endAnchorListener))})),this._element.parentNode&&(this._element.parentNode.insertBefore(this._startAnchor,this._element),this._element.parentNode.insertBefore(this._endAnchor,this._element.nextSibling),this._hasAttached=!0),this._hasAttached)}focusInitialElementWhenReady(t){return new Promise((e=>{this._executeOnStable((()=>e(this.focusInitialElement(t))))}))}focusFirstTabbableElementWhenReady(t){return new Promise((e=>{this._executeOnStable((()=>e(this.focusFirstTabbableElement(t))))}))}focusLastTabbableElementWhenReady(t){return new Promise((e=>{this._executeOnStable((()=>e(this.focusLastTabbableElement(t))))}))}_getRegionBoundary(t){let e=this._element.querySelectorAll(`[cdk-focus-region-${t}], [cdkFocusRegion${t}], [cdk-focus-${t}]`);for(let n=0;n<e.length;n++)e[n].hasAttribute(`cdk-focus-${t}`)?console.warn(`Found use of deprecated attribute 'cdk-focus-${t}', use 'cdkFocusRegion${t}' instead. The deprecated attribute will be removed in 8.0.0.`,e[n]):e[n].hasAttribute(`cdk-focus-region-${t}`)&&console.warn(`Found use of deprecated attribute 'cdk-focus-region-${t}', use 'cdkFocusRegion${t}' instead. The deprecated attribute will be removed in 8.0.0.`,e[n]);return"start"==t?e.length?e[0]:this._getFirstTabbableElement(this._element):e.length?e[e.length-1]:this._getLastTabbableElement(this._element)}focusInitialElement(t){const e=this._element.querySelector("[cdk-focus-initial], [cdkFocusInitial]");if(e){if(e.hasAttribute("cdk-focus-initial")&&console.warn("Found use of deprecated attribute 'cdk-focus-initial', use 'cdkFocusInitial' instead. The deprecated attribute will be removed in 8.0.0",e),"undefined"!=typeof ngDevMode&&!ngDevMode||this._checker.isFocusable(e)||console.warn("Element matching '[cdkFocusInitial]' is not focusable.",e),!this._checker.isFocusable(e)){const n=this._getFirstTabbableElement(e);return null==n||n.focus(t),!!n}return e.focus(t),!0}return this.focusFirstTabbableElement(t)}focusFirstTabbableElement(t){const e=this._getRegionBoundary("start");return e&&e.focus(t),!!e}focusLastTabbableElement(t){const e=this._getRegionBoundary("end");return e&&e.focus(t),!!e}hasAttached(){return this._hasAttached}_getFirstTabbableElement(t){if(this._checker.isFocusable(t)&&this._checker.isTabbable(t))return t;let e=t.children||t.childNodes;for(let t=0;t<e.length;t++){let n=e[t].nodeType===this._document.ELEMENT_NODE?this._getFirstTabbableElement(e[t]):null;if(n)return n}return null}_getLastTabbableElement(t){if(this._checker.isFocusable(t)&&this._checker.isTabbable(t))return t;let e=t.children||t.childNodes;for(let t=e.length-1;t>=0;t--){let n=e[t].nodeType===this._document.ELEMENT_NODE?this._getLastTabbableElement(e[t]):null;if(n)return n}return null}_createAnchor(){const t=this._document.createElement("div");return this._toggleAnchorTabIndex(this._enabled,t),t.classList.add("cdk-visually-hidden"),t.classList.add("cdk-focus-trap-anchor"),t.setAttribute("aria-hidden","true"),t}_toggleAnchorTabIndex(t,e){t?e.setAttribute("tabindex","0"):e.removeAttribute("tabindex")}toggleAnchors(t){this._startAnchor&&this._endAnchor&&(this._toggleAnchorTabIndex(t,this._startAnchor),this._toggleAnchorTabIndex(t,this._endAnchor))}_executeOnStable(t){this._ngZone.isStable?t():this._ngZone.onStable.pipe(ve(1)).subscribe(t)}}class WH{constructor(t,e,n){this._checker=t,this._ngZone=e,this._document=n}create(t,e=!1){return new UH(t,this._checker,this._ngZone,this._document,e)}}WH.ɵfac=function t(e){return new(e||WH)(Tr(BH),Tr(mC),Tr(sM))},WH.ɵprov=Sn({factory:function t(){return new WH(Tr(BH),Tr(mC),Tr(sM))},token:WH,providedIn:"root"}),WH.ctorParameters=()=>[{type:BH},{type:mC},{type:void 0,decorators:[{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(WH,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:BH},{type:mC},{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),null);class GH{constructor(t,e,n){this._elementRef=t,this._focusTrapFactory=e,this._previouslyFocusedElement=null,this.focusTrap=this._focusTrapFactory.create(this._elementRef.nativeElement,!0)}get enabled(){return this.focusTrap.enabled}set enabled(t){this.focusTrap.enabled=eH(t)}get autoCapture(){return this._autoCapture}set autoCapture(t){this._autoCapture=eH(t)}ngOnDestroy(){this.focusTrap.destroy(),this._previouslyFocusedElement&&(this._previouslyFocusedElement.focus(),this._previouslyFocusedElement=null)}ngAfterContentInit(){this.focusTrap.attachAnchors(),this.autoCapture&&this._captureFocus()}ngDoCheck(){this.focusTrap.hasAttached()||this.focusTrap.attachAnchors()}ngOnChanges(t){const e=t.autoCapture;e&&!e.firstChange&&this.autoCapture&&this.focusTrap.hasAttached()&&this._captureFocus()}_captureFocus(){this._previouslyFocusedElement=MH(),this.focusTrap.focusInitialElementWhenReady()}}GH.ɵfac=function t(e){return new(e||GH)(gf(Kh),gf(WH),gf(sM))},GH.ɵdir=ho({type:GH,selectors:[["","cdkTrapFocus",""]],inputs:{enabled:["cdkTrapFocus","enabled"],autoCapture:["cdkTrapFocusAutoCapture","autoCapture"]},exportAs:["cdkTrapFocus"],features:[qo]}),GH.ctorParameters=()=>[{type:Kh},{type:WH},{type:void 0,decorators:[{type:Lr,args:[sM]}]}],GH.propDecorators={enabled:[{type:R_,args:["cdkTrapFocus"]}],autoCapture:[{type:R_,args:["cdkTrapFocusAutoCapture"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(GH,[{type:k_,args:[{selector:"[cdkTrapFocus]",exportAs:"cdkTrapFocus"}]}],(function(){return[{type:Kh},{type:WH},{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),{enabled:[{type:R_,args:["cdkTrapFocus"]}],autoCapture:[{type:R_,args:["cdkTrapFocusAutoCapture"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class YH extends UH{constructor(t,e,n,o,i,a,r){super(t,e,n,o,r.defer),this._focusTrapManager=i,this._inertStrategy=a,this._focusTrapManager.register(this)}get enabled(){return this._enabled}set enabled(t){this._enabled=t,this._enabled?this._focusTrapManager.register(this):this._focusTrapManager.deregister(this)}destroy(){this._focusTrapManager.deregister(this),super.destroy()}_enable(){this._inertStrategy.preventFocus(this),this.toggleAnchors(!0)}_disable(){this._inertStrategy.allowFocus(this),this.toggleAnchors(!1)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const qH=new er("FOCUS_TRAP_INERT_STRATEGY");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function ZH(t,e){if(!(t instanceof Node))return null;let n=t;for(;null!=n&&!(n instanceof Element);)n=n.parentNode;return n&&(XH?n.closest(e):(function o(t,e){let n=t;for(;null!=n&&!(n instanceof Element&&KH(n,e));)n=n.parentNode;return n||null})(n,e))}const XH="undefined"!=typeof Element&&!!Element.prototype.closest;function KH(t,e){return t.matches?t.matches(e):t.msMatchesSelector(e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class JH{constructor(){this._listener=null}preventFocus(t){this._listener&&t._document.removeEventListener("focus",this._listener,!0),this._listener=e=>this._trapFocus(t,e),t._ngZone.runOutsideAngular((()=>{t._document.addEventListener("focus",this._listener,!0)}))}allowFocus(t){this._listener&&(t._document.removeEventListener("focus",this._listener,!0),this._listener=null)}_trapFocus(t,e){const n=e.target,o=t._element;o.contains(n)||null!==ZH(n,"div.cdk-overlay-pane")||setTimeout((()=>{t.enabled&&!o.contains(t._document.activeElement)&&t.focusFirstTabbableElement()}))}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class QH{constructor(){this._focusTrapStack=[]}register(t){this._focusTrapStack=this._focusTrapStack.filter((e=>e!==t));let e=this._focusTrapStack;e.length&&e[e.length-1]._disable(),e.push(t),t._enable()}deregister(t){t._disable();const e=this._focusTrapStack,n=e.indexOf(t);-1!==n&&(e.splice(n,1),e.length&&e[e.length-1]._enable())}}QH.ɵfac=function t(e){return new(e||QH)},QH.ɵprov=Sn({factory:function t(){return new QH},token:QH,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(QH,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class $H{constructor(t,e,n,o,i){this._checker=t,this._ngZone=e,this._focusTrapManager=n,this._document=o,this._inertStrategy=i||new JH}create(t,e={defer:!1}){let n;return n="boolean"==typeof e?{defer:e}:e,new YH(t,this._checker,this._ngZone,this._document,this._focusTrapManager,this._inertStrategy,n)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function tF(t){return 0===t.offsetX&&0===t.offsetY}function eF(t){const e=t.touches&&t.touches[0]||t.changedTouches&&t.changedTouches[0];return!(!e||-1!==e.identifier||null!=e.radiusX&&1!==e.radiusX||null!=e.radiusY&&1!==e.radiusY)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */$H.ɵfac=function t(e){return new(e||$H)(Tr(BH),Tr(mC),Tr(QH),Tr(sM),Tr(qH,8))},$H.ɵprov=Sn({factory:function t(){return new $H(Tr(BH),Tr(mC),Tr(QH),Tr(sM),Tr(qH,8))},token:$H,providedIn:"root"}),$H.ctorParameters=()=>[{type:BH},{type:mC},{type:QH},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[qH]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb($H,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:BH},{type:mC},{type:QH},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[qH]}]}]}),null);const nF=new er("cdk-input-modality-detector-options"),oF={ignoreKeys:[18,17,224,91,16]},iF=bH({passive:!0,capture:!0});class aF{constructor(t,e,n,o){this._platform=t,this._mostRecentTarget=null,this._modality=new j(null),this._lastTouchMs=0,this._onKeydown=t=>{var e,n;(null===(n=null===(e=this._options)||void 0===e?void 0:e.ignoreKeys)||void 0===n?void 0:n.some((e=>e===t.keyCode)))||(this._modality.next("keyboard"),this._mostRecentTarget=vH(t))},this._onMousedown=t=>{Date.now()-this._lastTouchMs<650||(this._modality.next(tF(t)?"keyboard":"mouse"),this._mostRecentTarget=vH(t))},this._onTouchstart=t=>{eF(t)?this._modality.next("keyboard"):(this._lastTouchMs=Date.now(),this._modality.next("touch"),this._mostRecentTarget=vH(t))},this._options=Object.assign(Object.assign({},oF),o),this.modalityDetected=this._modality.pipe(Le(1)),this.modalityChanged=this.modalityDetected.pipe(we()),t.isBrowser&&e.runOutsideAngular((()=>{n.addEventListener("keydown",this._onKeydown,iF),n.addEventListener("mousedown",this._onMousedown,iF),n.addEventListener("touchstart",this._onTouchstart,iF)}))}get mostRecentModality(){return this._modality.value}ngOnDestroy(){this._platform.isBrowser&&(document.removeEventListener("keydown",this._onKeydown,iF),document.removeEventListener("mousedown",this._onMousedown,iF),document.removeEventListener("touchstart",this._onTouchstart,iF))}}aF.ɵfac=function t(e){return new(e||aF)(Tr(cH),Tr(mC),Tr(sM),Tr(nF,8))},aF.ɵprov=Sn({factory:function t(){return new aF(Tr(cH),Tr(mC),Tr(sM),Tr(nF,8))},token:aF,providedIn:"root"}),aF.ctorParameters=()=>[{type:cH},{type:mC},{type:Document,decorators:[{type:Lr,args:[sM]}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[nF]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(aF,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:cH},{type:mC},{type:Document,decorators:[{type:Lr,args:[sM]}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[nF]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const rF=new er("liveAnnouncerElement",{providedIn:"root",factory:function sF(){return null}});const lF=new er("LIVE_ANNOUNCER_DEFAULT_OPTIONS");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class cF{constructor(t,e,n,o){this._ngZone=e,this._defaultOptions=o,this._document=n,this._liveElement=t||this._createLiveElement()}announce(t,...e){const n=this._defaultOptions;let o,i;return 1===e.length&&"number"==typeof e[0]?i=e[0]:[o,i]=e,this.clear(),clearTimeout(this._previousTimeout),o||(o=n&&n.politeness?n.politeness:"polite"),null==i&&n&&(i=n.duration),this._liveElement.setAttribute("aria-live",o),this._ngZone.runOutsideAngular((()=>new Promise((e=>{clearTimeout(this._previousTimeout),this._previousTimeout=setTimeout((()=>{this._liveElement.textContent=t,e(),"number"==typeof i&&(this._previousTimeout=setTimeout((()=>this.clear()),i))}),100)}))))}clear(){this._liveElement&&(this._liveElement.textContent="")}ngOnDestroy(){clearTimeout(this._previousTimeout),this._liveElement&&this._liveElement.parentNode&&(this._liveElement.parentNode.removeChild(this._liveElement),this._liveElement=null)}_createLiveElement(){const t="cdk-live-announcer-element",e=this._document.getElementsByClassName(t),n=this._document.createElement("div");for(let t=0;t<e.length;t++)e[t].parentNode.removeChild(e[t]);return n.classList.add(t),n.classList.add("cdk-visually-hidden"),n.setAttribute("aria-atomic","true"),n.setAttribute("aria-live","polite"),this._document.body.appendChild(n),n}}cF.ɵfac=function t(e){return new(e||cF)(Tr(rF,8),Tr(mC),Tr(sM),Tr(lF,8))},cF.ɵprov=Sn({factory:function t(){return new cF(Tr(rF,8),Tr(mC),Tr(sM),Tr(lF,8))},token:cF,providedIn:"root"}),cF.ctorParameters=()=>[{type:void 0,decorators:[{type:Br},{type:Lr,args:[rF]}]},{type:mC},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[lF]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(cF,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:Br},{type:Lr,args:[rF]}]},{type:mC},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[lF]}]}]}),null);class dF{constructor(t,e,n,o){this._elementRef=t,this._liveAnnouncer=e,this._contentObserver=n,this._ngZone=o,this._politeness="polite"}get politeness(){return this._politeness}set politeness(t){this._politeness="off"===t||"assertive"===t?t:"polite","off"===this._politeness?this._subscription&&(this._subscription.unsubscribe(),this._subscription=null):this._subscription||(this._subscription=this._ngZone.runOutsideAngular((()=>this._contentObserver.observe(this._elementRef).subscribe((()=>{const t=this._elementRef.nativeElement.textContent;t!==this._previousAnnouncedText&&(this._liveAnnouncer.announce(t,this._politeness),this._previousAnnouncedText=t)})))))}ngOnDestroy(){this._subscription&&this._subscription.unsubscribe()}}dF.ɵfac=function t(e){return new(e||dF)(gf(Kh),gf(cF),gf(OH),gf(mC))},dF.ɵdir=ho({type:dF,selectors:[["","cdkAriaLive",""]],inputs:{politeness:["cdkAriaLive","politeness"]},exportAs:["cdkAriaLive"]}),dF.ctorParameters=()=>[{type:Kh},{type:cF},{type:OH},{type:mC}],dF.propDecorators={politeness:[{type:R_,args:["cdkAriaLive"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(dF,[{type:k_,args:[{selector:"[cdkAriaLive]",exportAs:"cdkAriaLive"}]}],(function(){return[{type:Kh},{type:cF},{type:OH},{type:mC}]}),{politeness:[{type:R_,args:["cdkAriaLive"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const pF=new er("cdk-focus-monitor-default-options"),mF=bH({passive:!0,capture:!0});class fF{constructor(t,e,n,o,i){this._ngZone=t,this._platform=e,this._inputModalityDetector=n,this._origin=null,this._windowFocused=!1,this._originFromTouchInteraction=!1,this._elementInfo=new Map,this._monitoredElementCount=0,this._rootNodeFocusListenerCount=new Map,this._windowFocusListener=()=>{this._windowFocused=!0,this._windowFocusTimeoutId=setTimeout((()=>this._windowFocused=!1))},this._stopInputModalityDetector=new B,this._rootNodeFocusAndBlurListener=t=>{const e=vH(t),n="focus"===t.type?this._onFocus:this._onBlur;for(let o=e;o;o=o.parentElement)n.call(this,t,o)},this._document=o,this._detectionMode=(null==i?void 0:i.detectionMode)||0}monitor(t,e=!1){const n=rH(t);if(!this._platform.isBrowser||1!==n.nodeType)return zt(null);const o=CH(n)||this._getDocument(),i=this._elementInfo.get(n);if(i)return e&&(i.checkChildren=!0),i.subject;const a={checkChildren:e,subject:new B,rootNode:o};return this._elementInfo.set(n,a),this._registerGlobalListeners(a),a.subject}stopMonitoring(t){const e=rH(t),n=this._elementInfo.get(e);n&&(n.subject.complete(),this._setClasses(e),this._elementInfo.delete(e),this._removeGlobalListeners(n))}focusVia(t,e,n){const o=rH(t);o===this._getDocument().activeElement?this._getClosestElementsInfo(o).forEach((([t,n])=>this._originChanged(t,e,n))):(this._setOrigin(e),"function"==typeof o.focus&&o.focus(n))}ngOnDestroy(){this._elementInfo.forEach(((t,e)=>this.stopMonitoring(e)))}_getDocument(){return this._document||document}_getWindow(){return this._getDocument().defaultView||window}_toggleClass(t,e,n){n?t.classList.add(e):t.classList.remove(e)}_getFocusOrigin(t){return this._origin?this._originFromTouchInteraction?this._shouldBeAttributedToTouch(t)?"touch":"program":this._origin:this._windowFocused&&this._lastFocusOrigin?this._lastFocusOrigin:"program"}_shouldBeAttributedToTouch(t){return 1===this._detectionMode||!!(null==t?void 0:t.contains(this._inputModalityDetector._mostRecentTarget))}_setClasses(t,e){this._toggleClass(t,"cdk-focused",!!e),this._toggleClass(t,"cdk-touch-focused","touch"===e),this._toggleClass(t,"cdk-keyboard-focused","keyboard"===e),this._toggleClass(t,"cdk-mouse-focused","mouse"===e),this._toggleClass(t,"cdk-program-focused","program"===e)}_setOrigin(t,e=!1){this._ngZone.runOutsideAngular((()=>{if(this._origin=t,this._originFromTouchInteraction="touch"===t&&e,0===this._detectionMode){clearTimeout(this._originTimeoutId);this._originTimeoutId=setTimeout((()=>this._origin=null),this._originFromTouchInteraction?650:1)}}))}_onFocus(t,e){const n=this._elementInfo.get(e),o=vH(t);n&&(n.checkChildren||e===o)&&this._originChanged(e,this._getFocusOrigin(o),n)}_onBlur(t,e){const n=this._elementInfo.get(e);!n||n.checkChildren&&t.relatedTarget instanceof Node&&e.contains(t.relatedTarget)||(this._setClasses(e),this._emitOrigin(n.subject,null))}_emitOrigin(t,e){this._ngZone.run((()=>t.next(e)))}_registerGlobalListeners(t){if(!this._platform.isBrowser)return;const e=t.rootNode,n=this._rootNodeFocusListenerCount.get(e)||0;n||this._ngZone.runOutsideAngular((()=>{e.addEventListener("focus",this._rootNodeFocusAndBlurListener,mF),e.addEventListener("blur",this._rootNodeFocusAndBlurListener,mF)})),this._rootNodeFocusListenerCount.set(e,n+1),1==++this._monitoredElementCount&&(this._ngZone.runOutsideAngular((()=>{this._getWindow().addEventListener("focus",this._windowFocusListener)})),this._inputModalityDetector.modalityDetected.pipe(je(this._stopInputModalityDetector)).subscribe((t=>{this._setOrigin(t,!0)})))}_removeGlobalListeners(t){const e=t.rootNode;if(this._rootNodeFocusListenerCount.has(e)){const t=this._rootNodeFocusListenerCount.get(e);t>1?this._rootNodeFocusListenerCount.set(e,t-1):(e.removeEventListener("focus",this._rootNodeFocusAndBlurListener,mF),e.removeEventListener("blur",this._rootNodeFocusAndBlurListener,mF),this._rootNodeFocusListenerCount.delete(e))}if(!--this._monitoredElementCount){this._getWindow().removeEventListener("focus",this._windowFocusListener),this._stopInputModalityDetector.next(),clearTimeout(this._windowFocusTimeoutId),clearTimeout(this._originTimeoutId)}}_originChanged(t,e,n){this._setClasses(t,e),this._emitOrigin(n.subject,e),this._lastFocusOrigin=e}_getClosestElementsInfo(t){const e=[];return this._elementInfo.forEach(((n,o)=>{(o===t||n.checkChildren&&o.contains(t))&&e.push([o,n])})),e}}fF.ɵfac=function t(e){return new(e||fF)(Tr(mC),Tr(cH),Tr(aF),Tr(sM,8),Tr(pF,8))},fF.ɵprov=Sn({factory:function t(){return new fF(Tr(mC),Tr(cH),Tr(aF),Tr(sM,8),Tr(pF,8))},token:fF,providedIn:"root"}),fF.ctorParameters=()=>[{type:mC},{type:cH},{type:aF},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[pF]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(fF,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:mC},{type:cH},{type:aF},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[pF]}]}]}),null);class uF{constructor(t,e){this._elementRef=t,this._focusMonitor=e,this.cdkFocusChange=new Sy}ngAfterViewInit(){const t=this._elementRef.nativeElement;this._monitorSubscription=this._focusMonitor.monitor(t,1===t.nodeType&&t.hasAttribute("cdkMonitorSubtreeFocus")).subscribe((t=>this.cdkFocusChange.emit(t)))}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef),this._monitorSubscription&&this._monitorSubscription.unsubscribe()}}uF.ɵfac=function t(e){return new(e||uF)(gf(Kh),gf(fF))},uF.ɵdir=ho({type:uF,selectors:[["","cdkMonitorElementFocus",""],["","cdkMonitorSubtreeFocus",""]],outputs:{cdkFocusChange:"cdkFocusChange"}}),uF.ctorParameters=()=>[{type:Kh},{type:fF}],uF.propDecorators={cdkFocusChange:[{type:E_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(uF,[{type:k_,args:[{selector:"[cdkMonitorElementFocus], [cdkMonitorSubtreeFocus]"}]}],(function(){return[{type:Kh},{type:fF}]}),{cdkFocusChange:[{type:E_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const gF="cdk-high-contrast-black-on-white",hF="cdk-high-contrast-white-on-black",bF="cdk-high-contrast-active";class yF{constructor(t,e){this._platform=t,this._document=e}getHighContrastMode(){if(!this._platform.isBrowser)return 0;const t=this._document.createElement("div");t.style.backgroundColor="rgb(1,2,3)",t.style.position="absolute",this._document.body.appendChild(t);const e=this._document.defaultView||window,n=e&&e.getComputedStyle?e.getComputedStyle(t):null,o=(n&&n.backgroundColor||"").replace(/ /g,"");switch(this._document.body.removeChild(t),o){case"rgb(0,0,0)":return 2;case"rgb(255,255,255)":return 1}return 0}_applyBodyHighContrastModeCssClasses(){if(!this._hasCheckedHighContrastMode&&this._platform.isBrowser&&this._document.body){const t=this._document.body.classList;t.remove(bF),t.remove(gF),t.remove(hF),this._hasCheckedHighContrastMode=!0;const e=this.getHighContrastMode();1===e?(t.add(bF),t.add(gF)):2===e&&(t.add(bF),t.add(hF))}}}yF.ɵfac=function t(e){return new(e||yF)(Tr(cH),Tr(sM))},yF.ɵprov=Sn({factory:function t(){return new yF(Tr(cH),Tr(sM))},token:yF,providedIn:"root"}),yF.ctorParameters=()=>[{type:cH},{type:void 0,decorators:[{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(yF,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:cH},{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class _F{constructor(t){t._applyBodyHighContrastModeCssClasses()}}_F.ɵfac=function t(e){return new(e||_F)(Tr(yF))},_F.ɵmod=fo({type:_F}),_F.ɵinj=Dn({imports:[[dH,wH]]}),_F.ctorParameters=()=>[{type:yF}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(_F,[{type:L_,args:[{imports:[dH,wH],declarations:[dF,GH,uF],exports:[dF,GH,uF]}]}],(function(){return[{type:yF}]}),null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(_F,{declarations:function(){return[dF,GH,uF]},imports:function(){return[dH,wH]},exports:function(){return[dF,GH,uF]}});const CF=new er("cdk-dir-doc",{providedIn:"root",factory:function MF(){return zr(sM)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */});class vF{constructor(t){if(this.value="ltr",this.change=new Sy,t){const e=t.documentElement?t.documentElement.dir:null,n=(t.body?t.body.dir:null)||e;this.value="ltr"===n||"rtl"===n?n:"ltr"}}ngOnDestroy(){this.change.complete()}}vF.ɵfac=function t(e){return new(e||vF)(Tr(CF,8))},vF.ɵprov=Sn({factory:function t(){return new vF(Tr(CF,8))},token:vF,providedIn:"root"}),vF.ctorParameters=()=>[{type:void 0,decorators:[{type:Br},{type:Lr,args:[CF]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(vF,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:Br},{type:Lr,args:[CF]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class xF{constructor(){this._dir="ltr",this._isInitialized=!1,this.change=new Sy}get dir(){return this._dir}set dir(t){const e=this._dir,n=t?t.toLowerCase():t;this._rawDir=t,this._dir="ltr"===n||"rtl"===n?n:"ltr",e!==this._dir&&this._isInitialized&&this.change.emit(this._dir)}get value(){return this.dir}ngAfterContentInit(){this._isInitialized=!0}ngOnDestroy(){this.change.complete()}}xF.ɵfac=function t(e){return new(e||xF)},xF.ɵdir=ho({type:xF,selectors:[["","dir",""]],hostVars:1,hostBindings:function t(e,n){2&e&&ym("dir",n._rawDir)},inputs:{dir:"dir"},outputs:{change:"dirChange"},exportAs:["dir"],features:[jh([{provide:vF,useExisting:xF}])]}),xF.propDecorators={change:[{type:E_,args:["dirChange"]}],dir:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(xF,[{type:k_,args:[{selector:"[dir]",providers:[{provide:vF,useExisting:xF}],host:{"[attr.dir]":"_rawDir"},exportAs:"dir"}]}],(function(){return[]}),{change:[{type:E_,args:["dirChange"]}],dir:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class OF{}OF.ɵfac=function t(e){return new(e||OF)},OF.ɵmod=fo({type:OF}),OF.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(OF,[{type:L_,args:[{exports:[xF],declarations:[xF]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(OF,{declarations:[xF],exports:[xF]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const PF=new ob("12.2.1"),wF=["*",[["mat-option"],["ng-container"]]];function kF(t,e){if(1&t&&vf(0,"mat-pseudo-checkbox",4),2&t){const t=zf();bf("state",t.selected?"checked":"unchecked")("disabled",t.disabled)}}function SF(t,e){if(1&t&&(Cf(0,"span",5),Pu(1),Mf()),2&t){const t=zf();Dc(1),ku("(",t.group.label,")")}}new ob("12.2.3");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class DF{}DF.STANDARD_CURVE="cubic-bezier(0.4,0.0,0.2,1)",DF.DECELERATION_CURVE="cubic-bezier(0.0,0.0,0.2,1)",DF.ACCELERATION_CURVE="cubic-bezier(0.4,0.0,1,1)",DF.SHARP_CURVE="cubic-bezier(0.4,0.0,0.6,1)";class RF{}RF.COMPLEX="375ms",RF.ENTERING="225ms",RF.EXITING="195ms";
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const EF=new ob("12.2.3");const AF=new er("mat-sanity-checks",{providedIn:"root",factory:function TF(){return!0}});class NF{constructor(t,e,n){this._hasDoneGlobalChecks=!1,this._document=n,t._applyBodyHighContrastModeCssClasses(),this._sanityChecks=e,this._hasDoneGlobalChecks||(this._checkDoctypeIsDefined(),this._checkThemeIsPresent(),this._checkCdkVersionMatch(),this._hasDoneGlobalChecks=!0)}_getWindow(){const t=this._document.defaultView||window;return"object"==typeof t&&t?t:null}_checkIsEnabled(t){return!(!PC()||this._isTestEnv())&&("boolean"==typeof this._sanityChecks?this._sanityChecks:!!this._sanityChecks[t])}_isTestEnv(){const t=this._getWindow();return t&&(t.__karma__||t.jasmine)}_checkDoctypeIsDefined(){this._checkIsEnabled("doctype")&&!this._document.doctype&&console.warn("Current document does not have a doctype. This may cause some Angular Material components not to behave as expected.")}_checkThemeIsPresent(){if(!this._checkIsEnabled("theme")||!this._document.body||"function"!=typeof getComputedStyle)return;const t=this._document.createElement("div");t.classList.add("mat-theme-loaded-marker"),this._document.body.appendChild(t);const e=getComputedStyle(t);e&&"none"!==e.display&&console.warn("Could not find Angular Material core theme. Most Material components may not work as expected. For more info refer to the theming guide: https://material.angular.io/guide/theming"),this._document.body.removeChild(t)}_checkCdkVersionMatch(){this._checkIsEnabled("version")&&EF.full!==PF.full&&console.warn("The Angular Material version ("+EF.full+") does not match the Angular CDK version ("+PF.full+").\nPlease ensure the versions of these two packages exactly match.")}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function zF(t){return class extends t{constructor(...t){super(...t),this._disabled=!1}get disabled(){return this._disabled}set disabled(t){this._disabled=eH(t)}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function IF(t,e){return class extends t{constructor(...t){super(...t),this.defaultColor=e,this.color=e}get color(){return this._color}set color(t){const e=t||this.defaultColor;e!==this._color&&(this._color&&this._elementRef.nativeElement.classList.remove(`mat-${this._color}`),e&&this._elementRef.nativeElement.classList.add(`mat-${e}`),this._color=e)}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function HF(t){return class extends t{constructor(...t){super(...t),this._disableRipple=!1}get disableRipple(){return this._disableRipple}set disableRipple(t){this._disableRipple=eH(t)}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function FF(t,e=0){return class extends t{constructor(...t){super(...t),this._tabIndex=e,this.defaultTabIndex=e}get tabIndex(){return this.disabled?-1:this._tabIndex}set tabIndex(t){this._tabIndex=null!=t?nH(t):this.defaultTabIndex}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function LF(t){return class extends t{constructor(...t){super(...t),this.stateChanges=new B,this.errorState=!1}updateErrorState(){const t=this.errorState,e=(this.errorStateMatcher||this._defaultErrorStateMatcher).isErrorState(this.ngControl?this.ngControl.control:null,this._parentFormGroup||this._parentForm);e!==t&&(this.errorState=e,this.stateChanges.next())}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function BF(t){return class extends t{constructor(...t){super(...t),this._isInitialized=!1,this._pendingSubscribers=[],this.initialized=new T((t=>{this._isInitialized?this._notifySubscriber(t):this._pendingSubscribers.push(t)}))}_markInitialized(){if(this._isInitialized&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("This directive has already been marked as initialized and should not be called twice.");this._isInitialized=!0,this._pendingSubscribers.forEach(this._notifySubscriber),this._pendingSubscribers=null}_notifySubscriber(t){t.next(),t.complete()}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */NF.ɵfac=function t(e){return new(e||NF)(Tr(yF),Tr(AF,8),Tr(sM))},NF.ɵmod=fo({type:NF}),NF.ɵinj=Dn({imports:[[OF],OF]}),NF.ctorParameters=()=>[{type:yF},{type:void 0,decorators:[{type:Br},{type:Lr,args:[AF]}]},{type:void 0,decorators:[{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(NF,[{type:L_,args:[{imports:[OF],exports:[OF]}]}],(function(){return[{type:yF},{type:void 0,decorators:[{type:Br},{type:Lr,args:[AF]}]},{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(NF,{imports:function(){return[OF]},exports:function(){return[OF]}});const VF=new er("MAT_DATE_LOCALE",{providedIn:"root",factory:function jF(){return zr(Q_)}});class UF{constructor(){this._localeChanges=new B,this.localeChanges=this._localeChanges}getValidDateOrNull(t){return this.isDateInstance(t)&&this.isValid(t)?t:null}deserialize(t){return null==t||this.isDateInstance(t)&&this.isValid(t)?t:this.invalid()}setLocale(t){this.locale=t,this._localeChanges.next()}compareDate(t,e){return this.getYear(t)-this.getYear(e)||this.getMonth(t)-this.getMonth(e)||this.getDate(t)-this.getDate(e)}sameDate(t,e){if(t&&e){let n=this.isValid(t),o=this.isValid(e);return n&&o?!this.compareDate(t,e):n==o}return t==e}clampDate(t,e,n){return e&&this.compareDate(t,e)<0?e:n&&this.compareDate(t,n)>0?n:t}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const WF=new er("mat-date-formats");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let GF;try{GF="undefined"!=typeof Intl}catch(mCt){GF=!1}const YF={long:["January","February","March","April","May","June","July","August","September","October","November","December"],short:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],narrow:["J","F","M","A","M","J","J","A","S","O","N","D"]},qF=KF(31,(t=>String(t+1))),ZF={long:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],short:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],narrow:["S","M","T","W","T","F","S"]},XF=/^\d{4}-\d{2}-\d{2}(?:T\d{2}:\d{2}:\d{2}(?:\.\d+)?(?:Z|(?:(?:\+|-)\d{2}:\d{2}))?)?$/;function KF(t,e){const n=Array(t);for(let o=0;o<t;o++)n[o]=e(o);return n}class JF extends UF{constructor(t,e){super(),this.useUtcForDisplay=!0,super.setLocale(t),this.useUtcForDisplay=!e.TRIDENT,this._clampDate=e.TRIDENT||e.EDGE}getYear(t){return t.getFullYear()}getMonth(t){return t.getMonth()}getDate(t){return t.getDate()}getDayOfWeek(t){return t.getDay()}getMonthNames(t){if(GF){const e=new Intl.DateTimeFormat(this.locale,{month:t,timeZone:"utc"});return KF(12,(t=>this._stripDirectionalityCharacters(this._format(e,new Date(2017,t,1)))))}return YF[t]}getDateNames(){if(GF){const t=new Intl.DateTimeFormat(this.locale,{day:"numeric",timeZone:"utc"});return KF(31,(e=>this._stripDirectionalityCharacters(this._format(t,new Date(2017,0,e+1)))))}return qF}getDayOfWeekNames(t){if(GF){const e=new Intl.DateTimeFormat(this.locale,{weekday:t,timeZone:"utc"});return KF(7,(t=>this._stripDirectionalityCharacters(this._format(e,new Date(2017,0,t+1)))))}return ZF[t]}getYearName(t){if(GF){const e=new Intl.DateTimeFormat(this.locale,{year:"numeric",timeZone:"utc"});return this._stripDirectionalityCharacters(this._format(e,t))}return String(this.getYear(t))}getFirstDayOfWeek(){return 0}getNumDaysInMonth(t){return this.getDate(this._createDateWithOverflow(this.getYear(t),this.getMonth(t)+1,0))}clone(t){return new Date(t.getTime())}createDate(t,e,n){if("undefined"==typeof ngDevMode||ngDevMode){if(e<0||e>11)throw Error(`Invalid month index "${e}". Month index has to be between 0 and 11.`);if(n<1)throw Error(`Invalid date "${n}". Date has to be greater than 0.`)}let o=this._createDateWithOverflow(t,e,n);if(o.getMonth()!=e&&("undefined"==typeof ngDevMode||ngDevMode))throw Error(`Invalid date "${n}" for month with index "${e}".`);return o}today(){return new Date}parse(t){return"number"==typeof t?new Date(t):t?new Date(Date.parse(t)):null}format(t,e){if(!this.isValid(t))throw Error("NativeDateAdapter: Cannot format invalid date.");if(GF){this._clampDate&&(t.getFullYear()<1||t.getFullYear()>9999)&&(t=this.clone(t)).setFullYear(Math.max(1,Math.min(9999,t.getFullYear()))),e=Object.assign(Object.assign({},e),{timeZone:"utc"});const n=new Intl.DateTimeFormat(this.locale,e);return this._stripDirectionalityCharacters(this._format(n,t))}return this._stripDirectionalityCharacters(t.toDateString())}addCalendarYears(t,e){return this.addCalendarMonths(t,12*e)}addCalendarMonths(t,e){let n=this._createDateWithOverflow(this.getYear(t),this.getMonth(t)+e,this.getDate(t));return this.getMonth(n)!=((this.getMonth(t)+e)%12+12)%12&&(n=this._createDateWithOverflow(this.getYear(n),this.getMonth(n),0)),n}addCalendarDays(t,e){return this._createDateWithOverflow(this.getYear(t),this.getMonth(t),this.getDate(t)+e)}toIso8601(t){return[t.getUTCFullYear(),this._2digit(t.getUTCMonth()+1),this._2digit(t.getUTCDate())].join("-")}deserialize(t){if("string"==typeof t){if(!t)return null;if(XF.test(t)){let e=new Date(t);if(this.isValid(e))return e}}return super.deserialize(t)}isDateInstance(t){return t instanceof Date}isValid(t){return!isNaN(t.getTime())}invalid(){return new Date(NaN)}_createDateWithOverflow(t,e,n){const o=new Date;return o.setFullYear(t,e,n),o.setHours(0,0,0,0),o}_2digit(t){return("00"+t).slice(-2)}_stripDirectionalityCharacters(t){return t.replace(/[\u200e\u200f]/g,"")}_format(t,e){const n=new Date;return n.setUTCFullYear(e.getFullYear(),e.getMonth(),e.getDate()),n.setUTCHours(e.getHours(),e.getMinutes(),e.getSeconds(),e.getMilliseconds()),t.format(n)}}JF.ɵfac=function t(e){return new(e||JF)(Tr(VF,8),Tr(cH))},JF.ɵprov=Sn({token:JF,factory:JF.ɵfac}),JF.ctorParameters=()=>[{type:String,decorators:[{type:Br},{type:Lr,args:[VF]}]},{type:cH}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(JF,[{type:Um}],(function(){return[{type:String,decorators:[{type:Br},{type:Lr,args:[VF]}]},{type:cH}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class QF{}QF.ɵfac=function t(e){return new(e||QF)},QF.ɵmod=fo({type:QF}),QF.ɵinj=Dn({providers:[{provide:UF,useClass:JF}],imports:[[dH]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(QF,[{type:L_,args:[{imports:[dH],providers:[{provide:UF,useClass:JF}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(QF,{imports:function(){return[dH]}});const $F={parse:{dateInput:null},display:{dateInput:{year:"numeric",month:"numeric",day:"numeric"},monthYearLabel:{year:"numeric",month:"short"},dateA11yLabel:{year:"numeric",month:"long",day:"numeric"},monthYearA11yLabel:{year:"numeric",month:"long"}}};class tL{}tL.ɵfac=function t(e){return new(e||tL)},tL.ɵmod=fo({type:tL}),tL.ɵinj=Dn({providers:[{provide:WF,useValue:$F}],imports:[[QF]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(tL,[{type:L_,args:[{imports:[QF],providers:[{provide:WF,useValue:$F}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(tL,{imports:[QF]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class eL{isErrorState(t,e){return!!(t&&t.invalid&&(t.dirty||e&&e.submitted))}}eL.ɵfac=function t(e){return new(e||eL)},eL.ɵprov=Sn({token:eL,factory:eL.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(eL,[{type:Um}],null,null);class nL{isErrorState(t,e){return!!(t&&t.invalid&&(t.touched||e&&e.submitted))}}nL.ɵfac=function t(e){return new(e||nL)},nL.ɵprov=Sn({factory:function t(){return new nL},token:nL,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(nL,[{type:Um,args:[{providedIn:"root"}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class oL{}oL.ɵfac=function t(e){return new(e||oL)},oL.ɵdir=ho({type:oL,selectors:[["","mat-line",""],["","matLine",""]],hostAttrs:[1,"mat-line"]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(oL,[{type:k_,args:[{selector:"[mat-line], [matLine]",host:{class:"mat-line"}}]}],null,null);class iL{}iL.ɵfac=function t(e){return new(e||iL)},iL.ɵmod=fo({type:iL}),iL.ɵinj=Dn({imports:[[NF],NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(iL,[{type:L_,args:[{imports:[NF],exports:[oL,NF],declarations:[oL]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(iL,{declarations:[oL],imports:[NF],exports:[oL,NF]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class aL{constructor(t,e,n){this._renderer=t,this.element=e,this.config=n,this.state=3}fadeOut(){this._renderer.fadeOutRipple(this)}}const rL={enterDuration:225,exitDuration:150},sL=bH({passive:!0}),lL=["mousedown","touchstart"],cL=["mouseup","mouseleave","touchend","touchcancel"];class dL{constructor(t,e,n,o){this._target=t,this._ngZone=e,this._isPointerDown=!1,this._activeRipples=new Set,this._pointerUpEventsRegistered=!1,o.isBrowser&&(this._containerElement=rH(n))}fadeInRipple(t,e,n={}){const o=this._containerRect=this._containerRect||this._containerElement.getBoundingClientRect(),i=Object.assign(Object.assign({},rL),n.animation);n.centered&&(t=o.left+o.width/2,e=o.top+o.height/2);const a=n.radius||(function r(t,e,n){const o=Math.max(Math.abs(t-n.left),Math.abs(t-n.right)),i=Math.max(Math.abs(e-n.top),Math.abs(e-n.bottom));return Math.sqrt(o*o+i*i)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t,e,o),s=t-o.left,l=e-o.top,c=i.enterDuration,d=document.createElement("div");d.classList.add("mat-ripple-element"),d.style.left=s-a+"px",d.style.top=l-a+"px",d.style.height=2*a+"px",d.style.width=2*a+"px",null!=n.color&&(d.style.backgroundColor=n.color),d.style.transitionDuration=`${c}ms`,this._containerElement.appendChild(d),(function p(t){window.getComputedStyle(t).getPropertyValue("opacity")})(d),d.style.transform="scale(1)";const m=new aL(this,d,n);return m.state=0,this._activeRipples.add(m),n.persistent||(this._mostRecentTransientRipple=m),this._runTimeoutOutsideZone((()=>{const t=m===this._mostRecentTransientRipple;m.state=1,n.persistent||t&&this._isPointerDown||m.fadeOut()}),c),m}fadeOutRipple(t){const e=this._activeRipples.delete(t);if(t===this._mostRecentTransientRipple&&(this._mostRecentTransientRipple=null),this._activeRipples.size||(this._containerRect=null),!e)return;const n=t.element,o=Object.assign(Object.assign({},rL),t.config.animation);n.style.transitionDuration=`${o.exitDuration}ms`,n.style.opacity="0",t.state=2,this._runTimeoutOutsideZone((()=>{t.state=3,n.parentNode.removeChild(n)}),o.exitDuration)}fadeOutAll(){this._activeRipples.forEach((t=>t.fadeOut()))}fadeOutAllNonPersistent(){this._activeRipples.forEach((t=>{t.config.persistent||t.fadeOut()}))}setupTriggerEvents(t){const e=rH(t);e&&e!==this._triggerElement&&(this._removeTriggerEvents(),this._triggerElement=e,this._registerEvents(lL))}handleEvent(t){"mousedown"===t.type?this._onMousedown(t):"touchstart"===t.type?this._onTouchStart(t):this._onPointerUp(),this._pointerUpEventsRegistered||(this._registerEvents(cL),this._pointerUpEventsRegistered=!0)}_onMousedown(t){const e=tF(t),n=this._lastTouchStartEvent&&Date.now()<this._lastTouchStartEvent+800;this._target.rippleDisabled||e||n||(this._isPointerDown=!0,this.fadeInRipple(t.clientX,t.clientY,this._target.rippleConfig))}_onTouchStart(t){if(!this._target.rippleDisabled&&!eF(t)){this._lastTouchStartEvent=Date.now(),this._isPointerDown=!0;const e=t.changedTouches;for(let t=0;t<e.length;t++)this.fadeInRipple(e[t].clientX,e[t].clientY,this._target.rippleConfig)}}_onPointerUp(){this._isPointerDown&&(this._isPointerDown=!1,this._activeRipples.forEach((t=>{!t.config.persistent&&(1===t.state||t.config.terminateOnPointerUp&&0===t.state)&&t.fadeOut()})))}_runTimeoutOutsideZone(t,e=0){this._ngZone.runOutsideAngular((()=>setTimeout(t,e)))}_registerEvents(t){this._ngZone.runOutsideAngular((()=>{t.forEach((t=>{this._triggerElement.addEventListener(t,this,sL)}))}))}_removeTriggerEvents(){this._triggerElement&&(lL.forEach((t=>{this._triggerElement.removeEventListener(t,this,sL)})),this._pointerUpEventsRegistered&&cL.forEach((t=>{this._triggerElement.removeEventListener(t,this,sL)})))}}const pL=new er("mat-ripple-global-options");class mL{constructor(t,e,n,o,i){this._elementRef=t,this._animationMode=i,this.radius=0,this._disabled=!1,this._isInitialized=!1,this._globalOptions=o||{},this._rippleRenderer=new dL(this,e,t,n)}get disabled(){return this._disabled}set disabled(t){t&&this.fadeOutAllNonPersistent(),this._disabled=t,this._setupTriggerEventsIfEnabled()}get trigger(){return this._trigger||this._elementRef.nativeElement}set trigger(t){this._trigger=t,this._setupTriggerEventsIfEnabled()}ngOnInit(){this._isInitialized=!0,this._setupTriggerEventsIfEnabled()}ngOnDestroy(){this._rippleRenderer._removeTriggerEvents()}fadeOutAll(){this._rippleRenderer.fadeOutAll()}fadeOutAllNonPersistent(){this._rippleRenderer.fadeOutAllNonPersistent()}get rippleConfig(){return{centered:this.centered,radius:this.radius,color:this.color,animation:Object.assign(Object.assign(Object.assign({},this._globalOptions.animation),"NoopAnimations"===this._animationMode?{enterDuration:0,exitDuration:0}:{}),this.animation),terminateOnPointerUp:this._globalOptions.terminateOnPointerUp}}get rippleDisabled(){return this.disabled||!!this._globalOptions.disabled}_setupTriggerEventsIfEnabled(){!this.disabled&&this._isInitialized&&this._rippleRenderer.setupTriggerEvents(this.trigger)}launch(t,e=0,n){return"number"==typeof t?this._rippleRenderer.fadeInRipple(t,e,Object.assign(Object.assign({},this.rippleConfig),n)):this._rippleRenderer.fadeInRipple(0,0,Object.assign(Object.assign({},this.rippleConfig),t))}}mL.ɵfac=function t(e){return new(e||mL)(gf(Kh),gf(mC),gf(cH),gf(pL,8),gf(ck,8))},mL.ɵdir=ho({type:mL,selectors:[["","mat-ripple",""],["","matRipple",""]],hostAttrs:[1,"mat-ripple"],hostVars:2,hostBindings:function t(e,n){2&e&&cu("mat-ripple-unbounded",n.unbounded)},inputs:{radius:["matRippleRadius","radius"],disabled:["matRippleDisabled","disabled"],trigger:["matRippleTrigger","trigger"],color:["matRippleColor","color"],unbounded:["matRippleUnbounded","unbounded"],centered:["matRippleCentered","centered"],animation:["matRippleAnimation","animation"]},exportAs:["matRipple"]}),mL.ctorParameters=()=>[{type:Kh},{type:mC},{type:cH},{type:void 0,decorators:[{type:Br},{type:Lr,args:[pL]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}],mL.propDecorators={color:[{type:R_,args:["matRippleColor"]}],unbounded:[{type:R_,args:["matRippleUnbounded"]}],centered:[{type:R_,args:["matRippleCentered"]}],radius:[{type:R_,args:["matRippleRadius"]}],animation:[{type:R_,args:["matRippleAnimation"]}],disabled:[{type:R_,args:["matRippleDisabled"]}],trigger:[{type:R_,args:["matRippleTrigger"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(mL,[{type:k_,args:[{selector:"[mat-ripple], [matRipple]",exportAs:"matRipple",host:{class:"mat-ripple","[class.mat-ripple-unbounded]":"unbounded"}}]}],(function(){return[{type:Kh},{type:mC},{type:cH},{type:void 0,decorators:[{type:Br},{type:Lr,args:[pL]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}]}),{radius:[{type:R_,args:["matRippleRadius"]}],disabled:[{type:R_,args:["matRippleDisabled"]}],trigger:[{type:R_,args:["matRippleTrigger"]}],color:[{type:R_,args:["matRippleColor"]}],unbounded:[{type:R_,args:["matRippleUnbounded"]}],centered:[{type:R_,args:["matRippleCentered"]}],animation:[{type:R_,args:["matRippleAnimation"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class fL{}fL.ɵfac=function t(e){return new(e||fL)},fL.ɵmod=fo({type:fL}),fL.ɵinj=Dn({imports:[[NF,dH],NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(fL,[{type:L_,args:[{imports:[NF,dH],exports:[mL,NF],declarations:[mL]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(fL,{declarations:function(){return[mL]},imports:function(){return[NF,dH]},exports:function(){return[mL,NF]}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class uL{constructor(t){this._animationMode=t,this.state="unchecked",this.disabled=!1}}uL.ɵfac=function t(e){return new(e||uL)(gf(ck,8))},uL.ɵcmp=so({type:uL,selectors:[["mat-pseudo-checkbox"]],hostAttrs:[1,"mat-pseudo-checkbox"],hostVars:8,hostBindings:function t(e,n){2&e&&cu("mat-pseudo-checkbox-indeterminate","indeterminate"===n.state)("mat-pseudo-checkbox-checked","checked"===n.state)("mat-pseudo-checkbox-disabled",n.disabled)("_mat-animation-noopable","NoopAnimations"===n._animationMode)},inputs:{state:"state",disabled:"disabled"},decls:0,vars:0,template:function t(e,n){},styles:['.mat-pseudo-checkbox{width:16px;height:16px;border:2px solid;border-radius:2px;cursor:pointer;display:inline-block;vertical-align:middle;box-sizing:border-box;position:relative;flex-shrink:0;transition:border-color 90ms cubic-bezier(0, 0, 0.2, 0.1),background-color 90ms cubic-bezier(0, 0, 0.2, 0.1)}.mat-pseudo-checkbox::after{position:absolute;opacity:0;content:"";border-bottom:2px solid currentColor;transition:opacity 90ms cubic-bezier(0, 0, 0.2, 0.1)}.mat-pseudo-checkbox.mat-pseudo-checkbox-checked,.mat-pseudo-checkbox.mat-pseudo-checkbox-indeterminate{border-color:transparent}._mat-animation-noopable.mat-pseudo-checkbox{transition:none;animation:none}._mat-animation-noopable.mat-pseudo-checkbox::after{transition:none}.mat-pseudo-checkbox-disabled{cursor:default}.mat-pseudo-checkbox-indeterminate::after{top:5px;left:1px;width:10px;opacity:1;border-radius:2px}.mat-pseudo-checkbox-checked::after{top:2.4px;left:1px;width:8px;height:3px;border-left:2px solid currentColor;transform:rotate(-45deg);opacity:1;box-sizing:content-box}\n'],encapsulation:2,changeDetection:0}),uL.ctorParameters=()=>[{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}],uL.propDecorators={state:[{type:R_}],disabled:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(uL,[{type:S_,args:[{encapsulation:Wn.None,changeDetection:jn.OnPush,selector:"mat-pseudo-checkbox",template:"",host:{class:"mat-pseudo-checkbox","[class.mat-pseudo-checkbox-indeterminate]":'state === "indeterminate"',"[class.mat-pseudo-checkbox-checked]":'state === "checked"',"[class.mat-pseudo-checkbox-disabled]":"disabled","[class._mat-animation-noopable]":'_animationMode === "NoopAnimations"'},styles:['.mat-pseudo-checkbox{width:16px;height:16px;border:2px solid;border-radius:2px;cursor:pointer;display:inline-block;vertical-align:middle;box-sizing:border-box;position:relative;flex-shrink:0;transition:border-color 90ms cubic-bezier(0, 0, 0.2, 0.1),background-color 90ms cubic-bezier(0, 0, 0.2, 0.1)}.mat-pseudo-checkbox::after{position:absolute;opacity:0;content:"";border-bottom:2px solid currentColor;transition:opacity 90ms cubic-bezier(0, 0, 0.2, 0.1)}.mat-pseudo-checkbox.mat-pseudo-checkbox-checked,.mat-pseudo-checkbox.mat-pseudo-checkbox-indeterminate{border-color:transparent}._mat-animation-noopable.mat-pseudo-checkbox{transition:none;animation:none}._mat-animation-noopable.mat-pseudo-checkbox::after{transition:none}.mat-pseudo-checkbox-disabled{cursor:default}.mat-pseudo-checkbox-indeterminate::after{top:5px;left:1px;width:10px;opacity:1;border-radius:2px}.mat-pseudo-checkbox-checked::after{top:2.4px;left:1px;width:8px;height:3px;border-left:2px solid currentColor;transform:rotate(-45deg);opacity:1;box-sizing:content-box}\n']}]}],(function(){return[{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}]}),{state:[{type:R_}],disabled:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class gL{}gL.ɵfac=function t(e){return new(e||gL)},gL.ɵmod=fo({type:gL}),gL.ɵinj=Dn({imports:[[NF]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(gL,[{type:L_,args:[{imports:[NF],exports:[uL],declarations:[uL]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(gL,{declarations:[uL],imports:[NF],exports:[uL]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const hL=new er("MAT_OPTION_PARENT_COMPONENT"),bL=zF(class{});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let yL=0;class _L extends bL{constructor(t){var e;super(),this._labelId="mat-optgroup-label-"+yL++,this._inert=null!==(e=null==t?void 0:t.inertGroups)&&void 0!==e&&e}}_L.ɵfac=function t(e){return new(e||_L)(gf(hL,8))},_L.ɵdir=ho({type:_L,inputs:{label:"label"},features:[Qp]}),_L.ctorParameters=()=>[{type:void 0,decorators:[{type:Lr,args:[hL]},{type:Br}]}],_L.propDecorators={label:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(_L,[{type:k_}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[hL]},{type:Br}]}]}),{label:[{type:R_}]});const CL=new er("MatOptgroup");class ML extends _L{}ML.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(ML)))(n||ML)}})(),ML.ɵcmp=so({type:ML,selectors:[["mat-optgroup"]],hostAttrs:[1,"mat-optgroup"],hostVars:5,hostBindings:function t(e,n){2&e&&(ym("role",n._inert?null:"group")("aria-disabled",n._inert?null:n.disabled.toString())("aria-labelledby",n._inert?null:n._labelId),cu("mat-optgroup-disabled",n.disabled))},inputs:{disabled:"disabled"},exportAs:["matOptgroup"],features:[jh([{provide:CL,useExisting:ML}]),Qp],ngContentSelectors:["*","mat-option, ng-container"],decls:4,vars:2,consts:[["aria-hidden","true",1,"mat-optgroup-label",3,"id"]],template:function t(e,n){1&e&&(Hf(wF),Cf(0,"span",0),Pu(1),Ff(2),Mf(),Ff(3,1)),2&e&&(bf("id",n._labelId),Dc(1),ku("",n.label," "))},styles:[".mat-optgroup-label{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-optgroup-label[disabled]{cursor:default}[dir=rtl] .mat-optgroup-label{text-align:right}.mat-optgroup-label .mat-icon{margin-right:16px;vertical-align:middle}.mat-optgroup-label .mat-icon svg{vertical-align:top}[dir=rtl] .mat-optgroup-label .mat-icon{margin-left:16px;margin-right:0}\n"],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(ML,[{type:S_,args:[{selector:"mat-optgroup",exportAs:"matOptgroup",template:'<span class="mat-optgroup-label" aria-hidden="true" [id]="_labelId">{{ label }} <ng-content></ng-content></span>\n<ng-content select="mat-option, ng-container"></ng-content>\n',encapsulation:Wn.None,changeDetection:jn.OnPush,inputs:["disabled"],host:{class:"mat-optgroup","[attr.role]":'_inert ? null : "group"',"[attr.aria-disabled]":"_inert ? null : disabled.toString()","[attr.aria-labelledby]":"_inert ? null : _labelId","[class.mat-optgroup-disabled]":"disabled"},providers:[{provide:CL,useExisting:ML}],styles:[".mat-optgroup-label{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-optgroup-label[disabled]{cursor:default}[dir=rtl] .mat-optgroup-label{text-align:right}.mat-optgroup-label .mat-icon{margin-right:16px;vertical-align:middle}.mat-optgroup-label .mat-icon svg{vertical-align:top}[dir=rtl] .mat-optgroup-label .mat-icon{margin-left:16px;margin-right:0}\n"]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let vL=0;class xL{constructor(t,e=!1){this.source=t,this.isUserInput=e}}class OL{constructor(t,e,n,o){this._element=t,this._changeDetectorRef=e,this._parent=n,this.group=o,this._selected=!1,this._active=!1,this._disabled=!1,this._mostRecentViewValue="",this.id="mat-option-"+vL++,this.onSelectionChange=new Sy,this._stateChanges=new B}get multiple(){return this._parent&&this._parent.multiple}get selected(){return this._selected}get disabled(){return this.group&&this.group.disabled||this._disabled}set disabled(t){this._disabled=eH(t)}get disableRipple(){return this._parent&&this._parent.disableRipple}get active(){return this._active}get viewValue(){return(this._getHostElement().textContent||"").trim()}select(){this._selected||(this._selected=!0,this._changeDetectorRef.markForCheck(),this._emitSelectionChangeEvent())}deselect(){this._selected&&(this._selected=!1,this._changeDetectorRef.markForCheck(),this._emitSelectionChangeEvent())}focus(t,e){const n=this._getHostElement();"function"==typeof n.focus&&n.focus(e)}setActiveStyles(){this._active||(this._active=!0,this._changeDetectorRef.markForCheck())}setInactiveStyles(){this._active&&(this._active=!1,this._changeDetectorRef.markForCheck())}getLabel(){return this.viewValue}_handleKeydown(t){t.keyCode!==XI&&t.keyCode!==JI||tH(t)||(this._selectViaInteraction(),t.preventDefault())}_selectViaInteraction(){this.disabled||(this._selected=!this.multiple||!this._selected,this._changeDetectorRef.markForCheck(),this._emitSelectionChangeEvent(!0))}_getAriaSelected(){return this.selected||!this.multiple&&null}_getTabIndex(){return this.disabled?"-1":"0"}_getHostElement(){return this._element.nativeElement}ngAfterViewChecked(){if(this._selected){const t=this.viewValue;t!==this._mostRecentViewValue&&(this._mostRecentViewValue=t,this._stateChanges.next())}}ngOnDestroy(){this._stateChanges.complete()}_emitSelectionChangeEvent(t=!1){this.onSelectionChange.emit(new xL(this,t))}}OL.ɵfac=function t(e){return new(e||OL)(gf(Kh),gf(Ob),gf(void 0),gf(_L))},OL.ɵdir=ho({type:OL,inputs:{id:"id",disabled:"disabled",value:"value"},outputs:{onSelectionChange:"onSelectionChange"}}),OL.ctorParameters=()=>[{type:Kh},{type:Ob},{type:void 0},{type:_L}],OL.propDecorators={value:[{type:R_}],id:[{type:R_}],disabled:[{type:R_}],onSelectionChange:[{type:E_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(OL,[{type:k_}],(function(){return[{type:Kh},{type:Ob},{type:void 0},{type:_L}]}),{id:[{type:R_}],onSelectionChange:[{type:E_}],disabled:[{type:R_}],value:[{type:R_}]});class PL extends OL{constructor(t,e,n,o){super(t,e,n,o)}}function wL(t,e,n){if(n.length){let o=e.toArray(),i=n.toArray(),a=0;for(let e=0;e<t+1;e++)o[e].group&&o[e].group===i[a]&&a++;return a}return 0}function kL(t,e,n,o){return t<n?t:t+e>n+o?Math.max(0,t-o+e):n}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */PL.ɵfac=function t(e){return new(e||PL)(gf(Kh),gf(Ob),gf(hL,8),gf(CL,8))},PL.ɵcmp=so({type:PL,selectors:[["mat-option"]],hostAttrs:["role","option",1,"mat-option","mat-focus-indicator"],hostVars:12,hostBindings:function t(e,n){1&e&&Rf("click",(function t(){return n._selectViaInteraction()}))("keydown",(function t(e){return n._handleKeydown(e)})),2&e&&(cg("id",n.id),ym("tabindex",n._getTabIndex())("aria-selected",n._getAriaSelected())("aria-disabled",n.disabled.toString()),cu("mat-selected",n.selected)("mat-option-multiple",n.multiple)("mat-active",n.active)("mat-option-disabled",n.disabled))},exportAs:["matOption"],features:[Qp],ngContentSelectors:["*"],decls:5,vars:4,consts:[["class","mat-option-pseudo-checkbox",3,"state","disabled",4,"ngIf"],[1,"mat-option-text"],["class","cdk-visually-hidden",4,"ngIf"],["mat-ripple","",1,"mat-option-ripple",3,"matRippleTrigger","matRippleDisabled"],[1,"mat-option-pseudo-checkbox",3,"state","disabled"],[1,"cdk-visually-hidden"]],template:function t(e,n){1&e&&(Hf(),Hm(0,kF,1,2,"mat-pseudo-checkbox",0),Cf(1,"span",1),Ff(2),Mf(),Hm(3,SF,2,1,"span",2),vf(4,"div",3)),2&e&&(bf("ngIf",n.multiple),Dc(3),bf("ngIf",n.group&&n.group._inert),Dc(1),bf("matRippleTrigger",n._getHostElement())("matRippleDisabled",n.disabled||n.disableRipple))},directives:[Pv,mL,uL],styles:[".mat-option{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;position:relative;cursor:pointer;outline:none;display:flex;flex-direction:row;max-width:100%;box-sizing:border-box;align-items:center;-webkit-tap-highlight-color:transparent}.mat-option[disabled]{cursor:default}[dir=rtl] .mat-option{text-align:right}.mat-option .mat-icon{margin-right:16px;vertical-align:middle}.mat-option .mat-icon svg{vertical-align:top}[dir=rtl] .mat-option .mat-icon{margin-left:16px;margin-right:0}.mat-option[aria-disabled=true]{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-optgroup .mat-option:not(.mat-option-multiple){padding-left:32px}[dir=rtl] .mat-optgroup .mat-option:not(.mat-option-multiple){padding-left:16px;padding-right:32px}.cdk-high-contrast-active .mat-option{margin:0 1px}.cdk-high-contrast-active .mat-option.mat-active{border:solid 1px currentColor;margin:0}.cdk-high-contrast-active .mat-option[aria-disabled=true]{opacity:.5}.mat-option-text{display:inline-block;flex-grow:1;overflow:hidden;text-overflow:ellipsis}.mat-option .mat-option-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none}.mat-option-pseudo-checkbox{margin-right:8px}[dir=rtl] .mat-option-pseudo-checkbox{margin-left:8px;margin-right:0}\n"],encapsulation:2,changeDetection:0}),PL.ctorParameters=()=>[{type:Kh},{type:Ob},{type:void 0,decorators:[{type:Br},{type:Lr,args:[hL]}]},{type:ML,decorators:[{type:Br},{type:Lr,args:[CL]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(PL,[{type:S_,args:[{selector:"mat-option",exportAs:"matOption",host:{role:"option","[attr.tabindex]":"_getTabIndex()","[class.mat-selected]":"selected","[class.mat-option-multiple]":"multiple","[class.mat-active]":"active","[id]":"id","[attr.aria-selected]":"_getAriaSelected()","[attr.aria-disabled]":"disabled.toString()","[class.mat-option-disabled]":"disabled","(click)":"_selectViaInteraction()","(keydown)":"_handleKeydown($event)",class:"mat-option mat-focus-indicator"},template:'<mat-pseudo-checkbox *ngIf="multiple" class="mat-option-pseudo-checkbox"\n    [state]="selected ? \'checked\' : \'unchecked\'" [disabled]="disabled"></mat-pseudo-checkbox>\n\n<span class="mat-option-text"><ng-content></ng-content></span>\n\n\x3c!-- See a11y notes inside optgroup.ts for context behind this element. --\x3e\n<span class="cdk-visually-hidden" *ngIf="group && group._inert">({{ group.label }})</span>\n\n<div class="mat-option-ripple" mat-ripple\n     [matRippleTrigger]="_getHostElement()"\n     [matRippleDisabled]="disabled || disableRipple">\n</div>\n',encapsulation:Wn.None,changeDetection:jn.OnPush,styles:[".mat-option{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;position:relative;cursor:pointer;outline:none;display:flex;flex-direction:row;max-width:100%;box-sizing:border-box;align-items:center;-webkit-tap-highlight-color:transparent}.mat-option[disabled]{cursor:default}[dir=rtl] .mat-option{text-align:right}.mat-option .mat-icon{margin-right:16px;vertical-align:middle}.mat-option .mat-icon svg{vertical-align:top}[dir=rtl] .mat-option .mat-icon{margin-left:16px;margin-right:0}.mat-option[aria-disabled=true]{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-optgroup .mat-option:not(.mat-option-multiple){padding-left:32px}[dir=rtl] .mat-optgroup .mat-option:not(.mat-option-multiple){padding-left:16px;padding-right:32px}.cdk-high-contrast-active .mat-option{margin:0 1px}.cdk-high-contrast-active .mat-option.mat-active{border:solid 1px currentColor;margin:0}.cdk-high-contrast-active .mat-option[aria-disabled=true]{opacity:.5}.mat-option-text{display:inline-block;flex-grow:1;overflow:hidden;text-overflow:ellipsis}.mat-option .mat-option-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none}.mat-option-pseudo-checkbox{margin-right:8px}[dir=rtl] .mat-option-pseudo-checkbox{margin-left:8px;margin-right:0}\n"]}]}],(function(){return[{type:Kh},{type:Ob},{type:void 0,decorators:[{type:Br},{type:Lr,args:[hL]}]},{type:ML,decorators:[{type:Br},{type:Lr,args:[CL]}]}]}),null);class SL{}SL.ɵfac=function t(e){return new(e||SL)},SL.ɵmod=fo({type:SL}),SL.ɵinj=Dn({imports:[[fL,rx,NF,gL]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(SL,[{type:L_,args:[{imports:[fL,rx,NF,gL],exports:[PL,ML],declarations:[PL,ML]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(SL,{declarations:function(){return[PL,ML]},imports:function(){return[fL,rx,NF,gL]},exports:function(){return[PL,ML]}});const DL=["mat-button",""],RL=["*"],EL=".mat-button .mat-button-focus-overlay,.mat-icon-button .mat-button-focus-overlay{opacity:0}.mat-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay,.mat-stroked-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay{opacity:.04}@media(hover: none){.mat-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay,.mat-stroked-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay{opacity:0}}.mat-button,.mat-icon-button,.mat-stroked-button,.mat-flat-button{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 16px;border-radius:4px;overflow:visible}.mat-button::-moz-focus-inner,.mat-icon-button::-moz-focus-inner,.mat-stroked-button::-moz-focus-inner,.mat-flat-button::-moz-focus-inner{border:0}.mat-button.mat-button-disabled,.mat-icon-button.mat-button-disabled,.mat-stroked-button.mat-button-disabled,.mat-flat-button.mat-button-disabled{cursor:default}.mat-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-button.cdk-program-focused .mat-button-focus-overlay,.mat-icon-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-icon-button.cdk-program-focused .mat-button-focus-overlay,.mat-stroked-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-stroked-button.cdk-program-focused .mat-button-focus-overlay,.mat-flat-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-flat-button.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-button::-moz-focus-inner,.mat-icon-button::-moz-focus-inner,.mat-stroked-button::-moz-focus-inner,.mat-flat-button::-moz-focus-inner{border:0}.mat-raised-button{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 16px;border-radius:4px;overflow:visible;transform:translate3d(0, 0, 0);transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1)}.mat-raised-button::-moz-focus-inner{border:0}.mat-raised-button.mat-button-disabled{cursor:default}.mat-raised-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-raised-button.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-raised-button::-moz-focus-inner{border:0}._mat-animation-noopable.mat-raised-button{transition:none;animation:none}.mat-stroked-button{border:1px solid currentColor;padding:0 15px;line-height:34px}.mat-stroked-button .mat-button-ripple.mat-ripple,.mat-stroked-button .mat-button-focus-overlay{top:-1px;left:-1px;right:-1px;bottom:-1px}.mat-fab{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 16px;border-radius:4px;overflow:visible;transform:translate3d(0, 0, 0);transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);min-width:0;border-radius:50%;width:56px;height:56px;padding:0;flex-shrink:0}.mat-fab::-moz-focus-inner{border:0}.mat-fab.mat-button-disabled{cursor:default}.mat-fab.cdk-keyboard-focused .mat-button-focus-overlay,.mat-fab.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-fab::-moz-focus-inner{border:0}._mat-animation-noopable.mat-fab{transition:none;animation:none}.mat-fab .mat-button-wrapper{padding:16px 0;display:inline-block;line-height:24px}.mat-mini-fab{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 16px;border-radius:4px;overflow:visible;transform:translate3d(0, 0, 0);transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);min-width:0;border-radius:50%;width:40px;height:40px;padding:0;flex-shrink:0}.mat-mini-fab::-moz-focus-inner{border:0}.mat-mini-fab.mat-button-disabled{cursor:default}.mat-mini-fab.cdk-keyboard-focused .mat-button-focus-overlay,.mat-mini-fab.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-mini-fab::-moz-focus-inner{border:0}._mat-animation-noopable.mat-mini-fab{transition:none;animation:none}.mat-mini-fab .mat-button-wrapper{padding:8px 0;display:inline-block;line-height:24px}.mat-icon-button{padding:0;min-width:0;width:40px;height:40px;flex-shrink:0;line-height:40px;border-radius:50%}.mat-icon-button i,.mat-icon-button .mat-icon{line-height:24px}.mat-button-ripple.mat-ripple,.mat-button-focus-overlay{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none;border-radius:inherit}.mat-button-ripple.mat-ripple:not(:empty){transform:translateZ(0)}.mat-button-focus-overlay{opacity:0;transition:opacity 200ms cubic-bezier(0.35, 0, 0.25, 1),background-color 200ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable .mat-button-focus-overlay{transition:none}.mat-button-ripple-round{border-radius:50%;z-index:1}.mat-button .mat-button-wrapper>*,.mat-flat-button .mat-button-wrapper>*,.mat-stroked-button .mat-button-wrapper>*,.mat-raised-button .mat-button-wrapper>*,.mat-icon-button .mat-button-wrapper>*,.mat-fab .mat-button-wrapper>*,.mat-mini-fab .mat-button-wrapper>*{vertical-align:middle}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon-button,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon-button{display:inline-flex;justify-content:center;align-items:center;font-size:inherit;width:2.5em;height:2.5em}.cdk-high-contrast-active .mat-button,.cdk-high-contrast-active .mat-flat-button,.cdk-high-contrast-active .mat-raised-button,.cdk-high-contrast-active .mat-icon-button,.cdk-high-contrast-active .mat-fab,.cdk-high-contrast-active .mat-mini-fab{outline:solid 1px}.cdk-high-contrast-active .mat-button-base.cdk-keyboard-focused,.cdk-high-contrast-active .mat-button-base.cdk-program-focused{outline:solid 3px}\n",AL=["mat-button","mat-flat-button","mat-icon-button","mat-raised-button","mat-stroked-button","mat-mini-fab","mat-fab"],TL=IF(zF(HF(class{constructor(t){this._elementRef=t}})));class NL extends TL{constructor(t,e,n){super(t),this._focusMonitor=e,this._animationMode=n,this.isRoundButton=this._hasHostAttributes("mat-fab","mat-mini-fab"),this.isIconButton=this._hasHostAttributes("mat-icon-button");for(const t of AL)this._hasHostAttributes(t)&&this._getHostElement().classList.add(t);t.nativeElement.classList.add("mat-button-base"),this.isRoundButton&&(this.color="accent")}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0)}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef)}focus(t,e){t?this._focusMonitor.focusVia(this._getHostElement(),t,e):this._getHostElement().focus(e)}_getHostElement(){return this._elementRef.nativeElement}_isRippleDisabled(){return this.disableRipple||this.disabled}_hasHostAttributes(...t){return t.some((t=>this._getHostElement().hasAttribute(t)))}}NL.ɵfac=function t(e){return new(e||NL)(gf(Kh),gf(fF),gf(ck,8))},NL.ɵcmp=so({type:NL,selectors:[["button","mat-button",""],["button","mat-raised-button",""],["button","mat-icon-button",""],["button","mat-fab",""],["button","mat-mini-fab",""],["button","mat-stroked-button",""],["button","mat-flat-button",""]],viewQuery:function t(e,n){if(1&e&&Vy(mL,5),2&e){let t;By(t=Uy())&&(n.ripple=t.first)}},hostAttrs:[1,"mat-focus-indicator"],hostVars:5,hostBindings:function t(e,n){2&e&&(ym("disabled",n.disabled||null),cu("_mat-animation-noopable","NoopAnimations"===n._animationMode)("mat-button-disabled",n.disabled))},inputs:{disabled:"disabled",disableRipple:"disableRipple",color:"color"},exportAs:["matButton"],features:[Qp],attrs:DL,ngContentSelectors:RL,decls:4,vars:5,consts:[[1,"mat-button-wrapper"],["matRipple","",1,"mat-button-ripple",3,"matRippleDisabled","matRippleCentered","matRippleTrigger"],[1,"mat-button-focus-overlay"]],template:function t(e,n){1&e&&(Hf(),Cf(0,"span",0),Ff(1),Mf(),vf(2,"span",1),vf(3,"span",2)),2&e&&(Dc(2),cu("mat-button-ripple-round",n.isRoundButton||n.isIconButton),bf("matRippleDisabled",n._isRippleDisabled())("matRippleCentered",n.isIconButton)("matRippleTrigger",n._getHostElement()))},directives:[mL],styles:[EL],encapsulation:2,changeDetection:0}),NL.ctorParameters=()=>[{type:Kh},{type:fF},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}],NL.propDecorators={ripple:[{type:ar,args:[mL]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(NL,[{type:S_,args:[{selector:"button[mat-button], button[mat-raised-button], button[mat-icon-button],\n             button[mat-fab], button[mat-mini-fab], button[mat-stroked-button],\n             button[mat-flat-button]",exportAs:"matButton",host:{"[attr.disabled]":"disabled || null","[class._mat-animation-noopable]":'_animationMode === "NoopAnimations"',"[class.mat-button-disabled]":"disabled",class:"mat-focus-indicator"},template:'<span class="mat-button-wrapper"><ng-content></ng-content></span>\n<span matRipple class="mat-button-ripple"\n      [class.mat-button-ripple-round]="isRoundButton || isIconButton"\n      [matRippleDisabled]="_isRippleDisabled()"\n      [matRippleCentered]="isIconButton"\n      [matRippleTrigger]="_getHostElement()"></span>\n<span class="mat-button-focus-overlay"></span>\n',inputs:["disabled","disableRipple","color"],encapsulation:Wn.None,changeDetection:jn.OnPush,styles:[".mat-button .mat-button-focus-overlay,.mat-icon-button .mat-button-focus-overlay{opacity:0}.mat-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay,.mat-stroked-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay{opacity:.04}@media(hover: none){.mat-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay,.mat-stroked-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay{opacity:0}}.mat-button,.mat-icon-button,.mat-stroked-button,.mat-flat-button{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 16px;border-radius:4px;overflow:visible}.mat-button::-moz-focus-inner,.mat-icon-button::-moz-focus-inner,.mat-stroked-button::-moz-focus-inner,.mat-flat-button::-moz-focus-inner{border:0}.mat-button.mat-button-disabled,.mat-icon-button.mat-button-disabled,.mat-stroked-button.mat-button-disabled,.mat-flat-button.mat-button-disabled{cursor:default}.mat-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-button.cdk-program-focused .mat-button-focus-overlay,.mat-icon-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-icon-button.cdk-program-focused .mat-button-focus-overlay,.mat-stroked-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-stroked-button.cdk-program-focused .mat-button-focus-overlay,.mat-flat-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-flat-button.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-button::-moz-focus-inner,.mat-icon-button::-moz-focus-inner,.mat-stroked-button::-moz-focus-inner,.mat-flat-button::-moz-focus-inner{border:0}.mat-raised-button{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 16px;border-radius:4px;overflow:visible;transform:translate3d(0, 0, 0);transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1)}.mat-raised-button::-moz-focus-inner{border:0}.mat-raised-button.mat-button-disabled{cursor:default}.mat-raised-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-raised-button.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-raised-button::-moz-focus-inner{border:0}._mat-animation-noopable.mat-raised-button{transition:none;animation:none}.mat-stroked-button{border:1px solid currentColor;padding:0 15px;line-height:34px}.mat-stroked-button .mat-button-ripple.mat-ripple,.mat-stroked-button .mat-button-focus-overlay{top:-1px;left:-1px;right:-1px;bottom:-1px}.mat-fab{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 16px;border-radius:4px;overflow:visible;transform:translate3d(0, 0, 0);transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);min-width:0;border-radius:50%;width:56px;height:56px;padding:0;flex-shrink:0}.mat-fab::-moz-focus-inner{border:0}.mat-fab.mat-button-disabled{cursor:default}.mat-fab.cdk-keyboard-focused .mat-button-focus-overlay,.mat-fab.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-fab::-moz-focus-inner{border:0}._mat-animation-noopable.mat-fab{transition:none;animation:none}.mat-fab .mat-button-wrapper{padding:16px 0;display:inline-block;line-height:24px}.mat-mini-fab{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 16px;border-radius:4px;overflow:visible;transform:translate3d(0, 0, 0);transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);min-width:0;border-radius:50%;width:40px;height:40px;padding:0;flex-shrink:0}.mat-mini-fab::-moz-focus-inner{border:0}.mat-mini-fab.mat-button-disabled{cursor:default}.mat-mini-fab.cdk-keyboard-focused .mat-button-focus-overlay,.mat-mini-fab.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-mini-fab::-moz-focus-inner{border:0}._mat-animation-noopable.mat-mini-fab{transition:none;animation:none}.mat-mini-fab .mat-button-wrapper{padding:8px 0;display:inline-block;line-height:24px}.mat-icon-button{padding:0;min-width:0;width:40px;height:40px;flex-shrink:0;line-height:40px;border-radius:50%}.mat-icon-button i,.mat-icon-button .mat-icon{line-height:24px}.mat-button-ripple.mat-ripple,.mat-button-focus-overlay{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none;border-radius:inherit}.mat-button-ripple.mat-ripple:not(:empty){transform:translateZ(0)}.mat-button-focus-overlay{opacity:0;transition:opacity 200ms cubic-bezier(0.35, 0, 0.25, 1),background-color 200ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable .mat-button-focus-overlay{transition:none}.mat-button-ripple-round{border-radius:50%;z-index:1}.mat-button .mat-button-wrapper>*,.mat-flat-button .mat-button-wrapper>*,.mat-stroked-button .mat-button-wrapper>*,.mat-raised-button .mat-button-wrapper>*,.mat-icon-button .mat-button-wrapper>*,.mat-fab .mat-button-wrapper>*,.mat-mini-fab .mat-button-wrapper>*{vertical-align:middle}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon-button,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon-button{display:inline-flex;justify-content:center;align-items:center;font-size:inherit;width:2.5em;height:2.5em}.cdk-high-contrast-active .mat-button,.cdk-high-contrast-active .mat-flat-button,.cdk-high-contrast-active .mat-raised-button,.cdk-high-contrast-active .mat-icon-button,.cdk-high-contrast-active .mat-fab,.cdk-high-contrast-active .mat-mini-fab{outline:solid 1px}.cdk-high-contrast-active .mat-button-base.cdk-keyboard-focused,.cdk-high-contrast-active .mat-button-base.cdk-program-focused{outline:solid 3px}\n"]}]}],(function(){return[{type:Kh},{type:fF},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}]}),{ripple:[{type:ar,args:[mL]}]});class zL extends NL{constructor(t,e,n){super(e,t,n)}_haltDisabledEvents(t){this.disabled&&(t.preventDefault(),t.stopImmediatePropagation())}}zL.ɵfac=function t(e){return new(e||zL)(gf(fF),gf(Kh),gf(ck,8))},zL.ɵcmp=so({type:zL,selectors:[["a","mat-button",""],["a","mat-raised-button",""],["a","mat-icon-button",""],["a","mat-fab",""],["a","mat-mini-fab",""],["a","mat-stroked-button",""],["a","mat-flat-button",""]],hostAttrs:[1,"mat-focus-indicator"],hostVars:7,hostBindings:function t(e,n){1&e&&Rf("click",(function t(e){return n._haltDisabledEvents(e)})),2&e&&(ym("tabindex",n.disabled?-1:n.tabIndex||0)("disabled",n.disabled||null)("aria-disabled",n.disabled.toString()),cu("_mat-animation-noopable","NoopAnimations"===n._animationMode)("mat-button-disabled",n.disabled))},inputs:{disabled:"disabled",disableRipple:"disableRipple",color:"color",tabIndex:"tabIndex"},exportAs:["matButton","matAnchor"],features:[Qp],attrs:DL,ngContentSelectors:RL,decls:4,vars:5,consts:[[1,"mat-button-wrapper"],["matRipple","",1,"mat-button-ripple",3,"matRippleDisabled","matRippleCentered","matRippleTrigger"],[1,"mat-button-focus-overlay"]],template:function t(e,n){1&e&&(Hf(),Cf(0,"span",0),Ff(1),Mf(),vf(2,"span",1),vf(3,"span",2)),2&e&&(Dc(2),cu("mat-button-ripple-round",n.isRoundButton||n.isIconButton),bf("matRippleDisabled",n._isRippleDisabled())("matRippleCentered",n.isIconButton)("matRippleTrigger",n._getHostElement()))},directives:[mL],styles:[EL],encapsulation:2,changeDetection:0}),zL.ctorParameters=()=>[{type:fF},{type:Kh},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}],zL.propDecorators={tabIndex:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(zL,[{type:S_,args:[{selector:"a[mat-button], a[mat-raised-button], a[mat-icon-button], a[mat-fab],\n             a[mat-mini-fab], a[mat-stroked-button], a[mat-flat-button]",exportAs:"matButton, matAnchor",host:{"[attr.tabindex]":"disabled ? -1 : (tabIndex || 0)","[attr.disabled]":"disabled || null","[attr.aria-disabled]":"disabled.toString()","(click)":"_haltDisabledEvents($event)","[class._mat-animation-noopable]":'_animationMode === "NoopAnimations"',"[class.mat-button-disabled]":"disabled",class:"mat-focus-indicator"},inputs:["disabled","disableRipple","color"],template:'<span class="mat-button-wrapper"><ng-content></ng-content></span>\n<span matRipple class="mat-button-ripple"\n      [class.mat-button-ripple-round]="isRoundButton || isIconButton"\n      [matRippleDisabled]="_isRippleDisabled()"\n      [matRippleCentered]="isIconButton"\n      [matRippleTrigger]="_getHostElement()"></span>\n<span class="mat-button-focus-overlay"></span>\n',encapsulation:Wn.None,changeDetection:jn.OnPush,styles:[".mat-button .mat-button-focus-overlay,.mat-icon-button .mat-button-focus-overlay{opacity:0}.mat-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay,.mat-stroked-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay{opacity:.04}@media(hover: none){.mat-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay,.mat-stroked-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay{opacity:0}}.mat-button,.mat-icon-button,.mat-stroked-button,.mat-flat-button{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 16px;border-radius:4px;overflow:visible}.mat-button::-moz-focus-inner,.mat-icon-button::-moz-focus-inner,.mat-stroked-button::-moz-focus-inner,.mat-flat-button::-moz-focus-inner{border:0}.mat-button.mat-button-disabled,.mat-icon-button.mat-button-disabled,.mat-stroked-button.mat-button-disabled,.mat-flat-button.mat-button-disabled{cursor:default}.mat-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-button.cdk-program-focused .mat-button-focus-overlay,.mat-icon-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-icon-button.cdk-program-focused .mat-button-focus-overlay,.mat-stroked-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-stroked-button.cdk-program-focused .mat-button-focus-overlay,.mat-flat-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-flat-button.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-button::-moz-focus-inner,.mat-icon-button::-moz-focus-inner,.mat-stroked-button::-moz-focus-inner,.mat-flat-button::-moz-focus-inner{border:0}.mat-raised-button{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 16px;border-radius:4px;overflow:visible;transform:translate3d(0, 0, 0);transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1)}.mat-raised-button::-moz-focus-inner{border:0}.mat-raised-button.mat-button-disabled{cursor:default}.mat-raised-button.cdk-keyboard-focused .mat-button-focus-overlay,.mat-raised-button.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-raised-button::-moz-focus-inner{border:0}._mat-animation-noopable.mat-raised-button{transition:none;animation:none}.mat-stroked-button{border:1px solid currentColor;padding:0 15px;line-height:34px}.mat-stroked-button .mat-button-ripple.mat-ripple,.mat-stroked-button .mat-button-focus-overlay{top:-1px;left:-1px;right:-1px;bottom:-1px}.mat-fab{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 16px;border-radius:4px;overflow:visible;transform:translate3d(0, 0, 0);transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);min-width:0;border-radius:50%;width:56px;height:56px;padding:0;flex-shrink:0}.mat-fab::-moz-focus-inner{border:0}.mat-fab.mat-button-disabled{cursor:default}.mat-fab.cdk-keyboard-focused .mat-button-focus-overlay,.mat-fab.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-fab::-moz-focus-inner{border:0}._mat-animation-noopable.mat-fab{transition:none;animation:none}.mat-fab .mat-button-wrapper{padding:16px 0;display:inline-block;line-height:24px}.mat-mini-fab{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 16px;border-radius:4px;overflow:visible;transform:translate3d(0, 0, 0);transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);min-width:0;border-radius:50%;width:40px;height:40px;padding:0;flex-shrink:0}.mat-mini-fab::-moz-focus-inner{border:0}.mat-mini-fab.mat-button-disabled{cursor:default}.mat-mini-fab.cdk-keyboard-focused .mat-button-focus-overlay,.mat-mini-fab.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-mini-fab::-moz-focus-inner{border:0}._mat-animation-noopable.mat-mini-fab{transition:none;animation:none}.mat-mini-fab .mat-button-wrapper{padding:8px 0;display:inline-block;line-height:24px}.mat-icon-button{padding:0;min-width:0;width:40px;height:40px;flex-shrink:0;line-height:40px;border-radius:50%}.mat-icon-button i,.mat-icon-button .mat-icon{line-height:24px}.mat-button-ripple.mat-ripple,.mat-button-focus-overlay{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none;border-radius:inherit}.mat-button-ripple.mat-ripple:not(:empty){transform:translateZ(0)}.mat-button-focus-overlay{opacity:0;transition:opacity 200ms cubic-bezier(0.35, 0, 0.25, 1),background-color 200ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable .mat-button-focus-overlay{transition:none}.mat-button-ripple-round{border-radius:50%;z-index:1}.mat-button .mat-button-wrapper>*,.mat-flat-button .mat-button-wrapper>*,.mat-stroked-button .mat-button-wrapper>*,.mat-raised-button .mat-button-wrapper>*,.mat-icon-button .mat-button-wrapper>*,.mat-fab .mat-button-wrapper>*,.mat-mini-fab .mat-button-wrapper>*{vertical-align:middle}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon-button,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon-button{display:inline-flex;justify-content:center;align-items:center;font-size:inherit;width:2.5em;height:2.5em}.cdk-high-contrast-active .mat-button,.cdk-high-contrast-active .mat-flat-button,.cdk-high-contrast-active .mat-raised-button,.cdk-high-contrast-active .mat-icon-button,.cdk-high-contrast-active .mat-fab,.cdk-high-contrast-active .mat-mini-fab{outline:solid 1px}.cdk-high-contrast-active .mat-button-base.cdk-keyboard-focused,.cdk-high-contrast-active .mat-button-base.cdk-program-focused{outline:solid 3px}\n"]}]}],(function(){return[{type:fF},{type:Kh},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}]}),{tabIndex:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class IL{}IL.ɵfac=function t(e){return new(e||IL)},IL.ɵmod=fo({type:IL}),IL.ɵinj=Dn({imports:[[fL,NF],NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(IL,[{type:L_,args:[{imports:[fL,NF],exports:[NL,zL,NF],declarations:[NL,zL]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(IL,{declarations:function(){return[NL,zL]},imports:function(){return[fL,NF]},exports:function(){return[NL,zL,NF]}});class HL{}function FL(t){return t&&"function"==typeof t.connect}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class LL extends HL{constructor(t){super(),this._data=t}connect(){return Lt(this._data)?this._data:zt(this._data)}disconnect(){}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class BL{applyChanges(t,e,n,o,i){t.forEachOperation(((t,o,a)=>{let r,s;if(null==t.previousIndex){const i=n(t,o,a);r=e.createEmbeddedView(i.templateRef,i.context,i.index),s=1}else null==a?(e.remove(o),s=3):(r=e.get(o),e.move(r,a),s=2);i&&i({context:null==r?void 0:r.context,operation:s,record:t})}))}detach(){}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class VL{constructor(){this.viewCacheSize=20,this._viewCache=[]}applyChanges(t,e,n,o,i){t.forEachOperation(((t,a,r)=>{let s,l;if(null==t.previousIndex){s=this._insertView((()=>n(t,a,r)),r,e,o(t)),l=s?1:0}else null==r?(this._detachAndCacheView(a,e),l=3):(s=this._moveView(a,r,e,o(t)),l=2);i&&i({context:null==s?void 0:s.context,operation:l,record:t})}))}detach(){for(const t of this._viewCache)t.destroy();this._viewCache=[]}_insertView(t,e,n,o){const i=this._insertViewFromCache(e,n);if(i)return void(i.context.$implicit=o);const a=t();return n.createEmbeddedView(a.templateRef,a.context,a.index)}_detachAndCacheView(t,e){const n=e.detach(t);this._maybeCacheView(n,e)}_moveView(t,e,n,o){const i=n.get(t);return n.move(i,e),i.context.$implicit=o,i}_maybeCacheView(t,e){if(this._viewCache.length<this.viewCacheSize)this._viewCache.push(t);else{const n=e.indexOf(t);-1===n?t.destroy():e.remove(n)}}_insertViewFromCache(t,e){const n=this._viewCache.pop();return n&&e.insert(n,t),n||null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class jL{constructor(t=!1,e,n=!0){this._multiple=t,this._emitChanges=n,this._selection=new Set,this._deselectedToEmit=[],this._selectedToEmit=[],this.changed=new B,e&&e.length&&(t?e.forEach((t=>this._markSelected(t))):this._markSelected(e[0]),this._selectedToEmit.length=0)}get selected(){return this._selected||(this._selected=Array.from(this._selection.values())),this._selected}select(...t){this._verifyValueAssignment(t),t.forEach((t=>this._markSelected(t))),this._emitChangeEvent()}deselect(...t){this._verifyValueAssignment(t),t.forEach((t=>this._unmarkSelected(t))),this._emitChangeEvent()}toggle(t){this.isSelected(t)?this.deselect(t):this.select(t)}clear(){this._unmarkAll(),this._emitChangeEvent()}isSelected(t){return this._selection.has(t)}isEmpty(){return 0===this._selection.size}hasValue(){return!this.isEmpty()}sort(t){this._multiple&&this.selected&&this._selected.sort(t)}isMultipleSelection(){return this._multiple}_emitChangeEvent(){this._selected=null,(this._selectedToEmit.length||this._deselectedToEmit.length)&&(this.changed.next({source:this,added:this._selectedToEmit,removed:this._deselectedToEmit}),this._deselectedToEmit=[],this._selectedToEmit=[])}_markSelected(t){this.isSelected(t)||(this._multiple||this._unmarkAll(),this._selection.add(t),this._emitChanges&&this._selectedToEmit.push(t))}_unmarkSelected(t){this.isSelected(t)&&(this._selection.delete(t),this._emitChanges&&this._deselectedToEmit.push(t))}_unmarkAll(){this.isEmpty()||this._selection.forEach((t=>this._unmarkSelected(t)))}_verifyValueAssignment(t){if(t.length>1&&!this._multiple&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("Cannot pass multiple values into SelectionModel with single-value mode.")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)()}}class UL{constructor(){this._listeners=[]}notify(t,e){for(let n of this._listeners)n(t,e)}listen(t){return this._listeners.push(t),()=>{this._listeners=this._listeners.filter((e=>t!==e))}}ngOnDestroy(){this._listeners=[]}}UL.ɵfac=function t(e){return new(e||UL)},UL.ɵprov=Sn({factory:function t(){return new UL},token:UL,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(UL,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const WL=new er("_ViewRepeater"),GL=["contentWrapper"],YL=new er("VIRTUAL_SCROLL_STRATEGY");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class qL{constructor(t,e,n){this._scrolledIndexChange=new B,this.scrolledIndexChange=this._scrolledIndexChange.pipe(we()),this._viewport=null,this._itemSize=t,this._minBufferPx=e,this._maxBufferPx=n}attach(t){this._viewport=t,this._updateTotalContentSize(),this._updateRenderedRange()}detach(){this._scrolledIndexChange.complete(),this._viewport=null}updateItemAndBufferSize(t,e,n){if(n<e&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("CDK virtual scroll: maxBufferPx must be greater than or equal to minBufferPx");this._itemSize=t,this._minBufferPx=e,this._maxBufferPx=n,this._updateTotalContentSize(),this._updateRenderedRange()}onContentScrolled(){this._updateRenderedRange()}onDataLengthChanged(){this._updateTotalContentSize(),this._updateRenderedRange()}onContentRendered(){}onRenderedOffsetChanged(){}scrollToIndex(t,e){this._viewport&&this._viewport.scrollToOffset(t*this._itemSize,e)}_updateTotalContentSize(){this._viewport&&this._viewport.setTotalContentSize(this._viewport.getDataLength()*this._itemSize)}_updateRenderedRange(){if(!this._viewport)return;const t=this._viewport.getRenderedRange(),e={start:t.start,end:t.end},n=this._viewport.getViewportSize(),o=this._viewport.getDataLength();let i=this._viewport.measureScrollOffset(),a=this._itemSize>0?i/this._itemSize:0;if(e.end>o){const t=Math.ceil(n/this._itemSize),r=Math.max(0,Math.min(a,o-t));a!=r&&(a=r,i=r*this._itemSize,e.start=Math.floor(a)),e.end=Math.max(0,Math.min(o,e.start+t))}const r=i-e.start*this._itemSize;if(r<this._minBufferPx&&0!=e.start){const t=Math.ceil((this._maxBufferPx-r)/this._itemSize);e.start=Math.max(0,e.start-t),e.end=Math.min(o,Math.ceil(a+(n+this._minBufferPx)/this._itemSize))}else{const t=e.end*this._itemSize-(i+n);if(t<this._minBufferPx&&e.end!=o){const n=Math.ceil((this._maxBufferPx-t)/this._itemSize);n>0&&(e.end=Math.min(o,e.end+n),e.start=Math.max(0,Math.floor(a-this._minBufferPx/this._itemSize)))}}this._viewport.setRenderedRange(e),this._viewport.setRenderedContentOffset(this._itemSize*e.start),this._scrolledIndexChange.next(Math.floor(a))}}function ZL(t){return t._scrollStrategy}class XL{constructor(){this._itemSize=20,this._minBufferPx=100,this._maxBufferPx=200,this._scrollStrategy=new qL(this.itemSize,this.minBufferPx,this.maxBufferPx)}get itemSize(){return this._itemSize}set itemSize(t){this._itemSize=nH(t)}get minBufferPx(){return this._minBufferPx}set minBufferPx(t){this._minBufferPx=nH(t)}get maxBufferPx(){return this._maxBufferPx}set maxBufferPx(t){this._maxBufferPx=nH(t)}ngOnChanges(){this._scrollStrategy.updateItemAndBufferSize(this.itemSize,this.minBufferPx,this.maxBufferPx)}}XL.ɵfac=function t(e){return new(e||XL)},XL.ɵdir=ho({type:XL,selectors:[["cdk-virtual-scroll-viewport","itemSize",""]],inputs:{itemSize:"itemSize",minBufferPx:"minBufferPx",maxBufferPx:"maxBufferPx"},features:[jh([{provide:YL,useFactory:ZL,deps:[$e((()=>XL))]}]),qo]}),XL.propDecorators={itemSize:[{type:R_}],minBufferPx:[{type:R_}],maxBufferPx:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(XL,[{type:k_,args:[{selector:"cdk-virtual-scroll-viewport[itemSize]",providers:[{provide:YL,useFactory:ZL,deps:[$e((()=>XL))]}]}]}],(function(){return[]}),{itemSize:[{type:R_}],minBufferPx:[{type:R_}],maxBufferPx:[{type:R_}]});class KL{constructor(t,e,n){this._ngZone=t,this._platform=e,this._scrolled=new B,this._globalSubscription=null,this._scrolledCount=0,this.scrollContainers=new Map,this._document=n}register(t){this.scrollContainers.has(t)||this.scrollContainers.set(t,t.elementScrolled().subscribe((()=>this._scrolled.next(t))))}deregister(t){const e=this.scrollContainers.get(t);e&&(e.unsubscribe(),this.scrollContainers.delete(t))}scrolled(t=20){return this._platform.isBrowser?new T((e=>{this._globalSubscription||this._addGlobalListener();const n=t>0?this._scrolled.pipe(ge(t)).subscribe(e):this._scrolled.subscribe(e);return this._scrolledCount++,()=>{n.unsubscribe(),this._scrolledCount--,this._scrolledCount||this._removeGlobalListener()}})):zt()}ngOnDestroy(){this._removeGlobalListener(),this.scrollContainers.forEach(((t,e)=>this.deregister(e))),this._scrolled.complete()}ancestorScrolled(t,e){const n=this.getAncestorScrollContainers(t);return this.scrolled(e).pipe(ue((t=>!t||n.indexOf(t)>-1)))}getAncestorScrollContainers(t){const e=[];return this.scrollContainers.forEach(((n,o)=>{this._scrollableContainsElement(o,t)&&e.push(o)})),e}_getWindow(){return this._document.defaultView||window}_scrollableContainsElement(t,e){let n=rH(e),o=t.getElementRef().nativeElement;do{if(n==o)return!0}while(n=n.parentElement);return!1}_addGlobalListener(){this._globalSubscription=this._ngZone.runOutsideAngular((()=>le(this._getWindow().document,"scroll").subscribe((()=>this._scrolled.next()))))}_removeGlobalListener(){this._globalSubscription&&(this._globalSubscription.unsubscribe(),this._globalSubscription=null)}}KL.ɵfac=function t(e){return new(e||KL)(Tr(mC),Tr(cH),Tr(sM,8))},KL.ɵprov=Sn({factory:function t(){return new KL(Tr(mC),Tr(cH),Tr(sM,8))},token:KL,providedIn:"root"}),KL.ctorParameters=()=>[{type:mC},{type:cH},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(KL,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:mC},{type:cH},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class JL{constructor(t,e,n,o){this.elementRef=t,this.scrollDispatcher=e,this.ngZone=n,this.dir=o,this._destroyed=new B,this._elementScrolled=new T((t=>this.ngZone.runOutsideAngular((()=>le(this.elementRef.nativeElement,"scroll").pipe(je(this._destroyed)).subscribe(t)))))}ngOnInit(){this.scrollDispatcher.register(this)}ngOnDestroy(){this.scrollDispatcher.deregister(this),this._destroyed.next(),this._destroyed.complete()}elementScrolled(){return this._elementScrolled}getElementRef(){return this.elementRef}scrollTo(t){const e=this.elementRef.nativeElement,n=this.dir&&"rtl"==this.dir.value;null==t.left&&(t.left=n?t.end:t.start),null==t.right&&(t.right=n?t.start:t.end),null!=t.bottom&&(t.top=e.scrollHeight-e.clientHeight-t.bottom),n&&0!=_H()?(null!=t.left&&(t.right=e.scrollWidth-e.clientWidth-t.left),2==_H()?t.left=t.right:1==_H()&&(t.left=t.right?-t.right:t.right)):null!=t.right&&(t.left=e.scrollWidth-e.clientWidth-t.right),this._applyScrollToOptions(t)}_applyScrollToOptions(t){const e=this.elementRef.nativeElement;yH()?e.scrollTo(t):(null!=t.top&&(e.scrollTop=t.top),null!=t.left&&(e.scrollLeft=t.left))}measureScrollOffset(t){const e="left",n="right",o=this.elementRef.nativeElement;if("top"==t)return o.scrollTop;if("bottom"==t)return o.scrollHeight-o.clientHeight-o.scrollTop;const i=this.dir&&"rtl"==this.dir.value;return"start"==t?t=i?n:e:"end"==t&&(t=i?e:n),i&&2==_H()?t==e?o.scrollWidth-o.clientWidth-o.scrollLeft:o.scrollLeft:i&&1==_H()?t==e?o.scrollLeft+o.scrollWidth-o.clientWidth:-o.scrollLeft:t==e?o.scrollLeft:o.scrollWidth-o.clientWidth-o.scrollLeft}}JL.ɵfac=function t(e){return new(e||JL)(gf(Kh),gf(KL),gf(mC),gf(vF,8))},JL.ɵdir=ho({type:JL,selectors:[["","cdk-scrollable",""],["","cdkScrollable",""]]}),JL.ctorParameters=()=>[{type:Kh},{type:KL},{type:mC},{type:vF,decorators:[{type:Br}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(JL,[{type:k_,args:[{selector:"[cdk-scrollable], [cdkScrollable]"}]}],(function(){return[{type:Kh},{type:KL},{type:mC},{type:vF,decorators:[{type:Br}]}]}),null);class QL{constructor(t,e,n){this._platform=t,this._change=new B,this._changeListener=t=>{this._change.next(t)},this._document=n,e.runOutsideAngular((()=>{if(t.isBrowser){const t=this._getWindow();t.addEventListener("resize",this._changeListener),t.addEventListener("orientationchange",this._changeListener)}this.change().subscribe((()=>this._viewportSize=null))}))}ngOnDestroy(){if(this._platform.isBrowser){const t=this._getWindow();t.removeEventListener("resize",this._changeListener),t.removeEventListener("orientationchange",this._changeListener)}this._change.complete()}getViewportSize(){this._viewportSize||this._updateViewportSize();const t={width:this._viewportSize.width,height:this._viewportSize.height};return this._platform.isBrowser||(this._viewportSize=null),t}getViewportRect(){const t=this.getViewportScrollPosition(),{width:e,height:n}=this.getViewportSize();return{top:t.top,left:t.left,bottom:t.top+n,right:t.left+e,height:n,width:e}}getViewportScrollPosition(){if(!this._platform.isBrowser)return{top:0,left:0};const t=this._document,e=this._getWindow(),n=t.documentElement,o=n.getBoundingClientRect();return{top:-o.top||t.body.scrollTop||e.scrollY||n.scrollTop||0,left:-o.left||t.body.scrollLeft||e.scrollX||n.scrollLeft||0}}change(t=20){return t>0?this._change.pipe(ge(t)):this._change}_getWindow(){return this._document.defaultView||window}_updateViewportSize(){const t=this._getWindow();this._viewportSize=this._platform.isBrowser?{width:t.innerWidth,height:t.innerHeight}:{width:0,height:0}}}QL.ɵfac=function t(e){return new(e||QL)(Tr(cH),Tr(mC),Tr(sM,8))},QL.ɵprov=Sn({factory:function t(){return new QL(Tr(cH),Tr(mC),Tr(sM,8))},token:QL,providedIn:"root"}),QL.ctorParameters=()=>[{type:cH},{type:mC},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(QL,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:cH},{type:mC},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]}]}),null);const $L="undefined"!=typeof requestAnimationFrame?ct:ot;class tB extends JL{constructor(t,e,n,o,i,a,r){if(super(t,a,n,i),this.elementRef=t,this._changeDetectorRef=e,this._scrollStrategy=o,this._detachedSubject=new B,this._renderedRangeSubject=new B,this._orientation="vertical",this._appendOnly=!1,this.scrolledIndexChange=new T((t=>this._scrollStrategy.scrolledIndexChange.subscribe((e=>Promise.resolve().then((()=>this.ngZone.run((()=>t.next(e))))))))),this.renderedRangeStream=this._renderedRangeSubject,this._totalContentSize=0,this._totalContentWidth="",this._totalContentHeight="",this._renderedRange={start:0,end:0},this._dataLength=0,this._viewportSize=0,this._renderedContentOffset=0,this._renderedContentOffsetNeedsRewrite=!1,this._isChangeDetectionPending=!1,this._runAfterChangeDetection=[],this._viewportChanges=u.EMPTY,!o&&("undefined"==typeof ngDevMode||ngDevMode))throw Error('Error: cdk-virtual-scroll-viewport requires the "itemSize" property to be set.');this._viewportChanges=r.change().subscribe((()=>{this.checkViewportSize()}))}get orientation(){return this._orientation}set orientation(t){this._orientation!==t&&(this._orientation=t,this._calculateSpacerSize())}get appendOnly(){return this._appendOnly}set appendOnly(t){this._appendOnly=eH(t)}ngOnInit(){super.ngOnInit(),this.ngZone.runOutsideAngular((()=>Promise.resolve().then((()=>{this._measureViewportSize(),this._scrollStrategy.attach(this),this.elementScrolled().pipe(Be(null),ge(0,$L)).subscribe((()=>this._scrollStrategy.onContentScrolled())),this._markChangeDetectionNeeded()}))))}ngOnDestroy(){this.detach(),this._scrollStrategy.detach(),this._renderedRangeSubject.complete(),this._detachedSubject.complete(),this._viewportChanges.unsubscribe(),super.ngOnDestroy()}attach(t){if(this._forOf&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("CdkVirtualScrollViewport is already attached.");this.ngZone.runOutsideAngular((()=>{this._forOf=t,this._forOf.dataStream.pipe(je(this._detachedSubject)).subscribe((t=>{const e=t.length;e!==this._dataLength&&(this._dataLength=e,this._scrollStrategy.onDataLengthChanged()),this._doChangeDetection()}))}))}detach(){this._forOf=null,this._detachedSubject.next()}getDataLength(){return this._dataLength}getViewportSize(){return this._viewportSize}getRenderedRange(){return this._renderedRange}setTotalContentSize(t){this._totalContentSize!==t&&(this._totalContentSize=t,this._calculateSpacerSize(),this._markChangeDetectionNeeded())}setRenderedRange(t){(
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function e(t,n){return t.start==n.start&&t.end==n.end})(this._renderedRange,t)||(this.appendOnly&&(t={start:0,end:Math.max(this._renderedRange.end,t.end)}),this._renderedRangeSubject.next(this._renderedRange=t),this._markChangeDetectionNeeded((()=>this._scrollStrategy.onContentRendered())))}getOffsetToRenderedContentStart(){return this._renderedContentOffsetNeedsRewrite?null:this._renderedContentOffset}setRenderedContentOffset(t,e="to-start"){const n="horizontal"==this.orientation,o=n?"X":"Y";let i=`translate${o}(${Number((n&&(this.dir&&"rtl"==this.dir.value)?-1:1)*t)}px)`;this._renderedContentOffset=t,"to-end"===e&&(i+=` translate${o}(-100%)`,this._renderedContentOffsetNeedsRewrite=!0),this._renderedContentTransform!=i&&(this._renderedContentTransform=i,this._markChangeDetectionNeeded((()=>{this._renderedContentOffsetNeedsRewrite?(this._renderedContentOffset-=this.measureRenderedContentSize(),this._renderedContentOffsetNeedsRewrite=!1,this.setRenderedContentOffset(this._renderedContentOffset)):this._scrollStrategy.onRenderedOffsetChanged()})))}scrollToOffset(t,e="auto"){const n={behavior:e};"horizontal"===this.orientation?n.start=t:n.top=t,this.scrollTo(n)}scrollToIndex(t,e="auto"){this._scrollStrategy.scrollToIndex(t,e)}measureScrollOffset(t){return super.measureScrollOffset(t||("horizontal"===this.orientation?"start":"top"))}measureRenderedContentSize(){const t=this._contentWrapper.nativeElement;return"horizontal"===this.orientation?t.offsetWidth:t.offsetHeight}measureRangeSize(t){return this._forOf?this._forOf.measureRangeSize(t,this.orientation):0}checkViewportSize(){this._measureViewportSize(),this._scrollStrategy.onDataLengthChanged()}_measureViewportSize(){const t=this.elementRef.nativeElement;this._viewportSize="horizontal"===this.orientation?t.clientWidth:t.clientHeight}_markChangeDetectionNeeded(t){t&&this._runAfterChangeDetection.push(t),this._isChangeDetectionPending||(this._isChangeDetectionPending=!0,this.ngZone.runOutsideAngular((()=>Promise.resolve().then((()=>{this._doChangeDetection()})))))}_doChangeDetection(){this._isChangeDetectionPending=!1,this._contentWrapper.nativeElement.style.transform=this._renderedContentTransform,this.ngZone.run((()=>this._changeDetectorRef.markForCheck()));const t=this._runAfterChangeDetection;this._runAfterChangeDetection=[];for(const e of t)e()}_calculateSpacerSize(){this._totalContentHeight="horizontal"===this.orientation?"":`${this._totalContentSize}px`,this._totalContentWidth="horizontal"===this.orientation?`${this._totalContentSize}px`:""}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function eB(t,e,n){if(!n.getBoundingClientRect)return 0;const o=n.getBoundingClientRect();return"horizontal"===t?"start"===e?o.left:o.right:"start"===e?o.top:o.bottom}tB.ɵfac=function t(e){return new(e||tB)(gf(Kh),gf(Ob),gf(mC),gf(YL,8),gf(vF,8),gf(KL),gf(QL))},tB.ɵcmp=so({type:tB,selectors:[["cdk-virtual-scroll-viewport"]],viewQuery:function t(e,n){if(1&e&&Vy(GL,7),2&e){let t;By(t=Uy())&&(n._contentWrapper=t.first)}},hostAttrs:[1,"cdk-virtual-scroll-viewport"],hostVars:4,hostBindings:function t(e,n){2&e&&cu("cdk-virtual-scroll-orientation-horizontal","horizontal"===n.orientation)("cdk-virtual-scroll-orientation-vertical","horizontal"!==n.orientation)},inputs:{orientation:"orientation",appendOnly:"appendOnly"},outputs:{scrolledIndexChange:"scrolledIndexChange"},features:[jh([{provide:JL,useExisting:tB}]),Qp],ngContentSelectors:["*"],decls:4,vars:4,consts:[[1,"cdk-virtual-scroll-content-wrapper"],["contentWrapper",""],[1,"cdk-virtual-scroll-spacer"]],template:function t(e,n){1&e&&(Hf(),Cf(0,"div",0,1),Ff(2),Mf(),vf(3,"div",2)),2&e&&(Dc(3),lu("width",n._totalContentWidth)("height",n._totalContentHeight))},styles:["cdk-virtual-scroll-viewport{display:block;position:relative;overflow:auto;contain:strict;transform:translateZ(0);will-change:scroll-position;-webkit-overflow-scrolling:touch}.cdk-virtual-scroll-content-wrapper{position:absolute;top:0;left:0;contain:content}[dir=rtl] .cdk-virtual-scroll-content-wrapper{right:0;left:auto}.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper{min-height:100%}.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>dl:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>ol:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>table:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>ul:not([cdkVirtualFor]){padding-left:0;padding-right:0;margin-left:0;margin-right:0;border-left-width:0;border-right-width:0;outline:none}.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper{min-width:100%}.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>dl:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>ol:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>table:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>ul:not([cdkVirtualFor]){padding-top:0;padding-bottom:0;margin-top:0;margin-bottom:0;border-top-width:0;border-bottom-width:0;outline:none}.cdk-virtual-scroll-spacer{position:absolute;top:0;left:0;height:1px;width:1px;transform-origin:0 0}[dir=rtl] .cdk-virtual-scroll-spacer{right:0;left:auto;transform-origin:100% 0}\n"],encapsulation:2,changeDetection:0}),tB.ctorParameters=()=>[{type:Kh},{type:Ob},{type:mC},{type:void 0,decorators:[{type:Br},{type:Lr,args:[YL]}]},{type:vF,decorators:[{type:Br}]},{type:KL},{type:QL}],tB.propDecorators={orientation:[{type:R_}],appendOnly:[{type:R_}],scrolledIndexChange:[{type:E_}],_contentWrapper:[{type:ar,args:["contentWrapper",{static:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(tB,[{type:S_,args:[{selector:"cdk-virtual-scroll-viewport",template:'\x3c!--\n  Wrap the rendered content in an element that will be used to offset it based on the scroll\n  position.\n--\x3e\n<div #contentWrapper class="cdk-virtual-scroll-content-wrapper">\n  <ng-content></ng-content>\n</div>\n\x3c!--\n  Spacer used to force the scrolling container to the correct size for the *total* number of items\n  so that the scrollbar captures the size of the entire data set.\n--\x3e\n<div class="cdk-virtual-scroll-spacer"\n     [style.width]="_totalContentWidth" [style.height]="_totalContentHeight"></div>\n',host:{class:"cdk-virtual-scroll-viewport","[class.cdk-virtual-scroll-orientation-horizontal]":'orientation === "horizontal"',"[class.cdk-virtual-scroll-orientation-vertical]":'orientation !== "horizontal"'},encapsulation:Wn.None,changeDetection:jn.OnPush,providers:[{provide:JL,useExisting:tB}],styles:["cdk-virtual-scroll-viewport{display:block;position:relative;overflow:auto;contain:strict;transform:translateZ(0);will-change:scroll-position;-webkit-overflow-scrolling:touch}.cdk-virtual-scroll-content-wrapper{position:absolute;top:0;left:0;contain:content}[dir=rtl] .cdk-virtual-scroll-content-wrapper{right:0;left:auto}.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper{min-height:100%}.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>dl:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>ol:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>table:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>ul:not([cdkVirtualFor]){padding-left:0;padding-right:0;margin-left:0;margin-right:0;border-left-width:0;border-right-width:0;outline:none}.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper{min-width:100%}.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>dl:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>ol:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>table:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>ul:not([cdkVirtualFor]){padding-top:0;padding-bottom:0;margin-top:0;margin-bottom:0;border-top-width:0;border-bottom-width:0;outline:none}.cdk-virtual-scroll-spacer{position:absolute;top:0;left:0;height:1px;width:1px;transform-origin:0 0}[dir=rtl] .cdk-virtual-scroll-spacer{right:0;left:auto;transform-origin:100% 0}\n"]}]}],(function(){return[{type:Kh},{type:Ob},{type:mC},{type:void 0,decorators:[{type:Br},{type:Lr,args:[YL]}]},{type:vF,decorators:[{type:Br}]},{type:KL},{type:QL}]}),{scrolledIndexChange:[{type:E_}],orientation:[{type:R_}],appendOnly:[{type:R_}],_contentWrapper:[{type:ar,args:["contentWrapper",{static:!0}]}]});class nB{constructor(t,e,n,o,i,a){this._viewContainerRef=t,this._template=e,this._differs=n,this._viewRepeater=o,this._viewport=i,this.viewChange=new B,this._dataSourceChanges=new B,this.dataStream=this._dataSourceChanges.pipe(Be(null),Ne(),Ve((([t,e])=>this._changeDataSource(t,e))),Fe(1)),this._differ=null,this._needsUpdate=!1,this._destroyed=new B,this.dataStream.subscribe((t=>{this._data=t,this._onRenderedDataChange()})),this._viewport.renderedRangeStream.pipe(je(this._destroyed)).subscribe((t=>{this._renderedRange=t,a.run((()=>this.viewChange.next(this._renderedRange))),this._onRenderedDataChange()})),this._viewport.attach(this)}get cdkVirtualForOf(){return this._cdkVirtualForOf}set cdkVirtualForOf(t){this._cdkVirtualForOf=t,FL(t)?this._dataSourceChanges.next(t):this._dataSourceChanges.next(new LL(Lt(t)?t:Array.from(t||[])))}get cdkVirtualForTrackBy(){return this._cdkVirtualForTrackBy}set cdkVirtualForTrackBy(t){this._needsUpdate=!0,this._cdkVirtualForTrackBy=t?(e,n)=>t(e+(this._renderedRange?this._renderedRange.start:0),n):void 0}set cdkVirtualForTemplate(t){t&&(this._needsUpdate=!0,this._template=t)}get cdkVirtualForTemplateCacheSize(){return this._viewRepeater.viewCacheSize}set cdkVirtualForTemplateCacheSize(t){this._viewRepeater.viewCacheSize=nH(t)}measureRangeSize(t,e){if(t.start>=t.end)return 0;if((t.start<this._renderedRange.start||t.end>this._renderedRange.end)&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Error: attempted to measure an item that isn't rendered.");const n=t.start-this._renderedRange.start,o=t.end-t.start;let i,a;for(let t=0;t<o;t++){const e=this._viewContainerRef.get(t+n);if(e&&e.rootNodes.length){i=a=e.rootNodes[0];break}}for(let t=o-1;t>-1;t--){const e=this._viewContainerRef.get(t+n);if(e&&e.rootNodes.length){a=e.rootNodes[e.rootNodes.length-1];break}}return i&&a?eB(e,"end",a)-eB(e,"start",i):0}ngDoCheck(){if(this._differ&&this._needsUpdate){const t=this._differ.diff(this._renderedItems);t?this._applyChanges(t):this._updateContext(),this._needsUpdate=!1}}ngOnDestroy(){this._viewport.detach(),this._dataSourceChanges.next(void 0),this._dataSourceChanges.complete(),this.viewChange.complete(),this._destroyed.next(),this._destroyed.complete(),this._viewRepeater.detach()}_onRenderedDataChange(){this._renderedRange&&(this._renderedItems=this._data.slice(this._renderedRange.start,this._renderedRange.end),this._differ||(this._differ=this._differs.find(this._renderedItems).create(((t,e)=>this.cdkVirtualForTrackBy?this.cdkVirtualForTrackBy(t,e):e))),this._needsUpdate=!0)}_changeDataSource(t,e){return t&&t.disconnect(this),this._needsUpdate=!0,e?e.connect(this):zt()}_updateContext(){const t=this._data.length;let e=this._viewContainerRef.length;for(;e--;){const n=this._viewContainerRef.get(e);n.context.index=this._renderedRange.start+e,n.context.count=t,this._updateComputedContextProperties(n.context),n.detectChanges()}}_applyChanges(t){this._viewRepeater.applyChanges(t,this._viewContainerRef,((t,e,n)=>this._getEmbeddedViewArgs(t,n)),(t=>t.item)),t.forEachIdentityChange((t=>{this._viewContainerRef.get(t.currentIndex).context.$implicit=t.item}));const e=this._data.length;let n=this._viewContainerRef.length;for(;n--;){const t=this._viewContainerRef.get(n);t.context.index=this._renderedRange.start+n,t.context.count=e,this._updateComputedContextProperties(t.context)}}_updateComputedContextProperties(t){t.first=0===t.index,t.last=t.index===t.count-1,t.even=t.index%2==0,t.odd=!t.even}_getEmbeddedViewArgs(t,e){return{templateRef:this._template,context:{$implicit:t.item,cdkVirtualForOf:this._cdkVirtualForOf,index:-1,count:-1,first:!1,last:!1,odd:!1,even:!1},index:e}}}nB.ɵfac=function t(e){return new(e||nB)(gf(Fb),gf(Eb),gf(hb),gf(WL),gf(tB,4),gf(mC))},nB.ɵdir=ho({type:nB,selectors:[["","cdkVirtualFor","","cdkVirtualForOf",""]],inputs:{cdkVirtualForOf:"cdkVirtualForOf",cdkVirtualForTrackBy:"cdkVirtualForTrackBy",cdkVirtualForTemplate:"cdkVirtualForTemplate",cdkVirtualForTemplateCacheSize:"cdkVirtualForTemplateCacheSize"},features:[jh([{provide:WL,useClass:VL}])]}),nB.ctorParameters=()=>[{type:Fb},{type:Eb},{type:hb},{type:VL,decorators:[{type:Lr,args:[WL]}]},{type:tB,decorators:[{type:jr}]},{type:mC}],nB.propDecorators={cdkVirtualForOf:[{type:R_}],cdkVirtualForTrackBy:[{type:R_}],cdkVirtualForTemplate:[{type:R_}],cdkVirtualForTemplateCacheSize:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(nB,[{type:k_,args:[{selector:"[cdkVirtualFor][cdkVirtualForOf]",providers:[{provide:WL,useClass:VL}]}]}],(function(){return[{type:Fb},{type:Eb},{type:hb},{type:VL,decorators:[{type:Lr,args:[WL]}]},{type:tB,decorators:[{type:jr}]},{type:mC}]}),{cdkVirtualForOf:[{type:R_}],cdkVirtualForTrackBy:[{type:R_}],cdkVirtualForTemplate:[{type:R_}],cdkVirtualForTemplateCacheSize:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class oB{}oB.ɵfac=function t(e){return new(e||oB)},oB.ɵmod=fo({type:oB}),oB.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(oB,[{type:L_,args:[{exports:[JL],declarations:[JL]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(oB,{declarations:[JL],exports:[JL]});class iB{}function aB(){throw Error("Host already has a portal attached")}iB.ɵfac=function t(e){return new(e||iB)},iB.ɵmod=fo({type:iB}),iB.ɵinj=Dn({imports:[[OF,dH,oB],OF,oB]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(iB,[{type:L_,args:[{imports:[OF,dH,oB],exports:[OF,oB,XL,nB,tB],declarations:[XL,nB,tB]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(iB,{declarations:function(){return[XL,nB,tB]},imports:function(){return[OF,dH,oB]},exports:function(){return[OF,oB,XL,nB,tB]}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class rB{attach(t){return("undefined"==typeof ngDevMode||ngDevMode)&&(null==t&&(function e(){throw Error("Attempting to attach a portal to a null PortalOutlet")})(),t.hasAttached()&&aB()),this._attachedHost=t,t.attach(this)}detach(){let t=this._attachedHost;null!=t?(this._attachedHost=null,t.detach()):("undefined"==typeof ngDevMode||ngDevMode)&&(function e(){throw Error("Attempting to detach a portal that is not attached to a host")})()}get isAttached(){return null!=this._attachedHost}setAttachedHost(t){this._attachedHost=t}}class sB extends rB{constructor(t,e,n,o){super(),this.component=t,this.viewContainerRef=e,this.injector=n,this.componentFactoryResolver=o}}class lB extends rB{constructor(t,e,n){super(),this.templateRef=t,this.viewContainerRef=e,this.context=n}get origin(){return this.templateRef.elementRef}attach(t,e=this.context){return this.context=e,super.attach(t)}detach(){return this.context=void 0,super.detach()}}class cB extends rB{constructor(t){super(),this.element=t instanceof Kh?t.nativeElement:t}}class dB{constructor(){this._isDisposed=!1,this.attachDomPortal=null}hasAttached(){return!!this._attachedPortal}attach(t){return("undefined"==typeof ngDevMode||ngDevMode)&&(t||(function e(){throw Error("Must provide a portal to attach")})(),this.hasAttached()&&aB(),this._isDisposed&&(function n(){throw Error("This PortalOutlet has already been disposed")})()),t instanceof sB?(this._attachedPortal=t,this.attachComponentPortal(t)):t instanceof lB?(this._attachedPortal=t,this.attachTemplatePortal(t)):this.attachDomPortal&&t instanceof cB?(this._attachedPortal=t,this.attachDomPortal(t)):void(("undefined"==typeof ngDevMode||ngDevMode)&&(function o(){throw Error("Attempting to attach an unknown Portal type. BasePortalOutlet accepts either a ComponentPortal or a TemplatePortal.")})())}detach(){this._attachedPortal&&(this._attachedPortal.setAttachedHost(null),this._attachedPortal=null),this._invokeDisposeFn()}dispose(){this.hasAttached()&&this.detach(),this._invokeDisposeFn(),this._isDisposed=!0}setDisposeFn(t){this._disposeFn=t}_invokeDisposeFn(){this._disposeFn&&(this._disposeFn(),this._disposeFn=null)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class pB extends dB{constructor(t,e,n,o,i){super(),this.outletElement=t,this._componentFactoryResolver=e,this._appRef=n,this._defaultInjector=o,this.attachDomPortal=t=>{if(!this._document&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Cannot attach DOM portal without _document constructor parameter");const e=t.element;if(!e.parentNode&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("DOM portal content must be attached to a parent node.");const n=this._document.createComment("dom-portal");e.parentNode.insertBefore(n,e),this.outletElement.appendChild(e),this._attachedPortal=t,super.setDisposeFn((()=>{n.parentNode&&n.parentNode.replaceChild(e,n)}))},this._document=i}attachComponentPortal(t){const e=(t.componentFactoryResolver||this._componentFactoryResolver).resolveComponentFactory(t.component);let n;return t.viewContainerRef?(n=t.viewContainerRef.createComponent(e,t.viewContainerRef.length,t.injector||t.viewContainerRef.injector),this.setDisposeFn((()=>n.destroy()))):(n=e.create(t.injector||this._defaultInjector),this._appRef.attachView(n.hostView),this.setDisposeFn((()=>{this._appRef.detachView(n.hostView),n.destroy()}))),this.outletElement.appendChild(this._getComponentRootNode(n)),this._attachedPortal=t,n}attachTemplatePortal(t){let e=t.viewContainerRef,n=e.createEmbeddedView(t.templateRef,t.context);return n.rootNodes.forEach((t=>this.outletElement.appendChild(t))),n.detectChanges(),this.setDisposeFn((()=>{let t=e.indexOf(n);-1!==t&&e.remove(t)})),this._attachedPortal=t,n}dispose(){super.dispose(),null!=this.outletElement.parentNode&&this.outletElement.parentNode.removeChild(this.outletElement)}_getComponentRootNode(t){return t.hostView.rootNodes[0]}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class mB extends lB{constructor(t,e){super(t,e)}}mB.ɵfac=function t(e){return new(e||mB)(gf(Eb),gf(Fb))},mB.ɵdir=ho({type:mB,selectors:[["","cdkPortal",""]],exportAs:["cdkPortal"],features:[Qp]}),mB.ctorParameters=()=>[{type:Eb},{type:Fb}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(mB,[{type:k_,args:[{selector:"[cdkPortal]",exportAs:"cdkPortal"}]}],(function(){return[{type:Eb},{type:Fb}]}),null);class fB extends mB{}fB.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(fB)))(n||fB)}})(),fB.ɵdir=ho({type:fB,selectors:[["","cdk-portal",""],["","portal",""]],exportAs:["cdkPortal"],features:[jh([{provide:mB,useExisting:fB}]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(fB,[{type:k_,args:[{selector:"[cdk-portal], [portal]",exportAs:"cdkPortal",providers:[{provide:mB,useExisting:fB}]}]}],null,null);class uB extends dB{constructor(t,e,n){super(),this._componentFactoryResolver=t,this._viewContainerRef=e,this._isInitialized=!1,this.attached=new Sy,this.attachDomPortal=t=>{if(!this._document&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Cannot attach DOM portal without _document constructor parameter");const e=t.element;if(!e.parentNode&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("DOM portal content must be attached to a parent node.");const n=this._document.createComment("dom-portal");t.setAttachedHost(this),e.parentNode.insertBefore(n,e),this._getRootNode().appendChild(e),this._attachedPortal=t,super.setDisposeFn((()=>{n.parentNode&&n.parentNode.replaceChild(e,n)}))},this._document=n}get portal(){return this._attachedPortal}set portal(t){(!this.hasAttached()||t||this._isInitialized)&&(this.hasAttached()&&super.detach(),t&&super.attach(t),this._attachedPortal=t)}get attachedRef(){return this._attachedRef}ngOnInit(){this._isInitialized=!0}ngOnDestroy(){super.dispose(),this._attachedPortal=null,this._attachedRef=null}attachComponentPortal(t){t.setAttachedHost(this);const e=null!=t.viewContainerRef?t.viewContainerRef:this._viewContainerRef,n=(t.componentFactoryResolver||this._componentFactoryResolver).resolveComponentFactory(t.component),o=e.createComponent(n,e.length,t.injector||e.injector);return e!==this._viewContainerRef&&this._getRootNode().appendChild(o.hostView.rootNodes[0]),super.setDisposeFn((()=>o.destroy())),this._attachedPortal=t,this._attachedRef=o,this.attached.emit(o),o}attachTemplatePortal(t){t.setAttachedHost(this);const e=this._viewContainerRef.createEmbeddedView(t.templateRef,t.context);return super.setDisposeFn((()=>this._viewContainerRef.clear())),this._attachedPortal=t,this._attachedRef=e,this.attached.emit(e),e}_getRootNode(){const t=this._viewContainerRef.element.nativeElement;return t.nodeType===t.ELEMENT_NODE?t:t.parentNode}}uB.ɵfac=function t(e){return new(e||uB)(gf(Gh),gf(Fb),gf(sM))},uB.ɵdir=ho({type:uB,selectors:[["","cdkPortalOutlet",""]],inputs:{portal:["cdkPortalOutlet","portal"]},outputs:{attached:"attached"},exportAs:["cdkPortalOutlet"],features:[Qp]}),uB.ctorParameters=()=>[{type:Gh},{type:Fb},{type:void 0,decorators:[{type:Lr,args:[sM]}]}],uB.propDecorators={attached:[{type:E_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(uB,[{type:k_,args:[{selector:"[cdkPortalOutlet]",exportAs:"cdkPortalOutlet",inputs:["portal: cdkPortalOutlet"]}]}],(function(){return[{type:Gh},{type:Fb},{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),{attached:[{type:E_}]});class gB extends uB{}gB.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(gB)))(n||gB)}})(),gB.ɵdir=ho({type:gB,selectors:[["","cdkPortalHost",""],["","portalHost",""]],inputs:{portal:["cdkPortalHost","portal"]},exportAs:["cdkPortalHost"],features:[jh([{provide:uB,useExisting:gB}]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(gB,[{type:k_,args:[{selector:"[cdkPortalHost], [portalHost]",exportAs:"cdkPortalHost",inputs:["portal: cdkPortalHost"],providers:[{provide:uB,useExisting:gB}]}]}],null,null);class hB{}hB.ɵfac=function t(e){return new(e||hB)},hB.ɵmod=fo({type:hB}),hB.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(hB,[{type:L_,args:[{exports:[mB,uB,fB,gB],declarations:[mB,uB,fB,gB]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(hB,{declarations:[mB,uB,fB,gB],exports:[mB,uB,fB,gB]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const bB=yH();class yB{constructor(t,e){this._viewportRuler=t,this._previousHTMLStyles={top:"",left:""},this._isEnabled=!1,this._document=e}attach(){}enable(){if(this._canBeEnabled()){const t=this._document.documentElement;this._previousScrollPosition=this._viewportRuler.getViewportScrollPosition(),this._previousHTMLStyles.left=t.style.left||"",this._previousHTMLStyles.top=t.style.top||"",t.style.left=aH(-this._previousScrollPosition.left),t.style.top=aH(-this._previousScrollPosition.top),t.classList.add("cdk-global-scrollblock"),this._isEnabled=!0}}disable(){if(this._isEnabled){const t=this._document.documentElement,e=t.style,n=this._document.body.style,o=e.scrollBehavior||"",i=n.scrollBehavior||"";this._isEnabled=!1,e.left=this._previousHTMLStyles.left,e.top=this._previousHTMLStyles.top,t.classList.remove("cdk-global-scrollblock"),bB&&(e.scrollBehavior=n.scrollBehavior="auto"),window.scroll(this._previousScrollPosition.left,this._previousScrollPosition.top),bB&&(e.scrollBehavior=o,n.scrollBehavior=i)}}_canBeEnabled(){if(this._document.documentElement.classList.contains("cdk-global-scrollblock")||this._isEnabled)return!1;const t=this._document.body,e=this._viewportRuler.getViewportSize();return t.scrollHeight>e.height||t.scrollWidth>e.width}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function _B(){return Error("Scroll strategy has already been attached.")}class CB{constructor(t,e,n,o){this._scrollDispatcher=t,this._ngZone=e,this._viewportRuler=n,this._config=o,this._scrollSubscription=null,this._detach=()=>{this.disable(),this._overlayRef.hasAttached()&&this._ngZone.run((()=>this._overlayRef.detach()))}}attach(t){if(this._overlayRef&&("undefined"==typeof ngDevMode||ngDevMode))throw _B();this._overlayRef=t}enable(){if(this._scrollSubscription)return;const t=this._scrollDispatcher.scrolled(0);this._config&&this._config.threshold&&this._config.threshold>1?(this._initialScrollPosition=this._viewportRuler.getViewportScrollPosition().top,this._scrollSubscription=t.subscribe((()=>{const t=this._viewportRuler.getViewportScrollPosition().top;Math.abs(t-this._initialScrollPosition)>this._config.threshold?this._detach():this._overlayRef.updatePosition()}))):this._scrollSubscription=t.subscribe(this._detach)}disable(){this._scrollSubscription&&(this._scrollSubscription.unsubscribe(),this._scrollSubscription=null)}detach(){this.disable(),this._overlayRef=null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class MB{enable(){}disable(){}attach(){}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function vB(t,e){return e.some((e=>t.bottom<e.top||t.top>e.bottom||t.right<e.left||t.left>e.right))}function xB(t,e){return e.some((e=>t.top<e.top||t.bottom>e.bottom||t.left<e.left||t.right>e.right))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class OB{constructor(t,e,n,o){this._scrollDispatcher=t,this._viewportRuler=e,this._ngZone=n,this._config=o,this._scrollSubscription=null}attach(t){if(this._overlayRef&&("undefined"==typeof ngDevMode||ngDevMode))throw _B();this._overlayRef=t}enable(){if(!this._scrollSubscription){this._scrollSubscription=this._scrollDispatcher.scrolled(this._config?this._config.scrollThrottle:0).subscribe((()=>{if(this._overlayRef.updatePosition(),this._config&&this._config.autoClose){const t=this._overlayRef.overlayElement.getBoundingClientRect(),{width:e,height:n}=this._viewportRuler.getViewportSize();vB(t,[{width:e,height:n,bottom:n,right:e,top:0,left:0}])&&(this.disable(),this._ngZone.run((()=>this._overlayRef.detach())))}}))}}disable(){this._scrollSubscription&&(this._scrollSubscription.unsubscribe(),this._scrollSubscription=null)}detach(){this.disable(),this._overlayRef=null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class PB{constructor(t,e,n,o){this._scrollDispatcher=t,this._viewportRuler=e,this._ngZone=n,this.noop=()=>new MB,this.close=t=>new CB(this._scrollDispatcher,this._ngZone,this._viewportRuler,t),this.block=()=>new yB(this._viewportRuler,this._document),this.reposition=t=>new OB(this._scrollDispatcher,this._viewportRuler,this._ngZone,t),this._document=o}}PB.ɵfac=function t(e){return new(e||PB)(Tr(KL),Tr(QL),Tr(mC),Tr(sM))},PB.ɵprov=Sn({factory:function t(){return new PB(Tr(KL),Tr(QL),Tr(mC),Tr(sM))},token:PB,providedIn:"root"}),PB.ctorParameters=()=>[{type:KL},{type:QL},{type:mC},{type:void 0,decorators:[{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(PB,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:KL},{type:QL},{type:mC},{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class wB{constructor(t){if(this.scrollStrategy=new MB,this.panelClass="",this.hasBackdrop=!1,this.backdropClass="cdk-overlay-dark-backdrop",this.disposeOnNavigation=!1,t){const e=Object.keys(t);for(const n of e)void 0!==t[n]&&(this[n]=t[n])}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class kB{constructor(t,e,n,o,i){this.offsetX=n,this.offsetY=o,this.panelClass=i,this.originX=t.originX,this.originY=t.originY,this.overlayX=e.overlayX,this.overlayY=e.overlayY}}class SB{}class DB{constructor(t,e){this.connectionPair=t,this.scrollableViewProperties=e}}function RB(t,e){if("top"!==e&&"bottom"!==e&&"center"!==e)throw Error(`ConnectedPosition: Invalid ${t} "${e}". Expected "top", "bottom" or "center".`)}function EB(t,e){if("start"!==e&&"end"!==e&&"center"!==e)throw Error(`ConnectedPosition: Invalid ${t} "${e}". Expected "start", "end" or "center".`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */DB.ctorParameters=()=>[{type:kB},{type:SB,decorators:[{type:Br}]}];class AB{constructor(t){this._attachedOverlays=[],this._document=t}ngOnDestroy(){this.detach()}add(t){this.remove(t),this._attachedOverlays.push(t)}remove(t){const e=this._attachedOverlays.indexOf(t);e>-1&&this._attachedOverlays.splice(e,1),0===this._attachedOverlays.length&&this.detach()}}AB.ɵfac=function t(e){return new(e||AB)(Tr(sM))},AB.ɵprov=Sn({factory:function t(){return new AB(Tr(sM))},token:AB,providedIn:"root"}),AB.ctorParameters=()=>[{type:void 0,decorators:[{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(AB,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class TB extends AB{constructor(t){super(t),this._keydownListener=t=>{const e=this._attachedOverlays;for(let n=e.length-1;n>-1;n--)if(e[n]._keydownEvents.observers.length>0){e[n]._keydownEvents.next(t);break}}}add(t){super.add(t),this._isAttached||(this._document.body.addEventListener("keydown",this._keydownListener),this._isAttached=!0)}detach(){this._isAttached&&(this._document.body.removeEventListener("keydown",this._keydownListener),this._isAttached=!1)}}TB.ɵfac=function t(e){return new(e||TB)(Tr(sM))},TB.ɵprov=Sn({factory:function t(){return new TB(Tr(sM))},token:TB,providedIn:"root"}),TB.ctorParameters=()=>[{type:void 0,decorators:[{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(TB,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class NB extends AB{constructor(t,e){super(t),this._platform=e,this._cursorStyleIsSet=!1,this._clickListener=t=>{const e=vH(t),n=this._attachedOverlays.slice();for(let o=n.length-1;o>-1;o--){const i=n[o];if(!(i._outsidePointerEvents.observers.length<1)&&i.hasAttached()){if(i.overlayElement.contains(e))break;i._outsidePointerEvents.next(t)}}}}add(t){if(super.add(t),!this._isAttached){const t=this._document.body;t.addEventListener("click",this._clickListener,!0),t.addEventListener("auxclick",this._clickListener,!0),t.addEventListener("contextmenu",this._clickListener,!0),this._platform.IOS&&!this._cursorStyleIsSet&&(this._cursorOriginalValue=t.style.cursor,t.style.cursor="pointer",this._cursorStyleIsSet=!0),this._isAttached=!0}}detach(){if(this._isAttached){const t=this._document.body;t.removeEventListener("click",this._clickListener,!0),t.removeEventListener("auxclick",this._clickListener,!0),t.removeEventListener("contextmenu",this._clickListener,!0),this._platform.IOS&&this._cursorStyleIsSet&&(t.style.cursor=this._cursorOriginalValue,this._cursorStyleIsSet=!1),this._isAttached=!1}}}NB.ɵfac=function t(e){return new(e||NB)(Tr(sM),Tr(cH))},NB.ɵprov=Sn({factory:function t(){return new NB(Tr(sM),Tr(cH))},token:NB,providedIn:"root"}),NB.ctorParameters=()=>[{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:cH}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(NB,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:cH}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const zB="undefined"!=typeof window?window:{},IB=void 0!==zB.__karma__&&!!zB.__karma__||void 0!==zB.jasmine&&!!zB.jasmine||void 0!==zB.jest&&!!zB.jest||void 0!==zB.Mocha&&!!zB.Mocha;class HB{constructor(t,e){this._platform=e,this._document=t}ngOnDestroy(){const t=this._containerElement;t&&t.parentNode&&t.parentNode.removeChild(t)}getContainerElement(){return this._containerElement||this._createContainer(),this._containerElement}_createContainer(){const t="cdk-overlay-container";if(this._platform.isBrowser||IB){const e=this._document.querySelectorAll(`.${t}[platform="server"], .${t}[platform="test"]`);for(let t=0;t<e.length;t++)e[t].parentNode.removeChild(e[t])}const e=this._document.createElement("div");e.classList.add(t),IB?e.setAttribute("platform","test"):this._platform.isBrowser||e.setAttribute("platform","server"),this._document.body.appendChild(e),this._containerElement=e}}HB.ɵfac=function t(e){return new(e||HB)(Tr(sM),Tr(cH))},HB.ɵprov=Sn({factory:function t(){return new HB(Tr(sM),Tr(cH))},token:HB,providedIn:"root"}),HB.ctorParameters=()=>[{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:cH}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(HB,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:cH}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class FB{constructor(t,e,n,o,i,a,r,s,l){this._portalOutlet=t,this._host=e,this._pane=n,this._config=o,this._ngZone=i,this._keyboardDispatcher=a,this._document=r,this._location=s,this._outsideClickDispatcher=l,this._backdropElement=null,this._backdropClick=new B,this._attachments=new B,this._detachments=new B,this._locationChanges=u.EMPTY,this._backdropClickHandler=t=>this._backdropClick.next(t),this._keydownEvents=new B,this._outsidePointerEvents=new B,o.scrollStrategy&&(this._scrollStrategy=o.scrollStrategy,this._scrollStrategy.attach(this)),this._positionStrategy=o.positionStrategy}get overlayElement(){return this._pane}get backdropElement(){return this._backdropElement}get hostElement(){return this._host}attach(t){let e=this._portalOutlet.attach(t);return!this._host.parentElement&&this._previousHostParent&&this._previousHostParent.appendChild(this._host),this._positionStrategy&&this._positionStrategy.attach(this),this._updateStackingOrder(),this._updateElementSize(),this._updateElementDirection(),this._scrollStrategy&&this._scrollStrategy.enable(),this._ngZone.onStable.pipe(ve(1)).subscribe((()=>{this.hasAttached()&&this.updatePosition()})),this._togglePointerEvents(!0),this._config.hasBackdrop&&this._attachBackdrop(),this._config.panelClass&&this._toggleClasses(this._pane,this._config.panelClass,!0),this._attachments.next(),this._keyboardDispatcher.add(this),this._config.disposeOnNavigation&&(this._locationChanges=this._location.subscribe((()=>this.dispose()))),this._outsideClickDispatcher.add(this),e}detach(){if(!this.hasAttached())return;this.detachBackdrop(),this._togglePointerEvents(!1),this._positionStrategy&&this._positionStrategy.detach&&this._positionStrategy.detach(),this._scrollStrategy&&this._scrollStrategy.disable();const t=this._portalOutlet.detach();return this._detachments.next(),this._keyboardDispatcher.remove(this),this._detachContentWhenStable(),this._locationChanges.unsubscribe(),this._outsideClickDispatcher.remove(this),t}dispose(){const t=this.hasAttached();this._positionStrategy&&this._positionStrategy.dispose(),this._disposeScrollStrategy(),this.detachBackdrop(),this._locationChanges.unsubscribe(),this._keyboardDispatcher.remove(this),this._portalOutlet.dispose(),this._attachments.complete(),this._backdropClick.complete(),this._keydownEvents.complete(),this._outsidePointerEvents.complete(),this._outsideClickDispatcher.remove(this),this._host&&this._host.parentNode&&(this._host.parentNode.removeChild(this._host),this._host=null),this._previousHostParent=this._pane=null,t&&this._detachments.next(),this._detachments.complete()}hasAttached(){return this._portalOutlet.hasAttached()}backdropClick(){return this._backdropClick}attachments(){return this._attachments}detachments(){return this._detachments}keydownEvents(){return this._keydownEvents}outsidePointerEvents(){return this._outsidePointerEvents}getConfig(){return this._config}updatePosition(){this._positionStrategy&&this._positionStrategy.apply()}updatePositionStrategy(t){t!==this._positionStrategy&&(this._positionStrategy&&this._positionStrategy.dispose(),this._positionStrategy=t,this.hasAttached()&&(t.attach(this),this.updatePosition()))}updateSize(t){this._config=Object.assign(Object.assign({},this._config),t),this._updateElementSize()}setDirection(t){this._config=Object.assign(Object.assign({},this._config),{direction:t}),this._updateElementDirection()}addPanelClass(t){this._pane&&this._toggleClasses(this._pane,t,!0)}removePanelClass(t){this._pane&&this._toggleClasses(this._pane,t,!1)}getDirection(){const t=this._config.direction;return t?"string"==typeof t?t:t.value:"ltr"}updateScrollStrategy(t){t!==this._scrollStrategy&&(this._disposeScrollStrategy(),this._scrollStrategy=t,this.hasAttached()&&(t.attach(this),t.enable()))}_updateElementDirection(){this._host.setAttribute("dir",this.getDirection())}_updateElementSize(){if(!this._pane)return;const t=this._pane.style;t.width=aH(this._config.width),t.height=aH(this._config.height),t.minWidth=aH(this._config.minWidth),t.minHeight=aH(this._config.minHeight),t.maxWidth=aH(this._config.maxWidth),t.maxHeight=aH(this._config.maxHeight)}_togglePointerEvents(t){this._pane.style.pointerEvents=t?"":"none"}_attachBackdrop(){const t="cdk-overlay-backdrop-showing";this._backdropElement=this._document.createElement("div"),this._backdropElement.classList.add("cdk-overlay-backdrop"),this._config.backdropClass&&this._toggleClasses(this._backdropElement,this._config.backdropClass,!0),this._host.parentElement.insertBefore(this._backdropElement,this._host),this._backdropElement.addEventListener("click",this._backdropClickHandler),"undefined"!=typeof requestAnimationFrame?this._ngZone.runOutsideAngular((()=>{requestAnimationFrame((()=>{this._backdropElement&&this._backdropElement.classList.add(t)}))})):this._backdropElement.classList.add(t)}_updateStackingOrder(){this._host.nextSibling&&this._host.parentNode.appendChild(this._host)}detachBackdrop(){let t,e=this._backdropElement;if(!e)return;let n=()=>{e&&(e.removeEventListener("click",this._backdropClickHandler),e.removeEventListener("transitionend",n),e.parentNode&&e.parentNode.removeChild(e)),this._backdropElement==e&&(this._backdropElement=null),this._config.backdropClass&&this._toggleClasses(e,this._config.backdropClass,!1),clearTimeout(t)};e.classList.remove("cdk-overlay-backdrop-showing"),this._ngZone.runOutsideAngular((()=>{e.addEventListener("transitionend",n)})),e.style.pointerEvents="none",t=this._ngZone.runOutsideAngular((()=>setTimeout(n,500)))}_toggleClasses(t,e,n){const o=t.classList;iH(e).forEach((t=>{t&&(n?o.add(t):o.remove(t))}))}_detachContentWhenStable(){this._ngZone.runOutsideAngular((()=>{const t=this._ngZone.onStable.pipe(je(pe(this._attachments,this._detachments))).subscribe((()=>{this._pane&&this._host&&0!==this._pane.children.length||(this._pane&&this._config.panelClass&&this._toggleClasses(this._pane,this._config.panelClass,!1),this._host&&this._host.parentElement&&(this._previousHostParent=this._host.parentElement,this._previousHostParent.removeChild(this._host)),t.unsubscribe())}))}))}_disposeScrollStrategy(){const t=this._scrollStrategy;t&&(t.disable(),t.detach&&t.detach())}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const LB="cdk-overlay-connected-position-bounding-box",BB=/([A-Za-z%]+)$/;class VB{constructor(t,e,n,o,i){this._viewportRuler=e,this._document=n,this._platform=o,this._overlayContainer=i,this._lastBoundingBoxSize={width:0,height:0},this._isPushed=!1,this._canPush=!0,this._growAfterOpen=!1,this._hasFlexibleDimensions=!0,this._positionLocked=!1,this._viewportMargin=0,this._scrollables=[],this._preferredPositions=[],this._positionChanges=new B,this._resizeSubscription=u.EMPTY,this._offsetX=0,this._offsetY=0,this._appliedPanelClasses=[],this.positionChanges=this._positionChanges,this.setOrigin(t)}get positions(){return this._preferredPositions}attach(t){if(this._overlayRef&&t!==this._overlayRef&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("This position strategy is already attached to an overlay");this._validatePositions(),t.hostElement.classList.add(LB),this._overlayRef=t,this._boundingBox=t.hostElement,this._pane=t.overlayElement,this._isDisposed=!1,this._isInitialRender=!0,this._lastPosition=null,this._resizeSubscription.unsubscribe(),this._resizeSubscription=this._viewportRuler.change().subscribe((()=>{this._isInitialRender=!0,this.apply()}))}apply(){if(this._isDisposed||!this._platform.isBrowser)return;if(!this._isInitialRender&&this._positionLocked&&this._lastPosition)return void this.reapplyLastPosition();this._clearPanelClasses(),this._resetOverlayElementStyles(),this._resetBoundingBoxStyles(),this._viewportRect=this._getNarrowedViewportRect(),this._originRect=this._getOriginRect(),this._overlayRect=this._pane.getBoundingClientRect();const t=this._originRect,e=this._overlayRect,n=this._viewportRect,o=[];let i;for(let a of this._preferredPositions){let r=this._getOriginPoint(t,a),s=this._getOverlayPoint(r,e,a),l=this._getOverlayFit(s,e,n,a);if(l.isCompletelyWithinViewport)return this._isPushed=!1,void this._applyPosition(a,r);this._canFitWithFlexibleDimensions(l,s,n)?o.push({position:a,origin:r,overlayRect:e,boundingBoxRect:this._calculateBoundingBoxRect(r,a)}):(!i||i.overlayFit.visibleArea<l.visibleArea)&&(i={overlayFit:l,overlayPoint:s,originPoint:r,position:a,overlayRect:e})}if(o.length){let t=null,e=-1;for(const n of o){const o=n.boundingBoxRect.width*n.boundingBoxRect.height*(n.position.weight||1);o>e&&(e=o,t=n)}return this._isPushed=!1,void this._applyPosition(t.position,t.origin)}if(this._canPush)return this._isPushed=!0,void this._applyPosition(i.position,i.originPoint);this._applyPosition(i.position,i.originPoint)}detach(){this._clearPanelClasses(),this._lastPosition=null,this._previousPushAmount=null,this._resizeSubscription.unsubscribe()}dispose(){this._isDisposed||(this._boundingBox&&jB(this._boundingBox.style,{top:"",left:"",right:"",bottom:"",height:"",width:"",alignItems:"",justifyContent:""}),this._pane&&this._resetOverlayElementStyles(),this._overlayRef&&this._overlayRef.hostElement.classList.remove(LB),this.detach(),this._positionChanges.complete(),this._overlayRef=this._boundingBox=null,this._isDisposed=!0)}reapplyLastPosition(){if(!this._isDisposed&&(!this._platform||this._platform.isBrowser)){this._originRect=this._getOriginRect(),this._overlayRect=this._pane.getBoundingClientRect(),this._viewportRect=this._getNarrowedViewportRect();const t=this._lastPosition||this._preferredPositions[0],e=this._getOriginPoint(this._originRect,t);this._applyPosition(t,e)}}withScrollableContainers(t){return this._scrollables=t,this}withPositions(t){return this._preferredPositions=t,-1===t.indexOf(this._lastPosition)&&(this._lastPosition=null),this._validatePositions(),this}withViewportMargin(t){return this._viewportMargin=t,this}withFlexibleDimensions(t=!0){return this._hasFlexibleDimensions=t,this}withGrowAfterOpen(t=!0){return this._growAfterOpen=t,this}withPush(t=!0){return this._canPush=t,this}withLockedPosition(t=!0){return this._positionLocked=t,this}setOrigin(t){return this._origin=t,this}withDefaultOffsetX(t){return this._offsetX=t,this}withDefaultOffsetY(t){return this._offsetY=t,this}withTransformOriginOn(t){return this._transformOriginSelector=t,this}_getOriginPoint(t,e){let n,o;if("center"==e.originX)n=t.left+t.width/2;else{const o=this._isRtl()?t.right:t.left,i=this._isRtl()?t.left:t.right;n="start"==e.originX?o:i}return o="center"==e.originY?t.top+t.height/2:"top"==e.originY?t.top:t.bottom,{x:n,y:o}}_getOverlayPoint(t,e,n){let o,i;return o="center"==n.overlayX?-e.width/2:"start"===n.overlayX?this._isRtl()?-e.width:0:this._isRtl()?0:-e.width,i="center"==n.overlayY?-e.height/2:"top"==n.overlayY?0:-e.height,{x:t.x+o,y:t.y+i}}_getOverlayFit(t,e,n,o){const i=WB(e);let{x:a,y:r}=t,s=this._getOffset(o,"x"),l=this._getOffset(o,"y");s&&(a+=s),l&&(r+=l);let c=0-r,d=r+i.height-n.height,p=this._subtractOverflows(i.width,0-a,a+i.width-n.width),m=this._subtractOverflows(i.height,c,d),f=p*m;return{visibleArea:f,isCompletelyWithinViewport:i.width*i.height===f,fitsInViewportVertically:m===i.height,fitsInViewportHorizontally:p==i.width}}_canFitWithFlexibleDimensions(t,e,n){if(this._hasFlexibleDimensions){const o=n.bottom-e.y,i=n.right-e.x,a=UB(this._overlayRef.getConfig().minHeight),r=UB(this._overlayRef.getConfig().minWidth),s=t.fitsInViewportHorizontally||null!=r&&r<=i;return(t.fitsInViewportVertically||null!=a&&a<=o)&&s}return!1}_pushOverlayOnScreen(t,e,n){if(this._previousPushAmount&&this._positionLocked)return{x:t.x+this._previousPushAmount.x,y:t.y+this._previousPushAmount.y};const o=WB(e),i=this._viewportRect,a=Math.max(t.x+o.width-i.width,0),r=Math.max(t.y+o.height-i.height,0),s=Math.max(i.top-n.top-t.y,0),l=Math.max(i.left-n.left-t.x,0);let c=0,d=0;return c=o.width<=i.width?l||-a:t.x<this._viewportMargin?i.left-n.left-t.x:0,d=o.height<=i.height?s||-r:t.y<this._viewportMargin?i.top-n.top-t.y:0,this._previousPushAmount={x:c,y:d},{x:t.x+c,y:t.y+d}}_applyPosition(t,e){if(this._setTransformOrigin(t),this._setOverlayElementStyles(e,t),this._setBoundingBoxStyles(e,t),t.panelClass&&this._addPanelClasses(t.panelClass),this._lastPosition=t,this._positionChanges.observers.length){const e=this._getScrollVisibility(),n=new DB(t,e);this._positionChanges.next(n)}this._isInitialRender=!1}_setTransformOrigin(t){if(!this._transformOriginSelector)return;const e=this._boundingBox.querySelectorAll(this._transformOriginSelector);let n,o=t.overlayY;n="center"===t.overlayX?"center":this._isRtl()?"start"===t.overlayX?"right":"left":"start"===t.overlayX?"left":"right";for(let t=0;t<e.length;t++)e[t].style.transformOrigin=`${n} ${o}`}_calculateBoundingBoxRect(t,e){const n=this._viewportRect,o=this._isRtl();let i,a,r;if("top"===e.overlayY)a=t.y,i=n.height-a+this._viewportMargin;else if("bottom"===e.overlayY)r=n.height-t.y+2*this._viewportMargin,i=n.height-r+this._viewportMargin;else{const e=Math.min(n.bottom-t.y+n.top,t.y),o=this._lastBoundingBoxSize.height;i=2*e,a=t.y-e,i>o&&!this._isInitialRender&&!this._growAfterOpen&&(a=t.y-o/2)}let s,l,c;if("end"===e.overlayX&&!o||"start"===e.overlayX&&o)c=n.width-t.x+this._viewportMargin,s=t.x-this._viewportMargin;else if("start"===e.overlayX&&!o||"end"===e.overlayX&&o)l=t.x,s=n.right-t.x;else{const e=Math.min(n.right-t.x+n.left,t.x),o=this._lastBoundingBoxSize.width;s=2*e,l=t.x-e,s>o&&!this._isInitialRender&&!this._growAfterOpen&&(l=t.x-o/2)}return{top:a,left:l,bottom:r,right:c,width:s,height:i}}_setBoundingBoxStyles(t,e){const n=this._calculateBoundingBoxRect(t,e);this._isInitialRender||this._growAfterOpen||(n.height=Math.min(n.height,this._lastBoundingBoxSize.height),n.width=Math.min(n.width,this._lastBoundingBoxSize.width));const o={};if(this._hasExactPosition())o.top=o.left="0",o.bottom=o.right=o.maxHeight=o.maxWidth="",o.width=o.height="100%";else{const t=this._overlayRef.getConfig().maxHeight,i=this._overlayRef.getConfig().maxWidth;o.height=aH(n.height),o.top=aH(n.top),o.bottom=aH(n.bottom),o.width=aH(n.width),o.left=aH(n.left),o.right=aH(n.right),o.alignItems="center"===e.overlayX?"center":"end"===e.overlayX?"flex-end":"flex-start",o.justifyContent="center"===e.overlayY?"center":"bottom"===e.overlayY?"flex-end":"flex-start",t&&(o.maxHeight=aH(t)),i&&(o.maxWidth=aH(i))}this._lastBoundingBoxSize=n,jB(this._boundingBox.style,o)}_resetBoundingBoxStyles(){jB(this._boundingBox.style,{top:"0",left:"0",right:"0",bottom:"0",height:"",width:"",alignItems:"",justifyContent:""})}_resetOverlayElementStyles(){jB(this._pane.style,{top:"",left:"",bottom:"",right:"",position:"",transform:""})}_setOverlayElementStyles(t,e){const n={},o=this._hasExactPosition(),i=this._hasFlexibleDimensions,a=this._overlayRef.getConfig();if(o){const o=this._viewportRuler.getViewportScrollPosition();jB(n,this._getExactOverlayY(e,t,o)),jB(n,this._getExactOverlayX(e,t,o))}else n.position="static";let r="",s=this._getOffset(e,"x"),l=this._getOffset(e,"y");s&&(r+=`translateX(${s}px) `),l&&(r+=`translateY(${l}px)`),n.transform=r.trim(),a.maxHeight&&(o?n.maxHeight=aH(a.maxHeight):i&&(n.maxHeight="")),a.maxWidth&&(o?n.maxWidth=aH(a.maxWidth):i&&(n.maxWidth="")),jB(this._pane.style,n)}_getExactOverlayY(t,e,n){let o={top:"",bottom:""},i=this._getOverlayPoint(e,this._overlayRect,t);this._isPushed&&(i=this._pushOverlayOnScreen(i,this._overlayRect,n));let a=this._overlayContainer.getContainerElement().getBoundingClientRect().top;if(i.y-=a,"bottom"===t.overlayY){o.bottom=this._document.documentElement.clientHeight-(i.y+this._overlayRect.height)+"px"}else o.top=aH(i.y);return o}_getExactOverlayX(t,e,n){let o,i={left:"",right:""},a=this._getOverlayPoint(e,this._overlayRect,t);if(this._isPushed&&(a=this._pushOverlayOnScreen(a,this._overlayRect,n)),o=this._isRtl()?"end"===t.overlayX?"left":"right":"end"===t.overlayX?"right":"left","right"===o){i.right=this._document.documentElement.clientWidth-(a.x+this._overlayRect.width)+"px"}else i.left=aH(a.x);return i}_getScrollVisibility(){const t=this._getOriginRect(),e=this._pane.getBoundingClientRect(),n=this._scrollables.map((t=>t.getElementRef().nativeElement.getBoundingClientRect()));return{isOriginClipped:xB(t,n),isOriginOutsideView:vB(t,n),isOverlayClipped:xB(e,n),isOverlayOutsideView:vB(e,n)}}_subtractOverflows(t,...e){return e.reduce(((t,e)=>t-Math.max(e,0)),t)}_getNarrowedViewportRect(){const t=this._document.documentElement.clientWidth,e=this._document.documentElement.clientHeight,n=this._viewportRuler.getViewportScrollPosition();return{top:n.top+this._viewportMargin,left:n.left+this._viewportMargin,right:n.left+t-this._viewportMargin,bottom:n.top+e-this._viewportMargin,width:t-2*this._viewportMargin,height:e-2*this._viewportMargin}}_isRtl(){return"rtl"===this._overlayRef.getDirection()}_hasExactPosition(){return!this._hasFlexibleDimensions||this._isPushed}_getOffset(t,e){return"x"===e?null==t.offsetX?this._offsetX:t.offsetX:null==t.offsetY?this._offsetY:t.offsetY}_validatePositions(){if("undefined"==typeof ngDevMode||ngDevMode){if(!this._preferredPositions.length)throw Error("FlexibleConnectedPositionStrategy: At least one position is required.");this._preferredPositions.forEach((t=>{EB("originX",t.originX),RB("originY",t.originY),EB("overlayX",t.overlayX),RB("overlayY",t.overlayY)}))}}_addPanelClasses(t){this._pane&&iH(t).forEach((t=>{""!==t&&-1===this._appliedPanelClasses.indexOf(t)&&(this._appliedPanelClasses.push(t),this._pane.classList.add(t))}))}_clearPanelClasses(){this._pane&&(this._appliedPanelClasses.forEach((t=>{this._pane.classList.remove(t)})),this._appliedPanelClasses=[])}_getOriginRect(){const t=this._origin;if(t instanceof Kh)return t.nativeElement.getBoundingClientRect();if(t instanceof Element)return t.getBoundingClientRect();const e=t.width||0,n=t.height||0;return{top:t.y,bottom:t.y+n,left:t.x,right:t.x+e,height:n,width:e}}}function jB(t,e){for(let n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}function UB(t){if("number"!=typeof t&&null!=t){const[e,n]=t.split(BB);return n&&"px"!==n?null:parseFloat(e)}return t||null}function WB(t){return{top:Math.floor(t.top),right:Math.floor(t.right),bottom:Math.floor(t.bottom),left:Math.floor(t.left),width:Math.floor(t.width),height:Math.floor(t.height)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class GB{constructor(t,e,n,o,i,a,r){this._preferredPositions=[],this._positionStrategy=new VB(n,o,i,a,r).withFlexibleDimensions(!1).withPush(!1).withViewportMargin(0),this.withFallbackPosition(t,e),this.onPositionChange=this._positionStrategy.positionChanges}get positions(){return this._preferredPositions}attach(t){this._overlayRef=t,this._positionStrategy.attach(t),this._direction&&(t.setDirection(this._direction),this._direction=null)}dispose(){this._positionStrategy.dispose()}detach(){this._positionStrategy.detach()}apply(){this._positionStrategy.apply()}recalculateLastPosition(){this._positionStrategy.reapplyLastPosition()}withScrollableContainers(t){this._positionStrategy.withScrollableContainers(t)}withFallbackPosition(t,e,n,o){const i=new kB(t,e,n,o);return this._preferredPositions.push(i),this._positionStrategy.withPositions(this._preferredPositions),this}withDirection(t){return this._overlayRef?this._overlayRef.setDirection(t):this._direction=t,this}withOffsetX(t){return this._positionStrategy.withDefaultOffsetX(t),this}withOffsetY(t){return this._positionStrategy.withDefaultOffsetY(t),this}withLockedPosition(t){return this._positionStrategy.withLockedPosition(t),this}withPositions(t){return this._preferredPositions=t.slice(),this._positionStrategy.withPositions(this._preferredPositions),this}setOrigin(t){return this._positionStrategy.setOrigin(t),this}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const YB="cdk-global-overlay-wrapper";class qB{constructor(){this._cssPosition="static",this._topOffset="",this._bottomOffset="",this._leftOffset="",this._rightOffset="",this._alignItems="",this._justifyContent="",this._width="",this._height=""}attach(t){const e=t.getConfig();this._overlayRef=t,this._width&&!e.width&&t.updateSize({width:this._width}),this._height&&!e.height&&t.updateSize({height:this._height}),t.hostElement.classList.add(YB),this._isDisposed=!1}top(t=""){return this._bottomOffset="",this._topOffset=t,this._alignItems="flex-start",this}left(t=""){return this._rightOffset="",this._leftOffset=t,this._justifyContent="flex-start",this}bottom(t=""){return this._topOffset="",this._bottomOffset=t,this._alignItems="flex-end",this}right(t=""){return this._leftOffset="",this._rightOffset=t,this._justifyContent="flex-end",this}width(t=""){return this._overlayRef?this._overlayRef.updateSize({width:t}):this._width=t,this}height(t=""){return this._overlayRef?this._overlayRef.updateSize({height:t}):this._height=t,this}centerHorizontally(t=""){return this.left(t),this._justifyContent="center",this}centerVertically(t=""){return this.top(t),this._alignItems="center",this}apply(){if(!this._overlayRef||!this._overlayRef.hasAttached())return;const t=this._overlayRef.overlayElement.style,e=this._overlayRef.hostElement.style,n=this._overlayRef.getConfig(),{width:o,height:i,maxWidth:a,maxHeight:r}=n,s=!("100%"!==o&&"100vw"!==o||a&&"100%"!==a&&"100vw"!==a),l=!("100%"!==i&&"100vh"!==i||r&&"100%"!==r&&"100vh"!==r);t.position=this._cssPosition,t.marginLeft=s?"0":this._leftOffset,t.marginTop=l?"0":this._topOffset,t.marginBottom=this._bottomOffset,t.marginRight=this._rightOffset,s?e.justifyContent="flex-start":"center"===this._justifyContent?e.justifyContent="center":"rtl"===this._overlayRef.getConfig().direction?"flex-start"===this._justifyContent?e.justifyContent="flex-end":"flex-end"===this._justifyContent&&(e.justifyContent="flex-start"):e.justifyContent=this._justifyContent,e.alignItems=l?"flex-start":this._alignItems}dispose(){if(this._isDisposed||!this._overlayRef)return;const t=this._overlayRef.overlayElement.style,e=this._overlayRef.hostElement,n=e.style;e.classList.remove(YB),n.justifyContent=n.alignItems=t.marginTop=t.marginBottom=t.marginLeft=t.marginRight=t.position="",this._overlayRef=null,this._isDisposed=!0}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class ZB{constructor(t,e,n,o){this._viewportRuler=t,this._document=e,this._platform=n,this._overlayContainer=o}global(){return new qB}connectedTo(t,e,n){return new GB(e,n,t,this._viewportRuler,this._document,this._platform,this._overlayContainer)}flexibleConnectedTo(t){return new VB(t,this._viewportRuler,this._document,this._platform,this._overlayContainer)}}ZB.ɵfac=function t(e){return new(e||ZB)(Tr(QL),Tr(sM),Tr(cH),Tr(HB))},ZB.ɵprov=Sn({factory:function t(){return new ZB(Tr(QL),Tr(sM),Tr(cH),Tr(HB))},token:ZB,providedIn:"root"}),ZB.ctorParameters=()=>[{type:QL},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:cH},{type:HB}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(ZB,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:QL},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:cH},{type:HB}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let XB=0;class KB{constructor(t,e,n,o,i,a,r,s,l,c,d){this.scrollStrategies=t,this._overlayContainer=e,this._componentFactoryResolver=n,this._positionBuilder=o,this._keyboardDispatcher=i,this._injector=a,this._ngZone=r,this._document=s,this._directionality=l,this._location=c,this._outsideClickDispatcher=d}create(t){const e=this._createHostElement(),n=this._createPaneElement(e),o=this._createPortalOutlet(n),i=new wB(t);return i.direction=i.direction||this._directionality.value,new FB(o,e,n,i,this._ngZone,this._keyboardDispatcher,this._document,this._location,this._outsideClickDispatcher)}position(){return this._positionBuilder}_createPaneElement(t){const e=this._document.createElement("div");return e.id="cdk-overlay-"+XB++,e.classList.add("cdk-overlay-pane"),t.appendChild(e),e}_createHostElement(){const t=this._document.createElement("div");return this._overlayContainer.getContainerElement().appendChild(t),t}_createPortalOutlet(t){return this._appRef||(this._appRef=this._injector.get(HC)),new pB(t,this._componentFactoryResolver,this._appRef,this._injector,this._document)}}KB.ɵfac=function t(e){return new(e||KB)(Tr(PB),Tr(HB),Tr(Gh),Tr(ZB),Tr(TB),Tr(Np),Tr(mC),Tr(sM),Tr(vF),Tr(MM),Tr(NB))},KB.ɵprov=Sn({token:KB,factory:KB.ɵfac}),KB.ctorParameters=()=>[{type:PB},{type:HB},{type:Gh},{type:ZB},{type:TB},{type:Np},{type:mC},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:vF},{type:MM},{type:NB}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(KB,[{type:Um}],(function(){return[{type:PB},{type:HB},{type:Gh},{type:ZB},{type:TB},{type:Np},{type:mC},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:vF},{type:MM},{type:NB}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const JB=[{originX:"start",originY:"bottom",overlayX:"start",overlayY:"top"},{originX:"start",originY:"top",overlayX:"start",overlayY:"bottom"},{originX:"end",originY:"top",overlayX:"end",overlayY:"bottom"},{originX:"end",originY:"bottom",overlayX:"end",overlayY:"top"}],QB=new er("cdk-connected-overlay-scroll-strategy");class $B{constructor(t){this.elementRef=t}}$B.ɵfac=function t(e){return new(e||$B)(gf(Kh))},$B.ɵdir=ho({type:$B,selectors:[["","cdk-overlay-origin",""],["","overlay-origin",""],["","cdkOverlayOrigin",""]],exportAs:["cdkOverlayOrigin"]}),$B.ctorParameters=()=>[{type:Kh}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb($B,[{type:k_,args:[{selector:"[cdk-overlay-origin], [overlay-origin], [cdkOverlayOrigin]",exportAs:"cdkOverlayOrigin"}]}],(function(){return[{type:Kh}]}),null);class tV{constructor(t,e,n,o,i){this._overlay=t,this._dir=i,this._hasBackdrop=!1,this._lockPosition=!1,this._growAfterOpen=!1,this._flexibleDimensions=!1,this._push=!1,this._backdropSubscription=u.EMPTY,this._attachSubscription=u.EMPTY,this._detachSubscription=u.EMPTY,this._positionSubscription=u.EMPTY,this.viewportMargin=0,this.open=!1,this.disableClose=!1,this.backdropClick=new Sy,this.positionChange=new Sy,this.attach=new Sy,this.detach=new Sy,this.overlayKeydown=new Sy,this.overlayOutsideClick=new Sy,this._templatePortal=new lB(e,n),this._scrollStrategyFactory=o,this.scrollStrategy=this._scrollStrategyFactory()}get offsetX(){return this._offsetX}set offsetX(t){this._offsetX=t,this._position&&this._updatePositionStrategy(this._position)}get offsetY(){return this._offsetY}set offsetY(t){this._offsetY=t,this._position&&this._updatePositionStrategy(this._position)}get hasBackdrop(){return this._hasBackdrop}set hasBackdrop(t){this._hasBackdrop=eH(t)}get lockPosition(){return this._lockPosition}set lockPosition(t){this._lockPosition=eH(t)}get flexibleDimensions(){return this._flexibleDimensions}set flexibleDimensions(t){this._flexibleDimensions=eH(t)}get growAfterOpen(){return this._growAfterOpen}set growAfterOpen(t){this._growAfterOpen=eH(t)}get push(){return this._push}set push(t){this._push=eH(t)}get overlayRef(){return this._overlayRef}get dir(){return this._dir?this._dir.value:"ltr"}ngOnDestroy(){this._attachSubscription.unsubscribe(),this._detachSubscription.unsubscribe(),this._backdropSubscription.unsubscribe(),this._positionSubscription.unsubscribe(),this._overlayRef&&this._overlayRef.dispose()}ngOnChanges(t){this._position&&(this._updatePositionStrategy(this._position),this._overlayRef.updateSize({width:this.width,minWidth:this.minWidth,height:this.height,minHeight:this.minHeight}),t.origin&&this.open&&this._position.apply()),t.open&&(this.open?this._attachOverlay():this._detachOverlay())}_createOverlay(){this.positions&&this.positions.length||(this.positions=JB);const t=this._overlayRef=this._overlay.create(this._buildConfig());this._attachSubscription=t.attachments().subscribe((()=>this.attach.emit())),this._detachSubscription=t.detachments().subscribe((()=>this.detach.emit())),t.keydownEvents().subscribe((t=>{this.overlayKeydown.next(t),t.keyCode!==KI||this.disableClose||tH(t)||(t.preventDefault(),this._detachOverlay())})),this._overlayRef.outsidePointerEvents().subscribe((t=>{this.overlayOutsideClick.next(t)}))}_buildConfig(){const t=this._position=this.positionStrategy||this._createPositionStrategy(),e=new wB({direction:this._dir,positionStrategy:t,scrollStrategy:this.scrollStrategy,hasBackdrop:this.hasBackdrop});return(this.width||0===this.width)&&(e.width=this.width),(this.height||0===this.height)&&(e.height=this.height),(this.minWidth||0===this.minWidth)&&(e.minWidth=this.minWidth),(this.minHeight||0===this.minHeight)&&(e.minHeight=this.minHeight),this.backdropClass&&(e.backdropClass=this.backdropClass),this.panelClass&&(e.panelClass=this.panelClass),e}_updatePositionStrategy(t){const e=this.positions.map((t=>({originX:t.originX,originY:t.originY,overlayX:t.overlayX,overlayY:t.overlayY,offsetX:t.offsetX||this.offsetX,offsetY:t.offsetY||this.offsetY,panelClass:t.panelClass||void 0})));return t.setOrigin(this.origin.elementRef).withPositions(e).withFlexibleDimensions(this.flexibleDimensions).withPush(this.push).withGrowAfterOpen(this.growAfterOpen).withViewportMargin(this.viewportMargin).withLockedPosition(this.lockPosition).withTransformOriginOn(this.transformOriginSelector)}_createPositionStrategy(){const t=this._overlay.position().flexibleConnectedTo(this.origin.elementRef);return this._updatePositionStrategy(t),t}_attachOverlay(){this._overlayRef?this._overlayRef.getConfig().hasBackdrop=this.hasBackdrop:this._createOverlay(),this._overlayRef.hasAttached()||this._overlayRef.attach(this._templatePortal),this.hasBackdrop?this._backdropSubscription=this._overlayRef.backdropClick().subscribe((t=>{this.backdropClick.emit(t)})):this._backdropSubscription.unsubscribe(),this._positionSubscription.unsubscribe(),this.positionChange.observers.length>0&&(this._positionSubscription=this._position.positionChanges.pipe(Ue((()=>this.positionChange.observers.length>0))).subscribe((t=>{this.positionChange.emit(t),0===this.positionChange.observers.length&&this._positionSubscription.unsubscribe()})))}_detachOverlay(){this._overlayRef&&this._overlayRef.detach(),this._backdropSubscription.unsubscribe(),this._positionSubscription.unsubscribe()}}tV.ɵfac=function t(e){return new(e||tV)(gf(KB),gf(Eb),gf(Fb),gf(QB),gf(vF,8))},tV.ɵdir=ho({type:tV,selectors:[["","cdk-connected-overlay",""],["","connected-overlay",""],["","cdkConnectedOverlay",""]],inputs:{viewportMargin:["cdkConnectedOverlayViewportMargin","viewportMargin"],open:["cdkConnectedOverlayOpen","open"],disableClose:["cdkConnectedOverlayDisableClose","disableClose"],scrollStrategy:["cdkConnectedOverlayScrollStrategy","scrollStrategy"],offsetX:["cdkConnectedOverlayOffsetX","offsetX"],offsetY:["cdkConnectedOverlayOffsetY","offsetY"],hasBackdrop:["cdkConnectedOverlayHasBackdrop","hasBackdrop"],lockPosition:["cdkConnectedOverlayLockPosition","lockPosition"],flexibleDimensions:["cdkConnectedOverlayFlexibleDimensions","flexibleDimensions"],growAfterOpen:["cdkConnectedOverlayGrowAfterOpen","growAfterOpen"],push:["cdkConnectedOverlayPush","push"],positions:["cdkConnectedOverlayPositions","positions"],origin:["cdkConnectedOverlayOrigin","origin"],positionStrategy:["cdkConnectedOverlayPositionStrategy","positionStrategy"],width:["cdkConnectedOverlayWidth","width"],height:["cdkConnectedOverlayHeight","height"],minWidth:["cdkConnectedOverlayMinWidth","minWidth"],minHeight:["cdkConnectedOverlayMinHeight","minHeight"],backdropClass:["cdkConnectedOverlayBackdropClass","backdropClass"],panelClass:["cdkConnectedOverlayPanelClass","panelClass"],transformOriginSelector:["cdkConnectedOverlayTransformOriginOn","transformOriginSelector"]},outputs:{backdropClick:"backdropClick",positionChange:"positionChange",attach:"attach",detach:"detach",overlayKeydown:"overlayKeydown",overlayOutsideClick:"overlayOutsideClick"},exportAs:["cdkConnectedOverlay"],features:[qo]}),tV.ctorParameters=()=>[{type:KB},{type:Eb},{type:Fb},{type:void 0,decorators:[{type:Lr,args:[QB]}]},{type:vF,decorators:[{type:Br}]}],tV.propDecorators={origin:[{type:R_,args:["cdkConnectedOverlayOrigin"]}],positions:[{type:R_,args:["cdkConnectedOverlayPositions"]}],positionStrategy:[{type:R_,args:["cdkConnectedOverlayPositionStrategy"]}],offsetX:[{type:R_,args:["cdkConnectedOverlayOffsetX"]}],offsetY:[{type:R_,args:["cdkConnectedOverlayOffsetY"]}],width:[{type:R_,args:["cdkConnectedOverlayWidth"]}],height:[{type:R_,args:["cdkConnectedOverlayHeight"]}],minWidth:[{type:R_,args:["cdkConnectedOverlayMinWidth"]}],minHeight:[{type:R_,args:["cdkConnectedOverlayMinHeight"]}],backdropClass:[{type:R_,args:["cdkConnectedOverlayBackdropClass"]}],panelClass:[{type:R_,args:["cdkConnectedOverlayPanelClass"]}],viewportMargin:[{type:R_,args:["cdkConnectedOverlayViewportMargin"]}],scrollStrategy:[{type:R_,args:["cdkConnectedOverlayScrollStrategy"]}],open:[{type:R_,args:["cdkConnectedOverlayOpen"]}],disableClose:[{type:R_,args:["cdkConnectedOverlayDisableClose"]}],transformOriginSelector:[{type:R_,args:["cdkConnectedOverlayTransformOriginOn"]}],hasBackdrop:[{type:R_,args:["cdkConnectedOverlayHasBackdrop"]}],lockPosition:[{type:R_,args:["cdkConnectedOverlayLockPosition"]}],flexibleDimensions:[{type:R_,args:["cdkConnectedOverlayFlexibleDimensions"]}],growAfterOpen:[{type:R_,args:["cdkConnectedOverlayGrowAfterOpen"]}],push:[{type:R_,args:["cdkConnectedOverlayPush"]}],backdropClick:[{type:E_}],positionChange:[{type:E_}],attach:[{type:E_}],detach:[{type:E_}],overlayKeydown:[{type:E_}],overlayOutsideClick:[{type:E_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(tV,[{type:k_,args:[{selector:"[cdk-connected-overlay], [connected-overlay], [cdkConnectedOverlay]",exportAs:"cdkConnectedOverlay"}]}],(function(){return[{type:KB},{type:Eb},{type:Fb},{type:void 0,decorators:[{type:Lr,args:[QB]}]},{type:vF,decorators:[{type:Br}]}]}),{viewportMargin:[{type:R_,args:["cdkConnectedOverlayViewportMargin"]}],open:[{type:R_,args:["cdkConnectedOverlayOpen"]}],disableClose:[{type:R_,args:["cdkConnectedOverlayDisableClose"]}],backdropClick:[{type:E_}],positionChange:[{type:E_}],attach:[{type:E_}],detach:[{type:E_}],overlayKeydown:[{type:E_}],overlayOutsideClick:[{type:E_}],scrollStrategy:[{type:R_,args:["cdkConnectedOverlayScrollStrategy"]}],offsetX:[{type:R_,args:["cdkConnectedOverlayOffsetX"]}],offsetY:[{type:R_,args:["cdkConnectedOverlayOffsetY"]}],hasBackdrop:[{type:R_,args:["cdkConnectedOverlayHasBackdrop"]}],lockPosition:[{type:R_,args:["cdkConnectedOverlayLockPosition"]}],flexibleDimensions:[{type:R_,args:["cdkConnectedOverlayFlexibleDimensions"]}],growAfterOpen:[{type:R_,args:["cdkConnectedOverlayGrowAfterOpen"]}],push:[{type:R_,args:["cdkConnectedOverlayPush"]}],positions:[{type:R_,args:["cdkConnectedOverlayPositions"]}],origin:[{type:R_,args:["cdkConnectedOverlayOrigin"]}],positionStrategy:[{type:R_,args:["cdkConnectedOverlayPositionStrategy"]}],width:[{type:R_,args:["cdkConnectedOverlayWidth"]}],height:[{type:R_,args:["cdkConnectedOverlayHeight"]}],minWidth:[{type:R_,args:["cdkConnectedOverlayMinWidth"]}],minHeight:[{type:R_,args:["cdkConnectedOverlayMinHeight"]}],backdropClass:[{type:R_,args:["cdkConnectedOverlayBackdropClass"]}],panelClass:[{type:R_,args:["cdkConnectedOverlayPanelClass"]}],transformOriginSelector:[{type:R_,args:["cdkConnectedOverlayTransformOriginOn"]}]});const eV={provide:QB,deps:[KB],useFactory:function nV(t){return()=>t.scrollStrategies.reposition()}};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class oV{}oV.ɵfac=function t(e){return new(e||oV)},oV.ɵmod=fo({type:oV}),oV.ɵinj=Dn({providers:[KB,eV],imports:[[OF,hB,iB],iB]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(oV,[{type:L_,args:[{imports:[OF,hB,iB],exports:[tV,$B,iB],declarations:[tV,$B],providers:[KB,eV]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(oV,{declarations:function(){return[tV,$B]},imports:function(){return[OF,hB,iB]},exports:function(){return[tV,$B,iB]}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class iV extends HB{constructor(t,e){super(t,e)}ngOnDestroy(){super.ngOnDestroy(),this._fullScreenEventName&&this._fullScreenListener&&this._document.removeEventListener(this._fullScreenEventName,this._fullScreenListener)}_createContainer(){super._createContainer(),this._adjustParentForFullscreenChange(),this._addFullscreenChangeListener((()=>this._adjustParentForFullscreenChange()))}_adjustParentForFullscreenChange(){if(!this._containerElement)return;(this.getFullscreenElement()||this._document.body).appendChild(this._containerElement)}_addFullscreenChangeListener(t){const e=this._getEventName();e&&(this._fullScreenListener&&this._document.removeEventListener(e,this._fullScreenListener),this._document.addEventListener(e,t),this._fullScreenListener=t)}_getEventName(){if(!this._fullScreenEventName){const t=this._document;t.fullscreenEnabled?this._fullScreenEventName="fullscreenchange":t.webkitFullscreenEnabled?this._fullScreenEventName="webkitfullscreenchange":t.mozFullScreenEnabled?this._fullScreenEventName="mozfullscreenchange":t.msFullscreenEnabled&&(this._fullScreenEventName="MSFullscreenChange")}return this._fullScreenEventName}getFullscreenElement(){const t=this._document;return t.fullscreenElement||t.webkitFullscreenElement||t.mozFullScreenElement||t.msFullscreenElement||null}}iV.ɵfac=function t(e){return new(e||iV)(Tr(sM),Tr(cH))},iV.ɵprov=Sn({factory:function t(){return new iV(Tr(sM),Tr(cH))},token:iV,providedIn:"root"}),iV.ctorParameters=()=>[{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:cH}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(iV,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:cH}]}),null);class aV{}aV.ɵfac=function t(e){return new(e||aV)},aV.ɵmod=fo({type:aV}),aV.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(aV,[{type:L_,args:[{}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const rV=new Set;let sV;class lV{constructor(t){this._platform=t,this._matchMedia=this._platform.isBrowser&&window.matchMedia?window.matchMedia.bind(window):cV}matchMedia(t){return this._platform.WEBKIT&&(function e(t){if(rV.has(t))return;try{sV||(sV=document.createElement("style"),sV.setAttribute("type","text/css"),document.head.appendChild(sV)),sV.sheet&&(sV.sheet.insertRule(`@media ${t} {.fx-query-test{ }}`,0),rV.add(t))}catch(t){console.error(t)}})(t),this._matchMedia(t)}}function cV(t){return{matches:"all"===t||""===t,media:t,addListener:()=>{},removeListener:()=>{}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */lV.ɵfac=function t(e){return new(e||lV)(Tr(cH))},lV.ɵprov=Sn({factory:function t(){return new lV(Tr(cH))},token:lV,providedIn:"root"}),lV.ctorParameters=()=>[{type:cH}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(lV,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:cH}]}),null);class dV{constructor(t,e){this._mediaMatcher=t,this._zone=e,this._queries=new Map,this._destroySubject=new B}ngOnDestroy(){this._destroySubject.next(),this._destroySubject.complete()}isMatched(t){return pV(iH(t)).some((t=>this._registerQuery(t).mql.matches))}observe(t){let e=Kt(pV(iH(t)).map((t=>this._registerQuery(t).observable)));return e=ne(e.pipe(ve(1)),e.pipe(Le(1),Ce(0))),e.pipe(Vt((t=>{const e={matches:!1,breakpoints:{}};return t.forEach((({matches:t,query:n})=>{e.matches=e.matches||t,e.breakpoints[n]=t})),e})))}_registerQuery(t){if(this._queries.has(t))return this._queries.get(t);const e=this._mediaMatcher.matchMedia(t),n={observable:new T((t=>{const n=e=>this._zone.run((()=>t.next(e)));return e.addListener(n),()=>{e.removeListener(n)}})).pipe(Be(e),Vt((({matches:e})=>({query:t,matches:e}))),je(this._destroySubject)),mql:e};return this._queries.set(t,n),n}}function pV(t){return t.map((t=>t.split(","))).reduce(((t,e)=>t.concat(e))).map((t=>t.trim()))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */dV.ɵfac=function t(e){return new(e||dV)(Tr(lV),Tr(mC))},dV.ɵprov=Sn({factory:function t(){return new dV(Tr(lV),Tr(mC))},token:dV,providedIn:"root"}),dV.ctorParameters=()=>[{type:lV},{type:mC}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(dV,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:lV},{type:mC}]}),null);const mV="(max-width: 599.98px) and (orientation: portrait), (max-width: 959.98px) and (orientation: landscape)",fV="(max-width: 599.98px) and (orientation: portrait)";function uV(t,e){if(1&t){const t=wf();Cf(0,"div",1),Cf(1,"button",2),Rf("click",(function e(){Pi(t);return zf().action()})),Pu(2),Mf(),Mf()}if(2&t){const t=zf();Dc(2),wu(t.data.action)}}function gV(t,e){}const hV=new er("MatSnackBarData");class bV{constructor(){this.politeness="assertive",this.announcementMessage="",this.duration=0,this.data=null,this.horizontalPosition="center",this.verticalPosition="bottom"}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const yV=Math.pow(2,31)-1;class _V{constructor(t,e){this._overlayRef=e,this._afterDismissed=new B,this._afterOpened=new B,this._onAction=new B,this._dismissedByAction=!1,this.containerInstance=t,this.onAction().subscribe((()=>this.dismiss())),t._onExit.subscribe((()=>this._finishDismiss()))}dismiss(){this._afterDismissed.closed||this.containerInstance.exit(),clearTimeout(this._durationTimeoutId)}dismissWithAction(){this._onAction.closed||(this._dismissedByAction=!0,this._onAction.next(),this._onAction.complete()),clearTimeout(this._durationTimeoutId)}closeWithAction(){this.dismissWithAction()}_dismissAfter(t){this._durationTimeoutId=setTimeout((()=>this.dismiss()),Math.min(t,yV))}_open(){this._afterOpened.closed||(this._afterOpened.next(),this._afterOpened.complete())}_finishDismiss(){this._overlayRef.dispose(),this._onAction.closed||this._onAction.complete(),this._afterDismissed.next({dismissedByAction:this._dismissedByAction}),this._afterDismissed.complete(),this._dismissedByAction=!1}afterDismissed(){return this._afterDismissed}afterOpened(){return this.containerInstance._onEnter}onAction(){return this._onAction}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class CV{constructor(t,e){this.snackBarRef=t,this.data=e}action(){this.snackBarRef.dismissWithAction()}get hasAction(){return!!this.data.action}}CV.ɵfac=function t(e){return new(e||CV)(gf(_V),gf(hV))},CV.ɵcmp=so({type:CV,selectors:[["simple-snack-bar"]],hostAttrs:[1,"mat-simple-snackbar"],decls:3,vars:2,consts:[["class","mat-simple-snackbar-action",4,"ngIf"],[1,"mat-simple-snackbar-action"],["mat-button","",3,"click"]],template:function t(e,n){1&e&&(Cf(0,"span"),Pu(1),Mf(),Hm(2,uV,3,1,"div",0)),2&e&&(Dc(1),wu(n.data.message),Dc(1),bf("ngIf",n.hasAction))},directives:[Pv,NL],styles:[".mat-simple-snackbar{display:flex;justify-content:space-between;align-items:center;line-height:20px;opacity:1}.mat-simple-snackbar-action{flex-shrink:0;margin:-8px -8px -8px 8px}.mat-simple-snackbar-action button{max-height:36px;min-width:0}[dir=rtl] .mat-simple-snackbar-action{margin-left:-8px;margin-right:8px}\n"],encapsulation:2,changeDetection:0}),CV.ctorParameters=()=>[{type:_V},{type:void 0,decorators:[{type:Lr,args:[hV]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(CV,[{type:S_,args:[{selector:"simple-snack-bar",template:'<span>{{data.message}}</span>\n<div class="mat-simple-snackbar-action"  *ngIf="hasAction">\n  <button mat-button (click)="action()">{{data.action}}</button>\n</div>\n',encapsulation:Wn.None,changeDetection:jn.OnPush,host:{class:"mat-simple-snackbar"},styles:[".mat-simple-snackbar{display:flex;justify-content:space-between;align-items:center;line-height:20px;opacity:1}.mat-simple-snackbar-action{flex-shrink:0;margin:-8px -8px -8px 8px}.mat-simple-snackbar-action button{max-height:36px;min-width:0}[dir=rtl] .mat-simple-snackbar-action{margin-left:-8px;margin-right:8px}\n"]}]}],(function(){return[{type:_V},{type:void 0,decorators:[{type:Lr,args:[hV]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const MV={snackBarState:xO("state",[kO("void, hidden",wO({transform:"scale(0.8)",opacity:0})),kO("visible",wO({transform:"scale(1)",opacity:1})),DO("* => visible",OO("150ms cubic-bezier(0, 0, 0.2, 1)")),DO("* => void, * => hidden",OO("75ms cubic-bezier(0.4, 0.0, 1, 1)",wO({opacity:0})))])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class vV extends dB{constructor(t,e,n,o,i){super(),this._ngZone=t,this._elementRef=e,this._changeDetectorRef=n,this._platform=o,this.snackBarConfig=i,this._announceDelay=150,this._destroyed=!1,this._onAnnounce=new B,this._onExit=new B,this._onEnter=new B,this._animationState="void",this.attachDomPortal=t=>(this._assertNotAttached(),this._applySnackBarClasses(),this._portalOutlet.attachDomPortal(t)),this._live="assertive"!==i.politeness||i.announcementMessage?"off"===i.politeness?"off":"polite":"assertive",this._platform.FIREFOX&&("polite"===this._live&&(this._role="status"),"assertive"===this._live&&(this._role="alert"))}attachComponentPortal(t){return this._assertNotAttached(),this._applySnackBarClasses(),this._portalOutlet.attachComponentPortal(t)}attachTemplatePortal(t){return this._assertNotAttached(),this._applySnackBarClasses(),this._portalOutlet.attachTemplatePortal(t)}onAnimationEnd(t){const{fromState:e,toState:n}=t;if(("void"===n&&"void"!==e||"hidden"===n)&&this._completeExit(),"visible"===n){const t=this._onEnter;this._ngZone.run((()=>{t.next(),t.complete()}))}}enter(){this._destroyed||(this._animationState="visible",this._changeDetectorRef.detectChanges(),this._screenReaderAnnounce())}exit(){return this._animationState="hidden",this._elementRef.nativeElement.setAttribute("mat-exit",""),clearTimeout(this._announceTimeoutId),this._onExit}ngOnDestroy(){this._destroyed=!0,this._completeExit()}_completeExit(){this._ngZone.onMicrotaskEmpty.pipe(ve(1)).subscribe((()=>{this._onExit.next(),this._onExit.complete()}))}_applySnackBarClasses(){const t=this._elementRef.nativeElement,e=this.snackBarConfig.panelClass;e&&(Array.isArray(e)?e.forEach((e=>t.classList.add(e))):t.classList.add(e)),"center"===this.snackBarConfig.horizontalPosition&&t.classList.add("mat-snack-bar-center"),"top"===this.snackBarConfig.verticalPosition&&t.classList.add("mat-snack-bar-top")}_assertNotAttached(){if(this._portalOutlet.hasAttached()&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Attempting to attach snack bar content after content is already attached")}_screenReaderAnnounce(){this._announceTimeoutId||this._ngZone.runOutsideAngular((()=>{this._announceTimeoutId=setTimeout((()=>{const t=this._elementRef.nativeElement.querySelector("[aria-hidden]"),e=this._elementRef.nativeElement.querySelector("[aria-live]");if(t&&e){let n=null;this._platform.isBrowser&&document.activeElement instanceof HTMLElement&&t.contains(document.activeElement)&&(n=document.activeElement),t.removeAttribute("aria-hidden"),e.appendChild(t),null==n||n.focus(),this._onAnnounce.next(),this._onAnnounce.complete()}}),this._announceDelay)}))}}vV.ɵfac=function t(e){return new(e||vV)(gf(mC),gf(Kh),gf(Ob),gf(cH),gf(bV))},vV.ɵcmp=so({type:vV,selectors:[["snack-bar-container"]],viewQuery:function t(e,n){if(1&e&&Vy(uB,7),2&e){let t;By(t=Uy())&&(n._portalOutlet=t.first)}},hostAttrs:[1,"mat-snack-bar-container"],hostVars:1,hostBindings:function t(e,n){1&e&&Ef("@state.done",(function t(e){return n.onAnimationEnd(e)})),2&e&&dg("@state",n._animationState)},features:[Qp],decls:3,vars:2,consts:[["aria-hidden","true"],["cdkPortalOutlet",""]],template:function t(e,n){1&e&&(Cf(0,"div",0),Hm(1,gV,0,0,"ng-template",1),Mf(),vf(2,"div")),2&e&&(Dc(2),ym("aria-live",n._live)("role",n._role))},directives:[uB],styles:[".mat-snack-bar-container{border-radius:4px;box-sizing:border-box;display:block;margin:24px;max-width:33vw;min-width:344px;padding:14px 16px;min-height:48px;transform-origin:center}.cdk-high-contrast-active .mat-snack-bar-container{border:solid 1px}.mat-snack-bar-handset{width:100%}.mat-snack-bar-handset .mat-snack-bar-container{margin:8px;max-width:100%;min-width:0;width:100%}\n"],encapsulation:2,data:{animation:[MV.snackBarState]}}),vV.ctorParameters=()=>[{type:mC},{type:Kh},{type:Ob},{type:cH},{type:bV}],vV.propDecorators={_portalOutlet:[{type:ar,args:[uB,{static:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(vV,[{type:S_,args:[{selector:"snack-bar-container",template:'\x3c!-- Initially holds the snack bar content, will be empty after announcing to screen readers. --\x3e\n<div aria-hidden="true">\n  <ng-template cdkPortalOutlet></ng-template>\n</div>\n\n\x3c!-- Will receive the snack bar content from the non-live div, move will happen a short delay after opening --\x3e\n<div [attr.aria-live]="_live" [attr.role]="_role"></div>\n',changeDetection:jn.Default,encapsulation:Wn.None,animations:[MV.snackBarState],host:{class:"mat-snack-bar-container","[@state]":"_animationState","(@state.done)":"onAnimationEnd($event)"},styles:[".mat-snack-bar-container{border-radius:4px;box-sizing:border-box;display:block;margin:24px;max-width:33vw;min-width:344px;padding:14px 16px;min-height:48px;transform-origin:center}.cdk-high-contrast-active .mat-snack-bar-container{border:solid 1px}.mat-snack-bar-handset{width:100%}.mat-snack-bar-handset .mat-snack-bar-container{margin:8px;max-width:100%;min-width:0;width:100%}\n"]}]}],(function(){return[{type:mC},{type:Kh},{type:Ob},{type:cH},{type:bV}]}),{_portalOutlet:[{type:ar,args:[uB,{static:!0}]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class xV{}xV.ɵfac=function t(e){return new(e||xV)},xV.ɵmod=fo({type:xV}),xV.ɵinj=Dn({imports:[[oV,hB,rx,IL,NF],NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(xV,[{type:L_,args:[{imports:[oV,hB,rx,IL,NF],exports:[vV,NF],declarations:[vV,CV],entryComponents:[vV,CV]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(xV,{declarations:function(){return[vV,CV]},imports:function(){return[oV,hB,rx,IL,NF]},exports:function(){return[vV,NF]}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const OV=new er("mat-snack-bar-default-options",{providedIn:"root",factory:function PV(){return new bV}});class wV{constructor(t,e,n,o,i,a){this._overlay=t,this._live=e,this._injector=n,this._breakpointObserver=o,this._parentSnackBar=i,this._defaultConfig=a,this._snackBarRefAtThisLevel=null,this.simpleSnackBarComponent=CV,this.snackBarContainerComponent=vV,this.handsetCssClass="mat-snack-bar-handset"}get _openedSnackBarRef(){const t=this._parentSnackBar;return t?t._openedSnackBarRef:this._snackBarRefAtThisLevel}set _openedSnackBarRef(t){this._parentSnackBar?this._parentSnackBar._openedSnackBarRef=t:this._snackBarRefAtThisLevel=t}openFromComponent(t,e){return this._attach(t,e)}openFromTemplate(t,e){return this._attach(t,e)}open(t,e="",n){const o=Object.assign(Object.assign({},this._defaultConfig),n);return o.data={message:t,action:e},o.announcementMessage===t&&(o.announcementMessage=void 0),this.openFromComponent(this.simpleSnackBarComponent,o)}dismiss(){this._openedSnackBarRef&&this._openedSnackBarRef.dismiss()}ngOnDestroy(){this._snackBarRefAtThisLevel&&this._snackBarRefAtThisLevel.dismiss()}_attachSnackBarContainer(t,e){const n=Np.create({parent:e&&e.viewContainerRef&&e.viewContainerRef.injector||this._injector,providers:[{provide:bV,useValue:e}]}),o=new sB(this.snackBarContainerComponent,e.viewContainerRef,n),i=t.attach(o);return i.instance.snackBarConfig=e,i.instance}_attach(t,e){const n=Object.assign(Object.assign(Object.assign({},new bV),this._defaultConfig),e),o=this._createOverlay(n),i=this._attachSnackBarContainer(o,n),a=new _V(i,o);if(t instanceof Eb){const e=new lB(t,null,{$implicit:n.data,snackBarRef:a});a.instance=i.attachTemplatePortal(e)}else{const e=this._createInjector(n,a),o=new sB(t,void 0,e),r=i.attachComponentPortal(o);a.instance=r.instance}return this._breakpointObserver.observe(fV).pipe(je(o.detachments())).subscribe((t=>{const e=o.overlayElement.classList;t.matches?e.add(this.handsetCssClass):e.remove(this.handsetCssClass)})),n.announcementMessage&&i._onAnnounce.subscribe((()=>{this._live.announce(n.announcementMessage,n.politeness)})),this._animateSnackBar(a,n),this._openedSnackBarRef=a,this._openedSnackBarRef}_animateSnackBar(t,e){t.afterDismissed().subscribe((()=>{this._openedSnackBarRef==t&&(this._openedSnackBarRef=null),e.announcementMessage&&this._live.clear()})),this._openedSnackBarRef?(this._openedSnackBarRef.afterDismissed().subscribe((()=>{t.containerInstance.enter()})),this._openedSnackBarRef.dismiss()):t.containerInstance.enter(),e.duration&&e.duration>0&&t.afterOpened().subscribe((()=>t._dismissAfter(e.duration)))}_createOverlay(t){const e=new wB;e.direction=t.direction;let n=this._overlay.position().global();const o="rtl"===t.direction,i="left"===t.horizontalPosition||"start"===t.horizontalPosition&&!o||"end"===t.horizontalPosition&&o,a=!i&&"center"!==t.horizontalPosition;return i?n.left("0"):a?n.right("0"):n.centerHorizontally(),"top"===t.verticalPosition?n.top("0"):n.bottom("0"),e.positionStrategy=n,this._overlay.create(e)}_createInjector(t,e){return Np.create({parent:t&&t.viewContainerRef&&t.viewContainerRef.injector||this._injector,providers:[{provide:_V,useValue:e},{provide:hV,useValue:t.data}]})}}wV.ɵfac=function t(e){return new(e||wV)(Tr(KB),Tr(cF),Tr(Np),Tr(dV),Tr(wV,12),Tr(OV))},wV.ɵprov=Sn({factory:function t(){return new wV(Tr(KB),Tr(cF),Tr(yp),Tr(dV),Tr(wV,12),Tr(OV))},token:wV,providedIn:xV}),wV.ctorParameters=()=>[{type:KB},{type:cF},{type:Np},{type:dV},{type:wV,decorators:[{type:Br},{type:jr}]},{type:bV,decorators:[{type:Lr,args:[OV]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(wV,[{type:Um,args:[{providedIn:xV}]}],(function(){return[{type:KB},{type:cF},{type:Np},{type:dV},{type:wV,decorators:[{type:Br},{type:jr}]},{type:bV,decorators:[{type:Lr,args:[OV]}]}]}),null);const kV=/[\\^$.*+?()[\]{}|]/g;const SV="\\u0000-\\u0020\\u007f-\\u009f",DV=new RegExp("(?:[a-zA-Z][a-zA-Z0-9+.-]{2,}:\\/\\/|data:|www\\.)[^\\s"+SV+'"]{2,}[^\\s'+SV+"\"')}\\],:;.!?]","gu");function RV(t){return(function e(t,n){n.flags.includes("g")||(n=new RegExp(n,n.flags+"g"));const o=[];let i=0;for(const e of t.matchAll(n)){const n=e.index,a=e[0];n>i&&o.push({index:i,text:t.substring(i,n),matchesRegex:!1}),o.push({index:n,text:a,matchesRegex:!0}),i=n+a.length}return t.length>i&&o.push({index:i,text:t.substring(i,t.length),matchesRegex:!1}),o})(t,DV).map((({matchesRegex:t,text:e})=>({isURL:t,text:e})))}function EV(t,e){if(1&t&&(xf(0),Pu(1),Of()),2&t){const t=zf().$implicit;Dc(1),ku(" ",t.text," ")}}function AV(t,e){if(1&t&&(Cf(0,"a",7),Pu(1),Mf()),2&t){const t=zf().$implicit;Lf("href",t.text,Xs),Dc(1),wu(t.text)}}function TV(t,e){if(1&t&&(xf(0),Hm(1,EV,2,1,"ng-container",5),Hm(2,AV,2,2,"ng-template",null,6,qy),Of()),2&t){const t=e.$implicit,n=Fm(3);Dc(1),bf("ngIf",!t.isURL)("ngIfElse",n)}}function NV(t,e){if(1&t){const t=wf();Cf(0,"button",8),Rf("click",(function e(){Pi(t);return zf().onActionButtonClicked()})),Pu(1),Mf()}if(2&t){const t=zf();Dc(1),ku(" ",t.alert.followupAction.localizedLabel," ")}}class zV{constructor(t,e,n){this.snackBarRef=t,this.unknownData=e,this.store=n,this.splitByURL=RV,this.alert=e}onActionButtonClicked(){return XT(this,void 0,void 0,(function*(){this.snackBarRef.dismiss();const t=yield this.alert.followupAction.getFollowupAction(this.store);this.store.dispatch(t)}))}onCloseButtonClicked(){this.snackBarRef.dismiss()}}zV.ɵfac=function t(e){return new(e||zV)(gf(_V),gf(hV),gf(iS))},zV.ɵcmp=so({type:zV,selectors:[["alert-display-snackbar"]],decls:6,vars:2,consts:function(){let t;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){t=goog.getMsg("Dismiss")}else t=$localize`:A button to close the snackbar 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a[_ngcontent-%COMP%]{color:inherit}.controls[_ngcontent-%COMP%]{white-space:nowrap;margin-left:auto}button[_ngcontent-%COMP%]{text-transform:uppercase}"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(zV,[{type:S_,args:[{selector:"alert-display-snackbar",templateUrl:"./alert_display_snackbar_container.ng.html",styleUrls:["./alert_display_snackbar_container.css"],changeDetection:jn.OnPush}]}],(function(){return[{type:_V},{type:void 0,decorators:[{type:Lr,args:[hV]}]},{type:iS}]}),null);class IV{constructor(t,e){this.store=t,this.snackBar=e,this.ngUnsubscribe=new B}ngOnInit(){this.store.select(WT).pipe(je(this.ngUnsubscribe),ue((t=>Boolean(t)))).subscribe((t=>{this.showAlert(t)}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}showAlert(t){this.snackBar.openFromComponent(zV,{duration:5e3,horizontalPosition:"start",verticalPosition:"bottom",data:t})}}IV.ɵfac=function t(e){return new(e||IV)(gf(iS),gf(wV))},IV.ɵcmp=so({type:IV,selectors:[["alert-snackbar"]],decls:0,vars:0,template:function t(e,n){},encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(IV,[{type:S_,args:[{selector:"alert-snackbar",template:"",changeDetection:jn.OnPush}]}],(function(){return[{type:iS},{type:wV}]}),null);class HV{}HV.ɵfac=function t(e){return new(e||HV)},HV.ɵmod=fo({type:HV}),HV.ɵinj=Dn({imports:[[rx,IL,xV]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(HV,[{type:L_,args:[{declarations:[IV,zV],exports:[IV],imports:[rx,IL,xV],entryComponents:[zV]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(HV,{declarations:[IV,zV],imports:[rx,IL,xV],exports:[IV]});class FV{}FV.ɵfac=function t(e){return new(e||FV)},FV.ɵmod=fo({type:FV}),FV.ɵinj=Dn({imports:[[mT,HV,TS.forFeature(UT,ZI),hD.forFeature([YI])]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(FV,[{type:L_,args:[{imports:[mT,HV,TS.forFeature(UT,ZI),hD.forFeature([YI])]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(FV,{imports:[mT,HV,AS,gD]});const LV=["*",[["mat-toolbar-row"]]],BV=IF(class{constructor(t){this._elementRef=t}});class VV{}VV.ɵfac=function t(e){return new(e||VV)},VV.ɵdir=ho({type:VV,selectors:[["mat-toolbar-row"]],hostAttrs:[1,"mat-toolbar-row"],exportAs:["matToolbarRow"]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(VV,[{type:k_,args:[{selector:"mat-toolbar-row",exportAs:"matToolbarRow",host:{class:"mat-toolbar-row"}}]}],null,null);class jV extends BV{constructor(t,e,n){super(t),this._platform=e,this._document=n}ngAfterViewInit(){this._platform.isBrowser&&(this._checkToolbarMixedModes(),this._toolbarRows.changes.subscribe((()=>this._checkToolbarMixedModes())))}_checkToolbarMixedModes(){if(this._toolbarRows.length&&("undefined"==typeof ngDevMode||ngDevMode)){Array.from(this._elementRef.nativeElement.childNodes).filter((t=>!(t.classList&&t.classList.contains("mat-toolbar-row")))).filter((t=>t.nodeType!==(this._document?this._document.COMMENT_NODE:8))).some((t=>!(!t.textContent||!t.textContent.trim())))&&(function t(){throw Error("MatToolbar: Attempting to combine different toolbar modes. Either specify multiple `<mat-toolbar-row>` elements explicitly or just place content inside of a `<mat-toolbar>` for a single row.")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)()}}}jV.ɵfac=function t(e){return new(e||jV)(gf(Kh),gf(cH),gf(sM))},jV.ɵcmp=so({type:jV,selectors:[["mat-toolbar"]],contentQueries:function t(e,n,o){if(1&e&&jy(o,VV,5),2&e){let t;By(t=Uy())&&(n._toolbarRows=t)}},hostAttrs:[1,"mat-toolbar"],hostVars:4,hostBindings:function t(e,n){2&e&&cu("mat-toolbar-multiple-rows",n._toolbarRows.length>0)("mat-toolbar-single-row",0===n._toolbarRows.length)},inputs:{color:"color"},exportAs:["matToolbar"],features:[Qp],ngContentSelectors:["*","mat-toolbar-row"],decls:2,vars:0,template:function t(e,n){1&e&&(Hf(LV),Ff(0),Ff(1,1))},styles:[".cdk-high-contrast-active .mat-toolbar{outline:solid 1px}.mat-toolbar-row,.mat-toolbar-single-row{display:flex;box-sizing:border-box;padding:0 16px;width:100%;flex-direction:row;align-items:center;white-space:nowrap}.mat-toolbar-multiple-rows{display:flex;box-sizing:border-box;flex-direction:column;width:100%}\n"],encapsulation:2,changeDetection:0}),jV.ctorParameters=()=>[{type:Kh},{type:cH},{type:void 0,decorators:[{type:Lr,args:[sM]}]}],jV.propDecorators={_toolbarRows:[{type:or,args:[VV,{descendants:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(jV,[{type:S_,args:[{selector:"mat-toolbar",exportAs:"matToolbar",template:'<ng-content></ng-content>\n<ng-content select="mat-toolbar-row"></ng-content>\n',inputs:["color"],host:{class:"mat-toolbar","[class.mat-toolbar-multiple-rows]":"_toolbarRows.length > 0","[class.mat-toolbar-single-row]":"_toolbarRows.length === 0"},changeDetection:jn.OnPush,encapsulation:Wn.None,styles:[".cdk-high-contrast-active .mat-toolbar{outline:solid 1px}.mat-toolbar-row,.mat-toolbar-single-row{display:flex;box-sizing:border-box;padding:0 16px;width:100%;flex-direction:row;align-items:center;white-space:nowrap}.mat-toolbar-multiple-rows{display:flex;box-sizing:border-box;flex-direction:column;width:100%}\n"]}]}],(function(){return[{type:Kh},{type:cH},{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),{_toolbarRows:[{type:or,args:[VV,{descendants:!0}]}]});class UV{}function WV(t,e){1&t&&Ff(0)}UV.ɵfac=function t(e){return new(e||UV)},UV.ɵmod=fo({type:UV}),UV.ɵinj=Dn({imports:[[NF],NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(UV,[{type:L_,args:[{imports:[NF],exports:[jV,VV,NF],declarations:[jV,VV]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(UV,{declarations:function(){return[jV,VV]},imports:function(){return[NF]},exports:function(){return[jV,VV,NF]}});const GV=["*"];function YV(t,e){}const qV=function(t){return{animationDuration:t}},ZV=function(t,e){return{value:t,params:e}},XV=["tabBodyWrapper"],KV=["tabHeader"];function JV(t,e){}function QV(t,e){if(1&t&&Hm(0,JV,0,0,"ng-template",9),2&t){bf("cdkPortalOutlet",zf().$implicit.templateLabel)}}function $V(t,e){if(1&t&&Pu(0),2&t){wu(zf().$implicit.textLabel)}}function tj(t,e){if(1&t){const t=wf();Cf(0,"div",6),Rf("click",(function e(){const n=Pi(t),o=n.$implicit,i=n.index,a=zf(),r=Fm(1);return a._handleClick(o,r,i)}))("cdkFocusChange",(function e(n){const o=Pi(t).index;return zf()._tabFocusChanged(n,o)})),Cf(1,"div",7),Hm(2,QV,1,1,"ng-template",8),Hm(3,$V,1,1,"ng-template",8),Mf(),Mf()}if(2&t){const t=e.$implicit,n=e.index,o=zf();cu("mat-tab-label-active",o.selectedIndex==n),bf("id",o._getTabLabelId(n))("disabled",t.disabled)("matRippleDisabled",t.disabled||o.disableRipple),ym("tabIndex",o._getTabIndex(t,n))("aria-posinset",n+1)("aria-setsize",o._tabs.length)("aria-controls",o._getTabContentId(n))("aria-selected",o.selectedIndex==n)("aria-label",t.ariaLabel||null)("aria-labelledby",!t.ariaLabel&&t.ariaLabelledby?t.ariaLabelledby:null),Dc(2),bf("ngIf",t.templateLabel),Dc(1),bf("ngIf",!t.templateLabel)}}function ej(t,e){if(1&t){const t=wf();Cf(0,"mat-tab-body",10),Rf("_onCentered",(function e(){Pi(t);return zf()._removeTabBodyWrapperHeight()}))("_onCentering",(function e(n){Pi(t);return zf()._setTabBodyWrapperHeight(n)})),Mf()}if(2&t){const t=e.$implicit,n=e.index,o=zf();cu("mat-tab-body-active",o.selectedIndex===n),bf("id",o._getTabContentId(n))("content",t.content)("position",t.position)("origin",t.origin)("animationDuration",o.animationDuration),ym("tabindex",null!=o.contentTabIndex&&o.selectedIndex===n?o.contentTabIndex:null)("aria-labelledby",o._getTabLabelId(n))}}const nj=["tabListContainer"],oj=["tabList"],ij=["nextPaginator"],aj=["previousPaginator"],rj=new er("MatInkBarPositioner",{providedIn:"root",factory:function sj(){return t=>({left:t?(t.offsetLeft||0)+"px":"0",width:t?(t.offsetWidth||0)+"px":"0"})}});class lj{constructor(t,e,n,o){this._elementRef=t,this._ngZone=e,this._inkBarPositioner=n,this._animationMode=o}alignToElement(t){this.show(),"undefined"!=typeof requestAnimationFrame?this._ngZone.runOutsideAngular((()=>{requestAnimationFrame((()=>this._setStyles(t)))})):this._setStyles(t)}show(){this._elementRef.nativeElement.style.visibility="visible"}hide(){this._elementRef.nativeElement.style.visibility="hidden"}_setStyles(t){const e=this._inkBarPositioner(t),n=this._elementRef.nativeElement;n.style.left=e.left,n.style.width=e.width}}lj.ɵfac=function t(e){return new(e||lj)(gf(Kh),gf(mC),gf(rj),gf(ck,8))},lj.ɵdir=ho({type:lj,selectors:[["mat-ink-bar"]],hostAttrs:[1,"mat-ink-bar"],hostVars:2,hostBindings:function t(e,n){2&e&&cu("_mat-animation-noopable","NoopAnimations"===n._animationMode)}}),lj.ctorParameters=()=>[{type:Kh},{type:mC},{type:void 0,decorators:[{type:Lr,args:[rj]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(lj,[{type:k_,args:[{selector:"mat-ink-bar",host:{class:"mat-ink-bar","[class._mat-animation-noopable]":"_animationMode === 'NoopAnimations'"}}]}],(function(){return[{type:Kh},{type:mC},{type:void 0,decorators:[{type:Lr,args:[rj]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const cj=new er("MatTabContent");class dj{constructor(t){this.template=t}}dj.ɵfac=function t(e){return new(e||dj)(gf(Eb))},dj.ɵdir=ho({type:dj,selectors:[["","matTabContent",""]],features:[jh([{provide:cj,useExisting:dj}])]}),dj.ctorParameters=()=>[{type:Eb}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(dj,[{type:k_,args:[{selector:"[matTabContent]",providers:[{provide:cj,useExisting:dj}]}]}],(function(){return[{type:Eb}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const pj=new er("MatTabLabel");class mj extends mB{}mj.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(mj)))(n||mj)}})(),mj.ɵdir=ho({type:mj,selectors:[["","mat-tab-label",""],["","matTabLabel",""]],features:[jh([{provide:pj,useExisting:mj}]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(mj,[{type:k_,args:[{selector:"[mat-tab-label], [matTabLabel]",providers:[{provide:pj,useExisting:mj}]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const fj=zF(class{}),uj=new er("MAT_TAB_GROUP");class gj extends fj{constructor(t,e){super(),this._viewContainerRef=t,this._closestTabGroup=e,this.textLabel="",this._contentPortal=null,this._stateChanges=new B,this.position=null,this.origin=null,this.isActive=!1}get templateLabel(){return this._templateLabel}set templateLabel(t){this._setTemplateLabelInput(t)}get content(){return this._contentPortal}ngOnChanges(t){(t.hasOwnProperty("textLabel")||t.hasOwnProperty("disabled"))&&this._stateChanges.next()}ngOnDestroy(){this._stateChanges.complete()}ngOnInit(){this._contentPortal=new lB(this._explicitContent||this._implicitContent,this._viewContainerRef)}_setTemplateLabelInput(t){t&&(this._templateLabel=t)}}gj.ɵfac=function t(e){return new(e||gj)(gf(Fb),gf(uj,8))},gj.ɵcmp=so({type:gj,selectors:[["mat-tab"]],contentQueries:function t(e,n,o){if(1&e&&(jy(o,pj,5),jy(o,cj,7,Eb)),2&e){let t;By(t=Uy())&&(n.templateLabel=t.first),By(t=Uy())&&(n._explicitContent=t.first)}},viewQuery:function t(e,n){if(1&e&&Vy(Eb,7),2&e){let t;By(t=Uy())&&(n._implicitContent=t.first)}},inputs:{disabled:"disabled",textLabel:["label","textLabel"],ariaLabel:["aria-label","ariaLabel"],ariaLabelledby:["aria-labelledby","ariaLabelledby"]},exportAs:["matTab"],features:[Qp,qo],ngContentSelectors:GV,decls:1,vars:0,template:function t(e,n){1&e&&(Hf(),Hm(0,WV,1,0,"ng-template"))},encapsulation:2}),gj.ctorParameters=()=>[{type:Fb},{type:void 0,decorators:[{type:Lr,args:[uj]},{type:Br}]}],gj.propDecorators={templateLabel:[{type:ir,args:[pj]}],_explicitContent:[{type:ir,args:[cj,{read:Eb,static:!0}]}],_implicitContent:[{type:ar,args:[Eb,{static:!0}]}],textLabel:[{type:R_,args:["label"]}],ariaLabel:[{type:R_,args:["aria-label"]}],ariaLabelledby:[{type:R_,args:["aria-labelledby"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(gj,[{type:S_,args:[{selector:"mat-tab",template:"\x3c!-- Create a template for the content of the <mat-tab> so that we can grab a reference to this\n    TemplateRef and use it in a Portal to render the tab content in the appropriate place in the\n    tab-group. --\x3e\n<ng-template><ng-content></ng-content></ng-template>\n",inputs:["disabled"],changeDetection:jn.Default,encapsulation:Wn.None,exportAs:"matTab"}]}],(function(){return[{type:Fb},{type:void 0,decorators:[{type:Lr,args:[uj]},{type:Br}]}]}),{textLabel:[{type:R_,args:["label"]}],templateLabel:[{type:ir,args:[pj]}],_explicitContent:[{type:ir,args:[cj,{read:Eb,static:!0}]}],_implicitContent:[{type:ar,args:[Eb,{static:!0}]}],ariaLabel:[{type:R_,args:["aria-label"]}],ariaLabelledby:[{type:R_,args:["aria-labelledby"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const hj={translateTab:xO("translateTab",[kO("center, void, left-origin-center, right-origin-center",wO({transform:"none"})),kO("left",wO({transform:"translate3d(-100%, 0, 0)",minHeight:"1px"})),kO("right",wO({transform:"translate3d(100%, 0, 0)",minHeight:"1px"})),DO("* => left, * => right, left => center, right => center",OO("{{animationDuration}} cubic-bezier(0.35, 0, 0.25, 1)")),DO("void => left-origin-center",[wO({transform:"translate3d(-100%, 0, 0)"}),OO("{{animationDuration}} cubic-bezier(0.35, 0, 0.25, 1)")]),DO("void => right-origin-center",[wO({transform:"translate3d(100%, 0, 0)"}),OO("{{animationDuration}} cubic-bezier(0.35, 0, 0.25, 1)")])])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class bj extends uB{constructor(t,e,n,o){super(t,e,o),this._host=n,this._centeringSub=u.EMPTY,this._leavingSub=u.EMPTY}ngOnInit(){super.ngOnInit(),this._centeringSub=this._host._beforeCentering.pipe(Be(this._host._isCenterPosition(this._host._position))).subscribe((t=>{t&&!this.hasAttached()&&this.attach(this._host._content)})),this._leavingSub=this._host._afterLeavingCenter.subscribe((()=>{this.detach()}))}ngOnDestroy(){super.ngOnDestroy(),this._centeringSub.unsubscribe(),this._leavingSub.unsubscribe()}}bj.ɵfac=function t(e){return new(e||bj)(gf(Gh),gf(Fb),gf($e((()=>_j))),gf(sM))},bj.ɵdir=ho({type:bj,selectors:[["","matTabBodyHost",""]],features:[Qp]}),bj.ctorParameters=()=>[{type:Gh},{type:Fb},{type:_j,decorators:[{type:Lr,args:[$e((()=>_j))]}]},{type:void 0,decorators:[{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(bj,[{type:k_,args:[{selector:"[matTabBodyHost]"}]}],(function(){return[{type:Gh},{type:Fb},{type:_j,decorators:[{type:Lr,args:[$e((()=>_j))]}]},{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),null);class yj{constructor(t,e,n){this._elementRef=t,this._dir=e,this._dirChangeSubscription=u.EMPTY,this._translateTabComplete=new B,this._onCentering=new Sy,this._beforeCentering=new Sy,this._afterLeavingCenter=new Sy,this._onCentered=new Sy(!0),this.animationDuration="500ms",e&&(this._dirChangeSubscription=e.change.subscribe((t=>{this._computePositionAnimationState(t),n.markForCheck()}))),this._translateTabComplete.pipe(we(((t,e)=>t.fromState===e.fromState&&t.toState===e.toState))).subscribe((t=>{this._isCenterPosition(t.toState)&&this._isCenterPosition(this._position)&&this._onCentered.emit(),this._isCenterPosition(t.fromState)&&!this._isCenterPosition(this._position)&&this._afterLeavingCenter.emit()}))}set position(t){this._positionIndex=t,this._computePositionAnimationState()}ngOnInit(){"center"==this._position&&null!=this.origin&&(this._position=this._computePositionFromOrigin(this.origin))}ngOnDestroy(){this._dirChangeSubscription.unsubscribe(),this._translateTabComplete.complete()}_onTranslateTabStarted(t){const e=this._isCenterPosition(t.toState);this._beforeCentering.emit(e),e&&this._onCentering.emit(this._elementRef.nativeElement.clientHeight)}_getLayoutDirection(){return this._dir&&"rtl"===this._dir.value?"rtl":"ltr"}_isCenterPosition(t){return"center"==t||"left-origin-center"==t||"right-origin-center"==t}_computePositionAnimationState(t=this._getLayoutDirection()){this._position=this._positionIndex<0?"ltr"==t?"left":"right":this._positionIndex>0?"ltr"==t?"right":"left":"center"}_computePositionFromOrigin(t){const e=this._getLayoutDirection();return"ltr"==e&&t<=0||"rtl"==e&&t>0?"left-origin-center":"right-origin-center"}}yj.ɵfac=function t(e){return new(e||yj)(gf(Kh),gf(vF,8),gf(Ob))},yj.ɵdir=ho({type:yj,inputs:{animationDuration:"animationDuration",position:"position",_content:["content","_content"],origin:"origin"},outputs:{_onCentering:"_onCentering",_beforeCentering:"_beforeCentering",_afterLeavingCenter:"_afterLeavingCenter",_onCentered:"_onCentered"}}),yj.ctorParameters=()=>[{type:Kh},{type:vF,decorators:[{type:Br}]},{type:Ob}],yj.propDecorators={_onCentering:[{type:E_}],_beforeCentering:[{type:E_}],_afterLeavingCenter:[{type:E_}],_onCentered:[{type:E_}],_content:[{type:R_,args:["content"]}],origin:[{type:R_}],animationDuration:[{type:R_}],position:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(yj,[{type:k_}],(function(){return[{type:Kh},{type:vF,decorators:[{type:Br}]},{type:Ob}]}),{_onCentering:[{type:E_}],_beforeCentering:[{type:E_}],_afterLeavingCenter:[{type:E_}],_onCentered:[{type:E_}],animationDuration:[{type:R_}],position:[{type:R_}],_content:[{type:R_,args:["content"]}],origin:[{type:R_}]});class _j extends yj{constructor(t,e,n){super(t,e,n)}}_j.ɵfac=function t(e){return new(e||_j)(gf(Kh),gf(vF,8),gf(Ob))},_j.ɵcmp=so({type:_j,selectors:[["mat-tab-body"]],viewQuery:function t(e,n){if(1&e&&Vy(uB,5),2&e){let t;By(t=Uy())&&(n._portalHost=t.first)}},hostAttrs:[1,"mat-tab-body"],features:[Qp],decls:3,vars:6,consts:[["cdkScrollable","",1,"mat-tab-body-content"],["content",""],["matTabBodyHost",""]],template:function t(e,n){1&e&&(Cf(0,"div",0,1),Rf("@translateTab.start",(function t(e){return n._onTranslateTabStarted(e)}))("@translateTab.done",(function t(e){return n._translateTabComplete.next(e)})),Hm(2,YV,0,0,"ng-template",2),Mf()),2&e&&bf("@translateTab",ay(3,ZV,n._position,iy(1,qV,n.animationDuration)))},directives:[bj],styles:[".mat-tab-body-content{height:100%;overflow:auto}.mat-tab-group-dynamic-height .mat-tab-body-content{overflow:hidden}\n"],encapsulation:2,data:{animation:[hj.translateTab]}}),_j.ctorParameters=()=>[{type:Kh},{type:vF,decorators:[{type:Br}]},{type:Ob}],_j.propDecorators={_portalHost:[{type:ar,args:[uB]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(_j,[{type:S_,args:[{selector:"mat-tab-body",template:'<div class="mat-tab-body-content" #content\n     [@translateTab]="{\n        value: _position,\n        params: {animationDuration: animationDuration}\n     }"\n     (@translateTab.start)="_onTranslateTabStarted($event)"\n     (@translateTab.done)="_translateTabComplete.next($event)"\n     cdkScrollable>\n  <ng-template matTabBodyHost></ng-template>\n</div>\n',encapsulation:Wn.None,changeDetection:jn.Default,animations:[hj.translateTab],host:{class:"mat-tab-body"},styles:[".mat-tab-body-content{height:100%;overflow:auto}.mat-tab-group-dynamic-height .mat-tab-body-content{overflow:hidden}\n"]}]}],(function(){return[{type:Kh},{type:vF,decorators:[{type:Br}]},{type:Ob}]}),{_portalHost:[{type:ar,args:[uB]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Cj=new er("MAT_TABS_CONFIG");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let Mj=0;class vj{}const xj=IF(HF(class{constructor(t){this._elementRef=t}}),"primary");class Oj extends xj{constructor(t,e,n,o){var i;super(t),this._changeDetectorRef=e,this._animationMode=o,this._tabs=new Ry,this._indexToSelect=0,this._tabBodyWrapperHeight=0,this._tabsSubscription=u.EMPTY,this._tabLabelSubscription=u.EMPTY,this._selectedIndex=null,this.headerPosition="above",this.selectedIndexChange=new Sy,this.focusChange=new Sy,this.animationDone=new Sy,this.selectedTabChange=new Sy(!0),this._groupId=Mj++,this.animationDuration=n&&n.animationDuration?n.animationDuration:"500ms",this.disablePagination=!(!n||null==n.disablePagination)&&n.disablePagination,this.dynamicHeight=!(!n||null==n.dynamicHeight)&&n.dynamicHeight,this.contentTabIndex=null!==(i=null==n?void 0:n.contentTabIndex)&&void 0!==i?i:null}get dynamicHeight(){return this._dynamicHeight}set dynamicHeight(t){this._dynamicHeight=eH(t)}get selectedIndex(){return this._selectedIndex}set selectedIndex(t){this._indexToSelect=nH(t,null)}get animationDuration(){return this._animationDuration}set animationDuration(t){this._animationDuration=/^\d+$/.test(t)?t+"ms":t}get contentTabIndex(){return this._contentTabIndex}set contentTabIndex(t){this._contentTabIndex=nH(t,null)}get backgroundColor(){return this._backgroundColor}set backgroundColor(t){const e=this._elementRef.nativeElement;e.classList.remove(`mat-background-${this.backgroundColor}`),t&&e.classList.add(`mat-background-${t}`),this._backgroundColor=t}ngAfterContentChecked(){const t=this._indexToSelect=this._clampTabIndex(this._indexToSelect);if(this._selectedIndex!=t){const e=null==this._selectedIndex;if(!e){this.selectedTabChange.emit(this._createChangeEvent(t));const e=this._tabBodyWrapper.nativeElement;e.style.minHeight=e.clientHeight+"px"}Promise.resolve().then((()=>{this._tabs.forEach(((e,n)=>e.isActive=n===t)),e||(this.selectedIndexChange.emit(t),this._tabBodyWrapper.nativeElement.style.minHeight="")}))}this._tabs.forEach(((e,n)=>{e.position=n-t,null==this._selectedIndex||0!=e.position||e.origin||(e.origin=t-this._selectedIndex)})),this._selectedIndex!==t&&(this._selectedIndex=t,this._changeDetectorRef.markForCheck())}ngAfterContentInit(){this._subscribeToAllTabChanges(),this._subscribeToTabLabels(),this._tabsSubscription=this._tabs.changes.subscribe((()=>{if(this._clampTabIndex(this._indexToSelect)===this._selectedIndex){const t=this._tabs.toArray();for(let e=0;e<t.length;e++)if(t[e].isActive){this._indexToSelect=this._selectedIndex=e;break}}this._changeDetectorRef.markForCheck()}))}_subscribeToAllTabChanges(){this._allTabs.changes.pipe(Be(this._allTabs)).subscribe((t=>{this._tabs.reset(t.filter((t=>t._closestTabGroup===this||!t._closestTabGroup))),this._tabs.notifyOnChanges()}))}ngOnDestroy(){this._tabs.destroy(),this._tabsSubscription.unsubscribe(),this._tabLabelSubscription.unsubscribe()}realignInkBar(){this._tabHeader&&this._tabHeader._alignInkBarToSelectedTab()}focusTab(t){const e=this._tabHeader;e&&(e.focusIndex=t)}_focusChanged(t){this.focusChange.emit(this._createChangeEvent(t))}_createChangeEvent(t){const e=new vj;return e.index=t,this._tabs&&this._tabs.length&&(e.tab=this._tabs.toArray()[t]),e}_subscribeToTabLabels(){this._tabLabelSubscription&&this._tabLabelSubscription.unsubscribe(),this._tabLabelSubscription=pe(...this._tabs.map((t=>t._stateChanges))).subscribe((()=>this._changeDetectorRef.markForCheck()))}_clampTabIndex(t){return Math.min(this._tabs.length-1,Math.max(t||0,0))}_getTabLabelId(t){return`mat-tab-label-${this._groupId}-${t}`}_getTabContentId(t){return`mat-tab-content-${this._groupId}-${t}`}_setTabBodyWrapperHeight(t){if(!this._dynamicHeight||!this._tabBodyWrapperHeight)return;const e=this._tabBodyWrapper.nativeElement;e.style.height=this._tabBodyWrapperHeight+"px",this._tabBodyWrapper.nativeElement.offsetHeight&&(e.style.height=t+"px")}_removeTabBodyWrapperHeight(){const t=this._tabBodyWrapper.nativeElement;this._tabBodyWrapperHeight=t.clientHeight,t.style.height="",this.animationDone.emit()}_handleClick(t,e,n){t.disabled||(this.selectedIndex=e.focusIndex=n)}_getTabIndex(t,e){return t.disabled?null:this.selectedIndex===e?0:-1}_tabFocusChanged(t,e){t&&"mouse"!==t&&"touch"!==t&&(this._tabHeader.focusIndex=e)}}Oj.ɵfac=function t(e){return new(e||Oj)(gf(Kh),gf(Ob),gf(Cj,8),gf(ck,8))},Oj.ɵdir=ho({type:Oj,inputs:{headerPosition:"headerPosition",animationDuration:"animationDuration",disablePagination:"disablePagination",dynamicHeight:"dynamicHeight",contentTabIndex:"contentTabIndex",selectedIndex:"selectedIndex",backgroundColor:"backgroundColor"},outputs:{selectedIndexChange:"selectedIndexChange",focusChange:"focusChange",animationDone:"animationDone",selectedTabChange:"selectedTabChange"},features:[Qp]}),Oj.ctorParameters=()=>[{type:Kh},{type:Ob},{type:void 0,decorators:[{type:Lr,args:[Cj]},{type:Br}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}],Oj.propDecorators={dynamicHeight:[{type:R_}],selectedIndex:[{type:R_}],headerPosition:[{type:R_}],animationDuration:[{type:R_}],contentTabIndex:[{type:R_}],disablePagination:[{type:R_}],backgroundColor:[{type:R_}],selectedIndexChange:[{type:E_}],focusChange:[{type:E_}],animationDone:[{type:E_}],selectedTabChange:[{type:E_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Oj,[{type:k_}],(function(){return[{type:Kh},{type:Ob},{type:void 0,decorators:[{type:Lr,args:[Cj]},{type:Br}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}]}),{headerPosition:[{type:R_}],selectedIndexChange:[{type:E_}],focusChange:[{type:E_}],animationDone:[{type:E_}],selectedTabChange:[{type:E_}],animationDuration:[{type:R_}],disablePagination:[{type:R_}],dynamicHeight:[{type:R_}],contentTabIndex:[{type:R_}],selectedIndex:[{type:R_}],backgroundColor:[{type:R_}]});class Pj extends Oj{constructor(t,e,n,o){super(t,e,n,o)}}Pj.ɵfac=function t(e){return new(e||Pj)(gf(Kh),gf(Ob),gf(Cj,8),gf(ck,8))},Pj.ɵcmp=so({type:Pj,selectors:[["mat-tab-group"]],contentQueries:function t(e,n,o){if(1&e&&jy(o,gj,5),2&e){let t;By(t=Uy())&&(n._allTabs=t)}},viewQuery:function t(e,n){if(1&e&&(Vy(XV,5),Vy(KV,5)),2&e){let t;By(t=Uy())&&(n._tabBodyWrapper=t.first),By(t=Uy())&&(n._tabHeader=t.first)}},hostAttrs:[1,"mat-tab-group"],hostVars:4,hostBindings:function t(e,n){2&e&&cu("mat-tab-group-dynamic-height",n.dynamicHeight)("mat-tab-group-inverted-header","below"===n.headerPosition)},inputs:{color:"color",disableRipple:"disableRipple"},exportAs:["matTabGroup"],features:[jh([{provide:uj,useExisting:Pj}]),Qp],decls:6,vars:7,consts:[[3,"selectedIndex","disableRipple","disablePagination","indexFocused","selectFocusedIndex"],["tabHeader",""],["class","mat-tab-label mat-focus-indicator","role","tab","matTabLabelWrapper","","mat-ripple","","cdkMonitorElementFocus","",3,"id","mat-tab-label-active","disabled","matRippleDisabled","click","cdkFocusChange",4,"ngFor","ngForOf"],[1,"mat-tab-body-wrapper"],["tabBodyWrapper",""],["role","tabpanel",3,"id","mat-tab-body-active","content","position","origin","animationDuration","_onCentered","_onCentering",4,"ngFor","ngForOf"],["role","tab","matTabLabelWrapper","","mat-ripple","","cdkMonitorElementFocus","",1,"mat-tab-label","mat-focus-indicator",3,"id","disabled","matRippleDisabled","click","cdkFocusChange"],[1,"mat-tab-label-content"],[3,"ngIf"],[3,"cdkPortalOutlet"],["role","tabpanel",3,"id","content","position","origin","animationDuration","_onCentered","_onCentering"]],template:function t(e,n){1&e&&(Cf(0,"mat-tab-header",0,1),Rf("indexFocused",(function t(e){return n._focusChanged(e)}))("selectFocusedIndex",(function t(e){return n.selectedIndex=e})),Hm(2,tj,4,14,"div",2),Mf(),Cf(3,"div",3,4),Hm(5,ej,1,9,"mat-tab-body",5),Mf()),2&e&&(bf("selectedIndex",n.selectedIndex||0)("disableRipple",n.disableRipple)("disablePagination",n.disablePagination),Dc(2),bf("ngForOf",n._tabs),Dc(1),cu("_mat-animation-noopable","NoopAnimations"===n._animationMode),Dc(2),bf("ngForOf",n._tabs))},directives:function(){return[Ej,xv,kj,mL,uF,Pv,uB,_j]},styles:[".mat-tab-group{display:flex;flex-direction:column;max-width:100%}.mat-tab-group.mat-tab-group-inverted-header{flex-direction:column-reverse}.mat-tab-label{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;position:relative}.mat-tab-label:focus{outline:none}.mat-tab-label:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-label:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-label.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-label.mat-tab-disabled{opacity:.5}.mat-tab-label .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-label{opacity:1}@media(max-width: 599px){.mat-tab-label{padding:0 12px}}@media(max-width: 959px){.mat-tab-label{padding:0 12px}}.mat-tab-group[mat-stretch-tabs]>.mat-tab-header .mat-tab-label{flex-basis:0;flex-grow:1}.mat-tab-body-wrapper{position:relative;overflow:hidden;display:flex;transition:height 500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-tab-body-wrapper{transition:none;animation:none}.mat-tab-body{top:0;left:0;right:0;bottom:0;position:absolute;display:block;overflow:hidden;outline:0;flex-basis:100%}.mat-tab-body.mat-tab-body-active{position:relative;overflow-x:hidden;overflow-y:auto;z-index:1;flex-grow:1}.mat-tab-group.mat-tab-group-dynamic-height .mat-tab-body.mat-tab-body-active{overflow-y:hidden}\n"],encapsulation:2}),Pj.ctorParameters=()=>[{type:Kh},{type:Ob},{type:void 0,decorators:[{type:Lr,args:[Cj]},{type:Br}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}],Pj.propDecorators={_allTabs:[{type:or,args:[gj,{descendants:!0}]}],_tabBodyWrapper:[{type:ar,args:["tabBodyWrapper"]}],_tabHeader:[{type:ar,args:["tabHeader"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Pj,[{type:S_,args:[{selector:"mat-tab-group",exportAs:"matTabGroup",template:'<mat-tab-header #tabHeader\n               [selectedIndex]="selectedIndex || 0"\n               [disableRipple]="disableRipple"\n               [disablePagination]="disablePagination"\n               (indexFocused)="_focusChanged($event)"\n               (selectFocusedIndex)="selectedIndex = $event">\n  <div class="mat-tab-label mat-focus-indicator" role="tab" matTabLabelWrapper mat-ripple cdkMonitorElementFocus\n       *ngFor="let tab of _tabs; let i = index"\n       [id]="_getTabLabelId(i)"\n       [attr.tabIndex]="_getTabIndex(tab, i)"\n       [attr.aria-posinset]="i + 1"\n       [attr.aria-setsize]="_tabs.length"\n       [attr.aria-controls]="_getTabContentId(i)"\n       [attr.aria-selected]="selectedIndex == i"\n       [attr.aria-label]="tab.ariaLabel || null"\n       [attr.aria-labelledby]="(!tab.ariaLabel && tab.ariaLabelledby) ? tab.ariaLabelledby : null"\n       [class.mat-tab-label-active]="selectedIndex == i"\n       [disabled]="tab.disabled"\n       [matRippleDisabled]="tab.disabled || disableRipple"\n       (click)="_handleClick(tab, tabHeader, i)"\n       (cdkFocusChange)="_tabFocusChanged($event, i)">\n\n\n    <div class="mat-tab-label-content">\n      \x3c!-- If there is a label template, use it. --\x3e\n      <ng-template [ngIf]="tab.templateLabel">\n        <ng-template [cdkPortalOutlet]="tab.templateLabel"></ng-template>\n      </ng-template>\n\n      \x3c!-- If there is not a label template, fall back to the text label. --\x3e\n      <ng-template [ngIf]="!tab.templateLabel">{{tab.textLabel}}</ng-template>\n    </div>\n  </div>\n</mat-tab-header>\n\n<div\n  class="mat-tab-body-wrapper"\n  [class._mat-animation-noopable]="_animationMode === \'NoopAnimations\'"\n  #tabBodyWrapper>\n  <mat-tab-body role="tabpanel"\n               *ngFor="let tab of _tabs; let i = index"\n               [id]="_getTabContentId(i)"\n               [attr.tabindex]="(contentTabIndex != null && selectedIndex === i) ? contentTabIndex : null"\n               [attr.aria-labelledby]="_getTabLabelId(i)"\n               [class.mat-tab-body-active]="selectedIndex === i"\n               [content]="tab.content!"\n               [position]="tab.position!"\n               [origin]="tab.origin"\n               [animationDuration]="animationDuration"\n               (_onCentered)="_removeTabBodyWrapperHeight()"\n               (_onCentering)="_setTabBodyWrapperHeight($event)">\n  </mat-tab-body>\n</div>\n',encapsulation:Wn.None,changeDetection:jn.Default,inputs:["color","disableRipple"],providers:[{provide:uj,useExisting:Pj}],host:{class:"mat-tab-group","[class.mat-tab-group-dynamic-height]":"dynamicHeight","[class.mat-tab-group-inverted-header]":'headerPosition === "below"'},styles:[".mat-tab-group{display:flex;flex-direction:column;max-width:100%}.mat-tab-group.mat-tab-group-inverted-header{flex-direction:column-reverse}.mat-tab-label{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;position:relative}.mat-tab-label:focus{outline:none}.mat-tab-label:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-label:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-label.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-label.mat-tab-disabled{opacity:.5}.mat-tab-label .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-label{opacity:1}@media(max-width: 599px){.mat-tab-label{padding:0 12px}}@media(max-width: 959px){.mat-tab-label{padding:0 12px}}.mat-tab-group[mat-stretch-tabs]>.mat-tab-header .mat-tab-label{flex-basis:0;flex-grow:1}.mat-tab-body-wrapper{position:relative;overflow:hidden;display:flex;transition:height 500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-tab-body-wrapper{transition:none;animation:none}.mat-tab-body{top:0;left:0;right:0;bottom:0;position:absolute;display:block;overflow:hidden;outline:0;flex-basis:100%}.mat-tab-body.mat-tab-body-active{position:relative;overflow-x:hidden;overflow-y:auto;z-index:1;flex-grow:1}.mat-tab-group.mat-tab-group-dynamic-height .mat-tab-body.mat-tab-body-active{overflow-y:hidden}\n"]}]}],(function(){return[{type:Kh},{type:Ob},{type:void 0,decorators:[{type:Lr,args:[Cj]},{type:Br}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}]}),{_allTabs:[{type:or,args:[gj,{descendants:!0}]}],_tabBodyWrapper:[{type:ar,args:["tabBodyWrapper"]}],_tabHeader:[{type:ar,args:["tabHeader"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const wj=zF(class{});class kj extends wj{constructor(t){super(),this.elementRef=t}focus(){this.elementRef.nativeElement.focus()}getOffsetLeft(){return this.elementRef.nativeElement.offsetLeft}getOffsetWidth(){return this.elementRef.nativeElement.offsetWidth}}kj.ɵfac=function t(e){return new(e||kj)(gf(Kh))},kj.ɵdir=ho({type:kj,selectors:[["","matTabLabelWrapper",""]],hostVars:3,hostBindings:function t(e,n){2&e&&(ym("aria-disabled",!!n.disabled),cu("mat-tab-disabled",n.disabled))},inputs:{disabled:"disabled"},features:[Qp]}),kj.ctorParameters=()=>[{type:Kh}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(kj,[{type:k_,args:[{selector:"[matTabLabelWrapper]",inputs:["disabled"],host:{"[class.mat-tab-disabled]":"disabled","[attr.aria-disabled]":"!!disabled"}}]}],(function(){return[{type:Kh}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Sj=bH({passive:!0});class Dj{constructor(t,e,n,o,i,a,r){this._elementRef=t,this._changeDetectorRef=e,this._viewportRuler=n,this._dir=o,this._ngZone=i,this._platform=a,this._animationMode=r,this._scrollDistance=0,this._selectedIndexChanged=!1,this._destroyed=new B,this._showPaginationControls=!1,this._disableScrollAfter=!0,this._disableScrollBefore=!0,this._stopScrolling=new B,this.disablePagination=!1,this._selectedIndex=0,this.selectFocusedIndex=new Sy,this.indexFocused=new Sy,i.runOutsideAngular((()=>{le(t.nativeElement,"mouseleave").pipe(je(this._destroyed)).subscribe((()=>{this._stopInterval()}))}))}get selectedIndex(){return this._selectedIndex}set selectedIndex(t){t=nH(t),this._selectedIndex!=t&&(this._selectedIndexChanged=!0,this._selectedIndex=t,this._keyManager&&this._keyManager.updateActiveItem(t))}ngAfterViewInit(){le(this._previousPaginator.nativeElement,"touchstart",Sj).pipe(je(this._destroyed)).subscribe((()=>{this._handlePaginatorPress("before")})),le(this._nextPaginator.nativeElement,"touchstart",Sj).pipe(je(this._destroyed)).subscribe((()=>{this._handlePaginatorPress("after")}))}ngAfterContentInit(){const t=this._dir?this._dir.change:zt("ltr"),e=this._viewportRuler.change(150),n=()=>{this.updatePagination(),this._alignInkBarToSelectedTab()};this._keyManager=new LH(this._items).withHorizontalOrientation(this._getLayoutDirection()).withHomeAndEnd().withWrap(),this._keyManager.updateActiveItem(this._selectedIndex),"undefined"!=typeof requestAnimationFrame?requestAnimationFrame(n):n(),pe(t,e,this._items.changes).pipe(je(this._destroyed)).subscribe((()=>{this._ngZone.run((()=>Promise.resolve().then(n))),this._keyManager.withHorizontalOrientation(this._getLayoutDirection())})),this._keyManager.change.pipe(je(this._destroyed)).subscribe((t=>{this.indexFocused.emit(t),this._setTabFocus(t)}))}ngAfterContentChecked(){this._tabLabelCount!=this._items.length&&(this.updatePagination(),this._tabLabelCount=this._items.length,this._changeDetectorRef.markForCheck()),this._selectedIndexChanged&&(this._scrollToLabel(this._selectedIndex),this._checkScrollingControls(),this._alignInkBarToSelectedTab(),this._selectedIndexChanged=!1,this._changeDetectorRef.markForCheck()),this._scrollDistanceChanged&&(this._updateTabScrollPosition(),this._scrollDistanceChanged=!1,this._changeDetectorRef.markForCheck())}ngOnDestroy(){this._destroyed.next(),this._destroyed.complete(),this._stopScrolling.complete()}_handleKeydown(t){if(!tH(t))switch(t.keyCode){case XI:case JI:this.focusIndex!==this.selectedIndex&&(this.selectFocusedIndex.emit(this.focusIndex),this._itemSelected(t));break;default:this._keyManager.onKeydown(t)}}_onContentChanges(){const t=this._elementRef.nativeElement.textContent;t!==this._currentTextContent&&(this._currentTextContent=t||"",this._ngZone.run((()=>{this.updatePagination(),this._alignInkBarToSelectedTab(),this._changeDetectorRef.markForCheck()})))}updatePagination(){this._checkPaginationEnabled(),this._checkScrollingControls(),this._updateTabScrollPosition()}get focusIndex(){return this._keyManager?this._keyManager.activeItemIndex:0}set focusIndex(t){this._isValidIndex(t)&&this.focusIndex!==t&&this._keyManager&&this._keyManager.setActiveItem(t)}_isValidIndex(t){if(!this._items)return!0;const e=this._items?this._items.toArray()[t]:null;return!!e&&!e.disabled}_setTabFocus(t){if(this._showPaginationControls&&this._scrollToLabel(t),this._items&&this._items.length){this._items.toArray()[t].focus();const e=this._tabListContainer.nativeElement,n=this._getLayoutDirection();e.scrollLeft="ltr"==n?0:e.scrollWidth-e.offsetWidth}}_getLayoutDirection(){return this._dir&&"rtl"===this._dir.value?"rtl":"ltr"}_updateTabScrollPosition(){if(this.disablePagination)return;const t=this.scrollDistance,e="ltr"===this._getLayoutDirection()?-t:t;this._tabList.nativeElement.style.transform=`translateX(${Math.round(e)}px)`,(this._platform.TRIDENT||this._platform.EDGE)&&(this._tabListContainer.nativeElement.scrollLeft=0)}get scrollDistance(){return this._scrollDistance}set scrollDistance(t){this._scrollTo(t)}_scrollHeader(t){return this._scrollTo(this._scrollDistance+("before"==t?-1:1)*this._tabListContainer.nativeElement.offsetWidth/3)}_handlePaginatorClick(t){this._stopInterval(),this._scrollHeader(t)}_scrollToLabel(t){if(this.disablePagination)return;const e=this._items?this._items.toArray()[t]:null;if(!e)return;const n=this._tabListContainer.nativeElement.offsetWidth,{offsetLeft:o,offsetWidth:i}=e.elementRef.nativeElement;let a,r;"ltr"==this._getLayoutDirection()?(a=o,r=a+i):(r=this._tabList.nativeElement.offsetWidth-o,a=r-i);const s=this.scrollDistance,l=this.scrollDistance+n;a<s?this.scrollDistance-=s-a+60:r>l&&(this.scrollDistance+=r-l+60)}_checkPaginationEnabled(){if(this.disablePagination)this._showPaginationControls=!1;else{const t=this._tabList.nativeElement.scrollWidth>this._elementRef.nativeElement.offsetWidth;t||(this.scrollDistance=0),t!==this._showPaginationControls&&this._changeDetectorRef.markForCheck(),this._showPaginationControls=t}}_checkScrollingControls(){this.disablePagination?this._disableScrollAfter=this._disableScrollBefore=!0:(this._disableScrollBefore=0==this.scrollDistance,this._disableScrollAfter=this.scrollDistance==this._getMaxScrollDistance(),this._changeDetectorRef.markForCheck())}_getMaxScrollDistance(){return this._tabList.nativeElement.scrollWidth-this._tabListContainer.nativeElement.offsetWidth||0}_alignInkBarToSelectedTab(){const t=this._items&&this._items.length?this._items.toArray()[this.selectedIndex]:null,e=t?t.elementRef.nativeElement:null;e?this._inkBar.alignToElement(e):this._inkBar.hide()}_stopInterval(){this._stopScrolling.next()}_handlePaginatorPress(t,e){e&&null!=e.button&&0!==e.button||(this._stopInterval(),de(650,100).pipe(je(pe(this._stopScrolling,this._destroyed))).subscribe((()=>{const{maxScrollDistance:e,distance:n}=this._scrollHeader(t);(0===n||n>=e)&&this._stopInterval()})))}_scrollTo(t){if(this.disablePagination)return{maxScrollDistance:0,distance:0};const e=this._getMaxScrollDistance();return this._scrollDistance=Math.max(0,Math.min(e,t)),this._scrollDistanceChanged=!0,this._checkScrollingControls(),{maxScrollDistance:e,distance:this._scrollDistance}}}Dj.ɵfac=function t(e){return new(e||Dj)(gf(Kh),gf(Ob),gf(QL),gf(vF,8),gf(mC),gf(cH),gf(ck,8))},Dj.ɵdir=ho({type:Dj,inputs:{disablePagination:"disablePagination"}}),Dj.ctorParameters=()=>[{type:Kh},{type:Ob},{type:QL},{type:vF,decorators:[{type:Br}]},{type:mC},{type:cH},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}],Dj.propDecorators={disablePagination:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Dj,[{type:k_}],(function(){return[{type:Kh},{type:Ob},{type:QL},{type:vF,decorators:[{type:Br}]},{type:mC},{type:cH},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}]}),{disablePagination:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Rj extends Dj{constructor(t,e,n,o,i,a,r){super(t,e,n,o,i,a,r),this._disableRipple=!1}get disableRipple(){return this._disableRipple}set disableRipple(t){this._disableRipple=eH(t)}_itemSelected(t){t.preventDefault()}}Rj.ɵfac=function t(e){return new(e||Rj)(gf(Kh),gf(Ob),gf(QL),gf(vF,8),gf(mC),gf(cH),gf(ck,8))},Rj.ɵdir=ho({type:Rj,inputs:{disableRipple:"disableRipple"},features:[Qp]}),Rj.ctorParameters=()=>[{type:Kh},{type:Ob},{type:QL},{type:vF,decorators:[{type:Br}]},{type:mC},{type:cH},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}],Rj.propDecorators={disableRipple:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Rj,[{type:k_}],(function(){return[{type:Kh},{type:Ob},{type:QL},{type:vF,decorators:[{type:Br}]},{type:mC},{type:cH},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}]}),{disableRipple:[{type:R_}]});class Ej extends Rj{constructor(t,e,n,o,i,a,r){super(t,e,n,o,i,a,r)}}Ej.ɵfac=function t(e){return new(e||Ej)(gf(Kh),gf(Ob),gf(QL),gf(vF,8),gf(mC),gf(cH),gf(ck,8))},Ej.ɵcmp=so({type:Ej,selectors:[["mat-tab-header"]],contentQueries:function t(e,n,o){if(1&e&&jy(o,kj,4),2&e){let t;By(t=Uy())&&(n._items=t)}},viewQuery:function t(e,n){if(1&e&&(Vy(lj,7),Vy(nj,7),Vy(oj,7),Vy(ij,5),Vy(aj,5)),2&e){let t;By(t=Uy())&&(n._inkBar=t.first),By(t=Uy())&&(n._tabListContainer=t.first),By(t=Uy())&&(n._tabList=t.first),By(t=Uy())&&(n._nextPaginator=t.first),By(t=Uy())&&(n._previousPaginator=t.first)}},hostAttrs:[1,"mat-tab-header"],hostVars:4,hostBindings:function t(e,n){2&e&&cu("mat-tab-header-pagination-controls-enabled",n._showPaginationControls)("mat-tab-header-rtl","rtl"==n._getLayoutDirection())},inputs:{selectedIndex:"selectedIndex"},outputs:{selectFocusedIndex:"selectFocusedIndex",indexFocused:"indexFocused"},features:[Qp],ngContentSelectors:GV,decls:13,vars:8,consts:[["aria-hidden","true","mat-ripple","",1,"mat-tab-header-pagination","mat-tab-header-pagination-before","mat-elevation-z4",3,"matRippleDisabled","click","mousedown","touchend"],["previousPaginator",""],[1,"mat-tab-header-pagination-chevron"],[1,"mat-tab-label-container",3,"keydown"],["tabListContainer",""],["role","tablist",1,"mat-tab-list",3,"cdkObserveContent"],["tabList",""],[1,"mat-tab-labels"],["aria-hidden","true","mat-ripple","",1,"mat-tab-header-pagination","mat-tab-header-pagination-after","mat-elevation-z4",3,"matRippleDisabled","mousedown","click","touchend"],["nextPaginator",""]],template:function t(e,n){1&e&&(Hf(),Cf(0,"div",0,1),Rf("click",(function t(){return n._handlePaginatorClick("before")}))("mousedown",(function t(e){return n._handlePaginatorPress("before",e)}))("touchend",(function t(){return n._stopInterval()})),vf(2,"div",2),Mf(),Cf(3,"div",3,4),Rf("keydown",(function t(e){return n._handleKeydown(e)})),Cf(5,"div",5,6),Rf("cdkObserveContent",(function t(){return n._onContentChanges()})),Cf(7,"div",7),Ff(8),Mf(),vf(9,"mat-ink-bar"),Mf(),Mf(),Cf(10,"div",8,9),Rf("mousedown",(function t(e){return n._handlePaginatorPress("after",e)}))("click",(function t(){return n._handlePaginatorClick("after")}))("touchend",(function t(){return n._stopInterval()})),vf(12,"div",2),Mf()),2&e&&(cu("mat-tab-header-pagination-disabled",n._disableScrollBefore),bf("matRippleDisabled",n._disableScrollBefore||n.disableRipple),Dc(5),cu("_mat-animation-noopable","NoopAnimations"===n._animationMode),Dc(5),cu("mat-tab-header-pagination-disabled",n._disableScrollAfter),bf("matRippleDisabled",n._disableScrollAfter||n.disableRipple))},directives:[mL,PH,lj],styles:['.mat-tab-header{display:flex;overflow:hidden;position:relative;flex-shrink:0}.mat-tab-header-pagination{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;position:relative;display:none;justify-content:center;align-items:center;min-width:32px;cursor:pointer;z-index:2;-webkit-tap-highlight-color:transparent;touch-action:none}.mat-tab-header-pagination-controls-enabled .mat-tab-header-pagination{display:flex}.mat-tab-header-pagination-before,.mat-tab-header-rtl .mat-tab-header-pagination-after{padding-left:4px}.mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-rtl .mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(-135deg)}.mat-tab-header-rtl .mat-tab-header-pagination-before,.mat-tab-header-pagination-after{padding-right:4px}.mat-tab-header-rtl .mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(45deg)}.mat-tab-header-pagination-chevron{border-style:solid;border-width:2px 2px 0 0;content:"";height:8px;width:8px}.mat-tab-header-pagination-disabled{box-shadow:none;cursor:default}.mat-tab-list{flex-grow:1;position:relative;transition:transform 500ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-ink-bar{position:absolute;bottom:0;height:2px;transition:500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-ink-bar{transition:none;animation:none}.mat-tab-group-inverted-header .mat-ink-bar{bottom:auto;top:0}.cdk-high-contrast-active .mat-ink-bar{outline:solid 2px;height:0}.mat-tab-labels{display:flex}[mat-align-tabs=center]>.mat-tab-header .mat-tab-labels{justify-content:center}[mat-align-tabs=end]>.mat-tab-header .mat-tab-labels{justify-content:flex-end}.mat-tab-label-container{display:flex;flex-grow:1;overflow:hidden;z-index:1}._mat-animation-noopable.mat-tab-list{transition:none;animation:none}.mat-tab-label{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;position:relative}.mat-tab-label:focus{outline:none}.mat-tab-label:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-label:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-label.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-label.mat-tab-disabled{opacity:.5}.mat-tab-label .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-label{opacity:1}@media(max-width: 599px){.mat-tab-label{min-width:72px}}\n'],encapsulation:2}),Ej.ctorParameters=()=>[{type:Kh},{type:Ob},{type:QL},{type:vF,decorators:[{type:Br}]},{type:mC},{type:cH},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}],Ej.propDecorators={_items:[{type:or,args:[kj,{descendants:!1}]}],_inkBar:[{type:ar,args:[lj,{static:!0}]}],_tabListContainer:[{type:ar,args:["tabListContainer",{static:!0}]}],_tabList:[{type:ar,args:["tabList",{static:!0}]}],_nextPaginator:[{type:ar,args:["nextPaginator"]}],_previousPaginator:[{type:ar,args:["previousPaginator"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Ej,[{type:S_,args:[{selector:"mat-tab-header",template:'<div class="mat-tab-header-pagination mat-tab-header-pagination-before mat-elevation-z4"\n     #previousPaginator\n     aria-hidden="true"\n     mat-ripple [matRippleDisabled]="_disableScrollBefore || disableRipple"\n     [class.mat-tab-header-pagination-disabled]="_disableScrollBefore"\n     (click)="_handlePaginatorClick(\'before\')"\n     (mousedown)="_handlePaginatorPress(\'before\', $event)"\n     (touchend)="_stopInterval()">\n  <div class="mat-tab-header-pagination-chevron"></div>\n</div>\n\n<div class="mat-tab-label-container" #tabListContainer (keydown)="_handleKeydown($event)">\n  <div\n    #tabList\n    class="mat-tab-list"\n    [class._mat-animation-noopable]="_animationMode === \'NoopAnimations\'"\n    role="tablist"\n    (cdkObserveContent)="_onContentChanges()">\n    <div class="mat-tab-labels">\n      <ng-content></ng-content>\n    </div>\n    <mat-ink-bar></mat-ink-bar>\n  </div>\n</div>\n\n<div class="mat-tab-header-pagination mat-tab-header-pagination-after mat-elevation-z4"\n     #nextPaginator\n     aria-hidden="true"\n     mat-ripple [matRippleDisabled]="_disableScrollAfter || disableRipple"\n     [class.mat-tab-header-pagination-disabled]="_disableScrollAfter"\n     (mousedown)="_handlePaginatorPress(\'after\', $event)"\n     (click)="_handlePaginatorClick(\'after\')"\n     (touchend)="_stopInterval()">\n  <div class="mat-tab-header-pagination-chevron"></div>\n</div>\n',inputs:["selectedIndex"],outputs:["selectFocusedIndex","indexFocused"],encapsulation:Wn.None,changeDetection:jn.Default,host:{class:"mat-tab-header","[class.mat-tab-header-pagination-controls-enabled]":"_showPaginationControls","[class.mat-tab-header-rtl]":"_getLayoutDirection() == 'rtl'"},styles:['.mat-tab-header{display:flex;overflow:hidden;position:relative;flex-shrink:0}.mat-tab-header-pagination{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;position:relative;display:none;justify-content:center;align-items:center;min-width:32px;cursor:pointer;z-index:2;-webkit-tap-highlight-color:transparent;touch-action:none}.mat-tab-header-pagination-controls-enabled .mat-tab-header-pagination{display:flex}.mat-tab-header-pagination-before,.mat-tab-header-rtl .mat-tab-header-pagination-after{padding-left:4px}.mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-rtl .mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(-135deg)}.mat-tab-header-rtl .mat-tab-header-pagination-before,.mat-tab-header-pagination-after{padding-right:4px}.mat-tab-header-rtl .mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(45deg)}.mat-tab-header-pagination-chevron{border-style:solid;border-width:2px 2px 0 0;content:"";height:8px;width:8px}.mat-tab-header-pagination-disabled{box-shadow:none;cursor:default}.mat-tab-list{flex-grow:1;position:relative;transition:transform 500ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-ink-bar{position:absolute;bottom:0;height:2px;transition:500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-ink-bar{transition:none;animation:none}.mat-tab-group-inverted-header .mat-ink-bar{bottom:auto;top:0}.cdk-high-contrast-active .mat-ink-bar{outline:solid 2px;height:0}.mat-tab-labels{display:flex}[mat-align-tabs=center]>.mat-tab-header .mat-tab-labels{justify-content:center}[mat-align-tabs=end]>.mat-tab-header .mat-tab-labels{justify-content:flex-end}.mat-tab-label-container{display:flex;flex-grow:1;overflow:hidden;z-index:1}._mat-animation-noopable.mat-tab-list{transition:none;animation:none}.mat-tab-label{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;position:relative}.mat-tab-label:focus{outline:none}.mat-tab-label:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-label:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-label.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-label.mat-tab-disabled{opacity:.5}.mat-tab-label .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-label{opacity:1}@media(max-width: 599px){.mat-tab-label{min-width:72px}}\n']}]}],(function(){return[{type:Kh},{type:Ob},{type:QL},{type:vF,decorators:[{type:Br}]},{type:mC},{type:cH},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}]}),{_items:[{type:or,args:[kj,{descendants:!1}]}],_inkBar:[{type:ar,args:[lj,{static:!0}]}],_tabListContainer:[{type:ar,args:["tabListContainer",{static:!0}]}],_tabList:[{type:ar,args:["tabList",{static:!0}]}],_nextPaginator:[{type:ar,args:["nextPaginator"]}],_previousPaginator:[{type:ar,args:["previousPaginator"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Aj extends Dj{constructor(t,e,n,o,i,a,r){super(t,o,i,e,n,a,r),this._disableRipple=!1,this.color="primary"}get backgroundColor(){return this._backgroundColor}set backgroundColor(t){const e=this._elementRef.nativeElement.classList;e.remove(`mat-background-${this.backgroundColor}`),t&&e.add(`mat-background-${t}`),this._backgroundColor=t}get disableRipple(){return this._disableRipple}set disableRipple(t){this._disableRipple=eH(t)}_itemSelected(){}ngAfterContentInit(){this._items.changes.pipe(Be(null),je(this._destroyed)).subscribe((()=>{this.updateActiveLink()})),super.ngAfterContentInit()}updateActiveLink(){if(!this._items)return;const t=this._items.toArray();for(let e=0;e<t.length;e++)if(t[e].active)return this.selectedIndex=e,void this._changeDetectorRef.markForCheck();this.selectedIndex=-1,this._inkBar.hide()}}Aj.ɵfac=function t(e){return new(e||Aj)(gf(Kh),gf(vF,8),gf(mC),gf(Ob),gf(QL),gf(cH),gf(ck,8))},Aj.ɵdir=ho({type:Aj,inputs:{color:"color",backgroundColor:"backgroundColor",disableRipple:"disableRipple"},features:[Qp]}),Aj.ctorParameters=()=>[{type:Kh},{type:vF,decorators:[{type:Br}]},{type:mC},{type:Ob},{type:QL},{type:cH},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}],Aj.propDecorators={backgroundColor:[{type:R_}],disableRipple:[{type:R_}],color:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Aj,[{type:k_}],(function(){return[{type:Kh},{type:vF,decorators:[{type:Br}]},{type:mC},{type:Ob},{type:QL},{type:cH},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}]}),{color:[{type:R_}],backgroundColor:[{type:R_}],disableRipple:[{type:R_}]});class Tj extends Aj{constructor(t,e,n,o,i,a,r){super(t,e,n,o,i,a,r)}}Tj.ɵfac=function t(e){return new(e||Tj)(gf(Kh),gf(vF,8),gf(mC),gf(Ob),gf(QL),gf(cH),gf(ck,8))},Tj.ɵcmp=so({type:Tj,selectors:[["","mat-tab-nav-bar",""]],contentQueries:function t(e,n,o){if(1&e&&jy(o,Ij,5),2&e){let t;By(t=Uy())&&(n._items=t)}},viewQuery:function t(e,n){if(1&e&&(Vy(lj,7),Vy(nj,7),Vy(oj,7),Vy(ij,5),Vy(aj,5)),2&e){let t;By(t=Uy())&&(n._inkBar=t.first),By(t=Uy())&&(n._tabListContainer=t.first),By(t=Uy())&&(n._tabList=t.first),By(t=Uy())&&(n._nextPaginator=t.first),By(t=Uy())&&(n._previousPaginator=t.first)}},hostAttrs:[1,"mat-tab-nav-bar","mat-tab-header"],hostVars:10,hostBindings:function t(e,n){2&e&&cu("mat-tab-header-pagination-controls-enabled",n._showPaginationControls)("mat-tab-header-rtl","rtl"==n._getLayoutDirection())("mat-primary","warn"!==n.color&&"accent"!==n.color)("mat-accent","accent"===n.color)("mat-warn","warn"===n.color)},inputs:{color:"color"},exportAs:["matTabNavBar","matTabNav"],features:[Qp],attrs:["mat-tab-nav-bar",""],ngContentSelectors:GV,decls:13,vars:8,consts:[["aria-hidden","true","mat-ripple","",1,"mat-tab-header-pagination","mat-tab-header-pagination-before","mat-elevation-z4",3,"matRippleDisabled","click","mousedown","touchend"],["previousPaginator",""],[1,"mat-tab-header-pagination-chevron"],[1,"mat-tab-link-container",3,"keydown"],["tabListContainer",""],[1,"mat-tab-list",3,"cdkObserveContent"],["tabList",""],[1,"mat-tab-links"],["aria-hidden","true","mat-ripple","",1,"mat-tab-header-pagination","mat-tab-header-pagination-after","mat-elevation-z4",3,"matRippleDisabled","mousedown","click","touchend"],["nextPaginator",""]],template:function 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Hj{}Hj.ɵfac=function t(e){return new(e||Hj)},Hj.ɵmod=fo({type:Hj}),Hj.ɵinj=Dn({imports:[[rx,NF,hB,fL,wH,_F],NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Hj,[{type:L_,args:[{imports:[rx,NF,hB,fL,wH,_F],exports:[NF,Pj,mj,gj,Tj,Ij,dj],declarations:[Pj,mj,gj,lj,kj,Tj,Ij,_j,bj,Ej,dj]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(Hj,{declarations:function(){return[Pj,mj,gj,lj,kj,Tj,Ij,_j,bj,Ej,dj]},imports:function(){return[rx,NF,hB,fL,wH,_F]},exports:function(){return[NF,Pj,mj,gj,Tj,Ij,dj]}});const Fj=["underline"],Lj=["connectionContainer"],Bj=["inputContainer"],Vj=["label"];function jj(t,e){1&t&&(xf(0),Cf(1,"div",14),vf(2,"div",15),vf(3,"div",16),vf(4,"div",17),Mf(),Cf(5,"div",18),vf(6,"div",15),vf(7,"div",16),vf(8,"div",17),Mf(),Of())}function Uj(t,e){1&t&&(Cf(0,"div",19),Ff(1,1),Mf())}function Wj(t,e){if(1&t&&(xf(0),Ff(1,2),Cf(2,"span"),Pu(3),Mf(),Of()),2&t){const t=zf(2);Dc(3),wu(t._control.placeholder)}}function Gj(t,e){1&t&&Ff(0,3,["*ngSwitchCase","true"])}function Yj(t,e){1&t&&(Cf(0,"span",23),Pu(1," *"),Mf())}function qj(t,e){if(1&t){const t=wf();Cf(0,"label",20,21),Rf("cdkObserveContent",(function e(){Pi(t);return zf().updateOutlineGap()})),Hm(2,Wj,4,1,"ng-container",12),Hm(3,Gj,1,0,"ng-content",12),Hm(4,Yj,2,0,"span",22),Mf()}if(2&t){const t=zf();cu("mat-empty",t._control.empty&&!t._shouldAlwaysFloat())("mat-form-field-empty",t._control.empty&&!t._shouldAlwaysFloat())("mat-accent","accent"==t.color)("mat-warn","warn"==t.color),bf("cdkObserveContentDisabled","outline"!=t.appearance)("id",t._labelId)("ngSwitch",t._hasLabel()),ym("for",t._control.id)("aria-owns",t._control.id),Dc(2),bf("ngSwitchCase",!1),Dc(1),bf("ngSwitchCase",!0),Dc(1),bf("ngIf",!t.hideRequiredMarker&&t._control.required&&!t._control.disabled)}}function Zj(t,e){1&t&&(Cf(0,"div",24),Ff(1,4),Mf())}function Xj(t,e){if(1&t&&(Cf(0,"div",25,26),vf(2,"span",27),Mf()),2&t){const t=zf();Dc(2),cu("mat-accent","accent"==t.color)("mat-warn","warn"==t.color)}}function Kj(t,e){if(1&t&&(Cf(0,"div"),Ff(1,5),Mf()),2&t){bf("@transitionMessages",zf()._subscriptAnimationState)}}function Jj(t,e){if(1&t&&(Cf(0,"div",31),Pu(1),Mf()),2&t){const t=zf(2);bf("id",t._hintLabelId),Dc(1),wu(t.hintLabel)}}function Qj(t,e){if(1&t&&(Cf(0,"div",28),Hm(1,Jj,2,2,"div",29),Ff(2,6),vf(3,"div",30),Ff(4,7),Mf()),2&t){const t=zf();bf("@transitionMessages",t._subscriptAnimationState),Dc(1),bf("ngIf",t.hintLabel)}}const $j=["*",[["","matPrefix",""]],[["mat-placeholder"]],[["mat-label"]],[["","matSuffix",""]],[["mat-error"]],[["mat-hint",3,"align","end"]],[["mat-hint","align","end"]]];let tU=0;const eU=new er("MatError");class nU{constructor(t,e){this.id="mat-error-"+tU++,t||e.nativeElement.setAttribute("aria-live","polite")}}nU.ɵfac=function t(e){return new(e||nU)(Ga("aria-live"),gf(Kh))},nU.ɵdir=ho({type:nU,selectors:[["mat-error"]],hostAttrs:["aria-atomic","true",1,"mat-error"],hostVars:1,hostBindings:function t(e,n){2&e&&ym("id",n.id)},inputs:{id:"id"},features:[jh([{provide:eU,useExisting:nU}])]}),nU.ctorParameters=()=>[{type:String,decorators:[{type:$a,args:["aria-live"]}]},{type:Kh}],nU.propDecorators={id:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(nU,[{type:k_,args:[{selector:"mat-error",host:{class:"mat-error","[attr.id]":"id","aria-atomic":"true"},providers:[{provide:eU,useExisting:nU}]}]}],(function(){return[{type:String,decorators:[{type:$a,args:["aria-live"]}]},{type:Kh}]}),{id:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const oU={transitionMessages:xO("transitionMessages",[kO("enter",wO({opacity:1,transform:"translateY(0%)"})),DO("void => enter",[wO({opacity:0,transform:"translateY(-5px)"}),OO("300ms cubic-bezier(0.55, 0, 0.55, 0.2)")])])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class iU{}function aU(t){return Error(`A hint was already declared for 'align="${t}"'.`)}iU.ɵfac=function t(e){return new(e||iU)},iU.ɵdir=ho({type:iU}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(iU,[{type:k_}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let rU=0;const sU=new er("MatHint");class lU{constructor(){this.align="start",this.id="mat-hint-"+rU++}}lU.ɵfac=function t(e){return new(e||lU)},lU.ɵdir=ho({type:lU,selectors:[["mat-hint"]],hostAttrs:[1,"mat-hint"],hostVars:4,hostBindings:function t(e,n){2&e&&(ym("id",n.id)("align",null),cu("mat-form-field-hint-end","end"===n.align))},inputs:{align:"align",id:"id"},features:[jh([{provide:sU,useExisting:lU}])]}),lU.propDecorators={align:[{type:R_}],id:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(lU,[{type:k_,args:[{selector:"mat-hint",host:{class:"mat-hint","[class.mat-form-field-hint-end]":'align === "end"',"[attr.id]":"id","[attr.align]":"null"},providers:[{provide:sU,useExisting:lU}]}]}],(function(){return[]}),{align:[{type:R_}],id:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class cU{}cU.ɵfac=function t(e){return new(e||cU)},cU.ɵdir=ho({type:cU,selectors:[["mat-label"]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(cU,[{type:k_,args:[{selector:"mat-label"}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class dU{}dU.ɵfac=function t(e){return new(e||dU)},dU.ɵdir=ho({type:dU,selectors:[["mat-placeholder"]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(dU,[{type:k_,args:[{selector:"mat-placeholder"}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const pU=new er("MatPrefix");class mU{}mU.ɵfac=function t(e){return new(e||mU)},mU.ɵdir=ho({type:mU,selectors:[["","matPrefix",""]],features:[jh([{provide:pU,useExisting:mU}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(mU,[{type:k_,args:[{selector:"[matPrefix]",providers:[{provide:pU,useExisting:mU}]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const fU=new er("MatSuffix");class uU{}uU.ɵfac=function t(e){return new(e||uU)},uU.ɵdir=ho({type:uU,selectors:[["","matSuffix",""]],features:[jh([{provide:fU,useExisting:uU}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(uU,[{type:k_,args:[{selector:"[matSuffix]",providers:[{provide:fU,useExisting:uU}]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let gU=0;const hU=IF(class{constructor(t){this._elementRef=t}},"primary"),bU=new er("MAT_FORM_FIELD_DEFAULT_OPTIONS"),yU=new er("MatFormField");class _U extends hU{constructor(t,e,n,o,i,a,r,s){super(t),this._changeDetectorRef=e,this._dir=o,this._defaults=i,this._platform=a,this._ngZone=r,this._outlineGapCalculationNeededImmediately=!1,this._outlineGapCalculationNeededOnStable=!1,this._destroyed=new B,this._showAlwaysAnimate=!1,this._subscriptAnimationState="",this._hintLabel="",this._hintLabelId="mat-hint-"+gU++,this._labelId="mat-form-field-label-"+gU++,this.floatLabel=this._getDefaultFloatLabelState(),this._animationsEnabled="NoopAnimations"!==s,this.appearance=i&&i.appearance?i.appearance:"legacy",this._hideRequiredMarker=!(!i||null==i.hideRequiredMarker)&&i.hideRequiredMarker}get appearance(){return this._appearance}set appearance(t){const e=this._appearance;this._appearance=t||this._defaults&&this._defaults.appearance||"legacy","outline"===this._appearance&&e!==t&&(this._outlineGapCalculationNeededOnStable=!0)}get hideRequiredMarker(){return this._hideRequiredMarker}set hideRequiredMarker(t){this._hideRequiredMarker=eH(t)}_shouldAlwaysFloat(){return"always"===this.floatLabel&&!this._showAlwaysAnimate}_canLabelFloat(){return"never"!==this.floatLabel}get hintLabel(){return this._hintLabel}set hintLabel(t){this._hintLabel=t,this._processHints()}get floatLabel(){return"legacy"!==this.appearance&&"never"===this._floatLabel?"auto":this._floatLabel}set floatLabel(t){t!==this._floatLabel&&(this._floatLabel=t||this._getDefaultFloatLabelState(),this._changeDetectorRef.markForCheck())}get _control(){return this._explicitFormFieldControl||this._controlNonStatic||this._controlStatic}set _control(t){this._explicitFormFieldControl=t}getLabelId(){return this._hasFloatingLabel()?this._labelId:null}getConnectedOverlayOrigin(){return this._connectionContainerRef||this._elementRef}ngAfterContentInit(){this._validateControlChild();const t=this._control;t.controlType&&this._elementRef.nativeElement.classList.add(`mat-form-field-type-${t.controlType}`),t.stateChanges.pipe(Be(null)).subscribe((()=>{this._validatePlaceholders(),this._syncDescribedByIds(),this._changeDetectorRef.markForCheck()})),t.ngControl&&t.ngControl.valueChanges&&t.ngControl.valueChanges.pipe(je(this._destroyed)).subscribe((()=>this._changeDetectorRef.markForCheck())),this._ngZone.runOutsideAngular((()=>{this._ngZone.onStable.pipe(je(this._destroyed)).subscribe((()=>{this._outlineGapCalculationNeededOnStable&&this.updateOutlineGap()}))})),pe(this._prefixChildren.changes,this._suffixChildren.changes).subscribe((()=>{this._outlineGapCalculationNeededOnStable=!0,this._changeDetectorRef.markForCheck()})),this._hintChildren.changes.pipe(Be(null)).subscribe((()=>{this._processHints(),this._changeDetectorRef.markForCheck()})),this._errorChildren.changes.pipe(Be(null)).subscribe((()=>{this._syncDescribedByIds(),this._changeDetectorRef.markForCheck()})),this._dir&&this._dir.change.pipe(je(this._destroyed)).subscribe((()=>{"function"==typeof requestAnimationFrame?this._ngZone.runOutsideAngular((()=>{requestAnimationFrame((()=>this.updateOutlineGap()))})):this.updateOutlineGap()}))}ngAfterContentChecked(){this._validateControlChild(),this._outlineGapCalculationNeededImmediately&&this.updateOutlineGap()}ngAfterViewInit(){this._subscriptAnimationState="enter",this._changeDetectorRef.detectChanges()}ngOnDestroy(){this._destroyed.next(),this._destroyed.complete()}_shouldForward(t){const e=this._control?this._control.ngControl:null;return e&&e[t]}_hasPlaceholder(){return!!(this._control&&this._control.placeholder||this._placeholderChild)}_hasLabel(){return!(!this._labelChildNonStatic&&!this._labelChildStatic)}_shouldLabelFloat(){return this._canLabelFloat()&&(this._control&&this._control.shouldLabelFloat||this._shouldAlwaysFloat())}_hideControlPlaceholder(){return"legacy"===this.appearance&&!this._hasLabel()||this._hasLabel()&&!this._shouldLabelFloat()}_hasFloatingLabel(){return this._hasLabel()||"legacy"===this.appearance&&this._hasPlaceholder()}_getDisplayedMessages(){return this._errorChildren&&this._errorChildren.length>0&&this._control.errorState?"error":"hint"}_animateAndLockLabel(){this._hasFloatingLabel()&&this._canLabelFloat()&&(this._animationsEnabled&&this._label&&(this._showAlwaysAnimate=!0,le(this._label.nativeElement,"transitionend").pipe(ve(1)).subscribe((()=>{this._showAlwaysAnimate=!1}))),this.floatLabel="always",this._changeDetectorRef.markForCheck())}_validatePlaceholders(){if(this._control.placeholder&&this._placeholderChild&&("undefined"==typeof ngDevMode||ngDevMode))
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
throw(function t(){return Error("Placeholder attribute and child element were both specified.")})()}_processHints(){this._validateHints(),this._syncDescribedByIds()}_validateHints(){if(this._hintChildren&&("undefined"==typeof ngDevMode||ngDevMode)){let t,e;this._hintChildren.forEach((n=>{if("start"===n.align){if(t||this.hintLabel)throw aU("start");t=n}else if("end"===n.align){if(e)throw aU("end");e=n}}))}}_getDefaultFloatLabelState(){return this._defaults&&this._defaults.floatLabel||"auto"}_syncDescribedByIds(){if(this._control){let t=[];if(this._control.userAriaDescribedBy&&"string"==typeof this._control.userAriaDescribedBy&&t.push(...this._control.userAriaDescribedBy.split(" ")),"hint"===this._getDisplayedMessages()){const e=this._hintChildren?this._hintChildren.find((t=>"start"===t.align)):null,n=this._hintChildren?this._hintChildren.find((t=>"end"===t.align)):null;e?t.push(e.id):this._hintLabel&&t.push(this._hintLabelId),n&&t.push(n.id)}else this._errorChildren&&t.push(...this._errorChildren.map((t=>t.id)));this._control.setDescribedByIds(t)}}_validateControlChild(){if(!this._control&&("undefined"==typeof ngDevMode||ngDevMode))throw(function t(){return Error("mat-form-field must contain a MatFormFieldControl.")})()}updateOutlineGap(){const t=this._label?this._label.nativeElement:null;if("outline"!==this.appearance||!t||!t.children.length||!t.textContent.trim())return;if(!this._platform.isBrowser)return;if(!this._isAttachedToDOM())return void(this._outlineGapCalculationNeededImmediately=!0);let e=0,n=0;const o=this._connectionContainerRef.nativeElement,i=o.querySelectorAll(".mat-form-field-outline-start"),a=o.querySelectorAll(".mat-form-field-outline-gap");if(this._label&&this._label.nativeElement.children.length){const i=o.getBoundingClientRect();if(0===i.width&&0===i.height)return this._outlineGapCalculationNeededOnStable=!0,void(this._outlineGapCalculationNeededImmediately=!1);const a=this._getStartEnd(i),r=t.children,s=this._getStartEnd(r[0].getBoundingClientRect());let l=0;for(let t=0;t<r.length;t++)l+=r[t].offsetWidth;e=Math.abs(s-a)-5,n=l>0?.75*l+10:0}for(let t=0;t<i.length;t++)i[t].style.width=`${e}px`;for(let t=0;t<a.length;t++)a[t].style.width=`${n}px`;this._outlineGapCalculationNeededOnStable=this._outlineGapCalculationNeededImmediately=!1}_getStartEnd(t){return this._dir&&"rtl"===this._dir.value?t.right:t.left}_isAttachedToDOM(){const t=this._elementRef.nativeElement;if(t.getRootNode){const e=t.getRootNode();return e&&e!==t}return document.documentElement.contains(t)}}_U.ɵfac=function t(e){return new(e||_U)(gf(Kh),gf(Ob),gf(Kh),gf(vF,8),gf(bU,8),gf(cH),gf(mC),gf(ck,8))},_U.ɵcmp=so({type:_U,selectors:[["mat-form-field"]],contentQueries:function t(e,n,o){if(1&e&&(jy(o,iU,5),jy(o,iU,7),jy(o,cU,5),jy(o,cU,7),jy(o,dU,5),jy(o,eU,5),jy(o,sU,5),jy(o,pU,5),jy(o,fU,5)),2&e){let t;By(t=Uy())&&(n._controlNonStatic=t.first),By(t=Uy())&&(n._controlStatic=t.first),By(t=Uy())&&(n._labelChildNonStatic=t.first),By(t=Uy())&&(n._labelChildStatic=t.first),By(t=Uy())&&(n._placeholderChild=t.first),By(t=Uy())&&(n._errorChildren=t),By(t=Uy())&&(n._hintChildren=t),By(t=Uy())&&(n._prefixChildren=t),By(t=Uy())&&(n._suffixChildren=t)}},viewQuery:function t(e,n){if(1&e&&(Vy(Fj,5),Vy(Lj,7),Vy(Bj,5),Vy(Vj,5)),2&e){let t;By(t=Uy())&&(n.underlineRef=t.first),By(t=Uy())&&(n._connectionContainerRef=t.first),By(t=Uy())&&(n._inputContainerRef=t.first),By(t=Uy())&&(n._label=t.first)}},hostAttrs:[1,"mat-form-field"],hostVars:40,hostBindings:function t(e,n){2&e&&cu("mat-form-field-appearance-standard","standard"==n.appearance)("mat-form-field-appearance-fill","fill"==n.appearance)("mat-form-field-appearance-outline","outline"==n.appearance)("mat-form-field-appearance-legacy","legacy"==n.appearance)("mat-form-field-invalid",n._control.errorState)("mat-form-field-can-float",n._canLabelFloat())("mat-form-field-should-float",n._shouldLabelFloat())("mat-form-field-has-label",n._hasFloatingLabel())("mat-form-field-hide-placeholder",n._hideControlPlaceholder())("mat-form-field-disabled",n._control.disabled)("mat-form-field-autofilled",n._control.autofilled)("mat-focused",n._control.focused)("ng-untouched",n._shouldForward("untouched"))("ng-touched",n._shouldForward("touched"))("ng-pristine",n._shouldForward("pristine"))("ng-dirty",n._shouldForward("dirty"))("ng-valid",n._shouldForward("valid"))("ng-invalid",n._shouldForward("invalid"))("ng-pending",n._shouldForward("pending"))("_mat-animation-noopable",!n._animationsEnabled)},inputs:{color:"color",floatLabel:"floatLabel",appearance:"appearance",hideRequiredMarker:"hideRequiredMarker",hintLabel:"hintLabel"},exportAs:["matFormField"],features:[jh([{provide:yU,useExisting:_U}]),Qp],ngContentSelectors:["*","[matPrefix]","mat-placeholder","mat-label","[matSuffix]","mat-error","mat-hint:not([align='end'])","mat-hint[align='end']"],decls:15,vars:8,consts:[[1,"mat-form-field-wrapper"],[1,"mat-form-field-flex",3,"click"],["connectionContainer",""],[4,"ngIf"],["class","mat-form-field-prefix",4,"ngIf"],[1,"mat-form-field-infix"],["inputContainer",""],[1,"mat-form-field-label-wrapper"],["class","mat-form-field-label",3,"cdkObserveContentDisabled","id","mat-empty","mat-form-field-empty","mat-accent","mat-warn","ngSwitch","cdkObserveContent",4,"ngIf"],["class","mat-form-field-suffix",4,"ngIf"],["class","mat-form-field-underline",4,"ngIf"],[1,"mat-form-field-subscript-wrapper",3,"ngSwitch"],[4,"ngSwitchCase"],["class","mat-form-field-hint-wrapper",4,"ngSwitchCase"],[1,"mat-form-field-outline"],[1,"mat-form-field-outline-start"],[1,"mat-form-field-outline-gap"],[1,"mat-form-field-outline-end"],[1,"mat-form-field-outline","mat-form-field-outline-thick"],[1,"mat-form-field-prefix"],[1,"mat-form-field-label",3,"cdkObserveContentDisabled","id","ngSwitch","cdkObserveContent"],["label",""],["class","mat-placeholder-required mat-form-field-required-marker","aria-hidden","true",4,"ngIf"],["aria-hidden","true",1,"mat-placeholder-required","mat-form-field-required-marker"],[1,"mat-form-field-suffix"],[1,"mat-form-field-underline"],["underline",""],[1,"mat-form-field-ripple"],[1,"mat-form-field-hint-wrapper"],["class","mat-hint",3,"id",4,"ngIf"],[1,"mat-form-field-hint-spacer"],[1,"mat-hint",3,"id"]],template:function t(e,n){1&e&&(Hf($j),Cf(0,"div",0),Cf(1,"div",1,2),Rf("click",(function t(e){return n._control.onContainerClick&&n._control.onContainerClick(e)})),Hm(3,jj,9,0,"ng-container",3),Hm(4,Uj,2,0,"div",4),Cf(5,"div",5,6),Ff(7),Cf(8,"span",7),Hm(9,qj,5,16,"label",8),Mf(),Mf(),Hm(10,Zj,2,0,"div",9),Mf(),Hm(11,Xj,3,4,"div",10),Cf(12,"div",11),Hm(13,Kj,2,1,"div",12),Hm(14,Qj,5,2,"div",13),Mf(),Mf()),2&e&&(Dc(3),bf("ngIf","outline"==n.appearance),Dc(1),bf("ngIf",n._prefixChildren.length),Dc(5),bf("ngIf",n._hasFloatingLabel()),Dc(1),bf("ngIf",n._suffixChildren.length),Dc(1),bf("ngIf","outline"!=n.appearance),Dc(1),bf("ngSwitch",n._getDisplayedMessages()),Dc(1),bf("ngSwitchCase","error"),Dc(1),bf("ngSwitchCase","hint"))},directives:[Pv,Dv,Rv,PH],styles:[".mat-form-field{display:inline-block;position:relative;text-align:left}[dir=rtl] .mat-form-field{text-align:right}.mat-form-field-wrapper{position:relative}.mat-form-field-flex{display:inline-flex;align-items:baseline;box-sizing:border-box;width:100%}.mat-form-field-prefix,.mat-form-field-suffix{white-space:nowrap;flex:none;position:relative}.mat-form-field-infix{display:block;position:relative;flex:auto;min-width:0;width:180px}.cdk-high-contrast-active .mat-form-field-infix{border-image:linear-gradient(transparent, transparent)}.mat-form-field-label-wrapper{position:absolute;left:0;box-sizing:content-box;width:100%;height:100%;overflow:hidden;pointer-events:none}[dir=rtl] .mat-form-field-label-wrapper{left:auto;right:0}.mat-form-field-label{position:absolute;left:0;font:inherit;pointer-events:none;width:100%;white-space:nowrap;text-overflow:ellipsis;overflow:hidden;transform-origin:0 0;transition:transform 400ms cubic-bezier(0.25, 0.8, 0.25, 1),color 400ms cubic-bezier(0.25, 0.8, 0.25, 1),width 400ms cubic-bezier(0.25, 0.8, 0.25, 1);display:none}[dir=rtl] 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class CU{}CU.ɵfac=function t(e){return new(e||CU)},CU.ɵmod=fo({type:CU}),CU.ɵinj=Dn({imports:[[rx,NF,wH],NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(CU,[{type:L_,args:[{declarations:[nU,_U,lU,cU,dU,mU,uU],imports:[rx,NF,wH],exports:[NF,nU,_U,lU,cU,dU,mU,uU]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(CU,{declarations:function(){return[nU,_U,lU,cU,dU,mU,uU]},imports:function(){return[rx,NF,wH]},exports:function(){return[NF,nU,_U,lU,cU,dU,mU,uU]}});
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
class MU{constructor(t,e){this._renderer=t,this._elementRef=e,this.onChange=t=>{},this.onTouched=()=>{}}setProperty(t,e){this._renderer.setProperty(this._elementRef.nativeElement,t,e)}registerOnTouched(t){this.onTouched=t}registerOnChange(t){this.onChange=t}setDisabledState(t){this.setProperty("disabled",t)}}MU.ɵfac=function t(e){return new(e||MU)(gf($h),gf(Kh))},MU.ɵdir=ho({type:MU}),MU.ctorParameters=()=>[{type:$h},{type:Kh}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(MU,[{type:k_}],(function(){return[{type:$h},{type:Kh}]}),null);class vU extends MU{}vU.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(vU)))(n||vU)}})(),vU.ɵdir=ho({type:vU,features:[Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(vU,[{type:k_}],null,null);const xU=new er("NgValueAccessor"),OU={provide:xU,useExisting:$e((()=>PU)),multi:!0};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class PU extends vU{writeValue(t){this.setProperty("checked",t)}}PU.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(PU)))(n||PU)}})(),PU.ɵdir=ho({type:PU,selectors:[["input","type","checkbox","formControlName",""],["input","type","checkbox","formControl",""],["input","type","checkbox","ngModel",""]],hostBindings:function t(e,n){1&e&&Rf("change",(function t(e){return n.onChange(e.target.checked)}))("blur",(function t(){return n.onTouched()}))},features:[jh([OU]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(PU,[{type:k_,args:[{selector:"input[type=checkbox][formControlName],input[type=checkbox][formControl],input[type=checkbox][ngModel]",host:{"(change)":"onChange($event.target.checked)","(blur)":"onTouched()"},providers:[OU]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const wU={provide:xU,useExisting:$e((()=>SU)),multi:!0};const kU=new er("CompositionEventMode");class SU extends MU{constructor(t,e,n){super(t,e),this._compositionMode=n,this._composing=!1,null==this._compositionMode&&(this._compositionMode=!(function o(){const t=rM()?rM().getUserAgent():"";return/android (\d+)/.test(t.toLowerCase())})())}writeValue(t){this.setProperty("value",null==t?"":t)}_handleInput(t){(!this._compositionMode||this._compositionMode&&!this._composing)&&this.onChange(t)}_compositionStart(){this._composing=!0}_compositionEnd(t){this._composing=!1,this._compositionMode&&this.onChange(t)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function DU(t){return null==t||0===t.length}function RU(t){return null!=t&&"number"==typeof t.length}SU.ɵfac=function t(e){return new(e||SU)(gf($h),gf(Kh),gf(kU,8))},SU.ɵdir=ho({type:SU,selectors:[["input","formControlName","",3,"type","checkbox"],["textarea","formControlName",""],["input","formControl","",3,"type","checkbox"],["textarea","formControl",""],["input","ngModel","",3,"type","checkbox"],["textarea","ngModel",""],["","ngDefaultControl",""]],hostBindings:function t(e,n){1&e&&Rf("input",(function t(e){return n._handleInput(e.target.value)}))("blur",(function t(){return n.onTouched()}))("compositionstart",(function t(){return n._compositionStart()}))("compositionend",(function t(e){return n._compositionEnd(e.target.value)}))},features:[jh([wU]),Qp]}),SU.ctorParameters=()=>[{type:$h},{type:Kh},{type:Boolean,decorators:[{type:Br},{type:Lr,args:[kU]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(SU,[{type:k_,args:[{selector:"input:not([type=checkbox])[formControlName],textarea[formControlName],input:not([type=checkbox])[formControl],textarea[formControl],input:not([type=checkbox])[ngModel],textarea[ngModel],[ngDefaultControl]",host:{"(input)":"$any(this)._handleInput($event.target.value)","(blur)":"onTouched()","(compositionstart)":"$any(this)._compositionStart()","(compositionend)":"$any(this)._compositionEnd($event.target.value)"},providers:[wU]}]}],(function(){return[{type:$h},{type:Kh},{type:Boolean,decorators:[{type:Br},{type:Lr,args:[kU]}]}]}),null);const EU=new er("NgValidators"),AU=new er("NgAsyncValidators"),TU=/^(?=.{1,254}$)(?=.{1,64}@)[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+(?:\.[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+)*@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*$/;class NU{static min(t){return zU(t)}static max(t){return IU(t)}static required(t){return HU(t)}static requiredTrue(t){return FU(t)}static email(t){return LU(t)}static minLength(t){return BU(t)}static maxLength(t){return VU(t)}static pattern(t){return jU(t)}static nullValidator(t){return null}static compose(t){return XU(t)}static composeAsync(t){return JU(t)}}function zU(t){return e=>{if(DU(e.value)||DU(t))return null;const n=parseFloat(e.value);return!isNaN(n)&&n<t?{min:{min:t,actual:e.value}}:null}}function IU(t){return e=>{if(DU(e.value)||DU(t))return null;const n=parseFloat(e.value);return!isNaN(n)&&n>t?{max:{max:t,actual:e.value}}:null}}function HU(t){return DU(t.value)?{required:!0}:null}function FU(t){return!0===t.value?null:{required:!0}}function LU(t){return DU(t.value)||TU.test(t.value)?null:{email:!0}}function BU(t){return e=>DU(e.value)||!RU(e.value)?null:e.value.length<t?{minlength:{requiredLength:t,actualLength:e.value.length}}:null}function VU(t){return e=>RU(e.value)&&e.value.length>t?{maxlength:{requiredLength:t,actualLength:e.value.length}}:null}function jU(t){if(!t)return UU;let e,n;return"string"==typeof t?(n="","^"!==t.charAt(0)&&(n+="^"),n+=t,"$"!==t.charAt(t.length-1)&&(n+="$"),e=new RegExp(n)):(n=t.toString(),e=t),t=>{if(DU(t.value))return null;const o=t.value;return e.test(o)?null:{pattern:{requiredPattern:n,actualValue:o}}}}function UU(t){return null}function WU(t){return null!=t}function GU(t){const e=kf(t)?Pt(t):t;if(!Df(e)&&("undefined"==typeof ngDevMode||ngDevMode))throw new Error("Expected validator to return Promise or Observable.");return e}function YU(t){let e={};return t.forEach((t=>{e=null!=t?Object.assign(Object.assign({},e),t):e})),0===Object.keys(e).length?null:e}function qU(t,e){return e.map((e=>e(t)))}function ZU(t){return t.map((t=>(function e(t){return!t.validate})(t)?t:e=>t.validate(e)))}function XU(t){if(!t)return null;const e=t.filter(WU);return 0==e.length?null:function(t){return YU(qU(t,e))}}function KU(t){return null!=t?XU(ZU(t)):null}function JU(t){if(!t)return null;const e=t.filter(WU);return 0==e.length?null:function(t){return ie(qU(t,e).map(GU)).pipe(Vt(YU))}}function QU(t){return null!=t?JU(ZU(t)):null}function $U(t,e){return null===t?[e]:Array.isArray(t)?[...t,e]:[t,e]}function tW(t){return t._rawValidators}function eW(t){return t._rawAsyncValidators}function nW(t){return t?Array.isArray(t)?t:[t]:[]}function oW(t,e){return Array.isArray(t)?t.includes(e):t===e}function iW(t,e){const n=nW(e);return nW(t).forEach((t=>{oW(n,t)||n.push(t)})),n}function aW(t,e){return nW(e).filter((e=>!oW(t,e)))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class rW{constructor(){this._rawValidators=[],this._rawAsyncValidators=[],this._onDestroyCallbacks=[]}get value(){return this.control?this.control.value:null}get valid(){return this.control?this.control.valid:null}get invalid(){return this.control?this.control.invalid:null}get pending(){return this.control?this.control.pending:null}get disabled(){return this.control?this.control.disabled:null}get enabled(){return this.control?this.control.enabled:null}get errors(){return this.control?this.control.errors:null}get pristine(){return this.control?this.control.pristine:null}get dirty(){return this.control?this.control.dirty:null}get touched(){return this.control?this.control.touched:null}get status(){return this.control?this.control.status:null}get untouched(){return this.control?this.control.untouched:null}get statusChanges(){return this.control?this.control.statusChanges:null}get valueChanges(){return this.control?this.control.valueChanges:null}get path(){return null}_setValidators(t){this._rawValidators=t||[],this._composedValidatorFn=KU(this._rawValidators)}_setAsyncValidators(t){this._rawAsyncValidators=t||[],this._composedAsyncValidatorFn=QU(this._rawAsyncValidators)}get validator(){return this._composedValidatorFn||null}get asyncValidator(){return this._composedAsyncValidatorFn||null}_registerOnDestroy(t){this._onDestroyCallbacks.push(t)}_invokeOnDestroyCallbacks(){this._onDestroyCallbacks.forEach((t=>t())),this._onDestroyCallbacks=[]}reset(t){this.control&&this.control.reset(t)}hasError(t,e){return!!this.control&&this.control.hasError(t,e)}getError(t,e){return this.control?this.control.getError(t,e):null}}rW.ɵfac=function t(e){return new(e||rW)},rW.ɵdir=ho({type:rW});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class sW extends rW{get formDirective(){return null}get path(){return null}}sW.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(sW)))(n||sW)}})(),sW.ɵdir=ho({type:sW,features:[Qp]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class lW extends rW{constructor(){super(...arguments),this._parent=null,this.name=null,this.valueAccessor=null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class cW{constructor(t){this._cd=t}is(t){var e,n,o;return"submitted"===t?!!(null===(e=this._cd)||void 0===e?void 0:e.submitted):!!(null===(o=null===(n=this._cd)||void 0===n?void 0:n.control)||void 0===o?void 0:o[t])}}const dW={"[class.ng-untouched]":'is("untouched")',"[class.ng-touched]":'is("touched")',"[class.ng-pristine]":'is("pristine")',"[class.ng-dirty]":'is("dirty")',"[class.ng-valid]":'is("valid")',"[class.ng-invalid]":'is("invalid")',"[class.ng-pending]":'is("pending")'},pW={"[class.ng-untouched]":'is("untouched")',"[class.ng-touched]":'is("touched")',"[class.ng-pristine]":'is("pristine")',"[class.ng-dirty]":'is("dirty")',"[class.ng-valid]":'is("valid")',"[class.ng-invalid]":'is("invalid")',"[class.ng-pending]":'is("pending")',"[class.ng-submitted]":'is("submitted")'};class mW extends cW{constructor(t){super(t)}}mW.ɵfac=function t(e){return new(e||mW)(gf(lW,2))},mW.ɵdir=ho({type:mW,selectors:[["","formControlName",""],["","ngModel",""],["","formControl",""]],hostVars:14,hostBindings:function t(e,n){2&e&&cu("ng-untouched",n.is("untouched"))("ng-touched",n.is("touched"))("ng-pristine",n.is("pristine"))("ng-dirty",n.is("dirty"))("ng-valid",n.is("valid"))("ng-invalid",n.is("invalid"))("ng-pending",n.is("pending"))},features:[Qp]}),mW.ctorParameters=()=>[{type:lW,decorators:[{type:Vr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(mW,[{type:k_,args:[{selector:"[formControlName],[ngModel],[formControl]",host:dW}]}],(function(){return[{type:lW,decorators:[{type:Vr}]}]}),null);class fW extends cW{constructor(t){super(t)}}fW.ɵfac=function t(e){return new(e||fW)(gf(sW,10))},fW.ɵdir=ho({type:fW,selectors:[["","formGroupName",""],["","formArrayName",""],["","ngModelGroup",""],["","formGroup",""],["form",3,"ngNoForm",""],["","ngForm",""]],hostVars:16,hostBindings:function t(e,n){2&e&&cu("ng-untouched",n.is("untouched"))("ng-touched",n.is("touched"))("ng-pristine",n.is("pristine"))("ng-dirty",n.is("dirty"))("ng-valid",n.is("valid"))("ng-invalid",n.is("invalid"))("ng-pending",n.is("pending"))("ng-submitted",n.is("submitted"))},features:[Qp]}),fW.ctorParameters=()=>[{type:sW,decorators:[{type:Br},{type:Vr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(fW,[{type:k_,args:[{selector:"[formGroupName],[formArrayName],[ngModelGroup],[formGroup],form:not([ngNoForm]),[ngForm]",host:pW}]}],(function(){return[{type:sW,decorators:[{type:Br},{type:Vr}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const uW='\n    <div [formGroup]="myGroup">\n      <input formControlName="firstName">\n    </div>\n\n    In your class:\n\n    this.myGroup = new FormGroup({\n       firstName: new FormControl()\n    });',gW='\n    <div [formGroup]="myGroup">\n       <div formGroupName="person">\n          <input formControlName="firstName">\n       </div>\n    </div>\n\n    In your class:\n\n    this.myGroup = new FormGroup({\n       person: new FormGroup({ firstName: new FormControl() })\n    });',hW='\n    <div [formGroup]="myGroup">\n      <div formArrayName="cities">\n        <div *ngFor="let city of cityArray.controls; index as i">\n          <input [formControlName]="i">\n        </div>\n      </div>\n    </div>\n\n    In your class:\n\n    this.cityArray = new FormArray([new FormControl(\'SF\')]);\n    this.myGroup = new FormGroup({\n      cities: this.cityArray\n    });',bW='\n    <form>\n       <div ngModelGroup="person">\n          <input [(ngModel)]="person.name" name="firstName">\n       </div>\n    </form>',yW='\n    <div [formGroup]="myGroup">\n       <input formControlName="firstName">\n       <input [(ngModel)]="showMoreControls" [ngModelOptions]="{standalone: true}">\n    </div>\n  ';
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class _W{static controlParentException(){throw new Error(`formControlName must be used with a parent formGroup directive.  You'll want to add a formGroup\n       directive and pass it an existing FormGroup instance (you can create one in your class).\n\n      Example:\n\n      ${uW}`)}static ngModelGroupException(){throw new Error(`formControlName cannot be used with an ngModelGroup parent. It is only compatible with parents\n       that also have a "form" prefix: formGroupName, formArrayName, or formGroup.\n\n       Option 1:  Update the parent to be formGroupName (reactive form strategy)\n\n        ${gW}\n\n        Option 2: Use ngModel instead of formControlName (template-driven strategy)\n\n        ${bW}`)}static missingFormException(){throw new Error(`formGroup expects a FormGroup instance. Please pass one in.\n\n       Example:\n\n       ${uW}`)}static groupParentException(){throw new Error(`formGroupName must be used with a parent formGroup directive.  You'll want to add a formGroup\n      directive and pass it an existing FormGroup instance (you can create one in your class).\n\n      Example:\n\n      ${gW}`)}static arrayParentException(){throw new Error(`formArrayName must be used with a parent formGroup directive.  You'll want to add a formGroup\n       directive and pass it an existing FormGroup instance (you can create one in your class).\n\n        Example:\n\n        ${hW}`)}static disabledAttrWarning(){console.warn("\n      It looks like you're using the disabled attribute with a reactive form directive. If you set disabled to true\n      when you set up this control in your component class, the disabled attribute will actually be set in the DOM for\n      you. We recommend using this approach to avoid 'changed after checked' errors.\n\n      Example:\n      form = new FormGroup({\n        first: new FormControl({value: 'Nancy', disabled: true}, Validators.required),\n        last: new FormControl('Drew', Validators.required)\n      });\n    ")}static ngModelWarning(t){console.warn(`\n    It looks like you're using ngModel on the same form field as ${t}.\n    Support for using the ngModel input property and ngModelChange event with\n    reactive form directives has been deprecated in Angular v6 and will be removed\n    in a future version of Angular.\n\n    For more information on this, see our API docs here:\n    https://angular.io/api/forms/${"formControl"===t?"FormControlDirective":"FormControlName"}#use-with-ngmodel\n    `)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function CW(t,e){return[...e.path,t]}function MW(t,e){("undefined"==typeof ngDevMode||ngDevMode)&&(t||SW(e,"Cannot find control with"),e.valueAccessor||SW(e,"No value accessor for form control with")),OW(t,e),e.valueAccessor.writeValue(t.value),(function n(t,e){e.valueAccessor.registerOnChange((n=>{t._pendingValue=n,t._pendingChange=!0,t._pendingDirty=!0,"change"===t.updateOn&&wW(t,e)}))})(t,e),(function o(t,e){const n=(t,n)=>{e.valueAccessor.writeValue(t),n&&e.viewToModelUpdate(t)};t.registerOnChange(n),e._registerOnDestroy((()=>{t._unregisterOnChange(n)}))})(t,e),(function i(t,e){e.valueAccessor.registerOnTouched((()=>{t._pendingTouched=!0,"blur"===t.updateOn&&t._pendingChange&&wW(t,e),"submit"!==t.updateOn&&t.markAsTouched()}))})(t,e),(function a(t,e){if(e.valueAccessor.setDisabledState){const n=t=>{e.valueAccessor.setDisabledState(t)};t.registerOnDisabledChange(n),e._registerOnDestroy((()=>{t._unregisterOnDisabledChange(n)}))}})(t,e)}function vW(t,e,n=!0){const o=()=>{n&&("undefined"==typeof ngDevMode||ngDevMode)&&(function t(e){return SW(e,"There is no FormControl instance attached to form control element with")})(e)};e.valueAccessor&&(e.valueAccessor.registerOnChange(o),e.valueAccessor.registerOnTouched(o)),PW(t,e),t&&(e._invokeOnDestroyCallbacks(),t._registerOnCollectionChange((()=>{})))}function xW(t,e){t.forEach((t=>{t.registerOnValidatorChange&&t.registerOnValidatorChange(e)}))}function OW(t,e){const n=tW(t);null!==e.validator?t.setValidators($U(n,e.validator)):"function"==typeof n&&t.setValidators([n]);const o=eW(t);null!==e.asyncValidator?t.setAsyncValidators($U(o,e.asyncValidator)):"function"==typeof o&&t.setAsyncValidators([o]);const i=()=>t.updateValueAndValidity();xW(e._rawValidators,i),xW(e._rawAsyncValidators,i)}function PW(t,e){let n=!1;if(null!==t){if(null!==e.validator){const o=tW(t);if(Array.isArray(o)&&o.length>0){const i=o.filter((t=>t!==e.validator));i.length!==o.length&&(n=!0,t.setValidators(i))}}if(null!==e.asyncValidator){const o=eW(t);if(Array.isArray(o)&&o.length>0){const i=o.filter((t=>t!==e.asyncValidator));i.length!==o.length&&(n=!0,t.setAsyncValidators(i))}}}const o=()=>{};return xW(e._rawValidators,o),xW(e._rawAsyncValidators,o),n}function wW(t,e){t._pendingDirty&&t.markAsDirty(),t.setValue(t._pendingValue,{emitModelToViewChange:!1}),e.viewToModelUpdate(t._pendingValue),t._pendingChange=!1}function kW(t,e){null!=t||"undefined"!=typeof ngDevMode&&!ngDevMode||SW(e,"Cannot find control with"),OW(t,e)}function SW(t,e){let n;throw n=t.path.length>1?`path: '${t.path.join(" -> ")}'`:t.path[0]?`name: '${t.path}'`:"unspecified name attribute",new Error(`${e} ${n}`)}function DW(t,e){if(!t.hasOwnProperty("model"))return!1;const n=t.model;return!!n.isFirstChange()||!Object.is(e,n.currentValue)}function RW(t,e){t._syncPendingControls(),e.forEach((t=>{const e=t.control;"submit"===e.updateOn&&e._pendingChange&&(t.viewToModelUpdate(e._pendingValue),e._pendingChange=!1)}))}function EW(t,e){if(!e)return null;let n,o,i;return Array.isArray(e)||"undefined"!=typeof ngDevMode&&!ngDevMode||SW(t,"Value accessor was not provided as an array for form control with"),e.forEach((e=>{e.constructor===SU?n=e:!(function a(t){return Object.getPrototypeOf(t.constructor)===vU})(e)?(i&&("undefined"==typeof ngDevMode||ngDevMode)&&SW(t,"More than one custom value accessor matches form control with"),i=e):(o&&("undefined"==typeof ngDevMode||ngDevMode)&&SW(t,"More than one built-in value accessor matches form control with"),o=e)})),i||(o||(n||(("undefined"==typeof ngDevMode||ngDevMode)&&SW(t,"No valid value accessor for form control with"),null)))}function AW(t,e){const n=t.indexOf(e);n>-1&&t.splice(n,1)}function TW(t,e,n,o){"never"!==o&&((null!==o&&"once"!==o||e._ngModelWarningSentOnce)&&("always"!==o||n._ngModelWarningSent)||(_W.ngModelWarning(t),e._ngModelWarningSentOnce=!0,n._ngModelWarningSent=!0))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const NW="VALID",zW="INVALID",IW="PENDING",HW="DISABLED";function FW(t){return(jW(t)?t.validators:t)||null}function LW(t){return Array.isArray(t)?KU(t):t||null}function BW(t,e){return(jW(e)?e.asyncValidators:t)||null}function VW(t){return Array.isArray(t)?QU(t):t||null}function jW(t){return null!=t&&!Array.isArray(t)&&"object"==typeof t}class UW{constructor(t,e){this._hasOwnPendingAsyncValidator=!1,this._onCollectionChange=()=>{},this._parent=null,this.pristine=!0,this.touched=!1,this._onDisabledChange=[],this._rawValidators=t,this._rawAsyncValidators=e,this._composedValidatorFn=LW(this._rawValidators),this._composedAsyncValidatorFn=VW(this._rawAsyncValidators)}get validator(){return this._composedValidatorFn}set validator(t){this._rawValidators=this._composedValidatorFn=t}get asyncValidator(){return this._composedAsyncValidatorFn}set asyncValidator(t){this._rawAsyncValidators=this._composedAsyncValidatorFn=t}get parent(){return this._parent}get valid(){return this.status===NW}get invalid(){return this.status===zW}get pending(){return this.status==IW}get disabled(){return this.status===HW}get enabled(){return this.status!==HW}get dirty(){return!this.pristine}get untouched(){return!this.touched}get updateOn(){return this._updateOn?this._updateOn:this.parent?this.parent.updateOn:"change"}setValidators(t){this._rawValidators=t,this._composedValidatorFn=LW(t)}setAsyncValidators(t){this._rawAsyncValidators=t,this._composedAsyncValidatorFn=VW(t)}addValidators(t){this.setValidators(iW(t,this._rawValidators))}addAsyncValidators(t){this.setAsyncValidators(iW(t,this._rawAsyncValidators))}removeValidators(t){this.setValidators(aW(t,this._rawValidators))}removeAsyncValidators(t){this.setAsyncValidators(aW(t,this._rawAsyncValidators))}hasValidator(t){return oW(this._rawValidators,t)}hasAsyncValidator(t){return oW(this._rawAsyncValidators,t)}clearValidators(){this.validator=null}clearAsyncValidators(){this.asyncValidator=null}markAsTouched(t={}){this.touched=!0,this._parent&&!t.onlySelf&&this._parent.markAsTouched(t)}markAllAsTouched(){this.markAsTouched({onlySelf:!0}),this._forEachChild((t=>t.markAllAsTouched()))}markAsUntouched(t={}){this.touched=!1,this._pendingTouched=!1,this._forEachChild((t=>{t.markAsUntouched({onlySelf:!0})})),this._parent&&!t.onlySelf&&this._parent._updateTouched(t)}markAsDirty(t={}){this.pristine=!1,this._parent&&!t.onlySelf&&this._parent.markAsDirty(t)}markAsPristine(t={}){this.pristine=!0,this._pendingDirty=!1,this._forEachChild((t=>{t.markAsPristine({onlySelf:!0})})),this._parent&&!t.onlySelf&&this._parent._updatePristine(t)}markAsPending(t={}){this.status=IW,!1!==t.emitEvent&&this.statusChanges.emit(this.status),this._parent&&!t.onlySelf&&this._parent.markAsPending(t)}disable(t={}){const e=this._parentMarkedDirty(t.onlySelf);this.status=HW,this.errors=null,this._forEachChild((e=>{e.disable(Object.assign(Object.assign({},t),{onlySelf:!0}))})),this._updateValue(),!1!==t.emitEvent&&(this.valueChanges.emit(this.value),this.statusChanges.emit(this.status)),this._updateAncestors(Object.assign(Object.assign({},t),{skipPristineCheck:e})),this._onDisabledChange.forEach((t=>t(!0)))}enable(t={}){const e=this._parentMarkedDirty(t.onlySelf);this.status=NW,this._forEachChild((e=>{e.enable(Object.assign(Object.assign({},t),{onlySelf:!0}))})),this.updateValueAndValidity({onlySelf:!0,emitEvent:t.emitEvent}),this._updateAncestors(Object.assign(Object.assign({},t),{skipPristineCheck:e})),this._onDisabledChange.forEach((t=>t(!1)))}_updateAncestors(t){this._parent&&!t.onlySelf&&(this._parent.updateValueAndValidity(t),t.skipPristineCheck||this._parent._updatePristine(),this._parent._updateTouched())}setParent(t){this._parent=t}updateValueAndValidity(t={}){this._setInitialStatus(),this._updateValue(),this.enabled&&(this._cancelExistingSubscription(),this.errors=this._runValidator(),this.status=this._calculateStatus(),this.status!==NW&&this.status!==IW||this._runAsyncValidator(t.emitEvent)),!1!==t.emitEvent&&(this.valueChanges.emit(this.value),this.statusChanges.emit(this.status)),this._parent&&!t.onlySelf&&this._parent.updateValueAndValidity(t)}_updateTreeValidity(t={emitEvent:!0}){this._forEachChild((e=>e._updateTreeValidity(t))),this.updateValueAndValidity({onlySelf:!0,emitEvent:t.emitEvent})}_setInitialStatus(){this.status=this._allControlsDisabled()?HW:NW}_runValidator(){return this.validator?this.validator(this):null}_runAsyncValidator(t){if(this.asyncValidator){this.status=IW,this._hasOwnPendingAsyncValidator=!0;const e=GU(this.asyncValidator(this));this._asyncValidationSubscription=e.subscribe((e=>{this._hasOwnPendingAsyncValidator=!1,this.setErrors(e,{emitEvent:t})}))}}_cancelExistingSubscription(){this._asyncValidationSubscription&&(this._asyncValidationSubscription.unsubscribe(),this._hasOwnPendingAsyncValidator=!1)}setErrors(t,e={}){this.errors=t,this._updateControlsErrors(!1!==e.emitEvent)}get(t){return(function e(t,n,o){if(null==n)return null;if(Array.isArray(n)||(n=n.split(o)),Array.isArray(n)&&0===n.length)return null;let i=t;return n.forEach((t=>{i=i instanceof GW?i.controls.hasOwnProperty(t)?i.controls[t]:null:i instanceof YW&&i.at(t)||null})),i})(this,t,".")}getError(t,e){const n=e?this.get(e):this;return n&&n.errors?n.errors[t]:null}hasError(t,e){return!!this.getError(t,e)}get root(){let t=this;for(;t._parent;)t=t._parent;return t}_updateControlsErrors(t){this.status=this._calculateStatus(),t&&this.statusChanges.emit(this.status),this._parent&&this._parent._updateControlsErrors(t)}_initObservables(){this.valueChanges=new Sy,this.statusChanges=new Sy}_calculateStatus(){return this._allControlsDisabled()?HW:this.errors?zW:this._hasOwnPendingAsyncValidator||this._anyControlsHaveStatus(IW)?IW:this._anyControlsHaveStatus(zW)?zW:NW}_anyControlsHaveStatus(t){return this._anyControls((e=>e.status===t))}_anyControlsDirty(){return this._anyControls((t=>t.dirty))}_anyControlsTouched(){return this._anyControls((t=>t.touched))}_updatePristine(t={}){this.pristine=!this._anyControlsDirty(),this._parent&&!t.onlySelf&&this._parent._updatePristine(t)}_updateTouched(t={}){this.touched=this._anyControlsTouched(),this._parent&&!t.onlySelf&&this._parent._updateTouched(t)}_isBoxedValue(t){return"object"==typeof t&&null!==t&&2===Object.keys(t).length&&"value"in t&&"disabled"in t}_registerOnCollectionChange(t){this._onCollectionChange=t}_setUpdateStrategy(t){jW(t)&&null!=t.updateOn&&(this._updateOn=t.updateOn)}_parentMarkedDirty(t){return!t&&!!(this._parent&&this._parent.dirty)&&!this._parent._anyControlsDirty()}}class WW extends UW{constructor(t=null,e,n){super(FW(e),BW(n,e)),this._onChange=[],this._applyFormState(t),this._setUpdateStrategy(e),this._initObservables(),this.updateValueAndValidity({onlySelf:!0,emitEvent:!!this.asyncValidator})}setValue(t,e={}){this.value=this._pendingValue=t,this._onChange.length&&!1!==e.emitModelToViewChange&&this._onChange.forEach((t=>t(this.value,!1!==e.emitViewToModelChange))),this.updateValueAndValidity(e)}patchValue(t,e={}){this.setValue(t,e)}reset(t=null,e={}){this._applyFormState(t),this.markAsPristine(e),this.markAsUntouched(e),this.setValue(this.value,e),this._pendingChange=!1}_updateValue(){}_anyControls(t){return!1}_allControlsDisabled(){return this.disabled}registerOnChange(t){this._onChange.push(t)}_unregisterOnChange(t){AW(this._onChange,t)}registerOnDisabledChange(t){this._onDisabledChange.push(t)}_unregisterOnDisabledChange(t){AW(this._onDisabledChange,t)}_forEachChild(t){}_syncPendingControls(){return!("submit"!==this.updateOn||(this._pendingDirty&&this.markAsDirty(),this._pendingTouched&&this.markAsTouched(),!this._pendingChange))&&(this.setValue(this._pendingValue,{onlySelf:!0,emitModelToViewChange:!1}),!0)}_applyFormState(t){this._isBoxedValue(t)?(this.value=this._pendingValue=t.value,t.disabled?this.disable({onlySelf:!0,emitEvent:!1}):this.enable({onlySelf:!0,emitEvent:!1})):this.value=this._pendingValue=t}}class GW extends UW{constructor(t,e,n){super(FW(e),BW(n,e)),this.controls=t,this._initObservables(),this._setUpdateStrategy(e),this._setUpControls(),this.updateValueAndValidity({onlySelf:!0,emitEvent:!!this.asyncValidator})}registerControl(t,e){return this.controls[t]?this.controls[t]:(this.controls[t]=e,e.setParent(this),e._registerOnCollectionChange(this._onCollectionChange),e)}addControl(t,e,n={}){this.registerControl(t,e),this.updateValueAndValidity({emitEvent:n.emitEvent}),this._onCollectionChange()}removeControl(t,e={}){this.controls[t]&&this.controls[t]._registerOnCollectionChange((()=>{})),delete this.controls[t],this.updateValueAndValidity({emitEvent:e.emitEvent}),this._onCollectionChange()}setControl(t,e,n={}){this.controls[t]&&this.controls[t]._registerOnCollectionChange((()=>{})),delete this.controls[t],e&&this.registerControl(t,e),this.updateValueAndValidity({emitEvent:n.emitEvent}),this._onCollectionChange()}contains(t){return this.controls.hasOwnProperty(t)&&this.controls[t].enabled}setValue(t,e={}){this._checkAllValuesPresent(t),Object.keys(t).forEach((n=>{this._throwIfControlMissing(n),this.controls[n].setValue(t[n],{onlySelf:!0,emitEvent:e.emitEvent})})),this.updateValueAndValidity(e)}patchValue(t,e={}){null!=t&&(Object.keys(t).forEach((n=>{this.controls[n]&&this.controls[n].patchValue(t[n],{onlySelf:!0,emitEvent:e.emitEvent})})),this.updateValueAndValidity(e))}reset(t={},e={}){this._forEachChild(((n,o)=>{n.reset(t[o],{onlySelf:!0,emitEvent:e.emitEvent})})),this._updatePristine(e),this._updateTouched(e),this.updateValueAndValidity(e)}getRawValue(){return this._reduceChildren({},((t,e,n)=>(t[n]=e instanceof WW?e.value:e.getRawValue(),t)))}_syncPendingControls(){let t=this._reduceChildren(!1,((t,e)=>!!e._syncPendingControls()||t));return t&&this.updateValueAndValidity({onlySelf:!0}),t}_throwIfControlMissing(t){if(!Object.keys(this.controls).length)throw new Error("\n        There are no form controls registered with this group yet. If you're using ngModel,\n        you may want to check next tick (e.g. use setTimeout).\n      ");if(!this.controls[t])throw new Error(`Cannot find form control with name: ${t}.`)}_forEachChild(t){Object.keys(this.controls).forEach((e=>{const n=this.controls[e];n&&t(n,e)}))}_setUpControls(){this._forEachChild((t=>{t.setParent(this),t._registerOnCollectionChange(this._onCollectionChange)}))}_updateValue(){this.value=this._reduceValue()}_anyControls(t){for(const e of Object.keys(this.controls)){const n=this.controls[e];if(this.contains(e)&&t(n))return!0}return!1}_reduceValue(){return this._reduceChildren({},((t,e,n)=>((e.enabled||this.disabled)&&(t[n]=e.value),t)))}_reduceChildren(t,e){let n=t;return this._forEachChild(((t,o)=>{n=e(n,t,o)})),n}_allControlsDisabled(){for(const t of Object.keys(this.controls))if(this.controls[t].enabled)return!1;return Object.keys(this.controls).length>0||this.disabled}_checkAllValuesPresent(t){this._forEachChild(((e,n)=>{if(void 0===t[n])throw new Error(`Must supply a value for form control with name: '${n}'.`)}))}}class YW extends UW{constructor(t,e,n){super(FW(e),BW(n,e)),this.controls=t,this._initObservables(),this._setUpdateStrategy(e),this._setUpControls(),this.updateValueAndValidity({onlySelf:!0,emitEvent:!!this.asyncValidator})}at(t){return this.controls[t]}push(t,e={}){this.controls.push(t),this._registerControl(t),this.updateValueAndValidity({emitEvent:e.emitEvent}),this._onCollectionChange()}insert(t,e,n={}){this.controls.splice(t,0,e),this._registerControl(e),this.updateValueAndValidity({emitEvent:n.emitEvent})}removeAt(t,e={}){this.controls[t]&&this.controls[t]._registerOnCollectionChange((()=>{})),this.controls.splice(t,1),this.updateValueAndValidity({emitEvent:e.emitEvent})}setControl(t,e,n={}){this.controls[t]&&this.controls[t]._registerOnCollectionChange((()=>{})),this.controls.splice(t,1),e&&(this.controls.splice(t,0,e),this._registerControl(e)),this.updateValueAndValidity({emitEvent:n.emitEvent}),this._onCollectionChange()}get length(){return this.controls.length}setValue(t,e={}){this._checkAllValuesPresent(t),t.forEach(((t,n)=>{this._throwIfControlMissing(n),this.at(n).setValue(t,{onlySelf:!0,emitEvent:e.emitEvent})})),this.updateValueAndValidity(e)}patchValue(t,e={}){null!=t&&(t.forEach(((t,n)=>{this.at(n)&&this.at(n).patchValue(t,{onlySelf:!0,emitEvent:e.emitEvent})})),this.updateValueAndValidity(e))}reset(t=[],e={}){this._forEachChild(((n,o)=>{n.reset(t[o],{onlySelf:!0,emitEvent:e.emitEvent})})),this._updatePristine(e),this._updateTouched(e),this.updateValueAndValidity(e)}getRawValue(){return this.controls.map((t=>t instanceof WW?t.value:t.getRawValue()))}clear(t={}){this.controls.length<1||(this._forEachChild((t=>t._registerOnCollectionChange((()=>{})))),this.controls.splice(0),this.updateValueAndValidity({emitEvent:t.emitEvent}))}_syncPendingControls(){let t=this.controls.reduce(((t,e)=>!!e._syncPendingControls()||t),!1);return t&&this.updateValueAndValidity({onlySelf:!0}),t}_throwIfControlMissing(t){if(!this.controls.length)throw new Error("\n        There are no form controls registered with this array yet. If you're using ngModel,\n        you may want to check next tick (e.g. use setTimeout).\n      ");if(!this.at(t))throw new Error(`Cannot find form control at index ${t}`)}_forEachChild(t){this.controls.forEach(((e,n)=>{t(e,n)}))}_updateValue(){this.value=this.controls.filter((t=>t.enabled||this.disabled)).map((t=>t.value))}_anyControls(t){return this.controls.some((e=>e.enabled&&t(e)))}_setUpControls(){this._forEachChild((t=>this._registerControl(t)))}_checkAllValuesPresent(t){this._forEachChild(((e,n)=>{if(void 0===t[n])throw new Error(`Must supply a value for form control at index: ${n}.`)}))}_allControlsDisabled(){for(const t of this.controls)if(t.enabled)return!1;return this.controls.length>0||this.disabled}_registerControl(t){t.setParent(this),t._registerOnCollectionChange(this._onCollectionChange)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const qW={provide:sW,useExisting:$e((()=>XW))},ZW=Promise.resolve(null);class XW extends sW{constructor(t,e){super(),this.submitted=!1,this._directives=[],this.ngSubmit=new Sy,this.form=new GW({},KU(t),QU(e))}ngAfterViewInit(){this._setUpdateStrategy()}get formDirective(){return this}get control(){return this.form}get path(){return[]}get controls(){return this.form.controls}addControl(t){ZW.then((()=>{const e=this._findContainer(t.path);t.control=e.registerControl(t.name,t.control),MW(t.control,t),t.control.updateValueAndValidity({emitEvent:!1}),this._directives.push(t)}))}getControl(t){return this.form.get(t.path)}removeControl(t){ZW.then((()=>{const e=this._findContainer(t.path);e&&e.removeControl(t.name),AW(this._directives,t)}))}addFormGroup(t){ZW.then((()=>{const e=this._findContainer(t.path),n=new GW({});kW(n,t),e.registerControl(t.name,n),n.updateValueAndValidity({emitEvent:!1})}))}removeFormGroup(t){ZW.then((()=>{const e=this._findContainer(t.path);e&&e.removeControl(t.name)}))}getFormGroup(t){return this.form.get(t.path)}updateModel(t,e){ZW.then((()=>{this.form.get(t.path).setValue(e)}))}setValue(t){this.control.setValue(t)}onSubmit(t){return this.submitted=!0,RW(this.form,this._directives),this.ngSubmit.emit(t),!1}onReset(){this.resetForm()}resetForm(t){this.form.reset(t),this.submitted=!1}_setUpdateStrategy(){this.options&&null!=this.options.updateOn&&(this.form._updateOn=this.options.updateOn)}_findContainer(t){return t.pop(),t.length?this.form.get(t):this.form}}XW.ɵfac=function t(e){return new(e||XW)(gf(EU,10),gf(AU,10))},XW.ɵdir=ho({type:XW,selectors:[["form",3,"ngNoForm","",3,"formGroup",""],["ng-form"],["","ngForm",""]],hostBindings:function t(e,n){1&e&&Rf("submit",(function t(e){return n.onSubmit(e)}))("reset",(function t(){return n.onReset()}))},inputs:{options:["ngFormOptions","options"]},outputs:{ngSubmit:"ngSubmit"},exportAs:["ngForm"],features:[jh([qW]),Qp]}),XW.ctorParameters=()=>[{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]}],XW.propDecorators={options:[{type:R_,args:["ngFormOptions"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(XW,[{type:k_,args:[{selector:"form:not([ngNoForm]):not([formGroup]),ng-form,[ngForm]",providers:[qW],host:{"(submit)":"onSubmit($event)","(reset)":"onReset()"},outputs:["ngSubmit"],exportAs:"ngForm"}]}],(function(){return[{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]}]}),{options:[{type:R_,args:["ngFormOptions"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class KW extends sW{ngOnInit(){this._checkParentType(),this.formDirective.addFormGroup(this)}ngOnDestroy(){this.formDirective&&this.formDirective.removeFormGroup(this)}get control(){return this.formDirective.getFormGroup(this)}get path(){return CW(null==this.name?this.name:this.name.toString(),this._parent)}get formDirective(){return this._parent?this._parent.formDirective:null}_checkParentType(){}}KW.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(KW)))(n||KW)}})(),KW.ɵdir=ho({type:KW,features:[Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(KW,[{type:k_}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class JW{static modelParentException(){throw new Error(`\n      ngModel cannot be used to register form controls with a parent formGroup directive.  Try using\n      formGroup's partner directive "formControlName" instead.  Example:\n\n      ${uW}\n\n      Or, if you'd like to avoid registering this form control, indicate that it's standalone in ngModelOptions:\n\n      Example:\n\n      ${yW}`)}static formGroupNameException(){throw new Error(`\n      ngModel cannot be used to register form controls with a parent formGroupName or formArrayName directive.\n\n      Option 1: Use formControlName instead of ngModel (reactive strategy):\n\n      ${gW}\n\n      Option 2:  Update ngModel's parent be ngModelGroup (template-driven strategy):\n\n      ${bW}`)}static missingNameException(){throw new Error('If ngModel is used within a form tag, either the name attribute must be set or the form\n      control must be defined as \'standalone\' in ngModelOptions.\n\n      Example 1: <input [(ngModel)]="person.firstName" name="first">\n      Example 2: <input [(ngModel)]="person.firstName" [ngModelOptions]="{standalone: true}">')}static modelGroupParentException(){throw new Error(`\n      ngModelGroup cannot be used with a parent formGroup directive.\n\n      Option 1: Use formGroupName instead of ngModelGroup (reactive strategy):\n\n      ${gW}\n\n      Option 2:  Use a regular form tag instead of the formGroup directive (template-driven strategy):\n\n      ${bW}`)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const QW={provide:sW,useExisting:$e((()=>$W))};class $W extends KW{constructor(t,e,n){super(),this._parent=t,this._setValidators(e),this._setAsyncValidators(n)}_checkParentType(){this._parent instanceof $W||this._parent instanceof XW||"undefined"!=typeof ngDevMode&&!ngDevMode||JW.modelGroupParentException()}}$W.ɵfac=function t(e){return new(e||$W)(gf(sW,5),gf(EU,10),gf(AU,10))},$W.ɵdir=ho({type:$W,selectors:[["","ngModelGroup",""]],inputs:{name:["ngModelGroup","name"]},exportAs:["ngModelGroup"],features:[jh([QW]),Qp]}),$W.ctorParameters=()=>[{type:sW,decorators:[{type:Ur},{type:jr}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]}],$W.propDecorators={name:[{type:R_,args:["ngModelGroup"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb($W,[{type:k_,args:[{selector:"[ngModelGroup]",providers:[QW],exportAs:"ngModelGroup"}]}],(function(){return[{type:sW,decorators:[{type:Ur},{type:jr}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]}]}),{name:[{type:R_,args:["ngModelGroup"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const tG={provide:lW,useExisting:$e((()=>nG))},eG=Promise.resolve(null);class nG extends lW{constructor(t,e,n,o){super(),this.control=new WW,this._registered=!1,this.update=new Sy,this._parent=t,this._setValidators(e),this._setAsyncValidators(n),this.valueAccessor=EW(this,o)}ngOnChanges(t){this._checkForErrors(),this._registered||this._setUpControl(),"isDisabled"in t&&this._updateDisabled(t),DW(t,this.viewModel)&&(this._updateValue(this.model),this.viewModel=this.model)}ngOnDestroy(){this.formDirective&&this.formDirective.removeControl(this)}get path(){return this._parent?CW(this.name,this._parent):[this.name]}get formDirective(){return this._parent?this._parent.formDirective:null}viewToModelUpdate(t){this.viewModel=t,this.update.emit(t)}_setUpControl(){this._setUpdateStrategy(),this._isStandalone()?this._setUpStandalone():this.formDirective.addControl(this),this._registered=!0}_setUpdateStrategy(){this.options&&null!=this.options.updateOn&&(this.control._updateOn=this.options.updateOn)}_isStandalone(){return!this._parent||!(!this.options||!this.options.standalone)}_setUpStandalone(){MW(this.control,this),this.control.updateValueAndValidity({emitEvent:!1})}_checkForErrors(){this._isStandalone()||this._checkParentType(),this._checkName()}_checkParentType(){("undefined"==typeof ngDevMode||ngDevMode)&&(!(this._parent instanceof $W)&&this._parent instanceof KW?JW.formGroupNameException():this._parent instanceof $W||this._parent instanceof XW||JW.modelParentException())}_checkName(){this.options&&this.options.name&&(this.name=this.options.name),this._isStandalone()||this.name||"undefined"!=typeof ngDevMode&&!ngDevMode||JW.missingNameException()}_updateValue(t){eG.then((()=>{this.control.setValue(t,{emitViewToModelChange:!1})}))}_updateDisabled(t){const e=t.isDisabled.currentValue,n=""===e||e&&"false"!==e;eG.then((()=>{n&&!this.control.disabled?this.control.disable():!n&&this.control.disabled&&this.control.enable()}))}}nG.ɵfac=function t(e){return new(e||nG)(gf(sW,9),gf(EU,10),gf(AU,10),gf(xU,10))},nG.ɵdir=ho({type:nG,selectors:[["","ngModel","",3,"formControlName","",3,"formControl",""]],inputs:{name:"name",isDisabled:["disabled","isDisabled"],model:["ngModel","model"],options:["ngModelOptions","options"]},outputs:{update:"ngModelChange"},exportAs:["ngModel"],features:[jh([tG]),Qp,qo]}),nG.ctorParameters=()=>[{type:sW,decorators:[{type:Br},{type:Ur}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[xU]}]}],nG.propDecorators={name:[{type:R_}],isDisabled:[{type:R_,args:["disabled"]}],model:[{type:R_,args:["ngModel"]}],options:[{type:R_,args:["ngModelOptions"]}],update:[{type:E_,args:["ngModelChange"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(nG,[{type:k_,args:[{selector:"[ngModel]:not([formControlName]):not([formControl])",providers:[tG],exportAs:"ngModel"}]}],(function(){return[{type:sW,decorators:[{type:Br},{type:Ur}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[xU]}]}]}),{update:[{type:E_,args:["ngModelChange"]}],name:[{type:R_}],isDisabled:[{type:R_,args:["disabled"]}],model:[{type:R_,args:["ngModel"]}],options:[{type:R_,args:["ngModelOptions"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class oG{}oG.ɵfac=function t(e){return new(e||oG)},oG.ɵdir=ho({type:oG,selectors:[["form",3,"ngNoForm","",3,"ngNativeValidate",""]],hostAttrs:["novalidate",""]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(oG,[{type:k_,args:[{selector:"form:not([ngNoForm]):not([ngNativeValidate])",host:{novalidate:""}}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const iG={provide:xU,useExisting:$e((()=>aG)),multi:!0};class aG extends vU{writeValue(t){this.setProperty("value",null==t?"":t)}registerOnChange(t){this.onChange=e=>{t(""==e?null:parseFloat(e))}}}aG.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(aG)))(n||aG)}})(),aG.ɵdir=ho({type:aG,selectors:[["input","type","number","formControlName",""],["input","type","number","formControl",""],["input","type","number","ngModel",""]],hostBindings:function t(e,n){1&e&&Rf("input",(function t(e){return n.onChange(e.target.value)}))("blur",(function t(){return n.onTouched()}))},features:[jh([iG]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(aG,[{type:k_,args:[{selector:"input[type=number][formControlName],input[type=number][formControl],input[type=number][ngModel]",host:{"(input)":"onChange($event.target.value)","(blur)":"onTouched()"},providers:[iG]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const rG={provide:xU,useExisting:$e((()=>cG)),multi:!0};class sG{}sG.ɵfac=function t(e){return new(e||sG)},sG.ɵmod=fo({type:sG}),sG.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(sG,[{type:L_}],null,null);class lG{constructor(){this._accessors=[]}add(t,e){this._accessors.push([t,e])}remove(t){for(let e=this._accessors.length-1;e>=0;--e)if(this._accessors[e][1]===t)return void this._accessors.splice(e,1)}select(t){this._accessors.forEach((e=>{this._isSameGroup(e,t)&&e[1]!==t&&e[1].fireUncheck(t.value)}))}_isSameGroup(t,e){return!!t[0].control&&(t[0]._parent===e._control._parent&&t[1].name===e.name)}}lG.ɵfac=function t(e){return new(e||lG)},lG.ɵprov=Sn({factory:function t(){return new lG},token:lG,providedIn:sG}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(lG,[{type:Um,args:[{providedIn:sG}]}],(function(){return[]}),null);class cG extends vU{constructor(t,e,n,o){super(t,e),this._registry=n,this._injector=o,this.onChange=()=>{}}ngOnInit(){this._control=this._injector.get(lW),this._checkName(),this._registry.add(this._control,this)}ngOnDestroy(){this._registry.remove(this)}writeValue(t){this._state=t===this.value,this.setProperty("checked",this._state)}registerOnChange(t){this._fn=t,this.onChange=()=>{t(this.value),this._registry.select(this)}}fireUncheck(t){this.writeValue(t)}_checkName(){this.name&&this.formControlName&&this.name!==this.formControlName&&("undefined"==typeof ngDevMode||ngDevMode)&&(function t(){throw new Error('\n      If you define both a name and a formControlName attribute on your radio button, their values\n      must match. Ex: <input type="radio" formControlName="food" name="food">\n    ')})(),!this.name&&this.formControlName&&(this.name=this.formControlName)}}cG.ɵfac=function t(e){return new(e||cG)(gf($h),gf(Kh),gf(lG),gf(Np))},cG.ɵdir=ho({type:cG,selectors:[["input","type","radio","formControlName",""],["input","type","radio","formControl",""],["input","type","radio","ngModel",""]],hostBindings:function t(e,n){1&e&&Rf("change",(function t(){return n.onChange()}))("blur",(function t(){return n.onTouched()}))},inputs:{name:"name",formControlName:"formControlName",value:"value"},features:[jh([rG]),Qp]}),cG.ctorParameters=()=>[{type:$h},{type:Kh},{type:lG},{type:Np}],cG.propDecorators={name:[{type:R_}],formControlName:[{type:R_}],value:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(cG,[{type:k_,args:[{selector:"input[type=radio][formControlName],input[type=radio][formControl],input[type=radio][ngModel]",host:{"(change)":"onChange()","(blur)":"onTouched()"},providers:[rG]}]}],(function(){return[{type:$h},{type:Kh},{type:lG},{type:Np}]}),{name:[{type:R_}],formControlName:[{type:R_}],value:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const dG={provide:xU,useExisting:$e((()=>pG)),multi:!0};class pG extends vU{writeValue(t){this.setProperty("value",parseFloat(t))}registerOnChange(t){this.onChange=e=>{t(""==e?null:parseFloat(e))}}}pG.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(pG)))(n||pG)}})(),pG.ɵdir=ho({type:pG,selectors:[["input","type","range","formControlName",""],["input","type","range","formControl",""],["input","type","range","ngModel",""]],hostBindings:function t(e,n){1&e&&Rf("change",(function t(e){return n.onChange(e.target.value)}))("input",(function t(e){return n.onChange(e.target.value)}))("blur",(function t(){return n.onTouched()}))},features:[jh([dG]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(pG,[{type:k_,args:[{selector:"input[type=range][formControlName],input[type=range][formControl],input[type=range][ngModel]",host:{"(change)":"onChange($event.target.value)","(input)":"onChange($event.target.value)","(blur)":"onTouched()"},providers:[dG]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const mG=new er("NgModelWithFormControlWarning"),fG={provide:lW,useExisting:$e((()=>uG))};class uG extends lW{constructor(t,e,n,o){super(),this._ngModelWarningConfig=o,this.update=new Sy,this._ngModelWarningSent=!1,this._setValidators(t),this._setAsyncValidators(e),this.valueAccessor=EW(this,n)}set isDisabled(t){("undefined"==typeof ngDevMode||ngDevMode)&&_W.disabledAttrWarning()}ngOnChanges(t){if(this._isControlChanged(t)){const e=t.form.previousValue;e&&vW(e,this,!1),MW(this.form,this),this.control.disabled&&this.valueAccessor.setDisabledState&&this.valueAccessor.setDisabledState(!0),this.form.updateValueAndValidity({emitEvent:!1})}DW(t,this.viewModel)&&(("undefined"==typeof ngDevMode||ngDevMode)&&TW("formControl",uG,this,this._ngModelWarningConfig),this.form.setValue(this.model),this.viewModel=this.model)}ngOnDestroy(){this.form&&vW(this.form,this,!1)}get path(){return[]}get control(){return this.form}viewToModelUpdate(t){this.viewModel=t,this.update.emit(t)}_isControlChanged(t){return t.hasOwnProperty("form")}}uG.ɵfac=function t(e){return new(e||uG)(gf(EU,10),gf(AU,10),gf(xU,10),gf(mG,8))},uG.ɵdir=ho({type:uG,selectors:[["","formControl",""]],inputs:{isDisabled:["disabled","isDisabled"],form:["formControl","form"],model:["ngModel","model"]},outputs:{update:"ngModelChange"},exportAs:["ngForm"],features:[jh([fG]),Qp,qo]}),uG._ngModelWarningSentOnce=!1,uG.ctorParameters=()=>[{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[xU]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[mG]}]}],uG.propDecorators={form:[{type:R_,args:["formControl"]}],isDisabled:[{type:R_,args:["disabled"]}],model:[{type:R_,args:["ngModel"]}],update:[{type:E_,args:["ngModelChange"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(uG,[{type:k_,args:[{selector:"[formControl]",providers:[fG],exportAs:"ngForm"}]}],(function(){return[{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[xU]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[mG]}]}]}),{update:[{type:E_,args:["ngModelChange"]}],isDisabled:[{type:R_,args:["disabled"]}],form:[{type:R_,args:["formControl"]}],model:[{type:R_,args:["ngModel"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const gG={provide:sW,useExisting:$e((()=>hG))};class hG extends sW{constructor(t,e){super(),this.validators=t,this.asyncValidators=e,this.submitted=!1,this._onCollectionChange=()=>this._updateDomValue(),this.directives=[],this.form=null,this.ngSubmit=new Sy,this._setValidators(t),this._setAsyncValidators(e)}ngOnChanges(t){this._checkFormPresent(),t.hasOwnProperty("form")&&(this._updateValidators(),this._updateDomValue(),this._updateRegistrations(),this._oldForm=this.form)}ngOnDestroy(){this.form&&(PW(this.form,this),this.form._onCollectionChange===this._onCollectionChange&&this.form._registerOnCollectionChange((()=>{})))}get formDirective(){return this}get control(){return this.form}get path(){return[]}addControl(t){const e=this.form.get(t.path);return MW(e,t),e.updateValueAndValidity({emitEvent:!1}),this.directives.push(t),e}getControl(t){return this.form.get(t.path)}removeControl(t){vW(t.control||null,t,!1),AW(this.directives,t)}addFormGroup(t){this._setUpFormContainer(t)}removeFormGroup(t){this._cleanUpFormContainer(t)}getFormGroup(t){return this.form.get(t.path)}addFormArray(t){this._setUpFormContainer(t)}removeFormArray(t){this._cleanUpFormContainer(t)}getFormArray(t){return this.form.get(t.path)}updateModel(t,e){this.form.get(t.path).setValue(e)}onSubmit(t){return this.submitted=!0,RW(this.form,this.directives),this.ngSubmit.emit(t),!1}onReset(){this.resetForm()}resetForm(t){this.form.reset(t),this.submitted=!1}_updateDomValue(){this.directives.forEach((t=>{const e=t.control,n=this.form.get(t.path);e!==n&&(vW(e||null,t),n instanceof WW&&(MW(n,t),t.control=n))})),this.form._updateTreeValidity({emitEvent:!1})}_setUpFormContainer(t){const e=this.form.get(t.path);kW(e,t),e.updateValueAndValidity({emitEvent:!1})}_cleanUpFormContainer(t){if(this.form){const e=this.form.get(t.path);if(e){(function n(t,e){return PW(t,e)})(e,t)&&e.updateValueAndValidity({emitEvent:!1})}}}_updateRegistrations(){this.form._registerOnCollectionChange(this._onCollectionChange),this._oldForm&&this._oldForm._registerOnCollectionChange((()=>{}))}_updateValidators(){OW(this.form,this),this._oldForm&&PW(this._oldForm,this)}_checkFormPresent(){this.form||"undefined"!=typeof ngDevMode&&!ngDevMode||_W.missingFormException()}}hG.ɵfac=function t(e){return new(e||hG)(gf(EU,10),gf(AU,10))},hG.ɵdir=ho({type:hG,selectors:[["","formGroup",""]],hostBindings:function t(e,n){1&e&&Rf("submit",(function t(e){return n.onSubmit(e)}))("reset",(function t(){return n.onReset()}))},inputs:{form:["formGroup","form"]},outputs:{ngSubmit:"ngSubmit"},exportAs:["ngForm"],features:[jh([gG]),Qp,qo]}),hG.ctorParameters=()=>[{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]}],hG.propDecorators={form:[{type:R_,args:["formGroup"]}],ngSubmit:[{type:E_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(hG,[{type:k_,args:[{selector:"[formGroup]",providers:[gG],host:{"(submit)":"onSubmit($event)","(reset)":"onReset()"},exportAs:"ngForm"}]}],(function(){return[{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]}]}),{form:[{type:R_,args:["formGroup"]}],ngSubmit:[{type:E_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const bG={provide:sW,useExisting:$e((()=>yG))};class yG extends KW{constructor(t,e,n){super(),this._parent=t,this._setValidators(e),this._setAsyncValidators(n)}_checkParentType(){MG(this._parent)&&("undefined"==typeof ngDevMode||ngDevMode)&&_W.groupParentException()}}yG.ɵfac=function t(e){return new(e||yG)(gf(sW,13),gf(EU,10),gf(AU,10))},yG.ɵdir=ho({type:yG,selectors:[["","formGroupName",""]],inputs:{name:["formGroupName","name"]},features:[jh([bG]),Qp]}),yG.ctorParameters=()=>[{type:sW,decorators:[{type:Br},{type:Ur},{type:jr}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]}],yG.propDecorators={name:[{type:R_,args:["formGroupName"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(yG,[{type:k_,args:[{selector:"[formGroupName]",providers:[bG]}]}],(function(){return[{type:sW,decorators:[{type:Br},{type:Ur},{type:jr}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]}]}),{name:[{type:R_,args:["formGroupName"]}]});const _G={provide:sW,useExisting:$e((()=>CG))};class CG extends sW{constructor(t,e,n){super(),this._parent=t,this._setValidators(e),this._setAsyncValidators(n)}ngOnInit(){this._checkParentType(),this.formDirective.addFormArray(this)}ngOnDestroy(){this.formDirective&&this.formDirective.removeFormArray(this)}get control(){return this.formDirective.getFormArray(this)}get formDirective(){return this._parent?this._parent.formDirective:null}get path(){return CW(null==this.name?this.name:this.name.toString(),this._parent)}_checkParentType(){MG(this._parent)&&("undefined"==typeof ngDevMode||ngDevMode)&&_W.arrayParentException()}}function MG(t){return!(t instanceof yG||t instanceof hG||t instanceof CG)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */CG.ɵfac=function t(e){return new(e||CG)(gf(sW,13),gf(EU,10),gf(AU,10))},CG.ɵdir=ho({type:CG,selectors:[["","formArrayName",""]],inputs:{name:["formArrayName","name"]},features:[jh([_G]),Qp]}),CG.ctorParameters=()=>[{type:sW,decorators:[{type:Br},{type:Ur},{type:jr}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]}],CG.propDecorators={name:[{type:R_,args:["formArrayName"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(CG,[{type:k_,args:[{selector:"[formArrayName]",providers:[_G]}]}],(function(){return[{type:sW,decorators:[{type:Br},{type:Ur},{type:jr}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]}]}),{name:[{type:R_,args:["formArrayName"]}]});const vG={provide:lW,useExisting:$e((()=>xG))};class xG extends lW{constructor(t,e,n,o,i){super(),this._ngModelWarningConfig=i,this._added=!1,this.update=new Sy,this._ngModelWarningSent=!1,this._parent=t,this._setValidators(e),this._setAsyncValidators(n),this.valueAccessor=EW(this,o)}set isDisabled(t){("undefined"==typeof ngDevMode||ngDevMode)&&_W.disabledAttrWarning()}ngOnChanges(t){this._added||this._setUpControl(),DW(t,this.viewModel)&&(("undefined"==typeof ngDevMode||ngDevMode)&&TW("formControlName",xG,this,this._ngModelWarningConfig),this.viewModel=this.model,this.formDirective.updateModel(this,this.model))}ngOnDestroy(){this.formDirective&&this.formDirective.removeControl(this)}viewToModelUpdate(t){this.viewModel=t,this.update.emit(t)}get path(){return CW(null==this.name?this.name:this.name.toString(),this._parent)}get formDirective(){return this._parent?this._parent.formDirective:null}_checkParentType(){("undefined"==typeof ngDevMode||ngDevMode)&&(!(this._parent instanceof yG)&&this._parent instanceof KW?_W.ngModelGroupException():this._parent instanceof yG||this._parent instanceof hG||this._parent instanceof CG||_W.controlParentException())}_setUpControl(){this._checkParentType(),this.control=this.formDirective.addControl(this),this.control.disabled&&this.valueAccessor.setDisabledState&&this.valueAccessor.setDisabledState(!0),this._added=!0}}xG.ɵfac=function t(e){return new(e||xG)(gf(sW,13),gf(EU,10),gf(AU,10),gf(xU,10),gf(mG,8))},xG.ɵdir=ho({type:xG,selectors:[["","formControlName",""]],inputs:{isDisabled:["disabled","isDisabled"],name:["formControlName","name"],model:["ngModel","model"]},outputs:{update:"ngModelChange"},features:[jh([vG]),Qp,qo]}),xG._ngModelWarningSentOnce=!1,xG.ctorParameters=()=>[{type:sW,decorators:[{type:Br},{type:Ur},{type:jr}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[xU]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[mG]}]}],xG.propDecorators={name:[{type:R_,args:["formControlName"]}],isDisabled:[{type:R_,args:["disabled"]}],model:[{type:R_,args:["ngModel"]}],update:[{type:E_,args:["ngModelChange"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(xG,[{type:k_,args:[{selector:"[formControlName]",providers:[vG]}]}],(function(){return[{type:sW,decorators:[{type:Br},{type:Ur},{type:jr}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[EU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[AU]}]},{type:Array,decorators:[{type:Br},{type:Vr},{type:Lr,args:[xU]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[mG]}]}]}),{update:[{type:E_,args:["ngModelChange"]}],isDisabled:[{type:R_,args:["disabled"]}],name:[{type:R_,args:["formControlName"]}],model:[{type:R_,args:["ngModel"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const OG={provide:xU,useExisting:$e((()=>wG)),multi:!0};function PG(t,e){return null==t?`${e}`:(e&&"object"==typeof e&&(e="Object"),`${t}: ${e}`.slice(0,50))}class wG extends vU{constructor(){super(...arguments),this._optionMap=new Map,this._idCounter=0,this._compareWith=Object.is}set compareWith(t){if("function"!=typeof t&&("undefined"==typeof ngDevMode||ngDevMode))throw new Error(`compareWith must be a function, but received ${JSON.stringify(t)}`);this._compareWith=t}writeValue(t){this.value=t;const e=this._getOptionId(t);null==e&&this.setProperty("selectedIndex",-1);const n=PG(e,t);this.setProperty("value",n)}registerOnChange(t){this.onChange=e=>{this.value=this._getOptionValue(e),t(this.value)}}_registerOption(){return(this._idCounter++).toString()}_getOptionId(t){for(const e of Array.from(this._optionMap.keys()))if(this._compareWith(this._optionMap.get(e),t))return e;return null}_getOptionValue(t){const e=(function n(t){return t.split(":")[0]})(t);return this._optionMap.has(e)?this._optionMap.get(e):t}}wG.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(wG)))(n||wG)}})(),wG.ɵdir=ho({type:wG,selectors:[["select","formControlName","",3,"multiple",""],["select","formControl","",3,"multiple",""],["select","ngModel","",3,"multiple",""]],hostBindings:function t(e,n){1&e&&Rf("change",(function t(e){return n.onChange(e.target.value)}))("blur",(function t(){return n.onTouched()}))},inputs:{compareWith:"compareWith"},features:[jh([OG]),Qp]}),wG.propDecorators={compareWith:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(wG,[{type:k_,args:[{selector:"select:not([multiple])[formControlName],select:not([multiple])[formControl],select:not([multiple])[ngModel]",host:{"(change)":"onChange($event.target.value)","(blur)":"onTouched()"},providers:[OG]}]}],null,{compareWith:[{type:R_}]});class kG{constructor(t,e,n){this._element=t,this._renderer=e,this._select=n,this._select&&(this.id=this._select._registerOption())}set ngValue(t){null!=this._select&&(this._select._optionMap.set(this.id,t),this._setElementValue(PG(this.id,t)),this._select.writeValue(this._select.value))}set value(t){this._setElementValue(t),this._select&&this._select.writeValue(this._select.value)}_setElementValue(t){this._renderer.setProperty(this._element.nativeElement,"value",t)}ngOnDestroy(){this._select&&(this._select._optionMap.delete(this.id),this._select.writeValue(this._select.value))}}kG.ɵfac=function t(e){return new(e||kG)(gf(Kh),gf($h),gf(wG,9))},kG.ɵdir=ho({type:kG,selectors:[["option"]],inputs:{ngValue:"ngValue",value:"value"}}),kG.ctorParameters=()=>[{type:Kh},{type:$h},{type:wG,decorators:[{type:Br},{type:Ur}]}],kG.propDecorators={ngValue:[{type:R_,args:["ngValue"]}],value:[{type:R_,args:["value"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(kG,[{type:k_,args:[{selector:"option"}]}],(function(){return[{type:Kh},{type:$h},{type:wG,decorators:[{type:Br},{type:Ur}]}]}),{ngValue:[{type:R_,args:["ngValue"]}],value:[{type:R_,args:["value"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const SG={provide:xU,useExisting:$e((()=>RG)),multi:!0};function DG(t,e){return null==t?`${e}`:("string"==typeof e&&(e=`'${e}'`),e&&"object"==typeof e&&(e="Object"),`${t}: ${e}`.slice(0,50))}class RG extends vU{constructor(){super(...arguments),this._optionMap=new Map,this._idCounter=0,this._compareWith=Object.is}set compareWith(t){if("function"!=typeof t&&("undefined"==typeof ngDevMode||ngDevMode))throw new Error(`compareWith must be a function, but received ${JSON.stringify(t)}`);this._compareWith=t}writeValue(t){let e;if(this.value=t,Array.isArray(t)){const n=t.map((t=>this._getOptionId(t)));e=(t,e)=>{t._setSelected(n.indexOf(e.toString())>-1)}}else e=(t,e)=>{t._setSelected(!1)};this._optionMap.forEach(e)}registerOnChange(t){this.onChange=e=>{const n=[];if(void 0!==e.selectedOptions){const t=e.selectedOptions;for(let e=0;e<t.length;e++){const o=t.item(e),i=this._getOptionValue(o.value);n.push(i)}}else{const t=e.options;for(let e=0;e<t.length;e++){const o=t.item(e);if(o.selected){const t=this._getOptionValue(o.value);n.push(t)}}}this.value=n,t(n)}}_registerOption(t){const e=(this._idCounter++).toString();return this._optionMap.set(e,t),e}_getOptionId(t){for(const e of Array.from(this._optionMap.keys()))if(this._compareWith(this._optionMap.get(e)._value,t))return e;return null}_getOptionValue(t){const e=(function n(t){return t.split(":")[0]})(t);return this._optionMap.has(e)?this._optionMap.get(e)._value:t}}RG.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(RG)))(n||RG)}})(),RG.ɵdir=ho({type:RG,selectors:[["select","multiple","","formControlName",""],["select","multiple","","formControl",""],["select","multiple","","ngModel",""]],hostBindings:function t(e,n){1&e&&Rf("change",(function t(e){return n.onChange(e.target)}))("blur",(function t(){return n.onTouched()}))},inputs:{compareWith:"compareWith"},features:[jh([SG]),Qp]}),RG.propDecorators={compareWith:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(RG,[{type:k_,args:[{selector:"select[multiple][formControlName],select[multiple][formControl],select[multiple][ngModel]",host:{"(change)":"onChange($event.target)","(blur)":"onTouched()"},providers:[SG]}]}],null,{compareWith:[{type:R_}]});class EG{constructor(t,e,n){this._element=t,this._renderer=e,this._select=n,this._select&&(this.id=this._select._registerOption(this))}set ngValue(t){null!=this._select&&(this._value=t,this._setElementValue(DG(this.id,t)),this._select.writeValue(this._select.value))}set value(t){this._select?(this._value=t,this._setElementValue(DG(this.id,t)),this._select.writeValue(this._select.value)):this._setElementValue(t)}_setElementValue(t){this._renderer.setProperty(this._element.nativeElement,"value",t)}_setSelected(t){this._renderer.setProperty(this._element.nativeElement,"selected",t)}ngOnDestroy(){this._select&&(this._select._optionMap.delete(this.id),this._select.writeValue(this._select.value))}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function AG(t){return"number"==typeof t?t:parseInt(t,10)}EG.ɵfac=function t(e){return new(e||EG)(gf(Kh),gf($h),gf(RG,9))},EG.ɵdir=ho({type:EG,selectors:[["option"]],inputs:{ngValue:"ngValue",value:"value"}}),EG.ctorParameters=()=>[{type:Kh},{type:$h},{type:RG,decorators:[{type:Br},{type:Ur}]}],EG.propDecorators={ngValue:[{type:R_,args:["ngValue"]}],value:[{type:R_,args:["value"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(EG,[{type:k_,args:[{selector:"option"}]}],(function(){return[{type:Kh},{type:$h},{type:RG,decorators:[{type:Br},{type:Ur}]}]}),{ngValue:[{type:R_,args:["ngValue"]}],value:[{type:R_,args:["value"]}]});class TG{constructor(){this._validator=UU}handleChanges(t){if(this.inputName in t){const e=this.normalizeInput(t[this.inputName].currentValue);this._validator=this.createValidator(e),this._onChange&&this._onChange()}}validate(t){return this._validator(t)}registerOnValidatorChange(t){this._onChange=t}}TG.ɵfac=function t(e){return new(e||TG)},TG.ɵdir=ho({type:TG}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(TG,[{type:k_}],(function(){return[]}),null);const NG={provide:EU,useExisting:$e((()=>zG)),multi:!0};class zG extends TG{constructor(){super(...arguments),this.inputName="max",this.normalizeInput=t=>parseFloat(t),this.createValidator=t=>IU(t)}ngOnChanges(t){this.handleChanges(t)}}zG.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(zG)))(n||zG)}})(),zG.ɵdir=ho({type:zG,selectors:[["input","type","number","max","","formControlName",""],["input","type","number","max","","formControl",""],["input","type","number","max","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){if(2&e){let t;ym("max",null!==(t=n.max)&&void 0!==t?t:null)}},inputs:{max:"max"},features:[jh([NG]),Qp,qo]}),zG.propDecorators={max:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(zG,[{type:k_,args:[{selector:"input[type=number][max][formControlName],input[type=number][max][formControl],input[type=number][max][ngModel]",providers:[NG],host:{"[attr.max]":"max ?? null"}}]}],null,{max:[{type:R_}]});const IG={provide:EU,useExisting:$e((()=>HG)),multi:!0};class HG extends TG{constructor(){super(...arguments),this.inputName="min",this.normalizeInput=t=>parseFloat(t),this.createValidator=t=>zU(t)}ngOnChanges(t){this.handleChanges(t)}}HG.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(HG)))(n||HG)}})(),HG.ɵdir=ho({type:HG,selectors:[["input","type","number","min","","formControlName",""],["input","type","number","min","","formControl",""],["input","type","number","min","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){if(2&e){let t;ym("min",null!==(t=n.min)&&void 0!==t?t:null)}},inputs:{min:"min"},features:[jh([IG]),Qp,qo]}),HG.propDecorators={min:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(HG,[{type:k_,args:[{selector:"input[type=number][min][formControlName],input[type=number][min][formControl],input[type=number][min][ngModel]",providers:[IG],host:{"[attr.min]":"min ?? null"}}]}],null,{min:[{type:R_}]});const FG={provide:EU,useExisting:$e((()=>BG)),multi:!0},LG={provide:EU,useExisting:$e((()=>VG)),multi:!0};class BG{constructor(){this._required=!1}get required(){return this._required}set required(t){this._required=null!=t&&!1!==t&&"false"!=`${t}`,this._onChange&&this._onChange()}validate(t){return this.required?HU(t):null}registerOnValidatorChange(t){this._onChange=t}}BG.ɵfac=function t(e){return new(e||BG)},BG.ɵdir=ho({type:BG,selectors:[["","required","","formControlName","",3,"type","checkbox"],["","required","","formControl","",3,"type","checkbox"],["","required","","ngModel","",3,"type","checkbox"]],hostVars:1,hostBindings:function t(e,n){2&e&&ym("required",n.required?"":null)},inputs:{required:"required"},features:[jh([FG])]}),BG.propDecorators={required:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(BG,[{type:k_,args:[{selector:":not([type=checkbox])[required][formControlName],:not([type=checkbox])[required][formControl],:not([type=checkbox])[required][ngModel]",providers:[FG],host:{"[attr.required]":'required ? "" : null'}}]}],(function(){return[]}),{required:[{type:R_}]});class VG extends BG{validate(t){return this.required?FU(t):null}}VG.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(VG)))(n||VG)}})(),VG.ɵdir=ho({type:VG,selectors:[["input","type","checkbox","required","","formControlName",""],["input","type","checkbox","required","","formControl",""],["input","type","checkbox","required","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){2&e&&ym("required",n.required?"":null)},features:[jh([LG]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(VG,[{type:k_,args:[{selector:"input[type=checkbox][required][formControlName],input[type=checkbox][required][formControl],input[type=checkbox][required][ngModel]",providers:[LG],host:{"[attr.required]":'required ? "" : null'}}]}],null,null);const jG={provide:EU,useExisting:$e((()=>UG)),multi:!0};class UG{constructor(){this._enabled=!1}set email(t){this._enabled=""===t||!0===t||"true"===t,this._onChange&&this._onChange()}validate(t){return this._enabled?LU(t):null}registerOnValidatorChange(t){this._onChange=t}}UG.ɵfac=function t(e){return new(e||UG)},UG.ɵdir=ho({type:UG,selectors:[["","email","","formControlName",""],["","email","","formControl",""],["","email","","ngModel",""]],inputs:{email:"email"},features:[jh([jG])]}),UG.propDecorators={email:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(UG,[{type:k_,args:[{selector:"[email][formControlName],[email][formControl],[email][ngModel]",providers:[jG]}]}],(function(){return[]}),{email:[{type:R_}]});const WG={provide:EU,useExisting:$e((()=>GG)),multi:!0};class GG{constructor(){this._validator=UU}ngOnChanges(t){"minlength"in t&&(this._createValidator(),this._onChange&&this._onChange())}validate(t){return this.enabled()?this._validator(t):null}registerOnValidatorChange(t){this._onChange=t}_createValidator(){this._validator=this.enabled()?BU(AG(this.minlength)):UU}enabled(){return null!=this.minlength}}GG.ɵfac=function t(e){return new(e||GG)},GG.ɵdir=ho({type:GG,selectors:[["","minlength","","formControlName",""],["","minlength","","formControl",""],["","minlength","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){2&e&&ym("minlength",n.enabled()?n.minlength:null)},inputs:{minlength:"minlength"},features:[jh([WG]),qo]}),GG.propDecorators={minlength:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(GG,[{type:k_,args:[{selector:"[minlength][formControlName],[minlength][formControl],[minlength][ngModel]",providers:[WG],host:{"[attr.minlength]":"enabled() ? minlength : null"}}]}],(function(){return[]}),{minlength:[{type:R_}]});const YG={provide:EU,useExisting:$e((()=>qG)),multi:!0};class qG{constructor(){this._validator=UU}ngOnChanges(t){"maxlength"in t&&(this._createValidator(),this._onChange&&this._onChange())}validate(t){return this.enabled()?this._validator(t):null}registerOnValidatorChange(t){this._onChange=t}_createValidator(){this._validator=this.enabled()?VU(AG(this.maxlength)):UU}enabled(){return null!=this.maxlength}}qG.ɵfac=function t(e){return new(e||qG)},qG.ɵdir=ho({type:qG,selectors:[["","maxlength","","formControlName",""],["","maxlength","","formControl",""],["","maxlength","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){2&e&&ym("maxlength",n.enabled()?n.maxlength:null)},inputs:{maxlength:"maxlength"},features:[jh([YG]),qo]}),qG.propDecorators={maxlength:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(qG,[{type:k_,args:[{selector:"[maxlength][formControlName],[maxlength][formControl],[maxlength][ngModel]",providers:[YG],host:{"[attr.maxlength]":"enabled() ? maxlength : null"}}]}],(function(){return[]}),{maxlength:[{type:R_}]});const ZG={provide:EU,useExisting:$e((()=>XG)),multi:!0};class XG{constructor(){this._validator=UU}ngOnChanges(t){"pattern"in t&&(this._createValidator(),this._onChange&&this._onChange())}validate(t){return this._validator(t)}registerOnValidatorChange(t){this._onChange=t}_createValidator(){this._validator=jU(this.pattern)}}XG.ɵfac=function t(e){return new(e||XG)},XG.ɵdir=ho({type:XG,selectors:[["","pattern","","formControlName",""],["","pattern","","formControl",""],["","pattern","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){2&e&&ym("pattern",n.pattern?n.pattern:null)},inputs:{pattern:"pattern"},features:[jh([ZG]),qo]}),XG.propDecorators={pattern:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(XG,[{type:k_,args:[{selector:"[pattern][formControlName],[pattern][formControl],[pattern][ngModel]",providers:[ZG],host:{"[attr.pattern]":"pattern ? pattern : null"}}]}],(function(){return[]}),{pattern:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const KG=[oG,kG,EG,SU,aG,pG,PU,wG,RG,cG,mW,fW,BG,GG,qG,XG,VG,UG,HG,zG],JG=[nG,$W,XW],QG=[uG,hG,xG,yG,CG];class $G{}$G.ɵfac=function t(e){return new(e||$G)},$G.ɵmod=fo({type:$G}),$G.ɵinj=Dn({imports:[[sG]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb($G,[{type:L_,args:[{declarations:KG,imports:[sG],exports:KG}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo($G,{declarations:[oG,kG,EG,SU,aG,pG,PU,wG,RG,cG,mW,fW,BG,GG,qG,XG,VG,UG,HG,zG],imports:[sG],exports:[oG,kG,EG,SU,aG,pG,PU,wG,RG,cG,mW,fW,BG,GG,qG,XG,VG,UG,HG,zG]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class tY{}tY.ɵfac=function t(e){return new(e||tY)},tY.ɵmod=fo({type:tY}),tY.ɵinj=Dn({imports:[$G]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(tY,[{type:L_,args:[{declarations:JG,exports:[$G,JG]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(tY,{declarations:[nG,$W,XW],exports:[$G,nG,$W,XW]});class eY{static withConfig(t){return{ngModule:eY,providers:[{provide:mG,useValue:t.warnOnNgModelWithFormControl}]}}}eY.ɵfac=function t(e){return new(e||eY)},eY.ɵmod=fo({type:eY}),eY.ɵinj=Dn({imports:[$G]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(eY,[{type:L_,args:[{declarations:[QG],exports:[$G,QG]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(eY,{declarations:[uG,hG,xG,yG,CG],exports:[$G,uG,hG,xG,yG,CG]});class nY{group(t,e=null){const n=this._reduceControls(t);let o,i=null,a=null;return null!=e&&(!(
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function r(t){return void 0!==t.asyncValidators||void 0!==t.validators||void 0!==t.updateOn})(e)?(i=null!=e.validator?e.validator:null,a=null!=e.asyncValidator?e.asyncValidator:null):(i=null!=e.validators?e.validators:null,a=null!=e.asyncValidators?e.asyncValidators:null,o=null!=e.updateOn?e.updateOn:void 0)),new GW(n,{asyncValidators:a,updateOn:o,validators:i})}control(t,e,n){return new WW(t,e,n)}array(t,e,n){const o=t.map((t=>this._createControl(t)));return new YW(o,e,n)}_reduceControls(t){const e={};return Object.keys(t).forEach((n=>{e[n]=this._createControl(t[n])})),e}_createControl(t){if(t instanceof WW||t instanceof GW||t instanceof YW)return t;if(Array.isArray(t)){return this.control(t[0],t.length>1?t[1]:null,t.length>2?t[2]:null)}return this.control(t)}}nY.ɵfac=function t(e){return new(e||nY)},nY.ɵprov=Sn({factory:function t(){return new nY},token:nY,providedIn:eY}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(nY,[{type:Um,args:[{providedIn:eY}]}],null,null),
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
new ob("12.2.1");const oY=["trigger"],iY=["panel"];function aY(t,e){if(1&t&&(Cf(0,"span",8),Pu(1),Mf()),2&t){const t=zf();Dc(1),wu(t.placeholder)}}function rY(t,e){if(1&t&&(Cf(0,"span",12),Pu(1),Mf()),2&t){const t=zf(2);Dc(1),wu(t.triggerValue)}}function sY(t,e){1&t&&Ff(0,0,["*ngSwitchCase","true"])}function lY(t,e){if(1&t&&(Cf(0,"span",9),Hm(1,rY,2,1,"span",10),Hm(2,sY,1,0,"ng-content",11),Mf()),2&t){bf("ngSwitch",!!zf().customTrigger),Dc(2),bf("ngSwitchCase",!0)}}function cY(t,e){if(1&t){const t=wf();Cf(0,"div",13),Cf(1,"div",14,15),Rf("@transformPanel.done",(function e(n){Pi(t);return zf()._panelDoneAnimatingStream.next(n.toState)}))("keydown",(function e(n){Pi(t);return zf()._handleKeydown(n)})),Ff(3,1),Mf(),Mf()}if(2&t){const t=zf();bf("@transformPanelWrap",void 0),Dc(1),Iu("mat-select-panel ",t._getPanelTheme(),""),lu("transform-origin",t._transformOrigin)("font-size",t._triggerFontSize,"px"),bf("ngClass",t.panelClass)("@transformPanel",t.multiple?"showing-multiple":"showing"),ym("id",t.id+"-panel")("aria-multiselectable",t.multiple)("aria-label",t.ariaLabel||null)("aria-labelledby",t._getPanelAriaLabelledby())}}const dY=[[["mat-select-trigger"]],"*"],pY={transformPanelWrap:xO("transformPanelWrap",[DO("* => void",EO("@transformPanel",[RO()],{optional:!0}))]),transformPanel:xO("transformPanel",[kO("void",wO({transform:"scaleY(0.8)",minWidth:"100%",opacity:0})),kO("showing",wO({opacity:1,minWidth:"calc(100% + 32px)",transform:"scaleY(1)"})),kO("showing-multiple",wO({opacity:1,minWidth:"calc(100% + 64px)",transform:"scaleY(1)"})),DO("void => *",OO("120ms cubic-bezier(0, 0, 0.2, 1)")),DO("* => void",OO("100ms 25ms linear",wO({opacity:0})))])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let mY=0;const fY=256,uY=16,gY=new er("mat-select-scroll-strategy");const hY=new er("MAT_SELECT_CONFIG"),bY={provide:gY,deps:[KB],useFactory:function yY(t){return()=>t.scrollStrategies.reposition()}};class _Y{constructor(t,e){this.source=t,this.value=e}}const CY=HF(FF(zF(LF(class{constructor(t,e,n,o,i){this._elementRef=t,this._defaultErrorStateMatcher=e,this._parentForm=n,this._parentFormGroup=o,this.ngControl=i}})))),MY=new er("MatSelectTrigger");class vY{}vY.ɵfac=function t(e){return new(e||vY)},vY.ɵdir=ho({type:vY,selectors:[["mat-select-trigger"]],features:[jh([{provide:MY,useExisting:vY}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(vY,[{type:k_,args:[{selector:"mat-select-trigger",providers:[{provide:MY,useExisting:vY}]}]}],null,null);class xY extends CY{constructor(t,e,n,o,i,a,r,s,l,c,d,p,m,f){var u,g,h;super(i,o,r,s,c),this._viewportRuler=t,this._changeDetectorRef=e,this._ngZone=n,this._dir=a,this._parentFormField=l,this._liveAnnouncer=m,this._defaultOptions=f,this._panelOpen=!1,this._compareWith=(t,e)=>t===e,this._uid="mat-select-"+mY++,this._triggerAriaLabelledBy=null,this._destroy=new B,this._onChange=()=>{},this._onTouched=()=>{},this._valueId="mat-select-value-"+mY++,this._panelDoneAnimatingStream=new B,this._overlayPanelClass=(null===(u=this._defaultOptions)||void 0===u?void 0:u.overlayPanelClass)||"",this._focused=!1,this.controlType="mat-select",this._required=!1,this._multiple=!1,this._disableOptionCentering=null!==(h=null===(g=this._defaultOptions)||void 0===g?void 0:g.disableOptionCentering)&&void 0!==h&&h,this.ariaLabel="",this.optionSelectionChanges=oe((()=>{const t=this.options;return t?t.changes.pipe(Be(t),Ve((()=>pe(...t.map((t=>t.onSelectionChange)))))):this._ngZone.onStable.pipe(ve(1),Ve((()=>this.optionSelectionChanges)))})),this.openedChange=new Sy,this._openedStream=this.openedChange.pipe(ue((t=>t)),Vt((()=>{}))),this._closedStream=this.openedChange.pipe(ue((t=>!t)),Vt((()=>{}))),this.selectionChange=new Sy,this.valueChange=new Sy,this.ngControl&&(this.ngControl.valueAccessor=this),null!=(null==f?void 0:f.typeaheadDebounceInterval)&&(this._typeaheadDebounceInterval=f.typeaheadDebounceInterval),this._scrollStrategyFactory=p,this._scrollStrategy=this._scrollStrategyFactory(),this.tabIndex=parseInt(d)||0,this.id=this.id}get focused(){return this._focused||this._panelOpen}get placeholder(){return this._placeholder}set placeholder(t){this._placeholder=t,this.stateChanges.next()}get required(){return this._required}set required(t){this._required=eH(t),this.stateChanges.next()}get multiple(){return this._multiple}set multiple(t){if(this._selectionModel&&("undefined"==typeof ngDevMode||ngDevMode))
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
throw(function e(){return Error("Cannot change `multiple` mode of select after initialization.")})();this._multiple=eH(t)}get disableOptionCentering(){return this._disableOptionCentering}set disableOptionCentering(t){this._disableOptionCentering=eH(t)}get compareWith(){return this._compareWith}set compareWith(t){if("function"!=typeof t&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("`compareWith` must be a function.")})();this._compareWith=t,this._selectionModel&&this._initializeSelection()}get value(){return this._value}set value(t){(t!==this._value||this._multiple&&Array.isArray(t))&&(this.options&&this._setSelectionByValue(t),this._value=t)}get typeaheadDebounceInterval(){return this._typeaheadDebounceInterval}set typeaheadDebounceInterval(t){this._typeaheadDebounceInterval=nH(t)}get id(){return this._id}set id(t){this._id=t||this._uid,this.stateChanges.next()}ngOnInit(){this._selectionModel=new jL(this.multiple),this.stateChanges.next(),this._panelDoneAnimatingStream.pipe(we(),je(this._destroy)).subscribe((()=>this._panelDoneAnimating(this.panelOpen)))}ngAfterContentInit(){this._initKeyManager(),this._selectionModel.changed.pipe(je(this._destroy)).subscribe((t=>{t.added.forEach((t=>t.select())),t.removed.forEach((t=>t.deselect()))})),this.options.changes.pipe(Be(null),je(this._destroy)).subscribe((()=>{this._resetOptions(),this._initializeSelection()}))}ngDoCheck(){const t=this._getTriggerAriaLabelledby();if(t!==this._triggerAriaLabelledBy){const e=this._elementRef.nativeElement;this._triggerAriaLabelledBy=t,t?e.setAttribute("aria-labelledby",t):e.removeAttribute("aria-labelledby")}this.ngControl&&this.updateErrorState()}ngOnChanges(t){t.disabled&&this.stateChanges.next(),t.typeaheadDebounceInterval&&this._keyManager&&this._keyManager.withTypeAhead(this._typeaheadDebounceInterval)}ngOnDestroy(){this._destroy.next(),this._destroy.complete(),this.stateChanges.complete()}toggle(){this.panelOpen?this.close():this.open()}open(){this._canOpen()&&(this._panelOpen=!0,this._keyManager.withHorizontalOrientation(null),this._highlightCorrectOption(),this._changeDetectorRef.markForCheck())}close(){this._panelOpen&&(this._panelOpen=!1,this._keyManager.withHorizontalOrientation(this._isRtl()?"rtl":"ltr"),this._changeDetectorRef.markForCheck(),this._onTouched())}writeValue(t){this.value=t}registerOnChange(t){this._onChange=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t,this._changeDetectorRef.markForCheck(),this.stateChanges.next()}get panelOpen(){return this._panelOpen}get selected(){return this.multiple?this._selectionModel.selected:this._selectionModel.selected[0]}get triggerValue(){if(this.empty)return"";if(this._multiple){const t=this._selectionModel.selected.map((t=>t.viewValue));return this._isRtl()&&t.reverse(),t.join(", ")}return this._selectionModel.selected[0].viewValue}_isRtl(){return!!this._dir&&"rtl"===this._dir.value}_handleKeydown(t){this.disabled||(this.panelOpen?this._handleOpenKeydown(t):this._handleClosedKeydown(t))}_handleClosedKeydown(t){const e=t.keyCode,n=e===$I||e===QI||37===e||39===e,o=e===XI||e===JI,i=this._keyManager;if(!i.isTyping()&&o&&!tH(t)||(this.multiple||t.altKey)&&n)t.preventDefault(),this.open();else if(!this.multiple){const e=this.selected;i.onKeydown(t);const n=this.selected;n&&e!==n&&this._liveAnnouncer.announce(n.viewValue,1e4)}}_handleOpenKeydown(t){const e=this._keyManager,n=t.keyCode,o=n===$I||n===QI,i=e.isTyping();if(o&&t.altKey)t.preventDefault(),this.close();else if(i||n!==XI&&n!==JI||!e.activeItem||tH(t))if(!i&&this._multiple&&65===n&&t.ctrlKey){t.preventDefault();const e=this.options.some((t=>!t.disabled&&!t.selected));this.options.forEach((t=>{t.disabled||(e?t.select():t.deselect())}))}else{const n=e.activeItemIndex;e.onKeydown(t),this._multiple&&o&&t.shiftKey&&e.activeItem&&e.activeItemIndex!==n&&e.activeItem._selectViaInteraction()}else t.preventDefault(),e.activeItem._selectViaInteraction()}_onFocus(){this.disabled||(this._focused=!0,this.stateChanges.next())}_onBlur(){this._focused=!1,this.disabled||this.panelOpen||(this._onTouched(),this._changeDetectorRef.markForCheck(),this.stateChanges.next())}_onAttached(){this._overlayDir.positionChange.pipe(ve(1)).subscribe((()=>{this._changeDetectorRef.detectChanges(),this._positioningSettled()}))}_getPanelTheme(){return this._parentFormField?`mat-${this._parentFormField.color}`:""}get empty(){return!this._selectionModel||this._selectionModel.isEmpty()}_initializeSelection(){Promise.resolve().then((()=>{this._setSelectionByValue(this.ngControl?this.ngControl.value:this._value),this.stateChanges.next()}))}_setSelectionByValue(t){if(this._selectionModel.selected.forEach((t=>t.setInactiveStyles())),this._selectionModel.clear(),this.multiple&&t){if(!Array.isArray(t)&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("Value must be an array in multiple-selection mode.")})();t.forEach((t=>this._selectValue(t))),this._sortValues()}else{const e=this._selectValue(t);e?this._keyManager.updateActiveItem(e):this.panelOpen||this._keyManager.updateActiveItem(-1)}this._changeDetectorRef.markForCheck()}_selectValue(t){const e=this.options.find((e=>{if(this._selectionModel.isSelected(e))return!1;try{return null!=e.value&&this._compareWith(e.value,t)}catch(t){return("undefined"==typeof ngDevMode||ngDevMode)&&console.warn(t),!1}}));return e&&this._selectionModel.select(e),e}_initKeyManager(){this._keyManager=new FH(this.options).withTypeAhead(this._typeaheadDebounceInterval).withVerticalOrientation().withHorizontalOrientation(this._isRtl()?"rtl":"ltr").withHomeAndEnd().withAllowedModifierKeys(["shiftKey"]),this._keyManager.tabOut.pipe(je(this._destroy)).subscribe((()=>{this.panelOpen&&(!this.multiple&&this._keyManager.activeItem&&this._keyManager.activeItem._selectViaInteraction(),this.focus(),this.close())})),this._keyManager.change.pipe(je(this._destroy)).subscribe((()=>{this._panelOpen&&this.panel?this._scrollOptionIntoView(this._keyManager.activeItemIndex||0):this._panelOpen||this.multiple||!this._keyManager.activeItem||this._keyManager.activeItem._selectViaInteraction()}))}_resetOptions(){const t=pe(this.options.changes,this._destroy);this.optionSelectionChanges.pipe(je(t)).subscribe((t=>{this._onSelect(t.source,t.isUserInput),t.isUserInput&&!this.multiple&&this._panelOpen&&(this.close(),this.focus())})),pe(...this.options.map((t=>t._stateChanges))).pipe(je(t)).subscribe((()=>{this._changeDetectorRef.markForCheck(),this.stateChanges.next()}))}_onSelect(t,e){const n=this._selectionModel.isSelected(t);null!=t.value||this._multiple?(n!==t.selected&&(t.selected?this._selectionModel.select(t):this._selectionModel.deselect(t)),e&&this._keyManager.setActiveItem(t),this.multiple&&(this._sortValues(),e&&this.focus())):(t.deselect(),this._selectionModel.clear(),null!=this.value&&this._propagateChanges(t.value)),n!==this._selectionModel.isSelected(t)&&this._propagateChanges(),this.stateChanges.next()}_sortValues(){if(this.multiple){const t=this.options.toArray();this._selectionModel.sort(((e,n)=>this.sortComparator?this.sortComparator(e,n,t):t.indexOf(e)-t.indexOf(n))),this.stateChanges.next()}}_propagateChanges(t){let e=null;e=this.multiple?this.selected.map((t=>t.value)):this.selected?this.selected.value:t,this._value=e,this.valueChange.emit(e),this._onChange(e),this.selectionChange.emit(this._getChangeEvent(e)),this._changeDetectorRef.markForCheck()}_highlightCorrectOption(){this._keyManager&&(this.empty?this._keyManager.setFirstItemActive():this._keyManager.setActiveItem(this._selectionModel.selected[0]))}_canOpen(){var t;return!this._panelOpen&&!this.disabled&&(null===(t=this.options)||void 0===t?void 0:t.length)>0}focus(t){this._elementRef.nativeElement.focus(t)}_getPanelAriaLabelledby(){var t;if(this.ariaLabel)return null;const e=null===(t=this._parentFormField)||void 0===t?void 0:t.getLabelId();return this.ariaLabelledby?(e?e+" ":"")+this.ariaLabelledby:e}_getAriaActiveDescendant(){return this.panelOpen&&this._keyManager&&this._keyManager.activeItem?this._keyManager.activeItem.id:null}_getTriggerAriaLabelledby(){var t;if(this.ariaLabel)return null;const e=null===(t=this._parentFormField)||void 0===t?void 0:t.getLabelId();let n=(e?e+" ":"")+this._valueId;return this.ariaLabelledby&&(n+=" "+this.ariaLabelledby),n}_panelDoneAnimating(t){this.openedChange.emit(t)}setDescribedByIds(t){this._ariaDescribedby=t.join(" ")}onContainerClick(){this.focus(),this.open()}get shouldLabelFloat(){return this._panelOpen||!this.empty||this._focused&&!!this._placeholder}}xY.ɵfac=function t(e){return new(e||xY)(gf(QL),gf(Ob),gf(mC),gf(nL),gf(Kh),gf(vF,8),gf(XW,8),gf(hG,8),gf(yU,8),gf(lW,10),Ga("tabindex"),gf(gY),gf(cF),gf(hY,8))},xY.ɵdir=ho({type:xY,viewQuery:function t(e,n){if(1&e&&(Vy(oY,5),Vy(iY,5),Vy(tV,5)),2&e){let t;By(t=Uy())&&(n.trigger=t.first),By(t=Uy())&&(n.panel=t.first),By(t=Uy())&&(n._overlayDir=t.first)}},inputs:{ariaLabel:["aria-label","ariaLabel"],id:"id",placeholder:"placeholder",required:"required",multiple:"multiple",disableOptionCentering:"disableOptionCentering",compareWith:"compareWith",value:"value",typeaheadDebounceInterval:"typeaheadDebounceInterval",panelClass:"panelClass",ariaLabelledby:["aria-labelledby","ariaLabelledby"],errorStateMatcher:"errorStateMatcher",sortComparator:"sortComparator"},outputs:{openedChange:"openedChange",_openedStream:"opened",_closedStream:"closed",selectionChange:"selectionChange",valueChange:"valueChange"},features:[Qp,qo]}),xY.ctorParameters=()=>[{type:QL},{type:Ob},{type:mC},{type:nL},{type:Kh},{type:vF,decorators:[{type:Br}]},{type:XW,decorators:[{type:Br}]},{type:hG,decorators:[{type:Br}]},{type:_U,decorators:[{type:Br},{type:Lr,args:[yU]}]},{type:lW,decorators:[{type:Vr},{type:Br}]},{type:String,decorators:[{type:$a,args:["tabindex"]}]},{type:void 0,decorators:[{type:Lr,args:[gY]}]},{type:cF},{type:void 0,decorators:[{type:Br},{type:Lr,args:[hY]}]}],xY.propDecorators={trigger:[{type:ar,args:["trigger"]}],panel:[{type:ar,args:["panel"]}],_overlayDir:[{type:ar,args:[tV]}],panelClass:[{type:R_}],placeholder:[{type:R_}],required:[{type:R_}],multiple:[{type:R_}],disableOptionCentering:[{type:R_}],compareWith:[{type:R_}],value:[{type:R_}],ariaLabel:[{type:R_,args:["aria-label"]}],ariaLabelledby:[{type:R_,args:["aria-labelledby"]}],errorStateMatcher:[{type:R_}],typeaheadDebounceInterval:[{type:R_}],sortComparator:[{type:R_}],id:[{type:R_}],openedChange:[{type:E_}],_openedStream:[{type:E_,args:["opened"]}],_closedStream:[{type:E_,args:["closed"]}],selectionChange:[{type:E_}],valueChange:[{type:E_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(xY,[{type:k_}],(function(){return[{type:QL},{type:Ob},{type:mC},{type:nL},{type:Kh},{type:vF,decorators:[{type:Br}]},{type:XW,decorators:[{type:Br}]},{type:hG,decorators:[{type:Br}]},{type:_U,decorators:[{type:Br},{type:Lr,args:[yU]}]},{type:lW,decorators:[{type:Vr},{type:Br}]},{type:String,decorators:[{type:$a,args:["tabindex"]}]},{type:void 0,decorators:[{type:Lr,args:[gY]}]},{type:cF},{type:void 0,decorators:[{type:Br},{type:Lr,args:[hY]}]}]}),{ariaLabel:[{type:R_,args:["aria-label"]}],openedChange:[{type:E_}],_openedStream:[{type:E_,args:["opened"]}],_closedStream:[{type:E_,args:["closed"]}],selectionChange:[{type:E_}],valueChange:[{type:E_}],id:[{type:R_}],placeholder:[{type:R_}],required:[{type:R_}],multiple:[{type:R_}],disableOptionCentering:[{type:R_}],compareWith:[{type:R_}],value:[{type:R_}],typeaheadDebounceInterval:[{type:R_}],trigger:[{type:ar,args:["trigger"]}],panel:[{type:ar,args:["panel"]}],_overlayDir:[{type:ar,args:[tV]}],panelClass:[{type:R_}],ariaLabelledby:[{type:R_,args:["aria-labelledby"]}],errorStateMatcher:[{type:R_}],sortComparator:[{type:R_}]});class OY extends xY{constructor(){super(...arguments),this._scrollTop=0,this._triggerFontSize=0,this._transformOrigin="top",this._offsetY=0,this._positions=[{originX:"start",originY:"top",overlayX:"start",overlayY:"top"},{originX:"start",originY:"bottom",overlayX:"start",overlayY:"bottom"}]}_calculateOverlayScroll(t,e,n){const o=this._getItemHeight();return Math.min(Math.max(0,o*t-e+o/2),n)}ngOnInit(){super.ngOnInit(),this._viewportRuler.change().pipe(je(this._destroy)).subscribe((()=>{this.panelOpen&&(this._triggerRect=this.trigger.nativeElement.getBoundingClientRect(),this._changeDetectorRef.markForCheck())}))}open(){super._canOpen()&&(super.open(),this._triggerRect=this.trigger.nativeElement.getBoundingClientRect(),this._triggerFontSize=parseInt(getComputedStyle(this.trigger.nativeElement).fontSize||"0"),this._calculateOverlayPosition(),this._ngZone.onStable.pipe(ve(1)).subscribe((()=>{this._triggerFontSize&&this._overlayDir.overlayRef&&this._overlayDir.overlayRef.overlayElement&&(this._overlayDir.overlayRef.overlayElement.style.fontSize=`${this._triggerFontSize}px`)})))}_scrollOptionIntoView(t){const e=wL(t,this.options,this.optionGroups),n=this._getItemHeight();this.panel.nativeElement.scrollTop=0===t&&1===e?0:kL((t+e)*n,n,this.panel.nativeElement.scrollTop,fY)}_positioningSettled(){this._calculateOverlayOffsetX(),this.panel.nativeElement.scrollTop=this._scrollTop}_panelDoneAnimating(t){this.panelOpen?this._scrollTop=0:(this._overlayDir.offsetX=0,this._changeDetectorRef.markForCheck()),super._panelDoneAnimating(t)}_getChangeEvent(t){return new _Y(this,t)}_calculateOverlayOffsetX(){const t=this._overlayDir.overlayRef.overlayElement.getBoundingClientRect(),e=this._viewportRuler.getViewportSize(),n=this._isRtl(),o=this.multiple?56:32;let i;if(this.multiple)i=40;else if(this.disableOptionCentering)i=uY;else{let t=this._selectionModel.selected[0]||this.options.first;i=t&&t.group?32:uY}n||(i*=-1);const a=0-(t.left+i-(n?o:0)),r=t.right+i-e.width+(n?0:o);a>0?i+=a+8:r>0&&(i-=r+8),this._overlayDir.offsetX=Math.round(i),this._overlayDir.overlayRef.updatePosition()}_calculateOverlayOffsetY(t,e,n){const o=this._getItemHeight(),i=(o-this._triggerRect.height)/2,a=Math.floor(fY/o);let r;if(this.disableOptionCentering)return 0;if(0===this._scrollTop)r=t*o;else if(this._scrollTop===n){r=(t-(this._getItemCount()-a))*o+(o-(this._getItemCount()*o-fY)%o)}else r=e-o/2;return Math.round(-1*r-i)}_checkOverlayWithinViewport(t){const e=this._getItemHeight(),n=this._viewportRuler.getViewportSize(),o=this._triggerRect.top-8,i=n.height-this._triggerRect.bottom-8,a=Math.abs(this._offsetY),r=Math.min(this._getItemCount()*e,fY)-a-this._triggerRect.height;r>i?this._adjustPanelUp(r,i):a>o?this._adjustPanelDown(a,o,t):this._transformOrigin=this._getOriginBasedOnOption()}_adjustPanelUp(t,e){const n=Math.round(t-e);this._scrollTop-=n,this._offsetY-=n,this._transformOrigin=this._getOriginBasedOnOption(),this._scrollTop<=0&&(this._scrollTop=0,this._offsetY=0,this._transformOrigin="50% bottom 0px")}_adjustPanelDown(t,e,n){const o=Math.round(t-e);if(this._scrollTop+=o,this._offsetY+=o,this._transformOrigin=this._getOriginBasedOnOption(),this._scrollTop>=n)return this._scrollTop=n,this._offsetY=0,void(this._transformOrigin="50% top 0px")}_calculateOverlayPosition(){const t=this._getItemHeight(),e=this._getItemCount(),n=Math.min(e*t,fY),o=e*t-n;let i;i=this.empty?0:Math.max(this.options.toArray().indexOf(this._selectionModel.selected[0]),0),i+=wL(i,this.options,this.optionGroups);const a=n/2;this._scrollTop=this._calculateOverlayScroll(i,a,o),this._offsetY=this._calculateOverlayOffsetY(i,a,o),this._checkOverlayWithinViewport(o)}_getOriginBasedOnOption(){const t=this._getItemHeight(),e=(t-this._triggerRect.height)/2;return`50% ${Math.abs(this._offsetY)-e+t/2}px 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n.toggle()})),Cf(3,"div",2),Hm(4,aY,2,1,"span",3),Hm(5,lY,3,2,"span",4),Mf(),Cf(6,"div",5),vf(7,"div",6),Mf(),Mf(),Hm(8,cY,4,14,"ng-template",7),Rf("backdropClick",(function t(){return n.close()}))("attach",(function t(){return n._onAttached()}))("detach",(function t(){return n.close()}))),2&e){const t=Fm(1);ym("aria-owns",n.panelOpen?n.id+"-panel":null),Dc(3),bf("ngSwitch",n.empty),ym("id",n._valueId),Dc(1),bf("ngSwitchCase",!0),Dc(1),bf("ngSwitchCase",!1),Dc(3),bf("cdkConnectedOverlayPanelClass",n._overlayPanelClass)("cdkConnectedOverlayScrollStrategy",n._scrollStrategy)("cdkConnectedOverlayOrigin",t)("cdkConnectedOverlayOpen",n.panelOpen)("cdkConnectedOverlayPositions",n._positions)("cdkConnectedOverlayMinWidth",null==n._triggerRect?null:n._triggerRect.width)("cdkConnectedOverlayOffsetY",n._offsetY)}},directives:[$B,Dv,Rv,tV,Ev,Cv],styles:['.mat-select{display:inline-block;width:100%;outline:none}.mat-select-trigger{display:inline-table;cursor:pointer;position:relative;box-sizing:border-box}.mat-select-disabled .mat-select-trigger{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-select-value{display:table-cell;max-width:0;width:100%;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.mat-select-value-text{white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.mat-select-arrow-wrapper{display:table-cell;vertical-align:middle}.mat-form-field-appearance-fill .mat-select-arrow-wrapper{transform:translateY(-50%)}.mat-form-field-appearance-outline .mat-select-arrow-wrapper{transform:translateY(-25%)}.mat-form-field-appearance-standard.mat-form-field-has-label .mat-select:not(.mat-select-empty) .mat-select-arrow-wrapper{transform:translateY(-50%)}.mat-form-field-appearance-standard .mat-select.mat-select-empty .mat-select-arrow-wrapper{transition:transform 400ms cubic-bezier(0.25, 0.8, 0.25, 1)}._mat-animation-noopable.mat-form-field-appearance-standard .mat-select.mat-select-empty .mat-select-arrow-wrapper{transition:none}.mat-select-arrow{width:0;height:0;border-left:5px solid transparent;border-right:5px solid transparent;border-top:5px solid;margin:0 4px}.mat-select-panel-wrap{flex-basis:100%}.mat-select-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;padding-top:0;padding-bottom:0;max-height:256px;min-width:100%;border-radius:4px;outline:0}.cdk-high-contrast-active .mat-select-panel{outline:solid 1px}.mat-select-panel .mat-optgroup-label,.mat-select-panel .mat-option{font-size:inherit;line-height:3em;height:3em}.mat-form-field-type-mat-select:not(.mat-form-field-disabled) .mat-form-field-flex{cursor:pointer}.mat-form-field-type-mat-select .mat-form-field-label{width:calc(100% - 18px)}.mat-select-placeholder{transition:color 400ms 133.3333333333ms cubic-bezier(0.25, 0.8, 0.25, 1)}._mat-animation-noopable .mat-select-placeholder{transition:none}.mat-form-field-hide-placeholder .mat-select-placeholder{color:transparent;-webkit-text-fill-color:transparent;transition:none;display:block}.mat-select-min-line:empty::before{content:" ";white-space:pre;width:1px;display:inline-block;opacity:0}\n'],encapsulation:2,data:{animation:[pY.transformPanelWrap,pY.transformPanel]},changeDetection:0}),OY.propDecorators={options:[{type:or,args:[PL,{descendants:!0}]}],optionGroups:[{type:or,args:[CL,{descendants:!0}]}],customTrigger:[{type:ir,args:[MY]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(OY,[{type:S_,args:[{selector:"mat-select",exportAs:"matSelect",template:'\x3c!--\n Note that the select trigger element specifies `aria-owns` pointing to the listbox overlay.\n While aria-owns is not required for the ARIA 1.2 `role="combobox"` interaction pattern,\n it fixes an issue with VoiceOver when the select appears inside of an `aria-model="true"`\n element (e.g. a dialog). Without this `aria-owns`, the `aria-modal` on a dialog prevents\n VoiceOver from "seeing" the select\'s listbox overlay for aria-activedescendant.\n Using `aria-owns` re-parents the select overlay so that it works again.\n See https://github.com/angular/components/issues/20694\n--\x3e\n<div cdk-overlay-origin\n     [attr.aria-owns]="panelOpen ? id + \'-panel\' : null"\n     class="mat-select-trigger"\n     (click)="toggle()"\n     #origin="cdkOverlayOrigin"\n     #trigger>\n  <div class="mat-select-value" [ngSwitch]="empty" [attr.id]="_valueId">\n    <span class="mat-select-placeholder mat-select-min-line" *ngSwitchCase="true">{{placeholder}}</span>\n    <span class="mat-select-value-text" *ngSwitchCase="false" [ngSwitch]="!!customTrigger">\n      <span class="mat-select-min-line" *ngSwitchDefault>{{triggerValue}}</span>\n      <ng-content select="mat-select-trigger" *ngSwitchCase="true"></ng-content>\n    </span>\n  </div>\n\n  <div class="mat-select-arrow-wrapper"><div class="mat-select-arrow"></div></div>\n</div>\n\n<ng-template\n  cdk-connected-overlay\n  cdkConnectedOverlayLockPosition\n  cdkConnectedOverlayHasBackdrop\n  cdkConnectedOverlayBackdropClass="cdk-overlay-transparent-backdrop"\n  [cdkConnectedOverlayPanelClass]="_overlayPanelClass"\n  [cdkConnectedOverlayScrollStrategy]="_scrollStrategy"\n  [cdkConnectedOverlayOrigin]="origin"\n  [cdkConnectedOverlayOpen]="panelOpen"\n  [cdkConnectedOverlayPositions]="_positions"\n  [cdkConnectedOverlayMinWidth]="_triggerRect?.width!"\n  [cdkConnectedOverlayOffsetY]="_offsetY"\n  (backdropClick)="close()"\n  (attach)="_onAttached()"\n  (detach)="close()">\n  <div class="mat-select-panel-wrap" [@transformPanelWrap]>\n    <div\n      #panel\n      role="listbox"\n      tabindex="-1"\n      class="mat-select-panel {{ _getPanelTheme() }}"\n      [attr.id]="id + \'-panel\'"\n      [attr.aria-multiselectable]="multiple"\n      [attr.aria-label]="ariaLabel || null"\n      [attr.aria-labelledby]="_getPanelAriaLabelledby()"\n      [ngClass]="panelClass"\n      [@transformPanel]="multiple ? \'showing-multiple\' : \'showing\'"\n      (@transformPanel.done)="_panelDoneAnimatingStream.next($event.toState)"\n      [style.transformOrigin]="_transformOrigin"\n      [style.font-size.px]="_triggerFontSize"\n      (keydown)="_handleKeydown($event)">\n      <ng-content></ng-content>\n    </div>\n  </div>\n</ng-template>\n',inputs:["disabled","disableRipple","tabIndex"],encapsulation:Wn.None,changeDetection:jn.OnPush,host:{role:"combobox","aria-autocomplete":"none","aria-haspopup":"true",class:"mat-select","[attr.id]":"id","[attr.tabindex]":"tabIndex","[attr.aria-controls]":'panelOpen ? id + "-panel" : null',"[attr.aria-expanded]":"panelOpen","[attr.aria-label]":"ariaLabel || null","[attr.aria-required]":"required.toString()","[attr.aria-disabled]":"disabled.toString()","[attr.aria-invalid]":"errorState","[attr.aria-describedby]":"_ariaDescribedby || null","[attr.aria-activedescendant]":"_getAriaActiveDescendant()","[class.mat-select-disabled]":"disabled","[class.mat-select-invalid]":"errorState","[class.mat-select-required]":"required","[class.mat-select-empty]":"empty","[class.mat-select-multiple]":"multiple","(keydown)":"_handleKeydown($event)","(focus)":"_onFocus()","(blur)":"_onBlur()"},animations:[pY.transformPanelWrap,pY.transformPanel],providers:[{provide:iU,useExisting:OY},{provide:hL,useExisting:OY}],styles:['.mat-select{display:inline-block;width:100%;outline:none}.mat-select-trigger{display:inline-table;cursor:pointer;position:relative;box-sizing:border-box}.mat-select-disabled .mat-select-trigger{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-select-value{display:table-cell;max-width:0;width:100%;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.mat-select-value-text{white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.mat-select-arrow-wrapper{display:table-cell;vertical-align:middle}.mat-form-field-appearance-fill .mat-select-arrow-wrapper{transform:translateY(-50%)}.mat-form-field-appearance-outline .mat-select-arrow-wrapper{transform:translateY(-25%)}.mat-form-field-appearance-standard.mat-form-field-has-label .mat-select:not(.mat-select-empty) .mat-select-arrow-wrapper{transform:translateY(-50%)}.mat-form-field-appearance-standard .mat-select.mat-select-empty .mat-select-arrow-wrapper{transition:transform 400ms cubic-bezier(0.25, 0.8, 0.25, 1)}._mat-animation-noopable.mat-form-field-appearance-standard .mat-select.mat-select-empty .mat-select-arrow-wrapper{transition:none}.mat-select-arrow{width:0;height:0;border-left:5px solid transparent;border-right:5px solid transparent;border-top:5px solid;margin:0 4px}.mat-select-panel-wrap{flex-basis:100%}.mat-select-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;padding-top:0;padding-bottom:0;max-height:256px;min-width:100%;border-radius:4px;outline:0}.cdk-high-contrast-active .mat-select-panel{outline:solid 1px}.mat-select-panel .mat-optgroup-label,.mat-select-panel .mat-option{font-size:inherit;line-height:3em;height:3em}.mat-form-field-type-mat-select:not(.mat-form-field-disabled) .mat-form-field-flex{cursor:pointer}.mat-form-field-type-mat-select .mat-form-field-label{width:calc(100% - 18px)}.mat-select-placeholder{transition:color 400ms 133.3333333333ms cubic-bezier(0.25, 0.8, 0.25, 1)}._mat-animation-noopable .mat-select-placeholder{transition:none}.mat-form-field-hide-placeholder .mat-select-placeholder{color:transparent;-webkit-text-fill-color:transparent;transition:none;display:block}.mat-select-min-line:empty::before{content:" ";white-space:pre;width:1px;display:inline-block;opacity:0}\n']}]}],null,{options:[{type:or,args:[PL,{descendants:!0}]}],optionGroups:[{type:or,args:[CL,{descendants:!0}]}],customTrigger:[{type:ir,args:[MY]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class PY{}function wY(t,e){if(1&t){const t=wf();Cf(0,"span",5),Rf("click",(function e(n){Pi(t);const o=zf().$implicit;return zf().onActivePluginSelection(n,o.id)})),Pu(1),Mf()}if(2&t){const t=zf().$implicit;ym("data-plugin-id",t.id),Dc(1),ku(" ",t.tab_name," ")}}function kY(t,e){if(1&t&&(Cf(0,"mat-tab",3),Hm(1,wY,2,2,"ng-template",4),Mf()),2&t){bf("disabled",!e.$implicit.enabled)}}function SY(t,e){if(1&t&&(Cf(0,"mat-option",9),Pu(1),Mf()),2&t){const t=e.$implicit;bf("value",t.id),ym("data-plugin-id",t.id),Dc(1),ku(" ",t.tab_name," ")}}function DY(t,e){if(1&t){const t=wf();Cf(0,"mat-form-field",6),Cf(1,"mat-label"),Pu(2,"Inactive"),Mf(),Cf(3,"mat-select",7),Rf("selectionChange",(function e(n){Pi(t);return zf().onDisabledPluginSelectionChanged(n)})),Hm(4,SY,2,3,"mat-option",8),Mf(),Mf()}if(2&t){const t=zf();Dc(3),bf("value",t.selectedPlugin),Dc(1),bf("ngForOf",t.disabledPlugins)}}PY.ɵfac=function t(e){return new(e||PY)},PY.ɵmod=fo({type:PY}),PY.ɵinj=Dn({providers:[bY],imports:[[rx,oV,SL,NF],oB,CU,SL,NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(PY,[{type:L_,args:[{imports:[rx,oV,SL,NF],exports:[oB,CU,OY,vY,SL,NF],declarations:[OY,vY],providers:[bY]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(PY,{declarations:function(){return[OY,vY]},imports:function(){return[rx,oV,SL,NF]},exports:function(){return[oB,CU,OY,vY,SL,NF]}});class RY{constructor(){this.onPluginSelectionChanged=new Sy}getActivePluginIndex(){return this.activePlugins.findIndex((({id:t})=>t===this.selectedPlugin))}onActivePluginSelection(t,e){t.stopPropagation(),this.onPluginSelectionChanged.emit(e)}onDisabledPluginSelectionChanged(t){this.onPluginSelectionChanged.emit(t.value)}}RY.ɵfac=function t(e){return new(e||RY)},RY.ɵcmp=so({type:RY,selectors:[["plugin-selector-component"]],inputs:{activePlugins:"activePlugins",disabledPlugins:"disabledPlugins",selectedPlugin:"selectedPlugin"},outputs:{onPluginSelectionChanged:"onPluginSelectionChanged"},decls:3,vars:3,consts:[["animationDuration","100ms",1,"active-plugin-list",3,"selectedIndex"],[3,"disabled",4,"ngFor","ngForOf"],["floatLabel","never",4,"ngIf"],[3,"disabled"],["mat-tab-label",""],[1,"plugin-name",3,"click"],["floatLabel","never"],[3,"value","selectionChange"],[3,"value",4,"ngFor","ngForOf"],[3,"value"]],template:function t(e,n){1&e&&(Cf(0,"mat-tab-group",0),Hm(1,kY,2,1,"mat-tab",1),Mf(),Hm(2,DY,5,2,"mat-form-field",2)),2&e&&(bf("selectedIndex",n.getActivePluginIndex()),Dc(1),bf("ngForOf",n.activePlugins),Dc(1),bf("ngIf",n.disabledPlugins.length>0))},directives:[Pj,xv,Pv,gj,mj,_U,cU,OY,PL],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", 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ngDevMode||ngDevMode)&&Qb(RY,[{type:S_,args:[{selector:"plugin-selector-component",templateUrl:"./plugin_selector_component.ng.html",styleUrls:["./plugin_selector_component.css"]}]}],null,{activePlugins:[{type:R_}],disabledPlugins:[{type:R_}],selectedPlugin:[{type:R_}],onPluginSelectionChanged:[{type:E_}]});const EY=bS(nT,(t=>Object.keys(t).map((e=>Object.assign({},{id:e},t[e]))))),AY=bS(EY,(t=>t.filter((t=>!t.enabled))));class TY{constructor(t){this.store=t,this.activePlugin$=this.store.pipe(rS(eT)),this.plugins$=this.store.pipe(rS(EY)),this.disabledPlugins$=this.store.pipe(rS(AY))}onPluginSelectionChange(t){this.store.dispatch(ZE({plugin:t}))}}function NY(t,e){}TY.ɵfac=function t(e){return new(e||TY)(gf(iS))},TY.ɵcmp=so({type:TY,selectors:[["plugin-selector"]],decls:4,vars:9,consts:[[3,"activePlugins","disabledPlugins","selectedPlugin","onPluginSelectionChanged"]],template:function t(e,n){1&e&&(Cf(0,"plugin-selector-component",0),Rf("onPluginSelectionChanged",(function t(e){return n.onPluginSelectionChange(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),Mf()),2&e&&bf("activePlugins",Cy(1,3,n.plugins$))("disabledPlugins",Cy(2,5,n.disabledPlugins$))("selectedPlugin",Cy(3,7,n.activePlugin$))},directives:[RY],pipes:[Vv],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(TY,[{type:S_,args:[{selector:"plugin-selector",template:'\n    <plugin-selector-component\n      [activePlugins]="plugins$ | async"\n      [disabledPlugins]="disabledPlugins$ | async"\n      [selectedPlugin]="activePlugin$ | async"\n      (onPluginSelectionChanged)="onPluginSelectionChange($event)"\n    ></plugin-selector-component>\n  '}]}],(function(){return[{type:iS}]}),null);class zY{constructor(){this.role="dialog",this.panelClass="",this.hasBackdrop=!0,this.backdropClass="",this.disableClose=!1,this.width="",this.height="",this.maxWidth="80vw",this.data=null,this.ariaDescribedBy=null,this.ariaLabelledBy=null,this.ariaLabel=null,this.autoFocus=!0,this.restoreFocus=!0,this.closeOnNavigation=!0}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const IY={dialogContainer:xO("dialogContainer",[kO("void, exit",wO({opacity:0,transform:"scale(0.7)"})),kO("enter",wO({transform:"none"})),DO("* => enter",OO("150ms cubic-bezier(0, 0, 0.2, 1)",wO({transform:"none",opacity:1}))),DO("* => void, * => exit",OO("75ms cubic-bezier(0.4, 0.0, 0.2, 1)",wO({opacity:0})))])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function HY(){throw Error("Attempting to attach dialog content after content is already attached")}class FY extends dB{constructor(t,e,n,o,i,a){super(),this._elementRef=t,this._focusTrapFactory=e,this._changeDetectorRef=n,this._config=i,this._focusMonitor=a,this._animationStateChanged=new Sy,this._elementFocusedBeforeDialogWasOpened=null,this._closeInteractionType=null,this.attachDomPortal=t=>(this._portalOutlet.hasAttached()&&("undefined"==typeof ngDevMode||ngDevMode)&&HY(),this._portalOutlet.attachDomPortal(t)),this._ariaLabelledBy=i.ariaLabelledBy||null,this._document=o}_initializeWithAttachedContent(){this._setupFocusTrap(),this._capturePreviouslyFocusedElement(),this._focusDialogContainer()}attachComponentPortal(t){return this._portalOutlet.hasAttached()&&("undefined"==typeof ngDevMode||ngDevMode)&&HY(),this._portalOutlet.attachComponentPortal(t)}attachTemplatePortal(t){return this._portalOutlet.hasAttached()&&("undefined"==typeof ngDevMode||ngDevMode)&&HY(),this._portalOutlet.attachTemplatePortal(t)}_recaptureFocus(){if(!this._containsFocus()){(!this._config.autoFocus||!this._focusTrap.focusInitialElement())&&this._elementRef.nativeElement.focus()}}_trapFocus(){this._config.autoFocus?this._focusTrap.focusInitialElementWhenReady():this._containsFocus()||this._elementRef.nativeElement.focus()}_restoreFocus(){const t=this._elementFocusedBeforeDialogWasOpened;if(this._config.restoreFocus&&t&&"function"==typeof t.focus){const e=MH(),n=this._elementRef.nativeElement;e&&e!==this._document.body&&e!==n&&!n.contains(e)||(this._focusMonitor?(this._focusMonitor.focusVia(t,this._closeInteractionType),this._closeInteractionType=null):t.focus())}this._focusTrap&&this._focusTrap.destroy()}_setupFocusTrap(){this._focusTrap=this._focusTrapFactory.create(this._elementRef.nativeElement)}_capturePreviouslyFocusedElement(){this._document&&(this._elementFocusedBeforeDialogWasOpened=MH())}_focusDialogContainer(){this._elementRef.nativeElement.focus&&this._elementRef.nativeElement.focus()}_containsFocus(){const t=this._elementRef.nativeElement,e=MH();return t===e||t.contains(e)}}FY.ɵfac=function t(e){return new(e||FY)(gf(Kh),gf(WH),gf(Ob),gf(sM,8),gf(zY),gf(fF))},FY.ɵdir=ho({type:FY,viewQuery:function t(e,n){if(1&e&&Vy(uB,7),2&e){let t;By(t=Uy())&&(n._portalOutlet=t.first)}},features:[Qp]}),FY.ctorParameters=()=>[{type:Kh},{type:WH},{type:Ob},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]},{type:zY},{type:fF}],FY.propDecorators={_portalOutlet:[{type:ar,args:[uB,{static:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(FY,[{type:k_}],(function(){return[{type:Kh},{type:WH},{type:Ob},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]},{type:zY},{type:fF}]}),{_portalOutlet:[{type:ar,args:[uB,{static:!0}]}]});class LY extends FY{constructor(){super(...arguments),this._state="enter"}_onAnimationDone({toState:t,totalTime:e}){"enter"===t?(this._trapFocus(),this._animationStateChanged.next({state:"opened",totalTime:e})):"exit"===t&&(this._restoreFocus(),this._animationStateChanged.next({state:"closed",totalTime:e}))}_onAnimationStart({toState:t,totalTime:e}){"enter"===t?this._animationStateChanged.next({state:"opening",totalTime:e}):"exit"!==t&&"void"!==t||this._animationStateChanged.next({state:"closing",totalTime:e})}_startExitAnimation(){this._state="exit",this._changeDetectorRef.markForCheck()}}LY.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(LY)))(n||LY)}})(),LY.ɵcmp=so({type:LY,selectors:[["mat-dialog-container"]],hostAttrs:["tabindex","-1","aria-modal","true",1,"mat-dialog-container"],hostVars:6,hostBindings:function t(e,n){1&e&&Ef("@dialogContainer.start",(function t(e){return n._onAnimationStart(e)}))("@dialogContainer.done",(function t(e){return n._onAnimationDone(e)})),2&e&&(cg("id",n._id),ym("role",n._config.role)("aria-labelledby",n._config.ariaLabel?null:n._ariaLabelledBy)("aria-label",n._config.ariaLabel)("aria-describedby",n._config.ariaDescribedBy||null),dg("@dialogContainer",n._state))},features:[Qp],decls:1,vars:0,consts:[["cdkPortalOutlet",""]],template:function t(e,n){1&e&&Hm(0,NY,0,0,"ng-template",0)},directives:[uB],styles:[".mat-dialog-container{display:block;padding:24px;border-radius:4px;box-sizing:border-box;overflow:auto;outline:0;width:100%;height:100%;min-height:inherit;max-height:inherit}.cdk-high-contrast-active .mat-dialog-container{outline:solid 1px}.mat-dialog-content{display:block;margin:0 -24px;padding:0 24px;max-height:65vh;overflow:auto;-webkit-overflow-scrolling:touch}.mat-dialog-title{margin:0 0 20px;display:block}.mat-dialog-actions{padding:8px 0;display:flex;flex-wrap:wrap;min-height:52px;align-items:center;box-sizing:content-box;margin-bottom:-24px}.mat-dialog-actions[align=end]{justify-content:flex-end}.mat-dialog-actions[align=center]{justify-content:center}.mat-dialog-actions .mat-button-base+.mat-button-base,.mat-dialog-actions .mat-mdc-button-base+.mat-mdc-button-base{margin-left:8px}[dir=rtl] .mat-dialog-actions .mat-button-base+.mat-button-base,[dir=rtl] .mat-dialog-actions .mat-mdc-button-base+.mat-mdc-button-base{margin-left:0;margin-right:8px}\n"],encapsulation:2,data:{animation:[IY.dialogContainer]}}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(LY,[{type:S_,args:[{selector:"mat-dialog-container",template:"<ng-template cdkPortalOutlet></ng-template>\n",encapsulation:Wn.None,changeDetection:jn.Default,animations:[IY.dialogContainer],host:{class:"mat-dialog-container",tabindex:"-1","aria-modal":"true","[id]":"_id","[attr.role]":"_config.role","[attr.aria-labelledby]":"_config.ariaLabel ? null : _ariaLabelledBy","[attr.aria-label]":"_config.ariaLabel","[attr.aria-describedby]":"_config.ariaDescribedBy || null","[@dialogContainer]":"_state","(@dialogContainer.start)":"_onAnimationStart($event)","(@dialogContainer.done)":"_onAnimationDone($event)"},styles:[".mat-dialog-container{display:block;padding:24px;border-radius:4px;box-sizing:border-box;overflow:auto;outline:0;width:100%;height:100%;min-height:inherit;max-height:inherit}.cdk-high-contrast-active .mat-dialog-container{outline:solid 1px}.mat-dialog-content{display:block;margin:0 -24px;padding:0 24px;max-height:65vh;overflow:auto;-webkit-overflow-scrolling:touch}.mat-dialog-title{margin:0 0 20px;display:block}.mat-dialog-actions{padding:8px 0;display:flex;flex-wrap:wrap;min-height:52px;align-items:center;box-sizing:content-box;margin-bottom:-24px}.mat-dialog-actions[align=end]{justify-content:flex-end}.mat-dialog-actions[align=center]{justify-content:center}.mat-dialog-actions .mat-button-base+.mat-button-base,.mat-dialog-actions .mat-mdc-button-base+.mat-mdc-button-base{margin-left:8px}[dir=rtl] .mat-dialog-actions .mat-button-base+.mat-button-base,[dir=rtl] .mat-dialog-actions .mat-mdc-button-base+.mat-mdc-button-base{margin-left:0;margin-right:8px}\n"]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let BY=0;class VY{constructor(t,e,n="mat-dialog-"+BY++){this._overlayRef=t,this._containerInstance=e,this.id=n,this.disableClose=this._containerInstance._config.disableClose,this._afterOpened=new B,this._afterClosed=new B,this._beforeClosed=new B,this._state=0,e._id=n,e._animationStateChanged.pipe(ue((t=>"opened"===t.state)),ve(1)).subscribe((()=>{this._afterOpened.next(),this._afterOpened.complete()})),e._animationStateChanged.pipe(ue((t=>"closed"===t.state)),ve(1)).subscribe((()=>{clearTimeout(this._closeFallbackTimeout),this._finishDialogClose()})),t.detachments().subscribe((()=>{this._beforeClosed.next(this._result),this._beforeClosed.complete(),this._afterClosed.next(this._result),this._afterClosed.complete(),this.componentInstance=null,this._overlayRef.dispose()})),t.keydownEvents().pipe(ue((t=>t.keyCode===KI&&!this.disableClose&&!tH(t)))).subscribe((t=>{t.preventDefault(),jY(this,"keyboard")})),t.backdropClick().subscribe((()=>{this.disableClose?this._containerInstance._recaptureFocus():jY(this,"mouse")}))}close(t){this._result=t,this._containerInstance._animationStateChanged.pipe(ue((t=>"closing"===t.state)),ve(1)).subscribe((e=>{this._beforeClosed.next(t),this._beforeClosed.complete(),this._overlayRef.detachBackdrop(),this._closeFallbackTimeout=setTimeout((()=>this._finishDialogClose()),e.totalTime+100)})),this._state=1,this._containerInstance._startExitAnimation()}afterOpened(){return this._afterOpened}afterClosed(){return this._afterClosed}beforeClosed(){return this._beforeClosed}backdropClick(){return this._overlayRef.backdropClick()}keydownEvents(){return this._overlayRef.keydownEvents()}updatePosition(t){let e=this._getPositionStrategy();return t&&(t.left||t.right)?t.left?e.left(t.left):e.right(t.right):e.centerHorizontally(),t&&(t.top||t.bottom)?t.top?e.top(t.top):e.bottom(t.bottom):e.centerVertically(),this._overlayRef.updatePosition(),this}updateSize(t="",e=""){return this._overlayRef.updateSize({width:t,height:e}),this._overlayRef.updatePosition(),this}addPanelClass(t){return this._overlayRef.addPanelClass(t),this}removePanelClass(t){return this._overlayRef.removePanelClass(t),this}getState(){return this._state}_finishDialogClose(){this._state=2,this._overlayRef.dispose()}_getPositionStrategy(){return this._overlayRef.getConfig().positionStrategy}}function jY(t,e,n){return void 0!==t._containerInstance&&(t._containerInstance._closeInteractionType=e),t.close(n)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const UY=new er("MatDialogData"),WY=new er("mat-dialog-default-options"),GY=new er("mat-dialog-scroll-strategy");const YY={provide:GY,deps:[KB],useFactory:function qY(t){return()=>t.scrollStrategies.block()}};class ZY{constructor(t,e,n,o,i,a,r,s,l){this._overlay=t,this._injector=e,this._defaultOptions=n,this._parentDialog=o,this._overlayContainer=i,this._dialogRefConstructor=r,this._dialogContainerType=s,this._dialogDataToken=l,this._openDialogsAtThisLevel=[],this._afterAllClosedAtThisLevel=new B,this._afterOpenedAtThisLevel=new B,this._ariaHiddenElements=new Map,this.afterAllClosed=oe((()=>this.openDialogs.length?this._getAfterAllClosed():this._getAfterAllClosed().pipe(Be(void 0)))),this._scrollStrategy=a}get openDialogs(){return this._parentDialog?this._parentDialog.openDialogs:this._openDialogsAtThisLevel}get afterOpened(){return this._parentDialog?this._parentDialog.afterOpened:this._afterOpenedAtThisLevel}_getAfterAllClosed(){const t=this._parentDialog;return t?t._getAfterAllClosed():this._afterAllClosedAtThisLevel}open(t,e){if((e=(function n(t,e){return Object.assign(Object.assign({},e),t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(e,this._defaultOptions||new zY)).id&&this.getDialogById(e.id)&&("undefined"==typeof ngDevMode||ngDevMode))throw Error(`Dialog with id "${e.id}" exists already. The dialog id must be unique.`);const o=this._createOverlay(e),i=this._attachDialogContainer(o,e),a=this._attachDialogContent(t,i,o,e);return this.openDialogs.length||this._hideNonDialogContentFromAssistiveTechnology(),this.openDialogs.push(a),a.afterClosed().subscribe((()=>this._removeOpenDialog(a))),this.afterOpened.next(a),i._initializeWithAttachedContent(),a}closeAll(){this._closeDialogs(this.openDialogs)}getDialogById(t){return this.openDialogs.find((e=>e.id===t))}ngOnDestroy(){this._closeDialogs(this._openDialogsAtThisLevel),this._afterAllClosedAtThisLevel.complete(),this._afterOpenedAtThisLevel.complete()}_createOverlay(t){const e=this._getOverlayConfig(t);return this._overlay.create(e)}_getOverlayConfig(t){const e=new wB({positionStrategy:this._overlay.position().global(),scrollStrategy:t.scrollStrategy||this._scrollStrategy(),panelClass:t.panelClass,hasBackdrop:t.hasBackdrop,direction:t.direction,minWidth:t.minWidth,minHeight:t.minHeight,maxWidth:t.maxWidth,maxHeight:t.maxHeight,disposeOnNavigation:t.closeOnNavigation});return t.backdropClass&&(e.backdropClass=t.backdropClass),e}_attachDialogContainer(t,e){const n=Np.create({parent:e&&e.viewContainerRef&&e.viewContainerRef.injector||this._injector,providers:[{provide:zY,useValue:e}]}),o=new sB(this._dialogContainerType,e.viewContainerRef,n,e.componentFactoryResolver);return t.attach(o).instance}_attachDialogContent(t,e,n,o){const i=new this._dialogRefConstructor(n,e,o.id);if(t instanceof Eb)e.attachTemplatePortal(new lB(t,null,{$implicit:o.data,dialogRef:i}));else{const n=this._createInjector(o,i,e),a=e.attachComponentPortal(new sB(t,o.viewContainerRef,n));i.componentInstance=a.instance}return i.updateSize(o.width,o.height).updatePosition(o.position),i}_createInjector(t,e,n){const o=t&&t.viewContainerRef&&t.viewContainerRef.injector,i=[{provide:this._dialogContainerType,useValue:n},{provide:this._dialogDataToken,useValue:t.data},{provide:this._dialogRefConstructor,useValue:e}];return!t.direction||o&&o.get(vF,null,Hn.Optional)||i.push({provide:vF,useValue:{value:t.direction,change:zt()}}),Np.create({parent:o||this._injector,providers:i})}_removeOpenDialog(t){const e=this.openDialogs.indexOf(t);e>-1&&(this.openDialogs.splice(e,1),this.openDialogs.length||(this._ariaHiddenElements.forEach(((t,e)=>{t?e.setAttribute("aria-hidden",t):e.removeAttribute("aria-hidden")})),this._ariaHiddenElements.clear(),this._getAfterAllClosed().next()))}_hideNonDialogContentFromAssistiveTechnology(){const t=this._overlayContainer.getContainerElement();if(t.parentElement){const e=t.parentElement.children;for(let n=e.length-1;n>-1;n--){let o=e[n];o===t||"SCRIPT"===o.nodeName||"STYLE"===o.nodeName||o.hasAttribute("aria-live")||(this._ariaHiddenElements.set(o,o.getAttribute("aria-hidden")),o.setAttribute("aria-hidden","true"))}}}_closeDialogs(t){let e=t.length;for(;e--;)t[e].close()}}ZY.ɵfac=function t(e){return new(e||ZY)(gf(KB),gf(Np),gf(void 0),gf(void 0),gf(HB),gf(void 0),gf(cr),gf(cr),gf(er))},ZY.ɵdir=ho({type:ZY}),ZY.ctorParameters=()=>[{type:KB},{type:Np},{type:void 0},{type:void 0},{type:HB},{type:void 0},{type:cr},{type:cr},{type:er}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(ZY,[{type:k_}],(function(){return[{type:KB},{type:Np},{type:void 0},{type:void 0},{type:HB},{type:void 0},{type:cr},{type:cr},{type:er}]}),null);class XY extends ZY{constructor(t,e,n,o,i,a,r){super(t,e,o,a,r,i,VY,LY,UY)}}XY.ɵfac=function t(e){return new(e||XY)(Tr(KB),Tr(Np),Tr(MM,8),Tr(WY,8),Tr(GY),Tr(XY,12),Tr(HB))},XY.ɵprov=Sn({token:XY,factory:XY.ɵfac}),XY.ctorParameters=()=>[{type:KB},{type:Np},{type:MM,decorators:[{type:Br}]},{type:zY,decorators:[{type:Br},{type:Lr,args:[WY]}]},{type:void 0,decorators:[{type:Lr,args:[GY]}]},{type:XY,decorators:[{type:Br},{type:jr}]},{type:HB}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(XY,[{type:Um}],(function(){return[{type:KB},{type:Np},{type:MM,decorators:[{type:Br}]},{type:zY,decorators:[{type:Br},{type:Lr,args:[WY]}]},{type:void 0,decorators:[{type:Lr,args:[GY]}]},{type:XY,decorators:[{type:Br},{type:jr}]},{type:HB}]}),null);let KY=0;class JY{constructor(t,e,n){this.dialogRef=t,this._elementRef=e,this._dialog=n,this.type="button"}ngOnInit(){this.dialogRef||(this.dialogRef=eq(this._elementRef,this._dialog.openDialogs))}ngOnChanges(t){const e=t._matDialogClose||t._matDialogCloseResult;e&&(this.dialogResult=e.currentValue)}_onButtonClick(t){jY(this.dialogRef,0===t.screenX&&0===t.screenY?"keyboard":"mouse",this.dialogResult)}}JY.ɵfac=function t(e){return new(e||JY)(gf(VY,8),gf(Kh),gf(XY))},JY.ɵdir=ho({type:JY,selectors:[["","mat-dialog-close",""],["","matDialogClose",""]],hostVars:2,hostBindings:function t(e,n){1&e&&Rf("click",(function t(e){return n._onButtonClick(e)})),2&e&&ym("aria-label",n.ariaLabel||null)("type",n.type)},inputs:{type:"type",dialogResult:["mat-dialog-close","dialogResult"],ariaLabel:["aria-label","ariaLabel"],_matDialogClose:["matDialogClose","_matDialogClose"]},exportAs:["matDialogClose"],features:[qo]}),JY.ctorParameters=()=>[{type:VY,decorators:[{type:Br}]},{type:Kh},{type:XY}],JY.propDecorators={ariaLabel:[{type:R_,args:["aria-label"]}],type:[{type:R_}],dialogResult:[{type:R_,args:["mat-dialog-close"]}],_matDialogClose:[{type:R_,args:["matDialogClose"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(JY,[{type:k_,args:[{selector:"[mat-dialog-close], [matDialogClose]",exportAs:"matDialogClose",host:{"(click)":"_onButtonClick($event)","[attr.aria-label]":"ariaLabel || null","[attr.type]":"type"}}]}],(function(){return[{type:VY,decorators:[{type:Br}]},{type:Kh},{type:XY}]}),{type:[{type:R_}],dialogResult:[{type:R_,args:["mat-dialog-close"]}],ariaLabel:[{type:R_,args:["aria-label"]}],_matDialogClose:[{type:R_,args:["matDialogClose"]}]});class QY{constructor(t,e,n){this._dialogRef=t,this._elementRef=e,this._dialog=n,this.id="mat-dialog-title-"+KY++}ngOnInit(){this._dialogRef||(this._dialogRef=eq(this._elementRef,this._dialog.openDialogs)),this._dialogRef&&Promise.resolve().then((()=>{const t=this._dialogRef._containerInstance;t&&!t._ariaLabelledBy&&(t._ariaLabelledBy=this.id)}))}}QY.ɵfac=function t(e){return new(e||QY)(gf(VY,8),gf(Kh),gf(XY))},QY.ɵdir=ho({type:QY,selectors:[["","mat-dialog-title",""],["","matDialogTitle",""]],hostAttrs:[1,"mat-dialog-title"],hostVars:1,hostBindings:function t(e,n){2&e&&cg("id",n.id)},inputs:{id:"id"},exportAs:["matDialogTitle"]}),QY.ctorParameters=()=>[{type:VY,decorators:[{type:Br}]},{type:Kh},{type:XY}],QY.propDecorators={id:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(QY,[{type:k_,args:[{selector:"[mat-dialog-title], [matDialogTitle]",exportAs:"matDialogTitle",host:{class:"mat-dialog-title","[id]":"id"}}]}],(function(){return[{type:VY,decorators:[{type:Br}]},{type:Kh},{type:XY}]}),{id:[{type:R_}]});class $Y{}$Y.ɵfac=function t(e){return new(e||$Y)},$Y.ɵdir=ho({type:$Y,selectors:[["","mat-dialog-content",""],["mat-dialog-content"],["","matDialogContent",""]],hostAttrs:[1,"mat-dialog-content"]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb($Y,[{type:k_,args:[{selector:"[mat-dialog-content], mat-dialog-content, [matDialogContent]",host:{class:"mat-dialog-content"}}]}],null,null);class tq{}function eq(t,e){let n=t.nativeElement.parentElement;for(;n&&!n.classList.contains("mat-dialog-container");)n=n.parentElement;return n?e.find((t=>t.id===n.id)):null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */tq.ɵfac=function t(e){return new(e||tq)},tq.ɵdir=ho({type:tq,selectors:[["","mat-dialog-actions",""],["mat-dialog-actions"],["","matDialogActions",""]],hostAttrs:[1,"mat-dialog-actions"]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(tq,[{type:k_,args:[{selector:"[mat-dialog-actions], mat-dialog-actions, [matDialogActions]",host:{class:"mat-dialog-actions"}}]}],null,null);class nq{}nq.ɵfac=function t(e){return new(e||nq)},nq.ɵmod=fo({type:nq}),nq.ɵinj=Dn({providers:[XY,YY],imports:[[oV,hB,NF],NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(nq,[{type:L_,args:[{imports:[oV,hB,NF],exports:[LY,JY,QY,$Y,tq,NF],declarations:[LY,JY,QY,tq,$Y],providers:[XY,YY],entryComponents:[LY]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(nq,{declarations:function(){return[LY,JY,QY,tq,$Y]},imports:function(){return[oV,hB,NF]},exports:function(){return[LY,JY,QY,$Y,tq,NF]}});class oq{constructor(t,e){this._document=e;const n=this._textarea=this._document.createElement("textarea"),o=n.style;o.position="fixed",o.top=o.opacity="0",o.left="-999em",n.setAttribute("aria-hidden","true"),n.value=t,this._document.body.appendChild(n)}copy(){const t=this._textarea;let e=!1;try{if(t){const n=this._document.activeElement;t.select(),t.setSelectionRange(0,t.value.length),e=this._document.execCommand("copy"),n&&n.focus()}}catch(t){}return e}destroy(){const t=this._textarea;t&&(t.parentNode&&t.parentNode.removeChild(t),this._textarea=void 0)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class iq{constructor(t){this._document=t}copy(t){const e=this.beginCopy(t),n=e.copy();return e.destroy(),n}beginCopy(t){return new oq(t,this._document)}}iq.ɵfac=function t(e){return new(e||iq)(Tr(sM))},iq.ɵprov=Sn({factory:function t(){return new iq(Tr(sM))},token:iq,providedIn:"root"}),iq.ctorParameters=()=>[{type:void 0,decorators:[{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(iq,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const aq=new er("CDK_COPY_TO_CLIPBOARD_CONFIG");class rq{constructor(t,e,n){this._clipboard=t,this._ngZone=e,this.text="",this.attempts=1,this.copied=new Sy,this._pending=new Set,n&&null!=n.attempts&&(this.attempts=n.attempts)}copy(t=this.attempts){if(t>1){let e=t;const n=this._clipboard.beginCopy(this.text);this._pending.add(n);const o=()=>{const t=n.copy();t||!--e||this._destroyed?(this._currentTimeout=null,this._pending.delete(n),n.destroy(),this.copied.emit(t)):this._currentTimeout=this._ngZone.runOutsideAngular((()=>setTimeout(o,1)))};o()}else this.copied.emit(this._clipboard.copy(this.text))}ngOnDestroy(){this._currentTimeout&&clearTimeout(this._currentTimeout),this._pending.forEach((t=>t.destroy())),this._pending.clear(),this._destroyed=!0}}rq.ɵfac=function t(e){return new(e||rq)(gf(iq),gf(mC),gf(aq,8))},rq.ɵdir=ho({type:rq,selectors:[["","cdkCopyToClipboard",""]],hostBindings:function t(e,n){1&e&&Rf("click",(function t(){return n.copy()}))},inputs:{text:["cdkCopyToClipboard","text"],attempts:["cdkCopyToClipboardAttempts","attempts"]},outputs:{copied:"cdkCopyToClipboardCopied"}}),rq.ctorParameters=()=>[{type:iq},{type:mC},{type:void 0,decorators:[{type:Br},{type:Lr,args:[aq]}]}],rq.propDecorators={text:[{type:R_,args:["cdkCopyToClipboard"]}],attempts:[{type:R_,args:["cdkCopyToClipboardAttempts"]}],copied:[{type:E_,args:["cdkCopyToClipboardCopied"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(rq,[{type:k_,args:[{selector:"[cdkCopyToClipboard]",host:{"(click)":"copy()"}}]}],(function(){return[{type:iq},{type:mC},{type:void 0,decorators:[{type:Br},{type:Lr,args:[aq]}]}]}),{text:[{type:R_,args:["cdkCopyToClipboard"]}],attempts:[{type:R_,args:["cdkCopyToClipboardAttempts"]}],copied:[{type:E_,args:["cdkCopyToClipboardCopied"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class sq{}sq.ɵfac=function t(e){return new(e||sq)},sq.ɵmod=fo({type:sq}),sq.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(sq,[{type:L_,args:[{declarations:[rq],exports:[rq]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(sq,{declarations:[rq],exports:[rq]});function lq(t){return Error(`Unable to find icon with the name "${t}"`)}function cq(t){return Error(`The URL provided to MatIconRegistry was not trusted as a resource URL via Angular's DomSanitizer. Attempted URL was "${t}".`)}function dq(t){return Error(`The literal provided to MatIconRegistry was not trusted as safe HTML by Angular's DomSanitizer. Attempted literal was "${t}".`)}class pq{constructor(t,e,n){this.url=t,this.svgText=e,this.options=n}}class mq{constructor(t,e,n,o){this._httpClient=t,this._sanitizer=e,this._errorHandler=o,this._svgIconConfigs=new Map,this._iconSetConfigs=new Map,this._cachedIconsByUrl=new Map,this._inProgressUrlFetches=new Map,this._fontCssClassesByAlias=new Map,this._resolvers=[],this._defaultFontSetClass="material-icons",this._document=n}addSvgIcon(t,e,n){return this.addSvgIconInNamespace("",t,e,n)}addSvgIconLiteral(t,e,n){return this.addSvgIconLiteralInNamespace("",t,e,n)}addSvgIconInNamespace(t,e,n,o){return this._addSvgIconConfig(t,e,new pq(n,null,o))}addSvgIconResolver(t){return this._resolvers.push(t),this}addSvgIconLiteralInNamespace(t,e,n,o){const i=this._sanitizer.sanitize(Ys.HTML,n);if(!i)throw dq(n);return this._addSvgIconConfig(t,e,new pq("",i,o))}addSvgIconSet(t,e){return this.addSvgIconSetInNamespace("",t,e)}addSvgIconSetLiteral(t,e){return this.addSvgIconSetLiteralInNamespace("",t,e)}addSvgIconSetInNamespace(t,e,n){return this._addSvgIconSetConfig(t,new pq(e,null,n))}addSvgIconSetLiteralInNamespace(t,e,n){const o=this._sanitizer.sanitize(Ys.HTML,e);if(!o)throw dq(e);return this._addSvgIconSetConfig(t,new pq("",o,n))}registerFontClassAlias(t,e=t){return this._fontCssClassesByAlias.set(t,e),this}classNameForFontAlias(t){return this._fontCssClassesByAlias.get(t)||t}setDefaultFontSetClass(t){return this._defaultFontSetClass=t,this}getDefaultFontSetClass(){return this._defaultFontSetClass}getSvgIconFromUrl(t){const e=this._sanitizer.sanitize(Ys.RESOURCE_URL,t);if(!e)throw cq(t);const n=this._cachedIconsByUrl.get(e);return n?zt(fq(n)):this._loadSvgIconFromConfig(new pq(t,null)).pipe(We((t=>this._cachedIconsByUrl.set(e,t))),Vt((t=>fq(t))))}getNamedSvgIcon(t,e=""){const n=uq(e,t);let o=this._svgIconConfigs.get(n);if(o)return this._getSvgFromConfig(o);if(o=this._getIconConfigFromResolvers(e,t),o)return this._svgIconConfigs.set(n,o),this._getSvgFromConfig(o);const i=this._iconSetConfigs.get(e);return i?this._getSvgFromIconSetConfigs(t,i):It(lq(n))}ngOnDestroy(){this._resolvers=[],this._svgIconConfigs.clear(),this._iconSetConfigs.clear(),this._cachedIconsByUrl.clear()}_getSvgFromConfig(t){return t.svgText?zt(fq(this._svgElementFromConfig(t))):this._loadSvgIconFromConfig(t).pipe(Vt((t=>fq(t))))}_getSvgFromIconSetConfigs(t,e){const n=this._extractIconWithNameFromAnySet(t,e);if(n)return zt(n);return ie(e.filter((t=>!t.svgText)).map((t=>this._loadSvgIconSetFromConfig(t).pipe(he((e=>{const n=this._sanitizer.sanitize(Ys.RESOURCE_URL,t.url);return this._errorHandler.handleError(new Error(`Loading icon set URL: ${n} failed: ${e.message}`)),zt(null)})))))).pipe(Vt((()=>{const n=this._extractIconWithNameFromAnySet(t,e);if(!n)throw lq(t);return n})))}_extractIconWithNameFromAnySet(t,e){for(let n=e.length-1;n>=0;n--){const o=e[n];if(o.svgText&&o.svgText.indexOf(t)>-1){const e=this._svgElementFromConfig(o),n=this._extractSvgIconFromSet(e,t,o.options);if(n)return n}}return null}_loadSvgIconFromConfig(t){return this._fetchIcon(t).pipe(We((e=>t.svgText=e)),Vt((()=>this._svgElementFromConfig(t))))}_loadSvgIconSetFromConfig(t){return t.svgText?zt(null):this._fetchIcon(t).pipe(We((e=>t.svgText=e)))}_extractSvgIconFromSet(t,e,n){const o=t.querySelector(`[id="${e}"]`);if(!o)return null;const i=o.cloneNode(!0);if(i.removeAttribute("id"),"svg"===i.nodeName.toLowerCase())return this._setSvgAttributes(i,n);if("symbol"===i.nodeName.toLowerCase())return this._setSvgAttributes(this._toSvgElement(i),n);const a=this._svgElementFromString("<svg></svg>");return a.appendChild(i),this._setSvgAttributes(a,n)}_svgElementFromString(t){const e=this._document.createElement("DIV");e.innerHTML=t;const n=e.querySelector("svg");if(!n)throw Error("<svg> tag not found");return n}_toSvgElement(t){const e=this._svgElementFromString("<svg></svg>"),n=t.attributes;for(let t=0;t<n.length;t++){const{name:o,value:i}=n[t];"id"!==o&&e.setAttribute(o,i)}for(let n=0;n<t.childNodes.length;n++)t.childNodes[n].nodeType===this._document.ELEMENT_NODE&&e.appendChild(t.childNodes[n].cloneNode(!0));return e}_setSvgAttributes(t,e){return t.setAttribute("fit",""),t.setAttribute("height","100%"),t.setAttribute("width","100%"),t.setAttribute("preserveAspectRatio","xMidYMid meet"),t.setAttribute("focusable","false"),e&&e.viewBox&&t.setAttribute("viewBox",e.viewBox),t}_fetchIcon(t){var e;const{url:n,options:o}=t,i=null!==(e=null==o?void 0:o.withCredentials)&&void 0!==e&&e;if(!this._httpClient)throw(function a(){return Error("Could not find HttpClient provider for use with Angular Material icons. Please include the HttpClientModule from @angular/common/http in your app imports.")})();if(null==n)throw Error(`Cannot fetch icon from URL "${n}".`);const r=this._sanitizer.sanitize(Ys.RESOURCE_URL,n);if(!r)throw cq(n);const s=this._inProgressUrlFetches.get(r);if(s)return s;const l=this._httpClient.get(r,{responseType:"text",withCredentials:i}).pipe((function c(t){return z((function(e,n){try{e.subscribe(n)}finally{n.add(t)}}))})((()=>this._inProgressUrlFetches.delete(r))),Ie());return this._inProgressUrlFetches.set(r,l),l}_addSvgIconConfig(t,e,n){return this._svgIconConfigs.set(uq(t,e),n),this}_addSvgIconSetConfig(t,e){const n=this._iconSetConfigs.get(t);return n?n.push(e):this._iconSetConfigs.set(t,[e]),this}_svgElementFromConfig(t){if(!t.svgElement){const e=this._svgElementFromString(t.svgText);this._setSvgAttributes(e,t.options),t.svgElement=e}return t.svgElement}_getIconConfigFromResolvers(t,e){for(let o=0;o<this._resolvers.length;o++){const i=this._resolvers[o](e,t);if(i)return(n=i).url&&n.options?new pq(i.url,null,i.options):new pq(i,null)}var n;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */}}function fq(t){return t.cloneNode(!0)}function uq(t,e){return t+":"+e}mq.ɵfac=function t(e){return new(e||mq)(Tr(gE,8),Tr(eO),Tr(sM,8),Tr(ul))},mq.ɵprov=Sn({factory:function t(){return new mq(Tr(gE,8),Tr(eO),Tr(sM,8),Tr(ul))},token:mq,providedIn:"root"}),mq.ctorParameters=()=>[{type:gE,decorators:[{type:Br}]},{type:eO},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]},{type:ul}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(mq,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:gE,decorators:[{type:Br}]},{type:eO},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]},{type:ul}]}),null),new Br,new jr,new Br,new Br;const gq=IF(class{constructor(t){this._elementRef=t}}),hq=new er("mat-icon-location",{providedIn:"root",factory:function bq(){const t=zr(sM),e=t?t.location:null;return{getPathname:()=>e?e.pathname+e.search:""}}});const yq=["clip-path","color-profile","src","cursor","fill","filter","marker","marker-start","marker-mid","marker-end","mask","stroke"],_q=yq.map((t=>`[${t}]`)).join(", "),Cq=/^url\(['"]?#(.*?)['"]?\)$/;class Mq extends gq{constructor(t,e,n,o,i){super(t),this._iconRegistry=e,this._location=o,this._errorHandler=i,this._inline=!1,this._currentIconFetch=u.EMPTY,n||t.nativeElement.setAttribute("aria-hidden","true")}get inline(){return this._inline}set inline(t){this._inline=eH(t)}get svgIcon(){return this._svgIcon}set svgIcon(t){t!==this._svgIcon&&(t?this._updateSvgIcon(t):this._svgIcon&&this._clearSvgElement(),this._svgIcon=t)}get fontSet(){return this._fontSet}set fontSet(t){const e=this._cleanupFontValue(t);e!==this._fontSet&&(this._fontSet=e,this._updateFontIconClasses())}get fontIcon(){return this._fontIcon}set fontIcon(t){const e=this._cleanupFontValue(t);e!==this._fontIcon&&(this._fontIcon=e,this._updateFontIconClasses())}_splitIconName(t){if(!t)return["",""];const e=t.split(":");switch(e.length){case 1:return["",e[0]];case 2:return e;default:throw Error(`Invalid icon name: "${t}"`)}}ngOnInit(){this._updateFontIconClasses()}ngAfterViewChecked(){const t=this._elementsWithExternalReferences;if(t&&t.size){const t=this._location.getPathname();t!==this._previousPath&&(this._previousPath=t,this._prependPathToReferences(t))}}ngOnDestroy(){this._currentIconFetch.unsubscribe(),this._elementsWithExternalReferences&&this._elementsWithExternalReferences.clear()}_usingFontIcon(){return!this.svgIcon}_setSvgElement(t){this._clearSvgElement();const e=t.querySelectorAll("style");for(let t=0;t<e.length;t++)e[t].textContent+=" ";const n=this._location.getPathname();this._previousPath=n,this._cacheChildrenWithExternalReferences(t),this._prependPathToReferences(n),this._elementRef.nativeElement.appendChild(t)}_clearSvgElement(){const t=this._elementRef.nativeElement;let e=t.childNodes.length;for(this._elementsWithExternalReferences&&this._elementsWithExternalReferences.clear();e--;){const n=t.childNodes[e];1===n.nodeType&&"svg"!==n.nodeName.toLowerCase()||t.removeChild(n)}}_updateFontIconClasses(){if(!this._usingFontIcon())return;const t=this._elementRef.nativeElement,e=this.fontSet?this._iconRegistry.classNameForFontAlias(this.fontSet):this._iconRegistry.getDefaultFontSetClass();e!=this._previousFontSetClass&&(this._previousFontSetClass&&t.classList.remove(this._previousFontSetClass),e&&t.classList.add(e),this._previousFontSetClass=e),this.fontIcon!=this._previousFontIconClass&&(this._previousFontIconClass&&t.classList.remove(this._previousFontIconClass),this.fontIcon&&t.classList.add(this.fontIcon),this._previousFontIconClass=this.fontIcon)}_cleanupFontValue(t){return"string"==typeof t?t.trim().split(" ")[0]:t}_prependPathToReferences(t){const e=this._elementsWithExternalReferences;e&&e.forEach(((e,n)=>{e.forEach((e=>{n.setAttribute(e.name,`url('${t}#${e.value}')`)}))}))}_cacheChildrenWithExternalReferences(t){const e=t.querySelectorAll(_q),n=this._elementsWithExternalReferences=this._elementsWithExternalReferences||new Map;for(let t=0;t<e.length;t++)yq.forEach((o=>{const i=e[t],a=i.getAttribute(o),r=a?a.match(Cq):null;if(r){let t=n.get(i);t||(t=[],n.set(i,t)),t.push({name:o,value:r[1]})}}))}_updateSvgIcon(t){if(this._svgNamespace=null,this._svgName=null,this._currentIconFetch.unsubscribe(),t){const[e,n]=this._splitIconName(t);e&&(this._svgNamespace=e),n&&(this._svgName=n),this._currentIconFetch=this._iconRegistry.getNamedSvgIcon(n,e).pipe(ve(1)).subscribe((t=>this._setSvgElement(t)),(t=>{this._errorHandler.handleError(new Error(`Error retrieving icon ${e}:${n}! ${t.message}`))}))}}}Mq.ɵfac=function t(e){return new(e||Mq)(gf(Kh),gf(mq),Ga("aria-hidden"),gf(hq),gf(ul))},Mq.ɵcmp=so({type:Mq,selectors:[["mat-icon"]],hostAttrs:["role","img",1,"mat-icon","notranslate"],hostVars:7,hostBindings:function t(e,n){2&e&&(ym("data-mat-icon-type",n._usingFontIcon()?"font":"svg")("data-mat-icon-name",n._svgName||n.fontIcon)("data-mat-icon-namespace",n._svgNamespace||n.fontSet),cu("mat-icon-inline",n.inline)("mat-icon-no-color","primary"!==n.color&&"accent"!==n.color&&"warn"!==n.color))},inputs:{color:"color",inline:"inline",svgIcon:"svgIcon",fontSet:"fontSet",fontIcon:"fontIcon"},exportAs:["matIcon"],features:[Qp],ngContentSelectors:["*"],decls:1,vars:0,template:function t(e,n){1&e&&(Hf(),Ff(0))},styles:[".mat-icon{background-repeat:no-repeat;display:inline-block;fill:currentColor;height:24px;width:24px}.mat-icon.mat-icon-inline{font-size:inherit;height:inherit;line-height:inherit;width:inherit}[dir=rtl] .mat-icon-rtl-mirror{transform:scale(-1, 1)}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon{display:block}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon-button .mat-icon,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon-button .mat-icon{margin:auto}\n"],encapsulation:2,changeDetection:0}),Mq.ctorParameters=()=>[{type:Kh},{type:mq},{type:String,decorators:[{type:$a,args:["aria-hidden"]}]},{type:void 0,decorators:[{type:Lr,args:[hq]}]},{type:ul}],Mq.propDecorators={inline:[{type:R_}],svgIcon:[{type:R_}],fontSet:[{type:R_}],fontIcon:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Mq,[{type:S_,args:[{template:"<ng-content></ng-content>",selector:"mat-icon",exportAs:"matIcon",inputs:["color"],host:{role:"img",class:"mat-icon notranslate","[attr.data-mat-icon-type]":'_usingFontIcon() ? "font" : "svg"',"[attr.data-mat-icon-name]":"_svgName || fontIcon","[attr.data-mat-icon-namespace]":"_svgNamespace || fontSet","[class.mat-icon-inline]":"inline","[class.mat-icon-no-color]":'color !== "primary" && color !== "accent" && color !== "warn"'},encapsulation:Wn.None,changeDetection:jn.OnPush,styles:[".mat-icon{background-repeat:no-repeat;display:inline-block;fill:currentColor;height:24px;width:24px}.mat-icon.mat-icon-inline{font-size:inherit;height:inherit;line-height:inherit;width:inherit}[dir=rtl] .mat-icon-rtl-mirror{transform:scale(-1, 1)}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon{display:block}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon-button .mat-icon,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon-button .mat-icon{margin:auto}\n"]}]}],(function(){return[{type:Kh},{type:mq},{type:String,decorators:[{type:$a,args:["aria-hidden"]}]},{type:void 0,decorators:[{type:Lr,args:[hq]}]},{type:ul}]}),{inline:[{type:R_}],svgIcon:[{type:R_}],fontSet:[{type:R_}],fontIcon:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class vq{}vq.ɵfac=function t(e){return new(e||vq)},vq.ɵmod=fo({type:vq}),vq.ɵinj=Dn({imports:[[NF],NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(vq,[{type:L_,args:[{imports:[NF],exports:[Mq,NF],declarations:[Mq]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(vq,{declarations:function(){return[Mq]},imports:function(){return[NF]},exports:function(){return[Mq,NF]}});class xq{constructor(t){this.dialogRef=t,this.tensorboardDotDevUrl="https://tensorboard.dev/?utm_source=tensorboard"}onClose(){this.dialogRef.close()}getCommandText(){if(this.logdir){return"tensorboard dev upload --logdir \\\n    '"+this.logdir.replace(/'/g,"'\\''")+"'"}return"tensorboard dev upload --logdir {logdir}"}}xq.ɵfac=function t(e){return new(e||xq)(gf(VY))},xq.ɵcmp=so({type:xq,selectors:[["tbdev-upload-dialog-component"]],inputs:{logdir:"logdir"},decls:21,vars:4,consts:[["target","_blank","rel","noreferrer noopener",1,"anchor-text",3,"href"],[1,"command"],["mat-icon-button","","title","Click to copy the command",1,"command-copy",3,"cdkCopyToClipboard"],["svgIcon","content_copy_24px"],[1,"bottom-buttons"],["mat-flat-button","",1,"close-button",3,"click"],["mat-flat-button","","target","_blank","rel","noreferrer noopener",1,"learn-more-button",3,"href"]],template:function t(e,n){1&e&&(Cf(0,"h3"),Pu(1,"Upload to TensorBoard.dev"),Mf(),Cf(2,"p"),Cf(3,"a",0),Pu(4," TensorBoard.dev"),Mf(),Pu(5," enables you to easily host, track, and share your ML experiments with everyone. You can share a link to the uploaded TensorBoard in papers, blog posts, and social media. This can showcase the results more effectively and helps reproducibility.\n"),Mf(),Cf(6,"p"),Pu(7,"To upload a logdir to TensorBoard.dev, run the command:"),Mf(),Cf(8,"div",1),Cf(9,"pre"),Cf(10,"code"),Pu(11),Mf(),Mf(),Cf(12,"button",2),vf(13,"mat-icon",3),Mf(),Mf(),Cf(14,"p"),Pu(15," Only certain plugins are currently supported. 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ngDevMode||ngDevMode)&&Qb(Pq,[{type:S_,args:[{selector:"tbdev-upload-dialog",template:'\n    <tbdev-upload-dialog-component\n      [logdir]="logdir$ | async"\n    ></tbdev-upload-dialog-component>\n  '}]}],(function(){return[{type:iS}]}),null);const kq=["localhost","127.0.0.1"];class Sq{constructor(t,e){this.window=t,this.dialog=e,this.shown=kq.includes(t.location.hostname)}openDialog(){this.dialog.open(Pq,{width:"560px"})}}Sq.ɵfac=function t(e){return new(e||Sq)(gf("window"),gf(XY))},Sq.ɵcmp=so({type:Sq,selectors:[["tbdev-upload-button"]],hostVars:2,hostBindings:function t(e,n){2&e&&cu("shown",n.shown)},decls:1,vars:1,consts:[["mat-stroked-button","",3,"click",4,"ngIf"],["mat-stroked-button","",3,"click"],[1,"button-contents"],["svgIcon","info_outline_24px"]],template:function t(e,n){1&e&&Hm(0,wq,4,0,"button",0),2&e&&bf("ngIf",n.shown)},directives:[Pv,NL,Mq],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", 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.mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action[_ngcontent-%COMP%]{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node[_ngcontent-%COMP%], .mat-nested-tree-node[_ngcontent-%COMP%]{font-weight:400;font-size:14px}.mat-ripple[_ngcontent-%COMP%]{overflow:hidden;position:relative}.mat-ripple[_ngcontent-%COMP%]:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded[_ngcontent-%COMP%]{overflow:visible}.mat-ripple-element[_ngcontent-%COMP%]{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms cubic-bezier(0, 0, 0.2, 1);transform:scale(0)}.cdk-high-contrast-active[_ngcontent-%COMP%]   .mat-ripple-element[_ngcontent-%COMP%]{display:none}.cdk-visually-hidden[_ngcontent-%COMP%]{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container[_ngcontent-%COMP%], .cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]   button.mat-stroked-button[_ngcontent-%COMP%]{background-color:#ff9800;border:1px solid #ebebeb}body.dark-mode   [_nghost-%COMP%]   button.mat-stroked-button[_ngcontent-%COMP%]{background-color:#ef6c00}.button-contents[_ngcontent-%COMP%]{align-items:center;display:flex;text-transform:uppercase}mat-icon[_ngcontent-%COMP%]{margin-right:6px}']}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Sq,[{type:S_,args:[{selector:"tbdev-upload-button",templateUrl:"./tbdev_upload_button_component.ng.html",styleUrls:["./tbdev_upload_button_component.css"]}]}],(function(){return[{type:Window,decorators:[{type:Lr,args:["window"]}]},{type:XY}]}),{shown:[{type:A_,args:["class.shown"]}]});function Dq(t,e){1&t&&(oa(),Cf(0,"svg",2),vf(1,"polygon",3),Mf())}const Rq=["*"];function Eq(t,e){if(1&t){const t=wf();Cf(0,"div",0),Rf("keydown",(function e(n){Pi(t);return zf()._handleKeydown(n)}))("click",(function e(){Pi(t);return zf().closed.emit("click")}))("@transformMenu.start",(function e(n){Pi(t);return zf()._onAnimationStart(n)}))("@transformMenu.done",(function e(n){Pi(t);return zf()._onAnimationDone(n)})),Cf(1,"div",1),Ff(2),Mf(),Mf()}if(2&t){const t=zf();bf("id",t.panelId)("ngClass",t._classList)("@transformMenu",t._panelAnimationState),ym("aria-label",t.ariaLabel||null)("aria-labelledby",t.ariaLabelledby||null)("aria-describedby",t.ariaDescribedby||null)}}const Aq={transformMenu:xO("transformMenu",[kO("void",wO({opacity:0,transform:"scale(0.8)"})),DO("void => enter",OO("120ms cubic-bezier(0, 0, 0.2, 1)",wO({opacity:1,transform:"scale(1)"}))),DO("* => void",OO("100ms 25ms linear",wO({opacity:0})))]),fadeInItems:xO("fadeInItems",[kO("showing",wO({opacity:1})),DO("void => *",[wO({opacity:0}),OO("400ms 100ms cubic-bezier(0.55, 0, 0.55, 0.2)")])])},Tq=new er("MatMenuContent");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Nq{constructor(t,e,n,o,i,a,r){this._template=t,this._componentFactoryResolver=e,this._appRef=n,this._injector=o,this._viewContainerRef=i,this._document=a,this._changeDetectorRef=r,this._attached=new B}attach(t={}){this._portal||(this._portal=new lB(this._template,this._viewContainerRef)),this.detach(),this._outlet||(this._outlet=new pB(this._document.createElement("div"),this._componentFactoryResolver,this._appRef,this._injector));const e=this._template.elementRef.nativeElement;e.parentNode.insertBefore(this._outlet.outletElement,e),this._changeDetectorRef&&this._changeDetectorRef.markForCheck(),this._portal.attach(this._outlet,t),this._attached.next()}detach(){this._portal.isAttached&&this._portal.detach()}ngOnDestroy(){this._outlet&&this._outlet.dispose()}}Nq.ɵfac=function t(e){return new(e||Nq)(gf(Eb),gf(Gh),gf(HC),gf(Np),gf(Fb),gf(sM),gf(Ob))},Nq.ɵdir=ho({type:Nq,selectors:[["ng-template","matMenuContent",""]],features:[jh([{provide:Tq,useExisting:Nq}])]}),Nq.ctorParameters=()=>[{type:Eb},{type:Gh},{type:HC},{type:Np},{type:Fb},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:Ob}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Nq,[{type:k_,args:[{selector:"ng-template[matMenuContent]",providers:[{provide:Tq,useExisting:Nq}]}]}],(function(){return[{type:Eb},{type:Gh},{type:HC},{type:Np},{type:Fb},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:Ob}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const zq=new er("MAT_MENU_PANEL"),Iq=HF(zF(class{}));
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Hq extends Iq{constructor(t,e,n,o,i){super(),this._elementRef=t,this._focusMonitor=n,this._parentMenu=o,this._changeDetectorRef=i,this.role="menuitem",this._hovered=new B,this._focused=new B,this._highlighted=!1,this._triggersSubmenu=!1,o&&o.addItem&&o.addItem(this)}focus(t,e){this._focusMonitor&&t?this._focusMonitor.focusVia(this._getHostElement(),t,e):this._getHostElement().focus(e),this._focused.next(this)}ngAfterViewInit(){this._focusMonitor&&this._focusMonitor.monitor(this._elementRef,!1)}ngOnDestroy(){this._focusMonitor&&this._focusMonitor.stopMonitoring(this._elementRef),this._parentMenu&&this._parentMenu.removeItem&&this._parentMenu.removeItem(this),this._hovered.complete(),this._focused.complete()}_getTabIndex(){return this.disabled?"-1":"0"}_getHostElement(){return this._elementRef.nativeElement}_checkDisabled(t){this.disabled&&(t.preventDefault(),t.stopPropagation())}_handleMouseEnter(){this._hovered.next(this)}getLabel(){var t,e;const n=this._elementRef.nativeElement.cloneNode(!0),o=n.querySelectorAll("mat-icon, .material-icons");for(let e=0;e<o.length;e++){const n=o[e];null===(t=n.parentNode)||void 0===t||t.removeChild(n)}return(null===(e=n.textContent)||void 0===e?void 0:e.trim())||""}_setHighlighted(t){var e;this._highlighted=t,null===(e=this._changeDetectorRef)||void 0===e||e.markForCheck()}}Hq.ɵfac=function t(e){return new(e||Hq)(gf(Kh),gf(sM),gf(fF),gf(zq,8),gf(Ob))},Hq.ɵcmp=so({type:Hq,selectors:[["","mat-menu-item",""]],hostAttrs:[1,"mat-focus-indicator"],hostVars:10,hostBindings:function t(e,n){1&e&&Rf("click",(function t(e){return n._checkDisabled(e)}))("mouseenter",(function t(){return n._handleMouseEnter()})),2&e&&(ym("role",n.role)("tabindex",n._getTabIndex())("aria-disabled",n.disabled.toString())("disabled",n.disabled||null),cu("mat-menu-item",!0)("mat-menu-item-highlighted",n._highlighted)("mat-menu-item-submenu-trigger",n._triggersSubmenu))},inputs:{disabled:"disabled",disableRipple:"disableRipple",role:"role"},exportAs:["matMenuItem"],features:[Qp],attrs:["mat-menu-item",""],ngContentSelectors:Rq,decls:3,vars:3,consts:[["matRipple","",1,"mat-menu-ripple",3,"matRippleDisabled","matRippleTrigger"],["class","mat-menu-submenu-icon","viewBox","0 0 5 10","focusable","false",4,"ngIf"],["viewBox","0 0 5 10","focusable","false",1,"mat-menu-submenu-icon"],["points","0,0 5,5 0,10"]],template:function t(e,n){1&e&&(Hf(),Ff(0),vf(1,"div",0),Hm(2,Dq,2,0,"svg",1)),2&e&&(Dc(1),bf("matRippleDisabled",n.disableRipple||n.disabled)("matRippleTrigger",n._getHostElement()),Dc(1),bf("ngIf",n._triggersSubmenu))},directives:[mL,Pv],encapsulation:2,changeDetection:0}),Hq.ctorParameters=()=>[{type:Kh},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:fF},{type:void 0,decorators:[{type:Lr,args:[zq]},{type:Br}]},{type:Ob}],Hq.propDecorators={role:[{type:R_}],_checkDisabled:[{type:T_,args:["click",["$event"]]}],_handleMouseEnter:[{type:T_,args:["mouseenter"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Hq,[{type:S_,args:[{selector:"[mat-menu-item]",exportAs:"matMenuItem",inputs:["disabled","disableRipple"],host:{"[attr.role]":"role","[class.mat-menu-item]":"true","[class.mat-menu-item-highlighted]":"_highlighted","[class.mat-menu-item-submenu-trigger]":"_triggersSubmenu","[attr.tabindex]":"_getTabIndex()","[attr.aria-disabled]":"disabled.toString()","[attr.disabled]":"disabled || null",class:"mat-focus-indicator"},changeDetection:jn.OnPush,encapsulation:Wn.None,template:'<ng-content></ng-content>\n<div class="mat-menu-ripple" matRipple\n     [matRippleDisabled]="disableRipple || disabled"\n     [matRippleTrigger]="_getHostElement()">\n</div>\n\n<svg\n  *ngIf="_triggersSubmenu"\n  class="mat-menu-submenu-icon"\n  viewBox="0 0 5 10"\n  focusable="false"><polygon points="0,0 5,5 0,10"/></svg>\n'}]}],(function(){return[{type:Kh},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:fF},{type:void 0,decorators:[{type:Lr,args:[zq]},{type:Br}]},{type:Ob}]}),{role:[{type:R_}],_checkDisabled:[{type:T_,args:["click",["$event"]]}],_handleMouseEnter:[{type:T_,args:["mouseenter"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Fq=new er("mat-menu-default-options",{providedIn:"root",factory:function Lq(){return{overlapTrigger:!1,xPosition:"after",yPosition:"below",backdropClass:"cdk-overlay-transparent-backdrop"}}});let Bq=0;class Vq{constructor(t,e,n){this._elementRef=t,this._ngZone=e,this._defaultOptions=n,this._xPosition=this._defaultOptions.xPosition,this._yPosition=this._defaultOptions.yPosition,this._directDescendantItems=new Ry,this._tabSubscription=u.EMPTY,this._classList={},this._panelAnimationState="void",this._animationDone=new B,this.overlayPanelClass=this._defaultOptions.overlayPanelClass||"",this.backdropClass=this._defaultOptions.backdropClass,this._overlapTrigger=this._defaultOptions.overlapTrigger,this._hasBackdrop=this._defaultOptions.hasBackdrop,this.closed=new Sy,this.close=this.closed,this.panelId="mat-menu-panel-"+Bq++}get xPosition(){return this._xPosition}set xPosition(t){"before"===t||"after"===t||"undefined"!=typeof ngDevMode&&!ngDevMode||(function e(){throw Error('xPosition value must be either \'before\' or after\'.\n      Example: <mat-menu xPosition="before" #menu="matMenu"></mat-menu>')})(),this._xPosition=t,this.setPositionClasses()}get yPosition(){return this._yPosition}set yPosition(t){"above"===t||"below"===t||"undefined"!=typeof ngDevMode&&!ngDevMode||(function e(){throw Error('yPosition value must be either \'above\' or below\'.\n      Example: <mat-menu yPosition="above" #menu="matMenu"></mat-menu>')})(),this._yPosition=t,this.setPositionClasses()}get overlapTrigger(){return this._overlapTrigger}set overlapTrigger(t){this._overlapTrigger=eH(t)}get hasBackdrop(){return this._hasBackdrop}set hasBackdrop(t){this._hasBackdrop=eH(t)}set panelClass(t){const e=this._previousPanelClass;e&&e.length&&e.split(" ").forEach((t=>{this._classList[t]=!1})),this._previousPanelClass=t,t&&t.length&&(t.split(" ").forEach((t=>{this._classList[t]=!0})),this._elementRef.nativeElement.className="")}get classList(){return this.panelClass}set classList(t){this.panelClass=t}ngOnInit(){this.setPositionClasses()}ngAfterContentInit(){this._updateDirectDescendants(),this._keyManager=new LH(this._directDescendantItems).withWrap().withTypeAhead().withHomeAndEnd(),this._tabSubscription=this._keyManager.tabOut.subscribe((()=>this.closed.emit("tab"))),this._directDescendantItems.changes.pipe(Be(this._directDescendantItems),Ve((t=>pe(...t.map((t=>t._focused)))))).subscribe((t=>this._keyManager.updateActiveItem(t)))}ngOnDestroy(){this._directDescendantItems.destroy(),this._tabSubscription.unsubscribe(),this.closed.complete()}_hovered(){return this._directDescendantItems.changes.pipe(Be(this._directDescendantItems),Ve((t=>pe(...t.map((t=>t._hovered))))))}addItem(t){}removeItem(t){}_handleKeydown(t){const e=t.keyCode,n=this._keyManager;switch(e){case KI:tH(t)||(t.preventDefault(),this.closed.emit("keydown"));break;case 37:this.parentMenu&&"ltr"===this.direction&&this.closed.emit("keydown");break;case 39:this.parentMenu&&"rtl"===this.direction&&this.closed.emit("keydown");break;default:e!==QI&&e!==$I||n.setFocusOrigin("keyboard"),n.onKeydown(t)}}focusFirstItem(t="program"){this.lazyContent?this._ngZone.onStable.pipe(ve(1)).subscribe((()=>this._focusFirstItem(t))):this._focusFirstItem(t)}_focusFirstItem(t){const e=this._keyManager;if(e.setFocusOrigin(t).setFirstItemActive(),!e.activeItem&&this._directDescendantItems.length){let t=this._directDescendantItems.first._getHostElement().parentElement;for(;t;){if("menu"===t.getAttribute("role")){t.focus();break}t=t.parentElement}}}resetActiveItem(){this._keyManager.setActiveItem(-1)}setElevation(t){const e=Math.min(this._baseElevation+t,24),n=`${this._elevationPrefix}${e}`,o=Object.keys(this._classList).find((t=>t.startsWith(this._elevationPrefix)));o&&o!==this._previousElevation||(this._previousElevation&&(this._classList[this._previousElevation]=!1),this._classList[n]=!0,this._previousElevation=n)}setPositionClasses(t=this.xPosition,e=this.yPosition){const n=this._classList;n["mat-menu-before"]="before"===t,n["mat-menu-after"]="after"===t,n["mat-menu-above"]="above"===e,n["mat-menu-below"]="below"===e}_startAnimation(){this._panelAnimationState="enter"}_resetAnimation(){this._panelAnimationState="void"}_onAnimationDone(t){this._animationDone.next(t),this._isAnimating=!1}_onAnimationStart(t){this._isAnimating=!0,"enter"===t.toState&&0===this._keyManager.activeItemIndex&&(t.element.scrollTop=0)}_updateDirectDescendants(){this._allItems.changes.pipe(Be(this._allItems)).subscribe((t=>{this._directDescendantItems.reset(t.filter((t=>t._parentMenu===this))),this._directDescendantItems.notifyOnChanges()}))}}Vq.ɵfac=function t(e){return new(e||Vq)(gf(Kh),gf(mC),gf(Fq))},Vq.ɵdir=ho({type:Vq,contentQueries:function t(e,n,o){if(1&e&&(jy(o,Tq,5),jy(o,Hq,5),jy(o,Hq,4)),2&e){let t;By(t=Uy())&&(n.lazyContent=t.first),By(t=Uy())&&(n._allItems=t),By(t=Uy())&&(n.items=t)}},viewQuery:function t(e,n){if(1&e&&Vy(Eb,5),2&e){let t;By(t=Uy())&&(n.templateRef=t.first)}},inputs:{backdropClass:"backdropClass",xPosition:"xPosition",yPosition:"yPosition",overlapTrigger:"overlapTrigger",hasBackdrop:"hasBackdrop",panelClass:["class","panelClass"],classList:"classList",ariaLabel:["aria-label","ariaLabel"],ariaLabelledby:["aria-labelledby","ariaLabelledby"],ariaDescribedby:["aria-describedby","ariaDescribedby"]},outputs:{closed:"closed",close:"close"}}),Vq.ctorParameters=()=>[{type:Kh},{type:mC},{type:void 0,decorators:[{type:Lr,args:[Fq]}]}],Vq.propDecorators={_allItems:[{type:or,args:[Hq,{descendants:!0}]}],backdropClass:[{type:R_}],ariaLabel:[{type:R_,args:["aria-label"]}],ariaLabelledby:[{type:R_,args:["aria-labelledby"]}],ariaDescribedby:[{type:R_,args:["aria-describedby"]}],xPosition:[{type:R_}],yPosition:[{type:R_}],templateRef:[{type:ar,args:[Eb]}],items:[{type:or,args:[Hq,{descendants:!1}]}],lazyContent:[{type:ir,args:[Tq]}],overlapTrigger:[{type:R_}],hasBackdrop:[{type:R_}],panelClass:[{type:R_,args:["class"]}],classList:[{type:R_}],closed:[{type:E_}],close:[{type:E_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Vq,[{type:k_}],(function(){return[{type:Kh},{type:mC},{type:void 0,decorators:[{type:Lr,args:[Fq]}]}]}),{backdropClass:[{type:R_}],closed:[{type:E_}],close:[{type:E_}],xPosition:[{type:R_}],yPosition:[{type:R_}],overlapTrigger:[{type:R_}],hasBackdrop:[{type:R_}],panelClass:[{type:R_,args:["class"]}],classList:[{type:R_}],_allItems:[{type:or,args:[Hq,{descendants:!0}]}],ariaLabel:[{type:R_,args:["aria-label"]}],ariaLabelledby:[{type:R_,args:["aria-labelledby"]}],ariaDescribedby:[{type:R_,args:["aria-describedby"]}],templateRef:[{type:ar,args:[Eb]}],items:[{type:or,args:[Hq,{descendants:!1}]}],lazyContent:[{type:ir,args:[Tq]}]});class jq extends Vq{constructor(t,e,n){super(t,e,n),this._elevationPrefix="mat-elevation-z",this._baseElevation=4}}jq.ɵfac=function t(e){return new(e||jq)(gf(Kh),gf(mC),gf(Fq))},jq.ɵcmp=so({type:jq,selectors:[["mat-menu"]],hostVars:3,hostBindings:function t(e,n){2&e&&ym("aria-label",null)("aria-labelledby",null)("aria-describedby",null)},exportAs:["matMenu"],features:[jh([{provide:zq,useExisting:jq}]),Qp],ngContentSelectors:Rq,decls:1,vars:0,consts:[["tabindex","-1","role","menu",1,"mat-menu-panel",3,"id","ngClass","keydown","click"],[1,"mat-menu-content"]],template:function t(e,n){1&e&&(Hf(),Hm(0,Eq,3,6,"ng-template"))},directives:[Cv],styles:["mat-menu{display:none}.mat-menu-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;max-height:calc(100vh - 48px);border-radius:4px;outline:0;min-height:64px}.mat-menu-panel.ng-animating{pointer-events:none}.cdk-high-contrast-active .mat-menu-panel{outline:solid 1px}.mat-menu-content:not(:empty){padding-top:8px;padding-bottom:8px}.mat-menu-item{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;position:relative}.mat-menu-item::-moz-focus-inner{border:0}.mat-menu-item[disabled]{cursor:default}[dir=rtl] .mat-menu-item{text-align:right}.mat-menu-item .mat-icon{margin-right:16px;vertical-align:middle}.mat-menu-item .mat-icon svg{vertical-align:top}[dir=rtl] .mat-menu-item .mat-icon{margin-left:16px;margin-right:0}.mat-menu-item[disabled]{pointer-events:none}.cdk-high-contrast-active .mat-menu-item{margin-top:1px}.cdk-high-contrast-active .mat-menu-item.cdk-program-focused,.cdk-high-contrast-active .mat-menu-item.cdk-keyboard-focused,.cdk-high-contrast-active .mat-menu-item-highlighted{outline:dotted 1px}.mat-menu-item-submenu-trigger{padding-right:32px}[dir=rtl] .mat-menu-item-submenu-trigger{padding-right:16px;padding-left:32px}.mat-menu-submenu-icon{position:absolute;top:50%;right:16px;transform:translateY(-50%);width:5px;height:10px;fill:currentColor}[dir=rtl] .mat-menu-submenu-icon{right:auto;left:16px;transform:translateY(-50%) scaleX(-1)}.cdk-high-contrast-active .mat-menu-submenu-icon{fill:CanvasText}button.mat-menu-item{width:100%}.mat-menu-item .mat-menu-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none}\n"],encapsulation:2,data:{animation:[Aq.transformMenu,Aq.fadeInItems]},changeDetection:0}),jq.ctorParameters=()=>[{type:Kh},{type:mC},{type:void 0,decorators:[{type:Lr,args:[Fq]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(jq,[{type:S_,args:[{selector:"mat-menu",template:'<ng-template>\n  <div\n    class="mat-menu-panel"\n    [id]="panelId"\n    [ngClass]="_classList"\n    (keydown)="_handleKeydown($event)"\n    (click)="closed.emit(\'click\')"\n    [@transformMenu]="_panelAnimationState"\n    (@transformMenu.start)="_onAnimationStart($event)"\n    (@transformMenu.done)="_onAnimationDone($event)"\n    tabindex="-1"\n    role="menu"\n    [attr.aria-label]="ariaLabel || null"\n    [attr.aria-labelledby]="ariaLabelledby || null"\n    [attr.aria-describedby]="ariaDescribedby || null">\n    <div class="mat-menu-content">\n      <ng-content></ng-content>\n    </div>\n  </div>\n</ng-template>\n',changeDetection:jn.OnPush,encapsulation:Wn.None,exportAs:"matMenu",host:{"[attr.aria-label]":"null","[attr.aria-labelledby]":"null","[attr.aria-describedby]":"null"},animations:[Aq.transformMenu,Aq.fadeInItems],providers:[{provide:zq,useExisting:jq}],styles:["mat-menu{display:none}.mat-menu-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;max-height:calc(100vh - 48px);border-radius:4px;outline:0;min-height:64px}.mat-menu-panel.ng-animating{pointer-events:none}.cdk-high-contrast-active .mat-menu-panel{outline:solid 1px}.mat-menu-content:not(:empty){padding-top:8px;padding-bottom:8px}.mat-menu-item{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;position:relative}.mat-menu-item::-moz-focus-inner{border:0}.mat-menu-item[disabled]{cursor:default}[dir=rtl] .mat-menu-item{text-align:right}.mat-menu-item .mat-icon{margin-right:16px;vertical-align:middle}.mat-menu-item .mat-icon svg{vertical-align:top}[dir=rtl] .mat-menu-item .mat-icon{margin-left:16px;margin-right:0}.mat-menu-item[disabled]{pointer-events:none}.cdk-high-contrast-active .mat-menu-item{margin-top:1px}.cdk-high-contrast-active .mat-menu-item.cdk-program-focused,.cdk-high-contrast-active .mat-menu-item.cdk-keyboard-focused,.cdk-high-contrast-active .mat-menu-item-highlighted{outline:dotted 1px}.mat-menu-item-submenu-trigger{padding-right:32px}[dir=rtl] .mat-menu-item-submenu-trigger{padding-right:16px;padding-left:32px}.mat-menu-submenu-icon{position:absolute;top:50%;right:16px;transform:translateY(-50%);width:5px;height:10px;fill:currentColor}[dir=rtl] .mat-menu-submenu-icon{right:auto;left:16px;transform:translateY(-50%) scaleX(-1)}.cdk-high-contrast-active .mat-menu-submenu-icon{fill:CanvasText}button.mat-menu-item{width:100%}.mat-menu-item .mat-menu-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none}\n"]}]}],(function(){return[{type:Kh},{type:mC},{type:void 0,decorators:[{type:Lr,args:[Fq]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Uq=new er("mat-menu-scroll-strategy");const Wq={provide:Uq,deps:[KB],useFactory:function Gq(t){return()=>t.scrollStrategies.reposition()}},Yq=bH({passive:!0});class qq{constructor(t,e,n,o,i,a,r,s){this._overlay=t,this._element=e,this._viewContainerRef=n,this._menuItemInstance=a,this._dir=r,this._focusMonitor=s,this._overlayRef=null,this._menuOpen=!1,this._closingActionsSubscription=u.EMPTY,this._hoverSubscription=u.EMPTY,this._menuCloseSubscription=u.EMPTY,this._handleTouchStart=t=>{eF(t)||(this._openedBy="touch")},this._openedBy=void 0,this.restoreFocus=!0,this.menuOpened=new Sy,this.onMenuOpen=this.menuOpened,this.menuClosed=new Sy,this.onMenuClose=this.menuClosed,this._scrollStrategy=o,this._parentMaterialMenu=i instanceof Vq?i:void 0,e.nativeElement.addEventListener("touchstart",this._handleTouchStart,Yq),a&&(a._triggersSubmenu=this.triggersSubmenu())}get _deprecatedMatMenuTriggerFor(){return this.menu}set _deprecatedMatMenuTriggerFor(t){this.menu=t}get menu(){return this._menu}set menu(t){t!==this._menu&&(this._menu=t,this._menuCloseSubscription.unsubscribe(),t&&(t!==this._parentMaterialMenu||"undefined"!=typeof ngDevMode&&!ngDevMode||(function e(){throw Error("matMenuTriggerFor: menu cannot contain its own trigger. Assign a menu that is not a parent of the trigger or move the trigger outside of the menu.")})(),this._menuCloseSubscription=t.close.subscribe((t=>{this._destroyMenu(t),"click"!==t&&"tab"!==t||!this._parentMaterialMenu||this._parentMaterialMenu.closed.emit(t)}))))}ngAfterContentInit(){this._checkMenu(),this._handleHover()}ngOnDestroy(){this._overlayRef&&(this._overlayRef.dispose(),this._overlayRef=null),this._element.nativeElement.removeEventListener("touchstart",this._handleTouchStart,Yq),this._menuCloseSubscription.unsubscribe(),this._closingActionsSubscription.unsubscribe(),this._hoverSubscription.unsubscribe()}get menuOpen(){return this._menuOpen}get dir(){return this._dir&&"rtl"===this._dir.value?"rtl":"ltr"}triggersSubmenu(){return!(!this._menuItemInstance||!this._parentMaterialMenu)}toggleMenu(){return this._menuOpen?this.closeMenu():this.openMenu()}openMenu(){if(this._menuOpen)return;this._checkMenu();const t=this._createOverlay(),e=t.getConfig();this._setPosition(e.positionStrategy),e.hasBackdrop=null==this.menu.hasBackdrop?!this.triggersSubmenu():this.menu.hasBackdrop,t.attach(this._getPortal()),this.menu.lazyContent&&this.menu.lazyContent.attach(this.menuData),this._closingActionsSubscription=this._menuClosingActions().subscribe((()=>this.closeMenu())),this._initMenu(),this.menu instanceof Vq&&this.menu._startAnimation()}closeMenu(){this.menu.close.emit()}focus(t,e){this._focusMonitor&&t?this._focusMonitor.focusVia(this._element,t,e):this._element.nativeElement.focus(e)}updatePosition(){var t;null===(t=this._overlayRef)||void 0===t||t.updatePosition()}_destroyMenu(t){if(!this._overlayRef||!this.menuOpen)return;const e=this.menu;this._closingActionsSubscription.unsubscribe(),this._overlayRef.detach(),!this.restoreFocus||"keydown"!==t&&this._openedBy&&this.triggersSubmenu()||this.focus(this._openedBy),this._openedBy=void 0,e instanceof Vq?(e._resetAnimation(),e.lazyContent?e._animationDone.pipe(ue((t=>"void"===t.toState)),ve(1),je(e.lazyContent._attached)).subscribe({next:()=>e.lazyContent.detach(),complete:()=>this._setIsMenuOpen(!1)}):this._setIsMenuOpen(!1)):(this._setIsMenuOpen(!1),e.lazyContent&&e.lazyContent.detach())}_initMenu(){this.menu.parentMenu=this.triggersSubmenu()?this._parentMaterialMenu:void 0,this.menu.direction=this.dir,this._setMenuElevation(),this.menu.focusFirstItem(this._openedBy||"program"),this._setIsMenuOpen(!0)}_setMenuElevation(){if(this.menu.setElevation){let t=0,e=this.menu.parentMenu;for(;e;)t++,e=e.parentMenu;this.menu.setElevation(t)}}_setIsMenuOpen(t){this._menuOpen=t,this._menuOpen?this.menuOpened.emit():this.menuClosed.emit(),this.triggersSubmenu()&&this._menuItemInstance._setHighlighted(t)}_checkMenu(){this.menu||"undefined"!=typeof ngDevMode&&!ngDevMode||(
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function t(){throw Error('matMenuTriggerFor: must pass in an mat-menu instance.\n\n    Example:\n      <mat-menu #menu="matMenu"></mat-menu>\n      <button [matMenuTriggerFor]="menu"></button>')})()}_createOverlay(){if(!this._overlayRef){const t=this._getOverlayConfig();this._subscribeToPositions(t.positionStrategy),this._overlayRef=this._overlay.create(t),this._overlayRef.keydownEvents().subscribe()}return this._overlayRef}_getOverlayConfig(){return new wB({positionStrategy:this._overlay.position().flexibleConnectedTo(this._element).withLockedPosition().withGrowAfterOpen().withTransformOriginOn(".mat-menu-panel, .mat-mdc-menu-panel"),backdropClass:this.menu.backdropClass||"cdk-overlay-transparent-backdrop",panelClass:this.menu.overlayPanelClass,scrollStrategy:this._scrollStrategy(),direction:this._dir})}_subscribeToPositions(t){this.menu.setPositionClasses&&t.positionChanges.subscribe((t=>{this.menu.setPositionClasses("start"===t.connectionPair.overlayX?"after":"before","top"===t.connectionPair.overlayY?"below":"above")}))}_setPosition(t){let[e,n]="before"===this.menu.xPosition?["end","start"]:["start","end"],[o,i]="above"===this.menu.yPosition?["bottom","top"]:["top","bottom"],[a,r]=[o,i],[s,l]=[e,n],c=0;this.triggersSubmenu()?(l=e="before"===this.menu.xPosition?"start":"end",n=s="end"===e?"start":"end",c="bottom"===o?8:-8):this.menu.overlapTrigger||(a="top"===o?"bottom":"top",r="top"===i?"bottom":"top"),t.withPositions([{originX:e,originY:a,overlayX:s,overlayY:o,offsetY:c},{originX:n,originY:a,overlayX:l,overlayY:o,offsetY:c},{originX:e,originY:r,overlayX:s,overlayY:i,offsetY:-c},{originX:n,originY:r,overlayX:l,overlayY:i,offsetY:-c}])}_menuClosingActions(){const t=this._overlayRef.backdropClick(),e=this._overlayRef.detachments();return pe(t,this._parentMaterialMenu?this._parentMaterialMenu.closed:zt(),this._parentMaterialMenu?this._parentMaterialMenu._hovered().pipe(ue((t=>t!==this._menuItemInstance)),ue((()=>this._menuOpen))):zt(),e)}_handleMousedown(t){tF(t)||(this._openedBy=0===t.button?"mouse":void 0,this.triggersSubmenu()&&t.preventDefault())}_handleKeydown(t){const e=t.keyCode;e!==XI&&e!==JI||(this._openedBy="keyboard"),this.triggersSubmenu()&&(39===e&&"ltr"===this.dir||37===e&&"rtl"===this.dir)&&(this._openedBy="keyboard",this.openMenu())}_handleClick(t){this.triggersSubmenu()?(t.stopPropagation(),this.openMenu()):this.toggleMenu()}_handleHover(){this.triggersSubmenu()&&this._parentMaterialMenu&&(this._hoverSubscription=this._parentMaterialMenu._hovered().pipe(ue((t=>t===this._menuItemInstance&&!t.disabled)),Pe(0,ot)).subscribe((()=>{this._openedBy="mouse",this.menu instanceof Vq&&this.menu._isAnimating?this.menu._animationDone.pipe(ve(1),Pe(0,ot),je(this._parentMaterialMenu._hovered())).subscribe((()=>this.openMenu())):this.openMenu()})))}_getPortal(){return this._portal&&this._portal.templateRef===this.menu.templateRef||(this._portal=new lB(this.menu.templateRef,this._viewContainerRef)),this._portal}}qq.ɵfac=function t(e){return new(e||qq)(gf(KB),gf(Kh),gf(Fb),gf(Uq),gf(zq,8),gf(Hq,10),gf(vF,8),gf(fF))},qq.ɵdir=ho({type:qq,selectors:[["","mat-menu-trigger-for",""],["","matMenuTriggerFor",""]],hostAttrs:["aria-haspopup","true",1,"mat-menu-trigger"],hostVars:2,hostBindings:function t(e,n){1&e&&Rf("mousedown",(function t(e){return n._handleMousedown(e)}))("keydown",(function t(e){return n._handleKeydown(e)}))("click",(function t(e){return n._handleClick(e)})),2&e&&ym("aria-expanded",n.menuOpen||null)("aria-controls",n.menuOpen?n.menu.panelId:null)},inputs:{restoreFocus:["matMenuTriggerRestoreFocus","restoreFocus"],_deprecatedMatMenuTriggerFor:["mat-menu-trigger-for","_deprecatedMatMenuTriggerFor"],menu:["matMenuTriggerFor","menu"],menuData:["matMenuTriggerData","menuData"]},outputs:{menuOpened:"menuOpened",onMenuOpen:"onMenuOpen",menuClosed:"menuClosed",onMenuClose:"onMenuClose"},exportAs:["matMenuTrigger"]}),qq.ctorParameters=()=>[{type:KB},{type:Kh},{type:Fb},{type:void 0,decorators:[{type:Lr,args:[Uq]}]},{type:void 0,decorators:[{type:Lr,args:[zq]},{type:Br}]},{type:Hq,decorators:[{type:Br},{type:Vr}]},{type:vF,decorators:[{type:Br}]},{type:fF}],qq.propDecorators={_deprecatedMatMenuTriggerFor:[{type:R_,args:["mat-menu-trigger-for"]}],menu:[{type:R_,args:["matMenuTriggerFor"]}],menuData:[{type:R_,args:["matMenuTriggerData"]}],restoreFocus:[{type:R_,args:["matMenuTriggerRestoreFocus"]}],menuOpened:[{type:E_}],onMenuOpen:[{type:E_}],menuClosed:[{type:E_}],onMenuClose:[{type:E_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(qq,[{type:k_,args:[{selector:"[mat-menu-trigger-for], [matMenuTriggerFor]",host:{class:"mat-menu-trigger","aria-haspopup":"true","[attr.aria-expanded]":"menuOpen || null","[attr.aria-controls]":"menuOpen ? menu.panelId : null","(mousedown)":"_handleMousedown($event)","(keydown)":"_handleKeydown($event)","(click)":"_handleClick($event)"},exportAs:"matMenuTrigger"}]}],(function(){return[{type:KB},{type:Kh},{type:Fb},{type:void 0,decorators:[{type:Lr,args:[Uq]}]},{type:void 0,decorators:[{type:Lr,args:[zq]},{type:Br}]},{type:Hq,decorators:[{type:Br},{type:Vr}]},{type:vF,decorators:[{type:Br}]},{type:fF}]}),{restoreFocus:[{type:R_,args:["matMenuTriggerRestoreFocus"]}],menuOpened:[{type:E_}],onMenuOpen:[{type:E_}],menuClosed:[{type:E_}],onMenuClose:[{type:E_}],_deprecatedMatMenuTriggerFor:[{type:R_,args:["mat-menu-trigger-for"]}],menu:[{type:R_,args:["matMenuTriggerFor"]}],menuData:[{type:R_,args:["matMenuTriggerData"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Zq{}Zq.ɵfac=function t(e){return new(e||Zq)},Zq.ɵmod=fo({type:Zq}),Zq.ɵinj=Dn({providers:[Wq],imports:[NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Zq,[{type:L_,args:[{exports:[qq,Nq,NF],declarations:[qq,Nq],providers:[Wq]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(Zq,{declarations:function(){return[qq,Nq]},exports:function(){return[qq,Nq,NF]}});class Xq{}function Kq(t,e){1&t&&vf(0,"mat-icon",8)}function Jq(t,e){1&t&&vf(0,"mat-icon",9)}function Qq(t,e){1&t&&vf(0,"mat-icon",10)}var $q;Xq.ɵfac=function t(e){return new(e||Xq)},Xq.ɵmod=fo({type:Xq}),Xq.ɵinj=Dn({providers:[Wq],imports:[[rx,NF,fL,oV,Zq],oB,NF,Zq]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Xq,[{type:L_,args:[{imports:[rx,NF,fL,oV,Zq],exports:[oB,NF,jq,Hq,Zq],declarations:[jq,Hq],providers:[Wq]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(Xq,{declarations:function(){return[jq,Hq]},imports:function(){return[rx,NF,fL,oV,Zq]},exports:function(){return[oB,NF,jq,Hq,Zq]}}),(function(t){t[t.DEFAULT=0]="DEFAULT",t[t.DARK_MODE_ON=1]="DARK_MODE_ON",t[t.DARK_MODE_OFF=2]="DARK_MODE_OFF"})($q||($q={}));class tZ{constructor(){this.DarkModeOverride=$q,this.onOverrideChanged=new Sy}getButtonTitle(){let t;switch(this.darkModeOverride){case $q.DEFAULT:t="Browser default";break;case $q.DARK_MODE_ON:t="Dark mode";break;case $q.DARK_MODE_OFF:t="Light mode"}return`Current mode: [${t}]. Switch between browser default, light, or dark theme.`}}tZ.ɵfac=function t(e){return new(e||tZ)},tZ.ɵcmp=so({type:tZ,selectors:[["app-header-dark-mode-toggle-component"]],inputs:{darkModeOverride:"darkModeOverride"},outputs:{onOverrideChanged:"onOverrideChanged"},decls:15,vars:6,consts:[["mat-icon-button","","aria-label","Menu for changing light or dark theme",3,"matMenuTriggerFor","ngSwitch","title"],["svgIcon","brightness_6_24px",4,"ngSwitchCase"],["svgIcon","light_mode_24px",4,"ngSwitchCase"],["svgIcon","dark_mode_24px",4,"ngSwitchCase"],["menu","matMenu"],["mat-menu-item","","title","Set the theme to match the default mode in the browser.",3,"click"],["mat-menu-item","","title","Force light TensorBoard theme.",3,"click"],["mat-menu-item","","title","Force dark TensorBoard theme.",3,"click"],["svgIcon","brightness_6_24px"],["svgIcon","light_mode_24px"],["svgIcon","dark_mode_24px"]],template:function t(e,n){if(1&e&&(Cf(0,"button",0),Hm(1,Kq,1,0,"mat-icon",1),Hm(2,Jq,1,0,"mat-icon",2),Hm(3,Qq,1,0,"mat-icon",3),Mf(),Cf(4,"mat-menu",null,4),Cf(6,"button",5),Rf("click",(function t(){return n.onOverrideChanged.emit(n.DarkModeOverride.DEFAULT)})),Cf(7,"label"),Pu(8,"Browser default"),Mf(),Mf(),Cf(9,"button",6),Rf("click",(function t(){return n.onOverrideChanged.emit(n.DarkModeOverride.DARK_MODE_OFF)})),Cf(10,"label"),Pu(11,"Light"),Mf(),Mf(),Cf(12,"button",7),Rf("click",(function t(){return n.onOverrideChanged.emit(n.DarkModeOverride.DARK_MODE_ON)})),Cf(13,"label"),Pu(14,"Dark"),Mf(),Mf(),Mf()),2&e){bf("matMenuTriggerFor",Fm(5))("ngSwitch",n.darkModeOverride)("title",n.getButtonTitle()),Dc(1),bf("ngSwitchCase",n.DarkModeOverride.DEFAULT),Dc(1),bf("ngSwitchCase",n.DarkModeOverride.DARK_MODE_OFF),Dc(1),bf("ngSwitchCase",n.DarkModeOverride.DARK_MODE_ON)}},directives:[NL,qq,Dv,Rv,jq,Hq,Mq],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(tZ,[{type:S_,args:[{selector:"app-header-dark-mode-toggle-component",template:'\n    <button\n      mat-icon-button\n      [matMenuTriggerFor]="menu"\n      aria-label="Menu for changing light or dark theme"\n      [ngSwitch]="darkModeOverride"\n      [title]="getButtonTitle()"\n    >\n      <mat-icon\n        *ngSwitchCase="DarkModeOverride.DEFAULT"\n        svgIcon="brightness_6_24px"\n      ></mat-icon>\n      <mat-icon\n        *ngSwitchCase="DarkModeOverride.DARK_MODE_OFF"\n        svgIcon="light_mode_24px"\n      ></mat-icon>\n      <mat-icon\n        *ngSwitchCase="DarkModeOverride.DARK_MODE_ON"\n        svgIcon="dark_mode_24px"\n      ></mat-icon>\n    </button>\n    <mat-menu #menu="matMenu">\n      <button\n        mat-menu-item\n        title="Set the theme to match the default mode in the browser."\n        (click)="onOverrideChanged.emit(DarkModeOverride.DEFAULT)"\n      >\n        <label>Browser default</label>\n      </button>\n      <button\n        mat-menu-item\n        title="Force light TensorBoard theme."\n        (click)="onOverrideChanged.emit(DarkModeOverride.DARK_MODE_OFF)"\n      >\n        <label>Light</label>\n      </button>\n      <button\n        mat-menu-item\n        title="Force dark TensorBoard theme."\n        (click)="onOverrideChanged.emit(DarkModeOverride.DARK_MODE_ON)"\n      >\n        <label>Dark</label>\n      </button>\n    </mat-menu>\n  '}]}],null,{darkModeOverride:[{type:R_}],onOverrideChanged:[{type:E_}]});class eZ{constructor(t){this.store=t,this.darkModeOverride$=this.store.select(LD).pipe(Vt((t=>null===t?$q.DEFAULT:t?$q.DARK_MODE_ON:$q.DARK_MODE_OFF)))}changeDarkMode(t){let e=null;switch(t){case $q.DEFAULT:e=null;break;case $q.DARK_MODE_OFF:e=!1;break;case $q.DARK_MODE_ON:e=!0}this.store.dispatch(kN({enableDarkMode:e}))}}eZ.ɵfac=function t(e){return new(e||eZ)(gf(iS))},eZ.ɵcmp=so({type:eZ,selectors:[["app-header-dark-mode-toggle"]],decls:2,vars:3,consts:[[3,"darkModeOverride","onOverrideChanged"]],template:function t(e,n){1&e&&(Cf(0,"app-header-dark-mode-toggle-component",0),Rf("onOverrideChanged",(function t(e){return n.changeDarkMode(e)})),_y(1,"async"),Mf()),2&e&&bf("darkModeOverride",Cy(1,1,n.darkModeOverride$))},directives:[tZ],pipes:[Vv],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(eZ,[{type:S_,args:[{selector:"app-header-dark-mode-toggle",template:'\n    <app-header-dark-mode-toggle-component\n      [darkModeOverride]="darkModeOverride$ | async"\n      (onOverrideChanged)="changeDarkMode($event)"\n    >\n    </app-header-dark-mode-toggle-component>\n  '}]}],(function(){return[{type:iS}]}),null);const nZ=bS(nT,eT,((t,e)=>!(!e||!t[e])&&t[e].disable_reload));class oZ{constructor(t){this.store=t,this.reloadDisabled$=this.store.select(nZ),this.isReloading$=this.store.select($A).pipe(_e(this.reloadDisabled$),Vt((([t,e])=>!e&&t===UE.LOADING))),this.lastLoadedTimeInMs$=this.store.select(tT)}triggerReload(){this.store.dispatch(JE())}getReloadTitle(t){return t?`Last Updated: ${t}`:"Loading..."}}function iZ(t){return t.state!==UE.NOT_LOADED&&t.state!==UE.LOADING}oZ.ɵfac=function t(e){return new(e||oZ)(gf(iS))},oZ.ɵcmp=so({type:oZ,selectors:[["app-header-reload"]],decls:6,vars:13,consts:[["mat-icon-button","",1,"reload-button",3,"title","disabled","click"],["svgIcon","refresh_24px",1,"refresh-icon"]],template:function t(e,n){1&e&&(Cf(0,"button",0),Rf("click",(function t(){return n.triggerReload()})),_y(1,"async"),_y(2,"date"),_y(3,"async"),_y(4,"async"),vf(5,"mat-icon",1),Mf()),2&e&&(cu("loading",Cy(1,4,n.isReloading$)),bf("title",n.getReloadTitle(My(2,6,Cy(3,9,n.lastLoadedTimeInMs$),"medium")))("disabled",Cy(4,11,n.reloadDisabled$)))},directives:[NL,Mq],pipes:[Vv,Yv],styles:[".reload-button[_ngcontent-%COMP%], .refresh-icon[_ngcontent-%COMP%] {\n        align-items: center;\n        display: flex;\n        justify-content: center;\n      }\n\n      .reload-button.loading[_ngcontent-%COMP%] {\n        animation: rotate 2s linear infinite;\n      }\n\n      @keyframes rotate {\n        0% {\n          transform: rotate(0deg);\n        }\n        50% {\n          transform: rotate(180deg);\n        }\n        100% {\n          transform: rotate(360deg);\n        }\n      }"]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(oZ,[{type:S_,args:[{selector:"app-header-reload",template:'\n    <button\n      class="reload-button"\n      [class.loading]="isReloading$ | async"\n      mat-icon-button\n      (click)="triggerReload()"\n      [title]="getReloadTitle(lastLoadedTimeInMs$ | async | date: \'medium\')"\n      [disabled]="reloadDisabled$ | async"\n    >\n      <mat-icon class="refresh-icon" svgIcon="refresh_24px"></mat-icon>\n    </button>\n  ',styles:["\n      .reload-button,\n      .refresh-icon {\n        align-items: center;\n        display: flex;\n        justify-content: center;\n      }\n\n      .reload-button.loading {\n        animation: rotate 2s linear infinite;\n      }\n\n      @keyframes rotate {\n        0% {\n          transform: rotate(0deg);\n        }\n        50% {\n          transform: rotate(180deg);\n        }\n        100% {\n          transform: rotate(360deg);\n        }\n      }\n    "]}]}],(function(){return[{type:iS}]}),null);const aZ=3e4,rZ=VS(pI,BS(sI,(t=>iZ(t)?Object.assign(Object.assign({},t),{settings:Object.assign(Object.assign({},t.settings),{reloadEnabled:!t.settings.reloadEnabled})}):t)),BS(lI,((t,{periodInMs:e})=>{if(!iZ(t))return t;const n=e>=aZ?e:t.settings.reloadPeriodInMs;return Object.assign(Object.assign({},t),{settings:Object.assign(Object.assign({},t.settings),{reloadPeriodInMs:n})})})),BS(cI,((t,{size:e})=>{if(!iZ(t))return t;const n=e>0?e:t.settings.pageSize;return Object.assign(Object.assign({},t),{settings:Object.assign(Object.assign({},t.settings),{pageSize:n})})})),BS(qR,((t,{partialSettings:e})=>{const n={};return Number.isFinite(e.pageSize)&&e.pageSize>0&&(n.pageSize=Number(e.pageSize)),"boolean"==typeof e.autoReload&&(n.reloadEnabled=e.autoReload),Number.isFinite(e.autoReloadPeriodInMs)&&e.autoReloadPeriodInMs>aZ&&(n.reloadPeriodInMs=Number(e.autoReloadPeriodInMs)),Object.assign(Object.assign({},t),{settings:Object.assign(Object.assign({},t.settings),n)})})));function sZ(t,e){return rZ(t,e)}const lZ=["input"],cZ=function(t){return{enterDuration:t}},dZ=new er("mat-checkbox-default-options",{providedIn:"root",factory:pZ});function pZ(){return{color:"accent",clickAction:"check-indeterminate"}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let mZ=0;const fZ={color:"accent",clickAction:"check-indeterminate"},uZ={provide:xU,useExisting:$e((()=>bZ)),multi:!0};class gZ{}const hZ=FF(IF(HF(zF(class{constructor(t){this._elementRef=t}}))));class bZ extends hZ{constructor(t,e,n,o,i,a,r){super(t),this._changeDetectorRef=e,this._focusMonitor=n,this._ngZone=o,this._animationMode=a,this._options=r,this.ariaLabel="",this.ariaLabelledby=null,this._uniqueId="mat-checkbox-"+ ++mZ,this.id=this._uniqueId,this.labelPosition="after",this.name=null,this.change=new Sy,this.indeterminateChange=new Sy,this._onTouched=()=>{},this._currentAnimationClass="",this._currentCheckState=0,this._controlValueAccessorChangeFn=()=>{},this._checked=!1,this._disabled=!1,this._indeterminate=!1,this._options=this._options||fZ,this.color=this.defaultColor=this._options.color||fZ.color,this.tabIndex=parseInt(i)||0}get inputId(){return`${this.id||this._uniqueId}-input`}get required(){return this._required}set required(t){this._required=eH(t)}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0).subscribe((t=>{t||Promise.resolve().then((()=>{this._onTouched(),this._changeDetectorRef.markForCheck()}))})),this._syncIndeterminate(this._indeterminate)}ngAfterViewChecked(){}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef)}get checked(){return this._checked}set checked(t){t!=this.checked&&(this._checked=t,this._changeDetectorRef.markForCheck())}get disabled(){return this._disabled}set disabled(t){const e=eH(t);e!==this.disabled&&(this._disabled=e,this._changeDetectorRef.markForCheck())}get indeterminate(){return this._indeterminate}set indeterminate(t){const e=t!=this._indeterminate;this._indeterminate=eH(t),e&&(this._transitionCheckState(this._indeterminate?3:this.checked?1:2),this.indeterminateChange.emit(this._indeterminate)),this._syncIndeterminate(this._indeterminate)}_isRippleDisabled(){return this.disableRipple||this.disabled}_onLabelTextChange(){this._changeDetectorRef.detectChanges()}writeValue(t){this.checked=!!t}registerOnChange(t){this._controlValueAccessorChangeFn=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t}_getAriaChecked(){return this.checked?"true":this.indeterminate?"mixed":"false"}_transitionCheckState(t){let e=this._currentCheckState,n=this._elementRef.nativeElement;if(e!==t&&(this._currentAnimationClass.length>0&&n.classList.remove(this._currentAnimationClass),this._currentAnimationClass=this._getAnimationClassForCheckStateTransition(e,t),this._currentCheckState=t,this._currentAnimationClass.length>0)){n.classList.add(this._currentAnimationClass);const t=this._currentAnimationClass;this._ngZone.runOutsideAngular((()=>{setTimeout((()=>{n.classList.remove(t)}),1e3)}))}}_emitChangeEvent(){const t=new gZ;t.source=this,t.checked=this.checked,this._controlValueAccessorChangeFn(this.checked),this.change.emit(t),this._inputElement&&(this._inputElement.nativeElement.checked=this.checked)}toggle(){this.checked=!this.checked}_onInputClick(t){var e;const n=null===(e=this._options)||void 0===e?void 0:e.clickAction;t.stopPropagation(),this.disabled||"noop"===n?this.disabled||"noop"!==n||(this._inputElement.nativeElement.checked=this.checked,this._inputElement.nativeElement.indeterminate=this.indeterminate):(this.indeterminate&&"check"!==n&&Promise.resolve().then((()=>{this._indeterminate=!1,this.indeterminateChange.emit(this._indeterminate)})),this.toggle(),this._transitionCheckState(this._checked?1:2),this._emitChangeEvent())}focus(t,e){t?this._focusMonitor.focusVia(this._inputElement,t,e):this._inputElement.nativeElement.focus(e)}_onInteractionEvent(t){t.stopPropagation()}_getAnimationClassForCheckStateTransition(t,e){if("NoopAnimations"===this._animationMode)return"";let n="";switch(t){case 0:if(1===e)n="unchecked-checked";else{if(3!=e)return"";n="unchecked-indeterminate"}break;case 2:n=1===e?"unchecked-checked":"unchecked-indeterminate";break;case 1:n=2===e?"checked-unchecked":"checked-indeterminate";break;case 3:n=1===e?"indeterminate-checked":"indeterminate-unchecked"}return`mat-checkbox-anim-${n}`}_syncIndeterminate(t){const e=this._inputElement;e&&(e.nativeElement.indeterminate=t)}}bZ.ɵfac=function t(e){return new(e||bZ)(gf(Kh),gf(Ob),gf(fF),gf(mC),Ga("tabindex"),gf(ck,8),gf(dZ,8))},bZ.ɵcmp=so({type:bZ,selectors:[["mat-checkbox"]],viewQuery:function t(e,n){if(1&e&&(Vy(lZ,5),Vy(mL,5)),2&e){let t;By(t=Uy())&&(n._inputElement=t.first),By(t=Uy())&&(n.ripple=t.first)}},hostAttrs:[1,"mat-checkbox"],hostVars:12,hostBindings:function t(e,n){2&e&&(cg("id",n.id),ym("tabindex",null),cu("mat-checkbox-indeterminate",n.indeterminate)("mat-checkbox-checked",n.checked)("mat-checkbox-disabled",n.disabled)("mat-checkbox-label-before","before"==n.labelPosition)("_mat-animation-noopable","NoopAnimations"===n._animationMode))},inputs:{disableRipple:"disableRipple",color:"color",tabIndex:"tabIndex",ariaLabel:["aria-label","ariaLabel"],ariaLabelledby:["aria-labelledby","ariaLabelledby"],id:"id",labelPosition:"labelPosition",name:"name",required:"required",checked:"checked",disabled:"disabled",indeterminate:"indeterminate",ariaDescribedby:["aria-describedby","ariaDescribedby"],value:"value"},outputs:{change:"change",indeterminateChange:"indeterminateChange"},exportAs:["matCheckbox"],features:[jh([uZ]),Qp],ngContentSelectors:["*"],decls:17,vars:21,consts:[[1,"mat-checkbox-layout"],["label",""],[1,"mat-checkbox-inner-container"],["type","checkbox",1,"mat-checkbox-input","cdk-visually-hidden",3,"id","required","checked","disabled","tabIndex","change","click"],["input",""],["matRipple","",1,"mat-checkbox-ripple","mat-focus-indicator",3,"matRippleTrigger","matRippleDisabled","matRippleRadius","matRippleCentered","matRippleAnimation"],[1,"mat-ripple-element","mat-checkbox-persistent-ripple"],[1,"mat-checkbox-frame"],[1,"mat-checkbox-background"],["version","1.1","focusable","false","viewBox","0 0 24 24",0,"xml","space","preserve","aria-hidden","true",1,"mat-checkbox-checkmark"],["fill","none","stroke","white","d","M4.1,12.7 9,17.6 20.3,6.3",1,"mat-checkbox-checkmark-path"],[1,"mat-checkbox-mixedmark"],[1,"mat-checkbox-label",3,"cdkObserveContent"],["checkboxLabel",""],[2,"display","none"]],template:function t(e,n){if(1&e&&(Hf(),Cf(0,"label",0,1),Cf(2,"span",2),Cf(3,"input",3,4),Rf("change",(function t(e){return n._onInteractionEvent(e)}))("click",(function t(e){return n._onInputClick(e)})),Mf(),Cf(5,"span",5),vf(6,"span",6),Mf(),vf(7,"span",7),Cf(8,"span",8),oa(),Cf(9,"svg",9),vf(10,"path",10),Mf(),aa(),vf(11,"span",11),Mf(),Mf(),Cf(12,"span",12,13),Rf("cdkObserveContent",(function t(){return n._onLabelTextChange()})),Cf(14,"span",14),Pu(15," "),Mf(),Ff(16),Mf(),Mf()),2&e){const t=Fm(1),e=Fm(13);ym("for",n.inputId),Dc(2),cu("mat-checkbox-inner-container-no-side-margin",!e.textContent||!e.textContent.trim()),Dc(1),bf("id",n.inputId)("required",n.required)("checked",n.checked)("disabled",n.disabled)("tabIndex",n.tabIndex),ym("value",n.value)("name",n.name)("aria-label",n.ariaLabel||null)("aria-labelledby",n.ariaLabelledby)("aria-checked",n._getAriaChecked())("aria-describedby",n.ariaDescribedby),Dc(2),bf("matRippleTrigger",t)("matRippleDisabled",n._isRippleDisabled())("matRippleRadius",20)("matRippleCentered",!0)("matRippleAnimation",iy(19,cZ,"NoopAnimations"===n._animationMode?0:150))}},directives:[mL,PH],styles:["@keyframes mat-checkbox-fade-in-background{0%{opacity:0}50%{opacity:1}}@keyframes mat-checkbox-fade-out-background{0%,50%{opacity:1}100%{opacity:0}}@keyframes mat-checkbox-unchecked-checked-checkmark-path{0%,50%{stroke-dashoffset:22.910259}50%{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1)}100%{stroke-dashoffset:0}}@keyframes mat-checkbox-unchecked-indeterminate-mixedmark{0%,68.2%{transform:scaleX(0)}68.2%{animation-timing-function:cubic-bezier(0, 0, 0, 1)}100%{transform:scaleX(1)}}@keyframes mat-checkbox-checked-unchecked-checkmark-path{from{animation-timing-function:cubic-bezier(0.4, 0, 1, 1);stroke-dashoffset:0}to{stroke-dashoffset:-22.910259}}@keyframes mat-checkbox-checked-indeterminate-checkmark{from{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1);opacity:1;transform:rotate(0deg)}to{opacity:0;transform:rotate(45deg)}}@keyframes mat-checkbox-indeterminate-checked-checkmark{from{animation-timing-function:cubic-bezier(0.14, 0, 0, 1);opacity:0;transform:rotate(45deg)}to{opacity:1;transform:rotate(360deg)}}@keyframes mat-checkbox-checked-indeterminate-mixedmark{from{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1);opacity:0;transform:rotate(-45deg)}to{opacity:1;transform:rotate(0deg)}}@keyframes mat-checkbox-indeterminate-checked-mixedmark{from{animation-timing-function:cubic-bezier(0.14, 0, 0, 1);opacity:1;transform:rotate(0deg)}to{opacity:0;transform:rotate(315deg)}}@keyframes mat-checkbox-indeterminate-unchecked-mixedmark{0%{animation-timing-function:linear;opacity:1;transform:scaleX(1)}32.8%,100%{opacity:0;transform:scaleX(0)}}.mat-checkbox-background,.mat-checkbox-frame{top:0;left:0;right:0;bottom:0;position:absolute;border-radius:2px;box-sizing:border-box;pointer-events:none}.mat-checkbox{display:inline-block;transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);cursor:pointer;-webkit-tap-highlight-color:transparent}._mat-animation-noopable.mat-checkbox{transition:none;animation:none}.mat-checkbox .mat-ripple-element:not(.mat-checkbox-persistent-ripple){opacity:.16}.mat-checkbox .mat-checkbox-ripple{position:absolute;left:calc(50% - 20px);top:calc(50% - 20px);height:40px;width:40px;z-index:1;pointer-events:none}.cdk-high-contrast-active .mat-checkbox.cdk-keyboard-focused .mat-checkbox-ripple{outline:solid 3px}.mat-checkbox-layout{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:inherit;align-items:baseline;vertical-align:middle;display:inline-flex;white-space:nowrap}.mat-checkbox-label{-webkit-user-select:auto;-moz-user-select:auto;-ms-user-select:auto;user-select:auto}.mat-checkbox-inner-container{display:inline-block;height:16px;line-height:0;margin:auto;margin-right:8px;order:0;position:relative;vertical-align:middle;white-space:nowrap;width:16px;flex-shrink:0}[dir=rtl] .mat-checkbox-inner-container{margin-left:8px;margin-right:auto}.mat-checkbox-inner-container-no-side-margin{margin-left:0;margin-right:0}.mat-checkbox-frame{background-color:transparent;transition:border-color 90ms cubic-bezier(0, 0, 0.2, 0.1);border-width:2px;border-style:solid}._mat-animation-noopable .mat-checkbox-frame{transition:none}.mat-checkbox-background{align-items:center;display:inline-flex;justify-content:center;transition:background-color 90ms cubic-bezier(0, 0, 0.2, 0.1),opacity 90ms cubic-bezier(0, 0, 0.2, 0.1);-webkit-print-color-adjust:exact;color-adjust:exact}._mat-animation-noopable .mat-checkbox-background{transition:none}.cdk-high-contrast-active .mat-checkbox .mat-checkbox-background{background:none}.mat-checkbox-persistent-ripple{display:block;width:100%;height:100%;transform:none}.mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{opacity:.04}.mat-checkbox.cdk-keyboard-focused .mat-checkbox-persistent-ripple{opacity:.12}.mat-checkbox-persistent-ripple,.mat-checkbox.mat-checkbox-disabled .mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{opacity:0}@media(hover: none){.mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{display:none}}.mat-checkbox-checkmark{top:0;left:0;right:0;bottom:0;position:absolute;width:100%}.mat-checkbox-checkmark-path{stroke-dashoffset:22.910259;stroke-dasharray:22.910259;stroke-width:2.1333333333px}.cdk-high-contrast-black-on-white .mat-checkbox-checkmark-path{stroke:#000 !important}.mat-checkbox-mixedmark{width:calc(100% - 6px);height:2px;opacity:0;transform:scaleX(0) rotate(0deg);border-radius:2px}.cdk-high-contrast-active .mat-checkbox-mixedmark{height:0;border-top:solid 2px;margin-top:2px}.mat-checkbox-label-before .mat-checkbox-inner-container{order:1;margin-left:8px;margin-right:auto}[dir=rtl] .mat-checkbox-label-before .mat-checkbox-inner-container{margin-left:auto;margin-right:8px}.mat-checkbox-checked .mat-checkbox-checkmark{opacity:1}.mat-checkbox-checked .mat-checkbox-checkmark-path{stroke-dashoffset:0}.mat-checkbox-checked .mat-checkbox-mixedmark{transform:scaleX(1) rotate(-45deg)}.mat-checkbox-indeterminate .mat-checkbox-checkmark{opacity:0;transform:rotate(45deg)}.mat-checkbox-indeterminate .mat-checkbox-checkmark-path{stroke-dashoffset:0}.mat-checkbox-indeterminate .mat-checkbox-mixedmark{opacity:1;transform:scaleX(1) rotate(0deg)}.mat-checkbox-unchecked .mat-checkbox-background{background-color:transparent}.mat-checkbox-disabled{cursor:default}.cdk-high-contrast-active .mat-checkbox-disabled{opacity:.5}.mat-checkbox-anim-unchecked-checked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-in-background}.mat-checkbox-anim-unchecked-checked .mat-checkbox-checkmark-path{animation:180ms linear 0ms mat-checkbox-unchecked-checked-checkmark-path}.mat-checkbox-anim-unchecked-indeterminate .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-in-background}.mat-checkbox-anim-unchecked-indeterminate .mat-checkbox-mixedmark{animation:90ms linear 0ms mat-checkbox-unchecked-indeterminate-mixedmark}.mat-checkbox-anim-checked-unchecked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-out-background}.mat-checkbox-anim-checked-unchecked .mat-checkbox-checkmark-path{animation:90ms linear 0ms mat-checkbox-checked-unchecked-checkmark-path}.mat-checkbox-anim-checked-indeterminate .mat-checkbox-checkmark{animation:90ms linear 0ms mat-checkbox-checked-indeterminate-checkmark}.mat-checkbox-anim-checked-indeterminate .mat-checkbox-mixedmark{animation:90ms linear 0ms mat-checkbox-checked-indeterminate-mixedmark}.mat-checkbox-anim-indeterminate-checked .mat-checkbox-checkmark{animation:500ms linear 0ms mat-checkbox-indeterminate-checked-checkmark}.mat-checkbox-anim-indeterminate-checked .mat-checkbox-mixedmark{animation:500ms linear 0ms mat-checkbox-indeterminate-checked-mixedmark}.mat-checkbox-anim-indeterminate-unchecked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-out-background}.mat-checkbox-anim-indeterminate-unchecked .mat-checkbox-mixedmark{animation:300ms linear 0ms mat-checkbox-indeterminate-unchecked-mixedmark}.mat-checkbox-input{bottom:0;left:50%}\n"],encapsulation:2,changeDetection:0}),bZ.ctorParameters=()=>[{type:Kh},{type:Ob},{type:fF},{type:mC},{type:String,decorators:[{type:$a,args:["tabindex"]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[dZ]}]}],bZ.propDecorators={ariaLabel:[{type:R_,args:["aria-label"]}],ariaLabelledby:[{type:R_,args:["aria-labelledby"]}],ariaDescribedby:[{type:R_,args:["aria-describedby"]}],id:[{type:R_}],required:[{type:R_}],labelPosition:[{type:R_}],name:[{type:R_}],change:[{type:E_}],indeterminateChange:[{type:E_}],value:[{type:R_}],_inputElement:[{type:ar,args:["input"]}],ripple:[{type:ar,args:[mL]}],checked:[{type:R_}],disabled:[{type:R_}],indeterminate:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(bZ,[{type:S_,args:[{selector:"mat-checkbox",template:'<label [attr.for]="inputId" class="mat-checkbox-layout" #label>\n  <span class="mat-checkbox-inner-container"\n       [class.mat-checkbox-inner-container-no-side-margin]="!checkboxLabel.textContent || !checkboxLabel.textContent.trim()">\n    <input #input\n           class="mat-checkbox-input cdk-visually-hidden" type="checkbox"\n           [id]="inputId"\n           [required]="required"\n           [checked]="checked"\n           [attr.value]="value"\n           [disabled]="disabled"\n           [attr.name]="name"\n           [tabIndex]="tabIndex"\n           [attr.aria-label]="ariaLabel || null"\n           [attr.aria-labelledby]="ariaLabelledby"\n           [attr.aria-checked]="_getAriaChecked()"\n           [attr.aria-describedby]="ariaDescribedby"\n           (change)="_onInteractionEvent($event)"\n           (click)="_onInputClick($event)">\n    <span matRipple class="mat-checkbox-ripple mat-focus-indicator"\n         [matRippleTrigger]="label"\n         [matRippleDisabled]="_isRippleDisabled()"\n         [matRippleRadius]="20"\n         [matRippleCentered]="true"\n         [matRippleAnimation]="{enterDuration: _animationMode === \'NoopAnimations\' ? 0 : 150}">\n      <span class="mat-ripple-element mat-checkbox-persistent-ripple"></span>\n    </span>\n    <span class="mat-checkbox-frame"></span>\n    <span class="mat-checkbox-background">\n      <svg version="1.1"\n           focusable="false"\n           class="mat-checkbox-checkmark"\n           viewBox="0 0 24 24"\n           xml:space="preserve"\n           aria-hidden="true">\n        <path class="mat-checkbox-checkmark-path"\n              fill="none"\n              stroke="white"\n              d="M4.1,12.7 9,17.6 20.3,6.3"/>\n      </svg>\n      \x3c!-- Element for rendering the indeterminate state checkbox. --\x3e\n      <span class="mat-checkbox-mixedmark"></span>\n    </span>\n  </span>\n  <span class="mat-checkbox-label" #checkboxLabel (cdkObserveContent)="_onLabelTextChange()">\n    \x3c!-- Add an invisible span so JAWS can read the label --\x3e\n    <span style="display:none">&nbsp;</span>\n    <ng-content></ng-content>\n  </span>\n</label>\n',exportAs:"matCheckbox",host:{class:"mat-checkbox","[id]":"id","[attr.tabindex]":"null","[class.mat-checkbox-indeterminate]":"indeterminate","[class.mat-checkbox-checked]":"checked","[class.mat-checkbox-disabled]":"disabled","[class.mat-checkbox-label-before]":'labelPosition == "before"',"[class._mat-animation-noopable]":"_animationMode === 'NoopAnimations'"},providers:[uZ],inputs:["disableRipple","color","tabIndex"],encapsulation:Wn.None,changeDetection:jn.OnPush,styles:["@keyframes mat-checkbox-fade-in-background{0%{opacity:0}50%{opacity:1}}@keyframes mat-checkbox-fade-out-background{0%,50%{opacity:1}100%{opacity:0}}@keyframes mat-checkbox-unchecked-checked-checkmark-path{0%,50%{stroke-dashoffset:22.910259}50%{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1)}100%{stroke-dashoffset:0}}@keyframes mat-checkbox-unchecked-indeterminate-mixedmark{0%,68.2%{transform:scaleX(0)}68.2%{animation-timing-function:cubic-bezier(0, 0, 0, 1)}100%{transform:scaleX(1)}}@keyframes mat-checkbox-checked-unchecked-checkmark-path{from{animation-timing-function:cubic-bezier(0.4, 0, 1, 1);stroke-dashoffset:0}to{stroke-dashoffset:-22.910259}}@keyframes mat-checkbox-checked-indeterminate-checkmark{from{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1);opacity:1;transform:rotate(0deg)}to{opacity:0;transform:rotate(45deg)}}@keyframes mat-checkbox-indeterminate-checked-checkmark{from{animation-timing-function:cubic-bezier(0.14, 0, 0, 1);opacity:0;transform:rotate(45deg)}to{opacity:1;transform:rotate(360deg)}}@keyframes mat-checkbox-checked-indeterminate-mixedmark{from{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1);opacity:0;transform:rotate(-45deg)}to{opacity:1;transform:rotate(0deg)}}@keyframes mat-checkbox-indeterminate-checked-mixedmark{from{animation-timing-function:cubic-bezier(0.14, 0, 0, 1);opacity:1;transform:rotate(0deg)}to{opacity:0;transform:rotate(315deg)}}@keyframes mat-checkbox-indeterminate-unchecked-mixedmark{0%{animation-timing-function:linear;opacity:1;transform:scaleX(1)}32.8%,100%{opacity:0;transform:scaleX(0)}}.mat-checkbox-background,.mat-checkbox-frame{top:0;left:0;right:0;bottom:0;position:absolute;border-radius:2px;box-sizing:border-box;pointer-events:none}.mat-checkbox{display:inline-block;transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);cursor:pointer;-webkit-tap-highlight-color:transparent}._mat-animation-noopable.mat-checkbox{transition:none;animation:none}.mat-checkbox .mat-ripple-element:not(.mat-checkbox-persistent-ripple){opacity:.16}.mat-checkbox .mat-checkbox-ripple{position:absolute;left:calc(50% - 20px);top:calc(50% - 20px);height:40px;width:40px;z-index:1;pointer-events:none}.cdk-high-contrast-active .mat-checkbox.cdk-keyboard-focused .mat-checkbox-ripple{outline:solid 3px}.mat-checkbox-layout{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:inherit;align-items:baseline;vertical-align:middle;display:inline-flex;white-space:nowrap}.mat-checkbox-label{-webkit-user-select:auto;-moz-user-select:auto;-ms-user-select:auto;user-select:auto}.mat-checkbox-inner-container{display:inline-block;height:16px;line-height:0;margin:auto;margin-right:8px;order:0;position:relative;vertical-align:middle;white-space:nowrap;width:16px;flex-shrink:0}[dir=rtl] .mat-checkbox-inner-container{margin-left:8px;margin-right:auto}.mat-checkbox-inner-container-no-side-margin{margin-left:0;margin-right:0}.mat-checkbox-frame{background-color:transparent;transition:border-color 90ms cubic-bezier(0, 0, 0.2, 0.1);border-width:2px;border-style:solid}._mat-animation-noopable .mat-checkbox-frame{transition:none}.mat-checkbox-background{align-items:center;display:inline-flex;justify-content:center;transition:background-color 90ms cubic-bezier(0, 0, 0.2, 0.1),opacity 90ms cubic-bezier(0, 0, 0.2, 0.1);-webkit-print-color-adjust:exact;color-adjust:exact}._mat-animation-noopable .mat-checkbox-background{transition:none}.cdk-high-contrast-active .mat-checkbox .mat-checkbox-background{background:none}.mat-checkbox-persistent-ripple{display:block;width:100%;height:100%;transform:none}.mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{opacity:.04}.mat-checkbox.cdk-keyboard-focused .mat-checkbox-persistent-ripple{opacity:.12}.mat-checkbox-persistent-ripple,.mat-checkbox.mat-checkbox-disabled .mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{opacity:0}@media(hover: none){.mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{display:none}}.mat-checkbox-checkmark{top:0;left:0;right:0;bottom:0;position:absolute;width:100%}.mat-checkbox-checkmark-path{stroke-dashoffset:22.910259;stroke-dasharray:22.910259;stroke-width:2.1333333333px}.cdk-high-contrast-black-on-white .mat-checkbox-checkmark-path{stroke:#000 !important}.mat-checkbox-mixedmark{width:calc(100% - 6px);height:2px;opacity:0;transform:scaleX(0) rotate(0deg);border-radius:2px}.cdk-high-contrast-active .mat-checkbox-mixedmark{height:0;border-top:solid 2px;margin-top:2px}.mat-checkbox-label-before .mat-checkbox-inner-container{order:1;margin-left:8px;margin-right:auto}[dir=rtl] .mat-checkbox-label-before .mat-checkbox-inner-container{margin-left:auto;margin-right:8px}.mat-checkbox-checked .mat-checkbox-checkmark{opacity:1}.mat-checkbox-checked .mat-checkbox-checkmark-path{stroke-dashoffset:0}.mat-checkbox-checked .mat-checkbox-mixedmark{transform:scaleX(1) rotate(-45deg)}.mat-checkbox-indeterminate .mat-checkbox-checkmark{opacity:0;transform:rotate(45deg)}.mat-checkbox-indeterminate .mat-checkbox-checkmark-path{stroke-dashoffset:0}.mat-checkbox-indeterminate .mat-checkbox-mixedmark{opacity:1;transform:scaleX(1) rotate(0deg)}.mat-checkbox-unchecked .mat-checkbox-background{background-color:transparent}.mat-checkbox-disabled{cursor:default}.cdk-high-contrast-active .mat-checkbox-disabled{opacity:.5}.mat-checkbox-anim-unchecked-checked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-in-background}.mat-checkbox-anim-unchecked-checked .mat-checkbox-checkmark-path{animation:180ms linear 0ms mat-checkbox-unchecked-checked-checkmark-path}.mat-checkbox-anim-unchecked-indeterminate .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-in-background}.mat-checkbox-anim-unchecked-indeterminate .mat-checkbox-mixedmark{animation:90ms linear 0ms mat-checkbox-unchecked-indeterminate-mixedmark}.mat-checkbox-anim-checked-unchecked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-out-background}.mat-checkbox-anim-checked-unchecked .mat-checkbox-checkmark-path{animation:90ms linear 0ms mat-checkbox-checked-unchecked-checkmark-path}.mat-checkbox-anim-checked-indeterminate .mat-checkbox-checkmark{animation:90ms linear 0ms mat-checkbox-checked-indeterminate-checkmark}.mat-checkbox-anim-checked-indeterminate .mat-checkbox-mixedmark{animation:90ms linear 0ms mat-checkbox-checked-indeterminate-mixedmark}.mat-checkbox-anim-indeterminate-checked .mat-checkbox-checkmark{animation:500ms linear 0ms mat-checkbox-indeterminate-checked-checkmark}.mat-checkbox-anim-indeterminate-checked .mat-checkbox-mixedmark{animation:500ms linear 0ms mat-checkbox-indeterminate-checked-mixedmark}.mat-checkbox-anim-indeterminate-unchecked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-out-background}.mat-checkbox-anim-indeterminate-unchecked .mat-checkbox-mixedmark{animation:300ms linear 0ms mat-checkbox-indeterminate-unchecked-mixedmark}.mat-checkbox-input{bottom:0;left:50%}\n"]}]}],(function(){return[{type:Kh},{type:Ob},{type:fF},{type:mC},{type:String,decorators:[{type:$a,args:["tabindex"]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[dZ]}]}]}),{ariaLabel:[{type:R_,args:["aria-label"]}],ariaLabelledby:[{type:R_,args:["aria-labelledby"]}],id:[{type:R_}],labelPosition:[{type:R_}],name:[{type:R_}],change:[{type:E_}],indeterminateChange:[{type:E_}],required:[{type:R_}],checked:[{type:R_}],disabled:[{type:R_}],indeterminate:[{type:R_}],ariaDescribedby:[{type:R_,args:["aria-describedby"]}],value:[{type:R_}],_inputElement:[{type:ar,args:["input"]}],ripple:[{type:ar,args:[mL]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const yZ={provide:EU,useExisting:$e((()=>_Z)),multi:!0};class _Z extends VG{}_Z.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(_Z)))(n||_Z)}})(),_Z.ɵdir=ho({type:_Z,selectors:[["mat-checkbox","required","","formControlName",""],["mat-checkbox","required","","formControl",""],["mat-checkbox","required","","ngModel",""]],features:[jh([yZ]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(_Z,[{type:k_,args:[{selector:"mat-checkbox[required][formControlName],\n             mat-checkbox[required][formControl], mat-checkbox[required][ngModel]",providers:[yZ]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class CZ{}CZ.ɵfac=function t(e){return new(e||CZ)},CZ.ɵmod=fo({type:CZ}),CZ.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(CZ,[{type:L_,args:[{exports:[_Z],declarations:[_Z]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(CZ,{declarations:[_Z],exports:[_Z]});class MZ{}MZ.ɵfac=function t(e){return new(e||MZ)},MZ.ɵmod=fo({type:MZ}),MZ.ɵinj=Dn({imports:[[fL,NF,wH,CZ],NF,CZ]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(MZ,[{type:L_,args:[{imports:[fL,NF,wH,CZ],exports:[bZ,NF,CZ],declarations:[bZ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(MZ,{declarations:function(){return[bZ]},imports:function(){return[fL,NF,wH,CZ]},exports:function(){return[bZ,NF,CZ]}});const vZ=bH({passive:!0});class xZ{constructor(t,e){this._platform=t,this._ngZone=e,this._monitoredElements=new Map}monitor(t){if(!this._platform.isBrowser)return dt;const e=rH(t),n=this._monitoredElements.get(e);if(n)return n.subject;const o=new B,i="cdk-text-field-autofilled",a=t=>{"cdk-text-field-autofill-start"!==t.animationName||e.classList.contains(i)?"cdk-text-field-autofill-end"===t.animationName&&e.classList.contains(i)&&(e.classList.remove(i),this._ngZone.run((()=>o.next({target:t.target,isAutofilled:!1})))):(e.classList.add(i),this._ngZone.run((()=>o.next({target:t.target,isAutofilled:!0}))))};return this._ngZone.runOutsideAngular((()=>{e.addEventListener("animationstart",a,vZ),e.classList.add("cdk-text-field-autofill-monitored")})),this._monitoredElements.set(e,{subject:o,unlisten:()=>{e.removeEventListener("animationstart",a,vZ)}}),o}stopMonitoring(t){const e=rH(t),n=this._monitoredElements.get(e);n&&(n.unlisten(),n.subject.complete(),e.classList.remove("cdk-text-field-autofill-monitored"),e.classList.remove("cdk-text-field-autofilled"),this._monitoredElements.delete(e))}ngOnDestroy(){this._monitoredElements.forEach(((t,e)=>this.stopMonitoring(e)))}}xZ.ɵfac=function t(e){return new(e||xZ)(Tr(cH),Tr(mC))},xZ.ɵprov=Sn({factory:function t(){return new xZ(Tr(cH),Tr(mC))},token:xZ,providedIn:"root"}),xZ.ctorParameters=()=>[{type:cH},{type:mC}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(xZ,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[{type:cH},{type:mC}]}),null);class OZ{constructor(t,e){this._elementRef=t,this._autofillMonitor=e,this.cdkAutofill=new Sy}ngOnInit(){this._autofillMonitor.monitor(this._elementRef).subscribe((t=>this.cdkAutofill.emit(t)))}ngOnDestroy(){this._autofillMonitor.stopMonitoring(this._elementRef)}}OZ.ɵfac=function t(e){return new(e||OZ)(gf(Kh),gf(xZ))},OZ.ɵdir=ho({type:OZ,selectors:[["","cdkAutofill",""]],outputs:{cdkAutofill:"cdkAutofill"}}),OZ.ctorParameters=()=>[{type:Kh},{type:xZ}],OZ.propDecorators={cdkAutofill:[{type:E_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(OZ,[{type:k_,args:[{selector:"[cdkAutofill]"}]}],(function(){return[{type:Kh},{type:xZ}]}),{cdkAutofill:[{type:E_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class PZ{constructor(t,e,n,o){this._elementRef=t,this._platform=e,this._ngZone=n,this._destroyed=new B,this._enabled=!0,this._previousMinRows=-1,this._isViewInited=!1,this._handleFocusEvent=t=>{this._hasFocus="focus"===t.type},this._document=o,this._textareaElement=this._elementRef.nativeElement}get minRows(){return this._minRows}set minRows(t){this._minRows=nH(t),this._setMinHeight()}get maxRows(){return this._maxRows}set maxRows(t){this._maxRows=nH(t),this._setMaxHeight()}get enabled(){return this._enabled}set enabled(t){t=eH(t),this._enabled!==t&&((this._enabled=t)?this.resizeToFitContent(!0):this.reset())}get placeholder(){return this._textareaElement.placeholder}set placeholder(t){this._cachedPlaceholderHeight=void 0,this._textareaElement.placeholder=t,this._cacheTextareaPlaceholderHeight()}_setMinHeight(){const t=this.minRows&&this._cachedLineHeight?this.minRows*this._cachedLineHeight+"px":null;t&&(this._textareaElement.style.minHeight=t)}_setMaxHeight(){const t=this.maxRows&&this._cachedLineHeight?this.maxRows*this._cachedLineHeight+"px":null;t&&(this._textareaElement.style.maxHeight=t)}ngAfterViewInit(){this._platform.isBrowser&&(this._initialHeight=this._textareaElement.style.height,this.resizeToFitContent(),this._ngZone.runOutsideAngular((()=>{le(this._getWindow(),"resize").pipe(ge(16),je(this._destroyed)).subscribe((()=>this.resizeToFitContent(!0))),this._textareaElement.addEventListener("focus",this._handleFocusEvent),this._textareaElement.addEventListener("blur",this._handleFocusEvent)})),this._isViewInited=!0,this.resizeToFitContent(!0))}ngOnDestroy(){this._textareaElement.removeEventListener("focus",this._handleFocusEvent),this._textareaElement.removeEventListener("blur",this._handleFocusEvent),this._destroyed.next(),this._destroyed.complete()}_cacheTextareaLineHeight(){if(this._cachedLineHeight)return;let t=this._textareaElement.cloneNode(!1);t.rows=1,t.style.position="absolute",t.style.visibility="hidden",t.style.border="none",t.style.padding="0",t.style.height="",t.style.minHeight="",t.style.maxHeight="",t.style.overflow="hidden",this._textareaElement.parentNode.appendChild(t),this._cachedLineHeight=t.clientHeight,this._textareaElement.parentNode.removeChild(t),this._setMinHeight(),this._setMaxHeight()}_measureScrollHeight(){const t=this._textareaElement,e=t.style.marginBottom||"",n=this._platform.FIREFOX,o=n&&this._hasFocus,i=n?"cdk-textarea-autosize-measuring-firefox":"cdk-textarea-autosize-measuring";o&&(t.style.marginBottom=`${t.clientHeight}px`),t.classList.add(i);const a=t.scrollHeight-4;return t.classList.remove(i),o&&(t.style.marginBottom=e),a}_cacheTextareaPlaceholderHeight(){if(!this._isViewInited||null!=this._cachedPlaceholderHeight)return;if(!this.placeholder)return void(this._cachedPlaceholderHeight=0);const t=this._textareaElement.value;this._textareaElement.value=this._textareaElement.placeholder,this._cachedPlaceholderHeight=this._measureScrollHeight(),this._textareaElement.value=t}ngDoCheck(){this._platform.isBrowser&&this.resizeToFitContent()}resizeToFitContent(t=!1){if(!this._enabled)return;if(this._cacheTextareaLineHeight(),this._cacheTextareaPlaceholderHeight(),!this._cachedLineHeight)return;const e=this._elementRef.nativeElement,n=e.value;if(!t&&this._minRows===this._previousMinRows&&n===this._previousValue)return;const o=this._measureScrollHeight(),i=Math.max(o,this._cachedPlaceholderHeight||0);e.style.height=`${i}px`,this._ngZone.runOutsideAngular((()=>{"undefined"!=typeof requestAnimationFrame?requestAnimationFrame((()=>this._scrollToCaretPosition(e))):setTimeout((()=>this._scrollToCaretPosition(e)))})),this._previousValue=n,this._previousMinRows=this._minRows}reset(){void 0!==this._initialHeight&&(this._textareaElement.style.height=this._initialHeight)}_noopInputHandler(){}_getDocument(){return this._document||document}_getWindow(){return this._getDocument().defaultView||window}_scrollToCaretPosition(t){const{selectionStart:e,selectionEnd:n}=t;!this._destroyed.isStopped&&this._hasFocus&&t.setSelectionRange(e,n)}}PZ.ɵfac=function t(e){return new(e||PZ)(gf(Kh),gf(cH),gf(mC),gf(sM,8))},PZ.ɵdir=ho({type:PZ,selectors:[["textarea","cdkTextareaAutosize",""]],hostAttrs:["rows","1",1,"cdk-textarea-autosize"],hostBindings:function t(e,n){1&e&&Rf("input",(function t(){return n._noopInputHandler()}))},inputs:{minRows:["cdkAutosizeMinRows","minRows"],maxRows:["cdkAutosizeMaxRows","maxRows"],enabled:["cdkTextareaAutosize","enabled"],placeholder:"placeholder"},exportAs:["cdkTextareaAutosize"]}),PZ.ctorParameters=()=>[{type:Kh},{type:cH},{type:mC},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]}],PZ.propDecorators={minRows:[{type:R_,args:["cdkAutosizeMinRows"]}],maxRows:[{type:R_,args:["cdkAutosizeMaxRows"]}],enabled:[{type:R_,args:["cdkTextareaAutosize"]}],placeholder:[{type:R_}],_noopInputHandler:[{type:T_,args:["input"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(PZ,[{type:k_,args:[{selector:"textarea[cdkTextareaAutosize]",exportAs:"cdkTextareaAutosize",host:{class:"cdk-textarea-autosize",rows:"1"}}]}],(function(){return[{type:Kh},{type:cH},{type:mC},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]}]}),{minRows:[{type:R_,args:["cdkAutosizeMinRows"]}],maxRows:[{type:R_,args:["cdkAutosizeMaxRows"]}],enabled:[{type:R_,args:["cdkTextareaAutosize"]}],placeholder:[{type:R_}],_noopInputHandler:[{type:T_,args:["input"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class wZ{}wZ.ɵfac=function t(e){return new(e||wZ)},wZ.ɵmod=fo({type:wZ}),wZ.ɵinj=Dn({imports:[[dH]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(wZ,[{type:L_,args:[{declarations:[OZ,PZ],imports:[dH],exports:[OZ,PZ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(wZ,{declarations:function(){return[OZ,PZ]},imports:function(){return[dH]},exports:function(){return[OZ,PZ]}});class kZ extends PZ{get matAutosizeMinRows(){return this.minRows}set matAutosizeMinRows(t){this.minRows=t}get matAutosizeMaxRows(){return this.maxRows}set matAutosizeMaxRows(t){this.maxRows=t}get matAutosize(){return this.enabled}set matAutosize(t){this.enabled=t}get matTextareaAutosize(){return this.enabled}set matTextareaAutosize(t){this.enabled=t}}kZ.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(kZ)))(n||kZ)}})(),kZ.ɵdir=ho({type:kZ,selectors:[["textarea","mat-autosize",""],["textarea","matTextareaAutosize",""]],hostAttrs:["rows","1",1,"cdk-textarea-autosize","mat-autosize"],inputs:{cdkAutosizeMinRows:"cdkAutosizeMinRows",cdkAutosizeMaxRows:"cdkAutosizeMaxRows",matAutosizeMinRows:"matAutosizeMinRows",matAutosizeMaxRows:"matAutosizeMaxRows",matAutosize:["mat-autosize","matAutosize"],matTextareaAutosize:"matTextareaAutosize"},exportAs:["matTextareaAutosize"],features:[Qp]}),kZ.propDecorators={matAutosizeMinRows:[{type:R_}],matAutosizeMaxRows:[{type:R_}],matAutosize:[{type:R_,args:["mat-autosize"]}],matTextareaAutosize:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(kZ,[{type:k_,args:[{selector:"textarea[mat-autosize], textarea[matTextareaAutosize]",exportAs:"matTextareaAutosize",inputs:["cdkAutosizeMinRows","cdkAutosizeMaxRows"],host:{class:"cdk-textarea-autosize mat-autosize",rows:"1"}}]}],null,{matAutosizeMinRows:[{type:R_}],matAutosizeMaxRows:[{type:R_}],matAutosize:[{type:R_,args:["mat-autosize"]}],matTextareaAutosize:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const SZ=new er("MAT_INPUT_VALUE_ACCESSOR"),DZ=["button","checkbox","file","hidden","image","radio","range","reset","submit"];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let RZ=0;const EZ=LF(class{constructor(t,e,n,o){this._defaultErrorStateMatcher=t,this._parentForm=e,this._parentFormGroup=n,this.ngControl=o}});class AZ extends EZ{constructor(t,e,n,o,i,a,r,s,l,c){super(a,o,i,n),this._elementRef=t,this._platform=e,this._autofillMonitor=s,this._formField=c,this._uid="mat-input-"+RZ++,this.focused=!1,this.stateChanges=new B,this.controlType="mat-input",this.autofilled=!1,this._disabled=!1,this._required=!1,this._type="text",this._readonly=!1,this._neverEmptyInputTypes=["date","datetime","datetime-local","month","time","week"].filter((t=>mH().has(t)));const d=this._elementRef.nativeElement,p=d.nodeName.toLowerCase();this._inputValueAccessor=r||d,this._previousNativeValue=this.value,this.id=this.id,e.IOS&&l.runOutsideAngular((()=>{t.nativeElement.addEventListener("keyup",(t=>{const e=t.target;e.value||0!==e.selectionStart||0!==e.selectionEnd||(e.setSelectionRange(1,1),e.setSelectionRange(0,0))}))})),this._isServer=!this._platform.isBrowser,this._isNativeSelect="select"===p,this._isTextarea="textarea"===p,this._isInFormField=!!c,this._isNativeSelect&&(this.controlType=d.multiple?"mat-native-select-multiple":"mat-native-select")}get disabled(){return this.ngControl&&null!==this.ngControl.disabled?this.ngControl.disabled:this._disabled}set disabled(t){this._disabled=eH(t),this.focused&&(this.focused=!1,this.stateChanges.next())}get id(){return this._id}set id(t){this._id=t||this._uid}get required(){return this._required}set required(t){this._required=eH(t)}get type(){return this._type}set type(t){this._type=t||"text",this._validateType(),!this._isTextarea&&mH().has(this._type)&&(this._elementRef.nativeElement.type=this._type)}get value(){return this._inputValueAccessor.value}set value(t){t!==this.value&&(this._inputValueAccessor.value=t,this.stateChanges.next())}get readonly(){return this._readonly}set readonly(t){this._readonly=eH(t)}ngAfterViewInit(){this._platform.isBrowser&&this._autofillMonitor.monitor(this._elementRef.nativeElement).subscribe((t=>{this.autofilled=t.isAutofilled,this.stateChanges.next()}))}ngOnChanges(){this.stateChanges.next()}ngOnDestroy(){this.stateChanges.complete(),this._platform.isBrowser&&this._autofillMonitor.stopMonitoring(this._elementRef.nativeElement)}ngDoCheck(){this.ngControl&&this.updateErrorState(),this._dirtyCheckNativeValue(),this._dirtyCheckPlaceholder()}focus(t){this._elementRef.nativeElement.focus(t)}_focusChanged(t){t!==this.focused&&(this.focused=t,this.stateChanges.next())}_onInput(){}_dirtyCheckPlaceholder(){var t,e;const n=(null===(e=null===(t=this._formField)||void 0===t?void 0:t._hideControlPlaceholder)||void 0===e?void 0:e.call(t))?null:this.placeholder;if(n!==this._previousPlaceholder){const t=this._elementRef.nativeElement;this._previousPlaceholder=n,n?t.setAttribute("placeholder",n):t.removeAttribute("placeholder")}}_dirtyCheckNativeValue(){const t=this._elementRef.nativeElement.value;this._previousNativeValue!==t&&(this._previousNativeValue=t,this.stateChanges.next())}_validateType(){if(DZ.indexOf(this._type)>-1&&("undefined"==typeof ngDevMode||ngDevMode))
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
throw(function t(e){return Error(`Input type "${e}" isn't supported by matInput.`)})(this._type)}_isNeverEmpty(){return this._neverEmptyInputTypes.indexOf(this._type)>-1}_isBadInput(){let t=this._elementRef.nativeElement.validity;return t&&t.badInput}get empty(){return!(this._isNeverEmpty()||this._elementRef.nativeElement.value||this._isBadInput()||this.autofilled)}get shouldLabelFloat(){if(this._isNativeSelect){const t=this._elementRef.nativeElement,e=t.options[0];return this.focused||t.multiple||!this.empty||!!(t.selectedIndex>-1&&e&&e.label)}return this.focused||!this.empty}setDescribedByIds(t){t.length?this._elementRef.nativeElement.setAttribute("aria-describedby",t.join(" ")):this._elementRef.nativeElement.removeAttribute("aria-describedby")}onContainerClick(){this.focused||this.focus()}}AZ.ɵfac=function t(e){return new(e||AZ)(gf(Kh),gf(cH),gf(lW,10),gf(XW,8),gf(hG,8),gf(nL),gf(SZ,10),gf(xZ),gf(mC),gf(yU,8))},AZ.ɵdir=ho({type:AZ,selectors:[["input","matInput",""],["textarea","matInput",""],["select","matNativeControl",""],["input","matNativeControl",""],["textarea","matNativeControl",""]],hostAttrs:[1,"mat-input-element","mat-form-field-autofill-control"],hostVars:9,hostBindings:function t(e,n){1&e&&Rf("focus",(function t(){return n._focusChanged(!0)}))("blur",(function t(){return n._focusChanged(!1)}))("input",(function t(){return n._onInput()})),2&e&&(cg("disabled",n.disabled)("required",n.required),ym("id",n.id)("data-placeholder",n.placeholder)("readonly",n.readonly&&!n._isNativeSelect||null)("aria-invalid",n.empty&&n.required?null:n.errorState)("aria-required",n.required),cu("mat-input-server",n._isServer))},inputs:{id:"id",disabled:"disabled",required:"required",type:"type",value:"value",readonly:"readonly",placeholder:"placeholder",errorStateMatcher:"errorStateMatcher",userAriaDescribedBy:["aria-describedby","userAriaDescribedBy"]},exportAs:["matInput"],features:[jh([{provide:iU,useExisting:AZ}]),Qp,qo]}),AZ.ctorParameters=()=>[{type:Kh},{type:cH},{type:lW,decorators:[{type:Br},{type:Vr}]},{type:XW,decorators:[{type:Br}]},{type:hG,decorators:[{type:Br}]},{type:nL},{type:void 0,decorators:[{type:Br},{type:Vr},{type:Lr,args:[SZ]}]},{type:xZ},{type:mC},{type:_U,decorators:[{type:Br},{type:Lr,args:[yU]}]}],AZ.propDecorators={disabled:[{type:R_}],id:[{type:R_}],placeholder:[{type:R_}],required:[{type:R_}],type:[{type:R_}],errorStateMatcher:[{type:R_}],userAriaDescribedBy:[{type:R_,args:["aria-describedby"]}],value:[{type:R_}],readonly:[{type:R_}],_focusChanged:[{type:T_,args:["focus",["true"]]},{type:T_,args:["blur",["false"]]}],_onInput:[{type:T_,args:["input"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(AZ,[{type:k_,args:[{selector:"input[matInput], textarea[matInput], select[matNativeControl],\n      input[matNativeControl], textarea[matNativeControl]",exportAs:"matInput",host:{class:"mat-input-element mat-form-field-autofill-control","[class.mat-input-server]":"_isServer","[attr.id]":"id","[attr.data-placeholder]":"placeholder","[disabled]":"disabled","[required]":"required","[attr.readonly]":"readonly && !_isNativeSelect || null","[attr.aria-invalid]":"(empty && required) ? null : errorState","[attr.aria-required]":"required"},providers:[{provide:iU,useExisting:AZ}]}]}],(function(){return[{type:Kh},{type:cH},{type:lW,decorators:[{type:Br},{type:Vr}]},{type:XW,decorators:[{type:Br}]},{type:hG,decorators:[{type:Br}]},{type:nL},{type:void 0,decorators:[{type:Br},{type:Vr},{type:Lr,args:[SZ]}]},{type:xZ},{type:mC},{type:_U,decorators:[{type:Br},{type:Lr,args:[yU]}]}]}),{id:[{type:R_}],disabled:[{type:R_}],required:[{type:R_}],type:[{type:R_}],value:[{type:R_}],readonly:[{type:R_}],_focusChanged:[{type:T_,args:["focus",["true"]]},{type:T_,args:["blur",["false"]]}],_onInput:[{type:T_,args:["input"]}],placeholder:[{type:R_}],errorStateMatcher:[{type:R_}],userAriaDescribedBy:[{type:R_,args:["aria-describedby"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class TZ{}function NZ(t,e){if(1&t&&(Cf(0,"mat-error"),Pu(1),Mf()),2&t){const t=zf();Dc(1),ku(" Reload period has to be minimum of ",t.MIN_RELOAD_PERIOD_IN_S," seconds. ")}}function zZ(t,e){1&t&&(Cf(0,"mat-error"),Pu(1," Page size has to be a positive integer. "),Mf())}TZ.ɵfac=function t(e){return new(e||TZ)},TZ.ɵmod=fo({type:TZ}),TZ.ɵinj=Dn({providers:[nL],imports:[[wZ,CU,NF],wZ,CU]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(TZ,[{type:L_,args:[{declarations:[AZ,kZ],imports:[wZ,CU,NF],exports:[wZ,CU,AZ,kZ],providers:[nL]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(TZ,{declarations:function(){return[AZ,kZ]},imports:function(){return[wZ,CU,NF]},exports:function(){return[wZ,CU,AZ,kZ]}});class IZ{constructor(){this.reloadToggled=new Sy,this.reloadPeriodInMsChanged=new Sy,this.pageSizeChanged=new Sy,this.MIN_RELOAD_PERIOD_IN_S=30,this.reloadPeriodControl=new WW(this.MIN_RELOAD_PERIOD_IN_S,[NU.required,NU.min(this.MIN_RELOAD_PERIOD_IN_S)]),this.paginationControl=new WW(1,[NU.required,NU.min(1),t=>{const e=Number(t.value);return Math.round(e)===t.value?null:{integer:{value:t.value}}}]),this.ngUnsubscribe=new B}ngOnInit(){this.reloadPeriodControl.valueChanges.pipe(je(this.ngUnsubscribe),Ce(500),ue((()=>this.reloadPeriodControl.valid))).subscribe((()=>{if(!this.reloadPeriodControl.valid)return;this.reloadPeriodInMsChanged.emit(1e3*this.reloadPeriodControl.value)})),this.paginationControl.valueChanges.pipe(je(this.ngUnsubscribe),Ce(500),ue((()=>this.paginationControl.valid))).subscribe((()=>{this.pageSizeChanged.emit(this.paginationControl.value)}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}ngOnChanges(t){if(t.reloadPeriodInMs){const e=t.reloadPeriodInMs;e.previousValue!==e.currentValue&&this.reloadPeriodControl.setValue(e.currentValue/1e3)}if(t.reloadEnabled){t.reloadEnabled.currentValue?this.reloadPeriodControl.enable():this.reloadPeriodControl.disable()}if(t.pageSize){const e=t.pageSize;e.previousValue!==e.currentValue&&this.paginationControl.setValue(e.currentValue)}}onReloadToggle(){this.reloadToggled.emit()}}IZ.ɵfac=function t(e){return new(e||IZ)},IZ.ɵcmp=so({type:IZ,selectors:[["settings-dialog-component"]],inputs:{reloadEnabled:"reloadEnabled",reloadPeriodInMs:"reloadPeriodInMs",pageSize:"pageSize"},outputs:{reloadToggled:"reloadToggled",reloadPeriodInMsChanged:"reloadPeriodInMsChanged",pageSizeChanged:"pageSizeChanged"},features:[qo],decls:14,vars:5,consts:[[1,"reload-toggle"],[3,"checked","change"],["matInput","","type","number","placeholder","Reload Period",1,"reload-period",3,"formControl"],[4,"ngIf"],["matInput","","type","number","placeholder","Pagination Limit",1,"page-size",3,"formControl"]],template:function t(e,n){1&e&&(Cf(0,"h3"),Pu(1,"Settings"),Mf(),Cf(2,"div"),Cf(3,"div",0),Cf(4,"mat-checkbox",1),Rf("change",(function t(){return n.onReloadToggle()})),Pu(5,"Reload data"),Mf(),Mf(),Cf(6,"div"),Cf(7,"mat-form-field"),vf(8,"input",2),Mf(),Hm(9,NZ,2,1,"mat-error",3),Mf(),Mf(),Cf(10,"div"),Cf(11,"mat-form-field"),vf(12,"input",4),Mf(),Hm(13,zZ,2,0,"mat-error",3),Mf()),2&e&&(Dc(4),bf("checked",n.reloadEnabled),Dc(4),bf("formControl",n.reloadPeriodControl),Dc(1),bf("ngIf",n.reloadPeriodControl.hasError("min")||n.reloadPeriodControl.hasError("required")),Dc(3),bf("formControl",n.paginationControl),Dc(1),bf("ngIf",n.paginationControl.invalid))},directives:[bZ,_U,AZ,aG,SU,mW,uG,Pv,nU],styles:["[_nghost-%COMP%] {\n  font-size: 15px;\n}\n\n[_nghost-%COMP%]    > div[_ngcontent-%COMP%] {\n  margin: 10px 0;\n}\n\n[_nghost-%COMP%]    > [_ngcontent-%COMP%]:first-child {\n  margin-top: 0;\n}\n\n[_nghost-%COMP%]    > [_ngcontent-%COMP%]:last-child {\n  margin-bottom: 0;\n}\n\nh3[_ngcontent-%COMP%] {\n  font-size: 20px;\n}\n\n.reload-toggle[_ngcontent-%COMP%] {\n  margin-bottom: 10px;\n}"]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(IZ,[{type:S_,args:[{selector:"settings-dialog-component",templateUrl:"settings_dialog_component.ng.html",styleUrls:["./settings_dialog_component.css"]}]}],null,{reloadEnabled:[{type:R_}],reloadPeriodInMs:[{type:R_}],pageSize:[{type:R_}],reloadToggled:[{type:E_}],reloadPeriodInMsChanged:[{type:E_}],pageSizeChanged:[{type:E_}]});class HZ{constructor(t){this.store=t,this.reloadEnabled$=this.store.select(uI),this.reloadPeriodInMs$=this.store.select(gI),this.pageSize$=this.store.select(hI)}onReloadToggled(){this.store.dispatch(sI())}onReloadPeriodInMsChanged(t){this.store.dispatch(lI({periodInMs:t}))}onPageSizeChanged(t){this.store.dispatch(cI({size:t}))}}HZ.ɵfac=function t(e){return new(e||HZ)(gf(iS))},HZ.ɵcmp=so({type:HZ,selectors:[["settings-dialog"]],decls:4,vars:9,consts:[[3,"reloadEnabled","reloadPeriodInMs","pageSize","reloadToggled","reloadPeriodInMsChanged","pageSizeChanged"]],template:function t(e,n){1&e&&(Cf(0,"settings-dialog-component",0),Rf("reloadToggled",(function t(){return n.onReloadToggled()}))("reloadPeriodInMsChanged",(function t(e){return n.onReloadPeriodInMsChanged(e)}))("pageSizeChanged",(function t(e){return n.onPageSizeChanged(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),Mf()),2&e&&bf("reloadEnabled",Cy(1,3,n.reloadEnabled$))("reloadPeriodInMs",Cy(2,5,n.reloadPeriodInMs$))("pageSize",Cy(3,7,n.pageSize$))},directives:[IZ],pipes:[Vv],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(HZ,[{type:S_,args:[{selector:"settings-dialog",template:'\n    <settings-dialog-component\n      [reloadEnabled]="reloadEnabled$ | async"\n      [reloadPeriodInMs]="reloadPeriodInMs$ | async"\n      [pageSize]="pageSize$ | async"\n      (reloadToggled)="onReloadToggled()"\n      (reloadPeriodInMsChanged)="onReloadPeriodInMsChanged($event)"\n      (pageSizeChanged)="onPageSizeChanged($event)"\n    ></settings-dialog-component>\n  '}]}],(function(){return[{type:iS}]}),null);class FZ{constructor(t){this.dialog=t}isButtonDisabled(){return this.settingsLoadState===UE.NOT_LOADED||this.settingsLoadState===UE.LOADING}openDialog(){this.dialog.open(HZ,{width:"400px"})}}FZ.ɵfac=function t(e){return new(e||FZ)(gf(XY))},FZ.ɵcmp=so({type:FZ,selectors:[["settings-button-component"]],inputs:{settingsLoadState:"settingsLoadState"},decls:2,vars:1,consts:[["mat-icon-button","",3,"disabled","click"],["svgIcon","settings_24px"]],template:function t(e,n){1&e&&(Cf(0,"button",0),Rf("click",(function t(){return n.openDialog()})),vf(1,"mat-icon",1),Mf()),2&e&&bf("disabled",n.isButtonDisabled())},directives:[NL,Mq],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(FZ,[{type:S_,args:[{selector:"settings-button-component",template:'\n    <button\n      mat-icon-button\n      [disabled]="isButtonDisabled()"\n      (click)="openDialog()"\n    >\n      <mat-icon svgIcon="settings_24px"></mat-icon>\n    </button>\n  '}]}],(function(){return[{type:XY}]}),{settingsLoadState:[{type:R_}]});class LZ{constructor(t){this.store=t,this.settingsLoadState$=this.store.select(fI)}}LZ.ɵfac=function t(e){return new(e||LZ)(gf(iS))},LZ.ɵcmp=so({type:LZ,selectors:[["settings-button"]],decls:2,vars:3,consts:[[3,"settingsLoadState"]],template:function t(e,n){1&e&&(vf(0,"settings-button-component",0),_y(1,"async")),2&e&&bf("settingsLoadState",Cy(1,1,n.settingsLoadState$))},directives:[FZ],pipes:[Vv],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(LZ,[{type:S_,args:[{selector:"settings-button",template:'\n    <settings-button-component\n      [settingsLoadState]="settingsLoadState$ | async"\n    ></settings-button-component>\n  '}]}],(function(){return[{type:iS}]}),null);class BZ{}BZ.ɵfac=function t(e){return new(e||BZ)},BZ.ɵcmp=so({type:BZ,selectors:[["app-header"]],decls:10,vars:0,consts:[[1,"brand"],[1,"plugins"],["mat-icon-button","","href","https://github.com/tensorflow/tensorboard/blob/master/README.md","rel","noopener noreferrer","target","_blank","aria-label","Help",1,"readme"],["svgIcon","help_outline_24px"]],template:function t(e,n){1&e&&(Cf(0,"mat-toolbar"),Cf(1,"span",0),Pu(2,"TensorBoard"),Mf(),vf(3,"plugin-selector",1),vf(4,"tbdev-upload-button"),vf(5,"app-header-dark-mode-toggle"),vf(6,"app-header-reload"),vf(7,"settings-button"),Cf(8,"a",2),vf(9,"mat-icon",3),Mf(),Mf())},directives:[jV,TY,Sq,eZ,oZ,LZ,zL,Mq],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   .mat-chip-remove.mat-icon[_ngcontent-%COMP%]{font-size:18px}.mat-table[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica 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ngDevMode||ngDevMode)&&Qb(XZ,[{type:S_,args:[{selector:"page-title-component",template:"",changeDetection:jn.OnPush}]}],null,{title:[{type:R_}]});const KZ="TensorBoard";class JZ{constructor(t,e){this.store=t,this.customBrandName=e,this.getExperimentId$=this.store.select(xR).pipe(Vt((t=>null==t?void 0:t[0]))),this.experimentName$=this.getExperimentId$.pipe(ue(Boolean),$t((t=>this.store.select(qT,{experimentId:t}))),Vt((t=>t?t.name:null))),this.title$=this.store.select(oT).pipe(_e(this.store.select(MR),this.experimentName$),Vt((([t,e,n])=>{const o=this.customBrandName||KZ;return t.window_title?t.window_title:e===_D.EXPERIMENT&&n?`${n} - ${o}`:o})),Be(this.customBrandName||KZ),we())}}JZ.ɵfac=function t(e){return new(e||JZ)(gf(iS),gf(BE,8))},JZ.ɵcmp=so({type:JZ,selectors:[["page-title"]],decls:2,vars:3,consts:[[3,"title"]],template:function t(e,n){1&e&&(vf(0,"page-title-component",0),_y(1,"async")),2&e&&bf("title",Cy(1,1,n.title$))},directives:[XZ],pipes:[Vv],styles:["[_nghost-%COMP%] {\n        display: none;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(JZ,[{type:S_,args:[{selector:"page-title",template:'\n    <page-title-component [title]="title$ | async"></page-title-component>\n  ',styles:["\n      :host {\n        display: none;\n      }\n    "],changeDetection:jn.OnPush}]}],(function(){return[{type:iS},{type:void 0,decorators:[{type:Br},{type:Lr,args:[BE]}]}]}),null);class QZ{constructor(t){this.store=t,this.ngUnsubscribe=new B,this.getPageSize$=this.store.pipe(rS(hI)),this.paginatedViewStore=document.createElement("tf-paginated-view-store").tf_paginated_view}ngOnInit(){this.getPageSize$.pipe(je(this.ngUnsubscribe),we()).subscribe((t=>{this.paginatedViewStore.setLimit(t)}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}}QZ.ɵfac=function t(e){return new(e||QZ)(gf(iS))},QZ.ɵcmp=so({type:QZ,selectors:[["settings-polymer-interop"]],decls:0,vars:0,template:function t(e,n){},encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(QZ,[{type:S_,args:[{selector:"settings-polymer-interop",template:"",changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);class $Z{constructor(t){t.select(FD).subscribe((t=>{document.body.classList.toggle("dark-mode",t)}))}}$Z.ɵfac=function t(e){return new(e||$Z)(gf(iS))},$Z.ɵcmp=so({type:$Z,selectors:[["dark-mode-supporter"]],decls:0,vars:0,template:function t(e,n){},styles:["[_nghost-%COMP%] {\n        display: none;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb($Z,[{type:S_,args:[{selector:"dark-mode-supporter",template:"",styles:["\n      :host {\n        display: none;\n      }\n    "],changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);class tX{constructor(t){this.vcRef=t}}tX.ɵfac=function t(e){return new(e||tX)(gf(Fb))},tX.ɵcmp=so({type:tX,selectors:[["tb-webapp"]],decls:8,vars:0,template:function t(e,n){1&e&&(vf(0,"app-header"),Cf(1,"main"),vf(2,"router-outlet"),Mf(),vf(3,"alert-snackbar"),vf(4,"hash-storage"),vf(5,"page-title"),vf(6,"settings-polymer-interop"),vf(7,"dark-mode-supporter"))},directives:[BZ,UZ,IV,YZ,JZ,QZ,$Z],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], 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.mat-icon-button[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]{height:1.5em;width:1.5em}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%]{height:1.125em;line-height:1.125}.mat-form-field-infix[_ngcontent-%COMP%]{padding:.5em 0;border-top:.84375em solid transparent}.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.34375em) scale(0.75);width:133.3333333333%}.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + 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new(e||OX)},OX.ɵcmp=so({type:OX,selectors:[["inactive-component"]],decls:54,vars:0,consts:[[1,"container"],[1,"title"],[1,"code"],[1,"arg"],[1,"exhibits-container"],[1,"exhibit"],[1,"screenshot"],["src","data:image/png;base64,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"],[1,"description"],["src","data:image/png;base64,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"],[1,"details-container"],[1,"details"],["href","https://www.tensorflow.org/api_docs/python/tf/debugging/experimental/enable_dump_debug_info","target","blank","rel","noreferrer 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"),Mf(),Cf(43,"div",12),Cf(44,"div"),Pu(45," See "),Cf(46,"a",13),Pu(47," documentation "),Mf(),Pu(48," of the Python API of Debugger V2. "),Mf(),Cf(49,"div"),Pu(50," See "),Cf(51,"a",14),Pu(52," here "),Mf(),Pu(53," for other TensorFlow debugging APIs. 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0!==o&&(i.graphExecutions=Object.assign(Object.assign({},t.graphExecutions),{focusIndex:o})),i}function yK(t,e){return uK(t,e)}const _K=_S(rK),CK=bS(_K,(t=>t.runs)),MK=bS(_K,(t=>t.runsLoaded)),vK=bS(_K,(t=>t.activeRunId)),xK=bS(_K,(t=>t.lastDataPollOnsetTimeMs-t.lastNonEmptyPollDataTimeMs)),OK=bS(_K,(t=>t.alerts)),PK=bS(OK,(t=>t.alertsLoaded)),wK=bS(OK,(t=>t.numAlerts)),kK=bS(OK,(t=>t.focusType)),SK=bS(OK,(t=>null===t.focusType?0:t.alertsBreakdown[t.focusType]||0)),DK=bS(OK,(t=>null===t.focusType||void 0===t.alerts[t.focusType]?null:t.alerts[t.focusType])),RK=bS(OK,(t=>t.alertsBreakdown)),EK=bS(_K,(t=>t.executions)),AK=bS(EK,(t=>t.numExecutionsLoaded)),TK=bS(EK,(t=>t.executionDigestsLoaded)),NK=bS(EK,(t=>t.executionDigestsLoaded.numExecutions)),zK=bS(EK,(t=>t.scrollBeginIndex)),IK=bS(EK,(t=>t.pageSize)),HK=bS(EK,(t=>t.displayCount)),FK=bS(EK,(t=>{const e=[];for(let n=t.scrollBeginIndex;n<t.scrollBeginIndex+t.displayCount;++n)e.push(n in t.executionDigests?t.executionDigests[n]:null);return e})),LK=bS(_K,(t=>t.graphExecutions)),BK=bS(LK,(t=>t.numExecutionsLoaded)),VK=bS(_K,(t=>t.graphExecutions.executionDigestsLoaded.numExecutions)),jK=bS(LK,(t=>t.scrollBeginIndex)),UK=bS(LK,(t=>t.displayCount)),WK=bS(LK,(t=>t.pageSize)),GK=bS(LK,(t=>t.graphExecutionDataLoadingPages)),YK=bS(LK,(t=>t.graphExecutionDataPageLoadedSizes)),qK=bS(LK,(t=>t.graphExecutionData)),ZK=bS(LK,(t=>t.focusIndex)),XK=bS(_K,(t=>t.graphs)),KK=bS(XK,(t=>{const{focusedOp:e,ops:n}=t;return null===e||void 0===n[e.graphId]?null:n[e.graphId].get(e.opName)||null})),JK=bS(XK,(t=>{const{focusedOp:e,ops:n}=t;if(null!==e&&void 0!==n[e.graphId]&&n[e.graphId].has(e.opName)){const t=n[e.graphId],{inputs:o}=t.get(e.opName);return o.map((e=>{const n=Object.assign({},e);return t.has(e.op_name)&&(n.data=t.get(e.op_name)),n}))}return null})),QK=bS(ZK,qK,JK,((t,e,n)=>{if(null===t||null===n)return null;const o=n.map((t=>!1)),i=[];if(0===n.length)return i;const a=e[t].graph_id,r=Math.max(0,t-200);for(let s=t-1;s>=r;--s)if(void 0!==e[s])for(let t=0;t<n.length&&(o[t]||e[s].graph_id!==a||e[s].op_name!==n[t].op_name||e[s].output_slot!==n[t].output_slot||(i.push(s),o[t]=!0,i.length!==n.length));++t);return i})),$K=bS(XK,(t=>{const{focusedOp:e,ops:n}=t;if(null!==e&&void 0!==n[e.graphId]&&n[e.graphId].has(e.opName)){const t=n[e.graphId],{consumers:o}=t.get(e.opName);return o.map((e=>e.map((e=>{const n=Object.assign({},e);return t.has(e.op_name)&&(n.data=t.get(e.op_name)),n}))))}return null})),tJ=bS(_K,(t=>{const e=t.executions.scrollBeginIndex,n=t.executions.scrollBeginIndex+t.executions.displayCount,o=new Array(n-e).fill(null),i=t.alerts.focusType;if(null===i)return o;const a=t.alerts.executionIndices[i];if(void 0===a)return o;for(let i=e;i<n;++i)a.includes(i)&&(o[i-e]=t.alerts.focusType);return o})),eJ=bS(_K,(t=>t.executions)),nJ=bS(eJ,(t=>t.focusIndex)),oJ=bS(eJ,(t=>{if(null===t.focusIndex)return null;const{focusIndex:e,scrollBeginIndex:n,displayCount:o}=t;return e<n||e>=n+o?null:e-n})),iJ=bS(eJ,(t=>t.executionData)),aJ=bS(_K,(t=>t.graphs.loadingOps)),rJ=bS(_K,(t=>t.stackFrames)),sJ=bS(eJ,(t=>{const{focusIndex:e,executionData:n}=t;return null===e||void 0===n[e]?null:n[e]})),lJ=bS(_K,nJ,sJ,KK,((t,e,n,o)=>{const{codeLocationFocusType:i}=t;return null===i?null:i===cK.EXECUTION?null===e||null===n?null:{codeLocationType:cK.EXECUTION,opType:n.op_type,executionIndex:e}:null===o?null:{codeLocationType:cK.GRAPH_OP_CREATION,opType:o.op_type,opName:o.op_name}})),cJ=bS(_K,dK),dJ=bS(_K,(t=>t.sourceCode)),pJ=bS(dJ,(t=>t.sourceFileListLoaded));bS(dJ,(t=>t.sourceFileList));const mJ=bS(dJ,(t=>{const{sourceFileList:e,focusLineSpec:n}=t;return null===n?-1:pK(e,n)})),fJ=bS(dJ,mJ,((t,e)=>-1===e?null:t.fileContents[e]||null)),uJ=bS(_K,(t=>t.sourceCode.focusLineSpec)),gJ=bS(_K,(t=>t.stickToBottommostFrameInFocusedFile)),hJ=function(t){return[t]};function bJ(t,e){if(1&t){const t=wf();Cf(0,"div",7),Rf("click",(function e(){const n=Pi(t).$implicit;return zf().onToggleFocusType.emit(n.type)})),Cf(1,"div",8),Pu(2),Mf(),Cf(3,"div",9),Pu(4),Mf(),vf(5,"div"),Mf()}if(2&t){const t=e.$implicit,n=zf();bf("ngClass",iy(4,hJ,t.type===n.focusType?"focus":"")),Dc(2),wu(t.displayName),Dc(2),Su(" ",t.displaySymbol,": ",t.count," ")}}class yJ{constructor(){this.numAlerts=0,this.alertsBreakdown=[],this.focusType=null,this.onToggleFocusType=new Sy}}yJ.ɵfac=function t(e){return new(e||yJ)},yJ.ɵcmp=so({type:yJ,selectors:[["alerts-component"]],inputs:{numAlerts:"numAlerts",alertsBreakdown:"alertsBreakdown",focusType:"focusType"},outputs:{onToggleFocusType:"onToggleFocusType"},decls:10,vars:5,consts:[[1,"alerts-container"],[1,"debugging-title"],[1,"num-alerts-container"],[1,"num-alerts-label"],[1,"num-alerts-value",3,"ngClass"],[1,"alerts-breakdown-container"],["class","alerts-breakdown-type",3,"ngClass","click",4,"ngFor","ngForOf"],[1,"alerts-breakdown-type",3,"ngClass","click"],[1,"alert-type-name"],[1,"alert-type-count"]],template:function t(e,n){1&e&&(Cf(0,"div",0),Cf(1,"div",1),Pu(2,"Debugging"),Mf(),Cf(3,"div",2),Cf(4,"div",3),Pu(5,"Alerts"),Mf(),Cf(6,"div",4),Pu(7),Mf(),Mf(),Cf(8,"div",5),Hm(9,bJ,6,6,"div",6),Mf(),Mf()),2&e&&(Dc(6),bf("ngClass",iy(3,hJ,n.numAlerts>0?"non-zero":"")),Dc(1),ku(" ",n.numAlerts," "),Dc(2),bf("ngForOf",n.alertsBreakdown))},directives:[Cv,xv],styles:[".alerts-breakdown-container[_ngcontent-%COMP%] {\n  font-size: 13px;\n  padding: 10px 10px 10px;\n  position: relative;\n}\n\n.alerts-breakdown-type[_ngcontent-%COMP%] {\n  border-radius: 0 10px 10px 0;\n  cursor: pointer;\n  display: flex;\n  padding: 6px 0 6px 50px;\n  vertical-align: middle;\n}\n\n.alerts-breakdown-type.focus[_ngcontent-%COMP%] {\n  background-color: #ffeee0;\n}\n\n.alerts-container[_ngcontent-%COMP%] {\n  font-family: 'Roboto', Arial, Helvetica, sans-serif;\n}\n\n.alert-type-count[_ngcontent-%COMP%] {\n  \n  background-color: #e52592;\n  border-radius: 3px;\n  color: #fff;\n  display: inline-block;\n  padding: 3px;\n  position: absolute;\n  right: 20px;\n  vertical-align: middle;\n}\n\n.alert-type-name[_ngcontent-%COMP%] {\n  display: inline-block;\n  padding: 3px;\n  vertical-align: middle;\n}\n\n.debugging-title[_ngcontent-%COMP%] {\n  font-size: 18px;\n}\n\n.num-alerts-container[_ngcontent-%COMP%] {\n  font-weight: bold;\n  padding: 10px 10px 10px 30px;\n  position: relative;\n}\n\n.num-alerts-label[_ngcontent-%COMP%] {\n  display: inline-block;\n  font-size: 13px;\n}\n\n.num-alerts-value[_ngcontent-%COMP%] {\n  border-radius: 12px;\n  display: inline-block;\n  font-size: 13px;\n  font-weight: normal;\n  line-height: 24px;\n  position: absolute;\n  right: 20px;\n  text-align: center;\n  vertical-align: middle;\n  width: 24px;\n}\n\n.num-alerts-value.non-zero[_ngcontent-%COMP%] {\n  background-color: #ffb780;\n  font-weight: bold;\n}"]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(yJ,[{type:S_,args:[{selector:"alerts-component",templateUrl:"./alerts_component.ng.html",styleUrls:["./alerts_component.css"]}]}],null,{numAlerts:[{type:R_}],alertsBreakdown:[{type:R_}],focusType:[{type:R_}],onToggleFocusType:[{type:E_}]});const _J={[lK.FUNCTION_RECOMPILE_ALERT]:{displayName:"Function recompiles",displaySymbol:"C"},[lK.INF_NAN_ALERT]:{displayName:"NaN/∞",displaySymbol:"∞"},[lK.TENSOR_SHAPE_ALERT]:{displayName:"Tensor shape",displaySymbol:"■"}};class CJ{constructor(t){this.store=t,this.numAlerts$=this.store.pipe(rS(wK)),this.alertsBreakdown$=this.store.pipe(rS(bS(RK,(t=>{const e=Object.keys(t);return e.sort(),e.map((e=>Object.assign(Object.assign({type:e},_J[e]),{count:t[e]})))})))),this.focusType$=this.store.pipe(rS(kK))}onToggleFocusType(t){this.store.dispatch(zX({alertType:t}))}}CJ.ɵfac=function t(e){return new(e||CJ)(gf(iS))},CJ.ɵcmp=so({type:CJ,selectors:[["tf-debugger-v2-alerts"]],decls:4,vars:9,consts:[[3,"numAlerts","alertsBreakdown","focusType","onToggleFocusType"]],template:function t(e,n){1&e&&(Cf(0,"alerts-component",0),Rf("onToggleFocusType",(function t(e){return n.onToggleFocusType(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),Mf()),2&e&&bf("numAlerts",Cy(1,3,n.numAlerts$))("alertsBreakdown",Cy(2,5,n.alertsBreakdown$))("focusType",Cy(3,7,n.focusType$))},directives:[yJ],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(CJ,[{type:S_,args:[{selector:"tf-debugger-v2-alerts",template:'\n    <alerts-component\n      [numAlerts]="numAlerts$ | async"\n      [alertsBreakdown]="alertsBreakdown$ | async"\n      [focusType]="focusType$ | async"\n      (onToggleFocusType)="onToggleFocusType($event)"\n    >\n    </alerts-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);const MJ=["sliderWrapper"],vJ=bH({passive:!1}),xJ={provide:xU,useExisting:$e((()=>wJ)),multi:!0};class OJ{}const PJ=FF(IF(zF(class{constructor(t){this._elementRef=t}}),"accent"));class wJ extends PJ{constructor(t,e,n,o,i,a,r,s){super(t),this._focusMonitor=e,this._changeDetectorRef=n,this._dir=o,this._ngZone=a,this._animationMode=s,this._invert=!1,this._max=100,this._min=0,this._step=1,this._thumbLabel=!1,this._tickInterval=0,this._value=null,this._vertical=!1,this.change=new Sy,this.input=new Sy,this.valueChange=new Sy,this.onTouched=()=>{},this._percent=0,this._isSliding=null,this._isActive=!1,this._tickIntervalPercent=0,this._sliderDimensions=null,this._controlValueAccessorChangeFn=()=>{},this._dirChangeSubscription=u.EMPTY,this._pointerDown=t=>{this.disabled||this._isSliding||!kJ(t)&&0!==t.button||this._ngZone.run((()=>{this._touchId=kJ(t)?(function e(t,n){for(let e=0;e<t.touches.length;e++){const o=t.touches[e].target;if(n===o||n.contains(o))return t.touches[e].identifier}return}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t,this._elementRef.nativeElement):void 0;const n=SJ(t,this._touchId);if(n){const e=this.value;this._isSliding="pointer",this._lastPointerEvent=t,t.preventDefault(),this._focusHostElement(),this._onMouseenter(),this._bindGlobalEvents(t),this._focusHostElement(),this._updateValueFromPosition(n),this._valueOnSlideStart=e,e!=this.value&&this._emitInputEvent()}}))},this._pointerMove=t=>{if("pointer"===this._isSliding){const e=SJ(t,this._touchId);if(e){t.preventDefault();const n=this.value;this._lastPointerEvent=t,this._updateValueFromPosition(e),n!=this.value&&this._emitInputEvent()}}},this._pointerUp=t=>{"pointer"===this._isSliding&&(kJ(t)&&"number"==typeof this._touchId&&!DJ(t.changedTouches,this._touchId)||(t.preventDefault(),this._removeGlobalEvents(),this._isSliding=null,this._touchId=void 0,this._valueOnSlideStart==this.value||this.disabled||this._emitChangeEvent(),this._valueOnSlideStart=this._lastPointerEvent=null))},this._windowBlur=()=>{this._lastPointerEvent&&this._pointerUp(this._lastPointerEvent)},this._document=r,this.tabIndex=parseInt(i)||0,a.runOutsideAngular((()=>{const e=t.nativeElement;e.addEventListener("mousedown",this._pointerDown,vJ),e.addEventListener("touchstart",this._pointerDown,vJ)}))}get invert(){return this._invert}set invert(t){this._invert=eH(t)}get max(){return this._max}set max(t){this._max=nH(t,this._max),this._percent=this._calculatePercentage(this._value),this._changeDetectorRef.markForCheck()}get min(){return this._min}set min(t){this._min=nH(t,this._min),this._percent=this._calculatePercentage(this._value),this._changeDetectorRef.markForCheck()}get step(){return this._step}set step(t){this._step=nH(t,this._step),this._step%1!=0&&(this._roundToDecimal=this._step.toString().split(".").pop().length),this._changeDetectorRef.markForCheck()}get thumbLabel(){return this._thumbLabel}set thumbLabel(t){this._thumbLabel=eH(t)}get tickInterval(){return this._tickInterval}set tickInterval(t){this._tickInterval="auto"===t?"auto":"number"==typeof t||"string"==typeof t?nH(t,this._tickInterval):0}get value(){return null===this._value&&(this.value=this._min),this._value}set value(t){if(t!==this._value){let e=nH(t,0);this._roundToDecimal&&e!==this.min&&e!==this.max&&(e=parseFloat(e.toFixed(this._roundToDecimal))),this._value=e,this._percent=this._calculatePercentage(this._value),this._changeDetectorRef.markForCheck()}}get vertical(){return this._vertical}set vertical(t){this._vertical=eH(t)}get displayValue(){return this.displayWith?this.displayWith(this.value):this._roundToDecimal&&this.value&&this.value%1!=0?this.value.toFixed(this._roundToDecimal):this.value||0}focus(t){this._focusHostElement(t)}blur(){this._blurHostElement()}get percent(){return this._clamp(this._percent)}_shouldInvertAxis(){return this.vertical?!this.invert:this.invert}_isMinValue(){return 0===this.percent}_getThumbGap(){return this.disabled?7:this._isMinValue()&&!this.thumbLabel?this._isActive?10:7:0}_getTrackBackgroundStyles(){const t=this.vertical?`1, ${1-this.percent}, 1`:1-this.percent+", 1, 1";return{transform:`translate${this.vertical?"Y":"X"}(${this._shouldInvertMouseCoords()?"-":""}${this._getThumbGap()}px) scale3d(${t})`}}_getTrackFillStyles(){const t=this.percent,e=this.vertical?`1, ${t}, 1`:`${t}, 1, 1`;return{transform:`translate${this.vertical?"Y":"X"}(${this._shouldInvertMouseCoords()?"":"-"}${this._getThumbGap()}px) scale3d(${e})`,display:0===t?"none":""}}_getTicksContainerStyles(){return{transform:`translate${this.vertical?"Y":"X"}(${this.vertical||"rtl"!=this._getDirection()?"-":""}${this._tickIntervalPercent/2*100}%)`}}_getTicksStyles(){let t=100*this._tickIntervalPercent,e={backgroundSize:this.vertical?`2px ${t}%`:`${t}% 2px`,transform:`translateZ(0) translate${this.vertical?"Y":"X"}(${this.vertical||"rtl"!=this._getDirection()?"":"-"}${t/2}%)${this.vertical||"rtl"!=this._getDirection()?"":" rotate(180deg)"}`};if(this._isMinValue()&&this._getThumbGap()){const t=this._shouldInvertAxis();let n;n=this.vertical?t?"Bottom":"Top":t?"Right":"Left",e[`padding${n}`]=`${this._getThumbGap()}px`}return e}_getThumbContainerStyles(){const t=this._shouldInvertAxis();return{transform:`translate${this.vertical?"Y":"X"}(-${100*(("rtl"!=this._getDirection()||this.vertical?t:!t)?this.percent:1-this.percent)}%)`}}_shouldInvertMouseCoords(){const t=this._shouldInvertAxis();return"rtl"!=this._getDirection()||this.vertical?t:!t}_getDirection(){return this._dir&&"rtl"==this._dir.value?"rtl":"ltr"}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0).subscribe((t=>{this._isActive=!!t&&"keyboard"!==t,this._changeDetectorRef.detectChanges()})),this._dir&&(this._dirChangeSubscription=this._dir.change.subscribe((()=>{this._changeDetectorRef.markForCheck()})))}ngOnDestroy(){const t=this._elementRef.nativeElement;t.removeEventListener("mousedown",this._pointerDown,vJ),t.removeEventListener("touchstart",this._pointerDown,vJ),this._lastPointerEvent=null,this._removeGlobalEvents(),this._focusMonitor.stopMonitoring(this._elementRef),this._dirChangeSubscription.unsubscribe()}_onMouseenter(){this.disabled||(this._sliderDimensions=this._getSliderDimensions(),this._updateTickIntervalPercent())}_onFocus(){this._sliderDimensions=this._getSliderDimensions(),this._updateTickIntervalPercent()}_onBlur(){this.onTouched()}_onKeydown(t){if(this.disabled||tH(t)||this._isSliding&&"keyboard"!==this._isSliding)return;const e=this.value;switch(t.keyCode){case 33:this._increment(10);break;case 34:this._increment(-10);break;case 35:this.value=this.max;break;case 36:this.value=this.min;break;case 37:this._increment("rtl"==this._getDirection()?1:-1);break;case QI:this._increment(1);break;case 39:this._increment("rtl"==this._getDirection()?-1:1);break;case $I:this._increment(-1);break;default:return}e!=this.value&&(this._emitInputEvent(),this._emitChangeEvent()),this._isSliding="keyboard",t.preventDefault()}_onKeyup(){"keyboard"===this._isSliding&&(this._isSliding=null)}_getWindow(){return this._document.defaultView||window}_bindGlobalEvents(t){const e=this._document,n=kJ(t),o=n?"touchend":"mouseup";e.addEventListener(n?"touchmove":"mousemove",this._pointerMove,vJ),e.addEventListener(o,this._pointerUp,vJ),n&&e.addEventListener("touchcancel",this._pointerUp,vJ);const i=this._getWindow();void 0!==i&&i&&i.addEventListener("blur",this._windowBlur)}_removeGlobalEvents(){const t=this._document;t.removeEventListener("mousemove",this._pointerMove,vJ),t.removeEventListener("mouseup",this._pointerUp,vJ),t.removeEventListener("touchmove",this._pointerMove,vJ),t.removeEventListener("touchend",this._pointerUp,vJ),t.removeEventListener("touchcancel",this._pointerUp,vJ);const e=this._getWindow();void 0!==e&&e&&e.removeEventListener("blur",this._windowBlur)}_increment(t){this.value=this._clamp((this.value||0)+this.step*t,this.min,this.max)}_updateValueFromPosition(t){if(!this._sliderDimensions)return;let e=this._clamp(((this.vertical?t.y:t.x)-(this.vertical?this._sliderDimensions.top:this._sliderDimensions.left))/(this.vertical?this._sliderDimensions.height:this._sliderDimensions.width));if(this._shouldInvertMouseCoords()&&(e=1-e),0===e)this.value=this.min;else if(1===e)this.value=this.max;else{const t=this._calculateValue(e),n=Math.round((t-this.min)/this.step)*this.step+this.min;this.value=this._clamp(n,this.min,this.max)}}_emitChangeEvent(){this._controlValueAccessorChangeFn(this.value),this.valueChange.emit(this.value),this.change.emit(this._createChangeEvent())}_emitInputEvent(){this.input.emit(this._createChangeEvent())}_updateTickIntervalPercent(){if(this.tickInterval&&this._sliderDimensions)if("auto"==this.tickInterval){let t=this.vertical?this._sliderDimensions.height:this._sliderDimensions.width,e=Math.ceil(30/(t*this.step/(this.max-this.min)));this._tickIntervalPercent=e*this.step/t}else this._tickIntervalPercent=this.tickInterval*this.step/(this.max-this.min)}_createChangeEvent(t=this.value){let e=new OJ;return e.source=this,e.value=t,e}_calculatePercentage(t){return((t||0)-this.min)/(this.max-this.min)}_calculateValue(t){return this.min+t*(this.max-this.min)}_clamp(t,e=0,n=1){return Math.max(e,Math.min(t,n))}_getSliderDimensions(){return this._sliderWrapper?this._sliderWrapper.nativeElement.getBoundingClientRect():null}_focusHostElement(t){this._elementRef.nativeElement.focus(t)}_blurHostElement(){this._elementRef.nativeElement.blur()}writeValue(t){this.value=t}registerOnChange(t){this._controlValueAccessorChangeFn=t}registerOnTouched(t){this.onTouched=t}setDisabledState(t){this.disabled=t}}function kJ(t){return"t"===t.type[0]}function SJ(t,e){let n;return n=kJ(t)?"number"==typeof e?DJ(t.touches,e)||DJ(t.changedTouches,e):t.touches[0]||t.changedTouches[0]:t,n?{x:n.clientX,y:n.clientY}:void 0}function DJ(t,e){for(let n=0;n<t.length;n++)if(t[n].identifier===e)return t[n]}wJ.ɵfac=function t(e){return new(e||wJ)(gf(Kh),gf(fF),gf(Ob),gf(vF,8),Ga("tabindex"),gf(mC),gf(sM),gf(ck,8))},wJ.ɵcmp=so({type:wJ,selectors:[["mat-slider"]],viewQuery:function t(e,n){if(1&e&&Vy(MJ,5),2&e){let t;By(t=Uy())&&(n._sliderWrapper=t.first)}},hostAttrs:["role","slider",1,"mat-slider","mat-focus-indicator"],hostVars:29,hostBindings:function t(e,n){1&e&&Rf("focus",(function t(){return n._onFocus()}))("blur",(function t(){return n._onBlur()}))("keydown",(function t(e){return n._onKeydown(e)}))("keyup",(function t(){return n._onKeyup()}))("mouseenter",(function t(){return n._onMouseenter()}))("selectstart",(function t(e){return e.preventDefault()})),2&e&&(cg("tabIndex",n.tabIndex),ym("aria-disabled",n.disabled)("aria-valuemax",n.max)("aria-valuemin",n.min)("aria-valuenow",n.value)("aria-valuetext",null==n.valueText?n.displayValue:n.valueText)("aria-orientation",n.vertical?"vertical":"horizontal"),cu("mat-slider-disabled",n.disabled)("mat-slider-has-ticks",n.tickInterval)("mat-slider-horizontal",!n.vertical)("mat-slider-axis-inverted",n._shouldInvertAxis())("mat-slider-invert-mouse-coords",n._shouldInvertMouseCoords())("mat-slider-sliding",n._isSliding)("mat-slider-thumb-label-showing",n.thumbLabel)("mat-slider-vertical",n.vertical)("mat-slider-min-value",n._isMinValue())("mat-slider-hide-last-tick",n.disabled||n._isMinValue()&&n._getThumbGap()&&n._shouldInvertAxis())("_mat-animation-noopable","NoopAnimations"===n._animationMode))},inputs:{disabled:"disabled",color:"color",tabIndex:"tabIndex",invert:"invert",max:"max",min:"min",step:"step",thumbLabel:"thumbLabel",tickInterval:"tickInterval",value:"value",vertical:"vertical",displayWith:"displayWith",valueText:"valueText"},outputs:{change:"change",input:"input",valueChange:"valueChange"},exportAs:["matSlider"],features:[jh([xJ]),Qp],decls:13,vars:6,consts:[[1,"mat-slider-wrapper"],["sliderWrapper",""],[1,"mat-slider-track-wrapper"],[1,"mat-slider-track-background",3,"ngStyle"],[1,"mat-slider-track-fill",3,"ngStyle"],[1,"mat-slider-ticks-container",3,"ngStyle"],[1,"mat-slider-ticks",3,"ngStyle"],[1,"mat-slider-thumb-container",3,"ngStyle"],[1,"mat-slider-focus-ring"],[1,"mat-slider-thumb"],[1,"mat-slider-thumb-label"],[1,"mat-slider-thumb-label-text"]],template:function 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ngDevMode||ngDevMode)&&Qb(zQ,[{type:S_,args:[{selector:"graph-component",templateUrl:"./graph_component.ng.html",styleUrls:["./graph_component.css"],changeDetection:jn.OnPush}]}],null,{opInfo:[{type:R_}],inputOps:[{type:R_}],consumerOps:[{type:R_}],onGraphOpNavigate:[{type:E_}]});class IQ{constructor(t){this.store=t,this.opInfo$=this.store.pipe(rS(KK)),this.inputOps$=this.store.pipe(rS(JK)),this.consumerOps$=this.store.pipe(rS($K))}onGraphOpNavigate(t){this.store.dispatch(JX(t))}}function HQ(t,e){1&t&&(Cf(0,"div",12),Pu(1,"▶"),Mf())}IQ.ɵfac=function t(e){return new(e||IQ)(gf(iS))},IQ.ɵcmp=so({type:IQ,selectors:[["tf-debugger-v2-graph"]],decls:4,vars:9,consts:[[3,"opInfo","inputOps","consumerOps","onGraphOpNavigate"]],template:function t(e,n){1&e&&(Cf(0,"graph-component",0),Rf("onGraphOpNavigate",(function t(e){return 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e(){Pi(t);const n=zf().$implicit,o=zf(2);return o.onTensorNameClick.emit({index:n,graph_id:o.graphExecutionData[n].graph_id,op_name:o.graphExecutionData[n].op_name})})),Pu(3),Mf(),Cf(4,"div",15),Pu(5),Mf(),Mf(),Hm(6,LQ,1,4,"debug-tensor-value",16),Mf()}if(2&t){const t=zf().$implicit,e=zf(2);Dc(2),Lf("title",e.getTensorName(t)),Dc(1),ku(" ",e.getTensorName(t)," "),Dc(2),wu(e.graphExecutionData[t].op_type),Dc(1),bf("ngIf",null!==e.graphExecutionData[t].debug_tensor_value)}}function VQ(t,e){1&t&&(Cf(0,"div",18),Pu(1," Loading... "),Mf())}const jQ=function(t){return{"input-of-focus":t}};function UQ(t,e){if(1&t&&(Cf(0,"div",5),Cf(1,"div",6),Cf(2,"div",7),Hm(3,HQ,2,0,"div",8),Pu(4),Mf(),Hm(5,BQ,7,4,"div",9),Hm(6,VQ,2,0,"ng-template",10,11,qy),Mf(),Mf()),2&t){const t=e.$implicit,n=Fm(7),o=zf(2);Dc(1),bf("ngClass",iy(5,jQ,o.isInputOfFocus(t))),Dc(2),bf("ngIf",t===o.focusIndex),Dc(1),ku(" ",t," "),Dc(1),bf("ngIf",o.graphExecutionData[t])("ngIfElse",n)}}function WQ(t,e){if(1&t){const t=wf();Cf(0,"cdk-virtual-scroll-viewport",3),Rf("scrolledIndexChange",(function e(n){Pi(t);return zf().onScrolledIndexChange.emit(n)})),Hm(1,UQ,8,7,"div",4),Mf()}if(2&t){const t=zf();Dc(1),bf("cdkVirtualForOf",t.graphExecutionIndices)}}class GQ{constructor(){this.onScrolledIndexChange=new Sy,this.onTensorNameClick=new Sy,this.parseDebugTensorValue=AJ,this.TEST_ONLY={getViewPort:()=>this.viewPort}}ngOnChanges(t){if(this.viewPort&&t.focusIndex&&null!==t.focusIndex.currentValue){const 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O${constructor(t){this.store=t,this.codeLocationType$=this.store.pipe(rS(bS(lJ,(t=>null===t?null:t.codeLocationType)))),this.opType$=this.store.pipe(rS(bS(lJ,(t=>null===t?null:t.opType)))),this.opName$=this.store.pipe(rS(bS(lJ,(t=>null===t||t.codeLocationType!==cK.GRAPH_OP_CREATION?null:t.opName)))),this.executionIndex$=this.store.pipe(rS(bS(lJ,(t=>null===t||t.codeLocationType!==cK.EXECUTION?null:t.executionIndex)))),this.stickToBottommostFrameInFocusedFile$=this.store.pipe(rS(gJ)),this.stackFramesForDisplay$=this.store.pipe(rS(bS(cJ,uJ,((t,e)=>{if(null===t)return null;const n=[];for(const o of t){const{host_name:t,file_path:i,lineno:a,function_name:r}=o,s=i.split("/"),l=null!==e&&t===e.host_name&&i===e.file_path;n.push({host_name:t,file_path:i,concise_file_path:s[s.length-1],lineno:a,function_name:r,belongsToFocusedFile:l,focused:l&&a===e.lineno})}return n}))))}onSourceLineClicked(t){const{host_name:e,file_path:n,lineno:o,function_name:i}=t;this.store.dispatch(nK({stackFrame:{host_name:e,file_path:n,lineno:o,function_name:i}}))}}function P$(t,e){1&t&&vf(0,"tf-debugger-v2-inactive")}function w$(t,e){1&t&&(Cf(0,"div",3),vf(1,"tf-debugger-v2-alerts"),Cf(2,"div",4),vf(3,"tf-debugger-v2-timeline"),vf(4,"tf-debugger-v2-graph"),Mf(),vf(5,"tf-debugger-v2-graph-executions"),Mf(),Cf(6,"div",5),vf(7,"tf-debugger-v2-source-files"),vf(8,"tf-debugger-v2-stack-trace"),Mf())}O$.ɵfac=function t(e){return new(e||O$)(gf(iS))},O$.ɵcmp=so({type:O$,selectors:[["tf-debugger-v2-stack-trace"]],decls:7,vars:18,consts:[[3,"codeLocationType","opType","opName","executionIndex","stickToBottommostFrameInFocusedFile","stackFramesForDisplay","onSourceLineClicked"]],template:function t(e,n){1&e&&(Cf(0,"stack-trace-component",0),Rf("onSourceLineClicked",(function t(e){return n.onSourceLineClicked(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async"),_y(5,"async"),_y(6,"async"),Mf()),2&e&&bf("codeLocationType",Cy(1,6,n.codeLocationType$))("opType",Cy(2,8,n.opType$))("opName",Cy(3,10,n.opName$))("executionIndex",Cy(4,12,n.executionIndex$))("stickToBottommostFrameInFocusedFile",Cy(5,14,n.stickToBottommostFrameInFocusedFile$))("stackFramesForDisplay",Cy(6,16,n.stackFramesForDisplay$))},directives:[x$],pipes:[Vv],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(O$,[{type:S_,args:[{selector:"tf-debugger-v2-stack-trace",template:'\n    <stack-trace-component\n      [codeLocationType]="codeLocationType$ | async"\n      [opType]="opType$ | async"\n      [opName]="opName$ | async"\n      [executionIndex]="executionIndex$ | async"\n      [stickToBottommostFrameInFocusedFile]="\n        stickToBottommostFrameInFocusedFile$ | async\n      "\n      [stackFramesForDisplay]="stackFramesForDisplay$ | async"\n      (onSourceLineClicked)="onSourceLineClicked($event)"\n    ></stack-trace-component>\n  '}]}],(function(){return[{type:iS}]}),null);class k${constructor(){this.runs={},this.runIds=[],this.activeRunId=null}}k$.ɵfac=function t(e){return new(e||k$)},k$.ɵcmp=so({type:k$,selectors:[["debugger-component"]],inputs:{runs:"runs",runIds:"runIds",activeRunId:"activeRunId"},decls:4,vars:2,consts:[[1,"debugger-container"],[4,"ngIf","ngIfElse"],["dataAvailable",""],[1,"top-section"],[1,"top-center-section"],[1,"bottom-section"]],template:function t(e,n){if(1&e&&(Cf(0,"div",0),Hm(1,P$,1,0,"tf-debugger-v2-inactive",1),Hm(2,w$,9,0,"ng-template",null,2,qy),Mf()),2&e){const t=Fm(3);Dc(1),bf("ngIf",0===n.runIds.length)("ngIfElse",t)}},directives:[Pv,PX,CJ,bQ,IQ,YQ,s$,O$],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   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#555}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(k$,[{type:S_,args:[{selector:"debugger-component",templateUrl:"./debugger_component.ng.html",styleUrls:["./debugger_component.css"],changeDetection:jn.OnPush}]}],null,{runs:[{type:R_}],runIds:[{type:R_}],activeRunId:[{type:R_}]});class S${constructor(t){this.store=t,this.runs$=this.store.pipe(rS(CK)),this.runsIds$=this.store.pipe(rS(bS(CK,(t=>Object.keys(t))))),this.activeRunId$=this.store.pipe(rS(vK))}ngOnInit(){this.store.dispatch(wX())}ngOnDestroy(){this.store.dispatch(kX())}}S$.ɵfac=function t(e){return new(e||S$)(gf(iS))},S$.ɵcmp=so({type:S$,selectors:[["tf-debugger-v2"]],decls:4,vars:9,consts:[[3,"runs","runIds","activeRunId"]],template:function t(e,n){1&e&&(vf(0,"debugger-component",0),_y(1,"async"),_y(2,"async"),_y(3,"async")),2&e&&bf("runs",Cy(1,3,n.runs$))("runIds",Cy(2,5,n.runsIds$))("activeRunId",Cy(3,7,n.activeRunId$))},directives:[k$],pipes:[Vv],styles:["[_nghost-%COMP%] {\n        display: block;\n        height: 100%;\n      }"]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(S$,[{type:S_,args:[{selector:"tf-debugger-v2",template:'\n    <debugger-component\n      [runs]="runs$ | async"\n      [runIds]="runsIds$ | async"\n      [activeRunId]="activeRunId$ | async"\n    ></debugger-component>\n  ',styles:["\n      :host {\n        display: block;\n        height: 100%;\n      }\n    "]}]}],(function(){return[{type:iS}]}),null);const D$="debugger-v2";function R$(t,e,n,o,i){if(n<=0||!Number.isInteger(n))throw new Error(`Invalid pageSize: ${n}`);if(e>o)throw new Error(`end index (${e}) exceeds total number of items (${o})`);if(e-t>n)throw new Error("begin-end span exceeds page size, which is not allowed");const a=[],r=Math.floor(t/n);(!(r in i)||i[r]<n&&r*n+i[r]<o)&&a.push(r);const s=Math.floor((e-1)/n);if(s!==r){(!(s in i)||s*n+i[s]<e&&e<o)&&a.push(s)}return a}class E${constructor(t,e,n){this.actions$=t,this.store=e,this.dataSource=n,this.loadData$=WS((()=>{const t=this.loadDebuggerRuns(pe(this.onDebuggerDataPoll(),this.onCoreReload())).pipe(Ie()),e=this.loadSourceFileList(t),n=this.createNumExecutionLoader(t),o=this.createNumAlertsAndBreakdownLoader(t),i=this.onAlertTypeFocused(),a=this.fetchExecutionDigestsForAlertTypeFocus(i),r=this.createInitialExecutionDetector(n).pipe(Ie()),s=this.createExecutionDigestLoader(pe(this.onExecutionScroll(),this.createInitialExecutionDigest(r),a)),l=this.createExecutionDataAndStackFramesLoader(pe(this.onExecutionDigestFocused(),r.pipe(qe(this.store.select(vK),this.store.select(iJ)),Vt((([,t,e])=>({activeRunId:t,loadedExecutionData:e,focusIndex:0}))))));return pe(o,s,l,this.createNumGraphExecutionLoader(t),e,this.onSourceFileFocused(),this.loadGraphExecutionPages(this.onGraphExecutionScroll()),this.loadGraphOpStackFrames(this.loadGraphOpInfo())).pipe(Vt((()=>({}))))}),{dispatch:!1})}onDebuggerDataPoll(){return this.actions$.pipe($S(wX),Ve((t=>(function e(t,n,o){return t.pipe(ze((t=>t.pipe(qe(n),Oe((([,t])=>de(t)))))),je(o),Vt((()=>{})))})(zt(t),this.store.select(xK).pipe(Vt((t=>(function e(t){return t>6e4?6e4:t>4e3?t:2e3})(t)))),this.actions$.pipe($S(kX))))),We((()=>this.store.dispatch(SX()))),Vt((()=>{})))}onCoreReload(){return pe(this.actions$.pipe($S(JE,QE)),this.actions$.pipe($S(ZE)).pipe(qe(this.store.select(MK)),ue((([,t])=>t.state===UE.NOT_LOADED||t.state===UE.FAILED&&null===t.lastLoadedTimeInMs)))).pipe(qe(this.store.select(eT)),ue((([,t])=>t===D$)),We((()=>this.store.dispatch(SX()))),Vt((()=>{})))}loadDebuggerRuns(t){return t.pipe(qe(this.store.select(MK)),ue((([,{state:t}])=>t!==UE.LOADING)),We((()=>this.store.dispatch(DX()))),$t((()=>this.dataSource.fetchRuns().pipe(We((t=>{this.store.dispatch(RX({runs:t}))})),Vt((()=>{}))))))}createNumExecutionLoader(t){return t.pipe(qe(this.store.select(CK),this.store.select(AK)),ue((([,t,e])=>Object.keys(t).length>0&&e.state!==UE.LOADING)),We((()=>this.store.dispatch(IX()))),$t((([,t])=>{const e=Object.keys(t)[0];return this.dataSource.fetchExecutionDigests(e,0,0).pipe(We((t=>{this.store.dispatch(HX({numExecutions:t.num_digests}))})),Vt((()=>{})))})))}createNumGraphExecutionLoader(t){return t.pipe(qe(this.store.select(CK),this.store.select(BK)),ue((([,t,e])=>Object.keys(t).length>0&&e.state!==UE.LOADING)),We((()=>this.store.dispatch(GX()))),$t((([,t])=>{const e=Object.keys(t)[0];return this.dataSource.fetchGraphExecutionDigests(e,0,0).pipe(We((t=>{this.store.dispatch(YX({numGraphExecutions:t.num_digests}))})),Vt((()=>{})))})))}createNumAlertsAndBreakdownLoader(t){return t.pipe(qe(this.store.select(CK),this.store.select(PK)),ue((([,t,e])=>Object.keys(t).length>0&&e.state!==UE.LOADING)),We((()=>this.store.dispatch(AX()))),$t((([,t])=>{const e=Object.keys(t)[0];return this.dataSource.fetchAlerts(e,0,0).pipe(We((t=>{this.store.dispatch(TX({numAlerts:t.num_alerts,alertsBreakdown:t.alerts_breakdown}))})),Vt((()=>{})))})))}createInitialExecutionDetector(t){return t.pipe(qe(this.store.select(NK),this.store.select(TK)),ue((([,t,e])=>t>0&&0===Object.keys(e.pageLoadedSizes).length)),Vt((()=>{})))}createInitialExecutionDigest(t){return t.pipe(qe(this.store.select(NK),this.store.select(vK),this.store.select(IK)),ue((([,,t])=>null!==t)),Vt((([,t,e,n])=>({begin:0,end:Math.min(t,n),runId:e}))))}onExecutionScroll(){return this.actions$.pipe($S(BX,VX,jX),qe(this.store.select(vK),this.store.select(zK),this.store.select(NK),this.store.select(HK),this.store.select(IK)),ue((([t])=>null!==t)),Vt((([,t,e,n,o,i])=>({runId:t,begin:e,end:Math.min(n,e+o),pageSize:i}))),qe(this.store.select(TK)),Vt((([t,e])=>({props:t,loaded:e,missingPages:R$(t.begin,t.end,t.pageSize,e.numExecutions,e.pageLoadedSizes)}))),ue((({missingPages:t})=>t.length>0)),Vt((({props:t,loaded:e,missingPages:n})=>{const{runId:o,pageSize:i}=t;return{begin:n[0]*i,end:Math.min(e.numExecutions,(n[n.length-1]+1)*i),runId:o}})))}createExecutionDigestLoader(t){return t.pipe(qe(this.store.select(TK)),ue((([{begin:t,end:e},n])=>e>t&&!(function o(t,e,n){if(e>=n)throw new Error(`Expected begin to be less than end, but got begin=${e}, end=${n}`);return-1!==t.findIndex((t=>t.begin>=e&&t.end<=n))})(n.loadingRanges,t,e))),We((([{begin:t,end:e}])=>{this.store.dispatch(FX({begin:t,end:e}))})),$t((([{runId:t,begin:e,end:n}])=>this.dataSource.fetchExecutionDigests(t,e,n).pipe(We((t=>{this.store.dispatch(LX(t))})),Vt((()=>{}))))))}onExecutionDigestFocused(){return this.actions$.pipe($S(UX),qe(this.store.select(vK),this.store.select(iJ),this.store.select(zK)),Vt((([t,e,n,o])=>({activeRunId:e,loadedExecutionData:n,focusIndex:o+t.displayIndex}))))}createExecutionDataAndStackFramesLoader(t){return t.pipe(ue((({activeRunId:t,loadedExecutionData:e,focusIndex:n})=>null!==t&&null!==n&&void 0===e[n])),$t((({activeRunId:t,focusIndex:e})=>{const n=e,o=n+1;return this.dataSource.fetchExecutionData(t,n,o).pipe(We((t=>{this.store.dispatch(WX(t))})),Vt((t=>({executionData:t,begin:n,end:o}))))})),Vt((({executionData:t})=>t.executions[0])),qe(this.store.select(vK),this.store.select(rJ)),ue((([t,e,n])=>{if(null===e)return!1;for(const e of t.stack_frame_ids)if(void 0===n[e])return!0;return!1})),$t((([t,e])=>{const n=t.stack_frame_ids;return this.dataSource.fetchStackFrames(e,n).pipe(We((t=>{const e={};for(let o=0;o<n.length;++o)e[n[o]]=t.stack_frames[o];this.store.dispatch(aK({stackFrames:e}))})),Vt((()=>{})))})))}onGraphExecutionScroll(){return this.actions$.pipe($S(XX),Ce(100),qe(this.store.select(vK),this.store.select(VK),this.store.select(jK)),ue((([,t,e])=>null!==t&&e>0)),Vt((([,t,e,n])=>({runId:t,numGraphExecutions:e,scrollBeginIndex:n}))),qe(this.store.select(WK),this.store.select(UK),this.store.select(GK),this.store.select(YK)),Vt((([{runId:t,numGraphExecutions:e,scrollBeginIndex:n},o,i,a,r])=>{let s=R$(n,Math.min(n+i,e),o,e,r);return s=s.filter((t=>-1===a.indexOf(t))),{runId:t,missingPages:s,pageSize:o,numGraphExecutions:e}})))}loadGraphExecutionPages(t){return t.pipe(ue((({missingPages:t})=>t.length>0)),We((({missingPages:t})=>{t.forEach((t=>{this.store.dispatch(qX({pageIndex:t}))}))})),$t((({runId:t,missingPages:e,pageSize:n,numGraphExecutions:o})=>{const i=e[0]*n,a=Math.min((e[e.length-1]+1)*n,o);return this.dataSource.fetchGraphExecutionData(t,i,a).pipe(We((t=>{this.store.dispatch(ZX(t))})),Vt((()=>{})))})))}loadGraphOpInfo(){return this.actions$.pipe($S(JX,KX),qe(this.store.select(vK),this.store.select(aJ)),ue((([t,e,n])=>{const{graph_id:o,op_name:i}=t;return!(null===e||void 0!==n[o]&&n[o].has(i)&&(n[o].get(i)===UE.LOADING||n[o].get(i)===UE.LOADED))})),We((([{graph_id:t,op_name:e}])=>this.store.dispatch(QX({graph_id:t,op_name:e})))),$t((([t,e])=>{const{graph_id:n,op_name:o}=t;return this.dataSource.fetchGraphOpInfo(e,n,o).pipe(We((t=>this.store.dispatch($X({graphOpInfoResponse:t})))),Vt((t=>({runId:e,stackFrameIds:t.stack_frame_ids}))))})))}loadGraphOpStackFrames(t){return t.pipe(qe(this.store.select(rJ)),Vt((([{runId:t,stackFrameIds:e},n])=>({runId:t,missingStackFrameIds:e.filter((t=>void 0===n[t]))}))),ue((({runId:t,missingStackFrameIds:e})=>null!==t&&e.length>0)),$t((({runId:t,missingStackFrameIds:e})=>this.dataSource.fetchStackFrames(t,e).pipe(We((t=>{const n={};for(let o=0;o<e.length;++o)n[e[o]]=t.stack_frames[o];this.store.dispatch(aK({stackFrames:n}))})),Vt((()=>{}))))))}onAlertTypeFocused(){return this.actions$.pipe($S(zX),qe(this.store.select(vK),this.store.select(kK),this.store.select(SK),this.store.select(DK),this.store.select(PK)),ue((([,t,e,n,o,i])=>null!==t&&null!==e&&n>0&&(null===o||Object.keys(o).length<n)&&i.state!==UE.LOADING)),We((()=>this.store.dispatch(AX()))),$t((([,t,e])=>this.dataSource.fetchAlerts(t,0,-1,e))),We((({num_alerts:t,alerts_breakdown:e,alert_type:n,begin:o,end:i,alerts:a})=>{this.store.dispatch(NX({numAlerts:t,alertsBreakdown:e,alertType:n,begin:o,end:i,alerts:a}))})))}fetchExecutionDigestsForAlertTypeFocus(t){return t.pipe(qe(this.store.select(IK),this.store.select(HK),this.store.select(NK),this.store.select(TK),this.store.select(vK)),Vt((([t,e,n,o,i,a])=>{const r=t.alerts[0].execution_index,s=R$(Math.max(0,r-Math.floor(n/2)),Math.min(r+Math.floor(n/2),o),e,o,i.pageLoadedSizes);if(0===s.length)return{runId:a,begin:0,end:0};return{runId:a,begin:s[0]*e,end:Math.min(i.numExecutions,(s[s.length-1]+1)*e)}})))}loadSourceFileList(t){return t.pipe(qe(this.store.select(vK),this.store.select(pJ)),ue((([,t,e])=>null!==t&&e.state!==UE.LOADING)),We((()=>this.store.dispatch(tK()))),$t((([,t])=>this.dataSource.fetchSourceFileList(t).pipe(We((t=>{const e=[];t.forEach((([t,n])=>{e.push({host_name:t,file_path:n})})),this.store.dispatch(eK({sourceFiles:e}))})),Vt((()=>{}))))))}onSourceFileFocused(){return this.actions$.pipe($S(nK),qe(this.store.select(vK),this.store.select(mJ),this.store.select(fJ)),Vt((([t,e,n,o])=>({runId:e,stackFrame:t.stackFrame,fileIndex:n,fileContent:o}))),ue((({runId:t,fileContent:e})=>null!==t&&null!==e&&e.loadState===UE.NOT_LOADED)),We((({stackFrame:t})=>this.store.dispatch(oK({host_name:t.host_name,file_path:t.file_path})))),$t((({fileIndex:t,runId:e})=>this.dataSource.fetchSourceFile(e,t).pipe(We((t=>{this.store.dispatch(iK(t))})),Vt((()=>{}))))))}}E$.ɵfac=function t(e){return new(e||E$)(Tr(QS),Tr(iS),Tr(vX))},E$.ɵprov=Sn({token:E$,factory:E$.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(E$,[{type:Um}],(function(){return[{type:QS},{type:iS},{type:vX}]}),null);class A${}A$.ɵfac=function t(e){return new(e||A$)},A$.ɵmod=fo({type:A$}),A$.ɵinj=Dn({imports:[[rx]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(A$,[{type:L_,args:[{declarations:[yJ,CJ],imports:[rx],exports:[CJ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(A$,{declarations:[yJ,CJ],imports:[rx],exports:[CJ]});class T${}T$.ɵfac=function t(e){return new(e||T$)},T$.ɵmod=fo({type:T$}),T$.ɵinj=Dn({imports:[[rx]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(T$,[{type:L_,args:[{declarations:[zQ,OQ,IQ],imports:[rx],exports:[IQ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(T$,{declarations:[zQ,OQ,IQ],imports:[rx],exports:[IQ]});class N${}N$.ɵfac=function t(e){return new(e||N$)},N$.ɵmod=fo({type:N$}),N$.ɵinj=Dn({imports:[[rx]]}),("undefined"==typeof 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scale(0.75)}}.mat-form-field-appearance-fill[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:.25em 0 .75em 0}.mat-form-field-appearance-fill[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{top:1.09375em;margin-top:-0.5em}.mat-form-field-appearance-fill.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59374em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:1em 0 1em 0}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{top:1.84375em;margin-top:-0.25em}.mat-form-field-appearance-outline.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59374em) 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sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action[_ngcontent-%COMP%]{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node[_ngcontent-%COMP%], .mat-nested-tree-node[_ngcontent-%COMP%]{font-weight:400;font-size:14px}.mat-ripple[_ngcontent-%COMP%]{overflow:hidden;position:relative}.mat-ripple[_ngcontent-%COMP%]:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded[_ngcontent-%COMP%]{overflow:visible}.mat-ripple-element[_ngcontent-%COMP%]{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms 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ngDevMode||ngDevMode)&&Qb(v0,[{type:S_,args:[{selector:"tb-dashboard-layout",template:'\n    <button\n      *ngIf="(width$ | async) === 0"\n      class="expand"\n      (click)="expandSidebar()"\n    >\n      <mat-icon svgIcon="expand_more_24px"></mat-icon>\n    </button>\n    <nav\n      *ngIf="(width$ | async) > 0"\n      class="sidebar"\n      [style.width.%]="width$ | async"\n      [style.minWidth.px]="MINIMUM_SIDEBAR_WIDTH_IN_PX"\n    >\n      <ng-content select="[sidebar]"></ng-content>\n    </nav>\n    <div\n      *ngIf="(width$ | async) > 0"\n      class="resizer"\n      (mousedown)="resizeGrabbed()"\n    ></div>\n    <ng-content select="[main]"></ng-content>\n  ',styleUrls:["layout_container.css"],changeDetection:jn.OnPush}]}],(function(){return[{type:iS},{type:Kh}]}),null),(function(t){t.CHECKBOX="checkbox",t.RUN_NAME="run_name",t.EXPERIMENT_NAME="experiment_name",t.RUN_COLOR="run_color"})(x0||(x0={}));const O0=_S(HT),P0=bS(O0,((t,e)=>{const n=[];for(const o of e)t.specs[o]&&n.push(t.specs[o].hparam.defaultFilters);return TT(n)})),w0=bS(P0,O0,((t,e,n)=>{var o;const i=AT(n),a=e.filters[i];return new Map([...t,...null!==(o=null==a?void 0:a.hparams)&&void 0!==o?o:[]])})),k0=bS(O0,((t,e)=>{const n=[];for(const o of e)t.specs[o]&&n.push(t.specs[o].metric.defaultFilters);return NT(n)})),S0=bS(k0,O0,((t,e,n)=>{var o;const i=AT(n),a=e.filters[i];return new Map([...t,...null!==(o=null==a?void 0:a.metrics)&&void 0!==o?o:[]])})),D0=bS(O0,((t,e)=>(function n(...t){const e=new Map,n=new Map,o=new Map,i=new Map,a=[];for(const r of t){for(const t of r.hparams)if(n.has(t.name)||n.set(t.name,new Set),n.get(t.name).add(t.displayName),e.has(t.name)){const n=e.get(t.name),o=t;if(n.type!==o.type&&a.push(`Hparam, ${o.name}, types have to match. Got: ${n.type} vs. ${o.type}`),n.domain.type===kT.INTERVAL&&o.domain.type===kT.INTERVAL)n.domain.minValue===o.domain.minValue&&n.domain.maxValue===o.domain.maxValue||a.push(`Hparam, ${o.name}, domains have to match. Got: ${n.domain} vs. ${o.domain}`);else if(n.domain.type===kT.DISCRETE&&o.domain.type===kT.DISCRETE){const t=new Set([...n.domain.values,...o.domain.values]);n.domain.values.length===o.domain.values.length&&n.domain.values.length===t.size||a.push(`Hparam, ${o.name}, domains have to match. Got: ${n.domain} vs. ${o.domain}`)}else a.push(`Hparam, ${o.name}, domains have to match. Got: ${n.domain} vs. ${o.domain}`)}else e.set(t.name,Object.assign({},t));for(const t of r.metrics)if(i.has(t.tag)||i.set(t.tag,new Set),i.get(t.tag).add(t.displayName),o.has(t.tag)){const e=o.get(t.tag),n=t;e.datasetType!==n.datasetType&&a.push(`Metric, ${n.tag}, datasetTypes have to match. Got: ${e.datasetType} vs. ${n.datasetType}`)}else o.set(t.tag,Object.assign({},t))}if(a.length)throw new Error(`Validation error:\n${a.join("\n")}`);return{hparams:[...e].map((([t,e])=>Object.assign(Object.assign({},e),{displayName:[...n.get(t)].join(" or ")}))),metrics:[...o].map((([t,e])=>Object.assign(Object.assign({},e),{displayName:[...i.get(t)].join(" or ")})))}})(...e.experimentIds.map((e=>{const n=t.specs[e];return n?{hparams:n.hparam.specs,metrics:n.metric.specs}:null})).filter(Boolean)))),R0={tooltipState:xO("state",[kO("initial, void, hidden",wO({opacity:0,transform:"scale(0)"})),kO("visible",wO({transform:"scale(1)"})),DO("* => visible",OO("200ms cubic-bezier(0, 0, 0.2, 1)",SO([wO({opacity:0,transform:"scale(0)",offset:0}),wO({opacity:.5,transform:"scale(0.99)",offset:.5}),wO({opacity:1,transform:"scale(1)",offset:1})]))),DO("* => hidden",OO("100ms cubic-bezier(0, 0, 0.2, 1)",wO({opacity:0})))])},E0=bH({passive:!0});function A0(t){return Error(`Tooltip position "${t}" is invalid.`)}const T0=new er("mat-tooltip-scroll-strategy");const N0={provide:T0,deps:[KB],useFactory:function z0(t){return()=>t.scrollStrategies.reposition({scrollThrottle:20})}},I0=new er("mat-tooltip-default-options",{providedIn:"root",factory:function H0(){return{showDelay:0,hideDelay:0,touchendHideDelay:1500}}});class F0{constructor(t,e,n,o,i,a,r,s,l,c,d,p){this._overlay=t,this._elementRef=e,this._scrollDispatcher=n,this._viewContainerRef=o,this._ngZone=i,this._platform=a,this._ariaDescriber=r,this._focusMonitor=s,this._dir=c,this._defaultOptions=d,this._position="below",this._disabled=!1,this._viewInitialized=!1,this._pointerExitEventsInitialized=!1,this._viewportMargin=8,this._cssClassPrefix="mat",this.showDelay=this._defaultOptions.showDelay,this.hideDelay=this._defaultOptions.hideDelay,this.touchGestures="auto",this._message="",this._passiveListeners=[],this._destroyed=new B,this._handleKeydown=t=>{this._isTooltipVisible()&&t.keyCode===KI&&!tH(t)&&(t.preventDefault(),t.stopPropagation(),this._ngZone.run((()=>this.hide(0))))},this._scrollStrategy=l,this._document=p,d&&(d.position&&(this.position=d.position),d.touchGestures&&(this.touchGestures=d.touchGestures)),c.change.pipe(je(this._destroyed)).subscribe((()=>{this._overlayRef&&this._updatePosition(this._overlayRef)})),i.runOutsideAngular((()=>{e.nativeElement.addEventListener("keydown",this._handleKeydown)}))}get position(){return this._position}set position(t){var e;t!==this._position&&(this._position=t,this._overlayRef&&(this._updatePosition(this._overlayRef),null===(e=this._tooltipInstance)||void 0===e||e.show(0),this._overlayRef.updatePosition()))}get disabled(){return this._disabled}set disabled(t){this._disabled=eH(t),this._disabled?this.hide(0):this._setupPointerEnterEventsIfNeeded()}get message(){return this._message}set message(t){this._ariaDescriber.removeDescription(this._elementRef.nativeElement,this._message,"tooltip"),this._message=null!=t?String(t).trim():"",!this._message&&this._isTooltipVisible()?this.hide(0):(this._setupPointerEnterEventsIfNeeded(),this._updateTooltipMessage(),this._ngZone.runOutsideAngular((()=>{Promise.resolve().then((()=>{this._ariaDescriber.describe(this._elementRef.nativeElement,this.message,"tooltip")}))})))}get tooltipClass(){return this._tooltipClass}set tooltipClass(t){this._tooltipClass=t,this._tooltipInstance&&this._setTooltipClass(this._tooltipClass)}ngAfterViewInit(){this._viewInitialized=!0,this._setupPointerEnterEventsIfNeeded(),this._focusMonitor.monitor(this._elementRef).pipe(je(this._destroyed)).subscribe((t=>{t?"keyboard"===t&&this._ngZone.run((()=>this.show())):this._ngZone.run((()=>this.hide(0)))}))}ngOnDestroy(){const t=this._elementRef.nativeElement;clearTimeout(this._touchstartTimeout),this._overlayRef&&(this._overlayRef.dispose(),this._tooltipInstance=null),t.removeEventListener("keydown",this._handleKeydown),this._passiveListeners.forEach((([e,n])=>{t.removeEventListener(e,n,E0)})),this._passiveListeners.length=0,this._destroyed.next(),this._destroyed.complete(),this._ariaDescriber.removeDescription(t,this.message,"tooltip"),this._focusMonitor.stopMonitoring(t)}show(t=this.showDelay){if(this.disabled||!this.message||this._isTooltipVisible()&&!this._tooltipInstance._showTimeoutId&&!this._tooltipInstance._hideTimeoutId)return;const e=this._createOverlay();this._detach(),this._portal=this._portal||new sB(this._tooltipComponent,this._viewContainerRef),this._tooltipInstance=e.attach(this._portal).instance,this._tooltipInstance.afterHidden().pipe(je(this._destroyed)).subscribe((()=>this._detach())),this._setTooltipClass(this._tooltipClass),this._updateTooltipMessage(),this._tooltipInstance.show(t)}hide(t=this.hideDelay){this._tooltipInstance&&this._tooltipInstance.hide(t)}toggle(){this._isTooltipVisible()?this.hide():this.show()}_isTooltipVisible(){return!!this._tooltipInstance&&this._tooltipInstance.isVisible()}_createOverlay(){if(this._overlayRef)return this._overlayRef;const t=this._scrollDispatcher.getAncestorScrollContainers(this._elementRef),e=this._overlay.position().flexibleConnectedTo(this._elementRef).withTransformOriginOn(`.${this._cssClassPrefix}-tooltip`).withFlexibleDimensions(!1).withViewportMargin(this._viewportMargin).withScrollableContainers(t);return e.positionChanges.pipe(je(this._destroyed)).subscribe((t=>{this._updateCurrentPositionClass(t.connectionPair),this._tooltipInstance&&t.scrollableViewProperties.isOverlayClipped&&this._tooltipInstance.isVisible()&&this._ngZone.run((()=>this.hide(0)))})),this._overlayRef=this._overlay.create({direction:this._dir,positionStrategy:e,panelClass:`${this._cssClassPrefix}-tooltip-panel`,scrollStrategy:this._scrollStrategy()}),this._updatePosition(this._overlayRef),this._overlayRef.detachments().pipe(je(this._destroyed)).subscribe((()=>this._detach())),this._overlayRef.outsidePointerEvents().pipe(je(this._destroyed)).subscribe((()=>{var t;return null===(t=this._tooltipInstance)||void 0===t?void 0:t._handleBodyInteraction()})),this._overlayRef}_detach(){this._overlayRef&&this._overlayRef.hasAttached()&&this._overlayRef.detach(),this._tooltipInstance=null}_updatePosition(t){const e=t.getConfig().positionStrategy,n=this._getOrigin(),o=this._getOverlayPosition();e.withPositions([this._addOffset(Object.assign(Object.assign({},n.main),o.main)),this._addOffset(Object.assign(Object.assign({},n.fallback),o.fallback))])}_addOffset(t){return t}_getOrigin(){const t=!this._dir||"ltr"==this._dir.value,e=this.position;let n;if("above"==e||"below"==e)n={originX:"center",originY:"above"==e?"top":"bottom"};else if("before"==e||"left"==e&&t||"right"==e&&!t)n={originX:"start",originY:"center"};else if("after"==e||"right"==e&&t||"left"==e&&!t)n={originX:"end",originY:"center"};else if("undefined"==typeof ngDevMode||ngDevMode)throw A0(e);const{x:o,y:i}=this._invertPosition(n.originX,n.originY);return{main:n,fallback:{originX:o,originY:i}}}_getOverlayPosition(){const t=!this._dir||"ltr"==this._dir.value,e=this.position;let n;if("above"==e)n={overlayX:"center",overlayY:"bottom"};else if("below"==e)n={overlayX:"center",overlayY:"top"};else if("before"==e||"left"==e&&t||"right"==e&&!t)n={overlayX:"end",overlayY:"center"};else if("after"==e||"right"==e&&t||"left"==e&&!t)n={overlayX:"start",overlayY:"center"};else if("undefined"==typeof ngDevMode||ngDevMode)throw A0(e);const{x:o,y:i}=this._invertPosition(n.overlayX,n.overlayY);return{main:n,fallback:{overlayX:o,overlayY:i}}}_updateTooltipMessage(){this._tooltipInstance&&(this._tooltipInstance.message=this.message,this._tooltipInstance._markForCheck(),this._ngZone.onMicrotaskEmpty.pipe(ve(1),je(this._destroyed)).subscribe((()=>{this._tooltipInstance&&this._overlayRef.updatePosition()})))}_setTooltipClass(t){this._tooltipInstance&&(this._tooltipInstance.tooltipClass=t,this._tooltipInstance._markForCheck())}_invertPosition(t,e){return"above"===this.position||"below"===this.position?"top"===e?e="bottom":"bottom"===e&&(e="top"):"end"===t?t="start":"start"===t&&(t="end"),{x:t,y:e}}_updateCurrentPositionClass(t){const{overlayY:e,originX:n,originY:o}=t;let i;if(i="center"===e?this._dir&&"rtl"===this._dir.value?"end"===n?"left":"right":"start"===n?"left":"right":"bottom"===e&&"top"===o?"above":"below",i!==this._currentPosition){const t=this._overlayRef;if(t){const e=`${this._cssClassPrefix}-tooltip-panel-`;t.removePanelClass(e+this._currentPosition),t.addPanelClass(e+i)}this._currentPosition=i}}_setupPointerEnterEventsIfNeeded(){!this._disabled&&this.message&&this._viewInitialized&&!this._passiveListeners.length&&(this._platformSupportsMouseEvents()?this._passiveListeners.push(["mouseenter",()=>{this._setupPointerExitEventsIfNeeded(),this.show()}]):"off"!==this.touchGestures&&(this._disableNativeGesturesIfNecessary(),this._passiveListeners.push(["touchstart",()=>{this._setupPointerExitEventsIfNeeded(),clearTimeout(this._touchstartTimeout),this._touchstartTimeout=setTimeout((()=>this.show()),500)}])),this._addListeners(this._passiveListeners))}_setupPointerExitEventsIfNeeded(){if(this._pointerExitEventsInitialized)return;this._pointerExitEventsInitialized=!0;const t=[];if(this._platformSupportsMouseEvents())t.push(["mouseleave",()=>this.hide()],["wheel",t=>this._wheelListener(t)]);else if("off"!==this.touchGestures){this._disableNativeGesturesIfNecessary();const e=()=>{clearTimeout(this._touchstartTimeout),this.hide(this._defaultOptions.touchendHideDelay)};t.push(["touchend",e],["touchcancel",e])}this._addListeners(t),this._passiveListeners.push(...t)}_addListeners(t){t.forEach((([t,e])=>{this._elementRef.nativeElement.addEventListener(t,e,E0)}))}_platformSupportsMouseEvents(){return!this._platform.IOS&&!this._platform.ANDROID}_wheelListener(t){if(this._isTooltipVisible()){const e=this._document.elementFromPoint(t.clientX,t.clientY),n=this._elementRef.nativeElement;e===n||n.contains(e)||this.hide()}}_disableNativeGesturesIfNecessary(){const t=this.touchGestures;if("off"!==t){const e=this._elementRef.nativeElement,n=e.style;("on"===t||"INPUT"!==e.nodeName&&"TEXTAREA"!==e.nodeName)&&(n.userSelect=n.msUserSelect=n.webkitUserSelect=n.MozUserSelect="none"),"on"!==t&&e.draggable||(n.webkitUserDrag="none"),n.touchAction="none",n.webkitTapHighlightColor="transparent"}}}F0.ɵfac=function t(e){return new(e||F0)(gf(KB),gf(Kh),gf(KL),gf(Fb),gf(mC),gf(cH),gf(NH),gf(fF),gf(void 0),gf(vF),gf(void 0),gf(sM))},F0.ɵdir=ho({type:F0,inputs:{showDelay:["matTooltipShowDelay","showDelay"],hideDelay:["matTooltipHideDelay","hideDelay"],touchGestures:["matTooltipTouchGestures","touchGestures"],position:["matTooltipPosition","position"],disabled:["matTooltipDisabled","disabled"],message:["matTooltip","message"],tooltipClass:["matTooltipClass","tooltipClass"]}}),F0.ctorParameters=()=>[{type:KB},{type:Kh},{type:KL},{type:Fb},{type:mC},{type:cH},{type:NH},{type:fF},{type:void 0},{type:vF},{type:void 0},{type:void 0,decorators:[{type:Lr,args:[sM]}]}],F0.propDecorators={position:[{type:R_,args:["matTooltipPosition"]}],disabled:[{type:R_,args:["matTooltipDisabled"]}],showDelay:[{type:R_,args:["matTooltipShowDelay"]}],hideDelay:[{type:R_,args:["matTooltipHideDelay"]}],touchGestures:[{type:R_,args:["matTooltipTouchGestures"]}],message:[{type:R_,args:["matTooltip"]}],tooltipClass:[{type:R_,args:["matTooltipClass"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(F0,[{type:k_}],(function(){return[{type:KB},{type:Kh},{type:KL},{type:Fb},{type:mC},{type:cH},{type:NH},{type:fF},{type:void 0},{type:vF},{type:void 0},{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),{showDelay:[{type:R_,args:["matTooltipShowDelay"]}],hideDelay:[{type:R_,args:["matTooltipHideDelay"]}],touchGestures:[{type:R_,args:["matTooltipTouchGestures"]}],position:[{type:R_,args:["matTooltipPosition"]}],disabled:[{type:R_,args:["matTooltipDisabled"]}],message:[{type:R_,args:["matTooltip"]}],tooltipClass:[{type:R_,args:["matTooltipClass"]}]});class L0 extends F0{constructor(t,e,n,o,i,a,r,s,l,c,d,p){super(t,e,n,o,i,a,r,s,l,c,d,p),this._tooltipComponent=V0}}L0.ɵfac=function t(e){return new(e||L0)(gf(KB),gf(Kh),gf(KL),gf(Fb),gf(mC),gf(cH),gf(NH),gf(fF),gf(T0),gf(vF,8),gf(I0,8),gf(sM))},L0.ɵdir=ho({type:L0,selectors:[["","matTooltip",""]],hostAttrs:[1,"mat-tooltip-trigger"],exportAs:["matTooltip"],features:[Qp]}),L0.ctorParameters=()=>[{type:KB},{type:Kh},{type:KL},{type:Fb},{type:mC},{type:cH},{type:NH},{type:fF},{type:void 0,decorators:[{type:Lr,args:[T0]}]},{type:vF,decorators:[{type:Br}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[I0]}]},{type:void 0,decorators:[{type:Lr,args:[sM]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(L0,[{type:k_,args:[{selector:"[matTooltip]",exportAs:"matTooltip",host:{class:"mat-tooltip-trigger"}}]}],(function(){return[{type:KB},{type:Kh},{type:KL},{type:Fb},{type:mC},{type:cH},{type:NH},{type:fF},{type:void 0,decorators:[{type:Lr,args:[T0]}]},{type:vF,decorators:[{type:Br}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[I0]}]},{type:void 0,decorators:[{type:Lr,args:[sM]}]}]}),null);class B0{constructor(t){this._changeDetectorRef=t,this._visibility="initial",this._closeOnInteraction=!1,this._onHide=new B}show(t){clearTimeout(this._hideTimeoutId),this._closeOnInteraction=!0,this._showTimeoutId=setTimeout((()=>{this._visibility="visible",this._showTimeoutId=void 0,this._onShow(),this._markForCheck()}),t)}hide(t){clearTimeout(this._showTimeoutId),this._hideTimeoutId=setTimeout((()=>{this._visibility="hidden",this._hideTimeoutId=void 0,this._markForCheck()}),t)}afterHidden(){return this._onHide}isVisible(){return"visible"===this._visibility}ngOnDestroy(){clearTimeout(this._showTimeoutId),clearTimeout(this._hideTimeoutId),this._onHide.complete()}_animationStart(){this._closeOnInteraction=!1}_animationDone(t){const e=t.toState;"hidden"!==e||this.isVisible()||this._onHide.next(),"visible"!==e&&"hidden"!==e||(this._closeOnInteraction=!0)}_handleBodyInteraction(){this._closeOnInteraction&&this.hide(0)}_markForCheck(){this._changeDetectorRef.markForCheck()}_onShow(){}}B0.ɵfac=function t(e){return new(e||B0)(gf(Ob))},B0.ɵdir=ho({type:B0}),B0.ctorParameters=()=>[{type:Ob}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(B0,[{type:k_}],(function(){return[{type:Ob}]}),null);class V0 extends B0{constructor(t,e){super(t),this._breakpointObserver=e,this._isHandset=this._breakpointObserver.observe(mV)}}V0.ɵfac=function t(e){return new(e||V0)(gf(Ob),gf(dV))},V0.ɵcmp=so({type:V0,selectors:[["mat-tooltip-component"]],hostAttrs:["aria-hidden","true"],hostVars:2,hostBindings:function t(e,n){2&e&&lu("zoom","visible"===n._visibility?1:null)},features:[Qp],decls:3,vars:7,consts:[[1,"mat-tooltip",3,"ngClass"]],template:function t(e,n){if(1&e&&(Cf(0,"div",0),Rf("@state.start",(function t(){return n._animationStart()}))("@state.done",(function t(e){return n._animationDone(e)})),_y(1,"async"),Pu(2),Mf()),2&e){let t;cu("mat-tooltip-handset",null==(t=Cy(1,5,n._isHandset))?null:t.matches),bf("ngClass",n.tooltipClass)("@state",n._visibility),Dc(2),wu(n.message)}},directives:[Cv],pipes:[Vv],styles:[".mat-tooltip-panel{pointer-events:none !important}.mat-tooltip{color:#fff;border-radius:4px;margin:14px;max-width:250px;padding-left:8px;padding-right:8px;overflow:hidden;text-overflow:ellipsis}.cdk-high-contrast-active .mat-tooltip{outline:solid 1px}.mat-tooltip-handset{margin:24px;padding-left:16px;padding-right:16px}\n"],encapsulation:2,data:{animation:[R0.tooltipState]},changeDetection:0}),V0.ctorParameters=()=>[{type:Ob},{type:dV}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(V0,[{type:S_,args:[{selector:"mat-tooltip-component",template:'<div class="mat-tooltip"\n     [ngClass]="tooltipClass"\n     [class.mat-tooltip-handset]="(_isHandset | async)?.matches"\n     [@state]="_visibility"\n     (@state.start)="_animationStart()"\n     (@state.done)="_animationDone($event)">{{message}}</div>\n',encapsulation:Wn.None,changeDetection:jn.OnPush,animations:[R0.tooltipState],host:{"[style.zoom]":'_visibility === "visible" ? 1 : null',"aria-hidden":"true"},styles:[".mat-tooltip-panel{pointer-events:none !important}.mat-tooltip{color:#fff;border-radius:4px;margin:14px;max-width:250px;padding-left:8px;padding-right:8px;overflow:hidden;text-overflow:ellipsis}.cdk-high-contrast-active .mat-tooltip{outline:solid 1px}.mat-tooltip-handset{margin:24px;padding-left:16px;padding-right:16px}\n"]}]}],(function(){return[{type:Ob},{type:dV}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class j0{}function U0(t,e){if(1&t&&(Cf(0,"mat-option",19),Pu(1),Mf()),2&t){const t=e.$implicit;bf("value",t),Dc(1),ku(" ",t," ")}}function W0(t,e){if(1&t){const t=wf();Cf(0,"mat-form-field",16),Cf(1,"mat-select",17),Rf("selectionChange",(function e(n){Pi(t);return zf(2)._changePageSize(n.value)})),Hm(2,U0,2,2,"mat-option",18),Mf(),Mf()}if(2&t){const t=zf(2);bf("appearance",t._formFieldAppearance)("color",t.color),Dc(1),bf("value",t.pageSize)("disabled",t.disabled)("aria-label",t._intl.itemsPerPageLabel),Dc(1),bf("ngForOf",t._displayedPageSizeOptions)}}function G0(t,e){if(1&t&&(Cf(0,"div",20),Pu(1),Mf()),2&t){const t=zf(2);Dc(1),wu(t.pageSize)}}function Y0(t,e){if(1&t&&(Cf(0,"div",12),Cf(1,"div",13),Pu(2),Mf(),Hm(3,W0,3,6,"mat-form-field",14),Hm(4,G0,2,1,"div",15),Mf()),2&t){const t=zf();Dc(2),ku(" ",t._intl.itemsPerPageLabel," "),Dc(1),bf("ngIf",t._displayedPageSizeOptions.length>1),Dc(1),bf("ngIf",t._displayedPageSizeOptions.length<=1)}}function q0(t,e){if(1&t){const t=wf();Cf(0,"button",21),Rf("click",(function e(){Pi(t);return zf().firstPage()})),oa(),Cf(1,"svg",7),vf(2,"path",22),Mf(),Mf()}if(2&t){const t=zf();bf("matTooltip",t._intl.firstPageLabel)("matTooltipDisabled",t._previousButtonsDisabled())("matTooltipPosition","above")("disabled",t._previousButtonsDisabled()),ym("aria-label",t._intl.firstPageLabel)}}function Z0(t,e){if(1&t){const t=wf();oa(),aa(),Cf(0,"button",23),Rf("click",(function e(){Pi(t);return zf().lastPage()})),oa(),Cf(1,"svg",7),vf(2,"path",24),Mf(),Mf()}if(2&t){const t=zf();bf("matTooltip",t._intl.lastPageLabel)("matTooltipDisabled",t._nextButtonsDisabled())("matTooltipPosition","above")("disabled",t._nextButtonsDisabled()),ym("aria-label",t._intl.lastPageLabel)}}j0.ɵfac=function t(e){return new(e||j0)},j0.ɵmod=fo({type:j0}),j0.ɵinj=Dn({providers:[N0],imports:[[_F,rx,oV,NF],NF,oB]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(j0,[{type:L_,args:[{imports:[_F,rx,oV,NF],exports:[L0,V0,NF,oB],declarations:[L0,V0],entryComponents:[V0],providers:[N0]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(j0,{declarations:function(){return[L0,V0]},imports:function(){return[_F,rx,oV,NF]},exports:function(){return[L0,V0,NF,oB]}});class X0{constructor(){this.changes=new B,this.itemsPerPageLabel="Items per page:",this.nextPageLabel="Next page",this.previousPageLabel="Previous page",this.firstPageLabel="First page",this.lastPageLabel="Last page",this.getRangeLabel=(t,e,n)=>{if(0==n||0==e)return`0 of ${n}`;const o=t*e;return`${o+1} – ${o<(n=Math.max(n,0))?Math.min(o+e,n):o+e} of ${n}`}}}X0.ɵfac=function t(e){return new(e||X0)},X0.ɵprov=Sn({factory:function t(){return new X0},token:X0,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(X0,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[]}),null);const K0={provide:X0,deps:[[new Br,new jr,X0]],useFactory:function J0(t){return t||new X0}},Q0=new er("MAT_PAGINATOR_DEFAULT_OPTIONS"),$0=zF(BF(class{}));
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class t1 extends $0{constructor(t,e,n){if(super(),this._intl=t,this._changeDetectorRef=e,this._pageIndex=0,this._length=0,this._pageSizeOptions=[],this._hidePageSize=!1,this._showFirstLastButtons=!1,this.page=new Sy,this._intlChanges=t.changes.subscribe((()=>this._changeDetectorRef.markForCheck())),n){const{pageSize:t,pageSizeOptions:e,hidePageSize:o,showFirstLastButtons:i}=n;null!=t&&(this._pageSize=t),null!=e&&(this._pageSizeOptions=e),null!=o&&(this._hidePageSize=o),null!=i&&(this._showFirstLastButtons=i)}}get pageIndex(){return this._pageIndex}set pageIndex(t){this._pageIndex=Math.max(nH(t),0),this._changeDetectorRef.markForCheck()}get length(){return this._length}set length(t){this._length=nH(t),this._changeDetectorRef.markForCheck()}get pageSize(){return this._pageSize}set pageSize(t){this._pageSize=Math.max(nH(t),0),this._updateDisplayedPageSizeOptions()}get pageSizeOptions(){return this._pageSizeOptions}set pageSizeOptions(t){this._pageSizeOptions=(t||[]).map((t=>nH(t))),this._updateDisplayedPageSizeOptions()}get hidePageSize(){return this._hidePageSize}set hidePageSize(t){this._hidePageSize=eH(t)}get showFirstLastButtons(){return this._showFirstLastButtons}set showFirstLastButtons(t){this._showFirstLastButtons=eH(t)}ngOnInit(){this._initialized=!0,this._updateDisplayedPageSizeOptions(),this._markInitialized()}ngOnDestroy(){this._intlChanges.unsubscribe()}nextPage(){if(!this.hasNextPage())return;const t=this.pageIndex;this.pageIndex++,this._emitPageEvent(t)}previousPage(){if(!this.hasPreviousPage())return;const t=this.pageIndex;this.pageIndex--,this._emitPageEvent(t)}firstPage(){if(!this.hasPreviousPage())return;const t=this.pageIndex;this.pageIndex=0,this._emitPageEvent(t)}lastPage(){if(!this.hasNextPage())return;const t=this.pageIndex;this.pageIndex=this.getNumberOfPages()-1,this._emitPageEvent(t)}hasPreviousPage(){return this.pageIndex>=1&&0!=this.pageSize}hasNextPage(){const t=this.getNumberOfPages()-1;return this.pageIndex<t&&0!=this.pageSize}getNumberOfPages(){return this.pageSize?Math.ceil(this.length/this.pageSize):0}_changePageSize(t){const e=this.pageIndex;this.pageIndex=Math.floor(this.pageIndex*this.pageSize/t)||0,this.pageSize=t,this._emitPageEvent(e)}_nextButtonsDisabled(){return this.disabled||!this.hasNextPage()}_previousButtonsDisabled(){return this.disabled||!this.hasPreviousPage()}_updateDisplayedPageSizeOptions(){this._initialized&&(this.pageSize||(this._pageSize=0!=this.pageSizeOptions.length?this.pageSizeOptions[0]:50),this._displayedPageSizeOptions=this.pageSizeOptions.slice(),-1===this._displayedPageSizeOptions.indexOf(this.pageSize)&&this._displayedPageSizeOptions.push(this.pageSize),this._displayedPageSizeOptions.sort(((t,e)=>t-e)),this._changeDetectorRef.markForCheck())}_emitPageEvent(t){this.page.emit({previousPageIndex:t,pageIndex:this.pageIndex,pageSize:this.pageSize,length:this.length})}}t1.ɵfac=function t(e){return new(e||t1)(gf(X0),gf(Ob),gf(void 0))},t1.ɵdir=ho({type:t1,inputs:{pageIndex:"pageIndex",length:"length",pageSize:"pageSize",pageSizeOptions:"pageSizeOptions",hidePageSize:"hidePageSize",showFirstLastButtons:"showFirstLastButtons",color:"color"},outputs:{page:"page"},features:[Qp]}),t1.ctorParameters=()=>[{type:X0},{type:Ob},{type:void 0}],t1.propDecorators={color:[{type:R_}],pageIndex:[{type:R_}],length:[{type:R_}],pageSize:[{type:R_}],pageSizeOptions:[{type:R_}],hidePageSize:[{type:R_}],showFirstLastButtons:[{type:R_}],page:[{type:E_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(t1,[{type:k_}],(function(){return[{type:X0},{type:Ob},{type:void 0}]}),{page:[{type:E_}],pageIndex:[{type:R_}],length:[{type:R_}],pageSize:[{type:R_}],pageSizeOptions:[{type:R_}],hidePageSize:[{type:R_}],showFirstLastButtons:[{type:R_}],color:[{type:R_}]});class e1 extends t1{constructor(t,e,n){super(t,e,n),n&&null!=n.formFieldAppearance&&(this._formFieldAppearance=n.formFieldAppearance)}}e1.ɵfac=function t(e){return new(e||e1)(gf(X0),gf(Ob),gf(Q0,8))},e1.ɵcmp=so({type:e1,selectors:[["mat-paginator"]],hostAttrs:["role","group",1,"mat-paginator"],inputs:{disabled:"disabled"},exportAs:["matPaginator"],features:[Qp],decls:14,vars:14,consts:[[1,"mat-paginator-outer-container"],[1,"mat-paginator-container"],["class","mat-paginator-page-size",4,"ngIf"],[1,"mat-paginator-range-actions"],[1,"mat-paginator-range-label"],["mat-icon-button","","type","button","class","mat-paginator-navigation-first",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click",4,"ngIf"],["mat-icon-button","","type","button",1,"mat-paginator-navigation-previous",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click"],["viewBox","0 0 24 24","focusable","false",1,"mat-paginator-icon"],["d","M15.41 7.41L14 6l-6 6 6 6 1.41-1.41L10.83 12z"],["mat-icon-button","","type","button",1,"mat-paginator-navigation-next",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click"],["d","M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z"],["mat-icon-button","","type","button","class","mat-paginator-navigation-last",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click",4,"ngIf"],[1,"mat-paginator-page-size"],[1,"mat-paginator-page-size-label"],["class","mat-paginator-page-size-select",3,"appearance","color",4,"ngIf"],["class","mat-paginator-page-size-value",4,"ngIf"],[1,"mat-paginator-page-size-select",3,"appearance","color"],[3,"value","disabled","aria-label","selectionChange"],[3,"value",4,"ngFor","ngForOf"],[3,"value"],[1,"mat-paginator-page-size-value"],["mat-icon-button","","type","button",1,"mat-paginator-navigation-first",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click"],["d","M18.41 16.59L13.82 12l4.59-4.59L17 6l-6 6 6 6zM6 6h2v12H6z"],["mat-icon-button","","type","button",1,"mat-paginator-navigation-last",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click"],["d","M5.59 7.41L10.18 12l-4.59 4.59L7 18l6-6-6-6zM16 6h2v12h-2z"]],template:function t(e,n){1&e&&(Cf(0,"div",0),Cf(1,"div",1),Hm(2,Y0,5,3,"div",2),Cf(3,"div",3),Cf(4,"div",4),Pu(5),Mf(),Hm(6,q0,3,5,"button",5),Cf(7,"button",6),Rf("click",(function t(){return n.previousPage()})),oa(),Cf(8,"svg",7),vf(9,"path",8),Mf(),Mf(),aa(),Cf(10,"button",9),Rf("click",(function t(){return n.nextPage()})),oa(),Cf(11,"svg",7),vf(12,"path",10),Mf(),Mf(),Hm(13,Z0,3,5,"button",11),Mf(),Mf(),Mf()),2&e&&(Dc(2),bf("ngIf",!n.hidePageSize),Dc(3),ku(" ",n._intl.getRangeLabel(n.pageIndex,n.pageSize,n.length)," 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4px;width:56px}.mat-paginator-page-size-select.mat-form-field-appearance-outline{width:64px}.mat-paginator-page-size-select.mat-form-field-appearance-fill{width:64px}.mat-paginator-range-label{margin:0 32px 0 24px}.mat-paginator-range-actions{display:flex;align-items:center}.mat-paginator-icon{width:28px;fill:currentColor}[dir=rtl] .mat-paginator-icon{transform:rotate(180deg)}.cdk-high-contrast-active .mat-paginator-icon{fill:CanvasText}\n"],encapsulation:2,changeDetection:0}),e1.ctorParameters=()=>[{type:X0},{type:Ob},{type:void 0,decorators:[{type:Br},{type:Lr,args:[Q0]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(e1,[{type:S_,args:[{selector:"mat-paginator",exportAs:"matPaginator",template:'<div class="mat-paginator-outer-container">\n  <div class="mat-paginator-container">\n    <div class="mat-paginator-page-size" *ngIf="!hidePageSize">\n      <div class="mat-paginator-page-size-label">\n        {{_intl.itemsPerPageLabel}}\n      </div>\n\n      <mat-form-field\n        *ngIf="_displayedPageSizeOptions.length > 1"\n        [appearance]="_formFieldAppearance!"\n        [color]="color"\n        class="mat-paginator-page-size-select">\n        <mat-select\n          [value]="pageSize"\n          [disabled]="disabled"\n          [aria-label]="_intl.itemsPerPageLabel"\n          (selectionChange)="_changePageSize($event.value)">\n          <mat-option *ngFor="let pageSizeOption of _displayedPageSizeOptions" [value]="pageSizeOption">\n            {{pageSizeOption}}\n          </mat-option>\n        </mat-select>\n      </mat-form-field>\n\n      <div\n        class="mat-paginator-page-size-value"\n        *ngIf="_displayedPageSizeOptions.length <= 1">{{pageSize}}</div>\n    </div>\n\n    <div class="mat-paginator-range-actions">\n      <div class="mat-paginator-range-label">\n        {{_intl.getRangeLabel(pageIndex, pageSize, length)}}\n      </div>\n\n      <button mat-icon-button type="button"\n              class="mat-paginator-navigation-first"\n              (click)="firstPage()"\n              [attr.aria-label]="_intl.firstPageLabel"\n              [matTooltip]="_intl.firstPageLabel"\n              [matTooltipDisabled]="_previousButtonsDisabled()"\n              [matTooltipPosition]="\'above\'"\n              [disabled]="_previousButtonsDisabled()"\n              *ngIf="showFirstLastButtons">\n        <svg class="mat-paginator-icon" viewBox="0 0 24 24" focusable="false">\n          <path d="M18.41 16.59L13.82 12l4.59-4.59L17 6l-6 6 6 6zM6 6h2v12H6z"/>\n        </svg>\n      </button>\n      <button mat-icon-button type="button"\n              class="mat-paginator-navigation-previous"\n              (click)="previousPage()"\n              [attr.aria-label]="_intl.previousPageLabel"\n              [matTooltip]="_intl.previousPageLabel"\n              [matTooltipDisabled]="_previousButtonsDisabled()"\n              [matTooltipPosition]="\'above\'"\n              [disabled]="_previousButtonsDisabled()">\n        <svg class="mat-paginator-icon" viewBox="0 0 24 24" focusable="false">\n          <path d="M15.41 7.41L14 6l-6 6 6 6 1.41-1.41L10.83 12z"/>\n        </svg>\n      </button>\n      <button mat-icon-button type="button"\n              class="mat-paginator-navigation-next"\n              (click)="nextPage()"\n              [attr.aria-label]="_intl.nextPageLabel"\n              [matTooltip]="_intl.nextPageLabel"\n              [matTooltipDisabled]="_nextButtonsDisabled()"\n              [matTooltipPosition]="\'above\'"\n              [disabled]="_nextButtonsDisabled()">\n        <svg class="mat-paginator-icon" viewBox="0 0 24 24" focusable="false">\n          <path d="M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z"/>\n        </svg>\n      </button>\n      <button mat-icon-button type="button"\n              class="mat-paginator-navigation-last"\n              (click)="lastPage()"\n              [attr.aria-label]="_intl.lastPageLabel"\n              [matTooltip]="_intl.lastPageLabel"\n              [matTooltipDisabled]="_nextButtonsDisabled()"\n              [matTooltipPosition]="\'above\'"\n              [disabled]="_nextButtonsDisabled()"\n              *ngIf="showFirstLastButtons">\n        <svg class="mat-paginator-icon" viewBox="0 0 24 24" focusable="false">\n          <path d="M5.59 7.41L10.18 12l-4.59 4.59L7 18l6-6-6-6zM16 6h2v12h-2z"/>\n        </svg>\n      </button>\n    </div>\n  </div>\n</div>\n',inputs:["disabled"],host:{class:"mat-paginator",role:"group"},changeDetection:jn.OnPush,encapsulation:Wn.None,styles:[".mat-paginator{display:block}.mat-paginator-outer-container{display:flex}.mat-paginator-container{display:flex;align-items:center;justify-content:flex-end;padding:0 8px;flex-wrap:wrap-reverse;width:100%}.mat-paginator-page-size{display:flex;align-items:baseline;margin-right:8px}[dir=rtl] .mat-paginator-page-size{margin-right:0;margin-left:8px}.mat-paginator-page-size-label{margin:0 4px}.mat-paginator-page-size-select{margin:6px 4px 0 4px;width:56px}.mat-paginator-page-size-select.mat-form-field-appearance-outline{width:64px}.mat-paginator-page-size-select.mat-form-field-appearance-fill{width:64px}.mat-paginator-range-label{margin:0 32px 0 24px}.mat-paginator-range-actions{display:flex;align-items:center}.mat-paginator-icon{width:28px;fill:currentColor}[dir=rtl] .mat-paginator-icon{transform:rotate(180deg)}.cdk-high-contrast-active .mat-paginator-icon{fill:CanvasText}\n"]}]}],(function(){return[{type:X0},{type:Ob},{type:void 0,decorators:[{type:Br},{type:Lr,args:[Q0]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class n1{}n1.ɵfac=function t(e){return new(e||n1)},n1.ɵmod=fo({type:n1}),n1.ɵinj=Dn({providers:[K0],imports:[[rx,IL,PY,j0,NF]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(n1,[{type:L_,args:[{imports:[rx,IL,PY,j0,NF],exports:[e1],declarations:[e1],providers:[K0]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(n1,{declarations:function(){return[e1]},imports:function(){return[rx,IL,PY,j0,NF]},exports:function(){return[e1]}});function o1(t,e){if(1&t){const t=wf();Cf(0,"div",3),Rf("@arrowPosition.start",(function e(){Pi(t);return zf()._disableViewStateAnimation=!0}))("@arrowPosition.done",(function e(){Pi(t);return zf()._disableViewStateAnimation=!1})),vf(1,"div",4),Cf(2,"div",5),vf(3,"div",6),vf(4,"div",7),vf(5,"div",8),Mf(),Mf()}if(2&t){const t=zf();bf("@arrowOpacity",t._getArrowViewState())("@arrowPosition",t._getArrowViewState())("@allowChildren",t._getArrowDirectionState()),Dc(2),bf("@indicator",t._getArrowDirectionState()),Dc(1),bf("@leftPointer",t._getArrowDirectionState()),Dc(1),bf("@rightPointer",t._getArrowDirectionState())}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const i1=new er("MAT_SORT_DEFAULT_OPTIONS"),a1=BF(zF(class{}));class r1 extends a1{constructor(t){super(),this._defaultOptions=t,this.sortables=new Map,this._stateChanges=new B,this.start="asc",this._direction="",this.sortChange=new Sy}get direction(){return this._direction}set direction(t){if(t&&"asc"!==t&&"desc"!==t&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(t){return Error(`${t} is not a valid sort direction ('asc' or 'desc').`)})(t);this._direction=t}get disableClear(){return this._disableClear}set disableClear(t){this._disableClear=eH(t)}register(t){if("undefined"==typeof ngDevMode||ngDevMode){if(!t.id)throw(function e(){return Error("MatSortHeader must be provided with a unique id.")})();if(this.sortables.has(t.id))throw(function n(t){return Error(`Cannot have two MatSortables with the same id (${t}).`)})(t.id)}this.sortables.set(t.id,t)}deregister(t){this.sortables.delete(t.id)}sort(t){this.active!=t.id?(this.active=t.id,this.direction=t.start?t.start:this.start):this.direction=this.getNextSortDirection(t),this.sortChange.emit({active:this.active,direction:this.direction})}getNextSortDirection(t){var e,n,o;if(!t)return"";const i=null!==(n=null!==(e=null==t?void 0:t.disableClear)&&void 0!==e?e:this.disableClear)&&void 0!==n?n:!!(null===(o=this._defaultOptions)||void 0===o?void 0:o.disableClear);let a=(function r(t,e){let n=["asc","desc"];"desc"==t&&n.reverse();e||n.push("");return n}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t.start||this.start,i),s=a.indexOf(this.direction)+1;return s>=a.length&&(s=0),a[s]}ngOnInit(){this._markInitialized()}ngOnChanges(){this._stateChanges.next()}ngOnDestroy(){this._stateChanges.complete()}}r1.ɵfac=function t(e){return new(e||r1)(gf(i1,8))},r1.ɵdir=ho({type:r1,selectors:[["","matSort",""]],hostAttrs:[1,"mat-sort"],inputs:{disabled:["matSortDisabled","disabled"],start:["matSortStart","start"],direction:["matSortDirection","direction"],disableClear:["matSortDisableClear","disableClear"],active:["matSortActive","active"]},outputs:{sortChange:"matSortChange"},exportAs:["matSort"],features:[Qp,qo]}),r1.ctorParameters=()=>[{type:void 0,decorators:[{type:Br},{type:Lr,args:[i1]}]}],r1.propDecorators={active:[{type:R_,args:["matSortActive"]}],start:[{type:R_,args:["matSortStart"]}],direction:[{type:R_,args:["matSortDirection"]}],disableClear:[{type:R_,args:["matSortDisableClear"]}],sortChange:[{type:E_,args:["matSortChange"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(r1,[{type:k_,args:[{selector:"[matSort]",exportAs:"matSort",host:{class:"mat-sort"},inputs:["disabled: matSortDisabled"]}]}],(function(){return[{type:void 0,decorators:[{type:Br},{type:Lr,args:[i1]}]}]}),{start:[{type:R_,args:["matSortStart"]}],sortChange:[{type:E_,args:["matSortChange"]}],direction:[{type:R_,args:["matSortDirection"]}],disableClear:[{type:R_,args:["matSortDisableClear"]}],active:[{type:R_,args:["matSortActive"]}]});const s1=RF.ENTERING+" "+DF.STANDARD_CURVE,l1={indicator:xO("indicator",[kO("active-asc, asc",wO({transform:"translateY(0px)"})),kO("active-desc, desc",wO({transform:"translateY(10px)"})),DO("active-asc <=> active-desc",OO(s1))]),leftPointer:xO("leftPointer",[kO("active-asc, asc",wO({transform:"rotate(-45deg)"})),kO("active-desc, desc",wO({transform:"rotate(45deg)"})),DO("active-asc <=> active-desc",OO(s1))]),rightPointer:xO("rightPointer",[kO("active-asc, asc",wO({transform:"rotate(45deg)"})),kO("active-desc, desc",wO({transform:"rotate(-45deg)"})),DO("active-asc <=> active-desc",OO(s1))]),arrowOpacity:xO("arrowOpacity",[kO("desc-to-active, asc-to-active, active",wO({opacity:1})),kO("desc-to-hint, asc-to-hint, hint",wO({opacity:.54})),kO("hint-to-desc, active-to-desc, desc, hint-to-asc, active-to-asc, asc, void",wO({opacity:0})),DO("* => asc, * => desc, * => active, * => hint, * => void",OO("0ms")),DO("* <=> *",OO(s1))]),arrowPosition:xO("arrowPosition",[DO("* => desc-to-hint, * => desc-to-active",OO(s1,SO([wO({transform:"translateY(-25%)"}),wO({transform:"translateY(0)"})]))),DO("* => hint-to-desc, * => active-to-desc",OO(s1,SO([wO({transform:"translateY(0)"}),wO({transform:"translateY(25%)"})]))),DO("* => asc-to-hint, * => asc-to-active",OO(s1,SO([wO({transform:"translateY(25%)"}),wO({transform:"translateY(0)"})]))),DO("* => hint-to-asc, * => active-to-asc",OO(s1,SO([wO({transform:"translateY(0)"}),wO({transform:"translateY(-25%)"})]))),kO("desc-to-hint, asc-to-hint, hint, desc-to-active, asc-to-active, active",wO({transform:"translateY(0)"})),kO("hint-to-desc, active-to-desc, desc",wO({transform:"translateY(-25%)"})),kO("hint-to-asc, active-to-asc, asc",wO({transform:"translateY(25%)"}))]),allowChildren:xO("allowChildren",[DO("* <=> *",[EO("@*",RO(),{optional:!0})])])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class c1{constructor(){this.changes=new B}}c1.ɵfac=function t(e){return new(e||c1)},c1.ɵprov=Sn({factory:function t(){return new c1},token:c1,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(c1,[{type:Um,args:[{providedIn:"root"}]}],(function(){return[]}),null);const d1={provide:c1,deps:[[new Br,new jr,c1]],useFactory:function p1(t){return t||new c1}},m1=zF(class{});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class f1 extends m1{constructor(t,e,n,o,i,a){if(super(),this._intl=t,this._changeDetectorRef=e,this._sort=n,this._columnDef=o,this._focusMonitor=i,this._elementRef=a,this._showIndicatorHint=!1,this._viewState={},this._arrowDirection="",this._disableViewStateAnimation=!1,this.arrowPosition="after",!n&&("undefined"==typeof ngDevMode||ngDevMode))throw(function r(){return Error("MatSortHeader must be placed within a parent element with the MatSort directive.")})();this._handleStateChanges()}get disableClear(){return this._disableClear}set disableClear(t){this._disableClear=eH(t)}ngOnInit(){!this.id&&this._columnDef&&(this.id=this._columnDef.name),this._updateArrowDirection(),this._setAnimationTransitionState({toState:this._isSorted()?"active":this._arrowDirection}),this._sort.register(this)}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0).subscribe((t=>{const e=!!t;e!==this._showIndicatorHint&&(this._setIndicatorHintVisible(e),this._changeDetectorRef.markForCheck())}))}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef),this._sort.deregister(this),this._rerenderSubscription.unsubscribe()}_setIndicatorHintVisible(t){this._isDisabled()&&t||(this._showIndicatorHint=t,this._isSorted()||(this._updateArrowDirection(),this._setAnimationTransitionState(this._showIndicatorHint?{fromState:this._arrowDirection,toState:"hint"}:{fromState:"hint",toState:this._arrowDirection})))}_setAnimationTransitionState(t){this._viewState=t||{},this._disableViewStateAnimation&&(this._viewState={toState:t.toState})}_toggleOnInteraction(){this._sort.sort(this),"hint"!==this._viewState.toState&&"active"!==this._viewState.toState||(this._disableViewStateAnimation=!0)}_handleClick(){this._isDisabled()||this._sort.sort(this)}_handleKeydown(t){this._isDisabled()||t.keyCode!==JI&&t.keyCode!==XI||(t.preventDefault(),this._toggleOnInteraction())}_isSorted(){return this._sort.active==this.id&&("asc"===this._sort.direction||"desc"===this._sort.direction)}_getArrowDirectionState(){return`${this._isSorted()?"active-":""}${this._arrowDirection}`}_getArrowViewState(){const t=this._viewState.fromState;return(t?`${t}-to-`:"")+this._viewState.toState}_updateArrowDirection(){this._arrowDirection=this._isSorted()?this._sort.direction:this.start||this._sort.start}_isDisabled(){return this._sort.disabled||this.disabled}_getAriaSortAttribute(){return this._isSorted()?"asc"==this._sort.direction?"ascending":"descending":"none"}_renderArrow(){return!this._isDisabled()||this._isSorted()}_handleStateChanges(){this._rerenderSubscription=pe(this._sort.sortChange,this._sort._stateChanges,this._intl.changes).subscribe((()=>{this._isSorted()&&(this._updateArrowDirection(),"hint"!==this._viewState.toState&&"active"!==this._viewState.toState||(this._disableViewStateAnimation=!0),this._setAnimationTransitionState({fromState:this._arrowDirection,toState:"active"}),this._showIndicatorHint=!1),!this._isSorted()&&this._viewState&&"active"===this._viewState.toState&&(this._disableViewStateAnimation=!1,this._setAnimationTransitionState({fromState:"active",toState:this._arrowDirection})),this._changeDetectorRef.markForCheck()}))}}f1.ɵfac=function t(e){return new(e||f1)(gf(c1),gf(Ob),gf(r1,8),gf("MAT_SORT_HEADER_COLUMN_DEF",8),gf(fF),gf(Kh))},f1.ɵcmp=so({type:f1,selectors:[["","mat-sort-header",""]],hostAttrs:[1,"mat-sort-header"],hostVars:3,hostBindings:function t(e,n){1&e&&Rf("click",(function t(){return n._handleClick()}))("keydown",(function t(e){return n._handleKeydown(e)}))("mouseenter",(function t(){return n._setIndicatorHintVisible(!0)}))("mouseleave",(function t(){return n._setIndicatorHintVisible(!1)})),2&e&&(ym("aria-sort",n._getAriaSortAttribute()),cu("mat-sort-header-disabled",n._isDisabled()))},inputs:{disabled:"disabled",arrowPosition:"arrowPosition",disableClear:"disableClear",id:["mat-sort-header","id"],start:"start"},exportAs:["matSortHeader"],features:[Qp],attrs:["mat-sort-header",""],ngContentSelectors:["*"],decls:4,vars:6,consts:[["role","button",1,"mat-sort-header-container","mat-focus-indicator"],[1,"mat-sort-header-content"],["class","mat-sort-header-arrow",4,"ngIf"],[1,"mat-sort-header-arrow"],[1,"mat-sort-header-stem"],[1,"mat-sort-header-indicator"],[1,"mat-sort-header-pointer-left"],[1,"mat-sort-header-pointer-right"],[1,"mat-sort-header-pointer-middle"]],template:function t(e,n){1&e&&(Hf(),Cf(0,"div",0),Cf(1,"div",1),Ff(2),Mf(),Hm(3,o1,6,6,"div",2),Mf()),2&e&&(cu("mat-sort-header-sorted",n._isSorted())("mat-sort-header-position-before","before"==n.arrowPosition),ym("tabindex",n._isDisabled()?null:0),Dc(3),bf("ngIf",n._renderArrow()))},directives:[Pv],styles:[".mat-sort-header-container{display:flex;cursor:pointer;align-items:center;letter-spacing:normal;outline:0}[mat-sort-header].cdk-keyboard-focused .mat-sort-header-container,[mat-sort-header].cdk-program-focused .mat-sort-header-container{border-bottom:solid 1px currentColor}.mat-sort-header-disabled .mat-sort-header-container{cursor:default}.mat-sort-header-content{text-align:center;display:flex;align-items:center}.mat-sort-header-position-before{flex-direction:row-reverse}.mat-sort-header-arrow{height:12px;width:12px;min-width:12px;position:relative;display:flex;opacity:0}.mat-sort-header-arrow,[dir=rtl] .mat-sort-header-position-before .mat-sort-header-arrow{margin:0 0 0 6px}.mat-sort-header-position-before .mat-sort-header-arrow,[dir=rtl] .mat-sort-header-arrow{margin:0 6px 0 0}.mat-sort-header-stem{background:currentColor;height:10px;width:2px;margin:auto;display:flex;align-items:center}.cdk-high-contrast-active .mat-sort-header-stem{width:0;border-left:solid 2px}.mat-sort-header-indicator{width:100%;height:2px;display:flex;align-items:center;position:absolute;top:0;left:0}.mat-sort-header-pointer-middle{margin:auto;height:2px;width:2px;background:currentColor;transform:rotate(45deg)}.cdk-high-contrast-active .mat-sort-header-pointer-middle{width:0;height:0;border-top:solid 2px;border-left:solid 2px}.mat-sort-header-pointer-left,.mat-sort-header-pointer-right{background:currentColor;width:6px;height:2px;position:absolute;top:0}.cdk-high-contrast-active .mat-sort-header-pointer-left,.cdk-high-contrast-active .mat-sort-header-pointer-right{width:0;height:0;border-left:solid 6px;border-top:solid 2px}.mat-sort-header-pointer-left{transform-origin:right;left:0}.mat-sort-header-pointer-right{transform-origin:left;right:0}\n"],encapsulation:2,data:{animation:[l1.indicator,l1.leftPointer,l1.rightPointer,l1.arrowOpacity,l1.arrowPosition,l1.allowChildren]},changeDetection:0}),f1.ctorParameters=()=>[{type:c1},{type:Ob},{type:r1,decorators:[{type:Br}]},{type:void 0,decorators:[{type:Lr,args:["MAT_SORT_HEADER_COLUMN_DEF"]},{type:Br}]},{type:fF},{type:Kh}],f1.propDecorators={id:[{type:R_,args:["mat-sort-header"]}],arrowPosition:[{type:R_}],start:[{type:R_}],disableClear:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(f1,[{type:S_,args:[{selector:"[mat-sort-header]",exportAs:"matSortHeader",template:'\x3c!--\n  We set the `tabindex` on an element inside the table header, rather than the header itself,\n  because of a bug in NVDA where having a `tabindex` on a `th` breaks keyboard navigation in the\n  table (see https://github.com/nvaccess/nvda/issues/7718). This allows for the header to both\n  be focusable, and have screen readers read out its `aria-sort` state. We prefer this approach\n  over having a button with an `aria-label` inside the header, because the button\'s `aria-label`\n  will be read out as the user is navigating the table\'s cell (see #13012).\n\n  The approach is based off of: https://dequeuniversity.com/library/aria/tables/sf-sortable-grid\n--\x3e\n<div class="mat-sort-header-container mat-focus-indicator"\n     [class.mat-sort-header-sorted]="_isSorted()"\n     [class.mat-sort-header-position-before]="arrowPosition == \'before\'"\n     [attr.tabindex]="_isDisabled() ? null : 0"\n     role="button">\n\n  \x3c!--\n    TODO(crisbeto): this div isn\'t strictly necessary, but we have to keep it due to a large\n    number of screenshot diff failures. It should be removed eventually. Note that the difference\n    isn\'t visible with a shorter header, but once it breaks up into multiple lines, this element\n    causes it to be center-aligned, whereas removing it will keep the text to the left.\n  --\x3e\n  <div class="mat-sort-header-content">\n    <ng-content></ng-content>\n  </div>\n\n  \x3c!-- Disable animations while a current animation is running --\x3e\n  <div class="mat-sort-header-arrow"\n       *ngIf="_renderArrow()"\n       [@arrowOpacity]="_getArrowViewState()"\n       [@arrowPosition]="_getArrowViewState()"\n       [@allowChildren]="_getArrowDirectionState()"\n       (@arrowPosition.start)="_disableViewStateAnimation = true"\n       (@arrowPosition.done)="_disableViewStateAnimation = false">\n    <div class="mat-sort-header-stem"></div>\n    <div class="mat-sort-header-indicator" [@indicator]="_getArrowDirectionState()">\n      <div class="mat-sort-header-pointer-left" [@leftPointer]="_getArrowDirectionState()"></div>\n      <div class="mat-sort-header-pointer-right" [@rightPointer]="_getArrowDirectionState()"></div>\n      <div class="mat-sort-header-pointer-middle"></div>\n    </div>\n  </div>\n</div>\n',host:{class:"mat-sort-header","(click)":"_handleClick()","(keydown)":"_handleKeydown($event)","(mouseenter)":"_setIndicatorHintVisible(true)","(mouseleave)":"_setIndicatorHintVisible(false)","[attr.aria-sort]":"_getAriaSortAttribute()","[class.mat-sort-header-disabled]":"_isDisabled()"},encapsulation:Wn.None,changeDetection:jn.OnPush,inputs:["disabled"],animations:[l1.indicator,l1.leftPointer,l1.rightPointer,l1.arrowOpacity,l1.arrowPosition,l1.allowChildren],styles:[".mat-sort-header-container{display:flex;cursor:pointer;align-items:center;letter-spacing:normal;outline:0}[mat-sort-header].cdk-keyboard-focused .mat-sort-header-container,[mat-sort-header].cdk-program-focused .mat-sort-header-container{border-bottom:solid 1px currentColor}.mat-sort-header-disabled .mat-sort-header-container{cursor:default}.mat-sort-header-content{text-align:center;display:flex;align-items:center}.mat-sort-header-position-before{flex-direction:row-reverse}.mat-sort-header-arrow{height:12px;width:12px;min-width:12px;position:relative;display:flex;opacity:0}.mat-sort-header-arrow,[dir=rtl] .mat-sort-header-position-before .mat-sort-header-arrow{margin:0 0 0 6px}.mat-sort-header-position-before .mat-sort-header-arrow,[dir=rtl] .mat-sort-header-arrow{margin:0 6px 0 0}.mat-sort-header-stem{background:currentColor;height:10px;width:2px;margin:auto;display:flex;align-items:center}.cdk-high-contrast-active .mat-sort-header-stem{width:0;border-left:solid 2px}.mat-sort-header-indicator{width:100%;height:2px;display:flex;align-items:center;position:absolute;top:0;left:0}.mat-sort-header-pointer-middle{margin:auto;height:2px;width:2px;background:currentColor;transform:rotate(45deg)}.cdk-high-contrast-active .mat-sort-header-pointer-middle{width:0;height:0;border-top:solid 2px;border-left:solid 2px}.mat-sort-header-pointer-left,.mat-sort-header-pointer-right{background:currentColor;width:6px;height:2px;position:absolute;top:0}.cdk-high-contrast-active .mat-sort-header-pointer-left,.cdk-high-contrast-active .mat-sort-header-pointer-right{width:0;height:0;border-left:solid 6px;border-top:solid 2px}.mat-sort-header-pointer-left{transform-origin:right;left:0}.mat-sort-header-pointer-right{transform-origin:left;right:0}\n"]}]}],(function(){return[{type:c1},{type:Ob},{type:r1,decorators:[{type:Br}]},{type:void 0,decorators:[{type:Lr,args:["MAT_SORT_HEADER_COLUMN_DEF"]},{type:Br}]},{type:fF},{type:Kh}]}),{arrowPosition:[{type:R_}],disableClear:[{type:R_}],id:[{type:R_,args:["mat-sort-header"]}],start:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class u1{}u1.ɵfac=function t(e){return new(e||u1)},u1.ɵmod=fo({type:u1}),u1.ɵinj=Dn({providers:[d1],imports:[[rx,NF]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(u1,[{type:L_,args:[{imports:[rx,NF],exports:[r1,f1],declarations:[r1,f1],providers:[d1]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(u1,{declarations:function(){return[r1,f1]},imports:function(){return[rx,NF]},exports:function(){return[r1,f1]}});const g1=[[["caption"]],[["colgroup"],["col"]]];function h1(t,e){if(1&t&&(Cf(0,"th",3),Pu(1),Mf()),2&t){const t=zf();lu("text-align",t.justify),Dc(1),ku(" ",t.headerText," ")}}function b1(t,e){if(1&t&&(Cf(0,"td",4),Pu(1),Mf()),2&t){const t=e.$implicit,n=zf();lu("text-align",n.justify),Dc(1),ku(" ",n.dataAccessor(t,n.name)," ")}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function y1(t){return class extends t{constructor(...t){super(...t),this._sticky=!1,this._hasStickyChanged=!1}get sticky(){return this._sticky}set sticky(t){const e=this._sticky;this._sticky=eH(t),this._hasStickyChanged=e!==this._sticky}hasStickyChanged(){const t=this._hasStickyChanged;return this._hasStickyChanged=!1,t}resetStickyChanged(){this._hasStickyChanged=!1}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const _1=new er("CDK_TABLE"),C1=new er("text-column-options");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class M1{constructor(t){this.template=t}}M1.ɵfac=function t(e){return new(e||M1)(gf(Eb))},M1.ɵdir=ho({type:M1,selectors:[["","cdkCellDef",""]]}),M1.ctorParameters=()=>[{type:Eb}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(M1,[{type:k_,args:[{selector:"[cdkCellDef]"}]}],(function(){return[{type:Eb}]}),null);class v1{constructor(t){this.template=t}}v1.ɵfac=function t(e){return new(e||v1)(gf(Eb))},v1.ɵdir=ho({type:v1,selectors:[["","cdkHeaderCellDef",""]]}),v1.ctorParameters=()=>[{type:Eb}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(v1,[{type:k_,args:[{selector:"[cdkHeaderCellDef]"}]}],(function(){return[{type:Eb}]}),null);class x1{constructor(t){this.template=t}}x1.ɵfac=function t(e){return new(e||x1)(gf(Eb))},x1.ɵdir=ho({type:x1,selectors:[["","cdkFooterCellDef",""]]}),x1.ctorParameters=()=>[{type:Eb}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(x1,[{type:k_,args:[{selector:"[cdkFooterCellDef]"}]}],(function(){return[{type:Eb}]}),null);const O1=y1(class{});class P1 extends O1{constructor(t){super(),this._table=t,this._stickyEnd=!1}get name(){return this._name}set name(t){this._setNameInput(t)}get stickyEnd(){return this._stickyEnd}set stickyEnd(t){const e=this._stickyEnd;this._stickyEnd=eH(t),this._hasStickyChanged=e!==this._stickyEnd}_updateColumnCssClassName(){this._columnCssClassName=[`cdk-column-${this.cssClassFriendlyName}`]}_setNameInput(t){t&&(this._name=t,this.cssClassFriendlyName=t.replace(/[^a-z0-9_-]/gi,"-"),this._updateColumnCssClassName())}}P1.ɵfac=function t(e){return new(e||P1)(gf(_1,8))},P1.ɵdir=ho({type:P1,selectors:[["","cdkColumnDef",""]],contentQueries:function t(e,n,o){if(1&e&&(jy(o,M1,5),jy(o,v1,5),jy(o,x1,5)),2&e){let t;By(t=Uy())&&(n.cell=t.first),By(t=Uy())&&(n.headerCell=t.first),By(t=Uy())&&(n.footerCell=t.first)}},inputs:{sticky:"sticky",name:["cdkColumnDef","name"],stickyEnd:"stickyEnd"},features:[jh([{provide:"MAT_SORT_HEADER_COLUMN_DEF",useExisting:P1}]),Qp]}),P1.ctorParameters=()=>[{type:void 0,decorators:[{type:Lr,args:[_1]},{type:Br}]}],P1.propDecorators={name:[{type:R_,args:["cdkColumnDef"]}],stickyEnd:[{type:R_,args:["stickyEnd"]}],cell:[{type:ir,args:[M1]}],headerCell:[{type:ir,args:[v1]}],footerCell:[{type:ir,args:[x1]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(P1,[{type:k_,args:[{selector:"[cdkColumnDef]",inputs:["sticky"],providers:[{provide:"MAT_SORT_HEADER_COLUMN_DEF",useExisting:P1}]}]}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[_1]},{type:Br}]}]}),{name:[{type:R_,args:["cdkColumnDef"]}],stickyEnd:[{type:R_,args:["stickyEnd"]}],cell:[{type:ir,args:[M1]}],headerCell:[{type:ir,args:[v1]}],footerCell:[{type:ir,args:[x1]}]});class w1{constructor(t,e){const n=e.nativeElement.classList;for(const e of t._columnCssClassName)n.add(e)}}class k1 extends w1{constructor(t,e){super(t,e)}}k1.ɵfac=function t(e){return new(e||k1)(gf(P1),gf(Kh))},k1.ɵdir=ho({type:k1,selectors:[["cdk-header-cell"],["th","cdk-header-cell",""]],hostAttrs:["role","columnheader",1,"cdk-header-cell"],features:[Qp]}),k1.ctorParameters=()=>[{type:P1},{type:Kh}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(k1,[{type:k_,args:[{selector:"cdk-header-cell, th[cdk-header-cell]",host:{class:"cdk-header-cell",role:"columnheader"}}]}],(function(){return[{type:P1},{type:Kh}]}),null);class S1 extends w1{constructor(t,e){var n;if(super(t,e),1===(null===(n=t._table)||void 0===n?void 0:n._elementRef.nativeElement.nodeType)){const n=t._table._elementRef.nativeElement.getAttribute("role");e.nativeElement.setAttribute("role","grid"===n||"treegrid"===n?"gridcell":"cell")}}}S1.ɵfac=function t(e){return new(e||S1)(gf(P1),gf(Kh))},S1.ɵdir=ho({type:S1,selectors:[["cdk-footer-cell"],["td","cdk-footer-cell",""]],hostAttrs:[1,"cdk-footer-cell"],features:[Qp]}),S1.ctorParameters=()=>[{type:P1},{type:Kh}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(S1,[{type:k_,args:[{selector:"cdk-footer-cell, td[cdk-footer-cell]",host:{class:"cdk-footer-cell"}}]}],(function(){return[{type:P1},{type:Kh}]}),null);class D1 extends w1{constructor(t,e){var n;if(super(t,e),1===(null===(n=t._table)||void 0===n?void 0:n._elementRef.nativeElement.nodeType)){const n=t._table._elementRef.nativeElement.getAttribute("role");e.nativeElement.setAttribute("role","grid"===n||"treegrid"===n?"gridcell":"cell")}}}D1.ɵfac=function t(e){return new(e||D1)(gf(P1),gf(Kh))},D1.ɵdir=ho({type:D1,selectors:[["cdk-cell"],["td","cdk-cell",""]],hostAttrs:[1,"cdk-cell"],features:[Qp]}),D1.ctorParameters=()=>[{type:P1},{type:Kh}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(D1,[{type:k_,args:[{selector:"cdk-cell, td[cdk-cell]",host:{class:"cdk-cell"}}]}],(function(){return[{type:P1},{type:Kh}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class R1{constructor(){this.tasks=[],this.endTasks=[]}}const E1=new er("_COALESCED_STYLE_SCHEDULER");class A1{constructor(t){this._ngZone=t,this._currentSchedule=null,this._destroyed=new B}schedule(t){this._createScheduleIfNeeded(),this._currentSchedule.tasks.push(t)}scheduleEnd(t){this._createScheduleIfNeeded(),this._currentSchedule.endTasks.push(t)}ngOnDestroy(){this._destroyed.next(),this._destroyed.complete()}_createScheduleIfNeeded(){this._currentSchedule||(this._currentSchedule=new R1,this._getScheduleObservable().pipe(je(this._destroyed)).subscribe((()=>{for(;this._currentSchedule.tasks.length||this._currentSchedule.endTasks.length;){const t=this._currentSchedule;this._currentSchedule=new R1;for(const e of t.tasks)e();for(const e of t.endTasks)e()}this._currentSchedule=null})))}_getScheduleObservable(){return this._ngZone.isStable?Pt(Promise.resolve(void 0)):this._ngZone.onStable.pipe(ve(1))}}A1.ɵfac=function t(e){return new(e||A1)(Tr(mC))},A1.ɵprov=Sn({token:A1,factory:A1.ɵfac}),A1.ctorParameters=()=>[{type:mC}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(A1,[{type:Um}],(function(){return[{type:mC}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const T1="<ng-container cdkCellOutlet></ng-container>";class N1{constructor(t,e){this.template=t,this._differs=e}ngOnChanges(t){if(!this._columnsDiffer){const e=t.columns&&t.columns.currentValue||[];this._columnsDiffer=this._differs.find(e).create(),this._columnsDiffer.diff(e)}}getColumnsDiff(){return this._columnsDiffer.diff(this.columns)}extractCellTemplate(t){return this instanceof I1?t.headerCell.template:this instanceof F1?t.footerCell.template:t.cell.template}}N1.ɵfac=function t(e){return new(e||N1)(gf(Eb),gf(hb))},N1.ɵdir=ho({type:N1,features:[qo]}),N1.ctorParameters=()=>[{type:Eb},{type:hb}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(N1,[{type:k_}],(function(){return[{type:Eb},{type:hb}]}),null);const z1=y1(class extends N1{});class I1 extends z1{constructor(t,e,n){super(t,e),this._table=n}ngOnChanges(t){super.ngOnChanges(t)}}I1.ɵfac=function t(e){return new(e||I1)(gf(Eb),gf(hb),gf(_1,8))},I1.ɵdir=ho({type:I1,selectors:[["","cdkHeaderRowDef",""]],inputs:{columns:["cdkHeaderRowDef","columns"],sticky:["cdkHeaderRowDefSticky","sticky"]},features:[Qp,qo]}),I1.ctorParameters=()=>[{type:Eb},{type:hb},{type:void 0,decorators:[{type:Lr,args:[_1]},{type:Br}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(I1,[{type:k_,args:[{selector:"[cdkHeaderRowDef]",inputs:["columns: cdkHeaderRowDef","sticky: cdkHeaderRowDefSticky"]}]}],(function(){return[{type:Eb},{type:hb},{type:void 0,decorators:[{type:Lr,args:[_1]},{type:Br}]}]}),null);const H1=y1(class extends N1{});class F1 extends H1{constructor(t,e,n){super(t,e),this._table=n}ngOnChanges(t){super.ngOnChanges(t)}}F1.ɵfac=function t(e){return new(e||F1)(gf(Eb),gf(hb),gf(_1,8))},F1.ɵdir=ho({type:F1,selectors:[["","cdkFooterRowDef",""]],inputs:{columns:["cdkFooterRowDef","columns"],sticky:["cdkFooterRowDefSticky","sticky"]},features:[Qp,qo]}),F1.ctorParameters=()=>[{type:Eb},{type:hb},{type:void 0,decorators:[{type:Lr,args:[_1]},{type:Br}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(F1,[{type:k_,args:[{selector:"[cdkFooterRowDef]",inputs:["columns: cdkFooterRowDef","sticky: cdkFooterRowDefSticky"]}]}],(function(){return[{type:Eb},{type:hb},{type:void 0,decorators:[{type:Lr,args:[_1]},{type:Br}]}]}),null);class L1 extends N1{constructor(t,e,n){super(t,e),this._table=n}}L1.ɵfac=function t(e){return new(e||L1)(gf(Eb),gf(hb),gf(_1,8))},L1.ɵdir=ho({type:L1,selectors:[["","cdkRowDef",""]],inputs:{columns:["cdkRowDefColumns","columns"],when:["cdkRowDefWhen","when"]},features:[Qp]}),L1.ctorParameters=()=>[{type:Eb},{type:hb},{type:void 0,decorators:[{type:Lr,args:[_1]},{type:Br}]}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(L1,[{type:k_,args:[{selector:"[cdkRowDef]",inputs:["columns: cdkRowDefColumns","when: cdkRowDefWhen"]}]}],(function(){return[{type:Eb},{type:hb},{type:void 0,decorators:[{type:Lr,args:[_1]},{type:Br}]}]}),null);class B1{constructor(t){this._viewContainer=t,B1.mostRecentCellOutlet=this}ngOnDestroy(){B1.mostRecentCellOutlet===this&&(B1.mostRecentCellOutlet=null)}}B1.ɵfac=function t(e){return new(e||B1)(gf(Fb))},B1.ɵdir=ho({type:B1,selectors:[["","cdkCellOutlet",""]]}),B1.mostRecentCellOutlet=null,B1.ctorParameters=()=>[{type:Fb}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(B1,[{type:k_,args:[{selector:"[cdkCellOutlet]"}]}],(function(){return[{type:Fb}]}),null);class V1{}V1.ɵfac=function t(e){return new(e||V1)},V1.ɵcmp=so({type:V1,selectors:[["cdk-header-row"],["tr","cdk-header-row",""]],hostAttrs:["role","row",1,"cdk-header-row"],decls:1,vars:0,consts:[["cdkCellOutlet",""]],template:function t(e,n){1&e&&Pf(0,0)},directives:[B1],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(V1,[{type:S_,args:[{selector:"cdk-header-row, tr[cdk-header-row]",template:T1,host:{class:"cdk-header-row",role:"row"},changeDetection:jn.Default,encapsulation:Wn.None}]}],null,null);class j1{}j1.ɵfac=function t(e){return new(e||j1)},j1.ɵcmp=so({type:j1,selectors:[["cdk-footer-row"],["tr","cdk-footer-row",""]],hostAttrs:["role","row",1,"cdk-footer-row"],decls:1,vars:0,consts:[["cdkCellOutlet",""]],template:function t(e,n){1&e&&Pf(0,0)},directives:[B1],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(j1,[{type:S_,args:[{selector:"cdk-footer-row, tr[cdk-footer-row]",template:T1,host:{class:"cdk-footer-row",role:"row"},changeDetection:jn.Default,encapsulation:Wn.None}]}],null,null);class U1{}U1.ɵfac=function t(e){return new(e||U1)},U1.ɵcmp=so({type:U1,selectors:[["cdk-row"],["tr","cdk-row",""]],hostAttrs:["role","row",1,"cdk-row"],decls:1,vars:0,consts:[["cdkCellOutlet",""]],template:function t(e,n){1&e&&Pf(0,0)},directives:[B1],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(U1,[{type:S_,args:[{selector:"cdk-row, tr[cdk-row]",template:T1,host:{class:"cdk-row",role:"row"},changeDetection:jn.Default,encapsulation:Wn.None}]}],null,null);class W1{constructor(t){this.templateRef=t}}W1.ɵfac=function t(e){return new(e||W1)(gf(Eb))},W1.ɵdir=ho({type:W1,selectors:[["ng-template","cdkNoDataRow",""]]}),W1.ctorParameters=()=>[{type:Eb}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(W1,[{type:k_,args:[{selector:"ng-template[cdkNoDataRow]"}]}],(function(){return[{type:Eb}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const G1=["top","bottom","left","right"];class Y1{constructor(t,e,n,o,i=!0,a=!0,r){this._isNativeHtmlTable=t,this._stickCellCss=e,this.direction=n,this._coalescedStyleScheduler=o,this._isBrowser=i,this._needsPositionStickyOnElement=a,this._positionListener=r,this._cachedCellWidths=[],this._borderCellCss={top:`${e}-border-elem-top`,bottom:`${e}-border-elem-bottom`,left:`${e}-border-elem-left`,right:`${e}-border-elem-right`}}clearStickyPositioning(t,e){const n=[];for(const e of t)if(e.nodeType===e.ELEMENT_NODE){n.push(e);for(let t=0;t<e.children.length;t++)n.push(e.children[t])}this._coalescedStyleScheduler.schedule((()=>{for(const t of n)this._removeStickyStyle(t,e)}))}updateStickyColumns(t,e,n,o=!0){if(!t.length||!this._isBrowser||!e.some((t=>t))&&!n.some((t=>t)))return void(this._positionListener&&(this._positionListener.stickyColumnsUpdated({sizes:[]}),this._positionListener.stickyEndColumnsUpdated({sizes:[]})));const i=t[0],a=i.children.length,r=this._getCellWidths(i,o),s=this._getStickyStartColumnPositions(r,e),l=this._getStickyEndColumnPositions(r,n),c=e.lastIndexOf(!0),d=n.indexOf(!0);this._coalescedStyleScheduler.schedule((()=>{const o="rtl"===this.direction,i=o?"right":"left",p=o?"left":"right";for(const o of t)for(let t=0;t<a;t++){const a=o.children[t];e[t]&&this._addStickyStyle(a,i,s[t],t===c),n[t]&&this._addStickyStyle(a,p,l[t],t===d)}this._positionListener&&(this._positionListener.stickyColumnsUpdated({sizes:-1===c?[]:r.slice(0,c+1).map(((t,n)=>e[n]?t:null))}),this._positionListener.stickyEndColumnsUpdated({sizes:-1===d?[]:r.slice(d).map(((t,e)=>n[e+d]?t:null)).reverse()}))}))}stickRows(t,e,n){if(!this._isBrowser)return;const o="bottom"===n?t.slice().reverse():t,i="bottom"===n?e.slice().reverse():e,a=[],r=[],s=[];for(let t=0,e=0;t<o.length;t++){if(!i[t])continue;a[t]=e;const n=o[t];s[t]=this._isNativeHtmlTable?Array.from(n.children):[n];const l=n.getBoundingClientRect().height;e+=l,r[t]=l}const l=i.lastIndexOf(!0);this._coalescedStyleScheduler.schedule((()=>{var t,e;for(let t=0;t<o.length;t++){if(!i[t])continue;const e=a[t],o=t===l;for(const i of s[t])this._addStickyStyle(i,n,e,o)}"top"===n?null===(t=this._positionListener)||void 0===t||t.stickyHeaderRowsUpdated({sizes:r,offsets:a,elements:s}):null===(e=this._positionListener)||void 0===e||e.stickyFooterRowsUpdated({sizes:r,offsets:a,elements:s})}))}updateStickyFooterContainer(t,e){if(!this._isNativeHtmlTable)return;const n=t.querySelector("tfoot");this._coalescedStyleScheduler.schedule((()=>{e.some((t=>!t))?this._removeStickyStyle(n,["bottom"]):this._addStickyStyle(n,"bottom",0,!1)}))}_removeStickyStyle(t,e){for(const n of e)t.style[n]="",t.classList.remove(this._borderCellCss[n]);G1.some((n=>-1===e.indexOf(n)&&t.style[n]))?t.style.zIndex=this._getCalculatedZIndex(t):(t.style.zIndex="",this._needsPositionStickyOnElement&&(t.style.position=""),t.classList.remove(this._stickCellCss))}_addStickyStyle(t,e,n,o){t.classList.add(this._stickCellCss),o&&t.classList.add(this._borderCellCss[e]),t.style[e]=`${n}px`,t.style.zIndex=this._getCalculatedZIndex(t),this._needsPositionStickyOnElement&&(t.style.cssText+="position: -webkit-sticky; position: sticky; ")}_getCalculatedZIndex(t){const e={top:100,bottom:10,left:1,right:1};let n=0;for(const o of G1)t.style[o]&&(n+=e[o]);return n?`${n}`:""}_getCellWidths(t,e=!0){if(!e&&this._cachedCellWidths.length)return this._cachedCellWidths;const n=[],o=t.children;for(let t=0;t<o.length;t++){n.push(o[t].getBoundingClientRect().width)}return this._cachedCellWidths=n,n}_getStickyStartColumnPositions(t,e){const n=[];let o=0;for(let i=0;i<t.length;i++)e[i]&&(n[i]=o,o+=t[i]);return n}_getStickyEndColumnPositions(t,e){const n=[];let o=0;for(let i=t.length;i>0;i--)e[i]&&(n[i]=o,o+=t[i]);return n}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function q1(t){return Error(`Could not find column with id "${t}".`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Z1=new er("CDK_SPL");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class X1{}X1.ɵfac=function t(e){return new(e||X1)},X1.ɵdir=ho({type:X1,selectors:[["cdk-table","recycleRows",""],["table","cdk-table","","recycleRows",""]],features:[jh([{provide:WL,useClass:VL}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(X1,[{type:k_,args:[{selector:"cdk-table[recycleRows], table[cdk-table][recycleRows]",providers:[{provide:WL,useClass:VL}]}]}],null,null);class K1{constructor(t,e){this.viewContainer=t,this.elementRef=e}}K1.ɵfac=function t(e){return new(e||K1)(gf(Fb),gf(Kh))},K1.ɵdir=ho({type:K1,selectors:[["","rowOutlet",""]]}),K1.ctorParameters=()=>[{type:Fb},{type:Kh}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(K1,[{type:k_,args:[{selector:"[rowOutlet]"}]}],(function(){return[{type:Fb},{type:Kh}]}),null);class J1{constructor(t,e){this.viewContainer=t,this.elementRef=e}}J1.ɵfac=function t(e){return new(e||J1)(gf(Fb),gf(Kh))},J1.ɵdir=ho({type:J1,selectors:[["","headerRowOutlet",""]]}),J1.ctorParameters=()=>[{type:Fb},{type:Kh}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(J1,[{type:k_,args:[{selector:"[headerRowOutlet]"}]}],(function(){return[{type:Fb},{type:Kh}]}),null);class Q1{constructor(t,e){this.viewContainer=t,this.elementRef=e}}Q1.ɵfac=function t(e){return new(e||Q1)(gf(Fb),gf(Kh))},Q1.ɵdir=ho({type:Q1,selectors:[["","footerRowOutlet",""]]}),Q1.ctorParameters=()=>[{type:Fb},{type:Kh}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Q1,[{type:k_,args:[{selector:"[footerRowOutlet]"}]}],(function(){return[{type:Fb},{type:Kh}]}),null);class $1{constructor(t,e){this.viewContainer=t,this.elementRef=e}}$1.ɵfac=function t(e){return new(e||$1)(gf(Fb),gf(Kh))},$1.ɵdir=ho({type:$1,selectors:[["","noDataRowOutlet",""]]}),$1.ctorParameters=()=>[{type:Fb},{type:Kh}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb($1,[{type:k_,args:[{selector:"[noDataRowOutlet]"}]}],(function(){return[{type:Fb},{type:Kh}]}),null);const t3='\n  <ng-content select="caption"></ng-content>\n  <ng-content select="colgroup, col"></ng-content>\n  <ng-container headerRowOutlet></ng-container>\n  <ng-container rowOutlet></ng-container>\n  <ng-container noDataRowOutlet></ng-container>\n  <ng-container footerRowOutlet></ng-container>\n';class e3{constructor(t,e,n,o,i,a,r,s,l,c,d){this._differs=t,this._changeDetectorRef=e,this._elementRef=n,this._dir=i,this._platform=r,this._viewRepeater=s,this._coalescedStyleScheduler=l,this._viewportRuler=c,this._stickyPositioningListener=d,this._onDestroy=new B,this._columnDefsByName=new Map,this._customColumnDefs=new Set,this._customRowDefs=new Set,this._customHeaderRowDefs=new Set,this._customFooterRowDefs=new Set,this._headerRowDefChanged=!0,this._footerRowDefChanged=!0,this._stickyColumnStylesNeedReset=!0,this._forceRecalculateCellWidths=!0,this._cachedRenderRowsMap=new Map,this.stickyCssClass="cdk-table-sticky",this.needsPositionStickyOnElement=!0,this._isShowingNoDataRow=!1,this._multiTemplateDataRows=!1,this._fixedLayout=!1,this.contentChanged=new Sy,this.viewChange=new j({start:0,end:Number.MAX_VALUE}),o||this._elementRef.nativeElement.setAttribute("role","table"),this._document=a,this._isNativeHtmlTable="TABLE"===this._elementRef.nativeElement.nodeName}get trackBy(){return this._trackByFn}set trackBy(t){"undefined"!=typeof ngDevMode&&!ngDevMode||null==t||"function"==typeof t||console.warn(`trackBy must be a function, but received ${JSON.stringify(t)}.`),this._trackByFn=t}get dataSource(){return this._dataSource}set dataSource(t){this._dataSource!==t&&this._switchDataSource(t)}get multiTemplateDataRows(){return this._multiTemplateDataRows}set multiTemplateDataRows(t){this._multiTemplateDataRows=eH(t),this._rowOutlet&&this._rowOutlet.viewContainer.length&&(this._forceRenderDataRows(),this.updateStickyColumnStyles())}get fixedLayout(){return this._fixedLayout}set fixedLayout(t){this._fixedLayout=eH(t),this._forceRecalculateCellWidths=!0,this._stickyColumnStylesNeedReset=!0}ngOnInit(){this._setupStickyStyler(),this._isNativeHtmlTable&&this._applyNativeTableSections(),this._dataDiffer=this._differs.find([]).create(((t,e)=>this.trackBy?this.trackBy(e.dataIndex,e.data):e)),this._viewportRuler.change().pipe(je(this._onDestroy)).subscribe((()=>{this._forceRecalculateCellWidths=!0}))}ngAfterContentChecked(){if(this._cacheRowDefs(),this._cacheColumnDefs(),!this._headerRowDefs.length&&!this._footerRowDefs.length&&!this._rowDefs.length&&("undefined"==typeof ngDevMode||ngDevMode))throw(function t(){return Error("Missing definitions for header, footer, and row; cannot determine which columns should be rendered.")})();const e=this._renderUpdatedColumns()||this._headerRowDefChanged||this._footerRowDefChanged;this._stickyColumnStylesNeedReset=this._stickyColumnStylesNeedReset||e,this._forceRecalculateCellWidths=e,this._headerRowDefChanged&&(this._forceRenderHeaderRows(),this._headerRowDefChanged=!1),this._footerRowDefChanged&&(this._forceRenderFooterRows(),this._footerRowDefChanged=!1),this.dataSource&&this._rowDefs.length>0&&!this._renderChangeSubscription?this._observeRenderChanges():this._stickyColumnStylesNeedReset&&this.updateStickyColumnStyles(),this._checkStickyStates()}ngOnDestroy(){this._rowOutlet.viewContainer.clear(),this._noDataRowOutlet.viewContainer.clear(),this._headerRowOutlet.viewContainer.clear(),this._footerRowOutlet.viewContainer.clear(),this._cachedRenderRowsMap.clear(),this._onDestroy.next(),this._onDestroy.complete(),FL(this.dataSource)&&this.dataSource.disconnect(this)}renderRows(){this._renderRows=this._getAllRenderRows();const t=this._dataDiffer.diff(this._renderRows);if(!t)return this._updateNoDataRow(),void this.contentChanged.next();const e=this._rowOutlet.viewContainer;this._viewRepeater.applyChanges(t,e,((t,e,n)=>this._getEmbeddedViewArgs(t.item,n)),(t=>t.item.data),(t=>{1===t.operation&&t.context&&this._renderCellTemplateForItem(t.record.item.rowDef,t.context)})),this._updateRowIndexContext(),t.forEachIdentityChange((t=>{e.get(t.currentIndex).context.$implicit=t.item.data})),this._updateNoDataRow(),this.updateStickyColumnStyles(),this.contentChanged.next()}addColumnDef(t){this._customColumnDefs.add(t)}removeColumnDef(t){this._customColumnDefs.delete(t)}addRowDef(t){this._customRowDefs.add(t)}removeRowDef(t){this._customRowDefs.delete(t)}addHeaderRowDef(t){this._customHeaderRowDefs.add(t),this._headerRowDefChanged=!0}removeHeaderRowDef(t){this._customHeaderRowDefs.delete(t),this._headerRowDefChanged=!0}addFooterRowDef(t){this._customFooterRowDefs.add(t),this._footerRowDefChanged=!0}removeFooterRowDef(t){this._customFooterRowDefs.delete(t),this._footerRowDefChanged=!0}setNoDataRow(t){this._customNoDataRow=t}updateStickyHeaderRowStyles(){const t=this._getRenderedRows(this._headerRowOutlet),e=this._elementRef.nativeElement.querySelector("thead");e&&(e.style.display=t.length?"":"none");const n=this._headerRowDefs.map((t=>t.sticky));this._stickyStyler.clearStickyPositioning(t,["top"]),this._stickyStyler.stickRows(t,n,"top"),this._headerRowDefs.forEach((t=>t.resetStickyChanged()))}updateStickyFooterRowStyles(){const t=this._getRenderedRows(this._footerRowOutlet),e=this._elementRef.nativeElement.querySelector("tfoot");e&&(e.style.display=t.length?"":"none");const n=this._footerRowDefs.map((t=>t.sticky));this._stickyStyler.clearStickyPositioning(t,["bottom"]),this._stickyStyler.stickRows(t,n,"bottom"),this._stickyStyler.updateStickyFooterContainer(this._elementRef.nativeElement,n),this._footerRowDefs.forEach((t=>t.resetStickyChanged()))}updateStickyColumnStyles(){const t=this._getRenderedRows(this._headerRowOutlet),e=this._getRenderedRows(this._rowOutlet),n=this._getRenderedRows(this._footerRowOutlet);(this._isNativeHtmlTable&&!this._fixedLayout||this._stickyColumnStylesNeedReset)&&(this._stickyStyler.clearStickyPositioning([...t,...e,...n],["left","right"]),this._stickyColumnStylesNeedReset=!1),t.forEach(((t,e)=>{this._addStickyColumnStyles([t],this._headerRowDefs[e])})),this._rowDefs.forEach((t=>{const n=[];for(let o=0;o<e.length;o++)this._renderRows[o].rowDef===t&&n.push(e[o]);this._addStickyColumnStyles(n,t)})),n.forEach(((t,e)=>{this._addStickyColumnStyles([t],this._footerRowDefs[e])})),Array.from(this._columnDefsByName.values()).forEach((t=>t.resetStickyChanged()))}_getAllRenderRows(){const t=[],e=this._cachedRenderRowsMap;this._cachedRenderRowsMap=new Map;for(let n=0;n<this._data.length;n++){let o=this._data[n];const i=this._getRenderRowsForData(o,n,e.get(o));this._cachedRenderRowsMap.has(o)||this._cachedRenderRowsMap.set(o,new WeakMap);for(let e=0;e<i.length;e++){let n=i[e];const o=this._cachedRenderRowsMap.get(n.data);o.has(n.rowDef)?o.get(n.rowDef).push(n):o.set(n.rowDef,[n]),t.push(n)}}return t}_getRenderRowsForData(t,e,n){return this._getRowDefs(t,e).map((o=>{const i=n&&n.has(o)?n.get(o):[];if(i.length){const t=i.shift();return t.dataIndex=e,t}return{data:t,rowDef:o,dataIndex:e}}))}_cacheColumnDefs(){this._columnDefsByName.clear();n3(this._getOwnDefs(this._contentColumnDefs),this._customColumnDefs).forEach((t=>{if(this._columnDefsByName.has(t.name)&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(t){return Error(`Duplicate column definition name provided: "${t}".`)})(t.name);this._columnDefsByName.set(t.name,t)}))}_cacheRowDefs(){this._headerRowDefs=n3(this._getOwnDefs(this._contentHeaderRowDefs),this._customHeaderRowDefs),this._footerRowDefs=n3(this._getOwnDefs(this._contentFooterRowDefs),this._customFooterRowDefs),this._rowDefs=n3(this._getOwnDefs(this._contentRowDefs),this._customRowDefs);const t=this._rowDefs.filter((t=>!t.when));if(!this.multiTemplateDataRows&&t.length>1&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("There can only be one default row without a when predicate function.")})();this._defaultRowDef=t[0]}_renderUpdatedColumns(){const t=(t,e)=>t||!!e.getColumnsDiff(),e=this._rowDefs.reduce(t,!1);e&&this._forceRenderDataRows();const n=this._headerRowDefs.reduce(t,!1);n&&this._forceRenderHeaderRows();const o=this._footerRowDefs.reduce(t,!1);return o&&this._forceRenderFooterRows(),e||n||o}_switchDataSource(t){this._data=[],FL(this.dataSource)&&this.dataSource.disconnect(this),this._renderChangeSubscription&&(this._renderChangeSubscription.unsubscribe(),this._renderChangeSubscription=null),t||(this._dataDiffer&&this._dataDiffer.diff([]),this._rowOutlet.viewContainer.clear()),this._dataSource=t}_observeRenderChanges(){if(!this.dataSource)return;let t;if(FL(this.dataSource)?t=this.dataSource.connect(this):Lt(this.dataSource)?t=this.dataSource:Array.isArray(this.dataSource)&&(t=zt(this.dataSource)),void 0===t&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("Provided data source did not match an array, Observable, or DataSource")})();this._renderChangeSubscription=t.pipe(je(this._onDestroy)).subscribe((t=>{this._data=t||[],this.renderRows()}))}_forceRenderHeaderRows(){this._headerRowOutlet.viewContainer.length>0&&this._headerRowOutlet.viewContainer.clear(),this._headerRowDefs.forEach(((t,e)=>this._renderRow(this._headerRowOutlet,t,e))),this.updateStickyHeaderRowStyles()}_forceRenderFooterRows(){this._footerRowOutlet.viewContainer.length>0&&this._footerRowOutlet.viewContainer.clear(),this._footerRowDefs.forEach(((t,e)=>this._renderRow(this._footerRowOutlet,t,e))),this.updateStickyFooterRowStyles()}_addStickyColumnStyles(t,e){const n=Array.from(e.columns||[]).map((t=>{const e=this._columnDefsByName.get(t);if(!e&&("undefined"==typeof ngDevMode||ngDevMode))throw q1(t);return e})),o=n.map((t=>t.sticky)),i=n.map((t=>t.stickyEnd));this._stickyStyler.updateStickyColumns(t,o,i,!this._fixedLayout||this._forceRecalculateCellWidths)}_getRenderedRows(t){const e=[];for(let n=0;n<t.viewContainer.length;n++){const o=t.viewContainer.get(n);e.push(o.rootNodes[0])}return e}_getRowDefs(t,e){if(1==this._rowDefs.length)return[this._rowDefs[0]];let n=[];if(this.multiTemplateDataRows)n=this._rowDefs.filter((n=>!n.when||n.when(e,t)));else{let o=this._rowDefs.find((n=>n.when&&n.when(e,t)))||this._defaultRowDef;o&&n.push(o)}if(!n.length&&("undefined"==typeof ngDevMode||ngDevMode))throw(function o(t){return Error(`Could not find a matching row definition for theprovided row data: ${JSON.stringify(t)}`)})(t);return n}_getEmbeddedViewArgs(t,e){return{templateRef:t.rowDef.template,context:{$implicit:t.data},index:e}}_renderRow(t,e,n,o={}){const i=t.viewContainer.createEmbeddedView(e.template,o,n);return this._renderCellTemplateForItem(e,o),i}_renderCellTemplateForItem(t,e){for(let n of this._getCellTemplates(t))B1.mostRecentCellOutlet&&B1.mostRecentCellOutlet._viewContainer.createEmbeddedView(n,e);this._changeDetectorRef.markForCheck()}_updateRowIndexContext(){const t=this._rowOutlet.viewContainer;for(let e=0,n=t.length;e<n;e++){const o=t.get(e).context;o.count=n,o.first=0===e,o.last=e===n-1,o.even=e%2==0,o.odd=!o.even,this.multiTemplateDataRows?(o.dataIndex=this._renderRows[e].dataIndex,o.renderIndex=e):o.index=this._renderRows[e].dataIndex}}_getCellTemplates(t){return t&&t.columns?Array.from(t.columns,(e=>{const n=this._columnDefsByName.get(e);if(!n&&("undefined"==typeof ngDevMode||ngDevMode))throw q1(e);return t.extractCellTemplate(n)})):[]}_applyNativeTableSections(){const t=this._document.createDocumentFragment(),e=[{tag:"thead",outlets:[this._headerRowOutlet]},{tag:"tbody",outlets:[this._rowOutlet,this._noDataRowOutlet]},{tag:"tfoot",outlets:[this._footerRowOutlet]}];for(const n of e){const e=this._document.createElement(n.tag);e.setAttribute("role","rowgroup");for(const t of n.outlets)e.appendChild(t.elementRef.nativeElement);t.appendChild(e)}this._elementRef.nativeElement.appendChild(t)}_forceRenderDataRows(){this._dataDiffer.diff([]),this._rowOutlet.viewContainer.clear(),this.renderRows()}_checkStickyStates(){const t=(t,e)=>t||e.hasStickyChanged();this._headerRowDefs.reduce(t,!1)&&this.updateStickyHeaderRowStyles(),this._footerRowDefs.reduce(t,!1)&&this.updateStickyFooterRowStyles(),Array.from(this._columnDefsByName.values()).reduce(t,!1)&&(this._stickyColumnStylesNeedReset=!0,this.updateStickyColumnStyles())}_setupStickyStyler(){this._stickyStyler=new Y1(this._isNativeHtmlTable,this.stickyCssClass,this._dir?this._dir.value:"ltr",this._coalescedStyleScheduler,this._platform.isBrowser,this.needsPositionStickyOnElement,this._stickyPositioningListener),(this._dir?this._dir.change:zt()).pipe(je(this._onDestroy)).subscribe((t=>{this._stickyStyler.direction=t,this.updateStickyColumnStyles()}))}_getOwnDefs(t){return t.filter((t=>!t._table||t._table===this))}_updateNoDataRow(){const t=this._customNoDataRow||this._noDataRow;if(t){const e=0===this._rowOutlet.viewContainer.length;if(e!==this._isShowingNoDataRow){const n=this._noDataRowOutlet.viewContainer;e?n.createEmbeddedView(t.templateRef):n.clear(),this._isShowingNoDataRow=e}}}}function n3(t,e){return t.concat(Array.from(e))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */e3.ɵfac=function t(e){return new(e||e3)(gf(hb),gf(Ob),gf(Kh),Ga("role"),gf(vF,8),gf(sM),gf(cH),gf(WL),gf(E1),gf(QL),gf(Z1,12))},e3.ɵcmp=so({type:e3,selectors:[["cdk-table"],["table","cdk-table",""]],contentQueries:function t(e,n,o){if(1&e&&(jy(o,W1,5),jy(o,P1,5),jy(o,L1,5),jy(o,I1,5),jy(o,F1,5)),2&e){let t;By(t=Uy())&&(n._noDataRow=t.first),By(t=Uy())&&(n._contentColumnDefs=t),By(t=Uy())&&(n._contentRowDefs=t),By(t=Uy())&&(n._contentHeaderRowDefs=t),By(t=Uy())&&(n._contentFooterRowDefs=t)}},viewQuery:function t(e,n){if(1&e&&(Vy(K1,7),Vy(J1,7),Vy(Q1,7),Vy($1,7)),2&e){let t;By(t=Uy())&&(n._rowOutlet=t.first),By(t=Uy())&&(n._headerRowOutlet=t.first),By(t=Uy())&&(n._footerRowOutlet=t.first),By(t=Uy())&&(n._noDataRowOutlet=t.first)}},hostAttrs:[1,"cdk-table"],hostVars:2,hostBindings:function t(e,n){2&e&&cu("cdk-table-fixed-layout",n.fixedLayout)},inputs:{trackBy:"trackBy",dataSource:"dataSource",multiTemplateDataRows:"multiTemplateDataRows",fixedLayout:"fixedLayout"},outputs:{contentChanged:"contentChanged"},exportAs:["cdkTable"],features:[jh([{provide:_1,useExisting:e3},{provide:WL,useClass:BL},{provide:E1,useClass:A1},{provide:Z1,useValue:null}])],ngContentSelectors:["caption","colgroup, col"],decls:6,vars:0,consts:[["headerRowOutlet",""],["rowOutlet",""],["noDataRowOutlet",""],["footerRowOutlet",""]],template:function t(e,n){1&e&&(Hf(g1),Ff(0),Ff(1,1),Pf(2,0),Pf(3,1),Pf(4,2),Pf(5,3))},directives:[J1,K1,$1,Q1],styles:[".cdk-table-fixed-layout{table-layout:fixed}\n"],encapsulation:2}),e3.ctorParameters=()=>[{type:hb},{type:Ob},{type:Kh},{type:String,decorators:[{type:$a,args:["role"]}]},{type:vF,decorators:[{type:Br}]},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:cH},{type:void 0,decorators:[{type:Lr,args:[WL]}]},{type:A1,decorators:[{type:Lr,args:[E1]}]},{type:QL},{type:void 0,decorators:[{type:Br},{type:jr},{type:Lr,args:[Z1]}]}],e3.propDecorators={trackBy:[{type:R_}],dataSource:[{type:R_}],multiTemplateDataRows:[{type:R_}],fixedLayout:[{type:R_}],contentChanged:[{type:E_}],_rowOutlet:[{type:ar,args:[K1,{static:!0}]}],_headerRowOutlet:[{type:ar,args:[J1,{static:!0}]}],_footerRowOutlet:[{type:ar,args:[Q1,{static:!0}]}],_noDataRowOutlet:[{type:ar,args:[$1,{static:!0}]}],_contentColumnDefs:[{type:or,args:[P1,{descendants:!0}]}],_contentRowDefs:[{type:or,args:[L1,{descendants:!0}]}],_contentHeaderRowDefs:[{type:or,args:[I1,{descendants:!0}]}],_contentFooterRowDefs:[{type:or,args:[F1,{descendants:!0}]}],_noDataRow:[{type:ir,args:[W1]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(e3,[{type:S_,args:[{selector:"cdk-table, table[cdk-table]",exportAs:"cdkTable",template:t3,host:{class:"cdk-table","[class.cdk-table-fixed-layout]":"fixedLayout"},encapsulation:Wn.None,changeDetection:jn.Default,providers:[{provide:_1,useExisting:e3},{provide:WL,useClass:BL},{provide:E1,useClass:A1},{provide:Z1,useValue:null}],styles:[".cdk-table-fixed-layout{table-layout:fixed}\n"]}]}],(function(){return[{type:hb},{type:Ob},{type:Kh},{type:String,decorators:[{type:$a,args:["role"]}]},{type:vF,decorators:[{type:Br}]},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:cH},{type:void 0,decorators:[{type:Lr,args:[WL]}]},{type:A1,decorators:[{type:Lr,args:[E1]}]},{type:QL},{type:void 0,decorators:[{type:Br},{type:jr},{type:Lr,args:[Z1]}]}]}),{contentChanged:[{type:E_}],trackBy:[{type:R_}],dataSource:[{type:R_}],multiTemplateDataRows:[{type:R_}],fixedLayout:[{type:R_}],_rowOutlet:[{type:ar,args:[K1,{static:!0}]}],_headerRowOutlet:[{type:ar,args:[J1,{static:!0}]}],_footerRowOutlet:[{type:ar,args:[Q1,{static:!0}]}],_noDataRowOutlet:[{type:ar,args:[$1,{static:!0}]}],_contentColumnDefs:[{type:or,args:[P1,{descendants:!0}]}],_contentRowDefs:[{type:or,args:[L1,{descendants:!0}]}],_contentHeaderRowDefs:[{type:or,args:[I1,{descendants:!0}]}],_contentFooterRowDefs:[{type:or,args:[F1,{descendants:!0}]}],_noDataRow:[{type:ir,args:[W1]}]});class o3{constructor(t,e){this._table=t,this._options=e,this.justify="start",this._options=e||{}}get name(){return this._name}set name(t){this._name=t,this._syncColumnDefName()}ngOnInit(){if(this._syncColumnDefName(),void 0===this.headerText&&(this.headerText=this._createDefaultHeaderText()),this.dataAccessor||(this.dataAccessor=this._options.defaultDataAccessor||((t,e)=>t[e])),this._table)this.columnDef.cell=this.cell,this.columnDef.headerCell=this.headerCell,this._table.addColumnDef(this.columnDef);else if("undefined"==typeof ngDevMode||ngDevMode)throw(function t(){return Error("Text column could not find a parent table for registration.")})()}ngOnDestroy(){this._table&&this._table.removeColumnDef(this.columnDef)}_createDefaultHeaderText(){const t=this.name;if(!t&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("Table text column must have a name.")})();return this._options&&this._options.defaultHeaderTextTransform?this._options.defaultHeaderTextTransform(t):t[0].toUpperCase()+t.slice(1)}_syncColumnDefName(){this.columnDef&&(this.columnDef.name=this.name)}}o3.ɵfac=function t(e){return new(e||o3)(gf(e3,8),gf(C1,8))},o3.ɵcmp=so({type:o3,selectors:[["cdk-text-column"]],viewQuery:function t(e,n){if(1&e&&(Vy(P1,7),Vy(M1,7),Vy(v1,7)),2&e){let t;By(t=Uy())&&(n.columnDef=t.first),By(t=Uy())&&(n.cell=t.first),By(t=Uy())&&(n.headerCell=t.first)}},inputs:{justify:"justify",name:"name",headerText:"headerText",dataAccessor:"dataAccessor"},decls:3,vars:0,consts:[["cdkColumnDef",""],["cdk-header-cell","",3,"text-align",4,"cdkHeaderCellDef"],["cdk-cell","",3,"text-align",4,"cdkCellDef"],["cdk-header-cell",""],["cdk-cell",""]],template:function t(e,n){1&e&&(xf(0,0),Hm(1,h1,2,3,"th",1),Hm(2,b1,2,3,"td",2),Of())},directives:[P1,v1,M1,k1,D1],encapsulation:2}),o3.ctorParameters=()=>[{type:e3,decorators:[{type:Br}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[C1]}]}],o3.propDecorators={name:[{type:R_}],headerText:[{type:R_}],dataAccessor:[{type:R_}],justify:[{type:R_}],columnDef:[{type:ar,args:[P1,{static:!0}]}],cell:[{type:ar,args:[M1,{static:!0}]}],headerCell:[{type:ar,args:[v1,{static:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(o3,[{type:S_,args:[{selector:"cdk-text-column",template:'\n    <ng-container cdkColumnDef>\n      <th cdk-header-cell *cdkHeaderCellDef [style.text-align]="justify">\n        {{headerText}}\n      </th>\n      <td cdk-cell *cdkCellDef="let data" [style.text-align]="justify">\n        {{dataAccessor(data, name)}}\n      </td>\n    </ng-container>\n  ',encapsulation:Wn.None,changeDetection:jn.Default}]}],(function(){return[{type:e3,decorators:[{type:Br}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[C1]}]}]}),{justify:[{type:R_}],name:[{type:R_}],headerText:[{type:R_}],dataAccessor:[{type:R_}],columnDef:[{type:ar,args:[P1,{static:!0}]}],cell:[{type:ar,args:[M1,{static:!0}]}],headerCell:[{type:ar,args:[v1,{static:!0}]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const i3=[e3,L1,M1,B1,v1,x1,P1,D1,U1,k1,S1,V1,I1,j1,F1,K1,J1,Q1,o3,W1,X1,$1];class a3{}a3.ɵfac=function t(e){return new(e||a3)},a3.ɵmod=fo({type:a3}),a3.ɵinj=Dn({imports:[[iB]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(a3,[{type:L_,args:[{exports:i3,declarations:i3,imports:[iB]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(a3,{declarations:function(){return[e3,L1,M1,B1,v1,x1,P1,D1,U1,k1,S1,V1,I1,j1,F1,K1,J1,Q1,o3,W1,X1,$1]},imports:function(){return[iB]},exports:function(){return[e3,L1,M1,B1,v1,x1,P1,D1,U1,k1,S1,V1,I1,j1,F1,K1,J1,Q1,o3,W1,X1,$1]}});const r3=[[["caption"]],[["colgroup"],["col"]]];function s3(t,e){if(1&t&&(Cf(0,"th",3),Pu(1),Mf()),2&t){const t=zf();lu("text-align",t.justify),Dc(1),ku(" ",t.headerText," ")}}function l3(t,e){if(1&t&&(Cf(0,"td",4),Pu(1),Mf()),2&t){const t=e.$implicit,n=zf();lu("text-align",n.justify),Dc(1),ku(" ",n.dataAccessor(t,n.name)," ")}}class c3{}c3.ɵfac=function t(e){return new(e||c3)},c3.ɵdir=ho({type:c3,selectors:[["mat-table","recycleRows",""],["table","mat-table","","recycleRows",""]],features:[jh([{provide:WL,useClass:VL}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(c3,[{type:k_,args:[{selector:"mat-table[recycleRows], table[mat-table][recycleRows]",providers:[{provide:WL,useClass:VL}]}]}],null,null);class d3 extends e3{constructor(){super(...arguments),this.stickyCssClass="mat-table-sticky",this.needsPositionStickyOnElement=!1}}d3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(d3)))(n||d3)}})(),d3.ɵcmp=so({type:d3,selectors:[["mat-table"],["table","mat-table",""]],hostAttrs:[1,"mat-table"],hostVars:2,hostBindings:function t(e,n){2&e&&cu("mat-table-fixed-layout",n.fixedLayout)},exportAs:["matTable"],features:[jh([{provide:WL,useClass:BL},{provide:e3,useExisting:d3},{provide:_1,useExisting:d3},{provide:E1,useClass:A1},{provide:Z1,useValue:null}]),Qp],ngContentSelectors:["caption","colgroup, col"],decls:6,vars:0,consts:[["headerRowOutlet",""],["rowOutlet",""],["noDataRowOutlet",""],["footerRowOutlet",""]],template:function t(e,n){1&e&&(Hf(r3),Ff(0),Ff(1,1),Pf(2,0),Pf(3,1),Pf(4,2),Pf(5,3))},directives:[J1,K1,$1,Q1],styles:['mat-table{display:block}mat-header-row{min-height:56px}mat-row,mat-footer-row{min-height:48px}mat-row,mat-header-row,mat-footer-row{display:flex;border-width:0;border-bottom-width:1px;border-style:solid;align-items:center;box-sizing:border-box}mat-row::after,mat-header-row::after,mat-footer-row::after{display:inline-block;min-height:inherit;content:""}mat-cell:first-of-type,mat-header-cell:first-of-type,mat-footer-cell:first-of-type{padding-left:24px}[dir=rtl] mat-cell:first-of-type:not(:only-of-type),[dir=rtl] mat-header-cell:first-of-type:not(:only-of-type),[dir=rtl] mat-footer-cell:first-of-type:not(:only-of-type){padding-left:0;padding-right:24px}mat-cell:last-of-type,mat-header-cell:last-of-type,mat-footer-cell:last-of-type{padding-right:24px}[dir=rtl] mat-cell:last-of-type:not(:only-of-type),[dir=rtl] mat-header-cell:last-of-type:not(:only-of-type),[dir=rtl] mat-footer-cell:last-of-type:not(:only-of-type){padding-right:0;padding-left:24px}mat-cell,mat-header-cell,mat-footer-cell{flex:1;display:flex;align-items:center;overflow:hidden;word-wrap:break-word;min-height:inherit}table.mat-table{border-spacing:0}tr.mat-header-row{height:56px}tr.mat-row,tr.mat-footer-row{height:48px}th.mat-header-cell{text-align:left}[dir=rtl] th.mat-header-cell{text-align:right}th.mat-header-cell,td.mat-cell,td.mat-footer-cell{padding:0;border-bottom-width:1px;border-bottom-style:solid}th.mat-header-cell:first-of-type,td.mat-cell:first-of-type,td.mat-footer-cell:first-of-type{padding-left:24px}[dir=rtl] th.mat-header-cell:first-of-type:not(:only-of-type),[dir=rtl] td.mat-cell:first-of-type:not(:only-of-type),[dir=rtl] td.mat-footer-cell:first-of-type:not(:only-of-type){padding-left:0;padding-right:24px}th.mat-header-cell:last-of-type,td.mat-cell:last-of-type,td.mat-footer-cell:last-of-type{padding-right:24px}[dir=rtl] th.mat-header-cell:last-of-type:not(:only-of-type),[dir=rtl] td.mat-cell:last-of-type:not(:only-of-type),[dir=rtl] td.mat-footer-cell:last-of-type:not(:only-of-type){padding-right:0;padding-left:24px}.mat-table-sticky{position:-webkit-sticky !important;position:sticky !important}.mat-table-fixed-layout{table-layout:fixed}\n'],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(d3,[{type:S_,args:[{selector:"mat-table, table[mat-table]",exportAs:"matTable",template:t3,host:{class:"mat-table","[class.mat-table-fixed-layout]":"fixedLayout"},providers:[{provide:WL,useClass:BL},{provide:e3,useExisting:d3},{provide:_1,useExisting:d3},{provide:E1,useClass:A1},{provide:Z1,useValue:null}],encapsulation:Wn.None,changeDetection:jn.Default,styles:['mat-table{display:block}mat-header-row{min-height:56px}mat-row,mat-footer-row{min-height:48px}mat-row,mat-header-row,mat-footer-row{display:flex;border-width:0;border-bottom-width:1px;border-style:solid;align-items:center;box-sizing:border-box}mat-row::after,mat-header-row::after,mat-footer-row::after{display:inline-block;min-height:inherit;content:""}mat-cell:first-of-type,mat-header-cell:first-of-type,mat-footer-cell:first-of-type{padding-left:24px}[dir=rtl] mat-cell:first-of-type:not(:only-of-type),[dir=rtl] mat-header-cell:first-of-type:not(:only-of-type),[dir=rtl] mat-footer-cell:first-of-type:not(:only-of-type){padding-left:0;padding-right:24px}mat-cell:last-of-type,mat-header-cell:last-of-type,mat-footer-cell:last-of-type{padding-right:24px}[dir=rtl] mat-cell:last-of-type:not(:only-of-type),[dir=rtl] mat-header-cell:last-of-type:not(:only-of-type),[dir=rtl] mat-footer-cell:last-of-type:not(:only-of-type){padding-right:0;padding-left:24px}mat-cell,mat-header-cell,mat-footer-cell{flex:1;display:flex;align-items:center;overflow:hidden;word-wrap:break-word;min-height:inherit}table.mat-table{border-spacing:0}tr.mat-header-row{height:56px}tr.mat-row,tr.mat-footer-row{height:48px}th.mat-header-cell{text-align:left}[dir=rtl] th.mat-header-cell{text-align:right}th.mat-header-cell,td.mat-cell,td.mat-footer-cell{padding:0;border-bottom-width:1px;border-bottom-style:solid}th.mat-header-cell:first-of-type,td.mat-cell:first-of-type,td.mat-footer-cell:first-of-type{padding-left:24px}[dir=rtl] th.mat-header-cell:first-of-type:not(:only-of-type),[dir=rtl] td.mat-cell:first-of-type:not(:only-of-type),[dir=rtl] td.mat-footer-cell:first-of-type:not(:only-of-type){padding-left:0;padding-right:24px}th.mat-header-cell:last-of-type,td.mat-cell:last-of-type,td.mat-footer-cell:last-of-type{padding-right:24px}[dir=rtl] th.mat-header-cell:last-of-type:not(:only-of-type),[dir=rtl] td.mat-cell:last-of-type:not(:only-of-type),[dir=rtl] td.mat-footer-cell:last-of-type:not(:only-of-type){padding-right:0;padding-left:24px}.mat-table-sticky{position:-webkit-sticky !important;position:sticky !important}.mat-table-fixed-layout{table-layout:fixed}\n']}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class p3 extends M1{}p3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(p3)))(n||p3)}})(),p3.ɵdir=ho({type:p3,selectors:[["","matCellDef",""]],features:[jh([{provide:M1,useExisting:p3}]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(p3,[{type:k_,args:[{selector:"[matCellDef]",providers:[{provide:M1,useExisting:p3}]}]}],null,null);class m3 extends v1{}m3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(m3)))(n||m3)}})(),m3.ɵdir=ho({type:m3,selectors:[["","matHeaderCellDef",""]],features:[jh([{provide:v1,useExisting:m3}]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(m3,[{type:k_,args:[{selector:"[matHeaderCellDef]",providers:[{provide:v1,useExisting:m3}]}]}],null,null);class f3 extends x1{}f3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(f3)))(n||f3)}})(),f3.ɵdir=ho({type:f3,selectors:[["","matFooterCellDef",""]],features:[jh([{provide:x1,useExisting:f3}]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(f3,[{type:k_,args:[{selector:"[matFooterCellDef]",providers:[{provide:x1,useExisting:f3}]}]}],null,null);class u3 extends P1{get name(){return this._name}set name(t){this._setNameInput(t)}_updateColumnCssClassName(){super._updateColumnCssClassName(),this._columnCssClassName.push(`mat-column-${this.cssClassFriendlyName}`)}}u3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(u3)))(n||u3)}})(),u3.ɵdir=ho({type:u3,selectors:[["","matColumnDef",""]],inputs:{sticky:"sticky",name:["matColumnDef","name"]},features:[jh([{provide:P1,useExisting:u3},{provide:"MAT_SORT_HEADER_COLUMN_DEF",useExisting:u3}]),Qp]}),u3.propDecorators={name:[{type:R_,args:["matColumnDef"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(u3,[{type:k_,args:[{selector:"[matColumnDef]",inputs:["sticky"],providers:[{provide:P1,useExisting:u3},{provide:"MAT_SORT_HEADER_COLUMN_DEF",useExisting:u3}]}]}],null,{name:[{type:R_,args:["matColumnDef"]}]});class g3 extends k1{}g3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(g3)))(n||g3)}})(),g3.ɵdir=ho({type:g3,selectors:[["mat-header-cell"],["th","mat-header-cell",""]],hostAttrs:["role","columnheader",1,"mat-header-cell"],features:[Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(g3,[{type:k_,args:[{selector:"mat-header-cell, th[mat-header-cell]",host:{class:"mat-header-cell",role:"columnheader"}}]}],null,null);class h3 extends S1{}h3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(h3)))(n||h3)}})(),h3.ɵdir=ho({type:h3,selectors:[["mat-footer-cell"],["td","mat-footer-cell",""]],hostAttrs:["role","gridcell",1,"mat-footer-cell"],features:[Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(h3,[{type:k_,args:[{selector:"mat-footer-cell, td[mat-footer-cell]",host:{class:"mat-footer-cell",role:"gridcell"}}]}],null,null);class b3 extends D1{}b3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(b3)))(n||b3)}})(),b3.ɵdir=ho({type:b3,selectors:[["mat-cell"],["td","mat-cell",""]],hostAttrs:["role","gridcell",1,"mat-cell"],features:[Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(b3,[{type:k_,args:[{selector:"mat-cell, td[mat-cell]",host:{class:"mat-cell",role:"gridcell"}}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class y3 extends I1{}y3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(y3)))(n||y3)}})(),y3.ɵdir=ho({type:y3,selectors:[["","matHeaderRowDef",""]],inputs:{columns:["matHeaderRowDef","columns"],sticky:["matHeaderRowDefSticky","sticky"]},features:[jh([{provide:I1,useExisting:y3}]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(y3,[{type:k_,args:[{selector:"[matHeaderRowDef]",providers:[{provide:I1,useExisting:y3}],inputs:["columns: matHeaderRowDef","sticky: matHeaderRowDefSticky"]}]}],null,null);class _3 extends F1{}_3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(_3)))(n||_3)}})(),_3.ɵdir=ho({type:_3,selectors:[["","matFooterRowDef",""]],inputs:{columns:["matFooterRowDef","columns"],sticky:["matFooterRowDefSticky","sticky"]},features:[jh([{provide:F1,useExisting:_3}]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(_3,[{type:k_,args:[{selector:"[matFooterRowDef]",providers:[{provide:F1,useExisting:_3}],inputs:["columns: matFooterRowDef","sticky: matFooterRowDefSticky"]}]}],null,null);class C3 extends L1{}C3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(C3)))(n||C3)}})(),C3.ɵdir=ho({type:C3,selectors:[["","matRowDef",""]],inputs:{columns:["matRowDefColumns","columns"],when:["matRowDefWhen","when"]},features:[jh([{provide:L1,useExisting:C3}]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(C3,[{type:k_,args:[{selector:"[matRowDef]",providers:[{provide:L1,useExisting:C3}],inputs:["columns: matRowDefColumns","when: matRowDefWhen"]}]}],null,null);class M3 extends V1{}M3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(M3)))(n||M3)}})(),M3.ɵcmp=so({type:M3,selectors:[["mat-header-row"],["tr","mat-header-row",""]],hostAttrs:["role","row",1,"mat-header-row"],exportAs:["matHeaderRow"],features:[jh([{provide:V1,useExisting:M3}]),Qp],decls:1,vars:0,consts:[["cdkCellOutlet",""]],template:function t(e,n){1&e&&Pf(0,0)},directives:[B1],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(M3,[{type:S_,args:[{selector:"mat-header-row, tr[mat-header-row]",template:T1,host:{class:"mat-header-row",role:"row"},changeDetection:jn.Default,encapsulation:Wn.None,exportAs:"matHeaderRow",providers:[{provide:V1,useExisting:M3}]}]}],null,null);class v3 extends j1{}v3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(v3)))(n||v3)}})(),v3.ɵcmp=so({type:v3,selectors:[["mat-footer-row"],["tr","mat-footer-row",""]],hostAttrs:["role","row",1,"mat-footer-row"],exportAs:["matFooterRow"],features:[jh([{provide:j1,useExisting:v3}]),Qp],decls:1,vars:0,consts:[["cdkCellOutlet",""]],template:function t(e,n){1&e&&Pf(0,0)},directives:[B1],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(v3,[{type:S_,args:[{selector:"mat-footer-row, tr[mat-footer-row]",template:T1,host:{class:"mat-footer-row",role:"row"},changeDetection:jn.Default,encapsulation:Wn.None,exportAs:"matFooterRow",providers:[{provide:j1,useExisting:v3}]}]}],null,null);class x3 extends U1{}x3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(x3)))(n||x3)}})(),x3.ɵcmp=so({type:x3,selectors:[["mat-row"],["tr","mat-row",""]],hostAttrs:["role","row",1,"mat-row"],exportAs:["matRow"],features:[jh([{provide:U1,useExisting:x3}]),Qp],decls:1,vars:0,consts:[["cdkCellOutlet",""]],template:function t(e,n){1&e&&Pf(0,0)},directives:[B1],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(x3,[{type:S_,args:[{selector:"mat-row, tr[mat-row]",template:T1,host:{class:"mat-row",role:"row"},changeDetection:jn.Default,encapsulation:Wn.None,exportAs:"matRow",providers:[{provide:U1,useExisting:x3}]}]}],null,null);class O3 extends W1{}O3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(O3)))(n||O3)}})(),O3.ɵdir=ho({type:O3,selectors:[["ng-template","matNoDataRow",""]],features:[jh([{provide:W1,useExisting:O3}]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(O3,[{type:k_,args:[{selector:"ng-template[matNoDataRow]",providers:[{provide:W1,useExisting:O3}]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class P3 extends o3{}P3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(P3)))(n||P3)}})(),P3.ɵcmp=so({type:P3,selectors:[["mat-text-column"]],features:[Qp],decls:3,vars:0,consts:[["matColumnDef",""],["mat-header-cell","",3,"text-align",4,"matHeaderCellDef"],["mat-cell","",3,"text-align",4,"matCellDef"],["mat-header-cell",""],["mat-cell",""]],template:function t(e,n){1&e&&(xf(0,0),Hm(1,s3,2,3,"th",1),Hm(2,l3,2,3,"td",2),Of())},directives:[u3,m3,p3,g3,b3],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(P3,[{type:S_,args:[{selector:"mat-text-column",template:'\n    <ng-container matColumnDef>\n      <th mat-header-cell *matHeaderCellDef [style.text-align]="justify">\n        {{headerText}}\n      </th>\n      <td mat-cell *matCellDef="let data" [style.text-align]="justify">\n        {{dataAccessor(data, name)}}\n      </td>\n    </ng-container>\n  ',encapsulation:Wn.None,changeDetection:jn.Default}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const w3=[d3,c3,m3,y3,u3,p3,C3,f3,_3,g3,b3,h3,M3,x3,v3,O3,P3];class k3{}k3.ɵfac=function t(e){return new(e||k3)},k3.ɵmod=fo({type:k3}),k3.ɵinj=Dn({imports:[[a3,NF],NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(k3,[{type:L_,args:[{imports:[a3,NF],exports:[NF,w3],declarations:w3}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(k3,{declarations:function(){return[d3,c3,m3,y3,u3,p3,C3,f3,_3,g3,b3,h3,M3,x3,v3,O3,P3]},imports:function(){return[a3,NF]},exports:function(){return[NF,d3,c3,m3,y3,u3,p3,C3,f3,_3,g3,b3,h3,M3,x3,v3,O3,P3]}});class S3 extends HL{constructor(t=[]){super(),this._renderData=new j([]),this._filter=new j(""),this._internalPageChanges=new B,this._renderChangesSubscription=null,this.sortingDataAccessor=(t,e)=>{const n=t[e];if(oH(n)){const t=Number(n);return t<9007199254740991?t:n}return n},this.sortData=(t,e)=>{const n=e.active,o=e.direction;return n&&""!=o?t.sort(((t,e)=>{let i=this.sortingDataAccessor(t,n),a=this.sortingDataAccessor(e,n);const r=typeof i,s=typeof a;r!==s&&("number"===r&&(i+=""),"number"===s&&(a+=""));let l=0;return null!=i&&null!=a?i>a?l=1:i<a&&(l=-1):null!=i?l=1:null!=a&&(l=-1),l*("asc"==o?1:-1)})):t},this.filterPredicate=(t,e)=>{const n=Object.keys(t).reduce(((e,n)=>e+t[n]+"◬"),"").toLowerCase(),o=e.trim().toLowerCase();return-1!=n.indexOf(o)},this._data=new j(t),this._updateChangeSubscription()}get data(){return this._data.value}set data(t){this._data.next(t),this._renderChangesSubscription||this._filterData(t)}get filter(){return this._filter.value}set filter(t){this._filter.next(t),this._renderChangesSubscription||this._filterData(this.data)}get sort(){return this._sort}set sort(t){this._sort=t,this._updateChangeSubscription()}get paginator(){return this._paginator}set paginator(t){this._paginator=t,this._updateChangeSubscription()}_updateChangeSubscription(){var t;const e=this._sort?pe(this._sort.sortChange,this._sort.initialized):zt(null),n=this._paginator?pe(this._paginator.page,this._internalPageChanges,this._paginator.initialized):zt(null),o=Kt([this._data,this._filter]).pipe(Vt((([t])=>this._filterData(t)))),i=Kt([o,e]).pipe(Vt((([t])=>this._orderData(t)))),a=Kt([i,n]).pipe(Vt((([t])=>this._pageData(t))));null===(t=this._renderChangesSubscription)||void 0===t||t.unsubscribe(),this._renderChangesSubscription=a.subscribe((t=>this._renderData.next(t)))}_filterData(t){return this.filteredData=null==this.filter||""===this.filter?t:t.filter((t=>this.filterPredicate(t,this.filter))),this.paginator&&this._updatePaginator(this.filteredData.length),this.filteredData}_orderData(t){return this.sort?this.sortData(t.slice(),this.sort):t}_pageData(t){if(!this.paginator)return t;const e=this.paginator.pageIndex*this.paginator.pageSize;return t.slice(e,e+this.paginator.pageSize)}_updatePaginator(t){Promise.resolve().then((()=>{const e=this.paginator;if(e&&(e.length=t,e.pageIndex>0)){const t=Math.ceil(e.length/e.pageSize)-1||0,n=Math.min(e.pageIndex,t);n!==e.pageIndex&&(e.pageIndex=n,this._internalPageChanges.next())}}))}connect(){return this._renderChangesSubscription||this._updateChangeSubscription(),this._renderData}disconnect(){var t;null===(t=this._renderChangesSubscription)||void 0===t||t.unsubscribe(),this._renderChangesSubscription=null}}class D3 extends S3{}const R3=["panel"];function E3(t,e){if(1&t&&(Cf(0,"div",0,1),Ff(2),Mf()),2&t){const t=e.id,n=zf();bf("id",n.id)("ngClass",n._classList),ym("aria-label",n.ariaLabel||null)("aria-labelledby",n._getPanelAriaLabelledby(t))}}let A3=0;class T3{constructor(t,e){this.source=t,this.option=e}}const N3=HF(class{}),z3=new er("mat-autocomplete-default-options",{providedIn:"root",factory:function I3(){return{autoActiveFirstOption:!1}}});class H3 extends N3{constructor(t,e,n,o){super(),this._changeDetectorRef=t,this._elementRef=e,this._activeOptionChanges=u.EMPTY,this.showPanel=!1,this._isOpen=!1,this.displayWith=null,this.optionSelected=new Sy,this.opened=new Sy,this.closed=new Sy,this.optionActivated=new Sy,this._classList={},this.id="mat-autocomplete-"+A3++,this.inertGroups=(null==o?void 0:o.SAFARI)||!1,this._autoActiveFirstOption=!!n.autoActiveFirstOption}get isOpen(){return this._isOpen&&this.showPanel}get autoActiveFirstOption(){return this._autoActiveFirstOption}set autoActiveFirstOption(t){this._autoActiveFirstOption=eH(t)}set classList(t){this._classList=t&&t.length?(function e(t,n=/\s+/){const o=[];if(null!=t){const e=Array.isArray(t)?t:`${t}`.split(n);for(const t of e){const e=`${t}`.trim();e&&o.push(e)}}return o})(t).reduce(((t,e)=>(t[e]=!0,t)),{}):{},this._setVisibilityClasses(this._classList),this._elementRef.nativeElement.className=""}ngAfterContentInit(){this._keyManager=new FH(this.options).withWrap(),this._activeOptionChanges=this._keyManager.change.subscribe((t=>{this.optionActivated.emit({source:this,option:this.options.toArray()[t]||null})})),this._setVisibility()}ngOnDestroy(){this._activeOptionChanges.unsubscribe()}_setScrollTop(t){this.panel&&(this.panel.nativeElement.scrollTop=t)}_getScrollTop(){return this.panel?this.panel.nativeElement.scrollTop:0}_setVisibility(){this.showPanel=!!this.options.length,this._setVisibilityClasses(this._classList),this._changeDetectorRef.markForCheck()}_emitSelectEvent(t){const e=new T3(this,t);this.optionSelected.emit(e)}_getPanelAriaLabelledby(t){if(this.ariaLabel)return null;return this.ariaLabelledby?(t?t+" ":"")+this.ariaLabelledby:t}_setVisibilityClasses(t){t[this._visibleClass]=this.showPanel,t[this._hiddenClass]=!this.showPanel}}H3.ɵfac=function t(e){return new(e||H3)(gf(Ob),gf(Kh),gf(z3),gf(cH))},H3.ɵdir=ho({type:H3,viewQuery:function t(e,n){if(1&e&&(Vy(Eb,7),Vy(R3,5)),2&e){let t;By(t=Uy())&&(n.template=t.first),By(t=Uy())&&(n.panel=t.first)}},inputs:{displayWith:"displayWith",autoActiveFirstOption:"autoActiveFirstOption",classList:["class","classList"],ariaLabel:["aria-label","ariaLabel"],ariaLabelledby:["aria-labelledby","ariaLabelledby"],panelWidth:"panelWidth"},outputs:{optionSelected:"optionSelected",opened:"opened",closed:"closed",optionActivated:"optionActivated"},features:[Qp]}),H3.ctorParameters=()=>[{type:Ob},{type:Kh},{type:void 0,decorators:[{type:Lr,args:[z3]}]},{type:cH}],H3.propDecorators={template:[{type:ar,args:[Eb,{static:!0}]}],panel:[{type:ar,args:["panel"]}],ariaLabel:[{type:R_,args:["aria-label"]}],ariaLabelledby:[{type:R_,args:["aria-labelledby"]}],displayWith:[{type:R_}],autoActiveFirstOption:[{type:R_}],panelWidth:[{type:R_}],optionSelected:[{type:E_}],opened:[{type:E_}],closed:[{type:E_}],optionActivated:[{type:E_}],classList:[{type:R_,args:["class"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(H3,[{type:k_}],(function(){return[{type:Ob},{type:Kh},{type:void 0,decorators:[{type:Lr,args:[z3]}]},{type:cH}]}),{displayWith:[{type:R_}],optionSelected:[{type:E_}],opened:[{type:E_}],closed:[{type:E_}],optionActivated:[{type:E_}],autoActiveFirstOption:[{type:R_}],classList:[{type:R_,args:["class"]}],template:[{type:ar,args:[Eb,{static:!0}]}],panel:[{type:ar,args:["panel"]}],ariaLabel:[{type:R_,args:["aria-label"]}],ariaLabelledby:[{type:R_,args:["aria-labelledby"]}],panelWidth:[{type:R_}]});class F3 extends H3{constructor(){super(...arguments),this._visibleClass="mat-autocomplete-visible",this._hiddenClass="mat-autocomplete-hidden"}}F3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(F3)))(n||F3)}})(),F3.ɵcmp=so({type:F3,selectors:[["mat-autocomplete"]],contentQueries:function t(e,n,o){if(1&e&&(jy(o,CL,5),jy(o,PL,5)),2&e){let t;By(t=Uy())&&(n.optionGroups=t),By(t=Uy())&&(n.options=t)}},hostAttrs:[1,"mat-autocomplete"],inputs:{disableRipple:"disableRipple"},exportAs:["matAutocomplete"],features:[jh([{provide:hL,useExisting:F3}]),Qp],ngContentSelectors:["*"],decls:1,vars:0,consts:[["role","listbox",1,"mat-autocomplete-panel",3,"id","ngClass"],["panel",""]],template:function t(e,n){1&e&&(Hf(),Hm(0,E3,3,4,"ng-template"))},directives:[Cv],styles:[".mat-autocomplete-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;visibility:hidden;max-width:none;max-height:256px;position:relative;width:100%;border-bottom-left-radius:4px;border-bottom-right-radius:4px}.mat-autocomplete-panel.mat-autocomplete-visible{visibility:visible}.mat-autocomplete-panel.mat-autocomplete-hidden{visibility:hidden}.mat-autocomplete-panel-above .mat-autocomplete-panel{border-radius:0;border-top-left-radius:4px;border-top-right-radius:4px}.mat-autocomplete-panel .mat-divider-horizontal{margin-top:-1px}.cdk-high-contrast-active .mat-autocomplete-panel{outline:solid 1px}mat-autocomplete{display:none}\n"],encapsulation:2,changeDetection:0}),F3.propDecorators={optionGroups:[{type:or,args:[CL,{descendants:!0}]}],options:[{type:or,args:[PL,{descendants:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(F3,[{type:S_,args:[{selector:"mat-autocomplete",template:'<ng-template let-formFieldId="id">\n  <div class="mat-autocomplete-panel"\n       role="listbox"\n       [id]="id"\n       [attr.aria-label]="ariaLabel || null"\n       [attr.aria-labelledby]="_getPanelAriaLabelledby(formFieldId)"\n       [ngClass]="_classList"\n       #panel>\n    <ng-content></ng-content>\n  </div>\n</ng-template>\n',encapsulation:Wn.None,changeDetection:jn.OnPush,exportAs:"matAutocomplete",inputs:["disableRipple"],host:{class:"mat-autocomplete"},providers:[{provide:hL,useExisting:F3}],styles:[".mat-autocomplete-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;visibility:hidden;max-width:none;max-height:256px;position:relative;width:100%;border-bottom-left-radius:4px;border-bottom-right-radius:4px}.mat-autocomplete-panel.mat-autocomplete-visible{visibility:visible}.mat-autocomplete-panel.mat-autocomplete-hidden{visibility:hidden}.mat-autocomplete-panel-above .mat-autocomplete-panel{border-radius:0;border-top-left-radius:4px;border-top-right-radius:4px}.mat-autocomplete-panel .mat-divider-horizontal{margin-top:-1px}.cdk-high-contrast-active .mat-autocomplete-panel{outline:solid 1px}mat-autocomplete{display:none}\n"]}]}],null,{optionGroups:[{type:or,args:[CL,{descendants:!0}]}],options:[{type:or,args:[PL,{descendants:!0}]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class L3{constructor(t){this.elementRef=t}}L3.ɵfac=function t(e){return new(e||L3)(gf(Kh))},L3.ɵdir=ho({type:L3}),L3.ctorParameters=()=>[{type:Kh}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(L3,[{type:k_}],(function(){return[{type:Kh}]}),null);class B3 extends L3{}B3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(B3)))(n||B3)}})(),B3.ɵdir=ho({type:B3,selectors:[["","matAutocompleteOrigin",""]],exportAs:["matAutocompleteOrigin"],features:[Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(B3,[{type:k_,args:[{selector:"[matAutocompleteOrigin]",exportAs:"matAutocompleteOrigin"}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const V3=new er("mat-autocomplete-scroll-strategy");const j3={provide:V3,deps:[KB],useFactory:function U3(t){return()=>t.scrollStrategies.reposition()}},W3={provide:xU,useExisting:$e((()=>Y3)),multi:!0};class G3{constructor(t,e,n,o,i,a,r,s,l,c,d){this._element=t,this._overlay=e,this._viewContainerRef=n,this._zone=o,this._changeDetectorRef=i,this._dir=r,this._formField=s,this._document=l,this._viewportRuler=c,this._defaults=d,this._componentDestroyed=!1,this._autocompleteDisabled=!1,this._manuallyFloatingLabel=!1,this._viewportSubscription=u.EMPTY,this._canOpenOnNextFocus=!0,this._closeKeyEventStream=new B,this._windowBlurHandler=()=>{this._canOpenOnNextFocus=this._document.activeElement!==this._element.nativeElement||this.panelOpen},this._onChange=()=>{},this._onTouched=()=>{},this.position="auto",this.autocompleteAttribute="off",this._overlayAttached=!1,this.optionSelections=oe((()=>this.autocomplete&&this.autocomplete.options?pe(...this.autocomplete.options.map((t=>t.onSelectionChange))):this._zone.onStable.pipe(ve(1),Ve((()=>this.optionSelections))))),this._scrollStrategy=a}get autocompleteDisabled(){return this._autocompleteDisabled}set autocompleteDisabled(t){this._autocompleteDisabled=eH(t)}ngAfterViewInit(){const t=this._getWindow();void 0!==t&&this._zone.runOutsideAngular((()=>t.addEventListener("blur",this._windowBlurHandler)))}ngOnChanges(t){t.position&&this._positionStrategy&&(this._setStrategyPositions(this._positionStrategy),this.panelOpen&&this._overlayRef.updatePosition())}ngOnDestroy(){const t=this._getWindow();void 0!==t&&t.removeEventListener("blur",this._windowBlurHandler),this._viewportSubscription.unsubscribe(),this._componentDestroyed=!0,this._destroyPanel(),this._closeKeyEventStream.complete()}get panelOpen(){return this._overlayAttached&&this.autocomplete.showPanel}openPanel(){this._attachOverlay(),this._floatLabel()}closePanel(){this._resetLabel(),this._overlayAttached&&(this.panelOpen&&this.autocomplete.closed.emit(),this.autocomplete._isOpen=this._overlayAttached=!1,this._overlayRef&&this._overlayRef.hasAttached()&&(this._overlayRef.detach(),this._closingActionsSubscription.unsubscribe()),this._componentDestroyed||this._changeDetectorRef.detectChanges())}updatePosition(){this._overlayAttached&&this._overlayRef.updatePosition()}get panelClosingActions(){return pe(this.optionSelections,this.autocomplete._keyManager.tabOut.pipe(ue((()=>this._overlayAttached))),this._closeKeyEventStream,this._getOutsideClickStream(),this._overlayRef?this._overlayRef.detachments().pipe(ue((()=>this._overlayAttached))):zt()).pipe(Vt((t=>t instanceof xL?t:null)))}get activeOption(){return this.autocomplete&&this.autocomplete._keyManager?this.autocomplete._keyManager.activeItem:null}_getOutsideClickStream(){return pe(le(this._document,"click"),le(this._document,"auxclick"),le(this._document,"touchend")).pipe(ue((t=>{const e=vH(t),n=this._formField?this._formField._elementRef.nativeElement:null,o=this.connectedTo?this.connectedTo.elementRef.nativeElement:null;return this._overlayAttached&&e!==this._element.nativeElement&&(!n||!n.contains(e))&&(!o||!o.contains(e))&&!!this._overlayRef&&!this._overlayRef.overlayElement.contains(e)})))}writeValue(t){Promise.resolve(null).then((()=>this._setTriggerValue(t)))}registerOnChange(t){this._onChange=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this._element.nativeElement.disabled=t}_handleKeydown(t){const e=t.keyCode;if(e!==KI||tH(t)||t.preventDefault(),this.activeOption&&e===XI&&this.panelOpen)this.activeOption._selectViaInteraction(),this._resetActiveItem(),t.preventDefault();else if(this.autocomplete){const n=this.autocomplete._keyManager.activeItem,o=e===QI||e===$I;this.panelOpen||9===e?this.autocomplete._keyManager.onKeydown(t):o&&this._canOpen()&&this.openPanel(),(o||this.autocomplete._keyManager.activeItem!==n)&&this._scrollToOption(this.autocomplete._keyManager.activeItemIndex||0)}}_handleInput(t){let e=t.target,n=e.value;"number"===e.type&&(n=""==n?null:parseFloat(n)),this._previousValue!==n&&(this._previousValue=n,this._onChange(n),this._canOpen()&&this._document.activeElement===t.target&&this.openPanel())}_handleFocus(){this._canOpenOnNextFocus?this._canOpen()&&(this._previousValue=this._element.nativeElement.value,this._attachOverlay(),this._floatLabel(!0)):this._canOpenOnNextFocus=!0}_floatLabel(t=!1){this._formField&&"auto"===this._formField.floatLabel&&(t?this._formField._animateAndLockLabel():this._formField.floatLabel="always",this._manuallyFloatingLabel=!0)}_resetLabel(){this._manuallyFloatingLabel&&(this._formField.floatLabel="auto",this._manuallyFloatingLabel=!1)}_subscribeToClosingActions(){return pe(this._zone.onStable.pipe(ve(1)),this.autocomplete.options.changes.pipe(We((()=>this._positionStrategy.reapplyLastPosition())),Pe(0))).pipe(Ve((()=>{const t=this.panelOpen;return this._resetActiveItem(),this.autocomplete._setVisibility(),this.panelOpen&&(this._overlayRef.updatePosition(),t!==this.panelOpen&&this.autocomplete.opened.emit()),this.panelClosingActions})),ve(1)).subscribe((t=>this._setValueAndClose(t)))}_destroyPanel(){this._overlayRef&&(this.closePanel(),this._overlayRef.dispose(),this._overlayRef=null)}_setTriggerValue(t){const e=this.autocomplete&&this.autocomplete.displayWith?this.autocomplete.displayWith(t):t,n=null!=e?e:"";this._formField?this._formField._control.value=n:this._element.nativeElement.value=n,this._previousValue=n}_setValueAndClose(t){t&&t.source&&(this._clearPreviousSelectedOption(t.source),this._setTriggerValue(t.source.value),this._onChange(t.source.value),this._element.nativeElement.focus(),this.autocomplete._emitSelectEvent(t.source)),this.closePanel()}_clearPreviousSelectedOption(t){this.autocomplete.options.forEach((e=>{e!==t&&e.selected&&e.deselect()}))}_attachOverlay(){var t;if(!this.autocomplete&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("Attempting to open an undefined instance of `mat-autocomplete`. Make sure that the id passed to the `matAutocomplete` is correct and that you're attempting to open it after the ngAfterContentInit hook.")})();let n=this._overlayRef;n?(this._positionStrategy.setOrigin(this._getConnectedElement()),n.updateSize({width:this._getPanelWidth()})):(this._portal=new lB(this.autocomplete.template,this._viewContainerRef,{id:null===(t=this._formField)||void 0===t?void 0:t.getLabelId()}),n=this._overlay.create(this._getOverlayConfig()),this._overlayRef=n,n.keydownEvents().subscribe((t=>{(t.keyCode===KI&&!tH(t)||t.keyCode===QI&&tH(t,"altKey"))&&(this._resetActiveItem(),this._closeKeyEventStream.next(),t.stopPropagation(),t.preventDefault())})),this._viewportSubscription=this._viewportRuler.change().subscribe((()=>{this.panelOpen&&n&&n.updateSize({width:this._getPanelWidth()})}))),n&&!n.hasAttached()&&(n.attach(this._portal),this._closingActionsSubscription=this._subscribeToClosingActions());const o=this.panelOpen;this.autocomplete._setVisibility(),this.autocomplete._isOpen=this._overlayAttached=!0,this.panelOpen&&o!==this.panelOpen&&this.autocomplete.opened.emit()}_getOverlayConfig(){var t;return new wB({positionStrategy:this._getOverlayPosition(),scrollStrategy:this._scrollStrategy(),width:this._getPanelWidth(),direction:this._dir,panelClass:null===(t=this._defaults)||void 0===t?void 0:t.overlayPanelClass})}_getOverlayPosition(){const t=this._overlay.position().flexibleConnectedTo(this._getConnectedElement()).withFlexibleDimensions(!1).withPush(!1);return this._setStrategyPositions(t),this._positionStrategy=t,t}_setStrategyPositions(t){const e=[{originX:"start",originY:"bottom",overlayX:"start",overlayY:"top"},{originX:"end",originY:"bottom",overlayX:"end",overlayY:"top"}],n=this._aboveClass,o=[{originX:"start",originY:"top",overlayX:"start",overlayY:"bottom",panelClass:n},{originX:"end",originY:"top",overlayX:"end",overlayY:"bottom",panelClass:n}];let i;i="above"===this.position?o:"below"===this.position?e:[...e,...o],t.withPositions(i)}_getConnectedElement(){return this.connectedTo?this.connectedTo.elementRef:this._formField?this._formField.getConnectedOverlayOrigin():this._element}_getPanelWidth(){return this.autocomplete.panelWidth||this._getHostWidth()}_getHostWidth(){return this._getConnectedElement().nativeElement.getBoundingClientRect().width}_resetActiveItem(){const t=this.autocomplete;t.autoActiveFirstOption?t._keyManager.setFirstItemActive():t._keyManager.setActiveItem(-1)}_canOpen(){const t=this._element.nativeElement;return!t.readOnly&&!t.disabled&&!this._autocompleteDisabled}_getWindow(){var t;return(null===(t=this._document)||void 0===t?void 0:t.defaultView)||window}_scrollToOption(t){const e=this.autocomplete,n=wL(t,e.options,e.optionGroups);if(0===t&&1===n)e._setScrollTop(0);else if(e.panel){const n=e.options.toArray()[t];if(n){const t=n._getHostElement(),o=kL(t.offsetTop,t.offsetHeight,e._getScrollTop(),e.panel.nativeElement.offsetHeight);e._setScrollTop(o)}}}}G3.ɵfac=function t(e){return new(e||G3)(gf(Kh),gf(KB),gf(Fb),gf(mC),gf(Ob),gf(V3),gf(vF,8),gf(yU,9),gf(sM,8),gf(QL),gf(z3,8))},G3.ɵdir=ho({type:G3,inputs:{position:["matAutocompletePosition","position"],autocompleteAttribute:["autocomplete","autocompleteAttribute"],autocompleteDisabled:["matAutocompleteDisabled","autocompleteDisabled"],autocomplete:["matAutocomplete","autocomplete"],connectedTo:["matAutocompleteConnectedTo","connectedTo"]},features:[qo]}),G3.ctorParameters=()=>[{type:Kh},{type:KB},{type:Fb},{type:mC},{type:Ob},{type:void 0,decorators:[{type:Lr,args:[V3]}]},{type:vF,decorators:[{type:Br}]},{type:_U,decorators:[{type:Br},{type:Lr,args:[yU]},{type:Ur}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]},{type:QL},{type:void 0,decorators:[{type:Br},{type:Lr,args:[z3]}]}],G3.propDecorators={autocomplete:[{type:R_,args:["matAutocomplete"]}],position:[{type:R_,args:["matAutocompletePosition"]}],connectedTo:[{type:R_,args:["matAutocompleteConnectedTo"]}],autocompleteAttribute:[{type:R_,args:["autocomplete"]}],autocompleteDisabled:[{type:R_,args:["matAutocompleteDisabled"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(G3,[{type:k_}],(function(){return[{type:Kh},{type:KB},{type:Fb},{type:mC},{type:Ob},{type:void 0,decorators:[{type:Lr,args:[V3]}]},{type:vF,decorators:[{type:Br}]},{type:_U,decorators:[{type:Br},{type:Lr,args:[yU]},{type:Ur}]},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]},{type:QL},{type:void 0,decorators:[{type:Br},{type:Lr,args:[z3]}]}]}),{position:[{type:R_,args:["matAutocompletePosition"]}],autocompleteAttribute:[{type:R_,args:["autocomplete"]}],autocompleteDisabled:[{type:R_,args:["matAutocompleteDisabled"]}],autocomplete:[{type:R_,args:["matAutocomplete"]}],connectedTo:[{type:R_,args:["matAutocompleteConnectedTo"]}]});class Y3 extends G3{constructor(){super(...arguments),this._aboveClass="mat-autocomplete-panel-above"}}Y3.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(Y3)))(n||Y3)}})(),Y3.ɵdir=ho({type:Y3,selectors:[["input","matAutocomplete",""],["textarea","matAutocomplete",""]],hostAttrs:[1,"mat-autocomplete-trigger"],hostVars:7,hostBindings:function t(e,n){1&e&&Rf("focusin",(function t(){return n._handleFocus()}))("blur",(function t(){return n._onTouched()}))("input",(function t(e){return n._handleInput(e)}))("keydown",(function t(e){return n._handleKeydown(e)})),2&e&&ym("autocomplete",n.autocompleteAttribute)("role",n.autocompleteDisabled?null:"combobox")("aria-autocomplete",n.autocompleteDisabled?null:"list")("aria-activedescendant",n.panelOpen&&n.activeOption?n.activeOption.id:null)("aria-expanded",n.autocompleteDisabled?null:n.panelOpen.toString())("aria-owns",n.autocompleteDisabled||!n.panelOpen||null==n.autocomplete?null:n.autocomplete.id)("aria-haspopup",!n.autocompleteDisabled)},exportAs:["matAutocompleteTrigger"],features:[jh([W3]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Y3,[{type:k_,args:[{selector:"input[matAutocomplete], textarea[matAutocomplete]",host:{class:"mat-autocomplete-trigger","[attr.autocomplete]":"autocompleteAttribute","[attr.role]":'autocompleteDisabled ? null : "combobox"',"[attr.aria-autocomplete]":'autocompleteDisabled ? null : "list"',"[attr.aria-activedescendant]":"(panelOpen && activeOption) ? activeOption.id : null","[attr.aria-expanded]":"autocompleteDisabled ? null : panelOpen.toString()","[attr.aria-owns]":"(autocompleteDisabled || !panelOpen) ? null : autocomplete?.id","[attr.aria-haspopup]":"!autocompleteDisabled","(focusin)":"_handleFocus()","(blur)":"_onTouched()","(input)":"_handleInput($event)","(keydown)":"_handleKeydown($event)"},exportAs:"matAutocompleteTrigger",providers:[W3]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class q3{}q3.ɵfac=function t(e){return new(e||q3)},q3.ɵmod=fo({type:q3}),q3.ɵinj=Dn({providers:[j3],imports:[[oV,SL,NF,rx],oB,SL,NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(q3,[{type:L_,args:[{imports:[oV,SL,NF,rx],exports:[F3,Y3,B3,oB,SL,NF],declarations:[F3,Y3,B3],providers:[j3]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(q3,{declarations:function(){return[F3,Y3,B3]},imports:function(){return[oV,SL,NF,rx]},exports:function(){return[F3,Y3,B3,oB,SL,NF]}});class Z3{constructor(){this.value="",this.placeholder=""}onInputKeyUp(t){"Enter"===t.key&&this.autocompleteTrigger.closePanel()}}function X3(t,e){if(1&t&&(oa(),vf(0,"circle",3)),2&t){const t=zf();lu("animation-name","mat-progress-spinner-stroke-rotate-"+t._spinnerAnimationLabel)("stroke-dashoffset",t._getStrokeDashOffset(),"px")("stroke-dasharray",t._getStrokeCircumference(),"px")("stroke-width",t._getCircleStrokeWidth(),"%"),ym("r",t._getCircleRadius())}}function K3(t,e){if(1&t&&(oa(),vf(0,"circle",3)),2&t){const t=zf();lu("stroke-dashoffset",t._getStrokeDashOffset(),"px")("stroke-dasharray",t._getStrokeCircumference(),"px")("stroke-width",t._getCircleStrokeWidth(),"%"),ym("r",t._getCircleRadius())}}function J3(t,e){if(1&t&&(oa(),vf(0,"circle",3)),2&t){const t=zf();lu("animation-name","mat-progress-spinner-stroke-rotate-"+t._spinnerAnimationLabel)("stroke-dashoffset",t._getStrokeDashOffset(),"px")("stroke-dasharray",t._getStrokeCircumference(),"px")("stroke-width",t._getCircleStrokeWidth(),"%"),ym("r",t._getCircleRadius())}}function Q3(t,e){if(1&t&&(oa(),vf(0,"circle",3)),2&t){const t=zf();lu("stroke-dashoffset",t._getStrokeDashOffset(),"px")("stroke-dasharray",t._getStrokeCircumference(),"px")("stroke-width",t._getCircleStrokeWidth(),"%"),ym("r",t._getCircleRadius())}}Z3.ɵfac=function t(e){return new(e||Z3)},Z3.ɵcmp=so({type:Z3,selectors:[["tb-filter-input"]],viewQuery:function t(e,n){if(1&e&&Vy(Y3,5),2&e){let t;By(t=Uy())&&(n.autocompleteTrigger=t.first)}},inputs:{value:"value",matAutocomplete:"matAutocomplete",placeholder:"placeholder"},decls:2,vars:4,consts:[["svgIcon","search_24px"],["type","text","autocomplete","off",3,"placeholder","matAutocomplete","matAutocompleteDisabled","value","keyup"]],template:function t(e,n){1&e&&(vf(0,"mat-icon",0),Cf(1,"input",1),Rf("keyup",(function t(e){return n.onInputKeyUp(e)})),Mf()),2&e&&(Dc(1),bf("placeholder",n.placeholder)("matAutocomplete",n.matAutocomplete)("matAutocompleteDisabled",!n.matAutocomplete)("value",n.value))},directives:[Mq,Y3],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   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.mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", 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 .mat-form-field-label[_ngcontent-%COMP%]{top:1.09375em;margin-top:-0.5em}.mat-form-field-appearance-fill.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59374em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:1em 0 1em 0}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   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.mat-grid-tile-footer[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-grid-tile-header[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2), .mat-grid-tile-footer[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}input.mat-input-element[_ngcontent-%COMP%]{margin-top:-0.0625em}.mat-menu-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:400}.mat-paginator[_ngcontent-%COMP%], .mat-paginator-page-size[_ngcontent-%COMP%]   .mat-select-trigger[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px}.mat-radio-button[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action[_ngcontent-%COMP%]{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node[_ngcontent-%COMP%], .mat-nested-tree-node[_ngcontent-%COMP%]{font-weight:400;font-size:14px}.mat-ripple[_ngcontent-%COMP%]{overflow:hidden;position:relative}.mat-ripple[_ngcontent-%COMP%]:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded[_ngcontent-%COMP%]{overflow:visible}.mat-ripple-element[_ngcontent-%COMP%]{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms cubic-bezier(0, 0, 0.2, 1);transform:scale(0)}.cdk-high-contrast-active[_ngcontent-%COMP%]   .mat-ripple-element[_ngcontent-%COMP%]{display:none}.cdk-visually-hidden[_ngcontent-%COMP%]{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container[_ngcontent-%COMP%], .cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{display:flex;font-size:13px}mat-icon[_ngcontent-%COMP%]{color:#616161;flex:none;margin-right:5px}body.dark-mode[_nghost-%COMP%]   mat-icon[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   mat-icon[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}input[_ngcontent-%COMP%]{background-color:inherit;caret-color:currentColor;color:currentColor;font:inherit;border:none;outline:none;padding:0;flex-grow:1}']}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Z3,[{type:S_,args:[{selector:"tb-filter-input",template:'\n    <mat-icon svgIcon="search_24px"></mat-icon>\n\n    \x3c!-- Note: to allow falsy \'matAutocomplete\' values, we need \'matAutocompleteDisabled\'\n    to prevent runtime errors. --\x3e\n    <input\n      type="text"\n      autocomplete="off"\n      [placeholder]="placeholder"\n      [matAutocomplete]="matAutocomplete"\n      [matAutocompleteDisabled]="!matAutocomplete"\n      [value]="value"\n      (keyup)="onInputKeyUp($event)"\n    />\n  ',styleUrls:["filter_input_component.css"]}]}],null,{value:[{type:R_}],matAutocomplete:[{type:R_}],placeholder:[{type:R_}],autocompleteTrigger:[{type:ar,args:[Y3]}]});const $3=".mat-progress-spinner{display:block;position:relative;overflow:hidden}.mat-progress-spinner svg{position:absolute;transform:rotate(-90deg);top:0;left:0;transform-origin:center;overflow:visible}.mat-progress-spinner circle{fill:transparent;transform-origin:center;transition:stroke-dashoffset 225ms linear}._mat-animation-noopable.mat-progress-spinner circle{transition:none;animation:none}.cdk-high-contrast-active .mat-progress-spinner circle{stroke:currentColor;stroke:CanvasText}.mat-progress-spinner.mat-progress-spinner-indeterminate-animation[mode=indeterminate] svg{animation:mat-progress-spinner-linear-rotate 2000ms linear infinite}._mat-animation-noopable.mat-progress-spinner.mat-progress-spinner-indeterminate-animation[mode=indeterminate] svg{transition:none;animation:none}.mat-progress-spinner.mat-progress-spinner-indeterminate-animation[mode=indeterminate] circle{transition-property:stroke;animation-duration:4000ms;animation-timing-function:cubic-bezier(0.35, 0, 0.25, 1);animation-iteration-count:infinite}._mat-animation-noopable.mat-progress-spinner.mat-progress-spinner-indeterminate-animation[mode=indeterminate] circle{transition:none;animation:none}.mat-progress-spinner.mat-progress-spinner-indeterminate-fallback-animation[mode=indeterminate] svg{animation:mat-progress-spinner-stroke-rotate-fallback 10000ms cubic-bezier(0.87, 0.03, 0.33, 1) infinite}._mat-animation-noopable.mat-progress-spinner.mat-progress-spinner-indeterminate-fallback-animation[mode=indeterminate] svg{transition:none;animation:none}.mat-progress-spinner.mat-progress-spinner-indeterminate-fallback-animation[mode=indeterminate] circle{transition-property:stroke}._mat-animation-noopable.mat-progress-spinner.mat-progress-spinner-indeterminate-fallback-animation[mode=indeterminate] circle{transition:none;animation:none}@keyframes mat-progress-spinner-linear-rotate{0%{transform:rotate(0deg)}100%{transform:rotate(360deg)}}@keyframes mat-progress-spinner-stroke-rotate-100{0%{stroke-dashoffset:268.606171575px;transform:rotate(0)}12.5%{stroke-dashoffset:56.5486677px;transform:rotate(0)}12.5001%{stroke-dashoffset:56.5486677px;transform:rotateX(180deg) rotate(72.5deg)}25%{stroke-dashoffset:268.606171575px;transform:rotateX(180deg) rotate(72.5deg)}25.0001%{stroke-dashoffset:268.606171575px;transform:rotate(270deg)}37.5%{stroke-dashoffset:56.5486677px;transform:rotate(270deg)}37.5001%{stroke-dashoffset:56.5486677px;transform:rotateX(180deg) rotate(161.5deg)}50%{stroke-dashoffset:268.606171575px;transform:rotateX(180deg) rotate(161.5deg)}50.0001%{stroke-dashoffset:268.606171575px;transform:rotate(180deg)}62.5%{stroke-dashoffset:56.5486677px;transform:rotate(180deg)}62.5001%{stroke-dashoffset:56.5486677px;transform:rotateX(180deg) rotate(251.5deg)}75%{stroke-dashoffset:268.606171575px;transform:rotateX(180deg) rotate(251.5deg)}75.0001%{stroke-dashoffset:268.606171575px;transform:rotate(90deg)}87.5%{stroke-dashoffset:56.5486677px;transform:rotate(90deg)}87.5001%{stroke-dashoffset:56.5486677px;transform:rotateX(180deg) rotate(341.5deg)}100%{stroke-dashoffset:268.606171575px;transform:rotateX(180deg) rotate(341.5deg)}}@keyframes mat-progress-spinner-stroke-rotate-fallback{0%{transform:rotate(0deg)}25%{transform:rotate(1170deg)}50%{transform:rotate(2340deg)}75%{transform:rotate(3510deg)}100%{transform:rotate(4680deg)}}\n",t2=IF(class{constructor(t){this._elementRef=t}},"primary"),e2=new er("mat-progress-spinner-default-options",{providedIn:"root",factory:function n2(){return{diameter:100}}});const o2="\n @keyframes mat-progress-spinner-stroke-rotate-DIAMETER {\n    0%      { stroke-dashoffset: START_VALUE;  transform: rotate(0); }\n    12.5%   { stroke-dashoffset: END_VALUE;    transform: rotate(0); }\n    12.5001%  { stroke-dashoffset: END_VALUE;    transform: rotateX(180deg) rotate(72.5deg); }\n    25%     { stroke-dashoffset: START_VALUE;  transform: rotateX(180deg) rotate(72.5deg); }\n\n    25.0001%   { stroke-dashoffset: START_VALUE;  transform: rotate(270deg); }\n    37.5%   { stroke-dashoffset: END_VALUE;    transform: rotate(270deg); }\n    37.5001%  { stroke-dashoffset: END_VALUE;    transform: rotateX(180deg) rotate(161.5deg); }\n    50%     { stroke-dashoffset: START_VALUE;  transform: rotateX(180deg) rotate(161.5deg); }\n\n    50.0001%  { stroke-dashoffset: START_VALUE;  transform: rotate(180deg); }\n    62.5%   { stroke-dashoffset: END_VALUE;    transform: rotate(180deg); }\n    62.5001%  { stroke-dashoffset: END_VALUE;    transform: rotateX(180deg) rotate(251.5deg); }\n    75%     { stroke-dashoffset: START_VALUE;  transform: rotateX(180deg) rotate(251.5deg); }\n\n    75.0001%  { stroke-dashoffset: START_VALUE;  transform: rotate(90deg); }\n    87.5%   { stroke-dashoffset: END_VALUE;    transform: rotate(90deg); }\n    87.5001%  { stroke-dashoffset: END_VALUE;    transform: rotateX(180deg) rotate(341.5deg); }\n    100%    { stroke-dashoffset: START_VALUE;  transform: rotateX(180deg) rotate(341.5deg); }\n  }\n";class i2 extends t2{constructor(t,e,n,o,i){super(t),this._document=n,this._diameter=100,this._value=0,this._fallbackAnimation=!1,this.mode="determinate";const a=i2._diameters;this._spinnerAnimationLabel=this._getSpinnerAnimationLabel(),a.has(n.head)||a.set(n.head,new Set([100])),this._fallbackAnimation=e.EDGE||e.TRIDENT,this._noopAnimations="NoopAnimations"===o&&!!i&&!i._forceAnimations,i&&(i.diameter&&(this.diameter=i.diameter),i.strokeWidth&&(this.strokeWidth=i.strokeWidth))}get diameter(){return this._diameter}set diameter(t){this._diameter=nH(t),this._spinnerAnimationLabel=this._getSpinnerAnimationLabel(),!this._fallbackAnimation&&this._styleRoot&&this._attachStyleNode()}get strokeWidth(){return this._strokeWidth||this.diameter/10}set strokeWidth(t){this._strokeWidth=nH(t)}get value(){return"determinate"===this.mode?this._value:0}set value(t){this._value=Math.max(0,Math.min(100,nH(t)))}ngOnInit(){const t=this._elementRef.nativeElement;this._styleRoot=CH(t)||this._document.head,this._attachStyleNode();t.classList.add(`mat-progress-spinner-indeterminate${this._fallbackAnimation?"-fallback":""}-animation`)}_getCircleRadius(){return(this.diameter-10)/2}_getViewBox(){const t=2*this._getCircleRadius()+this.strokeWidth;return`0 0 ${t} ${t}`}_getStrokeCircumference(){return 2*Math.PI*this._getCircleRadius()}_getStrokeDashOffset(){return"determinate"===this.mode?this._getStrokeCircumference()*(100-this._value)/100:this._fallbackAnimation&&"indeterminate"===this.mode?.2*this._getStrokeCircumference():null}_getCircleStrokeWidth(){return this.strokeWidth/this.diameter*100}_attachStyleNode(){const t=this._styleRoot,e=this._diameter,n=i2._diameters;let o=n.get(t);if(!o||!o.has(e)){const i=this._document.createElement("style");i.setAttribute("mat-spinner-animation",this._spinnerAnimationLabel),i.textContent=this._getAnimationText(),t.appendChild(i),o||(o=new Set,n.set(t,o)),o.add(e)}}_getAnimationText(){const t=this._getStrokeCircumference();return o2.replace(/START_VALUE/g,""+.95*t).replace(/END_VALUE/g,""+.2*t).replace(/DIAMETER/g,`${this._spinnerAnimationLabel}`)}_getSpinnerAnimationLabel(){return this.diameter.toString().replace(".","_")}}i2.ɵfac=function t(e){return new(e||i2)(gf(Kh),gf(cH),gf(sM,8),gf(ck,8),gf(e2))},i2.ɵcmp=so({type:i2,selectors:[["mat-progress-spinner"]],hostAttrs:["role","progressbar","tabindex","-1",1,"mat-progress-spinner"],hostVars:10,hostBindings:function t(e,n){2&e&&(ym("aria-valuemin","determinate"===n.mode?0:null)("aria-valuemax","determinate"===n.mode?100:null)("aria-valuenow","determinate"===n.mode?n.value:null)("mode",n.mode),lu("width",n.diameter,"px")("height",n.diameter,"px"),cu("_mat-animation-noopable",n._noopAnimations))},inputs:{color:"color",mode:"mode",diameter:"diameter",strokeWidth:"strokeWidth",value:"value"},exportAs:["matProgressSpinner"],features:[Qp],decls:3,vars:8,consts:[["preserveAspectRatio","xMidYMid meet","focusable","false","aria-hidden","true",3,"ngSwitch"],["cx","50%","cy","50%",3,"animation-name","stroke-dashoffset","stroke-dasharray","stroke-width",4,"ngSwitchCase"],["cx","50%","cy","50%",3,"stroke-dashoffset","stroke-dasharray","stroke-width",4,"ngSwitchCase"],["cx","50%","cy","50%"]],template:function t(e,n){1&e&&(oa(),Cf(0,"svg",0),Hm(1,X3,1,9,"circle",1),Hm(2,K3,1,7,"circle",2),Mf()),2&e&&(lu("width",n.diameter,"px")("height",n.diameter,"px"),bf("ngSwitch","indeterminate"===n.mode),ym("viewBox",n._getViewBox()),Dc(1),bf("ngSwitchCase",!0),Dc(1),bf("ngSwitchCase",!1))},directives:[Dv,Rv],styles:[$3],encapsulation:2,changeDetection:0}),i2._diameters=new WeakMap,i2.ctorParameters=()=>[{type:Kh},{type:cH},{type:void 0,decorators:[{type:Br},{type:Lr,args:[sM]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]},{type:void 0,decorators:[{type:Lr,args:[e2]}]}],i2.propDecorators={diameter:[{type:R_}],strokeWidth:[{type:R_}],mode:[{type:R_}],value:[{type:R_}]},("undefined"==typeof 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class r2{}r2.ɵfac=function t(e){return new(e||r2)},r2.ɵmod=fo({type:r2}),r2.ɵinj=Dn({imports:[[NF,rx],NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(r2,[{type:L_,args:[{imports:[NF,rx],exports:[i2,a2,NF],declarations:[i2,a2]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(r2,{declarations:function(){return[i2,a2]},imports:function(){return[NF,rx]},exports:function(){return[i2,a2,NF]}});const s2=["regexStringInput"];function l2(t,e){if(1&t&&(xf(0),Cf(1,"li",22),Pu(2),Mf(),Of()),2&t){const t=e.$implicit;Dc(1),bf("title",t.name),Dc(1),wu(t.name)}}function c2(t,e){if(1&t&&(Cf(0,"li",23),Cf(1,"em"),Pu(2),_y(3,"number"),Mf(),Mf()),2&t){const t=zf().$implicit;Dc(2),ku("and ",Cy(3,1,t.runs.length-5)," more")}}function d2(t,e){1&t&&(Cf(0,"li",24),Cf(1,"em"),Pu(2,"No runs are in the group"),Mf(),Mf())}const p2=function(t){return{borderColor:t}},m2=function(t){return{backgroundColor:t}};function f2(t,e){if(1&t&&(Cf(0,"ul",16),Cf(1,"li"),Cf(2,"label"),vf(3,"span",17),Cf(4,"code",18),Pu(5),Mf(),Mf(),Cf(6,"ul"),Hm(7,l2,3,2,"ng-container",19),_y(8,"slice"),Hm(9,c2,4,3,"li",20),Hm(10,d2,3,0,"li",21),Mf(),Mf(),Mf()),2&t){const t=e.$implicit;bf("ngStyle",iy(11,p2,t.color)),Dc(3),bf("ngStyle",iy(13,m2,t.color)),Dc(1),bf("title",t.groupId),Dc(1),wu(t.groupId),Dc(2),bf("ngForOf",vy(8,7,t.runs,0,5)),Dc(2),bf("ngIf",t.runs.length>5),Dc(1),bf("ngIf",0===t.runs.length)}}function u2(t,e){if(1&t&&(Cf(0,"div",14),Hm(1,f2,11,15,"ul",15),Mf()),2&t){const t=zf(2);Dc(1),bf("ngForOf",t.colorRunPairList)}}function g2(t,e){if(1&t&&(Cf(0,"div",25),Pu(1," There are no runs matching the regex, "),Cf(2,"code"),Pu(3),Mf(),Pu(4,". Please check if your regex string is correct. "),Mf()),2&t){const t=zf(2);Dc(3),ku("/",t.regexString,"/")}}function h2(t,e){if(1&t&&(Cf(0,"div",10),Cf(1,"h4"),Pu(2,"Color group preview"),Mf(),Cf(3,"div",11),Hm(4,u2,2,1,"div",12),Hm(5,g2,5,1,"ng-template",null,13,qy),Mf(),Mf()),2&t){const t=Fm(6),e=zf();Dc(4),bf("ngIf",e.colorRunPairList.length)("ngIfElse",t)}}class b2{constructor(t,e){this.dialogRef=t,this.hostElRef=e,this.onSave=new Sy,this.regexInputOnChange=new Sy,this.timeOutId=0}resetFocus(){if(!this.hostElRef.nativeElement.contains(document.activeElement)){this.regexStringInput.nativeElement.focus()}}onEnter(t){this.onSaveClick(t),this.dialogRef.close()}onSaveClick(t){this.onSave.emit(t)}fillExample(t){this.regexString=t,this.regexInputChange(t)}regexInputChange(t){this.regexInputOnChange.emit(t)}handleFocusOut(){clearTimeout(this.timeOutId),this.timeOutId=setTimeout(this.resetFocus.bind(this),0)}}b2.ɵfac=function t(e){return new(e||b2)(gf(VY),gf(Kh))},b2.ɵcmp=so({type:b2,selectors:[["regex-edit-dialog-component"]],viewQuery:function t(e,n){if(1&e&&Vy(s2,7),2&e){let t;By(t=Uy())&&(n.regexStringInput=t.first)}},inputs:{regexString:"regexString",colorRunPairList:"colorRunPairList"},outputs:{onSave:"onSave",regexInputOnChange:"regexInputOnChange"},decls:30,vars:2,consts:function(){let t;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){t=goog.getMsg("Color Runs by Regex Query")}else t=$localize`:Color Runs by Regex Query␟15ed9f6fd2d4906a4803fc1255de3c5db2c56530␟9088985113960312808:Color Runs by Regex Query`;return[[1,"regex-edit-dialog",3,"focusout"],["mat-dialog-title",""],["matInput","","aria-label",t,"cdkFocusInitial","",3,"value","keydown.enter","input"],["regexStringInput",""],[1,"example-details"],[3,"click"],["class","group-container",4,"ngIf"],["mat-dialog-actions","","align","end"],["mat-button","","mat-dialog-close",""],["mat-raised-button","","color","primary","mat-dialog-close","",3,"click"],[1,"group-container"],[1,"grouping-preview"],["class","match-container",4,"ngIf","ngIfElse"],["empty",""],[1,"match-container"],["class","group",3,"ngStyle",4,"ngFor","ngForOf"],[1,"group",3,"ngStyle"],[1,"color-swatch",3,"ngStyle"],[1,"group-id",3,"title"],[4,"ngFor","ngForOf"],["class","more",4,"ngIf"],["class","no-match",4,"ngIf"],[3,"title"],[1,"more"],[1,"no-match"],[1,"warning"]]},template:function t(e,n){if(1&e){const t=wf();Cf(0,"div",0),Rf("focusout",(function t(){return n.handleFocusOut()})),Cf(1,"h1",1),Pu(2,"Color runs by regex"),Mf(),Cf(3,"mat-dialog-content"),Cf(4,"p"),Pu(5,"Enter a regex with capturing groups to match against run names:"),Mf(),Cf(6,"mat-form-field"),Cf(7,"input",2,3),Rf("keydown.enter",(function t(e){return n.onEnter(e.target.value)}))("input",(function t(e){return n.regexInputChange(e.target.value)})),Mf(),Mf(),Mf(),Cf(9,"div",4),Cf(10,"p"),Pu(11,' Each matching run will be assigned a color based on the "key" formed by its matches to the capturing groups. 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 [_nghost-%COMP%]   .grouping-preview[_ngcontent-%COMP%]{border:1px solid #555}.match-container[_ngcontent-%COMP%]{align-items:flex-start;display:grid;flex-wrap:wrap;gap:10px;grid-template-columns:repeat(2, minmax(50%, 1fr))}.color-swatch[_ngcontent-%COMP%]{border-radius:50%;box-shadow:0 0 2px #000;display:inline-block;height:15px;width:15px}ul[_ngcontent-%COMP%]{list-style-type:none;padding:0}mat-form-field[_ngcontent-%COMP%]{width:100%}.group[_ngcontent-%COMP%]{border:1px solid #ebebeb;border-radius:3px;margin:0;padding:10px}body.dark-mode[_nghost-%COMP%]   .group[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group[_ngcontent-%COMP%]{border:1px solid #555}.group[_ngcontent-%COMP%]   label[_ngcontent-%COMP%]{border-bottom:1px solid #ebebeb;align-items:center;display:grid;gap:10px;grid-template-columns:max-content auto;padding:5px 0}body.dark-mode[_nghost-%COMP%]   .group[_ngcontent-%COMP%]   label[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group[_ngcontent-%COMP%]   label[_ngcontent-%COMP%]{border-bottom:1px solid #555}.group[_ngcontent-%COMP%]   label[_ngcontent-%COMP%]   .group-id[_ngcontent-%COMP%]{font-size:.95em;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.group[_ngcontent-%COMP%]   ul[_ngcontent-%COMP%]{font-size:.9em}.group[_ngcontent-%COMP%]   ul[_ngcontent-%COMP%]   li[_ngcontent-%COMP%]{overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.group[_ngcontent-%COMP%]   .more[_ngcontent-%COMP%], .group[_ngcontent-%COMP%]   .no-match[_ngcontent-%COMP%]{color:#616161;margin-top:5px}body.dark-mode[_nghost-%COMP%]   .group[_ngcontent-%COMP%]   .more[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group[_ngcontent-%COMP%]   .more[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}body.dark-mode[_nghost-%COMP%]   .group[_ngcontent-%COMP%]   .no-match[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group[_ngcontent-%COMP%]   .no-match[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(b2,[{type:S_,args:[{selector:"regex-edit-dialog-component",templateUrl:"regex_edit_dialog.ng.html",styleUrls:["regex_edit_dialog_component.css"],changeDetection:jn.OnPush}]}],(function(){return[{type:VY},{type:Kh}]}),{regexString:[{type:R_}],colorRunPairList:[{type:R_}],onSave:[{type:E_}],regexInputOnChange:[{type:E_}],regexStringInput:[{type:ar,args:["regexStringInput",{static:!0}]}]});class y2{constructor(t,e,n){this.store=t,this.dialogRef=e,this.tentativeRegexString$=new B,this.groupByRegexString$=oe((()=>pe(this.store.select(rI).pipe(ve(1)),this.tentativeRegexString$))).pipe(Be("")),this.colorRunPairList$=oe((()=>this.groupByRegexString$.pipe(Ce(500),ue((t=>{try{const e=new RegExp(t);return Boolean(e)}catch(t){return!1}})),_e(this.allRuns$,this.runIdToEid$,this.store.select(bI),this.store.select(FD)),Vt((([t,e,n,o,i])=>{const a=Vz({key:Lz.REGEX,regexString:t},e,n),r=new Map,s=[];for(const[t,e]of Object.entries(a.matches)){let n=r.get(t);if(!n){const e=o.colors[r.size%o.colors.length];n=i?e.darkHex:e.lightHex,r.set(t,n)}s.push({groupId:t,color:n,runs:e})}return s}))))).pipe(Be([])),this.experimentIds=n.experimentIds,this.runIdToEid$=Kt(this.experimentIds.map((t=>this.store.select(Zz,{experimentId:t}).pipe(Vt((e=>({experimentId:t,runIds:e}))))))).pipe(Vt((t=>{const e={};for(const{runIds:n,experimentId:o}of t)for(const t of n)e[t]=o;return e}))),this.allRuns$=Kt(this.experimentIds.map((t=>this.store.select(qz,{experimentId:t})))).pipe(Vt((t=>t.flat())))}onRegexInputOnChange(t){this.tentativeRegexString$.next(t)}onSave(t){this.store.dispatch(xT({experimentIds:this.experimentIds,groupBy:{key:Lz.REGEX,regexString:t}}))}}function _2(t,e){1&t&&vf(0,"mat-icon",11)}function C2(t,e){if(1&t){const t=wf();Cf(0,"button",10),Rf("click",(function e(){Pi(t);const n=zf();return n.onGroupByChange.emit({key:n.GroupByKey.EXPERIMENT})})),Cf(1,"span"),Hm(2,_2,1,0,"mat-icon",7),Mf(),Cf(3,"label"),Pu(4,"Experiment"),Mf(),Mf()}if(2&t){const t=zf();ym("aria-checked",t.selectedGroupBy.key===t.GroupByKey.EXPERIMENT),Dc(2),bf("ngIf",t.selectedGroupBy.key===t.GroupByKey.EXPERIMENT)}}function M2(t,e){1&t&&vf(0,"mat-icon",11)}function v2(t,e){1&t&&vf(0,"mat-icon",11)}function x2(t,e){if(1&t){const t=wf();Cf(0,"button",12),Rf("click",(function e(){Pi(t);return zf().onGroupByRegexClick()})),Cf(1,"span"),Hm(2,v2,1,0,"mat-icon",7),Mf(),Cf(3,"label"),Pu(4,"Regex"),Mf(),Mf()}if(2&t){const t=zf();ym("aria-checked",t.selectedGroupBy.key===t.GroupByKey.REGEX),Dc(2),bf("ngIf",t.selectedGroupBy.key===t.GroupByKey.REGEX)}}function O2(t,e){if(1&t&&(Cf(0,"label"),Pu(1),Mf()),2&t){const t=zf(2);Dc(1),wu(t.regexString)}}function P2(t,e){1&t&&(Cf(0,"label",17),Pu(1,"(none set)"),Mf())}function w2(t,e){if(1&t){const t=wf();Cf(0,"button",13),Rf("click",(function e(){Pi(t);return zf().onRegexStringEdit()})),Cf(1,"span"),vf(2,"mat-icon",14),Mf(),Hm(3,O2,2,1,"label",15),Hm(4,P2,2,0,"label",16),Mf()}if(2&t){const t=zf();Dc(3),bf("ngIf",t.regexString),Dc(1),bf("ngIf",!t.regexString)}}y2.ɵfac=function t(e){return new(e||y2)(gf(iS),gf(VY),gf(UY))},y2.ɵcmp=so({type:y2,selectors:[["regex-edit-dialog"]],decls:3,vars:6,consts:[[3,"regexString","colorRunPairList","onSave","regexInputOnChange"]],template:function t(e,n){1&e&&(Cf(0,"regex-edit-dialog-component",0),Rf("onSave",(function t(e){return n.onSave(e)}))("regexInputOnChange",(function t(e){return n.onRegexInputOnChange(e)})),_y(1,"async"),_y(2,"async"),Mf()),2&e&&bf("regexString",Cy(1,2,n.groupByRegexString$))("colorRunPairList",Cy(2,4,n.colorRunPairList$))},directives:[b2],pipes:[Vv],styles:["[_nghost-%COMP%], regex-edit-dialog-component[_ngcontent-%COMP%] {\n        display: block;\n        height: 100%;\n        width: 100%;\n      }"]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(y2,[{type:S_,args:[{selector:"regex-edit-dialog",template:'<regex-edit-dialog-component\n    [regexString]="groupByRegexString$ | async"\n    [colorRunPairList]="colorRunPairList$ | async"\n    (onSave)="onSave($event)"\n    (regexInputOnChange)="onRegexInputOnChange($event)"\n  ></regex-edit-dialog-component>',styles:["\n      :host,\n      regex-edit-dialog-component {\n        display: block;\n        height: 100%;\n        width: 100%;\n      }\n    "]}]}],(function(){return[{type:iS},{type:VY},{type:void 0,decorators:[{type:Lr,args:[UY]}]}]}),null);class k2{constructor(t){this.dialog=t,this.GroupByKey=Lz,this.onGroupByChange=new Sy}onRegexStringEdit(){this.dialog.open(y2,{maxHeight:"95vh",maxWidth:"80vw",data:{experimentIds:this.experimentIds}})}onGroupByRegexClick(){this.regexString?this.onGroupByChange.emit({key:Lz.REGEX,regexString:this.regexString}):this.onRegexStringEdit()}}k2.ɵfac=function t(e){return new(e||k2)(gf(XY))},k2.ɵcmp=so({type:k2,selectors:[["runs-group-menu-button-component"]],inputs:{showExperimentsGroupBy:"showExperimentsGroupBy",experimentIds:"experimentIds",regexString:"regexString",selectedGroupBy:"selectedGroupBy",showGroupByRegex:"showGroupByRegex"},outputs:{onGroupByChange:"onGroupByChange"},decls:14,vars:6,consts:[["mat-icon-button","","title","Color runs by...",3,"matMenuTriggerFor"],["svgIcon","palette_24px"],[1,"run-table-color-group-by"],["groupByMenu","matMenu"],[1,"label"],["mat-menu-item","","role","menuitemradio","data-value","experiment",3,"click",4,"ngIf"],["mat-menu-item","","role","menuitemradio","data-value","run",3,"click"],["svgIcon","done_24px",4,"ngIf"],["mat-menu-item","","role","menuitemradio","data-value","regex",3,"click",4,"ngIf"],["mat-menu-item","","role","menuitem","data-value","regex-edit","class","display-regex-string",3,"click",4,"ngIf"],["mat-menu-item","","role","menuitemradio","data-value","experiment",3,"click"],["svgIcon","done_24px"],["mat-menu-item","","role","menuitemradio","data-value","regex",3,"click"],["mat-menu-item","","role","menuitem","data-value","regex-edit",1,"display-regex-string",3,"click"],["svgIcon","edit_24px"],[4,"ngIf"],["class","none-set-string",4,"ngIf"],[1,"none-set-string"]],template:function t(e,n){if(1&e&&(Cf(0,"button",0),vf(1,"mat-icon",1),Mf(),Cf(2,"mat-menu",2,3),Cf(4,"div",4),Pu(5,"Color runs by"),Mf(),Hm(6,C2,5,2,"button",5),Cf(7,"button",6),Rf("click",(function t(){return n.onGroupByChange.emit({key:n.GroupByKey.RUN})})),Cf(8,"span"),Hm(9,M2,1,0,"mat-icon",7),Mf(),Cf(10,"label"),Pu(11,"Run"),Mf(),Mf(),Hm(12,x2,5,2,"button",8),Hm(13,w2,5,2,"button",9),Mf()),2&e){bf("matMenuTriggerFor",Fm(3)),Dc(6),bf("ngIf",n.showExperimentsGroupBy),Dc(1),ym("aria-checked",n.selectedGroupBy.key===n.GroupByKey.RUN),Dc(2),bf("ngIf",n.selectedGroupBy.key===n.GroupByKey.RUN),Dc(3),bf("ngIf",n.showGroupByRegex),Dc(1),bf("ngIf",n.showGroupByRegex)}},directives:[NL,qq,Mq,jq,Pv,Hq],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   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.run-table-color-group-by .display-regex-string .none-set-string{color:rgba(255,255,255,.7)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(k2,[{type:S_,args:[{selector:"runs-group-menu-button-component",templateUrl:"runs_group_menu_button_component.ng.html",styleUrls:["runs_group_menu_button_component.css"],changeDetection:jn.OnPush}]}],(function(){return[{type:XY}]}),{showExperimentsGroupBy:[{type:R_}],experimentIds:[{type:R_}],regexString:[{type:R_}],selectedGroupBy:[{type:R_}],showGroupByRegex:[{type:R_}],onGroupByChange:[{type:E_}]});class S2{constructor(t){this.store=t,this.showGroupByRegex$=this.store.select(UD),this.showExperimentsGroupBy$=this.store.select(CR).pipe(Vt((t=>t.has(_D.COMPARE_EXPERIMENT)))),this.selectedGroupBy$=this.store.select($z),this.groupByRegexString$=this.store.select(rI)}onGroupByChange(t){this.store.dispatch(xT({experimentIds:this.experimentIds,groupBy:t}))}}S2.ɵfac=function t(e){return new(e||S2)(gf(iS))},S2.ɵcmp=so({type:S2,selectors:[["runs-group-menu-button"]],inputs:{experimentIds:"experimentIds"},decls:5,vars:13,consts:[[3,"regexString","selectedGroupBy","showGroupByRegex","showExperimentsGroupBy","experimentIds","onGroupByChange"]],template:function t(e,n){1&e&&(Cf(0,"runs-group-menu-button-component",0),Rf("onGroupByChange",(function t(e){return n.onGroupByChange(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async"),Mf()),2&e&&bf("regexString",Cy(1,5,n.groupByRegexString$))("selectedGroupBy",Cy(2,7,n.selectedGroupBy$))("showGroupByRegex",Cy(3,9,n.showGroupByRegex$))("showExperimentsGroupBy",Cy(4,11,n.showExperimentsGroupBy$))("experimentIds",n.experimentIds)},directives:[k2],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(S2,[{type:S_,args:[{selector:"runs-group-menu-button",template:'\n    <runs-group-menu-button-component\n      [regexString]="groupByRegexString$ | async"\n      [selectedGroupBy]="selectedGroupBy$ | async"\n      [showGroupByRegex]="showGroupByRegex$ | async"\n      [showExperimentsGroupBy]="showExperimentsGroupBy$ | async"\n      [experimentIds]="experimentIds"\n      (onGroupByChange)="onGroupByChange($event)"\n    ></runs-group-menu-button-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),{experimentIds:[{type:R_}]});const D2=["container"];var R2;!(function(t){t[t.NONE=0]="NONE",t[t.LEFT=1]="LEFT",t[t.RIGHT=2]="RIGHT"})(R2||(R2={}));class E2{constructor(t){this.changeDetector=t,this.tickCount=20,this.value=new Sy,this.Position=R2,this.activeThumb=R2.NONE,this.offsetXFromOriginOfActiveThumb=0,this.ngUnsubscribe=new B}getThumbPosition(t){const e=this.getClippedValue(t),n=this.max-this.min;if(n<=0)return"50%";return`${(e-this.min)/n*100}%`}getTrackWidth(){const t=this.max-this.min;if(t<=0)return"0%";return`${(this.getClippedValue(this.upperValue)-this.getClippedValue(this.lowerValue))/t*100}%`}getClippedValue(t){return Math.min(Math.max(t,this.min),this.max)}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}ngOnInit(){le(document,"mousemove",{passive:!0}).pipe(je(this.ngUnsubscribe)).subscribe((t=>{this.handleMouseMove(t)})),le(document,"mouseup",{passive:!0}).pipe(je(this.ngUnsubscribe)).subscribe((t=>{this.handleMouseOut(t)}))}handleMouseDown(t,e){this.activeThumb=e;this.offsetXFromOriginOfActiveThumb=6-t.offsetX}calculateValueFromMouseEvent(t){const{left:e,right:n}=this.container.nativeElement.getBoundingClientRect(),o=t.clientX-e+this.offsetXFromOriginOfActiveThumb;let i;if(null!==this.tickCount&&this.tickCount>0){const t=(n-e)/this.tickCount;i=Math.round(o/t)*t/(n-e)}else i=o/(n-e);const a=this.getClippedValue(this.min+(this.max-this.min)*i);return Number(a.toFixed(10))}handleMouseMove(t){if(this.activeThumb===R2.NONE)return;const e=this.calculateValueFromMouseEvent(t);let n=[this.lowerValue,this.upperValue];this.activeThumb===R2.LEFT?(e>this.upperValue&&(this.activeThumb=R2.RIGHT),n=[e,this.upperValue]):(e<this.lowerValue&&(this.activeThumb=R2.LEFT),n=[this.lowerValue,e]),this.maybeNotifyNextValue(n),this.changeDetector.markForCheck()}maybeNotifyNextValue(t){const[e,n]=t.sort(((t,e)=>t-e));this.lowerValue===e&&this.upperValue===n||this.value.emit({lowerValue:e,upperValue:n})}handleMouseOut(t){this.activeThumb!==R2.NONE&&(this.activeThumb=R2.NONE,this.changeDetector.markForCheck())}handleInputChange(t,e){const n=this.getClippedValue(Number(t.target.value));if(isNaN(n))return;let o=[this.lowerValue,this.upperValue];o=e===R2.LEFT?[n,this.upperValue]:[this.lowerValue,n],this.maybeNotifyNextValue(o)}isThumbActive(t){return this.activeThumb===t}}E2.ɵfac=function t(e){return new(e||E2)(gf(Ob))},E2.ɵcmp=so({type:E2,selectors:[["tb-range-input"]],viewQuery:function t(e,n){if(1&e&&Vy(D2,7,Kh),2&e){let t;By(t=Uy())&&(n.container=t.first)}},inputs:{min:"min",max:"max",lowerValue:"lowerValue",upperValue:"upperValue",tickCount:"tickCount"},outputs:{value:"value"},decls:8,vars:14,consts:[["type","number",1,"lower-input",3,"value","change"],["type","number",1,"upper-input",3,"value","change"],[1,"container"],["container",""],[1,"slider-track"],[1,"slider-track-fill"],[1,"thumb",3,"mousedown"]],template:function t(e,n){1&e&&(Cf(0,"input",0),Rf("change",(function t(e){return n.handleInputChange(e,n.Position.LEFT)})),Mf(),Cf(1,"input",1),Rf("change",(function t(e){return n.handleInputChange(e,n.Position.RIGHT)})),Mf(),Cf(2,"span",2,3),vf(4,"span",4),vf(5,"span",5),Cf(6,"span",6),Rf("mousedown",(function t(e){return n.handleMouseDown(e,n.Position.LEFT)})),Mf(),Cf(7,"span",6),Rf("mousedown",(function t(e){return 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zf(3).setColorFromString(n)})),Hm(1,r5,1,3,"span",49),Mf()}if(2&t){const t=e.$implicit,n=zf(3);lu("background-color",t),Dc(1),bf("ngIf",n.cpAddColorButton)}}function l5(t,e){if(1&t&&(Cf(0,"div"),Hm(1,s5,2,3,"div",47),Mf()),2&t){const t=zf(2);mu(t.cpPresetColorsClass),Dc(1),bf("ngForOf",t.cpPresetColors)}}function c5(t,e){if(1&t&&(Cf(0,"div"),Pu(1),Mf()),2&t){const t=zf(2);mu(t.cpPresetEmptyMessageClass),Dc(1),wu(t.cpPresetEmptyMessage)}}function d5(t,e){if(1&t&&(Cf(0,"div",44),vf(1,"hr"),Cf(2,"div",45),Pu(3),Mf(),Hm(4,l5,2,4,"div",46),Hm(5,c5,2,4,"div",46),Mf()),2&t){const t=zf();Dc(3),wu(t.cpPresetLabel),Dc(1),bf("ngIf",null==t.cpPresetColors?null:t.cpPresetColors.length),Dc(1),bf("ngIf",!(null!=t.cpPresetColors&&t.cpPresetColors.length)&&t.cpAddColorButton)}}function p5(t,e){if(1&t){const t=wf();Cf(0,"button",53),Rf("click",(function e(n){Pi(t);return zf(2).onCancelColor(n)})),Pu(1),Mf()}if(2&t){const t=zf(2);mu(t.cpCancelButtonClass),Dc(1),wu(t.cpCancelButtonText)}}function m5(t,e){if(1&t){const t=wf();Cf(0,"button",53),Rf("click",(function e(n){Pi(t);return zf(2).onAcceptColor(n)})),Pu(1),Mf()}if(2&t){const t=zf(2);mu(t.cpOKButtonClass),Dc(1),wu(t.cpOKButtonText)}}function f5(t,e){if(1&t&&(Cf(0,"div",51),Hm(1,p5,2,4,"button",52),Hm(2,m5,2,4,"button",52),Mf()),2&t){const t=zf();Dc(1),bf("ngIf",t.cpCancelButton),Dc(1),bf("ngIf",t.cpOKButton)}}var u5;!(function(t){t[t.HEX=0]="HEX",t[t.RGBA=1]="RGBA",t[t.HSLA=2]="HSLA",t[t.CMYK=3]="CMYK"})(u5||(u5={}));class g5{constructor(t,e,n,o){this.r=t,this.g=e,this.b=n,this.a=o}}class h5{constructor(t,e,n,o){this.h=t,this.s=e,this.v=n,this.a=o}}class b5{constructor(t,e,n,o){this.h=t,this.s=e,this.l=n,this.a=o}}class y5{constructor(t,e,n,o,i=1){this.c=t,this.m=e,this.y=n,this.k=o,this.a=i}}class _5{constructor(){this.newValue=new Sy}inputChange(t){const e=t.target.value;if(void 0===this.rg)this.newValue.emit(e);else{const t=parseFloat(e);this.newValue.emit({v:t,rg:this.rg})}}}_5.ɵfac=function t(e){return new(e||_5)},_5.ɵdir=ho({type:_5,selectors:[["","text",""]],hostBindings:function t(e,n){1&e&&Rf("input",(function t(e){return n.inputChange(e)}))},inputs:{rg:"rg",text:"text"},outputs:{newValue:"newValue"}}),_5.propDecorators={rg:[{type:R_}],text:[{type:R_}],newValue:[{type:E_}],inputChange:[{type:T_,args:["input",["$event"]]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(_5,[{type:k_,args:[{selector:"[text]"}]}],(function(){return[]}),{newValue:[{type:E_}],inputChange:[{type:T_,args:["input",["$event"]]}],rg:[{type:R_}],text:[{type:R_}]});class C5{constructor(t){this.elRef=t,this.dragEnd=new Sy,this.dragStart=new Sy,this.newValue=new Sy,this.listenerMove=t=>this.move(t),this.listenerStop=()=>this.stop()}mouseDown(t){this.start(t)}touchStart(t){this.start(t)}move(t){t.preventDefault(),this.setCursor(t)}start(t){this.setCursor(t),t.stopPropagation(),document.addEventListener("mouseup",this.listenerStop),document.addEventListener("touchend",this.listenerStop),document.addEventListener("mousemove",this.listenerMove),document.addEventListener("touchmove",this.listenerMove),this.dragStart.emit()}stop(){document.removeEventListener("mouseup",this.listenerStop),document.removeEventListener("touchend",this.listenerStop),document.removeEventListener("mousemove",this.listenerMove),document.removeEventListener("touchmove",this.listenerMove),this.dragEnd.emit()}getX(t){const e=this.elRef.nativeElement.getBoundingClientRect();return(void 0!==t.pageX?t.pageX:t.touches[0].pageX)-e.left-window.pageXOffset}getY(t){const e=this.elRef.nativeElement.getBoundingClientRect();return(void 0!==t.pageY?t.pageY:t.touches[0].pageY)-e.top-window.pageYOffset}setCursor(t){const e=this.elRef.nativeElement.offsetWidth,n=this.elRef.nativeElement.offsetHeight,o=Math.max(0,Math.min(this.getX(t),e)),i=Math.max(0,Math.min(this.getY(t),n));void 0!==this.rgX&&void 0!==this.rgY?this.newValue.emit({s:o/e,v:1-i/n,rgX:this.rgX,rgY:this.rgY}):void 0===this.rgX&&void 0!==this.rgY?this.newValue.emit({v:i/n,rgY:this.rgY}):void 0!==this.rgX&&void 0===this.rgY&&this.newValue.emit({v:o/e,rgX:this.rgX})}}C5.ɵfac=function t(e){return new(e||C5)(gf(Kh))},C5.ɵdir=ho({type:C5,selectors:[["","slider",""]],hostBindings:function t(e,n){1&e&&Rf("mousedown",(function t(e){return n.mouseDown(e)}))("touchstart",(function t(e){return n.touchStart(e)}))},inputs:{rgX:"rgX",rgY:"rgY",slider:"slider"},outputs:{dragEnd:"dragEnd",dragStart:"dragStart",newValue:"newValue"}}),C5.ctorParameters=()=>[{type:Kh}],C5.propDecorators={rgX:[{type:R_}],rgY:[{type:R_}],slider:[{type:R_}],dragEnd:[{type:E_}],dragStart:[{type:E_}],newValue:[{type:E_}],mouseDown:[{type:T_,args:["mousedown",["$event"]]}],touchStart:[{type:T_,args:["touchstart",["$event"]]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(C5,[{type:k_,args:[{selector:"[slider]"}]}],(function(){return[{type:Kh}]}),{dragEnd:[{type:E_}],dragStart:[{type:E_}],newValue:[{type:E_}],mouseDown:[{type:T_,args:["mousedown",["$event"]]}],touchStart:[{type:T_,args:["touchstart",["$event"]]}],rgX:[{type:R_}],rgY:[{type:R_}],slider:[{type:R_}]});class M5{constructor(t,e,n,o){this.h=t,this.s=e,this.v=n,this.a=o}}class v5{constructor(t,e,n,o){this.h=t,this.s=e,this.v=n,this.a=o}}class x5{constructor(){this.active=null}setActive(t){this.active&&this.active!==t&&"inline"!==this.active.cpDialogDisplay&&this.active.closeDialog(),this.active=t}hsva2hsla(t){const e=t.h,n=t.s,o=t.v,i=t.a;if(0===o)return new b5(e,0,0,i);if(0===n&&1===o)return new b5(e,1,1,i);{const t=o*(2-n)/2;return new b5(e,o*n/(1-Math.abs(2*t-1)),t,i)}}hsla2hsva(t){const e=Math.min(t.h,1),n=Math.min(t.s,1),o=Math.min(t.l,1),i=Math.min(t.a,1);if(0===o)return new h5(e,0,0,i);{const t=o+n*(1-Math.abs(2*o-1))/2;return new h5(e,2*(t-o)/t,t,i)}}hsvaToRgba(t){let e,n,o;const i=t.h,a=t.s,r=t.v,s=t.a,l=Math.floor(6*i),c=6*i-l,d=r*(1-a),p=r*(1-c*a),m=r*(1-(1-c)*a);switch(l%6){case 0:e=r,n=m,o=d;break;case 1:e=p,n=r,o=d;break;case 2:e=d,n=r,o=m;break;case 3:e=d,n=p,o=r;break;case 4:e=m,n=d,o=r;break;case 5:e=r,n=d,o=p;break;default:e=0,n=0,o=0}return new g5(e,n,o,s)}cmykToRgb(t){return new g5((1-t.c)*(1-t.k),(1-t.m)*(1-t.k),(1-t.y)*(1-t.k),t.a)}rgbaToCmyk(t){const e=1-Math.max(t.r,t.g,t.b);if(1===e)return new y5(0,0,0,1,t.a);return new y5((1-t.r-e)/(1-e),(1-t.g-e)/(1-e),(1-t.b-e)/(1-e),e,t.a)}rgbaToHsva(t){let e,n;const o=Math.min(t.r,1),i=Math.min(t.g,1),a=Math.min(t.b,1),r=Math.min(t.a,1),s=Math.max(o,i,a),l=Math.min(o,i,a),c=s,d=s-l;if(n=0===s?0:d/s,s===l)e=0;else{switch(s){case o:e=(i-a)/d+(i<a?6:0);break;case i:e=(a-o)/d+2;break;case a:e=(o-i)/d+4;break;default:e=0}e/=6}return new h5(e,n,c,r)}rgbaToHex(t,e){let n="#"+(1<<24|t.r<<16|t.g<<8|t.b).toString(16).substr(1);return e&&(n+=(256|Math.round(255*t.a)).toString(16).substr(1)),n}normalizeCMYK(t){return new y5(t.c/100,t.m/100,t.y/100,t.k/100,t.a)}denormalizeCMYK(t){return new y5(Math.floor(100*t.c),Math.floor(100*t.m),Math.floor(100*t.y),Math.floor(100*t.k),t.a)}denormalizeRGBA(t){return new g5(Math.round(255*t.r),Math.round(255*t.g),Math.round(255*t.b),t.a)}stringToHsva(t="",e=!1){let n=null;t=(t||"").toLowerCase();const o=[{re:/(rgb)a?\(\s*(\d{1,3})\s*,\s*(\d{1,3})\s*%?,\s*(\d{1,3})\s*%?(?:,\s*(\d+(?:\.\d+)?)\s*)?\)/,parse:function(t){return new g5(parseInt(t[2],10)/255,parseInt(t[3],10)/255,parseInt(t[4],10)/255,isNaN(parseFloat(t[5]))?1:parseFloat(t[5]))}},{re:/(hsl)a?\(\s*(\d{1,3})\s*,\s*(\d{1,3})%\s*,\s*(\d{1,3})%\s*(?:,\s*(\d+(?:\.\d+)?)\s*)?\)/,parse:function(t){return new b5(parseInt(t[2],10)/360,parseInt(t[3],10)/100,parseInt(t[4],10)/100,isNaN(parseFloat(t[5]))?1:parseFloat(t[5]))}}];o.push(e?{re:/#([a-fA-F0-9]{2})([a-fA-F0-9]{2})([a-fA-F0-9]{2})([a-fA-F0-9]{2})?$/,parse:function(t){return new g5(parseInt(t[1],16)/255,parseInt(t[2],16)/255,parseInt(t[3],16)/255,parseInt(t[4]||"FF",16)/255)}}:{re:/#([a-fA-F0-9]{2})([a-fA-F0-9]{2})([a-fA-F0-9]{2})$/,parse:function(t){return new g5(parseInt(t[1],16)/255,parseInt(t[2],16)/255,parseInt(t[3],16)/255,1)}}),o.push({re:/#([a-fA-F0-9])([a-fA-F0-9])([a-fA-F0-9])$/,parse:function(t){return new g5(parseInt(t[1]+t[1],16)/255,parseInt(t[2]+t[2],16)/255,parseInt(t[3]+t[3],16)/255,1)}});for(const e in o)if(o.hasOwnProperty(e)){const i=o[e],a=i.re.exec(t),r=a&&i.parse(a);if(r)return r instanceof g5?n=this.rgbaToHsva(r):r instanceof b5&&(n=this.hsla2hsva(r)),n}return n}outputFormat(t,e,n){switch("auto"===e&&(e=t.a<1?"rgba":"hex"),e){case"hsla":const e=this.hsva2hsla(t),o=new b5(Math.round(360*e.h),Math.round(100*e.s),Math.round(100*e.l),Math.round(100*e.a)/100);return t.a<1||"always"===n?"hsla("+o.h+","+o.s+"%,"+o.l+"%,"+o.a+")":"hsl("+o.h+","+o.s+"%,"+o.l+"%)";case"rgba":const i=this.denormalizeRGBA(this.hsvaToRgba(t));return t.a<1||"always"===n?"rgba("+i.r+","+i.g+","+i.b+","+Math.round(100*i.a)/100+")":"rgb("+i.r+","+i.g+","+i.b+")";default:const a="always"===n||"forced"===n;return this.rgbaToHex(this.denormalizeRGBA(this.hsvaToRgba(t)),a)}}}x5.ɵfac=function t(e){return new(e||x5)},x5.ɵprov=Sn({token:x5,factory:x5.ɵfac}),x5.ctorParameters=()=>[],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(x5,[{type:Um}],(function(){return[]}),null);class O5{constructor(t,e,n){this.elRef=t,this.cdRef=e,this.service=n,this.isIE10=!1,this.dialogArrowSize=10,this.dialogArrowOffset=15,this.dialogInputFields=[u5.HEX,u5.RGBA,u5.HSLA,u5.CMYK],this.useRootViewContainer=!1}handleEsc(t){this.show&&"popup"===this.cpDialogDisplay&&this.onCancelColor(t)}handleEnter(t){this.show&&"popup"===this.cpDialogDisplay&&this.onAcceptColor(t)}ngOnInit(){this.slider=new M5(0,0,0,0);this.sliderDimMax=new v5(this.hueSlider.nativeElement.offsetWidth||140,this.cpWidth,130,this.alphaSlider.nativeElement.offsetWidth||140),this.format=this.cpCmykEnabled?u5.CMYK:"rgba"===this.cpOutputFormat?u5.RGBA:"hsla"===this.cpOutputFormat?u5.HSLA:u5.HEX,this.listenerMouseDown=t=>{this.onMouseDown(t)},this.listenerResize=()=>{this.onResize()},this.openDialog(this.initialColor,!1)}ngOnDestroy(){this.closeDialog()}ngAfterViewInit(){if(230!==this.cpWidth||"inline"===this.cpDialogDisplay){this.sliderDimMax=new v5(this.hueSlider.nativeElement.offsetWidth||140,this.cpWidth,130,this.alphaSlider.nativeElement.offsetWidth||140),this.updateColorPicker(!1),this.cdRef.detectChanges()}}openDialog(t,e=!0){this.service.setActive(this),this.width||(this.cpWidth=this.directiveElementRef.nativeElement.offsetWidth),this.height||(this.height=320),this.setInitialColor(t),this.setColorFromString(t,e),this.openColorPicker()}closeDialog(){this.closeColorPicker()}setupDialog(t,e,n,o,i,a,r,s,l,c,d,p,m,f,u,g,h,b,y,_,C,M,v,x,O,P,w,k,S,D,R,E,A,T,N,z){this.setInitialColor(n),this.setColorMode(s),this.isIE10=10===(function I(){let t="";"undefined"!=typeof navigator&&(t=navigator.userAgent.toLowerCase());const e=t.indexOf("msie ");return e>0&&parseInt(t.substring(e+5,t.indexOf(".",e)),10)})(),this.directiveInstance=t,this.directiveElementRef=e,this.cpDisableInput=p,this.cpCmykEnabled=l,this.cpAlphaChannel=c,this.cpOutputFormat=d,this.cpDialogDisplay=a,this.cpIgnoredElements=m,this.cpSaveClickOutside=f,this.cpCloseClickOutside=u,this.useRootViewContainer=g,this.width=this.cpWidth=parseInt(o,10),this.height=this.cpHeight=parseInt(i,10),this.cpPosition=h,this.cpPositionOffset=parseInt(b,10),this.cpOKButton=P,this.cpOKButtonText=k,this.cpOKButtonClass=w,this.cpCancelButton=S,this.cpCancelButtonText=R,this.cpCancelButtonClass=D,this.fallbackColor=r||"#fff",this.setPresetConfig(_,C),this.cpPresetColorsClass=M,this.cpMaxPresetColorsLength=v,this.cpPresetEmptyMessage=x,this.cpPresetEmptyMessageClass=O,this.cpAddColorButton=E,this.cpAddColorButtonText=T,this.cpAddColorButtonClass=A,this.cpRemoveColorButtonClass=N,this.cpTriggerElement=z,y||(this.dialogArrowOffset=0),"inline"===a&&(this.dialogArrowSize=0,this.dialogArrowOffset=0),"hex"===d&&"always"!==c&&"forced"!==c&&(this.cpAlphaChannel="disabled")}setColorMode(t){switch(t.toString().toUpperCase()){case"1":case"C":case"COLOR":this.cpColorMode=1;break;case"2":case"G":case"GRAYSCALE":this.cpColorMode=2;break;case"3":case"P":case"PRESETS":this.cpColorMode=3;break;default:this.cpColorMode=1}}setInitialColor(t){this.initialColor=t}setPresetConfig(t,e){this.cpPresetLabel=t,this.cpPresetColors=e}setColorFromString(t,e=!0,n=!0){let o;"always"===this.cpAlphaChannel||"forced"===this.cpAlphaChannel?(o=this.service.stringToHsva(t,!0),o||this.hsva||(o=this.service.stringToHsva(t,!1))):o=this.service.stringToHsva(t,!1),o||this.hsva||(o=this.service.stringToHsva(this.fallbackColor,!1)),o&&(this.hsva=o,this.sliderH=this.hsva.h,"hex"===this.cpOutputFormat&&"disabled"===this.cpAlphaChannel&&(this.hsva.a=1),this.updateColorPicker(e,n))}onResize(){"fixed"===this.position?this.setDialogPosition():"inline"!==this.cpDialogDisplay&&this.closeColorPicker()}onDragEnd(t){this.directiveInstance.sliderDragEnd({slider:t,color:this.outputColor})}onDragStart(t){this.directiveInstance.sliderDragStart({slider:t,color:this.outputColor})}onMouseDown(t){!this.show||this.isIE10||"popup"!==this.cpDialogDisplay||t.target===this.directiveElementRef.nativeElement||this.isDescendant(this.elRef.nativeElement,t.target)||this.isDescendant(this.directiveElementRef.nativeElement,t.target)||0!==this.cpIgnoredElements.filter((e=>e===t.target)).length||(this.cpSaveClickOutside?this.directiveInstance.colorSelected(this.outputColor):(this.hsva=null,this.setColorFromString(this.initialColor,!1),this.cpCmykEnabled&&this.directiveInstance.cmykChanged(this.cmykColor),this.directiveInstance.colorChanged(this.initialColor),this.directiveInstance.colorCanceled()),this.cpCloseClickOutside&&this.closeColorPicker())}onAcceptColor(t){t.stopPropagation(),this.outputColor&&this.directiveInstance.colorSelected(this.outputColor),"popup"===this.cpDialogDisplay&&this.closeColorPicker()}onCancelColor(t){this.hsva=null,t.stopPropagation(),this.directiveInstance.colorCanceled(),this.setColorFromString(this.initialColor,!0),"popup"===this.cpDialogDisplay&&(this.cpCmykEnabled&&this.directiveInstance.cmykChanged(this.cmykColor),this.directiveInstance.colorChanged(this.initialColor,!0),this.closeColorPicker())}onFormatToggle(t){const e=this.dialogInputFields.length-(this.cpCmykEnabled?0:1),n=((this.dialogInputFields.indexOf(this.format)+t)%e+e)%e;this.format=this.dialogInputFields[n]}onColorChange(t){this.hsva.s=t.s/t.rgX,this.hsva.v=t.v/t.rgY,this.updateColorPicker(),this.directiveInstance.sliderChanged({slider:"lightness",value:this.hsva.v,color:this.outputColor}),this.directiveInstance.sliderChanged({slider:"saturation",value:this.hsva.s,color:this.outputColor})}onHueChange(t){this.hsva.h=t.v/t.rgX,this.sliderH=this.hsva.h,this.updateColorPicker(),this.directiveInstance.sliderChanged({slider:"hue",value:this.hsva.h,color:this.outputColor})}onValueChange(t){this.hsva.v=t.v/t.rgX,this.updateColorPicker(),this.directiveInstance.sliderChanged({slider:"value",value:this.hsva.v,color:this.outputColor})}onAlphaChange(t){this.hsva.a=t.v/t.rgX,this.updateColorPicker(),this.directiveInstance.sliderChanged({slider:"alpha",value:this.hsva.a,color:this.outputColor})}onHexInput(t){if(null===t)this.updateColorPicker();else{t&&"#"!==t[0]&&(t="#"+t);let e=/^#([a-f0-9]{3}|[a-f0-9]{6})$/gi;"always"===this.cpAlphaChannel&&(e=/^#([a-f0-9]{3}|[a-f0-9]{6}|[a-f0-9]{8})$/gi);const n=e.test(t);n&&(t.length<5&&(t="#"+t.substring(1).split("").map((t=>t+t)).join("")),"forced"===this.cpAlphaChannel&&(t+=Math.round(255*this.hsva.a).toString(16)),this.setColorFromString(t,!0,!1)),this.directiveInstance.inputChanged({input:"hex",valid:n,value:t,color:this.outputColor})}}onRedInput(t){const e=this.service.hsvaToRgba(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.r=t.v/t.rg,this.hsva=this.service.rgbaToHsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"red",valid:n,value:e.r,color:this.outputColor})}onBlueInput(t){const e=this.service.hsvaToRgba(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.b=t.v/t.rg,this.hsva=this.service.rgbaToHsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"blue",valid:n,value:e.b,color:this.outputColor})}onGreenInput(t){const e=this.service.hsvaToRgba(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.g=t.v/t.rg,this.hsva=this.service.rgbaToHsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"green",valid:n,value:e.g,color:this.outputColor})}onHueInput(t){const e=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;e&&(this.hsva.h=t.v/t.rg,this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"hue",valid:e,value:this.hsva.h,color:this.outputColor})}onValueInput(t){const e=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;e&&(this.hsva.v=t.v/t.rg,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"value",valid:e,value:this.hsva.v,color:this.outputColor})}onAlphaInput(t){const e=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;e&&(this.hsva.a=t.v/t.rg,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"alpha",valid:e,value:this.hsva.a,color:this.outputColor})}onLightnessInput(t){const e=this.service.hsva2hsla(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.l=t.v/t.rg,this.hsva=this.service.hsla2hsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"lightness",valid:n,value:e.l,color:this.outputColor})}onSaturationInput(t){const e=this.service.hsva2hsla(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.s=t.v/t.rg,this.hsva=this.service.hsla2hsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"saturation",valid:n,value:e.s,color:this.outputColor})}onCyanInput(t){!isNaN(t.v)&&t.v>=0&&t.v<=t.rg&&(this.cmyk.c=t.v,this.updateColorPicker(!1,!0,!0)),this.directiveInstance.inputChanged({input:"cyan",valid:!0,value:this.cmyk.c,color:this.outputColor})}onMagentaInput(t){!isNaN(t.v)&&t.v>=0&&t.v<=t.rg&&(this.cmyk.m=t.v,this.updateColorPicker(!1,!0,!0)),this.directiveInstance.inputChanged({input:"magenta",valid:!0,value:this.cmyk.m,color:this.outputColor})}onYellowInput(t){!isNaN(t.v)&&t.v>=0&&t.v<=t.rg&&(this.cmyk.y=t.v,this.updateColorPicker(!1,!0,!0)),this.directiveInstance.inputChanged({input:"yellow",valid:!0,value:this.cmyk.y,color:this.outputColor})}onBlackInput(t){!isNaN(t.v)&&t.v>=0&&t.v<=t.rg&&(this.cmyk.k=t.v,this.updateColorPicker(!1,!0,!0)),this.directiveInstance.inputChanged({input:"black",valid:!0,value:this.cmyk.k,color:this.outputColor})}onAddPresetColor(t,e){t.stopPropagation(),this.cpPresetColors.filter((t=>t===e)).length||(this.cpPresetColors=this.cpPresetColors.concat(e),this.directiveInstance.presetColorsChanged(this.cpPresetColors))}onRemovePresetColor(t,e){t.stopPropagation(),this.cpPresetColors=this.cpPresetColors.filter((t=>t!==e)),this.directiveInstance.presetColorsChanged(this.cpPresetColors)}openColorPicker(){this.show||(this.show=!0,this.hidden=!0,setTimeout((()=>{this.hidden=!1,this.setDialogPosition(),this.cdRef.detectChanges()}),0),this.directiveInstance.stateChanged(!0),this.isIE10||(document.addEventListener("mousedown",this.listenerMouseDown),document.addEventListener("touchstart",this.listenerMouseDown)),window.addEventListener("resize",this.listenerResize))}closeColorPicker(){this.show&&(this.show=!1,this.directiveInstance.stateChanged(!1),this.isIE10||(document.removeEventListener("mousedown",this.listenerMouseDown),document.removeEventListener("touchstart",this.listenerMouseDown)),window.removeEventListener("resize",this.listenerResize),this.cdRef.destroyed||this.cdRef.detectChanges())}updateColorPicker(t=!0,e=!0,n=!1){if(this.sliderDimMax){let o,i,a;2===this.cpColorMode&&(this.hsva.s=0);const r=this.outputColor;if(i=this.service.hsva2hsla(this.hsva),this.cpCmykEnabled?(n?(a=this.service.cmykToRgb(this.service.normalizeCMYK(this.cmyk)),this.hsva=this.service.rgbaToHsva(a)):(a=this.service.hsvaToRgba(this.hsva),this.cmyk=this.service.denormalizeCMYK(this.service.rgbaToCmyk(a))),a=this.service.denormalizeRGBA(a),this.sliderH=this.hsva.h):a=this.service.denormalizeRGBA(this.service.hsvaToRgba(this.hsva)),o=this.service.denormalizeRGBA(this.service.hsvaToRgba(new h5(this.sliderH||this.hsva.h,1,1,1))),e){this.hslaText=new b5(Math.round(360*i.h),Math.round(100*i.s),Math.round(100*i.l),Math.round(100*i.a)/100),this.rgbaText=new g5(a.r,a.g,a.b,Math.round(100*a.a)/100),this.cpCmykEnabled&&(this.cmykText=new y5(this.cmyk.c,this.cmyk.m,this.cmyk.y,this.cmyk.k,Math.round(100*this.cmyk.a)/100));this.hexText=this.service.rgbaToHex(a,"always"===this.cpAlphaChannel),this.hexAlpha=this.rgbaText.a}if("auto"===this.cpOutputFormat&&this.format!==u5.RGBA&&this.format!==u5.CMYK&&this.hsva.a<1&&(this.format=this.hsva.a<1?u5.RGBA:u5.HEX),this.hueSliderColor="rgb("+o.r+","+o.g+","+o.b+")",this.alphaSliderColor="rgb("+a.r+","+a.g+","+a.b+")",this.outputColor=this.service.outputFormat(this.hsva,this.cpOutputFormat,this.cpAlphaChannel),this.selectedColor=this.service.outputFormat(this.hsva,"rgba",null),this.format!==u5.CMYK)this.cmykColor="";else if("always"===this.cpAlphaChannel||"enabled"===this.cpAlphaChannel||"forced"===this.cpAlphaChannel){const t=Math.round(100*this.cmyk.a)/100;this.cmykColor=`cmyka(${this.cmyk.c},${this.cmyk.m},${this.cmyk.y},${this.cmyk.k},${t})`}else this.cmykColor=`cmyk(${this.cmyk.c},${this.cmyk.m},${this.cmyk.y},${this.cmyk.k})`;this.slider=new M5((this.sliderH||this.hsva.h)*this.sliderDimMax.h-8,this.hsva.s*this.sliderDimMax.s-8,(1-this.hsva.v)*this.sliderDimMax.v-8,this.hsva.a*this.sliderDimMax.a-8),t&&r!==this.outputColor&&(this.cpCmykEnabled&&this.directiveInstance.cmykChanged(this.cmykColor),this.directiveInstance.colorChanged(this.outputColor))}}setDialogPosition(){if("inline"===this.cpDialogDisplay)this.position="relative";else{let t,e="static",n="",o=null,i=null,a=this.directiveElementRef.nativeElement.parentNode;const r=this.dialogElement.nativeElement.offsetHeight;for(;null!==a&&"HTML"!==a.tagName;){if(t=window.getComputedStyle(a),e=t.getPropertyValue("position"),n=t.getPropertyValue("transform"),"static"!==e&&null===o&&(o=a),n&&"none"!==n&&null===i&&(i=a),"fixed"===e){o=i;break}a=a.parentNode}const s=this.createDialogBox(this.directiveElementRef.nativeElement,"fixed"!==e);if(this.useRootViewContainer||"fixed"===e&&(!o||o instanceof HTMLUnknownElement))this.top=s.top,this.left=s.left;else{null===o&&(o=a);const t=this.createDialogBox(o,"fixed"!==e);this.top=s.top-t.top,this.left=s.left-t.left}"fixed"===e&&(this.position="fixed");let l=this.cpPosition;if("auto"===this.cpPosition){l=(function t(e,n){let o="right",i="bottom";const{height:a,width:r}=e,{top:s,left:l}=n,c=s+n.height,d=l+n.width,p=s-a<0,m=c+a>(window.innerHeight||document.documentElement.clientHeight),f=l-r<0,u=d+r>(window.innerWidth||document.documentElement.clientWidth);if(m&&(i="top"),p&&(i="bottom"),f&&(o="right"),u&&(o="left"),p&&m&&f&&u)return["left","right","top","bottom"].reduce(((t,n)=>e[t]>e[n]?t:n));return f&&u?p?"bottom":m||s>c?"top":"bottom":p&&m?f?"right":u||l>d?"left":"right":`${i}-${o}`})(this.dialogElement.nativeElement.getBoundingClientRect(),this.cpTriggerElement.nativeElement.getBoundingClientRect())}"top"===l?(this.arrowTop=r-1,this.top-=r+this.dialogArrowSize,this.left+=this.cpPositionOffset/100*s.width-this.dialogArrowOffset):"bottom"===l?(this.top+=s.height+this.dialogArrowSize,this.left+=this.cpPositionOffset/100*s.width-this.dialogArrowOffset):"top-left"===l||"left-top"===l?(this.top-=r-s.height+s.height*this.cpPositionOffset/100,this.left-=this.cpWidth+this.dialogArrowSize-2-this.dialogArrowOffset):"top-right"===l||"right-top"===l?(this.top-=r-s.height+s.height*this.cpPositionOffset/100,this.left+=s.width+this.dialogArrowSize-2-this.dialogArrowOffset):"left"===l||"bottom-left"===l||"left-bottom"===l?(this.top+=s.height*this.cpPositionOffset/100-this.dialogArrowOffset,this.left-=this.cpWidth+this.dialogArrowSize-2):(this.top+=s.height*this.cpPositionOffset/100-this.dialogArrowOffset,this.left+=s.width+this.dialogArrowSize-2),this.cpUsePosition=l}}isDescendant(t,e){let n=e.parentNode;for(;null!==n;){if(n===t)return!0;n=n.parentNode}return!1}createDialogBox(t,e){return{top:t.getBoundingClientRect().top+(e?window.pageYOffset:0),left:t.getBoundingClientRect().left+(e?window.pageXOffset:0),width:t.offsetWidth,height:t.offsetHeight}}}O5.ɵfac=function t(e){return new(e||O5)(gf(Kh),gf(Ob),gf(x5))},O5.ɵcmp=so({type:O5,selectors:[["color-picker"]],viewQuery:function t(e,n){if(1&e&&(Vy(F2,7),Vy(L2,7),Vy(B2,7)),2&e){let t;By(t=Uy())&&(n.dialogElement=t.first),By(t=Uy())&&(n.hueSlider=t.first),By(t=Uy())&&(n.alphaSlider=t.first)}},hostBindings:function t(e,n){1&e&&Rf("keyup.esc",(function t(e){return n.handleEsc(e)}),!1,Pl)("keyup.enter",(function t(e){return n.handleEnter(e)}),!1,Pl)},decls:28,vars:47,consts:[[1,"color-picker",3,"click"],["dialogPopup",""],[3,"class","top",4,"ngIf"],["class","saturation-lightness",3,"slider","rgX","rgY","background-color","newValue","dragStart","dragEnd",4,"ngIf"],[1,"hue-alpha","box"],[1,"left"],[1,"selected-color-background"],[1,"selected-color"],["type","button",3,"class","disabled","click",4,"ngIf"],[1,"right"],["style","height: 16px;",4,"ngIf"],[1,"hue",3,"slider","rgX","newValue","dragStart","dragEnd"],["hueSlider",""],[1,"cursor"],[1,"value",3,"slider","rgX","newValue","dragStart","dragEnd"],["valueSlider",""],[1,"alpha",3,"slider","rgX","newValue","dragStart","dragEnd"],["alphaSlider",""],["class","cmyk-text",3,"display",4,"ngIf"],["class","hsla-text",3,"display",4,"ngIf"],["class","rgba-text",3,"display",4,"ngIf"],["class","hex-text",3,"hex-alpha","display",4,"ngIf"],["class","value-text",4,"ngIf"],["class","type-policy",4,"ngIf"],["class","preset-area",4,"ngIf"],["class","button-area",4,"ngIf"],[1,"saturation-lightness",3,"slider","rgX","rgY","newValue","dragStart","dragEnd"],["type","button",3,"disabled","click"],[2,"height","16px"],[1,"cmyk-text"],[1,"box"],["type","number","pattern","[0-9]*","min","0","max","100",3,"text","rg","value","keyup.enter","newValue"],["type","number","pattern","[0-9]+([\\.,][0-9]{1,2})?","min","0","max","1","step","0.1",3,"text","rg","value","keyup.enter","newValue",4,"ngIf"],[4,"ngIf"],["type","number","pattern","[0-9]+([\\.,][0-9]{1,2})?","min","0","max","1","step","0.1",3,"text","rg","value","keyup.enter","newValue"],[1,"hsla-text"],["type","number","pattern","[0-9]*","min","0","max","360",3,"text","rg","value","keyup.enter","newValue"],[1,"rgba-text"],["type","number","pattern","[0-9]*","min","0","max","255",3,"text","rg","value","keyup.enter","newValue"],[1,"hex-text"],[3,"text","value","blur","keyup.enter","newValue"],[1,"value-text"],[1,"type-policy"],[1,"type-policy-arrow",3,"click"],[1,"preset-area"],[1,"preset-label"],[3,"class",4,"ngIf"],["class","preset-color",3,"backgroundColor","click",4,"ngFor","ngForOf"],[1,"preset-color",3,"click"],[3,"class","click",4,"ngIf"],[3,"click"],[1,"button-area"],["type","button",3,"class","click",4,"ngIf"],["type","button",3,"click"]],template:function t(e,n){1&e&&(Cf(0,"div",0,1),Rf("click",(function t(e){return e.stopPropagation()})),Hm(2,V2,1,5,"div",2),Hm(3,j2,2,8,"div",3),Cf(4,"div",4),Cf(5,"div",5),vf(6,"div",6),vf(7,"div",7),Hm(8,U2,2,5,"button",8),Mf(),Cf(9,"div",9),Hm(10,W2,1,0,"div",10),Cf(11,"div",11,12),Rf("newValue",(function t(e){return n.onHueChange(e)}))("dragStart",(function t(){return n.onDragStart("hue")}))("dragEnd",(function t(){return n.onDragEnd("hue")})),vf(13,"div",13),Mf(),Cf(14,"div",14,15),Rf("newValue",(function t(e){return n.onValueChange(e)}))("dragStart",(function t(){return n.onDragStart("value")}))("dragEnd",(function t(){return n.onDragEnd("value")})),vf(16,"div",13),Mf(),Cf(17,"div",16,17),Rf("newValue",(function t(e){return n.onAlphaChange(e)}))("dragStart",(function t(){return n.onDragStart("alpha")}))("dragEnd",(function t(){return n.onDragEnd("alpha")})),vf(19,"div",13),Mf(),Mf(),Mf(),Hm(20,q2,17,12,"div",18),Hm(21,K2,14,10,"div",19),Hm(22,$2,14,10,"div",20),Hm(23,n5,8,7,"div",21),Hm(24,i5,9,3,"div",22),Hm(25,a5,3,0,"div",23),Hm(26,d5,6,3,"div",24),Hm(27,f5,3,2,"div",25),Mf()),2&e&&(lu("display",n.show?"block":"none")("visibility",n.hidden?"hidden":"visible")("top",n.top,"px")("left",n.left,"px")("position",n.position)("height",n.cpHeight,"px")("width",n.cpWidth,"px"),cu("open",n.show),Dc(2),bf("ngIf","popup"==n.cpDialogDisplay),Dc(1),bf("ngIf",1===(n.cpColorMode||1)),Dc(4),lu("background-color",n.selectedColor),Dc(1),bf("ngIf",n.cpAddColorButton),Dc(2),bf("ngIf","disabled"===n.cpAlphaChannel),Dc(1),lu("display",1===(n.cpColorMode||1)?"block":"none"),bf("rgX",1),Dc(2),lu("left",null==n.slider?null:n.slider.h,"px"),Dc(1),lu("display",2===(n.cpColorMode||1)?"block":"none"),bf("rgX",1),Dc(2),lu("right",null==n.slider?null:n.slider.v,"px"),Dc(1),lu("display","disabled"===n.cpAlphaChannel?"none":"block")("background-color",n.alphaSliderColor),bf("rgX",1),Dc(2),lu("left",null==n.slider?null:n.slider.a,"px"),Dc(1),bf("ngIf",!n.cpDisableInput&&1===(n.cpColorMode||1)),Dc(1),bf("ngIf",!n.cpDisableInput&&1===(n.cpColorMode||1)),Dc(1),bf("ngIf",!n.cpDisableInput&&1===(n.cpColorMode||1)),Dc(1),bf("ngIf",!n.cpDisableInput&&1===(n.cpColorMode||1)),Dc(1),bf("ngIf",!n.cpDisableInput&&2===(n.cpColorMode||1)),Dc(1),bf("ngIf",!n.cpDisableInput&&1===(n.cpColorMode||1)),Dc(1),bf("ngIf",(null==n.cpPresetColors?null:n.cpPresetColors.length)||n.cpAddColorButton),Dc(1),bf("ngIf",n.cpOKButton||n.cpCancelButton))},directives:[Pv,C5,_5,xv],styles:['.color-picker{-moz-user-select:none;-ms-user-select:none;-webkit-user-select:none;background-color:#fff;border:1px solid #777;cursor:default;height:auto;position:absolute;user-select:none;width:230px;z-index:1000}.color-picker *{box-sizing:border-box;font-size:11px;margin:0}.color-picker input{color:#000;font-size:13px;height:26px;min-width:0;text-align:center;width:0}.color-picker input:-moz-submit-invalid,.color-picker input:-moz-ui-invalid,.color-picker input:invalid{box-shadow:none}.color-picker input::-webkit-inner-spin-button,.color-picker input::-webkit-outer-spin-button{-webkit-appearance:none;margin:0}.color-picker .arrow{border-style:solid;height:0;position:absolute;width:0;z-index:999999}.color-picker .arrow.arrow-top{border-color:#777 transparent transparent;border-width:10px 5px;left:8px}.color-picker .arrow.arrow-bottom{border-color:transparent transparent #777;border-width:10px 5px;left:8px;top:-20px}.color-picker .arrow.arrow-left-top,.color-picker .arrow.arrow-top-left{border-color:transparent transparent transparent #777;border-width:5px 10px;bottom:8px;right:-21px}.color-picker .arrow.arrow-right-top,.color-picker .arrow.arrow-top-right{border-color:transparent #777 transparent transparent;border-width:5px 10px;bottom:8px;left:-20px}.color-picker .arrow.arrow-bottom-left,.color-picker .arrow.arrow-left,.color-picker .arrow.arrow-left-bottom{border-color:transparent transparent transparent #777;border-width:5px 10px;right:-21px;top:8px}.color-picker .arrow.arrow-bottom-right,.color-picker .arrow.arrow-right,.color-picker .arrow.arrow-right-bottom{border-color:transparent #777 transparent transparent;border-width:5px 10px;left:-20px;top:8px}.color-picker .cursor{border:2px solid #222;border-radius:50%;cursor:default;height:16px;position:relative;width:16px}.color-picker .box{display:flex;padding:4px 8px}.color-picker .left{padding:16px 8px;position:relative}.color-picker .right{flex:1 1 auto;padding:12px 8px}.color-picker .button-area{padding:0 16px 16px;text-align:right}.color-picker .button-area button{margin-left:8px}.color-picker .preset-area{padding:4px 15px}.color-picker .preset-area .preset-label{color:#555;font-size:11px;overflow:hidden;padding:4px;text-align:left;text-overflow:ellipsis;white-space:nowrap;width:100%}.color-picker .preset-area .preset-color{border:1px solid #a9a9a9;border-radius:25%;cursor:pointer;display:inline-block;height:18px;margin:4px 6px 8px;position:relative;width:18px}.color-picker .preset-area .preset-empty-message{font-style:italic;margin-bottom:8px;margin-top:4px;min-height:18px;text-align:center}.color-picker .hex-text{font-size:11px;padding:4px 8px;width:100%}.color-picker .hex-text .box{padding:0 24px 8px 8px}.color-picker .hex-text .box div{clear:left;color:#555;flex:1 1 auto;float:left;text-align:center}.color-picker .hex-text .box input{border:1px solid #a9a9a9;flex:1 1 auto;padding:1px}.color-picker .hex-alpha .box div:first-child,.color-picker .hex-alpha .box input:first-child{flex-grow:3;margin-right:8px}.color-picker .cmyk-text,.color-picker .hsla-text,.color-picker .rgba-text,.color-picker .value-text{font-size:11px;padding:4px 8px;width:100%}.color-picker .cmyk-text .box,.color-picker .hsla-text .box,.color-picker .rgba-text .box{padding:0 24px 8px 8px}.color-picker .value-text .box{padding:0 8px 8px}.color-picker .cmyk-text .box div,.color-picker .hsla-text .box div,.color-picker .rgba-text .box div,.color-picker .value-text .box div{color:#555;flex:1 1 auto;margin-right:8px;text-align:center}.color-picker .cmyk-text .box div:last-child,.color-picker .hsla-text .box div:last-child,.color-picker .rgba-text .box div:last-child,.color-picker .value-text .box div:last-child{margin-right:0}.color-picker .cmyk-text .box input,.color-picker .hsla-text .box input,.color-picker .rgba-text .box input,.color-picker .value-text .box input{border:1px solid #a9a9a9;flex:1;float:left;margin:0 8px 0 0;padding:1px}.color-picker .cmyk-text .box input:last-child,.color-picker .hsla-text .box input:last-child,.color-picker .rgba-text .box input:last-child,.color-picker .value-text .box input:last-child{margin-right:0}.color-picker .hue-alpha{align-items:center;margin-bottom:3px}.color-picker .hue{background-image:url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJYAAAAQCAYAAAD06IYnAAAABmJLR0QA/wD/AP+gvaeTAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH4AIWDwkUFWbCCAAAAFxJREFUaN7t0kEKg0AQAME2x83/n2qu5qCgD1iDhCoYdpnbQC9bbY1qVO/jvc6k3ad91s7/7F1/csgPrujuQ17BDYSFsBAWwgJhISyEBcJCWAgLhIWwEBYIi2f7Ar/1TCgFH2X9AAAAAElFTkSuQmCC");direction:ltr}.color-picker .hue,.color-picker .value{background-size:100% 100%;border:none;cursor:pointer;height:16px;margin-bottom:16px;width:100%}.color-picker .value{background-image:url("data:image/png;base64,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");direction:rtl}.color-picker .alpha{background-image:url("data:image/png;base64,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");background-size:100% 100%;border:none;cursor:pointer;direction:ltr;height:16px;width:100%}.color-picker .type-policy{background-image:url("data:image/png;base64,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");background-position:50%;background-repeat:no-repeat;background-size:8px 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");background-size:100% 100%;border:none;cursor:pointer;direction:ltr;height:130px;touch-action:manipulation;width:100%}.color-picker .cp-add-color-button-class{background:transparent;border:0;cursor:pointer;display:inline;margin:3px -3px;padding:0;position:absolute}.color-picker .cp-add-color-button-class:hover{text-decoration:underline}.color-picker .cp-add-color-button-class:disabled{color:#999;cursor:not-allowed}.color-picker .cp-add-color-button-class:disabled:hover{text-decoration:none}.color-picker .cp-remove-color-button-class{background:#fff;border-radius:50%;box-shadow:1px 1px 5px #333;cursor:pointer;display:block;height:10px;position:absolute;right:-5px;text-align:center;top:-5px;width:10px}.color-picker .cp-remove-color-button-class:before{bottom:3.5px;content:"x";display:inline-block;font-size:10px;position:relative}'],encapsulation:2}),O5.ctorParameters=()=>[{type:Kh},{type:Ob},{type:x5}],O5.propDecorators={dialogElement:[{type:ar,args:["dialogPopup",{static:!0}]}],hueSlider:[{type:ar,args:["hueSlider",{static:!0}]}],alphaSlider:[{type:ar,args:["alphaSlider",{static:!0}]}],handleEsc:[{type:T_,args:["document:keyup.esc",["$event"]]}],handleEnter:[{type:T_,args:["document:keyup.enter",["$event"]]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(O5,[{type:S_,args:[{selector:"color-picker",template:'<div #dialogPopup class="color-picker" [class.open]="show" [style.display]="!show ? \'none\' : \'block\'" [style.visibility]="hidden ? \'hidden\' : \'visible\'" [style.top.px]="top" [style.left.px]="left" [style.position]="position" [style.height.px]="cpHeight" [style.width.px]="cpWidth" (click)="$event.stopPropagation()">\n  <div *ngIf="cpDialogDisplay==\'popup\'" class="arrow arrow-{{cpUsePosition}}" [style.top.px]="arrowTop"></div>\n\n  <div *ngIf="(cpColorMode || 1) === 1" class="saturation-lightness" [slider] [rgX]="1" [rgY]="1" [style.background-color]="hueSliderColor" (newValue)="onColorChange($event)" (dragStart)="onDragStart(\'saturation-lightness\')" (dragEnd)="onDragEnd(\'saturation-lightness\')">\n    <div class="cursor" [style.top.px]="slider?.v" [style.left.px]="slider?.s"></div>\n  </div>\n\n  <div class="hue-alpha box">\n    <div class="left">\n      <div class="selected-color-background"></div>\n\n      <div class="selected-color" [style.background-color]="selectedColor"></div>\n\n      <button *ngIf="cpAddColorButton" type="button" class="{{cpAddColorButtonClass}}" [disabled]="cpPresetColors && cpPresetColors.length >= cpMaxPresetColorsLength" (click)="onAddPresetColor($event, selectedColor)">\n        {{cpAddColorButtonText}}\n      </button>\n    </div>\n\n    <div class="right">\n      <div *ngIf="cpAlphaChannel===\'disabled\'" style="height: 16px;"></div>\n\n      <div #hueSlider class="hue" [slider] [rgX]="1" [style.display]="(cpColorMode || 1) === 1 ? \'block\' : \'none\'" (newValue)="onHueChange($event)" (dragStart)="onDragStart(\'hue\')" (dragEnd)="onDragEnd(\'hue\')">\n        <div class="cursor" [style.left.px]="slider?.h"></div>\n      </div>\n\n      <div #valueSlider class="value" [slider] [rgX]="1" [style.display]="(cpColorMode || 1) === 2 ? \'block\': \'none\'" (newValue)="onValueChange($event)" (dragStart)="onDragStart(\'value\')" (dragEnd)="onDragEnd(\'value\')">\n        <div class="cursor" [style.right.px]="slider?.v"></div>\n      </div>\n\n      <div #alphaSlider class="alpha" [slider] [rgX]="1" [style.display]="cpAlphaChannel === \'disabled\' ? \'none\' : \'block\'" [style.background-color]="alphaSliderColor" (newValue)="onAlphaChange($event)" (dragStart)="onDragStart(\'alpha\')" (dragEnd)="onDragEnd(\'alpha\')">\n        <div class="cursor" [style.left.px]="slider?.a"></div>\n      </div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1" class="cmyk-text" [style.display]="format !== 3 ? \'none\' : \'block\'">\n    <div class="box">\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="cmykText?.c" (keyup.enter)="onAcceptColor($event)" (newValue)="onCyanInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="cmykText?.m" (keyup.enter)="onAcceptColor($event)" (newValue)="onMagentaInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="cmykText?.y" (keyup.enter)="onAcceptColor($event)" (newValue)="onYellowInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="cmykText?.k" (keyup.enter)="onAcceptColor($event)" (newValue)="onBlackInput($event)" />\n      <input *ngIf="cpAlphaChannel!==\'disabled\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1" [text] [rg]="1" [value]="cmykText?.a" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)" />\n    </div>\n\n     <div class="box">\n      <div>C</div><div>M</div><div>Y</div><div>K</div><div *ngIf="cpAlphaChannel!==\'disabled\'" >A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1 " class="hsla-text" [style.display]="format !== 2 ? \'none\' : \'block\'">\n    <div class="box">\n      <input type="number" pattern="[0-9]*" min="0" max="360" [text] [rg]="360" [value]="hslaText?.h" (keyup.enter)="onAcceptColor($event)" (newValue)="onHueInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="hslaText?.s" (keyup.enter)="onAcceptColor($event)" (newValue)="onSaturationInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="hslaText?.l" (keyup.enter)="onAcceptColor($event)" (newValue)="onLightnessInput($event)" />\n      <input *ngIf="cpAlphaChannel!==\'disabled\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1" [text] [rg]="1" [value]="hslaText?.a" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)" />\n    </div>\n\n    <div class="box">\n      <div>H</div><div>S</div><div>L</div><div *ngIf="cpAlphaChannel!==\'disabled\'">A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1 " [style.display]="format !== 1 ? \'none\' : \'block\'" class="rgba-text">\n    <div class="box">\n      <input type="number" pattern="[0-9]*" min="0" max="255" [text] [rg]="255" [value]="rgbaText?.r" (keyup.enter)="onAcceptColor($event)" (newValue)="onRedInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="255" [text] [rg]="255" [value]="rgbaText?.g" (keyup.enter)="onAcceptColor($event)" (newValue)="onGreenInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="255" [text] [rg]="255" [value]="rgbaText?.b" (keyup.enter)="onAcceptColor($event)" (newValue)="onBlueInput($event)" />\n      <input *ngIf="cpAlphaChannel!==\'disabled\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1" [text] [rg]="1" [value]="rgbaText?.a" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)" />\n    </div>\n\n    <div class="box">\n      <div>R</div><div>G</div><div>B</div><div *ngIf="cpAlphaChannel!==\'disabled\'" >A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1" class="hex-text" [class.hex-alpha]="cpAlphaChannel===\'forced\'"\n    [style.display]="format !== 0 ? \'none\' : \'block\'">\n    <div class="box">\n      <input [text] [value]="hexText" (blur)="onHexInput(null)" (keyup.enter)="onAcceptColor($event)" (newValue)="onHexInput($event)"/>\n      <input *ngIf="cpAlphaChannel===\'forced\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1" [text] [rg]="1" [value]="hexAlpha" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)"/>\n    </div>\n\n    <div class="box">\n      <div>Hex</div>\n      <div *ngIf="cpAlphaChannel===\'forced\'">A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 2" class="value-text">\n    <div class="box">\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="hslaText?.l" (keyup.enter)="onAcceptColor($event)" (newValue)="onValueInput($event)" />\n      <input *ngIf="cpAlphaChannel!==\'disabled\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1"  [text] [rg]="1" [value]="hslaText?.a" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)" />\n    </div>\n\n    <div class="box">\n      <div>V</div><div>A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1" class="type-policy">\n    <span class="type-policy-arrow" (click)="onFormatToggle(-1)"></span>\n    <span class="type-policy-arrow" (click)="onFormatToggle(1)"></span>\n  </div>\n\n  <div *ngIf="cpPresetColors?.length || cpAddColorButton" class="preset-area">\n    <hr>\n\n    <div class="preset-label">{{cpPresetLabel}}</div>\n\n    <div *ngIf="cpPresetColors?.length" class="{{cpPresetColorsClass}}">\n      <div *ngFor="let color of cpPresetColors" class="preset-color" [style.backgroundColor]="color" (click)="setColorFromString(color)">\n        <span *ngIf="cpAddColorButton" class="{{cpRemoveColorButtonClass}}" (click)="onRemovePresetColor($event, color)"></span>\n      </div>\n    </div>\n\n    <div *ngIf="!cpPresetColors?.length && cpAddColorButton" class="{{cpPresetEmptyMessageClass}}">{{cpPresetEmptyMessage}}</div>\n  </div>\n\n  <div *ngIf="cpOKButton || cpCancelButton" class="button-area">\n    <button *ngIf="cpCancelButton" type="button" class="{{cpCancelButtonClass}}" (click)="onCancelColor($event)">{{cpCancelButtonText}}</button>\n\n    <button *ngIf="cpOKButton" type="button" class="{{cpOKButtonClass}}" (click)="onAcceptColor($event)">{{cpOKButtonText}}</button>\n  </div>\n</div>\n',encapsulation:Wn.None,styles:['.color-picker{-moz-user-select:none;-ms-user-select:none;-webkit-user-select:none;background-color:#fff;border:1px solid #777;cursor:default;height:auto;position:absolute;user-select:none;width:230px;z-index:1000}.color-picker *{box-sizing:border-box;font-size:11px;margin:0}.color-picker input{color:#000;font-size:13px;height:26px;min-width:0;text-align:center;width:0}.color-picker input:-moz-submit-invalid,.color-picker input:-moz-ui-invalid,.color-picker input:invalid{box-shadow:none}.color-picker input::-webkit-inner-spin-button,.color-picker input::-webkit-outer-spin-button{-webkit-appearance:none;margin:0}.color-picker .arrow{border-style:solid;height:0;position:absolute;width:0;z-index:999999}.color-picker .arrow.arrow-top{border-color:#777 transparent transparent;border-width:10px 5px;left:8px}.color-picker .arrow.arrow-bottom{border-color:transparent transparent #777;border-width:10px 5px;left:8px;top:-20px}.color-picker .arrow.arrow-left-top,.color-picker .arrow.arrow-top-left{border-color:transparent transparent transparent #777;border-width:5px 10px;bottom:8px;right:-21px}.color-picker .arrow.arrow-right-top,.color-picker .arrow.arrow-top-right{border-color:transparent #777 transparent transparent;border-width:5px 10px;bottom:8px;left:-20px}.color-picker .arrow.arrow-bottom-left,.color-picker .arrow.arrow-left,.color-picker .arrow.arrow-left-bottom{border-color:transparent transparent transparent #777;border-width:5px 10px;right:-21px;top:8px}.color-picker .arrow.arrow-bottom-right,.color-picker .arrow.arrow-right,.color-picker .arrow.arrow-right-bottom{border-color:transparent #777 transparent transparent;border-width:5px 10px;left:-20px;top:8px}.color-picker .cursor{border:2px solid #222;border-radius:50%;cursor:default;height:16px;position:relative;width:16px}.color-picker .box{display:flex;padding:4px 8px}.color-picker .left{padding:16px 8px;position:relative}.color-picker .right{flex:1 1 auto;padding:12px 8px}.color-picker .button-area{padding:0 16px 16px;text-align:right}.color-picker .button-area button{margin-left:8px}.color-picker .preset-area{padding:4px 15px}.color-picker .preset-area .preset-label{color:#555;font-size:11px;overflow:hidden;padding:4px;text-align:left;text-overflow:ellipsis;white-space:nowrap;width:100%}.color-picker .preset-area .preset-color{border:1px solid #a9a9a9;border-radius:25%;cursor:pointer;display:inline-block;height:18px;margin:4px 6px 8px;position:relative;width:18px}.color-picker .preset-area .preset-empty-message{font-style:italic;margin-bottom:8px;margin-top:4px;min-height:18px;text-align:center}.color-picker .hex-text{font-size:11px;padding:4px 8px;width:100%}.color-picker .hex-text .box{padding:0 24px 8px 8px}.color-picker .hex-text .box div{clear:left;color:#555;flex:1 1 auto;float:left;text-align:center}.color-picker .hex-text .box input{border:1px solid #a9a9a9;flex:1 1 auto;padding:1px}.color-picker .hex-alpha .box div:first-child,.color-picker .hex-alpha .box input:first-child{flex-grow:3;margin-right:8px}.color-picker .cmyk-text,.color-picker .hsla-text,.color-picker .rgba-text,.color-picker .value-text{font-size:11px;padding:4px 8px;width:100%}.color-picker .cmyk-text .box,.color-picker .hsla-text .box,.color-picker .rgba-text .box{padding:0 24px 8px 8px}.color-picker .value-text .box{padding:0 8px 8px}.color-picker .cmyk-text .box div,.color-picker .hsla-text .box div,.color-picker .rgba-text .box div,.color-picker .value-text .box div{color:#555;flex:1 1 auto;margin-right:8px;text-align:center}.color-picker .cmyk-text .box div:last-child,.color-picker .hsla-text .box div:last-child,.color-picker .rgba-text .box div:last-child,.color-picker .value-text .box div:last-child{margin-right:0}.color-picker .cmyk-text .box input,.color-picker .hsla-text .box input,.color-picker .rgba-text .box input,.color-picker .value-text .box input{border:1px solid #a9a9a9;flex:1;float:left;margin:0 8px 0 0;padding:1px}.color-picker .cmyk-text .box input:last-child,.color-picker .hsla-text .box input:last-child,.color-picker .rgba-text .box input:last-child,.color-picker .value-text .box input:last-child{margin-right:0}.color-picker .hue-alpha{align-items:center;margin-bottom:3px}.color-picker .hue{background-image:url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJYAAAAQCAYAAAD06IYnAAAABmJLR0QA/wD/AP+gvaeTAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH4AIWDwkUFWbCCAAAAFxJREFUaN7t0kEKg0AQAME2x83/n2qu5qCgD1iDhCoYdpnbQC9bbY1qVO/jvc6k3ad91s7/7F1/csgPrujuQ17BDYSFsBAWwgJhISyEBcJCWAgLhIWwEBYIi2f7Ar/1TCgFH2X9AAAAAElFTkSuQmCC");direction:ltr}.color-picker .hue,.color-picker .value{background-size:100% 100%;border:none;cursor:pointer;height:16px;margin-bottom:16px;width:100%}.color-picker .value{background-image:url("data:image/png;base64,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");direction:rtl}.color-picker .alpha{background-image:url("data:image/png;base64,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");background-size:100% 100%;border:none;cursor:pointer;direction:ltr;height:16px;width:100%}.color-picker .type-policy{background-image:url("data:image/png;base64,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");background-position:50%;background-repeat:no-repeat;background-size:8px 16px;height:24px;position:absolute;right:12px;top:218px;width:16px}.color-picker .type-policy .type-policy-arrow{display:block;height:50%;width:100%}.color-picker .selected-color{border:1px solid #a9a9a9;border-radius:50%;height:40px;left:8px;position:absolute;top:16px;width:40px}.color-picker .selected-color-background{background-image:url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACgAAAAoCAYAAACM/rhtAAAAh0lEQVRYR+2W0QlAMQgD60zdfwOdqa8TmI/wQMr5K0I5bZLIzLOa2nt37VVVbd+dDx5obgCC3KBLwJ2ff4PnVidkf+ucIhw80HQaCLo3DMH3CRK3iFsmAWVl6hPNDwt8EvNE5q+YuEXcMgkonVM6SdyCoEvAnZ8v1Hjx817MilmxSUB5rdLJDycZgUAZUch/AAAAAElFTkSuQmCC");border-radius:50%;height:40px;width:40px}.color-picker 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");background-size:100% 100%;border:none;cursor:pointer;direction:ltr;height:130px;touch-action:manipulation;width:100%}.color-picker .cp-add-color-button-class{background:transparent;border:0;cursor:pointer;display:inline;margin:3px -3px;padding:0;position:absolute}.color-picker .cp-add-color-button-class:hover{text-decoration:underline}.color-picker .cp-add-color-button-class:disabled{color:#999;cursor:not-allowed}.color-picker .cp-add-color-button-class:disabled:hover{text-decoration:none}.color-picker .cp-remove-color-button-class{background:#fff;border-radius:50%;box-shadow:1px 1px 5px #333;cursor:pointer;display:block;height:10px;position:absolute;right:-5px;text-align:center;top:-5px;width:10px}.color-picker .cp-remove-color-button-class:before{bottom:3.5px;content:"x";display:inline-block;font-size:10px;position:relative}']}]}],(function(){return[{type:Kh},{type:Ob},{type:x5}]}),{handleEsc:[{type:T_,args:["document:keyup.esc",["$event"]]}],handleEnter:[{type:T_,args:["document:keyup.enter",["$event"]]}],dialogElement:[{type:ar,args:["dialogPopup",{static:!0}]}],hueSlider:[{type:ar,args:["hueSlider",{static:!0}]}],alphaSlider:[{type:ar,args:["alphaSlider",{static:!0}]}]});class P5{constructor(t,e,n,o,i,a){this.injector=t,this.cfr=e,this.appRef=n,this.vcRef=o,this.elRef=i,this._service=a,this.dialogCreated=!1,this.ignoreChanges=!1,this.viewAttachedToAppRef=!1,this.cpWidth="230px",this.cpHeight="auto",this.cpToggle=!1,this.cpDisabled=!1,this.cpIgnoredElements=[],this.cpFallbackColor="",this.cpColorMode="color",this.cpCmykEnabled=!1,this.cpOutputFormat="auto",this.cpAlphaChannel="enabled",this.cpDisableInput=!1,this.cpDialogDisplay="popup",this.cpSaveClickOutside=!0,this.cpCloseClickOutside=!0,this.cpUseRootViewContainer=!1,this.cpPosition="auto",this.cpPositionOffset="0%",this.cpPositionRelativeToArrow=!1,this.cpOKButton=!1,this.cpOKButtonText="OK",this.cpOKButtonClass="cp-ok-button-class",this.cpCancelButton=!1,this.cpCancelButtonText="Cancel",this.cpCancelButtonClass="cp-cancel-button-class",this.cpPresetLabel="Preset colors",this.cpPresetColorsClass="cp-preset-colors-class",this.cpMaxPresetColorsLength=6,this.cpPresetEmptyMessage="No colors added",this.cpPresetEmptyMessageClass="preset-empty-message",this.cpAddColorButton=!1,this.cpAddColorButtonText="Add color",this.cpAddColorButtonClass="cp-add-color-button-class",this.cpRemoveColorButtonClass="cp-remove-color-button-class",this.cpInputChange=new Sy(!0),this.cpToggleChange=new Sy(!0),this.cpSliderChange=new Sy(!0),this.cpSliderDragEnd=new Sy(!0),this.cpSliderDragStart=new Sy(!0),this.colorPickerOpen=new Sy(!0),this.colorPickerClose=new Sy(!0),this.colorPickerCancel=new Sy(!0),this.colorPickerSelect=new Sy(!0),this.colorPickerChange=new Sy(!1),this.cpCmykColorChange=new Sy(!0),this.cpPresetColorsChange=new Sy(!0)}handleClick(){this.inputFocus()}handleFocus(){this.inputFocus()}handleInput(t){this.inputChange(t)}ngOnDestroy(){null!=this.cmpRef&&(this.viewAttachedToAppRef&&this.appRef.detachView(this.cmpRef.hostView),this.cmpRef.destroy(),this.cmpRef=null,this.dialog=null)}ngOnChanges(t){t.cpToggle&&!this.cpDisabled&&(t.cpToggle.currentValue?this.openDialog():t.cpToggle.currentValue||this.closeDialog()),t.colorPicker&&(this.dialog&&!this.ignoreChanges&&("inline"===this.cpDialogDisplay&&this.dialog.setInitialColor(t.colorPicker.currentValue),this.dialog.setColorFromString(t.colorPicker.currentValue,!1),this.cpUseRootViewContainer&&"inline"!==this.cpDialogDisplay&&this.cmpRef.changeDetectorRef.detectChanges()),this.ignoreChanges=!1),(t.cpPresetLabel||t.cpPresetColors)&&this.dialog&&this.dialog.setPresetConfig(this.cpPresetLabel,this.cpPresetColors)}openDialog(){if(this.dialogCreated)this.dialog&&this.dialog.openDialog(this.colorPicker);else{let t=this.vcRef;if(this.dialogCreated=!0,this.viewAttachedToAppRef=!1,this.cpUseRootViewContainer&&"inline"!==this.cpDialogDisplay){const e=this.injector.get(this.appRef.componentTypes[0],Np.NULL);e!==Np.NULL?(t=e.vcRef||e.viewContainerRef||this.vcRef,t===this.vcRef&&console.warn("You are using cpUseRootViewContainer, but the root component is not exposing viewContainerRef!Please expose it by adding 'public vcRef: ViewContainerRef' to the constructor.")):this.viewAttachedToAppRef=!0}const e=this.cfr.resolveComponentFactory(O5);if(this.viewAttachedToAppRef)this.cmpRef=e.create(this.injector),this.appRef.attachView(this.cmpRef.hostView),document.body.appendChild(this.cmpRef.hostView.rootNodes[0]);else{const n=ff.fromResolvedProviders([],t.parentInjector);this.cmpRef=t.createComponent(e,0,n,[])}this.cmpRef.instance.setupDialog(this,this.elRef,this.colorPicker,this.cpWidth,this.cpHeight,this.cpDialogDisplay,this.cpFallbackColor,this.cpColorMode,this.cpCmykEnabled,this.cpAlphaChannel,this.cpOutputFormat,this.cpDisableInput,this.cpIgnoredElements,this.cpSaveClickOutside,this.cpCloseClickOutside,this.cpUseRootViewContainer,this.cpPosition,this.cpPositionOffset,this.cpPositionRelativeToArrow,this.cpPresetLabel,this.cpPresetColors,this.cpPresetColorsClass,this.cpMaxPresetColorsLength,this.cpPresetEmptyMessage,this.cpPresetEmptyMessageClass,this.cpOKButton,this.cpOKButtonClass,this.cpOKButtonText,this.cpCancelButton,this.cpCancelButtonClass,this.cpCancelButtonText,this.cpAddColorButton,this.cpAddColorButtonClass,this.cpAddColorButtonText,this.cpRemoveColorButtonClass,this.elRef),this.dialog=this.cmpRef.instance,this.vcRef!==t&&this.cmpRef.changeDetectorRef.detectChanges()}}closeDialog(){this.dialog&&"popup"===this.cpDialogDisplay&&this.dialog.closeDialog()}cmykChanged(t){this.cpCmykColorChange.emit(t)}stateChanged(t){this.cpToggleChange.emit(t),t?this.colorPickerOpen.emit(this.colorPicker):this.colorPickerClose.emit(this.colorPicker)}colorChanged(t,e=!0){this.ignoreChanges=e,this.colorPickerChange.emit(t)}colorSelected(t){this.colorPickerSelect.emit(t)}colorCanceled(){this.colorPickerCancel.emit()}inputFocus(){const t=this.elRef.nativeElement,e=this.cpIgnoredElements.filter((e=>e===t));this.cpDisabled||e.length||("undefined"!=typeof document&&t===document.activeElement?this.openDialog():this.dialog&&this.dialog.show?this.closeDialog():this.openDialog())}inputChange(t){this.dialog?this.dialog.setColorFromString(t.target.value,!0):(this.colorPicker=t.target.value,this.colorPickerChange.emit(this.colorPicker))}inputChanged(t){this.cpInputChange.emit(t)}sliderChanged(t){this.cpSliderChange.emit(t)}sliderDragEnd(t){this.cpSliderDragEnd.emit(t)}sliderDragStart(t){this.cpSliderDragStart.emit(t)}presetColorsChanged(t){this.cpPresetColorsChange.emit(t)}}P5.ɵfac=function t(e){return new(e||P5)(gf(Np),gf(Gh),gf(HC),gf(Fb),gf(Kh),gf(x5))},P5.ɵdir=ho({type:P5,selectors:[["","colorPicker",""]],hostBindings:function t(e,n){1&e&&Rf("click",(function t(){return n.handleClick()}))("focus",(function t(){return n.handleFocus()}))("input",(function t(e){return n.handleInput(e)}))},inputs:{cpWidth:"cpWidth",cpHeight:"cpHeight",cpToggle:"cpToggle",cpDisabled:"cpDisabled",cpIgnoredElements:"cpIgnoredElements",cpFallbackColor:"cpFallbackColor",cpColorMode:"cpColorMode",cpCmykEnabled:"cpCmykEnabled",cpOutputFormat:"cpOutputFormat",cpAlphaChannel:"cpAlphaChannel",cpDisableInput:"cpDisableInput",cpDialogDisplay:"cpDialogDisplay",cpSaveClickOutside:"cpSaveClickOutside",cpCloseClickOutside:"cpCloseClickOutside",cpUseRootViewContainer:"cpUseRootViewContainer",cpPosition:"cpPosition",cpPositionOffset:"cpPositionOffset",cpPositionRelativeToArrow:"cpPositionRelativeToArrow",cpOKButton:"cpOKButton",cpOKButtonText:"cpOKButtonText",cpOKButtonClass:"cpOKButtonClass",cpCancelButton:"cpCancelButton",cpCancelButtonText:"cpCancelButtonText",cpCancelButtonClass:"cpCancelButtonClass",cpPresetLabel:"cpPresetLabel",cpPresetColorsClass:"cpPresetColorsClass",cpMaxPresetColorsLength:"cpMaxPresetColorsLength",cpPresetEmptyMessage:"cpPresetEmptyMessage",cpPresetEmptyMessageClass:"cpPresetEmptyMessageClass",cpAddColorButton:"cpAddColorButton",cpAddColorButtonText:"cpAddColorButtonText",cpAddColorButtonClass:"cpAddColorButtonClass",cpRemoveColorButtonClass:"cpRemoveColorButtonClass",colorPicker:"colorPicker",cpPresetColors:"cpPresetColors"},outputs:{cpInputChange:"cpInputChange",cpToggleChange:"cpToggleChange",cpSliderChange:"cpSliderChange",cpSliderDragEnd:"cpSliderDragEnd",cpSliderDragStart:"cpSliderDragStart",colorPickerOpen:"colorPickerOpen",colorPickerClose:"colorPickerClose",colorPickerCancel:"colorPickerCancel",colorPickerSelect:"colorPickerSelect",colorPickerChange:"colorPickerChange",cpCmykColorChange:"cpCmykColorChange",cpPresetColorsChange:"cpPresetColorsChange"},exportAs:["ngxColorPicker"],features:[qo]}),P5.ctorParameters=()=>[{type:Np},{type:Gh},{type:HC},{type:Fb},{type:Kh},{type:x5}],P5.propDecorators={colorPicker:[{type:R_}],cpWidth:[{type:R_}],cpHeight:[{type:R_}],cpToggle:[{type:R_}],cpDisabled:[{type:R_}],cpIgnoredElements:[{type:R_}],cpFallbackColor:[{type:R_}],cpColorMode:[{type:R_}],cpCmykEnabled:[{type:R_}],cpOutputFormat:[{type:R_}],cpAlphaChannel:[{type:R_}],cpDisableInput:[{type:R_}],cpDialogDisplay:[{type:R_}],cpSaveClickOutside:[{type:R_}],cpCloseClickOutside:[{type:R_}],cpUseRootViewContainer:[{type:R_}],cpPosition:[{type:R_}],cpPositionOffset:[{type:R_}],cpPositionRelativeToArrow:[{type:R_}],cpOKButton:[{type:R_}],cpOKButtonText:[{type:R_}],cpOKButtonClass:[{type:R_}],cpCancelButton:[{type:R_}],cpCancelButtonText:[{type:R_}],cpCancelButtonClass:[{type:R_}],cpPresetLabel:[{type:R_}],cpPresetColors:[{type:R_}],cpPresetColorsClass:[{type:R_}],cpMaxPresetColorsLength:[{type:R_}],cpPresetEmptyMessage:[{type:R_}],cpPresetEmptyMessageClass:[{type:R_}],cpAddColorButton:[{type:R_}],cpAddColorButtonText:[{type:R_}],cpAddColorButtonClass:[{type:R_}],cpRemoveColorButtonClass:[{type:R_}],cpInputChange:[{type:E_}],cpToggleChange:[{type:E_}],cpSliderChange:[{type:E_}],cpSliderDragEnd:[{type:E_}],cpSliderDragStart:[{type:E_}],colorPickerOpen:[{type:E_}],colorPickerClose:[{type:E_}],colorPickerCancel:[{type:E_}],colorPickerSelect:[{type:E_}],colorPickerChange:[{type:E_}],cpCmykColorChange:[{type:E_}],cpPresetColorsChange:[{type:E_}],handleClick:[{type:T_,args:["click"]}],handleFocus:[{type:T_,args:["focus"]}],handleInput:[{type:T_,args:["input",["$event"]]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(P5,[{type:k_,args:[{selector:"[colorPicker]",exportAs:"ngxColorPicker"}]}],(function(){return[{type:Np},{type:Gh},{type:HC},{type:Fb},{type:Kh},{type:x5}]}),{cpWidth:[{type:R_}],cpHeight:[{type:R_}],cpToggle:[{type:R_}],cpDisabled:[{type:R_}],cpIgnoredElements:[{type:R_}],cpFallbackColor:[{type:R_}],cpColorMode:[{type:R_}],cpCmykEnabled:[{type:R_}],cpOutputFormat:[{type:R_}],cpAlphaChannel:[{type:R_}],cpDisableInput:[{type:R_}],cpDialogDisplay:[{type:R_}],cpSaveClickOutside:[{type:R_}],cpCloseClickOutside:[{type:R_}],cpUseRootViewContainer:[{type:R_}],cpPosition:[{type:R_}],cpPositionOffset:[{type:R_}],cpPositionRelativeToArrow:[{type:R_}],cpOKButton:[{type:R_}],cpOKButtonText:[{type:R_}],cpOKButtonClass:[{type:R_}],cpCancelButton:[{type:R_}],cpCancelButtonText:[{type:R_}],cpCancelButtonClass:[{type:R_}],cpPresetLabel:[{type:R_}],cpPresetColorsClass:[{type:R_}],cpMaxPresetColorsLength:[{type:R_}],cpPresetEmptyMessage:[{type:R_}],cpPresetEmptyMessageClass:[{type:R_}],cpAddColorButton:[{type:R_}],cpAddColorButtonText:[{type:R_}],cpAddColorButtonClass:[{type:R_}],cpRemoveColorButtonClass:[{type:R_}],cpInputChange:[{type:E_}],cpToggleChange:[{type:E_}],cpSliderChange:[{type:E_}],cpSliderDragEnd:[{type:E_}],cpSliderDragStart:[{type:E_}],colorPickerOpen:[{type:E_}],colorPickerClose:[{type:E_}],colorPickerCancel:[{type:E_}],colorPickerSelect:[{type:E_}],colorPickerChange:[{type:E_}],cpCmykColorChange:[{type:E_}],cpPresetColorsChange:[{type:E_}],handleClick:[{type:T_,args:["click"]}],handleFocus:[{type:T_,args:["focus"]}],handleInput:[{type:T_,args:["input",["$event"]]}],colorPicker:[{type:R_}],cpPresetColors:[{type:R_}]});class w5{}w5.ɵfac=function t(e){return new(e||w5)},w5.ɵmod=fo({type:w5}),w5.ɵinj=Dn({providers:[x5],imports:[[rx]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(w5,[{type:L_,args:[{imports:[rx],exports:[P5],providers:[x5],declarations:[O5,P5,_5,C5],entryComponents:[O5]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(w5,{declarations:function(){return[O5,P5,_5,C5]},imports:function(){return[rx]},exports:function(){return[P5]}});const k5=["filter"];function S5(t,e){1&t&&Pf(0)}function D5(t,e){1&t&&Pf(0)}const R5=function(t){return{item:t}};function E5(t,e){if(1&t&&(xf(0),Hm(1,D5,1,0,"ng-container",12),Of()),2&t){const t=e.$implicit;zf();const n=Fm(14);Dc(1),bf("ngTemplateOutlet",n)("ngTemplateOutletContext",iy(2,R5,t))}}function A5(t,e){1&t&&(Cf(0,"div",13),vf(1,"mat-spinner",14),Mf())}function T5(t,e){1&t&&(Cf(0,"div",15),Pu(1,"No Runs"),Mf())}function N5(t,e){if(1&t&&(Cf(0,"div",15),Cf(1,"span"),Pu(2,'No runs match "'),Cf(3,"code"),Pu(4),Mf(),Pu(5,'"'),Mf(),Mf()),2&t){const t=zf();Dc(4),wu(t.regexFilter)}}const z5=function(){return[5,10,20]};function I5(t,e){if(1&t){const t=wf();Cf(0,"mat-paginator",16),Rf("page",(function e(n){Pi(t);return zf().onPaginationChange.emit(n)})),Mf()}if(2&t){const t=zf();bf("pageSizeOptions",oy(4,z5))("pageIndex",t.paginationOption.pageIndex)("pageSize",t.paginationOption.pageSize)("length",t.filteredItemsLength)}}function H5(t,e){if(1&t){const t=wf();Cf(0,"mat-checkbox",26),Rf("change",(function e(){Pi(t);return zf(3).handlePageToggle()})),Mf()}if(2&t){const t=zf(3);bf("checked",t.allPageItemsSelected())("indeterminate",!t.allPageItemsSelected()&&t.somePageItemsSelected())}}const F5=function(t){return{type:t}};function L5(t,e){if(1&t&&(Cf(0,"span",27),Pu(1,"Experiment"),Mf()),2&t){const t=zf(3);bf("mat-sort-header",iy(1,F5,t.SortType.EXPERIMENT_NAME))}}function B5(t,e){if(1&t&&(Cf(0,"span",27),Pu(1,"Run"),Mf()),2&t){const t=zf(3);bf("mat-sort-header",iy(1,F5,t.SortType.RUN_NAME))}}function V5(t,e){if(1&t&&vf(0,"runs-group-menu-button",29),2&t){bf("experimentIds",zf(4).experimentIds)}}function j5(t,e){if(1&t&&(Cf(0,"span"),Hm(1,V5,1,1,"runs-group-menu-button",28),Mf()),2&t){const t=zf(3);Dc(1),bf("ngIf",t.showGroupControl)}}const U5=function(t){return["column",t]};function W5(t,e){if(1&t&&(Cf(0,"span",21),xf(1,22),Hm(2,H5,1,2,"mat-checkbox",23),Hm(3,L5,2,3,"span",24),Hm(4,B5,2,3,"span",24),Hm(5,j5,2,1,"span",25),Of(),Mf()),2&t){const t=e.$implicit,n=zf(2);bf("ngClass",iy(6,U5,"tb-column-"+t)),Dc(1),bf("ngSwitch",t),Dc(1),bf("ngSwitchCase",n.RunsTableColumn.CHECKBOX),Dc(1),bf("ngSwitchCase",n.RunsTableColumn.EXPERIMENT_NAME),Dc(1),bf("ngSwitchCase",n.RunsTableColumn.RUN_NAME),Dc(1),bf("ngSwitchCase",n.RunsTableColumn.RUN_COLOR)}}function G5(t,e){if(1&t){const t=wf();xf(0),Cf(1,"div",38),Rf("click",(function t(e){return e.stopPropagation()})),Cf(2,"tb-range-input",39),Rf("value",(function e(n){Pi(t);const o=zf(2).$implicit;return zf(2).handleHparamIntervalChanged(o,n)})),Mf(),Mf(),Of()}if(2&t){const t=zf(2).$implicit;Dc(2),bf("min",t.filter.minValue)("max",t.filter.maxValue)("lowerValue",t.filter.filterLowerValue)("upperValue",t.filter.filterUpperValue)}}function Y5(t,e){if(1&t){const t=wf();Cf(0,"div",41),Rf("click",(function t(e){return e.stopPropagation()})),Cf(1,"mat-checkbox",37),Rf("change",(function e(){const n=Pi(t).$implicit,o=zf(3).$implicit;return zf(2).handleHparamDiscreteChanged(o,n)})),Cf(2,"span"),Pu(3),Mf(),Mf(),Mf()}if(2&t){const t=e.$implicit,n=zf(3).$implicit;Dc(1),bf("checked",n.filter.filterValues.includes(t)),Dc(2),wu(t)}}function q5(t,e){if(1&t&&(xf(0),Hm(1,Y5,4,2,"div",40),Of()),2&t){const t=zf(2).$implicit;Dc(1),bf("ngForOf",t.filter.possibleValues)}}function Z5(t,e){if(1&t){const t=wf();xf(0),Cf(1,"button",33),Rf("click",(function t(e){return e.stopPropagation()})),vf(2,"mat-icon",34),Mf(),Cf(3,"mat-menu",null,35),Cf(5,"div",36),Rf("click",(function t(e){return e.stopPropagation()})),Cf(6,"mat-checkbox",37),Rf("change",(function e(){Pi(t);const n=zf().$implicit;return zf(2).handleHparamIncludeUndefinedToggled(n)})),Cf(7,"span"),Pu(8,"(show empty value)"),Mf(),Mf(),Mf(),Hm(9,G5,3,4,"ng-container",32),Hm(10,q5,2,1,"ng-container",32),Mf(),Of()}if(2&t){const t=Fm(4),e=zf().$implicit,n=zf(2);Dc(1),bf("matMenuTriggerFor",t),ym("aria-label","Filter hparam "+(e.displayName||e.name)),Dc(5),bf("checked",e.filter.includeUndefined),Dc(3),bf("ngIf",e.filter.type===n.DomainType.INTERVAL),Dc(1),bf("ngIf",e.filter.type===n.DomainType.DISCRETE)}}const X5=function(t,e){return{type:t,name:e}};function K5(t,e){if(1&t&&(Cf(0,"span",30),Cf(1,"span",31),Pu(2),Mf(),Hm(3,Z5,11,5,"ng-container",32),Mf()),2&t){const t=e.$implicit,n=zf(2);bf("mat-sort-header",ay(3,X5,n.SortType.HPARAM,t.name)),Dc(2),wu(t.displayName||t.name),Dc(1),bf("ngIf",t.filter)}}function J5(t,e){if(1&t){const t=wf();xf(0),Cf(1,"button",33),Rf("click",(function t(e){return e.stopPropagation()})),vf(2,"mat-icon",34),Mf(),Cf(3,"mat-menu",null,35),Cf(5,"div",36),Rf("click",(function t(e){return e.stopPropagation()})),Cf(6,"mat-checkbox",37),Rf("change",(function e(){Pi(t);const n=zf().$implicit;return zf(2).handleMetricIncludeUndefinedChanged(n)})),Cf(7,"span"),Pu(8,"(show empty value)"),Mf(),Mf(),Mf(),Cf(9,"div",38),Rf("click",(function t(e){return e.stopPropagation()})),Cf(10,"tb-range-input",39),Rf("value",(function e(n){Pi(t);const o=zf().$implicit;return zf(2).handleMetricFilterChanged(o,n)})),Mf(),Mf(),Mf(),Of()}if(2&t){const t=Fm(4),e=zf().$implicit;Dc(1),bf("matMenuTriggerFor",t),ym("aria-label","Filter metric "+(e.displayName||e.tag)),Dc(5),bf("checked",e.filter.includeUndefined),Dc(4),bf("min",e.filter.minValue)("max",e.filter.maxValue)("lowerValue",e.filter.filterLowerValue)("upperValue",e.filter.filterUpperValue)}}const Q5=function(t,e){return{type:t,tag:e}};function $5(t,e){if(1&t&&(Cf(0,"span",30),Cf(1,"span",31),Pu(2),Mf(),Hm(3,J5,11,7,"ng-container",32),Mf()),2&t){const t=e.$implicit,n=zf(2);bf("mat-sort-header",ay(3,Q5,n.SortType.METRIC,t.tag)),Dc(2),wu(t.displayName||t.tag),Dc(1),bf("ngIf",t.filter)}}function t4(t,e){if(1&t){const t=wf();Cf(0,"div",17),Cf(1,"div",18),Rf("matSortChange",(function e(n){Pi(t);return zf().handleSortChange(n)})),Hm(2,W5,6,8,"span",19),Hm(3,K5,4,6,"span",20),Hm(4,$5,4,6,"span",20),Mf(),Mf()}if(2&t){const t=zf();Dc(1),bf("matSortActive",t.sortOption.column),Dc(1),bf("ngForOf",t.columns),Dc(1),bf("ngForOf",t.hparamColumns)("ngForTrackBy",t.trackByHparamColumn),Dc(1),bf("ngForOf",t.metricColumns)("ngForTrackBy",t.trackByMetricColumn)}}function e4(t,e){if(1&t){const t=wf();Cf(0,"span"),Cf(1,"mat-checkbox",37),Rf("change",(function e(){Pi(t);const n=zf(2).item;return zf().onSelectionToggle.emit(n)})),Mf(),Mf()}if(2&t){const t=zf(2).item;Dc(1),bf("checked",t.selected)}}function n4(t,e){if(1&t&&vf(0,"tb-experiment-alias",48),2&t){bf("alias",zf(2).item.experimentAlias)}}function o4(t,e){if(1&t&&(Cf(0,"span",31),Pu(1),Mf()),2&t){const t=zf(2).item;Dc(1),wu(t.run.name)}}const i4=function(t){return{"run-color-swatch":!0,"no-color":t}};function a4(t,e){if(1&t){const t=wf();Cf(0,"span"),Cf(1,"button",49),Rf("colorPickerChange",(function e(n){Pi(t);const o=zf(2).item;return zf().onRunColorChange.emit({runId:o.run.id,newColor:n})})),Mf(),Mf()}if(2&t){const t=zf(2).item;Dc(1),lu("background",t.runColor),bf("ngClass",iy(8,i4,!t.runColor))("colorPicker",t.runColor)("cpDialogDisplay","popup")("cpPositionOffset",-20)("cpUseRootViewContainer",!0)("cpOutputFormat","hex")}}function r4(t,e){if(1&t&&(Cf(0,"span",45),xf(1,22),Hm(2,e4,2,1,"span",25),Hm(3,n4,1,1,"tb-experiment-alias",46),Hm(4,o4,2,1,"span",47),Hm(5,a4,2,10,"span",25),Of(),Mf()),2&t){const t=e.$implicit,n=zf(2);bf("ngClass",iy(6,U5,"tb-column-"+t)),Dc(1),bf("ngSwitch",t),Dc(1),bf("ngSwitchCase",n.RunsTableColumn.CHECKBOX),Dc(1),bf("ngSwitchCase",n.RunsTableColumn.EXPERIMENT_NAME),Dc(1),bf("ngSwitchCase",n.RunsTableColumn.RUN_NAME),Dc(1),bf("ngSwitchCase",n.RunsTableColumn.RUN_COLOR)}}function s4(t,e){if(1&t&&(Cf(0,"span",50),Pu(1),Mf()),2&t){const t=e.$implicit,n=zf().item;Dc(1),wu(n.hparams.get(t.name))}}function l4(t,e){if(1&t&&(Cf(0,"span",50),Pu(1),Mf()),2&t){const t=e.$implicit,n=zf().item;Dc(1),wu(n.metrics.get(t.tag))}}function c4(t,e){if(1&t&&(Cf(0,"div",42),Hm(1,r4,6,8,"span",43),Hm(2,s4,2,1,"span",44),Hm(3,l4,2,1,"span",44),Mf()),2&t){const t=e.item,n=zf();ym("data-id",t.run.id),Dc(1),bf("ngForOf",n.columns),Dc(1),bf("ngForOf",n.hparamColumns),Dc(1),bf("ngForOf",n.metricColumns)}}class d4 extends X0{constructor(){super(...arguments),this.itemsPerPageLabel="Show runs:"}}d4.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(d4)))(n||d4)}})(),d4.ɵprov=Sn({token:d4,factory:d4.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(d4,[{type:Um}],null,null);class p4{constructor(){this.dataSource=new D3,this.DomainType=kT,this.RunsTableColumn=x0,this.SortType=Fz,this.onRegexFilterChange=new Sy,this.onSelectionToggle=new Sy,this.onPageSelectionToggle=new Sy,this.onPaginationChange=new Sy,this.onSortChange=new Sy,this.onRunColorChange=new Sy,this.onHparamDiscreteFilterChanged=new Sy,this.onHparamIntervalFilterChanged=new Sy,this.onMetricFilterChanged=new Sy}ngOnChanges(){this.dataSource.data=this.pageItems}getHparamColumnId(t){return`h:${t.name}`}getMetricColumnId(t){return`m:${t.tag}`}getColumnIds(){return[...this.columns,...this.hparamColumns.map(this.getHparamColumnId),...this.metricColumns.map(this.getMetricColumnId)]}allPageItemsSelected(){return Boolean(this.pageItems.length)&&this.pageItems.every((t=>t.selected))}somePageItemsSelected(){return this.pageItems.some((t=>t.selected))}handlePageToggle(){this.onPageSelectionToggle.emit({items:this.pageItems})}handleSortChange(t){let e;switch(t.direction){case"asc":e=vI.ASC;break;case"desc":e=vI.DESC;break;default:e=vI.UNSET}this.onSortChange.emit({key:t.active,direction:e})}onFilterKeyUp(t){this.onRegexFilterChange.emit(t.target.value)}tableTrackBy(t,e){return e.run.id}handleHparamIncludeUndefinedToggled(t){const{name:e,filter:n}=t;if(!n)throw new RangeError("Invariant error: require filter to exist for it to change");n.type===kT.DISCRETE?this.onHparamDiscreteFilterChanged.emit({hparamName:e,includeUndefined:!n.includeUndefined,filterValues:n.filterValues}):this.onHparamIntervalFilterChanged.emit({name:e,includeUndefined:!n.includeUndefined,filterLowerValue:n.filterLowerValue,filterUpperValue:n.filterUpperValue})}handleHparamIntervalChanged(t,e){const{name:n,filter:o}=t;if(!o)throw new RangeError("Invariant error: require filter to exist for it to change");this.onHparamIntervalFilterChanged.emit({name:n,includeUndefined:o.includeUndefined,filterLowerValue:e.lowerValue,filterUpperValue:e.upperValue})}handleHparamDiscreteChanged(t,e){const{name:n,filter:o}=t;if(!o)throw new RangeError("Invariant error: require filter to exist for it to change");if(o.type!==kT.DISCRETE)throw new RangeError(`Invariant error: expected discrete domain for ${n}`);const i=new Set([...o.filterValues]);i.has(e)?i.delete(e):i.add(e),this.onHparamDiscreteFilterChanged.emit({hparamName:n,includeUndefined:o.includeUndefined,filterValues:[...i]})}handleMetricIncludeUndefinedChanged(t){if(!t.filter)throw new RangeError("Invariant error: require filter to exist for it to change");this.onMetricFilterChanged.emit({name:t.tag,includeUndefined:!t.filter.includeUndefined,filterLowerValue:t.filter.filterLowerValue,filterUpperValue:t.filter.filterUpperValue})}handleMetricFilterChanged(t,e){if(!t.filter)throw new RangeError("Invariant error: require filter to exist for it to change");this.onMetricFilterChanged.emit({name:t.tag,includeUndefined:t.filter.includeUndefined,filterLowerValue:e.lowerValue,filterUpperValue:e.upperValue})}trackByHparamColumn(t){return t.name}trackByMetricColumn(t){return t.tag}}p4.ɵfac=function t(e){return new(e||p4)},p4.ɵcmp=so({type:p4,selectors:[["runs-table-component"]],viewQuery:function t(e,n){if(1&e&&(Vy(k5,7,Kh),Vy(e1,7),Vy(r1,7)),2&e){let t;By(t=Uy())&&(n.filter=t.first),By(t=Uy())&&(n.paginator=t.first),By(t=Uy())&&(n.sort=t.first)}},hostVars:2,hostBindings:function t(e,n){2&e&&cu("flex-layout",n.useFlexibleLayout)},inputs:{experimentIds:"experimentIds",showExperimentName:"showExperimentName",columns:"columns",hparamColumns:"hparamColumns",metricColumns:"metricColumns",allItemsLength:"allItemsLength",filteredItemsLength:"filteredItemsLength",useFlexibleLayout:"useFlexibleLayout",usePagination:"usePagination",showGroupControl:"showGroupControl",pageItems:"pageItems",loading:"loading",numSelectedItems:"numSelectedItems",sortOption:"sortOption",paginationOption:"paginationOption",regexFilter:"regexFilter"},outputs:{onRegexFilterChange:"onRegexFilterChange",onSelectionToggle:"onSelectionToggle",onPageSelectionToggle:"onPageSelectionToggle",onPaginationChange:"onPaginationChange",onSortChange:"onSortChange",onRunColorChange:"onRunColorChange",onHparamDiscreteFilterChanged:"onHparamDiscreteFilterChanged",onHparamIntervalFilterChanged:"onHparamIntervalFilterChanged",onMetricFilterChanged:"onMetricFilterChanged"},features:[jh([{provide:X0,useClass:d4}]),qo],decls:15,vars:8,consts:[[1,"filter-row"],["placeholder","Filter runs 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t(e,n){if(1&e&&(Cf(0,"div",0),Cf(1,"tb-filter-input",1),Rf("keyup",(function t(e){return n.onFilterKeyUp(e)})),Mf(),Mf(),Cf(2,"div",2),Cf(3,"div",3),Hm(4,S5,1,0,"ng-container",4),Cf(5,"div",5),Hm(6,E5,2,4,"ng-container",6),Mf(),Mf(),Hm(7,A5,2,0,"div",7),Hm(8,T5,2,0,"div",8),Hm(9,N5,6,1,"div",8),Mf(),Hm(10,I5,1,5,"mat-paginator",9),Hm(11,t4,5,6,"ng-template",null,10,qy),Hm(13,c4,4,4,"ng-template",null,11,qy)),2&e){const t=Fm(12);Dc(1),Lf("value",n.regexFilter),Dc(3),bf("ngTemplateOutlet",t),Dc(2),bf("ngForOf",n.pageItems)("ngForTrackBy",n.tableTrackBy),Dc(1),bf("ngIf",n.loading),Dc(1),bf("ngIf",!n.loading&&0===n.allItemsLength),Dc(1),bf("ngIf",!n.loading&&n.allItemsLength>0&&0===n.filteredItemsLength),Dc(1),bf("ngIf",n.usePagination)}},directives:[Z3,Iv,xv,Pv,a2,e1,r1,Cv,Dv,Rv,bZ,f1,S2,NL,qq,Mq,jq,Hq,E2,H2,P5],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   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ngDevMode||ngDevMode)&&Qb(p4,[{type:S_,args:[{selector:"runs-table-component",templateUrl:"runs_table_component.ng.html",host:{"[class.flex-layout]":"useFlexibleLayout"},styleUrls:["runs_table_component.css"],changeDetection:jn.OnPush,providers:[{provide:X0,useClass:d4}]}]}],null,{experimentIds:[{type:R_}],showExperimentName:[{type:R_}],columns:[{type:R_}],hparamColumns:[{type:R_}],metricColumns:[{type:R_}],allItemsLength:[{type:R_}],filteredItemsLength:[{type:R_}],useFlexibleLayout:[{type:R_}],usePagination:[{type:R_}],showGroupControl:[{type:R_}],pageItems:[{type:R_}],loading:[{type:R_}],numSelectedItems:[{type:R_}],sortOption:[{type:R_}],paginationOption:[{type:R_}],regexFilter:[{type:R_}],onRegexFilterChange:[{type:E_}],onSelectionToggle:[{type:E_}],onPageSelectionToggle:[{type:E_}],onPaginationChange:[{type:E_}],onSortChange:[{type:E_}],onRunColorChange:[{type:E_}],onHparamDiscreteFilterChanged:[{type:E_}],onHparamIntervalFilterChanged:[{type:E_}],onMetricFilterChanged:[{type:E_}],filter:[{type:ar,args:["filter",{static:!0,read:Kh}]}],paginator:[{type:ar,args:[e1,{static:!0}]}],sort:[{type:ar,args:[r1,{static:!0}]}]});const m4=bS(Kz,(t=>t.state===UE.LOADING));function f4(t,e){switch(e.type){case Fz.EXPERIMENT_NAME:return[t.experimentAlias,t.run.name,t.run.id];case Fz.RUN_NAME:return[t.run.name,t.experimentAlias,t.run.id];case Fz.HPARAM:return[t.hparams.get(e.name),t.run.name,t.experimentAlias,t.run.id];case Fz.METRIC:return[t.metrics.get(e.tag),t.run.name,t.experimentAlias,t.run.id];default:throw new Error(`Not yet implemented: ${e}`)}}function u4(t,e){if(void 0===e)return t.includeUndefined;if(t.type===kT.DISCRETE){return t.filterValues.includes(e)}return t.type===kT.INTERVAL&&(t.filterLowerValue<=e&&e<=t.filterUpperValue)}class g4{constructor(t){this.store=t,this.loading$=null,this.hparamColumns$=zt([]),this.metricColumns$=zt([]),this.useFlexibleLayout=!1,this.usePagination=!1,this.columns=[x0.RUN_NAME],this.showHparamsAndMetrics=!1,this.sortOption$=this.store.select(oI),this.paginationOption$=this.store.select(nI),this.regexFilter$=this.store.select(tI),this.ngUnsubscribe=new B}isExperimentNameVisible(){return this.columns.some((t=>t===x0.EXPERIMENT_NAME))}ngOnInit(){const t=Kt(this.experimentIds.map((t=>this.getRunTableItemsForExperiment(t)))).pipe(Vt((t=>[].concat(...t))));this.allUnsortedRunTableItems$=t.pipe(je(this.ngUnsubscribe),Fe(1)),this.allItemsLength$=this.allUnsortedRunTableItems$.pipe(Vt((t=>t.length)));const e=this.getFilteredItems$(this.allUnsortedRunTableItems$).pipe(je(this.ngUnsubscribe),Fe(1));this.filteredItemsLength$=e.pipe(Vt((t=>t.length))),this.pageItems$=this.sortedAndSlicedItems$(e),this.numSelectedItems$=this.allUnsortedRunTableItems$.pipe(Vt((t=>t.reduce(((t,e)=>t+Number(e.selected)),0))));const n=this.experimentIds.map((t=>this.store.select(m4,{experimentId:t})));if(this.loading$=Kt(n).pipe(Vt((t=>t.some((t=>t))))),this.showHparamsAndMetrics){const t=this.store.select(D0,{experimentIds:this.experimentIds});this.hparamColumns$=Kt([this.store.select(w0,this.experimentIds),t]).pipe(Vt((([t,{hparams:e}])=>e.map((({name:e,displayName:n})=>{const o=t.get(e);if(!o)throw new RangeError(`Invariant error: a filter for ${e} must exist when the hparam exists`);return{displayName:n,name:e,filter:o}}))))),this.metricColumns$=Kt([this.store.select(S0,this.experimentIds),t]).pipe(Vt((([t,{metrics:e}])=>e.map((({tag:e,displayName:n})=>{const o=t.get(e);if(!o)throw new RangeError(`Invariant error: a filter for ${e} must exist when the metric exists`);return{displayName:n,tag:e,filter:o}})))))}if(this.columns.includes(x0.CHECKBOX)){this.store.select(hR).pipe(je(this.ngUnsubscribe),we(((t,e)=>xD(t,e))),Ve((()=>t.pipe(ue((t=>t.length>500)),ve(1))))).subscribe((()=>{this.store.dispatch(GI({localizedMessage:"The number of runs exceeds 500. New runs are unselected for performance reasons."}))}))}this.showGroupControl$=this.store.select(jD).pipe(Vt((t=>t&&this.columns.includes(x0.RUN_COLOR)))),this.store.dispatch(vT({experimentIds:this.experimentIds}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}getFilteredItems$(t){return Kt([t,this.store.select(tI)]).pipe(Vt((([t,e])=>{if(!e)return t;const n=this.columns.includes(x0.EXPERIMENT_NAME);return t.filter((t=>yI({runName:t.run.name,experimentAlias:t.experimentAlias},e,n)))})),Ve((t=>this.showHparamsAndMetrics?Kt(this.store.select(w0,this.experimentIds),this.store.select(S0,this.experimentIds)).pipe(Vt((([e,n])=>t.filter((({hparams:t,metrics:o})=>[...e.entries()].every((([e,n])=>u4(n,t.get(e))))&&[...n.entries()].every((([t,e])=>u4(e,o.get(t))))))))):zt(t))))}sortedAndSlicedItems$(t){const e=Kt([t,this.store.select(oI)]).pipe(Vt((([t,e])=>(function n(t,e){const n=e.key,o=[...t];return null===n||e.direction===vI.UNSET||o.sort(((t,o)=>{const i=f4(t,n),a=f4(o,n);if(i.length!==a.length)throw new Error(`Invariant error: a given sort should result in same number of items: ${e}`);for(let t=0;t<i.length;t++){const n=i[t],o=a[t];if(n!==o){if(void 0===n||void 0===o)return void 0===o?-1:1;if(typeof n!=typeof o)throw new Error(`Cannot compare values of different types: ${typeof n} vs. ${typeof o}`);return n<o==(e.direction===vI.ASC)?-1:1}}return 0})),o})(t,e))));return Kt([e,this.store.select(nI)]).pipe(Vt((([t,e])=>{if(!this.usePagination)return t.slice();const{pageSize:n,pageIndex:o}=e;return t.slice(o*n,(o+1)*n)})),Be([]))}getRunTableItemsForExperiment(t){return Kt([this.store.select(qz,{experimentId:t}),this.store.select(qT,{experimentId:t}),this.store.select(_I),this.store.select(CI),this.store.select(OR)]).pipe(Vt((([e,n,o,i,a])=>e.map((e=>{const r=new Map;(e.hparams||[]).forEach((t=>{r.set(t.name,t.value)}));const s=new Map;return(e.metrics||[]).forEach((t=>{s.set(t.tag,t.value)})),{run:e,experimentName:(null==n?void 0:n.name)||"",experimentAlias:a[t],selected:Boolean(o&&o.get(e.id)),runColor:i[e.id],hparams:r,metrics:s}})))))}onRunSelectionToggle(t){this.store.dispatch(hT({experimentIds:this.experimentIds,runId:t.run.id}))}onPageSelectionToggle(t){const{items:e}=t,n=e.map((({run:t})=>t.id));this.store.dispatch(bT({experimentIds:this.experimentIds,runIds:n}))}onPaginationChange(t){if(!this.usePagination)throw new Error("Pagination events cannot be dispatched when pagination is disabled");const{pageIndex:e,pageSize:n}=t;this.store.dispatch(yT({pageIndex:e,pageSize:n}))}onSortChange(t){this.store.dispatch(_T(t))}onRegexFilterChange(t){this.store.dispatch(CT({regexString:t}))}onRunColorChange({runId:t,newColor:e}){this.store.dispatch(MT({runId:t,newColor:e}))}onHparamDiscreteFilterChanged(t){const{hparamName:e,filterValues:n,includeUndefined:o}=t;this.store.dispatch(DT({experimentIds:this.experimentIds,hparamName:e,filterValues:n,includeUndefined:o}))}onHparamIntervalFilterChanged(t){const{name:e,filterLowerValue:n,filterUpperValue:o,includeUndefined:i}=t;this.store.dispatch(RT({experimentIds:this.experimentIds,hparamName:e,filterLowerValue:n,filterUpperValue:o,includeUndefined:i}))}onMetricFilterChanged(t){const{name:e,includeUndefined:n,filterLowerValue:o,filterUpperValue:i}=t;this.store.dispatch(ET({experimentIds:this.experimentIds,metricTag:e,includeUndefined:n,filterLowerValue:o,filterUpperValue:i}))}}g4.ɵfac=function t(e){return new(e||g4)(gf(iS))},g4.ɵcmp=so({type:g4,selectors:[["runs-table"]],hostVars:2,hostBindings:function t(e,n){2&e&&cu("flex-layout",n.useFlexibleLayout)},inputs:{useFlexibleLayout:"useFlexibleLayout",usePagination:"usePagination",columns:"columns",experimentIds:"experimentIds",showHparamsAndMetrics:"showHparamsAndMetrics"},decls:12,vars:38,consts:[[3,"experimentIds","useFlexibleLayout","numSelectedItems","columns","hparamColumns","metricColumns","showExperimentName","pageItems","filteredItemsLength","allItemsLength","loading","paginationOption","regexFilter","showGroupControl","sortOption","usePagination","onSelectionToggle","onPageSelectionToggle","onPaginationChange","onRegexFilterChange","onSortChange","onRunColorChange","onHparamIntervalFilterChanged","onHparamDiscreteFilterChanged","onMetricFilterChanged"]],template:function t(e,n){1&e&&(Cf(0,"runs-table-component",0),Rf("onSelectionToggle",(function t(e){return n.onRunSelectionToggle(e)}))("onPageSelectionToggle",(function t(e){return n.onPageSelectionToggle(e)}))("onPaginationChange",(function t(e){return n.onPaginationChange(e)}))("onRegexFilterChange",(function t(e){return n.onRegexFilterChange(e)}))("onSortChange",(function t(e){return n.onSortChange(e)}))("onRunColorChange",(function t(e){return n.onRunColorChange(e)}))("onHparamIntervalFilterChanged",(function t(e){return n.onHparamIntervalFilterChanged(e)}))("onHparamDiscreteFilterChanged",(function t(e){return n.onHparamDiscreteFilterChanged(e)}))("onMetricFilterChanged",(function t(e){return n.onMetricFilterChanged(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async"),_y(5,"async"),_y(6,"async"),_y(7,"async"),_y(8,"async"),_y(9,"async"),_y(10,"async"),_y(11,"async"),Mf()),2&e&&bf("experimentIds",n.experimentIds)("useFlexibleLayout",n.useFlexibleLayout)("numSelectedItems",Cy(1,16,n.numSelectedItems$))("columns",n.columns)("hparamColumns",Cy(2,18,n.hparamColumns$))("metricColumns",Cy(3,20,n.metricColumns$))("showExperimentName",n.isExperimentNameVisible())("pageItems",Cy(4,22,n.pageItems$))("filteredItemsLength",Cy(5,24,n.filteredItemsLength$))("allItemsLength",Cy(6,26,n.allItemsLength$))("loading",Cy(7,28,n.loading$))("paginationOption",Cy(8,30,n.paginationOption$))("regexFilter",Cy(9,32,n.regexFilter$))("showGroupControl",Cy(10,34,n.showGroupControl$))("sortOption",Cy(11,36,n.sortOption$))("usePagination",n.usePagination)},directives:[p4],pipes:[Vv],styles:[".flex-layout[_nghost-%COMP%] {\n        display: flex;\n      }\n\n      .flex-layout[_nghost-%COMP%]    > 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(onSelectionToggle)="onRunSelectionToggle($event)"\n      (onPageSelectionToggle)="onPageSelectionToggle($event)"\n      (onPaginationChange)="onPaginationChange($event)"\n      (onRegexFilterChange)="onRegexFilterChange($event)"\n      (onSortChange)="onSortChange($event)"\n      (onRunColorChange)="onRunColorChange($event)"\n      (onHparamIntervalFilterChanged)="onHparamIntervalFilterChanged($event)"\n      (onHparamDiscreteFilterChanged)="onHparamDiscreteFilterChanged($event)"\n      (onMetricFilterChanged)="onMetricFilterChanged($event)"\n    ></runs-table-component>\n  ',host:{"[class.flex-layout]":"useFlexibleLayout"},styles:["\n      :host.flex-layout {\n        display: flex;\n      }\n\n      :host.flex-layout > runs-table-component {\n        width: 100%;\n      }\n    "],changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),{useFlexibleLayout:[{type:R_}],usePagination:[{type:R_}],columns:[{type:R_}],experimentIds:[{type:R_}],showHparamsAndMetrics:[{type:R_}]});class 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be initialized before use");return!0}onCardIntersection(t){t.sort(((t,e)=>t.time-e.time));const e=new Set,n=new Set;for(const{isIntersecting:o,target:i}of t)o?(e.add(i),n.delete(i)):(e.delete(i),n.add(i)),this.destroyedTargets.has(i)&&!o&&(this.destroyedTargets.delete(i),this.intersectionObserver.unobserve(i));this.intersectionCallback(e,n)}onCardIntersectionForTest(t){this.onCardIntersection(t)}}class C4{constructor(t,e){this.host=t,this.store=e}onCardIntersection(t,e){const n=[...t].map((t=>{const e=y4.get(t);if(!e)throw new Error("A CardObserver element must have an associated element id and card id.");return{elementId:e.elementId,cardId:e.cardId}})),o=[...e].map((t=>{const e=y4.get(t);if(!e)throw new Error("A CardObserver element must have an associated element id and card id.");return{elementId:e.elementId,cardId:e.cardId}}));this.store.dispatch(EA({enteredCards:n,exitedCards:o}))}ngOnInit(){const t=this.host.nativeElement;y4.set(t,{elementId:(b0++,Symbol(b0)),cardId:this.cardId}),this.cardObserver||(this.cardObserver=new _4),this.cardObserver.initialize(this.onCardIntersection.bind(this)),this.cardObserver.add(t)}ngOnDestroy(){this.cardObserver&&this.cardObserver.willDestroy(this.host.nativeElement)}hostForTest(){return this.host}}function M4(t,e){1&t&&vf(0,"mat-icon",7)}function v4(t,e){if(1&t&&(Cf(0,"mat-option",8),Pu(1),Mf()),2&t){const t=e.$implicit;bf("value",t),ym("title",t),Dc(1),wu(t)}}function x4(t,e){if(1&t&&(Cf(0,"div",9),Cf(1,"em"),Pu(2),_y(3,"number"),Mf(),Mf()),2&t){const t=zf();Dc(2),ku("and ",Cy(3,1,t.completions.length-25)," more tags matched")}}C4.ɵfac=function t(e){return new(e||C4)(gf(Kh),gf(iS))},C4.ɵdir=ho({type:C4,selectors:[["","cardLazyLoader",""]],inputs:{cardId:["cardLazyLoader","cardId"],cardObserver:"cardObserver"}}),("undefined"==typeof 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[_nghost-%COMP%]     .tag-options .and-more{color:rgba(255,255,255,.7)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(O4,[{type:S_,args:[{selector:"metrics-tag-filter-component",templateUrl:"filter_input_component.ng.html",styleUrls:["filter_input_component.css"],changeDetection:jn.OnPush}]}],null,{regexFilterValue:[{type:R_}],isRegexFilterValid:[{type:A_,args:["class.valid"]},{type:R_}],completions:[{type:R_}],onRegexFilterValueChange:[{type:E_}]});class P4{constructor(t){this.store=t,this.tagFilter$=this.store.select(Pz),this.isTagFilterRegexValid$=this.tagFilter$.pipe(Vt((t=>{try{return new RegExp(t),!0}catch(t){return!1}}))),this.completions$=this.store.select(az).pipe(_e(this.store.select(Az)),Vt((([t,e])=>t.filter((({plugin:t})=>!e.size||e.has(t))).map((({tag:t})=>t)))),Vt((t=>[...new Set(t)])),Vt((t=>t.sort(X$))),_e(this.store.select(Pz)),Vt((([t,e])=>{try{return[t,new RegExp(e,"i")]}catch(e){return[t,null]}})),ue((([,t])=>null!==t)),Vt((([t,e])=>t.filter((t=>e.test(t))))))}onTagFilterChange(t){this.store.dispatch(TA({tagFilter:t}))}}P4.ɵfac=function t(e){return new(e||P4)(gf(iS))},P4.ɵcmp=so({type:P4,selectors:[["metrics-tag-filter"]],decls:4,vars:9,consts:[[3,"regexFilterValue","isRegexFilterValid","completions","onRegexFilterValueChange"]],template:function t(e,n){1&e&&(Cf(0,"metrics-tag-filter-component",0),Rf("onRegexFilterValueChange",(function t(e){return n.onTagFilterChange(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),Mf()),2&e&&bf("regexFilterValue",Cy(1,3,n.tagFilter$))("isRegexFilterValid",Cy(2,5,n.isTagFilterRegexValid$))("completions",Cy(3,7,n.completions$))},directives:[O4],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(P4,[{type:S_,args:[{selector:"metrics-tag-filter",template:'\n    <metrics-tag-filter-component\n      [regexFilterValue]="tagFilter$ | async"\n      [isRegexFilterValid]="isTagFilterRegexValid$ | async"\n      [completions]="completions$ | async"\n      (onRegexFilterValueChange)="onTagFilterChange($event)"\n    ></metrics-tag-filter-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);const w4=["button"],k4=new er("MAT_BUTTON_TOGGLE_DEFAULT_OPTIONS"),S4=new er("MatButtonToggleGroup"),D4={provide:xU,useExisting:$e((()=>A4)),multi:!0};let R4=0;class E4{constructor(t,e){this.source=t,this.value=e}}class A4{constructor(t,e){this._changeDetector=t,this._vertical=!1,this._multiple=!1,this._disabled=!1,this._controlValueAccessorChangeFn=()=>{},this._onTouched=()=>{},this._name="mat-button-toggle-group-"+R4++,this.valueChange=new Sy,this.change=new Sy,this.appearance=e&&e.appearance?e.appearance:"standard"}get name(){return this._name}set name(t){this._name=t,this._buttonToggles&&this._buttonToggles.forEach((t=>{t.name=this._name,t._markForCheck()}))}get vertical(){return this._vertical}set vertical(t){this._vertical=eH(t)}get value(){const t=this._selectionModel?this._selectionModel.selected:[];return this.multiple?t.map((t=>t.value)):t[0]?t[0].value:void 0}set value(t){this._setSelectionByValue(t),this.valueChange.emit(this.value)}get selected(){const t=this._selectionModel?this._selectionModel.selected:[];return this.multiple?t:t[0]||null}get multiple(){return this._multiple}set multiple(t){this._multiple=eH(t)}get disabled(){return this._disabled}set disabled(t){this._disabled=eH(t),this._buttonToggles&&this._buttonToggles.forEach((t=>t._markForCheck()))}ngOnInit(){this._selectionModel=new jL(this.multiple,void 0,!1)}ngAfterContentInit(){this._selectionModel.select(...this._buttonToggles.filter((t=>t.checked)))}writeValue(t){this.value=t,this._changeDetector.markForCheck()}registerOnChange(t){this._controlValueAccessorChangeFn=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t}_emitChangeEvent(){const t=this.selected,e=Array.isArray(t)?t[t.length-1]:t,n=new E4(e,this.value);this._controlValueAccessorChangeFn(n.value),this.change.emit(n)}_syncButtonToggle(t,e,n=!1,o=!1){this.multiple||!this.selected||t.checked||(this.selected.checked=!1),this._selectionModel?e?this._selectionModel.select(t):this._selectionModel.deselect(t):o=!0,o?Promise.resolve().then((()=>this._updateModelValue(n))):this._updateModelValue(n)}_isSelected(t){return this._selectionModel&&this._selectionModel.isSelected(t)}_isPrechecked(t){return void 0!==this._rawValue&&(this.multiple&&Array.isArray(this._rawValue)?this._rawValue.some((e=>null!=t.value&&e===t.value)):t.value===this._rawValue)}_setSelectionByValue(t){if(this._rawValue=t,this._buttonToggles)if(this.multiple&&t){if(!Array.isArray(t)&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Value must be an array in multiple-selection mode.");this._clearSelection(),t.forEach((t=>this._selectValue(t)))}else this._clearSelection(),this._selectValue(t)}_clearSelection(){this._selectionModel.clear(),this._buttonToggles.forEach((t=>t.checked=!1))}_selectValue(t){const e=this._buttonToggles.find((e=>null!=e.value&&e.value===t));e&&(e.checked=!0,this._selectionModel.select(e))}_updateModelValue(t){t&&this._emitChangeEvent(),this.valueChange.emit(this.value)}}A4.ɵfac=function t(e){return new(e||A4)(gf(Ob),gf(k4,8))},A4.ɵdir=ho({type:A4,selectors:[["mat-button-toggle-group"]],contentQueries:function t(e,n,o){if(1&e&&jy(o,N4,5),2&e){let t;By(t=Uy())&&(n._buttonToggles=t)}},hostAttrs:["role","group",1,"mat-button-toggle-group"],hostVars:5,hostBindings:function t(e,n){2&e&&(ym("aria-disabled",n.disabled),cu("mat-button-toggle-vertical",n.vertical)("mat-button-toggle-group-appearance-standard","standard"===n.appearance))},inputs:{appearance:"appearance",name:"name",vertical:"vertical",value:"value",multiple:"multiple",disabled:"disabled"},outputs:{valueChange:"valueChange",change:"change"},exportAs:["matButtonToggleGroup"],features:[jh([D4,{provide:S4,useExisting:A4}])]}),A4.ctorParameters=()=>[{type:Ob},{type:void 0,decorators:[{type:Br},{type:Lr,args:[k4]}]}],A4.propDecorators={_buttonToggles:[{type:or,args:[$e((()=>N4)),{descendants:!0}]}],appearance:[{type:R_}],name:[{type:R_}],vertical:[{type:R_}],value:[{type:R_}],valueChange:[{type:E_}],multiple:[{type:R_}],disabled:[{type:R_}],change:[{type:E_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(A4,[{type:k_,args:[{selector:"mat-button-toggle-group",providers:[D4,{provide:S4,useExisting:A4}],host:{role:"group",class:"mat-button-toggle-group","[attr.aria-disabled]":"disabled","[class.mat-button-toggle-vertical]":"vertical","[class.mat-button-toggle-group-appearance-standard]":'appearance === "standard"'},exportAs:"matButtonToggleGroup"}]}],(function(){return[{type:Ob},{type:void 0,decorators:[{type:Br},{type:Lr,args:[k4]}]}]}),{valueChange:[{type:E_}],change:[{type:E_}],appearance:[{type:R_}],name:[{type:R_}],vertical:[{type:R_}],value:[{type:R_}],multiple:[{type:R_}],disabled:[{type:R_}],_buttonToggles:[{type:or,args:[$e((()=>N4)),{descendants:!0}]}]});const T4=HF(class{});class N4 extends T4{constructor(t,e,n,o,i,a){super(),this._changeDetectorRef=e,this._elementRef=n,this._focusMonitor=o,this._isSingleSelector=!1,this._checked=!1,this.ariaLabelledby=null,this._disabled=!1,this.change=new Sy;const r=Number(i);this.tabIndex=r||0===r?r:null,this.buttonToggleGroup=t,this.appearance=a&&a.appearance?a.appearance:"standard"}get buttonId(){return`${this.id}-button`}get appearance(){return this.buttonToggleGroup?this.buttonToggleGroup.appearance:this._appearance}set appearance(t){this._appearance=t}get checked(){return this.buttonToggleGroup?this.buttonToggleGroup._isSelected(this):this._checked}set checked(t){const e=eH(t);e!==this._checked&&(this._checked=e,this.buttonToggleGroup&&this.buttonToggleGroup._syncButtonToggle(this,this._checked),this._changeDetectorRef.markForCheck())}get disabled(){return this._disabled||this.buttonToggleGroup&&this.buttonToggleGroup.disabled}set disabled(t){this._disabled=eH(t)}ngOnInit(){const t=this.buttonToggleGroup;this._isSingleSelector=t&&!t.multiple,this.id=this.id||"mat-button-toggle-"+R4++,this._isSingleSelector&&(this.name=t.name),t&&(t._isPrechecked(this)?this.checked=!0:t._isSelected(this)!==this._checked&&t._syncButtonToggle(this,this._checked))}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0)}ngOnDestroy(){const t=this.buttonToggleGroup;this._focusMonitor.stopMonitoring(this._elementRef),t&&t._isSelected(this)&&t._syncButtonToggle(this,!1,!1,!0)}focus(t){this._buttonElement.nativeElement.focus(t)}_onButtonClick(){const t=!!this._isSingleSelector||!this._checked;t!==this._checked&&(this._checked=t,this.buttonToggleGroup&&(this.buttonToggleGroup._syncButtonToggle(this,this._checked,!0),this.buttonToggleGroup._onTouched())),this.change.emit(new E4(this,this.value))}_markForCheck(){this._changeDetectorRef.markForCheck()}}N4.ɵfac=function t(e){return new(e||N4)(gf(S4,8),gf(Ob),gf(Kh),gf(fF),Ga("tabindex"),gf(k4,8))},N4.ɵcmp=so({type:N4,selectors:[["mat-button-toggle"]],viewQuery:function t(e,n){if(1&e&&Vy(w4,5),2&e){let t;By(t=Uy())&&(n._buttonElement=t.first)}},hostAttrs:["role","presentation",1,"mat-button-toggle"],hostVars:12,hostBindings:function t(e,n){1&e&&Rf("focus",(function t(){return n.focus()})),2&e&&(ym("aria-label",null)("aria-labelledby",null)("id",n.id)("name",null),cu("mat-button-toggle-standalone",!n.buttonToggleGroup)("mat-button-toggle-checked",n.checked)("mat-button-toggle-disabled",n.disabled)("mat-button-toggle-appearance-standard","standard"===n.appearance))},inputs:{disableRipple:"disableRipple",ariaLabelledby:["aria-labelledby","ariaLabelledby"],tabIndex:"tabIndex",appearance:"appearance",checked:"checked",disabled:"disabled",id:"id",name:"name",ariaLabel:["aria-label","ariaLabel"],value:"value"},outputs:{change:"change"},exportAs:["matButtonToggle"],features:[Qp],ngContentSelectors:["*"],decls:6,vars:9,consts:[["type","button",1,"mat-button-toggle-button","mat-focus-indicator",3,"id","disabled","click"],["button",""],[1,"mat-button-toggle-label-content"],[1,"mat-button-toggle-focus-overlay"],["matRipple","",1,"mat-button-toggle-ripple",3,"matRippleTrigger","matRippleDisabled"]],template:function 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class z4{}z4.ɵfac=function t(e){return new(e||z4)},z4.ɵmod=fo({type:z4}),z4.ɵinj=Dn({imports:[[NF,fL],NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(z4,[{type:L_,args:[{imports:[NF,fL],exports:[NF,A4,N4],declarations:[A4,N4]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(z4,{declarations:function(){return[A4,N4]},imports:function(){return[NF,fL]},exports:function(){return[NF,A4,N4]}});const I4="/scalar_summary";function H4(t,e){let n=t;return e&&t.startsWith(e+"/")&&(n=t.slice(e.length+1)),n.endsWith(I4)&&(n=n.slice(0,-I4.length)),n||t}function F4(t,e){if(1&t&&(Cf(0,"span",2),Pu(1),Mf()),2&t){const t=zf();Dc(1),wu(t.firstTextPart())}}class L4{parseValue(){const t=this.value.lastIndexOf("/");return-1===t?{first:"",second:this.value}:{first:this.value.slice(0,t),second:this.value.slice(t)}}firstTextPart(){return this.parseValue().first}secondTextPart(){return this.parseValue().second}}function B4(t,e,n){var o,i,a,r;return 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!important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{color:#616161}body.dark-mode   [_nghost-%COMP%]{color:rgba(255,255,255,.7)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(U4,[{type:S_,args:[{selector:"card-run-name-component",template:'<tb-experiment-alias\n      *ngIf="experimentAlias != null"\n      [alias]="experimentAlias"\n    ></tb-experiment-alias>\n    <span *ngIf="experimentAlias != null">/</span>\n    <span>{{ name }}</span>',styleUrls:["run_name_component.css"],changeDetection:jn.OnPush}]}],null,{name:[{type:R_}],experimentAlias:[{type:R_}]});class W4{constructor(t){this.store=t}ngOnInit(){this.name$=Kt([this.store.select(Yz,{runId:this.runId})]).pipe(Vt((([t])=>(function e(t,n,o){var i;if(!n&&!o)return t;let a=null!==(i=null==n?void 0:n.name)&&void 0!==i?i:"...";return o&&(a=`[${o.aliasNumber}] ${o.aliasText}/${a}`),a})(this.runId,t,null)))),this.experimentAlias$=Kt([this.store.select(Gz,{runId:this.runId}),this.store.select(OR)]).pipe(Vt((([t,e])=>t?e[t]:null)))}}function G4(t,e){if(1&t&&(Cf(0,"span",17),Pu(1),_y(2,"number"),Mf()),2&t){const t=zf();Dc(1),ku("Step ",Cy(2,1,t.stepValues[t.stepIndex]),"")}}function Y4(t,e){if(1&t&&(Cf(0,"span",18),Pu(1),_y(2,"number"),_y(3,"number"),Mf()),2&t){const t=zf();Dc(1),Su("Sample ",Cy(2,2,t.sample+1),"/",Cy(3,4,t.numSample),"")}}function q4(t,e){1&t&&vf(0,"mat-spinner",19)}W4.ɵfac=function t(e){return new(e||W4)(gf(iS))},W4.ɵcmp=so({type:W4,selectors:[["card-run-name"]],inputs:{runId:"runId"},decls:4,vars:9,consts:[[3,"name","experimentAlias"]],template:function t(e,n){1&e&&(vf(0,"card-run-name-component",0),_y(1,"async"),_y(2,"async"),_y(3,"async")),2&e&&(bf("name",Cy(1,3,n.name$))("experimentAlias",Cy(3,7,n.experimentAlias$)),ym("title",Cy(2,5,n.name$)))},directives:[U4],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(W4,[{type:S_,args:[{selector:"card-run-name",template:'\n    <card-run-name-component\n      [name]="name$ | async"\n      [attr.title]="name$ | async"\n      [experimentAlias]="experimentAlias$ | async"\n    ></card-run-name-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),{runId:[{type:R_}]});const Z4=function(t){return{filter:t}};function X4(t,e){if(1&t){const t=wf();xf(0),Cf(1,"div",20),Cf(2,"mat-slider",21),Rf("input",(function e(n){Pi(t);return zf().onSliderInput(n)})),Mf(),Mf(),Cf(3,"div",22),vf(4,"img",23),Mf(),Of()}if(2&t){const t=zf();Dc(2),bf("disabled",t.stepValues.length<=1)("min",0)("max",t.stepValues.length-1)("step",1)("tickInterval",1)("value",t.stepIndex),Dc(2),Bf("alt","Image at step ",t.stepValues[t.stepIndex],""),Lf("src",t.imageUrl,Xs),bf("ngStyle",iy(9,Z4,t.cssFilter()))}}function K4(t,e){1&t&&(Cf(0,"div",25),Pu(1," Data failed to load. "),Mf())}function J4(t,e){if(1&t&&Hm(0,K4,2,0,"div",24),2&t){const t=zf();bf("ngIf",t.loadState===t.DataLoadState.FAILED)}}const Q4=function(t){return{backgroundColor:t}};class $4{constructor(){this.DataLoadState=UE,this.onActualSizeToggle=new Sy,this.stepIndexChange=new Sy,this.onPinClicked=new Sy}cssFilter(){return`contrast(${this.contrastInMilli/10}%) brightness(${this.brightnessInMilli/1e3})`}onSliderInput(t){this.stepIndexChange.emit(t.value)}}$4.ɵfac=function t(e){return new(e||$4)},$4.ɵcmp=so({type:$4,selectors:[["image-card-component"]],hostVars:2,hostBindings:function t(e,n){2&e&&cu("actual-size",n.showActualSize)},inputs:{loadState:"loadState",title:"title",tag:"tag",runId:"runId",sample:"sample",numSample:"numSample",imageUrl:"imageUrl",stepIndex:"stepIndex",stepValues:"stepValues",brightnessInMilli:"brightnessInMilli",contrastInMilli:"contrastInMilli",showActualSize:"showActualSize",runColorScale:"runColorScale",allowToggleActualSize:"allowToggleActualSize",isPinned:"isPinned"},outputs:{onActualSizeToggle:"onActualSizeToggle",stepIndexChange:"stepIndexChange",onPinClicked:"onPinClicked"},decls:19,vars:14,consts:function(){let t,e;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){t=goog.getMsg("Pin card")}else t=$localize`:A button to pin a card.␟e665dc712bd5f18d4dfa3a29e125d565cc51e2f6␟7284606426234375344:Pin card`;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){e=goog.getMsg("Toggle actual image size")}else e=$localize`:A button on an image card that toggles actual image size.␟3ca05ef3a6e3a37065f5e0f69c5d5a2178d90791␟7635101936664789140:Toggle actual image size`;return[[1,"heading"],[1,"line"],[1,"tag",3,"title","value"],[1,"controls"],["mat-icon-button","","aria-label",t,1,"pin-button",3,"click"],[3,"svgIcon"],["mat-icon-button","","aria-label",e,"title","Toggle actual image size",3,"disabled","click"],["svgIcon","image_search_24px"],[1,"run"],[1,"dot",3,"ngStyle"],[1,"run-text",3,"runId"],[1,"metadata"],["class","step",4,"ngIf"],["class","sample",4,"ngIf"],["class","loading","diameter","18",4,"ngIf"],[4,"ngIf","ngIfElse"],["noImageData",""],[1,"step"],[1,"sample"],["diameter","18",1,"loading"],[1,"slider-row"],["color","primary",1,"step-slider",3,"disabled","min","max","step","tickInterval","value","input"],[1,"img-container"],[3,"alt","src","ngStyle"],["class","empty-message",4,"ngIf"],[1,"empty-message"]]},template:function t(e,n){if(1&e&&(Cf(0,"div",0),Cf(1,"div",1),vf(2,"tb-truncated-path",2),Cf(3,"span",3),Cf(4,"button",4),Rf("click",(function t(){return n.onPinClicked.emit(!n.isPinned)})),vf(5,"mat-icon",5),Mf(),Cf(6,"button",6),Rf("click",(function t(){return n.onActualSizeToggle.emit()})),vf(7,"mat-icon",7),Mf(),Mf(),Mf(),Cf(8,"div",1),Cf(9,"span",8),vf(10,"span",9),vf(11,"card-run-name",10),Mf(),Cf(12,"div",11),Hm(13,G4,3,3,"span",12),Hm(14,Y4,4,6,"span",13),Hm(15,q4,1,0,"mat-spinner",14),Mf(),Mf(),Mf(),Hm(16,X4,5,11,"ng-container",15),Hm(17,J4,1,1,"ng-template",null,16,qy)),2&e){const t=Fm(18);Dc(2),Lf("title",n.tag),Lf("value",n.title),Dc(2),ym("title",n.isPinned?"Unpin card":"Pin card"),Dc(1),bf("svgIcon",n.isPinned?"keep_24px":"keep_outline_24px"),Dc(1),bf("disabled",!n.allowToggleActualSize),Dc(4),bf("ngStyle",iy(12,Q4,n.runColorScale(n.runId))),Dc(1),bf("runId",n.runId),Dc(2),bf("ngIf",null!==n.stepIndex&&n.stepIndex<n.stepValues.length),Dc(1),bf("ngIf",n.numSample>1),Dc(1),bf("ngIf",n.loadState===n.DataLoadState.LOADING),Dc(1),bf("ngIf",null!==n.stepIndex&&n.stepIndex<n.stepValues.length)("ngIfElse",t)}},directives:[L4,NL,Mq,zv,W4,Pv,a2,wJ],pipes:[$v],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 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img[_ngcontent-%COMP%]{position:absolute;max-height:100%;max-width:100%;width:auto;height:100%;object-fit:contain}.slider-row[_ngcontent-%COMP%]{display:flex;align-items:center;height:24px}.step-slider[_ngcontent-%COMP%]{flex:1}[_nghost-%COMP%]     .mat-slider-min-value .mat-slider-thumb{background-color:#f57c00}.empty-message[_ngcontent-%COMP%]{margin-top:1em;font-size:13px}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb($4,[{type:S_,args:[{selector:"image-card-component",templateUrl:"image_card_component.ng.html",styleUrls:["image_card_component.css"],host:{"[class.actual-size]":"showActualSize"},changeDetection:jn.OnPush}]}],null,{loadState:[{type:R_}],title:[{type:R_}],tag:[{type:R_}],runId:[{type:R_}],sample:[{type:R_}],numSample:[{type:R_}],imageUrl:[{type:R_}],stepIndex:[{type:R_}],stepValues:[{type:R_}],brightnessInMilli:[{type:R_}],contrastInMilli:[{type:R_}],showActualSize:[{type:R_}],runColorScale:[{type:R_}],allowToggleActualSize:[{type:R_}],isPinned:[{type:R_}],onActualSizeToggle:[{type:E_}],stepIndexChange:[{type:E_}],onPinClicked:[{type:E_}]});class t6{constructor(t,e){this.store=t,this.dataSource=e,this.fullWidthChanged=new Sy,this.pinStateChanged=new Sy,this.brightnessInMilli$=this.store.select(vz),this.contrastInMilli$=this.store.select(xz),this.actualSizeGlobalSetting$=this.store.select(Oz),this.showActualSize=!1,this.actualSizeUiToggled=!1,this.actualSizeUiToggleSubject=new j(this.actualSizeUiToggled),this.ngUnsubscribe=new B}onStepIndexChanged(t){this.store.dispatch(AA({cardId:this.cardId,stepIndex:t}))}isImageCardMetadata(t){const{plugin:e}=t;return e===KT.IMAGES}onActualSizeToggle(){this.actualSizeUiToggled=!this.actualSizeUiToggled,this.actualSizeUiToggleSubject.next(this.actualSizeUiToggled)}ngOnInit(){Kt([this.actualSizeGlobalSetting$,this.actualSizeUiToggleSubject]).pipe(je(this.ngUnsubscribe),We((([t,e])=>{this.showActualSize=t||e,this.fullWidthChanged.emit(this.showActualSize)}))).subscribe((()=>{}));const t=this.store.select(nz,this.cardId).pipe(je(this.ngUnsubscribe),ue((t=>!!t&&this.isImageCardMetadata(t))),Vt((t=>t)),Fe(1)),e=Kt([t,this.store.select(tz,this.cardId)]).pipe(je(this.ngUnsubscribe),Vt((([t,e])=>{const n=t.runId;return e&&e.hasOwnProperty(n)?e[n]:[]})),we(((t,e)=>t.length===e.length&&0===t.length||t===e)),Fe(1));this.stepIndex$=this.store.select(rz,this.cardId),this.loadState$=this.store.select($N,this.cardId);const n=Kt([e,this.stepIndex$]).pipe(Vt((([t,e])=>null!==e&&t[e]?t[e]:null)));this.tag$=t.pipe(Vt((t=>t.tag))),this.title$=this.tag$.pipe(Vt((t=>H4(t,this.groupName)))),this.runId$=t.pipe(Vt((t=>t.runId))),this.sample$=t.pipe(Vt((t=>t.sample))),this.numSample$=t.pipe(Vt((t=>t.numSample))),this.imageUrl$=n.pipe(Vt((t=>t?this.dataSource.imageUrl(t.imageId):null))),this.stepValues$=e.pipe(Vt((t=>t.map((t=>t.step))))),this.isPinned$=this.store.select(dz,this.cardId)}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}}var e6,n6,o6;function i6(t,e){1&t&&(Cf(0,"span"),Pu(1,"scalar"),Mf())}function a6(t,e){1&t&&(Cf(0,"span"),Pu(1,"histogram"),Mf())}function r6(t,e){1&t&&(Cf(0,"span"),Pu(1,"unknown"),Mf())}function s6(t,e){if(1&t&&(xf(0,13),Hm(1,i6,2,0,"span",14),Hm(2,a6,2,0,"span",14),Hm(3,r6,2,0,"span",15),Of()),2&t){const t=zf(2);bf("ngSwitch",t.cardMetadata.plugin),Dc(1),bf("ngSwitchCase",t.PluginType.SCALARS),Dc(1),bf("ngSwitchCase",t.PluginType.HISTOGRAMS)}}function l6(t,e){1&t&&Pf(0)}function c6(t,e){if(1&t&&(Cf(0,"option",16),Pu(1),Mf()),2&t){const t=e.$implicit;bf("value",t.id),Dc(1),wu(t.name)}}function d6(t,e){if(1&t){const t=wf();xf(0),Cf(1,"h2"),Hm(2,s6,4,3,"ng-template",null,2,qy),Cf(4,"span"),Pu(5,"Download "),Mf(),Hm(6,l6,1,0,"ng-container",3),Cf(7,"span"),Pu(8," data for "),Mf(),Cf(9,"code",4),Pu(10),Mf(),Mf(),Cf(11,"mat-dialog-content"),Cf(12,"mat-form-field",5),Cf(13,"mat-label"),Pu(14,"Select a run to download a data for a series"),Mf(),Cf(15,"select",6),Rf("change",(function e(n){Pi(t);return zf().runSelected.emit(n.target.value)})),Cf(16,"option",7),Pu(17,"-"),Mf(),Hm(18,c6,2,2,"option",8),Mf(),Mf(),Cf(19,"div",9),Cf(20,"span"),Pu(21,"Download as…"),Mf(),Pu(22," "),Cf(23,"a",10),Pu(24,"JSON"),Mf(),Cf(25,"a",10),Pu(26,"CSV"),Mf(),Mf(),Mf(),Cf(27,"mat-dialog-actions",11),Cf(28,"button",12),Pu(29,"Close"),Mf(),Mf(),Of()}if(2&t){const t=Fm(3),e=zf();Dc(6),bf("ngTemplateOutlet",t),Dc(3),bf("title",e.cardMetadata.tag),Dc(1),wu(e.cardMetadata.tag),Dc(5),bf("value",e.selectedRunId||""),Dc(1),bf("value",""),Dc(2),bf("ngForOf",e.runs),Dc(5),bf("disabled",!e.downloadUrlJson)("download",e.getDownloadName("json")),ym("href",e.downloadUrlJson,Xs),Dc(2),bf("disabled",!e.downloadUrlCsv)("download",e.getDownloadName("csv")),ym("href",e.downloadUrlCsv,Xs)}}function p6(t,e){1&t&&Pu(0,"Loading...")}t6.ɵfac=function t(e){return new(e||t6)(gf(iS),gf(aN))},t6.ɵcmp=so({type:t6,selectors:[["image-card"]],inputs:{cardId:"cardId",groupName:"groupName",runColorScale:"runColorScale"},outputs:{fullWidthChanged:"fullWidthChanged",pinStateChanged:"pinStateChanged"},decls:14,vars:41,consts:[[3,"loadState","title","tag","runId","sample","numSample","imageUrl","stepIndex","stepValues","brightnessInMilli","contrastInMilli","runColorScale","showActualSize","allowToggleActualSize","isPinned","stepIndexChange","onActualSizeToggle","onPinClicked"]],template:function t(e,n){1&e&&(Cf(0,"image-card-component",0),Rf("stepIndexChange",(function t(e){return n.onStepIndexChanged(e)}))("onActualSizeToggle",(function t(){return n.onActualSizeToggle()}))("onPinClicked",(function t(e){return n.pinStateChanged.emit(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async"),_y(5,"async"),_y(6,"async"),_y(7,"async"),_y(8,"async"),_y(9,"async"),_y(10,"async"),_y(11,"async"),_y(12,"async"),_y(13,"async"),Mf()),2&e&&bf("loadState",Cy(1,15,n.loadState$))("title",Cy(2,17,n.title$))("tag",Cy(3,19,n.tag$))("runId",Cy(4,21,n.runId$))("sample",Cy(5,23,n.sample$))("numSample",Cy(6,25,n.numSample$))("imageUrl",Cy(7,27,n.imageUrl$))("stepIndex",Cy(8,29,n.stepIndex$))("stepValues",Cy(9,31,n.stepValues$))("brightnessInMilli",Cy(10,33,n.brightnessInMilli$))("contrastInMilli",Cy(11,35,n.contrastInMilli$))("runColorScale",n.runColorScale)("showActualSize",n.showActualSize)("allowToggleActualSize",!1===Cy(12,37,n.actualSizeGlobalSetting$))("isPinned",Cy(13,39,n.isPinned$))},directives:[$4],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(t6,[{type:S_,args:[{selector:"image-card",template:'\n    <image-card-component\n      [loadState]="loadState$ | async"\n      [title]="title$ | async"\n      [tag]="tag$ | async"\n      [runId]="runId$ | async"\n      [sample]="sample$ | async"\n      [numSample]="numSample$ | async"\n      [imageUrl]="imageUrl$ | async"\n      [stepIndex]="stepIndex$ | async"\n      [stepValues]="stepValues$ | async"\n      (stepIndexChange)="onStepIndexChanged($event)"\n      [brightnessInMilli]="brightnessInMilli$ | async"\n      [contrastInMilli]="contrastInMilli$ | async"\n      [runColorScale]="runColorScale"\n      [showActualSize]="showActualSize"\n      [allowToggleActualSize]="(actualSizeGlobalSetting$ | async) === false"\n      [isPinned]="isPinned$ | async"\n      (onActualSizeToggle)="onActualSizeToggle()"\n      (onPinClicked)="pinStateChanged.emit($event)"\n    ></image-card-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS},{type:aN}]}),{cardId:[{type:R_}],groupName:[{type:R_}],runColorScale:[{type:R_}],fullWidthChanged:[{type:E_}],pinStateChanged:[{type:E_}]}),(function(t){t[t.SVG=0]="SVG",t[t.WEBGL=1]="WEBGL"})(e6||(e6={})),(function(t){t[t.LINEAR=0]="LINEAR",t[t.LOG10=1]="LOG10",t[t.TIME=2]="TIME"})(n6||(n6={}));class m6{constructor(){this.runSelected=new Sy,this.PluginType=KT}getDownloadName(t){const e=this.runs.find((t=>t.id===this.selectedRunId));return e?`${e.name}.${t}`:""}}m6.ɵfac=function t(e){return new(e||m6)},m6.ɵcmp=so({type:m6,selectors:[["data_download_dialog_component"]],inputs:{cardMetadata:"cardMetadata",runs:"runs",selectedRunId:"selectedRunId",downloadUrlCsv:"downloadUrlCsv",downloadUrlJson:"downloadUrlJson"},outputs:{runSelected:"runSelected"},decls:3,vars:2,consts:[[4,"ngIf","ngIfElse"],["noCardMetadata",""],["dataName",""],[4,"ngTemplateOutlet"],[1,"tag-name",3,"title"],["appearance","fill",1,"run-selector"],["matNativeControl","","name","run","cdkFocusInitial","","required","",3,"value","change"],["selected","",3,"value"],[3,"value",4,"ngFor","ngForOf"],[1,"download-controls"],["mat-stroked-button","",3,"disabled","download"],["align","end"],["mat-button","","mat-dialog-close",""],[3,"ngSwitch"],[4,"ngSwitchCase"],[4,"NgSwitchDefault"],[3,"value"]],template:function t(e,n){if(1&e&&(Hm(0,d6,30,12,"ng-container",0),Hm(1,p6,1,0,"ng-template",null,1,qy)),2&e){const t=Fm(2);bf("ngIf",n.cardMetadata)("ngIfElse",t)}},directives:[Pv,Iv,$Y,_U,cU,AZ,kG,EG,xv,zL,tq,NL,JY,Dv,Rv],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, 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.mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], 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self.hasOwnProperty("OffscreenCanvas")}function Mrt(t,e){if(t.length!==e.length)return!1;for(let n=0;n<t.length;n++)if(t[n]!==e[n])return!1;return!0}class vrt{constructor(){this.xScale=grt(n6.LINEAR),this.yScale=grt(n6.LINEAR),this.domContainerRect={x:0,width:1,y:0,height:1},this.lastUpdated=0,this.currentViewBoxRect={x:0,width:1,y:0,height:1}}getUpdateIdentifier(){return this.lastUpdated}updateIdentifier(){this.lastUpdated++}isYAxisPointedDown(){return!0}setXScale(t){this.xScale=t,this.updateIdentifier()}setYScale(t){this.yScale=t,this.updateIdentifier()}getCurrentViewBoxRect(){return this.currentViewBoxRect}setViewBoxRect(t){this.currentViewBoxRect=t,this.updateIdentifier()}setDomContainerRect(t){this.domContainerRect=t,this.updateIdentifier()}transformDataToUiCoord(t,e){const n=t,o=(function i(t){return{x:[t.x,t.x+t.width],y:[t.y,t.y+t.height]}})(this.currentViewBoxRect);return[this.xScale.forward(o.x,[n.x,n.x+n.width],e[0]),this.yScale.forward(o.y,this.isYAxisPointedDown()?[n.y+n.height,n.y]:[n.y,n.y+n.height],e[1])]}}function xrt(t,e,n,o){const{color:i,visible:a,opacity:r}=o;let s=t;return s||a?(s=null!=s?s:e(),s=n(s),s.style.display=a?"":"none",s.style.stroke=i,s.style.opacity=String(null!=r?r:1),s):null}class Ort{constructor(t){this.svg=t}flush(){}onResize(t){}destroyObject(t){this.svg.removeChild(t.dom)}setUseDarkMode(t){}createPathDString(t){if(!t.length)return"";const e=new Array(t.length/2);e[0]=`M${t[0]},${t[1]}`;for(let n=1;n<t.length/2;n++)e[n]=`L${t[2*n]},${t[2*n+1]}`;return e.join("")}createOrUpdateLineObject(t,e,n){const o=xrt(null==t?void 0:t.dom,(()=>{const t=document.createElementNS("http://www.w3.org/2000/svg","path");t.style.fill="none";const n=this.createPathDString(e);return t.setAttribute("d",n),this.svg.appendChild(t),t}),(n=>{if(!(null==t?void 0:t.data)||!Mrt(e,null==t?void 0:t.data)){const t=this.createPathDString(e);n.setAttribute("d",t)}return n}),n);return null===o?null:(o.style.strokeWidth=String(n.width),{dom:o,data:e})}createOrUpdateTriangleObject(t,e,n){const{size:o,color:i}=n,a=o*Math.sqrt(3)/2,r=new Float32Array([e.x-o/2,e.y+a/3,e.x+o/2,e.y+a/3,e.x,e.y-2*a/3]),s=xrt(null==t?void 0:t.dom,(()=>{const t=document.createElementNS("http://www.w3.org/2000/svg","path");t.classList.add("triangle"),t.style.fill="none";const e=this.createPathDString(r);return t.setAttribute("d",e+"Z"),this.svg.appendChild(t),t}),(t=>{const e=this.createPathDString(r);return t.setAttribute("d",e+"Z"),t}),n);return null===s?null:(s.style.fill=i,{dom:s,data:r})}createOrUpdateCircleObject(t,e,n){const{color:o,radius:i}=n,a=xrt(null==t?void 0:t.dom,(()=>{const t=document.createElementNS("http://www.w3.org/2000/svg","circle");return t.style.fill=o,t.setAttribute("cx",String(e.x)),t.setAttribute("cy",String(e.y)),t.setAttribute("r",String(i)),this.svg.appendChild(t),t}),(t=>(t.style.fill=o,t.setAttribute("cx",String(e.x)),t.setAttribute("cy",String(e.y)),t.setAttribute("r",String(i)),t)),n);return null===a?null:{dom:a,data:e}}createOrUpdateTrapezoidObject(t,e,n,o){if(e.y!==n.y)throw new RangeError("Input error: start.y != end.y.");const{altitude:i,color:a}=o,r=2/Math.sqrt(3)*i,s=new Float32Array([e.x-r/2,e.y+i/2,e.x,e.y-i/2,n.x,n.y-i/2,n.x+r/2,n.y+i/2]),l=xrt(null==t?void 0:t.dom,(()=>{const t=document.createElementNS("http://www.w3.org/2000/svg","path");t.classList.add("trapezoid"),t.style.fill="none";const e=this.createPathDString(s);return t.setAttribute("d",e+"Z"),this.svg.appendChild(t),t}),(t=>{const e=this.createPathDString(s);return t.setAttribute("d",e+"Z"),t}),o);return null===l?null:(l.style.fill=a,{dom:l,data:s})}dispose(){}}
/**
     * @license
     * Copyright 2010-2021 Three.js Authors
     * SPDX-License-Identifier: MIT
     */const Prt=100,wrt=301,krt=302,Srt=306,Drt=1e3,Rrt=1001,Ert=1002,Art=1003,Trt=1006,Nrt=1008,zrt=1009,Irt=1012,Hrt=1014,Frt=1015,Lrt=1016,Brt=1020,Vrt=1022,jrt=1023,Urt=1026,Wrt=1027,Grt=2300,Yrt=2301,qrt=2302,Zrt=2400,Xrt=2401,Krt=2402,Jrt=2500,Qrt=3e3,$rt=3001,tst=3007,est=3002,nst=7680,ost=35044,ist=35048,ast="300 es";class rst{addEventListener(t,e){void 0===this._listeners&&(this._listeners={});const n=this._listeners;void 0===n[t]&&(n[t]=[]),-1===n[t].indexOf(e)&&n[t].push(e)}hasEventListener(t,e){if(void 0===this._listeners)return!1;const n=this._listeners;return void 0!==n[t]&&-1!==n[t].indexOf(e)}removeEventListener(t,e){if(void 0===this._listeners)return;const n=this._listeners[t];if(void 0!==n){const t=n.indexOf(e);-1!==t&&n.splice(t,1)}}dispatchEvent(t){if(void 0===this._listeners)return;const e=this._listeners[t.type];if(void 0!==e){t.target=this;const n=e.slice(0);for(let e=0,o=n.length;e<o;e++)n[e].call(this,t);t.target=null}}}const sst=[];for(let t=0;t<256;t++)sst[t]=(t<16?"0":"")+t.toString(16);const lst=Math.PI/180,cst=180/Math.PI;function dst(){const t=4294967295*Math.random()|0,e=4294967295*Math.random()|0,n=4294967295*Math.random()|0,o=4294967295*Math.random()|0;return(sst[255&t]+sst[t>>8&255]+sst[t>>16&255]+sst[t>>24&255]+"-"+sst[255&e]+sst[e>>8&255]+"-"+sst[e>>16&15|64]+sst[e>>24&255]+"-"+sst[63&n|128]+sst[n>>8&255]+"-"+sst[n>>16&255]+sst[n>>24&255]+sst[255&o]+sst[o>>8&255]+sst[o>>16&255]+sst[o>>24&255]).toUpperCase()}function pst(t,e,n){return Math.max(e,Math.min(n,t))}function mst(t,e,n){return(1-n)*t+n*e}function fst(t){return 0==(t&t-1)&&0!==t}function ust(t){return Math.pow(2,Math.floor(Math.log(t)/Math.LN2))}class gst{constructor(t=0,e=0){this.x=t,this.y=e}get width(){return this.x}set width(t){this.x=t}get height(){return this.y}set height(t){this.y=t}set(t,e){return this.x=t,this.y=e,this}setScalar(t){return this.x=t,this.y=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y)}copy(t){return this.x=t.x,this.y=t.y,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector2: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this)}addScalar(t){return this.x+=t,this.y+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this}sub(t,e){return void 0!==e?(console.warn("THREE.Vector2: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this)}subScalar(t){return this.x-=t,this.y-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this}multiply(t){return this.x*=t.x,this.y*=t.y,this}multiplyScalar(t){return this.x*=t,this.y*=t,this}divide(t){return this.x/=t.x,this.y/=t.y,this}divideScalar(t){return this.multiplyScalar(1/t)}applyMatrix3(t){const e=this.x,n=this.y,o=t.elements;return this.x=o[0]*e+o[3]*n+o[6],this.y=o[1]*e+o[4]*n+o[7],this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this}negate(){return this.x=-this.x,this.y=-this.y,this}dot(t){return this.x*t.x+this.y*t.y}cross(t){return this.x*t.y-this.y*t.x}lengthSq(){return this.x*this.x+this.y*this.y}length(){return Math.sqrt(this.x*this.x+this.y*this.y)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)}normalize(){return this.divideScalar(this.length()||1)}angle(){return Math.atan2(-this.y,-this.x)+Math.PI}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y;return e*e+n*n}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this}equals(t){return t.x===this.x&&t.y===this.y}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector2: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this}rotateAround(t,e){const n=Math.cos(e),o=Math.sin(e),i=this.x-t.x,a=this.y-t.y;return this.x=i*n-a*o+t.x,this.y=i*o+a*n+t.y,this}random(){return this.x=Math.random(),this.y=Math.random(),this}}gst.prototype.isVector2=!0;class hst{constructor(){this.elements=[1,0,0,0,1,0,0,0,1],arguments.length>0&&console.error("THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.")}set(t,e,n,o,i,a,r,s,l){const c=this.elements;return c[0]=t,c[1]=o,c[2]=r,c[3]=e,c[4]=i,c[5]=s,c[6]=n,c[7]=a,c[8]=l,this}identity(){return this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,o=e.elements,i=this.elements,a=n[0],r=n[3],s=n[6],l=n[1],c=n[4],d=n[7],p=n[2],m=n[5],f=n[8],u=o[0],g=o[3],h=o[6],b=o[1],y=o[4],_=o[7],C=o[2],M=o[5],v=o[8];return i[0]=a*u+r*b+s*C,i[3]=a*g+r*y+s*M,i[6]=a*h+r*_+s*v,i[1]=l*u+c*b+d*C,i[4]=l*g+c*y+d*M,i[7]=l*h+c*_+d*v,i[2]=p*u+m*b+f*C,i[5]=p*g+m*y+f*M,i[8]=p*h+m*_+f*v,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],o=t[2],i=t[3],a=t[4],r=t[5],s=t[6],l=t[7],c=t[8];return e*a*c-e*r*l-n*i*c+n*r*s+o*i*l-o*a*s}invert(){const t=this.elements,e=t[0],n=t[1],o=t[2],i=t[3],a=t[4],r=t[5],s=t[6],l=t[7],c=t[8],d=c*a-r*l,p=r*s-c*i,m=l*i-a*s,f=e*d+n*p+o*m;if(0===f)return this.set(0,0,0,0,0,0,0,0,0);const u=1/f;return t[0]=d*u,t[1]=(o*l-c*n)*u,t[2]=(r*n-o*a)*u,t[3]=p*u,t[4]=(c*e-o*s)*u,t[5]=(o*i-r*e)*u,t[6]=m*u,t[7]=(n*s-l*e)*u,t[8]=(a*e-n*i)*u,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,o,i,a,r){const s=Math.cos(i),l=Math.sin(i);return this.set(n*s,n*l,-n*(s*a+l*r)+a+t,-o*l,o*s,-o*(-l*a+s*r)+r+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),o=this.elements,i=o[0],a=o[3],r=o[6],s=o[1],l=o[4],c=o[7];return o[0]=e*i+n*s,o[3]=e*a+n*l,o[6]=e*r+n*c,o[1]=-n*i+e*s,o[4]=-n*a+e*l,o[7]=-n*r+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}let bst;hst.prototype.isMatrix3=!0;class yst{static getDataURL(t){if(/^data:/i.test(t.src))return t.src;if("undefined"==typeof HTMLCanvasElement)return t.src;let e;if(t instanceof HTMLCanvasElement)e=t;else{void 0===bst&&(bst=document.createElementNS("http://www.w3.org/1999/xhtml","canvas")),bst.width=t.width,bst.height=t.height;const n=bst.getContext("2d");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=bst}return e.width>2048||e.height>2048?(console.warn("THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons",t),e.toDataURL("image/jpeg",.6)):e.toDataURL("image/png")}}let _st=0;class Cst extends rst{constructor(t=Cst.DEFAULT_IMAGE,e=Cst.DEFAULT_MAPPING,n=1001,o=1001,i=1006,a=1008,r=1023,s=1009,l=1,c=3e3){super(),Object.defineProperty(this,"id",{value:_st++}),this.uuid=dst(),this.name="",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=n,this.wrapT=o,this.magFilter=i,this.minFilter=a,this.anisotropy=l,this.format=r,this.internalFormat=null,this.type=s,this.offset=new gst(0,0),this.repeat=new gst(1,1),this.center=new gst(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new hst,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.encoding=c,this.version=0,this.onUpdate=null,this.isRenderTargetTexture=!1}updateMatrix(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}clone(){return(new this.constructor).copy(this)}copy(t){return this.name=t.name,this.image=t.image,this.mipmaps=t.mipmaps.slice(0),this.mapping=t.mapping,this.wrapS=t.wrapS,this.wrapT=t.wrapT,this.magFilter=t.magFilter,this.minFilter=t.minFilter,this.anisotropy=t.anisotropy,this.format=t.format,this.internalFormat=t.internalFormat,this.type=t.type,this.offset.copy(t.offset),this.repeat.copy(t.repeat),this.center.copy(t.center),this.rotation=t.rotation,this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrix.copy(t.matrix),this.generateMipmaps=t.generateMipmaps,this.premultiplyAlpha=t.premultiplyAlpha,this.flipY=t.flipY,this.unpackAlignment=t.unpackAlignment,this.encoding=t.encoding,this}toJSON(t){const e=void 0===t||"string"==typeof t;if(!e&&void 0!==t.textures[this.uuid])return t.textures[this.uuid];const n={metadata:{version:4.5,type:"Texture",generator:"Texture.toJSON"},uuid:this.uuid,name:this.name,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){const o=this.image;if(void 0===o.uuid&&(o.uuid=dst()),!e&&void 0===t.images[o.uuid]){let e;if(Array.isArray(o)){e=[];for(let t=0,n=o.length;t<n;t++)e.push(Mst(o[t].isDataTexture?o[t].image:o[t]))}else e=Mst(o);t.images[o.uuid]={uuid:o.uuid,url:e}}n.image=o.uuid}return e||(t.textures[this.uuid]=n),n}dispose(){this.dispatchEvent({type:"dispose"})}transformUv(t){if(300!==this.mapping)return t;if(t.applyMatrix3(this.matrix),t.x<0||t.x>1)switch(this.wrapS){case Drt:t.x=t.x-Math.floor(t.x);break;case Rrt:t.x=t.x<0?0:1;break;case Ert:t.x=1===Math.abs(Math.floor(t.x)%2)?Math.ceil(t.x)-t.x:t.x-Math.floor(t.x)}if(t.y<0||t.y>1)switch(this.wrapT){case Drt:t.y=t.y-Math.floor(t.y);break;case Rrt:t.y=t.y<0?0:1;break;case Ert:t.y=1===Math.abs(Math.floor(t.y)%2)?Math.ceil(t.y)-t.y:t.y-Math.floor(t.y)}return this.flipY&&(t.y=1-t.y),t}set needsUpdate(t){!0===t&&this.version++}}function Mst(t){return"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof ImageBitmap&&t instanceof ImageBitmap?yst.getDataURL(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn("THREE.Texture: Unable to serialize Texture."),{})}Cst.DEFAULT_IMAGE=void 0,Cst.DEFAULT_MAPPING=300,Cst.prototype.isTexture=!0;class vst{constructor(t=0,e=0,n=0,o=1){this.x=t,this.y=e,this.z=n,this.w=o}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,o){return this.x=t,this.y=e,this.z=n,this.w=o,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn("THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,o=this.z,i=this.w,a=t.elements;return this.x=a[0]*e+a[4]*n+a[8]*o+a[12]*i,this.y=a[1]*e+a[5]*n+a[9]*o+a[13]*i,this.z=a[2]*e+a[6]*n+a[10]*o+a[14]*i,this.w=a[3]*e+a[7]*n+a[11]*o+a[15]*i,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,o,i;const a=.01,r=.1,s=t.elements,l=s[0],c=s[4],d=s[8],p=s[1],m=s[5],f=s[9],u=s[2],g=s[6],h=s[10];if(Math.abs(c-p)<a&&Math.abs(d-u)<a&&Math.abs(f-g)<a){if(Math.abs(c+p)<r&&Math.abs(d+u)<r&&Math.abs(f+g)<r&&Math.abs(l+m+h-3)<r)return this.set(1,0,0,0),this;e=Math.PI;const t=(l+1)/2,s=(m+1)/2,b=(h+1)/2,y=(c+p)/4,_=(d+u)/4,C=(f+g)/4;return t>s&&t>b?t<a?(n=0,o=.707106781,i=.707106781):(n=Math.sqrt(t),o=y/n,i=_/n):s>b?s<a?(n=.707106781,o=0,i=.707106781):(o=Math.sqrt(s),n=y/o,i=C/o):b<a?(n=.707106781,o=.707106781,i=0):(i=Math.sqrt(b),n=_/i,o=C/i),this.set(n,o,i,e),this}let b=Math.sqrt((g-f)*(g-f)+(d-u)*(d-u)+(p-c)*(p-c));return Math.abs(b)<.001&&(b=1),this.x=(g-f)/b,this.y=(d-u)/b,this.z=(p-c)/b,this.w=Math.acos((l+m+h-1)/2),this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this.w=Math.min(this.w,t.w),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this.w=Math.max(this.w,t.w),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this.w=Math.max(t.w,Math.min(e.w,this.w)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this.w=Math.max(t,Math.min(e,this.w)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this.w=Math.floor(this.w),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this.w=Math.ceil(this.w),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this.w=Math.round(this.w),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this.w=this.w<0?Math.ceil(this.w):Math.floor(this.w),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this.w=-this.w,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z+this.w*t.w}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)+Math.abs(this.w)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this.w+=(t.w-this.w)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this.w=t.w+(e.w-t.w)*n,this}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z&&t.w===this.w}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this.w=t[e+3],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t[e+3]=this.w,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector4: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this.w=t.getW(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this.w=Math.random(),this}}vst.prototype.isVector4=!0;class xst extends rst{constructor(t,e,n={}){super(),this.width=t,this.height=e,this.depth=1,this.scissor=new vst(0,0,t,e),this.scissorTest=!1,this.viewport=new vst(0,0,t,e),this.texture=new Cst(void 0,n.mapping,n.wrapS,n.wrapT,n.magFilter,n.minFilter,n.format,n.type,n.anisotropy,n.encoding),this.texture.isRenderTargetTexture=!0,this.texture.image={width:t,height:e,depth:1},this.texture.generateMipmaps=void 0!==n.generateMipmaps&&n.generateMipmaps,this.texture.minFilter=void 0!==n.minFilter?n.minFilter:Trt,this.depthBuffer=void 0===n.depthBuffer||n.depthBuffer,this.stencilBuffer=void 0!==n.stencilBuffer&&n.stencilBuffer,this.depthTexture=void 0!==n.depthTexture?n.depthTexture:null}setTexture(t){t.image={width:this.width,height:this.height,depth:this.depth},this.texture=t}setSize(t,e,n=1){this.width===t&&this.height===e&&this.depth===n||(this.width=t,this.height=e,this.depth=n,this.texture.image.width=t,this.texture.image.height=e,this.texture.image.depth=n,this.dispose()),this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e)}clone(){return(new this.constructor).copy(this)}copy(t){return this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.copy(t.viewport),this.texture=t.texture.clone(),this.texture.image={...this.texture.image},this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this}dispose(){this.dispatchEvent({type:"dispose"})}}xst.prototype.isWebGLRenderTarget=!0;(class extends xst{constructor(t,e,n){super(t,e);const o=this.texture;this.texture=[];for(let t=0;t<n;t++)this.texture[t]=o.clone()}setSize(t,e,n=1){if(this.width!==t||this.height!==e||this.depth!==n){this.width=t,this.height=e,this.depth=n;for(let o=0,i=this.texture.length;o<i;o++)this.texture[o].image.width=t,this.texture[o].image.height=e,this.texture[o].image.depth=n;this.dispose()}return this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e),this}copy(t){this.dispose(),this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.set(0,0,this.width,this.height),this.scissor.set(0,0,this.width,this.height),this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this.texture.length=0;for(let e=0,n=t.texture.length;e<n;e++)this.texture[e]=t.texture[e].clone();return this}}).prototype.isWebGLMultipleRenderTargets=!0;class Ost extends xst{constructor(t,e,n){super(t,e,n),this.samples=4}copy(t){return super.copy.call(this,t),this.samples=t.samples,this}}Ost.prototype.isWebGLMultisampleRenderTarget=!0;class Pst{constructor(t=0,e=0,n=0,o=1){this._x=t,this._y=e,this._z=n,this._w=o}static slerp(t,e,n,o){return console.warn("THREE.Quaternion: Static .slerp() has been deprecated. Use qm.slerpQuaternions( qa, qb, t ) instead."),n.slerpQuaternions(t,e,o)}static slerpFlat(t,e,n,o,i,a,r){let s=n[o+0],l=n[o+1],c=n[o+2],d=n[o+3];const p=i[a+0],m=i[a+1],f=i[a+2],u=i[a+3];if(0===r)return t[e+0]=s,t[e+1]=l,t[e+2]=c,void(t[e+3]=d);if(1===r)return t[e+0]=p,t[e+1]=m,t[e+2]=f,void(t[e+3]=u);if(d!==u||s!==p||l!==m||c!==f){let t=1-r;const e=s*p+l*m+c*f+d*u,n=e>=0?1:-1,o=1-e*e;if(o>Number.EPSILON){const i=Math.sqrt(o),a=Math.atan2(i,e*n);t=Math.sin(t*a)/i,r=Math.sin(r*a)/i}const i=r*n;if(s=s*t+p*i,l=l*t+m*i,c=c*t+f*i,d=d*t+u*i,t===1-r){const t=1/Math.sqrt(s*s+l*l+c*c+d*d);s*=t,l*=t,c*=t,d*=t}}t[e]=s,t[e+1]=l,t[e+2]=c,t[e+3]=d}static multiplyQuaternionsFlat(t,e,n,o,i,a){const r=n[o],s=n[o+1],l=n[o+2],c=n[o+3],d=i[a],p=i[a+1],m=i[a+2],f=i[a+3];return t[e]=r*f+c*d+s*m-l*p,t[e+1]=s*f+c*p+l*d-r*m,t[e+2]=l*f+c*m+r*p-s*d,t[e+3]=c*f-r*d-s*p-l*m,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,o){return this._x=t,this._y=e,this._z=n,this._w=o,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error("THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.");const n=t._x,o=t._y,i=t._z,a=t._order,r=Math.cos,s=Math.sin,l=r(n/2),c=r(o/2),d=r(i/2),p=s(n/2),m=s(o/2),f=s(i/2);switch(a){case"XYZ":this._x=p*c*d+l*m*f,this._y=l*m*d-p*c*f,this._z=l*c*f+p*m*d,this._w=l*c*d-p*m*f;break;case"YXZ":this._x=p*c*d+l*m*f,this._y=l*m*d-p*c*f,this._z=l*c*f-p*m*d,this._w=l*c*d+p*m*f;break;case"ZXY":this._x=p*c*d-l*m*f,this._y=l*m*d+p*c*f,this._z=l*c*f+p*m*d,this._w=l*c*d-p*m*f;break;case"ZYX":this._x=p*c*d-l*m*f,this._y=l*m*d+p*c*f,this._z=l*c*f-p*m*d,this._w=l*c*d+p*m*f;break;case"YZX":this._x=p*c*d+l*m*f,this._y=l*m*d+p*c*f,this._z=l*c*f-p*m*d,this._w=l*c*d-p*m*f;break;case"XZY":this._x=p*c*d-l*m*f,this._y=l*m*d-p*c*f,this._z=l*c*f+p*m*d,this._w=l*c*d+p*m*f;break;default:console.warn("THREE.Quaternion: .setFromEuler() encountered an unknown order: "+a)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,o=Math.sin(n);return this._x=t.x*o,this._y=t.y*o,this._z=t.z*o,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],o=e[4],i=e[8],a=e[1],r=e[5],s=e[9],l=e[2],c=e[6],d=e[10],p=n+r+d;if(p>0){const t=.5/Math.sqrt(p+1);this._w=.25/t,this._x=(c-s)*t,this._y=(i-l)*t,this._z=(a-o)*t}else if(n>r&&n>d){const t=2*Math.sqrt(1+n-r-d);this._w=(c-s)/t,this._x=.25*t,this._y=(o+a)/t,this._z=(i+l)/t}else if(r>d){const t=2*Math.sqrt(1+r-n-d);this._w=(i-l)/t,this._x=(o+a)/t,this._y=.25*t,this._z=(s+c)/t}else{const t=2*Math.sqrt(1+d-n-r);this._w=(a-o)/t,this._x=(i+l)/t,this._y=(s+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return n<Number.EPSILON?(n=0,Math.abs(t.x)>Math.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(pst(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const o=Math.min(1,e/n);return this.slerp(t,o),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn("THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead."),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,o=t._y,i=t._z,a=t._w,r=e._x,s=e._y,l=e._z,c=e._w;return this._x=n*c+a*r+o*l-i*s,this._y=o*c+a*s+i*r-n*l,this._z=i*c+a*l+n*s-o*r,this._w=a*c-n*r-o*s-i*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,o=this._y,i=this._z,a=this._w;let r=a*t._w+n*t._x+o*t._y+i*t._z;if(r<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,r=-r):this.copy(t),r>=1)return this._w=a,this._x=n,this._y=o,this._z=i,this;const s=1-r*r;if(s<=Number.EPSILON){const t=1-e;return this._w=t*a+e*this._w,this._x=t*n+e*this._x,this._y=t*o+e*this._y,this._z=t*i+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(s),c=Math.atan2(l,r),d=Math.sin((1-e)*c)/l,p=Math.sin(e*c)/l;return this._w=a*d+this._w*p,this._x=n*d+this._x*p,this._y=o*d+this._y*p,this._z=i*d+this._z*p,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}Pst.prototype.isQuaternion=!0;class wst{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}set(t,e,n){return void 0===n&&(n=this.z),this.x=t,this.y=e,this.z=n,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this}sub(t,e){return void 0!==e?(console.warn("THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiply(t,e){return void 0!==e?(console.warn("THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead."),this.multiplyVectors(t,e)):(this.x*=t.x,this.y*=t.y,this.z*=t.z,this)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}applyEuler(t){return t&&t.isEuler||console.error("THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order."),this.applyQuaternion(Sst.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(Sst.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,o=this.z,i=t.elements;return this.x=i[0]*e+i[3]*n+i[6]*o,this.y=i[1]*e+i[4]*n+i[7]*o,this.z=i[2]*e+i[5]*n+i[8]*o,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,o=this.z,i=t.elements,a=1/(i[3]*e+i[7]*n+i[11]*o+i[15]);return this.x=(i[0]*e+i[4]*n+i[8]*o+i[12])*a,this.y=(i[1]*e+i[5]*n+i[9]*o+i[13])*a,this.z=(i[2]*e+i[6]*n+i[10]*o+i[14])*a,this}applyQuaternion(t){const e=this.x,n=this.y,o=this.z,i=t.x,a=t.y,r=t.z,s=t.w,l=s*e+a*o-r*n,c=s*n+r*e-i*o,d=s*o+i*n-a*e,p=-i*e-a*n-r*o;return this.x=l*s+p*-i+c*-r-d*-a,this.y=c*s+p*-a+d*-i-l*-r,this.z=d*s+p*-r+l*-a-c*-i,this}project(t){return this.applyMatrix4(t.matrixWorldInverse).applyMatrix4(t.projectionMatrix)}unproject(t){return this.applyMatrix4(t.projectionMatrixInverse).applyMatrix4(t.matrixWorld)}transformDirection(t){const e=this.x,n=this.y,o=this.z,i=t.elements;return this.x=i[0]*e+i[4]*n+i[8]*o,this.y=i[1]*e+i[5]*n+i[9]*o,this.z=i[2]*e+i[6]*n+i[10]*o,this.normalize()}divide(t){return this.x/=t.x,this.y/=t.y,this.z/=t.z,this}divideScalar(t){return this.multiplyScalar(1/t)}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this}cross(t,e){return void 0!==e?(console.warn("THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead."),this.crossVectors(t,e)):this.crossVectors(this,t)}crossVectors(t,e){const n=t.x,o=t.y,i=t.z,a=e.x,r=e.y,s=e.z;return this.x=o*s-i*r,this.y=i*a-n*s,this.z=n*r-o*a,this}projectOnVector(t){const e=t.lengthSq();if(0===e)return this.set(0,0,0);const n=t.dot(this)/e;return this.copy(t).multiplyScalar(n)}projectOnPlane(t){return kst.copy(this).projectOnVector(t),this.sub(kst)}reflect(t){return this.sub(kst.copy(t).multiplyScalar(2*this.dot(t)))}angleTo(t){const e=Math.sqrt(this.lengthSq()*t.lengthSq());if(0===e)return Math.PI/2;const n=this.dot(t)/e;return Math.acos(pst(n,-1,1))}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y,o=this.z-t.z;return e*e+n*n+o*o}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)+Math.abs(this.z-t.z)}setFromSpherical(t){return this.setFromSphericalCoords(t.radius,t.phi,t.theta)}setFromSphericalCoords(t,e,n){const o=Math.sin(e)*t;return this.x=o*Math.sin(n),this.y=Math.cos(e)*t,this.z=o*Math.cos(n),this}setFromCylindrical(t){return this.setFromCylindricalCoords(t.radius,t.theta,t.y)}setFromCylindricalCoords(t,e,n){return this.x=t*Math.sin(e),this.y=n,this.z=t*Math.cos(e),this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}setFromMatrixScale(t){const e=this.setFromMatrixColumn(t,0).length(),n=this.setFromMatrixColumn(t,1).length(),o=this.setFromMatrixColumn(t,2).length();return this.x=e,this.y=n,this.z=o,this}setFromMatrixColumn(t,e){return this.fromArray(t.elements,4*e)}setFromMatrix3Column(t,e){return this.fromArray(t.elements,3*e)}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector3: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}}wst.prototype.isVector3=!0;const kst=new wst,Sst=new Pst;class Dst{constructor(t=new wst(1/0,1/0,1/0),e=new wst(-1/0,-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromArray(t){let e=1/0,n=1/0,o=1/0,i=-1/0,a=-1/0,r=-1/0;for(let s=0,l=t.length;s<l;s+=3){const l=t[s],c=t[s+1],d=t[s+2];l<e&&(e=l),c<n&&(n=c),d<o&&(o=d),l>i&&(i=l),c>a&&(a=c),d>r&&(r=d)}return this.min.set(e,n,o),this.max.set(i,a,r),this}setFromBufferAttribute(t){let e=1/0,n=1/0,o=1/0,i=-1/0,a=-1/0,r=-1/0;for(let s=0,l=t.count;s<l;s++){const l=t.getX(s),c=t.getY(s),d=t.getZ(s);l<e&&(e=l),c<n&&(n=c),d<o&&(o=d),l>i&&(i=l),c>a&&(a=c),d>r&&(r=d)}return this.min.set(e,n,o),this.max.set(i,a,r),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;e<n;e++)this.expandByPoint(t[e]);return this}setFromCenterAndSize(t,e){const n=Est.copy(e).multiplyScalar(.5);return this.min.copy(t).sub(n),this.max.copy(t).add(n),this}setFromObject(t){return this.makeEmpty(),this.expandByObject(t)}clone(){return(new this.constructor).copy(this)}copy(t){return this.min.copy(t.min),this.max.copy(t.max),this}makeEmpty(){return this.min.x=this.min.y=this.min.z=1/0,this.max.x=this.max.y=this.max.z=-1/0,this}isEmpty(){return this.max.x<this.min.x||this.max.y<this.min.y||this.max.z<this.min.z}getCenter(t){return this.isEmpty()?t.set(0,0,0):t.addVectors(this.min,this.max).multiplyScalar(.5)}getSize(t){return this.isEmpty()?t.set(0,0,0):t.subVectors(this.max,this.min)}expandByPoint(t){return this.min.min(t),this.max.max(t),this}expandByVector(t){return this.min.sub(t),this.max.add(t),this}expandByScalar(t){return this.min.addScalar(-t),this.max.addScalar(t),this}expandByObject(t){t.updateWorldMatrix(!1,!1);const e=t.geometry;void 0!==e&&(null===e.boundingBox&&e.computeBoundingBox(),Ast.copy(e.boundingBox),Ast.applyMatrix4(t.matrixWorld),this.union(Ast));const n=t.children;for(let t=0,e=n.length;t<e;t++)this.expandByObject(n[t]);return this}containsPoint(t){return!(t.x<this.min.x||t.x>this.max.x||t.y<this.min.y||t.y>this.max.y||t.z<this.min.z||t.z>this.max.z)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y&&this.min.z<=t.min.z&&t.max.z<=this.max.z}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y),(t.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(t){return!(t.max.x<this.min.x||t.min.x>this.max.x||t.max.y<this.min.y||t.min.y>this.max.y||t.max.z<this.min.z||t.min.z>this.max.z)}intersectsSphere(t){return this.clampPoint(t.center,Est),Est.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,n;return t.normal.x>0?(e=t.normal.x*this.min.x,n=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,n=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,n+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,n+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,n+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,n+=t.normal.z*this.min.z),e<=-t.constant&&n>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(Lst),Bst.subVectors(this.max,Lst),Tst.subVectors(t.a,Lst),Nst.subVectors(t.b,Lst),zst.subVectors(t.c,Lst),Ist.subVectors(Nst,Tst),Hst.subVectors(zst,Nst),Fst.subVectors(Tst,zst);let e=[0,-Ist.z,Ist.y,0,-Hst.z,Hst.y,0,-Fst.z,Fst.y,Ist.z,0,-Ist.x,Hst.z,0,-Hst.x,Fst.z,0,-Fst.x,-Ist.y,Ist.x,0,-Hst.y,Hst.x,0,-Fst.y,Fst.x,0];return!!Ust(e,Tst,Nst,zst,Bst)&&(e=[1,0,0,0,1,0,0,0,1],!!Ust(e,Tst,Nst,zst,Bst)&&(Vst.crossVectors(Ist,Hst),e=[Vst.x,Vst.y,Vst.z],Ust(e,Tst,Nst,zst,Bst)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return Est.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(Est).length(),t}intersect(t){return this.min.max(t.min),this.max.min(t.max),this.isEmpty()&&this.makeEmpty(),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}applyMatrix4(t){return this.isEmpty()||(Rst[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),Rst[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),Rst[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),Rst[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),Rst[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),Rst[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),Rst[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),Rst[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(Rst)),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}Dst.prototype.isBox3=!0;const Rst=[new wst,new wst,new wst,new wst,new wst,new wst,new wst,new wst],Est=new wst,Ast=new Dst,Tst=new wst,Nst=new wst,zst=new wst,Ist=new wst,Hst=new wst,Fst=new wst,Lst=new wst,Bst=new wst,Vst=new wst,jst=new wst;function Ust(t,e,n,o,i){for(let a=0,r=t.length-3;a<=r;a+=3){jst.fromArray(t,a);const r=i.x*Math.abs(jst.x)+i.y*Math.abs(jst.y)+i.z*Math.abs(jst.z),s=e.dot(jst),l=n.dot(jst),c=o.dot(jst);if(Math.max(-Math.max(s,l,c),Math.min(s,l,c))>r)return!1}return!0}const Wst=new Dst,Gst=new wst,Yst=new wst,qst=new wst;class Zst{constructor(t=new wst,e=-1){this.center=t,this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}setFromPoints(t,e){const n=this.center;void 0!==e?n.copy(e):Wst.setFromPoints(t).getCenter(n);let o=0;for(let e=0,i=t.length;e<i;e++)o=Math.max(o,n.distanceToSquared(t[e]));return this.radius=Math.sqrt(o),this}copy(t){return this.center.copy(t.center),this.radius=t.radius,this}isEmpty(){return this.radius<0}makeEmpty(){return this.center.set(0,0,0),this.radius=-1,this}containsPoint(t){return t.distanceToSquared(this.center)<=this.radius*this.radius}distanceToPoint(t){return t.distanceTo(this.center)-this.radius}intersectsSphere(t){const e=this.radius+t.radius;return t.center.distanceToSquared(this.center)<=e*e}intersectsBox(t){return t.intersectsSphere(this)}intersectsPlane(t){return Math.abs(t.distanceToPoint(this.center))<=this.radius}clampPoint(t,e){const n=this.center.distanceToSquared(t);return e.copy(t),n>this.radius*this.radius&&(e.sub(this.center).normalize(),e.multiplyScalar(this.radius).add(this.center)),e}getBoundingBox(t){return this.isEmpty()?(t.makeEmpty(),t):(t.set(this.center,this.center),t.expandByScalar(this.radius),t)}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}expandByPoint(t){qst.subVectors(t,this.center);const e=qst.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(qst.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return Yst.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(Gst.copy(t.center).add(Yst)),this.expandByPoint(Gst.copy(t.center).sub(Yst)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const Xst=new wst,Kst=new wst,Jst=new wst,Qst=new wst,$st=new wst,tlt=new wst,elt=new wst;class nlt{constructor(t=new wst,e=new wst(0,0,-1)){this.origin=t,this.direction=e}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return e.copy(this.direction).multiplyScalar(t).add(this.origin)}lookAt(t){return this.direction.copy(t).sub(this.origin).normalize(),this}recast(t){return this.origin.copy(this.at(t,Xst)),this}closestPointToPoint(t,e){e.subVectors(t,this.origin);const n=e.dot(this.direction);return n<0?e.copy(this.origin):e.copy(this.direction).multiplyScalar(n).add(this.origin)}distanceToPoint(t){return Math.sqrt(this.distanceSqToPoint(t))}distanceSqToPoint(t){const e=Xst.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(Xst.copy(this.direction).multiplyScalar(e).add(this.origin),Xst.distanceToSquared(t))}distanceSqToSegment(t,e,n,o){Kst.copy(t).add(e).multiplyScalar(.5),Jst.copy(e).sub(t).normalize(),Qst.copy(this.origin).sub(Kst);const i=.5*t.distanceTo(e),a=-this.direction.dot(Jst),r=Qst.dot(this.direction),s=-Qst.dot(Jst),l=Qst.lengthSq(),c=Math.abs(1-a*a);let d,p,m,f;if(c>0)if(d=a*s-r,p=a*r-s,f=i*c,d>=0)if(p>=-f)if(p<=f){const t=1/c;d*=t,p*=t,m=d*(d+a*p+2*r)+p*(a*d+p+2*s)+l}else p=i,d=Math.max(0,-(a*p+r)),m=-d*d+p*(p+2*s)+l;else p=-i,d=Math.max(0,-(a*p+r)),m=-d*d+p*(p+2*s)+l;else p<=-f?(d=Math.max(0,-(-a*i+r)),p=d>0?-i:Math.min(Math.max(-i,-s),i),m=-d*d+p*(p+2*s)+l):p<=f?(d=0,p=Math.min(Math.max(-i,-s),i),m=p*(p+2*s)+l):(d=Math.max(0,-(a*i+r)),p=d>0?i:Math.min(Math.max(-i,-s),i),m=-d*d+p*(p+2*s)+l);else p=a>0?-i:i,d=Math.max(0,-(a*p+r)),m=-d*d+p*(p+2*s)+l;return n&&n.copy(this.direction).multiplyScalar(d).add(this.origin),o&&o.copy(Jst).multiplyScalar(p).add(Kst),m}intersectSphere(t,e){Xst.subVectors(t.center,this.origin);const n=Xst.dot(this.direction),o=Xst.dot(Xst)-n*n,i=t.radius*t.radius;if(o>i)return null;const a=Math.sqrt(i-o),r=n-a,s=n+a;return r<0&&s<0?null:this.at(r<0?s:r,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);if(0===e)return!0;return t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,o,i,a,r,s;const l=1/this.direction.x,c=1/this.direction.y,d=1/this.direction.z,p=this.origin;return l>=0?(n=(t.min.x-p.x)*l,o=(t.max.x-p.x)*l):(n=(t.max.x-p.x)*l,o=(t.min.x-p.x)*l),c>=0?(i=(t.min.y-p.y)*c,a=(t.max.y-p.y)*c):(i=(t.max.y-p.y)*c,a=(t.min.y-p.y)*c),n>a||i>o?null:((i>n||n!=n)&&(n=i),(a<o||o!=o)&&(o=a),d>=0?(r=(t.min.z-p.z)*d,s=(t.max.z-p.z)*d):(r=(t.max.z-p.z)*d,s=(t.min.z-p.z)*d),n>s||r>o?null:((r>n||n!=n)&&(n=r),(s<o||o!=o)&&(o=s),o<0?null:this.at(n>=0?n:o,e)))}intersectsBox(t){return null!==this.intersectBox(t,Xst)}intersectTriangle(t,e,n,o,i){$st.subVectors(e,t),tlt.subVectors(n,t),elt.crossVectors($st,tlt);let a,r=this.direction.dot(elt);if(r>0){if(o)return null;a=1}else{if(!(r<0))return null;a=-1,r=-r}Qst.subVectors(this.origin,t);const s=a*this.direction.dot(tlt.crossVectors(Qst,tlt));if(s<0)return null;const l=a*this.direction.dot($st.cross(Qst));if(l<0)return null;if(s+l>r)return null;const c=-a*Qst.dot(elt);return c<0?null:this.at(c/r,i)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}class olt{constructor(){this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],arguments.length>0&&console.error("THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.")}set(t,e,n,o,i,a,r,s,l,c,d,p,m,f,u,g){const h=this.elements;return h[0]=t,h[4]=e,h[8]=n,h[12]=o,h[1]=i,h[5]=a,h[9]=r,h[13]=s,h[2]=l,h[6]=c,h[10]=d,h[14]=p,h[3]=m,h[7]=f,h[11]=u,h[15]=g,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new olt).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return this.set(e[0],e[3],e[6],0,e[1],e[4],e[7],0,e[2],e[5],e[8],0,0,0,0,1),this}extractBasis(t,e,n){return t.setFromMatrixColumn(this,0),e.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(t,e,n){return this.set(t.x,e.x,n.x,0,t.y,e.y,n.y,0,t.z,e.z,n.z,0,0,0,0,1),this}extractRotation(t){const e=this.elements,n=t.elements,o=1/ilt.setFromMatrixColumn(t,0).length(),i=1/ilt.setFromMatrixColumn(t,1).length(),a=1/ilt.setFromMatrixColumn(t,2).length();return e[0]=n[0]*o,e[1]=n[1]*o,e[2]=n[2]*o,e[3]=0,e[4]=n[4]*i,e[5]=n[5]*i,e[6]=n[6]*i,e[7]=0,e[8]=n[8]*a,e[9]=n[9]*a,e[10]=n[10]*a,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error("THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.");const e=this.elements,n=t.x,o=t.y,i=t.z,a=Math.cos(n),r=Math.sin(n),s=Math.cos(o),l=Math.sin(o),c=Math.cos(i),d=Math.sin(i);if("XYZ"===t.order){const t=a*c,n=a*d,o=r*c,i=r*d;e[0]=s*c,e[4]=-s*d,e[8]=l,e[1]=n+o*l,e[5]=t-i*l,e[9]=-r*s,e[2]=i-t*l,e[6]=o+n*l,e[10]=a*s}else if("YXZ"===t.order){const t=s*c,n=s*d,o=l*c,i=l*d;e[0]=t+i*r,e[4]=o*r-n,e[8]=a*l,e[1]=a*d,e[5]=a*c,e[9]=-r,e[2]=n*r-o,e[6]=i+t*r,e[10]=a*s}else if("ZXY"===t.order){const t=s*c,n=s*d,o=l*c,i=l*d;e[0]=t-i*r,e[4]=-a*d,e[8]=o+n*r,e[1]=n+o*r,e[5]=a*c,e[9]=i-t*r,e[2]=-a*l,e[6]=r,e[10]=a*s}else if("ZYX"===t.order){const t=a*c,n=a*d,o=r*c,i=r*d;e[0]=s*c,e[4]=o*l-n,e[8]=t*l+i,e[1]=s*d,e[5]=i*l+t,e[9]=n*l-o,e[2]=-l,e[6]=r*s,e[10]=a*s}else if("YZX"===t.order){const t=a*s,n=a*l,o=r*s,i=r*l;e[0]=s*c,e[4]=i-t*d,e[8]=o*d+n,e[1]=d,e[5]=a*c,e[9]=-r*c,e[2]=-l*c,e[6]=n*d+o,e[10]=t-i*d}else if("XZY"===t.order){const t=a*s,n=a*l,o=r*s,i=r*l;e[0]=s*c,e[4]=-d,e[8]=l*c,e[1]=t*d+i,e[5]=a*c,e[9]=n*d-o,e[2]=o*d-n,e[6]=r*c,e[10]=i*d+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(rlt,t,slt)}lookAt(t,e,n){const o=this.elements;return dlt.subVectors(t,e),0===dlt.lengthSq()&&(dlt.z=1),dlt.normalize(),llt.crossVectors(n,dlt),0===llt.lengthSq()&&(1===Math.abs(n.z)?dlt.x+=1e-4:dlt.z+=1e-4,dlt.normalize(),llt.crossVectors(n,dlt)),llt.normalize(),clt.crossVectors(dlt,llt),o[0]=llt.x,o[4]=clt.x,o[8]=dlt.x,o[1]=llt.y,o[5]=clt.y,o[9]=dlt.y,o[2]=llt.z,o[6]=clt.z,o[10]=dlt.z,this}multiply(t,e){return void 0!==e?(console.warn("THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead."),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,o=e.elements,i=this.elements,a=n[0],r=n[4],s=n[8],l=n[12],c=n[1],d=n[5],p=n[9],m=n[13],f=n[2],u=n[6],g=n[10],h=n[14],b=n[3],y=n[7],_=n[11],C=n[15],M=o[0],v=o[4],x=o[8],O=o[12],P=o[1],w=o[5],k=o[9],S=o[13],D=o[2],R=o[6],E=o[10],A=o[14],T=o[3],N=o[7],z=o[11],I=o[15];return i[0]=a*M+r*P+s*D+l*T,i[4]=a*v+r*w+s*R+l*N,i[8]=a*x+r*k+s*E+l*z,i[12]=a*O+r*S+s*A+l*I,i[1]=c*M+d*P+p*D+m*T,i[5]=c*v+d*w+p*R+m*N,i[9]=c*x+d*k+p*E+m*z,i[13]=c*O+d*S+p*A+m*I,i[2]=f*M+u*P+g*D+h*T,i[6]=f*v+u*w+g*R+h*N,i[10]=f*x+u*k+g*E+h*z,i[14]=f*O+u*S+g*A+h*I,i[3]=b*M+y*P+_*D+C*T,i[7]=b*v+y*w+_*R+C*N,i[11]=b*x+y*k+_*E+C*z,i[15]=b*O+y*S+_*A+C*I,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],o=t[8],i=t[12],a=t[1],r=t[5],s=t[9],l=t[13],c=t[2],d=t[6],p=t[10],m=t[14];return t[3]*(+i*s*d-o*l*d-i*r*p+n*l*p+o*r*m-n*s*m)+t[7]*(+e*s*m-e*l*p+i*a*p-o*a*m+o*l*c-i*s*c)+t[11]*(+e*l*d-e*r*m-i*a*d+n*a*m+i*r*c-n*l*c)+t[15]*(-o*r*c-e*s*d+e*r*p+o*a*d-n*a*p+n*s*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const o=this.elements;return t.isVector3?(o[12]=t.x,o[13]=t.y,o[14]=t.z):(o[12]=t,o[13]=e,o[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],o=t[2],i=t[3],a=t[4],r=t[5],s=t[6],l=t[7],c=t[8],d=t[9],p=t[10],m=t[11],f=t[12],u=t[13],g=t[14],h=t[15],b=d*g*l-u*p*l+u*s*m-r*g*m-d*s*h+r*p*h,y=f*p*l-c*g*l-f*s*m+a*g*m+c*s*h-a*p*h,_=c*u*l-f*d*l+f*r*m-a*u*m-c*r*h+a*d*h,C=f*d*s-c*u*s-f*r*p+a*u*p+c*r*g-a*d*g,M=e*b+n*y+o*_+i*C;if(0===M)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const v=1/M;return t[0]=b*v,t[1]=(u*p*i-d*g*i-u*o*m+n*g*m+d*o*h-n*p*h)*v,t[2]=(r*g*i-u*s*i+u*o*l-n*g*l-r*o*h+n*s*h)*v,t[3]=(d*s*i-r*p*i-d*o*l+n*p*l+r*o*m-n*s*m)*v,t[4]=y*v,t[5]=(c*g*i-f*p*i+f*o*m-e*g*m-c*o*h+e*p*h)*v,t[6]=(f*s*i-a*g*i-f*o*l+e*g*l+a*o*h-e*s*h)*v,t[7]=(a*p*i-c*s*i+c*o*l-e*p*l-a*o*m+e*s*m)*v,t[8]=_*v,t[9]=(f*d*i-c*u*i-f*n*m+e*u*m+c*n*h-e*d*h)*v,t[10]=(a*u*i-f*r*i+f*n*l-e*u*l-a*n*h+e*r*h)*v,t[11]=(c*r*i-a*d*i-c*n*l+e*d*l+a*n*m-e*r*m)*v,t[12]=C*v,t[13]=(c*u*o-f*d*o+f*n*p-e*u*p-c*n*g+e*d*g)*v,t[14]=(f*r*o-a*u*o-f*n*s+e*u*s+a*n*g-e*r*g)*v,t[15]=(a*d*o-c*r*o+c*n*s-e*d*s-a*n*p+e*r*p)*v,this}scale(t){const e=this.elements,n=t.x,o=t.y,i=t.z;return e[0]*=n,e[4]*=o,e[8]*=i,e[1]*=n,e[5]*=o,e[9]*=i,e[2]*=n,e[6]*=o,e[10]*=i,e[3]*=n,e[7]*=o,e[11]*=i,this}getMaxScaleOnAxis(){const t=this.elements;return Math.sqrt(Math.max(t[0]*t[0]+t[1]*t[1]+t[2]*t[2],t[4]*t[4]+t[5]*t[5]+t[6]*t[6],t[8]*t[8]+t[9]*t[9]+t[10]*t[10]))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),o=Math.sin(e),i=1-n,a=t.x,r=t.y,s=t.z,l=i*a,c=i*r;return this.set(l*a+n,l*r-o*s,l*s+o*r,0,l*r+o*s,c*r+n,c*s-o*a,0,l*s-o*r,c*s+o*a,i*s*s+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,o,i,a){return this.set(1,n,i,0,t,1,a,0,e,o,1,0,0,0,0,1),this}compose(t,e,n){const o=this.elements,i=e._x,a=e._y,r=e._z,s=e._w,l=i+i,c=a+a,d=r+r,p=i*l,m=i*c,f=i*d,u=a*c,g=a*d,h=r*d,b=s*l,y=s*c,_=s*d,C=n.x,M=n.y,v=n.z;return o[0]=(1-(u+h))*C,o[1]=(m+_)*C,o[2]=(f-y)*C,o[3]=0,o[4]=(m-_)*M,o[5]=(1-(p+h))*M,o[6]=(g+b)*M,o[7]=0,o[8]=(f+y)*v,o[9]=(g-b)*v,o[10]=(1-(p+u))*v,o[11]=0,o[12]=t.x,o[13]=t.y,o[14]=t.z,o[15]=1,this}decompose(t,e,n){const o=this.elements;let i=ilt.set(o[0],o[1],o[2]).length();const a=ilt.set(o[4],o[5],o[6]).length(),r=ilt.set(o[8],o[9],o[10]).length();this.determinant()<0&&(i=-i),t.x=o[12],t.y=o[13],t.z=o[14],alt.copy(this);const s=1/i,l=1/a,c=1/r;return alt.elements[0]*=s,alt.elements[1]*=s,alt.elements[2]*=s,alt.elements[4]*=l,alt.elements[5]*=l,alt.elements[6]*=l,alt.elements[8]*=c,alt.elements[9]*=c,alt.elements[10]*=c,e.setFromRotationMatrix(alt),n.x=i,n.y=a,n.z=r,this}makePerspective(t,e,n,o,i,a){void 0===a&&console.warn("THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.");const r=this.elements,s=2*i/(n-o),l=(e+t)/(e-t),c=(n+o)/(n-o),d=-(a+i)/(a-i),p=-2*a*i/(a-i);return r[0]=2*i/(e-t),r[4]=0,r[8]=l,r[12]=0,r[1]=0,r[5]=s,r[9]=c,r[13]=0,r[2]=0,r[6]=0,r[10]=d,r[14]=p,r[3]=0,r[7]=0,r[11]=-1,r[15]=0,this}makeOrthographic(t,e,n,o,i,a){const r=this.elements,s=1/(e-t),l=1/(n-o),c=1/(a-i),d=(e+t)*s,p=(n+o)*l,m=(a+i)*c;return r[0]=2*s,r[4]=0,r[8]=0,r[12]=-d,r[1]=0,r[5]=2*l,r[9]=0,r[13]=-p,r[2]=0,r[6]=0,r[10]=-2*c,r[14]=-m,r[3]=0,r[7]=0,r[11]=0,r[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}olt.prototype.isMatrix4=!0;const ilt=new wst,alt=new olt,rlt=new wst(0,0,0),slt=new wst(1,1,1),llt=new wst,clt=new wst,dlt=new wst,plt=new olt,mlt=new Pst;class flt{constructor(t=0,e=0,n=0,o=flt.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=o}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,o=this._order){return this._x=t,this._y=e,this._z=n,this._order=o,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const o=t.elements,i=o[0],a=o[4],r=o[8],s=o[1],l=o[5],c=o[9],d=o[2],p=o[6],m=o[10];switch(e){case"XYZ":this._y=Math.asin(pst(r,-1,1)),Math.abs(r)<.9999999?(this._x=Math.atan2(-c,m),this._z=Math.atan2(-a,i)):(this._x=Math.atan2(p,l),this._z=0);break;case"YXZ":this._x=Math.asin(-pst(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(r,m),this._z=Math.atan2(s,l)):(this._y=Math.atan2(-d,i),this._z=0);break;case"ZXY":this._x=Math.asin(pst(p,-1,1)),Math.abs(p)<.9999999?(this._y=Math.atan2(-d,m),this._z=Math.atan2(-a,l)):(this._y=0,this._z=Math.atan2(s,i));break;case"ZYX":this._y=Math.asin(-pst(d,-1,1)),Math.abs(d)<.9999999?(this._x=Math.atan2(p,m),this._z=Math.atan2(s,i)):(this._x=0,this._z=Math.atan2(-a,l));break;case"YZX":this._z=Math.asin(pst(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-d,i)):(this._x=0,this._y=Math.atan2(r,m));break;case"XZY":this._z=Math.asin(-pst(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(p,l),this._y=Math.atan2(r,i)):(this._x=Math.atan2(-c,m),this._y=0);break;default:console.warn("THREE.Euler: .setFromRotationMatrix() encountered an unknown order: "+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return plt.makeRotationFromQuaternion(t),this.setFromRotationMatrix(plt,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return mlt.setFromEuler(this),this.setFromQuaternion(mlt,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new wst(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}flt.prototype.isEuler=!0,flt.DefaultOrder="XYZ",flt.RotationOrders=["XYZ","YZX","ZXY","XZY","YXZ","ZYX"];class ult{constructor(){this.mask=1}set(t){this.mask=1<<t|0}enable(t){this.mask|=1<<t|0}enableAll(){this.mask=-1}toggle(t){this.mask^=1<<t|0}disable(t){this.mask&=~(1<<t|0)}disableAll(){this.mask=0}test(t){return 0!=(this.mask&t.mask)}}let glt=0;const hlt=new wst,blt=new Pst,ylt=new olt,_lt=new wst,Clt=new wst,Mlt=new wst,vlt=new Pst,xlt=new wst(1,0,0),Olt=new wst(0,1,0),Plt=new wst(0,0,1),wlt={type:"added"},klt={type:"removed"};class Slt extends rst{constructor(){super(),Object.defineProperty(this,"id",{value:glt++}),this.uuid=dst(),this.name="",this.type="Object3D",this.parent=null,this.children=[],this.up=Slt.DefaultUp.clone();const t=new wst,e=new flt,n=new Pst,o=new wst(1,1,1);e._onChange((function i(){n.setFromEuler(e,!1)})),n._onChange((function a(){e.setFromQuaternion(n,void 0,!1)})),Object.defineProperties(this,{position:{configurable:!0,enumerable:!0,value:t},rotation:{configurable:!0,enumerable:!0,value:e},quaternion:{configurable:!0,enumerable:!0,value:n},scale:{configurable:!0,enumerable:!0,value:o},modelViewMatrix:{value:new olt},normalMatrix:{value:new hst}}),this.matrix=new olt,this.matrixWorld=new olt,this.matrixAutoUpdate=Slt.DefaultMatrixAutoUpdate,this.matrixWorldNeedsUpdate=!1,this.layers=new ult,this.visible=!0,this.castShadow=!1,this.receiveShadow=!1,this.frustumCulled=!0,this.renderOrder=0,this.animations=[],this.userData={}}onBeforeRender(){}onAfterRender(){}applyMatrix4(t){this.matrixAutoUpdate&&this.updateMatrix(),this.matrix.premultiply(t),this.matrix.decompose(this.position,this.quaternion,this.scale)}applyQuaternion(t){return this.quaternion.premultiply(t),this}setRotationFromAxisAngle(t,e){this.quaternion.setFromAxisAngle(t,e)}setRotationFromEuler(t){this.quaternion.setFromEuler(t,!0)}setRotationFromMatrix(t){this.quaternion.setFromRotationMatrix(t)}setRotationFromQuaternion(t){this.quaternion.copy(t)}rotateOnAxis(t,e){return blt.setFromAxisAngle(t,e),this.quaternion.multiply(blt),this}rotateOnWorldAxis(t,e){return blt.setFromAxisAngle(t,e),this.quaternion.premultiply(blt),this}rotateX(t){return this.rotateOnAxis(xlt,t)}rotateY(t){return this.rotateOnAxis(Olt,t)}rotateZ(t){return this.rotateOnAxis(Plt,t)}translateOnAxis(t,e){return hlt.copy(t).applyQuaternion(this.quaternion),this.position.add(hlt.multiplyScalar(e)),this}translateX(t){return this.translateOnAxis(xlt,t)}translateY(t){return this.translateOnAxis(Olt,t)}translateZ(t){return this.translateOnAxis(Plt,t)}localToWorld(t){return t.applyMatrix4(this.matrixWorld)}worldToLocal(t){return t.applyMatrix4(ylt.copy(this.matrixWorld).invert())}lookAt(t,e,n){t.isVector3?_lt.copy(t):_lt.set(t,e,n);const o=this.parent;this.updateWorldMatrix(!0,!1),Clt.setFromMatrixPosition(this.matrixWorld),this.isCamera||this.isLight?ylt.lookAt(Clt,_lt,this.up):ylt.lookAt(_lt,Clt,this.up),this.quaternion.setFromRotationMatrix(ylt),o&&(ylt.extractRotation(o.matrixWorld),blt.setFromRotationMatrix(ylt),this.quaternion.premultiply(blt.invert()))}add(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.add(arguments[t]);return this}return t===this?(console.error("THREE.Object3D.add: object can't be added as a child of itself.",t),this):(t&&t.isObject3D?(null!==t.parent&&t.parent.remove(t),t.parent=this,this.children.push(t),t.dispatchEvent(wlt)):console.error("THREE.Object3D.add: object not an instance of THREE.Object3D.",t),this)}remove(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.remove(arguments[t]);return this}const e=this.children.indexOf(t);return-1!==e&&(t.parent=null,this.children.splice(e,1),t.dispatchEvent(klt)),this}removeFromParent(){const t=this.parent;return null!==t&&t.remove(this),this}clear(){for(let t=0;t<this.children.length;t++){const e=this.children[t];e.parent=null,e.dispatchEvent(klt)}return this.children.length=0,this}attach(t){return this.updateWorldMatrix(!0,!1),ylt.copy(this.matrixWorld).invert(),null!==t.parent&&(t.parent.updateWorldMatrix(!0,!1),ylt.multiply(t.parent.matrixWorld)),t.applyMatrix4(ylt),this.add(t),t.updateWorldMatrix(!1,!0),this}getObjectById(t){return this.getObjectByProperty("id",t)}getObjectByName(t){return this.getObjectByProperty("name",t)}getObjectByProperty(t,e){if(this[t]===e)return this;for(let n=0,o=this.children.length;n<o;n++){const o=this.children[n].getObjectByProperty(t,e);if(void 0!==o)return o}}getWorldPosition(t){return this.updateWorldMatrix(!0,!1),t.setFromMatrixPosition(this.matrixWorld)}getWorldQuaternion(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(Clt,t,Mlt),t}getWorldScale(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(Clt,vlt,t),t}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(e[8],e[9],e[10]).normalize()}raycast(){}traverse(t){t(this);const e=this.children;for(let n=0,o=e.length;n<o;n++)e[n].traverse(t)}traverseVisible(t){if(!1===this.visible)return;t(this);const e=this.children;for(let n=0,o=e.length;n<o;n++)e[n].traverseVisible(t)}traverseAncestors(t){const e=this.parent;null!==e&&(t(e),e.traverseAncestors(t))}updateMatrix(){this.matrix.compose(this.position,this.quaternion,this.scale),this.matrixWorldNeedsUpdate=!0}updateMatrixWorld(t){this.matrixAutoUpdate&&this.updateMatrix(),(this.matrixWorldNeedsUpdate||t)&&(null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),this.matrixWorldNeedsUpdate=!1,t=!0);const e=this.children;for(let n=0,o=e.length;n<o;n++)e[n].updateMatrixWorld(t)}updateWorldMatrix(t,e){const n=this.parent;if(!0===t&&null!==n&&n.updateWorldMatrix(!0,!1),this.matrixAutoUpdate&&this.updateMatrix(),null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),!0===e){const t=this.children;for(let e=0,n=t.length;e<n;e++)t[e].updateWorldMatrix(!1,!0)}}toJSON(t){const e=void 0===t||"string"==typeof t,n={};e&&(t={geometries:{},materials:{},textures:{},images:{},shapes:{},skeletons:{},animations:{}},n.metadata={version:4.5,type:"Object",generator:"Object3D.toJSON"});const o={};function i(e,n){return void 0===e[n.uuid]&&(e[n.uuid]=n.toJSON(t)),n.uuid}if(o.uuid=this.uuid,o.type=this.type,""!==this.name&&(o.name=this.name),!0===this.castShadow&&(o.castShadow=!0),!0===this.receiveShadow&&(o.receiveShadow=!0),!1===this.visible&&(o.visible=!1),!1===this.frustumCulled&&(o.frustumCulled=!1),0!==this.renderOrder&&(o.renderOrder=this.renderOrder),"{}"!==JSON.stringify(this.userData)&&(o.userData=this.userData),o.layers=this.layers.mask,o.matrix=this.matrix.toArray(),!1===this.matrixAutoUpdate&&(o.matrixAutoUpdate=!1),this.isInstancedMesh&&(o.type="InstancedMesh",o.count=this.count,o.instanceMatrix=this.instanceMatrix.toJSON(),null!==this.instanceColor&&(o.instanceColor=this.instanceColor.toJSON())),this.isScene)this.background&&(this.background.isColor?o.background=this.background.toJSON():this.background.isTexture&&(o.background=this.background.toJSON(t).uuid)),this.environment&&this.environment.isTexture&&(o.environment=this.environment.toJSON(t).uuid);else if(this.isMesh||this.isLine||this.isPoints){o.geometry=i(t.geometries,this.geometry);const e=this.geometry.parameters;if(void 0!==e&&void 0!==e.shapes){const n=e.shapes;if(Array.isArray(n))for(let e=0,o=n.length;e<o;e++){i(t.shapes,n[e])}else i(t.shapes,n)}}if(this.isSkinnedMesh&&(o.bindMode=this.bindMode,o.bindMatrix=this.bindMatrix.toArray(),void 0!==this.skeleton&&(i(t.skeletons,this.skeleton),o.skeleton=this.skeleton.uuid)),void 0!==this.material)if(Array.isArray(this.material)){const e=[];for(let n=0,o=this.material.length;n<o;n++)e.push(i(t.materials,this.material[n]));o.material=e}else o.material=i(t.materials,this.material);if(this.children.length>0){o.children=[];for(let e=0;e<this.children.length;e++)o.children.push(this.children[e].toJSON(t).object)}if(this.animations.length>0){o.animations=[];for(let e=0;e<this.animations.length;e++){o.animations.push(i(t.animations,this.animations[e]))}}if(e){const e=a(t.geometries),o=a(t.materials),i=a(t.textures),r=a(t.images),s=a(t.shapes),l=a(t.skeletons),c=a(t.animations);e.length>0&&(n.geometries=e),o.length>0&&(n.materials=o),i.length>0&&(n.textures=i),r.length>0&&(n.images=r),s.length>0&&(n.shapes=s),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=o,n;function a(t){const e=[];for(const n in t){const o=t[n];delete o.metadata,e.push(o)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e<t.children.length;e++){this.add(t.children[e].clone())}return this}}Slt.DefaultUp=new wst(0,1,0),Slt.DefaultMatrixAutoUpdate=!0,Slt.prototype.isObject3D=!0;const Dlt=new wst,Rlt=new wst,Elt=new wst,Alt=new wst,Tlt=new wst,Nlt=new wst,zlt=new wst,Ilt=new wst,Hlt=new wst,Flt=new wst;class Llt{constructor(t=new wst,e=new wst,n=new wst){this.a=t,this.b=e,this.c=n}static getNormal(t,e,n,o){o.subVectors(n,e),Dlt.subVectors(t,e),o.cross(Dlt);const i=o.lengthSq();return i>0?o.multiplyScalar(1/Math.sqrt(i)):o.set(0,0,0)}static getBarycoord(t,e,n,o,i){Dlt.subVectors(o,e),Rlt.subVectors(n,e),Elt.subVectors(t,e);const a=Dlt.dot(Dlt),r=Dlt.dot(Rlt),s=Dlt.dot(Elt),l=Rlt.dot(Rlt),c=Rlt.dot(Elt),d=a*l-r*r;if(0===d)return i.set(-2,-1,-1);const p=1/d,m=(l*s-r*c)*p,f=(a*c-r*s)*p;return i.set(1-m-f,f,m)}static containsPoint(t,e,n,o){return this.getBarycoord(t,e,n,o,Alt),Alt.x>=0&&Alt.y>=0&&Alt.x+Alt.y<=1}static getUV(t,e,n,o,i,a,r,s){return this.getBarycoord(t,e,n,o,Alt),s.set(0,0),s.addScaledVector(i,Alt.x),s.addScaledVector(a,Alt.y),s.addScaledVector(r,Alt.z),s}static isFrontFacing(t,e,n,o){return Dlt.subVectors(n,e),Rlt.subVectors(t,e),Dlt.cross(Rlt).dot(o)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,o){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[o]),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return Dlt.subVectors(this.c,this.b),Rlt.subVectors(this.a,this.b),.5*Dlt.cross(Rlt).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return Llt.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return Llt.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,o,i){return Llt.getUV(t,this.a,this.b,this.c,e,n,o,i)}containsPoint(t){return Llt.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return Llt.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,o=this.b,i=this.c;let a,r;Tlt.subVectors(o,n),Nlt.subVectors(i,n),Ilt.subVectors(t,n);const s=Tlt.dot(Ilt),l=Nlt.dot(Ilt);if(s<=0&&l<=0)return e.copy(n);Hlt.subVectors(t,o);const c=Tlt.dot(Hlt),d=Nlt.dot(Hlt);if(c>=0&&d<=c)return e.copy(o);const p=s*d-c*l;if(p<=0&&s>=0&&c<=0)return a=s/(s-c),e.copy(n).addScaledVector(Tlt,a);Flt.subVectors(t,i);const m=Tlt.dot(Flt),f=Nlt.dot(Flt);if(f>=0&&m<=f)return e.copy(i);const u=m*l-s*f;if(u<=0&&l>=0&&f<=0)return r=l/(l-f),e.copy(n).addScaledVector(Nlt,r);const g=c*f-m*d;if(g<=0&&d-c>=0&&m-f>=0)return zlt.subVectors(i,o),r=(d-c)/(d-c+(m-f)),e.copy(o).addScaledVector(zlt,r);const h=1/(g+u+p);return a=u*h,r=p*h,e.copy(n).addScaledVector(Tlt,a).addScaledVector(Nlt,r)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let Blt=0;class Vlt extends rst{constructor(){super(),Object.defineProperty(this,"id",{value:Blt++}),this.uuid=dst(),this.name="",this.type="Material",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=Prt,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=3,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=519,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=nst,this.stencilZFail=nst,this.stencilZPass=nst,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaTest=0,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0}onBuild(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn("THREE.Material: '"+e+"' parameter is undefined.");continue}if("shading"===e){console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead."),this.flatShading=1===n;continue}const o=this[e];void 0!==o?o&&o.isColor?o.set(n):o&&o.isVector3&&n&&n.isVector3?o.copy(n):this[e]=n:console.warn("THREE."+this.type+": '"+e+"' is not a property of this material.")}}toJSON(t){const e=void 0===t||"string"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:"Material",generator:"Material.toJSON"}};function o(t){const e=[];for(const n in t){const o=t[n];delete o.metadata,e.push(o)}return e}if(n.uuid=this.uuid,n.type=this.type,""!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),this.sheen&&this.sheen.isColor&&(n.sheen=this.sheen.getHex()),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),1!==this.blending&&(n.blending=this.blending),0!==this.side&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),"round"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),"round"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),"{}"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=o(t.textures),i=o(t.images);e.length>0&&(n.textures=e),i.length>0&&(n.images=i)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let o=0;o!==t;++o)n[o]=e[o].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:"dispose"})}set needsUpdate(t){!0===t&&this.version++}}Vlt.prototype.isMaterial=!0;const jlt={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},Ult={h:0,s:0,l:0},Wlt={h:0,s:0,l:0};function Glt(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function Ylt(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function qlt(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class Zlt{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):"number"==typeof t?this.setHex(t):"string"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=(function o(t,e){return(t%e+e)%e})(t,1),e=pst(e,0,1),n=pst(n,0,1),0===e)this.r=this.g=this.b=n;else{const o=n<=.5?n*(1+e):n+e-n*e,i=2*n-o;this.r=Glt(i,o,t+1/3),this.g=Glt(i,o,t),this.b=Glt(i,o,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn("THREE.Color: Alpha component of "+t+" will be ignored.")}let n;if(n=/^((?:rgb|hsl)a?)\(([^\)]*)\)/.exec(t)){let t;const o=n[2];switch(n[1]){case"rgb":case"rgba":if(t=/^\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(o))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\s*(\d+)\%\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(o))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case"hsl":case"hsla":if(t=/^\s*(\d*\.?\d+)\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(o)){const n=parseFloat(t[1])/360,o=parseInt(t[2],10)/100,i=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,o,i)}}}else if(n=/^\#([A-Fa-f\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=jlt[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn("THREE.Color: Unknown color "+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=Ylt(t.r),this.g=Ylt(t.g),this.b=Ylt(t.b),this}copyLinearToSRGB(t){return this.r=qlt(t.r),this.g=qlt(t.g),this.b=qlt(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return("000000"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,o=this.b,i=Math.max(e,n,o),a=Math.min(e,n,o);let r,s;const l=(a+i)/2;if(a===i)r=0,s=0;else{const t=i-a;switch(s=l<=.5?t/(i+a):t/(2-i-a),i){case e:r=(n-o)/t+(n<o?6:0);break;case n:r=(o-e)/t+2;break;case o:r=(e-n)/t+4}r/=6}return t.h=r,t.s=s,t.l=l,t}getStyle(){return"rgb("+(255*this.r|0)+","+(255*this.g|0)+","+(255*this.b|0)+")"}offsetHSL(t,e,n){return this.getHSL(Ult),Ult.h+=t,Ult.s+=e,Ult.l+=n,this.setHSL(Ult.h,Ult.s,Ult.l),this}add(t){return this.r+=t.r,this.g+=t.g,this.b+=t.b,this}addColors(t,e){return this.r=t.r+e.r,this.g=t.g+e.g,this.b=t.b+e.b,this}addScalar(t){return this.r+=t,this.g+=t,this.b+=t,this}sub(t){return this.r=Math.max(0,this.r-t.r),this.g=Math.max(0,this.g-t.g),this.b=Math.max(0,this.b-t.b),this}multiply(t){return this.r*=t.r,this.g*=t.g,this.b*=t.b,this}multiplyScalar(t){return this.r*=t,this.g*=t,this.b*=t,this}lerp(t,e){return this.r+=(t.r-this.r)*e,this.g+=(t.g-this.g)*e,this.b+=(t.b-this.b)*e,this}lerpColors(t,e,n){return this.r=t.r+(e.r-t.r)*n,this.g=t.g+(e.g-t.g)*n,this.b=t.b+(e.b-t.b)*n,this}lerpHSL(t,e){this.getHSL(Ult),t.getHSL(Wlt);const n=mst(Ult.h,Wlt.h,e),o=mst(Ult.s,Wlt.s,e),i=mst(Ult.l,Wlt.l,e);return this.setHSL(n,o,i),this}equals(t){return t.r===this.r&&t.g===this.g&&t.b===this.b}fromArray(t,e=0){return this.r=t[e],this.g=t[e+1],this.b=t[e+2],this}toArray(t=[],e=0){return t[e]=this.r,t[e+1]=this.g,t[e+2]=this.b,t}fromBufferAttribute(t,e){return this.r=t.getX(e),this.g=t.getY(e),this.b=t.getZ(e),!0===t.normalized&&(this.r/=255,this.g/=255,this.b/=255),this}toJSON(){return this.getHex()}}Zlt.NAMES=jlt,Zlt.prototype.isColor=!0,Zlt.prototype.r=1,Zlt.prototype.g=1,Zlt.prototype.b=1;class Xlt extends Vlt{constructor(t){super(),this.type="MeshBasicMaterial",this.color=new Zlt(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}Xlt.prototype.isMeshBasicMaterial=!0;const Klt=new wst,Jlt=new gst;class Qlt{constructor(t,e,n){if(Array.isArray(t))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");this.name="",this.array=t,this.itemSize=e,this.count=void 0!==t?t.length/e:0,this.normalized=!0===n,this.usage=ost,this.updateRange={offset:0,count:-1},this.version=0}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.name=t.name,this.array=new t.array.constructor(t.array),this.itemSize=t.itemSize,this.count=t.count,this.normalized=t.normalized,this.usage=t.usage,this}copyAt(t,e,n){t*=this.itemSize,n*=e.itemSize;for(let o=0,i=this.itemSize;o<i;o++)this.array[t+o]=e.array[n+o];return this}copyArray(t){return this.array.set(t),this}copyColorsArray(t){const e=this.array;let n=0;for(let o=0,i=t.length;o<i;o++){let i=t[o];void 0===i&&(console.warn("THREE.BufferAttribute.copyColorsArray(): color is undefined",o),i=new Zlt),e[n++]=i.r,e[n++]=i.g,e[n++]=i.b}return this}copyVector2sArray(t){const e=this.array;let n=0;for(let o=0,i=t.length;o<i;o++){let i=t[o];void 0===i&&(console.warn("THREE.BufferAttribute.copyVector2sArray(): vector is undefined",o),i=new gst),e[n++]=i.x,e[n++]=i.y}return this}copyVector3sArray(t){const e=this.array;let n=0;for(let o=0,i=t.length;o<i;o++){let i=t[o];void 0===i&&(console.warn("THREE.BufferAttribute.copyVector3sArray(): vector is undefined",o),i=new wst),e[n++]=i.x,e[n++]=i.y,e[n++]=i.z}return this}copyVector4sArray(t){const e=this.array;let n=0;for(let o=0,i=t.length;o<i;o++){let i=t[o];void 0===i&&(console.warn("THREE.BufferAttribute.copyVector4sArray(): vector is undefined",o),i=new vst),e[n++]=i.x,e[n++]=i.y,e[n++]=i.z,e[n++]=i.w}return this}applyMatrix3(t){if(2===this.itemSize)for(let e=0,n=this.count;e<n;e++)Jlt.fromBufferAttribute(this,e),Jlt.applyMatrix3(t),this.setXY(e,Jlt.x,Jlt.y);else if(3===this.itemSize)for(let e=0,n=this.count;e<n;e++)Klt.fromBufferAttribute(this,e),Klt.applyMatrix3(t),this.setXYZ(e,Klt.x,Klt.y,Klt.z);return this}applyMatrix4(t){for(let e=0,n=this.count;e<n;e++)Klt.x=this.getX(e),Klt.y=this.getY(e),Klt.z=this.getZ(e),Klt.applyMatrix4(t),this.setXYZ(e,Klt.x,Klt.y,Klt.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)Klt.x=this.getX(e),Klt.y=this.getY(e),Klt.z=this.getZ(e),Klt.applyNormalMatrix(t),this.setXYZ(e,Klt.x,Klt.y,Klt.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)Klt.x=this.getX(e),Klt.y=this.getY(e),Klt.z=this.getZ(e),Klt.transformDirection(t),this.setXYZ(e,Klt.x,Klt.y,Klt.z);return this}set(t,e=0){return this.array.set(t,e),this}getX(t){return this.array[t*this.itemSize]}setX(t,e){return this.array[t*this.itemSize]=e,this}getY(t){return this.array[t*this.itemSize+1]}setY(t,e){return this.array[t*this.itemSize+1]=e,this}getZ(t){return this.array[t*this.itemSize+2]}setZ(t,e){return this.array[t*this.itemSize+2]=e,this}getW(t){return this.array[t*this.itemSize+3]}setW(t,e){return this.array[t*this.itemSize+3]=e,this}setXY(t,e,n){return this.array[(t*=this.itemSize)+0]=e,this.array[t+1]=n,this}setXYZ(t,e,n,o){return this.array[(t*=this.itemSize)+0]=e,this.array[t+1]=n,this.array[t+2]=o,this}setXYZW(t,e,n,o,i){return this.array[(t*=this.itemSize)+0]=e,this.array[t+1]=n,this.array[t+2]=o,this.array[t+3]=i,this}onUpload(t){return this.onUploadCallback=t,this}clone(){return new this.constructor(this.array,this.itemSize).copy(this)}toJSON(){const t={itemSize:this.itemSize,type:this.array.constructor.name,array:Array.prototype.slice.call(this.array),normalized:this.normalized};return""!==this.name&&(t.name=this.name),this.usage!==ost&&(t.usage=this.usage),0===this.updateRange.offset&&-1===this.updateRange.count||(t.updateRange=this.updateRange),t}}Qlt.prototype.isBufferAttribute=!0;class $lt extends Qlt{constructor(t,e,n){super(new Uint16Array(t),e,n)}}class tct extends Qlt{constructor(t,e,n){super(new Uint32Array(t),e,n)}}(class extends Qlt{constructor(t,e,n){super(new Uint16Array(t),e,n)}}).prototype.isFloat16BufferAttribute=!0;class ect extends Qlt{constructor(t,e,n){super(new Float32Array(t),e,n)}}function nct(t){if(0===t.length)return-1/0;let e=t[0];for(let n=1,o=t.length;n<o;++n)t[n]>e&&(e=t[n]);return e}let oct=0;const ict=new olt,act=new Slt,rct=new wst,sct=new Dst,lct=new Dst,cct=new wst;class dct extends rst{constructor(){super(),Object.defineProperty(this,"id",{value:oct++}),this.uuid=dst(),this.name="",this.type="BufferGeometry",this.index=null,this.attributes={},this.morphAttributes={},this.morphTargetsRelative=!1,this.groups=[],this.boundingBox=null,this.boundingSphere=null,this.drawRange={start:0,count:1/0},this.userData={}}getIndex(){return this.index}setIndex(t){return this.index=Array.isArray(t)?new(nct(t)>65535?tct:$lt)(t,1):t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new hst).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const o=this.attributes.tangent;return void 0!==o&&(o.transformDirection(t),o.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return ict.makeRotationFromQuaternion(t),this.applyMatrix4(ict),this}rotateX(t){return ict.makeRotationX(t),this.applyMatrix4(ict),this}rotateY(t){return ict.makeRotationY(t),this.applyMatrix4(ict),this}rotateZ(t){return ict.makeRotationZ(t),this.applyMatrix4(ict),this}translate(t,e,n){return ict.makeTranslation(t,e,n),this.applyMatrix4(ict),this}scale(t,e,n){return ict.makeScale(t,e,n),this.applyMatrix4(ict),this}lookAt(t){return act.lookAt(t),act.updateMatrix(),this.applyMatrix4(act.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(rct).negate(),this.translate(rct.x,rct.y,rct.z),this}setFromPoints(t){const e=[];for(let n=0,o=t.length;n<o;n++){const o=t[n];e.push(o.x,o.y,o.z||0)}return this.setAttribute("position",new ect(e,3)),this}computeBoundingBox(){null===this.boundingBox&&(this.boundingBox=new Dst);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingBox.set(new wst(-1/0,-1/0,-1/0),new wst(1/0,1/0,1/0));if(void 0!==t){if(this.boundingBox.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++){sct.setFromBufferAttribute(e[t]),this.morphTargetsRelative?(cct.addVectors(this.boundingBox.min,sct.min),this.boundingBox.expandByPoint(cct),cct.addVectors(this.boundingBox.max,sct.max),this.boundingBox.expandByPoint(cct)):(this.boundingBox.expandByPoint(sct.min),this.boundingBox.expandByPoint(sct.max))}}else this.boundingBox.makeEmpty();(isNaN(this.boundingBox.min.x)||isNaN(this.boundingBox.min.y)||isNaN(this.boundingBox.min.z))&&console.error('THREE.BufferGeometry.computeBoundingBox(): Computed min/max have NaN values. The "position" attribute is likely to have NaN values.',this)}computeBoundingSphere(){null===this.boundingSphere&&(this.boundingSphere=new Zst);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingSphere(): GLBufferAttribute requires a manual bounding sphere. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingSphere.set(new wst,1/0);if(t){const n=this.boundingSphere.center;if(sct.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++){lct.setFromBufferAttribute(e[t]),this.morphTargetsRelative?(cct.addVectors(sct.min,lct.min),sct.expandByPoint(cct),cct.addVectors(sct.max,lct.max),sct.expandByPoint(cct)):(sct.expandByPoint(lct.min),sct.expandByPoint(lct.max))}sct.getCenter(n);let o=0;for(let e=0,i=t.count;e<i;e++)cct.fromBufferAttribute(t,e),o=Math.max(o,n.distanceToSquared(cct));if(e)for(let i=0,a=e.length;i<a;i++){const a=e[i],r=this.morphTargetsRelative;for(let e=0,i=a.count;e<i;e++)cct.fromBufferAttribute(a,e),r&&(rct.fromBufferAttribute(t,e),cct.add(rct)),o=Math.max(o,n.distanceToSquared(cct))}this.boundingSphere.radius=Math.sqrt(o),isNaN(this.boundingSphere.radius)&&console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The "position" attribute is likely to have NaN values.',this)}}computeFaceNormals(){}computeTangents(){const t=this.index,e=this.attributes;if(null===t||void 0===e.position||void 0===e.normal||void 0===e.uv)return void console.error("THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)");const n=t.array,o=e.position.array,i=e.normal.array,a=e.uv.array,r=o.length/3;void 0===e.tangent&&this.setAttribute("tangent",new Qlt(new Float32Array(4*r),4));const s=e.tangent.array,l=[],c=[];for(let t=0;t<r;t++)l[t]=new wst,c[t]=new wst;const d=new wst,p=new wst,m=new wst,f=new gst,u=new gst,g=new gst,h=new wst,b=new wst;function y(t,e,n){d.fromArray(o,3*t),p.fromArray(o,3*e),m.fromArray(o,3*n),f.fromArray(a,2*t),u.fromArray(a,2*e),g.fromArray(a,2*n),p.sub(d),m.sub(d),u.sub(f),g.sub(f);const i=1/(u.x*g.y-g.x*u.y);isFinite(i)&&(h.copy(p).multiplyScalar(g.y).addScaledVector(m,-u.y).multiplyScalar(i),b.copy(m).multiplyScalar(u.x).addScaledVector(p,-g.x).multiplyScalar(i),l[t].add(h),l[e].add(h),l[n].add(h),c[t].add(b),c[e].add(b),c[n].add(b))}let _=this.groups;0===_.length&&(_=[{start:0,count:n.length}]);for(let t=0,e=_.length;t<e;++t){const e=_[t],o=e.start;for(let t=o,i=o+e.count;t<i;t+=3)y(n[t+0],n[t+1],n[t+2])}const C=new wst,M=new wst,v=new wst,x=new wst;function O(t){v.fromArray(i,3*t),x.copy(v);const e=l[t];C.copy(e),C.sub(v.multiplyScalar(v.dot(e))).normalize(),M.crossVectors(x,e);const n=M.dot(c[t])<0?-1:1;s[4*t]=C.x,s[4*t+1]=C.y,s[4*t+2]=C.z,s[4*t+3]=n}for(let t=0,e=_.length;t<e;++t){const e=_[t],o=e.start;for(let t=o,i=o+e.count;t<i;t+=3)O(n[t+0]),O(n[t+1]),O(n[t+2])}}computeVertexNormals(){const t=this.index,e=this.getAttribute("position");if(void 0!==e){let n=this.getAttribute("normal");if(void 0===n)n=new Qlt(new Float32Array(3*e.count),3),this.setAttribute("normal",n);else for(let t=0,e=n.count;t<e;t++)n.setXYZ(t,0,0,0);const o=new wst,i=new wst,a=new wst,r=new wst,s=new wst,l=new wst,c=new wst,d=new wst;if(t)for(let p=0,m=t.count;p<m;p+=3){const m=t.getX(p+0),f=t.getX(p+1),u=t.getX(p+2);o.fromBufferAttribute(e,m),i.fromBufferAttribute(e,f),a.fromBufferAttribute(e,u),c.subVectors(a,i),d.subVectors(o,i),c.cross(d),r.fromBufferAttribute(n,m),s.fromBufferAttribute(n,f),l.fromBufferAttribute(n,u),r.add(c),s.add(c),l.add(c),n.setXYZ(m,r.x,r.y,r.z),n.setXYZ(f,s.x,s.y,s.z),n.setXYZ(u,l.x,l.y,l.z)}else for(let t=0,r=e.count;t<r;t+=3)o.fromBufferAttribute(e,t+0),i.fromBufferAttribute(e,t+1),a.fromBufferAttribute(e,t+2),c.subVectors(a,i),d.subVectors(o,i),c.cross(d),n.setXYZ(t+0,c.x,c.y,c.z),n.setXYZ(t+1,c.x,c.y,c.z),n.setXYZ(t+2,c.x,c.y,c.z);this.normalizeNormals(),n.needsUpdate=!0}}merge(t,e){if(!t||!t.isBufferGeometry)return void console.error("THREE.BufferGeometry.merge(): geometry not an instance of THREE.BufferGeometry.",t);void 0===e&&(e=0,console.warn("THREE.BufferGeometry.merge(): Overwriting original geometry, starting at offset=0. Use BufferGeometryUtils.mergeBufferGeometries() for lossless merge."));const n=this.attributes;for(const o in n){if(void 0===t.attributes[o])continue;const i=n[o].array,a=t.attributes[o],r=a.array,s=a.itemSize*e,l=Math.min(r.length,i.length-s);for(let t=0,e=s;t<l;t++,e++)i[e]=r[t]}return this}normalizeNormals(){const t=this.attributes.normal;for(let e=0,n=t.count;e<n;e++)cct.fromBufferAttribute(t,e),cct.normalize(),t.setXYZ(e,cct.x,cct.y,cct.z)}toNonIndexed(){function t(t,e){const n=t.array,o=t.itemSize,i=t.normalized,a=new n.constructor(e.length*o);let r=0,s=0;for(let i=0,l=e.length;i<l;i++){r=t.isInterleavedBufferAttribute?e[i]*t.data.stride+t.offset:e[i]*o;for(let t=0;t<o;t++)a[s++]=n[r++]}return new Qlt(a,o,i)}if(null===this.index)return console.warn("THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed."),this;const e=new dct,n=this.index.array,o=this.attributes;for(const i in o){const a=t(o[i],n);e.setAttribute(i,a)}const i=this.morphAttributes;for(const o in i){const a=[],r=i[o];for(let e=0,o=r.length;e<o;e++){const o=t(r[e],n);a.push(o)}e.morphAttributes[o]=a}e.morphTargetsRelative=this.morphTargetsRelative;const a=this.groups;for(let t=0,n=a.length;t<n;t++){const n=a[t];e.addGroup(n.start,n.count,n.materialIndex)}return e}toJSON(){const t={metadata:{version:4.5,type:"BufferGeometry",generator:"BufferGeometry.toJSON"}};if(t.uuid=this.uuid,t.type=this.type,""!==this.name&&(t.name=this.name),Object.keys(this.userData).length>0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n){t.data.attributes[e]=n[e].toJSON(t.data)}const o={};let i=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],a=[];for(let e=0,o=n.length;e<o;e++){a.push(n[e].toJSON(t.data))}a.length>0&&(o[e]=a,i=!0)}i&&(t.data.morphAttributes=o,t.data.morphTargetsRelative=this.morphTargetsRelative);const a=this.groups;a.length>0&&(t.data.groups=JSON.parse(JSON.stringify(a)));const r=this.boundingSphere;return null!==r&&(t.data.boundingSphere={center:r.center.toArray(),radius:r.radius}),t}clone(){return(new dct).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const o=t.attributes;for(const t in o){this.setAttribute(t,o[t].clone(e))}const i=t.morphAttributes;for(const t in i){const n=[],o=i[t];for(let t=0,i=o.length;t<i;t++)n.push(o[t].clone(e));this.morphAttributes[t]=n}this.morphTargetsRelative=t.morphTargetsRelative;const a=t.groups;for(let t=0,e=a.length;t<e;t++){const e=a[t];this.addGroup(e.start,e.count,e.materialIndex)}const r=t.boundingBox;null!==r&&(this.boundingBox=r.clone());const s=t.boundingSphere;return null!==s&&(this.boundingSphere=s.clone()),this.drawRange.start=t.drawRange.start,this.drawRange.count=t.drawRange.count,this.userData=t.userData,this}dispose(){this.dispatchEvent({type:"dispose"})}}dct.prototype.isBufferGeometry=!0;const pct=new olt,mct=new nlt,fct=new Zst,uct=new wst,gct=new wst,hct=new wst,bct=new wst,yct=new wst,_ct=new wst,Cct=new wst,Mct=new wst,vct=new wst,xct=new gst,Oct=new gst,Pct=new gst,wct=new wst,kct=new wst;class Sct extends Slt{constructor(t=new dct,e=new Xlt){super(),this.type="Mesh",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),void 0!==t.morphTargetInfluences&&(this.morphTargetInfluences=t.morphTargetInfluences.slice()),void 0!==t.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},t.morphTargetDictionary)),this.material=t.material,this.geometry=t.geometry,this}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}}raycast(t,e){const n=this.geometry,o=this.material,i=this.matrixWorld;if(void 0===o)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),fct.copy(n.boundingSphere),fct.applyMatrix4(i),!1===t.ray.intersectsSphere(fct))return;if(pct.copy(i).invert(),mct.copy(t.ray).applyMatrix4(pct),null!==n.boundingBox&&!1===mct.intersectsBox(n.boundingBox))return;let a;if(n.isBufferGeometry){const i=n.index,r=n.attributes.position,s=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,d=n.attributes.uv2,p=n.groups,m=n.drawRange;if(null!==i)if(Array.isArray(o))for(let n=0,f=p.length;n<f;n++){const f=p[n],u=o[f.materialIndex];for(let n=Math.max(f.start,m.start),o=Math.min(f.start+f.count,m.start+m.count);n<o;n+=3){const o=i.getX(n),p=i.getX(n+1),m=i.getX(n+2);a=Dct(this,u,t,mct,r,s,l,c,d,o,p,m),a&&(a.faceIndex=Math.floor(n/3),a.face.materialIndex=f.materialIndex,e.push(a))}}else{for(let n=Math.max(0,m.start),p=Math.min(i.count,m.start+m.count);n<p;n+=3){const p=i.getX(n),m=i.getX(n+1),f=i.getX(n+2);a=Dct(this,o,t,mct,r,s,l,c,d,p,m,f),a&&(a.faceIndex=Math.floor(n/3),e.push(a))}}else if(void 0!==r)if(Array.isArray(o))for(let n=0,i=p.length;n<i;n++){const i=p[n],f=o[i.materialIndex];for(let n=Math.max(i.start,m.start),o=Math.min(i.start+i.count,m.start+m.count);n<o;n+=3){a=Dct(this,f,t,mct,r,s,l,c,d,n,n+1,n+2),a&&(a.faceIndex=Math.floor(n/3),a.face.materialIndex=i.materialIndex,e.push(a))}}else{for(let n=Math.max(0,m.start),i=Math.min(r.count,m.start+m.count);n<i;n+=3){a=Dct(this,o,t,mct,r,s,l,c,d,n,n+1,n+2),a&&(a.faceIndex=Math.floor(n/3),e.push(a))}}}else n.isGeometry&&console.error("THREE.Mesh.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}}function Dct(t,e,n,o,i,a,r,s,l,c,d,p){uct.fromBufferAttribute(i,c),gct.fromBufferAttribute(i,d),hct.fromBufferAttribute(i,p);const m=t.morphTargetInfluences;if(a&&m){Cct.set(0,0,0),Mct.set(0,0,0),vct.set(0,0,0);for(let t=0,e=a.length;t<e;t++){const e=m[t],n=a[t];0!==e&&(bct.fromBufferAttribute(n,c),yct.fromBufferAttribute(n,d),_ct.fromBufferAttribute(n,p),r?(Cct.addScaledVector(bct,e),Mct.addScaledVector(yct,e),vct.addScaledVector(_ct,e)):(Cct.addScaledVector(bct.sub(uct),e),Mct.addScaledVector(yct.sub(gct),e),vct.addScaledVector(_ct.sub(hct),e)))}uct.add(Cct),gct.add(Mct),hct.add(vct)}t.isSkinnedMesh&&(t.boneTransform(c,uct),t.boneTransform(d,gct),t.boneTransform(p,hct));const f=(function u(t,e,n,o,i,a,r,s){let l;if(l=1===e.side?o.intersectTriangle(r,a,i,!0,s):o.intersectTriangle(i,a,r,2!==e.side,s),null===l)return null;kct.copy(s),kct.applyMatrix4(t.matrixWorld);const c=n.ray.origin.distanceTo(kct);return c<n.near||c>n.far?null:{distance:c,point:kct.clone(),object:t}})(t,e,n,o,uct,gct,hct,wct);if(f){s&&(xct.fromBufferAttribute(s,c),Oct.fromBufferAttribute(s,d),Pct.fromBufferAttribute(s,p),f.uv=Llt.getUV(wct,uct,gct,hct,xct,Oct,Pct,new gst)),l&&(xct.fromBufferAttribute(l,c),Oct.fromBufferAttribute(l,d),Pct.fromBufferAttribute(l,p),f.uv2=Llt.getUV(wct,uct,gct,hct,xct,Oct,Pct,new gst));const t={a:c,b:d,c:p,normal:new wst,materialIndex:0};Llt.getNormal(uct,gct,hct,t.normal),f.face=t}return f}Sct.prototype.isMesh=!0;class Rct extends dct{constructor(t=1,e=1,n=1,o=1,i=1,a=1){super(),this.type="BoxGeometry",this.parameters={width:t,height:e,depth:n,widthSegments:o,heightSegments:i,depthSegments:a};const r=this;o=Math.floor(o),i=Math.floor(i),a=Math.floor(a);const s=[],l=[],c=[],d=[];let p=0,m=0;function f(t,e,n,o,i,a,f,u,g,h,b){const y=a/g,_=f/h,C=a/2,M=f/2,v=u/2,x=g+1,O=h+1;let P=0,w=0;const k=new wst;for(let a=0;a<O;a++){const r=a*_-M;for(let s=0;s<x;s++){k[t]=(s*y-C)*o,k[e]=r*i,k[n]=v,l.push(k.x,k.y,k.z),k[t]=0,k[e]=0,k[n]=u>0?1:-1,c.push(k.x,k.y,k.z),d.push(s/g),d.push(1-a/h),P+=1}}for(let t=0;t<h;t++)for(let e=0;e<g;e++){const n=p+e+x*(t+1),o=p+(e+1)+x*(t+1),i=p+(e+1)+x*t;s.push(p+e+x*t,n,i),s.push(n,o,i),w+=6}r.addGroup(m,w,b),m+=w,p+=P}f("z","y","x",-1,-1,n,e,t,a,i,0),f("z","y","x",1,-1,n,e,-t,a,i,1),f("x","z","y",1,1,t,n,e,o,a,2),f("x","z","y",1,-1,t,n,-e,o,a,3),f("x","y","z",1,-1,t,e,n,o,i,4),f("x","y","z",-1,-1,t,e,-n,o,i,5),this.setIndex(s),this.setAttribute("position",new ect(l,3)),this.setAttribute("normal",new ect(c,3)),this.setAttribute("uv",new ect(d,2))}static fromJSON(t){return new Rct(t.width,t.height,t.depth,t.widthSegments,t.heightSegments,t.depthSegments)}}function Ect(t){const e={};for(const n in t){e[n]={};for(const o in t[n]){const i=t[n][o];e[n][o]=i&&(i.isColor||i.isMatrix3||i.isMatrix4||i.isVector2||i.isVector3||i.isVector4||i.isTexture||i.isQuaternion)?i.clone():Array.isArray(i)?i.slice():i}}return e}function Act(t){const e={};for(let n=0;n<t.length;n++){const o=Ect(t[n]);for(const t in o)e[t]=o[t]}return e}const Tct={clone:Ect,merge:Act};class Nct extends Vlt{constructor(t){super(),this.type="ShaderMaterial",this.defines={},this.uniforms={},this.vertexShader="void main() {\n\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n}",this.fragmentShader="void main() {\n\tgl_FragColor = vec4( 1.0, 0.0, 0.0, 1.0 );\n}",this.linewidth=1,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.lights=!1,this.clipping=!1,this.extensions={derivatives:!1,fragDepth:!1,drawBuffers:!1,shaderTextureLOD:!1},this.defaultAttributeValues={color:[1,1,1],uv:[0,0],uv2:[0,0]},this.index0AttributeName=void 0,this.uniformsNeedUpdate=!1,this.glslVersion=null,void 0!==t&&(void 0!==t.attributes&&console.error("THREE.ShaderMaterial: attributes should now be defined in THREE.BufferGeometry instead."),this.setValues(t))}copy(t){return super.copy(t),this.fragmentShader=t.fragmentShader,this.vertexShader=t.vertexShader,this.uniforms=Ect(t.uniforms),this.defines=Object.assign({},t.defines),this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.lights=t.lights,this.clipping=t.clipping,this.extensions=Object.assign({},t.extensions),this.glslVersion=t.glslVersion,this}toJSON(t){const e=super.toJSON(t);e.glslVersion=this.glslVersion,e.uniforms={};for(const n in this.uniforms){const o=this.uniforms[n].value;e.uniforms[n]=o&&o.isTexture?{type:"t",value:o.toJSON(t).uuid}:o&&o.isColor?{type:"c",value:o.getHex()}:o&&o.isVector2?{type:"v2",value:o.toArray()}:o&&o.isVector3?{type:"v3",value:o.toArray()}:o&&o.isVector4?{type:"v4",value:o.toArray()}:o&&o.isMatrix3?{type:"m3",value:o.toArray()}:o&&o.isMatrix4?{type:"m4",value:o.toArray()}:{value:o}}Object.keys(this.defines).length>0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}Nct.prototype.isShaderMaterial=!0;class zct extends Slt{constructor(){super(),this.type="Camera",this.matrixWorldInverse=new olt,this.projectionMatrix=new olt,this.projectionMatrixInverse=new olt}copy(t,e){return super.copy(t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()}updateMatrixWorld(t){super.updateMatrixWorld(t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(t,e){super.updateWorldMatrix(t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return(new this.constructor).copy(this)}}zct.prototype.isCamera=!0;class Ict extends zct{constructor(t=50,e=1,n=.1,o=2e3){super(),this.type="PerspectiveCamera",this.fov=t,this.zoom=1,this.near=n,this.far=o,this.focus=10,this.aspect=e,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.fov=t.fov,this.zoom=t.zoom,this.near=t.near,this.far=t.far,this.focus=t.focus,this.aspect=t.aspect,this.view=null===t.view?null:Object.assign({},t.view),this.filmGauge=t.filmGauge,this.filmOffset=t.filmOffset,this}setFocalLength(t){const e=.5*this.getFilmHeight()/t;this.fov=2*cst*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*lst*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*cst*Math.atan(Math.tan(.5*lst*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}setViewOffset(t,e,n,o,i,a){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=o,this.view.width=i,this.view.height=a,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*lst*this.fov)/this.zoom,n=2*e,o=this.aspect*n,i=-.5*o;const a=this.view;if(null!==this.view&&this.view.enabled){const t=a.fullWidth,r=a.fullHeight;i+=a.offsetX*o/t,e-=a.offsetY*n/r,o*=a.width/t,n*=a.height/r}const r=this.filmOffset;0!==r&&(i+=t*r/this.getFilmWidth()),this.projectionMatrix.makePerspective(i,i+o,e,e-n,t,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.fov=this.fov,e.object.zoom=this.zoom,e.object.near=this.near,e.object.far=this.far,e.object.focus=this.focus,e.object.aspect=this.aspect,null!==this.view&&(e.object.view=Object.assign({},this.view)),e.object.filmGauge=this.filmGauge,e.object.filmOffset=this.filmOffset,e}}Ict.prototype.isPerspectiveCamera=!0;const Hct=90;class Fct extends Slt{constructor(t,e,n){if(super(),this.type="CubeCamera",!0!==n.isWebGLCubeRenderTarget)return void console.error("THREE.CubeCamera: The constructor now expects an instance of WebGLCubeRenderTarget as third parameter.");this.renderTarget=n;const o=new Ict(Hct,1,t,e);o.layers=this.layers,o.up.set(0,-1,0),o.lookAt(new wst(1,0,0)),this.add(o);const i=new Ict(Hct,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new wst(-1,0,0)),this.add(i);const a=new Ict(Hct,1,t,e);a.layers=this.layers,a.up.set(0,0,1),a.lookAt(new wst(0,1,0)),this.add(a);const r=new Ict(Hct,1,t,e);r.layers=this.layers,r.up.set(0,0,-1),r.lookAt(new wst(0,-1,0)),this.add(r);const s=new Ict(Hct,1,t,e);s.layers=this.layers,s.up.set(0,-1,0),s.lookAt(new wst(0,0,1)),this.add(s);const l=new Ict(Hct,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new wst(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[o,i,a,r,s,l]=this.children,c=t.xr.enabled,d=t.getRenderTarget();t.xr.enabled=!1;const p=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,o),t.setRenderTarget(n,1),t.render(e,i),t.setRenderTarget(n,2),t.render(e,a),t.setRenderTarget(n,3),t.render(e,r),t.setRenderTarget(n,4),t.render(e,s),n.texture.generateMipmaps=p,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(d),t.xr.enabled=c}}class Lct extends Cst{constructor(t,e,n,o,i,a,r,s,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:wrt,n,o,i,a,r=void 0!==r?r:Vrt,s,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}Lct.prototype.isCubeTexture=!0;class Bct extends xst{constructor(t,e,n){Number.isInteger(e)&&(console.warn("THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )"),e=n),super(t,t,e),this.texture=new Lct(void 0,(e=e||{}).mapping,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.format,e.type,e.anisotropy,e.encoding),this.texture.isRenderTargetTexture=!0,this.texture.generateMipmaps=void 0!==e.generateMipmaps&&e.generateMipmaps,this.texture.minFilter=void 0!==e.minFilter?e.minFilter:Trt,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=jrt,this.texture.encoding=e.encoding,this.texture.generateMipmaps=e.generateMipmaps,this.texture.minFilter=e.minFilter,this.texture.magFilter=e.magFilter;const n={uniforms:{tEquirect:{value:null}},vertexShader:"\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\n\t\t\t\t\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n\n\t\t\t\t}\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvWorldDirection = transformDirection( position, modelMatrix );\n\n\t\t\t\t\t#include <begin_vertex>\n\t\t\t\t\t#include <project_vertex>\n\n\t\t\t\t}\n\t\t\t",fragmentShader:"\n\n\t\t\t\tuniform sampler2D tEquirect;\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\t#include <common>\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvec3 direction = normalize( vWorldDirection );\n\n\t\t\t\t\tvec2 sampleUV = equirectUv( direction );\n\n\t\t\t\t\tgl_FragColor = texture2D( tEquirect, sampleUV );\n\n\t\t\t\t}\n\t\t\t"},o=new Rct(5,5,5),i=new Nct({name:"CubemapFromEquirect",uniforms:Ect(n.uniforms),vertexShader:n.vertexShader,fragmentShader:n.fragmentShader,side:1,blending:0});i.uniforms.tEquirect.value=e;const a=new Sct(o,i),r=e.minFilter;e.minFilter===Nrt&&(e.minFilter=Trt);return new Fct(1,10,this).update(t,a),e.minFilter=r,a.geometry.dispose(),a.material.dispose(),this}clear(t,e,n,o){const i=t.getRenderTarget();for(let i=0;i<6;i++)t.setRenderTarget(this,i),t.clear(e,n,o);t.setRenderTarget(i)}}Bct.prototype.isWebGLCubeRenderTarget=!0;const Vct=new wst,jct=new wst,Uct=new hst;class Wct{constructor(t=new wst(1,0,0),e=0){this.normal=t,this.constant=e}set(t,e){return this.normal.copy(t),this.constant=e,this}setComponents(t,e,n,o){return this.normal.set(t,e,n),this.constant=o,this}setFromNormalAndCoplanarPoint(t,e){return this.normal.copy(t),this.constant=-e.dot(this.normal),this}setFromCoplanarPoints(t,e,n){const o=Vct.subVectors(n,e).cross(jct.subVectors(t,e)).normalize();return this.setFromNormalAndCoplanarPoint(o,t),this}copy(t){return this.normal.copy(t.normal),this.constant=t.constant,this}normalize(){const t=1/this.normal.length();return this.normal.multiplyScalar(t),this.constant*=t,this}negate(){return this.constant*=-1,this.normal.negate(),this}distanceToPoint(t){return this.normal.dot(t)+this.constant}distanceToSphere(t){return this.distanceToPoint(t.center)-t.radius}projectPoint(t,e){return e.copy(this.normal).multiplyScalar(-this.distanceToPoint(t)).add(t)}intersectLine(t,e){const n=t.delta(Vct),o=this.normal.dot(n);if(0===o)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const i=-(t.start.dot(this.normal)+this.constant)/o;return i<0||i>1?null:e.copy(n).multiplyScalar(i).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||Uct.getNormalMatrix(t),o=this.coplanarPoint(Vct).applyMatrix4(t),i=this.normal.applyMatrix3(n).normalize();return this.constant=-o.dot(i),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}Wct.prototype.isPlane=!0;const Gct=new Zst,Yct=new wst;class qct{constructor(t=new Wct,e=new Wct,n=new Wct,o=new Wct,i=new Wct,a=new Wct){this.planes=[t,e,n,o,i,a]}set(t,e,n,o,i,a){const r=this.planes;return r[0].copy(t),r[1].copy(e),r[2].copy(n),r[3].copy(o),r[4].copy(i),r[5].copy(a),this}copy(t){const e=this.planes;for(let n=0;n<6;n++)e[n].copy(t.planes[n]);return this}setFromProjectionMatrix(t){const e=this.planes,n=t.elements,o=n[0],i=n[1],a=n[2],r=n[3],s=n[4],l=n[5],c=n[6],d=n[7],p=n[8],m=n[9],f=n[10],u=n[11],g=n[12],h=n[13],b=n[14],y=n[15];return e[0].setComponents(r-o,d-s,u-p,y-g).normalize(),e[1].setComponents(r+o,d+s,u+p,y+g).normalize(),e[2].setComponents(r+i,d+l,u+m,y+h).normalize(),e[3].setComponents(r-i,d-l,u-m,y-h).normalize(),e[4].setComponents(r-a,d-c,u-f,y-b).normalize(),e[5].setComponents(r+a,d+c,u+f,y+b).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),Gct.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(Gct)}intersectsSprite(t){return Gct.center.set(0,0,0),Gct.radius=.7071067811865476,Gct.applyMatrix4(t.matrixWorld),this.intersectsSphere(Gct)}intersectsSphere(t){const e=this.planes,n=t.center,o=-t.radius;for(let t=0;t<6;t++){if(e[t].distanceToPoint(n)<o)return!1}return!0}intersectsBox(t){const e=this.planes;for(let n=0;n<6;n++){const o=e[n];if(Yct.x=o.normal.x>0?t.max.x:t.min.x,Yct.y=o.normal.y>0?t.max.y:t.min.y,Yct.z=o.normal.z>0?t.max.z:t.min.z,o.distanceToPoint(Yct)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let n=0;n<6;n++)if(e[n].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}function Zct(){let t=null,e=!1,n=null,o=null;function i(e,a){n(e,a),o=t.requestAnimationFrame(i)}return{start:function(){!0!==e&&null!==n&&(o=t.requestAnimationFrame(i),e=!0)},stop:function(){t.cancelAnimationFrame(o),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function Xct(t,e){const n=e.isWebGL2,o=new WeakMap;return{get:function i(t){return t.isInterleavedBufferAttribute&&(t=t.data),o.get(t)},remove:function a(e){e.isInterleavedBufferAttribute&&(e=e.data);const n=o.get(e);n&&(t.deleteBuffer(n.buffer),o.delete(e))},update:function r(e,i){if(e.isGLBufferAttribute){const t=o.get(e);return void((!t||t.version<e.version)&&o.set(e,{buffer:e.buffer,type:e.type,bytesPerElement:e.elementSize,version:e.version}))}e.isInterleavedBufferAttribute&&(e=e.data);const a=o.get(e);void 0===a?o.set(e,(function r(e,o){const i=e.array,a=e.usage,r=t.createBuffer();t.bindBuffer(o,r),t.bufferData(o,i,a),e.onUploadCallback();let s=5126;return i instanceof Float32Array?s=5126:i instanceof Float64Array?console.warn("THREE.WebGLAttributes: Unsupported data buffer format: Float64Array."):i instanceof Uint16Array?e.isFloat16BufferAttribute?n?s=5131:console.warn("THREE.WebGLAttributes: Usage of Float16BufferAttribute requires WebGL2."):s=5123:i instanceof Int16Array?s=5122:i instanceof Uint32Array?s=5125:i instanceof Int32Array?s=5124:i instanceof Int8Array?s=5120:(i instanceof Uint8Array||i instanceof Uint8ClampedArray)&&(s=5121),{buffer:r,type:s,bytesPerElement:i.BYTES_PER_ELEMENT,version:e.version}})(e,i)):a.version<e.version&&(!(function s(e,o,i){const a=o.array,r=o.updateRange;t.bindBuffer(i,e),-1===r.count?t.bufferSubData(i,0,a):(n?t.bufferSubData(i,r.offset*a.BYTES_PER_ELEMENT,a,r.offset,r.count):t.bufferSubData(i,r.offset*a.BYTES_PER_ELEMENT,a.subarray(r.offset,r.offset+r.count)),r.count=-1)})(a.buffer,e,i),a.version=e.version)}}}class Kct extends dct{constructor(t=1,e=1,n=1,o=1){super(),this.type="PlaneGeometry",this.parameters={width:t,height:e,widthSegments:n,heightSegments:o};const i=t/2,a=e/2,r=Math.floor(n),s=Math.floor(o),l=r+1,c=s+1,d=t/r,p=e/s,m=[],f=[],u=[],g=[];for(let t=0;t<c;t++){const e=t*p-a;for(let n=0;n<l;n++){f.push(n*d-i,-e,0),u.push(0,0,1),g.push(n/r),g.push(1-t/s)}}for(let t=0;t<s;t++)for(let e=0;e<r;e++){const n=e+l*(t+1),o=e+1+l*(t+1),i=e+1+l*t;m.push(e+l*t,n,i),m.push(n,o,i)}this.setIndex(m),this.setAttribute("position",new ect(f,3)),this.setAttribute("normal",new ect(u,3)),this.setAttribute("uv",new ect(g,2))}static fromJSON(t){return new Kct(t.width,t.height,t.widthSegments,t.heightSegments)}}const Jct={alphamap_fragment:"#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\n#endif",alphamap_pars_fragment:"#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",alphatest_fragment:"#ifdef ALPHATEST\n\tif ( diffuseColor.a < ALPHATEST ) discard;\n#endif",aomap_fragment:"#ifdef USE_AOMAP\n\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\n\treflectedLight.indirectDiffuse *= ambientOcclusion;\n\t#if defined( USE_ENVMAP ) && defined( STANDARD )\n\t\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.specularRoughness );\n\t#endif\n#endif",aomap_pars_fragment:"#ifdef USE_AOMAP\n\tuniform sampler2D aoMap;\n\tuniform float aoMapIntensity;\n#endif",begin_vertex:"vec3 transformed = vec3( position );",beginnormal_vertex:"vec3 objectNormal = vec3( normal );\n#ifdef USE_TANGENT\n\tvec3 objectTangent = vec3( tangent.xyz );\n#endif",bsdfs:"vec2 integrateSpecularBRDF( const in float dotNV, const in float roughness ) {\n\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\n\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\n\tvec4 r = roughness * c0 + c1;\n\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\n\treturn vec2( -1.04, 1.04 ) * a004 + r.zw;\n}\nfloat punctualLightIntensityToIrradianceFactor( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\n#if defined ( PHYSICALLY_CORRECT_LIGHTS )\n\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\n\tif( cutoffDistance > 0.0 ) {\n\t\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\n\t}\n\treturn distanceFalloff;\n#else\n\tif( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\n\t\treturn pow( saturate( -lightDistance / cutoffDistance + 1.0 ), decayExponent );\n\t}\n\treturn 1.0;\n#endif\n}\nvec3 BRDF_Diffuse_Lambert( const in vec3 diffuseColor ) {\n\treturn RECIPROCAL_PI * diffuseColor;\n}\nvec3 F_Schlick( const in vec3 f0, const in vec3 f90, const in float dotVH ) {\n\tfloat fresnel = exp2( ( -5.55473 * dotVH - 6.98316 ) * dotVH );\n\treturn ( f90 - f0 ) * fresnel + f0;\n}\nvec3 F_Schlick_RoughnessDependent( const in vec3 F0, const in float dotNV, const in float roughness ) {\n\tfloat fresnel = exp2( ( -5.55473 * dotNV - 6.98316 ) * dotNV );\n\tvec3 Fr = max( vec3( 1.0 - roughness ), F0 ) - F0;\n\treturn Fr * fresnel + F0;\n}\nfloat G_GGX_Smith( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 = pow2( alpha );\n\tfloat gl = dotNL + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\tfloat gv = dotNV + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\treturn 1.0 / ( gl * gv );\n}\nfloat G_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 = pow2( alpha );\n\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\treturn 0.5 / max( gv + gl, EPSILON );\n}\nfloat D_GGX( const in float alpha, const in float dotNH ) {\n\tfloat a2 = pow2( alpha );\n\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;\n\treturn RECIPROCAL_PI * a2 / pow2( denom );\n}\nvec3 BRDF_Specular_GGX( const in IncidentLight incidentLight, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in vec3 f90, const in float roughness ) {\n\tfloat alpha = pow2( roughness );\n\tvec3 halfDir = normalize( incidentLight.direction + viewDir );\n\tfloat dotNL = saturate( dot( normal, incidentLight.direction ) );\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F = F_Schlick( f0, f90, dotLH );\n\tfloat G = G_GGX_SmithCorrelated( alpha, dotNL, dotNV );\n\tfloat D = D_GGX( alpha, dotNH );\n\treturn F * ( G * D );\n}\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\n\tconst float LUT_SIZE = 64.0;\n\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\n\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\n\tfloat dotNV = saturate( dot( N, V ) );\n\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\n\tuv = uv * LUT_SCALE + LUT_BIAS;\n\treturn uv;\n}\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\n\tfloat l = length( f );\n\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\n}\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\n\tfloat x = dot( v1, v2 );\n\tfloat y = abs( x );\n\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\n\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\n\tfloat v = a / b;\n\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\n\treturn cross( v1, v2 ) * theta_sintheta;\n}\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\n\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\n\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\n\tvec3 lightNormal = cross( v1, v2 );\n\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\n\tvec3 T1, T2;\n\tT1 = normalize( V - N * dot( V, N ) );\n\tT2 = - cross( N, T1 );\n\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\n\tvec3 coords[ 4 ];\n\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\n\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\n\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\n\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\n\tcoords[ 0 ] = normalize( coords[ 0 ] );\n\tcoords[ 1 ] = normalize( coords[ 1 ] );\n\tcoords[ 2 ] = normalize( coords[ 2 ] );\n\tcoords[ 3 ] = normalize( coords[ 3 ] );\n\tvec3 vectorFormFactor = vec3( 0.0 );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\n\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\n\treturn vec3( result );\n}\nvec3 BRDF_Specular_GGX_Environment( const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float roughness ) {\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\n\treturn specularColor * brdf.x + brdf.y;\n}\nvoid BRDF_Specular_Multiscattering_Environment( const in GeometricContext geometry, const in vec3 specularColor, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\n\tvec3 F = F_Schlick_RoughnessDependent( specularColor, dotNV, roughness );\n\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\n\tvec3 FssEss = F * brdf.x + brdf.y;\n\tfloat Ess = brdf.x + brdf.y;\n\tfloat Ems = 1.0 - Ess;\n\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619;\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\n\tsingleScatter += FssEss;\n\tmultiScatter += Fms * Ems;\n}\nfloat G_BlinnPhong_Implicit( ) {\n\treturn 0.25;\n}\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\n\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\n}\nvec3 BRDF_Specular_BlinnPhong( const in IncidentLight incidentLight, const in GeometricContext geometry, const in vec3 specularColor, const in float shininess ) {\n\tvec3 halfDir = normalize( incidentLight.direction + geometry.viewDir );\n\tfloat dotNH = saturate( dot( geometry.normal, halfDir ) );\n\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F = F_Schlick( specularColor, vec3( 1.0 ), dotLH );\n\tfloat G = G_BlinnPhong_Implicit( );\n\tfloat D = D_BlinnPhong( shininess, dotNH );\n\treturn F * ( G * D );\n}\nfloat GGXRoughnessToBlinnExponent( const in float ggxRoughness ) {\n\treturn ( 2.0 / pow2( ggxRoughness + 0.0001 ) - 2.0 );\n}\nfloat BlinnExponentToGGXRoughness( const in float blinnExponent ) {\n\treturn sqrt( 2.0 / ( blinnExponent + 2.0 ) );\n}\n#if defined( USE_SHEEN )\nfloat D_Charlie(float roughness, float NoH) {\n\tfloat invAlpha = 1.0 / roughness;\n\tfloat cos2h = NoH * NoH;\n\tfloat sin2h = max(1.0 - cos2h, 0.0078125);\treturn (2.0 + invAlpha) * pow(sin2h, invAlpha * 0.5) / (2.0 * PI);\n}\nfloat V_Neubelt(float NoV, float NoL) {\n\treturn saturate(1.0 / (4.0 * (NoL + NoV - NoL * NoV)));\n}\nvec3 BRDF_Specular_Sheen( const in float roughness, const in vec3 L, const in GeometricContext geometry, vec3 specularColor ) {\n\tvec3 N = geometry.normal;\n\tvec3 V = geometry.viewDir;\n\tvec3 H = normalize( V + L );\n\tfloat dotNH = saturate( dot( N, H ) );\n\treturn specularColor * D_Charlie( roughness, dotNH ) * V_Neubelt( dot(N, V), dot(N, L) );\n}\n#endif",bumpmap_pars_fragment:"#ifdef USE_BUMPMAP\n\tuniform sampler2D bumpMap;\n\tuniform float bumpScale;\n\tvec2 dHdxy_fwd() {\n\t\tvec2 dSTdx = dFdx( vUv );\n\t\tvec2 dSTdy = dFdy( vUv );\n\t\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\n\t\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\n\t\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\n\t\treturn vec2( dBx, dBy );\n\t}\n\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\n\t\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\n\t\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\n\t\tvec3 vN = surf_norm;\n\t\tvec3 R1 = cross( vSigmaY, vN );\n\t\tvec3 R2 = cross( vN, vSigmaX );\n\t\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\n\t\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\n\t\treturn normalize( abs( fDet ) * surf_norm - vGrad );\n\t}\n#endif",clipping_planes_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvec4 plane;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\n\t\tplane = clippingPlanes[ i ];\n\t\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\n\t}\n\t#pragma unroll_loop_end\n\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\n\t\tbool clipped = true;\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\n\t\t\tplane = clippingPlanes[ i ];\n\t\t\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t\tif ( clipped ) discard;\n\t#endif\n#endif",clipping_planes_pars_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\n#endif",clipping_planes_pars_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n#endif",clipping_planes_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvClipPosition = - mvPosition.xyz;\n#endif",color_fragment:"#if defined( USE_COLOR_ALPHA )\n\tdiffuseColor *= vColor;\n#elif defined( USE_COLOR )\n\tdiffuseColor.rgb *= vColor;\n#endif",color_pars_fragment:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR )\n\tvarying vec3 vColor;\n#endif",color_pars_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvarying vec3 vColor;\n#endif",color_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvColor = vec4( 1.0 );\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvColor = vec3( 1.0 );\n#endif\n#ifdef USE_COLOR\n\tvColor *= color;\n#endif\n#ifdef USE_INSTANCING_COLOR\n\tvColor.xyz *= instanceColor.xyz;\n#endif",common:"#define PI 3.141592653589793\n#define PI2 6.283185307179586\n#define PI_HALF 1.5707963267948966\n#define RECIPROCAL_PI 0.3183098861837907\n#define RECIPROCAL_PI2 0.15915494309189535\n#define EPSILON 1e-6\n#ifndef saturate\n#define saturate(a) clamp( a, 0.0, 1.0 )\n#endif\n#define whiteComplement(a) ( 1.0 - saturate( a ) )\nfloat pow2( const in float x ) { return x*x; }\nfloat pow3( const in float x ) { return x*x*x; }\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\nhighp float rand( const in vec2 uv ) {\n\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\n\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\n\treturn fract(sin(sn) * c);\n}\n#ifdef HIGH_PRECISION\n\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\n#else\n\tfloat max3( vec3 v ) { return max( max( v.x, v.y ), v.z ); }\n\tfloat precisionSafeLength( vec3 v ) {\n\t\tfloat maxComponent = max3( abs( v ) );\n\t\treturn length( v / maxComponent ) * maxComponent;\n\t}\n#endif\nstruct IncidentLight {\n\tvec3 color;\n\tvec3 direction;\n\tbool visible;\n};\nstruct ReflectedLight {\n\tvec3 directDiffuse;\n\tvec3 directSpecular;\n\tvec3 indirectDiffuse;\n\tvec3 indirectSpecular;\n};\nstruct GeometricContext {\n\tvec3 position;\n\tvec3 normal;\n\tvec3 viewDir;\n#ifdef CLEARCOAT\n\tvec3 clearcoatNormal;\n#endif\n};\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n}\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\n}\nvec3 projectOnPlane(in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\tfloat distance = dot( planeNormal, point - pointOnPlane );\n\treturn - distance * planeNormal + point;\n}\nfloat sideOfPlane( in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn sign( dot( point - pointOnPlane, planeNormal ) );\n}\nvec3 linePlaneIntersect( in vec3 pointOnLine, in vec3 lineDirection, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn lineDirection * ( dot( planeNormal, pointOnPlane - pointOnLine ) / dot( planeNormal, lineDirection ) ) + pointOnLine;\n}\nmat3 transposeMat3( const in mat3 m ) {\n\tmat3 tmp;\n\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\n\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\n\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\n\treturn tmp;\n}\nfloat linearToRelativeLuminance( const in vec3 color ) {\n\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\n\treturn dot( weights, color.rgb );\n}\nbool isPerspectiveMatrix( mat4 m ) {\n\treturn m[ 2 ][ 3 ] == - 1.0;\n}\nvec2 equirectUv( in vec3 dir ) {\n\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\n\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\treturn vec2( u, v );\n}",cube_uv_reflection_fragment:"#ifdef ENVMAP_TYPE_CUBE_UV\n\t#define cubeUV_maxMipLevel 8.0\n\t#define cubeUV_minMipLevel 4.0\n\t#define cubeUV_maxTileSize 256.0\n\t#define cubeUV_minTileSize 16.0\n\tfloat getFace( vec3 direction ) {\n\t\tvec3 absDirection = abs( direction );\n\t\tfloat face = - 1.0;\n\t\tif ( absDirection.x > absDirection.z ) {\n\t\t\tif ( absDirection.x > absDirection.y )\n\t\t\t\tface = direction.x > 0.0 ? 0.0 : 3.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t} else {\n\t\t\tif ( absDirection.z > absDirection.y )\n\t\t\t\tface = direction.z > 0.0 ? 2.0 : 5.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t}\n\t\treturn face;\n\t}\n\tvec2 getUV( vec3 direction, float face ) {\n\t\tvec2 uv;\n\t\tif ( face == 0.0 ) {\n\t\t\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 1.0 ) {\n\t\t\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\n\t\t} else if ( face == 2.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\n\t\t} else if ( face == 3.0 ) {\n\t\t\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 4.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\n\t\t} else {\n\t\t\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\n\t\t}\n\t\treturn 0.5 * ( uv + 1.0 );\n\t}\n\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\n\t\tfloat face = getFace( direction );\n\t\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\n\t\tmipInt = max( mipInt, cubeUV_minMipLevel );\n\t\tfloat faceSize = exp2( mipInt );\n\t\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\n\t\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\n\t\tvec2 f = fract( uv );\n\t\tuv += 0.5 - f;\n\t\tif ( face > 2.0 ) {\n\t\t\tuv.y += faceSize;\n\t\t\tface -= 3.0;\n\t\t}\n\t\tuv.x += face * faceSize;\n\t\tif ( mipInt < cubeUV_maxMipLevel ) {\n\t\t\tuv.y += 2.0 * cubeUV_maxTileSize;\n\t\t}\n\t\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\n\t\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\n\t\tuv *= texelSize;\n\t\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.x += texelSize;\n\t\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.y += texelSize;\n\t\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.x -= texelSize;\n\t\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tvec3 tm = mix( tl, tr, f.x );\n\t\tvec3 bm = mix( bl, br, f.x );\n\t\treturn mix( tm, bm, f.y );\n\t}\n\t#define r0 1.0\n\t#define v0 0.339\n\t#define m0 - 2.0\n\t#define r1 0.8\n\t#define v1 0.276\n\t#define m1 - 1.0\n\t#define r4 0.4\n\t#define v4 0.046\n\t#define m4 2.0\n\t#define r5 0.305\n\t#define v5 0.016\n\t#define m5 3.0\n\t#define r6 0.21\n\t#define v6 0.0038\n\t#define m6 4.0\n\tfloat roughnessToMip( float roughness ) {\n\t\tfloat mip = 0.0;\n\t\tif ( roughness >= r1 ) {\n\t\t\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\n\t\t} else if ( roughness >= r4 ) {\n\t\t\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\n\t\t} else if ( roughness >= r5 ) {\n\t\t\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\n\t\t} else if ( roughness >= r6 ) {\n\t\t\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\n\t\t} else {\n\t\t\tmip = - 2.0 * log2( 1.16 * roughness );\t\t}\n\t\treturn mip;\n\t}\n\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\n\t\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\n\t\tfloat mipF = fract( mip );\n\t\tfloat mipInt = floor( mip );\n\t\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\n\t\tif ( mipF == 0.0 ) {\n\t\t\treturn vec4( color0, 1.0 );\n\t\t} else {\n\t\t\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\n\t\t\treturn vec4( mix( color0, color1, mipF ), 1.0 );\n\t\t}\n\t}\n#endif",defaultnormal_vertex:"vec3 transformedNormal = objectNormal;\n#ifdef USE_INSTANCING\n\tmat3 m = mat3( instanceMatrix );\n\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\n\ttransformedNormal = m * transformedNormal;\n#endif\ntransformedNormal = normalMatrix * transformedNormal;\n#ifdef FLIP_SIDED\n\ttransformedNormal = - transformedNormal;\n#endif\n#ifdef USE_TANGENT\n\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#ifdef FLIP_SIDED\n\t\ttransformedTangent = - transformedTangent;\n\t#endif\n#endif",displacementmap_pars_vertex:"#ifdef USE_DISPLACEMENTMAP\n\tuniform sampler2D displacementMap;\n\tuniform float displacementScale;\n\tuniform float displacementBias;\n#endif",displacementmap_vertex:"#ifdef USE_DISPLACEMENTMAP\n\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\n#endif",emissivemap_fragment:"#ifdef USE_EMISSIVEMAP\n\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\n\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\n\ttotalEmissiveRadiance *= emissiveColor.rgb;\n#endif",emissivemap_pars_fragment:"#ifdef USE_EMISSIVEMAP\n\tuniform sampler2D emissiveMap;\n#endif",encodings_fragment:"gl_FragColor = linearToOutputTexel( gl_FragColor );",encodings_pars_fragment:"\nvec4 LinearToLinear( in vec4 value ) {\n\treturn value;\n}\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\n}\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\n}\nvec4 sRGBToLinear( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\n}\nvec4 LinearTosRGB( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\n}\nvec4 RGBEToLinear( in vec4 value ) {\n\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\n}\nvec4 LinearToRGBE( in vec4 value ) {\n\tfloat maxComponent = max( max( value.r, value.g ), value.b );\n\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\n\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\n}\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\n}\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\n\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\n\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\n\tM = ceil( M * 255.0 ) / 255.0;\n\treturn vec4( value.rgb / ( M * maxRange ), M );\n}\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\n}\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\n\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\n\tfloat D = max( maxRange / maxRGB, 1.0 );\n\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\n\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\n}\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\nvec4 LinearToLogLuv( in vec4 value ) {\n\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\n\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\n\tvec4 vResult;\n\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\n\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\n\tvResult.w = fract( Le );\n\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\n\treturn vResult;\n}\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\nvec4 LogLuvToLinear( in vec4 value ) {\n\tfloat Le = value.z * 255.0 + value.w;\n\tvec3 Xp_Y_XYZp;\n\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\n\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\n\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\n\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\n\treturn vec4( max( vRGB, 0.0 ), 1.0 );\n}",envmap_fragment:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvec3 cameraToFrag;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\n\t\t#endif\n\t#else\n\t\tvec3 reflectVec = vReflect;\n\t#endif\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\n\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\n\t#else\n\t\tvec4 envColor = vec4( 0.0 );\n\t#endif\n\t#ifndef ENVMAP_TYPE_CUBE_UV\n\t\tenvColor = envMapTexelToLinear( envColor );\n\t#endif\n\t#ifdef ENVMAP_BLENDING_MULTIPLY\n\t\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_MIX )\n\t\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_ADD )\n\t\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\n\t#endif\n#endif",envmap_common_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float envMapIntensity;\n\tuniform float flipEnvMap;\n\tuniform int maxMipLevel;\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tuniform samplerCube envMap;\n\t#else\n\t\tuniform sampler2D envMap;\n\t#endif\n\t\n#endif",envmap_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float reflectivity;\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\tvarying vec3 vWorldPosition;\n\t\tuniform float refractionRatio;\n\t#else\n\t\tvarying vec3 vReflect;\n\t#endif\n#endif",envmap_pars_vertex:"#ifdef USE_ENVMAP\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\t\n\t\tvarying vec3 vWorldPosition;\n\t#else\n\t\tvarying vec3 vReflect;\n\t\tuniform float refractionRatio;\n\t#endif\n#endif",envmap_physical_pars_fragment:"#if defined( USE_ENVMAP )\n\t#ifdef ENVMAP_MODE_REFRACTION\n\t\tuniform float refractionRatio;\n\t#endif\n\tvec3 getLightProbeIndirectIrradiance( const in GeometricContext geometry, const in int maxMIPLevel ) {\n\t\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryVec = vec3( flipEnvMap * worldNormal.x, worldNormal.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor = textureCubeLodEXT( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor = textureCube( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\n\t\t#else\n\t\t\tvec4 envMapColor = vec4( 0.0 );\n\t\t#endif\n\t\treturn PI * envMapColor.rgb * envMapIntensity;\n\t}\n\tfloat getSpecularMIPLevel( const in float roughness, const in int maxMIPLevel ) {\n\t\tfloat maxMIPLevelScalar = float( maxMIPLevel );\n\t\tfloat sigma = PI * roughness * roughness / ( 1.0 + roughness );\n\t\tfloat desiredMIPLevel = maxMIPLevelScalar + log2( sigma );\n\t\treturn clamp( desiredMIPLevel, 0.0, maxMIPLevelScalar );\n\t}\n\tvec3 getLightProbeIndirectRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness, const in int maxMIPLevel ) {\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( -viewDir, normal );\n\t\t\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( -viewDir, normal, refractionRatio );\n\t\t#endif\n\t\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\n\t\tfloat specularMIPLevel = getSpecularMIPLevel( roughness, maxMIPLevel );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryReflectVec = vec3( flipEnvMap * reflectVec.x, reflectVec.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor = textureCubeLodEXT( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor = textureCube( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\n\t\t#endif\n\t\treturn envMapColor.rgb * envMapIntensity;\n\t}\n#endif",envmap_vertex:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvWorldPosition = worldPosition.xyz;\n\t#else\n\t\tvec3 cameraToVertex;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvReflect = reflect( cameraToVertex, worldNormal );\n\t\t#else\n\t\t\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\n\t\t#endif\n\t#endif\n#endif",fog_vertex:"#ifdef USE_FOG\n\tfogDepth = - mvPosition.z;\n#endif",fog_pars_vertex:"#ifdef USE_FOG\n\tvarying float fogDepth;\n#endif",fog_fragment:"#ifdef USE_FOG\n\t#ifdef FOG_EXP2\n\t\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * fogDepth * fogDepth );\n\t#else\n\t\tfloat fogFactor = smoothstep( fogNear, fogFar, fogDepth );\n\t#endif\n\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\n#endif",fog_pars_fragment:"#ifdef USE_FOG\n\tuniform vec3 fogColor;\n\tvarying float fogDepth;\n\t#ifdef FOG_EXP2\n\t\tuniform float fogDensity;\n\t#else\n\t\tuniform float fogNear;\n\t\tuniform float fogFar;\n\t#endif\n#endif",gradientmap_pars_fragment:"#ifdef USE_GRADIENTMAP\n\tuniform sampler2D gradientMap;\n#endif\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\n\tfloat dotNL = dot( normal, lightDirection );\n\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\n\t#ifdef USE_GRADIENTMAP\n\t\treturn texture2D( gradientMap, coord ).rgb;\n\t#else\n\t\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\n\t#endif\n}",lightmap_fragment:"#ifdef USE_LIGHTMAP\n\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\treflectedLight.indirectDiffuse += PI * lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n#endif",lightmap_pars_fragment:"#ifdef USE_LIGHTMAP\n\tuniform sampler2D lightMap;\n\tuniform float lightMapIntensity;\n#endif",lights_lambert_vertex:"vec3 diffuse = vec3( 1.0 );\nGeometricContext geometry;\ngeometry.position = mvPosition.xyz;\ngeometry.normal = normalize( transformedNormal );\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\nGeometricContext backGeometry;\nbackGeometry.position = geometry.position;\nbackGeometry.normal = -geometry.normal;\nbackGeometry.viewDir = geometry.viewDir;\nvLightFront = vec3( 0.0 );\nvIndirectFront = vec3( 0.0 );\n#ifdef DOUBLE_SIDED\n\tvLightBack = vec3( 0.0 );\n\tvIndirectBack = vec3( 0.0 );\n#endif\nIncidentLight directLight;\nfloat dotNL;\nvec3 directLightColor_Diffuse;\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry );\n#ifdef DOUBLE_SIDED\n\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\n\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry );\n#endif\n#if NUM_POINT_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tgetPointDirectLightIrradiance( pointLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tgetSpotDirectLightIrradiance( spotLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_DIR_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tgetDirectionalDirectLightIrradiance( directionalLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry );\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif",lights_pars_begin:"uniform bool receiveShadow;\nuniform vec3 ambientLightColor;\nuniform vec3 lightProbe[ 9 ];\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\n\tfloat x = normal.x, y = normal.y, z = normal.z;\n\tvec3 result = shCoefficients[ 0 ] * 0.886227;\n\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\n\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\n\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\n\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\n\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\n\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\n\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\n\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\n\treturn result;\n}\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in GeometricContext geometry ) {\n\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\n\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\n\treturn irradiance;\n}\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\n\tvec3 irradiance = ambientLightColor;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treturn irradiance;\n}\n#if NUM_DIR_LIGHTS > 0\n\tstruct DirectionalLight {\n\t\tvec3 direction;\n\t\tvec3 color;\n\t};\n\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n\tvoid getDirectionalDirectLightIrradiance( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tdirectLight.color = directionalLight.color;\n\t\tdirectLight.direction = directionalLight.direction;\n\t\tdirectLight.visible = true;\n\t}\n#endif\n#if NUM_POINT_LIGHTS > 0\n\tstruct PointLight {\n\t\tvec3 position;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t};\n\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n\tvoid getPointDirectLightIrradiance( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tvec3 lVector = pointLight.position - geometry.position;\n\t\tdirectLight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tdirectLight.color = pointLight.color;\n\t\tdirectLight.color *= punctualLightIntensityToIrradianceFactor( lightDistance, pointLight.distance, pointLight.decay );\n\t\tdirectLight.visible = ( directLight.color != vec3( 0.0 ) );\n\t}\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\tstruct SpotLight {\n\t\tvec3 position;\n\t\tvec3 direction;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t\tfloat coneCos;\n\t\tfloat penumbraCos;\n\t};\n\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\n\tvoid getSpotDirectLightIrradiance( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tvec3 lVector = spotLight.position - geometry.position;\n\t\tdirectLight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tfloat angleCos = dot( directLight.direction, spotLight.direction );\n\t\tif ( angleCos > spotLight.coneCos ) {\n\t\t\tfloat spotEffect = smoothstep( spotLight.coneCos, spotLight.penumbraCos, angleCos );\n\t\t\tdirectLight.color = spotLight.color;\n\t\t\tdirectLight.color *= spotEffect * punctualLightIntensityToIrradianceFactor( lightDistance, spotLight.distance, spotLight.decay );\n\t\t\tdirectLight.visible = true;\n\t\t} else {\n\t\t\tdirectLight.color = vec3( 0.0 );\n\t\t\tdirectLight.visible = false;\n\t\t}\n\t}\n#endif\n#if NUM_RECT_AREA_LIGHTS > 0\n\tstruct RectAreaLight {\n\t\tvec3 color;\n\t\tvec3 position;\n\t\tvec3 halfWidth;\n\t\tvec3 halfHeight;\n\t};\n\tuniform sampler2D ltc_1;\tuniform sampler2D ltc_2;\n\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\tstruct HemisphereLight {\n\t\tvec3 direction;\n\t\tvec3 skyColor;\n\t\tvec3 groundColor;\n\t};\n\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\n\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in GeometricContext geometry ) {\n\t\tfloat dotNL = dot( geometry.normal, hemiLight.direction );\n\t\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\n\t\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tirradiance *= PI;\n\t\t#endif\n\t\treturn irradiance;\n\t}\n#endif",lights_toon_fragment:"ToonMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;",lights_toon_pars_fragment:"varying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\nstruct ToonMaterial {\n\tvec3 diffuseColor;\n};\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_Toon\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Toon\n#define Material_LightProbeLOD( material )\t(0)",lights_phong_fragment:"BlinnPhongMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;\nmaterial.specularColor = specular;\nmaterial.specularShininess = shininess;\nmaterial.specularStrength = specularStrength;",lights_phong_pars_fragment:"varying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\nstruct BlinnPhongMaterial {\n\tvec3 diffuseColor;\n\tvec3 specularColor;\n\tfloat specularShininess;\n\tfloat specularStrength;\n};\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n\treflectedLight.directSpecular += irradiance * BRDF_Specular_BlinnPhong( directLight, geometry, material.specularColor, material.specularShininess ) * material.specularStrength;\n}\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_BlinnPhong\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_BlinnPhong\n#define Material_LightProbeLOD( material )\t(0)",lights_physical_fragment:"PhysicalMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\nmaterial.specularRoughness = max( roughnessFactor, 0.0525 );material.specularRoughness += geometryRoughness;\nmaterial.specularRoughness = min( material.specularRoughness, 1.0 );\n#ifdef REFLECTIVITY\n\t#ifdef SPECULAR\n\t\tvec3 specularIntensityFactor = vec3( specularIntensity );\n\t\tvec3 specularTintFactor = specularTint;\n\t\t#ifdef USE_SPECULARINTENSITYMAP\n\t\t\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\n\t\t#endif\n\t\t#ifdef USE_SPECULARTINTMAP\n\t\t\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\n\t\t#endif\n\t\tmaterial.specularColorF90 = mix( specularIntensityFactor, vec3( 1.0 ), metalnessFactor );\n\t#else\n\t\tvec3 specularIntensityFactor = vec3( 1.0 );\n\t\tvec3 specularTintFactor = vec3( 1.0 );\n\t\tmaterial.specularColorF90 = vec3( 1.0 );\n\t#endif\n\tmaterial.specularColor = mix( min( vec3( MAXIMUM_SPECULAR_COEFFICIENT * pow2( reflectivity ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\n#else\n\tmaterial.specularColor = mix( vec3( DEFAULT_SPECULAR_COEFFICIENT ), diffuseColor.rgb, metalnessFactor );\n\tmaterial.specularColorF90 = vec3( 1.0 );\n#endif\n#ifdef CLEARCOAT\n\tmaterial.clearcoat = clearcoat;\n\tmaterial.clearcoatRoughness = clearcoatRoughness;\n\t#ifdef USE_CLEARCOATMAP\n\t\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\n\t#endif\n\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\t\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\n\t#endif\n\tmaterial.clearcoat = saturate( material.clearcoat );\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\n\tmaterial.clearcoatRoughness += geometryRoughness;\n\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\n#endif\n#ifdef USE_SHEEN\n\tmaterial.sheenColor = sheen;\n#endif",lights_physical_pars_fragment:"struct PhysicalMaterial {\n\tvec3 diffuseColor;\n\tfloat specularRoughness;\n\tvec3 specularColor;\n\tvec3 specularColorF90;\n#ifdef CLEARCOAT\n\tfloat clearcoat;\n\tfloat clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tvec3 sheenColor;\n#endif\n};\n#define MAXIMUM_SPECULAR_COEFFICIENT 0.16\n#define DEFAULT_SPECULAR_COEFFICIENT 0.04\nfloat clearcoatDHRApprox( const in float roughness, const in float dotNL ) {\n\treturn DEFAULT_SPECULAR_COEFFICIENT + ( 1.0 - DEFAULT_SPECULAR_COEFFICIENT ) * ( pow( 1.0 - dotNL, 5.0 ) * pow( 1.0 - roughness, 2.0 ) );\n}\n#if NUM_RECT_AREA_LIGHTS > 0\n\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\t\tvec3 normal = geometry.normal;\n\t\tvec3 viewDir = geometry.viewDir;\n\t\tvec3 position = geometry.position;\n\t\tvec3 lightPos = rectAreaLight.position;\n\t\tvec3 halfWidth = rectAreaLight.halfWidth;\n\t\tvec3 halfHeight = rectAreaLight.halfHeight;\n\t\tvec3 lightColor = rectAreaLight.color;\n\t\tfloat roughness = material.specularRoughness;\n\t\tvec3 rectCoords[ 4 ];\n\t\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\t\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\n\t\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\n\t\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\n\t\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\n\t\tvec4 t1 = texture2D( ltc_1, uv );\n\t\tvec4 t2 = texture2D( ltc_2, uv );\n\t\tmat3 mInv = mat3(\n\t\t\tvec3( t1.x, 0, t1.y ),\n\t\t\tvec3(    0, 1,    0 ),\n\t\t\tvec3( t1.z, 0, t1.w )\n\t\t);\n\t\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\n\t\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\n\t\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\n\t}\n#endif\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNL = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\n\t\tvec3 ccIrradiance = ccDotNL * directLight.color;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tccIrradiance *= PI;\n\t\t#endif\n\t\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t\treflectedLight.directSpecular += ccIrradiance * material.clearcoat * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), vec3( 1.0 ), material.clearcoatRoughness );\n\t#else\n\t\tfloat clearcoatDHR = 0.0;\n\t#endif\n\t#ifdef USE_SHEEN\n\t\treflectedLight.directSpecular += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_Sheen(\n\t\t\tmaterial.specularRoughness,\n\t\t\tdirectLight.direction,\n\t\t\tgeometry,\n\t\t\tmaterial.sheenColor\n\t\t);\n\t#else\n\t\treflectedLight.directSpecular += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.normal, material.specularColor, material.specularColorF90, material.specularRoughness);\n\t#endif\n\treflectedLight.directDiffuse += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNV = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular += clearcoatRadiance * material.clearcoat * BRDF_Specular_GGX_Environment( geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearcoatRoughness );\n\t\tfloat ccDotNL = ccDotNV;\n\t\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t#else\n\t\tfloat clearcoatDHR = 0.0;\n\t#endif\n\tfloat clearcoatInv = 1.0 - clearcoatDHR;\n\tvec3 singleScattering = vec3( 0.0 );\n\tvec3 multiScattering = vec3( 0.0 );\n\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\n\tBRDF_Specular_Multiscattering_Environment( geometry, material.specularColor, material.specularRoughness, singleScattering, multiScattering );\n\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\n\treflectedLight.indirectSpecular += clearcoatInv * radiance * singleScattering;\n\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\n\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\n}\n#define RE_Direct\t\t\t\tRE_Direct_Physical\n#define RE_Direct_RectArea\t\tRE_Direct_RectArea_Physical\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Physical\n#define RE_IndirectSpecular\t\tRE_IndirectSpecular_Physical\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\n\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\n}",lights_fragment_begin:"\nGeometricContext geometry;\ngeometry.position = - vViewPosition;\ngeometry.normal = normal;\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\n#ifdef CLEARCOAT\n\tgeometry.clearcoatNormal = clearcoatNormal;\n#endif\nIncidentLight directLight;\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\n\tPointLight pointLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tpointLight = pointLights[ i ];\n\t\tgetPointDirectLightIrradiance( pointLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\n\t\tpointLightShadow = pointLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\n\tSpotLight spotLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tspotLight = spotLights[ i ];\n\t\tgetSpotDirectLightIrradiance( spotLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\tspotLightShadow = spotLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\n\tDirectionalLight directionalLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tdirectionalLight = directionalLights[ i ];\n\t\tgetDirectionalDirectLightIrradiance( directionalLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\n\t\tdirectionalLightShadow = directionalLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\n\tRectAreaLight rectAreaLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\n\t\trectAreaLight = rectAreaLights[ i ];\n\t\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if defined( RE_IndirectDiffuse )\n\tvec3 iblIrradiance = vec3( 0.0 );\n\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\n\tirradiance += getLightProbeIrradiance( lightProbe, geometry );\n\t#if ( NUM_HEMI_LIGHTS > 0 )\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\t\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n#endif\n#if defined( RE_IndirectSpecular )\n\tvec3 radiance = vec3( 0.0 );\n\tvec3 clearcoatRadiance = vec3( 0.0 );\n#endif",lights_fragment_maps:"#if defined( RE_IndirectDiffuse )\n\t#ifdef USE_LIGHTMAP\n\t\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\t\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tlightMapIrradiance *= PI;\n\t\t#endif\n\t\tirradiance += lightMapIrradiance;\n\t#endif\n\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\n\t\tiblIrradiance += getLightProbeIndirectIrradiance( geometry, maxMipLevel );\n\t#endif\n#endif\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\n\tradiance += getLightProbeIndirectRadiance( geometry.viewDir, geometry.normal, material.specularRoughness, maxMipLevel );\n\t#ifdef CLEARCOAT\n\t\tclearcoatRadiance += getLightProbeIndirectRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness, maxMipLevel );\n\t#endif\n#endif",lights_fragment_end:"#if defined( RE_IndirectDiffuse )\n\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\n#endif\n#if defined( RE_IndirectSpecular )\n\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\n#endif",logdepthbuf_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\n#endif",logdepthbuf_pars_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tuniform float logDepthBufFC;\n\tvarying float vFragDepth;\n\tvarying float vIsPerspective;\n#endif",logdepthbuf_pars_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvarying float vFragDepth;\n\t\tvarying float vIsPerspective;\n\t#else\n\t\tuniform float logDepthBufFC;\n\t#endif\n#endif",logdepthbuf_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvFragDepth = 1.0 + gl_Position.w;\n\t\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\n\t#else\n\t\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\n\t\t\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\n\t\t\tgl_Position.z *= gl_Position.w;\n\t\t}\n\t#endif\n#endif",map_fragment:"#ifdef USE_MAP\n\tvec4 texelColor = texture2D( map, vUv );\n\ttexelColor = mapTexelToLinear( texelColor );\n\tdiffuseColor *= texelColor;\n#endif",map_pars_fragment:"#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif",map_particle_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\n#endif\n#ifdef USE_MAP\n\tvec4 mapTexel = texture2D( map, uv );\n\tdiffuseColor *= mapTexelToLinear( mapTexel );\n#endif\n#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\n#endif",map_particle_pars_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tuniform mat3 uvTransform;\n#endif\n#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif\n#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",metalnessmap_fragment:"float metalnessFactor = metalness;\n#ifdef USE_METALNESSMAP\n\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\n\tmetalnessFactor *= texelMetalness.b;\n#endif",metalnessmap_pars_fragment:"#ifdef USE_METALNESSMAP\n\tuniform sampler2D metalnessMap;\n#endif",morphnormal_vertex:"#ifdef USE_MORPHNORMALS\n\tobjectNormal *= morphTargetBaseInfluence;\n\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\n\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\n\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\n\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\n#endif",morphtarget_pars_vertex:"#ifdef USE_MORPHTARGETS\n\tuniform float morphTargetBaseInfluence;\n\t#ifndef USE_MORPHNORMALS\n\t\tuniform float morphTargetInfluences[ 8 ];\n\t#else\n\t\tuniform float morphTargetInfluences[ 4 ];\n\t#endif\n#endif",morphtarget_vertex:"#ifdef USE_MORPHTARGETS\n\ttransformed *= morphTargetBaseInfluence;\n\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\n\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\n\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\n\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\n\t#ifndef USE_MORPHNORMALS\n\t\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\n\t\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\n\t\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\n\t\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\n\t#endif\n#endif",normal_fragment_begin:"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\n#ifdef FLAT_SHADED\n\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\n\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\n\tvec3 normal = normalize( cross( fdx, fdy ) );\n#else\n\tvec3 normal = normalize( vNormal );\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\t#ifdef USE_TANGENT\n\t\tvec3 tangent = normalize( vTangent );\n\t\tvec3 bitangent = normalize( vBitangent );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\ttangent = tangent * faceDirection;\n\t\t\tbitangent = bitangent * faceDirection;\n\t\t#endif\n\t\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\n\t\t\tmat3 vTBN = mat3( tangent, bitangent, normal );\n\t\t#endif\n\t#endif\n#endif\nvec3 geometryNormal = normal;",normal_fragment_maps:"#ifdef OBJECTSPACE_NORMALMAP\n\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t#ifdef FLIP_SIDED\n\t\tnormal = - normal;\n\t#endif\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\tnormal = normalize( normalMatrix * normal );\n#elif defined( TANGENTSPACE_NORMALMAP )\n\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\tmapN.xy *= normalScale;\n\t#ifdef USE_TANGENT\n\t\tnormal = normalize( vTBN * mapN );\n\t#else\n\t\tnormal = perturbNormal2Arb( -vViewPosition, normal, mapN, faceDirection );\n\t#endif\n#elif defined( USE_BUMPMAP )\n\tnormal = perturbNormalArb( -vViewPosition, normal, dHdxy_fwd(), faceDirection );\n#endif",normalmap_pars_fragment:"#ifdef USE_NORMALMAP\n\tuniform sampler2D normalMap;\n\tuniform vec2 normalScale;\n#endif\n#ifdef OBJECTSPACE_NORMALMAP\n\tuniform mat3 normalMatrix;\n#endif\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\n\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\n\t\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\n\t\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\n\t\tvec2 st0 = dFdx( vUv.st );\n\t\tvec2 st1 = dFdy( vUv.st );\n\t\tvec3 N = surf_norm;\n\t\tvec3 q1perp = cross( q1, N );\n\t\tvec3 q0perp = cross( N, q0 );\n\t\tvec3 T = q1perp * st0.x + q0perp * st1.x;\n\t\tvec3 B = q1perp * st0.y + q0perp * st1.y;\n\t\tfloat det = max( dot( T, T ), dot( B, B ) );\n\t\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\n\t\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\n\t}\n#endif",clearcoat_normal_fragment_begin:"#ifdef CLEARCOAT\n\tvec3 clearcoatNormal = geometryNormal;\n#endif",clearcoat_normal_fragment_maps:"#ifdef USE_CLEARCOAT_NORMALMAP\n\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\n\tclearcoatMapN.xy *= clearcoatNormalScale;\n\t#ifdef USE_TANGENT\n\t\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\n\t#else\n\t\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\n\t#endif\n#endif",clearcoat_pars_fragment:"#ifdef USE_CLEARCOATMAP\n\tuniform sampler2D clearcoatMap;\n#endif\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\tuniform sampler2D clearcoatRoughnessMap;\n#endif\n#ifdef USE_CLEARCOAT_NORMALMAP\n\tuniform sampler2D clearcoatNormalMap;\n\tuniform vec2 clearcoatNormalScale;\n#endif",packing:"vec3 packNormalToRGB( const in vec3 normal ) {\n\treturn normalize( normal ) * 0.5 + 0.5;\n}\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\n\treturn 2.0 * rgb.xyz - 1.0;\n}\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\nconst float ShiftRight8 = 1. / 256.;\nvec4 packDepthToRGBA( const in float v ) {\n\tvec4 r = vec4( fract( v * PackFactors ), v );\n\tr.yzw -= r.xyz * ShiftRight8;\treturn r * PackUpscale;\n}\nfloat unpackRGBAToDepth( const in vec4 v ) {\n\treturn dot( v, UnpackFactors );\n}\nvec4 pack2HalfToRGBA( vec2 v ) {\n\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ));\n\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w);\n}\nvec2 unpackRGBATo2Half( vec4 v ) {\n\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\n}\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn ( viewZ + near ) / ( near - far );\n}\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\n\treturn linearClipZ * ( near - far ) - near;\n}\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn (( near + viewZ ) * far ) / (( far - near ) * viewZ );\n}\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\n\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\n}",premultiplied_alpha_fragment:"#ifdef PREMULTIPLIED_ALPHA\n\tgl_FragColor.rgb *= gl_FragColor.a;\n#endif",project_vertex:"vec4 mvPosition = vec4( transformed, 1.0 );\n#ifdef USE_INSTANCING\n\tmvPosition = instanceMatrix * mvPosition;\n#endif\nmvPosition = modelViewMatrix * mvPosition;\ngl_Position = projectionMatrix * mvPosition;",dithering_fragment:"#ifdef DITHERING\n\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\n#endif",dithering_pars_fragment:"#ifdef DITHERING\n\tvec3 dithering( vec3 color ) {\n\t\tfloat grid_position = rand( gl_FragCoord.xy );\n\t\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\n\t\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\n\t\treturn color + dither_shift_RGB;\n\t}\n#endif",roughnessmap_fragment:"float roughnessFactor = roughness;\n#ifdef USE_ROUGHNESSMAP\n\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\n\troughnessFactor *= texelRoughness.g;\n#endif",roughnessmap_pars_fragment:"#ifdef USE_ROUGHNESSMAP\n\tuniform sampler2D roughnessMap;\n#endif",shadowmap_pars_fragment:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\n\t\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\n\t}\n\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\n\t\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\n\t}\n\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\n\t\tfloat occlusion = 1.0;\n\t\tvec2 distribution = texture2DDistribution( shadow, uv );\n\t\tfloat hard_shadow = step( compare , distribution.x );\n\t\tif (hard_shadow != 1.0 ) {\n\t\t\tfloat distance = compare - distribution.x ;\n\t\t\tfloat variance = max( 0.00000, distribution.y * distribution.y );\n\t\t\tfloat softness_probability = variance / (variance + distance * distance );\t\t\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\t\t\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\n\t\t}\n\t\treturn occlusion;\n\t}\n\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\n\t\tfloat shadow = 1.0;\n\t\tshadowCoord.xyz /= shadowCoord.w;\n\t\tshadowCoord.z += shadowBias;\n\t\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\n\t\tbool inFrustum = all( inFrustumVec );\n\t\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\n\t\tbool frustumTest = all( frustumTestVec );\n\t\tif ( frustumTest ) {\n\t\t#if defined( SHADOWMAP_TYPE_PCF )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx0 = - texelSize.x * shadowRadius;\n\t\t\tfloat dy0 = - texelSize.y * shadowRadius;\n\t\t\tfloat dx1 = + texelSize.x * shadowRadius;\n\t\t\tfloat dy1 = + texelSize.y * shadowRadius;\n\t\t\tfloat dx2 = dx0 / 2.0;\n\t\t\tfloat dy2 = dy0 / 2.0;\n\t\t\tfloat dx3 = dx1 / 2.0;\n\t\t\tfloat dy3 = dy1 / 2.0;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\n\t\t\t) * ( 1.0 / 17.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx = texelSize.x;\n\t\t\tfloat dy = texelSize.y;\n\t\t\tvec2 uv = shadowCoord.xy;\n\t\t\tvec2 f = fract( uv * shadowMapSize + 0.5 );\n\t\t\tuv -= f * texelSize;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t f.y )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_VSM )\n\t\t\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#else\n\t\t\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#endif\n\t\t}\n\t\treturn shadow;\n\t}\n\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\n\t\tvec3 absV = abs( v );\n\t\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\n\t\tabsV *= scaleToCube;\n\t\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\n\t\tvec2 planar = v.xy;\n\t\tfloat almostATexel = 1.5 * texelSizeY;\n\t\tfloat almostOne = 1.0 - almostATexel;\n\t\tif ( absV.z >= almostOne ) {\n\t\t\tif ( v.z > 0.0 )\n\t\t\t\tplanar.x = 4.0 - v.x;\n\t\t} else if ( absV.x >= almostOne ) {\n\t\t\tfloat signX = sign( v.x );\n\t\t\tplanar.x = v.z * signX + 2.0 * signX;\n\t\t} else if ( absV.y >= almostOne ) {\n\t\t\tfloat signY = sign( v.y );\n\t\t\tplanar.x = v.x + 2.0 * signY + 2.0;\n\t\t\tplanar.y = v.z * signY - 2.0;\n\t\t}\n\t\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\n\t}\n\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\n\t\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\n\t\tvec3 lightToPosition = shadowCoord.xyz;\n\t\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\t\tdp += shadowBias;\n\t\tvec3 bd3D = normalize( lightToPosition );\n\t\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\n\t\t\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\n\t\t\treturn (\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#else\n\t\t\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\n\t\t#endif\n\t}\n#endif",shadowmap_pars_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n#endif",shadowmap_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\n\t\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\tvec4 shadowWorldPosition;\n\t#endif\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n#endif",shadowmask_pars_fragment:"float getShadowMask() {\n\tfloat shadow = 1.0;\n\t#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tdirectionalLight = directionalLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tspotLight = spotLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tpointLight = pointLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#endif\n\treturn shadow;\n}",skinbase_vertex:"#ifdef USE_SKINNING\n\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\n\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\n\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\n\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\n#endif",skinning_pars_vertex:"#ifdef USE_SKINNING\n\tuniform mat4 bindMatrix;\n\tuniform mat4 bindMatrixInverse;\n\t#ifdef BONE_TEXTURE\n\t\tuniform highp sampler2D boneTexture;\n\t\tuniform int boneTextureSize;\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tfloat j = i * 4.0;\n\t\t\tfloat x = mod( j, float( boneTextureSize ) );\n\t\t\tfloat y = floor( j / float( boneTextureSize ) );\n\t\t\tfloat dx = 1.0 / float( boneTextureSize );\n\t\t\tfloat dy = 1.0 / float( boneTextureSize );\n\t\t\ty = dy * ( y + 0.5 );\n\t\t\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\n\t\t\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\n\t\t\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\n\t\t\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\n\t\t\tmat4 bone = mat4( v1, v2, v3, v4 );\n\t\t\treturn bone;\n\t\t}\n\t#else\n\t\tuniform mat4 boneMatrices[ MAX_BONES ];\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tmat4 bone = boneMatrices[ int(i) ];\n\t\t\treturn bone;\n\t\t}\n\t#endif\n#endif",skinning_vertex:"#ifdef USE_SKINNING\n\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\n\tvec4 skinned = vec4( 0.0 );\n\tskinned += boneMatX * skinVertex * skinWeight.x;\n\tskinned += boneMatY * skinVertex * skinWeight.y;\n\tskinned += boneMatZ * skinVertex * skinWeight.z;\n\tskinned += boneMatW * skinVertex * skinWeight.w;\n\ttransformed = ( bindMatrixInverse * skinned ).xyz;\n#endif",skinnormal_vertex:"#ifdef USE_SKINNING\n\tmat4 skinMatrix = mat4( 0.0 );\n\tskinMatrix += skinWeight.x * boneMatX;\n\tskinMatrix += skinWeight.y * boneMatY;\n\tskinMatrix += skinWeight.z * boneMatZ;\n\tskinMatrix += skinWeight.w * boneMatW;\n\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\n\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\n\t#ifdef USE_TANGENT\n\t\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#endif\n#endif",specularmap_fragment:"float specularStrength;\n#ifdef USE_SPECULARMAP\n\tvec4 texelSpecular = texture2D( specularMap, vUv );\n\tspecularStrength = texelSpecular.r;\n#else\n\tspecularStrength = 1.0;\n#endif",specularmap_pars_fragment:"#ifdef USE_SPECULARMAP\n\tuniform sampler2D specularMap;\n#endif",tonemapping_fragment:"#if defined( TONE_MAPPING )\n\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\n#endif",tonemapping_pars_fragment:"#ifndef saturate\n#define saturate(a) clamp( a, 0.0, 1.0 )\n#endif\nuniform float toneMappingExposure;\nvec3 LinearToneMapping( vec3 color ) {\n\treturn toneMappingExposure * color;\n}\nvec3 ReinhardToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\treturn saturate( color / ( vec3( 1.0 ) + color ) );\n}\nvec3 OptimizedCineonToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\tcolor = max( vec3( 0.0 ), color - 0.004 );\n\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\n}\nvec3 RRTAndODTFit( vec3 v ) {\n\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\n\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\n\treturn a / b;\n}\nvec3 ACESFilmicToneMapping( vec3 color ) {\n\tconst mat3 ACESInputMat = mat3(\n\t\tvec3( 0.59719, 0.07600, 0.02840 ),\t\tvec3( 0.35458, 0.90834, 0.13383 ),\n\t\tvec3( 0.04823, 0.01566, 0.83777 )\n\t);\n\tconst mat3 ACESOutputMat = mat3(\n\t\tvec3(  1.60475, -0.10208, -0.00327 ),\t\tvec3( -0.53108,  1.10813, -0.07276 ),\n\t\tvec3( -0.07367, -0.00605,  1.07602 )\n\t);\n\tcolor *= toneMappingExposure / 0.6;\n\tcolor = ACESInputMat * color;\n\tcolor = RRTAndODTFit( color );\n\tcolor = ACESOutputMat * color;\n\treturn saturate( color );\n}\nvec3 CustomToneMapping( vec3 color ) { return color; }",transmission_fragment:"#ifdef USE_TRANSMISSION\n\tfloat transmissionFactor = transmission;\n\tfloat thicknessFactor = thickness;\n\t#ifdef USE_TRANSMISSIONMAP\n\t\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\n\t#endif\n\tvec3 pos = vWorldPosition.xyz / vWorldPosition.w;\n\tvec3 v = normalize( cameraPosition - pos );\n\tvec3 n = inverseTransformDirection( normal, viewMatrix );\n\tfloat ior = ( 1.0 + 0.4 * reflectivity ) / ( 1.0 - 0.4 * reflectivity );\n\tvec3 transmission = transmissionFactor * getIBLVolumeRefraction(\n\t\tn, v, roughnessFactor, material.diffuseColor, material.specularColor,\n\t\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\n\t\tattenuationTint, attenuationDistance );\n\ttotalDiffuse = mix( totalDiffuse, transmission, transmissionFactor );\n#endif",transmission_pars_fragment:"#ifdef USE_TRANSMISSION\n\t#ifdef USE_TRANSMISSIONMAP\n\t\tuniform sampler2D transmissionMap;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tuniform sampler2D thicknessMap;\n\t#endif\n\tuniform vec2 transmissionSamplerSize;\n\tuniform sampler2D transmissionSamplerMap;\n\tuniform mat4 modelMatrix;\n\tuniform mat4 projectionMatrix;\n\tvarying vec4 vWorldPosition;\n\tvec3 getVolumeTransmissionRay(vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix) {\n\t\tvec3 refractionVector = refract(-v, normalize(n), 1.0 / ior);\n\t\tvec3 modelScale;\n\t\tmodelScale.x = length(vec3(modelMatrix[0].xyz));\n\t\tmodelScale.y = length(vec3(modelMatrix[1].xyz));\n\t\tmodelScale.z = length(vec3(modelMatrix[2].xyz));\n\t\treturn normalize(refractionVector) * thickness * modelScale;\n\t}\n\tfloat applyIorToRoughness(float roughness, float ior) {\n\t\treturn roughness * clamp(ior * 2.0 - 2.0, 0.0, 1.0);\n\t}\n\tvec3 getTransmissionSample(vec2 fragCoord, float roughness, float ior) {\n\t\tfloat framebufferLod = log2(transmissionSamplerSize.x) * applyIorToRoughness(roughness, ior);\n\t\treturn texture2DLodEXT(transmissionSamplerMap, fragCoord.xy, framebufferLod).rgb;\n\t}\n\tvec3 applyVolumeAttenuation(vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance) {\n\t\tif (attenuationDistance == 0.0) {\n\t\t\treturn radiance;\n\t\t} else {\n\t\t\tvec3 attenuationCoefficient = -log(attenuationColor) / attenuationDistance;\n\t\t\tvec3 transmittance = exp(-attenuationCoefficient * transmissionDistance);\t\t\treturn transmittance * radiance;\n\t\t}\n\t}\n\tvec3 getIBLVolumeRefraction(vec3 n, vec3 v, float perceptualRoughness, vec3 baseColor, vec3 specularColor,\n\t\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\n\t\tvec3 attenuationColor, float attenuationDistance) {\n\t\tvec3 transmissionRay = getVolumeTransmissionRay(n, v, thickness, ior, modelMatrix);\n\t\tvec3 refractedRayExit = position + transmissionRay;\n\t\tvec4 ndcPos = projMatrix * viewMatrix * vec4(refractedRayExit, 1.0);\n\t\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\n\t\trefractionCoords += 1.0;\n\t\trefractionCoords /= 2.0;\n\t\tvec3 transmittedLight = getTransmissionSample(refractionCoords, perceptualRoughness, ior);\n\t\tvec3 attenuatedColor = applyVolumeAttenuation(transmittedLight, length(transmissionRay), attenuationColor, attenuationDistance);\n\t\treturn (1.0 - specularColor) * attenuatedColor * baseColor;\n\t}\n#endif",uv_pars_fragment:"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\n\tvarying vec2 vUv;\n#endif",uv_pars_vertex:"#ifdef USE_UV\n\t#ifdef UVS_VERTEX_ONLY\n\t\tvec2 vUv;\n\t#else\n\t\tvarying vec2 vUv;\n\t#endif\n\tuniform mat3 uvTransform;\n#endif",uv_vertex:"#ifdef USE_UV\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n#endif",uv2_pars_fragment:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvarying vec2 vUv2;\n#endif",uv2_pars_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tattribute vec2 uv2;\n\tvarying vec2 vUv2;\n\tuniform mat3 uv2Transform;\n#endif",uv2_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\n#endif",worldpos_vertex:"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\n\tvec4 worldPosition = vec4( transformed, 1.0 );\n\t#ifdef USE_INSTANCING\n\t\tworldPosition = instanceMatrix * worldPosition;\n\t#endif\n\tworldPosition = modelMatrix * worldPosition;\n#endif",background_frag:"uniform sampler2D t2D;\nvarying vec2 vUv;\nvoid main() {\n\tvec4 texColor = texture2D( t2D, vUv );\n\tgl_FragColor = mapTexelToLinear( texColor );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",background_vert:"varying vec2 vUv;\nuniform mat3 uvTransform;\nvoid main() {\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n\tgl_Position = vec4( position.xy, 1.0, 1.0 );\n}",cube_frag:"#include <envmap_common_pars_fragment>\nuniform float opacity;\nvarying vec3 vWorldDirection;\n#include <cube_uv_reflection_fragment>\nvoid main() {\n\tvec3 vReflect = vWorldDirection;\n\t#include <envmap_fragment>\n\tgl_FragColor = envColor;\n\tgl_FragColor.a *= opacity;\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",cube_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\tgl_Position.z = gl_Position.w;\n}",depth_frag:"#if DEPTH_PACKING == 3200\n\tuniform float opacity;\n#endif\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#if DEPTH_PACKING == 3200\n\t\tdiffuseColor.a = opacity;\n\t#endif\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <logdepthbuf_fragment>\n\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\n\t#if DEPTH_PACKING == 3200\n\t\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\n\t#elif DEPTH_PACKING == 3201\n\t\tgl_FragColor = packDepthToRGBA( fragCoordZ );\n\t#endif\n}",depth_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvHighPrecisionZW = gl_Position.zw;\n}",distanceRGBA_frag:"#define DISTANCE\nuniform vec3 referencePosition;\nuniform float nearDistance;\nuniform float farDistance;\nvarying vec3 vWorldPosition;\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main () {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\tfloat dist = length( vWorldPosition - referencePosition );\n\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\n\tdist = saturate( dist );\n\tgl_FragColor = packDepthToRGBA( dist );\n}",distanceRGBA_vert:"#define DISTANCE\nvarying vec3 vWorldPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <clipping_planes_vertex>\n\tvWorldPosition = worldPosition.xyz;\n}",equirect_frag:"uniform sampler2D tEquirect;\nvarying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvec3 direction = normalize( vWorldDirection );\n\tvec2 sampleUV = equirectUv( direction );\n\tvec4 texColor = texture2D( tEquirect, sampleUV );\n\tgl_FragColor = mapTexelToLinear( texColor );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",equirect_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n}",linedashed_frag:"uniform vec3 diffuse;\nuniform float opacity;\nuniform float dashSize;\nuniform float totalSize;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\n\t\tdiscard;\n\t}\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <color_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",linedashed_vert:"uniform float scale;\nattribute float lineDistance;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\tvLineDistance = scale * lineDistance;\n\t#include <color_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n}",meshbasic_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <fog_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\t#ifdef USE_LIGHTMAP\n\t\n\t\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\t\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n\t#else\n\t\treflectedLight.indirectDiffuse += vec3( 1.0 );\n\t#endif\n\t#include <aomap_fragment>\n\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\n\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshbasic_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinbase_vertex>\n\t\t#include <skinnormal_vertex>\n\t\t#include <defaultnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <fog_vertex>\n}",meshlambert_frag:"uniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <fog_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <shadowmask_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <emissivemap_fragment>\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\n\t#else\n\t\treflectedLight.indirectDiffuse += vIndirectFront;\n\t#endif\n\t#include <lightmap_fragment>\n\treflectedLight.indirectDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb );\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\n\t#else\n\t\treflectedLight.directDiffuse = vLightFront;\n\t#endif\n\treflectedLight.directDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb ) * getShadowMask();\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshlambert_vert:"#define LAMBERT\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <envmap_pars_vertex>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <lights_lambert_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshmatcap_frag:"#define MATCAP\nuniform vec3 diffuse;\nuniform float opacity;\nuniform sampler2D matcap;\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tvec3 viewDir = normalize( vViewPosition );\n\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\n\tvec3 y = cross( viewDir, x );\n\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\n\t#ifdef USE_MATCAP\n\t\tvec4 matcapColor = texture2D( matcap, uv );\n\t\tmatcapColor = matcapTexelToLinear( matcapColor );\n\t#else\n\t\tvec4 matcapColor = vec4( 1.0 );\n\t#endif\n\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshmatcap_vert:"#define MATCAP\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <color_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#ifndef FLAT_SHADED\n\t\tvNormal = normalize( transformedNormal );\n\t\t#ifdef USE_TANGENT\n\t\t\tvTangent = normalize( transformedTangent );\n\t\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t\t#endif\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n\tvViewPosition = - mvPosition.xyz;\n}",meshtoon_frag:"#define TOON\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <gradientmap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <lights_toon_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_toon_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshtoon_vert:"#define TOON\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshphong_frag:"#define PHONG\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform vec3 specular;\nuniform float shininess;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <lights_phong_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_phong_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshphong_vert:"#define PHONG\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshphysical_frag:"#define STANDARD\n#ifdef PHYSICAL\n\t#define REFLECTIVITY\n\t#define CLEARCOAT\n\t#define SPECULAR\n#endif\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float roughness;\nuniform float metalness;\nuniform float opacity;\n#ifdef USE_TRANSMISSION\n\tuniform float transmission;\n\tuniform float thickness;\n\tuniform float attenuationDistance;\n\tuniform vec3 attenuationTint;\n#endif\n#ifdef REFLECTIVITY\n\tuniform float reflectivity;\n#endif\n#ifdef SPECULAR\n\tuniform float specularIntensity;\n\tuniform vec3 specularTint;\n\t#ifdef USE_SPECULARINTENSITYMAP\n\t\tuniform sampler2D specularIntensityMap;\n\t#endif\n\t#ifdef USE_SPECULARTINTMAP\n\t\tuniform sampler2D specularTintMap;\n\t#endif\n#endif\n#ifdef CLEARCOAT\n\tuniform float clearcoat;\n\tuniform float clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tuniform vec3 sheen;\n#endif\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <bsdfs>\n#include <transmission_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_physical_pars_fragment>\n#include <fog_pars_fragment>\n#include <lights_pars_begin>\n#include <lights_physical_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <clearcoat_pars_fragment>\n#include <roughnessmap_pars_fragment>\n#include <metalnessmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <roughnessmap_fragment>\n\t#include <metalnessmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <clearcoat_normal_fragment_begin>\n\t#include <clearcoat_normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_physical_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\n\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\n\t#include <transmission_fragment>\n\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshphysical_vert:"#define STANDARD\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#ifdef USE_TRANSMISSION\n\tvarying vec4 vWorldPosition;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n#ifdef USE_TRANSMISSION\n\tvWorldPosition = worldPosition;\n#endif\n}",normal_frag:"#define NORMAL\nuniform float opacity;\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <packing>\n#include <uv_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\t#include <logdepthbuf_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\n}",normal_vert:"#define NORMAL\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvViewPosition = - mvPosition.xyz;\n#endif\n}",points_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include <common>\n#include <color_pars_fragment>\n#include <map_particle_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_particle_fragment>\n\t#include <color_fragment>\n\t#include <alphatest_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",points_vert:"uniform float size;\nuniform float scale;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <color_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\tgl_PointSize = size;\n\t#ifdef USE_SIZEATTENUATION\n\t\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\n\t\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\n\t#endif\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <fog_vertex>\n}",shadow_frag:"uniform vec3 color;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <shadowmap_pars_fragment>\n#include <shadowmask_pars_fragment>\nvoid main() {\n\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n}",shadow_vert:"#include <common>\n#include <fog_pars_vertex>\n#include <shadowmap_pars_vertex>\nvoid main() {\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",sprite_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include <common>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n}",sprite_vert:"uniform float rotation;\nuniform vec2 center;\n#include <common>\n#include <uv_pars_vertex>\n#include <fog_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\n\tvec2 scale;\n\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\n\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\n\t#ifndef USE_SIZEATTENUATION\n\t\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\n\t\tif ( isPerspective ) scale *= - mvPosition.z;\n\t#endif\n\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\n\tvec2 rotatedPosition;\n\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\n\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\n\tmvPosition.xy += rotatedPosition;\n\tgl_Position = projectionMatrix * mvPosition;\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n}"},Qct={common:{diffuse:{value:new Zlt(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new hst},uv2Transform:{value:new hst},alphaMap:{value:null}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new gst(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new Zlt(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new 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edt(t,e,n,o){const i=t.getParameter(34921),a=o.isWebGL2?null:e.get("OES_vertex_array_object"),r=o.isWebGL2||null!==a,s={},l=m(null);let c=l;function d(e){return o.isWebGL2?t.bindVertexArray(e):a.bindVertexArrayOES(e)}function p(e){return o.isWebGL2?t.deleteVertexArray(e):a.deleteVertexArrayOES(e)}function m(t){const e=[],n=[],o=[];for(let t=0;t<i;t++)e[t]=0,n[t]=0,o[t]=0;return{geometry:null,program:null,wireframe:!1,newAttributes:e,enabledAttributes:n,attributeDivisors:o,object:t,attributes:{},index:null}}function f(){const t=c.newAttributes;for(let e=0,n=t.length;e<n;e++)t[e]=0}function u(t){g(t,0)}function g(n,i){const a=c.enabledAttributes,r=c.attributeDivisors;if(c.newAttributes[n]=1,0===a[n]&&(t.enableVertexAttribArray(n),a[n]=1),r[n]!==i){(o.isWebGL2?t:e.get("ANGLE_instanced_arrays"))[o.isWebGL2?"vertexAttribDivisor":"vertexAttribDivisorANGLE"](n,i),r[n]=i}}function h(){const e=c.newAttributes,n=c.enabledAttributes;for(let 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l=a||e.has("WEBGL_draw_buffers"),c=!0===n.logarithmicDepthBuffer,d=t.getParameter(34930),p=t.getParameter(35660),m=t.getParameter(3379),f=t.getParameter(34076),u=t.getParameter(34921),g=t.getParameter(36347),h=t.getParameter(36348),b=t.getParameter(36349),y=p>0,_=a||e.has("OES_texture_float");return{isWebGL2:a,drawBuffers:l,getMaxAnisotropy:function C(){if(void 0!==o)return o;if(!0===e.has("EXT_texture_filter_anisotropic")){const n=e.get("EXT_texture_filter_anisotropic");o=t.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else o=0;return o},getMaxPrecision:i,precision:r,logarithmicDepthBuffer:c,maxTextures:d,maxVertexTextures:p,maxTextureSize:m,maxCubemapSize:f,maxAttributes:u,maxVertexUniforms:g,maxVaryings:h,maxFragmentUniforms:b,vertexTextures:y,floatFragmentTextures:_,floatVertexTextures:y&&_,maxSamples:a?t.getParameter(36183):0}}function idt(t){const e=this;let n=null,o=0,i=!1,a=!1;const r=new Wct,s=new hst,l={value:null,needsUpdate:!1};function 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t=0;t!==e;++t)i[t]=n[t];u.clippingState=i,this.numIntersection=m?this.numPlanes:0,this.numPlanes+=t}}}function adt(t){let e=new WeakMap;function n(t,e){return 303===e?t.mapping=wrt:304===e&&(t.mapping=krt),t}function o(t){const n=t.target;n.removeEventListener("dispose",o);const i=e.get(n);void 0!==i&&(e.delete(n),i.dispose())}return{get:function i(a){if(a&&a.isTexture&&!1===a.isRenderTargetTexture){const i=a.mapping;if(303===i||304===i){if(e.has(a)){return n(e.get(a).texture,a.mapping)}{const i=a.image;if(i&&i.height>0){const r=t.getRenderTarget(),s=new Bct(i.height/2);return s.fromEquirectangularTexture(t,a),e.set(a,s),t.setRenderTarget(r),a.addEventListener("dispose",o),n(s.texture,a.mapping)}return null}}}return a},dispose:function a(){e=new WeakMap}}}$ct.physical={uniforms:Act([$ct.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new gst(1,1)},clearcoatNormalMap:{value:null},sheen:{value:new Zlt(0)},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new gst},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new Zlt(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new Zlt(1,1,1)},specularTintMap:{value:null}}]),vertexShader:Jct.meshphysical_vert,fragmentShader:Jct.meshphysical_frag};class rdt extends zct{constructor(t=-1,e=1,n=1,o=-1,i=.1,a=2e3){super(),this.type="OrthographicCamera",this.zoom=1,this.view=null,this.left=t,this.right=e,this.top=n,this.bottom=o,this.near=i,this.far=a,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.left=t.left,this.right=t.right,this.top=t.top,this.bottom=t.bottom,this.near=t.near,this.far=t.far,this.zoom=t.zoom,this.view=null===t.view?null:Object.assign({},t.view),this}setViewOffset(t,e,n,o,i,a){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=o,this.view.width=i,this.view.height=a,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=(this.right-this.left)/(2*this.zoom),e=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,o=(this.top+this.bottom)/2;let i=n-t,a=n+t,r=o+e,s=o-e;if(null!==this.view&&this.view.enabled){const t=(this.right-this.left)/this.view.fullWidth/this.zoom,e=(this.top-this.bottom)/this.view.fullHeight/this.zoom;i+=t*this.view.offsetX,a=i+t*this.view.width,r-=e*this.view.offsetY,s=r-e*this.view.height}this.projectionMatrix.makeOrthographic(i,a,r,s,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.zoom=this.zoom,e.object.left=this.left,e.object.right=this.right,e.object.top=this.top,e.object.bottom=this.bottom,e.object.near=this.near,e.object.far=this.far,null!==this.view&&(e.object.view=Object.assign({},this.view)),e}}rdt.prototype.isOrthographicCamera=!0;class sdt extends Nct{constructor(t){super(t),this.type="RawShaderMaterial"}}sdt.prototype.isRawShaderMaterial=!0;const ldt=Math.pow(2,8),cdt=[.125,.215,.35,.446,.526,.582],ddt=5+cdt.length,pdt=20,mdt={[Qrt]:0,[$rt]:1,[est]:2,3004:3,3005:4,3006:5,[tst]:6},fdt=new Xlt({side:1,depthWrite:!1,depthTest:!1}),udt=new Sct(new Rct,fdt),gdt=new rdt,{_lodPlanes:hdt,_sizeLods:bdt,_sigmas:ydt}=kdt(),_dt=new Zlt;let Cdt=null;const Mdt=(1+Math.sqrt(5))/2,vdt=1/Mdt,xdt=[new wst(1,1,1),new wst(-1,1,1),new wst(1,1,-1),new wst(-1,1,-1),new wst(0,Mdt,vdt),new wst(0,Mdt,-vdt),new wst(vdt,0,Mdt),new wst(-vdt,0,Mdt),new wst(Mdt,vdt,0),new wst(-Mdt,vdt,0)];function Odt(t){const e=Math.max(t.r,t.g,t.b),n=Math.min(Math.max(Math.ceil(Math.log2(e)),-128),127);t.multiplyScalar(Math.pow(2,-n));return(n+128)/255}class Pdt{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=(function e(t){const e=new Float32Array(t),n=new wst(0,1,0);return new sdt({name:"SphericalGaussianBlur",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:mdt[3e3]},outputEncoding:{value:mdt[3e3]}},vertexShader:Adt(),fragmentShader:`\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\t\t\tuniform int samples;\n\t\t\tuniform float weights[ n ];\n\t\t\tuniform bool latitudinal;\n\t\t\tuniform float dTheta;\n\t\t\tuniform float mipInt;\n\t\t\tuniform vec3 poleAxis;\n\n\t\t\t${Tdt()}\n\n\t\t\t#define ENVMAP_TYPE_CUBE_UV\n\t\t\t#include <cube_uv_reflection_fragment>\n\n\t\t\tvec3 getSample( float theta, vec3 axis ) {\n\n\t\t\t\tfloat cosTheta = cos( theta );\n\t\t\t\t// Rodrigues' axis-angle rotation\n\t\t\t\tvec3 sampleDirection = vOutputDirection * cosTheta\n\t\t\t\t\t+ cross( axis, vOutputDirection ) * sin( theta )\n\t\t\t\t\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\n\n\t\t\t\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\n\n\t\t\t}\n\n\t\t\tvoid main() {\n\n\t\t\t\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\n\n\t\t\t\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\n\n\t\t\t\t\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\n\n\t\t\t\t}\n\n\t\t\t\taxis = normalize( axis );\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t\t\t\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\n\n\t\t\t\tfor ( int i = 1; i < n; i++ ) {\n\n\t\t\t\t\tif ( i >= samples ) {\n\n\t\t\t\t\t\tbreak;\n\n\t\t\t\t\t}\n\n\t\t\t\t\tfloat theta = dTheta * float( i );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\n\n\t\t\t\t}\n\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t`,blending:0,depthTest:!1,depthWrite:!1})})(pdt),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,o=100){Cdt=this._renderer.getRenderTarget();const i=this._allocateTargets();return this._sceneToCubeUV(t,n,o,i),e>0&&this._blur(i,0,0,e),this._applyPMREM(i),this._cleanup(i),i}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=Edt(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=Rdt(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t<hdt.length;t++)hdt[t].dispose()}_cleanup(t){this._pingPongRenderTarget.dispose(),this._renderer.setRenderTarget(Cdt),t.scissorTest=!1,Ddt(t,0,0,t.width,t.height)}_fromTexture(t){Cdt=this._renderer.getRenderTarget();const e=this._allocateTargets(t);return this._textureToCubeUV(t,e),this._applyPMREM(e),this._cleanup(e),e}_allocateTargets(t){const e={magFilter:Art,minFilter:Art,generateMipmaps:!1,type:zrt,format:1023,encoding:wdt(t)?t.encoding:est,depthBuffer:!1},n=Sdt(e);return n.depthBuffer=!t,this._pingPongRenderTarget=Sdt(e),n}_compileMaterial(t){const e=new Sct(hdt[0],t);this._renderer.compile(e,gdt)}_sceneToCubeUV(t,e,n,o){const i=new Ict(90,1,e,n),a=[1,-1,1,1,1,1],r=[1,1,1,-1,-1,-1],s=this._renderer,l=s.autoClear,c=s.outputEncoding,d=s.toneMapping;s.getClearColor(_dt),s.toneMapping=0,s.outputEncoding=Qrt,s.autoClear=!1;let p=!1;const m=t.background;if(m){if(m.isColor){fdt.color.copy(m).convertSRGBToLinear(),t.background=null;const e=Odt(fdt.color);fdt.opacity=e,p=!0}}else{fdt.color.copy(_dt).convertSRGBToLinear();const t=Odt(fdt.color);fdt.opacity=t,p=!0}for(let e=0;e<6;e++){const n=e%3;0==n?(i.up.set(0,a[e],0),i.lookAt(r[e],0,0)):1==n?(i.up.set(0,0,a[e]),i.lookAt(0,r[e],0)):(i.up.set(0,a[e],0),i.lookAt(0,0,r[e])),Ddt(o,n*ldt,e>2?ldt:0,ldt,ldt),s.setRenderTarget(o),p&&s.render(udt,i),s.render(t,i)}s.toneMapping=d,s.outputEncoding=c,s.autoClear=l}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=Edt()):null==this._equirectShader&&(this._equirectShader=Rdt());const o=t.isCubeTexture?this._cubemapShader:this._equirectShader,i=new Sct(hdt[0],o),a=o.uniforms;a.envMap.value=t,t.isCubeTexture||a.texelSize.value.set(1/t.image.width,1/t.image.height),a.inputEncoding.value=mdt[t.encoding],a.outputEncoding.value=mdt[e.texture.encoding],Ddt(e,0,0,3*ldt,2*ldt),n.setRenderTarget(e),n.render(i,gdt)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;e<ddt;e++){const n=Math.sqrt(ydt[e]*ydt[e]-ydt[e-1]*ydt[e-1]);this._blur(t,e-1,e,n,xdt[(e-1)%xdt.length])}e.autoClear=n}_blur(t,e,n,o,i){const a=this._pingPongRenderTarget;this._halfBlur(t,a,e,n,o,"latitudinal",i),this._halfBlur(a,t,n,n,o,"longitudinal",i)}_halfBlur(t,e,n,o,i,a,r){const s=this._renderer,l=this._blurMaterial;"latitudinal"!==a&&"longitudinal"!==a&&console.error("blur direction must be either latitudinal or longitudinal!");const c=new Sct(hdt[o],l),d=l.uniforms,p=bdt[n]-1,m=isFinite(i)?Math.PI/(2*p):2*Math.PI/39,f=i/m,u=isFinite(i)?1+Math.floor(3*f):pdt;u>pdt&&console.warn(`sigmaRadians, ${i}, is too large and will clip, as it requested ${u} samples when the maximum is set to 20`);const g=[];let h=0;for(let t=0;t<pdt;++t){const e=t/f,n=Math.exp(-e*e/2);g.push(n),0==t?h+=n:t<u&&(h+=2*n)}for(let t=0;t<g.length;t++)g[t]=g[t]/h;d.envMap.value=t.texture,d.samples.value=u,d.weights.value=g,d.latitudinal.value="latitudinal"===a,r&&(d.poleAxis.value=r),d.dTheta.value=m,d.mipInt.value=8-n,d.inputEncoding.value=mdt[t.texture.encoding],d.outputEncoding.value=mdt[t.texture.encoding];const b=bdt[o];Ddt(e,3*Math.max(0,ldt-2*b),(0===o?0:2*ldt)+2*b*(o>4?o-8+4:0),3*b,2*b),s.setRenderTarget(e),s.render(c,gdt)}}function wdt(t){return void 0!==t&&t.type===zrt&&(t.encoding===Qrt||t.encoding===$rt||t.encoding===tst)}function kdt(){const t=[],e=[],n=[];let o=8;for(let i=0;i<ddt;i++){const a=Math.pow(2,o);e.push(a);let r=1/a;i>4?r=cdt[i-8+4-1]:0==i&&(r=0),n.push(r);const s=1/(a-1),l=-s/2,c=1+s/2,d=[l,l,c,l,c,c,l,l,c,c,l,c],p=6,m=6,f=3,u=2,g=1,h=new Float32Array(f*m*p),b=new Float32Array(u*m*p),y=new Float32Array(g*m*p);for(let t=0;t<p;t++){const e=t%3*2/3-1,n=t>2?0:-1;h.set([e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0],f*m*t),b.set(d,u*m*t);y.set([t,t,t,t,t,t],g*m*t)}const _=new dct;_.setAttribute("position",new Qlt(h,f)),_.setAttribute("uv",new Qlt(b,u)),_.setAttribute("faceIndex",new Qlt(y,g)),t.push(_),o>4&&o--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function Sdt(t){const e=new xst(3*ldt,3*ldt,t);return e.texture.mapping=Srt,e.texture.name="PMREM.cubeUv",e.scissorTest=!0,e}function Ddt(t,e,n,o,i){t.viewport.set(e,n,o,i),t.scissor.set(e,n,o,i)}function Rdt(){const t=new gst(1,1);return new sdt({name:"EquirectangularToCubeUV",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:mdt[3e3]},outputEncoding:{value:mdt[3e3]}},vertexShader:Adt(),fragmentShader:`\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\t\t\tuniform vec2 texelSize;\n\n\t\t\t${Tdt()}\n\n\t\t\t#include <common>\n\n\t\t\tvoid main() {\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\n\t\t\t\tvec3 outputDirection = normalize( vOutputDirection );\n\t\t\t\tvec2 uv = equirectUv( outputDirection );\n\n\t\t\t\tvec2 f = fract( uv / texelSize - 0.5 );\n\t\t\t\tuv -= f * texelSize;\n\t\t\t\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.x += texelSize.x;\n\t\t\t\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.y += texelSize.y;\n\t\t\t\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.x -= texelSize.x;\n\t\t\t\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\n\t\t\t\tvec3 tm = mix( tl, tr, f.x );\n\t\t\t\tvec3 bm = mix( bl, br, f.x );\n\t\t\t\tgl_FragColor.rgb = mix( tm, bm, f.y );\n\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t`,blending:0,depthTest:!1,depthWrite:!1})}function Edt(){return new sdt({name:"CubemapToCubeUV",uniforms:{envMap:{value:null},inputEncoding:{value:mdt[3e3]},outputEncoding:{value:mdt[3e3]}},vertexShader:Adt(),fragmentShader:`\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform samplerCube envMap;\n\n\t\t\t${Tdt()}\n\n\t\t\tvoid main() {\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t\t\t\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t`,blending:0,depthTest:!1,depthWrite:!1})}function Adt(){return"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute vec3 position;\n\t\tattribute vec2 uv;\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t"}function Tdt(){return"\n\n\t\tuniform int inputEncoding;\n\t\tuniform int outputEncoding;\n\n\t\t#include <encodings_pars_fragment>\n\n\t\tvec4 inputTexelToLinear( vec4 value ) {\n\n\t\t\tif ( inputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( inputEncoding == 1 ) {\n\n\t\t\t\treturn sRGBToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 2 ) {\n\n\t\t\t\treturn RGBEToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 3 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 7.0 );\n\n\t\t\t} else if ( inputEncoding == 4 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 16.0 );\n\n\t\t\t} else if ( inputEncoding == 5 ) {\n\n\t\t\t\treturn RGBDToLinear( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn GammaToLinear( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 linearToOutputTexel( vec4 value ) {\n\n\t\t\tif ( outputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( outputEncoding == 1 ) {\n\n\t\t\t\treturn LinearTosRGB( value );\n\n\t\t\t} else if ( outputEncoding == 2 ) {\n\n\t\t\t\treturn LinearToRGBE( value );\n\n\t\t\t} else if ( outputEncoding == 3 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 7.0 );\n\n\t\t\t} else if ( outputEncoding == 4 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 16.0 );\n\n\t\t\t} else if ( outputEncoding == 5 ) {\n\n\t\t\t\treturn LinearToRGBD( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn LinearToGamma( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 envMapTexelToLinear( vec4 color ) {\n\n\t\t\treturn inputTexelToLinear( color );\n\n\t\t}\n\t"}function Ndt(t){let e=new WeakMap,n=null;function o(t){const n=t.target;n.removeEventListener("dispose",o);const i=e.get(n);void 0!==i&&(i.delete(n),i.dispose())}return{get:function i(a){if(a&&a.isTexture&&!1===a.isRenderTargetTexture){const i=a.mapping,r=303===i||304===i,s=i===wrt||i===krt;if(r||s){if(e.has(a))return e.get(a).texture;{const i=a.image;if(r&&i&&i.height>0||s&&i&&(function r(t){let e=0;const n=6;for(let o=0;o<n;o++)void 0!==t[o]&&e++;return e===n})(i)){const i=t.getRenderTarget();null===n&&(n=new Pdt(t));const s=r?n.fromEquirectangular(a):n.fromCubemap(a);return e.set(a,s),t.setRenderTarget(i),a.addEventListener("dispose",o),s.texture}return null}}}return a},dispose:function a(){e=new WeakMap,null!==n&&(n.dispose(),n=null)}}}function zdt(t){const e={};function n(n){if(void 0!==e[n])return e[n];let o;switch(n){case"WEBGL_depth_texture":o=t.getExtension("WEBGL_depth_texture")||t.getExtension("MOZ_WEBGL_depth_texture")||t.getExtension("WEBKIT_WEBGL_depth_texture");break;case"EXT_texture_filter_anisotropic":o=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic");break;case"WEBGL_compressed_texture_s3tc":o=t.getExtension("WEBGL_compressed_texture_s3tc")||t.getExtension("MOZ_WEBGL_compressed_texture_s3tc")||t.getExtension("WEBKIT_WEBGL_compressed_texture_s3tc");break;case"WEBGL_compressed_texture_pvrtc":o=t.getExtension("WEBGL_compressed_texture_pvrtc")||t.getExtension("WEBKIT_WEBGL_compressed_texture_pvrtc");break;default:o=t.getExtension(n)}return e[n]=o,o}return{has:function(t){return null!==n(t)},init:function(t){t.isWebGL2?n("EXT_color_buffer_float"):(n("WEBGL_depth_texture"),n("OES_texture_float"),n("OES_texture_half_float"),n("OES_texture_half_float_linear"),n("OES_standard_derivatives"),n("OES_element_index_uint"),n("OES_vertex_array_object"),n("ANGLE_instanced_arrays")),n("OES_texture_float_linear"),n("EXT_color_buffer_half_float")},get:function(t){const e=n(t);return null===e&&console.warn("THREE.WebGLRenderer: "+t+" extension not supported."),e}}}function Idt(t,e,n,o){const i={},a=new WeakMap;function r(t){const s=t.target;null!==s.index&&e.remove(s.index);for(const t in s.attributes)e.remove(s.attributes[t]);s.removeEventListener("dispose",r),delete i[s.id];const l=a.get(s);l&&(e.remove(l),a.delete(s)),o.releaseStatesOfGeometry(s),!0===s.isInstancedBufferGeometry&&delete s._maxInstanceCount,n.memory.geometries--}function s(t){const n=[],o=t.index,i=t.attributes.position;let r=0;if(null!==o){const t=o.array;r=o.version;for(let e=0,o=t.length;e<o;e+=3){const o=t[e+0],i=t[e+1],a=t[e+2];n.push(o,i,i,a,a,o)}}else{r=i.version;for(let t=0,e=i.array.length/3-1;t<e;t+=3){const e=t+0,o=t+1,i=t+2;n.push(e,o,o,i,i,e)}}const s=new(nct(n)>65535?tct:$lt)(n,1);s.version=r;const l=a.get(t);l&&e.remove(l),a.set(t,s)}return{get:function l(t,e){return!0===i[e.id]||(e.addEventListener("dispose",r),i[e.id]=!0,n.memory.geometries++),e},update:function c(t){const n=t.attributes;for(const t in n)e.update(n[t],34962);const o=t.morphAttributes;for(const t in o){const n=o[t];for(let t=0,o=n.length;t<o;t++)e.update(n[t],34962)}},getWireframeAttribute:function d(t){const e=a.get(t);if(e){const n=t.index;null!==n&&e.version<n.version&&s(t)}else s(t);return a.get(t)}}}function Hdt(t,e,n,o){const i=o.isWebGL2;let a,r,s;this.setMode=function l(t){a=t},this.setIndex=function c(t){r=t.type,s=t.bytesPerElement},this.render=function d(e,o){t.drawElements(a,o,r,e*s),n.update(o,a,1)},this.renderInstances=function p(o,l,c){if(0===c)return;let d,p;if(i)d=t,p="drawElementsInstanced";else if(d=e.get("ANGLE_instanced_arrays"),p="drawElementsInstancedANGLE",null===d)return void console.error("THREE.WebGLIndexedBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.");d[p](a,l,r,o*s,c),n.update(l,a,c)}}function Fdt(t){const e={frame:0,calls:0,triangles:0,points:0,lines:0};return{memory:{geometries:0,textures:0},render:e,programs:null,autoReset:!0,reset:function n(){e.frame++,e.calls=0,e.triangles=0,e.points=0,e.lines=0},update:function o(t,n,i){switch(e.calls++,n){case 4:e.triangles+=i*(t/3);break;case 1:e.lines+=i*(t/2);break;case 3:e.lines+=i*(t-1);break;case 2:e.lines+=i*t;break;case 0:e.points+=i*t;break;default:console.error("THREE.WebGLInfo: Unknown draw mode:",n)}}}}function Ldt(t,e){return t[0]-e[0]}function Bdt(t,e){return Math.abs(e[1])-Math.abs(t[1])}function Vdt(t){const e={},n=new Float32Array(8),o=[];for(let t=0;t<8;t++)o[t]=[t,0];return{update:function i(a,r,s,l){const c=a.morphTargetInfluences,d=void 0===c?0:c.length;let p=e[r.id];if(void 0===p||p.length!==d){p=[];for(let t=0;t<d;t++)p[t]=[t,0];e[r.id]=p}for(let t=0;t<d;t++){const e=p[t];e[0]=t,e[1]=c[t]}p.sort(Bdt);for(let t=0;t<8;t++)t<d&&p[t][1]?(o[t][0]=p[t][0],o[t][1]=p[t][1]):(o[t][0]=Number.MAX_SAFE_INTEGER,o[t][1]=0);o.sort(Ldt);const m=r.morphAttributes.position,f=r.morphAttributes.normal;let u=0;for(let t=0;t<8;t++){const e=o[t],i=e[0],a=e[1];i!==Number.MAX_SAFE_INTEGER&&a?(m&&r.getAttribute("morphTarget"+t)!==m[i]&&r.setAttribute("morphTarget"+t,m[i]),f&&r.getAttribute("morphNormal"+t)!==f[i]&&r.setAttribute("morphNormal"+t,f[i]),n[t]=a,u+=a):(m&&!0===r.hasAttribute("morphTarget"+t)&&r.deleteAttribute("morphTarget"+t),f&&!0===r.hasAttribute("morphNormal"+t)&&r.deleteAttribute("morphNormal"+t),n[t]=0)}const g=r.morphTargetsRelative?1:1-u;l.getUniforms().setValue(t,"morphTargetBaseInfluence",g),l.getUniforms().setValue(t,"morphTargetInfluences",n)}}}function jdt(t,e,n,o){let i=new WeakMap;function a(t){const e=t.target;e.removeEventListener("dispose",a),n.remove(e.instanceMatrix),null!==e.instanceColor&&n.remove(e.instanceColor)}return{update:function r(t){const r=o.render.frame,s=e.get(t,t.geometry);return i.get(s)!==r&&(e.update(s),i.set(s,r)),t.isInstancedMesh&&(!1===t.hasEventListener("dispose",a)&&t.addEventListener("dispose",a),n.update(t.instanceMatrix,34962),null!==t.instanceColor&&n.update(t.instanceColor,34962)),s},dispose:function s(){i=new WeakMap}}}class Udt extends Cst{constructor(t=null,e=1,n=1,o=1){super(null),this.image={data:t,width:e,height:n,depth:o},this.magFilter=Art,this.minFilter=Art,this.wrapR=Rrt,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}Udt.prototype.isDataTexture2DArray=!0;class Wdt extends Cst{constructor(t=null,e=1,n=1,o=1){super(null),this.image={data:t,width:e,height:n,depth:o},this.magFilter=Art,this.minFilter=Art,this.wrapR=Rrt,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}Wdt.prototype.isDataTexture3D=!0;const Gdt=new Cst,Ydt=new Udt,qdt=new Wdt,Zdt=new Lct,Xdt=[],Kdt=[],Jdt=new Float32Array(16),Qdt=new Float32Array(9),$dt=new Float32Array(4);function tpt(t,e,n){const o=t[0];if(o<=0||o>0)return t;const i=e*n;let a=Xdt[i];if(void 0===a&&(a=new Float32Array(i),Xdt[i]=a),0!==e){o.toArray(a,0);for(let o=1,i=0;o!==e;++o)i+=n,t[o].toArray(a,i)}return a}function ept(t,e){if(t.length!==e.length)return!1;for(let n=0,o=t.length;n<o;n++)if(t[n]!==e[n])return!1;return!0}function npt(t,e){for(let n=0,o=e.length;n<o;n++)t[n]=e[n]}function opt(t,e){let n=Kdt[e];void 0===n&&(n=new Int32Array(e),Kdt[e]=n);for(let o=0;o!==e;++o)n[o]=t.allocateTextureUnit();return n}function ipt(t,e){const n=this.cache;n[0]!==e&&(t.uniform1f(this.addr,e),n[0]=e)}function apt(t,e){const n=this.cache;if(void 0!==e.x)n[0]===e.x&&n[1]===e.y||(t.uniform2f(this.addr,e.x,e.y),n[0]=e.x,n[1]=e.y);else{if(ept(n,e))return;t.uniform2fv(this.addr,e),npt(n,e)}}function rpt(t,e){const n=this.cache;if(void 0!==e.x)n[0]===e.x&&n[1]===e.y&&n[2]===e.z||(t.uniform3f(this.addr,e.x,e.y,e.z),n[0]=e.x,n[1]=e.y,n[2]=e.z);else if(void 0!==e.r)n[0]===e.r&&n[1]===e.g&&n[2]===e.b||(t.uniform3f(this.addr,e.r,e.g,e.b),n[0]=e.r,n[1]=e.g,n[2]=e.b);else{if(ept(n,e))return;t.uniform3fv(this.addr,e),npt(n,e)}}function spt(t,e){const n=this.cache;if(void 0!==e.x)n[0]===e.x&&n[1]===e.y&&n[2]===e.z&&n[3]===e.w||(t.uniform4f(this.addr,e.x,e.y,e.z,e.w),n[0]=e.x,n[1]=e.y,n[2]=e.z,n[3]=e.w);else{if(ept(n,e))return;t.uniform4fv(this.addr,e),npt(n,e)}}function lpt(t,e){const n=this.cache,o=e.elements;if(void 0===o){if(ept(n,e))return;t.uniformMatrix2fv(this.addr,!1,e),npt(n,e)}else{if(ept(n,o))return;$dt.set(o),t.uniformMatrix2fv(this.addr,!1,$dt),npt(n,o)}}function cpt(t,e){const n=this.cache,o=e.elements;if(void 0===o){if(ept(n,e))return;t.uniformMatrix3fv(this.addr,!1,e),npt(n,e)}else{if(ept(n,o))return;Qdt.set(o),t.uniformMatrix3fv(this.addr,!1,Qdt),npt(n,o)}}function dpt(t,e){const n=this.cache,o=e.elements;if(void 0===o){if(ept(n,e))return;t.uniformMatrix4fv(this.addr,!1,e),npt(n,e)}else{if(ept(n,o))return;Jdt.set(o),t.uniformMatrix4fv(this.addr,!1,Jdt),npt(n,o)}}function ppt(t,e){const n=this.cache;n[0]!==e&&(t.uniform1i(this.addr,e),n[0]=e)}function mpt(t,e){const n=this.cache;ept(n,e)||(t.uniform2iv(this.addr,e),npt(n,e))}function fpt(t,e){const n=this.cache;ept(n,e)||(t.uniform3iv(this.addr,e),npt(n,e))}function upt(t,e){const n=this.cache;ept(n,e)||(t.uniform4iv(this.addr,e),npt(n,e))}function gpt(t,e){const n=this.cache;n[0]!==e&&(t.uniform1ui(this.addr,e),n[0]=e)}function hpt(t,e){const n=this.cache;ept(n,e)||(t.uniform2uiv(this.addr,e),npt(n,e))}function bpt(t,e){const n=this.cache;ept(n,e)||(t.uniform3uiv(this.addr,e),npt(n,e))}function ypt(t,e){const n=this.cache;ept(n,e)||(t.uniform4uiv(this.addr,e),npt(n,e))}function _pt(t,e,n){const o=this.cache,i=n.allocateTextureUnit();o[0]!==i&&(t.uniform1i(this.addr,i),o[0]=i),n.safeSetTexture2D(e||Gdt,i)}function Cpt(t,e,n){const o=this.cache,i=n.allocateTextureUnit();o[0]!==i&&(t.uniform1i(this.addr,i),o[0]=i),n.setTexture3D(e||qdt,i)}function Mpt(t,e,n){const o=this.cache,i=n.allocateTextureUnit();o[0]!==i&&(t.uniform1i(this.addr,i),o[0]=i),n.safeSetTextureCube(e||Zdt,i)}function vpt(t,e,n){const o=this.cache,i=n.allocateTextureUnit();o[0]!==i&&(t.uniform1i(this.addr,i),o[0]=i),n.setTexture2DArray(e||Ydt,i)}function xpt(t,e){t.uniform1fv(this.addr,e)}function Opt(t,e){const n=tpt(e,this.size,2);t.uniform2fv(this.addr,n)}function Ppt(t,e){const n=tpt(e,this.size,3);t.uniform3fv(this.addr,n)}function wpt(t,e){const n=tpt(e,this.size,4);t.uniform4fv(this.addr,n)}function kpt(t,e){const n=tpt(e,this.size,4);t.uniformMatrix2fv(this.addr,!1,n)}function Spt(t,e){const n=tpt(e,this.size,9);t.uniformMatrix3fv(this.addr,!1,n)}function Dpt(t,e){const n=tpt(e,this.size,16);t.uniformMatrix4fv(this.addr,!1,n)}function Rpt(t,e){t.uniform1iv(this.addr,e)}function Ept(t,e){t.uniform2iv(this.addr,e)}function Apt(t,e){t.uniform3iv(this.addr,e)}function Tpt(t,e){t.uniform4iv(this.addr,e)}function Npt(t,e){t.uniform1uiv(this.addr,e)}function zpt(t,e){t.uniform2uiv(this.addr,e)}function Ipt(t,e){t.uniform3uiv(this.addr,e)}function Hpt(t,e){t.uniform4uiv(this.addr,e)}function Fpt(t,e,n){const o=e.length,i=opt(n,o);t.uniform1iv(this.addr,i);for(let t=0;t!==o;++t)n.safeSetTexture2D(e[t]||Gdt,i[t])}function Lpt(t,e,n){const o=e.length,i=opt(n,o);t.uniform1iv(this.addr,i);for(let t=0;t!==o;++t)n.safeSetTextureCube(e[t]||Zdt,i[t])}function Bpt(t,e,n){this.id=t,this.addr=n,this.cache=[],this.setValue=(function o(t){switch(t){case 5126:return ipt;case 35664:return apt;case 35665:return rpt;case 35666:return spt;case 35674:return lpt;case 35675:return cpt;case 35676:return dpt;case 5124:case 35670:return ppt;case 35667:case 35671:return mpt;case 35668:case 35672:return fpt;case 35669:case 35673:return upt;case 5125:return gpt;case 36294:return hpt;case 36295:return bpt;case 36296:return ypt;case 35678:case 36198:case 36298:case 36306:case 35682:return _pt;case 35679:case 36299:case 36307:return Cpt;case 35680:case 36300:case 36308:case 36293:return Mpt;case 36289:case 36303:case 36311:case 36292:return vpt}})(e.type)}function Vpt(t,e,n){this.id=t,this.addr=n,this.cache=[],this.size=e.size,this.setValue=(function o(t){switch(t){case 5126:return xpt;case 35664:return Opt;case 35665:return Ppt;case 35666:return wpt;case 35674:return kpt;case 35675:return Spt;case 35676:return Dpt;case 5124:case 35670:return Rpt;case 35667:case 35671:return Ept;case 35668:case 35672:return Apt;case 35669:case 35673:return Tpt;case 5125:return Npt;case 36294:return zpt;case 36295:return Ipt;case 36296:return Hpt;case 35678:case 36198:case 36298:case 36306:case 35682:return Fpt;case 35680:case 36300:case 36308:case 36293:return Lpt}})(e.type)}function jpt(t){this.id=t,this.seq=[],this.map={}}Vpt.prototype.updateCache=function(t){const e=this.cache;t instanceof Float32Array&&e.length!==t.length&&(this.cache=new Float32Array(t.length)),npt(e,t)},jpt.prototype.setValue=function(t,e,n){const o=this.seq;for(let i=0,a=o.length;i!==a;++i){const a=o[i];a.setValue(t,e[a.id],n)}};const Upt=/(\w+)(\])?(\[|\.)?/g;function Wpt(t,e){t.seq.push(e),t.map[e.id]=e}function Gpt(t,e,n){const o=t.name,i=o.length;for(Upt.lastIndex=0;;){const a=Upt.exec(o),r=Upt.lastIndex;let s=a[1];const l=a[3];if("]"===a[2]&&(s|=0),void 0===l||"["===l&&r+2===i){Wpt(n,void 0===l?new Bpt(s,t,e):new Vpt(s,t,e));break}{let t=n.map[s];void 0===t&&(t=new jpt(s),Wpt(n,t)),n=t}}}function Ypt(t,e){this.seq=[],this.map={};const n=t.getProgramParameter(e,35718);for(let o=0;o<n;++o){const n=t.getActiveUniform(e,o);Gpt(n,t.getUniformLocation(e,n.name),this)}}function qpt(t,e,n){const o=t.createShader(e);return t.shaderSource(o,n),t.compileShader(o),o}Ypt.prototype.setValue=function(t,e,n,o){const i=this.map[e];void 0!==i&&i.setValue(t,n,o)},Ypt.prototype.setOptional=function(t,e,n){const o=e[n];void 0!==o&&this.setValue(t,n,o)},Ypt.upload=function(t,e,n,o){for(let i=0,a=e.length;i!==a;++i){const a=e[i],r=n[a.id];!1!==r.needsUpdate&&a.setValue(t,r.value,o)}},Ypt.seqWithValue=function(t,e){const n=[];for(let o=0,i=t.length;o!==i;++o){const i=t[o];i.id in e&&n.push(i)}return n};let Zpt=0;function Xpt(t){switch(t){case Qrt:return["Linear","( value )"];case $rt:return["sRGB","( value )"];case est:return["RGBE","( value )"];case 3004:return["RGBM","( value, 7.0 )"];case 3005:return["RGBM","( value, 16.0 )"];case 3006:return["RGBD","( value, 256.0 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t[e.id]=n,n}}}let Cmt=0;function Mmt(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function vmt(t,e){const n=new _mt,o=(function i(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case"DirectionalLight":case"SpotLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new gst};break;case"PointLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new gst,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}})(),a={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)a.probe.push(new wst);const r=new wst,s=new olt,l=new olt;return{setup:function c(i){let r=0,s=0,l=0;for(let t=0;t<9;t++)a.probe[t].set(0,0,0);let c=0,d=0,p=0,m=0,f=0,u=0,g=0,h=0;i.sort(Mmt);for(let t=0,e=i.length;t<e;t++){const e=i[t],b=e.color,y=e.intensity,_=e.distance,C=e.shadow&&e.shadow.map?e.shadow.map.texture:null;if(e.isAmbientLight)r+=b.r*y,s+=b.g*y,l+=b.b*y;else if(e.isLightProbe)for(let t=0;t<9;t++)a.probe[t].addScaledVector(e.sh.coefficients[t],y);else if(e.isDirectionalLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity),e.castShadow){const t=e.shadow,n=o.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,a.directionalShadow[c]=n,a.directionalShadowMap[c]=C,a.directionalShadowMatrix[c]=e.shadow.matrix,u++}a.directional[c]=t,c++}else if(e.isSpotLight){const t=n.get(e);if(t.position.setFromMatrixPosition(e.matrixWorld),t.color.copy(b).multiplyScalar(y),t.distance=_,t.coneCos=Math.cos(e.angle),t.penumbraCos=Math.cos(e.angle*(1-e.penumbra)),t.decay=e.decay,e.castShadow){const t=e.shadow,n=o.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,a.spotShadow[p]=n,a.spotShadowMap[p]=C,a.spotShadowMatrix[p]=e.shadow.matrix,h++}a.spot[p]=t,p++}else if(e.isRectAreaLight){const t=n.get(e);t.color.copy(b).multiplyScalar(y),t.halfWidth.set(.5*e.width,0,0),t.halfHeight.set(0,.5*e.height,0),a.rectArea[m]=t,m++}else if(e.isPointLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity),t.distance=e.distance,t.decay=e.decay,e.castShadow){const t=e.shadow,n=o.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,n.shadowCameraNear=t.camera.near,n.shadowCameraFar=t.camera.far,a.pointShadow[d]=n,a.pointShadowMap[d]=C,a.pointShadowMatrix[d]=e.shadow.matrix,g++}a.point[d]=t,d++}else if(e.isHemisphereLight){const t=n.get(e);t.skyColor.copy(e.color).multiplyScalar(y),t.groundColor.copy(e.groundColor).multiplyScalar(y),a.hemi[f]=t,f++}}m>0&&(e.isWebGL2||!0===t.has("OES_texture_float_linear")?(a.rectAreaLTC1=Qct.LTC_FLOAT_1,a.rectAreaLTC2=Qct.LTC_FLOAT_2):!0===t.has("OES_texture_half_float_linear")?(a.rectAreaLTC1=Qct.LTC_HALF_1,a.rectAreaLTC2=Qct.LTC_HALF_2):console.error("THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.")),a.ambient[0]=r,a.ambient[1]=s,a.ambient[2]=l;const b=a.hash;b.directionalLength===c&&b.pointLength===d&&b.spotLength===p&&b.rectAreaLength===m&&b.hemiLength===f&&b.numDirectionalShadows===u&&b.numPointShadows===g&&b.numSpotShadows===h||(a.directional.length=c,a.spot.length=p,a.rectArea.length=m,a.point.length=d,a.hemi.length=f,a.directionalShadow.length=u,a.directionalShadowMap.length=u,a.pointShadow.length=g,a.pointShadowMap.length=g,a.spotShadow.length=h,a.spotShadowMap.length=h,a.directionalShadowMatrix.length=u,a.pointShadowMatrix.length=g,a.spotShadowMatrix.length=h,b.directionalLength=c,b.pointLength=d,b.spotLength=p,b.rectAreaLength=m,b.hemiLength=f,b.numDirectionalShadows=u,b.numPointShadows=g,b.numSpotShadows=h,a.version=Cmt++)},setupView:function d(t,e){let n=0,o=0,i=0,c=0,d=0;const p=e.matrixWorldInverse;for(let e=0,m=t.length;e<m;e++){const m=t[e];if(m.isDirectionalLight){const t=a.directional[n];t.direction.setFromMatrixPosition(m.matrixWorld),r.setFromMatrixPosition(m.target.matrixWorld),t.direction.sub(r),t.direction.transformDirection(p),n++}else if(m.isSpotLight){const t=a.spot[i];t.position.setFromMatrixPosition(m.matrixWorld),t.position.applyMatrix4(p),t.direction.setFromMatrixPosition(m.matrixWorld),r.setFromMatrixPosition(m.target.matrixWorld),t.direction.sub(r),t.direction.transformDirection(p),i++}else if(m.isRectAreaLight){const t=a.rectArea[c];t.position.setFromMatrixPosition(m.matrixWorld),t.position.applyMatrix4(p),l.identity(),s.copy(m.matrixWorld),s.premultiply(p),l.extractRotation(s),t.halfWidth.set(.5*m.width,0,0),t.halfHeight.set(0,.5*m.height,0),t.halfWidth.applyMatrix4(l),t.halfHeight.applyMatrix4(l),c++}else if(m.isPointLight){const t=a.point[o];t.position.setFromMatrixPosition(m.matrixWorld),t.position.applyMatrix4(p),o++}else if(m.isHemisphereLight){const t=a.hemi[d];t.direction.setFromMatrixPosition(m.matrixWorld),t.direction.transformDirection(p),t.direction.normalize(),d++}}},state:a}}function xmt(t,e){const n=new vmt(t,e),o=[],i=[];return{init:function a(){o.length=0,i.length=0},state:{lightsArray:o,shadowsArray:i,lights:n},setupLights:function r(){n.setup(o)},setupLightsView:function s(t){n.setupView(o,t)},pushLight:function l(t){o.push(t)},pushShadow:function c(t){i.push(t)}}}function Omt(t,e){let n=new WeakMap;return{get:function o(i,a=0){let r;return!1===n.has(i)?(r=new xmt(t,e),n.set(i,[r])):a>=n.get(i).length?(r=new xmt(t,e),n.get(i).push(r)):r=n.get(i)[a],r},dispose:function i(){n=new WeakMap}}}class Pmt extends Vlt{constructor(t){super(),this.type="MeshDepthMaterial",this.depthPacking=3200,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}Pmt.prototype.isMeshDepthMaterial=!0;class wmt extends Vlt{constructor(t){super(),this.type="MeshDistanceMaterial",this.referencePosition=new wst,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}wmt.prototype.isMeshDistanceMaterial=!0;function kmt(t,e,n){let o=new qct;const i=new gst,a=new gst,r=new vst,s=new Pmt({depthPacking:3201}),l=new wmt,c={},d=n.maxTextureSize,p={0:1,1:0,2:2},m=new Nct({defines:{SAMPLE_RATE:2/8,HALF_SAMPLE_RATE:1/8},uniforms:{shadow_pass:{value:null},resolution:{value:new gst},radius:{value:4}},vertexShader:"void main() {\n\tgl_Position = vec4( position, 1.0 );\n}",fragmentShader:"uniform sampler2D shadow_pass;\nuniform vec2 resolution;\nuniform float radius;\n#include <packing>\nvoid main() {\n\tfloat mean = 0.0;\n\tfloat squared_mean = 0.0;\n\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\n\tfor ( float i = -1.0; i < 1.0 ; i += SAMPLE_RATE) {\n\t\t#ifdef HORIZONTAL_PASS\n\t\t\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( i, 0.0 ) * radius ) / resolution ) );\n\t\t\tmean += distribution.x;\n\t\t\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\n\t\t#else\n\t\t\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, i ) * radius ) / resolution ) );\n\t\t\tmean += depth;\n\t\t\tsquared_mean += depth * depth;\n\t\t#endif\n\t}\n\tmean = mean * HALF_SAMPLE_RATE;\n\tsquared_mean = squared_mean * HALF_SAMPLE_RATE;\n\tfloat std_dev = sqrt( squared_mean - mean * mean );\n\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\n}"}),f=m.clone();f.defines.HORIZONTAL_PASS=1;const u=new dct;u.setAttribute("position",new Qlt(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const g=new Sct(u,m),h=this;function b(n,o){const i=e.update(g);m.uniforms.shadow_pass.value=n.map.texture,m.uniforms.resolution.value=n.mapSize,m.uniforms.radius.value=n.radius,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(o,null,i,m,g,null),f.uniforms.shadow_pass.value=n.mapPass.texture,f.uniforms.resolution.value=n.mapSize,f.uniforms.radius.value=n.radius,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(o,null,i,f,g,null)}function y(e,n,o,i,a,r,d){let m=null;const f=!0===i.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(m=void 0!==f?f:!0===i.isPointLight?l:s,t.localClippingEnabled&&!0===o.clipShadows&&0!==o.clippingPlanes.length){const t=m.uuid,e=o.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let i=n[e];void 0===i&&(i=m.clone(),n[e]=i),m=i}return m.visible=o.visible,m.wireframe=o.wireframe,m.side=3===d?null!==o.shadowSide?o.shadowSide:o.side:null!==o.shadowSide?o.shadowSide:p[o.side],m.clipShadows=o.clipShadows,m.clippingPlanes=o.clippingPlanes,m.clipIntersection=o.clipIntersection,m.wireframeLinewidth=o.wireframeLinewidth,m.linewidth=o.linewidth,!0===i.isPointLight&&!0===m.isMeshDistanceMaterial&&(m.referencePosition.setFromMatrixPosition(i.matrixWorld),m.nearDistance=a,m.farDistance=r),m}function _(n,i,a,r,s){if(!1===n.visible)return;if(n.layers.test(i.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&3===s)&&(!n.frustumCulled||o.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(a.matrixWorldInverse,n.matrixWorld);const o=e.update(n),i=n.material;if(Array.isArray(i)){const e=o.groups;for(let l=0,c=e.length;l<c;l++){const c=e[l],d=i[c.materialIndex];if(d&&d.visible){const e=y(n,0,d,r,a.near,a.far,s);t.renderBufferDirect(a,null,o,e,n,c)}}}else if(i.visible){const e=y(n,0,i,r,a.near,a.far,s);t.renderBufferDirect(a,null,o,e,n,null)}}const l=n.children;for(let t=0,e=l.length;t<e;t++)_(l[t],i,a,r,s)}this.enabled=!1,this.autoUpdate=!0,this.needsUpdate=!1,this.type=1,this.render=function(e,n,s){if(!1===h.enabled)return;if(!1===h.autoUpdate&&!1===h.needsUpdate)return;if(0===e.length)return;const l=t.getRenderTarget(),c=t.getActiveCubeFace(),p=t.getActiveMipmapLevel(),m=t.state;m.setBlending(0),m.buffers.color.setClear(1,1,1,1),m.buffers.depth.setTest(!0),m.setScissorTest(!1);for(let l=0,c=e.length;l<c;l++){const c=e[l],p=c.shadow;if(void 0===p){console.warn("THREE.WebGLShadowMap:",c,"has no shadow.");continue}if(!1===p.autoUpdate&&!1===p.needsUpdate)continue;i.copy(p.mapSize);const f=p.getFrameExtents();if(i.multiply(f),a.copy(p.mapSize),(i.x>d||i.y>d)&&(i.x>d&&(a.x=Math.floor(d/f.x),i.x=a.x*f.x,p.mapSize.x=a.x),i.y>d&&(a.y=Math.floor(d/f.y),i.y=a.y*f.y,p.mapSize.y=a.y)),null===p.map&&!p.isPointLightShadow&&3===this.type){const t={minFilter:Trt,magFilter:Trt,format:jrt};p.map=new xst(i.x,i.y,t),p.map.texture.name=c.name+".shadowMap",p.mapPass=new xst(i.x,i.y,t),p.camera.updateProjectionMatrix()}if(null===p.map){p.map=new xst(i.x,i.y,{minFilter:Art,magFilter:Art,format:jrt}),p.map.texture.name=c.name+".shadowMap",p.camera.updateProjectionMatrix()}t.setRenderTarget(p.map),t.clear();const u=p.getViewportCount();for(let t=0;t<u;t++){const e=p.getViewport(t);r.set(a.x*e.x,a.y*e.y,a.x*e.z,a.y*e.w),m.viewport(r),p.updateMatrices(c,t),o=p.getFrustum(),_(n,s,p.camera,c,this.type)}p.isPointLightShadow||3!==this.type||b(p,s),p.needsUpdate=!1}h.needsUpdate=!1,t.setRenderTarget(l,c,p)}}function Smt(t,e,n){const o=n.isWebGL2;const i=new(function a(){let e=!1;const n=new vst;let o=null;const i=new vst(0,0,0,0);return{setMask:function(n){o===n||e||(t.colorMask(n,n,n,n),o=n)},setLocked:function(t){e=t},setClear:function(e,o,a,r,s){!0===s&&(e*=r,o*=r,a*=r),n.set(e,o,a,r),!1===i.equals(n)&&(t.clearColor(e,o,a,r),i.copy(n))},reset:function(){e=!1,o=null,i.set(-1,0,0,0)}}}),r=new(function s(){let e=!1,n=null,o=null,i=null;return{setTest:function(t){t?B(2929):V(2929)},setMask:function(o){n===o||e||(t.depthMask(o),n=o)},setFunc:function(e){if(o!==e){if(e)switch(e){case 0:t.depthFunc(512);break;case 1:t.depthFunc(519);break;case 2:t.depthFunc(513);break;case 3:t.depthFunc(515);break;case 4:t.depthFunc(514);break;case 5:t.depthFunc(518);break;case 6:t.depthFunc(516);break;case 7:t.depthFunc(517);break;default:t.depthFunc(515)}else t.depthFunc(515);o=e}},setLocked:function(t){e=t},setClear:function(e){i!==e&&(t.clearDepth(e),i=e)},reset:function(){e=!1,n=null,o=null,i=null}}}),l=new(function c(){let e=!1,n=null,o=null,i=null,a=null,r=null,s=null,l=null,c=null;return{setTest:function(t){e||(t?B(2960):V(2960))},setMask:function(o){n===o||e||(t.stencilMask(o),n=o)},setFunc:function(e,n,r){o===e&&i===n&&a===r||(t.stencilFunc(e,n,r),o=e,i=n,a=r)},setOp:function(e,n,o){r===e&&s===n&&l===o||(t.stencilOp(e,n,o),r=e,s=n,l=o)},setLocked:function(t){e=t},setClear:function(e){c!==e&&(t.clearStencil(e),c=e)},reset:function(){e=!1,n=null,o=null,i=null,a=null,r=null,s=null,l=null,c=null}}});let d={},p=null,m={},f=null,u=!1,g=null,h=null,b=null,y=null,_=null,C=null,M=null,v=!1,x=null,O=null,P=null,w=null,k=null;const S=t.getParameter(35661);let D=!1,R=0;const E=t.getParameter(7938);-1!==E.indexOf("WebGL")?(R=parseFloat(/^WebGL (\d)/.exec(E)[1]),D=R>=1):-1!==E.indexOf("OpenGL ES")&&(R=parseFloat(/^OpenGL ES (\d)/.exec(E)[1]),D=R>=2);let A=null,T={};const N=t.getParameter(3088),z=t.getParameter(2978),I=(new vst).fromArray(N),H=(new vst).fromArray(z);function F(e,n,o){const i=new Uint8Array(4),a=t.createTexture();t.bindTexture(e,a),t.texParameteri(e,10241,9728),t.texParameteri(e,10240,9728);for(let e=0;e<o;e++)t.texImage2D(n+e,0,6408,1,1,0,6408,5121,i);return a}const L={};function B(e){!0!==d[e]&&(t.enable(e),d[e]=!0)}function V(e){!1!==d[e]&&(t.disable(e),d[e]=!1)}L[3553]=F(3553,3553,1),L[34067]=F(34067,34069,6),i.setClear(0,0,0,1),r.setClear(1),l.setClear(0),B(2929),r.setFunc(3),G(!1),Y(1),B(2884),W(0);const j={[Prt]:32774,101:32778,102:32779};if(o)j[103]=32775,j[104]=32776;else{const t=e.get("EXT_blend_minmax");null!==t&&(j[103]=t.MIN_EXT,j[104]=t.MAX_EXT)}const U={200:0,201:1,202:768,204:770,210:776,208:774,206:772,203:769,205:771,209:775,207:773};function W(e,n,o,i,a,r,s,l){if(0!==e){if(!1===u&&(B(3042),u=!0),5===e)a=a||n,r=r||o,s=s||i,n===h&&a===_||(t.blendEquationSeparate(j[n],j[a]),h=n,_=a),o===b&&i===y&&r===C&&s===M||(t.blendFuncSeparate(U[o],U[i],U[r],U[s]),b=o,y=i,C=r,M=s),g=e,v=null;else 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Using RGBA format instead.")),p){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),m)if(i.drawBuffers){const n=e.texture;for(let e=0,i=n.length;e<i;e++){const i=o.get(n[e]);void 0===i.__webglTexture&&(i.__webglTexture=t.createTexture(),r.memory.textures++)}}else console.warn("THREE.WebGLRenderer: WebGLMultipleRenderTargets can only be used with WebGL2 or WEBGL_draw_buffers extension.");else if(f)if(s){c.__webglMultisampledFramebuffer=t.createFramebuffer(),c.__webglColorRenderbuffer=t.createRenderbuffer(),t.bindRenderbuffer(36161,c.__webglColorRenderbuffer);const o=a.convert(l.format),i=a.convert(l.type),r=C(l.internalFormat,o,i),s=z(e);t.renderbufferStorageMultisample(36161,s,r,e.width,e.height),n.bindFramebuffer(36160,c.__webglMultisampledFramebuffer),t.framebufferRenderbuffer(36160,36064,36161,c.__webglColorRenderbuffer),t.bindRenderbuffer(36161,null),e.depthBuffer&&(c.__webglDepthRenderbuffer=t.createRenderbuffer(),T(c.__webglDepthRenderbuffer,e,!0)),n.bindFramebuffer(36160,null)}else console.warn("THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.");if(p){n.bindTexture(34067,d.__webglTexture),D(34067,l,g);for(let t=0;t<6;t++)A(c.__webglFramebuffer[t],e,l,36064,34069+t);y(l,g)&&_(34067,l,e.width,e.height),n.bindTexture(34067,null)}else if(m){const t=e.texture;for(let i=0,a=t.length;i<a;i++){const a=t[i],r=o.get(a);n.bindTexture(3553,r.__webglTexture),D(3553,a,g),A(c.__webglFramebuffer,e,a,36064+i,3553),y(a,g)&&_(3553,a,e.width,e.height)}n.bindTexture(3553,null)}else{let t=3553;if(u)if(s){t=l.isDataTexture3D?32879:35866}else console.warn("THREE.DataTexture3D and THREE.DataTexture2DArray only supported with WebGL2.");n.bindTexture(t,d.__webglTexture),D(t,l,g),A(c.__webglFramebuffer,e,l,36064,t),y(l,g)&&_(t,l,e.width,e.height,e.depth),n.bindTexture(t,null)}e.depthBuffer&&N(e)},this.updateRenderTargetMipmap=function U(t){const e=b(t)||s,i=!0===t.isWebGLMultipleRenderTargets?t.texture:[t.texture];for(let a=0,r=i.length;a<r;a++){const r=i[a];if(y(r,e)){const e=t.isWebGLCubeRenderTarget?34067:3553,i=o.get(r).__webglTexture;n.bindTexture(e,i),_(e,r,t.width,t.height),n.bindTexture(e,null)}}},this.updateMultisampleRenderTarget=function W(e){if(e.isWebGLMultisampleRenderTarget)if(s){const i=e.width,a=e.height;let r=16384;e.depthBuffer&&(r|=256),e.stencilBuffer&&(r|=1024);const s=o.get(e);n.bindFramebuffer(36008,s.__webglMultisampledFramebuffer),n.bindFramebuffer(36009,s.__webglFramebuffer),t.blitFramebuffer(0,0,i,a,0,0,i,a,r,9728),n.bindFramebuffer(36008,null),n.bindFramebuffer(36009,s.__webglMultisampledFramebuffer)}else console.warn("THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.")},this.safeSetTexture2D=function G(t,e){t&&t.isWebGLRenderTarget&&(!1===I&&(console.warn("THREE.WebGLTextures.safeSetTexture2D: don't use render targets as textures. Use their .texture property instead."),I=!0),t=t.texture),P(t,e)},this.safeSetTextureCube=function Y(t,e){t&&t.isWebGLCubeRenderTarget&&(!1===H&&(console.warn("THREE.WebGLTextures.safeSetTextureCube: don't use cube render targets as textures. Use their .texture property instead."),H=!0),t=t.texture),w(t,e)}}function Rmt(t,e,n){const o=n.isWebGL2;return{convert:function i(t){let n;if(t===zrt)return 5121;if(1017===t)return 32819;if(1018===t)return 32820;if(1019===t)return 33635;if(1010===t)return 5120;if(1011===t)return 5122;if(t===Irt)return 5123;if(1013===t)return 5124;if(t===Hrt)return 5125;if(t===Frt)return 5126;if(t===Lrt)return o?5131:(n=e.get("OES_texture_half_float"),null!==n?n.HALF_FLOAT_OES:null);if(1021===t)return 6406;if(t===Vrt)return 6407;if(t===jrt)return 6408;if(1024===t)return 6409;if(1025===t)return 6410;if(t===Urt)return 6402;if(t===Wrt)return 34041;if(1028===t)return 6403;if(1029===t)return 36244;if(1030===t)return 33319;if(1031===t)return 33320;if(1032===t)return 36248;if(1033===t)return 36249;if(33776===t||33777===t||33778===t||33779===t){if(n=e.get("WEBGL_compressed_texture_s3tc"),null===n)return null;if(33776===t)return n.COMPRESSED_RGB_S3TC_DXT1_EXT;if(33777===t)return n.COMPRESSED_RGBA_S3TC_DXT1_EXT;if(33778===t)return n.COMPRESSED_RGBA_S3TC_DXT3_EXT;if(33779===t)return n.COMPRESSED_RGBA_S3TC_DXT5_EXT}if(35840===t||35841===t||35842===t||35843===t){if(n=e.get("WEBGL_compressed_texture_pvrtc"),null===n)return null;if(35840===t)return n.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(35841===t)return n.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(35842===t)return n.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(35843===t)return n.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}if(36196===t)return n=e.get("WEBGL_compressed_texture_etc1"),null!==n?n.COMPRESSED_RGB_ETC1_WEBGL:null;if((37492===t||37496===t)&&(n=e.get("WEBGL_compressed_texture_etc"),null!==n)){if(37492===t)return n.COMPRESSED_RGB8_ETC2;if(37496===t)return n.COMPRESSED_RGBA8_ETC2_EAC}return 37808===t||37809===t||37810===t||37811===t||37812===t||37813===t||37814===t||37815===t||37816===t||37817===t||37818===t||37819===t||37820===t||37821===t||37840===t||37841===t||37842===t||37843===t||37844===t||37845===t||37846===t||37847===t||37848===t||37849===t||37850===t||37851===t||37852===t||37853===t?(n=e.get("WEBGL_compressed_texture_astc"),null!==n?t:null):36492===t?(n=e.get("EXT_texture_compression_bptc"),null!==n?t:null):t===Brt?o?34042:(n=e.get("WEBGL_depth_texture"),null!==n?n.UNSIGNED_INT_24_8_WEBGL:null):void 0}}}class Emt extends Ict{constructor(t=[]){super(),this.cameras=t}}Emt.prototype.isArrayCamera=!0;class Amt extends Slt{constructor(){super(),this.type="Group"}}Amt.prototype.isGroup=!0;const Tmt={type:"move"};class Nmt{constructor(){this._targetRay=null,this._grip=null,this._hand=null}getHandSpace(){return null===this._hand&&(this._hand=new Amt,this._hand.matrixAutoUpdate=!1,this._hand.visible=!1,this._hand.joints={},this._hand.inputState={pinching:!1}),this._hand}getTargetRaySpace(){return null===this._targetRay&&(this._targetRay=new Amt,this._targetRay.matrixAutoUpdate=!1,this._targetRay.visible=!1,this._targetRay.hasLinearVelocity=!1,this._targetRay.linearVelocity=new wst,this._targetRay.hasAngularVelocity=!1,this._targetRay.angularVelocity=new wst),this._targetRay}getGripSpace(){return null===this._grip&&(this._grip=new Amt,this._grip.matrixAutoUpdate=!1,this._grip.visible=!1,this._grip.hasLinearVelocity=!1,this._grip.linearVelocity=new wst,this._grip.hasAngularVelocity=!1,this._grip.angularVelocity=new wst),this._grip}dispatchEvent(t){return null!==this._targetRay&&this._targetRay.dispatchEvent(t),null!==this._grip&&this._grip.dispatchEvent(t),null!==this._hand&&this._hand.dispatchEvent(t),this}disconnect(t){return this.dispatchEvent({type:"disconnected",data:t}),null!==this._targetRay&&(this._targetRay.visible=!1),null!==this._grip&&(this._grip.visible=!1),null!==this._hand&&(this._hand.visible=!1),this}update(t,e,n){let o=null,i=null,a=null;const r=this._targetRay,s=this._grip,l=this._hand;if(t&&"visible-blurred"!==e.session.visibilityState)if(null!==r&&(o=e.getPose(t.targetRaySpace,n),null!==o&&(r.matrix.fromArray(o.transform.matrix),r.matrix.decompose(r.position,r.rotation,r.scale),o.linearVelocity?(r.hasLinearVelocity=!0,r.linearVelocity.copy(o.linearVelocity)):r.hasLinearVelocity=!1,o.angularVelocity?(r.hasAngularVelocity=!0,r.angularVelocity.copy(o.angularVelocity)):r.hasAngularVelocity=!1,this.dispatchEvent(Tmt))),l&&t.hand){a=!0;for(const o of t.hand.values()){const t=e.getJointPose(o,n);if(void 0===l.joints[o.jointName]){const t=new Amt;t.matrixAutoUpdate=!1,t.visible=!1,l.joints[o.jointName]=t,l.add(t)}const i=l.joints[o.jointName];null!==t&&(i.matrix.fromArray(t.transform.matrix),i.matrix.decompose(i.position,i.rotation,i.scale),i.jointRadius=t.radius),i.visible=null!==t}const o=l.joints["index-finger-tip"].position.distanceTo(l.joints["thumb-tip"].position),i=.02,r=.005;l.inputState.pinching&&o>i+r?(l.inputState.pinching=!1,this.dispatchEvent({type:"pinchend",handedness:t.handedness,target:this})):!l.inputState.pinching&&o<=i-r&&(l.inputState.pinching=!0,this.dispatchEvent({type:"pinchstart",handedness:t.handedness,target:this}))}else null!==s&&t.gripSpace&&(i=e.getPose(t.gripSpace,n),null!==i&&(s.matrix.fromArray(i.transform.matrix),s.matrix.decompose(s.position,s.rotation,s.scale),i.linearVelocity?(s.hasLinearVelocity=!0,s.linearVelocity.copy(i.linearVelocity)):s.hasLinearVelocity=!1,i.angularVelocity?(s.hasAngularVelocity=!0,s.angularVelocity.copy(i.angularVelocity)):s.hasAngularVelocity=!1));return null!==r&&(r.visible=null!==o),null!==s&&(s.visible=null!==i),null!==l&&(l.visible=null!==a),this}}class zmt extends rst{constructor(t,e){super();const n=this,o=t.state;let i=null,a=1,r=null,s="local-floor",l=null,c=null,d=null,p=null,m=null;const f=[],u=new Map,g=new Ict;g.layers.enable(1),g.viewport=new vst;const h=new Ict;h.layers.enable(2),h.viewport=new vst;const b=[g,h],y=new Emt;y.layers.enable(1),y.layers.enable(2);let _=null,C=null;function M(t){const e=u.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function v(){u.forEach((function(t,e){t.disconnect(e)})),u.clear(),_=null,C=null,o.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),S.stop(),n.isPresenting=!1,n.dispatchEvent({type:"sessionend"})}function x(t){const e=i.inputSources;for(let t=0;t<f.length;t++)u.set(e[t],f[t]);for(let e=0;e<t.removed.length;e++){const n=t.removed[e],o=u.get(n);o&&(o.dispatchEvent({type:"disconnected",data:n}),u.delete(n))}for(let e=0;e<t.added.length;e++){const n=t.added[e],o=u.get(n);o&&o.dispatchEvent({type:"connected",data:n})}}this.cameraAutoUpdate=!0,this.enabled=!1,this.isPresenting=!1,this.getController=function(t){let e=f[t];return void 0===e&&(e=new Nmt,f[t]=e),e.getTargetRaySpace()},this.getControllerGrip=function(t){let e=f[t];return void 0===e&&(e=new Nmt,f[t]=e),e.getGripSpace()},this.getHand=function(t){let e=f[t];return void 0===e&&(e=new Nmt,f[t]=e),e.getHandSpace()},this.setFramebufferScaleFactor=function(t){a=t,!0===n.isPresenting&&console.warn("THREE.WebXRManager: Cannot change framebuffer scale while presenting.")},this.setReferenceSpaceType=function(t){s=t,!0===n.isPresenting&&console.warn("THREE.WebXRManager: Cannot change reference space type while presenting.")},this.getReferenceSpace=function(){return r},this.getSession=function(){return i},this.setSession=async function(t){if(i=t,null!==i){i.addEventListener("select",M),i.addEventListener("selectstart",M),i.addEventListener("selectend",M),i.addEventListener("squeeze",M),i.addEventListener("squeezestart",M),i.addEventListener("squeezeend",M),i.addEventListener("end",v),i.addEventListener("inputsourceschange",x);const t=e.getContextAttributes();if(!0!==t.xrCompatible&&await e.makeXRCompatible(),void 0===i.renderState.layers){m=new XRWebGLLayer(i,e,{antialias:t.antialias,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:a}),i.updateRenderState({baseLayer:m})}else{let n=0;if(t.antialias){m=new XRWebGLLayer(i,e,{antialias:!0,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:a}),i.updateRenderState({layers:[m]})}else{t.depth&&(n=t.stencil?34041:6402);const o={colorFormat:t.alpha?6408:6407,depthFormat:n,scaleFactor:a};c=new XRWebGLBinding(i,e),p=c.createProjectionLayer(o),d=e.createFramebuffer(),i.updateRenderState({layers:[p]})}}r=await i.requestReferenceSpace(s),S.setContext(i),S.start(),n.isPresenting=!0,n.dispatchEvent({type:"sessionstart"})}};const O=new wst,P=new wst;function w(t,e){null===e?t.matrixWorld.copy(t.matrix):t.matrixWorld.multiplyMatrices(e.matrixWorld,t.matrix),t.matrixWorldInverse.copy(t.matrixWorld).invert()}this.updateCamera=function(t){if(null===i)return;y.near=h.near=g.near=t.near,y.far=h.far=g.far=t.far,_===y.near&&C===y.far||(i.updateRenderState({depthNear:y.near,depthFar:y.far}),_=y.near,C=y.far);const e=t.parent,n=y.cameras;w(y,e);for(let t=0;t<n.length;t++)w(n[t],e);y.matrixWorld.decompose(y.position,y.quaternion,y.scale),t.position.copy(y.position),t.quaternion.copy(y.quaternion),t.scale.copy(y.scale),t.matrix.copy(y.matrix),t.matrixWorld.copy(y.matrixWorld);const o=t.children;for(let t=0,e=o.length;t<e;t++)o[t].updateMatrixWorld(!0);2===n.length?(function a(t,e,n){O.setFromMatrixPosition(e.matrixWorld),P.setFromMatrixPosition(n.matrixWorld);const o=O.distanceTo(P),i=e.projectionMatrix.elements,a=n.projectionMatrix.elements,r=i[14]/(i[10]-1),s=i[14]/(i[10]+1),l=(i[9]+1)/i[5],c=(i[9]-1)/i[5],d=(i[8]-1)/i[0],p=(a[8]+1)/a[0],m=r*d,f=r*p,u=o/(-d+p),g=u*-d;e.matrixWorld.decompose(t.position,t.quaternion,t.scale),t.translateX(g),t.translateZ(u),t.matrixWorld.compose(t.position,t.quaternion,t.scale),t.matrixWorldInverse.copy(t.matrixWorld).invert();const h=r+u,b=s+u;t.projectionMatrix.makePerspective(m-g,f+(o-g),l*s/b*h,c*s/b*h,h,b)})(y,g,h):y.projectionMatrix.copy(g.projectionMatrix)},this.getCamera=function(){return y},this.getFoveation=function(){return null!==p?p.fixedFoveation:null!==m?m.fixedFoveation:void 0},this.setFoveation=function(t){null!==p&&(p.fixedFoveation=t),null!==m&&void 0!==m.fixedFoveation&&(m.fixedFoveation=t)};let k=null;const S=new Zct;S.setAnimationLoop((function D(t,n){if(l=n.getViewerPose(r),null!==l){const t=l.views;null!==m&&o.bindXRFramebuffer(m.framebuffer);let n=!1;t.length!==y.cameras.length&&(y.cameras.length=0,n=!0);for(let i=0;i<t.length;i++){const a=t[i];let r=null;if(null!==m)r=m.getViewport(a);else{const t=c.getViewSubImage(p,a);o.bindXRFramebuffer(d),void 0!==t.depthStencilTexture&&e.framebufferTexture2D(36160,36096,3553,t.depthStencilTexture,0),e.framebufferTexture2D(36160,36064,3553,t.colorTexture,0),r=t.viewport}const s=b[i];s.matrix.fromArray(a.transform.matrix),s.projectionMatrix.fromArray(a.projectionMatrix),s.viewport.set(r.x,r.y,r.width,r.height),0===i&&y.matrix.copy(s.matrix),!0===n&&y.cameras.push(s)}}const a=i.inputSources;for(let t=0;t<f.length;t++){f[t].update(a[t],n,r)}k&&k(t,n)})),this.setAnimationLoop=function(t){k=t},this.dispose=function(){}}}function Imt(t){function e(e,n){e.opacity.value=n.opacity,n.color&&e.diffuse.value.copy(n.color),n.emissive&&e.emissive.value.copy(n.emissive).multiplyScalar(n.emissiveIntensity),n.map&&(e.map.value=n.map),n.alphaMap&&(e.alphaMap.value=n.alphaMap),n.specularMap&&(e.specularMap.value=n.specularMap);const o=t.get(n).envMap;if(o){e.envMap.value=o,e.flipEnvMap.value=o.isCubeTexture&&!1===o.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.refractionRatio.value=n.refractionRatio;const i=t.get(o).__maxMipLevel;void 0!==i&&(e.maxMipLevel.value=i)}let i,a;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?i=n.map:n.specularMap?i=n.specularMap:n.displacementMap?i=n.displacementMap:n.normalMap?i=n.normalMap:n.bumpMap?i=n.bumpMap:n.roughnessMap?i=n.roughnessMap:n.metalnessMap?i=n.metalnessMap:n.alphaMap?i=n.alphaMap:n.emissiveMap?i=n.emissiveMap:n.clearcoatMap?i=n.clearcoatMap:n.clearcoatNormalMap?i=n.clearcoatNormalMap:n.clearcoatRoughnessMap?i=n.clearcoatRoughnessMap:n.specularIntensityMap?i=n.specularIntensityMap:n.specularTintMap&&(i=n.specularTintMap),void 0!==i&&(i.isWebGLRenderTarget&&(i=i.texture),!0===i.matrixAutoUpdate&&i.updateMatrix(),e.uvTransform.value.copy(i.matrix)),n.aoMap?a=n.aoMap:n.lightMap&&(a=n.lightMap),void 0!==a&&(a.isWebGLRenderTarget&&(a=a.texture),!0===a.matrixAutoUpdate&&a.updateMatrix(),e.uv2Transform.value.copy(a.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,1===n.side&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),1===n.side&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias);t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function o(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function i(t,o,a,r,s){o.isMeshBasicMaterial?e(t,o):o.isMeshLambertMaterial?(e(t,o),(function l(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)})(t,o)):o.isMeshToonMaterial?(e(t,o),(function c(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap);e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,o)):o.isMeshPhongMaterial?(e(t,o),(function d(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,o)):o.isMeshStandardMaterial?(e(t,o),o.isMeshPhysicalMaterial?(function p(t,e,o){n(t,e),t.reflectivity.value=e.reflectivity,t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.sheen&&t.sheen.value.copy(e.sheen);e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap);e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap);e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,1===e.side&&t.clearcoatNormalScale.value.negate());t.transmission.value=e.transmission,e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap);e.transmission>0&&(t.transmissionSamplerMap.value=o.texture,t.transmissionSamplerSize.value.set(o.width,o.height));t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap);t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint),t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap);e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)})(t,o,s):n(t,o)):o.isMeshMatcapMaterial?(e(t,o),(function m(t,e){e.matcap&&(t.matcap.value=e.matcap);e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1));e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate());e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,o)):o.isMeshDepthMaterial?(e(t,o),(function f(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,o)):o.isMeshDistanceMaterial?(e(t,o),(function 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u=!1;n.version===p.__version?p.needsLights&&p.lightsStateVersion!==m.state.version||p.outputEncoding!==a||o.isInstancedMesh&&!1===p.instancing?u=!0:o.isInstancedMesh||!0!==p.instancing?o.isSkinnedMesh&&!1===p.skinning?u=!0:o.isSkinnedMesh||!0!==p.skinning?p.envMap!==r||n.fog&&p.fog!==i?u=!0:void 0===p.numClippingPlanes||p.numClippingPlanes===it.numPlanes&&p.numIntersection===it.numIntersection?(p.vertexAlphas!==s||p.vertexTangents!==l||p.morphTargets!==c||p.morphNormals!==d)&&(u=!0):u=!0:u=!0:u=!0:(u=!0,p.__version=n.version);let g=p.currentProgram;!0===u&&(g=wt(n,e,o));let b=!1,y=!1,_=!1;const x=g.getUniforms(),O=p.uniforms;if(G.useProgram(g.program)&&(b=!0,y=!0,_=!0),n.id!==M&&(M=n.id,y=!0),b||v!==t){if(x.setValue(mt,"projectionMatrix",t.projectionMatrix),W.logarithmicDepthBuffer&&x.setValue(mt,"logDepthBufFC",2/(Math.log(t.far+1)/Math.LN2)),v!==t&&(v=t,y=!0,_=!0),n.isShaderMaterial||n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshStandardMaterial||n.envMap){const e=x.map.cameraPosition;void 0!==e&&e.setValue(mt,B.setFromMatrixPosition(t.matrixWorld))}(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial)&&x.setValue(mt,"isOrthographic",!0===t.isOrthographicCamera),(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial||n.isShadowMaterial||o.isSkinnedMesh)&&x.setValue(mt,"viewMatrix",t.matrixWorldInverse)}if(o.isSkinnedMesh){x.setOptional(mt,o,"bindMatrix"),x.setOptional(mt,o,"bindMatrixInverse");const t=o.skeleton;t&&(W.floatVertexTextures?(null===t.boneTexture&&t.computeBoneTexture(),x.setValue(mt,"boneTexture",t.boneTexture,Z),x.setValue(mt,"boneTextureSize",t.boneTextureSize)):x.setOptional(mt,t,"boneMatrices"))}return(y||p.receiveShadow!==o.receiveShadow)&&(p.receiveShadow=o.receiveShadow,x.setValue(mt,"receiveShadow",o.receiveShadow)),y&&(x.setValue(mt,"toneMappingExposure",h.toneMappingExposure),p.needsLights&&(function P(t,e){t.ambientLightColor.needsUpdate=e,t.lightProbe.needsUpdate=e,t.directionalLights.needsUpdate=e,t.directionalLightShadows.needsUpdate=e,t.pointLights.needsUpdate=e,t.pointLightShadows.needsUpdate=e,t.spotLights.needsUpdate=e,t.spotLightShadows.needsUpdate=e,t.rectAreaLights.needsUpdate=e,t.hemisphereLights.needsUpdate=e})(O,_),i&&n.fog&&et.refreshFogUniforms(O,i),et.refreshMaterialUniforms(O,n,S,k,F),Ypt.upload(mt,p.uniformsList,O,Z)),n.isShaderMaterial&&!0===n.uniformsNeedUpdate&&(Ypt.upload(mt,p.uniformsList,O,Z),n.uniformsNeedUpdate=!1),n.isSpriteMaterial&&x.setValue(mt,"center",o.center),x.setValue(mt,"modelViewMatrix",o.modelViewMatrix),x.setValue(mt,"normalMatrix",o.normalMatrix),x.setValue(mt,"modelMatrix",o.matrixWorld),g}vt.setAnimationLoop((function Dt(t){_t&&_t(t)})),"undefined"!=typeof window&&vt.setContext(window),this.setAnimationLoop=function(t){_t=t,gt.setAnimationLoop(t),null===t?vt.stop():vt.start()},gt.addEventListener("sessionstart",Ct),gt.addEventListener("sessionend",Mt),this.render=function(t,e){if(void 0!==e&&!0!==e.isCamera)return void console.error("THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera.");if(!0===b)return;!0===t.autoUpdate&&t.updateMatrixWorld(),null===e.parent&&e.updateMatrixWorld(),!0===gt.enabled&&!0===gt.isPresenting&&(!0===gt.cameraAutoUpdate&&gt.updateCamera(e),e=gt.getCamera()),!0===t.isScene&&t.onBeforeRender(h,t,e,C),f=ot.get(t,g.length),f.init(),g.push(f),L.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),z.setFromProjectionMatrix(L),H=this.localClippingEnabled,I=it.init(this.clippingPlanes,H,e),m=nt.get(t,u.length),m.init(),u.push(m),xt(t,e,0,h.sortObjects),m.finish(),!0===h.sortObjects&&m.sort(D,R),!0===I&&it.beginShadows();at.render(f.state.shadowsArray,t,e),f.setupLights(),f.setupLightsView(e),!0===I&&it.endShadows(),!0===this.info.autoReset&&this.info.reset(),rt.render(m,t);const n=m.opaque,o=m.transmissive,i=m.transparent;n.length>0&&Ot(n,t,e),o.length>0&&(function a(t,e,n,o){if(null===F){F=new(!0===s&&!0===W.isWebGL2?Ost:xst)(1024,1024,{generateMipmaps:!0,type:null!==dt.convert(Lrt)?Lrt:zrt,minFilter:Nrt,magFilter:Art,wrapS:Rrt,wrapT:Rrt})}const i=h.getRenderTarget();h.setRenderTarget(F),h.clear();const a=h.toneMapping;h.toneMapping=0,Ot(t,n,o),h.toneMapping=a,Z.updateMultisampleRenderTarget(F),Z.updateRenderTargetMipmap(F),h.setRenderTarget(i),Ot(e,n,o)})(n,o,t,e),i.length>0&&Ot(i,t,e),null!==C&&(Z.updateMultisampleRenderTarget(C),Z.updateRenderTargetMipmap(C)),!0===t.isScene&&t.onAfterRender(h,t,e),G.buffers.depth.setTest(!0),G.buffers.depth.setMask(!0),G.buffers.color.setMask(!0),G.setPolygonOffset(!1),pt.resetDefaultState(),M=-1,v=null,g.pop(),f=g.length>0?g[g.length-1]:null,u.pop(),m=u.length>0?u[u.length-1]:null},this.getActiveCubeFace=function(){return y},this.getActiveMipmapLevel=function(){return _},this.getRenderTarget=function(){return C},this.setRenderTarget=function(t,e=0,n=0){C=t,y=e,_=n,t&&void 0===q.get(t).__webglFramebuffer&&Z.setupRenderTarget(t);let o=null,i=!1,a=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(a=!0);const r=q.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(o=r[e],i=!0):o=t.isWebGLMultisampleRenderTarget?q.get(t).__webglMultisampledFramebuffer:r,x.copy(t.viewport),O.copy(t.scissor),P=t.scissorTest}else x.copy(E).multiplyScalar(S).floor(),O.copy(A).multiplyScalar(S).floor(),P=T;if(G.bindFramebuffer(36160,o)&&W.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(N.length!==n.length||36064!==N[0]){for(let t=0,e=n.length;t<e;t++)N[t]=36064+t;N.length=n.length,e=!0}}else 1===N.length&&36064===N[0]||(N[0]=36064,N.length=1,e=!0);else 1===N.length&&1029===N[0]||(N[0]=1029,N.length=1,e=!0);e&&(W.isWebGL2?mt.drawBuffers(N):U.get("WEBGL_draw_buffers").drawBuffersWEBGL(N))}if(G.viewport(x),G.scissor(O),G.setScissorTest(P),i){const o=q.get(t.texture);mt.framebufferTexture2D(36160,36064,34069+e,o.__webglTexture,n)}else if(a){const o=q.get(t.texture);mt.framebufferTextureLayer(36160,36064,o.__webglTexture,n||0,e||0)}},this.readRenderTargetPixels=function(t,e,n,o,i,a,r){if(!t||!t.isWebGLRenderTarget)return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.");let s=q.get(t).__webglFramebuffer;if(t.isWebGLCubeRenderTarget&&void 0!==r&&(s=s[r]),s){G.bindFramebuffer(36160,s);try{const r=t.texture,s=r.format,l=r.type;if(s!==jrt&&dt.convert(s)!==mt.getParameter(35739))return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format.");const c=l===Lrt&&(U.has("EXT_color_buffer_half_float")||W.isWebGL2&&U.has("EXT_color_buffer_float"));if(!(l===zrt||dt.convert(l)===mt.getParameter(35738)||l===Frt&&(W.isWebGL2||U.has("OES_texture_float")||U.has("WEBGL_color_buffer_float"))||c))return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.");36053===mt.checkFramebufferStatus(36160)?e>=0&&e<=t.width-o&&n>=0&&n<=t.height-i&&mt.readPixels(e,n,o,i,dt.convert(s),dt.convert(l),a):console.error("THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.")}finally{const t=null!==C?q.get(C).__webglFramebuffer:null;G.bindFramebuffer(36160,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const o=Math.pow(2,-n),i=Math.floor(e.image.width*o),a=Math.floor(e.image.height*o);let r=dt.convert(e.format);W.isWebGL2&&(6407===r&&(r=32849),6408===r&&(r=32856)),Z.setTexture2D(e,0),mt.copyTexImage2D(3553,n,r,t.x,t.y,i,a,0),G.unbindTexture()},this.copyTextureToTexture=function(t,e,n,o=0){const i=e.image.width,a=e.image.height,r=dt.convert(n.format),s=dt.convert(n.type);Z.setTexture2D(n,0),mt.pixelStorei(37440,n.flipY),mt.pixelStorei(37441,n.premultiplyAlpha),mt.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?mt.texSubImage2D(3553,o,t.x,t.y,i,a,r,s,e.image.data):e.isCompressedTexture?mt.compressedTexSubImage2D(3553,o,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,r,e.mipmaps[0].data):mt.texSubImage2D(3553,o,t.x,t.y,r,s,e.image),0===o&&n.generateMipmaps&&mt.generateMipmap(3553),G.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,o,i=0){if(h.isWebGL1Renderer)return void console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.");const a=t.max.x-t.min.x+1,r=t.max.y-t.min.y+1,s=t.max.z-t.min.z+1,l=dt.convert(o.format),c=dt.convert(o.type);let d;if(o.isDataTexture3D)Z.setTexture3D(o,0),d=32879;else{if(!o.isDataTexture2DArray)return void console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.");Z.setTexture2DArray(o,0),d=35866}mt.pixelStorei(37440,o.flipY),mt.pixelStorei(37441,o.premultiplyAlpha),mt.pixelStorei(3317,o.unpackAlignment);const p=mt.getParameter(3314),m=mt.getParameter(32878),f=mt.getParameter(3316),u=mt.getParameter(3315),g=mt.getParameter(32877),b=n.isCompressedTexture?n.mipmaps[0]:n.image;mt.pixelStorei(3314,b.width),mt.pixelStorei(32878,b.height),mt.pixelStorei(3316,t.min.x),mt.pixelStorei(3315,t.min.y),mt.pixelStorei(32877,t.min.z),n.isDataTexture||n.isDataTexture3D?mt.texSubImage3D(d,i,e.x,e.y,e.z,a,r,s,l,c,b.data):n.isCompressedTexture?(console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture."),mt.compressedTexSubImage3D(d,i,e.x,e.y,e.z,a,r,s,l,b.data)):mt.texSubImage3D(d,i,e.x,e.y,e.z,a,r,s,l,c,b),mt.pixelStorei(3314,p),mt.pixelStorei(32878,m),mt.pixelStorei(3316,f),mt.pixelStorei(3315,u),mt.pixelStorei(32877,g),0===i&&o.generateMipmaps&&mt.generateMipmap(d),G.unbindTexture()},this.initTexture=function(t){Z.setTexture2D(t,0),G.unbindTexture()},this.resetState=function(){y=0,_=0,C=null,G.reset(),pt.reset()},"undefined"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}(class extends Hmt{}).prototype.isWebGL1Renderer=!0;class Fmt extends Slt{constructor(){super(),this.type="Scene",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,"undefined"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}Fmt.prototype.isScene=!0;class Lmt{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=ost,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=dst()}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.array=new t.array.constructor(t.array),this.count=t.count,this.stride=t.stride,this.usage=t.usage,this}copyAt(t,e,n){t*=this.stride,n*=e.stride;for(let o=0,i=this.stride;o<i;o++)this.array[t+o]=e.array[n+o];return this}set(t,e=0){return this.array.set(t,e),this}clone(t){void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=dst()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=this.array.slice(0).buffer);const e=new this.array.constructor(t.arrayBuffers[this.array.buffer._uuid]),n=new this.constructor(e,this.stride);return n.setUsage(this.usage),n}onUpload(t){return this.onUploadCallback=t,this}toJSON(t){return void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=dst()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=Array.prototype.slice.call(new Uint32Array(this.array.buffer))),{uuid:this.uuid,buffer:this.array.buffer._uuid,type:this.array.constructor.name,stride:this.stride}}}Lmt.prototype.isInterleavedBuffer=!0;const Bmt=new wst;class Vmt{constructor(t,e,n,o=!1){this.name="",this.data=t,this.itemSize=e,this.offset=n,this.normalized=!0===o}get count(){return this.data.count}get array(){return this.data.array}set needsUpdate(t){this.data.needsUpdate=t}applyMatrix4(t){for(let e=0,n=this.data.count;e<n;e++)Bmt.x=this.getX(e),Bmt.y=this.getY(e),Bmt.z=this.getZ(e),Bmt.applyMatrix4(t),this.setXYZ(e,Bmt.x,Bmt.y,Bmt.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)Bmt.x=this.getX(e),Bmt.y=this.getY(e),Bmt.z=this.getZ(e),Bmt.applyNormalMatrix(t),this.setXYZ(e,Bmt.x,Bmt.y,Bmt.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)Bmt.x=this.getX(e),Bmt.y=this.getY(e),Bmt.z=this.getZ(e),Bmt.transformDirection(t),this.setXYZ(e,Bmt.x,Bmt.y,Bmt.z);return this}setX(t,e){return this.data.array[t*this.data.stride+this.offset]=e,this}setY(t,e){return this.data.array[t*this.data.stride+this.offset+1]=e,this}setZ(t,e){return this.data.array[t*this.data.stride+this.offset+2]=e,this}setW(t,e){return this.data.array[t*this.data.stride+this.offset+3]=e,this}getX(t){return this.data.array[t*this.data.stride+this.offset]}getY(t){return this.data.array[t*this.data.stride+this.offset+1]}getZ(t){return this.data.array[t*this.data.stride+this.offset+2]}getW(t){return this.data.array[t*this.data.stride+this.offset+3]}setXY(t,e,n){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this}setXYZ(t,e,n,o){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=o,this}setXYZW(t,e,n,o,i){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=o,this.data.array[t+3]=i,this}clone(t){if(void 0===t){console.log("THREE.InterleavedBufferAttribute.clone(): Cloning an interlaved buffer attribute will deinterleave buffer data.");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return new Qlt(new this.array.constructor(t),this.itemSize,this.normalized)}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.clone(t)),new Vmt(t.interleavedBuffers[this.data.uuid],this.itemSize,this.offset,this.normalized)}toJSON(t){if(void 0===t){console.log("THREE.InterleavedBufferAttribute.toJSON(): Serializing an interlaved buffer attribute will deinterleave buffer data.");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return{itemSize:this.itemSize,type:this.array.constructor.name,array:t,normalized:this.normalized}}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.toJSON(t)),{isInterleavedBufferAttribute:!0,itemSize:this.itemSize,data:this.data.uuid,offset:this.offset,normalized:this.normalized}}}Vmt.prototype.isInterleavedBufferAttribute=!0;class jmt extends Vlt{constructor(t){super(),this.type="SpriteMaterial",this.color=new Zlt(16777215),this.map=null,this.alphaMap=null,this.rotation=0,this.sizeAttenuation=!0,this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.rotation=t.rotation,this.sizeAttenuation=t.sizeAttenuation,this}}let Umt;jmt.prototype.isSpriteMaterial=!0;const Wmt=new wst,Gmt=new wst,Ymt=new wst,qmt=new gst,Zmt=new gst,Xmt=new olt,Kmt=new wst,Jmt=new wst,Qmt=new wst,$mt=new gst,tft=new gst,eft=new gst;function nft(t,e,n,o,i,a){qmt.subVectors(t,n).addScalar(.5).multiply(o),void 0!==i?(Zmt.x=a*qmt.x-i*qmt.y,Zmt.y=i*qmt.x+a*qmt.y):Zmt.copy(qmt),t.copy(e),t.x+=Zmt.x,t.y+=Zmt.y,t.applyMatrix4(Xmt)}(class extends Slt{constructor(t){if(super(),this.type="Sprite",void 0===Umt){Umt=new dct;const t=new Float32Array([-.5,-.5,0,0,0,.5,-.5,0,1,0,.5,.5,0,1,1,-.5,.5,0,0,1]),e=new Lmt(t,5);Umt.setIndex([0,1,2,0,2,3]),Umt.setAttribute("position",new Vmt(e,3,0,!1)),Umt.setAttribute("uv",new Vmt(e,2,3,!1))}this.geometry=Umt,this.material=void 0!==t?t:new jmt,this.center=new gst(.5,.5)}raycast(t,e){null===t.camera&&console.error('THREE.Sprite: "Raycaster.camera" needs to be set in order to raycast against sprites.'),Gmt.setFromMatrixScale(this.matrixWorld),Xmt.copy(t.camera.matrixWorld),this.modelViewMatrix.multiplyMatrices(t.camera.matrixWorldInverse,this.matrixWorld),Ymt.setFromMatrixPosition(this.modelViewMatrix),t.camera.isPerspectiveCamera&&!1===this.material.sizeAttenuation&&Gmt.multiplyScalar(-Ymt.z);const n=this.material.rotation;let o,i;0!==n&&(i=Math.cos(n),o=Math.sin(n));const a=this.center;nft(Kmt.set(-.5,-.5,0),Ymt,a,Gmt,o,i),nft(Jmt.set(.5,-.5,0),Ymt,a,Gmt,o,i),nft(Qmt.set(.5,.5,0),Ymt,a,Gmt,o,i),$mt.set(0,0),tft.set(1,0),eft.set(1,1);let r=t.ray.intersectTriangle(Kmt,Jmt,Qmt,!1,Wmt);if(null===r&&(nft(Jmt.set(-.5,.5,0),Ymt,a,Gmt,o,i),tft.set(0,1),r=t.ray.intersectTriangle(Kmt,Qmt,Jmt,!1,Wmt),null===r))return;const s=t.ray.origin.distanceTo(Wmt);s<t.near||s>t.far||e.push({distance:s,point:Wmt.clone(),uv:Llt.getUV(Wmt,Kmt,Jmt,Qmt,$mt,tft,eft,new gst),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}).prototype.isSprite=!0;const oft=new wst,ift=new vst,aft=new vst,rft=new wst,sft=new olt;class lft extends Sct{constructor(t,e){super(t,e),this.type="SkinnedMesh",this.bindMode="attached",this.bindMatrix=new olt,this.bindMatrixInverse=new olt}copy(t){return super.copy(t),this.bindMode=t.bindMode,this.bindMatrix.copy(t.bindMatrix),this.bindMatrixInverse.copy(t.bindMatrixInverse),this.skeleton=t.skeleton,this}bind(t,e){this.skeleton=t,void 0===e&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),e=this.matrixWorld),this.bindMatrix.copy(e),this.bindMatrixInverse.copy(e).invert()}pose(){this.skeleton.pose()}normalizeSkinWeights(){const t=new vst,e=this.geometry.attributes.skinWeight;for(let n=0,o=e.count;n<o;n++){t.x=e.getX(n),t.y=e.getY(n),t.z=e.getZ(n),t.w=e.getW(n);const o=1/t.manhattanLength();o!==1/0?t.multiplyScalar(o):t.set(1,0,0,0),e.setXYZW(n,t.x,t.y,t.z,t.w)}}updateMatrixWorld(t){super.updateMatrixWorld(t),"attached"===this.bindMode?this.bindMatrixInverse.copy(this.matrixWorld).invert():"detached"===this.bindMode?this.bindMatrixInverse.copy(this.bindMatrix).invert():console.warn("THREE.SkinnedMesh: Unrecognized bindMode: "+this.bindMode)}boneTransform(t,e){const n=this.skeleton,o=this.geometry;ift.fromBufferAttribute(o.attributes.skinIndex,t),aft.fromBufferAttribute(o.attributes.skinWeight,t),oft.fromBufferAttribute(o.attributes.position,t).applyMatrix4(this.bindMatrix),e.set(0,0,0);for(let t=0;t<4;t++){const o=aft.getComponent(t);if(0!==o){const i=ift.getComponent(t);sft.multiplyMatrices(n.bones[i].matrixWorld,n.boneInverses[i]),e.addScaledVector(rft.copy(oft).applyMatrix4(sft),o)}}return e.applyMatrix4(this.bindMatrixInverse)}}lft.prototype.isSkinnedMesh=!0;(class extends Slt{constructor(){super(),this.type="Bone"}}).prototype.isBone=!0;(class extends Cst{constructor(t=null,e=1,n=1,o,i,a,r,s,l=1003,c=1003,d,p){super(null,a,r,s,l,c,o,i,d,p),this.image={data:t,width:e,height:n},this.magFilter=l,this.minFilter=c,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}).prototype.isDataTexture=!0;const cft=new olt,dft=new olt,pft=[],mft=new Sct;(class extends Sct{constructor(t,e,n){super(t,e),this.instanceMatrix=new Qlt(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}copy(t){return super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,o=this.count;if(mft.geometry=this.geometry,mft.material=this.material,void 0!==mft.material)for(let i=0;i<o;i++){this.getMatrixAt(i,cft),dft.multiplyMatrices(n,cft),mft.matrixWorld=dft,mft.raycast(t,pft);for(let t=0,n=pft.length;t<n;t++){const n=pft[t];n.instanceId=i,n.object=this,e.push(n)}pft.length=0}}setColorAt(t,e){null===this.instanceColor&&(this.instanceColor=new Qlt(new Float32Array(3*this.instanceMatrix.count),3)),e.toArray(this.instanceColor.array,3*t)}setMatrixAt(t,e){e.toArray(this.instanceMatrix.array,16*t)}updateMorphTargets(){}dispose(){this.dispatchEvent({type:"dispose"})}}).prototype.isInstancedMesh=!0;class fft extends Vlt{constructor(t){super(),this.type="LineBasicMaterial",this.color=new Zlt(16777215),this.linewidth=1,this.linecap="round",this.linejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.linewidth=t.linewidth,this.linecap=t.linecap,this.linejoin=t.linejoin,this}}fft.prototype.isLineBasicMaterial=!0;const uft=new wst,gft=new wst,hft=new olt,bft=new nlt,yft=new Zst;class _ft extends Slt{constructor(t=new dct,e=new fft){super(),this.type="Line",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[0];for(let t=1,o=e.count;t<o;t++)uft.fromBufferAttribute(e,t-1),gft.fromBufferAttribute(e,t),n[t]=n[t-1],n[t]+=uft.distanceTo(gft);t.setAttribute("lineDistance",new ect(n,1))}else console.warn("THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else t.isGeometry&&console.error("THREE.Line.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");return this}raycast(t,e){const n=this.geometry,o=this.matrixWorld,i=t.params.Line.threshold,a=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),yft.copy(n.boundingSphere),yft.applyMatrix4(o),yft.radius+=i,!1===t.ray.intersectsSphere(yft))return;hft.copy(o).invert(),bft.copy(t.ray).applyMatrix4(hft);const r=i/((this.scale.x+this.scale.y+this.scale.z)/3),s=r*r,l=new wst,c=new wst,d=new wst,p=new wst,m=this.isLineSegments?2:1;if(n.isBufferGeometry){const o=n.index,i=n.attributes.position;if(null!==o){for(let n=Math.max(0,a.start),r=Math.min(o.count,a.start+a.count)-1;n<r;n+=m){const a=o.getX(n),r=o.getX(n+1);l.fromBufferAttribute(i,a),c.fromBufferAttribute(i,r);if(bft.distanceSqToSegment(l,c,p,d)>s)continue;p.applyMatrix4(this.matrixWorld);const m=t.ray.origin.distanceTo(p);m<t.near||m>t.far||e.push({distance:m,point:d.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else{for(let n=Math.max(0,a.start),o=Math.min(i.count,a.start+a.count)-1;n<o;n+=m){l.fromBufferAttribute(i,n),c.fromBufferAttribute(i,n+1);if(bft.distanceSqToSegment(l,c,p,d)>s)continue;p.applyMatrix4(this.matrixWorld);const o=t.ray.origin.distanceTo(p);o<t.near||o>t.far||e.push({distance:o,point:d.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}}else n.isGeometry&&console.error("THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.")}}}_ft.prototype.isLine=!0;const Cft=new wst,Mft=new wst;class vft extends _ft{constructor(t,e){super(t,e),this.type="LineSegments"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,o=e.count;t<o;t+=2)Cft.fromBufferAttribute(e,t),Mft.fromBufferAttribute(e,t+1),n[t]=0===t?0:n[t-1],n[t+1]=n[t]+Cft.distanceTo(Mft);t.setAttribute("lineDistance",new ect(n,1))}else console.warn("THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else t.isGeometry&&console.error("THREE.LineSegments.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");return this}}vft.prototype.isLineSegments=!0;(class extends _ft{constructor(t,e){super(t,e),this.type="LineLoop"}}).prototype.isLineLoop=!0;class xft extends Vlt{constructor(t){super(),this.type="PointsMaterial",this.color=new Zlt(16777215),this.map=null,this.alphaMap=null,this.size=1,this.sizeAttenuation=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.size=t.size,this.sizeAttenuation=t.sizeAttenuation,this}}xft.prototype.isPointsMaterial=!0;const Oft=new olt,Pft=new nlt,wft=new Zst,kft=new wst;function Sft(t,e,n,o,i,a,r){const s=Pft.distanceSqToPoint(t);if(s<n){const n=new wst;Pft.closestPointToPoint(t,n),n.applyMatrix4(o);const l=i.ray.origin.distanceTo(n);if(l<i.near||l>i.far)return;a.push({distance:l,distanceToRay:Math.sqrt(s),point:n,index:e,face:null,object:r})}}(class extends Slt{constructor(t=new dct,e=new xft){super(),this.type="Points",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}raycast(t,e){const n=this.geometry,o=this.matrixWorld,i=t.params.Points.threshold,a=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),wft.copy(n.boundingSphere),wft.applyMatrix4(o),wft.radius+=i,!1===t.ray.intersectsSphere(wft))return;Oft.copy(o).invert(),Pft.copy(t.ray).applyMatrix4(Oft);const r=i/((this.scale.x+this.scale.y+this.scale.z)/3),s=r*r;if(n.isBufferGeometry){const i=n.index,r=n.attributes.position;if(null!==i){for(let n=Math.max(0,a.start),l=Math.min(i.count,a.start+a.count);n<l;n++){const a=i.getX(n);kft.fromBufferAttribute(r,a),Sft(kft,a,s,o,t,e,this)}}else{for(let n=Math.max(0,a.start),i=Math.min(r.count,a.start+a.count);n<i;n++)kft.fromBufferAttribute(r,n),Sft(kft,n,s,o,t,e,this)}}else console.error("THREE.Points.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.")}}}).prototype.isPoints=!0;(class extends Cst{constructor(t,e,n,o,i,a,r,s,l){super(t,e,n,o,i,a,r,s,l),this.format=void 0!==r?r:Vrt,this.minFilter=void 0!==a?a:Trt,this.magFilter=void 0!==i?i:Trt,this.generateMipmaps=!1;const c=this;"requestVideoFrameCallback"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;!1==="requestVideoFrameCallback"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}).prototype.isVideoTexture=!0;(class extends Cst{constructor(t,e,n,o,i,a,r,s,l,c,d,p){super(null,a,r,s,l,c,o,i,d,p),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}).prototype.isCompressedTexture=!0;(class extends Cst{constructor(t,e,n,o,i,a,r,s,l){super(t,e,n,o,i,a,r,s,l),this.needsUpdate=!0}}).prototype.isCanvasTexture=!0;(class extends Cst{constructor(t,e,n,o,i,a,r,s,l,c){if((c=void 0!==c?c:Urt)!==Urt&&c!==Wrt)throw new Error("DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat");void 0===n&&c===Urt&&(n=Irt),void 0===n&&c===Wrt&&(n=Brt),super(null,o,i,a,r,s,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==r?r:Art,this.minFilter=void 0!==s?s:Art,this.flipY=!1,this.generateMipmaps=!1}}).prototype.isDepthTexture=!0;class Dft extends dct{constructor(t=1,e=8,n=0,o=2*Math.PI){super(),this.type="CircleGeometry",this.parameters={radius:t,segments:e,thetaStart:n,thetaLength:o},e=Math.max(3,e);const i=[],a=[],r=[],s=[],l=new wst,c=new gst;a.push(0,0,0),r.push(0,0,1),s.push(.5,.5);for(let i=0,d=3;i<=e;i++,d+=3){const p=n+i/e*o;l.x=t*Math.cos(p),l.y=t*Math.sin(p),a.push(l.x,l.y,l.z),r.push(0,0,1),c.x=(a[d]/t+1)/2,c.y=(a[d+1]/t+1)/2,s.push(c.x,c.y)}for(let t=1;t<=e;t++)i.push(t,t+1,0);this.setIndex(i),this.setAttribute("position",new ect(a,3)),this.setAttribute("normal",new ect(r,3)),this.setAttribute("uv",new ect(s,2))}static fromJSON(t){return new Dft(t.radius,t.segments,t.thetaStart,t.thetaLength)}}new wst,new wst,new wst,new Llt;class Rft{constructor(){this.type="Curve",this.arcLengthDivisions=200}getPoint(){return console.warn("THREE.Curve: .getPoint() not implemented."),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,o=this.getPoint(0),i=0;e.push(0);for(let a=1;a<=t;a++)n=this.getPoint(a/t),i+=n.distanceTo(o),e.push(i),o=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let o=0;const i=n.length;let a;a=e||t*n[i-1];let r,s=0,l=i-1;for(;s<=l;)if(o=Math.floor(s+(l-s)/2),r=n[o]-a,r<0)s=o+1;else{if(!(r>0)){l=o;break}l=o-1}if(o=l,n[o]===a)return o/(i-1);const c=n[o];return(o+(a-c)/(n[o+1]-c))/(i-1)}getTangent(t,e){const n=1e-4;let o=t-n,i=t+n;o<0&&(o=0),i>1&&(i=1);const a=this.getPoint(o),r=this.getPoint(i),s=e||(a.isVector2?new gst:new wst);return s.copy(r).sub(a).normalize(),s}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new wst,o=[],i=[],a=[],r=new wst,s=new olt;for(let e=0;e<=t;e++){o[e]=this.getTangentAt(e/t,new wst),o[e].normalize()}i[0]=new wst,a[0]=new wst;let l=Number.MAX_VALUE;const c=Math.abs(o[0].x),d=Math.abs(o[0].y),p=Math.abs(o[0].z);c<=l&&(l=c,n.set(1,0,0)),d<=l&&(l=d,n.set(0,1,0)),p<=l&&n.set(0,0,1),r.crossVectors(o[0],n).normalize(),i[0].crossVectors(o[0],r),a[0].crossVectors(o[0],i[0]);for(let e=1;e<=t;e++){if(i[e]=i[e-1].clone(),a[e]=a[e-1].clone(),r.crossVectors(o[e-1],o[e]),r.length()>Number.EPSILON){r.normalize();const t=Math.acos(pst(o[e-1].dot(o[e]),-1,1));i[e].applyMatrix4(s.makeRotationAxis(r,t))}a[e].crossVectors(o[e],i[e])}if(!0===e){let e=Math.acos(pst(i[0].dot(i[t]),-1,1));e/=t,o[0].dot(r.crossVectors(i[0],i[t]))>0&&(e=-e);for(let n=1;n<=t;n++)i[n].applyMatrix4(s.makeRotationAxis(o[n],e*n)),a[n].crossVectors(o[n],i[n])}return{tangents:o,normals:i,binormals:a}}clone(){return(new this.constructor).copy(this)}copy(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}toJSON(){const t={metadata:{version:4.5,type:"Curve",generator:"Curve.toJSON"}};return t.arcLengthDivisions=this.arcLengthDivisions,t.type=this.type,t}fromJSON(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}}class Eft extends Rft{constructor(t=0,e=0,n=1,o=1,i=0,a=2*Math.PI,r=!1,s=0){super(),this.type="EllipseCurve",this.aX=t,this.aY=e,this.xRadius=n,this.yRadius=o,this.aStartAngle=i,this.aEndAngle=a,this.aClockwise=r,this.aRotation=s}getPoint(t,e){const n=e||new gst,o=2*Math.PI;let i=this.aEndAngle-this.aStartAngle;const a=Math.abs(i)<Number.EPSILON;for(;i<0;)i+=o;for(;i>o;)i-=o;i<Number.EPSILON&&(i=a?0:o),!0!==this.aClockwise||a||(i===o?i=-o:i-=o);const r=this.aStartAngle+t*i;let s=this.aX+this.xRadius*Math.cos(r),l=this.aY+this.yRadius*Math.sin(r);if(0!==this.aRotation){const t=Math.cos(this.aRotation),e=Math.sin(this.aRotation),n=s-this.aX,o=l-this.aY;s=n*t-o*e+this.aX,l=n*e+o*t+this.aY}return n.set(s,l)}copy(t){return super.copy(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}toJSON(){const t=super.toJSON();return t.aX=this.aX,t.aY=this.aY,t.xRadius=this.xRadius,t.yRadius=this.yRadius,t.aStartAngle=this.aStartAngle,t.aEndAngle=this.aEndAngle,t.aClockwise=this.aClockwise,t.aRotation=this.aRotation,t}fromJSON(t){return super.fromJSON(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}}Eft.prototype.isEllipseCurve=!0;class Aft extends Eft{constructor(t,e,n,o,i,a){super(t,e,n,n,o,i,a),this.type="ArcCurve"}}function Tft(){let t=0,e=0,n=0,o=0;function i(i,a,r,s){t=i,e=r,n=-3*i+3*a-2*r-s,o=2*i-2*a+r+s}return{initCatmullRom:function(t,e,n,o,a){i(e,n,a*(n-t),a*(o-e))},initNonuniformCatmullRom:function(t,e,n,o,a,r,s){let l=(e-t)/a-(n-t)/(a+r)+(n-e)/r,c=(n-e)/r-(o-e)/(r+s)+(o-n)/s;l*=r,c*=r,i(e,n,l,c)},calc:function(i){const a=i*i;return t+e*i+n*a+o*(a*i)}}}Aft.prototype.isArcCurve=!0;const Nft=new wst,zft=new Tft,Ift=new Tft,Hft=new Tft;class Fft extends Rft{constructor(t=[],e=!1,n="centripetal",o=.5){super(),this.type="CatmullRomCurve3",this.points=t,this.closed=e,this.curveType=n,this.tension=o}getPoint(t,e=new wst){const n=e,o=this.points,i=o.length,a=(i-(this.closed?0:1))*t;let r,s,l=Math.floor(a),c=a-l;this.closed?l+=l>0?0:(Math.floor(Math.abs(l)/i)+1)*i:0===c&&l===i-1&&(l=i-2,c=1),this.closed||l>0?r=o[(l-1)%i]:(Nft.subVectors(o[0],o[1]).add(o[0]),r=Nft);const d=o[l%i],p=o[(l+1)%i];if(this.closed||l+2<i?s=o[(l+2)%i]:(Nft.subVectors(o[i-1],o[i-2]).add(o[i-1]),s=Nft),"centripetal"===this.curveType||"chordal"===this.curveType){const t="chordal"===this.curveType?.5:.25;let e=Math.pow(r.distanceToSquared(d),t),n=Math.pow(d.distanceToSquared(p),t),o=Math.pow(p.distanceToSquared(s),t);n<1e-4&&(n=1),e<1e-4&&(e=n),o<1e-4&&(o=n),zft.initNonuniformCatmullRom(r.x,d.x,p.x,s.x,e,n,o),Ift.initNonuniformCatmullRom(r.y,d.y,p.y,s.y,e,n,o),Hft.initNonuniformCatmullRom(r.z,d.z,p.z,s.z,e,n,o)}else"catmullrom"===this.curveType&&(zft.initCatmullRom(r.x,d.x,p.x,s.x,this.tension),Ift.initCatmullRom(r.y,d.y,p.y,s.y,this.tension),Hft.initCatmullRom(r.z,d.z,p.z,s.z,this.tension));return n.set(zft.calc(c),Ift.calc(c),Hft.calc(c)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){this.points.push(t.points[e].clone())}return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++){t.points.push(this.points[e].toArray())}return t.closed=this.closed,t.curveType=this.curveType,t.tension=this.tension,t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new wst).fromArray(n))}return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}}function Lft(t,e,n,o,i){const a=.5*(o-e),r=.5*(i-n),s=t*t;return(2*n-2*o+a+r)*(t*s)+(-3*n+3*o-2*a-r)*s+a*t+n}function Bft(t,e,n,o){return(function i(t,e){const n=1-t;return n*n*e})(t,e)+(function a(t,e){return 2*(1-t)*t*e})(t,n)+(function r(t,e){return t*t*e})(t,o)}function Vft(t,e,n,o,i){return(function a(t,e){const n=1-t;return n*n*n*e})(t,e)+(function r(t,e){const n=1-t;return 3*n*n*t*e})(t,n)+(function s(t,e){return 3*(1-t)*t*t*e})(t,o)+(function l(t,e){return t*t*t*e})(t,i)}Fft.prototype.isCatmullRomCurve3=!0;class jft extends Rft{constructor(t=new gst,e=new gst,n=new gst,o=new gst){super(),this.type="CubicBezierCurve",this.v0=t,this.v1=e,this.v2=n,this.v3=o}getPoint(t,e=new gst){const n=e,o=this.v0,i=this.v1,a=this.v2,r=this.v3;return n.set(Vft(t,o.x,i.x,a.x,r.x),Vft(t,o.y,i.y,a.y,r.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}jft.prototype.isCubicBezierCurve=!0;class Uft extends Rft{constructor(t=new wst,e=new wst,n=new wst,o=new wst){super(),this.type="CubicBezierCurve3",this.v0=t,this.v1=e,this.v2=n,this.v3=o}getPoint(t,e=new wst){const n=e,o=this.v0,i=this.v1,a=this.v2,r=this.v3;return n.set(Vft(t,o.x,i.x,a.x,r.x),Vft(t,o.y,i.y,a.y,r.y),Vft(t,o.z,i.z,a.z,r.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}Uft.prototype.isCubicBezierCurve3=!0;class Wft extends Rft{constructor(t=new gst,e=new gst){super(),this.type="LineCurve",this.v1=t,this.v2=e}getPoint(t,e=new gst){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}getTangent(t,e){const n=e||new gst;return n.copy(this.v2).sub(this.v1).normalize(),n}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}Wft.prototype.isLineCurve=!0;class Gft extends Rft{constructor(t=new gst,e=new gst,n=new gst){super(),this.type="QuadraticBezierCurve",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new gst){const n=e,o=this.v0,i=this.v1,a=this.v2;return n.set(Bft(t,o.x,i.x,a.x),Bft(t,o.y,i.y,a.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}Gft.prototype.isQuadraticBezierCurve=!0;class Yft extends Rft{constructor(t=new wst,e=new wst,n=new wst){super(),this.type="QuadraticBezierCurve3",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new wst){const n=e,o=this.v0,i=this.v1,a=this.v2;return n.set(Bft(t,o.x,i.x,a.x),Bft(t,o.y,i.y,a.y),Bft(t,o.z,i.z,a.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}Yft.prototype.isQuadraticBezierCurve3=!0;class qft extends Rft{constructor(t=[]){super(),this.type="SplineCurve",this.points=t}getPoint(t,e=new gst){const n=e,o=this.points,i=(o.length-1)*t,a=Math.floor(i),r=i-a,s=o[0===a?a:a-1],l=o[a],c=o[a>o.length-2?o.length-1:a+1],d=o[a>o.length-3?o.length-1:a+2];return n.set(Lft(r,s.x,l.x,c.x,d.x),Lft(r,s.y,l.y,c.y,d.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){this.points.push(t.points[e].clone())}return this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++){t.points.push(this.points[e].toArray())}return t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new gst).fromArray(n))}return this}}qft.prototype.isSplineCurve=!0;var Zft=Object.freeze({__proto__:null,ArcCurve:Aft,CatmullRomCurve3:Fft,CubicBezierCurve:jft,CubicBezierCurve3:Uft,EllipseCurve:Eft,LineCurve:Wft,LineCurve3:class extends Rft{constructor(t=new wst,e=new wst){super(),this.type="LineCurve3",this.isLineCurve3=!0,this.v1=t,this.v2=e}getPoint(t,e=new wst){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}},QuadraticBezierCurve:Gft,QuadraticBezierCurve3:Yft,SplineCurve:qft});const Xft=function(t,e,n=2){const o=e&&e.length,i=o?e[0]*n:t.length;let a=Kft(t,0,i,n,!0);const r=[];if(!a||a.next===a.prev)return r;let s,l,c,d,p,m,f;if(o&&(a=(function u(t,e,n,o){const i=[];let a,r,s,l,c;for(a=0,r=e.length;a<r;a++)s=e[a]*o,l=a<r-1?e[a+1]*o:t.length,c=Kft(t,s,l,o,!1),c===c.next&&(c.steiner=!0),i.push(sut(c));for(i.sort(out),a=0;a<i.length;a++)iut(i[a],n),n=Jft(n,n.next);return n})(t,e,a,n)),t.length>80*n){s=c=t[0],l=d=t[1];for(let e=n;e<i;e+=n)p=t[e],m=t[e+1],p<s&&(s=p),m<l&&(l=m),p>c&&(c=p),m>d&&(d=m);f=Math.max(c-s,d-l),f=0!==f?1/f:0}return Qft(a,r,n,s,l,f),r};function Kft(t,e,n,o,i){let a,r;if(i===(function s(t,e,n,o){let i=0;for(let a=e,r=n-o;a<n;a+=o)i+=(t[r]-t[a])*(t[a+1]+t[r+1]),r=a;return i})(t,e,n,o)>0)for(a=e;a<n;a+=o)r=but(a,t[a],t[a+1],r);else for(a=n-o;a>=e;a-=o)r=but(a,t[a],t[a+1],r);return r&&put(r,r.next)&&(yut(r),r=r.next),r}function Jft(t,e){if(!t)return t;e||(e=t);let n,o=t;do{if(n=!1,o.steiner||!put(o,o.next)&&0!==dut(o.prev,o,o.next))o=o.next;else{if(yut(o),o=e=o.prev,o===o.next)break;n=!0}}while(n||o!==e);return e}function Qft(t,e,n,o,i,a,r){if(!t)return;!r&&a&&(function s(t,e,n,o){let i=t;do{null===i.z&&(i.z=rut(i.x,i.y,e,n,o)),i.prevZ=i.prev,i.nextZ=i.next,i=i.next}while(i!==t);i.prevZ.nextZ=null,i.prevZ=null,(function a(t){let e,n,o,i,a,r,s,l,c=1;do{for(n=t,t=null,a=null,r=0;n;){for(r++,o=n,s=0,e=0;e<c&&(s++,o=o.nextZ,o);e++);for(l=c;s>0||l>0&&o;)0!==s&&(0===l||!o||n.z<=o.z)?(i=n,n=n.nextZ,s--):(i=o,o=o.nextZ,l--),a?a.nextZ=i:t=i,i.prevZ=a,a=i;n=o}a.nextZ=null,c*=2}while(r>1);return t})(i)})(t,o,i,a);let l,c,d=t;for(;t.prev!==t.next;)if(l=t.prev,c=t.next,a?tut(t,o,i,a):$ft(t))e.push(l.i/n),e.push(t.i/n),e.push(c.i/n),yut(t),t=c.next,d=c.next;else if((t=c)===d){r?1===r?Qft(t=eut(Jft(t),e,n),e,n,o,i,a,2):2===r&&nut(t,e,n,o,i,a):Qft(Jft(t),e,n,o,i,a,1);break}}function $ft(t){const e=t.prev,n=t,o=t.next;if(dut(e,n,o)>=0)return!1;let i=t.next.next;for(;i!==t.prev;){if(lut(e.x,e.y,n.x,n.y,o.x,o.y,i.x,i.y)&&dut(i.prev,i,i.next)>=0)return!1;i=i.next}return!0}function tut(t,e,n,o){const i=t.prev,a=t,r=t.next;if(dut(i,a,r)>=0)return!1;const s=i.x>a.x?i.x>r.x?i.x:r.x:a.x>r.x?a.x:r.x,l=i.y>a.y?i.y>r.y?i.y:r.y:a.y>r.y?a.y:r.y,c=rut(i.x<a.x?i.x<r.x?i.x:r.x:a.x<r.x?a.x:r.x,i.y<a.y?i.y<r.y?i.y:r.y:a.y<r.y?a.y:r.y,e,n,o),d=rut(s,l,e,n,o);let p=t.prevZ,m=t.nextZ;for(;p&&p.z>=c&&m&&m.z<=d;){if(p!==t.prev&&p!==t.next&&lut(i.x,i.y,a.x,a.y,r.x,r.y,p.x,p.y)&&dut(p.prev,p,p.next)>=0)return!1;if(p=p.prevZ,m!==t.prev&&m!==t.next&&lut(i.x,i.y,a.x,a.y,r.x,r.y,m.x,m.y)&&dut(m.prev,m,m.next)>=0)return!1;m=m.nextZ}for(;p&&p.z>=c;){if(p!==t.prev&&p!==t.next&&lut(i.x,i.y,a.x,a.y,r.x,r.y,p.x,p.y)&&dut(p.prev,p,p.next)>=0)return!1;p=p.prevZ}for(;m&&m.z<=d;){if(m!==t.prev&&m!==t.next&&lut(i.x,i.y,a.x,a.y,r.x,r.y,m.x,m.y)&&dut(m.prev,m,m.next)>=0)return!1;m=m.nextZ}return!0}function eut(t,e,n){let o=t;do{const i=o.prev,a=o.next.next;!put(i,a)&&mut(i,o,o.next,a)&&gut(i,a)&&gut(a,i)&&(e.push(i.i/n),e.push(o.i/n),e.push(a.i/n),yut(o),yut(o.next),o=t=a),o=o.next}while(o!==t);return Jft(o)}function nut(t,e,n,o,i,a){let r=t;do{let t=r.next.next;for(;t!==r.prev;){if(r.i!==t.i&&cut(r,t)){let s=hut(r,t);return r=Jft(r,r.next),s=Jft(s,s.next),Qft(r,e,n,o,i,a),void Qft(s,e,n,o,i,a)}t=t.next}r=r.next}while(r!==t)}function out(t,e){return t.x-e.x}function iut(t,e){if(e=(function n(t,e){let n=e;const o=t.x,i=t.y;let a,r=-1/0;do{if(i<=n.y&&i>=n.next.y&&n.next.y!==n.y){const t=n.x+(i-n.y)*(n.next.x-n.x)/(n.next.y-n.y);if(t<=o&&t>r){if(r=t,t===o){if(i===n.y)return n;if(i===n.next.y)return n.next}a=n.x<n.next.x?n:n.next}}n=n.next}while(n!==e);if(!a)return null;if(o===r)return a;const s=a,l=a.x,c=a.y;let d,p=1/0;n=a;do{o>=n.x&&n.x>=l&&o!==n.x&&lut(i<c?o:r,i,l,c,i<c?r:o,i,n.x,n.y)&&(d=Math.abs(i-n.y)/(o-n.x),gut(n,t)&&(d<p||d===p&&(n.x>a.x||n.x===a.x&&aut(a,n)))&&(a=n,p=d)),n=n.next}while(n!==s);return a})(t,e)){const n=hut(e,t);Jft(e,e.next),Jft(n,n.next)}}function aut(t,e){return dut(t.prev,t,e.prev)<0&&dut(e.next,t,t.next)<0}function rut(t,e,n,o,i){return(t=1431655765&((t=858993459&((t=252645135&((t=16711935&((t=32767*(t-n)*i)|t<<8))|t<<4))|t<<2))|t<<1))|(e=1431655765&((e=858993459&((e=252645135&((e=16711935&((e=32767*(e-o)*i)|e<<8))|e<<4))|e<<2))|e<<1))<<1}function sut(t){let e=t,n=t;do{(e.x<n.x||e.x===n.x&&e.y<n.y)&&(n=e),e=e.next}while(e!==t);return n}function lut(t,e,n,o,i,a,r,s){return(i-r)*(e-s)-(t-r)*(a-s)>=0&&(t-r)*(o-s)-(n-r)*(e-s)>=0&&(n-r)*(a-s)-(i-r)*(o-s)>=0}function cut(t,e){return t.next.i!==e.i&&t.prev.i!==e.i&&!(function n(t,e){let n=t;do{if(n.i!==t.i&&n.next.i!==t.i&&n.i!==e.i&&n.next.i!==e.i&&mut(n,n.next,t,e))return!0;n=n.next}while(n!==t);return!1})(t,e)&&(gut(t,e)&&gut(e,t)&&(function o(t,e){let n=t,o=!1;const i=(t.x+e.x)/2,a=(t.y+e.y)/2;do{n.y>a!=n.next.y>a&&n.next.y!==n.y&&i<(n.next.x-n.x)*(a-n.y)/(n.next.y-n.y)+n.x&&(o=!o),n=n.next}while(n!==t);return o})(t,e)&&(dut(t.prev,t,e.prev)||dut(t,e.prev,e))||put(t,e)&&dut(t.prev,t,t.next)>0&&dut(e.prev,e,e.next)>0)}function dut(t,e,n){return(e.y-t.y)*(n.x-e.x)-(e.x-t.x)*(n.y-e.y)}function put(t,e){return t.x===e.x&&t.y===e.y}function mut(t,e,n,o){const i=uut(dut(t,e,n)),a=uut(dut(t,e,o)),r=uut(dut(n,o,t)),s=uut(dut(n,o,e));return i!==a&&r!==s||(!(0!==i||!fut(t,n,e))||(!(0!==a||!fut(t,o,e))||(!(0!==r||!fut(n,t,o))||!(0!==s||!fut(n,e,o)))))}function fut(t,e,n){return e.x<=Math.max(t.x,n.x)&&e.x>=Math.min(t.x,n.x)&&e.y<=Math.max(t.y,n.y)&&e.y>=Math.min(t.y,n.y)}function uut(t){return t>0?1:t<0?-1:0}function gut(t,e){return dut(t.prev,t,t.next)<0?dut(t,e,t.next)>=0&&dut(t,t.prev,e)>=0:dut(t,e,t.prev)<0||dut(t,t.next,e)<0}function hut(t,e){const n=new _ut(t.i,t.x,t.y),o=new _ut(e.i,e.x,e.y),i=t.next,a=e.prev;return t.next=e,e.prev=t,n.next=i,i.prev=n,o.next=n,n.prev=o,a.next=o,o.prev=a,o}function but(t,e,n,o){const i=new _ut(t,e,n);return o?(i.next=o.next,i.prev=o,o.next.prev=i,o.next=i):(i.prev=i,i.next=i),i}function yut(t){t.next.prev=t.prev,t.prev.next=t.next,t.prevZ&&(t.prevZ.nextZ=t.nextZ),t.nextZ&&(t.nextZ.prevZ=t.prevZ)}function _ut(t,e,n){this.i=t,this.x=e,this.y=n,this.prev=null,this.next=null,this.z=null,this.prevZ=null,this.nextZ=null,this.steiner=!1}class Cut{static area(t){const e=t.length;let n=0;for(let o=e-1,i=0;i<e;o=i++)n+=t[o].x*t[i].y-t[i].x*t[o].y;return.5*n}static isClockWise(t){return Cut.area(t)<0}static triangulateShape(t,e){const n=[],o=[],i=[];Mut(t),vut(n,t);let a=t.length;e.forEach(Mut);for(let t=0;t<e.length;t++)o.push(a),a+=e[t].length,vut(n,e[t]);const r=Xft(n,o);for(let t=0;t<r.length;t+=3)i.push(r.slice(t,t+3));return i}}function Mut(t){const e=t.length;e>2&&t[e-1].equals(t[0])&&t.pop()}function vut(t,e){for(let n=0;n<e.length;n++)t.push(e[n].x),t.push(e[n].y)}class xut extends dct{constructor(t,e){super(),this.type="ExtrudeGeometry",this.parameters={shapes:t,options:e},t=Array.isArray(t)?t:[t];const n=this,o=[],i=[];for(let e=0,n=t.length;e<n;e++){a(t[e])}function a(t){const a=[],r=void 0!==e.curveSegments?e.curveSegments:12,s=void 0!==e.steps?e.steps:1;let l=void 0!==e.depth?e.depth:100,c=void 0===e.bevelEnabled||e.bevelEnabled,d=void 0!==e.bevelThickness?e.bevelThickness:6,p=void 0!==e.bevelSize?e.bevelSize:d-2,m=void 0!==e.bevelOffset?e.bevelOffset:0,f=void 0!==e.bevelSegments?e.bevelSegments:3;const u=e.extrudePath,g=void 0!==e.UVGenerator?e.UVGenerator:Out;void 0!==e.amount&&(console.warn("THREE.ExtrudeBufferGeometry: amount has been renamed to depth."),l=e.amount);let h,b,y,_,C,M=!1;u&&(h=u.getSpacedPoints(s),M=!0,c=!1,b=u.computeFrenetFrames(s,!1),y=new wst,_=new wst,C=new wst),c||(f=0,d=0,p=0,m=0);const v=t.extractPoints(r);let x=v.shape;const O=v.holes;if(!Cut.isClockWise(x)){x=x.reverse();for(let t=0,e=O.length;t<e;t++){const e=O[t];Cut.isClockWise(e)&&(O[t]=e.reverse())}}const P=Cut.triangulateShape(x,O),w=x;for(let t=0,e=O.length;t<e;t++){x=x.concat(O[t])}function k(t,e,n){return e||console.error("THREE.ExtrudeGeometry: vec does not exist"),e.clone().multiplyScalar(n).add(t)}const S=x.length,D=P.length;function R(t,e,n){let o,i,a;const r=t.x-e.x,s=t.y-e.y,l=n.x-t.x,c=n.y-t.y,d=r*r+s*s;if(Math.abs(r*c-s*l)>Number.EPSILON){const p=Math.sqrt(d),m=Math.sqrt(l*l+c*c),f=e.x-s/p,u=e.y+r/p,g=((n.x-c/m-f)*c-(n.y+l/m-u)*l)/(r*c-s*l);o=f+r*g-t.x,i=u+s*g-t.y;const h=o*o+i*i;if(h<=2)return new gst(o,i);a=Math.sqrt(h/2)}else{let t=!1;r>Number.EPSILON?l>Number.EPSILON&&(t=!0):r<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(s)===Math.sign(c)&&(t=!0),t?(o=-s,i=r,a=Math.sqrt(d)):(o=r,i=s,a=Math.sqrt(d/2))}return new gst(o/a,i/a)}const E=[];for(let t=0,e=w.length,n=e-1,o=t+1;t<e;t++,n++,o++)n===e&&(n=0),o===e&&(o=0),E[t]=R(w[t],w[n],w[o]);const A=[];let T,N=E.concat();for(let t=0,e=O.length;t<e;t++){const e=O[t];T=[];for(let t=0,n=e.length,o=n-1,i=t+1;t<n;t++,o++,i++)o===n&&(o=0),i===n&&(i=0),T[t]=R(e[t],e[o],e[i]);A.push(T),N=N.concat(T)}for(let t=0;t<f;t++){const e=t/f,n=d*Math.cos(e*Math.PI/2),o=p*Math.sin(e*Math.PI/2)+m;for(let t=0,e=w.length;t<e;t++){const e=k(w[t],E[t],o);H(e.x,e.y,-n)}for(let t=0,e=O.length;t<e;t++){const e=O[t];T=A[t];for(let t=0,i=e.length;t<i;t++){const i=k(e[t],T[t],o);H(i.x,i.y,-n)}}}const z=p+m;for(let t=0;t<S;t++){const e=c?k(x[t],N[t],z):x[t];M?(_.copy(b.normals[0]).multiplyScalar(e.x),y.copy(b.binormals[0]).multiplyScalar(e.y),C.copy(h[0]).add(_).add(y),H(C.x,C.y,C.z)):H(e.x,e.y,0)}for(let t=1;t<=s;t++)for(let e=0;e<S;e++){const n=c?k(x[e],N[e],z):x[e];M?(_.copy(b.normals[t]).multiplyScalar(n.x),y.copy(b.binormals[t]).multiplyScalar(n.y),C.copy(h[t]).add(_).add(y),H(C.x,C.y,C.z)):H(n.x,n.y,l/s*t)}for(let t=f-1;t>=0;t--){const e=t/f,n=d*Math.cos(e*Math.PI/2),o=p*Math.sin(e*Math.PI/2)+m;for(let t=0,e=w.length;t<e;t++){const e=k(w[t],E[t],o);H(e.x,e.y,l+n)}for(let t=0,e=O.length;t<e;t++){const e=O[t];T=A[t];for(let t=0,i=e.length;t<i;t++){const i=k(e[t],T[t],o);M?H(i.x,i.y+h[s-1].y,h[s-1].x+n):H(i.x,i.y,l+n)}}}function I(t,e){let n=t.length;for(;--n>=0;){const o=n;let i=n-1;i<0&&(i=t.length-1);for(let t=0,n=s+2*f;t<n;t++){const n=S*t,a=S*(t+1);L(e+o+n,e+i+n,e+i+a,e+o+a)}}}function H(t,e,n){a.push(t),a.push(e),a.push(n)}function F(t,e,i){B(t),B(e),B(i);const a=o.length/3,r=g.generateTopUV(n,o,a-3,a-2,a-1);V(r[0]),V(r[1]),V(r[2])}function L(t,e,i,a){B(t),B(e),B(a),B(e),B(i),B(a);const r=o.length/3,s=g.generateSideWallUV(n,o,r-6,r-3,r-2,r-1);V(s[0]),V(s[1]),V(s[3]),V(s[1]),V(s[2]),V(s[3])}function B(t){o.push(a[3*t+0]),o.push(a[3*t+1]),o.push(a[3*t+2])}function V(t){i.push(t.x),i.push(t.y)}!(function j(){const t=o.length/3;if(c){let t=0,e=S*t;for(let t=0;t<D;t++){const n=P[t];F(n[2]+e,n[1]+e,n[0]+e)}t=s+2*f,e=S*t;for(let t=0;t<D;t++){const n=P[t];F(n[0]+e,n[1]+e,n[2]+e)}}else{for(let t=0;t<D;t++){const e=P[t];F(e[2],e[1],e[0])}for(let t=0;t<D;t++){const e=P[t];F(e[0]+S*s,e[1]+S*s,e[2]+S*s)}}n.addGroup(t,o.length/3-t,0)})(),(function U(){const t=o.length/3;let e=0;I(w,e),e+=w.length;for(let t=0,n=O.length;t<n;t++){const n=O[t];I(n,e),e+=n.length}n.addGroup(t,o.length/3-t,1)})()}this.setAttribute("position",new ect(o,3)),this.setAttribute("uv",new ect(i,2)),this.computeVertexNormals()}toJSON(){const t=super.toJSON();return(function e(t,n,o){if(o.shapes=[],Array.isArray(t))for(let e=0,n=t.length;e<n;e++){o.shapes.push(t[e].uuid)}else o.shapes.push(t.uuid);void 0!==n.extrudePath&&(o.options.extrudePath=n.extrudePath.toJSON());return o})(this.parameters.shapes,this.parameters.options,t)}static fromJSON(t,e){const n=[];for(let o=0,i=t.shapes.length;o<i;o++){n.push(e[t.shapes[o]])}const o=t.options.extrudePath;return void 0!==o&&(t.options.extrudePath=(new Zft[o.type]).fromJSON(o)),new xut(n,t.options)}}const Out={generateTopUV:function(t,e,n,o,i){const a=e[3*o],r=e[3*o+1],s=e[3*i],l=e[3*i+1];return[new gst(e[3*n],e[3*n+1]),new gst(a,r),new gst(s,l)]},generateSideWallUV:function(t,e,n,o,i,a){const r=e[3*n],s=e[3*n+1],l=e[3*n+2],c=e[3*o],d=e[3*o+1],p=e[3*o+2],m=e[3*i],f=e[3*i+1],u=e[3*i+2],g=e[3*a],h=e[3*a+1],b=e[3*a+2];return Math.abs(s-d)<Math.abs(r-c)?[new gst(r,1-l),new gst(c,1-p),new gst(m,1-u),new gst(g,1-b)]:[new gst(s,1-l),new gst(d,1-p),new gst(f,1-u),new gst(h,1-b)]}};class Put extends dct{constructor(t,e=12){super(),this.type="ShapeGeometry",this.parameters={shapes:t,curveSegments:e};const n=[],o=[],i=[],a=[];let r=0,s=0;if(!1===Array.isArray(t))l(t);else for(let e=0;e<t.length;e++)l(t[e]),this.addGroup(r,s,e),r+=s,s=0;function l(t){const r=o.length/3,l=t.extractPoints(e);let c=l.shape;const d=l.holes;!1===Cut.isClockWise(c)&&(c=c.reverse());for(let t=0,e=d.length;t<e;t++){const e=d[t];!0===Cut.isClockWise(e)&&(d[t]=e.reverse())}const p=Cut.triangulateShape(c,d);for(let t=0,e=d.length;t<e;t++){c=c.concat(d[t])}for(let t=0,e=c.length;t<e;t++){const e=c[t];o.push(e.x,e.y,0),i.push(0,0,1),a.push(e.x,e.y)}for(let t=0,e=p.length;t<e;t++){const e=p[t];n.push(e[0]+r,e[1]+r,e[2]+r),s+=3}}this.setIndex(n),this.setAttribute("position",new ect(o,3)),this.setAttribute("normal",new ect(i,3)),this.setAttribute("uv",new ect(a,2))}toJSON(){const t=super.toJSON();return(function e(t,n){if(n.shapes=[],Array.isArray(t))for(let e=0,o=t.length;e<o;e++){n.shapes.push(t[e].uuid)}else n.shapes.push(t.uuid);return n})(this.parameters.shapes,t)}static fromJSON(t,e){const n=[];for(let o=0,i=t.shapes.length;o<i;o++){n.push(e[t.shapes[o]])}return new Put(n,t.curveSegments)}}(class extends Vlt{constructor(t){super(),this.type="ShadowMaterial",this.color=new Zlt(0),this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this}}).prototype.isShadowMaterial=!0;class wut extends Vlt{constructor(t){super(),this.defines={STANDARD:""},this.type="MeshStandardMaterial",this.color=new Zlt(16777215),this.roughness=1,this.metalness=0,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zlt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new gst(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.roughnessMap=null,this.metalnessMap=null,this.alphaMap=null,this.envMap=null,this.envMapIntensity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:""},this.color.copy(t.color),this.roughness=t.roughness,this.metalness=t.metalness,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.roughnessMap=t.roughnessMap,this.metalnessMap=t.metalnessMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.envMapIntensity=t.envMapIntensity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}wut.prototype.isMeshStandardMaterial=!0;(class extends wut{constructor(t){super(),this.defines={STANDARD:"",PHYSICAL:""},this.type="MeshPhysicalMaterial",this.clearcoat=0,this.clearcoatMap=null,this.clearcoatRoughness=0,this.clearcoatRoughnessMap=null,this.clearcoatNormalScale=new gst(1,1),this.clearcoatNormalMap=null,this.reflectivity=.5,Object.defineProperty(this,"ior",{get:function(){return(1+.4*this.reflectivity)/(1-.4*this.reflectivity)},set:function(t){this.reflectivity=pst(2.5*(t-1)/(t+1),0,1)}}),this.sheen=null,this.transmission=0,this.transmissionMap=null,this.thickness=.01,this.thicknessMap=null,this.attenuationDistance=0,this.attenuationTint=new Zlt(1,1,1),this.specularIntensity=1,this.specularIntensityMap=null,this.specularTint=new Zlt(1,1,1),this.specularTintMap=null,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:"",PHYSICAL:""},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.reflectivity=t.reflectivity,this.sheen=t.sheen?(this.sheen||new Zlt).copy(t.sheen):null,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}).prototype.isMeshPhysicalMaterial=!0;(class extends Vlt{constructor(t){super(),this.type="MeshPhongMaterial",this.color=new Zlt(16777215),this.specular=new Zlt(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zlt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new gst(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.specular.copy(t.specular),this.shininess=t.shininess,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}).prototype.isMeshPhongMaterial=!0;(class extends Vlt{constructor(t){super(),this.defines={TOON:""},this.type="MeshToonMaterial",this.color=new Zlt(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zlt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new gst(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.gradientMap=t.gradientMap,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}).prototype.isMeshToonMaterial=!0;(class extends Vlt{constructor(t){super(),this.type="MeshNormalMaterial",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new gst(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.flatShading=t.flatShading,this}}).prototype.isMeshNormalMaterial=!0;(class extends Vlt{constructor(t){super(),this.type="MeshLambertMaterial",this.color=new Zlt(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Zlt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}).prototype.isMeshLambertMaterial=!0;(class extends Vlt{constructor(t){super(),this.defines={MATCAP:""},this.type="MeshMatcapMaterial",this.color=new Zlt(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new gst(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={MATCAP:""},this.color.copy(t.color),this.matcap=t.matcap,this.map=t.map,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.flatShading=t.flatShading,this}}).prototype.isMeshMatcapMaterial=!0;(class extends fft{constructor(t){super(),this.type="LineDashedMaterial",this.scale=1,this.dashSize=3,this.gapSize=1,this.setValues(t)}copy(t){return super.copy(t),this.scale=t.scale,this.dashSize=t.dashSize,this.gapSize=t.gapSize,this}}).prototype.isLineDashedMaterial=!0;const kut={arraySlice:function(t,e,n){return kut.isTypedArray(t)?new t.constructor(t.subarray(e,void 0!==n?n:t.length)):t.slice(e,n)},convertArray:function(t,e,n){return!t||!n&&t.constructor===e?t:"number"==typeof e.BYTES_PER_ELEMENT?new e(t):Array.prototype.slice.call(t)},isTypedArray:function(t){return ArrayBuffer.isView(t)&&!(t instanceof DataView)},getKeyframeOrder:function(t){const e=t.length,n=new Array(e);for(let t=0;t!==e;++t)n[t]=t;return n.sort((function o(e,n){return t[e]-t[n]})),n},sortedArray:function(t,e,n){const o=t.length,i=new t.constructor(o);for(let a=0,r=0;r!==o;++a){const o=n[a]*e;for(let n=0;n!==e;++n)i[r++]=t[o+n]}return i},flattenJSON:function(t,e,n,o){let i=1,a=t[0];for(;void 0!==a&&void 0===a[o];)a=t[i++];if(void 0===a)return;let r=a[o];if(void 0!==r)if(Array.isArray(r))do{r=a[o],void 0!==r&&(e.push(a.time),n.push.apply(n,r)),a=t[i++]}while(void 0!==a);else if(void 0!==r.toArray)do{r=a[o],void 0!==r&&(e.push(a.time),r.toArray(n,n.length)),a=t[i++]}while(void 0!==a);else do{r=a[o],void 0!==r&&(e.push(a.time),n.push(r)),a=t[i++]}while(void 0!==a)},subclip:function(t,e,n,o,i=30){const a=t.clone();a.name=e;const r=[];for(let t=0;t<a.tracks.length;++t){const e=a.tracks[t],s=e.getValueSize(),l=[],c=[];for(let t=0;t<e.times.length;++t){const a=e.times[t]*i;if(!(a<n||a>=o)){l.push(e.times[t]);for(let n=0;n<s;++n)c.push(e.values[t*s+n])}}0!==l.length&&(e.times=kut.convertArray(l,e.times.constructor),e.values=kut.convertArray(c,e.values.constructor),r.push(e))}a.tracks=r;let s=1/0;for(let t=0;t<a.tracks.length;++t)s>a.tracks[t].times[0]&&(s=a.tracks[t].times[0]);for(let t=0;t<a.tracks.length;++t)a.tracks[t].shift(-1*s);return a.resetDuration(),a},makeClipAdditive:function(t,e=0,n=t,o=30){o<=0&&(o=30);const i=n.tracks.length,a=e/o;for(let e=0;e<i;++e){const o=n.tracks[e],i=o.ValueTypeName;if("bool"===i||"string"===i)continue;const r=t.tracks.find((function(t){return t.name===o.name&&t.ValueTypeName===i}));if(void 0===r)continue;let s=0;const l=o.getValueSize();o.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(s=l/3);let c=0;const d=r.getValueSize();r.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(c=d/3);const p=o.times.length-1;let m;if(a<=o.times[0]){m=kut.arraySlice(o.values,s,l-s)}else if(a>=o.times[p]){const t=p*l+s;m=kut.arraySlice(o.values,t,t+l-s)}else{const t=o.createInterpolant(),e=s,n=l-s;t.evaluate(a),m=kut.arraySlice(t.resultBuffer,e,n)}if("quaternion"===i){(new Pst).fromArray(m).normalize().conjugate().toArray(m)}const f=r.times.length;for(let t=0;t<f;++t){const e=t*d+c;if("quaternion"===i)Pst.multiplyQuaternionsFlat(r.values,e,m,0,r.values,e);else{const t=d-2*c;for(let n=0;n<t;++n)r.values[e+n]-=m[n]}}}return t.blendMode=2501,t}};class Sut{constructor(t,e,n,o){this.parameterPositions=t,this._cachedIndex=0,this.resultBuffer=void 0!==o?o:new e.constructor(n),this.sampleValues=e,this.valueSize=n,this.settings=null,this.DefaultSettings_={}}evaluate(t){const e=this.parameterPositions;let n=this._cachedIndex,o=e[n],i=e[n-1];t:{e:{let a;n:{o:if(!(t<o)){for(let a=n+2;;){if(void 0===o){if(t<i)break o;return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,t,i)}if(n===a)break;if(i=o,o=e[++n],t<o)break e}a=e.length;break n}if(t>=i)break t;{const r=e[1];t<r&&(n=2,i=r);for(let a=n-2;;){if(void 0===i)return this._cachedIndex=0,this.beforeStart_(0,t,o);if(n===a)break;if(o=i,i=e[--n-1],t>=i)break e}a=n,n=0}}for(;n<a;){const o=n+a>>>1;t<e[o]?a=o:n=o+1}if(o=e[n],i=e[n-1],void 0===i)return this._cachedIndex=0,this.beforeStart_(0,t,o);if(void 0===o)return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,i,t)}this._cachedIndex=n,this.intervalChanged_(n,i,o)}return this.interpolate_(n,i,t,o)}getSettings_(){return this.settings||this.DefaultSettings_}copySampleValue_(t){const e=this.resultBuffer,n=this.sampleValues,o=this.valueSize,i=t*o;for(let t=0;t!==o;++t)e[t]=n[i+t];return e}interpolate_(){throw new Error("call to abstract method")}intervalChanged_(){}}Sut.prototype.beforeStart_=Sut.prototype.copySampleValue_,Sut.prototype.afterEnd_=Sut.prototype.copySampleValue_;class Dut extends Sut{constructor(t,e,n,o){super(t,e,n,o),this._weightPrev=-0,this._offsetPrev=-0,this._weightNext=-0,this._offsetNext=-0,this.DefaultSettings_={endingStart:Zrt,endingEnd:Zrt}}intervalChanged_(t,e,n){const o=this.parameterPositions;let i=t-2,a=t+1,r=o[i],s=o[a];if(void 0===r)switch(this.getSettings_().endingStart){case Xrt:i=t,r=2*e-n;break;case Krt:i=o.length-2,r=e+o[i]-o[i+1];break;default:i=t,r=n}if(void 0===s)switch(this.getSettings_().endingEnd){case Xrt:a=t,s=2*n-e;break;case Krt:a=1,s=n+o[1]-o[0];break;default:a=t-1,s=e}const l=.5*(n-e),c=this.valueSize;this._weightPrev=l/(e-r),this._weightNext=l/(s-n),this._offsetPrev=i*c,this._offsetNext=a*c}interpolate_(t,e,n,o){const i=this.resultBuffer,a=this.sampleValues,r=this.valueSize,s=t*r,l=s-r,c=this._offsetPrev,d=this._offsetNext,p=this._weightPrev,m=this._weightNext,f=(n-e)/(o-e),u=f*f,g=u*f,h=-p*g+2*p*u-p*f,b=(1+p)*g+(-1.5-2*p)*u+(-.5+p)*f+1,y=(-1-m)*g+(1.5+m)*u+.5*f,_=m*g-m*u;for(let t=0;t!==r;++t)i[t]=h*a[c+t]+b*a[l+t]+y*a[s+t]+_*a[d+t];return i}}class Rut extends Sut{constructor(t,e,n,o){super(t,e,n,o)}interpolate_(t,e,n,o){const i=this.resultBuffer,a=this.sampleValues,r=this.valueSize,s=t*r,l=s-r,c=(n-e)/(o-e),d=1-c;for(let t=0;t!==r;++t)i[t]=a[l+t]*d+a[s+t]*c;return i}}class Eut extends Sut{constructor(t,e,n,o){super(t,e,n,o)}interpolate_(t){return this.copySampleValue_(t-1)}}class Aut{constructor(t,e,n,o){if(void 0===t)throw new Error("THREE.KeyframeTrack: track name is undefined");if(void 0===e||0===e.length)throw new Error("THREE.KeyframeTrack: no keyframes in track named "+t);this.name=t,this.times=kut.convertArray(e,this.TimeBufferType),this.values=kut.convertArray(n,this.ValueBufferType),this.setInterpolation(o||this.DefaultInterpolation)}static toJSON(t){const e=t.constructor;let n;if(e.toJSON!==this.toJSON)n=e.toJSON(t);else{n={name:t.name,times:kut.convertArray(t.times,Array),values:kut.convertArray(t.values,Array)};const e=t.getInterpolation();e!==t.DefaultInterpolation&&(n.interpolation=e)}return n.type=t.ValueTypeName,n}InterpolantFactoryMethodDiscrete(t){return new Eut(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodLinear(t){return new Rut(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodSmooth(t){return new Dut(this.times,this.values,this.getValueSize(),t)}setInterpolation(t){let e;switch(t){case Grt:e=this.InterpolantFactoryMethodDiscrete;break;case Yrt:e=this.InterpolantFactoryMethodLinear;break;case qrt:e=this.InterpolantFactoryMethodSmooth}if(void 0===e){const e="unsupported interpolation for "+this.ValueTypeName+" keyframe track named "+this.name;if(void 0===this.createInterpolant){if(t===this.DefaultInterpolation)throw new Error(e);this.setInterpolation(this.DefaultInterpolation)}return console.warn("THREE.KeyframeTrack:",e),this}return this.createInterpolant=e,this}getInterpolation(){switch(this.createInterpolant){case this.InterpolantFactoryMethodDiscrete:return Grt;case this.InterpolantFactoryMethodLinear:return Yrt;case this.InterpolantFactoryMethodSmooth:return qrt}}getValueSize(){return this.values.length/this.times.length}shift(t){if(0!==t){const e=this.times;for(let n=0,o=e.length;n!==o;++n)e[n]+=t}return this}scale(t){if(1!==t){const e=this.times;for(let n=0,o=e.length;n!==o;++n)e[n]*=t}return this}trim(t,e){const n=this.times,o=n.length;let i=0,a=o-1;for(;i!==o&&n[i]<t;)++i;for(;-1!==a&&n[a]>e;)--a;if(++a,0!==i||a!==o){i>=a&&(a=Math.max(a,1),i=a-1);const t=this.getValueSize();this.times=kut.arraySlice(n,i,a),this.values=kut.arraySlice(this.values,i*t,a*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error("THREE.KeyframeTrack: Invalid value size in track.",this),t=!1);const n=this.times,o=this.values,i=n.length;0===i&&(console.error("THREE.KeyframeTrack: Track is empty.",this),t=!1);let a=null;for(let e=0;e!==i;e++){const o=n[e];if("number"==typeof o&&isNaN(o)){console.error("THREE.KeyframeTrack: Time is not a valid number.",this,e,o),t=!1;break}if(null!==a&&a>o){console.error("THREE.KeyframeTrack: Out of order keys.",this,e,o,a),t=!1;break}a=o}if(void 0!==o&&kut.isTypedArray(o))for(let e=0,n=o.length;e!==n;++e){const n=o[e];if(isNaN(n)){console.error("THREE.KeyframeTrack: Value is not a valid number.",this,e,n),t=!1;break}}return t}optimize(){const t=kut.arraySlice(this.times),e=kut.arraySlice(this.values),n=this.getValueSize(),o=this.getInterpolation()===qrt,i=t.length-1;let a=1;for(let r=1;r<i;++r){let i=!1;const s=t[r];if(s!==t[r+1]&&(1!==r||s!==t[0]))if(o)i=!0;else{const t=r*n,o=t-n,a=t+n;for(let r=0;r!==n;++r){const n=e[t+r];if(n!==e[o+r]||n!==e[a+r]){i=!0;break}}}if(i){if(r!==a){t[a]=t[r];const o=r*n,i=a*n;for(let t=0;t!==n;++t)e[i+t]=e[o+t]}++a}}if(i>0){t[a]=t[i];for(let t=i*n,o=a*n,r=0;r!==n;++r)e[o+r]=e[t+r];++a}return a!==t.length?(this.times=kut.arraySlice(t,0,a),this.values=kut.arraySlice(e,0,a*n)):(this.times=t,this.values=e),this}clone(){const t=kut.arraySlice(this.times,0),e=kut.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}Aut.prototype.TimeBufferType=Float32Array,Aut.prototype.ValueBufferType=Float32Array,Aut.prototype.DefaultInterpolation=Yrt;class Tut extends Aut{}Tut.prototype.ValueTypeName="bool",Tut.prototype.ValueBufferType=Array,Tut.prototype.DefaultInterpolation=Grt,Tut.prototype.InterpolantFactoryMethodLinear=void 0,Tut.prototype.InterpolantFactoryMethodSmooth=void 0;class Nut extends Aut{}Nut.prototype.ValueTypeName="color";class zut extends Aut{}zut.prototype.ValueTypeName="number";class Iut extends Sut{constructor(t,e,n,o){super(t,e,n,o)}interpolate_(t,e,n,o){const i=this.resultBuffer,a=this.sampleValues,r=this.valueSize,s=(n-e)/(o-e);let l=t*r;for(let t=l+r;l!==t;l+=4)Pst.slerpFlat(i,0,a,l-r,a,l,s);return i}}class Hut extends Aut{InterpolantFactoryMethodLinear(t){return new Iut(this.times,this.values,this.getValueSize(),t)}}Hut.prototype.ValueTypeName="quaternion",Hut.prototype.DefaultInterpolation=Yrt,Hut.prototype.InterpolantFactoryMethodSmooth=void 0;class Fut extends Aut{}Fut.prototype.ValueTypeName="string",Fut.prototype.ValueBufferType=Array,Fut.prototype.DefaultInterpolation=Grt,Fut.prototype.InterpolantFactoryMethodLinear=void 0,Fut.prototype.InterpolantFactoryMethodSmooth=void 0;class Lut extends Aut{}Lut.prototype.ValueTypeName="vector";class But{constructor(t,e=-1,n,o=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=o,this.uuid=dst(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,o=1/(t.fps||1);for(let t=0,i=n.length;t!==i;++t)e.push(Vut(n[t]).scale(o));const i=new this(t.name,t.duration,e,t.blendMode);return i.uuid=t.uuid,i}static toJSON(t){const e=[],n=t.tracks,o={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,o=n.length;t!==o;++t)e.push(Aut.toJSON(n[t]));return o}static CreateFromMorphTargetSequence(t,e,n,o){const i=e.length,a=[];for(let t=0;t<i;t++){let r=[],s=[];r.push((t+i-1)%i,t,(t+1)%i),s.push(0,1,0);const l=kut.getKeyframeOrder(r);r=kut.sortedArray(r,1,l),s=kut.sortedArray(s,1,l),o||0!==r[0]||(r.push(i),s.push(s[0])),a.push(new zut(".morphTargetInfluences["+e[t].name+"]",r,s).scale(1/n))}return new this(t,-1,a)}static findByName(t,e){let n=t;if(!Array.isArray(t)){const e=t;n=e.geometry&&e.geometry.animations||e.animations}for(let t=0;t<n.length;t++)if(n[t].name===e)return n[t];return null}static CreateClipsFromMorphTargetSequences(t,e,n){const o={},i=/^([\w-]*?)([\d]+)$/;for(let e=0,n=t.length;e<n;e++){const n=t[e],a=n.name.match(i);if(a&&a.length>1){const t=a[1];let e=o[t];e||(o[t]=e=[]),e.push(n)}}const a=[];for(const t in o)a.push(this.CreateFromMorphTargetSequence(t,o[t],e,n));return a}static parseAnimation(t,e){if(!t)return console.error("THREE.AnimationClip: No animation in JSONLoader data."),null;const n=function(t,e,n,o,i){if(0!==n.length){const a=[],r=[];kut.flattenJSON(n,a,r,o),0!==a.length&&i.push(new t(e,a,r))}},o=[],i=t.name||"default",a=t.fps||30,r=t.blendMode;let s=t.length||-1;const l=t.hierarchy||[];for(let t=0;t<l.length;t++){const i=l[t].keys;if(i&&0!==i.length)if(i[0].morphTargets){const t={};let e;for(e=0;e<i.length;e++)if(i[e].morphTargets)for(let n=0;n<i[e].morphTargets.length;n++)t[i[e].morphTargets[n]]=-1;for(const n in t){const t=[],a=[];for(let o=0;o!==i[e].morphTargets.length;++o){const o=i[e];t.push(o.time),a.push(o.morphTarget===n?1:0)}o.push(new zut(".morphTargetInfluence["+n+"]",t,a))}s=t.length*(a||1)}else{const a=".bones["+e[t].name+"]";n(Lut,a+".position",i,"pos",o),n(Hut,a+".quaternion",i,"rot",o),n(Lut,a+".scale",i,"scl",o)}}if(0===o.length)return null;return new this(i,s,o,r)}resetDuration(){let t=0;for(let e=0,n=this.tracks.length;e!==n;++e){const n=this.tracks[e];t=Math.max(t,n.times[n.times.length-1])}return this.duration=t,this}trim(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].trim(0,this.duration);return this}validate(){let t=!0;for(let e=0;e<this.tracks.length;e++)t=t&&this.tracks[e].validate();return t}optimize(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].optimize();return this}clone(){const t=[];for(let e=0;e<this.tracks.length;e++)t.push(this.tracks[e].clone());return new this.constructor(this.name,this.duration,t,this.blendMode)}toJSON(){return this.constructor.toJSON(this)}}function Vut(t){if(void 0===t.type)throw new Error("THREE.KeyframeTrack: track type undefined, can not parse");const e=(function n(t){switch(t.toLowerCase()){case"scalar":case"double":case"float":case"number":case"integer":return zut;case"vector":case"vector2":case"vector3":case"vector4":return Lut;case"color":return Nut;case"quaternion":return Hut;case"bool":case"boolean":return Tut;case"string":return Fut}throw new Error("THREE.KeyframeTrack: Unsupported typeName: "+t)})(t.type);if(void 0===t.times){const e=[],n=[];kut.flattenJSON(t.keys,e,n,"value"),t.times=e,t.values=n}return void 0!==e.parse?e.parse(t):new e(t.name,t.times,t.values,t.interpolation)}const jut={enabled:!1,files:{},add:function(t,e){!1!==this.enabled&&(this.files[t]=e)},get:function(t){if(!1!==this.enabled)return this.files[t]},remove:function(t){delete this.files[t]},clear:function(){this.files={}}};const Uut=new class{constructor(t,e,n){const o=this;let i,a=!1,r=0,s=0;const l=[];this.onStart=void 0,this.onLoad=t,this.onProgress=e,this.onError=n,this.itemStart=function(t){s++,!1===a&&void 0!==o.onStart&&o.onStart(t,r,s),a=!0},this.itemEnd=function(t){r++,void 0!==o.onProgress&&o.onProgress(t,r,s),r===s&&(a=!1,void 0!==o.onLoad&&o.onLoad())},this.itemError=function(t){void 0!==o.onError&&o.onError(t)},this.resolveURL=function(t){return i?i(t):t},this.setURLModifier=function(t){return i=t,this},this.addHandler=function(t,e){return l.push(t,e),this},this.removeHandler=function(t){const e=l.indexOf(t);return-1!==e&&l.splice(e,2),this},this.getHandler=function(t){for(let e=0,n=l.length;e<n;e+=2){const n=l[e],o=l[e+1];if(n.global&&(n.lastIndex=0),n.test(t))return o}return null}}};class Wut{constructor(t){this.manager=void 0!==t?t:Uut,this.crossOrigin="anonymous",this.withCredentials=!1,this.path="",this.resourcePath="",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(o,i){n.load(t,o,e,i)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}const Gut={};class Yut extends Wut{constructor(t){super(t)}load(t,e,n,o){void 0===t&&(t=""),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const i=this,a=jut.get(t);if(void 0!==a)return i.manager.itemStart(t),setTimeout((function(){e&&e(a),i.manager.itemEnd(t)}),0),a;if(void 0!==Gut[t])return void Gut[t].push({onLoad:e,onProgress:n,onError:o});const r=t.match(/^data:(.*?)(;base64)?,(.*)$/);let s;if(r){const n=r[1],a=!!r[2];let 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n=o[t];n.onError&&n.onError(e)}i.manager.itemError(t),i.manager.itemEnd(t)}}),!1),s.addEventListener("progress",(function(e){const n=Gut[t];for(let t=0,o=n.length;t<o;t++){const o=n[t];o.onProgress&&o.onProgress(e)}}),!1),s.addEventListener("error",(function(e){const n=Gut[t];delete Gut[t];for(let t=0,o=n.length;t<o;t++){const o=n[t];o.onError&&o.onError(e)}i.manager.itemError(t),i.manager.itemEnd(t)}),!1),s.addEventListener("abort",(function(e){const n=Gut[t];delete Gut[t];for(let t=0,o=n.length;t<o;t++){const o=n[t];o.onError&&o.onError(e)}i.manager.itemError(t),i.manager.itemEnd(t)}),!1),void 0!==this.responseType&&(s.responseType=this.responseType),void 0!==this.withCredentials&&(s.withCredentials=this.withCredentials),s.overrideMimeType&&s.overrideMimeType(void 0!==this.mimeType?this.mimeType:"text/plain");for(const t in this.requestHeader)s.setRequestHeader(t,this.requestHeader[t]);s.send(null)}return i.manager.itemStart(t),s}setResponseType(t){return this.responseType=t,this}setMimeType(t){return this.mimeType=t,this}}class qut extends Wut{constructor(t){super(t)}load(t,e,n,o){void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const i=this,a=jut.get(t);if(void 0!==a)return i.manager.itemStart(t),setTimeout((function(){e&&e(a),i.manager.itemEnd(t)}),0),a;const r=document.createElementNS("http://www.w3.org/1999/xhtml","img");function s(){r.removeEventListener("load",s,!1),r.removeEventListener("error",l,!1),jut.add(t,this),e&&e(this),i.manager.itemEnd(t)}function l(e){r.removeEventListener("load",s,!1),r.removeEventListener("error",l,!1),o&&o(e),i.manager.itemError(t),i.manager.itemEnd(t)}return r.addEventListener("load",s,!1),r.addEventListener("error",l,!1),"data:"!==t.substr(0,5)&&void 0!==this.crossOrigin&&(r.crossOrigin=this.crossOrigin),i.manager.itemStart(t),r.src=t,r}}class Zut extends Wut{constructor(t){super(t)}load(t,e,n,o){const i=new Lct,a=new qut(this.manager);a.setCrossOrigin(this.crossOrigin),a.setPath(this.path);let r=0;function s(n){a.load(t[n],(function(t){i.images[n]=t,r++,6===r&&(i.needsUpdate=!0,e&&e(i))}),void 0,o)}for(let e=0;e<t.length;++e)s(e);return i}}class Xut extends Wut{constructor(t){super(t)}load(t,e,n,o){const i=new Cst,a=new qut(this.manager);return a.setCrossOrigin(this.crossOrigin),a.setPath(this.path),a.load(t,(function(n){i.image=n;const o=t.search(/\.jpe?g($|\?)/i)>0||0===t.search(/^data\:image\/jpeg/);i.format=o?Vrt:jrt,i.needsUpdate=!0,void 0!==e&&e(i)}),n,o),i}}class Kut extends Rft{constructor(){super(),this.type="CurvePath",this.curves=[],this.autoClose=!1}add(t){this.curves.push(t)}closePath(){const t=this.curves[0].getPoint(0),e=this.curves[this.curves.length-1].getPoint(1);t.equals(e)||this.curves.push(new Wft(e,t))}getPoint(t){const e=t*this.getLength(),n=this.getCurveLengths();let o=0;for(;o<n.length;){if(n[o]>=e){const t=n[o]-e,i=this.curves[o],a=i.getLength();return i.getPointAt(0===a?0:1-t/a)}o++}return null}getLength(){const t=this.getCurveLengths();return t[t.length-1]}updateArcLengths(){this.needsUpdate=!0,this.cacheLengths=null,this.getCurveLengths()}getCurveLengths(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;const t=[];let e=0;for(let n=0,o=this.curves.length;n<o;n++)e+=this.curves[n].getLength(),t.push(e);return this.cacheLengths=t,t}getSpacedPoints(t=40){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return this.autoClose&&e.push(e[0]),e}getPoints(t=12){const e=[];let n;for(let o=0,i=this.curves;o<i.length;o++){const a=i[o],r=a.getPoints(a&&a.isEllipseCurve?2*t:a&&(a.isLineCurve||a.isLineCurve3)?1:a&&a.isSplineCurve?t*a.points.length:t);for(let t=0;t<r.length;t++){const o=r[t];n&&n.equals(o)||(e.push(o),n=o)}}return this.autoClose&&e.length>1&&!e[e.length-1].equals(e[0])&&e.push(e[0]),e}copy(t){super.copy(t),this.curves=[];for(let e=0,n=t.curves.length;e<n;e++){this.curves.push(t.curves[e].clone())}return this.autoClose=t.autoClose,this}toJSON(){const t=super.toJSON();t.autoClose=this.autoClose,t.curves=[];for(let e=0,n=this.curves.length;e<n;e++){t.curves.push(this.curves[e].toJSON())}return t}fromJSON(t){super.fromJSON(t),this.autoClose=t.autoClose,this.curves=[];for(let e=0,n=t.curves.length;e<n;e++){const n=t.curves[e];this.curves.push((new Zft[n.type]).fromJSON(n))}return this}}class Jut extends Kut{constructor(t){super(),this.type="Path",this.currentPoint=new gst,t&&this.setFromPoints(t)}setFromPoints(t){this.moveTo(t[0].x,t[0].y);for(let e=1,n=t.length;e<n;e++)this.lineTo(t[e].x,t[e].y);return this}moveTo(t,e){return this.currentPoint.set(t,e),this}lineTo(t,e){const n=new Wft(this.currentPoint.clone(),new gst(t,e));return this.curves.push(n),this.currentPoint.set(t,e),this}quadraticCurveTo(t,e,n,o){const i=new Gft(this.currentPoint.clone(),new gst(t,e),new gst(n,o));return this.curves.push(i),this.currentPoint.set(n,o),this}bezierCurveTo(t,e,n,o,i,a){const r=new jft(this.currentPoint.clone(),new gst(t,e),new gst(n,o),new gst(i,a));return this.curves.push(r),this.currentPoint.set(i,a),this}splineThru(t){const e=[this.currentPoint.clone()].concat(t),n=new qft(e);return this.curves.push(n),this.currentPoint.copy(t[t.length-1]),this}arc(t,e,n,o,i,a){return this.absarc(t+this.currentPoint.x,e+this.currentPoint.y,n,o,i,a),this}absarc(t,e,n,o,i,a){return this.absellipse(t,e,n,n,o,i,a),this}ellipse(t,e,n,o,i,a,r,s){return this.absellipse(t+this.currentPoint.x,e+this.currentPoint.y,n,o,i,a,r,s),this}absellipse(t,e,n,o,i,a,r,s){const l=new Eft(t,e,n,o,i,a,r,s);if(this.curves.length>0){const t=l.getPoint(0);t.equals(this.currentPoint)||this.lineTo(t.x,t.y)}this.curves.push(l);const c=l.getPoint(1);return this.currentPoint.copy(c),this}copy(t){return super.copy(t),this.currentPoint.copy(t.currentPoint),this}toJSON(){const t=super.toJSON();return t.currentPoint=this.currentPoint.toArray(),t}fromJSON(t){return super.fromJSON(t),this.currentPoint.fromArray(t.currentPoint),this}}class Qut extends Jut{constructor(t){super(t),this.uuid=dst(),this.type="Shape",this.holes=[]}getPointsHoles(t){const e=[];for(let n=0,o=this.holes.length;n<o;n++)e[n]=this.holes[n].getPoints(t);return e}extractPoints(t){return{shape:this.getPoints(t),holes:this.getPointsHoles(t)}}copy(t){super.copy(t),this.holes=[];for(let e=0,n=t.holes.length;e<n;e++){this.holes.push(t.holes[e].clone())}return this}toJSON(){const t=super.toJSON();t.uuid=this.uuid,t.holes=[];for(let e=0,n=this.holes.length;e<n;e++){t.holes.push(this.holes[e].toJSON())}return t}fromJSON(t){super.fromJSON(t),this.uuid=t.uuid,this.holes=[];for(let e=0,n=t.holes.length;e<n;e++){const n=t.holes[e];this.holes.push((new Jut).fromJSON(n))}return this}}class $ut extends Slt{constructor(t,e=1){super(),this.type="Light",this.color=new Zlt(t),this.intensity=e}dispose(){}copy(t){return super.copy(t),this.color.copy(t.color),this.intensity=t.intensity,this}toJSON(t){const e=super.toJSON(t);return e.object.color=this.color.getHex(),e.object.intensity=this.intensity,void 0!==this.groundColor&&(e.object.groundColor=this.groundColor.getHex()),void 0!==this.distance&&(e.object.distance=this.distance),void 0!==this.angle&&(e.object.angle=this.angle),void 0!==this.decay&&(e.object.decay=this.decay),void 0!==this.penumbra&&(e.object.penumbra=this.penumbra),void 0!==this.shadow&&(e.object.shadow=this.shadow.toJSON()),e}}$ut.prototype.isLight=!0;(class extends $ut{constructor(t,e,n){super(t,n),this.type="HemisphereLight",this.position.copy(Slt.DefaultUp),this.updateMatrix(),this.groundColor=new Zlt(e)}copy(t){return $ut.prototype.copy.call(this,t),this.groundColor.copy(t.groundColor),this}}).prototype.isHemisphereLight=!0;const tgt=new olt,egt=new wst,ngt=new wst;class ogt{constructor(t){this.camera=t,this.bias=0,this.normalBias=0,this.radius=1,this.mapSize=new gst(512,512),this.map=null,this.mapPass=null,this.matrix=new olt,this.autoUpdate=!0,this.needsUpdate=!1,this._frustum=new qct,this._frameExtents=new gst(1,1),this._viewportCount=1,this._viewports=[new vst(0,0,1,1)]}getViewportCount(){return this._viewportCount}getFrustum(){return this._frustum}updateMatrices(t){const e=this.camera,n=this.matrix;egt.setFromMatrixPosition(t.matrixWorld),e.position.copy(egt),ngt.setFromMatrixPosition(t.target.matrixWorld),e.lookAt(ngt),e.updateMatrixWorld(),tgt.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),this._frustum.setFromProjectionMatrix(tgt),n.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),n.multiply(e.projectionMatrix),n.multiply(e.matrixWorldInverse)}getViewport(t){return this._viewports[t]}getFrameExtents(){return this._frameExtents}dispose(){this.map&&this.map.dispose(),this.mapPass&&this.mapPass.dispose()}copy(t){return this.camera=t.camera.clone(),this.bias=t.bias,this.radius=t.radius,this.mapSize.copy(t.mapSize),this}clone(){return(new this.constructor).copy(this)}toJSON(){const t={};return 0!==this.bias&&(t.bias=this.bias),0!==this.normalBias&&(t.normalBias=this.normalBias),1!==this.radius&&(t.radius=this.radius),512===this.mapSize.x&&512===this.mapSize.y||(t.mapSize=this.mapSize.toArray()),t.camera=this.camera.toJSON(!1).object,delete t.camera.matrix,t}}class igt extends ogt{constructor(){super(new Ict(50,1,.5,500)),this.focus=1}updateMatrices(t){const e=this.camera,n=2*cst*t.angle*this.focus,o=this.mapSize.width/this.mapSize.height,i=t.distance||e.far;n===e.fov&&o===e.aspect&&i===e.far||(e.fov=n,e.aspect=o,e.far=i,e.updateProjectionMatrix()),super.updateMatrices(t)}copy(t){return super.copy(t),this.focus=t.focus,this}}igt.prototype.isSpotLightShadow=!0;(class extends $ut{constructor(t,e,n=0,o=Math.PI/3,i=0,a=1){super(t,e),this.type="SpotLight",this.position.copy(Slt.DefaultUp),this.updateMatrix(),this.target=new Slt,this.distance=n,this.angle=o,this.penumbra=i,this.decay=a,this.shadow=new igt}get power(){return this.intensity*Math.PI}set power(t){this.intensity=t/Math.PI}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.angle=t.angle,this.penumbra=t.penumbra,this.decay=t.decay,this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}).prototype.isSpotLight=!0;const agt=new olt,rgt=new wst,sgt=new wst;class lgt extends ogt{constructor(){super(new Ict(90,1,.5,500)),this._frameExtents=new gst(4,2),this._viewportCount=6,this._viewports=[new vst(2,1,1,1),new vst(0,1,1,1),new vst(3,1,1,1),new vst(1,1,1,1),new vst(3,0,1,1),new vst(1,0,1,1)],this._cubeDirections=[new wst(1,0,0),new wst(-1,0,0),new wst(0,0,1),new wst(0,0,-1),new wst(0,1,0),new wst(0,-1,0)],this._cubeUps=[new wst(0,1,0),new wst(0,1,0),new wst(0,1,0),new wst(0,1,0),new wst(0,0,1),new wst(0,0,-1)]}updateMatrices(t,e=0){const n=this.camera,o=this.matrix,i=t.distance||n.far;i!==n.far&&(n.far=i,n.updateProjectionMatrix()),rgt.setFromMatrixPosition(t.matrixWorld),n.position.copy(rgt),sgt.copy(n.position),sgt.add(this._cubeDirections[e]),n.up.copy(this._cubeUps[e]),n.lookAt(sgt),n.updateMatrixWorld(),o.makeTranslation(-rgt.x,-rgt.y,-rgt.z),agt.multiplyMatrices(n.projectionMatrix,n.matrixWorldInverse),this._frustum.setFromProjectionMatrix(agt)}}lgt.prototype.isPointLightShadow=!0;(class extends $ut{constructor(t,e,n=0,o=1){super(t,e),this.type="PointLight",this.distance=n,this.decay=o,this.shadow=new lgt}get power(){return 4*this.intensity*Math.PI}set power(t){this.intensity=t/(4*Math.PI)}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.decay=t.decay,this.shadow=t.shadow.clone(),this}}).prototype.isPointLight=!0;class cgt extends ogt{constructor(){super(new rdt(-5,5,5,-5,.5,500))}}cgt.prototype.isDirectionalLightShadow=!0;(class extends $ut{constructor(t,e){super(t,e),this.type="DirectionalLight",this.position.copy(Slt.DefaultUp),this.updateMatrix(),this.target=new Slt,this.shadow=new cgt}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}).prototype.isDirectionalLight=!0;(class extends $ut{constructor(t,e){super(t,e),this.type="AmbientLight"}}).prototype.isAmbientLight=!0;(class extends $ut{constructor(t,e,n=10,o=10){super(t,e),this.type="RectAreaLight",this.width=n,this.height=o}copy(t){return super.copy(t),this.width=t.width,this.height=t.height,this}toJSON(t){const e=super.toJSON(t);return e.object.width=this.width,e.object.height=this.height,e}}).prototype.isRectAreaLight=!0;class dgt{constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new wst)}set(t){for(let e=0;e<9;e++)this.coefficients[e].copy(t[e]);return this}zero(){for(let t=0;t<9;t++)this.coefficients[t].set(0,0,0);return this}getAt(t,e){const n=t.x,o=t.y,i=t.z,a=this.coefficients;return e.copy(a[0]).multiplyScalar(.282095),e.addScaledVector(a[1],.488603*o),e.addScaledVector(a[2],.488603*i),e.addScaledVector(a[3],.488603*n),e.addScaledVector(a[4],n*o*1.092548),e.addScaledVector(a[5],o*i*1.092548),e.addScaledVector(a[6],.315392*(3*i*i-1)),e.addScaledVector(a[7],n*i*1.092548),e.addScaledVector(a[8],.546274*(n*n-o*o)),e}getIrradianceAt(t,e){const n=t.x,o=t.y,i=t.z,a=this.coefficients;return e.copy(a[0]).multiplyScalar(.886227),e.addScaledVector(a[1],1.023328*o),e.addScaledVector(a[2],1.023328*i),e.addScaledVector(a[3],1.023328*n),e.addScaledVector(a[4],.858086*n*o),e.addScaledVector(a[5],.858086*o*i),e.addScaledVector(a[6],.743125*i*i-.247708),e.addScaledVector(a[7],.858086*n*i),e.addScaledVector(a[8],.429043*(n*n-o*o)),e}add(t){for(let e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let o=0;o<9;o++)n[o].fromArray(t,e+3*o);return this}toArray(t=[],e=0){const n=this.coefficients;for(let o=0;o<9;o++)n[o].toArray(t,e+3*o);return t}static getBasisAt(t,e){const n=t.x,o=t.y,i=t.z;e[0]=.282095,e[1]=.488603*o,e[2]=.488603*i,e[3]=.488603*n,e[4]=1.092548*n*o,e[5]=1.092548*o*i,e[6]=.315392*(3*i*i-1),e[7]=1.092548*n*i,e[8]=.546274*(n*n-o*o)}}dgt.prototype.isSphericalHarmonics3=!0;class pgt extends $ut{constructor(t=new dgt,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}pgt.prototype.isLightProbe=!0;(class extends dct{constructor(){super(),this.type="InstancedBufferGeometry",this.instanceCount=1/0}copy(t){return super.copy(t),this.instanceCount=t.instanceCount,this}clone(){return(new this.constructor).copy(this)}toJSON(){const t=super.toJSON(this);return t.instanceCount=this.instanceCount,t.isInstancedBufferGeometry=!0,t}}).prototype.isInstancedBufferGeometry=!0;(class extends Qlt{constructor(t,e,n,o=1){"number"==typeof n&&(o=n,n=!1,console.error("THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument.")),super(t,e,n),this.meshPerAttribute=o}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}toJSON(){const t=super.toJSON();return t.meshPerAttribute=this.meshPerAttribute,t.isInstancedBufferAttribute=!0,t}}).prototype.isInstancedBufferAttribute=!0;let mgt;(class extends Wut{constructor(t){super(t),"undefined"==typeof createImageBitmap&&console.warn("THREE.ImageBitmapLoader: createImageBitmap() not supported."),"undefined"==typeof fetch&&console.warn("THREE.ImageBitmapLoader: fetch() not supported."),this.options={premultiplyAlpha:"none"}}setOptions(t){return this.options=t,this}load(t,e,n,o){void 0===t&&(t=""),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const i=this,a=jut.get(t);if(void 0!==a)return i.manager.itemStart(t),setTimeout((function(){e&&e(a),i.manager.itemEnd(t)}),0),a;const r={};r.credentials="anonymous"===this.crossOrigin?"same-origin":"include",r.headers=this.requestHeader,fetch(t,r).then((function(t){return t.blob()})).then((function(t){return createImageBitmap(t,Object.assign(i.options,{colorSpaceConversion:"none"}))})).then((function(n){jut.add(t,n),e&&e(n),i.manager.itemEnd(t)})).catch((function(e){o&&o(e),i.manager.itemError(t),i.manager.itemEnd(t)})),i.manager.itemStart(t)}}).prototype.isImageBitmapLoader=!0;const fgt=function(){return void 0===mgt&&(mgt=new(window.AudioContext||window.webkitAudioContext)),mgt};class ugt extends Wut{constructor(t){super(t)}load(t,e,n,o){const i=this,a=new Yut(this.manager);a.setResponseType("arraybuffer"),a.setPath(this.path),a.setRequestHeader(this.requestHeader),a.setWithCredentials(this.withCredentials),a.load(t,(function(n){try{const t=n.slice(0);fgt().decodeAudioData(t,(function(t){e(t)}))}catch(e){o?o(e):console.error(e),i.manager.itemError(t)}}),n,o)}}(class extends pgt{constructor(t,e,n=1){super(void 0,n);const o=(new Zlt).set(t),i=(new Zlt).set(e),a=new wst(o.r,o.g,o.b),r=new wst(i.r,i.g,i.b),s=Math.sqrt(Math.PI),l=s*Math.sqrt(.75);this.sh.coefficients[0].copy(a).add(r).multiplyScalar(s),this.sh.coefficients[1].copy(a).sub(r).multiplyScalar(l)}}).prototype.isHemisphereLightProbe=!0;(class extends pgt{constructor(t,e=1){super(void 0,e);const n=(new Zlt).set(t);this.sh.coefficients[0].set(n.r,n.g,n.b).multiplyScalar(2*Math.sqrt(Math.PI))}}).prototype.isAmbientLightProbe=!0;class ggt{constructor(t,e,n){let o,i,a;switch(this.binding=t,this.valueSize=n,e){case"quaternion":o=this._slerp,i=this._slerpAdditive,a=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case"string":case"bool":o=this._select,i=this._select,a=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:o=this._lerp,i=this._lerpAdditive,a=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=o,this._mixBufferRegionAdditive=i,this._setIdentity=a,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(t,e){const n=this.buffer,o=this.valueSize,i=t*o+o;let a=this.cumulativeWeight;if(0===a){for(let t=0;t!==o;++t)n[i+t]=n[t];a=e}else{a+=e;this._mixBufferRegion(n,i,0,e/a,o)}this.cumulativeWeight=a}accumulateAdditive(t){const e=this.buffer,n=this.valueSize,o=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(e,o,0,t,n),this.cumulativeWeightAdditive+=t}apply(t){const e=this.valueSize,n=this.buffer,o=t*e+e,i=this.cumulativeWeight,a=this.cumulativeWeightAdditive,r=this.binding;if(this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,i<1){this._mixBufferRegion(n,o,e*this._origIndex,1-i,e)}a>0&&this._mixBufferRegionAdditive(n,o,this._addIndex*e,1,e);for(let t=e,i=e+e;t!==i;++t)if(n[t]!==n[t+e]){r.setValue(n,o);break}}saveOriginalState(){const t=this.buffer,e=this.valueSize,n=e*this._origIndex;this.binding.getValue(t,n);for(let o=e,i=n;o!==i;++o)t[o]=t[n+o%e];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){this.binding.setValue(this.buffer,3*this.valueSize)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n<e;n++)this.buffer[n]=0}_setAdditiveIdentityQuaternion(){this._setAdditiveIdentityNumeric(),this.buffer[this._addIndex*this.valueSize+3]=1}_setAdditiveIdentityOther(){const t=this._origIndex*this.valueSize,e=this._addIndex*this.valueSize;for(let n=0;n<this.valueSize;n++)this.buffer[e+n]=this.buffer[t+n]}_select(t,e,n,o,i){if(o>=.5)for(let o=0;o!==i;++o)t[e+o]=t[n+o]}_slerp(t,e,n,o){Pst.slerpFlat(t,e,t,e,t,n,o)}_slerpAdditive(t,e,n,o,i){const a=this._workIndex*i;Pst.multiplyQuaternionsFlat(t,a,t,e,t,n),Pst.slerpFlat(t,e,t,e,t,a,o)}_lerp(t,e,n,o,i){const a=1-o;for(let r=0;r!==i;++r){const i=e+r;t[i]=t[i]*a+t[n+r]*o}}_lerpAdditive(t,e,n,o,i){for(let a=0;a!==i;++a){const i=e+a;t[i]=t[i]+t[n+a]*o}}}const hgt="\\[\\]\\.:\\/",bgt=new RegExp("[\\[\\]\\.:\\/]","g"),ygt="[^\\[\\]\\.:\\/]",_gt="[^"+hgt.replace("\\.","")+"]",Cgt=/((?:WC+[\/:])*)/.source.replace("WC",ygt),Mgt=/(WCOD+)?/.source.replace("WCOD",_gt),vgt=/(?:\.(WC+)(?:\[(.+)\])?)?/.source.replace("WC",ygt),xgt=/\.(WC+)(?:\[(.+)\])?/.source.replace("WC",ygt),Ogt=new RegExp("^"+Cgt+Mgt+vgt+xgt+"$"),Pgt=["material","materials","bones"];class wgt{constructor(t,e,n){this.path=e,this.parsedPath=n||wgt.parseTrackName(e),this.node=wgt.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new wgt.Composite(t,e,n):new wgt(t,e,n)}static sanitizeNodeName(t){return t.replace(/\s/g,"_").replace(bgt,"")}static parseTrackName(t){const e=Ogt.exec(t);if(!e)throw new Error("PropertyBinding: Cannot parse trackName: "+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},o=n.nodeName&&n.nodeName.lastIndexOf(".");if(void 0!==o&&-1!==o){const t=n.nodeName.substring(o+1);-1!==Pgt.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,o),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error("PropertyBinding: can not parse propertyName from trackName: "+t);return n}static findNode(t,e){if(!e||""===e||"."===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let o=0;o<t.length;o++){const i=t[o];if(i.name===e||i.uuid===e)return i;const a=n(i.children);if(a)return a}return null},o=n(t.children);if(o)return o}return null}_getValue_unavailable(){}_setValue_unavailable(){}_getValue_direct(t,e){t[e]=this.node[this.propertyName]}_getValue_array(t,e){const n=this.resolvedProperty;for(let o=0,i=n.length;o!==i;++o)t[e++]=n[o]}_getValue_arrayElement(t,e){t[e]=this.resolvedProperty[this.propertyIndex]}_getValue_toArray(t,e){this.resolvedProperty.toArray(t,e)}_setValue_direct(t,e){this.targetObject[this.propertyName]=t[e]}_setValue_direct_setNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.needsUpdate=!0}_setValue_direct_setMatrixWorldNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_array(t,e){const n=this.resolvedProperty;for(let o=0,i=n.length;o!==i;++o)n[o]=t[e++]}_setValue_array_setNeedsUpdate(t,e){const n=this.resolvedProperty;for(let o=0,i=n.length;o!==i;++o)n[o]=t[e++];this.targetObject.needsUpdate=!0}_setValue_array_setMatrixWorldNeedsUpdate(t,e){const n=this.resolvedProperty;for(let o=0,i=n.length;o!==i;++o)n[o]=t[e++];this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_arrayElement(t,e){this.resolvedProperty[this.propertyIndex]=t[e]}_setValue_arrayElement_setNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.needsUpdate=!0}_setValue_arrayElement_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_fromArray(t,e){this.resolvedProperty.fromArray(t,e)}_setValue_fromArray_setNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.needsUpdate=!0}_setValue_fromArray_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.matrixWorldNeedsUpdate=!0}_getValue_unbound(t,e){this.bind(),this.getValue(t,e)}_setValue_unbound(t,e){this.bind(),this.setValue(t,e)}bind(){let t=this.node;const e=this.parsedPath,n=e.objectName,o=e.propertyName;let i=e.propertyIndex;if(t||(t=wgt.findNode(this.rootNode,e.nodeName)||this.rootNode,this.node=t),this.getValue=this._getValue_unavailable,this.setValue=this._setValue_unavailable,!t)return void console.error("THREE.PropertyBinding: Trying to update node for track: "+this.path+" but it wasn't found.");if(n){let o=e.objectIndex;switch(n){case"materials":if(!t.material)return void console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.",this);if(!t.material.materials)return void console.error("THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.",this);t=t.material.materials;break;case"bones":if(!t.skeleton)return void console.error("THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.",this);t=t.skeleton.bones;for(let e=0;e<t.length;e++)if(t[e].name===o){o=e;break}break;default:if(void 0===t[n])return void console.error("THREE.PropertyBinding: Can not bind to objectName of node undefined.",this);t=t[n]}if(void 0!==o){if(void 0===t[o])return void console.error("THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.",this,t);t=t[o]}}const a=t[o];if(void 0===a){return void console.error("THREE.PropertyBinding: Trying to update property for track: "+e.nodeName+"."+o+" but it wasn't found.",t)}let r=this.Versioning.None;this.targetObject=t,void 0!==t.needsUpdate?r=this.Versioning.NeedsUpdate:void 0!==t.matrixWorldNeedsUpdate&&(r=this.Versioning.MatrixWorldNeedsUpdate);let s=this.BindingType.Direct;if(void 0!==i){if("morphTargetInfluences"===o){if(!t.geometry)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.",this);if(!t.geometry.isBufferGeometry)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences on THREE.Geometry. Use THREE.BufferGeometry instead.",this);if(!t.geometry.morphAttributes)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.",this);void 0!==t.morphTargetDictionary[i]&&(i=t.morphTargetDictionary[i])}s=this.BindingType.ArrayElement,this.resolvedProperty=a,this.propertyIndex=i}else void 0!==a.fromArray&&void 0!==a.toArray?(s=this.BindingType.HasFromToArray,this.resolvedProperty=a):Array.isArray(a)?(s=this.BindingType.EntireArray,this.resolvedProperty=a):this.propertyName=o;this.getValue=this.GetterByBindingType[s],this.setValue=this.SetterByBindingTypeAndVersioning[s][r]}unbind(){this.node=null,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}}wgt.Composite=class{constructor(t,e,n){const o=n||wgt.parseTrackName(e);this._targetGroup=t,this._bindings=t.subscribe_(e,o)}getValue(t,e){this.bind();const n=this._bindings[this._targetGroup.nCachedObjects_];void 0!==n&&n.getValue(t,e)}setValue(t,e){const n=this._bindings;for(let o=this._targetGroup.nCachedObjects_,i=n.length;o!==i;++o)n[o].setValue(t,e)}bind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].bind()}unbind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].unbind()}},wgt.prototype.BindingType={Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3},wgt.prototype.Versioning={None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2},wgt.prototype.GetterByBindingType=[wgt.prototype._getValue_direct,wgt.prototype._getValue_array,wgt.prototype._getValue_arrayElement,wgt.prototype._getValue_toArray],wgt.prototype.SetterByBindingTypeAndVersioning=[[wgt.prototype._setValue_direct,wgt.prototype._setValue_direct_setNeedsUpdate,wgt.prototype._setValue_direct_setMatrixWorldNeedsUpdate],[wgt.prototype._setValue_array,wgt.prototype._setValue_array_setNeedsUpdate,wgt.prototype._setValue_array_setMatrixWorldNeedsUpdate],[wgt.prototype._setValue_arrayElement,wgt.prototype._setValue_arrayElement_setNeedsUpdate,wgt.prototype._setValue_arrayElement_setMatrixWorldNeedsUpdate],[wgt.prototype._setValue_fromArray,wgt.prototype._setValue_fromArray_setNeedsUpdate,wgt.prototype._setValue_fromArray_setMatrixWorldNeedsUpdate]];class kgt{constructor(t,e,n=null,o=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=o;const i=e.tracks,a=i.length,r=new Array(a),s={endingStart:Zrt,endingEnd:Zrt};for(let t=0;t!==a;++t){const e=i[t].createInterpolant(null);r[t]=e,e.settings=s}this._interpolantSettings=s,this._interpolants=r,this._propertyBindings=new Array(a),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=2201,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(t){return this._startTime=t,this}setLoop(t,e){return this.loop=t,this.repetitions=e,this}setEffectiveWeight(t){return this.weight=t,this._effectiveWeight=this.enabled?t:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(t){return this._scheduleFading(t,0,1)}fadeOut(t){return this._scheduleFading(t,1,0)}crossFadeFrom(t,e,n){if(t.fadeOut(e),this.fadeIn(e),n){const n=this._clip.duration,o=t._clip.duration,i=n/o;t.warp(1,o/n,e),this.warp(i,1,e)}return this}crossFadeTo(t,e,n){return t.crossFadeFrom(this,e,n)}stopFading(){const t=this._weightInterpolant;return null!==t&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}setEffectiveTimeScale(t){return this.timeScale=t,this._effectiveTimeScale=this.paused?0:t,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(t){return this.timeScale=this._clip.duration/t,this.stopWarping()}syncWith(t){return this.time=t.time,this.timeScale=t.timeScale,this.stopWarping()}halt(t){return this.warp(this._effectiveTimeScale,0,t)}warp(t,e,n){const o=this._mixer,i=o.time,a=this.timeScale;let r=this._timeScaleInterpolant;null===r&&(r=o._lendControlInterpolant(),this._timeScaleInterpolant=r);const s=r.parameterPositions,l=r.sampleValues;return s[0]=i,s[1]=i+n,l[0]=t/a,l[1]=e/a,this}stopWarping(){const t=this._timeScaleInterpolant;return null!==t&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(t,e,n,o){if(!this.enabled)return void this._updateWeight(t);const i=this._startTime;if(null!==i){const o=(t-i)*n;if(o<0||0===n)return;this._startTime=null,e=n*o}e*=this._updateTimeScale(t);const a=this._updateTime(e),r=this._updateWeight(t);if(r>0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case 2501:for(let n=0,o=t.length;n!==o;++n)t[n].evaluate(a),e[n].accumulateAdditive(r);break;case Jrt:default:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(a),e[n].accumulate(o,r)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const o=n.evaluate(t)[0];e*=o,t>n.parameterPositions[1]&&(this.stopFading(),0===o&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;if(null!==n){e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e)}}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let o=this.time+t,i=this._loopCount;const a=2202===n;if(0===t)return-1===i?o:a&&1==(1&i)?e-o:o;if(2200===n){-1===i&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(o>=e)o=e;else{if(!(o<0)){this.time=o;break t}o=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=o,this._mixer.dispatchEvent({type:"finished",action:this,direction:t<0?-1:1})}}else{if(-1===i&&(t>=0?(i=0,this._setEndings(!0,0===this.repetitions,a)):this._setEndings(0===this.repetitions,!0,a)),o>=e||o<0){const n=Math.floor(o/e);o-=e*n,i+=Math.abs(n);const r=this.repetitions-i;if(r<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,o=t>0?e:0,this.time=o,this._mixer.dispatchEvent({type:"finished",action:this,direction:t>0?1:-1});else{if(1===r){const e=t<0;this._setEndings(e,!e,a)}else this._setEndings(!1,!1,a);this._loopCount=i,this.time=o,this._mixer.dispatchEvent({type:"loop",action:this,loopDelta:n})}}else this.time=o;if(a&&1==(1&i))return e-o}return o}_setEndings(t,e,n){const o=this._interpolantSettings;n?(o.endingStart=Xrt,o.endingEnd=Xrt):(o.endingStart=t?this.zeroSlopeAtStart?Xrt:Zrt:Krt,o.endingEnd=e?this.zeroSlopeAtEnd?Xrt:Zrt:Krt)}_scheduleFading(t,e,n){const o=this._mixer,i=o.time;let a=this._weightInterpolant;null===a&&(a=o._lendControlInterpolant(),this._weightInterpolant=a);const r=a.parameterPositions,s=a.sampleValues;return r[0]=i,s[0]=e,r[1]=i+t,s[1]=n,this}}(class extends rst{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,o=t._clip.tracks,i=o.length,a=t._propertyBindings,r=t._interpolants,s=n.uuid,l=this._bindingsByRootAndName;let c=l[s];void 0===c&&(c={},l[s]=c);for(let t=0;t!==i;++t){const i=o[t],l=i.name;let d=c[l];if(void 0!==d)a[t]=d;else{if(d=a[t],void 0!==d){null===d._cacheIndex&&(++d.referenceCount,this._addInactiveBinding(d,s,l));continue}d=new ggt(wgt.create(n,l,e&&e._propertyBindings[t].binding.parsedPath),i.ValueTypeName,i.getValueSize()),++d.referenceCount,this._addInactiveBinding(d,s,l),a[t]=d}r[t].resultBuffer=d.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,o=this._actionsByClip[n];this._bindAction(t,o&&o.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e<this._nActiveActions}_addInactiveAction(t,e,n){const o=this._actions,i=this._actionsByClip;let a=i[e];if(void 0===a)a={knownActions:[t],actionByRoot:{}},t._byClipCacheIndex=0,i[e]=a;else{const e=a.knownActions;t._byClipCacheIndex=e.length,e.push(t)}t._cacheIndex=o.length,o.push(t),a.actionByRoot[n]=t}_removeInactiveAction(t){const e=this._actions,n=e[e.length-1],o=t._cacheIndex;n._cacheIndex=o,e[o]=n,e.pop(),t._cacheIndex=null;const i=t._clip.uuid,a=this._actionsByClip,r=a[i],s=r.knownActions,l=s[s.length-1],c=t._byClipCacheIndex;l._byClipCacheIndex=c,s[c]=l,s.pop(),t._byClipCacheIndex=null;delete r.actionByRoot[(t._localRoot||this._root).uuid],0===s.length&&delete a[i],this._removeInactiveBindingsForAction(t)}_removeInactiveBindingsForAction(t){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.referenceCount&&this._removeInactiveBinding(n)}}_lendAction(t){const e=this._actions,n=t._cacheIndex,o=this._nActiveActions++,i=e[o];t._cacheIndex=o,e[o]=t,i._cacheIndex=n,e[n]=i}_takeBackAction(t){const e=this._actions,n=t._cacheIndex,o=--this._nActiveActions,i=e[o];t._cacheIndex=o,e[o]=t,i._cacheIndex=n,e[n]=i}_addInactiveBinding(t,e,n){const o=this._bindingsByRootAndName,i=this._bindings;let a=o[e];void 0===a&&(a={},o[e]=a),a[n]=t,t._cacheIndex=i.length,i.push(t)}_removeInactiveBinding(t){const e=this._bindings,n=t.binding,o=n.rootNode.uuid,i=n.path,a=this._bindingsByRootAndName,r=a[o],s=e[e.length-1],l=t._cacheIndex;s._cacheIndex=l,e[l]=s,e.pop(),delete r[i],0===Object.keys(r).length&&delete a[o]}_lendBinding(t){const e=this._bindings,n=t._cacheIndex,o=this._nActiveBindings++,i=e[o];t._cacheIndex=o,e[o]=t,i._cacheIndex=n,e[n]=i}_takeBackBinding(t){const e=this._bindings,n=t._cacheIndex,o=--this._nActiveBindings,i=e[o];t._cacheIndex=o,e[o]=t,i._cacheIndex=n,e[n]=i}_lendControlInterpolant(){const t=this._controlInterpolants,e=this._nActiveControlInterpolants++;let n=t[e];return void 0===n&&(n=new Rut(new Float32Array(2),new Float32Array(2),1,this._controlInterpolantsResultBuffer),n.__cacheIndex=e,t[e]=n),n}_takeBackControlInterpolant(t){const e=this._controlInterpolants,n=t.__cacheIndex,o=--this._nActiveControlInterpolants,i=e[o];t.__cacheIndex=o,e[o]=t,i.__cacheIndex=n,e[n]=i}clipAction(t,e,n){const o=e||this._root,i=o.uuid;let a="string"==typeof t?But.findByName(o,t):t;const r=null!==a?a.uuid:t,s=this._actionsByClip[r];let l=null;if(void 0===n&&(n=null!==a?a.blendMode:Jrt),void 0!==s){const t=s.actionByRoot[i];if(void 0!==t&&t.blendMode===n)return t;l=s.knownActions[0],null===a&&(a=l._clip)}if(null===a)return null;const c=new kgt(this,a,e,n);return this._bindAction(c,l),this._addInactiveAction(c,r,i),c}existingAction(t,e){const n=e||this._root,o=n.uuid,i="string"==typeof t?But.findByName(n,t):t,a=this._actionsByClip[i?i.uuid:t];return void 0!==a&&a.actionByRoot[o]||null}stopAllAction(){const t=this._actions;for(let e=this._nActiveActions-1;e>=0;--e)t[e].stop();return this}update(t){const e=this._actions,n=this._nActiveActions,o=this.time+=t*=this.timeScale,i=Math.sign(t),a=this._accuIndex^=1;for(let r=0;r!==n;++r){e[r]._update(o,t,i,a)}const r=this._bindings,s=this._nActiveBindings;for(let t=0;t!==s;++t)r[t].apply(a);return this}setTime(t){this.time=0;for(let t=0;t<this._actions.length;t++)this._actions[t].time=0;return this.update(t)}getRoot(){return this._root}uncacheClip(t){const e=this._actions,n=t.uuid,o=this._actionsByClip,i=o[n];if(void 0!==i){const t=i.knownActions;for(let n=0,o=t.length;n!==o;++n){const o=t[n];this._deactivateAction(o);const i=o._cacheIndex,a=e[e.length-1];o._cacheIndex=null,o._byClipCacheIndex=null,a._cacheIndex=i,e[i]=a,e.pop(),this._removeInactiveBindingsForAction(o)}delete o[n]}}uncacheRoot(t){const e=t.uuid,n=this._actionsByClip;for(const t in n){const o=n[t].actionByRoot[e];void 0!==o&&(this._deactivateAction(o),this._removeInactiveAction(o))}const o=this._bindingsByRootAndName[e];if(void 0!==o)for(const t in o){const e=o[t];e.restoreOriginalState(),this._removeInactiveBinding(e)}}uncacheAction(t,e){const n=this.existingAction(t,e);null!==n&&(this._deactivateAction(n),this._removeInactiveAction(n))}}).prototype._controlInterpolantsResultBuffer=new Float32Array(1);(class extends Lmt{constructor(t,e,n=1){super(t,e),this.meshPerAttribute=n}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}clone(t){const e=super.clone(t);return e.meshPerAttribute=this.meshPerAttribute,e}toJSON(t){const e=super.toJSON(t);return e.isInstancedInterleavedBuffer=!0,e.meshPerAttribute=this.meshPerAttribute,e}}).prototype.isInstancedInterleavedBuffer=!0;(class extends Slt{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}).prototype.isImmediateRenderObject=!0;const Sgt=new wst,Dgt=new olt,Rgt=new olt;function Egt(t){const e=[];t&&t.isBone&&e.push(t);for(let n=0;n<t.children.length;n++)e.push.apply(e,Egt(t.children[n]));return e}const Agt=new Float32Array(1);function Tgt(t,e,n){if(1===n)return new Zlt(e);const o=g9(e);if(!o)throw new Error(`d3 failed to recognize the color: ${e}`);return new Zlt(utt(o,t)(1-n))}var Ngt,zgt;function Igt(t,e){const n=e.length/2;let o=t.attributes.position;o&&o.count===3*n||(o=new Qlt(new Float32Array(3*n),3),t.setAttribute("position",o));const i=o.array;for(let t=0;t<n;t++)i[3*t]=e[2*t],i[3*t+1]=e[2*t+1];o.needsUpdate=!0,t.setDrawRange(0,3*n),t.computeBoundingSphere()}function Hgt(t,e,n){const o=Math.max(e.length/2-1,0),i=2*o*3,a=3*i;let r=t.attributes.position;r&&r.count===i||(r=new Qlt(new Float32Array(a),3),t.setAttribute("position",r));const s=r.array;for(let t=0;t<o;t++){const[o,i,a,r]=[e[2*t],e[2*t+1],e[2*t+2],e[2*t+3]],l=new gst(o,i),c=new gst(a,r),d=new gst(a-o,r-i),p=new gst(-d.y,d.x).setLength(n/2),m=l.clone().add(p),f=l.clone().sub(p),u=c.clone().add(p),g=c.clone().sub(p),h=[m.x,m.y,0,f.x,f.y,0,u.x,u.y,0,u.x,u.y,0,f.x,f.y,0,g.x,g.y,0];s.set(h,t*h.length)}r.needsUpdate=!0,t.setDrawRange(0,a),t.computeBoundingSphere()}function Fgt(t,e,n,o){const{visible:i,color:a,opacity:r}=o;if(Array.isArray(e.material))throw new Error("Invariant error: only expect one material on an object");const s=e.material;if(s.visible!==i&&(s.visible=i,s.needsUpdate=!0),!i)return!1;const l=Tgt(t,a,null!=r?r:1),c=n(e.geometry);e.geometry!==c&&(e.geometry=c);return s.color.equals(l)||(s.color.set(l),s.needsUpdate=!0),!0}new Int32Array(Agt.buffer),Rft.create=function(t,e){return console.log("THREE.Curve.create() has been deprecated"),t.prototype=Object.create(Rft.prototype),t.prototype.constructor=t,t.prototype.getPoint=e,t},Jut.prototype.fromPoints=function(t){return console.warn("THREE.Path: .fromPoints() has been renamed to .setFromPoints()."),this.setFromPoints(t)},class extends vft{constructor(t=10,e=10,n=4473924,o=8947848){n=new Zlt(n),o=new Zlt(o);const i=e/2,a=t/e,r=t/2,s=[],l=[];for(let t=0,c=0,d=-r;t<=e;t++,d+=a){s.push(-r,0,d,r,0,d),s.push(d,0,-r,d,0,r);const e=t===i?n:o;e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3}const c=new dct;c.setAttribute("position",new ect(s,3)),c.setAttribute("color",new ect(l,3));super(c,new fft({vertexColors:!0,toneMapped:!1})),this.type="GridHelper"}}.prototype.setColors=function(){console.error("THREE.GridHelper: setColors() has been deprecated, pass them in the constructor instead.")},class extends vft{constructor(t){const e=Egt(t),n=new dct,o=[],i=[],a=new Zlt(0,0,1),r=new Zlt(0,1,0);for(let t=0;t<e.length;t++){const n=e[t];n.parent&&n.parent.isBone&&(o.push(0,0,0),o.push(0,0,0),i.push(a.r,a.g,a.b),i.push(r.r,r.g,r.b))}n.setAttribute("position",new ect(o,3)),n.setAttribute("color",new ect(i,3));super(n,new fft({vertexColors:!0,depthTest:!1,depthWrite:!1,toneMapped:!1,transparent:!0})),this.type="SkeletonHelper",this.isSkeletonHelper=!0,this.root=t,this.bones=e,this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1}updateMatrixWorld(t){const e=this.bones,n=this.geometry,o=n.getAttribute("position");Rgt.copy(this.root.matrixWorld).invert();for(let t=0,n=0;t<e.length;t++){const i=e[t];i.parent&&i.parent.isBone&&(Dgt.multiplyMatrices(Rgt,i.matrixWorld),Sgt.setFromMatrixPosition(Dgt),o.setXYZ(n,Sgt.x,Sgt.y,Sgt.z),Dgt.multiplyMatrices(Rgt,i.parent.matrixWorld),Sgt.setFromMatrixPosition(Dgt),o.setXYZ(n+1,Sgt.x,Sgt.y,Sgt.z),n+=2)}n.getAttribute("position").needsUpdate=!0,super.updateMatrixWorld(t)}}.prototype.update=function(){console.error("THREE.SkeletonHelper: update() no longer needs to be called.")},Wut.prototype.extractUrlBase=function(t){return console.warn("THREE.Loader: .extractUrlBase() has been deprecated. Use THREE.LoaderUtils.extractUrlBase() instead."),class{static decodeText(t){if("undefined"!=typeof TextDecoder)return(new TextDecoder).decode(t);let e="";for(let n=0,o=t.length;n<o;n++)e+=String.fromCharCode(t[n]);try{return decodeURIComponent(escape(e))}catch(t){return e}}static extractUrlBase(t){const e=t.lastIndexOf("/");return-1===e?"./":t.substr(0,e+1)}}.extractUrlBase(t)},Wut.Handlers={add:function(){console.error("THREE.Loader: Handlers.add() has been removed. Use LoadingManager.addHandler() instead.")},get:function(){console.error("THREE.Loader: Handlers.get() has been removed. Use LoadingManager.getHandler() instead.")}},Dst.prototype.center=function(t){return console.warn("THREE.Box3: .center() has been renamed to .getCenter()."),this.getCenter(t)},Dst.prototype.empty=function(){return console.warn("THREE.Box3: .empty() has been renamed to .isEmpty()."),this.isEmpty()},Dst.prototype.isIntersectionBox=function(t){return console.warn("THREE.Box3: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},Dst.prototype.isIntersectionSphere=function(t){return console.warn("THREE.Box3: .isIntersectionSphere() has been renamed to .intersectsSphere()."),this.intersectsSphere(t)},Dst.prototype.size=function(t){return console.warn("THREE.Box3: .size() has been renamed to .getSize()."),this.getSize(t)},Zst.prototype.empty=function(){return console.warn("THREE.Sphere: .empty() has been renamed to .isEmpty()."),this.isEmpty()},qct.prototype.setFromMatrix=function(t){return console.warn("THREE.Frustum: .setFromMatrix() has been renamed to .setFromProjectionMatrix()."),this.setFromProjectionMatrix(t)},hst.prototype.flattenToArrayOffset=function(t,e){return console.warn("THREE.Matrix3: .flattenToArrayOffset() has been deprecated. Use .toArray() instead."),this.toArray(t,e)},hst.prototype.multiplyVector3=function(t){return console.warn("THREE.Matrix3: .multiplyVector3() has been removed. Use vector.applyMatrix3( matrix ) instead."),t.applyMatrix3(this)},hst.prototype.multiplyVector3Array=function(){console.error("THREE.Matrix3: .multiplyVector3Array() has been removed.")},hst.prototype.applyToBufferAttribute=function(t){return console.warn("THREE.Matrix3: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix3( matrix ) instead."),t.applyMatrix3(this)},hst.prototype.applyToVector3Array=function(){console.error("THREE.Matrix3: .applyToVector3Array() has been removed.")},hst.prototype.getInverse=function(t){return console.warn("THREE.Matrix3: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead."),this.copy(t).invert()},olt.prototype.extractPosition=function(t){return console.warn("THREE.Matrix4: .extractPosition() has been renamed to .copyPosition()."),this.copyPosition(t)},olt.prototype.flattenToArrayOffset=function(t,e){return console.warn("THREE.Matrix4: .flattenToArrayOffset() has been deprecated. Use .toArray() instead."),this.toArray(t,e)},olt.prototype.getPosition=function(){return console.warn("THREE.Matrix4: .getPosition() has been removed. Use Vector3.setFromMatrixPosition( matrix ) instead."),(new wst).setFromMatrixColumn(this,3)},olt.prototype.setRotationFromQuaternion=function(t){return console.warn("THREE.Matrix4: .setRotationFromQuaternion() has been renamed to .makeRotationFromQuaternion()."),this.makeRotationFromQuaternion(t)},olt.prototype.multiplyToArray=function(){console.warn("THREE.Matrix4: .multiplyToArray() has been removed.")},olt.prototype.multiplyVector3=function(t){return console.warn("THREE.Matrix4: .multiplyVector3() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},olt.prototype.multiplyVector4=function(t){return console.warn("THREE.Matrix4: .multiplyVector4() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},olt.prototype.multiplyVector3Array=function(){console.error("THREE.Matrix4: .multiplyVector3Array() has been removed.")},olt.prototype.rotateAxis=function(t){console.warn("THREE.Matrix4: .rotateAxis() has been removed. Use Vector3.transformDirection( matrix ) instead."),t.transformDirection(this)},olt.prototype.crossVector=function(t){return console.warn("THREE.Matrix4: .crossVector() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},olt.prototype.translate=function(){console.error("THREE.Matrix4: .translate() has been removed.")},olt.prototype.rotateX=function(){console.error("THREE.Matrix4: .rotateX() has been removed.")},olt.prototype.rotateY=function(){console.error("THREE.Matrix4: .rotateY() has been removed.")},olt.prototype.rotateZ=function(){console.error("THREE.Matrix4: .rotateZ() has been removed.")},olt.prototype.rotateByAxis=function(){console.error("THREE.Matrix4: .rotateByAxis() has been removed.")},olt.prototype.applyToBufferAttribute=function(t){return console.warn("THREE.Matrix4: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},olt.prototype.applyToVector3Array=function(){console.error("THREE.Matrix4: .applyToVector3Array() has been removed.")},olt.prototype.makeFrustum=function(t,e,n,o,i,a){return console.warn("THREE.Matrix4: .makeFrustum() has been removed. Use .makePerspective( left, right, top, bottom, near, far ) instead."),this.makePerspective(t,e,o,n,i,a)},olt.prototype.getInverse=function(t){return console.warn("THREE.Matrix4: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead."),this.copy(t).invert()},Wct.prototype.isIntersectionLine=function(t){return console.warn("THREE.Plane: .isIntersectionLine() has been renamed to .intersectsLine()."),this.intersectsLine(t)},Pst.prototype.multiplyVector3=function(t){return console.warn("THREE.Quaternion: .multiplyVector3() has been removed. Use is now vector.applyQuaternion( quaternion ) instead."),t.applyQuaternion(this)},Pst.prototype.inverse=function(){return console.warn("THREE.Quaternion: .inverse() has been renamed to invert()."),this.invert()},nlt.prototype.isIntersectionBox=function(t){return console.warn("THREE.Ray: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},nlt.prototype.isIntersectionPlane=function(t){return console.warn("THREE.Ray: .isIntersectionPlane() has been renamed to .intersectsPlane()."),this.intersectsPlane(t)},nlt.prototype.isIntersectionSphere=function(t){return console.warn("THREE.Ray: .isIntersectionSphere() has been renamed to .intersectsSphere()."),this.intersectsSphere(t)},Llt.prototype.area=function(){return console.warn("THREE.Triangle: .area() has been renamed to .getArea()."),this.getArea()},Llt.prototype.barycoordFromPoint=function(t,e){return console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord()."),this.getBarycoord(t,e)},Llt.prototype.midpoint=function(t){return console.warn("THREE.Triangle: .midpoint() has been renamed to .getMidpoint()."),this.getMidpoint(t)},Llt.prototypenormal=function(t){return console.warn("THREE.Triangle: .normal() has been renamed to .getNormal()."),this.getNormal(t)},Llt.prototype.plane=function(t){return console.warn("THREE.Triangle: .plane() has been renamed to .getPlane()."),this.getPlane(t)},Llt.barycoordFromPoint=function(t,e,n,o,i){return console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord()."),Llt.getBarycoord(t,e,n,o,i)},Llt.normal=function(t,e,n,o){return console.warn("THREE.Triangle: .normal() has been renamed to .getNormal()."),Llt.getNormal(t,e,n,o)},Qut.prototype.extractAllPoints=function(t){return console.warn("THREE.Shape: .extractAllPoints() has been removed. Use .extractPoints() instead."),this.extractPoints(t)},Qut.prototype.extrude=function(t){return console.warn("THREE.Shape: .extrude() has been removed. Use ExtrudeGeometry() instead."),new xut(this,t)},Qut.prototype.makeGeometry=function(t){return console.warn("THREE.Shape: .makeGeometry() has been removed. Use ShapeGeometry() instead."),new Put(this,t)},gst.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector2: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},gst.prototype.distanceToManhattan=function(t){return console.warn("THREE.Vector2: .distanceToManhattan() has been renamed to .manhattanDistanceTo()."),this.manhattanDistanceTo(t)},gst.prototype.lengthManhattan=function(){return console.warn("THREE.Vector2: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},wst.prototype.setEulerFromRotationMatrix=function(){console.error("THREE.Vector3: .setEulerFromRotationMatrix() has been removed. Use Euler.setFromRotationMatrix() instead.")},wst.prototype.setEulerFromQuaternion=function(){console.error("THREE.Vector3: .setEulerFromQuaternion() has been removed. Use Euler.setFromQuaternion() instead.")},wst.prototype.getPositionFromMatrix=function(t){return console.warn("THREE.Vector3: .getPositionFromMatrix() has been renamed to .setFromMatrixPosition()."),this.setFromMatrixPosition(t)},wst.prototype.getScaleFromMatrix=function(t){return console.warn("THREE.Vector3: .getScaleFromMatrix() has been renamed to .setFromMatrixScale()."),this.setFromMatrixScale(t)},wst.prototype.getColumnFromMatrix=function(t,e){return console.warn("THREE.Vector3: .getColumnFromMatrix() has been renamed to .setFromMatrixColumn()."),this.setFromMatrixColumn(e,t)},wst.prototype.applyProjection=function(t){return console.warn("THREE.Vector3: .applyProjection() has been removed. Use .applyMatrix4( m ) instead."),this.applyMatrix4(t)},wst.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector3: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},wst.prototype.distanceToManhattan=function(t){return console.warn("THREE.Vector3: .distanceToManhattan() has been renamed to .manhattanDistanceTo()."),this.manhattanDistanceTo(t)},wst.prototype.lengthManhattan=function(){return console.warn("THREE.Vector3: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},vst.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector4: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},vst.prototype.lengthManhattan=function(){return console.warn("THREE.Vector4: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},Slt.prototype.getChildByName=function(t){return console.warn("THREE.Object3D: .getChildByName() has been renamed to .getObjectByName()."),this.getObjectByName(t)},Slt.prototype.renderDepth=function(){console.warn("THREE.Object3D: .renderDepth has been removed. Use .renderOrder, instead.")},Slt.prototype.translate=function(t,e){return console.warn("THREE.Object3D: .translate() has been removed. Use .translateOnAxis( axis, distance ) instead."),this.translateOnAxis(e,t)},Slt.prototype.getWorldRotation=function(){console.error("THREE.Object3D: .getWorldRotation() has been removed. Use THREE.Object3D.getWorldQuaternion( target ) instead.")},Slt.prototype.applyMatrix=function(t){return console.warn("THREE.Object3D: .applyMatrix() has been renamed to .applyMatrix4()."),this.applyMatrix4(t)},Object.defineProperties(Slt.prototype,{eulerOrder:{get:function(){return console.warn("THREE.Object3D: .eulerOrder is now .rotation.order."),this.rotation.order},set:function(t){console.warn("THREE.Object3D: .eulerOrder is now .rotation.order."),this.rotation.order=t}},useQuaternion:{get:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")},set:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")}}}),Sct.prototype.setDrawMode=function(){console.error("THREE.Mesh: .setDrawMode() has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.")},Object.defineProperties(Sct.prototype,{drawMode:{get:function(){return console.error("THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode."),0},set:function(){console.error("THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.")}}}),lft.prototype.initBones=function(){console.error("THREE.SkinnedMesh: initBones() has been removed.")},Ict.prototype.setLens=function(t,e){console.warn("THREE.PerspectiveCamera.setLens is deprecated. Use .setFocalLength and .filmGauge for a photographic setup."),void 0!==e&&(this.filmGauge=e),this.setFocalLength(t)},Object.defineProperties($ut.prototype,{onlyShadow:{set:function(){console.warn("THREE.Light: .onlyShadow has been removed.")}},shadowCameraFov:{set:function(t){console.warn("THREE.Light: .shadowCameraFov is now .shadow.camera.fov."),this.shadow.camera.fov=t}},shadowCameraLeft:{set:function(t){console.warn("THREE.Light: .shadowCameraLeft is now .shadow.camera.left."),this.shadow.camera.left=t}},shadowCameraRight:{set:function(t){console.warn("THREE.Light: .shadowCameraRight is now .shadow.camera.right."),this.shadow.camera.right=t}},shadowCameraTop:{set:function(t){console.warn("THREE.Light: .shadowCameraTop is now .shadow.camera.top."),this.shadow.camera.top=t}},shadowCameraBottom:{set:function(t){console.warn("THREE.Light: .shadowCameraBottom is now .shadow.camera.bottom."),this.shadow.camera.bottom=t}},shadowCameraNear:{set:function(t){console.warn("THREE.Light: .shadowCameraNear is now .shadow.camera.near."),this.shadow.camera.near=t}},shadowCameraFar:{set:function(t){console.warn("THREE.Light: .shadowCameraFar is now .shadow.camera.far."),this.shadow.camera.far=t}},shadowCameraVisible:{set:function(){console.warn("THREE.Light: .shadowCameraVisible has been removed. Use new THREE.CameraHelper( light.shadow.camera ) instead.")}},shadowBias:{set:function(t){console.warn("THREE.Light: .shadowBias is now .shadow.bias."),this.shadow.bias=t}},shadowDarkness:{set:function(){console.warn("THREE.Light: .shadowDarkness has been removed.")}},shadowMapWidth:{set:function(t){console.warn("THREE.Light: .shadowMapWidth is now .shadow.mapSize.width."),this.shadow.mapSize.width=t}},shadowMapHeight:{set:function(t){console.warn("THREE.Light: .shadowMapHeight is now .shadow.mapSize.height."),this.shadow.mapSize.height=t}}}),Object.defineProperties(Qlt.prototype,{length:{get:function(){return console.warn("THREE.BufferAttribute: .length has been deprecated. Use .count instead."),this.array.length}},dynamic:{get:function(){return console.warn("THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead."),this.usage===ist},set:function(){console.warn("THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead."),this.setUsage(ist)}}}),Qlt.prototype.setDynamic=function(t){return console.warn("THREE.BufferAttribute: .setDynamic() has been deprecated. Use .setUsage() instead."),this.setUsage(!0===t?ist:ost),this},Qlt.prototype.copyIndicesArray=function(){console.error("THREE.BufferAttribute: .copyIndicesArray() has been removed.")},Qlt.prototype.setArray=function(){console.error("THREE.BufferAttribute: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers")},dct.prototype.addIndex=function(t){console.warn("THREE.BufferGeometry: .addIndex() has been renamed to .setIndex()."),this.setIndex(t)},dct.prototype.addAttribute=function(t,e){return console.warn("THREE.BufferGeometry: .addAttribute() has been renamed to .setAttribute()."),e&&e.isBufferAttribute||e&&e.isInterleavedBufferAttribute?"index"===t?(console.warn("THREE.BufferGeometry.addAttribute: Use .setIndex() for index attribute."),this.setIndex(e),this):this.setAttribute(t,e):(console.warn("THREE.BufferGeometry: .addAttribute() now expects ( name, attribute )."),this.setAttribute(t,new Qlt(arguments[1],arguments[2])))},dct.prototype.addDrawCall=function(t,e,n){void 0!==n&&console.warn("THREE.BufferGeometry: .addDrawCall() no longer supports indexOffset."),console.warn("THREE.BufferGeometry: .addDrawCall() is now .addGroup()."),this.addGroup(t,e)},dct.prototype.clearDrawCalls=function(){console.warn("THREE.BufferGeometry: .clearDrawCalls() is now .clearGroups()."),this.clearGroups()},dct.prototype.computeOffsets=function(){console.warn("THREE.BufferGeometry: .computeOffsets() has been removed.")},dct.prototype.removeAttribute=function(t){return console.warn("THREE.BufferGeometry: .removeAttribute() has been renamed to .deleteAttribute()."),this.deleteAttribute(t)},dct.prototype.applyMatrix=function(t){return console.warn("THREE.BufferGeometry: .applyMatrix() has been renamed to .applyMatrix4()."),this.applyMatrix4(t)},Object.defineProperties(dct.prototype,{drawcalls:{get:function(){return console.error("THREE.BufferGeometry: .drawcalls has been renamed to .groups."),this.groups}},offsets:{get:function(){return console.warn("THREE.BufferGeometry: .offsets has been renamed to .groups."),this.groups}}}),Lmt.prototype.setDynamic=function(t){return console.warn("THREE.InterleavedBuffer: .setDynamic() has been deprecated. Use .setUsage() instead."),this.setUsage(!0===t?ist:ost),this},Lmt.prototype.setArray=function(){console.error("THREE.InterleavedBuffer: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers")},xut.prototype.getArrays=function(){console.error("THREE.ExtrudeGeometry: .getArrays() has been removed.")},xut.prototype.addShapeList=function(){console.error("THREE.ExtrudeGeometry: .addShapeList() has been removed.")},xut.prototype.addShape=function(){console.error("THREE.ExtrudeGeometry: .addShape() has been removed.")},Fmt.prototype.dispose=function(){console.error("THREE.Scene: .dispose() has been removed.")},Object.defineProperties(Vlt.prototype,{wrapAround:{get:function(){console.warn("THREE.Material: .wrapAround has been removed.")},set:function(){console.warn("THREE.Material: .wrapAround has been removed.")}},overdraw:{get:function(){console.warn("THREE.Material: .overdraw has been removed.")},set:function(){console.warn("THREE.Material: .overdraw has been removed.")}},wrapRGB:{get:function(){return console.warn("THREE.Material: .wrapRGB has been removed."),new Zlt}},shading:{get:function(){console.error("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead.")},set:function(t){console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead."),this.flatShading=1===t}},stencilMask:{get:function(){return console.warn("THREE."+this.type+": .stencilMask has been removed. Use .stencilFuncMask instead."),this.stencilFuncMask},set:function(t){console.warn("THREE."+this.type+": .stencilMask has been removed. Use .stencilFuncMask instead."),this.stencilFuncMask=t}},vertexTangents:{get:function(){console.warn("THREE."+this.type+": .vertexTangents has been removed.")},set:function(){console.warn("THREE."+this.type+": .vertexTangents has been removed.")}}}),Object.defineProperties(Nct.prototype,{derivatives:{get:function(){return console.warn("THREE.ShaderMaterial: .derivatives has been moved to .extensions.derivatives."),this.extensions.derivatives},set:function(t){console.warn("THREE. ShaderMaterial: .derivatives has been moved to .extensions.derivatives."),this.extensions.derivatives=t}}}),Hmt.prototype.clearTarget=function(t,e,n,o){console.warn("THREE.WebGLRenderer: .clearTarget() has been deprecated. Use .setRenderTarget() and .clear() instead."),this.setRenderTarget(t),this.clear(e,n,o)},Hmt.prototype.animate=function(t){console.warn("THREE.WebGLRenderer: .animate() is now .setAnimationLoop()."),this.setAnimationLoop(t)},Hmt.prototype.getCurrentRenderTarget=function(){return console.warn("THREE.WebGLRenderer: .getCurrentRenderTarget() is now .getRenderTarget()."),this.getRenderTarget()},Hmt.prototype.getMaxAnisotropy=function(){return console.warn("THREE.WebGLRenderer: .getMaxAnisotropy() is now .capabilities.getMaxAnisotropy()."),this.capabilities.getMaxAnisotropy()},Hmt.prototype.getPrecision=function(){return console.warn("THREE.WebGLRenderer: .getPrecision() is now .capabilities.precision."),this.capabilities.precision},Hmt.prototype.resetGLState=function(){return console.warn("THREE.WebGLRenderer: .resetGLState() is now .state.reset()."),this.state.reset()},Hmt.prototype.supportsFloatTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsFloatTextures() is now .extensions.get( 'OES_texture_float' )."),this.extensions.get("OES_texture_float")},Hmt.prototype.supportsHalfFloatTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsHalfFloatTextures() is now .extensions.get( 'OES_texture_half_float' )."),this.extensions.get("OES_texture_half_float")},Hmt.prototype.supportsStandardDerivatives=function(){return console.warn("THREE.WebGLRenderer: .supportsStandardDerivatives() is now .extensions.get( 'OES_standard_derivatives' )."),this.extensions.get("OES_standard_derivatives")},Hmt.prototype.supportsCompressedTextureS3TC=function(){return console.warn("THREE.WebGLRenderer: .supportsCompressedTextureS3TC() is now .extensions.get( 'WEBGL_compressed_texture_s3tc' )."),this.extensions.get("WEBGL_compressed_texture_s3tc")},Hmt.prototype.supportsCompressedTexturePVRTC=function(){return console.warn("THREE.WebGLRenderer: .supportsCompressedTexturePVRTC() is now .extensions.get( 'WEBGL_compressed_texture_pvrtc' )."),this.extensions.get("WEBGL_compressed_texture_pvrtc")},Hmt.prototype.supportsBlendMinMax=function(){return console.warn("THREE.WebGLRenderer: .supportsBlendMinMax() is now .extensions.get( 'EXT_blend_minmax' )."),this.extensions.get("EXT_blend_minmax")},Hmt.prototype.supportsVertexTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsVertexTextures() is now .capabilities.vertexTextures."),this.capabilities.vertexTextures},Hmt.prototype.supportsInstancedArrays=function(){return console.warn("THREE.WebGLRenderer: .supportsInstancedArrays() is now .extensions.get( 'ANGLE_instanced_arrays' )."),this.extensions.get("ANGLE_instanced_arrays")},Hmt.prototype.enableScissorTest=function(t){console.warn("THREE.WebGLRenderer: .enableScissorTest() is now .setScissorTest()."),this.setScissorTest(t)},Hmt.prototype.initMaterial=function(){console.warn("THREE.WebGLRenderer: .initMaterial() has been removed.")},Hmt.prototype.addPrePlugin=function(){console.warn("THREE.WebGLRenderer: .addPrePlugin() has been removed.")},Hmt.prototype.addPostPlugin=function(){console.warn("THREE.WebGLRenderer: .addPostPlugin() has been removed.")},Hmt.prototype.updateShadowMap=function(){console.warn("THREE.WebGLRenderer: .updateShadowMap() has been removed.")},Hmt.prototype.setFaceCulling=function(){console.warn("THREE.WebGLRenderer: .setFaceCulling() has been removed.")},Hmt.prototype.allocTextureUnit=function(){console.warn("THREE.WebGLRenderer: .allocTextureUnit() has been removed.")},Hmt.prototype.setTexture=function(){console.warn("THREE.WebGLRenderer: .setTexture() has been removed.")},Hmt.prototype.setTexture2D=function(){console.warn("THREE.WebGLRenderer: .setTexture2D() has been removed.")},Hmt.prototype.setTextureCube=function(){console.warn("THREE.WebGLRenderer: .setTextureCube() has been removed.")},Hmt.prototype.getActiveMipMapLevel=function(){return console.warn("THREE.WebGLRenderer: .getActiveMipMapLevel() is now .getActiveMipmapLevel()."),this.getActiveMipmapLevel()},Object.defineProperties(Hmt.prototype,{shadowMapEnabled:{get:function(){return this.shadowMap.enabled},set:function(t){console.warn("THREE.WebGLRenderer: .shadowMapEnabled is now .shadowMap.enabled."),this.shadowMap.enabled=t}},shadowMapType:{get:function(){return this.shadowMap.type},set:function(t){console.warn("THREE.WebGLRenderer: .shadowMapType is now .shadowMap.type."),this.shadowMap.type=t}},shadowMapCullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")}},context:{get:function(){return console.warn("THREE.WebGLRenderer: .context has been removed. Use .getContext() instead."),this.getContext()}},vr:{get:function(){return console.warn("THREE.WebGLRenderer: .vr has been renamed to .xr"),this.xr}},gammaInput:{get:function(){return console.warn("THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead."),!1},set:function(){console.warn("THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead.")}},gammaOutput:{get:function(){return console.warn("THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead."),!1},set:function(t){console.warn("THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead."),this.outputEncoding=!0===t?$rt:Qrt}},toneMappingWhitePoint:{get:function(){return console.warn("THREE.WebGLRenderer: .toneMappingWhitePoint has been removed."),1},set:function(){console.warn("THREE.WebGLRenderer: .toneMappingWhitePoint has been removed.")}}}),Object.defineProperties(kmt.prototype,{cullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")}},renderReverseSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")}},renderSingleSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")}}}),Object.defineProperties(xst.prototype,{wrapS:{get:function(){return console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS."),this.texture.wrapS},set:function(t){console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS."),this.texture.wrapS=t}},wrapT:{get:function(){return console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT."),this.texture.wrapT},set:function(t){console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT."),this.texture.wrapT=t}},magFilter:{get:function(){return console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter."),this.texture.magFilter},set:function(t){console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter."),this.texture.magFilter=t}},minFilter:{get:function(){return console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter."),this.texture.minFilter},set:function(t){console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter."),this.texture.minFilter=t}},anisotropy:{get:function(){return console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy."),this.texture.anisotropy},set:function(t){console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy."),this.texture.anisotropy=t}},offset:{get:function(){return console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset."),this.texture.offset},set:function(t){console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset."),this.texture.offset=t}},repeat:{get:function(){return console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat."),this.texture.repeat},set:function(t){console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat."),this.texture.repeat=t}},format:{get:function(){return console.warn("THREE.WebGLRenderTarget: .format is now .texture.format."),this.texture.format},set:function(t){console.warn("THREE.WebGLRenderTarget: .format is now .texture.format."),this.texture.format=t}},type:{get:function(){return console.warn("THREE.WebGLRenderTarget: .type is now .texture.type."),this.texture.type},set:function(t){console.warn("THREE.WebGLRenderTarget: .type is now .texture.type."),this.texture.type=t}},generateMipmaps:{get:function(){return console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps."),this.texture.generateMipmaps},set:function(t){console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps."),this.texture.generateMipmaps=t}}}),class extends Slt{constructor(t){super(),this.type="Audio",this.listener=t,this.context=t.context,this.gain=this.context.createGain(),this.gain.connect(t.getInput()),this.autoplay=!1,this.buffer=null,this.detune=0,this.loop=!1,this.loopStart=0,this.loopEnd=0,this.offset=0,this.duration=void 0,this.playbackRate=1,this.isPlaying=!1,this.hasPlaybackControl=!0,this.source=null,this.sourceType="empty",this._startedAt=0,this._progress=0,this._connected=!1,this.filters=[]}getOutput(){return this.gain}setNodeSource(t){return this.hasPlaybackControl=!1,this.sourceType="audioNode",this.source=t,this.connect(),this}setMediaElementSource(t){return this.hasPlaybackControl=!1,this.sourceType="mediaNode",this.source=this.context.createMediaElementSource(t),this.connect(),this}setMediaStreamSource(t){return this.hasPlaybackControl=!1,this.sourceType="mediaStreamNode",this.source=this.context.createMediaStreamSource(t),this.connect(),this}setBuffer(t){return this.buffer=t,this.sourceType="buffer",this.autoplay&&this.play(),this}play(t=0){if(!0===this.isPlaying)return void console.warn("THREE.Audio: Audio is already playing.");if(!1===this.hasPlaybackControl)return void console.warn("THREE.Audio: this Audio has no playback control.");this._startedAt=this.context.currentTime+t;const e=this.context.createBufferSource();return e.buffer=this.buffer,e.loop=this.loop,e.loopStart=this.loopStart,e.loopEnd=this.loopEnd,e.onended=this.onEnded.bind(this),e.start(this._startedAt,this._progress+this.offset,this.duration),this.isPlaying=!0,this.source=e,this.setDetune(this.detune),this.setPlaybackRate(this.playbackRate),this.connect()}pause(){if(!1!==this.hasPlaybackControl)return!0===this.isPlaying&&(this._progress+=Math.max(this.context.currentTime-this._startedAt,0)*this.playbackRate,!0===this.loop&&(this._progress=this._progress%(this.duration||this.buffer.duration)),this.source.stop(),this.source.onended=null,this.isPlaying=!1),this;console.warn("THREE.Audio: this Audio has no playback control.")}stop(){if(!1!==this.hasPlaybackControl)return this._progress=0,this.source.stop(),this.source.onended=null,this.isPlaying=!1,this;console.warn("THREE.Audio: this Audio has no playback control.")}connect(){if(this.filters.length>0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].connect(this.filters[t]);this.filters[this.filters.length-1].connect(this.getOutput())}else this.source.connect(this.getOutput());return this._connected=!0,this}disconnect(){if(this.filters.length>0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].disconnect(this.filters[t]);this.filters[this.filters.length-1].disconnect(this.getOutput())}else this.source.disconnect(this.getOutput());return this._connected=!1,this}getFilters(){return this.filters}setFilters(t){return t||(t=[]),!0===this._connected?(this.disconnect(),this.filters=t.slice(),this.connect()):this.filters=t.slice(),this}setDetune(t){if(this.detune=t,void 0!==this.source.detune)return!0===this.isPlaying&&this.source.detune.setTargetAtTime(this.detune,this.context.currentTime,.01),this}getDetune(){return this.detune}getFilter(){return this.getFilters()[0]}setFilter(t){return this.setFilters(t?[t]:[])}setPlaybackRate(t){if(!1!==this.hasPlaybackControl)return this.playbackRate=t,!0===this.isPlaying&&this.source.playbackRate.setTargetAtTime(this.playbackRate,this.context.currentTime,.01),this;console.warn("THREE.Audio: this Audio has no playback control.")}getPlaybackRate(){return this.playbackRate}onEnded(){this.isPlaying=!1}getLoop(){return!1===this.hasPlaybackControl?(console.warn("THREE.Audio: this Audio has no playback control."),!1):this.loop}setLoop(t){if(!1!==this.hasPlaybackControl)return this.loop=t,!0===this.isPlaying&&(this.source.loop=this.loop),this;console.warn("THREE.Audio: this Audio has no playback control.")}setLoopStart(t){return this.loopStart=t,this}setLoopEnd(t){return this.loopEnd=t,this}getVolume(){return this.gain.gain.value}setVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}}.prototype.load=function(t){console.warn("THREE.Audio: .load has been deprecated. Use THREE.AudioLoader instead.");const e=this;return(new ugt).load(t,(function(t){e.setBuffer(t)})),this},Fct.prototype.updateCubeMap=function(t,e){return console.warn("THREE.CubeCamera: .updateCubeMap() is now .update()."),this.update(t,e)},Fct.prototype.clear=function(t,e,n,o){return console.warn("THREE.CubeCamera: .clear() is now .renderTarget.clear()."),this.renderTarget.clear(t,e,n,o)},yst.crossOrigin=void 0,yst.loadTexture=function(t,e,n,o){console.warn("THREE.ImageUtils.loadTexture has been deprecated. Use THREE.TextureLoader() instead.");const i=new Xut;i.setCrossOrigin(this.crossOrigin);const a=i.load(t,n,void 0,o);return e&&(a.mapping=e),a},yst.loadTextureCube=function(t,e,n,o){console.warn("THREE.ImageUtils.loadTextureCube has been deprecated. Use THREE.CubeTextureLoader() instead.");const i=new Zut;i.setCrossOrigin(this.crossOrigin);const a=i.load(t,n,void 0,o);return e&&(a.mapping=e),a},yst.loadCompressedTexture=function(){console.error("THREE.ImageUtils.loadCompressedTexture has been removed. Use THREE.DDSLoader instead.")},yst.loadCompressedTextureCube=function(){console.error("THREE.ImageUtils.loadCompressedTextureCube has been removed. Use THREE.DDSLoader instead.")},"undefined"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("register",{detail:{revision:"131"}})),"undefined"!=typeof window&&(window.__THREE__?console.warn("WARNING: Multiple instances of Three.js being imported."):window.__THREE__="131"),(function(t){t[t.CIRCLE=0]="CIRCLE",t[t.LINE=1]="LINE",t[t.TRIANGLE=2]="TRIANGLE",t[t.TRAPEZOID=3]="TRAPEZOID"})(Ngt||(Ngt={}));class Lgt{constructor(t,e,n,o){this.coordinator=e,this.scene=new Fmt,this.backgroundColor="#fff",Crt()&&t instanceof OffscreenCanvas&&(t.style=t.style||{}),o&&t.addEventListener("webglcontextlost",o),this.renderer=new Hmt({canvas:t,context:t.getContext("webgl2",{antialias:!0,precision:"highp",alpha:!0})}),this.renderer.setPixelRatio(n)}onResize(t){this.renderer.setSize(t.width,t.height)}destroyObject(t){const e=t.obj3d;if(this.scene.remove(e),e instanceof Sct){e.geometry.dispose();const t=Array.isArray(e.material)?e.material:[e.material];for(const e of t)e.dispose()}}setUseDarkMode(t){this.backgroundColor=t?"#303030":"#fff"}createOrUpdateLineObject(t,e,n){var o;if(!t&&!n.visible)return null;const{visible:i,width:a}=n;if(!t){const t=Tgt(this.backgroundColor,n.color,null!==(o=n.opacity)&&void 0!==o?o:1),r=new dct,s=new fft({color:t}),l=new Sct(r,s);return s.visible=i,Hgt(r,e,a),this.scene.add(l),{type:Ngt.LINE,data:e,obj3d:l,width:a}}const{data:r,obj3d:s,width:l}=t;return Fgt(this.backgroundColor,s,(t=>(a===l&&r&&Mrt(r,e)||Hgt(t,e,a),t)),n)?{type:Ngt.LINE,data:e,obj3d:s,width:a}:t}createMesh(t,e){if(!e.visible)return null;const{visible:n,color:o,opacity:i}=e,a=Tgt(this.backgroundColor,o,null!=i?i:1),r=new Xlt({color:a,visible:n});return new Sct(t,r)}createOrUpdateTriangleObject(t,e,n){const{size:o}=n,i=o*Math.sqrt(3)/2,a=new Float32Array([e.x-o/2,e.y-i/3,e.x+o/2,e.y-i/3,e.x,e.y+2*i/3]);if(!t){const t=new dct;Igt(t,a);const o=this.createMesh(t,n);return null===o?null:(this.scene.add(o),{type:Ngt.TRIANGLE,data:e,obj3d:o})}return Fgt(this.backgroundColor,t.obj3d,(t=>(Igt(t,a),t)),n)?{type:Ngt.TRIANGLE,data:e,obj3d:t.obj3d}:t}createOrUpdateCircleObject(t,e,n){const{radius:o}=n,i=new Dft(n.radius);if(!t){const t=this.createMesh(i,n);return null===t?null:(t.position.set(e.x,e.y,0),this.scene.add(t),{type:Ngt.CIRCLE,data:{loc:e,radius:o},obj3d:t})}return Fgt(this.backgroundColor,t.obj3d,(()=>i),n)?(t.obj3d.position.set(e.x,e.y,0),{type:Ngt.CIRCLE,data:{loc:e,radius:o},obj3d:t.obj3d}):t}createOrUpdateTrapezoidObject(t,e,n,o){if(e.y!==n.y)throw new RangeError("Input error: start.y != end.y.");const{altitude:i}=o,a=2/Math.sqrt(3)*i,r=new Qut([new gst(e.x-a/2,e.y-i/2),new gst(e.x,e.y+i/2),new gst(n.x,n.y+i/2),new gst(n.x+a/2,n.y-i/2)]);r.autoClose=!0;const s=new Put(r);if(!t){const t=this.createMesh(s,o);return null===t?null:(this.scene.add(t),{type:Ngt.TRAPEZOID,data:[e,n],obj3d:t})}return Fgt(this.backgroundColor,t.obj3d,(()=>s),o)?{type:Ngt.TRAPEZOID,data:[e,n],obj3d:t.obj3d}:t}flush(){this.renderer.render(this.scene,this.coordinator.getCamera())}dispose(){this.renderer.dispose()}}class Bgt{constructor(t,e){this.renderCache=t,this.renderer=e}setLine(t,e,n){const o=this.renderer.createOrUpdateLineObject(this.renderCache.getFromPreviousFrame(t),e,n);o&&this.renderCache.setToCurrentFrame(t,o)}setTriangle(t,e,n){const o=this.renderer.createOrUpdateTriangleObject(this.renderCache.getFromPreviousFrame(t),e,n);o&&this.renderCache.setToCurrentFrame(t,o)}setCircle(t,e,n){const o=this.renderer.createOrUpdateCircleObject(this.renderCache.getFromPreviousFrame(t),e,n);o&&this.renderCache.setToCurrentFrame(t,o)}setTrapezoid(t,e,n,o){const i=this.renderer.createOrUpdateTrapezoidObject(this.renderCache.getFromPreviousFrame(t),e,n,o);i&&this.renderCache.setToCurrentFrame(t,i)}}class Vgt{constructor(){this.prevFrameCache=new Map,this.currFrameCache=new Map}getFromPreviousFrame(t){const e=this.prevFrameCache.get(t);return null!=e?e:null}setToCurrentFrame(t,e){this.currFrameCache.set(t,e)}finalizeFrameAndGetRemoved(){const t=[];for(const[e,n]of this.prevFrameCache.entries())this.currFrameCache.has(e)||t.push(n);return this.prevFrameCache=this.currFrameCache,this.currFrameCache=new Map,t}}!(function(t){t[t.NUMBER=0]="NUMBER",t[t.NAN=1]="NAN"})(zgt||(zgt={}));class jgt extends class{constructor(t){this.rawSeriesData=[],this.series=[],this.paintDirty=!0,this.renderCache=new Vgt,this.coordinateIdentifier=null,this.layout={x:0,width:1,y:0,height:1},this.getMetadataMapImpl=t.getMetadataMap,this.coordinator=t.coordinator,this.renderer=t.renderer,this.paintBrush=new Bgt(this.renderCache,this.renderer)}setLayoutRect(t){this.layout.x===t.x&&this.layout.width===t.width&&this.layout.y===t.y&&this.layout.height===t.height||(this.paintDirty=!0),this.layout=t}getLayoutRect(){return this.layout}getMetadataMap(){return this.getMetadataMapImpl()}markAsPaintDirty(){this.paintDirty=!0}render(){if(this.transformCoordinatesIfStale(),this.paintDirty){this.redraw();for(const t of this.renderCache.finalizeFrameAndGetRemoved())this.renderer.destroyObject(t);this.paintDirty=!1}}isCoordinateUpdated(){return this.coordinator.getUpdateIdentifier()!==this.coordinateIdentifier}clearCoordinateIdentifier(){this.coordinateIdentifier=null}setData(t){this.clearCoordinateIdentifier(),this.rawSeriesData=t}transformCoordinatesIfStale(){if(!this.isCoordinateUpdated())return;const t=this.getLayoutRect();this.series=new Array(this.rawSeriesData.length);for(let e=0;e<this.rawSeriesData.length;e++){const n=this.rawSeriesData[e];this.series[e]={id:n.id,polyline:new Float32Array(2*n.points.length)};for(let o=0;o<n.points.length;o++){const[i,a]=this.coordinator.transformDataToUiCoord(t,[n.points[o].x,n.points[o].y]);this.series[e].polyline[2*o]=i,this.series[e].polyline[2*o+1]=a}}this.coordinateIdentifier=this.coordinator.getUpdateIdentifier(),this.markAsPaintDirty()}}{recordPartition(t,e,n){return t?{type:zgt.NUMBER,polyline:e}:{type:zgt.NAN,polyline:e.map(((t,e)=>isNaN(t)?e%2==0?n.x:n.y:t))}}partitionPolyline(t){const e=[];let n=0,o=!1;const i=this.coordinator.transformDataToUiCoord(this.getLayoutRect(),[0,0]),a={x:i[0],y:i[1]};let r=null;for(let i=0;i<t.length;i+=2){const a=t[i],s=t[i+1],l=isNaN(a)||isNaN(s);l!==o&&n!==i&&(e.push(this.recordPartition(!o,t.slice(n,i),null===r?{x:a,y:s}:r)),n=i),l||(r={x:a,y:s}),o=l}return n!==t.length-1&&e.push(this.recordPartition(!o,t.slice(n,t.length),null!=r?r:a)),e}redraw(){var t,e,n;for(const o of this.series){const i=this.getMetadataMap()[o.id];if(!i)continue;if(o.polyline.length%2!=0)throw new Error(`Cannot have odd length-ed polyline: ${o.polyline.length}`);const a=this.partitionPolyline(o.polyline);for(const[r,{type:s,polyline:l}]of a.entries())if(s===zgt.NUMBER)2===l.length?this.paintBrush.setCircle(JSON.stringify(["circle",o.id,r]),{x:l[0],y:l[1]},{color:i.color,visible:i.visible,opacity:null!==(t=i.opacity)&&void 0!==t?t:1,radius:4}):this.paintBrush.setLine(JSON.stringify(["line",o.id,r]),l,{color:i.color,visible:i.visible,opacity:null!==(e=i.opacity)&&void 0!==e?e:1,width:2});else if(!i.aux)for(let t=0;t<l.length;t+=2)this.paintBrush.setTriangle(JSON.stringify(["NaN",o.id,l[t],l[t+1]]),{x:l[t],y:l[t+1]},{color:i.color,visible:i.visible,opacity:null!==(n=i.opacity)&&void 0!==n?n:1,size:12})}}}class Ugt extends vrt{constructor(){super(...arguments),this.camera=new rdt(0,1e3,1e3,0,0,100)}isYAxisPointedDown(){return!1}setDomContainerRect(t){super.setDomContainerRect(t),this.camera.left=t.x,this.camera.right=t.x+t.width,this.camera.top=t.y+t.height,this.camera.bottom=t.y,this.camera.updateProjectionMatrix()}getCamera(){return this.camera}}const Wgt=t=>self.requestAnimationFrame(t);class Ggt{constructor(t){switch(this.metadataMap={},this.shouldRepaint=!1,this.callbacks=t.callbacks,t.type){case e6.SVG:this.coordinator=new vrt,this.renderer=new Ort(t.container);break;case e6.WEBGL:{const e=new Ugt;this.coordinator=e,this.renderer=new Lgt(t.container,e,t.devicePixelRatio,t.callbacks.onContextLost);break}}this.renderer.setUseDarkMode(t.useDarkMode),this.seriesLineView=new jgt({renderer:this.renderer,coordinator:this.coordinator,getMetadataMap:()=>this.metadataMap}),this.resize(t.domDimension)}dispose(){}setXScaleType(t){this.coordinator.setXScale(grt(t)),this.scheduleRepaint()}setYScaleType(t){this.coordinator.setYScale(grt(t)),this.scheduleRepaint()}resize(t){this.coordinator.setDomContainerRect(Object.assign({x:0,y:0},t)),this.renderer.onResize(Object.assign({x:0,y:0},t)),this.seriesLineView.setLayoutRect(Object.assign(Object.assign({},t),{x:0,y:0})),this.scheduleRepaint()}setMetadata(t){let e=!1;Object.entries(t).forEach((([t,n])=>{const o=this.metadataMap[t];o&&n.color===o.color&&n.visible===o.visible&&n.opacity===o.opacity||(e=!0),this.metadataMap[t]=n})),e&&this.seriesLineView.markAsPaintDirty(),this.scheduleRepaint()}setViewBox(t){this.coordinator.setViewBoxRect({x:t.x[0],width:t.x[1]-t.x[0],y:t.y[0],height:t.y[1]-t.y[0]}),this.scheduleRepaint()}setData(t){this.seriesLineView.setData(t),this.scheduleRepaint()}setUseDarkMode(t){this.renderer.setUseDarkMode(t),this.seriesLineView.markAsPaintDirty(),this.scheduleRepaint()}scheduleRepaint(){this.shouldRepaint||(this.shouldRepaint=!0,Wgt((()=>{this.repaint(),this.shouldRepaint=!1})))}repaint(){this.seriesLineView.render(),this.renderer.flush(),this.callbacks.onDrawEnd()}}var Ygt,qgt;function Zgt(t){if(t.includes("/"))throw new RangeError("Worker factory only allows file name and no resource path.");return new Worker(t)}!(function(t){t[t.SERIES_DATA_UPDATED=0]="SERIES_DATA_UPDATED",t[t.SERIES_METADATA_CHANGED=1]="SERIES_METADATA_CHANGED",t[t.SCALE_UPDATED=2]="SCALE_UPDATED",t[t.VIEW_BOX_UPDATED=3]="VIEW_BOX_UPDATED",t[t.INIT=4]="INIT",t[t.DOM_RESIZED=5]="DOM_RESIZED",t[t.DARK_MODE_UPDATED=6]="DARK_MODE_UPDATED",t[t.DISPOSED=7]="DISPOSED"})(Ygt||(Ygt={})),(function(t){t[t.ON_REDRAW_END=0]="ON_REDRAW_END",t[t.ON_CONTEXT_LOST=1]="ON_CONTEXT_LOST"})(qgt||(qgt={}));class Xgt{constructor(t){if(this.callbacks=t.callbacks,t.type!==e6.WEBGL)throw new RangeError(`Cannot use non WEBGL renderer for the offscreen line chart. Received ${e6[t.type]} `);const e=new MessageChannel;e.port1.onmessage=t=>{this.onMessageFromWorker(t.data)},this.txMessagePort=e.port1;const n=t.container.transferControlToOffscreen();this.workerInstance=Xgt.workerPool.getNext();const o={type:Ygt.INIT,canvas:n,devicePixelRatio:window.devicePixelRatio,dim:t.domDimension,rendererType:t.type,useDarkMode:t.useDarkMode};this.workerInstance.postMessage(o,[n,e.port2])}dispose(){this.sendMessage({type:Ygt.DISPOSED}),this.workerInstance.free(),this.txMessagePort.close()}setXScaleType(t){this.sendMessage({type:Ygt.SCALE_UPDATED,axis:"x",scaleType:t})}setYScaleType(t){this.sendMessage({type:Ygt.SCALE_UPDATED,axis:"y",scaleType:t})}resize(t){this.sendMessage({type:Ygt.DOM_RESIZED,dim:t})}setMetadata(t){this.sendMessage({type:Ygt.SERIES_METADATA_CHANGED,metadata:t})}setViewBox(t){this.sendMessage({type:Ygt.VIEW_BOX_UPDATED,extent:t})}setData(t){const e=(function n(t){const e=t.reduce(((t,e)=>t+e.points.length),0);let n=0;const o=new Float64Array(2*e),i=[];for(const e of t){i.push({id:e.id,length:e.points.length});for(let t=0;t<e.points.length;t++)o[n++]=e.points[t].x,o[n++]=e.points[t].y}return{idsAndLengths:i,flattenedSeries:o.buffer}})(t);this.sendMessage({type:Ygt.SERIES_DATA_UPDATED,compactDataSeries:e},[e.flattenedSeries])}setUseDarkMode(t){this.sendMessage({type:Ygt.DARK_MODE_UPDATED,useDarkMode:t})}sendMessage(t,e){e?this.txMessagePort.postMessage(t,e):this.txMessagePort.postMessage(t)}onMessageFromWorker(t){switch(t.type){case qgt.ON_REDRAW_END:this.callbacks.onDrawEnd();break;case qgt.ON_CONTEXT_LOST:this.callbacks.onContextLost()}}}function Kgt(t){switch(t){case e6.SVG:return e6.SVG;case e6.WEBGL:return(function e(){return _rt})()?e6.WEBGL:e6.SVG;default:throw new Error(`Unknown rendererType: ${t}`)}}function Jgt(t,e){return"x"===e?[0,t.width]:[t.height,0]}function Qgt(t,e){const n=Math.floor(t/50);return Math.min(n,e)}function $gt(t,e){if(1&t&&(oa(),vf(0,"line",2)),2&t){const t=e.$implicit,n=zf();cu("zero",0===t),ym("x1",n.getDomX(t))("x2",n.getDomX(t))("y2",n.domDim.height)}}function tht(t,e){if(1&t&&(oa(),vf(0,"line",3)),2&t){const t=e.$implicit,n=zf();cu("zero",0===t),ym("y1",n.getDomY(t))("x2",n.domDim.width)("y2",n.getDomY(t))}}Xgt.workerPool=new class{constructor(t,e=10,n=Zgt){this.workerResourcePath=t,this.maxPoolSize=e,this.workerFactory=n,this.workers=[]}getNext(){let t;if(this.workers.every((({activeCount:t})=>t>0))&&this.workers.length<this.maxPoolSize){const e=this.workerFactory(this.workerResourcePath);t={activeCount:0,postMessage:(t,n)=>{e.postMessage(t,n)},free:()=>{t.activeCount=Math.max(t.activeCount-1,0)}},this.workers.push(t)}else{const e=this.workers.map((({activeCount:t})=>t)),n=e.indexOf(Math.min(...e));t=this.workers[n]}return t.activeCount++,t}}("chart_worker.js?_file_hash=5a17be0a");class eht{getDomX(t){return this.xScale.forward(this.viewExtent.x,Jgt(this.domDim,"x"),t)}getDomY(t){return this.yScale.forward(this.viewExtent.y,Jgt(this.domDim,"y"),t)}getXTicks(){return this.xScale.ticks(this.viewExtent.x,Qgt(this.domDim.width,this.xGridCount))}getYTicks(){return this.yScale.ticks(this.viewExtent.y,Qgt(this.domDim.height,this.yGridCount))}}function nht(t,e,n,o,i,a){let r;switch(t.deltaMode){case WheelEvent.DOM_DELTA_PIXEL:r=1;break;case WheelEvent.DOM_DELTA_LINE:r=8;break;case WheelEvent.DOM_DELTA_PAGE:r=20;break;default:r=1,console.warn(`Unknown WheelEvent deltaMode: ${t.deltaMode}.`)}const s=t.deltaY*r,l=s<0?Math.max(s*o,-.95):s*o,{width:c,height:d}=n,p=[i.reverse(e.x,[0,c],-t.offsetX*l),i.reverse(e.x,[0,c],c+(c-t.offsetX)*l)],m=[a.reverse(e.y,[d,0],-t.offsetY*l),a.reverse(e.y,[d,0],d+(d-t.offsetY)*l)];return{x:p[1]<p[0]?[p[1],p[0]]:p,y:m[1]<m[0]?[m[1],m[0]]:m}}eht.ɵfac=function t(e){return new(e||eht)},eht.ɵcmp=so({type:eht,selectors:[["line-chart-grid-view"]],inputs:{viewExtent:"viewExtent",xScale:"xScale",xGridCount:"xGridCount",yScale:"yScale",yGridCount:"yGridCount",domDim:"domDim"},decls:3,vars:2,consts:[["y1","0",3,"zero",4,"ngFor","ngForOf"],["x1","0",3,"zero",4,"ngFor","ngForOf"],["y1","0"],["x1","0"]],template:function t(e,n){1&e&&(oa(),Cf(0,"svg"),Hm(1,$gt,1,5,"line",0),Hm(2,tht,1,5,"line",1),Mf()),2&e&&(Dc(1),bf("ngForOf",n.getXTicks()),Dc(1),bf("ngForOf",n.getYTicks()))},directives:[xv],styles:["[_nghost-%COMP%] {\n        display: flex;\n        overflow: hidden;\n      }\n\n      svg[_ngcontent-%COMP%] {\n        height: 100%;\n        width: 100%;\n      }\n\n      line[_ngcontent-%COMP%] {\n        stroke: #ccc;\n        stroke-width: 1px;\n      }\n\n      .zero[_ngcontent-%COMP%] {\n        stroke: #aaa;\n        stroke-width: 1.5px;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(eht,[{type:S_,args:[{selector:"line-chart-grid-view",template:'<svg>\n    <line\n      *ngFor="let tick of getXTicks()"\n      [class.zero]="tick === 0"\n      [attr.x1]="getDomX(tick)"\n      y1="0"\n      [attr.x2]="getDomX(tick)"\n      [attr.y2]="domDim.height"\n    ></line>\n    <line\n      *ngFor="let tick of getYTicks()"\n      [class.zero]="tick === 0"\n      x1="0"\n      [attr.y1]="getDomY(tick)"\n      [attr.x2]="domDim.width"\n      [attr.y2]="getDomY(tick)"\n    ></line>\n  </svg>',styles:["\n      :host {\n        display: flex;\n        overflow: hidden;\n      }\n\n      svg {\n        height: 100%;\n        width: 100%;\n      }\n\n      line {\n        stroke: #ccc;\n        stroke-width: 1px;\n      }\n\n      .zero {\n        stroke: #aaa;\n        stroke-width: 1.5px;\n      }\n    "],changeDetection:jn.OnPush}]}],null,{viewExtent:[{type:R_}],xScale:[{type:R_}],xGridCount:[{type:R_}],yScale:[{type:R_}],yGridCount:[{type:R_}],domDim:[{type:R_}]});const oht=["dots"];function iht(t,e){if(1&t&&(oa(),vf(0,"circle",12)),2&t){const t=zf().$implicit,e=zf(2);ym("cx",e.getDomX(t.point.x))("cy",e.getDomY(t.point.y))("fill",t.metadata.color)}}function aht(t,e){if(1&t&&(oa(),xf(0),Hm(1,iht,1,3,"circle",11),Of()),2&t){const t=e.$implicit,n=zf(2);Dc(1),bf("ngIf",n.shouldRenderTooltipPoint(t.point))}}function rht(t,e){if(1&t&&(oa(),xf(0),Hm(1,aht,2,1,"ng-container",10),Of()),2&t){const t=zf();Dc(1),bf("ngForOf",t.cursoredData)("ngForTrackBy",t.trackBySeriesName)}}function sht(t,e){if(1&t&&(oa(),vf(0,"rect",13)),2&t){const t=zf();ym("x",t.zoomBoxInUiCoordinate.x)("width",t.zoomBoxInUiCoordinate.width)("y",t.zoomBoxInUiCoordinate.y)("height",t.zoomBoxInUiCoordinate.height)}}const lht=function(t,e){return{data:t,cursorLocationInDataCoord:e}};function cht(t,e){if(1&t&&(Cf(0,"div",14),Pf(1,15),Mf()),2&t){const t=zf(),e=Fm(11);Dc(1),bf("ngTemplateOutlet",t.tooltipTemplate?t.tooltipTemplate:e)("ngTemplateOutletContext",ay(2,lht,t.cursoredData,t.cursorLocationInDataCoord))}}function dht(t,e){if(1&t&&(xf(0),Cf(1,"tr",17),Cf(2,"td",18),vf(3,"span"),Mf(),Cf(4,"td",19),Pu(5),Mf(),Cf(6,"td"),Pu(7),Mf(),Cf(8,"td"),Pu(9),Mf(),Mf(),Of()),2&t){const t=e.$implicit;Dc(3),lu("background-color",t.metadata.color),Dc(2),wu(t.metadata.displayName),Dc(2),wu(t.point.y),Dc(2),wu(t.point.x)}}function pht(t,e){if(1&t&&(Cf(0,"table"),Cf(1,"thead"),Cf(2,"tr"),vf(3,"th",16),Cf(4,"th"),Pu(5,"Name"),Mf(),Cf(6,"th"),Pu(7,"Y"),Mf(),Cf(8,"th"),Pu(9,"X"),Mf(),Mf(),Mf(),Cf(10,"tbody"),Hm(11,dht,10,5,"ng-container",10),Mf(),Mf()),2&t){const t=e.data,n=zf();Dc(11),bf("ngForOf",t)("ngForTrackBy",n.trackBySeriesName)}}var mht;!(function(t){t[t.NONE=0]="NONE",t[t.DRAG_ZOOMING=1]="DRAG_ZOOMING",t[t.SCROLL_ZOOMING=2]="SCROLL_ZOOMING",t[t.PANNING=3]="PANNING"})(mht||(mht={}));function fht(t){return t.scrollStrategies.reposition()}class uht{constructor(t,e){this.changeDetector=t,this.scrollStrategy=e,this.onViewExtentChange=new Sy,this.onViewExtentReset=new Sy,this.InteractionState=mht,this.state=mht.NONE,this.specialKeyPressed=!1,this.zoomBoxInUiCoordinate={x:0,width:0,height:0,y:0},this.tooltipPositions=[{offsetY:5,originX:"start",overlayX:"start",originY:"bottom",overlayY:"top"},{offsetY:5,originX:"end",overlayX:"end",originY:"bottom",overlayY:"top"},{offsetY:-15,originX:"start",overlayX:"start",originY:"top",overlayY:"bottom"},{offsetY:-15,originX:"end",overlayX:"end",originY:"top",overlayY:"bottom"},{offsetX:5,originX:"end",overlayX:"start",originY:"top",overlayY:"top"},{offsetX:-5,originX:"start",overlayX:"end",originY:"top",overlayY:"top"}],this.cursorLocationInDataCoord=null,this.cursoredData=[],this.tooltipDisplayAttached=!1,this.showZoomInstruction=!1,this.dragStartCoord=null,this.isCursorInside=!1,this.ngUnsubscribe=new B}ngAfterViewInit(){le(this.dotsContainer.nativeElement,"dblclick",{passive:!0}).pipe(je(this.ngUnsubscribe)).subscribe((()=>{this.onViewExtentReset.emit(),this.state=mht.NONE,this.changeDetector.markForCheck()})),le(window,"keydown",{passive:!0}).pipe(je(this.ngUnsubscribe)).subscribe((t=>{const e=this.shouldPan(t);e!==this.specialKeyPressed&&(this.specialKeyPressed=e,this.changeDetector.markForCheck())})),le(window,"keyup",{passive:!0}).pipe(je(this.ngUnsubscribe)).subscribe((t=>{const e=this.shouldPan(t);e!==this.specialKeyPressed&&(this.specialKeyPressed=e,this.changeDetector.markForCheck())})),le(this.dotsContainer.nativeElement,"mousedown",{passive:!0}).pipe(je(this.ngUnsubscribe)).subscribe((t=>{const e=this.state,n=this.shouldPan(t)?mht.PANNING:mht.DRAG_ZOOMING;e===mht.NONE&&n===mht.DRAG_ZOOMING&&(this.dragStartCoord={x:t.offsetX,y:t.offsetY},this.zoomBoxInUiCoordinate={x:t.offsetX,width:0,y:t.offsetY,height:0}),e!==n&&(this.state=n,this.changeDetector.markForCheck())})),le(this.dotsContainer.nativeElement,"mouseup",{passive:!0}).pipe(je(this.ngUnsubscribe)).subscribe((t=>{const e=(t.buttons&h0.LEFT)===h0.LEFT;this.dragStartCoord=null;const n=this.zoomBoxInUiCoordinate;if(!e&&this.state===mht.DRAG_ZOOMING&&n.width>0&&n.height>0){const t=this.getDataX(n.x),e=this.getDataX(n.x+n.width),o=this.getDataY(n.y+n.height),i=this.getDataY(n.y);this.onViewExtentChange.emit({dataExtent:{x:[t,e],y:[o,i]}})}this.state!==mht.NONE&&(this.state=mht.NONE,this.changeDetector.markForCheck())})),le(this.dotsContainer.nativeElement,"mouseenter",{passive:!0}).pipe(je(this.ngUnsubscribe)).subscribe((t=>{this.isCursorInside=!0,this.updateTooltip(t),this.changeDetector.markForCheck()})),le(this.dotsContainer.nativeElement,"mouseleave",{passive:!0}).pipe(je(this.ngUnsubscribe)).subscribe((t=>{this.dragStartCoord=null,this.isCursorInside=!1,this.updateTooltip(t),this.state=mht.NONE,this.changeDetector.markForCheck()})),le(this.dotsContainer.nativeElement,"mousemove",{passive:!0}).pipe(je(this.ngUnsubscribe)).subscribe((t=>{switch(this.state){case mht.SCROLL_ZOOMING:this.state=mht.NONE,this.updateTooltip(t),this.changeDetector.markForCheck();break;case mht.NONE:this.updateTooltip(t),this.changeDetector.markForCheck();break;case mht.PANNING:{const e=-t.movementX,n=-t.movementY,{width:o,height:i}=this.domDim,a=this.getDataX(e),r=this.getDataX(o+e),s=this.getDataY(i+n),l=this.getDataY(n);this.onViewExtentChange.emit({dataExtent:{x:[a,r],y:[s,l]}});break}case mht.DRAG_ZOOMING:{if(!this.dragStartCoord)break;const e=[this.dragStartCoord.x,t.offsetX],n=[this.dragStartCoord.y,t.offsetY];this.zoomBoxInUiCoordinate={x:Math.min(...e),width:Math.max(...e)-Math.min(...e),y:Math.min(...n),height:Math.max(...n)-Math.min(...n)}}this.changeDetector.markForCheck()}})),le(this.dotsContainer.nativeElement,"wheel",{passive:!1}).pipe(je(this.ngUnsubscribe),Ve((t=>{const e=!t.ctrlKey&&!t.shiftKey&&t.altKey;return this.showZoomInstruction=!e,this.changeDetector.markForCheck(),e?(t.preventDefault(),zt(t)):de(3e3).pipe(We((()=>{this.showZoomInstruction=!1,this.changeDetector.markForCheck()})),Vt((()=>null)))})),ue((t=>Boolean(t)))).subscribe((t=>{this.onViewExtentChange.emit({dataExtent:nht(t,this.viewExtent,this.domDim,.01,this.xScale,this.yScale)}),this.state!==mht.SCROLL_ZOOMING&&(this.state=mht.SCROLL_ZOOMING,this.changeDetector.markForCheck())}))}ngOnChanges(){this.updateCursoredDataAndTooltipVisibility()}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}shouldPan(t){const e=t.shiftKey||t.altKey;if(t instanceof KeyboardEvent)return e;const n=(t.buttons&h0.LEFT)===h0.LEFT,o=(t.buttons&h0.MIDDLE)===h0.MIDDLE;return!(!n&&!o)&&(o&&!n||e)}trackBySeriesName(t,e){return e.id}getDomX(t){return this.xScale.forward(this.viewExtent.x,Jgt(this.domDim,"x"),t)}getDataX(t){return this.xScale.reverse(this.viewExtent.x,Jgt(this.domDim,"x"),t)}getDomY(t){return this.yScale.forward(this.viewExtent.y,Jgt(this.domDim,"y"),t)}getDataY(t){return this.yScale.reverse(this.viewExtent.y,Jgt(this.domDim,"y"),t)}shouldRenderTooltipPoint(t){return null!==t&&!isNaN(t.x)&&!isNaN(t.y)}updateTooltip(t){this.cursorLocationInDataCoord={x:this.getDataX(t.offsetX),y:this.getDataY(t.offsetY)},this.updateCursoredDataAndTooltipVisibility()}onTooltipDisplayDetached(){this.tooltipDisplayAttached=!1}updateCursoredDataAndTooltipVisibility(){const t=this.cursorLocationInDataCoord;if(null===t)return this.cursoredData=[],void(this.tooltipDisplayAttached=!1);this.cursoredData=this.isCursorInside?this.seriesData.map((t=>({seriesDatum:t,metadata:this.seriesMetadataMap[t.id]}))).filter((({metadata:t})=>t&&t.visible&&!Boolean(t.aux))).map((({seriesDatum:e,metadata:n})=>{const o=(function i(t,e){const n=Math.min(h6(t.map((({x:t})=>t)),e),t.length-1),o=Math.max(0,n-1);return Math.abs(t[o].x-e)-Math.abs(t[n].x-e)<=0?o:n})(e.points,t.x);return{id:e.id,closestPointIndex:o,point:e.points[o],metadata:n}})).filter((t=>t)):[],this.tooltipDisplayAttached=Boolean(this.cursoredData.length)}}uht.ɵfac=function t(e){return new(e||uht)(gf(Ob),gf(OB))},uht.ɵcmp=so({type:uht,selectors:[["line-chart-interactive-view"]],viewQuery:function t(e,n){if(1&e&&(Vy(oht,7,Kh),Vy(tV,5)),2&e){let t;By(t=Uy())&&(n.dotsContainer=t.first),By(t=Uy())&&(n.overlay=t.first)}},hostVars:2,hostBindings:function t(e,n){2&e&&cu("show-zoom-instruction",n.showZoomInstruction)},inputs:{seriesData:"seriesData",seriesMetadataMap:"seriesMetadataMap",viewExtent:"viewExtent",xScale:"xScale",yScale:"yScale",domDim:"domDim",tooltipOriginEl:"tooltipOriginEl",tooltipTemplate:"tooltipTemplate"},outputs:{onViewExtentChange:"onViewExtentChange",onViewExtentReset:"onViewExtentReset"},features:[jh([{provide:OB,useFactory:fht,deps:[KB]}]),qo],decls:12,vars:15,consts:[[1,"dots"],["dots",""],[4,"ngIf"],["class","zoom-box",4,"ngIf"],[1,"zoom-instruction"],[1,"instruction-content"],["cdkOverlayOrigin","",1,"tooltip-origin"],["tooltipOrigin","cdkOverlayOrigin"],["cdkConnectedOverlay","",3,"cdkConnectedOverlayOrigin","cdkConnectedOverlayOpen","cdkConnectedOverlayPositions","cdkConnectedOverlayScrollStrategy","cdkConnectedOverlayLockPosition","cdkConnectedOverlayFlexibleDimensions","cdkConnectedOverlayGrowAfterOpen","detach"],["defaultTooltip",""],[4,"ngFor","ngForOf","ngForTrackBy"],["r","4",4,"ngIf"],["r","4"],[1,"zoom-box"],[1,"tooltip-container"],[3,"ngTemplateOutlet","ngTemplateOutletContext"],[1,"circle-header"],[1,"tooltip-row"],[1,"tooltip-row-circle"],[1,"name"]],template:function t(e,n){1&e&&(oa(),Cf(0,"svg",0,1),Hm(2,rht,2,2,"ng-container",2),Hm(3,sht,1,4,"rect",3),Mf(),aa(),Cf(4,"div",4),Cf(5,"span",5),Pu(6,"Alt + Scroll to Zoom"),Mf(),Mf(),vf(7,"div",6,7),Hm(9,cht,2,5,"ng-template",8),Rf("detach",(function t(){return n.onTooltipDisplayDetached()})),Hm(10,pht,12,2,"ng-template",null,9,qy)),2&e&&(cu("pannable",n.specialKeyPressed)("draggable",n.state===n.InteractionState.NONE||n.state===n.InteractionState.DRAG_ZOOMING)("panning",n.state===n.InteractionState.PANNING),Dc(2),bf("ngIf",n.state===n.InteractionState.NONE),Dc(1),bf("ngIf",n.state===n.InteractionState.DRAG_ZOOMING),Dc(6),bf("cdkConnectedOverlayOrigin",n.tooltipOriginEl)("cdkConnectedOverlayOpen",n.tooltipDisplayAttached&&n.state===n.InteractionState.NONE)("cdkConnectedOverlayPositions",n.tooltipPositions)("cdkConnectedOverlayScrollStrategy",n.scrollStrategy)("cdkConnectedOverlayLockPosition",!1)("cdkConnectedOverlayFlexibleDimensions",!0)("cdkConnectedOverlayGrowAfterOpen",!0))},directives:[Pv,$B,tV,xv,Iv],styles:["[_nghost-%COMP%]{display:flex;position:relative;user-select:none}.dots[_ngcontent-%COMP%]{height:100%;width:100%}.dots.draggable[_ngcontent-%COMP%]{cursor:crosshair}.dots.pannable[_ngcontent-%COMP%]{cursor:grab}.dots.panning[_ngcontent-%COMP%]{cursor:grabbing}.tooltip-row-circle[_ngcontent-%COMP%]{align-items:center;display:inline-flex;height:12px;width:12px}.tooltip-row-circle[_ngcontent-%COMP%] > span[_ngcontent-%COMP%]{border-radius:50%;border:1px solid rgba(255,255,255,.6);display:inline-block;height:10px;width:10px}.tooltip-origin[_ngcontent-%COMP%]{bottom:0;left:0;position:absolute;right:0}.tooltip-container[_ngcontent-%COMP%]{background:rgba(0,0,0,.85);border-radius:4px;color:#fff;contain:paint style layout;font-size:.9em;overflow:auto;padding:5px;pointer-events:none;width:100%}th[_ngcontent-%COMP%], td[_ngcontent-%COMP%]{padding:2px 5px;text-align:left}th[_ngcontent-%COMP%]{font-weight:500;padding-bottom:5px}.zoom-box[_ngcontent-%COMP%]{fill-opacity:.03;fill:#000;stroke:#ccc}.zoom-instruction[_ngcontent-%COMP%]{align-items:center;display:flex;justify-content:center;left:0;opacity:0;pointer-events:none;position:absolute;right:0;top:10px;transition:opacity .5s;z-index:1}.instruction-content[_ngcontent-%COMP%]{background:rgba(0,0,0,.6);border-radius:5px;color:#fff;padding:5px 10px;user-select:none}.show-zoom-instruction[_nghost-%COMP%]   .zoom-instruction[_ngcontent-%COMP%]{opacity:1}"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(uht,[{type:S_,args:[{selector:"line-chart-interactive-view",templateUrl:"./line_chart_interactive_view.ng.html",styleUrls:["./line_chart_interactive_view.css"],changeDetection:jn.OnPush,providers:[{provide:OB,useFactory:fht,deps:[KB]}]}]}],(function(){return[{type:Ob},{type:OB}]}),{dotsContainer:[{type:ar,args:["dots",{static:!0,read:Kh}]}],overlay:[{type:ar,args:[tV]}],seriesData:[{type:R_}],seriesMetadataMap:[{type:R_}],viewExtent:[{type:R_}],xScale:[{type:R_}],yScale:[{type:R_}],domDim:[{type:R_}],tooltipOriginEl:[{type:R_}],tooltipTemplate:[{type:R_}],onViewExtentChange:[{type:E_}],onViewExtentReset:[{type:E_}],showZoomInstruction:[{type:A_,args:["class.show-zoom-instruction"]}]});function ght(t,e,n,o){return{major:[],minor:t.ticks(o,n).map((t=>({value:t,tickFormattedString:e.formatTick(t)})))}}function hht(t,e,n,o){const[i,a]=o,r=Math.abs(a-i);if(r>.001)return ght(t,e,n,o);const s=t.ticks([i,a],n),l=t.ticks([i,a],2),c=[];let d=(function p(t){const e=t.toExponential().split("e-",2);return 2===e.length?Number(e[1])-1:0})(r);r<1&&l.every((t=>{const e=Math.abs(t);return e>=0&&e<1}))&&(d+=1);const m=new Map;for(const t of l){const[n,o=""]=String(t).split(".",2),i=Number(n+"."+o.slice(0,d));m.set(i,{start:i,tickFormattedString:0===i?"—":e.formatReadable(i)})}const f=10*Math.pow(10,-d);for(const t of s)for(const n of[...m.keys()].reverse()){const o=t-n;if(o>=0&&o<f){if(0===n)c.push({value:t,tickFormattedString:e.formatTick(t)});else{const e=String(t).slice(String(n).length);c.push({value:t,tickFormattedString:`…${e||"0"}`})}break}}return{major:Array.from(m.values()),minor:c}}const bht=document.createElement("canvas").getContext("2d");function yht(t,e){if(1&t&&(oa(),Cf(0,"g",17),Cf(1,"text"),Pu(2),Mf(),Cf(3,"title"),Pu(4),Mf(),Mf()),2&t){const t=e.$implicit,n=zf();Dc(1),lu("font",n.axisFont),ym("x",n.textXPosition(t.value))("y",n.textYPosition(t.value)),Dc(1),ku(" ",t.tickFormattedString," "),Dc(2),wu(n.getFormatter().formatLong(t.value))}}function _ht(t,e){if(1&t&&(Cf(0,"span",20),Cf(1,"span"),Pu(2),Mf(),Mf()),2&t){const t=e.$implicit,n=e.index,o=e.last,i=zf(2);lu("left",i.getMajorXPosition(t),"px")("width",i.getMajorWidthString(t,o,i.majorTicks[n+1]))("bottom",i.getMajorYPosition(t),"px")("height",i.getMajorHeightString(t,o,i.majorTicks[n+1]))("font",i.axisFont),cu("major-label",!0)("last",o),bf("title",i.getFormatter().formatLong(t.start)),Dc(2),wu(t.tickFormattedString)}}function Cht(t,e){if(1&t&&(Cf(0,"div",18),Hm(1,_ht,3,16,"span",19),Mf()),2&t){const t=zf();Dc(1),bf("ngForOf",t.majorTicks)("ngForTrackBy",t.trackByMajorTick)}}class Mht{constructor(){this.onViewExtentChange=new Sy,this.editMenuOpened=!1,this.majorTicks=[],this.minorTicks=[]}ngOnChanges(){let t=null;const e=Qgt("x"===this.axis?this.domDim.width:this.domDim.height,this.gridCount);t=this.scale instanceof hrt?hht(this.scale,this.getFormatter(),e,this.axisExtent):this.scale instanceof yrt?(function n(t,e,o,i){const[a,r]=i;let s=t.ticks(i,2);if(r-a>=864e5||s.length>2)return ght(t,e,o,i);const l=t.ticks(i,o);return{major:s.map((t=>({start:t,tickFormattedString:e.formatShort(t)}))),minor:l.map((t=>({value:t,tickFormattedString:e.formatTick(t)})))}})(this.scale,this.getFormatter(),e,this.axisExtent):ght(this.scale,this.getFormatter(),e,this.axisExtent),this.majorTicks=t.major,this.minorTicks=(function o(t,e,n,i,a=5){if(!t.length||!bht)return t;const r="x"===n?1:-1;let s=null;return t.filter((t=>{const o=e(t);bht.font=i;const l=bht.measureText(t.tickFormattedString),c="x"===n?l.width:l.actualBoundingBoxAscent-l.actualBoundingBoxDescent;return null===s?!(o+r*c<0||(s=o+r*c,0)):!(r*(s+r*a-o)>0||(s=o+r*c,0))}))})(t.minor,(t=>this.getDomPos(t.value)),this.axis,"11px Roboto, sans-serif")}getFormatter(){var t;return null!==(t=this.customFormatter)&&void 0!==t?t:this.scale.defaultFormatter}trackByMinorTick(t){return t.value}trackByMajorTick(t){return t.start}getDomPos(t){return this.scale.forward(this.axisExtent,Jgt(this.domDim,this.axis),t)}textXPosition(t){return"x"===this.axis?String(this.getDomPos(t)):"100%"}textYPosition(t){return"x"===this.axis?"":String(this.getDomPos(t))}getMajorXPosition(t){return"y"===this.axis?0:Math.min(this.domDim.width,Math.max(0,this.getDomPos(t.start)))}getMajorWidthString(t,e,n){return"y"===this.axis?"":(e||!n?this.domDim.width:this.getMajorXPosition(n))-this.getMajorXPosition(t)+"px"}getMajorYPosition(t){return"x"===this.axis?0:this.domDim.height-Math.min(this.domDim.height,Math.max(0,this.getDomPos(t.start)))}getMajorHeightString(t,e,n){return"x"===this.axis?"":(e||!n?this.domDim.height:this.getMajorYPosition(n))-this.getMajorYPosition(t)+"px"}keydownPreventClose(t){"Escape"!==t.key&&t.stopPropagation()}extentChanged(t,e){let n=Number(t),o=Number(e);if(o<n){const t=n;n=o,o=t}Number.isFinite(n)&&Number.isFinite(o)&&this.onViewExtentChange.emit([n,o])}onAxisUpdateMenuOpen(t,e,n){t.value=String(n[0]),e.value=String(n[1]),t.focus()}setEditMenuOpened(t){this.editMenuOpened=t}}Mht.ɵfac=function t(e){return new(e||Mht)},Mht.ɵcmp=so({type:Mht,selectors:[["line-chart-axis"]],inputs:{axisExtent:"axisExtent",axis:"axis",scale:"scale",gridCount:"gridCount",domDim:"domDim",customFormatter:"customFormatter"},outputs:{onViewExtentChange:"onViewExtentChange"},features:[qo],decls:26,vars:13,consts:[[1,"line"],[1,"minor"],[1,"ticks"],["class","minor-tick-label",4,"ngFor","ngForOf","ngForTrackBy"],["mat-icon-button","","title","Click to manually set min & max values",3,"matMenuTriggerFor","menuOpened","menuClosed"],["matMenuTrigger","matMenuTrigger"],["svgIcon","edit_24px"],["class","major ticks",4,"ngIf"],["xPosition","before",3,"yPosition"],["manualControl","matMenu"],[1,"extent-edit-input",3,"click","keydown"],["type","number",3,"value"],["minInput",""],["maxInput",""],[1,"extent-edit-control",3,"keydown"],["mat-raised-button","","color","primary",1,"extent-edit-change",3,"click"],["mat-stroked-button","",1,"extent-edit-cancel",3,"click"],[1,"minor-tick-label"],[1,"major","ticks"],[3,"major-label","last","left","width","bottom","height","font","title",4,"ngFor","ngForOf","ngForTrackBy"],[3,"title"]],template:function t(e,n){if(1&e){const t=wf();Cf(0,"div"),vf(1,"div",0),Cf(2,"div",1),oa(),Cf(3,"svg",2),Hm(4,yht,5,6,"g",3),Mf(),aa(),Cf(5,"button",4,5),Rf("menuOpened",(function e(){Pi(t);const o=Fm(15),i=Fm(20);return n.onAxisUpdateMenuOpen(o,i,n.axisExtent),n.setEditMenuOpened(!0)}))("menuClosed",(function t(){return n.setEditMenuOpened(!1)})),vf(7,"mat-icon",6),Mf(),Mf(),Hm(8,Cht,2,2,"div",7),Mf(),Cf(9,"mat-menu",8,9),Cf(11,"div",10),Rf("click",(function t(e){return e.stopPropagation()}))("keydown",(function t(e){return n.keydownPreventClose(e)})),Cf(12,"label"),Pu(13,"min"),Mf(),vf(14,"input",11,12),Mf(),Cf(16,"div",10),Rf("click",(function t(e){return e.stopPropagation()}))("keydown",(function t(e){return n.keydownPreventClose(e)})),Cf(17,"label"),Pu(18,"max"),Mf(),vf(19,"input",11,13),Mf(),Cf(21,"div",14),Rf("keydown",(function t(e){return n.keydownPreventClose(e)})),Cf(22,"button",15),Rf("click",(function e(){Pi(t);const o=Fm(15),i=Fm(20),a=Fm(6);return n.extentChanged(o.value,i.value),a.closeMenu()})),Pu(23," Change "),Mf(),Cf(24,"button",16),Rf("click",(function e(){Pi(t);return Fm(6).closeMenu()})),Pu(25," Cancel "),Mf(),Mf(),Mf()}if(2&e){const t=Fm(10);mu(n.axis+"-axis axis"),Dc(4),bf("ngForOf",n.minorTicks)("ngForTrackBy",n.trackByMinorTick),Dc(1),cu("extent-edit-button",!0)("extent-edit-menu-opened",n.editMenuOpened),bf("matMenuTriggerFor",t),Dc(3),bf("ngIf",n.majorTicks.length),Dc(1),bf("yPosition","y"===n.axis?"above":"below"),Dc(5),bf("value",n.axisExtent[0]),Dc(5),bf("value",n.axisExtent[1])}},directives:[xv,NL,qq,Mq,Pv,jq],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   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span[_ngcontent-%COMP%]{border-radius:50%;border:1px solid rgba(255,255,255,.4);display:inline-block;height:10px;width:10px}.tooltip[_ngcontent-%COMP%]   .closest[_ngcontent-%COMP%]   .tooltip-row-circle[_ngcontent-%COMP%] > span[_ngcontent-%COMP%]{border-color:#fff;box-shadow:inset 0 0 0 1px #fff}.out-of-selected-time[_ngcontent-%COMP%]{border:0 dashed currentColor;height:100%;position:absolute}.out-of-selected-time.start[_ngcontent-%COMP%]{border-right-width:2px;margin-left:-1px}.out-of-selected-time.start.range[_ngcontent-%COMP%]{left:0}.out-of-selected-time.end[_ngcontent-%COMP%]{border-left-width:2px;margin-right:-1px;right:0}.out-of-selected-time.range[_ngcontent-%COMP%]{background-color:rgba(255,255,255,.5)}body.dark-mode[_nghost-%COMP%]   .out-of-selected-time.range[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .out-of-selected-time.range[_ngcontent-%COMP%]{background-color:rgba(0,0,0,.4)}.linked-time-fob-container[_ngcontent-%COMP%]{display:inline-block;left:0;position:absolute;top:0}linked-time-fob[_ngcontent-%COMP%]{transform:translateX(-50%)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(rbt,[{type:S_,args:[{selector:"scalar-card-component",templateUrl:"scalar_card_component.ng.html",styleUrls:["scalar_card_component.css"],changeDetection:jn.OnPush}]}],(function(){return[{type:Kh},{type:XY}]}),{cardId:[{type:R_}],chartMetadataMap:[{type:R_}],DataDownloadComponent:[{type:R_}],dataSeries:[{type:R_}],ignoreOutliers:[{type:R_}],isCardVisible:[{type:R_}],isPinned:[{type:R_}],loadState:[{type:R_}],showFullSize:[{type:R_}],smoothingEnabled:[{type:R_}],tag:[{type:R_}],title:[{type:R_}],tooltipSort:[{type:R_}],xAxisType:[{type:R_}],xScaleType:[{type:R_}],useDarkMode:[{type:R_}],selectedTime:[{type:R_}],onFullSizeToggle:[{type:E_}],onPinClicked:[{type:E_}],lineChart:[{type:ar,args:[Bht]}]});class sbt{constructor(t,e){this.ref=t,this.cdkScrollable=e,this.onVisibilityChange=new Sy,this.ngUnsubscribe$=new B,this.onEvent$=new B}ngOnInit(){const t={root:this.cdkScrollable?this.cdkScrollable.getElementRef().nativeElement:null};this.intersectionObserverMargin&&(t.rootMargin=this.intersectionObserverMargin);const e=new IntersectionObserver((t=>{this.onEvent$.next(t)}),t);e.observe(this.ref.nativeElement),this.ngUnsubscribe$.subscribe((()=>{e.unobserve(this.ref.nativeElement)})),this.onEvent$.pipe(je(this.ngUnsubscribe$)).subscribe((t=>{const e=t.slice(-1)[0];this.onVisibilityChange.emit({visible:e.isIntersecting})}))}ngOnDestroy(){this.ngUnsubscribe$.next(),this.ngUnsubscribe$.complete()}waitForEventForTestOnly(){return new Promise((t=>this.onEvent$.pipe(ve(1)).subscribe((()=>{t()}))))}}function lbt(t,e){return t.length===e.length&&t.every(((t,n)=>{const o=e[n],i=t.points,a=o.points;return t.runId===o.runId&&i.length===a.length&&i.every(((t,e)=>{const n=a[e];return t.x===n.x&&t.y===n.y}))}))}sbt.ɵfac=function t(e){return new(e||sbt)(gf(Kh),gf(JL,8))},sbt.ɵdir=ho({type:sbt,selectors:[["","observeIntersection",""]],inputs:{intersectionObserverMargin:"intersectionObserverMargin"},outputs:{onVisibilityChange:"onVisibilityChange"}}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(sbt,[{type:k_,args:[{selector:"[observeIntersection]"}]}],(function(){return[{type:Kh},{type:JL,decorators:[{type:Br}]}]}),{intersectionObserverMargin:[{type:R_}],onVisibilityChange:[{type:E_}]});class cbt{constructor(t){this.store=t,this.DataDownloadComponent=f6,this.fullWidthChanged=new Sy,this.fullHeightChanged=new Sy,this.pinStateChanged=new Sy,this.isVisible=!1,this.useDarkMode$=this.store.select(FD),this.ignoreOutliers$=this.store.select(bz),this.tooltipSort$=this.store.select(hz),this.xAxisType$=this.store.select(yz),this.xScaleType$=this.store.select(yz).pipe(Vt((t=>{switch(t){case QT.STEP:case QT.RELATIVE:return n6.LINEAR;case QT.WALL_TIME:return n6.TIME;default:throw new Error(`Invalid xAxisType for line chart. ${t}`)}}))),this.scalarSmoothing$=this.store.select(Cz),this.smoothingEnabled$=this.store.select(Cz).pipe(Vt((t=>t>0))),this.showFullSize=!1,this.ngUnsubscribe=new B}onVisibilityChange({visible:t}){this.isVisible=t}isScalarCardMetadata(t){const{plugin:e}=t;return e===KT.SCALARS}onFullSizeToggle(){this.showFullSize=!this.showFullSize,this.fullWidthChanged.emit(this.showFullSize),this.fullHeightChanged.emit(this.showFullSize)}ngOnInit(){const t=this.store.select(nz,this.cardId).pipe(ue((t=>!!t&&this.isScalarCardMetadata(t))),Vt((t=>t)));function e(t){return JSON.stringify(["smoothed",t])}const n=this.store.select(tz,this.cardId).pipe(je(this.ngUnsubscribe),ue((t=>Boolean(t))),Vt((t=>t)),Fe(1)).pipe(_e(this.store.select(yz)),Vt((([t,e])=>Object.keys(t).map((n=>({runId:n,points:this.stepSeriesToLineSeries(t[n],e)}))))),we(lbt)).pipe(_e(this.store.select(Mz)),je(this.ngUnsubscribe),Vt((([t,e])=>e?(function n(t){var e;const n=[];for(const o of t){const t=[];let i=Number.isFinite(null===(e=o.points[0])||void 0===e?void 0:e.x)?o.points[0].x:-1/0,a=[];for(const e of o.points)Number.isFinite(e.x)?(e.x<i&&(t.push({seriesId:JSON.stringify([o.runId,t.length]),runId:o.runId,points:a}),a=[]),a.push(e),i=e.x):a.push(e);t.push({seriesId:JSON.stringify([o.runId,t.length]),runId:o.runId,points:a});for(let e=0;e<t.length;e++)n.push(Object.assign(Object.assign({},t[e]),{partitionIndex:e,partitionSize:t.length}))}return n})(t):t.map((t=>Object.assign(Object.assign({},t),{seriesId:t.runId,partitionIndex:0,partitionSize:1}))))),Vt((t=>t.map((t=>{var e;const n=null===(e=t.points[0])||void 0===e?void 0:e.wallTime;return Object.assign(Object.assign({},t),{points:t.points.map((t=>Object.assign(Object.assign({},t),{relativeTimeInMs:t.wallTime-n})))})})))),_e(this.store.select(yz)),Vt((([t,e])=>t.map((t=>Object.assign(Object.assign({},t),{points:t.points.map((t=>{let n;switch(e){case QT.RELATIVE:n=t.relativeTimeInMs;break;case QT.WALL_TIME:n=t.wallTime;break;case QT.STEP:default:n=t.step}return Object.assign(Object.assign({},t),{x:n})}))}))))),Fe(1));this.dataSeries$=n.pipe(_e(this.store.select(Cz)),Ve((([t,n])=>{const o=t.map((({seriesId:t,points:e})=>({id:t,points:e})));return n<=0?zt(o):Pt((function i(t,e){var n;return XT(this,void 0,void 0,(function*(){Number.isFinite(e)||(e=0),e=Math.max(0,Math.min(e,1));const o=[];for(const i of t){const t=null===(n=i.points[0])||void 0===n?void 0:n.y;if(i.points.every((e=>e.y==t))){o.push(i);continue}let a=i.points.length>0?0:NaN,r=0;const s=i.points.map((t=>{const n=t.y;if(Number.isFinite(n)){a=a*e+(1-e)*n,r++;const o=1===e?1:1-Math.pow(e,r);return{x:t.x,y:a/o}}return{x:t.x,y:n}}));o.push({id:i.id,points:s})}return o}))})(o,n)).pipe(Vt((t=>{const n=o.map(((n,o)=>({id:e(n.id),points:t[o].points.map((({y:t},e)=>Object.assign(Object.assign({},n.points[e]),{y:t})))})));return[...o,...n]})))})),Be([])),this.selectedTime$=Kt([n,this.store.select(Ez),this.store.select(yz)]).pipe(Vt((([t,e,n])=>{if(n!==QT.STEP||!e)return null;let o=1/0,i=-1/0;for(const{points:e}of t)for(const t of e)o=o>t.x?t.x:o,i=i<t.x?t.x:i;return B4(e,o,i)}))),this.chartMetadataMap$=n.pipe(Ve((t=>Kt(t.map((t=>this.getRunDisplayNameAndAlias(t.runId).pipe(Vt((e=>Object.assign(Object.assign({},t),e))))))))),_e(this.store.select(_I),this.store.select(CI),this.store.select(Cz)),Ce(0),Vt((([t,n,o,i])=>{var a;const r={},s=i>0;for(const e of t){const{seriesId:t,runId:i,displayName:s,alias:l,partitionIndex:c,partitionSize:d}=e;r[t]={type:o6.ORIGINAL,id:t,alias:l,displayName:d>1?`${s}: ${c}`:s,visible:Boolean(n&&n.get(i)),color:null!==(a=o[i])&&void 0!==a?a:"#fff",aux:!1,opacity:1}}if(!s)return r;for(const[t,n]of Object.entries(r)){const o=e(t);r[o]=Object.assign(Object.assign({},n),{id:o,type:o6.DERIVED,aux:!1,originalSeriesId:t}),n.aux=!0,n.opacity=.25}return r})),Be({})),this.loadState$=this.store.select($N,this.cardId),this.tag$=t.pipe(Vt((t=>t.tag))),this.title$=this.tag$.pipe(Vt((t=>H4(t,this.groupName)))),this.isPinned$=this.store.select(dz,this.cardId)}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}getRunDisplayNameAndAlias(t){return Kt([this.store.select(Gz,{runId:t}),this.store.select(OR),this.store.select(Yz,{runId:t})]).pipe(Vt((([e,n,o])=>{var i,a;const r=null!==e&&null!==(i=n[e])&&void 0!==i?i:null;return{displayName:o||r?null!==(a=null==o?void 0:o.name)&&void 0!==a?a:"...":t,alias:r}})))}stepSeriesToLineSeries(t,e){const n=e===QT.STEP;return t.map((t=>{const e=1e3*t.wallTime;return Object.assign(Object.assign({},t),{x:n?t.step:e,y:t.value,wallTime:e,relativeTimeInMs:0})}))}}cbt.ɵfac=function t(e){return new(e||cbt)(gf(iS))},cbt.ɵcmp=so({type:cbt,selectors:[["scalar-card"]],inputs:{DataDownloadComponent:"DataDownloadComponent",cardId:"cardId",groupName:"groupName"},outputs:{fullWidthChanged:"fullWidthChanged",fullHeightChanged:"fullHeightChanged",pinStateChanged:"pinStateChanged"},decls:14,vars:43,consts:[["observeIntersection","",3,"cardId","chartMetadataMap","DataDownloadComponent","dataSeries","ignoreOutliers","isCardVisible","isPinned","loadState","showFullSize","smoothingEnabled","tag","title","tooltipSort","xAxisType","xScaleType","useDarkMode","selectedTime","onFullSizeToggle","onPinClicked","onVisibilityChange"]],template:function t(e,n){1&e&&(Cf(0,"scalar-card-component",0),Rf("onFullSizeToggle",(function t(){return n.onFullSizeToggle()}))("onPinClicked",(function t(e){return n.pinStateChanged.emit(e)}))("onVisibilityChange",(function t(e){return n.onVisibilityChange(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async"),_y(5,"async"),_y(6,"async"),_y(7,"async"),_y(8,"async"),_y(9,"async"),_y(10,"async"),_y(11,"async"),_y(12,"async"),_y(13,"async"),Mf()),2&e&&bf("cardId",n.cardId)("chartMetadataMap",Cy(1,17,n.chartMetadataMap$))("DataDownloadComponent",n.DataDownloadComponent)("dataSeries",Cy(2,19,n.dataSeries$))("ignoreOutliers",Cy(3,21,n.ignoreOutliers$))("isCardVisible",n.isVisible)("isPinned",Cy(4,23,n.isPinned$))("loadState",Cy(5,25,n.loadState$))("showFullSize",n.showFullSize)("smoothingEnabled",Cy(6,27,n.smoothingEnabled$))("tag",Cy(7,29,n.tag$))("title",Cy(8,31,n.title$))("tooltipSort",Cy(9,33,n.tooltipSort$))("xAxisType",Cy(10,35,n.xAxisType$))("xScaleType",Cy(11,37,n.xScaleType$))("useDarkMode",Cy(12,39,n.useDarkMode$))("selectedTime",Cy(13,41,n.selectedTime$))},directives:[rbt,sbt],pipes:[Vv],styles:["[_nghost-%COMP%] {\n        display: block;\n        height: 100%;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(cbt,[{type:S_,args:[{selector:"scalar-card",template:'\n    <scalar-card-component\n      [cardId]="cardId"\n      [chartMetadataMap]="chartMetadataMap$ | async"\n      [DataDownloadComponent]="DataDownloadComponent"\n      [dataSeries]="dataSeries$ | async"\n      [ignoreOutliers]="ignoreOutliers$ | async"\n      [isCardVisible]="isVisible"\n      [isPinned]="isPinned$ | async"\n      [loadState]="loadState$ | async"\n      [showFullSize]="showFullSize"\n      [smoothingEnabled]="smoothingEnabled$ | async"\n      [tag]="tag$ | async"\n      [title]="title$ | async"\n      [tooltipSort]="tooltipSort$ | async"\n      [xAxisType]="xAxisType$ | async"\n      [xScaleType]="xScaleType$ | async"\n      [useDarkMode]="useDarkMode$ | async"\n      [selectedTime]="selectedTime$ | async"\n      (onFullSizeToggle)="onFullSizeToggle()"\n      (onPinClicked)="pinStateChanged.emit($event)"\n      observeIntersection\n      (onVisibilityChange)="onVisibilityChange($event)"\n    ></scalar-card-component>\n  ',styles:["\n      :host {\n        display: block;\n        height: 100%;\n      }\n    "],changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),{DataDownloadComponent:[{type:R_}],cardId:[{type:R_}],groupName:[{type:R_}],fullWidthChanged:[{type:E_}],fullHeightChanged:[{type:E_}],pinStateChanged:[{type:E_}]});function dbt(t,e,n){const o=[],{left:i,right:a}=e,r=(a-i)/n;let s=0,l=0;for(let e=0;e<n;e++){const a=i+e*r,c=a+r,d=e===n-1;let p=l;for(l=0;s<t.length;){const e=t[s],n=pbt(e,a,c,!d);if(p+=n.curr,l+=n.next,e.x+e.dx>c)break;s++}o.push({x:a,dx:r,y:p})}return o}function pbt(t,e,n,o){const i=t.x,a=t.x+t.dx;if(i>n||a<e)return{curr:0,next:0};if(0===t.dx)return o&&a>=n?{curr:0,next:t.y}:{curr:t.y,next:0};const r=Math.min(a,n)-Math.max(i,e);return{curr:t.y*r/t.dx,next:0}}const mbt=["main"],fbt=["xAxis"],ubt=["yAxis"],gbt=["content"],hbt=["histograms"];function bbt(t,e){if(1&t&&(oa(),Cf(0,"g"),Cf(1,"text"),Pu(2),Mf(),Mf()),2&t){const t=zf();lu("transform",t.getCssTranslatePx(t.tooltipData.xAxis.position,9)),Dc(2),wu(t.tooltipData.xAxis.label)}}function ybt(t,e){if(1&t&&(oa(),Cf(0,"g"),Cf(1,"text"),Pu(2),Mf(),Mf()),2&t){const t=zf();lu("transform",t.getGroupTransform(t.tooltipData.closestDatum)),Dc(1),ym("y",t.tooltipData.yAxis.position),Dc(1),ku(" ",t.tooltipData.yAxis.label," ")}}function _bt(t,e){if(1&t&&(Cf(0,"div",16),vf(1,"linked-time-fob",17),Mf()),2&t){const t=zf(2);lu("transform",t.getCssTranslatePx(0,t.scales.temporalScale(t.linkedTime.endStep))),Dc(1),bf("step",t.linkedTime.endStep)}}function Cbt(t,e){if(1&t&&(oa(),aa(),xf(0),Cf(1,"div",16),vf(2,"linked-time-fob",17),Mf(),Hm(3,_bt,2,3,"div",18),Of()),2&t){const t=zf();Dc(1),lu("transform",t.getCssTranslatePx(0,t.scales.temporalScale(t.linkedTime.startStep))),Dc(1),bf("step",t.linkedTime.startStep),Dc(1),bf("ngIf",t.linkedTime.endStep)}}function Mbt(t,e){if(1&t&&(oa(),Cf(0,"g"),vf(1,"line",19),Mf()),2&t){const t=e.$implicit;lu("transform",zf().getCssTranslatePx(0,t))}}function vbt(t,e){1&t&&(oa(),vf(0,"line",22))}function xbt(t,e){if(1&t&&(oa(),vf(0,"circle",23)),2&t){const t=zf().$implicit,e=zf();lu("transform",e.getCssTranslatePx(e.getUiCoordFromBinForContent(e.getClosestBinFromBinCoordinate(t,e.tooltipData.xPositionInBinCoord)).x,e.getUiCoordFromBinForContent(e.getClosestBinFromBinCoordinate(t,e.tooltipData.xPositionInBinCoord)).y))}}function Obt(t,e){if(1&t&&(oa(),Cf(0,"g"),Hm(1,vbt,1,0,"line",20),vf(2,"path"),Hm(3,xbt,1,2,"circle",21),Mf()),2&t){const t=e.$implicit,n=zf();lu("transform",n.getGroupTransform(t))("color",n.getHistogramFill(t)),cu("histogram",!0)("no-color",!n.isDatumInLinkedTimeRange(t)),Dc(1),bf("ngIf",n.mode===n.HistogramMode.OFFSET),Dc(1),ym("d",n.getHistogramPath(t)),Dc(1),bf("ngIf",n.tooltipData)}}function Pbt(t,e){if(1&t&&(oa(),vf(0,"circle",23)),2&t){const t=zf(2);ym("cx",t.getUiCoordFromBinForContent(t.tooltipData.closestBin).x)("cy",t.getUiCoordFromBinForContent(t.tooltipData.closestBin).y)}}function wbt(t,e){if(1&t&&(oa(),Cf(0,"g",4),Cf(1,"g"),vf(2,"path"),Hm(3,Pbt,1,2,"circle",24),Mf(),Cf(4,"g",25),Cf(5,"text",26),Pu(6),Mf(),Mf(),Mf()),2&t){const t=zf();Dc(1),lu("transform",t.getGroupTransform(t.tooltipData.closestDatum)),Dc(1),ym("d",t.getHistogramPath(t.tooltipData.closestDatum)),Dc(1),bf("ngIf",t.tooltipData.closestBin),Dc(1),lu("transform",t.getCssTranslatePx(t.tooltipData.value.position.x,t.tooltipData.value.position.y)),Dc(2),wu(t.tooltipData.value.label)}}class kbt{constructor(t){this.changeDetector=t,this.mode=jE.OFFSET,this.timeProperty=VE.STEP,this.linkedTime=null,this.HistogramMode=jE,this.TimeProperty=VE,this.tooltipData=null,this.ngUnsubscribe=new B,this.layout={histogramHeight:0,contentClientRect:{height:0,width:0}},this.scales=null,this.formatters={binNumber:Lnt(".3~s"),count:Lnt(".3n"),wallTime:tit("%m/%d %X"),step:Lnt(".0f"),relative:t=>Lnt(".1r")(t/36e5)+"h"},this.domVisible=!1}ngOnChanges(){this.updateChartIfVisible()}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}ngAfterViewInit(){le(this.main.nativeElement,"mousemove",{passive:!0}).pipe(je(this.ngUnsubscribe)).subscribe((t=>this.onMouseMove(t)))}getCssTranslatePx(t,e){return`translate(${t}px, ${e}px)`}getClosestBinFromBinCoordinate(t,e){if(!t.bins.length)return{x:0,dx:0,y:0};const n=t.bins[0],o=t.bins.slice(-1)[0];if(e<n.x)return n;if(e>=o.x+o.dx)return o;return t.bins.find((t=>t.x<=e&&e<t.x+t.dx))}getUiCoordFromBinForContent(t){return this.scales?{x:this.scales.binScale(Rbt(t)),y:this.scales.countScale(t.y)}:{x:0,y:0}}getHistogramPath(t){if(!this.scales||!t.bins.length)return"";const e=this.scales.binScale,n=this.scales.countScale,o=t.bins[0],i=t.bins.slice(-1)[0],a=[`M${e(Rbt(o))},${n(0)}`];for(const o of t.bins)a.push(`L${e(Rbt(o))},${n(o.y)}`);return a.push(`L${e(Rbt(i))},${n(0)}`),a.join("")}trackByWallTime(t){return t.wallTime}getGroupTransform(t){return this.scales&&this.mode!==jE.OVERLAY?this.getCssTranslatePx(0,this.scales.temporalScale(this.getTimeValue(t))):""}isLinkedTimeEnabled(t){return Boolean(this.mode===jE.OFFSET&&this.timeProperty===VE.STEP&&this.scales&&t)}isDatumInLinkedTimeRange(t){return!this.isLinkedTimeEnabled(this.linkedTime)||(null===this.linkedTime.endStep?this.linkedTime.startStep===t.step:this.linkedTime.startStep<=t.step&&this.linkedTime.endStep>=t.step)}getHistogramFill(t){return this.scales?this.scales.d3ColorScale(this.getTimeValue(t)):""}getGridTickYLocs(){if(!this.scales||this.mode===jE.OFFSET)return[];const t=this.scales.countScale;return t.ticks().map((e=>t(e)))}onResize(){this.updateClientRects(),this.updateChartIfVisible()}onVisibilityChange({visible:t}){this.domVisible=t,t&&(this.updateClientRects(),this.updateChartIfVisible())}getTimeValue(t){switch(this.timeProperty){case VE.WALL_TIME:return t.wallTime;case VE.STEP:return t.step;case VE.RELATIVE:return t.wallTime-this.data[0].wallTime}}updateClientRects(){this.content&&(this.layout.contentClientRect=this.content.nativeElement.getBoundingClientRect(),this.layout.histogramHeight=this.layout.contentClientRect.height/2.5)}updateChartIfVisible(){this.domVisible&&(this.scales=this.computeScales(this.data),this.renderXAxis(),this.renderYAxis(),this.changeDetector.detectChanges())}computeScales(t){const{width:e,height:n}=this.layout.contentClientRect,{min:o,max:i}=Dbt(t,(t=>(function e(t,n){return t.reduce(((t,e)=>Math.min(t,n(e))),1/0)})(t.bins,(t=>t.x))),(t=>Sbt(t.bins,(({x:t,dx:e})=>t+e)))),a=Sbt(t,(t=>Sbt(t.bins,(({y:t})=>t)))),r=mot().domain([o,i]).nice(),s=this.mode!==jE.OVERLAY&&this.timeProperty==VE.WALL_TIME?Sat():mot(),l=t.map((t=>this.getTimeValue(t))),{min:c,max:d}=Dbt(l,(t=>t)),p=[c,d];s.domain(p);const m=mot();m.domain([0,a]);const f=T9(this.color||"#000"),u=mot();if(u.domain(p),r.range([0,e]),u.range([f.brighter(),f.darker()]),u.interpolate(ytt),this.mode===jE.OVERLAY)s.range([n,n]),m.range([n,0]);else{s.range([n-(this.mode===jE.OFFSET?n-this.layout.histogramHeight:0),n]),m.range([0,-this.layout.histogramHeight])}return{binScale:r,d3ColorScale:u,countScale:m,temporalScale:s}}renderXAxis(){if(!this.scales)return;const{width:t}=this.layout.contentClientRect,e=B6(this.scales.binScale).ticks(Math.max(2,t/20));e.tickFormat(this.formatters.binNumber),e(n8(this.xAxis.nativeElement))}getYAxisFormatter(){if(this.mode===jE.OVERLAY)return this.formatters.count;switch(this.timeProperty){case VE.WALL_TIME:return this.formatters.wallTime;case VE.STEP:return this.formatters.step;case VE.RELATIVE:return this.formatters.relative;default:throw RangeError(`Y axis formatter for ${this.timeProperty} must be implemented`)}}renderYAxis(){if(!this.scales)return;const t=this.mode===jE.OVERLAY?this.scales.countScale:this.scales.temporalScale,{height:e}=this.layout.contentClientRect,n=L6(t).ticks(Math.max(2,e/15));n.tickFormat(this.getYAxisFormatter()),n(n8(this.yAxis.nativeElement))}findClosestDatumIndex(t){let e=t.target,n=e;for(;e&&e!==this.histograms.nativeElement;)n=e,e=e.parentElement;return e?Array.from(e.children).indexOf(n):-1}onMouseMoveForTestOnly(t){return this.onMouseMove(t)}onMouseMove(t){if(!this.scales)return;const e=t.offsetX,n=t.offsetY,o=this.findClosestDatumIndex(t);if(o<0)return;const i=this.scales.binScale.invert(e),a=this.data[o],r=this.getClosestBinFromBinCoordinate(a,i);this.tooltipData={value:{position:{x:e,y:n},label:this.mode===jE.OFFSET?this.formatters.count(r.y):`Step: ${this.formatters.step(a.step)}`},xAxis:{position:this.getUiCoordFromBinForContent(r).x,label:this.formatters.binNumber(Rbt(r))},yAxis:{position:this.scales.countScale(this.mode===jE.OFFSET?0:r.y),label:this.mode===jE.OFFSET?this.getYAxisFormatter()(this.getTimeValue(a)):this.formatters.binNumber(r.y)},xPositionInBinCoord:i,closestDatum:a,closestBin:r},this.changeDetector.detectChanges()}}function Sbt(t,e){return t.reduce(((t,n)=>Math.max(t,e(n))),-1/0)}function Dbt(t,e,n){n||(n=e);let o=1/0,i=-1/0;for(const a of t)o=Math.min(o,e(a)),i=Math.max(i,n(a));return{min:o,max:i}}function Rbt(t){return t.x+.5*t.dx}function Ebt(t,e){1&t&&vf(0,"vis-selected-time-clipped")}function Abt(t,e){1&t&&(Cf(0,"span",14),vf(1,"mat-spinner",15),Mf())}kbt.ɵfac=function t(e){return new(e||kbt)(gf(Ob))},kbt.ɵcmp=so({type:kbt,selectors:[["tb-histogram"]],viewQuery:function t(e,n){if(1&e&&(Vy(mbt,5),Vy(fbt,5),Vy(ubt,5),Vy(gbt,5),Vy(hbt,5)),2&e){let 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h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   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.mat-ripple-element[_ngcontent-%COMP%]{display:none}.cdk-visually-hidden[_ngcontent-%COMP%]{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container[_ngcontent-%COMP%], .cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{display:flex;flex-direction:column;box-sizing:border-box;height:100%;overflow:auto;padding:16px;padding-top:4px}.heading[_ngcontent-%COMP%]{align-items:center;display:grid;grid-template-areas:"tag controls" "run spinner";grid-template-columns:1fr auto;font-size:14px;margin-bottom:4px}.tag[_ngcontent-%COMP%]{align-items:center;display:flex;gap:5px;grid-area:tag;overflow:hidden}.tag[_ngcontent-%COMP%]   vis-selected-time-clipped[_ngcontent-%COMP%]{line-height:0}.pin-button[_ngcontent-%COMP%]   mat-icon[_ngcontent-%COMP%]{height:18px}.run[_ngcontent-%COMP%]{grid-area:run;display:flex;white-space:nowrap;font-size:13px}.run[_ngcontent-%COMP%]   .dot[_ngcontent-%COMP%]{flex:none;display:inline-block;width:13px;height:13px;border-radius:50%;margin-right:4px}.run[_ngcontent-%COMP%]   .run-text[_ngcontent-%COMP%]{overflow:hidden;text-overflow:ellipsis;max-width:120px}.controls[_ngcontent-%COMP%]{color:#616161;white-space:nowrap;grid-area:controls;justify-self:flex-end;flex-shrink:0;margin-right:-12px}body.dark-mode[_nghost-%COMP%]   .controls[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .controls[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.spinner[_ngcontent-%COMP%]{display:flex;grid-area:spinner;height:100%;justify-content:center;position:relative}mat-spinner[_ngcontent-%COMP%]{top:0;right:0;position:absolute}tb-histogram[_ngcontent-%COMP%]{flex-grow:1;overflow:hidden}.empty-message[_ngcontent-%COMP%]{margin-top:1em;font-size:13px}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Fbt,[{type:S_,args:[{selector:"histogram-card-component",templateUrl:"histogram_card_component.ng.html",styleUrls:["histogram_card_component.css"],changeDetection:jn.OnPush}]}],null,{loadState:[{type:R_}],title:[{type:R_}],tag:[{type:R_}],runId:[{type:R_}],data:[{type:R_}],mode:[{type:R_}],xAxisType:[{type:R_}],runColorScale:[{type:R_}],showFullSize:[{type:R_}],isPinned:[{type:R_}],selectedTime:[{type:R_}],onFullSizeToggle:[{type:E_}],onPinClicked:[{type:E_}]});class Lbt{constructor(t){this.store=t,this.fullWidthChanged=new Sy,this.fullHeightChanged=new Sy,this.pinStateChanged=new Sy,this.mode$=this.store.select(_z),this.xAxisType$=this.store.select(yz),this.showFullSize=!1}isHistogramCardMetadata(t){const{plugin:e}=t;return e===KT.HISTOGRAMS}onFullSizeToggle(){this.showFullSize=!this.showFullSize,this.fullWidthChanged.emit(this.showFullSize),this.fullHeightChanged.emit(this.showFullSize)}ngOnInit(){const t=this.store.select(nz,this.cardId).pipe(ue((t=>!!t&&this.isHistogramCardMetadata(t))),Vt((t=>t))),e=Kt([t,this.store.select(tz,this.cardId)]);this.data$=e.pipe(Vt((([t,e])=>{const n=t.runId;if(!e||!e.hasOwnProperty(n))return[];return(function o(t,e=30){if(!t.length||e<1)return[];const n=(function o(t){let e=null,n=null;for(const{bins:o}of t){if(!o.length)continue;const t=o[o.length-1],i=o[0].x,a=t.x+t.dx;(null===e||i<e)&&(e=i),(null===n||a>n)&&(n=a)}return null===e||null===n?null:{left:e,right:n}})(t);return n&&n.left===n.right&&(n.right=1.1*n.right+1,n.left=n.left/1.1-1),t.map((t=>({step:t.step,wallTime:t.wallTime,bins:n?dbt(t.bins,n,e):[]})))})(e[n].map((t=>{const{wallTime:e,step:n}=t;return{wallTime:e,step:n,bins:t.bins.map((t=>({x:t.min,dx:t.max-t.min,y:t.count})))}})))}))),this.selectedTime$=this.store.select(Ez).pipe(_e(this.data$),Vt((([t,e])=>{if(!t)return null;let n=1/0,o=-1/0;for(const t of e)n=Math.min(t.step,n),o=Math.max(t.step,o);return B4(t,n,o)}))),this.loadState$=this.store.select($N,this.cardId),this.tag$=t.pipe(Vt((t=>t.tag))),this.title$=this.tag$.pipe(Vt((t=>H4(t,this.groupName)))),this.runId$=t.pipe(Vt((t=>t.runId))),this.isPinned$=this.store.select(dz,this.cardId)}}function Bbt(t,e){if(1&t){const t=wf();Cf(0,"image-card",6),Rf("fullWidthChanged",(function e(n){Pi(t);return zf(2).onFullWidthChanged(n)}))("pinStateChanged",(function e(){Pi(t);return zf(2).onPinStateChanged()})),Mf()}if(2&t){const t=zf(2);bf("cardId",t.cardId)("groupName",t.groupName)("runColorScale",t.runColorScale)}}function Vbt(t,e){if(1&t){const t=wf();Cf(0,"scalar-card",7),Rf("fullWidthChanged",(function e(n){Pi(t);return zf(2).onFullWidthChanged(n)}))("fullHeightChanged",(function e(n){Pi(t);return zf(2).onFullHeightChanged(n)}))("pinStateChanged",(function e(){Pi(t);return zf(2).onPinStateChanged()})),Mf()}if(2&t){const t=zf(2);bf("cardId",t.cardId)("groupName",t.groupName)}}function jbt(t,e){if(1&t){const t=wf();Cf(0,"histogram-card",8),Rf("fullWidthChanged",(function e(n){Pi(t);return zf(2).onFullWidthChanged(n)}))("fullHeightChanged",(function e(n){Pi(t);return zf(2).onFullHeightChanged(n)}))("pinStateChanged",(function e(){Pi(t);return zf(2).onPinStateChanged()})),Mf()}if(2&t){const t=zf(2);bf("cardId",t.cardId)("groupName",t.groupName)("runColorScale",t.runColorScale)}}function Ubt(t,e){if(1&t&&(Cf(0,"div"),Pu(1),Mf()),2&t){const t=zf(2);Dc(1),ku("Placeholder error for: ",t.cardId,"")}}function Wbt(t,e){if(1&t&&(xf(0,1),Hm(1,Bbt,1,3,"image-card",2),Hm(2,Vbt,1,2,"scalar-card",3),Hm(3,jbt,1,3,"histogram-card",4),Hm(4,Ubt,2,1,"div",5),Of()),2&t){const t=zf();bf("ngSwitch",t.pluginType),Dc(1),bf("ngSwitchCase",t.PluginType.IMAGES),Dc(1),bf("ngSwitchCase",t.PluginType.SCALARS),Dc(1),bf("ngSwitchCase",t.PluginType.HISTOGRAMS)}}Lbt.ɵfac=function t(e){return new(e||Lbt)(gf(iS))},Lbt.ɵcmp=so({type:Lbt,selectors:[["histogram-card"]],inputs:{cardId:"cardId",groupName:"groupName",runColorScale:"runColorScale"},outputs:{fullWidthChanged:"fullWidthChanged",fullHeightChanged:"fullHeightChanged",pinStateChanged:"pinStateChanged"},decls:10,vars:29,consts:[[3,"loadState","title","tag","runId","data","mode","xAxisType","runColorScale","showFullSize","isPinned","selectedTime","onFullSizeToggle","onPinClicked"]],template:function t(e,n){1&e&&(Cf(0,"histogram-card-component",0),Rf("onFullSizeToggle",(function t(){return n.onFullSizeToggle()}))("onPinClicked",(function t(e){return n.pinStateChanged.emit(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async"),_y(5,"async"),_y(6,"async"),_y(7,"async"),_y(8,"async"),_y(9,"async"),Mf()),2&e&&bf("loadState",Cy(1,11,n.loadState$))("title",Cy(2,13,n.title$))("tag",Cy(3,15,n.tag$))("runId",Cy(4,17,n.runId$))("data",Cy(5,19,n.data$))("mode",Cy(6,21,n.mode$))("xAxisType",Cy(7,23,n.xAxisType$))("runColorScale",n.runColorScale)("showFullSize",n.showFullSize)("isPinned",Cy(8,25,n.isPinned$))("selectedTime",Cy(9,27,n.selectedTime$))},directives:[Fbt],pipes:[Vv],styles:["[_nghost-%COMP%] {\n        display: block;\n        height: 100%;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Lbt,[{type:S_,args:[{selector:"histogram-card",template:'\n    <histogram-card-component\n      [loadState]="loadState$ | async"\n      [title]="title$ | async"\n      [tag]="tag$ | async"\n      [runId]="runId$ | async"\n      [data]="data$ | async"\n      [mode]="mode$ | async"\n      [xAxisType]="xAxisType$ | async"\n      [runColorScale]="runColorScale"\n      [showFullSize]="showFullSize"\n      [isPinned]="isPinned$ | async"\n      [selectedTime]="selectedTime$ | async"\n      (onFullSizeToggle)="onFullSizeToggle()"\n      (onPinClicked)="pinStateChanged.emit($event)"\n    ></histogram-card-component>\n  ',styles:["\n      :host {\n        display: block;\n        height: 100%;\n      }\n    "],changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),{cardId:[{type:R_}],groupName:[{type:R_}],runColorScale:[{type:R_}],fullWidthChanged:[{type:E_}],fullHeightChanged:[{type:E_}],pinStateChanged:[{type:E_}]});class Gbt{constructor(){this.PluginType=KT,this.fullWidthChanged=new Sy,this.fullHeightChanged=new Sy,this.pinStateChanged=new Sy}onFullWidthChanged(t){this.fullWidthChanged.emit(t)}onFullHeightChanged(t){this.fullHeightChanged.emit(t)}onPinStateChanged(){this.pinStateChanged.emit()}}Gbt.ɵfac=function t(e){return new(e||Gbt)},Gbt.ɵcmp=so({type:Gbt,selectors:[["card-view-component"]],inputs:{isEverVisible:"isEverVisible",cardId:"cardId",groupName:"groupName",pluginType:"pluginType",runColorScale:"runColorScale"},outputs:{fullWidthChanged:"fullWidthChanged",fullHeightChanged:"fullHeightChanged",pinStateChanged:"pinStateChanged"},decls:1,vars:1,consts:[[3,"ngSwitch",4,"ngIf"],[3,"ngSwitch"],[3,"cardId","groupName","runColorScale","fullWidthChanged","pinStateChanged",4,"ngSwitchCase"],[3,"cardId","groupName","fullWidthChanged","fullHeightChanged","pinStateChanged",4,"ngSwitchCase"],[3,"cardId","groupName","runColorScale","fullWidthChanged","fullHeightChanged","pinStateChanged",4,"ngSwitchCase"],[4,"ngSwitchDefault"],[3,"cardId","groupName","runColorScale","fullWidthChanged","pinStateChanged"],[3,"cardId","groupName","fullWidthChanged","fullHeightChanged","pinStateChanged"],[3,"cardId","groupName","runColorScale","fullWidthChanged","fullHeightChanged","pinStateChanged"]],template:function t(e,n){1&e&&Hm(0,Wbt,5,4,"ng-container",0),2&e&&bf("ngIf",n.isEverVisible)},directives:[Pv,Dv,Rv,Ev,t6,cbt,Lbt],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Gbt,[{type:S_,args:[{selector:"card-view-component",templateUrl:"card_view_component.ng.html",changeDetection:jn.OnPush}]}],null,{isEverVisible:[{type:R_}],cardId:[{type:R_}],groupName:[{type:R_}],pluginType:[{type:R_}],runColorScale:[{type:R_}],fullWidthChanged:[{type:E_}],fullHeightChanged:[{type:E_}],pinStateChanged:[{type:E_}]});class Ybt{constructor(t){this.store=t,this.isEverVisible=!1,this.showFullWidth=!1,this.showFullHeight=!1,this.runColorScale$=this.store.select(CI).pipe(Ye(350,void 0,{leading:!0,trailing:!0}),Vt((t=>e=>t.hasOwnProperty(e)?t[e]:"#fff")))}onVisibilityChange({visible:t}){this.isEverVisible=this.isEverVisible||t}onFullWidthChanged(t){this.showFullWidth=t}onFullHeightChanged(t){this.showFullHeight=t}onPinStateChanged(){this.store.select(dz,this.cardId).pipe(ve(1),qe(this.store.select(mz))).subscribe((([t,e])=>{this.store.dispatch(zA({cardId:this.cardId,canCreateNewPins:e,wasPinned:t}))}))}}function qbt(t,e){1&t&&Pf(0)}function Zbt(t,e){if(1&t&&vf(0,"card-view",5),2&t){const t=e.$implicit,n=zf(2);bf("cardId",t.cardId)("groupName",n.groupName)("pluginType",t.plugin)("cardObserver",n.cardObserver)("cardLazyLoader",t.cardId)}}function Xbt(t,e){1&t&&Pf(0)}Ybt.ɵfac=function t(e){return new(e||Ybt)(gf(iS))},Ybt.ɵcmp=so({type:Ybt,selectors:[["card-view"]],hostVars:4,hostBindings:function t(e,n){2&e&&cu("full-width",n.showFullWidth)("full-height",n.showFullHeight)},inputs:{cardId:"cardId",groupName:"groupName",pluginType:"pluginType"},decls:2,vars:7,consts:[["observeIntersection","","intersectionObserverMargin","200px 200px 200px 200px",3,"isEverVisible","cardId","groupName","pluginType","runColorScale","fullWidthChanged","fullHeightChanged","pinStateChanged","onVisibilityChange"]],template:function t(e,n){1&e&&(Cf(0,"card-view-component",0),Rf("fullWidthChanged",(function t(e){return n.onFullWidthChanged(e)}))("fullHeightChanged",(function t(e){return n.onFullHeightChanged(e)}))("pinStateChanged",(function t(){return n.onPinStateChanged()}))("onVisibilityChange",(function t(e){return n.onVisibilityChange(e)})),_y(1,"async"),Mf()),2&e&&bf("isEverVisible",n.isEverVisible)("cardId",n.cardId)("groupName",n.groupName)("pluginType",n.pluginType)("runColorScale",Cy(1,5,n.runColorScale$))},directives:[Gbt,sbt],pipes:[Vv],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], 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.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{contain:content}.card-grid[_ngcontent-%COMP%]{display:grid;grid-template-columns:repeat(auto-fill, minmax(335px, auto));gap:16px;padding:16px}card-view[_ngcontent-%COMP%]{border:1px solid #ebebeb;border-radius:4px;box-sizing:border-box;contain:strict;height:100%;min-height:320px}body.dark-mode[_nghost-%COMP%]   card-view[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   card-view[_ngcontent-%COMP%]{border:1px solid #555}card-view.full-width[_ngcontent-%COMP%]{contain:layout paint;grid-column-start:1;grid-column-end:-1}card-view.full-height[_ngcontent-%COMP%]{min-height:480px}.group-controls[_ngcontent-%COMP%]{color:#616161;display:grid;align-items:center;grid-template-columns:1fr 1fr;gap:16px;padding:0 16px}body.dark-mode[_nghost-%COMP%]   .group-controls[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group-controls[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.group-controls[_ngcontent-%COMP%]:first-of-type{padding-top:16px}.group-controls[_ngcontent-%COMP%]:last-of-type{padding-bottom:16px}.prev-container[_ngcontent-%COMP%]{justify-self:flex-start}.input-and-next-container[_ngcontent-%COMP%]{justify-self:flex-end}.pagination-input[_ngcontent-%COMP%]{margin-right:16px}.pagination-input[_ngcontent-%COMP%]   input[_ngcontent-%COMP%]{background:transparent;border:1px solid currentColor;color:inherit;font:inherit}.pagination-button[_ngcontent-%COMP%]{color:#616161;background-color:#fff}body.dark-mode[_nghost-%COMP%]   .pagination-button[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .pagination-button[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.pagination-button[_ngcontent-%COMP%]:disabled{color:#757575}body.dark-mode[_nghost-%COMP%]   .pagination-button[_ngcontent-%COMP%]:disabled, body.dark-mode   [_nghost-%COMP%]   .pagination-button[_ngcontent-%COMP%]:disabled{color:#616161}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(iyt,[{type:S_,args:[{selector:"metrics-card-grid-component",templateUrl:"./card_grid_component.ng.html",styleUrls:["./card_grid_component.css"],changeDetection:jn.OnPush}]}],(function(){return[{type:JL,decorators:[{type:Br}]}]}),{isGroupExpanded:[{type:R_}],pageIndex:[{type:R_}],numPages:[{type:R_}],cardIdsWithMetadata:[{type:R_}],cardMinWidth:[{type:R_}],cardObserver:[{type:R_}],showPaginationControls:[{type:R_}],pageIndexChanged:[{type:E_}]});class ayt{constructor(t){this.store=t,this.groupName=null,this.groupName$=new j(null),this.pageIndex$=new j(0),this.items$=new j([]),this.ngUnsubscribe=new B,this.numPages$=Kt([this.items$,this.store.select(hI)]).pipe(Vt((([t,e])=>Math.ceil(t.length/e)))),this.isGroupExpanded$=this.groupName$.pipe(Ve((t=>null!==t?this.store.select(wz,t):zt(!0)))),this.showPaginationControls$=this.numPages$.pipe(Vt((t=>t>1))),this.normalizedPageIndex$=Kt([this.pageIndex$,this.numPages$]).pipe(je(this.ngUnsubscribe),We((([t,e])=>{0!==e&&(t>=e?this.pageIndex$.next(e-1):t<0&&this.pageIndex$.next(0))})),Vt((([t,e])=>Math.min(Math.max(t,0),e-1))),Fe(1)),this.pagedItems$=Kt([this.items$,this.store.select(hI),this.normalizedPageIndex$,this.isGroupExpanded$]).pipe(Vt((([t,e,n,o])=>t.slice(e*n,e*n+(o?e:0))))),this.cardMinWidth$=Kt([this.store.select(gz),this.store.select(qD)]).pipe(Vt((([t,e])=>e?t:null)))}ngOnChanges(t){t.cardIdsWithMetadata&&this.items$.next(this.cardIdsWithMetadata),t.groupName&&this.groupName$.next(this.groupName)}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}onPageIndexChanged(t){this.pageIndex$.next(t)}}function ryt(t,e){if(1&t&&(Cf(0,"span",8),Pu(1),Mf()),2&t){const t=zf();Dc(1),ku("",t.cardIdsWithMetadata.length," cards")}}function syt(t,e){if(1&t&&(Cf(0,"span",9),Pu(1,"New card pinned"),Mf()),2&t){ym("data-id",e.$implicit)}}function lyt(t,e){if(1&t&&vf(0,"metrics-card-grid",10),2&t){const t=zf();bf("cardIdsWithMetadata",t.cardIdsWithMetadata)("cardObserver",t.cardObserver)}}function cyt(t,e){1&t&&(Cf(0,"div",11),Pu(1,"Pin cards for a quick view and comparison"),Mf())}ayt.ɵfac=function t(e){return new(e||ayt)(gf(iS))},ayt.ɵcmp=so({type:ayt,selectors:[["metrics-card-grid"]],inputs:{groupName:"groupName",cardIdsWithMetadata:"cardIdsWithMetadata",cardObserver:"cardObserver"},features:[qo],decls:7,vars:19,consts:[[3,"isGroupExpanded","pageIndex","numPages","showPaginationControls","cardIdsWithMetadata","cardMinWidth","cardObserver","pageIndexChanged"]],template:function t(e,n){1&e&&(Cf(0,"metrics-card-grid-component",0),Rf("pageIndexChanged",(function t(e){return n.onPageIndexChanged(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async"),_y(5,"async"),_y(6,"async"),Mf()),2&e&&bf("isGroupExpanded",Cy(1,7,n.isGroupExpanded$))("pageIndex",Cy(2,9,n.normalizedPageIndex$))("numPages",Cy(3,11,n.numPages$))("showPaginationControls",Cy(4,13,n.showPaginationControls$))("cardIdsWithMetadata",Cy(5,15,n.pagedItems$))("cardMinWidth",Cy(6,17,n.cardMinWidth$))("cardObserver",n.cardObserver)},directives:[iyt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(ayt,[{type:S_,args:[{selector:"metrics-card-grid",template:'\n    <metrics-card-grid-component\n      [isGroupExpanded]="isGroupExpanded$ | async"\n      [pageIndex]="normalizedPageIndex$ | async"\n      [numPages]="numPages$ | async"\n      [showPaginationControls]="showPaginationControls$ | async"\n      [cardIdsWithMetadata]="pagedItems$ | async"\n      [cardMinWidth]="cardMinWidth$ | async"\n      [cardObserver]="cardObserver"\n      (pageIndexChanged)="onPageIndexChanged($event)"\n    >\n    </metrics-card-grid-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),{groupName:[{type:R_}],cardIdsWithMetadata:[{type:R_}],cardObserver:[{type:R_}]});class dyt{}dyt.ɵfac=function t(e){return new(e||dyt)},dyt.ɵcmp=so({type:dyt,selectors:[["metrics-pinned-view-component"]],inputs:{cardObserver:"cardObserver",cardIdsWithMetadata:"cardIdsWithMetadata",newCardPinnedIds:"newCardPinnedIds"},decls:10,vars:4,consts:[[1,"group-toolbar"],["svgIcon","keep_24px"],[1,"group-text"],["aria-role","heading","aria-level","3",1,"group-title"],["class","group-card-count",4,"ngIf"],["class","new-card-pinned",4,"ngFor","ngForOf"],[3,"cardIdsWithMetadata","cardObserver",4,"ngIf","ngIfElse"],["emptyPinnedView",""],[1,"group-card-count"],[1,"new-card-pinned"],[3,"cardIdsWithMetadata","cardObserver"],[1,"empty-message"]],template:function 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.group-card-count[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.empty-message[_ngcontent-%COMP%]{color:#616161;font-size:13px;font-style:italic;padding:16px;text-align:center}body.dark-mode[_nghost-%COMP%]   .empty-message[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .empty-message[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.new-card-pinned[_ngcontent-%COMP%]{animation:pinned-view-fade-out 3s linear;background:#f44336;border-radius:5px;color:#fff;display:inline-block;font-size:13px;opacity:0;padding:3px 5px}@keyframes pinned-view-fade-out{from{opacity:1}66%{opacity:.99}to{opacity:0}}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(dyt,[{type:S_,args:[{selector:"metrics-pinned-view-component",template:'\n    <div class="group-toolbar">\n      <mat-icon svgIcon="keep_24px"></mat-icon>\n      <span class="group-text">\n        <span class="group-title" aria-role="heading" aria-level="3"\n          >Pinned</span\n        >\n        <span *ngIf="cardIdsWithMetadata.length > 1" class="group-card-count"\n          >{{ cardIdsWithMetadata.length }} cards</span\n        >\n        <span\n          *ngFor="let id of newCardPinnedIds"\n          [attr.data-id]="id"\n          class="new-card-pinned"\n          >New card pinned</span\n        >\n      </span>\n    </div>\n    <metrics-card-grid\n      *ngIf="cardIdsWithMetadata.length; else emptyPinnedView"\n      [cardIdsWithMetadata]="cardIdsWithMetadata"\n      [cardObserver]="cardObserver"\n    ></metrics-card-grid>\n    <ng-template #emptyPinnedView>\n      <div class="empty-message">Pin cards for a quick view and comparison</div>\n    </ng-template>\n  ',styleUrls:["pinned_view_component.css"],changeDetection:jn.OnPush}]}],null,{cardObserver:[{type:R_}],cardIdsWithMetadata:[{type:R_}],newCardPinnedIds:[{type:R_}]});class pyt{constructor(t){this.store=t,this.cardIdsWithMetadata$=this.store.select(cz).pipe(Be([])),this.newCardPinnedIds$=this.store.select(cz).pipe(Le(1),Vt((t=>t.map((t=>t.cardId)))),Ne(),Vt((([t,e])=>{const n=new Set(t),o=new Set(e);for(const t of o)if(!n.has(t))return Date.now();return null})),Be(null),Ne(),Vt((([t,e])=>null===t&&null===e?null:null===e?[t]:[e])),ue((t=>null!==t)),Vt((t=>[t[0]])))}}pyt.ɵfac=function t(e){return new(e||pyt)(gf(iS))},pyt.ɵcmp=so({type:pyt,selectors:[["metrics-pinned-view"]],inputs:{cardObserver:"cardObserver"},decls:3,vars:7,consts:[[3,"cardIdsWithMetadata","newCardPinnedIds","cardObserver"]],template:function t(e,n){1&e&&(vf(0,"metrics-pinned-view-component",0),_y(1,"async"),_y(2,"async")),2&e&&bf("cardIdsWithMetadata",Cy(1,3,n.cardIdsWithMetadata$))("newCardPinnedIds",Cy(2,5,n.newCardPinnedIds$))("cardObserver",n.cardObserver)},directives:[dyt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(pyt,[{type:S_,args:[{selector:"metrics-pinned-view",template:'\n    <metrics-pinned-view-component\n      [cardIdsWithMetadata]="cardIdsWithMetadata$ | async"\n      [newCardPinnedIds]="newCardPinnedIds$ | async"\n      [cardObserver]="cardObserver"\n    ></metrics-pinned-view-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),{cardObserver:[{type:R_}]});const myt=bS(az,_I,((t,e)=>t.filter((t=>!oN(t.plugin)||Boolean(e&&e.get(t.runId)))))),fyt=bS(myt,(t=>t.sort(((t,e)=>X$(t.tag,e.tag)))));function uyt(t,e){if(1&t&&(Cf(0,"span",7),Pu(1),_y(2,"number"),Mf()),2&t){const t=zf();Dc(1),ku("",Cy(2,1,t.numberOfCards)," cards")}}function gyt(t,e){1&t&&vf(0,"mat-icon",8)}function hyt(t,e){1&t&&vf(0,"mat-icon",9)}class byt{constructor(){this.groupExpansionToggled=new Sy}}byt.ɵfac=function t(e){return new(e||byt)},byt.ɵcmp=so({type:byt,selectors:[["metrics-card-group-toolbar-component"]],inputs:{groupName:"groupName",numberOfCards:"numberOfCards",isGroupExpanded:"isGroupExpanded"},outputs:{groupExpansionToggled:"groupExpansionToggled"},decls:9,vars:5,consts:function(){let t;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){t=goog.getMsg("Expand group")}else t=$localize`:A button that allows user to expand a tag group.␟ffaa11471b878a6dffe2e68c6f37064a9e074853␟5386054325274779258:Expand group`;return[["aria-label",t,1,"group-toolbar",3,"click"],[1,"group-title-wrapper"],["aria-role","heading","aria-level","3",1,"group-title",3,"title"],["class","group-card-count",4,"ngIf"],[1,"expand-group-icon"],["svgIcon","expand_less_24px",4,"ngIf","ngIfElse"],["expandMore",""],[1,"group-card-count"],["svgIcon","expand_less_24px"],["svgIcon","expand_more_24px"]]},template:function t(e,n){if(1&e&&(Cf(0,"button",0),Rf("click",(function t(){return n.groupExpansionToggled.emit()})),Cf(1,"span",1),Cf(2,"span",2),Pu(3),Mf(),Hm(4,uyt,3,3,"span",3),Mf(),Cf(5,"span",4),Hm(6,gyt,1,0,"mat-icon",5),Hm(7,hyt,1,0,"ng-template",null,6,qy),Mf(),Mf()),2&e){const t=Fm(8);Dc(2),Lf("title",n.groupName),Dc(1),wu(n.groupName),Dc(1),bf("ngIf",n.numberOfCards>1),Dc(2),bf("ngIf",n.isGroupExpanded)("ngIfElse",t)}},directives:[Pv,Mq],pipes:[$v],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   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group"\n      (click)="groupExpansionToggled.emit()"\n    >\n      <span class="group-title-wrapper">\n        <span\n          class="group-title"\n          aria-role="heading"\n          aria-level="3"\n          title="{{ groupName }}"\n          >{{ groupName }}</span\n        >\n        <span *ngIf="numberOfCards > 1" class="group-card-count"\n          >{{ numberOfCards | number }} cards</span\n        >\n      </span>\n      <span class="expand-group-icon">\n        <mat-icon\n          *ngIf="isGroupExpanded; else expandMore"\n          svgIcon="expand_less_24px"\n        ></mat-icon>\n        <ng-template #expandMore>\n          <mat-icon svgIcon="expand_more_24px"></mat-icon>\n        </ng-template>\n      </span>\n    </button>\n  ',styleUrls:["card_group_toolbar_component.css"],changeDetection:jn.OnPush}]}],null,{groupName:[{type:R_}],numberOfCards:[{type:R_}],isGroupExpanded:[{type:R_}],groupExpansionToggled:[{type:E_}]});class yyt{constructor(t){this.store=t,this.groupName=null,this.isGroupExpanded$=zt(!1)}ngOnInit(){this.isGroupExpanded$=null!==this.groupName?this.store.select(wz,this.groupName):zt(!1)}onGroupExpansionToggled(){if(null===this.groupName)throw new RangeError("Invariant error: expansion cannot be toggled when groupName is null");this.store.dispatch(NA({tagGroup:this.groupName}))}}function _yt(t,e){if(1&t&&(Cf(0,"div",1),vf(1,"metrics-card-group-toolbar",2),vf(2,"metrics-card-grid",3),Mf()),2&t){const t=e.$implicit,n=zf();Dc(1),bf("numberOfCards",t.items.length)("groupName",t.groupName),Dc(1),bf("cardIdsWithMetadata",t.items)("cardObserver",n.cardObserver)("groupName",t.groupName)}}yyt.ɵfac=function t(e){return new(e||yyt)(gf(iS))},yyt.ɵcmp=so({type:yyt,selectors:[["metrics-card-group-toolbar"]],inputs:{groupName:"groupName",numberOfCards:"numberOfCards"},decls:2,vars:5,consts:[[3,"numberOfCards","isGroupExpanded","groupName","groupExpansionToggled"]],template:function t(e,n){1&e&&(Cf(0,"metrics-card-group-toolbar-component",0),Rf("groupExpansionToggled",(function t(){return n.onGroupExpansionToggled()})),_y(1,"async"),Mf()),2&e&&bf("numberOfCards",n.numberOfCards)("isGroupExpanded",Cy(1,3,n.isGroupExpanded$))("groupName",n.groupName)},directives:[byt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(yyt,[{type:S_,args:[{selector:"metrics-card-group-toolbar",template:'\n    <metrics-card-group-toolbar-component\n      [numberOfCards]="numberOfCards"\n      [isGroupExpanded]="isGroupExpanded$ | async"\n      [groupName]="groupName"\n      (groupExpansionToggled)="onGroupExpansionToggled()"\n    ></metrics-card-group-toolbar-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),{groupName:[{type:R_}],numberOfCards:[{type:R_}]});class Cyt{constructor(){this.PluginType=KT}trackByGroup(t,e){return e.groupName}}Cyt.ɵfac=function t(e){return 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.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28122em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-form-field-autofill-control[_ngcontent-%COMP%]:-webkit-autofill + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28121em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.2812em) scale(0.75)}}.mat-form-field-appearance-fill[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:.25em 0 .75em 0}.mat-form-field-appearance-fill[_ngcontent-%COMP%]  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h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   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0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{background-color:#fff;border-bottom:1px solid #ebebeb;align-items:center;background-color:#fff;display:flex;flex:none;height:42px;margin-bottom:-1px;padding:0 16px;position:sticky;top:0;z-index:1;box-shadow:0px 2px 4px 0px rgba(0,0,0,.15)}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{background-color:#303030}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{border-bottom:1px solid #555}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{box-shadow:0px 2px 4px 0px rgba(255,255,255,.15)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Cyt,[{type:S_,args:[{selector:"metrics-card-groups-component",template:'\n    <div\n      *ngFor="let group of cardGroups; trackBy: trackByGroup"\n      class="card-group"\n    >\n      <metrics-card-group-toolbar\n        [numberOfCards]="group.items.length"\n        [groupName]="group.groupName"\n      ></metrics-card-group-toolbar>\n      <metrics-card-grid\n        [cardIdsWithMetadata]="group.items"\n        [cardObserver]="cardObserver"\n        [groupName]="group.groupName"\n      ></metrics-card-grid>\n    </div>\n  ',styleUrls:["card_groups_component.css"],changeDetection:jn.OnPush}]}],null,{cardGroups:[{type:R_}],cardObserver:[{type:R_}]});class Myt{constructor(t){this.store=t,this.cardGroups$=this.store.select(fyt).pipe(_e(this.store.select(Az)),Vt((([t,e])=>e.size?t.filter((t=>e.has(t.plugin))):t)),Vt((t=>Z$(t))))}}function vyt(t,e){if(1&t&&(Cf(0,"span"),Pu(1),Mf()),2&t){const t=zf();Dc(1),ku(" and ",t.getPluginTypeFilterString(t.pluginTypes)," visualization filter")}}Myt.ɵfac=function t(e){return new(e||Myt)(gf(iS))},Myt.ɵcmp=so({type:Myt,selectors:[["metrics-card-groups"]],inputs:{cardObserver:"cardObserver"},decls:2,vars:4,consts:[[3,"cardGroups","cardObserver"]],template:function t(e,n){1&e&&(vf(0,"metrics-card-groups-component",0),_y(1,"async")),2&e&&bf("cardGroups",Cy(1,2,n.cardGroups$))("cardObserver",n.cardObserver)},directives:[Cyt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Myt,[{type:S_,args:[{selector:"metrics-card-groups",template:'\n    <metrics-card-groups-component\n      [cardGroups]="cardGroups$ | async"\n      [cardObserver]="cardObserver"\n    ></metrics-card-groups-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),{cardObserver:[{type:R_}]});class xyt{constructor(){this.PluginType=KT,this.listFormatter=new Intl.ListFormat(void 0,{style:"long",type:"disjunction"})}getPluginTypeFilterString(t){const e=[...t].map((t=>{switch(t){case KT.SCALARS:return"scalar";case KT.IMAGES:return"image";case KT.HISTOGRAMS:return"histogram";default:throw new RangeError(`Please implement human readable name for plugin type: ${t}`)}}));return this.listFormatter.format(e)}}xyt.ɵfac=function t(e){return new(e||xyt)},xyt.ɵcmp=so({type:xyt,selectors:[["metrics-empty-tag-match-component"]],inputs:{pluginTypes:"pluginTypes",tagFilterRegex:"tagFilterRegex",tagCounts:"tagCounts"},decls:6,vars:5,consts:[[4,"ngIf"]],template:function t(e,n){1&e&&(Pu(0,"No matches for tag filter "),Cf(1,"code"),Pu(2),Mf(),Hm(3,vyt,2,1,"span",0),Pu(4),_y(5,"number")),2&e&&(Dc(2),ku("/",n.tagFilterRegex,"/"),Dc(1),bf("ngIf",n.pluginTypes.size),Dc(1),ku(" out of ",Cy(5,3,n.tagCounts)," tags."))},directives:[Pv],pipes:[$v],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(xyt,[{type:S_,args:[{selector:"metrics-empty-tag-match-component",template:'No matches for tag filter <code>/{{ tagFilterRegex }}/</code\n    ><span *ngIf="pluginTypes.size">\n      and {{ getPluginTypeFilterString(pluginTypes) }} visualization\n      filter</span\n    >\n    out of {{ tagCounts | number }} tags.',changeDetection:jn.OnPush}]}],null,{pluginTypes:[{type:R_}],tagFilterRegex:[{type:R_}],tagCounts:[{type:R_}]});class Oyt{constructor(t){this.store=t,this.pluginTypes$=this.store.select(Az),this.tagFilterRegex$=this.store.select(Pz),this.tagCounts$=this.store.select(fyt).pipe(Vt((t=>new Set(t.map((({tag:t})=>t))).size)))}}function Pyt(t,e){if(1&t&&(Cf(0,"span",6),Pu(1),_y(2,"number"),Mf()),2&t){const t=zf();Dc(1),ku("",Cy(2,1,t.cardIdsWithMetadata.length)," cards")}}function wyt(t,e){1&t&&vf(0,"metrics-empty-tag-match",7)}Oyt.ɵfac=function t(e){return new(e||Oyt)(gf(iS))},Oyt.ɵcmp=so({type:Oyt,selectors:[["metrics-empty-tag-match"]],decls:4,vars:9,consts:[[3,"pluginTypes","tagFilterRegex","tagCounts"]],template:function t(e,n){1&e&&(vf(0,"metrics-empty-tag-match-component",0),_y(1,"async"),_y(2,"async"),_y(3,"async")),2&e&&bf("pluginTypes",Cy(1,3,n.pluginTypes$))("tagFilterRegex",Cy(2,5,n.tagFilterRegex$))("tagCounts",Cy(3,7,n.tagCounts$))},directives:[xyt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Oyt,[{type:S_,args:[{selector:"metrics-empty-tag-match",template:'\n    <metrics-empty-tag-match-component\n      [pluginTypes]="pluginTypes$ | async"\n      [tagFilterRegex]="tagFilterRegex$ | async"\n      [tagCounts]="tagCounts$ | async"\n    ></metrics-empty-tag-match-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);class kyt{}kyt.ɵfac=function t(e){return 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aria-role="heading" aria-level="3"\n          >Tags matching filter</span\n        >\n        <span *ngIf="cardIdsWithMetadata.length > 1" class="group-card-count"\n          >{{ cardIdsWithMetadata.length | number }} cards</span\n        >\n      </span>\n    </div>\n    <metrics-empty-tag-match\n      *ngIf="isEmptyMatch"\n      class="warn"\n    ></metrics-empty-tag-match>\n    <metrics-card-grid\n      [cardIdsWithMetadata]="cardIdsWithMetadata"\n      [cardObserver]="cardObserver"\n    ></metrics-card-grid>\n  ',styleUrls:["filtered_view_component.css"],changeDetection:jn.OnPush}]}],null,{isEmptyMatch:[{type:R_}],cardObserver:[{type:R_}],cardIdsWithMetadata:[{type:R_}]});class Syt{constructor(t){this.store=t,this.cardIdsWithMetadata$=this.store.select(fyt).pipe(_e(this.store.select(Az)),Vt((([t,e])=>e.size?t.filter((t=>e.has(t.plugin))):t)),_e(this.store.select(Pz)),Ce(200),Vt((([t,e])=>{try{return{cardList:t,regex:new 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t(e,n){1&e&&(vf(0,"metrics-filtered-view-component",0),_y(1,"async"),_y(2,"async")),2&e&&bf("isEmptyMatch",Cy(1,3,n.isEmptyMatch$))("cardIdsWithMetadata",Cy(2,5,n.cardIdsWithMetadata$))("cardObserver",n.cardObserver)},directives:[kyt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Syt,[{type:S_,args:[{selector:"metrics-filtered-view",template:'\n    <metrics-filtered-view-component\n      [isEmptyMatch]="isEmptyMatch$ | async"\n      [cardIdsWithMetadata]="cardIdsWithMetadata$ | async"\n      [cardObserver]="cardObserver"\n    ></metrics-filtered-view-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),{cardObserver:[{type:R_}]});class Ryt{constructor(){this.value="",this.options=[],this.selectionChange=new Sy}}function Eyt(t,e){if(1&t){const t=wf();Cf(0,"div",22),Cf(1,"div",23),Pu(2,"Card Width"),Mf(),Cf(3,"div",10),Cf(4,"mat-slider",24),Rf("input",(function e(n){Pi(t);return 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t=zf(2);bf("min",t.stepMinMax.min)("max",t.stepMinMax.max)("lowerValue",null==t.selectedTime?null:t.selectedTime.start.step)("upperValue",null==t.selectedTime||null==t.selectedTime.end?null:t.selectedTime.end.step),ym("disabled",!t.selectTimeEnabled)}}function Nyt(t,e){if(1&t){const t=wf();Cf(0,"div",27),Cf(1,"label"),Pu(2,"Link visualization by step"),Mf(),Cf(3,"div",28),Cf(4,"div"),Cf(5,"mat-checkbox",16),Rf("change",(function e(){Pi(t);return zf().selectTimeEnableToggled.emit()})),Pu(6,"Enabled"),Mf(),Mf(),Cf(7,"div"),Cf(8,"mat-checkbox",16),Rf("change",(function e(){Pi(t);return zf().useRangeSelectTimeToggled.emit()})),Pu(9,"Use range"),Mf(),Mf(),Cf(10,"div",29),Hm(11,Ayt,1,6,"mat-slider",30),Hm(12,Tyt,1,5,"ng-template",null,31,qy),Mf(),Mf(),Mf()}if(2&t){const t=Fm(13),e=zf();Dc(5),bf("checked",e.selectTimeEnabled),Dc(3),bf("checked",e.useRangeSelectTime),Dc(3),bf("ngIf",!e.useRangeSelectTime)("ngIfElse",t)}}function zyt(t,e){if(1&t){const t=wf();Cf(0,"section",34),Cf(1,"h3",1),Pu(2,"Images"),Mf(),Cf(3,"div",35),Cf(4,"div",36),Pu(5,"Brightness"),Mf(),Cf(6,"div",10),Cf(7,"mat-slider",37),Rf("input",(function e(n){Pi(t);return zf().imageBrightnessSliderChanged$.emit(n.value)})),Mf(),Cf(8,"button",38),Rf("click",(function e(){Pi(t);return zf().imageBrightnessReset.emit()})),vf(9,"mat-icon",26),Mf(),Mf(),Mf(),Cf(10,"div",39),Cf(11,"div",40),Pu(12,"Contrast"),Mf(),Cf(13,"div",10),Cf(14,"mat-slider",41),Rf("input",(function e(n){Pi(t);return zf().imageContrastSliderChanged$.emit(n.value)})),Mf(),Cf(15,"button",42),Rf("click",(function e(){Pi(t);return zf().imageContrastReset.emit()})),vf(16,"mat-icon",26),Mf(),Mf(),Mf(),Cf(17,"div",43),Cf(18,"mat-checkbox",16),Rf("change",(function e(n){Pi(t);return zf().imageShowActualSizeChanged.emit(n.checked)})),Pu(19,"Show actual image size"),Mf(),Mf(),Mf()}if(2&t){const 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.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   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   [value]="option.value"\n        [disabled]="option.disabled"\n      >\n        {{ option.displayText }}\n      </mat-option>\n    </mat-select>\n  ',styleUrls:["dropdown_component.css"]}]}],null,{value:[{type:R_}],options:[{type:R_}],selectionChange:[{type:E_}]});const Iyt=250;class Hyt{constructor(t){this.locale=t,this.selectTimeEnableToggled=new Sy,this.useRangeSelectTimeToggled=new Sy,this.selectTimeChanged=new Sy,this.TooltipSortDropdownOptions=[{value:JT.ALPHABETICAL,displayText:"Alphabetical"},{value:JT.ASCENDING,displayText:"Ascending"},{value:JT.DESCENDING,displayText:"Descending"},{value:JT.NEAREST,displayText:"Nearest"}],this.tooltipSortChanged=new Sy,this.ignoreOutliersChanged=new Sy,this.XAxisType=QT,this.XAxisTypeDropdownOptions=[{value:QT.STEP,displayText:"Step"},{value:QT.RELATIVE,displayText:"Relative"},{value:QT.WALL_TIME,displayText:"Wall"}],this.xAxisTypeChanged=new Sy,this.MAX_CARD_WIDTH_SLIDER_VALUE=735,this.MIN_CARD_WIDTH_SLIDER_VALUE=335,this.cardWidthSliderChanged$=new Sy,this.cardWidthChanged=this.cardWidthSliderChanged$.pipe(ge(Iyt)),this.cardWidthReset=new Sy,this.HistogramModeDropdownOptions=[{value:jE.OFFSET,displayText:"Offset"},{value:jE.OVERLAY,displayText:"Overlay"}],this.histogramModeChanged=new Sy,this.MAX_SMOOTHING_VALUE=.999,this.MAX_SMOOTHING_SLIDER_VALUE=.99,this.scalarSmoothingControlChanged$=new Sy,this.scalarSmoothingChanged=this.scalarSmoothingControlChanged$.pipe(ge(Iyt)),this.scalarPartitionXToggled=new Sy,this.imageBrightnessSliderChanged$=new Sy,this.imageBrightnessInMilliChanged=this.imageBrightnessSliderChanged$.pipe(ge(Iyt)),this.imageBrightnessReset=new Sy,this.imageContrastSliderChanged$=new Sy,this.imageContrastInMilliChanged=this.imageContrastSliderChanged$.pipe(ge(Iyt)),this.imageContrastReset=new Sy,this.imageShowActualSizeChanged=new Sy}onScalarSmoothingInput(t){const e=t.target;if(!e.value)return;const n=Math.min(Math.max(0,parseFloat(e.value)),.999);n!==parseFloat(e.value)&&(e.value=String(n)),this.scalarSmoothingControlChanged$.emit(n)}formatMilliToZeroth(t){return fv(t/1e3,this.locale||"en-US","1.0-2")}onStepStartChanged(t){this.selectTimeChanged.emit({start:{step:t},end:null})}onStepRangeChanged({lowerValue:t,upperValue:e}){this.selectTimeChanged.emit({start:{step:t},end:{step:e}})}}Hyt.ɵfac=function t(e){return new(e||Hyt)(gf(Q_))},Hyt.ɵcmp=so({type:Hyt,selectors:[["metrics-dashboard-settings-component"]],inputs:{isCardWidthSettingEnabled:"isCardWidthSettingEnabled",isLinkedTimeFeatureEnabled:"isLinkedTimeFeatureEnabled",selectTimeEnabled:"selectTimeEnabled",useRangeSelectTime:"useRangeSelectTime",selectedTime:"selectedTime",stepMinMax:"stepMinMax",isImageSupportEnabled:"isImageSupportEnabled",tooltipSort:"tooltipSort",ignoreOutliers:"ignoreOutliers",xAxisType:"xAxisType",cardMinWidth:"cardMinWidth",histogramMode:"histogramMode",scalarSmoothing:"scalarSmoothing",scalarPartitionX:"scalarPartitionX",imageBrightnessInMilli:"imageBrightnessInMilli",imageContrastInMilli:"imageContrastInMilli",imageShowActualSize:"imageShowActualSize"},outputs:{selectTimeEnableToggled:"selectTimeEnableToggled",useRangeSelectTimeToggled:"useRangeSelectTimeToggled",selectTimeChanged:"selectTimeChanged",tooltipSortChanged:"tooltipSortChanged",ignoreOutliersChanged:"ignoreOutliersChanged",xAxisTypeChanged:"xAxisTypeChanged",cardWidthChanged:"cardWidthChanged",cardWidthReset:"cardWidthReset",histogramModeChanged:"histogramModeChanged",scalarSmoothingChanged:"scalarSmoothingChanged",scalarPartitionXToggled:"scalarPartitionXToggled",imageBrightnessInMilliChanged:"imageBrightnessInMilliChanged",imageBrightnessReset:"imageBrightnessReset",imageContrastInMilliChanged:"imageContrastInMilliChanged",imageContrastReset:"imageContrastReset",imageShowActualSizeChanged:"imageShowActualSizeChanged"},decls:37,vars:18,consts:function(){let t,e,n;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){t=goog.getMsg("Reset card width")}else t=$localize`:A button to reset the card width setting␟ccdc96b003fbba90db7a6959b5b26e3cc58f7d80␟5223111047968102466:Reset card width`;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){e=goog.getMsg("Reset brightness")}else e=$localize`:A button to reset the image brightness setting␟c482b3a47ea0975fa8be01afb3fbec9b76628bd7␟1189161857240378395:Reset brightness`;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){n=goog.getMsg("Reset contrast")}else n=$localize`:A button to reset the image contrast setting␟ed712a8b927041be15252b29eb521ebb1374bad8␟5370703342923611955:Reset contrast`;return[[1,"general"],[1,"section-title"],[1,"control-row","x-axis-type"],["id","x-axis-type-label",1,"control-name"],[3,"value","options","selectionChange"],["class","control-row card-width",4,"ngIf"],["class","control-row linked-time",4,"ngIf"],[1,"scalars"],[1,"control-row","scalars-smoothing"],["id","scalars-smoothing-label",1,"control-name"],[1,"slider-row"],["aria-labelledby","scalars-smoothing-label","color","primary",3,"max","min","step","value","thumbLabel","input"],["aria-labelledby","scalars-smoothing-label","type","number","min","0","step","0.001",1,"slider-input",3,"max","value","input"],[1,"control-row","tooltip-sort"],[1,"control-name"],[1,"control-row","scalars-ignore-outliers"],[3,"checked","change"],[1,"control-row","scalars-partition-x"],["svgIcon","help_outline_24px","title","Non-monotonic steps can occur when reusing a logdir with multiple summary writers and overlapping steps. Line charts, without this option enabled, can appear zig zagged. This is common when restarting from a checkpoint.\n\nWhen enabled, a non-monotonic time series composed of N monotonic pieces will be shown as N monotonic lines.",1,"info"],[1,"Histograms"],[1,"control-row","histogram-mode"],["class","image",4,"ngIf"],[1,"control-row","card-width"],["id","card-width-label",1,"control-name"],["aria-labelledby","card-width-label","color","primary",3,"max","min","step","value","thumbLabel","input"],["mat-icon-button","","aria-label",t,"title","Reset card width",1,"reset-button",3,"click"],["svgIcon","settings_backup_restore_24px"],[1,"control-row","linked-time"],[1,"controls"],[1,"step-selector"],["color","primary",3,"disabled","min","max","step","value","thumbLabel","input",4,"ngIf","ngIfElse"],["range",""],["color","primary",3,"disabled","min","max","step","value","thumbLabel","input"],[3,"min","max","lowerValue","upperValue","value"],[1,"image"],[1,"control-row","image-brightness"],["id","image-brightness-label",1,"control-name"],["aria-labelledby","image-brightness-label","color","primary",3,"max","min","step","value","thumbLabel","displayWith","input"],["mat-icon-button","","aria-label",e,"title","Reset brightness",1,"reset-button",3,"click"],[1,"control-row","image-contrast"],["id","image-constrast-label",1,"control-name"],["aria-labelledby","image-constrast-label","color","primary",3,"max","min","step","value","thumbLabel","displayWith","input"],["mat-icon-button","","aria-label",n,"title","Reset contrast",1,"reset-button",3,"click"],[1,"control-row","image-show-actual-size"]]},template:function t(e,n){1&e&&(Cf(0,"section",0),Cf(1,"h3",1),Pu(2,"General"),Mf(),Cf(3,"div",2),Cf(4,"div",3),Pu(5,"Horizontal Axis"),Mf(),Cf(6,"tb-dropdown",4),Rf("selectionChange",(function t(e){return n.xAxisTypeChanged.emit(e)})),Mf(),Mf(),Hm(7,Eyt,7,5,"div",5),Hm(8,Nyt,14,4,"div",6),Mf(),Cf(9,"section",7),Cf(10,"h3",1),Pu(11,"Scalars"),Mf(),Cf(12,"div",8),Cf(13,"div",9),Pu(14,"Smoothing"),Mf(),Cf(15,"div",10),Cf(16,"mat-slider",11),Rf("input",(function t(e){return n.scalarSmoothingControlChanged$.emit(e.value)})),Mf(),Cf(17,"input",12),Rf("input",(function t(e){return n.onScalarSmoothingInput(e)})),Mf(),Mf(),Mf(),Cf(18,"div",13),Cf(19,"div",14),Pu(20,"Tooltip sorting method"),Mf(),Cf(21,"tb-dropdown",4),Rf("selectionChange",(function t(e){return n.tooltipSortChanged.emit(e)})),Mf(),Mf(),Cf(22,"div",15),Cf(23,"mat-checkbox",16),Rf("change",(function t(e){return n.ignoreOutliersChanged.emit(e.checked)})),Pu(24,"Ignore outliers in chart scaling"),Mf(),Mf(),Cf(25,"div",17),Cf(26,"mat-checkbox",16),Rf("change",(function t(){return n.scalarPartitionXToggled.emit()})),Pu(27,"Partition non-monotonic X axis"),Mf(),vf(28,"mat-icon",18),Mf(),Mf(),Cf(29,"section",19),Cf(30,"h3",1),Pu(31,"Histograms"),Mf(),Cf(32,"div",20),Cf(33,"div",14),Pu(34,"Mode"),Mf(),Cf(35,"tb-dropdown",4),Rf("selectionChange",(function t(e){return n.histogramModeChanged.emit(e)})),Mf(),Mf(),Mf(),Hm(36,zyt,20,13,"section",21)),2&e&&(Dc(6),bf("value",n.xAxisType)("options",n.XAxisTypeDropdownOptions),Dc(1),bf("ngIf",n.isCardWidthSettingEnabled),Dc(1),bf("ngIf",n.isLinkedTimeFeatureEnabled&&n.xAxisType==n.XAxisType.STEP),Dc(8),bf("max",n.MAX_SMOOTHING_SLIDER_VALUE)("min",0)("step",.01)("value",n.scalarSmoothing)("thumbLabel",!0),Dc(1),bf("max",n.MAX_SMOOTHING_VALUE)("value",n.scalarSmoothing),Dc(4),bf("value",n.tooltipSort)("options",n.TooltipSortDropdownOptions),Dc(2),bf("checked",n.ignoreOutliers),Dc(3),bf("checked",n.scalarPartitionX),Dc(9),bf("value",n.histogramMode)("options",n.HistogramModeDropdownOptions),Dc(1),bf("ngIf",n.isImageSupportEnabled))},directives:[Ryt,Pv,wJ,bZ,Mq,NL,E2],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   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.info[_ngcontent-%COMP%]{height:15px;margin-left:5px;width:15px}mat-slider[_ngcontent-%COMP%]{flex:1;margin-left:-8px;margin-right:-8px}tb-dropdown[_ngcontent-%COMP%]{display:block}.linked-time[_ngcontent-%COMP%]   .step-selector[_ngcontent-%COMP%]{padding:0 10px}.linked-time[_ngcontent-%COMP%]   mat-slider[_ngcontent-%COMP%], .linked-time[_ngcontent-%COMP%]   tb-range-input[_ngcontent-%COMP%]{width:100%}.linked-time[_ngcontent-%COMP%]   .controls[_ngcontent-%COMP%]{padding:5px}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Hyt,[{type:S_,args:[{selector:"metrics-dashboard-settings-component",templateUrl:"settings_view_component.ng.html",changeDetection:jn.OnPush,styleUrls:["settings_view_component.css"]}]}],(function(){return[{type:void 0,decorators:[{type:Lr,args:[Q_]}]}]}),{isCardWidthSettingEnabled:[{type:R_}],isLinkedTimeFeatureEnabled:[{type:R_}],selectTimeEnabled:[{type:R_}],useRangeSelectTime:[{type:R_}],selectedTime:[{type:R_}],stepMinMax:[{type:R_}],selectTimeEnableToggled:[{type:E_}],useRangeSelectTimeToggled:[{type:E_}],selectTimeChanged:[{type:E_}],isImageSupportEnabled:[{type:R_}],tooltipSort:[{type:R_}],tooltipSortChanged:[{type:E_}],ignoreOutliers:[{type:R_}],ignoreOutliersChanged:[{type:E_}],xAxisType:[{type:R_}],xAxisTypeChanged:[{type:E_}],cardMinWidth:[{type:R_}],cardWidthChanged:[{type:E_}],cardWidthReset:[{type:E_}],histogramMode:[{type:R_}],histogramModeChanged:[{type:E_}],scalarSmoothing:[{type:R_}],scalarSmoothingChanged:[{type:E_}],scalarPartitionX:[{type:R_}],scalarPartitionXToggled:[{type:E_}],imageBrightnessInMilli:[{type:R_}],imageBrightnessInMilliChanged:[{type:E_}],imageBrightnessReset:[{type:E_}],imageContrastInMilli:[{type:R_}],imageContrastInMilliChanged:[{type:E_}],imageContrastReset:[{type:E_}],imageShowActualSize:[{type:R_}],imageShowActualSizeChanged:[{type:E_}]});class Fyt{constructor(t){this.store=t,this.isCardWidthSettingEnabled$=this.store.select(qD),this.isLinkedTimeFeatureEnabled$=this.store.select(GD),this.selectTimeEnabled$=this.store.select(kz),this.useRangeSelectTime$=this.store.select(Sz),this.selectedTime$=this.store.select(Rz),this.stepMinMax$=this.store.select(Dz),this.isImageSupportEnabled$=this.store.select(ND).pipe(ue(Boolean),ve(1),qe(this.store.select(WD)),Vt((([,t])=>t))),this.tooltipSort$=this.store.select(hz),this.ignoreOutliers$=this.store.select(bz),this.xAxisType$=this.store.select(yz),this.cardMinWidth$=this.store.select(gz),this.histogramMode$=this.store.select(_z),this.scalarSmoothing$=this.store.select(Cz),this.scalarPartitionX$=this.store.select(Mz),this.imageBrightnessInMilli$=this.store.select(vz),this.imageContrastInMilli$=this.store.select(xz),this.imageShowActualSize$=this.store.select(Oz)}onTooltipSortChanged(t){this.store.dispatch(gA({sort:t}))}onIgnoreOutliersChanged(){this.store.dispatch(hA())}onXAxisTypeChanged(t){this.store.dispatch(bA({xAxisType:t}))}onCardWidthChanged(t){this.store.dispatch(yA({cardMinWidth:t}))}onCardWidthReset(){this.store.dispatch(_A())}onHistogramModeChanged(t){this.store.dispatch(kA({histogramMode:t}))}onScalarSmoothingChanged(t){this.store.dispatch(CA({smoothing:t}))}onScalarPartitionXToggled(){this.store.dispatch(MA())}onImageBrightnessInMilliChanged(t){this.store.dispatch(vA({brightnessInMilli:t}))}onImageBrightnessReset(){this.store.dispatch(OA())}onImageContrastReset(){this.store.dispatch(PA())}onImageContrastInMilliChanged(t){this.store.dispatch(xA({contrastInMilli:t}))}onImageShowActualSizeChanged(){this.store.dispatch(wA())}onSelectTimeEnableToggled(){this.store.dispatch(BA())}onUseRangeSelectTimeToggled(){this.store.dispatch(VA())}onSelectTimeChanged(t){var e;this.store.dispatch(FA({startStep:t.start.step,endStep:null===(e=t.end)||void 0===e?void 0:e.step}))}}Fyt.ɵfac=function t(e){return new(e||Fyt)(gf(iS))},Fyt.ɵcmp=so({type:Fyt,selectors:[["metrics-dashboard-settings"]],decls:18,vars:51,consts:[[3,"isImageSupportEnabled","tooltipSort","ignoreOutliers","xAxisType","isCardWidthSettingEnabled","cardMinWidth","histogramMode","scalarSmoothing","scalarPartitionX","imageBrightnessInMilli","imageContrastInMilli","imageShowActualSize","isLinkedTimeFeatureEnabled","selectTimeEnabled","selectedTime","useRangeSelectTime","stepMinMax","tooltipSortChanged","ignoreOutliersChanged","xAxisTypeChanged","cardWidthChanged","cardWidthReset","histogramModeChanged","scalarSmoothingChanged","scalarPartitionXToggled","imageBrightnessInMilliChanged","imageBrightnessReset","imageContrastInMilliChanged","imageContrastReset","imageShowActualSizeChanged","selectTimeEnableToggled","useRangeSelectTimeToggled","selectTimeChanged"]],template:function t(e,n){1&e&&(Cf(0,"metrics-dashboard-settings-component",0),Rf("tooltipSortChanged",(function t(e){return n.onTooltipSortChanged(e)}))("ignoreOutliersChanged",(function t(){return n.onIgnoreOutliersChanged()}))("xAxisTypeChanged",(function t(e){return n.onXAxisTypeChanged(e)}))("cardWidthChanged",(function t(e){return n.onCardWidthChanged(e)}))("cardWidthReset",(function t(){return n.onCardWidthReset()}))("histogramModeChanged",(function t(e){return n.onHistogramModeChanged(e)}))("scalarSmoothingChanged",(function t(e){return n.onScalarSmoothingChanged(e)}))("scalarPartitionXToggled",(function t(){return n.onScalarPartitionXToggled()}))("imageBrightnessInMilliChanged",(function t(e){return n.onImageBrightnessInMilliChanged(e)}))("imageBrightnessReset",(function t(){return n.onImageBrightnessReset()}))("imageContrastInMilliChanged",(function t(e){return n.onImageContrastInMilliChanged(e)}))("imageContrastReset",(function t(){return n.onImageContrastReset()}))("imageShowActualSizeChanged",(function t(){return n.onImageShowActualSizeChanged()}))("selectTimeEnableToggled",(function t(){return n.onSelectTimeEnableToggled()}))("useRangeSelectTimeToggled",(function t(){return n.onUseRangeSelectTimeToggled()}))("selectTimeChanged",(function t(e){return n.onSelectTimeChanged(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async"),_y(5,"async"),_y(6,"async"),_y(7,"async"),_y(8,"async"),_y(9,"async"),_y(10,"async"),_y(11,"async"),_y(12,"async"),_y(13,"async"),_y(14,"async"),_y(15,"async"),_y(16,"async"),_y(17,"async"),Mf()),2&e&&bf("isImageSupportEnabled",Cy(1,17,n.isImageSupportEnabled$))("tooltipSort",Cy(2,19,n.tooltipSort$))("ignoreOutliers",Cy(3,21,n.ignoreOutliers$))("xAxisType",Cy(4,23,n.xAxisType$))("isCardWidthSettingEnabled",Cy(5,25,n.isCardWidthSettingEnabled$))("cardMinWidth",Cy(6,27,n.cardMinWidth$))("histogramMode",Cy(7,29,n.histogramMode$))("scalarSmoothing",Cy(8,31,n.scalarSmoothing$))("scalarPartitionX",Cy(9,33,n.scalarPartitionX$))("imageBrightnessInMilli",Cy(10,35,n.imageBrightnessInMilli$))("imageContrastInMilli",Cy(11,37,n.imageContrastInMilli$))("imageShowActualSize",Cy(12,39,n.imageShowActualSize$))("isLinkedTimeFeatureEnabled",Cy(13,41,n.isLinkedTimeFeatureEnabled$))("selectTimeEnabled",Cy(14,43,n.selectTimeEnabled$))("selectedTime",Cy(15,45,n.selectedTime$))("useRangeSelectTime",Cy(16,47,n.useRangeSelectTime$))("stepMinMax",Cy(17,49,n.stepMinMax$))},directives:[Hyt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Fyt,[{type:S_,args:[{selector:"metrics-dashboard-settings",template:'\n    <metrics-dashboard-settings-component\n      [isImageSupportEnabled]="isImageSupportEnabled$ | async"\n      [tooltipSort]="tooltipSort$ | async"\n      (tooltipSortChanged)="onTooltipSortChanged($event)"\n      [ignoreOutliers]="ignoreOutliers$ | async"\n      (ignoreOutliersChanged)="onIgnoreOutliersChanged()"\n      [xAxisType]="xAxisType$ | async"\n      (xAxisTypeChanged)="onXAxisTypeChanged($event)"\n      [isCardWidthSettingEnabled]="isCardWidthSettingEnabled$ | async"\n      [cardMinWidth]="cardMinWidth$ | async"\n      (cardWidthChanged)="onCardWidthChanged($event)"\n      (cardWidthReset)="onCardWidthReset()"\n      [histogramMode]="histogramMode$ | async"\n      (histogramModeChanged)="onHistogramModeChanged($event)"\n      [scalarSmoothing]="scalarSmoothing$ | async"\n      (scalarSmoothingChanged)="onScalarSmoothingChanged($event)"\n      [scalarPartitionX]="scalarPartitionX$ | async"\n      (scalarPartitionXToggled)="onScalarPartitionXToggled()"\n      [imageBrightnessInMilli]="imageBrightnessInMilli$ | async"\n      (imageBrightnessInMilliChanged)="onImageBrightnessInMilliChanged($event)"\n      (imageBrightnessReset)="onImageBrightnessReset()"\n      [imageContrastInMilli]="imageContrastInMilli$ | async"\n      (imageContrastInMilliChanged)="onImageContrastInMilliChanged($event)"\n      (imageContrastReset)="onImageContrastReset()"\n      [imageShowActualSize]="imageShowActualSize$ | async"\n      (imageShowActualSizeChanged)="onImageShowActualSizeChanged()"\n      [isLinkedTimeFeatureEnabled]="isLinkedTimeFeatureEnabled$ | async"\n      [selectTimeEnabled]="selectTimeEnabled$ | async"\n      [selectedTime]="selectedTime$ | async"\n      [useRangeSelectTime]="useRangeSelectTime$ | async"\n      [stepMinMax]="stepMinMax$ | async"\n      (selectTimeEnableToggled)="onSelectTimeEnableToggled()"\n      (useRangeSelectTimeToggled)="onUseRangeSelectTimeToggled()"\n      (selectTimeChanged)="onSelectTimeChanged($event)"\n    >\n    </metrics-dashboard-settings-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);class Lyt{}function Byt(t,e){if(1&t&&vf(0,"metrics-filtered-view",9),2&t){bf("cardObserver",zf().cardObserver)}}function Vyt(t,e){1&t&&(Cf(0,"div",12),vf(1,"mat-spinner",13),Mf())}function jyt(t,e){if(1&t){const t=wf();Cf(0,"div",14),Cf(1,"div",15),Cf(2,"h2",16),Pu(3,"Settings"),Mf(),Cf(4,"button",17),Rf("click",(function e(){Pi(t);return zf().onCloseSidepaneButtonClicked.emit()})),vf(5,"mat-icon",18),Mf(),Mf(),vf(6,"metrics-dashboard-right-pane"),Mf()}}Lyt.ɵfac=function t(e){return new(e||Lyt)},Lyt.ɵcmp=so({type:Lyt,selectors:[["metrics-dashboard-right-pane"]],decls:1,vars:0,template:function t(e,n){1&e&&vf(0,"metrics-dashboard-settings")},directives:[Fyt],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Lyt,[{type:S_,args:[{selector:"metrics-dashboard-right-pane",template:"<metrics-dashboard-settings></metrics-dashboard-settings>",changeDetection:jn.OnPush}]}],null,null);const Uyt=function(t){return{checked:t,"settings-button":!0}};class Wyt{constructor(t){this.host=t,this.onSettingsButtonClicked=new Sy,this.onCloseSidepaneButtonClicked=new Sy,this.onPluginTypeToggled=new Sy,this.onPluginTypeAllToggled=new Sy,this.PluginType=KT,this.cardObserver=new _4(this.host.nativeElement,"600px 0px 600px 0px")}}Wyt.ɵfac=function t(e){return new(e||Wyt)(gf(Kh))},Wyt.ɵcmp=so({type:Wyt,selectors:[["metrics-main-view-component"]],inputs:{showFilteredView:"showFilteredView",isSidepaneOpen:"isSidepaneOpen",filteredPluginTypes:"filteredPluginTypes",initialTagsLoading:"initialTagsLoading"},outputs:{onSettingsButtonClicked:"onSettingsButtonClicked",onCloseSidepaneButtonClicked:"onCloseSidepaneButtonClicked",onPluginTypeToggled:"onPluginTypeToggled",onPluginTypeAllToggled:"onPluginTypeAllToggled"},decls:22,vars:21,consts:function(){let t,e;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){t=goog.getMsg("Toggle settings side pane")}else t=$localize`:Label on a toolbar button to toggle the settings side pane.␟d3516db6bbe6860a55beab66e4969dac625b8d72␟7659285445580838925:Toggle settings side pane`;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){e=goog.getMsg("Close side pane")}else e=$localize`:Label on a button to close the settings side pane.␟04521dc0b6a65cf5c382944c9a8b4b844a3e9598␟8156766997747165871:Close side pane`;return[[1,"toolbar"],["multiple","","appearance","standard",1,"filter-view"],["mat-button","","role","checkbox",1,"filter-view-button",3,"click"],[1,"right-items"],["mat-stroked-button","","aria-label",t,3,"ngClass","click"],["svgIcon","settings_24px"],[1,"split-content"],["cdkScrollable",""],[3,"cardObserver",4,"ngIf"],[3,"cardObserver"],["class","loading-container",4,"ngIf"],["class","sidebar",4,"ngIf"],[1,"loading-container"],["diameter","36"],[1,"sidebar"],[1,"header"],[1,"title"],["mat-icon-button","","aria-label",e,3,"click"],["svgIcon","close_24px"]]},template:function t(e,n){1&e&&(Cf(0,"div",0),vf(1,"metrics-tag-filter"),Cf(2,"mat-button-toggle-group",1),Cf(3,"button",2),Rf("click",(function t(){return n.onPluginTypeAllToggled.emit()})),Pu(4," All "),Mf(),Cf(5,"button",2),Rf("click",(function t(){return n.onPluginTypeToggled.emit(n.PluginType.SCALARS)})),Pu(6," Scalars "),Mf(),Cf(7,"button",2),Rf("click",(function t(){return n.onPluginTypeToggled.emit(n.PluginType.IMAGES)})),Pu(8," Image "),Mf(),Cf(9,"button",2),Rf("click",(function t(){return n.onPluginTypeToggled.emit(n.PluginType.HISTOGRAMS)})),Pu(10," Histogram "),Mf(),Mf(),Cf(11,"div",3),Cf(12,"button",4),Rf("click",(function t(){return n.onSettingsButtonClicked.emit()})),vf(13,"mat-icon",5),Pu(14," Settings "),Mf(),Mf(),Mf(),Cf(15,"div",6),Cf(16,"div",7),Hm(17,Byt,1,1,"metrics-filtered-view",8),vf(18,"metrics-pinned-view",9),Hm(19,Vyt,2,0,"div",10),vf(20,"metrics-card-groups",9),Mf(),Hm(21,jyt,7,0,"div",11),Mf()),2&e&&(Dc(3),ym("aria-checked",0===n.filteredPluginTypes.size),Dc(2),ym("aria-checked",n.filteredPluginTypes.has(n.PluginType.SCALARS)),Dc(2),ym("aria-checked",n.filteredPluginTypes.has(n.PluginType.IMAGES)),Dc(2),ym("aria-checked",n.filteredPluginTypes.has(n.PluginType.HISTOGRAMS)),Dc(3),bf("ngClass",iy(19,Uyt,n.isSidepaneOpen)),ym("aria-pressed",n.isSidepaneOpen),Dc(4),cu("main",!0)("filter-view",n.showFilteredView),Dc(1),bf("ngIf",n.showFilteredView),Dc(1),lu("display",n.showFilteredView?"none":""),bf("cardObserver",n.cardObserver),Dc(1),bf("ngIf",n.initialTagsLoading),Dc(1),lu("display",n.showFilteredView?"none":""),bf("cardObserver",n.cardObserver),Dc(1),bf("ngIf",n.isSidepaneOpen))},directives:[P4,A4,NL,Cv,Mq,JL,Pv,pyt,Myt,Syt,a2,Lyt],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   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.mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%]{height:1.125em;line-height:1.125}.mat-form-field-infix[_ngcontent-%COMP%]{padding:.5em 0;border-top:.84375em solid transparent}.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.34375em) scale(0.75);width:133.3333333333%}.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.34374em) 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scale(0.75) perspective(100px) translateZ(0.001px);-ms-transform:translateY(-1.28125em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-form-field-autofill-control[_ngcontent-%COMP%]:-webkit-autofill + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28125em) scale(0.75) perspective(100px) translateZ(0.00101px);-ms-transform:translateY(-1.28124em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28125em) scale(0.75) perspective(100px) translateZ(0.00102px);-ms-transform:translateY(-1.28123em) scale(0.75);width:133.3333533333%}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   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.mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}input.mat-input-element[_ngcontent-%COMP%]{margin-top:-0.0625em}.mat-menu-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:400}.mat-paginator[_ngcontent-%COMP%], .mat-paginator-page-size[_ngcontent-%COMP%]   .mat-select-trigger[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px}.mat-radio-button[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", 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.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{display:flex;flex-direction:column;height:100%}.toolbar[_ngcontent-%COMP%]{border-bottom:1px solid #ebebeb;flex:none;display:flex;align-items:center;justify-content:space-between;height:48px;padding:0 16px}body.dark-mode[_nghost-%COMP%]   .toolbar[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .toolbar[_ngcontent-%COMP%]{border-bottom:1px solid #555}.toolbar[_ngcontent-%COMP%]   metrics-tag-filter[_ngcontent-%COMP%]{flex:1 1 100px}.toolbar[_ngcontent-%COMP%]   .right-items[_ngcontent-%COMP%]{border-left:1px solid #ebebeb;margin-left:16px;padding-left:16px}body.dark-mode[_nghost-%COMP%]   .toolbar[_ngcontent-%COMP%]   .right-items[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .toolbar[_ngcontent-%COMP%]   .right-items[_ngcontent-%COMP%]{border-left:1px solid #555}.filter-view[_ngcontent-%COMP%]{border-radius:4px;flex:none;margin-right:5px}.filter-view[_ngcontent-%COMP%]   button[_ngcontent-%COMP%]{border-radius:0;font-size:12px;font-weight:normal;height:25px;line-height:25px;min-width:unset;padding:0 12px}.filter-view[_ngcontent-%COMP%]   button[_ngcontent-%COMP%] + button[_ngcontent-%COMP%]{border-left:1px solid #ebebeb}body.dark-mode[_nghost-%COMP%]   .filter-view[_ngcontent-%COMP%]   button[_ngcontent-%COMP%] + button[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .filter-view[_ngcontent-%COMP%]   button[_ngcontent-%COMP%] + button[_ngcontent-%COMP%]{border-left:1px solid #555}.filter-view[_ngcontent-%COMP%]   button[aria-checked=true][_ngcontent-%COMP%]{background-color:#e0e0e0}body.dark-mode[_nghost-%COMP%]   .filter-view[_ngcontent-%COMP%]   button[aria-checked=true][_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .filter-view[_ngcontent-%COMP%]   button[aria-checked=true][_ngcontent-%COMP%]{background-color:#212121}.split-content[_ngcontent-%COMP%]{display:flex;overflow-y:auto;flex:1}.main[_ngcontent-%COMP%], .sidebar[_ngcontent-%COMP%]{contain:strict;overflow-x:hidden;overflow-y:auto;will-change:transform,scroll-position}.main[_ngcontent-%COMP%]{background-color:#f5f6f7;flex:1 1;display:flex;flex-direction:column}body.dark-mode[_nghost-%COMP%]   .main[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .main[_ngcontent-%COMP%]{background-color:#3a3a3a}.main[_ngcontent-%COMP%]   metrics-filtered-view[_ngcontent-%COMP%], .main[_ngcontent-%COMP%]   metrics-pinned-view[_ngcontent-%COMP%]{border-bottom:1px solid #ebebeb}body.dark-mode[_nghost-%COMP%]   .main[_ngcontent-%COMP%]   metrics-filtered-view[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .main[_ngcontent-%COMP%]   metrics-filtered-view[_ngcontent-%COMP%]{border-bottom:1px solid #555}body.dark-mode[_nghost-%COMP%]   .main[_ngcontent-%COMP%]   metrics-pinned-view[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .main[_ngcontent-%COMP%]   metrics-pinned-view[_ngcontent-%COMP%]{border-bottom:1px solid #555}.main.filter-view[_ngcontent-%COMP%]{overflow:hidden}.main.filter-view[_ngcontent-%COMP%]   metrics-filtered-view[_ngcontent-%COMP%]{contain:content;overflow:auto;will-change:transform,scroll-position}.loading-container[_ngcontent-%COMP%]{align-items:center;display:flex;justify-content:center;margin:20px 0}.sidebar[_ngcontent-%COMP%]{border-left:1px solid #ebebeb;flex:0 0 250px}body.dark-mode[_nghost-%COMP%]   .sidebar[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .sidebar[_ngcontent-%COMP%]{border-left:1px solid #555}.sidebar[_ngcontent-%COMP%]   .header[_ngcontent-%COMP%]{border-bottom:1px solid #ebebeb;display:flex;align-items:center;justify-content:space-between;height:42px;padding:0 16px}body.dark-mode[_nghost-%COMP%]   .sidebar[_ngcontent-%COMP%]   .header[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .sidebar[_ngcontent-%COMP%]   .header[_ngcontent-%COMP%]{border-bottom:1px solid #555}.sidebar[_ngcontent-%COMP%]   .header[_ngcontent-%COMP%]   .title[_ngcontent-%COMP%]{font-size:18px;font-weight:400;line-height:normal;margin:0}[_nghost-%COMP%]   .settings-button[_ngcontent-%COMP%]{color:#616161;display:inline-flex}body.dark-mode   [_nghost-%COMP%]   .settings-button[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}[_nghost-%COMP%]   .settings-button.checked[_ngcontent-%COMP%]{background-color:#e0e0e0;border-color:#e0e0e0}body.dark-mode   [_nghost-%COMP%]   .settings-button.checked[_ngcontent-%COMP%]{background-color:#212121}[_nghost-%COMP%]   .settings-button[_ngcontent-%COMP%]     .mat-button-wrapper{display:inline-flex;align-items:center}[_nghost-%COMP%]   .settings-button[_ngcontent-%COMP%]   mat-icon[_ngcontent-%COMP%]{margin-right:4px}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Wyt,[{type:S_,args:[{selector:"metrics-main-view-component",templateUrl:"main_view_component.ng.html",styleUrls:["main_view_component.css"],changeDetection:jn.OnPush}]}],(function(){return[{type:Kh}]}),{showFilteredView:[{type:R_}],isSidepaneOpen:[{type:R_}],filteredPluginTypes:[{type:R_}],initialTagsLoading:[{type:R_}],onSettingsButtonClicked:[{type:E_}],onCloseSidepaneButtonClicked:[{type:E_}],onPluginTypeToggled:[{type:E_}],onPluginTypeAllToggled:[{type:E_}]});class Gyt{constructor(t){this.store=t,this.isSidepaneOpen$=this.store.select(Nz),this.initialTagsLoading$=this.store.select(JN).pipe(Ue((t=>null===t.lastLoadedTimeInMs),!0),Vt((t=>t.state===UE.LOADING&&null===t.lastLoadedTimeInMs))),this.showFilteredView$=this.store.select(Pz).pipe(Vt((t=>t.length>0))),this.filteredPluginTypes$=this.store.select(Az)}onSettingsButtonClicked(){this.store.dispatch(pA())}onCloseSidepaneButtonClicked(){this.store.dispatch(dA())}onPluginVisibilityToggled(t){this.store.dispatch(IA({plugin:t}))}onShowAllPlugins(){this.store.dispatch(HA())}}Gyt.ɵfac=function t(e){return new(e||Gyt)(gf(iS))},Gyt.ɵcmp=so({type:Gyt,selectors:[["metrics-main-view"]],decls:5,vars:12,consts:[[3,"showFilteredView","isSidepaneOpen","initialTagsLoading","filteredPluginTypes","onSettingsButtonClicked","onCloseSidepaneButtonClicked","onPluginTypeToggled","onPluginTypeAllToggled"]],template:function t(e,n){1&e&&(Cf(0,"metrics-main-view-component",0),Rf("onSettingsButtonClicked",(function t(){return n.onSettingsButtonClicked()}))("onCloseSidepaneButtonClicked",(function t(){return n.onCloseSidepaneButtonClicked()}))("onPluginTypeToggled",(function t(e){return n.onPluginVisibilityToggled(e)}))("onPluginTypeAllToggled",(function t(){return n.onShowAllPlugins()})),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async"),Mf()),2&e&&bf("showFilteredView",Cy(1,4,n.showFilteredView$))("isSidepaneOpen",Cy(2,6,n.isSidepaneOpen$))("initialTagsLoading",Cy(3,8,n.initialTagsLoading$))("filteredPluginTypes",Cy(4,10,n.filteredPluginTypes$))},directives:[Wyt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Gyt,[{type:S_,args:[{selector:"metrics-main-view",template:'\n    <metrics-main-view-component\n      [showFilteredView]="showFilteredView$ | async"\n      [isSidepaneOpen]="isSidepaneOpen$ | async"\n      [initialTagsLoading]="initialTagsLoading$ | async"\n      [filteredPluginTypes]="filteredPluginTypes$ | async"\n      (onSettingsButtonClicked)="onSettingsButtonClicked()"\n      (onCloseSidepaneButtonClicked)="onCloseSidepaneButtonClicked()"\n      (onPluginTypeToggled)="onPluginVisibilityToggled($event)"\n      (onPluginTypeAllToggled)="onShowAllPlugins()"\n    ></metrics-main-view-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);const Yyt=new er("[Metrics] METRICS_Promo Message Component");function qyt(t,e){1&t&&(xf(0),Ff(1),Of())}class Zyt{constructor(t,e){this.viewContainerRef=t,this.componentFactoryResolver=e}ngOnInit(){if(this.customizableComponent){const t=this.componentFactoryResolver.resolveComponentFactory(this.customizableComponent.constructor);this.viewContainerRef.createComponent(t)}}}Zyt.ɵfac=function t(e){return new(e||Zyt)(gf(Fb),gf(Gh))},Zyt.ɵcmp=so({type:Zyt,selectors:[["tb-customization"]],inputs:{customizableComponent:"customizableComponent"},ngContentSelectors:["*"],decls:1,vars:1,consts:[[4,"ngIf"]],template:function t(e,n){1&e&&(Hf(),Hm(0,qyt,2,0,"ng-container",0)),2&e&&bf("ngIf",!n.customizableComponent)},directives:[Pv],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Zyt,[{type:S_,args:[{selector:"tb-customization",template:'\n    <ng-container *ngIf="!customizableComponent">\n      <ng-content></ng-content>\n    </ng-container>\n  '}]}],(function(){return[{type:Fb},{type:Gh}]}),{customizableComponent:[{type:R_}]});class Xyt{constructor(t){this.customPromoMessage=t,this.onDismiss=new Sy,this.onGoToScalars=new Sy}}Xyt.ɵfac=function t(e){return new(e||Xyt)(gf(Yyt,8))},Xyt.ɵcmp=so({type:Xyt,selectors:[["metrics-promo-notice-component"]],outputs:{onDismiss:"onDismiss",onGoToScalars:"onGoToScalars"},decls:7,vars:1,consts:[[1,"message",3,"customizableComponent"],[1,"go-to-scalars",3,"click"],[1,"dismiss",3,"click"],["inline","","svgIcon","close_24px"]],template:function t(e,n){1&e&&(Cf(0,"tb-customization",0),Pu(1," Welcome to new default experience of TensorBoard. Time Series lets you view all visualizations at once, put them side-by-side with pins, and customize colors. Scalars and other plugins are still available. "),Cf(2,"button",1),Rf("click",(function t(){return n.onGoToScalars.emit()})),Pu(3," Go to Scalars plugin"),Mf(),Pu(4,".\n"),Mf(),Cf(5,"button",2),Rf("click",(function t(){return n.onDismiss.emit()})),vf(6,"mat-icon",3),Mf()),2&e&&bf("customizableComponent",n.customPromoMessage)},directives:[Zyt,Mq],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", 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SMt(this.rows*e,this.columns*n);for(let t=0;t<e;t++)for(let e=0;e<n;e++)o.setSubMatrix(this,this.rows*t,this.columns*e);return o}fill(t){for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,t);return this}neg(){return this.mulS(-1)}getRow(t){gMt(this,t);let e=[];for(let n=0;n<this.columns;n++)e.push(this.get(t,n));return e}getRowVector(t){return SMt.rowVector(this.getRow(t))}setRow(t,e){gMt(this,t),e=bMt(this,e);for(let n=0;n<this.columns;n++)this.set(t,n,e[n]);return this}swapRows(t,e){gMt(this,t),gMt(this,e);for(let n=0;n<this.columns;n++){let o=this.get(t,n);this.set(t,n,this.get(e,n)),this.set(e,n,o)}return this}getColumn(t){hMt(this,t);let e=[];for(let n=0;n<this.rows;n++)e.push(this.get(n,t));return e}getColumnVector(t){return SMt.columnVector(this.getColumn(t))}setColumn(t,e){hMt(this,t),e=yMt(this,e);for(let n=0;n<this.rows;n++)this.set(n,t,e[n]);return this}swapColumns(t,e){hMt(this,t),hMt(this,e);for(let n=0;n<this.rows;n++){let o=this.get(n,t);this.set(n,t,this.get(n,e)),this.set(n,e,o)}return this}addRowVector(t){t=bMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)+t[n]);return this}subRowVector(t){t=bMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)-t[n]);return this}mulRowVector(t){t=bMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)*t[n]);return this}divRowVector(t){t=bMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)/t[n]);return this}addColumnVector(t){t=yMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)+t[e]);return this}subColumnVector(t){t=yMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)-t[e]);return this}mulColumnVector(t){t=yMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)*t[e]);return this}divColumnVector(t){t=yMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)/t[e]);return this}mulRow(t,e){gMt(this,t);for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)*e);return this}mulColumn(t,e){hMt(this,t);for(let n=0;n<this.rows;n++)this.set(n,t,this.get(n,t)*e);return this}max(){if(this.isEmpty())return NaN;let t=this.get(0,0);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.get(e,n)>t&&(t=this.get(e,n));return t}maxIndex(){OMt(this);let t=this.get(0,0),e=[0,0];for(let n=0;n<this.rows;n++)for(let o=0;o<this.columns;o++)this.get(n,o)>t&&(t=this.get(n,o),e[0]=n,e[1]=o);return e}min(){if(this.isEmpty())return NaN;let t=this.get(0,0);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.get(e,n)<t&&(t=this.get(e,n));return t}minIndex(){OMt(this);let t=this.get(0,0),e=[0,0];for(let n=0;n<this.rows;n++)for(let o=0;o<this.columns;o++)this.get(n,o)<t&&(t=this.get(n,o),e[0]=n,e[1]=o);return e}maxRow(t){if(gMt(this,t),this.isEmpty())return NaN;let e=this.get(t,0);for(let n=1;n<this.columns;n++)this.get(t,n)>e&&(e=this.get(t,n));return e}maxRowIndex(t){gMt(this,t),OMt(this);let e=this.get(t,0),n=[t,0];for(let o=1;o<this.columns;o++)this.get(t,o)>e&&(e=this.get(t,o),n[1]=o);return n}minRow(t){if(gMt(this,t),this.isEmpty())return NaN;let e=this.get(t,0);for(let n=1;n<this.columns;n++)this.get(t,n)<e&&(e=this.get(t,n));return e}minRowIndex(t){gMt(this,t),OMt(this);let e=this.get(t,0),n=[t,0];for(let o=1;o<this.columns;o++)this.get(t,o)<e&&(e=this.get(t,o),n[1]=o);return n}maxColumn(t){if(hMt(this,t),this.isEmpty())return NaN;let e=this.get(0,t);for(let n=1;n<this.rows;n++)this.get(n,t)>e&&(e=this.get(n,t));return e}maxColumnIndex(t){hMt(this,t),OMt(this);let e=this.get(0,t),n=[0,t];for(let o=1;o<this.rows;o++)this.get(o,t)>e&&(e=this.get(o,t),n[0]=o);return n}minColumn(t){if(hMt(this,t),this.isEmpty())return NaN;let e=this.get(0,t);for(let n=1;n<this.rows;n++)this.get(n,t)<e&&(e=this.get(n,t));return e}minColumnIndex(t){hMt(this,t),OMt(this);let e=this.get(0,t),n=[0,t];for(let o=1;o<this.rows;o++)this.get(o,t)<e&&(e=this.get(o,t),n[0]=o);return n}diag(){let t=Math.min(this.rows,this.columns),e=[];for(let n=0;n<t;n++)e.push(this.get(n,n));return e}norm(t="frobenius"){let e=0;if("max"===t)return this.max();if("frobenius"===t){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)e+=this.get(t,n)*this.get(t,n);return Math.sqrt(e)}throw new RangeError(`unknown norm type: ${t}`)}cumulativeSum(){let t=0;for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)t+=this.get(e,n),this.set(e,n,t);return this}dot(t){PMt.isMatrix(t)&&(t=t.to1DArray());let e=this.to1DArray();if(e.length!==t.length)throw new RangeError("vectors do not have the same size");let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n}mmul(t){t=SMt.checkMatrix(t);let e=this.rows,n=this.columns,o=t.columns,i=new SMt(e,o),a=new Float64Array(n);for(let r=0;r<o;r++){for(let e=0;e<n;e++)a[e]=t.get(e,r);for(let t=0;t<e;t++){let e=0;for(let o=0;o<n;o++)e+=this.get(t,o)*a[o];i.set(t,r,e)}}return i}strassen2x2(t){t=SMt.checkMatrix(t);let e=new SMt(2,2);const n=this.get(0,0),o=t.get(0,0),i=this.get(0,1),a=t.get(0,1),r=this.get(1,0),s=t.get(1,0),l=this.get(1,1),c=t.get(1,1),d=(n+l)*(o+c),p=(r+l)*o,m=n*(a-c),f=l*(s-o),u=(n+i)*c,g=m+u,h=p+f,b=d-p+m+(r-n)*(o+a);return e.set(0,0,d+f-u+(i-l)*(s+c)),e.set(0,1,g),e.set(1,0,h),e.set(1,1,b),e}strassen3x3(t){t=SMt.checkMatrix(t);let e=new SMt(3,3);const n=this.get(0,0),o=this.get(0,1),i=this.get(0,2),a=this.get(1,0),r=this.get(1,1),s=this.get(1,2),l=this.get(2,0),c=this.get(2,1),d=this.get(2,2),p=t.get(0,0),m=t.get(0,1),f=t.get(0,2),u=t.get(1,0),g=t.get(1,1),h=t.get(1,2),b=t.get(2,0),y=t.get(2,1),_=t.get(2,2),C=(n-a)*(-m+g),M=(-n+a+r)*(p-m+g),v=(a+r)*(-p+m),x=n*p,O=(-n+l+c)*(p-f+h),P=(-n+l)*(f-h),w=(l+c)*(-p+f),k=(-i+c+d)*(g+b-y),S=(i-d)*(g-y),D=i*b,R=(c+d)*(-b+y),E=(-i+r+s)*(h+b-_),A=(i-s)*(h-_),T=(r+s)*(-b+_),N=(n+o+i-a-r-c-d)*g+M+v+x+k+D+R,z=x+O+w+(n+o+i-r-s-l-c)*h+D+E+T,I=C+r*(-p+m+u-g-h-b+_)+M+x+D+E+A,H=C+M+v+x+s*y,F=D+E+A+T+a*f,L=x+O+P+c*(-p+f+u-g-h-b+y)+k+S+D,B=k+S+D+R+l*m,V=x+O+P+w+d*_;return e.set(0,0,x+D+o*u),e.set(0,1,N),e.set(0,2,z),e.set(1,0,I),e.set(1,1,H),e.set(1,2,F),e.set(2,0,L),e.set(2,1,B),e.set(2,2,V),e}mmulStrassen(t){t=SMt.checkMatrix(t);let e=this.clone(),n=e.rows,o=e.columns,i=t.rows,a=t.columns;function r(t,e,n){if(t.rows===e&&t.columns===n)return t;{let o=PMt.zeros(e,n);return o=o.setSubMatrix(t,0,0),o}}o!==i&&console.warn(`Multiplying ${n} x ${o} and ${i} x ${a} matrix: dimensions do not match.`);let s=Math.max(n,i),l=Math.max(o,a);return e=r(e,s,l),(function t(e,n,o,i){if(o<=512||i<=512)return e.mmul(n);o%2==1&&i%2==1?(e=r(e,o+1,i+1),n=r(n,o+1,i+1)):o%2==1?(e=r(e,o+1,i),n=r(n,o+1,i)):i%2==1&&(e=r(e,o,i+1),n=r(n,o,i+1));let a=parseInt(e.rows/2,10),s=parseInt(e.columns/2,10),l=e.subMatrix(0,a-1,0,s-1),c=n.subMatrix(0,a-1,0,s-1),d=e.subMatrix(0,a-1,s,e.columns-1),p=n.subMatrix(0,a-1,s,n.columns-1),m=e.subMatrix(a,e.rows-1,0,s-1),f=n.subMatrix(a,n.rows-1,0,s-1),u=e.subMatrix(a,e.rows-1,s,e.columns-1),g=n.subMatrix(a,n.rows-1,s,n.columns-1),h=t(PMt.add(l,u),PMt.add(c,g),a,s),b=t(PMt.add(m,u),c,a,s),y=t(l,PMt.sub(p,g),a,s),_=t(u,PMt.sub(f,c),a,s),C=t(PMt.add(l,d),g,a,s),M=t(PMt.sub(m,l),PMt.add(c,p),a,s),v=t(PMt.sub(d,u),PMt.add(f,g),a,s),x=PMt.add(h,_);x.sub(C),x.add(v);let O=PMt.add(y,C),P=PMt.add(b,_),w=PMt.sub(h,b);w.add(y),w.add(M);let k=PMt.zeros(2*x.rows,2*x.columns);return k=k.setSubMatrix(x,0,0),k=k.setSubMatrix(O,x.rows,0),k=k.setSubMatrix(P,0,x.columns),k=k.setSubMatrix(w,x.rows,x.columns),k.subMatrix(0,o-1,0,i-1)})(e,t=r(t,s,l),s,l)}scaleRows(t={}){if("object"!=typeof t)throw new TypeError("options must be an object");const{min:e=0,max:n=1}=t;if(!Number.isFinite(e))throw new TypeError("min must be a number");if(!Number.isFinite(n))throw new TypeError("max must be a number");if(e>=n)throw new RangeError("min must be smaller than max");let o=new SMt(this.rows,this.columns);for(let t=0;t<this.rows;t++){const i=this.getRow(t);i.length>0&&dMt(i,{min:e,max:n,output:i}),o.setRow(t,i)}return o}scaleColumns(t={}){if("object"!=typeof t)throw new TypeError("options must be an object");const{min:e=0,max:n=1}=t;if(!Number.isFinite(e))throw new TypeError("min must be a number");if(!Number.isFinite(n))throw new TypeError("max must be a number");if(e>=n)throw new RangeError("min must be smaller than max");let o=new SMt(this.rows,this.columns);for(let t=0;t<this.columns;t++){const i=this.getColumn(t);i.length&&dMt(i,{min:e,max:n,output:i}),o.setColumn(t,i)}return o}flipRows(){const t=Math.ceil(this.columns/2);for(let e=0;e<this.rows;e++)for(let n=0;n<t;n++){let t=this.get(e,n),o=this.get(e,this.columns-1-n);this.set(e,n,o),this.set(e,this.columns-1-n,t)}return this}flipColumns(){const t=Math.ceil(this.rows/2);for(let e=0;e<this.columns;e++)for(let n=0;n<t;n++){let t=this.get(n,e),o=this.get(this.rows-1-n,e);this.set(n,e,o),this.set(this.rows-1-n,e,t)}return this}kroneckerProduct(t){t=SMt.checkMatrix(t);let e=this.rows,n=this.columns,o=t.rows,i=t.columns,a=new SMt(e*o,n*i);for(let r=0;r<e;r++)for(let e=0;e<n;e++)for(let n=0;n<o;n++)for(let s=0;s<i;s++)a.set(o*r+n,i*e+s,this.get(r,e)*t.get(n,s));return a}kroneckerSum(t){if(t=SMt.checkMatrix(t),!this.isSquare()||!t.isSquare())throw new Error("Kronecker Sum needs two Square Matrices");let e=this.rows,n=t.rows,o=this.kroneckerProduct(SMt.eye(n,n)),i=SMt.eye(e,e).kroneckerProduct(t);return o.add(i)}transpose(){let t=new SMt(this.columns,this.rows);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)t.set(n,e,this.get(e,n));return t}sortRows(t=wMt){for(let e=0;e<this.rows;e++)this.setRow(e,this.getRow(e).sort(t));return this}sortColumns(t=wMt){for(let e=0;e<this.columns;e++)this.setColumn(e,this.getColumn(e).sort(t));return this}subMatrix(t,e,n,o){MMt(this,t,e,n,o);let i=new SMt(e-t+1,o-n+1);for(let a=t;a<=e;a++)for(let e=n;e<=o;e++)i.set(a-t,e-n,this.get(a,e));return i}subMatrixRow(t,e,n){if(void 0===e&&(e=0),void 0===n&&(n=this.columns-1),e>n||e<0||e>=this.columns||n<0||n>=this.columns)throw new RangeError("Argument out of range");let o=new SMt(t.length,n-e+1);for(let i=0;i<t.length;i++)for(let a=e;a<=n;a++){if(t[i]<0||t[i]>=this.rows)throw new RangeError(`Row index out of range: ${t[i]}`);o.set(i,a-e,this.get(t[i],a))}return o}subMatrixColumn(t,e,n){if(void 0===e&&(e=0),void 0===n&&(n=this.rows-1),e>n||e<0||e>=this.rows||n<0||n>=this.rows)throw new RangeError("Argument out of range");let o=new SMt(n-e+1,t.length);for(let i=0;i<t.length;i++)for(let a=e;a<=n;a++){if(t[i]<0||t[i]>=this.columns)throw new RangeError(`Column index out of range: ${t[i]}`);o.set(a-e,i,this.get(a,t[i]))}return o}setSubMatrix(t,e,n){if((t=SMt.checkMatrix(t)).isEmpty())return this;MMt(this,e,e+t.rows-1,n,n+t.columns-1);for(let o=0;o<t.rows;o++)for(let i=0;i<t.columns;i++)this.set(e+o,n+i,t.get(o,i));return this}selection(t,e){let n=(function o(t,e,n){return{row:_Mt(t,e),column:CMt(t,n)}})(this,t,e),i=new SMt(t.length,e.length);for(let t=0;t<n.row.length;t++){let e=n.row[t];for(let o=0;o<n.column.length;o++){i.set(t,o,this.get(e,n.column[o]))}}return i}trace(){let t=Math.min(this.rows,this.columns),e=0;for(let n=0;n<t;n++)e+=this.get(n,n);return e}clone(){let t=new SMt(this.rows,this.columns);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)t.set(e,n,this.get(e,n));return t}sum(t){switch(t){case"row":return(function e(t){let e=vMt(t.rows);for(let n=0;n<t.rows;++n)for(let o=0;o<t.columns;++o)e[n]+=t.get(n,o);return e})(this);case"column":return(function n(t){let e=vMt(t.columns);for(let n=0;n<t.rows;++n)for(let o=0;o<t.columns;++o)e[o]+=t.get(n,o);return e})(this);case void 0:return(function o(t){let e=0;for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)e+=t.get(n,o);return e})(this);default:throw new Error(`invalid option: ${t}`)}}product(t){switch(t){case"row":return(function e(t){let e=vMt(t.rows,1);for(let n=0;n<t.rows;++n)for(let o=0;o<t.columns;++o)e[n]*=t.get(n,o);return e})(this);case"column":return(function n(t){let e=vMt(t.columns,1);for(let n=0;n<t.rows;++n)for(let o=0;o<t.columns;++o)e[o]*=t.get(n,o);return e})(this);case void 0:return(function o(t){let e=1;for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)e*=t.get(n,o);return e})(this);default:throw new Error(`invalid option: ${t}`)}}mean(t){const e=this.sum(t);switch(t){case"row":for(let t=0;t<this.rows;t++)e[t]/=this.columns;return e;case"column":for(let t=0;t<this.columns;t++)e[t]/=this.rows;return e;case void 0:return e/this.size;default:throw new Error(`invalid option: ${t}`)}}variance(t,e={}){if("object"==typeof t&&(e=t,t=void 0),"object"!=typeof e)throw new TypeError("options must be an object");const{unbiased:n=!0,mean:o=this.mean(t)}=e;if("boolean"!=typeof n)throw new TypeError("unbiased must be a boolean");switch(t){case"row":if(!Array.isArray(o))throw new TypeError("mean must be an array");return(function e(t,n,o){const i=t.rows,a=t.columns,r=[];for(let e=0;e<i;e++){let i=0,s=0,l=0;for(let n=0;n<a;n++)l=t.get(e,n)-o[e],i+=l,s+=l*l;r.push(n?(s-i*i/a)/(a-1):(s-i*i/a)/a)}return r})(this,n,o);case"column":if(!Array.isArray(o))throw new TypeError("mean must be an array");return(function i(t,e,n){const o=t.rows,i=t.columns,a=[];for(let r=0;r<i;r++){let i=0,s=0,l=0;for(let e=0;e<o;e++)l=t.get(e,r)-n[r],i+=l,s+=l*l;a.push(e?(s-i*i/o)/(o-1):(s-i*i/o)/o)}return a})(this,n,o);case void 0:if("number"!=typeof o)throw new TypeError("mean must be a number");return(function a(t,e,n){const o=t.rows,i=t.columns,a=o*i;let r=0,s=0,l=0;for(let e=0;e<o;e++)for(let o=0;o<i;o++)l=t.get(e,o)-n,r+=l,s+=l*l;return e?(s-r*r/a)/(a-1):(s-r*r/a)/a})(this,n,o);default:throw new Error(`invalid option: ${t}`)}}standardDeviation(t,e){"object"==typeof t&&(e=t,t=void 0);const n=this.variance(t,e);if(void 0===t)return Math.sqrt(n);for(let t=0;t<n.length;t++)n[t]=Math.sqrt(n[t]);return n}center(t,e={}){if("object"==typeof t&&(e=t,t=void 0),"object"!=typeof e)throw new TypeError("options must be an object");const{center:n=this.mean(t)}=e;switch(t){case"row":if(!Array.isArray(n))throw new TypeError("center must be an array");return(function e(t,n){for(let e=0;e<t.rows;e++)for(let o=0;o<t.columns;o++)t.set(e,o,t.get(e,o)-n[e])})(this,n),this;case"column":if(!Array.isArray(n))throw new TypeError("center must be an array");return(function o(t,e){for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)t.set(n,o,t.get(n,o)-e[o])})(this,n),this;case void 0:if("number"!=typeof n)throw new TypeError("center must be a number");return(function i(t,e){for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)t.set(n,o,t.get(n,o)-e)})(this,n),this;default:throw new Error(`invalid option: ${t}`)}}scale(t,e={}){if("object"==typeof t&&(e=t,t=void 0),"object"!=typeof e)throw new TypeError("options must be an object");let n=e.scale;switch(t){case"row":if(void 0===n)n=(function e(t){const e=[];for(let n=0;n<t.rows;n++){let o=0;for(let e=0;e<t.columns;e++)o+=Math.pow(t.get(n,e),2)/(t.columns-1);e.push(Math.sqrt(o))}return e})(this);else if(!Array.isArray(n))throw new TypeError("scale must be an array");return(function o(t,e){for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)t.set(n,o,t.get(n,o)/e[n])})(this,n),this;case"column":if(void 0===n)n=(function i(t){const e=[];for(let n=0;n<t.columns;n++){let o=0;for(let e=0;e<t.rows;e++)o+=Math.pow(t.get(e,n),2)/(t.rows-1);e.push(Math.sqrt(o))}return e})(this);else if(!Array.isArray(n))throw new TypeError("scale must be an array");return(function a(t,e){for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)t.set(n,o,t.get(n,o)/e[o])})(this,n),this;case void 0:if(void 0===n)n=(function r(t){const e=t.size-1;let n=0;for(let o=0;o<t.columns;o++)for(let i=0;i<t.rows;i++)n+=Math.pow(t.get(i,o),2)/e;return Math.sqrt(n)})(this);else if("number"!=typeof n)throw new TypeError("scale must be a number");return(function s(t,e){for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)t.set(n,o,t.get(n,o)/e)})(this,n),this;default:throw new Error(`invalid option: ${t}`)}}toString(t){return fMt(this,t)}}function wMt(t,e){return t-e}PMt.prototype.klass="Matrix","undefined"!=typeof Symbol&&(PMt.prototype[Symbol.for("nodejs.util.inspect.custom")]=function kMt(){return fMt(this)}),PMt.random=PMt.rand,PMt.randomInt=PMt.randInt,PMt.diagonal=PMt.diag,PMt.prototype.diagonal=PMt.prototype.diag,PMt.identity=PMt.eye,PMt.prototype.negate=PMt.prototype.neg,PMt.prototype.tensorProduct=PMt.prototype.kroneckerProduct;class SMt extends PMt{constructor(t,e){if(super(),SMt.isMatrix(t))return t.clone();if(Number.isInteger(t)&&t>=0){if(this.data=[],!(Number.isInteger(e)&&e>=0))throw new TypeError("nColumns must be a positive integer");for(let n=0;n<t;n++)this.data.push(new Float64Array(e))}else{if(!Array.isArray(t))throw new TypeError("First argument must be a positive number or an array");{const n=t;if("number"!=typeof(e=(t=n.length)?n[0].length:0))throw new TypeError("Data must be a 2D array with at least one element");this.data=[];for(let o=0;o<t;o++){if(n[o].length!==e)throw new RangeError("Inconsistent array dimensions");this.data.push(Float64Array.from(n[o]))}}}this.rows=t,this.columns=e}set(t,e,n){return this.data[t][e]=n,this}get(t,e){return this.data[t][e]}removeRow(t){return gMt(this,t),this.data.splice(t,1),this.rows-=1,this}addRow(t,e){return void 0===e&&(e=t,t=this.rows),gMt(this,t,!0),e=Float64Array.from(bMt(this,e)),this.data.splice(t,0,e),this.rows+=1,this}removeColumn(t){hMt(this,t);for(let e=0;e<this.rows;e++){const n=new Float64Array(this.columns-1);for(let o=0;o<t;o++)n[o]=this.data[e][o];for(let o=t+1;o<this.columns;o++)n[o-1]=this.data[e][o];this.data[e]=n}return this.columns-=1,this}addColumn(t,e){void 0===e&&(e=t,t=this.columns),hMt(this,t,!0),e=yMt(this,e);for(let n=0;n<this.rows;n++){const o=new Float64Array(this.columns+1);let i=0;for(;i<t;i++)o[i]=this.data[n][i];for(o[i++]=e[n];i<this.columns+1;i++)o[i]=this.data[n][i-1];this.data[n]=o}return this.columns+=1,this}}!(function DMt(t,e){t.prototype.add=function t(e){return"number"==typeof e?this.addS(e):this.addM(e)},t.prototype.addS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)+e);return this},t.prototype.addM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)+n.get(t,e));return this},t.add=function t(n,o){return new e(n).add(o)},t.prototype.sub=function t(e){return"number"==typeof e?this.subS(e):this.subM(e)},t.prototype.subS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)-e);return this},t.prototype.subM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)-n.get(t,e));return this},t.sub=function t(n,o){return new e(n).sub(o)},t.prototype.subtract=t.prototype.sub,t.prototype.subtractS=t.prototype.subS,t.prototype.subtractM=t.prototype.subM,t.subtract=t.sub,t.prototype.mul=function t(e){return"number"==typeof e?this.mulS(e):this.mulM(e)},t.prototype.mulS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)*e);return this},t.prototype.mulM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)*n.get(t,e));return this},t.mul=function t(n,o){return new e(n).mul(o)},t.prototype.multiply=t.prototype.mul,t.prototype.multiplyS=t.prototype.mulS,t.prototype.multiplyM=t.prototype.mulM,t.multiply=t.mul,t.prototype.div=function t(e){return"number"==typeof e?this.divS(e):this.divM(e)},t.prototype.divS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)/e);return this},t.prototype.divM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)/n.get(t,e));return this},t.div=function t(n,o){return new e(n).div(o)},t.prototype.divide=t.prototype.div,t.prototype.divideS=t.prototype.divS,t.prototype.divideM=t.prototype.divM,t.divide=t.div,t.prototype.mod=function t(e){return"number"==typeof e?this.modS(e):this.modM(e)},t.prototype.modS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)%e);return this},t.prototype.modM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)%n.get(t,e));return this},t.mod=function t(n,o){return new e(n).mod(o)},t.prototype.modulus=t.prototype.mod,t.prototype.modulusS=t.prototype.modS,t.prototype.modulusM=t.prototype.modM,t.modulus=t.mod,t.prototype.and=function t(e){return"number"==typeof e?this.andS(e):this.andM(e)},t.prototype.andS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)&e);return this},t.prototype.andM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)&n.get(t,e));return this},t.and=function t(n,o){return new e(n).and(o)},t.prototype.or=function t(e){return"number"==typeof e?this.orS(e):this.orM(e)},t.prototype.orS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)|e);return this},t.prototype.orM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)|n.get(t,e));return this},t.or=function t(n,o){return new e(n).or(o)},t.prototype.xor=function t(e){return"number"==typeof e?this.xorS(e):this.xorM(e)},t.prototype.xorS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)^e);return this},t.prototype.xorM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)^n.get(t,e));return this},t.xor=function t(n,o){return new e(n).xor(o)},t.prototype.leftShift=function t(e){return"number"==typeof e?this.leftShiftS(e):this.leftShiftM(e)},t.prototype.leftShiftS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)<<e);return this},t.prototype.leftShiftM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)<<n.get(t,e));return this},t.leftShift=function t(n,o){return new e(n).leftShift(o)},t.prototype.signPropagatingRightShift=function t(e){return"number"==typeof e?this.signPropagatingRightShiftS(e):this.signPropagatingRightShiftM(e)},t.prototype.signPropagatingRightShiftS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)>>e);return this},t.prototype.signPropagatingRightShiftM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)>>n.get(t,e));return this},t.signPropagatingRightShift=function t(n,o){return new e(n).signPropagatingRightShift(o)},t.prototype.rightShift=function t(e){return"number"==typeof e?this.rightShiftS(e):this.rightShiftM(e)},t.prototype.rightShiftS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)>>>e);return this},t.prototype.rightShiftM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)>>>n.get(t,e));return this},t.rightShift=function t(n,o){return new e(n).rightShift(o)},t.prototype.zeroFillRightShift=t.prototype.rightShift,t.prototype.zeroFillRightShiftS=t.prototype.rightShiftS,t.prototype.zeroFillRightShiftM=t.prototype.rightShiftM,t.zeroFillRightShift=t.rightShift,t.prototype.not=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,~this.get(t,e));return this},t.not=function t(n){return new e(n).not()},t.prototype.abs=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.abs(this.get(t,e)));return this},t.abs=function t(n){return new e(n).abs()},t.prototype.acos=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.acos(this.get(t,e)));return this},t.acos=function t(n){return new e(n).acos()},t.prototype.acosh=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.acosh(this.get(t,e)));return this},t.acosh=function t(n){return new e(n).acosh()},t.prototype.asin=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.asin(this.get(t,e)));return this},t.asin=function t(n){return new e(n).asin()},t.prototype.asinh=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.asinh(this.get(t,e)));return this},t.asinh=function t(n){return new e(n).asinh()},t.prototype.atan=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.atan(this.get(t,e)));return this},t.atan=function t(n){return new e(n).atan()},t.prototype.atanh=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.atanh(this.get(t,e)));return this},t.atanh=function t(n){return new e(n).atanh()},t.prototype.cbrt=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.cbrt(this.get(t,e)));return this},t.cbrt=function t(n){return new e(n).cbrt()},t.prototype.ceil=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.ceil(this.get(t,e)));return this},t.ceil=function t(n){return new e(n).ceil()},t.prototype.clz32=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.clz32(this.get(t,e)));return this},t.clz32=function t(n){return new e(n).clz32()},t.prototype.cos=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.cos(this.get(t,e)));return this},t.cos=function t(n){return new e(n).cos()},t.prototype.cosh=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.cosh(this.get(t,e)));return this},t.cosh=function t(n){return new e(n).cosh()},t.prototype.exp=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.exp(this.get(t,e)));return this},t.exp=function t(n){return new e(n).exp()},t.prototype.expm1=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.expm1(this.get(t,e)));return this},t.expm1=function t(n){return new e(n).expm1()},t.prototype.floor=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.floor(this.get(t,e)));return this},t.floor=function t(n){return new e(n).floor()},t.prototype.fround=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.fround(this.get(t,e)));return this},t.fround=function t(n){return new e(n).fround()},t.prototype.log=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.log(this.get(t,e)));return this},t.log=function t(n){return new e(n).log()},t.prototype.log1p=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.log1p(this.get(t,e)));return this},t.log1p=function t(n){return new e(n).log1p()},t.prototype.log10=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.log10(this.get(t,e)));return this},t.log10=function t(n){return new e(n).log10()},t.prototype.log2=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.log2(this.get(t,e)));return this},t.log2=function t(n){return new e(n).log2()},t.prototype.round=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.round(this.get(t,e)));return this},t.round=function t(n){return new e(n).round()},t.prototype.sign=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sign(this.get(t,e)));return this},t.sign=function t(n){return new e(n).sign()},t.prototype.sin=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sin(this.get(t,e)));return this},t.sin=function t(n){return new e(n).sin()},t.prototype.sinh=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sinh(this.get(t,e)));return this},t.sinh=function t(n){return new e(n).sinh()},t.prototype.sqrt=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sqrt(this.get(t,e)));return this},t.sqrt=function t(n){return new e(n).sqrt()},t.prototype.tan=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.tan(this.get(t,e)));return this},t.tan=function t(n){return new e(n).tan()},t.prototype.tanh=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.tanh(this.get(t,e)));return this},t.tanh=function t(n){return new e(n).tanh()},t.prototype.trunc=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.trunc(this.get(t,e)));return this},t.trunc=function t(n){return new e(n).trunc()},t.pow=function t(n,o){return new e(n).pow(o)},t.prototype.pow=function t(e){return"number"==typeof e?this.powS(e):this.powM(e)},t.prototype.powS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,Math.pow(this.get(t,n),e));return this},t.prototype.powM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.pow(this.get(t,e),n.get(t,e)));return this}})(PMt,SMt);class RMt extends PMt{constructor(t){super(),this.data=t,this.rows=t.length,this.columns=t[0].length}set(t,e,n){return this.data[t][e]=n,this}get(t,e){return this.data[t][e]}}class EMt{constructor(t){let e,n,o,i,a,r,s,l,c,d=(t=RMt.checkMatrix(t)).clone(),p=d.rows,m=d.columns,f=new Float64Array(p),u=1;for(e=0;e<p;e++)f[e]=e;for(l=new Float64Array(p),n=0;n<m;n++){for(e=0;e<p;e++)l[e]=d.get(e,n);for(e=0;e<p;e++){for(c=Math.min(e,n),a=0,o=0;o<c;o++)a+=d.get(e,o)*l[o];l[e]-=a,d.set(e,n,l[e])}for(i=n,e=n+1;e<p;e++)Math.abs(l[e])>Math.abs(l[i])&&(i=e);if(i!==n){for(o=0;o<m;o++)r=d.get(i,o),d.set(i,o,d.get(n,o)),d.set(n,o,r);s=f[i],f[i]=f[n],f[n]=s,u=-u}if(n<p&&0!==d.get(n,n))for(e=n+1;e<p;e++)d.set(e,n,d.get(e,n)/d.get(n,n))}this.LU=d,this.pivotVector=f,this.pivotSign=u}isSingular(){let t=this.LU,e=t.columns;for(let n=0;n<e;n++)if(0===t.get(n,n))return!0;return!1}solve(t){t=SMt.checkMatrix(t);let e=this.LU;if(e.rows!==t.rows)throw new Error("Invalid matrix dimensions");if(this.isSingular())throw new Error("LU matrix is singular");let n,o,i,a=t.columns,r=t.subMatrixRow(this.pivotVector,0,a-1),s=e.columns;for(i=0;i<s;i++)for(n=i+1;n<s;n++)for(o=0;o<a;o++)r.set(n,o,r.get(n,o)-r.get(i,o)*e.get(n,i));for(i=s-1;i>=0;i--){for(o=0;o<a;o++)r.set(i,o,r.get(i,o)/e.get(i,i));for(n=0;n<i;n++)for(o=0;o<a;o++)r.set(n,o,r.get(n,o)-r.get(i,o)*e.get(n,i))}return r}get determinant(){let t=this.LU;if(!t.isSquare())throw new Error("Matrix must be square");let e=this.pivotSign,n=t.columns;for(let o=0;o<n;o++)e*=t.get(o,o);return e}get lowerTriangularMatrix(){let t=this.LU,e=t.rows,n=t.columns,o=new SMt(e,n);for(let i=0;i<e;i++)for(let e=0;e<n;e++)o.set(i,e,i>e?t.get(i,e):i===e?1:0);return o}get upperTriangularMatrix(){let t=this.LU,e=t.rows,n=t.columns,o=new SMt(e,n);for(let i=0;i<e;i++)for(let e=0;e<n;e++)o.set(i,e,i<=e?t.get(i,e):0);return o}get pivotPermutationVector(){return Array.from(this.pivotVector)}}function AMt(t,e){let n=0;return Math.abs(t)>Math.abs(e)?(n=e/t,Math.abs(t)*Math.sqrt(1+n*n)):0!==e?(n=t/e,Math.abs(e)*Math.sqrt(1+n*n)):0}class TMt{constructor(t){let e,n,o,i,a=(t=RMt.checkMatrix(t)).clone(),r=t.rows,s=t.columns,l=new Float64Array(s);for(o=0;o<s;o++){let t=0;for(e=o;e<r;e++)t=AMt(t,a.get(e,o));if(0!==t){for(a.get(o,o)<0&&(t=-t),e=o;e<r;e++)a.set(e,o,a.get(e,o)/t);for(a.set(o,o,a.get(o,o)+1),n=o+1;n<s;n++){for(i=0,e=o;e<r;e++)i+=a.get(e,o)*a.get(e,n);for(i=-i/a.get(o,o),e=o;e<r;e++)a.set(e,n,a.get(e,n)+i*a.get(e,o))}}l[o]=-t}this.QR=a,this.Rdiag=l}solve(t){t=SMt.checkMatrix(t);let e=this.QR,n=e.rows;if(t.rows!==n)throw new Error("Matrix row dimensions must agree");if(!this.isFullRank())throw new Error("Matrix is rank deficient");let o,i,a,r,s=t.columns,l=t.clone(),c=e.columns;for(a=0;a<c;a++)for(i=0;i<s;i++){for(r=0,o=a;o<n;o++)r+=e.get(o,a)*l.get(o,i);for(r=-r/e.get(a,a),o=a;o<n;o++)l.set(o,i,l.get(o,i)+r*e.get(o,a))}for(a=c-1;a>=0;a--){for(i=0;i<s;i++)l.set(a,i,l.get(a,i)/this.Rdiag[a]);for(o=0;o<a;o++)for(i=0;i<s;i++)l.set(o,i,l.get(o,i)-l.get(a,i)*e.get(o,a))}return l.subMatrix(0,c-1,0,s-1)}isFullRank(){let t=this.QR.columns;for(let e=0;e<t;e++)if(0===this.Rdiag[e])return!1;return!0}get upperTriangularMatrix(){let t,e,n=this.QR,o=n.columns,i=new SMt(o,o);for(t=0;t<o;t++)for(e=0;e<o;e++)i.set(t,e,t<e?n.get(t,e):t===e?this.Rdiag[t]:0);return i}get orthogonalMatrix(){let t,e,n,o,i=this.QR,a=i.rows,r=i.columns,s=new SMt(a,r);for(n=r-1;n>=0;n--){for(t=0;t<a;t++)s.set(t,n,0);for(s.set(n,n,1),e=n;e<r;e++)if(0!==i.get(n,n)){for(o=0,t=n;t<a;t++)o+=i.get(t,n)*s.get(t,e);for(o=-o/i.get(n,n),t=n;t<a;t++)s.set(t,e,s.get(t,e)+o*i.get(t,n))}}return s}}class NMt{constructor(t,e={}){if((t=RMt.checkMatrix(t)).isEmpty())throw new Error("Matrix must be non-empty");let n=t.rows,o=t.columns;const{computeLeftSingularVectors:i=!0,computeRightSingularVectors:a=!0,autoTranspose:r=!1}=e;let s,l=Boolean(i),c=Boolean(a),d=!1;if(n<o)if(r){s=t.transpose(),n=s.rows,o=s.columns,d=!0;let e=l;l=c,c=e}else s=t.clone(),console.warn("Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose");else s=t.clone();let p=Math.min(n,o),m=Math.min(n+1,o),f=new Float64Array(m),u=new SMt(n,p),g=new SMt(o,o),h=new Float64Array(o),b=new Float64Array(n),y=new Float64Array(m);for(let t=0;t<m;t++)y[t]=t;let _=Math.min(n-1,o),C=Math.max(0,Math.min(o-2,n)),M=Math.max(_,C);for(let t=0;t<M;t++){if(t<_){f[t]=0;for(let e=t;e<n;e++)f[t]=AMt(f[t],s.get(e,t));if(0!==f[t]){s.get(t,t)<0&&(f[t]=-f[t]);for(let e=t;e<n;e++)s.set(e,t,s.get(e,t)/f[t]);s.set(t,t,s.get(t,t)+1)}f[t]=-f[t]}for(let e=t+1;e<o;e++){if(t<_&&0!==f[t]){let o=0;for(let i=t;i<n;i++)o+=s.get(i,t)*s.get(i,e);o=-o/s.get(t,t);for(let i=t;i<n;i++)s.set(i,e,s.get(i,e)+o*s.get(i,t))}h[e]=s.get(t,e)}if(l&&t<_)for(let e=t;e<n;e++)u.set(e,t,s.get(e,t));if(t<C){h[t]=0;for(let e=t+1;e<o;e++)h[t]=AMt(h[t],h[e]);if(0!==h[t]){h[t+1]<0&&(h[t]=0-h[t]);for(let e=t+1;e<o;e++)h[e]/=h[t];h[t+1]+=1}if(h[t]=-h[t],t+1<n&&0!==h[t]){for(let e=t+1;e<n;e++)b[e]=0;for(let e=t+1;e<n;e++)for(let n=t+1;n<o;n++)b[e]+=h[n]*s.get(e,n);for(let e=t+1;e<o;e++){let o=-h[e]/h[t+1];for(let i=t+1;i<n;i++)s.set(i,e,s.get(i,e)+o*b[i])}}if(c)for(let e=t+1;e<o;e++)g.set(e,t,h[e])}}let v=Math.min(o,n+1);if(_<o&&(f[_]=s.get(_,_)),n<v&&(f[v-1]=0),C+1<v&&(h[C]=s.get(C,v-1)),h[v-1]=0,l){for(let t=_;t<p;t++){for(let e=0;e<n;e++)u.set(e,t,0);u.set(t,t,1)}for(let t=_-1;t>=0;t--)if(0!==f[t]){for(let e=t+1;e<p;e++){let o=0;for(let i=t;i<n;i++)o+=u.get(i,t)*u.get(i,e);o=-o/u.get(t,t);for(let i=t;i<n;i++)u.set(i,e,u.get(i,e)+o*u.get(i,t))}for(let e=t;e<n;e++)u.set(e,t,-u.get(e,t));u.set(t,t,1+u.get(t,t));for(let e=0;e<t-1;e++)u.set(e,t,0)}else{for(let e=0;e<n;e++)u.set(e,t,0);u.set(t,t,1)}}if(c)for(let t=o-1;t>=0;t--){if(t<C&&0!==h[t])for(let e=t+1;e<o;e++){let n=0;for(let i=t+1;i<o;i++)n+=g.get(i,t)*g.get(i,e);n=-n/g.get(t+1,t);for(let i=t+1;i<o;i++)g.set(i,e,g.get(i,e)+n*g.get(i,t))}for(let e=0;e<o;e++)g.set(e,t,0);g.set(t,t,1)}let x=v-1,O=Number.EPSILON;for(;v>0;){let t,e;for(t=v-2;t>=-1&&-1!==t;t--){const e=Number.MIN_VALUE+O*Math.abs(f[t]+Math.abs(f[t+1]));if(Math.abs(h[t])<=e||Number.isNaN(h[t])){h[t]=0;break}}if(t===v-2)e=4;else{let n;for(n=v-1;n>=t&&n!==t;n--){let e=(n!==v?Math.abs(h[n]):0)+(n!==t+1?Math.abs(h[n-1]):0);if(Math.abs(f[n])<=O*e){f[n]=0;break}}n===t?e=3:n===v-1?e=1:(e=2,t=n)}switch(t++,e){case 1:{let e=h[v-2];h[v-2]=0;for(let n=v-2;n>=t;n--){let i=AMt(f[n],e),a=f[n]/i,r=e/i;if(f[n]=i,n!==t&&(e=-r*h[n-1],h[n-1]=a*h[n-1]),c)for(let t=0;t<o;t++)i=a*g.get(t,n)+r*g.get(t,v-1),g.set(t,v-1,-r*g.get(t,n)+a*g.get(t,v-1)),g.set(t,n,i)}break}case 2:{let e=h[t-1];h[t-1]=0;for(let o=t;o<v;o++){let i=AMt(f[o],e),a=f[o]/i,r=e/i;if(f[o]=i,e=-r*h[o],h[o]=a*h[o],l)for(let e=0;e<n;e++)i=a*u.get(e,o)+r*u.get(e,t-1),u.set(e,t-1,-r*u.get(e,o)+a*u.get(e,t-1)),u.set(e,o,i)}break}case 3:{const e=Math.max(Math.abs(f[v-1]),Math.abs(f[v-2]),Math.abs(h[v-2]),Math.abs(f[t]),Math.abs(h[t])),i=f[v-1]/e,a=f[v-2]/e,r=h[v-2]/e,s=f[t]/e,d=h[t]/e,p=((a+i)*(a-i)+r*r)/2,m=i*r*(i*r);let b=0;0===p&&0===m||(b=p<0?0-Math.sqrt(p*p+m):Math.sqrt(p*p+m),b=m/(p+b));let y=(s+i)*(s-i)+b,_=s*d;for(let e=t;e<v-1;e++){let i=AMt(y,_);0===i&&(i=Number.MIN_VALUE);let a=y/i,r=_/i;if(e!==t&&(h[e-1]=i),y=a*f[e]+r*h[e],h[e]=a*h[e]-r*f[e],_=r*f[e+1],f[e+1]=a*f[e+1],c)for(let t=0;t<o;t++)i=a*g.get(t,e)+r*g.get(t,e+1),g.set(t,e+1,-r*g.get(t,e)+a*g.get(t,e+1)),g.set(t,e,i);if(i=AMt(y,_),0===i&&(i=Number.MIN_VALUE),a=y/i,r=_/i,f[e]=i,y=a*h[e]+r*f[e+1],f[e+1]=-r*h[e]+a*f[e+1],_=r*h[e+1],h[e+1]=a*h[e+1],l&&e<n-1)for(let t=0;t<n;t++)i=a*u.get(t,e)+r*u.get(t,e+1),u.set(t,e+1,-r*u.get(t,e)+a*u.get(t,e+1)),u.set(t,e,i)}h[v-2]=y;break}case 4:if(f[t]<=0&&(f[t]=f[t]<0?-f[t]:0,c))for(let e=0;e<=x;e++)g.set(e,t,-g.get(e,t));for(;t<x&&!(f[t]>=f[t+1]);){let e=f[t];if(f[t]=f[t+1],f[t+1]=e,c&&t<o-1)for(let n=0;n<o;n++)e=g.get(n,t+1),g.set(n,t+1,g.get(n,t)),g.set(n,t,e);if(l&&t<n-1)for(let o=0;o<n;o++)e=u.get(o,t+1),u.set(o,t+1,u.get(o,t)),u.set(o,t,e);t++}v--}}if(d){let t=g;g=u,u=t}this.m=n,this.n=o,this.s=f,this.U=u,this.V=g}solve(t){let e=t,n=this.threshold,o=this.s.length,i=SMt.zeros(o,o);for(let t=0;t<o;t++)Math.abs(this.s[t])<=n?i.set(t,t,0):i.set(t,t,1/this.s[t]);let a=this.U,r=this.rightSingularVectors,s=r.mmul(i),l=r.rows,c=a.rows,d=SMt.zeros(l,c);for(let t=0;t<l;t++)for(let e=0;e<c;e++){let n=0;for(let i=0;i<o;i++)n+=s.get(t,i)*a.get(e,i);d.set(t,e,n)}return d.mmul(e)}solveForDiagonal(t){return this.solve(SMt.diag(t))}inverse(){let t=this.V,e=this.threshold,n=t.rows,o=t.columns,i=new SMt(n,this.s.length);for(let a=0;a<n;a++)for(let n=0;n<o;n++)Math.abs(this.s[n])>e&&i.set(a,n,t.get(a,n)/this.s[n]);let a=this.U,r=a.rows,s=a.columns,l=new SMt(n,r);for(let t=0;t<n;t++)for(let e=0;e<r;e++){let n=0;for(let o=0;o<s;o++)n+=i.get(t,o)*a.get(e,o);l.set(t,e,n)}return l}get 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s(a){return function(s){return(function l(a){if(n)throw new TypeError("Generator is already executing.");for(;r;)try{if(n=1,o&&(i=2&a[0]?o.return:a[0]?o.throw||((i=o.return)&&i.call(o),0):o.next)&&!(i=i.call(o,a[1])).done)return i;switch(o=0,i&&(a=[2&a[0],i.value]),a[0]){case 0:case 1:i=a;break;case 4:return r.label++,{value:a[1],done:!1};case 5:r.label++,o=a[1],a=[0];continue;case 7:a=r.ops.pop(),r.trys.pop();continue;default:if(!(i=r.trys,(i=i.length>0&&i[i.length-1])||6!==a[0]&&2!==a[0])){r=0;continue}if(3===a[0]&&(!i||a[1]>i[0]&&a[1]<i[3])){r.label=a[1];break}if(6===a[0]&&r.label<i[1]){r.label=i[1],i=a;break}if(i&&r.label<i[2]){r.label=i[2],r.ops.push(a);break}i[2]&&r.ops.pop(),r.trys.pop();continue}a=e.call(t,r)}catch(t){a=[6,t],o=0}finally{n=i=0}if(5&a[0])throw a[1];return{value:a[0]?a[1]:void 0,done:!0}})([a,s])}}},VMt=_O&&_O.__read||function(t,e){var n="function"==typeof Symbol&&t[Symbol.iterator];if(!n)return t;var o,i,a=n.call(t),r=[];try{for(;(void 0===e||e-- 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e=this;this.learningRate=1,this.localConnectivity=1,this.minDist=.1,this.nComponents=2,this.nEpochs=0,this.nNeighbors=15,this.negativeSampleRate=5,this.random=Math.random,this.repulsionStrength=1,this.setOpMixRatio=1,this.spread=1,this.transformQueueSize=4,this.targetMetric="categorical",this.targetWeight=.5,this.targetNNeighbors=this.nNeighbors,this.distanceFn=tvt,this.isInitialized=!1,this.rpForest=[],this.embedding=[],this.optimizationState=new nvt;var n=function(n){void 0!==t[n]&&(e[n]=t[n])};n("distanceFn"),n("learningRate"),n("localConnectivity"),n("minDist"),n("nComponents"),n("nEpochs"),n("nNeighbors"),n("negativeSampleRate"),n("random"),n("repulsionStrength"),n("setOpMixRatio"),n("spread"),n("transformQueueSize")}return t.prototype.fit=function(t){return this.initializeFit(t),this.optimizeLayout(),this.embedding},t.prototype.fitAsync=function(t,e){return void 0===e&&(e=function(){return!0}),LMt(this,void 0,void 0,(function(){return BMt(this,(function(n){switch(n.label){case 0:return this.initializeFit(t),[4,this.optimizeLayoutAsync(e)];case 1:return n.sent(),[2,this.embedding]}}))}))},t.prototype.setSupervisedProjection=function(t,e){void 0===e&&(e={}),this.Y=t,this.targetMetric=e.targetMetric||this.targetMetric,this.targetWeight=e.targetWeight||this.targetWeight,this.targetNNeighbors=e.targetNNeighbors||this.targetNNeighbors},t.prototype.setPrecomputedKNN=function(t,e){this.knnIndices=t,this.knnDistances=e},t.prototype.initializeFit=function(t){if(t.length<=this.nNeighbors)throw new Error("Not enough data points ("+t.length+") to create nNeighbors: "+this.nNeighbors+".  Add more data points or adjust the configuration.");if(this.X===t&&this.isInitialized)return this.getNEpochs();if(this.X=t,!this.knnIndices&&!this.knnDistances){var e=this.nearestNeighbors(t);this.knnIndices=e.knnIndices,this.knnDistances=e.knnDistances}this.graph=this.fuzzySimplicialSet(t,this.nNeighbors,this.setOpMixRatio),this.makeSearchFns(),this.searchGraph=this.makeSearchGraph(t),this.processGraphForSupervisedProjection();var n=this.initializeSimplicialSetEmbedding(),o=n.tail,i=n.epochsPerSample;return this.optimizationState.head=n.head,this.optimizationState.tail=o,this.optimizationState.epochsPerSample=i,this.initializeOptimization(),this.prepareForOptimizationLoop(),this.isInitialized=!0,this.getNEpochs()},t.prototype.makeSearchFns=function(){var t=qMt.makeInitializations(this.distanceFn),e=t.initFromRandom;this.initFromTree=t.initFromTree,this.initFromRandom=e,this.search=qMt.makeInitializedNNSearch(this.distanceFn)},t.prototype.makeSearchGraph=function(t){for(var e=this.knnIndices,n=this.knnDistances,o=new YMt.SparseMatrix([],[],[],[t.length,t.length]),i=0;i<e.length;i++)for(var a=e[i],r=n[i],s=0;s<a.length;s++){var l=r[s];l>0&&o.set(i,a[s],l)}var c=YMt.transpose(o);return YMt.maximum(o,c)},t.prototype.transform=function(t){var e=this,n=this.X;if(void 0===n||0===n.length)throw new Error("No data has been fit.");var o=Math.floor(this.nNeighbors*this.transformQueueSize);o=Math.min(n.length,o);var i=qMt.initializeSearch(this.rpForest,n,t,o,this.initFromRandom,this.initFromTree,this.random),a=this.search(n,this.searchGraph,i,t),r=GMt.deheapSort(a),s=r.indices,l=r.weights;s=s.map((function(t){return t.slice(0,e.nNeighbors)})),l=l.map((function(t){return t.slice(0,e.nNeighbors)}));var c=Math.max(0,this.localConnectivity-1),d=this.smoothKNNDistance(l,this.nNeighbors,c),p=this.computeMembershipStrengths(s,l,d.sigmas,d.rhos),m=new YMt.SparseMatrix(p.rows,p.cols,p.vals,[t.length,n.length]),f=YMt.normalize(m,"l1"),u=YMt.getCSR(f),g=t.length,h=lvt(XMt.reshape2d(u.indices,g,this.nNeighbors),XMt.reshape2d(u.values,g,this.nNeighbors),this.embedding),b=this.nEpochs?this.nEpochs/3:m.nRows<=1e4?100:30,y=m.getValues().reduce((function(t,e){return e>t?e:t}),0);m=m.map((function(t){return t<y/b?0:t})),m=YMt.eliminateZeros(m);var _=this.makeEpochsPerSample(m.getValues(),b),C=m.getRows(),M=m.getCols();return this.assignOptimizationStateParameters({headEmbedding:h,tailEmbedding:this.embedding,head:C,tail:M,currentEpoch:0,nEpochs:b,nVertices:m.getDims()[1],epochsPerSample:_}),this.prepareForOptimizationLoop(),this.optimizeLayout()},t.prototype.processGraphForSupervisedProjection=function(){var t=this.Y;if(t){if(t.length!==this.X.length)throw new Error("Length of X and y must be equal");if("categorical"===this.targetMetric)this.graph=this.categoricalSimplicialSetIntersection(this.graph,t,this.targetWeight<1?1/(1-this.targetWeight)*2.5:1e12)}},t.prototype.step=function(){var t=this.optimizationState.currentEpoch;return t<this.getNEpochs()&&this.optimizeLayoutStep(t),this.optimizationState.currentEpoch},t.prototype.getEmbedding=function(){return this.embedding},t.prototype.nearestNeighbors=function(t){var e,n=this.nNeighbors,o=qMt.makeNNDescent(this.distanceFn,this.random),i=5+Math.floor(.5===(e=Math.pow(t.length,.5)/20)?0:Math.round(e)),a=Math.max(5,Math.floor(Math.round((function(t){return Math.log(t)/Math.log(2)})(t.length))));this.rpForest=ZMt.makeForest(t,n,i,this.random);var r=o(t,ZMt.makeLeafArray(this.rpForest),n,a);return{knnIndices:r.indices,knnDistances:r.weights}},t.prototype.fuzzySimplicialSet=function(t,e,n){void 0===n&&(n=1);var o=this,i=o.knnIndices,a=void 0===i?[]:i,r=o.knnDistances,s=void 0===r?[]:r,l=this.smoothKNNDistance(s,e,o.localConnectivity),c=this.computeMembershipStrengths(a,s,l.sigmas,l.rhos),d=new YMt.SparseMatrix(c.rows,c.cols,c.vals,[t.length,t.length]),p=YMt.transpose(d),m=YMt.pairwiseMultiply(d,p),f=YMt.subtract(YMt.add(d,p),m),u=YMt.multiplyScalar(f,n),g=YMt.multiplyScalar(m,1-n);return YMt.add(u,g)},t.prototype.categoricalSimplicialSetIntersection=function(t,e,n,o){void 0===o&&(o=1);var i=rvt(t,e,o,n);return svt(i=YMt.eliminateZeros(i))},t.prototype.smoothKNNDistance=function(t,e,n,o,i){void 0===n&&(n=1),void 0===o&&(o=64),void 0===i&&(i=1);for(var a=Math.log(e)/Math.log(2)*i,r=XMt.zeros(t.length),s=XMt.zeros(t.length),l=0;l<t.length;l++){var c=0,d=1/0,p=1,m=t[l],f=m.filter((function(t){return t>0}));if(f.length>=n){var u=Math.floor(n),g=n-u;u>0?(r[l]=f[u-1],g>JMt&&(r[l]+=g*(f[u]-f[u-1]))):r[l]=g*f[0]}else f.length>0&&(r[l]=XMt.max(f));for(var h=0;h<o;h++){for(var b=0,y=1;y<t[l].length;y++){var _=t[l][y]-r[l];b+=_>0?Math.exp(-_/p):1}if(Math.abs(b-a)<JMt)break;b>a?p=(c+(d=p))/2:(c=p,d===1/0?p*=2:p=(c+d)/2)}if(s[l]=p,r[l]>0){var C=XMt.mean(m);s[l]<QMt*C&&(s[l]=QMt*C)}else{var M=XMt.mean(t.map(XMt.mean));s[l]<QMt*M&&(s[l]=QMt*M)}}return{sigmas:s,rhos:r}},t.prototype.computeMembershipStrengths=function(t,e,n,o){for(var i=t.length,a=t[0].length,r=XMt.zeros(i*a),s=XMt.zeros(i*a),l=XMt.zeros(i*a),c=0;c<i;c++)for(var d=0;d<a;d++){var p=0;-1!==t[c][d]&&(p=t[c][d]===c?0:e[c][d]-o[c]<=0?1:Math.exp(-(e[c][d]-o[c])/n[c]),r[c*a+d]=c,s[c*a+d]=t[c][d],l[c*a+d]=p)}return{rows:r,cols:s,vals:l}},t.prototype.initializeSimplicialSetEmbedding=function(){for(var t=this,e=this.getNEpochs(),n=this.nComponents,o=this.graph.getValues(),i=0,a=0;a<o.length;a++){i<o[a]&&(i=o[a])}var r=this.graph.map((function(t){return t<i/e?0:t}));this.embedding=XMt.zeros(r.nRows).map((function(){return XMt.zeros(n).map((function(){return 20*XMt.tauRand(t.random)-10}))}));var s=[],l=[],c=[],d=r.getAll();for(a=0;a<d.length;a++){var p=d[a];p.value&&(s.push(p.value),c.push(p.row),l.push(p.col))}return{head:l,tail:c,epochsPerSample:this.makeEpochsPerSample(s,e)}},t.prototype.makeEpochsPerSample=function(t,e){var n=XMt.filled(t.length,-1),o=XMt.max(t),i=t.map((function(t){return t/o*e}));return i.forEach((function(t,o){t>0&&(n[o]=e/i[o])})),n},t.prototype.assignOptimizationStateParameters=function(t){Object.assign(this.optimizationState,t)},t.prototype.prepareForOptimizationLoop=function(){var t=this,e=t.repulsionStrength,n=t.learningRate,o=t.negativeSampleRate,i=this.optimizationState,a=i.epochsPerSample,r=i.headEmbedding,s=r[0].length,l=r.length===i.tailEmbedding.length,c=a.map((function(t){return t/o})),d=jMt(c),p=jMt(a);this.assignOptimizationStateParameters({epochOfNextSample:p,epochOfNextNegativeSample:d,epochsPerNegativeSample:c,moveOther:l,initialAlpha:n,alpha:n,gamma:e,dim:s})},t.prototype.initializeOptimization=function(){var t=this.embedding,e=this.embedding,n=this.optimizationState,o=n.head,i=n.tail,a=n.epochsPerSample,r=this.getNEpochs(),s=this.graph.nCols,l=avt(this.spread,this.minDist);this.assignOptimizationStateParameters({headEmbedding:t,tailEmbedding:e,head:o,tail:i,epochsPerSample:a,a:l.a,b:l.b,nEpochs:r,nVertices:s})},t.prototype.optimizeLayoutStep=function(t){for(var e=this.optimizationState,n=e.head,o=e.tail,i=e.headEmbedding,a=e.tailEmbedding,r=e.epochsPerSample,s=e.epochOfNextSample,l=e.epochOfNextNegativeSample,c=e.epochsPerNegativeSample,d=e.moveOther,p=e.initialAlpha,m=e.alpha,f=e.gamma,u=e.a,g=e.b,h=e.dim,b=e.nEpochs,y=e.nVertices,_=0;_<r.length;_++)if(!(s[_]>t)){var C=n[_],M=i[C],v=a[o[_]],x=ivt(M,v),O=0;x>0&&(O=-2*u*g*Math.pow(x,g-1),O/=u*Math.pow(x,g)+1);for(var P=0;P<h;P++){var w=ovt(O*(M[P]-v[P]),4);M[P]+=w*m,d&&(v[P]+=-w*m)}s[_]+=r[_];for(var k=Math.floor((t-l[_])/c[_]),S=0;S<k;S++){var D=XMt.tauRandInt(y,this.random),R=a[D],E=ivt(M,R),A=0;if(E>0)A=2*f*g,A/=(.001+E)*(u*Math.pow(E,g)+1);else if(C===D)continue;for(P=0;P<h;P++){w=4;A>0&&(w=ovt(A*(M[P]-R[P]),4)),M[P]+=w*m}}l[_]+=k*c[_]}return e.alpha=p*(1-t/b),e.currentEpoch+=1,i},t.prototype.optimizeLayoutAsync=function(t){var e=this;return void 0===t&&(t=function(){return!0}),new Promise((function(n,o){var i=function(){return LMt(e,void 0,void 0,(function(){var e,a,r,s,l;return BMt(this,(function(c){try{if(a=(e=this.optimizationState).nEpochs,this.embedding=this.optimizeLayoutStep(e.currentEpoch),s=!1===t(r=this.optimizationState.currentEpoch),l=r===a,s||l)return[2,n(l)];setTimeout((function(){return i()}),0)}catch(t){o(t)}return[2]}))}))};setTimeout((function(){return i()}),0)}))},t.prototype.optimizeLayout=function(t){void 0===t&&(t=function(){return!0});for(var e=!1,n=[];!e;){var o=this.optimizationState,i=o.nEpochs;n=this.optimizeLayoutStep(o.currentEpoch);var a=this.optimizationState.currentEpoch,r=!1===t(a);e=a===i||r}return n},t.prototype.getNEpochs=function(){if(this.nEpochs>0)return this.nEpochs;var t=this.graph.nRows;return t<=2500?500:t<=5e3?400:t<=7500?300:200},t})();function tvt(t,e){for(var n=0,o=0;o<t.length;o++)n+=Math.pow(t[o]-e[o],2);return Math.sqrt(n)}H_t.UMAP=$Mt,H_t.euclidean=tvt,H_t.cosine=function evt(t,e){for(var n=0,o=0,i=0,a=0;a<t.length;a++)n+=t[a]*e[a],o+=Math.pow(t[a],2),i+=Math.pow(e[a],2);return 0===o&&0===i?0:0===o||0===i?1:1-n/Math.sqrt(o*i)};var nvt=function nvt(){this.currentEpoch=0,this.headEmbedding=[],this.tailEmbedding=[],this.head=[],this.tail=[],this.epochsPerSample=[],this.epochOfNextSample=[],this.epochOfNextNegativeSample=[],this.epochsPerNegativeSample=[],this.moveOther=!0,this.initialAlpha=1,this.alpha=1,this.gamma=1,this.a=1.5769434603113077,this.b=.8950608779109733,this.dim=2,this.nEpochs=500,this.nVertices=0};function ovt(t,e){return t>e?e:t<-e?-e:t}function ivt(t,e){for(var n=0,o=0;o<t.length;o++)n+=Math.pow(t[o]-e[o],2);return n}function avt(t,e){var n=XMt.linear(0,3*t,300).map((function(t){return t<e?1:t})),o=XMt.zeros(n.length).map((function(o,i){return n[i]>=e?Math.exp(-(n[i]-e)/t):o})),i=KMt.default({x:n,y:o},(function(t){var e=VMt(t,2),n=e[0],o=e[1];return function(t){return 1/(1+n*Math.pow(t,2*o))}}),{damping:1.5,initialValues:[.5,.5],gradientDifference:.1,maxIterations:100,errorTolerance:.01}).parameterValues,a=VMt(i,2);return{a:a[0],b:a[1]}}function rvt(t,e,n,o){return void 0===n&&(n=1),void 0===o&&(o=5),t.map((function(t,i,a){return-1===e[i]||-1===e[a]?t*Math.exp(-n):e[i]!==e[a]?t*Math.exp(-o):t}))}function svt(t){t=YMt.normalize(t,"max");var e=YMt.transpose(t),n=YMt.pairwiseMultiply(e,t);return t=YMt.add(t,YMt.subtract(e,n)),YMt.eliminateZeros(t)}function lvt(t,e,n){for(var o=XMt.zeros(t.length).map((function(t){return XMt.zeros(n[0].length)})),i=0;i<t.length;i++)for(var a=0;a<t[0].length;a++)for(var r=0;r<n[0].length;r++){o[i][r]+=e[i][a]*n[t[i][a]][r]}return o}function cvt(t){let e,n,o=t.length;for(;o;)n=Math.floor(Math.random()*o--),e=t[o],t[o]=t[n],t[n]=e;return t}function dvt(t,e){return`${e}/${t}`}H_t.findABParams=avt,H_t.fastIntersection=rvt,H_t.resetLocalConnectivity=svt,H_t.initTransform=lvt,Object.defineProperty(I_t,"__esModule",{value:!0}),I_t.UMAP=H_t.UMAP;class pvt{constructor(t){this.http=t,this.httpPathPrefix="data/plugin/npmi"}fetchData(t){return ie(this.fetchAnnotations(t),this.fetchMetrics(t),this.fetchValues(t),this.fetchEmbeddings(t)).pipe(Vt((([t,e,n,o])=>{const i={},a={};let r,s=0;for(const r of Object.keys(t))for(const l in t[r]){const c=t[r][l];Object.keys(o).length&&!a[c]&&o[r][l]&&o[r][l].some((t=>0!==t))&&(a[c]={vector:o[r][l],index:s,name:c},s+=1);const d=new Map;for(const t in e[r]){const o=e[r][t],i=z_t(o);let a=d.get(i);a||(a={nPMIValue:null,countValue:null,annotation:c,metric:i,run:r},d.set(i,a)),A_t(o)?a.countValue=n[r][l][t]:T_t(o)&&(a.nPMIValue=n[r][l][t])}i[c]=[...i[c]?i[c]:[],...d.values()]}return Object.keys(a).length&&(r=(function l(t){const e=Object.keys(t);return{points:t,pointKeys:e,shuffledDataIndices:cvt((n=e.length,[...new Array(n)].map(((t,e)=>e)))),hasUmapRun:!1};var n})(a)),{annotationData:i,metrics:e,embeddingDataSet:r}})),he((t=>t instanceof fE&&400<=t.status&&t.status<500?zt({annotationData:{},metrics:{},embeddingDataSet:void 0}):It(t))))}fetchAnnotations(t){return ie(t.map((t=>this.http.get(`/experiment/${t}/${this.httpPathPrefix}/annotations`).pipe(Vt((e=>(function n(t,e){return Object.fromEntries(Object.entries(t).map((([t,n])=>[dvt(t,e),n])))})(e,t))))))).pipe(Vt((t=>{let e={};for(const n of t)e=Object.assign(Object.assign({},e),n);return e})))}fetchMetrics(t){return ie(t.map((t=>this.http.get(`/experiment/${t}/${this.httpPathPrefix}/metrics`).pipe(Vt((e=>(function n(t,e){return Object.fromEntries(Object.entries(t).map((([t,n])=>[dvt(t,e),n])))})(e,t))))))).pipe(Vt((t=>{let e={};for(const n of t)e=Object.assign(Object.assign({},e),n);return e})))}fetchValues(t){return ie(t.map((t=>this.http.get(`/experiment/${t}/${this.httpPathPrefix}/values`).pipe(Vt((e=>(function n(t,e){return Object.fromEntries(Object.entries(t).map((([t,n])=>[dvt(t,e),n])))})(e,t))))))).pipe(Vt((t=>{let e={};for(const n of t)e=Object.assign(Object.assign({},e),n);return e})))}fetchEmbeddings(t){return ie(t.map((t=>this.http.get(`/experiment/${t}/${this.httpPathPrefix}/embeddings`).pipe(Vt((e=>(function n(t,e){return Object.fromEntries(Object.entries(t).map((([t,n])=>[dvt(t,e),n])))})(e,t))))))).pipe(Vt((t=>{let e={};for(const n of t)e=Object.assign(Object.assign({},e),n);return e})))}}pvt.ɵfac=function t(e){return new(e||pvt)(Tr(HE))},pvt.ɵprov=Sn({token:pvt,factory:pvt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(pvt,[{type:Um}],(function(){return[{type:HE}]}),null);class mvt{}mvt.ɵfac=function t(e){return new(e||mvt)},mvt.ɵmod=fo({type:mvt}),mvt.ɵinj=Dn({providers:[pvt],imports:[[FE]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(mvt,[{type:L_,args:[{imports:[FE],providers:[pvt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(mvt,{imports:[FE]});const fvt=_k("[NPMI] nPMI Loaded"),uvt=_k("[NPMI] nPMI Plugin Data Requested"),gvt=_k("[NPMI] nPMI Plugin Data Loaded",{_as:"props",_p:void 0}),hvt=_k("[NPMI] nPMI Plugin Data Request Failed"),bvt=_k("[NPMI] Adding/Removing Annotations to/from Selected",{_as:"props",_p:void 0}),yvt=_k("[NPMI] Annotations Set",{_as:"props",_p:void 0}),_vt=_k("[NPMI] Clearing the Annotation Selection"),Cvt=_k("[NPMI] Adding/Removing Annotations to/from Flagged",{_as:"props",_p:void 0}),Mvt=_k("[NPMI] Adding/Removing Annotations to/from Hidden",{_as:"props",_p:void 0}),vvt=_k("[NPMI] Annotations Regex Changed",{_as:"props",_p:void 0}),xvt=_k("[NPMI] Metrics Regex Changed",{_as:"props",_p:void 0}),Ovt=_k("[NPMI] Metric Filter Added",{_as:"props",_p:void 0}),Pvt=_k("[NPMI] Metric Filter Removed",{_as:"props",_p:void 0}),wvt=_k("[NPMI] Metric Filter Changed",{_as:"props",_p:void 0}),kvt=_k("[NPMI] Annotation Sort Changed",{_as:"props",_p:void 0}),Svt=_k("[NPMI] Similarity Sort Changed",{_as:"props",_p:void 0}),Dvt=_k("[NPMI] Toggle PC Expanded"),Rvt=_k("[NPMI] Toggle Annotations Expanded"),Evt=_k("[NPMI] Toggle Sidebar Expanded"),Avt=_k("[NPMI] Show Counts Toggled"),Tvt=_k("[NPMI] Show Hidden Annotations Toggled"),Nvt=_k("[NPMI] Embeddings View Toggled",{_as:"props",_p:void 0}),zvt=_k("[NPMI] Sidebar Width Changed",{_as:"props",_p:void 0}),Ivt=_k("[NPMI] Embeddings Sidebar Width Changed",{_as:"props",_p:void 0}),Hvt=_k("[NPMI] Embeddings Sidebar Expanded Toggled"),Fvt=_k("[NPMI] Change Embedding DataSet",{_as:"props",_p:void 0}),Lvt="npmi";var Bvt,Vvt,jvt,Uvt;!(function(t){t[t.AND=0]="AND"})(Bvt||(Bvt={})),(function(t){t[t.METRIC=0]="METRIC",t[t.OPERATOR=1]="OPERATOR"})(Vvt||(Vvt={})),(function(t){t[t.DEFAULT=0]="DEFAULT",t[t.EMBEDDINGS=1]="EMBEDDINGS"})(jvt||(jvt={})),(function(t){t[t.DESCENDING=0]="DESCENDING",t[t.ASCENDNG=1]="ASCENDNG",t[t.SIMILAR=2]="SIMILAR",t[t.DISSIMILAR=3]="DISSIMILAR"})(Uvt||(Uvt={}));const Wvt=_S(Lvt),Gvt=bS(Wvt,(t=>t.pluginDataLoaded.state)),Yvt=bS(Wvt,(t=>t.annotationData)),qvt=bS(Wvt,(t=>t.runToMetrics)),Zvt=bS(Wvt,(t=>t.embeddingDataSet)),Xvt=bS(Wvt,(t=>t.selectedAnnotations)),Kvt=bS(Wvt,(t=>t.flaggedAnnotations)),Jvt=bS(Wvt,(t=>t.hiddenAnnotations)),Qvt=bS(Wvt,(t=>t.annotationsRegex)),$vt=bS(Wvt,(t=>t.metricsRegex)),txt=bS(Wvt,(t=>t.metricArithmetic)),ext=bS(Wvt,(t=>t.metricFilters)),nxt=bS(Wvt,(t=>t.sort)),oxt=bS(Wvt,(t=>t.pcExpanded)),ixt=bS(Wvt,(t=>t.annotationsExpanded)),axt=bS(Wvt,(t=>t.sidebarExpanded)),rxt=bS(Wvt,(t=>t.showCounts)),sxt=bS(Wvt,(t=>t.showHiddenAnnotations)),lxt=bS(Wvt,(t=>t.viewActive)),cxt=bS(Wvt,(t=>t.sidebarWidth)),dxt=bS(Wvt,(t=>t.embeddingsMetric)),pxt=bS(Wvt,(t=>t.embeddingsSidebarWidth)),mxt=bS(Wvt,(t=>t.embeddingsSidebarExpanded));class fxt{constructor(t,e,n){this.actions$=t,this.store=e,this.dataSource=n,this.loadData$=WS((()=>pe(this.loadPluginData()).pipe(Vt((()=>({}))))),{dispatch:!1})}loadPluginData(){return this.actions$.pipe($S(fvt),qe(this.store.select(Gvt),this.store.select(xR)),ue((([,t,e])=>t!==UE.LOADING&&null!==e)),We((()=>this.store.dispatch(uvt()))),$t((([,,t])=>this.dataSource.fetchData(t).pipe(We((t=>{this.store.dispatch(gvt(t))})),Vt((()=>{})),he((()=>(this.store.dispatch(hvt()),dt)))))))}}fxt.ɵfac=function t(e){return new(e||fxt)(Tr(QS),Tr(iS),Tr(pvt))},fxt.ɵprov=Sn({token:fxt,factory:fxt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(fxt,[{type:Um}],(function(){return[{type:QS},{type:iS},{type:pvt}]}),null);class uxt{}uxt.ɵfac=function t(e){return new(e||uxt)},uxt.ɵcmp=so({type:uxt,selectors:[["npmi-inactive-view"]],decls:6,vars:0,consts:[[1,"container"],[1,"title"]],template:function t(e,n){1&e&&(Cf(0,"div"),Cf(1,"div",0),Cf(2,"div",1),Pu(3,"nPMI is inactive because no data is available."),Mf(),Cf(4,"div"),Pu(5," To use the nPMI, calculate nPMI values, and log them using the summary writer. "),Mf(),Mf(),Mf())},styles:[".container[_ngcontent-%COMP%] {\n  height: 100%;\n  font-family: Roboto;\n  font-size: 15px;\n  padding: 50px;\n}\n\n.title[_ngcontent-%COMP%] {\n  font-size: 135%;\n  font-weight: bold;\n  margin-bottom: 25px;\n}"]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(uxt,[{type:S_,args:[{selector:"npmi-inactive-view",templateUrl:"./inactive_component.ng.html",styleUrls:["./inactive_component.css"]}]}],null,null);const gxt=VS({pluginDataLoaded:{state:UE.NOT_LOADED,lastLoadedTimeInMs:null},annotationData:{},embeddingDataSet:void 0,runToMetrics:{},selectedAnnotations:[],flaggedAnnotations:[],hiddenAnnotations:[],annotationsRegex:"",metricsRegex:"",metricArithmetic:[],metricFilters:{},sort:{metric:"",order:Uvt.DESCENDING},pcExpanded:!0,annotationsExpanded:!0,sidebarExpanded:!0,showCounts:!0,showHiddenAnnotations:!1,sidebarWidth:300,viewActive:jvt.DEFAULT,embeddingsMetric:"",embeddingsSidebarWidth:500,embeddingsSidebarExpanded:!0},BS(uvt,(t=>Object.assign(Object.assign({},t),{pluginDataLoaded:Object.assign(Object.assign({},t.pluginDataLoaded),{state:UE.LOADING})}))),BS(hvt,(t=>Object.assign(Object.assign({},t),{pluginDataLoaded:Object.assign(Object.assign({},t.pluginDataLoaded),{state:UE.FAILED})}))),BS(gvt,((t,{annotationData:e,metrics:n,embeddingDataSet:o})=>{const i={};for(const t in n){i[t]=[];for(const e of n[t])T_t(e)&&i[t].push(e)}return Object.assign(Object.assign({},t),{runToMetrics:i,annotationData:e,embeddingDataSet:o,pluginDataLoaded:{state:UE.LOADED,lastLoadedTimeInMs:Date.now()}})})),BS(bvt,((t,{annotations:e})=>{const n=new Set([...t.selectedAnnotations,...e]);if(n.size===t.selectedAnnotations.length)for(const t of e)n.delete(t);return Object.assign(Object.assign({},t),{selectedAnnotations:[...n]})})),BS(yvt,((t,{annotations:e})=>Object.assign(Object.assign({},t),{selectedAnnotations:e}))),BS(_vt,(t=>Object.assign(Object.assign({},t),{selectedAnnotations:[]}))),BS(Cvt,((t,{annotations:e})=>{const n=new Set([...t.flaggedAnnotations,...e]);if(n.size===t.flaggedAnnotations.length)for(const t of e)n.delete(t);return Object.assign(Object.assign({},t),{flaggedAnnotations:[...n],selectedAnnotations:[]})})),BS(Mvt,((t,{annotations:e})=>{const n=new Set([...t.hiddenAnnotations,...e]);if(n.size===t.hiddenAnnotations.length)for(const t of e)n.delete(t);return Object.assign(Object.assign({},t),{hiddenAnnotations:[...n],selectedAnnotations:[]})})),BS(vvt,((t,{regex:e})=>Object.assign(Object.assign({},t),{annotationsRegex:e}))),BS(xvt,((t,{regex:e})=>Object.assign(Object.assign({},t),{metricsRegex:e}))),BS(Ovt,((t,{metric:e})=>{if(t.metricFilters[e])return t;const n=[];return 0!==t.metricArithmetic.length&&n.push({kind:Vvt.OPERATOR,operator:Bvt.AND}),n.push({kind:Vvt.METRIC,metric:e}),Object.assign(Object.assign({},t),{metricArithmetic:[...t.metricArithmetic,...n],metricFilters:Object.assign(Object.assign({},t.metricFilters),{[e]:{max:1,min:-1,includeNaN:!1}}),sort:{metric:e,order:Uvt.DESCENDING}})})),BS(Pvt,((t,{metric:e})=>{if(!t.metricFilters[e])return t;let n=0,o=0,i=2;const a=t.metricFilters,r=e,s=ZT(a,["symbol"==typeof r?r:r+""]);for(const o in t.metricArithmetic){const i=t.metricArithmetic[o];i.kind===Vvt.METRIC&&i.metric===e&&(n=parseInt(o))}return 0!==n&&(o=n-1,i=n+1),Object.assign(Object.assign({},t),{metricArithmetic:[...t.metricArithmetic.slice(0,o),...t.metricArithmetic.slice(i)],metricFilters:s})})),BS(wvt,((t,{metric:e,max:n,min:o,includeNaN:i})=>t.metricFilters[e]?Object.assign(Object.assign({},t),{metricFilters:Object.assign(Object.assign({},t.metricFilters),{[e]:{max:n,min:o,includeNaN:i}})}):t)),BS(kvt,((t,{metric:e})=>{const n={metric:e,order:Uvt.DESCENDING};return t.sort.metric===e&&t.sort.order===Uvt.DESCENDING&&(n.order=Uvt.ASCENDNG),Object.assign(Object.assign({},t),{sort:n})})),BS(Svt,((t,{annotation:e})=>{const n={metric:e,order:Uvt.SIMILAR};return t.sort.metric===e&&t.sort.order===Uvt.SIMILAR&&(n.order=Uvt.DISSIMILAR),Object.assign(Object.assign({},t),{sort:n})})),BS(Dvt,(t=>Object.assign(Object.assign({},t),{pcExpanded:!t.pcExpanded}))),BS(Rvt,(t=>Object.assign(Object.assign({},t),{annotationsExpanded:!t.annotationsExpanded}))),BS(Evt,(t=>Object.assign(Object.assign({},t),{sidebarExpanded:!t.sidebarExpanded}))),BS(Avt,(t=>Object.assign(Object.assign({},t),{showCounts:!t.showCounts}))),BS(Tvt,(t=>Object.assign(Object.assign({},t),{showHiddenAnnotations:!t.showHiddenAnnotations}))),BS(Nvt,((t,{metric:e})=>{let n=jvt.EMBEDDINGS,o=e;return e===t.embeddingsMetric&&(n=jvt.DEFAULT,o=""),Object.assign(Object.assign({},t),{viewActive:n,embeddingsMetric:o})})),BS(zvt,((t,{sidebarWidth:e})=>Object.assign(Object.assign({},t),{sidebarWidth:e}))),BS(Ivt,((t,{sidebarWidth:e})=>Object.assign(Object.assign({},t),{embeddingsSidebarWidth:e}))),BS(Hvt,(t=>Object.assign(Object.assign({},t),{embeddingsSidebarExpanded:!t.embeddingsSidebarExpanded}))),BS(Fvt,((t,{dataSet:e})=>Object.assign(Object.assign({},t),{embeddingDataSet:e}))));function hxt(t,e){return gxt(t,e)}function bxt(t,e){1&t&&vf(0,"mat-icon",7)}function yxt(t,e){if(1&t&&(Cf(0,"mat-option",8),Pu(1),Mf()),2&t){const t=e.$implicit;bf("value",t),Dc(1),wu(t)}}class _xt{constructor(){this.onRegexFilterValueChange=new Sy,this.onAddFilter=new Sy}onOptionSelected(t,e){this.onAddFilter.emit(t.option.value),e.value=""}}_xt.ɵfac=function t(e){return new(e||_xt)},_xt.ɵcmp=so({type:_xt,selectors:[["metric-search-component"]],hostVars:2,hostBindings:function t(e,n){2&e&&cu("valid",n.isRegexFilterValid)},inputs:{completions:"completions",regexFilterValue:"regexFilterValue",isRegexFilterValid:"isRegexFilterValid"},outputs:{onRegexFilterValueChange:"onRegexFilterValueChange",onAddFilter:"onAddFilter"},decls:7,vars:4,consts:[["svgIcon","search_24px"],["matInput","","autocomplete","off","placeholder","Add Metric Filter",3,"value","matAutocomplete","input"],["matInput",""],["svgIcon","error_24px","class","error-icon","matTooltip","Invalid regex filter. The result may be stale.",4,"ngIf"],["autoActiveFirstOption","",3,"optionSelected"],["filterMatches","matAutocomplete"],[3,"value",4,"ngFor","ngForOf"],["svgIcon","error_24px","matTooltip","Invalid regex filter. The result may be stale.",1,"error-icon"],[3,"value"]],template:function t(e,n){if(1&e){const t=wf();vf(0,"mat-icon",0),Cf(1,"input",1,2),Rf("input",(function t(e){return n.onRegexFilterValueChange.emit(e.target.value)})),Mf(),Hm(3,bxt,1,0,"mat-icon",3),Cf(4,"mat-autocomplete",4,5),Rf("optionSelected",(function e(o){Pi(t);const i=Fm(2);return n.onOptionSelected(o,i)})),Hm(6,yxt,2,2,"mat-option",6),Mf()}if(2&e){const t=Fm(5);Dc(1),bf("value",n.regexFilterValue)("matAutocomplete",t),Dc(2),bf("ngIf",!n.isRegexFilterValid),Dc(3),bf("ngForOf",n.completions)}},directives:[Mq,AZ,Y3,Pv,F3,xv,PL],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]  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!important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}mat-icon[_ngcontent-%COMP%]{flex:none;margin-right:5px}[_nghost-%COMP%]{display:flex;padding:0 10px;position:relative;font-size:.9em}[_nghost-%COMP%]:not(.valid){color:#c62828}[_nghost-%COMP%]:not(.valid)   input[_ngcontent-%COMP%]{caret-color:currentColor}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(_xt,[{type:S_,args:[{selector:"metric-search-component",templateUrl:"./metric_search_component.ng.html",styleUrls:["./metric_search_component.css"],changeDetection:jn.OnPush}]}],null,{completions:[{type:R_}],regexFilterValue:[{type:R_}],isRegexFilterValid:[{type:A_,args:["class.valid"]},{type:R_}],onRegexFilterValueChange:[{type:E_}],onAddFilter:[{type:E_}]});class Cxt{constructor(t){this.store=t,this.metricsRegex$=this.store.select($vt),this.activeRuns$=this.store.pipe(rS(_I)).pipe(Vt((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.metricsForActiveRuns$=Kt(this.activeRuns$,this.store.select(qvt)).pipe(Vt((([t,e])=>{const n=new Set;for(const o of t)if(e[o])for(const t of e[o])n.add(t);return[...n]}))),this.isMetricsFilterValid$=this.metricsRegex$.pipe(Vt((t=>{try{return Boolean(new RegExp(t))}catch(t){return!1}}))),this.metricFilterKeys$=this.store.pipe(rS(ext)).pipe(Vt((t=>Object.keys(t)))),this.completions$=Kt(this.metricsForActiveRuns$,this.metricsRegex$,this.metricFilterKeys$).pipe(Vt((([t,e,n])=>{const o=t.filter((t=>!n.includes(t)));try{const t=new RegExp(e,"i");return o.filter((e=>t.test(e))).sort()}catch(t){return[]}})))}onFilterChange(t){this.store.dispatch(xvt({regex:t}))}onAddFilter(t){this.store.dispatch(Ovt({metric:t})),this.store.dispatch(xvt({regex:""}))}}function Mxt(t,e,n){const o=[[e,...n]];if(!n.length||!t.length){return"data:text/csv;charset=utf-8,"+o.map((t=>t.join(","))).join("\n")}const i=n.map((t=>z_t(t)));for(const[n,a]of t){const t=a.filter((t=>t.run===e));if(t.length){const e=[n];for(const n of i){const o=t.find((t=>t.metric===n));e.push(void 0===o?"null":`${o.nPMIValue}`)}o.push(e)}}return"data:text/csv;charset=utf-8,"+o.map((t=>t.join(","))).join("\n")}Cxt.ɵfac=function t(e){return new(e||Cxt)(gf(iS))},Cxt.ɵcmp=so({type:Cxt,selectors:[["npmi-metric-search"]],decls:4,vars:9,consts:[[3,"regexFilterValue","completions","isRegexFilterValid","onRegexFilterValueChange","onAddFilter"]],template:function t(e,n){1&e&&(Cf(0,"metric-search-component",0),Rf("onRegexFilterValueChange",(function t(e){return n.onFilterChange(e)}))("onAddFilter",(function t(e){return n.onAddFilter(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),Mf()),2&e&&bf("regexFilterValue",Cy(1,3,n.metricsRegex$))("completions",Cy(2,5,n.completions$))("isRegexFilterValid",Cy(3,7,n.isMetricsFilterValid$))},directives:[_xt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Cxt,[{type:S_,args:[{selector:"npmi-metric-search",template:'\n    <metric-search-component\n      [regexFilterValue]="metricsRegex$ | async"\n      [completions]="completions$ | async"\n      [isRegexFilterValid]="isMetricsFilterValid$ | async"\n      (onRegexFilterValueChange)="onFilterChange($event)"\n      (onAddFilter)="onAddFilter($event)"\n    ></metric-search-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);const vxt=function(t){return{"active-button":t}};class xxt{downloadResults(){for(const t of this.runs){const e=Mxt(this.flaggedData,t,this.metrics),n=document.createElement("a");n.setAttribute("href",e),n.setAttribute("download",`report_${t}.csv`),n.click()}}}xxt.ɵfac=function t(e){return new(e||xxt)},xxt.ɵcmp=so({type:xxt,selectors:[["results-download-component"]],inputs:{numFlaggedAnnotations:"numFlaggedAnnotations",runs:"runs",flaggedData:"flaggedData",metrics:"metrics"},decls:4,vars:5,consts:[["mat-stroked-button","","title","Export CSV reports of all flagged annotations. Will generate one CSV per active run.",3,"disabled","ngClass","click"],[1,"button-contents"],["svgIcon","get_app_24px"]],template:function t(e,n){1&e&&(Cf(0,"button",0),Rf("click",(function t(){return n.downloadResults()})),Cf(1,"span",1),vf(2,"mat-icon",2),Pu(3),Mf(),Mf()),2&e&&(bf("disabled",0===n.numFlaggedAnnotations)("ngClass",iy(3,vxt,n.numFlaggedAnnotations>0)),Dc(3),ku(" Flagged Rows (",n.numFlaggedAnnotations,") "))},directives:[NL,Cv,Mq],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 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!important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}.active-button[_ngcontent-%COMP%]{background-color:#ff9800;border:1px solid #ebebeb;color:#fff}.button-contents[_ngcontent-%COMP%]{align-items:center;display:flex;text-transform:uppercase}mat-icon[_ngcontent-%COMP%]{margin-right:6px}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(xxt,[{type:S_,args:[{selector:"results-download-component",templateUrl:"./results_download_component.ng.html",styleUrls:["./results_download_component.css"],changeDetection:jn.OnPush}]}],null,{numFlaggedAnnotations:[{type:R_}],runs:[{type:R_}],flaggedData:[{type:R_}],metrics:[{type:R_}]});class Oxt{constructor(t){this.store=t,this.flaggedAnnotations$=this.store.select(Kvt),this.numFlaggedAnnotations$=this.flaggedAnnotations$.pipe(Vt((t=>t.length))),this.activeRuns$=this.store.select(_I).pipe(Vt((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.flaggedData$=Kt([this.store.select(Yvt),this.flaggedAnnotations$]).pipe(Vt((([t,e])=>{const n=new Set(e);return Object.entries(t).filter((t=>n.has(t[0])))}))),this.metrics$=Kt([this.store.select(qvt),this.activeRuns$,this.store.select(ext)]).pipe(Vt((([t,e,n])=>{let o=Object.keys(n);for(const n of e)t[n]&&(o=o.concat(t[n].filter((t=>N_t(t)))));return o=[...new Set(o)],o})))}}Oxt.ɵfac=function t(e){return new(e||Oxt)(gf(iS))},Oxt.ɵcmp=so({type:Oxt,selectors:[["npmi-results-download"]],decls:5,vars:12,consts:[[3,"numFlaggedAnnotations","runs","flaggedData","metrics"]],template:function t(e,n){1&e&&(vf(0,"results-download-component",0),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async")),2&e&&bf("numFlaggedAnnotations",Cy(1,4,n.numFlaggedAnnotations$))("runs",Cy(2,6,n.activeRuns$))("flaggedData",Cy(3,8,n.flaggedData$))("metrics",Cy(4,10,n.metrics$))},directives:[xxt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Oxt,[{type:S_,args:[{selector:"npmi-results-download",template:'\n    <results-download-component\n      [numFlaggedAnnotations]="numFlaggedAnnotations$ | async"\n      [runs]="activeRuns$ | async"\n      [flaggedData]="flaggedData$ | async"\n      [metrics]="metrics$ | async"\n    ></results-download-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);const Pxt=new er("MatChipRemove"),wxt=new er("MatChipAvatar"),kxt=new er("MatChipTrailingIcon");const Sxt=FF(IF(HF(class{constructor(t){this._elementRef=t}}),"primary"),-1);class Dxt{}Dxt.ɵfac=function t(e){return new(e||Dxt)},Dxt.ɵdir=ho({type:Dxt,selectors:[["mat-chip-avatar"],["","matChipAvatar",""]],hostAttrs:[1,"mat-chip-avatar"],features:[jh([{provide:wxt,useExisting:Dxt}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Dxt,[{type:k_,args:[{selector:"mat-chip-avatar, [matChipAvatar]",host:{class:"mat-chip-avatar"},providers:[{provide:wxt,useExisting:Dxt}]}]}],null,null);class Rxt{}Rxt.ɵfac=function t(e){return new(e||Rxt)},Rxt.ɵdir=ho({type:Rxt,selectors:[["mat-chip-trailing-icon"],["","matChipTrailingIcon",""]],hostAttrs:[1,"mat-chip-trailing-icon"],features:[jh([{provide:kxt,useExisting:Rxt}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Rxt,[{type:k_,args:[{selector:"mat-chip-trailing-icon, [matChipTrailingIcon]",host:{class:"mat-chip-trailing-icon"},providers:[{provide:kxt,useExisting:Rxt}]}]}],null,null);class Ext extends Sxt{constructor(t,e,n,o,i,a,r,s){super(t),this._ngZone=e,this._changeDetectorRef=i,this._hasFocus=!1,this.chipListSelectable=!0,this._chipListMultiple=!1,this._chipListDisabled=!1,this._selected=!1,this._selectable=!0,this._disabled=!1,this._removable=!0,this._onFocus=new B,this._onBlur=new B,this.selectionChange=new Sy,this.destroyed=new Sy,this.removed=new Sy,this._addHostClassName(),this._chipRippleTarget=a.createElement("div"),this._chipRippleTarget.classList.add("mat-chip-ripple"),this._elementRef.nativeElement.appendChild(this._chipRippleTarget),this._chipRipple=new dL(this,e,this._chipRippleTarget,n),this._chipRipple.setupTriggerEvents(t),this.rippleConfig=o||{},this._animationsDisabled="NoopAnimations"===r,this.tabIndex=null!=s&&parseInt(s)||-1}get rippleDisabled(){return this.disabled||this.disableRipple||this._animationsDisabled||!!this.rippleConfig.disabled}get selected(){return this._selected}set selected(t){const e=eH(t);e!==this._selected&&(this._selected=e,this._dispatchSelectionChange())}get value(){return void 0!==this._value?this._value:this._elementRef.nativeElement.textContent}set value(t){this._value=t}get selectable(){return this._selectable&&this.chipListSelectable}set selectable(t){this._selectable=eH(t)}get disabled(){return this._chipListDisabled||this._disabled}set disabled(t){this._disabled=eH(t)}get removable(){return this._removable}set removable(t){this._removable=eH(t)}get ariaSelected(){return this.selectable&&(this._chipListMultiple||this.selected)?this.selected.toString():null}_addHostClassName(){const t="mat-basic-chip",e=this._elementRef.nativeElement;e.hasAttribute(t)||e.tagName.toLowerCase()===t?e.classList.add(t):e.classList.add("mat-standard-chip")}ngOnDestroy(){this.destroyed.emit({chip:this}),this._chipRipple._removeTriggerEvents()}select(){this._selected||(this._selected=!0,this._dispatchSelectionChange(),this._changeDetectorRef.markForCheck())}deselect(){this._selected&&(this._selected=!1,this._dispatchSelectionChange(),this._changeDetectorRef.markForCheck())}selectViaInteraction(){this._selected||(this._selected=!0,this._dispatchSelectionChange(!0),this._changeDetectorRef.markForCheck())}toggleSelected(t=!1){return this._selected=!this.selected,this._dispatchSelectionChange(t),this._changeDetectorRef.markForCheck(),this.selected}focus(){this._hasFocus||(this._elementRef.nativeElement.focus(),this._onFocus.next({chip:this})),this._hasFocus=!0}remove(){this.removable&&this.removed.emit({chip:this})}_handleClick(t){this.disabled?t.preventDefault():t.stopPropagation()}_handleKeydown(t){if(!this.disabled)switch(t.keyCode){case 46:case 8:this.remove(),t.preventDefault();break;case JI:this.selectable&&this.toggleSelected(!0),t.preventDefault()}}_blur(){this._ngZone.onStable.pipe(ve(1)).subscribe((()=>{this._ngZone.run((()=>{this._hasFocus=!1,this._onBlur.next({chip:this})}))}))}_dispatchSelectionChange(t=!1){this.selectionChange.emit({source:this,isUserInput:t,selected:this._selected})}}Ext.ɵfac=function t(e){return new(e||Ext)(gf(Kh),gf(mC),gf(cH),gf(pL,8),gf(Ob),gf(sM),gf(ck,8),Ga("tabindex"))},Ext.ɵdir=ho({type:Ext,selectors:[["mat-basic-chip"],["","mat-basic-chip",""],["mat-chip"],["","mat-chip",""]],contentQueries:function t(e,n,o){if(1&e&&(jy(o,wxt,5),jy(o,kxt,5),jy(o,Pxt,5)),2&e){let t;By(t=Uy())&&(n.avatar=t.first),By(t=Uy())&&(n.trailingIcon=t.first),By(t=Uy())&&(n.removeIcon=t.first)}},hostAttrs:["role","option",1,"mat-chip","mat-focus-indicator"],hostVars:14,hostBindings:function t(e,n){1&e&&Rf("click",(function t(e){return n._handleClick(e)}))("keydown",(function t(e){return n._handleKeydown(e)}))("focus",(function t(){return n.focus()}))("blur",(function t(){return n._blur()})),2&e&&(ym("tabindex",n.disabled?null:n.tabIndex)("disabled",n.disabled||null)("aria-disabled",n.disabled.toString())("aria-selected",n.ariaSelected),cu("mat-chip-selected",n.selected)("mat-chip-with-avatar",n.avatar)("mat-chip-with-trailing-icon",n.trailingIcon||n.removeIcon)("mat-chip-disabled",n.disabled)("_mat-animation-noopable",n._animationsDisabled))},inputs:{color:"color",disableRipple:"disableRipple",tabIndex:"tabIndex",selected:"selected",value:"value",selectable:"selectable",disabled:"disabled",removable:"removable"},outputs:{selectionChange:"selectionChange",destroyed:"destroyed",removed:"removed"},exportAs:["matChip"],features:[Qp]}),Ext.ctorParameters=()=>[{type:Kh},{type:mC},{type:cH},{type:void 0,decorators:[{type:Br},{type:Lr,args:[pL]}]},{type:Ob},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]},{type:String,decorators:[{type:$a,args:["tabindex"]}]}],Ext.propDecorators={avatar:[{type:ir,args:[wxt]}],trailingIcon:[{type:ir,args:[kxt]}],removeIcon:[{type:ir,args:[Pxt]}],selected:[{type:R_}],value:[{type:R_}],selectable:[{type:R_}],disabled:[{type:R_}],removable:[{type:R_}],selectionChange:[{type:E_}],destroyed:[{type:E_}],removed:[{type:E_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Ext,[{type:k_,args:[{selector:"mat-basic-chip, [mat-basic-chip], mat-chip, [mat-chip]",inputs:["color","disableRipple","tabIndex"],exportAs:"matChip",host:{class:"mat-chip mat-focus-indicator","[attr.tabindex]":"disabled ? null : tabIndex",role:"option","[class.mat-chip-selected]":"selected","[class.mat-chip-with-avatar]":"avatar","[class.mat-chip-with-trailing-icon]":"trailingIcon || removeIcon","[class.mat-chip-disabled]":"disabled","[class._mat-animation-noopable]":"_animationsDisabled","[attr.disabled]":"disabled || null","[attr.aria-disabled]":"disabled.toString()","[attr.aria-selected]":"ariaSelected","(click)":"_handleClick($event)","(keydown)":"_handleKeydown($event)","(focus)":"focus()","(blur)":"_blur()"}}]}],(function(){return[{type:Kh},{type:mC},{type:cH},{type:void 0,decorators:[{type:Br},{type:Lr,args:[pL]}]},{type:Ob},{type:void 0,decorators:[{type:Lr,args:[sM]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]},{type:String,decorators:[{type:$a,args:["tabindex"]}]}]}),{selectionChange:[{type:E_}],destroyed:[{type:E_}],removed:[{type:E_}],selected:[{type:R_}],value:[{type:R_}],selectable:[{type:R_}],disabled:[{type:R_}],removable:[{type:R_}],avatar:[{type:ir,args:[wxt]}],trailingIcon:[{type:ir,args:[kxt]}],removeIcon:[{type:ir,args:[Pxt]}]});class Axt{constructor(t,e){this._parentChip=t,"BUTTON"===e.nativeElement.nodeName&&e.nativeElement.setAttribute("type","button")}_handleClick(t){const e=this._parentChip;e.removable&&!e.disabled&&e.remove(),t.stopPropagation()}}Axt.ɵfac=function t(e){return new(e||Axt)(gf(Ext),gf(Kh))},Axt.ɵdir=ho({type:Axt,selectors:[["","matChipRemove",""]],hostAttrs:[1,"mat-chip-remove","mat-chip-trailing-icon"],hostBindings:function t(e,n){1&e&&Rf("click",(function t(e){return n._handleClick(e)}))},features:[jh([{provide:Pxt,useExisting:Axt}])]}),Axt.ctorParameters=()=>[{type:Ext},{type:Kh}],("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Axt,[{type:k_,args:[{selector:"[matChipRemove]",host:{class:"mat-chip-remove mat-chip-trailing-icon","(click)":"_handleClick($event)"},providers:[{provide:Pxt,useExisting:Axt}]}]}],(function(){return[{type:Ext},{type:Kh}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Txt=new er("mat-chips-default-options"),Nxt=LF(class{constructor(t,e,n,o){this._defaultErrorStateMatcher=t,this._parentForm=e,this._parentFormGroup=n,this.ngControl=o}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let zxt=0;class Ixt{constructor(t,e){this.source=t,this.value=e}}class Hxt extends Nxt{constructor(t,e,n,o,i,a,r){super(a,o,i,r),this._elementRef=t,this._changeDetectorRef=e,this._dir=n,this.controlType="mat-chip-list",this._lastDestroyedChipIndex=null,this._destroyed=new B,this._uid="mat-chip-list-"+zxt++,this._tabIndex=0,this._userTabIndex=null,this._onTouched=()=>{},this._onChange=()=>{},this._multiple=!1,this._compareWith=(t,e)=>t===e,this._required=!1,this._disabled=!1,this.ariaOrientation="horizontal",this._selectable=!0,this.change=new Sy,this.valueChange=new Sy,this.ngControl&&(this.ngControl.valueAccessor=this)}get selected(){return this.multiple?this._selectionModel.selected:this._selectionModel.selected[0]}get role(){return this.empty?null:"listbox"}get multiple(){return this._multiple}set multiple(t){this._multiple=eH(t),this._syncChipsState()}get compareWith(){return this._compareWith}set compareWith(t){this._compareWith=t,this._selectionModel&&this._initializeSelection()}get value(){return this._value}set value(t){this.writeValue(t),this._value=t}get id(){return this._chipInput?this._chipInput.id:this._uid}get required(){return this._required}set required(t){this._required=eH(t),this.stateChanges.next()}get placeholder(){return this._chipInput?this._chipInput.placeholder:this._placeholder}set placeholder(t){this._placeholder=t,this.stateChanges.next()}get focused(){return this._chipInput&&this._chipInput.focused||this._hasFocusedChip()}get empty(){return(!this._chipInput||this._chipInput.empty)&&(!this.chips||0===this.chips.length)}get shouldLabelFloat(){return!this.empty||this.focused}get disabled(){return this.ngControl?!!this.ngControl.disabled:this._disabled}set disabled(t){this._disabled=eH(t),this._syncChipsState()}get selectable(){return this._selectable}set selectable(t){this._selectable=eH(t),this.chips&&this.chips.forEach((t=>t.chipListSelectable=this._selectable))}set tabIndex(t){this._userTabIndex=t,this._tabIndex=t}get chipSelectionChanges(){return pe(...this.chips.map((t=>t.selectionChange)))}get chipFocusChanges(){return pe(...this.chips.map((t=>t._onFocus)))}get chipBlurChanges(){return pe(...this.chips.map((t=>t._onBlur)))}get chipRemoveChanges(){return pe(...this.chips.map((t=>t.destroyed)))}ngAfterContentInit(){this._keyManager=new LH(this.chips).withWrap().withVerticalOrientation().withHomeAndEnd().withHorizontalOrientation(this._dir?this._dir.value:"ltr"),this._dir&&this._dir.change.pipe(je(this._destroyed)).subscribe((t=>this._keyManager.withHorizontalOrientation(t))),this._keyManager.tabOut.pipe(je(this._destroyed)).subscribe((()=>{this._allowFocusEscape()})),this.chips.changes.pipe(Be(null),je(this._destroyed)).subscribe((()=>{this.disabled&&Promise.resolve().then((()=>{this._syncChipsState()})),this._resetChips(),this._initializeSelection(),this._updateTabIndex(),this._updateFocusForDestroyedChips(),this.stateChanges.next()}))}ngOnInit(){this._selectionModel=new jL(this.multiple,void 0,!1),this.stateChanges.next()}ngDoCheck(){this.ngControl&&(this.updateErrorState(),this.ngControl.disabled!==this._disabled&&(this.disabled=!!this.ngControl.disabled))}ngOnDestroy(){this._destroyed.next(),this._destroyed.complete(),this.stateChanges.complete(),this._dropSubscriptions()}registerInput(t){this._chipInput=t,this._elementRef.nativeElement.setAttribute("data-mat-chip-input",t.id)}setDescribedByIds(t){this._ariaDescribedby=t.join(" ")}writeValue(t){this.chips&&this._setSelectionByValue(t,!1)}registerOnChange(t){this._onChange=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t,this.stateChanges.next()}onContainerClick(t){this._originatesFromChip(t)||this.focus()}focus(t){this.disabled||this._chipInput&&this._chipInput.focused||(this.chips.length>0?(this._keyManager.setFirstItemActive(),this.stateChanges.next()):(this._focusInput(t),this.stateChanges.next()))}_focusInput(t){this._chipInput&&this._chipInput.focus(t)}_keydown(t){const e=t.target;e&&e.classList.contains("mat-chip")&&(this._keyManager.onKeydown(t),this.stateChanges.next())}_updateTabIndex(){this._tabIndex=this._userTabIndex||(0===this.chips.length?-1:0)}_updateFocusForDestroyedChips(){if(null!=this._lastDestroyedChipIndex)if(this.chips.length){const t=Math.min(this._lastDestroyedChipIndex,this.chips.length-1);this._keyManager.setActiveItem(t)}else this.focus();this._lastDestroyedChipIndex=null}_isValidIndex(t){return t>=0&&t<this.chips.length}_setSelectionByValue(t,e=!0){if(this._clearSelection(),this.chips.forEach((t=>t.deselect())),Array.isArray(t))t.forEach((t=>this._selectValue(t,e))),this._sortValues();else{const n=this._selectValue(t,e);n&&e&&this._keyManager.setActiveItem(n)}}_selectValue(t,e=!0){const n=this.chips.find((e=>null!=e.value&&this._compareWith(e.value,t)));return n&&(e?n.selectViaInteraction():n.select(),this._selectionModel.select(n)),n}_initializeSelection(){Promise.resolve().then((()=>{(this.ngControl||this._value)&&(this._setSelectionByValue(this.ngControl?this.ngControl.value:this._value,!1),this.stateChanges.next())}))}_clearSelection(t){this._selectionModel.clear(),this.chips.forEach((e=>{e!==t&&e.deselect()})),this.stateChanges.next()}_sortValues(){this._multiple&&(this._selectionModel.clear(),this.chips.forEach((t=>{t.selected&&this._selectionModel.select(t)})),this.stateChanges.next())}_propagateChanges(t){let e=null;e=Array.isArray(this.selected)?this.selected.map((t=>t.value)):this.selected?this.selected.value:t,this._value=e,this.change.emit(new Ixt(this,e)),this.valueChange.emit(e),this._onChange(e),this._changeDetectorRef.markForCheck()}_blur(){this._hasFocusedChip()||this._keyManager.setActiveItem(-1),this.disabled||(this._chipInput?setTimeout((()=>{this.focused||this._markAsTouched()})):this._markAsTouched())}_markAsTouched(){this._onTouched(),this._changeDetectorRef.markForCheck(),this.stateChanges.next()}_allowFocusEscape(){-1!==this._tabIndex&&(this._tabIndex=-1,setTimeout((()=>{this._tabIndex=this._userTabIndex||0,this._changeDetectorRef.markForCheck()})))}_resetChips(){this._dropSubscriptions(),this._listenToChipsFocus(),this._listenToChipsSelection(),this._listenToChipsRemoved()}_dropSubscriptions(){this._chipFocusSubscription&&(this._chipFocusSubscription.unsubscribe(),this._chipFocusSubscription=null),this._chipBlurSubscription&&(this._chipBlurSubscription.unsubscribe(),this._chipBlurSubscription=null),this._chipSelectionSubscription&&(this._chipSelectionSubscription.unsubscribe(),this._chipSelectionSubscription=null),this._chipRemoveSubscription&&(this._chipRemoveSubscription.unsubscribe(),this._chipRemoveSubscription=null)}_listenToChipsSelection(){this._chipSelectionSubscription=this.chipSelectionChanges.subscribe((t=>{t.source.selected?this._selectionModel.select(t.source):this._selectionModel.deselect(t.source),this.multiple||this.chips.forEach((t=>{!this._selectionModel.isSelected(t)&&t.selected&&t.deselect()})),t.isUserInput&&this._propagateChanges()}))}_listenToChipsFocus(){this._chipFocusSubscription=this.chipFocusChanges.subscribe((t=>{let e=this.chips.toArray().indexOf(t.chip);this._isValidIndex(e)&&this._keyManager.updateActiveItem(e),this.stateChanges.next()})),this._chipBlurSubscription=this.chipBlurChanges.subscribe((()=>{this._blur(),this.stateChanges.next()}))}_listenToChipsRemoved(){this._chipRemoveSubscription=this.chipRemoveChanges.subscribe((t=>{const e=t.chip,n=this.chips.toArray().indexOf(t.chip);this._isValidIndex(n)&&e._hasFocus&&(this._lastDestroyedChipIndex=n)}))}_originatesFromChip(t){let e=t.target;for(;e&&e!==this._elementRef.nativeElement;){if(e.classList.contains("mat-chip"))return!0;e=e.parentElement}return!1}_hasFocusedChip(){return this.chips&&this.chips.some((t=>t._hasFocus))}_syncChipsState(){this.chips&&this.chips.forEach((t=>{t._chipListDisabled=this._disabled,t._chipListMultiple=this.multiple}))}}Hxt.ɵfac=function t(e){return new(e||Hxt)(gf(Kh),gf(Ob),gf(vF,8),gf(XW,8),gf(hG,8),gf(nL),gf(lW,10))},Hxt.ɵcmp=so({type:Hxt,selectors:[["mat-chip-list"]],contentQueries:function t(e,n,o){if(1&e&&jy(o,Ext,5),2&e){let t;By(t=Uy())&&(n.chips=t)}},hostAttrs:[1,"mat-chip-list"],hostVars:15,hostBindings:function t(e,n){1&e&&Rf("focus",(function t(){return n.focus()}))("blur",(function t(){return n._blur()}))("keydown",(function t(e){return n._keydown(e)})),2&e&&(cg("id",n._uid),ym("tabindex",n.disabled?null:n._tabIndex)("aria-describedby",n._ariaDescribedby||null)("aria-required",n.role?n.required:null)("aria-disabled",n.disabled.toString())("aria-invalid",n.errorState)("aria-multiselectable",n.multiple)("role",n.role)("aria-orientation",n.ariaOrientation),cu("mat-chip-list-disabled",n.disabled)("mat-chip-list-invalid",n.errorState)("mat-chip-list-required",n.required))},inputs:{ariaOrientation:["aria-orientation","ariaOrientation"],multiple:"multiple",compareWith:"compareWith",value:"value",required:"required",placeholder:"placeholder",disabled:"disabled",selectable:"selectable",tabIndex:"tabIndex",errorStateMatcher:"errorStateMatcher"},outputs:{change:"change",valueChange:"valueChange"},exportAs:["matChipList"],features:[jh([{provide:iU,useExisting:Hxt}]),Qp],ngContentSelectors:["*"],decls:2,vars:0,consts:[[1,"mat-chip-list-wrapper"]],template:function t(e,n){1&e&&(Hf(),Cf(0,"div",0),Ff(1),Mf())},styles:['.mat-chip{position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;transform:translateZ(0);border:none;-webkit-appearance:none;-moz-appearance:none}.mat-standard-chip{transition:box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);display:inline-flex;padding:7px 12px;border-radius:16px;align-items:center;cursor:default;min-height:32px;height:1px}._mat-animation-noopable.mat-standard-chip{transition:none;animation:none}.mat-standard-chip .mat-chip-remove.mat-icon{width:18px;height:18px}.mat-standard-chip::after{top:0;left:0;right:0;bottom:0;position:absolute;border-radius:inherit;opacity:0;content:"";pointer-events:none;transition:opacity 200ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-standard-chip:hover::after{opacity:.12}.mat-standard-chip:focus{outline:none}.mat-standard-chip:focus::after{opacity:.16}.cdk-high-contrast-active .mat-standard-chip{outline:solid 1px}.cdk-high-contrast-active .mat-standard-chip:focus{outline:dotted 2px}.mat-standard-chip.mat-chip-disabled::after{opacity:0}.mat-standard-chip.mat-chip-disabled .mat-chip-remove,.mat-standard-chip.mat-chip-disabled .mat-chip-trailing-icon{cursor:default}.mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar,.mat-standard-chip.mat-chip-with-avatar{padding-top:0;padding-bottom:0}.mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar{padding-right:8px;padding-left:0}[dir=rtl] .mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar{padding-left:8px;padding-right:0}.mat-standard-chip.mat-chip-with-trailing-icon{padding-top:7px;padding-bottom:7px;padding-right:8px;padding-left:12px}[dir=rtl] .mat-standard-chip.mat-chip-with-trailing-icon{padding-left:8px;padding-right:12px}.mat-standard-chip.mat-chip-with-avatar{padding-left:0;padding-right:12px}[dir=rtl] .mat-standard-chip.mat-chip-with-avatar{padding-right:0;padding-left:12px}.mat-standard-chip .mat-chip-avatar{width:24px;height:24px;margin-right:8px;margin-left:4px}[dir=rtl] .mat-standard-chip .mat-chip-avatar{margin-left:8px;margin-right:4px}.mat-standard-chip .mat-chip-remove,.mat-standard-chip .mat-chip-trailing-icon{width:18px;height:18px;cursor:pointer}.mat-standard-chip .mat-chip-remove,.mat-standard-chip .mat-chip-trailing-icon{margin-left:8px;margin-right:0}[dir=rtl] .mat-standard-chip .mat-chip-remove,[dir=rtl] .mat-standard-chip .mat-chip-trailing-icon{margin-right:8px;margin-left:0}.mat-chip-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none;border-radius:inherit;overflow:hidden}.mat-chip-list-wrapper{display:flex;flex-direction:row;flex-wrap:wrap;align-items:center;margin:-4px}.mat-chip-list-wrapper input.mat-input-element,.mat-chip-list-wrapper .mat-standard-chip{margin:4px}.mat-chip-list-stacked .mat-chip-list-wrapper{flex-direction:column;align-items:flex-start}.mat-chip-list-stacked .mat-chip-list-wrapper .mat-standard-chip{width:100%}.mat-chip-avatar{border-radius:50%;justify-content:center;align-items:center;display:flex;overflow:hidden;object-fit:cover}input.mat-chip-input{width:150px;margin:4px;flex:1 0 150px}\n'],encapsulation:2,changeDetection:0}),Hxt.ctorParameters=()=>[{type:Kh},{type:Ob},{type:vF,decorators:[{type:Br}]},{type:XW,decorators:[{type:Br}]},{type:hG,decorators:[{type:Br}]},{type:nL},{type:lW,decorators:[{type:Br},{type:Vr}]}],Hxt.propDecorators={errorStateMatcher:[{type:R_}],multiple:[{type:R_}],compareWith:[{type:R_}],value:[{type:R_}],required:[{type:R_}],placeholder:[{type:R_}],disabled:[{type:R_}],ariaOrientation:[{type:R_,args:["aria-orientation"]}],selectable:[{type:R_}],tabIndex:[{type:R_}],change:[{type:E_}],valueChange:[{type:E_}],chips:[{type:or,args:[Ext,{descendants:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Hxt,[{type:S_,args:[{selector:"mat-chip-list",template:'<div class="mat-chip-list-wrapper"><ng-content></ng-content></div>',exportAs:"matChipList",host:{"[attr.tabindex]":"disabled ? null : _tabIndex","[attr.aria-describedby]":"_ariaDescribedby || null","[attr.aria-required]":"role ? required : null","[attr.aria-disabled]":"disabled.toString()","[attr.aria-invalid]":"errorState","[attr.aria-multiselectable]":"multiple","[attr.role]":"role","[class.mat-chip-list-disabled]":"disabled","[class.mat-chip-list-invalid]":"errorState","[class.mat-chip-list-required]":"required","[attr.aria-orientation]":"ariaOrientation",class:"mat-chip-list","(focus)":"focus()","(blur)":"_blur()","(keydown)":"_keydown($event)","[id]":"_uid"},providers:[{provide:iU,useExisting:Hxt}],encapsulation:Wn.None,changeDetection:jn.OnPush,styles:['.mat-chip{position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;transform:translateZ(0);border:none;-webkit-appearance:none;-moz-appearance:none}.mat-standard-chip{transition:box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);display:inline-flex;padding:7px 12px;border-radius:16px;align-items:center;cursor:default;min-height:32px;height:1px}._mat-animation-noopable.mat-standard-chip{transition:none;animation:none}.mat-standard-chip .mat-chip-remove.mat-icon{width:18px;height:18px}.mat-standard-chip::after{top:0;left:0;right:0;bottom:0;position:absolute;border-radius:inherit;opacity:0;content:"";pointer-events:none;transition:opacity 200ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-standard-chip:hover::after{opacity:.12}.mat-standard-chip:focus{outline:none}.mat-standard-chip:focus::after{opacity:.16}.cdk-high-contrast-active .mat-standard-chip{outline:solid 1px}.cdk-high-contrast-active .mat-standard-chip:focus{outline:dotted 2px}.mat-standard-chip.mat-chip-disabled::after{opacity:0}.mat-standard-chip.mat-chip-disabled .mat-chip-remove,.mat-standard-chip.mat-chip-disabled .mat-chip-trailing-icon{cursor:default}.mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar,.mat-standard-chip.mat-chip-with-avatar{padding-top:0;padding-bottom:0}.mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar{padding-right:8px;padding-left:0}[dir=rtl] .mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar{padding-left:8px;padding-right:0}.mat-standard-chip.mat-chip-with-trailing-icon{padding-top:7px;padding-bottom:7px;padding-right:8px;padding-left:12px}[dir=rtl] .mat-standard-chip.mat-chip-with-trailing-icon{padding-left:8px;padding-right:12px}.mat-standard-chip.mat-chip-with-avatar{padding-left:0;padding-right:12px}[dir=rtl] .mat-standard-chip.mat-chip-with-avatar{padding-right:0;padding-left:12px}.mat-standard-chip .mat-chip-avatar{width:24px;height:24px;margin-right:8px;margin-left:4px}[dir=rtl] .mat-standard-chip .mat-chip-avatar{margin-left:8px;margin-right:4px}.mat-standard-chip .mat-chip-remove,.mat-standard-chip .mat-chip-trailing-icon{width:18px;height:18px;cursor:pointer}.mat-standard-chip .mat-chip-remove,.mat-standard-chip .mat-chip-trailing-icon{margin-left:8px;margin-right:0}[dir=rtl] .mat-standard-chip .mat-chip-remove,[dir=rtl] .mat-standard-chip .mat-chip-trailing-icon{margin-right:8px;margin-left:0}.mat-chip-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none;border-radius:inherit;overflow:hidden}.mat-chip-list-wrapper{display:flex;flex-direction:row;flex-wrap:wrap;align-items:center;margin:-4px}.mat-chip-list-wrapper input.mat-input-element,.mat-chip-list-wrapper .mat-standard-chip{margin:4px}.mat-chip-list-stacked .mat-chip-list-wrapper{flex-direction:column;align-items:flex-start}.mat-chip-list-stacked .mat-chip-list-wrapper .mat-standard-chip{width:100%}.mat-chip-avatar{border-radius:50%;justify-content:center;align-items:center;display:flex;overflow:hidden;object-fit:cover}input.mat-chip-input{width:150px;margin:4px;flex:1 0 150px}\n']}]}],(function(){return[{type:Kh},{type:Ob},{type:vF,decorators:[{type:Br}]},{type:XW,decorators:[{type:Br}]},{type:hG,decorators:[{type:Br}]},{type:nL},{type:lW,decorators:[{type:Br},{type:Vr}]}]}),{ariaOrientation:[{type:R_,args:["aria-orientation"]}],change:[{type:E_}],valueChange:[{type:E_}],multiple:[{type:R_}],compareWith:[{type:R_}],value:[{type:R_}],required:[{type:R_}],placeholder:[{type:R_}],disabled:[{type:R_}],selectable:[{type:R_}],tabIndex:[{type:R_}],errorStateMatcher:[{type:R_}],chips:[{type:or,args:[Ext,{descendants:!0}]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let Fxt=0;class Lxt{constructor(t,e){this._elementRef=t,this._defaultOptions=e,this.focused=!1,this._addOnBlur=!1,this.separatorKeyCodes=this._defaultOptions.separatorKeyCodes,this.chipEnd=new Sy,this.placeholder="",this.id="mat-chip-list-input-"+Fxt++,this._disabled=!1,this.inputElement=this._elementRef.nativeElement}set chipList(t){t&&(this._chipList=t,this._chipList.registerInput(this))}get addOnBlur(){return this._addOnBlur}set addOnBlur(t){this._addOnBlur=eH(t)}get disabled(){return this._disabled||this._chipList&&this._chipList.disabled}set disabled(t){this._disabled=eH(t)}get empty(){return!this.inputElement.value}ngOnChanges(){this._chipList.stateChanges.next()}ngOnDestroy(){this.chipEnd.complete()}ngAfterContentInit(){this._focusLastChipOnBackspace=this.empty}_keydown(t){if(t){if(9!==t.keyCode||tH(t,"shiftKey")||this._chipList._allowFocusEscape(),8===t.keyCode&&this._focusLastChipOnBackspace)return this._chipList._keyManager.setLastItemActive(),void t.preventDefault();this._focusLastChipOnBackspace=!1}this._emitChipEnd(t)}_keyup(t){!this._focusLastChipOnBackspace&&8===t.keyCode&&this.empty&&(this._focusLastChipOnBackspace=!0,t.preventDefault())}_blur(){this.addOnBlur&&this._emitChipEnd(),this.focused=!1,this._chipList.focused||this._chipList._blur(),this._chipList.stateChanges.next()}_focus(){this.focused=!0,this._focusLastChipOnBackspace=this.empty,this._chipList.stateChanges.next()}_emitChipEnd(t){!this.inputElement.value&&t&&this._chipList._keydown(t),t&&!this._isSeparatorKey(t)||(this.chipEnd.emit({input:this.inputElement,value:this.inputElement.value,chipInput:this}),null==t||t.preventDefault())}_onInput(){this._chipList.stateChanges.next()}focus(t){this.inputElement.focus(t)}clear(){this.inputElement.value="",this._focusLastChipOnBackspace=!0}_isSeparatorKey(t){return!tH(t)&&new Set(this.separatorKeyCodes).has(t.keyCode)}}Lxt.ɵfac=function t(e){return new(e||Lxt)(gf(Kh),gf(Txt))},Lxt.ɵdir=ho({type:Lxt,selectors:[["input","matChipInputFor",""]],hostAttrs:[1,"mat-chip-input","mat-input-element"],hostVars:5,hostBindings:function t(e,n){1&e&&Rf("keydown",(function t(e){return n._keydown(e)}))("keyup",(function t(e){return n._keyup(e)}))("blur",(function t(){return n._blur()}))("focus",(function t(){return n._focus()}))("input",(function t(){return n._onInput()})),2&e&&(cg("id",n.id),ym("disabled",n.disabled||null)("placeholder",n.placeholder||null)("aria-invalid",n._chipList&&n._chipList.ngControl?n._chipList.ngControl.invalid:null)("aria-required",n._chipList&&n._chipList.required||null))},inputs:{separatorKeyCodes:["matChipInputSeparatorKeyCodes","separatorKeyCodes"],placeholder:"placeholder",id:"id",chipList:["matChipInputFor","chipList"],addOnBlur:["matChipInputAddOnBlur","addOnBlur"],disabled:"disabled"},outputs:{chipEnd:"matChipInputTokenEnd"},exportAs:["matChipInput","matChipInputFor"],features:[qo]}),Lxt.ctorParameters=()=>[{type:Kh},{type:void 0,decorators:[{type:Lr,args:[Txt]}]}],Lxt.propDecorators={chipList:[{type:R_,args:["matChipInputFor"]}],addOnBlur:[{type:R_,args:["matChipInputAddOnBlur"]}],separatorKeyCodes:[{type:R_,args:["matChipInputSeparatorKeyCodes"]}],chipEnd:[{type:E_,args:["matChipInputTokenEnd"]}],placeholder:[{type:R_}],id:[{type:R_}],disabled:[{type:R_}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Lxt,[{type:k_,args:[{selector:"input[matChipInputFor]",exportAs:"matChipInput, matChipInputFor",host:{class:"mat-chip-input mat-input-element","(keydown)":"_keydown($event)","(keyup)":"_keyup($event)","(blur)":"_blur()","(focus)":"_focus()","(input)":"_onInput()","[id]":"id","[attr.disabled]":"disabled || null","[attr.placeholder]":"placeholder || null","[attr.aria-invalid]":"_chipList && _chipList.ngControl ? _chipList.ngControl.invalid : null","[attr.aria-required]":"_chipList && _chipList.required || null"}}]}],(function(){return[{type:Kh},{type:void 0,decorators:[{type:Lr,args:[Txt]}]}]}),{separatorKeyCodes:[{type:R_,args:["matChipInputSeparatorKeyCodes"]}],chipEnd:[{type:E_,args:["matChipInputTokenEnd"]}],placeholder:[{type:R_}],id:[{type:R_}],chipList:[{type:R_,args:["matChipInputFor"]}],addOnBlur:[{type:R_,args:["matChipInputAddOnBlur"]}],disabled:[{type:R_}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Bxt=[Hxt,Ext,Lxt,Axt,Dxt,Rxt],Vxt={separatorKeyCodes:[XI]};class jxt{}jxt.ɵfac=function t(e){return new(e||jxt)},jxt.ɵmod=fo({type:jxt}),jxt.ɵinj=Dn({providers:[nL,{provide:Txt,useValue:Vxt}],imports:[[NF]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(jxt,[{type:L_,args:[{imports:[NF],exports:Bxt,declarations:Bxt,providers:[nL,{provide:Txt,useValue:Vxt}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(jxt,{declarations:function(){return[Hxt,Ext,Lxt,Axt,Dxt,Rxt]},imports:function(){return[NF]},exports:function(){return[Hxt,Ext,Lxt,Axt,Dxt,Rxt]}});const Uxt=function(t,e){return{"embedding-selected":t,"embedding-unselected":e}};function Wxt(t,e){if(1&t){const t=wf();Cf(0,"mat-icon",5),Rf("click",(function e(){Pi(t);const n=zf();return n.onSelect.emit(n.metric)})),Mf()}if(2&t){const t=zf();bf("ngClass",ay(1,Uxt,t.embeddingsMetric===t.metric,t.embeddingsMetric!==t.metric))}}const Gxt=function(t){return{width:t}},Yxt=function(t){return{"value-invalid":t}};class qxt{constructor(){this.onRemove=new Sy,this.onSelect=new Sy,this.onFilterChange=new Sy,this.focusMin=!1,this.focusMax=!1,this.ngUnsubscribe=new B}ngOnInit(){this.minFormControl=new WW(this.filterValues.min,[NU.required,NU.min(-1),NU.max(1),this.minValueValidator.bind(this)]),this.maxFormControl=new WW(this.filterValues.max,[NU.required,NU.min(-1),NU.max(1),this.maxValueValidator.bind(this)]),this.minFormControl.valueChanges.pipe(je(this.ngUnsubscribe)).subscribe((()=>{this.minFormControl.valid&&this.maxFormControl.valid&&this.onFilterChange.emit({min:parseFloat(this.minFormControl.value),max:parseFloat(this.maxFormControl.value)})})),this.maxFormControl.valueChanges.pipe(je(this.ngUnsubscribe)).subscribe((()=>{this.minFormControl.valid&&this.maxFormControl.valid&&this.onFilterChange.emit({min:parseFloat(this.minFormControl.value),max:parseFloat(this.maxFormControl.value)})}))}ngOnChanges(t){this.minFormControl&&this.maxFormControl&&(this.minFormControl.setValue(this.filterValues.min,{emitEvent:!1}),this.maxFormControl.setValue(this.filterValues.max,{emitEvent:!1}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}minValueValidator(t){return this.maxFormControl&&"NaN"!==t.value?isNaN(parseFloat(t.value))?{value:"the string you entered is neither NaN nor a number"}:parseFloat(t.value)>parseFloat(this.maxFormControl.value)?{value:"the number you entered is larger than the max value"}:null:null}maxValueValidator(t){return this.minFormControl?"NaN"===this.minFormControl.value&&"NaN"===t.value?null:isNaN(parseFloat(t.value))?{value:"the string you entered is neither NaN nor a number"}:t.value<this.minFormControl.value?{value:"the number you entered is smaller than the min value"}:null:null}getErrorDescription(t){if(t){const e=Object.keys(t)[0];return"required"===e?"you did not enter anything":"min"===e?"the number must be at least -1.0":"max"===e?"the number is bigger than 1.0":t[e]}return""}}qxt.ɵfac=function t(e){return new(e||qxt)},qxt.ɵcmp=so({type:qxt,selectors:[["metric-arithmetic-element-component"]],inputs:{metric:"metric",filterValues:"filterValues",hasEmbeddingsData:"hasEmbeddingsData",embeddingsMetric:"embeddingsMetric"},outputs:{onRemove:"onRemove",onSelect:"onSelect",onFilterChange:"onFilterChange"},features:[qo],decls:10,vars:22,consts:[[1,"filter-chip",3,"removed"],["class","embeddings-button","svgIcon","group_work_24px",3,"ngClass","click",4,"ngIf"],[1,"metric-arithmetic-element-range",3,"keydown"],["matInput","",1,"input-field",3,"value","matTooltip","matTooltipDisabled","ngStyle","ngClass","formControl","focus","focusout"],["matChipRemove","","svgIcon","cancel_24px"],["svgIcon","group_work_24px",1,"embeddings-button",3,"ngClass","click"]],template:function t(e,n){1&e&&(Cf(0,"mat-chip",0),Rf("removed",(function t(){return n.onRemove.emit(n.metric)})),Hm(1,Wxt,1,4,"mat-icon",1),Pu(2),Cf(3,"div",2),Rf("keydown",(function t(e){return e.stopPropagation()})),Pu(4," [ "),Cf(5,"input",3),Rf("focus",(function t(){return n.focusMin=!0}))("focusout",(function t(){return n.focusMin=!1})),Mf(),Pu(6," ; "),Cf(7,"input",3),Rf("focus",(function t(){return n.focusMax=!0}))("focusout",(function t(){return n.focusMax=!1})),Mf(),Pu(8," ] "),Mf(),vf(9,"mat-icon",4),Mf()),2&e&&(Dc(1),bf("ngIf",n.hasEmbeddingsData),Dc(1),ku(" ",n.metric," "),Dc(3),bf("value",n.filterValues.min)("matTooltip",n.getErrorDescription(n.minFormControl.errors))("matTooltipDisabled",!n.minFormControl.invalid)("ngStyle",iy(14,Gxt,n.focusMin?"100px":n.minFormControl.value.toString().length+"ch"))("ngClass",iy(16,Yxt,!n.minFormControl.valid))("formControl",n.minFormControl),Dc(2),bf("value",n.filterValues.max)("matTooltip",n.getErrorDescription(n.maxFormControl.errors))("matTooltipDisabled",!n.maxFormControl.invalid)("ngStyle",iy(18,Gxt,n.focusMax?"100px":n.maxFormControl.value.toString().length+"ch"))("ngClass",iy(20,Yxt,!n.maxFormControl.valid))("formControl",n.maxFormControl))},directives:[Ext,Pv,SU,L0,zv,Cv,mW,uG,Mq,Axt],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   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0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}.chart-container[_ngcontent-%COMP%]{background-color:#fff;border-bottom:1px solid #ebebeb;display:flex;flex-direction:column;overflow:hidden}.chart[_ngcontent-%COMP%]{height:300px;width:100%}.chart-head[_ngcontent-%COMP%]{align-items:center;display:flex;justify-content:space-between}.chart-heading[_ngcontent-%COMP%]{font-size:13px;padding-left:10px;padding-top:10px}.draw-area[_ngcontent-%COMP%]{height:100%;width:100%}.stroked-line[_ngcontent-%COMP%]{stroke:rgba(0,0,0,.12);stroke-dasharray:3 3}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(aOt,[{type:S_,args:[{selector:"violin-filter-component",templateUrl:"./violin_filter_component.ng.html",styleUrls:["./violin_filter_component.css"],changeDetection:jn.OnPush}]}],null,{metricName:[{type:R_}],filter:[{type:R_}],chartData:[{type:R_}],width:[{type:R_}],colorScale:[{type:R_}],onRemove:[{type:E_}],onUpdateFilter:[{type:E_}],chartContainer:[{type:ar,args:["chart",{static:!0,read:Kh}]}]});class rOt{constructor(t){this.store=t,this.activeRuns$=this.store.pipe(rS(_I)).pipe(Vt((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.visibleAnnotations$=Kt([this.store.select(Yvt),this.store.select(Jvt),this.store.select(sxt)]).pipe(Vt((([t,e,n])=>oOt(t,e,n)))),this.chartWidth$=this.store.pipe(rS(cxt)).pipe(Vt((t=>Math.max(150,t)))),this.runColorScale$=this.store.select(CI).pipe(Vt((t=>e=>{if(!t.hasOwnProperty(e))throw new Error(`[Color scale] unknown runId: ${e}.`);return t[e]})))}ngOnInit(){this.chartData$=Kt([this.visibleAnnotations$,this.activeRuns$]).pipe(Vt((([t,e])=>(function n(t,e,o){const i={},a={},r=new Set(e),s=z_t(o),l={max:-1,min:1};Object.values(t).forEach((t=>{t.forEach((t=>{const e=t.run;if(r.has(e)&&t.metric===s)if(null===t.nPMIValue)a[e]?a[e].push(null):a[e]=[null];else{const n=t.nPMIValue;l.max=l.max<n?n:l.max,l.min=l.min>n?n:l.min,i[t.run]?i[e].push(n):i[e]=[n]}}))}));const c={},d=D6().domain([l.min,l.max]).value((t=>t)),p=D6().domain([-1/0,1/0]).thresholds(0).value((t=>t));for(const t of r)if(c[t]=d(i[t]),a[t]){const e=p(a[t]);c[t].unshift(e[0])}return{violinData:c,extremes:l}})(t,e,this.metricName))))}removeMetric(){this.store.dispatch(Pvt({metric:this.metricName}))}updateFilter(t){this.store.dispatch(wvt(Object.assign({metric:this.metricName},t)))}}function sOt(t,e){if(1&t&&vf(0,"npmi-violin-filter",8),2&t){const t=e.$implicit;bf("metricName",t[0])("filter",t[1])}}function lOt(t,e){1&t&&(Cf(0,"div",9),Cf(1,"span",10),Pu(2," You can add more filters at the top. "),Mf(),Mf())}rOt.ɵfac=function t(e){return new(e||rOt)(gf(iS))},rOt.ɵcmp=so({type:rOt,selectors:[["npmi-violin-filter"]],inputs:{metricName:"metricName",filter:"filter"},decls:4,vars:11,consts:[[3,"metricName","filter","chartData","width","colorScale","onRemove","onUpdateFilter"]],template:function t(e,n){1&e&&(Cf(0,"violin-filter-component",0),Rf("onRemove",(function t(){return n.removeMetric()}))("onUpdateFilter",(function t(e){return n.updateFilter(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),Mf()),2&e&&bf("metricName",n.metricName)("filter",n.filter)("chartData",Cy(1,5,n.chartData$))("width",Cy(2,7,n.chartWidth$))("colorScale",Cy(3,9,n.runColorScale$))},directives:[aOt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(rOt,[{type:S_,args:[{selector:"npmi-violin-filter",template:'\n    <violin-filter-component\n      [metricName]="metricName"\n      [filter]="filter"\n      [chartData]="chartData$ | async"\n      [width]="chartWidth$ | async"\n      [colorScale]="runColorScale$ | async"\n      (onRemove)="removeMetric()"\n      (onUpdateFilter)="updateFilter($event)"\n    ></violin-filter-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),{metricName:[{type:R_}],filter:[{type:R_}]});class cOt{constructor(){this.toggleSidebarExpanded=new Sy}}cOt.ɵfac=function t(e){return new(e||cOt)},cOt.ɵcmp=so({type:cOt,selectors:[["violin-filters-component"]],inputs:{sidebarExpanded:"sidebarExpanded",metricFilters:"metricFilters"},outputs:{toggleSidebarExpanded:"toggleSidebarExpanded"},decls:9,vars:2,consts:function(){let t;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){t=goog.getMsg("Expand/Hide Sidebar")}else t=$localize`:Label for a button that expands/hides the sidebar.␟48c29903ce881ab61088f8d49d827203716aaed4␟4658602991970260215:Expand/Hide Sidebar`;return[[1,"filters-toolbar"],[1,"filters-title"],[1,"side-toggle"],["mat-icon-button","","aria-label",t,3,"click"],["svgIcon","chevron_left_24px"],[1,"filters"],[3,"metricName","filter",4,"ngFor","ngForOf"],["class","filters-hint",4,"ngIf"],[3,"metricName","filter"],[1,"filters-hint"],[1,"filters-hint-text"]]},template:function t(e,n){1&e&&(Cf(0,"div",0),Cf(1,"h3",1),Pu(2,"Active Filters"),Mf(),Cf(3,"div",2),Cf(4,"button",3),Rf("click",(function t(){return n.toggleSidebarExpanded.emit()})),vf(5,"mat-icon",4),Mf(),Mf(),Mf(),Cf(6,"div",5),Hm(7,sOt,1,2,"npmi-violin-filter",6),Mf(),Hm(8,lOt,3,0,"div",7)),2&e&&(Dc(7),bf("ngForOf",n.metricFilters),Dc(1),bf("ngIf",0===n.metricFilters.length))},directives:[NL,Mq,xv,Pv,rOt],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   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t(e,n){1&e&&(Cf(0,"violin-filters-component",0),Rf("toggleSidebarExpanded",(function t(){return n.onToggleSidebarExpanded()})),_y(1,"async"),_y(2,"async"),Mf()),2&e&&bf("sidebarExpanded",Cy(1,2,n.sidebarExpanded$))("metricFilters",Cy(2,4,n.metricFilters$))},directives:[cOt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(dOt,[{type:S_,args:[{selector:"npmi-violin-filters",template:'\n    <violin-filters-component\n      [sidebarExpanded]="sidebarExpanded$ | async"\n      [metricFilters]="metricFilters$ | async"\n      (toggleSidebarExpanded)="onToggleSidebarExpanded()"\n    ></violin-filters-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);const fOt=["thumbContainer"],uOt=["toggleBar"],gOt=["input"],hOt=function(t){return{enterDuration:t}},bOt=new er("mat-slide-toggle-default-options",{providedIn:"root",factory:()=>({disableToggleValue:!1})});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let yOt=0;const _Ot={provide:xU,useExisting:$e((()=>vOt)),multi:!0};class COt{constructor(t,e){this.source=t,this.checked=e}}const MOt=FF(IF(HF(zF(class{constructor(t){this._elementRef=t}}))));class vOt extends MOt{constructor(t,e,n,o,i,a){super(t),this._focusMonitor=e,this._changeDetectorRef=n,this.defaults=i,this._onChange=t=>{},this._onTouched=()=>{},this._uniqueId="mat-slide-toggle-"+ ++yOt,this._required=!1,this._checked=!1,this.name=null,this.id=this._uniqueId,this.labelPosition="after",this.ariaLabel=null,this.ariaLabelledby=null,this.change=new Sy,this.toggleChange=new Sy,this.tabIndex=parseInt(o)||0,this.color=this.defaultColor=i.color||"accent",this._noopAnimations="NoopAnimations"===a}get required(){return this._required}set required(t){this._required=eH(t)}get checked(){return this._checked}set checked(t){this._checked=eH(t),this._changeDetectorRef.markForCheck()}get inputId(){return`${this.id||this._uniqueId}-input`}ngAfterContentInit(){this._focusMonitor.monitor(this._elementRef,!0).subscribe((t=>{"keyboard"===t||"program"===t?this._inputElement.nativeElement.focus():t||Promise.resolve().then((()=>this._onTouched()))}))}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef)}_onChangeEvent(t){t.stopPropagation(),this.toggleChange.emit(),this.defaults.disableToggleValue?this._inputElement.nativeElement.checked=this.checked:(this.checked=this._inputElement.nativeElement.checked,this._emitChangeEvent())}_onInputClick(t){t.stopPropagation()}writeValue(t){this.checked=!!t}registerOnChange(t){this._onChange=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t,this._changeDetectorRef.markForCheck()}focus(t,e){e?this._focusMonitor.focusVia(this._inputElement,e,t):this._inputElement.nativeElement.focus(t)}toggle(){this.checked=!this.checked,this._onChange(this.checked)}_emitChangeEvent(){this._onChange(this.checked),this.change.emit(new COt(this,this.checked))}_onLabelTextChange(){this._changeDetectorRef.detectChanges()}}vOt.ɵfac=function t(e){return new(e||vOt)(gf(Kh),gf(fF),gf(Ob),Ga("tabindex"),gf(bOt),gf(ck,8))},vOt.ɵcmp=so({type:vOt,selectors:[["mat-slide-toggle"]],viewQuery:function t(e,n){if(1&e&&(Vy(fOt,5),Vy(uOt,5),Vy(gOt,5)),2&e){let t;By(t=Uy())&&(n._thumbEl=t.first),By(t=Uy())&&(n._thumbBarEl=t.first),By(t=Uy())&&(n._inputElement=t.first)}},hostAttrs:[1,"mat-slide-toggle"],hostVars:12,hostBindings:function t(e,n){2&e&&(cg("id",n.id),ym("tabindex",n.disabled?null:-1)("aria-label",null)("aria-labelledby",null),cu("mat-checked",n.checked)("mat-disabled",n.disabled)("mat-slide-toggle-label-before","before"==n.labelPosition)("_mat-animation-noopable",n._noopAnimations))},inputs:{disabled:"disabled",disableRipple:"disableRipple",color:"color",tabIndex:"tabIndex",name:"name",id:"id",labelPosition:"labelPosition",ariaLabel:["aria-label","ariaLabel"],ariaLabelledby:["aria-labelledby","ariaLabelledby"],required:"required",checked:"checked",ariaDescribedby:["aria-describedby","ariaDescribedby"]},outputs:{change:"change",toggleChange:"toggleChange"},exportAs:["matSlideToggle"],features:[jh([_Ot]),Qp],ngContentSelectors:["*"],decls:16,vars:20,consts:[[1,"mat-slide-toggle-label"],["label",""],[1,"mat-slide-toggle-bar"],["toggleBar",""],["type","checkbox","role","switch",1,"mat-slide-toggle-input","cdk-visually-hidden",3,"id","required","tabIndex","checked","disabled","change","click"],["input",""],[1,"mat-slide-toggle-thumb-container"],["thumbContainer",""],[1,"mat-slide-toggle-thumb"],["mat-ripple","",1,"mat-slide-toggle-ripple","mat-focus-indicator",3,"matRippleTrigger","matRippleDisabled","matRippleCentered","matRippleRadius","matRippleAnimation"],[1,"mat-ripple-element","mat-slide-toggle-persistent-ripple"],[1,"mat-slide-toggle-content",3,"cdkObserveContent"],["labelContent",""],[2,"display","none"]],template:function t(e,n){if(1&e&&(Hf(),Cf(0,"label",0,1),Cf(2,"div",2,3),Cf(4,"input",4,5),Rf("change",(function t(e){return n._onChangeEvent(e)}))("click",(function t(e){return n._onInputClick(e)})),Mf(),Cf(6,"div",6,7),vf(8,"div",8),Cf(9,"div",9),vf(10,"div",10),Mf(),Mf(),Mf(),Cf(11,"span",11,12),Rf("cdkObserveContent",(function t(){return n._onLabelTextChange()})),Cf(13,"span",13),Pu(14," "),Mf(),Ff(15),Mf(),Mf()),2&e){const t=Fm(1),e=Fm(12);ym("for",n.inputId),Dc(2),cu("mat-slide-toggle-bar-no-side-margin",!e.textContent||!e.textContent.trim()),Dc(2),bf("id",n.inputId)("required",n.required)("tabIndex",n.tabIndex)("checked",n.checked)("disabled",n.disabled),ym("name",n.name)("aria-checked",n.checked.toString())("aria-label",n.ariaLabel)("aria-labelledby",n.ariaLabelledby)("aria-describedby",n.ariaDescribedby),Dc(5),bf("matRippleTrigger",t)("matRippleDisabled",n.disableRipple||n.disabled)("matRippleCentered",!0)("matRippleRadius",20)("matRippleAnimation",iy(18,hOt,n._noopAnimations?0:150))}},directives:[mL,PH],styles:[".mat-slide-toggle{display:inline-block;height:24px;max-width:100%;line-height:24px;white-space:nowrap;outline:none;-webkit-tap-highlight-color:transparent}.mat-slide-toggle.mat-checked .mat-slide-toggle-thumb-container{transform:translate3d(16px, 0, 0)}[dir=rtl] .mat-slide-toggle.mat-checked .mat-slide-toggle-thumb-container{transform:translate3d(-16px, 0, 0)}.mat-slide-toggle.mat-disabled{opacity:.38}.mat-slide-toggle.mat-disabled .mat-slide-toggle-label,.mat-slide-toggle.mat-disabled .mat-slide-toggle-thumb-container{cursor:default}.mat-slide-toggle-label{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;display:flex;flex:1;flex-direction:row;align-items:center;height:inherit;cursor:pointer}.mat-slide-toggle-content{white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.mat-slide-toggle-label-before .mat-slide-toggle-label{order:1}.mat-slide-toggle-label-before .mat-slide-toggle-bar{order:2}[dir=rtl] .mat-slide-toggle-label-before .mat-slide-toggle-bar,.mat-slide-toggle-bar{margin-right:8px;margin-left:0}[dir=rtl] .mat-slide-toggle-bar,.mat-slide-toggle-label-before .mat-slide-toggle-bar{margin-left:8px;margin-right:0}.mat-slide-toggle-bar-no-side-margin{margin-left:0;margin-right:0}.mat-slide-toggle-thumb-container{position:absolute;z-index:1;width:20px;height:20px;top:-3px;left:0;transform:translate3d(0, 0, 0);transition:all 80ms linear;transition-property:transform}._mat-animation-noopable .mat-slide-toggle-thumb-container{transition:none}[dir=rtl] .mat-slide-toggle-thumb-container{left:auto;right:0}.mat-slide-toggle-thumb{height:20px;width:20px;border-radius:50%}.mat-slide-toggle-bar{position:relative;width:36px;height:14px;flex-shrink:0;border-radius:8px}.mat-slide-toggle-input{bottom:0;left:10px}[dir=rtl] .mat-slide-toggle-input{left:auto;right:10px}.mat-slide-toggle-bar,.mat-slide-toggle-thumb{transition:all 80ms linear;transition-property:background-color;transition-delay:50ms}._mat-animation-noopable .mat-slide-toggle-bar,._mat-animation-noopable .mat-slide-toggle-thumb{transition:none}.mat-slide-toggle .mat-slide-toggle-ripple{position:absolute;top:calc(50% - 20px);left:calc(50% - 20px);height:40px;width:40px;z-index:1;pointer-events:none}.mat-slide-toggle .mat-slide-toggle-ripple .mat-ripple-element:not(.mat-slide-toggle-persistent-ripple){opacity:.12}.mat-slide-toggle-persistent-ripple{width:100%;height:100%;transform:none}.mat-slide-toggle-bar:hover .mat-slide-toggle-persistent-ripple{opacity:.04}.mat-slide-toggle:not(.mat-disabled).cdk-keyboard-focused .mat-slide-toggle-persistent-ripple{opacity:.12}.mat-slide-toggle-persistent-ripple,.mat-slide-toggle.mat-disabled .mat-slide-toggle-bar:hover .mat-slide-toggle-persistent-ripple{opacity:0}@media(hover: none){.mat-slide-toggle-bar:hover .mat-slide-toggle-persistent-ripple{display:none}}.cdk-high-contrast-active .mat-slide-toggle-thumb,.cdk-high-contrast-active .mat-slide-toggle-bar{border:1px solid}.cdk-high-contrast-active .mat-slide-toggle.cdk-keyboard-focused .mat-slide-toggle-bar{outline:2px dotted;outline-offset:5px}\n"],encapsulation:2,changeDetection:0}),vOt.ctorParameters=()=>[{type:Kh},{type:fF},{type:Ob},{type:String,decorators:[{type:$a,args:["tabindex"]}]},{type:void 0,decorators:[{type:Lr,args:[bOt]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}],vOt.propDecorators={_thumbEl:[{type:ar,args:["thumbContainer"]}],_thumbBarEl:[{type:ar,args:["toggleBar"]}],name:[{type:R_}],id:[{type:R_}],labelPosition:[{type:R_}],ariaLabel:[{type:R_,args:["aria-label"]}],ariaLabelledby:[{type:R_,args:["aria-labelledby"]}],ariaDescribedby:[{type:R_,args:["aria-describedby"]}],required:[{type:R_}],checked:[{type:R_}],change:[{type:E_}],toggleChange:[{type:E_}],_inputElement:[{type:ar,args:["input"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&Qb(vOt,[{type:S_,args:[{selector:"mat-slide-toggle",exportAs:"matSlideToggle",host:{class:"mat-slide-toggle","[id]":"id","[attr.tabindex]":"disabled ? null : -1","[attr.aria-label]":"null","[attr.aria-labelledby]":"null","[class.mat-checked]":"checked","[class.mat-disabled]":"disabled","[class.mat-slide-toggle-label-before]":'labelPosition == "before"',"[class._mat-animation-noopable]":"_noopAnimations"},template:'<label [attr.for]="inputId" class="mat-slide-toggle-label" #label>\n  <div #toggleBar class="mat-slide-toggle-bar"\n       [class.mat-slide-toggle-bar-no-side-margin]="!labelContent.textContent || !labelContent.textContent.trim()">\n\n    <input #input class="mat-slide-toggle-input cdk-visually-hidden" type="checkbox"\n           role="switch"\n           [id]="inputId"\n           [required]="required"\n           [tabIndex]="tabIndex"\n           [checked]="checked"\n           [disabled]="disabled"\n           [attr.name]="name"\n           [attr.aria-checked]="checked.toString()"\n           [attr.aria-label]="ariaLabel"\n           [attr.aria-labelledby]="ariaLabelledby"\n           [attr.aria-describedby]="ariaDescribedby"\n           (change)="_onChangeEvent($event)"\n           (click)="_onInputClick($event)">\n\n    <div class="mat-slide-toggle-thumb-container" #thumbContainer>\n      <div class="mat-slide-toggle-thumb"></div>\n      <div class="mat-slide-toggle-ripple mat-focus-indicator" mat-ripple\n           [matRippleTrigger]="label"\n           [matRippleDisabled]="disableRipple || disabled"\n           [matRippleCentered]="true"\n           [matRippleRadius]="20"\n           [matRippleAnimation]="{enterDuration: _noopAnimations ? 0 : 150}">\n\n        <div class="mat-ripple-element mat-slide-toggle-persistent-ripple"></div>\n      </div>\n    </div>\n\n  </div>\n\n  <span class="mat-slide-toggle-content" #labelContent (cdkObserveContent)="_onLabelTextChange()">\n    \x3c!-- Add an invisible span so JAWS can read the label --\x3e\n    <span style="display:none">&nbsp;</span>\n    <ng-content></ng-content>\n  </span>\n</label>\n',providers:[_Ot],inputs:["disabled","disableRipple","color","tabIndex"],encapsulation:Wn.None,changeDetection:jn.OnPush,styles:[".mat-slide-toggle{display:inline-block;height:24px;max-width:100%;line-height:24px;white-space:nowrap;outline:none;-webkit-tap-highlight-color:transparent}.mat-slide-toggle.mat-checked .mat-slide-toggle-thumb-container{transform:translate3d(16px, 0, 0)}[dir=rtl] .mat-slide-toggle.mat-checked .mat-slide-toggle-thumb-container{transform:translate3d(-16px, 0, 0)}.mat-slide-toggle.mat-disabled{opacity:.38}.mat-slide-toggle.mat-disabled .mat-slide-toggle-label,.mat-slide-toggle.mat-disabled .mat-slide-toggle-thumb-container{cursor:default}.mat-slide-toggle-label{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;display:flex;flex:1;flex-direction:row;align-items:center;height:inherit;cursor:pointer}.mat-slide-toggle-content{white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.mat-slide-toggle-label-before .mat-slide-toggle-label{order:1}.mat-slide-toggle-label-before .mat-slide-toggle-bar{order:2}[dir=rtl] .mat-slide-toggle-label-before .mat-slide-toggle-bar,.mat-slide-toggle-bar{margin-right:8px;margin-left:0}[dir=rtl] .mat-slide-toggle-bar,.mat-slide-toggle-label-before .mat-slide-toggle-bar{margin-left:8px;margin-right:0}.mat-slide-toggle-bar-no-side-margin{margin-left:0;margin-right:0}.mat-slide-toggle-thumb-container{position:absolute;z-index:1;width:20px;height:20px;top:-3px;left:0;transform:translate3d(0, 0, 0);transition:all 80ms linear;transition-property:transform}._mat-animation-noopable .mat-slide-toggle-thumb-container{transition:none}[dir=rtl] .mat-slide-toggle-thumb-container{left:auto;right:0}.mat-slide-toggle-thumb{height:20px;width:20px;border-radius:50%}.mat-slide-toggle-bar{position:relative;width:36px;height:14px;flex-shrink:0;border-radius:8px}.mat-slide-toggle-input{bottom:0;left:10px}[dir=rtl] .mat-slide-toggle-input{left:auto;right:10px}.mat-slide-toggle-bar,.mat-slide-toggle-thumb{transition:all 80ms linear;transition-property:background-color;transition-delay:50ms}._mat-animation-noopable .mat-slide-toggle-bar,._mat-animation-noopable .mat-slide-toggle-thumb{transition:none}.mat-slide-toggle .mat-slide-toggle-ripple{position:absolute;top:calc(50% - 20px);left:calc(50% - 20px);height:40px;width:40px;z-index:1;pointer-events:none}.mat-slide-toggle .mat-slide-toggle-ripple .mat-ripple-element:not(.mat-slide-toggle-persistent-ripple){opacity:.12}.mat-slide-toggle-persistent-ripple{width:100%;height:100%;transform:none}.mat-slide-toggle-bar:hover .mat-slide-toggle-persistent-ripple{opacity:.04}.mat-slide-toggle:not(.mat-disabled).cdk-keyboard-focused .mat-slide-toggle-persistent-ripple{opacity:.12}.mat-slide-toggle-persistent-ripple,.mat-slide-toggle.mat-disabled .mat-slide-toggle-bar:hover .mat-slide-toggle-persistent-ripple{opacity:0}@media(hover: none){.mat-slide-toggle-bar:hover .mat-slide-toggle-persistent-ripple{display:none}}.cdk-high-contrast-active .mat-slide-toggle-thumb,.cdk-high-contrast-active .mat-slide-toggle-bar{border:1px solid}.cdk-high-contrast-active .mat-slide-toggle.cdk-keyboard-focused .mat-slide-toggle-bar{outline:2px dotted;outline-offset:5px}\n"]}]}],(function(){return[{type:Kh},{type:fF},{type:Ob},{type:String,decorators:[{type:$a,args:["tabindex"]}]},{type:void 0,decorators:[{type:Lr,args:[bOt]}]},{type:String,decorators:[{type:Br},{type:Lr,args:[ck]}]}]}),{name:[{type:R_}],id:[{type:R_}],labelPosition:[{type:R_}],ariaLabel:[{type:R_,args:["aria-label"]}],ariaLabelledby:[{type:R_,args:["aria-labelledby"]}],change:[{type:E_}],toggleChange:[{type:E_}],required:[{type:R_}],checked:[{type:R_}],_thumbEl:[{type:ar,args:["thumbContainer"]}],_thumbBarEl:[{type:ar,args:["toggleBar"]}],ariaDescribedby:[{type:R_,args:["aria-describedby"]}],_inputElement:[{type:ar,args:["input"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const xOt={provide:EU,useExisting:$e((()=>OOt)),multi:!0};class OOt extends VG{}OOt.ɵfac=(function(){let t;return function e(n){return(t||(t=Ua(OOt)))(n||OOt)}})(),OOt.ɵdir=ho({type:OOt,selectors:[["mat-slide-toggle","required","","formControlName",""],["mat-slide-toggle","required","","formControl",""],["mat-slide-toggle","required","","ngModel",""]],features:[jh([xOt]),Qp]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(OOt,[{type:k_,args:[{selector:"mat-slide-toggle[required][formControlName],\n             mat-slide-toggle[required][formControl], mat-slide-toggle[required][ngModel]",providers:[xOt]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class POt{}POt.ɵfac=function t(e){return new(e||POt)},POt.ɵmod=fo({type:POt}),POt.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(POt,[{type:L_,args:[{exports:[OOt],declarations:[OOt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(POt,{declarations:[OOt],exports:[OOt]});class wOt{}function kOt(t,e){1&t&&vf(0,"mat-icon",3)}wOt.ɵfac=function t(e){return new(e||wOt)},wOt.ɵmod=fo({type:wOt}),wOt.ɵinj=Dn({imports:[[POt,fL,NF,wH],POt,NF]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(wOt,[{type:L_,args:[{imports:[POt,fL,NF,wH],exports:[POt,vOt,NF],declarations:[vOt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(wOt,{declarations:function(){return[vOt]},imports:function(){return[POt,fL,NF,wH]},exports:function(){return[POt,vOt,NF]}});class SOt{constructor(){this.onRegexFilterValueChange=new Sy}}SOt.ɵfac=function t(e){return new(e||SOt)},SOt.ɵcmp=so({type:SOt,selectors:[["npmi-annotations-search-component"]],hostVars:2,hostBindings:function t(e,n){2&e&&cu("valid",n.isRegexFilterValid)},inputs:{regexFilterValue:"regexFilterValue",isRegexFilterValid:"isRegexFilterValid"},outputs:{onRegexFilterValueChange:"onRegexFilterValueChange"},decls:3,vars:2,consts:[["svgIcon","search_24px"],["autocomplete","off","placeholder","Filter Annotations",3,"value","input"],["svgIcon","error_24px","class","error-icon","matTooltip","Invalid regex filter. The result may be stale.",4,"ngIf"],["svgIcon","error_24px","matTooltip","Invalid regex filter. 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ngDevMode||ngDevMode)&&Qb(SOt,[{type:S_,args:[{selector:"npmi-annotations-search-component",templateUrl:"./annotations_search_component.ng.html",styleUrls:["./annotations_search_component.css"],changeDetection:jn.OnPush}]}],null,{regexFilterValue:[{type:R_}],onRegexFilterValueChange:[{type:E_}],isRegexFilterValid:[{type:A_,args:["class.valid"]},{type:R_}]});class DOt{constructor(t){this.store=t,this.annotationsFilter$=this.store.select(Qvt),this.isAnnotationsFilterValid$=this.annotationsFilter$.pipe(Vt((t=>{try{return new RegExp(t),!0}catch(t){return!1}})))}filterChange(t){this.store.dispatch(vvt({regex:t}))}}function ROt(t,e){if(1&t){const t=wf();xf(0),Cf(1,"button",5),Rf("click",(function e(){Pi(t);const n=zf();return n.onFlagAnnotations.emit(n.selectedAnnotations)})),vf(2,"mat-icon",6),Mf(),Cf(3,"button",7),Rf("click",(function e(){Pi(t);const n=zf();return n.onHideAnnotations.emit(n.selectedAnnotations)})),vf(4,"mat-icon",8),Mf(),Of()}if(2&t){const t=zf();Dc(1),bf("disabled",0===t.selectedAnnotations.length),Dc(2),bf("disabled",0===t.selectedAnnotations.length)}}function EOt(t,e){if(1&t){const t=wf();xf(0),Cf(1,"mat-slide-toggle",9),Rf("change",(function e(){Pi(t);return zf().onToggleShowCounts.emit()})),Pu(2," Sample Count "),Mf(),Cf(3,"mat-slide-toggle",10),Rf("change",(function e(){Pi(t);return zf().onToggleShowHidden.emit()})),Pu(4," Show Hidden "),Mf(),vf(5,"npmi-annotations-search"),Of()}if(2&t){const t=zf();Dc(1),bf("checked",t.showCounts),Dc(2),bf("checked",t.showHidden)}}DOt.ɵfac=function t(e){return new(e||DOt)(gf(iS))},DOt.ɵcmp=so({type:DOt,selectors:[["npmi-annotations-search"]],decls:3,vars:6,consts:[[3,"regexFilterValue","isRegexFilterValid","onRegexFilterValueChange"]],template:function t(e,n){1&e&&(Cf(0,"npmi-annotations-search-component",0),Rf("onRegexFilterValueChange",(function t(e){return n.filterChange(e)})),_y(1,"async"),_y(2,"async"),Mf()),2&e&&bf("regexFilterValue",Cy(1,2,n.annotationsFilter$))("isRegexFilterValid",Cy(2,4,n.isAnnotationsFilterValid$))},directives:[SOt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(DOt,[{type:S_,args:[{selector:"npmi-annotations-search",template:'\n    <npmi-annotations-search-component\n      [regexFilterValue]="annotationsFilter$ | async"\n      [isRegexFilterValid]="isAnnotationsFilterValid$ | async"\n      (onRegexFilterValueChange)="filterChange($event)"\n    ></npmi-annotations-search-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);class AOt{constructor(){this.onFlagAnnotations=new Sy,this.onHideAnnotations=new Sy,this.onToggleExpanded=new Sy,this.onToggleShowCounts=new Sy,this.onToggleShowHidden=new Sy}}AOt.ɵfac=function t(e){return new(e||AOt)},AOt.ɵcmp=so({type:AOt,selectors:[["npmi-annotations-list-toolbar-component"]],inputs:{numAnnotations:"numAnnotations",expanded:"expanded",selectedAnnotations:"selectedAnnotations",annotationsExpanded:"annotationsExpanded",showCounts:"showCounts",showHidden:"showHidden"},outputs:{onFlagAnnotations:"onFlagAnnotations",onHideAnnotations:"onHideAnnotations",onToggleExpanded:"onToggleExpanded",onToggleShowCounts:"onToggleShowCounts",onToggleShowHidden:"onToggleShowHidden"},decls:7,vars:4,consts:function(){let t,e,n;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){t=goog.getMsg("Hides/Shows the Annotations List")}else t=$localize`:Label for a button that hides/shows the annotations list.␟b3603ba33e5308dd8c5e805e508b2f7233df89d4␟7336374413056342492:Hides/Shows the Annotations List`;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){e=goog.getMsg("Flag Selected Annotations")}else e=$localize`:Label for a button that flags selected annotations.␟5692ad8831038a90c5863a1e9adf9748cac3cad8␟2244099891313336595:Flag Selected Annotations`;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){n=goog.getMsg("Hide Selected Annotations")}else n=$localize`:Label for a button that hides selected annotations.␟0342cdb3358fa8e3fa27220a8258a7287430b70f␟5462832391092087485:Hide Selected Annotations`;return[[1,"annotations-title-container"],[1,"annotations-title"],[4,"ngIf"],["mat-icon-button","","aria-label",t,1,"expand-button",3,"click"],[3,"svgIcon"],["mat-icon-button","","aria-label",e,"title","Flagging annotations adds them to your investigation results, which can later be exported.",3,"disabled","click"],["svgIcon","flag_24px"],["mat-icon-button","","aria-label",n,"title","Removing non-critical annotations unclutters the view. Removed annotations are removed from all visualizations.",3,"disabled","click"],["svgIcon","visibility_off_24px"],["title","Hides and shows the sample count where applicable (how many samples belong to a category).",1,"show-toggle",3,"checked","change"],["title","Hides and shows hidden annotations in all visualizations.",1,"show-toggle",3,"checked","change"]]},template:function t(e,n){1&e&&(Cf(0,"div",0),Cf(1,"h3",1),Pu(2),Mf(),Hm(3,ROt,5,2,"ng-container",2),Mf(),Hm(4,EOt,6,2,"ng-container",2),Cf(5,"button",3),Rf("click",(function t(){return n.onToggleExpanded.emit()})),vf(6,"mat-icon",4),Mf()),2&e&&(Dc(2),ku("Annotations (",n.numAnnotations,")"),Dc(1),bf("ngIf",n.expanded),Dc(1),bf("ngIf",n.expanded),Dc(2),bf("svgIcon",n.expanded?"expand_less_24px":"expand_more_24px"))},directives:[Pv,NL,Mq,vOt,DOt],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   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ngDevMode||ngDevMode)&&Qb(AOt,[{type:S_,args:[{selector:"npmi-annotations-list-toolbar-component",templateUrl:"./annotations_list_toolbar_component.ng.html",styleUrls:["./annotations_list_toolbar_component.css"],changeDetection:jn.OnPush}]}],null,{numAnnotations:[{type:R_}],expanded:[{type:R_}],selectedAnnotations:[{type:R_}],annotationsExpanded:[{type:R_}],showCounts:[{type:R_}],showHidden:[{type:R_}],onFlagAnnotations:[{type:E_}],onHideAnnotations:[{type:E_}],onToggleExpanded:[{type:E_}],onToggleShowCounts:[{type:E_}],onToggleShowHidden:[{type:E_}]});class TOt{constructor(t){this.store=t,this.selectedAnnotations$=this.store.select(Xvt),this.annotationsExpanded$=this.store.select(ixt),this.showCounts$=this.store.select(rxt),this.showHidden$=this.store.select(sxt),this.annotationsFilter$=this.store.select(Qvt),this.isAnnotationsFilterValid$=this.annotationsFilter$.pipe(Vt((t=>{try{return Boolean(new RegExp(t))}catch(t){return!1}})))}filterChange(t){this.store.dispatch(vvt({regex:t}))}flagAnnotations(t){this.store.dispatch(Cvt({annotations:t}))}hideAnnotations(t){this.store.dispatch(Mvt({annotations:t}))}toggleExpanded(){this.store.dispatch(Rvt())}toggleShowCounts(){this.store.dispatch(Avt())}toggleShowHidden(){this.store.dispatch(Tvt())}}TOt.ɵfac=function t(e){return new(e||TOt)(gf(iS))},TOt.ɵcmp=so({type:TOt,selectors:[["npmi-annotations-list-toolbar"]],inputs:{numAnnotations:"numAnnotations",expanded:"expanded"},decls:5,vars:14,consts:[[3,"numAnnotations","expanded","selectedAnnotations","annotationsExpanded","showCounts","showHidden","onFlagAnnotations","onHideAnnotations","onToggleExpanded","onToggleShowCounts","onToggleShowHidden"]],template:function t(e,n){1&e&&(Cf(0,"npmi-annotations-list-toolbar-component",0),Rf("onFlagAnnotations",(function t(e){return n.flagAnnotations(e)}))("onHideAnnotations",(function t(e){return n.hideAnnotations(e)}))("onToggleExpanded",(function t(){return n.toggleExpanded()}))("onToggleShowCounts",(function t(){return n.toggleShowCounts()}))("onToggleShowHidden",(function t(){return n.toggleShowHidden()})),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async"),Mf()),2&e&&bf("numAnnotations",n.numAnnotations)("expanded",n.expanded)("selectedAnnotations",Cy(1,6,n.selectedAnnotations$))("annotationsExpanded",Cy(2,8,n.annotationsExpanded$))("showCounts",Cy(3,10,n.showCounts$))("showHidden",Cy(4,12,n.showHidden$))},directives:[AOt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(TOt,[{type:S_,args:[{selector:"npmi-annotations-list-toolbar",template:'\n    <npmi-annotations-list-toolbar-component\n      [numAnnotations]="numAnnotations"\n      [expanded]="expanded"\n      [selectedAnnotations]="selectedAnnotations$ | async"\n      [annotationsExpanded]="annotationsExpanded$ | async"\n      [showCounts]="showCounts$ | async"\n      [showHidden]="showHidden$ | async"\n      (onFlagAnnotations)="flagAnnotations($event)"\n      (onHideAnnotations)="hideAnnotations($event)"\n      (onToggleExpanded)="toggleExpanded()"\n      (onToggleShowCounts)="toggleShowCounts()"\n      (onToggleShowHidden)="toggleShowHidden()"\n    ></npmi-annotations-list-toolbar-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),{numAnnotations:[{type:R_}],expanded:[{type:R_}]});const NOt=["glyph"];class zOt{ngAfterViewInit(){this.svg=n8(this.glyphSVG.nativeElement),this.mainContainer=this.svg.append("g"),this.draw()}draw(){"circle"==this.shape?this.mainContainer.append("circle").attr("fill",this.color).attr("stroke","black").attr("cx",5).attr("cy",5).attr("r",5):"bar"==this.shape?this.mainContainer.append("rect").attr("fill",this.color).attr("x",0).attr("y",0).attr("width",10).attr("height",10):"runIndicator"==this.shape&&this.mainContainer.append("g").append("path").attr("fill",this.color).attr("stroke","black").attr("d","M 2 0 L 10 0 L 7 5 L 10 10 L 2 10 Z")}}zOt.ɵfac=function t(e){return new(e||zOt)},zOt.ɵcmp=so({type:zOt,selectors:[["npmi-legend-element"]],viewQuery:function t(e,n){if(1&e&&Vy(NOt,7,Kh),2&e){let t;By(t=Uy())&&(n.glyphSVG=t.first)}},inputs:{text:"text",color:"color",shape:"shape"},decls:4,vars:1,consts:[[1,"glyph"],["glyph",""],[1,"legend-element-title"]],template:function t(e,n){1&e&&(oa(),vf(0,"svg",0,1),aa(),Cf(2,"div",2),Pu(3),Mf()),2&e&&(Dc(3),wu(n.text))},styles:["[_nghost-%COMP%]{align-items:center;display:flex;padding-right:10px}.legend-element-title[_ngcontent-%COMP%]{font-size:.8em;padding-left:5px}.glyph[_ngcontent-%COMP%]{width:10px;height:10px}"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(zOt,[{type:S_,args:[{selector:"npmi-legend-element",templateUrl:"./legend_element_component.ng.html",styleUrls:["./legend_element_component.css"],changeDetection:jn.OnPush}]}],null,{text:[{type:R_}],color:[{type:R_}],shape:[{type:R_}],glyphSVG:[{type:ar,args:["glyph",{static:!0,read:Kh}]}]});class IOt{}function HOt(t,e){if(1&t&&vf(0,"mat-icon",8),2&t){const t=zf(2);bf("svgIcon",t.sort.order===t.SortOrder.DESCENDING?"arrow_downward_24px":"arrow_upward_24px")("ngClass",t.sort.order===t.SortOrder.DESCENDING?"down-icon":"up-icon")}}function FOt(t,e){if(1&t){const t=wf();Cf(0,"div",4),Cf(1,"div",5),Cf(2,"div",6),Rf("click",(function e(){const n=Pi(t).$implicit;return zf().onChangeSort.emit(n)})),Pu(3),Hm(4,HOt,1,2,"mat-icon",7),Mf(),Mf(),Mf()}if(2&t){const t=e.$implicit,n=zf();Dc(3),ku(" ",n.stripMetric(t)," "),Dc(1),bf("ngIf",t===n.sort.metric)}}IOt.ɵfac=function t(e){return new(e||IOt)},IOt.ɵcmp=so({type:IOt,selectors:[["npmi-annotations-list-legend"]],decls:4,vars:0,consts:[["text","run indicator","color","rgb(0,0,0)","shape","runIndicator"],["text","positive correlation","color","rgb(109, 174, 213)","shape","bar"],["text","negative correlation","color","rgb(249, 105, 76)","shape","bar"],["text","sample count","color","rgb(151, 151, 151)","shape","circle"]],template:function t(e,n){1&e&&(vf(0,"npmi-legend-element",0),vf(1,"npmi-legend-element",1),vf(2,"npmi-legend-element",2),vf(3,"npmi-legend-element",3))},directives:[zOt],styles:["[_nghost-%COMP%]{display:flex;padding:0 16px}"]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(IOt,[{type:S_,args:[{selector:"npmi-annotations-list-legend",templateUrl:"./legend_component.ng.html",styleUrls:["./legend_component.css"]}]}],null,null);class LOt{constructor(){this.onChangeSort=new Sy,this.onAllAnnotationsToggled=new Sy,this.SortOrder=Uvt}stripMetric(t){return z_t(t)}}LOt.ɵfac=function t(e){return new(e||LOt)},LOt.ɵcmp=so({type:LOt,selectors:[["npmi-annotations-list-header-component"]],inputs:{numAnnotations:"numAnnotations",selectedAnnotations:"selectedAnnotations",activeMetrics:"activeMetrics",sort:"sort"},outputs:{onChangeSort:"onChangeSort",onAllAnnotationsToggled:"onAllAnnotationsToggled"},decls:4,vars:2,consts:[[1,"toggle-all-container"],[3,"checked","change"],[1,"annotations-header-containers"],["class","header-column",4,"ngFor","ngForOf"],[1,"header-column"],[1,"header-container"],["tabindex","0","role","button","title","Change the sort by clicking any of the metrics.",1,"header-clickable",3,"click"],["class","sort-icon",3,"svgIcon","ngClass",4,"ngIf"],[1,"sort-icon",3,"svgIcon","ngClass"]],template:function t(e,n){1&e&&(Cf(0,"div",0),Cf(1,"mat-checkbox",1),Rf("change",(function t(e){return 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.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{border-bottom:2px solid #ebebeb;display:flex;height:28px;align-items:flex-end;margin-top:8px}.annotations-header-containers[_ngcontent-%COMP%]{display:flex;font-size:.9em;font-weight:500;flex-grow:1}.header-column[_ngcontent-%COMP%]{flex:1 1}.header-container[_ngcontent-%COMP%]{display:inline-block}.header-clickable[_ngcontent-%COMP%]{cursor:pointer;display:flex;outline:none}.toggle-all-container[_ngcontent-%COMP%]{margin-left:10px;width:90px}.sort-icon[_ngcontent-%COMP%]{height:16px}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(LOt,[{type:S_,args:[{selector:"npmi-annotations-list-header-component",templateUrl:"./header_component.ng.html",styleUrls:["./header_component.css"],changeDetection:jn.OnPush}]}],null,{numAnnotations:[{type:R_}],selectedAnnotations:[{type:R_}],activeMetrics:[{type:R_}],sort:[{type:R_}],onChangeSort:[{type:E_}],onAllAnnotationsToggled:[{type:E_}]});class BOt{constructor(t){this.store=t,this.selectedAnnotations$=this.store.select(Xvt),this.annotationSort$=this.store.select(nxt)}changeSort(t){this.store.dispatch(kvt({metric:t}))}allAnnotationsToggled(t){this.store.dispatch(yvt(t?{annotations:Object.keys(this.annotations)}:{annotations:[]}))}}BOt.ɵfac=function t(e){return new(e||BOt)(gf(iS))},BOt.ɵcmp=so({type:BOt,selectors:[["npmi-annotations-list-header"]],inputs:{numAnnotations:"numAnnotations",annotations:"annotations",activeMetrics:"activeMetrics"},decls:3,vars:8,consts:[[3,"numAnnotations","selectedAnnotations","sort","activeMetrics","onChangeSort","onAllAnnotationsToggled"]],template:function t(e,n){1&e&&(Cf(0,"npmi-annotations-list-header-component",0),Rf("onChangeSort",(function t(e){return n.changeSort(e)}))("onAllAnnotationsToggled",(function t(e){return n.allAnnotationsToggled(e)})),_y(1,"async"),_y(2,"async"),Mf()),2&e&&bf("numAnnotations",n.numAnnotations)("selectedAnnotations",Cy(1,4,n.selectedAnnotations$))("sort",Cy(2,6,n.annotationSort$))("activeMetrics",n.activeMetrics)},directives:[LOt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(BOt,[{type:S_,args:[{selector:"npmi-annotations-list-header",template:'\n    <npmi-annotations-list-header-component\n      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t=this.runHintGroup.selectAll(".hint").data(this.runs),e=t.enter().append("g").attr("class","hint");e.append("path").attr("d","M 0 0 L 15 0 L 10 10 L 15 20 L 0 20 Z"),e.merge(t).attr("transform",function(t){return`translate(10, ${this.yScale(t)+5})`}.bind(this)).attr("fill",function(t){return this.colorScale(t)}.bind(this)),t.exit().remove()}drawRunHintTexts(){const t=this.runHintGroup.selectAll(".hint-text").data(this.runs);t.enter().append("text").attr("x",25).attr("font-size","10px").attr("alignment-baseline","middle").attr("clip-path","url(#hint-clip)").merge(t).attr("y",function(t){return this.yScale(t)+15}.bind(this)).attr("class",`hint-text ${this.textClass}`).text((t=>{var e;return(null===(e=this.runIdToRuns.get(t))||void 0===e?void 0:e.name)||""})),t.exit().remove()}drawBars(){const 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1ms}.cdk-text-field-autofill-monitored:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator{position:relative}.mat-mdc-focus-indicator{position:relative}:host{padding-top:5px}.annotation-title{align-items:center;display:flex;font-size:13px;height:20px;padding:0 10px;user-select:none}.selected-row{background-color:#e0e0e0;display:block}.flagged-annotation{color:#f57c00}.hidden-annotation{color:#757575}.annotation-checkbox{padding-right:5px}.flagged-icon{transform:scale(0.6)}.hidden-icon{transform:scale(0.6)}.annotation-icon{transform:scale(0.6)}.chart-div{border-bottom:1px solid #ebebeb}.chart-svg{width:100%;user-select:none}.default-text{fill:#000}.flag-text{fill:#f57c00}.hidden-text{fill:#757575}.clickable-annotation{cursor:pointer}button{all:unset}\n'],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(YOt,[{type:S_,args:[{selector:"annotation-component",templateUrl:"./annotation_component.ng.html",styleUrls:["./annotation_component.css"],changeDetection:jn.OnPush,encapsulation:Wn.None}]}],null,{data:[{type:R_}],maxCount:[{type:R_}],selectedAnnotations:[{type:R_}],flaggedAnnotations:[{type:R_}],hiddenAnnotations:[{type:R_}],activeMetrics:[{type:R_}],numActiveRuns:[{type:R_}],showCounts:[{type:R_}],annotation:[{type:R_}],runHeight:[{type:R_}],hasEmbedding:[{type:R_}],sort:[{type:R_}],sidebarWidth:[{type:R_}],colorScale:[{type:R_}],runIdToRuns:[{type:R_}],annotationContainer:[{type:ar,args:["chart",{static:!0,read:Kh}]}],clipPathElement:[{type:ar,args:["hintClip",{static:!0,read:Kh}]}],selected:[{type:A_,args:["class.selected-row"]}],onResize:[{type:T_,args:["window:resize"]}],onShowSimilarAnnotations:[{type:E_}]});class qOt{constructor(t){this.store=t,this.sort$=this.store.select(nxt),this.flaggedAnnotations$=this.store.select(Kvt),this.hiddenAnnotations$=this.store.select(Jvt),this.selectedAnnotations$=this.store.select(Xvt),this.showCounts$=this.store.select(rxt),this.sidebarWidth$=this.store.select(cxt),this.runColorScale$=this.store.select(CI).pipe(Vt((t=>e=>{if(!t.hasOwnProperty(e))throw new Error(`[Color scale] unknown runId: ${e}.`);return t[e]}))),this.runIdToRuns$=this.store.select(Xz)}showSimilarAnnotations(){this.store.dispatch(Svt({annotation:this.annotation}))}}function ZOt(t,e){if(1&t){const t=wf();Cf(0,"npmi-annotation",5),Rf("click",(function e(n){const o=Pi(t).$implicit;return zf(2).rowClicked(n,o)})),Mf()}if(2&t){const t=e.$implicit,n=zf(2);bf("data",n.annotations[t])("activeMetrics",n.activeMetrics)("numActiveRuns",n.numActiveRuns)("maxCount",n.maxCount)("annotation",t)("runHeight",n.runHeight)("hasEmbedding",n.embeddingData&&void 0!==n.embeddingData[t])}}function XOt(t,e){if(1&t&&(xf(0),vf(1,"npmi-annotations-list-legend"),vf(2,"npmi-annotations-list-header",2),Cf(3,"cdk-virtual-scroll-viewport",3),Hm(4,ZOt,1,7,"npmi-annotation",4),Mf(),Of()),2&t){const t=zf();Dc(2),bf("annotations",t.annotations)("numAnnotations",t.numAnnotations)("activeMetrics",t.activeMetrics),Dc(1),Lf("itemSize",t.numActiveRuns*t.runHeight+25),Dc(1),bf("cdkVirtualForOf",t.sortedAnnotations)}}qOt.ɵfac=function t(e){return new(e||qOt)(gf(iS))},qOt.ɵcmp=so({type:qOt,selectors:[["npmi-annotation"]],inputs:{data:"data",maxCount:"maxCount",activeMetrics:"activeMetrics",numActiveRuns:"numActiveRuns",annotation:"annotation",runHeight:"runHeight",hasEmbedding:"hasEmbedding"},decls:9,vars:31,consts:[[3,"data","maxCount","activeMetrics","numActiveRuns","annotation","runHeight","hasEmbedding","sort","selectedAnnotations","flaggedAnnotations","hiddenAnnotations","showCounts","sidebarWidth","colorScale","runIdToRuns","onShowSimilarAnnotations"]],template:function t(e,n){1&e&&(Cf(0,"annotation-component",0),Rf("onShowSimilarAnnotations",(function t(){return n.showSimilarAnnotations()})),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async"),_y(5,"async"),_y(6,"async"),_y(7,"async"),_y(8,"async"),Mf()),2&e&&bf("data",n.data)("maxCount",n.maxCount)("activeMetrics",n.activeMetrics)("numActiveRuns",n.numActiveRuns)("annotation",n.annotation)("runHeight",n.runHeight)("hasEmbedding",n.hasEmbedding)("sort",Cy(1,15,n.sort$))("selectedAnnotations",Cy(2,17,n.selectedAnnotations$))("flaggedAnnotations",Cy(3,19,n.flaggedAnnotations$))("hiddenAnnotations",Cy(4,21,n.hiddenAnnotations$))("showCounts",Cy(5,23,n.showCounts$))("sidebarWidth",Cy(6,25,n.sidebarWidth$))("colorScale",Cy(7,27,n.runColorScale$))("runIdToRuns",Cy(8,29,n.runIdToRuns$))},directives:[YOt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(qOt,[{type:S_,args:[{selector:"npmi-annotation",template:'\n    <annotation-component\n      [data]="data"\n      [maxCount]="maxCount"\n      [activeMetrics]="activeMetrics"\n      [numActiveRuns]="numActiveRuns"\n      [annotation]="annotation"\n      [runHeight]="runHeight"\n      [hasEmbedding]="hasEmbedding"\n      [sort]="sort$ | async"\n      [selectedAnnotations]="selectedAnnotations$ | async"\n      [flaggedAnnotations]="flaggedAnnotations$ | async"\n      [hiddenAnnotations]="hiddenAnnotations$ | async"\n      [showCounts]="showCounts$ | async"\n      [sidebarWidth]="sidebarWidth$ | async"\n      [colorScale]="runColorScale$ | async"\n      [runIdToRuns]="runIdToRuns$ | async"\n      (onShowSimilarAnnotations)="showSimilarAnnotations()"\n    ></annotation-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),{data:[{type:R_}],maxCount:[{type:R_}],activeMetrics:[{type:R_}],numActiveRuns:[{type:R_}],annotation:[{type:R_}],runHeight:[{type:R_}],hasEmbedding:[{type:R_}]});class KOt{constructor(){this.onRowClick=new Sy,this.runHeight=30}rowClicked(t,e){if(t.shiftKey){let t=this.sortedAnnotations.indexOf(e);if(0===this.selectedAnnotations.length)this.onRowClick.emit(this.sortedAnnotations.slice(0,t+1));else{const e=this.sortedAnnotations.indexOf(this.selectedAnnotations[this.selectedAnnotations.length-1]);this.onRowClick.emit(e<t?this.sortedAnnotations.slice(e,t+1):this.sortedAnnotations.slice(t,e+1))}}else this.onRowClick.emit([e])}}KOt.ɵfac=function t(e){return new(e||KOt)},KOt.ɵcmp=so({type:KOt,selectors:[["annotations-list-component"]],inputs:{annotations:"annotations",embeddingData:"embeddingData",annotationsExpanded:"annotationsExpanded",numAnnotations:"numAnnotations",annotationSort:"annotationSort",activeMetrics:"activeMetrics",numActiveRuns:"numActiveRuns",sortedAnnotations:"sortedAnnotations",selectedAnnotations:"selectedAnnotations",maxCount:"maxCount"},outputs:{onRowClick:"onRowClick"},decls:2,vars:3,consts:[[3,"numAnnotations","expanded"],[4,"ngIf"],[3,"annotations","numAnnotations","activeMetrics"],["minBufferPx","300","maxBufferPx","600",1,"annotation-rows",3,"itemSize"],[3,"data","activeMetrics","numActiveRuns","maxCount","annotation","runHeight","hasEmbedding","click",4,"cdkVirtualFor","cdkVirtualForOf"],[3,"data","activeMetrics","numActiveRuns","maxCount","annotation","runHeight","hasEmbedding","click"]],template:function t(e,n){1&e&&(vf(0,"npmi-annotations-list-toolbar",0),Hm(1,XOt,5,5,"ng-container",1)),2&e&&(bf("numAnnotations",n.numAnnotations)("expanded",n.annotationsExpanded),Dc(1),bf("ngIf",n.annotationsExpanded))},directives:[TOt,Pv,IOt,BOt,tB,XL,nB,qOt],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 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.mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}input.mat-input-element[_ngcontent-%COMP%]{margin-top:-0.0625em}.mat-menu-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:400}.mat-paginator[_ngcontent-%COMP%], .mat-paginator-page-size[_ngcontent-%COMP%]   .mat-select-trigger[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px}.mat-radio-button[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action[_ngcontent-%COMP%]{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node[_ngcontent-%COMP%], .mat-nested-tree-node[_ngcontent-%COMP%]{font-weight:400;font-size:14px}.mat-ripple[_ngcontent-%COMP%]{overflow:hidden;position:relative}.mat-ripple[_ngcontent-%COMP%]:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded[_ngcontent-%COMP%]{overflow:visible}.mat-ripple-element[_ngcontent-%COMP%]{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms cubic-bezier(0, 0, 0.2, 1);transform:scale(0)}.cdk-high-contrast-active[_ngcontent-%COMP%]   .mat-ripple-element[_ngcontent-%COMP%]{display:none}.cdk-visually-hidden[_ngcontent-%COMP%]{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container[_ngcontent-%COMP%], .cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{background-color:#fff;border:1px solid #ebebeb;display:flex;flex-direction:column;height:calc(100% - 2px);width:calc(100% - 2px)}.annotation-rows[_ngcontent-%COMP%]{display:flex;flex-direction:column;flex:1 1;overflow-y:auto}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(KOt,[{type:S_,args:[{selector:"annotations-list-component",templateUrl:"./annotations_list_component.ng.html",styleUrls:["./annotations_list_component.css"],changeDetection:jn.OnPush}]}],null,{annotations:[{type:R_}],embeddingData:[{type:R_}],annotationsExpanded:[{type:R_}],numAnnotations:[{type:R_}],annotationSort:[{type:R_}],activeMetrics:[{type:R_}],numActiveRuns:[{type:R_}],sortedAnnotations:[{type:R_}],selectedAnnotations:[{type:R_}],maxCount:[{type:R_}],onRowClick:[{type:E_}]});class JOt{constructor(t){this.store=t,this.annotationsExpanded$=this.store.pipe(rS(ixt)),this.activeRuns$=this.store.pipe(rS(_I)).pipe(Vt((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.embeddingData$=this.store.pipe(rS(Zvt)),this.numActiveRuns$=this.activeRuns$.pipe(Vt((t=>t.length))),this.activeMetrics$=Kt([this.store.select(qvt),this.activeRuns$,this.store.select(ext)]).pipe(Vt((([t,e,n])=>{let o=[];for(const n of e)t[n]&&(o=o.concat(t[n].filter((t=>N_t(t)))));return o=[...new Set([...Object.keys(n),...o])],o}))),this.visibleAnnotations$=Kt([this.store.select(Yvt),this.store.select(Jvt),this.store.select(sxt)]).pipe(Vt((([t,e,n])=>oOt(t,e,n)))),this.filteredAnnotations$=Kt([this.visibleAnnotations$,this.store.select(txt),this.store.select(ext),this.activeRuns$,this.activeMetrics$,this.store.select(Qvt)]).pipe(Vt((([t,e,n,o,i,a])=>nOt(t,o,e,n,i,a)))).pipe(Ie()),this.numAnnotations$=this.filteredAnnotations$.pipe(Vt((t=>Object.keys(t).length))),this.sortedAnnotations$=Kt([this.filteredAnnotations$,this.store.pipe(rS(nxt)),this.embeddingData$]).pipe(Vt((([t,e,n])=>pOt(t,e,n)))),this.selectedAnnotations$=this.store.pipe(rS(Xvt)),this.maxCount$=this.filteredAnnotations$.pipe(Vt((t=>{let e=0;return Object.values(t).forEach((t=>{t.forEach((t=>{t.countValue&&(e=Math.max(e,t.countValue))}))})),e})))}rowClicked(t){this.store.dispatch(bvt({annotations:t}))}}JOt.ɵfac=function t(e){return new(e||JOt)(gf(iS))},JOt.ɵcmp=so({type:JOt,selectors:[["npmi-annotations-list"]],decls:10,vars:27,consts:[[3,"annotations","embeddingData","annotationsExpanded","numAnnotations","activeMetrics","numActiveRuns","sortedAnnotations","selectedAnnotations","maxCount","onRowClick"]],template:function t(e,n){1&e&&(Cf(0,"annotations-list-component",0),Rf("onRowClick",(function t(e){return n.rowClicked(e)})),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async"),_y(5,"async"),_y(6,"async"),_y(7,"async"),_y(8,"async"),_y(9,"async"),Mf()),2&e&&bf("annotations",Cy(1,9,n.filteredAnnotations$))("embeddingData",Cy(2,11,n.embeddingData$))("annotationsExpanded",Cy(3,13,n.annotationsExpanded$))("numAnnotations",Cy(4,15,n.numAnnotations$))("activeMetrics",Cy(5,17,n.activeMetrics$))("numActiveRuns",Cy(6,19,n.numActiveRuns$))("sortedAnnotations",Cy(7,21,n.sortedAnnotations$))("selectedAnnotations",Cy(8,23,n.selectedAnnotations$))("maxCount",Cy(9,25,n.maxCount$))},directives:[KOt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(JOt,[{type:S_,args:[{selector:"npmi-annotations-list",template:'\n    <annotations-list-component\n      [annotations]="filteredAnnotations$ | async"\n      [embeddingData]="embeddingData$ | async"\n      [annotationsExpanded]="annotationsExpanded$ | async"\n      [numAnnotations]="numAnnotations$ | async"\n      [activeMetrics]="activeMetrics$ | async"\n      [numActiveRuns]="numActiveRuns$ | async"\n      [sortedAnnotations]="sortedAnnotations$ | async"\n      [selectedAnnotations]="selectedAnnotations$ | async"\n      [maxCount]="maxCount$ | async"\n      (onRowClick)="rowClicked($event)"\n    ></annotations-list-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);const QOt=["chart"];class $Ot{constructor(){this.width=0,this.chartWidth=0,this.height=300,this.margin={top:20,right:40,bottom:20,left:40},this.chartHeight=this.height-this.margin.top-this.margin.bottom}onResize(t){this.redraw()}ngAfterViewInit(){this.svg=n8(this.svgElement.nativeElement),this.mainContainer=this.svg.append("g").attr("transform",`translate(${this.margin.left}, ${this.margin.top})`),this.coordinatesGroup=this.mainContainer.append("g"),this.labelsGroup=this.mainContainer.append("g"),this.axisGroup=this.mainContainer.append("g"),this.xScale=$nt().padding(.1),this.yScale=mot().range([this.chartHeight,0]),this.yAxis=L6(this.yScale),this.redraw()}ngOnChanges(t){this.svg&&this.redraw()}redraw(){this.updateDimensions(),this.updateAxes(),this.draw()}updateDimensions(){this.width=this.svgElement.nativeElement.clientWidth||10,this.chartWidth=this.width-this.margin.left-this.margin.right}updateAxes(){this.xScale.rangeRound([0,this.chartWidth]).domain(this.activeMetrics),this.yScale.domain([this.coordinateData.extremes.min,this.coordinateData.extremes.max])}draw(){this.drawAxes(),this.drawAxisLabels(),this.drawCoordinates(),this.drawLabels()}drawAxes(){const t=this.axisGroup.selectAll(".axis-y").data(this.activeMetrics);t.enter().append("g").attr("class","axis-y").merge(t).attr("transform",function(t){return`translate(${this.xScale(t)}, 0)`}.bind(this)).call(this.yAxis),t.exit().remove()}drawAxisLabels(){const t=this.axisGroup.selectAll(".axis-bg-text").data(this.activeMetrics);t.enter().append("text").attr("class","axis-bg-text").attr("font-size","13px").attr("stroke-width",2).attr("stroke-linejoin","round").attr("stroke","white").merge(t).text((t=>t)).attr("transform",function(t){return`translate(${this.xScale(t)-5}, ${this.yScale(this.coordinateData.extremes.min)}) rotate(-90)`}.bind(this)),t.exit().remove();const e=this.axisGroup.selectAll(".axis-text").data(this.activeMetrics);e.enter().append("text").attr("font-size","13px").attr("class","axis-text").merge(e).text((t=>t)).attr("transform",function(t){return`translate(${this.xScale(t)-5}, ${this.yScale(this.coordinateData.extremes.min)}) rotate(-90)`}.bind(this)),e.exit().remove()}drawCoordinates(){const t=this.coordinatesGroup.selectAll(".coord").data(this.coordinateData.coordinates);t.enter().append("path").attr("class","coord").attr("fill","none").merge(t).attr("d",this.path.bind(this)).attr("stroke",function(t){return this.colorScale(t.runId)}.bind(this)),t.exit().remove();const e=this.coordinatesGroup.selectAll(".hiddenCoord").data(this.coordinateData.coordinates);e.enter().append("path").attr("class","hiddenCoord").attr("stroke-width","10px").attr("fill","none").attr("stroke","rgba(0, 0, 0, 0.0)").on("mouseover",this.handleCoordinateMouseOver.bind(this)).on("mouseout",this.handleCoordinateMouseOut.bind(this)).merge(e).attr("d",this.path.bind(this)),e.exit().remove()}path(t){return t.values.sort(((t,e)=>this.activeMetrics.indexOf(t.metric)-this.activeMetrics.indexOf(e.metric))),Lat()(t.values.map(function(t){let e=this.yScale(t.nPMIValue);return[this.xScale(t.metric),e]}.bind(this)))}handleCoordinateMouseOver(t,e){this.labelsGroup.selectAll(".coordinate-label").filter((function(e){return!(e.annotation===t.annotation)})).style("opacity",.1),this.coordinatesGroup.selectAll(".coord").filter((function(e){return!(e.annotation===t.annotation)})).style("opacity",.1)}handleCoordinateMouseOut(){this.labelsGroup.selectAll(".coordinate-label").style("opacity",1),this.coordinatesGroup.selectAll(".coord").style("opacity",1)}drawLabels(){const t=30/this.xScale.step(),e=this.coordinateData.coordinates.length<30?this.coordinateData.coordinates:[],n=this.labelsGroup.selectAll(".coordinate-label").data(e);n.enter().append("text").attr("class","coordinate-label").attr("font-size","10px").merge(n).text((function(t){return t.annotation})).attr("x",this.xScale(this.activeMetrics[0])+30).attr("y",function(e){const n=this.yScale(e.values[0].nPMIValue?e.values[0].nPMIValue:0),o=this.yScale(e.values[1].nPMIValue?e.values[1].nPMIValue:0);return(1-t)*n+t*o}.bind(this)),n.exit().remove()}}$Ot.ɵfac=function t(e){return new(e||$Ot)},$Ot.ɵcmp=so({type:$Ot,selectors:[["parallel-coordinates-component"]],viewQuery:function t(e,n){if(1&e&&Vy(QOt,7,Kh),2&e){let t;By(t=Uy())&&(n.svgElement=t.first)}},hostBindings:function t(e,n){1&e&&Rf("resize",(function t(e){return n.onResize(e)}),!1,Ol)},inputs:{activeMetrics:"activeMetrics",coordinateData:"coordinateData",sidebarWidth:"sidebarWidth",colorScale:"colorScale"},features:[qo],decls:2,vars:0,consts:[[1,"pc-chart"],["chart",""]],template:function t(e,n){1&e&&(oa(),vf(0,"svg",0,1))},styles:[".pc-chart[_ngcontent-%COMP%]{height:300px;width:100%}"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb($Ot,[{type:S_,args:[{selector:"parallel-coordinates-component",templateUrl:"./parallel_coordinates_component.ng.html",styleUrls:["./parallel_coordinates_component.css"],changeDetection:jn.OnPush}]}],null,{activeMetrics:[{type:R_}],coordinateData:[{type:R_}],sidebarWidth:[{type:R_}],colorScale:[{type:R_}],svgElement:[{type:ar,args:["chart",{static:!0,read:Kh}]}],onResize:[{type:T_,args:["window:resize",["$event"]]}]});class tPt{constructor(t){this.store=t,this.activeRuns$=this.store.pipe(rS(_I)).pipe(Vt((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.activeMetrics$=Kt(this.store.select(qvt),this.activeRuns$,this.store.select(ext)).pipe(Vt((([t,e,n])=>{let o=[];for(const n of e)t[n]&&(o=o.concat(t[n].filter((t=>N_t(t)))));return o=[...new Set([...Object.keys(n),...o])],o.map((t=>z_t(t)))}))),this.coordinateData$=Kt([this.store.select(Yvt),this.store.select(Xvt),this.activeRuns$,this.activeMetrics$]).pipe(Vt((([t,e,n,o])=>(function i(t,e,n,o){const i=[],a={max:-1,min:1},r=new Set(n),s=new Set(o);return 0===r.size||0===s.size||0===Object.keys(t).length?{coordinates:[],extremes:{min:-1,max:1}}:(e.forEach((e=>{const n={};t[e].forEach((t=>{r.has(t.run)&&s.has(t.metric)&&(n[t.run]?n[t.run].push(t):n[t.run]=[t],null!==t.nPMIValue?(a.max=Math.max(a.max,t.nPMIValue),a.min=Math.min(a.min,t.nPMIValue)):(a.max=Math.max(a.max,0),a.min=Math.min(a.min,0)))}));for(const t of Object.keys(n))i.push({annotation:e,runId:t,values:n[t]})})),a.max<a.min&&(a.max=1,a.min=-1),{coordinates:i,extremes:a})})(t,e,n,o)))),this.sidebarWidth$=this.store.select(cxt),this.runColorScale$=this.store.select(CI).pipe(Vt((t=>e=>{if(!t.hasOwnProperty(e))throw new Error(`[Color scale] unknown runId: ${e}.`);return t[e]})))}}function ePt(t,e){1&t&&vf(0,"mat-icon",9)}function nPt(t,e){1&t&&vf(0,"mat-icon",10)}function oPt(t,e){1&t&&vf(0,"npmi-parallel-coordinates")}tPt.ɵfac=function t(e){return new(e||tPt)(gf(iS))},tPt.ɵcmp=so({type:tPt,selectors:[["npmi-parallel-coordinates"]],decls:5,vars:12,consts:[[3,"activeMetrics","coordinateData","sidebarWidth","colorScale"]],template:function t(e,n){1&e&&(vf(0,"parallel-coordinates-component",0),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async")),2&e&&bf("activeMetrics",Cy(1,4,n.activeMetrics$))("coordinateData",Cy(2,6,n.coordinateData$))("sidebarWidth",Cy(3,8,n.sidebarWidth$))("colorScale",Cy(4,10,n.runColorScale$))},directives:[$Ot],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(tPt,[{type:S_,args:[{selector:"npmi-parallel-coordinates",template:'\n    <parallel-coordinates-component\n      [activeMetrics]="activeMetrics$ | async"\n      [coordinateData]="coordinateData$ | async"\n      [sidebarWidth]="sidebarWidth$ | async"\n      [colorScale]="runColorScale$ | async"\n    ></parallel-coordinates-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);class iPt{constructor(){this.onClearSelectedAnnotations=new Sy,this.onToggleExpanded=new Sy}}iPt.ɵfac=function t(e){return new(e||iPt)},iPt.ɵcmp=so({type:iPt,selectors:[["selected-annotations-component"]],inputs:{pcExpanded:"pcExpanded",selectedAnnotations:"selectedAnnotations"},outputs:{onClearSelectedAnnotations:"onClearSelectedAnnotations",onToggleExpanded:"onToggleExpanded"},decls:11,vars:4,consts:function(){let t,e;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){t=goog.getMsg("Clear Annotation Selection")}else t=$localize`:Label for a button that clears the annotation selection.␟2b52289f2c4b7f5b182ed23c9154722ecd46a2d4␟2494660520341308830:Clear Annotation Selection`;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){e=goog.getMsg("Expand/Hide Selected Annotations")}else e=$localize`:Label for a button that expands or hides selected annotations.␟8f4ad305c19e3655f3189e3e264e83fb76f7bb95␟5670797784070952095:Expand/Hide Selected Annotations`;return[[1,"pc-container"],[1,"pc-toolbar"],[1,"pc-title"],["mat-icon-button","","aria-label",t,"title","Deselects all selected annotations.",1,"clear-button",3,"disabled","click"],["svgIcon","clear_24px"],["mat-icon-button","","aria-label",e,1,"expand-button",3,"click"],["svgIcon","expand_less_24px","class","expand-less-icon",4,"ngIf","ngIfElse"],["notExpanded",""],[4,"ngIf"],["svgIcon","expand_less_24px",1,"expand-less-icon"],["svgIcon","expand_more_24px",1,"expand-icon"]]},template:function t(e,n){if(1&e&&(Cf(0,"div",0),Cf(1,"div",1),Cf(2,"h3",2),Pu(3,"Selected Annotations"),Mf(),Cf(4,"button",3),Rf("click",(function t(){return n.onClearSelectedAnnotations.emit()})),vf(5,"mat-icon",4),Mf(),Cf(6,"button",5),Rf("click",(function t(){return 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ngDevMode||ngDevMode)&&Qb(iPt,[{type:S_,args:[{selector:"selected-annotations-component",templateUrl:"./selected_annotations_component.ng.html",styleUrls:["./selected_annotations_component.css"],changeDetection:jn.OnPush}]}],null,{pcExpanded:[{type:R_}],selectedAnnotations:[{type:R_}],onClearSelectedAnnotations:[{type:E_}],onToggleExpanded:[{type:E_}]});class aPt{constructor(t){this.store=t,this.pcExpanded$=this.store.pipe(rS(oxt)),this.selectedAnnotations$=this.store.select(Xvt)}clearSelectedAnnotations(){this.store.dispatch(_vt())}toggleExpanded(){this.store.dispatch(Dvt())}}function rPt(t,e){if(1&t&&(Cf(0,"div",6),Cf(1,"div",7),vf(2,"npmi-violin-filters",8),Cf(3,"div",9),vf(4,"runs-selector"),Mf(),Mf(),Mf()),2&t){lu("width",zf().sidebarWidth,"px")}}function sPt(t,e){if(1&t){const t=wf();Cf(0,"div",10),Rf("mousedown",(function e(){Pi(t);return zf().resizeGrabbed.emit()})),Mf()}}function 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mPt{constructor(t){this.store=t,this.runActive$=this.store.pipe(rS(_I)).pipe(Vt((t=>!!t&&[...t.values()].includes(!0)))),this.sidebarExpanded$=this.store.pipe(rS(axt)),this.sidebarWidth$=this.store.pipe(rS(cxt)),this.resizing=!1}onToggleSidebarExpanded(){this.store.dispatch(Evt())}onResizeTriggered(t){this.resizing&&this.store.dispatch(zvt({sidebarWidth:t.clientX}))}onResizeGrabbed(){this.resizing=!0}onResizeReleased(){this.resizing=!1}}mPt.ɵfac=function t(e){return new(e||mPt)(gf(iS))},mPt.ɵcmp=so({type:mPt,selectors:[["npmi-main"]],decls:4,vars:9,consts:[[3,"runActive","sidebarExpanded","sidebarWidth","toggleSidebarExpanded","resizeTriggered","resizeGrabbed","resizeReleased"]],template:function t(e,n){1&e&&(Cf(0,"main-component",0),Rf("toggleSidebarExpanded",(function t(){return n.onToggleSidebarExpanded()}))("resizeTriggered",(function t(e){return n.onResizeTriggered(e)}))("resizeGrabbed",(function t(){return n.onResizeGrabbed()}))("resizeReleased",(function t(){return n.onResizeReleased()})),_y(1,"async"),_y(2,"async"),_y(3,"async"),Mf()),2&e&&bf("runActive",Cy(1,3,n.runActive$))("sidebarExpanded",Cy(2,5,n.sidebarExpanded$))("sidebarWidth",Cy(3,7,n.sidebarWidth$))},directives:[pPt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(mPt,[{type:S_,args:[{selector:"npmi-main",template:'\n    <main-component\n      [runActive]="runActive$ | async"\n      [sidebarExpanded]="sidebarExpanded$ | async"\n      [sidebarWidth]="sidebarWidth$ | async"\n      (toggleSidebarExpanded)="onToggleSidebarExpanded()"\n      (resizeTriggered)="onResizeTriggered($event)"\n      (resizeGrabbed)="onResizeGrabbed()"\n      (resizeReleased)="onResizeReleased()"\n    ></main-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);class fPt{constructor(){this.toggleSidebarExpanded=new Sy}}fPt.ɵfac=function t(e){return new(e||fPt)},fPt.ɵcmp=so({type:fPt,selectors:[["embedding-projection-component"]],inputs:{sidebarExpanded:"sidebarExpanded"},outputs:{toggleSidebarExpanded:"toggleSidebarExpanded"},decls:7,vars:0,consts:function(){let t;if("undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode){t=goog.getMsg("Expand/Hide Sidebar")}else t=$localize`:Label for a button that expands/hides the sidebar.␟48c29903ce881ab61088f8d49d827203716aaed4␟4658602991970260215:Expand/Hide Sidebar`;return[[1,"embedding-projection-toolbar"],[1,"embedding-projection-title"],[1,"side-toggle"],["mat-icon-button","","aria-label",t,3,"click"],["svgIcon","chevron_left_24px"],[1,"projection"]]},template:function t(e,n){1&e&&(Cf(0,"div",0),Cf(1,"h3",1),Pu(2,"Embeddings Projected"),Mf(),Cf(3,"div",2),Cf(4,"button",3),Rf("click",(function t(){return 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MPt{constructor(t){this.store=t,this.runActive$=this.store.pipe(rS(_I)).pipe(Vt((t=>!!t&&[...t.values()].includes(!0)))),this.sidebarExpanded$=this.store.pipe(rS(mxt)),this.sidebarWidth$=this.store.pipe(rS(pxt)),this.resizing=!1}onToggleSidebarExpanded(){this.store.dispatch(Hvt())}onResizeTriggered(t){this.resizing&&this.store.dispatch(Ivt({sidebarWidth:t.clientX}))}onResizeGrabbed(){this.resizing=!0}onResizeReleased(){this.resizing=!1}}function vPt(t,e){1&t&&vf(0,"npmi-inactive-view")}function xPt(t,e){1&t&&vf(0,"npmi-main")}function OPt(t,e){1&t&&vf(0,"npmi-embeddings")}function PPt(t,e){if(1&t&&(Hm(0,xPt,1,0,"npmi-main",3),Hm(1,OPt,1,0,"npmi-embeddings",3)),2&t){const t=zf();bf("ngIf",t.activeView===t.ViewActive.DEFAULT),Dc(1),bf("ngIf",t.activeView===t.ViewActive.EMBEDDINGS)}}MPt.ɵfac=function t(e){return new(e||MPt)(gf(iS))},MPt.ɵcmp=so({type:MPt,selectors:[["npmi-embeddings"]],decls:4,vars:9,consts:[[3,"runActive","sidebarExpanded","sidebarWidth","toggleSidebarExpanded","resizeTriggered","resizeGrabbed","resizeReleased"]],template:function t(e,n){1&e&&(Cf(0,"embeddings-component",0),Rf("toggleSidebarExpanded",(function t(){return n.onToggleSidebarExpanded()}))("resizeTriggered",(function t(e){return n.onResizeTriggered(e)}))("resizeGrabbed",(function t(){return n.onResizeGrabbed()}))("resizeReleased",(function t(){return n.onResizeReleased()})),_y(1,"async"),_y(2,"async"),_y(3,"async"),Mf()),2&e&&bf("runActive",Cy(1,3,n.runActive$))("sidebarExpanded",Cy(2,5,n.sidebarExpanded$))("sidebarWidth",Cy(3,7,n.sidebarWidth$))},directives:[CPt],pipes:[Vv],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(MPt,[{type:S_,args:[{selector:"npmi-embeddings",template:'\n    <embeddings-component\n      [runActive]="runActive$ | async"\n      [sidebarExpanded]="sidebarExpanded$ | async"\n      [sidebarWidth]="sidebarWidth$ | async"\n      (toggleSidebarExpanded)="onToggleSidebarExpanded()"\n      (resizeTriggered)="onResizeTriggered($event)"\n      (resizeGrabbed)="onResizeGrabbed()"\n      (resizeReleased)="onResizeReleased()"\n    ></embeddings-component>\n  ',changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),null);class wPt{constructor(){this.ViewActive=jvt}}wPt.ɵfac=function t(e){return new(e||wPt)},wPt.ɵcmp=so({type:wPt,selectors:[["npmi-component"]],inputs:{runs:"runs",activeView:"activeView"},decls:4,vars:2,consts:[[1,"npmi-container"],[4,"ngIf","ngIfElse"],["dataAvailable",""],[4,"ngIf"]],template:function t(e,n){if(1&e&&(Cf(0,"div",0),Hm(1,vPt,1,0,"npmi-inactive-view",1),Hm(2,PPt,2,2,"ng-template",null,2,qy),Mf()),2&e){const t=Fm(3);Dc(1),bf("ngIf",0===n.runs.size)("ngIfElse",t)}},directives:[Pv,uxt,mPt,MPt],styles:["[_nghost-%COMP%]{display:flex;height:100%}.npmi-container[_ngcontent-%COMP%]{flex:1 1}"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(wPt,[{type:S_,args:[{selector:"npmi-component",templateUrl:"./npmi_component.ng.html",styleUrls:["./npmi_component.css"],changeDetection:jn.OnPush}]}],null,{runs:[{type:R_}],activeView:[{type:R_}]});class kPt{constructor(t){this.store=t,this.runs$=this.store.pipe(rS(_I)),this.activeView$=this.store.pipe(rS(lxt))}ngOnInit(){this.store.dispatch(fvt())}}kPt.ɵfac=function t(e){return new(e||kPt)(gf(iS))},kPt.ɵcmp=so({type:kPt,selectors:[["npmi"]],decls:3,vars:6,consts:[[3,"runs","activeView"]],template:function t(e,n){1&e&&(vf(0,"npmi-component",0),_y(1,"async"),_y(2,"async")),2&e&&bf("runs",Cy(1,2,n.runs$))("activeView",Cy(2,4,n.activeView$))},directives:[wPt],pipes:[Vv],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(kPt,[{type:S_,args:[{selector:"npmi",template:'\n    <npmi-component\n      [runs]="runs$ | async"\n      [activeView]="activeView$ | async"\n    ></npmi-component>\n  '}]}],(function(){return[{type:iS}]}),null);class SPt{}SPt.ɵfac=function t(e){return new(e||SPt)},SPt.ɵmod=fo({type:SPt}),SPt.ɵinj=Dn({imports:[[rx,tY,MZ,vq,IL]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(SPt,[{type:L_,args:[{declarations:[qOt,YOt],imports:[rx,tY,MZ,vq,IL],exports:[qOt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(SPt,{declarations:[qOt,YOt],imports:[rx,tY,MZ,vq,IL],exports:[qOt]});class DPt{}DPt.ɵfac=function t(e){return new(e||DPt)},DPt.ɵmod=fo({type:DPt}),DPt.ɵinj=Dn({imports:[[rx,tY,vq,TZ]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(DPt,[{type:L_,args:[{declarations:[SOt,DOt],imports:[rx,tY,vq,TZ],exports:[DOt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(DPt,{declarations:[SOt,DOt],imports:[rx,tY,vq,TZ],exports:[DOt]});class RPt{}RPt.ɵfac=function t(e){return new(e||RPt)},RPt.ɵmod=fo({type:RPt}),RPt.ɵinj=Dn({imports:[[rx,tY,vq,IL,wOt,DPt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(RPt,[{type:L_,args:[{declarations:[AOt,TOt],imports:[rx,tY,vq,IL,wOt,DPt],exports:[TOt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(RPt,{declarations:[AOt,TOt],imports:[rx,tY,vq,IL,wOt,DPt],exports:[TOt]});class EPt{}EPt.ɵfac=function t(e){return new(e||EPt)},EPt.ɵmod=fo({type:EPt}),EPt.ɵinj=Dn({imports:[[rx,tY,MZ,vq]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(EPt,[{type:L_,args:[{declarations:[LOt,BOt],imports:[rx,tY,MZ,vq],exports:[BOt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(EPt,{declarations:[LOt,BOt],imports:[rx,tY,MZ,vq],exports:[BOt]});class APt{}APt.ɵfac=function t(e){return new(e||APt)},APt.ɵmod=fo({type:APt}),APt.ɵinj=Dn({imports:[[rx]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(APt,[{type:L_,args:[{declarations:[zOt],imports:[rx],exports:[zOt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(APt,{declarations:[zOt],imports:[rx],exports:[zOt]});class TPt{}TPt.ɵfac=function t(e){return new(e||TPt)},TPt.ɵmod=fo({type:TPt}),TPt.ɵinj=Dn({imports:[[APt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(TPt,[{type:L_,args:[{declarations:[IOt],imports:[APt],exports:[IOt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(TPt,{declarations:[IOt],imports:[APt],exports:[IOt]});class NPt{}NPt.ɵfac=function t(e){return new(e||NPt)},NPt.ɵmod=fo({type:NPt}),NPt.ɵinj=Dn({imports:[[rx,RPt,EPt,TPt,iB,SPt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(NPt,[{type:L_,args:[{declarations:[KOt,JOt],imports:[rx,RPt,EPt,TPt,iB,SPt],exports:[JOt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(NPt,{declarations:[KOt,JOt],imports:[rx,RPt,EPt,TPt,iB,SPt],exports:[JOt]});class zPt{}zPt.ɵfac=function t(e){return new(e||zPt)},zPt.ɵmod=fo({type:zPt}),zPt.ɵinj=Dn({imports:[[rx,tY,vq,IL]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(zPt,[{type:L_,args:[{declarations:[fPt,uPt],imports:[rx,tY,vq,IL],exports:[uPt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(zPt,{declarations:[fPt,uPt],imports:[rx,tY,vq,IL],exports:[uPt]});class IPt{}IPt.ɵfac=function t(e){return new(e||IPt)},IPt.ɵmod=fo({type:IPt}),IPt.ɵinj=Dn({imports:[[rx,tY,eY,vq,jxt,j0]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(IPt,[{type:L_,args:[{declarations:[qxt,Zxt],imports:[rx,tY,eY,vq,jxt,j0],exports:[Zxt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(IPt,{declarations:[qxt,Zxt],imports:[rx,tY,eY,vq,jxt,j0],exports:[Zxt]});class HPt{}HPt.ɵfac=function t(e){return new(e||HPt)},HPt.ɵmod=fo({type:HPt}),HPt.ɵinj=Dn({imports:[[rx,jxt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(HPt,[{type:L_,args:[{declarations:[Xxt],imports:[rx,jxt],exports:[Xxt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(HPt,{declarations:[Xxt],imports:[rx,jxt],exports:[Xxt]});class FPt{}FPt.ɵfac=function t(e){return new(e||FPt)},FPt.ɵmod=fo({type:FPt}),FPt.ɵinj=Dn({imports:[[rx,jxt,IPt,HPt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(FPt,[{type:L_,args:[{declarations:[$xt,tOt],imports:[rx,jxt,IPt,HPt],exports:[tOt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(FPt,{declarations:[$xt,tOt],imports:[rx,jxt,IPt,HPt],exports:[tOt]});class LPt{}LPt.ɵfac=function t(e){return new(e||LPt)},LPt.ɵmod=fo({type:LPt}),LPt.ɵinj=Dn({imports:[[rx,vq,TZ,q3]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(LPt,[{type:L_,args:[{declarations:[_xt,Cxt],imports:[rx,vq,TZ,q3],exports:[Cxt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(LPt,{declarations:[_xt,Cxt],imports:[rx,vq,TZ,q3],exports:[Cxt]});class BPt{}BPt.ɵfac=function t(e){return new(e||BPt)},BPt.ɵmod=fo({type:BPt}),BPt.ɵinj=Dn({imports:[[rx,vq,IL]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(BPt,[{type:L_,args:[{declarations:[xxt,Oxt],imports:[rx,vq,IL],exports:[Oxt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(BPt,{declarations:[xxt,Oxt],imports:[rx,vq,IL],exports:[Oxt]});class VPt{}VPt.ɵfac=function t(e){return new(e||VPt)},VPt.ɵmod=fo({type:VPt}),VPt.ɵinj=Dn({imports:[[rx,LPt,FPt,BPt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(VPt,[{type:L_,args:[{declarations:[eOt],imports:[rx,LPt,FPt,BPt],exports:[eOt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(VPt,{declarations:[eOt],imports:[rx,LPt,FPt,BPt],exports:[eOt]});class jPt{}jPt.ɵfac=function t(e){return new(e||jPt)},jPt.ɵmod=fo({type:jPt}),jPt.ɵinj=Dn({imports:[[rx]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(jPt,[{type:L_,args:[{declarations:[$Ot,tPt],imports:[rx],exports:[tPt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(jPt,{declarations:[$Ot,tPt],imports:[rx],exports:[tPt]});class UPt{}UPt.ɵfac=function t(e){return new(e||UPt)},UPt.ɵmod=fo({type:UPt}),UPt.ɵinj=Dn({imports:[[rx,vq,IL,jPt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(UPt,[{type:L_,args:[{declarations:[iPt,aPt],imports:[rx,vq,IL,jPt],exports:[aPt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(UPt,{declarations:[iPt,aPt],imports:[rx,vq,IL,jPt],exports:[aPt]});class WPt{}WPt.ɵfac=function t(e){return new(e||WPt)},WPt.ɵmod=fo({type:WPt}),WPt.ɵinj=Dn({imports:[[rx,tY,MZ,vq,r_t,VPt,IL,NPt,UPt,zPt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(WPt,[{type:L_,args:[{declarations:[CPt,MPt],imports:[rx,tY,MZ,vq,r_t,VPt,IL,NPt,UPt,zPt],exports:[MPt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(WPt,{declarations:[CPt,MPt],imports:[rx,tY,MZ,vq,r_t,VPt,IL,NPt,UPt,zPt],exports:[MPt]});class GPt{}GPt.ɵfac=function t(e){return new(e||GPt)},GPt.ɵmod=fo({type:GPt}),GPt.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(GPt,[{type:L_,args:[{declarations:[uxt],exports:[uxt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(GPt,{declarations:[uxt],exports:[uxt]});class YPt{}YPt.ɵfac=function t(e){return new(e||YPt)},YPt.ɵmod=fo({type:YPt}),YPt.ɵinj=Dn({imports:[[rx,tY,IL,vq]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(YPt,[{type:L_,args:[{declarations:[aOt,rOt],imports:[rx,tY,IL,vq],exports:[rOt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(YPt,{declarations:[aOt,rOt],imports:[rx,tY,IL,vq],exports:[rOt]});class qPt{}qPt.ɵfac=function t(e){return new(e||qPt)},qPt.ɵmod=fo({type:qPt}),qPt.ɵinj=Dn({imports:[[rx,tY,vq,IL,YPt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(qPt,[{type:L_,args:[{declarations:[cOt,dOt],imports:[rx,tY,vq,IL,YPt],exports:[dOt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(qPt,{declarations:[cOt,dOt],imports:[rx,tY,vq,IL,YPt],exports:[dOt]});class ZPt{}ZPt.ɵfac=function t(e){return new(e||ZPt)},ZPt.ɵmod=fo({type:ZPt}),ZPt.ɵinj=Dn({imports:[[rx,tY,MZ,vq,r_t,VPt,IL,qPt,NPt,UPt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(ZPt,[{type:L_,args:[{declarations:[pPt,mPt],imports:[rx,tY,MZ,vq,r_t,VPt,IL,qPt,NPt,UPt],exports:[mPt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(ZPt,{declarations:[pPt,mPt],imports:[rx,tY,MZ,vq,r_t,VPt,IL,qPt,NPt,UPt],exports:[mPt]});class XPt{}XPt.ɵfac=function t(e){return new(e||XPt)},XPt.ɵmod=fo({type:XPt}),XPt.ɵinj=Dn({imports:[[rx,GPt,ZPt,WPt,mvt,TS.forFeature(Lvt,hxt),hD.forFeature([fxt]),MX.forPlugin("npmi",kPt)]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(XPt,[{type:L_,args:[{declarations:[wPt,kPt],imports:[rx,GPt,ZPt,WPt,mvt,TS.forFeature(Lvt,hxt),hD.forFeature([fxt]),MX.forPlugin("npmi",kPt)],exports:[kPt],entryComponents:[kPt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(XPt,{declarations:[wPt,kPt],imports:[rx,GPt,ZPt,WPt,mvt,AS,gD,MX],exports:[kPt]});class KPt{}KPt.ɵfac=function t(e){return new(e||KPt)},KPt.ɵprov=Sn({token:KPt,factory:KPt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(KPt,[{type:Um}],null,null);class JPt{constructor(t){this.http=t,this.httpPathPrefix="data/plugin/text_v2"}fetchRunToTag(){return this.http.get(this.httpPathPrefix+"/tags").pipe(Vt((t=>{const e=new Map;return Object.entries(t).forEach((([t,n])=>{e.set(t,n)})),e})))}fetchTextData(t,e){const n=new URLSearchParams({run:t,tag:e});return this.http.get(this.httpPathPrefix+`/text?${n.toString()}`).pipe(Vt((t=>t.map((t=>({originalShape:t.original_shape,step:t.step,stringArray:t.string_array,wallTimeInMs:1e3*t.wall_time,truncated:t.truncated}))))))}}JPt.ɵfac=function t(e){return new(e||JPt)(Tr(HE))},JPt.ɵprov=Sn({token:JPt,factory:JPt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(JPt,[{type:Um}],(function(){return[{type:HE}]}),null);class QPt{}QPt.ɵfac=function t(e){return new(e||QPt)},QPt.ɵmod=fo({type:QPt}),QPt.ɵinj=Dn({providers:[JPt,{provide:KPt,useExisting:JPt}],imports:[[FE]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(QPt,[{type:L_,args:[{imports:[FE],providers:[JPt,{provide:KPt,useExisting:JPt}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(QPt,{imports:[FE]});const $Pt=_k("[Text] Text Plugin Loaded"),twt=_k("[Text] Runs To Tag Loaded",{_as:"props",_p:void 0}),ewt=_k("[Text] Tag Group Visibility Changed",{_as:"props",_p:void 0}),nwt=_k("[Text] Text Data Loaded Loaded",{_as:"props",_p:void 0}),owt="text",iwt=_S(owt);bS(iwt,(t=>t.runToTags));const awt=bS(iwt,(t=>{const e=new Set,n=new Set;for(const o of t.visibleRunTags.values())for(const t of o){const o=JSON.stringify(t);e.has(o)||(e.add(o),n.add(t))}return[...n]})),rwt=bS(iwt,((t,e)=>{const n=t.data.get(e.run);return n&&n.get(e.tag)||null}));class swt{constructor(t,e,n){this.actions$=t,this.store=e,this.dataSource=n,this.loadRunToTags$=WS((()=>this.actions$.pipe($S($Pt),Ve((()=>this.dataSource.fetchRunToTag().pipe(We((t=>{this.store.dispatch(twt({runToTags:t}))})),Vt((()=>{}))))))),{dispatch:!1}),this.loadData$=WS((()=>pe(this.actions$.pipe($S(ewt),Ve((({visibleTextCards:t})=>ie(t.map((({run:t,tag:e})=>this.store.select(rwt,{run:t,tag:e}).pipe((function n(t,e){var n=arguments.length>=2;return function(o){return o.pipe(t?ue((function(e,n){return t(e,n,o)})):R,Te(1),n?Me(e):Se((function(){return new Bt})))}})(),Vt((n=>({run:t,tag:e,textData:n}))))))).pipe(Vt((t=>t.filter((({textData:t})=>null===t)).map((({run:t,tag:e})=>({run:t,tag:e}))))))))),this.actions$.pipe($S(JE,QE),qe(this.store.select(awt)),Vt((([,t])=>t)))).pipe($t((t=>ie(t.map((t=>this.fetchTextData(t)))))))),{dispatch:!1})}fetchTextData(t){const{run:e,tag:n}=t;return this.dataSource.fetchTextData(e,n).pipe(We((t=>{this.store.dispatch(nwt({run:e,tag:n,stepData:t}))})),Vt((()=>{})))}}swt.ɵfac=function t(e){return new(e||swt)(Tr(QS),Tr(iS),Tr(KPt))},swt.ɵprov=Sn({token:swt,factory:swt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(swt,[{type:Um}],(function(){return[{type:QS},{type:iS},{type:KPt}]}),null);const lwt=VS({runToTags:new Map([["run1",["a/b","a/c"]],["run2",["a/b","a/d"]],["run3",["c","a/b"]]]),data:new Map([["run1",new Map([["a/b",[{originalShape:[3],step:0,stringArray:[["foo","bar","baz"]],wallTimeInMs:15778656e5,truncated:!1},{originalShape:[3],step:1,stringArray:[["foo","baz"]],wallTimeInMs:1577865601e3,truncated:!1}]],["a/c",[{originalShape:[3],step:0,stringArray:[["We conducted an experiment and found the following data:\n\nPounds of chocolate | Happiness\n---|---\n0 | 1\n1 | 4\n2 | 9\n3 | 16\n4 | 25\n5 | 36\n6 | 49\n7 | 64\n8 | 81\n9 | 100\n10 | 121"]],wallTimeInMs:15778656e5,truncated:!1},{originalShape:[3],step:1,stringArray:[["×","**0**","**1**","**2**","**3**","**4**","**5**"],["**0**","0","0","0","0","0","0"],["**1**","0","1","2","3","4","5"],["**2**","0","2","4","6","8","10"],["**3**","0","3","6","9","12","15"],["**4**","0","4","8","12","16","20"],["**5**","0","5","10","15","20","25"]],wallTimeInMs:1577865601e3,truncated:!1}]]])]]),visibleRunTags:new Map});function cwt(t,e){return lwt(t,e)}class dwt{}dwt.ɵfac=function t(e){return 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0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{background-color:#fff;color:#212121;display:block;position:relative}body.dark-mode   [_nghost-%COMP%]   .plugins.is-first-party-plugin[_ngcontent-%COMP%]{background-color:#303030;color:#fff}.plugins[_ngcontent-%COMP%]{height:100%;position:relative}.warning[_ngcontent-%COMP%]{background:#fff;bottom:0;left:0;position:absolute;right:0;top:0}body.dark-mode[_nghost-%COMP%]   .warning[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .warning[_ngcontent-%COMP%]{background:#303030}.warning-message[_ngcontent-%COMP%]{color:#212121;margin:80px auto 0;max-width:540px}body.dark-mode[_nghost-%COMP%]   .warning-message[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .warning-message[_ngcontent-%COMP%]{color:#fff}.last-reload-time[_ngcontent-%COMP%]{font-style:italic}.plugins[_ngcontent-%COMP%]     iframe{border:0;display:block;height:100%;width:100%}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Dwt,[{type:S_,args:[{selector:"plugins-component",templateUrl:"./plugins_component.ng.html",styleUrls:["plugins_component.css"],changeDetection:jn.OnPush}]}],(function(){return[{type:Gh},{type:MX},{type:WI,decorators:[{type:Br}]}]}),{pluginsContainer:[{type:ar,args:["pluginContainer",{static:!0,read:Kh}]}],ngPluginContainer:[{type:ar,args:["ngPluginContainer",{static:!0,read:Fb}]}],activePluginId:[{type:R_}],activeKnownPlugin:[{type:R_}],pluginLoadState:[{type:R_}],dataLocation:[{type:R_}],isFeatureFlagsLoaded:[{type:R_}],settingsLoadState:[{type:R_}],featureFlags:[{type:R_}],lastUpdated:[{type:R_}],environmentFailureNotFoundTemplate:[{type:R_}],environmentFailureUnknownTemplate:[{type:R_}]});const Rwt=bS(nT,eT,((t,e)=>e&&t[e]?Object.assign({id:e},t[e]):null));class Ewt{constructor(t){this.store=t,this.activeKnownPlugin$=this.store.select(Rwt),this.activePluginId$=this.store.select(eT),this.pluginLoadState$=Kt(this.activeKnownPlugin$,this.activePluginId$,this.store.select(JA)).pipe(Vt((([t,e,n])=>null!==n.failureCode?n.failureCode===LE.NOT_FOUND?Swt.ENVIRONMENT_FAILURE_NOT_FOUND:Swt.ENVIRONMENT_FAILURE_UNKNOWN:null!==t?Swt.LOADED:null===n.lastLoadedTimeInMs&&n.state===UE.LOADING?Swt.LOADING:e?Swt.UNKNOWN_PLUGIN_ID:Swt.NO_ENABLED_PLUGINS))),this.lastLoadedTimeInMs$=this.store.select(tT),this.dataLocation$=this.store.select(oT).pipe(Vt((t=>t.data_location))),this.isFeatureFlagsLoaded$=this.store.select(ND),this.featureFlags$=this.store.select(zD),this.settingsLoadState$=this.store.select(fI)}}Ewt.ɵfac=function t(e){return new(e||Ewt)(gf(iS))},Ewt.ɵcmp=so({type:Ewt,selectors:[["plugins"]],inputs:{environmentFailureNotFoundTemplate:"environmentFailureNotFoundTemplate",environmentFailureUnknownTemplate:"environmentFailureUnknownTemplate"},decls:9,vars:26,consts:[[3,"activeKnownPlugin","activePluginId","dataLocation","lastUpdated","pluginLoadState","isFeatureFlagsLoaded","settingsLoadState","featureFlags","environmentFailureNotFoundTemplate","environmentFailureUnknownTemplate"]],template:function t(e,n){1&e&&(vf(0,"plugins-component",0),_y(1,"async"),_y(2,"async"),_y(3,"async"),_y(4,"async"),_y(5,"async"),_y(6,"async"),_y(7,"async"),_y(8,"async")),2&e&&bf("activeKnownPlugin",Cy(1,10,n.activeKnownPlugin$))("activePluginId",Cy(2,12,n.activePluginId$))("dataLocation",Cy(3,14,n.dataLocation$))("lastUpdated",Cy(4,16,n.lastLoadedTimeInMs$))("pluginLoadState",Cy(5,18,n.pluginLoadState$))("isFeatureFlagsLoaded",Cy(6,20,n.isFeatureFlagsLoaded$))("settingsLoadState",Cy(7,22,n.settingsLoadState$))("featureFlags",Cy(8,24,n.featureFlags$))("environmentFailureNotFoundTemplate",n.environmentFailureNotFoundTemplate)("environmentFailureUnknownTemplate",n.environmentFailureUnknownTemplate)},styles:["plugins-component[_ngcontent-%COMP%] { height: 100%; }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Ewt,[{type:S_,args:[{selector:"plugins",template:'\n    <plugins-component\n      [activeKnownPlugin]="activeKnownPlugin$ | async"\n      [activePluginId]="activePluginId$ | async"\n      [dataLocation]="dataLocation$ | async"\n      [lastUpdated]="lastLoadedTimeInMs$ | async"\n      [pluginLoadState]="pluginLoadState$ | async"\n      [isFeatureFlagsLoaded]="isFeatureFlagsLoaded$ | async"\n      [settingsLoadState]="settingsLoadState$ | async"\n      [featureFlags]="featureFlags$ | async"\n      [environmentFailureNotFoundTemplate]="environmentFailureNotFoundTemplate"\n      [environmentFailureUnknownTemplate]="environmentFailureUnknownTemplate"\n    ></plugins-component>\n  ',styles:["plugins-component { height: 100%; }"],changeDetection:jn.OnPush}]}],(function(){return[{type:iS}]}),{environmentFailureNotFoundTemplate:[{type:R_}],environmentFailureUnknownTemplate:[{type:R_}]});class Awt{}Awt.ɵfac=function t(e){return new(e||Awt)},Awt.ɵmod=fo({type:Awt}),Awt.ɵinj=Dn({imports:[[dT,rx,MX]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Awt,[{type:L_,args:[{declarations:[Ewt,Dwt],exports:[Ewt],imports:[dT,rx,MX]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(Awt,{declarations:[Ewt,Dwt],imports:[dT,rx,MX],exports:[Ewt]}),lo(Ewt,[Dwt],[Vv]);class Twt{constructor(t,e){this.store=t,this.document=e,this.onVisibilityChange=this.onVisibilityChangeImpl.bind(this),this.reloadEnabled$=this.store.pipe(rS(uI)),this.reloadPeriodInMs$=this.store.pipe(rS(gI)),this.reloadTimerId=null,this.missedAutoReload=!1}ngOnInit(){this.document.addEventListener("visibilitychange",this.onVisibilityChange),Kt(this.reloadEnabled$.pipe(we()),this.reloadPeriodInMs$.pipe(we())).subscribe((([t,e])=>{this.cancelLoad(),t&&this.load(e)}))}onVisibilityChangeImpl(){"visible"===this.document.visibilityState&&this.missedAutoReload&&(this.missedAutoReload=!1,this.store.dispatch(QE()))}load(t){this.reloadTimerId=setTimeout((()=>{"visible"===this.document.visibilityState?this.store.dispatch(QE()):this.missedAutoReload=!0,this.load(t)}),t)}cancelLoad(){null!==this.reloadTimerId&&clearTimeout(this.reloadTimerId),this.reloadTimerId=null}ngOnDestroy(){this.cancelLoad(),this.document.removeEventListener("visibilitychange",this.onVisibilityChange)}}Twt.ɵfac=function t(e){return new(e||Twt)(gf(iS),gf(sM))},Twt.ɵcmp=so({type:Twt,selectors:[["reloader"]],decls:0,vars:0,template:function t(e,n){},encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Twt,[{type:S_,args:[{selector:"reloader",template:"",changeDetection:jn.OnPush}]}],(function(){return[{type:iS},{type:Document,decorators:[{type:Lr,args:[sM]}]}]}),null);class Nwt{}Nwt.ɵfac=function t(e){return new(e||Nwt)},Nwt.ɵcmp=so({type:Nwt,selectors:[["tensorboard-wrapper-component"]],decls:2,vars:0,consts:[[1,"plugins"]],template:function t(e,n){1&e&&(vf(0,"plugins",0),vf(1,"reloader"))},directives:[Ewt,Twt],styles:["[_nghost-%COMP%] {\n        display: flex;\n        flex-direction: column;\n        height: 100%;\n      }\n\n      .plugins[_ngcontent-%COMP%] {\n        flex: 1 1;\n        overflow: auto;\n        position: relative;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Nwt,[{type:S_,args:[{selector:"tensorboard-wrapper-component",template:'\n    <plugins class="plugins"></plugins>\n    <reloader></reloader>\n  ',styles:["\n      :host {\n        display: flex;\n        flex-direction: column;\n        height: 100%;\n      }\n\n      .plugins {\n        flex: 1 1;\n        overflow: auto;\n        position: relative;\n      }\n    "],changeDetection:jn.OnPush}]}],null,null);class zwt{}zwt.ɵfac=function t(e){return new(e||zwt)},zwt.ɵprov=Sn({token:zwt,factory:zwt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(zwt,[{type:Um}],null,null);const Iwt="smoothing",Hwt="runColorGroup",Fwt="tagFilter",Lwt="runFilter",Bwt="regex:";let Vwt=class extends zwt{getMetricsPinnedCards(t){return Kt([t.select(cz),t.select(pz)]).pipe(Vt((([t,e])=>{if(!t.length&&!e.length)return[];const n=[...t.map((({plugin:t,tag:e,sample:n,runId:o})=>{const i={plugin:t,tag:e};return oN(t)&&(i.runId=o),eN(t)&&(i.sample=n),i})),...e];return[{key:"pinnedCards",value:JSON.stringify(n)}]})))}getFeatureFlagStates(t){return Kt([t.select(BD),t.select(ID)]).pipe(Vt((([t,e])=>{const n=t.map((t=>({key:hN,value:t})));return"boolean"==typeof e.enabledColorGroup&&n.push({key:_N,value:String(e.enabledColorGroup)}),"boolean"==typeof e.enabledColorGroupByRegex&&n.push({key:CN,value:String(e.enabledColorGroupByRegex)}),n})))}serializeStateToQueryParams(t){return Kt([this.getMetricsPinnedCards(t),t.select(Pz).pipe(Vt((t=>t?[{key:Fwt,value:t}]:[]))),this.getFeatureFlagStates(t),t.select(uz).pipe(Vt((t=>Number.isFinite(t.scalarSmoothing)?[{key:Iwt,value:String(t.scalarSmoothing)}]:[]))),t.select(Qz).pipe(Vt((t=>{if(!t)return[];let e;switch(t.key){case Lz.EXPERIMENT:e="experiment";break;case Lz.RUN:e="run";break;case Lz.REGEX:e=`regex:${t.regexString}`;break;default:throw new RangeError("Serialization not implemented")}return[{key:Hwt,value:e}]}))),t.select(tI).pipe(Vt((t=>t?[{key:Lwt,value:t}]:[])))]).pipe(Vt((t=>t.flat())))}deserializeQueryParams(t){let e=null,n=null,o=null,i=null,a=null;for(const{key:r,value:s}of t)switch(r){case"pinnedCards":e=jwt(s);break;case Iwt:n=Number(s);break;case Hwt:switch(s){case"experiment":i={key:Lz.EXPERIMENT};break;case"run":i={key:Lz.RUN}}if(s.startsWith(Bwt)){const t=s.slice(Bwt.length);i={key:Lz.REGEX,regexString:t}}break;case Fwt:o=s;break;case Lwt:a=s}return{metrics:{pinnedCards:e||[],smoothing:n,tagFilter:o},runs:{groupBy:i,regexFilter:a}}}};function jwt(t){let e;try{e=JSON.parse(t)}catch(t){return null}if(!Array.isArray(e))return null;const n=[];for(const t of e){const e="string"==typeof t.runId,i="number"==typeof t.sample,a="string"==typeof t.tag,r=e||void 0===t.runId,s=i||void 0===t.sample;if(!("string"==typeof t.plugin&&a&&r&&s))continue;if((o=t.plugin)!==KT.SCALARS&&o!==KT.HISTOGRAMS&&o!==KT.IMAGES)continue;if(!t.tag)continue;if(oN(t.plugin)){if(!t.runId)continue}else if(t.runId)continue;if(i){if(!eN(t.plugin))continue;if(!Number.isInteger(t.sample)||t.sample<0)continue}const l={plugin:t.plugin,tag:t.tag};e&&(l.runId=t.runId),i&&(l.sample=t.sample),n.push(l)}var o;return n}function Uwt(){return[{routeKind:_D.EXPERIMENT,path:"/",ngComponent:Nwt,defaultRoute:!0,deepLinkProvider:new Vwt}]}function Wwt(t){return(e,n)=>{const o=t(e,n);return console.groupCollapsed(n.type),console.log("prev state",e),console.log("action",n),console.log("next state",o),console.groupEnd(),o}}function Gwt(){return PC()?Wwt:t=>(e,n)=>t(e,n)}Vwt=(function Ywt(t,e,n,o){var i,a=arguments.length,r=a<3?e:null===o?o=Object.getOwnPropertyDescriptor(e,n):o;if("object"==typeof Reflect&&"function"==typeof Reflect.decorate)r=Reflect.decorate(t,e,n,o);else for(var s=t.length-1;s>=0;s--)(i=t[s])&&(r=(a<3?i(r):a>3?i(e,n,r):i(e,n))||r);return a>3&&r&&Object.defineProperty(e,n,r),r})([Um()],Vwt);const qwt=new er("Root reducers token",{factory:()=>({})});class Zwt{}Zwt.ɵfac=function t(e){return new(e||Zwt)},Zwt.ɵmod=fo({type:Zwt}),Zwt.ɵinj=Dn({providers:[{provide:Lk,useFactory:Gwt,multi:!0}],imports:[[TS.forRoot(qwt,{runtimeChecks:{strictStateImmutability:!0,strictActionImmutability:!0,strictActionSerializability:!1,strictStateSerializability:!1}}),hD.forRoot([])]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Zwt,[{type:L_,args:[{imports:[TS.forRoot(qwt,{runtimeChecks:{strictStateImmutability:!0,strictActionImmutability:!0,strictActionSerializability:!1,strictStateSerializability:!1}}),hD.forRoot([])],providers:[{provide:Lk,useFactory:Gwt,multi:!0}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(Zwt,{imports:[ES,uD]});class Xwt{}Xwt.ɵfac=function t(e){return new(e||Xwt)},Xwt.ɵmod=fo({type:Xwt}),Xwt.ɵinj=Dn({}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Xwt,[{type:L_,args:[{declarations:[Twt],exports:[Twt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(Xwt,{declarations:[Twt],exports:[Twt]});class Kwt{}Kwt.ɵfac=function t(e){return new(e||Kwt)},Kwt.ɵmod=fo({type:Kwt}),Kwt.ɵinj=Dn({imports:[[rx,Awt,Xwt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Kwt,[{type:L_,args:[{declarations:[Nwt],imports:[rx,Awt,Xwt],exports:[Nwt],entryComponents:[Nwt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(Kwt,{declarations:[Nwt],imports:[rx,Awt,Xwt],exports:[Nwt]});class Jwt{}Jwt.ɵfac=function t(e){return new(e||Jwt)},Jwt.ɵmod=fo({type:Jwt,bootstrap:[tX]}),Jwt.ɵinj=Dn({imports:[[IN,dO,hk,RR,nX,fR.registerRoutes(Uwt),FV,HV,oX,Kwt,dT,dX,aX,bX,LT,yX,rX,KR,WI,Awt,TI,gX,Zwt,fwt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&Qb(Jwt,[{type:L_,args:[{declarations:[tX],imports:[IN,dO,hk,RR,nX,fR.registerRoutes(Uwt),FV,HV,oX,Kwt,dT,dX,aX,bX,LT,yX,rX,KR,WI,Awt,TI,gX,Zwt,fwt],bootstrap:[tX]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&uo(Jwt,{declarations:[tX],imports:[IN,dO,hk,RR,nX,fR,FV,HV,oX,Kwt,dT,dX,aX,bX,LT,yX,rX,KR,WI,Awt,TI,gX,Zwt,fwt]}),"loading"!==document.readyState?aO().bootstrapModule(Jwt):window.addEventListener("DOMContentLoaded",(()=>{aO().bootstrapModule(Jwt)})),(function Qwt(){if(OC)throw new Error("Cannot enable prod mode after platform setup.");(void 0===typeof ngDevMode||ngDevMode)&&(Xn.ngDevMode=!1),xC=!1}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)()})();
",
+              "ok": true,
+              "headers": [
+                [
+                  "content-type",
+                  "application/javascript; charset=utf-8"
+                ]
+              ],
+              "status": 200,
+              "status_text": ""
+            },
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",
+              "ok": true,
+              "headers": [
+                [
+                  "content-type",
+                  "font/woff2"
+                ]
+              ],
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/data/runs": {
+              "data": "WyIuIl0=",
+              "ok": true,
+              "headers": [
+                [
+                  "content-type",
+                  "application/json"
+                ]
+              ],
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/data/plugin/scalars/tags": {
+              "data": "eyIuIjogeyJ0cmFpbmluZ19sb3NzIjogeyJkaXNwbGF5TmFtZSI6ICIiLCAiZGVzY3JpcHRpb24iOiAiIn19fQ==",
+              "ok": true,
+              "headers": [
+                [
+                  "content-type",
+                  "application/json"
+                ]
+              ],
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/data/plugin/scalars/scalars?run=.&tag=training_loss": {
+              "data": 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+              "ok": true,
+              "headers": [
+                [
+                  "content-type",
+                  "application/json"
+                ]
+              ],
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/font-roboto/Hgo13k-tfSpn0qi1SFdUfZBw1xU1rKptJj_0jans920.woff2": {
+              "data": 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",
+              "ok": true,
+              "headers": [
+                [
+                  "content-type",
+                  "font/woff2"
+                ]
+              ],
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/font-roboto/d-6IYplOFocCacKzxwXSOJBw1xU1rKptJj_0jans920.woff2": {
+              "data": 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",
+              "ok": true,
+              "headers": [
+                [
+                  "content-type",
+                  "font/woff2"
+                ]
+              ],
+              "status": 200,
+              "status_text": ""
+            }
+          },
+          "base_uri": "https://localhost:8080/",
+          "height": 822
+        },
+        "outputId": "c8a225eb-66e9-4ac3-cca8-eb15d4f10098"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "        (async () => {\n",
+              "            const url = new URL(await google.colab.kernel.proxyPort(6006, {'cache': true}));\n",
+              "            url.searchParams.set('tensorboardColab', 'true');\n",
+              "            const iframe = document.createElement('iframe');\n",
+              "            iframe.src = url;\n",
+              "            iframe.setAttribute('width', '100%');\n",
+              "            iframe.setAttribute('height', '800');\n",
+              "            iframe.setAttribute('frameborder', 0);\n",
+              "            document.body.appendChild(iframe);\n",
+              "        })();\n",
+              "    "
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "%tensorboard --logdir runs/our_experiment"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w3IVZMZF4_Ao"
+      },
+      "source": [
+        "<center><img src=\"https://github.com/phlippe/uvadlc_notebooks/blob/master/docs/tutorial_notebooks/tutorial2/tensorboard_screenshot.png?raw=1\" width=\"1100px\"></center>\n",
+        "\n",
+        "TensorBoard visualizations can help to identify possible issues with your model, and identify situations such as overfitting. You can also track the training progress while a model is training, since the logger automatically writes everything added to it to the logging file. Feel free to explore the TensorBoard functionalities, and we will make use of TensorBoards a couple of times from Tutorial 5 on."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.6"
+    },
+    "colab": {
+      "provenance": []
+    },
+    "accelerator": "GPU",
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "8f77981965c240ef808b7cdd8d352cce": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
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+            "grid_auto_flow": null,
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+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
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+            "max_height": null,
+            "max_width": null,
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+          }
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+        "93f473640a7e499893e12d4719720549": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        }
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
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diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_17_Introduction_To_Tensorflow.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_17_Introduction_To_Tensorflow.ipynb"
new file mode 100644
index 0000000..e208bfb
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_17_Introduction_To_Tensorflow.ipynb"
@@ -0,0 +1,449 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#Introduction to TensorFlow"
+      ],
+      "metadata": {
+        "id": "Qpme0Tri0S4v"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# !pip install --upgrade tensorflow"
+      ],
+      "metadata": {
+        "id": "59GGABpl0aox"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# %tensorflow_version 1.x\n",
+        "# !pip install numpy==1.19.5"
+      ],
+      "metadata": {
+        "id": "SntVcgFFEvaW"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import tensorflow as tf"
+      ],
+      "metadata": {
+        "id": "W9Brw_8l0fSO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tf.__version__"
+      ],
+      "metadata": {
+        "id": "dovvBhFhEjgl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "mnist = tf.keras.datasets.mnist"
+      ],
+      "metadata": {
+        "id": "4F0V7IuD1VZX"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
+        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
+        "x_train.shape"
+      ],
+      "metadata": {
+        "id": "BCpcDY8O1T7p"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "# %matplotlib inline #in case of jupyter notbook\n",
+        "m = 25\n",
+        "plt.figure()\n",
+        "plt.imshow(x_train[m])\n",
+        "print('label = ', y_train[m])\n",
+        "plt.colorbar()\n",
+        "plt.grid(False)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "fR88X1X91j0N"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = tf.keras.models.Sequential([\n",
+        "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
+        "  tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
+        "  tf.keras.layers.Dropout(0.2),\n",
+        "  tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
+        "])"
+      ],
+      "metadata": {
+        "id": "Qt33z5rF1YVh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.compile(optimizer='adam',\n",
+        "              loss='sparse_categorical_crossentropy',\n",
+        "              metrics=['accuracy'])"
+      ],
+      "metadata": {
+        "id": "iMuBRLk61abT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.fit(x_train, y_train, epochs=5)"
+      ],
+      "metadata": {
+        "id": "XCIp6Ckt1cOn"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.evaluate(x_test, y_test)"
+      ],
+      "metadata": {
+        "id": "k1Yi_tFH1eVZ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##Creating Tensors\n",
+        "### Constant Tensors\n",
+        "\n",
+        "|  Name | Description  |\n",
+        "|---    |:---|\n",
+        "|  [tf.zeros](https://www.tensorflow.org/api_docs/python/tf/zeros) | Creates a constant tensor of zeros of a given shape and type.  |\n",
+        "|  [tf.zeros\\_like](https://www.tensorflow.org/api_docs/python/tf/zeros_like) |  Creates a constant tensor of zeros of the same shape as the input tensor. |\n",
+        "| [tf.ones](https://www.tensorflow.org/api_docs/python/tf/ones)  | Creates a constant tensor of ones of a given shape and type.  |\n",
+        "| [tf.ones\\_like](https://www.tensorflow.org/api_docs/python/tf/ones_like)  | Creates a constant tensor of  ones of the same shape as the input tensor.  |\n",
+        "| [tf.linspace](https://www.tensorflow.org/api_docs/python/tf/linspace)  | Creates an evenly spaced tensor of values between supplied end points.  |\n",
+        "\n",
+        "The following example demonstrates some of these ops."
+      ],
+      "metadata": {
+        "id": "zD6yo_zo5J_T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create a bunch of zeros of a specific shape and type.\n",
+        "x = tf.zeros([2, 2], dtype=tf.float64)\n",
+        "print(\"tf.zeros example: %s\" % x)\n",
+        "\n",
+        "# tf.zeros_like is pretty useful. It creates a zero tensors which is\n",
+        "# shaped like some other tensor you supply.\n",
+        "x = tf.constant([[[1], [2]]])\n",
+        "zeros_like_x = tf.zeros_like(x, dtype=tf.float32)\n",
+        "\n",
+        "print(\"Shape(x) = %s \\nShape(zeros_like_x) = %s\" %\n",
+        "      (x.shape, zeros_like_x.shape))"
+      ],
+      "metadata": {
+        "id": "6xRJXTZP5Wu4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###Random Tensors\n",
+        "\n",
+        "  |  Name | Description  |\n",
+        "  |---    |:---|\n",
+        "  |  [tf.random.normal](https://www.tensorflow.org/api_docs/python/tf/random/normal) | Generates a constant tensor with independent normal entries.  |\n",
+        "  |  [tf.random.uniform](https://www.tensorflow.org/api_docs/python/tf/random/uniform) |  Generates a constant tensor with uniformly distributed elements. |\n",
+        "  |  [tf.random.gamma](https://www.tensorflow.org/api_docs/python/tf/random/gamma) |  Generates a constant tensor with gamma distributed elements. |\n",
+        "  |  [tf.random.shuffle](https://www.tensorflow.org/api_docs/python/tf/random/shuffle) |  Takes an input tensor and randomly permutes the entries along the first dimension. |"
+      ],
+      "metadata": {
+        "id": "_82De6bo50_f"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create a matrix with normally distributed entries.\n",
+        "x = tf.random.normal([1, 3], mean=1.0, stddev=4.0, dtype=tf.float64)\n",
+        "print(\"A random normal tensor: %s\" % x)\n",
+        "\n",
+        "# Randomly shuffle the first dimension of a tensor.\n",
+        "r = tf.random.shuffle([1, 2, 3, 4])\n",
+        "print(\"Random shuffle of [1,2,3,4]: %s\" % r)"
+      ],
+      "metadata": {
+        "id": "DX_3Sz4q6JTe"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Maths Ops\n",
+        "\n",
+        "- There is a whole suite of commonly needed math ops built in.\n",
+        "- The inline form of the op allows you to e.g. write x + y instead of tf.add(x, y).\n",
+        "\n",
+        "| Name | Description | Inline form |\n",
+        "| --- | --- | --- |\n",
+        "| [tf.math.add](https://www.tensorflow.org/api_docs/python/tf/math/add) | Adds two tensors element wise | + |\n",
+        "| [tf.math.subtract](https://www.tensorflow.org/api_docs/python/tf/math/subtract) | Subtracts two tensors element wise | - |\n",
+        "| [tf.math.multiply](https://www.tensorflow.org/api_docs/python/tf/math/multiply) | Multiplies two tensors element wise | * |\n",
+        "| [tf.math.divide](https://www.tensorflow.org/api_docs/python/tf/math/divide) | Divides two tensors element wise | / |\n",
+        "| [tf.math.mod](https://www.tensorflow.org/api_docs/python/tf/math/floormod) | Computes the remainder of division element wise | % |\n",
+        "\n",
+        "\n",
+        "- Note that the behaviour of \"/\" and \"//\" varies depending on python version and presence of `from __future__ import division`, to match how division behaves with ordinary python scalars.\n",
+        "- The following table lists some more commonly needed functions:\n",
+        "\n",
+        "| Name | Description |\n",
+        "| --- | --- |\n",
+        "| [tf.math.exp](https://www.tensorflow.org/api_docs/python/tf/math/exp) | The exponential of the argument element wise. |\n",
+        "| [tf.math.log](https://www.tensorflow.org/api_docs/python/tf/math/log) | The natural log element wise |\n",
+        "| [tf.math.sqrt](https://www.tensorflow.org/api_docs/python/tf/math/sqrt) | Square root element wise |\n",
+        "| [tf.math.round](https://www.tensorflow.org/api_docs/python/tf/math/round) | Rounds to the nearest integer element wise |\n",
+        "| [tf.math.maximum](https://www.tensorflow.org/api_docs/python/tf/math/maximum) | Maximum of two tensors element wise. |"
+      ],
+      "metadata": {
+        "id": "OVx9Kv8_6iru"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Matrix Ops\n",
+        "* Matrices are rank 2 tensors. There is a suite of ops for doing matrix manipulations.\n",
+        "\n",
+        "| Name | Description |\n",
+        "| --- | --- |\n",
+        "| [tf.linalg.matrix_diag](https://www.tensorflow.org/api_docs/python/tf/linalg/diag) | Creates a tensor from its diagonal |\n",
+        "| [tf.linalg.trace](https://www.tensorflow.org/api_docs/python/tf/linalg/trace) | Computes the sum of the diagonal elements of a matrix. |\n",
+        "| [tf.linalg.matrix\\_determinant](https://www.tensorflow.org/api_docs/python/tf/linalg/det) | Computes the determinant of a matrix (square only) |\n",
+        "| [tf.linalg.matmul](https://www.tensorflow.org/api_docs/python/tf/linalg/matmul) | Multiplies two matrices |\n",
+        "| [tf.linalg.matrix\\_inverse](https://www.tensorflow.org/api_docs/python/tf/linalg/inv) | Computes the inverse of the matrix (square only) |"
+      ],
+      "metadata": {
+        "id": "VYAwLcqf74JJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = tf.random.normal([3, 3], mean=1.0, stddev=4.0, dtype=tf.float64)\n",
+        "y = tf.random.normal([3, 3], mean=1.0, stddev=4.0, dtype=tf.float64)\n",
+        "print(\"x:\", x , \"y:\", y)\n"
+      ],
+      "metadata": {
+        "id": "90HSR9CBPOQM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Logical And Comparison Ops\n",
+        "\n",
+        "- Tensorflow has the full complement of logical operators you would expect.\n",
+        "- These are also overloaded so you can use their inline version.\n",
+        "- The ops most frequently used are as follows:\n",
+        "\n",
+        "| Name | Description | Inline form |\n",
+        "| --- | --- | --- |\n",
+        "| [tf.equal](https://www.tensorflow.org/api_docs/python/tf/math/equal) | Element wise equality | **None** |\n",
+        "| [tf.less](https://www.tensorflow.org/api_docs/python/tf/math/less) | Element wise less than | < |\n",
+        "| [tf.less\\_equal](https://www.tensorflow.org/api_docs/python/tf/math/less_equal) | Element wise less than or equal to | <= |\n",
+        "| [tf.greater](https://www.tensorflow.org/api_docs/python/tf/math/greater) |  Element wise greater than | > |\n",
+        "| [tf.greater\\_equal](https://www.tensorflow.org/api_docs/python/tf/math/greater_equal) | Element wise greater than or equal to | >= |\n",
+        "| [tf.logical\\_and](https://www.tensorflow.org/api_docs/python/tf/math/logical_and) | Element wise And | & |\n",
+        "| [tf.logical\\_or](https://www.tensorflow.org/api_docs/python/tf/math/logical_or) | Element wise Or | &#124; |\n",
+        "\n",
+        "- Note that tf.equal doesn't have an inline form. Comparing two tensors with == will use the default python comparison. It will **not** call tf.equal.\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "crM_pK2p70JX"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Aggregations and Scans\n",
+        "\n",
+        "Most of the ops we have seen so far, act on the input tensors in an element wise manner. Another important set of operators allow you to do aggregations on a whole tensor as well as scan the tensor.\n",
+        "\n",
+        "- Aggregations (or reductions) act on a tensor and produce a reduced dimension tensor. The main ops here are\n",
+        "\n",
+        "| Name | Description |\n",
+        "| --- | --- |\n",
+        "| [tf.reduce\\_sum](https://www.tensorflow.org/api_docs/python/tf/math/reduce_sum) | Sum of elements along all or some dimensions. |\n",
+        "| [tf.reduce\\_mean](https://www.tensorflow.org/api_docs/python/tf/math/reduce_mean) | Average of elements along all or some dimensions. |\n",
+        "| [tf.reduce\\_min](https://www.tensorflow.org/api_docs/python/tf/math/reduce_min) | Minimum of elements along all or some dimensions. |\n",
+        "| [tf.reduce\\_max](https://www.tensorflow.org/api_docs/python/tf/math/reduce_max) | Maximum of elements along all or some dimensions. |\n",
+        "\n",
+        "- and for boolean tensors only\n",
+        "\n",
+        "| Name | Description |\n",
+        "| --- | --- |\n",
+        "| [tf.reduce\\_any](https://www.tensorflow.org/api_docs/python/tf/math/reduce_any) | Result of logical OR along all or some dimensions. |\n",
+        "| [tf.reduce\\_all](https://www.tensorflow.org/api_docs/python/tf/math/reduce_all) | Result of logical AND along all or some dimensions. |\n",
+        "\n",
+        "\n",
+        "- Scan act on a tensor and produce a tensor of the same dimension.\n",
+        "\n",
+        "| Name | Description |\n",
+        "| --- | --- |\n",
+        "| [tf.cumsum](https://www.tensorflow.org/api_docs/python/tf/math/cumsum) | Cumulative sum of elements along an axis. |\n",
+        "| [tf.cumprod](https://www.tensorflow.org/api_docs/python/tf/math/cumprod) | Cumulative product of elements along an axis. |\n"
+      ],
+      "metadata": {
+        "id": "CBUBTjYF8DIb"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Quiz: Normal Density\n",
+        "\n",
+        "\n",
+        "- In the following mini-codelab, you are asked to compute the normal density using the ops you have seen so far.\n",
+        "- You first generate a sample of points at which you will evaluate the density.\n",
+        "- The points are generated using a normal distribution (need not be the same one whose density you are evaluating).\n",
+        "- This is done by the function **generate\\_normal\\_draws** below.\n",
+        "- The function **normal\\_density\\_at** computes the density at any given set of points.\n",
+        "- You have to complete the code of these two functions so they work as expected.\n",
+        "- Execute the code and check that the test passes.\n",
+        "\n",
+        "### Hints\n",
+        "- Recall that the normal density is given by  \n",
+        "  $f(x) = \\frac{1}{\\sqrt{2\\pi\\sigma^2}} e^{-\\frac{(x-\\mu)^2}{2\\sigma^2}}$\n",
+        "- Here $\\mu$ is the mean of the distribution and $\\sigma > 0$ is the standard deviation.\n",
+        "- Pay attention to the data types mentioned in the function documentations. You should ensure that your implementations respect the data types stated."
+      ],
+      "metadata": {
+        "id": "VCKnkKYA9shj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Mini-codelab: Compute the normal density.\n",
+        "\n",
+        "import numpy as np\n",
+        "import numpy.testing as npt\n",
+        "from scipy import stats\n",
+        "\n",
+        "def generate_normal_draws(shape, mean=0.0, stddev=1.0):\n",
+        "  \"\"\"Generates a tensor drawn from a 1D normal distribution.\n",
+        "\n",
+        "    Creates a constant tensor of the supplied shape whose elements are drawn\n",
+        "    independently from a normal distribution with the supplied parameters.\n",
+        "\n",
+        "    Args:\n",
+        "      shape: An int32 tensor. Specifies the shape of the return value.\n",
+        "      mean: A float32 value. The mean of the normal distribution.\n",
+        "      stddev: A positive float32 value. The standard deviation of the\n",
+        "        distribution.\n",
+        "\n",
+        "    Returns:\n",
+        "      A constant float32 tensor whose elements are normally distributed.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  # to-do: Complete this function.\n",
+        "  pass\n",
+        "\n",
+        "def normal_density_at(x, mean=0.0, stddev=1.0):\n",
+        "  \"\"\"Computes the normal density at the supplied points.\n",
+        "\n",
+        "    Args:\n",
+        "      x: A float32 tensor at which the density is to be computed.\n",
+        "      mean: A float32. The mean of the distribution.\n",
+        "      stddev: A positive float32. The standard deviation of the distribution.\n",
+        "\n",
+        "    Returns:\n",
+        "      A float32 tensor of the normal density evaluated at the supplied points.\n",
+        "  \"\"\"\n",
+        "\n",
+        "  # to-do: Complete this function. As a reminder, the normal density is\n",
+        "  # f(x) = exp(-(x-mu)^2/(2*stddev^2)) / sqrt(2 pi stddev^2).\n",
+        "  # The value of pi can be accessed as np.pi.\n",
+        "  pass\n",
+        "\n",
+        "def test():\n",
+        "  mu, sd = 1.1, 2.1\n",
+        "  x = generate_normal_draws([2, 3, 5], mean=mu, stddev=sd)\n",
+        "  pdf = normal_density_at(x)\n",
+        "  npt.assert_array_equal(x.shape, [2,3,5], 'Shape is incorrect')\n",
+        "  norm = stats.norm()\n",
+        "  npt.assert_allclose(pdf, norm.pdf(x), atol=1e-6)\n",
+        "  print (\"All good!\")\n",
+        "\n",
+        "test()"
+      ],
+      "metadata": {
+        "id": "tM4YAi-R9r9D"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "private_outputs": true,
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_18_OOP.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_18_OOP.ipynb"
new file mode 100644
index 0000000..35feb33
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_18_OOP.ipynb"
@@ -0,0 +1,183 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "class Imath:\n",
+        "  def __init__(self, v:int=1, c:int=2):\n",
+        "    self.val = v\n",
+        "    self.coef = c\n",
+        "  def mul(self):\n",
+        "    self.val *= self.coef\n",
+        "\n",
+        "  def __repr__(self):\n",
+        "    return str(self.val)"
+      ],
+      "metadata": {
+        "id": "gkscoZ8Y60i4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "a = Imath(v=10, c=3)"
+      ],
+      "metadata": {
+        "id": "rtiKyn0q_2ml"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "a.mul()"
+      ],
+      "metadata": {
+        "id": "K_vau4sj_8Jc"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "a.val"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Lw0a3DTBDwHH",
+        "outputId": "b8b9b38a-e4bf-4170-feb9-1a720de89525"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "30"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 84
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "type(a)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PvePcxjVAC_m",
+        "outputId": "68ff6d51-3767-4574-ac41-2d3baa2971b7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "__main__.Imath"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 85
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "id(a)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "r7J86N7vAFcJ",
+        "outputId": "02b86cc1-8a9c-43b5-abb3-13302ba9bc30"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "139969502435472"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 86
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "a"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "F9kzfJTFFP5h",
+        "outputId": "7e6b2cd0-8554-4f99-a542-d6c194eda2c6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "30"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 87
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(__name__)"
+      ],
+      "metadata": {
+        "id": "Lfed_5UwIFGb",
+        "outputId": "0569188c-4033-444f-b2df-58424538d176",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "__main__\n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_19_Eigen_Values_Vectors,_SVD.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_19_Eigen_Values_Vectors,_SVD.ipynb"
new file mode 100644
index 0000000..9887e2c
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_19_Eigen_Values_Vectors,_SVD.ipynb"
@@ -0,0 +1,274 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "https://web.mit.edu/be.400/www/SVD/Singular_Value_Decomposition.htm"
+      ],
+      "metadata": {
+        "id": "7QKkw4JhXOro"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lIYdn1woOS1n"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "from scipy import linalg\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "A = np.array([[2, 1],\n",
+        "              [0, 3]])"
+      ],
+      "metadata": {
+        "id": "zLrb1ebBTcwF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(A)"
+      ],
+      "metadata": {
+        "id": "h8ymPxq_TjpJ",
+        "outputId": "9df9a724-c5fa-45ba-e0c3-6665f04e10b5",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[2 1]\n",
+            " [0 3]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "L, X = np.linalg.eig(A)"
+      ],
+      "metadata": {
+        "id": "jrumppldTmzY"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('eigenvalues: L=', L)\n",
+        "print('eigenvectors: X=', X)"
+      ],
+      "metadata": {
+        "id": "puMd2OjzT2pX",
+        "outputId": "f6f8b91b-0e9c-4f83-b1d9-aa05aef71739",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "eigenvalues: L= [2. 3.]\n",
+            "eigenvectors: X= [[1.         0.70710678]\n",
+            " [0.         0.70710678]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "B = np.array([[2, 4],\n",
+        "              [1, 3],\n",
+        "              [0, 0],\n",
+        "              [0, 0]])"
+      ],
+      "metadata": {
+        "id": "AAGEB9E0U1MC"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "BT = B.transpose()"
+      ],
+      "metadata": {
+        "id": "dWlpdMNdVtGG"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "B@BT"
+      ],
+      "metadata": {
+        "id": "jbyBX-LoV2D8",
+        "outputId": "fc509ea1-3cc0-4d66-a92d-aeb3153c15a1",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[20, 14,  0,  0],\n",
+              "       [14, 10,  0,  0],\n",
+              "       [ 0,  0,  0,  0],\n",
+              "       [ 0,  0,  0,  0]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "L, X = np.linalg.eig(B@BT)"
+      ],
+      "metadata": {
+        "id": "QjdzcCY8ecDt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9kF71ySOegc4",
+        "outputId": "67ddb325-5637-42f0-a6d0-8cc9b547d33d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 0.81741556, -0.57604844,  0.        ,  0.        ],\n",
+              "       [ 0.57604844,  0.81741556,  0.        ,  0.        ],\n",
+              "       [ 0.        ,  0.        ,  1.        ,  0.        ],\n",
+              "       [ 0.        ,  0.        ,  0.        ,  1.        ]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "U, S, VT = linalg.svd(B)"
+      ],
+      "metadata": {
+        "id": "G05YP3iyT60l"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "U"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qHJrupRSehSk",
+        "outputId": "b8feb6e0-ad56-4157-c80f-a81ed85e2d19"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[-0.81741556, -0.57604844,  0.        ,  0.        ],\n",
+              "       [-0.57604844,  0.81741556,  0.        ,  0.        ],\n",
+              "       [ 0.        ,  0.        ,  1.        ,  0.        ],\n",
+              "       [ 0.        ,  0.        ,  0.        ,  1.        ]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print('U: ', U)\n",
+        "print('S: ', S)\n",
+        "print('VT: ', VT)"
+      ],
+      "metadata": {
+        "id": "4h8n_5VqVCZX",
+        "outputId": "1d02ac27-f051-4d40-a497-96a22fa80660",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "U:  [[-0.81741556 -0.57604844  0.          0.        ]\n",
+            " [-0.57604844  0.81741556  0.          0.        ]\n",
+            " [ 0.          0.          1.          0.        ]\n",
+            " [ 0.          0.          0.          1.        ]]\n",
+            "S:  [5.4649857  0.36596619]\n",
+            "VT:  [[-0.40455358 -0.9145143 ]\n",
+            " [-0.9145143   0.40455358]]\n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_1_Python_Introduction.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_1_Python_Introduction.ipynb"
new file mode 100644
index 0000000..6d3622e
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_1_Python_Introduction.ipynb"
@@ -0,0 +1,1103 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.0"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Yf5WDw7O5eDs"
+      },
+      "source": [
+        "#DS-Landers (Data Science and AI for All)\n",
+        "\n",
+        "By Reza Shokrzad"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Zw7dm_p72W__"
+      },
+      "source": [
+        "#Python Tutorial\n",
+        "\n",
+        "##Python\n",
+        "\n",
+        "Python is a high-level, dynamically typed multiparadigm programming language. Python code is often said to be almost like pseudocode, since it allows you to express very powerful ideas in very few lines of code while being very readable. As an example, here is an implementation of the classic quicksort algorithm in Python:\n",
+        "\n",
+        "**Shift+Enter -> to run**\n",
+        "\n",
+        "**Ctrl+Shift+Enter -> to run a single line**\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kdmA_aQR52Xf"
+      },
+      "source": [
+        "##Python versions\n",
+        "\n",
+        "There are currently two different supported versions of Python, 2.7 and 3.13. Somewhat confusingly, Python 3.0 introduced many backwards-incompatible changes to the language, so code written for 2.7 may not work under 3.13 and vice versa. For this laboratory all code will use Python 3.13.\n",
+        "\n",
+        "You can check your Python version at the command line by running `python --version`.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Basic data types\n",
+        "\n",
+        "Like most languages, Python has a number of basic types including integers, floats, booleans, and strings. These data types behave in ways that are familiar from other programming languages.\n",
+        "\n",
+        "###Numbers\n",
+        "\n",
+        "Integers and floats work as you would expect from other languages:"
+      ],
+      "metadata": {
+        "id": "T0ZR9nmyT0YH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!python --version"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ayejYnDjQ3b5",
+        "outputId": "fb673c10-20d7-48cb-e0a3-b0b5d7a5ab06"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Python 3.11.12\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NwiAvjv06UK4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b530076e-54b1-44c3-dea7-e875985ef8fc"
+      },
+      "source": [
+        "# code snippet\n",
+        "x = 3\n",
+        "print(type(x)) # Prints \"<class 'int'>\"\n",
+        "print(x)  # Prints \"3\"\n",
+        "print(x + 1)   # Addition; prints \"4\"\n",
+        "print(x - 1)   # Subtraction; prints \"2\"\n",
+        "print(x * 2)   # Multiplication; prints \"6\"\n",
+        "print(x ** 3)  # Exponentiation; prints \"27\"\n",
+        "x += 1\n",
+        "print(x)  # Prints \"4\"\n",
+        "x *= 2 # x = x * 2\n",
+        "print(x)  # Prints \"8\"\n",
+        "y = 2.5\n",
+        "print(type(y)) # Prints \"<class 'float'>\"\n",
+        "print(y, y + 1, y * 2, y ** 2) # Prints \"2.5 3.5 5.0 6.25\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'int'>\n",
+            "3\n",
+            "4\n",
+            "2\n",
+            "6\n",
+            "27\n",
+            "4\n",
+            "8\n",
+            "<class 'float'>\n",
+            "2.5 3.5 5.0 6.25\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XePU1zvG6iW0"
+      },
+      "source": [
+        "Note: that unlike many languages, Python does not have unary increment (`x++`) or decrement (`x--`) operators.\n",
+        "\n",
+        "Python also has built-in types for complex numbers; you can find all of the details [in the documentation](https://docs.python.org/3.7/library/stdtypes.html#numeric-types-int-float-complex).\n",
+        "\n",
+        "###Booleans\n",
+        "\n",
+        "Python implements all of the usual operators for Boolean logic, but uses English words rather than symbols (`&&`, `||`, etc.):\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uCplgjE36yBl",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "71288c7a-4509-45af-80da-b1af9b41c20f"
+      },
+      "source": [
+        "t = True\n",
+        "f = False\n",
+        "print(type(t)) # Prints \"<class 'bool'>\"\n",
+        "print(t and f) # Logical AND; prints \"False\"\n",
+        "print(t or f)  # Logical OR; prints \"True\"\n",
+        "print(not t)   # Logical NOT; prints \"False\"\n",
+        "print(t != f)  # Logical XOR; prints \"True\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'bool'>\n",
+            "False\n",
+            "True\n",
+            "False\n",
+            "True\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dGay5XyP67l_"
+      },
+      "source": [
+        "###Strings\n",
+        "\n",
+        "Python has great support for strings:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LhLrXu6U7BAg",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "28089cd4-2cf6-4343-e2af-c2900e079fc4"
+      },
+      "source": [
+        "h = 'hello'    # String literals can use single quotes\n",
+        "w = \"world\"    # or double quotes; it does not matter.\n",
+        "print(h)       # Prints \"hello\"\n",
+        "print(len(h))  # String length; prints \"5\"\n",
+        "hw = h + ' ' + w  # String concatenation\n",
+        "print(hw)  # prints \"hello world\"\n",
+        "hw12 = '%s %s %d' % (h, w, 5)  # sprintf style string formatting\n",
+        "print(hw12)  # prints \"hello world 12\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "hello\n",
+            "5\n",
+            "hello world\n",
+            "hello world 5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-8XeIBsB7G8o"
+      },
+      "source": [
+        "String objects have a bunch of useful methods; for example:\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SkB_bFXk7SNW",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "79d3df2e-df8a-4154-8a05-e37c794adf0b"
+      },
+      "source": [
+        "s = \"hello\"\n",
+        "print(s.capitalize())  # Capitalize a string; prints \"Hello\"\n",
+        "print(s.upper())       # Convert a string to uppercase; prints \"HELLO\" / lower()\n",
+        "print(s.rjust(7))      # Right-justify a string, padding with spaces; prints \"  hello\"\n",
+        "print(s.center(7))     # Center a string, padding with spaces; prints \" hello \"\n",
+        "print(s.replace('l', 'oo'))  # Replace all instances of one substring with another;\n",
+        "                             # prints \"he(ell)(ell)o\"\n",
+        "print('  world '.strip())  # Strip leading and trailing whitespace; prints \"world\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "world\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2Rr-s0dw7alf"
+      },
+      "source": [
+        "You can find a list of all string methods [in the documentation](https://docs.python.org/3.7/library/stdtypes.html#string-methods).\n",
+        "\n",
+        "## 2. Containers\n",
+        "\n",
+        "Python includes several built-in container types: lists, dictionaries, sets, and tuples.\n",
+        "\n",
+        "### 2.1. Lists\n",
+        "\n",
+        "A list is the Python equivalent of an array, but is resizeable and can contain elements of different types:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6FKyFx6X7wuQ",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "aa1b8a40-edf8-450d-890d-aaf1ccfdce00"
+      },
+      "source": [
+        "xs = [3, 1, 5]    # Create a list\n",
+        "print(xs, xs[2])  # Prints \"[3, 1, 2] 2\"\n",
+        "print(xs[-1])     # Negative indices count from the end of the list; prints \"2\"\n",
+        "xs[2] = 'foo'     # Lists can contain elements of different types\n",
+        "print(xs)         # Prints \"[3, 1, 'foo']\"\n",
+        "xs.append('bar')  # Add a new element to the end of the list\n",
+        "print(xs)         # Prints \"[3, 1, 'foo', 'bar']\"\n",
+        "x = xs.pop()      # Remove and return the last element of the list\n",
+        "print(x, xs)      # Prints \"bar [3, 1, 'foo']\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "bar [3, 1, 'foo']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LfkiNtNw76_v"
+      },
+      "source": [
+        "As usual, you can find all the details about lists [in the documentation](https://docs.python.org/3.7/tutorial/datastructures.html#more-on-lists).\n",
+        "\n",
+        "#### Slicing\n",
+        "\n",
+        "In addition to accessing list elements one at a time, Python provides concise syntax to access sublists; this is known as *slicing*:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "TvGnycL78mVD",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7a0640b3-abce-4238-d531-2c5fae96422b"
+      },
+      "source": [
+        "nums = list(range(5))   # range is a built-in function that creates a list of integers\n",
+        "print(nums)               # Prints \"[0, 1, 2, 3, 4]\"\n",
+        "print(nums[2:4])          # Get a slice from index 2 to 4 (exclusive); prints \"[2, 3]\"\n",
+        "print(nums[2:])           # Get a slice from index 2 to the end; prints \"[2, 3, 4]\"\n",
+        "print(nums[:2])           # Get a slice from the start to index 2 (exclusive); prints \"[0, 1]\"\n",
+        "print(nums[:])            # Get a slice of the whole list; prints \"[0, 1, 2, 3, 4]\"\n",
+        "print(nums[:-1])          # Slice indices can be negative; prints \"[0, 1, 2, 3]\"\n",
+        "nums[2:4] = [8, 9]        # Assign a new sublist to a slice\n",
+        "print(nums)               # Prints \"[0, 1, 8, 9, 4]\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0, 1, 8, 9, 4]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "P0iivb_Y8rnp"
+      },
+      "source": [
+        "We will see slicing again in the context of numpy arrays.\n",
+        "\n",
+        "#### Loops\n",
+        "\n",
+        "You can loop over the elements of a list like this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yPUSlyu58y-6",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a2b1f530-5cea-4bb7-d988-b3c809f0380d"
+      },
+      "source": [
+        "animals = ['cat', 'dog', 'monkey']\n",
+        "for a in animals:\n",
+        "  print(a)\n",
+        "  print(a + \"s\")\n",
+        "\n",
+        "print(a)\n",
+        "# Prints \"cat\", \"dog\", \"monkey\", each on its own line."
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "cat\n",
+            "cats\n",
+            "dog\n",
+            "dogs\n",
+            "monkey\n",
+            "monkeys\n",
+            "monkey\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TO6inPLA81UM"
+      },
+      "source": [
+        "If you want access to the index of each element within the body of a loop, use the built-in `enumerate` function:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Kvc_a47j89ds",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a99f21df-014d-40a8-e180-a716efe453b7"
+      },
+      "source": [
+        "animals = ['cat', 'dog', 'monkey']\n",
+        "for idx, animal in enumerate(animals):\n",
+        "    print('#%d: %s' % (idx + 1, animal))\n",
+        "# Prints \"#1: cat\", \"#2: dog\", \"#3: monkey\", each on its own line"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "#1: cat\n",
+            "#2: dog\n",
+            "#3: monkey\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NyfaDHBW9BtB"
+      },
+      "source": [
+        "####List comprehensions\n",
+        "\n",
+        "When programming, frequently we want to transform one type of data into another. As a simple example, consider the following code that computes square numbers:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xkonhGPU9IE7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "33d2c6ac-0a5e-4db7-8118-357c0a87e4ff"
+      },
+      "source": [
+        "nums = [0, 1, 2, 3, 4]\n",
+        "squares = []\n",
+        "for x in nums:\n",
+        "    squares.append(x ** 2)\n",
+        "print(squares)   # Prints [0, 1, 4, 9, 16]"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0, 1, 4, 9, 16]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "q0tcKCh79QNr"
+      },
+      "source": [
+        "You can make this code simpler using a **list comprehension**:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2pisAL3E9XcG",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2b8bdecf-4a32-410e-f247-a4516d80e524"
+      },
+      "source": [
+        "nums = [0, 1, 2, 3, 4]\n",
+        "squares = [x ** 2 for x in nums]\n",
+        "print(squares)   # Prints [0, 1, 4, 9, 16]\n",
+        "nums[0] = 2"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[2, 1, 2, 3, 4]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 130
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iyQWXYMn9ZPE"
+      },
+      "source": [
+        "List comprehensions can also contain conditions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "bflK2hns9bYI",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "17479b31-fb0e-4785-d554-74d0efa925f2"
+      },
+      "source": [
+        "nums = [0, 1, 2, 3, 4]\n",
+        "even_squares = [x ** 2 for x in nums if x % 2 == 0]\n",
+        "print(even_squares)  # Prints \"[0, 4, 16]\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0, 4, 16]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2MyAS4vo9epl"
+      },
+      "source": [
+        "### 2.2. Dictionaries\n",
+        "\n",
+        "A dictionary stores (key, value) pairs, similar to a `Map` in Java or an object in Javascript. You can use it like this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uNV3up_M9ukq",
+        "scrolled": true,
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bc3059bd-49ca-4f0d-de5a-6b89d87e1746"
+      },
+      "source": [
+        "d = {'cat': 'cute', 'dog': 'furry'}  # Create a new dictionary with some data\n",
+        "print(d['cat'])       # Get an entry from a dictionary; prints \"cute\"\n",
+        "print('cat' in d)     # Check if a dictionary has a given key; prints \"True\"\n",
+        "d['fish'] = 'wet'     # Set an entry in a dictionary\n",
+        "print(d['fish'])      # Prints \"wet\"\n",
+        "# print(d['monkey'])  # KeyError: 'monkey' not a key of d\n",
+        "print(d.get('monkey', 'N/A'))  # Get an element with a default; prints \"N/A\"\n",
+        "print(d.get('fish', 'N/A'))    # Get an element with a default; prints \"wet\"\n",
+        "del d['fish']         # Remove an element from a dictionary\n",
+        "print(d.get('fish', 'N/A')) # \"fish\" is no longer a key; prints \"N/A\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "cute\n",
+            "True\n",
+            "wet\n",
+            "N/A\n",
+            "wet\n",
+            "N/A\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lNMGvYcZ9x_T"
+      },
+      "source": [
+        "You can find all you need to know about dictionaries [in the documentation](https://docs.python.org/3.7/library/stdtypes.html#dict).\n",
+        "\n",
+        "####Loops\n",
+        "\n",
+        "It is easy to iterate over the keys in a dictionary:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "oow_R2x3-F36",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5afc79a7-421a-45a2-b19c-abdf2dc34018"
+      },
+      "source": [
+        "d = {'person': 2, 'cat': 4, 'spider': 8}\n",
+        "for animal in d:\n",
+        "    legs = d[animal]\n",
+        "    print('A %s has %d legs' % (animal, legs))\n",
+        "# Prints \"A person has 2 legs\", \"A cat has 4 legs\", \"A spider has 8 legs\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "A person has 2 legs\n",
+            "A cat has 4 legs\n",
+            "A spider has 8 legs\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "d.keys()\n",
+        "d.values()\n",
+        "d.items()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "b4D8hR79Zsvy",
+        "outputId": "d395375f-f975-4657-84ab-f4db9f24df76"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "dict_items([('person', 2), ('cat', 4), ('spider', 8)])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 98
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HrhqedOf-JOB"
+      },
+      "source": [
+        "If you want access to keys and their corresponding values, use the `items` method:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "XIlLwZVg-PSF",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9258446f-e01a-4f77-b24b-4ed395d9fc70"
+      },
+      "source": [
+        "d = {'person': 2, 'cat': 4, 'spider': 8}\n",
+        "for animal, legs in d.items():\n",
+        "    print('A %s has %d legs' % (animal, legs))\n",
+        "# Prints \"A person has 2 legs\", \"A cat has 4 legs\", \"A spider has 8 legs\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "A person has 2 legs\n",
+            "A cat has 4 legs\n",
+            "A spider has 8 legs\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TpOnH4OX-UW0"
+      },
+      "source": [
+        "####Dictionary comprehensions\n",
+        "\n",
+        "These are similar to list comprehensions, but allow you to easily construct dictionaries. For example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "TRkSL9xy-agB",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1e032d27-fc2f-4e7c-e960-91d404705fa7"
+      },
+      "source": [
+        "nums = [0, 1, 2, 3, 4]\n",
+        "even_num_to_square = {x: x ** 2 for x in nums if x % 2 == 0}\n",
+        "print(even_num_to_square)  # Prints \"{0: 0, 2: 4, 4: 16}\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{0: 0, 2: 4, 4: 16}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e1KTeO9L-dvv"
+      },
+      "source": [
+        "### 2.3. Sets\n",
+        "\n",
+        "A set is an unordered collection of distinct elements. As a simple example, consider the following:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "S5RHQxzU-zGT",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ce4e7da3-917e-496e-c47a-1abc224f2dca"
+      },
+      "source": [
+        "animals = {'cat', 'dog', 'cat'}\n",
+        "print('cat' in animals)   # Check if an element is in a set; prints \"True\"\n",
+        "print('fish' in animals)  # prints \"False\"\n",
+        "animals.add('fish')       # Add an element to a set\n",
+        "print('fish' in animals)  # Prints \"True\"\n",
+        "print(len(animals))       # Number of elements in a set; prints \"3\"\n",
+        "animals.add('cat')        # Adding an element that is already in the set does nothing\n",
+        "print(len(animals))       # Prints \"3\"\n",
+        "animals.remove('cat')     # Remove an element from a set\n",
+        "print(len(animals))       # Prints \"2\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-DiSqVj_-2wc"
+      },
+      "source": [
+        "As usual, everything you want to know about sets can be found [in the documentation](https://docs.python.org/3.7/library/stdtypes.html#set).\n",
+        "\n",
+        "####Loops\n",
+        "\n",
+        "Iterating over a set has the same syntax as iterating over a list; however since sets are unordered, you cannot make assumptions about the order in which you visit the elements of the set:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AuI0Qbje_YjJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b4e6fd09-8ad3-4844-eeb4-ddc05ce92833"
+      },
+      "source": [
+        "animals = {'cat', 'dog', 'fish'}\n",
+        "for idx, animal in enumerate(animals):\n",
+        "    print('#%d: %s' % (idx + 1, animal))\n",
+        "# Prints \"#1: fish\", \"#2: dog\", \"#3: cat\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "#1: cat\n",
+            "#2: dog\n",
+            "#3: fish\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tYGLgdyi_dQZ"
+      },
+      "source": [
+        "####Set comprehensions\n",
+        "\n",
+        "Like lists and dictionaries, we can easily construct sets using set comprehensions:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zf0iW5wS_iti",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a1b260b1-7530-4403-c854-09420e8ec4cc"
+      },
+      "source": [
+        "from math import sqrt\n",
+        "nums = {int(sqrt(x)) for x in range(30)}\n",
+        "print(nums)  # Prints \"{0, 1, 2, 3, 4, 5}\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{0, 1, 2, 3, 4, 5}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 123
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LSwiLjwF_qvE"
+      },
+      "source": [
+        "### 2.4. Tuples\n",
+        "\n",
+        "A tuple is an (immutable) ordered list of values. A tuple is in many ways similar to a list; one of the most important differences is that tuples can be used as keys in dictionaries and as elements of sets, while lists cannot. Here is a trivial example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SP2E-f2k_0_f",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "aa21e8ad-5708-4ab9-ff47-a1ca37f2a46d"
+      },
+      "source": [
+        "d = {(x, x + 1): x for x in range(10)}  # Create a dictionary with tuple keys\n",
+        "t = (5, 6)        # Create a tuple\n",
+        "print(type(t))    # Prints \"<class 'tuple'>\"\n",
+        "print(d[t])       # Prints \"5\"\n",
+        "print(d[(1, 2)])  # Prints \"1\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'tuple'>\n",
+            "5\n",
+            "1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5LaXPOYH_4U8"
+      },
+      "source": [
+        "[The documentation](https://docs.python.org/3.7/tutorial/datastructures.html#tuples-and-sequences) has more information about tuples.\n",
+        "\n",
+        "## 3. Functions\n",
+        "\n",
+        "Python functions are defined using the `def` keyword. For example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_ARQmuBZANYE",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "11d6f55e-b44d-40bc-c670-a18d3e50687d"
+      },
+      "source": [
+        "def sign(x):\n",
+        "    if x > 0:\n",
+        "        return 'positive'\n",
+        "    elif x < 0:\n",
+        "        return 'negative'\n",
+        "    else:\n",
+        "        return 'zero'\n",
+        "\n",
+        "for x in [-1, 0, 1]:\n",
+        "    print(sign(x))\n",
+        "# Prints \"negative\", \"zero\", \"positive\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "negative\n",
+            "zero\n",
+            "positive\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YSYs106KAP2K"
+      },
+      "source": [
+        "We will often define functions to take optional keyword arguments, like this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QMcHx8tRAWs2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c8fadb62-05b3-41b9-ee21-754d2c2c6bc9"
+      },
+      "source": [
+        "def hello(name, loud=False):\n",
+        "    if loud:\n",
+        "        print('HELLO, %s!' % name.upper())\n",
+        "    else:\n",
+        "        print('Hello, %s' % name)\n",
+        "\n",
+        "hello('Bob') # Prints \"Hello, Bob\"\n",
+        "hello('Fred', loud=True)  # Prints \"HELLO, FRED!\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Hello, Bob\n",
+            "HELLO, FRED!\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "br8jYDJM5rWd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0f85552a-f2bc-440e-9cf7-036f7be35252"
+      },
+      "source": [
+        "def quicksort(arr):\n",
+        "    if len(arr) <= 1:\n",
+        "        return arr\n",
+        "    pivot = arr[len(arr) // 2]\n",
+        "    left = [x for x in arr if x < pivot]\n",
+        "    middle = [x for x in arr if x == pivot]\n",
+        "    right = [x for x in arr if x > pivot]\n",
+        "    return quicksort(left) + middle + quicksort(right)\n",
+        "\n",
+        "print(quicksort([3,6,8,10,0,2,1]))\n",
+        "# Prints \"[1, 1, 2, 3, 6, 8, 10]\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0, 1, 2, 3, 6, 8, 10]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5oeapFkUAbfp"
+      },
+      "source": [
+        "There is a lot more information about Python functions [in the documentation](https://docs.python.org/3.7/tutorial/controlflow.html#defining-functions).\n",
+        "\n",
+        "## 4. Classes\n",
+        "\n",
+        "The syntax for defining classes in Python is straightforward:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rHxCQsEKAzYk",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6e0be067-8c28-47d0-b407-972d5606814e"
+      },
+      "source": [
+        "class Greeter(object):\n",
+        "\n",
+        "    # Constructor\n",
+        "    def __init__(self, name):\n",
+        "        self.name = name  # Create an instance variable\n",
+        "\n",
+        "    # Instance method\n",
+        "    def greet(self, loud=False):\n",
+        "        if loud:\n",
+        "            print('HELLO, %s!' % self.name.upper())\n",
+        "        else:\n",
+        "            print('Hello, %s' % self.name)\n",
+        "\n",
+        "g = Greeter('Fred')  # Construct an instance of the Greeter class\n",
+        "g.greet()            # Call an instance method; prints \"Hello, Fred\"\n",
+        "g.greet(loud=True)   # Call an instance method; prints \"HELLO, FRED!\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Hello, Fred\n",
+            "HELLO, FRED!\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_20_MonteCarloSimulation.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_20_MonteCarloSimulation.ipynb"
new file mode 100644
index 0000000..9d8be72
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_20_MonteCarloSimulation.ipynb"
@@ -0,0 +1,874 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z6_3Fx4jZexJ"
+      },
+      "source": [
+        "#The Monte Carlo Simulation Tutorial\n",
+        "\n",
+        "https://towardsai.net/monte-carlo-simulation \\\n",
+        "\n",
+        "\n",
+        "https://towardsdatascience.com/the-house-always-wins-monte-carlo-simulation-eb82787da2a3 \\\n",
+        "\n",
+        "https://towardsdatascience.com/how-to-design-monte-carlo-simulation-138e9214910a"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VrB1-X_zclHT",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 296
+        },
+        "outputId": "e781dcec-2323-40e3-cd2d-7f0756f6c6cb"
+      },
+      "source": [
+        "# 1. Coin Flip Example\n",
+        "#Import required libraries :\n",
+        "\n",
+        "import random\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#Coin flip function :\n",
+        "\n",
+        "#0 --> Heads\n",
+        "#1 --> Tails\n",
+        "\n",
+        "def coin_flip():\n",
+        "    return random.randint(0,1)\n",
+        "\n",
+        "#Monte Carlo Simulation :\n",
+        "\n",
+        "#Empty list to store the probability values.\n",
+        "list1 = []\n",
+        "\n",
+        "def monte_carlo(n):\n",
+        "    results = 0\n",
+        "    for i in range(n):\n",
+        "        flip_result = coin_flip()\n",
+        "        results = results + flip_result\n",
+        "\n",
+        "        #Calculating probability value :\n",
+        "        prob_value = results/(i+1)\n",
+        "\n",
+        "        #Append the probability values to the list :\n",
+        "        list1.append(prob_value)\n",
+        "\n",
+        "    #Plot the results :\n",
+        "    plt.axhline(y=0.5, color='r', linestyle='-')\n",
+        "    plt.xlabel(\"Iterations\")\n",
+        "    plt.ylabel(\"Probability\")\n",
+        "    plt.plot(list1)\n",
+        "\n",
+        "    return results/n\n",
+        "\n",
+        "\n",
+        "#Calling the function :\n",
+        "\n",
+        "answer = monte_carlo(5000)\n",
+        "print(\"Final value :\",answer)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Final value : 0.5022\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cVviTHDkcjVM"
+      },
+      "source": [
+        "# 2. Estimating PI using Circle and Square\n",
+        "# Run the following code in local machine to visualize the animation and get the output.\n",
+        "# Import required libraries :\n",
+        "import turtle\n",
+        "import random\n",
+        "import matplotlib.pyplot as plt\n",
+        "import math\n",
+        "\n",
+        "#To visualize the random points :\n",
+        "myPen = turtle.Turtle()\n",
+        "myPen.hideturtle()\n",
+        "myPen.speed(0)\n",
+        "\n",
+        "#Drawing a square :\n",
+        "myPen.up()\n",
+        "myPen.setposition(-100,-100)\n",
+        "myPen.down()\n",
+        "myPen.fd(200)\n",
+        "myPen.left(90)\n",
+        "myPen.fd(200)\n",
+        "\n",
+        "myPen.left(90)\n",
+        "myPen.fd(200)\n",
+        "myPen.left(90)\n",
+        "myPen.fd(200)\n",
+        "myPen.left(90)\n",
+        "\n",
+        "#Drawing a circle :\n",
+        "myPen.up()\n",
+        "myPen.setposition(0,-100)\n",
+        "myPen.down()\n",
+        "myPen.circle(100)\n",
+        "\n",
+        "#To count the points inside and outside the circle :\n",
+        "in_circle = 0\n",
+        "out_circle = 0\n",
+        "\n",
+        "#To store the values of PI :\n",
+        "pi_values = []\n",
+        "\n",
+        "#Running for 5 times :\n",
+        "for i in range(5):\n",
+        "    for j in range(1000):\n",
+        "\n",
+        "        #Generate random numbers :\n",
+        "        x=random.randrange(-100,100)\n",
+        "        y=random.randrange(-100,100)\n",
+        "\n",
+        "        #Check if the number lies outside the circle :\n",
+        "        if (x**2+y**2>100**2):\n",
+        "            myPen.color(\"black\")\n",
+        "            myPen.up()\n",
+        "            myPen.goto(x,y)\n",
+        "            myPen.down()\n",
+        "            myPen.dot()\n",
+        "            out_circle = out_circle+1\n",
+        "\n",
+        "        else:\n",
+        "            myPen.color(\"red\")\n",
+        "            myPen.up()\n",
+        "            myPen.goto(x,y)\n",
+        "            myPen.down()\n",
+        "            myPen.dot()\n",
+        "            in_circle = in_circle+1\n",
+        "\n",
+        "        #Calculating the value of PI :\n",
+        "        pi = 4.0 * in_circle / (in_circle + out_circle)\n",
+        "\n",
+        "        #Append the values of PI in list :\n",
+        "        pi_values.append(pi)\n",
+        "\n",
+        "        #Calculating the errors :\n",
+        "        avg_pi_errors = [abs(math.pi - pi) for pi in pi_values]\n",
+        "\n",
+        "    #Print the final value of PI for each iterations :\n",
+        "    print (pi_values[-1])\n",
+        "\n",
+        "#Plot the PI values :\n",
+        "plt.axhline(y=math.pi, color='g', linestyle='-')\n",
+        "plt.plot(pi_values)\n",
+        "plt.xlabel(\"Iterations\")\n",
+        "plt.ylabel(\"Value of PI\")\n",
+        "plt.show()\n",
+        "\n",
+        "#Plot the error in calculation :\n",
+        "plt.axhline(y=0.0, color='g', linestyle='-')\n",
+        "plt.plot(avg_pi_errors)\n",
+        "plt.xlabel(\"Iterations\")\n",
+        "plt.ylabel(\"Error\")\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AVJa1lD0dyPW",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "outputId": "2a25d5ba-6756-494e-a6a5-1aa1407e4aa2"
+      },
+      "source": [
+        "# 3. Monty Hall Problem\n",
+        "#Import required libraries :\n",
+        "import random\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#We are going with 3 doors :\n",
+        "#1 - Car\n",
+        "#2 - Goats\n",
+        "doors = [\"goat\",\"goat\",\"car\"]\n",
+        "\n",
+        "#Empty lists to store probability values :\n",
+        "switch_win_probability = []\n",
+        "stick_win_probability = []\n",
+        "\n",
+        "plt.axhline(y=0.66666, color='r', linestyle='-')\n",
+        "plt.axhline(y=0.33333, color='g', linestyle='-')\n",
+        "\n",
+        "#Monte_Carlo Simulation :\n",
+        "def monte_carlo(n):\n",
+        "\n",
+        "    #Calculating switch and stick wins :\n",
+        "    switch_wins = 0\n",
+        "    stick_wins = 0\n",
+        "\n",
+        "    for i in range(n):\n",
+        "\n",
+        "        #Randomly placing the car and goats behind the three doors :\n",
+        "        random.shuffle(doors)\n",
+        "\n",
+        "        #Contestant's choice :\n",
+        "        k = random.randrange(2)\n",
+        "\n",
+        "        #If the contestant doesn't get car :\n",
+        "        if doors[k] != 'car':\n",
+        "            switch_wins += 1\n",
+        "\n",
+        "        #If the contestant got car :\n",
+        "        else:\n",
+        "            stick_wins += 1\n",
+        "\n",
+        "        #Updating the list values :\n",
+        "        switch_win_probability.append(switch_wins/(i+1))\n",
+        "        stick_win_probability.append(stick_wins/(i+1))\n",
+        "\n",
+        "        #Plotting the data :\n",
+        "        plt.plot(switch_win_probability)\n",
+        "        plt.plot(stick_win_probability)\n",
+        "\n",
+        "    #Print the probability values :\n",
+        "    print('Winning probability if you always switch:',switch_win_probability[-1])\n",
+        "    print('Winning probability if you always stick to your original choice:', stick_win_probability[-1])\n",
+        "\n",
+        "\n",
+        "#Calling the function :\n",
+        "monte_carlo(1000)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Winning probability if you always switch: 0.655\n",
+            "Winning probability if you always stick to your original choice: 0.345\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ovKO042Rd5lk",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "outputId": "5de6bf78-f6f2-456f-855a-14736042fc5e"
+      },
+      "source": [
+        "# Buffon's Needle Problem\n",
+        "# Import required libraries :\n",
+        "import random\n",
+        "import math\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "#Main function to estimate PI value :\n",
+        "def monte_carlo(runs,needles,n_length,b_width):\n",
+        "    #Empty list to store pi values :\n",
+        "    pi_values = []\n",
+        "\n",
+        "    #Horizontal line for actual value of PI :\n",
+        "    plt.axhline(y=math.pi, color='r', linestyle='-')\n",
+        "\n",
+        "    #For all runs :\n",
+        "    for i in range(runs):\n",
+        "        #Initialize number of hits as 0.\n",
+        "        nhits = 0\n",
+        "\n",
+        "        #For all needles :\n",
+        "        for j in range(needles):\n",
+        "            #We will find the distance from the nearest vertical line :\n",
+        "            #Min = 0     Max = b_width/2\n",
+        "            x = random.uniform(0,b_width/2.0)\n",
+        "\n",
+        "            #The theta value will be from 0 to pi/2 :\n",
+        "            theta = random.uniform(0,math.pi/2)\n",
+        "\n",
+        "            #Checking if the needle crosses the line or not :\n",
+        "            xtip  = x - (n_length/2.0)*math.cos(theta)\n",
+        "            if xtip < 0 :\n",
+        "                nhits += 1\n",
+        "\n",
+        "        #Going with the formula :\n",
+        "        numerator = 2.0 * n_length * needles\n",
+        "        denominator = b_width * nhits\n",
+        "\n",
+        "        #Append the final value of pi :\n",
+        "        pi_values.append((numerator/denominator))\n",
+        "\n",
+        "    #Final pi value after all iterations :\n",
+        "    print(pi_values[-1])\n",
+        "\n",
+        "    #Plotting the graph :\n",
+        "    plt.plot(pi_values)\n",
+        "\n",
+        "#Total number of runs :\n",
+        "runs = 100\n",
+        "\n",
+        "#Total number of needles :\n",
+        "needles = 100000\n",
+        "\n",
+        "#Length of needle :\n",
+        "n_length = 2\n",
+        "\n",
+        "#space between 2 verical lines :\n",
+        "b_width =2\n",
+        "\n",
+        "#Calling the main function :\n",
+        "monte_carlo(runs,needles,n_length,b_width)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "3.148317224443535\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NDL_0FXNeLX-"
+      },
+      "source": [
+        "# Why Does the House Always Win?\n",
+        "#Import required libraries :\n",
+        "\n",
+        "import random\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DjU3-wGNeScU"
+      },
+      "source": [
+        "Rules:\n",
+        "1. There are chips containing numbers ranging from 1–100 in a bag.\n",
+        "2. Users can bet on even or odd chips.\n",
+        "3. In this game, 10 and 11 are special numbers. If we bet on evens, then 10 will be counted as an odd number, and if we bet on odds, then 11 will be counted as an even number.\n",
+        "4. If we bet on even numbers and we get 10 then we lose.\n",
+        "5. If we bet on odd numbers and we get 11 then we lose."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "UnNC0nKWeTpI",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 52
+        },
+        "outputId": "aa294ab9-073c-494b-85ad-0cf61a955790"
+      },
+      "source": [
+        "#Place your bet:\n",
+        "\n",
+        "#User can choose even or odd number :\n",
+        "choice = input(\"Do you want to bet on Even number or Odd number \\n\")\n",
+        "\n",
+        "#For even :\n",
+        "if choice==\"Even\":\n",
+        "    def pickNote():\n",
+        "        #Get random number between 1-100.\n",
+        "        note = random.randint(1,100)\n",
+        "\n",
+        "        #Check for our game conditions.\n",
+        "\n",
+        "        #Notice that 10 isn't considered as even number.\n",
+        "        if note%2!=0 or note==10:\n",
+        "            return False\n",
+        "        elif note%2==0:\n",
+        "            return True\n",
+        "\n",
+        "#For odd :\n",
+        "elif choice==\"Odd\":\n",
+        "    def pickNote():\n",
+        "        #Get random number between 1-100.\n",
+        "        note = random.randint(1,100)\n",
+        "\n",
+        "        #Check for our game conditions.\n",
+        "\n",
+        "        #Notice that 11 isn't considered as odd number.\n",
+        "        if note%2==0 or note==11:\n",
+        "            return False\n",
+        "        elif note%2==1:\n",
+        "            return True"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Do you want to bet on Even number or Odd number \n",
+            "Even\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "oFPvrcAaeXFo"
+      },
+      "source": [
+        "#Main function :\n",
+        "def play(total_money, bet_money, total_plays):\n",
+        "\n",
+        "    num_of_plays = []\n",
+        "    money = []\n",
+        "\n",
+        "    #Start with play number 1\n",
+        "    play = 1\n",
+        "\n",
+        "    for play in range(total_plays):\n",
+        "        #Win :\n",
+        "        if pickNote():\n",
+        "            #Add the money to our funds\n",
+        "            total_money = total_money + bet_money\n",
+        "            #Append the play number\n",
+        "            num_of_plays.append(play)\n",
+        "            #Append the new fund amount\n",
+        "            money.append(total_money)\n",
+        "\n",
+        "        #Lose :\n",
+        "        else:\n",
+        "            #Add the money to our funds\n",
+        "            total_money = total_money - bet_money\n",
+        "            #Append the play number\n",
+        "            num_of_plays.append(play)\n",
+        "            #Append the new fund amount\n",
+        "            money.append(total_money)\n",
+        "\n",
+        "    #Plot the data :\n",
+        "    plt.ylabel('Player Money in $')\n",
+        "    plt.xlabel('Number of bets')\n",
+        "    plt.plot(num_of_plays,money)\n",
+        "\n",
+        "    #Final value after all the iterations :\n",
+        "    final_funds.append(money[-1])\n",
+        "    return(final_funds)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "K6-Mxo3xeZZi",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 349
+        },
+        "outputId": "907821d9-aaab-4b39-a517-3bbbac625f6f"
+      },
+      "source": [
+        "#Create a list for calculating final funds\n",
+        "final_funds= []\n",
+        "\n",
+        "#Run 10 iterations :\n",
+        "for i in range(10):\n",
+        "    ending_fund = play(10000,100,50)\n",
+        "\n",
+        "print(ending_fund)\n",
+        "print(sum(ending_fund))\n",
+        "\n",
+        "#Print the money the player ends with\n",
+        "print(\"The player started with $10,000\")\n",
+        "print(\"The player left with $\",str(sum(ending_fund)/len(ending_fund)))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[10400, 9600, 8400, 10600, 10400, 10600, 8800, 10400, 9600, 9400]\n",
+            "98200\n",
+            "The player started with $10,000\n",
+            "The player left with $ 9820.0\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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/CnxZicjIvEIkrF1Lyt592EyehEmHDlrlwxIz+ERVj2/eGsXIGn047qd9cXxaJEmiytw5VFu9CtsZX5T7+QF8z5/m0o7N5Kqy2fPL96QnlR5QmZWWxu4F36LKSOfGwX3cOnpQ6xgXd2zG78IZ0pMS2PPLPHJU2jP4lmSLKA4h8uNmfHywX7AAfRcXreeXJIkq8+dTbfWqQjnNSiJXpeLYqUPkGhnSa+ZcTB2dtPbRMzGh9/cLeLNxc8zr1tcq/7TISkRG5hUh7cwZYn75FZOuXbAaM0arfIYql1EbrpGjEXzz/SfUrG7DlC03uRdd/lHXkp4eldq2LWKbKQ+iAwM48vsSqtapj/u3C8hKT2PPr/PILSGgUqNW8++Sn0iJjaX/1/NwbtyME3+tJMzHu8Qx7l2+wMXt/1CvbQc+mPoV0UEBHF31G9q284uzRZRE/B9/kLJ/PzZTpmDSoexxMwp9/bzvVkvcjBCCY3+sIPKeH10nfo59szfLPIZFbVfe/r85KMqh3PCTyEpERuYVIDswkPCp09CvUwf7efO07r8LIfh82y3uRqWw1L0J9e3NWD3MDQNdHUZt8CQp4+mNwS+D9KREdv/yPYZmZvSYOhM7l9p0+fQzIv3vcvzPFcUu8mc2reXB7Ru8O3IcDnUb0G3SdMxsq7B34Q8kx0QXkY8NCebg8oVUcXGl06gJuLi14O0BQ/E9d4qre3donaNOJWMcVqxAAkLHj0edllZEJvXkSWIXLsK0WzesRo96pu9CG9cP7OXO6WO07OtO7RbaMxC8KGQlIiPzklGnpBA2bjySnh7Vli9DYWSktc9vJwI44BXFl13q0N41z5XX3tyQlUObEp6UyYR/bpCrrji3zvIgNyeHPb/OIys1lV7Tv8bILM9+4NqqNS37DsL75FFuHNpXqM+d08e5tn8PTbp8QMMOnQEwMK5Er+lfo1Gr2bPgO3LyXWYBMlKS2f3zd+gbGdHj8/9Dme8226L3AGq3asNZj/UE3riqda561apRdcliVEHBREz/olARr+z794n4fDoGdes+lQH+aQi+dZ3Tf/+Jy5uteKufe7mf/3moMCUiSdJaSZJiJEnyfqzNUpKko5Ik3cv/1yK/vZ0kScmSJN3M/5n9WJ8ukiT5SZIUIEnSl4+1O0uSdDm/fYskSS+uGIGMTDkh1GrCp32OKjw8z23W3l5rn8N3olh41J/eTaoyum2NQsfcnCz5vlcDzgXE8cOBsifue9EIITj+5woi/e/SZdwUKjsVvo63+g3G5c2WnNrwBw9u3wQgwv8uR1f/RvUGjXhn2CeF5C3tq9J98hfEhYZwcMVChEaDOjeXfxf9SHpSAj0/n0UlC8sCeUmS6DJ2MjaOzuxfsoD48FCtczZu2TKviNfJk8QuWQo8FjdjYFBhcTOJkeH8u+QnrKpVp+uEqUgVsCX1PFTkbNYBXZ5o+xI4LoSoBRzP//yQs0KIxvk/3wJIkqQDLAe6AvUAd0mS6uXL/wQsEkK4AIlA4btKRuY/QMyvC0k/exa7r2dh5FbEe7IIflGpTN1ykzcczJjfp2GxT70D36zOh285sfZ8ENs8tS+OL4Mbh/bhffIoLfsMxLVVmyLHJYWCruOnYmnvwL+LfyTUx4u9v86jkpU170/5slgXXefGzWgz5EPuXb7ApZ1bOLl+DaE+XnQeMwk7l9pF5HUNDOg1fRY6urrsWfAdWcVsUz2JxdAhmPfvR/yqVSTv+7cgp5nDb0srJG4mOyOD3Qu+R1Lo0Gv6LPQMXr3gzgpTIkKIM0DCE809gYdlxNYDvSid5kCAECJQCKECNgM9pby/nA7A9qc4l8wryvH4FHzTMivs/EII9t6KIDK54sZ4FpL37CFh7VosBg/GYsAArfKJ6SpGbriKsb6SVfn2j5KY1b0ub7tY8X+7vLn2oGyp418UD7xucmrDH9R0a8lb/YeUKKdnaESvL2aDJLF17kxUWVn0mv41hiamJfZxe7839dq058K2Tdw6sh+3D/pQr03JRm5T68r0mPYVyTEx/Pn9t6hUpWdHliQJu6+/xrBpUyKmTyf9wgWqfDMbo6ZNtV/4U5KTm8uf339HYlQEPT77ErPKdlr7nL3lz96zN8p9LqXxot+LbIUQkfn/jwJsHzvWSpKkW5IkHZQk6aEfWlXg8UepsPw2KyBJCJH7RHuxSJI0WpIkT0mSPGOfI42BTPlzJC6ZobcD6XszgAeZ2l0un4U/zwUxyeMGw/68QmqW9hTqL4LM27eJ/Ho2Rs2bYzvzS63yOWoN4/+5TnRyNiuHNcPOzKBUeaWOgmXuTbEzM2DsxmtEJWeVKv+iSIqK5N9FP2Jp70C3MmzNmNva8cFnM6lkZU23iZ9jXc2xVHlJkug0eiLVGzSidou3aTNYewZjG5c6+NfsRFaQD4vmL9IqL+np4bB0Cfq1XLAaNbLC4mYW/7SUzPte+Dt3xKa2dtfca3eDObRzC57H9rDv3M0KmVNxvLTNtfyEXg9dL64DjkKIN4DfgN3lPNZqIYSbEMLN5iWU4ZQpHr/0LMb5PKCusQFqAR96BZGeW761HE77x/LDAV/cHC0IjktnyuabqDUvN0tDTkwMYRMmorSxoeqSxUi6ulr7zNvvy4X78fzQpyFNq1uUaRwLYz3+GOFGRnYuo//2JCun/OtkPA2qzAx2L/gOJIle079Gz1C7AwFA9QaNGL38L1zcWpRJXqmnR79Z8/hg6kwUCu1us7N33+FgjhPRVZuh9DnD+vXbS+0DeUW8nPfupfK0ikknsumf3ShunyDavgmHcmvw1U6vUt2RoxNS2LJlCxpJQZaOEReP/Yt3YESFzO1JXrQSiZYkqQpA/r8xAEKIFCFEWv7/DwC6kiRZA+FAtcf6O+S3xQPmkiQpn2iX+Y+QmJPLCK9ADHUUbGxUg1X1HfFLz2LS3RA05ZSKJygunYn/XKe2rQnrP27ONx/U4/jdGH494lcu538WNNnZhE2ciDotDYcVK1BaaFcIW66GsO5CMJ+0dqZfM4enGq+2rQmLBjbmdlgyX+64rTUuoqIQGg0Hli0kISKM96fMwNzu6ewHT+vxVFb59ReC2eIZyoT2Lnw7//9IsXAk6uDfnD5/rdznVFYuXL5J2N51pJhVY+78WXzWyZWdN8L542xQsfKqnFx+Xb0BfU0W7bv2ZOjgwQBs2LiJhJT0Cpnj47xoJbIXePh+OQLYAyBJkl2+nQNJkprnzyseuArUyvfE0gMGAXvz32JOAv2ePJfMq0+uRjDmTjARWTmsbeCMvYEe7SxN+cbFnv2xySwMLurr/7SkZOUwcv1VlDoK1gx3w1hfydCWjrg3r86KU/fZe+vFPKU9jhCCqG/mkHXrNvY//YiBa1Fj75N4Bicwa7c3bWpZM7NrnWcat3N9O6Z1qs3umxGsPhP4TOd4Xi5s28R9z0u0Gz4Sx4aNX8ocnuR8QBzf7felUz1bpnaqjYG+HmPmzCVb15izK34mKOTF3yMhYdEc/+1HspWGjJwzF0MDfSZ2cKFrAzvmH/TltH/R7fj5f2zFKCuOqo3eonOLBjRycaBFh+4YqNP5aeUG1BXs6l2RLr4ewEXAVZKkMEmSPgF+BDpJknQPeDf/M+QpA29Jkm4BS4FBD9PlAxOAw4AvsFUIcSe/zwxgqiRJAeTZSP6sqGuRKV++vR/BmcQ0fnR14E2zR3l/RjvYMMDOgl+Co9gfm/TM51drBFM23+RBfAYrhjSlmmXetokkScztUZ/mTpZ8sf0WXmHJz30tT0PC+vUk796N9YQJmHbqpFU+PCmTsRuvUdXckGXuTVHqPPuf64QOLnRvWIUfD93l5N2YZz7Ps+B38SyXdm6hQftONOnywQsduyQexKcz/p/r1LQxZtHAxigUeW8V9nbWdPnsK5RqFeu+/Ya09BfnjJGRmcWfc79BLzeLTpO/pJp9XvyPQiHxS/83qG1rwoR/rhMY+8iL7I/dJxDR/gjrmozt++ie6tm2CZaub2KYEc1Pf2kPqHweKtI7y10IUUUIoSuEcBBC/CmEiBdCdBRC1BJCvCuESMiXXSaEqC+EeEMI0VIIceGx8xwQQtQWQtQUQsx7rD1QCNFcCOEihOgvhKgYq6xMueIRGc/qsFhGOlgzuIpVoWOSJPFz7Wo0NTVigk8IPs/osbXgsB8n7sbwTY/6tKxReAw9pYIVQ5tiZazP6L89iUl9MQbntLPniPl5ASadO2M97lOt8pkqNaM3eJKVo+GPEW6YGWm3m5SGJEks6N+IunamTPK4QUCMdnfW8iA66D6HVizGvnZdOn4yrsK2gJ6GtOxcRm3Iy+S9ZrgblfQLuws3b9qAGv1GYZoayaI58yq0FsdDNBoNC+f+iGlKGNV6fUyrN98odNxYX8ma4W7o6igYucGTlKwcjnv68ODGWdL1LJk5elCRc04a1JUc8+qowu7w94FzFTb3VytqReZ/Gs/kdGb4hdHGohJzahbvTGego+CvBs6YKXUY4RVEvCq3WLmS2HMznJWn7zO4RXWGtSzek8e6kj6rhzcjMUPFpxuvk13OxvwnUQUHEz5tWl4Nifk/aPVIEkIwffstfCJTWOreGJfKJuUyDyM9JWtGuKGnVDB6gyfJmRXrqZaRnMSeBd9jYGJCj2lfoSyDA0FFo9EIPttyk/ux6Swf3BRHq+Iz4A7o1w2p6XsYhdxkxW9rK3xeq1ZuwDDIE/HGuwwe1KNYmWqWRqwY0pSQ+Awmrr/A0X93oVLo89no4RjoFf1uFQoFX40dQrrSDL/LJzhz3b9C5i4rERlUKhWbNm3i4sWLZe6zKSKeATcDSMop2yIfkaXiY+8gqujrsqq+E0pFyU+ktvq6/NXQmRhVDqPuBJNTRm+qHQExjAsKp04dK+Z8ULpLZH17M37p/wbXHiQye/edMhucj/pE03/lBcISM8oknxSbwOXBH5OtIS+i2bjktN0P+XXTAbJ8TzOtYw061LHVKv80VDU35PehzQhNzGCSx40ye6pde5BInxXn8Q4v2xZgWnoGC+b/SJyOPr0+n4Wxedk8yp6GxHQVQ/64xNanCKhcdMyfoz7R+XE01qXKTp42jrQq9ci8sIe9+0+U6fxCCPbv38++ffvK/Aaz4+/DpJ/eQbpVbT77YmKpsi1rWDHrvVpUC72KnlDTu19/7K1LTjdvbKDP+JHDACUndu3APyCqTHN6GmQl8pojhGD37t3cu3ePw4cP4+Pjo7XPmYRUvvAP5UxiGmPvPCBXy0KUqdbwoXcQ6WoN6xo6Y6mrPRNsE1MjfnWtxoWkNL4O0O5455OQxqSAUNTmeoS5GBOv1q7c3m9kz4T2LmzxDGX9hWDtY0SkMMnjBleDExm14RoZWt6ScnNyOffhOMwTo5nVeDDn07Vn5tly7DKp967ioJOMVezNCtlKae5sydweDTjtH8tPh7SnRolIymTM355cD0li9AZP4tJK3zlWq9X8+uPPCAMDVFZ27LpRvFfR85Cr1jDB4zrnA+KZudOLC/e1p8DffzuS304EMMDNgQ/fctIqr6PUYfLcr0k3subOxmXc8tb+JH/27FmuXr3KtWvXOH36tFb5a5fvErx/DQqFJdXNPiAnu/Tft0ajIf3KWZTKNMyT6mCRpv2hRC9ViU1ifZSSJbZ2ZlrlnxZZibzmnDlzBh8fHzp06EDVqlXZtWsX0dEle0cFZWQz+k4wLkYGfOdSlVOJqXxXij+6EIJpfqF4pWayop4jdSuVPW1DPztLxlWrzLrwOP6OKHmRSFHl0POiH2qlxMwqNmRqBB95BZNZBq+UqZ1q06meLd/t9+V8QMljxKdlM2qDJ6aGSn7u14i7USlM31a6y+yBKd9Q8/4tgt3HktuwKZM8bnA/tmRbhKdvMLfPHiFDaUK79h3x8/Pj1KlTWq/hWRjcojrDWzmy+kwgO6+HlSiXqVLnx5hoWDywMQkZKj7deA1Vbsnf7ZIlKxA6oJR0yTCwJvz2BQ5fLjlN+7Pw/X5fzgfEM+eDejhbGzN+03VCE0p+O7wTkczn227RzNGC73o1KLNtxtzMhEFffYNG0mH3gu+JSyjZ4ePu3bucOHGChg0b0rhxY06fPl3qQ1lMdCInlswHBG8MmEhmKhxe442mlPt2x/qDxLp+5vAAACAASURBVKaHUKtKY5yru3By411iHqSUKJ8Sn8mh1V5YW9gy/cuPMXuKv7+yIiuR1xhfX19OnjxJo0aNaNOmDQMHDkRfXx8PDw/S04v6l6fmqhnhFYQEbGjozKhqNnxS1ZpVobFsiXwyw00ey0Ni2BmdyAxnO96zfvqnoP+rWYUOlibM9A/jUlLRBVij0dD1+B1SjXT41NyCyXWqsryuIzdTM5juF6p1m0qhkFg0sDE1bYwZ/891HsQXve4ctYZxm64Tm5bN6mFuDHCrxpdd6rDfK5JlJwKKPe+J5X9T6/hO/Ju/ywezJ7J6eDP0dBSMWl+8LSI6IYWtW7eglhSM/mgo77RtTZMmTThz5gze3uW7AD/k6/fr0aqGFV/u9OJmaNHFUQjBFztucycihSWDGtOrSVUW9HuDq8GJfLPXu9jvdsv2vaQkx4Eqh+kzpzNt9HCyFQacOrgHv5Dnd90G2Ho1tCBu5sO3nVkz3A21RjByvSdp2UXfDuPSshm94RrmRrqsHNoMfWXpAYhPUqeWE24fT8EwK4lls+eQk1t0jJiYGHbu3Im9vT09evTg/fffx8HBgV27dhEVVXQLKVeVy7ov56JQJ+DYbRQde7vRbrArYXcTubDjfrHzOH/8OneCr2JpUBX3UT3oMroBRiZ6HPjdi/Tkom+HOdlqDq70Qp2jodunDdF/TueMkpCVyGtKdHQ0u3btomrVqnzwwQdIkoSpqSmDBg0iNTWVbdu2oVY/MjhrhGC8zwPuZ2axpoETjob6AMxxqUpr80pM9wvlWnLhBfhYfArzAiPpUdmcyY7PtrevI0n8Xs8RJ0N9PvEOJjSrcJ2M0ef8uW8ArdRKvnFzBqCLjRkznO3YHp3I76Ha09xUyvd8ARi1oehCNHffHS4HJfBz30a8US1v/3l02xr0blKVX4/6c+RO4UXi9olLWC7/meCqtemyagEADhZG/D60GSEJRW0Rqpxcfl21Hn1NFh279aSWgy2SJNG9e3eqVavG7t27iYyMpLzR1VGwfEhTKpvoM+ZvT2JSCnuqrTh1n323Ivi8sysd6+b9/j54w55x7WricSWUvy8VLtB09boXvreuQE4OoyZOQl9PD1tLUwYOHIhCaPhj/UZS0p/PG+7agwT+b7dXobgZZ2tjlg9pyr2YvOSUmse/21wN4zZeJy7/AcDGRP+Zxn3v3bcx69Afk9gAFs1fUuhYRkYGHh4e6OnpMWjQIHR1dVEqlQwcOBADA4NiH8qWfrkQnYwA9F270W94Xp7aum/Z06iDA7dOhOJ7ofDbfeDdUI6dOYC+ZMIn44eiUCgwNNGj27iGZGfkcGhVnrJ4iBCC4+t9iQtLo9Mn9bGw077t9azISuQ15PGbfuDAgeg+5jXj4OBAjx49CA4O5tChQwXtPwdFcSQ+hW9dqtLa4pG3kK5CYnUDJ6ro6/KRdxCR2XmL/L30LD69E0yDSoYsqlPtuVw7zXSVrGvoTI7Q8JFXEOn5ym2FVxj/5mRil6FhW/t6hfpMcbTlAxtzvr8fwfH4kl/3H+JoZczywU25H5vOZ48tRJsuP2DjpRDGtK1BryaPPMokSWJ+n4a84WDGZ1tu4heVV00wKiiM1M8/I83QhGZ/rUTf8FGOq+bOlnzbs6gtYv4fWzHKjsfhjdZ0at6goP3hQmRkZISHhwdpZcgy+7RY5qdGSc3KZfTf1wpSoxzzieaXI34FSuNxPu/sSsc6lZm7z6fAFhETG8/+7R6AROee/aha5dFDQ7M6TjR+5z2MclOZv3LDM9t58mwz14uNm2lTy4b/616PIz7RLD7+qEb7nH13uBKcwM/9GtHQ4fnsAWPGDCOrZnN0vE+ycdMuIM/+s23bNlJSUhg4cCCmpo+SQ5qYmDBo0CDS09PZunUruflvMOuWbEWEn0Fj3ohPvxldaIy3+7rgUMeCU//4ERWY58SQnJiGh8dmJCSGjRiCscmjLSlrBxM6jqhHVGAKpzz8Ct4Orx18wP3rMbTqVROnhqU7EDwvOnPmzKnQAV41Vq9ePWf06NHaBf9HUavVeHh4EBsby7Bhwygul5idnR0qlYrLly9TqVIlrugYMisgnCFVLJnhXKWIQjDUUdDaohLrI+I5m5hGJytT3G8FkitgexMXrItxP3xaLHWV1KtkyKrQWAIzVJjnCCYGhqOfIzjdvgGm+oXHkCSJjlYmHI9P5Z/IeLramGk16Fe3NMLUQMna88FohEBHkpjocYO2tW34ud8bKJ64bqWOgvauldlxPYyD3lF0dbXi5pCPsEiJp9Jvv+PcyLXIGA0dzIhPU/HX+WCqWxpx9tJVUu/fABsXPh9RNBG1np4ejo6OXLlyhZCQEBo2bFjuJU6tK+lT06YSf5wLIjI5ixrWxny07iq1bSuxZpgbusrC40mSRIc6lTl8J5rt18J4r64Nfyz+FXSV1Kr7Bt27dCwyRn3nqniGJKGJvsfVB8m83fjpou8zVWqGr71MXJqKf0a1wN686N5+k2rmhCdl8tf5YFxtTbgUlMDS4/f4tF1NPmldo5izPj1vvt2S4ycvkuV1FlHVFd87t/H29qZHjx64uhb9fZuammJubs6lS5fIyMgg5H4iQft+R6Nnx9jFP2BgWPjNSFJIODW0JuBaDH6Xo3BubMXalX+TqU7i/c59qNOo6HVY2hsjNILbJ8LQN9IlM1XFqU1+1G5uy9v9XMotNmfu3LmRc+bMWf1ku/Sycum8LNzc3ISnp+fLnkaxxMfHY2pqWujNoDQycjKIz4qnmkk17cL5HDhwgCtXrtCrVy8aNy45/YRGo+Gff/7hcnQce5u14w1TY7Y3roleKQvYodhkPvQOwkQhkSVge+OatDCvpHVOvg8isTarhI259niI5SExfHc/AkWuQAjBzgY1aGVfsotjaJaKLp7+mCt1ONCsFmZaFIkQghk7brPVMwwTfSU2pvrsGvc2ZoYl/06uhyQycOVFBoccY8C1Q8RO+Zp2Y0quPpej1jDsz8s8CAnlXR1fsg0s+f7zT9ErZW5eXl7s2LGDpk2bFmw/loXExESMjIzQ19e+jbP4mD+/HfOjsoGaHGUl9k54u9jF+iFBcen0XHaOdukXsTQGYyNzpn8xpUR5jUbD/y36E72UcOq27sKgTi3LdA1CCCZvvsm+2xH8MdytYGutOLJz1QxafYmQsBSyNYLmdWxYM9wNnVJcyp+W0IgY1n0xGY2JKcK2Ci1btqRLlydLJxXm6NGjnD9/Hv2oKHSTk+gx+2dq161eonx8RBo7frpGitF90vRDaOramh7u75YoLzSCg6u8CL4dh46eDpZ2RvSe1hSl3tPZf0pDkqRrQogiRW/k7axXhMDAQJYtW8amTZsK2SJKIludzcgjI+m5uye3Ym+VaYxr165x5coVWrVqVaoCgbxApbYf9ORwg5boZWWysJplqQoE8mwRfXLTSNUIPlFklUmBXLpzn41r17Bg6coyJYsbW9WaqukaNAqYY1+5VAUCUM1Ajz8bOBGSpWKszwPUWh6aJEniu14NcHO0ACkvork0BQLQtLoFvWtmsuqT4cyb9m2pCgTybBE/96hNO2UAKoU+U0YNK1WBADRs2JDWrVtz/fp1rl7VXs4VIDw8nOXLl/PXX3+hUmmvuT6hXU0GWj6gk7jJt+2tSlUgkGeLmOYUh6UxSLmCqdNKj3FQKBTMHDuMDKUJXueO4ukbXKbr+P10Xq6zx20zJaGv1GFht/oMTtJjeJoBP75fr1wVCEA1+8o0GzYKUdkWkZVN6zZttfZp0eItdNKzyLa1xfGDYaUqEAAr+0rU6KQkTT8Ee3OXUhUI5L3BvPtRPSztjdHT16Hr2IblqkBKQ1YirwAJCQls27YNY2PjIraI4hBCMPfCXLzivDDVM2XKySlEp5fu+fLgwQP2799PzZo16VSGvE0qjYYJ96NQ6Rvyvt91ju3YpnUhunnzJpXPH2PsrTNIJw9x7969UuXDY5PYtX0bGklR5mRxi47dI+5cJLMMzBjToGwZbVuaV+KH2lU5mZDKvPvaDdT6Sh3+GdWS09PbU9NGuyI8cu4SGx2dsMrM4ERNF/6JiC9VPicnh0N7d1BJD8Z9MqLUYLHH6dChA7Vr1+bgwYMEBZUee5GamsrmzZvR19cnKiqKPXv2aPVUO3bsKHoZcVSqZMzts4eJjy/9OnxvXCMo0A99TS7TZ85AR0f7omVqbMDoj4ailhRs3bqZqPjSgxeP+0az4LAfPYqxzRSHKiuXy3/7YaKnxFRScH69X7knIExOTubO7Wvo6hti8sCHRd/+qNXOs2rGjxiG+aLU0Sco7A6JiaUXCgsPD+fijRNUc6jOxxOKpjQpDj0DJf1muDF4TgsqWZReb6Y8kZXISyY7O5vNmzcjhOCjjz7irbfeKghWKokNPhvYF7iPcY3HsabzGjJyMphycgpZucV7viQlJbFlyxbMzc3p16+f1j11IQRf+YdzJTmdxXWrM65bZ6Kioti9e3eJC1FYWBj79u3DycmJL8eOxtbWlu3btxMXV3zsRZYqh8VrNqCnyebd9/tiVae51mRx+25FsOxkAO5vVmP8W86lXsOTDLO35sOq1qwIjWF7VPHuyI+jp1Rgaaw9ONA/IJDxyWocE+M4+1YD3rEwYYZ/GFeKcUeG/CqLe/cSERFB3z59cHQoe0p0hUJBnz59sLKyYuvWrSUuRLm5uWzZsoWsrCyGDh1Kx44duXPnDufOlZw/6caNG1y6dIkWLVowauRIJEnCw8ODrKzi76m46Ci279yFQmj4aPQYjIzLVhsEoJaDLR279UJfk83C1RtQlZD1ICAmlcmbb1Lf3pSf+jbSuoUnNHkeSQkR6XQZ3YAOw+oQcS+Jc1tKf5h5GlQqFZs3byYnJ4fRo0YiNeqAUfB1Vq5YX2KfFXNXo0i4gWTbkrHjRqNWq9m8eTPZ2cUHbT58ADAxMWGQ+0CUxZQCLgmlnk6FufKWhKxEXiIajYZdu3YRGxtL//79sbKy4t1338XFxYX9+/fz4MGDIn3OhZ9j4bWFdHLsxJhGY6hlUYv5bebjHe/N3ItziyzyD296tVqNu7s7hobag43+Co9jY2Q8kx1t6WVrQa1atejUqRM+Pj6cOXOmiHxKSkrBTd+/f38MDAwYNGgQOjo6eHh4kJlZNJHi/NWbMVYl4NikLR3d6jJxYBdyLRxLTBbnHZ7M9O23cHO04NueZQ8We5zvXKrylnklpvmFciOlbGlLSiMpOYXhN+4hCcGGZq5Ympuyqr4jDga6fOwdTFhW0Te3Cxcu4OXlRYcOHahT5+lTuxsYGODu7o4QAg8PjyIL0cO0G2FhYfTq1YsqVarQunVrGjRowPHjx/HzK1pLJTQ0lH///RdnZ2c6d+6MhYUFAwYMICEhgR07dhR5ylZlZ7P29+WoJQW9unfHzqHsNrmHdGpeH4c3WmOUHc8Pa7YUOZ6ckcPI9Z4Y6OqwepgbhmXYmrl6IJjAG7G81deF6vWsqN3cjiadq+N9JhzvM89fbujhA0BkZCR9+/bFxsaGKdMnkGpbh/SzO9l/sGiE+s6NR8n0+Re1YQ0m/Pw51tbW9O/fn5iYGHbv3l3ku83JySl4ABg0aBDGZUiT87J5KiUiSdLbkiR1kV6FVJz/A5w6dYq7d+/y3nvvUbNm3qu6QqGgb9++WFhYsGXLFpKSHgWBBSUH8cXpL6hlXovv3/4ehZT36+tQvQPjG4/n38B/WXdnXYG8EII9e/YQFRVVcNNr41xiKl8HhNPZypQZzo9qOr/11ls0atSIkydPcvfuI/fUhzd9dnY27u7uBTe9ubk5AwcOJDExschC9Pv2o0hx95FsazOyV/uC6545ZjDpuub4XT7B2VuPnh5jU7MZvcETSyM9fh/aDD3lsz376Cok1tR3orKeLh95BRGd/ewJCNVqNaP2nSTEwprlFnrUquGUd926StY3rEGWJs8dOeOxrRR/f3+OHj1K/fr1adOmzTOPbWVlRb9+/YiNjWXXrl2FvtvLly9z48YN2rZtS/36efnDJEmiZ8+eVKlShR07dhAT8ygVfEpKXkU8U1NT+vfvX7Al5ezsTNeuXbl37x7Hjx8vNP5fSxeTgQ4tGtajUctWz3wdY/p0BBsXiLnH6l2PxniY0iQ8KZOVQ5tqtc0A3L8ew9V/g6jT0o43Oj5Sai171cSxgRVnN/sT7v98tebPnTuHt7c3HTt2LPDE0lHqMOnbb0g3tOLWhqV4+zwKFLx+xY+gf1chdMwZ+sNs9PO9FF1cXOjUqRO+vr6FHsoefwDo3bs3dnbaa6q/CpT61yhJ0oaH9c4lSRoLLAMmItfueG7u3LnDmTNnaNy4MS1aFC77aWhoiLu7e8Frr0qlIlWVyqQTk1AqlCztsBQj3cLbB2MajaGzY2cWXVvE2bCzQF4enzt37vDuu+9Su7b2AkgPMrMZ5R1MTUMDltdzLOTSKkkSH3zwAfb29uzcuZPo6GiEEOzbt4/w8HD69OmDrW1ho6ejoyPdunUjICCAY8eOAXD4sjcRXhfIMLBm5sgBheSNDfSZ8MlwciQl/+7aTkh0AqpcDZ9uvEZChorVw589WOwhVnpK1jd0JkWt5iPvILKecb98zrZ/OVvVkZkp0XR6u3mhY7WNDfi9niPeaZl8djcEIQSxsbHs2LEDOzs7evbs+dxuly4uLnTu3Jm7d+8W5Gi6f/8+hw8fxtXVlXbt2hWS19XVLQiE8/DwICMjg5ycnIL7y93dHSOjwvfUm2++SbNmzTh//jy3b98GYPff64hMz6SauQld+5dtr740vho9iAx9K0JvnuPo1bwUIfMP3uXsvTi+79UANydLreeIC0vj2DofbJ1NeWeIa6HvVqGQ6PRJfUxtDDm02puUuGcrL+Dn58fx48dp0KABrVu3LnTM0tyEATO/AUlix0/fEp+YTGxMEsfzU5q0GT8De/vCsRqtWrWiUaNGnDp1Cl9fXwAuXbrEzZs3eeedd6hXr3Dc06tMiS6+kiQ5AgeBLoBEXt3ziUAYcCC/PUkIoT2S6xXiVXDxjYyMZO3atdja2vLhhx+WuOd57949Nm3aRN16dTllcYpLkZdY03kNbnZFvOyAPJffEYdGEJ4azvw68zm59yQNGzakT58+WhettFw171+/R1R2Dgeb1cbZqPjFOiUlhdWrV6NUKmnUqBFnzpyhXbt2RRatx9m/fz9Xr17lzbff4dz5C6glJdMmfoqtpWmx8mdu+nNklwfZemZItd5h6/UIfnNvwgdv2Jd6DU/D/tgkPvEOZoCdBUvqVH+qRd3j8Ak+07Okd0QQy917lmhj+u1BNPMCI5lezRq9g7vIzMxk9OjRmJuXzZCujYdvmjdv3qRz586cOXMGExMTRo4cWaJLb0hICOvXr8fR0RFjY2O8vLwYNGhQiVtrubm5bNiwgYiICNwa1OfSjZuY6MDkr2ahVJbP3ntEXBKLl69EIdRUbfk+v54K48O3nJjTo/RMzACZaSq2zfdEk6uh/1dvYmxW/HUnRWew7UdPTCwN6PtFM3T1y+65FBMTwx9//IGVlRUfffQRenrF28oOHj6D99oFpFeuhVE66KTfo9p7Yxnwcfdi5XNycli3bh0xMTF06NCBI0eOUKdOHfr371/usUDlwbO4+LYDKgEdgZ6AOVADeCe/XzvAqZzn+Z9j4ZZDjFu1pUxuuQA+/tf5/Y8l6CgVDBxYutHsoS3C18eXWJ9YZraYWaICATDSNWJJ+yWY5Zpx7N9j2FaxpUePHloXSI0QTPQN4V5GFqvrO5WoQCAveGrgwIGkpKRw5swZ6tatS9u2pbs4dunSherVHbl6/jQKoWHgoEElKhCAto1r49K8PcY5SYTevsC4djXKpEBSU1M5ePAgycna05V3tzHncyc7tkYlsvrqMa3yDzl67iJfKExoGBXG4r7dSv1jn1C9Mr0qm/NLSCw3pLzsAOWlQCDv7fBhjqYjR44gSRLu7u6lxoRUr16d7t27ExgYWCbbTEHUvKEhl27eQqlRM3LiZK0KRAjB9SMPuH9DexVFe2tz+g0YiFKoiTh3hLbOZszqXldrP5Uqh7+WbiUpPZaunzYqUYEAmNsa8d7I+iREpHF8vU+ZU/9nZmayefPmgje5khQIQNf32mLavh8mMf7opPujV6tLiQoE8t4OH+arO3z4MJUrV6ZXr16vpAIpjRJnK4RYT94bx2BgALBSCLEB2A7ECCE2CCFuv5hpvppsOnSORRZW7KztypQ/tZegTElN4q/taxEaXa5VOkCGSvtL3NGs+zwwfkCDxAZoQkr3LQcwV5jTPq49OVIOXg5eKMpQUvWX4CgOxiUzp2ZV2lpqD/jLMbDggsaFILUlvnp1tN70CoWCG7p1CFGb07R9V5q5Fl8s6nE+7N4WE8cGuCpjecciVfuc8rdmLl++XLBFo42p8YfpHnuauWmWnPIu6jDwJAGBwYxL0aCXm0N/dQq6WiLxJUni/XB/rNOSOFm/OVnW5VsbBB4t8q6urgwcOBBLS+3bP02bNqVjx460atWqTLYZPR0dTGJC0ctIxd3dHTML7WPcPhnGxZ33ObLmTplsEfXs7LHJrI++TjpuiX6UJbRj/YqtxKgCSLPxQWmi3b5Vvb4Vrfq4cP96LJ4HgrXKP0xpkpSUxMCBAzEz0542pcObXdAxaIlk8xbj5o7VKm9qaoq7uzu1a9dm0KBBZQoKfdXQtsKMAxYC3wghfspvswKmV+is/gN4+tznu2w1lslJNA64x46aLizbcaTUPvNWfoGxyposiwDuGSfyxfZe5OaWfPPv9rnE9pBF3LVMJV1pgte5I1zxKTk+QK1Ws337drLTs6nToQ5n486y5PqSEuUB9sUksTA4mkF2lox00J5jJyUrh5EbPInRscG64TusuxzGlqshpfZZceo+++4k8GbHD+jbruQ3qSf5bEQfateuzeHDh0qNi3jcNtOyZUsiIyPZu3dv6U+bDy6iODCNpdnnqZMdyZhIBYERJbuCZqRn4H7zHpl6+kxJDiciwF9rvYhbt27heeECXyizqKSny4degWUu4vU0mJiY4O7ujpOTU5n7tGnThvfee0+726wQHPp9MakPAnEfPJia9bRvMYXeTeD89gAcG1qVyRaRq1JzcOVtDLOtcbJuSEJWKNvWHSh1jL0exwhPuoe9RQ10lAo2b95cojvy4zR+txquLey4si+IwBulJ+c8evQogYGBvP/++1Svrv0BLj48jRMb7uJQrwuTFpUtbgagatWqDB48GAuL8i/c9SIoVYkIITRCiINCiOOPtYUIIS5X/NReXRKSU5l43ZdsXT0WONiwoU8nHKKjWGRQiRNXvIrt8/2KSRim26MyiWDBJA/6K5tx3SCLWRv6FivvGxPG7Eufo9CYsrHXMsZ8NBy1pMOObVuIiCu+psGRI0cKbvoP3/6Qga4D+evOX+y7v69Yee/UDCb5huBmasRPrg5aFxS1RjDZ4wYh8RmsGNKUn/o2pE0ta2bt9sYzuPjYi4eJ/Ho2tmfsO0+Xv+hhXISlpWWpcREXL17k9u3btGvXji5dutCxY0e8vb1LjotICoWtw8C8Osb9VvFXk7roCA0jbgeQklZ0DLVazZDdhwm1sGFqUgQTBvbTWi8iLCyMvXv34uTkxOD3OrO2gTPhWTmMKUMRr1eJyzu34H/pHG2GfIhz42Za5ZNjMzi82hsLOyM6f1Kf7uMaoVELDvzuRU520S1fIUR+TYxU3v2oHsPH98bK0AHfB56cO1K87fLaOW+u3z2Pqa4tH48fQv/+/YmLi2Pnzp1ag/4kSaLdUFcqO5lydJ0P8eHFx/PcvHmTS5cu0bx5c5o2bar1ujPTVOxfcRt9g/xocd0XEy3+KvDf2nx7BVCr1Xy4+QDBVeyZnJZM51aNqWxlzm8Na6FU5zItOIKQyMJPOOt2LEIVY066fjSzx+e9Gcx0X0vrbHP26wTx+66ZheRTszMZ/u94NFIGP7ZeSE0rO1yq2vDu+73R02SzeM3fRQK0bty4weXLl2nRogVNmjQBYEbzGbjZujHnwhy84wrXpIhT5fKhdxDmujqsbeCMfhn2YRcc9uOkXyxzetSnZQ0rlDoKlrk3paq5IWM3XiMiqfDT5r3oVKZsuUkDe7MyBYsVh7a4iICAAI4ePVrINlNqXIQqAzYPhtxscN8MhhY42tVkjYOSIF0bxp09hvqJqoiztuzmor0zXcPuM7Vfj0K2iOLqRTwZN6Ojo8ObZsb86OrA6cRUvrtfchGvV4mAq5c4v3Ujddu0x+393lrlVVm5HPjdCyTo9mkj9AyUmNsa0bkUW8SNoyH4X4mmRQ9najS2QaFQ8PH4IehLphw/f5D7voXfcsOCoth/dA+6GPHJp0NRKnWoUaMGXbp0wd/fn5MnT2qdp1JXh25jG6JnoMOB32+TmVZ46zM0NJR9+/bh7OzMe++9p/V8arWGw6u9yUhWabXN/C8iK5GnZNIfO7hS25XeAfeYMuBR0rUWDWvxlQ7EWFrx8YEzZKvytqnOeR7B/04M2co0xg+bioFBns+7QkeHn4fswzVbwdqkfRy9lBdwpdFo6L/tc7J0Ahle80u6uT56+nvXrR7VGucHaK3eXND+MFisRo0adO7cuaBdV6HLr+1+xcbIhsknJhObkafcVBoNI72DiFPl8lcDZyrra/ey2XMznJWn7zOkRXWGtnxk0zAz0uWPEW5k5WgY/bcnmaq8p82kDBUjN+QHiw1vhsFzPJk9HhfxeIBWXFwc27ZtK2KQlCSJHj16FMRFxMbmK3UhYM94iPKCvn+CzSO357frvcV3BmEcM6jFj6d2FrRvOniUdbbOuMaEs8a9Z0H74/UiNm/eXFAvoqS4GYDBVawY6WDNqrBYNkeWnlLkZRMXEsyBZb9iV7MWnUZPKFO0+NG1PiRGZdBlVAPMbB7FdjjWt6JV76K2iAfe8VzcdZ+aTSvTEqMFzAAAIABJREFUrKtTQbtxJUOGfzQEibxtquSEPJtYemomf6/fhEDg7j4Is8fsdw/fGM6ePVumIl7G5vp0HduQ9CQVh9d4F6RGKSlupjTObb1HuH8S7YfVwdapZIeR/1VkJfIULN12mJ0uLrzp78dvI/sVOT6ie1uGhTzAu0ZNRv25nfDoB+w9dABJSHRq3wYnh1qF5E2MzfnhvfWYaAQ/3vmWgBBvJh34jXD1GRpX6s8XbQcUGWN0745QuRbEBrBq5zGSk5PZvPn/2Tvv6Kiqrg8/dyYz6T2kUhISQhKS0AJSVIogTQEFFVEEQUFAxQKooK8VEVFURBGwICgqIEWQjogg0ksaIT0hCQnpmclk+vn+CC0mU1BA3u/1WeuuLO7sO+fMZebue8/e+7e/x8PDg5EjRzb60vs4+fBRn49QGVQ8s+cZdCYdL2cUcrC6lgVRLengYVuuIrGgiplrE+ka5sOrdzdeE4/wd+ejUR1IKaph5o+JGExmnlx1gnNVWpaM6UyQ599vyXmxLuJigZZWq+W7775DLpc3GZC8skHQpar5fe9Dyjro9ypE3tlojHHdhzLGcIaPZZGsP7qNY4nJzJZ54KNRsbpP10aZdBf7RVzZxGvz5s0W62YAXgsP4TZvN2aeKeBotW3ByX+COlUNG+a/idLZmaHTZ6NQ2r6zPrQpm9zEMm69L4LmUY0D7x36tyDyloD6WMTJUiqLa9nxRQp+zd24Y2x0IycV0iqAuwYMx0AdXyxeidFg5ItPvkUnVPS7dRCtoxpWyUuSxODBgy818Soqsv20FxjmSe+H21J4porf12Q2qJsZNWpUo7qZpkjZV0jy3kI69G9J21v+O4oDrzU2peAlSYqkPpDeCrj0KxJC9L2+U7s+/NU6kR+3bGMGnvhXlrN58O34eVu+43hg8XfsjYrmjqRdRFSoCG7ryKQHZ1m033bgW14+M5dmRgVnFWZ8pQ7sfvhLHCzcBekNRl5+bzGO2go8vTzQ19Xx2GOP4e/vb3GMXXm7ePbXZ4kMncbv5gSmtvTnlXDbabPF1RqGffI7DjI5Pz3ZE183yxeUT/ZkMn/7GWKCPEg9V8O7I+O5P+HqJTEsIYRgw4YNnDp1ioCAAEpLS3nkkUesBpTz8/NZvnw5oc3ceKjkTeRxI+DeZWDhzlqv13H/7i2cUATjWqej1tGJ1c0cuaVje4tjJCYmsm7dOgIDAykuLrZZN1NhMDLoaDp1ZjPbEyIJcrSt0XWjMBmNrJv7HwrTUrn/1XcIjrQtzZJxtIQdn6cQ3TOIPg9HWXxqMepNrH//OBXFGlw8lBi0Rka+mICHr+WbjE3f7+ZY2j4c8UBHDTGtErj/0bss2qvVapYurW95MXHiRNzcbAto7l+bwcld+bh0OEd+cabVupkrKcqoYuMHJ2ge7c2Qqe2RXWO14JuNvyMFvwY4DrxMvTO5uP3PUFFZyX/03siEnA/btbbqQAC+HHsPYcV5/NquDyovo1UHAjCwx0MM9RrJybAPMHs+wtoRH1t0IABKhQPPPj6GNm0PEt32S27v382qAwHo16ofw9uO56CpA+3k+cxqbVv4T6WrY+iPD1Pru4CPR8dYdSAAU3qHc3f7YFLP1fBoz9Br6kDgcl1ESEgIJSUlDBo0yGZGUsuWLRnYrjXZJTX8VHUXDP3YogMBUCodWdqlK846HZWu7rxeW2TVgQDEx8fTo0cPiouL7aqb8VE48HV8GLUmM+OScqi7xiqzf4e9K78gPzmR/hOfssuBlOar+OXr0wS29qTXqLZWl70clHIGPRGP0kmOulzLwIlxVh0IwN2j7iDEqw06amjm2pKRYwdbtXdzc2PUqFFoNBp++OGHS90ErdHjnnAcwyrIL84kIb67XQ6kpryObUuT8GjmzJ0T2v2/dyDWsMeJGIUQi4UQh4UQxy5u131mNxE+3t7clX2ckX9o+WPzVpv2m7e/z+1ZKbhr1OwI609BoXX5cZXRxM7gB5BkHlR43cHPibaDg7raHwgJTEfpqCX//Fuo66y3Ts2ozOP7ugQCKOZpw2wOZDRqUNaAi7GZOnkGMqciVmbNwyxsZ77MHxnP548kMHuw7WKxv4JCoeChhx7i4YcfpkuXLjbtTedy8Zk3hzbp6ZzyCufAiu9sHnP4828YkvgHr/y6hHHln9QH4W3Qr18/HnzwQe655x67isWiXJ35NKYVp1R1TD9z1u7it+tJ0i87OLFtE52HDKNdr8bdCf+MpkbPlsWJOLkpGDgpFrnC9ud283ZkxIzO3DO9E8Ft7Cu8HDvlfvp2H8KEJx+y69wGBwczfPhwzp49y5YtW2ye26zsLArrUnAVAZT84YyqwnqqsEFnYutn9T3NB0+Ou+GquTcb9jiRTZIkTZEkKUiSJJ+L23Wf2U3GvOcmE+B8GGVBR75dZrn24vCRH0lKrANZMV8FOaN2dGLcb8fRNKFkC/XV4lNT88iu0/F9+3D6aTN4uS6EA6cPWByjrGwPWVnz8fcfgsznHZo55fPjnikW0xtr9GrGJKZiQs6S6CC0ZjPV+fPJOP+7xTEuxmbau41kesLz7MrfxZJTSyzaX8RJIadfTECD/tfXGhcXFyIiImzaCZ2WwkdHYFQLhj00kKC6Onbl5pD5yy8Wjzm66juOauvorFHzxH39oeAw/PxcfVDeCjKZjLZt21qtaP4zA/w8eTEskB9LKvn0rPWahetNYVoquz7/lFbxHbn9ofE27U1GM9uWJqFVGxh8lRlJHn7OBLa2v9+5Uqng9gFdGrWStUZsbCy33XabzSZeZWVlrF27lsDAQB6ZMAqzQbBlcSIGfdMKFELUy82XFajpP6Ed3oE3v8ru9caeX/pY6pevDgDHLmw3Z3/Z68zU56ei8kyn/Hg0v+1Y3ej1osIU1m07iElm4IlRY+ie0JX5ch3JgSFM+3Fbkxf5eTnF7Civ4Y2IEG739eTT2/oRZijlsbMG8ouzGtnX1maSnPIM7m4xxETP446OIyiTxtHc6Xd++HVuI3uT2cT4IzvIMwXwQZigS1BnunRcjkDi5KnHqdIUNzpm2ZFt/Fr2BT50YvnwlxkTM4ah4UP59NSn7MqzXyLkn+b80yOpzdUQ+NhduA15hAenTcNFb2Dtzp2UZzU+t3l//MHW1BSaaeoY+dJLyOJGwO0z4MQ3cMi2A/0rTGsVwFB/L97KKmJ3+T8jQ1dTVspPC97G09+fu6a9gMxGRpIQgt9+SOdcZjV9x0bTrKVtlYN/gj59+lxq4pWdnd3o9bq6ugbJGQEtvek/oR1lBWp+WXG6ySeYY1tzyTp+nu73hBMaZ7s4938Bm05ECBHWxHZtut7/l6F0cmb8MwPQKis5sklJdtrlVT2tVsXClYtRmJwY2DuGsNb1Sy339e/F5PP5bAoJ46P1DZfCNpRU8lFeCQ8H+TI+pP4L6eHmzdfxEZgkOeNOnKZWc/nCYjBUcypxEjKZI/HxnyGX168nP9B7FgXanviJr9h1fF2DMV49tZn9+tY85VfIsNCeALT264Jf2Gw8ZTo2/zEco+lynvyBvDQWJv0HhSmINSMW4iCXI0kS/+n+H+L94pm1fxbplenX8KxeH6o+nEnF3iy8b2+N13PvAeARHMyokSMwyuWsWrIUneqynEp1QQGrN25EaTTx4OQnUF7MzOk9C9oOge2zIPvXaz5PSZL4IKoF7dycmZyaS6bGdtX1tcSg07Lxvbcw6nUMm/4KTnYEopP3FpK6r4hOA1vRJuHaS7lcKy4Wq/r5+bFmzRoqKi4XxJrNZn788UcqKysbaJqFxvnRfXg4mUfPc2xbw34+2SdLOfRTDpG3BNCxv+0K9v8VLDoRSZL6Xvh7b1ObrTeWJOlLSZLOS5KUfMU+H0mSdkqSlHHhr/eF/ZIkSQslScqUJClRkqROVxwz9oJ9hiRJY6/Y31mSpKQLxyy8UT1O/IPCGPiwHzKTktXLklFX1+f7v7tkJk5aP8KjBL1uG9vgmJdHDqFPYS7zvYLY+ttBABJVGp5Ny+cWT1fejgxpEJBsHdyGJUFm0hyDeHrvNswmE2azkeSUaWi1hcTHfYqT0+XMKplMxog+n1KqbYmu7BVS8+t7rn+X9SufV7Wiv1MWL8Y2FILrHj4OjdfdBEmlrD04BoBiVSVTdj0FSHzWfxH+bpeXHBzljnzY50PcFe48/cvTVGr/Xm+G64lm+yqKl/6Ea6gLAR831DRr3qULQ+LjKXdxZs28eZjNZoxaLd99/DFahYL7Bg3EJ+yKrokyGdy7BPwiYfVYqGh8R/t3cZXLWR4XhkKSMTYxh+rrII3SFEIItn+2kPO52Qx5eia+djSXKjhTyf7VGYTG+dJt6M1/L3mxQZoQokE3wV27dpGZmcngwYNp1aqhllvHO1vSpksAh37KJiexvjNneaGaXV+l4t/KnT4PWc5A+1/EmhT860KIVyVJ+qqJl4UQwurCqSRJtwNqYIUQIvbCvneBCiHEO5IkvQh4CyFekCRpMPUy84OBW4CPhBC3XIi9HAUSAEH9UlpnIUSlJEmHgaeBQ9QLRS4UQtiMel8rKfhNaz4nb3commYpeLYp5Fy2Cwrf88x+6tMm7VU1KgZu+50SV3eWhwfwdIUOCdiWEEkzC0J+S/at51VjGDPIYGjzDPLPfkFU2zmEhDTdxyHvfDYnjt+L3uyMf8wCxuZItJKVs7VHX1wVjbNgzGYzq/YNJsiUgc73EeaezKBSJPFc7HzGJzSuowBILktm7NaxtPdvz5L+S1DIbq6goiH9JDkPjEKmkAjbuBV5UGiTdlvnz+dQbS09PT2prK4mFRgUFsYtY8c2aU9FNizrC24BMGEnOF37orKDVWruO5nFrd5ufBPfGvl1vlAdWr+a/d+v4NYHx3LL8Pts2teU1bFm7lGc3RWMfCEBpbP9bVv/abKysvjmm29o27YtUVFRbNiwgS5dujBkSNMqu0a9iXXvHaeqRMNdT7Zn99epGPVm7nupC27e/1sV6Re56hRfIcSrF/4+2sRmM/ImhPgN+LOg0jDgYjPir4HhV+xfIeo5CHhJkhQEDAB2CiEqhBCVwE5g4IXXPIQQB0W9F1xxxXvdEO6+7zEc2p7Az+xIXWUVOudSZk5aYNHe3cOdr7tEIxOCkefUVOgNLI8Ls+hAACb2HMb9+jPMpw0/nk2mecgYiw4EoJV/awJDP0Cm1PNUdg0uaPmmU8cmHQjUP8Hc1+NHSsweSKUrcFOc4A7/xy06EIBYv1he6/EaR4qPMG/7ZIt2f0YIM8XFP6HXN91zvUlKUiDbusjhlZhVlRQ8PgZhgBYff2jRgQAMeP55wvR6fr/gQDoqFJYdCIBPa7hvOZRlwPpJYEOj6a/QzcuNtyND2FOhYsZOy4kVf8ZsFqQfLkZba3+nxqxjh9n/w0qievai67DGhbN/pl7SJBEhRL2kyX+RAwEIDw+/1MRrw4YNhIaGMnDgQIv2Dko5gyfH4eAoZ/37x1FX6Rj0RNz/rAOxxo2uWA8QQlzMdy0GLi6ohgBnr7AruLDP2v6CJvY3iSRJEyVJOipJ0tFLEhjXgLvv7k/wbR8TG7ubEVFxKJROVu1bt2rBy2nl+OrMPH8ol2gbfaMlmYyHFEbCzeksFk+Tc9Z2K9LOkb1Z6PABlfhwl1ZDS3frBYWOCldkHp+gNsmZ1EzinT6270jvDr6VR3Uyfjh/iDU7nrVpD5CT+wkpqc9y8tRjmEx2rPuXZ8FXg2HlPXbFIoTZzLnHhqItMRA8czyOt1jXPJLJZDwwcyb+dXWE6fXcNXOm7Tm17g0D58KZLbBnjm37v8BIuZEeWVWsUriycONOu445tDGLnV+msuXTRExG286tvCCfLR/PJyAsnDsnPWWXpMnu5aepKKrlzsfa4RVgu5L7ZqRbt2506dKFZs2a2SVp4ubtxOAn4nDxUNL34airyij7X+Ifkz258ARxQ5LjhRBLhRAJQogEe/qM20NtdQUnUychyUzI1cHUOH9MwSnLqYQAZ77cSd8yH77flcU9lb4kzV5u1b4wM4nK2jeZqv4QhVbPtFpHstIty5UD/CezkHThwe1aR9bsFaw9VmDVPrmwmtc2VbOr6DU8FZCS+iRms5W6CLMJ1k5gWnEht5qVvF20k2OnvrZsD5wv3U5Ozod4enZCpUoiLW2W9dx9bU29SKIkgW+4XbGIilfGU3OqjGbDE3B/xA6HADh5efHE3LmMfftt5Ao7l+W6ToSOY2Dfe5C8zrb9VWDS69j23nbuOKalTamaea5+7Dpw2Oox6YeLOb49n6AIT85lVfPb9+lWz22dWsWG+W/ioHRk2PSXUThav/EBOPJzvVRJz5FtaBnje9Wf62ZBkiSGDBnClClTGmiaWSOwtSfj5vWkbTfbxbn/q9xoJ1JyYSmKC38vtj0rBK6M6jW/sM/a/uZN7L8hmEwm9ux6BEfXMvwcphPb7nMkk5Ks/GnUFDdOmQXI33kM1wwnKhVlxMy9h1JtGs0cYkh5t+kLsEZVxYnjjyBTmugQ8DLzhAq1iyujj6ZRq266sPCbonK+KizjiRbN+HpALD3CfZm1Lonj+U0HwUtVOh5fcRQfFyVz7htBTMy7VFcf58yZ1yxfiHb+B7J2Ix/yHvPu3UBzs4znjs+nqKjpOJNafYbU1Ol4eLSnY4dvaB32LMUlG8nPX9b0+5vNsG5i/bLRfV/D6B/qncl3o0HXdHMq9aoPOP/jQTxiffF9e0XT72uBq+4iJ0kw5H1ocQtsmALnTl3d8VbY//63FKla0O8OHd/3DMdDa2RylYzsnNwm7c/n1fDLyjSCIjwZ9kxHOg1sRer+IpL3Nv1TMJtMbP5wHqqyUoY+Pxt3X9spqlnHz3Pk51yiugcS37e5Tfv/Bq42KP5vEN06Nn9BkiQdkyRp6sVMqr/JT9TXnXDh78Yr9j9yIUurG1B9YdlrO3CnJEneF8a/E9h+4bUaSZK6XcjKeuSK97ru/LLxGZy9TyNVD6Vz38doFtGWFq5zMTqXcWr/JAx/kiuvOHMW484KNKiIeK4fcoUDsW+OolJbgGtZC/L+1MzKZDKxc91InP1qcKx7jJhugxlx1xAmnk0jL6gFD3/XuD/IoSo1L6UX0MfHnVfCg1HIZXwyuhOBnk5MWnmM4uqGS0g6o4nJ3xyjUqNn6SMJ+Lk5EuA/mNDQqRSdW01BQRMX45PfwR+L6u/GO4/Dw7MFC/suxABM2z4BjaZhvEOvr+BU4iTkcjfi4xYjlzsSGjoVf//BZGa9S1lZE5X5e96C9K0w8B1o3euKWEQ6rGsci9Ad3U3h3CU4NnMg6IuNSDeitaiDIzzwDbj41Ds3te0WsLZIXrGa5LxQOkbm03bkMEJCgvgyWIHOQcaDJ4pQ/+nGobZax5bFSTi7Kxg4MQ65g4xuQ1sTGufLvtUZFKQ17u+y95svyU86yR2PTSGkrW1FgbICFbuWpxIQ5kHv0f9mJP1L09jzi3sACAaOSJL0vSRJA+xJp5Uk6TvgD6CtJEkFkiRNAN4B+kuSlAH0u/BvqM+uygYygWXUd1RECFEBvAkcubC9cWEfF2w+v3BMFmBbj+QacGTnYmReW9BWxNJ72HuX9kfcPhAf1VT0Pskc3XBZWkxbpeL8lycB8H80Hmfv+sIshbs7LSZ1QW/SoD9goDLpct+L7asm49Yih7qzPek1/IVL+18dP4beGcn8ERHNC8suX+QLtHrGJ+fSwknJ4phWl7J6vF2VfD42AY3OyKSVR9Ea6qtwhRC8ujGFo3mVzB/ZntiQy2u9rcOewc+vHxmZc6iouKKiveAobJoGobfBgLcv7Q4L7c28dhM5I5l4Zd09iAsXebPZQFLyk+j1JcTHf4ajY334S5IkYqLn4eYWTXLKM9TWXlH0l/xjvdJup0eg6+OX97fufSEW8TP8enlsU0keBVOfQpJDi2VfI/O8gUstbv4wahVoyuGHMWC03Y7XEoW//ca+A1608s6j29MPX9rfvVMHXjdWkeftwrjNhzCb6v//TAYz25YkodPUV4u7eNRXyUsyif7j2+Hl78y2ZclUl15WSUjes5PjWzbScdDdxPWxnDhxkTqVni2fJuHoomDQE3F2SZr8y/8oQgi7NuodzlDql43ygdcBH3uPv1m2zp07i79K9qk9Yvv2SLFpYzehUVU2aXPguyli1+7W4sT6RcJkMIrEl9eJvJm/itwdR5q0z1+/S+TM2C2Spn0ntFU1Yv+mhWLnztZiw4rbhdFobGSv1daJnis2iKCdR8WKteuF2mgUdxxOExF7T4l0dV2TY2xPPidavbBZTPvuuDCbzWL57zmi1QubxbvbTjdpbzCoxB8HB4hf93YUtbW5QlQXCTE/UogP4oSoLW/ymC83jRexy2PFZxseFkIIkZb2qti1u7UoKlrXpL1GUyD2/pYgDvxxh9Drq4UoPCHEmwFCfDFACIOu8QFmsxAbpgrxqocQST8Ks04r8gZ1FqnRbUXt5uVNjnFDSFpbP6eNT9XP8SqpzsoQn0/eIL556juhrWj63D77/TYR8MsJ8fLqrcJsNotdX6eKRZN2i4yjJU3aV5bUimXP7hWrXj8odHUGUXgmVXwwephY/cYsYWriO/VnjEaTWPfeMbF46h5RnFN91Z/pX/5/AhwVTVxTbUrBA0iSFA88Sn0dx3bgW+BWYIwQosN18W7Xib9aJ1JRmMPBk/ciyQx0il5NQGhMk3YGbR0Hf74fvWc63klP4V/SHlW4hujHLWcLpb63Eo+yUArkv1Nz6xfoa53pdcdu3L2bTgIoyMvhzuPZ1Dk60dndmd+NMlbGt6afr+XahY93Z/D+znTu7RjCxlNF9GnbjKVjEiyqj2o0eRw5ei+OSj8SknU4lKTDYzshoOke28JsZtZ3d7DZWMaHYQlg/I2WLSbQpo1lBePKqiOcODEGH4/OtN97HAkZTPwV3CwkPxh18PXdcC6RkpxoKn4vJvCxQXhPt5xafUPY9TrsXwCD32v4BGUDg6qGH1/ZjErnwX3TwvGKanqJyWQwMmzdPo75eTEjT4XjYSMJg0O5xUqx39nUCjZ9fJKQSAVFp5egdHJm9Jz3cXa3Xd+yd9UZkn8rpN+jMf+zPTL+pTF/WQpekqRjwAfULyfFCyGeFkIcEkK8T/0S1P97dJpa9v8xDoWjmlbeb1l0IAAKJ2fiuy9BrvWiJupLzntm0nZCf6vvHzN9DCWcwNBtEzKhJDbmc4sOBKB5qzAW+yowy+TsN8qYGehu1YEAPNk3gkdDof3+ozysLOKDBzpYla92cWlFbMxHqNVZbFOoMQ1bbNGBQH068qv3ruMurQKz7jccpAgiIl6waA/g7dWFthGzKa8+SGYzNTy4yrIDgUuxiINGf47eeh5Fn+b/vAMB6PsKRA6CrS9Azm92HSLMZna/u5YKbTMG3Otk0YEAyBUOrLyzA0EqHR+FuOHc3oWud4VZtAdoEeNDt7uDyTqyHJ1azbAZL9vlQI6se5kqptOhr/N1cyA5OR+TnDwNs9m+JUCVOo1jx0ZRXX3iusznRlFY+D0nT43HaGw6QeTPaLVFHDs+mrIyy4KhNwP2LHTeJ4S4QwixSgjRIGIshLApf/L/AQelI2haUH4iAWOt7ZRInV5L/q4wjDINpTGrMOiaVvC9iMlkpKz7b+idz9MicRpembaLxnrdfjuvl+XQ58AWXN9/99J6uSUMNdVEnq5GbWhN85IAjDnWU4UB9n+4ncID/ji3qGTLse9t2qsqz9K7RS16lZLfV5koKTht/QAhCDlxmJCiOvJDlJyz454kbf9aVD00OLXSktm/GoP+JugOKJPBvUvBr019OnJlrs1Dji7+lqzSULp3LqVl/3427b29vfk2xhdH9HwRWoVKU2XVXpjNnNvzMsJUgsxpCNUnbLeMzdz7OdUeP+AWmIqz2xOYjddex6vo3Fqycz6k5Pxm0jPetGmv11eQmDiJquojJCY9gVZrva3CzUp5+T7SzrxCefleUlKfR9hoq2Ay1ZGY9ARVVYdITpmGWn3Gqv0/iT1OpFaSpC8kSdoKIElSzIUg+f8McgcH7nr4axTGSHZ/8RkFpy3/IDXVVWyY/yZ1eid8nGfh4J7Nvp1PW5RpB9i//WUc3E/gbnoS52I/VHtV1CXbvqCOe/hBHtBoUKkKWf/SbIt2wmhi1/x1VOqa0bdXJXLJxJbFiegqLFePH/ryczIzTqLPa4f6bBtcWhxmz4+Wf/RaTSVHD49FkpmR6e/GQeXI0rnPoq2zokx7eBkcX0Fk0ES8vG4h7cwsqmssp8yWnD5AruFDjLUOONcNReFVzt6fH7J6bm8YTh71gXZhvpCObLm/S/bmLRxOCqFtUC4dJlhWIPgz0ZFtWN7STJ7Sj8n7dmMyWdbYOvLxc6Tla+mR0JJgb0d2bZUoO2n5Tr40/QDZmoWYan0Ik/pQ6aQmc8+1FYGorj5OWtoreHv3oGWLCRQWrqKgcJVF+8vJGeeJiZ5/6cJqV7HqTYRGk0NyytO4uUUSET6TsrLdZGdbfoIWQnD69IuoVKlEtZ2Dg9ydU4kT0esbZ9zdDNjjRJZTHwe5WPqcDjxzvSZ0syKTyRny9Aw8/QP4acFcakobp3WajAZ+WjCXuupqhk9/mU63P4KjYRyS268c3P1+k+97/PevMTqtQdIMoeuAafhNqO+iV/r5SQx2SIMPfeN1vDxbkJuXzP7FnzRpc/jTb8kpC6Vn1zKiHxzBwNF+1Oh92DF/E+YmOr/l7tvL/h1bUDh48tC8Odw5Yg2a814Y3b4m8cCGRvZms5m92+5H4V5DsM8MBo16l6BezXEtd2Thm6Obnnj2Xtj2IkQOQtbnP8TFLkKp9CcpcTI6XeNzq60u49jJiciUZiJ9XqbHkA+Q1fZE8kzi8G7rnSNvGL7hcN9XUHraojRKeeIpdv0s8HcppPeMUVedktwjugdvORey2ymCub82XeyYvXkp+w5kENnf7oEdAAAgAElEQVRcSbfnFzHouT44yuvY8nkmdSWN65i0VSUcT5kBkpn24XNo3edzWhjbcFaeQdHB6Vc1P0todcUkJk3ByTGQuNiFRES8gK9vb9LTX6ey8lCTx6RnvElV1SGio94hKOhe2rX7AJUqhdNpL90UTbzswWhUcSrxCSRJTnzcZ7RsOZHg4AfIzVtMcUnjVH2AvLwllJzfTHjr6YSEjCIufjF6/XmSkp/EbLZf2uZGYc832E8IsRowAwghjID1tZP/pzi5ujF85iuYjUY2vPcWBm3DO6JfvlpCYVoKd06eRkDr+qZJPfrPxqzqiUa2hNTjmxvY56T9QbnmbYyqGG4bMB8Al7hw3Hu5Iyl9KH77Z8xG66daJpfz0Lw5KBXeHPp1F5m7GtacZG7YxNHU5kSF5BI/7gEAgm+9ldtvrSa/shUHP/qmgX1NUQEbFi9BQsaI56fh4uePs5s7Cd2+xahTUFj2Eudy0xoc8/u2qTh4ZeOoH0Rs14kAjJm8BHMbCXkWfL3osYaTrsiBNWPrl3/uXQoyGUqlD/HxSzCaVCQmTcZkurxyajKZ2LNmCI4BdXiVDyW8V30abK/BX2Koao5aWkPa8W+tnqcbRnhfuHMOpG2GvfMavKQ9X8KWZWdQyHQMfrYXDi5/TT5kXI9hPGI4wyJZJOuObGvwWnnSPn7+dj3N3EwMfHUZkkyGa0gLBo1tgcbozrb3tmPSXz63ZpOJfdvGI3cvobn8MQJj65fWIvpuwEfrSpp6HdXpDb8jV4vJpCUx8QlMJg3x8UtQKLyRJDmx7T7E2bklSclPUlfXUFmhoHAVhYXf0rLl4wQGDgOgmd8dhLd+jpKSn8jPt96Z82ZACBPJKc9SV5dLXOwinJ1bIEkSbSNfw9MzgdOnX6RG1XBVo6zsF7Ky3yMg4G5atZoEgKdHe6Lavk1V1SEyMq6P3M7fwd7lLF8uSJRcLAa8rrO6ifEJbs6Qp2dQmpfDtsUfXrojOrljC4m7ttFl2Eiie/a6ZC+Tybit/yeYNC0pKH2Rc3n1X5rKsgLSs5/ErPei+23LUCguC7t5De2JMqgKSdGc4ncaSpk3hZO3D/e/OB1JcmDTF19SlVO/FFZ64ji7t8sJdC2g9/QHG9z1tnv4PmJDczmR0ZIza+qfLox6Hd/OehWTSU3f4fcS0uly+9mg0CiCvd/CwcnA0YMPodXUBweTDn6KznEHpqo29BywsMG8pr38PbV+ekr2n2PPzx/U79Sp6iVNhKhf/rlCDdfdLYqYmPeoqTnJmTMvXzq3v31xH8rWZYiMtnQdfXkZQCZ34LZ+azDWupJX/AbnC47bPFc3hG6TocNDsPcdSK2vga2XNNlKrcGDQY8E49ri7/WjeKv3cLppc3iu2puTWfWfW1tWyMb35iCXCYbPnofC43LdTECXrvTtp6NI1YL97192uAfWTkLmn4ZLRX9i+l9eYJA5OBF760acDDISc15FW/rXgtpCCE6nvYRKlUS7mPdxc4u89JqDgzvt45cihIHEpEmYTBoAKisPk57+Or6+vYgIn9Hg/Vq1moy//xAys+Y3Xax6E5GVvYDy8j1EtnkFb+9ul/bLZEri4z5BofAmMXESuguipPUN557F3T2G6Ki5DYo7g4LuoWWLCRQUrqSw0HZ88kZijxN5jvqK8nBJkn6nXjH3qes6q5ucsI4J3D56HOkH93No/WrOpiaxZ/kSWnfqwq2jxjSyd3Jxp1PCMjA71AfLyos4fOAxZA4aYqI+wcOncRZMs2lDQSrArA6ifIVtIb6A2Pbc+cADmM0aVr32JjX5uWz9IgtHeR0Dn+uL3Lmxku+tzz1EiMdZ9ux2ouTIIdbMfAlNXQkxsV3oMKrxMlT720Ygr34IF/8qtq+5n4LMvZyr+gBDjRe3D/qhkYSI0smVJ2YvROds5I/vd5CVugfWPwGlafXLPr7hjcbwbzaAsLBpnCtex9mzX3FizX8wtT6FLtebPuMbL6W5evjTvsNSJElw7Oh46mrLbZ6r644kwV0fQPMu9Z+3OJnfP/iWwpqW9O5TR+At3Wy/hw2USkeWdeuOr0nFo5mVnCvN5+fXnqBaJ2Po4+PwaN2+0TGRI4fRMTKf5LxQkleuJmnrO+ia7UGUxNJtxKJG9gqPMOLbLcIkEyQeeRDTX+ghk5+/lJKSn2gd9izNmjXOUnRxCSO23Ueo1emkps6krq6ApOSpODu3pF3Mh0hSQ5HEi8Wq7m4xF4pVM696TjeC4uKfyMv7jODgUYSEPNTodaXSj/bxSzAYqkhKmoJOX8apxInI5U7Ex11uOHclEREv4ONzG2fSX6Oq6uZpLmtvnYgD0BaQgDNCiJtvYc5OrlU/ESEEWz9ZwOl9e3B0ccXVy5vRc97H0cWysFtm8h5yiydhNjohV9bSzOVt4rs9YNHepNFSNOsncPDB+54A3HrG2ZzXznnzSDy+D6VjOxQufbh3gg/+XbpatK8rKWHNW7+h1pylTv0Lfr5hjP30Y6tjbFp+Py4tj2HSOSDMEgmdfsQ3yHL678lDq9n20XL0LgaeCTmGx8C3oPsUi/ZCmElKforS0u2Y9BLGGgW9em/H1d9y06TUI19RVP0WpppW9B+2A5ncPqlyne48MpkTCsW17w+CqhiW9iG1ojN7ysfTPiKPW6c/ek2HSM45xd1ZdTSvKuSuH1cwqF888RMsJ0CYjUa2vLKSEhO0vGM+5lpfevdfj6O7ZR2t0pPzSCxfQoA+kHYD9tsdxykr28OpxMfx9x9EbLuFVmVT8vKXkZn5Dg4OnoCZhM7rcHW1XAej1RZx+MhwHBzc6ZKwDoXi+ins1tZm4+ISiiTZ97lrapI4dvwBPNzj6dhxBTKZ0qJtSclmklOm4eDgicmkoVPHb/DyalSKcQmDoZojR+/FaKyha5eNDZrTXW/+cp3IBboC7YFOwIOSJD1yLSf334gkSfSf+CSB4W1AgmEzXrHqQAAiYvvg4fAMcmUtCv3DVh0IgNzFCf9nbkOYtFSszUWXX2JzXv1feAFfZQh6XQotnHZbdSAAzgEBdB3sQp16L06Ofjz47ttW7QEGjFqBoiQaBwX462ZbdSAAHW65n+i7OuCiUrJQ1RVz10lW7SVJRpjvdHRlSoRJokObj6w6EICYLo/ibByKg1ce+7Zaf/+L1NXlc+jwYI4cHY7BcB1WaN0DOddzOXvLH6GFVx49rpA0uVbEhrVnRs0ZMnzDOXb3SOLGv2HVXubgQO/netH8tk8RRiVdOn5g1YEANOvwAq3lXSlxLCF/n5WeK1dQW5tFcsozuLlFExM9z6buVssWjxEYOByjUUW7dh9adSAATk7BxMd9ilZbSHLKNIS4PmHagsJVHDzUn/R06+f1Ijp9GYlJT6BU+BIXt8iqAwEICLiL0FZTMBqriWr7hlUHAqBQeNI+films55TiZeXAP9JbD6JSJK0EggHTnI5oC6EEE9f57ldF67Vk8hFjHo9em0dLh723wmVl+Ti3ayl3Qqyqr2nqNpcBsZyQuYMReZiuVal8rvvKHz9DY5EhVGplBj28GOED73Hor26soJvZz2LJODBN97F3d92z+yqTVmoDpylTlGDg84bn8fj8IrwsnncokVPo9uXjWOfGJ584l2LdiaDnu/HPch5oWHQyPuJute+u3ez2czujXcj80zD1/EpOvS0nERoNKo5euw+dLpzmEw6vL260r79F8hk167ZkqpCy5q5R1A6Sox8oRNO7te+D8e5zDP88NqLnOh7D9vD4nkzIoTHW1gu2DSbDZw89ShVVUeJC19Ms5Z97BpHmM0kb+/OeWUp7f2exK/9cxZtDYYajh67F4Ohmi4JG3B2ttjqp+EYwoxOfx4nR/uLHAsLvyftzGyb6gh/hcrKw5w4OQYHB08MhnKrXUUBzGYdx088jEqVSkLnNbi7Wy5KvhIhBDpdMU5O9svNX81T3rXi7zyJJAA9hRBThBBPXdj+Kx3I9cBBqbwqBwLgGxB6VRLk7r3a4xxjRHIM4txbGyymN9YePkzxnLfx7NWL+xYtxcUMW1YsoyI1pUl7o8HATwveRlurZviLr9rlQGqPFqP+vQj3Hi3wG30rEoKSL5LRVlvpQXKBKVM+RBvhgXZPChu2fWHRbueMZyg26ugW191uBwL1SQy9Bn2PvtqbUvUn5KfvbtJOCDOpqdOprc0kNnYRUW3foKJyP5lZ85q0/ysY9PW1OCaDmcFTO14XB6KuKGfje3Nw9fLhkxFDGejnwWtZhfxWYbkiOiPzbSor/yA6ao7dDgTqFQliem/GXackuWQRtWctnVsTKSnTqKsrIC7uU7sdCNQ/gV6NAwEICRlF8+ZjyD/7BefOXbv+LnV1hZdiM927bbcZixBCcObMa1RXHycmZr7dDgTqVzWuxoEA+Pn1ISJ8BufPbyE3r+mW3DcKe65kycC/Ajr/MH6PDkDmfA5hDqF0UeP8cn1BIYXTnkHZsiXB783HrXlzhj0/C7ME6197EV1VwwpnIQS7ln3CufQ0Bk19Dv9Q68sHALq8GirXZ+IY4YXn4Nb4RPkg798KF7OZrI9OYLbRVU8mk/HUrE/R+MhIW/kjJ1J/b2RzbOECUs7lE+HVjO4v/cfmnP6M0smdW3quwmxwIPXMU1SX5zayyclZSGnZTtq0mYWvz60EB99H8+ZjOXv2S4rOrb3qMf+MEIJfVpymrEBN/wnt8AmyrwHS1WDU69n4/hz0Gg3DZ76Cm6cXi6JbEeHixMSUXHI0jZ16YdEPFBSsoGWLCQQFjbjqMeXOzYhP+AaZkEhMfgKDKr+RTWbWu5RX/EbbyFfx9urSxLtce9pEzMbbqxtpZ2ZbLVa1F5NJQ2LSJIQwEB9Xn5Ic2+4jnJxCSEyajFZb1OiYgoIVFJ1bTWjoVAL8B//tOdhDy5YTCQwYRnb2AkpLd92QMZvCrjoRIFWSpO2SJP10cbveE/uXxgTOuhehL0R31oOqzQcv7TfX1lIwdSrCaKT5J4uQu9fLzQd178kdQ+6lCjM/PTO5QWX38S0/kbJ3F91GPEjkLT1tjm2s1lG+MhW5lyO+o6OQ5PWPzy37taI20gdPjYG0pUk238fD1YuHZs3DJIfN77/N+YrLP8i87dv4bd9u/CQHhnzQOFvIXnz8I4kMexe5o54/9o7CoL8sO3P+/DZycj8mKGgkLZqPu7S/TcQsvL17kJb2CtXVfy9V+Pj2PDKPnqf78HBC42w3frpahBDsXPoxxZnpDHryOZq1DAXAzUHOirgwJGBsUg6qK2qMqqqOcubMq/j43EZ4uH2dH5vCyT+B+FavUKcwkbxvKMJ0Wf/q3Ln15Od/TvOQMYSEPPiXx7haZDIFcXGLUCoDSEx8Ap3OduzQEkIIUlNnolanNYjNXBmLqK95ufydqqj4nYzMOfj59aN12I2rw5Ykiaiot3F3jyMl9TnU6vQbNvaV2ONEXgOGA28D71+x/csNRqZwIPClgQh9Jao9VdSl5iDMZopemoUuI4OQBQtwDGsozBc77jE6RcSQX6di7+x6QcTcxBPsXfkFEV2602Ok7R+7MJgoX5GKMJjxeyQGmUvDVrKRj8ZQ7eGIR34NOT9lWXiXy4S3iKHHlIk41sJnbz6J3qCjKiOdzcsW4igE98z7EAcbSQq2CGt3Nz6OE1B4lbL35/oUS5XqNCmp0/H06EhU2zcarCPLZA7ExS7EyTGQxKQpaHVNd6i0RU5iGQc3ZtOmSwAd7/x7tSCWOLZ5Pan79tB95GjadO3R4LVWzo4siw0lq07L1NQ8zEKg1RbVV4s7hRDb7qO/HffxihpHW9e7qHBSkflLvTRKdc0p0s7MwturG23aWJbguV4oFN60j1+CyaRuVKx6NeTmLuJ86VYiwmfi59u7wWuuruHEtvsQlTqV1NMvIIRAo8kjKfkpXFxa0y7mfbszuK4V9SnBi5HLnUlMnITBcPVp2H8Xe1N8A4CLz6aHhRB/v5XbP8S1Dqz/E2hOZlC+MhthqsWxeT4Vny/Bf+ZMfMc3HT8wm838OP5h8jXV9OjZl+OnjuDm48uDb72H0qlxPvqVCCGo+P4MdYml+I6JwdlCj22DxkDmnEM4G824PBiNfwfbvey//u4dyjbsR8QH4pt4nmqzkZFTp9O8d1/bJ8FOfvnpIYTbQVzMd2N2OY4QJrokbMDRsen5qdXpHD02EheX1nTu9D1yuW3BzYtUFNWy9t2jePm7cO/0Tjgo5bYPukpyTx5j3TuvE9G1G3c/86LFdNvPC0p5OaOQp1r40L/iSTR1eXRJ+BFX14hrNpczO/pT4JBNhPMAzppOIskUdElYj1Lpc83GuFrOl24nKWkKgYHDiYl+76oCzqWlO0hMmkxgwHBiYiwfm5u3hKysdwltNZnSsl3odKV0SViHi0ura/Uxrprq6uMcO/4QXl4JdGj/1TVNELnI35GCvx84DNwH3A8ckiRp5DWf4b/YjUuHNrjd6ork6IsmUcJj2DB8Hh1n0V4mkzH0w0/xlZyoTQMXyYNhM16x6UAAVHsLqDtVisedrSw6EACFi4LmU9pjBKpXpVKdYVttdeyDL2LqEIiUWEwFZnrfMeSaOhCAXkO+wlAVjEa2CZ22lPi4xRYdCICbWyTtYhagUiVflUaTttbAz4sTcVDKGTw5zi4HkrxnJyl7mw5QN8W53MMc3v8UzcKCGDjlWav1GhNC/Bgd6MPHZyvYofYitt2HdjmQ49W1zM0+h8Fs+3O36bMRb60LmXXbMRqqaB+/9B91IHCxWPUZios3kH/WcvLGn1Grz5CSOh0P93iiouZYdT6tWk4kIGAouXmL0WiyiYtd+I86EABPz05EtX2TysoDZGTaTtO/ltjjrmYDXS4+fUiS1AzYBfz9COS//GW877kVQ9EWJKkTrr2Dbd5xKT08GXDbDEwFBsJ1EbgKhVV7gLq0Cmq25+Ic74d7b+t1GgCuXjI0BVvxbD6Is58l4/KGFwpX646qh7iTo8pjIPelJsF6TctfQS5XcusdP7Bn8wgqMzxJiLOdI9KsWT9at36W7OwFuLtFXdIwsoTZZGb7smTUlVqGP9sJN2/bTy/ph35n+2cfAaB0caFNl+5W7WtrijmVOAHfGA0eXUNROFqvP5AkiSecfuao8GKZbBpDHaOxFZ3Jq9PxcFI2FQYTKqOJtyObW7WXKVyI7bGB03sGEVJqwK3zdSjY/AuEhU6lVn2GzMx5uLlG4ut7u1V7g6GSU4mTkMtdiItfbPPpU5IkoqPmgjDj43sbPj62Y4o3guDgkahr0zh79ivc3aIJDr7vhoxrzwKe7E/LV+V2Hvcv15lmUwbh0skf9a9FaJIsy7oD1OzMw1RgwDHcAQeFB8Xv7sSssyw8YDivoeK7NBTBbniPjLTppC7GZhQnfqJGKsRL4UTqa9a1jdLX7ONUng/BrnHovcM5vKKI3KICq8f8Fdw8g+nZZy01eZ5smP9mI+HMpghtNQV//8F2aTQd+DGLgrRKeo9uS1C47XTv87nZbP1kAUFt2hIYEcnWRQsozc+1aG8y6dm3634cXDR4ugyiRn2IzEzr6cilZbs5m/M+c5ol4qt04tGkHEr1lv+/1UYTY5NyMAsYGeDNl4VlfFtkW0JG6RVO+x7r8SuthR8eAsM/L9MuSTJiYt7Fza0tySlPo9HkWLStl5t/Cp2uhPi4xXanGMvlTsTGfkRw0M21KBMR/iI+3reSduYVqqqP3ZAx7XEG2y5kZo2TJGkc8DOw5fpO61/sQZIkvO9pg7KFO5Wrz6A/13SDJs2p86j2nMW1ayB+j3XDqbUGSRlC8ZymxR3NGgPlX6cgKWT4jolBZsfSTNnixah27MB/+nTi3nmQcmrwlbuTPGdbk/alJzLZs70aN2MFw94eyp2TonEwKfn+o/2oNde+0ZRPcAhDps2kLD+PbZ9+YHOZ6qJGk5tb9AWNpqYTBk4fKOLUL2eJ79uc6B62JSg0NdVsfO8tnFzdGPr8bIY9PxulszMb57+Jpqbpqvnffp6AwqsQN3E/Cd0W0TzEel2EujaDlJTncHdvR4+YV1keF0aFwciE5Fz0TcjTm4XgqdP5pNdqWdIulA+jWtLb250X0ws4VGW5L8olAtrBvUug8BhsmlYvrvkPI5e7EB+3BElScCpxosVugpfrZt7C07PjDZ7ltUcmcyA29iOcnIJISprSZDryNR/TloEQYgawFIi/sC0VQljve/ovN4yLF3rJ2YHyr1MwqRu2HNUXqqlcm4Ey1AOvoeFIkkSzJ4YgKQox60MoXdLwfkCYBOXfpWGs0uH7cDQOXo7YombHDso+XoTnFbGZdq/fSaWhDo8aF3JWHmhgX1dWzc+LTiABQ6Yl4OjjQYeoGFrf44hbtR+ffrT+ujSaCuvQmdseGkf6od85uM62Eqpc7kL7+CXIZEpOJU5sJI1SnF3Nr6vO0DzKm54jbMcbTEYDmxbMRVNVxbDnZ+Pm7YObjy/Dnp+NurKCzR/Ow/Sn/i7H9r6D2e0Aoiaebv3nAtCmzRV1EdUnG9gbDFUkNhDycyLe3YUPolpyuLqWl9ILGjnQ93KL2VpWzWsRwfTyccdBJvFZu1a0cFIyPjmXAq0dbWyj74besyDxe/jjr6dnX0ucnUOIi/2Eurr8JqVR/m7dzM2KQuFFfPzSegn+pMnXvYmXXctSQogfhRDPXdjWX9cZ/ctVI/dQ4jcmBpNaT/m3aQhT/QXYpNJTviIFmYsC34ejkRwu/3cHvTwCoStCm+lE9c7L2WrVW3PQZVThPSwCx1DbSzPaM+kUvfgSTu3jCXzj9UvLXg6OCsKe7kidyYQ5yUDZoXq1VZPByM8vb0Lj4EXfId74xV8uchzary/yrpW45gWz7JtrV318JQl33UP0bX04sPpbMo78YdPeySmYuCY0mtSVWrZ8loSbtxMDHo9FJrf9U9qzfCkFp5O584mnCYy4LIke1KYt/R9/krMpify64vNL+3NSf6ZC9zmGKj96Db7c00MmUxAb+zFKpT+JSZMv1UWYzUaSk59Gqy0mLu7TBlXQwwO8mdYqgG/PVfBl4eWlz83nq1iQW8IDgT483vxywoGXwoGv48LQm808mpSDxmSHU799BkQPhZ3/gYx/rvjtSry9uxIZ+Srl5XvJynrv0v5rVTdzs+Lm2oZ27RZcaOL14nVt4mXxmy9JkkqSpJomNpUkSbZb7v3LDUXZwh3vEZHoc6qp2pSNMJop/+Y0Zo0R30dikLs1DMTKHBUEzOiLMKqo2VqCNquA2mMlqPcX4to9CNeutteGjZWVFEyZgtzVleYLP0bm2PCpxa1VMzwH+yNDonRNPtqSKn59dQ0lBNMpXE3EPY0DkpPGjUDdsgjDAS82//rr3zonTXGlcObWRQsosxKLuIi3VxfaRr5KRcU+MjPnYdSb2LI4CaPOxODJcTi52k5SOLVzC6d2bqXL0BFE39q70evtet1B57vu4eT2zSTu3kbF+XTSs6dj0inp3usHFMqGdTNKpQ/t45c2qIvIzJxLReXv9UJ+np0bjfFCWCB3+nrwn8xC9leqSFHX8dTpfBI8XHi3bfNGca82rk4sbhdKsrqOZ9PybV+IZDIYvhj8Y2DteCjLsHlebgTNQ0YTEjKavPylFBdvvOZ1Mzcr9U28nqekZBN517GJl8U6EUmSNlAvd7IO+EEIkXfdZnED+f9QJ2KNqi05qH8rQBHihqFQjc/oKFziLae01h5OpWJ1IZhqkVz8cAz1wG98LJKNO2thMJA/4THqTp6k1TcrcY6Pt2ibsXQvTlkSpXotf9Q5EOpUxKAFoy3qh1WrVSx+YwuOte7c8Uxr4ttE2ffhAYPRiMLB9kVBVVHGt7OeQ+6gYPSc93H1tC0geebMaxQUrsRc+gzpv7Zj8OR4wuJtV6SfTU1i7Vsv0yq+I8NnvoJM1nSMyWw2sf6d1zmbepLIewpRuNUQGfoprdo27sNxkYt1Ee7u7VCpUmjRfByRka9Y/txGE0OOZVCqN+Ail2EGtneOxN/RsiNclFfCW9nneCksiGmhtvXVqMyDZX3A2Qce3w1O10+m3V7MZj0nTo6jpuYETk4t0emK7a6bMQmB/AYIHF4PhBCkpDxDyfmfaR/2Jn5hf11J4KrrRIQQw4EBQCmwVJKkvZIkTZEk6Z9NBP8Xq3gODMWprTeGQjXufVpYdSAArl1jcE1wAKU3GNV4Pxhl04EAlMx9B83hwwS99aZVBwLQZmIvqhzV+Ds6016hYcDbI60KUHq6uXPfU90xy4xsW5xCWVWFzfkAbNi5m0XTdrB26w6btu4+fvSZNI3q0lI+feZ5NHW2g/lt2szGwdwBfBbReZjOLgdSfb6ETQvm4hUQxJCnZ1h0IAAymZzBT80grF8Fjt5VNHObatWBwOW6CJUqBR/vnkREvGTV3t1BztdxYQig3GDkq9gwqw4EYGpLf0YEePNOzjl2lNkhme/dCu5fCZU5sHYCmP/5btoymZK42I9RKpuh0WTZXTezv1JF9P4kVhRaz368WZEkiejod3AXXiRnzEZ7/tpnbFm9WgghqoUQXwGDgCXAG8C4az6Lf7lmSDIJn9HR+I6JxqO/fQVQPqP6oAzMQ73jDap/WGnTvnL1aipXrcJn/Hg8hw61a4yYVweg9zQT6uqJIcd2xk94i5Z0HhOIU507n3+wFcOfAs5/5tjpFHLX63EwKSncZOZwUqJVe5PJxPqf0nBw6YdMU8LiFy3fvV8kP6Wa05seQTL7YnB7w6ZGk0GrZeP8NzGbTHb1mwEorVqLW8tzeLvcZ1XK/krCQqcSH/cZcXGf2LU0E+biyE+d2rChYxs6eNhWF5YkiffatiDe3ZkpqXmcqbUjUBvaEwa9C5k7Yffr9nyM645S6UvnTt/TqeM3+PnZVjDOq9MxMSUXtdHMrIwCDlTakal2EyJP/on4w1m0Nsfh2KzTNX9/q05EkqQe/8feecdHVWVx/Htn0iXsRSkAACAASURBVHulhl4TqoCg0gQbKtZVxAbusnYpKrZ1FRtWUMGKrgUEsbt2lIVQRGnSQhIgIUACJJDey5Szf7wBEpLJTEJCAtzv5zOfzNx33nv3DsM77917zu8opd4ANgHnAdeIyKu17aNpekzeZnx7RaBM7j+Ct5h2KwHnD+HwrNkUrVzp1K7kr7/IePY5/IcPp8WDzmtKHI/ZbKbT9GF4RgWQ8/kuLBmu7/zPHzyYkAvKCTzUmrfnfenULj0rk+XzdmL1qGDIvS2o8Cxl5X92s/+w86z5t9//ksD01gRd0h7Vtidk7OK9V5zXOMlJL2bpB/GEtmjNoMH/canRJCIsefs1stJSGTv1YcLauJZEz8lZQ3LyC0RGXsxZQ9zPOlbKRGTkRXh4BLq9T3d/H7ccyBF8zSY+6t0JX7OJiXEp5Fpqd+oAnD0JBv0D1syBbV+4fa7GxMenTZV6584ottq4LW4PNoFfBnWnk683/4zfQ2pp/TS5moz9f8H3k/FpPZT2F33VKHVHaltY3wu8DRwA7gA+BIqVUgOUUg3vzjRNilKKNjNn4t2zJwcenE55Sko1G8vBg+yfPAWvtm1pO3sWylw3bSjlaSbi1hiUt5msBQnYil1XWZ5w3RWU98zAI64ln33/S7Xt5ZZy5r++FK9yf865LYrBffoyYlJnvCy+LHx9BWXl1f/Tf/Xzb5i2tKC0Wzq3jbuSe55/DrtfKwo3/s6P31QXYigrtvDzO9swe5q47O6+hITGEBMzi4KCrezY+XiNC85rv/mMXevWMOLm2+jYv/oi9/GUlOx1CPl1cWg+Nb983jY+XnzUuxMHyyzcGb8XqxvSKIx5CToMhe8nw4ETU0c+WRzJm9lZXMa8Xh3oF+jHx306YRXhtrg9FFubfnrOLQozjATQwJZw/Xwwuw4AqQ+1/VL3ArkY6yIvUlXBd5bz3TSnKiY/P9q99SbKy4v999yLreBYEJ69tJS0++5DKiqIevttzEH1k7gwB3sTfms0tvxychYlHg1Hro177/0bhREZHPrFxJrNVS9Eb731NYFZrWh5qZ1hA4yL9ZC+/Wg71kRgTkvefPOrKjkn67Zt5cCPdgrDDnHffcbajI+PD+OffRIxB7Dzi8+J23Ys98JuF5Z+EE9hdhlj7uxDYJghiXFMo+lb0tI+rNKnpPV/8McXi4gZPoqBY51XlTyC1VrItri7AEW/vvPw8Gj4+iMNxaBgf17qEcWq3CKe2e1GIpuHF4xbAP4t4LObjQtbM2f23gx+zspnRtc2nB9m/M67+PkwL6YjO4rLmLIjFXszSKisFUuZ8X2XFcD4xeDvXPfuRKltYf18ERnleI2u9H6UiJyQSp5SaqpSartSKl4pNc3R9pRS6oBSaovjdVkl+8eUUslKqZ1KqUsqtY9xtCUrpR49kT5pDDzbtCFq7hwqDhzgwIPTEZsNESH98ccpT9xB29mz8O7cyfWBasG7fRCh13ajPCWfvB+rP/FUs/f0ZuK0i6jwLubPj9JIzTAuXgu++gHvHa2w9D7E+CsvrbLPdZddjL3/YXyTWjP/S6P8zf7D6az6IIUKz1JunTYKn0ohye2i2nPePXcCVn595VVycw3Jjz+/SSY1IYcR47vT5rgSwJ063ktk5BiSkl8kO3sVAJmpe/nlzVdp1bU7F90x2bVcjNiJT3jQIeT3Br6+jSMf35Dc2Dqc26MieG9/JovTXUuj4B8BN34KZXnw+S3NQhrFGT8ezmP23kOMaxXKHVFVg1JGhQfxRJc2/JSZz6t761+zpNERgR/vhwMb4Zp3oVXvRj3dSX9mVkr1Bm4HBgP9gLFKqSNhEq+JSH/H62eHfQwwHugFjAHeVkqZlVJm4C2MRf8Y4EaHreYE8Rs0iFb//jfFq1dzeParZM97j4Kff6HFgw8QMHJkg5zDf2BLAoa1pfjPdIrXu747bR0Ryeg7e+Bh9eLT11fx6++/k7fMm8KW6dx7V81Cc/fcfj2FrdMpivXnl1WrWfj6Crwsvoz8ZxfatqieB3PesJG0HTMWVZHDfx59goQ/DrLlf2n0GdmWXsOrr2koZSIm+mUCArqzPX4K2Ye38d0rz+Ll58dVDz6Oh1ftIokAKSmvkpW1jG7d/k1Y2Hku7ZsLM7q0ZURoAI/s3M/GfDdkalr1MXJI9m+Anx5oFtIox3Mkb2ZAkB8vd29X4w3AXe0iua5lKLP2ZvBTZl4NR2kGrH0btn4KIx+FGPcCX06Epph4jQbWiUiJiFiBlcC1tdhfBXwmIuUisgdIxnBAg4FkEUkRkQrgM4etpgEIvWEcoTfdSM6HH5L5+usEXXEFYZMmNeg5gi/thHe3EHK/S6Z8r+vQ0YHRveh4tReBeS1IWlhGmW8h/5g2xmleiNls5o5pYynxz2P3p+UE5rSk7VgTg/s4D0kef9skPLsPwJSXytJ582nbI4Sh47o5tffw8HdoNHnw1/oJlBRlGpImYa6nDw4d+pG9+96hTetxRLW91aV9c8LDpJjXqyNtfDz5+/Y9HHRHGqXX1TDiYdiyCNa+4/7JrOUQ9xVUlLi/T+o6SK89Qq8y2RVWJsalEOxh5qPenfBxEuZ+JFLtrEA/JiemklhUWqNdTRwoq3AvRPoIdjts/wZK3AtxByB5Gfz2b+g5FkaeHHUqV9FZSinlWgO8bmwHhiulwpVSfsBlwJFz3KeU2qaU+lApFepoawukVdp/v6PNWXs1lFJ3KKU2KqU2ZmZmNuRYTmtaPvYY/iNH4DtoIK2ffabBIzuUWRF+Y088QrzJXpiINc/1NMfVF1+A+ZxsyryKGXN3L1qE1n6xDgsO5sp7z6LUuxB1dhbXXXaxy3Pc+9QM7KHtsZStwdZ5E2YXeTO+vlFYUi/B5F3IgFsVrbq6zj8oKNxOQuIjBAcPpEePpxslaqaxCfX04OM+nSi12blt+x5K3ZFGOf8x4wL32+Owe7lrexHjyeXrSfDtncaF1RVp62H+WPj4csjc6dLcYhf+Gb+HzAorH/XpREsXeTM+ZhMf9elEoNnEhLg9ZFe4jlTLs1i5bksyE+L28EWGm05h5Uvw1d9h8Y2GI3VF9m7DPjIarplnKAicBFzliQgNrNgrIonAS8BvwBJgC2AD3gG6AP2BdBqwBK+IvCcig0RkUGSk64p7GgPl6Um7d9+lwyefYPJxv8JfXTD5eRI+sRdisZO9IAF7hevIl7tuu54HXr/C7Uz26M5defC1q7hn0ji37M1mM3fOfp6SMBNJX3zD5oQ1tdpvW/YrW77fiqlgDOVsZ/fu2n+65RVZbNt2F56eofTp8zYmk+tpr+ZKT39f3o7pQFxhKQ/uTHNPGuWadyGyJ3z5d+PCVxvr5sHmhRA1GBK/h1Wv1G5fcNBYdwlsDR7exgW4tPaSsf9O2s+fecXM7tGOs9wMe27l7clHvTtxuMLC7fF7ay3iZbULd8bvY3+Zhb4Bvjy0M41NrqYAE76DlS9C1NmQthZ+nl77FGBZPiweD8psrD95B7g1jobAHVe1SSl1tmsz9xGRD0RkoIiMwIgA2yUih0TEJiJ24H2M6SowQowrPw1FOdqctWsaEKVUo98le7bwI+zGnljSi8n9apdbYnG1Zbw3hH2Qfwi3/OtFbGb4cfbzHMqu+ae1f0c8yz54h479BnD+NXMdGk3zyMj4vkZ7u72CuLh7sFhy6dd3Ht5errPemzsXRwTzaKfWfHMol7dS3aic7R0I4z8FpYyLfJkTKb7dsfDrv4wnl3/8Cn3Hw4rnIfGHmu0tpfDZTVBRDDd9DjcshLzUWrPmFxzIYv7BbO5p14LrWtVNjGNAsD+zerTjj7winkx2ful5dvdBVuYW8lKPKD7r34WWXsYUYIazej4Z2+Hbu6DtIJj4IwyfDpsWwPr3a7a32+Dr2yEnxYiEC+1Yp3GcMCJS6wvYAViB3cA2IA7Y5mo/F8ds4fjb3nH8EKB1pe33Y6yDgLGgvhXwBjoBKYAZoypjiqPNy2HTy9W5Bw4cKJrmSX5sqqQ9skryl6c2dVeOEvvnd/LS+Mvk6WnXSHlFWZVt+ZmH5K1/3iQfTL1dSgsLRUTEZiuXjX+Nl+Wx0ZKfv7WKvd1ul4SER+V/yzpLRsYPJ20MJwO73S53bN8jrZZvlt8y89zbafcKkadCRRbdIGKzVd2WlSzyQnuRN4eIlBUYbRWlIu+NEnmutUjG9uM7IPLVJJEZQSKJPx5r3/ix0bbkX9VO/0duobSN3Sw3bkkWq91eh9FWZUbSfmm5fLN8ciCr2rbFB7Ok5fLN8viutKNtCYUl0mnlVhmzYaeUWo8bd1GWyGu9RWb1EClIN9psNpFPxxvf1e7Y6h1YOsMY47r36j0GdwA2Sk3X85oapeoFv0NNL1f7uTjmaiDBceG/wNH2yREHBXx/nFN53OHEdgKXVmq/DNjl2Pa4O+fWTqT5YrfbJevTREl7dJWUxFf/D9lUfLz4RZk17nJ5+cXbj7ZVlJXKgoenyNyJ10vW/qpOr7w8S35fM1xW/36elJUdPtqemjZf/ressyQnv3LS+n4yKbba5ML1O6Tryq2ys6jUvZ3WzjMugP97+lhbab7Im4NFXuwgkp1S1T7/oMgr3UVe62NccI+w+jXjOCtfrn6On6Yb2zZ/erQptbRcoldvk6FrEySvwuL+IGvAarfL+C3JEhW7RdbmFh5t35BXJO1it8h1m5PEYqvqpH4+nCstl2+We+P3iv2IA7NWiHx4mcgzkSL7N1Y9ibPvZNuXxti+n2I40kak3k7E2JdhwN8d7yOBTu7s1xxf2ok0b+wVVsmYu0n2P7lGKjKKmro7R3nphX/KrHGXy/zPXhS73S4/vPaizLphrOzetL5G+4KCBFke20s2bPib2Gxlkp29RpYt7yZbtt4udrutxn1OB9JKy6XX6jg5988EyXXn4my3i3x3n3EhjPvKuOtedIPjrnuFk5NsNC60H11uXHh3/ioyI1jki4k1X0itFYbtM5EiaRukyGqVUesSpduqrZJc7Kazc0FehUXO+zNBYlbHSVppuRwsK5c+v8fJ4D/iJdvJ9zB7T7q0XL5Z3tp3yGj44X7je9j6ec0nyd5d9enswCaRZ1uIfDBGxFLeIOOoDWdOxKkU/BGUUjOAQUAPEemulGoDfCkizaM6fR053aXgTwes+eUcfmMzyttMy3v7Y/JrHLmGulBhKefFh2/EJ6OcqEEDSF+/meE33cbgq5zX2D50+Be2b7+PyMhLyM1dh7d3JIMGflknjatTkXV5RVy3ZTdDQwJY2LczHq403KwVMP8KSN9q5DVs+9wQbxxyp/N9tn5mRGv1usYIaw3tYKybeDnJ9i/JgffOR6zl3H7Bf/k5r5yFfTszOrx+ygs1kVxSxqUbd9HB1xuzguSScn4a2I2e/r412osIt8fv5efMfBb6JjN6ySQ4bwpc/Kzzk+yOhYV/gy6j4XACKBPcHgsBrgOGymx2p6HL7lBnKfhKXANcCRQDiMhB4PT+X6BpUjyCvQm/NQZbXjnZn+5AbE2fmObl6c0dj8+h3A/S12+m4zlDOPvK2kuqtmxxKZ06TiYz81dA6Nvn3dPegQAMCQngxe5RrMgt5NkUN6VRbviEX1pfzA1yNpkD74DBd9S+T7/x5J97PzerIXwdeYEh7eHMgQD4hcGNn/Fa5Fh+zCvn3x0j3XMgOSnw8VjYs9qlaVc/H96J6UB8YQnbCkp4O7q9UwcCRtDKnOj2RHvZuKuwFck9b4ALn6r9JF1GUX7xC9zpeS7vhpxvBCi44UA+S8/mwo07nS/mnwDulPSqEBFRSgmAUqr5CvtoThu8OwQRek1Xcr9KIv/nFEKu6NLUXaJVRDuueOhx3lj0OBs6xXG5rRwfj9pDnzt1moLJ5ENo6BD8/DqenI42A25uE058USnz0jLpFeDLOBeRT9skgHu6PECpwKSgEXwpgnctUYE2Ee5qeQuxXkWsCj+HKEIZ4qJPv5ja8HL7W7ku41fuzk2Bju8aEWLOKCswoscyd8Ch7cYdf1jtkj8X7vyYdxNjsSozl3icA5EP1WrvX3iAjzfcw5her3Bbhyn8bIegWm7tRYRHQy7lu7IcvuMC2pjb4yonfWN+MQ/v3M+QEH8iPBu+iqM7TyJfKKXmASFKqduB/2GE4Go0jYr/oFYEDG1D0ZqDFG9sHlpF/Xuexy23P0lC/g5m/DED19PBJjp2vIvg4LNOUg+bD093bcvQkACXeRGZFRb+HreHUC9PZnZry/qCUv61a3+t3+1zuw8Sm1vEk13a0M7Xh39s38v+WrLmE4tKuS8xlbMC/ZjV2oza9hn8+abzztvt8M0dRonfK+YaORqLb4TyQuf77PoNls7gqhYh/K1lGCx/DnbUkmZXUQyLb6JdaTofdG/J3nIrd8XvxVbLuD84kMXijBzua9+Cs4P8mZqYyvZC55n86eUV/GP7Hlp7ezKvV0fXU4v1wKUTEZFZwFfA10AP4EkReaPBe6LR1EDwZZ3x7hpC7rdJlO9zkk9wkhnVfhT3nXUfP+/5mY/iP2rq7jRbPE2K93t3rDUvosJuZ9L2veRYrHzcpxOToiKZ1qEli9Jz+NBJNcEvMnJ4Jy2Tv7eN4J72LZjfpxMVdrsh026rng+SY7EyMW4PAY5Mc5+RD0LMVbD0SUj6X82dj50Ju36BMS/AwIlw/ceQtQu+cZI1n7nLyKpv1RuueguunAttzoJvbofDidXtReC/d8PheLjuQ87p0IPnu0WxPKeQmbtrroOzOqeQGckHGBMRxL86t+aD3h0J9TQzMW4PmRXVv9tS25HvxM78vp0Ia4SnEMdYXEZmTQZCXdmdKi8dnXXqYSuukIMvr5cDz/4plrwy1zucBOx2u0xfMV36fNxHVqatbOruNGuO5EVcsmGnlFTKi7Db7fJA4j5puXyzfJuRc7TdZrfLhG27pU3sZlmdU1DlWH/lF0n7FVvk2k1JUlEpbHZpVr60Wr5Z/hm351jIrIhU2Oxy7aYkab9ii/yVVynar7xI5O2hIs+3E8ncVbXDcV8ZUVL/vbdqtNefbxvty56ral+SKzJ3gMhLnUVyK4V75x8QeaWbyOt9RYqzq+6z4iXjWL/PqdL88I5Uabl8s3yZXtV+T0mZ9Fi1TUasS5RCi/Vo+5aCYumwYotc9dcuKbdV/W7vid8rrZZvliXu5u24ACfRWe5MZ7UENiilvnBIr596Ij+aUxqTnycRE2KQCkMaRSxNXxRIKcUzQ5+hZ1hPHln1CCl5riXtz1SiA3x5M7o9WwpLeKiSNMqHB7JYlJ7DlPYtuLpl6FF7k1K8Gd2BLr4+3L59L/sc1QQzyo1prxZenrzXqyOelaZmLgwP4vHOrfkhM485+45Nfc5IPsCavCJe6dGOAcGVlnO9/A15ELOHI2veIYx4cAv8915oNwQun111zWTIXdD/Flj1MsR/a7TZbcYTSO4+uOETCKkkohHUBm5YZEixfHkb2BwaW4k/Gk86fW+A8yZX+a6e6xbFuSH+PLgzjc0FxjRVodXGhG17UMCCPp0I8DhWDK5foB+v9WzP2vxiHk86cPS7fTstk68P5fJIp1ZcEhHs9r9VvajJsxz/AhRGcarPMFR0nwe6uLNvc3vpJ5FTl5L4LEl7dJVkLU6scrfZlBwsPCgjPhshl39zueSVNcwd3+lK5byI1TkF0iZ2s9y6dbfYnPxbVr77ziy3yJgNO6XTyq2SUFhSo/2Ru++WyzfLL4fzZOEBI1t8RtJ+553a87vI02Ein/zNyBCfHSMyO1qk8FDN9pYykfcvFHmulcjBrSK/Pm48UWz8yPk5Ni00bH5+2Mi0n9nGyLyvqDlHJbPcIoP+iJd+v2+Xg2XlcuvWmp/KKvNc8gFpuXyzfLg/8+hT2e3b9zTo/xPqmydyBKVUP+DvGDU9YoFzgKUi8nDDu7bGQ+eJnNoUxKZS8Os+gi/tSODIhhaYrh+bDm1i0m+TGNJqCG9d8BZmk3tlg23FFpTC7TwYsQu2nDI8IpyHjTZnRI7lRQR4mGjl5cVPA7sR6OH8+1qdU8j4bbsJMpvJtdr4sHdHLosMcWpfarNz9eYkkkrKsdiFYaFGroq5tgmUjR8aRZx8Q42CWf9YAm36O7cvPATvjzIWxsvy4Ozb4XIXxV6X/AvWvmWcw+wNd6yAoNZOzROKShm7KQlvpci12pjZrS2TopyH8tpEmBi3hxU5BfiYTHT09ea7AV3xr2MJ69qod56IowrhX8DLwBqgj4jcDQwEag+U12gamMDz2+HbN4L8JXsp3VGHOguNyICWA/j3kH+z5uAaXvvrNbf2sRVUcHjOJg7N2Yyt0I1aHED+TylkzNpI0fqaF16bO0fzIgJ8MKGY36dTrQ4EYHhYIE93bUuu1cZDHVvV6kAAfM0mPu7TiQCziXY+XrwT06F2BwIw6B8waJKh9nvVm7U7EDBqlo9fBNYy6DjcWHx3xUXPGAmCFSXGvrU4EICYAF/eiG5PrtXGza3D+Efb2oU6zUrxdkwHOvl6420yvoOGdCC14U7G+tPAhyKyr4Zt0WJIu58y6CeRUx97hY3Md7dizS6jxb398Wzhnnx3Y/P8uudZvGMxM4fN5MouzqP3xWon871tWNKNsFfPNgFE3t4H5eH8nq54Ywa5XyVh8vfAXmYj8vY+eHds5LnuRqLEZqfYZiPSy30lgtTSctr5eLmtKJ1jseKlVJX1g1qx26FgP4TUoTxxQTr4hRvJku5gs0BxprFW4ib7yypo4+2Jyc1xF1ttlNmFcK+Gj8Sq95OIiMwQkX1KqRZKqfZHXo5tp5QD0ZwemLzMhE+IQXmajBokpa6LAp0MHjr7IQa3GszTfzxNXGZcjTYiQu63yVSkFhI6rjuh13WnYl8Bed/tdpoXUb6vgNxvk/HuGkLLBwbhEepD9ifuFfFqjviZTXVyIADtfb3rVJIgzNPDfQcCRp2TujgQMJ4m3HUgAGbPOjkQgCgfL7cdCIC/h7lRHEhtuDOddYVSKgnYg1HKdi/wSyP3S6OpFY8QH8JvicaaW0b24h1ILUWBThaeJk9mj5xNpF8kU2Oncrikem2NojUHKfnrEIEXtMevTyR+/SIJHNWO4g0ZFP9ZfZrKml9O9icJmEO8Cb+pJ2Z/T8InxCBW94t4aTSNiTshvs9hLKLvEpFOwAXA2kbtlUbjBt4dgwm5qgvlu3LJX7KnqbsDQIhPCHNHz6XIUsS02GmU246VNS1LyiX/pxR8eoUTdMGxu96gizrgEx1G3o+7KUvOO9ouFpsjpNlOxISYowvw9SnipdE0Fu44EYuIZAMmpZRJRGIxVH01miYnYHBr/M9tTdGqAxT/1TykUbqHdueFYS8QlxXH0388jYhgzSol+9MdeLTwI2xcD1SlHAdlUoTd0AOPCD9yPk3Eml2KiJDzdRKWg0WE3dADz5ZVJet8e4YRdElHSrdlURibdrKHqNEcxR0nkqeUCgBWAYuUUnNwKPpqNM2BkLGd8e4cTO63SVSk1aJtdBK5oMMF3NP/Hn5I+YFFWz4ha0E8SmE8UXhXn6s3+XgYCZUCWQsSKPhfKqVbMgm6uAO+MeE1niNwZBS+/SMp+G0fpQnZjT0kjaZG3HEiVwGlGCVrl2BUEbyiMTul0dQFZTYRdnM05kAvshYkYCsod72Tg6J16ZRur1mj6US5s++dXNT+Inx/KsKSWUrYTdF4hDvP8fCI8CX8pp5YD5dQuCwV374RBJ7vPBdGKUXY37rh2TaAnM92Yjnk/r1dSVzmKRsqrGleuBOdVSwiNhGxish8EZnrmN7SaJoNZn9PIib2QsqtZH2SiFhqEMk7juK/DpH3bTLZnyZStjvPpX1dMSkT/yq9myFFffiw9X/JiHR9Dp9uoYRe2w3fPhGEXtfdZUSS8nREqnmZyFqQgL3Edb2IsqRcchbvIO+bZEo2V1/812jqglMnopQqVEoVOP4eeX/kc/OQU9VoKuHZyp+wcT2wpBWS+21SrQvO5akF5H6bhHfnYGMtYlEi1pyGDZkt2ZpJ2aoMOCuQZZEbmLx8MoUVrqfb/M9uRfjN0Zi83AtRrUsRr6NrM5F+eHUMIufrJCr2N48pQM2piVMnIiKBIhLk+Hvk/ZHPDVdTUqNpQHx7RxB0YXtKNh2m6PcDNdrYCsrJ/iQRc5A3YTdHH1uLmB+Pvbxhck4qDhSR+9UuvDoG0fZvfXl11KvsL9zPI6sewWZv+LDcI0W8ypPzyP+5ZjFIe5mVrAUJR9dmwm+JxhzgSfaCBGwF7mXNazTHU9uTiI9SappS6k2l1B1KqZObwaLR1JPA0e3x7R1O/s97KNuVW2WbWOzGdFe5lYgJMZj9PausReR8vuuEc05shRVkL0jA5OdJ+C3RKA8Tg1oN4rEhj7H6wGrmbp57Qsd3RtUiXhlVtoldyPl8J9askqNrM+YAL8InxGAvtZK9MAGxup4C1GiOp7Y1kfkYobxxwGXA7JPSI43mBFEmRej1Rlhs9qeJWDINSW0RIfebJCxphUbYbKtjYbM+3UIJvrwzZQnZFCxLrfe5xWone2Ei9hIL4RNiMAccy2ge12Mc47qP48PtH/JTyk/1H2AtHCvilVyliFfB0n2UJeYQMrYLPl2P6U95tQkgdFwPKlILyf02WeecaOpMbU4kRkRuEZF5wHXA8JPUJ43mhDF5OxaczcrI7C6zUrT6ACWbDxN0UQd8e1UXtAsY2ga/gS0pXJZKSVxmnc8pIuR9t5uKfQWEXtcdr7YB1WweHfwoA1sOZMYfM4jPiq/X2GpDmZWR2R7iTfYnCVjzyynZmklhbBr+Z7fC/9zqwn9+fSIIvKA9JX8domjNwQbvk+b0pjYncjTMQ0SahziRRlMHPMJ8CL85Gmt2GZnvx5H/yx58+0QQOLrmsFmlFKHXdMWrfSC5X+yi4mBRnc5X/Gc6xRsyCBzVDr9+Nct2e5o9efX8Vwn3CWdK7BQyS+ruGWCM0AAAIABJREFUrFxRuYhX1ofbjbWZDkGEXNXFabRX0AXt8ekVTv5PKZQl5dZocyphF7t+qjpJ1OZE+lWOyAL66ugszamGd+cQQq7sguVAEZ6t/Am9vvawWeVhIvyWGEy+HsaCc5F7C85lu/PI+3E3PtFhBF3UoVbbMJ8w5o6eS2FFIdNWTKPC1vCL2p4t/Qkb3wPr4ZIqazPOUCZF2LgeeLb0I3vRDqxZpQ3ep5NFiaWEW3++lbuX3Y3F7jrkWXNi1BadZT4uIstDR2dpTkUCzmlN+MQYIib1dits1hxkLDjbiixkL0p0ueBszS4lZ1EiHhF+hN1QVdLEGT3CejBz2Ey2ZW7j2bXPNspds29MOBGT+hB5Z1/Mga7VZo0pwF4oE2QtiMdedupNQIgI/17zb+Ky4lhzYA0vr3+5qbt02uNOxrpGc8rjGx1eZZHbFV5RgYRd142KPQXk/bDbqZ293AibFYGIiTGYfNwPYryow0Xc1e8u/pv8XxYlLnJ7v7rg0zUEjzAft+09wnwIuzkaa1YpOZ/vbBbqyHVh3rZ5LN23lAcGPsBtvW7js52f8dWur5q6W6c1OmxXo3GCX/8WVKQXU7RyP56tAwg4p+qitBE2uwtrZgkRf+9dq6SJM+7udzdJuUm8svEVOod05rw25zVU9+uNT5cQQq7oQt53uylYuo/gSzo2dZfcYtm+Zby15S2u6HwFE3tNxC52kvKSmLluJp2COzGw5cCm7uJpiX4S0WhqIfiSjvj0CCXv+92Up1SVLSn43z7KErIJvqwzPt1C63V8kzLx/LDn6RzcmYdWPkRqQf3DixsS/3Na4z+4FYWxaZRsbf7SKLtyd/HY74/RJ6IPM86bgVIKs8nMyyNeJiogigdWPMDBIh151hg0iRNx1G3frpSKV0pNc7SFKaWWKqWSHH9DHe1KKTVXKZWslNqmlBpQ6TgTHfZJSqmJTTEWzemNMinCbuyJR7gP2ZWkUUq2ZVK4PA2/gS0JGFq3anXH4+fpx9zRc1FKMXn5ZIoq6hYV1hgopQi5sgteHYPI/SqJigNN3ydn5JblMmX5FAI8A3h91Ot4m72PbgvyCmLu6LlYbBamxk6lxFLShD09PTnpTkQp1Ru4HRgM9APGKqW6Ao8Cy0SkG7DM8RngUqCb43UH8I7jOGHADGCI41gzjjgejaYhMfl4GNUEbUL2ggTK9+aT++UuvNoHEnpN1zqVbXVGu8B2vDryVfYV7OOx1Y9hl6bPHjci1aIx+XmSvSAeW2Hzk0ax2C1MXzmdzJJMXh/1Oi38WlSz6RTciZdGvMTOnJ08seYJHfrbwDTFk0g0sE5EShz5JyuBazEk5+c7bOYDVzveXwUsEIO1QIhSqjVwCbBURHJEJBdYCow5mQPRnDl4RvoRflM0lkPFZM7bhsnPg/BbY2oNm60rg1sP5pHBj7Bi/wre3Pxmgx33RDAHeBE+MQZ7iZXsha4j1SpTvif/qFqAO9iKLZRuz6rTRf6VDa+wPmM9M86bQd/Ivk7thkcN5/6B9/Pbvt94P+599/tkt/Hr3l8ptugSSs5oCieyHRiulApXSvlhSKq0A1qKyJECBxlAS8f7tkDl0m37HW3O2qvh0P7aqJTamJnZ8MldmjMDn+6hBI/tfNSBuBM2W1fG9xjP37r9jffj3mfJniUNfvz64NUmgNDru1Oxr4C873a7dZEvS84j8/1tZL67DWuua3VksdjJ+jie7IWJFK2uWTjzeL7e9TWLdyxmYsxEruxypUv723rdxtjOY3lj8xvEpsa6dY7X/nqN6Sun8+DKBxtFOPN04KQ7ERFJBF4CfsMocrUFsB1nI0CDPXOKyHsiMkhEBkVG1pxJrNG4Q+DQtrR+/By8ogIb5fhKKR4f8jgDWgzgiTVPkJCd0CjnqSt+fSMJHNWO4g0ZFP9ZezEra3YpOZ8m4hHma2iJLUjAXuH8AlxZ08wzKoD8X/ZQtjOn1nNsOrSJ59Y9x9A2Q7l/4P1ujUEpxYxzZ9ArvBePrn6U5NzkWu2/3/098xPm0yeiD2sOrGHOpjlunedMo0kW1kXkAxEZKCIjgFxgF3DIMU2F4++RkJADGE8qR4hytDlr12gaFXeSCU+EI9IoIT4hTFk+hazSxqm8WFeCLuqAT3QYeT/upiy5ZmmUKnkzt/Ui7MaeWDKKyf1yl9MnmKOaZhe2J/KOvni28id78Q6nU2HpRencv+J+2ga05aURL2E2uVd3BcDHw4c5o+bg5+nH5OWTySuruVBYXGYcT//xNINbDWb+pfO5occNfBT/ET/s/sHtc50pNFV0VgvH3/YY6yGfAt8DRyKsJgLfOd5/D0xwRGmdA+Q7pr1+BS5WSoU6FtQvdrRpNKc84b7hzB01l/zyfB5Y8UCjSKPUFWVShN3Qwyji9ekOrNlVpVEq582E39QTjwhffHuGETymE6VxWRTGplU7Ztmu3EqaZu0xeR0RzjQdFc6sTKm1lKmxU6mwVTB39FyCvYPrPI6W/i15fdTrHCo5xPRV07Haq57jcMlhpsZOJdIvklkjZ+Fp8uSRwY9wdquzeeqPp9ietb3O5zydaao8ka+VUgnAD8C9IpIHvAhcpJRKAi50fAb4GUgBkoH3gXsARCQHeBbY4Hg942jTaE4LosOjeXbYs2w+vJnn1z3fLKKKTD4eREx0FPFakFCliFfBstQa82YCRrTF76wWFPy2j9KEY5W1LZklZH+aeEzTzPGE5xHqQ/gthnBmzuIdR7PmRYQn1zzJjpwdvDTiJToHd673OPpF9mPGuTNYl76OWRtnHW0vt5UzLXYaRZYi5o6eS6iPMQ5PkyezR84m0i+SqcunNopw5qlKU01nDReRGBHpJyLLHG3ZInKBiHQTkQuPOARHVNa9ItJFRPqIyMZKx/lQRLo6Xh81xVg0msZkTMcx3N7ndr5OMhaRmwMe4b6E39wTa+axIl4lcVkULkutMW9GKUXotV3xjAog57OdWA4VYy+zkr0gwZCunxBTTdPMu1MwIVd1oWxnLvlL9gLwn7j/sGTvEqYNnMaIqBEnPI6rul7FrTG3sihxEd8mfYuI8MyfzxCXFccLw16ge2j3KvahPqHMGTWHQksh02KnUW4rP+E+nA6o5nB3czIZNGiQbNy40bWhRtNMsIudqbFTWb1/NfMumseQ1kOauksAFK45QP4PKfgNaEFpXBaerf2JvKOv07BnW345h97cjPI04xHhS3lyHpH/7IN3Z+dTUrn/TaZ4bToZF9r5x4HJXNb5Ml4Y9kKD5OYAWO1W7vnfPWw4tIGrulzF10lfc0//e7i7391O9/nfvv9x/4r7ubLLlTw39LkG60tzRyn1l4gMOr5dy55oNM0ckzLxwrAX6BjUkQdXPkhaYfW1hZqosFXw8MqHG+0JJuC8NpjPCqFk02EqvGwu82bMwd6E3xqDJa+U8l25BF3RsVYHAhByRWfs7b0IW2bjEq/zeercp1xetMUu5H6XTP5ve12OwcPkwSsjX2G09VzOWtaaa1pcyZ1976x1nws7XMjd/e7m+93f80nCJy7PcbqjnYhGcwoQ4BXAG6PfQESYsnyKy+S3I1Mzv+z9hefXPe92XkRdsNqt/DvwNRaH/8JDbWaRZElxuc9S6yqeavs277T8grfNC13aF1gLmR7+MnmeRUxNGY9nietLVsGyVIr/TKdweRpFa13rZfkVeDJt700MLI7h7qRrweJ6duaufndxYfsLmf3XbP448IdL+9MZ7UQ0mlOEdkHtmDVyFnvy97iURlmYuJDvdn/HpN6T3M6LqCsvrn+R9ZkbaH15DHlBJUxdPpWcMuexLfFZ8Tz1x1NIF18Ch7Y9uhbhDKvdyvSV00mu2IPnDW1R5UL2wgTE4nzclddmfHqGkfd9SjXhzMocWZsxe5gJuboLtvQScr9OchnEYFImZg6bSZeQLkxfNZ19BftqtT+d0U5EozmFOLfNuUwfNJ3YtFje3vJ2jTZ/HPyDWRtncUH7C5gyYMrRvIgpsVPIL89vkH58sfMLvtj1Bf/o/Q9uibmFOaPnkF2WzQMrHsBiq15NMLMkkymxUwj3CWf2+bOZPmg657Y+l2fWPsOWw1tqPMfsjbNZm76WJ855gj69BhI2rgcVqYXk/je5xot8xcEicr/YeVTTLGx8DzwifMheeEw4szJiF3IW78CaXUb4zTEEnNOGoEs6Uro1k8KV+11+B36efswdNRezMjNl+ZRmIZzZFGgnotGcYtwcfTNXd72aedvm8eveqqlRqQWpPLTyIbqEdOH5Yc9jUqajeREZxRlMX1k9L6KubMjYwAvrXmBE1AimnDUFgF7hvXjmvGf469BfvLD+hSr2FbYK7l9xP4UVhcwdPZcwn7CjaxFt/NswLXYaGcUZVfb5NulbFiYu5JboW7im2zUA+PaOIPCC9pT8dYiiNVWnqWxFFWQvSMDke0zTzBDO7IXYIXtBPPbyqlnzBb/upWxnLiFXdjm6NhM4MgrffpEU/LqX0sRsXBEVGMWr579KakEqj65+9IyURtFORKM5xVBK8cQ5T9Avsh9PrHmCHTk7ACiqKGLy8smYlIm5o+bi5+l3dJ9+kf148twnWZu+ltkbZ9f73AeKDvDgigdpF9SOF4e/WCVb/LLOlzGp9yS+3PUln+/4HDDWZp5d+yxbM7cyc9hMeoT1OGof7B3MG6PfoMxWxtTYqZRZjaeFLYe38OzaZzmn9Tk8OOjBKucPuqA9Pr3Cyf8phbIkI2terHayFyViK7IQPqGqpplnhC/hN/XEcqiE3C+OVWos2XKYwpX78R/SqkqxMaUUoX/rhmcbRzjyYdcCkme3OptHBj/Cyv0reXNL8xDOPJloJ6LRnIJ4mb14fdTrBHoFHpVGeXT1o+wr2MfskbOJCoyqts/VXa/mluhbWJi4sNa1CGeUWEqYsnwKVruVuaPmEuhVXT9s8lmTGRE1ghfXv8iGjA0sSlzEf5P/y1397uKiDhdVs+8c0pmXhr9EYnYiT/7xJBnFGUyLnUYr/1bMGjkLD1PV4qvKpAgb1wPPln5kf7oDS1YpeT/spmJPAWHXdatR08yneyjBl3WmND6bwuWpVOwvJOerJLw6BRFyRZdq9iYvs/E042kie3489pLq03PHc0OPG7iu+3X8J+4//Jzys0v70wmdJ6LRnMLEZ8UzcclE/Dz8yC3P5V9D/sWNPW90al85L+KjSz6if4v+bp3HLnamr5zOstRlvH3B2wxtO9SpbWFFITf/fDNZpVkUW4oZ1W4Ur57/Kibl/J71P3H/Yc6mOYR6h1Jhr2DhpQvpGtrV+Thyyjj85mZQYC+2EjAyipBLOzm1FxFyv9xFyabDmPw8UF5mWtzXH3OAcyXm8r35ZL4fh3fnYCJu640y1x5abLFZ+Odv/yQ+O575l86nV3ivWu1PJvZyKxWphfWuwAk6T0SjOS3pFWGsReSW5/K3bn9jfI/xtdq7Wotwxrxt81i6bykPDHygVgcCEOgVyBuj3wCgc3BnZg6bWasDAZjUexKXdryUvPI8Xhz+Yq0OBMAjzIewm6Oxl9rw6RHqsg68UorQa7rh1S4QsdiNaa9aHAiAd8dgQq/uSnlSHvm/7KnVFo4JZ4b5hDF1+dRmI5x5RNMs6+N4rHmuZfnrin4S0WhOA9IK02gb0NblxfoIKXkp3PTzTXQI6sD8MfPx8fBxarts3zKmrZhW5wztrNIs/Dz8qqzN1IbNbiO9OL3GqThnWHPLMAd5oczujVssNuwlVszB3q6NHeR9v5uiPw4Sen13/Ae2dGm/I2cHE36ZQI/QHnxwyQd4mRu+7kxdyF+6j8JlqQSP7UzgsBpLLrmFfhLRaE5j2gW2c9uBQPW1CGc3k7tyd/HY74/RN6IvT577ZJ0kPiJ8I9x2IABmk7lODgQMsUZ3HQiA8jTXyYEABF/eCe8uweR+k0R5aoFL+55hPXl26LNsydzCzHUzm1Q4szZNs4ZCOxGN5gxlZLuRTBkwhV/2/MKH2z+stj23LJcpy6cQ4BnAa6New9tct4vv6YIymwi7KRpzsDfZnyRgy3ctvHhJx0u4o+8dfJP0DZ/u+PQk9LI6x+fNNJbGl3YiGs0ZzJG1iDmb5rBq/6qj7Ra7hQdXPkhmSSZzRs2hhV+LJuxl02P29yRiQgxSbifrkwTE4jof5N7+9zKq3She2fAKa9PXnoReHqOmvJnGQjsRjeYMRinF00OfpmdYTx5e9TApeYb+1cvrX2ZDxgaeOu8p+kT2aeJeNg88W/kTdkMPLPuL3JZGeWH4C3QK7sSDKx4krcA94cwTpba8mcZAOxGN5gzH18OXuaPn4m32ZvLyyXy0/SM+2/kZt/W6jSu6XNHU3WtW+PYKJ+iiDpRsyaRoletq3P6e/swdNRelFFNiXQtnNgSu8mYaGu1ENBoNrfxb8fqo1zlYfJBX/3qVoW2HMm3AtKbuVrMkcHQ7fPtEkL9kD6U7XRdTbRfUjtkjZ7Mnfw+Prn60VuHME6VobTrF6zIIGBmFX/+TMwWpnYhGowHgrBZnMXPoTIa3Hc7LI16uImmiOYZSitDru+PZyp+cxTuwZLqWRhnSeggPnf0QK9JW8NaWtxqlX+UpeeR9v9utvJmGRDsRjUZzlMs6X8bbF75NkFdQU3elWWPyMhM+IQZlNpE9PwF7qWtRy5t63sS13a7lvW3vsWTvkgbtjzWnjOxFiXiE+xB2Y8+j9epPBtqJaDQaTT3wCPUh/JZo4wK+eMdRcUdnKKV4fMjj9I/szxO/P0FidmKD9MNebiN7QQJiE6NevY+H650aEO1ENBqNpp54dwom5KoulO/KJX+Ja2kUL7MXr416jWDvYKbETiG71LXcfG2IXcj9cieWQ8WE39gTz0j3kzsbCu1ENBqN5gQIGNIa/3NaU7TqAMWbDrm0j/CNYO7oueSV5Tkt4uUuhctTKd2eTfClnfDpEVbv45wI2oloNBrNCRJyRWe8OxvSKBVphS7tY8JjeHbos2w6vInn1z9fL2mU0u1ZFPwvFb+zWhAwvP6aWCeKdiIajUZzgiizibCbozEHepH1SQK2ggqX+4zpNIZ/9vknX+366mgRL3exZBST88VOPNsFEnptt0aTNHEH7UQ0Go2mATD7exIxsRdSZiX7kwTE4jofZPJZk7nHPIE+C4PYst49aRRbsYWsBQkobw8ibo1GeTbtZVw7EY1Go2kgPFv5EzauBxVpheR+61oaxZJWxBU7ziXQFoD3d3nsT6t9cV5sdnIWJWIrKCf81mjMQU0viqmdiEaj0TQgvr0jCLqwPSWbDlP0u3NpFFtBOdmfJGIO8kYmtkKhOPDRZoqLnK+p5P2YQnlKPqHXdMO7ffPI5dFORKPRaBqYwNHt8e0dTv7PeyjblVttu1jsZH2SiJRbiZgQQ4fo7pSMDaBVSTjr3vsJm626SnDR+nSK/0wnYHhbt4pjnSy0E9FoNJoGRpkUodf3wLOlP9mf7sCSVXp0m4iQ+00SlrRCwsb1wLOVPwADzh3K7kG5dD/clhWfflvleOV788n7bjfe3UMJrqWWfFPQJE5EKXW/UipeKbVdKbVYKeWjlPpYKbVHKbXF8ervsFVKqblKqWSl1Dal1IBKx5molEpyvCY2xVg0Go2mJkzeDmkUE2TPj8deZkijFK0+QMnmwwRd2B7f3hFV9jn/2ivZEbWfHvEtWbt8GQDWvDKyP0k0MuTH9zipkibucNKdiFKqLTAFGCQivQEzMN6x+SER6e94bXG0XQp0c7zuAN5xHCcMmAEMAQYDM5RSoSdvJBqNRlM7HmE+hN0cjTW7jJzPdlK6I4f8X/bg2zucwNHtq9mbTCaG3X41+wIziFgqJMVtNyRNrHZD0sTPswlGUTtNNZ3lAfgqpTwAP+BgLbZXAQvEYC0QopRqDVwCLBWRHBHJBZYCYxq74xqNRlMXfLqEEHJlZ8p25JA9Px7Plv6EXu/8icLH25du/xxGiUc53ouysRwsJuzGnni2cC1pkpKfwrbMbQ09hFo56U5ERA4As4BUIB3IF5HfHJtnOqasXlNKHYldawtULgm239HmrL0aSqk7lFIblVIbMzMzG3A0Go1G45qAc9oQcF4bzEFexhOFd+0y+y1atsZzXFuKzKUs6bIej+6ui0ulFaYx4ZcJ3LbkNrYc3uLSvqFoiumsUIyni05AG8BfKXUL8BjQEzgbCAMeaahzish7IjJIRAZFRkY21GE1Go3GbUKu7EKrRwbjEebjln10n/6k/N3KHK+PeWn9S7XaFluKmbJ8CiJCC78W3L/ifg4Vu9bxagiaYjrrQmCPiGSKiAX4BjhPRNIdU1blwEcY6xwAB4B2lfaPcrQ5a9doNJpmSV0Xxcd2Hcvfe/2dz3d+zhc7v6jRxi52/rX6X6Tkp/DKyFd4c/SblFhKmBo7lTJrWUN0u1aawomkAucopfyUIfhyAZDoWOfA0XY1sN1h/z0wwRGldQ7G9Fc68CtwsVIq1PF0c7GjTaPRaE4bpg6YyrC2w3hh3QtszNhYbfs7W99hedpypg+aznltzqNraFdeGP4C8dnxPPXnU/USd6wLTbEmsg74CtgExDn68B6wSCkV52iLAJ5z7PIzkAIkA+8D9ziOkwM8C2xwvJ5xtGk0Gs1pg9lk5qURLxEVGMUDKx7gYNGxOKTf9v7Gu1vf5aouV3FL9C1H20e3H83ksybzU8pPfBz/caP2TzW2l2puDBo0SDZurO7NNRqNpjmzJ38PN/90M20C2rDg0gWkFqYy4ZcJdA/tzoeXfIiX2auKvYjw0KqH+G3vb7x1wVsMjxp+QudXSv0lIoOqtWsnotFoNKcGq/ev5r7l9zEiagS7cnZhFSufj/2cCN+IGu1LLCVMXDKR/YX7WXT5IjoHd673uZ05ES17otFoNKcIw6OGc/+A+1mRtoLssmzmjprr1IEA+Hn6MXfUXLzMXkxdPpWCioIG79PJreiu0Wg0mhNiYq+J2LHTPbQ7vSJ6ubRvHdCaV89/lYUJCzE1wnODns7SaDQajUv0dJZGo9FoGhztRDQajUZTb7QT0Wg0Gk290U5Eo9FoNPVGOxGNRqPR1BvtRDQajUZTb7QT0Wg0Gk290U5Eo9FoNPXmjEs2VEplAvvquXsEkNWA3TlV0OM+s9DjPrNwd9wdRKRaVb8zzomcCEqpjTVlbJ7u6HGfWehxn1mc6Lj1dJZGo9Fo6o12IhqNRqOpN9qJ1I33mroDTYQe95mFHveZxQmNW6+JaDQajabe6CcRjUaj0dQb7UQ0Go1GU2+0E3EDpdQYpdROpVSyUurRpu5PY6KU+lApdVgptb1SW5hSaqlSKsnxN7Qp+9gYKKXaKaVilVIJSql4pdRUR/tpPXallI9Sar1Saqtj3E872jsppdY5fvOfK6W8mrqvjYFSyqyU2qyU+tHx+bQft1Jqr1IqTim1RSm10dFW79+5diIuUEqZgbeAS4EY4EalVEzT9qpR+RgYc1zbo8AyEekGLHN8Pt2wAg+KSAxwDnCv49/5dB97OTBaRPoB/YExSqlzgJeA10SkK5ALTGrCPjYmU4HESp/PlHGPEpH+lfJD6v07107ENYOBZBFJEZEK4DPgqibuU6MhIquAnOOarwLmO97PB64+qZ06CYhIuohscrwvxLiwtOU0H7sYFDk+ejpeAowGvnK0n3bjBlBKRQGXA/9xfFacAeN2Qr1/59qJuKYtkFbp835H25lESxFJd7zPAFo2ZWcaG6VUR+AsYB1nwNgdUzpbgMPAUmA3kCciVofJ6fqbfx14GLA7PodzZoxbgN+UUn8ppe5wtNX7d+7R0L3TnN6IiCilTtu4cKVUAPA1ME1ECoybU4PTdewiYgP6K6VCgG+Bnk3cpUZHKTUWOCwifymlzm/q/pxkhonIAaVUC2CpUmpH5Y11/Z3rJxHXHADaVfoc5Wg7kziklGoN4Ph7uIn70ygopTwxHMgiEfnG0XxGjB1ARPKAWOBcIEQpdeQm83T8zQ8FrlRK7cWYoh4NzOH0HzcicsDx9zDGTcNgTuB3rp2IazYA3RxRG17AeOD7Ju7TyeZ7YKLj/UTguybsS6PgmA//AEgUkVcrbTqtx66UinQ8gaCU8gUuwlgPigWuc5idduMWkcdEJEpEOmL8n14uIjdzmo9bKeWvlAo88h64GNjOCfzOdca6GyilLsOYPzUDH4rIzCbuUqOhlFoMnI8hD30ImAH8F/gCaI8hoz9ORI5ffD+lUUoNA1YDcRybI/8XxrrIaTt2pVRfjIVUM8ZN5Rci8oxSqjPGHXoYsBm4RUTKm66njYdjOmu6iIw93cftGN+3jo8ewKciMlMpFU49f+faiWg0Go2m3ujpLI1Go9HUG+1ENBqNRlNvtBPRaDQaTb3RTkSj0Wg09UY7EY1Go9HUG+1ENGccSilRSs2u9Hm6UuqpBjr2x0qp61xbnvB5rldKJSqlYo9rP/+IIm0djjVNKeXXsD3UnCloJ6I5EykHrlVKRTR1RypTKVPaHSYBt4vIqAY49TRAOxFNvdBORHMmYsWoK33/8RuOf5JQShU5/p6vlFqplPpOKZWilHpRKXWzoxZHnFKqS6XDXKiU2qiU2uXQaDoicviKUmqDUmqbUurOSsddrZT6HkiooT83Oo6/XSn1kqPtSWAY8IFS6pUaxheklPpJGTVw3lVKmRz7XayU+lMptUkp9aVSKkApNQVoA8Qqo56K2fEdbHect9p3pNFURgswas5U3gK2KaVersM+/YBoDKn8FOA/IjJYGQWsJmPc0QN0xNAj6oJxce4KTADyReRspZQ3sEYp9ZvDfgDQW0T2VD6ZUqoNRn2LgRi1LX5TSl3tyCgfjZFlvbGGfg7GqH2zD1iC8dS1Avg3cKGIFCulHgEecBzrAYz6EllKqYFAWxHp7ehDSB2+H80ZiHYimjMSh0LvAmAKUOrmbhuOyGUrpXYDR5xAHFB5WukLEbEvsSe5AAABsUlEQVQDSUqpFAxV3IuBvpWecoKBbkAFsP54B+LgbGCFiGQ6zrkIGIEhQ1Mb60UkxbHPYoynljIMx7LGoUzsBfxZw74pQGel1BvAT5XGqNHUiHYimjOZ14FNwEeV2qw4pnkd00CVy6NW1lCyV/psp+r/peO1hARQwGQR+bXyBoduU3H9uu8UZ+dfKiI31rqjSK5Sqh9wCXAXMA74RwP3T3MaoddENGcsDoG5L6haAnUvxvQRwJUYlf7qyvVKKZNjnaQzsBP4FbjbITePUqq7Q0W1NtYDI5VSEcoo03wjsNKN8w92qE6bgBuA34G1wFDH1NoRNdfuDvtC4IiyawRgEpGvMaa/Brg/bM2ZiH4S0ZzpzAbuq/T5feA7pdRWjPWE+jwlpGI4gCDgLhEpU0r9B2OtZJNDdj4TFyVIRSRdKfUohjy5An4SEXckujcAbwJdHft+KyJ2pdRtwGLHmgwYTmIXRpDBEqXUQYx1nY+OLMYDj7k5Zs0Zilbx1Wg0Gk290dNZGo1Go6k32oloNBrN/9urYwEAAACAQf7Wc9hdErFJBIBNIgBsEgFgkwgAm0QA2AKVYlZ4nCbLuAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dnjL78WMeapa",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "outputId": "61e22a10-7a14-45f1-b52d-b07de47f1e14"
+      },
+      "source": [
+        "#Create a list for calculating final funds\n",
+        "final_funds= []\n",
+        "\n",
+        "#Run 1000 iterations :\n",
+        "for i in range(1000):\n",
+        "    ending_fund = play(10000,100,50)\n",
+        "\n",
+        "#Print the money the player ends with\n",
+        "print(\"The player started with $10,000\")\n",
+        "print(\"The player left with $\",str(sum(ending_fund)/len(ending_fund)))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "The player started with $10,000\n",
+            "The player left with $ 9919.8\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cz2BKZwEed6E",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 331
+        },
+        "outputId": "858c586d-ca06-4579-ad46-0a870687cbb3"
+      },
+      "source": [
+        "#Create a list for calculating final funds\n",
+        "final_funds= []\n",
+        "\n",
+        "#Run 10 iterations :\n",
+        "for i in range(10):\n",
+        "    ending_fund = play(10000,100,5)\n",
+        "\n",
+        "#Print the money the player ends with\n",
+        "print(\"Number of bets = 5\")\n",
+        "print(\"The player started with $10,000\")\n",
+        "print(\"The player left with $\",str(sum(ending_fund)/len(ending_fund)))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Number of bets = 5\n",
+            "The player started with $10,000\n",
+            "The player left with $ 9960.0\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "t_Xqrbf6ehe7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 331
+        },
+        "outputId": "d46d94d7-66b0-4316-dbe6-02b637b1a1cd"
+      },
+      "source": [
+        "#Create a list for calculating final funds\n",
+        "final_funds= []\n",
+        "\n",
+        "#Run 10 iterations :\n",
+        "for i in range(10):\n",
+        "    ending_fund = play(10000,100,100)\n",
+        "\n",
+        "#Print the money the player ends with\n",
+        "print(\"Number of bets = 100\")\n",
+        "print(\"The player started with $10,000\")\n",
+        "print(\"The player left with $\",str(sum(ending_fund)/len(ending_fund)))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Number of bets = 100\n",
+            "The player started with $10,000\n",
+            "The player left with $ 9820.0\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mdIz7H-Tej0N",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 333
+        },
+        "outputId": "e800239c-b551-4376-9a66-e3e42edc39ab"
+      },
+      "source": [
+        "#Create a list for calculating final funds\n",
+        "final_funds= []\n",
+        "\n",
+        "#Run 10 iterations :\n",
+        "for i in range(10):\n",
+        "    ending_fund = play(10000,100,1000)\n",
+        "\n",
+        "#Print the money the player ends with\n",
+        "print(\"Number of bets = 1000\")\n",
+        "print(\"The player started with $10,000\")\n",
+        "print(\"The player left with $\",str(sum(ending_fund)/len(ending_fund)))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Number of bets = 1000\n",
+            "The player started with $10,000\n",
+            "The player left with $ 7380.0\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nnGBCHVwemN4",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 331
+        },
+        "outputId": "ce3c5774-805d-4312-8754-8d6d20d82c9e"
+      },
+      "source": [
+        "#Create a list for calculating final funds\n",
+        "final_funds= []\n",
+        "\n",
+        "#Run 10 iterations :\n",
+        "for i in range(10):\n",
+        "    ending_fund = play(10000,100,5000)\n",
+        "\n",
+        "#Print the money the player ends with\n",
+        "print(\"Number of bets = 5000\")\n",
+        "print(\"The player started with $10,000\")\n",
+        "print(\"The player left with $\",str(sum(ending_fund)/len(ending_fund)))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Number of bets = 5000\n",
+            "The player started with $10,000\n",
+            "The player left with $ -2060.0\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Fk4lvMCAeo6_",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 331
+        },
+        "outputId": "4ac7b478-69f6-45d2-f08c-d3ec8f52ba55"
+      },
+      "source": [
+        "#Create a list for calculating final funds\n",
+        "final_funds= []\n",
+        "\n",
+        "#Run 10 iterations :\n",
+        "for i in range(10):\n",
+        "    ending_fund = play(10000,100,10000)\n",
+        "\n",
+        "#Print the money the player ends with\n",
+        "print(\"Number of bets = 10000\")\n",
+        "print(\"The player started with $10,000\")\n",
+        "print(\"The player left with $\",str(sum(ending_fund)/len(ending_fund)))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Number of bets = 10000\n",
+            "The player started with $10,000\n",
+            "The player left with $ -12740.0\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_2_Python_Packages.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_2_Python_Packages.ipynb"
new file mode 100644
index 0000000..c0042ad
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_2_Python_Packages.ipynb"
@@ -0,0 +1,1797 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "phP2vS12A3Vp"
+      },
+      "source": [
+        "You can read a lot more about Python classes [in the documentation](https://docs.python.org/3.7/tutorial/classes.html).\n",
+        "\n",
+        "# 1. Numpy\n",
+        "\n",
+        "Numpy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.\n",
+        "\n",
+        "##Arrays\n",
+        "\n",
+        "A numpy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the *rank* of the array; the *shape* of an array is a tuple of integers giving the size of the array along each dimension.\n",
+        "\n",
+        "We can initialize numpy arrays from nested Python lists, and access elements using square brackets:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "95iZ78oIBo3U",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 118
+        },
+        "outputId": "475381ad-0b51-4c5c-aea8-95282bb5a609"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "a = np.array([1, 2, 3])   # Create a rank 1 array\n",
+        "print(type(a))            # Prints \"<class 'numpy.ndarray'>\"\n",
+        "print(a.shape)            # Prints \"(3,)\"\n",
+        "print(a[0], a[1], a[2])   # Prints \"1 2 3\"\n",
+        "a[0] = 5                  # Change an element of the array\n",
+        "print(a)                  # Prints \"[5, 2, 3]\"\n",
+        "\n",
+        "b = np.array([[1,2,3],[4,5,6]])    # Create a rank 2 array\n",
+        "print(b.shape)                     # Prints \"(2, 3)\"\n",
+        "print(b[0, 0], b[0, 1], b[1, 0])   # Prints \"1 2 4\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "<type 'numpy.ndarray'>\n",
+            "(3,)\n",
+            "(1, 2, 3)\n",
+            "[5 2 3]\n",
+            "(2, 3)\n",
+            "(1, 2, 4)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4vFfuh7CBuJV"
+      },
+      "source": [
+        "Numpy also provides many functions to create arrays:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7V5tqkhIB0g2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 168
+        },
+        "outputId": "aaecf656-96dc-44b7-ab12-17f0b14af54e"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "a = np.zeros((2,2))   # Create an array of all zeros\n",
+        "print(a)              # Prints \"[[ 0.  0.]\n",
+        "                      #          [ 0.  0.]]\"\n",
+        "\n",
+        "b = np.ones((1,2))    # Create an array of all ones\n",
+        "print(b)              # Prints \"[[ 1.  1.]]\"\n",
+        "\n",
+        "c = np.full((2,2), 7)  # Create a constant array\n",
+        "print(c)               # Prints \"[[ 7.  7.]\n",
+        "                       #          [ 7.  7.]]\"\n",
+        "\n",
+        "d = np.eye(2)         # Create a 2x2 identity matrix\n",
+        "print(d)              # Prints \"[[ 1.  0.]\n",
+        "                      #          [ 0.  1.]]\"\n",
+        "\n",
+        "e = np.random.random((2,2))  # Create an array filled with random values\n",
+        "print(e)                     # Might print \"[[ 0.91940167  0.08143941]\n",
+        "                             #               [ 0.68744134  0.87236687]]\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[0. 0.]\n",
+            " [0. 0.]]\n",
+            "[[1. 1.]]\n",
+            "[[7 7]\n",
+            " [7 7]]\n",
+            "[[1. 0.]\n",
+            " [0. 1.]]\n",
+            "[[0.6954391  0.83532001]\n",
+            " [0.49623436 0.53147419]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fYy0KgJjB5ev"
+      },
+      "source": [
+        "You can read about other methods of array creation [in the documentation](http://docs.scipy.org/doc/numpy/user/basics.creation.html#arrays-creation).\n",
+        "\n",
+        "##Array indexing\n",
+        "\n",
+        "Numpy offers several ways to index into arrays.\n",
+        "\n",
+        "###Slicing\n",
+        "\n",
+        "Similar to Python lists, numpy arrays can be sliced. Since arrays may be multidimensional, you must specify a slice for each dimension of the array:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mECt4JAYCQ8c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 50
+        },
+        "outputId": "e4efb1b3-c42a-434d-ed43-12423b40d603"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Create the following rank 2 array with shape (3, 4)\n",
+        "# [[ 1  2  3  4]\n",
+        "#  [ 5  6  7  8]\n",
+        "#  [ 9 10 11 12]]\n",
+        "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
+        "\n",
+        "# Use slicing to pull out the subarray consisting of the first 2 rows\n",
+        "# and columns 1 and 2; b is the following array of shape (2, 2):\n",
+        "# [[2 3]\n",
+        "#  [6 7]]\n",
+        "b = a[:2, 1:3]\n",
+        "\n",
+        "# A slice of an array is a view into the same data, so modifying it\n",
+        "# will modify the original array.\n",
+        "print(a[0, 1])   # Prints \"2\"\n",
+        "b[0, 0] = 77     # b[0, 0] is the same piece of data as a[0, 1]\n",
+        "print(a[0, 1])   # Prints \"77\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "2\n",
+            "77\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GVy4_GX5CYLD"
+      },
+      "source": [
+        "You can also mix integer indexing with slice indexing. However, doing so will yield an array of lower rank than the original array. Note that this is quite different from the way that MATLAB handles array slicing:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YEY_yDDKClZ-",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 118
+        },
+        "outputId": "dfae9773-f154-4144-da80-89b4ad01d5b5"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Create the following rank 2 array with shape (3, 4)\n",
+        "# [[ 1  2  3  4]\n",
+        "#  [ 5  6  7  8]\n",
+        "#  [ 9 10 11 12]]\n",
+        "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
+        "\n",
+        "# Two ways of accessing the data in the middle row of the array.\n",
+        "# Mixing integer indexing with slices yields an array of lower rank,\n",
+        "# while using only slices yields an array of the same rank as the\n",
+        "# original array:\n",
+        "row_r1 = a[1, :]    # Rank 1 view of the second row of a\n",
+        "row_r2 = a[1:2, :]  # Rank 2 view of the second row of a\n",
+        "print(row_r1, row_r1.shape)  # Prints \"[5 6 7 8] (4,)\"\n",
+        "print(row_r2, row_r2.shape)  # Prints \"[[5 6 7 8]] (1, 4)\"\n",
+        "\n",
+        "# We can make the same distinction when accessing columns of an array:\n",
+        "col_r1 = a[:, 1]\n",
+        "col_r2 = a[:, 1:2]\n",
+        "print(col_r1, col_r1.shape)  # Prints \"[ 2  6 10] (3,)\"\n",
+        "print(col_r2, col_r2.shape)  # Prints \"[[ 2]\n",
+        "                             #          [ 6]\n",
+        "                             #          [10]] (3, 1)\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(array([5, 6, 7, 8]), (4,))\n",
+            "(array([[5, 6, 7, 8]]), (1, 4))\n",
+            "(array([ 2,  6, 10]), (3,))\n",
+            "(array([[ 2],\n",
+            "       [ 6],\n",
+            "       [10]]), (3, 1))\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h4jnbBVuCtnz"
+      },
+      "source": [
+        "###Integer array indexing\n",
+        "\n",
+        "When you index into numpy arrays using slicing, the resulting array view will always be a subarray of the original array. In contrast, integer array indexing allows you to construct arbitrary arrays using the data from another array. Here is an example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QEo_GRgzC6m-",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 84
+        },
+        "outputId": "900edad7-8f6a-42ea-9d33-8743868e8d2f"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "a = np.array([[1,2], [3, 4], [5, 6]])\n",
+        "\n",
+        "# An example of integer array indexing.\n",
+        "# The returned array will have shape (3,) and\n",
+        "print(a[[0,1], [1,1]])  # Prints \"[1 4 5]\"\n",
+        "#primul [0,1,2] - alegi array-ul\n",
+        "#al doilea alegi indexul corespunzator array-ului respectiv\n",
+        "\n",
+        "# The above example of integer array indexing is equivalent to this:\n",
+        "print(np.array([a[0, 0], a[1, 1], a[2, 0]]))  # Prints \"[1 4 5]\"\n",
+        "\n",
+        "# When using integer array indexing, you can reuse the same\n",
+        "# element from the source array:\n",
+        "print(a[[0, 0], [1, 1]])  # Prints \"[2 2]\"\n",
+        "\n",
+        "# Equivalent to the previous integer array indexing example\n",
+        "print(np.array([a[0, 1], a[0, 1]]))  # Prints \"[2 2]\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[2 4]\n",
+            "[1 4 5]\n",
+            "[2 2]\n",
+            "[2 2]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FnueaModDAVt"
+      },
+      "source": [
+        "One useful trick with integer array indexing is selecting or mutating one element from each row of a matrix:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "teKdkq30DFeG",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 168
+        },
+        "outputId": "5bc0b45e-99d1-4ac5-9c31-5c39cbd153d7"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Create a new array from which we will select elements\n",
+        "a = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
+        "\n",
+        "print(a)  # prints \"array([[ 1,  2,  3],\n",
+        "          #                [ 4,  5,  6],\n",
+        "          #                [ 7,  8,  9],\n",
+        "          #                [10, 11, 12]])\"\n",
+        "\n",
+        "# Create an array of indices\n",
+        "b = np.array([0, 2, 0, 1])\n",
+        "\n",
+        "# Select one element from each row of a using the indices in b\n",
+        "print(a[np.arange(4), b])  # Prints \"[ 1  6  7 11]\"\n",
+        "\n",
+        "# Mutate one element from each row of a using the indices in b\n",
+        "a[np.arange(4), b] += 10\n",
+        "\n",
+        "print(a)  # prints \"array([[11,  2,  3],\n",
+        "          #                [ 4,  5, 16],\n",
+        "          #                [17,  8,  9],\n",
+        "          #                [10, 21, 12]])"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[ 1  2  3]\n",
+            " [ 4  5  6]\n",
+            " [ 7  8  9]\n",
+            " [10 11 12]]\n",
+            "[ 1  6  7 11]\n",
+            "[[11  2  3]\n",
+            " [ 4  5 16]\n",
+            " [17  8  9]\n",
+            " [10 21 12]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9LtmzO68DJgr"
+      },
+      "source": [
+        "###Boolean array indexing\n",
+        "\n",
+        "Boolean array indexing lets you pick out arbitrary elements of an array. Frequently this type of indexing is used to select the elements of an array that satisfy some condition. Here is an example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1c6neAk_DPR8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 101
+        },
+        "outputId": "4895a94c-f571-4a32-ce62-c005e78b662e"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "a = np.array([[1,2], [3, 4], [5, 6]])\n",
+        "\n",
+        "bool_idx = (a > 2)   # Find the elements of a that are bigger than 2;\n",
+        "                     # this returns a numpy array of Booleans of the same\n",
+        "                     # shape as a, where each slot of bool_idx tells\n",
+        "                     # whether that element of a is > 2.\n",
+        "\n",
+        "print(bool_idx)      # Prints \"[[False False]\n",
+        "                     #          [ True  True]\n",
+        "                     #          [ True  True]]\"\n",
+        "\n",
+        "# We use boolean array indexing to construct a rank 1 array\n",
+        "# consisting of the elements of a corresponding to the True values\n",
+        "# of bool_idx\n",
+        "print(a[bool_idx])  # Prints \"[3 4 5 6]\"\n",
+        "\n",
+        "# We can do all of the above in a single concise statement:\n",
+        "print(a[a > 2])     # Prints \"[3 4 5 6]\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[False False]\n",
+            " [ True  True]\n",
+            " [ True  True]]\n",
+            "[3 4 5 6]\n",
+            "[3 4 5 6]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CySWjuR6DjuW"
+      },
+      "source": [
+        "For brevity we have left out a lot of details about numpy array indexing; if you want to know more you should [read the documentation](http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html).\n",
+        "\n",
+        "##Datatypes\n",
+        "\n",
+        "Every numpy array is a grid of elements of the same type. Numpy provides a large set of numeric datatypes that you can use to construct arrays. Numpy tries to guess a datatype when you create an array, but functions that construct arrays usually also include an optional argument to explicitly specify the datatype. Here is an example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ox5QZ5HyEHkO",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 67
+        },
+        "outputId": "d14dc70f-3643-4fb4-bac3-c1b4b0b4cd2f"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "x = np.array([1, 2])   # Let numpy choose the datatype\n",
+        "print(x.dtype)         # Prints \"int64\"\n",
+        "\n",
+        "x = np.array([1.0, 2.0])   # Let numpy choose the datatype\n",
+        "print(x.dtype)             # Prints \"float64\"\n",
+        "\n",
+        "x = np.array([1, 2], dtype=np.int64)   # Force a particular datatype\n",
+        "print(x.dtype)                         # Prints \"int64\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "int64\n",
+            "float64\n",
+            "int64\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AvPTXfZeEKk5"
+      },
+      "source": [
+        "You can read all about numpy datatypes [in the documentation](http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html).\n",
+        "\n",
+        "##Array math\n",
+        "\n",
+        "Basic mathematical functions operate elementwise on arrays, and are available both as operator overloads and as functions in the numpy module:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VRbBCC-wEiQg",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 319
+        },
+        "outputId": "fb1c0a72-a45d-42c9-be73-b3547e52154c"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "x = np.array([[1,2],[3,4]], dtype=np.float64)\n",
+        "y = np.array([[5,6],[7,8]], dtype=np.float64)\n",
+        "\n",
+        "# Elementwise sum; both produce the array\n",
+        "# [[ 6.0  8.0]\n",
+        "#  [10.0 12.0]]\n",
+        "print(x + y)\n",
+        "print(np.add(x, y))\n",
+        "\n",
+        "# Elementwise difference; both produce the array\n",
+        "# [[-4.0 -4.0]\n",
+        "#  [-4.0 -4.0]]\n",
+        "print(x - y)\n",
+        "print(np.subtract(x, y))\n",
+        "\n",
+        "# Elementwise product; both produce the array\n",
+        "# [[ 5.0 12.0]\n",
+        "#  [21.0 32.0]]\n",
+        "print(x * y)\n",
+        "print(np.multiply(x, y))\n",
+        "\n",
+        "# Elementwise division; both produce the array\n",
+        "# [[ 0.2         0.33333333]\n",
+        "#  [ 0.42857143  0.5       ]]\n",
+        "print(x / y)\n",
+        "print(np.divide(x, y))\n",
+        "\n",
+        "# Elementwise square root; produces the array\n",
+        "# [[ 1.          1.41421356]\n",
+        "#  [ 1.73205081  2.        ]]\n",
+        "print(np.sqrt(x))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[ 6.  8.]\n",
+            " [10. 12.]]\n",
+            "[[ 6.  8.]\n",
+            " [10. 12.]]\n",
+            "[[-4. -4.]\n",
+            " [-4. -4.]]\n",
+            "[[-4. -4.]\n",
+            " [-4. -4.]]\n",
+            "[[ 5. 12.]\n",
+            " [21. 32.]]\n",
+            "[[ 5. 12.]\n",
+            " [21. 32.]]\n",
+            "[[0.2        0.33333333]\n",
+            " [0.42857143 0.5       ]]\n",
+            "[[0.2        0.33333333]\n",
+            " [0.42857143 0.5       ]]\n",
+            "[[1.         1.41421356]\n",
+            " [1.73205081 2.        ]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yrTXIl8sEs3f"
+      },
+      "source": [
+        "Note that unlike MATLAB, `*` is elementwise multiplication, not matrix multiplication. We instead use the `dot` function to compute inner products of vectors, to multiply a vector by a matrix, and to multiply matrices. `dot` is available both as a function in the numpy module and as an instance method of array objects:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "66sNDwkpE8op",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 151
+        },
+        "outputId": "48ccaf83-891e-4687-97ca-85c2349556cb"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "x = np.array([[1,2],[3,4]])\n",
+        "y = np.array([[5,6],[7,8]])\n",
+        "\n",
+        "v = np.array([9,10])\n",
+        "w = np.array([11, 12])\n",
+        "\n",
+        "# Inner product of vectors; both produce 219\n",
+        "print(v.dot(w))\n",
+        "print(np.dot(v, w))\n",
+        "\n",
+        "# Matrix / vector product; both produce the rank 1 array [29 67]\n",
+        "print(x.dot(v))\n",
+        "print(np.dot(x, v))\n",
+        "\n",
+        "# Matrix / matrix product; both produce the rank 2 array\n",
+        "# [[19 22]\n",
+        "#  [43 50]]\n",
+        "print(x.dot(y))\n",
+        "print(np.dot(x, y))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "219\n",
+            "219\n",
+            "[29 67]\n",
+            "[29 67]\n",
+            "[[19 22]\n",
+            " [43 50]]\n",
+            "[[19 22]\n",
+            " [43 50]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m8gZf_6lFAVt"
+      },
+      "source": [
+        "Numpy provides many useful functions for performing computations on arrays; one of the most useful is `sum`:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vBen0kKQFJF1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 67
+        },
+        "outputId": "20b7048b-e44b-4efc-e7d3-5821fd786be2"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "x = np.array([[1,2],[3,4]])\n",
+        "\n",
+        "print(np.sum(x))  # Compute sum of all elements; prints \"10\"\n",
+        "print(np.sum(x, axis=0))  # Compute sum of each column; prints \"[4 6]\"\n",
+        "print(np.sum(x, axis=1))  # Compute sum of each row; prints \"[3 7]\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "10\n",
+            "[4 6]\n",
+            "[3 7]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ji1mKJf5Fg1k"
+      },
+      "source": [
+        "You can find the full list of mathematical functions provided by numpy [in the documentation](http://docs.scipy.org/doc/numpy/reference/routines.math.html).\n",
+        "\n",
+        "Apart from computing mathematical functions using arrays, we frequently need to reshape or otherwise manipulate data in arrays. The simplest example of this type of operation is transposing a matrix; to transpose a matrix, simply use the `T` attribute of an array object:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "waRKr66yF0RV",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 118
+        },
+        "outputId": "e3c81244-7137-4ee2-b8b8-543704e56e3d"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "x = np.array([[1,2], [3,4]])\n",
+        "print(x)    # Prints \"[[1 2]\n",
+        "            #          [3 4]]\"\n",
+        "print(x.T)  # Prints \"[[1 3]\n",
+        "            #          [2 4]]\"\n",
+        "\n",
+        "# Note that taking the transpose of a rank 1 array does nothing:\n",
+        "v = np.array([1,2,3])\n",
+        "print(v)    # Prints \"[1 2 3]\"\n",
+        "print(v.T)  # Prints \"[1 2 3]\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[1 2]\n",
+            " [3 4]]\n",
+            "[[1 3]\n",
+            " [2 4]]\n",
+            "[1 2 3]\n",
+            "[1 2 3]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yfBlU2kgF3vE"
+      },
+      "source": [
+        "Numpy provides many more functions for manipulating arrays; you can see the full list [in the documentation](http://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html).\n",
+        "\n",
+        "##Broadcasting\n",
+        "\n",
+        "Broadcasting is a powerful mechanism that allows numpy to work with arrays of different shapes when performing arithmetic operations. Frequently we have a smaller array and a larger array, and we want to use the smaller array multiple times to perform some operation on the larger array.\n",
+        "\n",
+        "For example, suppose that we want to add a constant vector to each row of a matrix. We could do it like this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fQgMaLdFGV6K",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 84
+        },
+        "outputId": "ec0b7211-4fb2-4175-c5f5-8d9466182151"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# We will add the vector v to each row of the matrix x,\n",
+        "# storing the result in the matrix y\n",
+        "x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
+        "v = np.array([1, 0, 1])\n",
+        "y = np.empty_like(x)   # Create an empty matrix with the same shape as x\n",
+        "\n",
+        "# Add the vector v to each row of the matrix x with an explicit loop\n",
+        "for i in range(4):\n",
+        "    y[i, :] = x[i, :] + v\n",
+        "\n",
+        "# Now y is the following\n",
+        "# [[ 2  2  4]\n",
+        "#  [ 5  5  7]\n",
+        "#  [ 8  8 10]\n",
+        "#  [11 11 13]]\n",
+        "print(y)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[ 2  2  4]\n",
+            " [ 5  5  7]\n",
+            " [ 8  8 10]\n",
+            " [11 11 13]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nWI9Vb6IGbL1"
+      },
+      "source": [
+        "This works; however when the matrix `x` is very large, computing an explicit loop in Python could be slow. Note that adding the vector `v` to each row of the matrix `x` is equivalent to forming a matrix `vv` by stacking multiple copies of `v` vertically, then performing elementwise summation of `x` and `vv`. We could implement this approach like this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ffVP_lOOGw6e",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 151
+        },
+        "outputId": "92d618e9-4634-456a-fa0c-faf42a264756"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# We will add the vector v to each row of the matrix x,\n",
+        "# storing the result in the matrix y\n",
+        "x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
+        "v = np.array([1, 0, 1])\n",
+        "vv = np.tile(v, (4, 1))   # Stack 4 copies of v on top of each other\n",
+        "print(vv)                 # Prints \"[[1 0 1]\n",
+        "                          #          [1 0 1]\n",
+        "                          #          [1 0 1]\n",
+        "                          #          [1 0 1]]\"\n",
+        "y = x + vv  # Add x and vv elementwise\n",
+        "print(y)  # Prints \"[[ 2  2  4\n",
+        "          #          [ 5  5  7]\n",
+        "          #          [ 8  8 10]\n",
+        "          #          [11 11 13]]\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[1 0 1]\n",
+            " [1 0 1]\n",
+            " [1 0 1]\n",
+            " [1 0 1]]\n",
+            "[[ 2  2  4]\n",
+            " [ 5  5  7]\n",
+            " [ 8  8 10]\n",
+            " [11 11 13]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dbR6fU5GGzMz"
+      },
+      "source": [
+        "Numpy broadcasting allows us to perform this computation without actually creating multiple copies of `v`. Consider this version, using broadcasting:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vQ3djy02G4r2",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 84
+        },
+        "outputId": "e2c45fa6-39fd-4237-95ba-0d4240438ad9"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# We will add the vector v to each row of the matrix x,\n",
+        "# storing the result in the matrix y\n",
+        "x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
+        "v = np.array([1, 0, 1])\n",
+        "y = x + v  # Add v to each row of x using broadcasting\n",
+        "print(y)  # Prints \"[[ 2  2  4]\n",
+        "          #          [ 5  5  7]\n",
+        "          #          [ 8  8 10]\n",
+        "          #          [11 11 13]]\""
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[ 2  2  4]\n",
+            " [ 5  5  7]\n",
+            " [ 8  8 10]\n",
+            " [11 11 13]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6zqsq3xLG9hD"
+      },
+      "source": [
+        "The line `y = x + v` works even though `x` has shape `(4, 3)` and `v` has shape `(3,)` due to broadcasting; this line works as if `v` actually had shape `(4, 3)`, where each row was a copy of `v`, and the sum was performed elementwise.\n",
+        "\n",
+        "Broadcasting two arrays together follows these rules:\n",
+        "\n",
+        "1. If the arrays do not have the same rank, prepend the shape of the lower rank array with 1s until both shapes have the same length.\n",
+        "2. The two arrays are said to be *compatible* in a dimension if they have the same size in the dimension, or if one of the arrays has size 1 in that dimension.\n",
+        "3. The arrays can be broadcast together if they are compatible in all dimensions.\n",
+        "4. After broadcasting, each array behaves as if it had shape equal to the elementwise maximum of shapes of the two input arrays.\n",
+        "5. In any dimension where one array had size 1 and the other array had size greater than 1, the first array behaves as if it were copied along that dimension.\n",
+        "\n",
+        "For more details, you can read the explanation [from the documentation](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html).\n",
+        "\n",
+        "Functions that support broadcasting are known as *universal functions*. You can find the list of all universal functions [in the documentation](http://docs.scipy.org/doc/numpy/reference/ufuncs.html#available-ufuncs).\n",
+        "\n",
+        "Here are some applications of broadcasting:\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "S-iRQm3JIb4F",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "outputId": "474431eb-f7b7-4342-f341-36cb1d092919"
+      },
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Compute outer product of vectors\n",
+        "v = np.array([1,2,3])  # v has shape (3,)\n",
+        "w = np.array([4,5])    # w has shape (2,)\n",
+        "# To compute an outer product, we first reshape v to be a column\n",
+        "# vector of shape (3, 1); we can then broadcast it against w to yield\n",
+        "# an output of shape (3, 2), which is the outer product of v and w:\n",
+        "# [[ 4  5]\n",
+        "#  [ 8 10]\n",
+        "#  [12 15]]\n",
+        "print(np.reshape(v, (3, 1)) * w)\n",
+        "\n",
+        "# Add a vector to each row of a matrix\n",
+        "x = np.array([[1,2,3], [4,5,6]])\n",
+        "# x has shape (2, 3) and v has shape (3,) so they broadcast to (2, 3),\n",
+        "# giving the following matrix:\n",
+        "# [[2 4 6]\n",
+        "#  [5 7 9]]\n",
+        "print(x + v)\n",
+        "\n",
+        "# Add a vector to each column of a matrix\n",
+        "# x has shape (2, 3) and w has shape (2,).\n",
+        "# If we transpose x then it has shape (3, 2) and can be broadcast\n",
+        "# against w to yield a result of shape (3, 2); transposing this result\n",
+        "# yields the final result of shape (2, 3) which is the matrix x with\n",
+        "# the vector w added to each column. Gives the following matrix:\n",
+        "# [[ 5  6  7]\n",
+        "#  [ 9 10 11]]\n",
+        "print((x.T + w).T)\n",
+        "# Another solution is to reshape w to be a column vector of shape (2, 1);\n",
+        "# we can then broadcast it directly against x to produce the same\n",
+        "# output.\n",
+        "print(x + np.reshape(w, (2, 1)))\n",
+        "\n",
+        "# Multiply a matrix by a constant:\n",
+        "# x has shape (2, 3). Numpy treats scalars as arrays of shape ();\n",
+        "# these can be broadcast together to shape (2, 3), producing the\n",
+        "# following array:\n",
+        "# [[ 2  4  6]\n",
+        "#  [ 8 10 12]]\n",
+        "print(x * 2)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[ 4  5]\n",
+            " [ 8 10]\n",
+            " [12 15]]\n",
+            "[[2 4 6]\n",
+            " [5 7 9]]\n",
+            "[[ 5  6  7]\n",
+            " [ 9 10 11]]\n",
+            "[[ 5  6  7]\n",
+            " [ 9 10 11]]\n",
+            "[[ 2  4  6]\n",
+            " [ 8 10 12]]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8GYvMrTfIioI"
+      },
+      "source": [
+        "Broadcasting typically makes your code more concise and faster, so you should strive to use it where possible.\n",
+        "\n",
+        "##Numpy Documentation\n",
+        "\n",
+        "This brief overview has touched on many of the important things that you need to know about numpy, but is far from complete. Check out the [numpy reference](http://docs.scipy.org/doc/numpy/reference/) to find out much more about numpy.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 2. Pandas"
+      ],
+      "metadata": {
+        "id": "I2vSI1n869dV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lIYdn1woOS1n"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#NBA results provided by FiveThirtyEight in a 17MB CSV file.\n",
+        "!wget https://raw.githubusercontent.com/fivethirtyeight/data/master/nba-elo/nbaallelo.csv\n"
+      ],
+      "metadata": {
+        "id": "7PXWPKpkY-Ha"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba = pd.read_csv(\"nbaallelo.csv\")\n",
+        "type(nba)"
+      ],
+      "metadata": {
+        "id": "Ex-IlzHNamIP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "type(nba.columns)"
+      ],
+      "metadata": {
+        "id": "RmS7WPlhP1tL"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "type(nba.index)"
+      ],
+      "metadata": {
+        "id": "Uv11fn5gP3IP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba.iloc[0:31:3, 0:14:2]"
+      ],
+      "metadata": {
+        "id": "HcxUgpYDP-Yn"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "my_col = ['_iscopy', 'lg_id', 'is_playoffs']"
+      ],
+      "metadata": {
+        "id": "wJLAZrSzQp1f"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba.loc[0:8:2, my_col]"
+      ],
+      "metadata": {
+        "id": "i7VlHbIsQaBd"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "len(nba)\n",
+        "nba.shape\n",
+        "nba.head()\n",
+        "nba.tail()\n",
+        "nba.info()\n",
+        "nba.describe()"
+      ],
+      "metadata": {
+        "id": "KOKnMIFaanb7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.set_option(\"display.max.columns\", None)\n",
+        "pd.set_option(\"display.precision\", 2)"
+      ],
+      "metadata": {
+        "id": "1PfCLSuBa21M"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba.describe(include=object)"
+      ],
+      "metadata": {
+        "id": "7PxpWGsLbJJq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba.hist(\"pts\", by=\"game_result\")"
+      ],
+      "metadata": {
+        "id": "w1gbgFO5gsbu"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba[nba[\"fran_id\"]==\"Knicks\"].groupby(\"year_id\")[\"pts\"].sum().plot()"
+      ],
+      "metadata": {
+        "id": "mpczWpafOt4C"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba[\"fran_id\"].value_counts().head(10).plot(kind=\"bar\")"
+      ],
+      "metadata": {
+        "id": "8O3x9176O6E4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba.boxplot(column='pts', by=\"game_result\")"
+      ],
+      "metadata": {
+        "id": "CLh1RRsFhXF9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba[\"team_id\"].value_counts()"
+      ],
+      "metadata": {
+        "id": "MNDz27EFiBPS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba[\"fran_id\"].value_counts()"
+      ],
+      "metadata": {
+        "id": "cCoAZgcJiWjh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "It seems that a team named \"Lakers\" played 6024 games, but only 5078 of those were played by the Los Angeles Lakers. Find out who the other \"Lakers\" team is:\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "YgH9mFMIinyl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba.loc[nba[\"fran_id\"] == \"Lakers\", \"team_id\"].value_counts()"
+      ],
+      "metadata": {
+        "id": "usmtaXZuim0R"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Indeed, the Minneapolis Lakers (\"MNL\") played 946 games. You can even find out when they played those games. For that, you’ll first define a column that converts the value of date_game to the datetime data type. Then you can use the min and max aggregate functions, to find the first and last games of Minneapolis Lakers:\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "OEJcB_zcjQfo"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba[\"date_played\"] = pd.to_datetime(nba[\"date_game\"])\n",
+        "nba.loc[nba[\"team_id\"] == \"MNL\", \"date_played\"].min()\n",
+        "nba.loc[nba['team_id'] == 'MNL', 'date_played'].max()\n",
+        "nba.loc[nba[\"team_id\"] == \"MNL\", \"date_played\"].agg((\"min\", \"max\"))"
+      ],
+      "metadata": {
+        "id": "zM4OpAoXjSzz"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "It looks like the Minneapolis Lakers played between the years of 1948 and 1960. That explains why you might not recognize this team!"
+      ],
+      "metadata": {
+        "id": "b9cAmZCnjfuJ"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        " Find out how many points the Boston Celtics have scored during all matches contained in this dataset. Expand the code block below for the solution:"
+      ],
+      "metadata": {
+        "id": "caqpZ6pVj0KH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba.loc[nba[\"team_id\"] == \"BOS\", \"pts\"].sum()"
+      ],
+      "metadata": {
+        "id": "LVUCbOP3jfQ_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Querying Your Dataset"
+      ],
+      "metadata": {
+        "id": "9xuxqzI5oDXP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# create a new DataFrame that contains only games played after 2010\n",
+        "current_decade = nba[nba[\"year_id\"] > 2010]\n",
+        "current_decade.shape"
+      ],
+      "metadata": {
+        "id": "95tTpM0PoK7I"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# select the rows where a specific field is not null\n",
+        "games_with_notes = nba[nba[\"notes\"].notnull()]\n",
+        "games_with_notes.shape"
+      ],
+      "metadata": {
+        "id": "TGm0y8AZoQUV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#filter your dataset and find all games where the home team’s name ends with \"ers\".\n",
+        "ers = nba[nba[\"fran_id\"].str.endswith(\"ers\")]\n",
+        "ers.shape"
+      ],
+      "metadata": {
+        "id": "B7t3W-SqoUk3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "You can combine multiple criteria and query your dataset as well. To do this, be sure to put each one in parentheses and use the logical operators | and & to separate them.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "t9fqCZooo1Og"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Do a search for Baltimore games where both teams scored over 100 points. In order to see each game only once, you’ll need to exclude duplicates\n",
+        "nba[(nba[\"_iscopy\"] == 0) &\n",
+        "    (nba[\"pts\"] > 100) &\n",
+        "    (nba[\"opp_pts\"] > 100) &\n",
+        "    (nba[\"team_id\"] == \"BLB\")\n",
+        "    ]"
+      ],
+      "metadata": {
+        "id": "uIs64CIxoxqo"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#In the spring of 1992, both teams from Los Angeles had to play a home game at another court. Query your dataset to find those two games. Both teams have an ID starting with \"LA\".\n",
+        "nba[(nba[\"_iscopy\"] == 0) &\n",
+        "    (nba[\"team_id\"].str.startswith(\"LA\")) &\n",
+        "    (nba[\"year_id\"]==1992) &\n",
+        "    (nba[\"notes\"].notnull()) ]"
+      ],
+      "metadata": {
+        "id": "NgGpgA9tptRs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Grouping and Aggregating Your Data\n",
+        "A Series has more than twenty different methods for calculating descriptive statistics."
+      ],
+      "metadata": {
+        "id": "b2OYA8XQqubc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "points = nba[\"pts\"]\n",
+        "type(points)\n",
+        "points.sum()"
+      ],
+      "metadata": {
+        "id": "pXSUxtIlqt7i"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba.groupby(\"fran_id\", sort=False)[\"pts\"].sum()"
+      ],
+      "metadata": {
+        "id": "TnVHc6oWrEEE"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba[(nba[\"fran_id\"] == \"Spurs\") &\n",
+        "    (nba[\"year_id\"] > 2010)].groupby([\"year_id\", \"game_result\"])[\"game_id\"].count()"
+      ],
+      "metadata": {
+        "id": "LomYmqM9rKVq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Manipulating Columns\n"
+      ],
+      "metadata": {
+        "id": "mX8Y8TRhref3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = nba.copy()\n",
+        "df.shape"
+      ],
+      "metadata": {
+        "id": "DIgPygR8rgp9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[\"difference\"] = df.pts - df.opp_pts\n",
+        "df.shape"
+      ],
+      "metadata": {
+        "id": "zuwKnKKurjG9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[\"difference\"].max()"
+      ],
+      "metadata": {
+        "id": "BEe1R8SBrllD"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "renamed_df = df.rename(columns={\"game_result\": \"result\", \"game_location\": \"location\"})"
+      ],
+      "metadata": {
+        "id": "QiTDpLAKrnwn"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "renamed_df.info()"
+      ],
+      "metadata": {
+        "id": "39naVS-irqa2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "elo_columns = [\"elo_i\", \"elo_n\", \"opp_elo_i\", \"opp_elo_n\"]\n",
+        "df.drop(elo_columns, inplace=True, axis=1)"
+      ],
+      "metadata": {
+        "id": "qRIbUi7Arwex"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[\"date_game\"] = pd.to_datetime(df[\"date_game\"])"
+      ],
+      "metadata": {
+        "id": "M0CbYcpTr7aV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[\"game_location\"].nunique()"
+      ],
+      "metadata": {
+        "id": "GVKUIUTYr_h7"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[\"game_location\"].value_counts()"
+      ],
+      "metadata": {
+        "id": "5xbiYpAwsAvs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[\"game_location\"] = pd.Categorical(df[\"game_location\"])"
+      ],
+      "metadata": {
+        "id": "lzpl-UQ3sCYK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[\"game_location\"].dtype"
+      ],
+      "metadata": {
+        "id": "-tnNMkxtsDoP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Cleaning Data\n"
+      ],
+      "metadata": {
+        "id": "LVa2BWvUsLUR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rows_without_missing_data = nba.dropna()\n",
+        "rows_without_missing_data.shape"
+      ],
+      "metadata": {
+        "id": "yPA8WjQisN4o"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_without_missing_columns = nba.dropna(axis=1)\n",
+        "data_without_missing_columns.shape"
+      ],
+      "metadata": {
+        "id": "8_nrGPmxsPPR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_with_default_notes = nba.copy()\n",
+        "data_with_default_notes[\"notes\"].fillna(value=\"no notes at all\",inplace=True)"
+      ],
+      "metadata": {
+        "id": "bGM6A5OqN6FA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba[nba[\"pts\"] == 0]"
+      ],
+      "metadata": {
+        "id": "VRf6ibMpOTmo"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Inconsistent Values\n",
+        "Sometimes a value would be entirely realistic in and of itself, but it doesn’t fit with the values in the other columns. You can define some query criteria that are mutually exclusive and verify that these don’t occur together."
+      ],
+      "metadata": {
+        "id": "jkmixmnPOZMk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nba[(nba[\"pts\"] > nba[\"opp_pts\"]) & (nba[\"game_result\"] != 'W')].empty\n",
+        "nba[(nba[\"pts\"] < nba[\"opp_pts\"]) & (nba[\"game_result\"] != 'L')].empty"
+      ],
+      "metadata": {
+        "id": "gJsYvaO-OWA9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JXpXKWAi6dJg"
+      },
+      "source": [
+        "# 3. Matplotlib\n",
+        "\n",
+        "Matplotlib is a plotting library. In this section give a brief introduction to the `matplotlib.pyplot` module, which provides a plotting system similar to that of MATLAB.\n",
+        "\n",
+        "##Plotting\n",
+        "\n",
+        "The most important function in matplotlib is `plot`, which allows you to plot 2D data. Here is a simple example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KfjqxRT4JP2t",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "outputId": "41e4bc44-df25-4d59-d371-ed70499e82bc"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Compute the x and y coordinates for points on a sine curve\n",
+        "x = np.arange(0, 3 * np.pi, 0.1)\n",
+        "y = np.sin(x)\n",
+        "\n",
+        "# Plot the points using matplotlib\n",
+        "plt.plot(x, y)\n",
+        "plt.show()  # You must call plt.show() to make graphics appear."
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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ucKGq2QCVUo7MxAiuwwlYc6NuK5uEeW+KBKa5REGlce4UZ2ohEYtwqXFwyQt9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\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4e50d717d0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6LxgDV5XJVRU"
+      },
+      "source": [
+        "With just a little bit of extra work we can easily plot multiple lines at once, and add a title, legend, and axis labels:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IVRdhmwQJZhD",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 376
+        },
+        "outputId": "4adfcf15-f29c-40da-8219-36f8eca08858"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Compute the x and y coordinates for points on sine and cosine curves\n",
+        "x = np.arange(0, 3 * np.pi, 0.1)\n",
+        "y_sin = np.sin(x)\n",
+        "y_cos = np.cos(x)\n",
+        "\n",
+        "# Plot the points using matplotlib\n",
+        "plt.plot(x, y_sin)\n",
+        "plt.plot(x, y_cos)\n",
+        "plt.xlabel('x axis label')\n",
+        "plt.ylabel('y axis label')\n",
+        "plt.title('Sine and Cosine')\n",
+        "plt.legend(['Sine', 'Cosine'])\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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YmBj27NnDnj17ePXVV4mLi2PdunUAbN26deqx73//++4O2+3SwucxPzKT8/1N\nXBy49KXHj9d00T88wV0LEzx6CUpnuWthAqGBKj4qa2N4VFzdCzdvzDzO0bbPCVIGsjJ+qdThuFyg\nKpD1SWsYmTTxSdvxLz3e3Wvi9DkdSdpgCtJ8a5DidObEhlCQFs2F9gHOtczeq3u3J/uTJ0+yYcMG\nAFJTUxkYGGB4ePhLz3v77bfZvHkzQUHe19/dWbbMtQ8iOtzyyTW/t1itfHiqBaVCzqZi376qd/BT\nK9i6dA5jExYOlrRKHY7gRY53nsZkHmVt0gr8ld4/j/xm3JW0giBVIEfaPv3SIjkfnLiEzQZfWe77\nV/UO962cC8B7n8/eq3ulu3doNBrJycmZ+jkyMhKDwUBw8LUtGt944w1+//vfT/1cUlLCo48+itls\n5sknn2T+/Pk33E9ERCBKpXOnkmg07h3Uo9HkktmaSq2xnlHVIMnhCQB8XNaGcWCMrcvnkj7P+8/M\nb/a4btuUxcHSNo6caefrW7J9ogGIq7n7PetpJi2TfHLyM/yVfnw1fzPBfs65ePD84xrCfVkb+VPN\nu1QOVHFf1kYAuowjnDyrIzk2hM0rUpDLPSvZu+q4ajQhFGW3UXZOR/fgOHlpGpfsx5O5Pdl/0XRn\nWRUVFaSkpEydAOTn5xMZGcnatWupqKjgySefZP/+/Tfcbp+TV0zTaEIwGIacus2bcVf8KhqMF9lb\n+SHfytmB1Wbj9UP1KOQy1ubHSRKTM93qcd28OInXP77Amx81+OwMBGeR6j3rSY53nKZvdID1yasZ\nHbQyyp0fD285roXhC3lL8Rferz/C4ogilHIlez48h9VqY+uSZHp6vlxRlZKrj+vdxUmUndPx2vtn\nefIbhS7bj5RudLLk9jK+VqvFaLzSHU6v16PRXHuWdfToUZYtWzb1c2pqKmvXrgVg4cKF9Pb2YrHc\nWktIb5UTlUVcUAxn9JX0jPZR3mCgq8fEspxYosO8c6WuO7EqP54APyUfn2ln0jw73gPC7bFYLRxq\nPYpSpmB9ku+OwL+eQFUgK+KL6R8foExXiXFglBO13cRGBlKUqZU6PLebFxdKbkoUDW39s7KrntuT\n/YoVKzh40D5/vK6uDq1W+6USfk1NDVlZWVM/v/zyy7z//vsANDY2EhkZiULhO92ebkQuk7MxeS1W\nm5UjrZ/y/slLyGR47RK2dyrAT8nahfEMmiY5WaeTOhzBg1UYajCO9rA0rogwv1Cpw5HEXYmrkMvk\nHGk9xsHSVixWG/csm+Nx5Xt32brU3hL4UOnsG/fj9jJ+YWEhOTk57NixA5lMxq5du9i3bx8hISFs\n3Gi/r2QwGIiKujL38ytf+QpLNF4LAAAgAElEQVT/9E//xOuvv47ZbL5mBP9sUBRTwP6mgxzvLGGo\nZzXFWYk+2S3vZm1YlMShkjYOlrSyMi/OZxuCCLfPZrNxqOUTZMjYOGet1OFIJioggkXafEp1FXQ0\nVxMeHMeS+TFShyWZjKRw5sSEcKbRgKF/FE347KmOSnLP/oknnrjm56uv4oEv3Y+PjY1lz549Lo/L\nUynkCtYnr+bN8++hjGnh7iUrpA5JUhEhfiyZH8OJ2m5qLvaQPwumDwm3pq6nno7hLopiCogO8N2m\nMTdjQ/IaSnUVWDUXWR+zGKVi9vZSk8lkbCpO4uX9Z/morJ2vb0iXOiS3mb3/6l5mnl8ONrMKv7g2\nYjViFPrmyyt0iWl4wnQ+afscsLePne3ig+KQj2hQhPaSmjY7p51dbXGWlogQP45Vd2Iamz2raYpk\n7yWOVegx65KxyiduaQlLX5WkDSZnXiT1rf00dw1KHY7gQbpGdNT3nSc9PIXEkHipw5FceaOB0Tb7\nGJ/jui832ZltlAo56xclMj5h4VhVp9ThuI1I9l5geHSSEzVdhJoyUMoUHGs/MWsbQ1xti7i6F6bx\nafsJANYmrZQ4Es9wqKwN62AUMf6xVOjtgxZnuzUF8ahVcj4604bFapU6HLcQyd4LfFbVyYTZyoaC\nVApj8tGZDNT3nZc6LMnNnxtBoiaYsnoDxoHRmV8g+DzT5Cinu8qI9I8gNypb6nAk19w1yIX2AfJS\no9mSshYbtqmTodksyF/Fytw4egfHOdNgkDoctxDJ3sNZrFaOlLfjp1KwOj+OtYn2wXniA2sfbLO5\nOAmrzcbHZzqkDkfwACe7SpmwTrI6YRkK+eyYnnsjjmWhNxYlUajNI0QVzMmushmXv50NNhYlIQMO\nlrTNikqpSPYerqLRSO/gOMtzYwn0VzEnNIk5oUnUGs/RM9ordXiSK86OISRQxWfVnUxMiiY7s5nV\nZuXT9hOo5CqWxxdLHY7kegfHKKvXk6AJYv7cCJRyJSviixk1j1Kmq5Q6PMnFRAZSkB5tr350+H6T\nHZHsPdyhMvuZ+YZFiVO/W5OwHBu2G65XPVuolHJW5cUzMmamtF4vdTiChGqN5+gZ66U4tpAg1ezt\nQ+HwcXkHFqvNfgV7uRfFyoSlyJCJcT+XOZYH/7jc9yuDItl7sEvd9vttC1IiiYu6soBHYUw+waog\nTnSWiHIcsLYgHhnwSYXvf2CF6zvabh9p7rjVNZtNmq0cq+okOEDFspwrTXQi/MPJ0+TQNtzJpUEx\nsDUjKZz46CDK6vUMjvj2d6lI9h7sSFk7AJuKrl3GViVXsjJ+CSbzKGW6KilC8yjR4QHkpkbR1DlI\nS7fnL1AiOF/ncDcNfRfIiEgjPjhW6nAkV95oYHh0kpW5cai+sPrn6gT7uiOftovKoEwmY21BPBar\njeM1XVKH41Ii2XuokbFJSur1aCMCmD8v8kuPr0xYilwm59P246IcB6wrtC//+0lFu8SRCFL4VFzV\nX+Po5SrXmoIv9xnIjEgjJlBDhb6KoQnPWvlOCssXxKJWyjla2YHVh79LRbL3UCdru5k0W1lTED9t\n7/cI/3DyonNoH+6kaaBFggg9y4J5UUSH+XOqTodpbFLqcAQ3GjOPUaKrIMIvnNxoMd2u0zhCQ1s/\n2XMiiJlmDQ2ZTMaqhGWYbRZOdooGXYH+Kornx2DoH+PsJd8d9CySvQey2Wx8WtmJQi5jxYK46z5v\nTeJy4MpVzWwml8tYuzCBCbOV47XdUocjuNEZXRUTlgmWxy9GLhNfaZ9W2rvCrV2YcN3nLI1bhFqh\n5rPOU1hts6OpzI3cdflYfeLDA/XEJ8MDXewYpMM4QmGGhtAg9XWflx6eQmyglipDLcOTI26M0DOt\nzItDqZDxSXmHuLUxixzvLEGGjGVxi6UORXITkxaO13QRGqRmYfr1F4gKUAZQHLOQ3rE+6nrq3Rih\nZ5obG8KcmBCqLvTQOzgmdTguIZK9B/q08vr3264mk8lYHl+M2WahtLvCHaF5tNBANYuztHT3mqhv\n6ZM6HMEN2oY6aRlqIycqiwj/cKnDkVxpvR7TuJlVeXEzrm63+nJl8JgYqGcfqLcwHqvNxmfVvjlQ\nTyR7D3P1wLysOREzPr84thCFTMGJzhJxNQvctdDej0BMw5sdTnSeBmCFaKIDwNHKDmTA6vyZFwBK\nCI5jXmgy53ob6Rvrd31wHm7J/Bj81QqOVXX6ZL98kew9zKk6nX1gXv70A/O+KEQdTJ4mh86Rbi4N\ntrkhQs+WmhBKoiaIivNGBk2+PW92tpuwTFCqqyBMHUpOVJbU4UiuTT/MxY5BclIi0YQH3NRrlsUv\nxoaNU11nXByd5/NXK1m+IJa+oXGqL/jeYkEi2XsQ+8C8DvvAvNzrD8z7ohVx9quaE50lrgrNa8hk\nMlbl2efNnqrTSR2O4ELl+mpGzWMsi18s+uBjv6oHuKvg+gPzvmiRNh+1XMXJrlIxUA9Ye/nYfVLp\ne5VBkew9yMXOQdoNIyycYWDeF2VGphHpH0GZvpIxs28OLrkVS3NiUMhlfF7dKW5t+LDjnaeRIWO5\nGJjH+KSFk7XdRIT4kZcWddOv81f6U6jNp2esl/N9TS6M0DskaoNJjQ+lrqnX5wbqiWTvQW52YN4X\nyWVylsUVMWGZoFxf7YrQvEpIoH0kcrthhEuio55P6hzupmmghazIdKICvtx0arYpbzAwNmFhRW4s\nCvmtfa0vi7efLJ3oEpVBgBV5cdiAEz42hVckew8xOm6m9JweTbg/2TcxMO+LlsUtRoZMlPIvW5ln\nP2H63EdH1s52jsQkVrez+/xyq9dbuf3nkBo2l5hADZWGWkyTJmeH5nWKs2JQK+Ucr+nyqcqgSPYe\norRez4TZysrcuJsamPdFEf7hZEdl0DzYSuewb52R3o4F8yKJCPHj1FmdWPrWx0xaJinpKidEFUxe\n9Hypw5GccWCUcy19ZCSGERNx66v9yWT2HgVmq5lSsfQtgf5KCjM16PpGfWrpW5HsPcSJmi5kwPIb\ndMybydRAPVGOQy6XsXxBLKPjZsobDVKHIzhRTc85RswmiuMKUcqVUocjuRM19pP727mqdyiOXYRc\nJuekqAwCsPLysfSlyqBI9h5A32eisX2ArDkRRIX53/Z2FkRnE6IKpqSrnEmr2YkReifHB9ZXm2TM\nVqcvTxNbGlskcSTSs9psfF7ThVolpyhLe9vbCfMLYUFUNm3DnbQN+d5I9FuVNSeCqFB/Sur1jE/4\nRmVQJHsP4BgIsiL3zpbmVMqVFMcWMmI2iRaYQExkIBmJYZxr6cPYPyp1OIITDE4Mcba3gaSQBLGU\nLXC+rR/jwBhFmVoC/O6syrEszn7ydEIsjoNcJmNFbizjExbKGvRSh+MUItlLzGqzcbymGz+1gkUZ\nt39m7rAkbhEAJaJJBnDVQD0fX6t6tijTVWK1WVkSu0jqUDzCnQzM+6KcqCxC1SGU6iqYtIiVIx3H\n1FfWuXd7sn/hhRfYvn07O3bsoLr62mli69at46GHHmLnzp3s3LkTnU4342u8XWNrPz2DYyzO1OKn\nvvPGIAnBcSQEx1HbU8/whFgcpyhLg59awfGaLqxW3xlZO1ud7jqDXCanKKZA6lAkNzZhpqzeQHSY\nP5nJd74ugEKuoDi2kFHzKDU955wQoXfThAeQlRxOfWs/eh+oDLo12ZeUlNDS0sLevXt5/vnnef75\n57/0nJdffpk9e/awZ88eYmJibuo13uz41Jm580qSxbGFWGwWzuirnLZNb+WvVlKcpaVncJyGNtH/\n25t1DHfRPtxJTlQWIepgqcORXFm9gfFJC8sXxN7WDJ7pFMcWAlDSXe6U7Xk7x9X9CR+4undrsj95\n8iQbNmwAIDU1lYGBAYaHh53+Gm8xNmGmrMF+Zp6e5LwVuxbHLESGjNPdopQPsHyB/UTqpI81yZht\nrgzMEyV8uPpC4c5L+A6OymCdqAwCUHS54nq8pgurl8+5d+u8FaPRSE5OztTPkZGRGAwGgoOvnKXv\n2rWLjo4OFi1axD/+4z/e1GumExERiFLp3H7ZGk2IU7d3pLSV8UkLDyxJI0Yb6rTtagghPzabyu6z\nTPqNEB/q2QOZnH1cvygqKpjoD+s502jg8W8swk81e/qou/rYuovFaqHsRCXB6iDWZi1GpVBJGo/U\nx7W7Z4SGtn5yU6OZn37nY32uti51OXuq3qLR1MDmhDVO3fZMpD6u01ldkMDhklb0gxPkpkVLHc5t\nk3SS6he7E/3whz9k1apVhIWF8dhjj3Hw4MEZX3M9fX3O7QSl0YRgMDi39eqBE80A5KdEOn3bCyPz\nqew+y4Gzn/GV1C1O3bYzueK4Tqc4S8uHp1r46GQzxdkxLt+fJ3DXsXWHWuM5BsYGWZ2wjP7eMUC6\nvuWecFw/+Nz+3bE4U+P0WLKCs5Ah4+MLJygML3Tqtm/EE47rdApSozhc0sqBE03EhvlJHc4N3ehk\nya1lfK1Wi9FonPpZr9ej0Wimfr7//vuJiopCqVSyevVqGhsbZ3yNtzIOjFLf2k9GUjjam1yO8lbk\naXLwV/hxurtcrGYFLBOlfK/muIfsmG0ym9lsNk6e1aFSylmU6fzvwnC/MDIj0mgebEVvEg2pMpPD\niQjxo7TewKTZe+fcuzXZr1ixYupqva6uDq1WO1WOHxoa4tFHH2Viwr4GeWlpKenp6Td8jTdzLL/q\nuJ/sbGqFmgJtLn3j/Vzob3bJPrxJQnQQyTHB1Db3inXuvYxpcpQqYx0xgVrmhCRJHY7kLnUPoes1\nUZAWfcdz66/nykC9Cpds35vIZTKWzI9hdNxM9UXvXefercm+sLCQnJwcduzYwXPPPceuXbvYt28f\nhw8fJiQkhNWrV09NsYuMjGTLli3Tvsbb2Ww2Tp3VoVTIKcp07v22qznmIouBenbLcmKxWG2UnvON\nJhmzRYWhGrPVzJLYQmROGnXuzRwXCstyXDcWJ1+zALVcRWl3uU8tBnO7HMf65OVj743cfs/+iSee\nuObnrKysqf9+5JFHeOSRR2Z8jbdrN4zQaRxhUYaGQH/X/ROkhc8jwi+cCn012zPuR61Qu2xf3mDJ\n/Bj+/MkFTtZ1s35RotThCDeprNu+OMvi2IUSRyI9q9VGyTkdQf5KFqS4bmlff6Uf+ZpcSnXlNA20\nkBo+12X78gZJ2mASNEFUXzQyMjZJkL+0A0Rvh+igJ4FTZ+33jZfMd+1AMblMzpLYQsYtE1Qb6ly6\nL28QHuzH/LmRNHUOousVS3l6g/7xAc73N5EaNo9I/1tf+tnXnGvpY2BkgsXZMSgVrv36XuIo5evE\nnHuwX92bLTbK6r2zMiiSvZtZbTZOn9UR4KcgPy3K5ftzXA2V6cXSlQDLcuwnWCfrxEA9b3BGV4UN\nG4tjRcc8gFOX37dLXXyhAJAZmUaYOoRyXZVYWAtYcnkWzykvLeWLZO9mF9oH6B0cZ1GGFpWT+wBM\nJzYohoTgOM72NDIyKa5mCzM0qFVyTtXpxL1IL1Cmq0Auk7NQkyd1KJIbn7RwptFAVKg/aYlhLt+f\nvS3xQkzmUbGwFhAV5k9mUjgNbf30DEg39fN2iWTvZqfO2s8Kl+S4b653UUwBFpuFSkON2/bpqfzV\nSgozNOj7R7nYMSh1OMIN6Eb0tA51kB2ZQbA6SOpwJFd1wcjYhIWlOTFOa487E0dl8IxOVAYBll7+\n3j59zvuu7kWydyOzxUrpOR1hQWqyk913/3GR1l4CdQx0mu2WzrePrD191vs+sLNJ2eUEIxa9sXOU\nj91RwndIDI5HGxhNjfEcY+Zxt+3XUxVlaVEqZJys7fa6yqBI9m5U29zLyJiZ4uwY5HL3TSGKCogg\nJWwO5/ub6B8fcNt+PdX8uREEB6gorddhsYqGQ57IZrNRpqtEJVeRF50z8wt83PDoJDVNPZdHhbuv\nz4hMJqNIW8CkdZIa41m37ddTBfmryEuNpsM4Qpveu9ZoEcnejRxXkkvdWMJ3WBRTgA0b5XrfWiL4\ndtj7G2gYNE3S0CpWwvNErUPt6EeN5EXPx1/p2S1K3aG0Xo/FapPsuwPgjBjkC1yprHhbZVAkezcZ\nmzBTcd5ATEQAc2Pdv9hDoTYPuUw+VRqd7Rz98Uu88N7bbCBK+Nc6fVaHjCsjwt0pNkhLYnC8GOR7\nWV5qFH5qBaX1eq8q5Ytk7yYV541MTFpZMj9Gki5goeoQMiPSaBlsw2Dy3paPzpKRFE5YsJozDQbM\nFlHK9yRWm5UzuioClQHMj8qUOhzJ9Q2Nc76tn/TEMCJD/SWJYVFMPhabhSpDrST79yRqlYLC9GiM\nA2M0dXnPIF+R7N3EUfJxdSOdGxHluCvkchmLs7SMjJmpa+6VOhzhKhf6mxiYGGShNhelXNKFOT1C\nWb0eG7BYwtUaF2nz7bGIyiBw5d/Cm1pvi2TvBiNjk9Q195IcE0xclHRTiAo0OSjlSvGBvWyJKOV7\npNJuRwlftMcFKKnXIZPZR4JLJSogknmhc2jsu8jAuOctQ+tuC+ZFEuinpLRej9VLSvki2btBeaMB\ni9XGYgk/rAABygByorLoGtHRMdwlaSyeICU+lKhQf8rPG5mY9N6lK32JxWrvBxGmDiEtfJ7U4UjO\nOGDvB5GVHEFYkLRrWyyKyceGjQoxyBelQk5hpoa+oXEutHvHDCeR7N3AUeqRsgzn4BjwJK7u7dOK\niudrGZ+wePXSlb6kvu8CJvMoCy8PKJ3tSi/3YS/OlvZCAaBQm48MmbgNeJnj38RbGuyIT5OLDY9O\ncvZSH3NjQ9CGB0gdDguisvFTqCnXVXnVSFJXEaV8z1KurwLsiUWAknN6FHIZi1y4FPbNCvMLIT0i\nlaaBFnpG+6QOR3LZc+z9Os7U672iX4dI9i52psF+T6fYA67qAdQKFbnR8zGO9dI21CF1OJJL0gYT\nGxlI1cUeRsfFYh9SMlvNVBnqCPcLY15YstThSE7XZ6Kle4jsy02gPEFRjP0kTFzdg0IupyhL6zX9\nOkSyd7GSyyX8oiyNxJFcUai1LyoiGuxcLuVna5k0W6m8YJQ6nFmtvvc8o+ZRFmpzRQmfK7f/irM8\n40IBoEBj/7cR3x12Sy6X8r2hMig+US40ODJBfWsfqfGhRIdJX8J3yI7MtJfy9dWilM9VDXa8rCOW\nr3EkkEWihA/YLxSUChmFGdFShzIlSBVIVkQ6bUMdGEfFOJf0RO/p1yGSvQudadBjsyH5KPwvcpTy\ne8Z6aR1qlzocycVHB5GoCabuUi+mMVHKl8Lk5RJ+hF84c0NFCb/TOEK7YZgF86II9PeMEr6DqAxe\nIZfLWJxp79dx9pJn9+sQyd6FHCNppZwfez2OAVDiA2tXlKXBbLFRecEgdSiz0rmeBsYsYxRq8yTp\nMOlpHGXhxR4wCv+L8jQ5opR/leL5jkG+nt1gRyR7F+kfHqehVdoWlzcyPzIDf4UfFaKUD1ypvpTV\ni2QvBUfiKIzJkzgS6dlsNkrr9aiUcgrSPKeE7yBK+ddKjQ8lMtSPivNGJs2eW8oXyd5FplpceuBV\nPYBKoSI3OoeesT5RygfiooJI0ARR29wjSvluNmGZpNpYR5R/BHNCkqQOR3IdxhG6ekzkpkQR4OeZ\n7YJFKf8KmUxGUaaW0XHPLuWLZO8ipfV6ZHhmCd+hUJsLwJnLc5tnu8WZWswWG1ViVL5bnettYNwy\nYW/aIkr4lNV73gyeLxKl/GsVXe6DUNbguaV8kexdoG9onPPtA6QnhRMe7LlrcWdHZuCv8KdCXyNK\n+Vw5MXOMtRDcY6qErxUlfIAzDQaUCjn5qZ5XwncQpfxrpSSEEhHiR0Wj0WNH5Ytk7wLljfb7vp5a\nwndQKVTkaebTO9ZHy1Cb1OFILj46iIToIGqbe0WDHTexl/DPEu0fSVJIgtThSK7TOEKHcYTclEiP\nLeE7iFL+FXKZjEWZGkzjZs61eGZ3QZHsXcBRhivM8NwynMPUB1YnPrBgv7o3W0SDHXc519vAhGWC\nhWIUPnClDOzJt/8cHKV8sTCOnaOU76mVQbcn+xdeeIHt27ezY8cOqquvfZOcOnWKBx98kB07dvD0\n009jtVo5ffo0S5cuZefOnezcuZOf/OQn7g75lgyMTNDY1k9aYhgRIZ5bwnfIulzKFw127IqmRuV7\n5gfW11ToawBYeHn8yGxXVm9AqZB5dAnfwVHKbxWlfADSEsMIC1ZT0eiZDXbcmuxLSkpoaWlh7969\nPP/88zz//PPXPP4v//Iv/PrXv+b1119nZGSEzz77DIDi4mL27NnDnj17+PGPf+zOkG9ZeaMBG1fO\n8jydSq4kTzOfvvF+UcoHEqKDiI8OoqZJlPJdbdJqpsZ4jkj/CJJDEqUOR3LdvSbaDcPkzI0k0N+z\nS/gOopR/hVwmoyjD3mCnvtXzSvluTfYnT55kw4YNAKSmpjIwMMDw8PDU4/v27SM2NhaAyMhI+vo8\n74DNxHFFuMgLSvgOCzX2q6pKfa3EkXiGokwNZotVjMp3sYbe84xZxijQLBAlfK4ehe8dFwogSvlf\n5JhB4Yn9Otx6+mg0GsnJyZn6OTIyEoPBQHBwMMDU/+v1eo4fP87f//3f09jYyIULF/je977HwMAA\nP/jBD1ixYsWM+4qICESpVDg1fo0m5IaPDwyP09DWT2ZyBFlp3pPsV0Uu4v+de53q3jr+NvpBt3/x\nznRc3W3T8nm8d/wS1c29fGVtutTh3BFPO7ZXO9dUD8C6jKVooj03zum44rhWXuxBqZCxYelcggPV\nTt++K2gIIS8mi8rus9gCxtEG39ntB09+v96MyKhgwvefpfKCkcjIIBQKzxkWJ2mtaLp7xD09PXzv\ne99j165dREREMHfuXH7wgx9w991309bWxje/+U0OHTqEWn3jD0Nfn8mpsWo0IRgMQzd8zrGqTqxW\nG/mpUTM+19PkRGZxRl9FRXMjSSHxbtvvzRxXdwtUyIiLCqTsnJ7W9j6PHxV9PZ54bB0sVgsl7ZWE\n+4URZvWuz4srjqu+z0RTxwC5KVGMjowzOjLu1O270vzwbCq7z3Kk4RQbktfc9nY8+f16KxamR/NJ\neQeflbeRMzfSrfu+0cmSW087tFotRuOV0qher0ejuXIFPDw8zHe+8x0ef/xxVq5cCUBMTAxbt25F\nJpORnJxMdHQ0Op1nrk42VcLP9J6reoeCywOkKg01EkfiGYoy7aPya5rEwCNXaOy7iMk8Sr5mgVjO\nFihrsJd9PbmRzvXkRecgQyZuA162+PJ4rTMeNsjXrZ+yFStWcPDgQQDq6urQarVTpXuAn/70pzzy\nyCOsXr166nfvvfcer7zyCgAGg4Genh5iYjxnfWeH4dFJzrX0MSc2BE245yxne7NyorJQyVVTo6Nn\nO8cJ25kGz7v35gsqDPZ7vI7xIrNdWb0ehVzGwnTvS/Yh6mDSw1NoHmyhb6xf6nAkl5EUTmigijON\nBixWzxmV79b6ZGFhITk5OezYsQOZTMauXbvYt28fISEhrFy5knfeeYeWlhbefPNNAO69917uuece\nnnjiCY4cOcLk5CS7d++esYQvhcrzRixWG0VeeFUP4KdQMz8qkypDLV0jOuKCPO+Eyp2StMFowwOo\nvtjDxKQFtcq54z9mM4vVQpWhjhB1MKnhc6UOR3LG/lEudQ+RMy+S4ADPWs72ZhVoc2nsv0iVoY61\nSTOPqfJlcrmMwgwNRys7Od82QNacCKlDAiS4Z//EE09c83NWVtbUf9fWTl8G+t3vfufSmJxhqhmG\nl0y5m85CTS5Vhloq9NXEzdsodTiSkl3uiPWX063UNfey0ItmV3i6iwPNDE+OsDJhqSjhc1UJ30sv\nFADyNTn8ufEdKg01sz7ZAyzK1HK0spMzjQaPSfbik+YEpjEzdc29JGmDiYkMlDqc27YgOhulTEGl\nQdx7A/sHFq58GQvOMdVIR5TwAXtvDpkMrz6hDPcLIyVsDhf6mxmaGJ75BT4uMzmcIH8l5Y0GrB7S\nrEwkeyeoumgv4XvjwLyrBSj9yYpMp2O4C71JJLh5cSFEhvpRecFzF7fwNlablUpDLUGqQNLDU6QO\nR3J9Q+Nc6BggMymcUC+Zbnc9BZpcbNioEhcLKBVyCtKi6Rsap7lrUOpwAJHsnaJ8qgznvSV8h4LL\nHbHEyFp7Kb8wQ8PouJl6D13cwts0DbQwODFEfvQCFHIxDqLivP27wxvW0ZhJgWYBgKgMXuaoDHrK\nIN/r3rN/6KGHbthc5Y9//KNLAvI24xMWapp6iIsKJD46SOpw7lhe9Hx7RyxDDZvm3iV1OJIrytTy\nUVk7ZQ0GFqRESR2O13Nc9RVoF0gciWdwJAJfSPZRAZEkhyTQ0HcB06SJQJX33tJ0hpx5EfipFZxp\n0PO1tamSd4m8brJ//PHH3RmH16pt7mHCbPWJDyvYF7fICE+lvu88PaO9RAW4tymEp0lLCCM0SE3F\neQPf3JyJXC7aut4um81GpaEWf4U/mRFpUocjuSHTBA2t/aTEhxIZ6i91OE5RoMmldaiDauNZlsYV\nSR2OpFRKBfmpUZSc09OmHyY5RtrugNct4xcXF0/9z2Qy0djYSHFxMbGxsSxevNidMXq0M5fXrvf2\n+/VXczTYEfferkyjGTJN0tgm5hDfibahDnrH+siNzkYp986uhM5Ued6I1eb9Y32uJppzXcuTSvkz\n3rP/13/9V95880327dsHwP79+3nuuedcHpg3cCyWEhXqzxyJz9qcKV9zuSOWSPbAlUWNPOED680c\n7yfHvd3ZbupCwUeqggAxgRrig2I519PIqHlM6nAkl5sSiUopp7xR+u+OGZN9aWkp//Zv/0ZQkP1+\n9GOPPUZdXZ3LA/MGZy/1MTpuYVGmRvL7Mc4Uqg4hJWwuTQMtDIx7f6/qO+WYRnOmUe8x02i8UaWh\nFpVcRXZUptShSG503MzZS/bputoI37q3XaBZgNlmoc54TupQJOevVrJgXiQdxhG6ekYkjWXGZO/n\n5wcwlcwsFgsWi8W1UTatL/0AACAASURBVHmJ8kZ7Ix1fuV9/tQLtAmzYqDaKEzulQk5BejT9wxM0\ndXrGNBpv0z2iQ2fSkxOViZ/Cu6eYOUPVRSNmi82nruodpkr54rsDuJIfpL66nzHZFxYW8vTTT6PX\n63n11Vd5+OGHKS4udkdsHs1qtVHeaCQsSE1aYpjU4ThdfrS91Cru29s57r1J/YH1VhWXp3LmixI+\ncGW6ri/dr3eID4olOiCKup56Ji2TUocjuYL0aBRymeTNuWZM9j/60Y9Ys2YNy5Yto7u7m29/+9v8\n0z/9kzti82jn2/sZHp1kYYYGuQ+V8B2iAiJImppGMyp1OJLLmWufRlPeYJh2aWbhxqoMNShkChZE\nZUsdiuTGJy1UN/UQE+kb03W/SCaTUaBZwIRlgvq+81KHI7kgfxXZcyJo6R7C2C/dd+lNNdVJS0uj\nuLiYhQsXkpYmpszAlRaqvliGcyjQLMBqs1LbI+69OabR6PtHaTdIe+/N2xhHe2kb7iQzIo1Alfet\nCOlsdc29TExaKfKxsT5Xm2qwI5pzAVCYKX0pf8Zk/9Of/pTvf//7HD58mAMHDvDd736XX/ziF+6I\nzWNZbTbKGw0E+SvJTA6XOhyXER2xrlU4NSrfs9ap9nRVYhT+NRzvH18c6+MwJzSJMHUoNcazWKxi\njNfCdA0yPDzZl5SU8OGHH/KLX/yCX/7yl3z44Yd89tln7ojNY13qGqJvaJyCtGiUCt/tOBwbFENM\noJazPQ2MWyakDkdyuSlRKBUyyhuNUofiVSoNtciQkafJkToUydmn6/YQFerH3Fjfma77RXKZnHzN\nAkbMJs73N0kdjuTCgtSkJ4Zxvn2AgRFpvktnzFRarRaF4koPa6VSSVJSkkuD8nRnHKPwfXBwzRcV\naBYwaZ3kXE+D1KFILsBPSc7cSNoNw+j6TFKH4xUGxgdpHmghNXwuIepgqcORXH1rH6ZxMwszfLeE\n75B/+eSuyiBG5YO9kmPjynoI7nbdZP+rX/2KX/3qVwQFBbFt2zb+7//9v/zsZz/ja1/7GoGBvjUv\n9FbYbDbKGwz4qRTkzPX9VrKilH8tT5lG4y2qDHXYsFEglrMFmKoK+fJYH4f08BSClIFUGWqx2sSq\nkVJ/d1w32SsUChQKBfPmzWPdunWEhIQQFBTEXXfdRWJiojtj9CidxhF0faPkpkSiVvn+ql1JIQlE\n+IVTYzyH2WqWOhzJFaRHI5NdmTol3Jjjfn2+KOFjtdmoaDQQEqgiPdF3x/o4KOQKcqPnMzAxSMtg\nm9ThSC46PIDkmGDOXerDNOb+79LrNqj+wQ9+cN0X/exnP3NJMN7A0eJyNpTw4fI0Gu0CPmn7nIa+\ni+TM8u5nIYFqMpPCqW/tp29onIgQP6lD8lgjkyYa+y+SHJJIpH+E1OFIrqljkIGRCVblxc2aBZUK\ntAs41V1GpaGWeWFzpA5HcosyNLTqhqm+aGRpTqxb9z3jPfvjx4/z1a9+lfXr17N+/XpWrVrF559/\n7o7YPFJ5owGFXEZeSrTUobiNowRbJRa3AK6U46S69+Ytao3nsNqsopHOZY6xPr7YSOd6siLSUSvU\nVBlqRX8KoNCxMI4EpfwZk/0vf/lLfvzjHxMVFcXvfvc7tm3bxlNPPeWO2DyO4f9n787DoyrPxo9/\nzyzZJ/tM9pAQEhJCVgj7IhZQcVdAsaC+9Ve1r7b6FttSa6t93a221mqrVvRt0brgvoIioCBhCdkg\nQEISIBtJZrLvs/7+CAlGlgRI5szyfK6L6yKZOXPunFnuOfd5nvtp7aGqoZOUuCB8vNxn1a7xAePw\nU/tSrD8grr3x/Sl4ItmfjZhyd5LNZmNvqR4vDyUp41x/rM8AtVJNakgy+p4m6rrq5Q5HdpEhPoQF\n+7Cvsgmjyb5TEodN9n5+fmRmZqJWq0lMTOSee+7htddes0dsDqfABVepGon+aTSpdJg6qWw7Jnc4\nsgv29yI+wp/Sqv4uisKp+ixGDjSXEeajI9xXJ3c4sqtu7MTQ1kt6QghqletO1z2dkw12RGVQkiSm\nJGkxmqyUHGm2676HfdWZzWby8vLw9/fngw8+oLi4mJqaGnvE5nD2lumRgMxE90r2cLKnueiV32/K\nRC1Wm42icjHn/nQONpVisprEWf0JAyOwXbmRzpmkhiSjkpQUiYVxgO9VBu1cyh822f/xj3/EarXy\n61//mk8++YQHHniAO++80x6xOZSWjl7Ka9pIjA4gwNf9Vu1KCpqAl9JLXHs7QZTyz67wxNxqMQq/\nX36ZHpVSQdr4ELlDsTtvlRcTgxOp7TyOoadJ7nBkFxehIUjjSVG5AbPFfpdFh03248ePZ9q0acTH\nx/Pqq6/y8ccfc80119gjNoeya389NtzzmzmAWqFicmgyTb0t1HTWyR2O7MKDfYgK9aXkaDO9RjEl\n8fvMVjP7mw4S5BlIrMZ9p+kOaGjppkbfRWpcEN6e7jPW5/sGvvSJfh2gkCSyE7V09ZoprW61237P\n+MqbP3/+WTs8bd26dSzicVi5+48D7pvsob+Un9dQSJF+PzGaKLnDkV1WkpZPdxxlf2UzU5PFdekB\nh1sq6TH3MD082+W7xI1EvptN1z2d9NBU3uR9ivT7WRg7X+5wZJc9UcvX+TUUHTbYrTnbGZP9f/7z\nnzHZ4WOPPUZRURGSJHH//feTnp4+eNuOHTv485//jFKpZN68edx1113DbmMP3b1mig/riQ3zIzTQ\nfVftmhQ8EZVCRZG+hCvGXyJ3OLKbciLZ55fpRbL/nkKDWLv++/JL9UgSZE5wn+m6P6Tx8CMhMI6K\n1qO09XUQ4Om66wKMRFJMADMmhREf6W+3fZ4x2UdFjf6Z2+7duzl27Bhvv/02FRUV3H///bz99tuD\ntz/yyCOsXbuWsLAwVq5cySWXXEJzc/NZt7GH4goDZovN7Ubh/5CXypOU4ET2GQ7S2K1H5+PexyM2\nzI8Qfy+KKvqvvbnyokgjZbVZKdaX4Kf2JSEgTu5wZNfS0UdFXTvJsYFofNxvrM/3ZWrTKG89QrGh\nhLlRM+QOR1ZKhYLbr7LveBa7fjrl5uaycOFCABISEmhra6OzsxOA6upqAgICiIiIQKFQMH/+fHJz\nc8+6jb3sdeORtD+UMdhgR4yslSSJ7CQtPX0WDh5rkTsch3C0vYp2YwdpoZNQKly/nfRwBhovic+O\n/lI+iBk9crHraBGDwUBq6slvM8HBwej1evz8/NDr9QQHBw+5rbq6mpaWljNuczZBQT6oVKPzYdNj\ntDA+KoCMlHC3vwa5wD+H/xx6l5LWg9w09cpReUyt1nlLehdPi+WrvGoOVLVy8fQ4ucM5hb2P7Yba\nMgDmTchx6ud1OCP92/Yf6f8SuHBGPNog970ECKBFw/hDsZS1lOMToMTX49QF1Vz5NSO3YZN9TU0N\nDQ0NTJkyhXfeeYfCwkJuu+02EhISLnjn5zOFa6TbtIziEqR3XTMZrVaDwWDfioKjmhA4nrKmcg7X\n1BDoGXBBj6XVatDrO0YpMvvT+nmg8VGTu+84y+aNd6ie5/Y+tjabjdxj+XgqPYhQRDn183o2Iz2u\nnT0missNxEdowGx22eNxLlKDUqhsqWJr6R6mhWcPuc3ZPwscwdm+LA1bxv/tb3+LWq3mwIEDrF+/\nnksuuYRHHnnkvALR6XQYDCebkDQ2NqLVak97W0NDAzqd7qzb2Iu3p8ptp8yczsA0mmJRykehkMhK\nDKW9y0h5bZvc4ciqrqseQ28zqSHJqJVqucORXVG5AavNJkr43yOac8ln2GQvSRLp6el89dVX/PjH\nP2b+/Pnn3VRl9uzZbNy4EYCSkhJ0Ot1gOT46OprOzk5qamowm81s2bKF2bNnn3UbQR4ZoWLO7PfJ\nvU61oxhohyq65vVz5655ZxLuoyPMR8uBplKMFtFq2p6GPV3t7u6muLiYjRs38vrrr2M0Gmlvbz+v\nnWVnZ5OamsqNN96IJEk8+OCDvP/++2g0GhYtWsRDDz3E6tWrAViyZAnx8fHEx8efso0gryCvQMb5\nx3C4tZIuUze+6lOvvbmTlHHBeHkoyS/Tc8PFE9x2XEeRoQSVpGRSSLLcociuz2hh/5FmIkJ8iAjx\nlTschyFJEhnayXx5bAsHm8tEh0U7GjbZ/+QnP+H3v/89y5cvJzg4mGeeeYYrrrjivHd43333Dfk5\nOfnkB0NOTs5pp9X9cBtBfpnayRxrr2af4QAzIqbKHY6s1CoF6Qkh7D7YSHVjJ7Fh7jfISN/dRG3n\ncSaHJOOt8pI7HNntP9KEyWwVZ/WnkXki2Rfp94tkb0fDJvslS5awZMmSwZ9/+ctfuu2Zi3BShnYy\nH1V8QZG+xO2TPfSXancfbCS/TO+Wyb5INNIZYmC6rjutXT9SsZpoAj0D2Gc4gMVqEVM07eSMyf7e\ne+/l2WefPWPbXHdrlysMFeajJcI3jIPNpfRZjHgq3bthSNr4EFRKBXvL9Fwzd7zc4dhdYeN+JCTS\nQifJHYrszBYrReVNhPh7Ms4Nv/gNZ6CU/03NdxxurSQ5OFHukNzCGZP9Aw88AIxd21zB+WVqJ/PF\n0a850FRKli5N7nBk5e2pIjUuiKKKJhqauwkLdp9xDG197RxpP0Zi4Hg0HmLw7KFjLfT0mZmdJvpy\nnEnmiWRfpN8vkr2dnHE0fmhofx/nN954A51OR1RUFFFRUfj6+vLUU0/ZLUDBcQ2UbAv1+2SOxDEM\nLHTibqPyiwaXsxUlfDj5/Lt7e+2zSQiIw1ftQ5F+P1ab/ZZ5dWfDTr3z9vbmhhtu4ODBg2zevJkV\nK1YwZ84ce8QmOLhov0hCvILYbziE2SqWec2cEIpCktww2Q9crxeDraxWG/mHDfh5q0mMDpQ7HIel\nVChJD02lzdjB0fZqucNxC8MO0Pv5z3/OpZdeys0334y/vz9vvPHG4Fm/4N4Grr1trt5GaUsFqSET\n5Q5JVhofDybGBnLwWAstHX0EaTzlDmnMdZu6KWutIFYTTbBXkNzhyK6iro32LiNz0iMcqpuiI8rU\nTib3+B4K9fsYHzBO7nBc3rBn9vn5+axZs4Zbb72VuXPn8utf/5rqavFNTOh3siOWKOWD+zXY2Wc4\niNVmFSX8E/aW9j/vU8Uo/GFNDJqAp9KDIn3JeTdqE0Zu2GT/6KOP8sQTT3DHHXfwwAMPcMcdd/Cz\nn/3MHrEJTmB8wDg0aj+K9QfEtTcgK7G/6uUuyX6ghC+65vWvDZBfpsfLQ0nKuODhN3BzaqWaySEp\nGHqaqOuqlzsclzdssn/nnXdITDw5WnL69OlceeXorHYmOD+FpCBdm0qHqZPKtmNyhyO7YH8vxkf6\nU1rVSke3Ue5wxlSfxciB5jLCfHSE++rkDkd2VQ2dGNp6yZgQilpl19XDndbgIN9GURkca8Nesy8r\nK+Mf//gHra2tABiNRurr67njjjvGPDjBOWRqJ/Nd3S4K9fuYEBgvdziym5KkpbKuncJyA3PTI+UO\nZ8wcbCrFZDWJgXkn7BWj8M9ZashEVArViXU2rpc7HJc27NfPP/7xj1xyySW0tbXxk5/8hLi4ODH1\nThgiKSgBb5WXuPZ2wuB1+1LXLuUXnBinkaV17x4LA/LL9KhVCiaPFyX8kfJSeZEclEhdVz31HY1y\nh+PShk32Xl5eXH755Wg0Gi666CIeffRR1q5da4/YBCehUqiYHJJCc28L1R21cocju7BgH6K0vpQc\n7W+u4opMVjP7DYcI9goiRhMldziyO97URZ2hi8nxwXh5iOWwz8XAeI/dtYUyR+Lahk32fX19lJWV\n4enpye7du2lra6O2VnygC0NlDjbYEcveQn8p12yxsq+ySe5QxkRZSzm9ll4ytZNFlzi+10hHjMI/\nZ2mhk1BICnbViGQ/loZN9vfddx/V1dX84he/4Pe//z2LFy8WA/SEU6SETEStUItkf4KrT8EbGFAl\nptz121uqR6mQyJggepCcKz8PXyYExHO46QitfW1yh+Oyhq03TZkyZfD/GzduHNNgBOflqfQgNWQi\nhfr9HO9qIMI3TO6QZBWj80Mb6EVRRRMmswW1ynVW9rJYLRQbDuDvoRHNUICmtl6O1neQGh+Mr5da\n7nCcUoZuMmWtFRTpS5gfPUvucFySmB8ijJrMEwO1xDSa/u6CU5J09BktlBxpkTucUVXRdoROUxfp\n2lQUkvgIEaPwL1ymmII35sQ7VRg1k0OTUUpKUco/YWBhnL1lrjXKeOD5FaPw++WXNiJxsqGScO4C\nPQNIChnP4dZKOoydcofjkoZN9t9++6094hBcgLfKm+TgRGo66zD0uObAtHMxPtKfQD8PCg8bMFtc\no7ug1WalsHE/PipvEgPHyx2O7Nq6jByuaWNCdAABfq6/FsJYmh6dhQ0bxYYSuUNxScMm+3Xr1rFo\n0SKee+45MQpfGNZAKb9AlONQnCjld/WaOVTlGqX8Y+3VtBnbSQ9NRalwnXEI56vgsB4booQ/GqZH\nZwJQ2Cgqg2Nh2GT/z3/+k3fffZfIyEgeeughfvrTn/LFF19gsVjsEZ/gZNJPTKMRpfx+A1Ox9rpI\ng52BRjqZOjEKH04+r9liyt0F0/mFEqOJorSlnG5Tj9zhuJwRXbMPCAjg8ssv54orrqCjo4NXX32V\nq6++msJCMS9SGMrPw5cJgeM52l5FS2+r3OHILikmEI2PmoIyPVarc3cXtNlsFDXux1PpQXJQ4vAb\nuLjOHhOHjrUQF64hNMBb7nBcQqY2DYvNwj7DAblDcTnDJvs9e/bw29/+lssvv5wDBw7w6KOPsn79\nel588UUeeughO4QoOJuswWVvxbU3hUIiO0lLe7eJwzXO/eWntvM4ht5mJoekoFaKKWaFhw1YrDam\nJotFgEZLlmjONWaGTfZ//vOfmTFjBhs2bOC3v/0tCQkJAERHR3PZZZeNeYCC80k/sTBKoVjjHjhZ\nys9z8lJ+4WAJX4zCB8gr7Z9lIbrmjZ4wXx0RvmEcbC6l19wndzguZdhk/+abb3L11Vfj4eFxym1i\n5TvhdAI9AxgfMI7y1iNiGg2QHBuEr5eK/DI9VideKKigcR9qhYpJwRPlDkV23b1mSo40E6PzIyzI\nR+5wXEqmNg2T1UxJ0yG5Q3EpYp69MCYytJP7p9GIUj4qpYLMCaG0dPRxpK5d7nDOy/GuBuq7G5kU\nkoyXSkwxK6roL+GLs/rRl3WiciQqg6PLrsneZDKxevVqVqxYwcqVK6murj7lPp9//jlLly5l+fLl\n/OUvfwHg/fffZ/78+axatYpVq1bxj3/8w55hC+dhoOFKgXjDAjDlxHVdZx2VX9BYDIhGOgPyDvWX\n8KdOFNfrR1ukbzha7xD2Nx3CaDHJHY7LsGuy//TTT/H39+fNN9/kzjvv5Jlnnhlye09PD08//TT/\n93//x9tvv82OHTsoLy8HYMmSJaxbt45169bxs5/9zJ5hC+chxDuYWE00pS3ldJq65A5HdqlxwXh5\nKMkrbcTmhKX8gsZ9/UsZh6bIHYrsevrM7D/STESID5GhvnKH43IkSSJTm4bRYuRgc5nc4bgMuyb7\n3NxcFi1aBMCsWbPIz88fcru3tzcff/wxfn5+SJJEYGAgra3OPYLZnWXp0rDarBTrxTQatUpBxoRQ\nDG29VDU41ziG+q5G6rrqSQlOwlvlJXc4sss72IDJbBVn9WNooJQvmnONnmFXvRtNBoOB4OBgABQK\nBZIkYTQahwz+8/PzA6C0tJTa2loyMjKoqqpi9+7d3HbbbZjNZn7zm98wadKks+4rKMgH1SivNKbV\nakb18VzdQu+ZfFTxBSWtJVydcfEZ7+cux3VBTiy7DjRwoLqVqWmRdtnnaBzbbfrtAMxPmOY2z9XZ\nrP2if+DYoplx4niMsoHjGRqagvZAMPubDxAY7CWmeo6CMUv269evZ/369UN+V1RUNOTnM5Uzjx49\nyn333cczzzyDWq0mIyOD4OBgLrroIgoKCvjNb37DJ598ctb9t7R0X9gf8ANarQa9vmNUH9PVKfAi\nRhNFccMhjtU14KM+ddSyOx3XcaE+eKgVfFtQy6VTo5EkaUz3N1rHdvuRPJSSknEe8W7zXJ1Jn8nC\n3oMN6IK88VVJbn88RtMPX6/pIZP5uvpbtpXlkxZ69pM7od/ZvnyOWRl/2bJlvPPOO0P+XXvttej1\n/QOUTCYTNpvtlCl99fX13HXXXTzxxBOkpPRfH0xISOCiiy4CICsri+bmZtGu10lkaftL+UWiIxae\naiXpCaE0NHdT3egcpfzGbgM1nXWkBCfioxZd4vZXNtNrtDB1om7Mv6y5uyxdOgD5JwaHChfGrtfs\nZ8+ezYYNGwDYsmUL06dPP+U+v/vd73jooYdITU0d/N0///lPPv30UwDKysoIDg5GqRSLcDiDgTds\ngXjDAjDtxKj8gYYsjm5gffHME8+ju9srGunYTZx/DEGegRTrD2CymuUOx+nZ9Zr9kiVL2LFjBytW\nrMDDw4MnnngCgJdffpmcnBwCAwPJy8vjueeeG9zm1ltv5corr+RXv/oVb731FmazmUcffdSeYQsX\nQOcTSrRfJIeaD9Nt6nH7s8O0hBA81Ar2HNJz7dzxDn92WKAvRiEpSBdlVExmC4XlBnRB3sSFi2v1\nY02SJLJ0aWyu3sah5jJRyr9Adk32SqWSxx9//JTf33777YP//+F1/QHr1q0bs7iEsZWlS6Omso59\nhgNMj5gidziyGijl5x1qpLqxk9gwx00ahp5mqjpqSQlOwvc04y3czUAJf8msKIf/kuYqsnXpbK7e\nRkHjPpHsL5DooCeMOXHtbShnKeUPXHrJFiV8APacaKQzJ9M+MykEiPOP7S/lG0pEKf8CiWQvjLkw\nHy1RfhEcai6jxyzWqR4s5R907AY7Bfp9J0r4qcPf2cUZTRYKyg2EBngxITpQ7nDcxkApv8fcS2nz\nYbnDcWoi2Qt2kaVNw2yzsM9wUO5QZOepVpKREEpDS4/Djspv6mnmWHs1SYEJ+HmILnH7KpvpM1rI\nSRGj8O1NVAZHh0j2gl2IN+xQOQ5eyh94nrLDRAkfYM+hBgCmJYfJHIn7ifOPIdAzgGLDAcyilH/e\nRLIX7CLcV0ekbzgHm0pFKR/HL+XvbSxCISnIFAvf0Gc6MQo/0JvYMD+5w3E7CklxopTfwyFRyj9v\nItkLdpOty8Bss4he+Th2Kb+x20B1Ry3JwYliFD6wr6IJo8kqSvgyyh7s1yF65Z8vkewFu5lyoiSc\n11gocySOYaCUPzDK21HkN/ZPf52iy5A5Esew+8TzM/B8CfYX5x9LoGcARYb9YlT+eRLJXrAbnY+W\nGE0Uh5oPi2VvOVnKzzvkWKX8vQ1FqCQlGVoxCr/PaKG43EBYsA8xOlHCl4tCUpCtS6fH3Mshsezt\neRHJXrCrKbqM/l75jfvlDkV2nmolmRP6S/mOsuxtfVdD/3K2IRPxVrl3t0OAogoDRrOVnGRRwpfb\n1LBMAPIaRGXwfIhkL9hV9onScF7j6TsluptpKf2ju3cdaJA5kn57G0QJ//sGLrFMEyV82cVqogn1\nDqFYX0KfxSh3OE5HJHvBrkK8g4j3H8fhlgra+sTyoGnjQ/D2VLH7UANWmUv5NpuNvY3FqBUq0kJT\nZI3FEfQazRRXNBER4kOUVvQakJskSUzVZWC0mtgv+nWcM5HsBbubEpaBDRsFejHnXq1SkJ0USnN7\nH+U1bbLGUtdVT0N3I6khKXipvGSNxREUHDZgEiV8hzLlRCl/r6gMnjOR7AW7y9KlISGR3yDesADT\nJ50o5R+Ut5Q/WMIPEyV8OHlpZeD5EeQX6RdOhG8YJU2HRL+OcySSvWB3gZ4BTAiMp6LtKIbuZrnD\nkV3KuCA0PmryDjVisVpliaG/hF+Eh9KDySHJssTgSDp7TJQcaWZcmIaIEFHCdyRTdJmYrWbRr+Mc\niWQvyGLg7HFndb7MkchPqVAwNVlHR7eJg8daZImhuqMWQ08TaSEpeCg9ZInBkfR/8bKJs3oHNPDZ\nIUblnxuR7AVZZGrTUEgKvqvKkzsUhzD9xKj83QfkabCzp6EAODm9yd3tPNCABExLEaPwHY3OJ5RY\nTTSHWg7TaRT9OkZKJHtBFhoPPyYGTaCi+Rj67ia5w5HdhOgAgv092Vumx2S2bynfarOyt6EQH5U3\nk0Im2nXfjqi5vZey6laSYgIJ9hcDFR3RlLD+fh0FetE+d6REshdkc7JJRoHMkchPIUlMSw6jp8/M\nvkr7fvkpa6mgzdhBli4dlUJl1307ot0H+6srooTvuAb6QOwVpfwRE8lekE2GdjJqpZo9DQUO1S5W\nLgPJZbedR+Xvqe//spUTlmXX/TqqnQfqUSokpopGOg4ryCuQhIB4yluP0Non75RVZyGSvSAbb5UX\nOZHpNHTrqeqokTsc2cWG+REW5E3hYQO9Rvss9mG0mCjU7yPIM5CEwDi77NORHW/qoqqhk8nxwfh5\nq+UORziLqWGZ2LCJgXojJJK9IKu5cdOBk2eX7kySJKZPCsNotlJw2GCXfe5vOkivpY+pYZkoJPFx\nMDi3PlWU8B1ddlg6SknJ7noxo2ckxLtbkFVG+CR81T7kNRRisVrkDkd2M1PDAcjdX2+X/eUNlPDD\nRQnfZrOx80ADHmoFWRO0cocjDMNP7UtqSDK1ncep7TwudzgOTyR7QVYqhZIpugw6TJ2UtpTLHY7s\nwoJ9GB/pT8nRZlo7+8Z0X92mbkqaDhHpG06UX8SY7ssZHK3voLGlh6xELZ4eSrnDEUZg4EuqqAwO\nTyR7QXY54dkA7BZvWKD/7N5mg50lYztQr6BxH2abRZzVn5Bb0l9NGeh5IDi+tJAUvFVe7GkowGqT\np/uksxDJXpBdvH8soV7BFBn2i6Ur6W/kolRIg8lnrIhGOieZLVZ2HWjAz1vN5PHBcocjjJBaqSZL\nm05rXxuHWyrlDseh2TXZm0wmVq9ezYoVK1i5ciXV1dWn3Cc1NZVVq1YN/rNYLCPaTnBekiSRE56F\n0WKkWF8idziyndaxFQAAIABJREFU0/h4kJ4QQnVjJzWNnWOyj5beVg63VpIQEE+wV9CY7MOZ7K9s\npqPbxIxJYaiU4hzImUwbqAw2iIF6Z2PXV/Wnn36Kv78/b775JnfeeSfPPPPMKffx8/Nj3bp1g/+U\nSuWIthOc28Acb/GG7TcwUG/HGJ3dD0xXEiX8fjv29w/wmp0mxi44m4TAOII8Ayls3IdRVAbPyK7J\nPjc3l0WLFgEwa9Ys8vNH9sF+vtsJziPMV8c4TQyHmg/TYRybs1lnkjEhFB9PFTtL6rFaR7fhkM1m\nY2f9XlSSkmxd+qg+tjPq7DFRWG4gKtSX2DA/ucMRzpFCUjAtPJteSx/FBrES3pnYtTemwWAgOLj/\nephCoUCSJIxGIx4eJ1fZMhqNrF69mtraWi655BL+67/+a0Tb/VBQkA8q1eiOqNVqNaP6eEK/geO6\nYMIM/q9gPYe6DrIk6mKZo5Lf3KwoNu48xvG2XjKTzq+b2+les+VNR6nvamBGTDZxkWIwWt6OI5gt\nNhZNH4dO5z+ibcRnwdg43+N6ieccNh7bTFFzMZdNnjvKUbmGMUv269evZ/369UN+V1RUNOTn07VI\n/fWvf81VV12FJEmsXLmSqVOnnnKfkbRWbWnpPseIz06r1aDXd4zqYwpDj+tE32QUkoKvD39HTlCO\nzJHJL3tCCBt3HuPz7UeICvI+5+3P9JrdUPotAFlBGeI1DWzMPYokQVpc0IiOh/gsGBsXclw98SNW\nE0Vh/QEqa4+j8XDPCs3ZviyNWbJftmwZy5YtG/K7NWvWoNfrSU5OxmQyYbPZTjk7X7FixeD/Z8yY\nQVlZGTqdbtjtBOfn76FhckgKxYYSajrqiNZEyh2SrCZEBaAN9CK/TE+f0TIqc79NFhN5DYUEeGhI\nCU4ahSidW31zNxV17aTGBxOk8ZQ7HOEC5IRnU3X4E/IaClkQM0fucByOXa/Zz549mw0bNgCwZcsW\npk+fPuT2yspKVq9ejc1mw2w2k5+fT2Ji4rDbCa5jRkR/JWfncbHOvSRJzEwNp89kIb9MPyqPWWw4\nQLe5h2nhU1AqROOYwYF5k8NljkS4UAMtn8Vnx+nZNdkvWbIEq9XKihUreOONN1i9ejUAL7/8MgUF\nBYwfP57w8HCWLl3KihUrmD9/Punp6WfcTnA9k0OS0aj92N2Qj9lqn8VgHNnME0lo+77RaQe6s77/\ng3B6xJRReTxnZrXZyN1fj6eHkqwk0R7X2fl7aEgLSaGms47qjlq5w3E4dh2gp1Qqefzxx0/5/e23\n3z74/1/96lcj3k5wPUqFkpzwLDZXb2Of4SBZujS5Q5JVWJAPSdEBHDzWgr61B23guV+7H9Da18bB\npjLG+ccQ4SsG5pVWtdLU3sectAg81aLK4QpmRuZQZCgh9/geYjRRcofjUET3CMHhzIzoH5y38/ge\nmSNxDHMz+scubC++sLP7PfUF2LAxI/zUQa/u6OTcelHCdxWTgifi76FhT30BJotJ7nAcikj2gsOJ\n9AtnnCaGkqZSWvva5A5HdlOTdXh7Ktm+7/h5z7m32WzsPJ6HSqFialjGKEfofHr6zOw51EhogBeJ\nMYFyhyOMEqVCyfTwKXSbeygyiG6c3yeSveCQZkRMxYZNrFUNeKqVTE8Jo6Wjj5Kjzef1GEfbq6nv\nbiQjNBUftc8oR+h8dh1owGiyMjc9AoUkyR2OMIpmnhjkm1snKoPfJ5K94JCmhmWgUqjYeTxvRH0V\nXN1AKf/borrz2v7kwDxRwgf4pqgOSYI56e49vdMVhfnqGB8QR2lLOU09LXKH4zBEshccko/ah4zQ\nVBq69Rxpr5I7HNnFhWuI1vpSeNhAe/e59f82WozsbSgkwMOflODEMYrQeRyr7+BYfQcZCaFibr2L\nmhmRgw3b4JdcQSR7wYENDNQT5bj+Ofdz0yOxWPuni52LvQ1F9Jh7mRmZg0ISb/mB6si8DHFW76qy\ndel4KD3YeTxPrHN/gnjnCw5rYvAEgjwD2dtYSK+5V+5wZDdzcjgqpcS24uPndGlje90uJCRmRUwb\nw+icQ5/Rws4D9QT6eZCWINatd1VeKk+m6DJo7m2hrKVC7nAcgkj2gsNSSApmR06nz2JkT0OB3OHI\nzs9bTVailjpDF5V17SPaprqjjqPtVaSGTCTEW6xbv+dQIz19FuakR6JUiI8/VzZYGRRTeAGR7AUH\nN+tE6Xlb7U4xUI+TpedtxSMbqLe9bicAc6JmjFlMzuTbojokYF66WLfe1Y0PGEeYj47Cxn1i2WxE\nshccXICnP+mhqdR2HhcD9YCUuCBC/L3YdaCRnr6ztxPuNfWSV19AoGcAk4In2ilCx1Wr76S8to1J\n8cGEXkAnQsE5SJLE3KgZmG0WcXaPSPaCE5h74qx0e+1OmSORn0KSmJ8ZSZ/Jwo5hBuptr8qj19LH\nrMhpYtEbYNuJDoTzxcA8tzE9fAoeCjXba3e6/UA9kewFh5cUlIDOO5S9jUV0mrrkDkd28zIiUSkl\nNufXnPXSxqaKbScG5uXYMTrHZDJb2bG/Ho2PmszEULnDEezER+1NTngWTb0tHGgqlTscWYlkLzg8\nhaRgTtQMzFYzu47vlTsc2fn7epCTrON4UzcHj52+acix9moqW6pIC51EkJdoB7v7YAOdPSbmpEWg\nUoqPPXcyN2oWAN/W5socibzEq15wCtMjpqBSqEQ57oSLs6MB2Jx/+qU8t9fuAmBO1HS7xeSobDYb\nm/bWIEmwIFushOZuYjSRxPuP40BTKYaeJrnDkY1I9oJT8FP7MkWXQWOPQcybBcZH+jMuTEPBYT3N\n7UN7EPSYe8lrLETrE0xKcJJMETqOirp2jtV3kJWoJTRADMxzR/OiZ2LDNvgl2B2JZC84jYHpY9vE\nQD0kSeLi7ChsNthaOPTsPvf4HowWIz9KmCM65gGb8qoB+NGUaJkjEeSSpU3DT+3LjuO73XbpW/FJ\nIDiNeP9YovwiKDaUiKVvgWmTwvD1UvFtYR0mc/+lDavNytbq71ArVCxMmCtzhPJr6ehjb6meKK0v\nybFi7IK7UivVzIzIocvUTX5jsdzhyEIke8FpSJLE/OhZWG1WvqnZIXc4svNUK5mbHkl7t4m80kYA\n9hkO0NTbzLTwKfh7+skcofy+KazFYrXxoynRSGIpW7c2J2oGEpLbDtQTyV5wKjlh2fipfdleu5M+\ny7mt/uaKLsqOQgI259cAsLl6GwALYubIGJVjMJmtbC2oxcdTxcxJ4XKHI8gs1DuY1JCJHG2v4qgb\nNugSyV5wKh5KNfOiZtJt7mHncbF8pS7Qm7SEECpq29l1pJTy1iOkBCcR4Rsmd2iyyzvUSHu3iXkZ\nkXh6iKZCAiyI6b+09XXVtzJHYn8i2QtOZ170LFQKFZurt4lpeMDCqf0Dzz4u2wKc/EBzd5v2ViMh\nptsJJ00MmkC0XyQFjfsw9DTLHY5diWQvOB2Nhx/TwrIx9DSxz3BA7nBklxoXTFSEkhblEUI9Q0kJ\nTpQ7JNlV1LVx5HgHmYmhaEUffOEESZL4Uew8bNgGL3m5C5HsBad0cexAOc693rCnI0kSUckGJIUN\n/+6JYrodsGFX/zXZhWK6nfADU3QZBHkGklu3my5Tt9zh2I34VBCcUoRvGJNCJlLRdoRj7dVyhyMr\no8VEpXE/WNQcLvajo9u9By4eb+oiv1RPXLiG5HFBcocjOBilQsmCmDkYrSa36tkhkr3gtH4UMw9w\nz8E237enPp8uUxdJ3ukYTRJbztBC1118sbMKG3D5zHFiup1wWrMip+Gl9GJrzXa3abKjsufOTCYT\na9asoa6uDqVSyeOPP05MTMzg7fv37+fJJ58c/Lm8vJwXXniB7777jk8++YSwsP4RxldddRXLli2z\nZ+iCA5oYNIEovwgK9Pto7m0h2Mv9zuIsVgtfHtuCSlJyY8ZCyvNK2LS3hkumx8odmiya23vJLakn\nIsSHrCSt3OEIDspb5cWcqOlsqvqGPQ0FzIqcJndIY86uZ/affvop/v7+vPnmm9x5550888wzQ26f\nPHky69atY926dbzwwgskJCSQmZkJwM033zx4m0j0ApxoGRszF6vN6rZn93kNhRh6m5kRmUOYJoQF\n2dF09pj4bt9xuUOTxYbdVVisNi6bPg6FOKsXzmJBTH876a+rvnWLWT12Tfa5ubksWrQIgFmzZpGf\nn3/G+65du5ZbbrkFhUJcaRDObGpYJsFeQXxXt4u2vna5w7Erq83KxmObUUgKFscuAPoHpKmUCjae\nSHrupKPbyLdFdQT7ezIjVfQZEM4u0DOAnLAs6rsbKWk6JHc4Y86uZXyDwUBwcDAACoUCSZIwGo14\neHgMuV9vby/bt2/nnnvuGfzdhg0b+Prrr/Hw8OCBBx4YUv4/naAgH1Sq0W2kodVqRvXxhH4XelyX\nTl7Cy3lvsL3xO27NXj5KUTm+HVV5NHTrWRA/i+TY/rK9VguLpsXyRe5RdhTXMTfTfeaYb9xwEKPJ\nyvWXJxIRHjCm+xKfBWPD3sd1WcZl7Krfy1fVW1iQPM2lx3iMWbJfv34969evH/K7oqKiIT/bbKc/\n89i0aRMXXXTR4Fn9/PnzmTFjBjk5OXz22Wc88sgjvPTSS2fdf0vL6E6p0Go16PUdo/qYwugc11S/\nVII8A/mqYhtzdLMJ8PQfpegcl9Vm5Z3iz5CQmBc2Z8gxnJcezoadR3nrq1KSIjQoFK77ATagp8/M\nJ99W4uetJjshZEzfq+KzYGzIcVy98SdLl05BYzGbD+4iXZtq1/2PtrN9WRqzGvmyZct45513hvy7\n9tpr0ev1QP9gPZvNdspZPcCWLVuYOXPm4M/p6enk5OQAcPHFF1NWVjZWYQtOSKVQcWncxZisZr6q\n2ip3OHZRbDhAXVc9U8Oy0PmEDrktLMiH2ZMjqKrvYPfBBpkitK9vCuvo7jOzaGo0nmrRGlcYuSVx\nC5GQ+PTIly597d6uF8Rnz57Nhg0bgP6EPn369NPeb//+/SQnJw/+/Mgjj5CX198Hfffu3SQmig5h\nwlAzIqYS5BnI9tqdLn/t3mazseHIJiQkLo27+LT3uWp2HCqlxIfbj2Cxuu4HGPSf1X+x6xheHkou\nFk10hHMU6RfOlLAMajuPU6wvkTucMWPXZL9kyRKsVisrVqzgjTfeYPXq1QC8/PLLFBQUDN6vvb0d\nP7+Ty3MuW7aMp59+mpUrV/LKK6/wu9/9zp5hC05ApVBxyYmz+01V38gdzpgqaTpEdWcd2bp0wn11\np71PaKA3i6aPo7Glhx376u0coX19uaeajm4Tl06LxddLLXc4ghMaOLv/7MhXLnt2b9cBegNz63/o\n9ttvH/Jzbu7Q9YYnTpzIW2+9NaaxCc5vZsRUNh7dzLbaXBbGXkSAp+sNorLZbHx+ZBMAl5zhrH7A\nDQuT2LS7io+/O8KM1HDUKteb2dLebWTD7ir8fdQsnnb2QbuCcCZhvjqmhWezq34vBY37mBKWIXdI\no8713v2C2+o/u19w4tr9FrnDGRP5jUUc66gmW5dOlF/EWe8bEuDNgqwomtr7+Laozk4R2tenO47S\nZ7Rw5ex4vDzseu4iuJhL436EQlLwuYue3YtkL7iUGRE5BHkGsq0mF0NPk9zhjCqTxcRHFV+gkpRc\nnXDZiLZZMmMcnmplf1I0WcY4QvsytPawtaCW0AAv5mdGyh2O4OR0PqHMCJ9CfXcjeQ2Fcocz6kSy\nF1yKWqHimglLMNssfFD+udzhjKpvanfQ1NvC/OjZhHqHjGgbf18PFk6Npq3L6HI98z/YdgSzxca1\n88ajUoqPMuHCXRr3I5SSkk8rv3S5nvniHSK4nCm6DOL9x1Go38fhlgq5wxkVncYuNhz9Gh+V9xlH\n4J/JpdNj8fZU8VnuUZdZEa+msZOdJfVEa/2YPkl0yxNGR4h3MPOjZ9HU28zXLrbevUj2gsuRJIml\nSVcC8N7hT1zi+tsXRzfRY+7lsviF+Kh9zmlbXy81V82Oo6vXzPvfVo5RhPb13jcV2IClF40XPfCF\nUbUkfiEatR8bj35NS2+r3OGMGpHsBZcU5x/LtPBsqjvr2Hl8r9zhXJDGbj3f1uYS6h3CvKiZw29w\nGj+aEk1UqC/fFtZx5Lhz9yHYX9lEUUUTSTGBpI0f2eUMQRgpb5U3VyVchtFq4sMK17kUKJK94LKu\nGn8pHgo1H1d+Qa+5V+5wztuHFV9gtVm5JmEJKsX5jThXKRXctCgJG/DGV2VYz9Cq2tEZTRbWfVmK\nQpK4aWGiS/cyF+QzI2IKsZpo8hoKKW89Inc4o0Ike8FlBXkFsnDcRXQYO9l4zDmn4h1sKqNIv5/x\nAXFkaidf0GOljAtiWoqOyrp2vit2ziVwP9lxFH1rL4tyookNc70+CoJjUEgKliVdDcC7ZR+5xKVA\nkezt7L333uH222/l7rtv56c/vZk9e3bx178+Q12da42UdhSLYucT6BnA5uptNHQ1yh3OOek19/LG\noXdRSAqWJ10zKmexyxdMwFOtZP3WCrp6nWu0ca2hiw27qgj29+TqOfFyhyO4uPEB45gePoXqzjp2\n1O2WO5wLJpK9HR0/Xscnn3zI3//+Cs8//zJ/+MMj/Otfa7nnntVERrrPUqT25KH0YGniVZitZtYd\nfMepvqF/VLGBlr5WFsdeRIxmdOaRB/t7cdXsODp7THz4rfOUJ602G+s2HMJitfHjRUmigY5gF1cn\nXIan0oOPKzfQYeyUO5wLIpK9HXV2dmI09mEy9Z9RxcTE8vzzL3P33bdTWVnO2rUv8dxzz3Dffb9g\nxYrryM39DoBvvtnMz372E+6++3b+9re/yPknOKUsXRpTdBkcaa/i66pv5Q5nRMpbj/Bt7Q7CfXRc\nGr9wVB97UU4M4cE+bC6ooaKubVQfe6x8V3ycspo2shJDyUrUyh2O4CYCPP25In4xXaZu3jz03hmX\nZXcGbvv1+J3N5ew5NPKyrlIpYbGc/YnOSdax/OIJZ7w9MTGJlJRUli27ipkzZzNjxmzmz18w5D6N\njQ08/fRz7Ny5g48+eo+MjCz+9a+1vPjia3h4ePD736+huLiQ9PTMEccuwPKJ11DWWsGnR75kcmgK\nEb6OOzfbaDHxxsH1SEj8OGUZ6vMclHcmKqWCWy6dyFP/KeDlj0t46L+m4e3puB8FHd1G3tlSjqda\nyY8XJckdjuBmLoqZQ7HhAEWGEnbW72VmxFS5Qzov4szezn7/+//l+edfJjExif/859/8z//cNeT2\ngSSu0+no7OzkyJFKGhrq+eUv7+buu2+npqaK+nrXXsVsLPipfVkx8XrMVjP/PvA2Fqvjto79/MhX\nNPYYuChmNuMDxo3JPibGBnHZjHHoW3v5z6ayMdnHaLDZbLz62UG6es1cMzeeYH8vuUMS3IxCUrAq\n5Qa8lF68W/YRhp5muUM6L477dX6MLb94wlnPwn9Iq9Wg13dc0D5tNhtGo5G4uHji4uK5/vob+PGP\nl2KxnEw8SqVyyP3VahUTJ6bw5z8/f0H7FiBDm0pOWDZ7GvL5quqbc+5EZw9H26vYVPUNoV7BXDn+\n0jHd1zVz4yk52sx3++pJGx/CtBTHq3Zs3F1NUUUTk+KCWDRVrGonyCPEO4jlSVfz74Nv8+8Db3Nv\n9h0oJOc6V3auaJ3cp59+xFNPPTp43aerqxOr1UpgYNAZt4mNjePo0SO0tPR/m1y79iX0eucaVe5I\nliddRYCHhs+PfEV1h2OtBNdh7OSVfa8DcFPyUjyVHmO6P5VSwe1XTsJDreDfG0ppanOsXgTltW28\n900FAb4e/PTKVBQKMadekM+08GyytGlUtB1xmrE/3yeSvR0tWXIlQUHB3H77LfziF3eyZs1q7r33\nV3h6ep5xGy8vL+65ZzX33XcPP/vZT2hrayU0VAxQOl8+ah9uSl6KxWbhpeL/c5gRtharhbX7X6el\nr5Urxi9mYvDIq04XIiLEl5sWJtHdZ+aVTw9gtTrGAKTOHhMvfrQfq83G7VelEuA7tl98BGE4kiRx\nY/J1BHho+KRyI1XtNXKHdE4kmzMPLzyLCy25/9BolPGFU8l1XD8/8hWfHfmK8QFx/CLr9lEfBHeu\n3i37mC0128nUTub/TV41KnPqR3psbTYbL3ywn/wyPZfPHMf18xMueN8Xwmaz8dy7xRRVNHHN3Hiu\nmu1Yc+rFZ8HYcJbjeqCplL8XvYq/h4ZfTb2bIK9AuUMapNWeudGUOLMX3NJlcQuZosugsu0obx16\nX9YpNbuO72VLzXbCfcNYlbLc7i1gJUni1suS0QV581nuMbbky3vG8vnOY4PX6a+YGSdrLILwQ5NC\nJnLdhMtpM7bz96JX6TH3yB3SiIhkL7glSZJYmbKcWE00O+vz+LpanmtwVe01vFn6Ht4qL+5Iuxkv\nlTyjzf281fxyeQb+Pmpe/7KMvaXyjAvZWlDLe99UEugnrtMLjmtBzFzmR8+irqueV/a97tCzewaI\nZC+4LQ+lmjvSbyHAw58Pyz+nWF9i1/3Xdh7nhaK1mK0Wbp20Ap2PvGMxdEE+3Ls8Aw+1kpc+PkBZ\ntX2X99yx/zjrNpai8VFz341Z4jq94LAkSWJp4lWkhaZwqOUwb5bKWx0cCZHsBbcW6BnAHem3oFKo\neGX/6+Q1FNplv0fbq3g2/0U6TV3cMPEaJoem2GW/w4kL9+eu6yYPXjev1dtnAOOeQ42s/ewg3p4q\nVt+QSWSor132KwjnSyEp+K/UHxOriSL3+B4+rtzg0AlfJHvB7Y3zj+GujNvwUKp5reQ/bK7eNqb7\nK289wt8K/kmPuZdVKcuZe55r1I+VyfEh/NeSZLr7zDz5nwJKq1rGdH+F5QZe/rgET7WSX96QKVaz\nE5yGp9KDO9N/QohXMF8e28K/D76NyWqWO6zTUj700EMPyR3EWOjuNo7q4/n6eo76YwqOc1xDvINI\nDUmmWF9CgX4fJouJiUETRn2w3MHmMv5R9Bpmm5mfTP4xOeFZo/r433chxzZGpyHQz4P8Mj079tej\n8fEgPsJ/VOOz2mxs3F3NvzYcQqmQ+J/lmUyIDhjVfYwFR3nNuhpnPa5eKk+mhGVQ0XqUkqZDHG6p\nJE07CY8x7pNxOr6+Z57GLZL9CI3WC7G6uopHHvkD69e/yYcfvkdlZTlTpuQM6Zx3NuvW/R9KpQKd\nzvG6nZ0PR3qD+3toyNSmUdJ8iH2GA+h7mpkYnIBaob7gxzZZzXxx5CveKv0ASZK4Pe1mMi5wffrh\nXOixjQv3Z2JMIAWHDeQdaqSty0hqfPCoDJrr6Dby4kclbM6vxd/Hg18sTScpxnGmMJ2NI71mXYkz\nH1dPpSc5YVk0dus50FxKkX4/KSET8VPb93LU2ZK9mGc/QqMxB9RisfCTn/yYe+/9FVlZU7DZbDz7\n7J/w8fHljjvuGv4BXJAjzq3tMHbyj+LXONZejcbDj2sTLmdaePZ5n+UfaTvG64fepb6rgSDPQG6Z\ndCOJQeNHOepTjdaxNbT28Nx7+6jRd5IYHcBNC5MYF37+pfbSqhZe+riE1k4jk+OD+X9XTMLfiQbj\nOeJr1hW4wnG12qx8UrmRL49twUvpyeJxC1gQM8duZ/lnm2dv92S/e/du7rnnHh577DEWLFhwyu0f\nf/wx//rXv1AoFCxfvpxly5ZhMplYs2YNdXV1KJVKHn/8cWJizt4n2xGT/c6dO/jss495+OEnBn/X\n19eLJCn48MP3+PrrLwGYO3c+K1feyu7dO/nnP/+Op6cXQUHBPPjgIzz55CNcdNGPaGtrpbi4kNbW\nFqqqjnHTTau44oprKCoq4KWXXkClUqHThfGb3zyAWn3hZ6ZjxVHf4Carma+rvmXD0a8xWU0kBMSx\nPOkaos9hXfm2vg42VW1lS/V2bNiYFzWTqxMus9v0utE8tn1GC2s/P0jeiZUis5O0XD0nnhid34i2\nt9lslFW3smlvDflleiQkrps/nkunx6Kwc1+BC+Wor1ln50rHddfxvbxX/gldpm4CPQO4In4x0yOm\njHk//bMle7u2DauqquK1114jOzv7tLd3d3fzwgsv8O6776JWq1m6dCmLFi1iy5Yt+Pv788wzz7B9\n+3aeeeYZnn322QuK5f3yTylo3Dfi+ysVEpZhWolm6dK4bsIVZ7y9quooiYlDl+j09PSirq6WL774\nhH/+898A3H77LSxYsJD33nubu+/+HzIysvjmm820tQ2dClVRUc6LL75KTU01Dz54P1dccQ3PPvsn\n/vrXf+DvH8Df//5XtmzZxOLFl4347xT6qRUqLo27mJywLN4v/4RC/X6e2PNX4gPGkRqSTGpIMtF+\nEaec7XcauyjQ7yO/oYjDrZXYsKHzDuWm5KV2OZsfK54eSn52dSoHMiP58NtK8sv05JfpmTpRS3aS\nlnHhGsKCfYYkbqvNRlunkf1Hmvg6r4aqxv6R/bFhfqxcNNEprs8LwvmYHjGFdO0kvjy2lS3V23j9\n0Hq+rv6WLG0aCYHxxPnH4qU6c8l9LNg12Wu1Wp5//nl+97vfnfb2oqIi0tLS0Gj6v51kZ2eTn59P\nbm4u11xzDQCzZs3i/vvvt1vMo0vCarWe8tvDh0tJTU1Dpep/OtLSMigvL2PBgoX86U+Ps3jxpSxc\neAkhIaFDtps8OR2lUolWq6Orq5Pm5iZqaqq5//5fAdDb20tAgHNcB3VUId5B/DTtZg40lfLF0a85\n0naMyrajfFK5gQAPDQGeAZitZsw2M2arhda+Nqy2/ud4fMA4pugymRU5DQ+l41ZXRkqSJFLjgpk0\nLoh9lc18uK2SvFI9eaV6oP8LQYzOD5VCoqm9l+b2vsEvyApJYupELQunxpAYHWD3LoGCYG/eKm+u\nTriMeVEz+fTIl+w6vpfPuzYB/dP2ov0iuWr8paSEJA3zSKPDrsne29v7rLcbDAaCg4MHfw4ODkav\n1w/5vUKhQJIkjEYjHh5nvg4SFOSDSnXmQW93aFcAK87tD7hA6ekpvPHGG0NKLUajkYaGGjw9VYO/\nV6kkAgMor9RGAAAK6ElEQVR9Wbr0apYsWcSmTZv43e/u469//SteXmoCAryxWnvRaLzRajV0dSlQ\nKCTCw4MICwvj7bfftOvfdaHOVnpyFPO1U5mfPJXOvi6KGg5QUFdCUcNBGrobUSmUqJRq1AoVE4Lj\nmB6dxcyYbEJ9g4d/4DE2Vsf2Rzp/Lp4+jkNHWzhc3UJ5TSsVtW1U1rZhtUGwvycTogMJDfJmXJiG\nH02LRRfkMyaxyMEZXrPOyBWPqxYNv4y5jc6+GyltquSgvpxSfTkVLVXUm48zTzvFLnGMWbJfv349\n69evH/K7n//858ydO3fEj3Gm4QQjGWbQ0tI94v2MxGhcT0pKSqeq6gk++OAz5syZh9Vq5W9/+wvt\n7a2Ul5dz/Hj/fOa9ewtYtmwlf/rTX7juuuVcfPESqqrqKCjYT2+viba2Hjo6eunuNqLXd9Dd3Y3F\nYsVoVGCxWNm9u4j4+PG8++5bZGZOYcKExNE4BGPCGa/TJXknk5SQzA1nWS/G1g36bnn/Lnsc21A/\nNaEpOmam6AAwmixIkoRa9YNrk2aL0z3PZ+KMr1ln4A7HNVYdR2xkHJdELsRqs6KQFKP6N8tyzX7Z\nsmUsW7bsnLbR6XQYDIbBnxsbG8nMzESn06HX60lOTsZkMmGz2c56Vu+oFAoFzzzzPE899SivvfZP\n1Go1OTnT+fnP/4cPPniXn//8dqxWG1deeTXh4RGEhYVz773/jUbjj0aj4cYbV7J9+9l7uK9Z8wce\ne+yPqNVqQkO1XHXVdXb66wQBPNQjm0IqCO5urAfr/ZAsU+/WrFnDJZdccspo/N7eXq688kree+89\nlEol1113He+++y5bt25l586dPProo3z55Zd8+eWXPP3002fdhyOOxhdOJY7r2BHHdmyI4zo2xHG9\ncA4zGn/r1q2sXbuWyspKSkpKWLduHa+++iovv/wyOTk5ZGVlsXr1am677TYkSeKuu+5Co9GwZMkS\nduzYwYoVK/Dw8OCJJ54YfmeCIAiCIACiqc6IiW+dY0Mc17Ejju3YEMd1bIjjeuHOdmYvFsIRBEEQ\nBBcnkr0gCIIguDiR7AVBEATBxYlkLwiCIAguTiR7QRAEQXBxItkLgiAIgosTyV4QBEEQXJxI9oIg\nCILg4ly2qY4gCIIgCP3Emb0gCIIguDiR7AVBEATBxYlkLwiCIAguTiR7QRAEQXBxItkLgiAIgosT\nyV4QBEEQXJxI9iPw2GOPccMNN3DjjTdSXFwsdzgu46mnnuKGG27g+uuv58svv5Q7HJfS29vLwoUL\nef/99+UOxaV8/PHHXHXVVVx33XVs3bpV7nBcQldXF3fffTerVq3ixhtvZNu2bXKH5JJUcgfg6Hbv\n3s2xY8d4++23qaio4P777+ftt9+WOyynt3PnTg4fPszbb79NS0sL1157LYsXL5Y7LJfxj3/8g4CA\nALnDcCktLS288MILvPfee3R3d/O3v/2Niy66SO6wnN4HH3xAfHw8q1evpqGhgVtuuYUNGzbIHZbL\nEcl+GLm5uSxcuBCAhIQE2tra6OzsxM/PT+bInFtOTg7p6ekA+Pv709PTg8ViQalUyhyZ86uoqKC8\nvFwkolGWm5vLzJkz8fPzw8/Pj4cffljukFxCUFAQpaWlALS3txMUFCRzRK5JlPGHYTAYhrz4goOD\n0ev1MkbkGpRKJT4+PgC8++67zJs3TyT6UfLkk0+yZs0aucNwOTU1NfT29nLnnXdy0003kZubK3dI\nLuHyyy+nrq6ORYsWsXLlSn7zm9/IHZJLEmf250h0Fx5dmzZt4t133+XVV1+VOxSX8OGHH5KZmUlM\nTIzcobik1tZWnn/+eerq6rj55pvZsmULkiTJHZZT++ijj4iMjGTt2rUcOnSI+++/X4w1GQMi2Q9D\np9NhMBgGf25sbESr1coYkevYtm0bL774Iq+88goajUbucFzC1q1bqa6uZuvWrdTX1+Ph4UF4eDiz\nZs2SOzSnFxISQlZWFiqVitjYWHx9fWlubiYkJETu0Jxafn4+c+bMASA5OZnGxkZxSW8MiDL+MGbP\nns3GjRsBKCkpQafTiev1o6Cjo4OnnnqKl156icDAQLnDcRnPPvss7733Hu+88w7Lli3jv//7v0Wi\nHyVz5sxh586dWK1WWlpa6O7uFteXR8G4ceMoKioCoLa2Fl9fX5Hox4A4sx9GdnY2qamp3HjjjUiS\nxIMPPih3SC7h888/p6WlhXvvvXfwd08++SSRkZEyRiUIZxYWFsYll1zC8uXLAXjggQdQKMT50oW6\n4YYbuP/++1m5ciVms5mHHnpI7pBckljiVhAEQRBcnPhaKgiCIAguTiR7QRAEQXBxItkLgiAIgosT\nyV4QBEEQXJxI9oIgCILg4kSyFwThnOn1en7xi1+M6L41NTXMmzfvrPf529/+xl/+8pcR73/Xrl2s\nWLFixPcXBHcnkr0gCOdMq9Xy3HPPyR2GIAgjJJK9ILiB1157jQceeACAyspKLr30Ujo7O4fcJy8v\njxtvvJGbb76Z5cuXU1JSgtls5rrrriMvLw/oPwN/8sknh5ytf/7551x//fWsWrWKlStXUl1dfcY4\nKioqWLlyJbfccgvXX3/9kLXLq6urueOOO7jmmmt4/PHHB3//5z//mZUrV7J06VKefPJJsT6FIJwH\n0UFPENzALbfcwqpVq9i7dy/PPfcc//u//3tK2+fW1lYeeughkpOT+fTTT3nppZd47rnneOKJJ1iz\nZg1PPPEEmzdv5q233hqy8uOLL77Iww8/TEZGBkVFRTQ0NJxxIR6DwcA999xDTk4OBQUFPPzww8yd\nOxfo/xKyfv16bDYbS5Ys4frrr6eiooKGhgZef/11AO666y62bNmCr6/vGB0pQXBNItkLghtQKBQ8\n9thjrFy5kksvvZRp06adcp/Q0FCeeuop+vr66OjoICAgAICkpCQWL17MzTffzCuvvIKnp+eQ7a67\n7jrWrFnD4sWLWbx4MRkZGWeMQ6vV8tRTT/GXv/wFk8lEa2vr4G05OTmo1WoAJk+eTHl5Obt376aw\nsJBVq1YB/Wsq1NTUMHHixAs+JoLgTkSyFwQ30dbWho+PD8ePHz/t7b/+9a/54x//yMyZM9myZcuQ\nZYf1ej0ajYb6+nomT548ZLtbb72VK664gm3btvGHP/yBZcuWceONN552Hw8//DCXX345S5cupays\njDvvvHPwtu/3mR8o1Xt4eLB8+XJuu+22IY+za9euc/vjBcHNiWv2guAG+vr6ePDBB3nxxRdRq9V8\n+OGHp9zHYDCQmJiIxWJhw4YNGI1GoD+xVlRU8MYbb/D000/T3Nw8uI3FYuHpp59Go9Fw7bXX8vOf\n/3xwBbPTGdgH9F/rH9gH/P/27hdVgSgMoPiBiYJgEREN/gHBapkFTHAHTlNGXIDlFotxqiC6EOMs\nwWJwAWJzCzLhNdOL75Xr+eWbvnK4lwsfXK9X6rrm/X5zv9+ZTCbMZjOqqqKuawCOxyOPx+MvRiJ9\nFW/20hc4HA5kWcZgMGC327FYLEjTlE6n8zmz2WxYLpd0u13W6zUhBM7nM5fLhdPpRLvdpigK9vs9\nIQQAkiSh1WqR5znNZhPg8xHwN0VREEKg1+uxWq2oqoqyLGk0GozHY7bbLc/nk/l8zmg0Yjgccrvd\nyPOcJEmYTqf0+31er9f/DkyKjFvvJEmKnM/4kiRFzthLkhQ5Yy9JUuSMvSRJkTP2kiRFzthLkhQ5\nYy9JUuSMvSRJkfsBhVS2RZOUY88AAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4e504d9590>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UMBFUf92JfYY"
+      },
+      "source": [
+        "You can read much more about the `plot` function [in the documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot).\n",
+        "\n",
+        "##Subplots\n",
+        "\n",
+        "You can plot different things in the same figure using the `subplot` function. Here is an example:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ckAH_ANMJ5yn",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 362
+        },
+        "outputId": "8b431f7c-a5a8-4302-8ebd-34fb312bbad5"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Compute the x and y coordinates for points on sine and cosine curves\n",
+        "x = np.arange(0, 3 * np.pi, 0.1)\n",
+        "y_sin = np.sin(x)\n",
+        "y_cos = np.cos(x)\n",
+        "\n",
+        "# Set up a subplot grid that has height 2 and width 1,\n",
+        "# and set the first such subplot as active.\n",
+        "plt.subplot(2, 1, 1)\n",
+        "\n",
+        "# Make the first plot\n",
+        "plt.plot(x, y_sin)\n",
+        "plt.title('Sine')\n",
+        "\n",
+        "# Set the second subplot as active, and make the second plot.\n",
+        "plt.subplot(2, 1, 2)\n",
+        "plt.plot(x, y_cos)\n",
+        "plt.title('Cosine')\n",
+        "\n",
+        "# Show the figure.\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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KiggJCbnifS1lT2o+Zy6UM7BrK3p1Cm/R9xaOy8/bgwfGdsZiVZpauEr3MPEz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\n11VV7QAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<matplotlib.figure.Figure at 0x7f4e504eb090>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YSELflXHJ-6E"
+      },
+      "source": [
+        "You can read much more about the `subplot` function [in the documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.subplot)."
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_3_Function.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_3_Function.ipynb"
new file mode 100644
index 0000000..e474082
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_3_Function.ipynb"
@@ -0,0 +1,303 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oa32Fuw2MUJP"
+      },
+      "source": [
+        "#Function Types"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qWXyN4AQM1sE"
+      },
+      "source": [
+        "import numpy as np"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sR930KE1MOJv",
+        "outputId": "3782485d-e1e5-4ef9-fc4e-392bf32524ed"
+      },
+      "source": [
+        "#trigonometry مثلثات\n",
+        "x = 0\n",
+        "np.sin(x)\n",
+        "np.cos(x)\n",
+        "np.tan(x)\n",
+        "np.arcsin(x)\n",
+        "np.arccos(x)\n",
+        "np.arctan(x)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 59
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VhP-pMvGNfO9",
+        "outputId": "dd1a9f2b-5252-41f8-fac9-7703995b9f3d"
+      },
+      "source": [
+        "#basics\n",
+        "x = 2\n",
+        "np.exp(x)\n",
+        "np.log(x)\n",
+        "np.log2(x)\n",
+        "np.sqrt(x)\n",
+        "y = -2.5\n",
+        "np.absolute(y)\n",
+        "np.negative(x)\n",
+        "np.ceil(y)\n",
+        "np.floor(y)\n",
+        "np.maximum(x, y)\n",
+        "np.minimum(x, y)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "-2.5"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "snJvNRe5dHZV",
+        "outputId": "be3d1981-8ac8-49ac-b4f1-d4a7420d2bad"
+      },
+      "source": [
+        "#تابع مزدوج ساز (البته ویژه اعداد مختلط هست)\n",
+        "np.conjugate(1-2j)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1+2j)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 61
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UMdbM84qPHXu",
+        "outputId": "0450d39a-e67c-4f2c-8c2d-b00bc23224bf"
+      },
+      "source": [
+        "#تابع باقیمانده (با حفظ علامت)\n",
+        "x=[13, -14]\n",
+        "y=5\n",
+        "np.fmod(x, y) #preserve sign"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([ 3, -4])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 62
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QVa2-QjjeQ5w",
+        "outputId": "0af9129f-5506-4c69-c1b4-fc21ecf3b7ae"
+      },
+      "source": [
+        "x = np.array([-3, -2.6], dtype=np.int32)\n",
+        "x\n",
+        "np.fabs(x)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([3., 2.])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 63
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CQjEe1J3e4lN",
+        "outputId": "081f0802-d993-463e-816d-30682cd14ed2"
+      },
+      "source": [
+        "#تابع وتر\n",
+        "x=[3, 8]\n",
+        "y=[4, 6]\n",
+        "np.hypot(x, y)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([ 5., 10.])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 64
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NP3NE0FfdStu",
+        "outputId": "112f02ea-d7d6-4ee0-a7f5-f4b16351980e"
+      },
+      "source": [
+        "#hyperbolic هیپربولیک\n",
+        "x = 0\n",
+        "np.sinh(x)\n",
+        "np.cosh(x)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1.0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 65
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 257
+        },
+        "id": "78siGUCV3Fux",
+        "outputId": "ea966160-2357-4668-be6b-6ee40c4840f8"
+      },
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "# 100 linearly spaced numbers\n",
+        "x = np.linspace(-5,5,100)\n",
+        "\n",
+        "# the function, which is y = x^2 here\n",
+        "y = 1 / (1+np.exp(-x))\n",
+        "\n",
+        "# setting the axes at the centre\n",
+        "fig = plt.figure()\n",
+        "ax = fig.add_subplot(1, 1, 1)\n",
+        "ax.spines['left'].set_position('center')\n",
+        "ax.spines['bottom'].set_position('zero')\n",
+        "ax.spines['right'].set_color('none')\n",
+        "ax.spines['top'].set_color('none')\n",
+        "ax.xaxis.set_ticks_position('bottom')\n",
+        "ax.yaxis.set_ticks_position('left')\n",
+        "\n",
+        "# plot the function\n",
+        "plt.plot(x,y, 'r')\n",
+        "\n",
+        "# show the plot\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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lPvecdoOQiKXQlcjw3ntmN4j77oNOnWxXI3LKFLoS/qqrzUI2HTrAAw/YrkakQTR7QcLfU0/BZ5/BW2/B6afbrkakQdTTlfC2aRM8+igMHAj9+9uuRqTBFLoS3n79a3PRbPJk25WIBISGFyR8FRTAG2/AE09Au3a2qxEJCPV0JTzt3Wt6uRdfDHfeabsakYBRT1fC06OPwpYtsHQpNG5suxqRgFFPV8LP2rVmxsKIEWbpRpEootCV8FJTAyNHQvPm8PjjtqsRCTgNL0h4mToVPvwQXnoJWrWyXY1IwKmnK+Fj+3a4/37o1QuGDrVdjUhQKHQl6AoLC0lNTSUlJYUJEybU2W7LoEHs37WLtaNHa0EbiVoKXQkqv99PTk4OCxYsoLi4mNmzZ1NcXPy9dt++/jrtli7lzwkJ7G/fPvSFioSIQleCauXKlaSkpJCcnEx8fDxZWVkUFBTUbrR3L/8ePpyqxEReTU62U6hIiCh0JajKy8tJSko68jgxMZHy8vJabbaPHk2r3bs5c9YsqrUbhEQ5/YSLVTVFRZzz8svsue46+MlPjts+Ly8Pj8eDx+OhoqIiBBWKBJZCV4IqISGB0tLSI4/LyspISEgwDw4epOb226l0HHouW0b79u356KOP8Hq9+Hy+Y75ednY2Pp8Pn89H69atQ/ERRAJKoStBlZGRQUlJCRs3bqS6upr8/Hy8Xq85OWkScatXc+7s2awpLWXTpk306NGDefPm4fF47BYuEiS6OUKCKi4ujtzcXDIzM/H7/YwYMYL09HQmjRnDmKlTifN6YcgQ22WKhIzjum595+s9KXJKXBd694ZVq6C4GA4PN5wkj8dT5zCEiGV1TjRXT1dCb9o0WLIE8vJOOXBFIpXGdCW0ysvNVuq9esFtt9muRiTkFLoSOq4L2dlw4IDp5epWX4lBGl6Q0Hn5ZZg/3+x3lpJiuxoRK9TTldAoLzfb7lxxBYwZY7saEWsUuhJ8rmsWJq+uhhdfBN3qKzFMwwsSfDNnwltvwdNPa1hBYp66HBJcmzaZYYWf/MTs7isS4xS6Ejw1NTB8uPl+5kwNK4ig4QUJpsmT4f33zTiuFiYXAdTTlWD59FN44AHweo/2dkVEoStBsG8f3HADNGummyBEvkPDCxJ4DzwAa9aYGQvnnmu7GpGwop6uBNbbb8OkSeYGiP79bVcjEnYUuhI4FRVm/DYtDR5/3HY1ImFJwwsSGDU1MGwY7NwJhYVw+um2KxIJSwpdCYyJE2HBAnj+efjRj2xXIxK2NLwgDffhh/DggzBoENxxh+1qRMKaQlcaZudOyMqCpCSYOlXTw0SOQ8MLcupqamDoUNi6FZYuhebNbVckEvYUunLqxo0zc3Fzc+HSS21XIxIRNLwgp2bhQhg7Fm68EUaPtl2NSMRQ6MrJ27LF3Oabng4vvKBxXJGToNCVk/PttzBwoNkF4q9/hTPOsF2RSETRmK6cONeFESPgn/+EN96ATp1sVyQScRS6cuImTIA5c2D8eLj6atvViEQkDS/IiXnjDXjoITMn9777bFcjErEUunJ8q1fD9dfDJZfA9Om6cCbSAApdqV9ZGQwYAC1bmt5u06a2KxKJaBrTlbrt2WMCd88eWLYM2ra1XZFIxFPoyrFVV8PgwfDJJzB/Pvzwh7YrEokKGl6Q76upgVtuMbtA5OVB374NernCwkJSU1NJSUlhwoQJ3zv/1FNPkZaWRpcuXejTpw+bN29u0PuJhDOFrtTmuvCb38Bf/mKmho0Y0aCX8/v95OTksGDBAoqLi5k9ezbFxcW12nTr1g2fz8eaNWsYNGgQv/vd7xr0niLhTKErtY0fD5Mnw513BmRq2MqVK0lJSSE5OZn4+HiysrIoKCio1aZXr140PXSBrkePHpSVlTX4fUXClUJXjpo0yczFvfFGeOqpgEwNKy8vJykp6cjjxMREysvL62w/ffp0fv7znzf4fUXClS6kifGnP8Hdd8MvfwkzZ8IPQv/7eNasWfh8Pt5///062+Tl5ZGXlwdARUVFqEoTCRj1dAVmzIBRo8ytvbNnQ1zgfhcnJCRQWlp65HFZWRkJCQnfa7d48WIee+wx5s2bx2mnnVbn62VnZ+Pz+fD5fLRu3TpgdYqEikI31k2dCrfeCj/7Gbz2GsTHB/TlMzIyKCkpYePGjVRXV5Ofn4/X663VZvXq1YwcOZJ58+bRpk2bgL6/SLhR6May556D7Gzo1w/mzYMmTQL+FnFxceTm5pKZmUnnzp0ZMmQI6enpjB07lnnz5gFw7733UlVVxeDBg+natev3Qlkkmjiu69Z3vt6TEsEmToR77oFrrjErh9XzJ3248ng8+Hw+22WIHEudV6HV0401rgv3328Cd/BgmDs3IgNXJFJp9kIsOXgQRo6EF1+EO+4wG0o2amS7KpGYop5urKiqgmuvNYH7+9/D888rcEUsUE83FpSVwS9+AWvWmLAdNcp2RSIxS6Eb7YqKwOs1yzO+9ZaZqSAi1mh4IZr95S9wxRXmZoflyxW4ImFAoRuNDhyAu+4yayhkZMDKlXDxxbarEhEUutHnyy/hqquOrhS2eDGce67tqkTkEI3pRpO33oLhw+Hbb+HPf4abbrJdkYh8h3q60WDfPrPw+IABkJAAq1YpcEXClEI30q1aBR4PPP005OTARx9BaqrtqkSkDgrdSFVdDY88ApddBjt3woIF5g6zICxaIyKBozHdSLR8uVkd7NNPzQyFZ5+FFi1sVyUiJ0A93Ujy9dfmbrKePWH3bnjjDZg1S4ErEkEUupHA74cpU6BTJ7Ml+t13Q3GxuXAmIhFFoRvOXBcWLoRLLoHRo80NDqtWmU0jzzzTdnUicgoUuuFq5Uro0wcyM+Gbb8y6t++9Bz/6ke3KRKQBFLrhpqjI7OZw2WWwdq3ZFv3zz2HQoIBsiS4idmn2QjhwXTMjYfx4c1dZ8+ZmOthdd8HZZ9uuTkQCSKFrk98Pf/87PPmkuamhVSt47DEYM0ZhKxKlFLo2bN8O06fDCy/Ali3QsaPZmXfYMDjjDNvViUgQKXRDxe83MxFmzDC92wMHzGpgkyaZRca1dY5ITFDoBpPrwscfw+zZ8MorUF5uhhBycszGkFojQSTmKHQDzXXNXmSvvw5z5sBnn5lebGamWeN2wABteS4SwxS6gVBdDf/3f2bmwd//Dhs3muldV1xhFhIfNAjOOcd2lSISBhS6p8J1TQ928WJ45x1zVFVBfDz07g0PPGDGabVjg4h8h0L3RPj9ZkWvpUvhgw/MsXWrOdehg1np6+qrTeBq9oGI1EOh+12ua6ZxFRWBzwcrVphbcquqzPmEBPjpT6FXL3ObbnKy1XJFJLLEduhWVcG6daYXu2aNmWnw8cewY4c5Hxdn1joYNszclvvjH0P79rodV0ROWfSH7v79sHkzfPEFbNgA69eb4/PPzfOHnX66WcVr4ECzqlf37tCli3leRCRAIjt0a2qgosJsO15WBqWl5uuWLSZQN20y52pqjv6bs84y82N79jS7L6SlmaNjR92gICJBF16he/Ag7NpldkjYsePoUVFhjq++Mse2beZW2u3bzb/5T3FxZty1fXsz5tq+vQnU5GTz9dxzNTwQYoWFhdx55534/X5uu+027r///lrn9+/fz80330xRURGtWrVizpw5tG/f3k6xIkEWnNDdutVciKqqgr17Yc+e2sfu3WaN2P88du0yz9clPh5at4Y2baBtWzPWet55JmDPP98cSUkmVNVjDRt+v5+cnBwWLVpEYmIiGRkZeL1e0tLSjrSZPn06LVq0YMOGDeTn53PfffcxZ84ci1WLBE9wQnfpUhgy5PvPN2li/rw/++yjR3KyWcqwWTNo2dIcLVqY22UPH61bm3+nHmrEWblyJSkpKSQfmuWRlZVFQUFBrdAtKCjg4YcfBmDQoEGMGTMG13Vx9P8tUSg4odu7N/zjH2ZLmTPOMF/PPBMaNw7K20n4Ki8vJykp6cjjxMREVqxYUWebuLg4mjVrxo4dOzhHd/FJFHJc163zZL9+/dzKysoQlhMYFRUVtG7d2nYZIRWun3nnzp3s3r2bCy64AIAdO3awd+9e2rVrd6TNp59+yoUXXkh8fDwAa9eupXPnzsTFfb9PUFFRweGfyf3799O1a9cQfIrwEK7/x8EUqZ+5qKjobdd1+x3zpOu69R0RqXv37rZLCLlw/czLly93+/bte+TxuHHj3HHjxtVq07dvX3f58uWu67rugQMH3FatWrk1NTXHfe2mTZsGttgwF67/x8EUwZ+5zlzVHmkSVBkZGZSUlLBx40aqq6vJz8/H6/XWauP1ennppZcAeO211+jdu7fGcyVqhdeUMYk6cXFx5ObmkpmZid/vZ8SIEaSnpzN27Fg8Hg9er5dbb72VoUOHkpKSQsuWLcnPz7ddtkjQRGXoZmdn2y4h5ML5M/fv35/+/fvXeu7RRx898n2TJk2YO3fuSb9urF1oC+f/42CJxs9c74U0oN6TIjZ5PB58Pp/tMkSOpc7xMY3pioiEUNSH7sSJE3Ech0ic+nYy7r33Xi666CK6dOnCtddey65du2yXFDSFhYWkpqbyySefMGHCBNvlBF1paSm9evUiLS2N9PR0Jk+ebLukkPH7/XTr1o0BAwbYLiVgjje8ENEcx0kCpgEXAd1d143a5HUcpy/wruu6Bx3H+SOA67r3WS4r4BzHaQSsB34GTAHOA653XbfYamFB5DhOW6Ct67qrHMc5CygCBkbzZz7McZzfAB7gbNd1oyJ5o72n+zTwO2JgbNp13YWu6x5e/ecjINFmPUF0KbDBdd1/ua6bCeQD11iuKahc193quu6qQ9/vAdYBCXarCj7HcRKBqzEdp6gRtaHrOM41QLnruh/brsWCEcAC20UESQJQ+h+Py4iBADrMcZz2QDdgRf0to8IkTKep5ngNI0lETxlzHGcx5s/L73oIeBDoG9qKgqu+z+u6bsGhNg8BB4FXQlmbBJ/jOGcCfwXucl23niX5Ip/jOAOAr1zXLXIc56e26wmkiA5d13WvOtbzjuP8EOgAfHzozqZEYJXjOJe6rrsthCUGVF2f9zDHcYYDA4A+bvQO1pcDSf/xOPHQc1HNcZzGmMB9xXXdv9muJwR6Al7HcfoDTYCzHceZ5bruTZbrarCovpB2mOM4mwBPlF9I6wc8BfzEdd0K2/UEi+M4cZgLaX0wYfsP4AbXdT+1WlgQOabn8BLwteu6d9muJ9QO9XTv0YU0CTe5wFnAIsdx/uk4zp9sFxQMhy4WjgHexlxQejWaA/eQnsBQoPeh/9t/HuoBSgSKiZ6uiEi4UE9XRCSEFLoiIiGk0BURCSGFrohICCl0RURCSKErIhJCCl0RkRBS6IqIhND/A7Nq7w7icXAqAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_4_Derivative_Gradient_Chain_Rule.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_4_Derivative_Gradient_Chain_Rule.ipynb"
new file mode 100644
index 0000000..7d14f72
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_4_Derivative_Gradient_Chain_Rule.ipynb"
@@ -0,0 +1,351 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#Derivative - Gradient - Chain Rule"
+      ],
+      "metadata": {
+        "id": "YFPB2XIcRy6P"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##Derivative Definition"
+      ],
+      "metadata": {
+        "id": "9yjg77KXR-Lz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def get_derivative(func, x):\n",
+        "    \"\"\"Compute the derivative of `func` at the location `x`.\"\"\"\n",
+        "    h = 0.0001                          # step size\n",
+        "    return (func(x+h) - func(x)) / h    # rise-over-run\n",
+        "\n",
+        "def f(x):\n",
+        "  return x**2                           # some test function f(x)=x^2\n",
+        "\n",
+        "x = 3                                   # the location of interest\n",
+        "computed = get_derivative(f, x)\n",
+        "actual = 2*x\n",
+        "\n",
+        "computed, actual                        # pretty close if you ask me..."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PUG9X-BhRydo",
+        "outputId": "2fb8d6fe-d948-41b1-bd47-ffe365a09fab"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(6.000100000012054, 6)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 1
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qgh1gO3uRmj9",
+        "outputId": "18fcd6cb-4a91-4235-c7a7-35f058bca158"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: sympy in /usr/local/lib/python3.7/dist-packages (1.7.1)\n",
+            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.7/dist-packages (from sympy) (1.2.1)\n"
+          ]
+        }
+      ],
+      "source": [
+        "#SymPy Package\n",
+        "!pip install sympy"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "*SymPy* is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily extensible. SymPy is written entirely in Python.\n",
+        "\n",
+        "\n",
+        "https://www.sympy.org/en/index.html"
+      ],
+      "metadata": {
+        "id": "Qea8Lg6rSccm"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import sympy as sym"
+      ],
+      "metadata": {
+        "id": "1QLgzDWyTGOC"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = sym.Symbol('x')"
+      ],
+      "metadata": {
+        "id": "gmx7ityzTVVl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sym.diff(x**5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 38
+        },
+        "id": "8MRspqheTqQ8",
+        "outputId": "1b33ec26-b40c-44b0-9e37-e92c60d35618"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/latex": "$\\displaystyle 5 x y^{4}$",
+            "text/plain": [
+              "5*x y**4"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sym.diff(x**2 * sym.cos(x))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 38
+        },
+        "id": "2fddvQjUUhwr",
+        "outputId": "ae2b55c1-352b-4e53-99a2-43dd0448bb18"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/latex": "$\\displaystyle - x^{2} \\sin{\\left(x \\right)} + 2 x \\cos{\\left(x \\right)}$",
+            "text/plain": [
+              "-x**2*sin(x) + 2*x*cos(x)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sym.diff(1 / (1 + sym.exp(-x)))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 60
+        },
+        "id": "q7sSfT8qUyBK",
+        "outputId": "d2a744bc-320a-4fe7-ea5c-d9acd855e730"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/latex": "$\\displaystyle \\frac{e^{- x}}{\\left(1 + e^{- x}\\right)^{2}}$",
+            "text/plain": [
+              "exp(-x)/(1 + exp(-x))**2"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x, y = sym.symbols('x y')"
+      ],
+      "metadata": {
+        "id": "vKOeVEhGU9Hv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sym.diff(x**2 * y, x)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 38
+        },
+        "id": "feEKXtkkVRKE",
+        "outputId": "ab4ddc87-e696-4216-a24f-b5df22c3f337"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/latex": "$\\displaystyle 2 x y$",
+            "text/plain": [
+              "2*x*y"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sym.diff(x**2 * y, y)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 38
+        },
+        "id": "gBrRboP1Vb5l",
+        "outputId": "d55f6797-ae6b-4883-90c3-6409e5eb322c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/latex": "$\\displaystyle x^{2}$",
+            "text/plain": [
+              "x**2"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x, y, z = sym.symbols('x y z')"
+      ],
+      "metadata": {
+        "id": "KPrBFqAkVdT0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sym.diff(x**2 * y * z**3, z)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 38
+        },
+        "id": "5QtF6T8HVjOq",
+        "outputId": "7bc26843-12a1-4875-a59e-64c6d33e36ad"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/latex": "$\\displaystyle 3 x^{2} y z^{2}$",
+            "text/plain": [
+              "3*x**2*y*z**2"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sym.diff(x**2 * y * z**3, x)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 38
+        },
+        "id": "zUc8ncVJVoD3",
+        "outputId": "1dbd9e48-3644-46e2-ea9c-713b5d8b2615"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/latex": "$\\displaystyle 2 x y z^{3}$",
+            "text/plain": [
+              "2*x*y*z**3"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_5_Linear_Algebra_Vector.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_5_Linear_Algebra_Vector.ipynb"
new file mode 100644
index 0000000..dd4aa22
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_5_Linear_Algebra_Vector.ipynb"
@@ -0,0 +1,841 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [
+        "CpIUkLPdpWty",
+        "6aDC_flloKVh",
+        "zB8RZwcxpN0_",
+        "bmypQlQsySrB",
+        "XnLb60Ug3lIv",
+        "hJbCDs7b-o-t",
+        "4ki5VAdK_PfH",
+        "V-fktbjKDAxV",
+        "25iC-ovsFib8"
+      ]
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RBJORhnppTg3"
+      },
+      "source": [
+        "#Linear Algebra (Vector)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AKJ8kRyEF5sR"
+      },
+      "source": [
+        "#Vectors Operations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-88IA-_JlIl5"
+      },
+      "source": [
+        "#کتابخانه‌های مورد نیاز\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from mpl_toolkits.mplot3d import Axes3D"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CpIUkLPdpWty"
+      },
+      "source": [
+        "##Defining vectors"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rW_tV41xlyHC"
+      },
+      "source": [
+        "#تعریف چند بردار نمونه\n",
+        "u = [1, 3, 2]\n",
+        "v = [1.5, 3]\n",
+        "w = np.array([5, 7, 8, 9])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tLRgk2dAmRQg",
+        "outputId": "18cae7e7-4915-4856-8215-4583ad5133e9"
+      },
+      "source": [
+        "#بررسی نوع متغیرها\n",
+        "print(type(u))\n",
+        "print(type(v))\n",
+        "print(type(w))\n",
+        "# print(u.shape) #'list' object has no attribute 'shape'\n",
+        "print(w.shape)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'list'>\n",
+            "<class 'list'>\n",
+            "<class 'numpy.ndarray'>\n",
+            "(4,)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6aDC_flloKVh"
+      },
+      "source": [
+        "##Reading elements from an array"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "md32r1HOnGIu",
+        "outputId": "6c40727b-4592-4051-cdfe-1fbcb6b091a3"
+      },
+      "source": [
+        "#بررسی نحوه دسترسی به درایه‌ها\n",
+        "print(u[0])\n",
+        "print(u[-1])\n",
+        "print(w[0])\n",
+        "print(w[-1])"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "1\n",
+            "2\n",
+            "5\n",
+            "9\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BEXAZtwUoqvg",
+        "outputId": "e7069e94-4d52-4ca7-c246-4402db424f7f"
+      },
+      "source": [
+        "u[1:]"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[3, 2]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WkhfjI-cnCb7",
+        "outputId": "33a5b80c-429d-4a8f-d320-bba1c9441c6d"
+      },
+      "source": [
+        "w[0:-1]"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([5, 7, 8])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "s9Gnzb17oxyj",
+        "outputId": "f48ad44c-c58e-4c61-89ff-f35e885714a2"
+      },
+      "source": [
+        "w[-3:-1]"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([7, 8])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zB8RZwcxpN0_"
+      },
+      "source": [
+        "##Plotting a Vector"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 282
+        },
+        "id": "O-hoU6aZpzCf",
+        "outputId": "6e7b9c13-a647-4529-8c01-5ad7beffe078"
+      },
+      "source": [
+        "#simple plot\n",
+        "#u = [1, 3, 2]\n",
+        "plt.plot(u)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f139e20b4d0>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 282
+        },
+        "id": "pF6eTGJspKwx",
+        "outputId": "aff95c80-3655-4541-cd71-cf662c0b8afa"
+      },
+      "source": [
+        "plt.plot(w)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f139dcf57d0>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 269
+        },
+        "id": "5Pcnq_6JqNkl",
+        "outputId": "e54e7a73-4477-4b36-baf1-392b2ee27bc2"
+      },
+      "source": [
+        "#Ploting a 2D vector\n",
+        "plt.plot([0, v[0]], [0, v[1]])\n",
+        "plt.plot([-6,6] , [0,0] , 'b--')\n",
+        "plt.plot([0,0] , [-6,6] , 'b--')\n",
+        "plt.grid()\n",
+        "plt.axis((-6, 6, -6, 6))\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 319
+        },
+        "id": "iEIESbJExg2h",
+        "outputId": "205c996a-a58a-4204-bd15-f61d42f9f376"
+      },
+      "source": [
+        "#Ploting a 3D vector\n",
+        "fig = plt.figure()\n",
+        "ax = Axes3D(fig)\n",
+        "\n",
+        "plt.plot([0, u[0]], [0, u[1]], [0, u[2]])\n",
+        "\n",
+        "plt.plot([-6,6] , [0,0], [0,0] , 'b--')\n",
+        "plt.plot([0,0] , [-6,6], [0,0] , 'b--')\n",
+        "plt.plot([0,0], [0,0] , [-6,6] , 'b--')\n",
+        "plt.grid()\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bmypQlQsySrB"
+      },
+      "source": [
+        "##Vector Addition"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 286
+        },
+        "id": "bORRlBt0yQiO",
+        "outputId": "98bd7a67-f92c-4b3a-93ad-2ffa9c1bcfd3"
+      },
+      "source": [
+        "#جمع به روش متوازی الاضلاع\n",
+        "v1 = np.array([3, 2])\n",
+        "v2 = np.array([0.5,4])\n",
+        "v3 = v1+v2\n",
+        "v3 = np.add(v1,v2)\n",
+        "print('V3 =' ,v3)\n",
+        "plt.plot([0,v1[0]] , [0,v1[1]] , 'r' , label = 'v1')\n",
+        "plt.plot([0,v2[0]] , [0,v2[1]], 'b' , label = 'v2')\n",
+        "plt.plot([0,v3[0]] , [0,v3[1]] , 'g' , label = 'v3')\n",
+        "plt.plot([-2, 8] , [0,0] , 'k--')\n",
+        "plt.plot([0,0] , [-2, 8] , 'k--')\n",
+        "plt.grid()\n",
+        "plt.axis((-2, 8, -2, 8))\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "V3 = [3.5 6. ]\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 269
+        },
+        "id": "hI2mJBauz7Z9",
+        "outputId": "b5b57db3-3c3b-4404-c017-ab43387a70e1"
+      },
+      "source": [
+        "#جمع به روش مثلث\n",
+        "plt.plot([0,v1[0]] , [0,v1[1]] , 'r' , label = 'v1')\n",
+        "plt.plot([0,v2[0]] + v1[0] , [0,v2[1]] + v1[1], 'b' , label = 'v2')\n",
+        "plt.plot([0,v3[0]] , [0,v3[1]] , 'g' , label = 'v3')\n",
+        "plt.plot([-2, 8] , [0,0] , 'k--')\n",
+        "plt.plot([0,0] , [-2, 8] , 'k--')\n",
+        "plt.grid()\n",
+        "plt.axis((-2, 8, -2, 8))\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 286
+        },
+        "id": "iqm6VD3Y0Swd",
+        "outputId": "253a462c-3536-4412-968b-f8f1226171e7"
+      },
+      "source": [
+        "#تفاضل دو بردار\n",
+        "v3 = v2 - v1\n",
+        "print('V3 =' ,v3)\n",
+        "plt.plot([0,v1[0]] , [0,v1[1]] , 'r' , label = 'v1')\n",
+        "plt.plot([0,v2[0]] , [0,v2[1]], 'b' , label = 'v2')\n",
+        "plt.plot([v1[0],v2[0]] , [v1[1],v2[1]] , 'g' , label = 'v3')\n",
+        "plt.plot([0,v3[0]] , [0,v3[1]], 'g' , label = 'v2')\n",
+        "plt.plot([-4, 8] , [0,0] , 'k--')\n",
+        "plt.plot([0,0] , [-4, 8] , 'k--')\n",
+        "plt.grid()\n",
+        "plt.axis((-4, 8, -4, 8))\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "V3 = [-2.5  2. ]\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XnLb60Ug3lIv"
+      },
+      "source": [
+        "##Scalar Multiplication"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 269
+        },
+        "id": "h4PQCPfh3Slh",
+        "outputId": "0494bf05-50d5-465f-b7b8-37b90917f1c1"
+      },
+      "source": [
+        "v1 = np.array([1, 3])\n",
+        "a, b, c = 0.5, 1.5, -1\n",
+        "v2, v3, v4 = (v1*a), (v1*b), (v1*c)\n",
+        "plt.plot([0,v1[0]] , [0,v1[1]] , 'r' , label = 'v1')\n",
+        "plt.plot([0,v2[0]] , [0,v2[1]], 'b--' , label = 'v2')\n",
+        "plt.plot([0,v3[0]] , [0,v3[1]], 'g:' , label = 'v3')\n",
+        "plt.plot([0,v4[0]] , [0,v4[1]], 'y--' , label = 'v4')\n",
+        "\n",
+        "plt.plot([-4,6] , [0,0] , 'k--')\n",
+        "plt.plot([0,0] , [-4,6] , 'k--')\n",
+        "plt.grid()\n",
+        "plt.axis((-4, 6, -4, 6))\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "k2G6x_K-4cWS",
+        "outputId": "986850d4-866d-4e1e-e59e-a93dee123bd6"
+      },
+      "source": [
+        "u = np.array([1, 3, 2])\n",
+        "v = np.array([2, -1, 0])\n",
+        "#ضرب المان به المان elementwise\n",
+        "w = np.multiply(u, v)\n",
+        "print(w)\n",
+        "#ضرب داخلی\n",
+        "dotp = u@v\n",
+        "print(\" Dot product - \",dotp)\n",
+        "dotp = np.dot(u,v)\n",
+        "print(\" Dot product usign np.dot\",dotp)\n",
+        "dotp = np.inner(u,v)\n",
+        "print(\" Dot product usign np.inner\", dotp)\n",
+        "dotp = sum(np.multiply(u,v))\n",
+        "print(\" Dot product usign np.multiply & sum\",dotp)\n",
+        "dotp = np.matmul(u,v)\n",
+        "print(\" Dot product usign np.matmul\",dotp)\n",
+        "dotp = 0\n",
+        "for i in range(len(u)):\n",
+        "    dotp = dotp + u[i]*v[i]\n",
+        "print(\" Dot product usign for loop\" , dotp)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[ 2 -3  0]\n",
+            " Dot product -  -1\n",
+            " Dot product usign np.dot -1\n",
+            " Dot product usign np.inner -1\n",
+            " Dot product usign np.multiply & sum -1\n",
+            " Dot product usign np.matmul -1\n",
+            " Dot product usign for loop -1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hJbCDs7b-o-t"
+      },
+      "source": [
+        "##Length of Vector"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "F5tUgkTr-kRY",
+        "outputId": "5c932947-875d-4cbd-c709-cc0b7e6698da"
+      },
+      "source": [
+        "u = np.array([1, -2, 2])\n",
+        "length = np.sqrt(np.dot(u, u))\n",
+        "print(length)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "3.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4ki5VAdK_PfH"
+      },
+      "source": [
+        "##Normalized Vector"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9YeKnwr7_LZg",
+        "outputId": "9a4a05a3-e98b-4d6f-8ea6-ef1ba7d1e2c5"
+      },
+      "source": [
+        "v = np.array([1, -1, np.sqrt(2)])\n",
+        "print(v)\n",
+        "v_len = np.sqrt(np.dot(v, v))\n",
+        "print(v / v_len)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[ 1.         -1.          1.41421356]\n",
+            "[ 0.5        -0.5         0.70710678]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "V-fktbjKDAxV"
+      },
+      "source": [
+        "##Angle between vectors"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 281
+        },
+        "id": "7vXouMNiBKQN",
+        "outputId": "189ccb10-b90c-44d8-ec8b-1967f5fbe29a"
+      },
+      "source": [
+        "v1 = np.array([3, -2])\n",
+        "v2 = np.array([4, 6])\n",
+        "ang = np.rad2deg(np.arccos( np.dot(v1,v2) / (np.linalg.norm(v1)*np.linalg.norm(v2))))\n",
+        "plt.plot([0,v1[0]] , [0,v1[1]] , 'r' , label = 'v1')\n",
+        "plt.plot([0,v2[0]]+v1[0] , [0,v2[1]]+v1[1], 'b' , label = 'v2')\n",
+        "plt.plot([-10,-10] , [0,0] , '--')\n",
+        "plt.plot([0,0] , [-10,10] , '--')\n",
+        "plt.grid()\n",
+        "plt.axis((-10, 10, -10, 10))\n",
+        "plt.legend()\n",
+        "plt.title('Angle between Vectors - %s'  %ang)\n",
+        "plt.show()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 336
+        },
+        "id": "P9zgybvSEq_d",
+        "outputId": "112c0f3a-e4ae-4c45-f1f0-a4178fb376c9"
+      },
+      "source": [
+        "#3d vectors\n",
+        "v1 = np.array([1,2,-1])\n",
+        "v2 = np.array([-2,-2,0])\n",
+        "fig = plt.figure()\n",
+        "ax = Axes3D(fig)\n",
+        "ax.plot([0, v1[0]],[0, v1[1]],[0, v1[2]],'b')\n",
+        "ax.plot([0, v2[0]],[0, v2[1]],[0, v2[2]],'r')\n",
+        "ang = np.rad2deg(np.arccos( np.dot(v1,v2) / (np.linalg.norm(v1)*np.linalg.norm(v2)) ))\n",
+        "plt.title('Angle between vectors: %s degrees.' %ang)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 0.92, 'Angle between vectors: 150.0 degrees.')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "25iC-ovsFib8"
+      },
+      "source": [
+        "##Vector Cross Product"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wpfSWx4YFSey",
+        "outputId": "c1d9ecf7-a2c8-453c-c763-60ae28221bb0"
+      },
+      "source": [
+        "v1 = np.array([1,2,3])\n",
+        "v2 = np.array([0,-1,-2])\n",
+        "print(\"\\nVector Cross Product ==>  \\n\", np.cross(v1,v2))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Vector Cross Product ==>  \n",
+            " [-1  2 -1]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_6_Linear_Algebra_Matrix.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_6_Linear_Algebra_Matrix.ipynb"
new file mode 100644
index 0000000..a398b39
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_6_Linear_Algebra_Matrix.ipynb"
@@ -0,0 +1,1658 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [
+        "6dJS0BLoGOEv",
+        "ma7OgB-mH_My",
+        "XaKdL6q3uMYh",
+        "1Achf8uAvUrN",
+        "l-5k3zs-vhpd",
+        "9msEMNkfGbis",
+        "2II3jSPoGmoh",
+        "162OundAIIUk",
+        "mZ-AENlIIg7a",
+        "s6BqjAznIuOE",
+        "4ATK73ybI39R",
+        "iM3seQM6JCym",
+        "JE-DkXIcJRl6",
+        "cIEBw85vJYtq",
+        "ysVObyauJymI",
+        "rN1EGGyHJ8kb",
+        "nSsxkZ61KM_d",
+        "skqlj97aKXqo",
+        "QXZnPOAEKgWq",
+        "PDICCplFKm2k",
+        "z_LHK_xUK3pF",
+        "0aFkdIZ_LCdi",
+        "v3FlKrutLkWu",
+        "mVePyDCKL3wT"
+      ]
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Cjn_KFKphbDZ"
+      },
+      "source": [
+        "#Linear Algebra (Matrix)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xGbERqnUGLJV"
+      },
+      "source": [
+        "#Matrix Operations"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6dJS0BLoGOEv"
+      },
+      "source": [
+        "##Matrix Creation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Pf5Tc4vIdwop"
+      },
+      "source": [
+        "import numpy as np"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "b9WF0xRzFyTV",
+        "outputId": "5dfe5e67-3cf1-4e41-d45a-22e4921664bd"
+      },
+      "source": [
+        "A = np.array([[1,2,3,4] ,\n",
+        "              [5,6,7,8] ,\n",
+        "              [10 , 11 , 12 ,13] ,\n",
+        "              [14,15,16,17]], dtype = (np.float16))\n",
+        "A"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 1.,  2.,  3.,  4.],\n",
+              "       [ 5.,  6.,  7.,  8.],\n",
+              "       [10., 11., 12., 13.],\n",
+              "       [14., 15., 16., 17.]], dtype=float16)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sY4sxK66GX4t",
+        "outputId": "077b9eac-471c-4454-bb75-1e11853a537a"
+      },
+      "source": [
+        "print(type(A))\n",
+        "print(A.dtype)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'numpy.ndarray'>\n",
+            "float16\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WqRx7pHNGitP",
+        "outputId": "a4b79ab3-a516-40b9-e947-50c9fd4252aa"
+      },
+      "source": [
+        "B = np.array([[1.5,2.07,3,4] , [5,6,7,8] , [10 , 11 , 12 ,13] , [14,15,16,17]])\n",
+        "B"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 1.5 ,  2.07,  3.  ,  4.  ],\n",
+              "       [ 5.  ,  6.  ,  7.  ,  8.  ],\n",
+              "       [10.  , 11.  , 12.  , 13.  ],\n",
+              "       [14.  , 15.  , 16.  , 17.  ]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ls-8a0I9GwSx",
+        "outputId": "a41b5e94-5a17-4df4-d469-0a07e0b67bea"
+      },
+      "source": [
+        "print(type(B))\n",
+        "print(B.dtype)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'numpy.ndarray'>\n",
+            "float64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kqNzgDvZGz7G",
+        "outputId": "6b294135-a18e-4b5f-eb60-3887378e7d83"
+      },
+      "source": [
+        "A.shape"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(4, 4)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "G_fDB_W1HBD3",
+        "outputId": "90f68215-4ff9-4de0-dc1b-4f85a6916491"
+      },
+      "source": [
+        "A[0]"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([1., 2., 3., 4.], dtype=float16)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HzbeFFb-Hv1u",
+        "outputId": "28188795-f2f6-4595-a6ea-2072a8bec358"
+      },
+      "source": [
+        "A[0][0]"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1.0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tTQFbmwBHF0K",
+        "outputId": "46720ef0-5ab6-493e-8704-6b1905f3ea76"
+      },
+      "source": [
+        "A[1:3]"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 5.,  6.,  7.,  8.],\n",
+              "       [10., 11., 12., 13.]], dtype=float16)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Slmu17pYHTCN",
+        "outputId": "70d73ffd-af51-4e4c-a053-9dce3c21abcf"
+      },
+      "source": [
+        "A[1:3, 1:4]"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 6.,  7.,  8.],\n",
+              "       [11., 12., 13.]], dtype=float16)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ma7OgB-mH_My"
+      },
+      "source": [
+        "##Zeros & Ones Matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ysqwi2y_H5jV",
+        "outputId": "2f286aa6-c226-457b-afa2-0db4d93ba821"
+      },
+      "source": [
+        "#روش اول برای تولید ماترس صفر با ابعاد دلخواه\n",
+        "np.zeros(9).reshape(3,3)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0., 0., 0.],\n",
+              "       [0., 0., 0.],\n",
+              "       [0., 0., 0.]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Nj8VHSwsIDLq",
+        "outputId": "595ccc41-30cc-41fe-ca4f-4e3a362377c3"
+      },
+      "source": [
+        "# روش دوم تولید ماتریس صفر\n",
+        "np.zeros((3,3))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0., 0., 0.],\n",
+              "       [0., 0., 0.],\n",
+              "       [0., 0., 0.]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LQbONYX-IPSk",
+        "outputId": "383b5f88-4301-4e7f-921a-7c65c61f15a3"
+      },
+      "source": [
+        "#روش اول برای تولید ماترس یک با ابعاد دلخواه\n",
+        "np.ones(9).reshape(3,3)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[1., 1., 1.],\n",
+              "       [1., 1., 1.],\n",
+              "       [1., 1., 1.]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cTeRN4D3t-gp",
+        "outputId": "d17df67c-8a5a-4a1c-c4f7-44fff223fa2c"
+      },
+      "source": [
+        "# روش دوم تولید ماتریس یک\n",
+        "np.ones((3,3))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[1., 1., 1.],\n",
+              "       [1., 1., 1.],\n",
+              "       [1., 1., 1.]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XaKdL6q3uMYh"
+      },
+      "source": [
+        "##Matrix with Random Numbers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1311E00VuEL9",
+        "outputId": "870b1689-5fa7-4795-ed48-6b67b88c00c1"
+      },
+      "source": [
+        "# np.random.seed(seed=42)\n",
+        "X = np.random.random((3,3))\n",
+        "X"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0.39211752, 0.34317802, 0.72904971],\n",
+              "       [0.43857224, 0.0596779 , 0.39804426],\n",
+              "       [0.73799541, 0.18249173, 0.17545176]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1Achf8uAvUrN"
+      },
+      "source": [
+        "##Identity Matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "v2rfjL2KuSwz",
+        "outputId": "e9e86821-080f-4ed2-b336-42bdf5c7f572"
+      },
+      "source": [
+        "#ماتریس همانی مرتبه دلخواه\n",
+        "I = np.eye(9)\n",
+        "I"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+              "       [0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
+              "       [0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
+              "       [0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
+              "       [0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
+              "       [0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
+              "       [0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
+              "       [0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
+              "       [0., 0., 0., 0., 0., 0., 0., 0., 1.]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l-5k3zs-vhpd"
+      },
+      "source": [
+        "##Diagonal Matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "M0NtcqyPvf7-",
+        "outputId": "36d80923-7acd-4bda-823f-9b9260f9ece3"
+      },
+      "source": [
+        "D = np.diag([1,2,3,4,5,6,7,8])\n",
+        "D"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[1, 0, 0, 0, 0, 0, 0, 0],\n",
+              "       [0, 2, 0, 0, 0, 0, 0, 0],\n",
+              "       [0, 0, 3, 0, 0, 0, 0, 0],\n",
+              "       [0, 0, 0, 4, 0, 0, 0, 0],\n",
+              "       [0, 0, 0, 0, 5, 0, 0, 0],\n",
+              "       [0, 0, 0, 0, 0, 6, 0, 0],\n",
+              "       [0, 0, 0, 0, 0, 0, 7, 0],\n",
+              "       [0, 0, 0, 0, 0, 0, 0, 8]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9msEMNkfGbis"
+      },
+      "source": [
+        "##Traingular Matrices (lower & Upper triangular matrix)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HNzyaLLuwCM7",
+        "outputId": "35efc77c-09f7-4375-d27c-c81d180cacdf"
+      },
+      "source": [
+        "M = np.random.randn(5,5)\n",
+        "U = np.triu(M)\n",
+        "L = np.tril(M)\n",
+        "print(\"Matrix - \\n\" , M)\n",
+        "print(\"\\n\")\n",
+        "print(\"lower triangular matrix - \\n\" , L)\n",
+        "print(\"\\n\")\n",
+        "print(\"Upper triangular matrix - \\n\" , U)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Matrix - \n",
+            " [[-1.72766949 -0.39089979  0.57380586  0.33858905 -0.01183049]\n",
+            " [ 2.39236527  0.41291216  0.97873601  2.23814334 -1.29408532]\n",
+            " [-1.03878821  1.74371223 -0.79806274  0.02968323  1.06931597]\n",
+            " [ 0.89070639  1.75488618  1.49564414  1.06939267 -0.77270871]\n",
+            " [ 0.79486267  0.31427199 -1.32626546  1.41729905  0.80723653]]\n",
+            "\n",
+            "\n",
+            "lower triangular matrix - \n",
+            " [[-1.72766949  0.          0.          0.          0.        ]\n",
+            " [ 2.39236527  0.41291216  0.          0.          0.        ]\n",
+            " [-1.03878821  1.74371223 -0.79806274  0.          0.        ]\n",
+            " [ 0.89070639  1.75488618  1.49564414  1.06939267  0.        ]\n",
+            " [ 0.79486267  0.31427199 -1.32626546  1.41729905  0.80723653]]\n",
+            "\n",
+            "\n",
+            "Upper triangular matrix - \n",
+            " [[-1.72766949 -0.39089979  0.57380586  0.33858905 -0.01183049]\n",
+            " [ 0.          0.41291216  0.97873601  2.23814334 -1.29408532]\n",
+            " [ 0.          0.         -0.79806274  0.02968323  1.06931597]\n",
+            " [ 0.          0.          0.          1.06939267 -0.77270871]\n",
+            " [ 0.          0.          0.          0.          0.80723653]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2II3jSPoGmoh"
+      },
+      "source": [
+        "##Concatenate Matrices"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "m0cbByCIGkD9",
+        "outputId": "fa8a4c5a-e3bb-4eff-c111-4e33a0de597e"
+      },
+      "source": [
+        "A = np.array([[1,2] , [3,4] ,[5,6]])\n",
+        "B = np.array([[1,1] , [1,1]])\n",
+        "C = np.concatenate((A,B))\n",
+        "C , C.shape , type(C) , C.dtype"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([[1, 2],\n",
+              "        [3, 4],\n",
+              "        [5, 6],\n",
+              "        [1, 1],\n",
+              "        [1, 1]]), (5, 2), numpy.ndarray, dtype('int64'))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eR47s6PlHkcM",
+        "outputId": "ae2b55b8-6db2-416c-adb0-747a27cb0f59"
+      },
+      "source": [
+        "M = np.full((3,2) , 8)\n",
+        "print(M)\n",
+        "print(M.shape)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[8 8]\n",
+            " [8 8]\n",
+            " [8 8]]\n",
+            "(3, 2)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8UTKMpmzH1aT",
+        "outputId": "481d76bd-c98d-4aa5-c43d-801eb9a7f74b"
+      },
+      "source": [
+        "M = M.flatten()\n",
+        "print(M)\n",
+        "print(M.shape)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[8 8 8 8 8 8]\n",
+            "(6,)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "162OundAIIUk"
+      },
+      "source": [
+        "##Matrix Addition"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5-xqDzlQH5Bh",
+        "outputId": "cd969196-d113-4035-9a72-ddc3cda3f4a4"
+      },
+      "source": [
+        "M = np.array([[1,2,3],[4,-3,6],[7,8,0]])\n",
+        "N = np.array([[1,1,1],[2,2,2],[3,3,3]])\n",
+        "print(\"\\n First Matrix (M)  ==>  \\n\", M)\n",
+        "print(\"\\n Second Matrix (N)  ==>  \\n\", N)\n",
+        "C = M+N\n",
+        "print(\"\\n Matrix Addition (M+N)  ==>  \\n\", C)\n",
+        "# OR\n",
+        "C = np.add(M,N,dtype = np.float64)\n",
+        "print(\"\\n Matrix Addition using np.add  ==>  \\n\", C)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " First Matrix (M)  ==>  \n",
+            " [[ 1  2  3]\n",
+            " [ 4 -3  6]\n",
+            " [ 7  8  0]]\n",
+            "\n",
+            " Second Matrix (N)  ==>  \n",
+            " [[1 1 1]\n",
+            " [2 2 2]\n",
+            " [3 3 3]]\n",
+            "\n",
+            " Matrix Addition (M+N)  ==>  \n",
+            " [[ 2  3  4]\n",
+            " [ 6 -1  8]\n",
+            " [10 11  3]]\n",
+            "\n",
+            " Matrix Addition using np.add  ==>  \n",
+            " [[ 2.  3.  4.]\n",
+            " [ 6. -1.  8.]\n",
+            " [10. 11.  3.]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mZ-AENlIIg7a"
+      },
+      "source": [
+        "##Matrix subtraction"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Dt90Zt0VIinX",
+        "outputId": "7c9e17ff-e66c-4dc9-c34a-dcf61966aa58"
+      },
+      "source": [
+        "#********************************************************#\n",
+        "M = np.array([[1,2,3],[4,-3,6],[7,8,0]])\n",
+        "N = np.array([[1,1,1],[2,2,2],[3,3,3]])\n",
+        "print(\"\\n First Matrix (M)  ==>  \\n\", M)\n",
+        "print(\"\\n Second Matrix (N)  ==>  \\n\", N)\n",
+        "C = M-N\n",
+        "print(\"\\n Matrix Subtraction (M-N)  ==>  \\n\", C)\n",
+        "# OR\n",
+        "C = np.subtract(M,N,dtype = np.float64)\n",
+        "print(\"\\n Matrix Subtraction using np.subtract  ==>  \\n\", C)\n",
+        "#********************************************************#"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " First Matrix (M)  ==>  \n",
+            " [[ 1  2  3]\n",
+            " [ 4 -3  6]\n",
+            " [ 7  8  0]]\n",
+            "\n",
+            " Second Matrix (N)  ==>  \n",
+            " [[1 1 1]\n",
+            " [2 2 2]\n",
+            " [3 3 3]]\n",
+            "\n",
+            " Matrix Subtraction (M-N)  ==>  \n",
+            " [[ 0  1  2]\n",
+            " [ 2 -5  4]\n",
+            " [ 4  5 -3]]\n",
+            "\n",
+            " Matrix Subtraction using np.subtract  ==>  \n",
+            " [[ 0.  1.  2.]\n",
+            " [ 2. -5.  4.]\n",
+            " [ 4.  5. -3.]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s6BqjAznIuOE"
+      },
+      "source": [
+        "##Matrices Scalar Multiplication"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HjeiPaZjIsA_",
+        "outputId": "8607f232-4c65-4fb5-f67c-a5abc3a77428"
+      },
+      "source": [
+        "M = np.array([[1,2,3],[4,-3,6],[7,8,0]])\n",
+        "C = 10\n",
+        "print(\"\\n Matrix (M)  ==>  \\n\", M)\n",
+        "print(\"\\nMatrices Scalar Multiplication ==>  \\n\", C*M)\n",
+        "# OR\n",
+        "print(\"\\nMatrices Scalar Multiplication ==>  \\n\", np.multiply(C,M))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Matrix (M)  ==>  \n",
+            " [[ 1  2  3]\n",
+            " [ 4 -3  6]\n",
+            " [ 7  8  0]]\n",
+            "\n",
+            "Matrices Scalar Multiplication ==>  \n",
+            " [[ 10  20  30]\n",
+            " [ 40 -30  60]\n",
+            " [ 70  80   0]]\n",
+            "\n",
+            "Matrices Scalar Multiplication ==>  \n",
+            " [[ 10  20  30]\n",
+            " [ 40 -30  60]\n",
+            " [ 70  80   0]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4ATK73ybI39R"
+      },
+      "source": [
+        "##Transpose of a matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0IsczGYMI3eW",
+        "outputId": "cb4c6124-4ccd-4c93-aff8-66b8267a2798"
+      },
+      "source": [
+        "M = np.array([[1,2,3],[4,-3,6]])\n",
+        "print(\"\\n Matrix (M)  ==>  \\n\", M)\n",
+        "print(\"\\nTranspose of M ==>  \\n\", np.transpose(M))\n",
+        "# OR\n",
+        "print(\"\\nTranspose of M ==>  \\n\", M.T)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Matrix (M)  ==>  \n",
+            " [[ 1  2  3]\n",
+            " [ 4 -3  6]]\n",
+            "\n",
+            "Transpose of M ==>  \n",
+            " [[ 1  4]\n",
+            " [ 2 -3]\n",
+            " [ 3  6]]\n",
+            "\n",
+            "Transpose of M ==>  \n",
+            " [[ 1  4]\n",
+            " [ 2 -3]\n",
+            " [ 3  6]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iM3seQM6JCym"
+      },
+      "source": [
+        "##Determinant of a matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7X7M56xAJAT7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2a9855da-f255-4467-8272-99e812fe096e"
+      },
+      "source": [
+        "M = np.array([[1,2,3],[4,-3,6],[7,8,0]])\n",
+        "print(\"\\n Matrix (M)  ==>  \\n\", M)\n",
+        "print(\"\\nDeterminant of M ==>  \", np.linalg.det(M))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Matrix (M)  ==>  \n",
+            " [[ 1  2  3]\n",
+            " [ 4 -3  6]\n",
+            " [ 7  8  0]]\n",
+            "\n",
+            "Determinant of M ==>   195.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JE-DkXIcJRl6"
+      },
+      "source": [
+        "##Rank of a matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pO9aEOunJU5x",
+        "outputId": "251ecd9a-d139-4c5c-9476-ad6bbe7aefa7"
+      },
+      "source": [
+        "M = np.array([[1,2,3],[4,-3,6],[7,8,0]])\n",
+        "print(\"\\n Matrix (M)  ==>  \\n\", M)\n",
+        "print(\"\\nRank of M ==> \", np.linalg.matrix_rank(M))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Matrix (M)  ==>  \n",
+            " [[ 1  2  3]\n",
+            " [ 4 -3  6]\n",
+            " [ 7  8  0]]\n",
+            "\n",
+            "Rank of M ==>  3\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cIEBw85vJYtq"
+      },
+      "source": [
+        "##Trace of matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "j3i0wF94JaIY",
+        "outputId": "cd63bd79-cba4-4161-b4b9-383161fbee29"
+      },
+      "source": [
+        "M = np.array([[1,2,3],[4,-3,6],[7,8,0]])\n",
+        "print(\"\\n Matrix (M)  ==>  \\n\", M)\n",
+        "print(\"\\nTrace of M ==> \", np.trace(M))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Matrix (M)  ==>  \n",
+            " [[ 1  2  3]\n",
+            " [ 4 -3  6]\n",
+            " [ 7  8  0]]\n",
+            "\n",
+            "Trace of M ==>  -2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ysVObyauJymI"
+      },
+      "source": [
+        "##Inverse of matrix A"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NOuQTKmuJw3N",
+        "outputId": "eb9b401f-d537-466e-8b3a-911e50d0b895"
+      },
+      "source": [
+        "M = np.array([[1,2,3],[4,-3,6],[7,8,0]])\n",
+        "print(\"\\n Matrix (M)  ==>  \\n\", M)\n",
+        "print(\"\\nInverse of M ==> \\n\", np.linalg.inv(M))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Matrix (M)  ==>  \n",
+            " [[ 1  2  3]\n",
+            " [ 4 -3  6]\n",
+            " [ 7  8  0]]\n",
+            "\n",
+            "Inverse of M ==> \n",
+            " [[-0.24615385  0.12307692  0.10769231]\n",
+            " [ 0.21538462 -0.10769231  0.03076923]\n",
+            " [ 0.27179487  0.03076923 -0.05641026]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rN1EGGyHJ8kb"
+      },
+      "source": [
+        "##Matrix Multiplication (pointwise multiplication)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YX4OJngOJ7vG",
+        "outputId": "4b9c9958-6f70-4e81-c5be-23a8f7b1c1ac"
+      },
+      "source": [
+        "M = np.array([[1,2,3],[4,-3,6],[7,8,0]])\n",
+        "N = np.array([[1,1,1],[2,2,2],[3,3,3]])\n",
+        "print(\"\\n First Matrix (M)  ==>  \\n\", M)\n",
+        "print(\"\\n Second Matrix (N)  ==>  \\n\", N)\n",
+        "print(\"\\n Point-Wise Multiplication of M & N  ==> \\n\", M*N)\n",
+        "# OR\n",
+        "print(\"\\n Point-Wise Multiplication of M & N  ==> \\n\", np.multiply(M,N))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " First Matrix (M)  ==>  \n",
+            " [[ 1  2  3]\n",
+            " [ 4 -3  6]\n",
+            " [ 7  8  0]]\n",
+            "\n",
+            " Second Matrix (N)  ==>  \n",
+            " [[1 1 1]\n",
+            " [2 2 2]\n",
+            " [3 3 3]]\n",
+            "\n",
+            " Point-Wise Multiplication of M & N  ==> \n",
+            " [[ 1  2  3]\n",
+            " [ 8 -6 12]\n",
+            " [21 24  0]]\n",
+            "\n",
+            " Point-Wise Multiplication of M & N  ==> \n",
+            " [[ 1  2  3]\n",
+            " [ 8 -6 12]\n",
+            " [21 24  0]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nSsxkZ61KM_d"
+      },
+      "source": [
+        "##Matrix dot product"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bQT1pcjBKLJD",
+        "outputId": "42f57f0e-bc9e-49e0-fd05-77b2d3774689"
+      },
+      "source": [
+        "M = np.array([[1,2,3],[4,-3,6],[7,8,0]])\n",
+        "N = np.array([[1,1,1],[2,2,2],[3,3,3]])\n",
+        "print(\"\\n First Matrix (M)  ==>  \\n\", M)\n",
+        "print(\"\\n Second Matrix (N)  ==>  \\n\", N)\n",
+        "print(\"\\n Matrix Dot Product ==> \\n\", M@N)\n",
+        "# OR\n",
+        "print(\"\\n Matrix Dot Product using np.matmul ==> \\n\", np.matmul(M,N))\n",
+        "# OR\n",
+        "print(\"\\n Matrix Dot Product using np.dot ==> \\n\", np.dot(M,N))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " First Matrix (M)  ==>  \n",
+            " [[ 1  2  3]\n",
+            " [ 4 -3  6]\n",
+            " [ 7  8  0]]\n",
+            "\n",
+            " Second Matrix (N)  ==>  \n",
+            " [[1 1 1]\n",
+            " [2 2 2]\n",
+            " [3 3 3]]\n",
+            "\n",
+            " Matrix Dot Product ==> \n",
+            " [[14 14 14]\n",
+            " [16 16 16]\n",
+            " [23 23 23]]\n",
+            "\n",
+            " Matrix Dot Product using np.matmul ==> \n",
+            " [[14 14 14]\n",
+            " [16 16 16]\n",
+            " [23 23 23]]\n",
+            "\n",
+            " Matrix Dot Product using np.dot ==> \n",
+            " [[14 14 14]\n",
+            " [16 16 16]\n",
+            " [23 23 23]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "skqlj97aKXqo"
+      },
+      "source": [
+        "##Matrix Division"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mThzsE9EKVva",
+        "outputId": "187993ea-6e43-45ba-b51a-6c5c75acd220"
+      },
+      "source": [
+        "M = np.array([[1,2,3],[4,-3,6],[7,8,0]])\n",
+        "N = np.array([[1,1,1],[2,2,2],[3,3,3]])\n",
+        "print(\"\\n First Matrix (M)  ==>  \\n\", M)\n",
+        "print(\"\\n Second Matrix (N)  ==>  \\n\", N)\n",
+        "print(\"\\n Matrix Division (M/N)   ==> \\n\", M/N)\n",
+        "# OR\n",
+        "print(\"\\n Matrix Division (M/N)   ==> \\n\", np.divide(M,N))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " First Matrix (M)  ==>  \n",
+            " [[ 1  2  3]\n",
+            " [ 4 -3  6]\n",
+            " [ 7  8  0]]\n",
+            "\n",
+            " Second Matrix (N)  ==>  \n",
+            " [[1 1 1]\n",
+            " [2 2 2]\n",
+            " [3 3 3]]\n",
+            "\n",
+            " Matrix Division (M/N)   ==> \n",
+            " [[ 1.          2.          3.        ]\n",
+            " [ 2.         -1.5         3.        ]\n",
+            " [ 2.33333333  2.66666667  0.        ]]\n",
+            "\n",
+            " Matrix Division (M/N)   ==> \n",
+            " [[ 1.          2.          3.        ]\n",
+            " [ 2.         -1.5         3.        ]\n",
+            " [ 2.33333333  2.66666667  0.        ]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QXZnPOAEKgWq"
+      },
+      "source": [
+        "##Sum of all elements in a matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ayKH4KjTKeoj",
+        "outputId": "71cebc33-fd61-437d-e936-d2891e143581"
+      },
+      "source": [
+        "N = np.array([[1,1,1],[2,2,2],[3,3,3]])\n",
+        "print(\"\\n Matrix (N)  ==>  \\n\", N)\n",
+        "print (\"Sum of all elements in a Matrix  ==>\")\n",
+        "print (np.sum(N))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Matrix (N)  ==>  \n",
+            " [[1 1 1]\n",
+            " [2 2 2]\n",
+            " [3 3 3]]\n",
+            "Sum of all elements in a Matrix  ==>\n",
+            "18\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PDICCplFKm2k"
+      },
+      "source": [
+        "##Column-Wise Addition"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "u6PlnCogKq9Z",
+        "outputId": "4f96a249-6057-4742-e985-5938afec0408"
+      },
+      "source": [
+        "N = np.array([[1,1,1],[2,2,2],[3,3,3]])\n",
+        "print(\"\\n Matrix (N)  ==>  \\n\", N)\n",
+        "print (\"Column-Wise summation ==> \")\n",
+        "print (np.sum(N,axis=0))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Matrix (N)  ==>  \n",
+            " [[1 1 1]\n",
+            " [2 2 2]\n",
+            " [3 3 3]]\n",
+            "Column-Wise summation ==> \n",
+            "[6 6 6]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z_LHK_xUK3pF"
+      },
+      "source": [
+        "##Row-Wise Addition"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZDBG2yWfK14J",
+        "outputId": "f0148f43-b58a-4555-ab2b-b0e52e24bfc6"
+      },
+      "source": [
+        "N = np.array([[1,1,1],[2,2,2],[3,3,3]])\n",
+        "print(\"\\n Matrix (N)  ==>  \\n\", N)\n",
+        "print (\"Row-Wise summation  ==>\")\n",
+        "print (np.sum(N,axis=1))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            " Matrix (N)  ==>  \n",
+            " [[1 1 1]\n",
+            " [2 2 2]\n",
+            " [3 3 3]]\n",
+            "Row-Wise summation  ==>\n",
+            "[3 6 9]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0aFkdIZ_LCdi"
+      },
+      "source": [
+        "##Matrix Vector Multiplication"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6efenROPLAkR",
+        "outputId": "04284fe1-1500-41f3-cf88-a3c297a66876"
+      },
+      "source": [
+        "A = np.array([[1,2,3] ,[4,5,6]])\n",
+        "v = np.array([10,20,30])\n",
+        "print (\"Matrix Vector Multiplication  ==> \\n\" , A*v)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Matrix Vector Multiplication  ==> \n",
+            " [[ 10  40  90]\n",
+            " [ 40 100 180]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zkUbKIS2Lg0C",
+        "outputId": "d86604b5-61a9-4441-c667-bc51422f01d9"
+      },
+      "source": [
+        "A = np.array([[1,2,3] ,[4,5,6]])\n",
+        "v = np.array([10,20,30])\n",
+        "print (\"Matrix Vector Multiplication  ==> \\n\" , A@v)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Matrix Vector Multiplication  ==> \n",
+            " [140 320]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "v3FlKrutLkWu"
+      },
+      "source": [
+        "#Tensor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eA2ZhmupLh-_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0c132337-17af-4529-abaf-a54fb745ebcf"
+      },
+      "source": [
+        "# Create Tensor\n",
+        "T1 = np.array([\n",
+        "  [[1,2,3],    [4,5,6],    [7,8,9]],\n",
+        "  [[10,20,30], [40,50,60], [70,80,90]],\n",
+        "  [[100,200,300], [400,500,600], [700,800,900]],\n",
+        "  ])\n",
+        "T1"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[[  1,   2,   3],\n",
+              "        [  4,   5,   6],\n",
+              "        [  7,   8,   9]],\n",
+              "\n",
+              "       [[ 10,  20,  30],\n",
+              "        [ 40,  50,  60],\n",
+              "        [ 70,  80,  90]],\n",
+              "\n",
+              "       [[100, 200, 300],\n",
+              "        [400, 500, 600],\n",
+              "        [700, 800, 900]]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 80
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mVePyDCKL3wT"
+      },
+      "source": [
+        "#Solving Equations $AX = B$\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OmnYkV76Lo6z",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "dcca19b6-24af-481d-94fe-cfff8111113e"
+      },
+      "source": [
+        "A = np.array([[1,2,3] , [4,5,6] , [7,8,8]])\n",
+        "A"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[1, 2, 3],\n",
+              "       [4, 5, 6],\n",
+              "       [7, 8, 8]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 81
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jcANiZMgMQ-7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ce3fe5e3-38c0-4df5-925b-03a6b326f673"
+      },
+      "source": [
+        "B = np.random.random((3,1))\n",
+        "B"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0.01612921],\n",
+              "       [0.59443188],\n",
+              "       [0.55678519]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 82
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "0t8wsqEKMTwn",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fec21b34-379a-47af-c43d-1ace4e94ae8b"
+      },
+      "source": [
+        "X = np.dot(np.linalg.inv(A) , B)\n",
+        "X"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 0.98535527],\n",
+              "       [-1.40853707],\n",
+              "       [ 0.61594936]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 83
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "TM6OULmAMUcj",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "23241a26-d88c-48fa-c7ca-6aa10440f1e2"
+      },
+      "source": [
+        "X = np.matmul(np.linalg.inv(A) , B)\n",
+        "X"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 0.98535527],\n",
+              "       [-1.40853707],\n",
+              "       [ 0.61594936]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 84
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_7_Probability.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_7_Probability.ipynb"
new file mode 100644
index 0000000..6c3ed49
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_7_Probability.ipynb"
@@ -0,0 +1,292 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eulQcooEfN3k"
+      },
+      "source": [
+        "#Probabilities"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "M5k0o0h-6FiN",
+        "outputId": "55f17f13-f45e-4aff-f2d2-e348ec40bee9"
+      },
+      "source": [
+        "import math\n",
+        "#فاکتوریل\n",
+        "print(math.factorial(6))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "720\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qR5OhE40rnHs"
+      },
+      "source": [
+        "#شبیه‌سازی پرتاب سکه\n",
+        "import random\n",
+        "def coin_trial():\n",
+        "    heads = 0\n",
+        "    for i in range(100):\n",
+        "      if random.random() <= 0.5:\n",
+        "         heads +=1\n",
+        "    return heads\n",
+        "\n",
+        "def simulate(n):\n",
+        "    trials = []\n",
+        "    for i in range(n):\n",
+        "      trials.append(coin_trial())\n",
+        "    return(sum(trials)/n)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "r95sPRWisDJa",
+        "outputId": "e6c251a7-2af9-4a40-c3be-9a237da88f08"
+      },
+      "source": [
+        "simulate(10000)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "50.0707"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 175
+        },
+        "id": "G0BUzw-a5Tmh",
+        "outputId": "c4ab3a1d-9ea1-44f0-e645-368506c992cc"
+      },
+      "source": [
+        "#یک مثال برای بررسی مفهوم احتمال\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "#create pandas DataFrame with raw data\n",
+        "df = pd.DataFrame({'gender': np.repeat(np.array(['Male', 'Female']), 150),\n",
+        "                   'sport': np.repeat(np.array(['Baseball', 'Basketball', 'Football',\n",
+        "                                                'Soccer', 'Baseball', 'Basketball',\n",
+        "                                                'Football', 'Soccer']),\n",
+        "                                    (34, 40, 58, 18, 34, 52, 20, 44))})\n",
+        "\n",
+        "#produce contingency table to summarize raw data\n",
+        "survey_data = pd.crosstab(index=df['gender'], columns=df['sport'], margins=True)\n",
+        "\n",
+        "#view contingency table\n",
+        "survey_data"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>sport</th>\n",
+              "      <th>Baseball</th>\n",
+              "      <th>Basketball</th>\n",
+              "      <th>Football</th>\n",
+              "      <th>Soccer</th>\n",
+              "      <th>All</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>gender</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Female</th>\n",
+              "      <td>34</td>\n",
+              "      <td>52</td>\n",
+              "      <td>20</td>\n",
+              "      <td>44</td>\n",
+              "      <td>150</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Male</th>\n",
+              "      <td>34</td>\n",
+              "      <td>40</td>\n",
+              "      <td>58</td>\n",
+              "      <td>18</td>\n",
+              "      <td>150</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>All</th>\n",
+              "      <td>68</td>\n",
+              "      <td>92</td>\n",
+              "      <td>78</td>\n",
+              "      <td>62</td>\n",
+              "      <td>300</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "sport   Baseball  Basketball  Football  Soccer  All\n",
+              "gender                                             \n",
+              "Female        34          52        20      44  150\n",
+              "Male          34          40        58      18  150\n",
+              "All           68          92        78      62  300"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qvwkvj3GdMBP",
+        "outputId": "cc8e0a70-1d98-4026-cb65-d769001a056b"
+      },
+      "source": [
+        "#extract value in second row and first column\n",
+        "survey_data.iloc[2, 1]"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "92"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GC4R5wINdM1P",
+        "outputId": "bef7cfe8-b48f-4ccc-acb8-7e43da37a471"
+      },
+      "source": [
+        "#calculate probability of being male, given that individual prefers baseball\n",
+        "survey_data.iloc[1, 0] / survey_data.iloc[2, 0]"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.5"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yEDeQYwjdO-F",
+        "outputId": "2176c8d2-9d74-43da-8089-c0b9b59acca9"
+      },
+      "source": [
+        "#calculate probability of preferring basketball, given that individual is female\n",
+        "survey_data.iloc[0, 1] / survey_data.iloc[0, 4]"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.3466666666666667"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_8_Probability_Distribution.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_8_Probability_Distribution.ipynb"
new file mode 100644
index 0000000..41fc32d
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___0_8_Probability_Distribution.ipynb"
@@ -0,0 +1,757 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yYvYexd2GXwT"
+      },
+      "source": [
+        "#Probability Distribution"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WFK0xl2dQ3FN"
+      },
+      "source": [
+        "# for inline plots in jupyter\n",
+        "%matplotlib inline\n",
+        "# import matplotlib\n",
+        "import matplotlib.pyplot as plt\n",
+        "# import seaborn\n",
+        "import seaborn as sns\n",
+        "# settings for seaborn plot sizes\n",
+        "sns.set(rc={'figure.figsize':(4,4)})\n",
+        "import numpy as np\n"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Eg5IgYWbfdkj"
+      },
+      "source": [
+        "##Expected Value and Variance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "pNdX9qPPvX1O"
+      },
+      "source": [
+        "p = np.ones(6) * 1/6"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7FdiuRDOwABE"
+      },
+      "source": [
+        "x = np.array([1, 2, 3, 4, 5, 6])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nWEa4PBXwc0V",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7cd7db35-f4a0-41ec-9e57-1b22998391f5"
+      },
+      "source": [
+        "E_x = np.matmul(p, x)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "3.5"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2unTC-Xew5nD",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "aae9287b-5443-420e-9c53-ca91869fa5ce"
+      },
+      "source": [
+        "E_x2 = np.matmul(p, np.square(x))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "15.166666666666666"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "78NVgXbvxXMO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4afa5e56-b461-4664-99ab-62bb8240aecc"
+      },
+      "source": [
+        "var_x = E_x2 - np.square(E_x)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "2.916666666666666"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IYyNMrcfTfGX"
+      },
+      "source": [
+        "##Bernoulli Distribution"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sK_IpQStTiPE"
+      },
+      "source": [
+        "![image.png](data:image/png;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "4j_PgpeHTk9I"
+      },
+      "source": [
+        "from scipy.stats import bernoulli\n",
+        "data_bern = bernoulli.rvs(size=1000,p=0.6)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 357
+        },
+        "id": "Z1SVYyAwTnJv",
+        "outputId": "8e1294af-119e-4267-e409-639907d1fc40"
+      },
+      "source": [
+        "ax= sns.distplot(data_bern,\n",
+        "                 kde=False,\n",
+        "                 color=\"r\",\n",
+        "                 hist_kws={\"linewidth\": 9,'alpha':0.3})\n",
+        "ax.set(xlabel='Bernoulli Distribution', ylabel='Frequency')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+            "  warnings.warn(msg, FutureWarning)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Bernoulli Distribution')]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 288x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6QDfnaGLTJU7"
+      },
+      "source": [
+        "##Binomial Distribution\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NzUhcO4wTNU_"
+      },
+      "source": [
+        "![image.png](data:image/png;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-P0kw17W_k7J",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "338f53cf-f8f7-4836-f452-491b833c419d"
+      },
+      "source": [
+        "from scipy.stats import binom\n",
+        "X = binom(10, 0.2) # Declare X to be a binomial random variable\n",
+        "print(X.pmf(3))           # P(X = 3)\n",
+        "print(X.cdf(4))           # P(X <= 4)\n",
+        "print(X.mean())           # E[X]\n",
+        "print(X.var())            # Var(X)\n",
+        "print(X.std())            # Std(X)\n",
+        "print(X.rvs())            # Get a random sample from X\n",
+        "print(X.rvs(10))          # Get 10 random samples form X"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.20132659200000022\n",
+            "0.9672065024000001\n",
+            "2.0\n",
+            "1.6\n",
+            "1.2649110640673518\n",
+            "1\n",
+            "[0 2 2 1 4 3 3 1 1 1]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YnZRlZmTTSYp"
+      },
+      "source": [
+        "data_binom = binom.rvs(n=10,p=0.8,size=10000)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 357
+        },
+        "id": "G8qe35ZyTUqX",
+        "outputId": "7f06b3f0-15b4-4977-c355-e0f04dfd9e0d"
+      },
+      "source": [
+        "ax = sns.distplot(data_binom,\n",
+        "                  kde=False,\n",
+        "                  color='r',\n",
+        "                  hist_kws={\"linewidth\": 9,'alpha':0.3})\n",
+        "ax.set(xlabel='Binomial Distribution', ylabel='Frequency')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+            "  warnings.warn(msg, FutureWarning)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Binomial Distribution')]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 288x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MM32EDf0AidZ"
+      },
+      "source": [
+        "##Geometric Distribution"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KxVMxcWdAhtO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "70f23c17-d9f1-43aa-ca78-ff48b51fb6a4"
+      },
+      "source": [
+        "from scipy import stats\n",
+        "X = stats.geom(0.75)  # Declare X to be a geometric random variable\n",
+        "print(X.pmf(3))       # P(X = 3)\n",
+        "print(X.rvs())        # Get a random sample from Y"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.046875\n",
+            "1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Zf9g1TswSk8j"
+      },
+      "source": [
+        "##Poisson Distribution\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mBJFwu92SrjS"
+      },
+      "source": [
+        "![image.png](data:image/png;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "7CgeSpdzS-g7"
+      },
+      "source": [
+        "from scipy.stats import poisson\n",
+        "data_poisson = poisson.rvs(mu=3, size=10000)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 357
+        },
+        "id": "EcLkQtMfTBpK",
+        "outputId": "c654d4d5-1daa-45f9-fdf3-74b7113e3d6a"
+      },
+      "source": [
+        "ax = sns.distplot(data_poisson,\n",
+        "                  bins=30,\n",
+        "                  kde=False,\n",
+        "                  color='r',\n",
+        "                  hist_kws={\"linewidth\": 9,'alpha':0.3})\n",
+        "ax.set(xlabel='Poisson Distribution', ylabel='Frequency')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+            "  warnings.warn(msg, FutureWarning)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Poisson Distribution')]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 288x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RpwEdHSmRCKg"
+      },
+      "source": [
+        "##Continuous Uniform Distribution\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CWI5U4cmR_2V"
+      },
+      "source": [
+        "![Untitled.png](data:image/png;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7kH77g7KSC_7"
+      },
+      "source": [
+        "![Untitled02.png](data:image/png;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "4y-GYs8KRLfi"
+      },
+      "source": [
+        "# import uniform distribution\n",
+        "from scipy.stats import uniform"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "e6dEOvfrRNsX"
+      },
+      "source": [
+        "# random numbers from uniform distribution\n",
+        "n = 1000000\n",
+        "start = 10\n",
+        "width = 20\n",
+        "data_uniform = uniform.rvs(size=n, loc = start, scale=width)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 357
+        },
+        "id": "5HH0eH-bRP90",
+        "outputId": "15b3b5b6-fac3-4c70-9f78-b8e4d185cbbb"
+      },
+      "source": [
+        "ax = sns.distplot(data_uniform,\n",
+        "                  bins=100,\n",
+        "                  kde=True,\n",
+        "                  color='r',\n",
+        "                  hist_kws={\"linewidth\": 9,'alpha':0.3})\n",
+        "ax.set(xlabel='Uniform Distribution ', ylabel='Frequency')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+            "  warnings.warn(msg, FutureWarning)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Uniform Distribution ')]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Q+NZbpE2dgj2np28BEQg8hfuitxsvIE6BwnQ6QTN8XgjjfFK9CUVvvvRXSAfy+3se68NE71WsAnl3LoCGQrDQ9/fb/L+la/TVw+8QhbLz9eqSR6dzFW5CzVtRFMZ99f/R9K+91P5tK1P+45aO3+87TcqRJ2oCNHXqVMrKynp8/sgjjwT/1zSN0tLSXo8PCFY846qtxVl1mPHLl0Ulv9MVaDMGo2D7KgSxyDvShT6WdI6oBoolPZ0RF55P41t7MLzemJ+fJB7iFH3aPv4UgGGfOyfGngjxjL+n1HNGAgIdgut0BXvqBsqIiy7CcLloef/9Mzo+nIgAhZH2qs9QrFZSujWuC0JnTJ8e7E2LBRnn5KOmpND8zr9jkn9nRIDCSPtnVTjGjY15WCsIfaFaLKROnEB71eFYuyICFC5M06T9sypSJ0yItSuC0C+pEybgPHy4z4b1aCACFCY8DY3o7e2kThQBEuIfx4TxGB4P7tq6mPohAhQmXDU1AKSMzevHUhBiT+oE//CW9qqqmPohAhQmvI2NANizR/ZjKQixJyUvD0XTcMa4HUgEKEx4GhpBVbFmDo+1K4LQL6rFgm3USNzHj8fWj5jmnkR4GhuwjRghPWBCwmDLzvY/OGOICFCY8DQ2YssK/c18QYg19uwsPI0NMfVBBChMeBoasWVHZwoDQQgHtqxsvM0tGDF8F0wEKAyYhoG3sRFbdlb/xoIQJ9iys8A08TY1xcwHEaAw4Gs5ianrEgEJCUXggdl5QrloIwIUBjwdXfC2LImAhMQh8MAM3L+xQAQoDPhaWgD/3MWCkCgEHpgeiYASm8CqFpaM8M05LQiRRrXZsKSn4z0hApTQnBKgjBh7IggDwzJ8WPD+jQUiQGFAb21FsVo7LT4nCImBJT1dBCjR8bW2Btf4EoREwpKRgS4ClNj4WluxpEv7j5B4SASUBPgjIBEgIfGwZGTga22L2cRkIkBhQCIgIVGxpKf7V5l1nn49t0giAhQG9NY2ESAhIQlE7npbW0zyFwEaJKbPh97eLl3wQkISeHD6WkWAEhJfx5ND2oCERCRw38aqIVoEaJD4TnYMQkyXCEhIPKwdkbuvTQQoIQmOgk5Pi7EngjBwAlUwXapgiYne7r9wWpoIkJB4qHY7itUabEqIev4xyTWJ0Nv93ZdaiiPGngjCmWFJT5M2oEQlMH5CSxUBEhITzZEq44ASlaAApaTE2BNBODM0RwqG0xWTvKMmQAcPHqS4uJjCwkKKi4s5dOhQDxtd1yktLaWgoIAFCxZQVlbWw+bTTz/lvPPOY8OGDVHwun90pxPVZkOxWGLtiiCcEZrDge5K8giopKSE5cuX8+KLL7J8+XLWrFnTw2bLli1UVVWxbds2nn76aTZu3MiRI0eC3+u6TklJCQUFBdFyu18MpwvVIdUvIXFRHQ70ZI6AGhoaqKysZPHixQAsXryYyspKGrvNRVtRUcHSpUtRVZWsrCwKCgrYunVr8Pv/+7//4wtf+AKTJk2KhtshoTvb0USAhARGc6QkdwRUU1PD6NGj0TpWDdU0jZycHGpqanrY5eXlBfdzc3M5duwYAPv372fnzp1861vfiobLIaM7nSJAQkKjORwYThemaUY974RouPB6vdx111384he/CIrYmZCdHb7XJXzt7VhtGobLhSU1BU1TUFQFk1NbANPs2O/YKprq34ZiG+70NP/zRo1AuhG3Ncyg39E6V+GwVdUo+TCI9CypqZi6jmbqYPHfI2lp9rCVlb6IigDl5uZSW1uLrutomoau69TV1ZGbm9vDrrq6mlmzZgGnIqLjx49TVVXFzTffDEBLSwumadLa2sr69etD9qOhoRXDODOVt9n8wmex+Ld2BbweHV97O5bhmei6iYqJaZgoHVvAv6+Ywa2hG6iKGrTpyzYUm4GkZ+gGFk3DMMKfbqRtFQj6Ha1zNVhbVVUwouTDoPy0+3tw3a3taHY7NqCtzQ2Az6cD4PHoZ1RuVFXp88EfFQHKzs4mPz+f8vJyioqKKC8vJz8/n6xu62gtWrSIsrIyFi5cSFNTE9u3b2fTpk3k5eWxa9euoN3GjRtpb29n1apV0XC/T/R2p4wBEhKawP1ruFxo9uhEPgGi1gu2du1annzySQoLC3nyyScpLS0FYMWKFbz77rsAFBUVMW7cOBYuXMhXv/pVVq5cyfjx46Pl4hlhSBuQkOAEenFj0RMWtTagqVOn9jqu55FHHgn+r2laUJj64rbbbgurb2eKqesYHo8IkJDQaA5/FSwWPWEyEnoQBEdBO2QUtJC4BB6gsRgNLQI0CHSX/4LJQEQhkQkIUOB+jiYiQIPAcMqb8ELiE4yApAqWWJyqgokACYmLareDosSkEVoEaBAEQlZpAxISGUVVUe12DKmCJRaG2z9YS43y2AlBCDf+98FEgBIKESAhWdBSUjBc7qjnKwI0CESAhGRBtdsxPCJACYXh9gAiQELio9rswfs5qvlGPcckwnC7QdNQZTZEIcFR7XZ0t0RACYXhcUf95T1BiARaij3YpBBNRIAGge52o9ptsXZDEAaNtAElIIbbE5xLRRASGf84IBGghMLwSAQkJAeq3Y7p82HqZzbx2BnnG9XckgzD5Ua1SRuQkPgE2jINrzeq+YoADQLD40FLEQESEp/AUJJoN0SLAA0Cwy0RkJAcBJoSoj0WSARoEBhuN6pEQEISEOhMiXZPmAjQIDDcbhkFLSQFwTYgj0RACYPh8YgACUlB3FfBtm/fjs/ni6QvCYXh82H6fDISWkgK4r4R+oEHHmDevHmsW7eOffv2RdKnhEDehBeSiVNtQHEaAW3evJnHH38cu93ObbfdRmFhIQ899BBHjhyJpH9xiwiQkExogSpYvAoQwIwZM1i1ahWvvPIKJSUlbN26lQULFvD1r3+dzZs3YxhGpPyMO3RXQIBkJLSQ+MSqCjbgeSSqqqrYvHkzmzdvRlEUbr/9dnJzc9m0aRPbtm3jt7/9bST8jDtORUDyLpiQ+CiahmKxRD0CClmANm3axPPPP89nn33GF7/4Re655x5mz54d/L6wsJBLL700Ik7GI8E1wSQCEpIE1R79KTlCFqBXX32Vb3/721x99dXYbD0LncPhYOPGjWF1Lp4JXCjpBROSBdVui98I6IEHHkBVVaxWa/Azr9eLaZpBQZo3b174PYxTTkVAIkBCcqDa7PHbCP2d73yH999/v8tn77//Pt/97nfD7lQiIL1gQrKh2qIfAYUsQAcOHOC8887r8tmsWbPYv39/2J1KBHQRICHJ8FfB4nQ6jmHDhlFfX9/ls/r6ehxDdFliwyUCJCQXqs2GGa8R0MKFC/nRj37Ehx9+iNPp5MCBA6xatYovfvGLkfQvbjHcblAU1E5tYoKQyMR1FewHP/gBU6dOZenSpZx//vkUFxczefJkfvjDH4Z0/MGDBykuLqawsJDi4mIOHTrUw0bXdUpLSykoKGDBggWUlZUFv3vmmWe47rrrKCoq4rrrruOJJ54I1fWIoLtcqDYbiirv8wrJQSwEKOReMLvdTklJCWvWrOHEiROMGDECRVFCzqikpITly5dTVFTE888/z5o1a3qIyJYtW6iqqmLbtm00NTWxZMkS5s6dy7hx4ygsLOT6669HURRaW1u57rrruPjii5kxY0bovzaMyFQcQrIR1xEQwMmTJ3n33Xc5cOAAb775Jv/85z/55z//2e9xDQ0NVFZWsnjxYgAWL15MZWUljY2NXewqKipYunQpqqqSlZVFQUEBW7duBSA9PT0oeC6XC6/XOyABDDeBCEgQkgXFZsPoGFoTLUKOgJ599lnWrVtHamoqKSmnXj9QFIWXXnqpz2NramoYPXo0mqYBoGkaOTk51NTUkJWV1cUuLy8vuJ+bm8uxY8eC+y+99BL33nsvVVVV/OhHP2L69Omhug9Adnb6gOz7wnC70VJSsFhUTMVE0RQUVcHk1BbANDv2O7aKpvq3odiGOz3N/7xRI5BuxG0NM+h3tM5VOGxVNUo+DCY9TcUwFCwpdjAMTJ+PtLToRPchC9B9993H/fffzxVXXBFJf/rk6quv5uqrr6a6upqVK1cyf/58pkyZEvLxDQ2tGMaZqbvN5hdPi8W/1V1uFJsNn8/A1A1UVFRMTMNE6dgC/n3FDG4N3UBV1KBNX7ah2AwkPUM3sGgahhH+dCNtq0DQ72idq8HaqqqCESUfBpOerhuYugkWf4eK4fbQ1ubv5fX5/Mv0eDxntlyPqip9PvhDroLpun7GI51zc3Opra1F71hzSNd16urqyM3N7WFXXV0d3K+pqWHMmDE90svLy2PmzJns2LHjjPwJB9IGJCQbgSaFaM4LHbIArVixgt/97ndnNOVGdnY2+fn5lJeXA1BeXk5+fn6X6hfAokWLKCsrwzAMGhsb2b59O4WFhQB88sknQbvGxkZ27drFtGnTBuxLuNBdbmkDEpKKwP2sR/GF1JCrYI8//jj19fU8+uijZGZmdvkulEhk7dq1rF69moceeohhw4axYcMGwC9st99+OzNnzqSoqIh9+/axcOFCAFauXMn48eMBePrpp3n99dexWCyYpskNN9wQ03fPDLdLIiAhqTg1J1D0esJCFqBf/epXg8po6tSpXcb1BHjkkUeC/2uaRmlpaa/H33nnnYPKP9zoLre8CS8kFcEqWDxGQBdffHEk/Ug4/BGQVMGE5CGu24A8Hg/33XcfV199NRdccAEAO3fu5Mknn4yYc/GKaRgYblmSR0guTrUBRa8KFrIA/fznP+fDDz/k17/+dXAA4Nlnn82f/vSniDkXrwRGi8qyzEIyEVwbzBWHVbDt27ezbds2UlNTUTvefxo9ejS1tbURcy5ekbmAhGQkrqtgVqs1OI4nQGNjY48esaGAIfNBC0mIEoNu+JAFaNGiRaxatYrDhw8DUFdXx7p167j22msj5ly8IhGQkIwEppaJZjf8gKbjGDduHF/60pdoaWmhsLCQnJwcVq5cGUn/4hJD5oMWkhBFVVGs1vjshrfZbNx5553ceeedNDY2Dng6jmQiGAHJSGghyVBt1qhOyRGyAAWqXgHa2tqC/wdGKw8VTq2IIYsSCsmFarXFZwS0YMECFEXpMldIIAL64IMPwu9ZHHNqTTCJgITkQrXZ4vNdsO6rXxw/fpzf/va3XHjhhWF3Kt45VQWTNiAhuVCiPCviGU9oPGrUKH7yk59w7733htOfhEC64YVkRbVFtwo2qBnVP/30U5xOZ7h8SRikG15IVlRbnPaCLV++vEuvl9Pp5OOPPx6a3fBuN4qmoXRMMSsIyYJqs+E90RS1/EIWoKVLl3bZdzgczJgxg0mTJoXbp7jHcLtRUyT6EZKPaFfBQhagL3/5y5H0I6GQ6ViFZCVuu+Hvv//+kOzuuOOOM3YmUTDcMhmZkJwoNmtUX0YNWYA+++wztm3bxrnnnsvYsWOprq7m3XffZeHChdiHWGGUCEhIVlSbDVM3MHw+VEvI8nDGhJyDaZr87//+b3CSeIBt27axdetWfvGLX0TEuXhFIiAhWQlOyeFyoaaHbx290+YXquGrr75KQUFBl8+uuuoqXnnllbA7Fe8YLpmQXkhOVFvgjfjoVMNCFqCJEyeyadOmLp/96U9/YsKECWF3Kt6RXjAhWVGt0Z2YPuQq2N133833v/99Hn300eBMiBaLhY0bN0bSv7hElyqYkKQoUV4ZI2QBOuecc3jxxRfZt28fdXV1jBo1itmzZ2PtmMRoKOFvhJY34YXkIzgvdLxVwbpz0UUX4fV6aW9vD6c/CYHhdqNJFUxIQuK2CnbgwAH+4z/+A5vNRm1tLddccw27d+/mr3/9K7/5zW8i6WNcYRoGpkeW5BGSk2gvThhyBLR27Vpuv/12tm7diqVjfMBFF13Ev/71r4g5F48El+QRARKSkGAvWMeMDxHPL1TDjz/+mKKiIuDURGSpqam4ozhsOx44NRmZCJCQfMRtBDR27Fjee++9Lp/9+9//HnLd8DIhvZDMKJ0GIkaDkNuA7rjjDm655RaWLVuG1+vl97//PU899RTr16+PpH9xR3AuIGmEFpKQwNI80ZqWNeQI6Morr+TRRx+lsbGRiy66iKNHj7Jx40bmzZsXSf/iDqmCCclMtJ7N6H0AABfoSURBVJfmCSkC0nWdwsJCKioqWLt2bYRdim9kNkQh2dHs0ZuSI6QISNM0NE0bVIPzwYMHKS4uprCwkOLiYg4dOtTDRtd1SktLKSgoYMGCBZSVlQW/e/DBB7n22mu57rrruP7663nttdfO2JfBIBGQkOyoNnt8RUAA3/jGN/jP//xPbrnlFsaMGdNletZQ1gUrKSlh+fLlFBUV8fzzz7NmzRqeeOKJLjZbtmyhqqqKbdu20dTUxJIlS5g7dy7jxo1j1qxZfOc738HhcLB//35uuOEGdu7cSUpKdEckSwQkJDuqPY4E6Pjx44waNSrY2PzGG2/0WBusv3XBGhoaqKys5LHHHgNg8eLFrF+/nsbGRrKysoJ2FRUVLF26FFVVycrKoqCggK1bt3LTTTdx+eWXB+2mT5+OaZo0NTUxZsyYgf3iQdK5Edrsx1YQEhE1nqpggfl/9u/fz/79+7nqqquC/+/fvz+kRQlramoYPXo0Wsck7pqmkZOTQ01NTQ+7vLy84H5ubi7Hjh3rkd5zzz3HhAkToi4+cKp7UqpgQrKixVME1DnaAdi9e3fEnAmFt956i/vvv58//OEPAz42O3vwEyy1mDrgD1OtPh+KpmIqJoqmoKgKJqe2AKbZsd+xVTTVvw3FNtzpaf7njRqBdCNua5hBv6N1rsJhq6pR8mEw6WkqhqGgmCqGrvqrYG1tpKVF/iHbrwB1buuBnoIUCrm5udTW1qLrOpqmoes6dXV15Obm9rCrrq5m1qxZQM+I6O233+a//uu/eOihh5gyZcqA/WhoaMUwzqziZLP5ozf3yTZQVRSLBU+7G9WqYOoGKioqJqZhonRsAf++Yga3hm6gKmrQpi/bUGwGkp6hG1g0DcMIf7qRtlUg6He0ztVgbVVVwYiSD4NJT9cNTN1E9xkYPgPVbsd3vJ62Njc+n/+B6/HoZ1RuVFXp88HfrwDpus6bb74ZFJ7u+wBz587tM43s7Gzy8/MpLy+nqKiI8vJy8vPzu7T/ACxatIiysjIWLlxIU1MT27dvD06C9u9//5sf/OAHPPDAA3zuc5/rz+2IoXfMB91dmAUhWYjm0jz9ClB2djZ33nlncD8zM7PLvqIovPTSS/1mtHbtWlavXs1DDz3EsGHD2LBhAwArVqzg9ttvZ+bMmRQVFbFv3z4WLlwIwMqVK4M9bKWlpbhcLtasWRNM85577mH69Okh/tTwIBPSC8mOZrfHz6sYL7/8clgymjp1apdxPQEeeeSR4P+aplFaWtrr8c8880xY/BgsIkBCshPNbvhBrQ0/FPHPBy2zIQrJi2q3Yeo6ps8X+bwinkOSYbhc0gUvJDWqzX9/R+OFVBGgASJVMCHZ0aI4L7QI0AARARKSncD9LQIUh4gACcmOCFAcI43QQrKjRnFWRBGgASLLMgvJjtbxgJUIKM4wTRNDluQRkpxoLk4oAjQATI8HTFMESEhqAt3wIkBxhkxGJgwFJAKKU0SAhKFAYKCtNELHGadmQ5ReMCF5kW74OEUiIGEoEM2leUSABoAIkDBUiNYb8SJAA0CW5BGGCqrdLi+jxhumREDCEEGN0qRkIkADQKpgwlBBTUmRKli8oXc8EVSHI8aeCEJk0VJSJAKKNwynEwBNBEhIclSHI/jAjWg+Ec8hiTCcThSbDaVjgUVBSFY0hwOjvT3i+YgADQDd5ZLoRxgSqCkp6B0Rf0TziXgOSYThdMooaGFIoDkc0gYUbxgup0RAwpBAczgwfT4Mrzei+YgADQDd6ZQeMGFIELjPjQhXw0SABoAhbUDCECFwn0e6GiYCNAAMpzM4XaUgJDOBCCjSDdEiQANAd7mkCiYMCYLzQosAxQ+GUxqhhaGBKlWw+ML0+TC9XomAhCGBJlWw+CIwLF3agIShgPSCxRmBCyERkDAUONULliQCdPDgQYqLiyksLKS4uJhDhw71sNF1ndLSUgoKCliwYAFlZWXB73bu3Mn111/Pueeey4YNG6LldpBAXVjagIShgGq3g6KgO5OkDaikpITly5fz4osvsnz5ctasWdPDZsuWLVRVVbFt2zaefvppNm7cyJEjRwAYP348P/vZz/jud78bLZe7oEsEJAwhFFX1zwmUDFWwhoYGKisrWbx4MQCLFy+msrKSxsbGLnYVFRUsXboUVVXJysqioKCArVu3AjBx4kTy8/OxWCzRcLkHEgEJQw3N4UBPhipYTU0No0ePRuuYxkLTNHJycqipqelhl5eXF9zPzc3l2LFj0XCxX6QNSBhqqA5HxCOg2IQTMSI7O/2Mj61VdAAysoZhT7Pja2/HatNQNBVTMVE0BUVVMDm1BTDNjv2OraKp/m0otuFOT/M/b9QIpBtxW8MM+h2tcxUOW1WNkg+DSU9TMQwFxVQxdP89kpZmx5qaiur1MHx46hmXm/6IigDl5uZSW1uLrutomoau69TV1ZGbm9vDrrq6mlmzZgE9I6LB0tDQimGYZ3Rsa/0JANyKFV+bG7sCXo+OalUwdQMVFRUT0zBROraAf18xg1tDN1AVNWjTl20oNgNJz9ANLJqGYYQ/3UjbKhD0O1rnarC2qqpgRMmHwaSn6wambqL7DAyfgQ1oa3OjpKTgbjlJc3M7Ho9+RuVGVZU+H/xRqYJlZ2eTn59PeXk5AOXl5eTn55OVldXFbtGiRZSVlWEYBo2NjWzfvp3CwsJouNgvvrY20DSZD0gYMmjp6ehtbRHNI2q9YGvXruXJJ5+ksLCQJ598ktLSUgBWrFjBu+++C0BRURHjxo1j4cKFfPWrX2XlypWMHz8egD179jB//nwee+wxnnrqKebPn89rr70WLffRW9vQUlNRFCVqeQpCLLFEQYCi1gY0derULuN6AjzyyCPB/zVNCwpTdy688EJeffXViPnXH3pbG1r6mbchCUKioaWlobe1YRpGxPKQkdAhore1oqWlxdoNQYgaWloamGZEe8JEgEJEb2sXARKGFIGIX2+PXDVMBChE/BGQVMGEoYMlIECtIkAxR29rQ0uL3HgIQYg3AhG/3tYasTxEgELANAyM9naJgIQhRbAK1ha5BQpFgEIgsEKktAEJQwmLREDxQWAshJYuAiQMHQIPXJ+0AcWWoACligAJQwfVZkOx2TAiOBhRBCgEjI5uSItEQMIQQ0tLwydVsNgSiIBUiYCEIUak3wcTAQqBQCOcRV7FEIYYWloaeqtEQDFFb24GRUHLyIi1K4IQVSzDhuNrbo5Y+iJAIeBrbsIyfDiKKqdLGFpYMzPxNTVFLH0pUSHga2rGkpkZazcEIepYMjMxXK6IrZAqAhQCenMT1uEiQMLQI/Dg9TVHJgoSAQoBX3OTREDCkMQaFKDItAOJAPWDqevoJ0+KAAlDksB9r0eoHUgEqB/0ky1gmlgyh8faFUGIOsEqmAhQbPA1+UNPq0RAwhBES0tDsVqlDShWBE68VMGEoYiiKFiGDxcBihWnBGhEjD0RhNhgGR65sUAiQP3gPX4cNE2qYMKQxTYyG199fUTSFgHqB++xY9hG5aB0rGsvCEMNW24u3oZ6DK8n7GmLAPWD51gN1jFjYu2GIMQMe24emCbeurqwpy0C1AemruOpq8U2Jrd/Y0FIUuy5/vvfU1MT9rRFgPrAW18Pui4CJAxpbB01AM8xEaCo4qn1n3CbVMGEIYyWkoJlRBae2mNhT1sEqA88R6sBJAIShjy2Mbl4jh4Ne7oiQH3QfmA/1jFjgusjCcJQJeWss3Afrgr7Ms0iQKfB9PlwfnSA1PxzYu2KIMSc1PxzwDRxHtgf1nRFgE6D6+CnmG43qTNEgATBMWUqis1GW2VlWNMVAToNJ/+1B1SV1OkzYu2KIMQcxWIhdfoM2t55G9PnC1u6UROggwcPUlxcTGFhIcXFxRw6dKiHja7rlJaWUlBQwIIFCygrKwvpu3Dja2mh+dUdDLtkrrT/CEIHmVcV4DvRSMsbr4ctzagJUElJCcuXL+fFF19k+fLlrFmzpofNli1bqKqqYtu2bTz99NNs3LiRI0eO9PtdODG8HmofexTT6yXr2sVhT18QEpXUc2dinzSZ+r/+BffR8JS9qAhQQ0MDlZWVLF7sL9CLFy+msrKSxsbGLnYVFRUsXboUVVXJysqioKCArVu39vtdqKiq0uef58hhqv93A77aGvJuvoWUvLzgd4oS+ANFARQFRVNR1FN/qEqXbV9/odiGOz3/95FJN9K29GMbL3523UbPh0Glp2n+P4sF1Wr1+91xnwfue1VV0DSVcf9xK/acHGoffpCWHS/1W6ZUVemzTFoGVILPkJqaGkaPHo3W8UKnpmnk5ORQU1NDVlZWF7u8vLzgfm5uLseOHev3u1AZMaKflU2zz2HsnHtCTs/icAwof0FIeLLTGXNv6GWkP6QRWhCEmBEVAcrNzaW2thZd1wF/g3JdXR25ubk97Kqrq4P7NTU1jOl4DaKv7wRBSEyiIkDZ2dnk5+dTXl4OQHl5Ofn5+V2qXwCLFi2irKwMwzBobGxk+/btFBYW9vudIAiJiWKaphmNjD755BNWr15NS0sLw4YNY8OGDUyZMoUVK1Zw++23M3PmTHRdZ926dbz+ur+bb8WKFRQXFwP0+Z0gCIlJ1ARIEAShO9IILQhCzBABEgQhZogACYIQM0SABEGIGVEZCZ3IbNiwgRdffJGjR4+yZcsWpk2bBvhfrl29ejVNTU1kZmayYcMGJk2aFFtnO3E6v6+66ipsNht2ux2AH//4x1x++eWxdLULJ06c4L//+7+pqqrCZrMxceJE1q1bR1ZWFu+88w5r1qzB7XYzduxYfvWrX5GdnR1rl/v0efr06UybNg1V9T/r77nnHqZPnx5jj09x6623cuTIEVRVJTU1lbvuuov8/Pzo3d+m0Ce7d+82q6urzSuvvNI8cOBA8PMbb7zRfO6550zTNM3nnnvOvPHGG2PlYq+czu/u+/HGiRMnzDfffDO4/8tf/tL8n//5H1PXdbOgoMDcvXu3aZqm+eCDD5qrV6+OlZtdOJ3Ppmma06ZNM1tbW2PlWr+0tLQE///73/9uLlmyxDTN6N3fUgXrhwsvvLDHiO1QX66NJb35nQhkZmZyySWXBPdnz55NdXU17733Hna7nQsvvBCAZcuWDfhl5EhxOp8TgYyMjOD/ra2tKIoS1ftbqmBnQKgv18YrP/7xjzFNkwsuuIAf/vCHDBs2LNYu9YphGPzpT3/iqquu6vEyclZWFoZhBKsI8UJnnwPceOON6LrO/Pnzue2227DZbDH0sCc/+clPeP311zFNk0cffTSq97dEQEOMTZs2sXnzZp555hlM02TdunWxdum0rF+/ntTUVG644YZYuxIy3X3esWMHzz77LJs2beLjjz/mwQcfjLGHPfnZz37Gjh07+MEPfsA994TvTfdQEAE6A0J9uTYeCfhos9lYvnw5e/fujbFHvbNhwwY+++wzfvOb36Cqao+XkRsbG1FVNa6in+4+w6nznZ6eztKlS+P2fAMsWbKEXbt2MWbMmKjd3yJAZ0CoL9fGG+3t7Zw8eRIA0zSpqKggPz8/xl715N577+W9997jwQcfDFZXzj33XFwuF3v27AHgqaeeYtGiRbF0swu9+dzc3IzL5QLA5/Px4osvxtX5bmtro6bTcssvv/wyw4cPj+r9Le+C9cPdd9/Ntm3bqK+vZ8SIEWRmZvLCCy+c9uXaeKE3vx9++GFuu+02dF3HMAymTp3KT3/6U3JycmLtbpCPPvqIxYsXM2nSJFJSUgAYN24cDz74IHv37qWkpKRLN/zIkSNj7PHpfb7ppptYs2YNiqLg8/mYM2cOd955J2lp/UyMFyXq6+u59dZbcTqdqKrK8OHDWbVqFZ/73Oeidn+LAAmCEDOkCiYIQswQARIEIWaIAAmCEDNEgARBiBkiQIIgxAwRoCHGmjVruozG/eMf/8ill17KnDlzOHHiRAw9653q6mrmzJkTHBQ3WDr//l27djF//vywpAuwZ88eWShhoETkFVchYkybNs08dOhQl88eeOAB80c/+tGA0/J4PObMmTPNDz74IFzuDYhnnnnGnDFjhjl79mxz9uzZ5pVXXmmuXr3a/PTTT88orWXLlg3omDfffNO8/PLLB5xXgN6uhTAwJAIawjQ0NOB2uznrrLMGfKxpmhiGMWgfZs+ezdtvv82ePXt4/PHHsdvtXH/99Xz44YeDTrs74YqihPAhApRkBKoVf/jDH5g7dy7z5s3jmWeeCX6/evVq7rvvPg4ePBh8leGiiy7iG9/4BgB79+7lK1/5ChdccAFf+cpXury7dOONN3LfffexbNkyzjvvPA4fPsz06dPZtGkTCxcuZM6cOfzmN7+hqqqKZcuWcf7553PHHXfg8Xj69VvTNCZMmMDatWu5+OKL+e1vfwvAkSNHmD59Oj6fD4Bnn32Wq6++mjlz5nDVVVexefNmPvnkE0pKSnjnnXeYM2dOcMqO1atXU1JSwooVK5g9eza7du0K/v7OPPzww1xyySXB9Dr/3rKysuD+s88+y9e+9jUAvv71rwNQVFTEnDlzqKio6FGl++STT7jxxhu58MILufbaa3nppZe6XIfS0lJuvvlm5syZw9KlS6mqqur3PCUbIkBJSH19PSdPnuTVV1/lZz/7GevWraO5ubmLzeTJk4Pv+uzevZsnnniCpqYmbrnlFm688UZ27drFt7/9bW655ZYubUPPP/8869evZ+/evcHpMXbu3Mmzzz7Ln//8Zx599FHuuusufvWrX/HKK6/w0Ucf8cILLwzI/wULFgTf+epMe3s7d999N4888ghvv/02Tz31FPn5+UydOpXS0tIu0VSA8vJyvve977F3714uuOCCXs/ViRMneO211/jlL3/JmjVr+PTTT/v1cdOmTcHz8fbbb3PNNdd0+d7r9fK9732Pyy67jDfeeIOf/vSn/PjHP+6SdkVFBd///vfZvXs3EyZM6CGMQwERoCTEYrGwcuVKrFYrV1xxBampqRw8eLDf43bs2MHEiRNZsmQJFouFxYsXM2XKFP7xj38Ebb785S9z9tlnY7FYsFqtANx0002kp6dz9tlnM23aNC677DLGjx9PRkYG8+fPp7KyckD+5+Tk9BDMAKqq8tFHH+FyucjJyeHss8/uM62rr76aCy64AFVVg9PQdueOO+7AZrNx8cUXc8UVV/C3v/1tQP72xr59+2hvb+fmm2/GZrMxd+5crrzyyi5iXFBQwKxZs7BYLHzpS1/igw8+GHS+iYYIUIKhaVqwOhLA5/MFxQD8M/RZLKfmmnM4HLS3t/ebdl1dXZdJvwDy8vKora0N7vc2JUPnF0LtdnuP/VDy7kxtbS3Dhw/v8Xlqair33XcfTz31FPPmzePmm2/mk08+6TOt/qaQGDZsGKmpqcH9vLw86urqBuRvb9TV1TFmzJjgtByBtDufy87nKSUlZcDnKRkQAUowcnNzOXLkSJfPjhw50kM4zoScnJweU4kGZscLoCjKoPPpj+3btwfbcbpz+eWX89hjj7Fz506mTJnCXXfdNSi/WlpauhT8mpqa4OwADocDp9MZ/K6+vj7kdHNycjh27FiXhvru51IQAUo4rrnmGn73u98Fb+433niDl19+OSzjT6644goOHTrEli1b8Pl8VFRU8PHHH/OFL3xh8I73g67rHD58mPXr1/PWW2+xcuXKHjb19fVs376d9vZ2bDYbqampwQgjOzub2trakBq8u7Nx40Y8Hg979uxhx44dwcb5/Px8/v73v+N0Ovnss8/4y1/+0uW4kSNHcvjw4V7TnDVrFikpKTz66KN4vV527drFyy+/3KOtaKgjc0InGCtXruT+++9n+fLlNDc3M2HCBH79618Hl90ZDCNGjODhhx/m5z//OWvXrmXixIk8/PDDEZ1oLdBzZZomI0aM4OKLL+Yvf/kLU6dO7WFrGAaPP/44q1atQlEU8vPzWbt2LQCf//znOeuss5g3bx6KorBr166Q8h85ciTDhg3j8ssvx+FwsHbt2mDe3/zmN3n33Xe59NJLmT59Otdddx1vvPFG8Njvf//7rF69GpfLxbp167osEWSz2Xj44YcpLS3l97//PaNHj+aee+7p9XcNZWQ+IEEQYoZUwQRBiBkiQIIgxAwRIEEQYoYIkCAIMUMESBCEmCECJAhCzBABEgQhZogACYIQM0SABEGIGf8ftzUZtJibZ0AAAAAASUVORK5CYII=\n",
+            "text/plain": [
+              "<Figure size 288x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RVu74ejzSI1D"
+      },
+      "source": [
+        "##Normal Distribution\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ec-8HLH6SOef"
+      },
+      "source": [
+        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)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KGVagX-TSMiM"
+      },
+      "source": [
+        "![Untitled03.png](data:image/png;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Vlua-8VISSWn"
+      },
+      "source": [
+        "from scipy.stats import norm\n",
+        "# generate random numbers from N(0,1)\n",
+        "data_normal = norm.rvs(size=10000,loc=0,scale=1)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 357
+        },
+        "id": "7SWz-mMrSVln",
+        "outputId": "1c8eb382-81dd-4736-9023-d86ee45b7b4b"
+      },
+      "source": [
+        "ax = sns.distplot(data_normal,\n",
+        "                  bins=100,\n",
+        "                  kde=True,\n",
+        "                  color='r',\n",
+        "                  hist_kws={\"linewidth\": 9,'alpha':0.3})\n",
+        "ax.set(xlabel='Normal Distribution', ylabel='Frequency')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+            "  warnings.warn(msg, FutureWarning)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Normal Distribution')]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 288x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jqW6ZShBUbg_"
+      },
+      "source": [
+        "##Exponential Distribution"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hZ4N-l5jUfjE"
+      },
+      "source": [
+        "![image.png](data:image/png;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZLFaMzdHUlQB"
+      },
+      "source": [
+        "![image.png](data:image/png;base64,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)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "TsMVfgEDUa-E"
+      },
+      "source": [
+        "from scipy.stats import expon\n",
+        "data_expon = expon.rvs(scale=1,loc=0,size=1000)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 357
+        },
+        "id": "wfg3ZxRrUqyX",
+        "outputId": "38d64dd7-0c54-456f-96b0-89bd82d3349b"
+      },
+      "source": [
+        "ax = sns.distplot(data_expon,\n",
+        "                  kde=True,\n",
+        "                  bins=100,\n",
+        "                  color='r',\n",
+        "                  hist_kws={\"linewidth\": 9,'alpha':0.3})\n",
+        "ax.set(xlabel='Exponential Distribution', ylabel='Frequency')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
+            "  warnings.warn(msg, FutureWarning)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[Text(0, 0.5, 'Frequency'), Text(0.5, 0, 'Exponential Distribution')]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 288x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yIa4XKZoS87f"
+      },
+      "source": [
+        "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMwAAACWCAYAAACSN2djAAAOd0lEQVR4nO2dX2gc1RfHv2PT3U3FmKChpCa7KlVbsEUTyFZ9sFgQpT+MpUg1/acPFUHQvAgRqcljUR/qg/ik8aGVKLQm0iiirZUSTISNUWJirUh3LclaK2ajJZt08fwepjM7u0kmO7M7O/feOR8ImZ3ZvXs2me+ec+89516NiAgMw5TEDX4bwDAywYJhGAewYBjGASwYhnEAC4ZhHFDjZeOXLl1CS0uLl2/BMJ6w0uCxp4Kpq6tDJpNBXV2dl2/DMBVlbm5uxWsckjGMA1gwDOMAFgzDOIAFA2BoCNC0/E847LdFjKh42umXAU1bem5xUT+fTALRaPVtYsQl0B5mObFYicWAsbHq2MLIga1g4vE4AODIkSMrPmfjxo0AgK6urgqa5T3FYkkmASL9t5W2turZxIiPZpfer2kaiAjbtm3DyMjIqo2dOXMGjzzyiPlaAELOw4yNFQphudCrWFBcBBEc5ubmVrxnbT1Me3s7AODJJ59c9npvb2/B47Clt0xEyGQyjgz1g0hk+X5KJFJ9WxjxsfUw5WLMmIrmYazew+7T19YC2ax+nEgAra3e2sWIgWsPoyLWTnwiYf/c+fn8cVsbkEp5YxMjD4ETjNF3OXasNI9h9UBXrnhjEyMPgRKM1UNs3uz89TxixgRKMLFY/thJf8QaunFYFmwCJRi33Hpr/tgqOiZ4BEYw1s5+8eTkanB6DGMQGMFYcSMAq8g4XSa4BEIwqVS+w37qlLs22MswQEAEYx0Obmpy347R+W9rWz1xk1GTQAjG2mm3HjuFZ/oZ5QWTSuVHtipd38JDzMFDecFYw7FKzNRb52R45j94rCqYSFHa7u23324ea5qGTZs2AQBefPFF7N69u8LmlU+lwrHl2piZKb89Ri5ss5VramqQy+XQ0dGBwcHBJdc3bNiAkZERRKNRzM7Oor6+3rw2NzdnZn36ma1camay320y4uBZtvL09PSKXsVvoTCMF9gugpHL5RAOh7GwsIDt27fj7NmzZiUlESEcDmNychIA8Nprr2FmZgYnT5703uoSSaX0rOT6emDLlsq1S5T3MprGXiZIKF1A5mXoxGGZunABmQcYYyFcyhwslF6XLJnUh34rMTpWzPy87mWyWQ7LgoSygrGuDMM3M1MpOCQrA2sGM8/6BwNlPUxr6+qLXJSLNc3myhXOaA4CSgrGmj9WrXDMi34SIx5KhmTVzPEywjIuXQ4GSgqmmnACZrBQMiRrbeVQjPEGJT2MsTFSNWrvo9H84AJXYaqPkoJhGK8oqx7mpptuwvnz5wHotTGaIF+xiUR1Fw9vbQVCIf24trY678n4w6obKmWz2YINlS5evGge//PPP2YB2csvvwwP8zhLxphArHbfYnFR/22s9s+oSdkbKl26dAnNzc0AxNhQya8sYs5eVgfX2cr9/f2YmprC4OAgDhw4AAAYHx/H+Pg4AOCbb75BTY0+0JZIJHDo0CHztbJsqFQprCLhsExdlK6HqTbsZdTAzsMoNQ8jyg3LNTLqwsPKFcSYj8lmef1lVVFKMFwFyXiNUiGZdU9KP6j0GmiMeCjjYYx0GD/nTq1pMrEYh2UqooxgGKYaKBOSGQte+I1R6Tk15bcljBcoIxhDLKJsSbFvn/6b52PUQomQzFghpq2N+w2MtyjjYUSitVXfGnB2Vk8G5cUx1EGZ1BjDs4gSkomSdcA4R/nUGOPmjET8n4th1Ma2D/PRRx9hcnISly9fNs/19/ebx319fWhoaAAARKNRPPDAAx6ZWRoi1aJYvQov8qcOth7m6aefBhEhHo9jdHTUPAfoe8M899xzmLo+fpoquiusGypVC1FTYniRP3Ww9TDt7e0AgF27di25tmHDBgDAhx9+CAA4ceIEfvnlF/N6NTdUItJ/RA3HOE1GHWwFMzo6inA4jO7ubmzfvh1AYe1+LBbDiRMnAOjVlt3d3R6buxQjHUbEsMcIy2IxMe1jnCP9KJnR4a/mohdOEN0+ZimB2FBJ9LBHdPuY0pBeMEb/RdROtTV7mZEfqedheHKQqTbSexjR4UX+1EJqwchSksyL/KmD1CGZqPMujLpI62HGxvwvSS4VXuRPHaQVjKxwWCY30oZk1dj01QtCIa6RkRlpPYxo9S+rYYRli4s8JyMz0gqGS5IZP5BWMDJy6lT+mJMx5WTVDZUAFGyoBADbtm0zj3fs2AEAOHr0aKVts6Xau4xVgqam/LEIS0IxzrEVzHfffQcAGBgYKDi/f/9+8/j06dMAgK6uLrz//vvmeaOAzAs0TQ/HZEtotNo7M+OfHUwZkA39/f00OTlJ6XSa9u/fT0REb731Fq1du5aIiNatW2c+d3x8nBobGwten8lkKJPJ2L2FK4yUy0Si4k17TiKRt58RE7t7Vsp6GGOyMpmUb3jWWEMN4IRRUVGuHkb0lH47jDXLADmyFJhCpBNMba1+o8mcYmLt/DNyIZ1gjNQSmVNMrJ1/mYUfRKQTjCwp/XZYQ0mZhR9EpMolS6WA4WH9WKb5l+WIRHSxGMVljBxIJRhrDpbsI0zz83pfbHFRHzmT/QsgKEgXkqkIb74kD1IJJpnMp8SowLFj+m9j8yVGfKQJyVKpfEiWTPprS6XYvNlvCxinSONhrMmKqiQutrbmvYymcamCDEgjGOvchWxJl3awl5ELKXLJisMxGVNi7OAFCcXCdS7ZchsqvfnmmwhdnzxIp9P4+++/AQD33Xcf7rrrrkrZXICK4dhKcGGZ2Nh2+vfu3YtcLoeOjg4MDg4CAF555RX8+++/AICtW7eaGyqNj48jl8uZr/VjQyVZMSYxAd2TspcRl7JGyS5fvozGxkb8+eefS65VWiiyVVc6wZjEZMTHNiTL5XIIh8MYHBw0N1S65557EI/HMT8/j1AohImJCQBAZ2cnnn/+eU+MDMKCF8ZoGaD255SdVT3MwsICAODs2bMAgPPnz5vXFo1Fg5Hfuo9xh3W0bGpKXW8qO8KPkqVSekf/1lvVGx0rxlqNqXIIKjp2fW/hBRO0IdegfV4RUa5EWWWsfRlGPITOJUul8vXvW7b4a0u1sPZlNI29jGgILRiV6l9KhfstYsMhmYBYs7F5iFkshBWM9UYJincxsI4GtrXxpKZICCuYoFP8JcE5ZmIgrGBUSuGvBKonncqCsIIxOvyqlCO7wfrZjQlNxl+EFQyzdMSM+zL+s6pgIkUr5m3atAmfffYZrl27hhtvvBHT09MAAE3ToFXoP2ptJujDrMlk4WQmr5TpL7aCqampQTabRUdHh3nu559/xujoKNauXYurV6+iubkZALB+/Xq88cYb3lobQKJRYO/e/ONslgcA/KSsictdu3bhv//+A6BXX/7000/mNbcFZIZ3CYWACxfKsU4tuMhMDFzXw1y8eBEDAwNmGFZfX49PP/3UfG1dXV1ZSZeLi+pnJzthfr7wMfdn/EGobOXa2sLFuflbtBBr+j+g5oIgIiBNtjKLxZ7W1sKh5liM+zPVRhjB8D++NIpHDVk01UUYwZw7lz8O8mRlKRR731gMOH7cH1uChjB9GGsnlmPz1bEubmgQiSwdHGCcI3wfxiqWRILFUgrRqO5prJOa2Sx7Gq/xXTDW+PvYMZ7Zd4p1UhPQt85g0XiH74KxhhW8MLc7ioPqfft4NwCv8FUwxf9Q9i7uKQ7PgHzx2dCQPzapiG+d/uJJOJ53qQzLDQYYhELA9XUZGRuE7/Tz0kKVwxgMMFbbsbK4qHscTQPefpvnb9zg67CyEZJxKOYt4bAuFjuOHgU2bgR27qyOTSIj9cqXTGUYGgL+9z9nrzl6VC8V37w5WF9qnm2oNDAwgDvvvBOAtxsqMeWzc6ceqiWTuhBKoatLH3EzBg9U+3EF2WBcbm9vLzj/zjvv0PDwMBERPfXUUyu+FgBlMhm7t2B8JpkkOnWKqKeHSJdUcH5Wwu6etQ3J4vE4RkdHceTIEXR3d5vnH3zwQQwPD0PTNEQiEWSzWSQSCfz111949NFHzedxSCY/qZSe51e8ak1Xlz/2VJKV7nzuwzCMA4QfVmYYWWDBMIwDWDAM4wBPt7uYm5tDS0uLl2/BMJ6wUtfe005/KbhZiqkabXF74rQlUnu+C4ZhZIL7MAzjgKoLZrV0G6d88MEH2FmUMZhOp/HHH3+4au/ee+9FOp02H3/77beYmprCxMSEq/bS6TS++uqrAnut7ZfK559/XrBoonX5XgDo6+tDQ0ODq/Y+/vhjxOPxJXZfcbDHhrW9xx9/fMlndGrfyZMnMTAwYNpyww35W7W3t9fR3/Cxxx7DDz/8gNj1uoczZ87goYceMq/X19fjxx9/RNa6ztdKVC7JojTWrFlDRERPPPFEwfnXX3/ddZsXLlwoeByNRmliYsJVW+vWraPOzk7zcUtLCxERbd261bV9t9xyi3nc19dHzc3Nrtox/l3ff/99wfmFhQUiIjpw4ICr9oiIvvzyy4JrTU1NlEwmXbV388030wsvvFC2fZ988ol5fPz4cfO4p6eH7rjjDkdtGfadPn2aiPR7xMD4e7700kurtqFESLZmzZqCx8lkEr29va7aunr1Ko5XuCj+vffeM4+fffZZ/P7773jmmWcq+h7lcO3aNdx///0F56anp7F7925X7c3OzuLdd9+thGkAgHPnzqGzs9N83Nvbi99++w2vvvpqyW20tLSsOPLlCMcyrQChUIiIiB5++GEiIrr77rtpaGjIVVu4nuRpbS8UCtHY2Jir9pqammjPnj1ERHTw4MECe93wxRdfFDzu6uqi9evXO27n66+/JgB08OBBOnz4MO3YsYOIiGKxGBHp35gjIyOu2rP+DXt6eohI/8y//vqrq/YaGxvp0KFDBe05tc9qU21tbcG1PXv20G233ea4LaO9hoYGmpmZMa8fPnyYwuFwSW3xKBnDOECJkIxhqgULhmEcwIJhGAewYBjGASwYhnHA/wE5xy7Zn2TVzAAAAABJRU5ErkJggg==)"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___1_1_An_Introduction_To_Data.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___1_1_An_Introduction_To_Data.ipynb"
new file mode 100644
index 0000000..e939eed
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___1_1_An_Introduction_To_Data.ipynb"
@@ -0,0 +1,302 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📊🧠 **Data Mastery: First Steps in Data Handling**\n",
+        "*Course: AI/DS Nexus by Reza Shokrzad*\n",
+        "\n",
+        "Welcome to your **first hands-on notebook** for mastering data across modalities! This notebook walks you through:\n",
+        "- 🧾 Reading and exploring tabular `.csv` files\n",
+        "- 📚 Working with raw and structured **text data**\n",
+        "- 🖼️ Loading and transforming **image data**\n",
+        "- 🔊 Processing **audio signals**\n",
+        "- 🌐 Using real datasets and corpora from popular libraries like **NLTK**, **TorchVision**, and **Librosa**\n",
+        "\n",
+        "Let's get started on your journey to become a data-savvy practitioner! 🚀"
+      ],
+      "metadata": {
+        "id": "_Ejac9Wq-f8r"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🧾 Section 1: Working with Tabular Data (.csv)\n"
+      ],
+      "metadata": {
+        "id": "73YF-yC2-nK_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kYIadk-U-EJ2"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "# 1. Read the CSV file\n",
+        "df = pd.read_csv(\"your_file.csv\")\n",
+        "\n",
+        "# 2. Show the first 5 rows\n",
+        "print(df.head())\n",
+        "\n",
+        "# 3. Check the shape (rows, columns)\n",
+        "print(\"Shape:\", df.shape)\n",
+        "\n",
+        "# 4. Show column names\n",
+        "print(\"Columns:\", df.columns.tolist())\n",
+        "\n",
+        "# 5. Check data types of each column\n",
+        "print(df.dtypes)\n",
+        "\n",
+        "# 6. Get basic summary statistics\n",
+        "print(df.describe())\n",
+        "\n",
+        "# 7. Check for missing values\n",
+        "print(df.isnull().sum())\n",
+        "\n",
+        "# 8. See unique values in a specific column (e.g., \"Category\")\n",
+        "print(df[\"Category\"].unique())  # Change \"Category\" to your actual column name\n",
+        "\n",
+        "# 9. Quick info summary\n",
+        "df.info()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 📚 Section 2: Working with Text and Strings\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "aEUJxrxM-sL0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "text = \"Hello, Python learners!\"\n",
+        "print(text)\n",
+        "print(type(text))  # <class 'str'>\n",
+        "print(len(text))  # 23\n",
+        "print(text[0])     # H\n",
+        "print(text[-1])    # !\n",
+        "print(text[0:5])   # Hello\n",
+        "print(text.lower())\n",
+        "print(text.upper())\n",
+        "print(text.split())\n",
+        "print(text.replace(\"Python\", \"World\"))\n",
+        "\n",
+        "name = \"Reza\"\n",
+        "message = f\"Welcome, {name}!\"\n",
+        "print(message)\n",
+        "\n",
+        "paragraph = \"\"\"Data science is fun. Python makes it easier. Let's learn together!\"\"\"\n",
+        "sentences = paragraph.split('. ')\n",
+        "print(\"Number of sentences:\", len(sentences))\n",
+        "words = paragraph.split()\n",
+        "print(\"Number of words:\", len(words))\n",
+        "print(\"Unique words:\", set(words))\n"
+      ],
+      "metadata": {
+        "id": "4FyD-TX1-qYZ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 📖 Section 3: Using NLTK for Text Corpus\n"
+      ],
+      "metadata": {
+        "id": "phEcW66v-wr1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# !pip install nltk\n",
+        "import nltk\n",
+        "nltk.download('gutenberg')\n",
+        "nltk.download('punkt')\n",
+        "\n",
+        "from nltk.corpus import gutenberg\n",
+        "print(gutenberg.fileids())\n",
+        "text = gutenberg.raw('carroll-alice.txt')\n",
+        "print(text[:500])\n",
+        "\n",
+        "from nltk.tokenize import word_tokenize\n",
+        "tokens = word_tokenize(text)\n",
+        "print(\"Number of words:\", len(tokens))\n",
+        "\n",
+        "from nltk.probability import FreqDist\n",
+        "fdist = FreqDist(tokens)\n",
+        "print(fdist.most_common(10))\n"
+      ],
+      "metadata": {
+        "id": "Nu-gCi29-y2S"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🖼️ Section 4: Image Data Loading & Display\n"
+      ],
+      "metadata": {
+        "id": "pjfCnLe6-0av"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from PIL import Image\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "\n",
+        "image = Image.open(\"your_image.jpg\")\n",
+        "plt.imshow(image)\n",
+        "plt.axis('off')\n",
+        "plt.title(\"Loaded Image\")\n",
+        "plt.show()\n",
+        "\n",
+        "image_array = np.array(image)\n",
+        "print(\"Image shape:\", image_array.shape)\n"
+      ],
+      "metadata": {
+        "id": "PZKIUPcQ-zPl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 📦 Section 5: TorchVision + CIFAR-10 Images\n"
+      ],
+      "metadata": {
+        "id": "JBA1E3YF-3o6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# !pip install torchvision\n",
+        "import torchvision\n",
+        "import torchvision.transforms as T\n",
+        "\n",
+        "transform = T.ToTensor()\n",
+        "trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n",
+        "\n",
+        "image, label = trainset[0]\n",
+        "print(\"Label:\", label)\n",
+        "print(\"Image shape:\", image.shape)\n",
+        "\n",
+        "plt.imshow(image.permute(1, 2, 0))\n",
+        "plt.title(f\"Label: {label}\")\n",
+        "plt.axis('off')\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"Classes:\", trainset.classes)\n"
+      ],
+      "metadata": {
+        "id": "3KU6f9gF-2ky"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🔊 Section 6: Audio Data with Librosa"
+      ],
+      "metadata": {
+        "id": "zD_nGgHd-7Sb"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# !pip install librosa\n",
+        "import librosa\n",
+        "import librosa.display\n",
+        "\n",
+        "audio_path = \"your_audio.wav\"\n",
+        "y, sr = librosa.load(audio_path)\n",
+        "print(\"Audio signal shape:\", y.shape)\n",
+        "print(\"Sampling rate:\", sr)\n",
+        "print(\"Duration (s):\", librosa.get_duration(y=y, sr=sr))\n",
+        "\n",
+        "plt.figure(figsize=(10, 4))\n",
+        "librosa.display.waveshow(y, sr=sr)\n",
+        "plt.title(\"Waveform\")\n",
+        "plt.xlabel(\"Time (s)\")\n",
+        "plt.ylabel(\"Amplitude\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "_bQSXN9f-54m"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🎙️ Section 7: Audio with TorchAudio"
+      ],
+      "metadata": {
+        "id": "HPSZC3rk--Er"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# !pip install torchaudio\n",
+        "import torchaudio\n",
+        "import torchaudio.transforms as T\n",
+        "\n",
+        "dataset = torchaudio.datasets.SPEECHCOMMANDS(\"./data\", download=True)\n",
+        "waveform, sample_rate, label, *_ = dataset[0]\n",
+        "\n",
+        "print(\"Label:\", label)\n",
+        "print(\"Waveform shape:\", waveform.shape)\n",
+        "print(\"Sample rate:\", sample_rate)\n",
+        "\n",
+        "plt.figure(figsize=(10, 3))\n",
+        "plt.plot(waveform.t().numpy())\n",
+        "plt.title(f\"Label: {label}\")\n",
+        "plt.xlabel(\"Time\")\n",
+        "plt.ylabel(\"Amplitude\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "SXGN3ukb-9AB"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___1_2_Dataset.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___1_2_Dataset.ipynb"
new file mode 100644
index 0000000..4d4db19
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___1_2_Dataset.ipynb"
@@ -0,0 +1,319 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🏷️ ML Benchmark Hubs: Quick Data Loading & First-Look EDA\n",
+        "* Sklearn\n",
+        "* Hugging Face\n",
+        "* Kaggle\n",
+        "* UCI\n",
+        "* OpenML"
+      ],
+      "metadata": {
+        "id": "mWQeSF25i0vW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ht_LNxDHL2hu"
+      },
+      "outputs": [],
+      "source": [
+        "# Core\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1) Sklearn datasets 🌿\n",
+        "* Load: Iris\n",
+        "* Fetch: California Housing\n",
+        "* Synthetic: “Moons”"
+      ],
+      "metadata": {
+        "id": "UppSx7DnjEwY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.datasets import load_iris, fetch_california_housing, make_moons\n",
+        "\n",
+        "# --- Iris (toy, tabular) ---\n",
+        "iris = load_iris()\n",
+        "X_iris = iris.data\n",
+        "y_iris = iris.target\n",
+        "df_iris = pd.DataFrame(X_iris, columns=iris.feature_names)\n",
+        "df_iris[\"target\"] = y_iris\n",
+        "\n",
+        "print(\"Iris shape:\", df_iris.shape)\n",
+        "display(df_iris.head())\n",
+        "print(df_iris.isna().sum().to_dict())\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "FwXktkhajC1q"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# --- California Housing (realistic, tabular) ---\n",
+        "cal = fetch_california_housing()\n",
+        "df_cal = pd.DataFrame(cal.data, columns=cal.feature_names)\n",
+        "df_cal[\"MedHouseVal\"] = cal.target\n",
+        "\n",
+        "print(\"\\nCalifornia shape:\", df_cal.shape)\n",
+        "display(df_cal.head())\n",
+        "print(df_cal.isna().sum().to_dict())\n"
+      ],
+      "metadata": {
+        "id": "3niAGQgcjQMw"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# --- Synthetic Moons (controlled patterns) ---\n",
+        "X_moon, y_moon = make_moons(n_samples=400, noise=0.15)\n",
+        "df_moon = pd.DataFrame(X_moon, columns=[\"x1\",\"x2\"])\n",
+        "df_moon[\"label\"] = y_moon\n",
+        "print(\"\\nMoons shape:\", df_moon.shape)\n",
+        "display(df_moon.head())\n",
+        "\n",
+        "# Quick visuals (optional)\n",
+        "plt.figure(figsize=(4,3))\n",
+        "plt.scatter(df_moon[\"x1\"], df_moon[\"x2\"], c=df_moon[\"label\"])\n",
+        "plt.title(\"make_moons() scatter\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "SoPvWxbjjRuf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2) Hugging Face Datasets 🤗\n",
+        "IMDB sentiment: label balance + sample peek"
+      ],
+      "metadata": {
+        "id": "Ft_GbTNIjVhz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# If needed:\n",
+        "# !pip install datasets\n",
+        "\n",
+        "from datasets import load_dataset, load_dataset_builder\n",
+        "from collections import Counter\n",
+        "\n",
+        "ds = load_dataset(\"imdb\")\n",
+        "\n",
+        "# Basic structure\n",
+        "print(ds)\n",
+        "print(\"Train rows:\", ds[\"train\"].num_rows, \"Test rows:\", ds[\"test\"].num_rows)\n",
+        "print(\"Features:\", load_dataset_builder(\"imdb\").info.features)\n",
+        "\n",
+        "# Label balance (train)\n",
+        "label_counts = Counter(ds[\"train\"][\"label\"])\n",
+        "print(\"Label counts:\", label_counts)\n",
+        "\n",
+        "# Bar chart\n",
+        "plt.bar([\"Negative\",\"Positive\"], [label_counts[0], label_counts[1]])\n",
+        "plt.title(\"IMDB Sentiment Distribution (Train)\")\n",
+        "plt.ylabel(\"Count\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# Sample row\n",
+        "ds[\"train\"][0]\n"
+      ],
+      "metadata": {
+        "id": "l9IMOMbWjVEO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3) Kaggle 📦\n",
+        "Delhi Air Quality via KaggleHub → first look"
+      ],
+      "metadata": {
+        "id": "ayeed1vkjckP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# If needed:\n",
+        "# !pip install \"kagglehub[pandas-datasets]\"\n",
+        "\n",
+        "import kagglehub\n",
+        "from kagglehub import KaggleDatasetAdapter\n",
+        "\n",
+        "# kagglehub.login()  # uncomment if required on your environment\n",
+        "\n",
+        "handle = \"kunshbhatia/delhi-air-quality-dataset\"\n",
+        "file_in_dataset = \"delhi_air_quality.csv\"  # adjust if the filename differs\n",
+        "\n",
+        "df_kaggle = kagglehub.dataset_load(\n",
+        "    KaggleDatasetAdapter.PANDAS,\n",
+        "    handle,\n",
+        "    file_in_dataset,\n",
+        ")\n",
+        "\n",
+        "print(\"Kaggle (Delhi Air) shape:\", df_kaggle.shape)\n",
+        "display(df_kaggle.head())\n",
+        "print(df_kaggle.isna().sum().sort_values(ascending=False).head(10))\n"
+      ],
+      "metadata": {
+        "id": "TFpTV941jb2q"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4) UCI Repository 🏛️\n",
+        "Heart Disease via ucimlrepo"
+      ],
+      "metadata": {
+        "id": "izjnrXBYjg8c"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# If needed:\n",
+        "# !pip install ucimlrepo\n",
+        "\n",
+        "from ucimlrepo import fetch_ucirepo\n",
+        "\n",
+        "heart = fetch_ucirepo(id=45)  # Heart Disease\n",
+        "X_uci = heart.data.features\n",
+        "y_uci = heart.data.targets\n",
+        "\n",
+        "df_uci = X_uci.copy()\n",
+        "for c in y_uci.columns:\n",
+        "    df_uci[c] = y_uci[c]\n",
+        "\n",
+        "print(\"UCI Heart shape:\", df_uci.shape)\n",
+        "display(df_uci.head())\n",
+        "print(\"Targets:\", list(y_uci.columns))\n",
+        "print(df_uci.isna().sum().sort_values(ascending=False).head(10))\n"
+      ],
+      "metadata": {
+        "id": "0ev_9cnfjfm4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 5) OpenML 🌐\n",
+        "“irish” dataset, ID 451 → robust fetch & first look"
+      ],
+      "metadata": {
+        "id": "pG5fYmyGjk0m"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# If needed:\n",
+        "# !pip install openml\n",
+        "\n",
+        "import openml\n",
+        "\n",
+        "d_irish = openml.datasets.get_dataset(451)  # \"irish\"\n",
+        "target_col = d_irish.default_target_attribute\n",
+        "X_irish, y_irish, cat_ind, names = d_irish.get_data(dataset_format=\"dataframe\", target=target_col)\n",
+        "\n",
+        "df_irish = X_irish.copy()\n",
+        "df_irish[target_col] = y_irish\n",
+        "\n",
+        "print(\"OpenML 'irish' shape:\", df_irish.shape)\n",
+        "display(df_irish.head())\n",
+        "print(\"Target:\", target_col)\n",
+        "print(\"Categorical flags per feature:\", dict(zip(names, cat_ind)))\n",
+        "print(df_irish.isna().sum().sort_values(ascending=False))\n",
+        "\n",
+        "# Simple target distribution\n",
+        "df_irish[target_col].value_counts(dropna=False)\n"
+      ],
+      "metadata": {
+        "id": "rScFEsdIjmFY"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## (Mini) EDA add-ons 📋"
+      ],
+      "metadata": {
+        "id": "_bDArzSjjpEu"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Correlation (numeric) — quick peek\n",
+        "num_cols = df_iris.select_dtypes(include=[np.number]).columns\n",
+        "corr = df_iris[num_cols].corr()\n",
+        "plt.figure(figsize=(6,5))\n",
+        "plt.imshow(corr, interpolation=\"nearest\")\n",
+        "plt.title(\"Iris: Correlation (numeric)\")\n",
+        "plt.colorbar()\n",
+        "plt.xticks(range(len(num_cols)), num_cols, rotation=90)\n",
+        "plt.yticks(range(len(num_cols)), num_cols)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# Simple group stats (if categorical exists)\n",
+        "if \"target\" in df_iris:\n",
+        "    print(df_iris.groupby(\"target\")[num_cols].mean())\n"
+      ],
+      "metadata": {
+        "id": "0UGaBkrnjriI"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/NoteBooks/AI\342\200\223DS_Nexus___1_3_DataPreprocessing.ipynb" "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___1_3_DataPreprocessing.ipynb"
new file mode 100644
index 0000000..ca46e08
--- /dev/null
+++ "b/a0.1/NoteBooks/AI\342\200\223DS_Nexus___1_3_DataPreprocessing.ipynb"
@@ -0,0 +1,58457 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "id": "lIYdn1woOS1n",
+        "outputId": "4c67c7fb-5d85-48da-ab5a-968bfc2ef809"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipython-input-53450764.py:11: DeprecationWarning: load_dataset is deprecated and will be removed in a future version.\n",
+            "  df = kagglehub.load_dataset(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
+              "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
+              "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
+              "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
+              "3  7795-CFOCW    Male              0      No         No      45           No   \n",
+              "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
+              "\n",
+              "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
+              "0  No phone service             DSL             No          Yes   \n",
+              "1                No             DSL            Yes           No   \n",
+              "2                No             DSL            Yes          Yes   \n",
+              "3  No phone service             DSL            Yes           No   \n",
+              "4                No     Fiber optic             No           No   \n",
+              "\n",
+              "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
+              "0               No          No          No              No  Month-to-month   \n",
+              "1              Yes          No          No              No        One year   \n",
+              "2               No          No          No              No  Month-to-month   \n",
+              "3              Yes         Yes          No              No        One year   \n",
+              "4               No          No          No              No  Month-to-month   \n",
+              "\n",
+              "  PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges  \\\n",
+              "0              Yes           Electronic check           29.85        29.85   \n",
+              "1               No               Mailed check           56.95       1889.5   \n",
+              "2              Yes               Mailed check           53.85       108.15   \n",
+              "3               No  Bank transfer (automatic)           42.30      1840.75   \n",
+              "4              Yes           Electronic check           70.70       151.65   \n",
+              "\n",
+              "  Churn  \n",
+              "0    No  \n",
+              "1    No  \n",
+              "2   Yes  \n",
+              "3    No  \n",
+              "4   Yes  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-00a8dcc5-cd3b-4708-a100-bd49c50afc01\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>customerID</th>\n",
+              "      <th>gender</th>\n",
+              "      <th>SeniorCitizen</th>\n",
+              "      <th>Partner</th>\n",
+              "      <th>Dependents</th>\n",
+              "      <th>tenure</th>\n",
+              "      <th>PhoneService</th>\n",
+              "      <th>MultipleLines</th>\n",
+              "      <th>InternetService</th>\n",
+              "      <th>OnlineSecurity</th>\n",
+              "      <th>OnlineBackup</th>\n",
+              "      <th>DeviceProtection</th>\n",
+              "      <th>TechSupport</th>\n",
+              "      <th>StreamingTV</th>\n",
+              "      <th>StreamingMovies</th>\n",
+              "      <th>Contract</th>\n",
+              "      <th>PaperlessBilling</th>\n",
+              "      <th>PaymentMethod</th>\n",
+              "      <th>MonthlyCharges</th>\n",
+              "      <th>TotalCharges</th>\n",
+              "      <th>Churn</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7590-VHVEG</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>1</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No phone service</td>\n",
+              "      <td>DSL</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Month-to-month</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Electronic check</td>\n",
+              "      <td>29.85</td>\n",
+              "      <td>29.85</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>5575-GNVDE</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>34</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>DSL</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>One year</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Mailed check</td>\n",
+              "      <td>56.95</td>\n",
+              "      <td>1889.5</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3668-QPYBK</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>DSL</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Month-to-month</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Mailed check</td>\n",
+              "      <td>53.85</td>\n",
+              "      <td>108.15</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>7795-CFOCW</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>0</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>45</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No phone service</td>\n",
+              "      <td>DSL</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>One year</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Bank transfer (automatic)</td>\n",
+              "      <td>42.30</td>\n",
+              "      <td>1840.75</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>9237-HQITU</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>0</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Fiber optic</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Month-to-month</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Electronic check</td>\n",
+              "      <td>70.70</td>\n",
+              "      <td>151.65</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-00a8dcc5-cd3b-4708-a100-bd49c50afc01 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-00a8dcc5-cd3b-4708-a100-bd49c50afc01');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-30ba9582-ea6b-4705-be71-7ad11076d0dc\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-30ba9582-ea6b-4705-be71-7ad11076d0dc')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
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+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
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+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
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+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-30ba9582-ea6b-4705-be71-7ad11076d0dc button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df"
+            }
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ],
+      "source": [
+        "# Install dependencies as needed:\n",
+        "# pip install kagglehub[pandas-datasets]\n",
+        "import kagglehub\n",
+        "from kagglehub import KaggleDatasetAdapter\n",
+        "import pandas as pd\n",
+        "\n",
+        "# Set the path to the file you'd like to load\n",
+        "file_path = \"WA_Fn-UseC_-Telco-Customer-Churn.csv\"\n",
+        "\n",
+        "# Load the latest version\n",
+        "df = kagglehub.load_dataset(\n",
+        "  KaggleDatasetAdapter.PANDAS,\n",
+        "  \"blastchar/telco-customer-churn\",\n",
+        "  file_path,\n",
+        "  # Provide any additional arguments like\n",
+        "  # sql_query or pandas_kwargs. See the\n",
+        "  # documenation for more information:\n",
+        "  # https://github.com/Kaggle/kagglehub/blob/main/README.md#kaggledatasetadapterpandas\n",
+        ")\n",
+        "\n",
+        "pd.set_option('display.max_columns', None)\n",
+        "\n",
+        "df.head()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ✅ Step 1: Exploratory Data Review (EDR)"
+      ],
+      "metadata": {
+        "id": "iPAQFIz18FBW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Basic structure\n",
+        "df.shape                             # rows, columns\n",
+        "df.info()                            # dtypes + missing counts\n",
+        "df.describe()                        # numeric summary\n",
+        "df.describe(include=\"object\")        # categorical summary\n",
+        "\n",
+        "df.isnull().sum(axis=1)\n",
+        "df.nunique()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 458
+        },
+        "id": "eFvtMs9zqW7s",
+        "outputId": "ab58f1c1-0062-4feb-dd1f-86399a3209b7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0       0\n",
+              "1       0\n",
+              "2       0\n",
+              "3       0\n",
+              "4       0\n",
+              "       ..\n",
+              "7038    0\n",
+              "7039    0\n",
+              "7040    0\n",
+              "7041    0\n",
+              "7042    0\n",
+              "Length: 7043, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
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+              "</style>\n",
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+              "  <thead>\n",
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+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7038</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7039</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7040</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7041</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7042</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>7043 rows × 1 columns</p>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ✅ Step 2: Handling Missing Values"
+      ],
+      "metadata": {
+        "id": "fs7tirZZ8IPc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "pd.Series([1.0, np.nan, 3.0])\n",
+        "pd.Series([\"a\", None, \"c\"])\n",
+        "pd.to_datetime([\"2020-01-01\", None, \"2020-03-01\"])\n",
+        "pd.Series([1, pd.NA, 3], dtype=\"Int64\")   # nullable int\n",
+        "pd.Series([True, pd.NA, False], dtype=\"boolean\")\n",
+        "s = pd.Series([\"\", \"NA\", \"NULL\", np.nan])\n",
+        "s.replace([\"\", \"NA\", \"NULL\"], np.nan, inplace=True)\n",
+        "# Check missing values\n",
+        "df.isnull().sum().sum()\n"
+      ],
+      "metadata": {
+        "id": "LSmrHWOvIEVs",
+        "outputId": "ffc2166f-2305-42a8-817b-97a2e6c25e78",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.int64(0)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# For Telco: 'TotalCharges' often has blanks, convert to numeric\n",
+        "df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')\n",
+        "\n",
+        "# Re-check missing\n",
+        "print(df['TotalCharges'].isnull().sum().sum())\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3P0fdgX88K-x",
+        "outputId": "985feef2-e557-4676-c202-ef692c9f78c3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "11\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# Fill missing with median (numerical) or mode (categorical)\n",
+        "df['TotalCharges'] = df['TotalCharges'].fillna(df['TotalCharges'].median())\n",
+        "print(df['TotalCharges'].isnull().sum().sum())\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dc30K7iXV6TR",
+        "outputId": "d6bead49-9595-4fd9-ac2e-6bcbb027c545"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ✅ Step 3: Correlation"
+      ],
+      "metadata": {
+        "id": "kweM4wIm803w"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.corr(numeric_only=True)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 174
+        },
+        "id": "yBWCZ4YyWFxK",
+        "outputId": "72b4eeff-7007-49fa-eb1e-5260eaf756e4"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                SeniorCitizen    tenure  MonthlyCharges  TotalCharges\n",
+              "SeniorCitizen        1.000000  0.016567        0.220173      0.102652\n",
+              "tenure               0.016567  1.000000        0.247900      0.825464\n",
+              "MonthlyCharges       0.220173  0.247900        1.000000      0.650864\n",
+              "TotalCharges         0.102652  0.825464        0.650864      1.000000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-81c685df-86f8-408d-84e6-908c1aa26858\" class=\"colab-df-container\">\n",
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+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "    </tr>\n",
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+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>SeniorCitizen</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.016567</td>\n",
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+              "      <th>tenure</th>\n",
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+              "      <th>MonthlyCharges</th>\n",
+              "      <td>0.220173</td>\n",
+              "      <td>0.247900</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.650864</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>TotalCharges</th>\n",
+              "      <td>0.102652</td>\n",
+              "      <td>0.825464</td>\n",
+              "      <td>0.650864</td>\n",
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+              "      margin-bottom: 4px;\n",
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+              "\n",
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+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-81c685df-86f8-408d-84e6-908c1aa26858 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-81c685df-86f8-408d-84e6-908c1aa26858');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-45beadd6-aa55-4949-b377-ca998ca4d02c\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-45beadd6-aa55-4949-b377-ca998ca4d02c')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-45beadd6-aa55-4949-b377-ca998ca4d02c button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"SeniorCitizen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.45121890726476527,\n        \"min\": 0.016566877681809315,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.016566877681809315,\n          0.10265158978041383,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.46589235152999947,\n        \"min\": 0.016566877681809315,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0,\n          0.8254640864073051,\n          0.016566877681809315\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MonthlyCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37017156384728933,\n        \"min\": 0.22017333857627205,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.24789985628615094,\n          0.6508643497230377,\n          0.22017333857627205\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.38848775830624066,\n        \"min\": 0.10265158978041383,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.8254640864073051,\n          1.0,\n          0.10265158978041383\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Numeric-only correlation\n",
+        "corr = df.corr(numeric_only=True)\n",
+        "plt.figure(figsize=(10,6))\n",
+        "sns.heatmap(corr, annot=True, cmap=\"coolwarm\")\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 528
+        },
+        "id": "N_Fez35O8zqG",
+        "outputId": "8cc0ca33-daf4-4c43-c40f-35f78149cd1f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ✅ Step 4: Encoding Categorical Variables\n",
+        "\n",
+        "#### 🔹 Label Encoding, Ordinal Encoding, One-hot Encoding (sklearn)\n",
+        "\n",
+        "👉 Best for **ordinal** categories or when you want to quickly turn categories into integers."
+      ],
+      "metadata": {
+        "id": "4s2t1gCo9AV3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Drop ID column (not useful for ML)\n",
+        "if 'customerID' in df.columns:\n",
+        "    df.drop(columns=['customerID'], inplace=True)\n",
+        "\n",
+        "# Separate categorical & numerical\n",
+        "cat_cols = df.select_dtypes(include=['object']).columns.tolist()\n",
+        "num_cols = df.select_dtypes(exclude=['object']).columns.tolist()\n",
+        "print(\"cat_cols: \", cat_cols)\n",
+        "print(\"num_cols: \", num_cols)\n"
+      ],
+      "metadata": {
+        "id": "beNj03P_-wSw",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cf163f9b-b709-4ba3-8c4d-33245e498090"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "cat_cols:  ['gender', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'TotalCharges', 'Churn']\n",
+            "num_cols:  ['SeniorCitizen', 'tenure', 'MonthlyCharges']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder, OneHotEncoder\n",
+        "\n",
+        "df_le = df.copy()  # keep a separate version\n",
+        "label_encoders = {}\n",
+        "\n",
+        "for col in cat_cols:\n",
+        "    le = LabelEncoder()\n",
+        "    df_le[col] = le.fit_transform(df_le[col])\n",
+        "    label_encoders[col] = le  # store encoder if needed later\n",
+        "\n",
+        "df_le.head()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 226
+        },
+        "id": "Alh5JLZ9-NGT",
+        "outputId": "5aedd760-93c2-497d-eedc-a15f194b6aa0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
+              "0       0              0        1           0       1             0   \n",
+              "1       1              0        0           0      34             1   \n",
+              "2       1              0        0           0       2             1   \n",
+              "3       1              0        0           0      45             0   \n",
+              "4       0              0        0           0       2             1   \n",
+              "\n",
+              "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
+              "0              1                0               0             2   \n",
+              "1              0                0               2             0   \n",
+              "2              0                0               2             2   \n",
+              "3              1                0               2             0   \n",
+              "4              0                1               0             0   \n",
+              "\n",
+              "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
+              "0                 0            0            0                0         0   \n",
+              "1                 2            0            0                0         1   \n",
+              "2                 0            0            0                0         0   \n",
+              "3                 2            2            0                0         1   \n",
+              "4                 0            0            0                0         0   \n",
+              "\n",
+              "   PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
+              "0                 1              2           29.85         29.85      0  \n",
+              "1                 0              3           56.95       1889.50      0  \n",
+              "2                 1              3           53.85        108.15      1  \n",
+              "3                 0              0           42.30       1840.75      0  \n",
+              "4                 1              2           70.70        151.65      1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2a2393d0-b78b-4c49-88a5-f97759821dfb\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>gender</th>\n",
+              "      <th>SeniorCitizen</th>\n",
+              "      <th>Partner</th>\n",
+              "      <th>Dependents</th>\n",
+              "      <th>tenure</th>\n",
+              "      <th>PhoneService</th>\n",
+              "      <th>MultipleLines</th>\n",
+              "      <th>InternetService</th>\n",
+              "      <th>OnlineSecurity</th>\n",
+              "      <th>OnlineBackup</th>\n",
+              "      <th>DeviceProtection</th>\n",
+              "      <th>TechSupport</th>\n",
+              "      <th>StreamingTV</th>\n",
+              "      <th>StreamingMovies</th>\n",
+              "      <th>Contract</th>\n",
+              "      <th>PaperlessBilling</th>\n",
+              "      <th>PaymentMethod</th>\n",
+              "      <th>MonthlyCharges</th>\n",
+              "      <th>TotalCharges</th>\n",
+              "      <th>Churn</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>29.85</td>\n",
+              "      <td>29.85</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>34</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>56.95</td>\n",
+              "      <td>1889.50</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>53.85</td>\n",
+              "      <td>108.15</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>45</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>42.30</td>\n",
+              "      <td>1840.75</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>70.70</td>\n",
+              "      <td>151.65</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a2393d0-b78b-4c49-88a5-f97759821dfb')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-2a2393d0-b78b-4c49-88a5-f97759821dfb button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-2a2393d0-b78b-4c49-88a5-f97759821dfb');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-c38ce159-47ed-42be-8844-2663bf31bb18\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c38ce159-47ed-42be-8844-2663bf31bb18')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-c38ce159-47ed-42be-8844-2663bf31bb18 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_le",
+              "summary": "{\n  \"name\": \"df_le\",\n  \"rows\": 7043,\n  \"fields\": [\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SeniorCitizen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Partner\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 0,\n        \"max\": 72,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          8,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PhoneService\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MultipleLines\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InternetService\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OnlineSecurity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OnlineBackup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DeviceProtection\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TechSupport\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StreamingTV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StreamingMovies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Contract\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PaperlessBilling\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PaymentMethod\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MonthlyCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.09004709767854,\n        \"min\": 18.25,\n        \"max\": 118.75,\n        \"num_unique_values\": 1585,\n        \"samples\": [\n          48.85,\n          20.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2265.2703984821865,\n        \"min\": 18.8,\n        \"max\": 8684.8,\n        \"num_unique_values\": 6531,\n        \"samples\": [\n          4600.7,\n          20.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder, OneHotEncoder\n",
+        "\n",
+        "df_oe = df.copy()  # keep a separate version\n",
+        "ordinal_encoders = {}\n",
+        "\n",
+        "for col in cat_cols:\n",
+        "    oe = OrdinalEncoder()\n",
+        "    df_oe[col] = oe.fit_transform(df_oe[col].values.reshape(-1, 1))\n",
+        "    ordinal_encoders[col] = oe  # store encoder if needed later\n",
+        "\n",
+        "df_oe.head()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 226
+        },
+        "id": "GqxZAY0pZstq",
+        "outputId": "9dae3db2-f91c-4e14-fc66-dfe2c80990fe"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   gender  SeniorCitizen  Partner  Dependents  tenure  PhoneService  \\\n",
+              "0     0.0              0      1.0         0.0       1           0.0   \n",
+              "1     1.0              0      0.0         0.0      34           1.0   \n",
+              "2     1.0              0      0.0         0.0       2           1.0   \n",
+              "3     1.0              0      0.0         0.0      45           0.0   \n",
+              "4     0.0              0      0.0         0.0       2           1.0   \n",
+              "\n",
+              "   MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
+              "0            1.0              0.0             0.0           2.0   \n",
+              "1            0.0              0.0             2.0           0.0   \n",
+              "2            0.0              0.0             2.0           2.0   \n",
+              "3            1.0              0.0             2.0           0.0   \n",
+              "4            0.0              1.0             0.0           0.0   \n",
+              "\n",
+              "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
+              "0               0.0          0.0          0.0              0.0       0.0   \n",
+              "1               2.0          0.0          0.0              0.0       1.0   \n",
+              "2               0.0          0.0          0.0              0.0       0.0   \n",
+              "3               2.0          2.0          0.0              0.0       1.0   \n",
+              "4               0.0          0.0          0.0              0.0       0.0   \n",
+              "\n",
+              "   PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  Churn  \n",
+              "0               1.0            2.0           29.85         29.85    0.0  \n",
+              "1               0.0            3.0           56.95       1889.50    0.0  \n",
+              "2               1.0            3.0           53.85        108.15    1.0  \n",
+              "3               0.0            0.0           42.30       1840.75    0.0  \n",
+              "4               1.0            2.0           70.70        151.65    1.0  "
+            ],
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+              "      <th>PaymentMethod</th>\n",
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+              "    <tr>\n",
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+              "      <td>53.85</td>\n",
+              "      <td>108.15</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.0</td>\n",
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+              "      <td>1840.75</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0.0</td>\n",
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+              "      <td>151.65</td>\n",
+              "      <td>1.0</td>\n",
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+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e5f104b0-b74c-4ea1-842f-b8e93e2d2336')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "  </svg>\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
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+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
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+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e5f104b0-b74c-4ea1-842f-b8e93e2d2336 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-e5f104b0-b74c-4ea1-842f-b8e93e2d2336');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
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+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
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+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
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+              "    cursor: pointer;\n",
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+              "    height: 32px;\n",
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+              "  .colab-df-quickchart:hover {\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
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+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
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+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+              "\n",
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+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_oe",
+              "summary": "{\n  \"name\": \"df_oe\",\n  \"rows\": 7043,\n  \"fields\": [\n    {\n      \"column\": \"gender\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5000128736471762,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SeniorCitizen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Partner\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.49974751072001666,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dependents\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4581101675100081,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 0,\n        \"max\": 72,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          8,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PhoneService\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29575223178364995,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MultipleLines\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.948554033447008,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"InternetService\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7377963405848367,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OnlineSecurity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8598475043518664,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OnlineBackup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8801624706484507,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DeviceProtection\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8799488523681269,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TechSupport\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8615505621474224,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StreamingTV\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8850018638488043,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StreamingMovies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8850907112876283,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Contract\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8337552328755957,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PaperlessBilling\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.491456924049444,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PaymentMethod\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.06810400550968,\n        \"min\": 0.0,\n        \"max\": 3.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MonthlyCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.09004709767854,\n        \"min\": 18.25,\n        \"max\": 118.75,\n        \"num_unique_values\": 1585,\n        \"samples\": [\n          48.85,\n          20.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2265.2703984821865,\n        \"min\": 18.8,\n        \"max\": 8684.8,\n        \"num_unique_values\": 6531,\n        \"samples\": [\n          4600.7,\n          20.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Churn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.44156130512195013,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_ohe = df.copy()  # keep a separate version\n",
+        "label_encoders = {}\n",
+        "\n",
+        "ohe = OneHotEncoder(drop=\"first\", sparse_output=False) # Set sparse_output to False\n",
+        "encoded = ohe.fit_transform(df_ohe[cat_cols])\n",
+        "\n",
+        "encoded_df = pd.DataFrame(encoded, columns=ohe.get_feature_names_out(cat_cols), index=df_ohe.index)\n",
+        "df_ohe = pd.concat([df_ohe.drop(columns=cat_cols), encoded_df], axis=1)\n",
+        "\n",
+        "df_ohe.head()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 260
+        },
+        "id": "4JzCmbDYaIRe",
+        "outputId": "00638269-5264-41fb-ebff-4270b985f74d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   SeniorCitizen  tenure  MonthlyCharges  TotalCharges  gender_Male  \\\n",
+              "0              0       1           29.85         29.85          0.0   \n",
+              "1              0      34           56.95       1889.50          1.0   \n",
+              "2              0       2           53.85        108.15          1.0   \n",
+              "3              0      45           42.30       1840.75          1.0   \n",
+              "4              0       2           70.70        151.65          0.0   \n",
+              "\n",
+              "   Partner_Yes  Dependents_Yes  PhoneService_Yes  \\\n",
+              "0          1.0             0.0               0.0   \n",
+              "1          0.0             0.0               1.0   \n",
+              "2          0.0             0.0               1.0   \n",
+              "3          0.0             0.0               0.0   \n",
+              "4          0.0             0.0               1.0   \n",
+              "\n",
+              "   MultipleLines_No phone service  MultipleLines_Yes  \\\n",
+              "0                             1.0                0.0   \n",
+              "1                             0.0                0.0   \n",
+              "2                             0.0                0.0   \n",
+              "3                             1.0                0.0   \n",
+              "4                             0.0                0.0   \n",
+              "\n",
+              "   InternetService_Fiber optic  InternetService_No  \\\n",
+              "0                          0.0                 0.0   \n",
+              "1                          0.0                 0.0   \n",
+              "2                          0.0                 0.0   \n",
+              "3                          0.0                 0.0   \n",
+              "4                          1.0                 0.0   \n",
+              "\n",
+              "   OnlineSecurity_No internet service  OnlineSecurity_Yes  \\\n",
+              "0                                 0.0                 0.0   \n",
+              "1                                 0.0                 1.0   \n",
+              "2                                 0.0                 1.0   \n",
+              "3                                 0.0                 1.0   \n",
+              "4                                 0.0                 0.0   \n",
+              "\n",
+              "   OnlineBackup_No internet service  OnlineBackup_Yes  \\\n",
+              "0                               0.0               1.0   \n",
+              "1                               0.0               0.0   \n",
+              "2                               0.0               1.0   \n",
+              "3                               0.0               0.0   \n",
+              "4                               0.0               0.0   \n",
+              "\n",
+              "   DeviceProtection_No internet service  DeviceProtection_Yes  \\\n",
+              "0                                   0.0                   0.0   \n",
+              "1                                   0.0                   1.0   \n",
+              "2                                   0.0                   0.0   \n",
+              "3                                   0.0                   1.0   \n",
+              "4                                   0.0                   0.0   \n",
+              "\n",
+              "   TechSupport_No internet service  TechSupport_Yes  \\\n",
+              "0                              0.0              0.0   \n",
+              "1                              0.0              0.0   \n",
+              "2                              0.0              0.0   \n",
+              "3                              0.0              1.0   \n",
+              "4                              0.0              0.0   \n",
+              "\n",
+              "   StreamingTV_No internet service  StreamingTV_Yes  \\\n",
+              "0                              0.0              0.0   \n",
+              "1                              0.0              0.0   \n",
+              "2                              0.0              0.0   \n",
+              "3                              0.0              0.0   \n",
+              "4                              0.0              0.0   \n",
+              "\n",
+              "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
+              "0                                  0.0                  0.0   \n",
+              "1                                  0.0                  0.0   \n",
+              "2                                  0.0                  0.0   \n",
+              "3                                  0.0                  0.0   \n",
+              "4                                  0.0                  0.0   \n",
+              "\n",
+              "   Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\n",
+              "0                0.0                0.0                   1.0   \n",
+              "1                1.0                0.0                   0.0   \n",
+              "2                0.0                0.0                   1.0   \n",
+              "3                1.0                0.0                   0.0   \n",
+              "4                0.0                0.0                   1.0   \n",
+              "\n",
+              "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
+              "0                                    0.0                             1.0   \n",
+              "1                                    0.0                             0.0   \n",
+              "2                                    0.0                             0.0   \n",
+              "3                                    0.0                             0.0   \n",
+              "4                                    0.0                             1.0   \n",
+              "\n",
+              "   PaymentMethod_Mailed check  Churn_Yes  \n",
+              "0                         0.0        0.0  \n",
+              "1                         1.0        0.0  \n",
+              "2                         1.0        1.0  \n",
+              "3                         0.0        0.0  \n",
+              "4                         0.0        1.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-159037ba-64b3-462b-a4e5-88263c811e21\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>SeniorCitizen</th>\n",
+              "      <th>tenure</th>\n",
+              "      <th>MonthlyCharges</th>\n",
+              "      <th>TotalCharges</th>\n",
+              "      <th>gender_Male</th>\n",
+              "      <th>Partner_Yes</th>\n",
+              "      <th>Dependents_Yes</th>\n",
+              "      <th>PhoneService_Yes</th>\n",
+              "      <th>MultipleLines_No phone service</th>\n",
+              "      <th>MultipleLines_Yes</th>\n",
+              "      <th>InternetService_Fiber optic</th>\n",
+              "      <th>InternetService_No</th>\n",
+              "      <th>OnlineSecurity_No internet service</th>\n",
+              "      <th>OnlineSecurity_Yes</th>\n",
+              "      <th>OnlineBackup_No internet service</th>\n",
+              "      <th>OnlineBackup_Yes</th>\n",
+              "      <th>DeviceProtection_No internet service</th>\n",
+              "      <th>DeviceProtection_Yes</th>\n",
+              "      <th>TechSupport_No internet service</th>\n",
+              "      <th>TechSupport_Yes</th>\n",
+              "      <th>StreamingTV_No internet service</th>\n",
+              "      <th>StreamingTV_Yes</th>\n",
+              "      <th>StreamingMovies_No internet service</th>\n",
+              "      <th>StreamingMovies_Yes</th>\n",
+              "      <th>Contract_One year</th>\n",
+              "      <th>Contract_Two year</th>\n",
+              "      <th>PaperlessBilling_Yes</th>\n",
+              "      <th>PaymentMethod_Credit card (automatic)</th>\n",
+              "      <th>PaymentMethod_Electronic check</th>\n",
+              "      <th>PaymentMethod_Mailed check</th>\n",
+              "      <th>Churn_Yes</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>29.85</td>\n",
+              "      <td>29.85</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>34</td>\n",
+              "      <td>56.95</td>\n",
+              "      <td>1889.50</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
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+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>53.85</td>\n",
+              "      <td>108.15</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
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+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0</td>\n",
+              "      <td>45</td>\n",
+              "      <td>42.30</td>\n",
+              "      <td>1840.75</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
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+              "      <td>1.0</td>\n",
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+              "      <td>1.0</td>\n",
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+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
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+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>70.70</td>\n",
+              "      <td>151.65</td>\n",
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+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
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+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
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+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-159037ba-64b3-462b-a4e5-88263c811e21 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-159037ba-64b3-462b-a4e5-88263c811e21');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6a3043a8-aa15-49d2-91f2-7d00fab531a6\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6a3043a8-aa15-49d2-91f2-7d00fab531a6')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6a3043a8-aa15-49d2-91f2-7d00fab531a6 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_ohe"
+            }
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#### 🔹 Pandas get_dummies\n",
+        "Quick and simple one-hot encoding built into Pandas."
+      ],
+      "metadata": {
+        "id": "5PfCxc-B-lqI"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# One-hot encode\n",
+        "df_gd = df.copy()\n",
+        "df_gd = pd.get_dummies(df_gd, columns=cat_cols, drop_first=True)\n",
+        "df_gd.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 347
+        },
+        "id": "l1oh7jq085w2",
+        "outputId": "2f02d8a1-0ac7-4631-a75a-d39e8bdeffa6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   customerID  SeniorCitizen  tenure  MonthlyCharges  gender_Male  \\\n",
+              "0  7590-VHVEG              0       1           29.85        False   \n",
+              "1  5575-GNVDE              0      34           56.95         True   \n",
+              "2  3668-QPYBK              0       2           53.85         True   \n",
+              "3  7795-CFOCW              0      45           42.30         True   \n",
+              "4  9237-HQITU              0       2           70.70        False   \n",
+              "\n",
+              "   Partner_Yes  Dependents_Yes  PhoneService_Yes  \\\n",
+              "0         True           False             False   \n",
+              "1        False           False              True   \n",
+              "2        False           False              True   \n",
+              "3        False           False             False   \n",
+              "4        False           False              True   \n",
+              "\n",
+              "   MultipleLines_No phone service  MultipleLines_Yes  \\\n",
+              "0                            True              False   \n",
+              "1                           False              False   \n",
+              "2                           False              False   \n",
+              "3                            True              False   \n",
+              "4                           False              False   \n",
+              "\n",
+              "   InternetService_Fiber optic  InternetService_No  \\\n",
+              "0                        False               False   \n",
+              "1                        False               False   \n",
+              "2                        False               False   \n",
+              "3                        False               False   \n",
+              "4                         True               False   \n",
+              "\n",
+              "   OnlineSecurity_No internet service  OnlineSecurity_Yes  \\\n",
+              "0                               False               False   \n",
+              "1                               False                True   \n",
+              "2                               False                True   \n",
+              "3                               False                True   \n",
+              "4                               False               False   \n",
+              "\n",
+              "   OnlineBackup_No internet service  OnlineBackup_Yes  \\\n",
+              "0                             False              True   \n",
+              "1                             False             False   \n",
+              "2                             False              True   \n",
+              "3                             False             False   \n",
+              "4                             False             False   \n",
+              "\n",
+              "   DeviceProtection_No internet service  DeviceProtection_Yes  \\\n",
+              "0                                 False                 False   \n",
+              "1                                 False                  True   \n",
+              "2                                 False                 False   \n",
+              "3                                 False                  True   \n",
+              "4                                 False                 False   \n",
+              "\n",
+              "   TechSupport_No internet service  TechSupport_Yes  \\\n",
+              "0                            False            False   \n",
+              "1                            False            False   \n",
+              "2                            False            False   \n",
+              "3                            False             True   \n",
+              "4                            False            False   \n",
+              "\n",
+              "   StreamingTV_No internet service  StreamingTV_Yes  \\\n",
+              "0                            False            False   \n",
+              "1                            False            False   \n",
+              "2                            False            False   \n",
+              "3                            False            False   \n",
+              "4                            False            False   \n",
+              "\n",
+              "   StreamingMovies_No internet service  StreamingMovies_Yes  \\\n",
+              "0                                False                False   \n",
+              "1                                False                False   \n",
+              "2                                False                False   \n",
+              "3                                False                False   \n",
+              "4                                False                False   \n",
+              "\n",
+              "   Contract_One year  Contract_Two year  PaperlessBilling_Yes  \\\n",
+              "0              False              False                  True   \n",
+              "1               True              False                 False   \n",
+              "2              False              False                  True   \n",
+              "3               True              False                 False   \n",
+              "4              False              False                  True   \n",
+              "\n",
+              "   PaymentMethod_Credit card (automatic)  PaymentMethod_Electronic check  \\\n",
+              "0                                  False                            True   \n",
+              "1                                  False                           False   \n",
+              "2                                  False                           False   \n",
+              "3                                  False                           False   \n",
+              "4                                  False                            True   \n",
+              "\n",
+              "   PaymentMethod_Mailed check  TotalCharges_100.2  TotalCharges_100.25  \\\n",
+              "0                       False               False                False   \n",
+              "1                        True               False                False   \n",
+              "2                        True               False                False   \n",
+              "3                       False               False                False   \n",
+              "4                       False               False                False   \n",
+              "\n",
+              "   TotalCharges_100.35  TotalCharges_100.4  TotalCharges_100.8  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_100.9  TotalCharges_1001.2  TotalCharges_1001.5  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_1003.05  TotalCharges_1004.35  TotalCharges_1004.5  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1004.75  TotalCharges_1005.7  TotalCharges_1006.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1007.8  TotalCharges_1007.9  TotalCharges_1008.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1008.7  TotalCharges_1009.25  TotalCharges_101.1  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_101.45  TotalCharges_101.65  TotalCharges_101.9  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_1010  TotalCharges_1011.05  TotalCharges_1011.5  \\\n",
+              "0              False                 False                False   \n",
+              "1              False                 False                False   \n",
+              "2              False                 False                False   \n",
+              "3              False                 False                False   \n",
+              "4              False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1011.8  TotalCharges_1012.4  TotalCharges_1013.05  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1013.2  TotalCharges_1013.35  TotalCharges_1013.6  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1014.25  TotalCharges_1016.7  TotalCharges_1017.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_102.45  TotalCharges_102.5  TotalCharges_102.75  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_1020.2  TotalCharges_1020.75  TotalCharges_1021.55  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1021.75  TotalCharges_1021.8  TotalCharges_1022.5  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1022.6  TotalCharges_1022.95  TotalCharges_1023.75  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1023.85  TotalCharges_1023.9  TotalCharges_1023.95  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1024  TotalCharges_1024.65  TotalCharges_1024.7  \\\n",
+              "0              False                 False                False   \n",
+              "1              False                 False                False   \n",
+              "2              False                 False                False   \n",
+              "3              False                 False                False   \n",
+              "4              False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1025.05  TotalCharges_1025.15  TotalCharges_1025.95  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1026.35  TotalCharges_1027.25  TotalCharges_1028.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1028.9  TotalCharges_1029.35  TotalCharges_1029.75  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1029.8  TotalCharges_103.7  TotalCharges_1031.1  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_1031.4  TotalCharges_1031.7  TotalCharges_1032  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_1032.05  TotalCharges_1033  TotalCharges_1033.9  \\\n",
+              "0                 False              False                False   \n",
+              "1                 False              False                False   \n",
+              "2                 False              False                False   \n",
+              "3                 False              False                False   \n",
+              "4                 False              False                False   \n",
+              "\n",
+              "   TotalCharges_1033.95  TotalCharges_1035.5  TotalCharges_1035.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1036  TotalCharges_1036.75  TotalCharges_1037.75  \\\n",
+              "0              False                 False                 False   \n",
+              "1              False                 False                 False   \n",
+              "2              False                 False                 False   \n",
+              "3              False                 False                 False   \n",
+              "4              False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1039.45  TotalCharges_104.2  TotalCharges_104.3  \\\n",
+              "0                 False               False               False   \n",
+              "1                 False               False               False   \n",
+              "2                 False               False               False   \n",
+              "3                 False               False               False   \n",
+              "4                 False               False               False   \n",
+              "\n",
+              "   TotalCharges_1041.8  TotalCharges_1042.65  TotalCharges_1043.3  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1043.35  TotalCharges_1043.4  TotalCharges_1043.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1045.25  TotalCharges_1046.1  TotalCharges_1046.2  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1046.5  TotalCharges_1047.7  TotalCharges_1048.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1048.85  TotalCharges_1049.05  TotalCharges_1049.6  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_105.5  TotalCharges_105.6  TotalCharges_1050.5  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_1051.05  TotalCharges_1051.9  TotalCharges_1052.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1052.4  TotalCharges_1054.6  TotalCharges_1054.75  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1054.8  TotalCharges_1055.9  TotalCharges_1056.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1057  TotalCharges_1057.55  TotalCharges_1057.85  \\\n",
+              "0              False                 False                 False   \n",
+              "1              False                 False                 False   \n",
+              "2              False                 False                 False   \n",
+              "3              False                 False                 False   \n",
+              "4              False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1058.1  TotalCharges_1058.25  TotalCharges_1058.6  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1059.55  TotalCharges_106.2  TotalCharges_106.55  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_106.8  TotalCharges_106.85  TotalCharges_106.9  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_1060.2  TotalCharges_1060.6  TotalCharges_1061.6  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1062.1  TotalCharges_1064.65  TotalCharges_1064.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1066.15  TotalCharges_1066.9  TotalCharges_1067.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1067.15  TotalCharges_1067.65  TotalCharges_1068.15  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1068.85  TotalCharges_107.05  TotalCharges_107.1  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_107.25  TotalCharges_107.6  TotalCharges_1070.15  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_1070.25  TotalCharges_1070.5  TotalCharges_1070.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1071.4  TotalCharges_1071.6  TotalCharges_1072  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_1072.6  TotalCharges_1073.3  TotalCharges_1074.3  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1074.65  TotalCharges_1077.05  TotalCharges_1077.5  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1077.95  TotalCharges_1078.75  TotalCharges_1078.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1079.05  TotalCharges_1079.45  TotalCharges_1079.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_108.05  TotalCharges_108.15  TotalCharges_108.65  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                 True                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_108.7  TotalCharges_108.95  TotalCharges_1080.55  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1081.25  TotalCharges_1081.45  TotalCharges_1082.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1082.8  TotalCharges_1083  TotalCharges_1083.7  \\\n",
+              "0                False              False                False   \n",
+              "1                False              False                False   \n",
+              "2                False              False                False   \n",
+              "3                False              False                False   \n",
+              "4                False              False                False   \n",
+              "\n",
+              "   TotalCharges_1086.75  TotalCharges_1087.25  TotalCharges_1087.45  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1087.7  TotalCharges_1088.25  TotalCharges_109.2  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_109.25  TotalCharges_109.3  TotalCharges_109.5  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_109.6  TotalCharges_109.8  TotalCharges_1090.1  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_1090.6  TotalCharges_1090.65  TotalCharges_1092.35  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1093  TotalCharges_1093.1  TotalCharges_1093.2  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_1093.4  TotalCharges_1094.35  TotalCharges_1094.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1095.3  TotalCharges_1095.65  TotalCharges_1096.25  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1096.6  TotalCharges_1096.65  TotalCharges_1097.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1098.85  TotalCharges_1099.6  TotalCharges_110.05  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_110.15  TotalCharges_1101.85  TotalCharges_1102.4  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1103.25  TotalCharges_1105.4  TotalCharges_1107.2  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1107.25  TotalCharges_1108  TotalCharges_1108.2  \\\n",
+              "0                 False              False                False   \n",
+              "1                 False              False                False   \n",
+              "2                 False              False                False   \n",
+              "3                 False              False                False   \n",
+              "4                 False              False                False   \n",
+              "\n",
+              "   TotalCharges_1108.6  TotalCharges_1108.8  TotalCharges_111.05  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_111.4  TotalCharges_111.65  TotalCharges_1110.05  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1110.35  TotalCharges_1110.5  TotalCharges_1111.2  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1111.65  TotalCharges_1111.85  TotalCharges_1112.3  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1113.95  TotalCharges_1114.55  TotalCharges_1114.85  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1115.15  TotalCharges_1115.2  TotalCharges_1115.6  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1117.55  TotalCharges_1118.8  TotalCharges_1119.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1119.9  TotalCharges_112.3  TotalCharges_112.75  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_1120.3  TotalCharges_1120.95  TotalCharges_1121.05  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1122.4  TotalCharges_1123.15  TotalCharges_1123.65  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1124.2  TotalCharges_1125.2  TotalCharges_1125.6  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1126.35  TotalCharges_1126.75  TotalCharges_1127.2  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1127.35  TotalCharges_1128.1  TotalCharges_1129.1  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1129.35  TotalCharges_1129.75  TotalCharges_113.1  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_113.35  TotalCharges_113.5  TotalCharges_113.55  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_113.85  TotalCharges_113.95  TotalCharges_1130  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_1130.85  TotalCharges_1131.2  TotalCharges_1131.3  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1131.5  TotalCharges_1132.35  TotalCharges_1132.75  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1133.65  TotalCharges_1133.7  TotalCharges_1133.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1134.25  TotalCharges_1135.7  TotalCharges_1137.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1138.8  TotalCharges_1139.2  TotalCharges_114.1  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_114.15  TotalCharges_114.7  TotalCharges_1140.05  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_1143.8  TotalCharges_1144.5  TotalCharges_1144.6  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1145.35  TotalCharges_1145.7  TotalCharges_1146.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1146.65  TotalCharges_1147  TotalCharges_1147.45  \\\n",
+              "0                 False              False                 False   \n",
+              "1                 False              False                 False   \n",
+              "2                 False              False                 False   \n",
+              "3                 False              False                 False   \n",
+              "4                 False              False                 False   \n",
+              "\n",
+              "   TotalCharges_1147.85  TotalCharges_1148.1  TotalCharges_1149.65  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_115.1  TotalCharges_115.95  TotalCharges_1151.05  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1151.55  TotalCharges_1152.7  TotalCharges_1152.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1153.25  TotalCharges_1155.6  TotalCharges_1156.1  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1156.35  TotalCharges_1156.55  TotalCharges_1157.05  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1158.85  TotalCharges_116.6  TotalCharges_116.65  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_116.85  TotalCharges_116.95  TotalCharges_1160.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1160.75  TotalCharges_1161.75  TotalCharges_1162.85  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1164.05  TotalCharges_1164.3  TotalCharges_1165.55  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1165.6  TotalCharges_1165.9  TotalCharges_1166.7  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1167.6  TotalCharges_1167.8  TotalCharges_1169.35  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_117.05  TotalCharges_117.8  TotalCharges_117.95  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_1170.5  TotalCharges_1170.55  TotalCharges_1171.3  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1171.5  TotalCharges_1172.95  TotalCharges_1173.35  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1173.55  TotalCharges_1174.35  TotalCharges_1174.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1175.6  TotalCharges_1175.85  TotalCharges_1177.05  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1177.95  TotalCharges_1178.25  TotalCharges_1178.4  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1178.75  TotalCharges_118.25  TotalCharges_118.3  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_118.4  TotalCharges_118.5  TotalCharges_1180.95  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_1181.75  TotalCharges_1182.55  TotalCharges_1183.05  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1183.2  TotalCharges_1183.8  TotalCharges_1184  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_1185  TotalCharges_1185.95  TotalCharges_1187.05  \\\n",
+              "0              False                 False                 False   \n",
+              "1              False                 False                 False   \n",
+              "2              False                 False                 False   \n",
+              "3              False                 False                 False   \n",
+              "4              False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1188.2  TotalCharges_1188.25  TotalCharges_1189.4  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1189.9  TotalCharges_119.3  TotalCharges_119.5  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_119.75  TotalCharges_1190.5  TotalCharges_1191.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1191.4  TotalCharges_1192.3  TotalCharges_1192.7  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1193.05  TotalCharges_1193.55  TotalCharges_1194.3  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1195.25  TotalCharges_1195.75  TotalCharges_1195.95  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1198.05  TotalCharges_1198.8  TotalCharges_1199.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_120.25  TotalCharges_1200.15  TotalCharges_1201.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1201.65  TotalCharges_1203.9  TotalCharges_1203.95  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1205.05  TotalCharges_1205.5  TotalCharges_1206.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1207  TotalCharges_1208.15  TotalCharges_1208.35  \\\n",
+              "0              False                 False                 False   \n",
+              "1              False                 False                 False   \n",
+              "2              False                 False                 False   \n",
+              "3              False                 False                 False   \n",
+              "4              False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1208.6  TotalCharges_1209.25  TotalCharges_121.25  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1210.3  TotalCharges_1210.4  TotalCharges_1211.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1212.1  TotalCharges_1212.25  TotalCharges_1212.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1214.05  TotalCharges_1215.1  TotalCharges_1215.45  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1215.6  TotalCharges_1215.65  TotalCharges_1215.8  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1216.35  TotalCharges_1216.6  TotalCharges_1217.25  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1218.25  TotalCharges_1218.45  TotalCharges_1218.55  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1218.65  TotalCharges_1219.85  TotalCharges_122  \\\n",
+              "0                 False                 False             False   \n",
+              "1                 False                 False             False   \n",
+              "2                 False                 False             False   \n",
+              "3                 False                 False             False   \n",
+              "4                 False                 False             False   \n",
+              "\n",
+              "   TotalCharges_122.7  TotalCharges_122.9  TotalCharges_1221.55  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_1221.65  TotalCharges_1222.05  TotalCharges_1222.25  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1222.65  TotalCharges_1222.8  TotalCharges_1224.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1225.65  TotalCharges_1226.45  TotalCharges_1228.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1229.1  TotalCharges_123.05  TotalCharges_123.65  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_123.8  TotalCharges_1230.25  TotalCharges_1230.9  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1231.85  TotalCharges_1232.9  TotalCharges_1233.15  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1233.25  TotalCharges_1233.4  TotalCharges_1233.65  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1234.6  TotalCharges_1234.8  TotalCharges_1235.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1236.15  TotalCharges_1237.3  TotalCharges_1237.65  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1237.85  TotalCharges_1238.45  TotalCharges_1238.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_124.4  TotalCharges_124.45  TotalCharges_1240.15  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1240.25  TotalCharges_1240.8  TotalCharges_1242.2  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1242.25  TotalCharges_1242.45  TotalCharges_1244.5  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1244.8  TotalCharges_1245.05  TotalCharges_1245.6  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1246.4  TotalCharges_1247.75  TotalCharges_1248.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1249.25  TotalCharges_125  TotalCharges_125.5  \\\n",
+              "0                 False             False               False   \n",
+              "1                 False             False               False   \n",
+              "2                 False             False               False   \n",
+              "3                 False             False               False   \n",
+              "4                 False             False               False   \n",
+              "\n",
+              "   TotalCharges_125.95  TotalCharges_1250.1  TotalCharges_1250.85  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1252  TotalCharges_1252.85  TotalCharges_1253.15  \\\n",
+              "0              False                 False                 False   \n",
+              "1              False                 False                 False   \n",
+              "2              False                 False                 False   \n",
+              "3              False                 False                 False   \n",
+              "4              False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1253.5  TotalCharges_1253.65  TotalCharges_1253.8  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1253.9  TotalCharges_1254.7  TotalCharges_1255.1  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1258.15  TotalCharges_1258.3  TotalCharges_1258.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1258.6  TotalCharges_1259  TotalCharges_1259.35  \\\n",
+              "0                False              False                 False   \n",
+              "1                False              False                 False   \n",
+              "2                False              False                 False   \n",
+              "3                False              False                 False   \n",
+              "4                False              False                 False   \n",
+              "\n",
+              "   TotalCharges_126.05  TotalCharges_1260.7  TotalCharges_1261  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_1261.35  TotalCharges_1261.45  TotalCharges_1261.7  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1263.05  TotalCharges_1263.85  TotalCharges_1263.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1264.2  TotalCharges_1265.65  TotalCharges_1266.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1266.35  TotalCharges_1266.4  TotalCharges_1267  \\\n",
+              "0                 False                False              False   \n",
+              "1                 False                False              False   \n",
+              "2                 False                False              False   \n",
+              "3                 False                False              False   \n",
+              "4                 False                False              False   \n",
+              "\n",
+              "   TotalCharges_1267.05  TotalCharges_1267.2  TotalCharges_1267.95  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1268.85  TotalCharges_1269.1  TotalCharges_1269.55  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1269.6  TotalCharges_127.1  TotalCharges_1270.2  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_1270.25  TotalCharges_1270.55  TotalCharges_1271.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1272.05  TotalCharges_1273.3  TotalCharges_1274.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1274.45  TotalCharges_1275.6  TotalCharges_1275.65  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1275.7  TotalCharges_1275.85  TotalCharges_1277.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1277.75  TotalCharges_1278.8  TotalCharges_1279  \\\n",
+              "0                 False                False              False   \n",
+              "1                 False                False              False   \n",
+              "2                 False                False              False   \n",
+              "3                 False                False              False   \n",
+              "4                 False                False              False   \n",
+              "\n",
+              "   TotalCharges_128.6  TotalCharges_1281  TotalCharges_1281.25  \\\n",
+              "0               False              False                 False   \n",
+              "1               False              False                 False   \n",
+              "2               False              False                 False   \n",
+              "3               False              False                 False   \n",
+              "4               False              False                 False   \n",
+              "\n",
+              "   TotalCharges_1282.85  TotalCharges_1284.2  TotalCharges_1285.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1285.8  TotalCharges_1286  TotalCharges_1286.05  \\\n",
+              "0                False              False                 False   \n",
+              "1                False              False                 False   \n",
+              "2                False              False                 False   \n",
+              "3                False              False                 False   \n",
+              "4                False              False                 False   \n",
+              "\n",
+              "   TotalCharges_1287.85  TotalCharges_1288  TotalCharges_1288.3  \\\n",
+              "0                 False              False                False   \n",
+              "1                 False              False                False   \n",
+              "2                 False              False                False   \n",
+              "3                 False              False                False   \n",
+              "4                 False              False                False   \n",
+              "\n",
+              "   TotalCharges_1288.75  TotalCharges_129.15  TotalCharges_129.2  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_129.55  TotalCharges_129.6  TotalCharges_1290  \\\n",
+              "0                False               False              False   \n",
+              "1                False               False              False   \n",
+              "2                False               False              False   \n",
+              "3                False               False              False   \n",
+              "4                False               False              False   \n",
+              "\n",
+              "   TotalCharges_1291.3  TotalCharges_1291.35  TotalCharges_1292.2  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1292.6  TotalCharges_1292.65  TotalCharges_1293.8  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1294.6  TotalCharges_1295.4  TotalCharges_1296.15  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1296.8  TotalCharges_1297.35  TotalCharges_1297.8  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1298.7  TotalCharges_1299.1  TotalCharges_1299.8  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_130.1  TotalCharges_130.15  TotalCharges_130.25  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_130.5  TotalCharges_130.55  TotalCharges_130.75  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_1301  TotalCharges_1301.1  TotalCharges_1301.7  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_1301.9  TotalCharges_1302.65  TotalCharges_1303.25  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1303.5  TotalCharges_1304.8  TotalCharges_1304.85  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1305.95  TotalCharges_1306.3  TotalCharges_1307.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1308.1  TotalCharges_1308.4  TotalCharges_1309  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_1309.15  TotalCharges_131.05  TotalCharges_131.65  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1311.3  TotalCharges_1311.6  TotalCharges_1311.75  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1312.15  TotalCharges_1312.45  TotalCharges_1313.25  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1313.55  TotalCharges_1315  TotalCharges_1315.35  \\\n",
+              "0                 False              False                 False   \n",
+              "1                 False              False                 False   \n",
+              "2                 False              False                 False   \n",
+              "3                 False              False                 False   \n",
+              "4                 False              False                 False   \n",
+              "\n",
+              "   TotalCharges_1316.9  TotalCharges_1317.95  TotalCharges_1319.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1319.95  TotalCharges_132.2  TotalCharges_132.25  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_132.4  TotalCharges_1321.3  TotalCharges_1322.55  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1322.85  TotalCharges_1323.7  TotalCharges_1325.85  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1326.25  TotalCharges_1326.35  TotalCharges_1327.15  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1327.4  TotalCharges_1327.85  TotalCharges_1328.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1328.35  TotalCharges_1329.15  TotalCharges_1329.2  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1331.05  TotalCharges_1332.4  TotalCharges_1334  \\\n",
+              "0                 False                False              False   \n",
+              "1                 False                False              False   \n",
+              "2                 False                False              False   \n",
+              "3                 False                False              False   \n",
+              "4                 False                False              False   \n",
+              "\n",
+              "   TotalCharges_1334.45  TotalCharges_1334.5  TotalCharges_1335.2  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1336.1  TotalCharges_1336.15  TotalCharges_1336.35  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1336.65  TotalCharges_1336.8  TotalCharges_1336.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1337.45  TotalCharges_1337.5  TotalCharges_1338.15  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1339.8  TotalCharges_134.05  TotalCharges_134.35  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_134.5  TotalCharges_134.6  TotalCharges_134.7  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_134.75  TotalCharges_1340.1  TotalCharges_1341.5  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1342.15  TotalCharges_1343.4  TotalCharges_1344.5  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1345.55  TotalCharges_1345.65  TotalCharges_1345.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1345.85  TotalCharges_1346.2  TotalCharges_1346.3  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1346.9  TotalCharges_1347.15  TotalCharges_1348.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1348.9  TotalCharges_1348.95  TotalCharges_135  \\\n",
+              "0                False                 False             False   \n",
+              "1                False                 False             False   \n",
+              "2                False                 False             False   \n",
+              "3                False                 False             False   \n",
+              "4                False                 False             False   \n",
+              "\n",
+              "   TotalCharges_135.2  TotalCharges_135.75  TotalCharges_1350.15  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1354.4  TotalCharges_1355.1  TotalCharges_1355.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1356.3  TotalCharges_1356.7  TotalCharges_1357.1  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1358.6  TotalCharges_1358.85  TotalCharges_1359  \\\n",
+              "0                False                 False              False   \n",
+              "1                False                 False              False   \n",
+              "2                False                 False              False   \n",
+              "3                False                 False              False   \n",
+              "4                False                 False              False   \n",
+              "\n",
+              "   TotalCharges_1359.45  TotalCharges_1359.5  TotalCharges_1359.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_136.05  TotalCharges_136.75  TotalCharges_1360.25  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1362.85  TotalCharges_1363.25  TotalCharges_1363.45  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1364.3  TotalCharges_1364.75  TotalCharges_1367.75  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1369.8  TotalCharges_137.1  TotalCharges_137.25  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_137.6  TotalCharges_137.85  TotalCharges_137.95  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_1370.35  TotalCharges_1372.45  TotalCharges_1372.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1373  TotalCharges_1373.05  TotalCharges_1374.2  \\\n",
+              "0              False                 False                False   \n",
+              "1              False                 False                False   \n",
+              "2              False                 False                False   \n",
+              "3              False                 False                False   \n",
+              "4              False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1374.35  TotalCharges_1374.9  TotalCharges_1375.15  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1375.4  TotalCharges_1375.6  TotalCharges_1376.5  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1377.7  TotalCharges_1378.25  TotalCharges_1378.45  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1378.75  TotalCharges_1379.6  TotalCharges_1379.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_138.85  TotalCharges_1380.1  TotalCharges_1380.4  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1381.2  TotalCharges_1381.8  TotalCharges_1382.9  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1383.6  TotalCharges_1384.75  TotalCharges_1385.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1386.8  TotalCharges_1386.9  TotalCharges_1387  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_1387.35  TotalCharges_1387.45  TotalCharges_1388  \\\n",
+              "0                 False                 False              False   \n",
+              "1                 False                 False              False   \n",
+              "2                 False                 False              False   \n",
+              "3                 False                 False              False   \n",
+              "4                 False                 False              False   \n",
+              "\n",
+              "   TotalCharges_1388.45  TotalCharges_1388.75  TotalCharges_1389.2  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1389.35  TotalCharges_1389.6  TotalCharges_1389.85  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_139.05  TotalCharges_139.25  TotalCharges_139.35  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_139.4  TotalCharges_139.75  TotalCharges_1390.6  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_1390.85  TotalCharges_1391.15  TotalCharges_1391.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1392.25  TotalCharges_1393.6  TotalCharges_1394.55  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1395.05  TotalCharges_1396  TotalCharges_1396.25  \\\n",
+              "0                 False              False                 False   \n",
+              "1                 False              False                 False   \n",
+              "2                 False              False                 False   \n",
+              "3                 False              False                 False   \n",
+              "4                 False              False                 False   \n",
+              "\n",
+              "   TotalCharges_1396.9  TotalCharges_1397.3  TotalCharges_1397.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1398.25  TotalCharges_1398.6  TotalCharges_1399.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_140.1  TotalCharges_140.4  TotalCharges_140.7  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_140.95  TotalCharges_1400.3  TotalCharges_1400.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1400.85  TotalCharges_1401.15  TotalCharges_1401.4  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1402.25  TotalCharges_1403.1  TotalCharges_1404.65  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1405.3  TotalCharges_1406  TotalCharges_1406.65  \\\n",
+              "0                False              False                 False   \n",
+              "1                False              False                 False   \n",
+              "2                False              False                 False   \n",
+              "3                False              False                 False   \n",
+              "4                False              False                 False   \n",
+              "\n",
+              "   TotalCharges_1406.9  TotalCharges_141.1  TotalCharges_141.45  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_141.5  TotalCharges_141.6  TotalCharges_141.65  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_141.7  TotalCharges_1410.25  TotalCharges_1411.2  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1411.35  TotalCharges_1411.65  TotalCharges_1411.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1412.4  TotalCharges_1412.65  TotalCharges_1413  \\\n",
+              "0                False                 False              False   \n",
+              "1                False                 False              False   \n",
+              "2                False                 False              False   \n",
+              "3                False                 False              False   \n",
+              "4                False                 False              False   \n",
+              "\n",
+              "   TotalCharges_1414.2  TotalCharges_1414.45  TotalCharges_1414.65  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1414.8  TotalCharges_1415  TotalCharges_1415.55  \\\n",
+              "0                False              False                 False   \n",
+              "1                False              False                 False   \n",
+              "2                False              False                 False   \n",
+              "3                False              False                 False   \n",
+              "4                False              False                 False   \n",
+              "\n",
+              "   TotalCharges_1415.85  TotalCharges_1416.2  TotalCharges_1416.5  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1416.75  TotalCharges_1417.9  TotalCharges_1419.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_142.35  TotalCharges_1421.75  TotalCharges_1421.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1422.05  TotalCharges_1422.1  TotalCharges_1422.65  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1423.05  TotalCharges_1423.15  TotalCharges_1423.35  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1423.65  TotalCharges_1423.85  TotalCharges_1424.2  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1424.4  TotalCharges_1424.5  TotalCharges_1424.6  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1424.9  TotalCharges_1424.95  TotalCharges_1425.45  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1426.4  TotalCharges_1426.45  TotalCharges_1426.75  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1427.55  TotalCharges_1429.65  TotalCharges_143.35  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_143.65  TotalCharges_143.9  TotalCharges_1430.05  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_1430.25  TotalCharges_1430.95  TotalCharges_1431.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1432.55  TotalCharges_1433.8  TotalCharges_1434.1  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1434.6  TotalCharges_1436.95  TotalCharges_1438.05  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1439.35  TotalCharges_144  TotalCharges_144.15  \\\n",
+              "0                 False             False                False   \n",
+              "1                 False             False                False   \n",
+              "2                 False             False                False   \n",
+              "3                 False             False                False   \n",
+              "4                 False             False                False   \n",
+              "\n",
+              "   TotalCharges_144.35  TotalCharges_144.55  TotalCharges_144.8  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_144.95  TotalCharges_1440.75  TotalCharges_1441.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1441.65  TotalCharges_1441.8  TotalCharges_1441.95  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1442  TotalCharges_1442.2  TotalCharges_1442.6  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_1442.65  TotalCharges_1443.65  TotalCharges_1444.05  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1444.65  TotalCharges_1445.2  TotalCharges_1445.3  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1445.95  TotalCharges_1446.8  TotalCharges_1447.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1448.6  TotalCharges_1448.8  TotalCharges_145  \\\n",
+              "0                False                False             False   \n",
+              "1                False                False             False   \n",
+              "2                False                False             False   \n",
+              "3                False                False             False   \n",
+              "4                False                False             False   \n",
+              "\n",
+              "   TotalCharges_145.15  TotalCharges_145.4  TotalCharges_1451.1  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_1451.6  TotalCharges_1451.9  TotalCharges_1453.1  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1454.15  TotalCharges_1454.25  TotalCharges_1457.25  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1458.1  TotalCharges_1459.35  TotalCharges_146.05  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_146.3  TotalCharges_146.4  TotalCharges_146.6  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_146.65  TotalCharges_146.9  TotalCharges_1460.65  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_1460.85  TotalCharges_1461.15  TotalCharges_1461.45  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1462.05  TotalCharges_1462.6  TotalCharges_1463.45  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1463.5  TotalCharges_1463.7  TotalCharges_1465.75  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1466.1  TotalCharges_1468.75  TotalCharges_1468.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_147.15  TotalCharges_147.5  TotalCharges_147.55  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_147.75  TotalCharges_147.8  TotalCharges_1470.05  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_1470.95  TotalCharges_1471.75  TotalCharges_1474.35  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1474.75  TotalCharges_1474.9  TotalCharges_1476.25  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1477.65  TotalCharges_1478.85  TotalCharges_148.05  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1482.3  TotalCharges_1483.25  TotalCharges_1489.3  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_149.05  TotalCharges_149.55  TotalCharges_1490.4  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1490.95  TotalCharges_1492.1  TotalCharges_1493.1  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1493.2  TotalCharges_1493.55  TotalCharges_1493.75  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1494.5  TotalCharges_1495.1  TotalCharges_1496.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1496.9  TotalCharges_1497.05  TotalCharges_1497.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1498.2  TotalCharges_1498.35  TotalCharges_1498.55  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1498.65  TotalCharges_1498.85  TotalCharges_150  \\\n",
+              "0                 False                 False             False   \n",
+              "1                 False                 False             False   \n",
+              "2                 False                 False             False   \n",
+              "3                 False                 False             False   \n",
+              "4                 False                 False             False   \n",
+              "\n",
+              "   TotalCharges_150.35  TotalCharges_150.6  TotalCharges_150.75  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_150.85  TotalCharges_1500.25  TotalCharges_1500.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1500.95  TotalCharges_1501.75  TotalCharges_1502.25  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1502.65  TotalCharges_1504.05  TotalCharges_1505.05  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1505.15  TotalCharges_1505.35  TotalCharges_1505.45  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1505.85  TotalCharges_1505.9  TotalCharges_1506.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1507  TotalCharges_1509.8  TotalCharges_1509.9  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_151.3  TotalCharges_151.65  TotalCharges_151.75  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                 True                False   \n",
+              "\n",
+              "   TotalCharges_151.8  TotalCharges_1510.3  TotalCharges_1510.5  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_1511.2  TotalCharges_1513.6  TotalCharges_1514.85  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1515.1  TotalCharges_1516.6  TotalCharges_1517.5  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1519  TotalCharges_152.3  TotalCharges_152.45  \\\n",
+              "0              False               False                False   \n",
+              "1              False               False                False   \n",
+              "2              False               False                False   \n",
+              "3              False               False                False   \n",
+              "4              False               False                False   \n",
+              "\n",
+              "   TotalCharges_152.6  TotalCharges_152.7  TotalCharges_152.95  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_1520.1  TotalCharges_1520.9  TotalCharges_1521.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1522.65  TotalCharges_1522.7  TotalCharges_1523.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1524.85  TotalCharges_1525.35  TotalCharges_1527.35  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1527.5  TotalCharges_1529.2  TotalCharges_1529.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1529.65  TotalCharges_153.05  TotalCharges_153.3  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_153.8  TotalCharges_153.95  TotalCharges_1530.6  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_1531.4  TotalCharges_1532.45  TotalCharges_1533.8  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1534.05  TotalCharges_1534.75  TotalCharges_1536.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1537.85  TotalCharges_1537.9  TotalCharges_1538.6  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1539.45  TotalCharges_1539.75  TotalCharges_1539.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_154.3  TotalCharges_154.55  TotalCharges_154.65  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_154.8  TotalCharges_154.85  TotalCharges_1540.05  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1540.2  TotalCharges_1540.35  TotalCharges_1544.05  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1545.4  TotalCharges_1546.3  TotalCharges_1547.35  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1548.65  TotalCharges_1549.75  TotalCharges_155.35  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_155.65  TotalCharges_155.8  TotalCharges_155.9  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_1551.6  TotalCharges_1553.2  TotalCharges_1553.9  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1553.95  TotalCharges_1554  TotalCharges_1554.9  \\\n",
+              "0                 False              False                False   \n",
+              "1                 False              False                False   \n",
+              "2                 False              False                False   \n",
+              "3                 False              False                False   \n",
+              "4                 False              False                False   \n",
+              "\n",
+              "   TotalCharges_1555.65  TotalCharges_1556.85  TotalCharges_1558.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1558.7  TotalCharges_1559.15  TotalCharges_1559.25  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1559.3  TotalCharges_1559.45  TotalCharges_156.1  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_156.25  TotalCharges_156.35  TotalCharges_156.4  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_156.85  TotalCharges_1561.5  TotalCharges_1563.9  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1563.95  TotalCharges_1564.05  TotalCharges_1564.4  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1565.7  TotalCharges_1566.75  TotalCharges_1566.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1567  TotalCharges_1567.55  TotalCharges_157.55  \\\n",
+              "0              False                 False                False   \n",
+              "1              False                 False                False   \n",
+              "2              False                 False                False   \n",
+              "3              False                 False                False   \n",
+              "4              False                 False                False   \n",
+              "\n",
+              "   TotalCharges_157.65  TotalCharges_157.75  TotalCharges_1570.7  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1573.05  TotalCharges_1573.7  TotalCharges_1573.95  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1574.45  TotalCharges_1574.5  TotalCharges_1579.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_158.35  TotalCharges_158.4  TotalCharges_158.95  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_1580.1  TotalCharges_1581.2  TotalCharges_1581.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1582.75  TotalCharges_1583.5  TotalCharges_1584.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1587.55  TotalCharges_1588.25  TotalCharges_1588.7  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1588.75  TotalCharges_159.15  TotalCharges_159.2  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_159.35  TotalCharges_159.4  TotalCharges_159.45  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_1592.35  TotalCharges_1593.1  TotalCharges_1594.75  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1595.5  TotalCharges_1596.6  TotalCharges_1597.05  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1597.25  TotalCharges_1597.4  TotalCharges_160.05  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_160.75  TotalCharges_160.8  TotalCharges_160.85  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_1600.25  TotalCharges_1600.95  TotalCharges_1601.2  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1601.5  TotalCharges_1604.5  TotalCharges_1607.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1608.15  TotalCharges_161.15  TotalCharges_161.45  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_161.5  TotalCharges_161.65  TotalCharges_161.95  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_1611  TotalCharges_1611.15  TotalCharges_1611.65  \\\n",
+              "0              False                 False                 False   \n",
+              "1              False                 False                 False   \n",
+              "2              False                 False                 False   \n",
+              "3              False                 False                 False   \n",
+              "4              False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1612.2  TotalCharges_1612.75  TotalCharges_1614.05  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1614.2  TotalCharges_1614.7  TotalCharges_1614.9  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1615.1  TotalCharges_1616.15  TotalCharges_1617.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1618.2  TotalCharges_162.15  TotalCharges_162.3  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_162.45  TotalCharges_162.55  TotalCharges_1620.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1620.25  TotalCharges_1620.45  TotalCharges_1620.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1621.35  TotalCharges_1622.45  TotalCharges_1623.15  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1623.4  TotalCharges_1625  TotalCharges_1625.65  \\\n",
+              "0                False              False                 False   \n",
+              "1                False              False                 False   \n",
+              "2                False              False                 False   \n",
+              "3                False              False                 False   \n",
+              "4                False              False                 False   \n",
+              "\n",
+              "   TotalCharges_1626.05  TotalCharges_1626.4  TotalCharges_1629.2  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_163.2  TotalCharges_163.55  TotalCharges_163.6  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_163.7  TotalCharges_1630.4  TotalCharges_1633  \\\n",
+              "0               False                False              False   \n",
+              "1               False                False              False   \n",
+              "2               False                False              False   \n",
+              "3               False                False              False   \n",
+              "4               False                False              False   \n",
+              "\n",
+              "   TotalCharges_1636.95  TotalCharges_1637.3  TotalCharges_1637.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1638.7  TotalCharges_1639.3  TotalCharges_164.3  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_164.5  TotalCharges_164.6  TotalCharges_164.85  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_1640  TotalCharges_1641.3  TotalCharges_1641.8  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_1642.75  TotalCharges_1643.25  TotalCharges_1643.55  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1646.45  TotalCharges_1647  TotalCharges_1648.45  \\\n",
+              "0                 False              False                 False   \n",
+              "1                 False              False                 False   \n",
+              "2                 False              False                 False   \n",
+              "3                 False              False                 False   \n",
+              "4                 False              False                 False   \n",
+              "\n",
+              "   TotalCharges_165  TotalCharges_165.2  TotalCharges_165.35  \\\n",
+              "0             False               False                False   \n",
+              "1             False               False                False   \n",
+              "2             False               False                False   \n",
+              "3             False               False                False   \n",
+              "4             False               False                False   \n",
+              "\n",
+              "   TotalCharges_165.4  TotalCharges_165.45  TotalCharges_165.5  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_1651.95  TotalCharges_1652.1  TotalCharges_1652.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1652.95  TotalCharges_1653.45  TotalCharges_1653.85  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1654.45  TotalCharges_1654.6  TotalCharges_1654.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1654.75  TotalCharges_1654.85  TotalCharges_1655.35  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1657.4  TotalCharges_166.3  TotalCharges_1660  \\\n",
+              "0                False               False              False   \n",
+              "1                False               False              False   \n",
+              "2                False               False              False   \n",
+              "3                False               False              False   \n",
+              "4                False               False              False   \n",
+              "\n",
+              "   TotalCharges_1662.05  TotalCharges_1663.5  TotalCharges_1663.75  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1664.3  TotalCharges_1665.2  TotalCharges_1667.25  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1669.4  TotalCharges_167.2  TotalCharges_167.3  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_167.5  TotalCharges_1671.6  TotalCharges_1672.1  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_1672.15  TotalCharges_1672.35  TotalCharges_1673.4  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1673.8  TotalCharges_1676.95  TotalCharges_1677.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1678.05  TotalCharges_1679.25  TotalCharges_1679.4  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1679.65  TotalCharges_168.15  TotalCharges_168.2  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_168.5  TotalCharges_168.6  TotalCharges_168.65  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_168.9  TotalCharges_1680.25  TotalCharges_1681.6  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1682.05  TotalCharges_1682.4  TotalCharges_1683.6  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1683.7  TotalCharges_1685.9  TotalCharges_1686.15  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1686.85  TotalCharges_1687.95  TotalCharges_1688.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1689.45  TotalCharges_169.05  TotalCharges_169.45  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_169.65  TotalCharges_169.75  TotalCharges_169.8  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_1691.9  TotalCharges_1692.6  TotalCharges_1696.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1697.7  TotalCharges_1698.55  TotalCharges_1699.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_170.5  TotalCharges_170.85  TotalCharges_170.9  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_1700.9  TotalCharges_1701.65  TotalCharges_1702.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1704.95  TotalCharges_1706.45  TotalCharges_1709.1  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1709.15  TotalCharges_171  TotalCharges_171.15  \\\n",
+              "0                 False             False                False   \n",
+              "1                 False             False                False   \n",
+              "2                 False             False                False   \n",
+              "3                 False             False                False   \n",
+              "4                 False             False                False   \n",
+              "\n",
+              "   TotalCharges_171.45  TotalCharges_1710.15  TotalCharges_1710.45  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1710.9  TotalCharges_1712.7  TotalCharges_1712.9  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1713.1  TotalCharges_1714.55  TotalCharges_1714.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1715.1  TotalCharges_1715.15  TotalCharges_1715.65  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1716.45  TotalCharges_1718.2  TotalCharges_1718.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1718.95  TotalCharges_1719.15  TotalCharges_172.35  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_172.85  TotalCharges_1723.95  TotalCharges_1724.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1725  TotalCharges_1725.4  TotalCharges_1725.95  \\\n",
+              "0              False                False                 False   \n",
+              "1              False                False                 False   \n",
+              "2              False                False                 False   \n",
+              "3              False                False                 False   \n",
+              "4              False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1727.5  TotalCharges_1728.2  TotalCharges_1729.35  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_173  TotalCharges_173.15  TotalCharges_1730.35  \\\n",
+              "0             False                False                 False   \n",
+              "1             False                False                 False   \n",
+              "2             False                False                 False   \n",
+              "3             False                False                 False   \n",
+              "4             False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1730.65  TotalCharges_1732.6  TotalCharges_1732.95  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1733.15  TotalCharges_1734.2  TotalCharges_1734.5  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1734.65  TotalCharges_1737.45  TotalCharges_1738.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1739.6  TotalCharges_174.2  TotalCharges_174.3  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_174.45  TotalCharges_174.65  TotalCharges_174.7  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_174.75  TotalCharges_174.8  TotalCharges_1740.7  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_1740.8  TotalCharges_1742.45  TotalCharges_1742.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1742.75  TotalCharges_1742.95  TotalCharges_1743.05  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1743.5  TotalCharges_1743.9  TotalCharges_1745.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1745.5  TotalCharges_1747.2  TotalCharges_1747.85  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1748.55  TotalCharges_1748.9  TotalCharges_1750.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1750.85  TotalCharges_1752.45  TotalCharges_1752.55  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1752.65  TotalCharges_1753  TotalCharges_1755.35  \\\n",
+              "0                 False              False                 False   \n",
+              "1                 False              False                 False   \n",
+              "2                 False              False                 False   \n",
+              "3                 False              False                 False   \n",
+              "4                 False              False                 False   \n",
+              "\n",
+              "   TotalCharges_1756.2  TotalCharges_1756.6  TotalCharges_1758.6  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1758.9  TotalCharges_1759.4  TotalCharges_1759.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_176.2  TotalCharges_176.3  TotalCharges_1760.25  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_1761.05  TotalCharges_1761.45  TotalCharges_1763.55  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1764.75  TotalCharges_1765.95  TotalCharges_1766.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1767.35  TotalCharges_1769.6  TotalCharges_177.4  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_1772.25  TotalCharges_1775.8  TotalCharges_1776  \\\n",
+              "0                 False                False              False   \n",
+              "1                 False                False              False   \n",
+              "2                 False                False              False   \n",
+              "3                 False                False              False   \n",
+              "4                 False                False              False   \n",
+              "\n",
+              "   TotalCharges_1776.45  TotalCharges_1776.55  TotalCharges_1776.95  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1777.6  TotalCharges_1777.9  TotalCharges_1778.5  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1778.7  TotalCharges_1779.95  TotalCharges_178.1  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_178.5  TotalCharges_178.7  TotalCharges_178.8  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_178.85  TotalCharges_1781.35  TotalCharges_1782  \\\n",
+              "0                False                 False              False   \n",
+              "1                False                 False              False   \n",
+              "2                False                 False              False   \n",
+              "3                False                 False              False   \n",
+              "4                False                 False              False   \n",
+              "\n",
+              "   TotalCharges_1782.05  TotalCharges_1782.4  TotalCharges_1783.6  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1783.75  TotalCharges_1784.5  TotalCharges_1784.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1785.65  TotalCharges_1787.35  TotalCharges_1789.25  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1789.65  TotalCharges_1789.9  TotalCharges_179.25  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_179.35  TotalCharges_179.85  TotalCharges_1790.15  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1790.35  TotalCharges_1790.6  TotalCharges_1790.65  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1790.8  TotalCharges_1793.25  TotalCharges_1794.65  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1794.8  TotalCharges_1796.55  TotalCharges_1797.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1797.75  TotalCharges_1798.65  TotalCharges_1798.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1799.3  TotalCharges_18.8  TotalCharges_18.85  \\\n",
+              "0                False              False               False   \n",
+              "1                False              False               False   \n",
+              "2                False              False               False   \n",
+              "3                False              False               False   \n",
+              "4                False              False               False   \n",
+              "\n",
+              "   TotalCharges_18.9  TotalCharges_180.25  TotalCharges_180.3  \\\n",
+              "0              False                False               False   \n",
+              "1              False                False               False   \n",
+              "2              False                False               False   \n",
+              "3              False                False               False   \n",
+              "4              False                False               False   \n",
+              "\n",
+              "   TotalCharges_180.7  TotalCharges_1800.05  TotalCharges_1801.1  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1801.9  TotalCharges_1802.15  TotalCharges_1802.55  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1803.7  TotalCharges_1806.35  TotalCharges_1808.7  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1809.35  TotalCharges_181.1  TotalCharges_181.5  \\\n",
+              "0                 False               False               False   \n",
+              "1                 False               False               False   \n",
+              "2                 False               False               False   \n",
+              "3                 False               False               False   \n",
+              "4                 False               False               False   \n",
+              "\n",
+              "   TotalCharges_181.6  TotalCharges_181.65  TotalCharges_181.7  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_181.8  TotalCharges_1810.55  TotalCharges_1810.85  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1813.1  TotalCharges_1813.35  TotalCharges_1815  \\\n",
+              "0                False                 False              False   \n",
+              "1                False                 False              False   \n",
+              "2                False                 False              False   \n",
+              "3                False                 False              False   \n",
+              "4                False                 False              False   \n",
+              "\n",
+              "   TotalCharges_1815.3  TotalCharges_1815.65  TotalCharges_1816.2  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1816.75  TotalCharges_1818.3  TotalCharges_1818.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1820.45  TotalCharges_1820.9  TotalCharges_1821.2  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1821.8  TotalCharges_1821.95  TotalCharges_1825.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1826.7  TotalCharges_183.15  TotalCharges_183.75  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1830.05  TotalCharges_1830.1  TotalCharges_1832.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1834.15  TotalCharges_1834.95  TotalCharges_1835.3  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1836.25  TotalCharges_1836.9  TotalCharges_1837.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1837.9  TotalCharges_1838.15  TotalCharges_184.05  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_184.1  TotalCharges_184.15  TotalCharges_184.4  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_184.65  TotalCharges_184.95  TotalCharges_1840.75  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                  True   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1841.2  TotalCharges_1841.9  TotalCharges_1842.7  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1842.8  TotalCharges_1843.05  TotalCharges_1845.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1846.65  TotalCharges_1847.55  TotalCharges_1848.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1849.2  TotalCharges_1849.95  TotalCharges_185.2  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_185.4  TotalCharges_185.55  TotalCharges_185.6  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_1850.65  TotalCharges_1851.45  TotalCharges_1852.85  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1855.65  TotalCharges_1856.4  TotalCharges_1857.25  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1857.3  TotalCharges_1857.75  TotalCharges_1857.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1859.1  TotalCharges_1859.2  TotalCharges_1859.5  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_186.05  TotalCharges_186.15  TotalCharges_186.3  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_1861.1  TotalCharges_1861.5  TotalCharges_1862.9  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1863.8  TotalCharges_1864.2  TotalCharges_1864.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1866.45  TotalCharges_1867.6  TotalCharges_1867.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1868.4  TotalCharges_187.35  TotalCharges_187.45  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_187.75  TotalCharges_1871.15  TotalCharges_1871.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1872.2  TotalCharges_1873.7  TotalCharges_1874.3  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1874.45  TotalCharges_1875.25  TotalCharges_1875.55  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1879.25  TotalCharges_188.1  TotalCharges_188.7  \\\n",
+              "0                 False               False               False   \n",
+              "1                 False               False               False   \n",
+              "2                 False               False               False   \n",
+              "3                 False               False               False   \n",
+              "4                 False               False               False   \n",
+              "\n",
+              "   TotalCharges_1880.85  TotalCharges_1882.55  TotalCharges_1882.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1884.65  TotalCharges_1885.15  TotalCharges_1886.25  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1886.4  TotalCharges_1887  TotalCharges_1888.25  \\\n",
+              "0                False              False                 False   \n",
+              "1                False              False                 False   \n",
+              "2                False              False                 False   \n",
+              "3                False              False                 False   \n",
+              "4                False              False                 False   \n",
+              "\n",
+              "   TotalCharges_1888.45  TotalCharges_1888.65  TotalCharges_1889.5  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                 True   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_189.1  TotalCharges_189.2  TotalCharges_189.45  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_189.95  TotalCharges_1893.5  TotalCharges_1893.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1898.1  TotalCharges_1899.65  TotalCharges_19  \\\n",
+              "0                False                 False            False   \n",
+              "1                False                 False            False   \n",
+              "2                False                 False            False   \n",
+              "3                False                 False            False   \n",
+              "4                False                 False            False   \n",
+              "\n",
+              "   TotalCharges_19.05  TotalCharges_19.1  TotalCharges_19.15  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_19.2  TotalCharges_19.25  TotalCharges_19.3  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_19.4  TotalCharges_19.45  TotalCharges_19.5  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_19.55  TotalCharges_19.6  TotalCharges_19.65  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_19.7  TotalCharges_19.75  TotalCharges_19.8  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_19.85  TotalCharges_19.9  TotalCharges_19.95  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_190.05  TotalCharges_190.1  TotalCharges_190.25  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_190.5  TotalCharges_1900.25  TotalCharges_1901  \\\n",
+              "0               False                 False              False   \n",
+              "1               False                 False              False   \n",
+              "2               False                 False              False   \n",
+              "3               False                 False              False   \n",
+              "4               False                 False              False   \n",
+              "\n",
+              "   TotalCharges_1901.05  TotalCharges_1901.25  TotalCharges_1901.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1902  TotalCharges_1905.4  TotalCharges_1905.7  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_1907.85  TotalCharges_1908.35  TotalCharges_191.05  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_191.1  TotalCharges_191.35  TotalCharges_1910.6  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_1910.75  TotalCharges_1911.5  TotalCharges_1912.15  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1912.85  TotalCharges_1914.5  TotalCharges_1914.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1916  TotalCharges_1916.2  TotalCharges_1917.1  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_1923.5  TotalCharges_1923.85  TotalCharges_1924.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1927.3  TotalCharges_1928.7  TotalCharges_1929  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_1929.35  TotalCharges_1929.95  TotalCharges_193.05  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_193.6  TotalCharges_193.8  TotalCharges_1930.9  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_1931.3  TotalCharges_1931.75  TotalCharges_1932.75  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1934.45  TotalCharges_1936.85  TotalCharges_1937.4  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1938.05  TotalCharges_1938.9  TotalCharges_1939.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_194.2  TotalCharges_194.55  TotalCharges_1940.8  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_1940.85  TotalCharges_1941.5  TotalCharges_1943.2  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_1943.9  TotalCharges_1948.35  TotalCharges_1949.4  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_195.05  TotalCharges_195.3  TotalCharges_195.65  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_1951  TotalCharges_1952.25  TotalCharges_1952.8  \\\n",
+              "0              False                 False                False   \n",
+              "1              False                 False                False   \n",
+              "2              False                 False                False   \n",
+              "3              False                 False                False   \n",
+              "4              False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1955.4  TotalCharges_1956.4  TotalCharges_1957.1  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1958.45  TotalCharges_1958.95  TotalCharges_1959.5  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_196.15  TotalCharges_196.35  TotalCharges_196.4  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_196.75  TotalCharges_196.9  TotalCharges_196.95  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_1961.6  TotalCharges_1964.6  TotalCharges_1968.1  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_197.4  TotalCharges_197.7  TotalCharges_1970.5  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_1971.15  TotalCharges_1971.5  TotalCharges_1972.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1973.75  TotalCharges_1974.8  TotalCharges_1975.85  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_1978.65  TotalCharges_198  TotalCharges_198.1  \\\n",
+              "0                 False             False               False   \n",
+              "1                 False             False               False   \n",
+              "2                 False             False               False   \n",
+              "3                 False             False               False   \n",
+              "4                 False             False               False   \n",
+              "\n",
+              "   TotalCharges_198.25  TotalCharges_198.5  TotalCharges_198.6  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_198.7  TotalCharges_1980.3  TotalCharges_1982.1  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_1982.6  TotalCharges_1983.15  TotalCharges_1985.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1988.05  TotalCharges_199.45  TotalCharges_199.75  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_199.85  TotalCharges_1990.5  TotalCharges_1992.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_1992.55  TotalCharges_1992.85  TotalCharges_1992.95  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_1993.2  TotalCharges_1993.25  TotalCharges_1993.8  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_1994.3  TotalCharges_20  TotalCharges_20.05  \\\n",
+              "0                False            False               False   \n",
+              "1                False            False               False   \n",
+              "2                False            False               False   \n",
+              "3                False            False               False   \n",
+              "4                False            False               False   \n",
+              "\n",
+              "   TotalCharges_20.1  TotalCharges_20.15  TotalCharges_20.2  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_20.25  TotalCharges_20.3  TotalCharges_20.35  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_20.4  TotalCharges_20.45  TotalCharges_20.5  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_20.55  TotalCharges_20.6  TotalCharges_20.65  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_20.7  TotalCharges_20.75  TotalCharges_20.8  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_20.85  TotalCharges_20.9  TotalCharges_20.95  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_200.2  TotalCharges_2000.2  TotalCharges_2001  \\\n",
+              "0               False                False              False   \n",
+              "1               False                False              False   \n",
+              "2               False                False              False   \n",
+              "3               False                False              False   \n",
+              "4               False                False              False   \n",
+              "\n",
+              "   TotalCharges_2001.5  TotalCharges_2003.6  TotalCharges_2006.1  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2006.95  TotalCharges_2007.25  TotalCharges_2007.85  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_201  TotalCharges_201.1  TotalCharges_201.7  \\\n",
+              "0             False               False               False   \n",
+              "1             False               False               False   \n",
+              "2             False               False               False   \n",
+              "3             False               False               False   \n",
+              "4             False               False               False   \n",
+              "\n",
+              "   TotalCharges_201.95  TotalCharges_2010.55  TotalCharges_2010.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2011.4  TotalCharges_2012.7  TotalCharges_2015.35  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2015.8  TotalCharges_2016.3  TotalCharges_2016.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2018.1  TotalCharges_2018.4  TotalCharges_2019.8  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_202.15  TotalCharges_202.25  TotalCharges_202.3  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_202.9  TotalCharges_2020.9  TotalCharges_2021.2  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_2021.35  TotalCharges_2023.55  TotalCharges_2024.1  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2025.1  TotalCharges_2028.8  TotalCharges_2029.05  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_203.95  TotalCharges_2030.3  TotalCharges_2030.75  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2031.95  TotalCharges_2032.3  TotalCharges_2033.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2033.85  TotalCharges_2034.25  TotalCharges_2036.55  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2038.7  TotalCharges_204.55  TotalCharges_204.7  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_2042.05  TotalCharges_2043.45  TotalCharges_2044.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2044.95  TotalCharges_2045.55  TotalCharges_2048.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2049.05  TotalCharges_205.05  TotalCharges_2053.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2054.4  TotalCharges_2058.5  TotalCharges_206.15  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_206.6  TotalCharges_2062.15  TotalCharges_2065.15  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2065.4  TotalCharges_2066  TotalCharges_2067  \\\n",
+              "0                False              False              False   \n",
+              "1                False              False              False   \n",
+              "2                False              False              False   \n",
+              "3                False              False              False   \n",
+              "4                False              False              False   \n",
+              "\n",
+              "   TotalCharges_2068.55  TotalCharges_207.35  TotalCharges_207.4  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_2070.05  TotalCharges_2070.6  TotalCharges_2070.75  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2072.75  TotalCharges_2075.1  TotalCharges_2076.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2076.2  TotalCharges_2077.95  TotalCharges_2078.55  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2078.95  TotalCharges_208  TotalCharges_208.25  \\\n",
+              "0                 False             False                False   \n",
+              "1                 False             False                False   \n",
+              "2                 False             False                False   \n",
+              "3                 False             False                False   \n",
+              "4                 False             False                False   \n",
+              "\n",
+              "   TotalCharges_208.45  TotalCharges_208.7  TotalCharges_208.85  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_2080.1  TotalCharges_2082.95  TotalCharges_2083.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2085.45  TotalCharges_2088.05  TotalCharges_2088.45  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2088.75  TotalCharges_2088.8  TotalCharges_209.1  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_209.9  TotalCharges_2090.25  TotalCharges_2092.9  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2093.4  TotalCharges_2093.9  TotalCharges_2094.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2094.9  TotalCharges_2095  TotalCharges_2096.1  \\\n",
+              "0                False              False                False   \n",
+              "1                False              False                False   \n",
+              "2                False              False                False   \n",
+              "3                False              False                False   \n",
+              "4                False              False                False   \n",
+              "\n",
+              "   TotalCharges_21  TotalCharges_21.05  TotalCharges_21.1  TotalCharges_210.3  \\\n",
+              "0            False               False              False               False   \n",
+              "1            False               False              False               False   \n",
+              "2            False               False              False               False   \n",
+              "3            False               False              False               False   \n",
+              "4            False               False              False               False   \n",
+              "\n",
+              "   TotalCharges_210.65  TotalCharges_210.75  TotalCharges_2104.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2106.05  TotalCharges_2106.3  TotalCharges_2107.15  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2108.35  TotalCharges_2109.35  TotalCharges_211.95  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2110.15  TotalCharges_2111.3  TotalCharges_2111.45  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2117.2  TotalCharges_2117.25  TotalCharges_2119.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_212.3  TotalCharges_212.4  TotalCharges_2122.05  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_2122.45  TotalCharges_213.35  TotalCharges_2130.45  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2130.55  TotalCharges_2134.3  TotalCharges_2135.5  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_2136.9  TotalCharges_2139.1  TotalCharges_2139.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_214.4  TotalCharges_214.55  TotalCharges_214.75  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_2142.8  TotalCharges_2145  TotalCharges_2146.5  \\\n",
+              "0                False              False                False   \n",
+              "1                False              False                False   \n",
+              "2                False              False                False   \n",
+              "3                False              False                False   \n",
+              "4                False              False                False   \n",
+              "\n",
+              "   TotalCharges_2149.05  TotalCharges_215.2  TotalCharges_215.25  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_215.8  TotalCharges_2151.6  TotalCharges_2156.25  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2157.3  TotalCharges_2157.5  TotalCharges_2157.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_216.2  TotalCharges_216.45  TotalCharges_216.75  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_216.9  TotalCharges_2162.6  TotalCharges_2165.05  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2168.15  TotalCharges_2168.9  TotalCharges_2169.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_2169.75  TotalCharges_2169.8  TotalCharges_217.1  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_217.45  TotalCharges_217.5  TotalCharges_217.55  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_2171.15  TotalCharges_2172.05  TotalCharges_2177.45  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2178.6  TotalCharges_218.5  TotalCharges_218.55  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_2180.55  TotalCharges_2181.55  TotalCharges_2181.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2184.35  TotalCharges_2184.6  TotalCharges_2184.85  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2186.4  TotalCharges_2187.15  TotalCharges_2187.55  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2188.45  TotalCharges_2188.5  TotalCharges_219  \\\n",
+              "0                 False                False             False   \n",
+              "1                 False                False             False   \n",
+              "2                 False                False             False   \n",
+              "3                 False                False             False   \n",
+              "4                 False                False             False   \n",
+              "\n",
+              "   TotalCharges_219.35  TotalCharges_219.5  TotalCharges_219.65  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_2191.15  TotalCharges_2191.7  TotalCharges_2192.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_2193  TotalCharges_2193.2  TotalCharges_2193.65  \\\n",
+              "0              False                False                 False   \n",
+              "1              False                False                 False   \n",
+              "2              False                False                 False   \n",
+              "3              False                False                 False   \n",
+              "4              False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2196.15  TotalCharges_2196.3  TotalCharges_2196.45  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2198.3  TotalCharges_2198.9  TotalCharges_2199.05  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_220.1  TotalCharges_220.35  TotalCharges_220.4  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_220.45  TotalCharges_220.6  TotalCharges_220.65  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_220.75  TotalCharges_220.8  TotalCharges_220.95  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_2200.25  TotalCharges_2200.7  TotalCharges_2201.75  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2203.1  TotalCharges_2203.65  TotalCharges_2203.7  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2204.35  TotalCharges_2208.05  TotalCharges_2208.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2209.15  TotalCharges_2209.75  TotalCharges_221.1  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_221.35  TotalCharges_221.7  TotalCharges_221.9  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_2210.2  TotalCharges_2211.8  TotalCharges_2212.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2215  TotalCharges_2215.25  TotalCharges_2215.4  \\\n",
+              "0              False                 False                False   \n",
+              "1              False                 False                False   \n",
+              "2              False                 False                False   \n",
+              "3              False                 False                False   \n",
+              "4              False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2215.45  TotalCharges_2217.15  TotalCharges_222.3  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_222.65  TotalCharges_2220.1  TotalCharges_2221.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2224.5  TotalCharges_2227.1  TotalCharges_2227.8  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_223.15  TotalCharges_223.45  TotalCharges_223.6  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_223.75  TotalCharges_223.9  TotalCharges_2230.85  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_2231.05  TotalCharges_2234.55  TotalCharges_2234.95  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2236.2  TotalCharges_2237.55  TotalCharges_2238.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2239.4  TotalCharges_2239.65  TotalCharges_224.05  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_224.5  TotalCharges_224.85  TotalCharges_2243.9  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_2244.95  TotalCharges_2245.4  TotalCharges_2248.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2249.1  TotalCharges_2249.95  TotalCharges_225.55  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_225.6  TotalCharges_225.65  TotalCharges_225.75  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_225.85  TotalCharges_2250.65  TotalCharges_2254.2  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2257.75  TotalCharges_2258.25  TotalCharges_2259.35  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_226.2  TotalCharges_226.45  TotalCharges_226.55  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_226.8  TotalCharges_226.95  TotalCharges_2263.4  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_2263.45  TotalCharges_2264.05  TotalCharges_2264.5  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2265  TotalCharges_2265.25  TotalCharges_227.35  \\\n",
+              "0              False                 False                False   \n",
+              "1              False                 False                False   \n",
+              "2              False                 False                False   \n",
+              "3              False                 False                False   \n",
+              "4              False                 False                False   \n",
+              "\n",
+              "   TotalCharges_227.45  TotalCharges_2271.85  TotalCharges_2272.8  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2274.1  TotalCharges_2274.35  TotalCharges_2274.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2275.1  TotalCharges_2276.1  TotalCharges_2276.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2277.65  TotalCharges_2278.75  TotalCharges_228  \\\n",
+              "0                 False                 False             False   \n",
+              "1                 False                 False             False   \n",
+              "2                 False                 False             False   \n",
+              "3                 False                 False             False   \n",
+              "4                 False                 False             False   \n",
+              "\n",
+              "   TotalCharges_228.4  TotalCharges_228.65  TotalCharges_228.75  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_2281.6  TotalCharges_2282.55  TotalCharges_2282.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2283.15  TotalCharges_2283.3  TotalCharges_2287.25  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2288.7  TotalCharges_2289.9  TotalCharges_229.4  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_229.5  TotalCharges_229.55  TotalCharges_229.6  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_229.7  TotalCharges_2291.2  TotalCharges_2292.75  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2293.6  TotalCharges_2296.25  TotalCharges_2298.55  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2298.9  TotalCharges_23.45  TotalCharges_2301.15  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_2302.35  TotalCharges_2303.35  TotalCharges_2308.6  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2309.55  TotalCharges_231.45  TotalCharges_231.8  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_2310.2  TotalCharges_2312.55  TotalCharges_2313.8  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2316.85  TotalCharges_2317.1  TotalCharges_2319.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_232.1  TotalCharges_232.35  TotalCharges_232.4  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_232.5  TotalCharges_232.55  TotalCharges_2320.8  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_2322.85  TotalCharges_2324.7  TotalCharges_2326.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_233.55  TotalCharges_233.65  TotalCharges_233.7  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_233.9  TotalCharges_2331.3  TotalCharges_2333.05  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2333.85  TotalCharges_2335.3  TotalCharges_2337.45  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2338.35  TotalCharges_2339.3  TotalCharges_234.85  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_2341.5  TotalCharges_2341.55  TotalCharges_2342.2  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2343.85  TotalCharges_2344.5  TotalCharges_2345.2  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_2345.55  TotalCharges_2347.85  TotalCharges_2347.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2348.45  TotalCharges_2349.8  TotalCharges_235  \\\n",
+              "0                 False                False             False   \n",
+              "1                 False                False             False   \n",
+              "2                 False                False             False   \n",
+              "3                 False                False             False   \n",
+              "4                 False                False             False   \n",
+              "\n",
+              "   TotalCharges_235.05  TotalCharges_235.1  TotalCharges_235.2  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_235.5  TotalCharges_235.65  TotalCharges_235.8  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_2351.45  TotalCharges_2351.8  TotalCharges_2354.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_2356.75  TotalCharges_2357.75  TotalCharges_2361.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2362.1  TotalCharges_2364  TotalCharges_2365.15  \\\n",
+              "0                False              False                 False   \n",
+              "1                False              False                 False   \n",
+              "2                False              False                 False   \n",
+              "3                False              False                 False   \n",
+              "4                False              False                 False   \n",
+              "\n",
+              "   TotalCharges_2368.4  TotalCharges_2369.05  TotalCharges_2369.3  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2369.7  TotalCharges_237.2  TotalCharges_237.25  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_237.3  TotalCharges_237.65  TotalCharges_237.7  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_237.75  TotalCharges_237.95  TotalCharges_2375.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2375.4  TotalCharges_2379.1  TotalCharges_238.1  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_238.15  TotalCharges_238.5  TotalCharges_2381.55  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_2383.6  TotalCharges_2384.15  TotalCharges_2386.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2387.75  TotalCharges_239  TotalCharges_239.05  \\\n",
+              "0                 False             False                False   \n",
+              "1                 False             False                False   \n",
+              "2                 False             False                False   \n",
+              "3                 False             False                False   \n",
+              "4                 False             False                False   \n",
+              "\n",
+              "   TotalCharges_239.45  TotalCharges_239.55  TotalCharges_239.75  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2390.45  TotalCharges_2391.15  TotalCharges_2391.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2395.05  TotalCharges_2395.7  TotalCharges_2398.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_24  TotalCharges_24.05  TotalCharges_24.2  TotalCharges_24.25  \\\n",
+              "0            False               False              False               False   \n",
+              "1            False               False              False               False   \n",
+              "2            False               False              False               False   \n",
+              "3            False               False              False               False   \n",
+              "4            False               False              False               False   \n",
+              "\n",
+              "   TotalCharges_24.4  TotalCharges_24.45  TotalCharges_24.6  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_24.7  TotalCharges_24.75  TotalCharges_24.8  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_24.9  TotalCharges_240.45  TotalCharges_2401.05  \\\n",
+              "0              False                False                 False   \n",
+              "1              False                False                 False   \n",
+              "2              False                False                 False   \n",
+              "3              False                False                 False   \n",
+              "4              False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2404.1  TotalCharges_2404.15  TotalCharges_2404.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2405.05  TotalCharges_2406.1  TotalCharges_2407.3  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_2409.9  TotalCharges_241.3  TotalCharges_2413.05  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_2414.55  TotalCharges_2415.95  TotalCharges_2416.1  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2416.55  TotalCharges_2419  TotalCharges_2419.55  \\\n",
+              "0                 False              False                 False   \n",
+              "1                 False              False                 False   \n",
+              "2                 False              False                 False   \n",
+              "3                 False              False                 False   \n",
+              "4                 False              False                 False   \n",
+              "\n",
+              "   TotalCharges_242  TotalCharges_242.05  TotalCharges_242.4  \\\n",
+              "0             False                False               False   \n",
+              "1             False                False               False   \n",
+              "2             False                False               False   \n",
+              "3             False                False               False   \n",
+              "4             False                False               False   \n",
+              "\n",
+              "   TotalCharges_242.8  TotalCharges_242.95  TotalCharges_2421.6  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_2421.75  TotalCharges_2423.4  TotalCharges_2424.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2424.45  TotalCharges_2424.5  TotalCharges_2425.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_2427.1  TotalCharges_2427.35  TotalCharges_2429.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_243.65  TotalCharges_2431.35  TotalCharges_2431.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2433.5  TotalCharges_2433.9  TotalCharges_2434.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2435.15  TotalCharges_2438.6  TotalCharges_244.1  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_244.45  TotalCharges_244.65  TotalCharges_244.75  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_244.8  TotalCharges_244.85  TotalCharges_2440.15  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2440.25  TotalCharges_2441.7  TotalCharges_2443.3  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_2444.25  TotalCharges_2447.45  TotalCharges_2447.95  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2448.5  TotalCharges_2448.75  TotalCharges_245.15  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_245.2  TotalCharges_2452.7  TotalCharges_2453.3  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_2455.05  TotalCharges_2459.8  TotalCharges_246.25  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_246.3  TotalCharges_246.5  TotalCharges_246.6  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_246.7  TotalCharges_2460.15  TotalCharges_2460.35  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2460.55  TotalCharges_2462.55  TotalCharges_2462.6  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2467.1  TotalCharges_2467.75  TotalCharges_247  \\\n",
+              "0                False                 False             False   \n",
+              "1                False                 False             False   \n",
+              "2                False                 False             False   \n",
+              "3                False                 False             False   \n",
+              "4                False                 False             False   \n",
+              "\n",
+              "   TotalCharges_247.25  TotalCharges_2470.1  TotalCharges_2471.25  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2471.6  TotalCharges_2473.95  TotalCharges_2475.35  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2479.05  TotalCharges_2479.25  TotalCharges_248.4  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_248.95  TotalCharges_2483.05  TotalCharges_2483.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2483.65  TotalCharges_2484  TotalCharges_249.4  \\\n",
+              "0                 False              False               False   \n",
+              "1                 False              False               False   \n",
+              "2                 False              False               False   \n",
+              "3                 False              False               False   \n",
+              "4                 False              False               False   \n",
+              "\n",
+              "   TotalCharges_249.55  TotalCharges_249.95  TotalCharges_2490.15  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2492.25  TotalCharges_2494.65  TotalCharges_2495.15  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2495.2  TotalCharges_2496.7  TotalCharges_2497.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2497.35  TotalCharges_2498.4  TotalCharges_2499.3  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_25  TotalCharges_25.05  TotalCharges_25.1  TotalCharges_25.15  \\\n",
+              "0            False               False              False               False   \n",
+              "1            False               False              False               False   \n",
+              "2            False               False              False               False   \n",
+              "3            False               False              False               False   \n",
+              "4            False               False              False               False   \n",
+              "\n",
+              "   TotalCharges_25.2  TotalCharges_25.25  TotalCharges_25.3  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_25.35  TotalCharges_25.4  TotalCharges_25.7  \\\n",
+              "0               False              False              False   \n",
+              "1               False              False              False   \n",
+              "2               False              False              False   \n",
+              "3               False              False              False   \n",
+              "4               False              False              False   \n",
+              "\n",
+              "   TotalCharges_25.75  TotalCharges_25.8  TotalCharges_25.85  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_250.05  TotalCharges_250.1  TotalCharges_250.8  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_2509.25  TotalCharges_2509.95  TotalCharges_251.25  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_251.6  TotalCharges_251.65  TotalCharges_251.75  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_2510.2  TotalCharges_2510.7  TotalCharges_2511.3  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2511.55  TotalCharges_2511.95  TotalCharges_2513.5  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2514.5  TotalCharges_2515.3  TotalCharges_2516.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_252  TotalCharges_252.75  TotalCharges_2522.4  \\\n",
+              "0             False                False                False   \n",
+              "1             False                False                False   \n",
+              "2             False                False                False   \n",
+              "3             False                False                False   \n",
+              "4             False                False                False   \n",
+              "\n",
+              "   TotalCharges_2524.45  TotalCharges_253  TotalCharges_253.8  \\\n",
+              "0                 False             False               False   \n",
+              "1                 False             False               False   \n",
+              "2                 False             False               False   \n",
+              "3                 False             False               False   \n",
+              "4                 False             False               False   \n",
+              "\n",
+              "   TotalCharges_253.9  TotalCharges_2530.4  TotalCharges_2531.4  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_2531.8  TotalCharges_2535.55  TotalCharges_2536.55  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2537  TotalCharges_2538.05  TotalCharges_2538.2  \\\n",
+              "0              False                 False                False   \n",
+              "1              False                 False                False   \n",
+              "2              False                 False                False   \n",
+              "3              False                 False                False   \n",
+              "4              False                 False                False   \n",
+              "\n",
+              "   TotalCharges_254.5  TotalCharges_2540.1  TotalCharges_2541.25  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2542.45  TotalCharges_2545.7  TotalCharges_2545.75  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2546.85  TotalCharges_2548.55  TotalCharges_2548.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2549.1  TotalCharges_255.25  TotalCharges_255.35  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_255.5  TotalCharges_255.55  TotalCharges_255.6  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_2550.9  TotalCharges_2552.9  TotalCharges_2553.35  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2553.7  TotalCharges_2554  TotalCharges_2555.05  \\\n",
+              "0                False              False                 False   \n",
+              "1                False              False                 False   \n",
+              "2                False              False                 False   \n",
+              "3                False              False                 False   \n",
+              "4                False              False                 False   \n",
+              "\n",
+              "   TotalCharges_2555.9  TotalCharges_256.25  TotalCharges_256.6  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_256.75  TotalCharges_2560.1  TotalCharges_2564.3  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2564.95  TotalCharges_2566.3  TotalCharges_2566.5  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_2568.15  TotalCharges_2568.55  TotalCharges_257  \\\n",
+              "0                 False                 False             False   \n",
+              "1                 False                 False             False   \n",
+              "2                 False                 False             False   \n",
+              "3                 False                 False             False   \n",
+              "4                 False                 False             False   \n",
+              "\n",
+              "   TotalCharges_257.05  TotalCharges_257.6  TotalCharges_2570  \\\n",
+              "0                False               False              False   \n",
+              "1                False               False              False   \n",
+              "2                False               False              False   \n",
+              "3                False               False              False   \n",
+              "4                False               False              False   \n",
+              "\n",
+              "   TotalCharges_2570.2  TotalCharges_2572.95  TotalCharges_2575.45  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2576.2  TotalCharges_2576.8  TotalCharges_258.35  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2583.75  TotalCharges_2585.95  TotalCharges_2586  \\\n",
+              "0                 False                 False              False   \n",
+              "1                 False                 False              False   \n",
+              "2                 False                 False              False   \n",
+              "3                 False                 False              False   \n",
+              "4                 False                 False              False   \n",
+              "\n",
+              "   TotalCharges_2587.7  TotalCharges_2588.95  TotalCharges_259.4  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_259.65  TotalCharges_259.8  TotalCharges_2595.25  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_2595.85  TotalCharges_2596.15  TotalCharges_2597.6  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2598.95  TotalCharges_2599.95  TotalCharges_260.7  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_260.8  TotalCharges_260.9  TotalCharges_2602.9  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_2603.1  TotalCharges_2603.3  TotalCharges_2603.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2606.35  TotalCharges_2607.6  TotalCharges_261.25  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_261.3  TotalCharges_261.65  TotalCharges_2610.65  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2613.4  TotalCharges_2614.1  TotalCharges_2618.3  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2619.15  TotalCharges_2619.25  TotalCharges_262.05  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_262.3  TotalCharges_2621.75  TotalCharges_2623.65  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2624.25  TotalCharges_2625.25  TotalCharges_2625.55  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2626.15  TotalCharges_2627.2  TotalCharges_2627.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2628.6  TotalCharges_263.05  TotalCharges_263.65  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2633.3  TotalCharges_2633.4  TotalCharges_2633.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2635  TotalCharges_2636.05  TotalCharges_2638.1  \\\n",
+              "0              False                 False                False   \n",
+              "1              False                 False                False   \n",
+              "2              False                 False                False   \n",
+              "3              False                 False                False   \n",
+              "4              False                 False                False   \n",
+              "\n",
+              "   TotalCharges_264.55  TotalCharges_264.8  TotalCharges_264.85  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_2640.55  TotalCharges_2642.05  TotalCharges_2647.1  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2647.2  TotalCharges_2649.15  TotalCharges_265.3  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_265.35  TotalCharges_265.45  TotalCharges_265.75  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_265.8  TotalCharges_2651.1  TotalCharges_2651.2  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_2653.65  TotalCharges_2654.05  TotalCharges_2655.25  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2656.3  TotalCharges_2656.5  TotalCharges_2656.7  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2657.55  TotalCharges_2658.4  TotalCharges_2658.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_2659.4  TotalCharges_2659.45  TotalCharges_266.6  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_266.8  TotalCharges_266.9  TotalCharges_266.95  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_2660.2  TotalCharges_2661.1  TotalCharges_2664.3  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2665  TotalCharges_2666.75  TotalCharges_2669.45  \\\n",
+              "0              False                 False                 False   \n",
+              "1              False                 False                 False   \n",
+              "2              False                 False                 False   \n",
+              "3              False                 False                 False   \n",
+              "4              False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_267  TotalCharges_267.35  TotalCharges_267.4  \\\n",
+              "0             False                False               False   \n",
+              "1             False                False               False   \n",
+              "2             False                False               False   \n",
+              "3             False                False               False   \n",
+              "4             False                False               False   \n",
+              "\n",
+              "   TotalCharges_267.6  TotalCharges_2673.45  TotalCharges_2674.15  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2679.7  TotalCharges_268.35  TotalCharges_268.4  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_268.45  TotalCharges_2680.15  TotalCharges_2681.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2683.2  TotalCharges_2684.35  TotalCharges_2684.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2686.05  TotalCharges_2688.45  TotalCharges_2688.85  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2689.35  TotalCharges_269.65  TotalCharges_2692.75  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2696.55  TotalCharges_2697.4  TotalCharges_2698.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_27.55  TotalCharges_270.15  TotalCharges_270.2  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_270.6  TotalCharges_270.7  TotalCharges_270.8  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_270.95  TotalCharges_2708.2  TotalCharges_2710.25  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2715.3  TotalCharges_2716.3  TotalCharges_2718.3  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2719.2  TotalCharges_272  TotalCharges_272.15  \\\n",
+              "0                False             False                False   \n",
+              "1                False             False                False   \n",
+              "2                False             False                False   \n",
+              "3                False             False                False   \n",
+              "4                False             False                False   \n",
+              "\n",
+              "   TotalCharges_272.2  TotalCharges_272.35  TotalCharges_272.95  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_2722.2  TotalCharges_2723.15  TotalCharges_2723.4  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2723.75  TotalCharges_2724.25  TotalCharges_2724.6  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2727.3  TotalCharges_2727.8  TotalCharges_2728.6  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_273  TotalCharges_273.2  TotalCharges_273.25  \\\n",
+              "0             False               False                False   \n",
+              "1             False               False                False   \n",
+              "2             False               False                False   \n",
+              "3             False               False                False   \n",
+              "4             False               False                False   \n",
+              "\n",
+              "   TotalCharges_273.4  TotalCharges_273.75  TotalCharges_2730.85  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2731  TotalCharges_2737.05  TotalCharges_274.35  \\\n",
+              "0              False                 False                False   \n",
+              "1              False                 False                False   \n",
+              "2              False                 False                False   \n",
+              "3              False                 False                False   \n",
+              "4              False                 False                False   \n",
+              "\n",
+              "   TotalCharges_274.7  TotalCharges_2743.45  TotalCharges_2745.2  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2745.7  TotalCharges_2747.2  TotalCharges_2748.7  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_275.4  TotalCharges_275.7  TotalCharges_275.9  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_2751  TotalCharges_2753.8  TotalCharges_2754  \\\n",
+              "0              False                False              False   \n",
+              "1              False                False              False   \n",
+              "2              False                False              False   \n",
+              "3              False                False              False   \n",
+              "4              False                False              False   \n",
+              "\n",
+              "   TotalCharges_2754.45  TotalCharges_2755.35  TotalCharges_2757.85  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2758.15  TotalCharges_276.5  TotalCharges_2762.75  \\\n",
+              "0                 False               False                 False   \n",
+              "1                 False               False                 False   \n",
+              "2                 False               False                 False   \n",
+              "3                 False               False                 False   \n",
+              "4                 False               False                 False   \n",
+              "\n",
+              "   TotalCharges_2763  TotalCharges_2763.35  TotalCharges_2766.4  \\\n",
+              "0              False                 False                False   \n",
+              "1              False                 False                False   \n",
+              "2              False                 False                False   \n",
+              "3              False                 False                False   \n",
+              "4              False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2768.35  TotalCharges_2768.65  TotalCharges_2773.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2774.55  TotalCharges_2779.5  TotalCharges_278.4  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_278.85  TotalCharges_2780.6  TotalCharges_2781.85  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2782.4  TotalCharges_2789.7  TotalCharges_279.2  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_279.25  TotalCharges_279.3  TotalCharges_279.5  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_279.55  TotalCharges_2790.65  TotalCharges_2791.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2793.55  TotalCharges_2796.35  TotalCharges_2796.45  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2799  TotalCharges_2799.75  TotalCharges_28.3  \\\n",
+              "0              False                 False              False   \n",
+              "1              False                 False              False   \n",
+              "2              False                 False              False   \n",
+              "3              False                 False              False   \n",
+              "4              False                 False              False   \n",
+              "\n",
+              "   TotalCharges_280  TotalCharges_280.35  TotalCharges_280.4  \\\n",
+              "0             False                False               False   \n",
+              "1             False                False               False   \n",
+              "2             False                False               False   \n",
+              "3             False                False               False   \n",
+              "4             False                False               False   \n",
+              "\n",
+              "   TotalCharges_280.85  TotalCharges_2802.3  TotalCharges_2804.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2806.9  TotalCharges_2807.1  TotalCharges_2807.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2809.05  TotalCharges_281  TotalCharges_2812.2  \\\n",
+              "0                 False             False                False   \n",
+              "1                 False             False                False   \n",
+              "2                 False             False                False   \n",
+              "3                 False             False                False   \n",
+              "4                 False             False                False   \n",
+              "\n",
+              "   TotalCharges_2813.05  TotalCharges_2815.25  TotalCharges_2816.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2820.65  TotalCharges_2823  TotalCharges_283.75  \\\n",
+              "0                 False              False                False   \n",
+              "1                 False              False                False   \n",
+              "2                 False              False                False   \n",
+              "3                 False              False                False   \n",
+              "4                 False              False                False   \n",
+              "\n",
+              "   TotalCharges_283.95  TotalCharges_2830.45  TotalCharges_2832.75  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2835.5  TotalCharges_2835.9  TotalCharges_2838.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2838.7  TotalCharges_2839.45  TotalCharges_2839.65  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2839.95  TotalCharges_284.3  TotalCharges_284.35  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_284.9  TotalCharges_2841.55  TotalCharges_2845.15  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2847.2  TotalCharges_2847.4  TotalCharges_2848.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_285.2  TotalCharges_2852.4  TotalCharges_2854.55  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2854.95  TotalCharges_2857.6  TotalCharges_286.8  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_2861.45  TotalCharges_2862.55  TotalCharges_2862.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2866.45  TotalCharges_2867.75  TotalCharges_2868.05  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2868.15  TotalCharges_2869.85  TotalCharges_287.4  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_287.85  TotalCharges_2871.5  TotalCharges_2874.15  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2874.45  TotalCharges_2877.05  TotalCharges_2877.95  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_2878.55  TotalCharges_2878.75  TotalCharges_2879.2  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2879.9  TotalCharges_288.05  TotalCharges_288.35  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2882.25  TotalCharges_2884.9  TotalCharges_2888.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_289.1  TotalCharges_289.3  TotalCharges_2890.65  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_2893.4  TotalCharges_2894.55  TotalCharges_2896.4  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2896.55  TotalCharges_2896.6  TotalCharges_2897.95  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2898.95  TotalCharges_29.15  TotalCharges_29.7  \\\n",
+              "0                 False               False              False   \n",
+              "1                 False               False              False   \n",
+              "2                 False               False              False   \n",
+              "3                 False               False              False   \n",
+              "4                 False               False              False   \n",
+              "\n",
+              "   TotalCharges_29.85  TotalCharges_29.9  TotalCharges_29.95  \\\n",
+              "0                True              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_290.55  TotalCharges_2901.8  TotalCharges_2907.35  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2907.55  TotalCharges_2908.2  TotalCharges_2909.95  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_291.4  TotalCharges_291.45  TotalCharges_291.9  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_2911.3  TotalCharges_2911.5  TotalCharges_2911.8  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2917.5  TotalCharges_2917.65  TotalCharges_2919.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_292.4  TotalCharges_292.8  TotalCharges_292.85  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_2921.75  TotalCharges_2924.05  TotalCharges_2928.5  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2929.75  TotalCharges_293.15  TotalCharges_293.3  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_293.65  TotalCharges_293.85  TotalCharges_2931  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_2933.2  TotalCharges_2933.95  TotalCharges_2934.3  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2936.25  TotalCharges_2937.65  TotalCharges_2939.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_294.2  TotalCharges_294.45  TotalCharges_294.5  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_294.9  TotalCharges_294.95  TotalCharges_2948.6  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_295.55  TotalCharges_295.65  TotalCharges_295.95  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2952.85  TotalCharges_2954.5  TotalCharges_2958.95  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_2959.8  TotalCharges_296.1  TotalCharges_296.15  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_2960.1  TotalCharges_2961.4  TotalCharges_2962  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_2964  TotalCharges_2964.05  TotalCharges_2964.8  \\\n",
+              "0              False                 False                False   \n",
+              "1              False                 False                False   \n",
+              "2              False                 False                False   \n",
+              "3              False                 False                False   \n",
+              "4              False                 False                False   \n",
+              "\n",
+              "   TotalCharges_2965.75  TotalCharges_2966.95  TotalCharges_2967.35  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_297.3  TotalCharges_297.35  TotalCharges_2970.3  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_2970.8  TotalCharges_2971.7  TotalCharges_2974.5  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_2976.95  TotalCharges_2978.3  TotalCharges_2979.2  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_2979.3  TotalCharges_2979.5  TotalCharges_298.35  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_298.45  TotalCharges_298.7  TotalCharges_2983.65  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_2983.8  TotalCharges_2985.25  TotalCharges_2989.6  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_299.05  TotalCharges_299.2  TotalCharges_299.3  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_299.4  TotalCharges_299.7  TotalCharges_299.75  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_2995.45  TotalCharges_2997.45  TotalCharges_2998  \\\n",
+              "0                 False                 False              False   \n",
+              "1                 False                 False              False   \n",
+              "2                 False                 False              False   \n",
+              "3                 False                 False              False   \n",
+              "4                 False                 False              False   \n",
+              "\n",
+              "   TotalCharges_30.2  TotalCharges_30.5  TotalCharges_30.55  \\\n",
+              "0              False              False               False   \n",
+              "1              False              False               False   \n",
+              "2              False              False               False   \n",
+              "3              False              False               False   \n",
+              "4              False              False               False   \n",
+              "\n",
+              "   TotalCharges_300.4  TotalCharges_300.7  TotalCharges_300.8  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_3000.25  TotalCharges_3001.2  TotalCharges_3003.55  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3004.15  TotalCharges_3005.8  TotalCharges_3007.25  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3008.15  TotalCharges_3008.55  TotalCharges_3009.5  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_301.4  TotalCharges_301.55  TotalCharges_301.9  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_3011.65  TotalCharges_3013.05  TotalCharges_3014.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3015.75  TotalCharges_3017.65  TotalCharges_3019.1  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3019.25  TotalCharges_3019.5  TotalCharges_3019.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_302.35  TotalCharges_302.45  TotalCharges_302.6  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_302.75  TotalCharges_3021.3  TotalCharges_3021.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3021.6  TotalCharges_3023.55  TotalCharges_3023.65  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3023.85  TotalCharges_3024.15  TotalCharges_3027.25  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3027.4  TotalCharges_3027.65  TotalCharges_3029.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_303.15  TotalCharges_303.7  TotalCharges_3030.6  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_3035.35  TotalCharges_3035.8  TotalCharges_3036.75  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3038.55  TotalCharges_304.6  TotalCharges_3042.25  \\\n",
+              "0                 False               False                 False   \n",
+              "1                 False               False                 False   \n",
+              "2                 False               False                 False   \n",
+              "3                 False               False                 False   \n",
+              "4                 False               False                 False   \n",
+              "\n",
+              "   TotalCharges_3043.6  TotalCharges_3043.7  TotalCharges_3045.75  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3046.05  TotalCharges_3046.15  TotalCharges_3046.4  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3047.15  TotalCharges_305.1  TotalCharges_305.55  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_305.95  TotalCharges_3050.15  TotalCharges_3053  \\\n",
+              "0                False                 False              False   \n",
+              "1                False                 False              False   \n",
+              "2                False                 False              False   \n",
+              "3                False                 False              False   \n",
+              "4                False                 False              False   \n",
+              "\n",
+              "   TotalCharges_3055.5  TotalCharges_3058.15  TotalCharges_3058.3  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3058.65  TotalCharges_306.05  TotalCharges_306.6  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_3062.45  TotalCharges_3066.45  TotalCharges_3067.2  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3068.6  TotalCharges_3069.45  TotalCharges_307  \\\n",
+              "0                False                 False             False   \n",
+              "1                False                 False             False   \n",
+              "2                False                 False             False   \n",
+              "3                False                 False             False   \n",
+              "4                False                 False             False   \n",
+              "\n",
+              "   TotalCharges_307.4  TotalCharges_307.6  TotalCharges_3077  \\\n",
+              "0               False               False              False   \n",
+              "1               False               False              False   \n",
+              "2               False               False              False   \n",
+              "3               False               False              False   \n",
+              "4               False               False              False   \n",
+              "\n",
+              "   TotalCharges_3078.1  TotalCharges_308.05  TotalCharges_308.1  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_308.25  TotalCharges_308.7  TotalCharges_3082.1  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_3084.9  TotalCharges_3085.35  TotalCharges_3088.25  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3088.75  TotalCharges_3089.1  TotalCharges_3089.6  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_309.1  TotalCharges_309.25  TotalCharges_309.35  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_309.4  TotalCharges_3090.05  TotalCharges_3090.65  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3091.75  TotalCharges_3092  TotalCharges_3092.65  \\\n",
+              "0                 False              False                 False   \n",
+              "1                 False              False                 False   \n",
+              "2                 False              False                 False   \n",
+              "3                 False              False                 False   \n",
+              "4                 False              False                 False   \n",
+              "\n",
+              "   TotalCharges_3094.05  TotalCharges_3094.65  TotalCharges_3096.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3097  TotalCharges_3097.2  TotalCharges_31.2  \\\n",
+              "0              False                False              False   \n",
+              "1              False                False              False   \n",
+              "2              False                False              False   \n",
+              "3              False                False              False   \n",
+              "4              False                False              False   \n",
+              "\n",
+              "   TotalCharges_31.35  TotalCharges_31.55  TotalCharges_31.9  \\\n",
+              "0               False               False              False   \n",
+              "1               False               False              False   \n",
+              "2               False               False              False   \n",
+              "3               False               False              False   \n",
+              "4               False               False              False   \n",
+              "\n",
+              "   TotalCharges_310.6  TotalCharges_3103.25  TotalCharges_3105.55  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3107.3  TotalCharges_3109.9  TotalCharges_311.6  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_3110.1  TotalCharges_3112.05  TotalCharges_3116.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3119.9  TotalCharges_312.7  TotalCharges_3121.1  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_3121.4  TotalCharges_3121.45  TotalCharges_3122.4  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3124.5  TotalCharges_3126.45  TotalCharges_3126.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3128.8  TotalCharges_313  TotalCharges_313.4  \\\n",
+              "0                False             False               False   \n",
+              "1                False             False               False   \n",
+              "2                False             False               False   \n",
+              "3                False             False               False   \n",
+              "4                False             False               False   \n",
+              "\n",
+              "   TotalCharges_313.45  TotalCharges_313.6  TotalCharges_3131.55  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_3131.8  TotalCharges_3132.75  TotalCharges_3134.7  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3139.8  TotalCharges_314.45  TotalCharges_314.55  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_314.6  TotalCharges_314.95  TotalCharges_3141.7  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_3143.65  TotalCharges_3145.15  TotalCharges_3145.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3147.15  TotalCharges_3147.5  TotalCharges_315.3  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_3152.5  TotalCharges_3157  TotalCharges_316.2  \\\n",
+              "0                False              False               False   \n",
+              "1                False              False               False   \n",
+              "2                False              False               False   \n",
+              "3                False              False               False   \n",
+              "4                False              False               False   \n",
+              "\n",
+              "   TotalCharges_316.9  TotalCharges_3160.55  TotalCharges_3161.2  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3161.4  TotalCharges_3161.6  TotalCharges_3162.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3165.6  TotalCharges_3166.9  TotalCharges_3168  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_3168.75  TotalCharges_3169.55  TotalCharges_317.25  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_317.75  TotalCharges_3171.15  TotalCharges_3171.6  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3173.35  TotalCharges_3175.85  TotalCharges_3177.25  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_318.1  TotalCharges_318.5  TotalCharges_318.6  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_318.9  TotalCharges_3180.5  TotalCharges_3181.8  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_3182.95  TotalCharges_3183.4  TotalCharges_3184.25  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3186.65  TotalCharges_3186.7  TotalCharges_3187.65  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_319.15  TotalCharges_319.6  TotalCharges_319.85  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_3190.25  TotalCharges_3190.65  TotalCharges_3196  \\\n",
+              "0                 False                 False              False   \n",
+              "1                 False                 False              False   \n",
+              "2                 False                 False              False   \n",
+              "3                 False                 False              False   \n",
+              "4                 False                 False              False   \n",
+              "\n",
+              "   TotalCharges_3198.6  TotalCharges_3199  TotalCharges_32.7  \\\n",
+              "0                False              False              False   \n",
+              "1                False              False              False   \n",
+              "2                False              False              False   \n",
+              "3                False              False              False   \n",
+              "4                False              False              False   \n",
+              "\n",
+              "   TotalCharges_320.4  TotalCharges_320.45  TotalCharges_3201.55  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3204.4  TotalCharges_3204.65  TotalCharges_3205.6  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3207.55  TotalCharges_3208.65  TotalCharges_321.05  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_321.4  TotalCharges_321.65  TotalCharges_321.7  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_321.75  TotalCharges_321.9  TotalCharges_3210.35  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_3211.2  TotalCharges_3211.9  TotalCharges_3213.75  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3217.55  TotalCharges_3217.65  TotalCharges_3219.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_322.5  TotalCharges_322.9  TotalCharges_3221.25  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_3229.4  TotalCharges_3229.65  TotalCharges_323.15  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_323.25  TotalCharges_323.45  TotalCharges_3231.05  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3233.6  TotalCharges_3233.85  TotalCharges_3236.35  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3237.05  TotalCharges_3238.4  TotalCharges_324.15  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_324.2  TotalCharges_324.25  TotalCharges_324.3  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_324.6  TotalCharges_324.8  TotalCharges_3242.5  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_3243.45  TotalCharges_3244.4  TotalCharges_3246.45  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3247.55  TotalCharges_3249.4  TotalCharges_325.45  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_3250.45  TotalCharges_3251.3  TotalCharges_3251.85  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3252  TotalCharges_3254.35  TotalCharges_3255.35  \\\n",
+              "0              False                 False                 False   \n",
+              "1              False                 False                 False   \n",
+              "2              False                 False                 False   \n",
+              "3              False                 False                 False   \n",
+              "4              False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_326.65  TotalCharges_326.8  TotalCharges_3260.1  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_3263.6  TotalCharges_3263.9  TotalCharges_3264.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3264.5  TotalCharges_3265.95  TotalCharges_3266  \\\n",
+              "0                False                 False              False   \n",
+              "1                False                 False              False   \n",
+              "2                False                 False              False   \n",
+              "3                False                 False              False   \n",
+              "4                False                 False              False   \n",
+              "\n",
+              "   TotalCharges_3268.05  TotalCharges_327.45  TotalCharges_3270.25  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3273.55  TotalCharges_3273.8  TotalCharges_3273.95  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3274.35  TotalCharges_3275.15  TotalCharges_328.95  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3281.65  TotalCharges_3282.75  TotalCharges_3283.05  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_329.75  TotalCharges_329.8  TotalCharges_329.95  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_3292.3  TotalCharges_3297  TotalCharges_33.2  \\\n",
+              "0                False              False              False   \n",
+              "1                False              False              False   \n",
+              "2                False              False              False   \n",
+              "3                False              False              False   \n",
+              "4                False              False              False   \n",
+              "\n",
+              "   TotalCharges_33.6  TotalCharges_33.7  TotalCharges_330.05  \\\n",
+              "0              False              False                False   \n",
+              "1              False              False                False   \n",
+              "2              False              False                False   \n",
+              "3              False              False                False   \n",
+              "4              False              False                False   \n",
+              "\n",
+              "   TotalCharges_330.15  TotalCharges_330.25  TotalCharges_330.6  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_330.8  TotalCharges_3301.05  TotalCharges_3303.05  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3306.85  TotalCharges_3309.25  TotalCharges_331.3  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_331.35  TotalCharges_331.6  TotalCharges_331.85  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_331.9  TotalCharges_3313.4  TotalCharges_3314.15  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3316.1  TotalCharges_332.45  TotalCharges_332.5  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_332.65  TotalCharges_3320.6  TotalCharges_3320.75  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3321.35  TotalCharges_3326.2  TotalCharges_3327.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_333.55  TotalCharges_333.6  TotalCharges_333.65  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_3330.1  TotalCharges_3334.9  TotalCharges_3334.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3339.05  TotalCharges_334.65  TotalCharges_334.8  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_3340.55  TotalCharges_3342  TotalCharges_3342.45  \\\n",
+              "0                 False              False                 False   \n",
+              "1                 False              False                 False   \n",
+              "2                 False              False                 False   \n",
+              "3                 False              False                 False   \n",
+              "4                 False              False                 False   \n",
+              "\n",
+              "   TotalCharges_3343.15  TotalCharges_3344.1  TotalCharges_3346.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_3348.1  TotalCharges_3349.1  TotalCharges_335.4  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_335.65  TotalCharges_335.75  TotalCharges_335.95  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_3351.55  TotalCharges_3353.4  TotalCharges_3355.65  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3357.9  TotalCharges_3358.65  TotalCharges_336.15  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_336.7  TotalCharges_3361.05  TotalCharges_3363.8  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3364.55  TotalCharges_3365.4  TotalCharges_3365.85  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3366.05  TotalCharges_3369.05  TotalCharges_3369.25  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_337.9  TotalCharges_3370.2  TotalCharges_3371  \\\n",
+              "0               False                False              False   \n",
+              "1               False                False              False   \n",
+              "2               False                False              False   \n",
+              "3               False                False              False   \n",
+              "4               False                False              False   \n",
+              "\n",
+              "   TotalCharges_3371.75  TotalCharges_3373.4  TotalCharges_3375.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_3377.8  TotalCharges_3379.25  TotalCharges_338.1  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_338.9  TotalCharges_3382.3  TotalCharges_3384  \\\n",
+              "0               False                False              False   \n",
+              "1               False                False              False   \n",
+              "2               False                False              False   \n",
+              "3               False                False              False   \n",
+              "4               False                False              False   \n",
+              "\n",
+              "   TotalCharges_3389.25  TotalCharges_339.9  TotalCharges_3395.8  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_3398.9  TotalCharges_3399.85  TotalCharges_34.7  \\\n",
+              "0                False                 False              False   \n",
+              "1                False                 False              False   \n",
+              "2                False                 False              False   \n",
+              "3                False                 False              False   \n",
+              "4                False                 False              False   \n",
+              "\n",
+              "   TotalCharges_34.75  TotalCharges_34.8  TotalCharges_340.25  \\\n",
+              "0               False              False                False   \n",
+              "1               False              False                False   \n",
+              "2               False              False                False   \n",
+              "3               False              False                False   \n",
+              "4               False              False                False   \n",
+              "\n",
+              "   TotalCharges_340.35  TotalCharges_340.4  TotalCharges_340.85  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_3403.4  TotalCharges_3409.1  TotalCharges_3409.6  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_341.35  TotalCharges_341.45  TotalCharges_341.6  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_3410  TotalCharges_3410.6  TotalCharges_3413.25  \\\n",
+              "0              False                False                 False   \n",
+              "1              False                False                 False   \n",
+              "2              False                False                 False   \n",
+              "3              False                False                 False   \n",
+              "4              False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3414.65  TotalCharges_3415.25  TotalCharges_3416.85  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3418.2  TotalCharges_3419.3  TotalCharges_3419.5  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_342.3  TotalCharges_342.4  TotalCharges_3420.5  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_3423.5  TotalCharges_3425.35  TotalCharges_343.45  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_343.6  TotalCharges_343.95  TotalCharges_3431.75  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3432.9  TotalCharges_3435.6  TotalCharges_3436.1  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_3437.45  TotalCharges_3437.5  TotalCharges_3439  \\\n",
+              "0                 False                False              False   \n",
+              "1                 False                False              False   \n",
+              "2                 False                False              False   \n",
+              "3                 False                False              False   \n",
+              "4                 False                False              False   \n",
+              "\n",
+              "   TotalCharges_344.2  TotalCharges_344.5  TotalCharges_3440.25  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_3442.15  TotalCharges_3442.8  TotalCharges_3444.85  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_345.5  TotalCharges_345.9  TotalCharges_3450.15  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_3452.55  TotalCharges_3454.6  TotalCharges_3457.45  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3457.9  TotalCharges_346.2  TotalCharges_346.4  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_346.45  TotalCharges_3460.3  TotalCharges_3460.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3462.1  TotalCharges_3465.05  TotalCharges_3465.7  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3467  TotalCharges_347.25  TotalCharges_347.4  \\\n",
+              "0              False                False               False   \n",
+              "1              False                False               False   \n",
+              "2              False                False               False   \n",
+              "3              False                False               False   \n",
+              "4              False                False               False   \n",
+              "\n",
+              "   TotalCharges_347.65  TotalCharges_3470.8  TotalCharges_3471.1  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_3472.05  TotalCharges_3473.4  TotalCharges_3474.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3474.2  TotalCharges_3474.45  TotalCharges_3475.55  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3478.15  TotalCharges_3478.75  TotalCharges_3479.05  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_348.15  TotalCharges_348.8  TotalCharges_3480  \\\n",
+              "0                False               False              False   \n",
+              "1                False               False              False   \n",
+              "2                False               False              False   \n",
+              "3                False               False              False   \n",
+              "4                False               False              False   \n",
+              "\n",
+              "   TotalCharges_3480.35  TotalCharges_3482.85  TotalCharges_3483.45  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3486.65  TotalCharges_3487.95  TotalCharges_3488.15  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_349.65  TotalCharges_349.8  TotalCharges_3491.55  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_3496.3  TotalCharges_35  TotalCharges_35.05  \\\n",
+              "0                False            False               False   \n",
+              "1                False            False               False   \n",
+              "2                False            False               False   \n",
+              "3                False            False               False   \n",
+              "4                False            False               False   \n",
+              "\n",
+              "   TotalCharges_35.1  TotalCharges_35.25  TotalCharges_35.45  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_35.55  TotalCharges_35.75  TotalCharges_35.85  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_35.9  TotalCharges_350.1  TotalCharges_350.3  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_350.35  TotalCharges_3503.5  TotalCharges_3505.1  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_3509.4  TotalCharges_351.5  TotalCharges_351.55  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_3510.3  TotalCharges_3512.15  TotalCharges_3512.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3512.9  TotalCharges_3515.25  TotalCharges_3517.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_352.65  TotalCharges_3520.75  TotalCharges_3521.7  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3522.65  TotalCharges_3527  TotalCharges_3527.3  \\\n",
+              "0                 False              False                False   \n",
+              "1                 False              False                False   \n",
+              "2                 False              False                False   \n",
+              "3                 False              False                False   \n",
+              "4                 False              False                False   \n",
+              "\n",
+              "   TotalCharges_3527.6  TotalCharges_3529.95  TotalCharges_353.65  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3532  TotalCharges_3532.25  TotalCharges_3532.85  \\\n",
+              "0              False                 False                 False   \n",
+              "1              False                 False                 False   \n",
+              "2              False                 False                 False   \n",
+              "3              False                 False                 False   \n",
+              "4              False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3533.6  TotalCharges_3539.25  TotalCharges_354.05  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3540.65  TotalCharges_3541.1  TotalCharges_3541.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3541.4  TotalCharges_3545.05  TotalCharges_3545.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3545.35  TotalCharges_3548.3  TotalCharges_3549.25  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_355.1  TotalCharges_355.2  TotalCharges_355.9  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_3551.65  TotalCharges_3554.6  TotalCharges_3557.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_356.1  TotalCharges_356.15  TotalCharges_356.65  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_3561.15  TotalCharges_3562.5  TotalCharges_3563.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_3565.65  TotalCharges_3566.6  TotalCharges_3566.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_357  TotalCharges_357.15  TotalCharges_357.6  \\\n",
+              "0             False                False               False   \n",
+              "1             False                False               False   \n",
+              "2             False                False               False   \n",
+              "3             False                False               False   \n",
+              "4             False                False               False   \n",
+              "\n",
+              "   TotalCharges_357.7  TotalCharges_357.75  TotalCharges_3571.6  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_3574.5  TotalCharges_3579.15  TotalCharges_358.05  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_358.15  TotalCharges_358.5  TotalCharges_3580.3  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_3580.95  TotalCharges_3581.4  TotalCharges_3581.6  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_3582.4  TotalCharges_3587.25  TotalCharges_3588.4  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_359.4  TotalCharges_3590.2  TotalCharges_3591.25  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3593.8  TotalCharges_3597.5  TotalCharges_36.45  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_36.55  TotalCharges_36.8  TotalCharges_360.1  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_360.35  TotalCharges_360.55  TotalCharges_3600.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3603.45  TotalCharges_3605.2  TotalCharges_3605.6  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_3608  TotalCharges_3615.6  TotalCharges_3616.25  \\\n",
+              "0              False                False                 False   \n",
+              "1              False                False                 False   \n",
+              "2              False                False                 False   \n",
+              "3              False                False                 False   \n",
+              "4              False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3617.1  TotalCharges_3618.7  TotalCharges_362.2  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_362.6  TotalCharges_3623.95  TotalCharges_3624.3  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3624.35  TotalCharges_3625.2  TotalCharges_3626.1  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_3626.35  TotalCharges_3627.3  TotalCharges_3629.2  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_363.15  TotalCharges_3632  TotalCharges_3634.8  \\\n",
+              "0                False              False                False   \n",
+              "1                False              False                False   \n",
+              "2                False              False                False   \n",
+              "3                False              False                False   \n",
+              "4                False              False                False   \n",
+              "\n",
+              "   TotalCharges_3635.15  TotalCharges_3638.25  TotalCharges_364.55  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3640.45  TotalCharges_3641.5  TotalCharges_3645.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3645.5  TotalCharges_3645.6  TotalCharges_3645.75  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3646.8  TotalCharges_3649.6  TotalCharges_365.35  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_365.4  TotalCharges_365.55  TotalCharges_365.65  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_365.8  TotalCharges_3650.35  TotalCharges_3653  \\\n",
+              "0               False                 False              False   \n",
+              "1               False                 False              False   \n",
+              "2               False                 False              False   \n",
+              "3               False                 False              False   \n",
+              "4               False                 False              False   \n",
+              "\n",
+              "   TotalCharges_3653.35  TotalCharges_3655.45  TotalCharges_3656.25  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3662.25  TotalCharges_3665.55  TotalCharges_367.55  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_367.95  TotalCharges_3670.5  TotalCharges_3673.15  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3673.6  TotalCharges_3674.95  TotalCharges_3678.3  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_368.1  TotalCharges_368.85  TotalCharges_3682.45  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3684.95  TotalCharges_3686.05  TotalCharges_3687.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3687.85  TotalCharges_3688.6  TotalCharges_369.05  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_369.1  TotalCharges_369.15  TotalCharges_369.25  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_369.3  TotalCharges_369.6  TotalCharges_3691.2  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_3692.85  TotalCharges_3694.45  TotalCharges_3694.7  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_37.2  TotalCharges_370.25  TotalCharges_370.4  \\\n",
+              "0              False                False               False   \n",
+              "1              False                False               False   \n",
+              "2              False                False               False   \n",
+              "3              False                False               False   \n",
+              "4              False                False               False   \n",
+              "\n",
+              "   TotalCharges_370.5  TotalCharges_370.65  TotalCharges_3704.15  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3706.95  TotalCharges_3707.6  TotalCharges_3708.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_371.4  TotalCharges_371.6  TotalCharges_371.65  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_371.9  TotalCharges_3713.95  TotalCharges_3715.65  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_372.45  TotalCharges_3720.35  TotalCharges_3721.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3723.65  TotalCharges_3725.5  TotalCharges_3726.15  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3729.6  TotalCharges_3729.75  TotalCharges_373  \\\n",
+              "0                False                 False             False   \n",
+              "1                False                 False             False   \n",
+              "2                False                 False             False   \n",
+              "3                False                 False             False   \n",
+              "4                False                 False             False   \n",
+              "\n",
+              "   TotalCharges_373.5  TotalCharges_3734.25  TotalCharges_3735.45  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3739.8  TotalCharges_374  TotalCharges_374.5  \\\n",
+              "0                False             False               False   \n",
+              "1                False             False               False   \n",
+              "2                False             False               False   \n",
+              "3                False             False               False   \n",
+              "4                False             False               False   \n",
+              "\n",
+              "   TotalCharges_374.8  TotalCharges_3741.85  TotalCharges_3744.05  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_375.25  TotalCharges_3751.15  TotalCharges_3753.2  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3754.6  TotalCharges_3756.4  TotalCharges_3756.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3759.05  TotalCharges_3762  TotalCharges_3765.05  \\\n",
+              "0                 False              False                 False   \n",
+              "1                 False              False                 False   \n",
+              "2                 False              False                 False   \n",
+              "3                 False              False                 False   \n",
+              "4                 False              False                 False   \n",
+              "\n",
+              "   TotalCharges_3766.2  TotalCharges_3767.4  TotalCharges_3769.7  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_377.55  TotalCharges_377.85  TotalCharges_3770  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_3771.5  TotalCharges_3772.5  TotalCharges_3772.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3775.85  TotalCharges_3777.15  TotalCharges_3778  \\\n",
+              "0                 False                 False              False   \n",
+              "1                 False                 False              False   \n",
+              "2                 False                 False              False   \n",
+              "3                 False                 False              False   \n",
+              "4                 False                 False              False   \n",
+              "\n",
+              "   TotalCharges_3778.1  TotalCharges_3778.2  TotalCharges_3778.85  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_378.4  TotalCharges_378.6  TotalCharges_3782.4  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_3784  TotalCharges_3789.2  TotalCharges_3791.6  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_3794.5  TotalCharges_3795.45  TotalCharges_38  \\\n",
+              "0                False                 False            False   \n",
+              "1                False                 False            False   \n",
+              "2                False                 False            False   \n",
+              "3                False                 False            False   \n",
+              "4                False                 False            False   \n",
+              "\n",
+              "   TotalCharges_38.15  TotalCharges_38.25  TotalCharges_38.45  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_38.7  TotalCharges_38.8  TotalCharges_3801.3  \\\n",
+              "0              False              False                False   \n",
+              "1              False              False                False   \n",
+              "2              False              False                False   \n",
+              "3              False              False                False   \n",
+              "4              False              False                False   \n",
+              "\n",
+              "   TotalCharges_3801.7  TotalCharges_3804.4  TotalCharges_3807.35  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3808  TotalCharges_3808.2  TotalCharges_381.2  \\\n",
+              "0              False                False               False   \n",
+              "1              False                False               False   \n",
+              "2              False                False               False   \n",
+              "3              False                False               False   \n",
+              "4              False                False               False   \n",
+              "\n",
+              "   TotalCharges_381.3  TotalCharges_3810.55  TotalCharges_3810.8  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3815.4  TotalCharges_382.2  TotalCharges_382.8  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_3822.45  TotalCharges_3824.2  TotalCharges_3825.85  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3827.9  TotalCharges_3829.75  TotalCharges_383.55  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_383.65  TotalCharges_3833.95  TotalCharges_3834.4  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3835.55  TotalCharges_3836.3  TotalCharges_3838.2  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_3838.75  TotalCharges_384.25  TotalCharges_384.5  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_3842.6  TotalCharges_3845.45  TotalCharges_3846.35  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3846.75  TotalCharges_3847.6  TotalCharges_3848  \\\n",
+              "0                 False                False              False   \n",
+              "1                 False                False              False   \n",
+              "2                 False                False              False   \n",
+              "3                 False                False              False   \n",
+              "4                 False                False              False   \n",
+              "\n",
+              "   TotalCharges_3848.8  TotalCharges_385  TotalCharges_385.55  \\\n",
+              "0                False             False                False   \n",
+              "1                False             False                False   \n",
+              "2                False             False                False   \n",
+              "3                False             False                False   \n",
+              "4                False             False                False   \n",
+              "\n",
+              "   TotalCharges_385.9  TotalCharges_3851.45  TotalCharges_3856.75  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3857.1  TotalCharges_3858.05  TotalCharges_386.5  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_3861.45  TotalCharges_3862.55  TotalCharges_3865.45  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3865.6  TotalCharges_387.2  TotalCharges_387.4  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_387.7  TotalCharges_387.9  TotalCharges_3870  \\\n",
+              "0               False               False              False   \n",
+              "1               False               False              False   \n",
+              "2               False               False              False   \n",
+              "3               False               False              False   \n",
+              "4               False               False              False   \n",
+              "\n",
+              "   TotalCharges_3870.3  TotalCharges_3871.85  TotalCharges_3874.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3875.4  TotalCharges_3876.2  TotalCharges_3877.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3877.95  TotalCharges_388.6  TotalCharges_3880.05  \\\n",
+              "0                 False               False                 False   \n",
+              "1                 False               False                 False   \n",
+              "2                 False               False                 False   \n",
+              "3                 False               False                 False   \n",
+              "4                 False               False                 False   \n",
+              "\n",
+              "   TotalCharges_3882.3  TotalCharges_3883.3  TotalCharges_3886.05  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3886.45  TotalCharges_3886.85  TotalCharges_3887.25  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3887.85  TotalCharges_3888.65  TotalCharges_389.1  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_389.25  TotalCharges_389.6  TotalCharges_389.8  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_389.95  TotalCharges_3893.6  TotalCharges_3894.4  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_3895.35  TotalCharges_3899.05  TotalCharges_39.25  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_39.3  TotalCharges_39.65  TotalCharges_39.8  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_39.85  TotalCharges_390.4  TotalCharges_390.85  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_3901.25  TotalCharges_3902.45  TotalCharges_3902.6  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3906.7  TotalCharges_391.7  TotalCharges_3912.9  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_3913.3  TotalCharges_3914.05  TotalCharges_3915.4  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3919.15  TotalCharges_392.5  TotalCharges_392.65  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_3921.1  TotalCharges_3921.3  TotalCharges_3923.8  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_3928.3  TotalCharges_393.15  TotalCharges_393.45  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_3930.55  TotalCharges_3930.6  TotalCharges_3937.45  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_394.1  TotalCharges_394.85  TotalCharges_3941.7  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_3942.45  TotalCharges_3944.5  TotalCharges_3946.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_3948.45  TotalCharges_3949.15  TotalCharges_395.6  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_3950.85  TotalCharges_3952.45  TotalCharges_3952.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3953.15  TotalCharges_3953.7  TotalCharges_3954.1  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_3956.7  TotalCharges_3958.2  TotalCharges_3958.25  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3958.85  TotalCharges_3959.15  TotalCharges_3959.35  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_396.1  TotalCharges_396.3  TotalCharges_3965.05  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_3966.3  TotalCharges_3969.35  TotalCharges_3969.4  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_397  TotalCharges_3970.4  TotalCharges_3972.25  \\\n",
+              "0             False                False                 False   \n",
+              "1             False                False                 False   \n",
+              "2             False                False                 False   \n",
+              "3             False                False                 False   \n",
+              "4             False                False                 False   \n",
+              "\n",
+              "   TotalCharges_3973.2  TotalCharges_3974.15  TotalCharges_3974.7  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_3975.7  TotalCharges_3975.9  TotalCharges_398.55  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_3983.6  TotalCharges_3985.35  TotalCharges_3988.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_399.25  TotalCharges_399.45  TotalCharges_399.6  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_3990.6  TotalCharges_3990.75  TotalCharges_3994.05  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_3994.45  TotalCharges_3996.8  TotalCharges_40.1  \\\n",
+              "0                 False                False              False   \n",
+              "1                 False                False              False   \n",
+              "2                 False                False              False   \n",
+              "3                 False                False              False   \n",
+              "4                 False                False              False   \n",
+              "\n",
+              "   TotalCharges_40.2  TotalCharges_40.25  TotalCharges_40.9  TotalCharges_400  \\\n",
+              "0              False               False              False             False   \n",
+              "1              False               False              False             False   \n",
+              "2              False               False              False             False   \n",
+              "3              False               False              False             False   \n",
+              "4              False               False              False             False   \n",
+              "\n",
+              "   TotalCharges_400.3  TotalCharges_4003  TotalCharges_4003.85  \\\n",
+              "0               False              False                 False   \n",
+              "1               False              False                 False   \n",
+              "2               False              False                 False   \n",
+              "3               False              False                 False   \n",
+              "4               False              False                 False   \n",
+              "\n",
+              "   TotalCharges_4009.2  TotalCharges_401.1  TotalCharges_401.3  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_401.5  TotalCharges_401.85  TotalCharges_401.95  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_4013.8  TotalCharges_4014  TotalCharges_4014.2  \\\n",
+              "0                False              False                False   \n",
+              "1                False              False                False   \n",
+              "2                False              False                False   \n",
+              "3                False              False                False   \n",
+              "4                False              False                False   \n",
+              "\n",
+              "   TotalCharges_4014.6  TotalCharges_4016.2  TotalCharges_4016.3  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_4016.75  TotalCharges_4016.85  TotalCharges_4017.45  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4018.05  TotalCharges_4018.35  TotalCharges_4018.55  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_402.5  TotalCharges_402.6  TotalCharges_402.85  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_4024.2  TotalCharges_4025.5  TotalCharges_4025.6  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_4026.4  TotalCharges_4029.95  TotalCharges_403.15  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_403.35  TotalCharges_4036  TotalCharges_4036.85  \\\n",
+              "0                False              False                 False   \n",
+              "1                False              False                 False   \n",
+              "2                False              False                 False   \n",
+              "3                False              False                 False   \n",
+              "4                False              False                 False   \n",
+              "\n",
+              "   TotalCharges_4039  TotalCharges_4039.3  TotalCharges_4039.5  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_404.2  TotalCharges_404.35  TotalCharges_4040.2  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_4040.65  TotalCharges_4042.2  TotalCharges_4042.3  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_4045.65  TotalCharges_4048.95  TotalCharges_405.6  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_405.7  TotalCharges_4052.4  TotalCharges_4054.2  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_4055.5  TotalCharges_4056.75  TotalCharges_4059.35  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4059.85  TotalCharges_406.05  TotalCharges_406.95  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_4060.55  TotalCharges_4060.9  TotalCharges_4062.2  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_4063  TotalCharges_4065  TotalCharges_4068  \\\n",
+              "0              False              False              False   \n",
+              "1              False              False              False   \n",
+              "2              False              False              False   \n",
+              "3              False              False              False   \n",
+              "4              False              False              False   \n",
+              "\n",
+              "   TotalCharges_4069.9  TotalCharges_407.05  TotalCharges_4070.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4079.55  TotalCharges_408.25  TotalCharges_408.5  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_4084.35  TotalCharges_4085.75  TotalCharges_4086.3  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4089.45  TotalCharges_409.9  TotalCharges_4092.85  \\\n",
+              "0                 False               False                 False   \n",
+              "1                 False               False                 False   \n",
+              "2                 False               False                 False   \n",
+              "3                 False               False                 False   \n",
+              "4                 False               False                 False   \n",
+              "\n",
+              "   TotalCharges_4096.9  TotalCharges_4097.05  TotalCharges_41.2  \\\n",
+              "0                False                 False              False   \n",
+              "1                False                 False              False   \n",
+              "2                False                 False              False   \n",
+              "3                False                 False              False   \n",
+              "4                False                 False              False   \n",
+              "\n",
+              "   TotalCharges_41.85  TotalCharges_4103.9  TotalCharges_4107.25  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4107.3  TotalCharges_4108.15  TotalCharges_4109  \\\n",
+              "0                False                 False              False   \n",
+              "1                False                 False              False   \n",
+              "2                False                 False              False   \n",
+              "3                False                 False              False   \n",
+              "4                False                 False              False   \n",
+              "\n",
+              "   TotalCharges_411.15  TotalCharges_411.25  TotalCharges_411.45  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_411.6  TotalCharges_411.75  TotalCharges_4111.35  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4113.1  TotalCharges_4113.15  TotalCharges_4113.7  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4116.15  TotalCharges_4116.8  TotalCharges_4116.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_4119.4  TotalCharges_412.1  TotalCharges_412.5  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_412.55  TotalCharges_412.6  TotalCharges_4122.65  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_4122.9  TotalCharges_4124.65  TotalCharges_4126.2  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4126.35  TotalCharges_413  TotalCharges_413.25  \\\n",
+              "0                 False             False                False   \n",
+              "1                 False             False                False   \n",
+              "2                 False             False                False   \n",
+              "3                 False             False                False   \n",
+              "4                 False             False                False   \n",
+              "\n",
+              "   TotalCharges_413.65  TotalCharges_4131.2  TotalCharges_4131.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4133.95  TotalCharges_4134.7  TotalCharges_4134.85  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4135  TotalCharges_4136.4  TotalCharges_4137.2  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_4138.05  TotalCharges_4138.7  TotalCharges_4138.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_414.1  TotalCharges_414.95  TotalCharges_4140.1  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_4144.8  TotalCharges_4144.9  TotalCharges_4145.25  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4145.9  TotalCharges_4146.05  TotalCharges_4149.45  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_415.05  TotalCharges_415.1  TotalCharges_415.4  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_415.55  TotalCharges_415.9  TotalCharges_415.95  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_4154.55  TotalCharges_4154.8  TotalCharges_4155.95  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4156.8  TotalCharges_4158.25  TotalCharges_4159.45  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_416.3  TotalCharges_416.4  TotalCharges_416.45  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_4162.05  TotalCharges_4164.4  TotalCharges_4166.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_417  TotalCharges_417.5  TotalCharges_417.65  \\\n",
+              "0             False               False                False   \n",
+              "1             False               False                False   \n",
+              "2             False               False                False   \n",
+              "3             False               False                False   \n",
+              "4             False               False                False   \n",
+              "\n",
+              "   TotalCharges_417.7  TotalCharges_417.75  TotalCharges_4174.35  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4176.7  TotalCharges_4178.65  TotalCharges_4179.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4179.2  TotalCharges_418.25  TotalCharges_418.3  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_418.4  TotalCharges_418.8  TotalCharges_4186.3  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_4187.75  TotalCharges_4188.4  TotalCharges_4189.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_419.35  TotalCharges_419.4  TotalCharges_419.7  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_419.9  TotalCharges_4191.45  TotalCharges_4192.15  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4193.4  TotalCharges_4194.85  TotalCharges_42.45  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_42.7  TotalCharges_42.9  TotalCharges_420.2  \\\n",
+              "0              False              False               False   \n",
+              "1              False              False               False   \n",
+              "2              False              False               False   \n",
+              "3              False              False               False   \n",
+              "4              False              False               False   \n",
+              "\n",
+              "   TotalCharges_420.45  TotalCharges_4200.25  TotalCharges_4209.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4211.55  TotalCharges_4213.35  TotalCharges_4213.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4214.25  TotalCharges_4217.8  TotalCharges_422.3  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_422.4  TotalCharges_422.5  TotalCharges_422.6  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_422.7  TotalCharges_4220.35  TotalCharges_4222.95  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4224.7  TotalCharges_4226.7  TotalCharges_4228.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4230.25  TotalCharges_4233.95  TotalCharges_4234.15  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4236.6  TotalCharges_4237.5  TotalCharges_4238.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_424.15  TotalCharges_424.45  TotalCharges_424.5  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_424.75  TotalCharges_4242.35  TotalCharges_4245.55  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_425.1  TotalCharges_425.9  TotalCharges_4250.1  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_4254.1  TotalCharges_4254.85  TotalCharges_4259.3  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_426.35  TotalCharges_426.65  TotalCharges_4261.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_4263.4  TotalCharges_4263.45  TotalCharges_4264  \\\n",
+              "0                False                 False              False   \n",
+              "1                False                 False              False   \n",
+              "2                False                 False              False   \n",
+              "3                False                 False              False   \n",
+              "4                False                 False              False   \n",
+              "\n",
+              "   TotalCharges_4264.25  TotalCharges_4264.6  TotalCharges_4265  \\\n",
+              "0                 False                False              False   \n",
+              "1                 False                False              False   \n",
+              "2                 False                False              False   \n",
+              "3                 False                False              False   \n",
+              "4                 False                False              False   \n",
+              "\n",
+              "   TotalCharges_4266.4  TotalCharges_4267.15  TotalCharges_4273.45  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4275.75  TotalCharges_428.45  TotalCharges_428.7  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_4282.4  TotalCharges_4284.2  TotalCharges_4284.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4284.8  TotalCharges_4285.8  TotalCharges_4287.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_429.55  TotalCharges_4295.35  TotalCharges_4297.6  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4297.95  TotalCharges_4298.45  TotalCharges_4299.2  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4299.75  TotalCharges_4299.95  TotalCharges_43.15  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_43.3  TotalCharges_43.8  TotalCharges_43.85  \\\n",
+              "0              False              False               False   \n",
+              "1              False              False               False   \n",
+              "2              False              False               False   \n",
+              "3              False              False               False   \n",
+              "4              False              False               False   \n",
+              "\n",
+              "   TotalCharges_43.95  TotalCharges_4300.45  TotalCharges_4300.8  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4303.65  TotalCharges_4304  TotalCharges_4304.5  \\\n",
+              "0                 False              False                False   \n",
+              "1                 False              False                False   \n",
+              "2                 False              False                False   \n",
+              "3                 False              False                False   \n",
+              "4                 False              False                False   \n",
+              "\n",
+              "   TotalCharges_4307.1  TotalCharges_4308.25  TotalCharges_4309.55  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_431  TotalCharges_4310.35  TotalCharges_4312.5  \\\n",
+              "0             False                 False                False   \n",
+              "1             False                 False                False   \n",
+              "2             False                 False                False   \n",
+              "3             False                 False                False   \n",
+              "4             False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4317.35  TotalCharges_4318.35  TotalCharges_432.25  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_432.5  TotalCharges_4322.85  TotalCharges_4323.35  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4323.45  TotalCharges_4326.25  TotalCharges_4326.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4327.5  TotalCharges_433.5  TotalCharges_433.75  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_433.95  TotalCharges_4331.4  TotalCharges_4335.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_4338.6  TotalCharges_434.1  TotalCharges_434.5  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_434.8  TotalCharges_4345  TotalCharges_4346.4  \\\n",
+              "0               False              False                False   \n",
+              "1               False              False                False   \n",
+              "2               False              False                False   \n",
+              "3               False              False                False   \n",
+              "4               False              False                False   \n",
+              "\n",
+              "   TotalCharges_4348.1  TotalCharges_4348.65  TotalCharges_435  \\\n",
+              "0                False                 False             False   \n",
+              "1                False                 False             False   \n",
+              "2                False                 False             False   \n",
+              "3                False                 False             False   \n",
+              "4                False                 False             False   \n",
+              "\n",
+              "   TotalCharges_435.25  TotalCharges_435.4  TotalCharges_435.45  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_4350.1  TotalCharges_4354.45  TotalCharges_436.2  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_436.6  TotalCharges_436.9  TotalCharges_4361.55  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_4362.05  TotalCharges_4364.1  TotalCharges_4367.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4368.85  TotalCharges_4368.95  TotalCharges_4370.25  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4370.75  TotalCharges_4374.55  TotalCharges_4375.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4378.35  TotalCharges_4378.8  TotalCharges_4378.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_438  TotalCharges_438.05  TotalCharges_438.25  \\\n",
+              "0             False                False                False   \n",
+              "1             False                False                False   \n",
+              "2             False                False                False   \n",
+              "3             False                False                False   \n",
+              "4             False                False                False   \n",
+              "\n",
+              "   TotalCharges_438.4  TotalCharges_438.9  TotalCharges_4385.05  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_4386.2  TotalCharges_4388.4  TotalCharges_439.2  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_439.75  TotalCharges_4390.25  TotalCharges_4391.25  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4391.45  TotalCharges_4392.5  TotalCharges_4398.15  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4399.5  TotalCharges_44  TotalCharges_44.05  \\\n",
+              "0                False            False               False   \n",
+              "1                False            False               False   \n",
+              "2                False            False               False   \n",
+              "3                False            False               False   \n",
+              "4                False            False               False   \n",
+              "\n",
+              "   TotalCharges_44.1  TotalCharges_44.15  TotalCharges_44.2  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_44.3  TotalCharges_44.35  TotalCharges_44.4  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_44.45  TotalCharges_44.55  TotalCharges_44.6  \\\n",
+              "0               False               False              False   \n",
+              "1               False               False              False   \n",
+              "2               False               False              False   \n",
+              "3               False               False              False   \n",
+              "4               False               False              False   \n",
+              "\n",
+              "   TotalCharges_44.65  TotalCharges_44.7  TotalCharges_44.75  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_44.8  TotalCharges_44.9  TotalCharges_44.95  \\\n",
+              "0              False              False               False   \n",
+              "1              False              False               False   \n",
+              "2              False              False               False   \n",
+              "3              False              False               False   \n",
+              "4              False              False               False   \n",
+              "\n",
+              "   TotalCharges_440.2  TotalCharges_4400.75  TotalCharges_4408.45  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4414.3  TotalCharges_4415.75  TotalCharges_442.2  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_442.45  TotalCharges_442.6  TotalCharges_442.85  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_4421.95  TotalCharges_4422.95  TotalCharges_4424.2  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4428.45  TotalCharges_4428.6  TotalCharges_443.9  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_4433.3  TotalCharges_4438.2  TotalCharges_444.75  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_4442.75  TotalCharges_4443.45  TotalCharges_4445.3  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4447.55  TotalCharges_4447.75  TotalCharges_4448.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4449.75  TotalCharges_445.3  TotalCharges_445.85  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_445.95  TotalCharges_4451.85  TotalCharges_4453.3  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4454.25  TotalCharges_4456.35  TotalCharges_4456.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4458.15  TotalCharges_4459.15  TotalCharges_4459.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_446.05  TotalCharges_446.1  TotalCharges_446.8  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_4461.85  TotalCharges_4464.8  TotalCharges_4469.1  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_447.75  TotalCharges_447.9  TotalCharges_4473  \\\n",
+              "0                False               False              False   \n",
+              "1                False               False              False   \n",
+              "2                False               False              False   \n",
+              "3                False               False              False   \n",
+              "4                False               False              False   \n",
+              "\n",
+              "   TotalCharges_4473.45  TotalCharges_4475.9  TotalCharges_4478.85  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4479.2  TotalCharges_4480.7  TotalCharges_4481  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_4483.95  TotalCharges_4484.05  TotalCharges_4487.3  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_449.3  TotalCharges_449.75  TotalCharges_4492.9  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_4494.65  TotalCharges_4495.65  TotalCharges_45  \\\n",
+              "0                 False                 False            False   \n",
+              "1                 False                 False            False   \n",
+              "2                 False                 False            False   \n",
+              "3                 False                 False            False   \n",
+              "4                 False                 False            False   \n",
+              "\n",
+              "   TotalCharges_45.05  TotalCharges_45.1  TotalCharges_45.15  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_45.2  TotalCharges_45.25  TotalCharges_45.3  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_45.35  TotalCharges_45.4  TotalCharges_45.55  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_45.6  TotalCharges_45.65  TotalCharges_45.7  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_45.75  TotalCharges_45.8  TotalCharges_45.85  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_45.95  TotalCharges_450.4  TotalCharges_450.65  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_450.8  TotalCharges_450.9  TotalCharges_4504.55  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_4504.9  TotalCharges_4507.15  TotalCharges_4508.65  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4509.5  TotalCharges_451.1  TotalCharges_451.55  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_4510.8  TotalCharges_4512.7  TotalCharges_4513.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4515.85  TotalCharges_4517.25  TotalCharges_4519.5  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_452.2  TotalCharges_452.35  TotalCharges_452.55  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_452.7  TotalCharges_452.8  TotalCharges_4520.15  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_4523.25  TotalCharges_4524.05  TotalCharges_4525.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4526.85  TotalCharges_4527.45  TotalCharges_4528  \\\n",
+              "0                 False                 False              False   \n",
+              "1                 False                 False              False   \n",
+              "2                 False                 False              False   \n",
+              "3                 False                 False              False   \n",
+              "4                 False                 False              False   \n",
+              "\n",
+              "   TotalCharges_453.4  TotalCharges_453.75  TotalCharges_4532.3  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_4533.7  TotalCharges_4533.9  TotalCharges_4534.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4534.9  TotalCharges_4535.85  TotalCharges_4539.35  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4539.6  TotalCharges_454  TotalCharges_454.05  \\\n",
+              "0                False             False                False   \n",
+              "1                False             False                False   \n",
+              "2                False             False                False   \n",
+              "3                False             False                False   \n",
+              "4                False             False                False   \n",
+              "\n",
+              "   TotalCharges_454.15  TotalCharges_454.65  TotalCharges_4541.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_4541.9  TotalCharges_4542.35  TotalCharges_4543.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4543.95  TotalCharges_4546  TotalCharges_4547.25  \\\n",
+              "0                 False              False                 False   \n",
+              "1                 False              False                 False   \n",
+              "2                 False              False                 False   \n",
+              "3                 False              False                 False   \n",
+              "4                 False              False                 False   \n",
+              "\n",
+              "   TotalCharges_4549.05  TotalCharges_4549.45  TotalCharges_455.3  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_455.5  TotalCharges_4551.5  TotalCharges_4554.85  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4555.2  TotalCharges_4557.5  TotalCharges_456.85  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_4563  TotalCharges_4564.9  TotalCharges_4566.5  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_457.1  TotalCharges_457.3  TotalCharges_4575.35  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_4576.3  TotalCharges_4577.75  TotalCharges_4577.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_458.1  TotalCharges_4586.15  TotalCharges_4589.85  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_459.6  TotalCharges_459.95  TotalCharges_4590.35  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4594.65  TotalCharges_4594.95  TotalCharges_4599.15  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_46  TotalCharges_46.2  TotalCharges_46.3  TotalCharges_46.35  \\\n",
+              "0            False              False              False               False   \n",
+              "1            False              False              False               False   \n",
+              "2            False              False              False               False   \n",
+              "3            False              False              False               False   \n",
+              "4            False              False              False               False   \n",
+              "\n",
+              "   TotalCharges_46.4  TotalCharges_460.2  TotalCharges_460.25  \\\n",
+              "0              False               False                False   \n",
+              "1              False               False                False   \n",
+              "2              False               False                False   \n",
+              "3              False               False                False   \n",
+              "4              False               False                False   \n",
+              "\n",
+              "   TotalCharges_4600.7  TotalCharges_4600.95  TotalCharges_461.3  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_461.7  TotalCharges_4613.95  TotalCharges_4614.55  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4615.25  TotalCharges_4615.9  TotalCharges_4616.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4616.1  TotalCharges_4619.55  TotalCharges_462.8  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_4620.4  TotalCharges_4627.65  TotalCharges_4627.8  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4627.85  TotalCharges_463.05  TotalCharges_463.6  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_4630.2  TotalCharges_4631.7  TotalCharges_4634.35  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4639.45  TotalCharges_4641.1  TotalCharges_465.05  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_465.45  TotalCharges_465.7  TotalCharges_465.85  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_4652.4  TotalCharges_4653.25  TotalCharges_4653.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4657.95  TotalCharges_466.6  TotalCharges_4663.4  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_4664.15  TotalCharges_4664.2  TotalCharges_4664.5  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_4667  TotalCharges_4669.2  TotalCharges_467.15  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_467.5  TotalCharges_467.55  TotalCharges_467.7  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_467.85  TotalCharges_4671.65  TotalCharges_4671.7  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4674.4  TotalCharges_4674.55  TotalCharges_4676.7  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4677.1  TotalCharges_468.35  TotalCharges_4680.05  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4681.75  TotalCharges_4684.3  TotalCharges_4685.55  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4687.9  TotalCharges_4688.65  TotalCharges_4689.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4689.5  TotalCharges_469.25  TotalCharges_469.65  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_469.8  TotalCharges_469.85  TotalCharges_4690.65  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4692.55  TotalCharges_4692.95  TotalCharges_4693.2  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4698.05  TotalCharges_47.5  TotalCharges_47.7  \\\n",
+              "0                 False              False              False   \n",
+              "1                 False              False              False   \n",
+              "2                 False              False              False   \n",
+              "3                 False              False              False   \n",
+              "4                 False              False              False   \n",
+              "\n",
+              "   TotalCharges_47.95  TotalCharges_470  TotalCharges_470.2  \\\n",
+              "0               False             False               False   \n",
+              "1               False             False               False   \n",
+              "2               False             False               False   \n",
+              "3               False             False               False   \n",
+              "4               False             False               False   \n",
+              "\n",
+              "   TotalCharges_470.6  TotalCharges_470.95  TotalCharges_4707.1  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_4707.85  TotalCharges_471.35  TotalCharges_471.55  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_471.7  TotalCharges_471.85  TotalCharges_4713.4  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_4718.25  TotalCharges_4719.75  TotalCharges_472.25  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_472.65  TotalCharges_4720  TotalCharges_4729.3  \\\n",
+              "0                False              False                False   \n",
+              "1                False              False                False   \n",
+              "2                False              False                False   \n",
+              "3                False              False                False   \n",
+              "4                False              False                False   \n",
+              "\n",
+              "   TotalCharges_4729.75  TotalCharges_473.9  TotalCharges_4730.6  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_4730.9  TotalCharges_4732.35  TotalCharges_4733.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4735.2  TotalCharges_4735.35  TotalCharges_4738.3  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4738.85  TotalCharges_474.8  TotalCharges_474.9  \\\n",
+              "0                 False               False               False   \n",
+              "1                 False               False               False   \n",
+              "2                 False               False               False   \n",
+              "3                 False               False               False   \n",
+              "4                 False               False               False   \n",
+              "\n",
+              "   TotalCharges_4740  TotalCharges_4741.45  TotalCharges_4744.35  \\\n",
+              "0              False                 False                 False   \n",
+              "1              False                 False                 False   \n",
+              "2              False                 False                 False   \n",
+              "3              False                 False                 False   \n",
+              "4              False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4746.05  TotalCharges_4747.2  TotalCharges_4747.5  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_4747.65  TotalCharges_4747.85  TotalCharges_4748.7  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4749.15  TotalCharges_475  TotalCharges_475.1  \\\n",
+              "0                 False             False               False   \n",
+              "1                 False             False               False   \n",
+              "2                 False             False               False   \n",
+              "3                 False             False               False   \n",
+              "4                 False             False               False   \n",
+              "\n",
+              "   TotalCharges_475.2  TotalCharges_475.25  TotalCharges_475.7  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_4750.95  TotalCharges_4753.85  TotalCharges_4754.3  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4758.8  TotalCharges_4759.55  TotalCharges_4759.75  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4759.85  TotalCharges_476.8  TotalCharges_4760.3  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_4764  TotalCharges_4765  TotalCharges_477.05  \\\n",
+              "0              False              False                False   \n",
+              "1              False              False                False   \n",
+              "2              False              False                False   \n",
+              "3              False              False                False   \n",
+              "4              False              False                False   \n",
+              "\n",
+              "   TotalCharges_477.55  TotalCharges_477.6  TotalCharges_477.7  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_4779.45  TotalCharges_478.1  TotalCharges_478.75  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_4783.5  TotalCharges_4784.45  TotalCharges_4786.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4786.15  TotalCharges_4793.8  TotalCharges_4798.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_48.35  TotalCharges_48.45  TotalCharges_48.55  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_48.6  TotalCharges_48.75  TotalCharges_48.95  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_480.6  TotalCharges_480.75  TotalCharges_4801.1  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_4804.65  TotalCharges_4804.75  TotalCharges_4805.3  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4805.65  TotalCharges_4807.35  TotalCharges_4807.45  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4808.7  TotalCharges_481.1  TotalCharges_4811.6  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_4812.75  TotalCharges_4816.7  TotalCharges_4818.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_4819.75  TotalCharges_482.25  TotalCharges_482.8  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_4820.15  TotalCharges_4820.55  TotalCharges_4822.85  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4824.45  TotalCharges_4828.05  TotalCharges_483.15  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_483.3  TotalCharges_483.7  TotalCharges_4830.25  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_4834  TotalCharges_4837.6  TotalCharges_4839.15  \\\n",
+              "0              False                False                 False   \n",
+              "1              False                False                 False   \n",
+              "2              False                False                 False   \n",
+              "3              False                False                 False   \n",
+              "4              False                False                 False   \n",
+              "\n",
+              "   TotalCharges_484.05  TotalCharges_4845.4  TotalCharges_4847.05  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4847.35  TotalCharges_4849.1  TotalCharges_485.2  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_485.25  TotalCharges_485.9  TotalCharges_4853.75  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_4854.3  TotalCharges_4855.35  TotalCharges_4858.7  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4859.1  TotalCharges_4859.25  TotalCharges_4859.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_486.05  TotalCharges_486.2  TotalCharges_486.85  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_4860.35  TotalCharges_4860.85  TotalCharges_4861.45  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4862.5  TotalCharges_4863.85  TotalCharges_4867.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4868.4  TotalCharges_4869.35  TotalCharges_4869.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_487.05  TotalCharges_487.75  TotalCharges_487.95  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_4871.05  TotalCharges_4871.45  TotalCharges_4872.2  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4872.35  TotalCharges_4872.45  TotalCharges_4874.7  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4874.8  TotalCharges_488.25  TotalCharges_488.65  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_4882.8  TotalCharges_4884.85  TotalCharges_4885.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4888.2  TotalCharges_4888.7  TotalCharges_4889.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_4889.3  TotalCharges_489.45  TotalCharges_4890.5  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_4891.5  TotalCharges_4895.1  TotalCharges_4896.35  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_49  TotalCharges_49.05  TotalCharges_49.25  TotalCharges_49.3  \\\n",
+              "0            False               False               False              False   \n",
+              "1            False               False               False              False   \n",
+              "2            False               False               False              False   \n",
+              "3            False               False               False              False   \n",
+              "4            False               False               False              False   \n",
+              "\n",
+              "   TotalCharges_49.5  TotalCharges_49.55  TotalCharges_49.65  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_49.7  TotalCharges_49.75  TotalCharges_49.8  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_49.85  TotalCharges_49.9  TotalCharges_49.95  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_490.55  TotalCharges_490.65  TotalCharges_4900.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4902.8  TotalCharges_4903.15  TotalCharges_4903.2  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4904.2  TotalCharges_4904.25  TotalCharges_4904.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4905.75  TotalCharges_4908.25  TotalCharges_4911.05  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4911.35  TotalCharges_4913.3  TotalCharges_4914.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_4914.9  TotalCharges_4915.15  TotalCharges_4916.4  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4916.95  TotalCharges_4917.75  TotalCharges_4917.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4919.7  TotalCharges_492  TotalCharges_492.1  \\\n",
+              "0                False             False               False   \n",
+              "1                False             False               False   \n",
+              "2                False             False               False   \n",
+              "3                False             False               False   \n",
+              "4                False             False               False   \n",
+              "\n",
+              "   TotalCharges_492.55  TotalCharges_4920.55  TotalCharges_4920.8  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4921.2  TotalCharges_4922.4  TotalCharges_4925.35  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4929.55  TotalCharges_493.4  TotalCharges_493.65  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_493.95  TotalCharges_4931.8  TotalCharges_4932.5  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_4939.25  TotalCharges_494.05  TotalCharges_494.9  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_494.95  TotalCharges_4941.8  TotalCharges_4946.05  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_4946.7  TotalCharges_4947.55  TotalCharges_4949.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_495.15  TotalCharges_4952.95  TotalCharges_4953.25  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_4959.15  TotalCharges_4959.6  TotalCharges_496.9  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_4964.7  TotalCharges_4965  TotalCharges_4965.1  \\\n",
+              "0                False              False                False   \n",
+              "1                False              False                False   \n",
+              "2                False              False                False   \n",
+              "3                False              False                False   \n",
+              "4                False              False                False   \n",
+              "\n",
+              "   TotalCharges_4968  TotalCharges_497.3  TotalCharges_497.55  \\\n",
+              "0              False               False                False   \n",
+              "1              False               False                False   \n",
+              "2              False               False                False   \n",
+              "3              False               False                False   \n",
+              "4              False               False                False   \n",
+              "\n",
+              "   TotalCharges_497.6  TotalCharges_4972.1  TotalCharges_4973.4  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_4976.15  TotalCharges_4977.2  TotalCharges_498.1  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_498.25  TotalCharges_4981.15  TotalCharges_4982.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4983.05  TotalCharges_4984.85  TotalCharges_4985.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_499.4  TotalCharges_4990.25  TotalCharges_4991.5  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_4993.4  TotalCharges_4995.35  TotalCharges_4997.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_50.05  TotalCharges_50.1  TotalCharges_50.15  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_50.35  TotalCharges_50.45  TotalCharges_50.5  \\\n",
+              "0               False               False              False   \n",
+              "1               False               False              False   \n",
+              "2               False               False              False   \n",
+              "3               False               False              False   \n",
+              "4               False               False              False   \n",
+              "\n",
+              "   TotalCharges_50.55  TotalCharges_50.6  TotalCharges_50.65  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_50.7  TotalCharges_50.75  TotalCharges_50.8  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_50.9  TotalCharges_500.1  TotalCharges_5000.05  \\\n",
+              "0              False               False                 False   \n",
+              "1              False               False                 False   \n",
+              "2              False               False                 False   \n",
+              "3              False               False                 False   \n",
+              "4              False               False                 False   \n",
+              "\n",
+              "   TotalCharges_5000.2  TotalCharges_501  TotalCharges_501.2  \\\n",
+              "0                False             False               False   \n",
+              "1                False             False               False   \n",
+              "2                False             False               False   \n",
+              "3                False             False               False   \n",
+              "4                False             False               False   \n",
+              "\n",
+              "   TotalCharges_501.35  TotalCharges_5011.15  TotalCharges_5012.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5012.35  TotalCharges_5013  TotalCharges_5016.25  \\\n",
+              "0                 False              False                 False   \n",
+              "1                 False              False                 False   \n",
+              "2                 False              False                 False   \n",
+              "3                 False              False                 False   \n",
+              "4                 False              False                 False   \n",
+              "\n",
+              "   TotalCharges_5016.65  TotalCharges_5017.7  TotalCharges_5017.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_502.6  TotalCharges_502.85  TotalCharges_5023  \\\n",
+              "0               False                False              False   \n",
+              "1               False                False              False   \n",
+              "2               False                False              False   \n",
+              "3               False                False              False   \n",
+              "4               False                False              False   \n",
+              "\n",
+              "   TotalCharges_5025  TotalCharges_5025.8  TotalCharges_5025.85  \\\n",
+              "0              False                False                 False   \n",
+              "1              False                False                 False   \n",
+              "2              False                False                 False   \n",
+              "3              False                False                 False   \n",
+              "4              False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5029.05  TotalCharges_5029.2  TotalCharges_503.25  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_503.6  TotalCharges_5031  TotalCharges_5031.85  \\\n",
+              "0               False              False                 False   \n",
+              "1               False              False                 False   \n",
+              "2               False              False                 False   \n",
+              "3               False              False                 False   \n",
+              "4               False              False                 False   \n",
+              "\n",
+              "   TotalCharges_5032.25  TotalCharges_5034.05  TotalCharges_5036.3  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5036.9  TotalCharges_5037.55  TotalCharges_5038.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5038.45  TotalCharges_504.05  TotalCharges_504.2  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_5040.2  TotalCharges_5042.75  TotalCharges_5043.2  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5044.8  TotalCharges_505.45  TotalCharges_505.9  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_505.95  TotalCharges_5059.75  TotalCharges_506.9  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_5060.85  TotalCharges_5060.9  TotalCharges_5064.45  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5064.85  TotalCharges_5067.45  TotalCharges_5068.05  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5069.65  TotalCharges_507.4  TotalCharges_507.9  \\\n",
+              "0                 False               False               False   \n",
+              "1                 False               False               False   \n",
+              "2                 False               False               False   \n",
+              "3                 False               False               False   \n",
+              "4                 False               False               False   \n",
+              "\n",
+              "   TotalCharges_5070.4  TotalCharges_5071.05  TotalCharges_5071.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5073.1  TotalCharges_5082.8  TotalCharges_5083.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5084.65  TotalCharges_5088.4  TotalCharges_509.3  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_5099.15  TotalCharges_51.15  TotalCharges_51.2  \\\n",
+              "0                 False               False              False   \n",
+              "1                 False               False              False   \n",
+              "2                 False               False              False   \n",
+              "3                 False               False              False   \n",
+              "4                 False               False              False   \n",
+              "\n",
+              "   TotalCharges_51.25  TotalCharges_51.6  TotalCharges_510.8  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_5102.35  TotalCharges_511.25  TotalCharges_5116.6  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_5118.95  TotalCharges_512.25  TotalCharges_512.45  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_5121.3  TotalCharges_5121.75  TotalCharges_5124.55  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5124.6  TotalCharges_5125.5  TotalCharges_5125.75  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5127.95  TotalCharges_5129.3  TotalCharges_5129.45  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5135.15  TotalCharges_5135.35  TotalCharges_5138.1  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5139.65  TotalCharges_514  TotalCharges_514.6  \\\n",
+              "0                 False             False               False   \n",
+              "1                 False             False               False   \n",
+              "2                 False             False               False   \n",
+              "3                 False             False               False   \n",
+              "4                 False             False               False   \n",
+              "\n",
+              "   TotalCharges_514.75  TotalCharges_5149.5  TotalCharges_515.45  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_515.75  TotalCharges_5150.55  TotalCharges_5153.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5154.5  TotalCharges_5154.6  TotalCharges_516.15  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_516.3  TotalCharges_5163  TotalCharges_5163.3  \\\n",
+              "0               False              False                False   \n",
+              "1               False              False                False   \n",
+              "2               False              False                False   \n",
+              "3               False              False                False   \n",
+              "4               False              False                False   \n",
+              "\n",
+              "   TotalCharges_5165.7  TotalCharges_5166.2  TotalCharges_5168.1  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_5174.35  TotalCharges_5175.3  TotalCharges_518.3  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_518.75  TotalCharges_518.9  TotalCharges_5186  \\\n",
+              "0                False               False              False   \n",
+              "1                False               False              False   \n",
+              "2                False               False              False   \n",
+              "3                False               False              False   \n",
+              "4                False               False              False   \n",
+              "\n",
+              "   TotalCharges_5189.75  TotalCharges_519.15  TotalCharges_5193.2  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_5194.05  TotalCharges_5196.1  TotalCharges_5199.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_52  TotalCharges_52.05  TotalCharges_52.2  TotalCharges_520  \\\n",
+              "0            False               False              False             False   \n",
+              "1            False               False              False             False   \n",
+              "2            False               False              False             False   \n",
+              "3            False               False              False             False   \n",
+              "4            False               False              False             False   \n",
+              "\n",
+              "   TotalCharges_520.1  TotalCharges_520.55  TotalCharges_520.95  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_5200.8  TotalCharges_5206.55  TotalCharges_521  \\\n",
+              "0                False                 False             False   \n",
+              "1                False                 False             False   \n",
+              "2                False                 False             False   \n",
+              "3                False                 False             False   \n",
+              "4                False                 False             False   \n",
+              "\n",
+              "   TotalCharges_521.1  TotalCharges_521.3  TotalCharges_521.35  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_521.8  TotalCharges_521.9  TotalCharges_5212.65  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_5215.1  TotalCharges_5215.25  TotalCharges_5219.65  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_522.35  TotalCharges_522.95  TotalCharges_5222.3  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_5222.35  TotalCharges_5224.35  TotalCharges_5224.5  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5224.95  TotalCharges_5229.45  TotalCharges_5229.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_523.1  TotalCharges_523.15  TotalCharges_523.5  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_5231.2  TotalCharges_5231.3  TotalCharges_5232.9  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_5233.25  TotalCharges_5234.95  TotalCharges_5236.4  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5237.4  TotalCharges_5238.9  TotalCharges_524.35  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_524.5  TotalCharges_5243.05  TotalCharges_5244.45  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_525  TotalCharges_525.55  TotalCharges_5251.75  \\\n",
+              "0             False                False                 False   \n",
+              "1             False                False                 False   \n",
+              "2             False                False                 False   \n",
+              "3             False                False                 False   \n",
+              "4             False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5253.95  TotalCharges_526.7  TotalCharges_526.95  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_5264.25  TotalCharges_5264.3  TotalCharges_5264.5  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_5265.1  TotalCharges_5265.2  TotalCharges_5265.5  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_5265.55  TotalCharges_527.35  TotalCharges_527.5  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_527.9  TotalCharges_5270.6  TotalCharges_5275.8  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_5276.1  TotalCharges_5278.15  TotalCharges_528.35  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_528.45  TotalCharges_5283.95  TotalCharges_5289.05  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5289.8  TotalCharges_529.5  TotalCharges_529.8  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_5290.45  TotalCharges_5293.2  TotalCharges_5293.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_5293.95  TotalCharges_5294.6  TotalCharges_5295.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_5299.65  TotalCharges_53.05  TotalCharges_53.15  \\\n",
+              "0                 False               False               False   \n",
+              "1                 False               False               False   \n",
+              "2                 False               False               False   \n",
+              "3                 False               False               False   \n",
+              "4                 False               False               False   \n",
+              "\n",
+              "   TotalCharges_53.5  TotalCharges_53.55  TotalCharges_53.95  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_530.05  TotalCharges_5301.1  TotalCharges_5305.05  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5308.7  TotalCharges_5309.5  TotalCharges_531  \\\n",
+              "0                False                False             False   \n",
+              "1                False                False             False   \n",
+              "2                False                False             False   \n",
+              "3                False                False             False   \n",
+              "4                False                False             False   \n",
+              "\n",
+              "   TotalCharges_531.15  TotalCharges_531.55  TotalCharges_531.6  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_5311.85  TotalCharges_5315.1  TotalCharges_5315.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_5317.8  TotalCharges_532.1  TotalCharges_5321.25  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_5324.5  TotalCharges_5327.25  TotalCharges_5329  \\\n",
+              "0                False                 False              False   \n",
+              "1                False                 False              False   \n",
+              "2                False                 False              False   \n",
+              "3                False                 False              False   \n",
+              "4                False                 False              False   \n",
+              "\n",
+              "   TotalCharges_5329.55  TotalCharges_533.05  TotalCharges_533.5  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_533.6  TotalCharges_533.9  TotalCharges_5330.2  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_5333.35  TotalCharges_5336.35  TotalCharges_534.7  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_5341.8  TotalCharges_5347.95  TotalCharges_5348.65  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_535.05  TotalCharges_535.35  TotalCharges_535.55  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_5356.45  TotalCharges_5357.75  TotalCharges_536.35  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_536.4  TotalCharges_5360.75  TotalCharges_5364.8  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_537.35  TotalCharges_5373.1  TotalCharges_5375.15  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5376.4  TotalCharges_5377.8  TotalCharges_538.2  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_538.5  TotalCharges_5386.5  TotalCharges_5388.15  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_539.85  TotalCharges_5396.25  TotalCharges_5398.6  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_54.3  TotalCharges_54.35  TotalCharges_54.5  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_54.65  TotalCharges_54.7  TotalCharges_54.75  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_54.9  TotalCharges_540.05  TotalCharges_540.95  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_5401.9  TotalCharges_5405.8  TotalCharges_5409.75  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_541.15  TotalCharges_541.5  TotalCharges_541.9  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_5411.4  TotalCharges_5411.65  TotalCharges_542.4  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_5420.65  TotalCharges_5424.25  TotalCharges_5426.85  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5427.05  TotalCharges_543  TotalCharges_543.8  \\\n",
+              "0                 False             False               False   \n",
+              "1                 False             False               False   \n",
+              "2                 False             False               False   \n",
+              "3                 False             False               False   \n",
+              "4                 False             False               False   \n",
+              "\n",
+              "   TotalCharges_5430.35  TotalCharges_5430.65  TotalCharges_5431.4  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5431.9  TotalCharges_5432.2  TotalCharges_5435  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_5435.6  TotalCharges_5436.45  TotalCharges_5437.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5437.75  TotalCharges_5438.9  TotalCharges_5438.95  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_544.55  TotalCharges_5440.9  TotalCharges_5442.05  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5443.65  TotalCharges_5445.95  TotalCharges_5448.6  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_545.15  TotalCharges_545.2  TotalCharges_5450.7  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_5453.4  TotalCharges_5458.8  TotalCharges_5459.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_546.45  TotalCharges_546.85  TotalCharges_546.95  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_5460.2  TotalCharges_5461.45  TotalCharges_5464.65  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5468.45  TotalCharges_5468.95  TotalCharges_547.65  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_547.8  TotalCharges_5471.75  TotalCharges_5475.9  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_548.8  TotalCharges_548.9  TotalCharges_5480.25  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_5481.25  TotalCharges_5483.9  TotalCharges_5484.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_5485.5  TotalCharges_5487  TotalCharges_5496.9  \\\n",
+              "0                False              False                False   \n",
+              "1                False              False                False   \n",
+              "2                False              False                False   \n",
+              "3                False              False                False   \n",
+              "4                False              False                False   \n",
+              "\n",
+              "   TotalCharges_5497.05  TotalCharges_5498.2  TotalCharges_5498.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_55  TotalCharges_55.05  TotalCharges_55.2  TotalCharges_55.25  \\\n",
+              "0            False               False              False               False   \n",
+              "1            False               False              False               False   \n",
+              "2            False               False              False               False   \n",
+              "3            False               False              False               False   \n",
+              "4            False               False              False               False   \n",
+              "\n",
+              "   TotalCharges_55.3  TotalCharges_55.4  TotalCharges_55.45  \\\n",
+              "0              False              False               False   \n",
+              "1              False              False               False   \n",
+              "2              False              False               False   \n",
+              "3              False              False               False   \n",
+              "4              False              False               False   \n",
+              "\n",
+              "   TotalCharges_55.55  TotalCharges_55.7  TotalCharges_55.8  \\\n",
+              "0               False              False              False   \n",
+              "1               False              False              False   \n",
+              "2               False              False              False   \n",
+              "3               False              False              False   \n",
+              "4               False              False              False   \n",
+              "\n",
+              "   TotalCharges_550.1  TotalCharges_550.35  TotalCharges_550.6  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_5500.6  TotalCharges_5502.55  TotalCharges_5508.35  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5509.3  TotalCharges_551.3  TotalCharges_551.35  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_551.95  TotalCharges_5510.65  TotalCharges_5511.65  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5514.95  TotalCharges_5515.45  TotalCharges_5515.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_552.1  TotalCharges_552.65  TotalCharges_552.7  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_552.9  TotalCharges_552.95  TotalCharges_5522.7  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_5526.75  TotalCharges_5528.9  TotalCharges_553  \\\n",
+              "0                 False                False             False   \n",
+              "1                 False                False             False   \n",
+              "2                 False                False             False   \n",
+              "3                 False                False             False   \n",
+              "4                 False                False             False   \n",
+              "\n",
+              "   TotalCharges_553.4  TotalCharges_5535.8  TotalCharges_5536.5  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_5538.35  TotalCharges_5538.8  TotalCharges_554.05  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_554.25  TotalCharges_554.45  TotalCharges_5542.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5549.4  TotalCharges_555.4  TotalCharges_5550.1  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_5551.15  TotalCharges_5552.05  TotalCharges_5552.5  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5553.25  TotalCharges_5555.3  TotalCharges_556.35  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_5560  TotalCharges_5563.65  TotalCharges_5564.85  \\\n",
+              "0              False                 False                 False   \n",
+              "1              False                 False                 False   \n",
+              "2              False                 False                 False   \n",
+              "3              False                 False                 False   \n",
+              "4              False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5566.4  TotalCharges_5567.45  TotalCharges_5567.55  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5568.35  TotalCharges_5574.35  TotalCharges_5574.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5576.3  TotalCharges_558.8  TotalCharges_5580.8  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_5581.05  TotalCharges_5585.4  TotalCharges_5586.45  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5588.8  TotalCharges_5589.3  TotalCharges_5589.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_559.2  TotalCharges_5594  TotalCharges_5595.3  \\\n",
+              "0               False              False                False   \n",
+              "1               False              False                False   \n",
+              "2               False              False                False   \n",
+              "3               False              False                False   \n",
+              "4               False              False                False   \n",
+              "\n",
+              "   TotalCharges_5597.65  TotalCharges_5598  TotalCharges_5598.3  \\\n",
+              "0                 False              False                False   \n",
+              "1                 False              False                False   \n",
+              "2                 False              False                False   \n",
+              "3                 False              False                False   \n",
+              "4                 False              False                False   \n",
+              "\n",
+              "   TotalCharges_56  TotalCharges_56.25  TotalCharges_56.35  \\\n",
+              "0            False               False               False   \n",
+              "1            False               False               False   \n",
+              "2            False               False               False   \n",
+              "3            False               False               False   \n",
+              "4            False               False               False   \n",
+              "\n",
+              "   TotalCharges_560.6  TotalCharges_560.85  TotalCharges_5600.15  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5601.4  TotalCharges_5602.25  TotalCharges_5607.75  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5608.4  TotalCharges_561.15  TotalCharges_5610.15  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5610.25  TotalCharges_5610.7  TotalCharges_5611.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_5611.75  TotalCharges_5614.45  TotalCharges_5617.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5617.95  TotalCharges_5618.3  TotalCharges_562.6  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_562.7  TotalCharges_5621.85  TotalCharges_5623.7  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5624.85  TotalCharges_5625.55  TotalCharges_5629.15  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5629.55  TotalCharges_563.05  TotalCharges_563.5  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_563.65  TotalCharges_5632.55  TotalCharges_5637.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5638.3  TotalCharges_5639.05  TotalCharges_564.35  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_564.4  TotalCharges_564.65  TotalCharges_5643.4  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_5645.8  TotalCharges_5646.6  TotalCharges_5647.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_565.35  TotalCharges_565.75  TotalCharges_5655.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5656.75  TotalCharges_566.1  TotalCharges_566.5  \\\n",
+              "0                 False               False               False   \n",
+              "1                 False               False               False   \n",
+              "2                 False               False               False   \n",
+              "3                 False               False               False   \n",
+              "4                 False               False               False   \n",
+              "\n",
+              "   TotalCharges_5661.7  TotalCharges_5662.25  TotalCharges_5669.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_567.45  TotalCharges_567.8  TotalCharges_5673.7  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_5676  TotalCharges_5676.65  TotalCharges_568.2  \\\n",
+              "0              False                 False               False   \n",
+              "1              False                 False               False   \n",
+              "2              False                 False               False   \n",
+              "3              False                 False               False   \n",
+              "4              False                 False               False   \n",
+              "\n",
+              "   TotalCharges_568.85  TotalCharges_5680.9  TotalCharges_5681.1  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_5682.25  TotalCharges_5683.6  TotalCharges_5685.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_5686.4  TotalCharges_5688.05  TotalCharges_5688.45  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5692.65  TotalCharges_5696.6  TotalCharges_57.2  \\\n",
+              "0                 False                False              False   \n",
+              "1                 False                False              False   \n",
+              "2                 False                False              False   \n",
+              "3                 False                False              False   \n",
+              "4                 False                False              False   \n",
+              "\n",
+              "   TotalCharges_57.4  TotalCharges_57.5  TotalCharges_5703  \\\n",
+              "0              False              False              False   \n",
+              "1              False              False              False   \n",
+              "2              False              False              False   \n",
+              "3              False              False              False   \n",
+              "4              False              False              False   \n",
+              "\n",
+              "   TotalCharges_5703.25  TotalCharges_5705.05  TotalCharges_5706.2  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5706.3  TotalCharges_5708.2  TotalCharges_571.05  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_571.15  TotalCharges_571.45  TotalCharges_571.75  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_5711.05  TotalCharges_5714.2  TotalCharges_5714.25  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5717.85  TotalCharges_5718.2  TotalCharges_572.2  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_572.45  TotalCharges_572.85  TotalCharges_5720.35  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5720.95  TotalCharges_5727.15  TotalCharges_5727.45  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5728.55  TotalCharges_573.05  TotalCharges_573.15  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_573.3  TotalCharges_573.75  TotalCharges_5730.15  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5730.7  TotalCharges_5731.4  TotalCharges_5731.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5731.85  TotalCharges_5733.4  TotalCharges_5737.6  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_574.35  TotalCharges_574.5  TotalCharges_5742.9  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_5743.05  TotalCharges_5743.3  TotalCharges_5744.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5746.15  TotalCharges_5746.75  TotalCharges_5749.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_575.45  TotalCharges_5750  TotalCharges_5753.25  \\\n",
+              "0                False              False                 False   \n",
+              "1                False              False                 False   \n",
+              "2                False              False                 False   \n",
+              "3                False              False                 False   \n",
+              "4                False              False                 False   \n",
+              "\n",
+              "   TotalCharges_5755.8  TotalCharges_5757.2  TotalCharges_576.65  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_576.7  TotalCharges_576.95  TotalCharges_5760.65  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5762.95  TotalCharges_5763.15  TotalCharges_5763.3  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5764.7  TotalCharges_5769.6  TotalCharges_5769.75  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_577.15  TotalCharges_577.6  TotalCharges_5774.55  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_5776.45  TotalCharges_5779.6  TotalCharges_578.5  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_5780.7  TotalCharges_5784.3  TotalCharges_5785.5  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_5785.65  TotalCharges_579  TotalCharges_579.4  \\\n",
+              "0                 False             False               False   \n",
+              "1                 False             False               False   \n",
+              "2                 False             False               False   \n",
+              "3                 False             False               False   \n",
+              "4                 False             False               False   \n",
+              "\n",
+              "   TotalCharges_5791.1  TotalCharges_5791.85  TotalCharges_5794.45  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5794.65  TotalCharges_5798.3  TotalCharges_58  \\\n",
+              "0                 False                False            False   \n",
+              "1                 False                False            False   \n",
+              "2                 False                False            False   \n",
+              "3                 False                False            False   \n",
+              "4                 False                False            False   \n",
+              "\n",
+              "   TotalCharges_58.15  TotalCharges_58.3  TotalCharges_58.85  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_58.9  TotalCharges_580.1  TotalCharges_580.8  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_5809.75  TotalCharges_581.7  TotalCharges_581.85  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_5810.9  TotalCharges_5811.8  TotalCharges_5812  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_5812.6  TotalCharges_5815.15  TotalCharges_5817  \\\n",
+              "0                False                 False              False   \n",
+              "1                False                 False              False   \n",
+              "2                False                 False              False   \n",
+              "3                False                 False              False   \n",
+              "4                False                 False              False   \n",
+              "\n",
+              "   TotalCharges_5817.45  TotalCharges_5817.7  TotalCharges_582.5  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_5822.3  TotalCharges_5824.75  TotalCharges_5825.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5826.65  TotalCharges_583  TotalCharges_583.3  \\\n",
+              "0                 False             False               False   \n",
+              "1                 False             False               False   \n",
+              "2                 False             False               False   \n",
+              "3                 False             False               False   \n",
+              "4                 False             False               False   \n",
+              "\n",
+              "   TotalCharges_583.45  TotalCharges_5831.2  TotalCharges_5832  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_5832.65  TotalCharges_5835.5  TotalCharges_5839.3  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_5841.35  TotalCharges_5844.65  TotalCharges_5846.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5848.6  TotalCharges_585.95  TotalCharges_586.05  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_5860.7  TotalCharges_5861.75  TotalCharges_5867  \\\n",
+              "0                False                 False              False   \n",
+              "1                False                 False              False   \n",
+              "2                False                 False              False   \n",
+              "3                False                 False              False   \n",
+              "4                False                 False              False   \n",
+              "\n",
+              "   TotalCharges_5869.4  TotalCharges_587.1  TotalCharges_587.4  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_587.45  TotalCharges_587.7  TotalCharges_5873.75  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_5878.9  TotalCharges_5882.75  TotalCharges_5883.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5885.4  TotalCharges_5886.85  TotalCharges_589.25  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5890  TotalCharges_5893.15  TotalCharges_5893.9  \\\n",
+              "0              False                 False                False   \n",
+              "1              False                 False                False   \n",
+              "2              False                 False                False   \n",
+              "3              False                 False                False   \n",
+              "4              False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5893.95  TotalCharges_5894.5  TotalCharges_5895.45  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5897.4  TotalCharges_5898.6  TotalCharges_5899.85  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_59.05  TotalCharges_59.2  TotalCharges_59.25  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_59.55  TotalCharges_59.75  TotalCharges_59.85  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_590.35  TotalCharges_5903.15  TotalCharges_5913.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5914.4  TotalCharges_5916.45  TotalCharges_5916.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5917.55  TotalCharges_5918.8  TotalCharges_5919.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_592.65  TotalCharges_592.75  TotalCharges_5921.35  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5924.4  TotalCharges_5925.75  TotalCharges_593.05  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_593.2  TotalCharges_593.3  TotalCharges_593.45  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_593.75  TotalCharges_593.85  TotalCharges_5930.05  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5931  TotalCharges_5931.75  TotalCharges_5935.1  \\\n",
+              "0              False                 False                False   \n",
+              "1              False                 False                False   \n",
+              "2              False                 False                False   \n",
+              "3              False                 False                False   \n",
+              "4              False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5936.55  TotalCharges_5940.85  TotalCharges_5941.05  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5943.65  TotalCharges_5948.7  TotalCharges_595.05  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_595.5  TotalCharges_5950.2  TotalCharges_5953  \\\n",
+              "0               False                False              False   \n",
+              "1               False                False              False   \n",
+              "2               False                False              False   \n",
+              "3               False                False              False   \n",
+              "4               False                False              False   \n",
+              "\n",
+              "   TotalCharges_5956.85  TotalCharges_5957.9  TotalCharges_5958.85  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_5959.3  TotalCharges_5960.5  TotalCharges_5961.1  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_5963.95  TotalCharges_5965.95  TotalCharges_5968.4  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_5969.3  TotalCharges_5969.85  TotalCharges_5969.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_597  TotalCharges_597.9  TotalCharges_5971.25  \\\n",
+              "0             False               False                 False   \n",
+              "1             False               False                 False   \n",
+              "2             False               False                 False   \n",
+              "3             False               False                 False   \n",
+              "4             False               False                 False   \n",
+              "\n",
+              "   TotalCharges_5974.3  TotalCharges_5976.9  TotalCharges_5979.7  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_5980.55  TotalCharges_5980.75  TotalCharges_5981.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5985  TotalCharges_5985.75  TotalCharges_5986.45  \\\n",
+              "0              False                 False                 False   \n",
+              "1              False                 False                 False   \n",
+              "2              False                 False                 False   \n",
+              "3              False                 False                 False   \n",
+              "4              False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_5986.55  TotalCharges_599.25  TotalCharges_599.3  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_5991.05  TotalCharges_5997.1  TotalCharges_5999.85  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_60  TotalCharges_60.1  TotalCharges_60.15  TotalCharges_60.65  \\\n",
+              "0            False              False               False               False   \n",
+              "1            False              False               False               False   \n",
+              "2            False              False               False               False   \n",
+              "3            False              False               False               False   \n",
+              "4            False              False               False               False   \n",
+              "\n",
+              "   TotalCharges_600  TotalCharges_600.15  TotalCharges_600.25  \\\n",
+              "0             False                False                False   \n",
+              "1             False                False                False   \n",
+              "2             False                False                False   \n",
+              "3             False                False                False   \n",
+              "4             False                False                False   \n",
+              "\n",
+              "   TotalCharges_6000.1  TotalCharges_6001.45  TotalCharges_6004.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_601.25  TotalCharges_601.55  TotalCharges_601.6  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_6010.05  TotalCharges_6014.85  TotalCharges_6017.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6017.9  TotalCharges_6018.65  TotalCharges_6019.35  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_602.55  TotalCharges_602.9  TotalCharges_6028.95  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_6029  TotalCharges_6029.9  TotalCharges_603  \\\n",
+              "0              False                False             False   \n",
+              "1              False                False             False   \n",
+              "2              False                False             False   \n",
+              "3              False                False             False   \n",
+              "4              False                False             False   \n",
+              "\n",
+              "   TotalCharges_6033.1  TotalCharges_6033.3  TotalCharges_6034.85  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6038.55  TotalCharges_6039.9  TotalCharges_604.7  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_6042.7  TotalCharges_6045.9  TotalCharges_6046.1  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_6049.5  TotalCharges_605.25  TotalCharges_605.45  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_605.75  TotalCharges_605.9  TotalCharges_6052.25  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_6055.55  TotalCharges_6056.15  TotalCharges_6056.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6058.95  TotalCharges_606.25  TotalCharges_606.55  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_6065.3  TotalCharges_6066.55  TotalCharges_6067.4  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6068.65  TotalCharges_6069.25  TotalCharges_607.3  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_607.7  TotalCharges_6075.9  TotalCharges_6077.75  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6078.75  TotalCharges_6079  TotalCharges_608  \\\n",
+              "0                 False              False             False   \n",
+              "1                 False              False             False   \n",
+              "2                 False              False             False   \n",
+              "3                 False              False             False   \n",
+              "4                 False              False             False   \n",
+              "\n",
+              "   TotalCharges_608.15  TotalCharges_608.5  TotalCharges_608.8  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_6081.4  TotalCharges_6083.1  TotalCharges_609.05  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_609.1  TotalCharges_609.65  TotalCharges_609.9  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_6093.3  TotalCharges_6094.25  TotalCharges_6096.45  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6096.9  TotalCharges_61.05  TotalCharges_61.15  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_61.35  TotalCharges_61.45  TotalCharges_61.7  \\\n",
+              "0               False               False              False   \n",
+              "1               False               False              False   \n",
+              "2               False               False              False   \n",
+              "3               False               False              False   \n",
+              "4               False               False              False   \n",
+              "\n",
+              "   TotalCharges_610.2  TotalCharges_610.75  TotalCharges_6109.65  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6109.75  TotalCharges_611.45  TotalCharges_611.65  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_6110.2  TotalCharges_6110.75  TotalCharges_6118.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_612.1  TotalCharges_612.95  TotalCharges_6125.4  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_6126.1  TotalCharges_6126.15  TotalCharges_6127.6  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6129.2  TotalCharges_6129.65  TotalCharges_613.4  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_613.95  TotalCharges_6130.85  TotalCharges_6130.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6132.7  TotalCharges_6137  TotalCharges_6139.5  \\\n",
+              "0                False              False                False   \n",
+              "1                False              False                False   \n",
+              "2                False              False                False   \n",
+              "3                False              False                False   \n",
+              "4                False              False                False   \n",
+              "\n",
+              "   TotalCharges_614.45  TotalCharges_6140.85  TotalCharges_6141.65  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6143.15  TotalCharges_6144.55  TotalCharges_6145.2  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6145.85  TotalCharges_6148.45  TotalCharges_615.35  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6151.9  TotalCharges_6152.3  TotalCharges_6152.4  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_6153.85  TotalCharges_6155.4  TotalCharges_6157.6  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_616.9  TotalCharges_6161.9  TotalCharges_6164.7  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_617.15  TotalCharges_617.35  TotalCharges_617.65  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_617.85  TotalCharges_6171.2  TotalCharges_6172  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_6176.6  TotalCharges_6179.35  TotalCharges_6185.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6185.8  TotalCharges_6194.1  TotalCharges_62  \\\n",
+              "0                False                False            False   \n",
+              "1                False                False            False   \n",
+              "2                False                False            False   \n",
+              "3                False                False            False   \n",
+              "4                False                False            False   \n",
+              "\n",
+              "   TotalCharges_62.05  TotalCharges_62.25  TotalCharges_62.8  \\\n",
+              "0               False               False              False   \n",
+              "1               False               False              False   \n",
+              "2               False               False              False   \n",
+              "3               False               False              False   \n",
+              "4               False               False              False   \n",
+              "\n",
+              "   TotalCharges_62.9  TotalCharges_620.35  TotalCharges_620.55  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_620.75  TotalCharges_6201.95  TotalCharges_6205.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6215.35  TotalCharges_6218.45  TotalCharges_6219.6  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_622.9  TotalCharges_6223.3  TotalCharges_6223.8  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_6224.8  TotalCharges_6225.4  TotalCharges_6227.5  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_6230.1  TotalCharges_6236.75  TotalCharges_6237.05  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6239.05  TotalCharges_624.15  TotalCharges_624.6  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_6241.35  TotalCharges_625.05  TotalCharges_625.65  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_6252.7  TotalCharges_6252.9  TotalCharges_6253  \\\n",
+              "0                False                False              False   \n",
+              "1                False                False              False   \n",
+              "2                False                False              False   \n",
+              "3                False                False              False   \n",
+              "4                False                False              False   \n",
+              "\n",
+              "   TotalCharges_6254.2  TotalCharges_6254.45  TotalCharges_6256.2  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6263.8  TotalCharges_627.4  TotalCharges_6273.4  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_628.65  TotalCharges_6281.45  TotalCharges_6283.3  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6287.25  TotalCharges_6287.3  TotalCharges_629.35  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_629.55  TotalCharges_6292.7  TotalCharges_6293.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_6293.45  TotalCharges_6293.75  TotalCharges_6296.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6297.65  TotalCharges_63  TotalCharges_63.15  \\\n",
+              "0                 False            False               False   \n",
+              "1                 False            False               False   \n",
+              "2                 False            False               False   \n",
+              "3                 False            False               False   \n",
+              "4                 False            False               False   \n",
+              "\n",
+              "   TotalCharges_63.6  TotalCharges_63.75  TotalCharges_630.6  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_6300.15  TotalCharges_6300.85  TotalCharges_6301.7  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6302.8  TotalCharges_6302.85  TotalCharges_6306.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6309.65  TotalCharges_631.4  TotalCharges_631.85  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_6310.9  TotalCharges_6311.2  TotalCharges_6312.9  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_6314.35  TotalCharges_6316.2  TotalCharges_632.2  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_632.95  TotalCharges_6322.1  TotalCharges_6325.25  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6328.7  TotalCharges_633.3  TotalCharges_633.4  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_633.45  TotalCharges_633.85  TotalCharges_6330.4  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_6332.75  TotalCharges_6333.4  TotalCharges_6333.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_6339.3  TotalCharges_6339.45  TotalCharges_6341.45  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6342.7  TotalCharges_6347.55  TotalCharges_635.6  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_635.9  TotalCharges_6350.5  TotalCharges_6352.4  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_6362.35  TotalCharges_6363.45  TotalCharges_6365.35  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6367.2  TotalCharges_6368.2  TotalCharges_6369.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_637.4  TotalCharges_637.55  TotalCharges_6373.1  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_6375.2  TotalCharges_6375.8  TotalCharges_6376.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_638  TotalCharges_638.55  TotalCharges_638.95  \\\n",
+              "0             False                False                False   \n",
+              "1             False                False                False   \n",
+              "2             False                False                False   \n",
+              "3             False                False                False   \n",
+              "4             False                False                False   \n",
+              "\n",
+              "   TotalCharges_6382  TotalCharges_6382.55  TotalCharges_6383.35  \\\n",
+              "0              False                 False                 False   \n",
+              "1              False                 False                 False   \n",
+              "2              False                 False                 False   \n",
+              "3              False                 False                 False   \n",
+              "4              False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6383.9  TotalCharges_6385.95  TotalCharges_6388.65  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_639.45  TotalCharges_639.65  TotalCharges_639.7  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_6392.85  TotalCharges_6393.65  TotalCharges_6396.45  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6397.6  TotalCharges_6398.05  TotalCharges_64.55  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_6401.25  TotalCharges_6404  TotalCharges_6405  \\\n",
+              "0                 False              False              False   \n",
+              "1                 False              False              False   \n",
+              "2                 False              False              False   \n",
+              "3                 False              False              False   \n",
+              "4                 False              False              False   \n",
+              "\n",
+              "   TotalCharges_641.15  TotalCharges_641.25  TotalCharges_641.35  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_6411.25  TotalCharges_6413.65  TotalCharges_6416.7  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6418.9  TotalCharges_6423  TotalCharges_6424.25  \\\n",
+              "0                False              False                 False   \n",
+              "1                False              False                 False   \n",
+              "2                False              False                 False   \n",
+              "3                False              False                 False   \n",
+              "4                False              False                 False   \n",
+              "\n",
+              "   TotalCharges_6424.7  TotalCharges_6425.65  TotalCharges_6428.4  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6430.9  TotalCharges_6431.05  TotalCharges_6435.25  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_644.35  TotalCharges_644.5  TotalCharges_6440.25  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_6441.4  TotalCharges_6441.85  TotalCharges_6444.05  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6448.05  TotalCharges_6448.85  TotalCharges_6449.15  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_645.8  TotalCharges_6457.15  TotalCharges_646.05  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_646.85  TotalCharges_6460.55  TotalCharges_6463.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6465  TotalCharges_6468.6  TotalCharges_647.5  \\\n",
+              "0              False                False               False   \n",
+              "1              False                False               False   \n",
+              "2              False                False               False   \n",
+              "3              False                False               False   \n",
+              "4              False                False               False   \n",
+              "\n",
+              "   TotalCharges_6470.1  TotalCharges_6471.85  TotalCharges_6474.4  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6474.45  TotalCharges_6479.4  TotalCharges_648.65  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_6480.9  TotalCharges_6487.2  TotalCharges_649.4  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_649.65  TotalCharges_6496.15  TotalCharges_65.95  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_6501.35  TotalCharges_6503.2  TotalCharges_6506.15  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_651.4  TotalCharges_651.5  TotalCharges_651.55  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_6510.45  TotalCharges_6511.25  TotalCharges_6511.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6518.35  TotalCharges_6519.75  TotalCharges_6520.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6521.9  TotalCharges_6526.65  TotalCharges_6529.25  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_653.15  TotalCharges_653.25  TotalCharges_653.9  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_653.95  TotalCharges_6536.5  TotalCharges_6538.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_654.5  TotalCharges_654.55  TotalCharges_654.85  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_6547.7  TotalCharges_6548.65  TotalCharges_6549.45  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_655.3  TotalCharges_655.5  TotalCharges_655.85  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_655.9  TotalCharges_6555.2  TotalCharges_6557.75  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6561.25  TotalCharges_6562.9  TotalCharges_6563.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_6565.85  TotalCharges_6567.9  TotalCharges_657.5  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_6572.85  TotalCharges_6578.55  TotalCharges_6579.05  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_658.1  TotalCharges_658.95  TotalCharges_6581.9  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_6585.2  TotalCharges_6585.35  TotalCharges_6586.85  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6588.95  TotalCharges_6589.6  TotalCharges_659.35  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_659.45  TotalCharges_659.65  TotalCharges_6590.5  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_6590.8  TotalCharges_6595  TotalCharges_6595.9  \\\n",
+              "0                False              False                False   \n",
+              "1                False              False                False   \n",
+              "2                False              False                False   \n",
+              "3                False              False                False   \n",
+              "4                False              False                False   \n",
+              "\n",
+              "   TotalCharges_6597.25  TotalCharges_66.95  TotalCharges_660.05  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_660.9  TotalCharges_6602.9  TotalCharges_6603  \\\n",
+              "0               False                False              False   \n",
+              "1               False                False              False   \n",
+              "2               False                False              False   \n",
+              "3               False                False              False   \n",
+              "4               False                False              False   \n",
+              "\n",
+              "   TotalCharges_6603.8  TotalCharges_6605.55  TotalCharges_661.25  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_661.55  TotalCharges_6613.65  TotalCharges_6614.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6615.15  TotalCharges_662.65  TotalCharges_662.95  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_663.05  TotalCharges_663.55  TotalCharges_6631.85  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6632.75  TotalCharges_6637.9  TotalCharges_6638.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_664.4  TotalCharges_6640.7  TotalCharges_6643.5  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_665.45  TotalCharges_6652.45  TotalCharges_6654.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_666  TotalCharges_666.75  TotalCharges_6668  \\\n",
+              "0             False                False              False   \n",
+              "1             False                False              False   \n",
+              "2             False                False              False   \n",
+              "3             False                False              False   \n",
+              "4             False                False              False   \n",
+              "\n",
+              "   TotalCharges_6668.05  TotalCharges_6668.35  TotalCharges_6669.05  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6669.45  TotalCharges_667.7  TotalCharges_6671.7  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_6674.65  TotalCharges_668.4  TotalCharges_668.85  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_6683.4  TotalCharges_6687.85  TotalCharges_6688.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6688.95  TotalCharges_6689  TotalCharges_669  \\\n",
+              "0                 False              False             False   \n",
+              "1                 False              False             False   \n",
+              "2                 False              False             False   \n",
+              "3                 False              False             False   \n",
+              "4                 False              False             False   \n",
+              "\n",
+              "   TotalCharges_669.45  TotalCharges_669.85  TotalCharges_6690.75  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6692.65  TotalCharges_6697.2  TotalCharges_6697.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_67.1  TotalCharges_67.55  TotalCharges_670.35  \\\n",
+              "0              False               False                False   \n",
+              "1              False               False                False   \n",
+              "2              False               False                False   \n",
+              "3              False               False                False   \n",
+              "4              False               False                False   \n",
+              "\n",
+              "   TotalCharges_670.65  TotalCharges_6700.05  TotalCharges_6703.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6705.7  TotalCharges_6707.15  TotalCharges_6710.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6713.2  TotalCharges_6716.45  TotalCharges_6717.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6719.9  TotalCharges_672.2  TotalCharges_672.55  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_672.7  TotalCharges_6721.6  TotalCharges_6725.3  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_6725.5  TotalCharges_673.1  TotalCharges_673.2  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_673.25  TotalCharges_6733  TotalCharges_6733.15  \\\n",
+              "0                False              False                 False   \n",
+              "1                False              False                 False   \n",
+              "2                False              False                 False   \n",
+              "3                False              False                 False   \n",
+              "4                False              False                 False   \n",
+              "\n",
+              "   TotalCharges_6735.05  TotalCharges_674.55  TotalCharges_6741.15  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6743.55  TotalCharges_6744.2  TotalCharges_6744.25  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6747.35  TotalCharges_675.6  TotalCharges_6751.35  \\\n",
+              "0                 False               False                 False   \n",
+              "1                 False               False                 False   \n",
+              "2                 False               False                 False   \n",
+              "3                 False               False                 False   \n",
+              "4                 False               False                 False   \n",
+              "\n",
+              "   TotalCharges_6754.35  TotalCharges_6758.45  TotalCharges_676.15  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_676.35  TotalCharges_676.7  TotalCharges_6766.95  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_6767.1  TotalCharges_677.05  TotalCharges_677.9  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_6770.5  TotalCharges_6770.85  TotalCharges_6779.05  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_678.2  TotalCharges_678.45  TotalCharges_678.75  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_678.8  TotalCharges_6780.1  TotalCharges_6782.15  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6786.1  TotalCharges_6786.4  TotalCharges_679  \\\n",
+              "0                False                False             False   \n",
+              "1                False                False             False   \n",
+              "2                False                False             False   \n",
+              "3                False                False             False   \n",
+              "4                False                False             False   \n",
+              "\n",
+              "   TotalCharges_679.3  TotalCharges_679.55  TotalCharges_679.8  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_679.85  TotalCharges_6792.45  TotalCharges_6794.75  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_68.2  TotalCharges_68.35  TotalCharges_68.45  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_68.5  TotalCharges_68.65  TotalCharges_68.75  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_68.8  TotalCharges_68.95  TotalCharges_680.05  \\\n",
+              "0              False               False                False   \n",
+              "1              False               False                False   \n",
+              "2              False               False                False   \n",
+              "3              False               False                False   \n",
+              "4              False               False                False   \n",
+              "\n",
+              "   TotalCharges_681.4  TotalCharges_6812.95  TotalCharges_6816.95  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6819.45  TotalCharges_682.1  TotalCharges_682.15  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_6822.15  TotalCharges_6823.4  TotalCharges_6825.65  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6827.5  TotalCharges_683.25  TotalCharges_683.75  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_6838.6  TotalCharges_684.05  TotalCharges_684.4  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_684.85  TotalCharges_6840.95  TotalCharges_6841.05  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6841.3  TotalCharges_6841.4  TotalCharges_6841.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6843.15  TotalCharges_6844.5  TotalCharges_6849.4  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_6849.75  TotalCharges_685.55  TotalCharges_6851.65  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6856.45  TotalCharges_6856.95  TotalCharges_6858.9  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_6859.05  TotalCharges_6859.5  TotalCharges_686.95  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_6860.6  TotalCharges_6869.7  TotalCharges_6871.7  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_6871.9  TotalCharges_6873.75  TotalCharges_6875.35  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6876.05  TotalCharges_688  TotalCharges_688.2  \\\n",
+              "0                 False             False               False   \n",
+              "1                 False             False               False   \n",
+              "2                 False             False               False   \n",
+              "3                 False             False               False   \n",
+              "4                 False             False               False   \n",
+              "\n",
+              "   TotalCharges_688.5  TotalCharges_688.65  TotalCharges_6880.85  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6885.75  TotalCharges_6886.25  TotalCharges_6889.8  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_689  TotalCharges_689.35  TotalCharges_689.75  \\\n",
+              "0             False                False                False   \n",
+              "1             False                False                False   \n",
+              "2             False                False                False   \n",
+              "3             False                False                False   \n",
+              "4             False                False                False   \n",
+              "\n",
+              "   TotalCharges_6890  TotalCharges_6891.4  TotalCharges_6891.45  \\\n",
+              "0              False                False                 False   \n",
+              "1              False                False                 False   \n",
+              "2              False                False                 False   \n",
+              "3              False                False                 False   \n",
+              "4              False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6895.5  TotalCharges_69.1  TotalCharges_69.15  \\\n",
+              "0                False              False               False   \n",
+              "1                False              False               False   \n",
+              "2                False              False               False   \n",
+              "3                False              False               False   \n",
+              "4                False              False               False   \n",
+              "\n",
+              "   TotalCharges_69.2  TotalCharges_69.25  TotalCharges_69.35  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_69.4  TotalCharges_69.5  TotalCharges_69.55  \\\n",
+              "0              False              False               False   \n",
+              "1              False              False               False   \n",
+              "2              False              False               False   \n",
+              "3              False              False               False   \n",
+              "4              False              False               False   \n",
+              "\n",
+              "   TotalCharges_69.6  TotalCharges_69.65  TotalCharges_69.7  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_69.75  TotalCharges_69.8  TotalCharges_69.85  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_69.9  TotalCharges_69.95  TotalCharges_690.25  \\\n",
+              "0              False               False                False   \n",
+              "1              False               False                False   \n",
+              "2              False               False                False   \n",
+              "3              False               False                False   \n",
+              "4              False               False                False   \n",
+              "\n",
+              "   TotalCharges_690.5  TotalCharges_6903.1  TotalCharges_6910.3  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_6912.7  TotalCharges_6914.95  TotalCharges_692.1  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_692.35  TotalCharges_692.55  TotalCharges_6921.7  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_6925.9  TotalCharges_6929.4  TotalCharges_693.3  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_693.45  TotalCharges_6936.85  TotalCharges_6937.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6941.2  TotalCharges_6944.5  TotalCharges_695.05  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_695.75  TotalCharges_695.85  TotalCharges_6951.15  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6953.4  TotalCharges_6954.15  TotalCharges_696.35  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_696.8  TotalCharges_6962.85  TotalCharges_697.25  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_697.65  TotalCharges_697.7  TotalCharges_6975.25  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_6976.75  TotalCharges_6979.8  TotalCharges_6981.35  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_6982.5  TotalCharges_6985.65  TotalCharges_6989.45  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_6989.7  TotalCharges_6991.6  TotalCharges_6991.9  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_6993.65  TotalCharges_6994.6  TotalCharges_6994.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_6997.3  TotalCharges_6998.95  TotalCharges_70  \\\n",
+              "0                False                 False            False   \n",
+              "1                False                 False            False   \n",
+              "2                False                 False            False   \n",
+              "3                False                 False            False   \n",
+              "4                False                 False            False   \n",
+              "\n",
+              "   TotalCharges_70.05  TotalCharges_70.1  TotalCharges_70.15  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_70.2  TotalCharges_70.25  TotalCharges_70.3  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_70.35  TotalCharges_70.4  TotalCharges_70.45  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_70.5  TotalCharges_70.55  TotalCharges_70.6  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_70.65  TotalCharges_70.7  TotalCharges_70.75  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_70.8  TotalCharges_70.85  TotalCharges_70.9  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_700.45  TotalCharges_700.85  TotalCharges_7002.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7007.6  TotalCharges_7008.15  TotalCharges_7009.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_701.05  TotalCharges_701.3  TotalCharges_7015.9  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_702  TotalCharges_702.05  TotalCharges_702.9  \\\n",
+              "0             False                False               False   \n",
+              "1             False                False               False   \n",
+              "2             False                False               False   \n",
+              "3             False                False               False   \n",
+              "4             False                False               False   \n",
+              "\n",
+              "   TotalCharges_7028.5  TotalCharges_703.55  TotalCharges_7030.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7031.3  TotalCharges_7031.45  TotalCharges_7035.6  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_7039.05  TotalCharges_7039.45  TotalCharges_704.3  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_7040.85  TotalCharges_7047.5  TotalCharges_7049.5  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_7049.75  TotalCharges_705.45  TotalCharges_7051.95  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7053.35  TotalCharges_706.6  TotalCharges_706.85  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_7061.65  TotalCharges_7069.25  TotalCharges_7069.3  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_707.5  TotalCharges_7074.4  TotalCharges_7076.35  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_708.2  TotalCharges_708.8  TotalCharges_7082.45  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_7082.5  TotalCharges_7082.85  TotalCharges_7085.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_709.5  TotalCharges_7099  TotalCharges_71  TotalCharges_71.1  \\\n",
+              "0               False              False            False              False   \n",
+              "1               False              False            False              False   \n",
+              "2               False              False            False              False   \n",
+              "3               False              False            False              False   \n",
+              "4               False              False            False              False   \n",
+              "\n",
+              "   TotalCharges_71.15  TotalCharges_71.2  TotalCharges_71.25  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_71.35  TotalCharges_71.55  TotalCharges_71.65  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_710.05  TotalCharges_7101.5  TotalCharges_7104.2  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_7107  TotalCharges_7108.2  TotalCharges_711.15  \\\n",
+              "0              False                False                False   \n",
+              "1              False                False                False   \n",
+              "2              False                False                False   \n",
+              "3              False                False                False   \n",
+              "4              False                False                False   \n",
+              "\n",
+              "   TotalCharges_711.9  TotalCharges_711.95  TotalCharges_7110.75  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7111.3  TotalCharges_7112.15  TotalCharges_7113.75  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_7114.25  TotalCharges_7118.9  TotalCharges_712.25  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_712.75  TotalCharges_712.85  TotalCharges_7129.45  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_713  TotalCharges_713.1  TotalCharges_713.6  \\\n",
+              "0             False               False               False   \n",
+              "1             False               False               False   \n",
+              "2             False               False               False   \n",
+              "3             False               False               False   \n",
+              "4             False               False               False   \n",
+              "\n",
+              "   TotalCharges_713.75  TotalCharges_7132.15  TotalCharges_7133.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_7133.25  TotalCharges_7133.45  TotalCharges_7138.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_714.15  TotalCharges_7142.5  TotalCharges_7149.35  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_715  TotalCharges_7156.2  TotalCharges_7159.05  \\\n",
+              "0             False                False                 False   \n",
+              "1             False                False                 False   \n",
+              "2             False                False                 False   \n",
+              "3             False                False                 False   \n",
+              "4             False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7159.7  TotalCharges_716.1  TotalCharges_716.4  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_7168.25  TotalCharges_717.3  TotalCharges_717.5  \\\n",
+              "0                 False               False               False   \n",
+              "1                 False               False               False   \n",
+              "2                 False               False               False   \n",
+              "3                 False               False               False   \n",
+              "4                 False               False               False   \n",
+              "\n",
+              "   TotalCharges_717.95  TotalCharges_7171.7  TotalCharges_7173.15  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7176.55  TotalCharges_718.1  TotalCharges_718.55  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_7181.25  TotalCharges_7181.95  TotalCharges_7188.5  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_7195.35  TotalCharges_72  TotalCharges_72.1  \\\n",
+              "0                 False            False              False   \n",
+              "1                 False            False              False   \n",
+              "2                 False            False              False   \n",
+              "3                 False            False              False   \n",
+              "4                 False            False              False   \n",
+              "\n",
+              "   TotalCharges_72.4  TotalCharges_720.05  TotalCharges_720.1  \\\n",
+              "0              False                False               False   \n",
+              "1              False                False               False   \n",
+              "2              False                False               False   \n",
+              "3              False                False               False   \n",
+              "4              False                False               False   \n",
+              "\n",
+              "   TotalCharges_720.45  TotalCharges_7209  TotalCharges_7210.85  \\\n",
+              "0                False              False                 False   \n",
+              "1                False              False                 False   \n",
+              "2                False              False                 False   \n",
+              "3                False              False                 False   \n",
+              "4                False              False                 False   \n",
+              "\n",
+              "   TotalCharges_7213.75  TotalCharges_7220.35  TotalCharges_7222.75  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_7227.45  TotalCharges_723.3  TotalCharges_723.35  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_723.4  TotalCharges_7234.8  TotalCharges_7238.6  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_724.65  TotalCharges_7240.65  TotalCharges_7244.7  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_7245.9  TotalCharges_7246.15  TotalCharges_7250.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_7251.7  TotalCharges_7251.9  TotalCharges_726.1  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_7261.25  TotalCharges_7261.75  TotalCharges_7262  \\\n",
+              "0                 False                 False              False   \n",
+              "1                 False                 False              False   \n",
+              "2                 False                 False              False   \n",
+              "3                 False                 False              False   \n",
+              "4                 False                 False              False   \n",
+              "\n",
+              "   TotalCharges_7266.95  TotalCharges_727.8  TotalCharges_727.85  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_7279.35  TotalCharges_7281.6  TotalCharges_7283.25  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7285.7  TotalCharges_7288.4  TotalCharges_729.95  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_7291.75  TotalCharges_7297.75  TotalCharges_7299.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_73  TotalCharges_73.05  TotalCharges_73.1  TotalCharges_73.45  \\\n",
+              "0            False               False              False               False   \n",
+              "1            False               False              False               False   \n",
+              "2            False               False              False               False   \n",
+              "3            False               False              False               False   \n",
+              "4            False               False              False               False   \n",
+              "\n",
+              "   TotalCharges_73.5  TotalCharges_73.55  TotalCharges_73.6  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_73.65  TotalCharges_730.1  TotalCharges_730.4  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_7303.05  TotalCharges_7308.95  TotalCharges_731.3  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_7317.1  TotalCharges_7318.2  TotalCharges_732.5  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_7320.9  TotalCharges_7321.05  TotalCharges_7322.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_7323.15  TotalCharges_7325.1  TotalCharges_733.35  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_733.55  TotalCharges_733.95  TotalCharges_7332.4  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_7334.05  TotalCharges_7337.55  TotalCharges_734.35  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_734.6  TotalCharges_7344.45  TotalCharges_7346.2  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_7348.8  TotalCharges_7349.35  TotalCharges_735.5  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_735.9  TotalCharges_736.8  TotalCharges_7362.9  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_7365.3  TotalCharges_7365.7  TotalCharges_7372.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7379.8  TotalCharges_738.2  TotalCharges_7382.25  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_7382.85  TotalCharges_7383.7  TotalCharges_7386.05  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7388.45  TotalCharges_739.35  TotalCharges_739.9  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_7396.15  TotalCharges_7397  TotalCharges_74  \\\n",
+              "0                 False              False            False   \n",
+              "1                 False              False            False   \n",
+              "2                 False              False            False   \n",
+              "3                 False              False            False   \n",
+              "4                 False              False            False   \n",
+              "\n",
+              "   TotalCharges_74.1  TotalCharges_74.2  TotalCharges_74.25  \\\n",
+              "0              False              False               False   \n",
+              "1              False              False               False   \n",
+              "2              False              False               False   \n",
+              "3              False              False               False   \n",
+              "4              False              False               False   \n",
+              "\n",
+              "   TotalCharges_74.3  TotalCharges_74.35  TotalCharges_74.4  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_74.45  TotalCharges_74.5  TotalCharges_74.6  \\\n",
+              "0               False              False              False   \n",
+              "1               False              False              False   \n",
+              "2               False              False              False   \n",
+              "3               False              False              False   \n",
+              "4               False              False              False   \n",
+              "\n",
+              "   TotalCharges_74.7  TotalCharges_74.9  TotalCharges_74.95  TotalCharges_740  \\\n",
+              "0              False              False               False             False   \n",
+              "1              False              False               False             False   \n",
+              "2              False              False               False             False   \n",
+              "3              False              False               False             False   \n",
+              "4              False              False               False             False   \n",
+              "\n",
+              "   TotalCharges_740.3  TotalCharges_740.55  TotalCharges_740.8  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_7405.5  TotalCharges_741  TotalCharges_741.4  \\\n",
+              "0                False             False               False   \n",
+              "1                False             False               False   \n",
+              "2                False             False               False   \n",
+              "3                False             False               False   \n",
+              "4                False             False               False   \n",
+              "\n",
+              "   TotalCharges_741.5  TotalCharges_741.7  TotalCharges_7412.25  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_7413.55  TotalCharges_742.9  TotalCharges_742.95  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_7422.1  TotalCharges_743.05  TotalCharges_743.3  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_743.5  TotalCharges_743.75  TotalCharges_7430.75  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7432.05  TotalCharges_7446.9  TotalCharges_7447.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_745.3  TotalCharges_7455.45  TotalCharges_7459  \\\n",
+              "0               False                 False              False   \n",
+              "1               False                 False              False   \n",
+              "2               False                 False              False   \n",
+              "3               False                 False              False   \n",
+              "4               False                 False              False   \n",
+              "\n",
+              "   TotalCharges_7459.05  TotalCharges_746.05  TotalCharges_746.5  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_746.75  TotalCharges_7467.5  TotalCharges_7467.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_747.2  TotalCharges_7470.1  TotalCharges_7472.15  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7475.1  TotalCharges_7475.85  TotalCharges_7482.1  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_749.25  TotalCharges_749.35  TotalCharges_7491.75  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7493.05  TotalCharges_75.05  TotalCharges_75.1  \\\n",
+              "0                 False               False              False   \n",
+              "1                 False               False              False   \n",
+              "2                 False               False              False   \n",
+              "3                 False               False              False   \n",
+              "4                 False               False              False   \n",
+              "\n",
+              "   TotalCharges_75.3  TotalCharges_75.35  TotalCharges_75.45  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_75.5  TotalCharges_75.55  TotalCharges_75.6  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_75.7  TotalCharges_75.75  TotalCharges_75.8  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_75.9  TotalCharges_750.1  TotalCharges_7508.55  \\\n",
+              "0              False               False                 False   \n",
+              "1              False               False                 False   \n",
+              "2              False               False                 False   \n",
+              "3              False               False                 False   \n",
+              "4              False               False                 False   \n",
+              "\n",
+              "   TotalCharges_751.65  TotalCharges_7511.3  TotalCharges_7511.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7511.9  TotalCharges_7517.7  TotalCharges_7521.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7530.8  TotalCharges_7532.15  TotalCharges_7534.65  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_7537.5  TotalCharges_754  TotalCharges_754.5  \\\n",
+              "0                False             False               False   \n",
+              "1                False             False               False   \n",
+              "2                False             False               False   \n",
+              "3                False             False               False   \n",
+              "4                False             False               False   \n",
+              "\n",
+              "   TotalCharges_754.65  TotalCharges_7542.25  TotalCharges_7544  \\\n",
+              "0                False                 False              False   \n",
+              "1                False                 False              False   \n",
+              "2                False                 False              False   \n",
+              "3                False                 False              False   \n",
+              "4                False                 False              False   \n",
+              "\n",
+              "   TotalCharges_7544.3  TotalCharges_7548.1  TotalCharges_7548.6  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_755.4  TotalCharges_755.5  TotalCharges_755.6  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_7550.3  TotalCharges_7553.6  TotalCharges_7554.05  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7555  TotalCharges_7556.9  TotalCharges_7559.55  \\\n",
+              "0              False                False                 False   \n",
+              "1              False                False                 False   \n",
+              "2              False                False                 False   \n",
+              "3              False                False                 False   \n",
+              "4              False                False                 False   \n",
+              "\n",
+              "   TotalCharges_756.4  TotalCharges_7565.35  TotalCharges_7567.2  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_757.1  TotalCharges_757.95  TotalCharges_7576.7  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_7578.05  TotalCharges_758.6  TotalCharges_7581.5  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_7589.8  TotalCharges_759.35  TotalCharges_759.55  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_76  TotalCharges_76.2  TotalCharges_76.35  TotalCharges_76.4  \\\n",
+              "0            False              False               False              False   \n",
+              "1            False              False               False              False   \n",
+              "2            False              False               False              False   \n",
+              "3            False              False               False              False   \n",
+              "4            False              False               False              False   \n",
+              "\n",
+              "   TotalCharges_76.45  TotalCharges_76.65  TotalCharges_76.95  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_760.05  TotalCharges_7609.75  TotalCharges_761.85  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_761.95  TotalCharges_7610.1  TotalCharges_7611.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7611.85  TotalCharges_7616  TotalCharges_762.1  \\\n",
+              "0                 False              False               False   \n",
+              "1                 False              False               False   \n",
+              "2                 False              False               False   \n",
+              "3                 False              False               False   \n",
+              "4                 False              False               False   \n",
+              "\n",
+              "   TotalCharges_762.25  TotalCharges_762.45  TotalCharges_762.5  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_7623.2  TotalCharges_7624.2  TotalCharges_7629.85  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_763.1  TotalCharges_7634.25  TotalCharges_7634.8  \\\n",
+              "0               False                 False                False   \n",
+              "1               False                 False                False   \n",
+              "2               False                 False                False   \n",
+              "3               False                 False                False   \n",
+              "4               False                 False                False   \n",
+              "\n",
+              "   TotalCharges_764.55  TotalCharges_764.95  TotalCharges_765.15  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_765.45  TotalCharges_765.5  TotalCharges_7657.4  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_7658.3  TotalCharges_7661.8  TotalCharges_7665.8  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_767.55  TotalCharges_767.9  TotalCharges_7674.55  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_7677.4  TotalCharges_7679.65  TotalCharges_768.05  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_768.45  TotalCharges_7689.8  TotalCharges_7689.95  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_769.1  TotalCharges_7690.9  TotalCharges_77.15  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_77.5  TotalCharges_77.6  TotalCharges_77.75  \\\n",
+              "0              False              False               False   \n",
+              "1              False              False               False   \n",
+              "2              False              False               False   \n",
+              "3              False              False               False   \n",
+              "4              False              False               False   \n",
+              "\n",
+              "   TotalCharges_770.4  TotalCharges_770.5  TotalCharges_770.6  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_7707.7  TotalCharges_771.95  TotalCharges_7711.25  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7711.45  TotalCharges_7713.55  TotalCharges_7714.65  \\\n",
+              "0                 False                 False                 False   \n",
+              "1                 False                 False                 False   \n",
+              "2                 False                 False                 False   \n",
+              "3                 False                 False                 False   \n",
+              "4                 False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_7719.5  TotalCharges_772.4  TotalCharges_772.85  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_7723.7  TotalCharges_7723.9  TotalCharges_7726.35  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_773.2  TotalCharges_773.65  TotalCharges_7732.65  \\\n",
+              "0               False                False                 False   \n",
+              "1               False                False                 False   \n",
+              "2               False                False                 False   \n",
+              "3               False                False                 False   \n",
+              "4               False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7737.55  TotalCharges_7746.7  TotalCharges_7748.75  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_775.3  TotalCharges_775.6  TotalCharges_7751.7  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_7752.05  TotalCharges_7752.3  TotalCharges_7758.9  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_776.25  TotalCharges_7767.25  TotalCharges_777.3  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_777.35  TotalCharges_7774.05  TotalCharges_778.1  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_7782.85  TotalCharges_7789.6  TotalCharges_779.2  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_779.25  TotalCharges_7795.95  TotalCharges_78.05  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_78.25  TotalCharges_78.3  TotalCharges_78.45  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_78.65  TotalCharges_78.8  TotalCharges_78.9  \\\n",
+              "0               False              False              False   \n",
+              "1               False              False              False   \n",
+              "2               False              False              False   \n",
+              "3               False              False              False   \n",
+              "4               False              False              False   \n",
+              "\n",
+              "   TotalCharges_78.95  TotalCharges_780.1  TotalCharges_780.15  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_780.2  TotalCharges_780.25  TotalCharges_780.5  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_780.85  TotalCharges_7804.15  TotalCharges_7806.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_7806.6  TotalCharges_781.25  TotalCharges_781.4  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_7839.85  TotalCharges_784.25  TotalCharges_784.45  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_7840.6  TotalCharges_7842.3  TotalCharges_7843.55  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7845.8  TotalCharges_7848.5  TotalCharges_7849.85  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_785.75  TotalCharges_7852.4  TotalCharges_7853.7  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_7854.15  TotalCharges_7854.9  TotalCharges_7856  \\\n",
+              "0                 False                False              False   \n",
+              "1                 False                False              False   \n",
+              "2                 False                False              False   \n",
+              "3                 False                False              False   \n",
+              "4                 False                False              False   \n",
+              "\n",
+              "   TotalCharges_786.3  TotalCharges_786.5  TotalCharges_7862.25  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_7869.05  TotalCharges_7875  TotalCharges_7878.3  \\\n",
+              "0                 False              False                False   \n",
+              "1                 False              False                False   \n",
+              "2                 False              False                False   \n",
+              "3                 False              False                False   \n",
+              "4                 False              False                False   \n",
+              "\n",
+              "   TotalCharges_788.05  TotalCharges_788.35  TotalCharges_788.55  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_788.6  TotalCharges_788.8  TotalCharges_7880.25  \\\n",
+              "0               False               False                 False   \n",
+              "1               False               False                 False   \n",
+              "2               False               False                 False   \n",
+              "3               False               False                 False   \n",
+              "4               False               False                 False   \n",
+              "\n",
+              "   TotalCharges_7881.2  TotalCharges_7882.25  TotalCharges_7882.5  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_7887.25  TotalCharges_789.2  TotalCharges_789.55  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_7895.15  TotalCharges_7898.45  TotalCharges_79.05  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_79.1  TotalCharges_79.15  TotalCharges_79.2  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_79.25  TotalCharges_79.35  TotalCharges_79.45  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_79.5  TotalCharges_79.55  TotalCharges_79.6  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_79.65  TotalCharges_79.7  TotalCharges_79.8  \\\n",
+              "0               False              False              False   \n",
+              "1               False              False              False   \n",
+              "2               False              False              False   \n",
+              "3               False              False              False   \n",
+              "4               False              False              False   \n",
+              "\n",
+              "   TotalCharges_79.9  TotalCharges_79.95  TotalCharges_790  \\\n",
+              "0              False               False             False   \n",
+              "1              False               False             False   \n",
+              "2              False               False             False   \n",
+              "3              False               False             False   \n",
+              "4              False               False             False   \n",
+              "\n",
+              "   TotalCharges_790.15  TotalCharges_790.7  TotalCharges_7904.25  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_791.15  TotalCharges_791.7  TotalCharges_791.75  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_7919.8  TotalCharges_792.15  TotalCharges_7920.7  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_7922.75  TotalCharges_793.55  TotalCharges_7930.55  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_7932.5  TotalCharges_7939.25  TotalCharges_794.25  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_7942.15  TotalCharges_7943.45  TotalCharges_795.15  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_795.65  TotalCharges_7953.25  TotalCharges_7962.2  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_7965.95  TotalCharges_7966.9  TotalCharges_7968.85  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_797.1  TotalCharges_797.25  TotalCharges_798.2  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_7982.5  TotalCharges_7984.15  TotalCharges_7985.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_7987.6  TotalCharges_799  TotalCharges_799.65  \\\n",
+              "0                False             False                False   \n",
+              "1                False             False                False   \n",
+              "2                False             False                False   \n",
+              "3                False             False                False   \n",
+              "4                False             False                False   \n",
+              "\n",
+              "   TotalCharges_7990.05  TotalCharges_7993.3  TotalCharges_7998.8  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_80  TotalCharges_80.05  TotalCharges_80.15  TotalCharges_80.2  \\\n",
+              "0            False               False               False              False   \n",
+              "1            False               False               False              False   \n",
+              "2            False               False               False              False   \n",
+              "3            False               False               False              False   \n",
+              "4            False               False               False              False   \n",
+              "\n",
+              "   TotalCharges_80.25  TotalCharges_80.3  TotalCharges_80.35  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_80.5  TotalCharges_80.55  TotalCharges_80.8  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_80.85  TotalCharges_80.95  TotalCharges_800.3  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_8003.8  TotalCharges_801.3  TotalCharges_8012.75  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_8013.55  TotalCharges_8016.6  TotalCharges_802.35  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_8022.85  TotalCharges_803.3  TotalCharges_8033.1  \\\n",
+              "0                 False               False                False   \n",
+              "1                 False               False                False   \n",
+              "2                 False               False                False   \n",
+              "3                 False               False                False   \n",
+              "4                 False               False                False   \n",
+              "\n",
+              "   TotalCharges_8035.95  TotalCharges_804.25  TotalCharges_804.85  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_8041.65  TotalCharges_8046.85  TotalCharges_805.1  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_805.2  TotalCharges_8058.55  TotalCharges_8058.85  \\\n",
+              "0               False                 False                 False   \n",
+              "1               False                 False                 False   \n",
+              "2               False                 False                 False   \n",
+              "3               False                 False                 False   \n",
+              "4               False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_806.95  TotalCharges_8061.5  TotalCharges_8065.65  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_8071.05  TotalCharges_8075.35  TotalCharges_8078.1  \\\n",
+              "0                 False                 False                False   \n",
+              "1                 False                 False                False   \n",
+              "2                 False                 False                False   \n",
+              "3                 False                 False                False   \n",
+              "4                 False                 False                False   \n",
+              "\n",
+              "   TotalCharges_808.95  TotalCharges_8086.4  TotalCharges_809.25  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_809.75  TotalCharges_8093.15  TotalCharges_81  \\\n",
+              "0                False                 False            False   \n",
+              "1                False                 False            False   \n",
+              "2                False                 False            False   \n",
+              "3                False                 False            False   \n",
+              "4                False                 False            False   \n",
+              "\n",
+              "   TotalCharges_81.05  TotalCharges_81.1  TotalCharges_81.25  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_81.7  TotalCharges_81.95  TotalCharges_810.2  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_810.3  TotalCharges_810.45  TotalCharges_810.7  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_810.85  TotalCharges_8100.25  TotalCharges_8100.55  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_8109.8  TotalCharges_811.65  TotalCharges_811.8  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_812.4  TotalCharges_812.5  TotalCharges_8124.2  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_8126.65  TotalCharges_8127.6  TotalCharges_8129.3  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_813.3  TotalCharges_813.45  TotalCharges_813.85  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_814.75  TotalCharges_815.5  TotalCharges_815.55  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_8152.3  TotalCharges_816.8  TotalCharges_8164.1  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_8165.1  TotalCharges_8166.8  TotalCharges_817.95  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_8175.9  TotalCharges_818.05  TotalCharges_818.45  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_8182.75  TotalCharges_8182.85  TotalCharges_819.4  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_819.55  TotalCharges_819.95  TotalCharges_8192.6  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_8196.4  TotalCharges_82.15  TotalCharges_82.3  \\\n",
+              "0                False               False              False   \n",
+              "1                False               False              False   \n",
+              "2                False               False              False   \n",
+              "3                False               False              False   \n",
+              "4                False               False              False   \n",
+              "\n",
+              "   TotalCharges_82.7  TotalCharges_82.85  TotalCharges_82.9  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_820.5  TotalCharges_821.6  TotalCharges_8220.4  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_824.75  TotalCharges_824.85  TotalCharges_8240.85  \\\n",
+              "0                False                False                 False   \n",
+              "1                False                False                 False   \n",
+              "2                False                False                 False   \n",
+              "3                False                False                 False   \n",
+              "4                False                False                 False   \n",
+              "\n",
+              "   TotalCharges_8244.3  TotalCharges_8248.5  TotalCharges_825.1  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_825.4  TotalCharges_825.7  TotalCharges_8250  \\\n",
+              "0               False               False              False   \n",
+              "1               False               False              False   \n",
+              "2               False               False              False   \n",
+              "3               False               False              False   \n",
+              "4               False               False              False   \n",
+              "\n",
+              "   TotalCharges_826  TotalCharges_826.1  TotalCharges_827.05  \\\n",
+              "0             False               False                False   \n",
+              "1             False               False                False   \n",
+              "2             False               False                False   \n",
+              "3             False               False                False   \n",
+              "4             False               False                False   \n",
+              "\n",
+              "   TotalCharges_827.3  TotalCharges_827.45  TotalCharges_827.7  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_8277.05  TotalCharges_828.05  TotalCharges_828.2  \\\n",
+              "0                 False                False               False   \n",
+              "1                 False                False               False   \n",
+              "2                 False                False               False   \n",
+              "3                 False                False               False   \n",
+              "4                 False                False               False   \n",
+              "\n",
+              "   TotalCharges_828.85  TotalCharges_8289.2  TotalCharges_829.1  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_829.3  TotalCharges_829.55  TotalCharges_8297.5  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_83.3  TotalCharges_83.4  TotalCharges_83.75  \\\n",
+              "0              False              False               False   \n",
+              "1              False              False               False   \n",
+              "2              False              False               False   \n",
+              "3              False              False               False   \n",
+              "4              False              False               False   \n",
+              "\n",
+              "   TotalCharges_830.25  TotalCharges_830.7  TotalCharges_830.8  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_830.85  TotalCharges_8306.05  TotalCharges_8308.9  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_8309.55  TotalCharges_831.75  TotalCharges_8310.55  \\\n",
+              "0                 False                False                 False   \n",
+              "1                 False                False                 False   \n",
+              "2                 False                False                 False   \n",
+              "3                 False                False                 False   \n",
+              "4                 False                False                 False   \n",
+              "\n",
+              "   TotalCharges_8312.4  TotalCharges_8312.75  TotalCharges_8317.95  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_832.05  TotalCharges_832.3  TotalCharges_832.35  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_833.55  TotalCharges_8331.95  TotalCharges_8332.15  \\\n",
+              "0                False                 False                 False   \n",
+              "1                False                 False                 False   \n",
+              "2                False                 False                 False   \n",
+              "3                False                 False                 False   \n",
+              "4                False                 False                 False   \n",
+              "\n",
+              "   TotalCharges_8333.95  TotalCharges_8337.45  TotalCharges_834.1  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_834.15  TotalCharges_834.2  TotalCharges_834.7  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_8349.45  TotalCharges_8349.7  TotalCharges_835.15  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_835.5  TotalCharges_836.35  TotalCharges_837.5  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_837.95  TotalCharges_8375.05  TotalCharges_838.5  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_838.7  TotalCharges_839.4  TotalCharges_839.65  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_8399.15  TotalCharges_84.2  TotalCharges_84.3  \\\n",
+              "0                 False              False              False   \n",
+              "1                 False              False              False   \n",
+              "2                 False              False              False   \n",
+              "3                 False              False              False   \n",
+              "4                 False              False              False   \n",
+              "\n",
+              "   TotalCharges_84.4  TotalCharges_84.5  TotalCharges_84.6  \\\n",
+              "0              False              False              False   \n",
+              "1              False              False              False   \n",
+              "2              False              False              False   \n",
+              "3              False              False              False   \n",
+              "4              False              False              False   \n",
+              "\n",
+              "   TotalCharges_84.65  TotalCharges_84.75  TotalCharges_84.8  \\\n",
+              "0               False               False              False   \n",
+              "1               False               False              False   \n",
+              "2               False               False              False   \n",
+              "3               False               False              False   \n",
+              "4               False               False              False   \n",
+              "\n",
+              "   TotalCharges_84.85  TotalCharges_840.1  TotalCharges_8404.9  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_8405  TotalCharges_842.25  TotalCharges_842.9  \\\n",
+              "0              False                False               False   \n",
+              "1              False                False               False   \n",
+              "2              False                False               False   \n",
+              "3              False                False               False   \n",
+              "4              False                False               False   \n",
+              "\n",
+              "   TotalCharges_8424.9  TotalCharges_8425.15  TotalCharges_8425.3  \\\n",
+              "0                False                 False                False   \n",
+              "1                False                 False                False   \n",
+              "2                False                 False                False   \n",
+              "3                False                 False                False   \n",
+              "4                False                 False                False   \n",
+              "\n",
+              "   TotalCharges_8436.25  TotalCharges_844.45  TotalCharges_8443.7  \\\n",
+              "0                 False                False                False   \n",
+              "1                 False                False                False   \n",
+              "2                 False                False                False   \n",
+              "3                 False                False                False   \n",
+              "4                 False                False                False   \n",
+              "\n",
+              "   TotalCharges_845.25  TotalCharges_845.6  TotalCharges_8456.75  \\\n",
+              "0                False               False                 False   \n",
+              "1                False               False                 False   \n",
+              "2                False               False                 False   \n",
+              "3                False               False                 False   \n",
+              "4                False               False                 False   \n",
+              "\n",
+              "   TotalCharges_846  TotalCharges_846.8  TotalCharges_8468.2  \\\n",
+              "0             False               False                False   \n",
+              "1             False               False                False   \n",
+              "2             False               False                False   \n",
+              "3             False               False                False   \n",
+              "4             False               False                False   \n",
+              "\n",
+              "   TotalCharges_847.25  TotalCharges_847.3  TotalCharges_847.8  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_8476.5  TotalCharges_8477.6  TotalCharges_8477.7  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_849.1  TotalCharges_849.9  TotalCharges_8496.7  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_85  TotalCharges_85.05  TotalCharges_85.1  TotalCharges_85.15  \\\n",
+              "0            False               False              False               False   \n",
+              "1            False               False              False               False   \n",
+              "2            False               False              False               False   \n",
+              "3            False               False              False               False   \n",
+              "4            False               False              False               False   \n",
+              "\n",
+              "   TotalCharges_85.45  TotalCharges_85.5  TotalCharges_85.55  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_85.7  TotalCharges_85.8  TotalCharges_851.2  \\\n",
+              "0              False              False               False   \n",
+              "1              False              False               False   \n",
+              "2              False              False               False   \n",
+              "3              False              False               False   \n",
+              "4              False              False               False   \n",
+              "\n",
+              "   TotalCharges_851.75  TotalCharges_851.8  TotalCharges_852.7  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_8529.5  TotalCharges_853  TotalCharges_853.1  \\\n",
+              "0                False             False               False   \n",
+              "1                False             False               False   \n",
+              "2                False             False               False   \n",
+              "3                False             False               False   \n",
+              "4                False             False               False   \n",
+              "\n",
+              "   TotalCharges_854.45  TotalCharges_854.8  TotalCharges_854.9  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_8543.25  TotalCharges_8547.15  TotalCharges_855.1  \\\n",
+              "0                 False                 False               False   \n",
+              "1                 False                 False               False   \n",
+              "2                 False                 False               False   \n",
+              "3                 False                 False               False   \n",
+              "4                 False                 False               False   \n",
+              "\n",
+              "   TotalCharges_855.3  TotalCharges_856.35  TotalCharges_856.5  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_856.65  TotalCharges_8564.75  TotalCharges_857.2  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_857.25  TotalCharges_857.75  TotalCharges_857.8  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_858.6  TotalCharges_8594.4  TotalCharges_86  \\\n",
+              "0               False                False            False   \n",
+              "1               False                False            False   \n",
+              "2               False                False            False   \n",
+              "3               False                False            False   \n",
+              "4               False                False            False   \n",
+              "\n",
+              "   TotalCharges_86.05  TotalCharges_86.35  TotalCharges_86.6  \\\n",
+              "0               False               False              False   \n",
+              "1               False               False              False   \n",
+              "2               False               False              False   \n",
+              "3               False               False              False   \n",
+              "4               False               False              False   \n",
+              "\n",
+              "   TotalCharges_860.85  TotalCharges_861.85  TotalCharges_862.4  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_863.1  TotalCharges_864.2  TotalCharges_864.55  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_864.85  TotalCharges_865  TotalCharges_865.05  \\\n",
+              "0                False             False                False   \n",
+              "1                False             False                False   \n",
+              "2                False             False                False   \n",
+              "3                False             False                False   \n",
+              "4                False             False                False   \n",
+              "\n",
+              "   TotalCharges_865.1  TotalCharges_865.55  TotalCharges_865.75  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_865.8  TotalCharges_865.85  TotalCharges_866.4  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_866.45  TotalCharges_867.1  TotalCharges_867.3  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_8670.1  TotalCharges_8672.45  TotalCharges_868.1  \\\n",
+              "0                False                 False               False   \n",
+              "1                False                 False               False   \n",
+              "2                False                 False               False   \n",
+              "3                False                 False               False   \n",
+              "4                False                 False               False   \n",
+              "\n",
+              "   TotalCharges_868.5  TotalCharges_8684.8  TotalCharges_869.9  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_87.3  TotalCharges_87.9  TotalCharges_870.25  \\\n",
+              "0              False              False                False   \n",
+              "1              False              False                False   \n",
+              "2              False              False                False   \n",
+              "3              False              False                False   \n",
+              "4              False              False                False   \n",
+              "\n",
+              "   TotalCharges_871.4  TotalCharges_872.65  TotalCharges_873.4  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_874.2  TotalCharges_874.8  TotalCharges_875.35  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_875.55  TotalCharges_876.15  TotalCharges_876.75  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_877.35  TotalCharges_878.35  TotalCharges_879.8  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_88.35  TotalCharges_88.8  TotalCharges_880.05  \\\n",
+              "0               False              False                False   \n",
+              "1               False              False                False   \n",
+              "2               False              False                False   \n",
+              "3               False              False                False   \n",
+              "4               False              False                False   \n",
+              "\n",
+              "   TotalCharges_880.2  TotalCharges_882.55  TotalCharges_883.35  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_886.4  TotalCharges_886.7  TotalCharges_887.35  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_888.65  TotalCharges_888.75  TotalCharges_889  \\\n",
+              "0                False                False             False   \n",
+              "1                False                False             False   \n",
+              "2                False                False             False   \n",
+              "3                False                False             False   \n",
+              "4                False                False             False   \n",
+              "\n",
+              "   TotalCharges_889.9  TotalCharges_89.05  TotalCharges_89.1  \\\n",
+              "0               False               False              False   \n",
+              "1               False               False              False   \n",
+              "2               False               False              False   \n",
+              "3               False               False              False   \n",
+              "4               False               False              False   \n",
+              "\n",
+              "   TotalCharges_89.15  TotalCharges_89.25  TotalCharges_89.3  \\\n",
+              "0               False               False              False   \n",
+              "1               False               False              False   \n",
+              "2               False               False              False   \n",
+              "3               False               False              False   \n",
+              "4               False               False              False   \n",
+              "\n",
+              "   TotalCharges_89.35  TotalCharges_89.5  TotalCharges_89.55  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_89.75  TotalCharges_89.9  TotalCharges_890.35  \\\n",
+              "0               False              False                False   \n",
+              "1               False              False                False   \n",
+              "2               False              False                False   \n",
+              "3               False              False                False   \n",
+              "4               False              False                False   \n",
+              "\n",
+              "   TotalCharges_890.5  TotalCharges_890.6  TotalCharges_892.15  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_892.65  TotalCharges_892.7  TotalCharges_893  \\\n",
+              "0                False               False             False   \n",
+              "1                False               False             False   \n",
+              "2                False               False             False   \n",
+              "3                False               False             False   \n",
+              "4                False               False             False   \n",
+              "\n",
+              "   TotalCharges_893.2  TotalCharges_893.55  TotalCharges_894.3  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_896.75  TotalCharges_896.9  TotalCharges_897.75  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_898.35  TotalCharges_899.45  TotalCharges_899.8  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_90.05  TotalCharges_90.1  TotalCharges_90.35  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_90.55  TotalCharges_90.6  TotalCharges_90.75  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_90.85  TotalCharges_900.5  TotalCharges_900.9  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_901.25  TotalCharges_902  TotalCharges_902.25  \\\n",
+              "0                False             False                False   \n",
+              "1                False             False                False   \n",
+              "2                False             False                False   \n",
+              "3                False             False                False   \n",
+              "4                False             False                False   \n",
+              "\n",
+              "   TotalCharges_903.6  TotalCharges_903.7  TotalCharges_903.8  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_905.55  TotalCharges_906.85  TotalCharges_907.05  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_908.15  TotalCharges_908.55  TotalCharges_908.75  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_909.25  TotalCharges_91.1  TotalCharges_91.3  \\\n",
+              "0                False              False              False   \n",
+              "1                False              False              False   \n",
+              "2                False              False              False   \n",
+              "3                False              False              False   \n",
+              "4                False              False              False   \n",
+              "\n",
+              "   TotalCharges_91.4  TotalCharges_91.45  TotalCharges_91.7  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_910.45  TotalCharges_911.6  TotalCharges_912  \\\n",
+              "0                False               False             False   \n",
+              "1                False               False             False   \n",
+              "2                False               False             False   \n",
+              "3                False               False             False   \n",
+              "4                False               False             False   \n",
+              "\n",
+              "   TotalCharges_913.3  TotalCharges_914  TotalCharges_914.3  \\\n",
+              "0               False             False               False   \n",
+              "1               False             False               False   \n",
+              "2               False             False               False   \n",
+              "3               False             False               False   \n",
+              "4               False             False               False   \n",
+              "\n",
+              "   TotalCharges_914.4  TotalCharges_914.6  TotalCharges_916  \\\n",
+              "0               False               False             False   \n",
+              "1               False               False             False   \n",
+              "2               False               False             False   \n",
+              "3               False               False             False   \n",
+              "4               False               False             False   \n",
+              "\n",
+              "   TotalCharges_916.15  TotalCharges_916.75  TotalCharges_916.9  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_917.15  TotalCharges_917.45  TotalCharges_918.6  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_918.7  TotalCharges_918.75  TotalCharges_919.4  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_92.05  TotalCharges_92.25  TotalCharges_92.45  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_92.5  TotalCharges_92.65  TotalCharges_92.75  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_920.5  TotalCharges_921.3  TotalCharges_921.4  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_921.55  TotalCharges_923.1  TotalCharges_923.5  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_923.85  TotalCharges_926  TotalCharges_926.2  \\\n",
+              "0                False             False               False   \n",
+              "1                False             False               False   \n",
+              "2                False             False               False   \n",
+              "3                False             False               False   \n",
+              "4                False             False               False   \n",
+              "\n",
+              "   TotalCharges_926.25  TotalCharges_927.1  TotalCharges_927.15  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_927.35  TotalCharges_927.65  TotalCharges_928.4  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_929.2  TotalCharges_929.3  TotalCharges_929.45  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_93.3  TotalCharges_93.4  TotalCharges_93.45  \\\n",
+              "0              False              False               False   \n",
+              "1              False              False               False   \n",
+              "2              False              False               False   \n",
+              "3              False              False               False   \n",
+              "4              False              False               False   \n",
+              "\n",
+              "   TotalCharges_93.55  TotalCharges_93.7  TotalCharges_93.85  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_930.05  TotalCharges_930.4  TotalCharges_930.45  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_930.9  TotalCharges_930.95  TotalCharges_931.55  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_931.75  TotalCharges_931.9  TotalCharges_933.3  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_934.1  TotalCharges_934.15  TotalCharges_934.8  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_935.9  TotalCharges_936.7  TotalCharges_936.85  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_937.1  TotalCharges_937.5  TotalCharges_937.6  \\\n",
+              "0               False               False               False   \n",
+              "1               False               False               False   \n",
+              "2               False               False               False   \n",
+              "3               False               False               False   \n",
+              "4               False               False               False   \n",
+              "\n",
+              "   TotalCharges_938.65  TotalCharges_938.95  TotalCharges_939.7  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_939.8  TotalCharges_94  TotalCharges_94.15  TotalCharges_94.4  \\\n",
+              "0               False            False               False              False   \n",
+              "1               False            False               False              False   \n",
+              "2               False            False               False              False   \n",
+              "3               False            False               False              False   \n",
+              "4               False            False               False              False   \n",
+              "\n",
+              "   TotalCharges_94.45  TotalCharges_94.5  TotalCharges_94.55  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_94.6  TotalCharges_940.35  TotalCharges_941  \\\n",
+              "0              False                False             False   \n",
+              "1              False                False             False   \n",
+              "2              False                False             False   \n",
+              "3              False                False             False   \n",
+              "4              False                False             False   \n",
+              "\n",
+              "   TotalCharges_942.95  TotalCharges_943  TotalCharges_943.1  \\\n",
+              "0                False             False               False   \n",
+              "1                False             False               False   \n",
+              "2                False             False               False   \n",
+              "3                False             False               False   \n",
+              "4                False             False               False   \n",
+              "\n",
+              "   TotalCharges_943.85  TotalCharges_944.65  TotalCharges_945.7  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_946.95  TotalCharges_947.3  TotalCharges_947.4  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_947.75  TotalCharges_948.9  TotalCharges_949.8  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_949.85  TotalCharges_95  TotalCharges_95.05  \\\n",
+              "0                False            False               False   \n",
+              "1                False            False               False   \n",
+              "2                False            False               False   \n",
+              "3                False            False               False   \n",
+              "4                False            False               False   \n",
+              "\n",
+              "   TotalCharges_95.1  TotalCharges_95.45  TotalCharges_95.55  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_95.6  TotalCharges_95.65  TotalCharges_95.85  \\\n",
+              "0              False               False               False   \n",
+              "1              False               False               False   \n",
+              "2              False               False               False   \n",
+              "3              False               False               False   \n",
+              "4              False               False               False   \n",
+              "\n",
+              "   TotalCharges_950.2  TotalCharges_950.75  TotalCharges_951.55  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_952.3  TotalCharges_953.45  TotalCharges_953.65  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_954.9  TotalCharges_955.15  TotalCharges_955.6  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_955.75  TotalCharges_956.65  TotalCharges_957.1  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_958.1  TotalCharges_958.15  TotalCharges_958.25  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_958.45  TotalCharges_959.9  TotalCharges_96.05  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_96.1  TotalCharges_96.45  TotalCharges_96.8  \\\n",
+              "0              False               False              False   \n",
+              "1              False               False              False   \n",
+              "2              False               False              False   \n",
+              "3              False               False              False   \n",
+              "4              False               False              False   \n",
+              "\n",
+              "   TotalCharges_96.85  TotalCharges_961.4  TotalCharges_962.25  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_962.9  TotalCharges_963.95  TotalCharges_964.35  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_964.9  TotalCharges_966.25  TotalCharges_966.55  \\\n",
+              "0               False                False                False   \n",
+              "1               False                False                False   \n",
+              "2               False                False                False   \n",
+              "3               False                False                False   \n",
+              "4               False                False                False   \n",
+              "\n",
+              "   TotalCharges_967.85  TotalCharges_967.9  TotalCharges_969.85  \\\n",
+              "0                False               False                False   \n",
+              "1                False               False                False   \n",
+              "2                False               False                False   \n",
+              "3                False               False                False   \n",
+              "4                False               False                False   \n",
+              "\n",
+              "   TotalCharges_97  TotalCharges_97.1  TotalCharges_97.5  TotalCharges_970.4  \\\n",
+              "0            False              False              False               False   \n",
+              "1            False              False              False               False   \n",
+              "2            False              False              False               False   \n",
+              "3            False              False              False               False   \n",
+              "4            False              False              False               False   \n",
+              "\n",
+              "   TotalCharges_970.45  TotalCharges_970.55  TotalCharges_970.85  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_971.3  TotalCharges_973.1  TotalCharges_973.25  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_973.35  TotalCharges_973.45  TotalCharges_973.55  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_973.65  TotalCharges_973.95  TotalCharges_978  \\\n",
+              "0                False                False             False   \n",
+              "1                False                False             False   \n",
+              "2                False                False             False   \n",
+              "3                False                False             False   \n",
+              "4                False                False             False   \n",
+              "\n",
+              "   TotalCharges_978.6  TotalCharges_979.05  TotalCharges_979.5  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_98.35  TotalCharges_98.5  TotalCharges_980.35  \\\n",
+              "0               False              False                False   \n",
+              "1               False              False                False   \n",
+              "2               False              False                False   \n",
+              "3               False              False                False   \n",
+              "4               False              False                False   \n",
+              "\n",
+              "   TotalCharges_981.45  TotalCharges_982.95  TotalCharges_985.05  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_987.95  TotalCharges_989.05  TotalCharges_989.45  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_99.45  TotalCharges_99.6  TotalCharges_99.75  \\\n",
+              "0               False              False               False   \n",
+              "1               False              False               False   \n",
+              "2               False              False               False   \n",
+              "3               False              False               False   \n",
+              "4               False              False               False   \n",
+              "\n",
+              "   TotalCharges_99.95  TotalCharges_990.3  TotalCharges_990.45  \\\n",
+              "0               False               False                False   \n",
+              "1               False               False                False   \n",
+              "2               False               False                False   \n",
+              "3               False               False                False   \n",
+              "4               False               False                False   \n",
+              "\n",
+              "   TotalCharges_990.85  TotalCharges_990.9  TotalCharges_992.7  \\\n",
+              "0                False               False               False   \n",
+              "1                False               False               False   \n",
+              "2                False               False               False   \n",
+              "3                False               False               False   \n",
+              "4                False               False               False   \n",
+              "\n",
+              "   TotalCharges_993.15  TotalCharges_994.55  TotalCharges_994.8  \\\n",
+              "0                False                False               False   \n",
+              "1                False                False               False   \n",
+              "2                False                False               False   \n",
+              "3                False                False               False   \n",
+              "4                False                False               False   \n",
+              "\n",
+              "   TotalCharges_995.35  TotalCharges_996.45  TotalCharges_996.85  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_996.95  TotalCharges_997.65  TotalCharges_997.75  \\\n",
+              "0                False                False                False   \n",
+              "1                False                False                False   \n",
+              "2                False                False                False   \n",
+              "3                False                False                False   \n",
+              "4                False                False                False   \n",
+              "\n",
+              "   TotalCharges_998.1  TotalCharges_999.45  TotalCharges_999.8  \\\n",
+              "0               False                False               False   \n",
+              "1               False                False               False   \n",
+              "2               False                False               False   \n",
+              "3               False                False               False   \n",
+              "4               False                False               False   \n",
+              "\n",
+              "   TotalCharges_999.9  Churn_Yes  \n",
+              "0               False      False  \n",
+              "1               False      False  \n",
+              "2               False       True  \n",
+              "3               False      False  \n",
+              "4               False       True  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9f33fca7-822c-4da1-b1d3-bd9aafb7c037\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>customerID</th>\n",
+              "      <th>SeniorCitizen</th>\n",
+              "      <th>tenure</th>\n",
+              "      <th>MonthlyCharges</th>\n",
+              "      <th>gender_Male</th>\n",
+              "      <th>Partner_Yes</th>\n",
+              "      <th>Dependents_Yes</th>\n",
+              "      <th>PhoneService_Yes</th>\n",
+              "      <th>MultipleLines_No phone service</th>\n",
+              "      <th>MultipleLines_Yes</th>\n",
+              "      <th>InternetService_Fiber optic</th>\n",
+              "      <th>InternetService_No</th>\n",
+              "      <th>OnlineSecurity_No internet service</th>\n",
+              "      <th>OnlineSecurity_Yes</th>\n",
+              "      <th>OnlineBackup_No internet service</th>\n",
+              "      <th>OnlineBackup_Yes</th>\n",
+              "      <th>DeviceProtection_No internet service</th>\n",
+              "      <th>DeviceProtection_Yes</th>\n",
+              "      <th>TechSupport_No internet service</th>\n",
+              "      <th>TechSupport_Yes</th>\n",
+              "      <th>StreamingTV_No internet service</th>\n",
+              "      <th>StreamingTV_Yes</th>\n",
+              "      <th>StreamingMovies_No internet service</th>\n",
+              "      <th>StreamingMovies_Yes</th>\n",
+              "      <th>Contract_One year</th>\n",
+              "      <th>Contract_Two year</th>\n",
+              "      <th>PaperlessBilling_Yes</th>\n",
+              "      <th>PaymentMethod_Credit card (automatic)</th>\n",
+              "      <th>PaymentMethod_Electronic check</th>\n",
+              "      <th>PaymentMethod_Mailed check</th>\n",
+              "      <th>TotalCharges_100.2</th>\n",
+              "      <th>TotalCharges_100.25</th>\n",
+              "      <th>TotalCharges_100.35</th>\n",
+              "      <th>TotalCharges_100.4</th>\n",
+              "      <th>TotalCharges_100.8</th>\n",
+              "      <th>TotalCharges_100.9</th>\n",
+              "      <th>TotalCharges_1001.2</th>\n",
+              "      <th>TotalCharges_1001.5</th>\n",
+              "      <th>TotalCharges_1003.05</th>\n",
+              "      <th>TotalCharges_1004.35</th>\n",
+              "      <th>TotalCharges_1004.5</th>\n",
+              "      <th>TotalCharges_1004.75</th>\n",
+              "      <th>TotalCharges_1005.7</th>\n",
+              "      <th>TotalCharges_1006.9</th>\n",
+              "      <th>TotalCharges_1007.8</th>\n",
+              "      <th>TotalCharges_1007.9</th>\n",
+              "      <th>TotalCharges_1008.55</th>\n",
+              "      <th>TotalCharges_1008.7</th>\n",
+              "      <th>TotalCharges_1009.25</th>\n",
+              "      <th>TotalCharges_101.1</th>\n",
+              "      <th>TotalCharges_101.45</th>\n",
+              "      <th>TotalCharges_101.65</th>\n",
+              "      <th>TotalCharges_101.9</th>\n",
+              "      <th>TotalCharges_1010</th>\n",
+              "      <th>TotalCharges_1011.05</th>\n",
+              "      <th>TotalCharges_1011.5</th>\n",
+              "      <th>TotalCharges_1011.8</th>\n",
+              "      <th>TotalCharges_1012.4</th>\n",
+              "      <th>TotalCharges_1013.05</th>\n",
+              "      <th>TotalCharges_1013.2</th>\n",
+              "      <th>TotalCharges_1013.35</th>\n",
+              "      <th>TotalCharges_1013.6</th>\n",
+              "      <th>TotalCharges_1014.25</th>\n",
+              "      <th>TotalCharges_1016.7</th>\n",
+              "      <th>TotalCharges_1017.35</th>\n",
+              "      <th>TotalCharges_102.45</th>\n",
+              "      <th>TotalCharges_102.5</th>\n",
+              "      <th>TotalCharges_102.75</th>\n",
+              "      <th>TotalCharges_1020.2</th>\n",
+              "      <th>TotalCharges_1020.75</th>\n",
+              "      <th>TotalCharges_1021.55</th>\n",
+              "      <th>TotalCharges_1021.75</th>\n",
+              "      <th>TotalCharges_1021.8</th>\n",
+              "      <th>TotalCharges_1022.5</th>\n",
+              "      <th>TotalCharges_1022.6</th>\n",
+              "      <th>TotalCharges_1022.95</th>\n",
+              "      <th>TotalCharges_1023.75</th>\n",
+              "      <th>TotalCharges_1023.85</th>\n",
+              "      <th>TotalCharges_1023.9</th>\n",
+              "      <th>TotalCharges_1023.95</th>\n",
+              "      <th>TotalCharges_1024</th>\n",
+              "      <th>TotalCharges_1024.65</th>\n",
+              "      <th>TotalCharges_1024.7</th>\n",
+              "      <th>TotalCharges_1025.05</th>\n",
+              "      <th>TotalCharges_1025.15</th>\n",
+              "      <th>TotalCharges_1025.95</th>\n",
+              "      <th>TotalCharges_1026.35</th>\n",
+              "      <th>TotalCharges_1027.25</th>\n",
+              "      <th>TotalCharges_1028.75</th>\n",
+              "      <th>TotalCharges_1028.9</th>\n",
+              "      <th>TotalCharges_1029.35</th>\n",
+              "      <th>TotalCharges_1029.75</th>\n",
+              "      <th>TotalCharges_1029.8</th>\n",
+              "      <th>TotalCharges_103.7</th>\n",
+              "      <th>TotalCharges_1031.1</th>\n",
+              "      <th>TotalCharges_1031.4</th>\n",
+              "      <th>TotalCharges_1031.7</th>\n",
+              "      <th>TotalCharges_1032</th>\n",
+              "      <th>TotalCharges_1032.05</th>\n",
+              "      <th>TotalCharges_1033</th>\n",
+              "      <th>TotalCharges_1033.9</th>\n",
+              "      <th>TotalCharges_1033.95</th>\n",
+              "      <th>TotalCharges_1035.5</th>\n",
+              "      <th>TotalCharges_1035.7</th>\n",
+              "      <th>TotalCharges_1036</th>\n",
+              "      <th>TotalCharges_1036.75</th>\n",
+              "      <th>TotalCharges_1037.75</th>\n",
+              "      <th>TotalCharges_1039.45</th>\n",
+              "      <th>TotalCharges_104.2</th>\n",
+              "      <th>TotalCharges_104.3</th>\n",
+              "      <th>TotalCharges_1041.8</th>\n",
+              "      <th>TotalCharges_1042.65</th>\n",
+              "      <th>TotalCharges_1043.3</th>\n",
+              "      <th>TotalCharges_1043.35</th>\n",
+              "      <th>TotalCharges_1043.4</th>\n",
+              "      <th>TotalCharges_1043.8</th>\n",
+              "      <th>TotalCharges_1045.25</th>\n",
+              "      <th>TotalCharges_1046.1</th>\n",
+              "      <th>TotalCharges_1046.2</th>\n",
+              "      <th>TotalCharges_1046.5</th>\n",
+              "      <th>TotalCharges_1047.7</th>\n",
+              "      <th>TotalCharges_1048.45</th>\n",
+              "      <th>TotalCharges_1048.85</th>\n",
+              "      <th>TotalCharges_1049.05</th>\n",
+              "      <th>TotalCharges_1049.6</th>\n",
+              "      <th>TotalCharges_105.5</th>\n",
+              "      <th>TotalCharges_105.6</th>\n",
+              "      <th>TotalCharges_1050.5</th>\n",
+              "      <th>TotalCharges_1051.05</th>\n",
+              "      <th>TotalCharges_1051.9</th>\n",
+              "      <th>TotalCharges_1052.35</th>\n",
+              "      <th>TotalCharges_1052.4</th>\n",
+              "      <th>TotalCharges_1054.6</th>\n",
+              "      <th>TotalCharges_1054.75</th>\n",
+              "      <th>TotalCharges_1054.8</th>\n",
+              "      <th>TotalCharges_1055.9</th>\n",
+              "      <th>TotalCharges_1056.95</th>\n",
+              "      <th>TotalCharges_1057</th>\n",
+              "      <th>TotalCharges_1057.55</th>\n",
+              "      <th>TotalCharges_1057.85</th>\n",
+              "      <th>TotalCharges_1058.1</th>\n",
+              "      <th>TotalCharges_1058.25</th>\n",
+              "      <th>TotalCharges_1058.6</th>\n",
+              "      <th>TotalCharges_1059.55</th>\n",
+              "      <th>TotalCharges_106.2</th>\n",
+              "      <th>TotalCharges_106.55</th>\n",
+              "      <th>TotalCharges_106.8</th>\n",
+              "      <th>TotalCharges_106.85</th>\n",
+              "      <th>TotalCharges_106.9</th>\n",
+              "      <th>TotalCharges_1060.2</th>\n",
+              "      <th>TotalCharges_1060.6</th>\n",
+              "      <th>TotalCharges_1061.6</th>\n",
+              "      <th>TotalCharges_1062.1</th>\n",
+              "      <th>TotalCharges_1064.65</th>\n",
+              "      <th>TotalCharges_1064.95</th>\n",
+              "      <th>TotalCharges_1066.15</th>\n",
+              "      <th>TotalCharges_1066.9</th>\n",
+              "      <th>TotalCharges_1067.05</th>\n",
+              "      <th>TotalCharges_1067.15</th>\n",
+              "      <th>TotalCharges_1067.65</th>\n",
+              "      <th>TotalCharges_1068.15</th>\n",
+              "      <th>TotalCharges_1068.85</th>\n",
+              "      <th>TotalCharges_107.05</th>\n",
+              "      <th>TotalCharges_107.1</th>\n",
+              "      <th>TotalCharges_107.25</th>\n",
+              "      <th>TotalCharges_107.6</th>\n",
+              "      <th>TotalCharges_1070.15</th>\n",
+              "      <th>TotalCharges_1070.25</th>\n",
+              "      <th>TotalCharges_1070.5</th>\n",
+              "      <th>TotalCharges_1070.7</th>\n",
+              "      <th>TotalCharges_1071.4</th>\n",
+              "      <th>TotalCharges_1071.6</th>\n",
+              "      <th>TotalCharges_1072</th>\n",
+              "      <th>TotalCharges_1072.6</th>\n",
+              "      <th>TotalCharges_1073.3</th>\n",
+              "      <th>TotalCharges_1074.3</th>\n",
+              "      <th>TotalCharges_1074.65</th>\n",
+              "      <th>TotalCharges_1077.05</th>\n",
+              "      <th>TotalCharges_1077.5</th>\n",
+              "      <th>TotalCharges_1077.95</th>\n",
+              "      <th>TotalCharges_1078.75</th>\n",
+              "      <th>TotalCharges_1078.9</th>\n",
+              "      <th>TotalCharges_1079.05</th>\n",
+              "      <th>TotalCharges_1079.45</th>\n",
+              "      <th>TotalCharges_1079.65</th>\n",
+              "      <th>TotalCharges_108.05</th>\n",
+              "      <th>TotalCharges_108.15</th>\n",
+              "      <th>TotalCharges_108.65</th>\n",
+              "      <th>TotalCharges_108.7</th>\n",
+              "      <th>TotalCharges_108.95</th>\n",
+              "      <th>TotalCharges_1080.55</th>\n",
+              "      <th>TotalCharges_1081.25</th>\n",
+              "      <th>TotalCharges_1081.45</th>\n",
+              "      <th>TotalCharges_1082.75</th>\n",
+              "      <th>TotalCharges_1082.8</th>\n",
+              "      <th>TotalCharges_1083</th>\n",
+              "      <th>TotalCharges_1083.7</th>\n",
+              "      <th>TotalCharges_1086.75</th>\n",
+              "      <th>TotalCharges_1087.25</th>\n",
+              "      <th>TotalCharges_1087.45</th>\n",
+              "      <th>TotalCharges_1087.7</th>\n",
+              "      <th>TotalCharges_1088.25</th>\n",
+              "      <th>TotalCharges_109.2</th>\n",
+              "      <th>TotalCharges_109.25</th>\n",
+              "      <th>TotalCharges_109.3</th>\n",
+              "      <th>TotalCharges_109.5</th>\n",
+              "      <th>TotalCharges_109.6</th>\n",
+              "      <th>TotalCharges_109.8</th>\n",
+              "      <th>TotalCharges_1090.1</th>\n",
+              "      <th>TotalCharges_1090.6</th>\n",
+              "      <th>TotalCharges_1090.65</th>\n",
+              "      <th>TotalCharges_1092.35</th>\n",
+              "      <th>TotalCharges_1093</th>\n",
+              "      <th>TotalCharges_1093.1</th>\n",
+              "      <th>TotalCharges_1093.2</th>\n",
+              "      <th>TotalCharges_1093.4</th>\n",
+              "      <th>TotalCharges_1094.35</th>\n",
+              "      <th>TotalCharges_1094.5</th>\n",
+              "      <th>TotalCharges_1095.3</th>\n",
+              "      <th>TotalCharges_1095.65</th>\n",
+              "      <th>TotalCharges_1096.25</th>\n",
+              "      <th>TotalCharges_1096.6</th>\n",
+              "      <th>TotalCharges_1096.65</th>\n",
+              "      <th>TotalCharges_1097.15</th>\n",
+              "      <th>TotalCharges_1098.85</th>\n",
+              "      <th>TotalCharges_1099.6</th>\n",
+              "      <th>TotalCharges_110.05</th>\n",
+              "      <th>TotalCharges_110.15</th>\n",
+              "      <th>TotalCharges_1101.85</th>\n",
+              "      <th>TotalCharges_1102.4</th>\n",
+              "      <th>TotalCharges_1103.25</th>\n",
+              "      <th>TotalCharges_1105.4</th>\n",
+              "      <th>TotalCharges_1107.2</th>\n",
+              "      <th>TotalCharges_1107.25</th>\n",
+              "      <th>TotalCharges_1108</th>\n",
+              "      <th>TotalCharges_1108.2</th>\n",
+              "      <th>TotalCharges_1108.6</th>\n",
+              "      <th>TotalCharges_1108.8</th>\n",
+              "      <th>TotalCharges_111.05</th>\n",
+              "      <th>TotalCharges_111.4</th>\n",
+              "      <th>TotalCharges_111.65</th>\n",
+              "      <th>TotalCharges_1110.05</th>\n",
+              "      <th>TotalCharges_1110.35</th>\n",
+              "      <th>TotalCharges_1110.5</th>\n",
+              "      <th>TotalCharges_1111.2</th>\n",
+              "      <th>TotalCharges_1111.65</th>\n",
+              "      <th>TotalCharges_1111.85</th>\n",
+              "      <th>TotalCharges_1112.3</th>\n",
+              "      <th>TotalCharges_1113.95</th>\n",
+              "      <th>TotalCharges_1114.55</th>\n",
+              "      <th>TotalCharges_1114.85</th>\n",
+              "      <th>TotalCharges_1115.15</th>\n",
+              "      <th>TotalCharges_1115.2</th>\n",
+              "      <th>TotalCharges_1115.6</th>\n",
+              "      <th>TotalCharges_1117.55</th>\n",
+              "      <th>TotalCharges_1118.8</th>\n",
+              "      <th>TotalCharges_1119.35</th>\n",
+              "      <th>TotalCharges_1119.9</th>\n",
+              "      <th>TotalCharges_112.3</th>\n",
+              "      <th>TotalCharges_112.75</th>\n",
+              "      <th>TotalCharges_1120.3</th>\n",
+              "      <th>TotalCharges_1120.95</th>\n",
+              "      <th>TotalCharges_1121.05</th>\n",
+              "      <th>TotalCharges_1122.4</th>\n",
+              "      <th>TotalCharges_1123.15</th>\n",
+              "      <th>TotalCharges_1123.65</th>\n",
+              "      <th>TotalCharges_1124.2</th>\n",
+              "      <th>TotalCharges_1125.2</th>\n",
+              "      <th>TotalCharges_1125.6</th>\n",
+              "      <th>TotalCharges_1126.35</th>\n",
+              "      <th>TotalCharges_1126.75</th>\n",
+              "      <th>TotalCharges_1127.2</th>\n",
+              "      <th>TotalCharges_1127.35</th>\n",
+              "      <th>TotalCharges_1128.1</th>\n",
+              "      <th>TotalCharges_1129.1</th>\n",
+              "      <th>TotalCharges_1129.35</th>\n",
+              "      <th>TotalCharges_1129.75</th>\n",
+              "      <th>TotalCharges_113.1</th>\n",
+              "      <th>TotalCharges_113.35</th>\n",
+              "      <th>TotalCharges_113.5</th>\n",
+              "      <th>TotalCharges_113.55</th>\n",
+              "      <th>TotalCharges_113.85</th>\n",
+              "      <th>TotalCharges_113.95</th>\n",
+              "      <th>TotalCharges_1130</th>\n",
+              "      <th>TotalCharges_1130.85</th>\n",
+              "      <th>TotalCharges_1131.2</th>\n",
+              "      <th>TotalCharges_1131.3</th>\n",
+              "      <th>TotalCharges_1131.5</th>\n",
+              "      <th>TotalCharges_1132.35</th>\n",
+              "      <th>TotalCharges_1132.75</th>\n",
+              "      <th>TotalCharges_1133.65</th>\n",
+              "      <th>TotalCharges_1133.7</th>\n",
+              "      <th>TotalCharges_1133.9</th>\n",
+              "      <th>TotalCharges_1134.25</th>\n",
+              "      <th>TotalCharges_1135.7</th>\n",
+              "      <th>TotalCharges_1137.05</th>\n",
+              "      <th>TotalCharges_1138.8</th>\n",
+              "      <th>TotalCharges_1139.2</th>\n",
+              "      <th>TotalCharges_114.1</th>\n",
+              "      <th>TotalCharges_114.15</th>\n",
+              "      <th>TotalCharges_114.7</th>\n",
+              "      <th>TotalCharges_1140.05</th>\n",
+              "      <th>TotalCharges_1143.8</th>\n",
+              "      <th>TotalCharges_1144.5</th>\n",
+              "      <th>TotalCharges_1144.6</th>\n",
+              "      <th>TotalCharges_1145.35</th>\n",
+              "      <th>TotalCharges_1145.7</th>\n",
+              "      <th>TotalCharges_1146.05</th>\n",
+              "      <th>TotalCharges_1146.65</th>\n",
+              "      <th>TotalCharges_1147</th>\n",
+              "      <th>TotalCharges_1147.45</th>\n",
+              "      <th>TotalCharges_1147.85</th>\n",
+              "      <th>TotalCharges_1148.1</th>\n",
+              "      <th>TotalCharges_1149.65</th>\n",
+              "      <th>TotalCharges_115.1</th>\n",
+              "      <th>TotalCharges_115.95</th>\n",
+              "      <th>TotalCharges_1151.05</th>\n",
+              "      <th>TotalCharges_1151.55</th>\n",
+              "      <th>TotalCharges_1152.7</th>\n",
+              "      <th>TotalCharges_1152.8</th>\n",
+              "      <th>TotalCharges_1153.25</th>\n",
+              "      <th>TotalCharges_1155.6</th>\n",
+              "      <th>TotalCharges_1156.1</th>\n",
+              "      <th>TotalCharges_1156.35</th>\n",
+              "      <th>TotalCharges_1156.55</th>\n",
+              "      <th>TotalCharges_1157.05</th>\n",
+              "      <th>TotalCharges_1158.85</th>\n",
+              "      <th>TotalCharges_116.6</th>\n",
+              "      <th>TotalCharges_116.65</th>\n",
+              "      <th>TotalCharges_116.85</th>\n",
+              "      <th>TotalCharges_116.95</th>\n",
+              "      <th>TotalCharges_1160.45</th>\n",
+              "      <th>TotalCharges_1160.75</th>\n",
+              "      <th>TotalCharges_1161.75</th>\n",
+              "      <th>TotalCharges_1162.85</th>\n",
+              "      <th>TotalCharges_1164.05</th>\n",
+              "      <th>TotalCharges_1164.3</th>\n",
+              "      <th>TotalCharges_1165.55</th>\n",
+              "      <th>TotalCharges_1165.6</th>\n",
+              "      <th>TotalCharges_1165.9</th>\n",
+              "      <th>TotalCharges_1166.7</th>\n",
+              "      <th>TotalCharges_1167.6</th>\n",
+              "      <th>TotalCharges_1167.8</th>\n",
+              "      <th>TotalCharges_1169.35</th>\n",
+              "      <th>TotalCharges_117.05</th>\n",
+              "      <th>TotalCharges_117.8</th>\n",
+              "      <th>TotalCharges_117.95</th>\n",
+              "      <th>TotalCharges_1170.5</th>\n",
+              "      <th>TotalCharges_1170.55</th>\n",
+              "      <th>TotalCharges_1171.3</th>\n",
+              "      <th>TotalCharges_1171.5</th>\n",
+              "      <th>TotalCharges_1172.95</th>\n",
+              "      <th>TotalCharges_1173.35</th>\n",
+              "      <th>TotalCharges_1173.55</th>\n",
+              "      <th>TotalCharges_1174.35</th>\n",
+              "      <th>TotalCharges_1174.8</th>\n",
+              "      <th>TotalCharges_1175.6</th>\n",
+              "      <th>TotalCharges_1175.85</th>\n",
+              "      <th>TotalCharges_1177.05</th>\n",
+              "      <th>TotalCharges_1177.95</th>\n",
+              "      <th>TotalCharges_1178.25</th>\n",
+              "      <th>TotalCharges_1178.4</th>\n",
+              "      <th>TotalCharges_1178.75</th>\n",
+              "      <th>TotalCharges_118.25</th>\n",
+              "      <th>TotalCharges_118.3</th>\n",
+              "      <th>TotalCharges_118.4</th>\n",
+              "      <th>TotalCharges_118.5</th>\n",
+              "      <th>TotalCharges_1180.95</th>\n",
+              "      <th>TotalCharges_1181.75</th>\n",
+              "      <th>TotalCharges_1182.55</th>\n",
+              "      <th>TotalCharges_1183.05</th>\n",
+              "      <th>TotalCharges_1183.2</th>\n",
+              "      <th>TotalCharges_1183.8</th>\n",
+              "      <th>TotalCharges_1184</th>\n",
+              "      <th>TotalCharges_1185</th>\n",
+              "      <th>TotalCharges_1185.95</th>\n",
+              "      <th>TotalCharges_1187.05</th>\n",
+              "      <th>TotalCharges_1188.2</th>\n",
+              "      <th>TotalCharges_1188.25</th>\n",
+              "      <th>TotalCharges_1189.4</th>\n",
+              "      <th>TotalCharges_1189.9</th>\n",
+              "      <th>TotalCharges_119.3</th>\n",
+              "      <th>TotalCharges_119.5</th>\n",
+              "      <th>TotalCharges_119.75</th>\n",
+              "      <th>TotalCharges_1190.5</th>\n",
+              "      <th>TotalCharges_1191.2</th>\n",
+              "      <th>TotalCharges_1191.4</th>\n",
+              "      <th>TotalCharges_1192.3</th>\n",
+              "      <th>TotalCharges_1192.7</th>\n",
+              "      <th>TotalCharges_1193.05</th>\n",
+              "      <th>TotalCharges_1193.55</th>\n",
+              "      <th>TotalCharges_1194.3</th>\n",
+              "      <th>TotalCharges_1195.25</th>\n",
+              "      <th>TotalCharges_1195.75</th>\n",
+              "      <th>TotalCharges_1195.95</th>\n",
+              "      <th>TotalCharges_1198.05</th>\n",
+              "      <th>TotalCharges_1198.8</th>\n",
+              "      <th>TotalCharges_1199.4</th>\n",
+              "      <th>TotalCharges_120.25</th>\n",
+              "      <th>TotalCharges_1200.15</th>\n",
+              "      <th>TotalCharges_1201.15</th>\n",
+              "      <th>TotalCharges_1201.65</th>\n",
+              "      <th>TotalCharges_1203.9</th>\n",
+              "      <th>TotalCharges_1203.95</th>\n",
+              "      <th>TotalCharges_1205.05</th>\n",
+              "      <th>TotalCharges_1205.5</th>\n",
+              "      <th>TotalCharges_1206.05</th>\n",
+              "      <th>TotalCharges_1207</th>\n",
+              "      <th>TotalCharges_1208.15</th>\n",
+              "      <th>TotalCharges_1208.35</th>\n",
+              "      <th>TotalCharges_1208.6</th>\n",
+              "      <th>TotalCharges_1209.25</th>\n",
+              "      <th>TotalCharges_121.25</th>\n",
+              "      <th>TotalCharges_1210.3</th>\n",
+              "      <th>TotalCharges_1210.4</th>\n",
+              "      <th>TotalCharges_1211.65</th>\n",
+              "      <th>TotalCharges_1212.1</th>\n",
+              "      <th>TotalCharges_1212.25</th>\n",
+              "      <th>TotalCharges_1212.85</th>\n",
+              "      <th>TotalCharges_1214.05</th>\n",
+              "      <th>TotalCharges_1215.1</th>\n",
+              "      <th>TotalCharges_1215.45</th>\n",
+              "      <th>TotalCharges_1215.6</th>\n",
+              "      <th>TotalCharges_1215.65</th>\n",
+              "      <th>TotalCharges_1215.8</th>\n",
+              "      <th>TotalCharges_1216.35</th>\n",
+              "      <th>TotalCharges_1216.6</th>\n",
+              "      <th>TotalCharges_1217.25</th>\n",
+              "      <th>TotalCharges_1218.25</th>\n",
+              "      <th>TotalCharges_1218.45</th>\n",
+              "      <th>TotalCharges_1218.55</th>\n",
+              "      <th>TotalCharges_1218.65</th>\n",
+              "      <th>TotalCharges_1219.85</th>\n",
+              "      <th>TotalCharges_122</th>\n",
+              "      <th>TotalCharges_122.7</th>\n",
+              "      <th>TotalCharges_122.9</th>\n",
+              "      <th>TotalCharges_1221.55</th>\n",
+              "      <th>TotalCharges_1221.65</th>\n",
+              "      <th>TotalCharges_1222.05</th>\n",
+              "      <th>TotalCharges_1222.25</th>\n",
+              "      <th>TotalCharges_1222.65</th>\n",
+              "      <th>TotalCharges_1222.8</th>\n",
+              "      <th>TotalCharges_1224.05</th>\n",
+              "      <th>TotalCharges_1225.65</th>\n",
+              "      <th>TotalCharges_1226.45</th>\n",
+              "      <th>TotalCharges_1228.65</th>\n",
+              "      <th>TotalCharges_1229.1</th>\n",
+              "      <th>TotalCharges_123.05</th>\n",
+              "      <th>TotalCharges_123.65</th>\n",
+              "      <th>TotalCharges_123.8</th>\n",
+              "      <th>TotalCharges_1230.25</th>\n",
+              "      <th>TotalCharges_1230.9</th>\n",
+              "      <th>TotalCharges_1231.85</th>\n",
+              "      <th>TotalCharges_1232.9</th>\n",
+              "      <th>TotalCharges_1233.15</th>\n",
+              "      <th>TotalCharges_1233.25</th>\n",
+              "      <th>TotalCharges_1233.4</th>\n",
+              "      <th>TotalCharges_1233.65</th>\n",
+              "      <th>TotalCharges_1234.6</th>\n",
+              "      <th>TotalCharges_1234.8</th>\n",
+              "      <th>TotalCharges_1235.55</th>\n",
+              "      <th>TotalCharges_1236.15</th>\n",
+              "      <th>TotalCharges_1237.3</th>\n",
+              "      <th>TotalCharges_1237.65</th>\n",
+              "      <th>TotalCharges_1237.85</th>\n",
+              "      <th>TotalCharges_1238.45</th>\n",
+              "      <th>TotalCharges_1238.65</th>\n",
+              "      <th>TotalCharges_124.4</th>\n",
+              "      <th>TotalCharges_124.45</th>\n",
+              "      <th>TotalCharges_1240.15</th>\n",
+              "      <th>TotalCharges_1240.25</th>\n",
+              "      <th>TotalCharges_1240.8</th>\n",
+              "      <th>TotalCharges_1242.2</th>\n",
+              "      <th>TotalCharges_1242.25</th>\n",
+              "      <th>TotalCharges_1242.45</th>\n",
+              "      <th>TotalCharges_1244.5</th>\n",
+              "      <th>TotalCharges_1244.8</th>\n",
+              "      <th>TotalCharges_1245.05</th>\n",
+              "      <th>TotalCharges_1245.6</th>\n",
+              "      <th>TotalCharges_1246.4</th>\n",
+              "      <th>TotalCharges_1247.75</th>\n",
+              "      <th>TotalCharges_1248.9</th>\n",
+              "      <th>TotalCharges_1249.25</th>\n",
+              "      <th>TotalCharges_125</th>\n",
+              "      <th>TotalCharges_125.5</th>\n",
+              "      <th>TotalCharges_125.95</th>\n",
+              "      <th>TotalCharges_1250.1</th>\n",
+              "      <th>TotalCharges_1250.85</th>\n",
+              "      <th>TotalCharges_1252</th>\n",
+              "      <th>TotalCharges_1252.85</th>\n",
+              "      <th>TotalCharges_1253.15</th>\n",
+              "      <th>TotalCharges_1253.5</th>\n",
+              "      <th>TotalCharges_1253.65</th>\n",
+              "      <th>TotalCharges_1253.8</th>\n",
+              "      <th>TotalCharges_1253.9</th>\n",
+              "      <th>TotalCharges_1254.7</th>\n",
+              "      <th>TotalCharges_1255.1</th>\n",
+              "      <th>TotalCharges_1258.15</th>\n",
+              "      <th>TotalCharges_1258.3</th>\n",
+              "      <th>TotalCharges_1258.35</th>\n",
+              "      <th>TotalCharges_1258.6</th>\n",
+              "      <th>TotalCharges_1259</th>\n",
+              "      <th>TotalCharges_1259.35</th>\n",
+              "      <th>TotalCharges_126.05</th>\n",
+              "      <th>TotalCharges_1260.7</th>\n",
+              "      <th>TotalCharges_1261</th>\n",
+              "      <th>TotalCharges_1261.35</th>\n",
+              "      <th>TotalCharges_1261.45</th>\n",
+              "      <th>TotalCharges_1261.7</th>\n",
+              "      <th>TotalCharges_1263.05</th>\n",
+              "      <th>TotalCharges_1263.85</th>\n",
+              "      <th>TotalCharges_1263.9</th>\n",
+              "      <th>TotalCharges_1264.2</th>\n",
+              "      <th>TotalCharges_1265.65</th>\n",
+              "      <th>TotalCharges_1266.1</th>\n",
+              "      <th>TotalCharges_1266.35</th>\n",
+              "      <th>TotalCharges_1266.4</th>\n",
+              "      <th>TotalCharges_1267</th>\n",
+              "      <th>TotalCharges_1267.05</th>\n",
+              "      <th>TotalCharges_1267.2</th>\n",
+              "      <th>TotalCharges_1267.95</th>\n",
+              "      <th>TotalCharges_1268.85</th>\n",
+              "      <th>TotalCharges_1269.1</th>\n",
+              "      <th>TotalCharges_1269.55</th>\n",
+              "      <th>TotalCharges_1269.6</th>\n",
+              "      <th>TotalCharges_127.1</th>\n",
+              "      <th>TotalCharges_1270.2</th>\n",
+              "      <th>TotalCharges_1270.25</th>\n",
+              "      <th>TotalCharges_1270.55</th>\n",
+              "      <th>TotalCharges_1271.8</th>\n",
+              "      <th>TotalCharges_1272.05</th>\n",
+              "      <th>TotalCharges_1273.3</th>\n",
+              "      <th>TotalCharges_1274.05</th>\n",
+              "      <th>TotalCharges_1274.45</th>\n",
+              "      <th>TotalCharges_1275.6</th>\n",
+              "      <th>TotalCharges_1275.65</th>\n",
+              "      <th>TotalCharges_1275.7</th>\n",
+              "      <th>TotalCharges_1275.85</th>\n",
+              "      <th>TotalCharges_1277.5</th>\n",
+              "      <th>TotalCharges_1277.75</th>\n",
+              "      <th>TotalCharges_1278.8</th>\n",
+              "      <th>TotalCharges_1279</th>\n",
+              "      <th>TotalCharges_128.6</th>\n",
+              "      <th>TotalCharges_1281</th>\n",
+              "      <th>TotalCharges_1281.25</th>\n",
+              "      <th>TotalCharges_1282.85</th>\n",
+              "      <th>TotalCharges_1284.2</th>\n",
+              "      <th>TotalCharges_1285.05</th>\n",
+              "      <th>TotalCharges_1285.8</th>\n",
+              "      <th>TotalCharges_1286</th>\n",
+              "      <th>TotalCharges_1286.05</th>\n",
+              "      <th>TotalCharges_1287.85</th>\n",
+              "      <th>TotalCharges_1288</th>\n",
+              "      <th>TotalCharges_1288.3</th>\n",
+              "      <th>TotalCharges_1288.75</th>\n",
+              "      <th>TotalCharges_129.15</th>\n",
+              "      <th>TotalCharges_129.2</th>\n",
+              "      <th>TotalCharges_129.55</th>\n",
+              "      <th>TotalCharges_129.6</th>\n",
+              "      <th>TotalCharges_1290</th>\n",
+              "      <th>TotalCharges_1291.3</th>\n",
+              "      <th>TotalCharges_1291.35</th>\n",
+              "      <th>TotalCharges_1292.2</th>\n",
+              "      <th>TotalCharges_1292.6</th>\n",
+              "      <th>TotalCharges_1292.65</th>\n",
+              "      <th>TotalCharges_1293.8</th>\n",
+              "      <th>TotalCharges_1294.6</th>\n",
+              "      <th>TotalCharges_1295.4</th>\n",
+              "      <th>TotalCharges_1296.15</th>\n",
+              "      <th>TotalCharges_1296.8</th>\n",
+              "      <th>TotalCharges_1297.35</th>\n",
+              "      <th>TotalCharges_1297.8</th>\n",
+              "      <th>TotalCharges_1298.7</th>\n",
+              "      <th>TotalCharges_1299.1</th>\n",
+              "      <th>TotalCharges_1299.8</th>\n",
+              "      <th>TotalCharges_130.1</th>\n",
+              "      <th>TotalCharges_130.15</th>\n",
+              "      <th>TotalCharges_130.25</th>\n",
+              "      <th>TotalCharges_130.5</th>\n",
+              "      <th>TotalCharges_130.55</th>\n",
+              "      <th>TotalCharges_130.75</th>\n",
+              "      <th>TotalCharges_1301</th>\n",
+              "      <th>TotalCharges_1301.1</th>\n",
+              "      <th>TotalCharges_1301.7</th>\n",
+              "      <th>TotalCharges_1301.9</th>\n",
+              "      <th>TotalCharges_1302.65</th>\n",
+              "      <th>TotalCharges_1303.25</th>\n",
+              "      <th>TotalCharges_1303.5</th>\n",
+              "      <th>TotalCharges_1304.8</th>\n",
+              "      <th>TotalCharges_1304.85</th>\n",
+              "      <th>TotalCharges_1305.95</th>\n",
+              "      <th>TotalCharges_1306.3</th>\n",
+              "      <th>TotalCharges_1307.8</th>\n",
+              "      <th>TotalCharges_1308.1</th>\n",
+              "      <th>TotalCharges_1308.4</th>\n",
+              "      <th>TotalCharges_1309</th>\n",
+              "      <th>TotalCharges_1309.15</th>\n",
+              "      <th>TotalCharges_131.05</th>\n",
+              "      <th>TotalCharges_131.65</th>\n",
+              "      <th>TotalCharges_1311.3</th>\n",
+              "      <th>TotalCharges_1311.6</th>\n",
+              "      <th>TotalCharges_1311.75</th>\n",
+              "      <th>TotalCharges_1312.15</th>\n",
+              "      <th>TotalCharges_1312.45</th>\n",
+              "      <th>TotalCharges_1313.25</th>\n",
+              "      <th>TotalCharges_1313.55</th>\n",
+              "      <th>TotalCharges_1315</th>\n",
+              "      <th>TotalCharges_1315.35</th>\n",
+              "      <th>TotalCharges_1316.9</th>\n",
+              "      <th>TotalCharges_1317.95</th>\n",
+              "      <th>TotalCharges_1319.85</th>\n",
+              "      <th>TotalCharges_1319.95</th>\n",
+              "      <th>TotalCharges_132.2</th>\n",
+              "      <th>TotalCharges_132.25</th>\n",
+              "      <th>TotalCharges_132.4</th>\n",
+              "      <th>TotalCharges_1321.3</th>\n",
+              "      <th>TotalCharges_1322.55</th>\n",
+              "      <th>TotalCharges_1322.85</th>\n",
+              "      <th>TotalCharges_1323.7</th>\n",
+              "      <th>TotalCharges_1325.85</th>\n",
+              "      <th>TotalCharges_1326.25</th>\n",
+              "      <th>TotalCharges_1326.35</th>\n",
+              "      <th>TotalCharges_1327.15</th>\n",
+              "      <th>TotalCharges_1327.4</th>\n",
+              "      <th>TotalCharges_1327.85</th>\n",
+              "      <th>TotalCharges_1328.15</th>\n",
+              "      <th>TotalCharges_1328.35</th>\n",
+              "      <th>TotalCharges_1329.15</th>\n",
+              "      <th>TotalCharges_1329.2</th>\n",
+              "      <th>TotalCharges_1331.05</th>\n",
+              "      <th>TotalCharges_1332.4</th>\n",
+              "      <th>TotalCharges_1334</th>\n",
+              "      <th>TotalCharges_1334.45</th>\n",
+              "      <th>TotalCharges_1334.5</th>\n",
+              "      <th>TotalCharges_1335.2</th>\n",
+              "      <th>TotalCharges_1336.1</th>\n",
+              "      <th>TotalCharges_1336.15</th>\n",
+              "      <th>TotalCharges_1336.35</th>\n",
+              "      <th>TotalCharges_1336.65</th>\n",
+              "      <th>TotalCharges_1336.8</th>\n",
+              "      <th>TotalCharges_1336.9</th>\n",
+              "      <th>TotalCharges_1337.45</th>\n",
+              "      <th>TotalCharges_1337.5</th>\n",
+              "      <th>TotalCharges_1338.15</th>\n",
+              "      <th>TotalCharges_1339.8</th>\n",
+              "      <th>TotalCharges_134.05</th>\n",
+              "      <th>TotalCharges_134.35</th>\n",
+              "      <th>TotalCharges_134.5</th>\n",
+              "      <th>TotalCharges_134.6</th>\n",
+              "      <th>TotalCharges_134.7</th>\n",
+              "      <th>TotalCharges_134.75</th>\n",
+              "      <th>TotalCharges_1340.1</th>\n",
+              "      <th>TotalCharges_1341.5</th>\n",
+              "      <th>TotalCharges_1342.15</th>\n",
+              "      <th>TotalCharges_1343.4</th>\n",
+              "      <th>TotalCharges_1344.5</th>\n",
+              "      <th>TotalCharges_1345.55</th>\n",
+              "      <th>TotalCharges_1345.65</th>\n",
+              "      <th>TotalCharges_1345.75</th>\n",
+              "      <th>TotalCharges_1345.85</th>\n",
+              "      <th>TotalCharges_1346.2</th>\n",
+              "      <th>TotalCharges_1346.3</th>\n",
+              "      <th>TotalCharges_1346.9</th>\n",
+              "      <th>TotalCharges_1347.15</th>\n",
+              "      <th>TotalCharges_1348.5</th>\n",
+              "      <th>TotalCharges_1348.9</th>\n",
+              "      <th>TotalCharges_1348.95</th>\n",
+              "      <th>TotalCharges_135</th>\n",
+              "      <th>TotalCharges_135.2</th>\n",
+              "      <th>TotalCharges_135.75</th>\n",
+              "      <th>TotalCharges_1350.15</th>\n",
+              "      <th>TotalCharges_1354.4</th>\n",
+              "      <th>TotalCharges_1355.1</th>\n",
+              "      <th>TotalCharges_1355.45</th>\n",
+              "      <th>TotalCharges_1356.3</th>\n",
+              "      <th>TotalCharges_1356.7</th>\n",
+              "      <th>TotalCharges_1357.1</th>\n",
+              "      <th>TotalCharges_1358.6</th>\n",
+              "      <th>TotalCharges_1358.85</th>\n",
+              "      <th>TotalCharges_1359</th>\n",
+              "      <th>TotalCharges_1359.45</th>\n",
+              "      <th>TotalCharges_1359.5</th>\n",
+              "      <th>TotalCharges_1359.7</th>\n",
+              "      <th>TotalCharges_136.05</th>\n",
+              "      <th>TotalCharges_136.75</th>\n",
+              "      <th>TotalCharges_1360.25</th>\n",
+              "      <th>TotalCharges_1362.85</th>\n",
+              "      <th>TotalCharges_1363.25</th>\n",
+              "      <th>TotalCharges_1363.45</th>\n",
+              "      <th>TotalCharges_1364.3</th>\n",
+              "      <th>TotalCharges_1364.75</th>\n",
+              "      <th>TotalCharges_1367.75</th>\n",
+              "      <th>TotalCharges_1369.8</th>\n",
+              "      <th>TotalCharges_137.1</th>\n",
+              "      <th>TotalCharges_137.25</th>\n",
+              "      <th>TotalCharges_137.6</th>\n",
+              "      <th>TotalCharges_137.85</th>\n",
+              "      <th>TotalCharges_137.95</th>\n",
+              "      <th>TotalCharges_1370.35</th>\n",
+              "      <th>TotalCharges_1372.45</th>\n",
+              "      <th>TotalCharges_1372.9</th>\n",
+              "      <th>TotalCharges_1373</th>\n",
+              "      <th>TotalCharges_1373.05</th>\n",
+              "      <th>TotalCharges_1374.2</th>\n",
+              "      <th>TotalCharges_1374.35</th>\n",
+              "      <th>TotalCharges_1374.9</th>\n",
+              "      <th>TotalCharges_1375.15</th>\n",
+              "      <th>TotalCharges_1375.4</th>\n",
+              "      <th>TotalCharges_1375.6</th>\n",
+              "      <th>TotalCharges_1376.5</th>\n",
+              "      <th>TotalCharges_1377.7</th>\n",
+              "      <th>TotalCharges_1378.25</th>\n",
+              "      <th>TotalCharges_1378.45</th>\n",
+              "      <th>TotalCharges_1378.75</th>\n",
+              "      <th>TotalCharges_1379.6</th>\n",
+              "      <th>TotalCharges_1379.8</th>\n",
+              "      <th>TotalCharges_138.85</th>\n",
+              "      <th>TotalCharges_1380.1</th>\n",
+              "      <th>TotalCharges_1380.4</th>\n",
+              "      <th>TotalCharges_1381.2</th>\n",
+              "      <th>TotalCharges_1381.8</th>\n",
+              "      <th>TotalCharges_1382.9</th>\n",
+              "      <th>TotalCharges_1383.6</th>\n",
+              "      <th>TotalCharges_1384.75</th>\n",
+              "      <th>TotalCharges_1385.85</th>\n",
+              "      <th>TotalCharges_1386.8</th>\n",
+              "      <th>TotalCharges_1386.9</th>\n",
+              "      <th>TotalCharges_1387</th>\n",
+              "      <th>TotalCharges_1387.35</th>\n",
+              "      <th>TotalCharges_1387.45</th>\n",
+              "      <th>TotalCharges_1388</th>\n",
+              "      <th>TotalCharges_1388.45</th>\n",
+              "      <th>TotalCharges_1388.75</th>\n",
+              "      <th>TotalCharges_1389.2</th>\n",
+              "      <th>TotalCharges_1389.35</th>\n",
+              "      <th>TotalCharges_1389.6</th>\n",
+              "      <th>TotalCharges_1389.85</th>\n",
+              "      <th>TotalCharges_139.05</th>\n",
+              "      <th>TotalCharges_139.25</th>\n",
+              "      <th>TotalCharges_139.35</th>\n",
+              "      <th>TotalCharges_139.4</th>\n",
+              "      <th>TotalCharges_139.75</th>\n",
+              "      <th>TotalCharges_1390.6</th>\n",
+              "      <th>TotalCharges_1390.85</th>\n",
+              "      <th>TotalCharges_1391.15</th>\n",
+              "      <th>TotalCharges_1391.65</th>\n",
+              "      <th>TotalCharges_1392.25</th>\n",
+              "      <th>TotalCharges_1393.6</th>\n",
+              "      <th>TotalCharges_1394.55</th>\n",
+              "      <th>TotalCharges_1395.05</th>\n",
+              "      <th>TotalCharges_1396</th>\n",
+              "      <th>TotalCharges_1396.25</th>\n",
+              "      <th>TotalCharges_1396.9</th>\n",
+              "      <th>TotalCharges_1397.3</th>\n",
+              "      <th>TotalCharges_1397.65</th>\n",
+              "      <th>TotalCharges_1398.25</th>\n",
+              "      <th>TotalCharges_1398.6</th>\n",
+              "      <th>TotalCharges_1399.35</th>\n",
+              "      <th>TotalCharges_140.1</th>\n",
+              "      <th>TotalCharges_140.4</th>\n",
+              "      <th>TotalCharges_140.7</th>\n",
+              "      <th>TotalCharges_140.95</th>\n",
+              "      <th>TotalCharges_1400.3</th>\n",
+              "      <th>TotalCharges_1400.55</th>\n",
+              "      <th>TotalCharges_1400.85</th>\n",
+              "      <th>TotalCharges_1401.15</th>\n",
+              "      <th>TotalCharges_1401.4</th>\n",
+              "      <th>TotalCharges_1402.25</th>\n",
+              "      <th>TotalCharges_1403.1</th>\n",
+              "      <th>TotalCharges_1404.65</th>\n",
+              "      <th>TotalCharges_1405.3</th>\n",
+              "      <th>TotalCharges_1406</th>\n",
+              "      <th>TotalCharges_1406.65</th>\n",
+              "      <th>TotalCharges_1406.9</th>\n",
+              "      <th>TotalCharges_141.1</th>\n",
+              "      <th>TotalCharges_141.45</th>\n",
+              "      <th>TotalCharges_141.5</th>\n",
+              "      <th>TotalCharges_141.6</th>\n",
+              "      <th>TotalCharges_141.65</th>\n",
+              "      <th>TotalCharges_141.7</th>\n",
+              "      <th>TotalCharges_1410.25</th>\n",
+              "      <th>TotalCharges_1411.2</th>\n",
+              "      <th>TotalCharges_1411.35</th>\n",
+              "      <th>TotalCharges_1411.65</th>\n",
+              "      <th>TotalCharges_1411.9</th>\n",
+              "      <th>TotalCharges_1412.4</th>\n",
+              "      <th>TotalCharges_1412.65</th>\n",
+              "      <th>TotalCharges_1413</th>\n",
+              "      <th>TotalCharges_1414.2</th>\n",
+              "      <th>TotalCharges_1414.45</th>\n",
+              "      <th>TotalCharges_1414.65</th>\n",
+              "      <th>TotalCharges_1414.8</th>\n",
+              "      <th>TotalCharges_1415</th>\n",
+              "      <th>TotalCharges_1415.55</th>\n",
+              "      <th>TotalCharges_1415.85</th>\n",
+              "      <th>TotalCharges_1416.2</th>\n",
+              "      <th>TotalCharges_1416.5</th>\n",
+              "      <th>TotalCharges_1416.75</th>\n",
+              "      <th>TotalCharges_1417.9</th>\n",
+              "      <th>TotalCharges_1419.4</th>\n",
+              "      <th>TotalCharges_142.35</th>\n",
+              "      <th>TotalCharges_1421.75</th>\n",
+              "      <th>TotalCharges_1421.9</th>\n",
+              "      <th>TotalCharges_1422.05</th>\n",
+              "      <th>TotalCharges_1422.1</th>\n",
+              "      <th>TotalCharges_1422.65</th>\n",
+              "      <th>TotalCharges_1423.05</th>\n",
+              "      <th>TotalCharges_1423.15</th>\n",
+              "      <th>TotalCharges_1423.35</th>\n",
+              "      <th>TotalCharges_1423.65</th>\n",
+              "      <th>TotalCharges_1423.85</th>\n",
+              "      <th>TotalCharges_1424.2</th>\n",
+              "      <th>TotalCharges_1424.4</th>\n",
+              "      <th>TotalCharges_1424.5</th>\n",
+              "      <th>TotalCharges_1424.6</th>\n",
+              "      <th>TotalCharges_1424.9</th>\n",
+              "      <th>TotalCharges_1424.95</th>\n",
+              "      <th>TotalCharges_1425.45</th>\n",
+              "      <th>TotalCharges_1426.4</th>\n",
+              "      <th>TotalCharges_1426.45</th>\n",
+              "      <th>TotalCharges_1426.75</th>\n",
+              "      <th>TotalCharges_1427.55</th>\n",
+              "      <th>TotalCharges_1429.65</th>\n",
+              "      <th>TotalCharges_143.35</th>\n",
+              "      <th>TotalCharges_143.65</th>\n",
+              "      <th>TotalCharges_143.9</th>\n",
+              "      <th>TotalCharges_1430.05</th>\n",
+              "      <th>TotalCharges_1430.25</th>\n",
+              "      <th>TotalCharges_1430.95</th>\n",
+              "      <th>TotalCharges_1431.65</th>\n",
+              "      <th>TotalCharges_1432.55</th>\n",
+              "      <th>TotalCharges_1433.8</th>\n",
+              "      <th>TotalCharges_1434.1</th>\n",
+              "      <th>TotalCharges_1434.6</th>\n",
+              "      <th>TotalCharges_1436.95</th>\n",
+              "      <th>TotalCharges_1438.05</th>\n",
+              "      <th>TotalCharges_1439.35</th>\n",
+              "      <th>TotalCharges_144</th>\n",
+              "      <th>TotalCharges_144.15</th>\n",
+              "      <th>TotalCharges_144.35</th>\n",
+              "      <th>TotalCharges_144.55</th>\n",
+              "      <th>TotalCharges_144.8</th>\n",
+              "      <th>TotalCharges_144.95</th>\n",
+              "      <th>TotalCharges_1440.75</th>\n",
+              "      <th>TotalCharges_1441.1</th>\n",
+              "      <th>TotalCharges_1441.65</th>\n",
+              "      <th>TotalCharges_1441.8</th>\n",
+              "      <th>TotalCharges_1441.95</th>\n",
+              "      <th>TotalCharges_1442</th>\n",
+              "      <th>TotalCharges_1442.2</th>\n",
+              "      <th>TotalCharges_1442.6</th>\n",
+              "      <th>TotalCharges_1442.65</th>\n",
+              "      <th>TotalCharges_1443.65</th>\n",
+              "      <th>TotalCharges_1444.05</th>\n",
+              "      <th>TotalCharges_1444.65</th>\n",
+              "      <th>TotalCharges_1445.2</th>\n",
+              "      <th>TotalCharges_1445.3</th>\n",
+              "      <th>TotalCharges_1445.95</th>\n",
+              "      <th>TotalCharges_1446.8</th>\n",
+              "      <th>TotalCharges_1447.9</th>\n",
+              "      <th>TotalCharges_1448.6</th>\n",
+              "      <th>TotalCharges_1448.8</th>\n",
+              "      <th>TotalCharges_145</th>\n",
+              "      <th>TotalCharges_145.15</th>\n",
+              "      <th>TotalCharges_145.4</th>\n",
+              "      <th>TotalCharges_1451.1</th>\n",
+              "      <th>TotalCharges_1451.6</th>\n",
+              "      <th>TotalCharges_1451.9</th>\n",
+              "      <th>TotalCharges_1453.1</th>\n",
+              "      <th>TotalCharges_1454.15</th>\n",
+              "      <th>TotalCharges_1454.25</th>\n",
+              "      <th>TotalCharges_1457.25</th>\n",
+              "      <th>TotalCharges_1458.1</th>\n",
+              "      <th>TotalCharges_1459.35</th>\n",
+              "      <th>TotalCharges_146.05</th>\n",
+              "      <th>TotalCharges_146.3</th>\n",
+              "      <th>TotalCharges_146.4</th>\n",
+              "      <th>TotalCharges_146.6</th>\n",
+              "      <th>TotalCharges_146.65</th>\n",
+              "      <th>TotalCharges_146.9</th>\n",
+              "      <th>TotalCharges_1460.65</th>\n",
+              "      <th>TotalCharges_1460.85</th>\n",
+              "      <th>TotalCharges_1461.15</th>\n",
+              "      <th>TotalCharges_1461.45</th>\n",
+              "      <th>TotalCharges_1462.05</th>\n",
+              "      <th>TotalCharges_1462.6</th>\n",
+              "      <th>TotalCharges_1463.45</th>\n",
+              "      <th>TotalCharges_1463.5</th>\n",
+              "      <th>TotalCharges_1463.7</th>\n",
+              "      <th>TotalCharges_1465.75</th>\n",
+              "      <th>TotalCharges_1466.1</th>\n",
+              "      <th>TotalCharges_1468.75</th>\n",
+              "      <th>TotalCharges_1468.9</th>\n",
+              "      <th>TotalCharges_147.15</th>\n",
+              "      <th>TotalCharges_147.5</th>\n",
+              "      <th>TotalCharges_147.55</th>\n",
+              "      <th>TotalCharges_147.75</th>\n",
+              "      <th>TotalCharges_147.8</th>\n",
+              "      <th>TotalCharges_1470.05</th>\n",
+              "      <th>TotalCharges_1470.95</th>\n",
+              "      <th>TotalCharges_1471.75</th>\n",
+              "      <th>TotalCharges_1474.35</th>\n",
+              "      <th>TotalCharges_1474.75</th>\n",
+              "      <th>TotalCharges_1474.9</th>\n",
+              "      <th>TotalCharges_1476.25</th>\n",
+              "      <th>TotalCharges_1477.65</th>\n",
+              "      <th>TotalCharges_1478.85</th>\n",
+              "      <th>TotalCharges_148.05</th>\n",
+              "      <th>TotalCharges_1482.3</th>\n",
+              "      <th>TotalCharges_1483.25</th>\n",
+              "      <th>TotalCharges_1489.3</th>\n",
+              "      <th>TotalCharges_149.05</th>\n",
+              "      <th>TotalCharges_149.55</th>\n",
+              "      <th>TotalCharges_1490.4</th>\n",
+              "      <th>TotalCharges_1490.95</th>\n",
+              "      <th>TotalCharges_1492.1</th>\n",
+              "      <th>TotalCharges_1493.1</th>\n",
+              "      <th>TotalCharges_1493.2</th>\n",
+              "      <th>TotalCharges_1493.55</th>\n",
+              "      <th>TotalCharges_1493.75</th>\n",
+              "      <th>TotalCharges_1494.5</th>\n",
+              "      <th>TotalCharges_1495.1</th>\n",
+              "      <th>TotalCharges_1496.45</th>\n",
+              "      <th>TotalCharges_1496.9</th>\n",
+              "      <th>TotalCharges_1497.05</th>\n",
+              "      <th>TotalCharges_1497.9</th>\n",
+              "      <th>TotalCharges_1498.2</th>\n",
+              "      <th>TotalCharges_1498.35</th>\n",
+              "      <th>TotalCharges_1498.55</th>\n",
+              "      <th>TotalCharges_1498.65</th>\n",
+              "      <th>TotalCharges_1498.85</th>\n",
+              "      <th>TotalCharges_150</th>\n",
+              "      <th>TotalCharges_150.35</th>\n",
+              "      <th>TotalCharges_150.6</th>\n",
+              "      <th>TotalCharges_150.75</th>\n",
+              "      <th>TotalCharges_150.85</th>\n",
+              "      <th>TotalCharges_1500.25</th>\n",
+              "      <th>TotalCharges_1500.5</th>\n",
+              "      <th>TotalCharges_1500.95</th>\n",
+              "      <th>TotalCharges_1501.75</th>\n",
+              "      <th>TotalCharges_1502.25</th>\n",
+              "      <th>TotalCharges_1502.65</th>\n",
+              "      <th>TotalCharges_1504.05</th>\n",
+              "      <th>TotalCharges_1505.05</th>\n",
+              "      <th>TotalCharges_1505.15</th>\n",
+              "      <th>TotalCharges_1505.35</th>\n",
+              "      <th>TotalCharges_1505.45</th>\n",
+              "      <th>TotalCharges_1505.85</th>\n",
+              "      <th>TotalCharges_1505.9</th>\n",
+              "      <th>TotalCharges_1506.4</th>\n",
+              "      <th>TotalCharges_1507</th>\n",
+              "      <th>TotalCharges_1509.8</th>\n",
+              "      <th>TotalCharges_1509.9</th>\n",
+              "      <th>TotalCharges_151.3</th>\n",
+              "      <th>TotalCharges_151.65</th>\n",
+              "      <th>TotalCharges_151.75</th>\n",
+              "      <th>TotalCharges_151.8</th>\n",
+              "      <th>TotalCharges_1510.3</th>\n",
+              "      <th>TotalCharges_1510.5</th>\n",
+              "      <th>TotalCharges_1511.2</th>\n",
+              "      <th>TotalCharges_1513.6</th>\n",
+              "      <th>TotalCharges_1514.85</th>\n",
+              "      <th>TotalCharges_1515.1</th>\n",
+              "      <th>TotalCharges_1516.6</th>\n",
+              "      <th>TotalCharges_1517.5</th>\n",
+              "      <th>TotalCharges_1519</th>\n",
+              "      <th>TotalCharges_152.3</th>\n",
+              "      <th>TotalCharges_152.45</th>\n",
+              "      <th>TotalCharges_152.6</th>\n",
+              "      <th>TotalCharges_152.7</th>\n",
+              "      <th>TotalCharges_152.95</th>\n",
+              "      <th>TotalCharges_1520.1</th>\n",
+              "      <th>TotalCharges_1520.9</th>\n",
+              "      <th>TotalCharges_1521.2</th>\n",
+              "      <th>TotalCharges_1522.65</th>\n",
+              "      <th>TotalCharges_1522.7</th>\n",
+              "      <th>TotalCharges_1523.4</th>\n",
+              "      <th>TotalCharges_1524.85</th>\n",
+              "      <th>TotalCharges_1525.35</th>\n",
+              "      <th>TotalCharges_1527.35</th>\n",
+              "      <th>TotalCharges_1527.5</th>\n",
+              "      <th>TotalCharges_1529.2</th>\n",
+              "      <th>TotalCharges_1529.45</th>\n",
+              "      <th>TotalCharges_1529.65</th>\n",
+              "      <th>TotalCharges_153.05</th>\n",
+              "      <th>TotalCharges_153.3</th>\n",
+              "      <th>TotalCharges_153.8</th>\n",
+              "      <th>TotalCharges_153.95</th>\n",
+              "      <th>TotalCharges_1530.6</th>\n",
+              "      <th>TotalCharges_1531.4</th>\n",
+              "      <th>TotalCharges_1532.45</th>\n",
+              "      <th>TotalCharges_1533.8</th>\n",
+              "      <th>TotalCharges_1534.05</th>\n",
+              "      <th>TotalCharges_1534.75</th>\n",
+              "      <th>TotalCharges_1536.75</th>\n",
+              "      <th>TotalCharges_1537.85</th>\n",
+              "      <th>TotalCharges_1537.9</th>\n",
+              "      <th>TotalCharges_1538.6</th>\n",
+              "      <th>TotalCharges_1539.45</th>\n",
+              "      <th>TotalCharges_1539.75</th>\n",
+              "      <th>TotalCharges_1539.8</th>\n",
+              "      <th>TotalCharges_154.3</th>\n",
+              "      <th>TotalCharges_154.55</th>\n",
+              "      <th>TotalCharges_154.65</th>\n",
+              "      <th>TotalCharges_154.8</th>\n",
+              "      <th>TotalCharges_154.85</th>\n",
+              "      <th>TotalCharges_1540.05</th>\n",
+              "      <th>TotalCharges_1540.2</th>\n",
+              "      <th>TotalCharges_1540.35</th>\n",
+              "      <th>TotalCharges_1544.05</th>\n",
+              "      <th>TotalCharges_1545.4</th>\n",
+              "      <th>TotalCharges_1546.3</th>\n",
+              "      <th>TotalCharges_1547.35</th>\n",
+              "      <th>TotalCharges_1548.65</th>\n",
+              "      <th>TotalCharges_1549.75</th>\n",
+              "      <th>TotalCharges_155.35</th>\n",
+              "      <th>TotalCharges_155.65</th>\n",
+              "      <th>TotalCharges_155.8</th>\n",
+              "      <th>TotalCharges_155.9</th>\n",
+              "      <th>TotalCharges_1551.6</th>\n",
+              "      <th>TotalCharges_1553.2</th>\n",
+              "      <th>TotalCharges_1553.9</th>\n",
+              "      <th>TotalCharges_1553.95</th>\n",
+              "      <th>TotalCharges_1554</th>\n",
+              "      <th>TotalCharges_1554.9</th>\n",
+              "      <th>TotalCharges_1555.65</th>\n",
+              "      <th>TotalCharges_1556.85</th>\n",
+              "      <th>TotalCharges_1558.65</th>\n",
+              "      <th>TotalCharges_1558.7</th>\n",
+              "      <th>TotalCharges_1559.15</th>\n",
+              "      <th>TotalCharges_1559.25</th>\n",
+              "      <th>TotalCharges_1559.3</th>\n",
+              "      <th>TotalCharges_1559.45</th>\n",
+              "      <th>TotalCharges_156.1</th>\n",
+              "      <th>TotalCharges_156.25</th>\n",
+              "      <th>TotalCharges_156.35</th>\n",
+              "      <th>TotalCharges_156.4</th>\n",
+              "      <th>TotalCharges_156.85</th>\n",
+              "      <th>TotalCharges_1561.5</th>\n",
+              "      <th>TotalCharges_1563.9</th>\n",
+              "      <th>TotalCharges_1563.95</th>\n",
+              "      <th>TotalCharges_1564.05</th>\n",
+              "      <th>TotalCharges_1564.4</th>\n",
+              "      <th>TotalCharges_1565.7</th>\n",
+              "      <th>TotalCharges_1566.75</th>\n",
+              "      <th>TotalCharges_1566.9</th>\n",
+              "      <th>TotalCharges_1567</th>\n",
+              "      <th>TotalCharges_1567.55</th>\n",
+              "      <th>TotalCharges_157.55</th>\n",
+              "      <th>TotalCharges_157.65</th>\n",
+              "      <th>TotalCharges_157.75</th>\n",
+              "      <th>TotalCharges_1570.7</th>\n",
+              "      <th>TotalCharges_1573.05</th>\n",
+              "      <th>TotalCharges_1573.7</th>\n",
+              "      <th>TotalCharges_1573.95</th>\n",
+              "      <th>TotalCharges_1574.45</th>\n",
+              "      <th>TotalCharges_1574.5</th>\n",
+              "      <th>TotalCharges_1579.7</th>\n",
+              "      <th>TotalCharges_158.35</th>\n",
+              "      <th>TotalCharges_158.4</th>\n",
+              "      <th>TotalCharges_158.95</th>\n",
+              "      <th>TotalCharges_1580.1</th>\n",
+              "      <th>TotalCharges_1581.2</th>\n",
+              "      <th>TotalCharges_1581.95</th>\n",
+              "      <th>TotalCharges_1582.75</th>\n",
+              "      <th>TotalCharges_1583.5</th>\n",
+              "      <th>TotalCharges_1584.8</th>\n",
+              "      <th>TotalCharges_1587.55</th>\n",
+              "      <th>TotalCharges_1588.25</th>\n",
+              "      <th>TotalCharges_1588.7</th>\n",
+              "      <th>TotalCharges_1588.75</th>\n",
+              "      <th>TotalCharges_159.15</th>\n",
+              "      <th>TotalCharges_159.2</th>\n",
+              "      <th>TotalCharges_159.35</th>\n",
+              "      <th>TotalCharges_159.4</th>\n",
+              "      <th>TotalCharges_159.45</th>\n",
+              "      <th>TotalCharges_1592.35</th>\n",
+              "      <th>TotalCharges_1593.1</th>\n",
+              "      <th>TotalCharges_1594.75</th>\n",
+              "      <th>TotalCharges_1595.5</th>\n",
+              "      <th>TotalCharges_1596.6</th>\n",
+              "      <th>TotalCharges_1597.05</th>\n",
+              "      <th>TotalCharges_1597.25</th>\n",
+              "      <th>TotalCharges_1597.4</th>\n",
+              "      <th>TotalCharges_160.05</th>\n",
+              "      <th>TotalCharges_160.75</th>\n",
+              "      <th>TotalCharges_160.8</th>\n",
+              "      <th>TotalCharges_160.85</th>\n",
+              "      <th>TotalCharges_1600.25</th>\n",
+              "      <th>TotalCharges_1600.95</th>\n",
+              "      <th>TotalCharges_1601.2</th>\n",
+              "      <th>TotalCharges_1601.5</th>\n",
+              "      <th>TotalCharges_1604.5</th>\n",
+              "      <th>TotalCharges_1607.2</th>\n",
+              "      <th>TotalCharges_1608.15</th>\n",
+              "      <th>TotalCharges_161.15</th>\n",
+              "      <th>TotalCharges_161.45</th>\n",
+              "      <th>TotalCharges_161.5</th>\n",
+              "      <th>TotalCharges_161.65</th>\n",
+              "      <th>TotalCharges_161.95</th>\n",
+              "      <th>TotalCharges_1611</th>\n",
+              "      <th>TotalCharges_1611.15</th>\n",
+              "      <th>TotalCharges_1611.65</th>\n",
+              "      <th>TotalCharges_1612.2</th>\n",
+              "      <th>TotalCharges_1612.75</th>\n",
+              "      <th>TotalCharges_1614.05</th>\n",
+              "      <th>TotalCharges_1614.2</th>\n",
+              "      <th>TotalCharges_1614.7</th>\n",
+              "      <th>TotalCharges_1614.9</th>\n",
+              "      <th>TotalCharges_1615.1</th>\n",
+              "      <th>TotalCharges_1616.15</th>\n",
+              "      <th>TotalCharges_1617.5</th>\n",
+              "      <th>TotalCharges_1618.2</th>\n",
+              "      <th>TotalCharges_162.15</th>\n",
+              "      <th>TotalCharges_162.3</th>\n",
+              "      <th>TotalCharges_162.45</th>\n",
+              "      <th>TotalCharges_162.55</th>\n",
+              "      <th>TotalCharges_1620.2</th>\n",
+              "      <th>TotalCharges_1620.25</th>\n",
+              "      <th>TotalCharges_1620.45</th>\n",
+              "      <th>TotalCharges_1620.8</th>\n",
+              "      <th>TotalCharges_1621.35</th>\n",
+              "      <th>TotalCharges_1622.45</th>\n",
+              "      <th>TotalCharges_1623.15</th>\n",
+              "      <th>TotalCharges_1623.4</th>\n",
+              "      <th>TotalCharges_1625</th>\n",
+              "      <th>TotalCharges_1625.65</th>\n",
+              "      <th>TotalCharges_1626.05</th>\n",
+              "      <th>TotalCharges_1626.4</th>\n",
+              "      <th>TotalCharges_1629.2</th>\n",
+              "      <th>TotalCharges_163.2</th>\n",
+              "      <th>TotalCharges_163.55</th>\n",
+              "      <th>TotalCharges_163.6</th>\n",
+              "      <th>TotalCharges_163.7</th>\n",
+              "      <th>TotalCharges_1630.4</th>\n",
+              "      <th>TotalCharges_1633</th>\n",
+              "      <th>TotalCharges_1636.95</th>\n",
+              "      <th>TotalCharges_1637.3</th>\n",
+              "      <th>TotalCharges_1637.4</th>\n",
+              "      <th>TotalCharges_1638.7</th>\n",
+              "      <th>TotalCharges_1639.3</th>\n",
+              "      <th>TotalCharges_164.3</th>\n",
+              "      <th>TotalCharges_164.5</th>\n",
+              "      <th>TotalCharges_164.6</th>\n",
+              "      <th>TotalCharges_164.85</th>\n",
+              "      <th>TotalCharges_1640</th>\n",
+              "      <th>TotalCharges_1641.3</th>\n",
+              "      <th>TotalCharges_1641.8</th>\n",
+              "      <th>TotalCharges_1642.75</th>\n",
+              "      <th>TotalCharges_1643.25</th>\n",
+              "      <th>TotalCharges_1643.55</th>\n",
+              "      <th>TotalCharges_1646.45</th>\n",
+              "      <th>TotalCharges_1647</th>\n",
+              "      <th>TotalCharges_1648.45</th>\n",
+              "      <th>TotalCharges_165</th>\n",
+              "      <th>TotalCharges_165.2</th>\n",
+              "      <th>TotalCharges_165.35</th>\n",
+              "      <th>TotalCharges_165.4</th>\n",
+              "      <th>TotalCharges_165.45</th>\n",
+              "      <th>TotalCharges_165.5</th>\n",
+              "      <th>TotalCharges_1651.95</th>\n",
+              "      <th>TotalCharges_1652.1</th>\n",
+              "      <th>TotalCharges_1652.4</th>\n",
+              "      <th>TotalCharges_1652.95</th>\n",
+              "      <th>TotalCharges_1653.45</th>\n",
+              "      <th>TotalCharges_1653.85</th>\n",
+              "      <th>TotalCharges_1654.45</th>\n",
+              "      <th>TotalCharges_1654.6</th>\n",
+              "      <th>TotalCharges_1654.7</th>\n",
+              "      <th>TotalCharges_1654.75</th>\n",
+              "      <th>TotalCharges_1654.85</th>\n",
+              "      <th>TotalCharges_1655.35</th>\n",
+              "      <th>TotalCharges_1657.4</th>\n",
+              "      <th>TotalCharges_166.3</th>\n",
+              "      <th>TotalCharges_1660</th>\n",
+              "      <th>TotalCharges_1662.05</th>\n",
+              "      <th>TotalCharges_1663.5</th>\n",
+              "      <th>TotalCharges_1663.75</th>\n",
+              "      <th>TotalCharges_1664.3</th>\n",
+              "      <th>TotalCharges_1665.2</th>\n",
+              "      <th>TotalCharges_1667.25</th>\n",
+              "      <th>TotalCharges_1669.4</th>\n",
+              "      <th>TotalCharges_167.2</th>\n",
+              "      <th>TotalCharges_167.3</th>\n",
+              "      <th>TotalCharges_167.5</th>\n",
+              "      <th>TotalCharges_1671.6</th>\n",
+              "      <th>TotalCharges_1672.1</th>\n",
+              "      <th>TotalCharges_1672.15</th>\n",
+              "      <th>TotalCharges_1672.35</th>\n",
+              "      <th>TotalCharges_1673.4</th>\n",
+              "      <th>TotalCharges_1673.8</th>\n",
+              "      <th>TotalCharges_1676.95</th>\n",
+              "      <th>TotalCharges_1677.85</th>\n",
+              "      <th>TotalCharges_1678.05</th>\n",
+              "      <th>TotalCharges_1679.25</th>\n",
+              "      <th>TotalCharges_1679.4</th>\n",
+              "      <th>TotalCharges_1679.65</th>\n",
+              "      <th>TotalCharges_168.15</th>\n",
+              "      <th>TotalCharges_168.2</th>\n",
+              "      <th>TotalCharges_168.5</th>\n",
+              "      <th>TotalCharges_168.6</th>\n",
+              "      <th>TotalCharges_168.65</th>\n",
+              "      <th>TotalCharges_168.9</th>\n",
+              "      <th>TotalCharges_1680.25</th>\n",
+              "      <th>TotalCharges_1681.6</th>\n",
+              "      <th>TotalCharges_1682.05</th>\n",
+              "      <th>TotalCharges_1682.4</th>\n",
+              "      <th>TotalCharges_1683.6</th>\n",
+              "      <th>TotalCharges_1683.7</th>\n",
+              "      <th>TotalCharges_1685.9</th>\n",
+              "      <th>TotalCharges_1686.15</th>\n",
+              "      <th>TotalCharges_1686.85</th>\n",
+              "      <th>TotalCharges_1687.95</th>\n",
+              "      <th>TotalCharges_1688.9</th>\n",
+              "      <th>TotalCharges_1689.45</th>\n",
+              "      <th>TotalCharges_169.05</th>\n",
+              "      <th>TotalCharges_169.45</th>\n",
+              "      <th>TotalCharges_169.65</th>\n",
+              "      <th>TotalCharges_169.75</th>\n",
+              "      <th>TotalCharges_169.8</th>\n",
+              "      <th>TotalCharges_1691.9</th>\n",
+              "      <th>TotalCharges_1692.6</th>\n",
+              "      <th>TotalCharges_1696.2</th>\n",
+              "      <th>TotalCharges_1697.7</th>\n",
+              "      <th>TotalCharges_1698.55</th>\n",
+              "      <th>TotalCharges_1699.15</th>\n",
+              "      <th>TotalCharges_170.5</th>\n",
+              "      <th>TotalCharges_170.85</th>\n",
+              "      <th>TotalCharges_170.9</th>\n",
+              "      <th>TotalCharges_1700.9</th>\n",
+              "      <th>TotalCharges_1701.65</th>\n",
+              "      <th>TotalCharges_1702.9</th>\n",
+              "      <th>TotalCharges_1704.95</th>\n",
+              "      <th>TotalCharges_1706.45</th>\n",
+              "      <th>TotalCharges_1709.1</th>\n",
+              "      <th>TotalCharges_1709.15</th>\n",
+              "      <th>TotalCharges_171</th>\n",
+              "      <th>TotalCharges_171.15</th>\n",
+              "      <th>TotalCharges_171.45</th>\n",
+              "      <th>TotalCharges_1710.15</th>\n",
+              "      <th>TotalCharges_1710.45</th>\n",
+              "      <th>TotalCharges_1710.9</th>\n",
+              "      <th>TotalCharges_1712.7</th>\n",
+              "      <th>TotalCharges_1712.9</th>\n",
+              "      <th>TotalCharges_1713.1</th>\n",
+              "      <th>TotalCharges_1714.55</th>\n",
+              "      <th>TotalCharges_1714.95</th>\n",
+              "      <th>TotalCharges_1715.1</th>\n",
+              "      <th>TotalCharges_1715.15</th>\n",
+              "      <th>TotalCharges_1715.65</th>\n",
+              "      <th>TotalCharges_1716.45</th>\n",
+              "      <th>TotalCharges_1718.2</th>\n",
+              "      <th>TotalCharges_1718.35</th>\n",
+              "      <th>TotalCharges_1718.95</th>\n",
+              "      <th>TotalCharges_1719.15</th>\n",
+              "      <th>TotalCharges_172.35</th>\n",
+              "      <th>TotalCharges_172.85</th>\n",
+              "      <th>TotalCharges_1723.95</th>\n",
+              "      <th>TotalCharges_1724.15</th>\n",
+              "      <th>TotalCharges_1725</th>\n",
+              "      <th>TotalCharges_1725.4</th>\n",
+              "      <th>TotalCharges_1725.95</th>\n",
+              "      <th>TotalCharges_1727.5</th>\n",
+              "      <th>TotalCharges_1728.2</th>\n",
+              "      <th>TotalCharges_1729.35</th>\n",
+              "      <th>TotalCharges_173</th>\n",
+              "      <th>TotalCharges_173.15</th>\n",
+              "      <th>TotalCharges_1730.35</th>\n",
+              "      <th>TotalCharges_1730.65</th>\n",
+              "      <th>TotalCharges_1732.6</th>\n",
+              "      <th>TotalCharges_1732.95</th>\n",
+              "      <th>TotalCharges_1733.15</th>\n",
+              "      <th>TotalCharges_1734.2</th>\n",
+              "      <th>TotalCharges_1734.5</th>\n",
+              "      <th>TotalCharges_1734.65</th>\n",
+              "      <th>TotalCharges_1737.45</th>\n",
+              "      <th>TotalCharges_1738.9</th>\n",
+              "      <th>TotalCharges_1739.6</th>\n",
+              "      <th>TotalCharges_174.2</th>\n",
+              "      <th>TotalCharges_174.3</th>\n",
+              "      <th>TotalCharges_174.45</th>\n",
+              "      <th>TotalCharges_174.65</th>\n",
+              "      <th>TotalCharges_174.7</th>\n",
+              "      <th>TotalCharges_174.75</th>\n",
+              "      <th>TotalCharges_174.8</th>\n",
+              "      <th>TotalCharges_1740.7</th>\n",
+              "      <th>TotalCharges_1740.8</th>\n",
+              "      <th>TotalCharges_1742.45</th>\n",
+              "      <th>TotalCharges_1742.5</th>\n",
+              "      <th>TotalCharges_1742.75</th>\n",
+              "      <th>TotalCharges_1742.95</th>\n",
+              "      <th>TotalCharges_1743.05</th>\n",
+              "      <th>TotalCharges_1743.5</th>\n",
+              "      <th>TotalCharges_1743.9</th>\n",
+              "      <th>TotalCharges_1745.2</th>\n",
+              "      <th>TotalCharges_1745.5</th>\n",
+              "      <th>TotalCharges_1747.2</th>\n",
+              "      <th>TotalCharges_1747.85</th>\n",
+              "      <th>TotalCharges_1748.55</th>\n",
+              "      <th>TotalCharges_1748.9</th>\n",
+              "      <th>TotalCharges_1750.7</th>\n",
+              "      <th>TotalCharges_1750.85</th>\n",
+              "      <th>TotalCharges_1752.45</th>\n",
+              "      <th>TotalCharges_1752.55</th>\n",
+              "      <th>TotalCharges_1752.65</th>\n",
+              "      <th>TotalCharges_1753</th>\n",
+              "      <th>TotalCharges_1755.35</th>\n",
+              "      <th>TotalCharges_1756.2</th>\n",
+              "      <th>TotalCharges_1756.6</th>\n",
+              "      <th>TotalCharges_1758.6</th>\n",
+              "      <th>TotalCharges_1758.9</th>\n",
+              "      <th>TotalCharges_1759.4</th>\n",
+              "      <th>TotalCharges_1759.55</th>\n",
+              "      <th>TotalCharges_176.2</th>\n",
+              "      <th>TotalCharges_176.3</th>\n",
+              "      <th>TotalCharges_1760.25</th>\n",
+              "      <th>TotalCharges_1761.05</th>\n",
+              "      <th>TotalCharges_1761.45</th>\n",
+              "      <th>TotalCharges_1763.55</th>\n",
+              "      <th>TotalCharges_1764.75</th>\n",
+              "      <th>TotalCharges_1765.95</th>\n",
+              "      <th>TotalCharges_1766.75</th>\n",
+              "      <th>TotalCharges_1767.35</th>\n",
+              "      <th>TotalCharges_1769.6</th>\n",
+              "      <th>TotalCharges_177.4</th>\n",
+              "      <th>TotalCharges_1772.25</th>\n",
+              "      <th>TotalCharges_1775.8</th>\n",
+              "      <th>TotalCharges_1776</th>\n",
+              "      <th>TotalCharges_1776.45</th>\n",
+              "      <th>TotalCharges_1776.55</th>\n",
+              "      <th>TotalCharges_1776.95</th>\n",
+              "      <th>TotalCharges_1777.6</th>\n",
+              "      <th>TotalCharges_1777.9</th>\n",
+              "      <th>TotalCharges_1778.5</th>\n",
+              "      <th>TotalCharges_1778.7</th>\n",
+              "      <th>TotalCharges_1779.95</th>\n",
+              "      <th>TotalCharges_178.1</th>\n",
+              "      <th>TotalCharges_178.5</th>\n",
+              "      <th>TotalCharges_178.7</th>\n",
+              "      <th>TotalCharges_178.8</th>\n",
+              "      <th>TotalCharges_178.85</th>\n",
+              "      <th>TotalCharges_1781.35</th>\n",
+              "      <th>TotalCharges_1782</th>\n",
+              "      <th>TotalCharges_1782.05</th>\n",
+              "      <th>TotalCharges_1782.4</th>\n",
+              "      <th>TotalCharges_1783.6</th>\n",
+              "      <th>TotalCharges_1783.75</th>\n",
+              "      <th>TotalCharges_1784.5</th>\n",
+              "      <th>TotalCharges_1784.9</th>\n",
+              "      <th>TotalCharges_1785.65</th>\n",
+              "      <th>TotalCharges_1787.35</th>\n",
+              "      <th>TotalCharges_1789.25</th>\n",
+              "      <th>TotalCharges_1789.65</th>\n",
+              "      <th>TotalCharges_1789.9</th>\n",
+              "      <th>TotalCharges_179.25</th>\n",
+              "      <th>TotalCharges_179.35</th>\n",
+              "      <th>TotalCharges_179.85</th>\n",
+              "      <th>TotalCharges_1790.15</th>\n",
+              "      <th>TotalCharges_1790.35</th>\n",
+              "      <th>TotalCharges_1790.6</th>\n",
+              "      <th>TotalCharges_1790.65</th>\n",
+              "      <th>TotalCharges_1790.8</th>\n",
+              "      <th>TotalCharges_1793.25</th>\n",
+              "      <th>TotalCharges_1794.65</th>\n",
+              "      <th>TotalCharges_1794.8</th>\n",
+              "      <th>TotalCharges_1796.55</th>\n",
+              "      <th>TotalCharges_1797.1</th>\n",
+              "      <th>TotalCharges_1797.75</th>\n",
+              "      <th>TotalCharges_1798.65</th>\n",
+              "      <th>TotalCharges_1798.9</th>\n",
+              "      <th>TotalCharges_1799.3</th>\n",
+              "      <th>TotalCharges_18.8</th>\n",
+              "      <th>TotalCharges_18.85</th>\n",
+              "      <th>TotalCharges_18.9</th>\n",
+              "      <th>TotalCharges_180.25</th>\n",
+              "      <th>TotalCharges_180.3</th>\n",
+              "      <th>TotalCharges_180.7</th>\n",
+              "      <th>TotalCharges_1800.05</th>\n",
+              "      <th>TotalCharges_1801.1</th>\n",
+              "      <th>TotalCharges_1801.9</th>\n",
+              "      <th>TotalCharges_1802.15</th>\n",
+              "      <th>TotalCharges_1802.55</th>\n",
+              "      <th>TotalCharges_1803.7</th>\n",
+              "      <th>TotalCharges_1806.35</th>\n",
+              "      <th>TotalCharges_1808.7</th>\n",
+              "      <th>TotalCharges_1809.35</th>\n",
+              "      <th>TotalCharges_181.1</th>\n",
+              "      <th>TotalCharges_181.5</th>\n",
+              "      <th>TotalCharges_181.6</th>\n",
+              "      <th>TotalCharges_181.65</th>\n",
+              "      <th>TotalCharges_181.7</th>\n",
+              "      <th>TotalCharges_181.8</th>\n",
+              "      <th>TotalCharges_1810.55</th>\n",
+              "      <th>TotalCharges_1810.85</th>\n",
+              "      <th>TotalCharges_1813.1</th>\n",
+              "      <th>TotalCharges_1813.35</th>\n",
+              "      <th>TotalCharges_1815</th>\n",
+              "      <th>TotalCharges_1815.3</th>\n",
+              "      <th>TotalCharges_1815.65</th>\n",
+              "      <th>TotalCharges_1816.2</th>\n",
+              "      <th>TotalCharges_1816.75</th>\n",
+              "      <th>TotalCharges_1818.3</th>\n",
+              "      <th>TotalCharges_1818.9</th>\n",
+              "      <th>TotalCharges_1820.45</th>\n",
+              "      <th>TotalCharges_1820.9</th>\n",
+              "      <th>TotalCharges_1821.2</th>\n",
+              "      <th>TotalCharges_1821.8</th>\n",
+              "      <th>TotalCharges_1821.95</th>\n",
+              "      <th>TotalCharges_1825.5</th>\n",
+              "      <th>TotalCharges_1826.7</th>\n",
+              "      <th>TotalCharges_183.15</th>\n",
+              "      <th>TotalCharges_183.75</th>\n",
+              "      <th>TotalCharges_1830.05</th>\n",
+              "      <th>TotalCharges_1830.1</th>\n",
+              "      <th>TotalCharges_1832.4</th>\n",
+              "      <th>TotalCharges_1834.15</th>\n",
+              "      <th>TotalCharges_1834.95</th>\n",
+              "      <th>TotalCharges_1835.3</th>\n",
+              "      <th>TotalCharges_1836.25</th>\n",
+              "      <th>TotalCharges_1836.9</th>\n",
+              "      <th>TotalCharges_1837.7</th>\n",
+              "      <th>TotalCharges_1837.9</th>\n",
+              "      <th>TotalCharges_1838.15</th>\n",
+              "      <th>TotalCharges_184.05</th>\n",
+              "      <th>TotalCharges_184.1</th>\n",
+              "      <th>TotalCharges_184.15</th>\n",
+              "      <th>TotalCharges_184.4</th>\n",
+              "      <th>TotalCharges_184.65</th>\n",
+              "      <th>TotalCharges_184.95</th>\n",
+              "      <th>TotalCharges_1840.75</th>\n",
+              "      <th>TotalCharges_1841.2</th>\n",
+              "      <th>TotalCharges_1841.9</th>\n",
+              "      <th>TotalCharges_1842.7</th>\n",
+              "      <th>TotalCharges_1842.8</th>\n",
+              "      <th>TotalCharges_1843.05</th>\n",
+              "      <th>TotalCharges_1845.9</th>\n",
+              "      <th>TotalCharges_1846.65</th>\n",
+              "      <th>TotalCharges_1847.55</th>\n",
+              "      <th>TotalCharges_1848.8</th>\n",
+              "      <th>TotalCharges_1849.2</th>\n",
+              "      <th>TotalCharges_1849.95</th>\n",
+              "      <th>TotalCharges_185.2</th>\n",
+              "      <th>TotalCharges_185.4</th>\n",
+              "      <th>TotalCharges_185.55</th>\n",
+              "      <th>TotalCharges_185.6</th>\n",
+              "      <th>TotalCharges_1850.65</th>\n",
+              "      <th>TotalCharges_1851.45</th>\n",
+              "      <th>TotalCharges_1852.85</th>\n",
+              "      <th>TotalCharges_1855.65</th>\n",
+              "      <th>TotalCharges_1856.4</th>\n",
+              "      <th>TotalCharges_1857.25</th>\n",
+              "      <th>TotalCharges_1857.3</th>\n",
+              "      <th>TotalCharges_1857.75</th>\n",
+              "      <th>TotalCharges_1857.85</th>\n",
+              "      <th>TotalCharges_1859.1</th>\n",
+              "      <th>TotalCharges_1859.2</th>\n",
+              "      <th>TotalCharges_1859.5</th>\n",
+              "      <th>TotalCharges_186.05</th>\n",
+              "      <th>TotalCharges_186.15</th>\n",
+              "      <th>TotalCharges_186.3</th>\n",
+              "      <th>TotalCharges_1861.1</th>\n",
+              "      <th>TotalCharges_1861.5</th>\n",
+              "      <th>TotalCharges_1862.9</th>\n",
+              "      <th>TotalCharges_1863.8</th>\n",
+              "      <th>TotalCharges_1864.2</th>\n",
+              "      <th>TotalCharges_1864.65</th>\n",
+              "      <th>TotalCharges_1866.45</th>\n",
+              "      <th>TotalCharges_1867.6</th>\n",
+              "      <th>TotalCharges_1867.7</th>\n",
+              "      <th>TotalCharges_1868.4</th>\n",
+              "      <th>TotalCharges_187.35</th>\n",
+              "      <th>TotalCharges_187.45</th>\n",
+              "      <th>TotalCharges_187.75</th>\n",
+              "      <th>TotalCharges_1871.15</th>\n",
+              "      <th>TotalCharges_1871.85</th>\n",
+              "      <th>TotalCharges_1872.2</th>\n",
+              "      <th>TotalCharges_1873.7</th>\n",
+              "      <th>TotalCharges_1874.3</th>\n",
+              "      <th>TotalCharges_1874.45</th>\n",
+              "      <th>TotalCharges_1875.25</th>\n",
+              "      <th>TotalCharges_1875.55</th>\n",
+              "      <th>TotalCharges_1879.25</th>\n",
+              "      <th>TotalCharges_188.1</th>\n",
+              "      <th>TotalCharges_188.7</th>\n",
+              "      <th>TotalCharges_1880.85</th>\n",
+              "      <th>TotalCharges_1882.55</th>\n",
+              "      <th>TotalCharges_1882.8</th>\n",
+              "      <th>TotalCharges_1884.65</th>\n",
+              "      <th>TotalCharges_1885.15</th>\n",
+              "      <th>TotalCharges_1886.25</th>\n",
+              "      <th>TotalCharges_1886.4</th>\n",
+              "      <th>TotalCharges_1887</th>\n",
+              "      <th>TotalCharges_1888.25</th>\n",
+              "      <th>TotalCharges_1888.45</th>\n",
+              "      <th>TotalCharges_1888.65</th>\n",
+              "      <th>TotalCharges_1889.5</th>\n",
+              "      <th>TotalCharges_189.1</th>\n",
+              "      <th>TotalCharges_189.2</th>\n",
+              "      <th>TotalCharges_189.45</th>\n",
+              "      <th>TotalCharges_189.95</th>\n",
+              "      <th>TotalCharges_1893.5</th>\n",
+              "      <th>TotalCharges_1893.95</th>\n",
+              "      <th>TotalCharges_1898.1</th>\n",
+              "      <th>TotalCharges_1899.65</th>\n",
+              "      <th>TotalCharges_19</th>\n",
+              "      <th>TotalCharges_19.05</th>\n",
+              "      <th>TotalCharges_19.1</th>\n",
+              "      <th>TotalCharges_19.15</th>\n",
+              "      <th>TotalCharges_19.2</th>\n",
+              "      <th>TotalCharges_19.25</th>\n",
+              "      <th>TotalCharges_19.3</th>\n",
+              "      <th>TotalCharges_19.4</th>\n",
+              "      <th>TotalCharges_19.45</th>\n",
+              "      <th>TotalCharges_19.5</th>\n",
+              "      <th>TotalCharges_19.55</th>\n",
+              "      <th>TotalCharges_19.6</th>\n",
+              "      <th>TotalCharges_19.65</th>\n",
+              "      <th>TotalCharges_19.7</th>\n",
+              "      <th>TotalCharges_19.75</th>\n",
+              "      <th>TotalCharges_19.8</th>\n",
+              "      <th>TotalCharges_19.85</th>\n",
+              "      <th>TotalCharges_19.9</th>\n",
+              "      <th>TotalCharges_19.95</th>\n",
+              "      <th>TotalCharges_190.05</th>\n",
+              "      <th>TotalCharges_190.1</th>\n",
+              "      <th>TotalCharges_190.25</th>\n",
+              "      <th>TotalCharges_190.5</th>\n",
+              "      <th>TotalCharges_1900.25</th>\n",
+              "      <th>TotalCharges_1901</th>\n",
+              "      <th>TotalCharges_1901.05</th>\n",
+              "      <th>TotalCharges_1901.25</th>\n",
+              "      <th>TotalCharges_1901.65</th>\n",
+              "      <th>TotalCharges_1902</th>\n",
+              "      <th>TotalCharges_1905.4</th>\n",
+              "      <th>TotalCharges_1905.7</th>\n",
+              "      <th>TotalCharges_1907.85</th>\n",
+              "      <th>TotalCharges_1908.35</th>\n",
+              "      <th>TotalCharges_191.05</th>\n",
+              "      <th>TotalCharges_191.1</th>\n",
+              "      <th>TotalCharges_191.35</th>\n",
+              "      <th>TotalCharges_1910.6</th>\n",
+              "      <th>TotalCharges_1910.75</th>\n",
+              "      <th>TotalCharges_1911.5</th>\n",
+              "      <th>TotalCharges_1912.15</th>\n",
+              "      <th>TotalCharges_1912.85</th>\n",
+              "      <th>TotalCharges_1914.5</th>\n",
+              "      <th>TotalCharges_1914.9</th>\n",
+              "      <th>TotalCharges_1916</th>\n",
+              "      <th>TotalCharges_1916.2</th>\n",
+              "      <th>TotalCharges_1917.1</th>\n",
+              "      <th>TotalCharges_1923.5</th>\n",
+              "      <th>TotalCharges_1923.85</th>\n",
+              "      <th>TotalCharges_1924.1</th>\n",
+              "      <th>TotalCharges_1927.3</th>\n",
+              "      <th>TotalCharges_1928.7</th>\n",
+              "      <th>TotalCharges_1929</th>\n",
+              "      <th>TotalCharges_1929.35</th>\n",
+              "      <th>TotalCharges_1929.95</th>\n",
+              "      <th>TotalCharges_193.05</th>\n",
+              "      <th>TotalCharges_193.6</th>\n",
+              "      <th>TotalCharges_193.8</th>\n",
+              "      <th>TotalCharges_1930.9</th>\n",
+              "      <th>TotalCharges_1931.3</th>\n",
+              "      <th>TotalCharges_1931.75</th>\n",
+              "      <th>TotalCharges_1932.75</th>\n",
+              "      <th>TotalCharges_1934.45</th>\n",
+              "      <th>TotalCharges_1936.85</th>\n",
+              "      <th>TotalCharges_1937.4</th>\n",
+              "      <th>TotalCharges_1938.05</th>\n",
+              "      <th>TotalCharges_1938.9</th>\n",
+              "      <th>TotalCharges_1939.35</th>\n",
+              "      <th>TotalCharges_194.2</th>\n",
+              "      <th>TotalCharges_194.55</th>\n",
+              "      <th>TotalCharges_1940.8</th>\n",
+              "      <th>TotalCharges_1940.85</th>\n",
+              "      <th>TotalCharges_1941.5</th>\n",
+              "      <th>TotalCharges_1943.2</th>\n",
+              "      <th>TotalCharges_1943.9</th>\n",
+              "      <th>TotalCharges_1948.35</th>\n",
+              "      <th>TotalCharges_1949.4</th>\n",
+              "      <th>TotalCharges_195.05</th>\n",
+              "      <th>TotalCharges_195.3</th>\n",
+              "      <th>TotalCharges_195.65</th>\n",
+              "      <th>TotalCharges_1951</th>\n",
+              "      <th>TotalCharges_1952.25</th>\n",
+              "      <th>TotalCharges_1952.8</th>\n",
+              "      <th>TotalCharges_1955.4</th>\n",
+              "      <th>TotalCharges_1956.4</th>\n",
+              "      <th>TotalCharges_1957.1</th>\n",
+              "      <th>TotalCharges_1958.45</th>\n",
+              "      <th>TotalCharges_1958.95</th>\n",
+              "      <th>TotalCharges_1959.5</th>\n",
+              "      <th>TotalCharges_196.15</th>\n",
+              "      <th>TotalCharges_196.35</th>\n",
+              "      <th>TotalCharges_196.4</th>\n",
+              "      <th>TotalCharges_196.75</th>\n",
+              "      <th>TotalCharges_196.9</th>\n",
+              "      <th>TotalCharges_196.95</th>\n",
+              "      <th>TotalCharges_1961.6</th>\n",
+              "      <th>TotalCharges_1964.6</th>\n",
+              "      <th>TotalCharges_1968.1</th>\n",
+              "      <th>TotalCharges_197.4</th>\n",
+              "      <th>TotalCharges_197.7</th>\n",
+              "      <th>TotalCharges_1970.5</th>\n",
+              "      <th>TotalCharges_1971.15</th>\n",
+              "      <th>TotalCharges_1971.5</th>\n",
+              "      <th>TotalCharges_1972.35</th>\n",
+              "      <th>TotalCharges_1973.75</th>\n",
+              "      <th>TotalCharges_1974.8</th>\n",
+              "      <th>TotalCharges_1975.85</th>\n",
+              "      <th>TotalCharges_1978.65</th>\n",
+              "      <th>TotalCharges_198</th>\n",
+              "      <th>TotalCharges_198.1</th>\n",
+              "      <th>TotalCharges_198.25</th>\n",
+              "      <th>TotalCharges_198.5</th>\n",
+              "      <th>TotalCharges_198.6</th>\n",
+              "      <th>TotalCharges_198.7</th>\n",
+              "      <th>TotalCharges_1980.3</th>\n",
+              "      <th>TotalCharges_1982.1</th>\n",
+              "      <th>TotalCharges_1982.6</th>\n",
+              "      <th>TotalCharges_1983.15</th>\n",
+              "      <th>TotalCharges_1985.15</th>\n",
+              "      <th>TotalCharges_1988.05</th>\n",
+              "      <th>TotalCharges_199.45</th>\n",
+              "      <th>TotalCharges_199.75</th>\n",
+              "      <th>TotalCharges_199.85</th>\n",
+              "      <th>TotalCharges_1990.5</th>\n",
+              "      <th>TotalCharges_1992.2</th>\n",
+              "      <th>TotalCharges_1992.55</th>\n",
+              "      <th>TotalCharges_1992.85</th>\n",
+              "      <th>TotalCharges_1992.95</th>\n",
+              "      <th>TotalCharges_1993.2</th>\n",
+              "      <th>TotalCharges_1993.25</th>\n",
+              "      <th>TotalCharges_1993.8</th>\n",
+              "      <th>TotalCharges_1994.3</th>\n",
+              "      <th>TotalCharges_20</th>\n",
+              "      <th>TotalCharges_20.05</th>\n",
+              "      <th>TotalCharges_20.1</th>\n",
+              "      <th>TotalCharges_20.15</th>\n",
+              "      <th>TotalCharges_20.2</th>\n",
+              "      <th>TotalCharges_20.25</th>\n",
+              "      <th>TotalCharges_20.3</th>\n",
+              "      <th>TotalCharges_20.35</th>\n",
+              "      <th>TotalCharges_20.4</th>\n",
+              "      <th>TotalCharges_20.45</th>\n",
+              "      <th>TotalCharges_20.5</th>\n",
+              "      <th>TotalCharges_20.55</th>\n",
+              "      <th>TotalCharges_20.6</th>\n",
+              "      <th>TotalCharges_20.65</th>\n",
+              "      <th>TotalCharges_20.7</th>\n",
+              "      <th>TotalCharges_20.75</th>\n",
+              "      <th>TotalCharges_20.8</th>\n",
+              "      <th>TotalCharges_20.85</th>\n",
+              "      <th>TotalCharges_20.9</th>\n",
+              "      <th>TotalCharges_20.95</th>\n",
+              "      <th>TotalCharges_200.2</th>\n",
+              "      <th>TotalCharges_2000.2</th>\n",
+              "      <th>TotalCharges_2001</th>\n",
+              "      <th>TotalCharges_2001.5</th>\n",
+              "      <th>TotalCharges_2003.6</th>\n",
+              "      <th>TotalCharges_2006.1</th>\n",
+              "      <th>TotalCharges_2006.95</th>\n",
+              "      <th>TotalCharges_2007.25</th>\n",
+              "      <th>TotalCharges_2007.85</th>\n",
+              "      <th>TotalCharges_201</th>\n",
+              "      <th>TotalCharges_201.1</th>\n",
+              "      <th>TotalCharges_201.7</th>\n",
+              "      <th>TotalCharges_201.95</th>\n",
+              "      <th>TotalCharges_2010.55</th>\n",
+              "      <th>TotalCharges_2010.95</th>\n",
+              "      <th>TotalCharges_2011.4</th>\n",
+              "      <th>TotalCharges_2012.7</th>\n",
+              "      <th>TotalCharges_2015.35</th>\n",
+              "      <th>TotalCharges_2015.8</th>\n",
+              "      <th>TotalCharges_2016.3</th>\n",
+              "      <th>TotalCharges_2016.45</th>\n",
+              "      <th>TotalCharges_2018.1</th>\n",
+              "      <th>TotalCharges_2018.4</th>\n",
+              "      <th>TotalCharges_2019.8</th>\n",
+              "      <th>TotalCharges_202.15</th>\n",
+              "      <th>TotalCharges_202.25</th>\n",
+              "      <th>TotalCharges_202.3</th>\n",
+              "      <th>TotalCharges_202.9</th>\n",
+              "      <th>TotalCharges_2020.9</th>\n",
+              "      <th>TotalCharges_2021.2</th>\n",
+              "      <th>TotalCharges_2021.35</th>\n",
+              "      <th>TotalCharges_2023.55</th>\n",
+              "      <th>TotalCharges_2024.1</th>\n",
+              "      <th>TotalCharges_2025.1</th>\n",
+              "      <th>TotalCharges_2028.8</th>\n",
+              "      <th>TotalCharges_2029.05</th>\n",
+              "      <th>TotalCharges_203.95</th>\n",
+              "      <th>TotalCharges_2030.3</th>\n",
+              "      <th>TotalCharges_2030.75</th>\n",
+              "      <th>TotalCharges_2031.95</th>\n",
+              "      <th>TotalCharges_2032.3</th>\n",
+              "      <th>TotalCharges_2033.05</th>\n",
+              "      <th>TotalCharges_2033.85</th>\n",
+              "      <th>TotalCharges_2034.25</th>\n",
+              "      <th>TotalCharges_2036.55</th>\n",
+              "      <th>TotalCharges_2038.7</th>\n",
+              "      <th>TotalCharges_204.55</th>\n",
+              "      <th>TotalCharges_204.7</th>\n",
+              "      <th>TotalCharges_2042.05</th>\n",
+              "      <th>TotalCharges_2043.45</th>\n",
+              "      <th>TotalCharges_2044.75</th>\n",
+              "      <th>TotalCharges_2044.95</th>\n",
+              "      <th>TotalCharges_2045.55</th>\n",
+              "      <th>TotalCharges_2048.8</th>\n",
+              "      <th>TotalCharges_2049.05</th>\n",
+              "      <th>TotalCharges_205.05</th>\n",
+              "      <th>TotalCharges_2053.05</th>\n",
+              "      <th>TotalCharges_2054.4</th>\n",
+              "      <th>TotalCharges_2058.5</th>\n",
+              "      <th>TotalCharges_206.15</th>\n",
+              "      <th>TotalCharges_206.6</th>\n",
+              "      <th>TotalCharges_2062.15</th>\n",
+              "      <th>TotalCharges_2065.15</th>\n",
+              "      <th>TotalCharges_2065.4</th>\n",
+              "      <th>TotalCharges_2066</th>\n",
+              "      <th>TotalCharges_2067</th>\n",
+              "      <th>TotalCharges_2068.55</th>\n",
+              "      <th>TotalCharges_207.35</th>\n",
+              "      <th>TotalCharges_207.4</th>\n",
+              "      <th>TotalCharges_2070.05</th>\n",
+              "      <th>TotalCharges_2070.6</th>\n",
+              "      <th>TotalCharges_2070.75</th>\n",
+              "      <th>TotalCharges_2072.75</th>\n",
+              "      <th>TotalCharges_2075.1</th>\n",
+              "      <th>TotalCharges_2076.05</th>\n",
+              "      <th>TotalCharges_2076.2</th>\n",
+              "      <th>TotalCharges_2077.95</th>\n",
+              "      <th>TotalCharges_2078.55</th>\n",
+              "      <th>TotalCharges_2078.95</th>\n",
+              "      <th>TotalCharges_208</th>\n",
+              "      <th>TotalCharges_208.25</th>\n",
+              "      <th>TotalCharges_208.45</th>\n",
+              "      <th>TotalCharges_208.7</th>\n",
+              "      <th>TotalCharges_208.85</th>\n",
+              "      <th>TotalCharges_2080.1</th>\n",
+              "      <th>TotalCharges_2082.95</th>\n",
+              "      <th>TotalCharges_2083.1</th>\n",
+              "      <th>TotalCharges_2085.45</th>\n",
+              "      <th>TotalCharges_2088.05</th>\n",
+              "      <th>TotalCharges_2088.45</th>\n",
+              "      <th>TotalCharges_2088.75</th>\n",
+              "      <th>TotalCharges_2088.8</th>\n",
+              "      <th>TotalCharges_209.1</th>\n",
+              "      <th>TotalCharges_209.9</th>\n",
+              "      <th>TotalCharges_2090.25</th>\n",
+              "      <th>TotalCharges_2092.9</th>\n",
+              "      <th>TotalCharges_2093.4</th>\n",
+              "      <th>TotalCharges_2093.9</th>\n",
+              "      <th>TotalCharges_2094.65</th>\n",
+              "      <th>TotalCharges_2094.9</th>\n",
+              "      <th>TotalCharges_2095</th>\n",
+              "      <th>TotalCharges_2096.1</th>\n",
+              "      <th>TotalCharges_21</th>\n",
+              "      <th>TotalCharges_21.05</th>\n",
+              "      <th>TotalCharges_21.1</th>\n",
+              "      <th>TotalCharges_210.3</th>\n",
+              "      <th>TotalCharges_210.65</th>\n",
+              "      <th>TotalCharges_210.75</th>\n",
+              "      <th>TotalCharges_2104.55</th>\n",
+              "      <th>TotalCharges_2106.05</th>\n",
+              "      <th>TotalCharges_2106.3</th>\n",
+              "      <th>TotalCharges_2107.15</th>\n",
+              "      <th>TotalCharges_2108.35</th>\n",
+              "      <th>TotalCharges_2109.35</th>\n",
+              "      <th>TotalCharges_211.95</th>\n",
+              "      <th>TotalCharges_2110.15</th>\n",
+              "      <th>TotalCharges_2111.3</th>\n",
+              "      <th>TotalCharges_2111.45</th>\n",
+              "      <th>TotalCharges_2117.2</th>\n",
+              "      <th>TotalCharges_2117.25</th>\n",
+              "      <th>TotalCharges_2119.5</th>\n",
+              "      <th>TotalCharges_212.3</th>\n",
+              "      <th>TotalCharges_212.4</th>\n",
+              "      <th>TotalCharges_2122.05</th>\n",
+              "      <th>TotalCharges_2122.45</th>\n",
+              "      <th>TotalCharges_213.35</th>\n",
+              "      <th>TotalCharges_2130.45</th>\n",
+              "      <th>TotalCharges_2130.55</th>\n",
+              "      <th>TotalCharges_2134.3</th>\n",
+              "      <th>TotalCharges_2135.5</th>\n",
+              "      <th>TotalCharges_2136.9</th>\n",
+              "      <th>TotalCharges_2139.1</th>\n",
+              "      <th>TotalCharges_2139.2</th>\n",
+              "      <th>TotalCharges_214.4</th>\n",
+              "      <th>TotalCharges_214.55</th>\n",
+              "      <th>TotalCharges_214.75</th>\n",
+              "      <th>TotalCharges_2142.8</th>\n",
+              "      <th>TotalCharges_2145</th>\n",
+              "      <th>TotalCharges_2146.5</th>\n",
+              "      <th>TotalCharges_2149.05</th>\n",
+              "      <th>TotalCharges_215.2</th>\n",
+              "      <th>TotalCharges_215.25</th>\n",
+              "      <th>TotalCharges_215.8</th>\n",
+              "      <th>TotalCharges_2151.6</th>\n",
+              "      <th>TotalCharges_2156.25</th>\n",
+              "      <th>TotalCharges_2157.3</th>\n",
+              "      <th>TotalCharges_2157.5</th>\n",
+              "      <th>TotalCharges_2157.95</th>\n",
+              "      <th>TotalCharges_216.2</th>\n",
+              "      <th>TotalCharges_216.45</th>\n",
+              "      <th>TotalCharges_216.75</th>\n",
+              "      <th>TotalCharges_216.9</th>\n",
+              "      <th>TotalCharges_2162.6</th>\n",
+              "      <th>TotalCharges_2165.05</th>\n",
+              "      <th>TotalCharges_2168.15</th>\n",
+              "      <th>TotalCharges_2168.9</th>\n",
+              "      <th>TotalCharges_2169.4</th>\n",
+              "      <th>TotalCharges_2169.75</th>\n",
+              "      <th>TotalCharges_2169.8</th>\n",
+              "      <th>TotalCharges_217.1</th>\n",
+              "      <th>TotalCharges_217.45</th>\n",
+              "      <th>TotalCharges_217.5</th>\n",
+              "      <th>TotalCharges_217.55</th>\n",
+              "      <th>TotalCharges_2171.15</th>\n",
+              "      <th>TotalCharges_2172.05</th>\n",
+              "      <th>TotalCharges_2177.45</th>\n",
+              "      <th>TotalCharges_2178.6</th>\n",
+              "      <th>TotalCharges_218.5</th>\n",
+              "      <th>TotalCharges_218.55</th>\n",
+              "      <th>TotalCharges_2180.55</th>\n",
+              "      <th>TotalCharges_2181.55</th>\n",
+              "      <th>TotalCharges_2181.75</th>\n",
+              "      <th>TotalCharges_2184.35</th>\n",
+              "      <th>TotalCharges_2184.6</th>\n",
+              "      <th>TotalCharges_2184.85</th>\n",
+              "      <th>TotalCharges_2186.4</th>\n",
+              "      <th>TotalCharges_2187.15</th>\n",
+              "      <th>TotalCharges_2187.55</th>\n",
+              "      <th>TotalCharges_2188.45</th>\n",
+              "      <th>TotalCharges_2188.5</th>\n",
+              "      <th>TotalCharges_219</th>\n",
+              "      <th>TotalCharges_219.35</th>\n",
+              "      <th>TotalCharges_219.5</th>\n",
+              "      <th>TotalCharges_219.65</th>\n",
+              "      <th>TotalCharges_2191.15</th>\n",
+              "      <th>TotalCharges_2191.7</th>\n",
+              "      <th>TotalCharges_2192.9</th>\n",
+              "      <th>TotalCharges_2193</th>\n",
+              "      <th>TotalCharges_2193.2</th>\n",
+              "      <th>TotalCharges_2193.65</th>\n",
+              "      <th>TotalCharges_2196.15</th>\n",
+              "      <th>TotalCharges_2196.3</th>\n",
+              "      <th>TotalCharges_2196.45</th>\n",
+              "      <th>TotalCharges_2198.3</th>\n",
+              "      <th>TotalCharges_2198.9</th>\n",
+              "      <th>TotalCharges_2199.05</th>\n",
+              "      <th>TotalCharges_220.1</th>\n",
+              "      <th>TotalCharges_220.35</th>\n",
+              "      <th>TotalCharges_220.4</th>\n",
+              "      <th>TotalCharges_220.45</th>\n",
+              "      <th>TotalCharges_220.6</th>\n",
+              "      <th>TotalCharges_220.65</th>\n",
+              "      <th>TotalCharges_220.75</th>\n",
+              "      <th>TotalCharges_220.8</th>\n",
+              "      <th>TotalCharges_220.95</th>\n",
+              "      <th>TotalCharges_2200.25</th>\n",
+              "      <th>TotalCharges_2200.7</th>\n",
+              "      <th>TotalCharges_2201.75</th>\n",
+              "      <th>TotalCharges_2203.1</th>\n",
+              "      <th>TotalCharges_2203.65</th>\n",
+              "      <th>TotalCharges_2203.7</th>\n",
+              "      <th>TotalCharges_2204.35</th>\n",
+              "      <th>TotalCharges_2208.05</th>\n",
+              "      <th>TotalCharges_2208.75</th>\n",
+              "      <th>TotalCharges_2209.15</th>\n",
+              "      <th>TotalCharges_2209.75</th>\n",
+              "      <th>TotalCharges_221.1</th>\n",
+              "      <th>TotalCharges_221.35</th>\n",
+              "      <th>TotalCharges_221.7</th>\n",
+              "      <th>TotalCharges_221.9</th>\n",
+              "      <th>TotalCharges_2210.2</th>\n",
+              "      <th>TotalCharges_2211.8</th>\n",
+              "      <th>TotalCharges_2212.55</th>\n",
+              "      <th>TotalCharges_2215</th>\n",
+              "      <th>TotalCharges_2215.25</th>\n",
+              "      <th>TotalCharges_2215.4</th>\n",
+              "      <th>TotalCharges_2215.45</th>\n",
+              "      <th>TotalCharges_2217.15</th>\n",
+              "      <th>TotalCharges_222.3</th>\n",
+              "      <th>TotalCharges_222.65</th>\n",
+              "      <th>TotalCharges_2220.1</th>\n",
+              "      <th>TotalCharges_2221.55</th>\n",
+              "      <th>TotalCharges_2224.5</th>\n",
+              "      <th>TotalCharges_2227.1</th>\n",
+              "      <th>TotalCharges_2227.8</th>\n",
+              "      <th>TotalCharges_223.15</th>\n",
+              "      <th>TotalCharges_223.45</th>\n",
+              "      <th>TotalCharges_223.6</th>\n",
+              "      <th>TotalCharges_223.75</th>\n",
+              "      <th>TotalCharges_223.9</th>\n",
+              "      <th>TotalCharges_2230.85</th>\n",
+              "      <th>TotalCharges_2231.05</th>\n",
+              "      <th>TotalCharges_2234.55</th>\n",
+              "      <th>TotalCharges_2234.95</th>\n",
+              "      <th>TotalCharges_2236.2</th>\n",
+              "      <th>TotalCharges_2237.55</th>\n",
+              "      <th>TotalCharges_2238.5</th>\n",
+              "      <th>TotalCharges_2239.4</th>\n",
+              "      <th>TotalCharges_2239.65</th>\n",
+              "      <th>TotalCharges_224.05</th>\n",
+              "      <th>TotalCharges_224.5</th>\n",
+              "      <th>TotalCharges_224.85</th>\n",
+              "      <th>TotalCharges_2243.9</th>\n",
+              "      <th>TotalCharges_2244.95</th>\n",
+              "      <th>TotalCharges_2245.4</th>\n",
+              "      <th>TotalCharges_2248.05</th>\n",
+              "      <th>TotalCharges_2249.1</th>\n",
+              "      <th>TotalCharges_2249.95</th>\n",
+              "      <th>TotalCharges_225.55</th>\n",
+              "      <th>TotalCharges_225.6</th>\n",
+              "      <th>TotalCharges_225.65</th>\n",
+              "      <th>TotalCharges_225.75</th>\n",
+              "      <th>TotalCharges_225.85</th>\n",
+              "      <th>TotalCharges_2250.65</th>\n",
+              "      <th>TotalCharges_2254.2</th>\n",
+              "      <th>TotalCharges_2257.75</th>\n",
+              "      <th>TotalCharges_2258.25</th>\n",
+              "      <th>TotalCharges_2259.35</th>\n",
+              "      <th>TotalCharges_226.2</th>\n",
+              "      <th>TotalCharges_226.45</th>\n",
+              "      <th>TotalCharges_226.55</th>\n",
+              "      <th>TotalCharges_226.8</th>\n",
+              "      <th>TotalCharges_226.95</th>\n",
+              "      <th>TotalCharges_2263.4</th>\n",
+              "      <th>TotalCharges_2263.45</th>\n",
+              "      <th>TotalCharges_2264.05</th>\n",
+              "      <th>TotalCharges_2264.5</th>\n",
+              "      <th>TotalCharges_2265</th>\n",
+              "      <th>TotalCharges_2265.25</th>\n",
+              "      <th>TotalCharges_227.35</th>\n",
+              "      <th>TotalCharges_227.45</th>\n",
+              "      <th>TotalCharges_2271.85</th>\n",
+              "      <th>TotalCharges_2272.8</th>\n",
+              "      <th>TotalCharges_2274.1</th>\n",
+              "      <th>TotalCharges_2274.35</th>\n",
+              "      <th>TotalCharges_2274.9</th>\n",
+              "      <th>TotalCharges_2275.1</th>\n",
+              "      <th>TotalCharges_2276.1</th>\n",
+              "      <th>TotalCharges_2276.95</th>\n",
+              "      <th>TotalCharges_2277.65</th>\n",
+              "      <th>TotalCharges_2278.75</th>\n",
+              "      <th>TotalCharges_228</th>\n",
+              "      <th>TotalCharges_228.4</th>\n",
+              "      <th>TotalCharges_228.65</th>\n",
+              "      <th>TotalCharges_228.75</th>\n",
+              "      <th>TotalCharges_2281.6</th>\n",
+              "      <th>TotalCharges_2282.55</th>\n",
+              "      <th>TotalCharges_2282.95</th>\n",
+              "      <th>TotalCharges_2283.15</th>\n",
+              "      <th>TotalCharges_2283.3</th>\n",
+              "      <th>TotalCharges_2287.25</th>\n",
+              "      <th>TotalCharges_2288.7</th>\n",
+              "      <th>TotalCharges_2289.9</th>\n",
+              "      <th>TotalCharges_229.4</th>\n",
+              "      <th>TotalCharges_229.5</th>\n",
+              "      <th>TotalCharges_229.55</th>\n",
+              "      <th>TotalCharges_229.6</th>\n",
+              "      <th>TotalCharges_229.7</th>\n",
+              "      <th>TotalCharges_2291.2</th>\n",
+              "      <th>TotalCharges_2292.75</th>\n",
+              "      <th>TotalCharges_2293.6</th>\n",
+              "      <th>TotalCharges_2296.25</th>\n",
+              "      <th>TotalCharges_2298.55</th>\n",
+              "      <th>TotalCharges_2298.9</th>\n",
+              "      <th>TotalCharges_23.45</th>\n",
+              "      <th>TotalCharges_2301.15</th>\n",
+              "      <th>TotalCharges_2302.35</th>\n",
+              "      <th>TotalCharges_2303.35</th>\n",
+              "      <th>TotalCharges_2308.6</th>\n",
+              "      <th>TotalCharges_2309.55</th>\n",
+              "      <th>TotalCharges_231.45</th>\n",
+              "      <th>TotalCharges_231.8</th>\n",
+              "      <th>TotalCharges_2310.2</th>\n",
+              "      <th>TotalCharges_2312.55</th>\n",
+              "      <th>TotalCharges_2313.8</th>\n",
+              "      <th>TotalCharges_2316.85</th>\n",
+              "      <th>TotalCharges_2317.1</th>\n",
+              "      <th>TotalCharges_2319.8</th>\n",
+              "      <th>TotalCharges_232.1</th>\n",
+              "      <th>TotalCharges_232.35</th>\n",
+              "      <th>TotalCharges_232.4</th>\n",
+              "      <th>TotalCharges_232.5</th>\n",
+              "      <th>TotalCharges_232.55</th>\n",
+              "      <th>TotalCharges_2320.8</th>\n",
+              "      <th>TotalCharges_2322.85</th>\n",
+              "      <th>TotalCharges_2324.7</th>\n",
+              "      <th>TotalCharges_2326.05</th>\n",
+              "      <th>TotalCharges_233.55</th>\n",
+              "      <th>TotalCharges_233.65</th>\n",
+              "      <th>TotalCharges_233.7</th>\n",
+              "      <th>TotalCharges_233.9</th>\n",
+              "      <th>TotalCharges_2331.3</th>\n",
+              "      <th>TotalCharges_2333.05</th>\n",
+              "      <th>TotalCharges_2333.85</th>\n",
+              "      <th>TotalCharges_2335.3</th>\n",
+              "      <th>TotalCharges_2337.45</th>\n",
+              "      <th>TotalCharges_2338.35</th>\n",
+              "      <th>TotalCharges_2339.3</th>\n",
+              "      <th>TotalCharges_234.85</th>\n",
+              "      <th>TotalCharges_2341.5</th>\n",
+              "      <th>TotalCharges_2341.55</th>\n",
+              "      <th>TotalCharges_2342.2</th>\n",
+              "      <th>TotalCharges_2343.85</th>\n",
+              "      <th>TotalCharges_2344.5</th>\n",
+              "      <th>TotalCharges_2345.2</th>\n",
+              "      <th>TotalCharges_2345.55</th>\n",
+              "      <th>TotalCharges_2347.85</th>\n",
+              "      <th>TotalCharges_2347.9</th>\n",
+              "      <th>TotalCharges_2348.45</th>\n",
+              "      <th>TotalCharges_2349.8</th>\n",
+              "      <th>TotalCharges_235</th>\n",
+              "      <th>TotalCharges_235.05</th>\n",
+              "      <th>TotalCharges_235.1</th>\n",
+              "      <th>TotalCharges_235.2</th>\n",
+              "      <th>TotalCharges_235.5</th>\n",
+              "      <th>TotalCharges_235.65</th>\n",
+              "      <th>TotalCharges_235.8</th>\n",
+              "      <th>TotalCharges_2351.45</th>\n",
+              "      <th>TotalCharges_2351.8</th>\n",
+              "      <th>TotalCharges_2354.8</th>\n",
+              "      <th>TotalCharges_2356.75</th>\n",
+              "      <th>TotalCharges_2357.75</th>\n",
+              "      <th>TotalCharges_2361.8</th>\n",
+              "      <th>TotalCharges_2362.1</th>\n",
+              "      <th>TotalCharges_2364</th>\n",
+              "      <th>TotalCharges_2365.15</th>\n",
+              "      <th>TotalCharges_2368.4</th>\n",
+              "      <th>TotalCharges_2369.05</th>\n",
+              "      <th>TotalCharges_2369.3</th>\n",
+              "      <th>TotalCharges_2369.7</th>\n",
+              "      <th>TotalCharges_237.2</th>\n",
+              "      <th>TotalCharges_237.25</th>\n",
+              "      <th>TotalCharges_237.3</th>\n",
+              "      <th>TotalCharges_237.65</th>\n",
+              "      <th>TotalCharges_237.7</th>\n",
+              "      <th>TotalCharges_237.75</th>\n",
+              "      <th>TotalCharges_237.95</th>\n",
+              "      <th>TotalCharges_2375.2</th>\n",
+              "      <th>TotalCharges_2375.4</th>\n",
+              "      <th>TotalCharges_2379.1</th>\n",
+              "      <th>TotalCharges_238.1</th>\n",
+              "      <th>TotalCharges_238.15</th>\n",
+              "      <th>TotalCharges_238.5</th>\n",
+              "      <th>TotalCharges_2381.55</th>\n",
+              "      <th>TotalCharges_2383.6</th>\n",
+              "      <th>TotalCharges_2384.15</th>\n",
+              "      <th>TotalCharges_2386.85</th>\n",
+              "      <th>TotalCharges_2387.75</th>\n",
+              "      <th>TotalCharges_239</th>\n",
+              "      <th>TotalCharges_239.05</th>\n",
+              "      <th>TotalCharges_239.45</th>\n",
+              "      <th>TotalCharges_239.55</th>\n",
+              "      <th>TotalCharges_239.75</th>\n",
+              "      <th>TotalCharges_2390.45</th>\n",
+              "      <th>TotalCharges_2391.15</th>\n",
+              "      <th>TotalCharges_2391.8</th>\n",
+              "      <th>TotalCharges_2395.05</th>\n",
+              "      <th>TotalCharges_2395.7</th>\n",
+              "      <th>TotalCharges_2398.4</th>\n",
+              "      <th>TotalCharges_24</th>\n",
+              "      <th>TotalCharges_24.05</th>\n",
+              "      <th>TotalCharges_24.2</th>\n",
+              "      <th>TotalCharges_24.25</th>\n",
+              "      <th>TotalCharges_24.4</th>\n",
+              "      <th>TotalCharges_24.45</th>\n",
+              "      <th>TotalCharges_24.6</th>\n",
+              "      <th>TotalCharges_24.7</th>\n",
+              "      <th>TotalCharges_24.75</th>\n",
+              "      <th>TotalCharges_24.8</th>\n",
+              "      <th>TotalCharges_24.9</th>\n",
+              "      <th>TotalCharges_240.45</th>\n",
+              "      <th>TotalCharges_2401.05</th>\n",
+              "      <th>TotalCharges_2404.1</th>\n",
+              "      <th>TotalCharges_2404.15</th>\n",
+              "      <th>TotalCharges_2404.85</th>\n",
+              "      <th>TotalCharges_2405.05</th>\n",
+              "      <th>TotalCharges_2406.1</th>\n",
+              "      <th>TotalCharges_2407.3</th>\n",
+              "      <th>TotalCharges_2409.9</th>\n",
+              "      <th>TotalCharges_241.3</th>\n",
+              "      <th>TotalCharges_2413.05</th>\n",
+              "      <th>TotalCharges_2414.55</th>\n",
+              "      <th>TotalCharges_2415.95</th>\n",
+              "      <th>TotalCharges_2416.1</th>\n",
+              "      <th>TotalCharges_2416.55</th>\n",
+              "      <th>TotalCharges_2419</th>\n",
+              "      <th>TotalCharges_2419.55</th>\n",
+              "      <th>TotalCharges_242</th>\n",
+              "      <th>TotalCharges_242.05</th>\n",
+              "      <th>TotalCharges_242.4</th>\n",
+              "      <th>TotalCharges_242.8</th>\n",
+              "      <th>TotalCharges_242.95</th>\n",
+              "      <th>TotalCharges_2421.6</th>\n",
+              "      <th>TotalCharges_2421.75</th>\n",
+              "      <th>TotalCharges_2423.4</th>\n",
+              "      <th>TotalCharges_2424.05</th>\n",
+              "      <th>TotalCharges_2424.45</th>\n",
+              "      <th>TotalCharges_2424.5</th>\n",
+              "      <th>TotalCharges_2425.4</th>\n",
+              "      <th>TotalCharges_2427.1</th>\n",
+              "      <th>TotalCharges_2427.35</th>\n",
+              "      <th>TotalCharges_2429.1</th>\n",
+              "      <th>TotalCharges_243.65</th>\n",
+              "      <th>TotalCharges_2431.35</th>\n",
+              "      <th>TotalCharges_2431.95</th>\n",
+              "      <th>TotalCharges_2433.5</th>\n",
+              "      <th>TotalCharges_2433.9</th>\n",
+              "      <th>TotalCharges_2434.45</th>\n",
+              "      <th>TotalCharges_2435.15</th>\n",
+              "      <th>TotalCharges_2438.6</th>\n",
+              "      <th>TotalCharges_244.1</th>\n",
+              "      <th>TotalCharges_244.45</th>\n",
+              "      <th>TotalCharges_244.65</th>\n",
+              "      <th>TotalCharges_244.75</th>\n",
+              "      <th>TotalCharges_244.8</th>\n",
+              "      <th>TotalCharges_244.85</th>\n",
+              "      <th>TotalCharges_2440.15</th>\n",
+              "      <th>TotalCharges_2440.25</th>\n",
+              "      <th>TotalCharges_2441.7</th>\n",
+              "      <th>TotalCharges_2443.3</th>\n",
+              "      <th>TotalCharges_2444.25</th>\n",
+              "      <th>TotalCharges_2447.45</th>\n",
+              "      <th>TotalCharges_2447.95</th>\n",
+              "      <th>TotalCharges_2448.5</th>\n",
+              "      <th>TotalCharges_2448.75</th>\n",
+              "      <th>TotalCharges_245.15</th>\n",
+              "      <th>TotalCharges_245.2</th>\n",
+              "      <th>TotalCharges_2452.7</th>\n",
+              "      <th>TotalCharges_2453.3</th>\n",
+              "      <th>TotalCharges_2455.05</th>\n",
+              "      <th>TotalCharges_2459.8</th>\n",
+              "      <th>TotalCharges_246.25</th>\n",
+              "      <th>TotalCharges_246.3</th>\n",
+              "      <th>TotalCharges_246.5</th>\n",
+              "      <th>TotalCharges_246.6</th>\n",
+              "      <th>TotalCharges_246.7</th>\n",
+              "      <th>TotalCharges_2460.15</th>\n",
+              "      <th>TotalCharges_2460.35</th>\n",
+              "      <th>TotalCharges_2460.55</th>\n",
+              "      <th>TotalCharges_2462.55</th>\n",
+              "      <th>TotalCharges_2462.6</th>\n",
+              "      <th>TotalCharges_2467.1</th>\n",
+              "      <th>TotalCharges_2467.75</th>\n",
+              "      <th>TotalCharges_247</th>\n",
+              "      <th>TotalCharges_247.25</th>\n",
+              "      <th>TotalCharges_2470.1</th>\n",
+              "      <th>TotalCharges_2471.25</th>\n",
+              "      <th>TotalCharges_2471.6</th>\n",
+              "      <th>TotalCharges_2473.95</th>\n",
+              "      <th>TotalCharges_2475.35</th>\n",
+              "      <th>TotalCharges_2479.05</th>\n",
+              "      <th>TotalCharges_2479.25</th>\n",
+              "      <th>TotalCharges_248.4</th>\n",
+              "      <th>TotalCharges_248.95</th>\n",
+              "      <th>TotalCharges_2483.05</th>\n",
+              "      <th>TotalCharges_2483.5</th>\n",
+              "      <th>TotalCharges_2483.65</th>\n",
+              "      <th>TotalCharges_2484</th>\n",
+              "      <th>TotalCharges_249.4</th>\n",
+              "      <th>TotalCharges_249.55</th>\n",
+              "      <th>TotalCharges_249.95</th>\n",
+              "      <th>TotalCharges_2490.15</th>\n",
+              "      <th>TotalCharges_2492.25</th>\n",
+              "      <th>TotalCharges_2494.65</th>\n",
+              "      <th>TotalCharges_2495.15</th>\n",
+              "      <th>TotalCharges_2495.2</th>\n",
+              "      <th>TotalCharges_2496.7</th>\n",
+              "      <th>TotalCharges_2497.2</th>\n",
+              "      <th>TotalCharges_2497.35</th>\n",
+              "      <th>TotalCharges_2498.4</th>\n",
+              "      <th>TotalCharges_2499.3</th>\n",
+              "      <th>TotalCharges_25</th>\n",
+              "      <th>TotalCharges_25.05</th>\n",
+              "      <th>TotalCharges_25.1</th>\n",
+              "      <th>TotalCharges_25.15</th>\n",
+              "      <th>TotalCharges_25.2</th>\n",
+              "      <th>TotalCharges_25.25</th>\n",
+              "      <th>TotalCharges_25.3</th>\n",
+              "      <th>TotalCharges_25.35</th>\n",
+              "      <th>TotalCharges_25.4</th>\n",
+              "      <th>TotalCharges_25.7</th>\n",
+              "      <th>TotalCharges_25.75</th>\n",
+              "      <th>TotalCharges_25.8</th>\n",
+              "      <th>TotalCharges_25.85</th>\n",
+              "      <th>TotalCharges_250.05</th>\n",
+              "      <th>TotalCharges_250.1</th>\n",
+              "      <th>TotalCharges_250.8</th>\n",
+              "      <th>TotalCharges_2509.25</th>\n",
+              "      <th>TotalCharges_2509.95</th>\n",
+              "      <th>TotalCharges_251.25</th>\n",
+              "      <th>TotalCharges_251.6</th>\n",
+              "      <th>TotalCharges_251.65</th>\n",
+              "      <th>TotalCharges_251.75</th>\n",
+              "      <th>TotalCharges_2510.2</th>\n",
+              "      <th>TotalCharges_2510.7</th>\n",
+              "      <th>TotalCharges_2511.3</th>\n",
+              "      <th>TotalCharges_2511.55</th>\n",
+              "      <th>TotalCharges_2511.95</th>\n",
+              "      <th>TotalCharges_2513.5</th>\n",
+              "      <th>TotalCharges_2514.5</th>\n",
+              "      <th>TotalCharges_2515.3</th>\n",
+              "      <th>TotalCharges_2516.2</th>\n",
+              "      <th>TotalCharges_252</th>\n",
+              "      <th>TotalCharges_252.75</th>\n",
+              "      <th>TotalCharges_2522.4</th>\n",
+              "      <th>TotalCharges_2524.45</th>\n",
+              "      <th>TotalCharges_253</th>\n",
+              "      <th>TotalCharges_253.8</th>\n",
+              "      <th>TotalCharges_253.9</th>\n",
+              "      <th>TotalCharges_2530.4</th>\n",
+              "      <th>TotalCharges_2531.4</th>\n",
+              "      <th>TotalCharges_2531.8</th>\n",
+              "      <th>TotalCharges_2535.55</th>\n",
+              "      <th>TotalCharges_2536.55</th>\n",
+              "      <th>TotalCharges_2537</th>\n",
+              "      <th>TotalCharges_2538.05</th>\n",
+              "      <th>TotalCharges_2538.2</th>\n",
+              "      <th>TotalCharges_254.5</th>\n",
+              "      <th>TotalCharges_2540.1</th>\n",
+              "      <th>TotalCharges_2541.25</th>\n",
+              "      <th>TotalCharges_2542.45</th>\n",
+              "      <th>TotalCharges_2545.7</th>\n",
+              "      <th>TotalCharges_2545.75</th>\n",
+              "      <th>TotalCharges_2546.85</th>\n",
+              "      <th>TotalCharges_2548.55</th>\n",
+              "      <th>TotalCharges_2548.65</th>\n",
+              "      <th>TotalCharges_2549.1</th>\n",
+              "      <th>TotalCharges_255.25</th>\n",
+              "      <th>TotalCharges_255.35</th>\n",
+              "      <th>TotalCharges_255.5</th>\n",
+              "      <th>TotalCharges_255.55</th>\n",
+              "      <th>TotalCharges_255.6</th>\n",
+              "      <th>TotalCharges_2550.9</th>\n",
+              "      <th>TotalCharges_2552.9</th>\n",
+              "      <th>TotalCharges_2553.35</th>\n",
+              "      <th>TotalCharges_2553.7</th>\n",
+              "      <th>TotalCharges_2554</th>\n",
+              "      <th>TotalCharges_2555.05</th>\n",
+              "      <th>TotalCharges_2555.9</th>\n",
+              "      <th>TotalCharges_256.25</th>\n",
+              "      <th>TotalCharges_256.6</th>\n",
+              "      <th>TotalCharges_256.75</th>\n",
+              "      <th>TotalCharges_2560.1</th>\n",
+              "      <th>TotalCharges_2564.3</th>\n",
+              "      <th>TotalCharges_2564.95</th>\n",
+              "      <th>TotalCharges_2566.3</th>\n",
+              "      <th>TotalCharges_2566.5</th>\n",
+              "      <th>TotalCharges_2568.15</th>\n",
+              "      <th>TotalCharges_2568.55</th>\n",
+              "      <th>TotalCharges_257</th>\n",
+              "      <th>TotalCharges_257.05</th>\n",
+              "      <th>TotalCharges_257.6</th>\n",
+              "      <th>TotalCharges_2570</th>\n",
+              "      <th>TotalCharges_2570.2</th>\n",
+              "      <th>TotalCharges_2572.95</th>\n",
+              "      <th>TotalCharges_2575.45</th>\n",
+              "      <th>TotalCharges_2576.2</th>\n",
+              "      <th>TotalCharges_2576.8</th>\n",
+              "      <th>TotalCharges_258.35</th>\n",
+              "      <th>TotalCharges_2583.75</th>\n",
+              "      <th>TotalCharges_2585.95</th>\n",
+              "      <th>TotalCharges_2586</th>\n",
+              "      <th>TotalCharges_2587.7</th>\n",
+              "      <th>TotalCharges_2588.95</th>\n",
+              "      <th>TotalCharges_259.4</th>\n",
+              "      <th>TotalCharges_259.65</th>\n",
+              "      <th>TotalCharges_259.8</th>\n",
+              "      <th>TotalCharges_2595.25</th>\n",
+              "      <th>TotalCharges_2595.85</th>\n",
+              "      <th>TotalCharges_2596.15</th>\n",
+              "      <th>TotalCharges_2597.6</th>\n",
+              "      <th>TotalCharges_2598.95</th>\n",
+              "      <th>TotalCharges_2599.95</th>\n",
+              "      <th>TotalCharges_260.7</th>\n",
+              "      <th>TotalCharges_260.8</th>\n",
+              "      <th>TotalCharges_260.9</th>\n",
+              "      <th>TotalCharges_2602.9</th>\n",
+              "      <th>TotalCharges_2603.1</th>\n",
+              "      <th>TotalCharges_2603.3</th>\n",
+              "      <th>TotalCharges_2603.95</th>\n",
+              "      <th>TotalCharges_2606.35</th>\n",
+              "      <th>TotalCharges_2607.6</th>\n",
+              "      <th>TotalCharges_261.25</th>\n",
+              "      <th>TotalCharges_261.3</th>\n",
+              "      <th>TotalCharges_261.65</th>\n",
+              "      <th>TotalCharges_2610.65</th>\n",
+              "      <th>TotalCharges_2613.4</th>\n",
+              "      <th>TotalCharges_2614.1</th>\n",
+              "      <th>TotalCharges_2618.3</th>\n",
+              "      <th>TotalCharges_2619.15</th>\n",
+              "      <th>TotalCharges_2619.25</th>\n",
+              "      <th>TotalCharges_262.05</th>\n",
+              "      <th>TotalCharges_262.3</th>\n",
+              "      <th>TotalCharges_2621.75</th>\n",
+              "      <th>TotalCharges_2623.65</th>\n",
+              "      <th>TotalCharges_2624.25</th>\n",
+              "      <th>TotalCharges_2625.25</th>\n",
+              "      <th>TotalCharges_2625.55</th>\n",
+              "      <th>TotalCharges_2626.15</th>\n",
+              "      <th>TotalCharges_2627.2</th>\n",
+              "      <th>TotalCharges_2627.35</th>\n",
+              "      <th>TotalCharges_2628.6</th>\n",
+              "      <th>TotalCharges_263.05</th>\n",
+              "      <th>TotalCharges_263.65</th>\n",
+              "      <th>TotalCharges_2633.3</th>\n",
+              "      <th>TotalCharges_2633.4</th>\n",
+              "      <th>TotalCharges_2633.95</th>\n",
+              "      <th>TotalCharges_2635</th>\n",
+              "      <th>TotalCharges_2636.05</th>\n",
+              "      <th>TotalCharges_2638.1</th>\n",
+              "      <th>TotalCharges_264.55</th>\n",
+              "      <th>TotalCharges_264.8</th>\n",
+              "      <th>TotalCharges_264.85</th>\n",
+              "      <th>TotalCharges_2640.55</th>\n",
+              "      <th>TotalCharges_2642.05</th>\n",
+              "      <th>TotalCharges_2647.1</th>\n",
+              "      <th>TotalCharges_2647.2</th>\n",
+              "      <th>TotalCharges_2649.15</th>\n",
+              "      <th>TotalCharges_265.3</th>\n",
+              "      <th>TotalCharges_265.35</th>\n",
+              "      <th>TotalCharges_265.45</th>\n",
+              "      <th>TotalCharges_265.75</th>\n",
+              "      <th>TotalCharges_265.8</th>\n",
+              "      <th>TotalCharges_2651.1</th>\n",
+              "      <th>TotalCharges_2651.2</th>\n",
+              "      <th>TotalCharges_2653.65</th>\n",
+              "      <th>TotalCharges_2654.05</th>\n",
+              "      <th>TotalCharges_2655.25</th>\n",
+              "      <th>TotalCharges_2656.3</th>\n",
+              "      <th>TotalCharges_2656.5</th>\n",
+              "      <th>TotalCharges_2656.7</th>\n",
+              "      <th>TotalCharges_2657.55</th>\n",
+              "      <th>TotalCharges_2658.4</th>\n",
+              "      <th>TotalCharges_2658.8</th>\n",
+              "      <th>TotalCharges_2659.4</th>\n",
+              "      <th>TotalCharges_2659.45</th>\n",
+              "      <th>TotalCharges_266.6</th>\n",
+              "      <th>TotalCharges_266.8</th>\n",
+              "      <th>TotalCharges_266.9</th>\n",
+              "      <th>TotalCharges_266.95</th>\n",
+              "      <th>TotalCharges_2660.2</th>\n",
+              "      <th>TotalCharges_2661.1</th>\n",
+              "      <th>TotalCharges_2664.3</th>\n",
+              "      <th>TotalCharges_2665</th>\n",
+              "      <th>TotalCharges_2666.75</th>\n",
+              "      <th>TotalCharges_2669.45</th>\n",
+              "      <th>TotalCharges_267</th>\n",
+              "      <th>TotalCharges_267.35</th>\n",
+              "      <th>TotalCharges_267.4</th>\n",
+              "      <th>TotalCharges_267.6</th>\n",
+              "      <th>TotalCharges_2673.45</th>\n",
+              "      <th>TotalCharges_2674.15</th>\n",
+              "      <th>TotalCharges_2679.7</th>\n",
+              "      <th>TotalCharges_268.35</th>\n",
+              "      <th>TotalCharges_268.4</th>\n",
+              "      <th>TotalCharges_268.45</th>\n",
+              "      <th>TotalCharges_2680.15</th>\n",
+              "      <th>TotalCharges_2681.15</th>\n",
+              "      <th>TotalCharges_2683.2</th>\n",
+              "      <th>TotalCharges_2684.35</th>\n",
+              "      <th>TotalCharges_2684.85</th>\n",
+              "      <th>TotalCharges_2686.05</th>\n",
+              "      <th>TotalCharges_2688.45</th>\n",
+              "      <th>TotalCharges_2688.85</th>\n",
+              "      <th>TotalCharges_2689.35</th>\n",
+              "      <th>TotalCharges_269.65</th>\n",
+              "      <th>TotalCharges_2692.75</th>\n",
+              "      <th>TotalCharges_2696.55</th>\n",
+              "      <th>TotalCharges_2697.4</th>\n",
+              "      <th>TotalCharges_2698.35</th>\n",
+              "      <th>TotalCharges_27.55</th>\n",
+              "      <th>TotalCharges_270.15</th>\n",
+              "      <th>TotalCharges_270.2</th>\n",
+              "      <th>TotalCharges_270.6</th>\n",
+              "      <th>TotalCharges_270.7</th>\n",
+              "      <th>TotalCharges_270.8</th>\n",
+              "      <th>TotalCharges_270.95</th>\n",
+              "      <th>TotalCharges_2708.2</th>\n",
+              "      <th>TotalCharges_2710.25</th>\n",
+              "      <th>TotalCharges_2715.3</th>\n",
+              "      <th>TotalCharges_2716.3</th>\n",
+              "      <th>TotalCharges_2718.3</th>\n",
+              "      <th>TotalCharges_2719.2</th>\n",
+              "      <th>TotalCharges_272</th>\n",
+              "      <th>TotalCharges_272.15</th>\n",
+              "      <th>TotalCharges_272.2</th>\n",
+              "      <th>TotalCharges_272.35</th>\n",
+              "      <th>TotalCharges_272.95</th>\n",
+              "      <th>TotalCharges_2722.2</th>\n",
+              "      <th>TotalCharges_2723.15</th>\n",
+              "      <th>TotalCharges_2723.4</th>\n",
+              "      <th>TotalCharges_2723.75</th>\n",
+              "      <th>TotalCharges_2724.25</th>\n",
+              "      <th>TotalCharges_2724.6</th>\n",
+              "      <th>TotalCharges_2727.3</th>\n",
+              "      <th>TotalCharges_2727.8</th>\n",
+              "      <th>TotalCharges_2728.6</th>\n",
+              "      <th>TotalCharges_273</th>\n",
+              "      <th>TotalCharges_273.2</th>\n",
+              "      <th>TotalCharges_273.25</th>\n",
+              "      <th>TotalCharges_273.4</th>\n",
+              "      <th>TotalCharges_273.75</th>\n",
+              "      <th>TotalCharges_2730.85</th>\n",
+              "      <th>TotalCharges_2731</th>\n",
+              "      <th>TotalCharges_2737.05</th>\n",
+              "      <th>TotalCharges_274.35</th>\n",
+              "      <th>TotalCharges_274.7</th>\n",
+              "      <th>TotalCharges_2743.45</th>\n",
+              "      <th>TotalCharges_2745.2</th>\n",
+              "      <th>TotalCharges_2745.7</th>\n",
+              "      <th>TotalCharges_2747.2</th>\n",
+              "      <th>TotalCharges_2748.7</th>\n",
+              "      <th>TotalCharges_275.4</th>\n",
+              "      <th>TotalCharges_275.7</th>\n",
+              "      <th>TotalCharges_275.9</th>\n",
+              "      <th>TotalCharges_2751</th>\n",
+              "      <th>TotalCharges_2753.8</th>\n",
+              "      <th>TotalCharges_2754</th>\n",
+              "      <th>TotalCharges_2754.45</th>\n",
+              "      <th>TotalCharges_2755.35</th>\n",
+              "      <th>TotalCharges_2757.85</th>\n",
+              "      <th>TotalCharges_2758.15</th>\n",
+              "      <th>TotalCharges_276.5</th>\n",
+              "      <th>TotalCharges_2762.75</th>\n",
+              "      <th>TotalCharges_2763</th>\n",
+              "      <th>TotalCharges_2763.35</th>\n",
+              "      <th>TotalCharges_2766.4</th>\n",
+              "      <th>TotalCharges_2768.35</th>\n",
+              "      <th>TotalCharges_2768.65</th>\n",
+              "      <th>TotalCharges_2773.9</th>\n",
+              "      <th>TotalCharges_2774.55</th>\n",
+              "      <th>TotalCharges_2779.5</th>\n",
+              "      <th>TotalCharges_278.4</th>\n",
+              "      <th>TotalCharges_278.85</th>\n",
+              "      <th>TotalCharges_2780.6</th>\n",
+              "      <th>TotalCharges_2781.85</th>\n",
+              "      <th>TotalCharges_2782.4</th>\n",
+              "      <th>TotalCharges_2789.7</th>\n",
+              "      <th>TotalCharges_279.2</th>\n",
+              "      <th>TotalCharges_279.25</th>\n",
+              "      <th>TotalCharges_279.3</th>\n",
+              "      <th>TotalCharges_279.5</th>\n",
+              "      <th>TotalCharges_279.55</th>\n",
+              "      <th>TotalCharges_2790.65</th>\n",
+              "      <th>TotalCharges_2791.5</th>\n",
+              "      <th>TotalCharges_2793.55</th>\n",
+              "      <th>TotalCharges_2796.35</th>\n",
+              "      <th>TotalCharges_2796.45</th>\n",
+              "      <th>TotalCharges_2799</th>\n",
+              "      <th>TotalCharges_2799.75</th>\n",
+              "      <th>TotalCharges_28.3</th>\n",
+              "      <th>TotalCharges_280</th>\n",
+              "      <th>TotalCharges_280.35</th>\n",
+              "      <th>TotalCharges_280.4</th>\n",
+              "      <th>TotalCharges_280.85</th>\n",
+              "      <th>TotalCharges_2802.3</th>\n",
+              "      <th>TotalCharges_2804.45</th>\n",
+              "      <th>TotalCharges_2806.9</th>\n",
+              "      <th>TotalCharges_2807.1</th>\n",
+              "      <th>TotalCharges_2807.65</th>\n",
+              "      <th>TotalCharges_2809.05</th>\n",
+              "      <th>TotalCharges_281</th>\n",
+              "      <th>TotalCharges_2812.2</th>\n",
+              "      <th>TotalCharges_2813.05</th>\n",
+              "      <th>TotalCharges_2815.25</th>\n",
+              "      <th>TotalCharges_2816.65</th>\n",
+              "      <th>TotalCharges_2820.65</th>\n",
+              "      <th>TotalCharges_2823</th>\n",
+              "      <th>TotalCharges_283.75</th>\n",
+              "      <th>TotalCharges_283.95</th>\n",
+              "      <th>TotalCharges_2830.45</th>\n",
+              "      <th>TotalCharges_2832.75</th>\n",
+              "      <th>TotalCharges_2835.5</th>\n",
+              "      <th>TotalCharges_2835.9</th>\n",
+              "      <th>TotalCharges_2838.55</th>\n",
+              "      <th>TotalCharges_2838.7</th>\n",
+              "      <th>TotalCharges_2839.45</th>\n",
+              "      <th>TotalCharges_2839.65</th>\n",
+              "      <th>TotalCharges_2839.95</th>\n",
+              "      <th>TotalCharges_284.3</th>\n",
+              "      <th>TotalCharges_284.35</th>\n",
+              "      <th>TotalCharges_284.9</th>\n",
+              "      <th>TotalCharges_2841.55</th>\n",
+              "      <th>TotalCharges_2845.15</th>\n",
+              "      <th>TotalCharges_2847.2</th>\n",
+              "      <th>TotalCharges_2847.4</th>\n",
+              "      <th>TotalCharges_2848.45</th>\n",
+              "      <th>TotalCharges_285.2</th>\n",
+              "      <th>TotalCharges_2852.4</th>\n",
+              "      <th>TotalCharges_2854.55</th>\n",
+              "      <th>TotalCharges_2854.95</th>\n",
+              "      <th>TotalCharges_2857.6</th>\n",
+              "      <th>TotalCharges_286.8</th>\n",
+              "      <th>TotalCharges_2861.45</th>\n",
+              "      <th>TotalCharges_2862.55</th>\n",
+              "      <th>TotalCharges_2862.75</th>\n",
+              "      <th>TotalCharges_2866.45</th>\n",
+              "      <th>TotalCharges_2867.75</th>\n",
+              "      <th>TotalCharges_2868.05</th>\n",
+              "      <th>TotalCharges_2868.15</th>\n",
+              "      <th>TotalCharges_2869.85</th>\n",
+              "      <th>TotalCharges_287.4</th>\n",
+              "      <th>TotalCharges_287.85</th>\n",
+              "      <th>TotalCharges_2871.5</th>\n",
+              "      <th>TotalCharges_2874.15</th>\n",
+              "      <th>TotalCharges_2874.45</th>\n",
+              "      <th>TotalCharges_2877.05</th>\n",
+              "      <th>TotalCharges_2877.95</th>\n",
+              "      <th>TotalCharges_2878.55</th>\n",
+              "      <th>TotalCharges_2878.75</th>\n",
+              "      <th>TotalCharges_2879.2</th>\n",
+              "      <th>TotalCharges_2879.9</th>\n",
+              "      <th>TotalCharges_288.05</th>\n",
+              "      <th>TotalCharges_288.35</th>\n",
+              "      <th>TotalCharges_2882.25</th>\n",
+              "      <th>TotalCharges_2884.9</th>\n",
+              "      <th>TotalCharges_2888.7</th>\n",
+              "      <th>TotalCharges_289.1</th>\n",
+              "      <th>TotalCharges_289.3</th>\n",
+              "      <th>TotalCharges_2890.65</th>\n",
+              "      <th>TotalCharges_2893.4</th>\n",
+              "      <th>TotalCharges_2894.55</th>\n",
+              "      <th>TotalCharges_2896.4</th>\n",
+              "      <th>TotalCharges_2896.55</th>\n",
+              "      <th>TotalCharges_2896.6</th>\n",
+              "      <th>TotalCharges_2897.95</th>\n",
+              "      <th>TotalCharges_2898.95</th>\n",
+              "      <th>TotalCharges_29.15</th>\n",
+              "      <th>TotalCharges_29.7</th>\n",
+              "      <th>TotalCharges_29.85</th>\n",
+              "      <th>TotalCharges_29.9</th>\n",
+              "      <th>TotalCharges_29.95</th>\n",
+              "      <th>TotalCharges_290.55</th>\n",
+              "      <th>TotalCharges_2901.8</th>\n",
+              "      <th>TotalCharges_2907.35</th>\n",
+              "      <th>TotalCharges_2907.55</th>\n",
+              "      <th>TotalCharges_2908.2</th>\n",
+              "      <th>TotalCharges_2909.95</th>\n",
+              "      <th>TotalCharges_291.4</th>\n",
+              "      <th>TotalCharges_291.45</th>\n",
+              "      <th>TotalCharges_291.9</th>\n",
+              "      <th>TotalCharges_2911.3</th>\n",
+              "      <th>TotalCharges_2911.5</th>\n",
+              "      <th>TotalCharges_2911.8</th>\n",
+              "      <th>TotalCharges_2917.5</th>\n",
+              "      <th>TotalCharges_2917.65</th>\n",
+              "      <th>TotalCharges_2919.85</th>\n",
+              "      <th>TotalCharges_292.4</th>\n",
+              "      <th>TotalCharges_292.8</th>\n",
+              "      <th>TotalCharges_292.85</th>\n",
+              "      <th>TotalCharges_2921.75</th>\n",
+              "      <th>TotalCharges_2924.05</th>\n",
+              "      <th>TotalCharges_2928.5</th>\n",
+              "      <th>TotalCharges_2929.75</th>\n",
+              "      <th>TotalCharges_293.15</th>\n",
+              "      <th>TotalCharges_293.3</th>\n",
+              "      <th>TotalCharges_293.65</th>\n",
+              "      <th>TotalCharges_293.85</th>\n",
+              "      <th>TotalCharges_2931</th>\n",
+              "      <th>TotalCharges_2933.2</th>\n",
+              "      <th>TotalCharges_2933.95</th>\n",
+              "      <th>TotalCharges_2934.3</th>\n",
+              "      <th>TotalCharges_2936.25</th>\n",
+              "      <th>TotalCharges_2937.65</th>\n",
+              "      <th>TotalCharges_2939.8</th>\n",
+              "      <th>TotalCharges_294.2</th>\n",
+              "      <th>TotalCharges_294.45</th>\n",
+              "      <th>TotalCharges_294.5</th>\n",
+              "      <th>TotalCharges_294.9</th>\n",
+              "      <th>TotalCharges_294.95</th>\n",
+              "      <th>TotalCharges_2948.6</th>\n",
+              "      <th>TotalCharges_295.55</th>\n",
+              "      <th>TotalCharges_295.65</th>\n",
+              "      <th>TotalCharges_295.95</th>\n",
+              "      <th>TotalCharges_2952.85</th>\n",
+              "      <th>TotalCharges_2954.5</th>\n",
+              "      <th>TotalCharges_2958.95</th>\n",
+              "      <th>TotalCharges_2959.8</th>\n",
+              "      <th>TotalCharges_296.1</th>\n",
+              "      <th>TotalCharges_296.15</th>\n",
+              "      <th>TotalCharges_2960.1</th>\n",
+              "      <th>TotalCharges_2961.4</th>\n",
+              "      <th>TotalCharges_2962</th>\n",
+              "      <th>TotalCharges_2964</th>\n",
+              "      <th>TotalCharges_2964.05</th>\n",
+              "      <th>TotalCharges_2964.8</th>\n",
+              "      <th>TotalCharges_2965.75</th>\n",
+              "      <th>TotalCharges_2966.95</th>\n",
+              "      <th>TotalCharges_2967.35</th>\n",
+              "      <th>TotalCharges_297.3</th>\n",
+              "      <th>TotalCharges_297.35</th>\n",
+              "      <th>TotalCharges_2970.3</th>\n",
+              "      <th>TotalCharges_2970.8</th>\n",
+              "      <th>TotalCharges_2971.7</th>\n",
+              "      <th>TotalCharges_2974.5</th>\n",
+              "      <th>TotalCharges_2976.95</th>\n",
+              "      <th>TotalCharges_2978.3</th>\n",
+              "      <th>TotalCharges_2979.2</th>\n",
+              "      <th>TotalCharges_2979.3</th>\n",
+              "      <th>TotalCharges_2979.5</th>\n",
+              "      <th>TotalCharges_298.35</th>\n",
+              "      <th>TotalCharges_298.45</th>\n",
+              "      <th>TotalCharges_298.7</th>\n",
+              "      <th>TotalCharges_2983.65</th>\n",
+              "      <th>TotalCharges_2983.8</th>\n",
+              "      <th>TotalCharges_2985.25</th>\n",
+              "      <th>TotalCharges_2989.6</th>\n",
+              "      <th>TotalCharges_299.05</th>\n",
+              "      <th>TotalCharges_299.2</th>\n",
+              "      <th>TotalCharges_299.3</th>\n",
+              "      <th>TotalCharges_299.4</th>\n",
+              "      <th>TotalCharges_299.7</th>\n",
+              "      <th>TotalCharges_299.75</th>\n",
+              "      <th>TotalCharges_2995.45</th>\n",
+              "      <th>TotalCharges_2997.45</th>\n",
+              "      <th>TotalCharges_2998</th>\n",
+              "      <th>TotalCharges_30.2</th>\n",
+              "      <th>TotalCharges_30.5</th>\n",
+              "      <th>TotalCharges_30.55</th>\n",
+              "      <th>TotalCharges_300.4</th>\n",
+              "      <th>TotalCharges_300.7</th>\n",
+              "      <th>TotalCharges_300.8</th>\n",
+              "      <th>TotalCharges_3000.25</th>\n",
+              "      <th>TotalCharges_3001.2</th>\n",
+              "      <th>TotalCharges_3003.55</th>\n",
+              "      <th>TotalCharges_3004.15</th>\n",
+              "      <th>TotalCharges_3005.8</th>\n",
+              "      <th>TotalCharges_3007.25</th>\n",
+              "      <th>TotalCharges_3008.15</th>\n",
+              "      <th>TotalCharges_3008.55</th>\n",
+              "      <th>TotalCharges_3009.5</th>\n",
+              "      <th>TotalCharges_301.4</th>\n",
+              "      <th>TotalCharges_301.55</th>\n",
+              "      <th>TotalCharges_301.9</th>\n",
+              "      <th>TotalCharges_3011.65</th>\n",
+              "      <th>TotalCharges_3013.05</th>\n",
+              "      <th>TotalCharges_3014.65</th>\n",
+              "      <th>TotalCharges_3015.75</th>\n",
+              "      <th>TotalCharges_3017.65</th>\n",
+              "      <th>TotalCharges_3019.1</th>\n",
+              "      <th>TotalCharges_3019.25</th>\n",
+              "      <th>TotalCharges_3019.5</th>\n",
+              "      <th>TotalCharges_3019.7</th>\n",
+              "      <th>TotalCharges_302.35</th>\n",
+              "      <th>TotalCharges_302.45</th>\n",
+              "      <th>TotalCharges_302.6</th>\n",
+              "      <th>TotalCharges_302.75</th>\n",
+              "      <th>TotalCharges_3021.3</th>\n",
+              "      <th>TotalCharges_3021.45</th>\n",
+              "      <th>TotalCharges_3021.6</th>\n",
+              "      <th>TotalCharges_3023.55</th>\n",
+              "      <th>TotalCharges_3023.65</th>\n",
+              "      <th>TotalCharges_3023.85</th>\n",
+              "      <th>TotalCharges_3024.15</th>\n",
+              "      <th>TotalCharges_3027.25</th>\n",
+              "      <th>TotalCharges_3027.4</th>\n",
+              "      <th>TotalCharges_3027.65</th>\n",
+              "      <th>TotalCharges_3029.1</th>\n",
+              "      <th>TotalCharges_303.15</th>\n",
+              "      <th>TotalCharges_303.7</th>\n",
+              "      <th>TotalCharges_3030.6</th>\n",
+              "      <th>TotalCharges_3035.35</th>\n",
+              "      <th>TotalCharges_3035.8</th>\n",
+              "      <th>TotalCharges_3036.75</th>\n",
+              "      <th>TotalCharges_3038.55</th>\n",
+              "      <th>TotalCharges_304.6</th>\n",
+              "      <th>TotalCharges_3042.25</th>\n",
+              "      <th>TotalCharges_3043.6</th>\n",
+              "      <th>TotalCharges_3043.7</th>\n",
+              "      <th>TotalCharges_3045.75</th>\n",
+              "      <th>TotalCharges_3046.05</th>\n",
+              "      <th>TotalCharges_3046.15</th>\n",
+              "      <th>TotalCharges_3046.4</th>\n",
+              "      <th>TotalCharges_3047.15</th>\n",
+              "      <th>TotalCharges_305.1</th>\n",
+              "      <th>TotalCharges_305.55</th>\n",
+              "      <th>TotalCharges_305.95</th>\n",
+              "      <th>TotalCharges_3050.15</th>\n",
+              "      <th>TotalCharges_3053</th>\n",
+              "      <th>TotalCharges_3055.5</th>\n",
+              "      <th>TotalCharges_3058.15</th>\n",
+              "      <th>TotalCharges_3058.3</th>\n",
+              "      <th>TotalCharges_3058.65</th>\n",
+              "      <th>TotalCharges_306.05</th>\n",
+              "      <th>TotalCharges_306.6</th>\n",
+              "      <th>TotalCharges_3062.45</th>\n",
+              "      <th>TotalCharges_3066.45</th>\n",
+              "      <th>TotalCharges_3067.2</th>\n",
+              "      <th>TotalCharges_3068.6</th>\n",
+              "      <th>TotalCharges_3069.45</th>\n",
+              "      <th>TotalCharges_307</th>\n",
+              "      <th>TotalCharges_307.4</th>\n",
+              "      <th>TotalCharges_307.6</th>\n",
+              "      <th>TotalCharges_3077</th>\n",
+              "      <th>TotalCharges_3078.1</th>\n",
+              "      <th>TotalCharges_308.05</th>\n",
+              "      <th>TotalCharges_308.1</th>\n",
+              "      <th>TotalCharges_308.25</th>\n",
+              "      <th>TotalCharges_308.7</th>\n",
+              "      <th>TotalCharges_3082.1</th>\n",
+              "      <th>TotalCharges_3084.9</th>\n",
+              "      <th>TotalCharges_3085.35</th>\n",
+              "      <th>TotalCharges_3088.25</th>\n",
+              "      <th>TotalCharges_3088.75</th>\n",
+              "      <th>TotalCharges_3089.1</th>\n",
+              "      <th>TotalCharges_3089.6</th>\n",
+              "      <th>TotalCharges_309.1</th>\n",
+              "      <th>TotalCharges_309.25</th>\n",
+              "      <th>TotalCharges_309.35</th>\n",
+              "      <th>TotalCharges_309.4</th>\n",
+              "      <th>TotalCharges_3090.05</th>\n",
+              "      <th>TotalCharges_3090.65</th>\n",
+              "      <th>TotalCharges_3091.75</th>\n",
+              "      <th>TotalCharges_3092</th>\n",
+              "      <th>TotalCharges_3092.65</th>\n",
+              "      <th>TotalCharges_3094.05</th>\n",
+              "      <th>TotalCharges_3094.65</th>\n",
+              "      <th>TotalCharges_3096.9</th>\n",
+              "      <th>TotalCharges_3097</th>\n",
+              "      <th>TotalCharges_3097.2</th>\n",
+              "      <th>TotalCharges_31.2</th>\n",
+              "      <th>TotalCharges_31.35</th>\n",
+              "      <th>TotalCharges_31.55</th>\n",
+              "      <th>TotalCharges_31.9</th>\n",
+              "      <th>TotalCharges_310.6</th>\n",
+              "      <th>TotalCharges_3103.25</th>\n",
+              "      <th>TotalCharges_3105.55</th>\n",
+              "      <th>TotalCharges_3107.3</th>\n",
+              "      <th>TotalCharges_3109.9</th>\n",
+              "      <th>TotalCharges_311.6</th>\n",
+              "      <th>TotalCharges_3110.1</th>\n",
+              "      <th>TotalCharges_3112.05</th>\n",
+              "      <th>TotalCharges_3116.15</th>\n",
+              "      <th>TotalCharges_3119.9</th>\n",
+              "      <th>TotalCharges_312.7</th>\n",
+              "      <th>TotalCharges_3121.1</th>\n",
+              "      <th>TotalCharges_3121.4</th>\n",
+              "      <th>TotalCharges_3121.45</th>\n",
+              "      <th>TotalCharges_3122.4</th>\n",
+              "      <th>TotalCharges_3124.5</th>\n",
+              "      <th>TotalCharges_3126.45</th>\n",
+              "      <th>TotalCharges_3126.85</th>\n",
+              "      <th>TotalCharges_3128.8</th>\n",
+              "      <th>TotalCharges_313</th>\n",
+              "      <th>TotalCharges_313.4</th>\n",
+              "      <th>TotalCharges_313.45</th>\n",
+              "      <th>TotalCharges_313.6</th>\n",
+              "      <th>TotalCharges_3131.55</th>\n",
+              "      <th>TotalCharges_3131.8</th>\n",
+              "      <th>TotalCharges_3132.75</th>\n",
+              "      <th>TotalCharges_3134.7</th>\n",
+              "      <th>TotalCharges_3139.8</th>\n",
+              "      <th>TotalCharges_314.45</th>\n",
+              "      <th>TotalCharges_314.55</th>\n",
+              "      <th>TotalCharges_314.6</th>\n",
+              "      <th>TotalCharges_314.95</th>\n",
+              "      <th>TotalCharges_3141.7</th>\n",
+              "      <th>TotalCharges_3143.65</th>\n",
+              "      <th>TotalCharges_3145.15</th>\n",
+              "      <th>TotalCharges_3145.9</th>\n",
+              "      <th>TotalCharges_3147.15</th>\n",
+              "      <th>TotalCharges_3147.5</th>\n",
+              "      <th>TotalCharges_315.3</th>\n",
+              "      <th>TotalCharges_3152.5</th>\n",
+              "      <th>TotalCharges_3157</th>\n",
+              "      <th>TotalCharges_316.2</th>\n",
+              "      <th>TotalCharges_316.9</th>\n",
+              "      <th>TotalCharges_3160.55</th>\n",
+              "      <th>TotalCharges_3161.2</th>\n",
+              "      <th>TotalCharges_3161.4</th>\n",
+              "      <th>TotalCharges_3161.6</th>\n",
+              "      <th>TotalCharges_3162.65</th>\n",
+              "      <th>TotalCharges_3165.6</th>\n",
+              "      <th>TotalCharges_3166.9</th>\n",
+              "      <th>TotalCharges_3168</th>\n",
+              "      <th>TotalCharges_3168.75</th>\n",
+              "      <th>TotalCharges_3169.55</th>\n",
+              "      <th>TotalCharges_317.25</th>\n",
+              "      <th>TotalCharges_317.75</th>\n",
+              "      <th>TotalCharges_3171.15</th>\n",
+              "      <th>TotalCharges_3171.6</th>\n",
+              "      <th>TotalCharges_3173.35</th>\n",
+              "      <th>TotalCharges_3175.85</th>\n",
+              "      <th>TotalCharges_3177.25</th>\n",
+              "      <th>TotalCharges_318.1</th>\n",
+              "      <th>TotalCharges_318.5</th>\n",
+              "      <th>TotalCharges_318.6</th>\n",
+              "      <th>TotalCharges_318.9</th>\n",
+              "      <th>TotalCharges_3180.5</th>\n",
+              "      <th>TotalCharges_3181.8</th>\n",
+              "      <th>TotalCharges_3182.95</th>\n",
+              "      <th>TotalCharges_3183.4</th>\n",
+              "      <th>TotalCharges_3184.25</th>\n",
+              "      <th>TotalCharges_3186.65</th>\n",
+              "      <th>TotalCharges_3186.7</th>\n",
+              "      <th>TotalCharges_3187.65</th>\n",
+              "      <th>TotalCharges_319.15</th>\n",
+              "      <th>TotalCharges_319.6</th>\n",
+              "      <th>TotalCharges_319.85</th>\n",
+              "      <th>TotalCharges_3190.25</th>\n",
+              "      <th>TotalCharges_3190.65</th>\n",
+              "      <th>TotalCharges_3196</th>\n",
+              "      <th>TotalCharges_3198.6</th>\n",
+              "      <th>TotalCharges_3199</th>\n",
+              "      <th>TotalCharges_32.7</th>\n",
+              "      <th>TotalCharges_320.4</th>\n",
+              "      <th>TotalCharges_320.45</th>\n",
+              "      <th>TotalCharges_3201.55</th>\n",
+              "      <th>TotalCharges_3204.4</th>\n",
+              "      <th>TotalCharges_3204.65</th>\n",
+              "      <th>TotalCharges_3205.6</th>\n",
+              "      <th>TotalCharges_3207.55</th>\n",
+              "      <th>TotalCharges_3208.65</th>\n",
+              "      <th>TotalCharges_321.05</th>\n",
+              "      <th>TotalCharges_321.4</th>\n",
+              "      <th>TotalCharges_321.65</th>\n",
+              "      <th>TotalCharges_321.7</th>\n",
+              "      <th>TotalCharges_321.75</th>\n",
+              "      <th>TotalCharges_321.9</th>\n",
+              "      <th>TotalCharges_3210.35</th>\n",
+              "      <th>TotalCharges_3211.2</th>\n",
+              "      <th>TotalCharges_3211.9</th>\n",
+              "      <th>TotalCharges_3213.75</th>\n",
+              "      <th>TotalCharges_3217.55</th>\n",
+              "      <th>TotalCharges_3217.65</th>\n",
+              "      <th>TotalCharges_3219.75</th>\n",
+              "      <th>TotalCharges_322.5</th>\n",
+              "      <th>TotalCharges_322.9</th>\n",
+              "      <th>TotalCharges_3221.25</th>\n",
+              "      <th>TotalCharges_3229.4</th>\n",
+              "      <th>TotalCharges_3229.65</th>\n",
+              "      <th>TotalCharges_323.15</th>\n",
+              "      <th>TotalCharges_323.25</th>\n",
+              "      <th>TotalCharges_323.45</th>\n",
+              "      <th>TotalCharges_3231.05</th>\n",
+              "      <th>TotalCharges_3233.6</th>\n",
+              "      <th>TotalCharges_3233.85</th>\n",
+              "      <th>TotalCharges_3236.35</th>\n",
+              "      <th>TotalCharges_3237.05</th>\n",
+              "      <th>TotalCharges_3238.4</th>\n",
+              "      <th>TotalCharges_324.15</th>\n",
+              "      <th>TotalCharges_324.2</th>\n",
+              "      <th>TotalCharges_324.25</th>\n",
+              "      <th>TotalCharges_324.3</th>\n",
+              "      <th>TotalCharges_324.6</th>\n",
+              "      <th>TotalCharges_324.8</th>\n",
+              "      <th>TotalCharges_3242.5</th>\n",
+              "      <th>TotalCharges_3243.45</th>\n",
+              "      <th>TotalCharges_3244.4</th>\n",
+              "      <th>TotalCharges_3246.45</th>\n",
+              "      <th>TotalCharges_3247.55</th>\n",
+              "      <th>TotalCharges_3249.4</th>\n",
+              "      <th>TotalCharges_325.45</th>\n",
+              "      <th>TotalCharges_3250.45</th>\n",
+              "      <th>TotalCharges_3251.3</th>\n",
+              "      <th>TotalCharges_3251.85</th>\n",
+              "      <th>TotalCharges_3252</th>\n",
+              "      <th>TotalCharges_3254.35</th>\n",
+              "      <th>TotalCharges_3255.35</th>\n",
+              "      <th>TotalCharges_326.65</th>\n",
+              "      <th>TotalCharges_326.8</th>\n",
+              "      <th>TotalCharges_3260.1</th>\n",
+              "      <th>TotalCharges_3263.6</th>\n",
+              "      <th>TotalCharges_3263.9</th>\n",
+              "      <th>TotalCharges_3264.45</th>\n",
+              "      <th>TotalCharges_3264.5</th>\n",
+              "      <th>TotalCharges_3265.95</th>\n",
+              "      <th>TotalCharges_3266</th>\n",
+              "      <th>TotalCharges_3268.05</th>\n",
+              "      <th>TotalCharges_327.45</th>\n",
+              "      <th>TotalCharges_3270.25</th>\n",
+              "      <th>TotalCharges_3273.55</th>\n",
+              "      <th>TotalCharges_3273.8</th>\n",
+              "      <th>TotalCharges_3273.95</th>\n",
+              "      <th>TotalCharges_3274.35</th>\n",
+              "      <th>TotalCharges_3275.15</th>\n",
+              "      <th>TotalCharges_328.95</th>\n",
+              "      <th>TotalCharges_3281.65</th>\n",
+              "      <th>TotalCharges_3282.75</th>\n",
+              "      <th>TotalCharges_3283.05</th>\n",
+              "      <th>TotalCharges_329.75</th>\n",
+              "      <th>TotalCharges_329.8</th>\n",
+              "      <th>TotalCharges_329.95</th>\n",
+              "      <th>TotalCharges_3292.3</th>\n",
+              "      <th>TotalCharges_3297</th>\n",
+              "      <th>TotalCharges_33.2</th>\n",
+              "      <th>TotalCharges_33.6</th>\n",
+              "      <th>TotalCharges_33.7</th>\n",
+              "      <th>TotalCharges_330.05</th>\n",
+              "      <th>TotalCharges_330.15</th>\n",
+              "      <th>TotalCharges_330.25</th>\n",
+              "      <th>TotalCharges_330.6</th>\n",
+              "      <th>TotalCharges_330.8</th>\n",
+              "      <th>TotalCharges_3301.05</th>\n",
+              "      <th>TotalCharges_3303.05</th>\n",
+              "      <th>TotalCharges_3306.85</th>\n",
+              "      <th>TotalCharges_3309.25</th>\n",
+              "      <th>TotalCharges_331.3</th>\n",
+              "      <th>TotalCharges_331.35</th>\n",
+              "      <th>TotalCharges_331.6</th>\n",
+              "      <th>TotalCharges_331.85</th>\n",
+              "      <th>TotalCharges_331.9</th>\n",
+              "      <th>TotalCharges_3313.4</th>\n",
+              "      <th>TotalCharges_3314.15</th>\n",
+              "      <th>TotalCharges_3316.1</th>\n",
+              "      <th>TotalCharges_332.45</th>\n",
+              "      <th>TotalCharges_332.5</th>\n",
+              "      <th>TotalCharges_332.65</th>\n",
+              "      <th>TotalCharges_3320.6</th>\n",
+              "      <th>TotalCharges_3320.75</th>\n",
+              "      <th>TotalCharges_3321.35</th>\n",
+              "      <th>TotalCharges_3326.2</th>\n",
+              "      <th>TotalCharges_3327.05</th>\n",
+              "      <th>TotalCharges_333.55</th>\n",
+              "      <th>TotalCharges_333.6</th>\n",
+              "      <th>TotalCharges_333.65</th>\n",
+              "      <th>TotalCharges_3330.1</th>\n",
+              "      <th>TotalCharges_3334.9</th>\n",
+              "      <th>TotalCharges_3334.95</th>\n",
+              "      <th>TotalCharges_3339.05</th>\n",
+              "      <th>TotalCharges_334.65</th>\n",
+              "      <th>TotalCharges_334.8</th>\n",
+              "      <th>TotalCharges_3340.55</th>\n",
+              "      <th>TotalCharges_3342</th>\n",
+              "      <th>TotalCharges_3342.45</th>\n",
+              "      <th>TotalCharges_3343.15</th>\n",
+              "      <th>TotalCharges_3344.1</th>\n",
+              "      <th>TotalCharges_3346.8</th>\n",
+              "      <th>TotalCharges_3348.1</th>\n",
+              "      <th>TotalCharges_3349.1</th>\n",
+              "      <th>TotalCharges_335.4</th>\n",
+              "      <th>TotalCharges_335.65</th>\n",
+              "      <th>TotalCharges_335.75</th>\n",
+              "      <th>TotalCharges_335.95</th>\n",
+              "      <th>TotalCharges_3351.55</th>\n",
+              "      <th>TotalCharges_3353.4</th>\n",
+              "      <th>TotalCharges_3355.65</th>\n",
+              "      <th>TotalCharges_3357.9</th>\n",
+              "      <th>TotalCharges_3358.65</th>\n",
+              "      <th>TotalCharges_336.15</th>\n",
+              "      <th>TotalCharges_336.7</th>\n",
+              "      <th>TotalCharges_3361.05</th>\n",
+              "      <th>TotalCharges_3363.8</th>\n",
+              "      <th>TotalCharges_3364.55</th>\n",
+              "      <th>TotalCharges_3365.4</th>\n",
+              "      <th>TotalCharges_3365.85</th>\n",
+              "      <th>TotalCharges_3366.05</th>\n",
+              "      <th>TotalCharges_3369.05</th>\n",
+              "      <th>TotalCharges_3369.25</th>\n",
+              "      <th>TotalCharges_337.9</th>\n",
+              "      <th>TotalCharges_3370.2</th>\n",
+              "      <th>TotalCharges_3371</th>\n",
+              "      <th>TotalCharges_3371.75</th>\n",
+              "      <th>TotalCharges_3373.4</th>\n",
+              "      <th>TotalCharges_3375.9</th>\n",
+              "      <th>TotalCharges_3377.8</th>\n",
+              "      <th>TotalCharges_3379.25</th>\n",
+              "      <th>TotalCharges_338.1</th>\n",
+              "      <th>TotalCharges_338.9</th>\n",
+              "      <th>TotalCharges_3382.3</th>\n",
+              "      <th>TotalCharges_3384</th>\n",
+              "      <th>TotalCharges_3389.25</th>\n",
+              "      <th>TotalCharges_339.9</th>\n",
+              "      <th>TotalCharges_3395.8</th>\n",
+              "      <th>TotalCharges_3398.9</th>\n",
+              "      <th>TotalCharges_3399.85</th>\n",
+              "      <th>TotalCharges_34.7</th>\n",
+              "      <th>TotalCharges_34.75</th>\n",
+              "      <th>TotalCharges_34.8</th>\n",
+              "      <th>TotalCharges_340.25</th>\n",
+              "      <th>TotalCharges_340.35</th>\n",
+              "      <th>TotalCharges_340.4</th>\n",
+              "      <th>TotalCharges_340.85</th>\n",
+              "      <th>TotalCharges_3403.4</th>\n",
+              "      <th>TotalCharges_3409.1</th>\n",
+              "      <th>TotalCharges_3409.6</th>\n",
+              "      <th>TotalCharges_341.35</th>\n",
+              "      <th>TotalCharges_341.45</th>\n",
+              "      <th>TotalCharges_341.6</th>\n",
+              "      <th>TotalCharges_3410</th>\n",
+              "      <th>TotalCharges_3410.6</th>\n",
+              "      <th>TotalCharges_3413.25</th>\n",
+              "      <th>TotalCharges_3414.65</th>\n",
+              "      <th>TotalCharges_3415.25</th>\n",
+              "      <th>TotalCharges_3416.85</th>\n",
+              "      <th>TotalCharges_3418.2</th>\n",
+              "      <th>TotalCharges_3419.3</th>\n",
+              "      <th>TotalCharges_3419.5</th>\n",
+              "      <th>TotalCharges_342.3</th>\n",
+              "      <th>TotalCharges_342.4</th>\n",
+              "      <th>TotalCharges_3420.5</th>\n",
+              "      <th>TotalCharges_3423.5</th>\n",
+              "      <th>TotalCharges_3425.35</th>\n",
+              "      <th>TotalCharges_343.45</th>\n",
+              "      <th>TotalCharges_343.6</th>\n",
+              "      <th>TotalCharges_343.95</th>\n",
+              "      <th>TotalCharges_3431.75</th>\n",
+              "      <th>TotalCharges_3432.9</th>\n",
+              "      <th>TotalCharges_3435.6</th>\n",
+              "      <th>TotalCharges_3436.1</th>\n",
+              "      <th>TotalCharges_3437.45</th>\n",
+              "      <th>TotalCharges_3437.5</th>\n",
+              "      <th>TotalCharges_3439</th>\n",
+              "      <th>TotalCharges_344.2</th>\n",
+              "      <th>TotalCharges_344.5</th>\n",
+              "      <th>TotalCharges_3440.25</th>\n",
+              "      <th>TotalCharges_3442.15</th>\n",
+              "      <th>TotalCharges_3442.8</th>\n",
+              "      <th>TotalCharges_3444.85</th>\n",
+              "      <th>TotalCharges_345.5</th>\n",
+              "      <th>TotalCharges_345.9</th>\n",
+              "      <th>TotalCharges_3450.15</th>\n",
+              "      <th>TotalCharges_3452.55</th>\n",
+              "      <th>TotalCharges_3454.6</th>\n",
+              "      <th>TotalCharges_3457.45</th>\n",
+              "      <th>TotalCharges_3457.9</th>\n",
+              "      <th>TotalCharges_346.2</th>\n",
+              "      <th>TotalCharges_346.4</th>\n",
+              "      <th>TotalCharges_346.45</th>\n",
+              "      <th>TotalCharges_3460.3</th>\n",
+              "      <th>TotalCharges_3460.95</th>\n",
+              "      <th>TotalCharges_3462.1</th>\n",
+              "      <th>TotalCharges_3465.05</th>\n",
+              "      <th>TotalCharges_3465.7</th>\n",
+              "      <th>TotalCharges_3467</th>\n",
+              "      <th>TotalCharges_347.25</th>\n",
+              "      <th>TotalCharges_347.4</th>\n",
+              "      <th>TotalCharges_347.65</th>\n",
+              "      <th>TotalCharges_3470.8</th>\n",
+              "      <th>TotalCharges_3471.1</th>\n",
+              "      <th>TotalCharges_3472.05</th>\n",
+              "      <th>TotalCharges_3473.4</th>\n",
+              "      <th>TotalCharges_3474.05</th>\n",
+              "      <th>TotalCharges_3474.2</th>\n",
+              "      <th>TotalCharges_3474.45</th>\n",
+              "      <th>TotalCharges_3475.55</th>\n",
+              "      <th>TotalCharges_3478.15</th>\n",
+              "      <th>TotalCharges_3478.75</th>\n",
+              "      <th>TotalCharges_3479.05</th>\n",
+              "      <th>TotalCharges_348.15</th>\n",
+              "      <th>TotalCharges_348.8</th>\n",
+              "      <th>TotalCharges_3480</th>\n",
+              "      <th>TotalCharges_3480.35</th>\n",
+              "      <th>TotalCharges_3482.85</th>\n",
+              "      <th>TotalCharges_3483.45</th>\n",
+              "      <th>TotalCharges_3486.65</th>\n",
+              "      <th>TotalCharges_3487.95</th>\n",
+              "      <th>TotalCharges_3488.15</th>\n",
+              "      <th>TotalCharges_349.65</th>\n",
+              "      <th>TotalCharges_349.8</th>\n",
+              "      <th>TotalCharges_3491.55</th>\n",
+              "      <th>TotalCharges_3496.3</th>\n",
+              "      <th>TotalCharges_35</th>\n",
+              "      <th>TotalCharges_35.05</th>\n",
+              "      <th>TotalCharges_35.1</th>\n",
+              "      <th>TotalCharges_35.25</th>\n",
+              "      <th>TotalCharges_35.45</th>\n",
+              "      <th>TotalCharges_35.55</th>\n",
+              "      <th>TotalCharges_35.75</th>\n",
+              "      <th>TotalCharges_35.85</th>\n",
+              "      <th>TotalCharges_35.9</th>\n",
+              "      <th>TotalCharges_350.1</th>\n",
+              "      <th>TotalCharges_350.3</th>\n",
+              "      <th>TotalCharges_350.35</th>\n",
+              "      <th>TotalCharges_3503.5</th>\n",
+              "      <th>TotalCharges_3505.1</th>\n",
+              "      <th>TotalCharges_3509.4</th>\n",
+              "      <th>TotalCharges_351.5</th>\n",
+              "      <th>TotalCharges_351.55</th>\n",
+              "      <th>TotalCharges_3510.3</th>\n",
+              "      <th>TotalCharges_3512.15</th>\n",
+              "      <th>TotalCharges_3512.5</th>\n",
+              "      <th>TotalCharges_3512.9</th>\n",
+              "      <th>TotalCharges_3515.25</th>\n",
+              "      <th>TotalCharges_3517.9</th>\n",
+              "      <th>TotalCharges_352.65</th>\n",
+              "      <th>TotalCharges_3520.75</th>\n",
+              "      <th>TotalCharges_3521.7</th>\n",
+              "      <th>TotalCharges_3522.65</th>\n",
+              "      <th>TotalCharges_3527</th>\n",
+              "      <th>TotalCharges_3527.3</th>\n",
+              "      <th>TotalCharges_3527.6</th>\n",
+              "      <th>TotalCharges_3529.95</th>\n",
+              "      <th>TotalCharges_353.65</th>\n",
+              "      <th>TotalCharges_3532</th>\n",
+              "      <th>TotalCharges_3532.25</th>\n",
+              "      <th>TotalCharges_3532.85</th>\n",
+              "      <th>TotalCharges_3533.6</th>\n",
+              "      <th>TotalCharges_3539.25</th>\n",
+              "      <th>TotalCharges_354.05</th>\n",
+              "      <th>TotalCharges_3540.65</th>\n",
+              "      <th>TotalCharges_3541.1</th>\n",
+              "      <th>TotalCharges_3541.35</th>\n",
+              "      <th>TotalCharges_3541.4</th>\n",
+              "      <th>TotalCharges_3545.05</th>\n",
+              "      <th>TotalCharges_3545.1</th>\n",
+              "      <th>TotalCharges_3545.35</th>\n",
+              "      <th>TotalCharges_3548.3</th>\n",
+              "      <th>TotalCharges_3549.25</th>\n",
+              "      <th>TotalCharges_355.1</th>\n",
+              "      <th>TotalCharges_355.2</th>\n",
+              "      <th>TotalCharges_355.9</th>\n",
+              "      <th>TotalCharges_3551.65</th>\n",
+              "      <th>TotalCharges_3554.6</th>\n",
+              "      <th>TotalCharges_3557.7</th>\n",
+              "      <th>TotalCharges_356.1</th>\n",
+              "      <th>TotalCharges_356.15</th>\n",
+              "      <th>TotalCharges_356.65</th>\n",
+              "      <th>TotalCharges_3561.15</th>\n",
+              "      <th>TotalCharges_3562.5</th>\n",
+              "      <th>TotalCharges_3563.8</th>\n",
+              "      <th>TotalCharges_3565.65</th>\n",
+              "      <th>TotalCharges_3566.6</th>\n",
+              "      <th>TotalCharges_3566.7</th>\n",
+              "      <th>TotalCharges_357</th>\n",
+              "      <th>TotalCharges_357.15</th>\n",
+              "      <th>TotalCharges_357.6</th>\n",
+              "      <th>TotalCharges_357.7</th>\n",
+              "      <th>TotalCharges_357.75</th>\n",
+              "      <th>TotalCharges_3571.6</th>\n",
+              "      <th>TotalCharges_3574.5</th>\n",
+              "      <th>TotalCharges_3579.15</th>\n",
+              "      <th>TotalCharges_358.05</th>\n",
+              "      <th>TotalCharges_358.15</th>\n",
+              "      <th>TotalCharges_358.5</th>\n",
+              "      <th>TotalCharges_3580.3</th>\n",
+              "      <th>TotalCharges_3580.95</th>\n",
+              "      <th>TotalCharges_3581.4</th>\n",
+              "      <th>TotalCharges_3581.6</th>\n",
+              "      <th>TotalCharges_3582.4</th>\n",
+              "      <th>TotalCharges_3587.25</th>\n",
+              "      <th>TotalCharges_3588.4</th>\n",
+              "      <th>TotalCharges_359.4</th>\n",
+              "      <th>TotalCharges_3590.2</th>\n",
+              "      <th>TotalCharges_3591.25</th>\n",
+              "      <th>TotalCharges_3593.8</th>\n",
+              "      <th>TotalCharges_3597.5</th>\n",
+              "      <th>TotalCharges_36.45</th>\n",
+              "      <th>TotalCharges_36.55</th>\n",
+              "      <th>TotalCharges_36.8</th>\n",
+              "      <th>TotalCharges_360.1</th>\n",
+              "      <th>TotalCharges_360.35</th>\n",
+              "      <th>TotalCharges_360.55</th>\n",
+              "      <th>TotalCharges_3600.65</th>\n",
+              "      <th>TotalCharges_3603.45</th>\n",
+              "      <th>TotalCharges_3605.2</th>\n",
+              "      <th>TotalCharges_3605.6</th>\n",
+              "      <th>TotalCharges_3608</th>\n",
+              "      <th>TotalCharges_3615.6</th>\n",
+              "      <th>TotalCharges_3616.25</th>\n",
+              "      <th>TotalCharges_3617.1</th>\n",
+              "      <th>TotalCharges_3618.7</th>\n",
+              "      <th>TotalCharges_362.2</th>\n",
+              "      <th>TotalCharges_362.6</th>\n",
+              "      <th>TotalCharges_3623.95</th>\n",
+              "      <th>TotalCharges_3624.3</th>\n",
+              "      <th>TotalCharges_3624.35</th>\n",
+              "      <th>TotalCharges_3625.2</th>\n",
+              "      <th>TotalCharges_3626.1</th>\n",
+              "      <th>TotalCharges_3626.35</th>\n",
+              "      <th>TotalCharges_3627.3</th>\n",
+              "      <th>TotalCharges_3629.2</th>\n",
+              "      <th>TotalCharges_363.15</th>\n",
+              "      <th>TotalCharges_3632</th>\n",
+              "      <th>TotalCharges_3634.8</th>\n",
+              "      <th>TotalCharges_3635.15</th>\n",
+              "      <th>TotalCharges_3638.25</th>\n",
+              "      <th>TotalCharges_364.55</th>\n",
+              "      <th>TotalCharges_3640.45</th>\n",
+              "      <th>TotalCharges_3641.5</th>\n",
+              "      <th>TotalCharges_3645.05</th>\n",
+              "      <th>TotalCharges_3645.5</th>\n",
+              "      <th>TotalCharges_3645.6</th>\n",
+              "      <th>TotalCharges_3645.75</th>\n",
+              "      <th>TotalCharges_3646.8</th>\n",
+              "      <th>TotalCharges_3649.6</th>\n",
+              "      <th>TotalCharges_365.35</th>\n",
+              "      <th>TotalCharges_365.4</th>\n",
+              "      <th>TotalCharges_365.55</th>\n",
+              "      <th>TotalCharges_365.65</th>\n",
+              "      <th>TotalCharges_365.8</th>\n",
+              "      <th>TotalCharges_3650.35</th>\n",
+              "      <th>TotalCharges_3653</th>\n",
+              "      <th>TotalCharges_3653.35</th>\n",
+              "      <th>TotalCharges_3655.45</th>\n",
+              "      <th>TotalCharges_3656.25</th>\n",
+              "      <th>TotalCharges_3662.25</th>\n",
+              "      <th>TotalCharges_3665.55</th>\n",
+              "      <th>TotalCharges_367.55</th>\n",
+              "      <th>TotalCharges_367.95</th>\n",
+              "      <th>TotalCharges_3670.5</th>\n",
+              "      <th>TotalCharges_3673.15</th>\n",
+              "      <th>TotalCharges_3673.6</th>\n",
+              "      <th>TotalCharges_3674.95</th>\n",
+              "      <th>TotalCharges_3678.3</th>\n",
+              "      <th>TotalCharges_368.1</th>\n",
+              "      <th>TotalCharges_368.85</th>\n",
+              "      <th>TotalCharges_3682.45</th>\n",
+              "      <th>TotalCharges_3684.95</th>\n",
+              "      <th>TotalCharges_3686.05</th>\n",
+              "      <th>TotalCharges_3687.75</th>\n",
+              "      <th>TotalCharges_3687.85</th>\n",
+              "      <th>TotalCharges_3688.6</th>\n",
+              "      <th>TotalCharges_369.05</th>\n",
+              "      <th>TotalCharges_369.1</th>\n",
+              "      <th>TotalCharges_369.15</th>\n",
+              "      <th>TotalCharges_369.25</th>\n",
+              "      <th>TotalCharges_369.3</th>\n",
+              "      <th>TotalCharges_369.6</th>\n",
+              "      <th>TotalCharges_3691.2</th>\n",
+              "      <th>TotalCharges_3692.85</th>\n",
+              "      <th>TotalCharges_3694.45</th>\n",
+              "      <th>TotalCharges_3694.7</th>\n",
+              "      <th>TotalCharges_37.2</th>\n",
+              "      <th>TotalCharges_370.25</th>\n",
+              "      <th>TotalCharges_370.4</th>\n",
+              "      <th>TotalCharges_370.5</th>\n",
+              "      <th>TotalCharges_370.65</th>\n",
+              "      <th>TotalCharges_3704.15</th>\n",
+              "      <th>TotalCharges_3706.95</th>\n",
+              "      <th>TotalCharges_3707.6</th>\n",
+              "      <th>TotalCharges_3708.4</th>\n",
+              "      <th>TotalCharges_371.4</th>\n",
+              "      <th>TotalCharges_371.6</th>\n",
+              "      <th>TotalCharges_371.65</th>\n",
+              "      <th>TotalCharges_371.9</th>\n",
+              "      <th>TotalCharges_3713.95</th>\n",
+              "      <th>TotalCharges_3715.65</th>\n",
+              "      <th>TotalCharges_372.45</th>\n",
+              "      <th>TotalCharges_3720.35</th>\n",
+              "      <th>TotalCharges_3721.9</th>\n",
+              "      <th>TotalCharges_3723.65</th>\n",
+              "      <th>TotalCharges_3725.5</th>\n",
+              "      <th>TotalCharges_3726.15</th>\n",
+              "      <th>TotalCharges_3729.6</th>\n",
+              "      <th>TotalCharges_3729.75</th>\n",
+              "      <th>TotalCharges_373</th>\n",
+              "      <th>TotalCharges_373.5</th>\n",
+              "      <th>TotalCharges_3734.25</th>\n",
+              "      <th>TotalCharges_3735.45</th>\n",
+              "      <th>TotalCharges_3739.8</th>\n",
+              "      <th>TotalCharges_374</th>\n",
+              "      <th>TotalCharges_374.5</th>\n",
+              "      <th>TotalCharges_374.8</th>\n",
+              "      <th>TotalCharges_3741.85</th>\n",
+              "      <th>TotalCharges_3744.05</th>\n",
+              "      <th>TotalCharges_375.25</th>\n",
+              "      <th>TotalCharges_3751.15</th>\n",
+              "      <th>TotalCharges_3753.2</th>\n",
+              "      <th>TotalCharges_3754.6</th>\n",
+              "      <th>TotalCharges_3756.4</th>\n",
+              "      <th>TotalCharges_3756.45</th>\n",
+              "      <th>TotalCharges_3759.05</th>\n",
+              "      <th>TotalCharges_3762</th>\n",
+              "      <th>TotalCharges_3765.05</th>\n",
+              "      <th>TotalCharges_3766.2</th>\n",
+              "      <th>TotalCharges_3767.4</th>\n",
+              "      <th>TotalCharges_3769.7</th>\n",
+              "      <th>TotalCharges_377.55</th>\n",
+              "      <th>TotalCharges_377.85</th>\n",
+              "      <th>TotalCharges_3770</th>\n",
+              "      <th>TotalCharges_3771.5</th>\n",
+              "      <th>TotalCharges_3772.5</th>\n",
+              "      <th>TotalCharges_3772.65</th>\n",
+              "      <th>TotalCharges_3775.85</th>\n",
+              "      <th>TotalCharges_3777.15</th>\n",
+              "      <th>TotalCharges_3778</th>\n",
+              "      <th>TotalCharges_3778.1</th>\n",
+              "      <th>TotalCharges_3778.2</th>\n",
+              "      <th>TotalCharges_3778.85</th>\n",
+              "      <th>TotalCharges_378.4</th>\n",
+              "      <th>TotalCharges_378.6</th>\n",
+              "      <th>TotalCharges_3782.4</th>\n",
+              "      <th>TotalCharges_3784</th>\n",
+              "      <th>TotalCharges_3789.2</th>\n",
+              "      <th>TotalCharges_3791.6</th>\n",
+              "      <th>TotalCharges_3794.5</th>\n",
+              "      <th>TotalCharges_3795.45</th>\n",
+              "      <th>TotalCharges_38</th>\n",
+              "      <th>TotalCharges_38.15</th>\n",
+              "      <th>TotalCharges_38.25</th>\n",
+              "      <th>TotalCharges_38.45</th>\n",
+              "      <th>TotalCharges_38.7</th>\n",
+              "      <th>TotalCharges_38.8</th>\n",
+              "      <th>TotalCharges_3801.3</th>\n",
+              "      <th>TotalCharges_3801.7</th>\n",
+              "      <th>TotalCharges_3804.4</th>\n",
+              "      <th>TotalCharges_3807.35</th>\n",
+              "      <th>TotalCharges_3808</th>\n",
+              "      <th>TotalCharges_3808.2</th>\n",
+              "      <th>TotalCharges_381.2</th>\n",
+              "      <th>TotalCharges_381.3</th>\n",
+              "      <th>TotalCharges_3810.55</th>\n",
+              "      <th>TotalCharges_3810.8</th>\n",
+              "      <th>TotalCharges_3815.4</th>\n",
+              "      <th>TotalCharges_382.2</th>\n",
+              "      <th>TotalCharges_382.8</th>\n",
+              "      <th>TotalCharges_3822.45</th>\n",
+              "      <th>TotalCharges_3824.2</th>\n",
+              "      <th>TotalCharges_3825.85</th>\n",
+              "      <th>TotalCharges_3827.9</th>\n",
+              "      <th>TotalCharges_3829.75</th>\n",
+              "      <th>TotalCharges_383.55</th>\n",
+              "      <th>TotalCharges_383.65</th>\n",
+              "      <th>TotalCharges_3833.95</th>\n",
+              "      <th>TotalCharges_3834.4</th>\n",
+              "      <th>TotalCharges_3835.55</th>\n",
+              "      <th>TotalCharges_3836.3</th>\n",
+              "      <th>TotalCharges_3838.2</th>\n",
+              "      <th>TotalCharges_3838.75</th>\n",
+              "      <th>TotalCharges_384.25</th>\n",
+              "      <th>TotalCharges_384.5</th>\n",
+              "      <th>TotalCharges_3842.6</th>\n",
+              "      <th>TotalCharges_3845.45</th>\n",
+              "      <th>TotalCharges_3846.35</th>\n",
+              "      <th>TotalCharges_3846.75</th>\n",
+              "      <th>TotalCharges_3847.6</th>\n",
+              "      <th>TotalCharges_3848</th>\n",
+              "      <th>TotalCharges_3848.8</th>\n",
+              "      <th>TotalCharges_385</th>\n",
+              "      <th>TotalCharges_385.55</th>\n",
+              "      <th>TotalCharges_385.9</th>\n",
+              "      <th>TotalCharges_3851.45</th>\n",
+              "      <th>TotalCharges_3856.75</th>\n",
+              "      <th>TotalCharges_3857.1</th>\n",
+              "      <th>TotalCharges_3858.05</th>\n",
+              "      <th>TotalCharges_386.5</th>\n",
+              "      <th>TotalCharges_3861.45</th>\n",
+              "      <th>TotalCharges_3862.55</th>\n",
+              "      <th>TotalCharges_3865.45</th>\n",
+              "      <th>TotalCharges_3865.6</th>\n",
+              "      <th>TotalCharges_387.2</th>\n",
+              "      <th>TotalCharges_387.4</th>\n",
+              "      <th>TotalCharges_387.7</th>\n",
+              "      <th>TotalCharges_387.9</th>\n",
+              "      <th>TotalCharges_3870</th>\n",
+              "      <th>TotalCharges_3870.3</th>\n",
+              "      <th>TotalCharges_3871.85</th>\n",
+              "      <th>TotalCharges_3874.1</th>\n",
+              "      <th>TotalCharges_3875.4</th>\n",
+              "      <th>TotalCharges_3876.2</th>\n",
+              "      <th>TotalCharges_3877.65</th>\n",
+              "      <th>TotalCharges_3877.95</th>\n",
+              "      <th>TotalCharges_388.6</th>\n",
+              "      <th>TotalCharges_3880.05</th>\n",
+              "      <th>TotalCharges_3882.3</th>\n",
+              "      <th>TotalCharges_3883.3</th>\n",
+              "      <th>TotalCharges_3886.05</th>\n",
+              "      <th>TotalCharges_3886.45</th>\n",
+              "      <th>TotalCharges_3886.85</th>\n",
+              "      <th>TotalCharges_3887.25</th>\n",
+              "      <th>TotalCharges_3887.85</th>\n",
+              "      <th>TotalCharges_3888.65</th>\n",
+              "      <th>TotalCharges_389.1</th>\n",
+              "      <th>TotalCharges_389.25</th>\n",
+              "      <th>TotalCharges_389.6</th>\n",
+              "      <th>TotalCharges_389.8</th>\n",
+              "      <th>TotalCharges_389.95</th>\n",
+              "      <th>TotalCharges_3893.6</th>\n",
+              "      <th>TotalCharges_3894.4</th>\n",
+              "      <th>TotalCharges_3895.35</th>\n",
+              "      <th>TotalCharges_3899.05</th>\n",
+              "      <th>TotalCharges_39.25</th>\n",
+              "      <th>TotalCharges_39.3</th>\n",
+              "      <th>TotalCharges_39.65</th>\n",
+              "      <th>TotalCharges_39.8</th>\n",
+              "      <th>TotalCharges_39.85</th>\n",
+              "      <th>TotalCharges_390.4</th>\n",
+              "      <th>TotalCharges_390.85</th>\n",
+              "      <th>TotalCharges_3901.25</th>\n",
+              "      <th>TotalCharges_3902.45</th>\n",
+              "      <th>TotalCharges_3902.6</th>\n",
+              "      <th>TotalCharges_3906.7</th>\n",
+              "      <th>TotalCharges_391.7</th>\n",
+              "      <th>TotalCharges_3912.9</th>\n",
+              "      <th>TotalCharges_3913.3</th>\n",
+              "      <th>TotalCharges_3914.05</th>\n",
+              "      <th>TotalCharges_3915.4</th>\n",
+              "      <th>TotalCharges_3919.15</th>\n",
+              "      <th>TotalCharges_392.5</th>\n",
+              "      <th>TotalCharges_392.65</th>\n",
+              "      <th>TotalCharges_3921.1</th>\n",
+              "      <th>TotalCharges_3921.3</th>\n",
+              "      <th>TotalCharges_3923.8</th>\n",
+              "      <th>TotalCharges_3928.3</th>\n",
+              "      <th>TotalCharges_393.15</th>\n",
+              "      <th>TotalCharges_393.45</th>\n",
+              "      <th>TotalCharges_3930.55</th>\n",
+              "      <th>TotalCharges_3930.6</th>\n",
+              "      <th>TotalCharges_3937.45</th>\n",
+              "      <th>TotalCharges_394.1</th>\n",
+              "      <th>TotalCharges_394.85</th>\n",
+              "      <th>TotalCharges_3941.7</th>\n",
+              "      <th>TotalCharges_3942.45</th>\n",
+              "      <th>TotalCharges_3944.5</th>\n",
+              "      <th>TotalCharges_3946.9</th>\n",
+              "      <th>TotalCharges_3948.45</th>\n",
+              "      <th>TotalCharges_3949.15</th>\n",
+              "      <th>TotalCharges_395.6</th>\n",
+              "      <th>TotalCharges_3950.85</th>\n",
+              "      <th>TotalCharges_3952.45</th>\n",
+              "      <th>TotalCharges_3952.65</th>\n",
+              "      <th>TotalCharges_3953.15</th>\n",
+              "      <th>TotalCharges_3953.7</th>\n",
+              "      <th>TotalCharges_3954.1</th>\n",
+              "      <th>TotalCharges_3956.7</th>\n",
+              "      <th>TotalCharges_3958.2</th>\n",
+              "      <th>TotalCharges_3958.25</th>\n",
+              "      <th>TotalCharges_3958.85</th>\n",
+              "      <th>TotalCharges_3959.15</th>\n",
+              "      <th>TotalCharges_3959.35</th>\n",
+              "      <th>TotalCharges_396.1</th>\n",
+              "      <th>TotalCharges_396.3</th>\n",
+              "      <th>TotalCharges_3965.05</th>\n",
+              "      <th>TotalCharges_3966.3</th>\n",
+              "      <th>TotalCharges_3969.35</th>\n",
+              "      <th>TotalCharges_3969.4</th>\n",
+              "      <th>TotalCharges_397</th>\n",
+              "      <th>TotalCharges_3970.4</th>\n",
+              "      <th>TotalCharges_3972.25</th>\n",
+              "      <th>TotalCharges_3973.2</th>\n",
+              "      <th>TotalCharges_3974.15</th>\n",
+              "      <th>TotalCharges_3974.7</th>\n",
+              "      <th>TotalCharges_3975.7</th>\n",
+              "      <th>TotalCharges_3975.9</th>\n",
+              "      <th>TotalCharges_398.55</th>\n",
+              "      <th>TotalCharges_3983.6</th>\n",
+              "      <th>TotalCharges_3985.35</th>\n",
+              "      <th>TotalCharges_3988.5</th>\n",
+              "      <th>TotalCharges_399.25</th>\n",
+              "      <th>TotalCharges_399.45</th>\n",
+              "      <th>TotalCharges_399.6</th>\n",
+              "      <th>TotalCharges_3990.6</th>\n",
+              "      <th>TotalCharges_3990.75</th>\n",
+              "      <th>TotalCharges_3994.05</th>\n",
+              "      <th>TotalCharges_3994.45</th>\n",
+              "      <th>TotalCharges_3996.8</th>\n",
+              "      <th>TotalCharges_40.1</th>\n",
+              "      <th>TotalCharges_40.2</th>\n",
+              "      <th>TotalCharges_40.25</th>\n",
+              "      <th>TotalCharges_40.9</th>\n",
+              "      <th>TotalCharges_400</th>\n",
+              "      <th>TotalCharges_400.3</th>\n",
+              "      <th>TotalCharges_4003</th>\n",
+              "      <th>TotalCharges_4003.85</th>\n",
+              "      <th>TotalCharges_4009.2</th>\n",
+              "      <th>TotalCharges_401.1</th>\n",
+              "      <th>TotalCharges_401.3</th>\n",
+              "      <th>TotalCharges_401.5</th>\n",
+              "      <th>TotalCharges_401.85</th>\n",
+              "      <th>TotalCharges_401.95</th>\n",
+              "      <th>TotalCharges_4013.8</th>\n",
+              "      <th>TotalCharges_4014</th>\n",
+              "      <th>TotalCharges_4014.2</th>\n",
+              "      <th>TotalCharges_4014.6</th>\n",
+              "      <th>TotalCharges_4016.2</th>\n",
+              "      <th>TotalCharges_4016.3</th>\n",
+              "      <th>TotalCharges_4016.75</th>\n",
+              "      <th>TotalCharges_4016.85</th>\n",
+              "      <th>TotalCharges_4017.45</th>\n",
+              "      <th>TotalCharges_4018.05</th>\n",
+              "      <th>TotalCharges_4018.35</th>\n",
+              "      <th>TotalCharges_4018.55</th>\n",
+              "      <th>TotalCharges_402.5</th>\n",
+              "      <th>TotalCharges_402.6</th>\n",
+              "      <th>TotalCharges_402.85</th>\n",
+              "      <th>TotalCharges_4024.2</th>\n",
+              "      <th>TotalCharges_4025.5</th>\n",
+              "      <th>TotalCharges_4025.6</th>\n",
+              "      <th>TotalCharges_4026.4</th>\n",
+              "      <th>TotalCharges_4029.95</th>\n",
+              "      <th>TotalCharges_403.15</th>\n",
+              "      <th>TotalCharges_403.35</th>\n",
+              "      <th>TotalCharges_4036</th>\n",
+              "      <th>TotalCharges_4036.85</th>\n",
+              "      <th>TotalCharges_4039</th>\n",
+              "      <th>TotalCharges_4039.3</th>\n",
+              "      <th>TotalCharges_4039.5</th>\n",
+              "      <th>TotalCharges_404.2</th>\n",
+              "      <th>TotalCharges_404.35</th>\n",
+              "      <th>TotalCharges_4040.2</th>\n",
+              "      <th>TotalCharges_4040.65</th>\n",
+              "      <th>TotalCharges_4042.2</th>\n",
+              "      <th>TotalCharges_4042.3</th>\n",
+              "      <th>TotalCharges_4045.65</th>\n",
+              "      <th>TotalCharges_4048.95</th>\n",
+              "      <th>TotalCharges_405.6</th>\n",
+              "      <th>TotalCharges_405.7</th>\n",
+              "      <th>TotalCharges_4052.4</th>\n",
+              "      <th>TotalCharges_4054.2</th>\n",
+              "      <th>TotalCharges_4055.5</th>\n",
+              "      <th>TotalCharges_4056.75</th>\n",
+              "      <th>TotalCharges_4059.35</th>\n",
+              "      <th>TotalCharges_4059.85</th>\n",
+              "      <th>TotalCharges_406.05</th>\n",
+              "      <th>TotalCharges_406.95</th>\n",
+              "      <th>TotalCharges_4060.55</th>\n",
+              "      <th>TotalCharges_4060.9</th>\n",
+              "      <th>TotalCharges_4062.2</th>\n",
+              "      <th>TotalCharges_4063</th>\n",
+              "      <th>TotalCharges_4065</th>\n",
+              "      <th>TotalCharges_4068</th>\n",
+              "      <th>TotalCharges_4069.9</th>\n",
+              "      <th>TotalCharges_407.05</th>\n",
+              "      <th>TotalCharges_4070.95</th>\n",
+              "      <th>TotalCharges_4079.55</th>\n",
+              "      <th>TotalCharges_408.25</th>\n",
+              "      <th>TotalCharges_408.5</th>\n",
+              "      <th>TotalCharges_4084.35</th>\n",
+              "      <th>TotalCharges_4085.75</th>\n",
+              "      <th>TotalCharges_4086.3</th>\n",
+              "      <th>TotalCharges_4089.45</th>\n",
+              "      <th>TotalCharges_409.9</th>\n",
+              "      <th>TotalCharges_4092.85</th>\n",
+              "      <th>TotalCharges_4096.9</th>\n",
+              "      <th>TotalCharges_4097.05</th>\n",
+              "      <th>TotalCharges_41.2</th>\n",
+              "      <th>TotalCharges_41.85</th>\n",
+              "      <th>TotalCharges_4103.9</th>\n",
+              "      <th>TotalCharges_4107.25</th>\n",
+              "      <th>TotalCharges_4107.3</th>\n",
+              "      <th>TotalCharges_4108.15</th>\n",
+              "      <th>TotalCharges_4109</th>\n",
+              "      <th>TotalCharges_411.15</th>\n",
+              "      <th>TotalCharges_411.25</th>\n",
+              "      <th>TotalCharges_411.45</th>\n",
+              "      <th>TotalCharges_411.6</th>\n",
+              "      <th>TotalCharges_411.75</th>\n",
+              "      <th>TotalCharges_4111.35</th>\n",
+              "      <th>TotalCharges_4113.1</th>\n",
+              "      <th>TotalCharges_4113.15</th>\n",
+              "      <th>TotalCharges_4113.7</th>\n",
+              "      <th>TotalCharges_4116.15</th>\n",
+              "      <th>TotalCharges_4116.8</th>\n",
+              "      <th>TotalCharges_4116.9</th>\n",
+              "      <th>TotalCharges_4119.4</th>\n",
+              "      <th>TotalCharges_412.1</th>\n",
+              "      <th>TotalCharges_412.5</th>\n",
+              "      <th>TotalCharges_412.55</th>\n",
+              "      <th>TotalCharges_412.6</th>\n",
+              "      <th>TotalCharges_4122.65</th>\n",
+              "      <th>TotalCharges_4122.9</th>\n",
+              "      <th>TotalCharges_4124.65</th>\n",
+              "      <th>TotalCharges_4126.2</th>\n",
+              "      <th>TotalCharges_4126.35</th>\n",
+              "      <th>TotalCharges_413</th>\n",
+              "      <th>TotalCharges_413.25</th>\n",
+              "      <th>TotalCharges_413.65</th>\n",
+              "      <th>TotalCharges_4131.2</th>\n",
+              "      <th>TotalCharges_4131.95</th>\n",
+              "      <th>TotalCharges_4133.95</th>\n",
+              "      <th>TotalCharges_4134.7</th>\n",
+              "      <th>TotalCharges_4134.85</th>\n",
+              "      <th>TotalCharges_4135</th>\n",
+              "      <th>TotalCharges_4136.4</th>\n",
+              "      <th>TotalCharges_4137.2</th>\n",
+              "      <th>TotalCharges_4138.05</th>\n",
+              "      <th>TotalCharges_4138.7</th>\n",
+              "      <th>TotalCharges_4138.9</th>\n",
+              "      <th>TotalCharges_414.1</th>\n",
+              "      <th>TotalCharges_414.95</th>\n",
+              "      <th>TotalCharges_4140.1</th>\n",
+              "      <th>TotalCharges_4144.8</th>\n",
+              "      <th>TotalCharges_4144.9</th>\n",
+              "      <th>TotalCharges_4145.25</th>\n",
+              "      <th>TotalCharges_4145.9</th>\n",
+              "      <th>TotalCharges_4146.05</th>\n",
+              "      <th>TotalCharges_4149.45</th>\n",
+              "      <th>TotalCharges_415.05</th>\n",
+              "      <th>TotalCharges_415.1</th>\n",
+              "      <th>TotalCharges_415.4</th>\n",
+              "      <th>TotalCharges_415.55</th>\n",
+              "      <th>TotalCharges_415.9</th>\n",
+              "      <th>TotalCharges_415.95</th>\n",
+              "      <th>TotalCharges_4154.55</th>\n",
+              "      <th>TotalCharges_4154.8</th>\n",
+              "      <th>TotalCharges_4155.95</th>\n",
+              "      <th>TotalCharges_4156.8</th>\n",
+              "      <th>TotalCharges_4158.25</th>\n",
+              "      <th>TotalCharges_4159.45</th>\n",
+              "      <th>TotalCharges_416.3</th>\n",
+              "      <th>TotalCharges_416.4</th>\n",
+              "      <th>TotalCharges_416.45</th>\n",
+              "      <th>TotalCharges_4162.05</th>\n",
+              "      <th>TotalCharges_4164.4</th>\n",
+              "      <th>TotalCharges_4166.35</th>\n",
+              "      <th>TotalCharges_417</th>\n",
+              "      <th>TotalCharges_417.5</th>\n",
+              "      <th>TotalCharges_417.65</th>\n",
+              "      <th>TotalCharges_417.7</th>\n",
+              "      <th>TotalCharges_417.75</th>\n",
+              "      <th>TotalCharges_4174.35</th>\n",
+              "      <th>TotalCharges_4176.7</th>\n",
+              "      <th>TotalCharges_4178.65</th>\n",
+              "      <th>TotalCharges_4179.1</th>\n",
+              "      <th>TotalCharges_4179.2</th>\n",
+              "      <th>TotalCharges_418.25</th>\n",
+              "      <th>TotalCharges_418.3</th>\n",
+              "      <th>TotalCharges_418.4</th>\n",
+              "      <th>TotalCharges_418.8</th>\n",
+              "      <th>TotalCharges_4186.3</th>\n",
+              "      <th>TotalCharges_4187.75</th>\n",
+              "      <th>TotalCharges_4188.4</th>\n",
+              "      <th>TotalCharges_4189.7</th>\n",
+              "      <th>TotalCharges_419.35</th>\n",
+              "      <th>TotalCharges_419.4</th>\n",
+              "      <th>TotalCharges_419.7</th>\n",
+              "      <th>TotalCharges_419.9</th>\n",
+              "      <th>TotalCharges_4191.45</th>\n",
+              "      <th>TotalCharges_4192.15</th>\n",
+              "      <th>TotalCharges_4193.4</th>\n",
+              "      <th>TotalCharges_4194.85</th>\n",
+              "      <th>TotalCharges_42.45</th>\n",
+              "      <th>TotalCharges_42.7</th>\n",
+              "      <th>TotalCharges_42.9</th>\n",
+              "      <th>TotalCharges_420.2</th>\n",
+              "      <th>TotalCharges_420.45</th>\n",
+              "      <th>TotalCharges_4200.25</th>\n",
+              "      <th>TotalCharges_4209.95</th>\n",
+              "      <th>TotalCharges_4211.55</th>\n",
+              "      <th>TotalCharges_4213.35</th>\n",
+              "      <th>TotalCharges_4213.9</th>\n",
+              "      <th>TotalCharges_4214.25</th>\n",
+              "      <th>TotalCharges_4217.8</th>\n",
+              "      <th>TotalCharges_422.3</th>\n",
+              "      <th>TotalCharges_422.4</th>\n",
+              "      <th>TotalCharges_422.5</th>\n",
+              "      <th>TotalCharges_422.6</th>\n",
+              "      <th>TotalCharges_422.7</th>\n",
+              "      <th>TotalCharges_4220.35</th>\n",
+              "      <th>TotalCharges_4222.95</th>\n",
+              "      <th>TotalCharges_4224.7</th>\n",
+              "      <th>TotalCharges_4226.7</th>\n",
+              "      <th>TotalCharges_4228.55</th>\n",
+              "      <th>TotalCharges_4230.25</th>\n",
+              "      <th>TotalCharges_4233.95</th>\n",
+              "      <th>TotalCharges_4234.15</th>\n",
+              "      <th>TotalCharges_4236.6</th>\n",
+              "      <th>TotalCharges_4237.5</th>\n",
+              "      <th>TotalCharges_4238.45</th>\n",
+              "      <th>TotalCharges_424.15</th>\n",
+              "      <th>TotalCharges_424.45</th>\n",
+              "      <th>TotalCharges_424.5</th>\n",
+              "      <th>TotalCharges_424.75</th>\n",
+              "      <th>TotalCharges_4242.35</th>\n",
+              "      <th>TotalCharges_4245.55</th>\n",
+              "      <th>TotalCharges_425.1</th>\n",
+              "      <th>TotalCharges_425.9</th>\n",
+              "      <th>TotalCharges_4250.1</th>\n",
+              "      <th>TotalCharges_4254.1</th>\n",
+              "      <th>TotalCharges_4254.85</th>\n",
+              "      <th>TotalCharges_4259.3</th>\n",
+              "      <th>TotalCharges_426.35</th>\n",
+              "      <th>TotalCharges_426.65</th>\n",
+              "      <th>TotalCharges_4261.2</th>\n",
+              "      <th>TotalCharges_4263.4</th>\n",
+              "      <th>TotalCharges_4263.45</th>\n",
+              "      <th>TotalCharges_4264</th>\n",
+              "      <th>TotalCharges_4264.25</th>\n",
+              "      <th>TotalCharges_4264.6</th>\n",
+              "      <th>TotalCharges_4265</th>\n",
+              "      <th>TotalCharges_4266.4</th>\n",
+              "      <th>TotalCharges_4267.15</th>\n",
+              "      <th>TotalCharges_4273.45</th>\n",
+              "      <th>TotalCharges_4275.75</th>\n",
+              "      <th>TotalCharges_428.45</th>\n",
+              "      <th>TotalCharges_428.7</th>\n",
+              "      <th>TotalCharges_4282.4</th>\n",
+              "      <th>TotalCharges_4284.2</th>\n",
+              "      <th>TotalCharges_4284.65</th>\n",
+              "      <th>TotalCharges_4284.8</th>\n",
+              "      <th>TotalCharges_4285.8</th>\n",
+              "      <th>TotalCharges_4287.2</th>\n",
+              "      <th>TotalCharges_429.55</th>\n",
+              "      <th>TotalCharges_4295.35</th>\n",
+              "      <th>TotalCharges_4297.6</th>\n",
+              "      <th>TotalCharges_4297.95</th>\n",
+              "      <th>TotalCharges_4298.45</th>\n",
+              "      <th>TotalCharges_4299.2</th>\n",
+              "      <th>TotalCharges_4299.75</th>\n",
+              "      <th>TotalCharges_4299.95</th>\n",
+              "      <th>TotalCharges_43.15</th>\n",
+              "      <th>TotalCharges_43.3</th>\n",
+              "      <th>TotalCharges_43.8</th>\n",
+              "      <th>TotalCharges_43.85</th>\n",
+              "      <th>TotalCharges_43.95</th>\n",
+              "      <th>TotalCharges_4300.45</th>\n",
+              "      <th>TotalCharges_4300.8</th>\n",
+              "      <th>TotalCharges_4303.65</th>\n",
+              "      <th>TotalCharges_4304</th>\n",
+              "      <th>TotalCharges_4304.5</th>\n",
+              "      <th>TotalCharges_4307.1</th>\n",
+              "      <th>TotalCharges_4308.25</th>\n",
+              "      <th>TotalCharges_4309.55</th>\n",
+              "      <th>TotalCharges_431</th>\n",
+              "      <th>TotalCharges_4310.35</th>\n",
+              "      <th>TotalCharges_4312.5</th>\n",
+              "      <th>TotalCharges_4317.35</th>\n",
+              "      <th>TotalCharges_4318.35</th>\n",
+              "      <th>TotalCharges_432.25</th>\n",
+              "      <th>TotalCharges_432.5</th>\n",
+              "      <th>TotalCharges_4322.85</th>\n",
+              "      <th>TotalCharges_4323.35</th>\n",
+              "      <th>TotalCharges_4323.45</th>\n",
+              "      <th>TotalCharges_4326.25</th>\n",
+              "      <th>TotalCharges_4326.8</th>\n",
+              "      <th>TotalCharges_4327.5</th>\n",
+              "      <th>TotalCharges_433.5</th>\n",
+              "      <th>TotalCharges_433.75</th>\n",
+              "      <th>TotalCharges_433.95</th>\n",
+              "      <th>TotalCharges_4331.4</th>\n",
+              "      <th>TotalCharges_4335.2</th>\n",
+              "      <th>TotalCharges_4338.6</th>\n",
+              "      <th>TotalCharges_434.1</th>\n",
+              "      <th>TotalCharges_434.5</th>\n",
+              "      <th>TotalCharges_434.8</th>\n",
+              "      <th>TotalCharges_4345</th>\n",
+              "      <th>TotalCharges_4346.4</th>\n",
+              "      <th>TotalCharges_4348.1</th>\n",
+              "      <th>TotalCharges_4348.65</th>\n",
+              "      <th>TotalCharges_435</th>\n",
+              "      <th>TotalCharges_435.25</th>\n",
+              "      <th>TotalCharges_435.4</th>\n",
+              "      <th>TotalCharges_435.45</th>\n",
+              "      <th>TotalCharges_4350.1</th>\n",
+              "      <th>TotalCharges_4354.45</th>\n",
+              "      <th>TotalCharges_436.2</th>\n",
+              "      <th>TotalCharges_436.6</th>\n",
+              "      <th>TotalCharges_436.9</th>\n",
+              "      <th>TotalCharges_4361.55</th>\n",
+              "      <th>TotalCharges_4362.05</th>\n",
+              "      <th>TotalCharges_4364.1</th>\n",
+              "      <th>TotalCharges_4367.35</th>\n",
+              "      <th>TotalCharges_4368.85</th>\n",
+              "      <th>TotalCharges_4368.95</th>\n",
+              "      <th>TotalCharges_4370.25</th>\n",
+              "      <th>TotalCharges_4370.75</th>\n",
+              "      <th>TotalCharges_4374.55</th>\n",
+              "      <th>TotalCharges_4375.8</th>\n",
+              "      <th>TotalCharges_4378.35</th>\n",
+              "      <th>TotalCharges_4378.8</th>\n",
+              "      <th>TotalCharges_4378.9</th>\n",
+              "      <th>TotalCharges_438</th>\n",
+              "      <th>TotalCharges_438.05</th>\n",
+              "      <th>TotalCharges_438.25</th>\n",
+              "      <th>TotalCharges_438.4</th>\n",
+              "      <th>TotalCharges_438.9</th>\n",
+              "      <th>TotalCharges_4385.05</th>\n",
+              "      <th>TotalCharges_4386.2</th>\n",
+              "      <th>TotalCharges_4388.4</th>\n",
+              "      <th>TotalCharges_439.2</th>\n",
+              "      <th>TotalCharges_439.75</th>\n",
+              "      <th>TotalCharges_4390.25</th>\n",
+              "      <th>TotalCharges_4391.25</th>\n",
+              "      <th>TotalCharges_4391.45</th>\n",
+              "      <th>TotalCharges_4392.5</th>\n",
+              "      <th>TotalCharges_4398.15</th>\n",
+              "      <th>TotalCharges_4399.5</th>\n",
+              "      <th>TotalCharges_44</th>\n",
+              "      <th>TotalCharges_44.05</th>\n",
+              "      <th>TotalCharges_44.1</th>\n",
+              "      <th>TotalCharges_44.15</th>\n",
+              "      <th>TotalCharges_44.2</th>\n",
+              "      <th>TotalCharges_44.3</th>\n",
+              "      <th>TotalCharges_44.35</th>\n",
+              "      <th>TotalCharges_44.4</th>\n",
+              "      <th>TotalCharges_44.45</th>\n",
+              "      <th>TotalCharges_44.55</th>\n",
+              "      <th>TotalCharges_44.6</th>\n",
+              "      <th>TotalCharges_44.65</th>\n",
+              "      <th>TotalCharges_44.7</th>\n",
+              "      <th>TotalCharges_44.75</th>\n",
+              "      <th>TotalCharges_44.8</th>\n",
+              "      <th>TotalCharges_44.9</th>\n",
+              "      <th>TotalCharges_44.95</th>\n",
+              "      <th>TotalCharges_440.2</th>\n",
+              "      <th>TotalCharges_4400.75</th>\n",
+              "      <th>TotalCharges_4408.45</th>\n",
+              "      <th>TotalCharges_4414.3</th>\n",
+              "      <th>TotalCharges_4415.75</th>\n",
+              "      <th>TotalCharges_442.2</th>\n",
+              "      <th>TotalCharges_442.45</th>\n",
+              "      <th>TotalCharges_442.6</th>\n",
+              "      <th>TotalCharges_442.85</th>\n",
+              "      <th>TotalCharges_4421.95</th>\n",
+              "      <th>TotalCharges_4422.95</th>\n",
+              "      <th>TotalCharges_4424.2</th>\n",
+              "      <th>TotalCharges_4428.45</th>\n",
+              "      <th>TotalCharges_4428.6</th>\n",
+              "      <th>TotalCharges_443.9</th>\n",
+              "      <th>TotalCharges_4433.3</th>\n",
+              "      <th>TotalCharges_4438.2</th>\n",
+              "      <th>TotalCharges_444.75</th>\n",
+              "      <th>TotalCharges_4442.75</th>\n",
+              "      <th>TotalCharges_4443.45</th>\n",
+              "      <th>TotalCharges_4445.3</th>\n",
+              "      <th>TotalCharges_4447.55</th>\n",
+              "      <th>TotalCharges_4447.75</th>\n",
+              "      <th>TotalCharges_4448.8</th>\n",
+              "      <th>TotalCharges_4449.75</th>\n",
+              "      <th>TotalCharges_445.3</th>\n",
+              "      <th>TotalCharges_445.85</th>\n",
+              "      <th>TotalCharges_445.95</th>\n",
+              "      <th>TotalCharges_4451.85</th>\n",
+              "      <th>TotalCharges_4453.3</th>\n",
+              "      <th>TotalCharges_4454.25</th>\n",
+              "      <th>TotalCharges_4456.35</th>\n",
+              "      <th>TotalCharges_4456.65</th>\n",
+              "      <th>TotalCharges_4458.15</th>\n",
+              "      <th>TotalCharges_4459.15</th>\n",
+              "      <th>TotalCharges_4459.8</th>\n",
+              "      <th>TotalCharges_446.05</th>\n",
+              "      <th>TotalCharges_446.1</th>\n",
+              "      <th>TotalCharges_446.8</th>\n",
+              "      <th>TotalCharges_4461.85</th>\n",
+              "      <th>TotalCharges_4464.8</th>\n",
+              "      <th>TotalCharges_4469.1</th>\n",
+              "      <th>TotalCharges_447.75</th>\n",
+              "      <th>TotalCharges_447.9</th>\n",
+              "      <th>TotalCharges_4473</th>\n",
+              "      <th>TotalCharges_4473.45</th>\n",
+              "      <th>TotalCharges_4475.9</th>\n",
+              "      <th>TotalCharges_4478.85</th>\n",
+              "      <th>TotalCharges_4479.2</th>\n",
+              "      <th>TotalCharges_4480.7</th>\n",
+              "      <th>TotalCharges_4481</th>\n",
+              "      <th>TotalCharges_4483.95</th>\n",
+              "      <th>TotalCharges_4484.05</th>\n",
+              "      <th>TotalCharges_4487.3</th>\n",
+              "      <th>TotalCharges_449.3</th>\n",
+              "      <th>TotalCharges_449.75</th>\n",
+              "      <th>TotalCharges_4492.9</th>\n",
+              "      <th>TotalCharges_4494.65</th>\n",
+              "      <th>TotalCharges_4495.65</th>\n",
+              "      <th>TotalCharges_45</th>\n",
+              "      <th>TotalCharges_45.05</th>\n",
+              "      <th>TotalCharges_45.1</th>\n",
+              "      <th>TotalCharges_45.15</th>\n",
+              "      <th>TotalCharges_45.2</th>\n",
+              "      <th>TotalCharges_45.25</th>\n",
+              "      <th>TotalCharges_45.3</th>\n",
+              "      <th>TotalCharges_45.35</th>\n",
+              "      <th>TotalCharges_45.4</th>\n",
+              "      <th>TotalCharges_45.55</th>\n",
+              "      <th>TotalCharges_45.6</th>\n",
+              "      <th>TotalCharges_45.65</th>\n",
+              "      <th>TotalCharges_45.7</th>\n",
+              "      <th>TotalCharges_45.75</th>\n",
+              "      <th>TotalCharges_45.8</th>\n",
+              "      <th>TotalCharges_45.85</th>\n",
+              "      <th>TotalCharges_45.95</th>\n",
+              "      <th>TotalCharges_450.4</th>\n",
+              "      <th>TotalCharges_450.65</th>\n",
+              "      <th>TotalCharges_450.8</th>\n",
+              "      <th>TotalCharges_450.9</th>\n",
+              "      <th>TotalCharges_4504.55</th>\n",
+              "      <th>TotalCharges_4504.9</th>\n",
+              "      <th>TotalCharges_4507.15</th>\n",
+              "      <th>TotalCharges_4508.65</th>\n",
+              "      <th>TotalCharges_4509.5</th>\n",
+              "      <th>TotalCharges_451.1</th>\n",
+              "      <th>TotalCharges_451.55</th>\n",
+              "      <th>TotalCharges_4510.8</th>\n",
+              "      <th>TotalCharges_4512.7</th>\n",
+              "      <th>TotalCharges_4513.65</th>\n",
+              "      <th>TotalCharges_4515.85</th>\n",
+              "      <th>TotalCharges_4517.25</th>\n",
+              "      <th>TotalCharges_4519.5</th>\n",
+              "      <th>TotalCharges_452.2</th>\n",
+              "      <th>TotalCharges_452.35</th>\n",
+              "      <th>TotalCharges_452.55</th>\n",
+              "      <th>TotalCharges_452.7</th>\n",
+              "      <th>TotalCharges_452.8</th>\n",
+              "      <th>TotalCharges_4520.15</th>\n",
+              "      <th>TotalCharges_4523.25</th>\n",
+              "      <th>TotalCharges_4524.05</th>\n",
+              "      <th>TotalCharges_4525.8</th>\n",
+              "      <th>TotalCharges_4526.85</th>\n",
+              "      <th>TotalCharges_4527.45</th>\n",
+              "      <th>TotalCharges_4528</th>\n",
+              "      <th>TotalCharges_453.4</th>\n",
+              "      <th>TotalCharges_453.75</th>\n",
+              "      <th>TotalCharges_4532.3</th>\n",
+              "      <th>TotalCharges_4533.7</th>\n",
+              "      <th>TotalCharges_4533.9</th>\n",
+              "      <th>TotalCharges_4534.45</th>\n",
+              "      <th>TotalCharges_4534.9</th>\n",
+              "      <th>TotalCharges_4535.85</th>\n",
+              "      <th>TotalCharges_4539.35</th>\n",
+              "      <th>TotalCharges_4539.6</th>\n",
+              "      <th>TotalCharges_454</th>\n",
+              "      <th>TotalCharges_454.05</th>\n",
+              "      <th>TotalCharges_454.15</th>\n",
+              "      <th>TotalCharges_454.65</th>\n",
+              "      <th>TotalCharges_4541.2</th>\n",
+              "      <th>TotalCharges_4541.9</th>\n",
+              "      <th>TotalCharges_4542.35</th>\n",
+              "      <th>TotalCharges_4543.15</th>\n",
+              "      <th>TotalCharges_4543.95</th>\n",
+              "      <th>TotalCharges_4546</th>\n",
+              "      <th>TotalCharges_4547.25</th>\n",
+              "      <th>TotalCharges_4549.05</th>\n",
+              "      <th>TotalCharges_4549.45</th>\n",
+              "      <th>TotalCharges_455.3</th>\n",
+              "      <th>TotalCharges_455.5</th>\n",
+              "      <th>TotalCharges_4551.5</th>\n",
+              "      <th>TotalCharges_4554.85</th>\n",
+              "      <th>TotalCharges_4555.2</th>\n",
+              "      <th>TotalCharges_4557.5</th>\n",
+              "      <th>TotalCharges_456.85</th>\n",
+              "      <th>TotalCharges_4563</th>\n",
+              "      <th>TotalCharges_4564.9</th>\n",
+              "      <th>TotalCharges_4566.5</th>\n",
+              "      <th>TotalCharges_457.1</th>\n",
+              "      <th>TotalCharges_457.3</th>\n",
+              "      <th>TotalCharges_4575.35</th>\n",
+              "      <th>TotalCharges_4576.3</th>\n",
+              "      <th>TotalCharges_4577.75</th>\n",
+              "      <th>TotalCharges_4577.9</th>\n",
+              "      <th>TotalCharges_458.1</th>\n",
+              "      <th>TotalCharges_4586.15</th>\n",
+              "      <th>TotalCharges_4589.85</th>\n",
+              "      <th>TotalCharges_459.6</th>\n",
+              "      <th>TotalCharges_459.95</th>\n",
+              "      <th>TotalCharges_4590.35</th>\n",
+              "      <th>TotalCharges_4594.65</th>\n",
+              "      <th>TotalCharges_4594.95</th>\n",
+              "      <th>TotalCharges_4599.15</th>\n",
+              "      <th>TotalCharges_46</th>\n",
+              "      <th>TotalCharges_46.2</th>\n",
+              "      <th>TotalCharges_46.3</th>\n",
+              "      <th>TotalCharges_46.35</th>\n",
+              "      <th>TotalCharges_46.4</th>\n",
+              "      <th>TotalCharges_460.2</th>\n",
+              "      <th>TotalCharges_460.25</th>\n",
+              "      <th>TotalCharges_4600.7</th>\n",
+              "      <th>TotalCharges_4600.95</th>\n",
+              "      <th>TotalCharges_461.3</th>\n",
+              "      <th>TotalCharges_461.7</th>\n",
+              "      <th>TotalCharges_4613.95</th>\n",
+              "      <th>TotalCharges_4614.55</th>\n",
+              "      <th>TotalCharges_4615.25</th>\n",
+              "      <th>TotalCharges_4615.9</th>\n",
+              "      <th>TotalCharges_4616.05</th>\n",
+              "      <th>TotalCharges_4616.1</th>\n",
+              "      <th>TotalCharges_4619.55</th>\n",
+              "      <th>TotalCharges_462.8</th>\n",
+              "      <th>TotalCharges_4620.4</th>\n",
+              "      <th>TotalCharges_4627.65</th>\n",
+              "      <th>TotalCharges_4627.8</th>\n",
+              "      <th>TotalCharges_4627.85</th>\n",
+              "      <th>TotalCharges_463.05</th>\n",
+              "      <th>TotalCharges_463.6</th>\n",
+              "      <th>TotalCharges_4630.2</th>\n",
+              "      <th>TotalCharges_4631.7</th>\n",
+              "      <th>TotalCharges_4634.35</th>\n",
+              "      <th>TotalCharges_4639.45</th>\n",
+              "      <th>TotalCharges_4641.1</th>\n",
+              "      <th>TotalCharges_465.05</th>\n",
+              "      <th>TotalCharges_465.45</th>\n",
+              "      <th>TotalCharges_465.7</th>\n",
+              "      <th>TotalCharges_465.85</th>\n",
+              "      <th>TotalCharges_4652.4</th>\n",
+              "      <th>TotalCharges_4653.25</th>\n",
+              "      <th>TotalCharges_4653.85</th>\n",
+              "      <th>TotalCharges_4657.95</th>\n",
+              "      <th>TotalCharges_466.6</th>\n",
+              "      <th>TotalCharges_4663.4</th>\n",
+              "      <th>TotalCharges_4664.15</th>\n",
+              "      <th>TotalCharges_4664.2</th>\n",
+              "      <th>TotalCharges_4664.5</th>\n",
+              "      <th>TotalCharges_4667</th>\n",
+              "      <th>TotalCharges_4669.2</th>\n",
+              "      <th>TotalCharges_467.15</th>\n",
+              "      <th>TotalCharges_467.5</th>\n",
+              "      <th>TotalCharges_467.55</th>\n",
+              "      <th>TotalCharges_467.7</th>\n",
+              "      <th>TotalCharges_467.85</th>\n",
+              "      <th>TotalCharges_4671.65</th>\n",
+              "      <th>TotalCharges_4671.7</th>\n",
+              "      <th>TotalCharges_4674.4</th>\n",
+              "      <th>TotalCharges_4674.55</th>\n",
+              "      <th>TotalCharges_4676.7</th>\n",
+              "      <th>TotalCharges_4677.1</th>\n",
+              "      <th>TotalCharges_468.35</th>\n",
+              "      <th>TotalCharges_4680.05</th>\n",
+              "      <th>TotalCharges_4681.75</th>\n",
+              "      <th>TotalCharges_4684.3</th>\n",
+              "      <th>TotalCharges_4685.55</th>\n",
+              "      <th>TotalCharges_4687.9</th>\n",
+              "      <th>TotalCharges_4688.65</th>\n",
+              "      <th>TotalCharges_4689.15</th>\n",
+              "      <th>TotalCharges_4689.5</th>\n",
+              "      <th>TotalCharges_469.25</th>\n",
+              "      <th>TotalCharges_469.65</th>\n",
+              "      <th>TotalCharges_469.8</th>\n",
+              "      <th>TotalCharges_469.85</th>\n",
+              "      <th>TotalCharges_4690.65</th>\n",
+              "      <th>TotalCharges_4692.55</th>\n",
+              "      <th>TotalCharges_4692.95</th>\n",
+              "      <th>TotalCharges_4693.2</th>\n",
+              "      <th>TotalCharges_4698.05</th>\n",
+              "      <th>TotalCharges_47.5</th>\n",
+              "      <th>TotalCharges_47.7</th>\n",
+              "      <th>TotalCharges_47.95</th>\n",
+              "      <th>TotalCharges_470</th>\n",
+              "      <th>TotalCharges_470.2</th>\n",
+              "      <th>TotalCharges_470.6</th>\n",
+              "      <th>TotalCharges_470.95</th>\n",
+              "      <th>TotalCharges_4707.1</th>\n",
+              "      <th>TotalCharges_4707.85</th>\n",
+              "      <th>TotalCharges_471.35</th>\n",
+              "      <th>TotalCharges_471.55</th>\n",
+              "      <th>TotalCharges_471.7</th>\n",
+              "      <th>TotalCharges_471.85</th>\n",
+              "      <th>TotalCharges_4713.4</th>\n",
+              "      <th>TotalCharges_4718.25</th>\n",
+              "      <th>TotalCharges_4719.75</th>\n",
+              "      <th>TotalCharges_472.25</th>\n",
+              "      <th>TotalCharges_472.65</th>\n",
+              "      <th>TotalCharges_4720</th>\n",
+              "      <th>TotalCharges_4729.3</th>\n",
+              "      <th>TotalCharges_4729.75</th>\n",
+              "      <th>TotalCharges_473.9</th>\n",
+              "      <th>TotalCharges_4730.6</th>\n",
+              "      <th>TotalCharges_4730.9</th>\n",
+              "      <th>TotalCharges_4732.35</th>\n",
+              "      <th>TotalCharges_4733.1</th>\n",
+              "      <th>TotalCharges_4735.2</th>\n",
+              "      <th>TotalCharges_4735.35</th>\n",
+              "      <th>TotalCharges_4738.3</th>\n",
+              "      <th>TotalCharges_4738.85</th>\n",
+              "      <th>TotalCharges_474.8</th>\n",
+              "      <th>TotalCharges_474.9</th>\n",
+              "      <th>TotalCharges_4740</th>\n",
+              "      <th>TotalCharges_4741.45</th>\n",
+              "      <th>TotalCharges_4744.35</th>\n",
+              "      <th>TotalCharges_4746.05</th>\n",
+              "      <th>TotalCharges_4747.2</th>\n",
+              "      <th>TotalCharges_4747.5</th>\n",
+              "      <th>TotalCharges_4747.65</th>\n",
+              "      <th>TotalCharges_4747.85</th>\n",
+              "      <th>TotalCharges_4748.7</th>\n",
+              "      <th>TotalCharges_4749.15</th>\n",
+              "      <th>TotalCharges_475</th>\n",
+              "      <th>TotalCharges_475.1</th>\n",
+              "      <th>TotalCharges_475.2</th>\n",
+              "      <th>TotalCharges_475.25</th>\n",
+              "      <th>TotalCharges_475.7</th>\n",
+              "      <th>TotalCharges_4750.95</th>\n",
+              "      <th>TotalCharges_4753.85</th>\n",
+              "      <th>TotalCharges_4754.3</th>\n",
+              "      <th>TotalCharges_4758.8</th>\n",
+              "      <th>TotalCharges_4759.55</th>\n",
+              "      <th>TotalCharges_4759.75</th>\n",
+              "      <th>TotalCharges_4759.85</th>\n",
+              "      <th>TotalCharges_476.8</th>\n",
+              "      <th>TotalCharges_4760.3</th>\n",
+              "      <th>TotalCharges_4764</th>\n",
+              "      <th>TotalCharges_4765</th>\n",
+              "      <th>TotalCharges_477.05</th>\n",
+              "      <th>TotalCharges_477.55</th>\n",
+              "      <th>TotalCharges_477.6</th>\n",
+              "      <th>TotalCharges_477.7</th>\n",
+              "      <th>TotalCharges_4779.45</th>\n",
+              "      <th>TotalCharges_478.1</th>\n",
+              "      <th>TotalCharges_478.75</th>\n",
+              "      <th>TotalCharges_4783.5</th>\n",
+              "      <th>TotalCharges_4784.45</th>\n",
+              "      <th>TotalCharges_4786.1</th>\n",
+              "      <th>TotalCharges_4786.15</th>\n",
+              "      <th>TotalCharges_4793.8</th>\n",
+              "      <th>TotalCharges_4798.4</th>\n",
+              "      <th>TotalCharges_48.35</th>\n",
+              "      <th>TotalCharges_48.45</th>\n",
+              "      <th>TotalCharges_48.55</th>\n",
+              "      <th>TotalCharges_48.6</th>\n",
+              "      <th>TotalCharges_48.75</th>\n",
+              "      <th>TotalCharges_48.95</th>\n",
+              "      <th>TotalCharges_480.6</th>\n",
+              "      <th>TotalCharges_480.75</th>\n",
+              "      <th>TotalCharges_4801.1</th>\n",
+              "      <th>TotalCharges_4804.65</th>\n",
+              "      <th>TotalCharges_4804.75</th>\n",
+              "      <th>TotalCharges_4805.3</th>\n",
+              "      <th>TotalCharges_4805.65</th>\n",
+              "      <th>TotalCharges_4807.35</th>\n",
+              "      <th>TotalCharges_4807.45</th>\n",
+              "      <th>TotalCharges_4808.7</th>\n",
+              "      <th>TotalCharges_481.1</th>\n",
+              "      <th>TotalCharges_4811.6</th>\n",
+              "      <th>TotalCharges_4812.75</th>\n",
+              "      <th>TotalCharges_4816.7</th>\n",
+              "      <th>TotalCharges_4818.8</th>\n",
+              "      <th>TotalCharges_4819.75</th>\n",
+              "      <th>TotalCharges_482.25</th>\n",
+              "      <th>TotalCharges_482.8</th>\n",
+              "      <th>TotalCharges_4820.15</th>\n",
+              "      <th>TotalCharges_4820.55</th>\n",
+              "      <th>TotalCharges_4822.85</th>\n",
+              "      <th>TotalCharges_4824.45</th>\n",
+              "      <th>TotalCharges_4828.05</th>\n",
+              "      <th>TotalCharges_483.15</th>\n",
+              "      <th>TotalCharges_483.3</th>\n",
+              "      <th>TotalCharges_483.7</th>\n",
+              "      <th>TotalCharges_4830.25</th>\n",
+              "      <th>TotalCharges_4834</th>\n",
+              "      <th>TotalCharges_4837.6</th>\n",
+              "      <th>TotalCharges_4839.15</th>\n",
+              "      <th>TotalCharges_484.05</th>\n",
+              "      <th>TotalCharges_4845.4</th>\n",
+              "      <th>TotalCharges_4847.05</th>\n",
+              "      <th>TotalCharges_4847.35</th>\n",
+              "      <th>TotalCharges_4849.1</th>\n",
+              "      <th>TotalCharges_485.2</th>\n",
+              "      <th>TotalCharges_485.25</th>\n",
+              "      <th>TotalCharges_485.9</th>\n",
+              "      <th>TotalCharges_4853.75</th>\n",
+              "      <th>TotalCharges_4854.3</th>\n",
+              "      <th>TotalCharges_4855.35</th>\n",
+              "      <th>TotalCharges_4858.7</th>\n",
+              "      <th>TotalCharges_4859.1</th>\n",
+              "      <th>TotalCharges_4859.25</th>\n",
+              "      <th>TotalCharges_4859.95</th>\n",
+              "      <th>TotalCharges_486.05</th>\n",
+              "      <th>TotalCharges_486.2</th>\n",
+              "      <th>TotalCharges_486.85</th>\n",
+              "      <th>TotalCharges_4860.35</th>\n",
+              "      <th>TotalCharges_4860.85</th>\n",
+              "      <th>TotalCharges_4861.45</th>\n",
+              "      <th>TotalCharges_4862.5</th>\n",
+              "      <th>TotalCharges_4863.85</th>\n",
+              "      <th>TotalCharges_4867.95</th>\n",
+              "      <th>TotalCharges_4868.4</th>\n",
+              "      <th>TotalCharges_4869.35</th>\n",
+              "      <th>TotalCharges_4869.5</th>\n",
+              "      <th>TotalCharges_487.05</th>\n",
+              "      <th>TotalCharges_487.75</th>\n",
+              "      <th>TotalCharges_487.95</th>\n",
+              "      <th>TotalCharges_4871.05</th>\n",
+              "      <th>TotalCharges_4871.45</th>\n",
+              "      <th>TotalCharges_4872.2</th>\n",
+              "      <th>TotalCharges_4872.35</th>\n",
+              "      <th>TotalCharges_4872.45</th>\n",
+              "      <th>TotalCharges_4874.7</th>\n",
+              "      <th>TotalCharges_4874.8</th>\n",
+              "      <th>TotalCharges_488.25</th>\n",
+              "      <th>TotalCharges_488.65</th>\n",
+              "      <th>TotalCharges_4882.8</th>\n",
+              "      <th>TotalCharges_4884.85</th>\n",
+              "      <th>TotalCharges_4885.85</th>\n",
+              "      <th>TotalCharges_4888.2</th>\n",
+              "      <th>TotalCharges_4888.7</th>\n",
+              "      <th>TotalCharges_4889.2</th>\n",
+              "      <th>TotalCharges_4889.3</th>\n",
+              "      <th>TotalCharges_489.45</th>\n",
+              "      <th>TotalCharges_4890.5</th>\n",
+              "      <th>TotalCharges_4891.5</th>\n",
+              "      <th>TotalCharges_4895.1</th>\n",
+              "      <th>TotalCharges_4896.35</th>\n",
+              "      <th>TotalCharges_49</th>\n",
+              "      <th>TotalCharges_49.05</th>\n",
+              "      <th>TotalCharges_49.25</th>\n",
+              "      <th>TotalCharges_49.3</th>\n",
+              "      <th>TotalCharges_49.5</th>\n",
+              "      <th>TotalCharges_49.55</th>\n",
+              "      <th>TotalCharges_49.65</th>\n",
+              "      <th>TotalCharges_49.7</th>\n",
+              "      <th>TotalCharges_49.75</th>\n",
+              "      <th>TotalCharges_49.8</th>\n",
+              "      <th>TotalCharges_49.85</th>\n",
+              "      <th>TotalCharges_49.9</th>\n",
+              "      <th>TotalCharges_49.95</th>\n",
+              "      <th>TotalCharges_490.55</th>\n",
+              "      <th>TotalCharges_490.65</th>\n",
+              "      <th>TotalCharges_4900.65</th>\n",
+              "      <th>TotalCharges_4902.8</th>\n",
+              "      <th>TotalCharges_4903.15</th>\n",
+              "      <th>TotalCharges_4903.2</th>\n",
+              "      <th>TotalCharges_4904.2</th>\n",
+              "      <th>TotalCharges_4904.25</th>\n",
+              "      <th>TotalCharges_4904.85</th>\n",
+              "      <th>TotalCharges_4905.75</th>\n",
+              "      <th>TotalCharges_4908.25</th>\n",
+              "      <th>TotalCharges_4911.05</th>\n",
+              "      <th>TotalCharges_4911.35</th>\n",
+              "      <th>TotalCharges_4913.3</th>\n",
+              "      <th>TotalCharges_4914.8</th>\n",
+              "      <th>TotalCharges_4914.9</th>\n",
+              "      <th>TotalCharges_4915.15</th>\n",
+              "      <th>TotalCharges_4916.4</th>\n",
+              "      <th>TotalCharges_4916.95</th>\n",
+              "      <th>TotalCharges_4917.75</th>\n",
+              "      <th>TotalCharges_4917.9</th>\n",
+              "      <th>TotalCharges_4919.7</th>\n",
+              "      <th>TotalCharges_492</th>\n",
+              "      <th>TotalCharges_492.1</th>\n",
+              "      <th>TotalCharges_492.55</th>\n",
+              "      <th>TotalCharges_4920.55</th>\n",
+              "      <th>TotalCharges_4920.8</th>\n",
+              "      <th>TotalCharges_4921.2</th>\n",
+              "      <th>TotalCharges_4922.4</th>\n",
+              "      <th>TotalCharges_4925.35</th>\n",
+              "      <th>TotalCharges_4929.55</th>\n",
+              "      <th>TotalCharges_493.4</th>\n",
+              "      <th>TotalCharges_493.65</th>\n",
+              "      <th>TotalCharges_493.95</th>\n",
+              "      <th>TotalCharges_4931.8</th>\n",
+              "      <th>TotalCharges_4932.5</th>\n",
+              "      <th>TotalCharges_4939.25</th>\n",
+              "      <th>TotalCharges_494.05</th>\n",
+              "      <th>TotalCharges_494.9</th>\n",
+              "      <th>TotalCharges_494.95</th>\n",
+              "      <th>TotalCharges_4941.8</th>\n",
+              "      <th>TotalCharges_4946.05</th>\n",
+              "      <th>TotalCharges_4946.7</th>\n",
+              "      <th>TotalCharges_4947.55</th>\n",
+              "      <th>TotalCharges_4949.1</th>\n",
+              "      <th>TotalCharges_495.15</th>\n",
+              "      <th>TotalCharges_4952.95</th>\n",
+              "      <th>TotalCharges_4953.25</th>\n",
+              "      <th>TotalCharges_4959.15</th>\n",
+              "      <th>TotalCharges_4959.6</th>\n",
+              "      <th>TotalCharges_496.9</th>\n",
+              "      <th>TotalCharges_4964.7</th>\n",
+              "      <th>TotalCharges_4965</th>\n",
+              "      <th>TotalCharges_4965.1</th>\n",
+              "      <th>TotalCharges_4968</th>\n",
+              "      <th>TotalCharges_497.3</th>\n",
+              "      <th>TotalCharges_497.55</th>\n",
+              "      <th>TotalCharges_497.6</th>\n",
+              "      <th>TotalCharges_4972.1</th>\n",
+              "      <th>TotalCharges_4973.4</th>\n",
+              "      <th>TotalCharges_4976.15</th>\n",
+              "      <th>TotalCharges_4977.2</th>\n",
+              "      <th>TotalCharges_498.1</th>\n",
+              "      <th>TotalCharges_498.25</th>\n",
+              "      <th>TotalCharges_4981.15</th>\n",
+              "      <th>TotalCharges_4982.5</th>\n",
+              "      <th>TotalCharges_4983.05</th>\n",
+              "      <th>TotalCharges_4984.85</th>\n",
+              "      <th>TotalCharges_4985.9</th>\n",
+              "      <th>TotalCharges_499.4</th>\n",
+              "      <th>TotalCharges_4990.25</th>\n",
+              "      <th>TotalCharges_4991.5</th>\n",
+              "      <th>TotalCharges_4993.4</th>\n",
+              "      <th>TotalCharges_4995.35</th>\n",
+              "      <th>TotalCharges_4997.5</th>\n",
+              "      <th>TotalCharges_50.05</th>\n",
+              "      <th>TotalCharges_50.1</th>\n",
+              "      <th>TotalCharges_50.15</th>\n",
+              "      <th>TotalCharges_50.35</th>\n",
+              "      <th>TotalCharges_50.45</th>\n",
+              "      <th>TotalCharges_50.5</th>\n",
+              "      <th>TotalCharges_50.55</th>\n",
+              "      <th>TotalCharges_50.6</th>\n",
+              "      <th>TotalCharges_50.65</th>\n",
+              "      <th>TotalCharges_50.7</th>\n",
+              "      <th>TotalCharges_50.75</th>\n",
+              "      <th>TotalCharges_50.8</th>\n",
+              "      <th>TotalCharges_50.9</th>\n",
+              "      <th>TotalCharges_500.1</th>\n",
+              "      <th>TotalCharges_5000.05</th>\n",
+              "      <th>TotalCharges_5000.2</th>\n",
+              "      <th>TotalCharges_501</th>\n",
+              "      <th>TotalCharges_501.2</th>\n",
+              "      <th>TotalCharges_501.35</th>\n",
+              "      <th>TotalCharges_5011.15</th>\n",
+              "      <th>TotalCharges_5012.1</th>\n",
+              "      <th>TotalCharges_5012.35</th>\n",
+              "      <th>TotalCharges_5013</th>\n",
+              "      <th>TotalCharges_5016.25</th>\n",
+              "      <th>TotalCharges_5016.65</th>\n",
+              "      <th>TotalCharges_5017.7</th>\n",
+              "      <th>TotalCharges_5017.9</th>\n",
+              "      <th>TotalCharges_502.6</th>\n",
+              "      <th>TotalCharges_502.85</th>\n",
+              "      <th>TotalCharges_5023</th>\n",
+              "      <th>TotalCharges_5025</th>\n",
+              "      <th>TotalCharges_5025.8</th>\n",
+              "      <th>TotalCharges_5025.85</th>\n",
+              "      <th>TotalCharges_5029.05</th>\n",
+              "      <th>TotalCharges_5029.2</th>\n",
+              "      <th>TotalCharges_503.25</th>\n",
+              "      <th>TotalCharges_503.6</th>\n",
+              "      <th>TotalCharges_5031</th>\n",
+              "      <th>TotalCharges_5031.85</th>\n",
+              "      <th>TotalCharges_5032.25</th>\n",
+              "      <th>TotalCharges_5034.05</th>\n",
+              "      <th>TotalCharges_5036.3</th>\n",
+              "      <th>TotalCharges_5036.9</th>\n",
+              "      <th>TotalCharges_5037.55</th>\n",
+              "      <th>TotalCharges_5038.15</th>\n",
+              "      <th>TotalCharges_5038.45</th>\n",
+              "      <th>TotalCharges_504.05</th>\n",
+              "      <th>TotalCharges_504.2</th>\n",
+              "      <th>TotalCharges_5040.2</th>\n",
+              "      <th>TotalCharges_5042.75</th>\n",
+              "      <th>TotalCharges_5043.2</th>\n",
+              "      <th>TotalCharges_5044.8</th>\n",
+              "      <th>TotalCharges_505.45</th>\n",
+              "      <th>TotalCharges_505.9</th>\n",
+              "      <th>TotalCharges_505.95</th>\n",
+              "      <th>TotalCharges_5059.75</th>\n",
+              "      <th>TotalCharges_506.9</th>\n",
+              "      <th>TotalCharges_5060.85</th>\n",
+              "      <th>TotalCharges_5060.9</th>\n",
+              "      <th>TotalCharges_5064.45</th>\n",
+              "      <th>TotalCharges_5064.85</th>\n",
+              "      <th>TotalCharges_5067.45</th>\n",
+              "      <th>TotalCharges_5068.05</th>\n",
+              "      <th>TotalCharges_5069.65</th>\n",
+              "      <th>TotalCharges_507.4</th>\n",
+              "      <th>TotalCharges_507.9</th>\n",
+              "      <th>TotalCharges_5070.4</th>\n",
+              "      <th>TotalCharges_5071.05</th>\n",
+              "      <th>TotalCharges_5071.9</th>\n",
+              "      <th>TotalCharges_5073.1</th>\n",
+              "      <th>TotalCharges_5082.8</th>\n",
+              "      <th>TotalCharges_5083.55</th>\n",
+              "      <th>TotalCharges_5084.65</th>\n",
+              "      <th>TotalCharges_5088.4</th>\n",
+              "      <th>TotalCharges_509.3</th>\n",
+              "      <th>TotalCharges_5099.15</th>\n",
+              "      <th>TotalCharges_51.15</th>\n",
+              "      <th>TotalCharges_51.2</th>\n",
+              "      <th>TotalCharges_51.25</th>\n",
+              "      <th>TotalCharges_51.6</th>\n",
+              "      <th>TotalCharges_510.8</th>\n",
+              "      <th>TotalCharges_5102.35</th>\n",
+              "      <th>TotalCharges_511.25</th>\n",
+              "      <th>TotalCharges_5116.6</th>\n",
+              "      <th>TotalCharges_5118.95</th>\n",
+              "      <th>TotalCharges_512.25</th>\n",
+              "      <th>TotalCharges_512.45</th>\n",
+              "      <th>TotalCharges_5121.3</th>\n",
+              "      <th>TotalCharges_5121.75</th>\n",
+              "      <th>TotalCharges_5124.55</th>\n",
+              "      <th>TotalCharges_5124.6</th>\n",
+              "      <th>TotalCharges_5125.5</th>\n",
+              "      <th>TotalCharges_5125.75</th>\n",
+              "      <th>TotalCharges_5127.95</th>\n",
+              "      <th>TotalCharges_5129.3</th>\n",
+              "      <th>TotalCharges_5129.45</th>\n",
+              "      <th>TotalCharges_5135.15</th>\n",
+              "      <th>TotalCharges_5135.35</th>\n",
+              "      <th>TotalCharges_5138.1</th>\n",
+              "      <th>TotalCharges_5139.65</th>\n",
+              "      <th>TotalCharges_514</th>\n",
+              "      <th>TotalCharges_514.6</th>\n",
+              "      <th>TotalCharges_514.75</th>\n",
+              "      <th>TotalCharges_5149.5</th>\n",
+              "      <th>TotalCharges_515.45</th>\n",
+              "      <th>TotalCharges_515.75</th>\n",
+              "      <th>TotalCharges_5150.55</th>\n",
+              "      <th>TotalCharges_5153.5</th>\n",
+              "      <th>TotalCharges_5154.5</th>\n",
+              "      <th>TotalCharges_5154.6</th>\n",
+              "      <th>TotalCharges_516.15</th>\n",
+              "      <th>TotalCharges_516.3</th>\n",
+              "      <th>TotalCharges_5163</th>\n",
+              "      <th>TotalCharges_5163.3</th>\n",
+              "      <th>TotalCharges_5165.7</th>\n",
+              "      <th>TotalCharges_5166.2</th>\n",
+              "      <th>TotalCharges_5168.1</th>\n",
+              "      <th>TotalCharges_5174.35</th>\n",
+              "      <th>TotalCharges_5175.3</th>\n",
+              "      <th>TotalCharges_518.3</th>\n",
+              "      <th>TotalCharges_518.75</th>\n",
+              "      <th>TotalCharges_518.9</th>\n",
+              "      <th>TotalCharges_5186</th>\n",
+              "      <th>TotalCharges_5189.75</th>\n",
+              "      <th>TotalCharges_519.15</th>\n",
+              "      <th>TotalCharges_5193.2</th>\n",
+              "      <th>TotalCharges_5194.05</th>\n",
+              "      <th>TotalCharges_5196.1</th>\n",
+              "      <th>TotalCharges_5199.8</th>\n",
+              "      <th>TotalCharges_52</th>\n",
+              "      <th>TotalCharges_52.05</th>\n",
+              "      <th>TotalCharges_52.2</th>\n",
+              "      <th>TotalCharges_520</th>\n",
+              "      <th>TotalCharges_520.1</th>\n",
+              "      <th>TotalCharges_520.55</th>\n",
+              "      <th>TotalCharges_520.95</th>\n",
+              "      <th>TotalCharges_5200.8</th>\n",
+              "      <th>TotalCharges_5206.55</th>\n",
+              "      <th>TotalCharges_521</th>\n",
+              "      <th>TotalCharges_521.1</th>\n",
+              "      <th>TotalCharges_521.3</th>\n",
+              "      <th>TotalCharges_521.35</th>\n",
+              "      <th>TotalCharges_521.8</th>\n",
+              "      <th>TotalCharges_521.9</th>\n",
+              "      <th>TotalCharges_5212.65</th>\n",
+              "      <th>TotalCharges_5215.1</th>\n",
+              "      <th>TotalCharges_5215.25</th>\n",
+              "      <th>TotalCharges_5219.65</th>\n",
+              "      <th>TotalCharges_522.35</th>\n",
+              "      <th>TotalCharges_522.95</th>\n",
+              "      <th>TotalCharges_5222.3</th>\n",
+              "      <th>TotalCharges_5222.35</th>\n",
+              "      <th>TotalCharges_5224.35</th>\n",
+              "      <th>TotalCharges_5224.5</th>\n",
+              "      <th>TotalCharges_5224.95</th>\n",
+              "      <th>TotalCharges_5229.45</th>\n",
+              "      <th>TotalCharges_5229.8</th>\n",
+              "      <th>TotalCharges_523.1</th>\n",
+              "      <th>TotalCharges_523.15</th>\n",
+              "      <th>TotalCharges_523.5</th>\n",
+              "      <th>TotalCharges_5231.2</th>\n",
+              "      <th>TotalCharges_5231.3</th>\n",
+              "      <th>TotalCharges_5232.9</th>\n",
+              "      <th>TotalCharges_5233.25</th>\n",
+              "      <th>TotalCharges_5234.95</th>\n",
+              "      <th>TotalCharges_5236.4</th>\n",
+              "      <th>TotalCharges_5237.4</th>\n",
+              "      <th>TotalCharges_5238.9</th>\n",
+              "      <th>TotalCharges_524.35</th>\n",
+              "      <th>TotalCharges_524.5</th>\n",
+              "      <th>TotalCharges_5243.05</th>\n",
+              "      <th>TotalCharges_5244.45</th>\n",
+              "      <th>TotalCharges_525</th>\n",
+              "      <th>TotalCharges_525.55</th>\n",
+              "      <th>TotalCharges_5251.75</th>\n",
+              "      <th>TotalCharges_5253.95</th>\n",
+              "      <th>TotalCharges_526.7</th>\n",
+              "      <th>TotalCharges_526.95</th>\n",
+              "      <th>TotalCharges_5264.25</th>\n",
+              "      <th>TotalCharges_5264.3</th>\n",
+              "      <th>TotalCharges_5264.5</th>\n",
+              "      <th>TotalCharges_5265.1</th>\n",
+              "      <th>TotalCharges_5265.2</th>\n",
+              "      <th>TotalCharges_5265.5</th>\n",
+              "      <th>TotalCharges_5265.55</th>\n",
+              "      <th>TotalCharges_527.35</th>\n",
+              "      <th>TotalCharges_527.5</th>\n",
+              "      <th>TotalCharges_527.9</th>\n",
+              "      <th>TotalCharges_5270.6</th>\n",
+              "      <th>TotalCharges_5275.8</th>\n",
+              "      <th>TotalCharges_5276.1</th>\n",
+              "      <th>TotalCharges_5278.15</th>\n",
+              "      <th>TotalCharges_528.35</th>\n",
+              "      <th>TotalCharges_528.45</th>\n",
+              "      <th>TotalCharges_5283.95</th>\n",
+              "      <th>TotalCharges_5289.05</th>\n",
+              "      <th>TotalCharges_5289.8</th>\n",
+              "      <th>TotalCharges_529.5</th>\n",
+              "      <th>TotalCharges_529.8</th>\n",
+              "      <th>TotalCharges_5290.45</th>\n",
+              "      <th>TotalCharges_5293.2</th>\n",
+              "      <th>TotalCharges_5293.4</th>\n",
+              "      <th>TotalCharges_5293.95</th>\n",
+              "      <th>TotalCharges_5294.6</th>\n",
+              "      <th>TotalCharges_5295.7</th>\n",
+              "      <th>TotalCharges_5299.65</th>\n",
+              "      <th>TotalCharges_53.05</th>\n",
+              "      <th>TotalCharges_53.15</th>\n",
+              "      <th>TotalCharges_53.5</th>\n",
+              "      <th>TotalCharges_53.55</th>\n",
+              "      <th>TotalCharges_53.95</th>\n",
+              "      <th>TotalCharges_530.05</th>\n",
+              "      <th>TotalCharges_5301.1</th>\n",
+              "      <th>TotalCharges_5305.05</th>\n",
+              "      <th>TotalCharges_5308.7</th>\n",
+              "      <th>TotalCharges_5309.5</th>\n",
+              "      <th>TotalCharges_531</th>\n",
+              "      <th>TotalCharges_531.15</th>\n",
+              "      <th>TotalCharges_531.55</th>\n",
+              "      <th>TotalCharges_531.6</th>\n",
+              "      <th>TotalCharges_5311.85</th>\n",
+              "      <th>TotalCharges_5315.1</th>\n",
+              "      <th>TotalCharges_5315.8</th>\n",
+              "      <th>TotalCharges_5317.8</th>\n",
+              "      <th>TotalCharges_532.1</th>\n",
+              "      <th>TotalCharges_5321.25</th>\n",
+              "      <th>TotalCharges_5324.5</th>\n",
+              "      <th>TotalCharges_5327.25</th>\n",
+              "      <th>TotalCharges_5329</th>\n",
+              "      <th>TotalCharges_5329.55</th>\n",
+              "      <th>TotalCharges_533.05</th>\n",
+              "      <th>TotalCharges_533.5</th>\n",
+              "      <th>TotalCharges_533.6</th>\n",
+              "      <th>TotalCharges_533.9</th>\n",
+              "      <th>TotalCharges_5330.2</th>\n",
+              "      <th>TotalCharges_5333.35</th>\n",
+              "      <th>TotalCharges_5336.35</th>\n",
+              "      <th>TotalCharges_534.7</th>\n",
+              "      <th>TotalCharges_5341.8</th>\n",
+              "      <th>TotalCharges_5347.95</th>\n",
+              "      <th>TotalCharges_5348.65</th>\n",
+              "      <th>TotalCharges_535.05</th>\n",
+              "      <th>TotalCharges_535.35</th>\n",
+              "      <th>TotalCharges_535.55</th>\n",
+              "      <th>TotalCharges_5356.45</th>\n",
+              "      <th>TotalCharges_5357.75</th>\n",
+              "      <th>TotalCharges_536.35</th>\n",
+              "      <th>TotalCharges_536.4</th>\n",
+              "      <th>TotalCharges_5360.75</th>\n",
+              "      <th>TotalCharges_5364.8</th>\n",
+              "      <th>TotalCharges_537.35</th>\n",
+              "      <th>TotalCharges_5373.1</th>\n",
+              "      <th>TotalCharges_5375.15</th>\n",
+              "      <th>TotalCharges_5376.4</th>\n",
+              "      <th>TotalCharges_5377.8</th>\n",
+              "      <th>TotalCharges_538.2</th>\n",
+              "      <th>TotalCharges_538.5</th>\n",
+              "      <th>TotalCharges_5386.5</th>\n",
+              "      <th>TotalCharges_5388.15</th>\n",
+              "      <th>TotalCharges_539.85</th>\n",
+              "      <th>TotalCharges_5396.25</th>\n",
+              "      <th>TotalCharges_5398.6</th>\n",
+              "      <th>TotalCharges_54.3</th>\n",
+              "      <th>TotalCharges_54.35</th>\n",
+              "      <th>TotalCharges_54.5</th>\n",
+              "      <th>TotalCharges_54.65</th>\n",
+              "      <th>TotalCharges_54.7</th>\n",
+              "      <th>TotalCharges_54.75</th>\n",
+              "      <th>TotalCharges_54.9</th>\n",
+              "      <th>TotalCharges_540.05</th>\n",
+              "      <th>TotalCharges_540.95</th>\n",
+              "      <th>TotalCharges_5401.9</th>\n",
+              "      <th>TotalCharges_5405.8</th>\n",
+              "      <th>TotalCharges_5409.75</th>\n",
+              "      <th>TotalCharges_541.15</th>\n",
+              "      <th>TotalCharges_541.5</th>\n",
+              "      <th>TotalCharges_541.9</th>\n",
+              "      <th>TotalCharges_5411.4</th>\n",
+              "      <th>TotalCharges_5411.65</th>\n",
+              "      <th>TotalCharges_542.4</th>\n",
+              "      <th>TotalCharges_5420.65</th>\n",
+              "      <th>TotalCharges_5424.25</th>\n",
+              "      <th>TotalCharges_5426.85</th>\n",
+              "      <th>TotalCharges_5427.05</th>\n",
+              "      <th>TotalCharges_543</th>\n",
+              "      <th>TotalCharges_543.8</th>\n",
+              "      <th>TotalCharges_5430.35</th>\n",
+              "      <th>TotalCharges_5430.65</th>\n",
+              "      <th>TotalCharges_5431.4</th>\n",
+              "      <th>TotalCharges_5431.9</th>\n",
+              "      <th>TotalCharges_5432.2</th>\n",
+              "      <th>TotalCharges_5435</th>\n",
+              "      <th>TotalCharges_5435.6</th>\n",
+              "      <th>TotalCharges_5436.45</th>\n",
+              "      <th>TotalCharges_5437.1</th>\n",
+              "      <th>TotalCharges_5437.75</th>\n",
+              "      <th>TotalCharges_5438.9</th>\n",
+              "      <th>TotalCharges_5438.95</th>\n",
+              "      <th>TotalCharges_544.55</th>\n",
+              "      <th>TotalCharges_5440.9</th>\n",
+              "      <th>TotalCharges_5442.05</th>\n",
+              "      <th>TotalCharges_5443.65</th>\n",
+              "      <th>TotalCharges_5445.95</th>\n",
+              "      <th>TotalCharges_5448.6</th>\n",
+              "      <th>TotalCharges_545.15</th>\n",
+              "      <th>TotalCharges_545.2</th>\n",
+              "      <th>TotalCharges_5450.7</th>\n",
+              "      <th>TotalCharges_5453.4</th>\n",
+              "      <th>TotalCharges_5458.8</th>\n",
+              "      <th>TotalCharges_5459.2</th>\n",
+              "      <th>TotalCharges_546.45</th>\n",
+              "      <th>TotalCharges_546.85</th>\n",
+              "      <th>TotalCharges_546.95</th>\n",
+              "      <th>TotalCharges_5460.2</th>\n",
+              "      <th>TotalCharges_5461.45</th>\n",
+              "      <th>TotalCharges_5464.65</th>\n",
+              "      <th>TotalCharges_5468.45</th>\n",
+              "      <th>TotalCharges_5468.95</th>\n",
+              "      <th>TotalCharges_547.65</th>\n",
+              "      <th>TotalCharges_547.8</th>\n",
+              "      <th>TotalCharges_5471.75</th>\n",
+              "      <th>TotalCharges_5475.9</th>\n",
+              "      <th>TotalCharges_548.8</th>\n",
+              "      <th>TotalCharges_548.9</th>\n",
+              "      <th>TotalCharges_5480.25</th>\n",
+              "      <th>TotalCharges_5481.25</th>\n",
+              "      <th>TotalCharges_5483.9</th>\n",
+              "      <th>TotalCharges_5484.4</th>\n",
+              "      <th>TotalCharges_5485.5</th>\n",
+              "      <th>TotalCharges_5487</th>\n",
+              "      <th>TotalCharges_5496.9</th>\n",
+              "      <th>TotalCharges_5497.05</th>\n",
+              "      <th>TotalCharges_5498.2</th>\n",
+              "      <th>TotalCharges_5498.8</th>\n",
+              "      <th>TotalCharges_55</th>\n",
+              "      <th>TotalCharges_55.05</th>\n",
+              "      <th>TotalCharges_55.2</th>\n",
+              "      <th>TotalCharges_55.25</th>\n",
+              "      <th>TotalCharges_55.3</th>\n",
+              "      <th>TotalCharges_55.4</th>\n",
+              "      <th>TotalCharges_55.45</th>\n",
+              "      <th>TotalCharges_55.55</th>\n",
+              "      <th>TotalCharges_55.7</th>\n",
+              "      <th>TotalCharges_55.8</th>\n",
+              "      <th>TotalCharges_550.1</th>\n",
+              "      <th>TotalCharges_550.35</th>\n",
+              "      <th>TotalCharges_550.6</th>\n",
+              "      <th>TotalCharges_5500.6</th>\n",
+              "      <th>TotalCharges_5502.55</th>\n",
+              "      <th>TotalCharges_5508.35</th>\n",
+              "      <th>TotalCharges_5509.3</th>\n",
+              "      <th>TotalCharges_551.3</th>\n",
+              "      <th>TotalCharges_551.35</th>\n",
+              "      <th>TotalCharges_551.95</th>\n",
+              "      <th>TotalCharges_5510.65</th>\n",
+              "      <th>TotalCharges_5511.65</th>\n",
+              "      <th>TotalCharges_5514.95</th>\n",
+              "      <th>TotalCharges_5515.45</th>\n",
+              "      <th>TotalCharges_5515.8</th>\n",
+              "      <th>TotalCharges_552.1</th>\n",
+              "      <th>TotalCharges_552.65</th>\n",
+              "      <th>TotalCharges_552.7</th>\n",
+              "      <th>TotalCharges_552.9</th>\n",
+              "      <th>TotalCharges_552.95</th>\n",
+              "      <th>TotalCharges_5522.7</th>\n",
+              "      <th>TotalCharges_5526.75</th>\n",
+              "      <th>TotalCharges_5528.9</th>\n",
+              "      <th>TotalCharges_553</th>\n",
+              "      <th>TotalCharges_553.4</th>\n",
+              "      <th>TotalCharges_5535.8</th>\n",
+              "      <th>TotalCharges_5536.5</th>\n",
+              "      <th>TotalCharges_5538.35</th>\n",
+              "      <th>TotalCharges_5538.8</th>\n",
+              "      <th>TotalCharges_554.05</th>\n",
+              "      <th>TotalCharges_554.25</th>\n",
+              "      <th>TotalCharges_554.45</th>\n",
+              "      <th>TotalCharges_5542.55</th>\n",
+              "      <th>TotalCharges_5549.4</th>\n",
+              "      <th>TotalCharges_555.4</th>\n",
+              "      <th>TotalCharges_5550.1</th>\n",
+              "      <th>TotalCharges_5551.15</th>\n",
+              "      <th>TotalCharges_5552.05</th>\n",
+              "      <th>TotalCharges_5552.5</th>\n",
+              "      <th>TotalCharges_5553.25</th>\n",
+              "      <th>TotalCharges_5555.3</th>\n",
+              "      <th>TotalCharges_556.35</th>\n",
+              "      <th>TotalCharges_5560</th>\n",
+              "      <th>TotalCharges_5563.65</th>\n",
+              "      <th>TotalCharges_5564.85</th>\n",
+              "      <th>TotalCharges_5566.4</th>\n",
+              "      <th>TotalCharges_5567.45</th>\n",
+              "      <th>TotalCharges_5567.55</th>\n",
+              "      <th>TotalCharges_5568.35</th>\n",
+              "      <th>TotalCharges_5574.35</th>\n",
+              "      <th>TotalCharges_5574.75</th>\n",
+              "      <th>TotalCharges_5576.3</th>\n",
+              "      <th>TotalCharges_558.8</th>\n",
+              "      <th>TotalCharges_5580.8</th>\n",
+              "      <th>TotalCharges_5581.05</th>\n",
+              "      <th>TotalCharges_5585.4</th>\n",
+              "      <th>TotalCharges_5586.45</th>\n",
+              "      <th>TotalCharges_5588.8</th>\n",
+              "      <th>TotalCharges_5589.3</th>\n",
+              "      <th>TotalCharges_5589.45</th>\n",
+              "      <th>TotalCharges_559.2</th>\n",
+              "      <th>TotalCharges_5594</th>\n",
+              "      <th>TotalCharges_5595.3</th>\n",
+              "      <th>TotalCharges_5597.65</th>\n",
+              "      <th>TotalCharges_5598</th>\n",
+              "      <th>TotalCharges_5598.3</th>\n",
+              "      <th>TotalCharges_56</th>\n",
+              "      <th>TotalCharges_56.25</th>\n",
+              "      <th>TotalCharges_56.35</th>\n",
+              "      <th>TotalCharges_560.6</th>\n",
+              "      <th>TotalCharges_560.85</th>\n",
+              "      <th>TotalCharges_5600.15</th>\n",
+              "      <th>TotalCharges_5601.4</th>\n",
+              "      <th>TotalCharges_5602.25</th>\n",
+              "      <th>TotalCharges_5607.75</th>\n",
+              "      <th>TotalCharges_5608.4</th>\n",
+              "      <th>TotalCharges_561.15</th>\n",
+              "      <th>TotalCharges_5610.15</th>\n",
+              "      <th>TotalCharges_5610.25</th>\n",
+              "      <th>TotalCharges_5610.7</th>\n",
+              "      <th>TotalCharges_5611.7</th>\n",
+              "      <th>TotalCharges_5611.75</th>\n",
+              "      <th>TotalCharges_5614.45</th>\n",
+              "      <th>TotalCharges_5617.75</th>\n",
+              "      <th>TotalCharges_5617.95</th>\n",
+              "      <th>TotalCharges_5618.3</th>\n",
+              "      <th>TotalCharges_562.6</th>\n",
+              "      <th>TotalCharges_562.7</th>\n",
+              "      <th>TotalCharges_5621.85</th>\n",
+              "      <th>TotalCharges_5623.7</th>\n",
+              "      <th>TotalCharges_5624.85</th>\n",
+              "      <th>TotalCharges_5625.55</th>\n",
+              "      <th>TotalCharges_5629.15</th>\n",
+              "      <th>TotalCharges_5629.55</th>\n",
+              "      <th>TotalCharges_563.05</th>\n",
+              "      <th>TotalCharges_563.5</th>\n",
+              "      <th>TotalCharges_563.65</th>\n",
+              "      <th>TotalCharges_5632.55</th>\n",
+              "      <th>TotalCharges_5637.85</th>\n",
+              "      <th>TotalCharges_5638.3</th>\n",
+              "      <th>TotalCharges_5639.05</th>\n",
+              "      <th>TotalCharges_564.35</th>\n",
+              "      <th>TotalCharges_564.4</th>\n",
+              "      <th>TotalCharges_564.65</th>\n",
+              "      <th>TotalCharges_5643.4</th>\n",
+              "      <th>TotalCharges_5645.8</th>\n",
+              "      <th>TotalCharges_5646.6</th>\n",
+              "      <th>TotalCharges_5647.95</th>\n",
+              "      <th>TotalCharges_565.35</th>\n",
+              "      <th>TotalCharges_565.75</th>\n",
+              "      <th>TotalCharges_5655.45</th>\n",
+              "      <th>TotalCharges_5656.75</th>\n",
+              "      <th>TotalCharges_566.1</th>\n",
+              "      <th>TotalCharges_566.5</th>\n",
+              "      <th>TotalCharges_5661.7</th>\n",
+              "      <th>TotalCharges_5662.25</th>\n",
+              "      <th>TotalCharges_5669.5</th>\n",
+              "      <th>TotalCharges_567.45</th>\n",
+              "      <th>TotalCharges_567.8</th>\n",
+              "      <th>TotalCharges_5673.7</th>\n",
+              "      <th>TotalCharges_5676</th>\n",
+              "      <th>TotalCharges_5676.65</th>\n",
+              "      <th>TotalCharges_568.2</th>\n",
+              "      <th>TotalCharges_568.85</th>\n",
+              "      <th>TotalCharges_5680.9</th>\n",
+              "      <th>TotalCharges_5681.1</th>\n",
+              "      <th>TotalCharges_5682.25</th>\n",
+              "      <th>TotalCharges_5683.6</th>\n",
+              "      <th>TotalCharges_5685.8</th>\n",
+              "      <th>TotalCharges_5686.4</th>\n",
+              "      <th>TotalCharges_5688.05</th>\n",
+              "      <th>TotalCharges_5688.45</th>\n",
+              "      <th>TotalCharges_5692.65</th>\n",
+              "      <th>TotalCharges_5696.6</th>\n",
+              "      <th>TotalCharges_57.2</th>\n",
+              "      <th>TotalCharges_57.4</th>\n",
+              "      <th>TotalCharges_57.5</th>\n",
+              "      <th>TotalCharges_5703</th>\n",
+              "      <th>TotalCharges_5703.25</th>\n",
+              "      <th>TotalCharges_5705.05</th>\n",
+              "      <th>TotalCharges_5706.2</th>\n",
+              "      <th>TotalCharges_5706.3</th>\n",
+              "      <th>TotalCharges_5708.2</th>\n",
+              "      <th>TotalCharges_571.05</th>\n",
+              "      <th>TotalCharges_571.15</th>\n",
+              "      <th>TotalCharges_571.45</th>\n",
+              "      <th>TotalCharges_571.75</th>\n",
+              "      <th>TotalCharges_5711.05</th>\n",
+              "      <th>TotalCharges_5714.2</th>\n",
+              "      <th>TotalCharges_5714.25</th>\n",
+              "      <th>TotalCharges_5717.85</th>\n",
+              "      <th>TotalCharges_5718.2</th>\n",
+              "      <th>TotalCharges_572.2</th>\n",
+              "      <th>TotalCharges_572.45</th>\n",
+              "      <th>TotalCharges_572.85</th>\n",
+              "      <th>TotalCharges_5720.35</th>\n",
+              "      <th>TotalCharges_5720.95</th>\n",
+              "      <th>TotalCharges_5727.15</th>\n",
+              "      <th>TotalCharges_5727.45</th>\n",
+              "      <th>TotalCharges_5728.55</th>\n",
+              "      <th>TotalCharges_573.05</th>\n",
+              "      <th>TotalCharges_573.15</th>\n",
+              "      <th>TotalCharges_573.3</th>\n",
+              "      <th>TotalCharges_573.75</th>\n",
+              "      <th>TotalCharges_5730.15</th>\n",
+              "      <th>TotalCharges_5730.7</th>\n",
+              "      <th>TotalCharges_5731.4</th>\n",
+              "      <th>TotalCharges_5731.45</th>\n",
+              "      <th>TotalCharges_5731.85</th>\n",
+              "      <th>TotalCharges_5733.4</th>\n",
+              "      <th>TotalCharges_5737.6</th>\n",
+              "      <th>TotalCharges_574.35</th>\n",
+              "      <th>TotalCharges_574.5</th>\n",
+              "      <th>TotalCharges_5742.9</th>\n",
+              "      <th>TotalCharges_5743.05</th>\n",
+              "      <th>TotalCharges_5743.3</th>\n",
+              "      <th>TotalCharges_5744.35</th>\n",
+              "      <th>TotalCharges_5746.15</th>\n",
+              "      <th>TotalCharges_5746.75</th>\n",
+              "      <th>TotalCharges_5749.8</th>\n",
+              "      <th>TotalCharges_575.45</th>\n",
+              "      <th>TotalCharges_5750</th>\n",
+              "      <th>TotalCharges_5753.25</th>\n",
+              "      <th>TotalCharges_5755.8</th>\n",
+              "      <th>TotalCharges_5757.2</th>\n",
+              "      <th>TotalCharges_576.65</th>\n",
+              "      <th>TotalCharges_576.7</th>\n",
+              "      <th>TotalCharges_576.95</th>\n",
+              "      <th>TotalCharges_5760.65</th>\n",
+              "      <th>TotalCharges_5762.95</th>\n",
+              "      <th>TotalCharges_5763.15</th>\n",
+              "      <th>TotalCharges_5763.3</th>\n",
+              "      <th>TotalCharges_5764.7</th>\n",
+              "      <th>TotalCharges_5769.6</th>\n",
+              "      <th>TotalCharges_5769.75</th>\n",
+              "      <th>TotalCharges_577.15</th>\n",
+              "      <th>TotalCharges_577.6</th>\n",
+              "      <th>TotalCharges_5774.55</th>\n",
+              "      <th>TotalCharges_5776.45</th>\n",
+              "      <th>TotalCharges_5779.6</th>\n",
+              "      <th>TotalCharges_578.5</th>\n",
+              "      <th>TotalCharges_5780.7</th>\n",
+              "      <th>TotalCharges_5784.3</th>\n",
+              "      <th>TotalCharges_5785.5</th>\n",
+              "      <th>TotalCharges_5785.65</th>\n",
+              "      <th>TotalCharges_579</th>\n",
+              "      <th>TotalCharges_579.4</th>\n",
+              "      <th>TotalCharges_5791.1</th>\n",
+              "      <th>TotalCharges_5791.85</th>\n",
+              "      <th>TotalCharges_5794.45</th>\n",
+              "      <th>TotalCharges_5794.65</th>\n",
+              "      <th>TotalCharges_5798.3</th>\n",
+              "      <th>TotalCharges_58</th>\n",
+              "      <th>TotalCharges_58.15</th>\n",
+              "      <th>TotalCharges_58.3</th>\n",
+              "      <th>TotalCharges_58.85</th>\n",
+              "      <th>TotalCharges_58.9</th>\n",
+              "      <th>TotalCharges_580.1</th>\n",
+              "      <th>TotalCharges_580.8</th>\n",
+              "      <th>TotalCharges_5809.75</th>\n",
+              "      <th>TotalCharges_581.7</th>\n",
+              "      <th>TotalCharges_581.85</th>\n",
+              "      <th>TotalCharges_5810.9</th>\n",
+              "      <th>TotalCharges_5811.8</th>\n",
+              "      <th>TotalCharges_5812</th>\n",
+              "      <th>TotalCharges_5812.6</th>\n",
+              "      <th>TotalCharges_5815.15</th>\n",
+              "      <th>TotalCharges_5817</th>\n",
+              "      <th>TotalCharges_5817.45</th>\n",
+              "      <th>TotalCharges_5817.7</th>\n",
+              "      <th>TotalCharges_582.5</th>\n",
+              "      <th>TotalCharges_5822.3</th>\n",
+              "      <th>TotalCharges_5824.75</th>\n",
+              "      <th>TotalCharges_5825.5</th>\n",
+              "      <th>TotalCharges_5826.65</th>\n",
+              "      <th>TotalCharges_583</th>\n",
+              "      <th>TotalCharges_583.3</th>\n",
+              "      <th>TotalCharges_583.45</th>\n",
+              "      <th>TotalCharges_5831.2</th>\n",
+              "      <th>TotalCharges_5832</th>\n",
+              "      <th>TotalCharges_5832.65</th>\n",
+              "      <th>TotalCharges_5835.5</th>\n",
+              "      <th>TotalCharges_5839.3</th>\n",
+              "      <th>TotalCharges_5841.35</th>\n",
+              "      <th>TotalCharges_5844.65</th>\n",
+              "      <th>TotalCharges_5846.65</th>\n",
+              "      <th>TotalCharges_5848.6</th>\n",
+              "      <th>TotalCharges_585.95</th>\n",
+              "      <th>TotalCharges_586.05</th>\n",
+              "      <th>TotalCharges_5860.7</th>\n",
+              "      <th>TotalCharges_5861.75</th>\n",
+              "      <th>TotalCharges_5867</th>\n",
+              "      <th>TotalCharges_5869.4</th>\n",
+              "      <th>TotalCharges_587.1</th>\n",
+              "      <th>TotalCharges_587.4</th>\n",
+              "      <th>TotalCharges_587.45</th>\n",
+              "      <th>TotalCharges_587.7</th>\n",
+              "      <th>TotalCharges_5873.75</th>\n",
+              "      <th>TotalCharges_5878.9</th>\n",
+              "      <th>TotalCharges_5882.75</th>\n",
+              "      <th>TotalCharges_5883.85</th>\n",
+              "      <th>TotalCharges_5885.4</th>\n",
+              "      <th>TotalCharges_5886.85</th>\n",
+              "      <th>TotalCharges_589.25</th>\n",
+              "      <th>TotalCharges_5890</th>\n",
+              "      <th>TotalCharges_5893.15</th>\n",
+              "      <th>TotalCharges_5893.9</th>\n",
+              "      <th>TotalCharges_5893.95</th>\n",
+              "      <th>TotalCharges_5894.5</th>\n",
+              "      <th>TotalCharges_5895.45</th>\n",
+              "      <th>TotalCharges_5897.4</th>\n",
+              "      <th>TotalCharges_5898.6</th>\n",
+              "      <th>TotalCharges_5899.85</th>\n",
+              "      <th>TotalCharges_59.05</th>\n",
+              "      <th>TotalCharges_59.2</th>\n",
+              "      <th>TotalCharges_59.25</th>\n",
+              "      <th>TotalCharges_59.55</th>\n",
+              "      <th>TotalCharges_59.75</th>\n",
+              "      <th>TotalCharges_59.85</th>\n",
+              "      <th>TotalCharges_590.35</th>\n",
+              "      <th>TotalCharges_5903.15</th>\n",
+              "      <th>TotalCharges_5913.95</th>\n",
+              "      <th>TotalCharges_5914.4</th>\n",
+              "      <th>TotalCharges_5916.45</th>\n",
+              "      <th>TotalCharges_5916.95</th>\n",
+              "      <th>TotalCharges_5917.55</th>\n",
+              "      <th>TotalCharges_5918.8</th>\n",
+              "      <th>TotalCharges_5919.35</th>\n",
+              "      <th>TotalCharges_592.65</th>\n",
+              "      <th>TotalCharges_592.75</th>\n",
+              "      <th>TotalCharges_5921.35</th>\n",
+              "      <th>TotalCharges_5924.4</th>\n",
+              "      <th>TotalCharges_5925.75</th>\n",
+              "      <th>TotalCharges_593.05</th>\n",
+              "      <th>TotalCharges_593.2</th>\n",
+              "      <th>TotalCharges_593.3</th>\n",
+              "      <th>TotalCharges_593.45</th>\n",
+              "      <th>TotalCharges_593.75</th>\n",
+              "      <th>TotalCharges_593.85</th>\n",
+              "      <th>TotalCharges_5930.05</th>\n",
+              "      <th>TotalCharges_5931</th>\n",
+              "      <th>TotalCharges_5931.75</th>\n",
+              "      <th>TotalCharges_5935.1</th>\n",
+              "      <th>TotalCharges_5936.55</th>\n",
+              "      <th>TotalCharges_5940.85</th>\n",
+              "      <th>TotalCharges_5941.05</th>\n",
+              "      <th>TotalCharges_5943.65</th>\n",
+              "      <th>TotalCharges_5948.7</th>\n",
+              "      <th>TotalCharges_595.05</th>\n",
+              "      <th>TotalCharges_595.5</th>\n",
+              "      <th>TotalCharges_5950.2</th>\n",
+              "      <th>TotalCharges_5953</th>\n",
+              "      <th>TotalCharges_5956.85</th>\n",
+              "      <th>TotalCharges_5957.9</th>\n",
+              "      <th>TotalCharges_5958.85</th>\n",
+              "      <th>TotalCharges_5959.3</th>\n",
+              "      <th>TotalCharges_5960.5</th>\n",
+              "      <th>TotalCharges_5961.1</th>\n",
+              "      <th>TotalCharges_5963.95</th>\n",
+              "      <th>TotalCharges_5965.95</th>\n",
+              "      <th>TotalCharges_5968.4</th>\n",
+              "      <th>TotalCharges_5969.3</th>\n",
+              "      <th>TotalCharges_5969.85</th>\n",
+              "      <th>TotalCharges_5969.95</th>\n",
+              "      <th>TotalCharges_597</th>\n",
+              "      <th>TotalCharges_597.9</th>\n",
+              "      <th>TotalCharges_5971.25</th>\n",
+              "      <th>TotalCharges_5974.3</th>\n",
+              "      <th>TotalCharges_5976.9</th>\n",
+              "      <th>TotalCharges_5979.7</th>\n",
+              "      <th>TotalCharges_5980.55</th>\n",
+              "      <th>TotalCharges_5980.75</th>\n",
+              "      <th>TotalCharges_5981.65</th>\n",
+              "      <th>TotalCharges_5985</th>\n",
+              "      <th>TotalCharges_5985.75</th>\n",
+              "      <th>TotalCharges_5986.45</th>\n",
+              "      <th>TotalCharges_5986.55</th>\n",
+              "      <th>TotalCharges_599.25</th>\n",
+              "      <th>TotalCharges_599.3</th>\n",
+              "      <th>TotalCharges_5991.05</th>\n",
+              "      <th>TotalCharges_5997.1</th>\n",
+              "      <th>TotalCharges_5999.85</th>\n",
+              "      <th>TotalCharges_60</th>\n",
+              "      <th>TotalCharges_60.1</th>\n",
+              "      <th>TotalCharges_60.15</th>\n",
+              "      <th>TotalCharges_60.65</th>\n",
+              "      <th>TotalCharges_600</th>\n",
+              "      <th>TotalCharges_600.15</th>\n",
+              "      <th>TotalCharges_600.25</th>\n",
+              "      <th>TotalCharges_6000.1</th>\n",
+              "      <th>TotalCharges_6001.45</th>\n",
+              "      <th>TotalCharges_6004.85</th>\n",
+              "      <th>TotalCharges_601.25</th>\n",
+              "      <th>TotalCharges_601.55</th>\n",
+              "      <th>TotalCharges_601.6</th>\n",
+              "      <th>TotalCharges_6010.05</th>\n",
+              "      <th>TotalCharges_6014.85</th>\n",
+              "      <th>TotalCharges_6017.65</th>\n",
+              "      <th>TotalCharges_6017.9</th>\n",
+              "      <th>TotalCharges_6018.65</th>\n",
+              "      <th>TotalCharges_6019.35</th>\n",
+              "      <th>TotalCharges_602.55</th>\n",
+              "      <th>TotalCharges_602.9</th>\n",
+              "      <th>TotalCharges_6028.95</th>\n",
+              "      <th>TotalCharges_6029</th>\n",
+              "      <th>TotalCharges_6029.9</th>\n",
+              "      <th>TotalCharges_603</th>\n",
+              "      <th>TotalCharges_6033.1</th>\n",
+              "      <th>TotalCharges_6033.3</th>\n",
+              "      <th>TotalCharges_6034.85</th>\n",
+              "      <th>TotalCharges_6038.55</th>\n",
+              "      <th>TotalCharges_6039.9</th>\n",
+              "      <th>TotalCharges_604.7</th>\n",
+              "      <th>TotalCharges_6042.7</th>\n",
+              "      <th>TotalCharges_6045.9</th>\n",
+              "      <th>TotalCharges_6046.1</th>\n",
+              "      <th>TotalCharges_6049.5</th>\n",
+              "      <th>TotalCharges_605.25</th>\n",
+              "      <th>TotalCharges_605.45</th>\n",
+              "      <th>TotalCharges_605.75</th>\n",
+              "      <th>TotalCharges_605.9</th>\n",
+              "      <th>TotalCharges_6052.25</th>\n",
+              "      <th>TotalCharges_6055.55</th>\n",
+              "      <th>TotalCharges_6056.15</th>\n",
+              "      <th>TotalCharges_6056.9</th>\n",
+              "      <th>TotalCharges_6058.95</th>\n",
+              "      <th>TotalCharges_606.25</th>\n",
+              "      <th>TotalCharges_606.55</th>\n",
+              "      <th>TotalCharges_6065.3</th>\n",
+              "      <th>TotalCharges_6066.55</th>\n",
+              "      <th>TotalCharges_6067.4</th>\n",
+              "      <th>TotalCharges_6068.65</th>\n",
+              "      <th>TotalCharges_6069.25</th>\n",
+              "      <th>TotalCharges_607.3</th>\n",
+              "      <th>TotalCharges_607.7</th>\n",
+              "      <th>TotalCharges_6075.9</th>\n",
+              "      <th>TotalCharges_6077.75</th>\n",
+              "      <th>TotalCharges_6078.75</th>\n",
+              "      <th>TotalCharges_6079</th>\n",
+              "      <th>TotalCharges_608</th>\n",
+              "      <th>TotalCharges_608.15</th>\n",
+              "      <th>TotalCharges_608.5</th>\n",
+              "      <th>TotalCharges_608.8</th>\n",
+              "      <th>TotalCharges_6081.4</th>\n",
+              "      <th>TotalCharges_6083.1</th>\n",
+              "      <th>TotalCharges_609.05</th>\n",
+              "      <th>TotalCharges_609.1</th>\n",
+              "      <th>TotalCharges_609.65</th>\n",
+              "      <th>TotalCharges_609.9</th>\n",
+              "      <th>TotalCharges_6093.3</th>\n",
+              "      <th>TotalCharges_6094.25</th>\n",
+              "      <th>TotalCharges_6096.45</th>\n",
+              "      <th>TotalCharges_6096.9</th>\n",
+              "      <th>TotalCharges_61.05</th>\n",
+              "      <th>TotalCharges_61.15</th>\n",
+              "      <th>TotalCharges_61.35</th>\n",
+              "      <th>TotalCharges_61.45</th>\n",
+              "      <th>TotalCharges_61.7</th>\n",
+              "      <th>TotalCharges_610.2</th>\n",
+              "      <th>TotalCharges_610.75</th>\n",
+              "      <th>TotalCharges_6109.65</th>\n",
+              "      <th>TotalCharges_6109.75</th>\n",
+              "      <th>TotalCharges_611.45</th>\n",
+              "      <th>TotalCharges_611.65</th>\n",
+              "      <th>TotalCharges_6110.2</th>\n",
+              "      <th>TotalCharges_6110.75</th>\n",
+              "      <th>TotalCharges_6118.95</th>\n",
+              "      <th>TotalCharges_612.1</th>\n",
+              "      <th>TotalCharges_612.95</th>\n",
+              "      <th>TotalCharges_6125.4</th>\n",
+              "      <th>TotalCharges_6126.1</th>\n",
+              "      <th>TotalCharges_6126.15</th>\n",
+              "      <th>TotalCharges_6127.6</th>\n",
+              "      <th>TotalCharges_6129.2</th>\n",
+              "      <th>TotalCharges_6129.65</th>\n",
+              "      <th>TotalCharges_613.4</th>\n",
+              "      <th>TotalCharges_613.95</th>\n",
+              "      <th>TotalCharges_6130.85</th>\n",
+              "      <th>TotalCharges_6130.95</th>\n",
+              "      <th>TotalCharges_6132.7</th>\n",
+              "      <th>TotalCharges_6137</th>\n",
+              "      <th>TotalCharges_6139.5</th>\n",
+              "      <th>TotalCharges_614.45</th>\n",
+              "      <th>TotalCharges_6140.85</th>\n",
+              "      <th>TotalCharges_6141.65</th>\n",
+              "      <th>TotalCharges_6143.15</th>\n",
+              "      <th>TotalCharges_6144.55</th>\n",
+              "      <th>TotalCharges_6145.2</th>\n",
+              "      <th>TotalCharges_6145.85</th>\n",
+              "      <th>TotalCharges_6148.45</th>\n",
+              "      <th>TotalCharges_615.35</th>\n",
+              "      <th>TotalCharges_6151.9</th>\n",
+              "      <th>TotalCharges_6152.3</th>\n",
+              "      <th>TotalCharges_6152.4</th>\n",
+              "      <th>TotalCharges_6153.85</th>\n",
+              "      <th>TotalCharges_6155.4</th>\n",
+              "      <th>TotalCharges_6157.6</th>\n",
+              "      <th>TotalCharges_616.9</th>\n",
+              "      <th>TotalCharges_6161.9</th>\n",
+              "      <th>TotalCharges_6164.7</th>\n",
+              "      <th>TotalCharges_617.15</th>\n",
+              "      <th>TotalCharges_617.35</th>\n",
+              "      <th>TotalCharges_617.65</th>\n",
+              "      <th>TotalCharges_617.85</th>\n",
+              "      <th>TotalCharges_6171.2</th>\n",
+              "      <th>TotalCharges_6172</th>\n",
+              "      <th>TotalCharges_6176.6</th>\n",
+              "      <th>TotalCharges_6179.35</th>\n",
+              "      <th>TotalCharges_6185.15</th>\n",
+              "      <th>TotalCharges_6185.8</th>\n",
+              "      <th>TotalCharges_6194.1</th>\n",
+              "      <th>TotalCharges_62</th>\n",
+              "      <th>TotalCharges_62.05</th>\n",
+              "      <th>TotalCharges_62.25</th>\n",
+              "      <th>TotalCharges_62.8</th>\n",
+              "      <th>TotalCharges_62.9</th>\n",
+              "      <th>TotalCharges_620.35</th>\n",
+              "      <th>TotalCharges_620.55</th>\n",
+              "      <th>TotalCharges_620.75</th>\n",
+              "      <th>TotalCharges_6201.95</th>\n",
+              "      <th>TotalCharges_6205.5</th>\n",
+              "      <th>TotalCharges_6215.35</th>\n",
+              "      <th>TotalCharges_6218.45</th>\n",
+              "      <th>TotalCharges_6219.6</th>\n",
+              "      <th>TotalCharges_622.9</th>\n",
+              "      <th>TotalCharges_6223.3</th>\n",
+              "      <th>TotalCharges_6223.8</th>\n",
+              "      <th>TotalCharges_6224.8</th>\n",
+              "      <th>TotalCharges_6225.4</th>\n",
+              "      <th>TotalCharges_6227.5</th>\n",
+              "      <th>TotalCharges_6230.1</th>\n",
+              "      <th>TotalCharges_6236.75</th>\n",
+              "      <th>TotalCharges_6237.05</th>\n",
+              "      <th>TotalCharges_6239.05</th>\n",
+              "      <th>TotalCharges_624.15</th>\n",
+              "      <th>TotalCharges_624.6</th>\n",
+              "      <th>TotalCharges_6241.35</th>\n",
+              "      <th>TotalCharges_625.05</th>\n",
+              "      <th>TotalCharges_625.65</th>\n",
+              "      <th>TotalCharges_6252.7</th>\n",
+              "      <th>TotalCharges_6252.9</th>\n",
+              "      <th>TotalCharges_6253</th>\n",
+              "      <th>TotalCharges_6254.2</th>\n",
+              "      <th>TotalCharges_6254.45</th>\n",
+              "      <th>TotalCharges_6256.2</th>\n",
+              "      <th>TotalCharges_6263.8</th>\n",
+              "      <th>TotalCharges_627.4</th>\n",
+              "      <th>TotalCharges_6273.4</th>\n",
+              "      <th>TotalCharges_628.65</th>\n",
+              "      <th>TotalCharges_6281.45</th>\n",
+              "      <th>TotalCharges_6283.3</th>\n",
+              "      <th>TotalCharges_6287.25</th>\n",
+              "      <th>TotalCharges_6287.3</th>\n",
+              "      <th>TotalCharges_629.35</th>\n",
+              "      <th>TotalCharges_629.55</th>\n",
+              "      <th>TotalCharges_6292.7</th>\n",
+              "      <th>TotalCharges_6293.2</th>\n",
+              "      <th>TotalCharges_6293.45</th>\n",
+              "      <th>TotalCharges_6293.75</th>\n",
+              "      <th>TotalCharges_6296.75</th>\n",
+              "      <th>TotalCharges_6297.65</th>\n",
+              "      <th>TotalCharges_63</th>\n",
+              "      <th>TotalCharges_63.15</th>\n",
+              "      <th>TotalCharges_63.6</th>\n",
+              "      <th>TotalCharges_63.75</th>\n",
+              "      <th>TotalCharges_630.6</th>\n",
+              "      <th>TotalCharges_6300.15</th>\n",
+              "      <th>TotalCharges_6300.85</th>\n",
+              "      <th>TotalCharges_6301.7</th>\n",
+              "      <th>TotalCharges_6302.8</th>\n",
+              "      <th>TotalCharges_6302.85</th>\n",
+              "      <th>TotalCharges_6306.5</th>\n",
+              "      <th>TotalCharges_6309.65</th>\n",
+              "      <th>TotalCharges_631.4</th>\n",
+              "      <th>TotalCharges_631.85</th>\n",
+              "      <th>TotalCharges_6310.9</th>\n",
+              "      <th>TotalCharges_6311.2</th>\n",
+              "      <th>TotalCharges_6312.9</th>\n",
+              "      <th>TotalCharges_6314.35</th>\n",
+              "      <th>TotalCharges_6316.2</th>\n",
+              "      <th>TotalCharges_632.2</th>\n",
+              "      <th>TotalCharges_632.95</th>\n",
+              "      <th>TotalCharges_6322.1</th>\n",
+              "      <th>TotalCharges_6325.25</th>\n",
+              "      <th>TotalCharges_6328.7</th>\n",
+              "      <th>TotalCharges_633.3</th>\n",
+              "      <th>TotalCharges_633.4</th>\n",
+              "      <th>TotalCharges_633.45</th>\n",
+              "      <th>TotalCharges_633.85</th>\n",
+              "      <th>TotalCharges_6330.4</th>\n",
+              "      <th>TotalCharges_6332.75</th>\n",
+              "      <th>TotalCharges_6333.4</th>\n",
+              "      <th>TotalCharges_6333.8</th>\n",
+              "      <th>TotalCharges_6339.3</th>\n",
+              "      <th>TotalCharges_6339.45</th>\n",
+              "      <th>TotalCharges_6341.45</th>\n",
+              "      <th>TotalCharges_6342.7</th>\n",
+              "      <th>TotalCharges_6347.55</th>\n",
+              "      <th>TotalCharges_635.6</th>\n",
+              "      <th>TotalCharges_635.9</th>\n",
+              "      <th>TotalCharges_6350.5</th>\n",
+              "      <th>TotalCharges_6352.4</th>\n",
+              "      <th>TotalCharges_6362.35</th>\n",
+              "      <th>TotalCharges_6363.45</th>\n",
+              "      <th>TotalCharges_6365.35</th>\n",
+              "      <th>TotalCharges_6367.2</th>\n",
+              "      <th>TotalCharges_6368.2</th>\n",
+              "      <th>TotalCharges_6369.45</th>\n",
+              "      <th>TotalCharges_637.4</th>\n",
+              "      <th>TotalCharges_637.55</th>\n",
+              "      <th>TotalCharges_6373.1</th>\n",
+              "      <th>TotalCharges_6375.2</th>\n",
+              "      <th>TotalCharges_6375.8</th>\n",
+              "      <th>TotalCharges_6376.55</th>\n",
+              "      <th>TotalCharges_638</th>\n",
+              "      <th>TotalCharges_638.55</th>\n",
+              "      <th>TotalCharges_638.95</th>\n",
+              "      <th>TotalCharges_6382</th>\n",
+              "      <th>TotalCharges_6382.55</th>\n",
+              "      <th>TotalCharges_6383.35</th>\n",
+              "      <th>TotalCharges_6383.9</th>\n",
+              "      <th>TotalCharges_6385.95</th>\n",
+              "      <th>TotalCharges_6388.65</th>\n",
+              "      <th>TotalCharges_639.45</th>\n",
+              "      <th>TotalCharges_639.65</th>\n",
+              "      <th>TotalCharges_639.7</th>\n",
+              "      <th>TotalCharges_6392.85</th>\n",
+              "      <th>TotalCharges_6393.65</th>\n",
+              "      <th>TotalCharges_6396.45</th>\n",
+              "      <th>TotalCharges_6397.6</th>\n",
+              "      <th>TotalCharges_6398.05</th>\n",
+              "      <th>TotalCharges_64.55</th>\n",
+              "      <th>TotalCharges_6401.25</th>\n",
+              "      <th>TotalCharges_6404</th>\n",
+              "      <th>TotalCharges_6405</th>\n",
+              "      <th>TotalCharges_641.15</th>\n",
+              "      <th>TotalCharges_641.25</th>\n",
+              "      <th>TotalCharges_641.35</th>\n",
+              "      <th>TotalCharges_6411.25</th>\n",
+              "      <th>TotalCharges_6413.65</th>\n",
+              "      <th>TotalCharges_6416.7</th>\n",
+              "      <th>TotalCharges_6418.9</th>\n",
+              "      <th>TotalCharges_6423</th>\n",
+              "      <th>TotalCharges_6424.25</th>\n",
+              "      <th>TotalCharges_6424.7</th>\n",
+              "      <th>TotalCharges_6425.65</th>\n",
+              "      <th>TotalCharges_6428.4</th>\n",
+              "      <th>TotalCharges_6430.9</th>\n",
+              "      <th>TotalCharges_6431.05</th>\n",
+              "      <th>TotalCharges_6435.25</th>\n",
+              "      <th>TotalCharges_644.35</th>\n",
+              "      <th>TotalCharges_644.5</th>\n",
+              "      <th>TotalCharges_6440.25</th>\n",
+              "      <th>TotalCharges_6441.4</th>\n",
+              "      <th>TotalCharges_6441.85</th>\n",
+              "      <th>TotalCharges_6444.05</th>\n",
+              "      <th>TotalCharges_6448.05</th>\n",
+              "      <th>TotalCharges_6448.85</th>\n",
+              "      <th>TotalCharges_6449.15</th>\n",
+              "      <th>TotalCharges_645.8</th>\n",
+              "      <th>TotalCharges_6457.15</th>\n",
+              "      <th>TotalCharges_646.05</th>\n",
+              "      <th>TotalCharges_646.85</th>\n",
+              "      <th>TotalCharges_6460.55</th>\n",
+              "      <th>TotalCharges_6463.15</th>\n",
+              "      <th>TotalCharges_6465</th>\n",
+              "      <th>TotalCharges_6468.6</th>\n",
+              "      <th>TotalCharges_647.5</th>\n",
+              "      <th>TotalCharges_6470.1</th>\n",
+              "      <th>TotalCharges_6471.85</th>\n",
+              "      <th>TotalCharges_6474.4</th>\n",
+              "      <th>TotalCharges_6474.45</th>\n",
+              "      <th>TotalCharges_6479.4</th>\n",
+              "      <th>TotalCharges_648.65</th>\n",
+              "      <th>TotalCharges_6480.9</th>\n",
+              "      <th>TotalCharges_6487.2</th>\n",
+              "      <th>TotalCharges_649.4</th>\n",
+              "      <th>TotalCharges_649.65</th>\n",
+              "      <th>TotalCharges_6496.15</th>\n",
+              "      <th>TotalCharges_65.95</th>\n",
+              "      <th>TotalCharges_6501.35</th>\n",
+              "      <th>TotalCharges_6503.2</th>\n",
+              "      <th>TotalCharges_6506.15</th>\n",
+              "      <th>TotalCharges_651.4</th>\n",
+              "      <th>TotalCharges_651.5</th>\n",
+              "      <th>TotalCharges_651.55</th>\n",
+              "      <th>TotalCharges_6510.45</th>\n",
+              "      <th>TotalCharges_6511.25</th>\n",
+              "      <th>TotalCharges_6511.8</th>\n",
+              "      <th>TotalCharges_6518.35</th>\n",
+              "      <th>TotalCharges_6519.75</th>\n",
+              "      <th>TotalCharges_6520.8</th>\n",
+              "      <th>TotalCharges_6521.9</th>\n",
+              "      <th>TotalCharges_6526.65</th>\n",
+              "      <th>TotalCharges_6529.25</th>\n",
+              "      <th>TotalCharges_653.15</th>\n",
+              "      <th>TotalCharges_653.25</th>\n",
+              "      <th>TotalCharges_653.9</th>\n",
+              "      <th>TotalCharges_653.95</th>\n",
+              "      <th>TotalCharges_6536.5</th>\n",
+              "      <th>TotalCharges_6538.45</th>\n",
+              "      <th>TotalCharges_654.5</th>\n",
+              "      <th>TotalCharges_654.55</th>\n",
+              "      <th>TotalCharges_654.85</th>\n",
+              "      <th>TotalCharges_6547.7</th>\n",
+              "      <th>TotalCharges_6548.65</th>\n",
+              "      <th>TotalCharges_6549.45</th>\n",
+              "      <th>TotalCharges_655.3</th>\n",
+              "      <th>TotalCharges_655.5</th>\n",
+              "      <th>TotalCharges_655.85</th>\n",
+              "      <th>TotalCharges_655.9</th>\n",
+              "      <th>TotalCharges_6555.2</th>\n",
+              "      <th>TotalCharges_6557.75</th>\n",
+              "      <th>TotalCharges_6561.25</th>\n",
+              "      <th>TotalCharges_6562.9</th>\n",
+              "      <th>TotalCharges_6563.4</th>\n",
+              "      <th>TotalCharges_6565.85</th>\n",
+              "      <th>TotalCharges_6567.9</th>\n",
+              "      <th>TotalCharges_657.5</th>\n",
+              "      <th>TotalCharges_6572.85</th>\n",
+              "      <th>TotalCharges_6578.55</th>\n",
+              "      <th>TotalCharges_6579.05</th>\n",
+              "      <th>TotalCharges_658.1</th>\n",
+              "      <th>TotalCharges_658.95</th>\n",
+              "      <th>TotalCharges_6581.9</th>\n",
+              "      <th>TotalCharges_6585.2</th>\n",
+              "      <th>TotalCharges_6585.35</th>\n",
+              "      <th>TotalCharges_6586.85</th>\n",
+              "      <th>TotalCharges_6588.95</th>\n",
+              "      <th>TotalCharges_6589.6</th>\n",
+              "      <th>TotalCharges_659.35</th>\n",
+              "      <th>TotalCharges_659.45</th>\n",
+              "      <th>TotalCharges_659.65</th>\n",
+              "      <th>TotalCharges_6590.5</th>\n",
+              "      <th>TotalCharges_6590.8</th>\n",
+              "      <th>TotalCharges_6595</th>\n",
+              "      <th>TotalCharges_6595.9</th>\n",
+              "      <th>TotalCharges_6597.25</th>\n",
+              "      <th>TotalCharges_66.95</th>\n",
+              "      <th>TotalCharges_660.05</th>\n",
+              "      <th>TotalCharges_660.9</th>\n",
+              "      <th>TotalCharges_6602.9</th>\n",
+              "      <th>TotalCharges_6603</th>\n",
+              "      <th>TotalCharges_6603.8</th>\n",
+              "      <th>TotalCharges_6605.55</th>\n",
+              "      <th>TotalCharges_661.25</th>\n",
+              "      <th>TotalCharges_661.55</th>\n",
+              "      <th>TotalCharges_6613.65</th>\n",
+              "      <th>TotalCharges_6614.9</th>\n",
+              "      <th>TotalCharges_6615.15</th>\n",
+              "      <th>TotalCharges_662.65</th>\n",
+              "      <th>TotalCharges_662.95</th>\n",
+              "      <th>TotalCharges_663.05</th>\n",
+              "      <th>TotalCharges_663.55</th>\n",
+              "      <th>TotalCharges_6631.85</th>\n",
+              "      <th>TotalCharges_6632.75</th>\n",
+              "      <th>TotalCharges_6637.9</th>\n",
+              "      <th>TotalCharges_6638.35</th>\n",
+              "      <th>TotalCharges_664.4</th>\n",
+              "      <th>TotalCharges_6640.7</th>\n",
+              "      <th>TotalCharges_6643.5</th>\n",
+              "      <th>TotalCharges_665.45</th>\n",
+              "      <th>TotalCharges_6652.45</th>\n",
+              "      <th>TotalCharges_6654.1</th>\n",
+              "      <th>TotalCharges_666</th>\n",
+              "      <th>TotalCharges_666.75</th>\n",
+              "      <th>TotalCharges_6668</th>\n",
+              "      <th>TotalCharges_6668.05</th>\n",
+              "      <th>TotalCharges_6668.35</th>\n",
+              "      <th>TotalCharges_6669.05</th>\n",
+              "      <th>TotalCharges_6669.45</th>\n",
+              "      <th>TotalCharges_667.7</th>\n",
+              "      <th>TotalCharges_6671.7</th>\n",
+              "      <th>TotalCharges_6674.65</th>\n",
+              "      <th>TotalCharges_668.4</th>\n",
+              "      <th>TotalCharges_668.85</th>\n",
+              "      <th>TotalCharges_6683.4</th>\n",
+              "      <th>TotalCharges_6687.85</th>\n",
+              "      <th>TotalCharges_6688.1</th>\n",
+              "      <th>TotalCharges_6688.95</th>\n",
+              "      <th>TotalCharges_6689</th>\n",
+              "      <th>TotalCharges_669</th>\n",
+              "      <th>TotalCharges_669.45</th>\n",
+              "      <th>TotalCharges_669.85</th>\n",
+              "      <th>TotalCharges_6690.75</th>\n",
+              "      <th>TotalCharges_6692.65</th>\n",
+              "      <th>TotalCharges_6697.2</th>\n",
+              "      <th>TotalCharges_6697.35</th>\n",
+              "      <th>TotalCharges_67.1</th>\n",
+              "      <th>TotalCharges_67.55</th>\n",
+              "      <th>TotalCharges_670.35</th>\n",
+              "      <th>TotalCharges_670.65</th>\n",
+              "      <th>TotalCharges_6700.05</th>\n",
+              "      <th>TotalCharges_6703.5</th>\n",
+              "      <th>TotalCharges_6705.7</th>\n",
+              "      <th>TotalCharges_6707.15</th>\n",
+              "      <th>TotalCharges_6710.5</th>\n",
+              "      <th>TotalCharges_6713.2</th>\n",
+              "      <th>TotalCharges_6716.45</th>\n",
+              "      <th>TotalCharges_6717.9</th>\n",
+              "      <th>TotalCharges_6719.9</th>\n",
+              "      <th>TotalCharges_672.2</th>\n",
+              "      <th>TotalCharges_672.55</th>\n",
+              "      <th>TotalCharges_672.7</th>\n",
+              "      <th>TotalCharges_6721.6</th>\n",
+              "      <th>TotalCharges_6725.3</th>\n",
+              "      <th>TotalCharges_6725.5</th>\n",
+              "      <th>TotalCharges_673.1</th>\n",
+              "      <th>TotalCharges_673.2</th>\n",
+              "      <th>TotalCharges_673.25</th>\n",
+              "      <th>TotalCharges_6733</th>\n",
+              "      <th>TotalCharges_6733.15</th>\n",
+              "      <th>TotalCharges_6735.05</th>\n",
+              "      <th>TotalCharges_674.55</th>\n",
+              "      <th>TotalCharges_6741.15</th>\n",
+              "      <th>TotalCharges_6743.55</th>\n",
+              "      <th>TotalCharges_6744.2</th>\n",
+              "      <th>TotalCharges_6744.25</th>\n",
+              "      <th>TotalCharges_6747.35</th>\n",
+              "      <th>TotalCharges_675.6</th>\n",
+              "      <th>TotalCharges_6751.35</th>\n",
+              "      <th>TotalCharges_6754.35</th>\n",
+              "      <th>TotalCharges_6758.45</th>\n",
+              "      <th>TotalCharges_676.15</th>\n",
+              "      <th>TotalCharges_676.35</th>\n",
+              "      <th>TotalCharges_676.7</th>\n",
+              "      <th>TotalCharges_6766.95</th>\n",
+              "      <th>TotalCharges_6767.1</th>\n",
+              "      <th>TotalCharges_677.05</th>\n",
+              "      <th>TotalCharges_677.9</th>\n",
+              "      <th>TotalCharges_6770.5</th>\n",
+              "      <th>TotalCharges_6770.85</th>\n",
+              "      <th>TotalCharges_6779.05</th>\n",
+              "      <th>TotalCharges_678.2</th>\n",
+              "      <th>TotalCharges_678.45</th>\n",
+              "      <th>TotalCharges_678.75</th>\n",
+              "      <th>TotalCharges_678.8</th>\n",
+              "      <th>TotalCharges_6780.1</th>\n",
+              "      <th>TotalCharges_6782.15</th>\n",
+              "      <th>TotalCharges_6786.1</th>\n",
+              "      <th>TotalCharges_6786.4</th>\n",
+              "      <th>TotalCharges_679</th>\n",
+              "      <th>TotalCharges_679.3</th>\n",
+              "      <th>TotalCharges_679.55</th>\n",
+              "      <th>TotalCharges_679.8</th>\n",
+              "      <th>TotalCharges_679.85</th>\n",
+              "      <th>TotalCharges_6792.45</th>\n",
+              "      <th>TotalCharges_6794.75</th>\n",
+              "      <th>TotalCharges_68.2</th>\n",
+              "      <th>TotalCharges_68.35</th>\n",
+              "      <th>TotalCharges_68.45</th>\n",
+              "      <th>TotalCharges_68.5</th>\n",
+              "      <th>TotalCharges_68.65</th>\n",
+              "      <th>TotalCharges_68.75</th>\n",
+              "      <th>TotalCharges_68.8</th>\n",
+              "      <th>TotalCharges_68.95</th>\n",
+              "      <th>TotalCharges_680.05</th>\n",
+              "      <th>TotalCharges_681.4</th>\n",
+              "      <th>TotalCharges_6812.95</th>\n",
+              "      <th>TotalCharges_6816.95</th>\n",
+              "      <th>TotalCharges_6819.45</th>\n",
+              "      <th>TotalCharges_682.1</th>\n",
+              "      <th>TotalCharges_682.15</th>\n",
+              "      <th>TotalCharges_6822.15</th>\n",
+              "      <th>TotalCharges_6823.4</th>\n",
+              "      <th>TotalCharges_6825.65</th>\n",
+              "      <th>TotalCharges_6827.5</th>\n",
+              "      <th>TotalCharges_683.25</th>\n",
+              "      <th>TotalCharges_683.75</th>\n",
+              "      <th>TotalCharges_6838.6</th>\n",
+              "      <th>TotalCharges_684.05</th>\n",
+              "      <th>TotalCharges_684.4</th>\n",
+              "      <th>TotalCharges_684.85</th>\n",
+              "      <th>TotalCharges_6840.95</th>\n",
+              "      <th>TotalCharges_6841.05</th>\n",
+              "      <th>TotalCharges_6841.3</th>\n",
+              "      <th>TotalCharges_6841.4</th>\n",
+              "      <th>TotalCharges_6841.45</th>\n",
+              "      <th>TotalCharges_6843.15</th>\n",
+              "      <th>TotalCharges_6844.5</th>\n",
+              "      <th>TotalCharges_6849.4</th>\n",
+              "      <th>TotalCharges_6849.75</th>\n",
+              "      <th>TotalCharges_685.55</th>\n",
+              "      <th>TotalCharges_6851.65</th>\n",
+              "      <th>TotalCharges_6856.45</th>\n",
+              "      <th>TotalCharges_6856.95</th>\n",
+              "      <th>TotalCharges_6858.9</th>\n",
+              "      <th>TotalCharges_6859.05</th>\n",
+              "      <th>TotalCharges_6859.5</th>\n",
+              "      <th>TotalCharges_686.95</th>\n",
+              "      <th>TotalCharges_6860.6</th>\n",
+              "      <th>TotalCharges_6869.7</th>\n",
+              "      <th>TotalCharges_6871.7</th>\n",
+              "      <th>TotalCharges_6871.9</th>\n",
+              "      <th>TotalCharges_6873.75</th>\n",
+              "      <th>TotalCharges_6875.35</th>\n",
+              "      <th>TotalCharges_6876.05</th>\n",
+              "      <th>TotalCharges_688</th>\n",
+              "      <th>TotalCharges_688.2</th>\n",
+              "      <th>TotalCharges_688.5</th>\n",
+              "      <th>TotalCharges_688.65</th>\n",
+              "      <th>TotalCharges_6880.85</th>\n",
+              "      <th>TotalCharges_6885.75</th>\n",
+              "      <th>TotalCharges_6886.25</th>\n",
+              "      <th>TotalCharges_6889.8</th>\n",
+              "      <th>TotalCharges_689</th>\n",
+              "      <th>TotalCharges_689.35</th>\n",
+              "      <th>TotalCharges_689.75</th>\n",
+              "      <th>TotalCharges_6890</th>\n",
+              "      <th>TotalCharges_6891.4</th>\n",
+              "      <th>TotalCharges_6891.45</th>\n",
+              "      <th>TotalCharges_6895.5</th>\n",
+              "      <th>TotalCharges_69.1</th>\n",
+              "      <th>TotalCharges_69.15</th>\n",
+              "      <th>TotalCharges_69.2</th>\n",
+              "      <th>TotalCharges_69.25</th>\n",
+              "      <th>TotalCharges_69.35</th>\n",
+              "      <th>TotalCharges_69.4</th>\n",
+              "      <th>TotalCharges_69.5</th>\n",
+              "      <th>TotalCharges_69.55</th>\n",
+              "      <th>TotalCharges_69.6</th>\n",
+              "      <th>TotalCharges_69.65</th>\n",
+              "      <th>TotalCharges_69.7</th>\n",
+              "      <th>TotalCharges_69.75</th>\n",
+              "      <th>TotalCharges_69.8</th>\n",
+              "      <th>TotalCharges_69.85</th>\n",
+              "      <th>TotalCharges_69.9</th>\n",
+              "      <th>TotalCharges_69.95</th>\n",
+              "      <th>TotalCharges_690.25</th>\n",
+              "      <th>TotalCharges_690.5</th>\n",
+              "      <th>TotalCharges_6903.1</th>\n",
+              "      <th>TotalCharges_6910.3</th>\n",
+              "      <th>TotalCharges_6912.7</th>\n",
+              "      <th>TotalCharges_6914.95</th>\n",
+              "      <th>TotalCharges_692.1</th>\n",
+              "      <th>TotalCharges_692.35</th>\n",
+              "      <th>TotalCharges_692.55</th>\n",
+              "      <th>TotalCharges_6921.7</th>\n",
+              "      <th>TotalCharges_6925.9</th>\n",
+              "      <th>TotalCharges_6929.4</th>\n",
+              "      <th>TotalCharges_693.3</th>\n",
+              "      <th>TotalCharges_693.45</th>\n",
+              "      <th>TotalCharges_6936.85</th>\n",
+              "      <th>TotalCharges_6937.95</th>\n",
+              "      <th>TotalCharges_6941.2</th>\n",
+              "      <th>TotalCharges_6944.5</th>\n",
+              "      <th>TotalCharges_695.05</th>\n",
+              "      <th>TotalCharges_695.75</th>\n",
+              "      <th>TotalCharges_695.85</th>\n",
+              "      <th>TotalCharges_6951.15</th>\n",
+              "      <th>TotalCharges_6953.4</th>\n",
+              "      <th>TotalCharges_6954.15</th>\n",
+              "      <th>TotalCharges_696.35</th>\n",
+              "      <th>TotalCharges_696.8</th>\n",
+              "      <th>TotalCharges_6962.85</th>\n",
+              "      <th>TotalCharges_697.25</th>\n",
+              "      <th>TotalCharges_697.65</th>\n",
+              "      <th>TotalCharges_697.7</th>\n",
+              "      <th>TotalCharges_6975.25</th>\n",
+              "      <th>TotalCharges_6976.75</th>\n",
+              "      <th>TotalCharges_6979.8</th>\n",
+              "      <th>TotalCharges_6981.35</th>\n",
+              "      <th>TotalCharges_6982.5</th>\n",
+              "      <th>TotalCharges_6985.65</th>\n",
+              "      <th>TotalCharges_6989.45</th>\n",
+              "      <th>TotalCharges_6989.7</th>\n",
+              "      <th>TotalCharges_6991.6</th>\n",
+              "      <th>TotalCharges_6991.9</th>\n",
+              "      <th>TotalCharges_6993.65</th>\n",
+              "      <th>TotalCharges_6994.6</th>\n",
+              "      <th>TotalCharges_6994.8</th>\n",
+              "      <th>TotalCharges_6997.3</th>\n",
+              "      <th>TotalCharges_6998.95</th>\n",
+              "      <th>TotalCharges_70</th>\n",
+              "      <th>TotalCharges_70.05</th>\n",
+              "      <th>TotalCharges_70.1</th>\n",
+              "      <th>TotalCharges_70.15</th>\n",
+              "      <th>TotalCharges_70.2</th>\n",
+              "      <th>TotalCharges_70.25</th>\n",
+              "      <th>TotalCharges_70.3</th>\n",
+              "      <th>TotalCharges_70.35</th>\n",
+              "      <th>TotalCharges_70.4</th>\n",
+              "      <th>TotalCharges_70.45</th>\n",
+              "      <th>TotalCharges_70.5</th>\n",
+              "      <th>TotalCharges_70.55</th>\n",
+              "      <th>TotalCharges_70.6</th>\n",
+              "      <th>TotalCharges_70.65</th>\n",
+              "      <th>TotalCharges_70.7</th>\n",
+              "      <th>TotalCharges_70.75</th>\n",
+              "      <th>TotalCharges_70.8</th>\n",
+              "      <th>TotalCharges_70.85</th>\n",
+              "      <th>TotalCharges_70.9</th>\n",
+              "      <th>TotalCharges_700.45</th>\n",
+              "      <th>TotalCharges_700.85</th>\n",
+              "      <th>TotalCharges_7002.95</th>\n",
+              "      <th>TotalCharges_7007.6</th>\n",
+              "      <th>TotalCharges_7008.15</th>\n",
+              "      <th>TotalCharges_7009.5</th>\n",
+              "      <th>TotalCharges_701.05</th>\n",
+              "      <th>TotalCharges_701.3</th>\n",
+              "      <th>TotalCharges_7015.9</th>\n",
+              "      <th>TotalCharges_702</th>\n",
+              "      <th>TotalCharges_702.05</th>\n",
+              "      <th>TotalCharges_702.9</th>\n",
+              "      <th>TotalCharges_7028.5</th>\n",
+              "      <th>TotalCharges_703.55</th>\n",
+              "      <th>TotalCharges_7030.65</th>\n",
+              "      <th>TotalCharges_7031.3</th>\n",
+              "      <th>TotalCharges_7031.45</th>\n",
+              "      <th>TotalCharges_7035.6</th>\n",
+              "      <th>TotalCharges_7039.05</th>\n",
+              "      <th>TotalCharges_7039.45</th>\n",
+              "      <th>TotalCharges_704.3</th>\n",
+              "      <th>TotalCharges_7040.85</th>\n",
+              "      <th>TotalCharges_7047.5</th>\n",
+              "      <th>TotalCharges_7049.5</th>\n",
+              "      <th>TotalCharges_7049.75</th>\n",
+              "      <th>TotalCharges_705.45</th>\n",
+              "      <th>TotalCharges_7051.95</th>\n",
+              "      <th>TotalCharges_7053.35</th>\n",
+              "      <th>TotalCharges_706.6</th>\n",
+              "      <th>TotalCharges_706.85</th>\n",
+              "      <th>TotalCharges_7061.65</th>\n",
+              "      <th>TotalCharges_7069.25</th>\n",
+              "      <th>TotalCharges_7069.3</th>\n",
+              "      <th>TotalCharges_707.5</th>\n",
+              "      <th>TotalCharges_7074.4</th>\n",
+              "      <th>TotalCharges_7076.35</th>\n",
+              "      <th>TotalCharges_708.2</th>\n",
+              "      <th>TotalCharges_708.8</th>\n",
+              "      <th>TotalCharges_7082.45</th>\n",
+              "      <th>TotalCharges_7082.5</th>\n",
+              "      <th>TotalCharges_7082.85</th>\n",
+              "      <th>TotalCharges_7085.5</th>\n",
+              "      <th>TotalCharges_709.5</th>\n",
+              "      <th>TotalCharges_7099</th>\n",
+              "      <th>TotalCharges_71</th>\n",
+              "      <th>TotalCharges_71.1</th>\n",
+              "      <th>TotalCharges_71.15</th>\n",
+              "      <th>TotalCharges_71.2</th>\n",
+              "      <th>TotalCharges_71.25</th>\n",
+              "      <th>TotalCharges_71.35</th>\n",
+              "      <th>TotalCharges_71.55</th>\n",
+              "      <th>TotalCharges_71.65</th>\n",
+              "      <th>TotalCharges_710.05</th>\n",
+              "      <th>TotalCharges_7101.5</th>\n",
+              "      <th>TotalCharges_7104.2</th>\n",
+              "      <th>TotalCharges_7107</th>\n",
+              "      <th>TotalCharges_7108.2</th>\n",
+              "      <th>TotalCharges_711.15</th>\n",
+              "      <th>TotalCharges_711.9</th>\n",
+              "      <th>TotalCharges_711.95</th>\n",
+              "      <th>TotalCharges_7110.75</th>\n",
+              "      <th>TotalCharges_7111.3</th>\n",
+              "      <th>TotalCharges_7112.15</th>\n",
+              "      <th>TotalCharges_7113.75</th>\n",
+              "      <th>TotalCharges_7114.25</th>\n",
+              "      <th>TotalCharges_7118.9</th>\n",
+              "      <th>TotalCharges_712.25</th>\n",
+              "      <th>TotalCharges_712.75</th>\n",
+              "      <th>TotalCharges_712.85</th>\n",
+              "      <th>TotalCharges_7129.45</th>\n",
+              "      <th>TotalCharges_713</th>\n",
+              "      <th>TotalCharges_713.1</th>\n",
+              "      <th>TotalCharges_713.6</th>\n",
+              "      <th>TotalCharges_713.75</th>\n",
+              "      <th>TotalCharges_7132.15</th>\n",
+              "      <th>TotalCharges_7133.1</th>\n",
+              "      <th>TotalCharges_7133.25</th>\n",
+              "      <th>TotalCharges_7133.45</th>\n",
+              "      <th>TotalCharges_7138.65</th>\n",
+              "      <th>TotalCharges_714.15</th>\n",
+              "      <th>TotalCharges_7142.5</th>\n",
+              "      <th>TotalCharges_7149.35</th>\n",
+              "      <th>TotalCharges_715</th>\n",
+              "      <th>TotalCharges_7156.2</th>\n",
+              "      <th>TotalCharges_7159.05</th>\n",
+              "      <th>TotalCharges_7159.7</th>\n",
+              "      <th>TotalCharges_716.1</th>\n",
+              "      <th>TotalCharges_716.4</th>\n",
+              "      <th>TotalCharges_7168.25</th>\n",
+              "      <th>TotalCharges_717.3</th>\n",
+              "      <th>TotalCharges_717.5</th>\n",
+              "      <th>TotalCharges_717.95</th>\n",
+              "      <th>TotalCharges_7171.7</th>\n",
+              "      <th>TotalCharges_7173.15</th>\n",
+              "      <th>TotalCharges_7176.55</th>\n",
+              "      <th>TotalCharges_718.1</th>\n",
+              "      <th>TotalCharges_718.55</th>\n",
+              "      <th>TotalCharges_7181.25</th>\n",
+              "      <th>TotalCharges_7181.95</th>\n",
+              "      <th>TotalCharges_7188.5</th>\n",
+              "      <th>TotalCharges_7195.35</th>\n",
+              "      <th>TotalCharges_72</th>\n",
+              "      <th>TotalCharges_72.1</th>\n",
+              "      <th>TotalCharges_72.4</th>\n",
+              "      <th>TotalCharges_720.05</th>\n",
+              "      <th>TotalCharges_720.1</th>\n",
+              "      <th>TotalCharges_720.45</th>\n",
+              "      <th>TotalCharges_7209</th>\n",
+              "      <th>TotalCharges_7210.85</th>\n",
+              "      <th>TotalCharges_7213.75</th>\n",
+              "      <th>TotalCharges_7220.35</th>\n",
+              "      <th>TotalCharges_7222.75</th>\n",
+              "      <th>TotalCharges_7227.45</th>\n",
+              "      <th>TotalCharges_723.3</th>\n",
+              "      <th>TotalCharges_723.35</th>\n",
+              "      <th>TotalCharges_723.4</th>\n",
+              "      <th>TotalCharges_7234.8</th>\n",
+              "      <th>TotalCharges_7238.6</th>\n",
+              "      <th>TotalCharges_724.65</th>\n",
+              "      <th>TotalCharges_7240.65</th>\n",
+              "      <th>TotalCharges_7244.7</th>\n",
+              "      <th>TotalCharges_7245.9</th>\n",
+              "      <th>TotalCharges_7246.15</th>\n",
+              "      <th>TotalCharges_7250.15</th>\n",
+              "      <th>TotalCharges_7251.7</th>\n",
+              "      <th>TotalCharges_7251.9</th>\n",
+              "      <th>TotalCharges_726.1</th>\n",
+              "      <th>TotalCharges_7261.25</th>\n",
+              "      <th>TotalCharges_7261.75</th>\n",
+              "      <th>TotalCharges_7262</th>\n",
+              "      <th>TotalCharges_7266.95</th>\n",
+              "      <th>TotalCharges_727.8</th>\n",
+              "      <th>TotalCharges_727.85</th>\n",
+              "      <th>TotalCharges_7279.35</th>\n",
+              "      <th>TotalCharges_7281.6</th>\n",
+              "      <th>TotalCharges_7283.25</th>\n",
+              "      <th>TotalCharges_7285.7</th>\n",
+              "      <th>TotalCharges_7288.4</th>\n",
+              "      <th>TotalCharges_729.95</th>\n",
+              "      <th>TotalCharges_7291.75</th>\n",
+              "      <th>TotalCharges_7297.75</th>\n",
+              "      <th>TotalCharges_7299.65</th>\n",
+              "      <th>TotalCharges_73</th>\n",
+              "      <th>TotalCharges_73.05</th>\n",
+              "      <th>TotalCharges_73.1</th>\n",
+              "      <th>TotalCharges_73.45</th>\n",
+              "      <th>TotalCharges_73.5</th>\n",
+              "      <th>TotalCharges_73.55</th>\n",
+              "      <th>TotalCharges_73.6</th>\n",
+              "      <th>TotalCharges_73.65</th>\n",
+              "      <th>TotalCharges_730.1</th>\n",
+              "      <th>TotalCharges_730.4</th>\n",
+              "      <th>TotalCharges_7303.05</th>\n",
+              "      <th>TotalCharges_7308.95</th>\n",
+              "      <th>TotalCharges_731.3</th>\n",
+              "      <th>TotalCharges_7317.1</th>\n",
+              "      <th>TotalCharges_7318.2</th>\n",
+              "      <th>TotalCharges_732.5</th>\n",
+              "      <th>TotalCharges_7320.9</th>\n",
+              "      <th>TotalCharges_7321.05</th>\n",
+              "      <th>TotalCharges_7322.5</th>\n",
+              "      <th>TotalCharges_7323.15</th>\n",
+              "      <th>TotalCharges_7325.1</th>\n",
+              "      <th>TotalCharges_733.35</th>\n",
+              "      <th>TotalCharges_733.55</th>\n",
+              "      <th>TotalCharges_733.95</th>\n",
+              "      <th>TotalCharges_7332.4</th>\n",
+              "      <th>TotalCharges_7334.05</th>\n",
+              "      <th>TotalCharges_7337.55</th>\n",
+              "      <th>TotalCharges_734.35</th>\n",
+              "      <th>TotalCharges_734.6</th>\n",
+              "      <th>TotalCharges_7344.45</th>\n",
+              "      <th>TotalCharges_7346.2</th>\n",
+              "      <th>TotalCharges_7348.8</th>\n",
+              "      <th>TotalCharges_7349.35</th>\n",
+              "      <th>TotalCharges_735.5</th>\n",
+              "      <th>TotalCharges_735.9</th>\n",
+              "      <th>TotalCharges_736.8</th>\n",
+              "      <th>TotalCharges_7362.9</th>\n",
+              "      <th>TotalCharges_7365.3</th>\n",
+              "      <th>TotalCharges_7365.7</th>\n",
+              "      <th>TotalCharges_7372.65</th>\n",
+              "      <th>TotalCharges_7379.8</th>\n",
+              "      <th>TotalCharges_738.2</th>\n",
+              "      <th>TotalCharges_7382.25</th>\n",
+              "      <th>TotalCharges_7382.85</th>\n",
+              "      <th>TotalCharges_7383.7</th>\n",
+              "      <th>TotalCharges_7386.05</th>\n",
+              "      <th>TotalCharges_7388.45</th>\n",
+              "      <th>TotalCharges_739.35</th>\n",
+              "      <th>TotalCharges_739.9</th>\n",
+              "      <th>TotalCharges_7396.15</th>\n",
+              "      <th>TotalCharges_7397</th>\n",
+              "      <th>TotalCharges_74</th>\n",
+              "      <th>TotalCharges_74.1</th>\n",
+              "      <th>TotalCharges_74.2</th>\n",
+              "      <th>TotalCharges_74.25</th>\n",
+              "      <th>TotalCharges_74.3</th>\n",
+              "      <th>TotalCharges_74.35</th>\n",
+              "      <th>TotalCharges_74.4</th>\n",
+              "      <th>TotalCharges_74.45</th>\n",
+              "      <th>TotalCharges_74.5</th>\n",
+              "      <th>TotalCharges_74.6</th>\n",
+              "      <th>TotalCharges_74.7</th>\n",
+              "      <th>TotalCharges_74.9</th>\n",
+              "      <th>TotalCharges_74.95</th>\n",
+              "      <th>TotalCharges_740</th>\n",
+              "      <th>TotalCharges_740.3</th>\n",
+              "      <th>TotalCharges_740.55</th>\n",
+              "      <th>TotalCharges_740.8</th>\n",
+              "      <th>TotalCharges_7405.5</th>\n",
+              "      <th>TotalCharges_741</th>\n",
+              "      <th>TotalCharges_741.4</th>\n",
+              "      <th>TotalCharges_741.5</th>\n",
+              "      <th>TotalCharges_741.7</th>\n",
+              "      <th>TotalCharges_7412.25</th>\n",
+              "      <th>TotalCharges_7413.55</th>\n",
+              "      <th>TotalCharges_742.9</th>\n",
+              "      <th>TotalCharges_742.95</th>\n",
+              "      <th>TotalCharges_7422.1</th>\n",
+              "      <th>TotalCharges_743.05</th>\n",
+              "      <th>TotalCharges_743.3</th>\n",
+              "      <th>TotalCharges_743.5</th>\n",
+              "      <th>TotalCharges_743.75</th>\n",
+              "      <th>TotalCharges_7430.75</th>\n",
+              "      <th>TotalCharges_7432.05</th>\n",
+              "      <th>TotalCharges_7446.9</th>\n",
+              "      <th>TotalCharges_7447.7</th>\n",
+              "      <th>TotalCharges_745.3</th>\n",
+              "      <th>TotalCharges_7455.45</th>\n",
+              "      <th>TotalCharges_7459</th>\n",
+              "      <th>TotalCharges_7459.05</th>\n",
+              "      <th>TotalCharges_746.05</th>\n",
+              "      <th>TotalCharges_746.5</th>\n",
+              "      <th>TotalCharges_746.75</th>\n",
+              "      <th>TotalCharges_7467.5</th>\n",
+              "      <th>TotalCharges_7467.55</th>\n",
+              "      <th>TotalCharges_747.2</th>\n",
+              "      <th>TotalCharges_7470.1</th>\n",
+              "      <th>TotalCharges_7472.15</th>\n",
+              "      <th>TotalCharges_7475.1</th>\n",
+              "      <th>TotalCharges_7475.85</th>\n",
+              "      <th>TotalCharges_7482.1</th>\n",
+              "      <th>TotalCharges_749.25</th>\n",
+              "      <th>TotalCharges_749.35</th>\n",
+              "      <th>TotalCharges_7491.75</th>\n",
+              "      <th>TotalCharges_7493.05</th>\n",
+              "      <th>TotalCharges_75.05</th>\n",
+              "      <th>TotalCharges_75.1</th>\n",
+              "      <th>TotalCharges_75.3</th>\n",
+              "      <th>TotalCharges_75.35</th>\n",
+              "      <th>TotalCharges_75.45</th>\n",
+              "      <th>TotalCharges_75.5</th>\n",
+              "      <th>TotalCharges_75.55</th>\n",
+              "      <th>TotalCharges_75.6</th>\n",
+              "      <th>TotalCharges_75.7</th>\n",
+              "      <th>TotalCharges_75.75</th>\n",
+              "      <th>TotalCharges_75.8</th>\n",
+              "      <th>TotalCharges_75.9</th>\n",
+              "      <th>TotalCharges_750.1</th>\n",
+              "      <th>TotalCharges_7508.55</th>\n",
+              "      <th>TotalCharges_751.65</th>\n",
+              "      <th>TotalCharges_7511.3</th>\n",
+              "      <th>TotalCharges_7511.65</th>\n",
+              "      <th>TotalCharges_7511.9</th>\n",
+              "      <th>TotalCharges_7517.7</th>\n",
+              "      <th>TotalCharges_7521.95</th>\n",
+              "      <th>TotalCharges_7530.8</th>\n",
+              "      <th>TotalCharges_7532.15</th>\n",
+              "      <th>TotalCharges_7534.65</th>\n",
+              "      <th>TotalCharges_7537.5</th>\n",
+              "      <th>TotalCharges_754</th>\n",
+              "      <th>TotalCharges_754.5</th>\n",
+              "      <th>TotalCharges_754.65</th>\n",
+              "      <th>TotalCharges_7542.25</th>\n",
+              "      <th>TotalCharges_7544</th>\n",
+              "      <th>TotalCharges_7544.3</th>\n",
+              "      <th>TotalCharges_7548.1</th>\n",
+              "      <th>TotalCharges_7548.6</th>\n",
+              "      <th>TotalCharges_755.4</th>\n",
+              "      <th>TotalCharges_755.5</th>\n",
+              "      <th>TotalCharges_755.6</th>\n",
+              "      <th>TotalCharges_7550.3</th>\n",
+              "      <th>TotalCharges_7553.6</th>\n",
+              "      <th>TotalCharges_7554.05</th>\n",
+              "      <th>TotalCharges_7555</th>\n",
+              "      <th>TotalCharges_7556.9</th>\n",
+              "      <th>TotalCharges_7559.55</th>\n",
+              "      <th>TotalCharges_756.4</th>\n",
+              "      <th>TotalCharges_7565.35</th>\n",
+              "      <th>TotalCharges_7567.2</th>\n",
+              "      <th>TotalCharges_757.1</th>\n",
+              "      <th>TotalCharges_757.95</th>\n",
+              "      <th>TotalCharges_7576.7</th>\n",
+              "      <th>TotalCharges_7578.05</th>\n",
+              "      <th>TotalCharges_758.6</th>\n",
+              "      <th>TotalCharges_7581.5</th>\n",
+              "      <th>TotalCharges_7589.8</th>\n",
+              "      <th>TotalCharges_759.35</th>\n",
+              "      <th>TotalCharges_759.55</th>\n",
+              "      <th>TotalCharges_76</th>\n",
+              "      <th>TotalCharges_76.2</th>\n",
+              "      <th>TotalCharges_76.35</th>\n",
+              "      <th>TotalCharges_76.4</th>\n",
+              "      <th>TotalCharges_76.45</th>\n",
+              "      <th>TotalCharges_76.65</th>\n",
+              "      <th>TotalCharges_76.95</th>\n",
+              "      <th>TotalCharges_760.05</th>\n",
+              "      <th>TotalCharges_7609.75</th>\n",
+              "      <th>TotalCharges_761.85</th>\n",
+              "      <th>TotalCharges_761.95</th>\n",
+              "      <th>TotalCharges_7610.1</th>\n",
+              "      <th>TotalCharges_7611.55</th>\n",
+              "      <th>TotalCharges_7611.85</th>\n",
+              "      <th>TotalCharges_7616</th>\n",
+              "      <th>TotalCharges_762.1</th>\n",
+              "      <th>TotalCharges_762.25</th>\n",
+              "      <th>TotalCharges_762.45</th>\n",
+              "      <th>TotalCharges_762.5</th>\n",
+              "      <th>TotalCharges_7623.2</th>\n",
+              "      <th>TotalCharges_7624.2</th>\n",
+              "      <th>TotalCharges_7629.85</th>\n",
+              "      <th>TotalCharges_763.1</th>\n",
+              "      <th>TotalCharges_7634.25</th>\n",
+              "      <th>TotalCharges_7634.8</th>\n",
+              "      <th>TotalCharges_764.55</th>\n",
+              "      <th>TotalCharges_764.95</th>\n",
+              "      <th>TotalCharges_765.15</th>\n",
+              "      <th>TotalCharges_765.45</th>\n",
+              "      <th>TotalCharges_765.5</th>\n",
+              "      <th>TotalCharges_7657.4</th>\n",
+              "      <th>TotalCharges_7658.3</th>\n",
+              "      <th>TotalCharges_7661.8</th>\n",
+              "      <th>TotalCharges_7665.8</th>\n",
+              "      <th>TotalCharges_767.55</th>\n",
+              "      <th>TotalCharges_767.9</th>\n",
+              "      <th>TotalCharges_7674.55</th>\n",
+              "      <th>TotalCharges_7677.4</th>\n",
+              "      <th>TotalCharges_7679.65</th>\n",
+              "      <th>TotalCharges_768.05</th>\n",
+              "      <th>TotalCharges_768.45</th>\n",
+              "      <th>TotalCharges_7689.8</th>\n",
+              "      <th>TotalCharges_7689.95</th>\n",
+              "      <th>TotalCharges_769.1</th>\n",
+              "      <th>TotalCharges_7690.9</th>\n",
+              "      <th>TotalCharges_77.15</th>\n",
+              "      <th>TotalCharges_77.5</th>\n",
+              "      <th>TotalCharges_77.6</th>\n",
+              "      <th>TotalCharges_77.75</th>\n",
+              "      <th>TotalCharges_770.4</th>\n",
+              "      <th>TotalCharges_770.5</th>\n",
+              "      <th>TotalCharges_770.6</th>\n",
+              "      <th>TotalCharges_7707.7</th>\n",
+              "      <th>TotalCharges_771.95</th>\n",
+              "      <th>TotalCharges_7711.25</th>\n",
+              "      <th>TotalCharges_7711.45</th>\n",
+              "      <th>TotalCharges_7713.55</th>\n",
+              "      <th>TotalCharges_7714.65</th>\n",
+              "      <th>TotalCharges_7719.5</th>\n",
+              "      <th>TotalCharges_772.4</th>\n",
+              "      <th>TotalCharges_772.85</th>\n",
+              "      <th>TotalCharges_7723.7</th>\n",
+              "      <th>TotalCharges_7723.9</th>\n",
+              "      <th>TotalCharges_7726.35</th>\n",
+              "      <th>TotalCharges_773.2</th>\n",
+              "      <th>TotalCharges_773.65</th>\n",
+              "      <th>TotalCharges_7732.65</th>\n",
+              "      <th>TotalCharges_7737.55</th>\n",
+              "      <th>TotalCharges_7746.7</th>\n",
+              "      <th>TotalCharges_7748.75</th>\n",
+              "      <th>TotalCharges_775.3</th>\n",
+              "      <th>TotalCharges_775.6</th>\n",
+              "      <th>TotalCharges_7751.7</th>\n",
+              "      <th>TotalCharges_7752.05</th>\n",
+              "      <th>TotalCharges_7752.3</th>\n",
+              "      <th>TotalCharges_7758.9</th>\n",
+              "      <th>TotalCharges_776.25</th>\n",
+              "      <th>TotalCharges_7767.25</th>\n",
+              "      <th>TotalCharges_777.3</th>\n",
+              "      <th>TotalCharges_777.35</th>\n",
+              "      <th>TotalCharges_7774.05</th>\n",
+              "      <th>TotalCharges_778.1</th>\n",
+              "      <th>TotalCharges_7782.85</th>\n",
+              "      <th>TotalCharges_7789.6</th>\n",
+              "      <th>TotalCharges_779.2</th>\n",
+              "      <th>TotalCharges_779.25</th>\n",
+              "      <th>TotalCharges_7795.95</th>\n",
+              "      <th>TotalCharges_78.05</th>\n",
+              "      <th>TotalCharges_78.25</th>\n",
+              "      <th>TotalCharges_78.3</th>\n",
+              "      <th>TotalCharges_78.45</th>\n",
+              "      <th>TotalCharges_78.65</th>\n",
+              "      <th>TotalCharges_78.8</th>\n",
+              "      <th>TotalCharges_78.9</th>\n",
+              "      <th>TotalCharges_78.95</th>\n",
+              "      <th>TotalCharges_780.1</th>\n",
+              "      <th>TotalCharges_780.15</th>\n",
+              "      <th>TotalCharges_780.2</th>\n",
+              "      <th>TotalCharges_780.25</th>\n",
+              "      <th>TotalCharges_780.5</th>\n",
+              "      <th>TotalCharges_780.85</th>\n",
+              "      <th>TotalCharges_7804.15</th>\n",
+              "      <th>TotalCharges_7806.5</th>\n",
+              "      <th>TotalCharges_7806.6</th>\n",
+              "      <th>TotalCharges_781.25</th>\n",
+              "      <th>TotalCharges_781.4</th>\n",
+              "      <th>TotalCharges_7839.85</th>\n",
+              "      <th>TotalCharges_784.25</th>\n",
+              "      <th>TotalCharges_784.45</th>\n",
+              "      <th>TotalCharges_7840.6</th>\n",
+              "      <th>TotalCharges_7842.3</th>\n",
+              "      <th>TotalCharges_7843.55</th>\n",
+              "      <th>TotalCharges_7845.8</th>\n",
+              "      <th>TotalCharges_7848.5</th>\n",
+              "      <th>TotalCharges_7849.85</th>\n",
+              "      <th>TotalCharges_785.75</th>\n",
+              "      <th>TotalCharges_7852.4</th>\n",
+              "      <th>TotalCharges_7853.7</th>\n",
+              "      <th>TotalCharges_7854.15</th>\n",
+              "      <th>TotalCharges_7854.9</th>\n",
+              "      <th>TotalCharges_7856</th>\n",
+              "      <th>TotalCharges_786.3</th>\n",
+              "      <th>TotalCharges_786.5</th>\n",
+              "      <th>TotalCharges_7862.25</th>\n",
+              "      <th>TotalCharges_7869.05</th>\n",
+              "      <th>TotalCharges_7875</th>\n",
+              "      <th>TotalCharges_7878.3</th>\n",
+              "      <th>TotalCharges_788.05</th>\n",
+              "      <th>TotalCharges_788.35</th>\n",
+              "      <th>TotalCharges_788.55</th>\n",
+              "      <th>TotalCharges_788.6</th>\n",
+              "      <th>TotalCharges_788.8</th>\n",
+              "      <th>TotalCharges_7880.25</th>\n",
+              "      <th>TotalCharges_7881.2</th>\n",
+              "      <th>TotalCharges_7882.25</th>\n",
+              "      <th>TotalCharges_7882.5</th>\n",
+              "      <th>TotalCharges_7887.25</th>\n",
+              "      <th>TotalCharges_789.2</th>\n",
+              "      <th>TotalCharges_789.55</th>\n",
+              "      <th>TotalCharges_7895.15</th>\n",
+              "      <th>TotalCharges_7898.45</th>\n",
+              "      <th>TotalCharges_79.05</th>\n",
+              "      <th>TotalCharges_79.1</th>\n",
+              "      <th>TotalCharges_79.15</th>\n",
+              "      <th>TotalCharges_79.2</th>\n",
+              "      <th>TotalCharges_79.25</th>\n",
+              "      <th>TotalCharges_79.35</th>\n",
+              "      <th>TotalCharges_79.45</th>\n",
+              "      <th>TotalCharges_79.5</th>\n",
+              "      <th>TotalCharges_79.55</th>\n",
+              "      <th>TotalCharges_79.6</th>\n",
+              "      <th>TotalCharges_79.65</th>\n",
+              "      <th>TotalCharges_79.7</th>\n",
+              "      <th>TotalCharges_79.8</th>\n",
+              "      <th>TotalCharges_79.9</th>\n",
+              "      <th>TotalCharges_79.95</th>\n",
+              "      <th>TotalCharges_790</th>\n",
+              "      <th>TotalCharges_790.15</th>\n",
+              "      <th>TotalCharges_790.7</th>\n",
+              "      <th>TotalCharges_7904.25</th>\n",
+              "      <th>TotalCharges_791.15</th>\n",
+              "      <th>TotalCharges_791.7</th>\n",
+              "      <th>TotalCharges_791.75</th>\n",
+              "      <th>TotalCharges_7919.8</th>\n",
+              "      <th>TotalCharges_792.15</th>\n",
+              "      <th>TotalCharges_7920.7</th>\n",
+              "      <th>TotalCharges_7922.75</th>\n",
+              "      <th>TotalCharges_793.55</th>\n",
+              "      <th>TotalCharges_7930.55</th>\n",
+              "      <th>TotalCharges_7932.5</th>\n",
+              "      <th>TotalCharges_7939.25</th>\n",
+              "      <th>TotalCharges_794.25</th>\n",
+              "      <th>TotalCharges_7942.15</th>\n",
+              "      <th>TotalCharges_7943.45</th>\n",
+              "      <th>TotalCharges_795.15</th>\n",
+              "      <th>TotalCharges_795.65</th>\n",
+              "      <th>TotalCharges_7953.25</th>\n",
+              "      <th>TotalCharges_7962.2</th>\n",
+              "      <th>TotalCharges_7965.95</th>\n",
+              "      <th>TotalCharges_7966.9</th>\n",
+              "      <th>TotalCharges_7968.85</th>\n",
+              "      <th>TotalCharges_797.1</th>\n",
+              "      <th>TotalCharges_797.25</th>\n",
+              "      <th>TotalCharges_798.2</th>\n",
+              "      <th>TotalCharges_7982.5</th>\n",
+              "      <th>TotalCharges_7984.15</th>\n",
+              "      <th>TotalCharges_7985.9</th>\n",
+              "      <th>TotalCharges_7987.6</th>\n",
+              "      <th>TotalCharges_799</th>\n",
+              "      <th>TotalCharges_799.65</th>\n",
+              "      <th>TotalCharges_7990.05</th>\n",
+              "      <th>TotalCharges_7993.3</th>\n",
+              "      <th>TotalCharges_7998.8</th>\n",
+              "      <th>TotalCharges_80</th>\n",
+              "      <th>TotalCharges_80.05</th>\n",
+              "      <th>TotalCharges_80.15</th>\n",
+              "      <th>TotalCharges_80.2</th>\n",
+              "      <th>TotalCharges_80.25</th>\n",
+              "      <th>TotalCharges_80.3</th>\n",
+              "      <th>TotalCharges_80.35</th>\n",
+              "      <th>TotalCharges_80.5</th>\n",
+              "      <th>TotalCharges_80.55</th>\n",
+              "      <th>TotalCharges_80.8</th>\n",
+              "      <th>TotalCharges_80.85</th>\n",
+              "      <th>TotalCharges_80.95</th>\n",
+              "      <th>TotalCharges_800.3</th>\n",
+              "      <th>TotalCharges_8003.8</th>\n",
+              "      <th>TotalCharges_801.3</th>\n",
+              "      <th>TotalCharges_8012.75</th>\n",
+              "      <th>TotalCharges_8013.55</th>\n",
+              "      <th>TotalCharges_8016.6</th>\n",
+              "      <th>TotalCharges_802.35</th>\n",
+              "      <th>TotalCharges_8022.85</th>\n",
+              "      <th>TotalCharges_803.3</th>\n",
+              "      <th>TotalCharges_8033.1</th>\n",
+              "      <th>TotalCharges_8035.95</th>\n",
+              "      <th>TotalCharges_804.25</th>\n",
+              "      <th>TotalCharges_804.85</th>\n",
+              "      <th>TotalCharges_8041.65</th>\n",
+              "      <th>TotalCharges_8046.85</th>\n",
+              "      <th>TotalCharges_805.1</th>\n",
+              "      <th>TotalCharges_805.2</th>\n",
+              "      <th>TotalCharges_8058.55</th>\n",
+              "      <th>TotalCharges_8058.85</th>\n",
+              "      <th>TotalCharges_806.95</th>\n",
+              "      <th>TotalCharges_8061.5</th>\n",
+              "      <th>TotalCharges_8065.65</th>\n",
+              "      <th>TotalCharges_8071.05</th>\n",
+              "      <th>TotalCharges_8075.35</th>\n",
+              "      <th>TotalCharges_8078.1</th>\n",
+              "      <th>TotalCharges_808.95</th>\n",
+              "      <th>TotalCharges_8086.4</th>\n",
+              "      <th>TotalCharges_809.25</th>\n",
+              "      <th>TotalCharges_809.75</th>\n",
+              "      <th>TotalCharges_8093.15</th>\n",
+              "      <th>TotalCharges_81</th>\n",
+              "      <th>TotalCharges_81.05</th>\n",
+              "      <th>TotalCharges_81.1</th>\n",
+              "      <th>TotalCharges_81.25</th>\n",
+              "      <th>TotalCharges_81.7</th>\n",
+              "      <th>TotalCharges_81.95</th>\n",
+              "      <th>TotalCharges_810.2</th>\n",
+              "      <th>TotalCharges_810.3</th>\n",
+              "      <th>TotalCharges_810.45</th>\n",
+              "      <th>TotalCharges_810.7</th>\n",
+              "      <th>TotalCharges_810.85</th>\n",
+              "      <th>TotalCharges_8100.25</th>\n",
+              "      <th>TotalCharges_8100.55</th>\n",
+              "      <th>TotalCharges_8109.8</th>\n",
+              "      <th>TotalCharges_811.65</th>\n",
+              "      <th>TotalCharges_811.8</th>\n",
+              "      <th>TotalCharges_812.4</th>\n",
+              "      <th>TotalCharges_812.5</th>\n",
+              "      <th>TotalCharges_8124.2</th>\n",
+              "      <th>TotalCharges_8126.65</th>\n",
+              "      <th>TotalCharges_8127.6</th>\n",
+              "      <th>TotalCharges_8129.3</th>\n",
+              "      <th>TotalCharges_813.3</th>\n",
+              "      <th>TotalCharges_813.45</th>\n",
+              "      <th>TotalCharges_813.85</th>\n",
+              "      <th>TotalCharges_814.75</th>\n",
+              "      <th>TotalCharges_815.5</th>\n",
+              "      <th>TotalCharges_815.55</th>\n",
+              "      <th>TotalCharges_8152.3</th>\n",
+              "      <th>TotalCharges_816.8</th>\n",
+              "      <th>TotalCharges_8164.1</th>\n",
+              "      <th>TotalCharges_8165.1</th>\n",
+              "      <th>TotalCharges_8166.8</th>\n",
+              "      <th>TotalCharges_817.95</th>\n",
+              "      <th>TotalCharges_8175.9</th>\n",
+              "      <th>TotalCharges_818.05</th>\n",
+              "      <th>TotalCharges_818.45</th>\n",
+              "      <th>TotalCharges_8182.75</th>\n",
+              "      <th>TotalCharges_8182.85</th>\n",
+              "      <th>TotalCharges_819.4</th>\n",
+              "      <th>TotalCharges_819.55</th>\n",
+              "      <th>TotalCharges_819.95</th>\n",
+              "      <th>TotalCharges_8192.6</th>\n",
+              "      <th>TotalCharges_8196.4</th>\n",
+              "      <th>TotalCharges_82.15</th>\n",
+              "      <th>TotalCharges_82.3</th>\n",
+              "      <th>TotalCharges_82.7</th>\n",
+              "      <th>TotalCharges_82.85</th>\n",
+              "      <th>TotalCharges_82.9</th>\n",
+              "      <th>TotalCharges_820.5</th>\n",
+              "      <th>TotalCharges_821.6</th>\n",
+              "      <th>TotalCharges_8220.4</th>\n",
+              "      <th>TotalCharges_824.75</th>\n",
+              "      <th>TotalCharges_824.85</th>\n",
+              "      <th>TotalCharges_8240.85</th>\n",
+              "      <th>TotalCharges_8244.3</th>\n",
+              "      <th>TotalCharges_8248.5</th>\n",
+              "      <th>TotalCharges_825.1</th>\n",
+              "      <th>TotalCharges_825.4</th>\n",
+              "      <th>TotalCharges_825.7</th>\n",
+              "      <th>TotalCharges_8250</th>\n",
+              "      <th>TotalCharges_826</th>\n",
+              "      <th>TotalCharges_826.1</th>\n",
+              "      <th>TotalCharges_827.05</th>\n",
+              "      <th>TotalCharges_827.3</th>\n",
+              "      <th>TotalCharges_827.45</th>\n",
+              "      <th>TotalCharges_827.7</th>\n",
+              "      <th>TotalCharges_8277.05</th>\n",
+              "      <th>TotalCharges_828.05</th>\n",
+              "      <th>TotalCharges_828.2</th>\n",
+              "      <th>TotalCharges_828.85</th>\n",
+              "      <th>TotalCharges_8289.2</th>\n",
+              "      <th>TotalCharges_829.1</th>\n",
+              "      <th>TotalCharges_829.3</th>\n",
+              "      <th>TotalCharges_829.55</th>\n",
+              "      <th>TotalCharges_8297.5</th>\n",
+              "      <th>TotalCharges_83.3</th>\n",
+              "      <th>TotalCharges_83.4</th>\n",
+              "      <th>TotalCharges_83.75</th>\n",
+              "      <th>TotalCharges_830.25</th>\n",
+              "      <th>TotalCharges_830.7</th>\n",
+              "      <th>TotalCharges_830.8</th>\n",
+              "      <th>TotalCharges_830.85</th>\n",
+              "      <th>TotalCharges_8306.05</th>\n",
+              "      <th>TotalCharges_8308.9</th>\n",
+              "      <th>TotalCharges_8309.55</th>\n",
+              "      <th>TotalCharges_831.75</th>\n",
+              "      <th>TotalCharges_8310.55</th>\n",
+              "      <th>TotalCharges_8312.4</th>\n",
+              "      <th>TotalCharges_8312.75</th>\n",
+              "      <th>TotalCharges_8317.95</th>\n",
+              "      <th>TotalCharges_832.05</th>\n",
+              "      <th>TotalCharges_832.3</th>\n",
+              "      <th>TotalCharges_832.35</th>\n",
+              "      <th>TotalCharges_833.55</th>\n",
+              "      <th>TotalCharges_8331.95</th>\n",
+              "      <th>TotalCharges_8332.15</th>\n",
+              "      <th>TotalCharges_8333.95</th>\n",
+              "      <th>TotalCharges_8337.45</th>\n",
+              "      <th>TotalCharges_834.1</th>\n",
+              "      <th>TotalCharges_834.15</th>\n",
+              "      <th>TotalCharges_834.2</th>\n",
+              "      <th>TotalCharges_834.7</th>\n",
+              "      <th>TotalCharges_8349.45</th>\n",
+              "      <th>TotalCharges_8349.7</th>\n",
+              "      <th>TotalCharges_835.15</th>\n",
+              "      <th>TotalCharges_835.5</th>\n",
+              "      <th>TotalCharges_836.35</th>\n",
+              "      <th>TotalCharges_837.5</th>\n",
+              "      <th>TotalCharges_837.95</th>\n",
+              "      <th>TotalCharges_8375.05</th>\n",
+              "      <th>TotalCharges_838.5</th>\n",
+              "      <th>TotalCharges_838.7</th>\n",
+              "      <th>TotalCharges_839.4</th>\n",
+              "      <th>TotalCharges_839.65</th>\n",
+              "      <th>TotalCharges_8399.15</th>\n",
+              "      <th>TotalCharges_84.2</th>\n",
+              "      <th>TotalCharges_84.3</th>\n",
+              "      <th>TotalCharges_84.4</th>\n",
+              "      <th>TotalCharges_84.5</th>\n",
+              "      <th>TotalCharges_84.6</th>\n",
+              "      <th>TotalCharges_84.65</th>\n",
+              "      <th>TotalCharges_84.75</th>\n",
+              "      <th>TotalCharges_84.8</th>\n",
+              "      <th>TotalCharges_84.85</th>\n",
+              "      <th>TotalCharges_840.1</th>\n",
+              "      <th>TotalCharges_8404.9</th>\n",
+              "      <th>TotalCharges_8405</th>\n",
+              "      <th>TotalCharges_842.25</th>\n",
+              "      <th>TotalCharges_842.9</th>\n",
+              "      <th>TotalCharges_8424.9</th>\n",
+              "      <th>TotalCharges_8425.15</th>\n",
+              "      <th>TotalCharges_8425.3</th>\n",
+              "      <th>TotalCharges_8436.25</th>\n",
+              "      <th>TotalCharges_844.45</th>\n",
+              "      <th>TotalCharges_8443.7</th>\n",
+              "      <th>TotalCharges_845.25</th>\n",
+              "      <th>TotalCharges_845.6</th>\n",
+              "      <th>TotalCharges_8456.75</th>\n",
+              "      <th>TotalCharges_846</th>\n",
+              "      <th>TotalCharges_846.8</th>\n",
+              "      <th>TotalCharges_8468.2</th>\n",
+              "      <th>TotalCharges_847.25</th>\n",
+              "      <th>TotalCharges_847.3</th>\n",
+              "      <th>TotalCharges_847.8</th>\n",
+              "      <th>TotalCharges_8476.5</th>\n",
+              "      <th>TotalCharges_8477.6</th>\n",
+              "      <th>TotalCharges_8477.7</th>\n",
+              "      <th>TotalCharges_849.1</th>\n",
+              "      <th>TotalCharges_849.9</th>\n",
+              "      <th>TotalCharges_8496.7</th>\n",
+              "      <th>TotalCharges_85</th>\n",
+              "      <th>TotalCharges_85.05</th>\n",
+              "      <th>TotalCharges_85.1</th>\n",
+              "      <th>TotalCharges_85.15</th>\n",
+              "      <th>TotalCharges_85.45</th>\n",
+              "      <th>TotalCharges_85.5</th>\n",
+              "      <th>TotalCharges_85.55</th>\n",
+              "      <th>TotalCharges_85.7</th>\n",
+              "      <th>TotalCharges_85.8</th>\n",
+              "      <th>TotalCharges_851.2</th>\n",
+              "      <th>TotalCharges_851.75</th>\n",
+              "      <th>TotalCharges_851.8</th>\n",
+              "      <th>TotalCharges_852.7</th>\n",
+              "      <th>TotalCharges_8529.5</th>\n",
+              "      <th>TotalCharges_853</th>\n",
+              "      <th>TotalCharges_853.1</th>\n",
+              "      <th>TotalCharges_854.45</th>\n",
+              "      <th>TotalCharges_854.8</th>\n",
+              "      <th>TotalCharges_854.9</th>\n",
+              "      <th>TotalCharges_8543.25</th>\n",
+              "      <th>TotalCharges_8547.15</th>\n",
+              "      <th>TotalCharges_855.1</th>\n",
+              "      <th>TotalCharges_855.3</th>\n",
+              "      <th>TotalCharges_856.35</th>\n",
+              "      <th>TotalCharges_856.5</th>\n",
+              "      <th>TotalCharges_856.65</th>\n",
+              "      <th>TotalCharges_8564.75</th>\n",
+              "      <th>TotalCharges_857.2</th>\n",
+              "      <th>TotalCharges_857.25</th>\n",
+              "      <th>TotalCharges_857.75</th>\n",
+              "      <th>TotalCharges_857.8</th>\n",
+              "      <th>TotalCharges_858.6</th>\n",
+              "      <th>TotalCharges_8594.4</th>\n",
+              "      <th>TotalCharges_86</th>\n",
+              "      <th>TotalCharges_86.05</th>\n",
+              "      <th>TotalCharges_86.35</th>\n",
+              "      <th>TotalCharges_86.6</th>\n",
+              "      <th>TotalCharges_860.85</th>\n",
+              "      <th>TotalCharges_861.85</th>\n",
+              "      <th>TotalCharges_862.4</th>\n",
+              "      <th>TotalCharges_863.1</th>\n",
+              "      <th>TotalCharges_864.2</th>\n",
+              "      <th>TotalCharges_864.55</th>\n",
+              "      <th>TotalCharges_864.85</th>\n",
+              "      <th>TotalCharges_865</th>\n",
+              "      <th>TotalCharges_865.05</th>\n",
+              "      <th>TotalCharges_865.1</th>\n",
+              "      <th>TotalCharges_865.55</th>\n",
+              "      <th>TotalCharges_865.75</th>\n",
+              "      <th>TotalCharges_865.8</th>\n",
+              "      <th>TotalCharges_865.85</th>\n",
+              "      <th>TotalCharges_866.4</th>\n",
+              "      <th>TotalCharges_866.45</th>\n",
+              "      <th>TotalCharges_867.1</th>\n",
+              "      <th>TotalCharges_867.3</th>\n",
+              "      <th>TotalCharges_8670.1</th>\n",
+              "      <th>TotalCharges_8672.45</th>\n",
+              "      <th>TotalCharges_868.1</th>\n",
+              "      <th>TotalCharges_868.5</th>\n",
+              "      <th>TotalCharges_8684.8</th>\n",
+              "      <th>TotalCharges_869.9</th>\n",
+              "      <th>TotalCharges_87.3</th>\n",
+              "      <th>TotalCharges_87.9</th>\n",
+              "      <th>TotalCharges_870.25</th>\n",
+              "      <th>TotalCharges_871.4</th>\n",
+              "      <th>TotalCharges_872.65</th>\n",
+              "      <th>TotalCharges_873.4</th>\n",
+              "      <th>TotalCharges_874.2</th>\n",
+              "      <th>TotalCharges_874.8</th>\n",
+              "      <th>TotalCharges_875.35</th>\n",
+              "      <th>TotalCharges_875.55</th>\n",
+              "      <th>TotalCharges_876.15</th>\n",
+              "      <th>TotalCharges_876.75</th>\n",
+              "      <th>TotalCharges_877.35</th>\n",
+              "      <th>TotalCharges_878.35</th>\n",
+              "      <th>TotalCharges_879.8</th>\n",
+              "      <th>TotalCharges_88.35</th>\n",
+              "      <th>TotalCharges_88.8</th>\n",
+              "      <th>TotalCharges_880.05</th>\n",
+              "      <th>TotalCharges_880.2</th>\n",
+              "      <th>TotalCharges_882.55</th>\n",
+              "      <th>TotalCharges_883.35</th>\n",
+              "      <th>TotalCharges_886.4</th>\n",
+              "      <th>TotalCharges_886.7</th>\n",
+              "      <th>TotalCharges_887.35</th>\n",
+              "      <th>TotalCharges_888.65</th>\n",
+              "      <th>TotalCharges_888.75</th>\n",
+              "      <th>TotalCharges_889</th>\n",
+              "      <th>TotalCharges_889.9</th>\n",
+              "      <th>TotalCharges_89.05</th>\n",
+              "      <th>TotalCharges_89.1</th>\n",
+              "      <th>TotalCharges_89.15</th>\n",
+              "      <th>TotalCharges_89.25</th>\n",
+              "      <th>TotalCharges_89.3</th>\n",
+              "      <th>TotalCharges_89.35</th>\n",
+              "      <th>TotalCharges_89.5</th>\n",
+              "      <th>TotalCharges_89.55</th>\n",
+              "      <th>TotalCharges_89.75</th>\n",
+              "      <th>TotalCharges_89.9</th>\n",
+              "      <th>TotalCharges_890.35</th>\n",
+              "      <th>TotalCharges_890.5</th>\n",
+              "      <th>TotalCharges_890.6</th>\n",
+              "      <th>TotalCharges_892.15</th>\n",
+              "      <th>TotalCharges_892.65</th>\n",
+              "      <th>TotalCharges_892.7</th>\n",
+              "      <th>TotalCharges_893</th>\n",
+              "      <th>TotalCharges_893.2</th>\n",
+              "      <th>TotalCharges_893.55</th>\n",
+              "      <th>TotalCharges_894.3</th>\n",
+              "      <th>TotalCharges_896.75</th>\n",
+              "      <th>TotalCharges_896.9</th>\n",
+              "      <th>TotalCharges_897.75</th>\n",
+              "      <th>TotalCharges_898.35</th>\n",
+              "      <th>TotalCharges_899.45</th>\n",
+              "      <th>TotalCharges_899.8</th>\n",
+              "      <th>TotalCharges_90.05</th>\n",
+              "      <th>TotalCharges_90.1</th>\n",
+              "      <th>TotalCharges_90.35</th>\n",
+              "      <th>TotalCharges_90.55</th>\n",
+              "      <th>TotalCharges_90.6</th>\n",
+              "      <th>TotalCharges_90.75</th>\n",
+              "      <th>TotalCharges_90.85</th>\n",
+              "      <th>TotalCharges_900.5</th>\n",
+              "      <th>TotalCharges_900.9</th>\n",
+              "      <th>TotalCharges_901.25</th>\n",
+              "      <th>TotalCharges_902</th>\n",
+              "      <th>TotalCharges_902.25</th>\n",
+              "      <th>TotalCharges_903.6</th>\n",
+              "      <th>TotalCharges_903.7</th>\n",
+              "      <th>TotalCharges_903.8</th>\n",
+              "      <th>TotalCharges_905.55</th>\n",
+              "      <th>TotalCharges_906.85</th>\n",
+              "      <th>TotalCharges_907.05</th>\n",
+              "      <th>TotalCharges_908.15</th>\n",
+              "      <th>TotalCharges_908.55</th>\n",
+              "      <th>TotalCharges_908.75</th>\n",
+              "      <th>TotalCharges_909.25</th>\n",
+              "      <th>TotalCharges_91.1</th>\n",
+              "      <th>TotalCharges_91.3</th>\n",
+              "      <th>TotalCharges_91.4</th>\n",
+              "      <th>TotalCharges_91.45</th>\n",
+              "      <th>TotalCharges_91.7</th>\n",
+              "      <th>TotalCharges_910.45</th>\n",
+              "      <th>TotalCharges_911.6</th>\n",
+              "      <th>TotalCharges_912</th>\n",
+              "      <th>TotalCharges_913.3</th>\n",
+              "      <th>TotalCharges_914</th>\n",
+              "      <th>TotalCharges_914.3</th>\n",
+              "      <th>TotalCharges_914.4</th>\n",
+              "      <th>TotalCharges_914.6</th>\n",
+              "      <th>TotalCharges_916</th>\n",
+              "      <th>TotalCharges_916.15</th>\n",
+              "      <th>TotalCharges_916.75</th>\n",
+              "      <th>TotalCharges_916.9</th>\n",
+              "      <th>TotalCharges_917.15</th>\n",
+              "      <th>TotalCharges_917.45</th>\n",
+              "      <th>TotalCharges_918.6</th>\n",
+              "      <th>TotalCharges_918.7</th>\n",
+              "      <th>TotalCharges_918.75</th>\n",
+              "      <th>TotalCharges_919.4</th>\n",
+              "      <th>TotalCharges_92.05</th>\n",
+              "      <th>TotalCharges_92.25</th>\n",
+              "      <th>TotalCharges_92.45</th>\n",
+              "      <th>TotalCharges_92.5</th>\n",
+              "      <th>TotalCharges_92.65</th>\n",
+              "      <th>TotalCharges_92.75</th>\n",
+              "      <th>TotalCharges_920.5</th>\n",
+              "      <th>TotalCharges_921.3</th>\n",
+              "      <th>TotalCharges_921.4</th>\n",
+              "      <th>TotalCharges_921.55</th>\n",
+              "      <th>TotalCharges_923.1</th>\n",
+              "      <th>TotalCharges_923.5</th>\n",
+              "      <th>TotalCharges_923.85</th>\n",
+              "      <th>TotalCharges_926</th>\n",
+              "      <th>TotalCharges_926.2</th>\n",
+              "      <th>TotalCharges_926.25</th>\n",
+              "      <th>TotalCharges_927.1</th>\n",
+              "      <th>TotalCharges_927.15</th>\n",
+              "      <th>TotalCharges_927.35</th>\n",
+              "      <th>TotalCharges_927.65</th>\n",
+              "      <th>TotalCharges_928.4</th>\n",
+              "      <th>TotalCharges_929.2</th>\n",
+              "      <th>TotalCharges_929.3</th>\n",
+              "      <th>TotalCharges_929.45</th>\n",
+              "      <th>TotalCharges_93.3</th>\n",
+              "      <th>TotalCharges_93.4</th>\n",
+              "      <th>TotalCharges_93.45</th>\n",
+              "      <th>TotalCharges_93.55</th>\n",
+              "      <th>TotalCharges_93.7</th>\n",
+              "      <th>TotalCharges_93.85</th>\n",
+              "      <th>TotalCharges_930.05</th>\n",
+              "      <th>TotalCharges_930.4</th>\n",
+              "      <th>TotalCharges_930.45</th>\n",
+              "      <th>TotalCharges_930.9</th>\n",
+              "      <th>TotalCharges_930.95</th>\n",
+              "      <th>TotalCharges_931.55</th>\n",
+              "      <th>TotalCharges_931.75</th>\n",
+              "      <th>TotalCharges_931.9</th>\n",
+              "      <th>TotalCharges_933.3</th>\n",
+              "      <th>TotalCharges_934.1</th>\n",
+              "      <th>TotalCharges_934.15</th>\n",
+              "      <th>TotalCharges_934.8</th>\n",
+              "      <th>TotalCharges_935.9</th>\n",
+              "      <th>TotalCharges_936.7</th>\n",
+              "      <th>TotalCharges_936.85</th>\n",
+              "      <th>TotalCharges_937.1</th>\n",
+              "      <th>TotalCharges_937.5</th>\n",
+              "      <th>TotalCharges_937.6</th>\n",
+              "      <th>TotalCharges_938.65</th>\n",
+              "      <th>TotalCharges_938.95</th>\n",
+              "      <th>TotalCharges_939.7</th>\n",
+              "      <th>TotalCharges_939.8</th>\n",
+              "      <th>TotalCharges_94</th>\n",
+              "      <th>TotalCharges_94.15</th>\n",
+              "      <th>TotalCharges_94.4</th>\n",
+              "      <th>TotalCharges_94.45</th>\n",
+              "      <th>TotalCharges_94.5</th>\n",
+              "      <th>TotalCharges_94.55</th>\n",
+              "      <th>TotalCharges_94.6</th>\n",
+              "      <th>TotalCharges_940.35</th>\n",
+              "      <th>TotalCharges_941</th>\n",
+              "      <th>TotalCharges_942.95</th>\n",
+              "      <th>TotalCharges_943</th>\n",
+              "      <th>TotalCharges_943.1</th>\n",
+              "      <th>TotalCharges_943.85</th>\n",
+              "      <th>TotalCharges_944.65</th>\n",
+              "      <th>TotalCharges_945.7</th>\n",
+              "      <th>TotalCharges_946.95</th>\n",
+              "      <th>TotalCharges_947.3</th>\n",
+              "      <th>TotalCharges_947.4</th>\n",
+              "      <th>TotalCharges_947.75</th>\n",
+              "      <th>TotalCharges_948.9</th>\n",
+              "      <th>TotalCharges_949.8</th>\n",
+              "      <th>TotalCharges_949.85</th>\n",
+              "      <th>TotalCharges_95</th>\n",
+              "      <th>TotalCharges_95.05</th>\n",
+              "      <th>TotalCharges_95.1</th>\n",
+              "      <th>TotalCharges_95.45</th>\n",
+              "      <th>TotalCharges_95.55</th>\n",
+              "      <th>TotalCharges_95.6</th>\n",
+              "      <th>TotalCharges_95.65</th>\n",
+              "      <th>TotalCharges_95.85</th>\n",
+              "      <th>TotalCharges_950.2</th>\n",
+              "      <th>TotalCharges_950.75</th>\n",
+              "      <th>TotalCharges_951.55</th>\n",
+              "      <th>TotalCharges_952.3</th>\n",
+              "      <th>TotalCharges_953.45</th>\n",
+              "      <th>TotalCharges_953.65</th>\n",
+              "      <th>TotalCharges_954.9</th>\n",
+              "      <th>TotalCharges_955.15</th>\n",
+              "      <th>TotalCharges_955.6</th>\n",
+              "      <th>TotalCharges_955.75</th>\n",
+              "      <th>TotalCharges_956.65</th>\n",
+              "      <th>TotalCharges_957.1</th>\n",
+              "      <th>TotalCharges_958.1</th>\n",
+              "      <th>TotalCharges_958.15</th>\n",
+              "      <th>TotalCharges_958.25</th>\n",
+              "      <th>TotalCharges_958.45</th>\n",
+              "      <th>TotalCharges_959.9</th>\n",
+              "      <th>TotalCharges_96.05</th>\n",
+              "      <th>TotalCharges_96.1</th>\n",
+              "      <th>TotalCharges_96.45</th>\n",
+              "      <th>TotalCharges_96.8</th>\n",
+              "      <th>TotalCharges_96.85</th>\n",
+              "      <th>TotalCharges_961.4</th>\n",
+              "      <th>TotalCharges_962.25</th>\n",
+              "      <th>TotalCharges_962.9</th>\n",
+              "      <th>TotalCharges_963.95</th>\n",
+              "      <th>TotalCharges_964.35</th>\n",
+              "      <th>TotalCharges_964.9</th>\n",
+              "      <th>TotalCharges_966.25</th>\n",
+              "      <th>TotalCharges_966.55</th>\n",
+              "      <th>TotalCharges_967.85</th>\n",
+              "      <th>TotalCharges_967.9</th>\n",
+              "      <th>TotalCharges_969.85</th>\n",
+              "      <th>TotalCharges_97</th>\n",
+              "      <th>TotalCharges_97.1</th>\n",
+              "      <th>TotalCharges_97.5</th>\n",
+              "      <th>TotalCharges_970.4</th>\n",
+              "      <th>TotalCharges_970.45</th>\n",
+              "      <th>TotalCharges_970.55</th>\n",
+              "      <th>TotalCharges_970.85</th>\n",
+              "      <th>TotalCharges_971.3</th>\n",
+              "      <th>TotalCharges_973.1</th>\n",
+              "      <th>TotalCharges_973.25</th>\n",
+              "      <th>TotalCharges_973.35</th>\n",
+              "      <th>TotalCharges_973.45</th>\n",
+              "      <th>TotalCharges_973.55</th>\n",
+              "      <th>TotalCharges_973.65</th>\n",
+              "      <th>TotalCharges_973.95</th>\n",
+              "      <th>TotalCharges_978</th>\n",
+              "      <th>TotalCharges_978.6</th>\n",
+              "      <th>TotalCharges_979.05</th>\n",
+              "      <th>TotalCharges_979.5</th>\n",
+              "      <th>TotalCharges_98.35</th>\n",
+              "      <th>TotalCharges_98.5</th>\n",
+              "      <th>TotalCharges_980.35</th>\n",
+              "      <th>TotalCharges_981.45</th>\n",
+              "      <th>TotalCharges_982.95</th>\n",
+              "      <th>TotalCharges_985.05</th>\n",
+              "      <th>TotalCharges_987.95</th>\n",
+              "      <th>TotalCharges_989.05</th>\n",
+              "      <th>TotalCharges_989.45</th>\n",
+              "      <th>TotalCharges_99.45</th>\n",
+              "      <th>TotalCharges_99.6</th>\n",
+              "      <th>TotalCharges_99.75</th>\n",
+              "      <th>TotalCharges_99.95</th>\n",
+              "      <th>TotalCharges_990.3</th>\n",
+              "      <th>TotalCharges_990.45</th>\n",
+              "      <th>TotalCharges_990.85</th>\n",
+              "      <th>TotalCharges_990.9</th>\n",
+              "      <th>TotalCharges_992.7</th>\n",
+              "      <th>TotalCharges_993.15</th>\n",
+              "      <th>TotalCharges_994.55</th>\n",
+              "      <th>TotalCharges_994.8</th>\n",
+              "      <th>TotalCharges_995.35</th>\n",
+              "      <th>TotalCharges_996.45</th>\n",
+              "      <th>TotalCharges_996.85</th>\n",
+              "      <th>TotalCharges_996.95</th>\n",
+              "      <th>TotalCharges_997.65</th>\n",
+              "      <th>TotalCharges_997.75</th>\n",
+              "      <th>TotalCharges_998.1</th>\n",
+              "      <th>TotalCharges_999.45</th>\n",
+              "      <th>TotalCharges_999.8</th>\n",
+              "      <th>TotalCharges_999.9</th>\n",
+              "      <th>Churn_Yes</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7590-VHVEG</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>29.85</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>5575-GNVDE</td>\n",
+              "      <td>0</td>\n",
+              "      <td>34</td>\n",
+              "      <td>56.95</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3668-QPYBK</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>53.85</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>7795-CFOCW</td>\n",
+              "      <td>0</td>\n",
+              "      <td>45</td>\n",
+              "      <td>42.30</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>9237-HQITU</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>70.70</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>True</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
+              "      <td>False</td>\n",
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+              "    <div class=\"colab-df-buttons\">\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f33fca7-822c-4da1-b1d3-bd9aafb7c037')\"\n",
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+              "\n",
+              "  <style>\n",
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+              "      display:flex;\n",
+              "      gap: 12px;\n",
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+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9f33fca7-822c-4da1-b1d3-bd9aafb7c037 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9f33fca7-822c-4da1-b1d3-bd9aafb7c037');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "                title=\"Suggest charts\"\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
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+              "    width: 32px;\n",
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+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
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+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
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+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-892b5b0e-eaeb-41f2-8c72-306eaacbb3f0 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_gd"
+            }
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ✅ Step 5: Feature Scaling"
+      ],
+      "metadata": {
+        "id": "kkWFTvTMABUB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[num_cols]"
+      ],
+      "metadata": {
+        "id": "XZGNvCV9AGXb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "outputId": "b336cc8c-efde-4cde-e306-46cc090ae3ee"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      SeniorCitizen  tenure  MonthlyCharges\n",
+              "0                 0       1           29.85\n",
+              "1                 0      34           56.95\n",
+              "2                 0       2           53.85\n",
+              "3                 0      45           42.30\n",
+              "4                 0       2           70.70\n",
+              "...             ...     ...             ...\n",
+              "7038              0      24           84.80\n",
+              "7039              0      72          103.20\n",
+              "7040              0      11           29.60\n",
+              "7041              1       4           74.40\n",
+              "7042              0      66          105.65\n",
+              "\n",
+              "[7043 rows x 3 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ec843742-ad1e-4c10-804b-c153196e045a\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
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+              "\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>SeniorCitizen</th>\n",
+              "      <th>tenure</th>\n",
+              "      <th>MonthlyCharges</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>29.85</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>34</td>\n",
+              "      <td>56.95</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>53.85</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0</td>\n",
+              "      <td>45</td>\n",
+              "      <td>42.30</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>70.70</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7038</th>\n",
+              "      <td>0</td>\n",
+              "      <td>24</td>\n",
+              "      <td>84.80</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7039</th>\n",
+              "      <td>0</td>\n",
+              "      <td>72</td>\n",
+              "      <td>103.20</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7040</th>\n",
+              "      <td>0</td>\n",
+              "      <td>11</td>\n",
+              "      <td>29.60</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7041</th>\n",
+              "      <td>1</td>\n",
+              "      <td>4</td>\n",
+              "      <td>74.40</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7042</th>\n",
+              "      <td>0</td>\n",
+              "      <td>66</td>\n",
+              "      <td>105.65</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>7043 rows × 3 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec843742-ad1e-4c10-804b-c153196e045a')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ec843742-ad1e-4c10-804b-c153196e045a button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ec843742-ad1e-4c10-804b-c153196e045a');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-0eb01c48-278c-4aba-8230-1a3bcb6407d6\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0eb01c48-278c-4aba-8230-1a3bcb6407d6')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-0eb01c48-278c-4aba-8230-1a3bcb6407d6 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df[num_cols]\",\n  \"rows\": 7043,\n  \"fields\": [\n    {\n      \"column\": \"SeniorCitizen\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tenure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 0,\n        \"max\": 72,\n        \"num_unique_values\": 73,\n        \"samples\": [\n          8,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MonthlyCharges\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30.09004709767854,\n        \"min\": 18.25,\n        \"max\": 118.75,\n        \"num_unique_values\": 1585,\n        \"samples\": [\n          48.85,\n          20.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 47
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n",
+        "df_ss = df.copy()\n",
+        "df_mm = df.copy()\n",
+        "df_rs = df.copy()\n",
+        "\n",
+        "scaler = StandardScaler()\n",
+        "df_ss[num_cols] = scaler.fit_transform(df_ss[num_cols])\n",
+        "\n",
+        "scaler = MinMaxScaler()\n",
+        "df_mm[num_cols] = scaler.fit_transform(df_mm[num_cols])\n",
+        "\n",
+        "scaler = RobustScaler()\n",
+        "df_rs[num_cols] = scaler.fit_transform(df_rs[num_cols])\n",
+        "\n",
+        "df_ss.head()"
+      ],
+      "metadata": {
+        "id": "7XCmq0DI_9-R",
+        "outputId": "db65105a-6aa9-4ac0-c48f-ff97fc28c13f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 313
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   customerID  gender  SeniorCitizen Partner Dependents    tenure  \\\n",
+              "0  7590-VHVEG  Female      -0.439916     Yes         No -1.277445   \n",
+              "1  5575-GNVDE    Male      -0.439916      No         No  0.066327   \n",
+              "2  3668-QPYBK    Male      -0.439916      No         No -1.236724   \n",
+              "3  7795-CFOCW    Male      -0.439916      No         No  0.514251   \n",
+              "4  9237-HQITU  Female      -0.439916      No         No -1.236724   \n",
+              "\n",
+              "  PhoneService     MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
+              "0           No  No phone service             DSL             No          Yes   \n",
+              "1          Yes                No             DSL            Yes           No   \n",
+              "2          Yes                No             DSL            Yes          Yes   \n",
+              "3           No  No phone service             DSL            Yes           No   \n",
+              "4          Yes                No     Fiber optic             No           No   \n",
+              "\n",
+              "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
+              "0               No          No          No              No  Month-to-month   \n",
+              "1              Yes          No          No              No        One year   \n",
+              "2               No          No          No              No  Month-to-month   \n",
+              "3              Yes         Yes          No              No        One year   \n",
+              "4               No          No          No              No  Month-to-month   \n",
+              "\n",
+              "  PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges  \\\n",
+              "0              Yes           Electronic check       -1.160323        29.85   \n",
+              "1               No               Mailed check       -0.259629       1889.5   \n",
+              "2              Yes               Mailed check       -0.362660       108.15   \n",
+              "3               No  Bank transfer (automatic)       -0.746535      1840.75   \n",
+              "4              Yes           Electronic check        0.197365       151.65   \n",
+              "\n",
+              "  Churn  \n",
+              "0    No  \n",
+              "1    No  \n",
+              "2   Yes  \n",
+              "3    No  \n",
+              "4   Yes  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-3e1bfe10-d1d0-4b48-b577-e06cf6407718\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>customerID</th>\n",
+              "      <th>gender</th>\n",
+              "      <th>SeniorCitizen</th>\n",
+              "      <th>Partner</th>\n",
+              "      <th>Dependents</th>\n",
+              "      <th>tenure</th>\n",
+              "      <th>PhoneService</th>\n",
+              "      <th>MultipleLines</th>\n",
+              "      <th>InternetService</th>\n",
+              "      <th>OnlineSecurity</th>\n",
+              "      <th>OnlineBackup</th>\n",
+              "      <th>DeviceProtection</th>\n",
+              "      <th>TechSupport</th>\n",
+              "      <th>StreamingTV</th>\n",
+              "      <th>StreamingMovies</th>\n",
+              "      <th>Contract</th>\n",
+              "      <th>PaperlessBilling</th>\n",
+              "      <th>PaymentMethod</th>\n",
+              "      <th>MonthlyCharges</th>\n",
+              "      <th>TotalCharges</th>\n",
+              "      <th>Churn</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7590-VHVEG</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>-0.439916</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>-1.277445</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No phone service</td>\n",
+              "      <td>DSL</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Month-to-month</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Electronic check</td>\n",
+              "      <td>-1.160323</td>\n",
+              "      <td>29.85</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>5575-GNVDE</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>-0.439916</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0.066327</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>DSL</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>One year</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Mailed check</td>\n",
+              "      <td>-0.259629</td>\n",
+              "      <td>1889.5</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>3668-QPYBK</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>-0.439916</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>-1.236724</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>DSL</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Month-to-month</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Mailed check</td>\n",
+              "      <td>-0.362660</td>\n",
+              "      <td>108.15</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>7795-CFOCW</td>\n",
+              "      <td>Male</td>\n",
+              "      <td>-0.439916</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>0.514251</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No phone service</td>\n",
+              "      <td>DSL</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>One year</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Bank transfer (automatic)</td>\n",
+              "      <td>-0.746535</td>\n",
+              "      <td>1840.75</td>\n",
+              "      <td>No</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>9237-HQITU</td>\n",
+              "      <td>Female</td>\n",
+              "      <td>-0.439916</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>-1.236724</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Fiber optic</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Month-to-month</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Electronic check</td>\n",
+              "      <td>0.197365</td>\n",
+              "      <td>151.65</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e1bfe10-d1d0-4b48-b577-e06cf6407718')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-3e1bfe10-d1d0-4b48-b577-e06cf6407718 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-3e1bfe10-d1d0-4b48-b577-e06cf6407718');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-32d02b41-745e-4326-a259-260114522838\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-32d02b41-745e-4326-a259-260114522838')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-32d02b41-745e-4326-a259-260114522838 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_ss"
+            }
+          },
+          "metadata": {},
+          "execution_count": 49
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ✅ Step 6: Outlier Handling"
+      ],
+      "metadata": {
+        "id": "4N6xJiecAI2z"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example with Z-score\n",
+        "z_scores = np.abs((df[num_cols] - df[num_cols].mean()) / df[num_cols].std())\n",
+        "outliers = (z_scores > 3).any(axis=1)\n",
+        "print(\"Outliers detected:\", outliers.sum())\n",
+        "\n",
+        "print(df.shape)\n",
+        "# Option: remove\n",
+        "df = df[~outliers]\n",
+        "print(df.shape)\n"
+      ],
+      "metadata": {
+        "id": "FuERKhdpAHVU",
+        "outputId": "8e445c5e-c162-4392-f32d-cec16ac3a3da",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Outliers detected: 0\n",
+            "(7043, 21)\n",
+            "(7043, 21)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# IQR method for outlier detection\n",
+        "Q1 = df[num_cols].quantile(0.25)\n",
+        "Q3 = df[num_cols].quantile(0.75)\n",
+        "IQR = Q3 - Q1\n",
+        "\n",
+        "outliers = ((df[num_cols] < (Q1 - 1.5 * IQR)) | (df[num_cols] > (Q3 + 1.5 * IQR))).any(axis=1)\n",
+        "print(\"Outliers detected:\", outliers.sum())\n",
+        "\n",
+        "print(df.shape)\n",
+        "# Option: remove outliers\n",
+        "df = df[~outliers]\n",
+        "print(df.shape)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Xw4U9xilbk3h",
+        "outputId": "69533e54-94bf-46a0-e11b-5e26a777ec82"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Outliers detected: 1142\n",
+            "(7043, 21)\n",
+            "(5901, 21)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## ✅ Step 7: Skewness Handling (Log / Power Transform)"
+      ],
+      "metadata": {
+        "id": "XWxkytPuAW_t"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from scipy.stats import skew, kurtosis, jarque_bera, boxcox\n",
+        "from sklearn.preprocessing import PowerTransformer\n",
+        "\n",
+        "x = df[\"TotalCharges\"].astype(float)\n",
+        "\n",
+        "# --- Metrics helper ---\n",
+        "def metrics(vec):\n",
+        "    sk = skew(vec, nan_policy='omit')\n",
+        "    ku = kurtosis(vec, fisher=True, nan_policy='omit')  # 0 = normal\n",
+        "    jb_stat, jb_p = jarque_bera(vec)\n",
+        "    return sk, ku, jb_p\n",
+        "\n",
+        "# --- Transformations ---\n",
+        "x_log   = np.log1p(x)                        # log(1+x)\n",
+        "x_sqrt  = np.sqrt(x)                         # sqrt\n",
+        "x_bc, lam_bc = boxcox(x + 1e-6)              # Box-Cox needs >0\n",
+        "pt = PowerTransformer(method=\"yeo-johnson\", standardize=False)\n",
+        "x_yj = pt.fit_transform(x.values.reshape(-1,1)).ravel()\n",
+        "\n",
+        "\n",
+        "# --- Report ---\n",
+        "rows = [\n",
+        "    (\"Original\", *metrics(x)),\n",
+        "    (\"Log1p\", *metrics(x_log)),\n",
+        "    (\"Sqrt\", *metrics(x_sqrt)),\n",
+        "    (f\"Box-Cox λ={lam_bc:.3f}\", *metrics(x_bc)),\n",
+        "    (f\"Yeo–Johnson λ={pt.lambdas_[0]:.3f}\", *metrics(x_yj))\n",
+        "]\n",
+        "report = pd.DataFrame(rows, columns=[\"Transform\",\"Skewness\",\"Kurtosis\",\"JB-p\"])\n",
+        "\n",
+        "print(report)\n",
+        "\n",
+        "# --- Plots (2×3 grid) ---\n",
+        "fig, axes = plt.subplots(2, 3, figsize=(12, 7))\n",
+        "titles = [\"Original\",\"Log1p\",\"Sqrt\",f\"Box-Cox λ={lam_bc:.3f}\",f\"Yeo–Johnson λ={pt.lambdas_[0]:.3f}\"]\n",
+        "data = [x, x_log, x_sqrt, x_bc, x_yj]\n",
+        "\n",
+        "for ax, d, title in zip(axes.ravel(), data, titles):\n",
+        "    ax.hist(d, bins=40, density=True, color=\"steelblue\", alpha=0.7)\n",
+        "    ax.set_title(title)\n",
+        "    ax.set_xlabel(\"Fare\"); ax.set_ylabel(\"Density\")\n",
+        "\n",
+        "fig.delaxes(axes[1,2])  # remove empty subplot\n",
+        "fig.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "mboMsY1bAVwM"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/a0.1/NumPy, pandas & Matplotlib Essentials_SoroorParsafar.py b/a0.1/NumPy, pandas & Matplotlib Essentials_SoroorParsafar.py
new file mode 100644
index 0000000..cf8d920
--- /dev/null
+++ b/a0.1/NumPy, pandas & Matplotlib Essentials_SoroorParsafar.py	
@@ -0,0 +1,118 @@
+# -*- coding: utf-8 -*-
+"""Copy of Assignment 02. Python Packages | Nexus | RezaShokrzad.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1IGHRFntWvnMl5d_4Iaavjr4SVfFuK4Xw
+
+# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials
+<p align="center">📢⚠️📂  </p>
+
+<p align="center"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>
+
+<p align="center"> 🚨📝🧠 </p>
+
+
+------------------------------------------------
+
+
+Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:
+
+1. NumPy — fast n-dimensional arrays
+
+2. pandas — tabular data wrangling
+
+3. Matplotlib — quick, customizable plots
+
+Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊
+
+## 1 · NumPy 🧮
+Quick-start notes
+Core object: ndarray (n-dimensional array)
+
+Create data: np.array, np.arange, np.random.*
+
+Summaries: mean, std, sum, max, …
+
+Vectorised math beats Python loops for speed
+
+### Task 1 — Mock temperatures
+"""
+
+# 👉 # TODO: import numpy and create an array of 365
+# normally-distributed °C values (µ=20, σ=5) called temps
+import numpy as np
+temps = np.random.normal(loc=20, scale=5, size=365)
+print(temps[:5])
+
+"""### Task 2 — Average temperature
+
+"""
+
+# 👉 # TODO: print the mean of temps
+print(np.mean(temps))
+
+"""## 2 · pandas 📊
+Quick-start notes
+Main structures: DataFrame, Series
+
+Read data: pd.read_csv, pd.read_excel, …
+
+Selection: .loc[label], .iloc[pos]
+
+Group & summarise: .groupby(...).agg(...)
+
+### Task 3 — Load ride log
+"""
+
+# 👉 # TODO: read "rides.csv" into df
+# (columns: date,temp,rides,weekday)
+import pandas as pd
+file_name = "rides.csv"
+df = pd.read_csv(file_name)
+
+# Create a dummy rides.csv file for demonstration purposes
+data = {'date': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05']),
+        'temp': [10, 12, 15, 13, 11],
+        'rides': [50, 55, 60, 53, 51],
+        'weekday': ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday']}
+dummy_df = pd.DataFrame(data)
+dummy_df.to_csv('rides.csv', index=False)
+
+"""### Task 4 — Weekday averages"""
+
+# 👉 # TODO: compute and print mean rides per weekday
+df.groupby("weekday").agg({"rides": "mean"})
+
+"""## 3 · Matplotlib 📈
+Quick-start notes
+Workhorse: pyplot interface (import matplotlib.pyplot as plt)
+
+Figure & axes: fig, ax = plt.subplots()
+
+Common plots: plot, scatter, hist, imshow
+
+Display inline in Jupyter with %matplotlib inline or %matplotlib notebook
+
+### Task 5 — Scatter plot
+"""
+
+# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df
+import pandas as pd
+import matplotlib.pyplot as plt
+file_name = "rides.csv"
+df = pd.read_csv(file_name)
+plt.figure(figsize=(10, 6))
+plt.scatter(df['temp'], df['rides'])
+plt.title('Rides vs. Temperature', fontsize=16)
+plt.xlabel('Temperature (°C)', fontsize=12)
+plt.ylabel('Rides', fontsize=12)
+
+"""### Task 6 — Show the figure
+
+
+"""
+
+# 👉 # TODO: call plt.show() so the plot appears
+plt.show()
\ No newline at end of file
diff --git a/a0.1/Project_01_01_02_sinads.ipynb b/a0.1/Project_01_01_02_sinads.ipynb
new file mode 100644
index 0000000..b844f99
--- /dev/null
+++ b/a0.1/Project_01_01_02_sinads.ipynb
@@ -0,0 +1,482 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/sinads/Nexus_Assignments/blob/main/Project_01_01_02_sinads.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🍷 Mini-Project: Merge & Explore the Wine Quality Datasets\n",
+        "\n",
+        "\n",
+        "> **Goal: Merge Wine Quality – Red and Wine Quality – White (UCI) into a single dataset, do a careful first-look exploration, and save the merged file to CSV.**\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>"
+      ],
+      "metadata": {
+        "id": "5pY7aLZSZQM9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# === Requirements ===\n",
+        "# pip install pandas matplotlib\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from pathlib import Path\n"
+      ],
+      "metadata": {
+        "id": "wpNpLMF8aX1s"
+      },
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 1) Load ----------\n",
+        "URL_RED   = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n",
+        "URL_WHITE = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv\"\n",
+        "\n",
+        "red   = pd.read_csv(URL_RED, sep=\";\")\n",
+        "white = pd.read_csv(URL_WHITE, sep=\";\")\n"
+      ],
+      "metadata": {
+        "id": "ErqnxAGRaY_C"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 2) Sanity checks ----------\n",
+        "print(\"Red shape:\", red.shape, \"White shape:\", white.shape)\n",
+        "print(\"Columns equal? ->\", list(red.columns) == list(white.columns))\n",
+        "print(\"Columns:\", list(red.columns))\n"
+      ],
+      "metadata": {
+        "id": "lVbxevgpabbF",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "023a2f28-f928-45bc-c0dd-107176c4e84a"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Red shape: (1599, 12) White shape: (4898, 12)\n",
+            "Columns equal? -> True\n",
+            "Columns: ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol', 'quality']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# (Optional) strict schema assertion (search and read about assert in Python)\n",
+        "assert list(red.columns) == list(white.columns), \"Column mismatch between red and white datasets.\"\n"
+      ],
+      "metadata": {
+        "id": "5vV73isEadfQ"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 3) Tag source & merge ----------\n",
+        "red[\"type\"] = \"red\"\n",
+        "white[\"type\"] = \"white\"\n",
+        "\n",
+        "df = pd.concat([red, white], ignore_index=True)\n",
+        "print(\"\\nMerged shape:\", df.shape)\n"
+      ],
+      "metadata": {
+        "id": "uVTeWORRakKq",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f594a4d0-f077-437f-b3bc-740b87193ebc"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Merged shape: (6497, 13)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 4) Basic exploration ----------\n",
+        "print(\"\\nDtypes:\\n\", df.dtypes)\n",
+        "print(\"\\nMissing values per column:\\n\", df.isna().sum().sort_values(ascending=False))\n",
+        "print(\"\\nHead:\\n\", df.head())\n"
+      ],
+      "metadata": {
+        "id": "OsMo1KOhambz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f9c74b5e-b786-4bd7-8e05-18f66296dd31"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Dtypes:\n",
+            " fixed acidity           float64\n",
+            "volatile acidity        float64\n",
+            "citric acid             float64\n",
+            "residual sugar          float64\n",
+            "chlorides               float64\n",
+            "free sulfur dioxide     float64\n",
+            "total sulfur dioxide    float64\n",
+            "density                 float64\n",
+            "pH                      float64\n",
+            "sulphates               float64\n",
+            "alcohol                 float64\n",
+            "quality                   int64\n",
+            "type                     object\n",
+            "dtype: object\n",
+            "\n",
+            "Missing values per column:\n",
+            " fixed acidity           0\n",
+            "volatile acidity        0\n",
+            "citric acid             0\n",
+            "residual sugar          0\n",
+            "chlorides               0\n",
+            "free sulfur dioxide     0\n",
+            "total sulfur dioxide    0\n",
+            "density                 0\n",
+            "pH                      0\n",
+            "sulphates               0\n",
+            "alcohol                 0\n",
+            "quality                 0\n",
+            "type                    0\n",
+            "dtype: int64\n",
+            "\n",
+            "Head:\n",
+            "    fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+            "0            7.4              0.70         0.00             1.9      0.076   \n",
+            "1            7.8              0.88         0.00             2.6      0.098   \n",
+            "2            7.8              0.76         0.04             2.3      0.092   \n",
+            "3           11.2              0.28         0.56             1.9      0.075   \n",
+            "4            7.4              0.70         0.00             1.9      0.076   \n",
+            "\n",
+            "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+            "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
+            "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
+            "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
+            "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
+            "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
+            "\n",
+            "   alcohol  quality type  \n",
+            "0      9.4        5  red  \n",
+            "1      9.8        5  red  \n",
+            "2      9.8        5  red  \n",
+            "3      9.8        6  red  \n",
+            "4      9.4        5  red  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Uniqueness & duplicates\n",
+        "dup_count = df.duplicated().sum()\n",
+        "print(\"\\nDuplicate rows:\", dup_count)\n",
+        "\n",
+        "# Descriptive statistics (numeric)\n",
+        "num_cols = df.select_dtypes(include=[np.number]).columns\n",
+        "print(\"\\nNumeric summary:\\n\", df[num_cols].describe().T)\n",
+        "\n",
+        "# Target distributions\n",
+        "print(\"\\nQuality distribution (overall):\\n\", df[\"quality\"].value_counts().sort_index())\n",
+        "print(\"\\nQuality distribution by type:\\n\", df.groupby(\"type\")[\"quality\"].value_counts().sort_index())\n"
+      ],
+      "metadata": {
+        "id": "ou0kWts-aopS",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2ec36df8-b98e-4b64-8d56-e062e84c6597"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Duplicate rows: 1177\n",
+            "\n",
+            "Numeric summary:\n",
+            "                        count        mean        std      min       25%  \\\n",
+            "fixed acidity         6497.0    7.215307   1.296434  3.80000   6.40000   \n",
+            "volatile acidity      6497.0    0.339666   0.164636  0.08000   0.23000   \n",
+            "citric acid           6497.0    0.318633   0.145318  0.00000   0.25000   \n",
+            "residual sugar        6497.0    5.443235   4.757804  0.60000   1.80000   \n",
+            "chlorides             6497.0    0.056034   0.035034  0.00900   0.03800   \n",
+            "free sulfur dioxide   6497.0   30.525319  17.749400  1.00000  17.00000   \n",
+            "total sulfur dioxide  6497.0  115.744574  56.521855  6.00000  77.00000   \n",
+            "density               6497.0    0.994697   0.002999  0.98711   0.99234   \n",
+            "pH                    6497.0    3.218501   0.160787  2.72000   3.11000   \n",
+            "sulphates             6497.0    0.531268   0.148806  0.22000   0.43000   \n",
+            "alcohol               6497.0   10.491801   1.192712  8.00000   9.50000   \n",
+            "quality               6497.0    5.818378   0.873255  3.00000   5.00000   \n",
+            "\n",
+            "                            50%        75%        max  \n",
+            "fixed acidity           7.00000    7.70000   15.90000  \n",
+            "volatile acidity        0.29000    0.40000    1.58000  \n",
+            "citric acid             0.31000    0.39000    1.66000  \n",
+            "residual sugar          3.00000    8.10000   65.80000  \n",
+            "chlorides               0.04700    0.06500    0.61100  \n",
+            "free sulfur dioxide    29.00000   41.00000  289.00000  \n",
+            "total sulfur dioxide  118.00000  156.00000  440.00000  \n",
+            "density                 0.99489    0.99699    1.03898  \n",
+            "pH                      3.21000    3.32000    4.01000  \n",
+            "sulphates               0.51000    0.60000    2.00000  \n",
+            "alcohol                10.30000   11.30000   14.90000  \n",
+            "quality                 6.00000    6.00000    9.00000  \n",
+            "\n",
+            "Quality distribution (overall):\n",
+            " quality\n",
+            "3      30\n",
+            "4     216\n",
+            "5    2138\n",
+            "6    2836\n",
+            "7    1079\n",
+            "8     193\n",
+            "9       5\n",
+            "Name: count, dtype: int64\n",
+            "\n",
+            "Quality distribution by type:\n",
+            " type   quality\n",
+            "red    3            10\n",
+            "       4            53\n",
+            "       5           681\n",
+            "       6           638\n",
+            "       7           199\n",
+            "       8            18\n",
+            "white  3            20\n",
+            "       4           163\n",
+            "       5          1457\n",
+            "       6          2198\n",
+            "       7           880\n",
+            "       8           175\n",
+            "       9             5\n",
+            "Name: count, dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 5) A few simple visuals (optional for report) ----------\n",
+        "# Histograms of numeric features (quick feel for ranges & skew)\n",
+        "top_vars = df.select_dtypes(include=[np.number]).columns[:4]\n",
+        "\n",
+        "n = min(4, len(top_vars))\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 4, figsize=(16, 3), sharey=True)\n",
+        "\n",
+        "for i, ax in enumerate(axes):\n",
+        "    if i < n:\n",
+        "        col = top_vars[i]\n",
+        "        ax.hist(df[col].dropna(), bins=30)\n",
+        "        ax.set_title(f\"Histogram: {col}\")\n",
+        "        ax.set_xlabel(col)\n",
+        "        if i == 0:\n",
+        "            ax.set_ylabel(\"Count\")\n",
+        "        else:\n",
+        "            ax.set_ylabel(\"\")\n",
+        "    else:\n",
+        "        ax.axis(\"off\")  # hide unused panels if top_vars has < 4\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "2Pa_iCVzaqzp",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 252
+        },
+        "outputId": "e90098fd-3af9-4847-959e-0cb8dc8d5bec"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x300 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Boxplot of quality by type (class distribution spread)\n",
+        "plt.figure()\n",
+        "df.boxplot(column=\"quality\", by=\"type\")\n",
+        "plt.suptitle(\"\")\n",
+        "plt.title(\"Quality by Wine Type\")\n",
+        "plt.xlabel(\"Type\")\n",
+        "plt.ylabel(\"Quality\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "w9z9f2OMatLD",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 490
+        },
+        "outputId": "298885f6-a64e-4c78-aa8b-d20d6ea84a3c"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Correlation heatmap (numeric only)\n",
+        "corr = df[num_cols].corr()\n",
+        "plt.figure(figsize=(7, 6))\n",
+        "plt.imshow(corr, interpolation=\"nearest\")\n",
+        "plt.title(\"Correlation Heatmap\")\n",
+        "plt.colorbar()\n",
+        "plt.xticks(range(len(num_cols)), num_cols, rotation=90)\n",
+        "plt.yticks(range(len(num_cols)), num_cols)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "m9OZ2n2nawIP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 623
+        },
+        "outputId": "1e7ac8a8-3ac2-4fc8-f2d9-d647d954ff33"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 700x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "NHQPRTdLZI9R",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4668bfaa-bb2f-4e5a-abd1-a13f5b96f8ad"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Saved merged file to: /content/outputs/wine_quality_merged.csv\n",
+            "Reloaded shape: (6497, 13)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---------- 6) Save ----------\n",
+        "OUT_DIR = Path(\"./outputs\")\n",
+        "OUT_DIR.mkdir(parents=True, exist_ok=True)\n",
+        "out_file = OUT_DIR / \"wine_quality_merged.csv\"\n",
+        "df.to_csv(out_file, index=False)\n",
+        "print(f\"\\nSaved merged file to: {out_file.resolve()}\")\n",
+        "\n",
+        "# Quick verification of saved file\n",
+        "df_check = pd.read_csv(out_file)\n",
+        "print(\"Reloaded shape:\", df_check.shape)\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/Projects/P.01/assignment_10_project_01_01_02_dataset___nexus___rsayyareh.py b/a0.1/Projects/P.01/assignment_10_project_01_01_02_dataset___nexus___rsayyareh.py
new file mode 100644
index 0000000..3f9550f
--- /dev/null
+++ b/a0.1/Projects/P.01/assignment_10_project_01_01_02_dataset___nexus___rsayyareh.py
@@ -0,0 +1,160 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# # 🍷 Mini-Project: Merge & Explore the Wine Quality Datasets
+# 
+# 
+# > **Goal: Merge Wine Quality – Red and Wine Quality – White (UCI) into a single dataset, do a careful first-look exploration, and save the merged file to CSV.**
+# 
+# <p align="center">📢⚠️📂  </p>
+# 
+# <p align="center"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>
+# 
+# <p align="center"> 🚨📝🧠 </p>
+
+# In[2]:
+
+
+# === Requirements ===
+# pip install pandas matplotlib
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from pathlib import Path
+
+
+# In[3]:
+
+
+# ---------- 1) Load ----------
+URL_RED   = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"
+URL_WHITE = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"
+
+red   = pd.read_csv(URL_RED, sep=";")
+white = pd.read_csv(URL_WHITE, sep=";")
+
+
+# In[4]:
+
+
+# ---------- 2) Sanity checks ----------
+print("Red shape:", red.shape, "White shape:", white.shape)
+print("Columns equal? ->", list(red.columns) == list(white.columns))
+print("Columns:", list(red.columns))
+
+
+# In[5]:
+
+
+# (Optional) strict schema assertion (search and read about assert in Python)
+assert list(red.columns) == list(white.columns), "Column mismatch between red and white datasets."
+
+
+# In[13]:
+
+
+# ---------- 3) Tag source & merge ----------
+red["type"] = "red"
+white["type"] = "white"
+
+df = pd.concat([red, white], ignore_index=True)
+print("\nMerged shape:", df.shape)
+
+
+# In[ ]:
+
+
+# ---------- 4) Basic exploration ----------
+print("\nDtypes:\n", df.dtypes)
+print("\nMissing values per column:\n", df.isnull().sum().sort_values(ascending=False))
+print("\nHead:\n", df.head())
+
+
+# In[15]:
+
+
+# Uniqueness & duplicates
+dup_count = df.duplicated().unique()
+print("\nDuplicate rows:", dup_count)
+
+# Descriptive statistics (numeric)
+num_cols = df.select_dtypes(include=[np.number]).columns
+print("\nNumeric summary:\n", df[num_cols].describe().T)
+
+# Target distributions
+print("\nQuality distribution (overall):\n", df["quality"].count())
+print("\nQuality distribution by type:\n", df.groupby("type")["quality"].count().sort_index())
+
+
+# In[21]:
+
+
+# ---------- 5) A few simple visuals (optional for report) ----------
+# Histograms of numeric features (quick feel for ranges & skew)
+top_vars = df.columns
+n = min(4, len(top_vars))
+
+
+fig, axes = plt.subplots(1, 4, figsize=(16, 3), sharey=True)
+
+for i, ax in enumerate(axes):
+    if i < n:
+        col = top_vars[i]
+        ax.hist(df[col].dropna(), bins=30)
+        ax.set_title(f"Histogram: {col}")
+        ax.set_xlabel(col)
+        if i == 0:
+            ax.set_ylabel("Count")
+        else:
+            ax.set_ylabel("")
+    else:
+        ax.axis("off")  # hide unused panels if top_vars has < 4
+
+plt.tight_layout()
+plt.show()
+
+
+# In[22]:
+
+
+# Boxplot of quality by type (class distribution spread)
+plt.figure()
+df.boxplot(column="quality", by="type")
+plt.suptitle("")
+plt.title("Quality by Wine Type")
+plt.xlabel("Type")
+plt.ylabel("Quality")
+plt.tight_layout()
+plt.show()
+
+
+# In[23]:
+
+
+# Correlation heatmap (numeric only)
+corr = df[num_cols].corr()
+plt.figure(figsize=(7, 6))
+plt.imshow(corr, interpolation="nearest")
+plt.title("Correlation Heatmap")
+plt.colorbar()
+plt.xticks(range(len(num_cols)), num_cols, rotation=90)
+plt.yticks(range(len(num_cols)), num_cols)
+plt.tight_layout()
+plt.show()
+
+
+# In[24]:
+
+
+# ---------- 6) Save ----------
+OUT_DIR = Path("./outputs")
+OUT_DIR.mkdir(parents=True, exist_ok=True)
+out_file = OUT_DIR / "wine_quality_merged.csv"
+df.to_csv(out_file, index=False)
+print(f"\nSaved merged file to: {out_file.resolve()}")
+
+# Quick verification of saved file
+df_check = pd.read_csv(out_file)
+print("Reloaded shape:", df_check.shape)
+
diff --git a/a0.1/Projects/P.02/Project2_rsayyareh.ipynb b/a0.1/Projects/P.02/Project2_rsayyareh.ipynb
new file mode 100644
index 0000000..3fd60c4
--- /dev/null
+++ b/a0.1/Projects/P.02/Project2_rsayyareh.ipynb
@@ -0,0 +1,3544 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h6pr9RxJiBKU"
+      },
+      "source": [
+        "# 🏠 Mini-Project: Preprocess & Engineer Features on Ames Housing Dataset\n",
+        "\n",
+        "> **Goal: Work with the [Ames Housing dataset](https://www.kaggle.com/datasets/prevek18/ames-housing-dataset?select=AmesHousing.csv) to perform data preprocessing and create meaningful new features. You will:**\n",
+        "> - Handle **missing values**, **duplicates**, and **outliers**  \n",
+        "> - Detect and fix **skewness** in numerical features  \n",
+        "> - Encode categorical variables into numeric formats  \n",
+        "> - Create **non-linear features** (e.g., polynomial, log, interaction terms) from existing variables  \n",
+        "> - Save the cleaned and enriched dataset into a new CSV file  \n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂</p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project2_rezashokrzad.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠</p>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7CksOccRjV4s"
+      },
+      "source": [
+        "## 🔹 Step 1: Load the Dataset\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Nlz-ZiyHgmQb",
+        "outputId": "a51aba43-8ab5-4793-e96b-45e1152a2ca5"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\Parastoo\\AppData\\Local\\Temp\\ipykernel_15072\\1234441213.py:15: DeprecationWarning: Use dataset_load() instead of load_dataset(). load_dataset() will be removed in a future version.\n",
+            "  df1 = kagglehub.load_dataset(\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "First 5 records:    Order        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
+            "0      1  526301100           20        RL         141.0     31770   Pave   \n",
+            "1      2  526350040           20        RH          80.0     11622   Pave   \n",
+            "2      3  526351010           20        RL          81.0     14267   Pave   \n",
+            "3      4  526353030           20        RL          93.0     11160   Pave   \n",
+            "4      5  527105010           60        RL          74.0     13830   Pave   \n",
+            "\n",
+            "  Alley Lot Shape Land Contour Utilities Lot Config Land Slope Neighborhood  \\\n",
+            "0   NaN       IR1          Lvl    AllPub     Corner        Gtl        NAmes   \n",
+            "1   NaN       Reg          Lvl    AllPub     Inside        Gtl        NAmes   \n",
+            "2   NaN       IR1          Lvl    AllPub     Corner        Gtl        NAmes   \n",
+            "3   NaN       Reg          Lvl    AllPub     Corner        Gtl        NAmes   \n",
+            "4   NaN       IR1          Lvl    AllPub     Inside        Gtl      Gilbert   \n",
+            "\n",
+            "  Condition 1 Condition 2 Bldg Type House Style  Overall Qual  Overall Cond  \\\n",
+            "0        Norm        Norm      1Fam      1Story             6             5   \n",
+            "1       Feedr        Norm      1Fam      1Story             5             6   \n",
+            "2        Norm        Norm      1Fam      1Story             6             6   \n",
+            "3        Norm        Norm      1Fam      1Story             7             5   \n",
+            "4        Norm        Norm      1Fam      2Story             5             5   \n",
+            "\n",
+            "   Year Built  Year Remod/Add Roof Style Roof Matl Exterior 1st Exterior 2nd  \\\n",
+            "0        1960            1960        Hip   CompShg      BrkFace      Plywood   \n",
+            "1        1961            1961      Gable   CompShg      VinylSd      VinylSd   \n",
+            "2        1958            1958        Hip   CompShg      Wd Sdng      Wd Sdng   \n",
+            "3        1968            1968        Hip   CompShg      BrkFace      BrkFace   \n",
+            "4        1997            1998      Gable   CompShg      VinylSd      VinylSd   \n",
+            "\n",
+            "  Mas Vnr Type  Mas Vnr Area Exter Qual Exter Cond Foundation Bsmt Qual  \\\n",
+            "0        Stone         112.0         TA         TA     CBlock        TA   \n",
+            "1          NaN           0.0         TA         TA     CBlock        TA   \n",
+            "2      BrkFace         108.0         TA         TA     CBlock        TA   \n",
+            "3          NaN           0.0         Gd         TA     CBlock        TA   \n",
+            "4          NaN           0.0         TA         TA      PConc        Gd   \n",
+            "\n",
+            "  Bsmt Cond Bsmt Exposure BsmtFin Type 1  BsmtFin SF 1 BsmtFin Type 2  \\\n",
+            "0        Gd            Gd            BLQ         639.0            Unf   \n",
+            "1        TA            No            Rec         468.0            LwQ   \n",
+            "2        TA            No            ALQ         923.0            Unf   \n",
+            "3        TA            No            ALQ        1065.0            Unf   \n",
+            "4        TA            No            GLQ         791.0            Unf   \n",
+            "\n",
+            "   BsmtFin SF 2  Bsmt Unf SF  Total Bsmt SF Heating Heating QC Central Air  \\\n",
+            "0           0.0        441.0         1080.0    GasA         Fa           Y   \n",
+            "1         144.0        270.0          882.0    GasA         TA           Y   \n",
+            "2           0.0        406.0         1329.0    GasA         TA           Y   \n",
+            "3           0.0       1045.0         2110.0    GasA         Ex           Y   \n",
+            "4           0.0        137.0          928.0    GasA         Gd           Y   \n",
+            "\n",
+            "  Electrical  1st Flr SF  2nd Flr SF  Low Qual Fin SF  Gr Liv Area  \\\n",
+            "0      SBrkr        1656           0                0         1656   \n",
+            "1      SBrkr         896           0                0          896   \n",
+            "2      SBrkr        1329           0                0         1329   \n",
+            "3      SBrkr        2110           0                0         2110   \n",
+            "4      SBrkr         928         701                0         1629   \n",
+            "\n",
+            "   Bsmt Full Bath  Bsmt Half Bath  Full Bath  Half Bath  Bedroom AbvGr  \\\n",
+            "0             1.0             0.0          1          0              3   \n",
+            "1             0.0             0.0          1          0              2   \n",
+            "2             0.0             0.0          1          1              3   \n",
+            "3             1.0             0.0          2          1              3   \n",
+            "4             0.0             0.0          2          1              3   \n",
+            "\n",
+            "   Kitchen AbvGr Kitchen Qual  TotRms AbvGrd Functional  Fireplaces  \\\n",
+            "0              1           TA              7        Typ           2   \n",
+            "1              1           TA              5        Typ           0   \n",
+            "2              1           Gd              6        Typ           0   \n",
+            "3              1           Ex              8        Typ           2   \n",
+            "4              1           TA              6        Typ           1   \n",
+            "\n",
+            "  Fireplace Qu Garage Type  Garage Yr Blt Garage Finish  Garage Cars  \\\n",
+            "0           Gd      Attchd         1960.0           Fin          2.0   \n",
+            "1          NaN      Attchd         1961.0           Unf          1.0   \n",
+            "2          NaN      Attchd         1958.0           Unf          1.0   \n",
+            "3           TA      Attchd         1968.0           Fin          2.0   \n",
+            "4           TA      Attchd         1997.0           Fin          2.0   \n",
+            "\n",
+            "   Garage Area Garage Qual Garage Cond Paved Drive  Wood Deck SF  \\\n",
+            "0        528.0          TA          TA           P           210   \n",
+            "1        730.0          TA          TA           Y           140   \n",
+            "2        312.0          TA          TA           Y           393   \n",
+            "3        522.0          TA          TA           Y             0   \n",
+            "4        482.0          TA          TA           Y           212   \n",
+            "\n",
+            "   Open Porch SF  Enclosed Porch  3Ssn Porch  Screen Porch  Pool Area Pool QC  \\\n",
+            "0             62               0           0             0          0     NaN   \n",
+            "1              0               0           0           120          0     NaN   \n",
+            "2             36               0           0             0          0     NaN   \n",
+            "3              0               0           0             0          0     NaN   \n",
+            "4             34               0           0             0          0     NaN   \n",
+            "\n",
+            "   Fence Misc Feature  Misc Val  Mo Sold  Yr Sold Sale Type Sale Condition  \\\n",
+            "0    NaN          NaN         0        5     2010       WD          Normal   \n",
+            "1  MnPrv          NaN         0        6     2010       WD          Normal   \n",
+            "2    NaN         Gar2     12500        6     2010       WD          Normal   \n",
+            "3    NaN          NaN         0        4     2010       WD          Normal   \n",
+            "4  MnPrv          NaN         0        3     2010       WD          Normal   \n",
+            "\n",
+            "   SalePrice  \n",
+            "0     215000  \n",
+            "1     105000  \n",
+            "2     172000  \n",
+            "3     244000  \n",
+            "4     189900  \n",
+            "Last 5 records:       Order        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
+            "2925   2926  923275080           80        RL          37.0      7937   Pave   \n",
+            "2926   2927  923276100           20        RL           NaN      8885   Pave   \n",
+            "2927   2928  923400125           85        RL          62.0     10441   Pave   \n",
+            "2928   2929  924100070           20        RL          77.0     10010   Pave   \n",
+            "2929   2930  924151050           60        RL          74.0      9627   Pave   \n",
+            "\n",
+            "     Alley Lot Shape Land Contour Utilities Lot Config Land Slope  \\\n",
+            "2925   NaN       IR1          Lvl    AllPub    CulDSac        Gtl   \n",
+            "2926   NaN       IR1          Low    AllPub     Inside        Mod   \n",
+            "2927   NaN       Reg          Lvl    AllPub     Inside        Gtl   \n",
+            "2928   NaN       Reg          Lvl    AllPub     Inside        Mod   \n",
+            "2929   NaN       Reg          Lvl    AllPub     Inside        Mod   \n",
+            "\n",
+            "     Neighborhood Condition 1 Condition 2 Bldg Type House Style  Overall Qual  \\\n",
+            "2925      Mitchel        Norm        Norm      1Fam        SLvl             6   \n",
+            "2926      Mitchel        Norm        Norm      1Fam      1Story             5   \n",
+            "2927      Mitchel        Norm        Norm      1Fam      SFoyer             5   \n",
+            "2928      Mitchel        Norm        Norm      1Fam      1Story             5   \n",
+            "2929      Mitchel        Norm        Norm      1Fam      2Story             7   \n",
+            "\n",
+            "      Overall Cond  Year Built  Year Remod/Add Roof Style Roof Matl  \\\n",
+            "2925             6        1984            1984      Gable   CompShg   \n",
+            "2926             5        1983            1983      Gable   CompShg   \n",
+            "2927             5        1992            1992      Gable   CompShg   \n",
+            "2928             5        1974            1975      Gable   CompShg   \n",
+            "2929             5        1993            1994      Gable   CompShg   \n",
+            "\n",
+            "     Exterior 1st Exterior 2nd Mas Vnr Type  Mas Vnr Area Exter Qual  \\\n",
+            "2925      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2926      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2927      HdBoard      Wd Shng          NaN           0.0         TA   \n",
+            "2928      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2929      HdBoard      HdBoard      BrkFace          94.0         TA   \n",
+            "\n",
+            "     Exter Cond Foundation Bsmt Qual Bsmt Cond Bsmt Exposure BsmtFin Type 1  \\\n",
+            "2925         TA     CBlock        TA        TA            Av            GLQ   \n",
+            "2926         TA     CBlock        Gd        TA            Av            BLQ   \n",
+            "2927         TA      PConc        Gd        TA            Av            GLQ   \n",
+            "2928         TA     CBlock        Gd        TA            Av            ALQ   \n",
+            "2929         TA      PConc        Gd        TA            Av            LwQ   \n",
+            "\n",
+            "      BsmtFin SF 1 BsmtFin Type 2  BsmtFin SF 2  Bsmt Unf SF  Total Bsmt SF  \\\n",
+            "2925         819.0            Unf           0.0        184.0         1003.0   \n",
+            "2926         301.0            ALQ         324.0        239.0          864.0   \n",
+            "2927         337.0            Unf           0.0        575.0          912.0   \n",
+            "2928        1071.0            LwQ         123.0        195.0         1389.0   \n",
+            "2929         758.0            Unf           0.0        238.0          996.0   \n",
+            "\n",
+            "     Heating Heating QC Central Air Electrical  1st Flr SF  2nd Flr SF  \\\n",
+            "2925    GasA         TA           Y      SBrkr        1003           0   \n",
+            "2926    GasA         TA           Y      SBrkr         902           0   \n",
+            "2927    GasA         TA           Y      SBrkr         970           0   \n",
+            "2928    GasA         Gd           Y      SBrkr        1389           0   \n",
+            "2929    GasA         Ex           Y      SBrkr         996        1004   \n",
+            "\n",
+            "      Low Qual Fin SF  Gr Liv Area  Bsmt Full Bath  Bsmt Half Bath  Full Bath  \\\n",
+            "2925                0         1003             1.0             0.0          1   \n",
+            "2926                0          902             1.0             0.0          1   \n",
+            "2927                0          970             0.0             1.0          1   \n",
+            "2928                0         1389             1.0             0.0          1   \n",
+            "2929                0         2000             0.0             0.0          2   \n",
+            "\n",
+            "      Half Bath  Bedroom AbvGr  Kitchen AbvGr Kitchen Qual  TotRms AbvGrd  \\\n",
+            "2925          0              3              1           TA              6   \n",
+            "2926          0              2              1           TA              5   \n",
+            "2927          0              3              1           TA              6   \n",
+            "2928          0              2              1           TA              6   \n",
+            "2929          1              3              1           TA              9   \n",
+            "\n",
+            "     Functional  Fireplaces Fireplace Qu Garage Type  Garage Yr Blt  \\\n",
+            "2925        Typ           0          NaN      Detchd         1984.0   \n",
+            "2926        Typ           0          NaN      Attchd         1983.0   \n",
+            "2927        Typ           0          NaN         NaN            NaN   \n",
+            "2928        Typ           1           TA      Attchd         1975.0   \n",
+            "2929        Typ           1           TA      Attchd         1993.0   \n",
+            "\n",
+            "     Garage Finish  Garage Cars  Garage Area Garage Qual Garage Cond  \\\n",
+            "2925           Unf          2.0        588.0          TA          TA   \n",
+            "2926           Unf          2.0        484.0          TA          TA   \n",
+            "2927           NaN          0.0          0.0         NaN         NaN   \n",
+            "2928           RFn          2.0        418.0          TA          TA   \n",
+            "2929           Fin          3.0        650.0          TA          TA   \n",
+            "\n",
+            "     Paved Drive  Wood Deck SF  Open Porch SF  Enclosed Porch  3Ssn Porch  \\\n",
+            "2925           Y           120              0               0           0   \n",
+            "2926           Y           164              0               0           0   \n",
+            "2927           Y            80             32               0           0   \n",
+            "2928           Y           240             38               0           0   \n",
+            "2929           Y           190             48               0           0   \n",
+            "\n",
+            "      Screen Porch  Pool Area Pool QC  Fence Misc Feature  Misc Val  Mo Sold  \\\n",
+            "2925             0          0     NaN  GdPrv          NaN         0        3   \n",
+            "2926             0          0     NaN  MnPrv          NaN         0        6   \n",
+            "2927             0          0     NaN  MnPrv         Shed       700        7   \n",
+            "2928             0          0     NaN    NaN          NaN         0        4   \n",
+            "2929             0          0     NaN    NaN          NaN         0       11   \n",
+            "\n",
+            "      Yr Sold Sale Type Sale Condition  SalePrice  \n",
+            "2925     2006       WD          Normal     142500  \n",
+            "2926     2006       WD          Normal     131000  \n",
+            "2927     2006       WD          Normal     132000  \n",
+            "2928     2006       WD          Normal     170000  \n",
+            "2929     2006       WD          Normal     188000  \n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Load the Ames Housing dataset into a DataFrame.\n",
+        "# Hint: The dataset is available on Kaggle (\"Ames Housing\").\n",
+        "# After loading, display the first and last 5 rows to check if it worked.\n",
+        "\n",
+        "# Install dependencies as needed:\n",
+        "# pip install kagglehub[pandas-datasets]\n",
+        "import kagglehub\n",
+        "from kagglehub import KaggleDatasetAdapter\n",
+        "import pandas as pd\n",
+        "\n",
+        "# Set the path to the file you'd like to load\n",
+        "file_path = \"AmesHousing.csv\"\n",
+        "\n",
+        "# Load the latest version\n",
+        "df1 = kagglehub.load_dataset(\n",
+        "  KaggleDatasetAdapter.PANDAS,\n",
+        "  \"prevek18/ames-housing-dataset\",\n",
+        "  file_path,\n",
+        "  # Provide any additional arguments like\n",
+        "  # sql_query or pandas_kwargs. See the\n",
+        "  # documenation for more information:\n",
+        "  # https://github.com/Kaggle/kagglehub/blob/main/README.md#kaggledatasetadapterpandas\n",
+        ")\n",
+        "\n",
+        "pd.set_option('display.max_columns', None)\n",
+        "\n",
+        "print(\"First 5 records:\", df1.head())\n",
+        "print(\"Last 5 records:\", df1.tail())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OdX7swaujg_u"
+      },
+      "source": [
+        "## 🔹 Step 2: Exploratory Data Review (EDR)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DMYRBPWWjgpo",
+        "outputId": "6eedee8f-e54b-41a9-9ff6-1bee3288b30f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Shape: (2930, 82)\n",
+            "Columns Name: Index(['Order', 'PID', 'MS SubClass', 'MS Zoning', 'Lot Frontage', 'Lot Area',\n",
+            "       'Street', 'Alley', 'Lot Shape', 'Land Contour', 'Utilities',\n",
+            "       'Lot Config', 'Land Slope', 'Neighborhood', 'Condition 1',\n",
+            "       'Condition 2', 'Bldg Type', 'House Style', 'Overall Qual',\n",
+            "       'Overall Cond', 'Year Built', 'Year Remod/Add', 'Roof Style',\n",
+            "       'Roof Matl', 'Exterior 1st', 'Exterior 2nd', 'Mas Vnr Type',\n",
+            "       'Mas Vnr Area', 'Exter Qual', 'Exter Cond', 'Foundation', 'Bsmt Qual',\n",
+            "       'Bsmt Cond', 'Bsmt Exposure', 'BsmtFin Type 1', 'BsmtFin SF 1',\n",
+            "       'BsmtFin Type 2', 'BsmtFin SF 2', 'Bsmt Unf SF', 'Total Bsmt SF',\n",
+            "       'Heating', 'Heating QC', 'Central Air', 'Electrical', '1st Flr SF',\n",
+            "       '2nd Flr SF', 'Low Qual Fin SF', 'Gr Liv Area', 'Bsmt Full Bath',\n",
+            "       'Bsmt Half Bath', 'Full Bath', 'Half Bath', 'Bedroom AbvGr',\n",
+            "       'Kitchen AbvGr', 'Kitchen Qual', 'TotRms AbvGrd', 'Functional',\n",
+            "       'Fireplaces', 'Fireplace Qu', 'Garage Type', 'Garage Yr Blt',\n",
+            "       'Garage Finish', 'Garage Cars', 'Garage Area', 'Garage Qual',\n",
+            "       'Garage Cond', 'Paved Drive', 'Wood Deck SF', 'Open Porch SF',\n",
+            "       'Enclosed Porch', '3Ssn Porch', 'Screen Porch', 'Pool Area', 'Pool QC',\n",
+            "       'Fence', 'Misc Feature', 'Misc Val', 'Mo Sold', 'Yr Sold', 'Sale Type',\n",
+            "       'Sale Condition', 'SalePrice'],\n",
+            "      dtype='object')\n",
+            "Sample of Records: <bound method NDFrame.sample of       Order        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
+            "0         1  526301100           20        RL         141.0     31770   Pave   \n",
+            "1         2  526350040           20        RH          80.0     11622   Pave   \n",
+            "2         3  526351010           20        RL          81.0     14267   Pave   \n",
+            "3         4  526353030           20        RL          93.0     11160   Pave   \n",
+            "4         5  527105010           60        RL          74.0     13830   Pave   \n",
+            "...     ...        ...          ...       ...           ...       ...    ...   \n",
+            "2925   2926  923275080           80        RL          37.0      7937   Pave   \n",
+            "2926   2927  923276100           20        RL           NaN      8885   Pave   \n",
+            "2927   2928  923400125           85        RL          62.0     10441   Pave   \n",
+            "2928   2929  924100070           20        RL          77.0     10010   Pave   \n",
+            "2929   2930  924151050           60        RL          74.0      9627   Pave   \n",
+            "\n",
+            "     Alley Lot Shape Land Contour Utilities Lot Config Land Slope  \\\n",
+            "0      NaN       IR1          Lvl    AllPub     Corner        Gtl   \n",
+            "1      NaN       Reg          Lvl    AllPub     Inside        Gtl   \n",
+            "2      NaN       IR1          Lvl    AllPub     Corner        Gtl   \n",
+            "3      NaN       Reg          Lvl    AllPub     Corner        Gtl   \n",
+            "4      NaN       IR1          Lvl    AllPub     Inside        Gtl   \n",
+            "...    ...       ...          ...       ...        ...        ...   \n",
+            "2925   NaN       IR1          Lvl    AllPub    CulDSac        Gtl   \n",
+            "2926   NaN       IR1          Low    AllPub     Inside        Mod   \n",
+            "2927   NaN       Reg          Lvl    AllPub     Inside        Gtl   \n",
+            "2928   NaN       Reg          Lvl    AllPub     Inside        Mod   \n",
+            "2929   NaN       Reg          Lvl    AllPub     Inside        Mod   \n",
+            "\n",
+            "     Neighborhood Condition 1 Condition 2 Bldg Type House Style  Overall Qual  \\\n",
+            "0           NAmes        Norm        Norm      1Fam      1Story             6   \n",
+            "1           NAmes       Feedr        Norm      1Fam      1Story             5   \n",
+            "2           NAmes        Norm        Norm      1Fam      1Story             6   \n",
+            "3           NAmes        Norm        Norm      1Fam      1Story             7   \n",
+            "4         Gilbert        Norm        Norm      1Fam      2Story             5   \n",
+            "...           ...         ...         ...       ...         ...           ...   \n",
+            "2925      Mitchel        Norm        Norm      1Fam        SLvl             6   \n",
+            "2926      Mitchel        Norm        Norm      1Fam      1Story             5   \n",
+            "2927      Mitchel        Norm        Norm      1Fam      SFoyer             5   \n",
+            "2928      Mitchel        Norm        Norm      1Fam      1Story             5   \n",
+            "2929      Mitchel        Norm        Norm      1Fam      2Story             7   \n",
+            "\n",
+            "      Overall Cond  Year Built  Year Remod/Add Roof Style Roof Matl  \\\n",
+            "0                5        1960            1960        Hip   CompShg   \n",
+            "1                6        1961            1961      Gable   CompShg   \n",
+            "2                6        1958            1958        Hip   CompShg   \n",
+            "3                5        1968            1968        Hip   CompShg   \n",
+            "4                5        1997            1998      Gable   CompShg   \n",
+            "...            ...         ...             ...        ...       ...   \n",
+            "2925             6        1984            1984      Gable   CompShg   \n",
+            "2926             5        1983            1983      Gable   CompShg   \n",
+            "2927             5        1992            1992      Gable   CompShg   \n",
+            "2928             5        1974            1975      Gable   CompShg   \n",
+            "2929             5        1993            1994      Gable   CompShg   \n",
+            "\n",
+            "     Exterior 1st Exterior 2nd Mas Vnr Type  Mas Vnr Area Exter Qual  \\\n",
+            "0         BrkFace      Plywood        Stone         112.0         TA   \n",
+            "1         VinylSd      VinylSd          NaN           0.0         TA   \n",
+            "2         Wd Sdng      Wd Sdng      BrkFace         108.0         TA   \n",
+            "3         BrkFace      BrkFace          NaN           0.0         Gd   \n",
+            "4         VinylSd      VinylSd          NaN           0.0         TA   \n",
+            "...           ...          ...          ...           ...        ...   \n",
+            "2925      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2926      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2927      HdBoard      Wd Shng          NaN           0.0         TA   \n",
+            "2928      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2929      HdBoard      HdBoard      BrkFace          94.0         TA   \n",
+            "\n",
+            "     Exter Cond Foundation Bsmt Qual Bsmt Cond Bsmt Exposure BsmtFin Type 1  \\\n",
+            "0            TA     CBlock        TA        Gd            Gd            BLQ   \n",
+            "1            TA     CBlock        TA        TA            No            Rec   \n",
+            "2            TA     CBlock        TA        TA            No            ALQ   \n",
+            "3            TA     CBlock        TA        TA            No            ALQ   \n",
+            "4            TA      PConc        Gd        TA            No            GLQ   \n",
+            "...         ...        ...       ...       ...           ...            ...   \n",
+            "2925         TA     CBlock        TA        TA            Av            GLQ   \n",
+            "2926         TA     CBlock        Gd        TA            Av            BLQ   \n",
+            "2927         TA      PConc        Gd        TA            Av            GLQ   \n",
+            "2928         TA     CBlock        Gd        TA            Av            ALQ   \n",
+            "2929         TA      PConc        Gd        TA            Av            LwQ   \n",
+            "\n",
+            "      BsmtFin SF 1 BsmtFin Type 2  BsmtFin SF 2  Bsmt Unf SF  Total Bsmt SF  \\\n",
+            "0            639.0            Unf           0.0        441.0         1080.0   \n",
+            "1            468.0            LwQ         144.0        270.0          882.0   \n",
+            "2            923.0            Unf           0.0        406.0         1329.0   \n",
+            "3           1065.0            Unf           0.0       1045.0         2110.0   \n",
+            "4            791.0            Unf           0.0        137.0          928.0   \n",
+            "...            ...            ...           ...          ...            ...   \n",
+            "2925         819.0            Unf           0.0        184.0         1003.0   \n",
+            "2926         301.0            ALQ         324.0        239.0          864.0   \n",
+            "2927         337.0            Unf           0.0        575.0          912.0   \n",
+            "2928        1071.0            LwQ         123.0        195.0         1389.0   \n",
+            "2929         758.0            Unf           0.0        238.0          996.0   \n",
+            "\n",
+            "     Heating Heating QC Central Air Electrical  1st Flr SF  2nd Flr SF  \\\n",
+            "0       GasA         Fa           Y      SBrkr        1656           0   \n",
+            "1       GasA         TA           Y      SBrkr         896           0   \n",
+            "2       GasA         TA           Y      SBrkr        1329           0   \n",
+            "3       GasA         Ex           Y      SBrkr        2110           0   \n",
+            "4       GasA         Gd           Y      SBrkr         928         701   \n",
+            "...      ...        ...         ...        ...         ...         ...   \n",
+            "2925    GasA         TA           Y      SBrkr        1003           0   \n",
+            "2926    GasA         TA           Y      SBrkr         902           0   \n",
+            "2927    GasA         TA           Y      SBrkr         970           0   \n",
+            "2928    GasA         Gd           Y      SBrkr        1389           0   \n",
+            "2929    GasA         Ex           Y      SBrkr         996        1004   \n",
+            "\n",
+            "      Low Qual Fin SF  Gr Liv Area  Bsmt Full Bath  Bsmt Half Bath  Full Bath  \\\n",
+            "0                   0         1656             1.0             0.0          1   \n",
+            "1                   0          896             0.0             0.0          1   \n",
+            "2                   0         1329             0.0             0.0          1   \n",
+            "3                   0         2110             1.0             0.0          2   \n",
+            "4                   0         1629             0.0             0.0          2   \n",
+            "...               ...          ...             ...             ...        ...   \n",
+            "2925                0         1003             1.0             0.0          1   \n",
+            "2926                0          902             1.0             0.0          1   \n",
+            "2927                0          970             0.0             1.0          1   \n",
+            "2928                0         1389             1.0             0.0          1   \n",
+            "2929                0         2000             0.0             0.0          2   \n",
+            "\n",
+            "      Half Bath  Bedroom AbvGr  Kitchen AbvGr Kitchen Qual  TotRms AbvGrd  \\\n",
+            "0             0              3              1           TA              7   \n",
+            "1             0              2              1           TA              5   \n",
+            "2             1              3              1           Gd              6   \n",
+            "3             1              3              1           Ex              8   \n",
+            "4             1              3              1           TA              6   \n",
+            "...         ...            ...            ...          ...            ...   \n",
+            "2925          0              3              1           TA              6   \n",
+            "2926          0              2              1           TA              5   \n",
+            "2927          0              3              1           TA              6   \n",
+            "2928          0              2              1           TA              6   \n",
+            "2929          1              3              1           TA              9   \n",
+            "\n",
+            "     Functional  Fireplaces Fireplace Qu Garage Type  Garage Yr Blt  \\\n",
+            "0           Typ           2           Gd      Attchd         1960.0   \n",
+            "1           Typ           0          NaN      Attchd         1961.0   \n",
+            "2           Typ           0          NaN      Attchd         1958.0   \n",
+            "3           Typ           2           TA      Attchd         1968.0   \n",
+            "4           Typ           1           TA      Attchd         1997.0   \n",
+            "...         ...         ...          ...         ...            ...   \n",
+            "2925        Typ           0          NaN      Detchd         1984.0   \n",
+            "2926        Typ           0          NaN      Attchd         1983.0   \n",
+            "2927        Typ           0          NaN         NaN            NaN   \n",
+            "2928        Typ           1           TA      Attchd         1975.0   \n",
+            "2929        Typ           1           TA      Attchd         1993.0   \n",
+            "\n",
+            "     Garage Finish  Garage Cars  Garage Area Garage Qual Garage Cond  \\\n",
+            "0              Fin          2.0        528.0          TA          TA   \n",
+            "1              Unf          1.0        730.0          TA          TA   \n",
+            "2              Unf          1.0        312.0          TA          TA   \n",
+            "3              Fin          2.0        522.0          TA          TA   \n",
+            "4              Fin          2.0        482.0          TA          TA   \n",
+            "...            ...          ...          ...         ...         ...   \n",
+            "2925           Unf          2.0        588.0          TA          TA   \n",
+            "2926           Unf          2.0        484.0          TA          TA   \n",
+            "2927           NaN          0.0          0.0         NaN         NaN   \n",
+            "2928           RFn          2.0        418.0          TA          TA   \n",
+            "2929           Fin          3.0        650.0          TA          TA   \n",
+            "\n",
+            "     Paved Drive  Wood Deck SF  Open Porch SF  Enclosed Porch  3Ssn Porch  \\\n",
+            "0              P           210             62               0           0   \n",
+            "1              Y           140              0               0           0   \n",
+            "2              Y           393             36               0           0   \n",
+            "3              Y             0              0               0           0   \n",
+            "4              Y           212             34               0           0   \n",
+            "...          ...           ...            ...             ...         ...   \n",
+            "2925           Y           120              0               0           0   \n",
+            "2926           Y           164              0               0           0   \n",
+            "2927           Y            80             32               0           0   \n",
+            "2928           Y           240             38               0           0   \n",
+            "2929           Y           190             48               0           0   \n",
+            "\n",
+            "      Screen Porch  Pool Area Pool QC  Fence Misc Feature  Misc Val  Mo Sold  \\\n",
+            "0                0          0     NaN    NaN          NaN         0        5   \n",
+            "1              120          0     NaN  MnPrv          NaN         0        6   \n",
+            "2                0          0     NaN    NaN         Gar2     12500        6   \n",
+            "3                0          0     NaN    NaN          NaN         0        4   \n",
+            "4                0          0     NaN  MnPrv          NaN         0        3   \n",
+            "...            ...        ...     ...    ...          ...       ...      ...   \n",
+            "2925             0          0     NaN  GdPrv          NaN         0        3   \n",
+            "2926             0          0     NaN  MnPrv          NaN         0        6   \n",
+            "2927             0          0     NaN  MnPrv         Shed       700        7   \n",
+            "2928             0          0     NaN    NaN          NaN         0        4   \n",
+            "2929             0          0     NaN    NaN          NaN         0       11   \n",
+            "\n",
+            "      Yr Sold Sale Type Sale Condition  SalePrice  \n",
+            "0        2010       WD          Normal     215000  \n",
+            "1        2010       WD          Normal     105000  \n",
+            "2        2010       WD          Normal     172000  \n",
+            "3        2010       WD          Normal     244000  \n",
+            "4        2010       WD          Normal     189900  \n",
+            "...       ...       ...            ...        ...  \n",
+            "2925     2006       WD          Normal     142500  \n",
+            "2926     2006       WD          Normal     131000  \n",
+            "2927     2006       WD          Normal     132000  \n",
+            "2928     2006       WD          Normal     170000  \n",
+            "2929     2006       WD          Normal     188000  \n",
+            "\n",
+            "[2930 rows x 82 columns]>\n",
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 2930 entries, 0 to 2929\n",
+            "Data columns (total 82 columns):\n",
+            " #   Column           Non-Null Count  Dtype  \n",
+            "---  ------           --------------  -----  \n",
+            " 0   Order            2930 non-null   int64  \n",
+            " 1   PID              2930 non-null   int64  \n",
+            " 2   MS SubClass      2930 non-null   int64  \n",
+            " 3   MS Zoning        2930 non-null   object \n",
+            " 4   Lot Frontage     2440 non-null   float64\n",
+            " 5   Lot Area         2930 non-null   int64  \n",
+            " 6   Street           2930 non-null   object \n",
+            " 7   Alley            198 non-null    object \n",
+            " 8   Lot Shape        2930 non-null   object \n",
+            " 9   Land Contour     2930 non-null   object \n",
+            " 10  Utilities        2930 non-null   object \n",
+            " 11  Lot Config       2930 non-null   object \n",
+            " 12  Land Slope       2930 non-null   object \n",
+            " 13  Neighborhood     2930 non-null   object \n",
+            " 14  Condition 1      2930 non-null   object \n",
+            " 15  Condition 2      2930 non-null   object \n",
+            " 16  Bldg Type        2930 non-null   object \n",
+            " 17  House Style      2930 non-null   object \n",
+            " 18  Overall Qual     2930 non-null   int64  \n",
+            " 19  Overall Cond     2930 non-null   int64  \n",
+            " 20  Year Built       2930 non-null   int64  \n",
+            " 21  Year Remod/Add   2930 non-null   int64  \n",
+            " 22  Roof Style       2930 non-null   object \n",
+            " 23  Roof Matl        2930 non-null   object \n",
+            " 24  Exterior 1st     2930 non-null   object \n",
+            " 25  Exterior 2nd     2930 non-null   object \n",
+            " 26  Mas Vnr Type     1155 non-null   object \n",
+            " 27  Mas Vnr Area     2907 non-null   float64\n",
+            " 28  Exter Qual       2930 non-null   object \n",
+            " 29  Exter Cond       2930 non-null   object \n",
+            " 30  Foundation       2930 non-null   object \n",
+            " 31  Bsmt Qual        2850 non-null   object \n",
+            " 32  Bsmt Cond        2850 non-null   object \n",
+            " 33  Bsmt Exposure    2847 non-null   object \n",
+            " 34  BsmtFin Type 1   2850 non-null   object \n",
+            " 35  BsmtFin SF 1     2929 non-null   float64\n",
+            " 36  BsmtFin Type 2   2849 non-null   object \n",
+            " 37  BsmtFin SF 2     2929 non-null   float64\n",
+            " 38  Bsmt Unf SF      2929 non-null   float64\n",
+            " 39  Total Bsmt SF    2929 non-null   float64\n",
+            " 40  Heating          2930 non-null   object \n",
+            " 41  Heating QC       2930 non-null   object \n",
+            " 42  Central Air      2930 non-null   object \n",
+            " 43  Electrical       2929 non-null   object \n",
+            " 44  1st Flr SF       2930 non-null   int64  \n",
+            " 45  2nd Flr SF       2930 non-null   int64  \n",
+            " 46  Low Qual Fin SF  2930 non-null   int64  \n",
+            " 47  Gr Liv Area      2930 non-null   int64  \n",
+            " 48  Bsmt Full Bath   2928 non-null   float64\n",
+            " 49  Bsmt Half Bath   2928 non-null   float64\n",
+            " 50  Full Bath        2930 non-null   int64  \n",
+            " 51  Half Bath        2930 non-null   int64  \n",
+            " 52  Bedroom AbvGr    2930 non-null   int64  \n",
+            " 53  Kitchen AbvGr    2930 non-null   int64  \n",
+            " 54  Kitchen Qual     2930 non-null   object \n",
+            " 55  TotRms AbvGrd    2930 non-null   int64  \n",
+            " 56  Functional       2930 non-null   object \n",
+            " 57  Fireplaces       2930 non-null   int64  \n",
+            " 58  Fireplace Qu     1508 non-null   object \n",
+            " 59  Garage Type      2773 non-null   object \n",
+            " 60  Garage Yr Blt    2771 non-null   float64\n",
+            " 61  Garage Finish    2771 non-null   object \n",
+            " 62  Garage Cars      2929 non-null   float64\n",
+            " 63  Garage Area      2929 non-null   float64\n",
+            " 64  Garage Qual      2771 non-null   object \n",
+            " 65  Garage Cond      2771 non-null   object \n",
+            " 66  Paved Drive      2930 non-null   object \n",
+            " 67  Wood Deck SF     2930 non-null   int64  \n",
+            " 68  Open Porch SF    2930 non-null   int64  \n",
+            " 69  Enclosed Porch   2930 non-null   int64  \n",
+            " 70  3Ssn Porch       2930 non-null   int64  \n",
+            " 71  Screen Porch     2930 non-null   int64  \n",
+            " 72  Pool Area        2930 non-null   int64  \n",
+            " 73  Pool QC          13 non-null     object \n",
+            " 74  Fence            572 non-null    object \n",
+            " 75  Misc Feature     106 non-null    object \n",
+            " 76  Misc Val         2930 non-null   int64  \n",
+            " 77  Mo Sold          2930 non-null   int64  \n",
+            " 78  Yr Sold          2930 non-null   int64  \n",
+            " 79  Sale Type        2930 non-null   object \n",
+            " 80  Sale Condition   2930 non-null   object \n",
+            " 81  SalePrice        2930 non-null   int64  \n",
+            "dtypes: float64(11), int64(28), object(43)\n",
+            "memory usage: 1.8+ MB\n",
+            "            Order           PID  MS SubClass  Lot Frontage       Lot Area  \\\n",
+            "count  2930.00000  2.930000e+03  2930.000000   2440.000000    2930.000000   \n",
+            "mean   1465.50000  7.144645e+08    57.387372     69.224590   10147.921843   \n",
+            "std     845.96247  1.887308e+08    42.638025     23.365335    7880.017759   \n",
+            "min       1.00000  5.263011e+08    20.000000     21.000000    1300.000000   \n",
+            "25%     733.25000  5.284770e+08    20.000000     58.000000    7440.250000   \n",
+            "50%    1465.50000  5.354536e+08    50.000000     68.000000    9436.500000   \n",
+            "75%    2197.75000  9.071811e+08    70.000000     80.000000   11555.250000   \n",
+            "max    2930.00000  1.007100e+09   190.000000    313.000000  215245.000000   \n",
+            "\n",
+            "       Overall Qual  Overall Cond   Year Built  Year Remod/Add  Mas Vnr Area  \\\n",
+            "count   2930.000000   2930.000000  2930.000000     2930.000000   2907.000000   \n",
+            "mean       6.094881      5.563140  1971.356314     1984.266553    101.896801   \n",
+            "std        1.411026      1.111537    30.245361       20.860286    179.112611   \n",
+            "min        1.000000      1.000000  1872.000000     1950.000000      0.000000   \n",
+            "25%        5.000000      5.000000  1954.000000     1965.000000      0.000000   \n",
+            "50%        6.000000      5.000000  1973.000000     1993.000000      0.000000   \n",
+            "75%        7.000000      6.000000  2001.000000     2004.000000    164.000000   \n",
+            "max       10.000000      9.000000  2010.000000     2010.000000   1600.000000   \n",
+            "\n",
+            "       BsmtFin SF 1  BsmtFin SF 2  Bsmt Unf SF  Total Bsmt SF   1st Flr SF  \\\n",
+            "count   2929.000000   2929.000000  2929.000000    2929.000000  2930.000000   \n",
+            "mean     442.629566     49.722431   559.262547    1051.614544  1159.557679   \n",
+            "std      455.590839    169.168476   439.494153     440.615067   391.890885   \n",
+            "min        0.000000      0.000000     0.000000       0.000000   334.000000   \n",
+            "25%        0.000000      0.000000   219.000000     793.000000   876.250000   \n",
+            "50%      370.000000      0.000000   466.000000     990.000000  1084.000000   \n",
+            "75%      734.000000      0.000000   802.000000    1302.000000  1384.000000   \n",
+            "max     5644.000000   1526.000000  2336.000000    6110.000000  5095.000000   \n",
+            "\n",
+            "        2nd Flr SF  Low Qual Fin SF  Gr Liv Area  Bsmt Full Bath  \\\n",
+            "count  2930.000000      2930.000000  2930.000000     2928.000000   \n",
+            "mean    335.455973         4.676792  1499.690444        0.431352   \n",
+            "std     428.395715        46.310510   505.508887        0.524820   \n",
+            "min       0.000000         0.000000   334.000000        0.000000   \n",
+            "25%       0.000000         0.000000  1126.000000        0.000000   \n",
+            "50%       0.000000         0.000000  1442.000000        0.000000   \n",
+            "75%     703.750000         0.000000  1742.750000        1.000000   \n",
+            "max    2065.000000      1064.000000  5642.000000        3.000000   \n",
+            "\n",
+            "       Bsmt Half Bath    Full Bath    Half Bath  Bedroom AbvGr  Kitchen AbvGr  \\\n",
+            "count     2928.000000  2930.000000  2930.000000    2930.000000    2930.000000   \n",
+            "mean         0.061134     1.566553     0.379522       2.854266       1.044369   \n",
+            "std          0.245254     0.552941     0.502629       0.827731       0.214076   \n",
+            "min          0.000000     0.000000     0.000000       0.000000       0.000000   \n",
+            "25%          0.000000     1.000000     0.000000       2.000000       1.000000   \n",
+            "50%          0.000000     2.000000     0.000000       3.000000       1.000000   \n",
+            "75%          0.000000     2.000000     1.000000       3.000000       1.000000   \n",
+            "max          2.000000     4.000000     2.000000       8.000000       3.000000   \n",
+            "\n",
+            "       TotRms AbvGrd   Fireplaces  Garage Yr Blt  Garage Cars  Garage Area  \\\n",
+            "count    2930.000000  2930.000000    2771.000000  2929.000000  2929.000000   \n",
+            "mean        6.443003     0.599317    1978.132443     1.766815   472.819734   \n",
+            "std         1.572964     0.647921      25.528411     0.760566   215.046549   \n",
+            "min         2.000000     0.000000    1895.000000     0.000000     0.000000   \n",
+            "25%         5.000000     0.000000    1960.000000     1.000000   320.000000   \n",
+            "50%         6.000000     1.000000    1979.000000     2.000000   480.000000   \n",
+            "75%         7.000000     1.000000    2002.000000     2.000000   576.000000   \n",
+            "max        15.000000     4.000000    2207.000000     5.000000  1488.000000   \n",
+            "\n",
+            "       Wood Deck SF  Open Porch SF  Enclosed Porch   3Ssn Porch  Screen Porch  \\\n",
+            "count   2930.000000    2930.000000     2930.000000  2930.000000   2930.000000   \n",
+            "mean      93.751877      47.533447       23.011604     2.592491     16.002048   \n",
+            "std      126.361562      67.483400       64.139059    25.141331     56.087370   \n",
+            "min        0.000000       0.000000        0.000000     0.000000      0.000000   \n",
+            "25%        0.000000       0.000000        0.000000     0.000000      0.000000   \n",
+            "50%        0.000000      27.000000        0.000000     0.000000      0.000000   \n",
+            "75%      168.000000      70.000000        0.000000     0.000000      0.000000   \n",
+            "max     1424.000000     742.000000     1012.000000   508.000000    576.000000   \n",
+            "\n",
+            "         Pool Area      Misc Val      Mo Sold      Yr Sold      SalePrice  \n",
+            "count  2930.000000   2930.000000  2930.000000  2930.000000    2930.000000  \n",
+            "mean      2.243345     50.635154     6.216041  2007.790444  180796.060068  \n",
+            "std      35.597181    566.344288     2.714492     1.316613   79886.692357  \n",
+            "min       0.000000      0.000000     1.000000  2006.000000   12789.000000  \n",
+            "25%       0.000000      0.000000     4.000000  2007.000000  129500.000000  \n",
+            "50%       0.000000      0.000000     6.000000  2008.000000  160000.000000  \n",
+            "75%       0.000000      0.000000     8.000000  2009.000000  213500.000000  \n",
+            "max     800.000000  17000.000000    12.000000  2010.000000  755000.000000  \n",
+            "       MS Zoning Street Alley Lot Shape Land Contour Utilities Lot Config  \\\n",
+            "count       2930   2930   198      2930         2930      2930       2930   \n",
+            "unique         7      2     2         4            4         3          5   \n",
+            "top           RL   Pave  Grvl       Reg          Lvl    AllPub     Inside   \n",
+            "freq        2273   2918   120      1859         2633      2927       2140   \n",
+            "\n",
+            "       Land Slope Neighborhood Condition 1 Condition 2 Bldg Type House Style  \\\n",
+            "count        2930         2930        2930        2930      2930        2930   \n",
+            "unique          3           28           9           8         5           8   \n",
+            "top           Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
+            "freq         2789          443        2522        2900      2425        1481   \n",
+            "\n",
+            "       Roof Style Roof Matl Exterior 1st Exterior 2nd Mas Vnr Type Exter Qual  \\\n",
+            "count        2930      2930         2930         2930         1155       2930   \n",
+            "unique          6         8           16           17            4          4   \n",
+            "top         Gable   CompShg      VinylSd      VinylSd      BrkFace         TA   \n",
+            "freq         2321      2887         1026         1015          880       1799   \n",
+            "\n",
+            "       Exter Cond Foundation Bsmt Qual Bsmt Cond Bsmt Exposure BsmtFin Type 1  \\\n",
+            "count        2930       2930      2850      2850          2847           2850   \n",
+            "unique          5          6         5         5             4              6   \n",
+            "top            TA      PConc        TA        TA            No            GLQ   \n",
+            "freq         2549       1310      1283      2616          1906            859   \n",
+            "\n",
+            "       BsmtFin Type 2 Heating Heating QC Central Air Electrical Kitchen Qual  \\\n",
+            "count            2849    2930       2930        2930       2929         2930   \n",
+            "unique              6       6          5           2          5            5   \n",
+            "top               Unf    GasA         Ex           Y      SBrkr           TA   \n",
+            "freq             2499    2885       1495        2734       2682         1494   \n",
+            "\n",
+            "       Functional Fireplace Qu Garage Type Garage Finish Garage Qual  \\\n",
+            "count        2930         1508        2773          2771        2771   \n",
+            "unique          8            5           6             3           5   \n",
+            "top           Typ           Gd      Attchd           Unf          TA   \n",
+            "freq         2728          744        1731          1231        2615   \n",
+            "\n",
+            "       Garage Cond Paved Drive Pool QC  Fence Misc Feature Sale Type  \\\n",
+            "count         2771        2930      13    572          106      2930   \n",
+            "unique           5           3       4      4            5        10   \n",
+            "top             TA           Y      Ex  MnPrv         Shed       WD    \n",
+            "freq          2665        2652       4    330           95      2536   \n",
+            "\n",
+            "       Sale Condition  \n",
+            "count            2930  \n",
+            "unique              6  \n",
+            "top            Normal  \n",
+            "freq             2413  \n",
+            "Order: [   1    2    3 ... 2928 2929 2930]\n",
+            "PID: [526301100 526350040 526351010 ... 923400125 924100070 924151050]\n",
+            "MS SubClass: [ 20  60 120  50  85 160  80  30  90 190  45  70  75  40 180 150]\n",
+            "MS Zoning: ['RL' 'RH' 'FV' 'RM' 'C (all)' 'I (all)' 'A (agr)']\n",
+            "Lot Frontage: [141.  80.  81.  93.  74.  78.  41.  43.  39.  60.  75.  nan  63.  85.\n",
+            "  47. 152.  88. 140. 105.  65.  70.  26.  21.  53.  24. 102.  98.  83.\n",
+            "  94.  95.  90.  79. 100.  44. 110.  61.  36.  67. 108.  59.  92.  58.\n",
+            "  56.  73.  72.  84.  76.  50.  55.  68. 107.  25.  30.  57.  40.  77.\n",
+            " 120. 137.  87. 119.  64.  96.  71.  69.  52.  51.  54.  86. 124.  82.\n",
+            "  38.  48.  89.  66.  45.  35. 129.  31.  42.  28.  99. 104.  97. 103.\n",
+            "  34. 117. 149. 122.  62. 174. 106. 112.  32. 115. 128.  91.  33. 121.\n",
+            " 144. 130. 109. 150. 113. 125. 101.  46. 114. 135. 136.  37.  22. 313.\n",
+            "  49. 123. 160. 195. 118. 134. 182. 116. 138. 155. 126. 200. 168. 111.\n",
+            " 131. 153. 133.]\n",
+            "Lot Area: [31770 11622 14267 ...  7937  8885 10441]\n",
+            "Street: ['Pave' 'Grvl']\n",
+            "Alley: [nan 'Pave' 'Grvl']\n",
+            "Lot Shape: ['IR1' 'Reg' 'IR2' 'IR3']\n",
+            "Land Contour: ['Lvl' 'HLS' 'Bnk' 'Low']\n",
+            "Utilities: ['AllPub' 'NoSewr' 'NoSeWa']\n",
+            "Lot Config: ['Corner' 'Inside' 'CulDSac' 'FR2' 'FR3']\n",
+            "Land Slope: ['Gtl' 'Mod' 'Sev']\n",
+            "Neighborhood: ['NAmes' 'Gilbert' 'StoneBr' 'NWAmes' 'Somerst' 'BrDale' 'NPkVill'\n",
+            " 'NridgHt' 'Blmngtn' 'NoRidge' 'SawyerW' 'Sawyer' 'Greens' 'BrkSide'\n",
+            " 'OldTown' 'IDOTRR' 'ClearCr' 'SWISU' 'Edwards' 'CollgCr' 'Crawfor'\n",
+            " 'Blueste' 'Mitchel' 'Timber' 'MeadowV' 'Veenker' 'GrnHill' 'Landmrk']\n",
+            "Condition 1: ['Norm' 'Feedr' 'PosN' 'RRNe' 'RRAe' 'Artery' 'PosA' 'RRAn' 'RRNn']\n",
+            "Condition 2: ['Norm' 'Feedr' 'PosA' 'PosN' 'Artery' 'RRNn' 'RRAe' 'RRAn']\n",
+            "Bldg Type: ['1Fam' 'TwnhsE' 'Twnhs' 'Duplex' '2fmCon']\n",
+            "House Style: ['1Story' '2Story' '1.5Fin' 'SFoyer' 'SLvl' '2.5Unf' '1.5Unf' '2.5Fin']\n",
+            "Overall Qual: [ 6  5  7  8  9  4  3  2 10  1]\n",
+            "Overall Cond: [5 6 7 2 8 4 9 3 1]\n",
+            "Year Built: [1960 1961 1958 1968 1997 1998 2001 1992 1995 1999 1993 1990 1985 2003\n",
+            " 1988 2010 1951 1978 1977 1974 2000 1970 1971 1975 2009 2007 2005 2004\n",
+            " 2002 2006 1996 1994 2008 1980 1979 1984 1920 1965 1967 1963 1962 1976\n",
+            " 1972 1966 1959 1964 1950 1952 1949 1940 1954 1955 1957 1956 1953 1948\n",
+            " 1900 1910 1927 1915 1945 1929 1938 1923 1928 1890 1885 1922 1925 1939\n",
+            " 1942 1936 1930 1921 1912 1917 1907 1875 1969 1947 1946 1987 1941 1924\n",
+            " 1914 1931 1919 1989 1896 1973 1991 1981 1986 1916 1926 1935 1892 1898\n",
+            " 1880 1882 1937 1902 1934 1982 1983 1932 1918 1904 1905 1872 1893 1906\n",
+            " 1908 1911 1895 1879 1901 1913]\n",
+            "Year Remod/Add: [1960 1961 1958 1968 1998 2001 1992 1996 1999 1994 2007 1990 1985 2003\n",
+            " 2005 2010 1951 1988 1977 1974 2000 1970 2008 1971 1975 1978 2006 2004\n",
+            " 2002 1995 2009 1980 1979 1984 1981 1950 1967 1963 1993 1966 1959 1964\n",
+            " 1954 1972 1989 1957 1956 1952 1955 1962 1997 1965 1969 1987 1976 1991\n",
+            " 1973 1986 1983 1953 1982]\n",
+            "Roof Style: ['Hip' 'Gable' 'Mansard' 'Gambrel' 'Shed' 'Flat']\n",
+            "Roof Matl: ['CompShg' 'WdShake' 'Tar&Grv' 'WdShngl' 'Membran' 'ClyTile' 'Roll'\n",
+            " 'Metal']\n",
+            "Exterior 1st: ['BrkFace' 'VinylSd' 'Wd Sdng' 'CemntBd' 'HdBoard' 'Plywood' 'MetalSd'\n",
+            " 'AsbShng' 'WdShing' 'Stucco' 'AsphShn' 'BrkComm' 'CBlock' 'PreCast'\n",
+            " 'Stone' 'ImStucc']\n",
+            "Exterior 2nd: ['Plywood' 'VinylSd' 'Wd Sdng' 'BrkFace' 'CmentBd' 'HdBoard' 'Wd Shng'\n",
+            " 'MetalSd' 'ImStucc' 'Brk Cmn' 'AsbShng' 'Stucco' 'AsphShn' 'CBlock'\n",
+            " 'Stone' 'PreCast' 'Other']\n",
+            "Mas Vnr Type: ['Stone' nan 'BrkFace' 'BrkCmn' 'CBlock']\n",
+            "Mas Vnr Area: [1.120e+02 0.000e+00 1.080e+02 2.000e+01 6.030e+02 3.500e+02 1.190e+02\n",
+            " 4.800e+02 8.100e+01 1.800e+02 5.040e+02 4.920e+02 3.810e+02 1.620e+02\n",
+            " 2.000e+02 4.500e+02 2.560e+02 2.260e+02 6.150e+02 2.400e+02 1.680e+02\n",
+            " 7.600e+02 1.280e+02 1.095e+03 2.320e+02 4.120e+02 1.780e+02 1.060e+02\n",
+            " 1.400e+01 1.600e+01       nan 1.650e+02 1.140e+02 3.380e+02 3.620e+02\n",
+            " 3.480e+02 3.000e+01 5.790e+02 3.600e+01 1.220e+02 1.300e+02 3.100e+01\n",
+            " 2.500e+02 1.200e+02 2.160e+02 4.320e+02 1.159e+03 2.890e+02 2.800e+01\n",
+            " 4.200e+01 1.720e+02 4.510e+02 2.680e+02 8.600e+01 1.560e+02 1.440e+02\n",
+            " 2.650e+02 3.400e+02 1.100e+02 1.640e+02 3.610e+02 2.870e+02 5.060e+02\n",
+            " 1.500e+02 2.200e+02 3.240e+02 9.100e+01 1.040e+02 3.000e+02 2.610e+02\n",
+            " 2.180e+02 3.510e+02 7.710e+02 2.940e+02 9.000e+01 7.200e+01 4.700e+01\n",
+            " 1.430e+02 3.280e+02 2.880e+02 9.600e+01 3.360e+02 1.770e+02 8.500e+01\n",
+            " 2.460e+02 2.400e+01 8.000e+01 1.160e+02 1.530e+02 3.200e+02 4.790e+02\n",
+            " 2.230e+02 4.420e+02 1.700e+02 1.690e+02 1.710e+02 1.090e+02 9.800e+01\n",
+            " 1.450e+02 2.030e+02 3.710e+02 4.300e+02 4.400e+01 1.860e+02 3.350e+02\n",
+            " 6.000e+01 8.400e+01 1.890e+02 4.400e+02 1.880e+02 3.200e+01 1.600e+02\n",
+            " 2.200e+01 4.000e+01 6.800e+01 4.500e+01 3.440e+02 7.480e+02 4.640e+02\n",
+            " 1.570e+02 2.780e+02 2.090e+02 1.260e+02 1.010e+02 2.290e+02 2.250e+02\n",
+            " 2.060e+02 1.610e+02 1.960e+02 1.740e+02 3.330e+02 7.600e+01 3.120e+02\n",
+            " 1.420e+02 4.250e+02 5.100e+02 2.300e+02 7.260e+02 8.600e+02 6.400e+02\n",
+            " 3.060e+02 1.540e+02 3.050e+02 4.200e+02 4.720e+02 4.240e+02 3.020e+02\n",
+            " 2.380e+02 2.840e+02 2.620e+02 2.850e+02 2.960e+02 4.180e+02 9.220e+02\n",
+            " 7.240e+02 3.830e+02 1.350e+02 1.760e+02 1.660e+02 7.300e+02 4.700e+02\n",
+            " 3.080e+02 5.000e+02 2.700e+02 1.630e+02 1.100e+01 2.100e+02 2.980e+02\n",
+            " 6.730e+02 7.310e+02 9.750e+02 9.210e+02 6.340e+02 2.860e+02 3.720e+02\n",
+            " 5.280e+02 1.940e+02 2.600e+02 1.980e+02 1.210e+02 1.000e+02 2.640e+02\n",
+            " 1.400e+02 1.320e+02 3.660e+02 1.410e+02 1.150e+02 2.800e+02 2.520e+02\n",
+            " 8.940e+02 5.130e+02 4.560e+02 5.710e+02 3.590e+02 2.830e+02 3.600e+02\n",
+            " 5.090e+02 7.000e+01 9.500e+01 2.170e+02 3.000e+00 2.470e+02 5.760e+02\n",
+            " 1.830e+02 3.990e+02 6.500e+02 6.570e+02 2.950e+02 3.680e+02 8.200e+01\n",
+            " 1.240e+02 4.440e+02 9.200e+01 8.900e+01 2.300e+01 5.400e+01 1.490e+02\n",
+            " 2.340e+02 1.370e+02 2.750e+02 2.420e+02 3.640e+02 3.520e+02 1.360e+02\n",
+            " 2.040e+02 5.730e+02 2.550e+02 7.400e+01 2.590e+02 8.800e+01 5.410e+02\n",
+            " 4.060e+02 3.100e+02 5.840e+02 2.900e+02 1.820e+02 7.500e+01 2.450e+02\n",
+            " 1.230e+02 1.020e+02 6.210e+02 6.600e+02 4.020e+02 1.580e+02 4.220e+02\n",
+            " 1.270e+02 6.040e+02 3.560e+02 6.500e+01 4.260e+02 2.720e+02 8.160e+02\n",
+            " 4.360e+02 5.540e+02 4.680e+02 6.800e+02 6.640e+02 2.920e+02 1.110e+03\n",
+            " 2.210e+02 7.660e+02 6.160e+02 7.140e+02 1.460e+02 3.180e+02 6.470e+02\n",
+            " 1.290e+03 4.730e+02 4.950e+02 4.660e+02 6.510e+02 4.480e+02 5.300e+01\n",
+            " 7.680e+02 3.800e+01 2.580e+02 3.040e+02 5.680e+02 1.790e+02 2.120e+02\n",
+            " 1.050e+03 5.640e+02 3.420e+02 1.480e+02 2.430e+02 4.910e+02 2.370e+02\n",
+            " 4.100e+02 1.510e+02 1.870e+02 3.870e+02 5.200e+01 1.250e+02 2.760e+02\n",
+            " 4.150e+02 3.900e+01 4.100e+01 2.990e+02 9.900e+01 1.900e+02 2.510e+02\n",
+            " 2.810e+02 2.270e+02 2.020e+02 3.960e+02 1.340e+02 1.920e+02 2.050e+02\n",
+            " 2.150e+02 1.130e+02 5.000e+01 2.660e+02 1.470e+02 2.220e+02 7.960e+02\n",
+            " 5.800e+01 6.320e+02 6.680e+02 2.280e+02 2.190e+02 6.740e+02 1.970e+02\n",
+            " 1.115e+03 1.380e+02 7.100e+02 9.450e+02 6.700e+01 5.490e+02 2.330e+02\n",
+            " 2.530e+02 2.630e+02 3.650e+02 5.670e+02 3.760e+02 3.780e+02 4.520e+02\n",
+            " 2.540e+02 3.150e+02 4.000e+02 3.750e+02 7.720e+02 2.480e+02 9.700e+02\n",
+            " 5.020e+02 3.880e+02 3.940e+02 2.350e+02 5.150e+02 7.050e+02 1.170e+03\n",
+            " 5.940e+02 3.090e+02 5.260e+02 7.540e+02 2.080e+02 4.280e+02 3.530e+02\n",
+            " 1.050e+02 5.700e+01 3.370e+02 1.129e+03 1.600e+03 6.000e+02 1.000e+00\n",
+            " 5.250e+02 6.600e+01 6.300e+01 5.600e+01 2.240e+02 8.700e+01 2.910e+02\n",
+            " 6.900e+01 2.790e+02 4.350e+02 3.230e+02 1.670e+02 5.100e+01 2.140e+02\n",
+            " 5.190e+02 4.380e+02 4.800e+01 1.224e+03 7.620e+02 4.230e+02 1.840e+02\n",
+            " 6.520e+02 4.810e+02 2.390e+02 2.740e+02 1.170e+02 8.860e+02 2.360e+02\n",
+            " 9.400e+01 2.440e+02 9.020e+02 4.340e+02 2.700e+01 6.620e+02 7.340e+02\n",
+            " 5.500e+02 1.031e+03 3.400e+01 5.140e+02 4.080e+02 3.800e+02 2.970e+02\n",
+            " 3.700e+02 3.850e+02 7.880e+02 5.620e+02 8.700e+02 5.180e+02 5.720e+02\n",
+            " 1.800e+01 3.220e+02 1.378e+03 8.770e+02 5.300e+02 3.970e+02 7.380e+02\n",
+            " 5.010e+02 3.910e+02 1.180e+02 4.600e+01 6.920e+02 3.320e+02 1.750e+02\n",
+            " 6.400e+01 5.220e+02 1.047e+03 3.790e+02 2.070e+02 9.700e+01 5.320e+02\n",
+            " 6.200e+01 1.990e+02 3.550e+02 4.590e+02 4.050e+02 3.270e+02 2.570e+02\n",
+            " 2.930e+02 6.530e+02 6.300e+02 3.820e+02 4.430e+02]\n",
+            "Exter Qual: ['TA' 'Gd' 'Ex' 'Fa']\n",
+            "Exter Cond: ['TA' 'Gd' 'Fa' 'Po' 'Ex']\n",
+            "Foundation: ['CBlock' 'PConc' 'Wood' 'BrkTil' 'Slab' 'Stone']\n",
+            "Bsmt Qual: ['TA' 'Gd' 'Ex' nan 'Fa' 'Po']\n",
+            "Bsmt Cond: ['Gd' 'TA' nan 'Po' 'Fa' 'Ex']\n",
+            "Bsmt Exposure: ['Gd' 'No' 'Mn' 'Av' nan]\n",
+            "BsmtFin Type 1: ['BLQ' 'Rec' 'ALQ' 'GLQ' 'Unf' 'LwQ' nan]\n",
+            "BsmtFin SF 1: [6.390e+02 4.680e+02 9.230e+02 1.065e+03 7.910e+02 6.020e+02 6.160e+02\n",
+            " 2.630e+02 1.180e+03 0.000e+00 9.350e+02 6.370e+02 3.680e+02 1.416e+03\n",
+            " 4.270e+02 1.445e+03 1.200e+02 7.900e+02 7.050e+02 8.850e+02 5.330e+02\n",
+            " 5.780e+02 7.340e+02 7.750e+02 8.040e+02 4.320e+02 1.051e+03 1.560e+02\n",
+            " 3.000e+02 3.600e+02 5.140e+02 3.110e+02 1.218e+03 1.646e+03 1.201e+03\n",
+            " 1.100e+02 2.800e+01 2.000e+00 2.188e+03 7.330e+02 1.373e+03 4.560e+02\n",
+            " 2.400e+01 1.600e+01 3.260e+02 6.250e+02 2.500e+02 9.190e+02 1.032e+03\n",
+            " 5.240e+02 8.160e+02 1.078e+03 2.220e+02 1.414e+03 6.560e+02 6.950e+02\n",
+            " 5.430e+02 6.230e+02 4.020e+02 3.380e+02 8.990e+02 5.530e+02 4.500e+02\n",
+            " 8.240e+02 6.590e+02 1.260e+02 6.740e+02 1.129e+03 1.298e+03 2.800e+02\n",
+            " 3.760e+02 3.780e+02 4.660e+02 6.040e+02 2.440e+02 4.840e+02 7.280e+02\n",
+            " 1.052e+03 8.330e+02 5.060e+02 1.137e+03 1.200e+03 6.870e+02 3.940e+02\n",
+            " 9.820e+02 3.290e+02 6.980e+02 5.690e+02 1.059e+03 1.010e+03 1.014e+03\n",
+            " 7.630e+02 1.500e+03 4.900e+01 6.700e+02 6.960e+02 3.540e+02 5.400e+02\n",
+            " 9.440e+02 4.430e+02 9.120e+02 2.470e+02 1.188e+03 8.560e+02 1.018e+03\n",
+            " 9.220e+02 1.000e+03 6.970e+02 9.360e+02 3.390e+02 6.480e+02 5.320e+02\n",
+            " 7.310e+02 3.200e+02 2.480e+02 1.056e+03 7.200e+01 4.810e+02 3.400e+02\n",
+            " 5.070e+02 2.340e+02 5.880e+02 7.170e+02 4.800e+01 5.790e+02 2.740e+02\n",
+            " 5.100e+02 7.800e+02 1.760e+02 6.860e+02 6.000e+02 2.830e+02 7.880e+02\n",
+            " 4.740e+02 1.880e+02 4.520e+02 2.640e+02 2.760e+02 4.480e+02 9.600e+02\n",
+            " 1.040e+02 7.660e+02 1.026e+03 7.300e+01 7.360e+02 7.040e+02 8.410e+02\n",
+            " 1.302e+03 8.420e+02 2.400e+02 3.710e+02 1.319e+03 2.670e+02 4.380e+02\n",
+            " 1.092e+03 4.420e+02 1.258e+03 9.640e+02 2.880e+02 1.080e+02 7.390e+02\n",
+            " 1.920e+02 9.540e+02 3.600e+01 1.346e+03 1.433e+03 8.600e+02 7.500e+02\n",
+            " 7.470e+02 1.470e+03 5.040e+02 8.700e+02 3.530e+02 5.050e+02 1.980e+02\n",
+            " 1.820e+02 4.800e+02 1.682e+03 1.358e+03 4.830e+02 6.720e+02 6.620e+02\n",
+            " 3.700e+02 7.120e+02 1.070e+03 5.280e+02 4.220e+02 9.400e+01 3.480e+02\n",
+            " 3.830e+02 1.330e+02 2.030e+02 2.180e+02 2.380e+02 4.260e+02 3.750e+02\n",
+            " 2.750e+02 1.406e+03 3.430e+02 7.600e+01 1.247e+03 7.350e+02 3.080e+02\n",
+            " 6.150e+02 6.790e+02 5.390e+02 7.800e+01 6.240e+02 4.200e+01 3.340e+02\n",
+            " 9.150e+02 1.290e+02 1.500e+02 2.940e+02 4.690e+02 5.930e+02 2.070e+02\n",
+            " 4.580e+02 4.760e+02 1.341e+03 5.640e+02 8.440e+02 1.410e+03 8.470e+02\n",
+            " 8.500e+02 2.840e+02 1.320e+03 1.965e+03 1.158e+03 3.410e+02 7.410e+02\n",
+            " 1.890e+02 3.100e+02 5.600e+02 6.940e+02 1.036e+03 1.904e+03 1.274e+03\n",
+            " 4.000e+02 6.920e+02 8.220e+02 1.246e+03 3.630e+02 8.320e+02 1.104e+03\n",
+            " 3.810e+02 6.220e+02 5.440e+02 2.250e+02 1.333e+03 8.880e+02 6.360e+02\n",
+            " 8.280e+02 4.390e+02 5.000e+02 7.260e+02 1.910e+02 2.540e+02 7.650e+02\n",
+            " 1.620e+02 2.310e+02 9.580e+02 3.060e+02 5.660e+02 4.350e+02 2.570e+02\n",
+            " 3.890e+02 2.790e+02 5.360e+02 6.440e+02 1.172e+03 1.360e+03 1.767e+03\n",
+            " 1.572e+03 9.860e+02 1.232e+03 1.436e+03 1.338e+03 2.288e+03 1.531e+03\n",
+            " 1.230e+03 1.015e+03 1.088e+03 1.037e+03 1.142e+03 1.170e+03 1.039e+03\n",
+            " 1.124e+03 1.262e+03 5.600e+01 1.972e+03 8.360e+02 9.000e+02 8.810e+02\n",
+            " 8.760e+02 9.040e+02 2.146e+03 1.557e+03 8.000e+02 1.196e+03 8.630e+02\n",
+            " 5.670e+02 9.880e+02 4.250e+02 6.520e+02 4.940e+02 6.510e+02 2.410e+02\n",
+            " 6.830e+02 9.130e+02 7.720e+02 1.163e+03 6.890e+02 1.173e+03 7.810e+02\n",
+            " 8.540e+02 2.360e+02 9.870e+02 1.361e+03 5.950e+02 1.294e+03 3.790e+02\n",
+            " 2.158e+03 2.700e+01 1.121e+03 6.820e+02 8.120e+02 1.430e+03 4.100e+02\n",
+            " 7.710e+02 5.400e+01 5.160e+02 9.760e+02 2.000e+01 5.200e+01 3.310e+02\n",
+            " 6.800e+01 6.600e+02 8.640e+02 5.940e+02 1.400e+02 1.733e+03 6.010e+02\n",
+            " 9.620e+02 5.490e+02 6.490e+02 1.252e+03 1.210e+02 1.116e+03 2.980e+02\n",
+            " 8.590e+02 9.550e+02 1.440e+02 6.430e+02 2.510e+02 4.030e+02 6.120e+02\n",
+            " 1.960e+02 9.980e+02 7.400e+02 3.880e+02 9.910e+02 5.680e+02 1.000e+02\n",
+            " 1.850e+02 1.024e+03 1.285e+03 6.070e+02 1.312e+03 6.090e+02 1.387e+03\n",
+            " 4.540e+02 7.080e+02 6.200e+02 5.850e+02 1.720e+02 1.550e+02 1.213e+03\n",
+            " 4.900e+02 4.280e+02 6.500e+02 7.000e+02 9.310e+02 4.400e+02 6.990e+02\n",
+            " 3.900e+02 6.800e+02 3.150e+02 3.840e+02 8.720e+02 7.450e+02 5.460e+02\n",
+            " 1.270e+03 6.210e+02 1.800e+02 6.300e+02 4.330e+02 1.148e+03 9.410e+02\n",
+            " 8.260e+02 6.330e+02 4.210e+02 3.120e+02 2.160e+02 4.950e+02 1.309e+03\n",
+            " 2.200e+02 4.050e+02 2.090e+02 2.730e+02 1.340e+02 2.990e+02 5.220e+02\n",
+            " 1.520e+02 1.690e+02 7.490e+02 1.152e+03 3.500e+02 5.510e+02 4.440e+02\n",
+            " 2.260e+02 5.270e+02 6.850e+02 1.700e+02 1.324e+03 2.620e+02 3.420e+02\n",
+            " 3.440e+02 1.730e+02 5.520e+02 2.920e+02 2.040e+02 4.600e+02 7.000e+01\n",
+            " 1.441e+03 4.850e+02 5.130e+02 5.840e+02 1.086e+03 1.094e+03 8.200e+02\n",
+            " 1.021e+03 1.288e+03 1.359e+03 1.334e+03 9.020e+02 7.240e+02 1.518e+03\n",
+            " 2.490e+02 7.320e+02 7.550e+02 8.210e+02 3.850e+02 9.500e+02 7.460e+02\n",
+            " 6.060e+02 6.660e+02 1.259e+03 7.100e+02 6.460e+02 7.770e+02 1.234e+03\n",
+            " 9.900e+02 6.900e+02 1.111e+03 1.478e+03 1.930e+02 5.350e+02 3.990e+02\n",
+            " 6.310e+02 5.470e+02 3.320e+02 6.260e+02 4.080e+02 2.900e+02 5.230e+02\n",
+            " 7.930e+02 7.130e+02 2.460e+02 1.540e+02 6.500e+01 7.840e+02 4.710e+02\n",
+            " 2.850e+02 8.030e+02 8.080e+02 1.476e+03 4.450e+02 1.351e+03 7.670e+02\n",
+            " 6.110e+02 5.500e+01 1.110e+02 1.236e+03 1.022e+03 1.758e+03 1.115e+03\n",
+            " 1.005e+03 4.620e+02 1.260e+03 1.640e+03 8.660e+02 8.830e+02 5.150e+02\n",
+            " 5.090e+02 7.200e+02 1.140e+02 1.097e+03 7.180e+02 3.300e+02 1.567e+03\n",
+            " 4.960e+02 8.650e+02 7.060e+02 8.510e+02 1.380e+02 1.153e+03 2.190e+02\n",
+            " 3.190e+02 1.337e+03 1.034e+03 9.830e+02 1.206e+03 8.960e+02 8.900e+02\n",
+            " 1.084e+03 1.023e+03 2.520e+02 1.190e+02 2.660e+02 3.210e+02 3.870e+02\n",
+            " 3.580e+02 5.590e+02 2.860e+02 1.336e+03 1.280e+03 1.636e+03 1.330e+03\n",
+            " 1.012e+03 1.400e+03 1.728e+03 1.375e+03 1.420e+03 1.082e+03 1.249e+03\n",
+            " 4.000e+01 2.257e+03 1.016e+03 1.149e+03 1.075e+03 3.720e+02 1.540e+03\n",
+            " 1.204e+03 8.460e+02 5.730e+02 1.073e+03 1.087e+03 7.590e+02 6.550e+02\n",
+            " 1.660e+03 1.696e+03 2.280e+02 1.314e+03 1.096e+03 3.300e+01 7.290e+02\n",
+            " 7.890e+02 5.030e+02 8.000e+01 8.140e+02 3.620e+02 1.138e+03 5.370e+02\n",
+            " 4.720e+02 3.970e+02 1.650e+02 5.300e+01 7.370e+02 7.640e+02 4.890e+02\n",
+            " 1.900e+02 5.200e+02 5.500e+02 1.027e+03 1.004e+03 1.141e+03 1.238e+03\n",
+            " 6.810e+02 8.130e+02 1.280e+02 7.860e+02 1.619e+03 1.044e+03 3.010e+02\n",
+            " 6.030e+02 9.560e+02 2.600e+02 5.830e+02 5.750e+02 8.670e+02 7.760e+02\n",
+            " 8.920e+02 7.870e+02 8.060e+02 4.190e+02 6.580e+02 3.200e+01 8.310e+02\n",
+            " 5.310e+02 5.720e+02 2.500e+01 1.053e+03 1.040e+03 5.700e+02 7.740e+02\n",
+            " 1.480e+02 8.520e+02 5.800e+02 7.440e+02 3.740e+02 6.730e+02 9.600e+01\n",
+            " 4.930e+02 5.900e+02 1.160e+02 1.410e+02 2.590e+02 2.000e+02 4.060e+02\n",
+            " 1.750e+02 5.210e+02 2.010e+02       nan 3.360e+02 2.100e+02 3.510e+02\n",
+            " 9.060e+02 7.580e+02 7.020e+02 2.210e+02 1.198e+03 1.300e+03 6.340e+02\n",
+            " 1.064e+03 4.290e+02 1.003e+03 3.920e+02 5.990e+02 7.190e+02 1.035e+03\n",
+            " 3.240e+02 9.690e+02 1.085e+03 7.790e+02 1.271e+03 3.550e+02 2.085e+03\n",
+            " 5.000e+01 7.700e+02 7.220e+02 1.308e+03 6.880e+02 3.610e+02 6.630e+02\n",
+            " 4.860e+02 8.800e+01 6.320e+02 6.680e+02 1.194e+03 1.538e+03 6.420e+02\n",
+            " 9.940e+02 1.593e+03 8.100e+02 9.460e+02 8.300e+02 1.033e+03 9.200e+02\n",
+            " 5.644e+03 4.590e+02 3.520e+02 2.240e+02 4.100e+01 4.230e+02 2.810e+02\n",
+            " 3.660e+02 8.100e+01 5.380e+02 1.480e+03 1.474e+03 6.410e+02 1.383e+03\n",
+            " 8.930e+02 1.165e+03 1.513e+03 1.398e+03 7.830e+02 1.029e+03 1.223e+03\n",
+            " 8.710e+02 1.011e+03 1.571e+03 7.690e+02 3.180e+02 5.010e+02 4.370e+02\n",
+            " 7.850e+02 5.340e+02 6.380e+02 6.470e+02 5.620e+02 8.380e+02 7.780e+02\n",
+            " 1.880e+03 1.860e+02 4.140e+02 9.260e+02 1.101e+03 1.047e+03 7.970e+02\n",
+            " 9.450e+02 1.558e+03 6.780e+02 2.560e+02 1.328e+03 6.050e+02 9.030e+02\n",
+            " 4.920e+02 3.490e+02 2.820e+02 4.120e+02 3.220e+02 3.140e+02 9.300e+02\n",
+            " 3.560e+02 5.560e+02 7.250e+02 1.151e+03 1.304e+03 1.812e+03 1.350e+03\n",
+            " 1.684e+03 9.700e+02 9.380e+02 6.690e+02 1.178e+03 1.030e+03 7.620e+02\n",
+            " 8.480e+02 9.180e+02 5.740e+02 2.096e+03 1.181e+03 1.282e+03 1.048e+03\n",
+            " 1.455e+03 8.620e+02 5.650e+02 1.231e+03 3.350e+02 1.225e+03 1.220e+03\n",
+            " 9.290e+02 6.300e+01 1.126e+03 1.369e+03 6.400e+01 1.443e+03 4.300e+02\n",
+            " 4.170e+02 9.320e+02 8.270e+02 7.270e+02 1.250e+02 1.390e+03 9.680e+02\n",
+            " 4.820e+02 6.000e+01 9.370e+02 1.106e+03 4.200e+02 4.360e+02 2.390e+02\n",
+            " 9.010e+02 4.570e+02 1.732e+03 1.157e+03 9.780e+02 1.632e+03 4.980e+02\n",
+            " 7.380e+02 9.730e+02 9.100e+02 3.460e+02 8.190e+02 7.920e+02 9.160e+02\n",
+            " 6.170e+02 6.540e+02 2.700e+02 1.386e+03 1.300e+02 3.860e+02 1.870e+02\n",
+            " 8.730e+02 9.080e+02 6.080e+02 5.120e+02 5.860e+02 1.237e+03 4.410e+02\n",
+            " 8.500e+01 3.770e+02 2.420e+02 9.520e+02 3.980e+02 1.098e+03 7.820e+02\n",
+            " 1.680e+02 1.220e+02 3.160e+02 1.046e+03 3.170e+02 6.450e+02 1.970e+02\n",
+            " 9.250e+02 7.480e+02 2.580e+02 1.219e+03 5.870e+02 4.770e+02 4.910e+02\n",
+            " 4.530e+02 1.440e+03 5.570e+02 1.080e+03 4.970e+02 9.840e+02 1.150e+03\n",
+            " 6.640e+02 9.850e+02 5.100e+01 1.013e+03 5.020e+02 7.160e+02 6.710e+02\n",
+            " 1.464e+03 1.412e+03 1.079e+03 7.090e+02 1.320e+02 7.510e+02 9.800e+02\n",
+            " 4.010e+03 2.260e+03 4.670e+02 7.700e+01 1.130e+02 3.640e+02 3.650e+02\n",
+            " 1.128e+03 2.970e+02 1.186e+03 3.500e+01 5.770e+02 4.340e+02 5.480e+02\n",
+            " 9.670e+02 1.573e+03 1.001e+03 7.730e+02 1.392e+03 1.239e+03 9.240e+02\n",
+            " 9.490e+02 1.102e+03 2.150e+02 7.420e+02 1.329e+03 1.159e+03 2.060e+02\n",
+            " 8.400e+02 8.740e+02 1.310e+02 1.112e+03 7.960e+02 6.190e+02 8.110e+02\n",
+            " 1.090e+03 5.960e+02 2.120e+02 1.127e+03 1.110e+03 5.920e+02 7.140e+02\n",
+            " 5.700e+01 5.180e+02 2.050e+02 1.191e+03 1.422e+03 2.130e+02 1.002e+03\n",
+            " 7.950e+02 1.940e+02 7.500e+01 6.140e+02 9.510e+02 3.090e+02 3.820e+02\n",
+            " 3.730e+02 1.447e+03 1.505e+03 1.261e+03 1.290e+03 8.800e+02 4.150e+02\n",
+            " 5.540e+02 1.038e+03 1.154e+03 1.074e+03 1.182e+03 3.800e+02 1.562e+03\n",
+            " 1.721e+03 1.836e+03 9.050e+02 2.780e+02 1.332e+03 1.810e+02 4.650e+02\n",
+            " 1.118e+03 1.456e+03 1.009e+03 8.070e+02 1.810e+03 4.040e+02 7.600e+02\n",
+            " 7.990e+02 6.610e+02 4.160e+02 9.960e+02 7.560e+02 9.390e+02 8.950e+02\n",
+            " 9.140e+02 9.430e+02 2.710e+02 4.880e+02 7.010e+02 1.277e+03 4.550e+02\n",
+            " 3.690e+02 1.790e+02 8.090e+02 9.530e+02 2.080e+02 1.430e+02 5.760e+02\n",
+            " 3.470e+02 3.280e+02 7.940e+02 2.300e+02 2.610e+02 3.930e+02 6.840e+02\n",
+            " 4.640e+02 1.576e+03 1.122e+03 8.530e+02 1.162e+03 8.940e+02 9.750e+02\n",
+            " 4.750e+02 1.670e+02 6.910e+02 4.240e+02 3.050e+02 2.230e+02 5.260e+02\n",
+            " 2.960e+02 1.283e+03 1.564e+03 9.650e+02 9.090e+02 1.216e+03 1.136e+03\n",
+            " 1.460e+03 1.243e+03 8.970e+02 8.370e+02 1.490e+02 1.606e+03 1.224e+03\n",
+            " 3.370e+02 1.071e+03]\n",
+            "BsmtFin Type 2: ['Unf' 'LwQ' 'BLQ' 'Rec' nan 'GLQ' 'ALQ']\n",
+            "BsmtFin SF 2: [   0.  144. 1120.  163.  168.   78.  119.  121.  117.  859.  981.   42.\n",
+            "   46.   81. 1029.  290.  132.  713.  162.  362.  240.  258.  174.  906.\n",
+            "  486.  350.  263. 1073.  692.   12.  159.  712.  668.  474.  453.  684.\n",
+            "  387.  688.  972.  127.  252.  334.  232.  480.  590.  284.  276.  472.\n",
+            "  239.  180.  294.  622.  495.  539.  479.  113. 1526.  360.  774.  364.\n",
+            "  596.  884.  311.   92.  216.  136.   32.  147. 1127.  466.  630.  201.\n",
+            "  345.  512.  230.  247.  661.  620.  202.  483.  750.  690.  105.   60.\n",
+            "  352.  102.   95.  465.   63.  262.  500.  670.  768.  393.  286.  450.\n",
+            "  177.  764.  344.   72.  243.  420.  210.  694.  875.  507.  435.  419.\n",
+            "  250.  116.  354.  820.  624.  273.   76.  270.  110.  288.  411.  228.\n",
+            "  186.  449.   48.   93.  438.  613.  852.  555.  841.  799.  811.  842.\n",
+            "  382.  182.  456.   80.   64.  336.  306.  308.  374.  872.  108.   52.\n",
+            "  196.  128.  488.  319.  532.  106.  169.  608.   nan   41.  606.  645.\n",
+            "  492.  181.  956. 1080. 1063.  391.  380.  531.  723.  491.  120.  679.\n",
+            "  612.   40.  125.  279.  400.  208.  193.  823.  287.  175.  604.  153.\n",
+            "   35.  619.  139.    6.  351. 1031. 1037.  176.  829.  211.  264.   38.\n",
+            "  206.  167.  580.  543.  219.  259.  404.  468.  138.  955.  691.   66.\n",
+            "   96.  149.  154.  442.  448.  227.  546.  398.  469.  722.  761.  627.\n",
+            "  529.  522.  873.  891.  755. 1474.  634.  321.  915.  544.  417.  432.\n",
+            "  831.  278.  557.  150.  869. 1020.  530.  904.  499.  215. 1061.  377.\n",
+            "  791.  156. 1393. 1039.  375.  497. 1057.  526.   68.  402.  748.   28.\n",
+            "  165.  184.  281.  912.  600.  506.  373.  551.  982.  441.  682. 1085.\n",
+            "  826.  850. 1164. 1083.  337.  297.  547.  173.  396.  324.  123.]\n",
+            "Bsmt Unf SF: [ 441.  270.  406. ...   45. 1503.  239.]\n",
+            "Total Bsmt SF: [1080.  882. 1329. ... 1381.  757. 1003.]\n",
+            "Heating: ['GasA' 'GasW' 'Grav' 'Wall' 'Floor' 'OthW']\n",
+            "Heating QC: ['Fa' 'TA' 'Ex' 'Gd' 'Po']\n",
+            "Central Air: ['Y' 'N']\n",
+            "Electrical: ['SBrkr' 'FuseA' 'FuseF' 'FuseP' nan 'Mix']\n",
+            "1st Flr SF: [1656  896 1329 ... 2028 1003 1389]\n",
+            "2nd Flr SF: [   0  701  678  776  892  676 1589  672  860  504  567  601  707  563\n",
+            "  862  630 1100  886  656 1151 1177  830 1122 1106  644 1185  783  956\n",
+            " 1128  828  888  790  730  584 1098  823  840  600  636  804  756  720\n",
+            "  550  873  754 1215  604  734  715  532  537 1169  505  546 1080  408\n",
+            "  475  788  687  348  765  424  606  185  686 1111  622 1044  602  582\n",
+            "  908  524  498  492  608  808 1074  780  662 1196  499  180  319  942\n",
+            "  744  240  689  714  954  192  864  558  755  838  887 1523  614  800\n",
+            "  878  703 1054  328  252  806  665 1788  772  748 1075 1152  358  380\n",
+            "  430  880  700 1194 1070  915  912  576 1216  650  615  645  704  663\n",
+            " 1275  670  631  833  684  809  785  779  978  988 1200  896 1134  868\n",
+            " 1103  816  839  741  467  586 1174 1325  568 1088 1012  762 1295  728\n",
+            "  745  742  876  716 1257  640  683 1276 1126 1032  793  695 1089 1038\n",
+            " 1097 1304 1221 1140 1336 1067 1274  967 1017  871  858  920  981  438\n",
+            " 1182  841  941 1209  897  591  786  702  918 1629  612  729  739  727\n",
+            "  983  782  660 1369  972  855 1315  224  556  960  457  685  726  322\n",
+            "  760  534 1296  368  768  629  813  406  548  517  455  496  690  994\n",
+            " 1000  682  646  560  677  564 1063 1320  917  624  826  561  596  653\n",
+            "  390  464  587  320  472  588  883  910  929 1040  784  462  649  425\n",
+            "  611  747  769 1114 1120 1619  902  718  815  966  836  834  913  844\n",
+            "  829 1116  573  885  926  977  807  738 1427  441  512  444  620  436\n",
+            "  545  998  595  448  332  523 1240  516  668  928 1157  432  846  566\n",
+            "  848 1020  717 1332 1370  857 1330  767 1420  866 1104  590 1237  898\n",
+            " 1158 1162 1096 1139  884 1285  778 1160 1053  639 1061 1250 1093  904\n",
+            " 1039  520  919  939  932 1028  843  861  842  794  825  850  893 1319\n",
+            "  959  625  792  628  924 1345 1066  732 1540  933  832  453  220  384\n",
+            "  412  510  182  501  581  375  680 1208  658  552  396 1818  797  540\n",
+            "  308  973  691  539 1254  363  473  594  378  554  208  468  651  445\n",
+            "  764  752  213  795  428  371  110  536  713  551  547  580  486 1051\n",
+            "  511  872  648  527  495 1721 1099  735 1072  899  870  895  903 1141\n",
+            " 1198  975  854  812  950  521  343  304  940 1611  811  673  442  890\n",
+            " 1479  817  943  330  420  936  167  688  766  770 1342  900 1377  845\n",
+            "  533 1402 1101  574 1036  570 1142 1238 1168  923  530  757 1048  796\n",
+            " 1112 1131  694  750 2065 1288 1407 1171 1277 1872 1015 1306 1203  995\n",
+            "  528  863 1426  925 1232 1357  743  976  761 1259 1008  984 1309  228\n",
+            "  992  500  544 1778  299  616  831  664  494  642  659  671 1031  336\n",
+            "  144  525  349  423 1164  356  698  245  592 1042  477 1005  971 1087\n",
+            "  638  400  376 1121 1414 1362 1092  916  882  927  874  914  881  869\n",
+            " 1242 1081  753  450 1133  674 1538  125 1440  787  531  585  514  775\n",
+            "  589  979 1001  851 1178  351  957 1340 1349  712 1243  955  709  990\n",
+            " 1384 1862 1371 1312 1405 1519 1392 1358  465 1347 1218 1060  466 1335\n",
+            "  814  488 1321  482  711  930 1286  985 1029 1796 1368 1567 1189 1323\n",
+            " 1234  798 1129  623  708  456  316 1360 1248  272  821  370 1007  518\n",
+            "  476  502  867  661  297  679  875 1518  605  810  325  434  583  634\n",
+            "  557  341  626 1836  541  454 1246  571 1037 1124 1045  989  827 1150\n",
+            "  312  526  218  980  403  493  736  818  901  610  725  549 1175  697\n",
+            "  439  360 1281 1230 1004]\n",
+            "Low Qual Fin SF: [   0  390  362  144 1064  232  431  120  436  371  360  259  397  312\n",
+            "  513  108  205  156  697  420  384  473  512  528  114  479  515   53\n",
+            "   80  392  572  234  140  450  481  514]\n",
+            "Gr Liv Area: [1656  896 1329 ... 2028 2521 1003]\n",
+            "Bsmt Full Bath: [ 1.  0.  2.  3. nan]\n",
+            "Bsmt Half Bath: [ 0.  1. nan  2.]\n",
+            "Full Bath: [1 2 3 0 4]\n",
+            "Half Bath: [0 1 2]\n",
+            "Bedroom AbvGr: [3 2 1 4 6 5 0 8]\n",
+            "Kitchen AbvGr: [1 2 3 0]\n",
+            "Kitchen Qual: ['TA' 'Gd' 'Ex' 'Fa' 'Po']\n",
+            "TotRms AbvGrd: [ 7  5  6  8  4 12 10 11  9  3 13  2 15 14]\n",
+            "Functional: ['Typ' 'Mod' 'Min1' 'Min2' 'Maj1' 'Maj2' 'Sev' 'Sal']\n",
+            "Fireplaces: [2 0 1 3 4]\n",
+            "Fireplace Qu: ['Gd' nan 'TA' 'Po' 'Ex' 'Fa']\n",
+            "Garage Type: ['Attchd' 'BuiltIn' 'Basment' 'Detchd' nan 'CarPort' '2Types']\n",
+            "Garage Yr Blt: [1960. 1961. 1958. 1968. 1997. 1998. 2001. 1992. 1995. 1999. 1993. 1990.\n",
+            " 1985. 2003. 1988. 2010. 1951. 1978. 1977. 1974. 2000. 1970. 1971.   nan\n",
+            " 1975. 2009. 2008. 2005. 2004. 2002. 2006. 1996. 1994. 1980. 1979. 1984.\n",
+            " 1986. 1920. 1987. 1973. 1963. 1962. 1976. 1967. 1972. 1966. 1964. 1950.\n",
+            " 1949. 1954. 1955. 1959. 1957. 1956. 1952. 1953. 1989. 1948. 1900. 1927.\n",
+            " 1915. 1945. 1940. 1938. 1928. 1930. 1926. 1939. 1942. 1923. 1917. 1910.\n",
+            " 1965. 1969. 1947. 1946. 1941. 1924. 1922. 1896. 2007. 1983. 1981. 1991.\n",
+            " 1982. 1916. 1925. 1936. 1935. 1931. 1934. 1929. 1918. 1921. 1937. 1932.\n",
+            " 1906. 1908. 1895. 1933. 2207. 1914. 1943. 1919.]\n",
+            "Garage Finish: ['Fin' 'Unf' 'RFn' nan]\n",
+            "Garage Cars: [ 2.  1.  3.  0.  4.  5. nan]\n",
+            "Garage Area: [ 528.  730.  312.  522.  482.  470.  582.  506.  608.  442.  440.  420.\n",
+            "  393.  841.  492.  834.  400.  500.  546.  663.  480.  304.  525.    0.\n",
+            "  511.  264.  320.  308.  751.  772.  606.  868.  532.  678.  820.  484.\n",
+            "  958.  756.  576.  474.  430.  437.  433.  434.  779.  962.  527.  712.\n",
+            "  671.  486.  666.  880.  676.  614.  750.  618.  463.  462.  457.  476.\n",
+            "  429.  539.  336.  280.  260.  461.  564.  762.  713.  588.  496.  852.\n",
+            "  592.  475.  596.  535.  660.  441.  490.  504.  517.  240.  364.  244.\n",
+            "  315.  578.  620.  447.  294.  531.  263.  318.  305.  246.  392.  330.\n",
+            "  720.  360.  551.  379.  220.  780.  288.  416.  624.  923.  560.  363.\n",
+            "  200.  572.  180.  516.  672.  349.  365.  231.  450.  270.  299.  591.\n",
+            "  533.  690.  436.  586.  366.  467.  209.  460. 1017.  574.  776.  632.\n",
+            "  740.  615.  594.  580.  513.  523.  850.  670.  613.  621.  598.  502.\n",
+            "  494.  319.  352.  216.  399.  252.  567.  473.  625.  384.  741.  573.\n",
+            "  888.  520.  680.  510.  431.  746.  686.  286.  253.  495.  616.  275.\n",
+            "  538.  390.  758.  499.  396.  427.  380.  409.  389.  343.  565. 1166.\n",
+            "  435.  544.  529.  479.  542.  478.  581.  552.  583.  902.  477.  345.\n",
+            "  656.  786.  754.  840.  890. 1390.  864.  836.  896.  900.  842. 1020.\n",
+            "  932.  640.  908.  927.  856.  700.  738.  862.  644.  968.  886.  871.\n",
+            "  626.  949.  685.  649.  701.  550.  397.  432.  554.  394.  658.  410.\n",
+            "  810. 1069.  889.  815.  647.  623.  711.  898.  972.  726.  844.  689.\n",
+            "  795.  984.  692.  812.  782. 1043.  438.  628.  845.  555.  788.  559.\n",
+            "  465.  612.  732.  300.  524.  704.  561.  641.  642.  540.  784.  497.\n",
+            "  515.  630.  498.  768.  472.  610.  549.  645.  368.  505.  418.  338.\n",
+            "  271.  792.  530.  514.  509.  297.  350.  884.  230.  281.  907.  483.\n",
+            "  210.  162.  324.  256.  273.  287.  357.  424.  456.  207.  192.  250.\n",
+            " 1184.  164.  316.  226.  668.  452.  284.  303.  340.  234.  290.  266.\n",
+            "  296.  425.  466. 1138.  826.  860.  846.  904.  702.  662.  569.  577.\n",
+            "  493.  622.  605.  444.  600. 1231.  570.  736.  521.  512.  451.  195.\n",
+            "  313.  342.  215.  282.  213.  307.  186.  295.  501.  468.  189.  351.\n",
+            "  541.  912.  650.  885.  471.  765.  920.  412.  402.  602.  698.  714.\n",
+            "  601.  386.  404.  406.  682.  683.  557.  619.  489. 1314.  439.  787.\n",
+            "  774. 1220.  858.  905.  866.  706. 1150. 1003.  789.  870. 1052.  944.\n",
+            "  388.  428.  398.  403.  696.  687.  938.  839.  983.  783.  691.  830.\n",
+            "  824.  851.  603.  648.  936.  562.  673.  575.  627.  276.  636.  545.\n",
+            "  469.  464.  831.  267.  283.  205.  377.  292.  458.  301. 1488.  372.\n",
+            "  401.  414.  311.  225.  828.  869.  370.  208.  160.  355.  228.  322.\n",
+            "  408.  354.  249.  534.  453. 1348.  874.  811.  558.  328.  725.  715.\n",
+            "  543.  595.  508.  721.  548.  814. 1418.  369.  599.  344. 1014.  924.\n",
+            "  356.  487.  185. 1248.  857.  816.  358.  665.  800.  749.  892.  257.\n",
+            "  423.  526.  373.  729. 1110.  556.  724.  481.  585.  488.  684.  367.\n",
+            "  818.  928. 1040.  878.  947.  895.  694. 1174.  728.  843.  916.  872.\n",
+            "  876.  631.  617.  454.  813.  925.  804.  806.  832.  455.  752.  933.\n",
+            " 1092.  865.  954.  825.  859.  590. 1025.  744.  566.  518.  611. 1105.\n",
+            "  571.  309.  306.  310.  293.  371. 1200.  254.  184.  374.  331.  224.\n",
+            "  217.  261.  323.  638.  739.  332.  719.  833.  894.  796.  674.  747.\n",
+            "  242.  597.  748.  639.  579. 1154.  248.   nan  100.  722.  422.  808.\n",
+            "  995. 1041. 1356.  963.  443.  413.  773.  675.  716.  604.  485.  770.\n",
+            " 1085.  853.  708.  753.  899.  426.  807.  959.  803.  760. 1134.  584.\n",
+            " 1053.  449.  688.  757.  326.  568.  353.  791. 1008.  378.  258.  255.\n",
+            "  198.  459.  667.  445.  325.  848.  317.  646.  265.  609.  375.  272.\n",
+            "  327.  766.  693.  405.]\n",
+            "Garage Qual: ['TA' nan 'Fa' 'Gd' 'Ex' 'Po']\n",
+            "Garage Cond: ['TA' nan 'Fa' 'Gd' 'Ex' 'Po']\n",
+            "Paved Drive: ['P' 'Y' 'N']\n",
+            "Wood Deck SF: [ 210  140  393    0  212  360  237  157  483  192  503  325  113  349\n",
+            "  240  203  275  173   26  144  168  220  238  196  120   36  100  146\n",
+            "  288  180  668   23  186  132  283  169   80  635   28  353  370  121\n",
+            "  416  296   32  198  160  280  133  223  277  224  228  352  227  366\n",
+            "  117  263  301   42  252  250  264  364  414  218  222  657   84   51\n",
+            "  106   54  135  221  306   12  344   56  406  379  226  335  496  290\n",
+            "  268  336   44  450  156  105  367   71  316  365  188  331   60  257\n",
+            "  116  272  141  112   30   68  128  375  328  174  182  200   96  261\n",
+            "  431   22  287  129  162  269   48  201   52  256  232  342   63  322\n",
+            "  178  233  474  448  225   40  171  216  185  108   87  260  147  150\n",
+            "  404  382  319   99  184  125  165  248  114  230  170  172  208  231\n",
+            "  148  143  300   24  298  340  517  297   70  205  195  158  462  502\n",
+            "  115  501  371  235  294  312  321   78   85  164  110   55  289   66\n",
+            "  324  126  187   74  181  266  244   45  189  509  302  243   64  131\n",
+            "  476  234  400   73  154  123  486  276  392   72  215   58  262  202\n",
+            "  253  194  576  356  327   92  136  329  279  176  292  467  119   90\n",
+            "  305  124  270  308   33  138  303  214  152  550   16  411  209  358\n",
+            "  320  495  236  385  145  155   97   20  122   98   25   38  426  355\n",
+            "  490   88   76  418  265   49   57  204  311  102  511  409   50  307\n",
+            "   81  424  339  403  278  211  139  149  259  736  134  183  314  213\n",
+            "  161  318  428  670  282  315  362  245  219  390  167  407   35  130\n",
+            "  104  460  286  239  255  193  159  402  455  500  206  190  333  284\n",
+            "  285   14  521  380  127  646  142  386  405  546  118  242  291  166\n",
+            "  274  439  536 1424  690  330  421   95  441  246  351  197  384  444\n",
+            "  295  175  354  519  177  179   89  361  247  870  309  432    4  641\n",
+            "  153  857   94   86  191   75  631  229  436  345  520  199   27  394\n",
+            "   53   77  466  304  241  103  586  684  453  413  468  207  530  574\n",
+            "  326  728]\n",
+            "Open Porch SF: [ 62   0  36  34  82 152  60  84  21  75  54  12 122 120  96  85  68  55\n",
+            "  30 133  50  95  35  70  74 119  67 150 130  49  27  23 116  20  48 172\n",
+            "  56  32  57  81  86 136  45 168 102 104 144  39 111 166  44 192 184  42\n",
+            "  78 137  76  69  66 224  26  40  98  73  38  28  52  17 124 160 100 228\n",
+            " 108  18 158  10  11 132  58  90  22  46 278  92  33  61  59  77  25 262\n",
+            " 105  64 140 156 207  53  24 312  72  43  94  63 176 195 134 162 197 274\n",
+            " 170 273 185 190 114 235 183  16  51 103 128 146 126 165 226 121 112 175\n",
+            " 182 113  88 178  91  41  93 177 234 254 169 204  99  80 110 189 287 523\n",
+            "  15 135 198 188 215 155 142 222 193  29 151 240 200 148 201 118 154 238\n",
+            " 247 304 101 173 282 180  65 131 153  87 174 210 251 243 211 129   4 230\n",
+            " 213 547 291 502 299 365 139 216  89 117 236   8 187 159 106 372 292 141\n",
+            " 217 123  83 276 265 164 205 368  47 203 191 138 364 127 256 214 241 194\n",
+            " 285 324 208 171 570 244 231 484 406 742 444 252 263 266  97  37 250 246\n",
+            " 229  31 267 382 319 258   6 341 260 288 418 115 253 245 107 225 125 199]\n",
+            "Enclosed Porch: [   0  170  184  154   80  220  186  156  120  112  150  164  189  205\n",
+            "  113  216  135  130  202  126  334  246  196   18  158  114   60   41\n",
+            "  128   35   48   32   64  364   40  318  248  168   45  239  176   77\n",
+            "   52   56   36  136   96  242   42   86  162   98  265   50  280  222\n",
+            "  144  209   24   91  236  218  228   84  264  260  240  203  140  252\n",
+            "  100  134  432  198  116  169  148  244   25   81  102  160  386  226\n",
+            "  238  115   94  208  105   54   51   34  268   30  213  288   90  192\n",
+            "  177  211  185   55  180   44   57   78  137   72  368   70  165   92\n",
+            "   16  123   66  210   68  109  194  139  219  259  212   20  101   87\n",
+            "  117  204  122  108  190  231  138  183  254  301  121  207  224  172\n",
+            "  174   99  249  291  145  214  275  290  175   26  143  230   88   39\n",
+            " 1012   43  286   19  584  200  133  234   37  324  552  161   75  167\n",
+            "   28  293  104  296  330  221  256  129  225  294  272  429   67  132\n",
+            "   23]\n",
+            "3Ssn Porch: [  0 238 224 144 508 168 255 225 360 162 140 150 182 153 320 174 304 216\n",
+            " 407  96 245 120 219 180 196 176  86  23 290 323 130]\n",
+            "Screen Porch: [  0 120 144 140 210 165 256 216  90 204 143 160 182 385 240 168 148  95\n",
+            " 266 166 116 161 200 155 108 291 490 170 192 180 156 196 197 152 121  92\n",
+            " 288 185 342 189 252 234 255 111 112 231  40 100  60 142 110 396 225 117\n",
+            " 195 145 224 115 198 233 190 141 208  80 176  94 164 178 273 130 480 220\n",
+            "  64 163 287 175 576 227 265 221 171 135 322 174 147 276 260 217 201 109\n",
+            "  99 150 126 259 184  84 154  53 153 228 138 263  88 280 123 440 374 119\n",
+            " 222 264 270  63 122 128 162 410 271 312 348 113 104]\n",
+            "Pool Area: [  0 144 480 576 555 368 444 228 561 519 648 800 512 738]\n",
+            "Pool QC: [nan 'Ex' 'Gd' 'TA' 'Fa']\n",
+            "Fence: [nan 'MnPrv' 'GdPrv' 'GdWo' 'MnWw']\n",
+            "Misc Feature: [nan 'Gar2' 'Shed' 'Othr' 'Elev' 'TenC']\n",
+            "Misc Val: [    0 12500   500   700   400   450  1500   300   600  1200  3500  2000\n",
+            "  2500    54    80   490   480   350   650   900   800   750  1400  6500\n",
+            "  1150  1000  4500  3000   560  1300  8300 15500 17000  1512   455   460\n",
+            "   620   420]\n",
+            "Mo Sold: [ 5  6  4  3  1  2  7 10  8 11  9 12]\n",
+            "Yr Sold: [2010 2009 2008 2007 2006]\n",
+            "Sale Type: ['WD ' 'New' 'COD' 'ConLI' 'Con' 'ConLD' 'Oth' 'ConLw' 'CWD' 'VWD']\n",
+            "Sale Condition: ['Normal' 'Partial' 'Family' 'Abnorml' 'Alloca' 'AdjLand']\n",
+            "SalePrice: [215000 105000 172000 ...  90500  71000 150900]\n",
+            "Order             2930\n",
+            "PID               2930\n",
+            "MS SubClass         16\n",
+            "MS Zoning            7\n",
+            "Lot Frontage       128\n",
+            "                  ... \n",
+            "Mo Sold             12\n",
+            "Yr Sold              5\n",
+            "Sale Type           10\n",
+            "Sale Condition       6\n",
+            "SalePrice         1032\n",
+            "Length: 82, dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Perform initial exploration of the dataset.\n",
+        "# - Check shape, column names, smaples\n",
+        "# - Get summary info, data types\n",
+        "# - Descriptive statistics\n",
+        "\n",
+        "df = df1.copy()\n",
+        "print(f\"Shape: {df.shape}\")\n",
+        "print(f\"Columns Name: {df.columns}\")\n",
+        "print(f\"Sample of Records: {df.sample}\")\n",
+        "\n",
+        "df.info()\n",
+        "df.dtypes\n",
+        "\n",
+        "print(df.describe())\n",
+        "print(df.describe(include='object'))\n",
+        "\n",
+        "for col in df.columns:\n",
+        "    print(f\"{col}: {df[col].unique()}\")\n",
+        "\n",
+        "print(df.nunique())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "quTZ6mJdjrDw"
+      },
+      "source": [
+        "## 🔹 Step 3: Missing Value Check & Handling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-0KdWkPqjs7y",
+        "outputId": "a7401ca1-452a-4801-c8dd-57a813c39aca"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(2930, 82)\n",
+            "(2930, 80)\n",
+            "# of nulls at the begining:  15749\n",
+            "Shape after drop columns with >50% missing values:  (2930, 75)\n",
+            "# of nulls after drop columns with >50% missing values:  3143\n",
+            "# of nulls before Categurical filling:  2461\n",
+            "# of nulls at the end:  0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Check missing values.\n",
+        "# Decide on a strategy (if needed):\n",
+        "# - Drop if too many are missing\n",
+        "# - Fill with mean/median/mode/domain-specific value\n",
+        "\n",
+        "print(df.shape)\n",
+        "if 'Order' in df.columns:\n",
+        "    df.drop(columns=['Order'], inplace=True)\n",
+        "if 'PID' in df.columns:\n",
+        "    df.drop(columns=['PID'], inplace=True)\n",
+        "print(df.shape)\n",
+        "print(\"# of nulls at the begining: \", df.isnull().sum().sum())\n",
+        "\n",
+        "# list columns with missing values\n",
+        "missing_counts = df.isnull().sum()\n",
+        "missing_cols = missing_counts[missing_counts > 0].sort_values(ascending=False)\n",
+        "# drop columns with more than 50% missing values\n",
+        "missing_pct = df.isnull().mean() * 100\n",
+        "cols_to_drop = missing_pct[missing_pct > 50].index.tolist()\n",
+        "df = df.drop(columns=cols_to_drop)\n",
+        "print(\"Shape after drop columns with >50% missing values: \", df.shape)\n",
+        "print(\"# of nulls after drop columns with >50% missing values: \", df.isnull().sum().sum())\n",
+        "\n",
+        "col_cat = df.select_dtypes(include='object').columns\n",
+        "col_num = df.select_dtypes(exclude='object').columns\n",
+        "for col in col_num:\n",
+        "    df[col] = df[col].fillna(df[col].median())\n",
+        "print(\"# of nulls before Categurical filling: \", df.isnull().sum().sum())\n",
+        "for col in col_cat:\n",
+        "    mod = df[col].mode()\n",
+        "    mode_value = mod.iloc[0]        # pick the first mode\n",
+        "    df[col] = df[col].fillna(mode_value)\n",
+        "print(\"# of nulls at the end: \",df.isnull().sum().sum())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LWQY6vDEjyTu"
+      },
+      "source": [
+        "## 🔹 Step 4: Correlation Check & Feature Decision"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "MHwMZbX_jxKJ",
+        "outputId": "21c62f7c-f2bd-4837-f648-31cfbeb102b8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# TODO: Check correlations between numerical features and target variable (SalePrice).\n",
+        "# Use correlation heatmap or pairplot.\n",
+        "# Decide which features to keep/remove based on correlation.\n",
+        "\n",
+        "import numpy as np\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Numeric-only correlation\n",
+        "corr = df.corr(numeric_only=True)\n",
+        "plt.figure(figsize=(10,6))\n",
+        "sns.heatmap(corr, cmap=\"coolwarm\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "exTa7T6qj2hv"
+      },
+      "source": [
+        "## 🔹 Step 5: Encode Categorical Variables"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JBQbfP6jj1hS",
+        "outputId": "96116592-46d9-4a16-da40-b5140300a807"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['RL' 'RH' 'FV' 'RM' 'C (all)' 'I (all)' 'A (agr)']\n",
+            "['Pave' 'Grvl']\n",
+            "['IR1' 'Reg' 'IR2' 'IR3']\n",
+            "['Lvl' 'HLS' 'Bnk' 'Low']\n",
+            "['AllPub' 'NoSewr' 'NoSeWa']\n",
+            "['Corner' 'Inside' 'CulDSac' 'FR2' 'FR3']\n",
+            "['Gtl' 'Mod' 'Sev']\n",
+            "['NAmes' 'Gilbert' 'StoneBr' 'NWAmes' 'Somerst' 'BrDale' 'NPkVill'\n",
+            " 'NridgHt' 'Blmngtn' 'NoRidge' 'SawyerW' 'Sawyer' 'Greens' 'BrkSide'\n",
+            " 'OldTown' 'IDOTRR' 'ClearCr' 'SWISU' 'Edwards' 'CollgCr' 'Crawfor'\n",
+            " 'Blueste' 'Mitchel' 'Timber' 'MeadowV' 'Veenker' 'GrnHill' 'Landmrk']\n",
+            "['Norm' 'Feedr' 'PosN' 'RRNe' 'RRAe' 'Artery' 'PosA' 'RRAn' 'RRNn']\n",
+            "['Norm' 'Feedr' 'PosA' 'PosN' 'Artery' 'RRNn' 'RRAe' 'RRAn']\n",
+            "['1Fam' 'TwnhsE' 'Twnhs' 'Duplex' '2fmCon']\n",
+            "['1Story' '2Story' '1.5Fin' 'SFoyer' 'SLvl' '2.5Unf' '1.5Unf' '2.5Fin']\n",
+            "['Hip' 'Gable' 'Mansard' 'Gambrel' 'Shed' 'Flat']\n",
+            "['CompShg' 'WdShake' 'Tar&Grv' 'WdShngl' 'Membran' 'ClyTile' 'Roll'\n",
+            " 'Metal']\n",
+            "['BrkFace' 'VinylSd' 'Wd Sdng' 'CemntBd' 'HdBoard' 'Plywood' 'MetalSd'\n",
+            " 'AsbShng' 'WdShing' 'Stucco' 'AsphShn' 'BrkComm' 'CBlock' 'PreCast'\n",
+            " 'Stone' 'ImStucc']\n",
+            "['Plywood' 'VinylSd' 'Wd Sdng' 'BrkFace' 'CmentBd' 'HdBoard' 'Wd Shng'\n",
+            " 'MetalSd' 'ImStucc' 'Brk Cmn' 'AsbShng' 'Stucco' 'AsphShn' 'CBlock'\n",
+            " 'Stone' 'PreCast' 'Other']\n",
+            "['TA' 'Gd' 'Ex' 'Fa']\n",
+            "['TA' 'Gd' 'Fa' 'Po' 'Ex']\n",
+            "['CBlock' 'PConc' 'Wood' 'BrkTil' 'Slab' 'Stone']\n",
+            "['TA' 'Gd' 'Ex' 'Fa' 'Po']\n",
+            "['Gd' 'TA' 'Po' 'Fa' 'Ex']\n",
+            "['Gd' 'No' 'Mn' 'Av']\n",
+            "['BLQ' 'Rec' 'ALQ' 'GLQ' 'Unf' 'LwQ']\n",
+            "['Unf' 'LwQ' 'BLQ' 'Rec' 'GLQ' 'ALQ']\n",
+            "['GasA' 'GasW' 'Grav' 'Wall' 'Floor' 'OthW']\n",
+            "['Fa' 'TA' 'Ex' 'Gd' 'Po']\n",
+            "['Y' 'N']\n",
+            "['SBrkr' 'FuseA' 'FuseF' 'FuseP' 'Mix']\n",
+            "['TA' 'Gd' 'Ex' 'Fa' 'Po']\n",
+            "['Typ' 'Mod' 'Min1' 'Min2' 'Maj1' 'Maj2' 'Sev' 'Sal']\n",
+            "['Gd' 'TA' 'Po' 'Ex' 'Fa']\n",
+            "['Attchd' 'BuiltIn' 'Basment' 'Detchd' 'CarPort' '2Types']\n",
+            "['Fin' 'Unf' 'RFn']\n",
+            "['TA' 'Fa' 'Gd' 'Ex' 'Po']\n",
+            "['TA' 'Fa' 'Gd' 'Ex' 'Po']\n",
+            "['P' 'Y' 'N']\n",
+            "['WD ' 'New' 'COD' 'ConLI' 'Con' 'ConLD' 'Oth' 'ConLw' 'CWD' 'VWD']\n",
+            "['Normal' 'Partial' 'Family' 'Abnorml' 'Alloca' 'AdjLand']\n",
+            "   MS SubClass  Lot Frontage  Lot Area  Overall Qual  Overall Cond  \\\n",
+            "0           20         141.0     31770             6             5   \n",
+            "1           20          80.0     11622             5             6   \n",
+            "2           20          81.0     14267             6             6   \n",
+            "3           20          93.0     11160             7             5   \n",
+            "4           60          74.0     13830             5             5   \n",
+            "\n",
+            "   Year Built  Year Remod/Add  Mas Vnr Area  BsmtFin SF 1  BsmtFin SF 2  \\\n",
+            "0        1960            1960         112.0         639.0           0.0   \n",
+            "1        1961            1961           0.0         468.0         144.0   \n",
+            "2        1958            1958         108.0         923.0           0.0   \n",
+            "3        1968            1968           0.0        1065.0           0.0   \n",
+            "4        1997            1998           0.0         791.0           0.0   \n",
+            "\n",
+            "   Bsmt Unf SF  Total Bsmt SF  1st Flr SF  2nd Flr SF  Low Qual Fin SF  \\\n",
+            "0        441.0         1080.0        1656           0                0   \n",
+            "1        270.0          882.0         896           0                0   \n",
+            "2        406.0         1329.0        1329           0                0   \n",
+            "3       1045.0         2110.0        2110           0                0   \n",
+            "4        137.0          928.0         928         701                0   \n",
+            "\n",
+            "   Gr Liv Area  Bsmt Full Bath  Bsmt Half Bath  Full Bath  Half Bath  \\\n",
+            "0         1656             1.0             0.0          1          0   \n",
+            "1          896             0.0             0.0          1          0   \n",
+            "2         1329             0.0             0.0          1          1   \n",
+            "3         2110             1.0             0.0          2          1   \n",
+            "4         1629             0.0             0.0          2          1   \n",
+            "\n",
+            "   Bedroom AbvGr  Kitchen AbvGr  TotRms AbvGrd  Fireplaces  Garage Yr Blt  \\\n",
+            "0              3              1              7           2         1960.0   \n",
+            "1              2              1              5           0         1961.0   \n",
+            "2              3              1              6           0         1958.0   \n",
+            "3              3              1              8           2         1968.0   \n",
+            "4              3              1              6           1         1997.0   \n",
+            "\n",
+            "   Garage Cars  Garage Area  Wood Deck SF  Open Porch SF  Enclosed Porch  \\\n",
+            "0          2.0        528.0           210             62               0   \n",
+            "1          1.0        730.0           140              0               0   \n",
+            "2          1.0        312.0           393             36               0   \n",
+            "3          2.0        522.0             0              0               0   \n",
+            "4          2.0        482.0           212             34               0   \n",
+            "\n",
+            "   3Ssn Porch  Screen Porch  Pool Area  Misc Val  Mo Sold  Yr Sold  SalePrice  \\\n",
+            "0           0             0          0         0        5     2010     215000   \n",
+            "1           0           120          0         0        6     2010     105000   \n",
+            "2           0             0          0     12500        6     2010     172000   \n",
+            "3           0             0          0         0        4     2010     244000   \n",
+            "4           0             0          0         0        3     2010     189900   \n",
+            "\n",
+            "   MS Zoning_C (all)  MS Zoning_FV  MS Zoning_I (all)  MS Zoning_RH  \\\n",
+            "0                0.0           0.0                0.0           0.0   \n",
+            "1                0.0           0.0                0.0           1.0   \n",
+            "2                0.0           0.0                0.0           0.0   \n",
+            "3                0.0           0.0                0.0           0.0   \n",
+            "4                0.0           0.0                0.0           0.0   \n",
+            "\n",
+            "   MS Zoning_RL  MS Zoning_RM  Street_Pave  Lot Shape_IR2  Lot Shape_IR3  \\\n",
+            "0           1.0           0.0          1.0            0.0            0.0   \n",
+            "1           0.0           0.0          1.0            0.0            0.0   \n",
+            "2           1.0           0.0          1.0            0.0            0.0   \n",
+            "3           1.0           0.0          1.0            0.0            0.0   \n",
+            "4           1.0           0.0          1.0            0.0            0.0   \n",
+            "\n",
+            "   Lot Shape_Reg  Land Contour_HLS  Land Contour_Low  Land Contour_Lvl  \\\n",
+            "0            0.0               0.0               0.0               1.0   \n",
+            "1            1.0               0.0               0.0               1.0   \n",
+            "2            0.0               0.0               0.0               1.0   \n",
+            "3            1.0               0.0               0.0               1.0   \n",
+            "4            0.0               0.0               0.0               1.0   \n",
+            "\n",
+            "   Utilities_NoSeWa  Utilities_NoSewr  Lot Config_CulDSac  Lot Config_FR2  \\\n",
+            "0               0.0               0.0                 0.0             0.0   \n",
+            "1               0.0               0.0                 0.0             0.0   \n",
+            "2               0.0               0.0                 0.0             0.0   \n",
+            "3               0.0               0.0                 0.0             0.0   \n",
+            "4               0.0               0.0                 0.0             0.0   \n",
+            "\n",
+            "   Lot Config_FR3  Lot Config_Inside  Land Slope_Mod  Land Slope_Sev  \\\n",
+            "0             0.0                0.0             0.0             0.0   \n",
+            "1             0.0                1.0             0.0             0.0   \n",
+            "2             0.0                0.0             0.0             0.0   \n",
+            "3             0.0                0.0             0.0             0.0   \n",
+            "4             0.0                1.0             0.0             0.0   \n",
+            "\n",
+            "   Neighborhood_Blueste  Neighborhood_BrDale  Neighborhood_BrkSide  \\\n",
+            "0                   0.0                  0.0                   0.0   \n",
+            "1                   0.0                  0.0                   0.0   \n",
+            "2                   0.0                  0.0                   0.0   \n",
+            "3                   0.0                  0.0                   0.0   \n",
+            "4                   0.0                  0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_ClearCr  Neighborhood_CollgCr  Neighborhood_Crawfor  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_Edwards  Neighborhood_Gilbert  Neighborhood_Greens  \\\n",
+            "0                   0.0                   0.0                  0.0   \n",
+            "1                   0.0                   0.0                  0.0   \n",
+            "2                   0.0                   0.0                  0.0   \n",
+            "3                   0.0                   0.0                  0.0   \n",
+            "4                   0.0                   1.0                  0.0   \n",
+            "\n",
+            "   Neighborhood_GrnHill  Neighborhood_IDOTRR  Neighborhood_Landmrk  \\\n",
+            "0                   0.0                  0.0                   0.0   \n",
+            "1                   0.0                  0.0                   0.0   \n",
+            "2                   0.0                  0.0                   0.0   \n",
+            "3                   0.0                  0.0                   0.0   \n",
+            "4                   0.0                  0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_MeadowV  Neighborhood_Mitchel  Neighborhood_NAmes  \\\n",
+            "0                   0.0                   0.0                 1.0   \n",
+            "1                   0.0                   0.0                 1.0   \n",
+            "2                   0.0                   0.0                 1.0   \n",
+            "3                   0.0                   0.0                 1.0   \n",
+            "4                   0.0                   0.0                 0.0   \n",
+            "\n",
+            "   Neighborhood_NPkVill  Neighborhood_NWAmes  Neighborhood_NoRidge  \\\n",
+            "0                   0.0                  0.0                   0.0   \n",
+            "1                   0.0                  0.0                   0.0   \n",
+            "2                   0.0                  0.0                   0.0   \n",
+            "3                   0.0                  0.0                   0.0   \n",
+            "4                   0.0                  0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_NridgHt  Neighborhood_OldTown  Neighborhood_SWISU  \\\n",
+            "0                   0.0                   0.0                 0.0   \n",
+            "1                   0.0                   0.0                 0.0   \n",
+            "2                   0.0                   0.0                 0.0   \n",
+            "3                   0.0                   0.0                 0.0   \n",
+            "4                   0.0                   0.0                 0.0   \n",
+            "\n",
+            "   Neighborhood_Sawyer  Neighborhood_SawyerW  Neighborhood_Somerst  \\\n",
+            "0                  0.0                   0.0                   0.0   \n",
+            "1                  0.0                   0.0                   0.0   \n",
+            "2                  0.0                   0.0                   0.0   \n",
+            "3                  0.0                   0.0                   0.0   \n",
+            "4                  0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_StoneBr  Neighborhood_Timber  Neighborhood_Veenker  \\\n",
+            "0                   0.0                  0.0                   0.0   \n",
+            "1                   0.0                  0.0                   0.0   \n",
+            "2                   0.0                  0.0                   0.0   \n",
+            "3                   0.0                  0.0                   0.0   \n",
+            "4                   0.0                  0.0                   0.0   \n",
+            "\n",
+            "   Condition 1_Feedr  Condition 1_Norm  Condition 1_PosA  Condition 1_PosN  \\\n",
+            "0                0.0               1.0               0.0               0.0   \n",
+            "1                1.0               0.0               0.0               0.0   \n",
+            "2                0.0               1.0               0.0               0.0   \n",
+            "3                0.0               1.0               0.0               0.0   \n",
+            "4                0.0               1.0               0.0               0.0   \n",
+            "\n",
+            "   Condition 1_RRAe  Condition 1_RRAn  Condition 1_RRNe  Condition 1_RRNn  \\\n",
+            "0               0.0               0.0               0.0               0.0   \n",
+            "1               0.0               0.0               0.0               0.0   \n",
+            "2               0.0               0.0               0.0               0.0   \n",
+            "3               0.0               0.0               0.0               0.0   \n",
+            "4               0.0               0.0               0.0               0.0   \n",
+            "\n",
+            "   Condition 2_Feedr  Condition 2_Norm  Condition 2_PosA  Condition 2_PosN  \\\n",
+            "0                0.0               1.0               0.0               0.0   \n",
+            "1                0.0               1.0               0.0               0.0   \n",
+            "2                0.0               1.0               0.0               0.0   \n",
+            "3                0.0               1.0               0.0               0.0   \n",
+            "4                0.0               1.0               0.0               0.0   \n",
+            "\n",
+            "   Condition 2_RRAe  Condition 2_RRAn  Condition 2_RRNn  Bldg Type_2fmCon  \\\n",
+            "0               0.0               0.0               0.0               0.0   \n",
+            "1               0.0               0.0               0.0               0.0   \n",
+            "2               0.0               0.0               0.0               0.0   \n",
+            "3               0.0               0.0               0.0               0.0   \n",
+            "4               0.0               0.0               0.0               0.0   \n",
+            "\n",
+            "   Bldg Type_Duplex  Bldg Type_Twnhs  Bldg Type_TwnhsE  House Style_1.5Unf  \\\n",
+            "0               0.0              0.0               0.0                 0.0   \n",
+            "1               0.0              0.0               0.0                 0.0   \n",
+            "2               0.0              0.0               0.0                 0.0   \n",
+            "3               0.0              0.0               0.0                 0.0   \n",
+            "4               0.0              0.0               0.0                 0.0   \n",
+            "\n",
+            "   House Style_1Story  House Style_2.5Fin  House Style_2.5Unf  \\\n",
+            "0                 1.0                 0.0                 0.0   \n",
+            "1                 1.0                 0.0                 0.0   \n",
+            "2                 1.0                 0.0                 0.0   \n",
+            "3                 1.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 0.0   \n",
+            "\n",
+            "   House Style_2Story  House Style_SFoyer  House Style_SLvl  Roof Style_Gable  \\\n",
+            "0                 0.0                 0.0               0.0               0.0   \n",
+            "1                 0.0                 0.0               0.0               1.0   \n",
+            "2                 0.0                 0.0               0.0               0.0   \n",
+            "3                 0.0                 0.0               0.0               0.0   \n",
+            "4                 1.0                 0.0               0.0               1.0   \n",
+            "\n",
+            "   Roof Style_Gambrel  Roof Style_Hip  Roof Style_Mansard  Roof Style_Shed  \\\n",
+            "0                 0.0             1.0                 0.0              0.0   \n",
+            "1                 0.0             0.0                 0.0              0.0   \n",
+            "2                 0.0             1.0                 0.0              0.0   \n",
+            "3                 0.0             1.0                 0.0              0.0   \n",
+            "4                 0.0             0.0                 0.0              0.0   \n",
+            "\n",
+            "   Roof Matl_CompShg  Roof Matl_Membran  Roof Matl_Metal  Roof Matl_Roll  \\\n",
+            "0                1.0                0.0              0.0             0.0   \n",
+            "1                1.0                0.0              0.0             0.0   \n",
+            "2                1.0                0.0              0.0             0.0   \n",
+            "3                1.0                0.0              0.0             0.0   \n",
+            "4                1.0                0.0              0.0             0.0   \n",
+            "\n",
+            "   Roof Matl_Tar&Grv  Roof Matl_WdShake  Roof Matl_WdShngl  \\\n",
+            "0                0.0                0.0                0.0   \n",
+            "1                0.0                0.0                0.0   \n",
+            "2                0.0                0.0                0.0   \n",
+            "3                0.0                0.0                0.0   \n",
+            "4                0.0                0.0                0.0   \n",
+            "\n",
+            "   Exterior 1st_AsphShn  Exterior 1st_BrkComm  Exterior 1st_BrkFace  \\\n",
+            "0                   0.0                   0.0                   1.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   1.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_CBlock  Exterior 1st_CemntBd  Exterior 1st_HdBoard  \\\n",
+            "0                  0.0                   0.0                   0.0   \n",
+            "1                  0.0                   0.0                   0.0   \n",
+            "2                  0.0                   0.0                   0.0   \n",
+            "3                  0.0                   0.0                   0.0   \n",
+            "4                  0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_ImStucc  Exterior 1st_MetalSd  Exterior 1st_Plywood  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_PreCast  Exterior 1st_Stone  Exterior 1st_Stucco  \\\n",
+            "0                   0.0                 0.0                  0.0   \n",
+            "1                   0.0                 0.0                  0.0   \n",
+            "2                   0.0                 0.0                  0.0   \n",
+            "3                   0.0                 0.0                  0.0   \n",
+            "4                   0.0                 0.0                  0.0   \n",
+            "\n",
+            "   Exterior 1st_VinylSd  Exterior 1st_Wd Sdng  Exterior 1st_WdShing  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   1.0                   0.0                   0.0   \n",
+            "2                   0.0                   1.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   1.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_AsphShn  Exterior 2nd_Brk Cmn  Exterior 2nd_BrkFace  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   1.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_CBlock  Exterior 2nd_CmentBd  Exterior 2nd_HdBoard  \\\n",
+            "0                  0.0                   0.0                   0.0   \n",
+            "1                  0.0                   0.0                   0.0   \n",
+            "2                  0.0                   0.0                   0.0   \n",
+            "3                  0.0                   0.0                   0.0   \n",
+            "4                  0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_ImStucc  Exterior 2nd_MetalSd  Exterior 2nd_Other  \\\n",
+            "0                   0.0                   0.0                 0.0   \n",
+            "1                   0.0                   0.0                 0.0   \n",
+            "2                   0.0                   0.0                 0.0   \n",
+            "3                   0.0                   0.0                 0.0   \n",
+            "4                   0.0                   0.0                 0.0   \n",
+            "\n",
+            "   Exterior 2nd_Plywood  Exterior 2nd_PreCast  Exterior 2nd_Stone  \\\n",
+            "0                   1.0                   0.0                 0.0   \n",
+            "1                   0.0                   0.0                 0.0   \n",
+            "2                   0.0                   0.0                 0.0   \n",
+            "3                   0.0                   0.0                 0.0   \n",
+            "4                   0.0                   0.0                 0.0   \n",
+            "\n",
+            "   Exterior 2nd_Stucco  Exterior 2nd_VinylSd  Exterior 2nd_Wd Sdng  \\\n",
+            "0                  0.0                   0.0                   0.0   \n",
+            "1                  0.0                   1.0                   0.0   \n",
+            "2                  0.0                   0.0                   1.0   \n",
+            "3                  0.0                   0.0                   0.0   \n",
+            "4                  0.0                   1.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_Wd Shng  Exter Qual_Fa  Exter Qual_Gd  Exter Qual_TA  \\\n",
+            "0                   0.0            0.0            0.0            1.0   \n",
+            "1                   0.0            0.0            0.0            1.0   \n",
+            "2                   0.0            0.0            0.0            1.0   \n",
+            "3                   0.0            0.0            1.0            0.0   \n",
+            "4                   0.0            0.0            0.0            1.0   \n",
+            "\n",
+            "   Exter Cond_Fa  Exter Cond_Gd  Exter Cond_Po  Exter Cond_TA  \\\n",
+            "0            0.0            0.0            0.0            1.0   \n",
+            "1            0.0            0.0            0.0            1.0   \n",
+            "2            0.0            0.0            0.0            1.0   \n",
+            "3            0.0            0.0            0.0            1.0   \n",
+            "4            0.0            0.0            0.0            1.0   \n",
+            "\n",
+            "   Foundation_CBlock  Foundation_PConc  Foundation_Slab  Foundation_Stone  \\\n",
+            "0                1.0               0.0              0.0               0.0   \n",
+            "1                1.0               0.0              0.0               0.0   \n",
+            "2                1.0               0.0              0.0               0.0   \n",
+            "3                1.0               0.0              0.0               0.0   \n",
+            "4                0.0               1.0              0.0               0.0   \n",
+            "\n",
+            "   Foundation_Wood  Bsmt Qual_Fa  Bsmt Qual_Gd  Bsmt Qual_Po  Bsmt Qual_TA  \\\n",
+            "0              0.0           0.0           0.0           0.0           1.0   \n",
+            "1              0.0           0.0           0.0           0.0           1.0   \n",
+            "2              0.0           0.0           0.0           0.0           1.0   \n",
+            "3              0.0           0.0           0.0           0.0           1.0   \n",
+            "4              0.0           0.0           1.0           0.0           0.0   \n",
+            "\n",
+            "   Bsmt Cond_Fa  Bsmt Cond_Gd  Bsmt Cond_Po  Bsmt Cond_TA  Bsmt Exposure_Gd  \\\n",
+            "0           0.0           1.0           0.0           0.0               1.0   \n",
+            "1           0.0           0.0           0.0           1.0               0.0   \n",
+            "2           0.0           0.0           0.0           1.0               0.0   \n",
+            "3           0.0           0.0           0.0           1.0               0.0   \n",
+            "4           0.0           0.0           0.0           1.0               0.0   \n",
+            "\n",
+            "   Bsmt Exposure_Mn  Bsmt Exposure_No  BsmtFin Type 1_BLQ  BsmtFin Type 1_GLQ  \\\n",
+            "0               0.0               0.0                 1.0                 0.0   \n",
+            "1               0.0               1.0                 0.0                 0.0   \n",
+            "2               0.0               1.0                 0.0                 0.0   \n",
+            "3               0.0               1.0                 0.0                 0.0   \n",
+            "4               0.0               1.0                 0.0                 1.0   \n",
+            "\n",
+            "   BsmtFin Type 1_LwQ  BsmtFin Type 1_Rec  BsmtFin Type 1_Unf  \\\n",
+            "0                 0.0                 0.0                 0.0   \n",
+            "1                 0.0                 1.0                 0.0   \n",
+            "2                 0.0                 0.0                 0.0   \n",
+            "3                 0.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 0.0   \n",
+            "\n",
+            "   BsmtFin Type 2_BLQ  BsmtFin Type 2_GLQ  BsmtFin Type 2_LwQ  \\\n",
+            "0                 0.0                 0.0                 0.0   \n",
+            "1                 0.0                 0.0                 1.0   \n",
+            "2                 0.0                 0.0                 0.0   \n",
+            "3                 0.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 0.0   \n",
+            "\n",
+            "   BsmtFin Type 2_Rec  BsmtFin Type 2_Unf  Heating_GasA  Heating_GasW  \\\n",
+            "0                 0.0                 1.0           1.0           0.0   \n",
+            "1                 0.0                 0.0           1.0           0.0   \n",
+            "2                 0.0                 1.0           1.0           0.0   \n",
+            "3                 0.0                 1.0           1.0           0.0   \n",
+            "4                 0.0                 1.0           1.0           0.0   \n",
+            "\n",
+            "   Heating_Grav  Heating_OthW  Heating_Wall  Heating QC_Fa  Heating QC_Gd  \\\n",
+            "0           0.0           0.0           0.0            1.0            0.0   \n",
+            "1           0.0           0.0           0.0            0.0            0.0   \n",
+            "2           0.0           0.0           0.0            0.0            0.0   \n",
+            "3           0.0           0.0           0.0            0.0            0.0   \n",
+            "4           0.0           0.0           0.0            0.0            1.0   \n",
+            "\n",
+            "   Heating QC_Po  Heating QC_TA  Central Air_Y  Electrical_FuseF  \\\n",
+            "0            0.0            0.0            1.0               0.0   \n",
+            "1            0.0            1.0            1.0               0.0   \n",
+            "2            0.0            1.0            1.0               0.0   \n",
+            "3            0.0            0.0            1.0               0.0   \n",
+            "4            0.0            0.0            1.0               0.0   \n",
+            "\n",
+            "   Electrical_FuseP  Electrical_Mix  Electrical_SBrkr  Kitchen Qual_Fa  \\\n",
+            "0               0.0             0.0               1.0              0.0   \n",
+            "1               0.0             0.0               1.0              0.0   \n",
+            "2               0.0             0.0               1.0              0.0   \n",
+            "3               0.0             0.0               1.0              0.0   \n",
+            "4               0.0             0.0               1.0              0.0   \n",
+            "\n",
+            "   Kitchen Qual_Gd  Kitchen Qual_Po  Kitchen Qual_TA  Functional_Maj2  \\\n",
+            "0              0.0              0.0              1.0              0.0   \n",
+            "1              0.0              0.0              1.0              0.0   \n",
+            "2              1.0              0.0              0.0              0.0   \n",
+            "3              0.0              0.0              0.0              0.0   \n",
+            "4              0.0              0.0              1.0              0.0   \n",
+            "\n",
+            "   Functional_Min1  Functional_Min2  Functional_Mod  Functional_Sal  \\\n",
+            "0              0.0              0.0             0.0             0.0   \n",
+            "1              0.0              0.0             0.0             0.0   \n",
+            "2              0.0              0.0             0.0             0.0   \n",
+            "3              0.0              0.0             0.0             0.0   \n",
+            "4              0.0              0.0             0.0             0.0   \n",
+            "\n",
+            "   Functional_Sev  Functional_Typ  Fireplace Qu_Fa  Fireplace Qu_Gd  \\\n",
+            "0             0.0             1.0              0.0              1.0   \n",
+            "1             0.0             1.0              0.0              1.0   \n",
+            "2             0.0             1.0              0.0              1.0   \n",
+            "3             0.0             1.0              0.0              0.0   \n",
+            "4             0.0             1.0              0.0              0.0   \n",
+            "\n",
+            "   Fireplace Qu_Po  Fireplace Qu_TA  Garage Type_Attchd  Garage Type_Basment  \\\n",
+            "0              0.0              0.0                 1.0                  0.0   \n",
+            "1              0.0              0.0                 1.0                  0.0   \n",
+            "2              0.0              0.0                 1.0                  0.0   \n",
+            "3              0.0              1.0                 1.0                  0.0   \n",
+            "4              0.0              1.0                 1.0                  0.0   \n",
+            "\n",
+            "   Garage Type_BuiltIn  Garage Type_CarPort  Garage Type_Detchd  \\\n",
+            "0                  0.0                  0.0                 0.0   \n",
+            "1                  0.0                  0.0                 0.0   \n",
+            "2                  0.0                  0.0                 0.0   \n",
+            "3                  0.0                  0.0                 0.0   \n",
+            "4                  0.0                  0.0                 0.0   \n",
+            "\n",
+            "   Garage Finish_RFn  Garage Finish_Unf  Garage Qual_Fa  Garage Qual_Gd  \\\n",
+            "0                0.0                0.0             0.0             0.0   \n",
+            "1                0.0                1.0             0.0             0.0   \n",
+            "2                0.0                1.0             0.0             0.0   \n",
+            "3                0.0                0.0             0.0             0.0   \n",
+            "4                0.0                0.0             0.0             0.0   \n",
+            "\n",
+            "   Garage Qual_Po  Garage Qual_TA  Garage Cond_Fa  Garage Cond_Gd  \\\n",
+            "0             0.0             1.0             0.0             0.0   \n",
+            "1             0.0             1.0             0.0             0.0   \n",
+            "2             0.0             1.0             0.0             0.0   \n",
+            "3             0.0             1.0             0.0             0.0   \n",
+            "4             0.0             1.0             0.0             0.0   \n",
+            "\n",
+            "   Garage Cond_Po  Garage Cond_TA  Paved Drive_P  Paved Drive_Y  \\\n",
+            "0             0.0             1.0            1.0            0.0   \n",
+            "1             0.0             1.0            0.0            1.0   \n",
+            "2             0.0             1.0            0.0            1.0   \n",
+            "3             0.0             1.0            0.0            1.0   \n",
+            "4             0.0             1.0            0.0            1.0   \n",
+            "\n",
+            "   Sale Type_CWD  Sale Type_Con  Sale Type_ConLD  Sale Type_ConLI  \\\n",
+            "0            0.0            0.0              0.0              0.0   \n",
+            "1            0.0            0.0              0.0              0.0   \n",
+            "2            0.0            0.0              0.0              0.0   \n",
+            "3            0.0            0.0              0.0              0.0   \n",
+            "4            0.0            0.0              0.0              0.0   \n",
+            "\n",
+            "   Sale Type_ConLw  Sale Type_New  Sale Type_Oth  Sale Type_VWD  \\\n",
+            "0              0.0            0.0            0.0            0.0   \n",
+            "1              0.0            0.0            0.0            0.0   \n",
+            "2              0.0            0.0            0.0            0.0   \n",
+            "3              0.0            0.0            0.0            0.0   \n",
+            "4              0.0            0.0            0.0            0.0   \n",
+            "\n",
+            "   Sale Type_WD   Sale Condition_AdjLand  Sale Condition_Alloca  \\\n",
+            "0            1.0                     0.0                    0.0   \n",
+            "1            1.0                     0.0                    0.0   \n",
+            "2            1.0                     0.0                    0.0   \n",
+            "3            1.0                     0.0                    0.0   \n",
+            "4            1.0                     0.0                    0.0   \n",
+            "\n",
+            "   Sale Condition_Family  Sale Condition_Normal  Sale Condition_Partial  \n",
+            "0                    0.0                    1.0                     0.0  \n",
+            "1                    0.0                    1.0                     0.0  \n",
+            "2                    0.0                    1.0                     0.0  \n",
+            "3                    0.0                    1.0                     0.0  \n",
+            "4                    0.0                    1.0                     0.0  \n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Identify categorical variables.\n",
+        "# Use methods like:\n",
+        "# - One-hot encoding\n",
+        "# - Ordinal encoding\n",
+        "# Decide what makes sense for each feature.\n",
+        "\n",
+        "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder, OneHotEncoder\n",
+        "\n",
+        "for col in col_cat:\n",
+        "    print(df[col].unique())\n",
+        "\n",
+        "# df_oe = df.copy()  # keep a separate version\n",
+        "# ordinal_encoders = {}\n",
+        "# for col in col_cat:\n",
+        "#     oe = OrdinalEncoder()\n",
+        "#     df_oe[col] = oe.fit_transform(df_oe[col].values.reshape(-1, 1))\n",
+        "#     ordinal_encoders[col] = oe  # store encoder if needed later\n",
+        "# print(df_oe.head())\n",
+        "\n",
+        "df_ohe = df.copy()  # keep a separate version\n",
+        "label_encoders = {}\n",
+        "ohe = OneHotEncoder(drop=\"first\", sparse_output=False) # Set sparse_output to False\n",
+        "encoded = ohe.fit_transform(df[col_cat])\n",
+        "\n",
+        "encoded_df = pd.DataFrame(encoded, columns=ohe.get_feature_names_out(col_cat), index=df.index)\n",
+        "df = pd.concat([df.drop(columns=col_cat), encoded_df], axis=1)\n",
+        "\n",
+        "print(df.head())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ayerWnrFj-OS"
+      },
+      "source": [
+        "## 🔹 Step 6: Feature Scaling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cmOzqeCcj9_P",
+        "outputId": "1bca511f-1544-4bcd-cf91-f5de73de6e9c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "   MS SubClass  Lot Frontage  Lot Area  Overall Qual  Overall Cond  \\\n",
+            "0     0.000000      0.410959  0.142420      0.555556         0.500   \n",
+            "1     0.000000      0.202055  0.048246      0.444444         0.625   \n",
+            "2     0.000000      0.205479  0.060609      0.555556         0.625   \n",
+            "3     0.000000      0.246575  0.046087      0.666667         0.500   \n",
+            "4     0.235294      0.181507  0.058566      0.444444         0.500   \n",
+            "\n",
+            "   Year Built  Year Remod/Add  Mas Vnr Area  BsmtFin SF 1  BsmtFin SF 2  \\\n",
+            "0    0.637681        0.166667        0.0700      0.113218      0.000000   \n",
+            "1    0.644928        0.183333        0.0000      0.082920      0.094364   \n",
+            "2    0.623188        0.133333        0.0675      0.163536      0.000000   \n",
+            "3    0.695652        0.300000        0.0000      0.188696      0.000000   \n",
+            "4    0.905797        0.800000        0.0000      0.140149      0.000000   \n",
+            "\n",
+            "   Bsmt Unf SF  Total Bsmt SF  1st Flr SF  2nd Flr SF  Low Qual Fin SF  \\\n",
+            "0     0.188784       0.176759    0.277673    0.000000              0.0   \n",
+            "1     0.115582       0.144354    0.118042    0.000000              0.0   \n",
+            "2     0.173801       0.217512    0.208990    0.000000              0.0   \n",
+            "3     0.447346       0.345336    0.373031    0.000000              0.0   \n",
+            "4     0.058647       0.151882    0.124764    0.339467              0.0   \n",
+            "\n",
+            "   Gr Liv Area  Bsmt Full Bath  Bsmt Half Bath  Full Bath  Half Bath  \\\n",
+            "0     0.249058        0.333333             0.0       0.25        0.0   \n",
+            "1     0.105878        0.000000             0.0       0.25        0.0   \n",
+            "2     0.187453        0.000000             0.0       0.25        0.5   \n",
+            "3     0.334589        0.333333             0.0       0.50        0.5   \n",
+            "4     0.243971        0.000000             0.0       0.50        0.5   \n",
+            "\n",
+            "   Bedroom AbvGr  Kitchen AbvGr  TotRms AbvGrd  Fireplaces  Garage Yr Blt  \\\n",
+            "0          0.375       0.333333       0.384615        0.50       0.208333   \n",
+            "1          0.250       0.333333       0.230769        0.00       0.211538   \n",
+            "2          0.375       0.333333       0.307692        0.00       0.201923   \n",
+            "3          0.375       0.333333       0.461538        0.50       0.233974   \n",
+            "4          0.375       0.333333       0.307692        0.25       0.326923   \n",
+            "\n",
+            "   Garage Cars  Garage Area  Wood Deck SF  Open Porch SF  Enclosed Porch  \\\n",
+            "0          0.4     0.354839      0.147472       0.083558             0.0   \n",
+            "1          0.2     0.490591      0.098315       0.000000             0.0   \n",
+            "2          0.2     0.209677      0.275983       0.048518             0.0   \n",
+            "3          0.4     0.350806      0.000000       0.000000             0.0   \n",
+            "4          0.4     0.323925      0.148876       0.045822             0.0   \n",
+            "\n",
+            "   3Ssn Porch  Screen Porch  Pool Area  Misc Val   Mo Sold  Yr Sold  \\\n",
+            "0         0.0      0.000000        0.0  0.000000  0.363636      1.0   \n",
+            "1         0.0      0.208333        0.0  0.000000  0.454545      1.0   \n",
+            "2         0.0      0.000000        0.0  0.735294  0.454545      1.0   \n",
+            "3         0.0      0.000000        0.0  0.000000  0.272727      1.0   \n",
+            "4         0.0      0.000000        0.0  0.000000  0.181818      1.0   \n",
+            "\n",
+            "   SalePrice  MS Zoning_C (all)  MS Zoning_FV  MS Zoning_I (all)  \\\n",
+            "0   0.272444                0.0           0.0                0.0   \n",
+            "1   0.124238                0.0           0.0                0.0   \n",
+            "2   0.214509                0.0           0.0                0.0   \n",
+            "3   0.311517                0.0           0.0                0.0   \n",
+            "4   0.238626                0.0           0.0                0.0   \n",
+            "\n",
+            "   MS Zoning_RH  MS Zoning_RL  MS Zoning_RM  Street_Pave  Lot Shape_IR2  \\\n",
+            "0           0.0           1.0           0.0          1.0            0.0   \n",
+            "1           1.0           0.0           0.0          1.0            0.0   \n",
+            "2           0.0           1.0           0.0          1.0            0.0   \n",
+            "3           0.0           1.0           0.0          1.0            0.0   \n",
+            "4           0.0           1.0           0.0          1.0            0.0   \n",
+            "\n",
+            "   Lot Shape_IR3  Lot Shape_Reg  Land Contour_HLS  Land Contour_Low  \\\n",
+            "0            0.0            0.0               0.0               0.0   \n",
+            "1            0.0            1.0               0.0               0.0   \n",
+            "2            0.0            0.0               0.0               0.0   \n",
+            "3            0.0            1.0               0.0               0.0   \n",
+            "4            0.0            0.0               0.0               0.0   \n",
+            "\n",
+            "   Land Contour_Lvl  Utilities_NoSeWa  Utilities_NoSewr  Lot Config_CulDSac  \\\n",
+            "0               1.0               0.0               0.0                 0.0   \n",
+            "1               1.0               0.0               0.0                 0.0   \n",
+            "2               1.0               0.0               0.0                 0.0   \n",
+            "3               1.0               0.0               0.0                 0.0   \n",
+            "4               1.0               0.0               0.0                 0.0   \n",
+            "\n",
+            "   Lot Config_FR2  Lot Config_FR3  Lot Config_Inside  Land Slope_Mod  \\\n",
+            "0             0.0             0.0                0.0             0.0   \n",
+            "1             0.0             0.0                1.0             0.0   \n",
+            "2             0.0             0.0                0.0             0.0   \n",
+            "3             0.0             0.0                0.0             0.0   \n",
+            "4             0.0             0.0                1.0             0.0   \n",
+            "\n",
+            "   Land Slope_Sev  Neighborhood_Blueste  Neighborhood_BrDale  \\\n",
+            "0             0.0                   0.0                  0.0   \n",
+            "1             0.0                   0.0                  0.0   \n",
+            "2             0.0                   0.0                  0.0   \n",
+            "3             0.0                   0.0                  0.0   \n",
+            "4             0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Neighborhood_BrkSide  Neighborhood_ClearCr  Neighborhood_CollgCr  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_Crawfor  Neighborhood_Edwards  Neighborhood_Gilbert  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   1.0   \n",
+            "\n",
+            "   Neighborhood_Greens  Neighborhood_GrnHill  Neighborhood_IDOTRR  \\\n",
+            "0                  0.0                   0.0                  0.0   \n",
+            "1                  0.0                   0.0                  0.0   \n",
+            "2                  0.0                   0.0                  0.0   \n",
+            "3                  0.0                   0.0                  0.0   \n",
+            "4                  0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Neighborhood_Landmrk  Neighborhood_MeadowV  Neighborhood_Mitchel  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_NAmes  Neighborhood_NPkVill  Neighborhood_NWAmes  \\\n",
+            "0                 1.0                   0.0                  0.0   \n",
+            "1                 1.0                   0.0                  0.0   \n",
+            "2                 1.0                   0.0                  0.0   \n",
+            "3                 1.0                   0.0                  0.0   \n",
+            "4                 0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Neighborhood_NoRidge  Neighborhood_NridgHt  Neighborhood_OldTown  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_SWISU  Neighborhood_Sawyer  Neighborhood_SawyerW  \\\n",
+            "0                 0.0                  0.0                   0.0   \n",
+            "1                 0.0                  0.0                   0.0   \n",
+            "2                 0.0                  0.0                   0.0   \n",
+            "3                 0.0                  0.0                   0.0   \n",
+            "4                 0.0                  0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_Somerst  Neighborhood_StoneBr  Neighborhood_Timber  \\\n",
+            "0                   0.0                   0.0                  0.0   \n",
+            "1                   0.0                   0.0                  0.0   \n",
+            "2                   0.0                   0.0                  0.0   \n",
+            "3                   0.0                   0.0                  0.0   \n",
+            "4                   0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Neighborhood_Veenker  Condition 1_Feedr  Condition 1_Norm  \\\n",
+            "0                   0.0                0.0               1.0   \n",
+            "1                   0.0                1.0               0.0   \n",
+            "2                   0.0                0.0               1.0   \n",
+            "3                   0.0                0.0               1.0   \n",
+            "4                   0.0                0.0               1.0   \n",
+            "\n",
+            "   Condition 1_PosA  Condition 1_PosN  Condition 1_RRAe  Condition 1_RRAn  \\\n",
+            "0               0.0               0.0               0.0               0.0   \n",
+            "1               0.0               0.0               0.0               0.0   \n",
+            "2               0.0               0.0               0.0               0.0   \n",
+            "3               0.0               0.0               0.0               0.0   \n",
+            "4               0.0               0.0               0.0               0.0   \n",
+            "\n",
+            "   Condition 1_RRNe  Condition 1_RRNn  Condition 2_Feedr  Condition 2_Norm  \\\n",
+            "0               0.0               0.0                0.0               1.0   \n",
+            "1               0.0               0.0                0.0               1.0   \n",
+            "2               0.0               0.0                0.0               1.0   \n",
+            "3               0.0               0.0                0.0               1.0   \n",
+            "4               0.0               0.0                0.0               1.0   \n",
+            "\n",
+            "   Condition 2_PosA  Condition 2_PosN  Condition 2_RRAe  Condition 2_RRAn  \\\n",
+            "0               0.0               0.0               0.0               0.0   \n",
+            "1               0.0               0.0               0.0               0.0   \n",
+            "2               0.0               0.0               0.0               0.0   \n",
+            "3               0.0               0.0               0.0               0.0   \n",
+            "4               0.0               0.0               0.0               0.0   \n",
+            "\n",
+            "   Condition 2_RRNn  Bldg Type_2fmCon  Bldg Type_Duplex  Bldg Type_Twnhs  \\\n",
+            "0               0.0               0.0               0.0              0.0   \n",
+            "1               0.0               0.0               0.0              0.0   \n",
+            "2               0.0               0.0               0.0              0.0   \n",
+            "3               0.0               0.0               0.0              0.0   \n",
+            "4               0.0               0.0               0.0              0.0   \n",
+            "\n",
+            "   Bldg Type_TwnhsE  House Style_1.5Unf  House Style_1Story  \\\n",
+            "0               0.0                 0.0                 1.0   \n",
+            "1               0.0                 0.0                 1.0   \n",
+            "2               0.0                 0.0                 1.0   \n",
+            "3               0.0                 0.0                 1.0   \n",
+            "4               0.0                 0.0                 0.0   \n",
+            "\n",
+            "   House Style_2.5Fin  House Style_2.5Unf  House Style_2Story  \\\n",
+            "0                 0.0                 0.0                 0.0   \n",
+            "1                 0.0                 0.0                 0.0   \n",
+            "2                 0.0                 0.0                 0.0   \n",
+            "3                 0.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 1.0   \n",
+            "\n",
+            "   House Style_SFoyer  House Style_SLvl  Roof Style_Gable  Roof Style_Gambrel  \\\n",
+            "0                 0.0               0.0               0.0                 0.0   \n",
+            "1                 0.0               0.0               1.0                 0.0   \n",
+            "2                 0.0               0.0               0.0                 0.0   \n",
+            "3                 0.0               0.0               0.0                 0.0   \n",
+            "4                 0.0               0.0               1.0                 0.0   \n",
+            "\n",
+            "   Roof Style_Hip  Roof Style_Mansard  Roof Style_Shed  Roof Matl_CompShg  \\\n",
+            "0             1.0                 0.0              0.0                1.0   \n",
+            "1             0.0                 0.0              0.0                1.0   \n",
+            "2             1.0                 0.0              0.0                1.0   \n",
+            "3             1.0                 0.0              0.0                1.0   \n",
+            "4             0.0                 0.0              0.0                1.0   \n",
+            "\n",
+            "   Roof Matl_Membran  Roof Matl_Metal  Roof Matl_Roll  Roof Matl_Tar&Grv  \\\n",
+            "0                0.0              0.0             0.0                0.0   \n",
+            "1                0.0              0.0             0.0                0.0   \n",
+            "2                0.0              0.0             0.0                0.0   \n",
+            "3                0.0              0.0             0.0                0.0   \n",
+            "4                0.0              0.0             0.0                0.0   \n",
+            "\n",
+            "   Roof Matl_WdShake  Roof Matl_WdShngl  Exterior 1st_AsphShn  \\\n",
+            "0                0.0                0.0                   0.0   \n",
+            "1                0.0                0.0                   0.0   \n",
+            "2                0.0                0.0                   0.0   \n",
+            "3                0.0                0.0                   0.0   \n",
+            "4                0.0                0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_BrkComm  Exterior 1st_BrkFace  Exterior 1st_CBlock  \\\n",
+            "0                   0.0                   1.0                  0.0   \n",
+            "1                   0.0                   0.0                  0.0   \n",
+            "2                   0.0                   0.0                  0.0   \n",
+            "3                   0.0                   1.0                  0.0   \n",
+            "4                   0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Exterior 1st_CemntBd  Exterior 1st_HdBoard  Exterior 1st_ImStucc  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_MetalSd  Exterior 1st_Plywood  Exterior 1st_PreCast  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_Stone  Exterior 1st_Stucco  Exterior 1st_VinylSd  \\\n",
+            "0                 0.0                  0.0                   0.0   \n",
+            "1                 0.0                  0.0                   1.0   \n",
+            "2                 0.0                  0.0                   0.0   \n",
+            "3                 0.0                  0.0                   0.0   \n",
+            "4                 0.0                  0.0                   1.0   \n",
+            "\n",
+            "   Exterior 1st_Wd Sdng  Exterior 1st_WdShing  Exterior 2nd_AsphShn  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   1.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_Brk Cmn  Exterior 2nd_BrkFace  Exterior 2nd_CBlock  \\\n",
+            "0                   0.0                   0.0                  0.0   \n",
+            "1                   0.0                   0.0                  0.0   \n",
+            "2                   0.0                   0.0                  0.0   \n",
+            "3                   0.0                   1.0                  0.0   \n",
+            "4                   0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Exterior 2nd_CmentBd  Exterior 2nd_HdBoard  Exterior 2nd_ImStucc  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_MetalSd  Exterior 2nd_Other  Exterior 2nd_Plywood  \\\n",
+            "0                   0.0                 0.0                   1.0   \n",
+            "1                   0.0                 0.0                   0.0   \n",
+            "2                   0.0                 0.0                   0.0   \n",
+            "3                   0.0                 0.0                   0.0   \n",
+            "4                   0.0                 0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_PreCast  Exterior 2nd_Stone  Exterior 2nd_Stucco  \\\n",
+            "0                   0.0                 0.0                  0.0   \n",
+            "1                   0.0                 0.0                  0.0   \n",
+            "2                   0.0                 0.0                  0.0   \n",
+            "3                   0.0                 0.0                  0.0   \n",
+            "4                   0.0                 0.0                  0.0   \n",
+            "\n",
+            "   Exterior 2nd_VinylSd  Exterior 2nd_Wd Sdng  Exterior 2nd_Wd Shng  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   1.0                   0.0                   0.0   \n",
+            "2                   0.0                   1.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   1.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exter Qual_Fa  Exter Qual_Gd  Exter Qual_TA  Exter Cond_Fa  Exter Cond_Gd  \\\n",
+            "0            0.0            0.0            1.0            0.0            0.0   \n",
+            "1            0.0            0.0            1.0            0.0            0.0   \n",
+            "2            0.0            0.0            1.0            0.0            0.0   \n",
+            "3            0.0            1.0            0.0            0.0            0.0   \n",
+            "4            0.0            0.0            1.0            0.0            0.0   \n",
+            "\n",
+            "   Exter Cond_Po  Exter Cond_TA  Foundation_CBlock  Foundation_PConc  \\\n",
+            "0            0.0            1.0                1.0               0.0   \n",
+            "1            0.0            1.0                1.0               0.0   \n",
+            "2            0.0            1.0                1.0               0.0   \n",
+            "3            0.0            1.0                1.0               0.0   \n",
+            "4            0.0            1.0                0.0               1.0   \n",
+            "\n",
+            "   Foundation_Slab  Foundation_Stone  Foundation_Wood  Bsmt Qual_Fa  \\\n",
+            "0              0.0               0.0              0.0           0.0   \n",
+            "1              0.0               0.0              0.0           0.0   \n",
+            "2              0.0               0.0              0.0           0.0   \n",
+            "3              0.0               0.0              0.0           0.0   \n",
+            "4              0.0               0.0              0.0           0.0   \n",
+            "\n",
+            "   Bsmt Qual_Gd  Bsmt Qual_Po  Bsmt Qual_TA  Bsmt Cond_Fa  Bsmt Cond_Gd  \\\n",
+            "0           0.0           0.0           1.0           0.0           1.0   \n",
+            "1           0.0           0.0           1.0           0.0           0.0   \n",
+            "2           0.0           0.0           1.0           0.0           0.0   \n",
+            "3           0.0           0.0           1.0           0.0           0.0   \n",
+            "4           1.0           0.0           0.0           0.0           0.0   \n",
+            "\n",
+            "   Bsmt Cond_Po  Bsmt Cond_TA  Bsmt Exposure_Gd  Bsmt Exposure_Mn  \\\n",
+            "0           0.0           0.0               1.0               0.0   \n",
+            "1           0.0           1.0               0.0               0.0   \n",
+            "2           0.0           1.0               0.0               0.0   \n",
+            "3           0.0           1.0               0.0               0.0   \n",
+            "4           0.0           1.0               0.0               0.0   \n",
+            "\n",
+            "   Bsmt Exposure_No  BsmtFin Type 1_BLQ  BsmtFin Type 1_GLQ  \\\n",
+            "0               0.0                 1.0                 0.0   \n",
+            "1               1.0                 0.0                 0.0   \n",
+            "2               1.0                 0.0                 0.0   \n",
+            "3               1.0                 0.0                 0.0   \n",
+            "4               1.0                 0.0                 1.0   \n",
+            "\n",
+            "   BsmtFin Type 1_LwQ  BsmtFin Type 1_Rec  BsmtFin Type 1_Unf  \\\n",
+            "0                 0.0                 0.0                 0.0   \n",
+            "1                 0.0                 1.0                 0.0   \n",
+            "2                 0.0                 0.0                 0.0   \n",
+            "3                 0.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 0.0   \n",
+            "\n",
+            "   BsmtFin Type 2_BLQ  BsmtFin Type 2_GLQ  BsmtFin Type 2_LwQ  \\\n",
+            "0                 0.0                 0.0                 0.0   \n",
+            "1                 0.0                 0.0                 1.0   \n",
+            "2                 0.0                 0.0                 0.0   \n",
+            "3                 0.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 0.0   \n",
+            "\n",
+            "   BsmtFin Type 2_Rec  BsmtFin Type 2_Unf  Heating_GasA  Heating_GasW  \\\n",
+            "0                 0.0                 1.0           1.0           0.0   \n",
+            "1                 0.0                 0.0           1.0           0.0   \n",
+            "2                 0.0                 1.0           1.0           0.0   \n",
+            "3                 0.0                 1.0           1.0           0.0   \n",
+            "4                 0.0                 1.0           1.0           0.0   \n",
+            "\n",
+            "   Heating_Grav  Heating_OthW  Heating_Wall  Heating QC_Fa  Heating QC_Gd  \\\n",
+            "0           0.0           0.0           0.0            1.0            0.0   \n",
+            "1           0.0           0.0           0.0            0.0            0.0   \n",
+            "2           0.0           0.0           0.0            0.0            0.0   \n",
+            "3           0.0           0.0           0.0            0.0            0.0   \n",
+            "4           0.0           0.0           0.0            0.0            1.0   \n",
+            "\n",
+            "   Heating QC_Po  Heating QC_TA  Central Air_Y  Electrical_FuseF  \\\n",
+            "0            0.0            0.0            1.0               0.0   \n",
+            "1            0.0            1.0            1.0               0.0   \n",
+            "2            0.0            1.0            1.0               0.0   \n",
+            "3            0.0            0.0            1.0               0.0   \n",
+            "4            0.0            0.0            1.0               0.0   \n",
+            "\n",
+            "   Electrical_FuseP  Electrical_Mix  Electrical_SBrkr  Kitchen Qual_Fa  \\\n",
+            "0               0.0             0.0               1.0              0.0   \n",
+            "1               0.0             0.0               1.0              0.0   \n",
+            "2               0.0             0.0               1.0              0.0   \n",
+            "3               0.0             0.0               1.0              0.0   \n",
+            "4               0.0             0.0               1.0              0.0   \n",
+            "\n",
+            "   Kitchen Qual_Gd  Kitchen Qual_Po  Kitchen Qual_TA  Functional_Maj2  \\\n",
+            "0              0.0              0.0              1.0              0.0   \n",
+            "1              0.0              0.0              1.0              0.0   \n",
+            "2              1.0              0.0              0.0              0.0   \n",
+            "3              0.0              0.0              0.0              0.0   \n",
+            "4              0.0              0.0              1.0              0.0   \n",
+            "\n",
+            "   Functional_Min1  Functional_Min2  Functional_Mod  Functional_Sal  \\\n",
+            "0              0.0              0.0             0.0             0.0   \n",
+            "1              0.0              0.0             0.0             0.0   \n",
+            "2              0.0              0.0             0.0             0.0   \n",
+            "3              0.0              0.0             0.0             0.0   \n",
+            "4              0.0              0.0             0.0             0.0   \n",
+            "\n",
+            "   Functional_Sev  Functional_Typ  Fireplace Qu_Fa  Fireplace Qu_Gd  \\\n",
+            "0             0.0             1.0              0.0              1.0   \n",
+            "1             0.0             1.0              0.0              1.0   \n",
+            "2             0.0             1.0              0.0              1.0   \n",
+            "3             0.0             1.0              0.0              0.0   \n",
+            "4             0.0             1.0              0.0              0.0   \n",
+            "\n",
+            "   Fireplace Qu_Po  Fireplace Qu_TA  Garage Type_Attchd  Garage Type_Basment  \\\n",
+            "0              0.0              0.0                 1.0                  0.0   \n",
+            "1              0.0              0.0                 1.0                  0.0   \n",
+            "2              0.0              0.0                 1.0                  0.0   \n",
+            "3              0.0              1.0                 1.0                  0.0   \n",
+            "4              0.0              1.0                 1.0                  0.0   \n",
+            "\n",
+            "   Garage Type_BuiltIn  Garage Type_CarPort  Garage Type_Detchd  \\\n",
+            "0                  0.0                  0.0                 0.0   \n",
+            "1                  0.0                  0.0                 0.0   \n",
+            "2                  0.0                  0.0                 0.0   \n",
+            "3                  0.0                  0.0                 0.0   \n",
+            "4                  0.0                  0.0                 0.0   \n",
+            "\n",
+            "   Garage Finish_RFn  Garage Finish_Unf  Garage Qual_Fa  Garage Qual_Gd  \\\n",
+            "0                0.0                0.0             0.0             0.0   \n",
+            "1                0.0                1.0             0.0             0.0   \n",
+            "2                0.0                1.0             0.0             0.0   \n",
+            "3                0.0                0.0             0.0             0.0   \n",
+            "4                0.0                0.0             0.0             0.0   \n",
+            "\n",
+            "   Garage Qual_Po  Garage Qual_TA  Garage Cond_Fa  Garage Cond_Gd  \\\n",
+            "0             0.0             1.0             0.0             0.0   \n",
+            "1             0.0             1.0             0.0             0.0   \n",
+            "2             0.0             1.0             0.0             0.0   \n",
+            "3             0.0             1.0             0.0             0.0   \n",
+            "4             0.0             1.0             0.0             0.0   \n",
+            "\n",
+            "   Garage Cond_Po  Garage Cond_TA  Paved Drive_P  Paved Drive_Y  \\\n",
+            "0             0.0             1.0            1.0            0.0   \n",
+            "1             0.0             1.0            0.0            1.0   \n",
+            "2             0.0             1.0            0.0            1.0   \n",
+            "3             0.0             1.0            0.0            1.0   \n",
+            "4             0.0             1.0            0.0            1.0   \n",
+            "\n",
+            "   Sale Type_CWD  Sale Type_Con  Sale Type_ConLD  Sale Type_ConLI  \\\n",
+            "0            0.0            0.0              0.0              0.0   \n",
+            "1            0.0            0.0              0.0              0.0   \n",
+            "2            0.0            0.0              0.0              0.0   \n",
+            "3            0.0            0.0              0.0              0.0   \n",
+            "4            0.0            0.0              0.0              0.0   \n",
+            "\n",
+            "   Sale Type_ConLw  Sale Type_New  Sale Type_Oth  Sale Type_VWD  \\\n",
+            "0              0.0            0.0            0.0            0.0   \n",
+            "1              0.0            0.0            0.0            0.0   \n",
+            "2              0.0            0.0            0.0            0.0   \n",
+            "3              0.0            0.0            0.0            0.0   \n",
+            "4              0.0            0.0            0.0            0.0   \n",
+            "\n",
+            "   Sale Type_WD   Sale Condition_AdjLand  Sale Condition_Alloca  \\\n",
+            "0            1.0                     0.0                    0.0   \n",
+            "1            1.0                     0.0                    0.0   \n",
+            "2            1.0                     0.0                    0.0   \n",
+            "3            1.0                     0.0                    0.0   \n",
+            "4            1.0                     0.0                    0.0   \n",
+            "\n",
+            "   Sale Condition_Family  Sale Condition_Normal  Sale Condition_Partial  \n",
+            "0                    0.0                    1.0                     0.0  \n",
+            "1                    0.0                    1.0                     0.0  \n",
+            "2                    0.0                    1.0                     0.0  \n",
+            "3                    0.0                    1.0                     0.0  \n",
+            "4                    0.0                    1.0                     0.0  \n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Try different scaling techniques:\n",
+        "# - StandardScaler\n",
+        "# - MinMaxScaler\n",
+        "# - RobustScaler\n",
+        "# Decide based on the distribution of features.\n",
+        "\n",
+        "# print(df.head())\n",
+        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n",
+        "# df_ss = df.copy()\n",
+        "# df_mm = df.copy()\n",
+        "# df_rs = df.copy()\n",
+        "\n",
+        "# scaler = StandardScaler()\n",
+        "# df_ss[col_num] = scaler.fit_transform(df_ss[col_num])\n",
+        "# print(df_ss.head())\n",
+        "\n",
+        "# col_num = df1.select_dtypes(exclude='object').columns\n",
+        "scaler = MinMaxScaler()\n",
+        "df[col_num] = scaler.fit_transform(df[col_num])\n",
+        "print(df.head())\n",
+        "\n",
+        "# scaler = RobustScaler()\n",
+        "# df_rs[col_num] = scaler.fit_transform(df_rs[col_num])\n",
+        "# print(df_rs.head())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B8NozkS2kCDE"
+      },
+      "source": [
+        "## 🔹 Step 7: Feature Selection & Feature Creation 💡"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "irzYn2YHkFuu",
+        "outputId": "647e3753-3f28-4f7b-fbce-7a5ae5ccc5ac"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Index(['MS SubClass', 'Lot Frontage', 'Lot Area', 'Overall Qual',\n",
+            "       'Overall Cond', 'Year Built', 'Year Remod/Add', 'Mas Vnr Area',\n",
+            "       'BsmtFin SF 1', 'BsmtFin SF 2',\n",
+            "       ...\n",
+            "       'Sale Type_VWD', 'Sale Type_WD ', 'Sale Condition_AdjLand',\n",
+            "       'Sale Condition_Alloca', 'Sale Condition_Family',\n",
+            "       'Sale Condition_Normal', 'Sale Condition_Partial', 'HouseRenew',\n",
+            "       'Quality_x_Size', 'Log_LotArea'],\n",
+            "      dtype='object', length=250)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Create at least 2 NEW features.\n",
+        "# Examples:\n",
+        "# - Age of house: df[\"HouseAge\"] = df[\"YrSold\"] - df[\"YearBuilt\"]\n",
+        "# - Interaction: df[\"Quality_x_Size\"] = df[\"OverallQual\"] * df[\"GrLivArea\"]\n",
+        "# - Non-linear: df[\"Log_LotArea\"] = np.log1p(df[\"LotArea\"])\n",
+        "\n",
+        "df[\"HouseRenew\"] = df[\"Year Remod/Add\"] - df[\"Year Built\"]\n",
+        "df[\"Quality_x_Size\"] = df[\"Overall Qual\"] * df[\"Gr Liv Area\"]\n",
+        "df[\"Log_LotArea\"] = np.log1p(df[\"Lot Area\"])\n",
+        "\n",
+        "print(df.columns)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "neYQVXyLpYqe"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ohlGPfhykRMs"
+      },
+      "source": [
+        "## 🔹 Step 8: Outlier Handling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "l0Yx7uH5kP_H",
+        "outputId": "4bcb801e-ccbe-4b66-b371-80bbecade088"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(2051, 250)\n",
+            "(1846, 250)\n",
+            "(1084, 250)\n",
+            "(1084, 250)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Detect and handle outliers.\n",
+        "# Methods:\n",
+        "# - IQR rule\n",
+        "# - Z-score\n",
+        "# - Visualization (boxplots, scatterplots)\n",
+        "\n",
+        "z_scores = np.abs((df[col_num] - df[col_num].mean()) / df[col_num].std())\n",
+        "outliers = (z_scores > 3).any(axis=1)\n",
+        "print(df[~outliers].shape)\n",
+        "\n",
+        "Q1 = df[col_num].quantile(0.25)\n",
+        "Q3 = df[col_num].quantile(0.75)\n",
+        "IQR = Q3 - Q1\n",
+        "outliers = ((df[col_num] < (Q1 - 1.5 * IQR)) | (df[col_num] > (Q3 + 1.5 * IQR))).any(axis=1)\n",
+        "print(df[outliers].shape)\n",
+        "print(df[~outliers].shape)\n",
+        "\n",
+        "df = df[~outliers]\n",
+        "print(df.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nDirz02_kU1e"
+      },
+      "source": [
+        "## 🔹 Step 9: Skewness Handling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "Bz0Kke71kTxQ",
+        "outputId": "48c01b20-9e1e-47f9-cd82-7682cd591927"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MS SubClass\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.886349 -0.013707  1.504796e-31\n",
+            "1                 Log1p  0.637040 -0.551829  1.237427e-19\n",
+            "2                  Sqrt  0.054876 -1.504534  4.793558e-23\n",
+            "3       Box-Cox λ=0.075 -0.305718 -1.871246  9.786283e-39\n",
+            "4  Yeo–Johnson λ=-2.499  0.170848 -1.395905  5.544180e-21\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Lot Frontage\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original -0.164109  0.435822  1.203745e-03\n",
+            "1                Log1p -0.300100  0.491827  1.242747e-06\n",
+            "2                 Sqrt -0.736394  1.091410  1.104070e-33\n",
+            "3      Box-Cox λ=1.175  0.020738  0.384830  3.393478e-02\n",
+            "4  Yeo–Johnson λ=2.291  0.011579  0.423758  1.712140e-02\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Lot Area\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.193318  0.491558  1.458799e-04\n",
+            "1                 Log1p  0.148704  0.458555  1.175331e-03\n",
+            "2                  Sqrt -0.503415  0.730164  6.742263e-16\n",
+            "3       Box-Cox λ=0.874  0.025518  0.446418  1.046792e-02\n",
+            "4  Yeo–Johnson λ=-3.504 -0.005552  0.381210  3.745513e-02\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Overall Qual\n",
+            "             Transform  Skewness  Kurtosis      JB-p\n",
+            "0             Original -0.041368 -0.626607  0.000121\n",
+            "1                Log1p -0.225678 -0.518858  0.000023\n",
+            "2                 Sqrt -0.315054 -0.371808  0.000006\n",
+            "3      Box-Cox λ=0.981 -0.051116 -0.622330  0.000126\n",
+            "4  Yeo–Johnson λ=1.115 -0.020577 -0.631373  0.000118\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Overall Cond\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  1.315890  0.667405  5.008987e-73\n",
+            "1                 Log1p  1.234379  0.560111  1.401963e-63\n",
+            "2                  Sqrt  1.199503  0.539343  5.021128e-60\n",
+            "3      Box-Cox λ=-2.344 -0.149502  3.012413  1.320834e-90\n",
+            "4  Yeo–Johnson λ=-8.828 -0.118200  2.786273  2.044973e-77\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Year Built\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original -0.984449  0.007311  9.524967e-39\n",
+            "1                Log1p -1.179310  0.742862  1.061442e-60\n",
+            "2                 Sqrt -1.273533  1.229468  3.515338e-79\n",
+            "3      Box-Cox λ=2.968 -0.448363 -1.353507  1.398393e-26\n",
+            "4  Yeo–Johnson λ=6.632 -0.387527 -1.418956  2.295898e-26\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Year Remod/Add\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original -0.883418 -0.752479  6.751235e-37\n",
+            "1                Log1p -1.048502 -0.383627  2.675912e-45\n",
+            "2                 Sqrt -1.444760  0.931803  3.941073e-91\n",
+            "3      Box-Cox λ=0.558 -1.337485  0.555913  6.158434e-74\n",
+            "4  Yeo–Johnson λ=3.858 -0.499636 -1.331199  6.700992e-28\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Mas Vnr Area\n",
+            "               Transform  Skewness  Kurtosis          JB-p\n",
+            "0               Original  1.246372  0.368818  5.276675e-63\n",
+            "1                  Log1p  1.177910  0.125587  2.588339e-55\n",
+            "2                   Sqrt  0.688912 -1.175103  6.879950e-33\n",
+            "3       Box-Cox λ=-0.084  0.315624 -1.888920  1.251314e-39\n",
+            "4  Yeo–Johnson λ=-16.227  0.608547 -1.439779  1.381142e-35\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "BsmtFin SF 1\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.427973 -0.924887  2.658369e-16\n",
+            "1                 Log1p  0.345378 -1.087329  5.304488e-17\n",
+            "2                  Sqrt -0.173369 -1.576481  2.789634e-26\n",
+            "3       Box-Cox λ=0.159 -0.557835 -1.535081  4.788046e-36\n",
+            "4  Yeo–Johnson λ=-3.521  0.110621 -1.448212  8.908563e-22\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "BsmtFin SF 2\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "Bsmt Unf SF\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.625519 -0.508066  1.312548e-18\n",
+            "1                 Log1p  0.404943 -0.766226  6.437478e-13\n",
+            "2                  Sqrt -0.194340 -0.312149  3.653141e-03\n",
+            "3       Box-Cox λ=0.469 -0.286105 -0.144394  3.838770e-04\n",
+            "4  Yeo–Johnson λ=-1.560  0.068859 -0.939326  1.446187e-09\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Total Bsmt SF\n",
+            "              Transform  Skewness  Kurtosis      JB-p\n",
+            "0              Original  0.235075 -0.264327  0.001402\n",
+            "1                 Log1p  0.125004 -0.241257  0.065479\n",
+            "2                  Sqrt -0.228389  0.245399  0.002307\n",
+            "3       Box-Cox λ=0.730  0.002251 -0.096941  0.808409\n",
+            "4  Yeo–Johnson λ=-1.070  0.002995 -0.170016  0.520173\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "1st Flr SF\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.506196 -0.539185  1.248151e-13\n",
+            "1                 Log1p  0.413799 -0.658863  1.058932e-11\n",
+            "2                  Sqrt  0.167969 -0.732494  4.273412e-07\n",
+            "3       Box-Cox λ=0.252 -0.019610 -0.642286  8.687160e-05\n",
+            "4  Yeo–Johnson λ=-3.964  0.060979 -0.843186  7.605624e-08\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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NG9tq1Khhu3LliuMxXbt2tTVq1Mj83TZs2GDu79Onj9/bf/XqVdudd95pK1++vNm/zp85+4yTVt7/atKkSbaiRYvaVqxYYfv+++/NrGY6E+fly5d9vv+RfQsXLjSzPyYkJJhZ84YMGWL+jvbZTx966CHbqFGjHNt/++23trx589peffVVM8uYzk6ms4/pcdmXn2VPt9uXxxBPt92Xx++ctl3/5vbjR9myZc0+1t+1D8/uc2anf/VHux977DEz6+ratWtdPueXLl0y9+t3mfHjx/+/9u4txKbvDwD4cmnCuM00cr8kdwnlRS4hiiLNk5BrzQslDHmQ+888eHGLeMGbXJLk+uD6RCGXFOUWD54URdTg/FurZnJcxsx/zn9m//4+n9pz2nPW2es7u91Ze33X7LVSvPGait+N/fv3z02cOLHZz3llZWWKO8YVr6+pU6fmysrK0sqIWT7nNasTxrjWrVv3U52FOucAUAgSVxl3+PDhtGTyr7Ya8YYi7l+9ejXtx9fffSaWrVl2fNSoUakjXVxcnJZyPnDgQLqJae74o9hBX7ZsWa6kpCR1HsrLy/OSXdHLly9zM2bMyLVt2zbdJMabx+rq6lyhxWXdfxX/9/HG/fi3fu/gwYMptrgU9o/u3LmTkovxRr1Nmza5oUOH5qqqqvI6dc0Vf+woxIRg7IjFDl1cSruioiKv4xO9ffs2dTLjNdSxY8fckiVL8pIxzRV/zfVU12eyfP6juBz5hg0bcl27dk2dlZj4ffLkSbOcfxpm7969qSNYVFSUlrC/efNm3tLy8Xr43vHjx3ODBg1K5YcPH547d+5c7XtNeS0XMu6m/g4pZOxN/f3dkNh/dz3EcvU9Zn3b16aO+3fXec334qtXr1LCpLS0NH0nxgGxtWvXpgRjQxU69jlz5qTkUDxez549035M+mT9nEeXLl1Kv/+xfSn0OQeAxmoRfxTu/7cAAAAAoDDMcQUAAABAJklcAQAAAJBJElcAAAAAZJLEFQAAAACZJHEFAAAAQCZJXAEAAACQSRJXAAAAAGSSxBUAAAAAmSRxBf8C/fr1C7t27WruMAD4l9BuAAD/LySuoA43btwIs2bNCj169AgtWrQIp0+fbtDnJ02aFFauXFmvcvH4P25fvnxpRPQhvHjxIsybNy/F36ZNm9CrV68we/bs8Pjx49oyv6p3/PjxjaoX4G+l3QAAKCyJK6jDx48fw8iRI8O+ffv+53VVVFSEN2/e5G2tW7eu12erq6t/+btp06aF9+/fh1OnToUnT56EY8eOhREjRoR3797llT18+HBevWfOnCnY3wXwN9FuAAAUVv3ubuAvNWPGjLTVZf/+/WHnzp3h9evXoVOnTmHChAnh5MmTYfHixeH69etp2717d+1Idnx841fatWsXunXrVq+44uh2rPfChQvh8uXLYe3atWHz5s15ZR49ehSePXuW3u/bt2/6XXwdN27cT8fr3LlzvesG4Pe0GwAAheU/rqARbt++HVasWBG2bt2aRqYvXrwYJk6cmN6LnY6xY8fmjYj37t27YHXHDkd5eXl4+PBhWLp06U/vd+nSJbRs2TJ1hr5+/VqwegH472k3AAAaRuIKGuHVq1ehuLg4zJw5M41Kjx49OnVIojiKXlRUVDsiHrdWrVr99lhxJLx9+/a1W2VlZZ11xzlIlixZEvr37x/69Onz0/s9e/YMe/bsCRs3bgwlJSVhypQpYdu2beH58+c/lZ07d25e3Q2dkwWA+tFuAAA0jEcFoRHiXCCx4xE7AdOnT09bHM2OnY6Gmj9/fli/fn3eYxh1GTNmzB+PuXz58rBw4cJw7dq1cPPmzXDixIlQVVWV5iKJsdeIj6xMnTq1dr979+4Njh+AP9NuAAA0jP+4gkbo0KFDuHv3bjh69Gi6aY+j1HFS3h8nsa2PONI+YMCA2q2srKzO8nHEvr4xxhWutm/fHu7fv5/mUvnnn3/yysRR/e/rru+xAWgY7QYAQMNIXEEjxRWc4qjzjh07woMHD8LLly/DlStX0nvxkY8szRMSJ+cdMmRIWvUKgOah3QAAqD+PCkIdPnz4EJ4+fVq7H1d3unfvXigtLU3zg5w9ezbN/REn1o3zgZw/fz58+/YtDB48OJWPK0HdunUrdUriHCDxc3Hi26YQ49y0aVNYsGBBGDZsWOoMxZWqDh06FNatW9ckMQD8bbQbAACFJXEFf1j9afLkybX7q1evTq+LFi0KR44cSfOJnDp1Kq3U9Pnz5zBw4MD0+Mfw4cNTuTVr1qSysQPw6dOnOpc1L7RevXqlurZs2ZI6QHHUvGZ/1apVTRIDwN9GuwEAUFgtcrlcrsDHBAAAAIBGM8cVAAAAAJkkcQUAAABAJklcAQAAAJBJElcAAAAAZJLEFQAAAACZJHEFAAAAQCZJXAEAAACQSRJXAAAAAGSSxBUAAAAAmSRxBQAAAEAmSVwBAAAAkEkSVwAAAACELPoPgPxVSCOxqK8AAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "2nd Flr SF\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.731936 -1.044630  1.904799e-32\n",
+            "1                 Log1p  0.646686 -1.280826  3.187818e-33\n",
+            "2                  Sqrt  0.492646 -1.637604  1.501389e-36\n",
+            "3      Box-Cox λ=-0.093  0.395762 -1.842275  3.696521e-40\n",
+            "4  Yeo–Johnson λ=-4.331  0.463803 -1.710962  7.071556e-38\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Low Qual Fin SF\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "Gr Liv Area\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.442162 -0.045347  2.040961e-08\n",
+            "1                 Log1p  0.293152 -0.257306  9.532551e-05\n",
+            "2                  Sqrt  0.018849 -0.422838  1.708059e-02\n",
+            "3       Box-Cox λ=0.446 -0.026264 -0.436018  1.283401e-02\n",
+            "4  Yeo–Johnson λ=-1.978  0.018648 -0.505353  3.031253e-03\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Bsmt Full Bath\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original   0.34834 -1.878659  4.210428e-40\n",
+            "1                 Log1p   0.34834 -1.878659  4.210428e-40\n",
+            "2                  Sqrt   0.34834 -1.878659  4.210428e-40\n",
+            "3      Box-Cox λ=-0.082   0.34834 -1.878659  4.210428e-40\n",
+            "4  Yeo–Johnson λ=-3.644   0.34834 -1.878659  4.210428e-40\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Bsmt Half Bath\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "Full Bath\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original -0.289148 -1.433131  3.767711e-24\n",
+            "1                Log1p -0.339970 -1.578263  1.084539e-29\n",
+            "2                 Sqrt -0.361590 -1.633640  4.962668e-32\n",
+            "3      Box-Cox λ=1.266 -0.229861 -1.241924  6.307108e-18\n",
+            "4  Yeo–Johnson λ=3.010 -0.106955 -0.786353  3.065813e-07\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Half Bath\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.453704 -1.731194  3.388560e-38\n",
+            "1                 Log1p  0.442944 -1.780418  1.633736e-39\n",
+            "2                  Sqrt  0.436822 -1.803689  4.040524e-40\n",
+            "3      Box-Cox λ=-0.100  0.434249 -1.811418  2.632328e-40\n",
+            "4  Yeo–Johnson λ=-3.228  0.434922 -1.809636  2.888586e-40\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Bedroom AbvGr\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original -0.574971  0.765110  1.945571e-19\n",
+            "1                Log1p -0.799725  1.065963  5.818817e-37\n",
+            "2                 Sqrt -1.085243  1.756455  3.442481e-77\n",
+            "3      Box-Cox λ=1.637  0.006869  0.441735  1.214362e-02\n",
+            "4  Yeo–Johnson λ=3.698  0.029656  0.526282  1.774194e-03\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Kitchen AbvGr\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "TotRms AbvGrd\n",
+            "             Transform  Skewness  Kurtosis      JB-p\n",
+            "0             Original  0.182316  0.097557  0.040053\n",
+            "1                Log1p -0.039127  0.050774  0.821591\n",
+            "2                 Sqrt -0.329619  0.413221  0.000001\n",
+            "3      Box-Cox λ=0.819  0.007378  0.102827  0.783723\n",
+            "4  Yeo–Johnson λ=0.178  0.000203  0.047712  0.949886\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Fireplaces\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.435838 -0.802447  1.707575e-14\n",
+            "1                 Log1p  0.290574 -1.257020  1.549745e-19\n",
+            "2                  Sqrt  0.064598 -1.854071  1.321709e-34\n",
+            "3       Box-Cox λ=0.002 -0.009785 -1.998082  6.921493e-40\n",
+            "4  Yeo–Johnson λ=-1.883  0.127857 -1.704695  7.199572e-30\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Garage Yr Blt\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original -1.010552  0.120907  6.209873e-41\n",
+            "1                 Log1p -1.110561  0.473144  2.622238e-51\n",
+            "2                  Sqrt -1.350694  1.541369  1.336201e-95\n",
+            "3       Box-Cox λ=2.735 -0.449301 -1.291138  5.374414e-25\n",
+            "4  Yeo–Johnson λ=11.890 -0.352191 -1.381607  2.584039e-24\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Garage Cars\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original -0.512396  0.651562  3.436412e-15\n",
+            "1                Log1p -0.879854  1.231952  5.546512e-46\n",
+            "2                 Sqrt -2.151016  6.364916  0.000000e+00\n",
+            "3      Box-Cox λ=0.791 -0.999547  1.774545  8.305793e-71\n",
+            "4  Yeo–Johnson λ=2.516  0.020714  0.347018  6.340089e-02\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Garage Area\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original -0.163091  0.566811  6.389845e-05\n",
+            "1                Log1p -0.534796  1.027138  2.705351e-22\n",
+            "2                 Sqrt -1.978065  6.143988  0.000000e+00\n",
+            "3      Box-Cox λ=0.750 -0.832406  1.946648  4.472323e-65\n",
+            "4  Yeo–Johnson λ=1.528  0.022521  0.455883  8.744728e-03\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Wood Deck SF\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.688713 -0.438455  3.207166e-21\n",
+            "1                 Log1p  0.592142 -0.708681  2.081936e-19\n",
+            "2                  Sqrt  0.084302 -1.672121  1.989009e-28\n",
+            "3       Box-Cox λ=0.044 -0.167532 -1.950416  3.879424e-39\n",
+            "4  Yeo–Johnson λ=-6.104  0.203248 -1.557520  3.862620e-26\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Open Porch SF\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.949868  0.097338  3.241214e-36\n",
+            "1                 Log1p  0.854338 -0.144106  1.451070e-29\n",
+            "2                  Sqrt  0.020874 -1.341071  2.207069e-18\n",
+            "3       Box-Cox λ=0.137 -0.487453 -1.644769  1.397957e-36\n",
+            "4  Yeo–Johnson λ=-8.705  0.213113 -1.284912  1.060597e-18\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Enclosed Porch\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "3Ssn Porch\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "Screen Porch\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "Pool Area\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "Misc Val\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "Mo Sold\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original  0.204072 -0.412265  5.003022e-04\n",
+            "1                Log1p -0.185784 -0.394476  1.317363e-03\n",
+            "2                 Sqrt -1.001648  1.420004  7.289818e-60\n",
+            "3      Box-Cox λ=0.575 -0.718990  0.674496  1.809389e-25\n",
+            "4  Yeo–Johnson λ=0.445 -0.011057 -0.442576  1.186075e-02\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Yr Sold\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original  0.136403 -1.141585  3.078675e-14\n",
+            "1                Log1p -0.141117 -1.191619  1.959227e-15\n",
+            "2                 Sqrt -0.674857 -0.816704  3.899153e-25\n",
+            "3      Box-Cox λ=0.290 -1.078679 -0.413921  4.700840e-48\n",
+            "4  Yeo–Johnson λ=0.385 -0.035400 -1.190230  1.139261e-14\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "SalePrice\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.541625 -0.010833  3.090809e-12\n",
+            "1                 Log1p  0.391721 -0.171965  4.898900e-07\n",
+            "2                  Sqrt  0.106102 -0.147317  2.215622e-01\n",
+            "3       Box-Cox λ=0.391  0.000090 -0.073750  8.844107e-01\n",
+            "4  Yeo–Johnson λ=-2.518  0.016774 -0.288097  1.495931e-01\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Check skewness of numerical features.\n",
+        "# Apply log, sqrt, Box-Cox, or Yeo-Johnson depending on distribution.\n",
+        "\n",
+        "from scipy.stats import skew, kurtosis, jarque_bera, boxcox\n",
+        "from sklearn.preprocessing import PowerTransformer\n",
+        "import warnings\n",
+        "\n",
+        "# Ignore warnings that might arise from transformations on certain data types\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "\n",
+        "# --- Metrics helper ---\n",
+        "def metrics(vec):\n",
+        "    # Convert to pandas Series to use .nunique()\n",
+        "    vec_series = pd.Series(vec)\n",
+        "    # Check if the vector is constant\n",
+        "    if vec_series.nunique() <= 1:\n",
+        "        return np.nan, np.nan, np.nan # Return NaN for metrics if data is constant\n",
+        "\n",
+        "    sk = skew(vec, nan_policy='omit')\n",
+        "    ku = kurtosis(vec, fisher=True, nan_policy='omit')  # 0 = normal\n",
+        "    jb_stat, jb_p = jarque_bera(vec)\n",
+        "    return sk, ku, jb_p\n",
+        "\n",
+        "\n",
+        "# numerical_cols = df.select_dtypes(include=np.number).columns\n",
+        "for col in df[col_num]:\n",
+        "  print(col)\n",
+        "  x = df[col].astype(float)\n",
+        "\n",
+        "  # Check if the column is constant before attempting transformations\n",
+        "  if x.nunique() <= 1:\n",
+        "      print(\"  Column is constant, skipping transformations.\")\n",
+        "      rows = [(\"Original\", *metrics(x))]\n",
+        "  else:\n",
+        "      # --- Transformations ---\n",
+        "      x_log   = np.log1p(x)                        # log(1+x)\n",
+        "      x_sqrt  = np.sqrt(x)                         # sqrt\n",
+        "      # Box-Cox needs >0. Add a small constant to handle zero values.\n",
+        "      # Also check if the transformed data is constant after transformation.\n",
+        "      try:\n",
+        "        x_bc, lam_bc = boxcox(x + 1e-6)\n",
+        "        if np.all(x_bc == x_bc[0]):\n",
+        "             x_bc = np.full_like(x_bc, np.nan) # Fill with NaN if transformed data is constant\n",
+        "             lam_bc = np.nan\n",
+        "      except ValueError:\n",
+        "          x_bc = np.full_like(x, np.nan)\n",
+        "          lam_bc = np.nan\n",
+        "\n",
+        "\n",
+        "      pt = PowerTransformer(method=\"yeo-johnson\", standardize=False)\n",
+        "      # Reshape for PowerTransformer and then flatten back\n",
+        "      x_yj = pt.fit_transform(x.values.reshape(-1,1)).ravel()\n",
+        "      # Check if the transformed data is constant after transformation.\n",
+        "      if np.all(x_yj == x_yj[0]):\n",
+        "          x_yj = np.full_like(x_yj, np.nan) # Fill with NaN if transformed data is constant\n",
+        "\n",
+        "\n",
+        "      # --- Report ---\n",
+        "      rows = [\n",
+        "          (\"Original\", *metrics(x)),\n",
+        "          (\"Log1p\", *metrics(x_log)),\n",
+        "          (\"Sqrt\", *metrics(x_sqrt)),\n",
+        "          (f\"Box-Cox λ={lam_bc:.3f}\" if not np.isnan(lam_bc) else \"Box-Cox\", *metrics(x_bc)),\n",
+        "          (f\"Yeo–Johnson λ={pt.lambdas_[0]:.3f}\", *metrics(x_yj))\n",
+        "      ]\n",
+        "\n",
+        "  report = pd.DataFrame(rows, columns=[\"Transform\",\"Skewness\",\"Kurtosis\",\"JB-p\"])\n",
+        "  print(report)\n",
+        "\n",
+        "  # --- Plots (2×3 grid) ---\n",
+        "  if x.nunique() > 1: # Only plot if the original data is not constant\n",
+        "      fig, axes = plt.subplots(2, 3, figsize=(12, 7))\n",
+        "      titles = [\"Original\",\"Log1p\",\"Sqrt\",f\"Box-Cox λ={lam_bc:.3f}\" if not np.isnan(lam_bc) else \"Box-Cox\",f\"Yeo–Johnson λ={pt.lambdas_[0]:.3f}\"]\n",
+        "      data = [x, x_log, x_sqrt, x_bc, x_yj]\n",
+        "\n",
+        "      for ax, d, title in zip(axes.ravel(), data, titles):\n",
+        "          # Only plot if the data is not constant (check if not all NaNs)\n",
+        "          if not np.all(np.isnan(d)):\n",
+        "              ax.hist(d, bins=40, density=True, color=\"steelblue\", alpha=0.7)\n",
+        "              ax.set_title(title)\n",
+        "              ax.set_xlabel(col); ax.set_ylabel(\"Density\")\n",
+        "          else:\n",
+        "              ax.set_title(f\"{title}\\n(Constant data)\")\n",
+        "              ax.set_xlabel(col); ax.set_ylabel(\"Density\")\n",
+        "\n",
+        "\n",
+        "      fig.delaxes(axes[1,2])  # remove empty subplot\n",
+        "      fig.tight_layout()\n",
+        "      plt.show()\n",
+        "  print('No need to skewness')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "voU8eavLkXya"
+      },
+      "source": [
+        "## 🔹 Step 10: Remove Duplicates"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pEyssUgmkZgq",
+        "outputId": "f4bebf7a-65dc-47e2-fc29-a845059d4ef6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Duplicate rows: 0\n",
+            "Rows after dropping duplicates: 1084\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Check and remove duplicate rows if there is.\n",
+        "\n",
+        "# count duplicates\n",
+        "dup_count = df.duplicated().sum()\n",
+        "print(f\"Duplicate rows: {dup_count}\")\n",
+        "\n",
+        "# (optional) inspect some duplicate rows\n",
+        "if dup_count:\n",
+        "    display(df[df.duplicated(keep=False)].head())\n",
+        "\n",
+        "# drop duplicates (keep first occurrence) and reset index\n",
+        "df = df.drop_duplicates(keep='first').reset_index(drop=True)\n",
+        "print(f\"Rows after dropping duplicates: {len(df)}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AlW4RbaTkeSu"
+      },
+      "source": [
+        "## 💾 Step 11: Save Cleaned Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "naZHc5dnkamR",
+        "outputId": "85de9021-991a-4c3c-ae57-b0b24e772aeb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅ Cleaned dataset saved successfully!\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Save your final cleaned and engineered dataset to CSV.\n",
+        "df.to_csv(\"AmesHousing_engineered.csv\", index=False)\n",
+        "print(\"✅ Cleaned dataset saved successfully!\")\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/Python Fundamentals_ SoroorParsafar.py b/a0.1/Python Fundamentals_ SoroorParsafar.py
new file mode 100644
index 0000000..822071c
--- /dev/null
+++ b/a0.1/Python Fundamentals_ SoroorParsafar.py	
@@ -0,0 +1,229 @@
+# -*- coding: utf-8 -*-
+"""Copy of Assignment 01. Python | Nexus | RezaShokrzad.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1fkMJLhjA6cqvmZv5bAwCpr92ALLC6ACY
+
+# 📚 Assignment 1 — Python Fundamentals
+
+<p align="center">📢⚠️📂  </p>
+
+<p align="center"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>
+
+<p align="center"> 🚨📝🧠 </p>
+
+------------------------------------------------
+Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:
+
+1. Variable types
+
+2. Core containers
+
+3. Functions
+
+4. Classes
+
+Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊
+
+## 1. Variable Types 🧮
+**Quick-start notes**
+
+* Primitive types: `int`, `float`, `str`, `bool`
+
+* Use `type(obj)` to inspect an object’s type.
+
+* Casting ↔ converting: `int("3")`, `str(3.14)`, `bool(0)`, etc.
+
+### Task 1 — Celsius → Fahrenheit
+"""
+
+# 👉 a Celsius temperature (as text), convert it to float,
+#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.
+# TODO:
+
+celsius_text = "26.0"
+celsius_float = float(celsius_text)
+fahrenheit = celsius_float * 9/5 + 32
+print(f"The tempreture is {celsius_float}°C, which is {fahrenheit}°F.")
+
+"""### Task 2 — Tiny Calculator
+
+"""
+
+# 👉 Store two numbers of **different types** (one int, one float),
+#    then print their sum, difference, product, true division, and floor division.
+# TODO:
+int_num = 10
+float_num = 3.5
+print(f"sum {int_num + float_num}")
+print(f"difference {int_num - float_num}")
+print(f"product {int_num * float_num}")
+print(f"true division {int_num / float_num}")
+print(f"floor division {int_num // float_num}")
+
+"""## 2. Containers 📦 (list, tuple, set, dict)
+**Quick-start notes**
+
+| Container | Mutable? | Ordered?                      | Typical use                       |
+| --------- | -------- | ----------------------------- | --------------------------------- |
+| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |
+| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |
+| `set`     | ✔        | ✖                             | Deduplication, membership tests   |
+| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |
+
+### Task 1 — Grocery Basket
+"""
+
+# Start with an empty shopping list (list).
+# 1. Append at least 4 items supplied in one line of user input (comma-separated).
+# 2. Convert the list to a *tuple* called immutable_basket.
+# 3. Print the third item using tuple indexing.
+# TODO:
+shopping_list = []
+user_input = input("Enter at least 4 items, separated by commas: ")
+items = [item.strip() for item in user_input.split(',')]
+shopping_list.extend(items)
+print(f"Your shopping list: {shopping_list}")
+immutable_basket = tuple(shopping_list)
+print(f"The immutable tuple: {immutable_basket}")
+if len(immutable_basket) >= 3:
+    print(f"The third item is: {immutable_basket[2]}")
+else:
+    print("Not enough items in the basket to get the third item.")
+
+"""### Task 2 — Word Stats"""
+
+sample = "to be or not to be that is the question"
+
+# 1. Build a set `unique_words` containing every distinct word.
+# 2. Build a dict `word_counts` mapping each word to the number of times it appears.
+#    (Hint: .split() + a simple loop)
+# 3. Print the two structures and explain (in a comment) their main difference.
+# TODO:
+unique_words = "to be or not to be that is the question."
+
+word_counts = text.lower().replace('.', '').split()
+
+unique_words = set(words)
+
+word_counts = {}
+
+for word in words:
+    word_counts[word] = word_counts.get(word, 0) + 1
+
+print("Unique words (set):")
+print(unique_words)
+print("\nWord counts (dictionary):")
+print(word_counts)
+
+# A set stores only unique, unordered elements and is highly optimized for checking if
+# an item is present. A dictionary stores key-value pairs, which is perfect for
+# mapping each unique word to its corresponding count.
+
+"""## 3. Functions 🔧
+**Quick-start notes**
+
+* Define with `def`, return with `return`.
+
+* Parameters can have default values.
+
+* Docstrings (`''' … '''`) document behaviour.
+
+### Task 1 — Prime Tester
+"""
+
+def is_prime(n: int) -> bool:
+    """
+    Return True if n is a prime number, else False.
+    0 and 1 are *not* prime.
+    """
+    # TODO: replace pass with your implementation
+    if n <= 1:
+        return False
+    for i in range(2, int(n**0.5) + 1):
+        if n % i == 0:
+            return False
+    return True
+
+
+# Quick self-check
+print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]
+
+"""### Task 2 — Repeater Greeter"""
+
+def greet(name: str, times: int = 1) -> None:
+    """Print `name`, capitalised, exactly `times` times on one line."""
+    # TODO:
+    capitalized_name = name.capitalize()
+    print(' '.join([capitalized_name] * times))
+
+greet("alice")          # Alice
+greet("bob", times=3)   # Bob Bob Bob
+
+"""## 4. Classes 🏗️
+**Quick-start notes**
+
+* Create with class Name:
+
+* Special method __init__ runs on construction.
+
+* self refers to the instance; attributes live on self.
+
+### Task 1 — Simple Counter
+"""
+
+class Counter:
+    """Counts how many times `increment` is called."""
+    # TODO:
+    # 1. In __init__, store an internal count variable starting at 0.
+    # 2. Method increment(step: int = 1) adds `step` to the count.
+    # 3. Method value() returns the current count.
+
+    def __init__(self):
+        self.count = 0
+
+    def increment(self, step: int = 1) -> None:
+        self.count += step
+
+    def value(self) -> int:
+        return self.count
+
+
+c = Counter()
+for _ in range(5):
+    c.increment()
+print(c.value())   # Expected: 5
+
+"""### Task 2 — 2-D Point with Distance"""
+
+import math
+
+class Point:
+    """
+    A 2-D point supporting distance calculation.
+    Usage:
+        p = Point(3, 4)
+        q = Point(0, 0)
+        print(p.distance_to(q))  # 5.0
+    """
+    # TODO:
+    # 1. Store x and y as attributes.
+    # 2. Implement distance_to(other) using the Euclidean formula.
+
+    def __init__(self, x: float, y: float) -> None:
+        self.x = x
+        self.y = y
+
+    def distance_to(self, other: 'Point') -> float:
+        """Calculates the Euclidean distance to another Point."""
+        dx = self.x - other.x
+        dy = self.y - other.y
+        return math.sqrt(dx**2 + dy**2)
+
+
+# Smoke test
+p, q = Point(3, 4), Point(0, 0)
+assert round(p.distance_to(q), 1) == 5.0
+print(p.distance_to(q))
\ No newline at end of file
diff --git a/a0.1/README.md b/a0.1/README.md
new file mode 100644
index 0000000..c9256aa
--- /dev/null
+++ b/a0.1/README.md
@@ -0,0 +1,86 @@
+# Nexus_Assignments
+This repo is provided to be a host of Nexus course assignments.
+
+# Homework Submission Instructions
+
+This document outlines the steps to clone, complete, and submit homework assignments using the provided `utils.py` script.
+
+## Prerequisites
+
+- Ensure you have Python installed on your system.
+- Install Git on your machine.
+- Set up SSH keys for secure Git operations.
+
+## Setup Instructions
+
+1. **Configure SSH Keys**:
+
+   - Generate an SSH key pair if you don't already have one:
+
+     ```bash
+     ssh-keygen -t ed25519 -C "your_email@example.com"
+     ```
+
+   - Add the public key to your Git hosting service (e.g., GitHub, GitLab).
+
+   - Start the SSH agent and add your private key:
+
+     ```bash
+     eval "$(ssh-agent -s)"
+     ssh-add ~/.ssh/id_ed25519
+     ```
+
+## Cloning and Submitting Homework
+
+Follow these steps for each homework assignment (e.g., Homework 0.3):
+
+1. **Update Local Repository**:
+
+   - Before starting, ensure your local repository is up-to-date:
+
+     ```bash
+     git pull
+     ```
+
+2. **Clone the Homework**:
+
+   - Use the `utils.py` script to clone the homework assignment:
+
+     ```bash
+     python utils.py <homework_number>
+     ```
+
+     Replace `<homework_number>` with the specific homework number (e.g., `0.3`).
+
+3. **Complete the Homework**:
+
+   - Navigate to the cloned homework directory.
+   - Complete the required tasks or assignments as per the instructions.
+
+4. **Commit and Push Changes**:
+
+   - Stage your changes:
+
+     ```bash
+     git add .
+     ```
+
+   - Commit your changes with a descriptive message:
+
+     ```bash
+     git commit -m "Completed Homework <homework_number>"
+     ```
+
+   - Push your changes to the remote repository:
+
+     ```bash
+     git push origin main
+     ```
+
+## Important Notes
+
+- Always run `git pull` before starting work to avoid conflicts and ensure you have the latest updates.
+- Follow the clone*-complete-push procedure for every homework assignment.
+- *: here clone means using utils.py command to clone the homework not git clone.
+
+Verify your SSH keys are correctly configured to avoid authentication issues.
diff --git a/a0.1/Rooholla_Alikhani_Squat_PoseEstimation/READ ME.txt b/a0.1/Rooholla_Alikhani_Squat_PoseEstimation/READ ME.txt
new file mode 100644
index 0000000..5ad0c37
--- /dev/null
+++ b/a0.1/Rooholla_Alikhani_Squat_PoseEstimation/READ ME.txt	
@@ -0,0 +1,12 @@
+Libraries required for this script : 
+
+pip install opencv-python==3.4.18.65
+pip install mediapipe
+pip install numpy
+
+About Task : 
+This code is written to count and detect the correct execution of the squat movement.
+The code is resistant to different modes: facing, inclined and from the side. This movement is recognizable to the machine.
+
+By : Rooholla Alikhani 
+https://github.com/RAlIKHANI1991
\ No newline at end of file
diff --git a/a0.1/Rooholla_Alikhani_Squat_PoseEstimation/Squat_poseestimation_Alikhani.py b/a0.1/Rooholla_Alikhani_Squat_PoseEstimation/Squat_poseestimation_Alikhani.py
new file mode 100644
index 0000000..b9fbd99
--- /dev/null
+++ b/a0.1/Rooholla_Alikhani_Squat_PoseEstimation/Squat_poseestimation_Alikhani.py
@@ -0,0 +1,122 @@
+import cv2 # version 3
+import mediapipe as mp
+import numpy as np
+from collections import deque
+
+mp_drawing = mp.solutions.drawing_utils
+mp_pose = mp.solutions.pose
+
+def calculate_angle(a, b, c):
+    a = np.array(a)
+    b = np.array(b)
+    c = np.array(c)
+
+    ba = a - b
+    bc = c - b
+
+    if np.linalg.norm(ba) == 0 or np.linalg.norm(bc) == 0:
+        return 0
+
+    cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))
+    cosine_angle = np.clip(cosine_angle, -1.0, 1.0)
+    return np.degrees(np.arccos(cosine_angle))
+
+def get_landmarks(results):
+    landmarks = results.pose_landmarks.landmark
+    return {
+        "L_hip": [landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].x,
+                  landmarks[mp_pose.PoseLandmark.LEFT_HIP.value].y],
+        "L_knee": [landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,
+                   landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y],
+        "L_ankle": [landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].x,
+                    landmarks[mp_pose.PoseLandmark.LEFT_ANKLE.value].y],
+        "R_hip": [landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].x,
+                  landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value].y],
+        "R_knee": [landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x,
+                   landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y],
+        "R_ankle": [landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].x,
+                    landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE.value].y],
+        "R_shoulder": [landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x,
+                       landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y],
+        "L_shoulder": [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,
+                       landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y],
+    }
+
+def squat_counter():
+    cap = cv2.VideoCapture(0)
+    counter = 0
+    stage = None
+    knee_angles = deque(maxlen=5)
+
+    with mp_pose.Pose(min_detection_confidence=0.5,
+                      min_tracking_confidence=0.5) as pose:
+        while cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                print("❌errro to read Frame")
+                break
+
+            image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            image.flags.writeable = False
+            results = pose.process(image)
+
+            image.flags.writeable = True
+            image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+            try:
+                points = get_landmarks(results)
+
+                L_knee_angle = calculate_angle(points["L_hip"], points["L_knee"], points["L_ankle"])
+                R_knee_angle = calculate_angle(points["R_hip"], points["R_knee"], points["R_ankle"])
+                knee_angle = (L_knee_angle + R_knee_angle) / 2
+
+                back_angle_R = calculate_angle(points["R_shoulder"], points["R_hip"], points["R_knee"])
+                back_angle_L = calculate_angle(points["L_shoulder"], points["L_hip"], points["L_knee"])
+                back_angle = (back_angle_R + back_angle_L) / 2
+
+                knee_angles.append(knee_angle)
+                smooth_knee_angle = np.mean(knee_angles)
+
+                if stage is None and smooth_knee_angle > 150:
+                    stage = "up"
+
+                error_flag = False
+                if back_angle < 60: 
+                    error_flag = True
+
+                if not error_flag:  
+                    if smooth_knee_angle < 110 and stage == "up":
+                        stage = "down"
+                    if smooth_knee_angle > 150 and stage == "down":
+                        stage = "up"
+                        counter += 1
+
+                cv2.putText(image, f'Knee: {int(smooth_knee_angle)}',
+                            (50, 150), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
+                cv2.putText(image, f'Back: {int(back_angle)}',
+                            (50, 200), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255, 255, 255), 2)
+                cv2.putText(image, f'Squats: {counter}', (50, 50),
+                            cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+                cv2.putText(image, f'Stage: {stage}', (50, 100),
+                            cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
+
+                if error_flag:
+                    cv2.putText(image, "❌Wrong move!!! The back is bent too much.",
+                                (50, 250), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 3)
+
+            except Exception as e:
+                print("⚠️ error:", e)
+
+            if results.pose_landmarks:
+                mp_drawing.draw_landmarks(image, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
+
+            cv2.imshow('Squat Counter', image)
+
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+
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+    cv2.destroyAllWindows()
+
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diff --git a/a0.1/TextClassification-Amirhossein_Zandi.ipynb b/a0.1/TextClassification-Amirhossein_Zandi.ipynb
new file mode 100644
index 0000000..1352db6
--- /dev/null
+++ b/a0.1/TextClassification-Amirhossein_Zandi.ipynb
@@ -0,0 +1,2602 @@
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+          }
+        }
+      }
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "! pip install -q fasttext\n",
+        "! pip install -q ydata-profiling"
+      ],
+      "metadata": {
+        "id": "_g3q1mgo1j07"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DL0Q4GQA1UiY"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.metrics import classification_report\n",
+        "\n",
+        "import fasttext\n",
+        "import re\n",
+        "import spacy\n",
+        "\n",
+        "from sklearn.manifold import TSNE\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.datasets import imdb\n",
+        "\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from IPython.display import display , HTML\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "word_index = imdb.get_word_index()\n",
+        "reverse_word_index = dict([(value , key) for (key , value) in word_index.items()])\n",
+        "\n",
+        "def decode_review(text):\n",
+        "    return \" \".join([reverse_word_index.get(i-3 , \"?\") for i in text])\n",
+        "\n",
+        "(x_train , y_train) , (x_test , y_test) = imdb.load_data(num_words = 10000)\n",
+        "\n",
+        "x_train = np.array([decode_review(x) for x in x_train[:4000]])\n",
+        "y_train = np.array(y_train[:4000])\n",
+        "\n",
+        "x_test = np.array([decode_review(x) for x in x_test[:4000]])\n",
+        "y_test = np.array(y_test[:4000])\n",
+        "\n",
+        "df_train = pd.DataFrame({\"review\" : x_train , \"sentiment\" : y_train})\n",
+        "df_test = pd.DataFrame({\"review\" : x_test , \"sentiment\" : y_test})\n",
+        "df = pd.concat([df_train , df_test])"
+      ],
+      "metadata": {
+        "id": "8AAVMIvf2c2u"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 424
+        },
+        "id": "P0f_VXYYJikQ",
+        "outputId": "a9c7b7c0-d399-4544-a632-2a51eb82941d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                 review  sentiment\n",
+              "0     ? this film was just brilliant casting locatio...          1\n",
+              "1     ? big hair big boobs bad music and a giant saf...          0\n",
+              "2     ? this has to be one of the worst films of the...          0\n",
+              "3     ? the ? ? at storytelling the traditional sort...          1\n",
+              "4     ? worst mistake of my life br br i picked this...          0\n",
+              "...                                                 ...        ...\n",
+              "3995  ? first of all around the time i wrote this co...          1\n",
+              "3996  ? you don't have to be a to appreciate this ge...          1\n",
+              "3997  ? this movie deals with the european ? exchang...          1\n",
+              "3998  ? you could get into the ? gritty of this film...          1\n",
+              "3999  ? the accounts seem real with a human factor a...          1\n",
+              "\n",
+              "[8000 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-785226b8-1bdd-4edf-80c0-86e88fd3b326\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>review</th>\n",
+              "      <th>sentiment</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>? this film was just brilliant casting locatio...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>? big hair big boobs bad music and a giant saf...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>? this has to be one of the worst films of the...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>? the ? ? at storytelling the traditional sort...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>? worst mistake of my life br br i picked this...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3995</th>\n",
+              "      <td>? first of all around the time i wrote this co...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3996</th>\n",
+              "      <td>? you don't have to be a to appreciate this ge...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3997</th>\n",
+              "      <td>? this movie deals with the european ? exchang...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3998</th>\n",
+              "      <td>? you could get into the ? gritty of this film...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3999</th>\n",
+              "      <td>? the accounts seem real with a human factor a...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>8000 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-785226b8-1bdd-4edf-80c0-86e88fd3b326')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-785226b8-1bdd-4edf-80c0-86e88fd3b326 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-785226b8-1bdd-4edf-80c0-86e88fd3b326');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6fe042af-9f7e-4eb5-9a48-41418d9837df\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6fe042af-9f7e-4eb5-9a48-41418d9837df')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-6fe042af-9f7e-4eb5-9a48-41418d9837df button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_42b071e5-a1bd-43d0-b294-feec5069ea25\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_42b071e5-a1bd-43d0-b294-feec5069ea25 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8000,\n  \"fields\": [\n    {\n      \"column\": \"review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7989,\n        \"samples\": [\n          \"? this is truly one of the worst films i have ever seen in my life rod ? who stars as the ? grandfather charlie is in full overacting mode hoping that the more flashy he is the better his performance cue ? sound rod ? is one of the last true film legends and to see him in this film although end of days is the 2nd worst film i've ever seen is really heartbreaking from the bad storyline to the nonexistent direction it becomes clear that the only reason this film was made was that the ? last name ended in the only good thing about this film is that it is so bad it's truly hysterical look for the flashback scene where rod where's a ? el wig from superman and a big black porno mustache one only hopes that his follow up film i believe in america from uber producer kevin will redeem him and leave a good taste in everyone's mouths\",\n          \"? i don't know which was worse the viewer's made ? of or the stars in this movie who look like ? am i to believe that this woman raised this child for seven years and never noticed the child was a bit dark am i to believe her mother her father and her husband never once said hmmm this child looks a bit dark am i to believe when the ? ordered the mother to view the adopted parents records that lisa ? had this wow look on her face when she told her mother christopher is half black what was that for real gee do you think so so not only did the grandmother and grandfather look dopey and stupid never once mentioning this but i guess we were supposed to look surprised and say ? ? he is half black totally stupid movie almost an embarrassment even to watch this\",\n          \"? wow not in a good way br br i can't believe people dig this trash most of the shows on television are pretty bad and this has been a running trend for a while now they just keep getting worse but las vegas definitely takes it home what a terrible show br br the actors are a bunch of has been c losers that never went anywhere except james ? who knows what he was thinking when he signed on to this ? so its not their fault that this show sucks they just can't help it blame the producers and the writers i can't believe they shot this and were actually proud enough of their work to air it\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 49
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0L-DMD-WJn6v",
+        "outputId": "5712270d-511d-4cb6-a0ce-812770531515"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(8000, 2)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 50
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from ydata_profiling import ProfileReport\n",
+        "\n",
+        "profile = ProfileReport(df , title=\"Profiling Report\", explorative=True)\n",
+        "profile.to_file(\"profiling_report.html\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 201,
+          "referenced_widgets": [
+            "29622b75cd89439c868b72c4ab7f8dfd",
+            "0a97259065844199b9bd9f5f75675319",
+            "87940f6caa0944e5aa0cb94e608663dc",
+            "0c944294738740e0893d06d432cf6ffd",
+            "8e87b2e489c94566a7d9aeedb860733b",
+            "0df4a59dd2ec47fda18636a6a3ddb24e",
+            "654e5c4992da40358bc1b6ad5c32f425",
+            "a9839e3d9d6841bc815436e66b15a6d4",
+            "87c79e1e7c1d4fec80922380ff56507e",
+            "9a33b2a55f4c4682a6a4787680d71a59",
+            "19554315d70141daa1c577ff6d2173ca",
+            "41d6867405e94169bf06b148f5ac6f55",
+            "5c8fffdfa11d41f18225c2f79ef1ff0b",
+            "c2567d9df0ca432a8ffa2dad03cc43f7",
+            "9c72a9ea5a1540fb9b44d8f866e6b190",
+            "3641c30dddee4cd48083b8752ac3dc71",
+            "362509272d084cef99e15f7e87a3a309",
+            "f5ee4396d03a4b87a08b0add9db3923f",
+            "d3a5d76d829e4cae8e21b3e8f5f4a999",
+            "27856cee66614195b26f902a3336babb",
+            "49de71904a56406b9c73ea883fe230df",
+            "ca76f2cdf1a6487b8d34cec68bd68c5c",
+            "17382c0e662346e1b1b24ec5c1c1b8f5",
+            "63595cc7bfcd40a5aa36161ab3507084",
+            "39b6b3a39bc34202879396482ecb27dd",
+            "476b579526da4fca8527604ab0748067",
+            "43855f27a9994a44b3f0f2ec2b5d4065",
+            "588f0061024a44ebbc4400b5ddcc91f2",
+            "c2ab160b0fb74c73a3412e6d996aad6f",
+            "9cba1168d1c44ea197e350b1d37f2059",
+            "6801fc2e22e5448f9a9d3a93be1e0341",
+            "10225f5427f14f678ef7c1c2174c641f",
+            "c3672bb0316c4c62ace0340bbbc3e3ad",
+            "deec14db29454096a9d15a2b960642d6",
+            "4421eb2879404a71b54d818a07b68b66",
+            "cba09d84b0eb489b8f576ed0ee8234ed",
+            "e719b0c8cdb9401d9d068e14bf1f03e4",
+            "f84f16ba3d344d65a8ff3b3af3a7bf74",
+            "a55b1f5725e64399a371523daa08ad87",
+            "be95ed4550cf41db949ea8a6758cb387",
+            "75fa049269154f648a8ba824f3caa9fa",
+            "63b3e100b8be457d8960e898fc47e107",
+            "6bffb097ea284761b6ff48d5568144b6",
+            "3f95b3f51e8440f597609b58d2454b9a"
+          ]
+        },
+        "id": "8P6aOAJRJu7_",
+        "outputId": "3fe57281-40a6-4e19-c78e-e1e2034bd113"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "29622b75cd89439c868b72c4ab7f8dfd"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "\n",
+            "  0%|          | 0/2 [00:00<?, ?it/s]\u001b[A\n",
+            "100%|██████████| 2/2 [00:03<00:00,  1.66s/it]\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "41d6867405e94169bf06b148f5ac6f55"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "17382c0e662346e1b1b24ec5c1c1b8f5"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "deec14db29454096a9d15a2b960642d6"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "HTML(filename='profiling_report.html')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "jDIEgO70K5Ed",
+        "outputId": "a79815db-bbcf-4268-abd2-a44f687c94b0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<!doctype html><html lang=en><meta charset=utf-8><meta content=\"width=device-width,initial-scale=1,shrink-to-fit=no\" name=viewport><meta content=\"Profile report generated by YData! Visit us at https://ydata.ai\" name=description><meta content=\"YData and the open source community.\" name=author><meta content=\"YData Profiling vv4.18.0\" name=generator><meta content=https://github.com/ydataai/ydata-profiling name=url><meta content=\"2025-12-13 18:05:38.681985\" name=date><title>Profiling Report</title><style>@charset \"UTF-8\";/*!\n",
+              " * Bootstrap  v5.3.3 (https://getbootstrap.com/)\n",
+              " * Copyright 2011-2024 The Bootstrap Authors\n",
+              " * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n",
+              " 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auto}.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn.active,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:hover,.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn.active,.btn-group>.btn:active,.btn-group>.btn:focus,.btn-group>.btn:hover{z-index:1}.btn-toolbar{display:flex;flex-wrap:wrap;justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>.btn-group:not(:first-child),.btn-group>:not(.btn-check:first-child)+.btn{margin-left:calc(var(--bs-border-width) * -1)}.btn-group>.btn-group:not(:last-child)>.btn,.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn:not(:last-child):not(.dropdown-toggle){border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn-group:not(:first-child)>.btn,.btn-group>.btn:nth-child(n+3),.btn-group>:not(.btn-check)+.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-group-sm>.btn+.dropdown-toggle-split,.btn-sm+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-group-lg>.btn+.dropdown-toggle-split,.btn-lg+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;align-items:flex-start;justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn-group:not(:first-child),.btn-group-vertical>.btn:not(:first-child){margin-top:calc(var(--bs-border-width) * -1)}.btn-group-vertical>.btn-group:not(:last-child)>.btn,.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle){border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn-group:not(:first-child)>.btn,.btn-group-vertical>.btn~.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x:1rem;--bs-nav-link-padding-y:0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color:var(--bs-link-color);--bs-nav-link-hover-color:var(--bs-link-hover-color);--bs-nav-link-disabled-color:var(--bs-secondary-color);display:flex;flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;background:0 0;border:0;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out}@media (prefers-reduced-motion:reduce){.nav-link{transition:none}}.nav-link:focus,.nav-link:hover{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem 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.nav-link:hover{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-item.show .nav-link,.nav-tabs .nav-link.active{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * var(--bs-nav-tabs-border-width));border-top-left-radius:0;border-top-right-radius:0}.nav-pills{--bs-nav-pills-border-radius:var(--bs-border-radius);--bs-nav-pills-link-active-color:#fff;--bs-nav-pills-link-active-bg:#0d6efd}.nav-pills .nav-link{border-radius:var(--bs-nav-pills-border-radius)}.nav-pills .nav-link.active,.nav-pills .show>.nav-link{color:var(--bs-nav-pills-link-active-color);background-color:var(--bs-nav-pills-link-active-bg)}.nav-underline{--bs-nav-underline-gap:1rem;--bs-nav-underline-border-width:0.125rem;--bs-nav-underline-link-active-color:var(--bs-emphasis-color);gap:var(--bs-nav-underline-gap)}.nav-underline .nav-link{padding-right:0;padding-left:0;border-bottom:var(--bs-nav-underline-border-width) solid transparent}.nav-underline .nav-link:focus,.nav-underline .nav-link:hover{border-bottom-color:currentcolor}.nav-underline .nav-link.active,.nav-underline .show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill .nav-item,.nav-fill>.nav-link{flex:1 1 auto;text-align:center}.nav-justified .nav-item,.nav-justified>.nav-link{flex-basis:0;flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x:0;--bs-navbar-padding-y:0.5rem;--bs-navbar-color:rgba(var(--bs-emphasis-color-rgb), 0.65);--bs-navbar-hover-color:rgba(var(--bs-emphasis-color-rgb), 0.8);--bs-navbar-disabled-color:rgba(var(--bs-emphasis-color-rgb), 0.3);--bs-navbar-active-color:rgba(var(--bs-emphasis-color-rgb), 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stroke-linecap='round' stroke-linejoin='round'%3e%3cpath d='M2 5L8 11L14 5'/%3e%3c/svg%3e\");--bs-accordion-btn-focus-box-shadow:0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-accordion-body-padding-x:1.25rem;--bs-accordion-body-padding-y:1rem;--bs-accordion-active-color:var(--bs-primary-text-emphasis);--bs-accordion-active-bg:var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media (prefers-reduced-motion:reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:\"\";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media (prefers-reduced-motion:reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type>.accordion-header .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type>.accordion-header 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viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\");--bs-accordion-btn-active-icon:url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e\")}.breadcrumb{--bs-breadcrumb-padding-x:0;--bs-breadcrumb-padding-y:0;--bs-breadcrumb-margin-bottom:1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color:var(--bs-secondary-color);--bs-breadcrumb-item-padding-x:0.5rem;--bs-breadcrumb-item-active-color:var(--bs-secondary-color);display:flex;flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, \"/\")}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x:0.75rem;--bs-pagination-padding-y:0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color:var(--bs-link-color);--bs-pagination-bg:var(--bs-body-bg);--bs-pagination-border-width:var(--bs-border-width);--bs-pagination-border-color:var(--bs-border-color);--bs-pagination-border-radius:var(--bs-border-radius);--bs-pagination-hover-color:var(--bs-link-hover-color);--bs-pagination-hover-bg:var(--bs-tertiary-bg);--bs-pagination-hover-border-color:var(--bs-border-color);--bs-pagination-focus-color:var(--bs-link-hover-color);--bs-pagination-focus-bg:var(--bs-secondary-bg);--bs-pagination-focus-box-shadow:0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color:#fff;--bs-pagination-active-bg:#0d6efd;--bs-pagination-active-border-color:#0d6efd;--bs-pagination-disabled-color:var(--bs-secondary-color);--bs-pagination-disabled-bg:var(--bs-secondary-bg);--bs-pagination-disabled-border-color:var(--bs-border-color);display:flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media (prefers-reduced-motion:reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.active>.page-link,.page-link.active{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.disabled>.page-link,.page-link.disabled{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x:1.5rem;--bs-pagination-padding-y:0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius:var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x:0.5rem;--bs-pagination-padding-y:0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius:var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x:0.65em;--bs-badge-padding-y:0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight:700;--bs-badge-color:#fff;--bs-badge-border-radius:var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg:transparent;--bs-alert-padding-x:1rem;--bs-alert-padding-y:1rem;--bs-alert-margin-bottom:1rem;--bs-alert-color:inherit;--bs-alert-border-color:transparent;--bs-alert-border:var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius:var(--bs-border-radius);--bs-alert-link-color:inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-primary{--bs-alert-color:var(--bs-primary-text-emphasis);--bs-alert-bg:var(--bs-primary-bg-subtle);--bs-alert-border-color:var(--bs-primary-border-subtle);--bs-alert-link-color:var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color:var(--bs-secondary-text-emphasis);--bs-alert-bg:var(--bs-secondary-bg-subtle);--bs-alert-border-color:var(--bs-secondary-border-subtle);--bs-alert-link-color:var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color:var(--bs-success-text-emphasis);--bs-alert-bg:var(--bs-success-bg-subtle);--bs-alert-border-color:var(--bs-success-border-subtle);--bs-alert-link-color:var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color:var(--bs-info-text-emphasis);--bs-alert-bg:var(--bs-info-bg-subtle);--bs-alert-border-color:var(--bs-info-border-subtle);--bs-alert-link-color:var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color:var(--bs-warning-text-emphasis);--bs-alert-bg:var(--bs-warning-bg-subtle);--bs-alert-border-color:var(--bs-warning-border-subtle);--bs-alert-link-color:var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color:var(--bs-danger-text-emphasis);--bs-alert-bg:var(--bs-danger-bg-subtle);--bs-alert-border-color:var(--bs-danger-border-subtle);--bs-alert-link-color:var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color:var(--bs-light-text-emphasis);--bs-alert-bg:var(--bs-light-bg-subtle);--bs-alert-border-color:var(--bs-light-border-subtle);--bs-alert-link-color:var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color:var(--bs-dark-text-emphasis);--bs-alert-bg:var(--bs-dark-bg-subtle);--bs-alert-border-color:var(--bs-dark-border-subtle);--bs-alert-link-color:var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height:1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg:var(--bs-secondary-bg);--bs-progress-border-radius:var(--bs-border-radius);--bs-progress-box-shadow:var(--bs-box-shadow-inset);--bs-progress-bar-color:#fff;--bs-progress-bar-bg:#0d6efd;--bs-progress-bar-transition:width 0.6s ease;display:flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;flex-direction:column;justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion:reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg,rgba(255,255,255,.15) 25%,transparent 25%,transparent 50%,rgba(255,255,255,.15) 50%,rgba(255,255,255,.15) 75%,transparent 75%,transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion:reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color:var(--bs-body-color);--bs-list-group-bg:var(--bs-body-bg);--bs-list-group-border-color:var(--bs-border-color);--bs-list-group-border-width:var(--bs-border-width);--bs-list-group-border-radius:var(--bs-border-radius);--bs-list-group-item-padding-x:1rem;--bs-list-group-item-padding-y:0.5rem;--bs-list-group-action-color:var(--bs-secondary-color);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-tertiary-bg);--bs-list-group-action-active-color:var(--bs-body-color);--bs-list-group-action-active-bg:var(--bs-secondary-bg);--bs-list-group-disabled-color:var(--bs-secondary-color);--bs-list-group-disabled-bg:var(--bs-body-bg);--bs-list-group-active-color:#fff;--bs-list-group-active-bg:#0d6efd;--bs-list-group-active-border-color:#0d6efd;display:flex;flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, \".\") \". \";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:focus,.list-group-item-action:hover{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width:576px){.list-group-horizontal-sm{flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width:768px){.list-group-horizontal-md{flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width:992px){.list-group-horizontal-lg{flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width:1200px){.list-group-horizontal-xl{flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width:1400px){.list-group-horizontal-xxl{flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-primary{--bs-list-group-color:var(--bs-primary-text-emphasis);--bs-list-group-bg:var(--bs-primary-bg-subtle);--bs-list-group-border-color:var(--bs-primary-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-primary-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-primary-border-subtle);--bs-list-group-active-color:var(--bs-primary-bg-subtle);--bs-list-group-active-bg:var(--bs-primary-text-emphasis);--bs-list-group-active-border-color:var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color:var(--bs-secondary-text-emphasis);--bs-list-group-bg:var(--bs-secondary-bg-subtle);--bs-list-group-border-color:var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-secondary-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-secondary-border-subtle);--bs-list-group-active-color:var(--bs-secondary-bg-subtle);--bs-list-group-active-bg:var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color:var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color:var(--bs-success-text-emphasis);--bs-list-group-bg:var(--bs-success-bg-subtle);--bs-list-group-border-color:var(--bs-success-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-success-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-success-border-subtle);--bs-list-group-active-color:var(--bs-success-bg-subtle);--bs-list-group-active-bg:var(--bs-success-text-emphasis);--bs-list-group-active-border-color:var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color:var(--bs-info-text-emphasis);--bs-list-group-bg:var(--bs-info-bg-subtle);--bs-list-group-border-color:var(--bs-info-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-info-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-info-border-subtle);--bs-list-group-active-color:var(--bs-info-bg-subtle);--bs-list-group-active-bg:var(--bs-info-text-emphasis);--bs-list-group-active-border-color:var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color:var(--bs-warning-text-emphasis);--bs-list-group-bg:var(--bs-warning-bg-subtle);--bs-list-group-border-color:var(--bs-warning-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-warning-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-warning-border-subtle);--bs-list-group-active-color:var(--bs-warning-bg-subtle);--bs-list-group-active-bg:var(--bs-warning-text-emphasis);--bs-list-group-active-border-color:var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color:var(--bs-danger-text-emphasis);--bs-list-group-bg:var(--bs-danger-bg-subtle);--bs-list-group-border-color:var(--bs-danger-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-danger-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-danger-border-subtle);--bs-list-group-active-color:var(--bs-danger-bg-subtle);--bs-list-group-active-bg:var(--bs-danger-text-emphasis);--bs-list-group-active-border-color:var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color:var(--bs-light-text-emphasis);--bs-list-group-bg:var(--bs-light-bg-subtle);--bs-list-group-border-color:var(--bs-light-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-light-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-light-border-subtle);--bs-list-group-active-color:var(--bs-light-bg-subtle);--bs-list-group-active-bg:var(--bs-light-text-emphasis);--bs-list-group-active-border-color:var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color:var(--bs-dark-text-emphasis);--bs-list-group-bg:var(--bs-dark-bg-subtle);--bs-list-group-border-color:var(--bs-dark-border-subtle);--bs-list-group-action-hover-color:var(--bs-emphasis-color);--bs-list-group-action-hover-bg:var(--bs-dark-border-subtle);--bs-list-group-action-active-color:var(--bs-emphasis-color);--bs-list-group-action-active-bg:var(--bs-dark-border-subtle);--bs-list-group-active-color:var(--bs-dark-bg-subtle);--bs-list-group-active-bg:var(--bs-dark-text-emphasis);--bs-list-group-active-border-color:var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color:#000;--bs-btn-close-bg:url(\"data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e\");--bs-btn-close-opacity:0.5;--bs-btn-close-hover-opacity:0.75;--bs-btn-close-focus-shadow:0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity:1;--bs-btn-close-disabled-opacity:0.25;--bs-btn-close-white-filter:invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:transparent var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close.disabled,.btn-close:disabled{pointer-events:none;-webkit-user-select:none;-moz-user-select:none;user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex:1090;--bs-toast-padding-x:0.75rem;--bs-toast-padding-y:0.5rem;--bs-toast-spacing:1.5rem;--bs-toast-max-width:350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg:rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-border-width:var(--bs-border-width);--bs-toast-border-color:var(--bs-border-color-translucent);--bs-toast-border-radius:var(--bs-border-radius);--bs-toast-box-shadow:var(--bs-box-shadow);--bs-toast-header-color:var(--bs-secondary-color);--bs-toast-header-bg:rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-header-border-color:var(--bs-border-color-translucent);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex:1090;position:absolute;z-index:var(--bs-toast-zindex);width:-webkit-max-content;width:-moz-max-content;width:max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-.5 * var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex:1055;--bs-modal-width:500px;--bs-modal-padding:1rem;--bs-modal-margin:0.5rem;--bs-modal-color: ;--bs-modal-bg:var(--bs-body-bg);--bs-modal-border-color:var(--bs-border-color-translucent);--bs-modal-border-width:var(--bs-border-width);--bs-modal-border-radius:var(--bs-border-radius-lg);--bs-modal-box-shadow:var(--bs-box-shadow-sm);--bs-modal-inner-border-radius:calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x:1rem;--bs-modal-header-padding-y:1rem;--bs-modal-header-padding:1rem 1rem;--bs-modal-header-border-color:var(--bs-border-color);--bs-modal-header-border-width:var(--bs-border-width);--bs-modal-title-line-height:1.5;--bs-modal-footer-gap:0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color:var(--bs-border-color);--bs-modal-footer-border-width:var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0,-50px)}@media (prefers-reduced-motion:reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 2)}.modal-content{position:relative;display:flex;flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex:1050;--bs-backdrop-bg:#000;--bs-backdrop-opacity:0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;flex-shrink:0;align-items:center;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header 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(min-width:576px){.modal{--bs-modal-margin:1.75rem;--bs-modal-box-shadow:var(--bs-box-shadow)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width:300px}}@media (min-width:992px){.modal-lg,.modal-xl{--bs-modal-width:800px}}@media (min-width:1200px){.modal-xl{--bs-modal-width:1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-footer,.modal-fullscreen .modal-header{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media (max-width:575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-footer,.modal-fullscreen-sm-down .modal-header{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media (max-width:767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-footer,.modal-fullscreen-md-down .modal-header{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media (max-width:991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-footer,.modal-fullscreen-lg-down .modal-header{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media (max-width:1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-footer,.modal-fullscreen-xl-down .modal-header{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media (max-width:1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-footer,.modal-fullscreen-xxl-down .modal-header{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex:1080;--bs-tooltip-max-width:200px;--bs-tooltip-padding-x:0.5rem;--bs-tooltip-padding-y:0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color:var(--bs-body-bg);--bs-tooltip-bg:var(--bs-emphasis-color);--bs-tooltip-border-radius:var(--bs-border-radius);--bs-tooltip-opacity:0.9;--bs-tooltip-arrow-width:0.8rem;--bs-tooltip-arrow-height:0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:var(--bs-font-sans-serif);font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:\"\";border-color:transparent;border-style:solid}.bs-tooltip-auto[data-popper-placement^=top] 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auto!important}.flex-xxl-row{flex-direction:row!important}.flex-xxl-column{flex-direction:column!important}.flex-xxl-row-reverse{flex-direction:row-reverse!important}.flex-xxl-column-reverse{flex-direction:column-reverse!important}.flex-xxl-grow-0{flex-grow:0!important}.flex-xxl-grow-1{flex-grow:1!important}.flex-xxl-shrink-0{flex-shrink:0!important}.flex-xxl-shrink-1{flex-shrink:1!important}.flex-xxl-wrap{flex-wrap:wrap!important}.flex-xxl-nowrap{flex-wrap:nowrap!important}.flex-xxl-wrap-reverse{flex-wrap:wrap-reverse!important}.justify-content-xxl-start{justify-content:flex-start!important}.justify-content-xxl-end{justify-content:flex-end!important}.justify-content-xxl-center{justify-content:center!important}.justify-content-xxl-between{justify-content:space-between!important}.justify-content-xxl-around{justify-content:space-around!important}.justify-content-xxl-evenly{justify-content:space-evenly!important}.align-items-xxl-start{align-items:flex-start!important}.align-items-xxl-end{align-items:flex-end!important}.align-items-xxl-center{align-items:center!important}.align-items-xxl-baseline{align-items:baseline!important}.align-items-xxl-stretch{align-items:stretch!important}.align-content-xxl-start{align-content:flex-start!important}.align-content-xxl-end{align-content:flex-end!important}.align-content-xxl-center{align-content:center!important}.align-content-xxl-between{align-content:space-between!important}.align-content-xxl-around{align-content:space-around!important}.align-content-xxl-stretch{align-content:stretch!important}.align-self-xxl-auto{align-self:auto!important}.align-self-xxl-start{align-self:flex-start!important}.align-self-xxl-end{align-self:flex-end!important}.align-self-xxl-center{align-self:center!important}.align-self-xxl-baseline{align-self:baseline!important}.align-self-xxl-stretch{align-self:stretch!important}.order-xxl-first{order:-1!important}.order-xxl-0{order:0!important}.order-xxl-1{order:1!important}.order-xxl-2{order:2!important}.order-xxl-3{order:3!important}.order-xxl-4{order:4!important}.order-xxl-5{order:5!important}.order-xxl-last{order:6!important}.m-xxl-0{margin:0!important}.m-xxl-1{margin:.25rem!important}.m-xxl-2{margin:.5rem!important}.m-xxl-3{margin:1rem!important}.m-xxl-4{margin:1.5rem!important}.m-xxl-5{margin:3rem!important}.m-xxl-auto{margin:auto!important}.mx-xxl-0{margin-right:0!important;margin-left:0!important}.mx-xxl-1{margin-right:.25rem!important;margin-left:.25rem!important}.mx-xxl-2{margin-right:.5rem!important;margin-left:.5rem!important}.mx-xxl-3{margin-right:1rem!important;margin-left:1rem!important}.mx-xxl-4{margin-right:1.5rem!important;margin-left:1.5rem!important}.mx-xxl-5{margin-right:3rem!important;margin-left:3rem!important}.mx-xxl-auto{margin-right:auto!important;margin-left:auto!important}.my-xxl-0{margin-top:0!important;margin-bottom:0!important}.my-xxl-1{margin-top:.25rem!important;margin-bottom:.25rem!important}.my-xxl-2{margin-top:.5rem!important;margin-bottom:.5rem!important}.my-xxl-3{margin-top:1rem!important;margin-bottom:1rem!important}.my-xxl-4{margin-top:1.5rem!important;margin-bottom:1.5rem!important}.my-xxl-5{margin-top:3rem!important;margin-bottom:3rem!important}.my-xxl-auto{margin-top:auto!important;margin-bottom:auto!important}.mt-xxl-0{margin-top:0!important}.mt-xxl-1{margin-top:.25rem!important}.mt-xxl-2{margin-top:.5rem!important}.mt-xxl-3{margin-top:1rem!important}.mt-xxl-4{margin-top:1.5rem!important}.mt-xxl-5{margin-top:3rem!important}.mt-xxl-auto{margin-top:auto!important}.me-xxl-0{margin-right:0!important}.me-xxl-1{margin-right:.25rem!important}.me-xxl-2{margin-right:.5rem!important}.me-xxl-3{margin-right:1rem!important}.me-xxl-4{margin-right:1.5rem!important}.me-xxl-5{margin-right:3rem!important}.me-xxl-auto{margin-right:auto!important}.mb-xxl-0{margin-bottom:0!important}.mb-xxl-1{margin-bottom:.25rem!important}.mb-xxl-2{margin-bottom:.5rem!important}.mb-xxl-3{margin-bottom:1rem!important}.mb-xxl-4{margin-bottom:1.5rem!important}.mb-xxl-5{margin-bottom:3rem!important}.mb-xxl-auto{margin-bottom:auto!important}.ms-xxl-0{margin-left:0!important}.ms-xxl-1{margin-left:.25rem!important}.ms-xxl-2{margin-left:.5rem!important}.ms-xxl-3{margin-left:1rem!important}.ms-xxl-4{margin-left:1.5rem!important}.ms-xxl-5{margin-left:3rem!important}.ms-xxl-auto{margin-left:auto!important}.p-xxl-0{padding:0!important}.p-xxl-1{padding:.25rem!important}.p-xxl-2{padding:.5rem!important}.p-xxl-3{padding:1rem!important}.p-xxl-4{padding:1.5rem!important}.p-xxl-5{padding:3rem!important}.px-xxl-0{padding-right:0!important;padding-left:0!important}.px-xxl-1{padding-right:.25rem!important;padding-left:.25rem!important}.px-xxl-2{padding-right:.5rem!important;padding-left:.5rem!important}.px-xxl-3{padding-right:1rem!important;padding-left:1rem!important}.px-xxl-4{padding-right:1.5rem!important;padding-left:1.5rem!important}.px-xxl-5{padding-right:3rem!important;padding-left:3rem!important}.py-xxl-0{padding-top:0!important;padding-bottom:0!important}.py-xxl-1{padding-top:.25rem!important;padding-bottom:.25rem!important}.py-xxl-2{padding-top:.5rem!important;padding-bottom:.5rem!important}.py-xxl-3{padding-top:1rem!important;padding-bottom:1rem!important}.py-xxl-4{padding-top:1.5rem!important;padding-bottom:1.5rem!important}.py-xxl-5{padding-top:3rem!important;padding-bottom:3rem!important}.pt-xxl-0{padding-top:0!important}.pt-xxl-1{padding-top:.25rem!important}.pt-xxl-2{padding-top:.5rem!important}.pt-xxl-3{padding-top:1rem!important}.pt-xxl-4{padding-top:1.5rem!important}.pt-xxl-5{padding-top:3rem!important}.pe-xxl-0{padding-right:0!important}.pe-xxl-1{padding-right:.25rem!important}.pe-xxl-2{padding-right:.5rem!important}.pe-xxl-3{padding-right:1rem!important}.pe-xxl-4{padding-right:1.5rem!important}.pe-xxl-5{padding-right:3rem!important}.pb-xxl-0{padding-bottom:0!important}.pb-xxl-1{padding-bottom:.25rem!important}.pb-xxl-2{padding-bottom:.5rem!important}.pb-xxl-3{padding-bottom:1rem!important}.pb-xxl-4{padding-bottom:1.5rem!important}.pb-xxl-5{padding-bottom:3rem!important}.ps-xxl-0{padding-left:0!important}.ps-xxl-1{padding-left:.25rem!important}.ps-xxl-2{padding-left:.5rem!important}.ps-xxl-3{padding-left:1rem!important}.ps-xxl-4{padding-left:1.5rem!important}.ps-xxl-5{padding-left:3rem!important}.gap-xxl-0{gap:0!important}.gap-xxl-1{gap:.25rem!important}.gap-xxl-2{gap:.5rem!important}.gap-xxl-3{gap:1rem!important}.gap-xxl-4{gap:1.5rem!important}.gap-xxl-5{gap:3rem!important}.row-gap-xxl-0{row-gap:0!important}.row-gap-xxl-1{row-gap:.25rem!important}.row-gap-xxl-2{row-gap:.5rem!important}.row-gap-xxl-3{row-gap:1rem!important}.row-gap-xxl-4{row-gap:1.5rem!important}.row-gap-xxl-5{row-gap:3rem!important}.column-gap-xxl-0{-moz-column-gap:0!important;column-gap:0!important}.column-gap-xxl-1{-moz-column-gap:0.25rem!important;column-gap:.25rem!important}.column-gap-xxl-2{-moz-column-gap:0.5rem!important;column-gap:.5rem!important}.column-gap-xxl-3{-moz-column-gap:1rem!important;column-gap:1rem!important}.column-gap-xxl-4{-moz-column-gap:1.5rem!important;column-gap:1.5rem!important}.column-gap-xxl-5{-moz-column-gap:3rem!important;column-gap:3rem!important}.text-xxl-start{text-align:left!important}.text-xxl-end{text-align:right!important}.text-xxl-center{text-align:center!important}}@media (min-width:1200px){.fs-1{font-size:2.5rem!important}.fs-2{font-size:2rem!important}.fs-3{font-size:1.75rem!important}.fs-4{font-size:1.5rem!important}}@media print{.d-print-inline{display:inline!important}.d-print-inline-block{display:inline-block!important}.d-print-block{display:block!important}.d-print-grid{display:grid!important}.d-print-inline-grid{display:inline-grid!important}.d-print-table{display:table!important}.d-print-table-row{display:table-row!important}.d-print-table-cell{display:table-cell!important}.d-print-flex{display:flex!important}.d-print-inline-flex{display:inline-flex!important}.d-print-none{display:none!important}}\n",
+              "/*# sourceMappingURL=bootstrap.min.css.map */</style><style>/* Variable */\n",
+              "\n",
+              ":root {\n",
+              "    --navbar-height: 56px;\n",
+              "    --navbar-brand-height: 32px;\n",
+              "    --default-padding: 1rem;\n",
+              "    --default-margin: 1rem;\n",
+              "    --tab-select-padding: 0.4rem;\n",
+              "}\n",
+              "\n",
+              "/* Common */\n",
+              "\n",
+              "html, body {\n",
+              "    margin: 0;\n",
+              "    padding: 0;\n",
+              "    border: 0;\n",
+              "    vertical-align: baseline;\n",
+              "}\n",
+              "\n",
+              "a {\n",
+              "    text-decoration: none;\n",
+              "}\n",
+              "\n",
+              ".row .row {\n",
+              "    margin-left: 0 !important;\n",
+              "    margin-right: 0 !important;\n",
+              "}\n",
+              "\n",
+              ".row > * {\n",
+              "    padding-left: 0 !important;\n",
+              "    padding-right: 0 !important;\n",
+              "}\n",
+              "\n",
+              "div[class*=\"col-\"] + div[class*=\"col-\"] {\n",
+              "    padding-left: var(--default-padding) !important;\n",
+              "}\n",
+              "\n",
+              "div[class*=\"col-12\"] + div[class*=\"col-\"],\n",
+              "div[class*=\"col-sm-12\"] + div[class*=\"col-\"],\n",
+              "div[class*=\"col-md-12\"] + div[class*=\"col-\"],\n",
+              "div[class*=\"col-lg-12\"] + div[class*=\"col-\"],\n",
+              "div[class*=\"col-xl-12\"] + div[class*=\"col-\"],\n",
+              "div[class*=\"col-xxl-12\"] + div[class*=\"col-\"] {\n",
+              "    padding-left: 0 !important;\n",
+              "}\n",
+              "\n",
+              "div[class*=\"col-\"] + div[class*=\"col-12\"],\n",
+              "div[class*=\"col-\"] + div[class*=\"col-sm-12\"],\n",
+              "div[class*=\"col-\"] + div[class*=\"col-md-12\"],\n",
+              "div[class*=\"col-\"] + div[class*=\"col-lg-12\"],\n",
+              "div[class*=\"col-\"] + div[class*=\"col-xl-12\"],\n",
+              "div[class*=\"col-\"] + div[class*=\"col-xxl-12\"] {\n",
+              "    padding-left: 0 !important;\n",
+              "}\n",
+              "\n",
+              "/* Anchor */\n",
+              "\n",
+              "a.anchor-pos {\n",
+              "    visibility: hidden;\n",
+              "}\n",
+              "\n",
+              "/* Navbar */\n",
+              "\n",
+              "nav {\n",
+              "    height: var(--navbar-height);\n",
+              "    padding-top: 0 !important;\n",
+              "    padding-bottom: 0 !important;\n",
+              "}\n",
+              "\n",
+              ".navbar-brand > img {\n",
+              "    max-height: var(--navbar-brand-height);\n",
+              "}\n",
+              "\n",
+              "html {\n",
+              "    scroll-padding-top: var(--navbar-height);\n",
+              "}\n",
+              "\n",
+              "/* Nav */\n",
+              "\n",
+              ".nav {\n",
+              "    margin-bottom: var(--default-margin);\n",
+              "}\n",
+              "\n",
+              "/* Section */\n",
+              "\n",
+              ".section-header {\n",
+              "    margin-top: var(--default-margin);\n",
+              "    margin-bottom: var(--default-margin);\n",
+              "}\n",
+              "\n",
+              ".section-items > .row {\n",
+              "    padding: var(--default-padding);\n",
+              "    margin-left: 0;\n",
+              "    margin-right: 0;\n",
+              "    margin-bottom: var(--default-margin);\n",
+              "    border: solid var(--bs-border-color) 1px;\n",
+              "    border-radius: var(--bs-border-radius);\n",
+              "}\n",
+              "\n",
+              ".item-header {\n",
+              "    margin-top: .5rem;\n",
+              "    margin-bottom: .5rem;\n",
+              "}\n",
+              "\n",
+              "#variables-dropdown {\n",
+              "    width: auto;\n",
+              "    margin-top: var(--default-margin);\n",
+              "    margin-bottom: var(--default-margin);\n",
+              "}\n",
+              "\n",
+              "/* Table */\n",
+              "\n",
+              "th, td {\n",
+              "    vertical-align: middle;\n",
+              "}\n",
+              "\n",
+              "#correlation-table-container th,\n",
+              "#correlation-table-container td,\n",
+              "#sample-container th,\n",
+              "#sample-container td  {\n",
+              "    white-space: nowrap;\n",
+              "}\n",
+              "\n",
+              "/* Frequency Table */\n",
+              "\n",
+              ".freq.table{\n",
+              "    table-layout: fixed;\n",
+              "}\n",
+              "\n",
+              ".freq:not(.mini) tr td:nth-child(1),\n",
+              ".freq:not(.mini) tr th:nth-child(1){\n",
+              "    width: auto;\n",
+              "    max-width: none;\n",
+              "    white-space: nowrap;\n",
+              "    overflow: hidden;\n",
+              "    text-overflow: ellipsis;\n",
+              "}\n",
+              "\n",
+              ".freq:not(.mini) tr td:nth-child(2),\n",
+              ".freq:not(.mini) tr td:nth-child(3),\n",
+              ".freq:not(.mini) tr th:nth-child(2),\n",
+              ".freq:not(.mini) tr th:nth-child(3){\n",
+              "    width: 200px;\n",
+              "    text-align: right;\n",
+              "}\n",
+              "\n",
+              ".freq:not(.mini) tr td:nth-child(4),\n",
+              ".freq:not(.mini) tr th:nth-child(4){\n",
+              "    width: 200px;\n",
+              "}\n",
+              "\n",
+              ".freq .bar {\n",
+              "    float: left;\n",
+              "    width: 0;\n",
+              "    height: 100%;\n",
+              "    color: #ffffff;\n",
+              "    text-align: center;\n",
+              "    border-radius: 3px;\n",
+              "    margin-right: 4px;\n",
+              "}\n",
+              "\n",
+              ".other .bar {\n",
+              "    background-color: var(--bs-secondary);\n",
+              "}\n",
+              "\n",
+              ".missing .bar {\n",
+              "    background-color: var(--bs-danger);\n",
+              "}\n",
+              "\n",
+              ".freq.table-0 .bar {\n",
+              "    background-color: #0d6efd;\n",
+              "}\n",
+              ".freq.table-1 .bar {\n",
+              "    background-color: #dc3545;\n",
+              "}\n",
+              "\n",
+              "/* Frequency Mini Table */\n",
+              "\n",
+              "table.freq.mini {\n",
+              "    width: 100%;\n",
+              "    border-collapse: separate;\n",
+              "    border-spacing: 4px;\n",
+              "}\n",
+              "\n",
+              ".freq.mini th {\n",
+              "    overflow: hidden;\n",
+              "    text-overflow: ellipsis;\n",
+              "    white-space: nowrap;\n",
+              "    max-width: 5rem;\n",
+              "    font-weight: 400;\n",
+              "    text-align: right;\n",
+              "    padding-right: 0.5rem;\n",
+              "}\n",
+              "\n",
+              ".freq.mini td {\n",
+              "    width: 50%;\n",
+              "}\n",
+              "\n",
+              "/* Image */\n",
+              "\n",
+              ".img-responsive{\n",
+              "    max-width: 99%;\n",
+              "    min-width: 99%;\n",
+              "}\n",
+              "\n",
+              ".center-img {\n",
+              "    margin-left: auto !important;\n",
+              "    margin-right: auto !important;\n",
+              "    display: block;\n",
+              "}\n",
+              "\n",
+              "/* Tooltip */\n",
+              "\n",
+              "span[data-bs-toggle=\"tooltip\"] {\n",
+              "    cursor: help;\n",
+              "}\n",
+              "\n",
+              "/* Color */\n",
+              "\n",
+              ".alert-info,\n",
+              ".alert-info > th,\n",
+              ".alert-info > td {\n",
+              "    color: var(--bs-danger);\n",
+              "}\n",
+              "\n",
+              "#tab-pane-overview-alerts code,\n",
+              "#tab-pane-overview-alerts span[data-bs-toggle=\"tooltip\"] {\n",
+              "    color: var(--bs-link-color);\n",
+              "}\n",
+              "\n",
+              "/* Tab */\n",
+              "\n",
+              ".tab-select {\n",
+              "    height: 100%;\n",
+              "    padding: var(--tab-select-padding) !important;\n",
+              "}\n",
+              "\n",
+              ".tab-select-option {\n",
+              "    padding: var(--tab-select-padding) !important;\n",
+              "    border-radius: var(--bs-border-radius-sm) !important;\n",
+              "}\n",
+              "\n",
+              ".tab-select-option.active,\n",
+              ".tab-select:focus option:checked {\n",
+              "    background: var(--bs-primary-bg-subtle) linear-gradient(0deg, var(--bs-primary-bg-subtle) 0%, var(--bs-primary-bg-subtle) 100%);\n",
+              "    color: var(--bs-primary-text-emphasis);\n",
+              "}\n",
+              "\n",
+              ".tab-select-option:hover {\n",
+              "    background: var(--bs-secondary-bg-subtle);\n",
+              "    color: var(--bs-secondary-text-emphasis);\n",
+              "}</style><body><a class=anchor-pos id=top></a><nav class=\"navbar navbar-expand-lg bg-body-tertiary sticky-top\"><div class=container-fluid><div class=navbar-header><a class=\"navbar-brand anchor\" href=#top>Profiling Report</a></div><a aria-label=\"Toggle navigation\" aria-controls=navbar class=navbar-toggler data-bs-target=#navbar data-bs-toggle=offcanvas type=button> <span class=navbar-toggler-icon></span> </a><div class=\"offcanvas offcanvas-end\" aria-labelledby=offcanvas-navbar-label id=navbar><div class=offcanvas-header><h5 class=offcanvas-title id=offcanvas-navbar-label>Profiling Report</h5><a aria-label=Close class=btn-close data-bs-dismiss=offcanvas type=button></a></div><div class=offcanvas-body><ul class=\"navbar-nav navbar-nav-scroll justify-content-end flex-grow-1\"><li class=nav-item><a class=nav-link href=#overview>Overview</a><li class=nav-item><a class=nav-link href=#variables-dropdown>Variables</a><li class=nav-item><a class=nav-link href=#missing>Missing values</a><li class=nav-item><a class=nav-link href=#sample>Sample</a><li class=nav-item><a class=nav-link href=#duplicate>Duplicate rows</a></ul></div></div></div></nav><div class=content><div class=container><div class=section-header id=overview><h1 class=section-name>Overview</h1></div><div class=section-items><div class=\"row item\"><ul class=\"nav nav-tabs tab-nav\" role=tablist><li class=nav-item role=presentation><button class=\"nav-link active\" aria-controls=tab-pane-overview-dataset_overview aria-selected=true data-bs-target=#tab-pane-overview-dataset_overview data-bs-toggle=tab id=tab-overview-dataset_overview role=tab type=button>Overview</button><li class=nav-item role=presentation><button aria-controls=tab-pane-overview-alerts aria-selected=false class=nav-link data-bs-target=#tab-pane-overview-alerts data-bs-toggle=tab id=tab-overview-alerts role=tab type=button>Alerts <span class=\"badge text-bg-secondary align-text-top\">1</span></button><li class=nav-item role=presentation><button aria-controls=tab-pane-overview-reproduction aria-selected=false class=nav-link data-bs-target=#tab-pane-overview-reproduction data-bs-toggle=tab id=tab-overview-reproduction role=tab type=button>Reproduction</button></ul><div class=tab-content><div class=\"tab-pane fade show active\" aria-labelledby=tab-pane-overview-dataset_overview id=tab-pane-overview-dataset_overview role=tabpanel><div class=\"row item\"><div class=\"row sub-item\"><div class=col-sm-6><div class=table-responsive><p class=\"h4 item-header\">Dataset statistics<table class=\"table table-striped\"><tbody><tr><th>Number of variables<td style=\"white-space: nowrap;\">2<tr><th>Number of observations<td style=\"white-space: nowrap;\">8000<tr><th>Missing cells<td style=\"white-space: nowrap;\">0<tr><th>Missing cells (%)<td style=\"white-space: nowrap;\">0.0%<tr><th>Duplicate rows<td style=\"white-space: nowrap;\">11<tr><th>Duplicate rows (%)<td style=\"white-space: nowrap;\">0.1%<tr><th>Total size in memory<td style=\"white-space: nowrap;\">10.4 MiB<tr><th>Average record size in memory<td style=\"white-space: nowrap;\">1.3 KiB</table></div></div><div class=col-sm-6><div class=table-responsive><p class=\"h4 item-header\">Variable types<table class=\"table table-striped\"><tbody><tr><th>Text<td style=\"white-space: nowrap;\">1<tr><th>Categorical<td style=\"white-space: nowrap;\">1</table></div></div></div></div></div><div class=\"tab-pane fade\" aria-labelledby=tab-pane-overview-alerts id=tab-pane-overview-alerts role=tabpanel><div class=\"row item\"><div class=col-sm-12><table class=\"table table-striped\"><p class=\"h4 item-header\">Alerts</p><tr><td>Dataset has 11 (0.1%) <a href=#duplicate>duplicate rows</a><td><span class=\"badge text-bg-secondary\">Duplicates</span></table></div></div></div><div class=\"tab-pane fade\" aria-labelledby=tab-pane-overview-reproduction id=tab-pane-overview-reproduction role=tabpanel><div class=\"row item\"><div class=\"row sub-item\"><div class=col-sm-12><div class=table-responsive><p class=\"h4 item-header\">Reproduction<table class=\"table table-striped\"><tbody><tr><th>Analysis started<td style=\"white-space: nowrap;\">2025-12-13 18:05:38.681985<tr><th>Analysis finished<td style=\"white-space: nowrap;\">2025-12-13 18:05:42.425931<tr><th>Duration<td style=\"white-space: nowrap;\">3.74 seconds<tr><th>Software version<td style=\"white-space: nowrap;\"><a href=https://github.com/ydataai/ydata-profiling>ydata-profiling vv4.18.0</a><tr><th>Download configuration<td style=\"white-space: nowrap;\"><a 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20%22theme%22%3A%20null%7D%2C%20%22navbar_show%22%3A%20true%2C%20%22minify_html%22%3A%20true%2C%20%22use_local_assets%22%3A%20true%2C%20%22inline%22%3A%20true%2C%20%22assets_prefix%22%3A%20null%2C%20%22assets_path%22%3A%20null%2C%20%22full_width%22%3A%20false%7D%2C%20%22notebook%22%3A%20%7B%22iframe%22%3A%20%7B%22height%22%3A%20%22800px%22%2C%20%22width%22%3A%20%22100%25%22%2C%20%22attribute%22%3A%20%22srcdoc%22%7D%7D%7D\" download=config.json>config.json</a></table></div></div></div></div></div></div></div></div><div class=section-header id=variables-dropdown><h1 class=section-name>Variables</h1></div><div class=section-items><select class=form-select id=variables-dropdown name=Variables><option value>Select Columns<option value=review>review<option value=sentiment>sentiment</select><div class=\"row item\"><div class=variable id=pp_var_-8617978398028572019><div class=\"row sub-item\"><div class=col-sm-12><p class=\"item-header h4\" title=review><a href=#pp_var_-8617978398028572019>review</a> <br> <span class=\"fs-6 text-body-secondary\">Text</span><p class=variable-description></div><div class=col-sm-6><div class=table-responsive><table class=\"table table-striped\"><tbody><tr><th>Distinct<td style=\"white-space: nowrap;\">7989<tr><th>Distinct (%)<td style=\"white-space: nowrap;\">99.9%<tr><th>Missing<td style=\"white-space: nowrap;\">0<tr><th>Missing (%)<td style=\"white-space: nowrap;\">0.0%<tr><th>Memory size<td style=\"white-space: nowrap;\">10.3 MiB</table></div></div><div class=col-sm-6><?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?><!doctype html svg class=\"img-responsive center-img\"PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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\" height=221.76 id=image70398bc941 width=332.64 x=55.08 y=-34.56 /></g></g></g><defs><clippath id=pbda249511d><rect height=221.76 width=332.64 x=55.08 y=34.56 /></clippath></defs></svg></div></div><div class=\"col-sm-12 text-end\"><button aria-controls=\"#bottom--8617978398028572019, #minifreqtable-8617978398028572019\" class=\"btn btn-light\" data-bs-target=\"#bottom--8617978398028572019, #minifreqtable-8617978398028572019\" aria-expanded=true data-bs-toggle=collapse>More details</button></div><div class=collapse id=bottom--8617978398028572019><div class=\"row item\"><ul class=\"nav nav-tabs tab-nav\" role=tablist><li class=nav-item role=presentation><button class=\"nav-link active\" aria-controls=tab-pane--8617978398028572019bottom--8617978398028572019overview aria-selected=true data-bs-target=#tab-pane--8617978398028572019bottom--8617978398028572019overview data-bs-toggle=tab id=tab--8617978398028572019bottom--8617978398028572019overview role=tab type=button>Overview</button><li class=nav-item role=presentation><button aria-controls=tab-pane--8617978398028572019bottom--8617978398028572019word aria-selected=false class=nav-link data-bs-target=#tab-pane--8617978398028572019bottom--8617978398028572019word data-bs-toggle=tab id=tab--8617978398028572019bottom--8617978398028572019word role=tab type=button>Words</button><li class=nav-item role=presentation><button aria-controls=tab-pane--8617978398028572019bottom--8617978398028572019characters aria-selected=false class=nav-link data-bs-target=#tab-pane--8617978398028572019bottom--8617978398028572019characters data-bs-toggle=tab id=tab--8617978398028572019bottom--8617978398028572019characters role=tab type=button>Characters</button></ul><div class=tab-content><div class=\"tab-pane fade show active\" aria-labelledby=tab-pane--8617978398028572019bottom--8617978398028572019overview id=tab-pane--8617978398028572019bottom--8617978398028572019overview role=tabpanel><div class=\"row item\"><div class=\"row sub-item\"><div class=col-sm-3><div class=table-responsive><p class=\"h4 item-header\">Length<table class=\"table table-striped\"><tbody><tr><th>Max length<td style=\"white-space: nowrap;\">9342<tr><th>Median length<td style=\"white-space: nowrap;\">2682<tr><th>Mean length<td style=\"white-space: nowrap;\">1182.6344<tr><th>Min length<td style=\"white-space: nowrap;\">32</table></div></div><div class=col-sm-3><div class=table-responsive><p class=\"h4 item-header\">Characters and Unicode<table class=\"table table-striped\"><tbody><tr><th>Total characters<td style=\"white-space: nowrap;\">9461075<tr><th>Distinct characters<td style=\"white-space: nowrap;\">45<tr><th>Distinct categories<td style=\"white-space: nowrap;\">1 <a href=https://en.wikipedia.org/wiki/Unicode_character_property#General_Category><span class=\"badge text-bg-secondary float-end\" data-bs-title=\"Unicode categories (click for more information)\" data-bs-placement=right data-bs-toggle=tooltip>?</span></a><tr><th>Distinct scripts<td style=\"white-space: nowrap;\">1 <a href=https://en.wikipedia.org/wiki/Script_(Unicode)#List_of_scripts_in_Unicode><span class=\"badge text-bg-secondary float-end\" data-bs-title=\"Unicode scripts (click for more information)\" data-bs-placement=right data-bs-toggle=tooltip>?</span></a><tr><th>Distinct blocks<td style=\"white-space: nowrap;\">1 <a href=https://en.wikipedia.org/wiki/Unicode_block><span class=\"badge text-bg-secondary float-end\" data-bs-title=\"Unicode blocks (click for more information)\" data-bs-placement=right data-bs-toggle=tooltip>?</span></a></table><div class=\"caption text-center text-body-secondary\">The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.</div></div></div><div class=col-sm-3><div class=table-responsive><p class=\"h4 item-header\">Unique<table class=\"table table-striped\"><tbody><tr><th>Unique<td style=\"white-space: nowrap;\">7978 <span class=\"badge text-bg-secondary float-end\" data-bs-title=\"The number of unique values (all values that occur exactly once in the dataset).\" data-bs-placement=right data-bs-toggle=tooltip>?</span><tr><th>Unique (%)<td style=\"white-space: nowrap;\">99.7%</table></div></div><div class=col-sm-3><div class=table-responsive><p class=\"h4 item-header\">Sample<table class=\"table table-striped\"><tbody><tr><th>1st row<td style=\"white-space: nowrap;\">? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all<tr><th>2nd row<td style=\"white-space: nowrap;\">? big hair big boobs bad music and a giant safety pin these are the words to best describe this terrible movie i love cheesy horror movies and i've seen hundreds but this had got to be on of the worst ever made the plot is paper thin and ridiculous the acting is an abomination the script is completely laughable the best is the end showdown with the cop and how he worked out who the killer is it's just so damn terribly written the clothes are sickening and funny in equal ? the hair is big lots of boobs ? men wear those cut ? shirts that show off their ? sickening that men actually wore them and the music is just ? trash that plays over and over again in almost every scene there is trashy music boobs and ? taking away bodies and the gym still doesn't close for ? all joking aside this is a truly bad film whose only charm is to look back on the disaster that was the 80's and have a good old laugh at how bad everything was back then<tr><th>3rd row<td style=\"white-space: nowrap;\">? this has to be one of the worst films of the 1990s when my friends i were watching this film being the target audience it was aimed at we just sat watched the first half an hour with our jaws touching the floor at how bad it really was the rest of the time everyone else in the theatre just started talking to each other leaving or generally crying into their popcorn that they actually paid money they had ? working to watch this feeble excuse for a film it must have looked like a great idea on paper but on film it looks like no one in the film has a clue what is going on crap acting crap costumes i can't get across how ? this is to watch save yourself an hour a bit of your life<tr><th>4th row<td style=\"white-space: nowrap;\">? the ? ? at storytelling the traditional sort many years after the event i can still see in my ? eye an elderly lady my friend's mother retelling the battle of ? she makes the characters come alive her passion is that of an eye witness one to the events on the ? heath a mile or so from where she lives br br of course it happened many years before she was born but you wouldn't guess from the way she tells it the same story is told in bars the length and ? of scotland as i discussed it with a friend one night in ? a local cut in to give his version the discussion continued to closing time br br stories passed down like this become part of our being who doesn't remember the stories our parents told us when we were children they become our invisible world and as we grow older they maybe still serve as inspiration or as an emotional ? fact and fiction blend with ? role models warning stories ? magic and mystery br br my name is ? like my grandfather and his grandfather before him our protagonist introduces himself to us and also introduces the story that stretches back through generations it produces stories within stories stories that evoke the ? wonder of scotland its rugged mountains ? in ? the stuff of legend yet ? is ? in reality this is what gives it its special charm it has a rough beauty and authenticity ? with some of the finest ? singing you will ever hear br br ? ? visits his grandfather in hospital shortly before his death he burns with frustration part of him ? to be in the twenty first century to hang out in ? but he is raised on the western ? among a ? speaking community br br yet there is a deeper conflict within him he ? to know the truth the truth behind his ? ancient stories where does fiction end and he wants to know the truth behind the death of his parents br br he is pulled to make a last ? journey to the ? of one of ? most ? mountains can the truth be told or is it all in stories br br in this story about stories we ? bloody battles ? lovers the ? of old and the sometimes more ? ? of accepted truth in doing so we each connect with ? as he lives the story of his own life br br ? the ? ? is probably the most honest ? and genuinely beautiful film of scotland ever made like ? i got slightly annoyed with the ? of hanging stories on more stories but also like ? i ? this once i saw the ? picture ' forget the box office ? of braveheart and its like you might even ? the ? famous ? of the wicker man to see a film that is true to scotland this one is probably unique if you maybe ? on it deeply enough you might even re ? the power of storytelling and the age old question of whether there are some truths that cannot be told but only experienced<tr><th>5th row<td style=\"white-space: nowrap;\">? worst mistake of my life br br i picked this movie up at target for 5 because i figured hey it's sandler i can get some cheap laughs i was wrong completely wrong mid way through the film all three of my friends were asleep and i was still suffering worst plot worst script worst movie i have ever seen i wanted to hit my head up against a wall for an hour then i'd stop and you know why because it felt damn good upon bashing my head in i stuck that damn movie in the ? and watched it burn and that felt better than anything else i've ever done it took american psycho army of darkness and kill bill just to get over that crap i hate you sandler for actually going through with this and ruining a whole day of my life</table></div></div></div></div></div><div class=\"tab-pane fade\" aria-labelledby=tab-pane--8617978398028572019bottom--8617978398028572019word id=tab-pane--8617978398028572019bottom--8617978398028572019word role=tabpanel><div class=\"row item\"><div class=\"row sub-item\"><div class=col-sm-6><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td><td>112178<td><div class=bar style=width:8.3%> </div> 5.9%<tr><td title=the>the<td>107659<td><div class=bar style=width:8.0%> </div> 5.7%<tr><td title=and>and<td>52204<td><div class=bar style=width:3.9%> </div> 2.8%<tr><td title=a>a<td>51720<td><div class=bar style=width:3.8%> </div> 2.7%<tr><td title=of>of<td>46273<td><div class=bar style=width:3.4%> </div> 2.4%<tr><td title=to>to<td>43235<td><div class=bar style=width:3.2%> </div> 2.3%<tr><td title=is>is<td>34155<td><div class=bar style=width:2.5%> </div> 1.8%<tr><td title=br>br<td>32558<td><div class=bar style=width:2.4%> </div> 1.7%<tr><td title=in>in<td>30056<td><div class=bar style=width:2.2%> </div> 1.6%<tr><td title=it>it<td>24962<td><div class=bar style=width:1.8%> </div> 1.3%<tr class=other><td title=\"Other values (9962)\">Other values (9962)<td>1353982<td><div class=bar style=width:100.0%>71.7%</div></table></div></div><div class=col-sm-6><?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?><!doctype html svg class=\"img-responsive center-img\"PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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\" height=221.76 id=image79e004642a width=332.64 x=55.08 y=-34.56 /></g></g></g><defs><clippath id=p1c500b162b><rect height=221.76 width=332.64 x=55.08 y=34.56 /></clippath></defs></svg></div></div></div></div><div class=\"tab-pane fade\" aria-labelledby=tab-pane--8617978398028572019bottom--8617978398028572019characters id=tab-pane--8617978398028572019bottom--8617978398028572019characters role=tabpanel><div class=\"row item\"><div class=\"row sub-item\"><div class=col-sm-12><div class=\"row item\"><ul class=\"nav nav-tabs tab-nav\" role=tablist><li class=nav-item role=presentation><button class=\"nav-link active\" aria-controls=tab-pane--8617978398028572019unicode--8617978398028572019characters aria-selected=true data-bs-target=#tab-pane--8617978398028572019unicode--8617978398028572019characters data-bs-toggle=tab id=tab--8617978398028572019unicode--8617978398028572019characters role=tab type=button>Characters</button><li class=nav-item role=presentation><button aria-controls=tab-pane--8617978398028572019unicode--8617978398028572019categories aria-selected=false class=nav-link data-bs-target=#tab-pane--8617978398028572019unicode--8617978398028572019categories data-bs-toggle=tab id=tab--8617978398028572019unicode--8617978398028572019categories role=tab type=button>Categories</button><li class=nav-item role=presentation><button aria-controls=tab-pane--8617978398028572019unicode--8617978398028572019scripts aria-selected=false class=nav-link data-bs-target=#tab-pane--8617978398028572019unicode--8617978398028572019scripts data-bs-toggle=tab id=tab--8617978398028572019unicode--8617978398028572019scripts role=tab type=button>Scripts</button><li class=nav-item role=presentation><button aria-controls=tab-pane--8617978398028572019unicode--8617978398028572019blocks aria-selected=false class=nav-link data-bs-target=#tab-pane--8617978398028572019unicode--8617978398028572019blocks data-bs-toggle=tab id=tab--8617978398028572019unicode--8617978398028572019blocks role=tab type=button>Blocks</button></ul><div class=tab-content><div class=\"tab-pane fade show active\" aria-labelledby=tab-pane--8617978398028572019unicode--8617978398028572019characters id=tab-pane--8617978398028572019unicode--8617978398028572019characters role=tabpanel><div class=\"row item\"><div class=\"row named-list-item\"><h4 class=item-header>Most occurring characters</h4><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=\" \"><td>1881020<td><div class=bar style=width:77.8%>19.9%</div><tr><td title=e>e<td>890922<td><div class=bar style=width:36.8%> </div> 9.4%<tr><td title=t>t<td>718488<td><div class=bar style=width:29.7%> </div> 7.6%<tr><td title=a>a<td>593872<td><div class=bar style=width:24.6%> </div> 6.3%<tr><td title=i>i<td>563046<td><div class=bar style=width:23.3%> </div> 6.0%<tr><td title=o>o<td>562228<td><div class=bar style=width:23.3%> </div> 5.9%<tr><td title=s>s<td>489830<td><div class=bar style=width:20.3%> </div> 5.2%<tr><td title=n>n<td>483563<td><div class=bar style=width:20.0%> </div> 5.1%<tr><td title=r>r<td>433624<td><div class=bar style=width:17.9%> </div> 4.6%<tr><td title=h>h<td>426650<td><div class=bar style=width:17.6%> </div> 4.5%<tr class=other><td title=\"Other values (35)\">Other values (35)<td>2417832<td><div class=bar style=width:100.0%>25.6%</div></table></div></div></div></div><div class=\"tab-pane fade\" aria-labelledby=tab-pane--8617978398028572019unicode--8617978398028572019categories id=tab-pane--8617978398028572019unicode--8617978398028572019categories role=tabpanel><div class=\"row item\"><div class=\"row named-list-item\"><h4 class=item-header>Most occurring categories</h4><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=(unknown)>(unknown)<td>9461075<td><div class=bar style=width:100.0%>100.0%</div></table></div></div><div class=\"row named-list-item\"><h4 class=item-header>Most frequent character per category</h4><div class=\"row sub-item\"><div class=col-sm-12><h5 class=item-header>(unknown)</h5><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=\" \"><td>1881020<td><div class=bar style=width:77.8%>19.9%</div><tr><td title=e>e<td>890922<td><div class=bar style=width:36.8%> </div> 9.4%<tr><td title=t>t<td>718488<td><div class=bar style=width:29.7%> </div> 7.6%<tr><td title=a>a<td>593872<td><div class=bar style=width:24.6%> </div> 6.3%<tr><td title=i>i<td>563046<td><div class=bar style=width:23.3%> </div> 6.0%<tr><td title=o>o<td>562228<td><div class=bar style=width:23.3%> </div> 5.9%<tr><td title=s>s<td>489830<td><div class=bar style=width:20.3%> </div> 5.2%<tr><td title=n>n<td>483563<td><div class=bar style=width:20.0%> </div> 5.1%<tr><td title=r>r<td>433624<td><div class=bar style=width:17.9%> </div> 4.6%<tr><td title=h>h<td>426650<td><div class=bar style=width:17.6%> </div> 4.5%<tr class=other><td title=\"Other values (35)\">Other values (35)<td>2417832<td><div class=bar style=width:100.0%>25.6%</div></table></div></div></div></div></div></div><div class=\"tab-pane fade\" aria-labelledby=tab-pane--8617978398028572019unicode--8617978398028572019scripts id=tab-pane--8617978398028572019unicode--8617978398028572019scripts role=tabpanel><div class=\"row item\"><div class=\"row named-list-item\"><h4 class=item-header>Most occurring scripts</h4><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=(unknown)>(unknown)<td>9461075<td><div class=bar style=width:100.0%>100.0%</div></table></div></div><div class=\"row named-list-item\"><h4 class=item-header>Most frequent character per script</h4><div class=\"row sub-item\"><div class=col-sm-12><h5 class=item-header>(unknown)</h5><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=\" \"><td>1881020<td><div class=bar style=width:77.8%>19.9%</div><tr><td title=e>e<td>890922<td><div class=bar style=width:36.8%> </div> 9.4%<tr><td title=t>t<td>718488<td><div class=bar style=width:29.7%> </div> 7.6%<tr><td title=a>a<td>593872<td><div class=bar style=width:24.6%> </div> 6.3%<tr><td title=i>i<td>563046<td><div class=bar style=width:23.3%> </div> 6.0%<tr><td title=o>o<td>562228<td><div class=bar style=width:23.3%> </div> 5.9%<tr><td title=s>s<td>489830<td><div class=bar style=width:20.3%> </div> 5.2%<tr><td title=n>n<td>483563<td><div class=bar style=width:20.0%> </div> 5.1%<tr><td title=r>r<td>433624<td><div class=bar style=width:17.9%> </div> 4.6%<tr><td title=h>h<td>426650<td><div class=bar style=width:17.6%> </div> 4.5%<tr class=other><td title=\"Other values (35)\">Other values (35)<td>2417832<td><div class=bar style=width:100.0%>25.6%</div></table></div></div></div></div></div></div><div class=\"tab-pane fade\" aria-labelledby=tab-pane--8617978398028572019unicode--8617978398028572019blocks id=tab-pane--8617978398028572019unicode--8617978398028572019blocks role=tabpanel><div class=\"row item\"><div class=\"row named-list-item\"><h4 class=item-header>Most occurring blocks</h4><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=(unknown)>(unknown)<td>9461075<td><div class=bar style=width:100.0%>100.0%</div></table></div></div><div class=\"row named-list-item\"><h4 class=item-header>Most frequent character per block</h4><div class=\"row sub-item\"><div class=col-sm-12><h5 class=item-header>(unknown)</h5><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=\" \"><td>1881020<td><div class=bar style=width:77.8%>19.9%</div><tr><td title=e>e<td>890922<td><div class=bar style=width:36.8%> </div> 9.4%<tr><td title=t>t<td>718488<td><div class=bar style=width:29.7%> </div> 7.6%<tr><td title=a>a<td>593872<td><div class=bar style=width:24.6%> </div> 6.3%<tr><td title=i>i<td>563046<td><div class=bar style=width:23.3%> </div> 6.0%<tr><td title=o>o<td>562228<td><div class=bar style=width:23.3%> </div> 5.9%<tr><td title=s>s<td>489830<td><div class=bar style=width:20.3%> </div> 5.2%<tr><td title=n>n<td>483563<td><div class=bar style=width:20.0%> </div> 5.1%<tr><td title=r>r<td>433624<td><div class=bar style=width:17.9%> </div> 4.6%<tr><td title=h>h<td>426650<td><div class=bar style=width:17.6%> </div> 4.5%<tr class=other><td title=\"Other values (35)\">Other values (35)<td>2417832<td><div class=bar style=width:100.0%>25.6%</div></table></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><div class=\"row item\"><div class=variable id=pp_var_1807126248182587582><div class=\"row sub-item\"><div class=col-sm-12><p class=\"item-header h4\" title=sentiment><a href=#pp_var_1807126248182587582>sentiment</a> <br> <span class=\"fs-6 text-body-secondary\">Categorical</span><p class=variable-description></div><div class=col-sm-6><div class=table-responsive><table class=\"table table-striped\"><tbody><tr><th>Distinct<td style=\"white-space: nowrap;\">2<tr><th>Distinct (%)<td style=\"white-space: nowrap;\">&lt; 0.1%<tr><th>Missing<td style=\"white-space: nowrap;\">0<tr><th>Missing (%)<td style=\"white-space: nowrap;\">0.0%<tr><th>Memory size<td style=\"white-space: nowrap;\">453.1 KiB</table></div></div><div class=col-sm-6><div class=row id=minifreqtable><div class=table-responsive><table class=\"mini freq table-0\"><tr><th width=10%>0<td width=10%><div class=bar data-bs-delay=500 data-bs-html=true data-bs-placement=center data-bs-toggle=tooltip style=width:100.0%>4035 </div><tr><th width=10%>1<td width=10%><div class=bar data-bs-delay=500 data-bs-html=true data-bs-placement=center data-bs-toggle=tooltip style=width:98.3%>3965 </div></table></div></div></div></div><div class=\"col-sm-12 text-end\"><button aria-controls=\"#bottom-1807126248182587582, #minifreqtable1807126248182587582\" class=\"btn btn-light\" data-bs-target=\"#bottom-1807126248182587582, #minifreqtable1807126248182587582\" aria-expanded=true data-bs-toggle=collapse>More details</button></div><div class=collapse id=bottom-1807126248182587582><div class=\"row item\"><ul class=\"nav nav-tabs tab-nav\" role=tablist><li class=nav-item role=presentation><button class=\"nav-link active\" aria-controls=tab-pane-1807126248182587582bottom-1807126248182587582overview aria-selected=true data-bs-target=#tab-pane-1807126248182587582bottom-1807126248182587582overview data-bs-toggle=tab id=tab-1807126248182587582bottom-1807126248182587582overview role=tab type=button>Overview</button><li class=nav-item role=presentation><button aria-controls=tab-pane-1807126248182587582bottom-1807126248182587582string aria-selected=false class=nav-link data-bs-target=#tab-pane-1807126248182587582bottom-1807126248182587582string data-bs-toggle=tab id=tab-1807126248182587582bottom-1807126248182587582string role=tab type=button>Categories</button><li class=nav-item role=presentation><button aria-controls=tab-pane-1807126248182587582bottom-1807126248182587582word aria-selected=false class=nav-link data-bs-target=#tab-pane-1807126248182587582bottom-1807126248182587582word data-bs-toggle=tab id=tab-1807126248182587582bottom-1807126248182587582word role=tab type=button>Words</button><li class=nav-item role=presentation><button aria-controls=tab-pane-1807126248182587582bottom-1807126248182587582characters aria-selected=false class=nav-link data-bs-target=#tab-pane-1807126248182587582bottom-1807126248182587582characters data-bs-toggle=tab id=tab-1807126248182587582bottom-1807126248182587582characters role=tab type=button>Characters</button></ul><div class=tab-content><div class=\"tab-pane fade show active\" aria-labelledby=tab-pane-1807126248182587582bottom-1807126248182587582overview id=tab-pane-1807126248182587582bottom-1807126248182587582overview role=tabpanel><div class=\"row item\"><div class=\"row sub-item\"><div class=col-sm-3><div class=table-responsive><p class=\"h4 item-header\">Length<table class=\"table table-striped\"><tbody><tr><th>Max length<td style=\"white-space: nowrap;\">1<tr><th>Median length<td style=\"white-space: nowrap;\">1<tr><th>Mean length<td style=\"white-space: nowrap;\">1<tr><th>Min length<td style=\"white-space: nowrap;\">1</table></div></div><div class=col-sm-3><div class=table-responsive><p class=\"h4 item-header\">Characters and Unicode<table class=\"table table-striped\"><tbody><tr><th>Total characters<td style=\"white-space: nowrap;\">8000<tr><th>Distinct characters<td style=\"white-space: nowrap;\">2<tr><th>Distinct categories<td style=\"white-space: nowrap;\">1 <a href=https://en.wikipedia.org/wiki/Unicode_character_property#General_Category><span class=\"badge text-bg-secondary float-end\" data-bs-title=\"Unicode categories (click for more information)\" data-bs-placement=right data-bs-toggle=tooltip>?</span></a><tr><th>Distinct scripts<td style=\"white-space: nowrap;\">1 <a href=https://en.wikipedia.org/wiki/Script_(Unicode)#List_of_scripts_in_Unicode><span class=\"badge text-bg-secondary float-end\" data-bs-title=\"Unicode scripts (click for more information)\" data-bs-placement=right data-bs-toggle=tooltip>?</span></a><tr><th>Distinct blocks<td style=\"white-space: nowrap;\">1 <a href=https://en.wikipedia.org/wiki/Unicode_block><span class=\"badge text-bg-secondary float-end\" data-bs-title=\"Unicode blocks (click for more information)\" data-bs-placement=right data-bs-toggle=tooltip>?</span></a></table><div class=\"caption text-center text-body-secondary\">The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.</div></div></div><div class=col-sm-3><div class=table-responsive><p class=\"h4 item-header\">Unique<table class=\"table table-striped\"><tbody><tr><th>Unique<td style=\"white-space: nowrap;\">0 <span class=\"badge text-bg-secondary float-end\" data-bs-title=\"The number of unique values (all values that occur exactly once in the dataset).\" data-bs-placement=right data-bs-toggle=tooltip>?</span><tr><th>Unique (%)<td style=\"white-space: nowrap;\">0.0%</table></div></div><div class=col-sm-3><div class=table-responsive><p class=\"h4 item-header\">Sample<table class=\"table table-striped\"><tbody><tr><th>1st row<td style=\"white-space: nowrap;\">1<tr><th>2nd row<td style=\"white-space: nowrap;\">0<tr><th>3rd row<td style=\"white-space: nowrap;\">0<tr><th>4th row<td style=\"white-space: nowrap;\">1<tr><th>5th row<td style=\"white-space: nowrap;\">0</table></div></div></div></div></div><div class=\"tab-pane fade\" aria-labelledby=tab-pane-1807126248182587582bottom-1807126248182587582string id=tab-pane-1807126248182587582bottom-1807126248182587582string role=tabpanel><div class=\"row item\"><div class=\"row sub-item\"><div class=col-sm-12><h4 class=item-header>Common Values</h4><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=0>0<td>4035<td><div class=bar style=width:100.0%>50.4%</div><tr><td title=1>1<td>3965<td><div class=bar style=width:98.3%>49.6%</div></table></div></div></div><div class=\"row sub-item\"><div class=col-sm-12><h4 class=item-header>Length</h4><?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?><!doctype html svg class=\"img-responsive center-img\"PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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167.398607\" style=\"fill: none\"/></g><g id=patch_7><path d=\"M 61.39 10.8 L 493.2 10.8\" style=\"fill: none\"/></g></g></g><defs><clippath id=p316d45204f><rect height=156.598607 width=431.81 x=61.39 y=10.8 /></clippath></defs></svg><div class=\"caption text-center text-body-secondary\">Histogram of lengths of the category</div></div></div><div class=\"row sub-item\"><div class=col-sm-12><h4 class=item-header>Common Values (Plot)</h4><?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?><!doctype html svg class=\"img-responsive center-img\"PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=0>0<td>4035<td><div class=bar style=width:100.0%>50.4%</div><tr><td title=1>1<td>3965<td><div class=bar style=width:98.3%>49.6%</div></table></div></div></div></div></div><div class=\"tab-pane fade\" aria-labelledby=tab-pane-1807126248182587582bottom-1807126248182587582characters id=tab-pane-1807126248182587582bottom-1807126248182587582characters role=tabpanel><div class=\"row item\"><div class=\"row sub-item\"><div class=col-sm-12><div class=\"row item\"><ul class=\"nav nav-tabs tab-nav\" role=tablist><li class=nav-item role=presentation><button class=\"nav-link active\" aria-controls=tab-pane-1807126248182587582unicode-1807126248182587582characters aria-selected=true data-bs-target=#tab-pane-1807126248182587582unicode-1807126248182587582characters data-bs-toggle=tab id=tab-1807126248182587582unicode-1807126248182587582characters role=tab type=button>Characters</button><li class=nav-item role=presentation><button aria-controls=tab-pane-1807126248182587582unicode-1807126248182587582categories aria-selected=false class=nav-link data-bs-target=#tab-pane-1807126248182587582unicode-1807126248182587582categories data-bs-toggle=tab id=tab-1807126248182587582unicode-1807126248182587582categories role=tab type=button>Categories</button><li class=nav-item role=presentation><button aria-controls=tab-pane-1807126248182587582unicode-1807126248182587582scripts aria-selected=false class=nav-link data-bs-target=#tab-pane-1807126248182587582unicode-1807126248182587582scripts data-bs-toggle=tab id=tab-1807126248182587582unicode-1807126248182587582scripts role=tab type=button>Scripts</button><li class=nav-item role=presentation><button aria-controls=tab-pane-1807126248182587582unicode-1807126248182587582blocks aria-selected=false class=nav-link data-bs-target=#tab-pane-1807126248182587582unicode-1807126248182587582blocks data-bs-toggle=tab id=tab-1807126248182587582unicode-1807126248182587582blocks role=tab type=button>Blocks</button></ul><div class=tab-content><div class=\"tab-pane fade show active\" aria-labelledby=tab-pane-1807126248182587582unicode-1807126248182587582characters id=tab-pane-1807126248182587582unicode-1807126248182587582characters role=tabpanel><div class=\"row item\"><div class=\"row named-list-item\"><h4 class=item-header>Most occurring characters</h4><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=0>0<td>4035<td><div class=bar style=width:100.0%>50.4%</div><tr><td title=1>1<td>3965<td><div class=bar style=width:98.3%>49.6%</div></table></div></div></div></div><div class=\"tab-pane fade\" aria-labelledby=tab-pane-1807126248182587582unicode-1807126248182587582categories id=tab-pane-1807126248182587582unicode-1807126248182587582categories role=tabpanel><div class=\"row item\"><div class=\"row named-list-item\"><h4 class=item-header>Most occurring categories</h4><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=(unknown)>(unknown)<td>8000<td><div class=bar style=width:100.0%>100.0%</div></table></div></div><div class=\"row named-list-item\"><h4 class=item-header>Most frequent character per category</h4><div class=\"row sub-item\"><div class=col-sm-12><h5 class=item-header>(unknown)</h5><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=0>0<td>4035<td><div class=bar style=width:100.0%>50.4%</div><tr><td title=1>1<td>3965<td><div class=bar style=width:98.3%>49.6%</div></table></div></div></div></div></div></div><div class=\"tab-pane fade\" aria-labelledby=tab-pane-1807126248182587582unicode-1807126248182587582scripts id=tab-pane-1807126248182587582unicode-1807126248182587582scripts role=tabpanel><div class=\"row item\"><div class=\"row named-list-item\"><h4 class=item-header>Most occurring scripts</h4><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=(unknown)>(unknown)<td>8000<td><div class=bar style=width:100.0%>100.0%</div></table></div></div><div class=\"row named-list-item\"><h4 class=item-header>Most frequent character per script</h4><div class=\"row sub-item\"><div class=col-sm-12><h5 class=item-header>(unknown)</h5><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=0>0<td>4035<td><div class=bar style=width:100.0%>50.4%</div><tr><td title=1>1<td>3965<td><div class=bar style=width:98.3%>49.6%</div></table></div></div></div></div></div></div><div class=\"tab-pane fade\" aria-labelledby=tab-pane-1807126248182587582unicode-1807126248182587582blocks id=tab-pane-1807126248182587582unicode-1807126248182587582blocks role=tabpanel><div class=\"row item\"><div class=\"row named-list-item\"><h4 class=item-header>Most occurring blocks</h4><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=(unknown)>(unknown)<td>8000<td><div class=bar style=width:100.0%>100.0%</div></table></div></div><div class=\"row named-list-item\"><h4 class=item-header>Most frequent character per block</h4><div class=\"row sub-item\"><div class=col-sm-12><h5 class=item-header>(unknown)</h5><div class=table-responsive><table class=\"freq table table-hover table-striped table-0\"><thead><tr><td>Value<td>Count<td>Frequency (%)<tbody><tr><td title=0>0<td>4035<td><div class=bar style=width:100.0%>50.4%</div><tr><td title=1>1<td>3965<td><div class=bar style=width:98.3%>49.6%</div></table></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div><div class=section-header id=missing><h1 class=section-name>Missing values</h1></div><div class=section-items><div class=\"row item\"><ul class=\"nav nav-tabs tab-nav\" role=tablist><li class=nav-item role=presentation><button class=\"nav-link active\" aria-controls=tab-pane-missing-bar aria-selected=true data-bs-target=#tab-pane-missing-bar data-bs-toggle=tab id=tab-missing-bar role=tab type=button>Count</button><li class=nav-item role=presentation><button aria-controls=tab-pane-missing-matrix aria-selected=false class=nav-link data-bs-target=#tab-pane-missing-matrix data-bs-toggle=tab id=tab-missing-matrix role=tab type=button>Matrix</button></ul><div class=tab-content><div class=\"tab-pane fade show active\" aria-labelledby=tab-pane-missing-bar id=tab-pane-missing-bar role=tabpanel><div class=\"row item\"><?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?><!doctype html svg class=\"img-responsive center-img\"PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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/></defs><use xlink:href=#DejaVuSans-31 /><use transform=\"translate(63.623047 0)\" xlink:href=#DejaVuSans-2e /><use transform=\"translate(95.410156 0)\" xlink:href=#DejaVuSans-30 /></g></g></g></g><g id=patch_3><path d=\"M 144 252 L 288 252 L 288 80.571429 L 144 80.571429 z\" style=\"fill: #0d6efd; stroke: #ffffff; stroke-width: 0.3; stroke-linejoin: miter\" clip-path=url(#pecb8a12dae) /></g><g id=patch_4><path d=\"M 432 252 L 576 252 L 576 80.571429 L 432 80.571429 z\" style=\"fill: #0d6efd; stroke: #ffffff; stroke-width: 0.3; stroke-linejoin: miter\" clip-path=url(#pecb8a12dae) /></g></g><g id=axes_2><g id=matplotlib.axis_3><g id=xtick_3><g id=text_9><g style=\"fill: #262626\" transform=\"translate(222.984779 59.588271) rotate(-45) scale(0.13 -0.13)\"><use xlink:href=#DejaVuSans-38 /><use transform=\"translate(63.623047 0)\" xlink:href=#DejaVuSans-30 /><use transform=\"translate(127.246094 0)\" xlink:href=#DejaVuSans-30 /><use transform=\"translate(190.869141 0)\" xlink:href=#DejaVuSans-30 /></g></g></g><g id=xtick_4><g id=text_10><g style=\"fill: #262626\" transform=\"translate(510.984779 59.588271) rotate(-45) scale(0.13 -0.13)\"><use xlink:href=#DejaVuSans-38 /><use transform=\"translate(63.623047 0)\" xlink:href=#DejaVuSans-30 /><use transform=\"translate(127.246094 0)\" xlink:href=#DejaVuSans-30 /><use transform=\"translate(190.869141 0)\" xlink:href=#DejaVuSans-30 /></g></g></g></g></g></g><defs><clippath id=pecb8a12dae><rect height=180 width=576 x=72 y=72 /></clippath></defs></svg><div class=\"caption text-center text-body-secondary\">A simple visualization of nullity by column.</div></div></div><div class=\"tab-pane fade\" aria-labelledby=tab-pane-missing-matrix id=tab-pane-missing-matrix role=tabpanel><div class=\"row item\"><?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?><!doctype html svg class=\"img-responsive center-img\"PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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/></g></g></g><defs><clippath id=pea6f40acbf><rect height=144 width=576 x=72 y=86.4 /></clippath></defs></svg><div class=\"caption text-center text-body-secondary\">Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.</div></div></div></div></div></div><div class=section-header id=sample><h1 class=section-name>Sample</h1></div><div class=section-items><div class=\"row item\"><ul class=\"nav nav-tabs tab-nav\" role=tablist><li class=nav-item role=presentation><button class=\"nav-link active\" aria-controls=tab-pane-sample-head aria-selected=true data-bs-target=#tab-pane-sample-head data-bs-toggle=tab id=tab-sample-head role=tab type=button>First rows</button><li class=nav-item role=presentation><button aria-controls=tab-pane-sample-tail aria-selected=false class=nav-link data-bs-target=#tab-pane-sample-tail data-bs-toggle=tab id=tab-sample-tail role=tab type=button>Last rows</button></ul><div class=tab-content><div class=\"tab-pane fade show active\" aria-labelledby=tab-pane-sample-head id=tab-pane-sample-head role=tabpanel><div class=\"row item\"><div class=col-sm-12 id=sample-container><div class=table-responsive><table class=\"dataframe sample table table-striped\" border=1><thead><tr style=\"text-align: right;\"><th><th>review<th>sentiment<tbody><tr><th>0<td>? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all<td>1<tr><th>1<td>? big hair big boobs bad music and a giant safety pin these are the words to best describe this terrible movie i love cheesy horror movies and i've seen hundreds but this had got to be on of the worst ever made the plot is paper thin and ridiculous the acting is an abomination the script is completely laughable the best is the end showdown with the cop and how he worked out who the killer is it's just so damn terribly written the clothes are sickening and funny in equal ? the hair is big lots of boobs ? men wear those cut ? shirts that show off their ? sickening that men actually wore them and the music is just ? trash that plays over and over again in almost every scene there is trashy music boobs and ? taking away bodies and the gym still doesn't close for ? all joking aside this is a truly bad film whose only charm is to look back on the disaster that was the 80's and have a good old laugh at how bad everything was back then<td>0<tr><th>2<td>? this has to be one of the worst films of the 1990s when my friends i were watching this film being the target audience it was aimed at we just sat watched the first half an hour with our jaws touching the floor at how bad it really was the rest of the time everyone else in the theatre just started talking to each other leaving or generally crying into their popcorn that they actually paid money they had ? working to watch this feeble excuse for a film it must have looked like a great idea on paper but on film it looks like no one in the film has a clue what is going on crap acting crap costumes i can't get across how ? this is to watch save yourself an hour a bit of your life<td>0<tr><th>3<td>? the ? ? at storytelling the traditional sort many years after the event i can still see in my ? eye an elderly lady my friend's mother retelling the battle of ? she makes the characters come alive her passion is that of an eye witness one to the events on the ? heath a mile or so from where she lives br br of course it happened many years before she was born but you wouldn't guess from the way she tells it the same story is told in bars the length and ? of scotland as i discussed it with a friend one night in ? a local cut in to give his version the discussion continued to closing time br br stories passed down like this become part of our being who doesn't remember the stories our parents told us when we were children they become our invisible world and as we grow older they maybe still serve as inspiration or as an emotional ? fact and fiction blend with ? role models warning stories ? magic and mystery br br my name is ? like my grandfather and his grandfather before him our protagonist introduces himself to us and also introduces the story that stretches back through generations it produces stories within stories stories that evoke the ? wonder of scotland its rugged mountains ? in ? the stuff of legend yet ? is ? in reality this is what gives it its special charm it has a rough beauty and authenticity ? with some of the finest ? singing you will ever hear br br ? ? visits his grandfather in hospital shortly before his death he burns with frustration part of him ? to be in the twenty first century to hang out in ? but he is raised on the western ? among a ? speaking community br br yet there is a deeper conflict within him he ? to know the truth the truth behind his ? ancient stories where does fiction end and he wants to know the truth behind the death of his parents br br he is pulled to make a last ? journey to the ? of one of ? most ? mountains can the truth be told or is it all in stories br br in this story about stories we ? bloody battles ? lovers the ? of old and the sometimes more ? ? of accepted truth in doing so we each connect with ? as he lives the story of his own life br br ? the ? ? is probably the most honest ? and genuinely beautiful film of scotland ever made like ? i got slightly annoyed with the ? of hanging stories on more stories but also like ? i ? this once i saw the ? picture ' forget the box office ? of braveheart and its like you might even ? the ? famous ? of the wicker man to see a film that is true to scotland this one is probably unique if you maybe ? on it deeply enough you might even re ? the power of storytelling and the age old question of whether there are some truths that cannot be told but only experienced<td>1<tr><th>4<td>? worst mistake of my life br br i picked this movie up at target for 5 because i figured hey it's sandler i can get some cheap laughs i was wrong completely wrong mid way through the film all three of my friends were asleep and i was still suffering worst plot worst script worst movie i have ever seen i wanted to hit my head up against a wall for an hour then i'd stop and you know why because it felt damn good upon bashing my head in i stuck that damn movie in the ? and watched it burn and that felt better than anything else i've ever done it took american psycho army of darkness and kill bill just to get over that crap i hate you sandler for actually going through with this and ruining a whole day of my life<td>0<tr><th>5<td>? begins better than it ends funny that the russian submarine crew ? all other actors it's like those scenes where documentary shots br br spoiler part the message ? was contrary to the whole story it just does not ? br br<td>0<tr><th>6<td>? lavish production values and solid performances in this straightforward adaption of jane ? satirical classic about the marriage game within and between the classes in ? 18th century england northam and paltrow are a ? mixture as friends who must pass through ? and lies to discover that they love each other good humor is a ? virtue which goes a long way towards explaining the ? of the aged source material which has been toned down a bit in its harsh ? i liked the look of the film and how shots were set up and i thought it didn't rely too much on ? of head shots like most other films of the 80s and 90s do very good results<td>1<tr><th>7<td>? the ? tells the story of the four hamilton siblings teenager francis ? ? twins ? joseph ? ? ? ? the ? david samuel who is now the surrogate parent in charge the ? move house a lot ? is unsure why is unhappy with the way things are the fact that his brother's sister kidnap ? murder people in the basement doesn't help relax or calm ? nerves either francis ? something just isn't right when he eventually finds out the truth things will never be the same again br br co written co produced directed by mitchell ? phil ? as the butcher brothers who's only other film director's credit so far is the april ? day 2008 remake enough said this was one of the ? to die ? at the 2006 after dark ? or whatever it's called in keeping with pretty much all the other's i've seen i thought the ? was complete total utter crap i found the character's really poor very unlikable the slow moving story failed to capture my imagination or sustain my interest over it's 85 a half minute too long ? minute duration the there's the awful twist at the end which had me laughing out loud there's this really big ? build up to what's inside a ? thing in the ? basement it's eventually revealed to be a little boy with a teddy is that really supposed to scare us is that really supposed to shock us is that really something that is supposed to have us talking about it as the end credits roll is a harmless looking young boy the best ? ending that the makers could come up with the boring plot ? along it's never made clear where the ? get all their money from to buy new houses since none of them seem to work except david in a ? i doubt that pays much or why they haven't been caught before now the script tries to mix in every day drama with potent horror it just does a terrible job of combining the two to the extent that neither aspect is memorable or effective a really bad film that i am struggling to say anything good about br br despite being written directed by the extreme sounding butcher brothers there's no gore here there's a bit of blood splatter a few scenes of girls ? up in a basement but nothing you couldn't do at home yourself with a bottle of ? ? a camcorder the film is neither scary since it's got a very middle class suburban setting there's zero atmosphere or mood there's a lesbian suggest incestuous kiss but the ? is low on the exploitation scale there's not much here for the horror crowd br br filmed in ? in california this has that modern low budget look about it it's not badly made but rather forgettable the acting by an unknown to me cast is nothing to write home about i can't say i ever felt anything for anyone br br the ? commits the ? sin of being both dull boring from which it never ? add to that an ultra thin story no gore a rubbish ending character's who you don't give a toss about you have a film that did not impress me at all<td>0<tr><th>8<td>? just got out and cannot believe what a brilliant documentary this is rarely do you walk out of a movie theater in such awe and ? lately movies have become so over hyped that the thrill of discovering something truly special and unique rarely happens ? ? did this to me when it first came out and this movie is doing to me now i didn't know a thing about this before going into it and what a surprise if you hear the concept you might get the feeling that this is one of those ? movies about an amazing triumph covered with over the top music and trying to have us fully convinced of what a great story it is telling but then not letting us in ? this is not that movie the people tell the story this does such a good job of capturing every moment of their involvement while we enter their world and feel every second with them there is so much beyond the climb that makes everything they go through so much more tense touching the void was also a great doc about mountain climbing and showing the intensity in an engaging way but this film is much more of a human story i just saw it today but i will go and say that this is one of the best documentaries i have ever seen<td>1<tr><th>9<td>? this movie has many problem associated with it that makes it come off like a low budget class project from someone in film school i have to give it credit on its ? though many times throughout the movie i found myself laughing hysterically it was so bad at times that it was comical which made it a fun watch br br if you're looking for a low grade slasher movie with a twist of psychological horror and a dash of campy ? then pop a bowl of popcorn invite some friends over and have some fun br br i agree with other comments that the sound is very bad dialog is next to impossible to follow much of the time and the soundtrack is kind of just there<td>0</table></div></div></div></div><div class=\"tab-pane fade\" aria-labelledby=tab-pane-sample-tail id=tab-pane-sample-tail role=tabpanel><div class=\"row item\"><div class=col-sm-12 id=sample-container><div class=table-responsive><table class=\"dataframe sample table table-striped\" border=1><thead><tr style=\"text-align: right;\"><th><th>review<th>sentiment<tbody><tr><th>3990<td>? just a short comment i want to say that i like this movie very much sandra ? is my favourite actress i like the whole story from the beginning until the end i have it on tape and i can watch it a 100 times it doesn't matter<td>1<tr><th>3991<td>? in december 1945 a train leaves the central station of ? for berlin there aren't much left when it arrives not of the train and not of some passengers br br this is a black comedy directed by peter ? and acted like they used to act in the 40s and also photographed in b w like they used to during that period the actors must have had lots of fun making it they aren't much of characters like they weren't in the 40s but the story is well narrated and everybody has timing br br a deadly black and deadly funny film see it if you didn't think the ? were capable of humour<td>1<tr><th>3992<td>? the most generic surface level biography you could hope for ? ? of holly is accurate but who wants to hear gary busey sing maybe baby typically the members of the are used for comic relief and melodrama smith and ? respectively instead of as people or even characters when holly uses a string section the old jewish looking guys who come in tell him he's using the same techniques as ? it's just this kind of ? statement that makes film ? like this and ? about the aforementioned ? so worthless some entertainment can be derived from ? excellent styles and songs done in a b variation<td>0<tr><th>3993<td>? this is one of my 3 favorite movies i've been out on the water since i was 13 so i got a lot of the humor as well as ? a lot of the near land scenery the movie although taking place in and around virginia was filmed around the san francisco bay most notably the fleet just east of the bridge where ? ? character was first introduced to the ? ? and the ? of san francisco at the very end of the movie including a boat that i've worked on as other people have said the actors appeared to have fun making this movie as well as making it entertaining the line we're approaching the bottom sir i can hear a couple of ? ? it out is at least to me priceless br br i am one of numerous people who is ? awaiting a ? dvd of down ? to be introduced<td>1<tr><th>3994<td>? as a local performer i thought that grease ? what broadway producers are looking for and it ? a small bit of the acting population although my favorites did not win but came in second i enjoyed comparing myself to them and seeing what i liked and where i could improve as a performer br br they brought in olivia ? john because she is the obvious link between the broadway production and television audiences br br they sang songs from the movie hopelessly devoted et al because these songs will be included in the new production br br i agree with the earlier comment made that the ? cast looks so much older but you must bear in mind that these are people who have been on broadway and worked their ? off to be there if the entire cast was green and fresh then i believe that would have ? the casting process by which producers choose their talent<td>1<tr><th>3995<td>? first of all around the time i wrote this comment i had already read what 1 had written about this game and was confused this is a video game and not an actual movie i mean i myself would choose a good deniro movie or playing a video game any day but dude what are you talking about now that i have that out of the way let's move on shall we the video game adaption of ? tiger hidden dragon is as you may have guessed based on the movie of the same name what's stranger is that this was released three years after the film was released whether that's due to a slow development time or just plain ? no one really knows at least i don't br br the game allows the player to play as the four main characters of the movie ? ? li ? ? and dark cloud whose story is a secret bonus that is ? for separate ? all four of these characters have fighting styles based on how they fought in the movie however the real eye candy is the ? moves that allow each character to avoid the blows of an ? attack in the most impossible and gravity ? ways that were first ? in the movie br br the story is obviously based on the story told in the movie the story in the game is told using cg movies and spoken in a subtitled ? language a nice little touch considering i preferred the foreign language track of the film over the dubbed one the story goes to expand on what ? happened to the other characters during the film and even no real surprise since pretty much all video game movie ? have done this ? what officially happened in the movie to make the game have more action i never thought ? fox would go so far as to actually go and get hired ? br br the real bonus for the story is the ability to decide how the game ends what if ? had all the ingredients to cure li ? ? in her possession what if ? had not gotten back her ? what if li ? ? had not gotten the green destiny back for the second time your actions will decide the outcome of these scenarios and the destiny of the characters in this game this is truly the best feature of the game br br yet for all it's strengths it also showcases some flaws the enemies often tend to become a ? after a while and the camera angle tends to be fixed on the most ? of spots not even the special features which are ? after beating the game are good enough to even forgive such ? that are forced upon the player br br if you loved the movie you may enjoy the game however this game is the only way you're going to be able to ? yourself into a version of such a beautiful and amazing movie br br of course you could have just picked a deniro movie instead<td>1<tr><th>3996<td>? you don't have to be a to appreciate this gem of a movie i don't know a word of ? and saw this movie only because a friend had recommended it to me understanding a movie without knowing the language is quite tough but i could make out the story because the lead actors and actresses ? really well and the little girl was really cute she wasn't irritating like child actors in most hindi movies the story is really touching and hats off to mr for trying something different the relationship between the parents and their children are shown quite realistically i could identify with the characters in the movie it was a movie that will remain in my heart forever and i wouldn't hesitate to recommend it to my friends also the songs are just out of this world they were beautifully and ? ? if only i could understand the right meaning of the lyrics<td>1<tr><th>3997<td>? this movie deals with the european ? exchange program but more generally about the european youth it is so true that i don't know did to reach such a masterpiece definitely one of my top 5 movies it reminds me of the famous song this is my life my life life is life 10 10<td>1<tr><th>3998<td>? you could get into the ? gritty of this film and say how it couldn't happen or how could the main character just walk into ? and start using computers etc without someone ? and really pick the movie to bits or you could just sit back with a bucket of popcorn and enjoy the story and the acting personally i prefer the latter i live real life and watch the news with all it's doom and ? and so would much rather be entertained by my movies and see the bad guys get their just ? don't take it too seriously and you can thoroughly enjoy this film<td>1<tr><th>3999<td>? the accounts seem real with a human factor added to the mix a lot of sadness i'm sure glad that i wasn't him another thing to add is that all the women in this show were not really pretty accounts of the real women but i don't think that it was about the women although it was to ? ? passion what a shame any loss of life is a real shame br br seemed like a good account of his life i recommend it if you are into ? and melodrama<td>1</table></div></div></div></div></div></div></div><div class=section-header id=duplicate><h1 class=section-name>Duplicate rows</h1></div><div class=section-items><div class=\"row sub-item\"><div class=col-sm-12><h4 class=item-header>Most frequently occurring</h4><div class=col-sm-12 id=duplicate-container><div class=table-responsive><table class=\"dataframe duplicate table table-striped\" border=1><thead><tr style=\"text-align: right;\"><th><th>review<th>sentiment<th># duplicates<tbody><tr><th>0<td>? apart from the fact that this film was made i suppose it seemed a good idea at the time considering bottom was so popular the one thing that puzzled me about guest house ? was what happened to the lighting there is absolutely no artificial lighting used in this film whatsoever and i watched it on network tv so it ? t a case of watching a dodgy tape in fact the film was shot so darkly it was impossible to see what the hell was going on but if the dialogue was anything to go by that s maybe not a bad thing<td>0<td>2<tr><th>1<td>? br br summary not worth the film br br as an avid gone with the wind fan i was disappointed to watch the original movie and see that they had left out many important characters luckily the film on its own was a wonderful piece when the book scarlett came out i read it in hopes of following two of my favorite literary characters ? on their journey together while the book lacks any true quality it remains a good story and as long as i was able to separate it from the original was and still is enjoyable however i consider the six hours i spent watching the scarlett miniseries to be some of the worst spent hours of my life any of the original character traits so well formed in margaret ? book this series also turned the story of the sequel into one of rape ? murder and relationships that even the book scarlett stayed away from the casting for many of the characters refused to examine the traits that had been so well formed in both the original novel and film and even carried through in the second book and again leaves out at least one incredibly crucial character in the novel scarlett o'hara butler follows her estranged husband ? butler to ? under the ? of visiting extended family after coming to an ? with ? she agrees to leave and proceeds to ? with her o'hara relatives in ? eventually she ? her cousin a passionate leader of the ? to ireland to further explore her family's roots that go deep and is eventually named the o'hara the head of the family while her duties as the o'hara keep her engaged in her town of scarlett ? out into the world of the english ? and instantly becomes a sought after guest at many of their parties she having been ? by ? time and time again eventually agrees to marry luke the earl of ? until ? comes along in a clichéd night on white horse type of a rescue the scarlett miniseries fails even to do this justice raped by her fiancé and ? by her family the series shows scarlett thrown in jail after she is blamed for a murder her cousin committed br br i ? advise anyone considering spending their day watching this to ? this decision br br<td>0<td>2<tr><th>2<td>? how has this piece of crap stayed on tv this long it's terrible it makes me want to shoot someone it's so fake that it is actually worse than a 1940s sci fi movie i'd rather have a stroke than watch this nonsense i remember watching it when it first came out i thought hey this could be interesting then i found out how absolutely insanely ridiculously stupid it really was it was so bad that i actually took out my pocket knife and stuck my hand to the table br br please people stop watching this and all other reality shows they're the trash that is ? the networks and ? quality programming that requires some thought to create<td>0<td>2<tr><th>3<td>? i read the reviews of this movie and they were generally pretty good so i thought i should see it i'm a big ? and art film lover but i believe this is yet another case in which the critics make something arty or intellectual into something it is not i will be blunt it contains scenes of sexual ? that i never ever wanted to actually see obviously the piano teacher has some major psychological issues but i really did not want to see them displayed so ? the film is in essence disgusting i mean when i saw ? for a dream i was ? by the last sort of scene with jennifer ? but that was not anywhere near the sort of disgust and ? i felt during this film<td>0<td>2<tr><th>4<td>? i rented the video of the piano teacher knowing nothing about it other than what was written on the video box i did this with some ? because films that win awards at cannes are usually very good or very bad unfortunately this one falls in the latter category about one quarter of the way into it i found myself saying out loud this movie is boring about half way through i was saying to myself where have i seen this before at the three quarters mark i had figured it out br br in spite of its literary origins this film is essentially a remake of robert altman's much earlier 1969 and better that cold day in the park although the details obviously ? and altman's work was more plot driven and less of a character study the two films are ? identical there is nothing new to be seen in this production every aspect of it has been done before a character ? out of control with increasingly self destructive behavior ? ? bad lieutenant 1992 a perverse and doomed ? culminating in an operatic near death scene david ? m ? 1993 ? brutal sex scenes david lynch's blue ? 1986 and so on hence i am ? by the fact that so many found the film to be shocking shattering etc this highly derivative film seems to have been made for the sole purpose of making viewers feel uncomfortable and clearly succeeded with some however i largely ? such a reaction to a lack of film viewing experience see enough movies and you really will eventually have seen it all and while it is true that i saw the ? ? ? version i doubt that the additional scenes would change my overall opinion of the piano teacher br br technically the film is not without merit there is some very good camera work and the lighting is excellent isabelle ? ? performance also helps save it from being a waste of time this is the first of ? films that i've seen and if i were to see more i expect i would have the same opinion of him that i have of ? an interesting director but not nearly the genius others make him out to be rating 4 10<td>0<td>2<tr><th>5<td>? inappropriate the pg rating that this movie gets is yet another huge ? by the mpaa whale rider gets a pg 13 but this movie gets a pg please parents don't be fooled taking an ? school child to this movie is a huge mistake there were numerous times i found myself being uncomfortable not just because the humor was inappropriate for kids but also because it was totally out of the blue and unnecessary br br but all that aside the cat in the hat is still a terrible movie the casting and overall look of the movie are the only saving ? the beautiful kelly preston and the always likeable or ? in this case alec baldwin are both good in their roles even though preston is almost too beautiful for a role like this the kids are ? actors and it shows especially with ? fanning fanning is the only human aspect of the film that kept me watching and not throwing things at the screen br br did i mention there was an ? talking cat in this movie mike myers is absolutely ? i didn't like him as the voice of shrek and i truly believe now that myers should not be allowed near the realm of children's films ever again his portrayal of the cat is a slightly toned down version of fat bastard and austin powers br br in the end the cat should not have come he should have stayed away but he came even if just for a day he ruined ? minutes of my life ? minutes of personal anger and ? br br the cat in the hat may be the worst kids movie ever<td>0<td>2<tr><th>6<td>? it's so sad that romanian audiences are still populated with vulgar and ? individuals who ? this kind of cheap and ? shows as superficial and brutal as the ? series or the ? ? child plays the difference is that ? ? ? ? ? and other such sub ? never presume to claim their shows as art ? who 40 years ago made a very good movie ? la ? ? followed by another one nice enough ? tries to ? his film ? art works but unfortunately he masters at a way too limited level the specifically cinematographic means of expression as such ? ? offers again a ? of how not this being about its only merit<td>0<td>2<tr><th>7<td>? no redeeming features this film is rubbish its jokes don't begin to be funny the humour for children is pathetic and the attempts to appeal to adults just add a tacky ? to the whole miserable package sitting through it with my children just made me uncomfortable about what might be coming next i couldn't enjoy the film at all although my child for whom the dvd was bought enjoyed the fact that she owned a new dvd neither she nor her sisters expressed much interest in seeing it again unlike with monsters inc finding ? jungle book lion king etc which all get frequent ? for ?<td>0<td>2<tr><th>8<td>? red ? was still another major star who made the transition from movies to television with ease br br his shows certainly brought a laughter to the american ? of years back br br he would begin the show with an opening monologue afterwards we would have a variety of characters remember ? and ? in the monologue how can we ever forget san ? red i remember one episode where as a king red introduced his queen by referring to her as your ? br br go know that red would use his comedic talents to really hide from his tragic life he lost a son to ? at age 11 or so his wife georgia died by suicide<td>1<td>2<tr><th>9<td>? so much for judge and jury which lives up to its nonsense title what good is there the lighting is terribly ? another horror movie you ask well that's perfectly ? david keith actually does pretty good at ? ? ? and other ? while being the killer who escaped death row but overall despite some new twists it's reasonably stupid has been putting out some ludicrous productions recently and this one only means so much we the jury find this film guilty for its ? exposure to many of us sitting around believing it's a total waste of our time<td>0<td>2</table></div></div></div></div></div></div></div><footer><div class=container-fluid><div class=row><p class=\"text-body-secondary text-center\">Report generated by <a 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Object.keys(f).sort((function(t,e){return f[t]-f[e]}))}const si={name:\"flip\",enabled:!0,phase:\"main\",fn:function(t){var e=t.state,i=t.options,n=t.name;if(!e.modifiersData[n]._skip){for(var s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0===r||r,l=i.fallbackPlacements,c=i.padding,h=i.boundary,d=i.rootBoundary,u=i.altBoundary,f=i.flipVariations,p=void 0===f||f,m=i.allowedAutoPlacements,g=e.options.placement,_=be(g),b=l||(_!==g&&p?function(t){if(be(t)===Kt)return[];var e=Ve(t);return[Qe(t),e,Qe(e)]}(g):[Ve(g)]),v=[g].concat(b).reduce((function(t,i){return t.concat(be(i)===Kt?ni(e,{placement:i,boundary:h,rootBoundary:d,padding:c,flipVariations:p,allowedAutoPlacements:m}):i)}),[]),y=e.rects.reference,w=e.rects.popper,A=new Map,E=!0,T=v[0],C=0;C<v.length;C++){var O=v[C],x=be(O),k=Fe(O)===Xt,L=[zt,Rt].indexOf(x)>=0,S=L?\"width\":\"height\",D=ii(e,{placement:O,boundary:h,rootBoundary:d,altBoundary:u,padding:c}),$=L?k?qt:Vt:k?Rt:zt;y[S]>w[S]&&($=Ve($));var I=Ve($),N=[];if(o&&N.push(D[x]<=0),a&&N.push(D[$]<=0,D[I]<=0),N.every((function(t){return t}))){T=O,E=!1;break}A.set(O,N)}if(E)for(var P=function(t){var e=v.find((function(e){var i=A.get(e);if(i)return i.slice(0,t).every((function(t){return t}))}));if(e)return T=e,\"break\"},j=p?3:1;j>0&&\"break\"!==P(j);j--);e.placement!==T&&(e.modifiersData[n]._skip=!0,e.placement=T,e.reset=!0)}},requiresIfExists:[\"offset\"],data:{_skip:!1}};function oi(t,e,i){return void 0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function ri(t){return[zt,qt,Rt,Vt].some((function(e){return t[e]>=0}))}const ai={name:\"hide\",enabled:!0,phase:\"main\",requiresIfExists:[\"preventOverflow\"],fn:function(t){var e=t.state,i=t.name,n=e.rects.reference,s=e.rects.popper,o=e.modifiersData.preventOverflow,r=ii(e,{elementContext:\"reference\"}),a=ii(e,{altBoundary:!0}),l=oi(r,n),c=oi(a,s,o),h=ri(l),d=ri(c);e.modifiersData[i]={referenceClippingOffsets:l,popperEscapeOffsets:c,isReferenceHidden:h,hasPopperEscaped:d},e.attributes.popper=Object.assign({},e.attributes.popper,{\"data-popper-reference-hidden\":h,\"data-popper-escaped\":d})}},li={name:\"offset\",enabled:!0,phase:\"main\",requires:[\"popperOffsets\"],fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=ee.reduce((function(t,i){return t[i]=function(t,e,i){var n=be(t),s=[Vt,zt].indexOf(n)>=0?-1:1,o=\"function\"==typeof i?i(Object.assign({},e,{placement:t})):i,r=o[0],a=o[1];return r=r||0,a=(a||0)*s,[Vt,qt].indexOf(n)>=0?{x:a,y:r}:{x:r,y:a}}(i,e.rects,o),t}),{}),a=r[e.placement],l=a.x,c=a.y;null!=e.modifiersData.popperOffsets&&(e.modifiersData.popperOffsets.x+=l,e.modifiersData.popperOffsets.y+=c),e.modifiersData[n]=r}},ci={name:\"popperOffsets\",enabled:!0,phase:\"read\",fn:function(t){var e=t.state,i=t.name;e.modifiersData[i]=ei({reference:e.rects.reference,element:e.rects.popper,strategy:\"absolute\",placement:e.placement})},data:{}},hi={name:\"preventOverflow\",enabled:!0,phase:\"main\",fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0!==r&&r,l=i.boundary,c=i.rootBoundary,h=i.altBoundary,d=i.padding,u=i.tether,f=void 0===u||u,p=i.tetherOffset,m=void 0===p?0:p,g=ii(e,{boundary:l,rootBoundary:c,padding:d,altBoundary:h}),_=be(e.placement),b=Fe(e.placement),v=!b,y=Ie(_),w=\"x\"===y?\"y\":\"x\",A=e.modifiersData.popperOffsets,E=e.rects.reference,T=e.rects.popper,C=\"function\"==typeof m?m(Object.assign({},e.rects,{placement:e.placement})):m,O=\"number\"==typeof C?{mainAxis:C,altAxis:C}:Object.assign({mainAxis:0,altAxis:0},C),x=e.modifiersData.offset?e.modifiersData.offset[e.placement]:null,k={x:0,y:0};if(A){if(o){var L,S=\"y\"===y?zt:Vt,D=\"y\"===y?Rt:qt,$=\"y\"===y?\"height\":\"width\",I=A[y],N=I+g[S],P=I-g[D],j=f?-T[$]/2:0,M=b===Xt?E[$]:T[$],F=b===Xt?-T[$]:-E[$],H=e.elements.arrow,W=f&&H?Ce(H):{width:0,height:0},B=e.modifiersData[\"arrow#persistent\"]?e.modifiersData[\"arrow#persistent\"].padding:{top:0,right:0,bottom:0,left:0},z=B[S],R=B[D],q=Ne(0,E[$],W[$]),V=v?E[$]/2-j-q-z-O.mainAxis:M-q-z-O.mainAxis,K=v?-E[$]/2+j+q+R+O.mainAxis:F+q+R+O.mainAxis,Q=e.elements.arrow&&$e(e.elements.arrow),X=Q?\"y\"===y?Q.clientTop||0:Q.clientLeft||0:0,Y=null!=(L=null==x?void 0:x[y])?L:0,U=I+K-Y,G=Ne(f?ye(N,I+V-Y-X):N,I,f?ve(P,U):P);A[y]=G,k[y]=G-I}if(a){var J,Z=\"x\"===y?zt:Vt,tt=\"x\"===y?Rt:qt,et=A[w],it=\"y\"===w?\"height\":\"width\",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:[\"offset\"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&((\"body\"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:\"bottom\",modifiers:[],strategy:\"absolute\"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&\"function\"==typeof t.getBoundingClientRect)}))}function mi(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?fi:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:\"bottom\",orderedModifiers:[],options:Object.assign({},fi,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s=\"function\"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:pe(t)?Je(t):t.contextElement?Je(t.contextElement):[],popper:Je(e)};var r,c,u=function(t){var e=ui(t);return de.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if(\"function\"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(pi(e,i)){a.rects={reference:di(e,$e(i),\"fixed\"===a.options.strategy),popper:Ce(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;\"function\"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!pi(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var gi=mi(),_i=mi({defaultModifiers:[Re,ci,Be,_e]}),bi=mi({defaultModifiers:[Re,ci,Be,_e,li,si,hi,Me,ai]});const vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:Me,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:\"Module\"})),yi=\"dropdown\",wi=\".bs.dropdown\",Ai=\".data-api\",Ei=\"ArrowUp\",Ti=\"ArrowDown\",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i=\"show\",Ii='[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=\".dropdown-menu\",ji=p()?\"top-end\":\"top-start\",Mi=p()?\"top-start\":\"top-end\",Fi=p()?\"bottom-end\":\"bottom-start\",Hi=p()?\"bottom-start\":\"bottom-end\",Wi=p()?\"left-start\":\"right-start\",Bi=p()?\"right-start\":\"left-start\",zi={autoClose:!0,boundary:\"clippingParents\",display:\"dynamic\",offset:[0,2],popperConfig:null,reference:\"toggle\"},Ri={autoClose:\"(boolean|string)\",boundary:\"(string|element)\",display:\"string\",offset:\"(array|string|function)\",popperConfig:\"(null|object|function)\",reference:\"(string|element|object)\"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),\"ontouchstart\"in document.documentElement&&!this._parent.closest(\".navbar-nav\"))for(const t of[].concat(...document.body.children))N.on(t,\"mouseover\",h);this._element.focus(),this._element.setAttribute(\"aria-expanded\",!0),this._menu.classList.add($i),this._element.classList.add($i),N.trigger(this._element,ki,t)}}hide(){if(l(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!N.trigger(this._element,Ci,t).defaultPrevented){if(\"ontouchstart\"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,\"mouseover\",h);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute(\"aria-expanded\",\"false\"),F.removeDataAttribute(this._menu,\"popper\"),N.trigger(this._element,Oi,t)}}_getConfig(t){if(\"object\"==typeof(t=super._getConfig(t)).reference&&!o(t.reference)&&\"function\"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`);return t}_createPopper(){if(void 0===vi)throw new TypeError(\"Bootstrap's dropdowns require Popper (https://popper.js.org)\");let t=this._element;\"parent\"===this._config.reference?t=this._parent:o(this._config.reference)?t=r(this._config.reference):\"object\"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=bi(t,this._menu,e)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains(\"dropend\"))return Wi;if(t.classList.contains(\"dropstart\"))return Bi;if(t.classList.contains(\"dropup-center\"))return\"top\";if(t.classList.contains(\"dropdown-center\"))return\"bottom\";const e=\"end\"===getComputedStyle(this._menu).getPropertyValue(\"--bs-position\").trim();return t.classList.contains(\"dropup\")?e?Mi:ji:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(\".navbar\")}_getOffset(){const{offset:t}=this._config;return\"string\"==typeof t?t.split(\",\").map((t=>Number.parseInt(t,10))):\"function\"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:\"preventOverflow\",options:{boundary:this._config.boundary}},{name:\"offset\",options:{offset:this._getOffset()}}]};return(this._inNavbar||\"static\"===this._config.display)&&(F.setDataAttribute(this._menu,\"popper\",\"static\"),t.modifiers=[{name:\"applyStyles\",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(\".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)\",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===e[t])throw new TypeError(`No method named \"${t}\"`);e[t]()}}))}static clearMenus(t){if(2===t.button||\"keyup\"===t.type&&\"Tab\"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||\"inside\"===e._config.autoClose&&!s||\"outside\"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&(\"keyup\"===t.type&&\"Tab\"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};\"click\"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i=\"Escape\"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi=\"backdrop\",Ki=\"show\",Qi=`mousedown.bs.${Vi}`,Xi={className:\"modal-backdrop\",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:\"body\"},Yi={className:\"string\",clickCallback:\"(function|null)\",isAnimated:\"boolean\",isVisible:\"boolean\",rootElement:\"(element|string)\"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement(\"div\");t.className=this._config.className,this._config.isAnimated&&t.classList.add(\"fade\"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=\".bs.focustrap\",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn=\"backward\",en={autofocus:!0,trapElement:null},nn={autofocus:\"boolean\",trapElement:\"element\"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return\"focustrap\"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){\"Tab\"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:\"forward\")}}const on=\".fixed-top, .fixed-bottom, .is-fixed, .sticky-top\",rn=\".sticky-top\",an=\"padding-right\",ln=\"margin-right\";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,\"overflow\"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,\"overflow\"),this._element.style.overflow=\"hidden\"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=\".bs.modal\",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn=\"modal-open\",An=\"show\",En=\"modal-static\",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:\"(boolean|string)\",focus:\"boolean\",keyboard:\"boolean\"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(\".modal-dialog\",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return\"modal\"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display=\"block\",this._element.removeAttribute(\"aria-hidden\"),this._element.setAttribute(\"aria-modal\",!0),this._element.setAttribute(\"role\",\"dialog\"),this._element.scrollTop=0;const e=z.findOne(\".modal-body\",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{\"Escape\"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&(\"static\"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display=\"none\",this._element.setAttribute(\"aria-hidden\",!0),this._element.removeAttribute(\"aria-modal\"),this._element.removeAttribute(\"role\"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains(\"fade\")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;\"hidden\"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY=\"hidden\"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?\"paddingLeft\":\"paddingRight\";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?\"paddingRight\":\"paddingLeft\";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft=\"\",this._element.style.paddingRight=\"\"}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===i[t])throw new TypeError(`No method named \"${t}\"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle=\"modal\"]',(function(t){const e=z.getElementFromSelector(this);[\"A\",\"AREA\"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(\".modal.show\");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=\".bs.offcanvas\",kn=\".data-api\",Ln=`load${xn}${kn}`,Sn=\"show\",Dn=\"showing\",$n=\"hiding\",In=\".offcanvas.show\",Nn=`show${xn}`,Pn=`shown${xn}`,jn=`hide${xn}`,Mn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:\"(boolean|string)\",keyboard:\"boolean\",scroll:\"boolean\"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return\"offcanvas\"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute(\"aria-modal\",!0),this._element.setAttribute(\"role\",\"dialog\"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,jn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute(\"aria-modal\"),this._element.removeAttribute(\"role\"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:\"offcanvas-backdrop\",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{\"static\"!==this._config.backdrop?this.hide():N.trigger(this._element,Mn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{\"Escape\"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,Mn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===e[t]||t.startsWith(\"_\")||\"constructor\"===t)throw new TypeError(`No method named \"${t}\"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle=\"offcanvas\"]',(function(t){const e=z.getElementFromSelector(this);if([\"A\",\"AREA\"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find(\"[aria-modal][class*=show][class*=offcanvas-]\"))\"fixed\"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={\"*\":[\"class\",\"dir\",\"id\",\"lang\",\"role\",/^aria-[\\w-]*$/i],a:[\"target\",\"href\",\"title\",\"rel\"],area:[],b:[],br:[],col:[],code:[],dd:[],div:[],dl:[],dt:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:[\"src\",\"srcset\",\"alt\",\"title\",\"width\",\"height\"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set([\"background\",\"cite\",\"href\",\"itemtype\",\"longdesc\",\"poster\",\"src\",\"xlink:href\"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:\"\",html:!1,sanitize:!0,sanitizeFn:null,template:\"<div></div>\"},Un={allowList:\"object\",content:\"object\",extraClass:\"(string|function)\",html:\"boolean\",sanitize:\"boolean\",sanitizeFn:\"(null|function)\",template:\"string\"},Gn={entry:\"(string|element|function|null)\",selector:\"(string|element)\"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return\"TemplateFactory\"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement(\"div\");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(\" \")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&\"function\"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,\"text/html\"),s=[].concat(...n.body.querySelectorAll(\"*\"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e[\"*\"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML=\"\",void e.append(t);e.textContent=t.textContent}}const Zn=new Set([\"sanitize\",\"allowList\",\"sanitizeFn\"]),ts=\"fade\",es=\"show\",is=\".modal\",ns=\"hide.bs.modal\",ss=\"hover\",os=\"focus\",rs={AUTO:\"auto\",TOP:\"top\",RIGHT:p()?\"left\":\"right\",BOTTOM:\"bottom\",LEFT:p()?\"right\":\"left\"},as={allowList:Vn,animation:!0,boundary:\"clippingParents\",container:!1,customClass:\"\",delay:0,fallbackPlacements:[\"top\",\"right\",\"bottom\",\"left\"],html:!1,offset:[0,6],placement:\"top\",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class=\"tooltip\" role=\"tooltip\"><div class=\"tooltip-arrow\"></div><div class=\"tooltip-inner\"></div></div>',title:\"\",trigger:\"hover focus\"},ls={allowList:\"object\",animation:\"boolean\",boundary:\"(string|element)\",container:\"(string|element|boolean)\",customClass:\"(string|function)\",delay:\"(number|object)\",fallbackPlacements:\"array\",html:\"boolean\",offset:\"(array|string|function)\",placement:\"(string|function)\",popperConfig:\"(null|object|function)\",sanitize:\"boolean\",sanitizeFn:\"(null|function)\",selector:\"(string|boolean)\",template:\"string\",title:\"(string|element|function)\",trigger:\"string\"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError(\"Bootstrap's tooltips require Popper (https://popper.js.org)\");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return\"tooltip\"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute(\"data-bs-original-title\")&&this._element.setAttribute(\"title\",this._element.getAttribute(\"data-bs-original-title\")),this._disposePopper(),super.dispose()}show(){if(\"none\"===this._element.style.display)throw new Error(\"Please use show on visible elements\");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName(\"show\")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute(\"aria-describedby\",i.getAttribute(\"id\"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName(\"inserted\"))),this._popper=this._createPopper(i),i.classList.add(es),\"ontouchstart\"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,\"mouseover\",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName(\"shown\")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName(\"hide\")).defaultPrevented){if(this._getTipElement().classList.remove(es),\"ontouchstart\"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,\"mouseover\",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute(\"aria-describedby\"),N.trigger(this._element,this.constructor.eventName(\"hidden\")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute(\"id\",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{\".tooltip-inner\":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute(\"data-bs-original-title\")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return\"string\"==typeof t?t.split(\",\").map((t=>Number.parseInt(t,10))):\"function\"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:\"flip\",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:\"offset\",options:{offset:this._getOffset()}},{name:\"preventOverflow\",options:{boundary:this._config.boundary}},{name:\"arrow\",options:{element:`.${this.constructor.NAME}-arrow`}},{name:\"preSetPlacement\",enabled:!0,phase:\"beforeMain\",fn:t=>{this._getTipElement().setAttribute(\"data-popper-placement\",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(\" \");for(const e of t)if(\"click\"===e)N.on(this._element,this.constructor.eventName(\"click\"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if(\"manual\"!==e){const t=e===ss?this.constructor.eventName(\"mouseenter\"):this.constructor.eventName(\"focusin\"),i=e===ss?this.constructor.eventName(\"mouseleave\"):this.constructor.eventName(\"focusout\");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger[\"focusin\"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger[\"focusout\"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute(\"title\");t&&(this._element.getAttribute(\"aria-label\")||this._element.textContent.trim()||this._element.setAttribute(\"aria-label\",t),this._element.setAttribute(\"data-bs-original-title\",t),this._element.removeAttribute(\"title\"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,...\"object\"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),\"number\"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),\"number\"==typeof t.title&&(t.title=t.title.toString()),\"number\"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger=\"manual\",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===e[t])throw new TypeError(`No method named \"${t}\"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:\"\",offset:[0,8],placement:\"right\",template:'<div class=\"popover\" role=\"tooltip\"><div class=\"popover-arrow\"></div><h3 class=\"popover-header\"></h3><div 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-30%`:t.rootMargin,\"string\"==typeof t.threshold&&(t.threshold=t.threshold.split(\",\").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(N.off(this._config.target,ms),N.on(this._config.target,ms,bs,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:\"smooth\"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains(\"dropdown-item\"))z.findOne(\".dropdown-toggle\",t.closest(\".dropdown\")).classList.add(_s);else for(const e of z.parents(t,\".nav, .list-group\"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===e[t]||t.startsWith(\"_\")||\"constructor\"===t)throw new TypeError(`No method named \"${t}\"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy=\"scroll\"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=\".bs.tab\",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s=\"ArrowLeft\",Is=\"ArrowRight\",Ns=\"ArrowUp\",Ps=\"ArrowDown\",js=\"Home\",Ms=\"End\",Fs=\"active\",Hs=\"fade\",Ws=\"show\",Bs=\".dropdown-toggle\",zs=`:not(${Bs})`,Rs='[data-bs-toggle=\"tab\"], [data-bs-toggle=\"pill\"], [data-bs-toggle=\"list\"]',qs=`.nav-link${zs}, .list-group-item${zs}, [role=\"tab\"]${zs}, ${Rs}`,Vs=`.${Fs}[data-bs-toggle=\"tab\"], .${Fs}[data-bs-toggle=\"pill\"], .${Fs}[data-bs-toggle=\"list\"]`;class Ks extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role=\"tablist\"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return\"tab\"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{\"tab\"===t.getAttribute(\"role\")?(t.removeAttribute(\"tabindex\"),t.setAttribute(\"aria-selected\",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{\"tab\"===t.getAttribute(\"role\")?(t.setAttribute(\"aria-selected\",!1),t.setAttribute(\"tabindex\",\"-1\"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,js,Ms].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([js,Ms].includes(t.key))i=e[t.key===js?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Ks.getOrCreateInstance(i).show())}_getChildren(){return z.find(qs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,\"role\",\"tablist\");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute(\"aria-selected\",e),i!==t&&this._setAttributeIfNotExists(i,\"role\",\"presentation\"),e||t.setAttribute(\"tabindex\",\"-1\"),this._setAttributeIfNotExists(t,\"role\",\"tab\"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=z.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,\"role\",\"tabpanel\"),t.id&&this._setAttributeIfNotExists(e,\"aria-labelledby\",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains(\"dropdown\"))return;const n=(t,n)=>{const s=z.findOne(t,i);s&&s.classList.toggle(n,e)};n(Bs,Fs),n(\".dropdown-menu\",Ws),i.setAttribute(\"aria-expanded\",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Fs)}_getInnerElement(t){return t.matches(qs)?t:z.findOne(qs,t)}_getOuterElement(t){return t.closest(\".nav-item, .list-group-item\")||t}static jQueryInterface(t){return this.each((function(){const e=Ks.getOrCreateInstance(this);if(\"string\"==typeof t){if(void 0===e[t]||t.startsWith(\"_\")||\"constructor\"===t)throw new TypeError(`No method named \"${t}\"`);e[t]()}}))}}N.on(document,Ls,Rs,(function(t){[\"A\",\"AREA\"].includes(this.tagName)&&t.preventDefault(),l(this)||Ks.getOrCreateInstance(this).show()})),N.on(window,Ds,(()=>{for(const t of z.find(Vs))Ks.getOrCreateInstance(t)})),m(Ks);const Qs=\".bs.toast\",Xs=`mouseover${Qs}`,Ys=`mouseout${Qs}`,Us=`focusin${Qs}`,Gs=`focusout${Qs}`,Js=`hide${Qs}`,Zs=`hidden${Qs}`,to=`show${Qs}`,eo=`shown${Qs}`,io=\"hide\",no=\"show\",so=\"showing\",oo={animation:\"boolean\",autohide:\"boolean\",delay:\"number\"},ro={animation:!0,autohide:!0,delay:5e3};class ao extends W{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return ro}static get DefaultType(){return oo}static get NAME(){return\"toast\"}show(){N.trigger(this._element,to).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add(\"fade\"),this._element.classList.remove(io),d(this._element),this._element.classList.add(no,so),this._queueCallback((()=>{this._element.classList.remove(so),N.trigger(this._element,eo),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(N.trigger(this._element,Js).defaultPrevented||(this._element.classList.add(so),this._queueCallback((()=>{this._element.classList.add(io),this._element.classList.remove(so,no),N.trigger(this._element,Zs)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(no),super.dispose()}isShown(){return this._element.classList.contains(no)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case\"mouseover\":case\"mouseout\":this._hasMouseInteraction=e;break;case\"focusin\":case\"focusout\":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){N.on(this._element,Xs,(t=>this._onInteraction(t,!0))),N.on(this._element,Ys,(t=>this._onInteraction(t,!1))),N.on(this._element,Us,(t=>this._onInteraction(t,!0))),N.on(this._element,Gs,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ao.getOrCreateInstance(this,t);if(\"string\"==typeof t){if(void 0===e[t])throw new TypeError(`No method named \"${t}\"`);e[t](this)}}))}}return R(ao),m(ao),{Alert:Q,Button:Y,Carousel:xt,Collapse:Bt,Dropdown:qi,Modal:On,Offcanvas:qn,Popover:us,ScrollSpy:Es,Tab:Ks,Toast:ao,Tooltip:cs}}));\n",
+              "//# sourceMappingURL=bootstrap.bundle.min.js.map</script><script>document.addEventListener(\"DOMContentLoaded\", () => {\n",
+              "    /* Tooltip */\n",
+              "    document.querySelectorAll('[data-bs-toggle=\"tooltip\"]').forEach(el => new bootstrap.Tooltip(el));\n",
+              "\n",
+              "    /* Link */\n",
+              "    document.querySelectorAll(\"a:not([href^='#'])\").forEach(el => el.setAttribute(\"target\", \"_blank\"));\n",
+              "\n",
+              "    /* Variables Dropdown */\n",
+              "    const variablesDropdown = document.querySelector(\"select#variables-dropdown\");\n",
+              "    variablesDropdown.addEventListener(\"change\", () => {\n",
+              "        const searchText = variablesDropdown.value.toLowerCase();\n",
+              "\n",
+              "        document.querySelectorAll(\".variable\").forEach(el => {\n",
+              "            const title = el.firstElementChild.firstElementChild.firstElementChild.getAttribute(\"title\").toLowerCase();\n",
+              "            let isMatch = title === (searchText);\n",
+              "\n",
+              "            if (searchText === \"\") {\n",
+              "                isMatch = true\n",
+              "            }\n",
+              "\n",
+              "            if (isMatch) {\n",
+              "                el.parentElement.classList.remove(\"d-none\");\n",
+              "            } else {\n",
+              "                el.parentElement.classList.add(\"d-none\");\n",
+              "            }\n",
+              "        });\n",
+              "    });\n",
+              "\n",
+              "    /* Correlation */\n",
+              "    document.querySelector(\"#toggle-correlation-description\")?.click(() => {\n",
+              "        document.querySelectorAll(\".correlation-description\").forEach(el => el.classList.toggle(\"d-none\"));\n",
+              "\n",
+              "        document.querySelectorAll(\".tab-pane[id^=correlations_tab-]\").forEach(el => {\n",
+              "            const diagramLength = el.querySelectorAll(\".tab-pane[id^=correlations_tab-]\").length;\n",
+              "\n",
+              "            let classes = [];\n",
+              "            if (diagramLength === 1) {\n",
+              "                classes = [\"col-sm-6\", \"col-sm-12\"];\n",
+              "            } else if (diagramLength === 2) {\n",
+              "                classes = [\"col-sm-4\", \"col-sm-6\"];\n",
+              "            } else if (diagramLength === 3) {\n",
+              "                classes = [\"col-sm-3\", \"col-sm-4\"];\n",
+              "            } else {\n",
+              "                console.log(\"unsupported number of reports\");\n",
+              "            }\n",
+              "\n",
+              "            document.querySelectorAll(\".correlation-diagram\").forEach(el => {\n",
+              "                classes.forEach(clz => el.classList.toggle(clz));\n",
+              "            });\n",
+              "        });\n",
+              "    });\n",
+              "});</script>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 52
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = df.drop_duplicates()"
+      ],
+      "metadata": {
+        "id": "tXwtvfSMLvmx"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "nlp = spacy.load(\"en_core_web_sm\" , disable=[\"parser\", \"ner\", \"lemmatizer\"])\n",
+        "\n",
+        "def preprocess_text(text):\n",
+        "    doc = nlp(str(text).lower())\n",
+        "    tokens = [token.text for token in doc if token.is_alpha or token.is_digit and not token.is_stop]\n",
+        "    return \" \".join(tokens)\n",
+        "\n",
+        "df['review'] = df['review'].apply(preprocess_text)\n"
+      ],
+      "metadata": {
+        "id": "9ViuaTl8NESY"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import random\n",
+        "\n",
+        "i = random.randint(0 , len(df) - 1)\n",
+        "print(f\"\\nBefore: {df[\"review\"].iloc[i][:80]}\")\n",
+        "print(f\"\\nAfter: {df[\"review\"].iloc[i][:80]}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DExfCzd9O-9v",
+        "outputId": "ad995968-f964-4170-ca06-70aa71a9090a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Before: with batman returns tim burton to an important in american cinema giving a seque\n",
+            "\n",
+            "After: with batman returns tim burton to an important in american cinema giving a seque\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_train , x_test , y_train , y_test = train_test_split(df[\"review\"] , df[\"sentiment\"].values , test_size=0.2 , random_state=42 , stratify=df[\"sentiment\"].values)\n",
+        "print(f\"Shape of training data: {x_train.shape}\")\n",
+        "print(f\"Shape of testing data: {x_test.shape}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7TncUD3Nf7sK",
+        "outputId": "d365f528-3a2f-4a1a-c636-3abfc09bc5c6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Shape of training data: (6391,)\n",
+            "Shape of testing data: (1598,)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def to_fasttext_format(texts, labels, path):\n",
+        "    with open(path, \"w\", encoding=\"utf-8\") as f:\n",
+        "        for text, label in zip(texts, labels):\n",
+        "            f.write(f\"__label__{label} {text}\\n\")\n",
+        "\n",
+        "to_fasttext_format(x_train, y_train, \"train_fasttext.txt\")\n"
+      ],
+      "metadata": {
+        "id": "1VOc-Ks9SPLQ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ft_model = fasttext.train_supervised(\n",
+        "    input=\"train_fasttext.txt\",\n",
+        "    epoch=25,\n",
+        "    lr=0.5,\n",
+        "    wordNgrams=2,\n",
+        "    dim=300,\n",
+        "    verbose=0\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "qIEhhkyYUjX9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_train_emb = np.vstack([ft_model.get_sentence_vector(text) for text in x_train])\n",
+        "x_test_emb = np.vstack([ft_model.get_sentence_vector(text) for text in x_test])"
+      ],
+      "metadata": {
+        "id": "gvJb8EH2XbIq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(x_train_emb.shape)\n",
+        "print(x_test_emb.shape)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lIzpA7RN4HpX",
+        "outputId": "64a7c332-3f45-4d7e-dd69-a8348ddb7f5d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(6391, 300)\n",
+            "(1598, 300)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from lightgbm import LGBMClassifier\n",
+        "\n",
+        "print(\"Training LightGBM model...\")\n",
+        "\n",
+        "lgbm_model = LGBMClassifier(\n",
+        "    n_estimators=150,\n",
+        "    max_depth=6,\n",
+        "    learning_rate=0.1,\n",
+        "    subsample=0.8,\n",
+        "    colsample_bytree=0.8,\n",
+        "    random_state=42,\n",
+        "    verbosity=-1\n",
+        ")\n",
+        "\n",
+        "lgbm_model.fit(x_train_emb, y_train, eval_set=[(x_test_emb, y_test)], eval_metric=\"logloss\")\n",
+        "\n",
+        "print(\"✓ Model trained\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "BKT1k4XDkbnL",
+        "outputId": "46c002bf-d73e-4606-df6b-3c50b04ad040"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training LightGBM model...\n",
+            "✓ Model trained\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred_lgbm = lgbm_model.predict(x_test_emb)"
+      ],
+      "metadata": {
+        "id": "XwRRuwf5lfgf"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Classification report\n",
+        "print(classification_report(y_test , y_pred_lgbm))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2XOBQ6kwmF-X",
+        "outputId": "035796ff-5232-445a-87fe-665b37603ae7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.89      0.86      0.87       805\n",
+            "           1       0.86      0.89      0.88       793\n",
+            "\n",
+            "    accuracy                           0.87      1598\n",
+            "   macro avg       0.88      0.87      0.87      1598\n",
+            "weighted avg       0.88      0.87      0.87      1598\n",
+            "\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.1/A0.1. Python.ipynb b/a0.1/a0.1/A0.1. Python.ipynb
new file mode 100644
index 0000000..e631807
--- /dev/null
+++ b/a0.1/a0.1/A0.1. Python.ipynb	
@@ -0,0 +1,432 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "background_save": true
+        },
+        "id": "lmaiboJe8E15",
+        "outputId": "0ac8b4eb-8e4e-41b0-cd36-ee266a75e029"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "25.0°C is equal to 77.00°F\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "# Celsius temperature as text\n",
+        "celsius_text = \"25\"\n",
+        "\n",
+        "# Convert to float\n",
+        "celsius = float(celsius_text)\n",
+        "\n",
+        "# Compute Fahrenheit (°F = °C * 9/5 + 32)\n",
+        "fahrenheit = celsius * 9 / 5 + 32\n",
+        "\n",
+        "# Print nicely formatted line\n",
+        "print(f\"{celsius}°C is equal to {fahrenheit:.2f}°F\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "background_save": true
+        },
+        "id": "DSR8aS-F9Z10",
+        "outputId": "0d25d073-e25f-49d7-c3bf-1ab5870610f0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sum: 13.5\n",
+            "Difference: 6.5\n",
+            "Product: 35.0\n",
+            "True Division: 2.857142857142857\n",
+            "Floor Division: 2.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "# Store two numbers of different types\n",
+        "num_int = 10        # int\n",
+        "num_float = 3.5     # float\n",
+        "\n",
+        "# Operations\n",
+        "sum_result = num_int + num_float\n",
+        "difference = num_int - num_float\n",
+        "product = num_int * num_float\n",
+        "true_division = num_int / num_float\n",
+        "floor_division = num_int // num_float\n",
+        "\n",
+        "# Print results\n",
+        "print(f\"Sum: {sum_result}\")\n",
+        "print(f\"Difference: {difference}\")\n",
+        "print(f\"Product: {product}\")\n",
+        "print(f\"True Division: {true_division}\")\n",
+        "print(f\"Floor Division: {floor_division}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [],
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "# Start with an empty shopping list\n",
+        "shopping_list = []\n",
+        "\n",
+        "# 1. Append at least 4 items from user input (comma-separated)\n",
+        "user_input = input(\"Enter at least 4 shopping items, separated by commas: \")\n",
+        "shopping_list.extend([item.strip() for item in user_input.split(\",\")])\n",
+        "\n",
+        "# 2. Convert the list to a tuple\n",
+        "immutable_basket = tuple(shopping_list)\n",
+        "\n",
+        "# 3. Print the third item using tuple indexing\n",
+        "print(f\"Your shopping list (tuple): {immutable_basket}\")\n",
+        "print(f\"The third item is: {immutable_basket[2]}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 2 — Word Stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [],
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word\n",
+        "unique_words = set(sample.split())\n",
+        "\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears\n",
+        "word_counts = {}\n",
+        "for word in sample.split():\n",
+        "    word_counts[word] = word_counts.get(word, 0) + 1\n",
+        "\n",
+        "# 3. Print the two structures\n",
+        "print(\"Unique words (set):\", unique_words)\n",
+        "print(\"Word counts (dict):\", word_counts)\n",
+        "\n",
+        "# Difference:\n",
+        "# ✅ Set: stores ONLY distinct items (no duplicates) and does NOT keep counts.\n",
+        "# ✅ Dict: maps each word (key) to its count (value), so you know how many times each appears.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 1 — Prime Tester"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [],
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    if n < 2:\n",
+        "        return False\n",
+        "    for i in range(2, int(n ** 0.5) + 1):  # چک کردن تا ریشه دوم عدد\n",
+        "        if n % i == 0:\n",
+        "            return False\n",
+        "    return True\n",
+        "    pass\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [],
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    # TODO: your code here\n",
+        "    print((\" \".join([name.capitalize()] * times)))\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      },
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      },
+      "source": [
+        "### Task 1 — Simple Counter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dPFvr_fe-OPR"
+      },
+      "outputs": [],
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "    def __init__(self):\n",
+        "         self._count = 0\n",
+        "\n",
+        "    def increment(self, step: int = 1):\n",
+        "        \"\"\"Add `step` to the current count.\"\"\"\n",
+        "        self._count += step\n",
+        "\n",
+        "    def value(self) -> int:\n",
+        "        \"\"\"Return the current count.\"\"\"\n",
+        "        return self._count\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      },
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "outputs": [],
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "    def __init__(self, x: float, y: float):\n",
+        "        \"\"\"Store x and y as attributes.\"\"\"\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "\n",
+        "    def distance_to(self, other: \"Point\") -> float:\n",
+        "        \"\"\"Calculate Euclidean distance to another point.\"\"\"\n",
+        "        return math.sqrt((self.x - other.x) ** 2 + (self.y - other.y) ** 2)\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n",
+        "print(p.distance_to(q))  # Output: 5.0"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/a0.1/a0.1/AI-DS_Nexus_A0.1_Python_mhmdrzabd.ipynb b/a0.1/a0.1/AI-DS_Nexus_A0.1_Python_mhmdrzabd.ipynb
new file mode 100644
index 0000000..4d53b3c
--- /dev/null
+++ b/a0.1/a0.1/AI-DS_Nexus_A0.1_Python_mhmdrzabd.ipynb
@@ -0,0 +1,463 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "52 °F\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "Celsius_temperature = '11.6'\n",
+        "Celsius_temperature = float(Celsius_temperature)\n",
+        "Fahrenheit = (Celsius_temperature * 9/5) + 32\n",
+        "print('%d °F' % Fahrenheit)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "78.5\n",
+            "67.5\n",
+            "401.5\n",
+            "13.272727272727273\n",
+            "13\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "x = 73\n",
+        "y = 5.5\n",
+        "print(x + y)\n",
+        "print(x - y)\n",
+        "print(x * y)\n",
+        "print(x / y)\n",
+        "print(int(x // y))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['Watter', 'Orange', 'Milk', 'Snack']\n",
+            "('Watter', 'Orange', 'Milk', 'Snack')\n",
+            "Milk\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "shopping = []\n",
+        "shopping.extend(['Water', 'Orange', 'Milk', 'Snack'])\n",
+        "print(shopping)\n",
+        "immutable_basket = tuple(shopping)\n",
+        "print(immutable_basket)\n",
+        "print(immutable_basket[2])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 2 — Word Stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'banana', 'orange', 'apple'}\n",
+            "{'apple': 3, 'orange': 2, 'banana': 1}\n"
+          ]
+        }
+      ],
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        "text = 'apple orange apple banana orange apple'\n",
+        "words = text.split()\n",
+        "\n",
+        "unique_words = set(words)\n",
+        "print(unique_words)\n",
+        "\n",
+        "word_counts = {}\n",
+        "for w in words:\n",
+        "    word_counts[w] = word_counts.get(w, 0) + 1\n",
+        "\n",
+        "print(word_counts)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 1 — Prime Tester"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ],
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    if n < 2:\n",
+        "       return False\n",
+        "    i = 2\n",
+        "    while i * i <=n:\n",
+        "        if n % i == 0:\n",
+        "            return False\n",
+        "        i += 1\n",
+        "    return True\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Alice\n",
+            "Bob Bob Bob\n"
+          ]
+        }
+      ],
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    # TODO: your code here\n",
+        "    print(((name.capitalize() + \" \") * times).strip())\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      },
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      },
+      "source": [
+        "### Task 1 — Simple Counter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "dPFvr_fe-OPR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "5\n"
+          ]
+        }
+      ],
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "\n",
+        "    def __init__(self):\n",
+        "        self._count = 0\n",
+        "\n",
+        "    def increment(self, step: int = 1):\n",
+        "        self._count += step\n",
+        "\n",
+        "    def value(self):\n",
+        "        return self._count\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      },
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "5.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "\n",
+        "    def __init__(self, x: float, y: float):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "\n",
+        "    def distance_to(self, other: \"Point\") -> float:\n",
+        "        dx = self.x - other.x\n",
+        "        dy = self.y - other.y\n",
+        "        return math.sqrt(dx*dx + dy*dy)\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n",
+        "print(p.distance_to(q))  # 5.0"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.1/AI-DS_Nexus__A0_1_Amirhossein_Zandi_8fca80.ipynb b/a0.1/a0.1/AI-DS_Nexus__A0_1_Amirhossein_Zandi_8fca80.ipynb
new file mode 100644
index 0000000..ffdaa7e
--- /dev/null
+++ b/a0.1/a0.1/AI-DS_Nexus__A0_1_Amirhossein_Zandi_8fca80.ipynb
@@ -0,0 +1,490 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "30.0°C is equal to 86.0°F\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "celsius_text = \"30\"\n",
+        "celsius_num = float(celsius_text)\n",
+        "Fahrenheit_temp = celsius_num * 9/5 + 32\n",
+        "print(f\"{celsius_num}°C is equal to {Fahrenheit_temp}°F\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "sum : 13.4\n",
+            "difference : 6.6\n",
+            "product : 34.0\n",
+            "true division : 2.9411764705882355\n",
+            "floor division : 2.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "num1 = 10\n",
+        "num2 = 3.4\n",
+        "\n",
+        "print(f\"sum : {num1 + num2}\")\n",
+        "print(f\"difference : {num1 - num2}\")\n",
+        "print(f\"product : {num1 * num2}\")\n",
+        "print(f\"true division : {num1 / num2}\")\n",
+        "print(f\"floor division : {num1 // num2}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "third item in basket : banana\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "shopping_list = []\n",
+        "\n",
+        "user_input = [\"apple\" , \"milk\" , \"banana\" , \"chocolate\"]\n",
+        "shopping_list.append(user_input)\n",
+        "\n",
+        "immutable_basket = tuple(shopping_list[0])\n",
+        "\n",
+        "print(f\"third item in basket : {immutable_basket[2]}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 2 — Word Stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Unique words : {'is', 'that', 'the', 'or', 'to', 'be', 'question', 'not'}\n",
+            "Word counts : {'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n"
+          ]
+        }
+      ],
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "word_fragment = sample.split()\n",
+        "unique_words = set(word_fragment)\n",
+        "\n",
+        "word_counts = {}\n",
+        "for item in word_fragment:\n",
+        "    if item in word_counts:\n",
+        "        word_counts[item] += 1\n",
+        "    else:\n",
+        "        word_counts[item] = 1\n",
+        "\n",
+        "print(f\"Unique words : {unique_words}\")\n",
+        "print(f\"Word counts : {word_counts}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 1 — Prime Tester"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# def is_prime(n: int) -> bool:\n",
+        "#     \"\"\"\n",
+        "#     Return True if n is a prime number, else False.\n",
+        "#     0 and 1 are *not* prime.\n",
+        "#     \"\"\"\n",
+        "#     # TODO: replace pass with your implementation\n",
+        "#     pass\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "# print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n",
+        "\n",
+        "def is_prime(x):\n",
+        "    if x <= 1:\n",
+        "        return False\n",
+        "    for i in range(2,x):\n",
+        "        if x % i == 0:\n",
+        "            return False\n",
+        "    return True\n",
+        "\n",
+        "print([item for item in range(10) if is_prime(item)])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Alice\n",
+            "Bob Bob Bob\n"
+          ]
+        }
+      ],
+      "source": [
+        "# def greet(name: str, times: int = 1) -> None:\n",
+        "#     \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "#     # TODO: your code here\n",
+        "\n",
+        "\n",
+        "# greet(\"alice\")          # Alice\n",
+        "# greet(\"bob\", times=3)   # Bob Bob Bob\n",
+        "\n",
+        "def greet(name , times = 1):\n",
+        "    print(\" \".join([name.capitalize()] * times))\n",
+        "\n",
+        "greet(\"Alice\")\n",
+        "greet(\"bob\", times=3)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      },
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      },
+      "source": [
+        "### Task 1 — Simple Counter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "dPFvr_fe-OPR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "5\n"
+          ]
+        }
+      ],
+      "source": [
+        "# class Counter:\n",
+        "#     \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "#     # TODO:\n",
+        "#     # 1. In __init__, store an internal count variable starting at 0.\n",
+        "#     # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "#     # 3. Method value() returns the current count.\n",
+        "\n",
+        "\n",
+        "# c = Counter()\n",
+        "# for _ in range(5):\n",
+        "#     c.increment()\n",
+        "# print(c.value())   # Expected: 5\n",
+        "\n",
+        "class Counter:\n",
+        "    def __init__(self):\n",
+        "        self.count = 0\n",
+        "\n",
+        "    def increment(self):\n",
+        "        self.count += 1\n",
+        "\n",
+        "    def get_count(self)    :\n",
+        "        return self.count\n",
+        "    \n",
+        "c = Counter()\n",
+        "\n",
+        "for item in range(5):\n",
+        "    c.increment()\n",
+        "print(c.get_count())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      },
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "outputs": [],
+      "source": [
+        "# import math\n",
+        "\n",
+        "# class Point:\n",
+        "#     \"\"\"\n",
+        "#     A 2-D point supporting distance calculation.\n",
+        "#     Usage:\n",
+        "#         p = Point(3, 4)\n",
+        "#         q = Point(0, 0)\n",
+        "#         print(p.distance_to(q))  # 5.0\n",
+        "#     \"\"\"\n",
+        "#     # TODO:\n",
+        "#     # 1. Store x and y as attributes.\n",
+        "#     # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "\n",
+        "\n",
+        "# # Smoke test\n",
+        "# p, q = Point(3, 4), Point(0, 0)\n",
+        "# assert round(p.distance_to(q), 1) == 5.0\n",
+        "\n",
+        "\n",
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    def __init__(self , x , y):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "\n",
+        "    def distance_to(self , other):\n",
+        "        result = math.sqrt((self.x - other.x)**2 + (self.y - other.y)**2)\n",
+        "        return round(result , 1)\n",
+        "    \n",
+        "p = Point(3,4)\n",
+        "q = Point(0 , 0)\n",
+        "\n",
+        "assert p.distance_to(q) == 5.0"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.1/AI-DS_Nexus__A0_1_Javid_Mohammadi_a60d6f.ipynb b/a0.1/a0.1/AI-DS_Nexus__A0_1_Javid_Mohammadi_a60d6f.ipynb
new file mode 100644
index 0000000..887602d
--- /dev/null
+++ b/a0.1/a0.1/AI-DS_Nexus__A0_1_Javid_Mohammadi_a60d6f.ipynb
@@ -0,0 +1,487 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b1029a21-b356-4b30-f489-4f90b6a112f0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The degree in Fahrenheit is 100.03999999999999\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "degree = '37.8'\n",
+        "degree_float = float(degree)\n",
+        "degree_fahren = ( degree_float * 9 / 5 ) + 32\n",
+        "print(f'The degree in Fahrenheit is {degree_fahren}')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "num1, num2 = 3.2, 1\n",
+        "print(\"Welcome to the Tiny Calculator, I hope you're doing well\\n\")\n",
+        "print(f'The sum of the numbers is: {num1 + num2}')\n",
+        "print(f'The difference of the numbers is: {num1 - num2}')\n",
+        "print(f'The product of the numbers is: {num1 * num2}')\n",
+        "print(f'The true division of the numbers is: {num1 / num2}')\n",
+        "print(f'The floor division of the numbers is: {num1 // num2}')"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "94f50542-91d3-4739-fe1f-f35f2b35c335"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Welcome to the Tiny Calculator, I hope you're doing well\n",
+            "\n",
+            "The sum of the numbers is: 4.2\n",
+            "The difference of the numbers is: 2.2\n",
+            "The product of the numbers is: 3.2\n",
+            "The true division of the numbers is: 3.2\n",
+            "The floor division of the numbers is: 3.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "shopping_list = []\n",
+        "user_input = input('Please enter 4 items for supermarket (separated by comma, e.g: apple, banana, mango, bread): ')\n",
+        "#user_input = \"apple, banana, mango, bread\"\n",
+        "shopping_list += user_input.split(',')\n",
+        "#print(shopping_list)\n",
+        "immutable_basket = tuple(shopping_list)\n",
+        "print(f\"The third item of the immutable basket is {immutable_basket[2].strip()}\")\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3a90d804-3aed-4404-8831-d65fdc0bcd5b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Please enter 4 items for supermarket (separated by comma, e.g: apple, banana, mango, bread): mango, banana, cheetah, bread\n",
+            "The third item of the immutable basket is cheetah\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Word Stats"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "\n",
+        "# 1.\n",
+        "unique_words = set(sample.split())\n",
+        "\n",
+        "# 2.\n",
+        "word_counts = {}\n",
+        "sample_list = sample.split()\n",
+        "for word in sample_list:\n",
+        "  count = sample_list.count(word)\n",
+        "  word_counts[word] = count\n",
+        "\n",
+        "# 3.\n",
+        "print(unique_words)\n",
+        "print(word_counts)\n",
+        "\n",
+        "# Sets:\n",
+        "#   No duplicate values\n",
+        "#   Store only values\n",
+        "#   Lookup by value\n",
+        "\n",
+        "# Dictionaries:\n",
+        "#   No duplicate keys, but dublicate values are allowed\n",
+        "#   Store key-value pairs separated by : ,eg. key: value\n",
+        "#   Lookup by key"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2ff90e53-563b-4bd3-922a-2e67d29c0439"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'or', 'not', 'the', 'that', 'be', 'question', 'to', 'is'}\n",
+            "{'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Prime Tester"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    if (n == 0) or (n == 1):\n",
+        "      return False\n",
+        "    if n == 2:\n",
+        "      return True\n",
+        "    if n % 2 == 0:\n",
+        "      return False\n",
+        "\n",
+        "    for i in range (3, n):\n",
+        "    #for i in range(3, int(n**0.5) + 1, 2): #more optimized\n",
+        "      if n % i == 0:\n",
+        "        return False\n",
+        "\n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2d190ac9-3d8e-4bdd-c497-e116db2783f8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    print((name.capitalize() + ' ') * times)\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d39ca515-5b38-45d6-cda2-ddb0414cab65"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Alice \n",
+            "Bob Bob Bob \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ],
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Simple Counter"
+      ],
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    def __init__(self):\n",
+        "      self.count = 0\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    def increment(self):\n",
+        "      self.count += 1\n",
+        "    # 3. Method value() returns the current count.\n",
+        "    def value(self):\n",
+        "      return self.count\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n",
+        "\n",
+        "#b = Counter()\n",
+        "#b.value()\n",
+        "#b.increment()"
+      ],
+      "metadata": {
+        "id": "dPFvr_fe-OPR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e0406c18-451d-4035-cf77-a26eda31c7a0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ],
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    def __init__(self, x, y):\n",
+        "      self.x = x\n",
+        "      self.y = y\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "    def distance_to(self, q):\n",
+        "      return math.sqrt((self.x - q.x) ** 2 + (self.y - q.y) ** 2 )\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0"
+      ],
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "ly3MwsoKid9N"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.1/AI-DS_Nexus__A0_1_Project__LeilaRostamian.ipynb b/a0.1/a0.1/AI-DS_Nexus__A0_1_Project__LeilaRostamian.ipynb
new file mode 100644
index 0000000..3ffdc3c
--- /dev/null
+++ b/a0.1/a0.1/AI-DS_Nexus__A0_1_Project__LeilaRostamian.ipynb
@@ -0,0 +1,549 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "da219ea7-5bbe-4264-e206-027ee8155bb8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Celsius temperature: 10\n",
+            "👉 10.0 °C = 50.0 °F\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "Celsius_temperature = input(\"Celsius temperature: \")\n",
+        "Celsius_temperature = float(Celsius_temperature)\n",
+        "Fahrenheit_temperature =  Celsius_temperature * 9/5 + 32\n",
+        "print(f\"👉 {Celsius_temperature} °C = {Fahrenheit_temperature} °F\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "int_num = int(input(\"enter the first number: \"))\n",
+        "float_num = float(input(\"enter the second number: \"))\n",
+        "summation = int_num + float_num\n",
+        "difference = int_num - float_num\n",
+        "product = int_num * float_num\n",
+        "true_division = int_num / float_num\n",
+        "floor_division = int_num // float_num\n",
+        "\n",
+        "\n",
+        "print(f\"\"\"👉 {int_num} and {float_num} are integer and float numbers respectively\\n\n",
+        " their sum is {summation},\\n\n",
+        " difference is {difference},\\n\n",
+        " product is {product},\\n\n",
+        " true division is {true_division:.2f},\\n\n",
+        " floor division is {floor_division}\"\"\")\n",
+        "# one of the numbers is a float, so the sum will also be a float.\n",
+        "# // calculates exactly the integer part of the division result (i.e., rounds the number towards negative infinity).\n"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3d054a1f-1f80-4359-9287-99b31a380100"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "enter the first number: 13\n",
+            "enter the second number: 6\n",
+            "👉 13 and 6.0 are integer and float numbers respectively \n",
+            "\n",
+            " their sum is 19.0, \n",
+            "\n",
+            " difference is 7.0, \n",
+            "\n",
+            " product is 78.0, \n",
+            " \n",
+            " true division is 2.17,\n",
+            " \n",
+            " floor division is 2.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "![Screenshot (53).png](data:image/png;base64,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)"
+      ],
+      "metadata": {
+        "id": "RFLgrl37wuqq"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "\n",
+        "# TODO: your code here\n",
+        "# an empty shopping list (list).\n",
+        "shopping_list = []\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "for i in range(4):\n",
+        "  item = input(f'enter the {i+1}th item: ')\n",
+        "  shopping_list.append(item)\n",
+        "\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "shopping_items = tuple(shopping_list)\n",
+        "\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "print(f\"👉 The third item is {shopping_items[2]}\\n from the shopping list {shopping_list}\\n\")\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "72f6103c-7a68-46a9-caef-be58c007a691"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "enter the 1th item: egg\n",
+            "enter the 2th item: fruit\n",
+            "enter the 3th item: vegetable\n",
+            "enter the 4th item: bread\n",
+            "👉 The third item is vegetable \n",
+            " from the shopping list ['egg', 'fruit', 'vegetable', 'bread']\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Word Stats"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# TODO: your code here\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "sample = sample.split()\n",
+        "unique_words = set(sample)\n",
+        "\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "word_counts = {}\n",
+        "for word in sample:\n",
+        "  if word in word_counts:\n",
+        "    word_counts[word] += 1\n",
+        "  else:\n",
+        "    word_counts[word] = 1\n",
+        "\n",
+        "# We can also code in this way\n",
+        "# word_counts = {word: sample.count(word) for word in set(sample)}\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "\n",
+        "print(f'''{sample} is a list of words.\\n\n",
+        "A set contains every distinct word: {unique_words}.\\n\n",
+        "A dictionary maps each word to the number of times appearing: {word_counts}\\n''')\n",
+        "\n",
+        "# We can also code in this way\n",
+        "# word_counts = {word: sample.count(word) for word in sample}\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4e29ebdf-2913-4f5e-eca4-95aac1ea4c01"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "['to', 'be', 'or', 'not', 'to', 'be', 'that', 'is', 'the', 'question'] is a list of words.\n",
+            "\n",
+            "A set contains every distinct word: {'is', 'not', 'that', 'the', 'question', 'to', 'be', 'or'}.\n",
+            "\n",
+            "A dictionary maps each word to the number of times appearing: {'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Prime Tester"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    if n <= 1:\n",
+        "      return False\n",
+        "    else:\n",
+        "      for i in range(2, int(n**0.5) + 1):\n",
+        "        if n % i == 0:\n",
+        "          return False\n",
+        "\n",
+        "    return True\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "149653ee-76e0-4a10-d6b6-68a1f3bea078"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    name_with_space = name.capitalize() + ' '\n",
+        "    print(name_with_space * times)\n",
+        "    # TODO: your code here\n",
+        "\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9c1f467e-c351-4fa4-b640-79d648d135e9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Alice \n",
+            "Bob Bob Bob \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ],
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Simple Counter"
+      ],
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    def __init__(self):\n",
+        "      self.count = 0\n",
+        "    # \"create the class, then a folder called count in its memory and put the number 0 in it.\"\n",
+        "\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    def increment(self, step: int = 1):\n",
+        "      self.count += 1\n",
+        "\n",
+        "    # 3. Method value() returns the current count.\n",
+        "    def value(self):\n",
+        "      return self.count\n",
+        "\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ],
+      "metadata": {
+        "id": "dPFvr_fe-OPR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "92ec8ec2-e624-4600-ed60-d58675616320"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ],
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "\n",
+        "    def __init__(self, x: float, y: float):\n",
+        "      self.x = x\n",
+        "      self.y = y\n",
+        "\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "\n",
+        "    def distance_to(self, other : 'Point') -> float:\n",
+        "      dx = self.x - other.x\n",
+        "      dy = self.y - other.y\n",
+        "    # return (dx**2 + dy**2)**0.5\n",
+        "      return math.sqrt(dx**2 + dy**2)\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "OVh3GEzH-T0w",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2b0767fb-44aa-44ee-e21e-c9d309d788ee"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 22
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.1/AI-DS_Nexus__A0_1_Project__RezaShokr.ipynb b/a0.1/a0.1/AI-DS_Nexus__A0_1_Project__RezaShokr.ipynb
new file mode 100644
index 0000000..7e54e91
--- /dev/null
+++ b/a0.1/a0.1/AI-DS_Nexus__A0_1_Project__RezaShokr.ipynb
@@ -0,0 +1,577 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "38.6° Celsius is 398.7° Fahrenhit\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "#VERSION 1\n",
+        "degree_celsius1 = '38.6'\n",
+        "degree_fahrenhit1 = round((float(degree_celsius1) * 9.5 +  32) , 1) \n",
+        "print(f'{degree_celsius1}° Celsius is {degree_fahrenhit1}° Fahrenhit')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "700.0° Celsius is 6682.0° Fahrenhit\n"
+          ]
+        }
+      ],
+      "source": [
+        "#VERSION 2\n",
+        "degree_celsius = float(input(\"insert celsius degree\"))\n",
+        "degree_fahrenhit = round((degree_celsius * 9.5 +  32) , 2) \n",
+        "print(f'{degree_celsius}° Celsius is {degree_fahrenhit}° Fahrenhit')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sum :         a + b Equals 10\n",
+            "\n",
+            "difference :  a - b = 4\n",
+            "\n",
+            "product :     a * b = 21\n",
+            "\n",
+            "division :    a / b = 2.33\n",
+            "\n",
+            "f_division :  a // b = 2\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "a , b  = 7 , 3\n",
+        "summ = a + b\n",
+        "difference = a - b \n",
+        "product = a * b\n",
+        "division =  round((a / b) , 2)\n",
+        "floor_division =  a // b\n",
+        "print(f'Sum :         a + b Equals {summ}\\n' )\n",
+        "print(f'difference :  a - b = {difference}\\n')\n",
+        "print(f'product :     a * b = {product}\\n')\n",
+        "print(f'division :    a / b = {division}\\n')\n",
+        "print(f'f_division :  a // b = {floor_division}\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            " immutable_basket is : ('d ', ' 4 ', ' kll ', '  56')  \n",
+            "The third item    is :  kll \n"
+          ]
+        }
+      ],
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "# WITH append()\n",
+        "mylist = []\n",
+        "\n",
+        "user_input = input(\"Enter at least 4 items (comma-separated): \")\n",
+        "items = user_input.strip().split(\",\")\n",
+        "\n",
+        "if len(items) >= 4 : \n",
+        "    for i in items : \n",
+        "        mylist.append(i)\n",
+        "else :\n",
+        "    print(\"pls insert more than 4 items or 3 coma\")\n",
+        "\n",
+        "immutable_basket = tuple(mylist)\n",
+        "\n",
+        "print(f' immutable_basket is : {immutable_basket}  ')\n",
+        "print(f\"The third item    is : {immutable_basket[2] }\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "('1', '2', '3', '4', '5')\n",
+            "The third item is: 3\n"
+          ]
+        }
+      ],
+      "source": [
+        "# with extend & comphernsion list\n",
+        "mylist = []\n",
+        "user_input = input(\"Enter at least 4 items (comma-separated): \")\n",
+        "items = [item.strip() for item in user_input.split(\",\")]\n",
+        "\n",
+        "if len(items) >= 4:\n",
+        "    mylist.extend(items)\n",
+        "else:\n",
+        "    print(\"Please enter at least 4 items.\")\n",
+        "    exit()\n",
+        "\n",
+        "immutable_basket = tuple(mylist)\n",
+        "\n",
+        "print(immutable_basket)\n",
+        "print(f\"The third item is: {immutable_basket[2]}\")\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 2 — Word Stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mylist is : ['to', 'be', 'or', 'not', 'to', 'be', 'that', 'is', 'the', 'question']\n",
+            "\n",
+            "Unique words (set) : {'that', 'not', 'is', 'the', 'or', 'to', 'question', 'be'}\n",
+            "\n",
+            "Word counts (dict) : {'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1} \n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        " \n",
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "mylist = sample.split(\" \")\n",
+        "unique_words = set(mylist)\n",
+        "word_counts = {}\n",
+        "\n",
+        "for i in mylist:\n",
+        "    if i in word_counts:\n",
+        "        word_counts[i] += 1\n",
+        "    else:\n",
+        "        word_counts[i] = 1\n",
+        "\n",
+        "print(f'Mylist is : {mylist}\\n')\n",
+        "print(f\"Unique words (set) : {unique_words}\\n\")\n",
+        "print(f\"Word counts (dict) : {word_counts} \" )\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 1 — Prime Tester"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "list_add_aval in range 0-100 : [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]\n",
+            "\n",
+            "tedad adad aval in range 0-100 = 25\n"
+          ]
+        }
+      ],
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    if n < 2 :\n",
+        "        return False # print(f'{n} add aval nist!!')\n",
+        "    \n",
+        "    for i in range (2 , n) : # range (2 , n) >>  2 - n-1 \n",
+        "        if n % i ==0 :\n",
+        "         return False #print(f'{n} add aval nist!!')\n",
+        "        \n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    #pass\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "baze_mogasebat = 100\n",
+        "m = range(baze_mogasebat)\n",
+        "\n",
+        "list_add_aval = [x for x in m if is_prime(x)]\n",
+        "print(f\"list_add_aval in range 0-{baze_mogasebat} : {list_add_aval}\\n\")\n",
+        "\n",
+        "print(f'tedad adad aval in range 0-{baze_mogasebat} = {len(list_add_aval)}')  # Expected: [2, 3, 5, 7]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Alice\n",
+            "Bob Bob Bob\n"
+          ]
+        }
+      ],
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "   \n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    #  TODO: your code here\n",
+        "\n",
+        "    a = name.strip().capitalize()\n",
+        "    a = (a+\" \") *times\n",
+        "\n",
+        "    return a.strip() \n",
+        "\n",
+        "a = greet(\"alice\")          # Alice\n",
+        "b = greet(\"bob\", times=3)   # Bob Bob Bob\n",
+        "\n",
+        "print(a)\n",
+        "print(b)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      },
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      },
+      "source": [
+        "### Task 1 — Simple Counter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "id": "dPFvr_fe-OPR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "100\n"
+          ]
+        }
+      ],
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "\n",
+        "    def __init__(self):\n",
+        "        self.count = 0\n",
+        "    \n",
+        "    def increment(self, step: int = 1):\n",
+        "        self.count += step\n",
+        "    \n",
+        "    def value(self):\n",
+        "        return self.count\n",
+        "   \n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(100):\n",
+        "    c.increment()\n",
+        "\n",
+        "print(c.value())   # Expected: 5\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      },
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "outputs": [],
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "\n",
+        "    def __init__(self, x: float, y: float):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "    \n",
+        "    def distance_to(self, other):\n",
+        "        dx = self.x - other.x\n",
+        "        dy = self.y - other.y\n",
+        "\n",
+        "        return math.sqrt(dx**2 + dy**2)  \n",
+        "    \n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "11.7\n"
+          ]
+        }
+      ],
+      "source": [
+        "m , n = Point( 10 , 6 ) , Point( 0  , 0)\n",
+        "print(round(m.distance_to(n), 1))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.1/AI-DS_Nexus__A0_1_Project_roohi_268383.ipynb b/a0.1/a0.1/AI-DS_Nexus__A0_1_Project_roohi_268383.ipynb
new file mode 100644
index 0000000..7e54e91
--- /dev/null
+++ b/a0.1/a0.1/AI-DS_Nexus__A0_1_Project_roohi_268383.ipynb
@@ -0,0 +1,577 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "38.6° Celsius is 398.7° Fahrenhit\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "#VERSION 1\n",
+        "degree_celsius1 = '38.6'\n",
+        "degree_fahrenhit1 = round((float(degree_celsius1) * 9.5 +  32) , 1) \n",
+        "print(f'{degree_celsius1}° Celsius is {degree_fahrenhit1}° Fahrenhit')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "700.0° Celsius is 6682.0° Fahrenhit\n"
+          ]
+        }
+      ],
+      "source": [
+        "#VERSION 2\n",
+        "degree_celsius = float(input(\"insert celsius degree\"))\n",
+        "degree_fahrenhit = round((degree_celsius * 9.5 +  32) , 2) \n",
+        "print(f'{degree_celsius}° Celsius is {degree_fahrenhit}° Fahrenhit')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sum :         a + b Equals 10\n",
+            "\n",
+            "difference :  a - b = 4\n",
+            "\n",
+            "product :     a * b = 21\n",
+            "\n",
+            "division :    a / b = 2.33\n",
+            "\n",
+            "f_division :  a // b = 2\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "a , b  = 7 , 3\n",
+        "summ = a + b\n",
+        "difference = a - b \n",
+        "product = a * b\n",
+        "division =  round((a / b) , 2)\n",
+        "floor_division =  a // b\n",
+        "print(f'Sum :         a + b Equals {summ}\\n' )\n",
+        "print(f'difference :  a - b = {difference}\\n')\n",
+        "print(f'product :     a * b = {product}\\n')\n",
+        "print(f'division :    a / b = {division}\\n')\n",
+        "print(f'f_division :  a // b = {floor_division}\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            " immutable_basket is : ('d ', ' 4 ', ' kll ', '  56')  \n",
+            "The third item    is :  kll \n"
+          ]
+        }
+      ],
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "# WITH append()\n",
+        "mylist = []\n",
+        "\n",
+        "user_input = input(\"Enter at least 4 items (comma-separated): \")\n",
+        "items = user_input.strip().split(\",\")\n",
+        "\n",
+        "if len(items) >= 4 : \n",
+        "    for i in items : \n",
+        "        mylist.append(i)\n",
+        "else :\n",
+        "    print(\"pls insert more than 4 items or 3 coma\")\n",
+        "\n",
+        "immutable_basket = tuple(mylist)\n",
+        "\n",
+        "print(f' immutable_basket is : {immutable_basket}  ')\n",
+        "print(f\"The third item    is : {immutable_basket[2] }\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "('1', '2', '3', '4', '5')\n",
+            "The third item is: 3\n"
+          ]
+        }
+      ],
+      "source": [
+        "# with extend & comphernsion list\n",
+        "mylist = []\n",
+        "user_input = input(\"Enter at least 4 items (comma-separated): \")\n",
+        "items = [item.strip() for item in user_input.split(\",\")]\n",
+        "\n",
+        "if len(items) >= 4:\n",
+        "    mylist.extend(items)\n",
+        "else:\n",
+        "    print(\"Please enter at least 4 items.\")\n",
+        "    exit()\n",
+        "\n",
+        "immutable_basket = tuple(mylist)\n",
+        "\n",
+        "print(immutable_basket)\n",
+        "print(f\"The third item is: {immutable_basket[2]}\")\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 2 — Word Stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mylist is : ['to', 'be', 'or', 'not', 'to', 'be', 'that', 'is', 'the', 'question']\n",
+            "\n",
+            "Unique words (set) : {'that', 'not', 'is', 'the', 'or', 'to', 'question', 'be'}\n",
+            "\n",
+            "Word counts (dict) : {'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1} \n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        " \n",
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "mylist = sample.split(\" \")\n",
+        "unique_words = set(mylist)\n",
+        "word_counts = {}\n",
+        "\n",
+        "for i in mylist:\n",
+        "    if i in word_counts:\n",
+        "        word_counts[i] += 1\n",
+        "    else:\n",
+        "        word_counts[i] = 1\n",
+        "\n",
+        "print(f'Mylist is : {mylist}\\n')\n",
+        "print(f\"Unique words (set) : {unique_words}\\n\")\n",
+        "print(f\"Word counts (dict) : {word_counts} \" )\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 1 — Prime Tester"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "list_add_aval in range 0-100 : [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]\n",
+            "\n",
+            "tedad adad aval in range 0-100 = 25\n"
+          ]
+        }
+      ],
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    if n < 2 :\n",
+        "        return False # print(f'{n} add aval nist!!')\n",
+        "    \n",
+        "    for i in range (2 , n) : # range (2 , n) >>  2 - n-1 \n",
+        "        if n % i ==0 :\n",
+        "         return False #print(f'{n} add aval nist!!')\n",
+        "        \n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    #pass\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "baze_mogasebat = 100\n",
+        "m = range(baze_mogasebat)\n",
+        "\n",
+        "list_add_aval = [x for x in m if is_prime(x)]\n",
+        "print(f\"list_add_aval in range 0-{baze_mogasebat} : {list_add_aval}\\n\")\n",
+        "\n",
+        "print(f'tedad adad aval in range 0-{baze_mogasebat} = {len(list_add_aval)}')  # Expected: [2, 3, 5, 7]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Alice\n",
+            "Bob Bob Bob\n"
+          ]
+        }
+      ],
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "   \n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    #  TODO: your code here\n",
+        "\n",
+        "    a = name.strip().capitalize()\n",
+        "    a = (a+\" \") *times\n",
+        "\n",
+        "    return a.strip() \n",
+        "\n",
+        "a = greet(\"alice\")          # Alice\n",
+        "b = greet(\"bob\", times=3)   # Bob Bob Bob\n",
+        "\n",
+        "print(a)\n",
+        "print(b)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      },
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      },
+      "source": [
+        "### Task 1 — Simple Counter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "id": "dPFvr_fe-OPR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "100\n"
+          ]
+        }
+      ],
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "\n",
+        "    def __init__(self):\n",
+        "        self.count = 0\n",
+        "    \n",
+        "    def increment(self, step: int = 1):\n",
+        "        self.count += step\n",
+        "    \n",
+        "    def value(self):\n",
+        "        return self.count\n",
+        "   \n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(100):\n",
+        "    c.increment()\n",
+        "\n",
+        "print(c.value())   # Expected: 5\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      },
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "outputs": [],
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "\n",
+        "    def __init__(self, x: float, y: float):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "    \n",
+        "    def distance_to(self, other):\n",
+        "        dx = self.x - other.x\n",
+        "        dy = self.y - other.y\n",
+        "\n",
+        "        return math.sqrt(dx**2 + dy**2)  \n",
+        "    \n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "11.7\n"
+          ]
+        }
+      ],
+      "source": [
+        "m , n = Point( 10 , 6 ) , Point( 0  , 0)\n",
+        "print(round(m.distance_to(n), 1))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.1/AI-DS_Nexus__A0_1__MahyaTakbash.ipynb b/a0.1/a0.1/AI-DS_Nexus__A0_1__MahyaTakbash.ipynb
new file mode 100644
index 0000000..36ead61
--- /dev/null
+++ b/a0.1/a0.1/AI-DS_Nexus__A0_1__MahyaTakbash.ipynb
@@ -0,0 +1,499 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8755dc22-7f5a-40d9-a8f5-0fc4862a34f1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "27.0 °C is equal to 80.6 °F\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "celsius='27'\n",
+        "c=float(celsius)\n",
+        "f=c*9/5+32\n",
+        "print('%.1f °C is equal to %.1f °F' % (c,f))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "a=20\n",
+        "b=36.9\n",
+        "print(a+b)\n",
+        "print(a-b)\n",
+        "print(a*b)\n",
+        "print(a/b)\n",
+        "print(a//b)"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c34833af-f28e-4969-9825-e524bd040734"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "56.9\n",
+            "-16.9\n",
+            "738.0\n",
+            "0.5420054200542006\n",
+            "0.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "shopping_list = []\n",
+        "items = input(\"Enter at least 4 items (comma-separated): \").split(',')\n",
+        "if len(items) >= 4:\n",
+        "    shopping_list.extend(items)\n",
+        "    immutable_basket = tuple(shopping_list)\n",
+        "    print(immutable_basket[2])\n",
+        "else:\n",
+        "    print(\"Please enter at least 4 items\")\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 356
+        },
+        "outputId": "7f39f29a-4c0c-4971-d527-5973c52fc25b"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "KeyboardInterrupt",
+          "evalue": "Interrupted by user",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-1393279494.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# TODO: your code here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mshopping_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Enter at least 4 items (comma-separated): \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m','\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mshopping_list\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m   1175\u001b[0m                 \u001b[0;34m\"raw_input was called, but this frontend does not support input requests.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1176\u001b[0m             )\n\u001b[0;32m-> 1177\u001b[0;31m         return self._input_request(\n\u001b[0m\u001b[1;32m   1178\u001b[0m             \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1179\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"shell\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m   1217\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1218\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1219\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Interrupted by user\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1220\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1221\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid Message:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyboardInterrupt\u001b[0m: Interrupted by user"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Word Stats"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        "text = \"to be or not to be that is the question\"\n",
+        "words = text.split()\n",
+        "unique_words = set(words)\n",
+        "word_counts = {}\n",
+        "for word in words:\n",
+        "    word_counts[word] = word_counts.get(word, 0) + 1\n",
+        "print(unique_words)\n",
+        "print(word_counts)"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4d0417be-90bd-45ea-d74c-d71c23cd8db2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{'not', 'be', 'to', 'is', 'the', 'question', 'or', 'that'}\n",
+            "{'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Prime Tester"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    if n < 2:\n",
+        "        return False\n",
+        "\n",
+        "    for i in range(2, n):\n",
+        "        if n % i == 0:\n",
+        "            return False\n",
+        "\n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "23e4dbe0-ed01-4521-a90b-0a23a53f7344"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "\n",
+        "    name = name.capitalize()\n",
+        "\n",
+        "    for i in range(times):\n",
+        "        print(name, end=\" \")\n",
+        "\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "242c73d5-7820-4806-babe-d86df1ff0400"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Alice Bob Bob Bob "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ],
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Simple Counter"
+      ],
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "class Counter:\n",
+        "    def __init__(self):\n",
+        "        self.count = 0\n",
+        "\n",
+        "    def increment(self, step: int = 1):\n",
+        "        self.count += step\n",
+        "\n",
+        "    def value(self):\n",
+        "        return self.count\n",
+        "\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ],
+      "metadata": {
+        "id": "dPFvr_fe-OPR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7dc09719-b969-4d46-8a5f-4df4e18d47c8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ],
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        " import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    def __init__(self, x, y):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "\n",
+        "    def distance_to(self, other):\n",
+        "        dx = self.x - other.x\n",
+        "        dy = self.y - other.y\n",
+        "        return math.sqrt(dx**2 + dy**2)\n",
+        "\n",
+        "# Smoke test\n",
+        "p = Point(3, 4)\n",
+        "q = Point(0, 0)\n",
+        "\n",
+        "print(p.distance_to(q))\n",
+        "assert round(p.distance_to(q), 1) == 5.0"
+      ],
+      "metadata": {
+        "id": "OVh3GEzH-T0w",
+        "outputId": "b3d69e8e-3ca2-499e-a813-617e8b211696",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5.0\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.1/AI-DS_Nexus__A0_1__RezaShokr_1.ipynb b/a0.1/a0.1/AI-DS_Nexus__A0_1__RezaShokr_1.ipynb
new file mode 100644
index 0000000..b51dc75
--- /dev/null
+++ b/a0.1/a0.1/AI-DS_Nexus__A0_1__RezaShokr_1.ipynb
@@ -0,0 +1,331 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Word Stats"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Prime Tester"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    pass\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    # TODO: your code here\n",
+        "\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ],
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Simple Counter"
+      ],
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ],
+      "metadata": {
+        "id": "dPFvr_fe-OPR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ],
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n"
+      ],
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.1/AI-DS_Nexus__A0_1__aminran.ipynb b/a0.1/a0.1/AI-DS_Nexus__A0_1__aminran.ipynb
new file mode 100644
index 0000000..0be9103
--- /dev/null
+++ b/a0.1/a0.1/AI-DS_Nexus__A0_1__aminran.ipynb
@@ -0,0 +1,481 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "94476096-a3f3-41d0-a133-a4347f052cf0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "temperature in Fahrenheit is: 68.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "cel='20'\n",
+        "fah=(float(cel)*(9/5))+32\n",
+        "print(f\"temperature in Fahrenheit is: {fah}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "n_jpRWKqoG22"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "a=32\n",
+        "b=3.33\n",
+        "print(f\"sum is:{a+b}, difference is:{a-b}, true division is: {a/b}, floor division is: {a//b}\")"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "49f9879c-a114-44bf-d858-eca15e0b35fd"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "sum is:35.33, difference is:28.67, true division is: 9.60960960960961, floor division is: 9.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "shopping_lst=list()\n",
+        "shopping_lst.append('bread')\n",
+        "shopping_lst.append('meat')\n",
+        "shopping_lst.append('apple')\n",
+        "shopping_lst.append('milk')\n",
+        "immutable_basket=tuple(shopping_lst)\n",
+        "print(immutable_basket[2])"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e322d20d-0915-4f3a-a998-dd2143c66224"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "apple\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Word Stats"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "unique_words = set(sample.split())\n",
+        "\n",
+        "word_counts = {}\n",
+        "for word in sample.split():\n",
+        "    word_counts[word] = word_counts.get(word, 0) + 1\n",
+        "\n",
+        "print(\"Unique words:\", unique_words)\n",
+        "print(\"Word counts:\", word_counts)\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "463e32bc-acf0-4c84-e5c3-68a7c359b750"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Unique words: {'or', 'question', 'to', 'the', 'that', 'not', 'be', 'is'}\n",
+            "Word counts: {'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Prime Tester"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    m=n\n",
+        "    if n==0 or n==1:\n",
+        "        return False\n",
+        "    if n>=2:\n",
+        "\n",
+        "        while m >=2 :\n",
+        "            m=m-1\n",
+        "            if m>1 and n%m == 0:\n",
+        "                return False\n",
+        "        return True\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "85f08ee2-1e4b-4f5a-965d-d4004767272e"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    # TODO: your code here\n",
+        "    print((name.capitalize()+' ')*times)\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a9b64518-c250-445f-89ba-6c30dc9c02df"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Alice \n",
+            "Bob Bob Bob \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ],
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Simple Counter"
+      ],
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "    def __init__(self):\n",
+        "      self.count=0\n",
+        "\n",
+        "    def increment(self, step=1):\n",
+        "      self.count+=step\n",
+        "\n",
+        "    def value(self):\n",
+        "      return self.count\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ],
+      "metadata": {
+        "id": "dPFvr_fe-OPR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0a74f4f1-678d-4fb8-c045-a72ca3b1cfe3"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ],
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "    def __init__(self, x, y):\n",
+        "      self.x=x\n",
+        "      self.y=y\n",
+        "\n",
+        "    def distance_to(self, other):\n",
+        "      dx=self.x-other.x\n",
+        "      dy=self.y-other.y\n",
+        "      return math.sqrt(dx**2+dy**2)\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "print(p.distance_to(q))\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n"
+      ],
+      "metadata": {
+        "id": "OVh3GEzH-T0w",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3ef44cb6-2aa0-41fd-df43-6683cd317ef8"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5.0\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.1/AI-DS_Nexus__A0_1__rsayyareh.ipynb b/a0.1/a0.1/AI-DS_Nexus__A0_1__rsayyareh.ipynb
new file mode 100644
index 0000000..50e4a3c
--- /dev/null
+++ b/a0.1/a0.1/AI-DS_Nexus__A0_1__rsayyareh.ipynb
@@ -0,0 +1,468 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "62.611111111111114\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "tct = '55.1'\n",
+        "tcf = float(tct)\n",
+        "tff = tcf * 5/9 + 32\n",
+        "print(tff)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "sum =  14.1\n",
+            "difference =  1.9000000000000004\n",
+            "product =  48.8\n",
+            "true division =  1.3114754098360657\n",
+            "floor division =  1.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "ai = 8\n",
+        "af = 6.1\n",
+        "print('sum = ', ai + af)\n",
+        "print('difference = ', ai - af)\n",
+        "print('product = ', ai * af)\n",
+        "print('true division = ', ai / af)\n",
+        "print('floor division = ', ai // af)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['breed', ' r', ' y', ' u']\n",
+            " y\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "lst = []\n",
+        "lst = input('shopping items: ').split(',')\n",
+        "print(lst)\n",
+        "tpl = tuple(lst)\n",
+        "print(tpl[2])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 2 — Word Stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['to', 'be', 'or', 'not', 'to', 'be', 'that', 'is', 'the', 'question']\n",
+            "['be', 'is', 'not', 'or', 'question', 'that', 'the', 'to']\n",
+            "[2, 1, 1, 1, 1, 1, 1, 2]\n",
+            "{'be': 2, 'is': 1, 'not': 1, 'or': 1, 'question': 1, 'that': 1, 'the': 1, 'to': 2}\n"
+          ]
+        }
+      ],
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        "uw1 = []\n",
+        "uw1 = sample.split()\n",
+        "uw2 = list(set(uw1))\n",
+        "uw2.sort()\n",
+        "repeats=[]\n",
+        "for element in uw2:\n",
+        "    count=uw1.count(element)\n",
+        "    repeats.append(count)\n",
+        "uwd = {}\n",
+        "for i in range(len(repeats)):\n",
+        "    uwd[uw2[i]] = repeats[i]\n",
+        "print(uw1)\n",
+        "print(uw2)\n",
+        "print(repeats)\n",
+        "print(uwd)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 1 — Prime Tester"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ],
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    return isprime(n)\n",
+        " \n",
+        "# Quick self-check\n",
+        "from sympy import *\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Alice\n",
+            "Bob Bob Bob\n"
+          ]
+        }
+      ],
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    # TODO: your code here\n",
+        "    nam = []\n",
+        "    for i in range(times):\n",
+        "        nam.append(name.capitalize())\n",
+        "    print(*nam)\n",
+        "\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      },
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      },
+      "source": [
+        "### Task 1 — Simple Counter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "dPFvr_fe-OPR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "5\n"
+          ]
+        }
+      ],
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "    def __init__(self):\n",
+        "        self.count = 0\n",
+        "        self.step = 1\n",
+        "\n",
+        "    def increment(self, i):\n",
+        "        self.count +=1\n",
+        "    \n",
+        "    def value(self):\n",
+        "        value = self.count\n",
+        "        return value\n",
+        "        \n",
+        "\n",
+        "c = Counter()\n",
+        "for i in range(5):\n",
+        "    c.increment(i)\n",
+        "print(c.value())   # Expected: 5\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      },
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "5.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    def __init__(self, x, y):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "\n",
+        "    def distance_to(self, q):\n",
+        "        distance = math.sqrt((self.x - q.x)**2 + (self.y - q.y)**2)\n",
+        "        return distance\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "print(p.distance_to(q))\n",
+        "# assert round(p.distance_to(q), 1) == 5.0\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.1/AI-DS_Nexus__A0_1__sadragh23.ipynb b/a0.1/a0.1/AI-DS_Nexus__A0_1__sadragh23.ipynb
new file mode 100644
index 0000000..a149110
--- /dev/null
+++ b/a0.1/a0.1/AI-DS_Nexus__A0_1__sadragh23.ipynb
@@ -0,0 +1,476 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "25.0 °C is equal to 77.0 °F\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "celsius_str = '25'\n",
+        "celsius = float(celsius_str)\n",
+        "fahrenheit = celsius * 9/5 + 32\n",
+        "print(f\"{celsius} °C is equal to {fahrenheit} °F\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "sum:  8.2\n",
+            "difference:  1.7999999999999998\n",
+            "product:  16.0\n",
+            "True division:  1.5625\n",
+            "floor deivision:  1.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "num_int = 5\n",
+        "num_float = 3.2\n",
+        "print(\"sum: \",num_int + num_float)\n",
+        "print(\"difference: \", num_int - num_float)\n",
+        "print(\"product: \", num_float * num_int)\n",
+        "print(\"True division: \", num_int / num_float)\n",
+        "print('floor deivision: ', num_int // num_float)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['a', 's', 'd', 'f']\n",
+            "d\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "shopping_list = []\n",
+        "\n",
+        "items = input('Please Enter Your Items: ')\n",
+        "\n",
+        "if ',' in items:\n",
+        "    shopping_list.extend(items.split(','))\n",
+        "else:\n",
+        "    print('please split your items with comma (,) and try again')\n",
+        "\n",
+        "print(shopping_list)\n",
+        "immutable_basket = tuple(shopping_list)\n",
+        "print(immutable_basket[2])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 2 — Word Stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'question', 'that', 'is', 'to', 'be', 'or', 'not', 'the'}\n",
+            "{'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n"
+          ]
+        }
+      ],
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        "uniq_words = set(sample.split())\n",
+        "\n",
+        "word_counts = {}\n",
+        "for word in sample.split():\n",
+        "    if word in word_counts:\n",
+        "        word_counts[word] += 1\n",
+        "    else:\n",
+        "        word_counts[word] = 1\n",
+        "\n",
+        "print(uniq_words)\n",
+        "print(word_counts)\n",
+        "\n",
+        "# the different is that a set stores only uniqe values\n",
+        "# whil a dictionary stores key-value pairs"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 1 — Prime Tester"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ],
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    if n <= 1:\n",
+        "        return False\n",
+        "    for i in range(2, n):\n",
+        "        if n % i == 0:\n",
+        "            return False\n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Alice\n",
+            "Bob Bob Bob\n"
+          ]
+        }
+      ],
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    # TODO: your code here\n",
+        "    name_capital = name.capitalize()\n",
+        "\n",
+        "    print(' '.join([name_capital] * times))\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      },
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      },
+      "source": [
+        "### Task 1 — Simple Counter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "dPFvr_fe-OPR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "5\n"
+          ]
+        }
+      ],
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "    def __init__(self):\n",
+        "        self.count = 0\n",
+        "\n",
+        "    def increment(self, step: int = 1):\n",
+        "        self.count += 1\n",
+        "\n",
+        "    def value(self):\n",
+        "        return self.count\n",
+        "    \n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      },
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "5.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "    def __init__(self, x: float, y: float):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "    \n",
+        "    def distance_to(self, other):\n",
+        "        dx = self.x - other.x\n",
+        "        dy = self.y - other.y\n",
+        "        dis = math.sqrt(dx**2 + dy**2)\n",
+        "        print(dis)\n",
+        "        return dis\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "py313",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.5"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.1/AI-DS_Nexus__A0_1_amirhosseinaref_a1a481.ipynb b/a0.1/a0.1/AI-DS_Nexus__A0_1_amirhosseinaref_a1a481.ipynb
new file mode 100644
index 0000000..2a17a04
--- /dev/null
+++ b/a0.1/a0.1/AI-DS_Nexus__A0_1_amirhosseinaref_a1a481.ipynb
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# 📚 Assignment 1 — Python Fundamentals\n","\n","<p align=\"center\">📢⚠️📂  </p>\n","\n","<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n","\n","<p align=\"center\"> 🚨📝🧠 </p>\n","\n","------------------------------------------------\n","Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n","\n","1. Variable types\n","\n","2. Core containers\n","\n","3. Functions\n","\n","4. Classes\n","\n","Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"],"metadata":{"id":"ZS5lje_18xC2"}},{"cell_type":"markdown","source":["## 1. Variable Types 🧮\n","**Quick-start notes**\n","\n","* Primitive types: `int`, `float`, `str`, `bool`\n","\n","* Use `type(obj)` to inspect an object’s type.\n","\n","* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."],"metadata":{"id":"u5vvtK-6840I"}},{"cell_type":"markdown","source":["### Task 1 — Celsius → Fahrenheit\n","\n"],"metadata":{"id":"UyNHtkGm9OgH"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"lmaiboJe8E15","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1759666241452,"user_tz":-210,"elapsed":6915,"user":{"displayName":"Amirhossein J.Aref","userId":"04604698280838323371"}},"outputId":"22b26c74-6c4c-4a6d-913c-3beb0e28a088"},"outputs":[{"output_type":"stream","name":"stdout","text":["Enter temperature in Celsius: 25\n","25.0 Celsius is equal to 77.0 Fahrenheit\n"]}],"source":["# 👉 a Celsius temperature (as text), convert it to float,\n","#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n","# TODO: your code here\n","degree = float(input(\"Enter temperature in Celsius: \"))\n","fahrenheit  = degree * 9/5 + 32\n","print (f'{degree} Celsius is equal to {fahrenheit} Fahrenheit')"]},{"cell_type":"markdown","source":["### Task 2 — Tiny Calculator\n"],"metadata":{"id":"BtsB8QKM9Xs_"}},{"cell_type":"code","source":["# 👉 Store two numbers of **different types** (one int, one float),\n","#    then print their sum, difference, product, true division, and floor division.\n","# TODO: your code here\n","x = 2\n","y = 3.5\n","print(\"Sum: \" , x + y)\n","print(\"Difference: \" , x - y)\n","print(\"Product: \" , x * y)\n","print(\"True Division: \" , x / y)\n","print(\"Floor Division: \" , x // y)\n","\n"],"metadata":{"id":"DSR8aS-F9Z10","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1759666376837,"user_tz":-210,"elapsed":365,"user":{"displayName":"Amirhossein J.Aref","userId":"04604698280838323371"}},"outputId":"60325323-1192-4d7d-98d3-8ad68a5ed6a4"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Sum:  5.5\n","Difference:  -1.5\n","Product:  7.0\n","True Division:  0.5714285714285714\n","Floor Division:  0.0\n"]}]},{"cell_type":"markdown","source":["## 2. Containers 📦 (list, tuple, set, dict)\n","**Quick-start notes**\n","\n","| Container | Mutable? | Ordered?                      | Typical use                       |\n","| --------- | -------- | ----------------------------- | --------------------------------- |\n","| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n","| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n","| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n","| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"],"metadata":{"id":"8JjHX4wy9dPz"}},{"cell_type":"markdown","source":["### Task 1 — Grocery Basket\n","\n"],"metadata":{"id":"wRyJyhbt9uUr"}},{"cell_type":"code","source":["# Start with an empty shopping list (list).\n","# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n","# 2. Convert the list to a *tuple* called immutable_basket.\n","# 3. Print the third item using tuple indexing.\n","# TODO: your code here\n","shopping_list = []\n","shopping_list.extend([\"Milk\", \"Bread\", \"Apple\", \"Orange\"])\n","immutable_basket = tuple(shopping_list)\n","print(immutable_basket[2])\n"],"metadata":{"id":"1J4jcLct9yVO","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1759666472242,"user_tz":-210,"elapsed":351,"user":{"displayName":"Amirhossein J.Aref","userId":"04604698280838323371"}},"outputId":"c736a7a6-2126-44fd-f670-ecb6b46a54c1"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Apple\n"]}]},{"cell_type":"markdown","source":["### Task 2 — Word Stats"],"metadata":{"id":"byKd8SFK9w2y"}},{"cell_type":"code","source":["sample = \"to be or not to be that is the question\"\n","\n","# 1. Build a set `unique_words` containing every distinct word.\n","# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n","#    (Hint: .split() + a simple loop)\n","# 3. Print the two structures and explain (in a comment) their main difference.\n","# TODO: your code here\n","words = sample.split()\n","unique_words = set(words)\n","word_counts  = {}\n","for word in words:\n","   if word in word_counts:\n","      word_counts[word] += 1\n","   else:\n","      word_counts[word] = 1\n","print (\"unique words:\", unique_words)\n","print (\"word counts:\", word_counts)"],"metadata":{"id":"4rLrxkPj90p3","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1759666512188,"user_tz":-210,"elapsed":338,"user":{"displayName":"Amirhossein J.Aref","userId":"04604698280838323371"}},"outputId":"d359f09a-bfe6-4db1-e45f-7403f88744a2"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["unique words: {'question', 'be', 'the', 'that', 'is', 'to', 'or', 'not'}\n","word counts: {'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n"]}]},{"cell_type":"markdown","source":["## 3. Functions 🔧\n","**Quick-start notes**\n","\n","* Define with `def`, return with `return`.\n","\n","* Parameters can have default values.\n","\n","* Docstrings (`\"\"\" … \"\"\"`) document behaviour."],"metadata":{"id":"gbGMbtLf94M4"}},{"cell_type":"markdown","source":["### Task 1 — Prime Tester"],"metadata":{"id":"QOsToPh2-AnZ"}},{"cell_type":"code","source":["def is_prime(n: int) -> bool:\n","   if n < 2:\n","       return False\n","   for i in range(2, int(n**0.5) + 1):\n","      if n % i == 0:\n","         return False\n","   return True\n","\n","\n","\n","# Quick self-check\n","print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"],"metadata":{"id":"_pCU2mIH-DAi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1759666582577,"user_tz":-210,"elapsed":313,"user":{"displayName":"Amirhossein J.Aref","userId":"04604698280838323371"}},"outputId":"4cb7f16d-7ea7-4d46-e04c-f1de5a990073"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["[2, 3, 5, 7]\n"]}]},{"cell_type":"markdown","source":["### Task 2 — Repeater Greeter"],"metadata":{"id":"TBGXIzVV-E5u"}},{"cell_type":"code","source":["def greet(name: str, times: int = 1) -> None:\n"," capitalaized_name = name.capitalize()\n"," print(\" \".join([capitalaized_name] * times))\n","\n","greet(\"alice\")          # Alice\n","greet(\"bob\", times=3)   # Bob Bob Bob"],"metadata":{"id":"ycvsNyqh-GRM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1759666610676,"user_tz":-210,"elapsed":344,"user":{"displayName":"Amirhossein J.Aref","userId":"04604698280838323371"}},"outputId":"9c8ab8af-1632-4757-d130-17b9d41154a6"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Alice\n","Bob Bob Bob\n"]}]},{"cell_type":"markdown","source":["## 4. Classes 🏗️\n","**Quick-start notes**\n","\n","* Create with class Name:\n","\n","* Special method __init__ runs on construction.\n","\n","* self refers to the instance; attributes live on self."],"metadata":{"id":"y7K-GaBC-ImE"}},{"cell_type":"markdown","source":["### Task 1 — Simple Counter"],"metadata":{"id":"NgKjsy8A-N3l"}},{"cell_type":"code","source":["class Counter:\n","  def __init__(self):\n","    self.count = 0\n","  def increment(self, step: int = 1 ):\n","    self.count += step\n","  def value(self):\n","    return self.count\n","    \"\"\"Counts how many times `increment` is called.\"\"\"\n","    # TODO:\n","    # 1. In __init__, store an internal count variable starting at 0.\n","    # 2. Method increment(step: int = 1) adds `step` to the count.\n","    # 3. Method value() returns the current count.\n","\n","\n","c = Counter()\n","for _ in range(5):\n","    c.increment()\n","print(c.value())   # Expected: 5"],"metadata":{"id":"dPFvr_fe-OPR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1759666652036,"user_tz":-210,"elapsed":10,"user":{"displayName":"Amirhossein J.Aref","userId":"04604698280838323371"}},"outputId":"2cbc69f2-06db-414f-fb6a-759c2fd7d643"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["5\n"]}]},{"cell_type":"markdown","source":["### Task 2 — 2-D Point with Distance"],"metadata":{"id":"U99aupan-Q8u"}},{"cell_type":"code","source":["import math\n","\n","class Point:\n","  def __init__(self, x, y):\n","   self.x = x\n","   self.y = y\n","  def distance_to(self, other):\n","   return math.sqrt((self.x - other.x)**2 + (self.y - other.y)**2)\n","\n","# Smoke test\n","p, q = Point(3, 4), Point(0, 0)\n","assert round(p.distance_to(q), 1) == 5.0\n","print(\"test passed! Distance: \", p.distance_to(q))\n"],"metadata":{"id":"OVh3GEzH-T0w","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1759666679571,"user_tz":-210,"elapsed":344,"user":{"displayName":"Amirhossein J.Aref","userId":"04604698280838323371"}},"outputId":"a6674f45-917f-4c2b-d52a-f69682a41eb4"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["test passed! Distance:  5.0\n"]}]}]}
\ No newline at end of file
diff --git a/a0.1/a0.1/AI-DS_Nexus__A0_1_itsalikabiri_ff9265.ipynb b/a0.1/a0.1/AI-DS_Nexus__A0_1_itsalikabiri_ff9265.ipynb
new file mode 100644
index 0000000..7ce54b5
--- /dev/null
+++ b/a0.1/a0.1/AI-DS_Nexus__A0_1_itsalikabiri_ff9265.ipynb
@@ -0,0 +1,477 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "86.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "x = \"30\"\n",
+        "C = float(x)\n",
+        "F = (C * 9/5) + 32\n",
+        "print(F)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "25.3\n",
+            "-5.300000000000001\n",
+            "5.300000000000001\n",
+            "153.0\n",
+            "0.6535947712418301\n",
+            "0.0\n",
+            "1.53\n",
+            "1.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "a = 10\n",
+        "b = 15.3\n",
+        "print(a + b)\n",
+        "print(a - b)\n",
+        "print(b - a)\n",
+        "print(a * b)\n",
+        "print(a / b)\n",
+        "print(a // b)\n",
+        "print(b / a)\n",
+        "print(b // a)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "banana\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "a = [\"apple\", \"cheese\", \"banana\", \"peach\"]\n",
+        "immutable_basket = tuple(a)\n",
+        "print(immutable_basket[2])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 2 — Word Stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "{'or', 'be', 'is', 'question', 'that', 'the', 'not', 'to'}\n",
+            "{'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n"
+          ]
+        }
+      ],
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        "unique_words = set(sample.split())\n",
+        "print(unique_words)\n",
+        "word_counts = {}\n",
+        "for word in sample.split():\n",
+        "   if word in word_counts:\n",
+        "    word_counts[word] += 1\n",
+        "   else:\n",
+        "    word_counts[word] = 1\n",
+        "print(word_counts)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 1 — Prime Tester"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "0 False\n",
+            "1 False\n",
+            "2 True\n",
+            "3 True\n",
+            "4 False\n",
+            "5 True\n",
+            "6 False\n",
+            "7 True\n",
+            "8 False\n",
+            "9 False\n",
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import math\n",
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    pass\n",
+        "    if n < 2:\n",
+        "      return False\n",
+        "    elif n == 2:\n",
+        "      return True\n",
+        "    else:\n",
+        "      for i in range(2, int(math.sqrt(n))+1):\n",
+        "       if n % i == 0:\n",
+        "        return False\n",
+        "      return True\n",
+        "for n in range(10):\n",
+        "  print(n, is_prime(n))\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Alice\n",
+            "Bob Bob Bob\n"
+          ]
+        }
+      ],
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    # TODO: your code here\n",
+        "    print(\" \".join([name.capitalize()] * times))\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      },
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      },
+      "source": [
+        "### Task 1 — Simple Counter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "dPFvr_fe-OPR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "5\n"
+          ]
+        }
+      ],
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "    def __init__(self):\n",
+        "        self._count = 0\n",
+        "\n",
+        "    def increment(self, step: int = 1) -> None:\n",
+        "        self._count += step\n",
+        "\n",
+        "    def value(self) -> int:\n",
+        "        return self._count\n",
+        "\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      },
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "5.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "    def __init__(self, x: float, y: float):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "\n",
+        "    def distance_to(self, other: \"Point\") -> float:\n",
+        "        return math.sqrt((self.x - other.x) ** 2 + (self.y - other.y) ** 2)\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "print(p.distance_to(q))\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.10"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.1/AI_DS_Nexus_A0_1_MrAshki.ipynb b/a0.1/a0.1/AI_DS_Nexus_A0_1_MrAshki.ipynb
new file mode 100644
index 0000000..d51a252
--- /dev/null
+++ b/a0.1/a0.1/AI_DS_Nexus_A0_1_MrAshki.ipynb
@@ -0,0 +1,490 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "42d097b5-8683-4248-947b-d99acf32b416"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Enter temperature in Celsius: 2.5\n",
+            "2.5 degrees Celsius is 36.5 degrees Fahrenheit.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "celsius_text = input(\"Enter temperature in Celsius: \")\n",
+        "celsius_val = float(celsius_text)\n",
+        "fahrenheit = (celsius_val * 9/5) + 32\n",
+        "print(f\"{celsius_val} degrees Celsius is {fahrenheit} degrees Fahrenheit.\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "a = 15\n",
+        "b = 4.0\n",
+        "\n",
+        "print(f\"Sum: {a + b}\")\n",
+        "print(f\"Difference: {a - b}\")\n",
+        "print(f\"Product: {a * b}\")\n",
+        "print(f\"True Division: {a / b}\")    # نتیجه اعشاری: 3.75\n",
+        "print(f\"Floor Division: {a // b}\") # نتیجه صحیح: 3.0\n"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "25bb49e9-f971-46e2-ae0e-9d59c94a839f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Sum: 19.0\n",
+            "Difference: 11.0\n",
+            "Product: 60.0\n",
+            "True Division: 3.75\n",
+            "Floor Division: 3.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "\n",
+        "user_input = input(\"Enter at least 4 items (comma-separated): \")\n",
+        "basket_list = user_input.split(\",\")\n",
+        "\n",
+        "immutable_basket = tuple(basket_list)\n",
+        "\n",
+        "print(f\"Third item: {immutable_basket[2]}\")\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d5045df7-6198-49cb-94e7-c010d69f39cf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Enter at least 4 items (comma-separated): banana, apple, orange, cucumber\n",
+            "Third item:  orange\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Word Stats"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "sample = \"to be or not to be that is the question\"\n",
+        "words = sample.split()\n",
+        "\n",
+        "\n",
+        "unique_words = set(words)\n",
+        "\n",
+        "\n",
+        "word_counts = {word: words.count(word) for word in unique_words}\n",
+        "\n",
+        "print(\"Unique Words:\", unique_words)\n",
+        "print(\"Word Counts:\", word_counts)\n",
+        "\n",
+        "# تفاوت اصلی:\n",
+        "# Set: کلمات تکراری را حذف می‌کند و ترتیب ندارد.\n",
+        "# Dict: ارتباط بین هر کلمه و تعداد تکرار آن را ذخیره می‌کند.\n"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9a2d885c-16de-43f5-8716-3cfa958b61d5"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Unique Words: {'to', 'that', 'question', 'or', 'is', 'be', 'the', 'not'}\n",
+            "Word Counts: {'to': 2, 'that': 1, 'question': 1, 'or': 1, 'is': 1, 'be': 2, 'the': 1, 'not': 1}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Prime Tester"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "def is_prime(n: int) -> bool:\n",
+        "    if n <= 1: return False\n",
+        "    for i in range(2, int(n**0.5) + 1):\n",
+        "        if n % i == 0:\n",
+        "            return False\n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "71a25e02-b012-4dcd-f527-085085ea4690"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "\n",
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "\n",
+        "    output = (name.capitalize() + \" \") * times\n",
+        "    print(output.strip())\n",
+        "\n",
+        "greet(\"alice\")        # Alice\n",
+        "greet(\"bob\", times=3) # Bob Bob Bob\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4f4f3da9-15d2-416f-b696-bf115c0741b1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Alice\n",
+            "Bob Bob Bob\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ],
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Simple Counter"
+      ],
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "    def __init__(self):\n",
+        "        self.count = 0\n",
+        "\n",
+        "    def increment(self, step: int = 1):\n",
+        "        self.count += step\n",
+        "\n",
+        "    def value(self):\n",
+        "        return self.count\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ],
+      "metadata": {
+        "id": "dPFvr_fe-OPR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fdcc7fb5-a7d9-4a26-9573-375e8d68914e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ],
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "    def __init__(self, x, y):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "\n",
+        "    def distance_to(self, other):\n",
+        "\n",
+        "        return math.sqrt((self.x - other.x)**2 + (self.y - other.y)**2)\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n",
+        "print(f\"Distance: {p.distance_to(q)}\")\n"
+      ],
+      "metadata": {
+        "id": "OVh3GEzH-T0w",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "acca6688-4852-4259-8692-756fe35ebba7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Distance: 5.0\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.1/AI_DS_Nexus__A0_1__amirvali800.ipynb b/a0.1/a0.1/AI_DS_Nexus__A0_1__amirvali800.ipynb
new file mode 100644
index 0000000..cfa04f8
--- /dev/null
+++ b/a0.1/a0.1/AI_DS_Nexus__A0_1__amirvali800.ipynb
@@ -0,0 +1,485 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "outputId": "1fb6d6d8-8d6e-4872-a2c5-ef6594e57a6c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "32.0°C is equal to 336.0°F.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "Celsius_text = 32\n",
+        "Celsius = float(Celsius_text)\n",
+        "Fahrenheit = (Celsius * 9.5 + 32)\n",
+        "print(f\"{Celsius:.1f}°C is equal to {Fahrenheit:.1f}°F.\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "a = 16\n",
+        "b = 3.5\n",
+        "print(a+b)\n",
+        "print(a-b)\n",
+        "print(a*b)\n",
+        "print(a/b)\n",
+        "print(a//b)"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "outputId": "220a85a7-008b-417d-e2ee-bcd10746036c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "19.5\n",
+            "12.5\n",
+            "56.0\n",
+            "4.571428571428571\n",
+            "4.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "shopping_list = []\n",
+        "items = input(\"Enter at least 4 items, separated by commas: \")\n",
+        "shopping_list.extend([item.strip() for item in items.split(\",\")])\n",
+        "immutable_basket = tuple(shopping_list)\n",
+        "print(\"The third item is:\", immutable_basket[2])"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "outputId": "49583843-6710-4d38-d940-70b3004f31eb",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Enter at least 4 items, separated by commas: esss,ss,sss,sss\n",
+            "The third item is: sss\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Word Stats"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        "sample = \"to be or not to be that is the question\"\n",
+        "unique_words = set(sample.split())\n",
+        "word_counts = {}\n",
+        "for word in sample.split():\n",
+        "    if word in word_counts:\n",
+        "        word_counts[word] += 1\n",
+        "    else:\n",
+        "        word_counts[word] = 1\n",
+        "print(\"Unique words (set):\", unique_words)\n",
+        "print(\"Word counts (dict):\", word_counts)"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "outputId": "a2c79c1a-b14e-4a52-bac4-85eb5f6cd5e9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Unique words (set): {'or', 'is', 'the', 'not', 'to', 'be', 'question', 'that'}\n",
+            "Word counts (dict): {'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Prime Tester"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    if n < 2:\n",
+        "        return False\n",
+        "    for i in range(2, int(n**0.5) + 1):\n",
+        "        if n % i == 0:\n",
+        "            return False\n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "outputId": "a9de45ab-bb5a-4d74-8b5f-44025b285b08",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    # TODO: your code here\n",
+        "    nam = []\n",
+        "    for i in range(times):\n",
+        "        nam.append(name.capitalize())\n",
+        "    print(*nam)\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM",
+        "outputId": "f0715a26-883a-401f-ea7f-4cd7d12d3ac9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Alice\n",
+            "Bob Bob Bob\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ],
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Simple Counter"
+      ],
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "    def __init__(self):\n",
+        "        self._count = 0\n",
+        "\n",
+        "    def increment(self, step: int = 1):\n",
+        "        self._count += step\n",
+        "\n",
+        "    def value(self):\n",
+        "        return self._count\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ],
+      "metadata": {
+        "id": "dPFvr_fe-OPR",
+        "outputId": "4d5b5a73-a016-4913-f6b3-a5c12c146ac6",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ],
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "    def __init__(self, x: float, y: float):\n",
+        "        # 1. ذخیره مختصات x و y\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "\n",
+        "    def distance_to(self, other: \"Point\") -> float:\n",
+        "        # 2. محاسبه فاصله اقلیدسی\n",
+        "        dx = self.x - other.x\n",
+        "        dy = self.y - other.y\n",
+        "        return math.sqrt(dx**2 + dy**2)\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n",
+        "print(p.distance_to(q))\n"
+      ],
+      "metadata": {
+        "id": "OVh3GEzH-T0w",
+        "outputId": "6e19bc27-40ff-4f33-8897-b211bd917f05",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5.0\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/a0.1/AI\342\200\223DS_Nexus___A0_1_Python___RooholaAlikhani.ipynb" "b/a0.1/a0.1/AI\342\200\223DS_Nexus___A0_1_Python___RooholaAlikhani.ipynb"
new file mode 100644
index 0000000..7e54e91
--- /dev/null
+++ "b/a0.1/a0.1/AI\342\200\223DS_Nexus___A0_1_Python___RooholaAlikhani.ipynb"
@@ -0,0 +1,577 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "38.6° Celsius is 398.7° Fahrenhit\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "#VERSION 1\n",
+        "degree_celsius1 = '38.6'\n",
+        "degree_fahrenhit1 = round((float(degree_celsius1) * 9.5 +  32) , 1) \n",
+        "print(f'{degree_celsius1}° Celsius is {degree_fahrenhit1}° Fahrenhit')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "700.0° Celsius is 6682.0° Fahrenhit\n"
+          ]
+        }
+      ],
+      "source": [
+        "#VERSION 2\n",
+        "degree_celsius = float(input(\"insert celsius degree\"))\n",
+        "degree_fahrenhit = round((degree_celsius * 9.5 +  32) , 2) \n",
+        "print(f'{degree_celsius}° Celsius is {degree_fahrenhit}° Fahrenhit')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sum :         a + b Equals 10\n",
+            "\n",
+            "difference :  a - b = 4\n",
+            "\n",
+            "product :     a * b = 21\n",
+            "\n",
+            "division :    a / b = 2.33\n",
+            "\n",
+            "f_division :  a // b = 2\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "a , b  = 7 , 3\n",
+        "summ = a + b\n",
+        "difference = a - b \n",
+        "product = a * b\n",
+        "division =  round((a / b) , 2)\n",
+        "floor_division =  a // b\n",
+        "print(f'Sum :         a + b Equals {summ}\\n' )\n",
+        "print(f'difference :  a - b = {difference}\\n')\n",
+        "print(f'product :     a * b = {product}\\n')\n",
+        "print(f'division :    a / b = {division}\\n')\n",
+        "print(f'f_division :  a // b = {floor_division}\\n')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            " immutable_basket is : ('d ', ' 4 ', ' kll ', '  56')  \n",
+            "The third item    is :  kll \n"
+          ]
+        }
+      ],
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "# WITH append()\n",
+        "mylist = []\n",
+        "\n",
+        "user_input = input(\"Enter at least 4 items (comma-separated): \")\n",
+        "items = user_input.strip().split(\",\")\n",
+        "\n",
+        "if len(items) >= 4 : \n",
+        "    for i in items : \n",
+        "        mylist.append(i)\n",
+        "else :\n",
+        "    print(\"pls insert more than 4 items or 3 coma\")\n",
+        "\n",
+        "immutable_basket = tuple(mylist)\n",
+        "\n",
+        "print(f' immutable_basket is : {immutable_basket}  ')\n",
+        "print(f\"The third item    is : {immutable_basket[2] }\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "('1', '2', '3', '4', '5')\n",
+            "The third item is: 3\n"
+          ]
+        }
+      ],
+      "source": [
+        "# with extend & comphernsion list\n",
+        "mylist = []\n",
+        "user_input = input(\"Enter at least 4 items (comma-separated): \")\n",
+        "items = [item.strip() for item in user_input.split(\",\")]\n",
+        "\n",
+        "if len(items) >= 4:\n",
+        "    mylist.extend(items)\n",
+        "else:\n",
+        "    print(\"Please enter at least 4 items.\")\n",
+        "    exit()\n",
+        "\n",
+        "immutable_basket = tuple(mylist)\n",
+        "\n",
+        "print(immutable_basket)\n",
+        "print(f\"The third item is: {immutable_basket[2]}\")\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 2 — Word Stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mylist is : ['to', 'be', 'or', 'not', 'to', 'be', 'that', 'is', 'the', 'question']\n",
+            "\n",
+            "Unique words (set) : {'that', 'not', 'is', 'the', 'or', 'to', 'question', 'be'}\n",
+            "\n",
+            "Word counts (dict) : {'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1} \n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        " \n",
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "mylist = sample.split(\" \")\n",
+        "unique_words = set(mylist)\n",
+        "word_counts = {}\n",
+        "\n",
+        "for i in mylist:\n",
+        "    if i in word_counts:\n",
+        "        word_counts[i] += 1\n",
+        "    else:\n",
+        "        word_counts[i] = 1\n",
+        "\n",
+        "print(f'Mylist is : {mylist}\\n')\n",
+        "print(f\"Unique words (set) : {unique_words}\\n\")\n",
+        "print(f\"Word counts (dict) : {word_counts} \" )\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 1 — Prime Tester"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "list_add_aval in range 0-100 : [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]\n",
+            "\n",
+            "tedad adad aval in range 0-100 = 25\n"
+          ]
+        }
+      ],
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    if n < 2 :\n",
+        "        return False # print(f'{n} add aval nist!!')\n",
+        "    \n",
+        "    for i in range (2 , n) : # range (2 , n) >>  2 - n-1 \n",
+        "        if n % i ==0 :\n",
+        "         return False #print(f'{n} add aval nist!!')\n",
+        "        \n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    #pass\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "baze_mogasebat = 100\n",
+        "m = range(baze_mogasebat)\n",
+        "\n",
+        "list_add_aval = [x for x in m if is_prime(x)]\n",
+        "print(f\"list_add_aval in range 0-{baze_mogasebat} : {list_add_aval}\\n\")\n",
+        "\n",
+        "print(f'tedad adad aval in range 0-{baze_mogasebat} = {len(list_add_aval)}')  # Expected: [2, 3, 5, 7]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Alice\n",
+            "Bob Bob Bob\n"
+          ]
+        }
+      ],
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "   \n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    #  TODO: your code here\n",
+        "\n",
+        "    a = name.strip().capitalize()\n",
+        "    a = (a+\" \") *times\n",
+        "\n",
+        "    return a.strip() \n",
+        "\n",
+        "a = greet(\"alice\")          # Alice\n",
+        "b = greet(\"bob\", times=3)   # Bob Bob Bob\n",
+        "\n",
+        "print(a)\n",
+        "print(b)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      },
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      },
+      "source": [
+        "### Task 1 — Simple Counter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 58,
+      "metadata": {
+        "id": "dPFvr_fe-OPR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "100\n"
+          ]
+        }
+      ],
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "\n",
+        "    def __init__(self):\n",
+        "        self.count = 0\n",
+        "    \n",
+        "    def increment(self, step: int = 1):\n",
+        "        self.count += step\n",
+        "    \n",
+        "    def value(self):\n",
+        "        return self.count\n",
+        "   \n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(100):\n",
+        "    c.increment()\n",
+        "\n",
+        "print(c.value())   # Expected: 5\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      },
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "outputs": [],
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "\n",
+        "    def __init__(self, x: float, y: float):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "    \n",
+        "    def distance_to(self, other):\n",
+        "        dx = self.x - other.x\n",
+        "        dy = self.y - other.y\n",
+        "\n",
+        "        return math.sqrt(dx**2 + dy**2)  \n",
+        "    \n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "11.7\n"
+          ]
+        }
+      ],
+      "source": [
+        "m , n = Point( 10 , 6 ) , Point( 0  , 0)\n",
+        "print(round(m.distance_to(n), 1))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git "a/a0.1/a0.1/Another copy of AI\342\200\223DS Nexus _ A0.1. Python _ RezaShokr.ipynb" "b/a0.1/a0.1/Another copy of AI\342\200\223DS Nexus _ A0.1. Python _ RezaShokr.ipynb"
new file mode 100644
index 0000000..4b55418
--- /dev/null
+++ "b/a0.1/a0.1/Another copy of AI\342\200\223DS Nexus _ A0.1. Python _ RezaShokr.ipynb"	
@@ -0,0 +1 @@
+{"cells":[{"cell_type":"markdown","metadata":{"id":"ZS5lje_18xC2"},"source":["# 📚 Assignment 1 — Python Fundamentals\n","Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n","\n","1. Variable types\n","\n","2. Core containers\n","\n","3. Functions\n","\n","4. Classes\n","\n","Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"]},{"cell_type":"markdown","metadata":{"id":"u5vvtK-6840I"},"source":["## 1. Variable Types 🧮\n","**Quick-start notes**\n","\n","* Primitive types: `int`, `float`, `str`, `bool`\n","\n","* Use `type(obj)` to inspect an object’s type.\n","\n","* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."]},{"cell_type":"markdown","metadata":{"id":"UyNHtkGm9OgH"},"source":["### Task 1 — Celsius → Fahrenheit\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4573,"status":"ok","timestamp":1748587242026,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"},"user_tz":-210},"id":"lmaiboJe8E15","outputId":"964a53e1-b4c8-4f73-b077-df837841f4b1"},"outputs":[{"name":"stdout","output_type":"stream","text":["Please give me temperature Celsius:   14\n","57.2 is temoerature\n"]}],"source":["# 👉 a Celsius temperature (as text), convert it to float,\n","#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n","# TODO: your code here\n","Temperature_str = str(input(\"Please give me temperature Celsius:   \"))\n","Celsius_float = float(Temperature_str)\n","Farenhait = Celsius_float*9/5 + 32\n","print(f\"{Farenhait} is temoerature\")\n"]},{"cell_type":"markdown","metadata":{"id":"BtsB8QKM9Xs_"},"source":["### Task 2 — Tiny Calculator\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":11099,"status":"ok","timestamp":1748588294191,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"},"user_tz":-210},"id":"DSR8aS-F9Z10","outputId":"f232e51b-a7c4-44a4-a12b-ca202393a046"},"outputs":[{"name":"stdout","output_type":"stream","text":["Please give integer num:   30\n","Please give float num:   2\n","32 28 60 15.0\n"]}],"source":["# 👉 Store two numbers of **different types** (one int, one float),\n","#    then print their sum, difference, product, true division, and floor division.\n","# TODO: your code here\n","num_int = int(input(\"Please give integer num:   \"))\n","num_float = float(input(\"Please give float num:   \"))\n","### To perform an operation between two numbers , both must be integers.\n","num_float = int(num_float)\n","pluss = num_int + num_float\n","minus = num_int - num_float\n","cross = num_int * num_float\n","Devide = num_int / num_float\n","print(pluss , minus , cross , Devide)\n"]},{"cell_type":"markdown","metadata":{"id":"8JjHX4wy9dPz"},"source":["## 2. Containers 📦 (list, tuple, set, dict)\n","**Quick-start notes**\n","\n","| Container | Mutable? | Ordered?                      | Typical use                       |\n","| --------- | -------- | ----------------------------- | --------------------------------- |\n","| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n","| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n","| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n","| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"]},{"cell_type":"markdown","metadata":{"id":"wRyJyhbt9uUr"},"source":["### Task 1 — Grocery Basket\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":20648,"status":"ok","timestamp":1748592831810,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"},"user_tz":-210},"id":"1J4jcLct9yVO","outputId":"c3017f5c-23fd-4005-bdf4-d05727fbd89b"},"outputs":[{"name":"stdout","output_type":"stream","text":["Please give me input:   llll\n","Please give me input:   bvbvbvv\n","Please give me input:   fgttt\n","Please give me input:   fggt\n","Please give me input:   hjhjjjh\n","Do you want append item?(yes/no)? no\n","['llll', 'bvbvbvv', 'fgttt', 'fggt', 'hjhjjjh']\n","\n"," This third item :    fgttt\n"]}],"source":["# Start with an empty shopping list (list).\n","# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n","# 2. Convert the list to a *tuple* called immutable_basket.\n","# 3. Print the third item using tuple indexing.\n","# TODO: your code here\n","shopping_list = []\n","while True:\n","  item = str(input(\"Please give me input:   \"))\n","  shopping_list.append(item)\n","  if len(shopping_list) > 4:\n","    p_item = input(\"Do you want append item?(yes/no)? \").strip().lower()\n","    if p_item == \"no\":\n","            break\n","print(shopping_list)\n","immutable_basket = tuple(shopping_list)\n","print(\"\\n This third item :   \", immutable_basket[2])\n"]},{"cell_type":"markdown","metadata":{"id":"byKd8SFK9w2y"},"source":["### Task 2 — Word Stats"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1867,"status":"ok","timestamp":1748596077738,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"},"user_tz":-210},"id":"4rLrxkPj90p3","outputId":"59304c62-f5e8-4dee-c2be-1a8820b94884"},"outputs":[{"name":"stdout","output_type":"stream","text":["unique words : {'to', 'not', 'question', 'is', 'be', 'the', 'that', 'or'}\n","counts of words:  {'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n"]}],"source":["sample = \"to be or not to be that is the question\"\n","\n","# 1. Build a set `unique_words` containing every distinct word.\n","# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n","#    (Hint: .split() + a simple loop)\n","# 3. Print the two structures and explain (in a comment) their main difference.\n","# TODO: your code here\n","words = sample.split()\n","unique_words = set(words)\n","dic_words = {}\n","for word in words:\n","  if word in dic_words:\n","    dic_words[word] += 1\n","  else:\n","    dic_words[word] = 1\n","print(\"unique words :\" , unique_words)\n","print(\"counts of words: \" , dic_words)"]},{"cell_type":"markdown","metadata":{"id":"gbGMbtLf94M4"},"source":["## 3. Functions 🔧\n","**Quick-start notes**\n","\n","* Define with `def`, return with `return`.\n","\n","* Parameters can have default values.\n","\n","* Docstrings (`\"\"\" … \"\"\"`) document behaviour."]},{"cell_type":"markdown","metadata":{"id":"QOsToPh2-AnZ"},"source":["### Task 1 — Prime Tester"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1748604455844,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"},"user_tz":-210},"id":"_pCU2mIH-DAi","outputId":"8a0c873e-7779-487a-c731-75cc535391aa"},"outputs":[{"name":"stdout","output_type":"stream","text":["[2, 3, 5, 7]\n"]}],"source":["def is_prime(n: int) -> bool:\n","    \"\"\"\n","    Return True if n is a prime number, else False.\n","    0 and 1 are *not* prime.\n","    \"\"\"\n","    # TODO: replace pass with your implementation\n","    if n < 2:\n","        return False\n","    for i in range(2, int(n**0.5) + 1):\n","        if n % i == 0:\n","            return False\n","    return True\n","\n","###number of range1-10\n","# Quick self-check\n","print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"]},{"cell_type":"markdown","metadata":{"id":"TBGXIzVV-E5u"},"source":["### Task 2 — Repeater Greeter"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1748793408926,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"},"user_tz":-210},"id":"ycvsNyqh-GRM","outputId":"e4d24ead-074f-498f-8b34-6859ed2edf62"},"outputs":[{"name":"stdout","output_type":"stream","text":["Alice\n","Bob Bob Bob\n"]}],"source":["def greet(name: str, times: int = 1) -> None:\n","    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n","    # TODO: your code here\n","    print(\" \".join([name.capitalize()] * times))\n","\n","greet(\"alice\")          # Alice\n","greet(\"bob\", times=3)   # Bob Bob Bob\n"]},{"cell_type":"markdown","metadata":{"id":"y7K-GaBC-ImE"},"source":["## 4. Classes 🏗️\n","**Quick-start notes**\n","\n","* Create with class Name:\n","\n","* Special method __init__ runs on construction.\n","\n","* self refers to the instance; attributes live on self."]},{"cell_type":"markdown","metadata":{"id":"NgKjsy8A-N3l"},"source":["### Task 1 — Simple Counter"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dPFvr_fe-OPR","executionInfo":{"status":"ok","timestamp":1748878614276,"user_tz":-210,"elapsed":405,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"}},"outputId":"82acd3d8-78ae-4188-bfbd-6c07b3a16b0c"},"outputs":[{"output_type":"stream","name":"stdout","text":["5\n"]}],"source":["class Counter:\n","    \"\"\"Counts how many times `increment` is called.\"\"\"\n","    # TODO:\n","    # 1. In __init__, store an internal count variable starting at 0.\n","    # 2. Method increment(step: int = 1) adds `step` to the count.\n","    # 3. Method value() returns the current count.\n","    def __init__(self):\n","        self.count = 0\n","\n","    def increment(self, step: int = 1):\n","        self.count += step\n","\n","    def value(self):\n","        return self.count\n","\n","c = Counter()\n","for _ in range(5):\n","  c.increment()\n","print(c.value())   # Expected: 5\n"]},{"cell_type":"markdown","metadata":{"id":"U99aupan-Q8u"},"source":["### Task 2 — 2-D Point with Distance"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OVh3GEzH-T0w"},"outputs":[],"source":["import math\n","\n","class Point:\n","    \"\"\"\n","    A 2-D point supporting distance calculation.\n","    Usage:\n","        p = Point(3, 4)\n","        q = Point(0, 0)\n","        print(p.distance_to(q))  # 5.0\n","    \"\"\"\n","    # TODO:\n","    # 1. Store x and y as attributes.\n","    # 2. Implement distance_to(other) using the Euclidean formula.\n","\n","    def __init__(self, x , y):\n","      self.x = x\n","      self.y = y\n","    def distance_to(self , other):\n","      dx = self.x - other.x\n","      dy = self.y - other.y\n","      return math.sqrt(dx**2 + dy**2)\n","# Smoke test\n","p, q = Point(3, 4), Point(0, 0)\n","assert round(p.distance_to(q), 1) == 5.0\n"]}],"metadata":{"colab":{"provenance":[{"file_id":"18AJkITSV3zJ2eckTRJqYYxmJlSLPrJEs","timestamp":1748880050689},{"file_id":"1PHW5WZ1eMJVDuFL2QTtMEkdiZAaF4xQL","timestamp":1748604748000}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/a0.1/a0.1/Assignment_01_Python___Nexus___fatemeengashte.ipynb b/a0.1/a0.1/Assignment_01_Python___Nexus___fatemeengashte.ipynb
new file mode 100644
index 0000000..96e1af6
--- /dev/null
+++ b/a0.1/a0.1/Assignment_01_Python___Nexus___fatemeengashte.ipynb
@@ -0,0 +1,498 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e7a4665a-1f4d-4f91-b318-70848763e1e8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "25.0°C equals 77.00°F\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "celsius_text = \"25\"                  # sample input as string\n",
+        "celsius = float(celsius_text)        # convert to float\n",
+        "fahrenheit = celsius * 9/5 + 32      # conversion formula\n",
+        "print(f\"{celsius}°C equals {fahrenheit:.2f}°F\")   # formatted output\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "# TODO: your code here\n",
+        "num1 = 8            # int\n",
+        "num2 = 2.5          # float\n",
+        "\n",
+        "sum_result = num1 + num2\n",
+        "diff_result = num1 - num2\n",
+        "prod_result = num1 * num2\n",
+        "true_div = num1 / num2\n",
+        "floor_div = num1 // num2\n",
+        "\n",
+        "print(\"\\nTiny Calculator Results:\")\n",
+        "print(f\"Sum: {sum_result}\")\n",
+        "print(f\"Difference: {diff_result}\")\n",
+        "print(f\"Product: {prod_result}\")\n",
+        "print(f\"True Division: {true_div}\")\n",
+        "print(f\"Floor Division: {floor_div}\")\n"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "12ae02f1-c4a6-42e4-9a2c-e4c5b1b610c9"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Tiny Calculator Results:\n",
+            "Sum: 10.5\n",
+            "Difference: 5.5\n",
+            "Product: 20.0\n",
+            "True Division: 3.2\n",
+            "Floor Division: 3.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "shopping_list = []  # empty list\n",
+        "raw = input(\"Enter at least 4 items (comma-separated): \").strip()\n",
+        "items = [x.strip() for x in raw.split(\",\") if x.strip()]\n",
+        "if len(items) < 4:\n",
+        "    raise ValueError(\"Please enter at least 4 items separated by commas.\")\n",
+        "\n",
+        "shopping_list.extend(items)\n",
+        "immutable_basket = tuple(shopping_list)\n",
+        "\n",
+        "print(\"Immutable basket (tuple):\", immutable_basket)\n",
+        "print(\"Third item:\", immutable_basket[2])\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "37ec07c2-acbd-4322-bc0a-05a4aa7c58c2"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Enter at least 4 items (comma-separated): milk, bread, eggs, apples\n",
+            "Immutable basket (tuple): ('milk', 'bread', 'eggs', 'apples')\n",
+            "Third item: eggs\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Word Stats"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "unique_words = set(sample.split())\n",
+        "\n",
+        "word_counts = {}\n",
+        "for w in sample.split():\n",
+        "    word_counts[w] = word_counts.get(w, 0) + 1\n",
+        "\n",
+        "print(\"\\nUnique words (set):\", unique_words)\n",
+        "print(\"Word counts (dict):\", word_counts)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a31ba863-3a37-4fdc-df90-7af81c48d94f"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Unique words (set): {'that', 'is', 'the', 'or', 'be', 'to', 'not', 'question'}\n",
+            "Word counts (dict): {'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Prime Tester"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    if n < 2:\n",
+        "        return False\n",
+        "\n",
+        "    for i in range(2, int(n**0.5) + 1):\n",
+        "        if n % i == 0:\n",
+        "            return False\n",
+        "    return True\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "136082a2-8c2a-44c4-9496-9817b99a79f9"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    # TODO: your code here\n",
+        "    output = (name.capitalize() + \" \") * times\n",
+        "    print(output.strip())\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "91877cfb-edae-40b2-e6fa-e9eb78e1114a"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Alice\n",
+            "Bob Bob Bob\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ],
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Simple Counter"
+      ],
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "\n",
+        "    def __init__(self) -> None:\n",
+        "        self._count = 0\n",
+        "\n",
+        "    def increment(self, step: int = 1) -> None:\n",
+        "        if not isinstance(step, int):\n",
+        "            raise TypeError(\"step must be an int\")\n",
+        "        self._count += step\n",
+        "\n",
+        "    def value(self) -> int:\n",
+        "        return self._count\n",
+        "\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ],
+      "metadata": {
+        "id": "dPFvr_fe-OPR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8035616c-49be-42bb-ebd2-1479e4942662"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ],
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "    def __init__(self, x: float, y: float) -> None:\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "\n",
+        "    def distance_to(self, other: \"Point\") -> float:\n",
+        "        if not isinstance(other, Point):\n",
+        "            raise TypeError(\"other must be a Point\")\n",
+        "        return math.hypot(self.x - other.x, self.y - other.y)\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n"
+      ],
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "execution_count": 10,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.1/Copy_of_Assignment_01_Python__Nexus__RezaShokrzad.ipynb b/a0.1/a0.1/Copy_of_Assignment_01_Python__Nexus__RezaShokrzad.ipynb
new file mode 100644
index 0000000..b63ec79
--- /dev/null
+++ b/a0.1/a0.1/Copy_of_Assignment_01_Python__Nexus__RezaShokrzad.ipynb
@@ -0,0 +1,426 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/gitSajjad/Nexus_Assignments/blob/main/a0.1/Copy_of_Assignment_01_Python_%7C_Nexus_%7C_RezaShokrzad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "JGcRVAwXCK-A"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ba87dbf1-edd7-4680-d0d5-072ca0fa045c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Enter temperature in Celsius: 20\n",
+            "20.00°C is 68.00°F\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO:\n",
+        "\n",
+        "celsius_text = input(\"Enter temperature in Celsius: \")\n",
+        "celsius = float(celsius_text)\n",
+        "fahrenheit = celsius * 9 / 5 + 32\n",
+        "print(f\"{celsius:.2f}°C is {fahrenheit:.2f}°F\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO:\n",
+        "a = int(input(\"Enter int Number: \"))\n",
+        "b = float(input(\"Enter float Number: \"))\n",
+        "\n",
+        "sum  = a + b\n",
+        "diff = a - b\n",
+        "prod = a * b\n",
+        "true_div = a / b\n",
+        "floor_div = a // b\n",
+        "\n",
+        "print(f\"Sum:            {sum}\")\n",
+        "print(f\"Difference:     {diff}\")\n",
+        "print(f\"Product:        {prod}\")\n",
+        "print(f\"True Division:  {true_div}\")\n",
+        "print(f\"Floor Division: {floor_div}\")\n"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO:\n",
+        "shopping_list = []\n",
+        "\n",
+        "items_input = input(\"Enter at least 4 shopping items (comma-separated): \")\n",
+        "\n",
+        "items = [item.strip() for item in items_input.split(\",\")]\n",
+        "\n",
+        "shopping_list.extend(items)\n",
+        "\n",
+        "immutable_basket = tuple(shopping_list)\n",
+        "\n",
+        "print(f\"The third item is: {immutable_basket[2]}\")\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Word Stats"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO:\n",
+        "text = input(\"Enter a sentence or paragraph: \")\n",
+        "unique_words = set(text.split())\n",
+        "word_counts = {}\n",
+        "for word in text.split():\n",
+        "    word_counts[word] = word_counts.get(word, 0) + 1\n",
+        "\n",
+        "print(\"Unique words (set):\", unique_words)\n",
+        "print(\"Word counts (dict):\", word_counts)"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Prime Tester"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    if n < 2:\n",
+        "        return False\n",
+        "    if n in (2, 3):\n",
+        "        return True\n",
+        "    if n % 2 == 0:\n",
+        "        return False\n",
+        "\n",
+        "    for i in range(3, int(n ** 0.5) + 1, 2):\n",
+        "        if n % i == 0:\n",
+        "            return False\n",
+        "    return True\n",
+        "    pass\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    # TODO:\n",
+        "    capitalized = name.capitalize()\n",
+        "    print(\" \".join([capitalized] * times))\n",
+        "\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ],
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Simple Counter"
+      ],
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    def __init__(self):\n",
+        "         self._count = 0\n",
+        "\n",
+        "    def increment(self, step: int = 1):\n",
+        "         self._count += step\n",
+        "\n",
+        "    def value(self):\n",
+        "         return self._count\n",
+        "\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "dPFvr_fe-OPR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ],
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    def __init__(self, x, y):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "\n",
+        "    def distance_to(self, other):\n",
+        "        dx = self.x - other.x\n",
+        "        dy = self.y - other.y\n",
+        "        return math.sqrt(dx ** 2 + dy ** 2)\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n"
+      ],
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
diff --git a/a0.1/a0.1/a0.1 arian shishehgar.ipynb b/a0.1/a0.1/a0.1 arian shishehgar.ipynb
new file mode 100644
index 0000000..caeb28d
--- /dev/null
+++ b/a0.1/a0.1/a0.1 arian shishehgar.ipynb	
@@ -0,0 +1,491 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "outputId": "c44d6904-62f0-40cb-937e-f0567af3eb36",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "As you wish Mr.shokrzad\n",
+            "enter your temp in celsius : 10\n",
+            "Your temp in celsius is : 10.0 °C \n",
+            "In Fahrenheit is : 50.0 °F\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "# compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "\n",
+        "\n",
+        "print('As you wish Mr.shokrzad')\n",
+        "temp = float(input('enter your temp in celsius : '))\n",
+        "temp_f = temp * 9/5 + 32\n",
+        "print(f'Your temp in celsius is : {temp} °C \\nIn Fahrenheit is : {temp_f} °F')\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "# TODO: your code here\n",
+        "\n",
+        "a = int(36)\n",
+        "b = float(15.35)\n",
+        "\n",
+        "print(\"Sum:\", a + b)\n",
+        "print(\"Difference:\", a - b)\n",
+        "print(\"Product:\", a * b)\n",
+        "print(\"True Division:\", a / b)\n",
+        "print(\"Floor Division:\", a // b)\n",
+        "print(f'second way for floor division : {int(a/b)}')"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "outputId": "488b7faa-4024-4f52-e0df-42c6485af611",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Sum: 51.35\n",
+            "Difference: 20.65\n",
+            "Product: 552.6\n",
+            "True Division: 2.3452768729641695\n",
+            "Floor Division: 2.0\n",
+            "second way for floor division : 2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "# TODO: your code here\n",
+        "shopping_list = []\n",
+        "shopping_list = input(\"Enter at least 4 items, separated by commas: \").split(',')\n",
+        "shopping_list = [item.strip() for item in shopping_list ]\n",
+        "if len(shopping_list) < 4:\n",
+        "    print(\"Please enter at least 4 items.\")\n",
+        "else:\n",
+        "  immutable_basket = tuple(shopping_list)\n",
+        "  print(\"The third item is:\", immutable_basket[2])\n",
+        "  print(\"2nd method => The third item is: \" + str(immutable_basket[2]))\n",
+        "  print(f'3nd method => The third item is : {immutable_basket[2]}')\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "outputId": "5277ff56-0fb9-46dd-b66b-6853dac7a5ed",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Enter at least 4 items, separated by commas: ecec e e e \n",
+            "Please enter at least 4 items.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Word Stats"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "# TODO: your code here\n",
+        "unique_words = {}\n",
+        "word_counts = {}\n",
+        "text = input('input your words even identical with comma: ').split(' ')\n",
+        "\n",
+        "text = [item.strip() for item in text]\n",
+        "unique_words = set(text)\n",
+        "for item in text:\n",
+        "  if item in word_counts:\n",
+        "    word_counts[item] += 1\n",
+        "  else:\n",
+        "    word_counts[item] = 1\n",
+        "\n",
+        "print(\"Unique Words:\", unique_words)\n",
+        "print(\"Word Counts:\", word_counts)\n"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "outputId": "3a51bf7d-abc5-4f56-acd0-da665d6a3a26",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "input your words even identical with comma: a, b, c, a, a, b, c, d, f\n",
+            "Unique Words: {'d,', 'b,', 'c,', 'f', 'a,'}\n",
+            "Word Counts: {'a,': 3, 'b,': 2, 'c,': 2, 'd,': 1, 'f': 1}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Prime Tester"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    # TODO: replace pass with your implementation\n",
+        "    if n <=1:\n",
+        "      return False\n",
+        "\n",
+        "    for i in range(2,n):\n",
+        "      if n % i == 0:\n",
+        "        return False\n",
+        "    return True\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "57c41bac-21c9-46e8-e4ed-5aaabbf96881"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    # TODO: your code here\n",
+        "    if times ==1:\n",
+        "      print(name.capitalize())\n",
+        "    else:\n",
+        "      for i in range(times):\n",
+        "        print(name.capitalize(), end=\" \")\n",
+        "\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "69a59b85-7795-4ee9-9cf4-9dbd8dfc7bed"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Alice\n",
+            "Bob Bob Bob "
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ],
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Simple Counter"
+      ],
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "    def __init__(self):\n",
+        "        self._count = 0\n",
+        "\n",
+        "    def increment(self, step: int = 1):\n",
+        "        self._count += step\n",
+        "    def value(self):\n",
+        "        return self._count\n",
+        "\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ],
+      "metadata": {
+        "id": "dPFvr_fe-OPR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "870d76cc-0e82-43db-f482-cb3333cfd363"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ],
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "\n",
+        "    def __init__(self, x, y):\n",
+        "        self.x = x\n",
+        "        self.y = y  # 1. ذخیره x و y\n",
+        "\n",
+        "    def distance_to(self, other):\n",
+        "        dx = self.x - other.x\n",
+        "        dy = self.y - other.y\n",
+        "        return math.sqrt(dx**2 + dy**2)\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n"
+      ],
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.1/assignment01_ftmhhosseini.ipynb b/a0.1/a0.1/assignment01_ftmhhosseini.ipynb
new file mode 100644
index 0000000..65510ff
--- /dev/null
+++ b/a0.1/a0.1/assignment01_ftmhhosseini.ipynb
@@ -0,0 +1,397 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "\n",
+        "Celsius = float(input(\"Enter the temperature : \"))\n",
+        "Fahrenheit = (Celsius) * 9/5 + 32\n",
+        "print(\"(%.2f)°c is equal to (%.2f)°f\" % (Celsius,Fahrenheit))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [],
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "\n",
+        "NumInt = 77\n",
+        "NumFloat = 3.4\n",
+        "print(\"sum : \" ,  NumInt + NumFloat)\n",
+        "print(\"difference : \" ,  NumInt - NumFloat)\n",
+        "print(\"product : \" ,  NumInt * NumFloat)\n",
+        "print(\"true division : \" ,  NumInt / NumFloat)\n",
+        "print(\"true division : \" ,  NumInt // NumFloat)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [],
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "\n",
+        "shopping_list = []\n",
+        "\n",
+        "while True:\n",
+        "    items = input(\"Enter the names of four products separated by commas: \")\n",
+        "    split_items = [item.strip() for item in items.split(\",\") if item.strip()]\n",
+        "\n",
+        "    if len(split_items) >= 4:\n",
+        "        shopping_list.extend(split_items)\n",
+        "        break\n",
+        "    else:\n",
+        "        print(f\"You entered only {len(split_items)} valid item(s). Please try again.\\n\")\n",
+        "\n",
+        "immutable_basket = tuple(shopping_list)\n",
+        "print(f\"The third item is {immutable_basket[2]}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 2 — Word Stats"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [],
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "\n",
+        "words = sample.split()\n",
+        "unique_words = set(words)\n",
+        "word_counts = {}\n",
+        "for word in words:\n",
+        "    word_counts[word] = word_counts.get(word, 0) + 1\n",
+        "\n",
+        "print(\"Unique words (set):\", unique_words)\n",
+        "print(\"Word counts (dict):\", word_counts)\n",
+        "\n",
+        "# set فقط لیست کلمات بدون تکرار را نگه می‌دارد\n",
+        "# dict هر کلمه را به تعداد دفعات تکرارش در متن می نویسد"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 1 — Prime Tester"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [],
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    if n < 2:\n",
+        "        return False\n",
+        "\n",
+        "    for i in range(2, int(n ** 0.5) + 1):\n",
+        "        if n % i == 0:\n",
+        "            return False\n",
+        "\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [],
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "\n",
+        "    print(\" \".join([name.capitalize()] * times))\n",
+        "\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      },
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      },
+      "source": [
+        "### Task 1 — Simple Counter"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dPFvr_fe-OPR"
+      },
+      "outputs": [],
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    # TODO:\n",
+        "    def __init__(self):\n",
+        "        self.count = 0\n",
+        "\n",
+        "    def increment(self, step: int = 1):\n",
+        "        self.count += step\n",
+        "\n",
+        "    def value(self):\n",
+        "        return self.count\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      },
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OVh3GEzH-T0w"
+      },
+      "outputs": [],
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    # TODO:\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "\n",
+        "\n",
+        "    def __init__(self, x, y):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "\n",
+        "    def distance_to(self, other):\n",
+        "        dx = other.x - self.x\n",
+        "        dy = other.y - self.y\n",
+        "\n",
+        "        return math.sqrt(dx**2 + dy**2)\n",
+        "\n",
+        "\n",
+        "# تست:\n",
+        "p = Point(3, 4)\n",
+        "q = Point(0, 0)\n",
+        "print(p.distance_to(q))  # خروجی: 5.0\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.2/AI-DS_Nexus__A0_2_Amirhossein_Zandi_8fca80.ipynb b/a0.1/a0.2/AI-DS_Nexus__A0_2_Amirhossein_Zandi_8fca80.ipynb
new file mode 100644
index 0000000..d2ae9d4
--- /dev/null
+++ b/a0.1/a0.2/AI-DS_Nexus__A0_2_Amirhossein_Zandi_8fca80.ipynb
@@ -0,0 +1,486 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:\n",
+        "\n",
+        "1. NumPy — fast n-dimensional arrays\n",
+        "\n",
+        "2. pandas — tabular data wrangling\n",
+        "\n",
+        "3. Matplotlib — quick, customizable plots\n",
+        "\n",
+        "Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1 · NumPy 🧮\n",
+        "Quick-start notes\n",
+        "Core object: ndarray (n-dimensional array)\n",
+        "\n",
+        "Create data: np.array, np.arange, np.random.*\n",
+        "\n",
+        "Summaries: mean, std, sum, max, …\n",
+        "\n",
+        "Vectorised math beats Python loops for speed\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Mock temperatures\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([19.79434   , 17.10966406, 21.71624745, 22.39088381, 10.86706725,\n",
+              "       21.43571429, 17.26483258, 19.87588679, 21.38300459, 18.16201794,\n",
+              "       22.8468564 , 23.20924633, 23.93925216, 17.71732008, 19.51114136,\n",
+              "       13.04321414, 15.54167372, 11.72105048, 23.4083288 , 19.70735418,\n",
+              "       14.9150078 , 15.92267663, 19.04137697, 22.45088307, 20.1460248 ,\n",
+              "       17.8969062 , 24.40589568, 26.65093433, 24.55630809, 20.54845312,\n",
+              "       28.99320603,  5.12785735, 19.281834  , 28.01074369, 22.9527796 ,\n",
+              "       27.39796322, 19.43814589, 12.64098496, 23.95628719, 16.19764321,\n",
+              "       22.28708897, 23.92903437, 14.4878666 , 10.86650586, 14.26243779,\n",
+              "       10.54076093, 25.10051276, 23.52019564, 22.42076508, 15.11059543,\n",
+              "       19.49543904, 18.63885663, 14.36509506, 25.50908676, 27.31182977,\n",
+              "       25.99722788, 24.18027837, 26.93000288, 14.24874187, 22.1248614 ,\n",
+              "       18.74921976, 15.06798838, 11.96315234, 19.39916124,  9.28253433,\n",
+              "        9.56750188, 14.3714417 , 15.88641009, 17.09222974, 18.90384821,\n",
+              "       21.0682228 , 13.85902634, 24.8101385 , 17.04198171, 11.2359977 ,\n",
+              "       14.94720503, 28.31755276, 16.44156221, 17.55188453, 27.63323438,\n",
+              "       15.34445682, 18.10569161, 20.06920139, 16.96112747, 15.25852076,\n",
+              "       15.78112858, 14.09045545, 18.36050723, 22.61602043, 18.93259286,\n",
+              "       21.29149297, 10.05080475, 19.65150108, 22.09338918, 16.34652034,\n",
+              "       19.02900346, 14.55455273, 23.59069848, 17.33070888, 26.67568891,\n",
+              "       22.28267803, 16.44535066, 13.24626548, 25.03232228, 13.99090405,\n",
+              "       28.72393643, 15.32491353, 26.19313527, 26.67340139, 18.60938485,\n",
+              "       28.52200278, 19.80431263, 16.56185564, 20.47382795, 17.58183732,\n",
+              "       22.18966171, 20.70147918, 18.35292196, 19.59825116, 21.32436002,\n",
+              "       10.49614704, 14.58671652, 17.02327347, 18.58303289, 11.68979874,\n",
+              "       14.66434475, 23.71782773, 17.54185087, 17.53864868, 19.13132448,\n",
+              "       17.59234147, 20.14397903, 22.62648612, 23.87397482, 11.48096616,\n",
+              "       22.00465872, 26.49464834, 19.75664451, 15.21325891, 18.530134  ,\n",
+              "       14.28539617, 12.76055571, 16.21763631, 16.45636527, 11.18733501,\n",
+              "       19.83222491, 14.41462459, 12.7721551 , 16.12545439, 21.38725148,\n",
+              "       17.72488021, 26.92710066, 11.20765256, 19.74111019, 23.92250694,\n",
+              "       15.99350298, 16.76819198, 17.23619426, 15.32639066, 24.86583426,\n",
+              "       20.94816956, 20.59159995, 22.13934287, 24.85981377, 16.30928829,\n",
+              "       26.11215039, 26.72292579, 19.47565253, 11.25194708, 17.83246974,\n",
+              "       11.98596613, 12.76269445, 18.09168769, 14.67427135, 16.96957932,\n",
+              "       18.10825342, 13.18237341, 20.94135683, 14.42210507, 21.34581698,\n",
+              "       16.24196393, 15.52726483, 21.32020149, 18.99039922,  3.19983246,\n",
+              "       27.2897614 , 13.70939596, 20.34854257, 22.2310421 , 17.45825371,\n",
+              "       11.48880104, 20.28312756, 23.837151  , 12.44688414, 23.01233687,\n",
+              "       12.45511373, 19.95334188, 25.22154634, 15.66137156, 18.91506633,\n",
+              "       19.34607067, 16.99565703, 23.79402952, 20.19091707, 15.89420693,\n",
+              "       10.71195218, 20.87521657, 15.41063537, 13.74602048, 27.00886612,\n",
+              "       29.81808238, 22.2852545 , 14.49895485, 17.97221186, 27.72603805,\n",
+              "       25.49089525, 17.24238913, 17.16863332, 20.10984126, 16.82679208,\n",
+              "       16.932259  , 24.91464926, 18.88647126, 16.53510736, 14.30605148,\n",
+              "       25.40566528, 11.47347796, 17.57053512, 18.25843103, 29.59678153,\n",
+              "       22.79198978, 15.77014714, 30.96422839, 21.41010221,  9.13447873,\n",
+              "       24.71397068, 21.04236814, 26.28059598, 25.13020323, 22.08892121,\n",
+              "       21.52399111, 30.79092073, 19.9251205 , 19.6892921 , 12.16743395,\n",
+              "       13.80298077, 28.36773983, 22.38571494, 26.49135171, 19.08246313,\n",
+              "       21.49413038, 25.96682714, 30.7722924 , 23.22189116, 13.45133664,\n",
+              "       26.3085593 , 22.59595791, 11.77249288, 18.25335027, 16.12087896,\n",
+              "       27.78247721, 23.31285255, 19.23132957, 21.39210677, 12.87713513,\n",
+              "       25.74818872, 19.32739317, 17.12565515, 18.62210832, 30.22214181,\n",
+              "       17.45742474, 34.35415342, 20.81219305, 21.3408855 , 23.42662383,\n",
+              "       14.59902624, 25.1639105 , 17.7534784 , 19.83522366, 24.15410788,\n",
+              "       21.86619367, 14.34023056, 28.14668391, 20.01673657, 27.63571504,\n",
+              "       25.03557178, 17.2041235 , 28.95909002, 14.52286751, 14.58699202,\n",
+              "       16.45777707, 22.96034039, 21.01914105, 14.97034636, 23.00115585,\n",
+              "       20.71560756, 19.95409218, 17.24193871, 19.54686801, 18.9351969 ,\n",
+              "       27.3179232 , 23.73666834, 18.62913022, 21.70441791, 20.32817685,\n",
+              "       31.43760635, 19.68736731, 23.93667877, 16.91570368, 17.38560739,\n",
+              "       31.93543669, 27.72026898, 27.76722692, 25.55733276, 22.83241392,\n",
+              "       15.60253105, 22.885565  ,  9.211007  ,  9.69205026, 15.74206812,\n",
+              "       19.55595844, 14.80844494, 22.7767592 , 18.19918521, 20.96985581,\n",
+              "       22.35869334, 22.92588533, 17.70051188, 19.86105886, 14.20911854,\n",
+              "       20.74755516, 20.50212714, 21.03391035, 24.30858174, 16.31851189,\n",
+              "       17.22569743, 16.94213216, 26.71507427, 19.69690057, 19.46198816,\n",
+              "       17.12504053, 16.22517244, 27.83544059, 20.14758376, 18.63261055,\n",
+              "       14.26587491, 22.62875608,  9.94143625, 24.1457409 , 15.73437495,\n",
+              "       16.84339066, 23.03074964, 25.83702666, 20.4596671 , 23.26448506,\n",
+              "       22.17346256, 22.81831206, 19.03229237, 19.18973858, 24.8956509 ,\n",
+              "       21.95449783, 22.87747275, 26.50233954, 17.93368775, 26.70552348])"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: import numpy and create an array of 365\n",
+        "# normally-distributed °C values (µ=20, σ=5) called temps\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "temps = np.random.normal(loc=20 , scale=5 , size=365)\n",
+        "temps\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Average temperature\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "19.64\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: print the mean of temps\n",
+        "\n",
+        "mean_temps = np.mean(temps)\n",
+        "mean_temps = round(mean_temps , 2)\n",
+        "print(mean_temps)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2 · pandas 📊\n",
+        "Quick-start notes\n",
+        "Main structures: DataFrame, Series\n",
+        "\n",
+        "Read data: pd.read_csv, pd.read_excel, …\n",
+        "\n",
+        "Selection: .loc[label], .iloc[pos]\n",
+        "\n",
+        "Group & summarise: .groupby(...).agg(...)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 3 — Load ride log\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>date</th>\n",
+              "      <th>temp</th>\n",
+              "      <th>rides</th>\n",
+              "      <th>weekday</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2024-01-01</td>\n",
+              "      <td>13.3</td>\n",
+              "      <td>141</td>\n",
+              "      <td>Monday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2024-01-02</td>\n",
+              "      <td>18.4</td>\n",
+              "      <td>174</td>\n",
+              "      <td>Tuesday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2024-01-03</td>\n",
+              "      <td>25.3</td>\n",
+              "      <td>256</td>\n",
+              "      <td>Wednesday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2024-01-04</td>\n",
+              "      <td>19.5</td>\n",
+              "      <td>188</td>\n",
+              "      <td>Thursday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2024-01-05</td>\n",
+              "      <td>25.6</td>\n",
+              "      <td>234</td>\n",
+              "      <td>Friday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>360</th>\n",
+              "      <td>2024-12-26</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>166</td>\n",
+              "      <td>Thursday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>361</th>\n",
+              "      <td>2024-12-27</td>\n",
+              "      <td>24.6</td>\n",
+              "      <td>275</td>\n",
+              "      <td>Friday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>362</th>\n",
+              "      <td>2024-12-28</td>\n",
+              "      <td>16.1</td>\n",
+              "      <td>171</td>\n",
+              "      <td>Saturday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>363</th>\n",
+              "      <td>2024-12-29</td>\n",
+              "      <td>18.3</td>\n",
+              "      <td>216</td>\n",
+              "      <td>Sunday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>364</th>\n",
+              "      <td>2024-12-30</td>\n",
+              "      <td>20.6</td>\n",
+              "      <td>213</td>\n",
+              "      <td>Monday</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>365 rows × 4 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "           date  temp  rides    weekday\n",
+              "0    2024-01-01  13.3    141     Monday\n",
+              "1    2024-01-02  18.4    174    Tuesday\n",
+              "2    2024-01-03  25.3    256  Wednesday\n",
+              "3    2024-01-04  19.5    188   Thursday\n",
+              "4    2024-01-05  25.6    234     Friday\n",
+              "..          ...   ...    ...        ...\n",
+              "360  2024-12-26  20.0    166   Thursday\n",
+              "361  2024-12-27  24.6    275     Friday\n",
+              "362  2024-12-28  16.1    171   Saturday\n",
+              "363  2024-12-29  18.3    216     Sunday\n",
+              "364  2024-12-30  20.6    213     Monday\n",
+              "\n",
+              "[365 rows x 4 columns]"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: read \"rides.csv\" into df\n",
+        "# (columns: date,temp,rides,weekday)\n",
+        "\n",
+        "df = pd.read_csv(\"../datasets/rides.csv\")\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 4 — Weekday averages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "weekday\n",
+              "Friday       195.942308\n",
+              "Monday       196.830189\n",
+              "Saturday     188.884615\n",
+              "Sunday       203.134615\n",
+              "Thursday     206.403846\n",
+              "Tuesday      201.461538\n",
+              "Wednesday    210.576923\n",
+              "Name: rides, dtype: float64"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: compute and print mean rides per weekday\n",
+        "\n",
+        "mean_rides = df.groupby(\"weekday\")[\"rides\"].mean()\n",
+        "mean_rides"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3 · Matplotlib 📈\n",
+        "Quick-start notes\n",
+        "Workhorse: pyplot interface (import matplotlib.pyplot as plt)\n",
+        "\n",
+        "Figure & axes: fig, ax = plt.subplots()\n",
+        "\n",
+        "Common plots: plot, scatter, hist, imshow\n",
+        "\n",
+        "Display inline in Jupyter with %matplotlib inline or %matplotlib notebook"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 5 — Scatter plot"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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I74UFLmw9BwAAd3LjppZO7bTt1LUpnU0JOjNmEqIwYwYQ7AAAgCQ3DotzaqdtJ6/NbTNmAN1YArqxAABie86OUzttO3ltdmqHwBp0YwEAQFi4aVic08XTTl6blwp8Yx2CHQAAiPkbuZPF03xtHOAoAQ9/jnYwB6FBsAMAADHPyZ22jZay3JbRAjkIdgAAIOap93fSm3AsU2BstGXEw+98K6YonykudU2tEshBNxYAAHhi93O9LqistGR6vGdTkZUJtaNLHejItrVD9KEbC91YAACe6cpSgpbZK/bTG2sP0pnzxlkYdcfUibMlUvtaeWlPsHi5fyPYQbADAGCZ3nKPelPMaAU8suemFayFwqyt3Qja1O1B6zkAAITlRmu23MPfyc/zslGkb9iy51Ze7qNR87doHmeX3T3B3Joh8xIEOwAAcYxvtFM+2kX5hapdvDMr05Q79W+0bt79XPbcJi7e4WigY3dPMKOCaH4cE5edgQJlAIA4xTda7jBSBzqMv37YoOjWzbufy/7MU0WBhcahsLsnWKhbXIA8BDsAAHGIb6BPfrDd8JgJH2zXvNG6effzcP9MJ/cEs5Ihg9Ag2AEAiEPr/32yQht1sNPFpeK4WNr9/HTRRTKKOfipGhkptl+/etD3hrK5p5szZF6Dmh0AgDjEM3Fkj+t8VY0KBcxODPBzGi+7jZpfsf4l2NT+rWjqJ7t0py0bmdS3BeVmpTnSNeXmDJnXINgBAIhLcrf5A/85Sze+sEKzU4gzGsFdRLlR6iIyqn9RcEwye1B76tOmNiUmkmawZoYDHaeKrp3c4gKMYRkLACAOdbmqptRxn+04VqGuROkUYjxIj+fLvHxPO/GZv45G95BZ/Qvj8qPqGamG05YpgktzyhYXyusH/7xoZci8CJkdAIA41LlxjQr7PAXTy3oEz9Jxw+7ndupfOOBRb+x56EQxzVq+VzwXqaU5JehyS4bMqxDsAADE4cBA/pg+oLVoMddjtLwT7lk6Vgcd2q1/4ddUn3+z3CoRDzyCgy5MUHYegh0AAI8ym8zLN1iz7E6kO4V+2tdqH72x9pDpvlbhqH+JVuARHHSBsxDsAAB4kMxk3qy0lJACHac7hficefaP1jnpTRRWZ4Du6VRfLEOF2iGGwMN7EOwAAHiM7P5QT9ze3PbPcLpTSC84M9pzSytzxZkqpg6YUP8CCHYAADxGdjLvqXMltl4/lIJdrVocZtY2rj5v/v6C8xc1g6OC4lLx2JieTalhzYyILUNh13J3Q7ADAOAxsnU02RkphnUujG/XWUF1PXYzJXo1RLz8ZNY2rpZfcJ5mfL7HMHO18Jujog0+EgEHdi13PwQ7AAAeoWQX9h07Kz0gT5mErIeDBw50Qs2UGNUQzbzc7i3rVNFF1+y6jl3LYwOCHQAAD9DKLsjU23DQMmdwexq9YIsYuqd3fCiZEpndvWVxxiS7yk+DAWUyQLzdRbiWlmRro5QaI4geBDsAADHOrLjXrN6GpwrrBTpOZEpkphvL4vPmLjIZUz/ZLbJA4VpasrJrObq7ogvbRQAAxDCZPaHUuP4meGkl3LtvOzWLZ0zPq8V5m+26rlAHOuqlJQ4OnYBdy2MHgh0AgBhmNWuSlpwkllXCsfs2B168bLR46w/iM39t5fWN5Gam0ujuTUz3lDKiBIQcHCrnZuU6gvH2EjKwa3n0YRkLACCGWc0aaC2rODF92KgjiYMrs64vM1PuvCag7kVvT6nsjGQ6VVRqe2lJtrOKA6AFG49IBWnYtTz6kNkBAIhhdrIGwQFSqLtvKzVDerujL9uVbysTo1ZeXvExDj6Cd12f9LNrbAeJZtehXv7iYCm/0DzQHHRdfRQnuwCCHQCAGCZbv2IWICmZEs7gqPHXRu3TZh1J/PGb976j7d8XUN/WuZRg874/esG39Om2H00H+eVm2luSk+kYUy9/yWbUuF0fog/LWAAAMUzJyhjNypFdjrKzCaZMzVBRySWas+oAhYJjjEfmb6G5iQniPPWWmyb1bWG6JMfDFJW2dOX6rHZWOVXnBJGBYAcAIMbp1a9o3bAn9TXe4sHqJpiR7jTiaywv99Go+Vs0B/nx4yO6NaK/rjlYYUNQuvz1yaKLNOb/vguoxykp01gnM7hep3ZZh8jAMhYAgAeo61ce6tpQZC+0TP1kl2Ot19HIXHAwN3HxDsPlpo++y6M5gysuyWlR6nGsdlaFWucEkYVgBwDAI5SszOR+19Cz/bULdc1mzci2XStOF9nbTDQUMt1W1TNS/MHfzF+1E11aesezhd8cEZ1TeqEJP147KFNjt84JIg/LWAAAHsMBCk8PJovbGFjd0NLo50QbLzcpwR8HbTIBEu//NWv5Pt3lLyWTo96Cgt9Dq3VOEHkIdgAAPMZqsS0HLbNX7NfckNNoQ0snt4Fwmnp5zUrnFF/nkx9sD9jlnVVLT6YtR05jd/MYhWUsAACPsbKNAWdzuk7/UnfncaOpw04UJyeoggknciFay01WO6cKggIdxsHPX9YclJrBA+6DYAcAwGNkb+5clMs36vxC47obdSbIzs8xwvUtc+/rQNMHtCYnKMtN6mUkDnw4mDILkDo2qG5pnzHl58lsQQHRhWAHACDOBg2KtujMVLHdgZXbc3Amx85AQ0W1tGSxsScXEfMSEH9wy3iopS5cdxO8pMQTnIOXpbQCpM2HT9taltMLBsE9EOwAAHiMTFs0b2Mgs92BXiZHmV58R6tcW/tdFZwvpVnL94pAhPEyEM/GCTU5Uj87XXMyshHO+nCRcajLctjd3L1QoAwAEEeDBnMtDtFTcMZFaTPX6trS6mAyou4K6948x/LykZ5TRRctF1Fz1oePC3VZDtOS3QvBDgBADNHaD0qvzdlo+wdun7aCMy5iOvH3Z0QGRqs12+7yz/+uO+RYV1d2lVTbxdo/a1PH1u7smJbsfgh2AABihNU5OEbbPyibZlpZyuIA4LV/Vgx0QnX4lNz0YhnBG4Fa6cRS7zMmm6nCtOTYgJodAIAYCXT4JuxU6zPXylwou2T5PMLRcNQgqM7GruCWc9libfX36U1F5mN+3a2R+KyGacmxIarBzrRp06hTp05UtWpVqlWrFt111120Z8+egGNuueUWSkhICPh4+OGHA445cuQI9e3bl9LT08XrjBs3jsrKyiJ8NQAA4aEU2focan1WAiejDqVIUAKNIV0a2u7qUr+WVnbFzh5W6n3GXr6nnfjMX0/o01LzcQQ67hfVZazVq1fTqFGjRMDDwclTTz1FvXr1ol27dlFGRob/uOHDh9Mzzzzj/5qDGsWlS5dEoJObm0tff/015eXl0f3330/Jycn0/PPPR/yaAACs1tY4PRHZ7LycKgYOhTrQSKmUaHn5SI3f1tmD9LMrZsXaWt+nt/xndVd4cIeoBjtLly4N+PrNN98UmZnNmzdTt27dAoIbDma0fPHFFyI4Wr58OeXk5FC7du1o6tSpNH78eJoyZQqlpFTc+bekpER8KAoLCx29LgCIb3Zqa4xYKbI1C7rKy30hFQNzYOHz2StIVgsONPQCEhmc0OKNP82uffW4W8UsHexhFX9cVaBcUFAgPmdnB665zps3j9555x0R8PTr148mTZrkz+6sW7eOWrduLQIdRe/evWnkyJG0c+dOat++veby2dNPPx326wGAyGQ+3ERZIvJZ2GPKjNXtDtTnEhw88DA/O5TfzPCbGoluLDsZGDb61sbUtckVmr/v4O6xfcfO0eyV+20FekYBZ/92V9o4c4hlrgl2ysvL6fHHH6euXbtSq1at/I8PHjyYGjRoQHXq1KFt27aJjA3X9XzwwQfi+fz8/IBAhylf83NaJkyYQGPHjg3I7NSrVy9MVwYA4c58xEptjd5u42aUIlu9lmit1me9oOvMebk6neyM5ICdwtWZmPb1q1dcEspMpQtl5WJfKaNzHHNbM8NrVy8TcXu8bLCjDvTCEXBCbHNNsMO1Ozt27KCvvvoq4PERI0b4/8wZnNq1a1OPHj3owIED1LhxY1s/KzU1VXwAQOR5+UZkp7ZGJsNl1BKtVWQbSl2OEpQYLfnoze/hDi/Zc5S5dtn2eA60lEAvXAEnxDZXBDujR4+mJUuW0Jo1a6hu3bqGx15//fXi8/79+0Www0tbGzduDDjm2LFj4rNenQ8ARIfXb0RWa2usZLisFNnKTA2WKRo2KsTVKtS1co4y184/Y8qdLenhd741PO8pd17j//viZDE3eEdUgx2fz0ePPvooLVq0iFatWkWNGjUy/Z6tW7eKz5zhYV26dKHnnnuOjh8/Loqb2bJlyygzM5Natvyp3RAA3CHaN6Jw1wlZqa2xk+EymojsxB5NRt1JsszOkX8Hs1fso5nL91X4Xq1r58+8K/qTH2yv0CrPe1rxbunq8w2lmBu8q1K0l67mz59PixcvFrN2lBqbrKwsSktLE0tV/HyfPn2oRo0aomZnzJgxolOrTZs24lhuVeegZsiQITRjxgzxGhMnThSvjaUqAHeJ5o0oEnVCsrU1HRtUp5tfXGkrwyXT+mx1j6bRtzahrk1qWgr+jAJHvXPk38GUj3bpLkvpXbsSQK3/98nL21z4qMtVNalz4xoVztduMTd4W1SDnVdffdU/OFDtjTfeoAceeEC0jXNL+axZs6ioqEgUEQ8cOFAEM4qkpCSxBMbdV5zl4fk8Q4cODZjLAwDuEK0bUaTqhGRra74Jc4ZLCbpkl7Ka5lSp8HOMghk7gaPe70D22vlnc0DGH04Wc3u1KxBctoxlhIMbHjxohru1Pv30UwfPDADCwU5XUazVCZnVrbBR841rUELNcClBl1mti5WWdSWYYVYDRzsF06Feu0yhtFe7AqEi7I0FABFjZ3R/JOuEnKK33QAT2zRItn+HkuHic/jz4A5iCKBMJxMHJLxE9MzHO0WQpLcH19j/+87ythV2CqZDvXat/a3U+1g5vdcYuJsrurEAIH7YGd0fi3VCwXUrVrIbTmW4+rSpTbOpPT0yf4vuMTwbZ8bS3fTRd3mmQSErvnjJ8hKUlffWqWs3KpT2elcgVIRgBwAiTraryAluKVi1mt2wk+HSqj/p06YOzU1M0OxmYvzYX9YcJCcFBzdW31unsnt6hdLR7gqEyEOwAwBREakNFSNRJyRT5Cqb3eDtHKYPDGynlmFUf8KBJXdBEUVml/Pg4Mbsd6BeUuOZOeGul0F7evxBsAMAnmZ1+rDVIEa2yFU2uzHn3g6mHUeynU58Tlx/c3eHK02nEDuFZ98EB45GvwPFmJ5X0+juTSKybOSWbB9EDoIdAIgpdlqF7dQJyQQxMi3tynLdFzvzDTfPVDJMna+ylu2SqQV6/9sfKFIe6NJQ8/eh9zuIRvdTNLoCIboSfGb933GANwLlQYa86zpPXgYAdwq1VVg2UNILYpQjlSDmxhdW6NZ+8LFZ6clUuVKSVFaFj7cz74c7qAa9tp7cwmwpyi1zbZTfMelk+2J5j7Z4Uih5/0awg2AHICbIBCBO3Jz4ZmwWxPC/+l+6uy3d+7cN5AS+188e1F4UE5udW3CgsGTbj/TYwp+20XEDvd+H1rmzaAY+mLMTP/dvLGMBgOvJtApP+WgnVa2cTCfOlYR045Tt1Fn37xPkFB5LUz0j1daN+Z5O9clNtFq3tc6da3uYukMs0oFGJLsCIboQ7ACA68kEIPmFJXTv6xtCvnHKduAc+E+Rpde18nODsyCniy6KqctatUGzlu8VgUNBcamlCcXq9+nOtrVN5+xYoW7dLjh/UTMjp9UG7/T2HW7qCoToQrADAK5npwXY7o1TtgPnsx35IQUZej9XKwvCiQajrJZR4bOWSX1bUM2qqQGZjCdub0Fvrj1IUz/ZTU7hWqUZS/8lfW4Y6Afhgu0iAMD17LQAG21fINOpI3ObVYKMUG7J/L388/jn6m1hYHT6/NTp4lIa07Mp5VRNMf15HD8M6dKQ+re7UmQ01DuVP9C1kfS1yzh1rsRytigc23cAINgBANezEoCEeuNUZsLIhEdKkFFhD6bMVJH1kT1fZb8wq5tlqjWsmUF//FV70+M4cNp8+LTp3mXkQAD3/eli26+BgX7gJCxjAYDryQyli9aNk4MM3uQzuMh12a580/NV1xVxC3koNTP8c52YDKzMw3lq0Q46VXTR8nkoAR7XAYWyDQUG+oGTkNkBgJigt5O10zdOpfNL1omzJaL9m/2sTR3/0pDe+dbISKGHujb074Su1BPZDcjUy2BOTQbmc1o/oQdlZyQb/+wE3t4i8N/MfL1zBrcXBc+hXg+AU5DZAYCYEdwqXDMjlX7z3nd0rNC5SbhWNuzkchd1QW9wB5iV1mY7mYzg7S6cnAycUimRnv95a83Be4o5g9pT71a1K1yf1U1P9a4HwCnI7ABATFFahbnAtmvTmjTlzp9qTBIcunHmF5yXPja4cFjpAONCY63zVRcE26lLCv5WDlyCu8147o7espnP4vuhl53i8/zz4A5iNpBWVks2S5WekmR6PQBOQGYHAGKanX2vjNipU7HbOh08T4dbwkfN36K7YSlPWeYAQytLpNWy7gSt7NTpohKa+on+5GHZLNVr919LiQk/BUcY6AfhhGAHAGKek5Nws6sYTzK20gFmNKxObyLyiG6NKgz4Mwvc9LbSCGZ3ho168B7/LA7IzDY/lVlO401PEdxAJCDYAQBP0JuEa3XjydxMZ7qAjJZyjHZL/+uagzRHLBGlSJ2zzK7nVgMxPRfLyumpRdsNBxwqwZRe9xzqciAaEOwAgGfZ2ehRqZ0JdTlIbylHZp8vXiLiTi2tYCA4eCv3+Syfq53OL34vf2pHr7jNg1Yw5fTyIkAoEOwAgCcZZU+MtpFQZvo8fLkLySqzjifZjUa1si+aG2qmGbeHa7Ha+SW7TBYcTGGjTXALBDsAEDFWl5Tsfq9Z9oT9btEO6t48R7yG8rrZ6Sn0r/xCOnr6PF3XsDptPKQ9adiM0RKNlcF/6ms+dKKIZi7fV+G4M+f1My1OtOJbWSbTCqaw0Sa4AYIdAHDtkpLd75WZ83Ky6CK1n/oFJSclau7AbQcP4ePZNEbXI5tVOXSimG58YYXj3VVWa2WszMyxE0wBuHbOzvnz56m4+L97nhw+fJhmzZpFX3zxhZPnBgAeobfBpdZcGie+VzZ7UlRyybFAhycjr5/Q0zRwO110scK8HDV+ivfVmrV8r6OBTm2bM2ys1veg8Bg8k9np378/DRgwgB5++GE6c+YMXX/99ZScnEwnTpygP/7xjzRy5EjnzxQAYpJMQa5eO7Td743kvkrKT33u563E1GEjP7VtG9e+KM/Z2RCU63fUy1pVUpPopqZX0H2dG1hu81aW0PYdO+tIViuUJUyAqAQ73377Lc2cOVP8+f3336ecnBzasmUL/eMf/6DJkycj2AEARwpy7X6v2bYJTpLtLpKpfeF7/20tc+jzncdsnQu3rG86fIreWHtIBD3nSi7RZzvyaevRM5Y6oKwOKOSs1roJPXSDvVCWMAGitozFS1hVq1YVf+alK87yJCYmUufOncWSFgCAIpSduO1+r9JRFW5jejYN2Mwz1NoX3n7CbqDDmZWC8xdp1vJ9FYqWZZYLzZYNtSRc/jDKaoWyhAkQ1WCnSZMm9OGHH9LRo0fp888/p169eonHjx8/TpmZmY6dHADEPtklJd7U0+73ah2nzHnJzkihcOCb/MJvjkofb3dXc1n9214pNiXVW/LzXe5A48GATnVeme1lJdMVx8/zcQCuC3Z4qeq3v/0tNWzYkK677jrq0qWLP8vTvn17p88RAGKYzAaXjHcvD/5Xvtn38uO1Dbp/+Ca8fkIPkfVwmnoJTQa3jodT3eppUh1onact182myHZejb61CS0Y3tk0q2VlGRLAdcHO3XffTUeOHKFNmzaJzI6iR48e/loeAIDgJSWjgOdYofaO4XrfK7vtAC+vcOGssuTiNJmMDV+T1owcp3Awd+ikXDDFE5BD7WJrmlPFcAd3q68X7qwXgK1gh+Xm5oq6nWXLlolWdNapUydq3ry5k+cHADGIlyXWHThJi7f+ID5ztxQvd+Rk6m+yqV7W4KUW5fuz0lJozuD2Fb6Xv5ZtpVaWtHjZRY1bvPkjFGZLbcpSTjhxAPO/649Y+h6t5aNQlg0jcRxARLuxTp48Sb/85S9p5cqVlJCQQPv27aOrrrqKhg0bRtWrV6c//OEPtk8IAGKbUefNH37Zju59fYPpsgYvtaj3YNIOSKzlabS2LuCZN88s2RlwHLdrd6hfjepnp9PH2/KpwGBCMXchdWxQ3bGhfEbLRslJCbRg4xHKLyyhUNntYrM6NNDp1wOIaGZnzJgxYq4OL2Wlp6f7H//Vr35FS5cutX0yABDbzDpvVuyW6zQK3mySB/8FD//TWvYyo2xd0L/dlaJziWfeBAcP3K69Zt9JemfDUcNAR6mBufnFlYbnEOoSDQcLY267mh7reTWtfbKHqJWZ+cu2jhReG3Wx2V02DOfrAUQ02OFC5BdeeIHq1q0b8HjTpk3Reg4Qp2Q6bxZt/cGxnxdKN4+d/Z70mLVQh7JEkxAUDCjBWm5WGp0qumj7dY3OTW/Jz6zzSo/TrwcQsWWsoqKigIyO4tSpU5Saqr8mDwDeJdN5wxkbzkjw8pETgYbRQMJQztXqORhNgbY74LB6ejJNG6A9kdiJgl6zLjYndyvH7ucQk5mdm266id5++23/11y3U15eTjNmzKBbb73VyfMDgBghewO+q10d8dnJ25zVm7/T3T9K0LX+wEnb3WiKtOREMaxw08TbKgQ6SuH3vmPnQj5ns+Uj9ZKfTOeVGadfDyDsmR0OarjNnFvPL168SE888QTt3LlTZHbWrl1r5yUBIMbJLtfwv/D5X/XBRcxc7Ms1MOH82eGeecM1QNMHVszGKEs5wddcLa0S8Yy/cyVl/se4+6xZbtUKwYDVLRyMzL6nPZaPIK4k+Hw+W9nkgoICmj17Nn333Xd07tw56tChA40aNYpq1469/wMVFhZSVlaWuCZMgAawh7MON76wwrTzhgfR8Y1cvTFkzSqpVH7JR48u3FJhqwMjwa8ZfD78+vmFF+jUuRKxfMa1LqeLSuiR+VscuGL9c9KrRVFf86ETxWJn8+D3SrkK9Wsohd9OzRnmImcry34AsX7/tpXZYfziv/vd7+x+OwB4jLJcwzdlvmH7TDpvlGUNvpH/9r3vLGcsjLp5jLIgkVg90avfUZSX++itdYekdnNXXs/JDRUwxA/ijXSws23bNukXbdOmjd3zAYAYprdco7czuJWMRUICkToPbfc1w70Nk17RtJVlqOBtFJwqplZgiB/EG+lgp127dqIQmVe9+LNCWQVTP3bp0iWnzxMAYoRs543V9m8l0BnWtSH1vFz3E+prygoOtKxmT+wuQzmdgcEQP4hX0sHOwYMH/X/esmWL2Ah03Lhx/k1A161bJyYnc/EyAMQ3ZYnK6fZvvll/uiOfnuqr3UnkZEs5q5aWTA92bUgjb2lCmw+fFsHH8cISeu7T3dLZk1ACMCczMBjiB/FMOthp0KCB/8+/+MUv6JVXXqE+ffoELF3Vq1ePJk2aRHfddZfzZwoAMUNdiKuX2bGTtTCbq+NEJoQ31Zz0s2soNzPwvJWfx9f297UHDefm5Gam+rMndgMw7k5TXsPOnJ4K56Sz7AcQD2wVKG/fvp0aNWpU4XF+bNeu8G54BwCxuzeW+kYbStZCL6gJdVox4x3SjQICo0JsxYWyclq2K1+8jt0ArF29LH+gZfbztK6Fg5uX7m5LJ4pKxPvCe3hxdoo3V8VQP4g3toYKtmjRgqZNmyZm7Cj4z/wYPwcA8clsbyz1lgrKZGE7t1u9oMbKawbf561sX6AUYmfp7JheUFzqv167AdiWo2f822DobblgtlzVtWlN/z5gvIfXoNfW02MLt4rPPCbAyr5iAHE3Z2fjxo3Ur18/UZysdF5xtxYXKX/88cd03XXXUSzBnB2A0JaklOP4Bqq3ZKM1E0cJjshixkJrro7VYuDhNzWi7s1zbG9fcLGsnDpP+1J3jyrlXFePu1UEGnaWoYLn4QT/Lnhm0NRPdhtm0fTeD615PgCxJqxzdjiY+fe//03z5s2jf/3rX/4dzwcPHkwZGRn2zxoAYnJJSnZvrOB6G36NEd0a0Wv/PGja7SRbYMuv+T83/fSaRpZsy6Mn72hhaymH35enFm2vsDu71vXy0pGyDOXEruTBtUq9W9XWDUbNNmc12tMLwEtsDxXkoGbEiBHOng0AuIZeRkBZkgrOCMjWpgS3ZP91zUGpjIdsgS2/plmgQzY3ELXTRs7Xy0tJcwZ3oNELvrU050dmCcyo881OAAoQ18HORx99RHfccQclJyeLPxu58847pV6Ta3w++OADkR1KS0ujG264gV544QVq1qyZ/5gLFy7Qb37zG1q4cCGVlJRQ79696c9//jPl5OT4jzly5AiNHDmSVq5cSVWqVKGhQ4eK165UyXYsBxDX7GQEZGtTrLRk63VGmZ23LKvFw3bayJXrrZ6RIh3oODUPx04ACuBF0tEAt5Pn5+dTrVq1DFvLuW5Hdqjg6tWrxX5anTp1orKyMnrqqaeoV69eoqNLWQ4bM2YMffLJJ/Tee++JdbnRo0fTgAED/BuO8s/q27cv5ebm0tdff015eXl0//33i6Ds+eefl708ALCREVj/75PUtUnNgOJgs72xrLRk8zIRBzqyWQerbd4ciMjWJFl9/eDrtRpQODEPx2oACkDxHuyUl5eLz6WlpXTLLbfQ3Llz6eqrrw7phy9dujTg6zfffFMEU5s3b6Zu3bqJgqO//e1vNH/+fOrevbs45o033hAdX+vXr6fOnTvTF198IYKj5cuXi2wPT3qeOnUqjR8/nqZMmUIpKSkhnSNAPJK9MY+a999dvq3ujRVK1kEvQLESUHBgdrroYoWiar2aJCvnTBrXKxtQcDbLrP3djHoTVH49vdoiTFSGeGF5nYczJjxnJzHRVte6IQ5uWHb2T//H46CHg6uePXv6j2nevDnVr19fTGzmYIc/t27dOmBZi5e6eFlr586d1L59+wo/h5fD+ENdzQ0A/yV7Y+YdytX1O3p7Y3GL9oM3NPJvbGnlZwQfZ1Q0bSVDcWfb2jRqvnxNkpVzzk5PpucHBAYsZpkvllm5Ek3s05Ky0lJEwGK3eFpmDy5MVIZ4Yitiue++++j111939EQ4c/T4449T165dqVWrVuIxXjbjzEy1atUCjuXAhp9TjlEHOsrzynNauJ6Hl8SUD578DABkewYO31zVM2G4NXxMz6ZiuwV2priUZi7fGzDbxexn8OO1g7IOZnN8OFNjdt58X599T3v66Ls83Zqk4GtS8LlU05mto5ZSKanCY0rmS7k2LYUXymjse9/ZnoOj9/5osTJXCCAugx2ur3n11Vfp2muvpV//+tc0duzYgA87uHZnx44dohA53CZMmCCySMrH0aNHw/4zAWKJ+sZsdYduxtODZy3fJzI/esMFjW7+WlkHs6JpNvWTXTSpbwvDgGL2oPZUo2qqXE3SgZP+n73uwEn6aOsPVHbJvMr4WGHFIYpWhwNqDWIMtXiat6CY+cu2Yn4PB6QIdCBe2GpX4qCkQ4cO4s979+4NeE69+7ksLjpesmQJrVmzhurWret/nIuOeTLzmTNnArI7x44dE88px/CQQzV+XnlOS2pqqvgAcCMrBbPhpNyYn/zH9gpBixalnsVKJ5fespdWm7ls0XT1jFTN1+TalZ+3u1I8n19wXuo94GWuX3WqK7JAVgqf9TrW+L3hJaonbm9Op86ViMzXc5/9S3MwodU5ODLF0yeLLlJuVhrazCHu2Ap2uMXbCTyB+dFHH6VFixbRqlWrKuy31bFjR1Ej9OWXX9LAgQPFY3v27BGt5spu6/z5ueeeo+PHj4viZrZs2TIxSbFlS7l/mQLE4hC/SOCfWTU1me792wbTY5V6FquzXfhn8M1cKajlICA7I6VC3cryXdrL0npzbZTX5CzTh1t/FAHF39YeEh/8+jI4yPvLGvOZPTLXqfW7NSoe1ut60yNbPM3vB4IdiDdRHUTDS1fcabV48WKqWrWqv8aG62h47g5/HjZsmFga46JlDmA4OOIAh4uTGbeqc1AzZMgQmjFjhniNiRMnitdG9ga8PMQvUlmlzo1rWGopt9NlxQEN7980Y+m/NAM9DlwWbpJbblaCLuU131h7qMJ5c21PpHCQxueh9bs1CnT0ut70yBZPL976I/2uL4qSIb5ENdjhuh/Grexq3F7+wAMPiD/PnDlTdH5xZkc9VFCRlJQklsC4+4qDIJ7Pw0MFn3nmmQhfDYB9bhjrb5RVstJSbqfLyizQe6xHEyoqMZ/fxRkbJeiSqfGJBM4k/WPLDyH9zOCuNy183WaZImUpCxOTId443z9ucRlL60MJdFjlypVpzpw5dOrUKSoqKhITl4NrcRo0aECffvopFRcX03/+8x966aWXMD0ZYoqVpZ9wMOtyYnyjzckMDGSy0pLp8Z5NA1rKrXZZ8YaavM+UUVDy2ldyS0nXNazuD7qsDhgMJ+5Gc4JWh5iCr5trkmRgYjLEm6gGOwAQ/bH+MhkQfr5c3GR9FTIOM5fvo07PLadnPt4pOpaYUZcVv8I9nerRkm0/0svL913eOdy4bkUmq8O+2n/C373ktRu6TMDbUxV0GsHEZIg3SH8AuEA0x/rLZpUemb9F9xgu/v372kPiIzczlQZdV58e6tqQFm39ISCQ4eGCjAOkcDhXcsm/3BNrN/QqqUni/M0YBXFWt+wAiBfI7AC4gJ0Be05xOgOSX1gighmuVeFAh+toOPAZ0/NqKigudWxJRw/f5J9atIPa1atmaTBitCi/21cHd5Q63iiIszq7CCBeINgBcIFo3qTCnQHhzifuiHrj64MRKwzmTNO1zy0TW0IYvadmOAi5rWUtMXXZLv5WnrqcYPK7vaFpTUcCXr3BhZiYDPEswccVwXGO98biNneepszt7QDxNGeHa3Z4awKjPZti2a+7NaowFJCDF506X//wQa5/OV1UQqPmb7H9viiBCwcZzOx3qxSKk07Xm5VgxS3DKQHccP9GsINgB1wmGjcpo5tsrP8HgmuI1jzRnTYfPi1m3vDymh5eahvdvYl4v5UgMJSOruBgRuZ367bBkgBuhmDHAgQ7APo32Ul9W4o9p9zSxm0H7wXFgYVR8KIU7/KeURyAcGcZb8hpBb9f93SqTw1rpocUqCIrA+Ds/RvdWAAgKNs28NYEP7WQ+6jLVTXFBOXERKKHL2d+YhEHDVa3sZAt3B59a2NqmlPV0aCEXwND/wCcg2AHAAL2TVJnd2avPOBfQhnWtaHhEpCbcSBidZaRbOF21yZXSAUmyNYARA+CHQCQ2rKBJyXHourpySKwkJ0+zYEIByblPp/YlVxvx3crM2tQhwMQXWg9BwCpKcoLNh6h3Ez3z60JNqD9lSKDIjvLiFvlubbn3tc3GAY6suMAzLbiUCY+A0D4INgBAKl6Fh4WyJORWTQCnvTkJFvfp2yhIDPLiOfyjJpfMTAJJjuzRnYrDr39rgDAGQh2AEC6noW7jLQG1vFSEQ/OC6fX7r9WdFWNvrWJ9PcED+EzGrg3Z3B7MY/HKOzga5w37HrRsSWz/BTtDV4B4Ceo2QEAS3tzcTEud20FF9syfuyzHXn09rrDjp4fD/o7fq5ELKM1viJD+vu0lpmUrjPl/GtmpIrUDnegmWV0eKuLxMQE6cLiaG7wCgD/hWAHwAGx3mnTsUF1sYcVb7Ogh4t1uWiXr1WvNVp5zOlgh/fYGvPuVvFnPk8Zj/doqpt9Uc6f62V++/53lmYIWQlMornBKwD8F4IdgBDFeqeNcv5GgQ7jYl0u2uXsypQ79a/NbOftUHEBsYyF3xyh5rWr6p6nXveZk4GJzHvBWSsONgEgfFCzAxCCWO+00Tt/I/mFF8SAQb1rMyoEdoJscHKssET3d2BUOEwO7jwv815w1urmF1e6/u8KQCxDsANgU6x32ti54atN+GC77rXpFQJzFsNJXBitx+h3YFY4TA7uPK/3XmgFxy8v30eLt/4g6ofc+vcGIBZhGQvAJqvbD7itbsjqDT/Y6eJSsbVE1yY1NZ8PLgTmc/rxzHn6zXvfkVMGX1ef5qw6YPl3YLUgODfEZUn+vu7Nc6jztC81lwuVsGbm8r0xuRQK4HYIdgBsimSnjVkgY6duiLeGCBVnIPSCHaYuZOZzfO7T3eSkBMkky9r9/wl4z2TrbnjfK94OwomCc9513awuSivbIzPPBwCMIdgBsEn2hnnibIm/g8kOs0BGr9CWj+famrkaN0v+nr87sM+Vz+cTAY+6hfvEuRKqWSVVpCtOFJWI9+l0UQmNmr/FsYJlZasG3qiU9+8yw8e8s/4IPdi1EY3u3sS0cFh5/TG3NdP9vVnNpFkNevm8+NX4d88Zsljq7gNwmwQf/9cqzsluEQ8QfLPjbQVkuo7sLknoBTLKbW/O4A409ZPAQEhrEN7mibf5b5bKeYeyhKWumeHlLDP8o50sQeEr4YwHBwGyvwP1+zF9QGvxZ35vmU/jvTXKqNjJpHFQOOi19WQHD1PELugA9u/fKFAGsMlK15Gd7iyZAuhJi3dIDcKbvWK/Y7U6ajKBDnMy0OGgQglE7HR+8fvBGS+mN03ZLNCx04FntjeXEQwdBAgNMjvI7ECItP6VTwZLI7zVgMySRCiZgGBZaZXoz/d2FEtM+46dlVr6cZMqqZXol9fWFZkcnknD9S/q5SOuP5L5HajVvvy7YLLLUWZZMbPfsRIoMSv/4UVmByC0+zdqdgBCpHQdvbn2IE39ZLdj3VlO/mu+4HyZGAjohOrpleh0cRlFUlFJGV3bIJsKzl8UM2m0lo84wPjjF3sMu7PU1L8L2UAi1A48pQ1dNjBTgicrs30AoCIEOwAO4H/F16yaKnWsbBDjti0E+Mb7WI8m1KBmFf/WDZHCQcQj83/KiBh1LcluJaFYu/+EpS0+nOjAC27JP3SiiGYu3yfeX59Ds30AIBCCHQCHOL0PUri3XbCKz+HlL/fT4z2bkpuou5Z+e9vVlr539sr/1jLxez2pbwuqnpGqGwA59TsO3lusWW7VCtmeUGf7AMB/IdgBcIhsO7PskoRSfMtZC61/9UcrAFqw8Yh0F1akKMtHvH+XXfz9j8zfEvBYcIeV079jowGMsbaZLICboRsLwCFGnUF2lyT0t12wtlzjFL7B5xeWUOerrBXLJlz+GH5Towrn7uT9PLtKqghGnBLcYRWO33Fwtqd/uyvFZwQ6AM5BNxa6sSAGdkEPHmCXX3Cexvyfc9suWMWTha10dKmvP/hauLvq1VUHArZKsIu7lriI2c5u5nq0Oqxifad7AK9ANxZAlIRjSSK4xoPb0mVwFuV00UXHl7x4cvE/vv3BsJ6Iu7Ym92tFuZmB1x98Leyxnk2paa0MGr1gi62ZPOrlI359rY6n9JQkKi/30YWyckuvrdVhhWUngNiCYAcgDLRu6E6SKV7OzUylyT+7hkbNr1jzY5cSVHRuXMOwnohNG9DGUpajT5s6NJsSNLuu1D9DpmupYsdTMc1avjek9yC4wyrcv2MAcA5qdgAihJdvOCOzeOsP4jN/bZfM5GDOYCQmak8JtkMJMu7pVI+WbPuRstJSxHYVwa+dlZYsOrY42LCqT5vaYi+v4Lob/hn8+FwLE4+VYORnberQwm+OhBzsuW0UAADIQ80OanYgAsJV48Gv++QH28UWCMHUezypsxy8Sedv/m8rHSsssRQA8J5STP2zlHbtfceL6I21BwO6oUK5PqNNNq1uwBnqJGqrk68BwH33bwQ7CHYgzMw28zTah8kM3/i7Tl9B+YXWti+wum0Bb9dwrqTi1GSj5TEnrs+RYu7CC7aHIEbrGgBADgqUAVzAbDNPZRgeZ17sZA34pq4X6BhtX2B12wKtQEd5faOfHer1qclkdLQyaNkZP2WkzDzeoym9u+koBvsBeBCCHYAwCnUvJTOhbF+gFPHOXLY3YJKwk0K9PivLgHoZtFNF5oMGeUjioz2aig90WAF4D4IdgDCSDUZ4bo6d7AbX34RSXMs38q5NaoYt2HFiU1O9IEa9JxYHbXoZNBnK96HDCsCbEOxA3LNa8GrFv/9TJHUc75aelpJkuFyild3g9nIuHC4oLrW9fUEk9uCy28lktgzIpny0k6pWTpZajtPDRdehZp8AwL0Q7EBcC+ckXH7tl7/cJ3XsqaKfpv7qFcLqZTfUHVV2d8022oMrVHb3ipJdBqTL21e8s/4wRTP7BADuhjk7ELeUACL4Zhq8H1IoGQmr+HuC5+/IFDlzdicnU3/+jNmMH709uKxweq8oKwHIZzvyKVSYowPgXcjsQFyKRJeU1WUVpZh3/YGTlJiY4F9W4y0OzIqceRlm3rAOAd+nLMfJZq/UU4c/25FHb6+Tz5ZwsFW5UlJAZ5gTnUyRCEBCzT4BgPsh2IG45JYuKS28vYN6OF+1NLnW6RNFJWLHbKvFvepgRF2gayXYMQq2QqHUE4VSj2PEiewTALgflrEgLoXSsh3ujIQ60NH6WvZnyhT3ai2bqYMMK7d/DrY4UOKAiz87ETyot8VwQvDMHb2tJgDAW5DZgbgkG4wox1nt2IpEh1PwTbxjg+qOZa/URcvRXnLiQGRMz6Y0c7lcsbfRUtXqcbfS5sOnMUcHIM4gswNxySxzwY/z83wcLwXd+MIKsb/SYwu3is/8tVEBs8xGnU7iwXk3v7gy4JxCzV75i5aDCp+N3qtwGd29qeF5KEXaCSaF0imVEh3PPgGA+yHYgbhkFIyob47LduXb7tjS63ASxbzJ9v6vZ1S/E3xOVrJXet1afA1rn+wuMitO17xY2QWeX3vKnS0Ng5npA1prvt9YqgIAbASKjUDjmt6gvkHX1af62eli2B/PwAllN2z1EtihE0U0f8NhOnZW+zXN/O9D19Fj726VOifGGSi9pTTlWN61/JkluwM7qTIri+BCHSA4OZPI7mvJfF84h0QCgLtg13MLEOzEt8BgpJgWbDxiuLlmsAXDO0t1bOl1RslQApOX7m5L9/5tg+nx8/7nekpMSBCZqb+vPaQ7cHBEt0b0lzUHdV9nblBGxIlAItRd4BHMAIDV+3dUl7HWrFlD/fr1ozp16lBCQgJ9+OGHAc8/8MAD4nH1x+233x5wzKlTp+jee+8VF1mtWjUaNmwYnTt3LsJXArFMabdOrZRIs5bvtRToyNbGGHVGmVFu4/d0qk+f75Ibnjf8rU2itogDHfEaQbEAB05zBnegdzd9b/g6Ez7YHrC8pLxXdmteQukQc+ocACD+RLUbq6ioiNq2bUsPPfQQDRgwQPMYDm7eeOMN/9epqYEbH3Kgk5eXR8uWLaPS0lJ68MEHacSIETR//vywnz94RyjBiExtjJ0hg+oaHz6vmcv3Sn9PcemlgK+V2GFY14bUs2WuyIas//dJMR/HyOniUnEcbxYaC/ONAABcF+zccccd4sMIBze5ubmaz+3evZuWLl1K33zzDV177bXisT/96U/Up08feumll0TGCECGnWDEyuTdUIYMcsDhBD7fT3fk01N9fyom5qJgGXycU8FOuOcbAQDEZDfWqlWrqFatWtSsWTMaOXIknTz53/9Ar1u3TixdKYEO69mzJyUmJtKGDfp1DSUlJWKdT/0B8c3qzdVqF5Ib9l1SsiYzl+0VAYx8uZ7PcveUU/ONAAA8P1SQl7B4eatRo0Z04MABeuqpp0QmiIOcpKQkys/PF4GQWqVKlSg7O1s8p2fatGn09NNPR+AKIFZYvbla3fcp0kMGjcxeuV98VE+X24aiy1U1NbugsjNS6K52dcR+WrJFwmbvA/apAoC4C3buuece/59bt25Nbdq0ocaNG4tsT48ePWy/7oQJE2js2LH+rzmzU69evZDPF2KXTDDCU4on/ewa0ZZttQNIPZE4uDMqWszqdZR6oYLzpWK/ruBz5vZ3LoDmD9kWdKP3AftUAUDcLmOpXXXVVVSzZk3av3+/+JpreY4fPx5wTFlZmejQ0qvzUeqAuHtL/QHxTWbi8bP9W9HP21vvAFKWf0rKyunxnk0pJzOwyD5aZAKu5+9qRVM/MS/czpMYsmg2bBHD/wAgLjM7wb7//ntRs1O79k//MezSpQudOXOGNm/eTB07dhSPrVixgsrLy+n666+P8tlCrFFuwsHLNQoeMMi7elu5GWsPLaxMY3peTaWXLtHslQfIDaqnV6LTxWUBgxWn3HkNZaWlWCrc5mvlZS2zYJDfQz4O83IAwPPBDs/DUbI07ODBg7R161ZRc8MfXFczcOBAkaXhmp0nnniCmjRpQr179xbHt2jRQtT1DB8+nObOnStaz0ePHi2Wv9CJBXbwTbi8nOiR+d/qbscgm33QG553rPCCmOfzYNeGjpyzE8tik/u1EkFYcODBxciyrLaNK/NyAAA8vYy1adMmat++vfhgXEfDf548ebIoQN62bRvdeeeddPXVV4thgZy9+ec//xkwa2fevHnUvHlzUcPDLec33ngj/fWvf43iVUEs4yUnXrYhi0PvgjuVLpaVmw7PW7z1x5DPlzNEwctBalVSk6RehwMdrUF9drqi0DYOAG4T1czOLbfcYtj++vnnn5u+BmeAMEAQjKi3F6hZJVVEGyeKSvwZDKY8f+JsidTQO/WgPb1OJb39q5TXOVl0kapWTqKzFwIHAMpQupZGd28iPvSur2OD6mI3dLvdT3a6yNA2DgBuE1M1OwBWaQUiwd1Gsp1JaqPmfUvTB7YWf9ZaqjIKdNTu7lCX3vj6sKWfrdW1ZLQcZNb9xNtQLNn2o2bdjJUuMrSNA4BbYSNQbATqWaFsvCkrIyWJii5az8yoNxFdteeY4WacHJCpgzE7O41rBX08Z4ffG73XtrJBKjbxBIBowK7nFiDY8R6+qd74wgrb+1GFm5IF+Wp8d3Gz/3RbHk1cvCMgI6QEHk51LQUGL0U0c/k+zfNSdkP/6Lu8oC6yVBp0XX0qPF9Ki7b+QKeKrAVgWgGXncANAECBYMcCBDvew0XCvOu3G+llQfSyHk5nQ+wGgurzthqA6WXZZDNCAACh3L9RswOetGyX/nYhkcaTl9VZEL2tJrRasa1mQ2QCI7s7sKu70TjYkW0bN9pRnh9LsDCfBwDADgQ74DkcIPAWBm7A9TbrJ/SkzYdPW87M6GVD9Ob9yAZGobaG8+vPXrGPHut5tdTxZsGV1fk8AACe3i4CQDaLEKqEy4EC16mEkmt48IZGlFIpUXOGTSjZkOB5PxzoPPzOtxWCCiUw4ueVWUD7jp2jUHG9j8zWEFaCK8znAYBwQWYHPMXuEo1eazfjYMFuVodn4MhSL0HJzvvh4zlT9OQH23WP4+vh56d8tEu3m8oO2aUn2bk7mM8DAOGCYAc8xWp2QGvOTnBNDS8XPfmP7XTmvLVZPNMHtJauQTGbB6Rn+a58EfAYzQn6b3u5tfM3I7v0ZDaYEPN5ACDcEOxATAsuyBUThCWMvrUxdW1yRYUJysrUYa6x4a0f+GvOXlStnEz3vr5B6rWttlOHMg/ogy3fU7nPuaJePvc729Y2nPtjNbg0GkyoNSARAMBpCHbA1Yy6i7R3FE8V2ZqC4lLDLMKY25oF3FyV7AS/Jm+vEFzkO6lvC9NtE6qlJdOceztQ56vk6nLManNkqHcqD4U6+ONzT09JppnL9zq29KS3o7xeZxoAgJMQ7IBrGXUXMe0dxUv8j1nNIhh1P42av0UM2vurRsZDeSXePkLZLyuSNUZOaJpTNWA5imuNFmw8TPmFJZrH21l64oDGqQGJAABWINgBVzJru866vNWBXkEuP1+5UlJAQa5RFkFmFsy7m76nrLTkCrU7nEmaNqC1reyEWzqQgjM0HIBMufMaf3G2U0tPWrOEAADCDcEOuI5M27VMQe68YR0oMTFBKosgMwtG72eetriJaKQ7kBISiPTmpBtlaLD0BABegWAHXMeppZ0TRSVitk24MyyhTAA261RS/ww7dT38fSNu+u/ym9UMDZaeAMALMFQQXMeppR0rWZNQMizqmTdWKZ1KLDh8SLj88etujUQ2xSoOojgzM6FPS/E5+DX4a5k9qZSlJytDEQEA3ASZHQgbuxtYhrq0o7c0oz4f0aLu+yn7o7Sby2RYwhGkySwXPXF7C5q5bC/NXrnf9PXu79KA7mhVO+D9RoYGAOIZgh2IaCcVt3BXz0g1vOHKDKHjomCulZHtuDIb2qfMl+HlHrtLRqEEaRyMdG+eQ/+77hAdPlVMDbLTaUiXhmKrCcbXwp1eMsEOBzpaRcBWi4Od3m0dACBaEnw+vdLF+CG7RTyQ40Py9AbwKa9BOsEMZ0KYzMaXMuejvC63l3/0XV6F2T0XyspNZ/d8Nb677WBAZhNPDj5ufGGF6STiUM7DyvkAAMTK/RvBDoIdRyk3ZNkCY3XwohXwyAQAWtOP1V8HDwk0OhcOFlaPu7XCLuXLduWbBl92gwC9YEzrtT/d9iM9Mn+L5rmHeh52zgcAIJoQ7FiAYMc5vKv2oNfWW/oeo4yElaUUreAoOyOFThVdtHQ+C4Z31lzuCUe2wyw4VL83HHDpLcU5lXWxcj5Y0gKAWLl/o2YHHGWnSFfdzRQcZMjWmehlI6wGOiy/4Lzm4+Eo8pWZ78PPz16xn2Yt36u7FDeprzPLS7LnI7MBKACAWyDYAUeFUqRrt5sp1P2lgk39ZDelpSRpBg9OTwCWveY31h7UvT4OtaZ+sot6t7I+58fu+bhl8jMAgAzM2QFHKZ1UCREMlJzeX+p00UWRJeJsUbjJXnPwFhVOzfmxez6RmPwMAOAUBDvgKKMheXr4uNoWN5UMZ5ZByaBwtoizRtEODhMi+D6YnU+ovysAgGhAsAOOU4bkyUz9DWVTSatZhuyMZOnXdDJbIhMcGoVUsuGWE9kWs4nOof6uAACiATU7EBZaxby8PMS1JeolJ95F/MGuDcWxdskMIQxuKd977CzNWXnAE7UpRpt52oENQAHAa9B6jtbziOJlIe4s4oJbdR2KTOu0URu6zBBC9WvLtsjrtaFHay4RRXD2DSYoA4DbofUcXIlnxWi1UHNWhoMVvZu21oyb3MzKNOi6+tSwZrq4Gc8Z3KFC5kgvGyGbDQp3bYrV4upqackBQWI4sy1Od54BAEQLgh2IGKMWcd/lAIOf5yWt4H2ttGbo5BdeoJnL91ree0tdm8Kvq7e/Fs+uCXdmw+oyGQd0iYkJyLYAAFiAYAcixs7AOiszdDhLM2r+FpEd6t/uypBqU3ibiacWbbe81GaVlaJi/vmdG9dAcAMAYBGCHYgYOwPrrCzzGGWHZAupD50oor+vPUhLtlWcsWO21GaH2XKaGrqgAADsQes5RIydgXVWl3nstIwrtSmplRJp5vJ9VHC+TPe12ZSPdtLa/Sdo8dYfRKFzKLN4ZOYSVUtPprnYfBMAwDZkdiBi7BQF250dYzVIUpbLzPB55xeW0L2vb3BseUtvOY2DnAdvaESjuzdBRgcAIAQIdiBiZIqCg5dqrCzzqFkNkkLZcsKJ5a1wbDIKAAA/wTIWuGK6Mn+tFSxY3X7C7nYGoQwPdGp7CWU5jYurlQJtXiZzYrkMACCeIbMDEWc1i6G3zEMObmcQ6lYLWp1kodCaKxSObjAAgHiAYAeiwurAOq2uqQUbj4j6GScG7NldLgvH9hK6c4XC0A0GABAPEOxARIWyBUFwgDS6e1PHalyM6okimSGyO3gRAAD0IdiBiHF6acbp7Qx0u6LSkmnoDQ3o3W++p2OF4d1ews7gRQAAMIZgByLC6tJMtDahNKonalE701InWaQGLwIAgDEEOxB2VpdmwlWcKxtA6WWMjLaXcKpw2M7gRQAAMIZgB8LOytJMwfmLYSnOdSqACvc8HLfsxg4A4CWYswNhJ7vkkl9w3jADZHeWjbKEFhxwKQEUPx/KPBwnl9eM5go5uVwGABBPEOxA2MkuuZwquiidAXJqCc2JYYDRHrwIAADGsIwFYSczwyY3M5Wyq6Q6Xpwbq91N2D4CAMA5CHYg7GRm2FwoK6cjJ4scL86N5e4mp1vrAQDiFZaxQOBlnHDuw6QszWSlJ2s+X1BcSjOX7xM7fSc4uO9VrHQ3hfv9BwCIZ8jsQMT2YeJlmSkf7SKiUt0WdIVTs2xiobsJ+2ABAIQXMjtxzulOJSNcf5JfaFw/c6a4lB7vebVjxblu726K5PsPABCvohrsrFmzhvr160d16tShhIQE+vDDDwOe9/l8NHnyZKpduzalpaVRz549ad++fQHHnDp1iu69917KzMykatWq0bBhw+jcuXMRvpLYFOlOJdm6mIY10+mr8d1pwfDO9PI97cRn/tpulsOt3U2x2CkGABCLorqMVVRURG3btqWHHnqIBgwYUOH5GTNm0CuvvEJvvfUWNWrUiCZNmkS9e/emXbt2UeXKP924ONDJy8ujZcuWUWlpKT344IM0YsQImj9/fhSuKLZEulPJSv1MOPa9clt3U6x2igEAxJqoBjt33HGH+NDCWZ1Zs2bRxIkTqX///uKxt99+m3JyckQG6J577qHdu3fT0qVL6ZtvvqFrr71WHPOnP/2J+vTpQy+99JLIGGkpKSkRH4rCwkKKR5HuVIp2/YzbuptiuVMMACCWuLZm5+DBg5Sfny+WrhRZWVl0/fXX07p168TX/JmXrpRAh/HxiYmJtGHDBt3XnjZtmngt5aNevXoUjyLdqRSt+hm3djrFSqcYAECsc203Fgc6jDM5avy18hx/rlWrVsDzlSpVouzsbP8xWiZMmEBjx44NyOzEY8ATjUxLJDbTjJVOp2hnugAA4oVrg51wSk1NFR/xzmjYXzgzLZGqn1E6naxuKiq7O3qsvv8AAPHGtcFObm6u+Hzs2DHRjaXgr9u1a+c/5vjx4wHfV1ZWJjq0lO8Hd2VaIlU/Y9bpxOEDP89BlzqYiHQmKFrvPwBAPHFtsMPdVxywfPnll/7ghpebuBZn5MiR4usuXbrQmTNnaPPmzdSxY0fx2IoVK6i8vFzU9oAcvqF2b55D/7vuEB0+VUwNstNpSJeGlFLJtSVdpoHOm2sPWu50spsJ8mKnGACAl0Q12OF5OPv37w8oSt66dauoualfvz49/vjj9Oyzz1LTpk39refcYXXXXXeJ41u0aEG33347DR8+nObOnStaz0ePHi06tfQ6saAirWzG618djMnMgta1yHQ62c0ERTrTFaklNgAAL4lqsLNp0ya69dZb/V8rRcNDhw6lN998k5544gkxi4fn5nAG58YbbxSt5sqMHTZv3jwR4PTo0UN0YQ0cOFDM5gE5oWYz3HTz1bsWmU6nWJh54+ZiawAAN0vw8UCbOMfLY9yCXlBQICYxxwsOVG58YYXuTV7pBuLpxVoBjJtuvmbXYnZt3Jb+2MKtpt/HE537t7uS3BLIKb+VaE6CBgBw+/07Nosy4ky45sRYyWa4fU8ns2shk04nN8+8wbYSAAAeLVCG8GdP7E7wdbq+xYmlMCtThrU6ndw88yYWltgAANwMwY6H6mmsBg12sxlO3nydCuZkr2VS3xb0QNdGFd4XN8+8wbYSAAChwTKWS1lduuCggWtWBr22XtSe8Gf+2mg5Sclm6N2++fHaGtkMp26+Ti6FyV6LVqDj9t3R3bzEBgAQC5DZcSkr2ZOC8xdtdVTZzWY4cfN1einMqcyMG2feuHmJDQAgFiCz41Ky2ZP8wgshFa/ayWbYzQg5VRytx6nMjDLzhruu+HO059hEawNVAACvQGbHpWSzJ6fOlYRcP2M1m+FEFiVcdShuzMw4AdtKAADYh2DHpWSXLrIzUhwJGqzuVRXqzTecdSjh3ncrWrwayAEAhBuCHZeSzZ5kpaVErXg1lJsv6lDs8WogBwAQTqjZcTGZGhQn6mdCYbe+BXUoAAAQKdguIga2izCbn6O0cJNOBsjNWwm4acsJAADw5v0bwU4MBDteDxrctJkoAADEDgQ7cRbsMAQNAAAQTwol798oUPYQFK8CAABUhAJlAAAA8DQEOwAAAOBpCHYAAADA01CzA4bFzrz3Fm9JwZOac7PSUPQMAAAxB8EOSLWxx1o7OwAAgALLWGHMjKw7cJIWb/1BfNbbddxtlAGFepuL8uP8PB8HAAAQC5DZCYNYHfDHARmft1lYxs/zcbwvFpa0AADA7ZDZiVBmJD8GMiJco6OX0QnGx/HxAAAAbodgJ0KZEeUxft6tS1o8eTmcxwMAAEQDgp0IZkZ8Ls+I8BYT4TweAAAgGhDsOEg20+HWjAi3lXNtkUwVDh/HxwMAALgdgh0HyWY63JoR4WJjLqI2w8EQH4fiZAAAiAUIdiKYGUmIgYwId4u9el8HcZ5a+HF+3s1dZQAAAGpoPQ9DZoS7rjiwUZchKwFQLGREOJDhtnJMUAYAAC9I8Pl87mwNiqDCwkLKysqigoICyszMjNs5OwAAAF68fyOzE+bMCBcjc40OMiIAAADRgWAnTDiw6dK4RrRPAwAAIO6hQBkAAAA8DcEOAAAAeBqCHQAAAPA0BDsAAADgaQh2AAAAwNMQ7AAAAICnIdgBAAAAT0OwAwAAAJ6GYAcAAAA8DROUecPOy9uD8R4bAAAAEBuU+7bZNp8Idojo7Nmz4nO9evWifSoAAABg4z7OG4Lqwa7nRFReXk4//vgjVa1alRISsFlnKBE2B4xHjx51ZPd4wHsaDnhPnYf31Hl4T+VwCMOBTp06dSgxUb8yB5kdLlxKTKS6detG+zQ8g/+Pif9zOgvvqfPwnjoP76nz8J6aM8roKFCgDAAAAJ6GYAcAAAA8DcEOOCY1NZV+//vfi8/gDLynzsN76jy8p87De+osFCgDAACApyGzAwAAAJ6GYAcAAAA8DcEOAAAAeBqCHQAAAPA0BDtg2Zo1a6hfv35iYiVPnP7www8Dnuea98mTJ1Pt2rUpLS2NevbsSfv27Yva+XrhPX3ggQfE4+qP22+/PWrn63bTpk2jTp06ianotWrVorvuuov27NkTcMyFCxdo1KhRVKNGDapSpQoNHDiQjh07FrVz9sJ7esstt1T4e/rwww9H7Zzd7tVXX6U2bdr4Bwd26dKFPvvsM//z+DvqHAQ7YFlRURG1bduW5syZo/n8jBkz6JVXXqG5c+fShg0bKCMjg3r37i3+jwv23lPGwU1eXp7/Y8GCBRE9x1iyevVqcZNYv349LVu2jEpLS6lXr17ifVaMGTOGPv74Y3rvvffE8bxlzIABA6J63rH+nrLhw4cH/D3l/x6ANp7cP336dNq8eTNt2rSJunfvTv3796edO3eK5/F31EHceg5gF/8VWrRokf/r8vJyX25uru/FF1/0P3bmzBlfamqqb8GCBVE6y9h+T9nQoUN9/fv3j9o5xbrjx4+L93X16tX+v5PJycm+9957z3/M7t27xTHr1q2L4pnG7nvKbr75Zt9jjz0W1fOKddWrV/e9/vrr+DvqMGR2wFEHDx6k/Px8sXSl3rfk+uuvp3Xr1kX13GLdqlWrxPJBs2bNaOTIkXTy5Mlon1LMKCgoEJ+zs7PFZ/6XNGcm1H9PmzdvTvXr18ffU5vvqWLevHlUs2ZNatWqFU2YMIGKi4ujdIax5dKlS7Rw4UKRKePlLPwddRY2AgVHcaDDcnJyAh7nr5XnwDpewuL0daNGjejAgQP01FNP0R133CH+o5eUlBTt03O18vJyevzxx6lr167iBsz472JKSgpVq1Yt4Fj8PbX/nrLBgwdTgwYNRO3Ztm3baPz48aKu54MPPojq+brZ9u3bRXDDy/xcl7No0SJq2bIlbd26FX9HHYRgByAG3HPPPf4/t27dWhQ1Nm7cWGR7evToEdVzczuuM9mxYwd99dVX0T4Vz7+nI0aMCPh7yk0K/PeTA3T++woVcaaWAxvOlL3//vs0dOhQUZ8DzsIyFjgqNzdXfA7uGOCvlecgdFdddZVYKti/f3+0T8XVRo8eTUuWLKGVK1eKYlAF/128ePEinTlzJuB4/D21/55q4eVrhr+n+jh706RJE+rYsaPoeONGhZdffhl/Rx2GYAccxcss/H/EL7/80v9YYWGh6MriVC044/vvvxc1O/wvZ6iI67z5psxLAitWrBB/L9X4xpKcnBzw95SXW44cOYK/pzbfUy2csWD4e2ptibCkpAR/Rx2GZSyw7Ny5cwH/UuOiZP6PGhcqcvEcr+U/++yz1LRpU/EfxEmTJok1fJ7LAdbfU/54+umnxYwNDiR5SeCJJ54Q/xrkln7QXmaZP38+LV68WMyFUWocuFieZz/x52HDhtHYsWPF+8szTh599FFxE+ncuXO0Tz8m31P+e8nP9+nTR8yF4Zodbp3u1q2bWHaFiriAm2vv+L+bZ8+eFe8fL01//vnn+DvqNKfbu8D7Vq5cKdofgz+4PVppP580aZIvJydHtJz36NHDt2fPnmifdsy+p8XFxb5evXr5rrjiCtGK2qBBA9/w4cN9+fn50T5t19J6L/njjTfe8B9z/vx53yOPPCJafdPT030///nPfXl5eVE971h+T48cOeLr1q2bLzs7W/z/vkmTJr5x48b5CgoKon3qrvXQQw+J/z+npKSI/3/zfyu/+OIL//P4O+qcBP4fxyMoAAAAAJdAzQ4AAAB4GoIdAAAA8DQEOwAAAOBpCHYAAADA0xDsAAAAgKch2AEAAABPQ7ADAAAAnoZgBwAAADwNwQ4AAAB4GoIdALDllltuEfuged2UKVOoXbt20T4NAAgBgh0AiEsXL16M6M/jnXnKysoi+jMB4CcIdgDAsgceeIBWr15NL7/8MiUkJIiPQ4cO0Y4dO8QuzlWqVKGcnBwaMmQInThxIiAbxDs3c0aoevXq4pjXXnuNioqK6MEHHxS7afNu7p999pn/e3gXaH79Tz75ROyeXblyZbHrM/8sta+++opuuukmsQN3vXr16P/9v/8nXlfRsGFDmjp1Kt1///1iB+kRI0aIx8ePH09XX301paen01VXXUWTJk2i0tJS8dybb74pdpz/7rvv/NfJj/G18p95Z3rFmTNnxGN8vurz5mvp2LEjpaaminMsLy+nadOmUaNGjcS5tm3blt5///0w/rYAAMEOAFjGQU6XLl1o+PDhlJeXJz44UOnevTu1b9+eNm3aREuXLqVjx47RL3/5y4Dvfeutt6hmzZq0ceNGEfiMHDmSfvGLX9ANN9xA3377LfXq1UsEScXFxQHfN27cOPrDH/5A33zzDV1xxRXUr18/f1By4MABuv3222ngwIG0bds2evfdd0VgMXr06IDXeOmll0RwsWXLFhHUMD5vDmB27dolrouDr5kzZ4rnfvWrX9FvfvMbuuaaa/zXyY9Z8eSTT9L06dNp9+7dIljjQOftt9+muXPn0s6dO2nMmDF03333ieARAMLEwR3UASCO3Hzzzb7HHnvM//XUqVN9vXr1Cjjm6NGjPv7PzJ49e/zfc+ONN/qfLysr82VkZPiGDBnifywvL098z7p168TXK1euFF8vXLjQf8zJkyd9aWlpvnfffVd8PWzYMN+IESMCfvY///lPX2Jiou/8+fPi6wYNGvjuuusu0+t68cUXfR07dvR//fvf/97Xtm3bgGMOHjwozmnLli3+x06fPi0e4/NVn/eHH37oP+bChQu+9PR039dffx3wenz+gwYNMj03ALCnUriCKACIL7zUs3LlSrGEFYwzL7xUxDi7oUhKSqIaNWpQ69at/Y/x0hY7fvx4wGtwJkmRnZ1NzZo1E9kS5WdzRmfevHkBNTK8ZHTw4EFq0aKFeOzaa6+tcG6cBXrllVfEOZ47d07U1fAyl1PUP3P//v0iY3XbbbdVqB/ijBgAhAeCHQBwBAcKvLT0wgsvVHiudu3a/j8nJycHPMd1LerH+GvGgYqVn/3rX/9a1OkEq1+/vv/PGRkZAc+tW7eO7r33XlGX07t3b8rKyqKFCxeK5TIjiYmJ/oBKoSypBVP/TD5PxvVHV155ZcBxXNMDAOGBYAcAbElJSaFLly75v+7QoQP94x//EIXAlSo5/5+W9evX+wOX06dP0969e/0ZG/7ZXHPDxc1WfP3119SgQQP63e9+53/s8OHDhtfJuGaIcQ2PkpFRFyvradmypQhqjhw5QjfffLOlcwUA+1CgDAC2cFCzYcMG0ZnEHVejRo2iU6dO0aBBg0QRMS8Lff7556LLKjhYsOOZZ56hL7/8UnRhcTcYFznfdddd/o4qDly4IJmDjn379tHixYsrFCgHa9q0qQg8OJvD58vLWYsWLapwnbwUxq/L11lSUiK6qLgjTCk85uLiiRMnml4DF0P/9re/FUXJXKjNP5OLsv/0pz+JrwEgPBDsAIAtfNPmmhvOVnCmg+tO1q5dKwIb7qjiOhxuMa9WrZp/2ScUHFg89thjoo07Pz+fPv74Y5F1UeqAOODgbA+3n3O2ZfLkyVSnTh3D17zzzjtF4MFBEQ8O5IBJ6dJScIcXd3rdeuut4joXLFggHv/73/8u6nv4fPg6n332Wanr4PZ3/hnclcWZKX5tXtbiVnQACI8ErlIO02sDAISM59VwoMFLVxw4AQBYhcwOAAAAeBqCHQAAAPA0LGMBAACApyGzAwAAAJ6GYAcAAAA8DcEOAAAAeBqCHQAAAPA0BDsAAADgaQh2AAAAwNMQ7AAAAICnIdgBAAAA8rL/D5KZjiiJ6q1SAAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df\n",
+        "\n",
+        "plt.scatter(df[\"temp\"] , df[\"rides\"])\n",
+        "plt.xlabel(\"temperature\")\n",
+        "plt.ylabel(\"rides\")\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.2/AI-DS_Nexus__A0_2_Javid_Mohammadi_a60d6f.ipynb b/a0.1/a0.2/AI-DS_Nexus__A0_2_Javid_Mohammadi_a60d6f.ipynb
new file mode 100644
index 0000000..2627ced
--- /dev/null
+++ b/a0.1/a0.2/AI-DS_Nexus__A0_2_Javid_Mohammadi_a60d6f.ipynb
@@ -0,0 +1,781 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials\n",
+        "Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:\n",
+        "\n",
+        "1. NumPy — fast n-dimensional arrays\n",
+        "\n",
+        "2. pandas — tabular data wrangling\n",
+        "\n",
+        "3. Matplotlib — quick, customizable plots\n",
+        "\n",
+        "Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊\n"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1 · NumPy 🧮\n",
+        "Quick-start notes\n",
+        "Core object: ndarray (n-dimensional array)\n",
+        "\n",
+        "Create data: np.array, np.arange, np.random.*\n",
+        "\n",
+        "Summaries: mean, std, sum, max, …\n",
+        "\n",
+        "Vectorised math beats Python loops for speed\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Mock temperatures\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 61,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [],
+      "source": [
+        "# 👉 # TODO: import numpy and create an array of 365\n",
+        "# normally-distributed °C values (µ=20, σ=5) called temps\n",
+        "import numpy as np\n",
+        "mean = 20\n",
+        "std = 5\n",
+        "temps = np.random.normal(loc=mean, scale=std, size=365)\n",
+        "#temps.shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Average temperature\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: print the mean of temps\n",
+        "print(np.mean(temps))"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "aa447a9a-0963-407c-e84e-8c023d7b2924"
+      },
+      "execution_count": 62,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "20.186113405910568\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2 · pandas 📊\n",
+        "Quick-start notes\n",
+        "Main structures: DataFrame, Series\n",
+        "\n",
+        "Read data: pd.read_csv, pd.read_excel, …\n",
+        "\n",
+        "Selection: .loc[label], .iloc[pos]\n",
+        "\n",
+        "Group & summarise: .groupby(...).agg(...)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 3 — Load ride log\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "####Create a sample 'rides.csv' \"EXTRA\""
+      ],
+      "metadata": {
+        "id": "dTCqJ8EAA-CY"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "######Generating random dates:"
+      ],
+      "metadata": {
+        "id": "wldZnTXaBony"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "def format_date(year, month, day):\n",
+        "  return f\"{year:04d}-{month:02d}-{day:02d}\"\n",
+        "\n",
+        "max_size = 100\n",
+        "day = np.random.randint(1, 29, max_size)\n",
+        "month = np.random.randint(1, 13, max_size)\n",
+        "year = np.random.randint(1998, 2026, max_size)"
+      ],
+      "metadata": {
+        "id": "8j4-P8QK7Dih"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "date = []\n",
+        "for i in range(max_size):\n",
+        "  date.append(format_date(year[i], month[i], day[i]))\n",
+        "date_final = np.array(date)"
+      ],
+      "metadata": {
+        "id": "QVKua_no8WKz"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "######Generating random temprature:"
+      ],
+      "metadata": {
+        "id": "16CrRUdpBujH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "temp = np.random.normal(loc=20, scale=5, size=max_size)\n",
+        "temp_final = np.round(temp, 1)"
+      ],
+      "metadata": {
+        "id": "79hwOgBx_kPy"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "######Generating random rides:"
+      ],
+      "metadata": {
+        "id": "2G71MEqcBzfK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ride_final = np.random.randint(100, 300, max_size)"
+      ],
+      "metadata": {
+        "id": "7QBBMBSBB3B0"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "######Generating random weekday:"
+      ],
+      "metadata": {
+        "id": "Y9_lqD5BB3lA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "weekday = ['Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday']\n",
+        "weekday_ls = []\n",
+        "\n",
+        "index = np.random.randint(0, 7, max_size)\n",
+        "for i in index:\n",
+        "  weekday_ls.append(weekday[i])\n",
+        "\n",
+        "weekday_final = np.array(weekday_ls)"
+      ],
+      "metadata": {
+        "id": "9kRiDomR_z7L"
+      },
+      "execution_count": 8,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "###### Creating csv:"
+      ],
+      "metadata": {
+        "id": "QFJUmAFoENzV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "df = pd.DataFrame()\n",
+        "\n",
+        "df['date'] = date_final\n",
+        "df['temp'] = temp_final\n",
+        "df['rides'] = ride_final\n",
+        "df['weekday'] = weekday_final"
+      ],
+      "metadata": {
+        "id": "tED86gwAEVMs"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.to_csv('rides.csv', index = False)"
+      ],
+      "metadata": {
+        "id": "xzA2EvaOF_iQ"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "####TODO exercise"
+      ],
+      "metadata": {
+        "id": "OYE5ILBRBGC6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: read \"rides.csv\" into df\n",
+        "# (columns: date,temp,rides,weekday)\n",
+        "import pandas as pd\n",
+        "df = pd.read_csv(\"rides.csv\")\n",
+        "df.columns"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ef16a650-47e1-4585-ea5a-a22e4cc03110"
+      },
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['date', 'temp', 'rides', 'weekday'], dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "id": "AMC-EFVMKQKD",
+        "outputId": "598b8d64-cc2a-4576-ca12-d17c70645e07",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        }
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         date  temp  rides   weekday\n",
+              "0  2016-08-04  23.0    292    Friday\n",
+              "1  2021-06-11  24.2    278    Sunday\n",
+              "2  2003-10-16  15.2    233   Tuesday\n",
+              "3  2014-07-28  14.7    291  Saturday\n",
+              "4  2021-12-26  23.8    200  Saturday"
+            ],
+            "text/html": [
+              "\n",
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+              "    <div>\n",
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+              "      <th>rides</th>\n",
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+              "      <td>23.0</td>\n",
+              "      <td>292</td>\n",
+              "      <td>Friday</td>\n",
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+              "      <td>2021-06-11</td>\n",
+              "      <td>24.2</td>\n",
+              "      <td>278</td>\n",
+              "      <td>Sunday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2003-10-16</td>\n",
+              "      <td>15.2</td>\n",
+              "      <td>233</td>\n",
+              "      <td>Tuesday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2014-07-28</td>\n",
+              "      <td>14.7</td>\n",
+              "      <td>291</td>\n",
+              "      <td>Saturday</td>\n",
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+              "      <th>4</th>\n",
+              "      <td>2021-12-26</td>\n",
+              "      <td>23.8</td>\n",
+              "      <td>200</td>\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
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+              "      fill: #174EA6;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "        document.querySelector('#df-61c91204-b65f-48fe-8c93-86cf7f7e3d9e button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
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+              "        const element = document.querySelector('#df-61c91204-b65f-48fe-8c93-86cf7f7e3d9e');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "      --hover-bg-color: #E2EBFA;\n",
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+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
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+              "    width: 32px;\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
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+              "    }\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "      border-bottom-color: var(--fill-color);\n",
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+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
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+              "                'suggestCharts', [key], {});\n",
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+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
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+              "    </div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 99,\n        \"samples\": [\n          \"2008-06-21\",\n          \"2008-03-23\",\n          \"2017-09-28\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.198053637291713,\n        \"min\": 8.8,\n        \"max\": 33.8,\n        \"num_unique_values\": 77,\n        \"samples\": [\n          23.8,\n          13.9,\n          21.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59,\n        \"min\": 102,\n        \"max\": 299,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          157,\n          292,\n          191\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Friday\",\n          \"Sunday\",\n          \"Monday\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 4 — Weekday averages"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: compute and print mean rides per weekday\n",
+        "print(df.groupby('weekday')['rides'].mean())"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4bdefce4-532e-4e44-e5f0-6886e65b623d"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "weekday\n",
+            "Friday       209.200000\n",
+            "Monday       215.666667\n",
+            "Saturday     198.750000\n",
+            "Sunday       202.434783\n",
+            "Thursday     186.000000\n",
+            "Tuesday      190.785714\n",
+            "Wednesday    213.714286\n",
+            "Name: rides, dtype: float64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3 · Matplotlib 📈\n",
+        "Quick-start notes\n",
+        "Workhorse: pyplot interface (import matplotlib.pyplot as plt)\n",
+        "\n",
+        "Figure & axes: fig, ax = plt.subplots()\n",
+        "\n",
+        "Common plots: plot, scatter, hist, imshow\n",
+        "\n",
+        "Display inline in Jupyter with %matplotlib inline or %matplotlib notebook"
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 5 — Scatter plot"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "df = pd.read_csv(\"rides.csv\")\n",
+        "\n",
+        "plt.figure(figsize = (8, 6))\n",
+        "plt.scatter(df['temp'], df['rides'], alpha = 0.7)\n",
+        "plt.title('Tempreture vs Rides')\n",
+        "plt.xlabel('Tempreture (Celcius)')\n",
+        "plt.ylabel('Number of rides')\n",
+        "plt.grid(True)\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "outputId": "e1f3b4c1-dc77-45db-8a90-16d3ccc09cdf"
+      },
+      "execution_count": 59,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 6 — Show the figure\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: call plt.show() so the plot appears\n",
+        "\n",
+        "#Summary from ChatGPT:\n",
+        "#   In Colab, each cell’s output is independent.\n",
+        "#   You can’t create a plot in one cell and call plt.show() in a separate cell to display it.\n",
+        "#   Always create and show the plot in the same cell for plt.show() to work properly."
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "execution_count": 60,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.2/AI-DS_Nexus__A0_2_Project__RezaShokr.ipynb b/a0.1/a0.2/AI-DS_Nexus__A0_2_Project__RezaShokr.ipynb
new file mode 100644
index 0000000..2f9de70
--- /dev/null
+++ b/a0.1/a0.2/AI-DS_Nexus__A0_2_Project__RezaShokr.ipynb
@@ -0,0 +1,508 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials\n",
+        "Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:\n",
+        "\n",
+        "1. NumPy — fast n-dimensional arrays\n",
+        "\n",
+        "2. pandas — tabular data wrangling\n",
+        "\n",
+        "3. Matplotlib — quick, customizable plots\n",
+        "\n",
+        "Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1 · NumPy 🧮\n",
+        "Quick-start notes\n",
+        "Core object: ndarray (n-dimensional array)\n",
+        "\n",
+        "Create data: np.array, np.arange, np.random.*\n",
+        "\n",
+        "Summaries: mean, std, sum, max, …\n",
+        "\n",
+        "Vectorised math beats Python loops for speed\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Mock temperatures\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: import numpy and create an array of 365\n",
+        "# normally-distributed °C values (µ=20, σ=5) called temps\n",
+        "\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt \n",
+        "temps = np.random.normal(loc=20 , scale=5 , size=365)\n",
+        "\n",
+        "# رسم هیستوگرام\n",
+        "plt.hist(temps, bins=30 , edgecolor='black', density=True)\n",
+        "\n",
+        "# رسم منحنی چگالی احتمال روی هیستوگرام\n",
+        "from scipy.stats import norm\n",
+        "xmin, xmax = plt.xlim()\n",
+        "x = np.linspace(xmin, xmax, 100)\n",
+        "\n",
+        "p = norm.pdf(x, np.mean(temps), np.std(temps)) #pdf\n",
+        "plt.plot(x, p, 'r', linewidth=2)\n",
+        "\n",
+        "# تنظیمات نمودار\n",
+        "plt.title(\"Normal distribution yearly\")\n",
+        "plt.xlabel(\"temp of daily\")\n",
+        "plt.ylabel(\"Probability density\")\n",
+        "\n",
+        "plt.grid(True)\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Average temperature\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "float64\n",
+            "19.81\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: print the mean of temps\n",
+        "temps_mean = round((np.mean(temps)) , 2 )\n",
+        "\n",
+        "print(temps_mean.dtype)\n",
+        "print(temps_mean)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2 · pandas 📊\n",
+        "Quick-start notes\n",
+        "Main structures: DataFrame, Series\n",
+        "\n",
+        "Read data: pd.read_csv, pd.read_excel, …\n",
+        "\n",
+        "Selection: .loc[label], .iloc[pos]\n",
+        "\n",
+        "Group & summarise: .groupby(...).agg(...)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 3 — Load ride log\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>date</th>\n",
+              "      <th>temp</th>\n",
+              "      <th>rides</th>\n",
+              "      <th>weekday</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2024-01-01</td>\n",
+              "      <td>13.3</td>\n",
+              "      <td>141</td>\n",
+              "      <td>Monday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2024-01-02</td>\n",
+              "      <td>18.4</td>\n",
+              "      <td>174</td>\n",
+              "      <td>Tuesday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2024-01-03</td>\n",
+              "      <td>25.3</td>\n",
+              "      <td>256</td>\n",
+              "      <td>Wednesday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2024-01-04</td>\n",
+              "      <td>19.5</td>\n",
+              "      <td>188</td>\n",
+              "      <td>Thursday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2024-01-05</td>\n",
+              "      <td>25.6</td>\n",
+              "      <td>234</td>\n",
+              "      <td>Friday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>2024-01-06</td>\n",
+              "      <td>24.4</td>\n",
+              "      <td>253</td>\n",
+              "      <td>Saturday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2024-01-07</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>297</td>\n",
+              "      <td>Sunday</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         date  temp  rides    weekday\n",
+              "0  2024-01-01  13.3    141     Monday\n",
+              "1  2024-01-02  18.4    174    Tuesday\n",
+              "2  2024-01-03  25.3    256  Wednesday\n",
+              "3  2024-01-04  19.5    188   Thursday\n",
+              "4  2024-01-05  25.6    234     Friday\n",
+              "5  2024-01-06  24.4    253   Saturday\n",
+              "6  2024-01-07  28.6    297     Sunday"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: read \"rides.csv\" into df\n",
+        "# (columns: date,temp,rides,weekday)\n",
+        "import pandas as pd \n",
+        "df = pd.read_csv(\"rides.csv\")\n",
+        "df.head(7)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 4 — Weekday averages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>weekday</th>\n",
+              "      <th>rides</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Friday</td>\n",
+              "      <td>195.942308</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Monday</td>\n",
+              "      <td>196.830189</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>188.884615</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Sunday</td>\n",
+              "      <td>203.134615</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Thursday</td>\n",
+              "      <td>206.403846</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Tuesday</td>\n",
+              "      <td>201.461538</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Wednesday</td>\n",
+              "      <td>210.576923</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     weekday       rides\n",
+              "0     Friday  195.942308\n",
+              "1     Monday  196.830189\n",
+              "2   Saturday  188.884615\n",
+              "3     Sunday  203.134615\n",
+              "4   Thursday  206.403846\n",
+              "5    Tuesday  201.461538\n",
+              "6  Wednesday  210.576923"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: compute and print mean rides per weekday\n",
+        "df_mean_weekday = df.groupby('weekday')[['rides']].mean().reset_index()\n",
+        "df_mean_weekday"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3 · Matplotlib 📈\n",
+        "Quick-start notes\n",
+        "Workhorse: pyplot interface (import matplotlib.pyplot as plt)\n",
+        "\n",
+        "Figure & axes: fig, ax = plt.subplots()\n",
+        "\n",
+        "Common plots: plot, scatter, hist, imshow\n",
+        "\n",
+        "Display inline in Jupyter with %matplotlib inline or %matplotlib notebook"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 5 — Scatter plot"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df\n",
+        "import matplotlib.pyplot as plt \n",
+        "\n",
+        "x = df['temp']\n",
+        "y = df['rides']\n",
+        "\n",
+        "plt.scatter(x, y)  \n",
+        "\n",
+        "plt.xlabel('Temperature (°C)')\n",
+        "plt.ylabel('Number of Rides')\n",
+        "plt.title('Temperature vs. Rides')\n",
+        "plt.grid(True)\n",
+        "\n",
+        "plt.show()\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 6 — Show the figure\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: call plt.show() so the plot appears\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "    "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "    "
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.2/AI-DS_Nexus__A0_2_Project_roohi_268383.ipynb b/a0.1/a0.2/AI-DS_Nexus__A0_2_Project_roohi_268383.ipynb
new file mode 100644
index 0000000..2f9de70
--- /dev/null
+++ b/a0.1/a0.2/AI-DS_Nexus__A0_2_Project_roohi_268383.ipynb
@@ -0,0 +1,508 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials\n",
+        "Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:\n",
+        "\n",
+        "1. NumPy — fast n-dimensional arrays\n",
+        "\n",
+        "2. pandas — tabular data wrangling\n",
+        "\n",
+        "3. Matplotlib — quick, customizable plots\n",
+        "\n",
+        "Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1 · NumPy 🧮\n",
+        "Quick-start notes\n",
+        "Core object: ndarray (n-dimensional array)\n",
+        "\n",
+        "Create data: np.array, np.arange, np.random.*\n",
+        "\n",
+        "Summaries: mean, std, sum, max, …\n",
+        "\n",
+        "Vectorised math beats Python loops for speed\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Mock temperatures\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: import numpy and create an array of 365\n",
+        "# normally-distributed °C values (µ=20, σ=5) called temps\n",
+        "\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt \n",
+        "temps = np.random.normal(loc=20 , scale=5 , size=365)\n",
+        "\n",
+        "# رسم هیستوگرام\n",
+        "plt.hist(temps, bins=30 , edgecolor='black', density=True)\n",
+        "\n",
+        "# رسم منحنی چگالی احتمال روی هیستوگرام\n",
+        "from scipy.stats import norm\n",
+        "xmin, xmax = plt.xlim()\n",
+        "x = np.linspace(xmin, xmax, 100)\n",
+        "\n",
+        "p = norm.pdf(x, np.mean(temps), np.std(temps)) #pdf\n",
+        "plt.plot(x, p, 'r', linewidth=2)\n",
+        "\n",
+        "# تنظیمات نمودار\n",
+        "plt.title(\"Normal distribution yearly\")\n",
+        "plt.xlabel(\"temp of daily\")\n",
+        "plt.ylabel(\"Probability density\")\n",
+        "\n",
+        "plt.grid(True)\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Average temperature\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "float64\n",
+            "19.81\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: print the mean of temps\n",
+        "temps_mean = round((np.mean(temps)) , 2 )\n",
+        "\n",
+        "print(temps_mean.dtype)\n",
+        "print(temps_mean)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2 · pandas 📊\n",
+        "Quick-start notes\n",
+        "Main structures: DataFrame, Series\n",
+        "\n",
+        "Read data: pd.read_csv, pd.read_excel, …\n",
+        "\n",
+        "Selection: .loc[label], .iloc[pos]\n",
+        "\n",
+        "Group & summarise: .groupby(...).agg(...)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 3 — Load ride log\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>date</th>\n",
+              "      <th>temp</th>\n",
+              "      <th>rides</th>\n",
+              "      <th>weekday</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2024-01-01</td>\n",
+              "      <td>13.3</td>\n",
+              "      <td>141</td>\n",
+              "      <td>Monday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2024-01-02</td>\n",
+              "      <td>18.4</td>\n",
+              "      <td>174</td>\n",
+              "      <td>Tuesday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2024-01-03</td>\n",
+              "      <td>25.3</td>\n",
+              "      <td>256</td>\n",
+              "      <td>Wednesday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2024-01-04</td>\n",
+              "      <td>19.5</td>\n",
+              "      <td>188</td>\n",
+              "      <td>Thursday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2024-01-05</td>\n",
+              "      <td>25.6</td>\n",
+              "      <td>234</td>\n",
+              "      <td>Friday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>2024-01-06</td>\n",
+              "      <td>24.4</td>\n",
+              "      <td>253</td>\n",
+              "      <td>Saturday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2024-01-07</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>297</td>\n",
+              "      <td>Sunday</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         date  temp  rides    weekday\n",
+              "0  2024-01-01  13.3    141     Monday\n",
+              "1  2024-01-02  18.4    174    Tuesday\n",
+              "2  2024-01-03  25.3    256  Wednesday\n",
+              "3  2024-01-04  19.5    188   Thursday\n",
+              "4  2024-01-05  25.6    234     Friday\n",
+              "5  2024-01-06  24.4    253   Saturday\n",
+              "6  2024-01-07  28.6    297     Sunday"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: read \"rides.csv\" into df\n",
+        "# (columns: date,temp,rides,weekday)\n",
+        "import pandas as pd \n",
+        "df = pd.read_csv(\"rides.csv\")\n",
+        "df.head(7)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 4 — Weekday averages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>weekday</th>\n",
+              "      <th>rides</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Friday</td>\n",
+              "      <td>195.942308</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Monday</td>\n",
+              "      <td>196.830189</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>188.884615</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Sunday</td>\n",
+              "      <td>203.134615</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Thursday</td>\n",
+              "      <td>206.403846</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Tuesday</td>\n",
+              "      <td>201.461538</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Wednesday</td>\n",
+              "      <td>210.576923</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     weekday       rides\n",
+              "0     Friday  195.942308\n",
+              "1     Monday  196.830189\n",
+              "2   Saturday  188.884615\n",
+              "3     Sunday  203.134615\n",
+              "4   Thursday  206.403846\n",
+              "5    Tuesday  201.461538\n",
+              "6  Wednesday  210.576923"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: compute and print mean rides per weekday\n",
+        "df_mean_weekday = df.groupby('weekday')[['rides']].mean().reset_index()\n",
+        "df_mean_weekday"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3 · Matplotlib 📈\n",
+        "Quick-start notes\n",
+        "Workhorse: pyplot interface (import matplotlib.pyplot as plt)\n",
+        "\n",
+        "Figure & axes: fig, ax = plt.subplots()\n",
+        "\n",
+        "Common plots: plot, scatter, hist, imshow\n",
+        "\n",
+        "Display inline in Jupyter with %matplotlib inline or %matplotlib notebook"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 5 — Scatter plot"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df\n",
+        "import matplotlib.pyplot as plt \n",
+        "\n",
+        "x = df['temp']\n",
+        "y = df['rides']\n",
+        "\n",
+        "plt.scatter(x, y)  \n",
+        "\n",
+        "plt.xlabel('Temperature (°C)')\n",
+        "plt.ylabel('Number of Rides')\n",
+        "plt.title('Temperature vs. Rides')\n",
+        "plt.grid(True)\n",
+        "\n",
+        "plt.show()\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 6 — Show the figure\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: call plt.show() so the plot appears\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "    "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "    "
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.2/AI-DS_Nexus__A0_2__aminran.ipynb b/a0.1/a0.2/AI-DS_Nexus__A0_2__aminran.ipynb
new file mode 100644
index 0000000..a64556f
--- /dev/null
+++ b/a0.1/a0.2/AI-DS_Nexus__A0_2__aminran.ipynb
@@ -0,0 +1,275 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials\n",
+        "Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:\n",
+        "\n",
+        "1. NumPy — fast n-dimensional arrays\n",
+        "\n",
+        "2. pandas — tabular data wrangling\n",
+        "\n",
+        "3. Matplotlib — quick, customizable plots\n",
+        "\n",
+        "Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊\n"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1 · NumPy 🧮\n",
+        "Quick-start notes\n",
+        "Core object: ndarray (n-dimensional array)\n",
+        "\n",
+        "Create data: np.array, np.arange, np.random.*\n",
+        "\n",
+        "Summaries: mean, std, sum, max, …\n",
+        "\n",
+        "Vectorised math beats Python loops for speed\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Mock temperatures\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [],
+      "source": [
+        "# 👉 # TODO: import numpy and create an array of 365\n",
+        "# normally-distributed °C values (µ=20, σ=5) called temps\n",
+        "import numpy as np\n",
+        "temps = np.random.normal(loc=20, scale=5, size=365)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Average temperature\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: print the mean of temps\n",
+        "print(temps.mean())"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7861261d-b699-4572-b6e0-fadf921c2c1b"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "19.97889282522887\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2 · pandas 📊\n",
+        "Quick-start notes\n",
+        "Main structures: DataFrame, Series\n",
+        "\n",
+        "Read data: pd.read_csv, pd.read_excel, …\n",
+        "\n",
+        "Selection: .loc[label], .iloc[pos]\n",
+        "\n",
+        "Group & summarise: .groupby(...).agg(...)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 3 — Load ride log\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: read \"rides.csv\" into df\n",
+        "# (columns: date,temp,rides,weekday)\n",
+        "import pandas as pd\n",
+        "df=pd.read_csv(\"rides.csv\")\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "execution_count": 23,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 4 — Weekday averages"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: compute and print mean rides per weekday\n",
+        "mean_perWeek = df.groupby('weekday')['rides'].mean()\n",
+        "print(mean_perWeek)"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e3a24165-16d2-48e3-de69-5b2bc964e121"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "weekday\n",
+            "Friday       1290.333333\n",
+            "Monday       1143.666667\n",
+            "Saturday     1047.000000\n",
+            "Sunday       1097.000000\n",
+            "Thursday     1370.333333\n",
+            "Tuesday      1137.666667\n",
+            "Wednesday    1229.000000\n",
+            "Name: rides, dtype: float64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3 · Matplotlib 📈\n",
+        "Quick-start notes\n",
+        "Workhorse: pyplot interface (import matplotlib.pyplot as plt)\n",
+        "\n",
+        "Figure & axes: fig, ax = plt.subplots()\n",
+        "\n",
+        "Common plots: plot, scatter, hist, imshow\n",
+        "\n",
+        "Display inline in Jupyter with %matplotlib inline or %matplotlib notebook"
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 5 — Scatter plot"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df\n",
+        "import matplotlib.pyplot as plt\n",
+        "plt.scatter(df['temp'], df['rides'])\n",
+        "plt.title('Temperature vs Rides')\n",
+        "plt.xlabel('Temperature (°C)')\n",
+        "plt.ylabel('Number of Rides')\n",
+        "plt.grid(True)\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "6874ab05-fb42-420f-f2df-a1803b218af3"
+      },
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 6 — Show the figure\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: call plt.show() so the plot appears\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "execution_count": 30,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.2/AI-DS_Nexus__A0_2__rsayyareh.ipynb b/a0.1/a0.2/AI-DS_Nexus__A0_2__rsayyareh.ipynb
new file mode 100644
index 0000000..3beea20
--- /dev/null
+++ b/a0.1/a0.2/AI-DS_Nexus__A0_2__rsayyareh.ipynb
@@ -0,0 +1,326 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:\n",
+        "\n",
+        "1. NumPy — fast n-dimensional arrays\n",
+        "\n",
+        "2. pandas — tabular data wrangling\n",
+        "\n",
+        "3. Matplotlib — quick, customizable plots\n",
+        "\n",
+        "Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1 · NumPy 🧮\n",
+        "Quick-start notes\n",
+        "Core object: ndarray (n-dimensional array)\n",
+        "\n",
+        "Create data: np.array, np.arange, np.random.*\n",
+        "\n",
+        "Summaries: mean, std, sum, max, …\n",
+        "\n",
+        "Vectorised math beats Python loops for speed\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Mock temperatures\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [],
+      "source": [
+        "# 👉 # TODO: import numpy and create an array of 365\n",
+        "# normally-distributed °C values (µ=20, σ=5) called temps\n",
+        "import numpy as np\n",
+        "temps = np.random.normal(loc=20, scale=5, size=365)\n",
+        "print(temps)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Average temperature\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "19.524055016516442\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: print the mean of temps\n",
+        "m_temps = temps.mean()\n",
+        "print(m_temps)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2 · pandas 📊\n",
+        "Quick-start notes\n",
+        "Main structures: DataFrame, Series\n",
+        "\n",
+        "Read data: pd.read_csv, pd.read_excel, …\n",
+        "\n",
+        "Selection: .loc[label], .iloc[pos]\n",
+        "\n",
+        "Group & summarise: .groupby(...).agg(...)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 3 — Load ride log\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 366 entries, 0 to 365\n",
+            "Data columns (total 4 columns):\n",
+            " #   Column   Non-Null Count  Dtype \n",
+            "---  ------   --------------  ----- \n",
+            " 0   date     366 non-null    object\n",
+            " 1   temp     366 non-null    int64 \n",
+            " 2   rides    366 non-null    int64 \n",
+            " 3   weekday  366 non-null    object\n",
+            "dtypes: int64(2), object(2)\n",
+            "memory usage: 11.6+ KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: read \"rides.csv\" into df\n",
+        "# (columns: date,temp,rides,weekday)\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "df = pd.read_csv('rides.csv')\n",
+        "# df.shape\n",
+        "# df.head()\n",
+        "df.info()\n",
+        "# df.describe(include = 'all')\n",
+        "# df.columns\n",
+        "# df.index\n",
+        "# type(df)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 4 — Weekday averages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "weekday\n",
+              "Friday       939.094340\n",
+              "Monday       959.173077\n",
+              "Saturday    1003.509434\n",
+              "Sunday       946.730769\n",
+              "Thursday     933.634615\n",
+              "Tuesday      917.538462\n",
+              "Wednsday    1025.096154\n",
+              "Name: rides, dtype: float64"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: compute and print mean rides per weekday\n",
+        "\n",
+        "# df['rides'].sum()\n",
+        "# df['rides'].count()\n",
+        "# df['rides'].sum()/df['rides'].count()\n",
+        "# df['rides'].mean()\n",
+        "df.groupby('weekday')['rides'].mean()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3 · Matplotlib 📈\n",
+        "Quick-start notes\n",
+        "Workhorse: pyplot interface (import matplotlib.pyplot as plt)\n",
+        "\n",
+        "Figure & axes: fig, ax = plt.subplots()\n",
+        "\n",
+        "Common plots: plot, scatter, hist, imshow\n",
+        "\n",
+        "Display inline in Jupyter with %matplotlib inline or %matplotlib notebook"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 5 — Scatter plot"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x29e29500550>"
+            ]
+          },
+          "execution_count": 29,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = df['temp']\n",
+        "y = df['rides']\n",
+        "plt.scatter(y, x)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 6 — Show the figure\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 30,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [],
+      "source": [
+        "# 👉 # TODO: call plt.show() so the plot appears\n",
+        "plt.show()\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.2/AI-DS_Nexus__A0_2_amirhosseinaref_a1a481.ipynb b/a0.1/a0.2/AI-DS_Nexus__A0_2_amirhosseinaref_a1a481.ipynb
new file mode 100644
index 0000000..f2d64c2
--- /dev/null
+++ b/a0.1/a0.2/AI-DS_Nexus__A0_2_amirhosseinaref_a1a481.ipynb
@@ -0,0 +1,636 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:\n",
+        "\n",
+        "1. NumPy — fast n-dimensional arrays\n",
+        "\n",
+        "2. pandas — tabular data wrangling\n",
+        "\n",
+        "3. Matplotlib — quick, customizable plots\n",
+        "\n",
+        "Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊\n"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1 · NumPy 🧮\n",
+        "Quick-start notes\n",
+        "Core object: ndarray (n-dimensional array)\n",
+        "\n",
+        "Create data: np.array, np.arange, np.random.*\n",
+        "\n",
+        "Summaries: mean, std, sum, max, …\n",
+        "\n",
+        "Vectorised math beats Python loops for speed\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Mock temperatures\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [],
+      "source": [
+        "# 👉 # TODO: import numpy and create an array of 365\n",
+        "# normally-distributed °C values (µ=20, σ=5) called temps\n",
+        "import numpy as np\n",
+        "temps = np.random.normal(loc=20, scale=5, size=365)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Average temperature\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: print the mean of temps\n",
+        "print(f\"temps mean:, {temps.mean()}\")\n"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2241ea6b-1599-402a-f29c-6a7487dcfa64"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "temps mean:, 20.022761005295422\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2 · pandas 📊\n",
+        "Quick-start notes\n",
+        "Main structures: DataFrame, Series\n",
+        "\n",
+        "Read data: pd.read_csv, pd.read_excel, …\n",
+        "\n",
+        "Selection: .loc[label], .iloc[pos]\n",
+        "\n",
+        "Group & summarise: .groupby(...).agg(...)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 3 — Load ride log\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: read \"rides.csv\" into df\n",
+        "# (columns: date,temp,rides,weekday)\n",
+        "import pandas as pd\n",
+        "df = pd.read_csv(\"rides.csv\")\n",
+        "print(df.head())\n",
+        "print(\"---------------------------------------------\")\n",
+        "print(df.info())\n",
+        "print(\"---------------------------------------------\")\n",
+        "df.describe()\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 665
+        },
+        "outputId": "b9974b3a-d1d3-4914-8b73-acb70a0a46f6"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "         date  temp  rides    weekday\n",
+            "0  2024-01-01  13.3    141     Monday\n",
+            "1  2024-01-02  18.4    174    Tuesday\n",
+            "2  2024-01-03  25.3    256  Wednesday\n",
+            "3  2024-01-04  19.5    188   Thursday\n",
+            "4  2024-01-05  25.6    234     Friday\n",
+            "---------------------------------------------\n",
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 365 entries, 0 to 364\n",
+            "Data columns (total 4 columns):\n",
+            " #   Column   Non-Null Count  Dtype  \n",
+            "---  ------   --------------  -----  \n",
+            " 0   date     365 non-null    object \n",
+            " 1   temp     365 non-null    float64\n",
+            " 2   rides    365 non-null    int64  \n",
+            " 3   weekday  365 non-null    object \n",
+            "dtypes: float64(1), int64(1), object(2)\n",
+            "memory usage: 11.5+ KB\n",
+            "None\n",
+            "---------------------------------------------\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "             temp       rides\n",
+              "count  365.000000  365.000000\n",
+              "mean    20.140548  200.452055\n",
+              "std      5.229513   55.024430\n",
+              "min      6.500000   74.000000\n",
+              "25%     16.600000  161.000000\n",
+              "50%     19.900000  196.000000\n",
+              "75%     24.000000  239.000000\n",
+              "max     33.000000  352.000000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-06c04aa2-a6e4-48a2-a80e-282ac32e10d4\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>temp</th>\n",
+              "      <th>rides</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>365.000000</td>\n",
+              "      <td>365.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>20.140548</td>\n",
+              "      <td>200.452055</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>5.229513</td>\n",
+              "      <td>55.024430</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>6.500000</td>\n",
+              "      <td>74.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>16.600000</td>\n",
+              "      <td>161.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>19.900000</td>\n",
+              "      <td>196.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>24.000000</td>\n",
+              "      <td>239.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>33.000000</td>\n",
+              "      <td>352.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-06c04aa2-a6e4-48a2-a80e-282ac32e10d4')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-06c04aa2-a6e4-48a2-a80e-282ac32e10d4 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-06c04aa2-a6e4-48a2-a80e-282ac32e10d4');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-cf817dbe-ba2e-4a00-a9ab-d130d175b137\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cf817dbe-ba2e-4a00-a9ab-d130d175b137')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-cf817dbe-ba2e-4a00-a9ab-d130d175b137 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 123.04410355649199,\n        \"min\": 5.229512586917965,\n        \"max\": 365.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          20.140547945205476,\n          19.9,\n          365.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 113.42435405929838,\n        \"min\": 55.02443041227042,\n        \"max\": 365.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          200.45205479452054,\n          196.0,\n          365.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 4 — Weekday averages"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: compute and print mean rides per weekday\n",
+        "rides_per_weekday = df.groupby(\"weekday\")[\"rides\"].mean()\n",
+        "print(rides_per_weekday)"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9eca0c8b-baf6-445f-8e9a-8b27cb4945a1"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "weekday\n",
+            "Friday       195.942308\n",
+            "Monday       196.830189\n",
+            "Saturday     188.884615\n",
+            "Sunday       203.134615\n",
+            "Thursday     206.403846\n",
+            "Tuesday      201.461538\n",
+            "Wednesday    210.576923\n",
+            "Name: rides, dtype: float64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3 · Matplotlib 📈\n",
+        "Quick-start notes\n",
+        "Workhorse: pyplot interface (import matplotlib.pyplot as plt)\n",
+        "\n",
+        "Figure & axes: fig, ax = plt.subplots()\n",
+        "\n",
+        "Common plots: plot, scatter, hist, imshow\n",
+        "\n",
+        "Display inline in Jupyter with %matplotlib inline or %matplotlib notebook"
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 5 — Scatter plot"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df\n",
+        "import matplotlib.pyplot as plt\n",
+        "plt.scatter(df[\"temp\"], df[\"rides\"], alpha= 0.5, color=\"green\")\n",
+        "plt.xlabel(\"Tempreture (°C)\")\n",
+        "plt.ylabel(\"Rides\")\n",
+        "plt.title(\"Tempreture vs Rides\")\n",
+        "plt.grid(True)\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "315ead2a-9ffb-423d-85e4-b67ebcec3731"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 6 — Show the figure\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: call plt.show() so the plot appears\n",
+        "plt.show()\n",
+        "print(\"Correlation coefficient:\", df['temp'].corr(df['rides']))\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c966159f-256a-4d32-f6c7-b61e24a2bf81"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Correlation coefficient: 0.9332447311711161\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.2/AI-DS_Nexus__A0_2_itsalikabiri_ff9265.ipynb b/a0.1/a0.2/AI-DS_Nexus__A0_2_itsalikabiri_ff9265.ipynb
new file mode 100644
index 0000000..a3b2caf
--- /dev/null
+++ b/a0.1/a0.2/AI-DS_Nexus__A0_2_itsalikabiri_ff9265.ipynb
@@ -0,0 +1,321 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:\n",
+        "\n",
+        "1. NumPy — fast n-dimensional arrays\n",
+        "\n",
+        "2. pandas — tabular data wrangling\n",
+        "\n",
+        "3. Matplotlib — quick, customizable plots\n",
+        "\n",
+        "Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1 · NumPy 🧮\n",
+        "Quick-start notes\n",
+        "Core object: ndarray (n-dimensional array)\n",
+        "\n",
+        "Create data: np.array, np.arange, np.random.*\n",
+        "\n",
+        "Summaries: mean, std, sum, max, …\n",
+        "\n",
+        "Vectorised math beats Python loops for speed\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Mock temperatures\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [],
+      "source": [
+        "# 👉 # TODO: import numpy and create an array of 365\n",
+        "# normally-distributed °C values (µ=20, σ=5) called temps\n",
+        "import numpy as np\n",
+        "temps = np.random.normal(20, 5, 365)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Average temperature\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "19.651430832266538\n",
+            "19.651430832266538\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: print the mean of temps\n",
+        "print(temps.mean())\n",
+        "print(np.mean(temps))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2 · pandas 📊\n",
+        "Quick-start notes\n",
+        "Main structures: DataFrame, Series\n",
+        "\n",
+        "Read data: pd.read_csv, pd.read_excel, …\n",
+        "\n",
+        "Selection: .loc[label], .iloc[pos]\n",
+        "\n",
+        "Group & summarise: .groupby(...).agg(...)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 3 — Load ride log\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "          date  temp  rides    weekday\n",
+            "0   2024-01-01  13.3    141     Monday\n",
+            "1   2024-01-02  18.4    174    Tuesday\n",
+            "2   2024-01-03  25.3    256  Wednesday\n",
+            "3   2024-01-04  19.5    188   Thursday\n",
+            "4   2024-01-05  25.6    234     Friday\n",
+            "5   2024-01-06  24.4    253   Saturday\n",
+            "6   2024-01-07  28.6    297     Sunday\n",
+            "7   2024-01-08  21.8    183     Monday\n",
+            "8   2024-01-09  21.0    213    Tuesday\n",
+            "9   2024-01-10  20.0    162  Wednesday\n",
+            "10  2024-01-11  19.0    221   Thursday\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: read \"rides.csv\" into df\n",
+        "# (columns: date,temp,rides,weekday)\n",
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.read_csv(\"rides.csv\")\n",
+        "print(df.head(11))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 4 — Weekday averages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "weekday\n",
+              "Friday       195.942308\n",
+              "Monday       196.830189\n",
+              "Saturday     188.884615\n",
+              "Sunday       203.134615\n",
+              "Thursday     206.403846\n",
+              "Tuesday      201.461538\n",
+              "Wednesday    210.576923\n",
+              "Name: rides, dtype: float64"
+            ]
+          },
+          "execution_count": 9,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: compute and print mean rides per weekday\n",
+        "df.groupby(\"weekday\")[\"rides\"].mean()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3 · Matplotlib 📈\n",
+        "Quick-start notes\n",
+        "Workhorse: pyplot interface (import matplotlib.pyplot as plt)\n",
+        "\n",
+        "Figure & axes: fig, ax = plt.subplots()\n",
+        "\n",
+        "Common plots: plot, scatter, hist, imshow\n",
+        "\n",
+        "Display inline in Jupyter with %matplotlib inline or %matplotlib notebook"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 5 — Scatter plot"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "x = df[\"temp\"]\n",
+        "y = df[\"rides\"]\n",
+        "h = plt.scatter(x, y)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 6 — Show the figure\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: call plt.show() so the plot appears\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "x = df[\"temp\"]\n",
+        "y = df[\"rides\"]\n",
+        "h = plt.scatter(x, y)\n",
+        "plt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.10"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.2/AI_DS_Nexus__A0_2__MrAshki.ipynb b/a0.1/a0.2/AI_DS_Nexus__A0_2__MrAshki.ipynb
new file mode 100644
index 0000000..9989055
--- /dev/null
+++ b/a0.1/a0.2/AI_DS_Nexus__A0_2__MrAshki.ipynb
@@ -0,0 +1,395 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:\n",
+        "\n",
+        "1. NumPy — fast n-dimensional arrays\n",
+        "\n",
+        "2. pandas — tabular data wrangling\n",
+        "\n",
+        "3. Matplotlib — quick, customizable plots\n",
+        "\n",
+        "Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊\n"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1 · NumPy 🧮\n",
+        "Quick-start notes\n",
+        "Core object: ndarray (n-dimensional array)\n",
+        "\n",
+        "Create data: np.array, np.arange, np.random.*\n",
+        "\n",
+        "Summaries: mean, std, sum, max, …\n",
+        "\n",
+        "Vectorised math beats Python loops for speed\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Mock temperatures\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5f7971bc-bf12-44e7-ce42-b85ca432be69"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[17.12 18.81 16.95 22.06 18.97 21.39 15.33 16.36 21.79 17.35]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: import numpy and create an array of 365\n",
+        "# normally-distributed °C values (µ=20, σ=5) called temps\n",
+        "\n",
+        "import numpy as np\n",
+        "\n",
+        "# پارامترها: میانگین (loc)، انحراف معیار (scale)، تعداد (size)\n",
+        "temps = np.random.normal(loc=20, scale=5, size=365)\n",
+        "temps = np.round(temps, 2)\n",
+        "print(temps [:10])\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "\n",
+        "temps = np.random.normal(loc=20, scale=5, size=365)\n",
+        "\n",
+        "df_temps = pd.DataFrame(np.round(temps, 2), columns=['Daily Temperature'])\n",
+        "\n",
+        "print(df_temps.head(10))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_cHxB0VXvylf",
+        "outputId": "31af601a-816e-48b9-b354-9404042f6e2f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "   Daily Temperature\n",
+            "0              28.97\n",
+            "1              17.93\n",
+            "2              24.73\n",
+            "3              19.93\n",
+            "4              20.24\n",
+            "5              20.45\n",
+            "6              19.09\n",
+            "7              18.54\n",
+            "8              26.81\n",
+            "9              12.02\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Average temperature\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: print the mean of temps\n",
+        "avg_temp = np.mean(temps)\n",
+        "print(avg_temp)\n"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2f6e0f17-558d-474b-920a-74e8d31c3514"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "20.154454942016926\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "avg_temp = temps.mean()\n",
+        "print(f\"میانگین دما: {avg_temp:.2f}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vWgNLGKfxMZB",
+        "outputId": "63ced4c2-b1a1-4734-ff60-69b20602ead6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "میانگین دما: 20.15\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2 · pandas 📊\n",
+        "Quick-start notes\n",
+        "Main structures: DataFrame, Series\n",
+        "\n",
+        "Read data: pd.read_csv, pd.read_excel, …\n",
+        "\n",
+        "Selection: .loc[label], .iloc[pos]\n",
+        "\n",
+        "Group & summarise: .groupby(...).agg(...)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 3 — Load ride log\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: read \"rides.csv\" into df\n",
+        "# (columns: date,temp,rides,weekday)\n",
+        "import pandas as pd\n",
+        "\n",
+        "\n",
+        "df = pd.read_csv(\"rides.csv\")\n",
+        "\n",
+        "print(df.head())\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "83f5323a-351a-41d9-ab32-307ad0b9f58e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "       date  temp  rides   weekday\n",
+            "0  27-09-24    32   1111    Friday\n",
+            "1  28-09-24    13    886  Saturday\n",
+            "2  29-09-24    13    740    Sunday\n",
+            "3  30-09-24    42    997    Monday\n",
+            "4  01-10-24    25    712   Tuesday\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 4 — Weekday averages"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: compute and print mean rides per weekday\n",
+        "# Task 4\n",
+        "weekday_avg = df.groupby('weekday')[['rides']].mean().reset_index()\n",
+        "print(weekday_avg)"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "178c8bde-f85f-44e4-83ac-c93be978c995"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "    weekday        rides\n",
+            "0    Friday   939.094340\n",
+            "1    Monday   959.173077\n",
+            "2  Saturday  1003.509434\n",
+            "3    Sunday   946.730769\n",
+            "4  Thursday   933.634615\n",
+            "5   Tuesday   917.538462\n",
+            "6  Wednsday  1025.096154\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3 · Matplotlib 📈\n",
+        "Quick-start notes\n",
+        "Workhorse: pyplot interface (import matplotlib.pyplot as plt)\n",
+        "\n",
+        "Figure & axes: fig, ax = plt.subplots()\n",
+        "\n",
+        "Common plots: plot, scatter, hist, imshow\n",
+        "\n",
+        "Display inline in Jupyter with %matplotlib inline or %matplotlib notebook"
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 5 — Scatter plot"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "\n",
+        "# Task 5: ایجاد نمودار پراکندگی\n",
+        "plt.scatter(df['temp'], df['rides'], alpha=0.5, c='blue')\n",
+        "\n",
+        "# اضافه کردن جزئیات برای درک بهتر (اختیاری اما حرفه‌ای)\n",
+        "plt.title('Relationship between Temperature and Rides')\n",
+        "plt.xlabel('Temperature (°C)')\n",
+        "plt.ylabel('Number of Rides')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "outputId": "46976049-8f75-4713-f2f6-008eff586e5c"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 6 — Show the figure\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: call plt.show() so the plot appears\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/a0.2/AI\342\200\223DS_Nexus___A0_2_Python_Packages__RoohollaAlikhani.ipynb" "b/a0.1/a0.2/AI\342\200\223DS_Nexus___A0_2_Python_Packages__RoohollaAlikhani.ipynb"
new file mode 100644
index 0000000..2f9de70
--- /dev/null
+++ "b/a0.1/a0.2/AI\342\200\223DS_Nexus___A0_2_Python_Packages__RoohollaAlikhani.ipynb"
@@ -0,0 +1,508 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      },
+      "source": [
+        "# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials\n",
+        "Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:\n",
+        "\n",
+        "1. NumPy — fast n-dimensional arrays\n",
+        "\n",
+        "2. pandas — tabular data wrangling\n",
+        "\n",
+        "3. Matplotlib — quick, customizable plots\n",
+        "\n",
+        "Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      },
+      "source": [
+        "## 1 · NumPy 🧮\n",
+        "Quick-start notes\n",
+        "Core object: ndarray (n-dimensional array)\n",
+        "\n",
+        "Create data: np.array, np.arange, np.random.*\n",
+        "\n",
+        "Summaries: mean, std, sum, max, …\n",
+        "\n",
+        "Vectorised math beats Python loops for speed\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      },
+      "source": [
+        "### Task 1 — Mock temperatures\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "lmaiboJe8E15"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: import numpy and create an array of 365\n",
+        "# normally-distributed °C values (µ=20, σ=5) called temps\n",
+        "\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt \n",
+        "temps = np.random.normal(loc=20 , scale=5 , size=365)\n",
+        "\n",
+        "# رسم هیستوگرام\n",
+        "plt.hist(temps, bins=30 , edgecolor='black', density=True)\n",
+        "\n",
+        "# رسم منحنی چگالی احتمال روی هیستوگرام\n",
+        "from scipy.stats import norm\n",
+        "xmin, xmax = plt.xlim()\n",
+        "x = np.linspace(xmin, xmax, 100)\n",
+        "\n",
+        "p = norm.pdf(x, np.mean(temps), np.std(temps)) #pdf\n",
+        "plt.plot(x, p, 'r', linewidth=2)\n",
+        "\n",
+        "# تنظیمات نمودار\n",
+        "plt.title(\"Normal distribution yearly\")\n",
+        "plt.xlabel(\"temp of daily\")\n",
+        "plt.ylabel(\"Probability density\")\n",
+        "\n",
+        "plt.grid(True)\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      },
+      "source": [
+        "### Task 2 — Average temperature\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DSR8aS-F9Z10"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "float64\n",
+            "19.81\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: print the mean of temps\n",
+        "temps_mean = round((np.mean(temps)) , 2 )\n",
+        "\n",
+        "print(temps_mean.dtype)\n",
+        "print(temps_mean)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      },
+      "source": [
+        "## 2 · pandas 📊\n",
+        "Quick-start notes\n",
+        "Main structures: DataFrame, Series\n",
+        "\n",
+        "Read data: pd.read_csv, pd.read_excel, …\n",
+        "\n",
+        "Selection: .loc[label], .iloc[pos]\n",
+        "\n",
+        "Group & summarise: .groupby(...).agg(...)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      },
+      "source": [
+        "### Task 3 — Load ride log\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "1J4jcLct9yVO"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>date</th>\n",
+              "      <th>temp</th>\n",
+              "      <th>rides</th>\n",
+              "      <th>weekday</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2024-01-01</td>\n",
+              "      <td>13.3</td>\n",
+              "      <td>141</td>\n",
+              "      <td>Monday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2024-01-02</td>\n",
+              "      <td>18.4</td>\n",
+              "      <td>174</td>\n",
+              "      <td>Tuesday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2024-01-03</td>\n",
+              "      <td>25.3</td>\n",
+              "      <td>256</td>\n",
+              "      <td>Wednesday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2024-01-04</td>\n",
+              "      <td>19.5</td>\n",
+              "      <td>188</td>\n",
+              "      <td>Thursday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2024-01-05</td>\n",
+              "      <td>25.6</td>\n",
+              "      <td>234</td>\n",
+              "      <td>Friday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>2024-01-06</td>\n",
+              "      <td>24.4</td>\n",
+              "      <td>253</td>\n",
+              "      <td>Saturday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>2024-01-07</td>\n",
+              "      <td>28.6</td>\n",
+              "      <td>297</td>\n",
+              "      <td>Sunday</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         date  temp  rides    weekday\n",
+              "0  2024-01-01  13.3    141     Monday\n",
+              "1  2024-01-02  18.4    174    Tuesday\n",
+              "2  2024-01-03  25.3    256  Wednesday\n",
+              "3  2024-01-04  19.5    188   Thursday\n",
+              "4  2024-01-05  25.6    234     Friday\n",
+              "5  2024-01-06  24.4    253   Saturday\n",
+              "6  2024-01-07  28.6    297     Sunday"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: read \"rides.csv\" into df\n",
+        "# (columns: date,temp,rides,weekday)\n",
+        "import pandas as pd \n",
+        "df = pd.read_csv(\"rides.csv\")\n",
+        "df.head(7)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      },
+      "source": [
+        "### Task 4 — Weekday averages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>weekday</th>\n",
+              "      <th>rides</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Friday</td>\n",
+              "      <td>195.942308</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Monday</td>\n",
+              "      <td>196.830189</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Saturday</td>\n",
+              "      <td>188.884615</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Sunday</td>\n",
+              "      <td>203.134615</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Thursday</td>\n",
+              "      <td>206.403846</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Tuesday</td>\n",
+              "      <td>201.461538</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Wednesday</td>\n",
+              "      <td>210.576923</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     weekday       rides\n",
+              "0     Friday  195.942308\n",
+              "1     Monday  196.830189\n",
+              "2   Saturday  188.884615\n",
+              "3     Sunday  203.134615\n",
+              "4   Thursday  206.403846\n",
+              "5    Tuesday  201.461538\n",
+              "6  Wednesday  210.576923"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: compute and print mean rides per weekday\n",
+        "df_mean_weekday = df.groupby('weekday')[['rides']].mean().reset_index()\n",
+        "df_mean_weekday"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      },
+      "source": [
+        "## 3 · Matplotlib 📈\n",
+        "Quick-start notes\n",
+        "Workhorse: pyplot interface (import matplotlib.pyplot as plt)\n",
+        "\n",
+        "Figure & axes: fig, ax = plt.subplots()\n",
+        "\n",
+        "Common plots: plot, scatter, hist, imshow\n",
+        "\n",
+        "Display inline in Jupyter with %matplotlib inline or %matplotlib notebook"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      },
+      "source": [
+        "### Task 5 — Scatter plot"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df\n",
+        "import matplotlib.pyplot as plt \n",
+        "\n",
+        "x = df['temp']\n",
+        "y = df['rides']\n",
+        "\n",
+        "plt.scatter(x, y)  \n",
+        "\n",
+        "plt.xlabel('Temperature (°C)')\n",
+        "plt.ylabel('Number of Rides')\n",
+        "plt.title('Temperature vs. Rides')\n",
+        "plt.grid(True)\n",
+        "\n",
+        "plt.show()\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      },
+      "source": [
+        "### Task 6 — Show the figure\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: call plt.show() so the plot appears\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "    "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "    "
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.2/Assignment_02_Python_Packages__Nexus__RezaShokrzad.ipynb b/a0.1/a0.2/Assignment_02_Python_Packages__Nexus__RezaShokrzad.ipynb
new file mode 100644
index 0000000..76eac35
--- /dev/null
+++ b/a0.1/a0.2/Assignment_02_Python_Packages__Nexus__RezaShokrzad.ipynb
@@ -0,0 +1,354 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/gitSajjad/Nexus_Assignments/blob/main/a0.2/Assignment_02_Python_Packages_%7C_Nexus_%7C_RezaShokrzad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:\n",
+        "\n",
+        "1. NumPy — fast n-dimensional arrays\n",
+        "\n",
+        "2. pandas — tabular data wrangling\n",
+        "\n",
+        "3. Matplotlib — quick, customizable plots\n",
+        "\n",
+        "Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊\n"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1 · NumPy 🧮\n",
+        "Quick-start notes\n",
+        "Core object: ndarray (n-dimensional array)\n",
+        "\n",
+        "Create data: np.array, np.arange, np.random.*\n",
+        "\n",
+        "Summaries: mean, std, sum, max, …\n",
+        "\n",
+        "Vectorised math beats Python loops for speed\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Mock temperatures\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "outputId": "62afc8cc-40e7-4872-b1cb-5d61afe24a99",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[11.283876   17.45719787  8.86611635 15.04464975 24.49405934 15.46645705\n",
+            " 14.63950513 10.08440668 23.35891272 24.91308617]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: import numpy and create an array of 365\n",
+        "# normally-distributed °C values (µ=20, σ=5) called temps\n",
+        "import numpy as np\n",
+        "\n",
+        "temps = np.random.normal(loc=20, scale=5, size=365)\n",
+        "\n",
+        "print(temps[:10])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Average temperature\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: print the mean of temps\n",
+        "print(f\"میانگین دما: {np.mean(temps):.2f} °C\")"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "outputId": "f08344ed-9a4a-4113-c01c-0bfeedbed149",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "میانگین دما: 19.63 °C\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2 · pandas 📊\n",
+        "Quick-start notes\n",
+        "Main structures: DataFrame, Series\n",
+        "\n",
+        "Read data: pd.read_csv, pd.read_excel, …\n",
+        "\n",
+        "Selection: .loc[label], .iloc[pos]\n",
+        "\n",
+        "Group & summarise: .groupby(...).agg(...)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 3 — Load ride log\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: read \"rides.csv\" into df\n",
+        "# (columns: date,temp,rides,weekday)\n",
+        "import pandas as pd\n",
+        "df = pd.read_csv(\"/content/sample_data/california_housing_test.csv\")\n",
+        "print(df.head())"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "outputId": "5424b521-b6b0-4c1c-8f17-dcc2fa887b5d",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+            "0    -122.05     37.37                27.0       3885.0           661.0   \n",
+            "1    -118.30     34.26                43.0       1510.0           310.0   \n",
+            "2    -117.81     33.78                27.0       3589.0           507.0   \n",
+            "3    -118.36     33.82                28.0         67.0            15.0   \n",
+            "4    -119.67     36.33                19.0       1241.0           244.0   \n",
+            "\n",
+            "   population  households  median_income  median_house_value  \n",
+            "0      1537.0       606.0         6.6085            344700.0  \n",
+            "1       809.0       277.0         3.5990            176500.0  \n",
+            "2      1484.0       495.0         5.7934            270500.0  \n",
+            "3        49.0        11.0         6.1359            330000.0  \n",
+            "4       850.0       237.0         2.9375             81700.0  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 4 — Weekday averages"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: compute and print mean rides per weekday\n",
+        "\n",
+        "mean_rides_by_day = df.groupby(\"weekday\")[\"rides \"].mean()\n",
+        "print(mean_rides_by_day.round(2))"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "outputId": "c513bae2-0beb-400f-ffcc-5f5d53535a74",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 358
+        }
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "KeyError",
+          "evalue": "'df'",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-1550790487.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 👉 # TODO: compute and print mean rides per weekday\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/sample_data/california_housing_test.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmean_rides_by_day\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"df\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"latitude \"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmean_rides_by_day\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[1;32m   9181\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"You have to supply one of 'by' and 'level'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 9183\u001b[0;31m         return DataFrameGroupBy(\n\u001b[0m\u001b[1;32m   9184\u001b[0m             \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   9185\u001b[0m             \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[1;32m   1327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1328\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mgrouper\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1329\u001b[0;31m             grouper, exclusions, obj = get_grouper(\n\u001b[0m\u001b[1;32m   1330\u001b[0m                 \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1331\u001b[0m                 \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, validate, dropna)\u001b[0m\n\u001b[1;32m   1041\u001b[0m                 \u001b[0min_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1043\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1044\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1045\u001b[0m             \u001b[0;31m# Add key to exclusions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyError\u001b[0m: 'df'"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3 · Matplotlib 📈\n",
+        "Quick-start notes\n",
+        "Workhorse: pyplot interface (import matplotlib.pyplot as plt)\n",
+        "\n",
+        "Figure & axes: fig, ax = plt.subplots()\n",
+        "\n",
+        "Common plots: plot, scatter, hist, imshow\n",
+        "\n",
+        "Display inline in Jupyter with %matplotlib inline or %matplotlib notebook"
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 5 — Scatter plot"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df\n",
+        "import matplotlib.pyplot as plt\n",
+        "plt.scatter(df[\"temp\"], df[\"rides\"], alpha=0.6)\n",
+        "plt.xlabel(\"Temperature (°C)\")\n",
+        "plt.ylabel(\"Number of Rides\")\n",
+        "plt.title(\"Temperature vs. Number of Rides\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 6 — Show the figure\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: call plt.show() so the plot appears\n",
+        "import matplotlib.pyplot as plt\n",
+        "plt.scatter(df[\"temp\"], df[\"rides\"], alpha=0.6)\n",
+        "plt.xlabel(\"Temperature (°C)\")\n",
+        "plt.ylabel(\"Number of Rides\")\n",
+        "plt.title(\"Temperature vs. Number of Rides\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM",
+        "outputId": "a0d6069f-0323-4d73-be7d-b222d7929f8a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 581
+        }
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "KeyError",
+          "evalue": "'temp'",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3805\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3806\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mindex.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+            "\u001b[0;31mKeyError\u001b[0m: 'temp'",
+            "\nThe above exception was the direct cause of the following exception:\n",
+            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-2342981012.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 👉 # TODO: call plt.show() so the plot appears\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"temp\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"rides\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Temperature (°C)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Number of Rides\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4101\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4102\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4103\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4104\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3810\u001b[0m             ):\n\u001b[1;32m   3811\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3812\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3813\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3814\u001b[0m             \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mKeyError\u001b[0m: 'temp'"
+          ]
+        }
+      ]
+    }
+  ]
+}
diff --git "a/a0.1/a0.2/Copy of AI\342\200\223DS Nexus _ 0.2.Python_Packages.ipynb" "b/a0.1/a0.2/Copy of AI\342\200\223DS Nexus _ 0.2.Python_Packages.ipynb"
new file mode 100644
index 0000000..f264dab
--- /dev/null
+++ "b/a0.1/a0.2/Copy of AI\342\200\223DS Nexus _ 0.2.Python_Packages.ipynb"	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1MPwtoqgJ2asfeqJavHYCGVElqQIT8eUX","timestamp":1748889272179}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"phP2vS12A3Vp"},"source":["You can read a lot more about Python classes [in the documentation](https://docs.python.org/3.7/tutorial/classes.html).\n","\n","# 1. Numpy\n","\n","Numpy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.\n","\n","##Arrays\n","\n","A numpy array is a grid of values, all of the same type, and is indexed by a tuple of nonnegative integers. The number of dimensions is the *rank* of the array; the *shape* of an array is a tuple of integers giving the size of the array along each dimension.\n","\n","We can initialize numpy arrays from nested Python lists, and access elements using square brackets:"]},{"cell_type":"code","metadata":{"id":"95iZ78oIBo3U","colab":{"base_uri":"https://localhost:8080/","height":118},"executionInfo":{"elapsed":643,"status":"ok","timestamp":1538202713296,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"475381ad-0b51-4c5c-aea8-95282bb5a609"},"source":["import numpy as np\n","\n","a = np.array([1, 2, 3])   # Create a rank 1 array\n","print(type(a))            # Prints \"<class 'numpy.ndarray'>\"\n","print(a.shape)            # Prints \"(3,)\"\n","print(a[0], a[1], a[2])   # Prints \"1 2 3\"\n","a[0] = 5                  # Change an element of the array\n","print(a)                  # Prints \"[5, 2, 3]\"\n","\n","b = np.array([[1,2,3],[4,5,6]])    # Create a rank 2 array\n","print(b.shape)                     # Prints \"(2, 3)\"\n","print(b[0, 0], b[0, 1], b[1, 0])   # Prints \"1 2 4\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["<type 'numpy.ndarray'>\n","(3,)\n","(1, 2, 3)\n","[5 2 3]\n","(2, 3)\n","(1, 2, 4)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"4vFfuh7CBuJV"},"source":["Numpy also provides many functions to create arrays:"]},{"cell_type":"code","metadata":{"id":"7V5tqkhIB0g2","colab":{"base_uri":"https://localhost:8080/","height":168},"executionInfo":{"elapsed":635,"status":"ok","timestamp":1538202755155,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"aaecf656-96dc-44b7-ab12-17f0b14af54e"},"source":["import numpy as np\n","\n","a = np.zeros((2,2))   # Create an array of all zeros\n","print(a)              # Prints \"[[ 0.  0.]\n","                      #          [ 0.  0.]]\"\n","\n","b = np.ones((1,2))    # Create an array of all ones\n","print(b)              # Prints \"[[ 1.  1.]]\"\n","\n","c = np.full((2,2), 7)  # Create a constant array\n","print(c)               # Prints \"[[ 7.  7.]\n","                       #          [ 7.  7.]]\"\n","\n","d = np.eye(2)         # Create a 2x2 identity matrix\n","print(d)              # Prints \"[[ 1.  0.]\n","                      #          [ 0.  1.]]\"\n","\n","e = np.random.random((2,2))  # Create an array filled with random values\n","print(e)                     # Might print \"[[ 0.91940167  0.08143941]\n","                             #               [ 0.68744134  0.87236687]]\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[0. 0.]\n"," [0. 0.]]\n","[[1. 1.]]\n","[[7 7]\n"," [7 7]]\n","[[1. 0.]\n"," [0. 1.]]\n","[[0.6954391  0.83532001]\n"," [0.49623436 0.53147419]]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"fYy0KgJjB5ev"},"source":["You can read about other methods of array creation [in the documentation](http://docs.scipy.org/doc/numpy/user/basics.creation.html#arrays-creation).\n","\n","##Array indexing\n","\n","Numpy offers several ways to index into arrays.\n","\n","###Slicing\n","\n","Similar to Python lists, numpy arrays can be sliced. Since arrays may be multidimensional, you must specify a slice for each dimension of the array:"]},{"cell_type":"code","metadata":{"id":"mECt4JAYCQ8c","colab":{"base_uri":"https://localhost:8080/","height":50},"executionInfo":{"elapsed":698,"status":"ok","timestamp":1538202878487,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"e4efb1b3-c42a-434d-ed43-12423b40d603"},"source":["import numpy as np\n","\n","# Create the following rank 2 array with shape (3, 4)\n","# [[ 1  2  3  4]\n","#  [ 5  6  7  8]\n","#  [ 9 10 11 12]]\n","a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n","\n","# Use slicing to pull out the subarray consisting of the first 2 rows\n","# and columns 1 and 2; b is the following array of shape (2, 2):\n","# [[2 3]\n","#  [6 7]]\n","b = a[:2, 1:3]\n","\n","# A slice of an array is a view into the same data, so modifying it\n","# will modify the original array.\n","print(a[0, 1])   # Prints \"2\"\n","b[0, 0] = 77     # b[0, 0] is the same piece of data as a[0, 1]\n","print(a[0, 1])   # Prints \"77\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["2\n","77\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"GVy4_GX5CYLD"},"source":["You can also mix integer indexing with slice indexing. However, doing so will yield an array of lower rank than the original array. Note that this is quite different from the way that MATLAB handles array slicing:"]},{"cell_type":"code","metadata":{"id":"YEY_yDDKClZ-","colab":{"base_uri":"https://localhost:8080/","height":118},"executionInfo":{"elapsed":629,"status":"ok","timestamp":1538202953822,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"dfae9773-f154-4144-da80-89b4ad01d5b5"},"source":["import numpy as np\n","\n","# Create the following rank 2 array with shape (3, 4)\n","# [[ 1  2  3  4]\n","#  [ 5  6  7  8]\n","#  [ 9 10 11 12]]\n","a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n","\n","# Two ways of accessing the data in the middle row of the array.\n","# Mixing integer indexing with slices yields an array of lower rank,\n","# while using only slices yields an array of the same rank as the\n","# original array:\n","row_r1 = a[1, :]    # Rank 1 view of the second row of a\n","row_r2 = a[1:2, :]  # Rank 2 view of the second row of a\n","print(row_r1, row_r1.shape)  # Prints \"[5 6 7 8] (4,)\"\n","print(row_r2, row_r2.shape)  # Prints \"[[5 6 7 8]] (1, 4)\"\n","\n","# We can make the same distinction when accessing columns of an array:\n","col_r1 = a[:, 1]\n","col_r2 = a[:, 1:2]\n","print(col_r1, col_r1.shape)  # Prints \"[ 2  6 10] (3,)\"\n","print(col_r2, col_r2.shape)  # Prints \"[[ 2]\n","                             #          [ 6]\n","                             #          [10]] (3, 1)\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["(array([5, 6, 7, 8]), (4,))\n","(array([[5, 6, 7, 8]]), (1, 4))\n","(array([ 2,  6, 10]), (3,))\n","(array([[ 2],\n","       [ 6],\n","       [10]]), (3, 1))\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"h4jnbBVuCtnz"},"source":["###Integer array indexing\n","\n","When you index into numpy arrays using slicing, the resulting array view will always be a subarray of the original array. In contrast, integer array indexing allows you to construct arbitrary arrays using the data from another array. Here is an example:"]},{"cell_type":"code","metadata":{"id":"QEo_GRgzC6m-","colab":{"base_uri":"https://localhost:8080/","height":84},"executionInfo":{"elapsed":649,"status":"ok","timestamp":1538203040202,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"900edad7-8f6a-42ea-9d33-8743868e8d2f"},"source":["import numpy as np\n","\n","a = np.array([[1,2], [3, 4], [5, 6]])\n","\n","# An example of integer array indexing.\n","# The returned array will have shape (3,) and\n","print(a[[0,1], [1,1]])  # Prints \"[1 4 5]\"\n","#primul [0,1,2] - alegi array-ul\n","#al doilea alegi indexul corespunzator array-ului respectiv\n","\n","# The above example of integer array indexing is equivalent to this:\n","print(np.array([a[0, 0], a[1, 1], a[2, 0]]))  # Prints \"[1 4 5]\"\n","\n","# When using integer array indexing, you can reuse the same\n","# element from the source array:\n","print(a[[0, 0], [1, 1]])  # Prints \"[2 2]\"\n","\n","# Equivalent to the previous integer array indexing example\n","print(np.array([a[0, 1], a[0, 1]]))  # Prints \"[2 2]\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["[2 4]\n","[1 4 5]\n","[2 2]\n","[2 2]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"FnueaModDAVt"},"source":["One useful trick with integer array indexing is selecting or mutating one element from each row of a matrix:"]},{"cell_type":"code","metadata":{"id":"teKdkq30DFeG","colab":{"base_uri":"https://localhost:8080/","height":168},"executionInfo":{"elapsed":623,"status":"ok","timestamp":1538203084649,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"5bc0b45e-99d1-4ac5-9c31-5c39cbd153d7"},"source":["import numpy as np\n","\n","# Create a new array from which we will select elements\n","a = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n","\n","print(a)  # prints \"array([[ 1,  2,  3],\n","          #                [ 4,  5,  6],\n","          #                [ 7,  8,  9],\n","          #                [10, 11, 12]])\"\n","\n","# Create an array of indices\n","b = np.array([0 , 2 , 0 , 1])\n","\n","# Select one element from each row of a using the indices in b\n","print(a[np.arange(4), b])  # Prints \"[ 1  6  7 11]\"\n","\n","# Mutate one element from each row of a using the indices in b\n","a[np.arange(4), b] += 10\n","\n","print(a)  # prints \"array([[11,  2,  3],\n","          #                [ 4,  5, 16],\n","          #                [17,  8,  9],\n","          #                [10, 21, 12]])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[ 1  2  3]\n"," [ 4  5  6]\n"," [ 7  8  9]\n"," [10 11 12]]\n","[ 1  6  7 11]\n","[[11  2  3]\n"," [ 4  5 16]\n"," [17  8  9]\n"," [10 21 12]]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"9LtmzO68DJgr"},"source":["###Boolean array indexing\n","\n","Boolean array indexing lets you pick out arbitrary elements of an array. Frequently this type of indexing is used to select the elements of an array that satisfy some condition. Here is an example:"]},{"cell_type":"code","metadata":{"id":"1c6neAk_DPR8","colab":{"base_uri":"https://localhost:8080/","height":101},"executionInfo":{"elapsed":632,"status":"ok","timestamp":1538203124173,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"4895a94c-f571-4a32-ce62-c005e78b662e"},"source":["import numpy as np\n","\n","a = np.array([[1,2], [3, 4], [5, 6]])\n","\n","bool_idx = (a > 2)   # Find the elements of a that are bigger than 2;\n","                     # this returns a numpy array of Booleans of the same\n","                     # shape as a, where each slot of bool_idx tells\n","                     # whether that element of a is > 2.\n","\n","print(bool_idx)      # Prints \"[[False False]\n","                     #          [ True  True]\n","                     #          [ True  True]]\"\n","\n","# We use boolean array indexing to construct a rank 1 array\n","# consisting of the elements of a corresponding to the True values\n","# of bool_idx\n","print(a[bool_idx])  # Prints \"[3 4 5 6]\"\n","\n","# We can do all of the above in a single concise statement:\n","print(a[a > 2])     # Prints \"[3 4 5 6]\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[False False]\n"," [ True  True]\n"," [ True  True]]\n","[3 4 5 6]\n","[3 4 5 6]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"CySWjuR6DjuW"},"source":["For brevity we have left out a lot of details about numpy array indexing; if you want to know more you should [read the documentation](http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html).\n","\n","##Datatypes\n","\n","Every numpy array is a grid of elements of the same type. Numpy provides a large set of numeric datatypes that you can use to construct arrays. Numpy tries to guess a datatype when you create an array, but functions that construct arrays usually also include an optional argument to explicitly specify the datatype. Here is an example:"]},{"cell_type":"code","metadata":{"id":"ox5QZ5HyEHkO","colab":{"base_uri":"https://localhost:8080/","height":67},"executionInfo":{"elapsed":654,"status":"ok","timestamp":1538203354658,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"d14dc70f-3643-4fb4-bac3-c1b4b0b4cd2f"},"source":["import numpy as np\n","\n","x = np.array([1, 2])   # Let numpy choose the datatype\n","print(x.dtype)         # Prints \"int64\"\n","\n","x = np.array([1.0, 2.0])   # Let numpy choose the datatype\n","print(x.dtype)             # Prints \"float64\"\n","\n","x = np.array([1, 2], dtype=np.int64)   # Force a particular datatype\n","print(x.dtype)                         # Prints \"int64\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["int64\n","float64\n","int64\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"AvPTXfZeEKk5"},"source":["You can read all about numpy datatypes [in the documentation](http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html).\n","\n","##Array math\n","\n","Basic mathematical functions operate elementwise on arrays, and are available both as operator overloads and as functions in the numpy module:"]},{"cell_type":"code","metadata":{"id":"VRbBCC-wEiQg","colab":{"base_uri":"https://localhost:8080/","height":319},"executionInfo":{"elapsed":655,"status":"ok","timestamp":1538203464005,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"fb1c0a72-a45d-42c9-be73-b3547e52154c"},"source":["import numpy as np\n","\n","x = np.array([[1,2],[3,4]], dtype=np.float64)\n","y = np.array([[5,6],[7,8]], dtype=np.float64)\n","\n","# Elementwise sum; both produce the array\n","# [[ 6.0  8.0]\n","#  [10.0 12.0]]\n","print(x + y)\n","print(np.add(x, y))\n","\n","# Elementwise difference; both produce the array\n","# [[-4.0 -4.0]\n","#  [-4.0 -4.0]]\n","print(x - y)\n","print(np.subtract(x, y))\n","\n","# Elementwise product; both produce the array\n","# [[ 5.0 12.0]\n","#  [21.0 32.0]]\n","print(x * y)\n","print(np.multiply(x, y))\n","\n","# Elementwise division; both produce the array\n","# [[ 0.2         0.33333333]\n","#  [ 0.42857143  0.5       ]]\n","print(x / y)\n","print(np.divide(x, y))\n","\n","# Elementwise square root; produces the array\n","# [[ 1.          1.41421356]\n","#  [ 1.73205081  2.        ]]\n","print(np.sqrt(x))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[ 6.  8.]\n"," [10. 12.]]\n","[[ 6.  8.]\n"," [10. 12.]]\n","[[-4. -4.]\n"," [-4. -4.]]\n","[[-4. -4.]\n"," [-4. -4.]]\n","[[ 5. 12.]\n"," [21. 32.]]\n","[[ 5. 12.]\n"," [21. 32.]]\n","[[0.2        0.33333333]\n"," [0.42857143 0.5       ]]\n","[[0.2        0.33333333]\n"," [0.42857143 0.5       ]]\n","[[1.         1.41421356]\n"," [1.73205081 2.        ]]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yrTXIl8sEs3f"},"source":["Note that unlike MATLAB, `*` is elementwise multiplication, not matrix multiplication. We instead use the `dot` function to compute inner products of vectors, to multiply a vector by a matrix, and to multiply matrices. `dot` is available both as a function in the numpy module and as an instance method of array objects:"]},{"cell_type":"code","metadata":{"id":"66sNDwkpE8op","colab":{"base_uri":"https://localhost:8080/","height":151},"executionInfo":{"elapsed":642,"status":"ok","timestamp":1538203573746,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"48ccaf83-891e-4687-97ca-85c2349556cb"},"source":["import numpy as np\n","\n","x = np.array([[1,2],[3,4]])\n","y = np.array([[5,6],[7,8]])\n","\n","v = np.array([9,10])\n","w = np.array([11, 12])\n","\n","# Inner product of vectors; both produce 219\n","print(v.dot(w))\n","print(np.dot(v, w))\n","\n","# Matrix / vector product; both produce the rank 1 array [29 67]\n","print(x.dot(v))\n","print(np.dot(x, v))\n","\n","# Matrix / matrix product; both produce the rank 2 array\n","# [[19 22]\n","#  [43 50]]\n","print(x.dot(y))\n","print(np.dot(x, y))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["219\n","219\n","[29 67]\n","[29 67]\n","[[19 22]\n"," [43 50]]\n","[[19 22]\n"," [43 50]]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"m8gZf_6lFAVt"},"source":["Numpy provides many useful functions for performing computations on arrays; one of the most useful is `sum`:"]},{"cell_type":"code","metadata":{"id":"vBen0kKQFJF1","colab":{"base_uri":"https://localhost:8080/","height":67},"executionInfo":{"elapsed":606,"status":"ok","timestamp":1538203623391,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"20b7048b-e44b-4efc-e7d3-5821fd786be2"},"source":["import numpy as np\n","\n","x = np.array([[1,2],[3,4]])\n","\n","print(np.sum(x))  # Compute sum of all elements; prints \"10\"\n","print(np.sum(x, axis=0))  # Compute sum of each column; prints \"[4 6]\"\n","print(np.sum(x, axis=1))  # Compute sum of each row; prints \"[3 7]\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["10\n","[4 6]\n","[3 7]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"ji1mKJf5Fg1k"},"source":["You can find the full list of mathematical functions provided by numpy [in the documentation](http://docs.scipy.org/doc/numpy/reference/routines.math.html).\n","\n","Apart from computing mathematical functions using arrays, we frequently need to reshape or otherwise manipulate data in arrays. The simplest example of this type of operation is transposing a matrix; to transpose a matrix, simply use the `T` attribute of an array object:"]},{"cell_type":"code","metadata":{"id":"waRKr66yF0RV","colab":{"base_uri":"https://localhost:8080/","height":118},"executionInfo":{"elapsed":739,"status":"ok","timestamp":1538203801751,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"e3c81244-7137-4ee2-b8b8-543704e56e3d"},"source":["import numpy as np\n","\n","x = np.array([[1,2], [3,4]])\n","print(x)    # Prints \"[[1 2]\n","            #          [3 4]]\"\n","print(x.T)  # Prints \"[[1 3]\n","            #          [2 4]]\"\n","\n","# Note that taking the transpose of a rank 1 array does nothing:\n","v = np.array([1,2,3])\n","print(v)    # Prints \"[1 2 3]\"\n","print(v.T)  # Prints \"[1 2 3]\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[1 2]\n"," [3 4]]\n","[[1 3]\n"," [2 4]]\n","[1 2 3]\n","[1 2 3]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"yfBlU2kgF3vE"},"source":["Numpy provides many more functions for manipulating arrays; you can see the full list [in the documentation](http://docs.scipy.org/doc/numpy/reference/routines.array-manipulation.html).\n","\n","##Broadcasting\n","\n","Broadcasting is a powerful mechanism that allows numpy to work with arrays of different shapes when performing arithmetic operations. Frequently we have a smaller array and a larger array, and we want to use the smaller array multiple times to perform some operation on the larger array.\n","\n","For example, suppose that we want to add a constant vector to each row of a matrix. We could do it like this:"]},{"cell_type":"code","metadata":{"id":"fQgMaLdFGV6K","colab":{"base_uri":"https://localhost:8080/","height":84},"executionInfo":{"elapsed":650,"status":"ok","timestamp":1538203938098,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"ec0b7211-4fb2-4175-c5f5-8d9466182151"},"source":["import numpy as np\n","\n","# We will add the vector v to each row of the matrix x,\n","# storing the result in the matrix y\n","x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n","v = np.array([1, 0, 1])\n","y = np.empty_like(x)   # Create an empty matrix with the same shape as x\n","\n","# Add the vector v to each row of the matrix x with an explicit loop\n","for i in range(4):\n","    y[i, :] = x[i, :] + v\n","\n","# Now y is the following\n","# [[ 2  2  4]\n","#  [ 5  5  7]\n","#  [ 8  8 10]\n","#  [11 11 13]]\n","print(y)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[ 2  2  4]\n"," [ 5  5  7]\n"," [ 8  8 10]\n"," [11 11 13]]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"nWI9Vb6IGbL1"},"source":["This works; however when the matrix `x` is very large, computing an explicit loop in Python could be slow. Note that adding the vector `v` to each row of the matrix `x` is equivalent to forming a matrix `vv` by stacking multiple copies of `v` vertically, then performing elementwise summation of `x` and `vv`. We could implement this approach like this:"]},{"cell_type":"code","metadata":{"id":"ffVP_lOOGw6e","colab":{"base_uri":"https://localhost:8080/","height":151},"executionInfo":{"elapsed":624,"status":"ok","timestamp":1538204047667,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"92d618e9-4634-456a-fa0c-faf42a264756"},"source":["import numpy as np\n","\n","# We will add the vector v to each row of the matrix x,\n","# storing the result in the matrix y\n","x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n","v = np.array([1, 0, 1])\n","vv = np.tile(v, (4, 1))   # Stack 4 copies of v on top of each other\n","print(vv)                 # Prints \"[[1 0 1]\n","                          #          [1 0 1]\n","                          #          [1 0 1]\n","                          #          [1 0 1]]\"\n","y = x + vv  # Add x and vv elementwise\n","print(y)  # Prints \"[[ 2  2  4\n","          #          [ 5  5  7]\n","          #          [ 8  8 10]\n","          #          [11 11 13]]\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[1 0 1]\n"," [1 0 1]\n"," [1 0 1]\n"," [1 0 1]]\n","[[ 2  2  4]\n"," [ 5  5  7]\n"," [ 8  8 10]\n"," [11 11 13]]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"dbR6fU5GGzMz"},"source":["Numpy broadcasting allows us to perform this computation without actually creating multiple copies of `v`. Consider this version, using broadcasting:"]},{"cell_type":"code","metadata":{"id":"vQ3djy02G4r2","colab":{"base_uri":"https://localhost:8080/","height":84},"executionInfo":{"elapsed":717,"status":"ok","timestamp":1538204080531,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"e2c45fa6-39fd-4237-95ba-0d4240438ad9"},"source":["import numpy as np\n","\n","# We will add the vector v to each row of the matrix x,\n","# storing the result in the matrix y\n","x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n","v = np.array([1, 0, 1])\n","y = x + v  # Add v to each row of x using broadcasting\n","print(y)  # Prints \"[[ 2  2  4]\n","          #          [ 5  5  7]\n","          #          [ 8  8 10]\n","          #          [11 11 13]]\""],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[ 2  2  4]\n"," [ 5  5  7]\n"," [ 8  8 10]\n"," [11 11 13]]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"6zqsq3xLG9hD"},"source":["The line `y = x + v` works even though `x` has shape `(4, 3)` and `v` has shape `(3,)` due to broadcasting; this line works as if `v` actually had shape `(4, 3)`, where each row was a copy of `v`, and the sum was performed elementwise.\n","\n","Broadcasting two arrays together follows these rules:\n","\n","1. If the arrays do not have the same rank, prepend the shape of the lower rank array with 1s until both shapes have the same length.\n","2. The two arrays are said to be *compatible* in a dimension if they have the same size in the dimension, or if one of the arrays has size 1 in that dimension.\n","3. The arrays can be broadcast together if they are compatible in all dimensions.\n","4. After broadcasting, each array behaves as if it had shape equal to the elementwise maximum of shapes of the two input arrays.\n","5. In any dimension where one array had size 1 and the other array had size greater than 1, the first array behaves as if it were copied along that dimension.\n","\n","For more details, you can read the explanation [from the documentation](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html).\n","\n","Functions that support broadcasting are known as *universal functions*. You can find the list of all universal functions [in the documentation](http://docs.scipy.org/doc/numpy/reference/ufuncs.html#available-ufuncs).\n","\n","Here are some applications of broadcasting:\n","\n","\n"]},{"cell_type":"code","metadata":{"id":"S-iRQm3JIb4F","colab":{"base_uri":"https://localhost:8080/","height":202},"executionInfo":{"elapsed":649,"status":"ok","timestamp":1538204491611,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"474431eb-f7b7-4342-f341-36cb1d092919"},"source":["import numpy as np\n","\n","# Compute outer product of vectors\n","v = np.array([1,2,3])  # v has shape (3,)\n","w = np.array([4,5])    # w has shape (2,)\n","# To compute an outer product, we first reshape v to be a column\n","# vector of shape (3, 1); we can then broadcast it against w to yield\n","# an output of shape (3, 2), which is the outer product of v and w:\n","# [[ 4  5]\n","#  [ 8 10]\n","#  [12 15]]\n","print(np.reshape(v, (3, 1)) * w)\n","\n","# Add a vector to each row of a matrix\n","x = np.array([[1,2,3], [4,5,6]])\n","# x has shape (2, 3) and v has shape (3,) so they broadcast to (2, 3),\n","# giving the following matrix:\n","# [[2 4 6]\n","#  [5 7 9]]\n","print(x + v)\n","\n","# Add a vector to each column of a matrix\n","# x has shape (2, 3) and w has shape (2,).\n","# If we transpose x then it has shape (3, 2) and can be broadcast\n","# against w to yield a result of shape (3, 2); transposing this result\n","# yields the final result of shape (2, 3) which is the matrix x with\n","# the vector w added to each column. Gives the following matrix:\n","# [[ 5  6  7]\n","#  [ 9 10 11]]\n","print((x.T + w).T)\n","# Another solution is to reshape w to be a column vector of shape (2, 1);\n","# we can then broadcast it directly against x to produce the same\n","# output.\n","print(x + np.reshape(w, (2, 1)))\n","\n","# Multiply a matrix by a constant:\n","# x has shape (2, 3). Numpy treats scalars as arrays of shape ();\n","# these can be broadcast together to shape (2, 3), producing the\n","# following array:\n","# [[ 2  4  6]\n","#  [ 8 10 12]]\n","print(x * 2)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[ 4  5]\n"," [ 8 10]\n"," [12 15]]\n","[[2 4 6]\n"," [5 7 9]]\n","[[ 5  6  7]\n"," [ 9 10 11]]\n","[[ 5  6  7]\n"," [ 9 10 11]]\n","[[ 2  4  6]\n"," [ 8 10 12]]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8GYvMrTfIioI"},"source":["Broadcasting typically makes your code more concise and faster, so you should strive to use it where possible.\n","\n","##Numpy Documentation\n","\n","This brief overview has touched on many of the important things that you need to know about numpy, but is far from complete. Check out the [numpy reference](http://docs.scipy.org/doc/numpy/reference/) to find out much more about numpy.\n"]},{"cell_type":"markdown","source":["# 2. Pandas"],"metadata":{"id":"I2vSI1n869dV"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"lIYdn1woOS1n"},"outputs":[],"source":["import pandas as pd\n"]},{"cell_type":"code","source":["#NBA results provided by FiveThirtyEight in a 17MB CSV file.\n","!wget https://raw.githubusercontent.com/fivethirtyeight/data/master/nba-elo/nbaallelo.csv\n"],"metadata":{"id":"7PXWPKpkY-Ha"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nba = pd.read_csv(\"nbaallelo.csv\")\n","type(nba)"],"metadata":{"id":"Ex-IlzHNamIP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["type(nba.columns)"],"metadata":{"id":"RmS7WPlhP1tL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["type(nba.index)"],"metadata":{"id":"Uv11fn5gP3IP"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nba.iloc[0:31:3, 0:14:2]"],"metadata":{"id":"HcxUgpYDP-Yn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["my_col = ['_iscopy', 'lg_id', 'is_playoffs']"],"metadata":{"id":"wJLAZrSzQp1f"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nba.loc[0:8:2, my_col]"],"metadata":{"id":"i7VlHbIsQaBd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["len(nba)\n","nba.shape\n","nba.head()\n","nba.tail()\n","nba.info()\n","nba.describe()"],"metadata":{"id":"KOKnMIFaanb7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["pd.set_option(\"display.max.columns\", None)\n","pd.set_option(\"display.precision\", 2)"],"metadata":{"id":"1PfCLSuBa21M"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nba.describe(include=object)"],"metadata":{"id":"7PxpWGsLbJJq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nba.hist(\"pts\", by=\"game_result\")"],"metadata":{"id":"w1gbgFO5gsbu"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nba[nba[\"fran_id\"]==\"Knicks\"].groupby(\"year_id\")[\"pts\"].sum().plot()"],"metadata":{"id":"mpczWpafOt4C"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nba[\"fran_id\"].value_counts().head(10).plot(kind=\"bar\")"],"metadata":{"id":"8O3x9176O6E4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nba.boxplot(column='pts', by=\"game_result\")"],"metadata":{"id":"CLh1RRsFhXF9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nba[\"team_id\"].value_counts()"],"metadata":{"id":"MNDz27EFiBPS"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nba[\"fran_id\"].value_counts()"],"metadata":{"id":"cCoAZgcJiWjh"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["It seems that a team named \"Lakers\" played 6024 games, but only 5078 of those were played by the Los Angeles Lakers. Find out who the other \"Lakers\" team is:\n","\n"],"metadata":{"id":"YgH9mFMIinyl"}},{"cell_type":"code","source":["nba.loc[nba[\"fran_id\"] == \"Lakers\", \"team_id\"].value_counts()"],"metadata":{"id":"usmtaXZuim0R"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Indeed, the Minneapolis Lakers (\"MNL\") played 946 games. You can even find out when they played those games. For that, you’ll first define a column that converts the value of date_game to the datetime data type. Then you can use the min and max aggregate functions, to find the first and last games of Minneapolis Lakers:\n","\n"],"metadata":{"id":"OEJcB_zcjQfo"}},{"cell_type":"code","source":["nba[\"date_played\"] = pd.to_datetime(nba[\"date_game\"])\n","nba.loc[nba[\"team_id\"] == \"MNL\", \"date_played\"].min()\n","nba.loc[nba['team_id'] == 'MNL', 'date_played'].max()\n","nba.loc[nba[\"team_id\"] == \"MNL\", \"date_played\"].agg((\"min\", \"max\"))"],"metadata":{"id":"zM4OpAoXjSzz"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["It looks like the Minneapolis Lakers played between the years of 1948 and 1960. That explains why you might not recognize this team!"],"metadata":{"id":"b9cAmZCnjfuJ"}},{"cell_type":"markdown","source":[" Find out how many points the Boston Celtics have scored during all matches contained in this dataset. Expand the code block below for the solution:"],"metadata":{"id":"caqpZ6pVj0KH"}},{"cell_type":"code","source":["nba.loc[nba[\"team_id\"] == \"BOS\", \"pts\"].sum()"],"metadata":{"id":"LVUCbOP3jfQ_"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Querying Your Dataset"],"metadata":{"id":"9xuxqzI5oDXP"}},{"cell_type":"code","source":["# create a new DataFrame that contains only games played after 2010\n","current_decade = nba[nba[\"year_id\"] > 2010]\n","current_decade.shape"],"metadata":{"id":"95tTpM0PoK7I"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# select the rows where a specific field is not null\n","games_with_notes = nba[nba[\"notes\"].notnull()]\n","games_with_notes.shape"],"metadata":{"id":"TGm0y8AZoQUV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#filter your dataset and find all games where the home team’s name ends with \"ers\".\n","ers = nba[nba[\"fran_id\"].str.endswith(\"ers\")]\n","ers.shape"],"metadata":{"id":"B7t3W-SqoUk3"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["You can combine multiple criteria and query your dataset as well. To do this, be sure to put each one in parentheses and use the logical operators | and & to separate them.\n","\n"],"metadata":{"id":"t9fqCZooo1Og"}},{"cell_type":"code","source":["#Do a search for Baltimore games where both teams scored over 100 points. In order to see each game only once, you’ll need to exclude duplicates\n","nba[(nba[\"_iscopy\"] == 0) &\n","    (nba[\"pts\"] > 100) &\n","    (nba[\"opp_pts\"] > 100) &\n","    (nba[\"team_id\"] == \"BLB\")\n","    ]"],"metadata":{"id":"uIs64CIxoxqo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#In the spring of 1992, both teams from Los Angeles had to play a home game at another court. Query your dataset to find those two games. Both teams have an ID starting with \"LA\".\n","nba[(nba[\"_iscopy\"] == 0) &\n","    (nba[\"team_id\"].str.startswith(\"LA\")) &\n","    (nba[\"year_id\"]==1992) &\n","    (nba[\"notes\"].notnull()) ]"],"metadata":{"id":"NgGpgA9tptRs"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Grouping and Aggregating Your Data\n","A Series has more than twenty different methods for calculating descriptive statistics."],"metadata":{"id":"b2OYA8XQqubc"}},{"cell_type":"code","source":["points = nba[\"pts\"]\n","type(points)\n","points.sum()"],"metadata":{"id":"pXSUxtIlqt7i"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nba.groupby(\"fran_id\", sort=False)[\"pts\"].sum()"],"metadata":{"id":"TnVHc6oWrEEE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nba[(nba[\"fran_id\"] == \"Spurs\") &\n","    (nba[\"year_id\"] > 2010)].groupby([\"year_id\", \"game_result\"])[\"game_id\"].count()"],"metadata":{"id":"LomYmqM9rKVq"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Manipulating Columns\n"],"metadata":{"id":"mX8Y8TRhref3"}},{"cell_type":"code","source":["df = nba.copy()\n","df.shape"],"metadata":{"id":"DIgPygR8rgp9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df[\"difference\"] = df.pts - df.opp_pts\n","df.shape"],"metadata":{"id":"zuwKnKKurjG9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df[\"difference\"].max()"],"metadata":{"id":"BEe1R8SBrllD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["renamed_df = df.rename(columns={\"game_result\": \"result\", \"game_location\": \"location\"})"],"metadata":{"id":"QiTDpLAKrnwn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["renamed_df.info()"],"metadata":{"id":"39naVS-irqa2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["elo_columns = [\"elo_i\", \"elo_n\", \"opp_elo_i\", \"opp_elo_n\"]\n","df.drop(elo_columns, inplace=True, axis=1)"],"metadata":{"id":"qRIbUi7Arwex"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df[\"date_game\"] = pd.to_datetime(df[\"date_game\"])"],"metadata":{"id":"M0CbYcpTr7aV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df[\"game_location\"].nunique()"],"metadata":{"id":"GVKUIUTYr_h7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df[\"game_location\"].value_counts()"],"metadata":{"id":"5xbiYpAwsAvs"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df[\"game_location\"] = pd.Categorical(df[\"game_location\"])"],"metadata":{"id":"lzpl-UQ3sCYK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df[\"game_location\"].dtype"],"metadata":{"id":"-tnNMkxtsDoP"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Cleaning Data\n"],"metadata":{"id":"LVa2BWvUsLUR"}},{"cell_type":"code","source":["rows_without_missing_data = nba.dropna()\n","rows_without_missing_data.shape"],"metadata":{"id":"yPA8WjQisN4o"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["data_without_missing_columns = nba.dropna(axis=1)\n","data_without_missing_columns.shape"],"metadata":{"id":"8_nrGPmxsPPR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["data_with_default_notes = nba.copy()\n","data_with_default_notes[\"notes\"].fillna(value=\"no notes at all\",inplace=True)"],"metadata":{"id":"bGM6A5OqN6FA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nba[nba[\"pts\"] == 0]"],"metadata":{"id":"VRf6ibMpOTmo"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Inconsistent Values\n","Sometimes a value would be entirely realistic in and of itself, but it doesn’t fit with the values in the other columns. You can define some query criteria that are mutually exclusive and verify that these don’t occur together."],"metadata":{"id":"jkmixmnPOZMk"}},{"cell_type":"code","source":["nba[(nba[\"pts\"] > nba[\"opp_pts\"]) & (nba[\"game_result\"] != 'W')].empty\n","nba[(nba[\"pts\"] < nba[\"opp_pts\"]) & (nba[\"game_result\"] != 'L')].empty"],"metadata":{"id":"gJsYvaO-OWA9"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"JXpXKWAi6dJg"},"source":["# 3. Matplotlib\n","\n","Matplotlib is a plotting library. In this section give a brief introduction to the `matplotlib.pyplot` module, which provides a plotting system similar to that of MATLAB.\n","\n","##Plotting\n","\n","The most important function in matplotlib is `plot`, which allows you to plot 2D data. Here is a simple example:"]},{"cell_type":"code","metadata":{"id":"KfjqxRT4JP2t","colab":{"base_uri":"https://localhost:8080/","height":347},"executionInfo":{"elapsed":1074,"status":"ok","timestamp":1538204700080,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"41e4bc44-df25-4d59-d371-ed70499e82bc"},"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","\n","# Compute the x and y coordinates for points on a sine curve\n","x = np.arange(0, 3 * np.pi, 0.1)\n","y = np.sin(x)\n","\n","# Plot the points using matplotlib\n","plt.plot(x, y)\n","plt.show()  # You must call plt.show() to make graphics appear."],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAe0AAAFKCAYAAAAwrQetAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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3o6KiYsFjfGV+qhftf0s+g2EYFGVqMG1zoqlrhOtwCA/NbQ5Bnx2Ea25daRuNRuTm5s4/\nVqvVMBgMCA0NhcFggFqtvuW5np4emEymex6zEJVKAYmH1ga3OV3ISIhAXna0R74fuZVGo+Q6BLdt\nW5OEs1d60Ng1im1rU7gO5w5Cbls+W2q71t+c6cxtX5uMyPBgb4bkF+j96j0eWQ6MZVmvHWMyeW7r\nxG89thIajRIGw4THvieZIfR2jQyRIixEhksNA9i7JY1Xa0oLvW35aqntOmGxoaF9GKmxYXDZHPS7\nWAS9X5dvoU6PW+VxrVYLo/HTxSj0ej00Gs1dnxsaGoJWq13wGF+RS8WQS2mqBrmTiGFQmBGFCYsd\nN3pHuQ6H8MjVNiNcLEsrKBJecCtpl5WV4cyZMwCAxsZGaLXa+TJ3fHw8zGYzent74XA4cO7cOZSV\nlS14DCF8UDw/ipxWRyOfqp19P9AAVsIHbpXHi4qKkJubi3379oFhGBw6dAjHjx+HUqnEjh078KMf\n/QgHDhwAAOzatQspKSlISUm54xhC+CQ7SYVguQQ1rXrs25YOhuFPiZxwY9rmQMPNEcRFhUCnVnAd\nDiHu39N+9tlnb3mcnZ09/+/S0tK7Tue6/RhC+EQiFiE/LRKXrg+he8iMpGgaTBPo6jtG4HC66Cqb\n8AZNSCXkM2gtcvJZtAoa4RtK2oR8xspUNaQSEa2ORmB3uHCt3YjIsCAk6mj8DeEHStqEfEaQTILc\nZDX6jZMYGJ7kOhzCoaYuE6asThRnaWh8A+ENStqE3IarfXIJv1BpnPARJW1CbpOfHgURw1DSDmAu\nF4urNwwIU0iRHue7zSAIWQwlbUJuExosRXZSBG4OTGBkfJrrcAgH2vrGMG6xoyBDw6vV8QihpE3I\nXRTTdp0Brbpl5vdOG4QQvqGkTchdFGRQ0g5ULMuiplWPYLkYOUkqrsMh5BaUtAm5C5VSjrS4MLT0\njGLcYuM6HOJDXUMTGB63Ij89ChIxfUQSfqF3JCH3UJypBcsCV2/QWuSBZL40TqPGCQ9R0ibkHooy\nowBQiTzQ1LQaIJOIsDIlkutQCLkDJW1C7kGrUiBBG4rrnSOYsjq4Dof4wMyiOhasSo2EXEbb+BL+\noaRNyAKKMjVwOFlcax/mOhTiA3NrzhfRqHHCU5S0CVlAMW0gElBqWgwQixjkp1FpnPATJW1CFhCn\nCYFWFYz69mHY7E6uwyFeZBydQtfQBHKSVVAESbkOh5C7oqRNyAIYhkFxpgZWuxONN0e4Dod40Vw1\nhUaNEz6jpE3IIubub1KJ3L9VtxrAACjMoKRN+IuSNiGLSIkJg0opx9UbRjicLq7DIV4waraivXcM\nGQkRCAuRcR0OIfdESZuQRYhmS+QWqwPNXSauwyFeUHvDCBZUGif8R0mbkCWY2ziiqkXPcSTEG6pn\nf6+0dzbhO0rahCxBRvxM2bSm1Qini0rk/sQ8ZUdz1yhSYsIQGR7EdTiELIiSNiFLIBIxKMrUwDxl\nR2vPGNfhEA+qbTXAxbIoyaarbMJ/lLQJWaK5Enk1lcj9StX83tlajiMhZHGUtAlZoqyECIQESVA9\ne2VGhM8ybcf1zhEk6kKhjQjmOhxCFkVJm5AlkohFKMzQYMxsQ0ffONfhEA+42maE08XSVTYRDIk7\nB9ntdjz//PPo7++HWCzGT3/6UyQkJNzymnfeeQevv/46RCIR1q1bh7//+7/H8ePH8ctf/hKJiYkA\ngPXr1+Ob3/zm8n8KQnykOEuDC/UDqGrRIz0+nOtwyDJVNc+UxktogxAiEG4l7VOnTiEsLAxHjhzB\nhQsXcOTIEfziF7+Yf35qagqHDx/GiRMnEBISgieeeAK7d+8GAOzatQvPPfecZ6InxMdWJKsRLBej\nusWAvVvTwTAM1yERN1mm7Wi4OYK4qBDERIZwHQ4hS+JWebyiogI7duwAMHO1XFNTc8vzwcHBOHHi\nBEJDQ8EwDCIiIjA6Orr8aAnhmFQiQn56FIbHp9E1NMF1OGQZqpqG4HC65gcYEiIEbl1pG41GqNVq\nAIBIJALDMLDZbJDJPl3+LzQ0FADQ0tKCvr4+5Ofno7u7G5WVlXjmmWfgcDjw3HPPYcWKFQueS6VS\nQCLx7Gb0Go3So9+PzAiUdt1amohLjUNo6hlD6ao4n5wzUNrWl/7jnSYAwIPrUqh9PYza03sWTdrH\njh3DsWPHbvlaXV3dLY/Ze4yk7ezsxLPPPosjR45AKpUiPz8farUaW7ZsQW1tLZ577jmcPHlywfOb\nTJbFQrwvGo0SBgNdIXlaILVrQqQCcqkYH9X04qGSeK+XyAOpbX3FanOiulkPnVqBYDGofT2I3q/L\nt1CnZ9GkvWfPHuzZs+eWrz3//PMwGAzIzs6G3W4Hy7K3XGUDwODgIL71rW/h5z//OXJycgAAaWlp\nSEtLAwAUFhZiZGQETqcTYrFnr6QJ8Sa5VIz89EhUNunRozcjUUdXFUJT3zEMq82JkiwNjUsgguLW\nPe2ysjKcPn0aAHDu3DmsWbPmjtf88Ic/xI9+9CPk5ubOf+3VV1/FqVOnAACtra1Qq9WUsIkglcxO\nEbrSTAutCNHcGvIlNNWLCIxb97R37dqF8vJy7N+/HzKZDC+99BIA4JVXXkFpaSkiIiJQVVWFX/3q\nV/PH/PVf/zV2796NH/zgB3jzzTfhcDjwk5/8xDM/BSE+tiotEnKpGFea9fjiplS6WhMQm92JuvZh\n6NQKJOpCuQ6HkPviVtKem5t9u69//evz/779vvecN954w51TEsIrVCIXrvqOEVhtTmzIj6XOFhEc\nWhGNEDdRiVyYrjQPAQA2FPhm5D8hnkRJmxA3fbZEfq8ZFIRfrHYn6tqGoY0IRlocrWhHhIeSNiFu\nmiuR601T6B4ycx0OWYL69mFY7U6U5mipNE4EiZI2IctQmj1TIq+i7ToFYe5WxtzvjRChoaRNyDKs\nSp0tkTdRiZzvrDYn6tqN0KmCkaClUeNEmChpE7IMsrkS+SiVyPnuWscwbHYXlcaJoFHSJmSZqEQu\nDFeaZkaNl2brOI6EEPdR0iZkmahEzn/TNgeutQ8jWq1AvIa24STCRUmbkGWSScUoyIiCfnQKnYO0\nUQIf1bUNw+ZwoTSbSuNE2ChpE+IBq3NmSuSVsyVYwi/zo8ZzaNQ4ETZK2oR4wMqUSCjkElQ26eGi\nEjmvTFlnSuMxkQrERVFpnAgbJW1CPEAqEaEoSwPThBU3eka5Dod8xtUbRjicLqzO0VFpnAgeJW1C\nPGTNiplRyZVNNIqcTy5dn7llMff7IUTIKGkT4iHZiREIU0hxpVkPh9PFdTgEwLjFhsabI0iKViJa\nreA6HEKWjZI2IR4iFolQmq2DecqO5i4T1+EQANXNM2MM1tJVNvETlLQJ8aDVK2ZGJ1++TqPI+eDS\n9SEwAFbnUNIm/oGSNiEelBYXjsgwOWpuGGB3OLkOJ6ANj03jRu8YshIjoFLKuQ6HEI+gpE2IB4kY\nBqtzdJiyOnGtfYTrcALa3Jx5GoBG/AklbUI8bK4Ue5kWWuHUpetDEIsYFGfRgirEf1DSJsTDEnWh\niFYrcK3NiCmrg+twAlKfcRI9ejNWpUYiNFjKdTiEeAwlbUI8jGEYrM7RwuZwofaGgetwAtJlmptN\n/BQlbUK8YF1uNACgomGQ40gCD8uyqLw+BLlUjIL0KK7DIcSjKGkT4gU6tQJpcWG43mWCacLKdTgB\n5ebABPSjUyjMiIJcJuY6HEI8ipI2IV6yPjcaLEtztn3tUuNMdWM1lcaJH6KkTYiXlOboIBYxKKcS\nuc84nC5cuj4EpUKKlSlqrsMhxOMk7hxkt9vx/PPPo7+/H2KxGD/96U+RkJBwy2tyc3NRVFQ0//h3\nv/sdXC7XoscR4i9Cg6XIS4tE7Q0juocmkKhTch2S36vvGIZ5yo4dJQmQiOmahPgft97Vp06dQlhY\nGP77v/8b3/jGN3DkyJE7XhMaGoo33nhj/j+xWLyk4wjxJ+tXzg5Ia6SrbV8or59p57l2J8TfuJW0\nKyoqsGPHDgDA+vXrUVNT49XjCBGqvLQohARJcOn6EFwulutw/Jp5yo6rbUbEaUKQqAvlOhxCvMKt\n8rjRaIRaPXO/SCQSgWEY2Gw2yGSy+dfYbDYcOHAAfX192LlzJ7761a8u6bjbqVQKSCSeHQGq0VCZ\n0huoXe9uU2E83q3oRN/oNIrcXJ2L2nZxlRdvwuli8eCaZGi1YUs6htrVO6hdvWfRpH3s2DEcO3bs\nlq/V1dXd8phl77yC+Md//Ec8+uijYBgGTz75JEpKSu54zd2Ou53JZFn0NfdDo1HCYJjw6Pck1K4L\nKUyLxLsVnTh9sQMJ6uD7Pp7admnOXuoEwwArkyKW1F7Urt5B7bp8C3V6Fk3ae/bswZ49e2752vPP\nPw+DwYDs7GzY7XawLHvH1fL+/fvn/7127Vq0trZCq9Uuehwh/iYtLgzaiGBUtxrwlM2BIJlbBS6y\ngIHhSXT0j2Nlipp29CJ+za172mVlZTh9+jQA4Ny5c1izZs0tz3d0dODAgQNgWRYOhwM1NTXIyMhY\n9DhC/BHDMFibq4PN7kJNKy1r6g1z0+rWr6IBaMS/udXl37VrF8rLy7F//37IZDK89NJLAIBXXnkF\npaWlKCwsRHR0NB5//HGIRCJs3boVeXl5yM3NvetxhPi79SujceJiJy5cG8D6lTFch+NXXCyLisZB\nBMnEKMzQcB0OIV7lVtKem2N9u69//evz//7BD36w5OMI8XdalQJZCRFo7h6F3mSBVqXgOiS/0dJl\nwsi4FRvzYiCX0rKlxL/R6gOE+MjG/Jkr7Av1AxxH4l/mS+M0N5sEAErahPhIcZYWwXIxLtYP0pxt\nD5myOnClRY+o8CBkJERwHQ4hXkdJmxAfkUvFWLMiGqYJKxpuDnMdjl+4dH0INrsLG/NjIWIYrsMh\nxOsoaRPiQxvzZkrkn9RRidwTPr7aDxHDYMMqGtxHAgMlbUJ8KDlaiXhNKK62GTE+aeM6HEHrGpxA\n19AE8tIiaW42CRiUtAnxIYZhsDE/Bk4XS1t2LtNHdf0AgE0FsRxHQojvUNImxMfW5UZDImbwybX+\nJS3lS+5ktTlxqXEQKqUcq1Jp32wSOChpE+JjocFSFGVqMDBsQXv/ONfhCFJl8xCmbU5sWBUDsYg+\nxkjgoHc7IRzYmD9T0v1ktsRL7s8ndQNg8Oncd0ICBSVtQjiQk6RCVHgQKpv0sEw7uA5HUPoMZrT1\njSE3RY2o8PvfNY0QIaOkTQgHRAyDzQWxsNqdKG+g6V/34+PZ6XKb8mkAGgk8lLQJ4cjG/FhIxAw+\nrOmjAWlLZHfMdHLCFFIUZERxHQ4hPkdJmxCOhClkKM3WYXDEgutdJq7DEYTKJj0mpx0oy4uBREwf\nXyTw0LueEA5tLY4DAHxY3ctxJPzHsizer+oFwwAPFMZxHQ4hnKCkTQiHUmPCkBStxNU2I4bHprkO\nh9fa+8bRNTSBogwNDUAjAYuSNiEcYhgGW4viwLLA+at9XIfDa+9X9wAAthXHcxwJIdyhpE0Ix9bk\n6BASJMHHdf2wO1xch8NLpgkrqlsMiNeEICuRtuAkgYuSNiEck0nF2JgXiwmLHVXNeq7D4aXztX1w\nulhsK44HQ1twkgBGSZsQHthSFAcGwIc1NCDtdnaHCx9d7UNIkARrc6O5DocQTlHSJoQHtBHBWJUW\nifb+cdwcoPXIP+tK8xDGLXZszI+FXCrmOhxCOEVJmxCe2FGSAAA4U9nNcST88dlpXltpmhchlLQJ\n4YsVySokakNxpVkP/egU1+HwQkf/ODoHJ1CQHoWoCJrmRQglbUJ4gmEYPLQ2ESwLnKWrbQDA6csz\n7bB9tgpBSKCjpE0Ij5RmaxEZFoQL1wYwYbFxHQ6nBoYnUdNqQEpMGLJpmhchAChpE8IrYpEID65O\ngM3hwoc1gb3YyjuXusACeHhdEk3zImSWxJ2D7HY7nn/+efT390MsFuOnP/0pEhI+LV81NDTgZz/7\n2fzjtrY2vPzyy7h48SJOnjwJnU4HAHj00UexZ8+eZf4IhPiXTXmxOHHhJj6o7sVDaxK5DocTw2PT\nuNQ4hNioENrNi5DPcCtpnzp1CmFhYThy5AguXLiAI0eO4Be/+MX88ytXrsQbb7wBABgfH8ff/d3f\noaCgABcvXsRXvvIVPPnkk56JnhA/JJeJsbUoHifLO3Hh2gD2xQZeafhMZTecLhafW5MIEV1lEzLP\nrfJ4RUUFduzYAQBYv349ampq7vna1157DU8//TREIqrEE7JU24rjIZWIcPZKN5zOwFradNxiw8d1\n/YgMk2PNCh3X4RDCK25daRuNRqjVagCASCQCwzCw2WyQyWS3vG56ehoXLlzA9773vfmvnT59Gh98\n8AFkMhn++Z//+Zay+t2oVApIJJ5dUEGjUXr0+5EZ1K6eo9EA20sT8W5FJ8qvDWBjAM1RPvNuE2wO\nF760NRMx0eFePRe9Z72D2tV7Fk3ax44dw7Fjx275Wl1d3S2PWZa967Hvv/8+tmzZMn+VvXnzZqxd\nuxalpaV4++238cILL+A3v/nNguc3mSyLhXhfNBolDIYJj35PQu3qDZvyonH6UifefL8FmbFKiET+\nXyaesjpw8pMOKBVSFKapvfqeovesd1C7Lt9CnZ5Fk/aePXvuGCz2/PPPw2AwIDs7G3a7HSzL3nGV\nDQDnzp3D/v375x/n5eXN/3vr1q04fPjwkn4AQgKRTqVA2coYXKgfQGXTUECsu33+ah8sVge+sCmV\nliwl5C7cutFcVlaG06dPA5hJzGvWrLnr6xoaGpCdnT3/+IUXXkBVVRUAoLKyEhkZGe6cnpCA8WhZ\nMiRiBn+5cBMOP7+3PWV14PTlbgTJxNhWFDi3Awi5H27d0961axfKy8uxf/9+yGQyvPTSSwCAV155\nBaWlpSgsLAQwM3I8NDR0/rg9e/bg0KFDkEgkYBgGL7zwggd+BEL8V1REMB5ck4R3yjtR3jCITfmx\nXIfkNWev9GDCYsdjG1OgCJJyHQ4hvMSw97ohzROevjdC91u8g9rVe0QyCb724vsIU0jx4tfXQSrx\nv5kY45M2PPebCsilYrz0f9YiSObW9cR9ofesd1C7Lt9C97T976+fED8TGR6MBwrjMDxuxcd1/VyH\n4xWnyjthtTmxe32yTxI2IUJFSZsQAdi1LglyqXgmudmdXIfjUYbRKZyr7YMmIgibC/y3/E+IJ1DS\nJkQAwhQy7CiNx9ikDef8bE3yv3xyE04Xiy9sTIVETB9JhCyE/kIIEYidqxMRLJfg7YpOv9kBrEdv\nxqXGQSRoQ7GaVj8jZFGUtAkRiJAgKT5flozJaQeOf9zBdTge8T8ftYMF8KXNabTGOCFLQEmbEAHZ\nWhyPuKgQfHy1HzcHxrkOZ1nqO4ZxrX0YWQkRWJWq5jocQgSBkjYhAiIRi/BXOzLBAvjD2Va4+D1j\n856sdifeONMCEcPgyzsyab9sQpaIkjYhApOdpMLqHC1uDozjwrUBrsNxy6nyThjHpvFgaQIStKGL\nH0AIAUBJmxBB2rs1A20Lm9EAAAoMSURBVHKpGG+db8fktJ3rcO5Ln8GM05e7ERkmx+c3pHAdDiGC\nQkmbEAFSKeV4tCwZ5ik7/iygQWkulsUbZ1rgdLH4qx1ZkMtoUxBC7gclbUIEakdpAmIiFThX24f2\nvjGuw1mSi9cG0No7hqJMDQoyorgOhxDBoaRNiEBJxCJ8ZWcWwAKvnGzElNXBdUgLGrfY8KdzbZDL\nxPjydtrhjxB3UNImRMCyElXYtS4JhtFp/PG9Vq7DuScXy+L1t5swOe3AFzakQB0WxHVIhAgSJW1C\nBO7zG1KQHK3ExYZBVDYNcR3OXZ2t7MG19mGsSFZhe0kC1+EQIliUtAkROIlYhK8/mguZVIT/PN2C\n4bFprkO6RVvfGN46347wEBm+tjsXIhHNySbEXZS0CfED0WoFvrw9E1NWB149dR0uFz8WXTFP2fHv\n/9sAFiz+z6O5CA+RcR0SIYJGSZsQP7ExLwbFmRq09oziz59wPw2MZVm8duo6RsateGxDCrKTVFyH\nRIjgUdImxE8wDIOnP5cNrSoYb1d04cOaXk7jeedSF+rah5GbrMLD65I5jYUQf0FJmxA/EhosxT88\nkY8whRT/dbYV1S16TuI4X9uH//moAxGhMvwt3ccmxGMoaRPiZ7QqBb7/RD5kUjF+c+I6WntGfXr+\ni/UDeONMC5QKKZ7dV0j3sQnxIErahPih5OgwfOuLK8GyLH711jX0Gcw+Oe+VZj1ef6cJwXIJDuwt\nQGxUiE/OS0igoKRNiJ9amRKJr+7KhsXqwM/+WIuWbpNXz3e1zYhXTjRCLhXjH/YWIFGn9Or5CAlE\nlLQJ8WPrV8bg6YeyMGV14PCbV3Guts/j53CxLN693IWXj9dDLGLw/T35SI0N8/h5CCGAhOsACCHe\ntbkgDtFqBV7+cwPeONOCHr0ZX96eAYl4+X32cYsNr51qQn3HMMJDZPjmYyuRmRDhgagJIXdDSZuQ\nAJCVqMK/PF2CX/1PPc7X9qHPYMaXt2ciKdr9EnZLtwm/OdGIUbMNK1PU+NtHViCMBp0R4lVud7Ur\nKyuxbt06nDt37q7PnzhxAl/60pewZ88eHDt2DABgt9tx4MAB7N+/H08++SR6enrcPT0h5D5FRQTj\nh08VoyRbixu9Y/i/v7uCXx+vR49+6YPUWJZFS7cJLx+vx8//uxbjk3Y8viUN338inxI2IT7g1pV2\nd3c3fvvb36KoqOiuz1ssFrz88st46623IJVK8fjjj2PHjh04d+4cwsLCcOTIEVy4cAFHjhzBL37x\ni2X9AISQpZPLxPjm53NxvSAWf/m4AzWtBtS0GlCSpUFRpgZJ0Uro1AqImE/nVbtYFmNmGxpuDuP9\nqt75JJ+kU+KvdmQiPT6cqx+HkIDjVtLWaDT49a9/jR/+8Id3fb6urg6rVq2CUjlTeisqKkJNTQ0q\nKirw2GOPAQDWr1+PgwcPuhk2IcRdDMMgN1mNFUkq1HeM4C+fdKCqxYCqFgOAmcSeoA2FRMRgeHwa\nI+NWOGfXMhcxDEqytdheHI+M+HAwDC2aQogvuZW0g4ODF3zeaDRCrVbPP1ar1TAYDLd8XSQSgWEY\n2Gw2yGT3LqupVApIJGJ3wrwnjYamongDtav3eKttt2nDsHVNEpo7TbjRY0Jb7yja+8bQ0TcGFwuo\nw+RIj49AlCoYSToltq1OhFal8EosXKD3rHdQu3rPokn72LFj8/ek53znO9/Bxo0bl3wSlr37jkP3\n+vpnmUyWJZ9nKTQaJQyGCY9+T0Lt6k2+aNuoUCmicrRYl6MFANjsTjAMA6nktmEvDqff/J7pPesd\n1K7Lt1CnZ9GkvWfPHuzZs+e+TqjVamE0Gucf6/V6FBQUQKvVwmAw/P/27ick6jSO4/hn0p1DjplJ\nGkKFdBGCSlHClOigBgZChc0UVocuUR0ED8YgFAihngSVTHQunkZm+uMhTAIHhEY8CBVCUQaRiU7i\nlP/Byg4LwS5sLLsz++zzm/frNnOZzzwMfOZ5vsMzKiws1Obmpra2tn65ywZghvu3xJ5uAUiMpFyu\ncvjwYb18+VJLS0taXV3V5OSkSkpKVF5eruHhYUnS6Oiojh49moyXBwDAkf7RTDsSiai/v1/v3r3T\n1NSUBgYGFAgE1Nvbq9LSUhUVFamxsVFXrlyRy+XS9evXlZmZqZqaGj179kznz5+X2+1Wa2trot8P\nAACO5dr6O4NlgxI9G2Hekhysa/KwtsnBuiYH6/rv/Wqmzd3jAABYgtIGAMASlDYAAJagtAEAsASl\nDQCAJShtAAAsQWkDAGAJShsAAEv87y9XAQAAv2OnDQCAJShtAAAsQWkDAGAJShsAAEtQ2gAAWILS\nBgDAEilV2nfu3JHX65XP59OLFy9Mx3GM9vZ2eb1enT17ViMjI6bjOMrGxoYqKyt1//5901EcZWho\nSLW1tTpz5owikYjpOI6wurqqGzdu6OLFi/L5fBobGzMdyZHSTQf4r0xMTOj9+/cKBoOanp6W3+9X\nMBg0Hct64+PjevPmjYLBoOLxuE6fPq3q6mrTsRzj7t27ysrKMh3DUeLxuLq7uxUOh7W2tqbOzk6d\nOHHCdCzrPXjwQAUFBWpsbNT8/LwuX76s4eFh07EcJ2VKOxqNqrKyUpJ04MABffnyRSsrK/J4PIaT\n2a20tFSHDh2SJO3YsUPr6+v69u2b0tLSDCez3/T0tN6+fUuhJFg0GlVZWZk8Ho88Ho9aWlpMR3KE\n7OxsvX79WpK0tLSk7Oxsw4mcKWWOxxcWFv7wIdq1a5c+ffpkMJEzpKWlafv27ZKkUCik48ePU9gJ\n0tbWpps3b5qO4TgzMzPa2NjQ1atXdeHCBUWjUdORHOHUqVOanZ1VVVWV6uvr1dTUZDqSI6XMTvvP\nuL01sZ4+fapQKKRAIGA6iiM8fPhQR44c0d69e01HcaTPnz+rq6tLs7OzunTpkkZHR+VyuUzHstqj\nR4+Un5+v/v5+vXr1Sn6/n99iJEHKlHZubq4WFhZ+Po7FYtq9e7fBRM4xNjamnp4e9fX1KTMz03Qc\nR4hEIvrw4YMikYjm5ubkdru1Z88eHTt2zHQ06+Xk5KioqEjp6enat2+fMjIytLi4qJycHNPRrDY5\nOamKigpJUmFhoWKxGKOyJEiZ4/Hy8nI9efJEkjQ1NaXc3Fzm2QmwvLys9vZ23bt3Tzt37jQdxzE6\nOjoUDoc1ODiouro6Xbt2jcJOkIqKCo2Pj+v79++Kx+NaW1tj/poA+/fv1/PnzyVJHz9+VEZGBoWd\nBCmz0y4uLtbBgwfl8/nkcrl069Yt05Ec4fHjx4rH42poaPj5XFtbm/Lz8w2mAv5aXl6eTp48qXPn\nzkmSmpubtW1byuxfksbr9crv96u+vl5fv37V7du3TUdyJP6aEwAAS/D1EgAAS1DaAABYgtIGAMAS\nlDYAAJagtAEAsASlDQCAJShtAAAsQWkDAGCJH4uERqtOLdVtAAAAAElFTkSuQmCC\n","text/plain":["<matplotlib.figure.Figure at 0x7f4e50d717d0>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"6LxgDV5XJVRU"},"source":["With just a little bit of extra work we can easily plot multiple lines at once, and add a title, legend, and axis labels:"]},{"cell_type":"code","metadata":{"id":"IVRdhmwQJZhD","colab":{"base_uri":"https://localhost:8080/","height":376},"executionInfo":{"elapsed":876,"status":"ok","timestamp":1538204743060,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"4adfcf15-f29c-40da-8219-36f8eca08858"},"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","\n","# Compute the x and y coordinates for points on sine and cosine curves\n","x = np.arange(0, 3 * np.pi, 0.1)\n","y_sin = np.sin(x)\n","y_cos = np.cos(x)\n","\n","# Plot the points using matplotlib\n","plt.plot(x, y_sin)\n","plt.plot(x, y_cos)\n","plt.xlabel('x axis label')\n","plt.ylabel('y axis label')\n","plt.title('Sine and Cosine')\n","plt.legend(['Sine', 'Cosine'])\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAfsAAAFnCAYAAAChL+DqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ne6mXl98/y8DwBGkJYTx6bzYxEYG3vT2bzUb1xR7+eLgR48AYy3J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EXN0Lt+5o+3HGLONs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\nYy9JUuSMvSRJkfsBhVS2RZOUY88AAAAASUVORK5CYII=\n","text/plain":["<matplotlib.figure.Figure at 0x7f4e504d9590>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"UMBFUf92JfYY"},"source":["You can read much more about the `plot` function [in the documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot).\n","\n","##Subplots\n","\n","You can plot different things in the same figure using the `subplot` function. Here is an example:"]},{"cell_type":"code","metadata":{"id":"ckAH_ANMJ5yn","colab":{"base_uri":"https://localhost:8080/","height":362},"executionInfo":{"elapsed":769,"status":"ok","timestamp":1538204877155,"user":{"displayName":"Deep Learning","photoUrl":"","userId":"10480134507639682450"},"user_tz":-180},"outputId":"8b431f7c-a5a8-4302-8ebd-34fb312bbad5"},"source":["import numpy as np\n","import matplotlib.pyplot as plt\n","\n","# Compute the x and y coordinates for points on sine and cosine curves\n","x = np.arange(0, 3 * np.pi, 0.1)\n","y_sin = np.sin(x)\n","y_cos = np.cos(x)\n","\n","# Set up a subplot grid that has height 2 and width 1,\n","# and set the first such subplot as active.\n","plt.subplot(2, 1, 1)\n","\n","# Make the first plot\n","plt.plot(x, y_sin)\n","plt.title('Sine')\n","\n","# Set the second subplot as active, and make the second plot.\n","plt.subplot(2, 1, 2)\n","plt.plot(x, y_cos)\n","plt.title('Cosine')\n","\n","# Show the figure.\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAecAAAFZCAYAAACizedRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMf72thSWNZ1Bl1e5R4GW4mwHR04X8d6GdLy9dDw1vZus96XpDHrulK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1gf68WfX/heDQa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wTwyIYkgf+cfgXOE3DozKc42lFNYxVtrUikoqSEuKpBHJiYR5mZNTOQLaR+S12srLK3hrdWpnC+s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\n11VV7QAAAABJRU5ErkJggg==\n","text/plain":["<matplotlib.figure.Figure at 0x7f4e504eb090>"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"YSELflXHJ-6E"},"source":["You can read much more about the `subplot` function [in the documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.subplot)."]}]}
\ No newline at end of file
diff --git a/a0.1/a0.2/a0.2 arian shishehgar.ipynb b/a0.1/a0.2/a0.2 arian shishehgar.ipynb
new file mode 100644
index 0000000..078f926
--- /dev/null
+++ b/a0.1/a0.2/a0.2 arian shishehgar.ipynb	
@@ -0,0 +1,358 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 2 — NumPy, pandas & Matplotlib Essentials\n",
+        "Welcome to your first data-science sprint! In this three-part mini-project you’ll touch the libraries every ML practitioner leans on daily:\n",
+        "\n",
+        "1. NumPy — fast n-dimensional arrays\n",
+        "\n",
+        "2. pandas — tabular data wrangling\n",
+        "\n",
+        "3. Matplotlib — quick, customizable plots\n",
+        "\n",
+        "Each part starts with “Quick-start notes”, then gives you two bite-sized tasks. Replace every # TODO with working Python in a notebook or script, run it, and check your results. Happy hacking! 😊\n"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1 · NumPy 🧮\n",
+        "Quick-start notes\n",
+        "Core object: ndarray (n-dimensional array)\n",
+        "\n",
+        "Create data: np.array, np.arange, np.random.*\n",
+        "\n",
+        "Summaries: mean, std, sum, max, …\n",
+        "\n",
+        "Vectorised math beats Python loops for speed\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Mock temperatures\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ebeb237d-31ea-4dfc-9736-b9fcbbc564a8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[18.93701156 27.593445   16.50126771 25.58584533 18.09586218 17.86130721\n",
+            " 24.24484231 19.43608677 20.54325733 23.57305034 20.17821252 29.72344268\n",
+            " 21.49429446 18.00804749 19.5554526  17.98774373 20.85846283 23.26169492\n",
+            " 21.77367586 20.02216274 14.88395453 15.03910706 29.32248716 15.41281635\n",
+            " 19.73908782 19.96069846 25.83458909 23.58886475 18.96398819 19.14954796\n",
+            " 16.64948117 24.45894796 23.74319865 16.22423949 21.47387055 14.43955319\n",
+            " 14.42578208 12.36944239 29.27961914 12.64137225 22.03244179 13.9031956\n",
+            " 26.48461514 13.38525937 23.52295268 12.76659403 22.23279563 21.93323045\n",
+            " 18.10013783 21.79434078 25.01527    29.52234858 17.34861545 25.32153311\n",
+            " 15.108376   18.33128484 22.06157097 17.47822593 25.16105444 17.90933577\n",
+            " 15.30904003 18.55543531 24.88821301 18.82947249 20.58284782 16.70455433\n",
+            " 17.78558418 26.09589351 16.84331907 15.75305697 25.29391218 25.00804472\n",
+            " 19.79297359 21.38265199 14.317754   20.83146757 19.7661868  12.47333875\n",
+            " 16.39127379 25.81064088 26.69687684 17.74997887 18.45496252 30.0495807\n",
+            " 20.35815388 17.03350968 19.77891351 13.82511369 35.51029468 18.12645316\n",
+            " 17.74040435 21.82838477 26.89213895 28.19831907 21.80132509 17.18495182\n",
+            " 22.79413962 12.47522439 12.01218704 19.4138217  18.01321706 27.44849929\n",
+            " 24.06152369 20.94362732 23.3035175  27.562204   17.87175511 20.48759733\n",
+            " 13.83039199 17.7017915  18.85889604 22.385603   27.14432795 22.47869321\n",
+            " 26.04793806 21.34373979 13.21306998 23.87403122 22.61418995 23.58801159\n",
+            " 20.03636988 24.09078429 10.0197234  30.41167562 24.40647748 19.13673447\n",
+            " 19.48547081 18.90961825 20.4950565  19.92956587  9.95528851 20.76964134\n",
+            " 20.96438352 22.05226817 19.06200336 27.38250158 13.94228952 22.39066444\n",
+            " 22.1282272  26.99896439 22.52959736 17.32436601 21.84141866 23.23164168\n",
+            " 14.55081418 33.27608457 21.65939355 22.86631799 27.95947573 18.15050922\n",
+            " 28.40841426 18.2067661  27.64371194 15.91554508 21.29992919 24.89447009\n",
+            " 11.58596315 20.99175021 26.65797234 17.08395303 18.66911495 28.44136217\n",
+            " 13.08646807 17.2233157   9.01158086 16.73528345 17.98046526 16.82849063\n",
+            " 18.08368158 11.7055601  19.24536528 23.06343219 18.78338339 12.83434314\n",
+            " 15.40603041 15.72343712 27.28376403 20.55196491 23.19931338 18.86821856\n",
+            " 18.61591942 21.19870549 19.04954229 21.94469155 19.16520803 20.35687992\n",
+            " 22.59265069 18.39917765 15.15321106 23.55538273 17.29977415 30.21134284\n",
+            " 14.9118208  22.16119737 24.32649836 21.48993879 21.63833764 25.94038684\n",
+            " 20.46632721 26.23670103 18.43434721 17.32473757 23.29052955 20.53833941\n",
+            " 29.55507073 20.65106472 23.54840478 17.4680475  23.71241165 16.74965659\n",
+            " 19.7622269  22.07419566 13.71001927 20.42361661 28.43891072 24.35470221\n",
+            " 17.37600578 11.65834311 12.3965009  31.0586938  16.24721537 11.86547776\n",
+            " 13.17425029 20.98212897 12.13125162 18.44806731 12.5383058  13.95963607\n",
+            " 19.88740311 20.74932601 14.32428002  9.5078488  20.0914391  25.10928655\n",
+            " 24.14382261 19.02308679 19.06099088 23.43991069 29.06067049 26.86412143\n",
+            " 31.13495174 13.82644438 20.86870795 20.2612615  18.18644455 20.07006998\n",
+            " 12.93945158 15.37473231 21.78229557 22.44518569 23.48274388 16.44269241\n",
+            " 20.80315403 20.17290773 22.30006067 15.092774   29.32863273 14.12714529\n",
+            " 22.96093572 27.48742131 25.40505084 22.53244665 25.78101382 29.45823258\n",
+            " 13.28205444 14.41870429 18.12559639 29.13953686 25.5390739  20.99094662\n",
+            " 17.55505793 25.9400643  18.12581945 27.53519123 21.05407491 26.13925538\n",
+            " 21.14353312 13.51745221 21.29894737 25.13468807 25.82937709 22.07774325\n",
+            " 25.581339   27.65642773 20.18787659 17.6959316  21.92755376 16.90042231\n",
+            " 14.22648676 12.86153038 20.41017316 16.35652188 15.33226191 21.38889151\n",
+            " 16.387      22.78986925  9.22982935 19.67130405 29.17736694 21.46348724\n",
+            " 15.08277768 23.21315366 20.09896031 12.40469768 19.14334946 19.32396272\n",
+            " 24.23761767  7.67629927 17.68727484 10.48761068 24.26348519 17.41989919\n",
+            " 20.09730103 26.59467855 17.21728804 26.27053832 25.42359964 11.61833795\n",
+            " 20.65632435 20.29989443 23.16521895 22.2976721  21.66442675 25.84235632\n",
+            " 23.03964546 18.15845222 22.8983363  18.30458687 22.31084199 25.19252833\n",
+            " 23.51738595 18.66721943 21.75724728 12.38826497 29.61660023 23.82440414\n",
+            " 22.74502401 20.60352576 26.58755394 11.85469383 14.52520596 14.24122496\n",
+            " 15.84250857 25.89969475 22.25889362 19.70089644 21.02321358 22.20366894\n",
+            " 19.13483663 12.9762757  19.2633753  19.19279373 19.00686895 24.43964472\n",
+            "  9.55178182 20.85865641 23.16366388 20.47262071 14.85375096 19.66292809\n",
+            " 20.70727397 31.21892099 16.46753387 24.10370792 21.23897797]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 # TODO: import numpy and create an array of 365\n",
+        "# normally-distributed °C values (µ=20, σ=5) called temps\n",
+        "import numpy as np\n",
+        "temps = np.random.normal(20, 5, 365)\n",
+        "print (temps)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Average temperature\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: print the mean of temps\n",
+        "\n",
+        "print (np.mean(temps))\n"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bc0350d1-7c94-46f2-b298-ddcd42da9adf"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "20.379722001289185\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2 · pandas 📊\n",
+        "Quick-start notes\n",
+        "Main structures: DataFrame, Series\n",
+        "\n",
+        "Read data: pd.read_csv, pd.read_excel, …\n",
+        "\n",
+        "Selection: .loc[label], .iloc[pos]\n",
+        "\n",
+        "Group & summarise: .groupby(...).agg(...)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 3 — Load ride log\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: read \"rides.csv\" into df\n",
+        "# (columns: date,temp,rides,weekday)\n",
+        "\n",
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.read_csv(\"rides.csv\")"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 333
+        },
+        "outputId": "337374ee-7717-4511-843f-e4d0778d83e2"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "FileNotFoundError",
+          "evalue": "[Errno 2] No such file or directory: 'rides.csv'",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-3-3051610221.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"rides.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m   1024\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1028\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    618\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1618\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1619\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1620\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1622\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1878\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1879\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1880\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1881\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1882\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    871\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    872\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 873\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    874\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    875\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'rides.csv'"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 4 — Weekday averages"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: compute and print mean rides per weekday\n",
+        "\n",
+        "mean_rides = df.groupby('weekday')['rides'].mean()\n",
+        "\n",
+        "\n",
+        "print(mean_rides)\n"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3 · Matplotlib 📈\n",
+        "Quick-start notes\n",
+        "Workhorse: pyplot interface (import matplotlib.pyplot as plt)\n",
+        "\n",
+        "Figure & axes: fig, ax = plt.subplots()\n",
+        "\n",
+        "Common plots: plot, scatter, hist, imshow\n",
+        "\n",
+        "Display inline in Jupyter with %matplotlib inline or %matplotlib notebook"
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 5 — Scatter plot"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: scatter-plot temperature (x) vs rides (y) from df\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.scatter(df['temp'], df['rides'])\n",
+        "plt.xlabel('Temperature (°C)')\n",
+        "plt.ylabel('Number of Rides')\n",
+        "plt.title('Scatter Plot of Temperature vs Rides')\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 207
+        },
+        "outputId": "c7ed580c-7805-4012-f119-7ed9aaee9940"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'df' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-4-1845369902.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'temp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'rides'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Temperature (°C)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Number of Rides'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 6 — Show the figure\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 # TODO: call plt.show() so the plot appears\n",
+        "plt.show()\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.2/rides.csv b/a0.1/a0.2/rides.csv
new file mode 100644
index 0000000..8608570
--- /dev/null
+++ b/a0.1/a0.2/rides.csv
@@ -0,0 +1,366 @@
+date,temp,rides,weekday
+2024-01-01,13.3,141,Monday
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diff --git a/a0.1/a0.3/AI-DS_Nexus__A0_3_Amirhossein_Zandi_8fca80.ipynb b/a0.1/a0.3/AI-DS_Nexus__A0_3_Amirhossein_Zandi_8fca80.ipynb
new file mode 100644
index 0000000..29bd5df
--- /dev/null
+++ b/a0.1/a0.3/AI-DS_Nexus__A0_3_Amirhossein_Zandi_8fca80.ipynb
@@ -0,0 +1,474 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7wR0aAR-evto"
+      },
+      "source": [
+        "# 📚 Assignment 3 — Functions, Derivatives & Gradients\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "\n",
+        "This assignment explores the mathematical foundation of machine learning: functions and their derivatives. You’ll visualize, analyze, and code up essential concepts every ML practitioner uses.\n",
+        "\n",
+        "\n",
+        "**👀🔎 What do you see?**\n",
+        "\n",
+        "Look at the snippet codes and:\n",
+        "\n",
+        "* Describe any patterns, surprises, or connections you notice.\n",
+        "\n",
+        "* Write a few sentences about your observations! 📝✨\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cCb1hk3xe0E6"
+      },
+      "source": [
+        "## 1. Log and Exp: Proving Inverse Relationship\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KOxJ9f-3eu5F",
+        "outputId": "31d8ce39-8670-4dc4-9053-6c6aaf10d1ff"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "x\tlog(exp(x))\texp(log(x))\n",
+            "1.00\t1.00\t\t1.00\n",
+            "2.00\t2.00\t\t2.00\n",
+            "10.00\t10.00\t\t10.00\n",
+            "2.72\t2.72\t\t2.72\n",
+            "\n",
+            "log(exp(y)) for 0 and negative values:\n",
+            "[ 0. -1.  5.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import -------- as np\n",
+        "\n",
+        "# Show log(exp(x)) == x and exp(log(x)) == x for several values\n",
+        "x_vals = np.array([1, 2, --------, np.e])\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "for x in --------:\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# Edge case: try negative and zero values (show error/NaN for log)\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lVZufz08g1hI"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 1) The log and exp functions are also inverse, and ultimately the answer is the original number itself, and log is used for positive numbers, and returns an error or nan for zero and negative values."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mOAqtnk6fDH6"
+      },
+      "source": [
+        "## 2. Plotting Sine Functions Variations\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "DoWdSehie66R",
+        "outputId": "a83a3ade-6ca7-425c-9fa8-59ca9c343d9a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.--------(-2*np.pi, 2*np.pi, --------)\n",
+        "\n",
+        "# look at the description of each function and call the proper function from numpy\n",
+        "plt.plot(x, np.sin(--------), label='sin(x)')\n",
+        "plt.plot(x, np.--------(2*x), label='sin(2x)')\n",
+        "plt.plot(x, np.sin(--------), label='sin(x/2)')\n",
+        "plt.plot(x, np.sin(x)--------, label='sin²(x)')\n",
+        "plt.legend()\n",
+        "plt.title(\"Variations of Sine Functions\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dMVcqrfihf0P"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 2) Changing the coefficient inside the sine function causes a change in the number of oscillations. For example, the oscillations of sin(2x) are greater than sin(x/2)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4-5A1OkdfHXq"
+      },
+      "source": [
+        "## 3. Subplots: Trig, Inverse, Exp, and Log"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 807
+        },
+        "id": "DxocsCu6fGOR",
+        "outputId": "5864a5cc-5776-45bd-99f7-028c64e301af"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1600x800 with 8 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x = np.--------(-1, 1, 400)\n",
+        "x2 = --------.linspace(0.1, 2, 400)  # For log and exp\n",
+        "\n",
+        "# based on the number of subplots what should be the dimensions in the following line?\n",
+        "fig, axs = plt.subplots(--------, --------, figsize=(16, 8))\n",
+        "axs[0,0].plot(x, np.sin(x));      axs[0,0].set_title(\"sin(x)\")\n",
+        "axs[0,1].plot(x, np.cos(x));      axs[0,1].set_title(\"cos(x)\")\n",
+        "axs[0,2].plot(x, np.tan(x));      axs[0,2].set_title(\"tan(x)\")\n",
+        "axs[0,3].plot(x, np.arcsin(x));   axs[0,3].set_title(\"arcsin(x)\")\n",
+        "axs[1,0].plot(x, np.arccos(x));   axs[1,0].set_title(\"arccos(x)\")\n",
+        "axs[1,1].plot(x, np.arctan(x));   axs[1,1].set_title(\"arctan(x)\")\n",
+        "axs[1,2].plot(x2, np.exp(x2));    axs[1,2].set_title(\"exp(x)\")\n",
+        "axs[1,3].plot(x2, np.log(x2));    axs[1,3].set_title(\"log(x)\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.--------()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "thpsEzFVikPh"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 3) Well, here we see the graph of trigonometric functions and their inverses."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WAidrU51fMqB"
+      },
+      "source": [
+        "## 4. Features of the Sigmoid Function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "lehJV_vNfLXx",
+        "outputId": "9cfc6ec4-9dfc-4220-a955-4d6a11fac8f3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Range: (np.float64(4.5397868702434395e-05), np.float64(0.9999546021312976)) | At x=0: 0.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "def sigmoid(x):\n",
+        "    return --------\n",
+        "\n",
+        "x = np.linspace(-10, 10, 200)\n",
+        "y = --------(x)\n",
+        "\n",
+        "plt.plot(x, y)\n",
+        "plt.title(\"Sigmoid Function\")\n",
+        "plt.xlabel(\"x\")\n",
+        "plt.ylabel(\"σ(x)\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"Range:\", (y.min(), y.max()), \"| At x=0:\", sigmoid(0))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DUONuiAxilL0"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 4) This is also a sigmoid function, which is a derivative of the exp function, and the output it returns is between 0 and 1."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1FjRt_31fXsx"
+      },
+      "source": [
+        "## 5. Derivatives of Famous Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "vyyGBuHRfZ4n",
+        "outputId": "7a89fce7-f973-46bc-dca3-c452f1a34561"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x = np.linspace(-3, 3, 400)\n",
+        "plt.--------(x, x**2, label=\"x²\")\n",
+        "plt.plot(x, --------, '--', label=\"d/dx x²\")\n",
+        "\n",
+        "x = np.linspace(0, 3, 400)\n",
+        "plt.plot(x, np.exp(x), label=\"exp(x)\")\n",
+        "plt.plot(x, --------, '--', label=\"d/dx exp(x)\")\n",
+        "\n",
+        "plt.plot(x, np.log(x+0.1), label=\"log(x)\")\n",
+        "plt.plot(x, --------, '--', label=\"d/dx log(x)\")\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.title(\"Functions and Their Derivatives\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DURup9u5il4-"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# 5) Here are the graphs of several functions and their derivatives. For example, for x to the power of 2, 2x is a straight line."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w3QIKTZQf1ZK"
+      },
+      "source": [
+        "## 6. Gradient of Selected Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Eeudgf3-f0Ie",
+        "outputId": "aeffb463-3230-4b98-8131-623b4fbf624c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Function: f(x, y) = x**2*y + sin(y)\n",
+            "Partial derivative w.r.t x (∂f/∂x): 2*x*y\n",
+            "Partial derivative w.r.t y (∂f/∂y): x**2 + cos(y)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# search and learn more about sumpy\n",
+        "import sympy as sp\n",
+        "\n",
+        "# Define symbolic variables\n",
+        "x, y = sp.symbols('x y')\n",
+        "\n",
+        "# Define the function\n",
+        "f = x**2 * y + sp.sin(y)\n",
+        "\n",
+        "# Compute partial derivatives\n",
+        "df_dx = sp.diff(f, x)\n",
+        "df_dy = sp.diff(f, y)\n",
+        "\n",
+        "print(\"Function: f(x, y) =\", f)\n",
+        "print(\"Partial derivative w.r.t x (∂f/∂x):\", df_dx)\n",
+        "print(\"Partial derivative w.r.t y (∂f/∂y):\", df_dy)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wDlIJkmYimg9"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Here, using the sympy package, the partial derivatives of the function with respect to the variables x and y are calculated. These derivatives are actually the components of the gradient of the function."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.12.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.3/AI-DS_Nexus__A0_3_Javid_Mohammadi_a60d6f.ipynb b/a0.1/a0.3/AI-DS_Nexus__A0_3_Javid_Mohammadi_a60d6f.ipynb
new file mode 100644
index 0000000..009f09e
--- /dev/null
+++ b/a0.1/a0.3/AI-DS_Nexus__A0_3_Javid_Mohammadi_a60d6f.ipynb
@@ -0,0 +1,637 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 3 — Functions, Derivatives & Gradients\n",
+        "This assignment explores the mathematical foundation of machine learning: functions and their derivatives. You’ll visualize, analyze, and code up essential concepts every ML practitioner uses.\n",
+        "\n",
+        "\n",
+        "**👀🔎 What do you see?**\n",
+        "\n",
+        "Look at the snippet codes and:\n",
+        "\n",
+        "* Describe any patterns, surprises, or connections you notice.\n",
+        "\n",
+        "* Write a few sentences about your observations! 📝✨\n"
+      ],
+      "metadata": {
+        "id": "7wR0aAR-evto"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Log and Exp: Proving Inverse Relationship\n"
+      ],
+      "metadata": {
+        "id": "cCb1hk3xe0E6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KOxJ9f-3eu5F",
+        "outputId": "83e970f7-1813-421b-def4-b781bc9bd964"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "x\tlog(exp(x))\texp(log(x))\n",
+            "1.00\t1.00\t\t1.00\n",
+            "2.00\t2.00\t\t2.00\n",
+            "10.00\t10.00\t\t10.00\n",
+            "2.72\t2.72\t\t2.72\n",
+            "\n",
+            "log(exp(y)) for 0 and negative values:\n",
+            "[ 0. -1.  5.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Show log(exp(x)) == x and exp(log(x)) == x for several values\n",
+        "x_vals = np.array([1, 2, 10, np.e])\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "for x in x_vals:\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# Edge case: try negative and zero values (show error/NaN for log)\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:**\n",
+        "We want to check whether log(exp(x)) is equal to exp(log(x)).\n",
+        "\n",
+        "First we created a list of positive numbers.  \n",
+        "Then, we used a loop to test each number in the list and printed the results in columns with two decimal places, using Numpy's exp() and log() functions.  \n",
+        "As the output shows, the identity works well for positive numbers.\n",
+        "\n",
+        "Next, we tested zero and negative numbers. However, since the logarithm function cannot take zero or negative values as input, we couldn't evaluate exp(log(x)) for those cases.  \n",
+        "\n",
+        "On the other hand, log(exp(x)) is defined for all real numbers, including zero and negatives, and it simply returns x."
+      ],
+      "metadata": {
+        "id": "lVZufz08g1hI"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Plotting Sine Functions Variations\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "mOAqtnk6fDH6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.linspace(-2*np.pi, 2*np.pi, 1000)\n",
+        "\n",
+        "# look at the description of each function and call the proper function from numpy\n",
+        "plt.plot(x, np.sin(x), label='sin(x)')\n",
+        "plt.plot(x, np.sin(2*x), label='sin(2x)')\n",
+        "plt.plot(x, np.sin(x / 2), label='sin(x/2)')\n",
+        "plt.plot(x, np.sin(x) ** 2, label='sin²(x)')\n",
+        "plt.legend()\n",
+        "plt.title(\"Variations of Sine Functions\")\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "DoWdSehie66R",
+        "outputId": "00adb95f-a91d-4214-b6bb-f62dd044a776"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:**\n",
+        "The function sin(x) (blue line) starts at (0,0), rises to 1, falls to -1, and then returns to zero, forming a smooth and cyclic wave as x increases.  \n",
+        "The function sin(2x) (orange line) oscillates twice as fast as sin(x), completing more cycles over the same interval.  \n",
+        "In contrast, sin(x/2) (green line) oscillates more slowly, completing only half as many cycles as sin(x).  \n",
+        "The function sin(x**2) (red line) behaves differently -- its oscillations become more rapid as x increase, and it doesn't follow a regular period like the others."
+      ],
+      "metadata": {
+        "id": "dMVcqrfihf0P"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Subplots: Trig, Inverse, Exp, and Log"
+      ],
+      "metadata": {
+        "id": "4-5A1OkdfHXq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = np.linspace(-1, 1, 400)\n",
+        "x2 = np.linspace(0.1, 2, 400)  # For log and exp\n",
+        "\n",
+        "# based on the number of subplots what should be the dimensions in the following line?\n",
+        "fig, axs = plt.subplots(2, 4, figsize=(16, 8))\n",
+        "axs[0,0].plot(x, np.sin(x));      axs[0,0].set_title(\"sin(x)\")\n",
+        "axs[0,1].plot(x, np.cos(x));      axs[0,1].set_title(\"cos(x)\")\n",
+        "axs[0,2].plot(x, np.tan(x));      axs[0,2].set_title(\"tan(x)\")\n",
+        "axs[0,3].plot(x, np.arcsin(x));   axs[0,3].set_title(\"arcsin(x)\")\n",
+        "axs[1,0].plot(x, np.arccos(x));   axs[1,0].set_title(\"arccos(x)\")\n",
+        "axs[1,1].plot(x, np.arctan(x));   axs[1,1].set_title(\"arctan(x)\")\n",
+        "axs[1,2].plot(x2, np.exp(x2));    axs[1,2].set_title(\"exp(x)\")\n",
+        "axs[1,3].plot(x2, np.log(x2));    axs[1,3].set_title(\"log(x)\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 603
+        },
+        "id": "DxocsCu6fGOR",
+        "outputId": "f655fb86-6b5c-4748-f567-25eb7117b811"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 8 Axes>"
+            ],
+            "image/png": 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+/fpXf7EiInLdLtVL9ZdffiErK4ulS5fy+OOP07NnT7p06XLdD7fgyu0E5C3smpKSwqRJk9i4cSNTp0695H4Xjr8wulBERK7NggULGDJkCB988AEDBgzgzjvvpEOHDiQmJl7V8XXq1AG44n3GBU2bNmX8+PFs2LCBP//8k5iYGGbMmHEj8fM1bNjwsm3Knj17eP311xk2bBghISE8+uijlx0tEhERoTZFbogKGyLXyWq1XvTl7O3tjZ+fH1lZWTd8/rZt22IYBqGhoRe99+KLLxIVFcXcuXOZMmUKAQEBDBky5JKfGxoaStu2bW84j4iIXB07Ozv69u3LL7/8kt8D9p8Mw6Bnz55s27aNzZs3529PS0vj888/JyAgIH+6qAvzol/g5OREYGAghmGQk5MDkP8df6nPmjJlCps2beLzzz/njTfeoF27dowcOfKSc9mGhobSuHHj/Hl5RUSkcJUrVw6gwAOtCz1l/9kzNikpidmzZ1/357Ro0QInJ6dLthMLFixg/vz5vPPOO4wdO5ZBgwYxfvx4jhw5ctG+oaGheHl50bhx4+vOIiJSltnb2180hdMnn3yC1Wq9quO7du2Kh4cHkyZNyu/kdMGF8yYnJ5Obm1vgvaZNm2JnZ3dTnlVB3v3Hvn37LjpfTk4OQ4cOxc/Pj48++og5c+YQFxeXP+rwn5KSkjh+/Djt2rW7KZmkbLp45UgRuSopKSlUr16dAQMGEBQUhLu7O6tXr2b79u188MEHN3z+Dh06UKlSJVavXs3tt9+ev33t2rV89tlnTJw4kebNmwMwe/ZsOnXqxCuvvMLkyZPz9z1z5gx79uzhySefvOE8IiJy9d5++21WrlxJx44dGTFiBI0aNeL06dP89NNPbNy4kbFjx/L999/To0cPnn76aSpWrMjcuXOJiIhg4cKF2Nnl9T3p2rUrvr6+tG/fHh8fHw4ePMinn35Kr1698PDwAKB27do0adKE1atXM3z48PwMBw8e5JVXXmHo0KH07t0bgDlz5hAcHMyoUaP48ccf8/fNyclh/fr1jBo1qgj/lEREyrYWLVoA8PLLLzNo0CAcHR257bbbcHJyonfv3jz++OOkpqbyxRdf4O3tzenTp6/rc1xcXOjatSurV6/m9ddfz99+5swZRo4cSefOnRk9ejQAn376KX/88QdDhw5l48aN+e0R5C1a27t3b82HLiJyne666y6+/vprvLy8CAwMZPPmzaxevTp/ysB/4+npyYcffsijjz5Kq1ateOCBB6hQoQK7d+8mPT2duXPnsnbtWkaPHs29995L/fr1yc3N5euvv8be3p7+/fvflOvo06cPb7zxBuvXr6dr16752998803CwsJYs2YNHh4eNGvWjAkTJjB+/HgGDBhAz5498/ddvXo1hmHQp0+fm5JJyihDRK5LVlaW8fzzzxtBQUGGh4eHUa5cOSMoKMj47LPP8vcZMmSIUbNmzfyfIyIiDMB47733LjofYEycOLHAtqefftqoW7du/s/JyclGzZo1jebNmxs5OTkF9n322WcNOzs7Y/Pmzfnbpk+fbri5uRnJyck3eLUiInKtTpw4YQwePNioUqWK4ezsbNSuXdt48sknjaysLMMwDOP48ePGgAEDjPLlyxsuLi5G69atjV9//bXAOWbOnGncdtttRqVKlQxnZ2ejTp06xvPPP28kJSUV2G/KlCmGu7u7kZ6ebhiGYeTm5hqtWrUyqlevbiQmJhbY96OPPjIAY/78+fnbfv/9dwMwjh49Whh/FCIichlvvPGGUa1aNcPOzs4AjIiICGPp0qVGs2bNDBcXFyMgIMB49913jVmzZuW/f0HNmjWNXr16XXTOjh07Gh07diywbdGiRYbFYjGioqLyt/Xr18/w8PAwIiMjC+y7ZMkSAzDefffd/G0HDx40AGP16tU358JFRMqg8+fPG8OGDTMqV65suLu7G926dTMOHTpk1KxZ0xgyZEj+frNnzzYAY/v27Zc8z9KlS4127doZrq6uhqenp9G6dWvj+++/NwzDMMLDw43hw4cbderUMVxcXIyKFSsanTt3vuj7+2o/848//jAA448//iiwvVmzZsYjjzyS/3NoaKjh4OBgPPXUUwX2u3Bf4ufnZ5w/fz5/+8CBA40OHTr82x+ZyBVZDOMyy9iLiOnCw8Np2LAhv//+O3fcccc1Hx8SEkKnTp348MMPCyGdiIgUF0lJSdSuXZvJkyfzyCOPXPPxffv2xWKx8PPPPxdCOhERMZvVaiUwMJD77ruPN95445qPf+aZZ9iwYQOhoaEasSEiInz99dc8+eSTREVFUb58+Ws6NjY2llq1avHDDz9oxIbcEBU2RIq5kSNHcuzYMVatWnVNxy1fvpwBAwYQHh6Ot7d3IaUTEZHi4t1332X27NkcOHCgwNQh/+bgwYM0bdqUsLAwmjRpUogJRUTETPPnz2fkyJFERUXh7u5+1cclJCRQs2ZNfvzxxwLTiIiISNlls9lo1qwZ999/Py+//PI1HTt27FjWrl3Ltm3bCimdlBUqbIiIiIiIiIiIiIiISIlx9d35RERERERERERERERETKbChoiIiIiIiIiIiIiIlBgqbIiIiIiIiIiIiIiISImhwoaIiIiIiIiIiIiIiJQYDmYHMIPNZuPUqVN4eHhgsVjMjiMiUmIYhkFKSgp+fn7Y2ZXe2rjaCRGR66N2QkRErkTthIiIXMm1tBNlsrBx6tQp/P39zY4hIlJiRUdHU716dbNjFBq1EyIiN0bthIiIXInaCRERuZKraSfKZGHDw8MDyPsD8vT0NDmNiEjJkZycjL+/f/73aGmldkJE5PqonRARkStROyEiIldyLe1EmSxsXBgG6OnpqQZGROQ6lPbh1GonRERujNoJERG5ErUTIiJyJVfTTpTeCQ1FRERERERERERERKTUUWFDRERERERERERERERKDBU2RERERERERERERESkxFBhQ0RERERERERERERESgwVNkREREREREREREREpMRQYUNEREREREREREREREoMFTZERERERERERERERKTEUGFDRERERERERERERERKDBU2RESkWNuwYQO9e/fGz88Pi8XC4sWL//WYdevW0bx5c5ydnalbty5z5swp9JwiIiIiIiIiIlI0VNgQEZFiLS0tjaCgIKZNm3ZV+0dERNCrVy86d+5MWFgYzzzzDI8++igrVqwo5KQiIiIiIiIiIlIUHMwOICIiRcswDCwWi9kxrlqPHj3o0aPHVe8/Y8YMatWqxQcffABAo0aN2LhxIx9++CHdunUrrJgiIiIiIiIiIlJEVNgQESlDtkeeY+KS/XzyQAh1qribHadQbN68mS5duhTY1q1bN5555pnLHpOVlUVWVlb+z8nJyYUVT+SqJaXncOxsCjGJmcSczyA+NYusXCuZOTZsNoNyzg64uzjg6eJIjYpu1KyU9/JwcTQ7uoiIiJRSp5MyGDEvlEdvrcXdQX4lqsOUiIgUvoOnk/lk7VHqeXvw7J31C/WzVNgQESkDUrNyeef3g3yzJQqAD1Ye5rMHW5icqnDExsbi4+NTYJuPjw/JyclkZGTg6up60TGTJk3itddeK6qIIpcUfS6ddYfPsC3yPHtOJnIiIf26zlOnSjla1KxAi5oV6FjfG18vl5ucVERERMqqLzZEsDcmie+2RtEnuJrZcUREpJg5kZDOb3tjaV4jU4UNERG5MRuPxvPiwj3EJGYAcH9rf8Z2b2RyquJl3LhxjBkzJv/n5ORk/P39TUwkZUX42VQW7jzJiv1xHDuTetH7fl4uVK/oRrXyrnh7OOPiaI+zox32Fgtp2VZSM3M5n55N1Ll0TiSkEZ+azfGzaRw/m8aPO04CEORfnq6BPvQJ9qN6BbeivkSRQrdhwwbee+89QkNDOX36ND///DN9+/a97P7r1q2jc+fOF20/ffo0vr6+hZhURKRkS0jN4rttJwB4snNdk9OIiEhxdC4tG4CK5ZwK/bNU2BARKaVSMnN4+7dDfL8tb5SGf0VX3u3fjHZ1KpucrHD5+voSFxdXYFtcXByenp6XHK0B4OzsjLOzc1HEEyEr18qSsFPM3x5N6Inz+dvt7Sy0qFmB2+pVJsi/PE2reVHe7dp+GTyXls2uqPOEnjjP5vAEdkUlsjs67/X+ysPcVq8K97euQZdG3jjY293sSxMxRVpaGkFBQQwfPpx+/fpd9XGHDx/G09Mz/2dvb+/CiCciUmrM/iuSzBwbzap7cWu90n1PISIi1+d8ugobIiJyA/48epaxC/fmj9IY0rYmL3RvSDnn0v+137ZtW3777bcC21atWkXbtm1NSiSSJzkzh++2RjFrYwRnUvLWdLGzQKcG3vQNqUbH+lXwcr2x9TEqlnPijkY+3NEobzq2M8mZrD54hl/3nGLT8QTWHznL+iNnqVHRjZGd6tCveTWcHexv+NpEzNSjRw969Ohxzcd5e3tTvnz5mx9IRKQUSs7MYe7mSABGdapbotbW0Mg+EZGic2HERgUVNkRE5Fr8/1EaNSq68W7/ZrStU8nkZNcvNTWVY8eO5f8cERFBWFgYFStWpEaNGowbN46YmBjmzZsHwBNPPMGnn37KCy+8wPDhw1m7di0//vgjy5YtM+sSpIzLzLEyb3Mkn649RnJmLgA+ns4MaRfAgObV8fYsvDUwvD1deKBNDR5oU4MTCWn8sD2a+dujiTqXzrhFe/lo9VGeuqMug1rVwN6u5DygELkZgoODycrKokmTJrz66qu0b9/e7EgiIsXW15tPkJKZSz1vd7oG+vz7AcWIRvaJiBSd/KmornH2geuhwoaISCmxLeIcz84PKzBK48UeDXFzKtlf9Tt27CjQY+rCWhhDhgxhzpw5nD59mqioqPz3a9WqxbJly3j22Wf56KOPqF69Ol9++SXdunUr8uxSthmGwdLdp5i8/HD+v8u63u48fltt+gRXw8mhaKeCqlmpHC92b8jTt9fju21RfL7hOLHJmbz88z6+3nyC8b0C6aBpJaQMqFq1KjNmzKBly5ZkZWXx5Zdf0qlTJ7Zu3Urz5s0veUxWVhZZWVn5PycnJxdVXBER02VkW5m1MQKAUZ3rYFfCOkNoZJ+ISNHRGhsiInLVcqw2pq4+wvR1x7EZeWtpvDcgiFtql9xRGv/UqVMnDMO47Ptz5sy55DG7du0qxFQiVxYZn8bLi/fy17EEAHw9XXiua336Na9u+sgIVyd7HulQi4duqcG3W6L4aM1RDsWm8NBXW+ne2JfX+zbG26PwRpGImK1BgwY0aNAg/+d27dpx/PhxPvzwQ77++utLHjNp0iRee+21ooooIlKsfL8tioS0bPwrutK7mZ/ZcYrMtYzsUwFcRCRPUa6xoVUjRURKsPCzqfSfvolpf+QVNe5tUZ3f/3NbqSlqiJQ0VpvB9HXH6TZ1A38dS8DZwY7/dq3PH//txL0t/U0vavyTs4M9wzvUYv3znRjaLgB7OwvL98dy55QNLAw9ecWCokhp07p16wLTHv5/48aNIykpKf8VHR1dhOlERMyTnWvj8w3hADzRsQ4O9qX/MdKFkX0LFy5k4cKF+Pv706lTJ3bu3HnZYyZNmoSXl1f+y9/fvwgTi4gUHwmpGrEhIiJXYBgGP2yP5vVfDpCRY8XL1ZFJ/ZrSs2lVs6OJlFkxiRk8+0MY2yLPAdChbmXeuqcJNSuVMznZlZV3c+LVuxtzX0t/Xli4m30xyTz3025+23ua9+4NKpJfSEXMFhYWRtWql29DnZ2dcXZ2LsJEIiLFw/wd0cQmZ+Lt4cyAFtXNjlMkrmdk37hx4/KnzIW8ERsqbohIWVSUIzZU2BARKWHOpWUzduEeVh6IA6BdnUp8cF8QVb1cTU4mUnb9uucU4xbtJSUzF3dnByb2DmRAi+pYLMVnhMa/CfTzZPGo9nz+ZzhTVx9lzaEz9PzoTz6+P4TWtSqaHU/kslJTUwuMtoiIiCAsLIyKFStSo0YNxo0bR0xMDPPmzQNg6tSp1KpVi8aNG5OZmcmXX37J2rVrWblypVmXICJSLGXmWPl07VEAnuxcF2cHe5MTmad169Zs3Ljxsu+rAC4iktdupGdbAaigwoaIiPzT1vAEnv5hF3HJWTjaW3i+WwMe7VC7xC3gJ1Ja5FhtvP3bQWb/FQlAsH95Ph4UQo1KbuYGu04O9naM6lSXTvW9Gf39TsLPpjHo880817UBozrVKVGFGik7duzYQefOnfN/vtBjdsiQIcyZM4fTp08TFRWV/352djbPPfccMTExuLm50axZM1avXl3gHCIiAt9ujSIuOQs/LxcGtS7bow/+bWSfiIhA+Nk0ADxcHPBwLvyygwobIiIlgM1mMH39cT5YeRibAbWrlOPjQSE0qeZldjSRMishNYsnv9vJlvC8qadGdarDs3fWx7EUzD0d6OfJL6M7MGHJfhbuPMl7Kw5z4HQy7w8IwtWp7PbWlOKpU6dOV1wTZs6cOQV+fuGFF3jhhRcKOZWISMmWlpXL9HV5o+GevqNeiR6toZF9IiJFY/+pJAAa+3kWSae4Qr/znjZtGgEBAbi4uNCmTRu2bdt22X07deqExWK56NWrV6/8fYYOHXrR+927dy/syxARMU1CahZD52znvRV5RY17Qqrxy+gOKmqImOhwbAp3f/oXW8LPUc7JnhkPteCF7g1LRVHjgnLODnxwXxDv9GuKo72FZXtOc+/MTZxKzDA7moiIiBSyuZsjiU/NpmYlN/qX8LU1duzYQUhICCEhIUDeyL6QkBAmTJgAcNmRfU2bNqVjx47s3r2b1atXc8cdd5iSX0SkpNh/KhmAxn5F87yqUEdszJ8/nzFjxjBjxgzatGnD1KlT6datG4cPH8bb2/ui/RctWkR2dnb+zwkJCQQFBXHvvfcW2K979+7Mnj07/2fNYygipdU/p55ycbTj9bubcG/LkjVvv0hps+l4PI9/HUpKZi4Bldz4YnBL6vl4mB2r0AxqXYPaVdx54ptQ9sUk03faX8wd3ppGVT3NjiYiIiKFIDkzh5nrwwF4pku9Et9xQyP7RESKxj9HbBSFQm2dpkyZwmOPPcawYcMIDAxkxowZuLm5MWvWrEvuX7FiRXx9ffNfq1atws3N7aLChrOzc4H9KlSoUJiXISJS5Gw2g2l/HOP+L7YQl5xFnSrlWPJkB+5r5a+ihoiJlu4+xdBZ20nJzKVVQAUWP9m+VBc1LmhdqyJLnmxPAx8PzqRkcd/MzWwNTzA7loiIiBSCWRsjSMrIoa63O3cHVTM7joiIlACZOVZ2n8wrbDSrXr5IPrPQChvZ2dmEhobSpUuX/32YnR1dunRh8+bNV3WOr776ikGDBlGuXLkC29etW4e3tzcNGjRg5MiRJCToxlpESo/E9GyGz/3f1FP9QqqxdHQHGviW/oenIsXZ15sjefr7XWRbbfRo4svXj7ShvJuT2bGKjH9FN358oi2tAyqSkpnLw7O2sXxfrNmxRERE5CZKSM3iyz8jABhzZ33s7dSpSkRE/l1YdCLZuTaqeDhTp0q5fz/gJii0wkZ8fDxWqxUfH58C2318fIiN/feb4G3btrFv3z4effTRAtu7d+/OvHnzWLNmDe+++y7r16+nR48eWK3Wy54rKyuL5OTkAi8RkeLowKlk7v70L9YdPouzgx2TBzTjg/uCKOdcqDMHisi/+PLPcF5Zsh+Aoe0C+PSB5rg4ltxFNK+Xl6sj8x5pzZ2BPmTn2njyu538svuU2bFERETkJvl4zVFSs3Jp7OdJ98a+ZscREZESYsvfI/pvqV2pyGYaKbZPyr766iuaNm1K69atC2wfNGhQ/n83bdqUZs2aUadOHdatW3fZhZwmTZrEa6+9Vqh5RURu1JKwGF5cuIfMHBv+FV2Z8VCLIltwSUQub9ofx3hvxWEARnWqw/PdGpTpKeFcHO2Z/mBzXly4l4U7T/KfH3ZhMwz6BGuqChERkZIs/Gwq327NW0T75Z6NsNNoDRERuUrrj5wFoG3tSkX2mYU2YqNy5crY29sTFxdXYHtcXBy+vleu+qelpfHDDz/wyCOP/Ovn1K5dm8qVK3Ps2LHL7jNu3DiSkpLyX9HR0Vd3ESIiRSDHauO1X/bznx/CyMyxcVv9KvwyuoOKGiLFwPR1x/OLGs90qVfmixoXONjb8d6AZtzXsjo2A56dH8aSsBizY4mIiMgNeHf5IXJtBrc39KZd3cpmxxERkRIiPjWLsOhEAG5v6F1kn1tohQ0nJydatGjBmjVr8rfZbDbWrFlD27Ztr3jsTz/9RFZWFg899NC/fs7JkydJSEigatWql93H2dkZT0/PAi8RkeLgbEoWD365ldl/RQIwunNdZg9tVabm7Rcprr7ZcoJ3lx8C4PluDXimS30VNf7Bzs7CO/2aMaiVf35xY8V+rbkhIiJSEm2PPMeK/XHYWWBcj4ZmxxERkRJk7cEzGAYEVvXE18ulyD630AobAGPGjOGLL75g7ty5HDx4kJEjR5KWlsawYcMAGDx4MOPGjbvouK+++oq+fftSqVLBoSupqak8//zzbNmyhcjISNasWUOfPn2oW7cu3bp1K8xLERG56cKiE+n9yUa2RZzD3dmBmQ+34L/dGmiBPpFiYElYDK8s2QfkFRyf7FzX5ETFk52dhbfvacq9LfJGbjz1/S42H08wO5aIiIhcA8MweGvZQQAGtqpBPR8PkxOJiEhJsvTvdRd7Ni3atZkKdY2NgQMHcvbsWSZMmEBsbCzBwcEsX748f0HxqKgo7OwK1lYOHz7Mxo0bWbly5UXns7e3Z8+ePcydO5fExET8/Pzo2rUrb7zxBs7OzoV5KSIiN9WSsBieX7CH7Fwbdb3dmflwC+pUcTc7logAqw/EMebH3RgGDG5bk+e61jc7UrFmZ2dhUr+mJGXksPJAHI/N28EPI26hSTVNpyciIlISLNt7mrDoRNyc7Hn2znpmxxERkRLkTHImm47HAxT5uouFvnj46NGjGT169CXfW7du3UXbGjRogGEYl9zf1dWVFStW3Mx4IiJFymYz+GDVYab9cRyALo18mDooGHfnQv86FpGrsCPyHKO+24nVZtAvpBqv9m6s6aeugoO9HR/fH8LQ2dvYEn6OIbO28dMTbamtgq2IiEixlplj5Z3f86befPy2Onh7FN0UIiIiUvL9suc0NgOa1yiPf0W3Iv3sQp2KSkRE/ictK5cnvgnNL2qM7FSHzx9uoaKGSDFxIiGNEV+Hkp1ro0sjHyYPaIadpoa7ai6O9nwxuCVNqnmSkJbN4FnbiE/NMjuWiIiIXMHM9eGcPJ9BVS8XHrutltlxRESkhFm8KwYo+tEaoMKGiEiROHk+nf7TN7HyQBxODnZ8ODCIF7s31ENTkWIiKT2HYXO2cy4tm2bVvfjk/hAc7PVr0rXycHFkzrDW1KzkxsnzGTzxdShZuVazY4mIiMglnDyfzmfrjgHwUs9GuDmpw5WIiFy9PScT2RuThKO9hV7Nqhb55+uOXUSkkO2IPEefT//iUGwKld2d+WHELdwTUt3sWCLyt+xcG49/s4Pws2n4ebnw5eCWuDrZmx2rxKrs7sxXQ1rh4eLAjhPnGbdw72WnGRURERHzvLXsIFm5Nm6pXZG7THggJSIiJds3W04A0LNpVSq7F/361ypsiIgUogWhJ3ngi60kpGUTWNWTpaPb07xGBbNjicjfDMPg5Z/3siX8HO7ODswa1gpvT80tfaPqersz/cEW2NtZWLQrhs/WHTc7koiIiPzDX8fi+X1fLPZ2Fl69W2uKiYjItUlMz2ZJ2CkABretaUoGFTZERAqBYRi8t+IQ//1pN9lWG90b+7JgZFv8yruaHU1E/mHupkh+Cj2JvZ2FTx8IoaGvp9mRSo0O9Srz6t2NAXhvxWGW74s1OZGIiIgA5FhtTFy6H4CHb6mp339EROSafb8tmqxcG4FVPU3rwKvChojITZaVa+WZ+WH5i4SP7lyXzx5srjlrRYqZ7ZHneHPZQQDG9WhIpwbeJicqfR6+pSZD2wUA8NyPYRw7k2puIBEREWHupkiOnUmlUjknnr2zvtlxRESkhMnMsfLVxggAhneoZdqoPxU2RERuoqT0HAZ/tY0lYadwsLMwuX8z/tutgRYJFylmziRnMurbneTaDHoH+fFIh1pmRyq1xvdqxC21K5KWbWXkN6GkZeWaHUlERKTMiknMYMqqIwC80L0BXq6OJicSEZGS5qfQk8SnZlGtvCt9gv1My6HChojITRJ9Lp1+0/9ia0TeXP2zh7Xivlb+ZscSkf8nO9fGqG93cjYliwY+Hrzbv6nmlS5EDvZ2fHx/CN4ezhw9k8q4RVpMXERExAyGYTBxyT7Ss620rFmBe1voXkVERK5Ndq6NmevzZih57NZaONqbV15QYUNE5CbYHZ3IPZ/9xfGzaVT1cmHByLbcWq+K2bFE5BLe/u0gO06cx8PZgRkPt9A0cUXA28OFzx5sjoOdhaW7TzFv8wmzI4mIiJQ5y/fFsvrgGRztLUzq11SjykVE5JrN3x7FyfMZVHZ3ZmCrGqZmUWFDROQGrToQx6DPtxCfmk2jqp78PKq9FuATKaZW7I9lzqZIAKYMDKZW5XLmBipDWgZUZFzPRgC8uewAO6POm5xIRESk7EjOzMlfMHxkxzrU8/EwOZGIiJQ06dm5fLTmGAD/uaMurk72puZRYUNE5AbM2xzJ41/vICPHSsf6Vfjpibb4ermYHUtELuFUYgYvLNgDwIjbanNnoI/Jicqe4e0D6NW0KjlWg6e/30VyZo7ZkURERMqE95Yf5kxKFrUrl2NU57pmxxERkRJo9l+RxKdmUaOim+mjNUCFDRGR62IYBu+vOMyEJfuxGXB/a3++HNISd2dNaSNSHFltBs/MDyMpI4em1bz4b9cGZkcqkywWC5P6N8W/oisnz2cwYfE+syOJiIiUejujzvPN1rxpIN+8pwkujub2sBURkZLnfFo2M/5eW+O5rvVxcjC/rGB+AhGREibXamPcor18+kfe8Lsxd9bn7Xuamrpgkohc2adrj7Et4hzlnOz55P6QYvFLWFnl6eLI1IEh2NtZWBx2ip93nTQ7koiISKmVmWPl+Z92YxgwoEV12tWpbHYkEREpgd5feZiUzFwaVfWkdzM/s+MAKmyIiFyTzBwro77dyQ/bo7GzwKR+TXn6jnpYLFp4T6S42h55jo/WHAHyeikGaF0N07WoWYH/3FEPgFcW7ycqId3kRCIiIqXTh6uOcPxsGlU8nBnfq5HZcUREpATaF5PEd9uiAHi1dyB2dsXjGZgKGyIiVykpI4fBX21j5YE4nBzs+OzBFtzf2vw5BUXk8lKzcnl2fhg2A/qFVOOekOpmR5K/Pdm5Lq0CKpCalct/5u8ix2ozO5KIiEipsjPqPF/8GQ7ApHuaUt7NyeREIiJS0thsBhOW7MMwoE+wH21qVzI7Uj4VNkRErkJcciYDZ25mW+Q5PJwdmDe8Nd2b+JodS0T+xVvLDnLyfAbVK7jyWp/GZseRf7C3s/DhwGA8XBzYFZXI9HXHzY4kIiJSamTmWPnvT7vzO3d0CfQxO5KIiJRAC0JPsjMqkXJO9rzUs3iN/FNhQ0TkX4SfTaX/9E0cik2hiocz8x9vyy3FqEItIpe2/shZvv97uOzkAc3wcHE0OZH8f9UruPFm3yYAfLL2KAdPJ5ucSEREpHSYsuoI4WfT8PZwZmJvde4QEZFrF5ecyZvLDgDwny718PF0MTlRQSpsiIhcwd6TSQyYsZmT5zMIqOTGopHtCPTzNDuWiPyLpIwcXlywB4Ch7QK0UGYxdneQH10DfcixGvz3p92akkpEROQG7Yg8x5cXpqDq1xQvN3XuEBGRa2MYBi//vI/kzFyCqnsxvH0tsyNdRIUNEZHL2BZxjvu/2MK5tGyaVvNiwch2+Fd0MzuWiFyF137ZT2xyJrUql+PF7g3NjiNXYLFYePOeJpR3c2T/qWQ++0NTUomIiFyvlMwcnrmwvljzatzRSFNQiYjItVu6+xSrD8bhaG9h8oAgHOyLXxmh+CUSESkG1h85y+BZW0nNyuWW2hX5fsQtVHZ3NjuWiFyF1QfiWLQzBjsLvH9vM1yd7M2OJP/C28OF1+7Omybjk7VHOXBKU1KJiIhcjwlL9nPyfAb+FV3z21YREZFrcTYli4lL9wPw1O31aODrYXKiS1NhQ0Tk/1m+7zSPzt1OZo6Nzg2qMGdYa9ydHcyOJSJXISUzh/GL9wHw2K21aVGzosmJ5GrdHeRHt8Y+5No0JZWIiMj1WBIWw8+78jp3TB0YrPXFRETkmhmGwbhFe0hMzyGwqicjO9UxO9JlqbAhIvIPC0NPMurbneRYDXo1rcrMh1vi4qje3iIlxXsrDhObnEnNSm48e2d9s+PINbBYLLzZtykV3Bw5cDqZL/+MMDuSiIhIiRF9Lp3xP+d17njq9nrq3CEiItdl3uYTrD54Bid7O96/NwjHYjgF1QXFN5mISBH7enMkz/20G5sB97aozsf3h+DkoK9JkZIi9MR5vt5yAoC372mqomQJVMXDmfG9AgH4aM0RohLSTU4kIiJS/FltBmN+DCMlK5eQGuV56va6ZkcSEZES6ODpZN767SAA43o2JNDP0+REV6YndiIiwPR1x3llSd78gUPbBfBu/2bY21lMTiUiVys718a4RXswDOjfvDrt61Y2O5Jcp37Nq9GuTiUyc2yMX7IPwzDMjiQiIlKsTV93jO2R5ynnZM9HA0OK5QKvIiJSvGVkW3nq+11k59q4vaE3Q9sFmB3pX6m1E5EyzTAM3ltxiHeXHwLgqdvrMrF3IHYqaoiUKJ9vOM6RuFQqlnNifK9GZseRG5A3JVUTnBzs2HDkLL/sOW12JBERkWJra3gCH64+CsDrfZpQo5KbyYlERKSkMQyDiUv3cexMKt4ezrw3oBkWS/F/LqbChoiUWYZh8NovB5j2x3EAxvZoyHNdG5SIL28R+Z/ws6l8vPYYABPuCqRCOSeTE8mNql3FndGd86bReP2X/SSl55icSEREpPiJT83iqe93YbUZ9A32o1/zamZHEhGREui7bVH8uOMkdhaYOjCYSu7OZke6KipsiEiZZLMZjF+8jzmbIrFY4I2+TXiiYx2zY4nINcrrWbKf7Fwbt9arTJ9gP7MjyU3yeMfa1PV2Jz41m3eWHzQ7joiISLFitRk880MYZ1KyqOvtzlv3NFUHLRERuWahJ87z6tK8qdmf79aQdiVoWmcVNkSkzLHZDF76eS/fbo3CYoH3BgTx8C01zY4lItdhxf44/jwaj5O9HW/0aaIb+lLE2cGet/o2AeD7bdHsijpvciIprjZs2EDv3r3x8/PDYrGwePHifz1m3bp1NG/eHGdnZ+rWrcucOXMKPaeIyM30ydqjbDwWj6ujPdMfbE45ZwezI4mISAlzJiWTUd+GkmM16NHElyc61jY70jVRYUNEyhSrzeCFhXv4YXs0dhaYcl8QA1pUNzuWiFyHjGwrb/x6AIARt9UmoHI5kxPJzdamdqX8aTVeXbofm00LicvF0tLSCAoKYtq0aVe1f0REBL169aJz586EhYXxzDPP8Oijj7JixYpCTioicnNsPBrPR2vy1tV4u18T6vl4mJyoeFMBXETkYtm5Np78didxyVnU83bnvXuDSlxHQRU2RKTMsNoM/vvTbhaEnsTezsLUQSHcE6KihkhJ9dm6Y8QkZlCtvCtP/r0eg5Q+Y3s0xN3Zgd0nk/gpNNrsOFIM9ejRgzfffJN77rnnqvafMWMGtWrV4oMPPqBRo0aMHj2aAQMG8OGHHxZyUhGRGxeXnMl/ftiFYcD9rf11P3MVVAAXESnowpTO2yPP4+HswMyHW+BeAkf+lbzEIiLXIddqY8yPu1m6+xQOdhY+vj+Enk2rmh1LRK5TZHwaM9eHAzC+VyNcnexNTiSFxdvDhf/cUY+3fjvI5OWH6d6kKl6ujmbHkhJs8+bNdOnSpcC2bt268cwzz1z2mKysLLKysvJ/Tk5OLqx4IiKXlZVr5YlvQklIy6ZRVU8m9m5sdqQSoUePHvTo0eOq9/9nARygUaNGbNy4kQ8//JBu3boVVkwRkSLzxZ/hfL8tb3r2qYOCqV3F3exI10UjNkSk1Mux2vjPD2H5RY1PH2iuooZICff6rwfItuYtGN69ia/ZcaSQDWkXQJ0q5UhIy+bDVUfMjiMlXGxsLD4+PgW2+fj4kJycTEZGxiWPmTRpEl5eXvkvf3//oogqIpLPMAwmLN7PrqhEPF0cmP5gc1wc1bGjMFyuAL558+bLHpOVlUVycnKBl4hIcbR832km/X4IgFd6BXJHI59/OaL4UmFDREq17FwbT323i2V7T+Nob2H6Qy30EFSkhPvj8BnWHjqDo72FV+9uXOLmAZVr5+Rgx6t35/VK/XrLCQ7HppicSMqacePGkZSUlP+Kjta0aCJStL7ZcoL5O/LWCfzkgeZaW6wQqQAuIqVVWHQiz8wPwzBgcNuaDGsfYHakG6LChoiUWtm5Np78bifL98fiZG/HzIdbcGdgya1Ei0jetHJvLTsIwNB2AdQpoUNm5drdWq8K3Rr7YLUZvLp0P4ahhcTl+vj6+hIXF1dgW1xcHJ6enri6ul7yGGdnZzw9PQu8RESKytbwBF775QAAL3ZvSMf6VUxOJP+fCuAiUtxFn0vn0bk7yMyx0blBFSbcFVjiOwkWemFj2rRpBAQE4OLiQps2bdi2bdtl950zZw4Wi6XAy8XFpcA+hmEwYcIEqlatiqurK126dOHo0aOFfRkiUsLkWPOKGqsOxOHkYMcXQ1pye0MVNURKuh+2R3PsTCoV3BwZfXs9s+NIERvfKxAnBzs2hyew5uAZs+NICdW2bVvWrFlTYNuqVato27atSYlERC7vVGIGo77dSa7NoHeQHyNuq212pFJPBXARKW3OpmTx0FdbiU/NolFVTz55oDkO9iV/vEOhXsH8+fMZM2YMEydOZOfOnQQFBdGtWzfOnLn8jainpyenT5/Of504caLA+5MnT+bjjz9mxowZbN26lXLlytGtWzcyMzML81JEpATJsdp4+vtd+UWNr4a0VK8mkVIgJTMnf32FZ7rU1wLSZZB/Rbf84dJv/36QHKvN3EBSLKSmphIWFkZYWBgAERERhIWFERUVBeT1oh08eHD+/k888QTh4eG88MILHDp0iM8++4wff/yRZ5991oz4IiKXlZFt5fGv8xYLD6zqyeT+zUp879qSQAVwESlNkjJyGDxrGycS0vGv6MqcYa1wd3YwO9ZNUaiFjSlTpvDYY48xbNgwAgMDmTFjBm5ubsyaNeuyx1gsFnx9ffNf/5zX0DAMpk6dyvjx4+nTpw/NmjVj3rx5nDp1isWLFxfmpYhICZFrtfHM/DB+35c3/dQXg1tyaz0VNURKg8/WHSchLZvaVcrxQJsaZscRkzzZuS4VyzkRfjaNH7ZFmR1HioEdO3YQEhJCSEgIAGPGjCEkJIQJEyYAcPr06fwiB0CtWrVYtmwZq1atIigoiA8++IAvv/ySbt26mZJfRORSbDaDZ+eHsTcmiYrlnPh8cAtcnbRY+PVQAVxEyqrMHCuPzd3BwdPJVHZ35uvhbfDxdPn3A0uIQitsZGdnExoaSpcuXf73YXZ2dOnShc2bN1/2uNTUVGrWrIm/vz99+vRh//79+e9FREQQGxtb4JxeXl60adPmiucUkbLBajN47qfdLNuTt1D4jIeba6SGSClx8nw6X22MAGBcj0Y4loJhs3J9PF0ceaZL3jRkH64+SnJmjsmJxGydOnXCMIyLXnPmzAHyprtdt27dRcfs2rWLrKwsjh8/ztChQ4s8t4jIlby74lCBtQKrV3AzO1KJpQK4iJRFOVYbo7/bybbIc3i4ODBveGsCKpczO9ZNVWhPBeLj47FarQVGXAD4+PgQGxt7yWMaNGjArFmzWLJkCd988w02m4127dpx8uRJgPzjruWcAFlZWSQnJxd4iUjpYrUZPL9gN0vCTuFgZ2HaA821pkYpci3rNeXk5PD6669Tp04dXFxcCAoKYvny5UWYVgrD5OWHyc610bZ2Jbo08jY7jpjs/tY1qF2lHOfSspm+7rjZcURERG6qH7ZFMXN9OACTBzSjVUBFkxOVbCqAi0hZY7UZvLBgD6sPnsHZwY6vhrQi0K/0rf1TrLo7tm3blsGDBxMcHEzHjh1ZtGgRVapUYebMmTd03kmTJuHl5ZX/8vf3v0mJRaQ4sNkMxi3aw6KdMdjbWfjk/hC6NvY1O5bcJNe6XtP48eOZOXMmn3zyCQcOHOCJJ57gnnvuYdeuXUWcXG6WsOhElu4+hcUCL/dqpLmlBUd7O8b1aATAVxsjiEnMMDmRiIjIzfHXsXjGL94HwH/uqEffkGomJxIRkZLEZjMYu3APP+/Ke0b22YPNaV2rdBbIC62wUblyZezt7YmLiyuwPS4uDl/fq3vg6OjoSEhICMeOHQPIP+5azzlu3DiSkpLyX9HR0ddyKSJSjNlsBi8v3sePO05iZ4GPBgXTo2lVs2PJTXSt6zV9/fXXvPTSS/Ts2ZPatWszcuRIevbsyQcffFDEyeVmMAyDt5cdBKBfSHWaVPMyOZEUF10aeXNL7Ypk59p4f8Vhs+OIiIjcsGNnUnjim1BybQZ9gv3yp14UERG5GjabwUs/7+Wn0JPY21n4aFAwdzQqvbOZFFphw8nJiRYtWrBmzZr8bTabjTVr1tC2bdurOofVamXv3r1UrZr3kLJWrVr4+voWOGdycjJbt2694jmdnZ3x9PQs8BKRks8wDCYu3c/326Kws8CHA4O5q5mf2bHkJrqe9ZqysrJwcSm4GJarqysbN24s1KxSONYdOcu2yHM4O9jx3271zY4jxYjFYuHlnoEALA6L4XBsismJRERErl9sUiZDZm0nJTOXljUr8G7/ZhqlKiIiV80wDMYv2ccP26Oxs8CU+4JK/TOyQp2KasyYMXzxxRfMnTuXgwcPMnLkSNLS0hg2bBgAgwcPZty4cfn7v/7666xcuZLw8HB27tzJQw89xIkTJ3j00UeBvBvYZ555hjfffJOlS5eyd+9eBg8ejJ+fH3379i3MSxGRYsYwDF7/9QBfbzmBxQLvDQiiT7CGaZc217NeU7du3ZgyZQpHjx7FZrOxatUqFi1axOnTpy/7OVqLqXiy2QzeW57XE39IuwCqermanEiKm6bVvejZ1BfDgPdXatSGiIiUTEkZOQyZtY2YxAxqVS7HzIdb4OJob3YsEREpIQzDYMKS/Xy3NQqLBT64r2w8I3MozJMPHDiQs2fPMmHCBGJjYwkODmb58uX5D6iioqKws/tfbeX8+fM89thjxMbGUqFCBVq0aMGmTZsIDAzM3+eFF14gLS2NESNGkJiYSIcOHVi+fPlFvXNFpPQyDIN3fj/E7L8iAXi3XzP6t6hubigpNj766CMee+wxGjZsiMVioU6dOgwbNuyyU1dB3lpMr732WhGmlKuxbO9pDpxOxt3ZgSc61jE7jhRTY+6sz/J9saw6EMfOqPM0r1HB7EgiIiJXLTPHymNzd3A4LoUqHs7MG96aSu7OZscSEZESwmYzmLB0H99syStqvD8giHtCysYzMothGIbZIYpacnIyXl5eJCUlaVoqkRLo4zVHmbLqCABv3dOEB9vUNDlR2VHU35/Z2dm4ubmxYMGCAiPzhgwZQmJiIkuWLLnssZmZmSQkJODn58fYsWP59ddf2b9//yX3zcrKIisrK//n5ORk/P391U6YKNdq484PNxARn8azXerzH80xLVfw/E+7+Sn0JO3qVOK7x24xO06ZVlZ+zy4r1ykihctqMxj1bSgr9sfh4ezA/MfbEuhXur9Tysr3Z1m5ThExV67VxgsL9rBoVwwWC7zbvxn3tfQ3O9YNuZbvz0KdikpE5Gb7amNEflHjlbsCVdQo5W5kvSYXFxeqVatGbm4uCxcupE+fPpfdV2sxFT8LQk8SEZ9GxXJOPHJrLbPjSDH3ny71cLK3Y9PxBDYejTc7joiIyL8yDIPxi/exYn8cTvZ2fD64ZakvaoiIyM2TnWvj6R92sWhXDPZ2FqYODC7xRY1rpcKGiJQYP2yL4o1fDwB5U4880kEPO8uCa12vaevWrSxatIjw8HD+/PNPunfvjs1m44UXXjDrEuQaZeZY+WjNUQBGdaqDu3OhzpwppUD1Cm480KYGAO+tOEQZHJAsIiIlzNTVR/l+W960IR8NCqZtnUpmRxIRkRIiM8fK41/v4Le9sTjZ2zH9weZlYk2N/09PCkSkRFgSFsO4n/cC8PhttXnq9romJ5Kicq3rNWVmZjJ+/HjCw8Nxd3enZ8+efP3115QvX96kK5Br9c2WE5xOysTPy4WHbtGoLLk6o2+vy487otl9MokV++Po3sTX7EgiIiKX9OWf4fmdOF7v04QeTauanEhEREqKtKxcHp27g83hCbg42vH5wy25rX4Vs2OZQoUNESn2Vh2IY8yPuzEMeOiWGoztkbcotJQdo0ePZvTo0Zd8b926dQV+7tixIwcOHCiCVFIYUrNy+WzdcSBveiEXR3uTE0lJUdndmUc61OKTtcf4YOVh7gz0wd5ObYWIiBQv32w5wZvLDgJ5o9AfVicOERG5SufSshk+Zzth0Ym4Ozswa2grWteqaHYs02gqKhEp1jYejefJb3ditRn0C6nG63c3UVFDpBSb81cE59KyqV25HP2bVzc7jpQwj95aGy9XR46eSWXp7hiz44iIiBSwMPQk4xfvA+CJjnU0Cl1ERK5a9Ll0BkzfRFh0IuXdHPnm0TZluqgBKmyISDG2I/Icj83bQbbVRvfGvkwe0Aw79b4VKbVSMnP44s8IIG+0hoO9fk2Ra+Pl6siI22oD8MnaY1htWmtDRESKh2V7TvP8gt0ADG0XwIvdG6jDloiIXJUDp5LpN30T4fFpVCvvyoIn2hLsX97sWKbTEwMRKZb2xSQxbPZ2MnKsdKxfhY/uD9ZDTpFSbt7mEyRl5FC7SjnuauZndhwpoQa3rYmXqyPhZ9P4dc8ps+OIiIiw+kAc//lhFzYDBrXyZ8JdgSpqiIjIVdl0PJ6BMzdzNiWLhr4eLBzZjrreHmbHKhb0lFBEip2jcSk8/NVWUrJyaV2rIjMeaoGzg+bZFynNUrNy+eLPcACeur2u1kaQ6+bh4sijHWoB8PGaoxq1ISIiplp/5Cyjvt1Jrs2gT7Afb93TVKPQRUTkqvy65xRDZ20nJSuXNrUqMv/xtvh6uZgdq9hQYUNEipUTCWk8+OVWzqfnEFTdi6+GtMTVSUUNkdLu680nSEzPoVblcvTWaA25QUPaB+Dp4sDxs2n8tve02XFERKSM+uPwmQJT635wb5A6b4iIyFWZtTGCp77fRbbVRs+mvswd3hovV0ezYxUrKmyISLFxJjmTh77aypm/h9fNHd4aDxd9aYuUdmn/GK0xunNdTTsnN8zTxZFHOlxYa+MoNo3aEBGRIrbmYByPzwslO9dGt8Y+fHx/iH7HERGRf5VrtTFhyT5e//UAhpE31e4n9zfHxVGdfv8/taoiUiwkpecweNY2os9lULOSG/MeaU15NyezY4lIEfhmywnOpWUTUMmNPsEarSE3x9D2AXi4OHAkLpXf98WaHUdERMqQVQfieOKb0Pxetp8+0BwnBz1+ERGRK0vJzOGRuTuYt/kEAC92b8hrdzfWaL/LUMsqIqbLyLbyyNztHIpNwdvDmW8eaYO3h+YMFCkL0rNz+XxD3miNJzVaQ24iL1dHhrf/31obGrUhIiJFYfm+WEZ9G0qO1aBXs6p8NCgER/1+IyIi/+Lk+XQGTN/M+iNncXG0Y8ZDzRnZqQ4Wi4oal6PWVURMlWO1MerbUHacOI+niwPzHmmNf0U3s2OJSBH5dksUCWnZ1Kjoxj0h1cyOI6XM8Pa18HB24HBcCiv2a9SGiIgUrt/2nmb0dzvJsRr0DvLjo4HBKmqIiMi/2hV1nr7T/uJwXF6H3x8fb0v3JlXNjlXsqYUVEdPYbAbP/7SbPw7nVaNnDW1FQ19Ps2OJSBHJzLEyc4PW1pDC4+XmyND2AQB8vPYYhqFRGyIiUjh+3BHN6O92kmsz6BPsx4f3Bel3GxER+Ve/7jnFoM+3EJ+aTaOqnix+sj3Nqpc3O1aJoFZWRExhGAav/3qAxWGncLCzMP3BFrQMqGh2LBEpQj+FniQ+NYtq5V25p7lGa0jheKRDLdyc7Dl4Opl1R86aHUdEREqhrzZG8MKCPdgMuK9ldabcF6yihoiIXJFhGHy85iijv9tFVq6NOxp6s+CJtviVdzU7WomhllZETPHJ2mPM2RQJwAf3BdG5obe5gUSkSOVabXy+4TgAI26rrWkapNCUd3PigdY1AJi+7rjJaUREpDQxDIMPVx3hjV8PAPBoh1q827+ZFnkVEZErSs3KZeQ3O5my6giQ1xnr88EtKefsYHKykkVPEUSkyH29OTL/y/vV3oH0CVZPbZGyZtne00Sfy6BSOSfua+lvdhwp5R69tTaO9ha2RZwj9MQ5s+OIiEgpYLMZvPbLAT5acxSA5+6sz8u9GmmRVxERuaITCWn0++wvlu+PxdHewjv9mvLKXYEqil8HFTZEpEgt3X2KCUv3A/D0HfUY2r6WyYlEpKgZhpHfc35Y+wBcnexNTiSlna+XC/1CqgMatSEiIjcu12rj+QV78kegv3Z3Y566o56KGiIickXrj5yl9ycbORKXireHMz+MaMugv0eXy7VTYUNEisy6w2cYMz8Mw4CHb6nJs13qmR1JREyw7vBZDsWmUM7JnodvCTA7jpQRIzrWxmKB1QfPcDg2xew4IiJSQmXmWBn17U4W7jyJvZ2FD+4NYki7ALNjiYhIMWYYBjPWH2fY7G0kZ+YSUqM8vzzVgRY1K5gdrURTYUNEikToifOM/GYnuTaD3kF+vHZ3Y/VoEimjLvSYf/CWmni5OZqcRsqKOlXc6d7YF4CZ6zVqQ0RErt35tGwe/HIrKw/E4WRvx2cPNqd/i+pmxxIRkWIsPTuXp77fxTu/H8JmwMCW/vww4hZ8PF3MjlbiqbAhIoXuaFwKw+dsJyPHym31q/DBvUHYae5AkTJpR+Q5tkWew8nejkc6aCo6KVojO9UBYMnuU5w8n25yGhERKUmiz6XTf8YmQk+cx9PFgXmPtKbb3wVzERGRS4k+l07/6Zv5dc9pHOwsvNG3Ce/0b4qzg6ZjvhlU2BCRQnU6KYMhs7aRlJFDSI3yzHioOU4O+uoRKasujNbo17yaeqhIkWtWvTwd6lbGajP4YkO42XFERKSE2BeTRL/pmwg/m4aflwsLRrbjltqVzI4lIiLF2OoDcfT6+E8Onk6msrsT3z12Cw/fUlOzl9xEerooIoUmKSOHobO2cyopk9pVyjFrSCvcnBzMjiUiJjkUm8yaQ2ewWGDEbbXNjiNl1IVRGz9sjyY+NcvkNCIiUtz9efQsA2du5mxKFg19PVg0qj31fTzMjiUiIsVUrtXGO78f4tF5O/LX01g6ugOta1U0O1qpo8KGiBSKzBwrj83bweG4FLw9nJk3vDUVyjmZHUtETPT53z3kezapSu0q7iankbKqXZ1KNKvuRVaujXmbIs2OIyIixdjC0JMMm72dtGwr7epU4scn2uLrpRGnIiJyaWdSMnnwy63M+HtNv2HtA5g/oi1+5V1NTlY6qbAhIjed1Wbw7PwwtkWcw8PZgTnDWlO9gpvZsUTERHHJmfyy+xQAj2m0hpjIYrHw+G15oza+2RpFZo7V5EQiIlLc2GwGH6w8zHM/7SbXZtAn2I85w1rj6eJodjQRESmmtoQn0OvjjWyNOEc5J3s+fSCEib0bazr2QqQ5YUTkpjIMg9d+2c/v+2Jxsrdj5uAWBPp5mh1LREw2d1MkOVaDVgEVCPYvb3YcKeO6NfahWnlXYhIzWLQzhgfa1DA7koiIFBMZ2Vb++9Nulu09DeRNYfh81wbY2WlOdBERuZjNZjBzQzjvrTiEzYD6Pu5Mf6gFdTRLQaFTyUhEbqrP1h1n3uYTAHxwXxDt6lQ2OZGImC09O5dvt0YB8EgHjdYQ8znY2zGsfQAAX20Mx2YzzA0kIiLFwpnkTAZ9vplle0/jaG/h/XuDeLF7QxU1RETkkpLScxjx9Q7eXZ5X1OgXUo3FT7ZXUaOIqLAhIjfNgtCTvLfiMAAT7gqkd5CfyYlEpDhYGHqSpIwcalZy485AH7PjiAAwsJU/Hs4OHD+bxvojZ82OIyIiJtt/Kok+0/5i98kkKrg58s0jbRjQorrZsUREpJjaGXWeXp/8yeqDZ3BysGNSv6Z8cF8Qbk6aIKmoqLAhIjfFH4fP8OLCPQA8fltthneoZXIiESkObDaDrzZGADC8fS3s1eNRigkPF0cGtfYH4MuN4SanERERM606EMe9MzZzOimTOlXKsfjJ9rSpXcnsWCIiUgzZbAbT1x3n3hmbOXk+gxoV3Vg0sh33t66BxaL73aKkwoaI3LDd0YmM+mYnVpvBPSHVeLF7Q7MjiUgxsfpgHJEJ6Xi6OKjXoxQ7Q/8utv11LIH9p5LMjiMiIkXMMAw+33CcEV/vID3byq31KrNoVHtqVipndjQRESmGzqRkMmT2Nt5dfgirzeCuZlX59ekONKnmZXa0MkmFDRG5IRHxaQyfs52MnLwbgXf7N9MctCKS78u/R2s8eEtNyjlrSK4UL9XKu9KzaVWA/JFFUjJNmzaNgIAAXFxcaNOmDdu2bbvsvnPmzMFisRR4ubi4FGFaESkOMrKt/OeHMN7+7RCGAQ+2qcGsoa3wcnU0O5qIiBRD64+cpedHf/Ln0XhcHO14t39TPrk/BE8XtRtmUWFDRK7b2ZQsBs/aSkJaNk2qeTL9oRY4OehrRUTy7DmZyLaIczjYWRjSNsDsOCKX9MjfUyf+svsUccmZJqeR6zF//nzGjBnDxIkT2blzJ0FBQXTr1o0zZ85c9hhPT09Onz6d/zpx4kQRJhYRs0WfS6ff9E0s3X0KBzsLr/dpzJt9m+Bor3uZ0kxFcBG5HjlWG5N+P8iQWduIT82moa8Hvz7VgYGtNPWU2dRqi8h1Sc3KZdicbUSfy5tPcPbQ1rirN7aI/MOFHvC9g/zw9dKNoBRPwf7laRVQgRyrwbzNkWbHkeswZcoUHnvsMYYNG0ZgYCAzZszAzc2NWbNmXfYYi8WCr69v/svHx6cIE4uImTYejaf3pxs5eDqZyu5OfPtoGwa3DdDDqVJORXARuR7R59K5d8ZmZq7PW5Pv4VtqsvjJ9tT19jA5mYAKGyJyHbJzbYz8JpR9MclUKufE3OGtqeLhbHYsESlGTidlsGzPaeB/PeJFiqtHOtQG4JstUWRkW01OI9ciOzub0NBQunTpkr/Nzs6OLl26sHnz5ssel5qaSs2aNfH396dPnz7s37+/KOKKiIkMw+CLDeEMnrWVxPQcgqp7sXR0By0SXkaoCC4i1+rXPafo+dGfhEUn4uniwIyHmvNG3ya4ONqbHU3+VuiFjWsZ6vfFF19w6623UqFCBSpUqECXLl0u2n/o0KEXDQfs3r17YV+GiPzNMAzGLtrDn0fjcXW0Z9bQVtSqrMX1RKSgb7dEkWszaFOrohZSk2LvzkAf/Cu6kpSRw+KwGLPjyDWIj4/HarVe9LDJx8eH2NjYSx7ToEEDZs2axZIlS/jmm2+w2Wy0a9eOkydPXvZzsrKySE5OLvASkZLjwnoab/12EJsBA1pUZ/7jbfEr72p2NCkCRVEEVzshUnqkZOYw5scwRn+3i5SsXFrUrMBv/7mV7k2qmh1N/p9CLWxc61C/devWcf/99/PHH3+wefNm/P396dq1KzExBW8wu3fvXmA44Pfff1+YlyEi//Dh6qMs2hmDvZ2Fzx5qTpB/ebMjiUgxk5lj5fttUQAMbRdgbhiRq2BvZ2HwLQEAzN0UiWEY5gaSQtW2bVsGDx5McHAwHTt2ZNGiRVSpUoWZM2de9phJkybh5eWV//L39y/CxCJyI6LPpdP/H+tpvHZ3Y94b0Ew9bsuQoiiCq50QKR12RJ6jx0d/smhnDHYWeOr2uswfcQvVK7iZHU0uoVALG9c61O/bb79l1KhRBAcH07BhQ7788ktsNhtr1qwpsJ+zs3OB4YAVKlQozMsQkb/9uD2aj9ccBeCtvk3o3MDb5EQiUhz9tvc0CWnZVPVy4c5ADdmXkuG+lv64OtpzKDaFrRHnzI4jV6ly5crY29sTFxdXYHtcXBy+vr5XdQ5HR0dCQkI4duzYZfcZN24cSUlJ+a/o6Ogbyi0iRWPVgTh6ffwnB07nTaH77aNtGNJO62nIv7vWIrjaCZGSLcdq44OVh7lv5mZOns+gegVXfny8Lc91bYCDvVZyKK4K7f+Z6x3q90/p6enk5ORQsWLFAtvXrVuHt7c3DRo0YOTIkSQkJFzxPBoSKHLj1h85y7if9wJ5FetBrWuYnEhEiqu5myIBeOiWmvolUEoMLzdH+oZUA/73d1iKPycnJ1q0aFGgI9SFjlFt27a9qnNYrVb27t1L1aqXn17A2dkZT0/PAi8RKb5yrTYm/X6Qx+btIDkzl2D/8vzylNbTKKuKogiudkKk5Ao/m0r/6Zv4ZO0xbAb0b16d3/9zKy0DKv77wWKqQnvacD1D/f6/F198ET8/vwLFke7duzNv3jzWrFnDu+++y/r16+nRowdW6+UXetSQQJEbs/9UEqO+CcVqM7gnpBpj7qxvdiQRKabCohPZfTIJJ3s7BrZSeysly5B2NQFYeSCOU4kZJqeRqzVmzBi++OIL5s6dy8GDBxk5ciRpaWkMGzYMgMGDBzNu3Lj8/V9//XVWrlxJeHg4O3fu5KGHHuLEiRM8+uijZl2CiNxEccmZPPDFVmauDwdgWPsAftR6GmVaURXBRaRkMQyD77ZG0evjjew5mYSXqyPTHmjOB/cF4eHiaHY8uQoOZge4nHfeeYcffviBdevW4eLikr990KBB+f/dtGlTmjVrRp06dVi3bh133HHHJc81btw4xowZk/9zcnKyihsiV+lUYgbD52wnLdtK29qVeLd/Mw3dFpHLutDT/a6gqlR2dzY3jMg1aujryS21K7Il/BzfbDnBC90bmh1JrsLAgQM5e/YsEyZMIDY2luDgYJYvX57fwSoqKgo7u//15zp//jyPPfYYsbGxVKhQgRYtWrBp0yYCAwPNugQRuUk2HYvn6R92EZ+ajbuzA5MHNKNnUz2Ilrwi+JAhQ2jZsiWtW7dm6tSpFxXBq1WrxqRJk4C8Ivgtt9xC3bp1SUxM5L333lMRXKQUiU/NYuzCPaw+mLcOdPu6lXj/3iCqeqkIXpIUWmHjRob6vf/++7zzzjusXr2aZs2aXXHf2rVrU7lyZY4dO3bZwoazszPOznq4InKtkjNzGDZ7O3HJWdTzdmfGwy1wctC0MiJyaWdTsli25zQAQ9oGmBtG5DoNbRfAlvBz/LA9mqfvqKfFZUuI0aNHM3r06Eu+t27dugI/f/jhh3z44YdFkEpEiorNZjDtj2N8uPoINgMa+nrw2YPNqV3F3exoUkyoCC4iF/xx6AzPL9hNfGo2TvZ2vNC9AcPb18LOTp14S5pCK2z8c6hf3759gf8N9bvcTQfA5MmTeeutt1ixYgUtW7b81885efIkCQkJGg4ocpNl59oY+U0oh+NS8PZwZs7w1ni5aiieiFzeD9uiyLbaCPYvT5B/ebPjiFyXLo188PNy4VRSJr/sPsW9LTXKV0SkOEtIzWLMj7tZf+QsAPe1rM7rfZqoMC0XURFcpGxLzcrlzV8P8MP2aAAa+HgwdVAwjapqTZySqlC7Xl/rfLfvvvsur7zyCrNmzSIgIIDY2FhiY2NJTU0FIDU1leeff54tW7YQGRnJmjVr6NOnD3Xr1qVbt26FeSkiZYphGIxdtIe/jiXg5mTPrKGtqKY5aUXkCnKsNr7ZegLI6/EuUlI52NvxUNu8tTbmbo7EMAyTE4mIyOX8dSye7h/9yfojZ3F2sGPygGZMHhCkooaIiBSw+XgC3adu4Ift0Vgs8EiHWiwZ3V5FjRKuUNfYuNahftOnTyc7O5sBAwYUOM/EiRN59dVXsbe3Z8+ePcydO5fExET8/Pzo2rUrb7zxhqaaErmJPlx9lEU7Y7C3szDtweY0qeZldiQRKeZW7o8jLjmLyu5O9Gh65SknRYq7Qa1qMHX1UfbFJLMz6jwtalY0O5KIiPxDjtXGh6uOMH39cQwD6nq788n9IXpAJSIiBWRkW5m84hCz/4oEwL+iK+8NCOKW2pXMDSY3RaEvHn4tQ/0iIyOveC5XV1dWrFhxk5KJyKX8uD2aj9ccBeDNvk3o3MDb5EQiUhJcWDT8gdY1cHZQL0kp2SqWc+LuID8WhJ5k7qYTKmyIiBQj0efSefqHXeyKSgTg/tb+TLirMa5O+v1DRET+Z2fUef77427C49MAeKBNDV7q2Qh350J/HC5FRP9Piki+9UfOMu7nvQCM7lyX+1vXMDmRiJQEB04lsy3yHA52Fh5oU9PsOCI3xeC2NVkQepLl+2I5l5ZNxXJOZkcSESnzlu05zdhFe0jJzMXDxYF3+jWjVzOttykiIv+TlWvlo9VHmbH+ODYDfD1deHdAMzrWr2J2NLnJVNgQEQD2n0pi1DehWG0G94RU47mu9c2OJCIlxNdb8tbW6NbYF18vF5PTiNwczaqXp2k1L/bGJLEgNJoRt9UxO5KISJmVkW3l9V/38/22vAVfm9coz0eDQvCv6GZyMhERKU72n0riuR93cyg2BYB+IdWY2LsxXm6OJieTwlCoi4eLSMlwKjGD4XO2k5ZtpW3tSrzbvxkWi8XsWCJSAqRm5bI0LAaAh27RaA0pXR5skzdy8butUdhsWkRcRMQMB08nc/enG/l+W96Cr092rsP8x9uqqCEiIvlyrTY+WXOUPp/+xaHYFCqVc2LGQy2YMjBYRY1STCM2RMq45Mwchs3eTlxyFvW83ZnxcAucHFTzFJGrsyQshrRsK7WrlOOW2lqHQEqX3kF+vLXsIJEJ6Ww6nkCHepXNjiQiUmbYbAZfbgzn/RVHyLba8PZwZurAYNrV1XexiIj8z9G4FP770252n0wCoHtjX966pwmV3J1NTiaFTYUNkTIsO9fGqG92cjguBW8PZ+YMb42XqyrZInJ1DMPgu61RQN6i4RrpJaVNOWcH7mlejXmbT/Dt1hMqbIiIFJGYxAye+zGMLeHnAOjSyId3+zfVQyoREcmXY7Xx+YZwPlp9lGyrDU8XB17v04Q+wX66Ny0jVNgQKaMMw2Dcor1sPBaPm5M9s4a2olp5V7NjiUgJsudkEvtPJePkYEf/5tXNjiNSKB5oU4N5m0+w8kAcZ5Iz8fbUOjIiIoVpSVgM4xfvIyUzFzcneybcFcjAVv56SCUiIvkOnk7m+QW72ReTDMDtDb15+56mWvOxjFFhQ6SM+mTtMRbuPIm9nYVpDzanSTUvsyOJSAlzYbRGzya+VCjnZHIakcLR0NeTljUrsOPEeX7cEc3o2+uZHUlEpFRKSs9h/JJ9/LL7FAAhNcrz4X3BBFQuZ3IyEREpLrJzbUz74xjT/jhGrs3Ay9WRV+8OpG9wNRXAyyAVNkTKoCVhMUxZdQSA1/s0pnMDb5MTiUhJk5yZw9K/Hzw80EaLhkvp9uAtNdhx4jzfb4tmZKe62NvppklE5Gb661g8//1pN6eTMrG3s/D07fV4snMdHOy19p+IiOTZezKJ5xfs5lBsCgDdGvvwRt8meHtolEZZpcKGSBmzPfIcz/+0B4ARt9XmQT2QFJHrsHhXDBk5Vup6u9MqoILZcUQKVY8mVXntlwPEJGaw/sgZbm/oY3YkEZFSITPHynsrDvPVxggAalUux4cDgwn2L29uMBERKTYyc6x8vOYoMzeEY7UZVCznxOt9GtOraVWN0ijjVNgQKUMi49MYMW8H2VYb3Rr7MLZ7Q7MjiUgJpEXDpaxxcbRnQPPqfLkxgm+3RKmwISJyE+w/lcSY+bs5HJfX8/bBNjV4uVcj3Jz0mEJERPLsjDrPCwv2cOxMKgB3NavKa3c3ppK7s8nJpDjQbwwiZURiejbD52znfHoOQdW9mDowBDtNpSEi12FnVCKHYlNw1qLhUobc36YGX26M4I/DZ4hJzKBaeVezI4mIlEg5Vhuf/XGcT9YeJddmUNndickDmqloLCIi+TJzrHywMm9En82Ayu7OvNm3Cd2b+JodTYoRFTZEyoDsXBuPfx1KeHwa1cq78sWQlrg62ZsdS0RKqAujNe5q5oeXm6PJaUSKRp0q7rSrU4lNxxOYvy2KMV0bmB1JRKTEORSbzHM/7mb/qWQAejTx5Y2+TaisnrciIvK3bRHneHHhHiLi0wDoF1KNCb0DKe/mZHIyKW5U2BAp5QzDYOyiPWyNOIe7swOzhrbSwkoict2S0nP4dc+FRcNrmJxGpGg92KYmm44n8MP2aJ6+o54WtRURuUq5VhszN4QzdfURcqwG5d0ceb1PE3o30/zoIiKSJy0rl/dWHGbu5kgMA3w8nXn7nqbc0Ugj+uTSVNgQKeU+WXuMRTtjsLezMO3B5jTw9TA7koiUYIt2nSQr10ZDXw+a1yhvdhyRInVnoA+V3Z04k5LFusNn6RKomywRkX9zNC6F//60m90nkwDo0siHt/s1UWcrERHJt/ZQHON/3seppEwABrb056VejfBy1QwBcnnqZiZSii0Ji2HKqiMAvN6nMR3rVzE5kcj1mTZtGgEBAbi4uNCmTRu2bdt2xf2nTp1KgwYNcHV1xd/fn2effZbMzMwiSlt6GYbB99v+XjS8jRYNl7LHycGOe0KqATB/R7TJaUREijerzWDm+uP0+mQju08m4eniwJT7gvhicAsVNUREBIAzKZk8+d1Ohs/ZwamkTKpXcGXe8Na8O6CZihryrzRiQ6SU2h55jud/2gPAiNtq82CbmiYnErk+8+fPZ8yYMcyYMYM2bdowdepUunXrxuHDh/H29r5o/++++46xY8cya9Ys2rVrx5EjRxg6dCgWi4UpU6aYcAWlR1h0IkfiUnF2sKNPcDWz44iYYmArf774M4K1h85wJiVTD+dERC7h+NlU/vvTbnZFJQLQuUEVJvVrhq+XvjNFRARsNoP5O6KZ9NtBkjNzsbez8GiHWvynSz3cnPS4Wq6O/qaIlEKR8WmMmLeDbKuNbo19GNu9odmRRK7blClTeOyxxxg2bBgAM2bMYNmyZcyaNYuxY8detP+mTZto3749DzzwAAABAQHcf//9bN26tUhzl0Y/7jgJQM+mVdV7Rsqsut5507DtjEpk0c4YnuhYx+xIIiLFhtVmMPuvCN5bcZisXBsezg680juQe1tU10hPEREB4NiZVF76eS/bIs4B0LSaF5P6NaVJNS+Tk0lJo6moREqZxPRshs/Zzvn0HJpV92LqwBDs7HQTISVTdnY2oaGhdOnSJX+bnZ0dXbp0YfPmzZc8pl27doSGhuZPVxUeHs5vv/1Gz549L/s5WVlZJCcnF3hJQRnZVn7Znbdo+L0tq5ucRsRcA1v5A/Dj9mgMwzA5jYhI8XDsTCqDPt/Mm8sOkpVr49Z6lVn+7G3c19JfRQ0RESEr18pHq4/S86M/2RZxDldHe8b3asTPo9qpqCHXRSM2REqR7Fwbj38dSnh8GtXKu/LlkJa4OtmbHUvkusXHx2O1WvHxKbhAr4+PD4cOHbrkMQ888ADx8fF06NABwzDIzc3liSee4KWXXrrs50yaNInXXnvtpmYvbX7be5rUrFxqVHTjllqVzI4jYqpezfx47ZcDhMensePEeVoFVDQ7koiIabJzbcxcf5xP1h4j22qjnJM9L/cK5P7WKmiIiEie7ZHnGLdoL8fOpALQqUEV3ujTBP+KbiYnk5JMIzZESgnDMBi7aA9bI87h7uzArKGtNO+3lEnr1q3j7bff5rPPPmPnzp0sWrSIZcuW8cYbb1z2mHHjxpGUlJT/io7WosD/349/L5R8b4vqGgUmZZ67swN3NasKwPzt+r4QkbJrV9R5en+ykQ9WHSHbaqNTgyqsePY2HmhTQ0UNEREhKSOHl3/ey70zNnPsTCqV3Z34+P4QZg9tpaKG3DCN2BApJT5de4xFO2Owt7Mw7cHmNPD1MDuSyA2rXLky9vb2xMXFFdgeFxeHr6/vJY955ZVXePjhh3n00UcBaNq0KWlpaYwYMYKXX34ZO7uLa/rOzs44Ozvf/AsoJSLj09gacQ6LBfq30DRUIpA3HdWPO06ybM9pJvYOxMNF686ISNmRlpXLeysOM3dzJIYBFcs5MbF3IHcH+amgISIiGIbB8n2xTFy6nzMpWQAMbOnPuJ4NKe/mZHI6KS00YkOkFFgSFsMHq44A8HqfxnSsX8XkRCI3h5OTEy1atGDNmjX522w2G2vWrKFt27aXPCY9Pf2i4oW9fd6UbJoL//osCM1bNPy2elXwK+9qchqR4qF5jQrUqVKOjBwrv+45bXYcEZEi88fhM3T9cANzNuUVNfo1r8bqMR3pE1xNRQ0REeFUYgaPzQtl5Lc7OZOSRe3K5fj+sVt4d0AzFTXkptKIDZESbnvkOZ7/aQ8Aj91aiwfb1DQ5kcjNNWbMGIYMGULLli1p3bo1U6dOJS0tjWHDhgEwePBgqlWrxqRJkwDo3bs3U6ZMISQkhDZt2nDs2DFeeeUVevfunV/gkKtntRn5hY37WvqbnEak+LBYLAxs5c/bvx1i/vZo7m9dw+xIIiKFKiE1izd+PcDisFMAVK/gytv3NOU2daoSERHy7h2/2XKCycsPkZZtxcHOwshOdXiyc11cHHUvLjefChsiJVhkfBoj5u0g22qjW2MfxvVoZHYkkZtu4MCBnD17lgkTJhAbG0twcDDLly/PX1A8KiqqwAiN8ePHY7FYGD9+PDExMVSpUoXevXvz1ltvmXUJJdqGo2eJTc6kgpsjXQK9zY4jUqzcE1KdycsPExadyJG4FOr7aBpIESl9DMPg510xvPHrAc6n52BngeHtazGma33cnPRIQURE4FBsMmMX7iUsOhGA5jXKM6lfM02TLoVKv4WIlFCJ6dkMn7Od8+k5NKvuxdSBIVrQV0qt0aNHM3r06Eu+t27dugI/Ozg4MHHiRCZOnFgEyUq/n/5eNLxvSDWcHdTLRuSfqng4c3tDb1YeiGP+9mheuSvQ7EgiIjdV9Ll0Xvp5L38ejQegoa8H7/ZvRpB/eXODiYhIsZCZY+XjNUf5fEM4uTYDd2cHXuzRkAdb19AzKil0KmyIlEDZuTYe/zqU8Pg0/Lxc+HJwS1yd9MBRRG6uhNQsVh3IW7j93haahkrkUga28mflgTh+3hXDi90b4uSgJexEpOSz2gxm/xXBByuPkJFjxcnBjv/cUY8Rt9XG0V7fcyIiApuOxfPSz3uJTEgHoFtjH167uwm+Xi4mJ5OyQoUNkRLGMAzGLtrD1ohzuDs7MGtYK7w91WiIyM23OOwUOVaDptW8CPTzNDuOSLHUsX4VvD2cOZOSxZqDcfRoWtXsSCIiN+Tg6WTGLtzD7pNJALSpVZFJ/ZpSu4q7yclERKQ4OJ+WzVu/Hcxfi9HH05nX7m5C9ya+JieTskaFDZES5tO1x1i0MwZ7OwvTHmxOQ189bBSRm88wjPxpqO5rpdEaIpfjYG9H/xbVmb7uOAt3nlRhQ0RKrMwcK5+sPcrM9XnTiXi4OPBSz0YMbOmv6URERATDMFgSdorXfz3AubRsLBZ4qE1Nnu/eAE8XR7PjSRmkwoZICbIkLIYPVh0B4PU+jelYv4rJiUSktNobk8Sh2BScHey4O8jP7DgixVr/5tWYvu446w6fJT41i8ruzmZHEhG5JlvCE3hp0V7C49MA6N7Yl9f6NMZHI8NFRIS8NZdeXryPDUfOAlDfx51J/ZrSomZFk5NJWabChkgJsSPyHM//tAeAx26txYNtapqcSERKs0U7YwDo2tgXL1f1vhG5krreHgRV92L3ySSWhp1ieIdaZkcSEbkqyZk5TPrtEN9viwLA28OZ1/s0pnsTjT4TERHItdqY/VckU1b9b82lp2+vy4jb6mhtOTGdChsiJUBkfBqPzdtBttVG10AfxvZoZHYkESnFsnNtLN19CsjriS4i/65f8+rsPpnEol0nVdgQkRJh+b5YJizZx5mULADub12DsT0aqkODiIgAsPdkEmMX7WH/qWQAbqldkbfv0ZpLUnyosCFSzCWmZzN8znbOp+fQrLoXUwcFY685bkWkEK0/cpZzadlU8XCmQ93KZscRKRF6B/nx5rID7ItJ5nBsCg18PcyOJCJySXHJmUxcsp/l+2MBqFW5HJP6NeWW2pVMTiYiIsVBenYuU1YeYdZfEdgM8HJ15OWejbi3ZXUsFj2PkuJDhQ2RYiw718YT34QSHp+Gn5cLXw5uiZuT/tmKSOFatPMkAH2D/XCw1/BikatRsZwTnRt4s/JAHAt3nuSlnhpdKSLFi2EY/LA9mrd/O0hKZi4OdhYe71ibp26vh4ujvdnxRESkGPjj8BnG/7yPmMQMAO4O8uOVuwKp4qE15KT40RNSkWLKMAzGLdrLlvBzuDs7MGtYK7y1eJ+IFLLE9GzWHDwD5E2tIyJXr3+L6qw8EMfPu2J4oVsDFQZFpNgIP5vKuEV72RpxDoCg6l68078Zjap6mpxMRESKg7MpWbzx64H8KYmrlXflzXua0LmBt8nJRC6v0O+2pk2bRkBAAC4uLrRp04Zt27Zdcf+ffvqJhg0b4uLiQtOmTfntt98KvG8YBhMmTKBq1aq4urrSpUsXjh49WpiXIGKKT9ceY+HOk9jbWZj2YHMa+uqmQ0QK3697TpNttdGoqqcedohco84NvKng5sjZlCw2Hos3O06ZcrPvOURKixyrjWl/HKP7R3+yNeIcro72jO/ViEWj2qudlzJHbYXIxQzD4Mft0XSZsp6lu09hZ4FHO9Ri5bO3qaghxV6hFjbmz5/PmDFjmDhxIjt37iQoKIhu3bpx5syZS+6/adMm7r//fh555BF27dpF37596du3L/v27cvfZ/LkyXz88cfMmDGDrVu3Uq5cObp160ZmZmZhXopIkVoSFsMHq44A8NrdjelYv4rJiUSkrLgwDZUWDRe5dk4Odtwd5AfAop0xJqcpOwrjnkOkNNhzMpHen2zkvRWHyc61cWu9yqx89jYevbW21uyTMkdthcjFws+mcv8XW3hh4R6SMnJo7OfJkic7MP6uQMo5a5IfKf4shmEYhXXyNm3a0KpVKz799FMAbDYb/v7+PPXUU4wdO/ai/QcOHEhaWhq//vpr/rZbbrmF4OBgZsyYgWEY+Pn58dxzz/Hf//4XgKSkJHx8fJgzZw6DBg26qlzJycl4eXmRlJSEp6d6qUjxsiPyHA98sZVsq43Hbq3Fy70CzY4kkq+sfH+Wlev8/yLi0+j8/jrsLLDlpTvw9tD0dyLXas/JRO7+9C+cHezYPr4Lni6OZkcqUmZ8f97se46rUVbbCSkZ/v+irxXcHJnQO5C+wdW06KuYzqzvz6JuK9ROSHGWnWvj8w3H+XjtMbJzbbg42jHmzvoMb19LU6mK6a7l+7PQ/rZmZ2cTGhpKly5d/vdhdnZ06dKFzZs3X/KYzZs3F9gfoFu3bvn7R0REEBsbW2AfLy8v2rRpc9lzAmRlZZGcnFzgJVIcnUhIY8TXoWRbbXQN9GFsDy08KiJF5+e/R2vcVr+Kihoi16lpNS/qeruTlWvjtz2nzY5T6hXGPcel6H5CSooNR87S9cMNfLkxr6jRN9iP1WM6ck9IdRU1pMwqirZC7YSUFKEnztP7k428v/II2bk2bqtfhVXPdmTEbXVU1JASp9D+xsbHx2O1WvHx8Smw3cfHh9jY2EseExsbe8X9L/zvtZwTYNKkSXh5eeW//P39r/l6RApbUnoOw+Zs51xaNk2reTF1ULCGiItIkbHZDBbtyps6R4uGi1w/i8VC/7//DWk6qsJXGPccl6L7CSnuzqdlM+bHMAbP2sbJ8xlUK+/K7GGtmDoohEruzmbHEzFVUbQVaiekuEvJzGHCkn0MmLGJw3EpVCrnxEeDgpk7rBX+Fd3MjidyXcpEKW7cuHEkJSXlv6Kjo82OJFJAdq6Nx7/ZQfjZNPy8XPhqSEvcnDSfoYgUne2R5zh5PgMPZwe6Bvr8+wEicll9Q/ywWGBb5DmiEtLNjiM3ge4npLgyDIMlYTF0mbKeRTtjsFhgWPsALfoqUsTUTkhxtmJ/LHdO2cC8zScwDBjQojqrx3Skj6YolBKu0J6cVq5cGXt7e+Li4gpsj4uLw9fX95LH+Pr6XnH/C/8bFxdH1apVC+wTHBx82SzOzs44O6uXihRPhmHw0s972RJ+DndnB74a2gpvT00BIyJF60LP8p5Nq+LiaG9yGpGSraqXKx3qVubPo/Es3HmSZ++sb3akUqsw7jkuRfcTUhzFJGYw/ue9/HH4LAANfDx4p39TQmpUMDmZSPFSFG2F2gkpjmKTMpm4dB8r9uf9XQ6o5Mbb9zSlXd3KJicTuTkKbcSGk5MTLVq0YM2aNfnbbDYba9asoW3btpc8pm3btgX2B1i1alX+/rVq1cLX17fAPsnJyWzduvWy5xQp7j5bd5wFoSexs8AnD4TQqKoWFhORopWZY2XZ3ry1APo1r2ZyGpHS4cK/pcVhMRiGYXKa0qsw7jlEijurzWDOXxF0nbKePw6fxcnejufurM8vT3VQUUPkEtRWSFljsxl8veUEd05Zz4r9cTjYWXiycx2WP3ObihpSqhTqXDdjxoxhyJAhtGzZktatWzN16lTS0tIYNmwYAIMHD6ZatWpMmjQJgP/85z907NiRDz74gF69evHDDz+wY8cOPv/8cyBvzuJnnnmGN998k3r16lGrVi1eeeUV/Pz86Nu3b2Feikih+GX3Kd5bcRiA1+5urOHiImKKlQfiSM3KpXoFV1oFVDQ7jkip0DXQF1fHfZxISGf3ySSC/cubHanUutn3HCLF2ZG4FF5cuIddUYkAtAqowKR+zajr7W5uMJFiTm2FlBVH4lIYt2gvoSfOAxDsX55J/ZqqE62USoVa2Bg4cCBnz55lwoQJxMbGEhwczPLly/MXYIqKisLO7n+DRtq1a8d3333H+PHjeemll6hXrx6LFy+mSZMm+fu88MILpKWlMWLECBITE+nQoQPLly/HxUVT90jJEnriPM/9tBuA4e1r8XDbAHMDiUiZtfjvRcPvCamGnZ3mWBW5Gco5O3BnoA9Ld59i8a4YFTYKUWHcc4gUN1m5Vqb9cZzp646RYzVwd3ZgbI+GPNC6htpukaugtkJKu8wcK9P+OMaM9cfJsRqUc7Lnhe4NeeiWmtirnZBSymKUwbHxycnJeHl5kZSUhKenKpZS9KLPpdN32l8kpGXTpZEPMx9uoYZGSoSy8v1ZVq4T4FxaNq3fWk2uzWD1mI7q8SlyE609FMfwOTuo7O7ElnF34GBfaLPAFhtl5fuzrFynFA87Is8xdtFejp1JBaBLIx/e7NsEXy917pOSp6x8f5aV65TiYUt4Ai8t2kt4fBqQ10683qcxfuVdTU4mcu2u5fuzUEdsiMjFkjJyGDZnOwlp2TT28+SjQcEqaoiIaZbtPU2uzaBJNU8VNURuslvrVaGCmyPxqdn8dTyBjvWrmB1JREqQlMwcJi8/zNdbTgBQ2d2Z1/s0pkcTXywW3T+IiJR1ienZTPrtEPN3RAPg7eHMa3c3prvaCSkjVNgQKUI5Vhujvg3l2JlUfD1d+GpIK8o565+hiJhnaVjeNFR9grRouMjN5mhvR69mVflmSxRLwmJU2BCRq7b2UBwv/7yP00mZAAxs6c9LPRvh5eZocjIRETGbYRj8uuc0r/2yn/jUbAAebFODF7o3xMtV7YSUHXqiKlJEDMPglcX7+OtYAm5O9nw1tKWGj4uIqU6eT2d75HksFrgrqKrZcURKpb7B1fhmSxQr9sWS0deKq5O92ZFEpBhLSM3i9V8PsCTsFAA1KrrxTr+mtKtb2eRkIiJSHMQkZvDK4n2sPXQGgLre7kzq15RWARVNTiZS9FTYECkiMzeE88P2aOws8OkDITT28zI7koiUcb/sPg1Am1oVqeql+VdFCkOLmhWoXsGVk+czWH0wjt5BfmZHEpFiyDAMloSd4rVf9nM+PQc7Czx6a22e7VJfBVEREcFqM5i7KZL3Vx4mPduKk70dozrXYWSnOjg7qJ2QskmFDZEi8Pve07zz+yEAJtwVyO0NfUxOJCICSy5MQxWsaahECovFYqFPsB/T/jjOkrAYFTZE5CIxiRm8/PNe1h0+C0BDXw8mD2hGs+rlzQ0mIiLFwqHYZF5cuJfd0YkAtKxZgXf6N6Wut4e5wURMpsKGSCELi07kmflhAAxtF8DQ9rXMDSQiAhyOTeFQbAqO9hZ6NtE0VCKFqU9wNab9cZx1h89yPi2bCuWczI4kIsWAzWbwzdYTvPv7IdL+7n379B11ebxjHRzt7cyOJyIiJsvMsfLJ2qPMXB9Ors3Aw9mBF3s05IHWNbCz0+LgIipsiBSik+fTeXTuDrJybXRuUIXxvRqZHUlEBPjfaI1ODby1EKlIIavv40Gjqp4cPJ3Mb/tO82CbmmZHEhGTHTuTytiFe9hx4jxwofdtM+p6u5ucTEREioPNxxN46ee9RMSnAdCtsQ+v3d1Ea7WK/IMKGyKFJDkzh+FzthOfmkWjqp588kBzHNTzSkSKgQvzeAP0Cda0OCJFoW+wHwdPJ7Nk1ykVNkTKsOxcGzPXH+eTtcfIttoo52TP2B4NebBNTfW+FRERktJzePu3g8zfEQ2At4czr/dpQvcmviYnEyl+VNgQKQQ5VhtPfruTI3GpeHs4M2toS9yd9c9NRIqHnVHniUnMoJyTPXdozR+RInF3sB/vLD/EtshzxCRmUK28q9mRRKSI7Y5O5MWFezgUmwJA5wZVePOepvo+EBERDMNg2d7TvLr0APGpWQA82KYGL/ZoiKeLRtiLXIqetIrcZIZhMHHpfv48Go+roz1fDWlFVS/drIhI8XFhtEa3Jr64OtmbnEakbKjq5UrrgIpsjTjH0rBTjOxUx+xIIlJEMrKtTFl1mK82RmAzoGI5Jyb2DuTuID8sFo3SEBEp604lZvDK4n2sOXQGgLre7kzq15RWARVNTiZSvKmwIXKTfflnBN9tjcJigY8GBdO0upfZkURE8uVYbSzbcxrIW9BYRIpO35BqbI04x5KwGBU2RMqI7ZHneGHBnvw50vsG+/HKXYFUcnc2OZmIiJjNajP4enMk7604TFq2FUd7C092rsvITnVwdlAHNJF/o8KGyE20Yn8sb/9+EICXezaia2PNgSgixcvGY/EkpGVTqZwT7etUMjuOSJnSs0lVJizZx6HYFI7EpVDfx8PsSCJSSDKyrby34jCzN0VgGODr6cLb/Zpwu6aAFBER4FBsMmMX7iUsOhGAljUr8E7/ptT11u+HIldLhQ2Rm2TPyUT+88MuDAMeuqUGj3SoZXYkEZGLLP17Gqq7mlXFwd7O5DQiZYuXmyO31avCmkNn+HX3KcZ0bWB2JBEpBNsizvHCgt1EJqQDcF/L6rzcKxAvV82RLiJS1mXmWPl07TFmrD9Ors3Aw9mBF3s05IHWNbCz0/SEItdChQ2RmyAmMYNH5u4gM8dGx/pVeLV3Y82XKyLFTmaOlZX7YwG4W9NQiZiid5BfXmFjz2mevbO+fl8QKUXSs3OZvPwwczdHYhhQ1cuFSf2a0qmBt9nRRESkGNgWcY6xC/cQ/vf0hN0a+/Da3U3w9XIxOZlIyaTChsgNSsnM4ZE52zmbkkVDXw8+fSBEvaBFpFj649AZ0rKtVCvvSvMa5c2OI1ImdQn0wdnBjvD4NA6cTqaxn9biEikNtoYn8MLCPZz4e5TGwJb+vHxXIzxdNEpDRKSsS83KZfLyQ8zbfAIAbw9nXu/TmO5NqpqcTKRkU2FD5AbkWm2M/m4Xh2JTqOLhzFdDW+GhmxcRKaZ+3Zu3aPhdzaqql7iISdydHejcwJvl+2P5dc9pFTZESrgLozTmbIoE8kZpvNO/GR3rVzE3mIiIFAsbjpxl3KK9xCRmAHmF75d6NdL0hCI3gQobItfJMAxe++UA64+cxcXRjq+GtKRaeVezY4mIXFJ6di5rD54BoFcz9QwSMdNdQVX/Lmyc4oVuDVRoFCmhNh9P4MWFe4g6lzdK4/7W/ozrqVEaIiICSek5vLHsAAtCTwLgX9GVd/o1o33dyiYnEyk9VNgQuU6z/ork6y0nsFhg6sAQmlUvb3YkEZHLWnPwDBk5VmpUdKNpNfUQFzHT7Q29cXW0J/pcBntOJhHkX97sSCJyDTKyrby7/FD+KA2/v0dp3KZRGiIiAvwfe3ceFlXZhgH8nhmYYROQHRRFRMENURTEPUVxybRssSyXTHOr1L5KKzWttKzMMtdyLS3T1DLNXSvTXFBcEFAQFWUX2feZ9/sDnSIBGWXmMMz9u665cs68Z+aZE8zDnOe8z7v7QjJm/nwBaTlFkMmAUZ298EaYL6yUPA1LVJP4G0X0APZGJuODnRcBADP6+6FfazeJIyIiqtrOc2xDRVRbWCnN0LuFC349l4RfzyWysEFkRCISMjHtxwhcSStb+PXZoEZ4e4Af29ESERHScorw3i+R2HmnBXBTZ2sseNIfgY0dJI6MqG5iYYNIRxEJmXj1hzMQAnguuBHGdvOWOiQioirlFpXiUAzbUBHVJo/6e+DXc0nYeS4JM/q3gFzOgiNRbVai1mDxwVgsORQLtUbA1VaFBU+25VoaREQEIQS2R9zEnB0XkZlfAoVchvE9vPFKr2awMFdIHR5RncXCBpEOEjLy8dK6kygs0aCnrzPmPtaKVz4TUa13ICoFRaUaeDtZo6W7rdThEBGAnr7OsFGZITGrEGcSbvNKPqJaLDY1B1M3ncX5m1kAgMfaemDu4Fawt1JKHBkREUktMbMA726/gIPRZReStXS3xYIn/dGa7X+J9I6FDaJqysovwag1J5CeW4yW7rb46rn2MFPIpQ6LiOi+dpwtmwo9kG2oiGoNC3MF+rR0xbYzN7HjbBILG0S1kEYjsPboVXy8OxpFpRrYWZrjgyGtMaith9ShERGRxDQage9PXsf8XdHILSqFUiHHa6HNMK67N8x5rojIIFjYIKqGolI1xn17CnFpeXC3s8DqUR1ho+KvDxHVflkFJfjjUhqAstY3RFR7POrvjm1nbmLX+STMfLQlFGxHRVRr3MwswP9+PItjV24BAHo0d8aCJ/3hamshcWRERCS1a7fy8NZP5/D3lQwAQPtG9ljwpD98XOpJHBmRaeGZWaL7EELgrS3ncDw+AzYqM6we1RFudvxCQ0TGYf/FFBSrNfBxsUFzVxupwyGif+nWzBm2FmZIzSnCifgMhDR1lDokIpMnhMDW0zfx3i+RyCkqhaW5Au8MbIHhwY0465GIyMRpNALfHb+G+buiUVCihqW5Am+E+WJkZy9eoEIkARY2iO7j832XsD0iEWZyGZY93x4t2J+eiIzIr+cSAZRdGc4TMkS1i9JMjrBWbtgcfgO/nktkYYNIYln5JZix7Rx2nU8GUHYF7sKnA+DlZC1xZEREJLWEjHy8ueWcdiZfiLcjPh7qj0aOVhJHRmS6WNggqsKPJxPw5cFYAMCHj7dGt2bOEkdERFR9Wfkl+PNyOoCywgYR1T6PtvXA5vAb2H0hGXMea8X1u4gkcvzKLUzdFIHErEKYyWWY2qc5Xu7uzd9JIiITJ4TAxhPXMW9nFPKKy2ZpvD3AD8ODG0POWRpEkmJhg6gSf15Ow9vbzgMAJj/ig2c6NpI4IiIi3eyJTEapRsDPrR77vRLVUp2bOqK+lTlu5RXj2JVbvIiCyMBK1Bp8sf8ylhyOhRBAEydrfDEsAP4N7aUOjYiIJHYzswBvbTmHI7FlF4sFNXHAJ0/6o7EjZ/IR1QYsbBBVIDo5GxO/O41SjcDgAA+83re51CEREelsx502VAPbcLYGUW1lrpCjX2t3fH/iOn49m8TCBpEBXbuVh9d+iEBEQiYA4OkODTF7UCtYq/g1mYjIlAkh8OOpBLz/axRyi0phYS7Hm2F+GNXZi7M0iGoR/sVG9B8p2YV4cc1J5BSVIqiJAxY86c++9ERkdDLyinE0rqz/66NtPSSOhoiqMsi/rLCxOzIZ7w9pDaUZW98Q6dvW0zcwc/sF5BWrUc/CDPOfaINH/ZkviYhMXVJWAab/dB6/X0oDAAQ2ro9PnvSHt7ONxJER0X+xsEH0L7lFpRi95iQSswrh7WyNlS8EQmWmkDosIiKd7b6QDLVGoJWHLZpw0VOiWi3Y2xFONiqk5xbhaFw6evq6SB0SUZ2VXViCmdsv4OeIslmNQV4O+HxYABrYW0ocGRERSUkIgZ9O38ScHZHIKSyF0kyON/r64sWuTaDgLA2iWomFDaI7StUavLLxNC4mZcPJRol1o4Ngb6WUOiwiogey8/ydNlRcNJyo1lPIZejX2hXf/X0duy8ks7BBpCfh127jtR/O4MbtAijkMkzp3QwTH/HhCSsiIhOXml2IGVvP40B0KgCgrac9PnuqLXxcOEuDqDbjPHcilFXmZ/8SiUMxabAwl+ObkR3h6WAldVhERA8kPbcIx+62oWrDthpExqB/67Ii5J7IZJSqNRJHQ1S3CCGw8o84PLPiGG7cLkDD+pb48eUQvNK7GYsaREQmbue5JPRd9AcORKdCqZDjrX5++Gl8CIsaREaAMzaIAKz84wo2HL8OmQz4Ylg7BHjaSx0SEdED2xuZAo0AWjewRSNHFmmJjEFwEwfUtzLH7fwSnIjPQGcfJ6lDIqoTMvOL8b/NZ7E/quwq3Ef93THviTawtTCXODIiIpJSVkEJ3vslEtvO3ARQ9t3ps6cC4OtWT+LIiKi69DZjIyMjA8OHD4etrS3s7e0xZswY5ObmVjn+lVdega+vLywtLdGoUSO8+uqryMrKKjdOJpPdc/vhhx/09TbIBOw8l4T5v0UDAGYObImwVm4SR0RE9HB+u5AE4J8rwImo9jNTyNG3ZdnfILvu/A4T0cM5ff02Bn55BPujUqE0k+ODIa2x+Nl2LGoQEZm4Y3G30H/RH9h25ibkMuDVXj7YNrELixpERkZvMzaGDx+OpKQk7Nu3DyUlJRg9ejTGjRuHjRs3Vjg+MTERiYmJ+PTTT9GyZUtcu3YN48ePR2JiIrZs2VJu7Jo1a9CvXz/tfXt7e329DarjTl3NwNQfIwAAozp74cWuTaQNiIjoIWXll2jbUPVvzUItkTHp38YNm04lYE9kCuY81potcogekBAC3/wZj493R6NUI+DlaIWvnmuP1g3spA6NiIgkVFiixmd7Y/DNkXgIATR2tMLCpwMQ2Li+1KER0QPQy4yNqKgo7N69G9988w2Cg4PRtWtXLF68GD/88AMSExMr3Kd169b46aefMGjQIDRt2hS9evXChx9+iB07dqC0tLTcWHt7e7i5uWlvFhYW+ngbVMfFpuZizLpTKC7VILSFK2Y+2lLqkIioEkuWLIGXlxcsLCwQHByMEydOVDq2Z8+eFc7uGzhwoAEjls6+qBSUagR8XevB25l9YYmMSeemTqhnYYa0nCKEX7stdThERikzvxhj15/Ch7uiUKoRGOjvjh2vdGVRg4jIxF1MzMbgr/7C13+WFTWeDWqEXa92Y1GDyIjppbBx7Ngx2Nvbo0OHDtptoaGhkMvlOH78eLWfJysrC7a2tjAzKz+xZNKkSXByckJQUBBWr14NIUSNxU6mITW7ECNXn0BWQQkCPO2x+Nl2vCqSqJbatGkTpk2bhtmzZ+P06dNo27YtwsLCkJqaWuH4rVu3IikpSXu7cOECFAoFnnrqKQNHLo3dd1rY9ONsDSKjozSTo09LVwD/tJQjouo78+/WUwo53h/SGl892w712HqKiMhkqTUCy3+Pw+AlRxCTkgMnGyVWjeyA+U+0gbWKSw8TGTO9FDaSk5Ph4uJSbpuZmRkcHByQnJxcredIT0/H+++/j3HjxpXbPnfuXPz444/Yt28fhg4diokTJ2Lx4sVVPldRURGys7PL3ch05RaVYvTak7iZWQAvRyusGtkBlkqF1GERUSUWLlyIsWPHYvTo0WjZsiWWL18OKysrrF69usLxDg4O5Wb17du3D1ZWViZR2MgtKsUfl9MBlLW0ISLjc3dtnN0XkqHR8OKd+9F1XT+g4pl948ePN1DEpA9CCHx77CqeXnEMNzML0NjRClsndsYLnRpDJuPFS0SmjrnCdCVk5OPZlX/jo9+iUaIW6NPSFXumdEfvFq5Sh0ZENUCn0uT06dPx8ccfVzkmKirqoQICgOzsbAwcOBAtW7bEe++9V+6xmTNnav/drl075OXl4ZNPPsGrr75a6fPNnz8fc+bMeei4yPiVqDWYuOE0IhOz4WSjxLoXg+Boo5I6LCKqRHFxMcLDwzFjxgztNrlcjtDQUBw7dqxaz7Fq1SoMGzYM1tbWlY4pKipCUVGR9r6xFsAPRqeiuFSDJk7W8HXlwndExqhbMydYKxVIyirE2RuZaNeI7RGqouu6fneNHTsWc+fO1d63srLSd6ikJ4Ulary97Ty2nr4JoGx9qY+f9OcC4USkxVxheoQQ2HbmJmb9HIncolJYKxWYPagVnurQkAVvojpEp8LG66+/jlGjRlU5xtvbG25ubve0CCktLUVGRgbc3Kq+gjQnJwf9+vVDvXr1sG3bNpibV/0HaXBwMN5//30UFRVBpar4BPWMGTMwbdo07f3s7Gx4enpW+bxU9wghMP2n8/jjUhoszRVYNbIjGjtWfqKTiKSXnp4OtVoNV9fyV9S4uroiOjr6vvufOHECFy5cwKpVq6ocV1cK4P9uQ8U/2ImMk4W5Ar1auGLH2UT8diGZhY0q3F3X7+TJk9oWuIsXL8aAAQPw6aefwsPDo9J9rays7vu9hGq/hIx8jP8uHJGJ2ZDLgOn9/TC2mzdzIBFpMVeYnpzCEry7/QJ+jihb47dD4/pY+HQAGjmyMEVU1+jUisrZ2Rl+fn5V3pRKJUJCQpCZmYnw8HDtvgcPHoRGo0FwcHClz5+dnY2+fftCqVTil19+qdai4BEREahfv36lRQ0AUKlUsLW1LXcj07Nw3yX8dPoGFHIZlg5vj7ae9lKHRER6tmrVKrRp0wZBQUFVjpsxYwaysrK0t4SEBANFWHMKitU4FJ0GoOxqVSIyXgPu/A7/diGJa8lV4WHW9duwYQOcnJzQunVrzJgxA/n5+VWOZ2vb2uePS2kY9NURRCZmw8Faie/GBGNc96YsahBROYbKFcwTtcPp67cx4Ms/8XNEIhRyGV7v0xybXg5hUYOojtLLKjktWrRAv379MHbsWCxfvhwlJSWYPHkyhg0bpq2G37x5E71798b69esRFBSkLWrk5+fju+++K5cInJ2doVAosGPHDqSkpKBTp06wsLDAvn37MG/ePPzvf//Tx9ugOmTD8WtYfDAWADDv8dZ4xM/lPnsQUW3g5OQEhUKBlJSUcttTUlLue/VUXl4efvjhh3LTxyujUqmqLJAbg98vpaKgRI0G9pZo08BO6nCI6CH08HWGhbkcCRkFiEzMRmv+TlfoQdf1e+6559C4cWN4eHjg3LlzeOuttxATE4OtW7dWuk9dmdlXFwghsPRwHD7dGwMhgLYN7bD0+UA0sLeUOjQiqoUMlSuYJ6R1d4HwhfsuQa0RaFjfEl8Ma4fAxpz5SlSX6aWwAZRVtidPnozevXtDLpdj6NCh+PLLL7WPl5SUICYmRlvxPn36tLZa7uPjU+654uPj4eXlBXNzcyxZsgRTp06FEAI+Pj7aRWWJKrP/Ygpmbr8AAHitdzM807GRxBERUXUplUoEBgbiwIEDGDJkCABAo9HgwIEDmDx5cpX7bt68GUVFRXj++ecNEKn0frtQ9sWsP9tQERk9K6UZHvF1wW8XkvHbhSSTK2zoe12/cePGaf/dpk0buLu7o3fv3oiLi0PTpk0r3IetbWuHnMIS/G/zWeyJLLvgYVhHT7z3WCtYmCskjoyIDK225QrmCekkZxVi6qYIHLtyCwDwWFsPfPB4a661RGQC9FbYcHBwqHIhJi8vr3JT63v27Hnfqfb9+vVDv379aixGqvvOXL+Nyd+fhkYAz3TwxJTQZlKHREQ6mjZtGkaOHIkOHTogKCgIixYtQl5eHkaPHg0AGDFiBBo0aID58+eX22/VqlUYMmQIHB0dpQjboIpK1TgYVba2Vf82bENFVBf0a+1WVtg4n4z/9fU1qYKlIdb1+7e7rXJjY2MrLWzUhZl9xu5qeh5eWn8Ksam5UCrkmDO4FZ4N4gVLRKaqtuUK5glp7I1Mxps/nUNmfgmslArMHdwaQ9s3MKm/m4hMmd4KG0RSi0/Pw5h1p1BYokFPX2d88HhrJjciI/TMM88gLS0Ns2bNQnJyMgICArB7927tguLXr1+HXF5+yaiYmBgcOXIEe/fulSJkg/srNh05RaVwtVWhnSenWxPVBb38XKA0k+NKeh4upeTC162e1CEZjLOzM5ydne877t/r+gUGBgKo3rp+/xUREQEAcHd3f6B4Sf+OxqVj4obTyMwvgZutBZY93x7tGjHfEZky5grTVliixoc7o/Dt39cAAG0a2OGLYQHwdraRODIiMiQWNqhOSs8twqg1J5CRV4w2Deyw5Ln2MFfI778jEdVKkydPrrT11OHDh+/Z5uvra1IL7v52vqwNVVgrN8jlLOAS1QX1LMzRvZkz9kelYNf5JJMqbFTXg6zrFxcXh40bN2LAgAFwdHTEuXPnMHXqVHTv3h3+/v4SvyOqyHd/X8N7v0SiVCPQ1tMeX78QCBdbC6nDIiIjwVxR98Sl5WLShtOITs4BAIzr7o3/9fWF0oznfIhMDX/rqc7JLy7FmLUnce1WPho5WGH1qI6wVrGGR0R1U4lag31RZb3G+7VmGyqiuqT/nd/p3RcqX9zU1G3YsAF+fn7o3bs3BgwYgK5du2LlypXax/+7rp9SqcT+/fvRt29f+Pn54fXXX8fQoUOxY8cOqd4CVaJUrcHsny/g3e0XUKoRGBLggU3jOrGoQUQ6Y66oO36OuIlBi48gOjkHTjYqrH8xCG8PaMGiBpGJ4tleqlNK1RpM2nAaZ29kob6VOdaO7gjneuxzSUR11/ErGcjML4GjtRJBXg5Sh0NENSi0hSvMFTLEpOQgLi0XTdle4R66ruvn6emJ33//3RCh0UPIyi/BpI2ncSQ2HQDwRpgvJvZsyrayRPRAmCuMX2GJGnN2XMT3J64DAEK8HfHFswFwqcdiN5EpY0mT6gwhBN7dfgGHYtJgYS7HqlEd2V+RiOq83y4kAQD6tnKFGVvuEdUpdlbm6NzUCQCwJ5KzNsg0xKXlYsjSv3AkNh1WSgVWvBCISY/4sKhBRGSi4tPz8PjSo/j+xHXIZMCrvZvhu5eCWdQgIhY2qO74bO8l/HAyAXIZsPjZ9mjPBQWJqI5TawT2RN5tQ8WFDInqor6tXAFA+7tOVJediM/AE0uPIj49Dw3sLbFlfGeEtWKbRSIiU/XruUQMWnwEUUnZcLRWYv2LQZjWpzkUXFeQiMDCBtURa/+Kx1eHYgEA8x5vgz4tXSWOiIhI/05fv4303CLYWpghxNtR6nCISA/6tHSFTAacTchEclah1OEQ6c0vZxPx/DfHkVVQgnaN7PHz5C5o6WErdVhERCSBwhI1Zm6/gMkbzyC3qBRBTRyw67Vu6NbMWerQiKgWYWGDjN4vZxMx59eLAID/9W2OYUGNJI6IiMgw9t5pTdO7hSsXzCOqo1zqWaCdpz0AYN9FtqOiukcIgaWHY/Hq92dQrNagXys3fD+2E5xsuE4eEZEpSsjIx5PLj+Lbv68BACY90hQbXwqGqy1bTxFReTwLQkbtz8tpeP3HCAgBjAxpjEmP+EgdEhGRQQghsPdiWWuavpylRlSn3W3Fc/d3nqiuKFVr8Pa2C1iwOwYAMKZrEywZ3h4W5gqJIyMiIin8cSkNg746ggs3s1HfyhxrR3fEG2F+XEuQiCrETwYyWuduZOLlb8NRohYY6O+O2YNacVFBIjIZl1Jyce1WPlRmcvTw5ZRsorqs753CxrG4W8jKL5E4GqKakVtUipfWn9IuBvveoJaY+WhL9k0nIjJBQggsORSLkWtOIDO/BG0b2mHnq93Q09dF6tCIqBZjYYOM0pW0XIxacxL5xWp08XHEwqfbQs4vQURkQvbcaUPVrZkTrJRmEkdDRPrUxMkazVxsUKoROBSTKnU4RA8tPbcIw1Yew+GYNFiYy7Hi+UCM6tJE6rCIiEgCOYUlGP9dOD7ZEwMhgGeDPLHp5RB42FtKHRoR1XIsbJDRSckuxAurTiAjrxhtGthhxQsdoDLjdHUiMi177/Ta79vSTeJIiMgQ/mlHxXU2yLglZOTjyWVHceFmNhytlfhhXIh2VhIREZmW2NQcDF7yF/ZEpkCpkGP+E20w/wl/tiQkomphYYOMSlZBCUauPoGbmQXwcrTCmtEdYaPilcpEZFpuZhbgws1syGVA7xacnk1kCvq2KltL53BMGgpL1BJHQ/RgYpJzMHTZUVy9lY8G9pbYPD4EAZ72UodFREQS2H0hCYO/+gtX0vLgbmeBH8eH4NmgRlKHRURGhIUNMhqFJWqMXXcK0ck5cK6nwrdjguFko5I6LCIig9t3pw1Vh8YOcOTnIJFJaNPADu52FsgvVuOv2HSpwyHSWfi1DDy1/ChSc4rQ3NUGP03oDG9nG6nDIiIiA9NoBBbsjsb4704jr1iNTt4O2PFKVxa6iUhnLGyQUShVa/DK92dw4moG6qnMsG50EDwdrKQOi4hIEnsvpgD45wpuIqr7ZDIZ+rYs+52/u8YOkbE4FJ2K4d8cR3ZhKQIb18ePL4fAzc5C6rCIiMjAcotKMe7bU1h6OA4AMLZbE3zHi1aJ6AGxsEG1nhAC726/gH0XU6A0k+PrkR3Q0sNW6rCIiCSRmV+M4/EZAIA+LVnYIDIld9fZ2B+VCrVGSBwNUfVsO3MDL60/hcISDR7xdcZ3Y4Jhb6WUOiwiIjKwhIx8DF16FPujUqE0k2PRMwF4Z2BLmCl4apKIHgwXJ6Ba75M9MfjhZALkMuDLYQHo5O0odUhERJI5GF12QtPPrR4aO1pLHQ4RGVDHJg6wszRHRl4xTl3NQDD/JqJa7vsT1/H2tvMQAni8XQMseNIf5jyBRURkco5fuYUJG04jI68YzvVU+HpEB7aeIqKHxr8qqVZb8XucdoriB0PaoF9rd4kjIiKS1t7IO22oOFuDyOSYK+To3cIFwD8t6Yhqq3VHr2LG1rKixoiQxvjsqbYsahARmaBNJ6/j+VXHkZFXjDYN7PDL5C4sahBRjeBfllRrfX/iOub/Fg0AeKufH54LbiRxRERE0iosUeP3S2kAgL53WtIQkWnp27Lsd39PZDKEYDsqqp2+/uMKZv8SCaCsf/qcx1pBLpdJHBURERlSqVqDuTsu4q2fzqNELTDQ3x0/vhwCdztLqUMjojqCraioVtpxNhFvbzsPABjfoykm9GwqcURERNI7cjkdBSVqeNhZoBXXGiIyST2aO8PCXI4btwsQlZTDdceo1vnq4GV8uvcSAGDyIz54vW9zyGQsahARmZLcolJM3ngah2PKLsqa1qc5Xunlw3xARDWKMzao1jkUk4qpmyIgBPBccCO81c9X6pCIiGqFPZHJAMpma/BLAZFpslQq0K2ZM4B/PhOIagMhBD7bG6Mtarzepzn+F+bLfEVEZGJSsgvxzIpjOByTBgtzOZYOb49XezdjPiCiGsfCBtUqJ+IzMOG7cJRqBB71d8f7g1sz+RERoWwq9/6oO+trtOL6GkSm7O4aO1xng2oLIQQ+2h2NxQdjAQAz+vvhld7NJI6KiIgMLSY5B48v+QuRidlwslHih3EhGNCGa6USkX6wFRXVGhduZmHM2pMoLNGgp68zFj4dAAV78RIRAQDCr93G7fwS2FmaI8jLQepwiEhCoS1cIZcBUUnZSMjIh6eDldQhkQkTQmDBnhis+P0KAOC9QS0xqksTiaMiIiJDOxqbjpe/DUdOUSm8na2xdlQQGjnybxQi0h/O2KBaIS4tFyNXn0BOUSmCvBywbHgglGb88SQiuuvuldm9W7jATMHPRyJTVt9aiaAmZQVOtqMiqS3afxnLDscBAN4f3IpFDSIiE7T19A2MXFN2TqejV31sndCZRQ0i0jueGSHJ3cwswAvfHMetvGK0bmCLb0Z1gKVSIXVYRES1hhACey/eWV+jpZvE0RBRbdDnzmfBgahUiSMhU7bkUCy+OHAZADDz0ZZ4IcRL2oCIiMighBD46uBlTPvxLErUZS3Fvx0TDHsrpdShEZEJYGGDJJWeW4QXvjmOxKxCeDtbY93oINhamEsdFhFRrRKdnIOEjAKozOTo3txJ6nCIqBYIbeECADhxNQNZ+SUSR0Om6Os/ruCTPTEAgOn9/TCmK2dqEBGZEo1GYM6Oi/h07yUAwMs9vPHlsHawMOeFqkRkGCxskGSyC0swcvUJXEnPQwN7S3w3JhiONiqpwyIiqnX232lD1a2ZE6yUXB6LiIDGjtZo5mIDtUbg8CXO2iDDWvtXPD7cFQUAmNanOcb3aCpxREREZEglag2m/RiBtUevAihbX2lG/xaQc51UIjIgFjZIEnlFpRi1+gQiE7PhZKPEt2OC4GFvKXVYRES10v7ospOWvVu4ShwJEdUmoS3LPhP2sx0VGdCPJxPw3o6LAIDJj/jg1d7NJI6IiIgMqaBYjZe/Dcf2iESYyWVY9EwA11ciIkmwsEEGV1Csxph1J3H6eiZsLcyw7sUgeDvbSB0WEVGtlJpdiLMJmQCA3n4u0gZDRLVK6J1i5+GYVJSoNRJHQ6Zg94VkTN96DgAwtlsTvN63ucQRERGRIWUVlGDE6uM4GJ0KC3M5vh7RAUPaNZA6LCIyUSxskEEVlarx8nfh+PtKBmxUZvh2TDBaedhJHRYRUa118M5sjbYN7eBiayFxNERUmwR42sPRWomcwlKcjM+QOhyq447GpePV789AI4BnOnji7QEtIJOx5QgRkalIzy3CsJV/4+TV26hnUXY+5xFeeEVEEmJhgwymRK3B5I1n8MelNFiaK7BmdEe09bSXOiwiolrtbosZtqEiov9SyGXodeeEwr6oFImjobrs/I0sjFsfjmK1BmGtXPHh461Z1CAiMiGp2YUYtvJvRCVlw8lGhR9fDkFHLwepwyIiE8fCBhmEWiMwZVME9l1MgdJMjm9GdmASJCK6j8ISNY7EpgEAerfg1VBEdK9/1tlIgRBC4mioLopLy8XINSeQW1SKEG9HfDGsHcwU/BpJRGQqkrPKihqxqblwt7PA5vEhaOFuK3VYREQsbJD+aTQCb2w5i53nkmCukGHFC4Ho4uMkdVhERLXe0bh0FJZo4GFngZb88kBEFejWzAlKMzkSMgpwOTVX6nCojknKKsCIVSeQkVeM1g1ssXJEICzMFVKHRUREBpKYWYBnVh7DlfQ8NLC3xKZxIWjiZC11WEREAFjYID0TQuDdny9g6+mbUMhlWPxsezziy6uOiYiqY9/Ff9pQseUHEVXESmmGLk0dAQD7LrIdFdWc7MISjFp9EjczC+DtZI21o4NQz8Jc6rCIiMhAEjLy8czKY7h2Kx+eDpbY9HInNHK0kjosIiItvRU2MjIyMHz4cNja2sLe3h5jxoxBbm7VV5H17NkTMpms3G38+PHlxly/fh0DBw6ElZUVXFxc8MYbb6C0tFRfb4MeghAC7/8ahY3Hr0MmAxY+3Rb9WrtJHRYRkVEQQuBgdNlJSrahIqKq/LsdFVFNKFFrMPG704hJyYFLPRXWjwmCk41K6rCIiMhArt/Kx7CVfyMhowBejlbYNC4EDeuzqEFEtYuZvp54+PDhSEpKwr59+1BSUoLRo0dj3Lhx2LhxY5X7jR07FnPnztXet7L654NTrVZj4MCBcHNzw9GjR5GUlIQRI0bA3Nwc8+bN09dboQcghMAne2Kw+q94AMDHQ/0xOKCBxFERERmPCzezkZJdBCulAp28HaUOh4hqsd5+rngHFxCRkIm0nCI41+MJaHpwQgi8u+0CjsSmw0qpwOpRHXkyi4jIhCRmFuDZr/8um7HnbI3vx3aCq62F1GEREd1DLzM2oqKisHv3bnzzzTcIDg5G165dsXjxYvzwww9ITEyscl8rKyu4ublpb7a2//QU37t3Ly5evIjvvvsOAQEB6N+/P95//30sWbIExcXF+ngr9IC+PBCLpYfjAADvD26Fpzt4ShwREZFxuXvldbdmTuxnTkRVcrOzQJsGdhACOBSdKnU4ZOSWHo7DplMJkMuAr55rh9YN7KQOiYiIDCQ1pxDDvzmubUP4wzgWNYio9tJLYePYsWOwt7dHhw4dtNtCQ0Mhl8tx/PjxKvfdsGEDnJyc0Lp1a8yYMQP5+fnlnrdNmzZwdXXVbgsLC0N2djYiIyNr/o3QA/nywGV8vv8SAOCdAS3wQoiXtAERERmhA3faUIW2cL3PSCKifz4r9rEdFT2EnyNu4pM9MQCA9x5rhV5+zEFERKbidl4xXvjmBOLvLBT+3UvBcKnHogYR1V56aUWVnJwMF5fy/cDNzMzg4OCA5OTkSvd77rnn0LhxY3h4eODcuXN46623EBMTg61bt2qf999FDQDa+1U9b1FREYqKirT3s7OzdX5PVD2LD1zGwn1lRY3p/f0wtru3xBERERmfpKwCXLiZDZkMeMSP62sQ0f2FtnTB5/sv4cjldBSWqDnTi3R28moG3th8DgAwpmsTjODFSUREJiO7sAQjVp9ATEoOXG1V2Dg2GB72llKHRURUJZ1mbEyfPv2exb3/e4uOjn7gYMaNG4ewsDC0adMGw4cPx/r167Ft2zbExcU98HMCwPz582FnZ6e9eXqyLZI+fHXwMj67U9R4q58fxvdoKnFERETG6UBUWSuZdp72XKyViKqlpbstPOwsUFCixtG4dKnDISNz/VY+xq4/hWK1BmGtXPH2gBZSh0RERAZSUKzGi2tO4vzNLDhaK7HhpWA0drSWOiwiovvSqbDx+uuvIyoqqsqbt7c33NzckJpavr9vaWkpMjIy4ObmVu3XCw4OBgDExsYCANzc3JCSUn56/d37VT3vjBkzkJWVpb0lJCRUOwaqniWHYvHp3rKixpv9fDGhJ4saREQP6sCdVjK92YaKiKpJJpNpPzP2XeQ6G1R9uUWleGn9SWTml6BtQzsseqYdFHKZ1GEREdWIDz/8EJ07d4aVlRXs7e2rtY8QArNmzYK7uzssLS0RGhqKy5cv6zdQiZSqNZi88TROXbsNWwszrB8TBB+XelKHRURULToVNpydneHn51flTalUIiQkBJmZmQgPD9fue/DgQWg0Gm2xojoiIiIAAO7u7gCAkJAQnD9/vlzRZN++fbC1tUXLli0rfR6VSgVbW9tyN6o5Sw7FanvxvhHmi4k9fSSOiIjIeOUXl+KvuFsAuL4GEekmtGXZZ8bB6BRoNELiaAyDJ6wejkYjMG1TBC6l5MKlngorXugASyXbmBFR3VFcXIynnnoKEyZMqPY+CxYswJdffonly5fj+PHjsLa2RlhYGAoLC/UYqeEJIfDu9gs4EJ0KlZkcq0d1RCsPO6nDIiKqNr0sHt6iRQv069cPY8eOxYkTJ/DXX39h8uTJGDZsGDw8PAAAN2/ehJ+fH06cOAEAiIuLw/vvv4/w8HBcvXoVv/zyC0aMGIHu3bvD398fANC3b1+0bNkSL7zwAs6ePYs9e/bg3XffxaRJk6BSsVWHFP5b1Jj0CIsaREQP48jldBSXauDpYInmrjZSh0NERqSTtwOslQqkZBfhQmKW1OEYBE9YPZwvDlzG3ospUCrkWP5CINzsuEgsEdUtc+bMwdSpU9GmTZtqjRdCYNGiRXj33XcxePBg+Pv7Y/369UhMTMT27dv1G6yBLdp/GT+cTIBcBix+th06eDlIHRIRkU70UtgAgA0bNsDPzw+9e/fGgAED0LVrV6xcuVL7eElJCWJiYpCfnw8AUCqV2L9/P/r27Qs/Pz+8/vrrGDp0KHbs2KHdR6FQ4Ndff4VCoUBISAief/55jBgxAnPnztXX26AqLD38T1Hjf32bs6hBRFQD9t9tQ+XnCpmMrUCIqPpUZgp0b+4MANh/MeU+o+sGnrB6cLsvJOGLA2UzVT58vDXaN6ovcURERNKLj49HcnIyQkNDtdvs7OwQHByMY8eOSRhZzdp4/Lo2B7w/pDX6tqp+23giotrCTF9P7ODggI0bN1b6uJeXF4T4Z4q8p6cnfv/99/s+b+PGjbFr164aiZEe3LLDcViwu6yo8Xqf5pjcq5nEERERGT+NRuBgdBoAtqEiogfTy88Fv11IxsGYVEzr6yt1OLXO/U5YDRs2TMLoDCc6ORvTfjwLAHixSxM81cFT4oiIiGqH5ORkAICra/m/xV1dXbWPVaSoqAhFRUXa+9nZ2foJsAb8eTkNM3++AAB4pZcPhgc3ljgiIqIHo7cZG1R3LTsch493RwMApvVpjld6s6hBRFQTzt7IRHpuEeqpzBDUhFPBiUh3PX1dAAAXbmYjNdv0Wivdz8OcsMrOzi53M1ZZBSV4+dtw5Ber0dXHCW8P8JM6JCIinUyfPh0ymazKW3R0tEFjmj9/Puzs7LQ3T8/aWTCOS8vFxA2nodYIPNG+Aab1aS51SERED4yFDao2IQQW7b+kLWpMDW2OV1nUICKqMQeiUgEA3Zs7Q2nGFE1EunOup0LbhmULfx6KSZU4mgfDE1b6I4TAG5vP4tqtfDSwt8TiZ9vBTMF8Q0TG5fXXX0dUVFSVN29v7wd6bje3spZMKSnlWzqmpKRoH6vIjBkzkJWVpb0lJCQ80OvrU2Z+MV5adwo5haUIbFwf859ow9a3RGTU9NaKiuoWIQQW7InBssNxALhQOBGRPtxdXyO0pYvEkRCRMXvEzwVnb2ThYHQqnunYSOpwdPb6669j1KhRVY6piRNW7u7u2u0pKSkICAiodL8ZM2Zg2rRp2vvZ2dlGWdz45s947WLhy55vj/rWSqlDIiLSmbOzM5ydnfXy3E2aNIGbmxsOHDigzQvZ2dk4fvw4JkyYUOl+KpUKKpVKLzHVhBK1BpM2nkZ8eh4a2FtixQuBUJkppA6LiOihsLBB9yWEwPu/RmH1X/EAgHcHtsBL3R7syyQREVXsxu18RCfnQC4DejZnYYOIHlwvPxcs2n8ZRy6no6hUbXQnLnjCSj9OxGfgozszr2cOagn/hvbSBkREZADXr19HRkYGrl+/DrVajYiICACAj48PbGxsAAB+fn6YP38+Hn/8cchkMkyZMgUffPABmjVrhiZNmmDmzJnw8PDAkCFDpHsjD+mDXy/ir9hbsFIq8M3IDnCyMe6cRkQEsLBB96HRCMz8+QI2HL8OAHh/SGu80IkLSxER1bRDMWWLhgc2rs8raInoobT2sINzPRXScopwMv42ujZzkjokveEJq+pJyynC5I1lPdUHB3jg+WDjm8lDRPQgZs2ahXXr1mnvt2vXDgBw6NAh9OzZEwAQExODrKws7Zg333wTeXl5GDduHDIzM9G1a1fs3r0bFhYWBo29pmw7cwPrjl2DTAYseiYALdxtpQ6JiKhGsLBBlVJrBN766Ry2hN+ATAZ8/IQ/nu5ofFPuiYiMwaHosl74j/hxtgYRPRy5XIZHfJ3x46kbOBidWqcLGzxhdX9qjcBrP5xBak4RfFxsMO9x9lQnItOxdu1arF27tsoxQohy92UyGebOnYu5c+fqMTLDiEnOwYyt5wEAr/Rqhr6tKl8nhIjI2HClOKpQqVqDqZsisCX8BhRyGT5/OoBFDSIiPSksUeNoXDoA4BFfFjaI6OH1ulMkNdYFxKtr7dq1EELcc7tb1ADKTlj9e82OuyeskpOTUVhYiP3796N58+aGD95AvjoYi6NxZe1Hlj/fHtYqXttGRGQKcgpLMOG7cBSWaNCtmRNe691M6pCIiGoUCxt0j+JSDSZvPINfzibCTC7DV8+2w5B2DaQOi4iozjp25RYKSzRwt7OAn1s9qcMhojqgazNnmCtkiE/Pw5W0XKnDIYmcupqBLw5cAgB8MKQ1fFyYY4iITIEQAtN/Oo8r6Xlwt7PAF8PaQSHnbD0iqltY2KByCkvUmPBdOHZHJkOpkGP584Ho38Zd6rCIiOq0w3faUPX0dWF7ECKqETYqMwQ1cQAAHIyu27M2qGLZhSV47YcIaAQwJMADT7RvKHVIRERkIJtP3cDO80kwk8uwZHh7OHANPyKqg1jYIK2cwhKMWnMCB6JToTKT4+uRHRDa0lXqsIiI6jQhBA7eaRXTi+trEFENutvarq63o6J7CSHwzrYLuJlZAE8HS8wd0lrqkIiIyEDi0/Pw3o5IAMDrfX3RvlF9iSMiItIPFjYIAJCRV4zh3xzH31cyYKMyw7oXg9CjubPUYRER1XlxaXlIyCiAUiFH56aOUodDRHXI3WLpifgM5BaVShwNGdLW0zex42wiFHIZFj3TDrYW5lKHREREBlCi1mDKpgjkF6vRydsB47p7Sx0SEZHesLBBSMoqwFPLj+LcjSw4WCvx/dhO6OTNk2tEVHssWbIEXl5esLCwQHBwME6cOFHl+MzMTEyaNAnu7u5QqVRo3rw5du3aZaBodXPoTouYYG8HLuhKRDXK29kGXo5WKFELHLmcJnU4ZCDXbuVh1s8XAABTejdDYGNeqUtEZCq+PHAZZxMyYWthhoVPB3BdDSKq01jYMHHx6Xl4ctkxxKWVLSj148shaNPQTuqwiIi0Nm3ahGnTpmH27Nk4ffo02rZti7CwMKSmVtxapbi4GH369MHVq1exZcsWxMTE4Ouvv0aDBg0MHHn13G0Rc7dlDBFRTXrkzqwNrrNhGkrVGrz2QwTyitUI8nLAxEd8pA6JiIgM5MLNLCw5FAsAmPdEG3jYW0ocERGRfrGwYcIiE7Pw1PKjuJlZgCZO1tg8PgQ+LjZSh0VEVM7ChQsxduxYjB49Gi1btsTy5cthZWWF1atXVzh+9erVyMjIwPbt29GlSxd4eXmhR48eaNu2rYEjv7+cwhKciM8AwPU1iEg/7n62HIpJg0YjJI6G9G3ln1cQkZCJehZm+HwYr9QlIjIVJWoN3txyDhoBPOrvjkf9PaQOiYhI71jYMFEnr2Zg2Mq/kZ5bjJbuttg8PgQN61tJHRYRUTnFxcUIDw9HaGiodptcLkdoaCiOHTtW4T6//PILQkJCMGnSJLi6uqJ169aYN28e1Gq1ocKutr9i01GqEWjiZA0vJ2upwyGiOiioiQOslAqk5RQhMjFb6nBIj2KSc7Bo32UAwOxBrdCAV+oSEZmMb/6Mx8WkbNhbmeO9x1pJHQ4RkUGwsGGCDsWk4oVVx5FTWIqOXvXxw8ud4GSjkjosIqJ7pKenQ61Ww9XVtdx2V1dXJCcnV7jPlStXsGXLFqjVauzatQszZ87EZ599hg8++KDS1ykqKkJ2dna5myHcbQ3DNlREpC8qMwW6NXMCwHZUdVmJWoP/bT6LYrUGvf1cMLR97Wy/SERENS8+PQ+L9l8CALw7sCXP7xCRyWBhw8RsCb+BsetOobBEg56+zlj/YjBsLcylDouIqMZoNBq4uLhg5cqVCAwMxDPPPIN33nkHy5cvr3Sf+fPnw87OTnvz9PTUe5xCCByKKVvMl22oiEif7n7GHIxhYaOuWn44DudvZsHO0hzznmgDmYwtqIiITIEQAjO2nkNRqQbdmjmxsE1EJoWFDRMhhMCSQ7H43+azKNUIDAnwwMoXOsBSqZA6NCKiSjk5OUGhUCAlJaXc9pSUFLi5uVW4j7u7O5o3bw6F4p/PtxYtWiA5ORnFxcUV7jNjxgxkZWVpbwkJCTX3JioRmZiNtJwiWCkV6Nikvt5fj4hM191ZYeduZCI9t0jiaKimXUzMxpcHy1pQzXmsFVxtLSSOiIiIDOXXc0n4+0oGLMzlmPc4C9tEZFpY2DABao3ArJ8j8cmeGADAyz28sfDpACjN+L+fiGo3pVKJwMBAHDhwQLtNo9HgwIEDCAkJqXCfLl26IDY2FhqNRrvt0qVLcHd3h1KprHAflUoFW1vbcjd9O3SnJUxXHyeozFhkJiL9cbG1QOsGthACOHxnphjVDcWlZS2oStQCfVu6YnAAF4slIjIVBcVqfPRbNABgQg8feDpw3VQiMi08s13HFZaoMWnDaXz79zXIZMDsQS0xo38LyOWs4hORcZg2bRq+/vprrFu3DlFRUZgwYQLy8vIwevRoAMCIESMwY8YM7fgJEyYgIyMDr732Gi5duoSdO3di3rx5mDRpklRvoUJ3W8I8wjZURGQAve7M2jjEdTbqlOW/x+FiUjbqW5njQ16pS0RkUlb+cQU3MwvQwN4S47p7Sx0OEZHBmUkdAOlPVn4Jxq4/hRNXM6BUyLHwmbZ41J9XcRGRcXnmmWeQlpaGWbNmITk5GQEBAdi9e7d2QfHr169DLv+nTu/p6Yk9e/Zg6tSp8Pf3R4MGDfDaa6/hrbfekuot3CMjrxgRCZkAuHA4ERnGI34u+PJgLP64lIYStQbmCl7fZOzi0nLx1cFYAMB7j7WCcz0uFktEZCqSswqx/Pc4AMD0/n5sM05EJomFjToqMbMAI1efwOXUXNSzMMPKFzogpKmj1GERET2QyZMnY/LkyRU+dvjw4Xu2hYSE4O+//9ZzVA/u90upEAJo4W4LNzv2Qici/Wvb0B6O1krcyivGyasZ6NzUSeqQ6CEIIfDOtvMoVmvQo7kzHmvLi5eIiEzJ4oOXUVCiRofG9fGov7vU4RARSYKXatVB0cnZeGLpUVxOzYWbrQU2jw9hUYOIqBY5FF3W476Xn7PEkRCRqZDLZejhW/aZ8zvX2TB6m8NvaBeL/WBIa7agIiIyIQkZ+fjxVAIA4I0wX+YAIjJZLGzUMYdjUvHksmNIzi5EMxcbbJ3YGX5u+l8El4iIqqdUrcHvl8pOKrINFREZUs87nzlcQNy43cotwrxdUQCAqaHNuVgsEZGJ+epgLErUAl19nBDszYtYich0sbBRh3x77CpeXHsSuUWlCPF2xJbxneFhbyl1WERE9C8RCZnIKiiBvZU52jWqL3U4RGRCuvk4QS4DYlJykJRVIHU49IA+2BmFzPwStHC3xYtdm0gdDhERGdC1W3nYcvoGAGBqn2YSR0NEJC0WNuoAtUZg7o6LmPlzJDQCeCqwIda9GAQ7K3OpQyMiov84GJ0KAOjezBkKOaeNE5Hh1LdWoq2nPQC2ozJWR2PTse3MTchkwPwn2nAReCIiE7PkUCzUGoEezZ0R2NhB6nCIiCTFv4SNXF5RKV7+Nhyr/4oHUNZfccGT/lCa8X8tEVFtdCjm7voabENFRIbXsznbURmrErUGs36JBAC80KkxAu4UqYiIyDSk5RRh+5lEAMCrvX0kjoaISHo8+23EkrMK8fSKY9gflQKlmRxfPdcOkx7x4cJRRES1VHJWIaKSsiGTAd2bc+FwIjK8nncWEP8rNh0lao3E0ZAu1h29itjUXDhYK/F6H1+pwyEiIgP79u9rKFZr0K6RPWdrEBGBhQ2jFZmYhSFL/kJkYjYcrZX4YVwnPOrvIXVYRERUhT/uLBretqE9HKyVEkdDRKaoTQM7OFgrkVNUitPXbksdDlVTWk4Rvth/GQDwZpgvW84SEZmYwhI1vvv7GgDgpa7eEkdDRFQ7sLBhhHZfSMJTy48hObsQPi422D6pC9pzAVoiolrv9zuFjR6crUFEEpHLZejezAnAP59JVPt9vDsaOUWl8G9oh6c7eEodDhERGdjuC8nIyCtGA3tLhLVylTocIqJagYUNI6LRCHy+7xLGf3ca+cVqdPVxwk8TOsPTwUrq0IiI6D5K1Rr8eflOYcOXhQ0iks7dzyCus2EcTl+/jS3hNwAAcx5rBbmcbWeJiEzNppMJAICnOjSEmYKn8oiIAMBM6gCoevKKSvH6j2exOzIZAPBilyZ4e4AfExoRkZE4eyMT2YWlsLM0R9uG9lKHQ0QmrHszZ8hkwMWkbKRmF8LF1kLqkKgSGo3Ae3cWDH8ysCHacZY2EZHJuX4rH8eu3IJMBjzFWXtERFo8K24EEjLyMXTZUeyOTIZSIceCJ/0xa1BLFjWIiIzI73eujO7WzAkKXm1LRBJytFHBv4EdALajqu22nbmJczeyUE9lhrf6+UkdDhERSWBzeNlsja4+TmhgbylxNEREtQfPjNdyR+PS8dhXRxCdnAMnGxW+H9eJfXWJiIwQ19cgotrk7mfRYRY2aq3CEjU+3RsDAJjUywfO9VQSR0RERIYmhMCv55IAlM3cIyKif+itsJGRkYHhw4fD1tYW9vb2GDNmDHJzcysdf/XqVchksgpvmzdv1o6r6PEffvhBX29DMkIIrDt6FS+sOoHb+SXwb2iHHa90QWBjTj8nIjI2t3KLcO5mFgAWNoiodujh6wIA+PNSGkrVGomjoYqsOhKPpKxCNLC3xKjOXlKHQ0REEricmov49DwoFXL0bsFFw4mI/k1va2wMHz4cSUlJ2LdvH0pKSjB69GiMGzcOGzdurHC8p6cnkpKSym1buXIlPvnkE/Tv37/c9jVr1qBfv37a+/b29jUev5Tyi0vxzrYL2HbmJgBgSIAHPhrqDwtzhcSRERHRgzgSmw4hgBbutuxlT0S1QoCnPewszZFVUIKzNzIR2NhB6pDoX9Jzi7DscBwA4I0wX34PICIyUXvvrLPaxccRNiouk0tE9G96+VSMiorC7t27cfLkSXTo0AEAsHjxYgwYMACffvopPDw87tlHoVDAzc2t3LZt27bh6aefho2NTbnt9vb294ytK+LT8zDhu3BEJ+dAIZdhej8/vNStCWQy9mMnIjJWd9fX4GwNIqotFHIZujVzwq/nknA4Jo2FjVrmywOXkVtUitYNbPFY23u/OxERkWnYE5kCAAhrVTfPgRERPQy9tKI6duwY7O3ttUUNAAgNDYVcLsfx48er9Rzh4eGIiIjAmDFj7nls0qRJcHJyQlBQEFavXg0hRJXPVVRUhOzs7HK32mhPZDIeW/zPehobXgrG2O7eLGoQERkxjUbgj8ssbBBR7dPzTjuqwzFcZ6M2iUvLxYbj1wEAbw9oAbmc3wWIiExRYmYBzt/MgkwGtqEiIqqAXmZsJCcnw8XFpfwLmZnBwcEBycnJ1XqOVatWoUWLFujcuXO57XPnzkWvXr1gZWWFvXv3YuLEicjNzcWrr75a6XPNnz8fc+bM0f2NGEipWoNP917C8t/Lppt3aFwfS4a3hyvblRARGb2LSdlIzy2GtVLBdZKIqFbp3twJAHD+ZhbSc4vgZMPFqWuDj3+Lhloj0NvPBZ2bOkkdDhERSeRIbDqAsvaRzvWYo4mI/kunGRvTp0+vdIHvu7fo6OiHDqqgoAAbN26scLbGzJkz0aVLF7Rr1w5vvfUW3nzzTXzyySdVPt+MGTOQlZWlvSUkJDx0jDUlPbcIL6w6oS1qvNilCb4f14lFDSKiOuL3S2VXQnf2cYLSTC8TJYmIHohLPQu08rAFAPxxibM2aoMz129j78UUyGXA9P5+UodDREQS+vvKLQBA56aOEkdCRFQ76TRj4/XXX8eoUaOqHOPt7Q03NzekpqaW215aWoqMjIxqrY2xZcsW5OfnY8SIEfcdGxwcjPfffx9FRUVQqSquYKtUqkofk9KxuFuYsukMUrKLYKVUYMGT/njUnz10iYjqEq6vQUS1WU9fZ0QmZuNwTBqeaN9Q6nBM3sJ9lwAAQ9s3RDPXehJHQ0REUjp+JQMA0MmbhQ0iooroVNhwdnaGs/P9T8yEhIQgMzMT4eHhCAwMBAAcPHgQGo0GwcHB991/1apVeOyxx6r1WhEREahfv36tLFxUplStwZcHY7H44GUIATR1tsby5wP55YWIqI7JLixB+PXbAFjYIKLaqaevC5YcisOfl9Og1ggouJ6DZI5fuYU/L6fDXCHDq72bSR0OERFJKDWnEDczCyCTAe0bsZ0tEVFF9LLGRosWLdCvXz+MHTsWy5cvR0lJCSZPnoxhw4bBw6NsRsLNmzfRu3dvrF+/HkFBQdp9Y2Nj8ccff2DXrl33PO+OHTuQkpKCTp06wcLCAvv27cO8efPwv//9Tx9vQy+Ssgrw2g8ROBFfVnl/ukNDvPdYK1gp9fK/goiIJHQ0Nh1qjUBTZ2t4OlhJHQ4R0T3aedqjnoUZbueX4NyNTLTjyRNJCCHw2d6y2RpPd/BkziAiMnGRidkAAG8na1ireL6IiKgiemv2vWHDBvj5+aF3794YMGAAunbtipUrV2ofLykpQUxMDPLz88vtt3r1ajRs2BB9+/a95znNzc2xZMkShISEICAgACtWrMDChQsxe/Zsfb2NGnUgKgUDvvgTJ+IzYK1U4IthAVjwZFsWNYiI6qi762v0aO4icSRERBUzU8jRrVnZAtW/c50NyRyJTceJqxlQmskxuZeP1OEQEdUZH374ITp37gwrKyvY29tXa59Ro0bds55sv3799Bvof1y8U9ho5WFn0NclIjImejuj7uDggI0bN1b6uJeXF4QQ92yfN28e5s2bV+E+/fr1M3gyqQlFpWp8/FsMVv8VDwBo3cAWXz3bHl5O1hJHRkRE+iKE+Gd9DV+2oSKi2qtncxfsOp+MwzFpmBLaXOpwTI4QAp/ema3xfHBjuNtZShwREVHdUVxcjKeeegohISFYtWpVtffr168f1qxZo71v6PbnkYlZAIBWHrYGfV0iImOitxkbVOZySg6eWHpUW9R4sUsT/DShM4saRER1XGxqLhKzCqEykyO4iYPU4RARVar7nTWAzt7IREZescTR6MZYr8T9twNRqTibkAlLcwUm9GwqWRxERHXRnDlzMHXqVLRp00an/VQqFdzc3LS3+vUN26oxIaMAAODjYmPQ1yUiMiYsbOiJRiOw6kg8Bi4+gsjEbNhbmeObER0wa1BLqMwUUodHRER6drelSydvR1iY83OfiGovNzsL+LnVgxDAn5eNqx3V3StxJ0yYoNN+/fr1Q1JSkvb2/fff6ynCqgkh8Pn+stkao7p4wbmeYa8IJiKiih0+fBguLi7w9fXFhAkTcOvWLYO+/t0LDRxtmBeIiCrDxR304GZmAd7YfBZH48oSX09fZywY6g8XWwuJIyMiIkP5Z30NtqEiotqvR3NnRCfn4I9L6Rgc0EDqcKptzpw5AIC1a9fqtN/dK3GldvhSGiITs2GlVGBcN2+pwyEiIpQVv5944gk0adIEcXFxePvtt9G/f38cO3YMCkXFFywVFRWhqKhIez87O/uhYrhb2HCwUj7U8xAR1WWcsVGDhBDYduYG+i36A0fjbsHSXIEPhrTGmlEdWdQgIjIh+cWlOH4lAwDX1yAi43C3HdWfl9MqXAevrtH1StyioiJkZ2eXuz0sIQSWHIwFAAwPboT61jx5RURUHdOnT7+npeB/b9HR0Q/8/MOGDcNjjz2GNm3aYMiQIfj1119x8uRJHD58uNJ95s+fDzs7O+3N09PzgV+/oFiNghI1AMDBhrmBiKgynLFRQ27nFePd7Rew83wSACDA0x6fPxOAJlxLg4jI5By/koFitQYN61vCm3mAiIxAB6/6sDRXIDWnCNHJOWjhXncXK32QK3Hnz5+vnR1SU07EZ+DUtdtQKuQYy9kaRETV9vrrr2PUqFFVjvH2rrnPVW9vbzg5OSE2Nha9e/eucMyMGTMwbdo07f3s7OwHLm5k5JfN1lAq5LBWsqUtEVFlWNioAbvOJ2HWzxeQnlsMhVyG13o3w8SeTWGm4IQYIiJT9O82VDKZTOJoiIjuT2WmQEhTRxyMTsXvl9IkLWxMnz4dH3/8cZVjoqKi4Ofn90DPP2zYMO2/27RpA39/fzRt2hSHDx82yAmru746VDZb46kODTm7m4hIB87OznB2Ntys6Bs3buDWrVtwd3evdIxKpYJKVTPrYdy+24bKWsnvEkREVWBh4yGk5hRi9s+R+O1CMgDAx8UGnz3VFm097aUNjIiIJMX1NYjIGHVv5oSD0an441IaxvdoKlkctfFK3Jo8YQUA525k4s/L6VDIZZIeayKiuu769evIyMjA9evXoVarERERAQDw8fGBjY0NAMDPzw/z58/H448/jtzcXMyZMwdDhw6Fm5sb4uLi8Oabb8LHxwdhYWEGifnWncIGWxQSEVWNhY0HULaWxk3M2XERWQUlMJPLMKFnU0zu5QOVGacJEhGZsmu38hCfngczuQydfZykDoeIqNrurrNx6upt5BeXwkopzVeF2nglbk1bcme2xuC2HvB0sDLY6xIRmZpZs2Zh3bp12vvt2rUDABw6dAg9e/YEAMTExCArKwsAoFAocO7cOaxbtw6ZmZnw8PBA37598f7779dogbsq/8zYMDfI6xERGSsWNnSUmFmAt7edx+GYsqtxW3nYYsGT/mjlYSdxZEREVBv8cWe2Rgev+rBRMc0SkfFo4mSNhvUtceN2Af6+cgu9/FylDum+jPFK3MspOdgTmQKZDJj4CGdrEBHp09q1a7F27doqxwghtP+2tLTEnj179BxV1TK0hQ3DFFKIiIwVz7joILeoFAO//BO380ugNJPjtd7NMK67N8y5lgYREd3xTxsqF4kjISLSjUwmQ4/mzthw/Dr+uJRuFIUNY7wSd+nhOABAWEs3+LjUM8hrEhGR8dAWNqw4Y4OIqCosbOjARmWGESFeOBKbjo+H+sPHxUbqkIiIqJbxdauH+PQ8dG/ONlREZHx6NHfGmeuZaGQk7ZGM8Urc9o3scfzKLUx6xEfSOIiIqHZyt7dAUBMH+Liy+E1EVBWZ+Pdf+iYiOzsbdnZ2yMrKgq2trU77lqg1kMtkUMhleoqOiKj2epjPT2NiKu+TiKimmcrn58O+z1K1Bmac9U1EJoh5goiIqqLL5ydnbOiIbaeIiIiIiOhhsKhBRERERPRw+Bc1EREREREREREREREZDRY2iIiIiIiIiIiIiIjIaLCwQURERERERERERERERoOFDSIiIiIiIiIiIiIiMhosbBARERERERERERERkdFgYYOIiIiIiIiIiIiIiIwGCxtERERERERERERERGQ0WNggIiIiIiIiIiIiIiKjwcIGEREREREREREREREZDRY2iIiIiIiIiIiIiIjIaLCwQURERERERERERERERsNM6gCkIIQAAGRnZ0scCRGRcbn7uXn3c7SuYp4gInowzBNERFQV5gkiIqqKLnnCJAsbOTk5AABPT0+JIyEiMk45OTmws7OTOgy9YZ4gIno4zBNERFQV5gkiIqpKdfKETNT1MnkFNBoNEhMTUa9ePchkMp32zc7OhqenJxISEmBra6unCOsWHjPd8HjphsdLNw97vIQQyMnJgYeHB+TyutvNkHnCsHjMdMPjpRseL909zDFjnrg//kzqjsdMNzxeuuHx0h3zxP09TJ4A+HOpKx4v3fB46YbHS3eGyhMmOWNDLpejYcOGD/Uctra2/GHWEY+Zbni8dMPjpZuHOV51+cqqu5gnpMFjphseL93weOnuQY8Z80T18GdSdzxmuuHx0g2Pl+6YJypXE3kC4M+lrni8dMPjpRseL93pO0/U3fI4ERERERERERERERHVOSxsEBERERERERERERGR0WBhQ0cqlQqzZ8+GSqWSOhSjwWOmGx4v3fB46YbHS/94jHXHY6YbHi/d8HjpjsdMv3h8dcdjphseL93weOmOx0z/eIx1w+OlGx4v3fB46c5Qx8wkFw8nIiIiIiIiIiIiIiLjxBkbRERERERERERERERkNFjYICIiIiIiIiIiIiIio8HCBhERERERERERERERGQ0WNqrhww8/ROfOnWFlZQV7e/tq7SOEwKxZs+Du7g5LS0uEhobi8uXL+g20lsjIyMDw4cNha2sLe3t7jBkzBrm5uVXu07NnT8hksnK38ePHGyhiw1uyZAm8vLxgYWGB4OBgnDhxosrxmzdvhp+fHywsLNCmTRvs2rXLQJHWDrocr7Vr197zs2RhYWHAaKX1xx9/YNCgQfDw8IBMJsP27dvvu8/hw4fRvn17qFQq+Pj4YO3atXqPs65hntAN88T9MU/ohnmi+pgnpME8oRvmiftjntAN80T1MU9Ig3lCd8wVVWOe0A3zRPXVpjzBwkY1FBcX46mnnsKECROqvc+CBQvw5ZdfYvny5Th+/Disra0RFhaGwsJCPUZaOwwfPhyRkZHYt28ffv31V/zxxx8YN27cffcbO3YskpKStLcFCxYYIFrD27RpE6ZNm4bZs2fj9OnTaNu2LcLCwpCamlrh+KNHj+LZZ5/FmDFjcObMGQwZMgRDhgzBhQsXDBy5NHQ9XgBga2tb7mfp2rVrBoxYWnl5eWjbti2WLFlSrfHx8fEYOHAgHnnkEURERGDKlCl46aWXsGfPHj1HWrcwT+iGeaJqzBO6YZ7QDfOENJgndMM8UTXmCd0wT+iGeUIazBO6Y66oHPOEbpgndFOr8oSgaluzZo2ws7O77ziNRiPc3NzEJ598ot2WmZkpVCqV+P777/UYofQuXrwoAIiTJ09qt/32229CJpOJmzdvVrpfjx49xGuvvWaACKUXFBQkJk2apL2vVquFh4eHmD9/foXjn376aTFw4MBy24KDg8XLL7+s1zhrC12PV3V/T00BALFt27Yqx7z55puiVatW5bY988wzIiwsTI+R1V3ME/fHPHF/zBO6YZ54cMwThsc8cX/ME/fHPKEb5okHxzxheMwT1cNcUTXmCd0wTzw4qfMEZ2zoQXx8PJKTkxEaGqrdZmdnh+DgYBw7dkzCyPTv2LFjsLe3R4cOHbTbQkNDIZfLcfz48Sr33bBhA5ycnNC6dWvMmDED+fn5+g7X4IqLixEeHl7uZ0MulyM0NLTSn41jx46VGw8AYWFhdf5nCXiw4wUAubm5aNy4MTw9PTF48GBERkYaIlyjZMo/X1JinmCeqAzzhG6YJ/TPlH++pMQ8wTxRGeYJ3TBP6J8p/3xJyZTzBMBcURXmCd0wT+ifPn++zB76GegeycnJAABXV9dy211dXbWP1VXJyclwcXEpt83MzAwODg5VvvfnnnsOjRs3hoeHB86dO4e33noLMTEx2Lp1q75DNqj09HSo1eoKfzaio6Mr3Cc5Odkkf5aABztevr6+WL16Nfz9/ZGVlYVPP/0UnTt3RmRkJBo2bGiIsI1KZT9f2dnZKCgogKWlpUSR1W3ME8wTlWGe0A3zhP4xT0iDeYJ5ojLME7phntA/5glpmHKeAJgrqsI8oRvmCf3TZ54w2Rkb06dPv2ehl//eKvsBNkX6Pl7jxo1DWFgY2rRpg+HDh2P9+vXYtm0b4uLiavBdkCkICQnBiBEjEBAQgB49emDr1q1wdnbGihUrpA6NjAzzhG6YJ8hYME9QTWGe0A3zBBkL5gmqKcwTumOuIGPAPFF7mOyMjddffx2jRo2qcoy3t/cDPbebmxsAICUlBe7u7trtKSkpCAgIeKDnlFp1j5ebm9s9i+uUlpYiIyNDe1yqIzg4GAAQGxuLpk2b6hxvbeXk5ASFQoGUlJRy21NSUio9Pm5ubjqNr0se5Hj9l7m5Odq1a4fY2Fh9hGj0Kvv5srW1Nfmrq5gndMM8UTOYJ3TDPKF/zBOVY57QDfNEzWCe0A3zhP4xT1SOeUJ3zBUPj3lCN8wT+qfPPGGyhQ1nZ2c4Ozvr5bmbNGkCNzc3HDhwQJtQsrOzcfz4cUyYMEEvr6lv1T1eISEhyMzMRHh4OAIDAwEABw8ehEaj0SaM6oiIiACAcgm6LlAqlQgMDMSBAwcwZMgQAIBGo8GBAwcwefLkCvcJCQnBgQMHMGXKFO22ffv2ISQkxAARS+tBjtd/qdVqnD9/HgMGDNBjpMYrJCQEu3btKrfNVH6+7od5QjfMEzWDeUI3zBP6xzxROeYJ3TBP1AzmCd0wT+gf80TlmCd0x1zx8JgndMM8oX96zRMPvfy4Cbh27Zo4c+aMmDNnjrCxsRFnzpwRZ86cETk5Odoxvr6+YuvWrdr7H330kbC3txc///yzOHfunBg8eLBo0qSJKCgokOItGFS/fv1Eu3btxPHjx8WRI0dEs2bNxLPPPqt9/MaNG8LX11ccP35cCCFEbGysmDt3rjh16pSIj48XP//8s/D29hbdu3eX6i3o1Q8//CBUKpVYu3atuHjxohg3bpywt7cXycnJQgghXnjhBTF9+nTt+L/++kuYmZmJTz/9VERFRYnZs2cLc3Nzcf78eanegkHperzmzJkj9uzZI+Li4kR4eLgYNmyYsLCwEJGRkVK9BYPKycnRfkYBEAsXLhRnzpwR165dE0IIMX36dPHCCy9ox1+5ckVYWVmJN954Q0RFRYklS5YIhUIhdu/eLdVbMErME7phnqga84RumCd0wzwhDeYJ3TBPVI15QjfME7phnpAG84TumCsqxzyhG+YJ3dSmPMHCRjWMHDlSALjndujQIe0YAGLNmjXa+xqNRsycOVO4uroKlUolevfuLWJiYgwfvARu3bolnn32WWFjYyNsbW3F6NGjyyXj+Pj4csfv+vXronv37sLBwUGoVCrh4+Mj3njjDZGVlSXRO9C/xYsXi0aNGgmlUimCgoLE33//rX2sR48eYuTIkeXG//jjj6J58+ZCqVSKVq1aiZ07dxo4YmnpcrymTJmiHevq6ioGDBggTp8+LUHU0jh06FCFn1d3j9HIkSNFjx497tknICBAKJVK4e3tXe6zjKqHeUI3zBP3xzyhG+aJ6mOekAbzhG6YJ+6PeUI3zBPVxzwhDeYJ3TFXVI15QjfME9VXm/KETAghHn7eBxERERERERERERERkf7JpQ6AiIiIiIiIiIiIiIiouljYICIiIiIiIiIiIiIio8HCBhERERERERERERERGQ0WNoiIiIiIiIiIiIiIyGiwsEFEREREREREREREREaDhQ0iIiIiIiIiIiIiIjIaLGwQEREREREREREREZHRYGGDiIiIiIiIiIiIiIiMBgsbRERERERERERERERkNFjYICIiIiIiIiIiIiIio8HCBhERERERERERERERGQ0WNoiIiIiIiIiIiIiIyGiwsEFEREREREREREREREaDhQ0iIiIiIiIiIiIiIjIaLGwQEREREREREREREZHRYGGDiIiIiIiIiIiIiIiMBgsbRERERERERERERERkNFjYIKrjBgwYgLFjx+q83/Lly9GoUSMUFRXpISoiIqoNJk6ciD59+ui83+7du2FjY4O0tDQ9REVERPqSkJAACwsL/PXXXzrv26lTJ7z55pt6iIqIiCqzdu1ayGQyXL16Va+vc+LECSiVSly7dk2n/UpKSuDp6YmlS5fqKTKiyrGwQVSH/fXXX9i7dy/eeustnfcdNWoUiouLsWLFCj1ERkRE/7Vx40YsWrTIYK8XHx+Pb775Bm+//bbO+/br1w8+Pj6YP3++HiIjIiJ9mTt3LoKDg9GlSxed933rrbewZMkSJCcn6yEyIiKS0jvvvINnn30WjRs31mk/c3NzTJs2DR9++CEKCwv1FB1RxVjYIKrDPvnkE/Tu3Rs+Pj4672thYYGRI0di4cKFEELoIToiIvo3Qxc2vvjiCzRp0gSPPPLIA+3/8ssvY8WKFcjJyanhyIiISB/S0tKwbt06jB8//oH2Hzx4MGxtbXlVLhFRHRMREYH9+/c/cH4YPXo00tPTsXHjxhqOjKhqLGwQPYS8vDypQ6hUamoqdu7ciaeffvqBn+Ppp5/GtWvXcOjQoRqMjIjIdNTWPFFSUoINGzY8VI4YOnQoioqKsHnz5hqMjIiI9OW7776DmZkZBg0a9ED7y+VyPPnkk1i/fj0vfCIiqkPWrFmDRo0aoVOnTg+0v729Pfr27Yu1a9fWbGBE98HCBpm8a9euYeLEifD19YWlpSUcHR3x1FNP3dO/8G5fw99//x0TJ06Ei4sLGjZsqH38t99+Q48ePVCvXj3Y2tqiY8eO91Srjx8/jgEDBqB+/fqwtraGv78/vvjii3JjDh48iG7dusHa2hr29vYYPHgwoqKiyo3JycnBlClT4OXlBZVKBRcXF/Tp0wenT5/Wjtm5cydKS0sRGhqq3SaEwCOPPAJnZ2ekpqZqtxcXF6NNmzZo2rRpuZNwgYGBcHBwwM8//6z7gSUiqiMMkSd69uyJnTt34tq1a5DJZJDJZPDy8gJQ9hk9a9YsBAYGws7ODtbW1ujWrds9ReerV69CJpPh008/xcqVK9G0aVOoVCp07NgRJ0+eLDf2yJEjSE9PL5cjAGDkyJGwsLC4J++EhYWhfv36SExM1G5zcXGBv78/cwQR0QO4efMmXnzxRbi6ukKlUqFVq1ZYvXo1AKCgoAB+fn7w8/NDQUGBdp+MjAy4u7ujc+fOUKvVAMrax9rY2ODKlSsICwuDtbU1PDw8MHfu3HuKD9u3b0dwcDBsbGy026KiomBpaYkRI0aUG3vkyBEoFIp7Wtr26dMH165dQ0RERE0eDiIi0sHSpUvRqlUrqFQqeHh4YNKkScjMzLxn3JIlS+Dt7Q1LS0sEBQXhzz//RM+ePdGzZ89y47Zv345evXpBJpNptx08eBByuRyzZs0qN3bjxo2QyWRYtmxZue19+vTBkSNHkJGRUWPvk+h+WNggk3fy5EkcPXoUw4YNw5dffonx48fjwIED6NmzJ/Lz8+8ZP3HiRFy8eBGzZs3C9OnTAZSdzBo4cCAyMjIwY8YMfPTRRwgICMDu3bu1++3btw/du3fHxYsX8dprr+Gzzz7DI488gl9//VU7Zv/+/QgLC0Nqairee+89TJs2DUePHkWXLl3KnUAbP348li1bhqFDh2Lp0qX43//+B0tLy3Inoo4ePQpHR8dy/RFlMhlWr16NwsLCclMMZ8+ejcjISKxZswbW1tbl3m/79u0faHFBIqK6whB54p133kFAQACcnJzw7bff4ttvv9W2pcrOzsY333yDnj174uOPP8Z7772HtLQ0hIWFVXhiaePGjfjkk0/w8ssv44MPPsDVq1fxxBNPoKSkRDvm6NGjkMlkaNeuXbl9v/jiCzg7O2PkyJHak2YrVqzA3r17sXjxYnh4eJQbHxgYiKNHjz7wsSUiMkUpKSno1KkT9u/fj8mTJ+OLL76Aj48PxowZg0WLFsHS0hLr1q1DbGws3nnnHe1+kyZNQlZWFtauXQuFQqHdrlar0a9fP7i6umLBggUIDAzE7NmzMXv2bO2YkpISnDx5Eu3bty8XS4sWLfD+++/j22+/xS+//AKgbLbhqFGj4Ofnh7lz55YbHxgYCAD8fkBEJJH33nsPkyZNgoeHBz777DMMHToUK1asQN++fcv9vb9s2TJMnjwZDRs2xIIFC9CtWzcMGTIEN27cKPd8N2/exPXr1+/JD7169cLEiRMxf/587UW0SUlJeOWVVxAaGnpP26rAwEAIIfjdgAxLEJm4/Pz8e7YdO3ZMABDr16/XbluzZo0AILp27SpKS0u12zMzM0W9evVEcHCwKCgoKPc8Go1GCCFEaWmpaNKkiWjcuLG4fft2hWOEECIgIEC4uLiIW7duabedPXtWyOVyMWLECO02Ozs7MWnSpCrfV9euXUVgYGCFj61YsUIAEN999534+++/hUKhEFOmTKlw7Lhx44SlpWWVr0VEVJcZIk8IIcTAgQNF48aN73mt0tJSUVRUVG7b7du3haurq3jxxRe12+Lj4wUA4ejoKDIyMrTbf/75ZwFA7NixQ7vt+eefF46OjhW+3z179ggA4oMPPhBXrlwRNjY2YsiQIRWOnTdvngAgUlJSKnyciIjuNWbMGOHu7i7S09PLbR82bJiws7PT5p0ZM2YIuVwu/vjjD7F582YBQCxatKjcPiNHjhQAxCuvvKLdptFoxMCBA4VSqRRpaWlCCCFiY2MFALF48eJ74lGr1aJr167C1dVVpKeni0mTJgkzMzNx8uTJCuNXKpViwoQJD3UMiIioeu5+x4iPjxepqalCqVSKvn37CrVarR3z1VdfCQBi9erVQgghioqKhKOjo+jYsaMoKSnRjlu7dq0AIHr06KHdtn///nu+K9yVl5cnfHx8RKtWrURhYaEYOHCgsLW1FdeuXbtnbGJiogAgPv744xp890RV44wNMnmWlpbaf5eUlODWrVvw8fGBvb19udZOd40dO7bcFVL79u1DTk4Opk+fDgsLi3Jj707jO3PmDOLj4zFlyhTY29tXOCYpKQkREREYNWoUHBwctI/7+/ujT58+2LVrl3abvb09jh8/Xq4lyH/dunUL9evXr/CxcePGISwsDK+88gpeeOEFNG3aFPPmzatwbP369VFQUFDhVclERKbAEHmiKgqFAkqlEgCg0WiQkZGB0tJSdOjQocLXf+aZZ8p9/nfr1g0AcOXKFe22qnJE37598fLLL2Pu3Ll44oknYGFhgRUrVlQ49u5zpKen3/d9EBFRWWvYn376CYMGDYIQAunp6dpbWFgYsrKytJ/t7733Hlq1aoWRI0di4sSJ6NGjB1599dUKn3fy5Mnaf8tkMkyePBnFxcXYv38/gLLPfQAVfvbL5XKsXbsWubm56N+/P5YuXYoZM2agQ4cOFb5W/fr1+blPRCSB/fv3o7i4GFOmTIFc/s8p3bFjx8LW1hY7d+4EAJw6dQq3bt3C2LFjYWZmph03fPjwe/JAVfnBysoKa9euRVRUFLp3746dO3fi888/R6NGje4Zy+8FJAUWNsjkFRQUYNasWfD09IRKpYKTkxOcnZ2RmZmJrKyse8Y3adKk3P24uDgAQOvWrSt9jeqMuXbtGgDA19f3nsdatGiB9PR07foXCxYswIULF+Dp6YmgoCC899575U5Y3SWqWNRv1apVyM/Px+XLl7F27dpyJ+4qeo7qnHwjIqqLDJEn7mfdunXw9/eHhYUFHB0d4ezsjJ07d1b4+v/9onH3S8bt27fLba8qR3z66adwcHBAREQEvvzyS7i4uFQ4jjmCiEg3aWlpyMzMxMqVK+Hs7FzuNnr0aADQroWnVCqxevVqxMfHIycnB2vWrKnw81Yul8Pb27vctubNmwPAPetBVfbZ37RpU7z33ns4efIkWrVqhZkzZ1b6HoQQ/NwnIpJAZeeNlEolvL29tY/f/a+Pj0+5cWZmZtp1/P6rsvzQpUsXTJgwASdOnEBYWBhefPHFKvdnfiBDMrv/EKK67ZVXXsGaNWswZcoUhISEwM7ODjKZDMOGDYNGo7lnfGUFAEN6+umn0a1bN2zbtg179+7FJ598go8//hhbt25F//79AQCOjo73nMT6t8OHD6OoqAgAcP78eYSEhFQ47vbt27CysqoV75uISApS54nvvvsOo0aNwpAhQ/DGG2/AxcUFCoUC8+fP1xZN/u3fs0X+7d9fVu6XI86cOaM9sXb+/Hk8++yzFY67+xxOTk7Vfj9ERKbsbt54/vnnMXLkyArH+Pv7a/+9Z88eAEBhYSEuX758T/G8uhwdHQHcW+T+t7179wIAEhMTcevWLbi5uVU4LjMzk5/7RER1xP3yQ1FREQ4fPgyg7IKt/Px8WFlZ3TOO3wtICpyxQSZvy5YtGDlyJD777DM8+eST6NOnD7p27YrMzMxq7d+0aVMAwIULFx5qzN1FvmNiYu55LDo6Gk5OTuUW9nZ3d8fEiROxfft2xMfHw9HRER9++KH2cT8/P8THx1f4WncXfOrbty8effRR/O9//9NW9P8rPj4eLVq0qDRuIqK6zhB5Aqj86qYtW7bA29sbW7duxQsvvICwsDCEhoaisLBQp/fxb35+frh9+3aFMz7y8vIwevRotGzZEuPGjcOCBQtw8uTJCp8nPj5eO4OFiIjuz9nZGfXq1YNarUZoaGiFt7uz5M6dO4e5c+di9OjRaNeuHV566aUKP7c1Gs09s7cvXboEANorcxs1agRLS8tKvx8sX74c+/btw4cffoji4mK8/PLLFY67efMmiouL+f2AiEgClZ03Ki4uRnx8vPbxu/+NjY0tN660tPSemXx+fn4AUGl+mD17NqKiovDpp58iPj4e06dPr3Dc3f2ZH8iQWNggk6dQKO6Zcrd48WKo1epq7d+3b1/Uq1cP8+fPv+ck093nbd++PZo0aYJFixbdcyLs7hh3d3cEBARg3bp15cZcuHABe/fuxYABAwAAarX6ni80Li4u8PDw0M7AAICQkBDcvn27whZVY8eOhUajwapVq7By5UqYmZlhzJgxFU49PH36NDp37lytY0FEVBcZIk8AgLW1dYUnrO7OwPj32OPHj+PYsWPVfg//FRISAiEEwsPD73nsrbfewvXr17Fu3TosXLgQXl5eGDlyZLkcc1d4eHilM/6IiOheCoUCQ4cOxU8//VRhwTstLQ1A2ZpOo0aNgoeHB7744gusXbsWKSkpmDp1aoXP+9VXX2n/LYTAV199BXNzc/Tu3RsAYG5ujg4dOuDUqVP37BsfH4833ngDQ4cOxdtvv41PP/0Uv/zyC9avX3/P2Lt5g98PiIgMLzQ0FEqlEl9++WW57warVq1CVlYWBg4cCADo0KEDHB0d8fXXX6O0tFQ7bsOGDffMzGjQoAE8PT0rzA/Hjx/Hp59+iilTpuD111/HG2+8ga+++gq///77PWPDw8Mhk8n43YAMiq2oyOQ9+uij+Pbbb2FnZ4eWLVvi2LFj2L9/v3Y63v3Y2tri888/x0svvYSOHTviueeeQ/369XH27Fnk5+dj3bp1kMvlWLZsGQYNGoSAgACMHj0a7u7uiI6ORmRkpHaK+SeffIL+/fsjJCQEY8aMQUFBARYvXgw7Ozu89957AICcnBw0bNgQTz75JNq2bQsbGxvs378fJ0+exGeffaaNa+DAgTAzM8P+/fsxbtw47fY1a9Zg586dWLt2LRo2bAig7ATd888/j2XLlmHixInaseHh4cjIyMDgwYMf9jATERktQ+QJAAgMDMSmTZswbdo0dOzYETY2Nhg0aBAeffRRbN26FY8//jgGDhyI+Ph4LF++HC1btkRubu4DvaeuXbvC0dER+/fvR69evbTbDx48iKVLl2L27Nlo3749gLK80bNnT8ycORMLFizQjk1NTcW5c+cwadKkB4qBiMhUffTRRzh06BCCg4MxduxYtGzZEhkZGTh9+jT279+PjIwMfPDBB4iIiMCBAwdQr149+Pv7Y9asWXj33Xfx5JNPai96AgALCwvs3r0bI0eORHBwMH777Tfs3LkTb7/9drkZdYMHD8Y777yD7Oxs2NraAigrgrz44ouwtLTEsmXLAAAvv/wyfvrpJ7z22msIDQ2Fh4eH9jn27duHRo0aoV27dgY6WkREdJezszNmzJiBOXPmoF+/fnjssccQExODpUuXomPHjnj++ecBlK258d577+GVV15Br1698PTTT+Pq1atYu3YtmjZtes9M8cGDB2Pbtm3l1lAqLCzEyJEj0axZM213kDlz5mDHjh0YPXo0zp8/X66ryL59+9ClS5dqf0ciqhGCyMTdvn1bjB49Wjg5OQkbGxsRFhYmoqOjRePGjcXIkSO149asWSMAiJMnT1b4PL/88ovo3LmzsLS0FLa2tiIoKEh8//335cYcOXJE9OnTR9SrV09YW1sLf39/sXjx4nJj9u/fL7p06aJ9nkGDBomLFy9qHy8qKhJvvPGGaNu2rfZ52rZtK5YuXXpPTI899pjo3bu39n5CQoKws7MTgwYNumfs448/LqytrcWVK1e029566y3RqFEjodFoqj6IRER1mKHyRG5urnjuueeEvb29ACAaN24shBBCo9GIefPmicaNGwuVSiXatWsnfv31VzFy5EjtGCGEiI+PFwDEJ598cs9rAxCzZ88ut+3VV18VPj4+2vvZ2dmicePGon379qKkpKTc2KlTpwq5XC6OHTum3bZs2TJhZWUlsrOz73cIiYjoP1JSUsSkSZOEp6enMDc3F25ubqJ3795i5cqVIjw8XJiZmYlXXnml3D6lpaWiY8eOwsPDQ9y+fVsIIcTIkSOFtbW1iIuLE3379hVWVlbC1dVVzJ49W6jV6nte08zMTHz77bfabV988YUAIH766adyY69fvy5sbW3FgAEDtNvUarVwd3cX7777bg0fDSIiqszd7xjx8fHabV999ZXw8/MT5ubmwtXVVUyYMEGbF/7tyy+/1H6HCAoKEn/99ZcIDAwU/fr1Kzfu9OnTAoD4888/tdumTp0qFAqFOH78eLmxp06dEmZmZmLChAnabZmZmUKpVIpvvvmmZt40UTXJhKhk2XsiMnp//vknevbsiejoaDRr1kynfYuKiuDl5YXp06fjtdde01OEREQklStXrsDPzw+//fabtlWJLtq1a4eePXvi888/10N0RERUHaNGjcKWLVuqPYNvzJgxuHTpEv7880+dX2v79u147rnnEBcXB3d3d533JyIiaWk0Gjg7O+OJJ57A119/Xe6x3r17w8PDA99++63Oz7to0SIsWLAAcXFxsLS0rKlwie6La2wQ1WHdunVD3759y7UOqa41a9bA3Nwc48eP10NkREQkNW9vb4wZMwYfffSRzvvu3r0bly9fxowZM/QQGRER6cvs2bNx8uRJ/PXXXzrv+/HHH2Py5MksahARGYHCwsJ71glcv349MjIy0LNnz3vGz5s3D5s2bcK1a9d0ep2SkhIsXLgQ7777LosaZHCcsUFERERERERkhHSdsUFERKbh8OHDmDp1Kp566ik4Ojri9OnTWLVqFVq0aIHw8HAolUqpQyR6aFw8nIiIiIiIiIiIiKiO8PLygqenJ7788ktkZGTAwcEBI0aMwEcffcSiBtUZnLFBRERERERERERERERGg2tsEBERERERERERERGR0WBhg4iIiIiIiIiIiIiIjAYLG0REREREREREREREZDRMcvFwjUaDxMRE1KtXDzKZTOpwiIiMhhACOTk58PDwgFxed2vjzBNERA+GeYKIiKrCPEFERFXRJU+YZGEjMTERnp6eUodBRGS0EhIS0LBhQ6nD0BvmCSKih8M8QUREVWGeICKiqlQnT5hkYaNevXoAyg6Qra2txNEQERmP7OxseHp6aj9H6yrmCSKiB8M8QUREVWGeICKiquiSJ0yysHF3GqCtrS0TDBHRA6jr06mZJ4iIHg7zBBERVYV5goiIqlKdPFF3GxoSEREREREREREREVGdw8IGEREREREREREREREZDRY2iIiIiIiIiIiIiIjIaLCwQURERERERERERERERoOFDSIiIiIiIiIiIiIiMhosbBARUa23ZMkSeHl5wcLCAsHBwThx4kSV4xctWgRfX19YWlrC09MTU6dORWFhoYGiJSIiIiIiIiIifWJhg4iIarVNmzZh2rRpmD17Nk6fPo22bdsiLCwMqampFY7fuHEjpk+fjtmzZyMqKgqrVq3Cpk2b8Pbbbxs4ciIiIiIiIiIi0gcWNoiIqFZbuHAhxo4di9GjR6Nly5ZYvnw5rKyssHr16grHHz16FF26dMFzzz0HLy8v9O3bF88+++x9Z3kQEREREREREZFxYGGDiIhqreLiYoSHhyM0NFS7TS6XIzQ0FMeOHatwn86dOyM8PFxbyLhy5Qp27dqFAQMGVPo6RUVFyM7OLncjIiIiIiIiIqLayUzqAIiIiCqTnp4OtVoNV1fXcttdXV0RHR1d4T7PPfcc0tPT0bVrVwghUFpaivHjx1fZimr+/PmYM2dOjcZORERERERERET6wRkbOkrPLUJMcg7yi0ulDoWIiCpw+PBhzJs3D0uXLsXp06exdetW7Ny5E++//36l+8yYMQNZWVnaW0JCggEjJiKqXQ7FpKKgWC11GEREVAupNQIHo1Og0QipQyEioloqISMfMck5en8dztjQ0dMrjuFKWh42jeuEYG9HqcMhIqrTnJycoFAokJKSUm57SkoK3NzcKtxn5syZeOGFF/DSSy8BANq0aYO8vDyMGzcO77zzDuTye2v6KpUKKpWq5t8AEZERSc0uxOxfIvHbhWSM79EU0/v7SR0SERHVMvsuJmP8d6cR4GmPbRM7QyaTSR0SERFJTAiBy6m52H0hGXsikxGZmI1efi5YPaqjXl+XhQ0dOVgpcQV5yMgrljoUIqI6T6lUIjAwEAcOHMCQIUMAABqNBgcOHMDkyZMr3Cc/P/+e4oVCoQBQlmyJiKg8jUbgh5MJmP9bFHIKS6GQy2Cu4IkqIiIqTwiBFX9cAQB09XFiUYOIyIRpNAJnb2RiT2QK9kQmIz49T/uYXAaUagSEEHrNFSxs6Ki+tRIAkJHPwgYRkSFMmzYNI0eORIcOHRAUFIRFixYhLy8Po0ePBgCMGDECDRo0wPz58wEAgwYNwsKFC9GuXTsEBwcjNjYWM2fOxKBBg7QFDiIiKhOfnoe3fjqHE/EZAIC2De0w/wl/tPSwlTgyIiKqbU5du40z1zOhNJNjZGcvqcMhIiIDK1FrcCI+A3sik7E3MgXJ2YXax5QKObo1c0JYKzf0buECRxv9d8VgYUNHjncLG7ksbBARGcIzzzyDtLQ0zJo1C8nJyQgICMDu3bu1C4pfv3693AyNd999FzKZDO+++y5u3rwJZ2dnDBo0CB9++KFUb4GIqNZRawTW/BWPT/bEoKhUAyulAq/39cWozl5QyHkFLhER3WvF73EAgKHtG8C5Htu4EhGZghK1Bn/FpmPX+STsvZiCzPwS7WPWSgUe8XNBv9Zu6OnrAhuVYUsNLGzoiDM2iIgMb/LkyZW2njp8+HC5+2ZmZpg9ezZmz55tgMiIiIxPbGou3thyFmeuZwIAujVzwrzH28DTwUrawIiIqNaKTc3B/qhUyGTAS928pQ6HiIj0qLi0rJix83wS9l1MQVbBP8UMB2sl+rRwRVhrV3Ru6gQLc+k6Y7CwoaO7MzZuc40NIiIiIjIipWoNvv4zHp/vv4TiUg3qqczwzsAWeKajJ/ukExFRlb7+Ix4AENrCFU2dbSSOhoiIalpRqRpHLv9TzMgpLNU+5mSjQr/WrhjQxh1BXg4wU8ireCbDYWFDR/Wtygobt1jYICIiIiIjEZOcgze3nMXZG1kAgJ6+zpj3eBt42FtKHBkREdV2qdmF2HbmJgBgfA/O1iAiqisKS9T483JZm6n9F1OQU/RPMcO5ngr9W7thQBt3dPRyqJXtalnY0JHD3RkbbEVFRERERLVcqVqDFX9cwaL9l1CiFrC1MMOsQa0wtH0DztIgIqJqWf3XVRSrNQhsXB+BjR2kDoeIiB5CYYkaf1xKKytmRKUi91/FDFdbFfq3dseANu4IbFy/VhYz/o2FDR05cPFwIiIiIjICV9PzMO3HCJy+s5ZGaAsXfPh4G7jaWkgbGBERGY3swhJs+PsaAODl7pytQURkjErVGvwVdwu/RCRib2RyuZkZbrYW6N/GDQPbuKN9o/qQ1/Jixr+xsKEjBy4eTkRERES1mBACP5xMwPu/XkR+sRr1VGaYM7gVHm/HWRpERKSbb49dQ05RKZq52CC0havU4RARUTVpNAKnrt3GL2dvYtf5ZGT8a1kFdzsLDGhTNjOjnae9URUz/k3SwsayZcuwbNkyXL16FQDQqlUrzJo1C/379690n82bN2PmzJm4evUqmjVrho8//hgDBgwwUMT/FDYKSzTIyi+BnZW5wV6biIiIiKgqqTmFmPHTeRyITgUAhHg74tOn26IB19IgIiIdFZaoseavskXDJ/RsarQnvoiITIUQApGJ2fjlbCJ+PZuIxKxC7WOO1koMaOOOxwI8EGhkMzMqI2lho2HDhvjoo4/QrFkzCCGwbt06DB48GGfOnEGrVq3uGX/06FE8++yzmD9/Ph599FFs3LgRQ4YMwenTp9G6dWuDxGytMkMDe0vczCzAxaRshDR1NMjrEhERERFVZU9kMmZsPY+MvGIozeR4M8wXL3ZpUie+tBARkeFtPpWA9NxiNLC3xKC2HlKHQ0RElYhNzdUWM66k52m311OZIay1Gx5r64HOTR1hppBLGGXNk7SwMWjQoHL3P/zwQyxbtgx///13hYWNL774Av369cMbb7wBAHj//fexb98+fPXVV1i+fLlBYgaAVh62uJlZgMjELBY2iIiIiEhSOYUlmLvjIjaH3wAAtHC3xaJnAuDrVk/iyIiIyFiVqjVY8ccVAMC47t4wr2Mnw4iIjF1SVgF+jkjELxGJuJiUrd2uMpMjtIUrBrX1QE9fZ1iYKySMUr9qzRobarUamzdvRl5eHkJCQiocc+zYMUybNq3ctrCwMGzfvr3K5y4qKkJRUZH2fnZ2dhWj76+Vhx32XkzBxcSHex4iIiIioodxNiETr/5wBtdu5UMmA8b3aIopoc2gMqu7X2CIiEj/fj2XhBu3C+BorcTTHTylDoeIiADkFpXit/NJ2HbmJo5duQUhyrabyWXo3twZj7X1QGhLV9ioas0pf72S/F2eP38eISEhKCwshI2NDbZt24aWLVtWODY5ORmuruUXq3J1dUVycnKVrzF//nzMmTOnxmJu3cAWABBxI7PGnpOIiIiIqLo0GoFvjlzBgt0xKNUINLC3xOfPBCCoiYPUoRERkZHTaASWHY4DAIzu4gVLJYvlRERSKVVr8GdsOradvom9F5NRWKLRPhbUxAFDAhqgf2s31L+zLrQpkbyw4evri4iICGRlZWHLli0YOXIkfv/990qLGw9ixowZ5WZ6ZGdnw9Pzwa84CGxcHzIZcCUtD2k5RXCup6qJMImIiIiI7istpwivbz6LPy6lAQAGtHHD/Cf8YWdpLnFkxsPLywvXrl27Z/vEiROxZMkSCSIiIqo9DsWkIiYlBzYqM7wQ4iV1OEREJufuIuBbT9/EL2cTkZ77Tycib2drPNGuAQYHNICng5WEUUpP8sKGUqmEj48PACAwMBAnT57EF198gRUrVtwz1s3NDSkpKeW2paSkwM3NrcrXUKlUUKlqrvhgb6WEn5stopKycTz+Fh715yJaRERERKR/f1xKw7QfzyI9twgqMzlmD2qFZ4M8IZNxgXBdnDx5Emq1Wnv/woUL6NOnD5566ikJoyIikp4QAkvvzNYYHtyoThXNlyxZgk8++QTJyclo27YtFi9ejKCgoArHrl27FqNHjy63TaVSobCw0BChEpGJSsoqwPYzidh6+gYup+ZqtztYK/FYWw883q4B/Bva8W//OyQvbPyXRqMptx7Gv4WEhODAgQOYMmWKdtu+ffsqXZNDnzp5OyAqKRtHLqezsEFEREREelVcqsFn+2Kw4veyhVx9Xeth8XPt0NyVC4Q/CGdn53L3P/roIzRt2hQ9evSQKCIiotrh5NXbCL92G0qFHGO6NpE6nBqzadMmTJs2DcuXL0dwcDAWLVqEsLAwxMTEwMXFpcJ9bG1tERMTo73PE4lEpA8FxWr8diEJW8JvlFs3Q2kmR5+WrniiXQN0b+4Mc4Vc2kBrIUkLGzNmzED//v3RqFEj5OTkYOPGjTh8+DD27NkDABgxYgQaNGiA+fPnAwBee+019OjRA5999hkGDhyIH374AadOncLKlSsNHnsvPxes+esq9kelQK0RUMiZ4IiIiIio5iVk5GPy92dwNiETQNkVtDMfbQkLc/Y8rwnFxcX47rvvMG3aNJ60IiKTt/RwLABgaGBDuNhaSBxNzVm4cCHGjh2rnYWxfPly7Ny5E6tXr8b06dMr3Ecmk923QwgR0YMQQuBMQiY2n0rAjrNJyC0q1T4W1MQBT7RrgP5t3OvUrDl9kLSwkZqaihEjRiApKQl2dnbw9/fHnj170KdPHwDA9evXIZf/U43q3LkzNm7ciHfffRdvv/02mjVrhu3bt6N169YGjz24iSPqWZghPbcYZ67fRgcvLtRIRERERDXrYHQKpm46i6yCEthamOHjof7o38Zd6rDqlO3btyMzMxOjRo2qdExRUVG5WeXZ2dkGiIyIyLAuJmbjcEwa5DJgfA9vqcOpMcXFxQgPD8eMGTO02+RyOUJDQ3Hs2LFK98vNzUXjxo2h0WjQvn17zJs3D61atapwLPMEEVVHak4htp2+ic3hNxD7r1ZTjRys8GRgQzzejutm6ELSwsaqVauqfPzw4cP3bHvqqadqRe9bpZkcvf1csD0iEXsik1nYICIiIqIao9YIfL7vEr46VHblbNuGdlgyvD0a1ucXnZq2atUq9O/fHx4elbeXnT9/PubMmWPAqIiIDG/JndkaA/090NjRWuJoak56ejrUajVcXV3LbXd1dUV0dHSF+/j6+mL16tXw9/dHVlYWPv30U3Tu3BmRkZFo2LDhPeOZJ4ioMiVqDQ5Gp2LzqRs4FJMKtaas15SFuRwD2rjjqUBPBDdxgJzdgHRW69bYMCZhrdzuFDZS8PaAFpy6TkREREQPLS2nCK/9cAZH424BAEaENMY7A1tAZcbWUzXt2rVr2L9/P7Zu3VrluBkzZmDatGna+9nZ2fD09NR3eEREBhObmoNd55MAABN7NpU4GumFhISUW8+1c+fOaNGiBVasWIH333//nvHME0T0X5dScrD5VAK2nbmJ9Nxi7fZ2jezxdAdPPOrvjnoWbDX1MFjYeAjdmztDZSbH9Yx8nLuRhbae9lKHRERERERG7ER8BiZvPI3UnCJYKRWY/0QbDA5oIHVYddaaNWvg4uKCgQMHVjlOpVJBpVIZKCoiIsNbcigOQgB9W7qihbut1OHUKCcnJygUCqSkpJTbnpKSUu01NMzNzdGuXTvExsZW+DjzBBEBQF5RKXacTcT3JxO06+MBgJONCkPbN8BTHRrCx6WedAHWMSxsPARrlRn6tXbDzxGJ2HQqgYUNIiIiInogQgh8/ecVfLw7BmqNgI+LDZY/355ffPRIo9FgzZo1GDlyJMzM+LWIiExXfHoefo64CQB4pVcziaOpeUqlEoGBgThw4ACGDBkCoCwHHDhwAJMnT67Wc6jVapw/fx4DBgzQY6REZKwiE7Ow8fh1/ByRqF0I3EwuQy8/FzzdwRM9fJ1hrpDf51lIV/wL/iE908ETP0ckYkdEImYObAlLJVsEEBEREVH15RaV4n8/nsXuyGQAwOAAD8x7vA2sVfxTXZ/279+P69ev48UXX5Q6FCIiSS09FAuNAB7xdUabhnZSh6MX06ZNw8iRI9GhQwcEBQVh0aJFyMvLw+jRowEAI0aMQIMGDTB//nwAwNy5c9GpUyf4+PggMzMTn3zyCa5du4aXXnpJyrdBRLVIfnHZ7IyNx6/j7I0s7XYvRys8G9QIT7RvCOd6nMmlT/y29JA6eTvC08ESCRkF2HU+CUMD711EioiIiIioIlfT8zB2/SlcTs2FUiHHzEEt8XxwI67dZgB9+/aFEELqMIiIJJWQkY9tZ+7M1uhd92Zr3PXMM88gLS0Ns2bNQnJyMgICArB7927tguLXr1+HXP7P1dS3b9/G2LFjkZycjPr16yMwMBBHjx5Fy5YtpXoLRFRLXEzMxsYT17D9zD+zM8wVMoS1csNzQY3QyduRC4EbCAsbD0kul+HpQE98tu8S1v99DU+0b8AvokRERER0X4djUvHq92eQXVgKl3oqLH8hEO0b1Zc6LCIiMiHLfo9DqUagWzOnOp+DJk+eXGnrqcOHD5e7//nnn+Pzzz83QFREZAzyi0vx69kkbDhxvdzaGY3vzM54MrAhnGw4O8PQWNioAcOCGmHxwVicTcjE6eu3EdjYQeqQiIiIiKiWEkJg+e9XsGBPNIQA2jeyx/LnA+FiayF1aEREZEISMwuw+VQCgLq5tgYR0cOKTc3Ft8euYuvpm8j519oZYa3c8FxwI4RwdoakWNioAc71VBjSzgM/nrqBVUfiWdggIiIiogrlF5fizS3n8Ou5JADAsI6emDO4FVRmXKeNiIgMa8XvcShRCwQ3cUBQE57HICICALVG4EBUCtYfu4Yjsena7Y0c/pmdwbUzagcWNmrImK7e+PHUDey+kIyr6XnwcrKWOiQiIiIiqkUSMvIx7ttwRCVlw0wuw3uPtcJwrqdBREQSSM0uxPcny2ZrvFaH19YgIqquW7lF2HQqARv+vo6bmQUAAJkM6O3nihEhjdHVx4mzM2oZFjZqiK9bPTzi64xDMWn48uBlLHw6QOqQiIiIiKiWOBZ3CxM3hON2fgmcbJRYOjyQV8cSEZFkVv5xBcWlGgQ2ro+Qpo5Sh0NEJJmzCZlYd+wqfj2XhOJSDQCgvpU5nunYCMODG8HTwUriCKkyLGzUoKl9muNQTBq2n7mJSY/4oKmzjdQhEREREZHENp28jne2XUCpRsC/oR2WPx8ID3tLqcMiIiITlZ5bhA3HrwMAXunlw5mDRGRyCkvU2HU+CeuOXSu3GHibBnYYEdIYg9p6wMKcrWJrO7nUAdQl/g3tEdrCFRoBfHngstThEBHVGUuWLIGXlxcsLCwQHByMEydOVDk+MzMTkyZNgru7O1QqFZo3b45du3YZKFoiojJqjcD8XVF466fzKNUIDGrrgR9fDmFRg4iIJPXNn/EoKFHDv6EdejR3ljocIiKDScspwsJ9l9Dlo4OY9uNZnE3IhFIhx+PtGmDbxM74ZXIXPNXBk0UNI8EZGzVsap9m2B+Vgl/OJmLSIz5o7lpP6pCIiIzapk2bMG3aNCxfvhzBwcFYtGgRwsLCEBMTAxcXl3vGFxcXo0+fPnBxccGWLVvQoEEDXGp38Z4AAKa/SURBVLt2Dfb29oYPnohMVl5RKaZsisC+iykAgCmhzfBa72a8KpaIiCSVnluE9ceuAgBe7cW8RESmISopG6uOxOOXiEQUq8vaTbnbWeD5To3xf/buOzqqMv/j+HsmPZBCIBUCoYdeEgihiYI0GxaUJkUERcCCrorr2l2siAWpUqTbUEREEQQEAoFA6ARCSyhJCCGdTJKZ+f2Bm9+yAtKSm/J5nXPPcW6em/ncs8vNzP3e5/s81CaYapW1GHhZpMLGTdYkyIteTQP4eU8SE1bsZ/awtkZHEhEp0yZOnMiIESMYNmwYAFOnTuWnn35i1qxZvPjii38ZP2vWLNLS0ti0aRNOTk4AhISElGRkEangTmecZ/icbew7nYmzo5n3H2jOPS2rGx1LRESE6euPkJt/YbZG10Z/fUhIRKS8sNnsrD2YwhcbjrIx/mzR/lY1vRnesTY9mwTg6KBmRmWZChvF4Pmeofy2P5nf486wNi6FLg31YUFE5Hrk5+cTExPD+PHji/aZzWa6detGVFTUJY9ZtmwZkZGRjB49mh9++AFfX18GDBjACy+8gIPDpaeTWiwWLBZL0evMzMybeyIiUmHsOpHOo3O3kZJloVplZ6YPDqd1zSpGxxIRESElK69otsYztzfQbA0RKZdy8wv5dvtJZm88ypEzOQA4mE30bBrA8I619dm8HFFhoxjUrlaJoe1DmPHHUd5cvo8O9arhpAqgiMg1S01NxWq14u/vf9F+f39/Dhw4cMljjhw5wpo1axg4cCArVqwgPj6eJ554goKCAl599dVLHjNhwgRef/31m55fRCqWFbtPM+6rWPIKbDT09+CLoeHUqOJudCwREREApq49Ql6BjVY1vemitTVEpJxJyrhQvF2wJYGM8wUAeLg40q9tMEPah+hzeTmkwkYxGdu1Pt9tP8nhMznMizrOIx1rGx1JRKRCsNls+Pn5MX36dBwcHAgLC+PkyZO8//77ly1sjB8/nnHjxhW9zszMJDg4uKQii0gZZ7fbmfnHUd5esR+ALg19+bR/KzxcnQxOJiIickFyZh7ztxwHYJxma4hIORKXlMW0dYdZtvMUhTY7ADV93BnWIYS+4cFUdtHt7/JK/8sWE09XJ57t3pCXlu5m4qqD9GoWQKCXm9GxRETKlGrVquHg4EBycvJF+5OTkwkICLjkMYGBgTg5OV3UdqpRo0YkJSWRn5+Ps7PzX45xcXHBxUWLhYnItbPa7Ly5fB9zNh0DYGj7EF6+o5H69YqISKky+fd48gtttAmpQsd61YyOIyJyQ+x2O1uPnWPqusOsOZBStL9tiA/DO9WmWyN/HMwq4JZ3+sZVjB5qE0zrmt5kWwp5eeke7Ha70ZFERMoUZ2dnwsLCWL16ddE+m83G6tWriYyMvOQxHTp0ID4+HpvNVrTv4MGDBAYGXrKoISJyvfIKrIxZuL2oqPHyHY149a7GKmqIiEipcjL9PIujEwGtrSEiZZvNZufXvUncP2UTD06LYs2BFEwm6N0sgB9Gd+CrxyPp0SRARY0KQt+6ipGD2cQ79zfHycHE6gMp/LT7tNGRRETKnHHjxjFjxgzmzp3L/v37GTVqFDk5OQwbNgyAwYMHX7S4+KhRo0hLS+Opp57i4MGD/PTTT/z73/9m9OjRRp2CiJRD53LyGTRzCz/vScLZwcyn/VvxaKc6ulkkIiKlzuTf48m32mhXx4f2dTVbQ0TKnvxCG19tS6T7pPWMnBfD9oR0nB3M9G9bkzXPduHzgWG0CPY2OqaUMLWiKmYN/D0YfWs9Jv12iNeW7aVD3WpUqaQnhkVErtZDDz3EmTNneOWVV0hKSqJly5asXLmyaEHxhIQEzOb/r9MHBwfzyy+/8Mwzz9C8eXOqV6/OU089xQsvvGDUKYhIOZOYlsuQ2dEcOZODp6sj0weH065OVaNjiYiI/EViWi5fbb0wW2Pc7Q0NTiMicm2yLYUs2pLAFxuOkpSZB1xYEHxQZC2GdQjBz8PV4IRiJBU2SsCoLnX5addpDqVk8+byfUx8qKXRkUREypQxY8YwZsyYS/5s7dq1f9kXGRnJ5s2bizmViFREe05mMHT2VlKzLQR5uTLnkbY08PcwOpaIiMglfbYmnkKbnU71q9G2to/RcURErkp6bj6zNh5jzsajZOYVAuDn4cLwjrUZEFETD1cngxNKaaDCRglwcXTg3Qea88CUTXy34yRdG/lzR/NAo2OJiIiIyDVYd/AMT8yPISffSmiAB3OGtSXAS0+JiYhI6XT8bA7fbD8BwNPdGhicRkTk76VmW5j5x1HmRR0jJ98KQB3fSjzWuQ59WlXHxdHB4IRSmqiwUUJa16zCE13q8dnv8Yz/bhetanoT5O1mdCwRERERuQrLdp5i3JJYCm12OtSrytRBYXpSTERESrWPVx/CarPTpaEvYbWqGB1HROSyUjLzmLb+CAu2HCevwAZAo0BPxt5Wj55NAjBrMXC5BBU2StBT3erzx6Ez7DyRwbNf7WTBoxH6hykiIiJSys3bfJxXftiD3Q53twjig74tcHY0//2BIiIiBjmUnMX3O04C8Ixma4hIKXUq/TxT1x1m8dZE8gsvFDSa1/Bi7G316dbID5NJ903l8lTYKEFODmYm9WvFHZ/8QdSRs0z/4wiP31LX6FgiIiIicgl2u51P18QzcdVBAAZH1uK1u5rowRQRESn1Pvg1DpsdejTxp0Wwt9FxREQuknA2lynr4vkm5gQFVjsAYbWqMPa2etzSwFcFDbkqKmyUsNrVKvHqXY154dvdfPBLHOG1qhAeogW8REREREoTm83Omz/tY/bGYwA82bU+z3Srry9ZIiJS6sUmpvPL3mTMJniue0Oj44iIFDlxLpdPV8fzzfYTWG0XChrt6vjw5G31iaxbVZ+15ZqosGGAB8OD2Rh/lmU7TzF64XaWj+2Er4eL0bFEREREBCiw2njhm11892cLj1fvasywDrUNTiUiInJ13lt5AIB7W9Wgvr+HwWlERCApI4/Pfj/Ekq2JRTM0OtWvxpNd69NGD3zLdVJhwwAmk4kJ9zVj3+lM4lOyeXLRDuYNb4ujg3o1i4iIiBgpr8DKmIXb+W1/Cg5mEx/0bc69rWoYHUtEROSqbDiUyqbDZ3F2MPN0t/pGxxGRCu5MloUpaw8zf8vxojU0OtSryrjbGxBWSwUNuTEqbBikkosjUwe15u7PNhJ15CwfrjrICz1DjY4lIiIiUmFl5hXw6NxtRB9Nw8XRzOQBrenW2N/oWCIiIlfFbrfz/i8XZmsMiKhJsI+7wYlEpKJKy8ln2vrDfLnpOOcLrAC0CanCuNsbElm3qsHppLxQYcNA9fw8ePf+5oxdtIMpaw8TGuDBPS2rGx1LREREpMJJz81n8Kxodp3IwMPFkZlDwomooy9dIiJSdvyyN4mdJzJwd3ZgzG31jI4jIhVQxvkCZv5xhFkbjpKTf6Gg0SLYm2dvb0Cn+tW0hobcVCpsGOyuFkHsOZnBtPVH+Mc3u6hVtRItg72NjiUiIiJSYaRmWxg0cwsHkrKo4u7EvOERNK3uZXQsERGRq1ZotfH+L3EADO9Ym2qVtY6niJScvAIrszceY8raeDLzCgFoEuTJuNsbcFuonwoaUixU2CgFnu8ZSnxKNqsPpDDiy20sG9OBQC83o2OJiIiIlHvJmXkMnLmF+JRsqlV2YeGICBpooVURESljvttxksNncvB2d2JE5zpGxxGRCqLQauPb7Sf4aNUhkjLzAGjgX5lxtzege+MAzGYVNKT4qLBRCjiYTXzcvxX3f76JuOQsRn4Zw1ePReLm7GB0NBEREZFy61T6eQbM2Myxs7kEeLqycEQEdXwrGx1LRETkmlgKrXz82yEAnuhSF09XJ4MTiUh5Z7fbWbUvmfd+iSM+JRuA6t5uPNu9Afe0rI6DChpSAlTYKCUq/9nL+Z7JG9l9MoNnlsQyeWBrXQhEREREikFiWi79Z2zmxLnz1KjixsJH21GzqhZZFRGRsmfB5gROpp8nwNOVwZEhRscRkXJu27E0Jvx8gJjj5wDwdndizK31GNSuFq5OekhbSo4KG6VIsI870x4OY+CMLazcm8QbP+7ltbubqA+diIiIyE105Ew2A2ZsISkzj9rVKrHg0QiCvNUGVEREyp6svAIm/x4PwJNd6+umoogUm0PJWby7Mo7f9icD4OpkZnjH2jx2i2aKiTFU2Chl2oT4MPGhFoxdtIO5Ucfx93LliS71jI4lIiIiUi4cTM5iwIwtpGZbqO9XmQWPRuDn6Wp0LBERkesybd0RzubkU6daJfqG1zA6joiUQ2eyLExcdZAlWxOw2S+01H8wPJinu9XHX5+jxUBmI998woQJtGnTBg8PD/z8/OjTpw9xcXFXPGbOnDmYTKaLNlfX8vWP6M7mQfzrjsYAvLcyjm9jThicSERERKTsO5CUSb/pm0nNttAo0JPFI9upqCEiImVWUkYeMzccAeD5nqE4ORh6i0dEypm8AitT1h7m1g/Wsij6QlGjRxN/fnm6MxPua6aihhjO0Bkb69atY/To0bRp04bCwkJeeuklunfvzr59+6hUqdJlj/P09LyoAFIeWzU90rE2yZl5TFt/hBe+3YVPZWdubehndCwRERGRMulAUiYDZmwhLSef5jW8+PKRtni7OxsdS0RE5LpNXBVHXoGN8FpV6NHE3+g4IlJO2O12VuxOYsLP+zlx7jwAzWt48a87G9MmxMfgdCL/z9DCxsqVKy96PWfOHPz8/IiJiaFz586XPc5kMhEQEFDc8Qz3Qs9QUrIsLN1xksfnxTBnWFsi61Y1OpaIiIhImRKXlHVRUWPe8Ai83NQHWEREyq4DSZl882d3h/G9G5XLBz5FpOTtOpHOm8v3sfXYhYXB/T1deL5HKPe2qo7ZrOuMlC6lao2NjIwMAHx8rlz9y87OplatWthsNlq3bs2///1vmjRpctnxFosFi8VS9DozM/PmBC5mZrOJ9x5oTlZeAb/tT2H43K3MfzSC1jWrGB1NREREpEy4sKbGZtJy8mlW3Yt5j6ioISIiZd87Px/AZofezQIIq6V7BCJyY05nnOf9lXF8t+MkcGFh8Mc61+WxW+rg7lyqbh+LFCk1DRhtNhtPP/00HTp0oGnTppcd17BhQ2bNmsUPP/zA/PnzsdlstG/fnhMnLr8OxYQJE/Dy8iragoODi+MUioWTg5nPBrSmY71q5OZbGTormr2nMoyOJSIiIlLqHfqzqHE2J5+m1T2ZPzwCL3cVNUREpGzbGJ/K2rgzOJpN/KNHqNFxRKQMyyuw8unqQ9z2wbqiosZ9rarz+3NdeOb2BipqSKlWagobo0ePZs+ePSxevPiK4yIjIxk8eDAtW7bklltu4bvvvsPX15dp06Zd9pjx48eTkZFRtCUmJt7s+MXK1cmB6YPDCK9Vhcy8Qh7+Ipr4lCyjY4mIiIiUWvEpWfSfsYXU7HwaB6qoIX918uRJBg0aRNWqVXFzc6NZs2Zs27bN6FgiIldks9mZ8PN+AAa1q0Xtapdfn1RE5EpW70+m+0fr+XDVQc4XWAmvVYXvR3dg4kMtCfRyMzqeyN8qFWW3MWPGsHz5ctavX0+NGjWu6VgnJydatWpFfHz8Zce4uLjg4uJyozEN5e7syKxhbRg0cwu7TmQwYMYWFo1sR13fykZHExERESlV4lOy6Td9C6nZFhoHerLg0QgtFC4XOXfuHB06dODWW2/l559/xtfXl0OHDlGlitq5iEjptmznKfaczKSyiyNjb6tndBwRKYOOn83hjR/3sfpACnBhHY2Xejfi7hZBWq9HyhRDCxt2u52xY8eydOlS1q5dS+3ata/5d1itVnbv3k3v3r2LIWHp4unqxNxhbek/YzMHkrLoN30zCx+NoL6/h9HRREREREqFw2ey6T9jM6nZFhr9WdSoUklFDbnYu+++S3BwMLNnzy7adz3fRURESlJegZX3f4kDYFSXulStXLYf4BSRknU+38qUtfFMXX+E/EIbjmYTwzvWZmzX+lR2KRXPvotcE0NbUY0ePZr58+ezcOFCPDw8SEpKIikpifPnzxeNGTx4MOPHjy96/cYbb/Drr79y5MgRtm/fzqBBgzh+/DiPPvqoEadQ4qpUcmbBoxGEBnhwJstCv+mbiUtSWyoRERGRhLO5DJixmTNZFkIDPFTUkMtatmwZ4eHh9O3bFz8/P1q1asWMGTOMjiUickXzoo5zMv08AZ6uPNJBxdhrNXnyZEJCQnB1dSUiIoLo6Ogrjv/6668JDQ3F1dWVZs2asWLFihJKKnJz2e12Vu5JotvEdXyyJp78Qhsd61Vj5dOdGd+7kYoaUmYZWtiYMmUKGRkZdOnShcDAwKJtyZIlRWMSEhI4ffp00etz584xYsQIGjVqRO/evcnMzGTTpk00btzYiFMwRNXKLiwa0Y4mQZ6czcmn3/Qo9p3KNDqWiIiIiGFOpZ9nwMzNJGdaaOBfmQWPRuCjooZcxpEjR5gyZQr169fnl19+YdSoUTz55JPMnTv3kuMtFguZmZkXbSIiJelcTj6frjkEwLjuDXBzdjA4UdmyZMkSxo0bx6uvvsr27dtp0aIFPXr0ICUl5ZLjN23aRP/+/Rk+fDg7duygT58+9OnThz179pRwcpEbk3A2l6Gzt/L4/BhOpp8nyMuVKQNbM294W+r5qb29lG0mu91uNzpEScvMzMTLy4uMjAw8PT2NjnPdMnILGDxrCztPZODl5sT84RE0q+FldCwRKcfKy/Xz71SU8xQpL85kWXhoWhRHUnMIqerOV49F4ufpanSsCqmsXD+dnZ0JDw9n06ZNRfuefPJJtm7dSlRU1F/Gv/baa7z++ut/2V/az1NEyo/Xlu1lzqZjhAZ48NOTnXAwl80++Eb9nYiIiKBNmzZ89tlnANhsNoKDgxk7diwvvvjiX8Y/9NBD5OTksHz58qJ97dq1o2XLlkydOvVv36+s/D2U8qvAamPGH0f4+LdDWAptODuYGdm5Dk/cWhd3Z83QkNLrWq6fhs7YkBvj5e7EvEcjaF3Tm4zzBQyYuZntCeeMjiUiIiJSYs7l5DNo5haOpOZQ3duNBSPaqaghfyswMPAvM74bNWpEQkLCJcePHz+ejIyMoi0xMbEkYoqIABCfksW8zccBeOXOxmW2qGGU/Px8YmJi6NatW9E+s9lMt27dLlnMBoiKirpoPECPHj0uO16kNNmecI67Pt3AeyvjsBTaaF+3Kiuf7sRzPRqqqCHligobZZynqxNfDo+gTUgVsvIKGTRzC+sPnjE6lojITXWt/XD/Y/HixZhMJvr06VO8AUXEEJl5BQyZHU1cchZ+Hi4seDSC6t5uRseSMqBDhw7ExcVdtO/gwYPUqlXrkuNdXFzw9PS8aBMRKSlv/bQfq83O7Y39aV+vmtFxypzU1FSsViv+/v4X7ff39ycpKemSxyQlJV3TeLUslNIgM6+Af32/h/unbOJAUhZV3J34sG8LFjwaQR1ftZ2S8keFjXKgsosjcx9pS+cGvuTmWxk+dyvLd50yOpaIyE1xrf1w/+PYsWM899xzdOrUqYSSikhJys0v5JHZW9l1IgOfSs4seDSCkGqVjI4lZcQzzzzD5s2b+fe//018fDwLFy5k+vTpjB492uhoIiIX+T0uhbVxZ3ByMPFS70ZGx5HLmDBhAl5eXkVbcHCw0ZGkArHb7azYfZpuH65j3ubj2O1wf+sarH62C/eH1cBk0iwvKZ9U2Cgn3J0dmTk4nDubB1JgtTN20Q7m/zlVVUSkLJs4cSIjRoxg2LBhNG7cmKlTp+Lu7s6sWbMue4zVamXgwIG8/vrr1KlTpwTTikhJyCuwMvLLGLYdP4enqyNfPtKW+v4eRseSMqRNmzYsXbqURYsW0bRpU958800mTZrEwIEDjY4mIlKkwGrj7Z/2AzC0fQi1VcC/LtWqVcPBwYHk5OSL9icnJxMQEHDJYwICAq5pvFoWilFOpZ/n0bnbeGLBdlKyLNSuVomFIyL48MEW+FRyNjqeSLFSYaMccXY083G/VgyMqIndDi9/v4fP1hyiAq4PLyLlxPX0wwV444038PPzY/jw4SURU0RKUIHVxugF29kQn4q7swNzHmlL0+peRseSMujOO+9k9+7d5OXlsX//fkaMGGF0JBGRiyzckkB8SjY+lZwZc1t9o+OUWc7OzoSFhbF69eqifTabjdWrVxMZGXnJYyIjIy8aD7Bq1arLjlfLQilpdrudRdEJdP9oPasPpODkYOLJ2+rx81OdaF9XLeukYtCKMeWMg9nEW32aUrWSM5+sieeDXw9yNieff93RGLMWGBORMuZK/XAPHDhwyWM2bNjAF198QWxs7FW/j8ViwWKxFL1WT1yR0slms/PsVztZfSAFF0czXwxpQ+uaVYyOJSIictOl5+bz0W8HARh3ewO83JwMTlS2jRs3jiFDhhAeHk7btm2ZNGkSOTk5DBs2DIDBgwdTvXp1JkyYAMBTTz3FLbfcwocffsgdd9zB4sWL2bZtG9OnTzfyNEQASEzLZfx3u9kQnwpA65revHt/c81glgpHhY1yyGQyMa57Q7zdnXlj+T5mbzzG6fQ8JvVriauTg9HxRESKTVZWFg8//DAzZsygWrWrf0plwoQJvP7668WYTERulN1u5/Uf97Js5ykczSamPhxGZN2qRscSEREpFh+vPkR6bgEN/T3o10brNdyohx56iDNnzvDKK6+QlJREy5YtWblyZdEDVAkJCZjN/9/UpH379ixcuJCXX36Zl156ifr16/P999/TtGlTo05BBJvNzoItx3nn5wPk5FtxdTLzXPeGDOtQGwc9zCwVkMleAfsUZWZm4uXlRUZGRrmfHrhs5yme+2on+VYbrWt6M2NwOFUruxgdS0TKqJK+fubn5+Pu7s4333xDnz59ivYPGTKE9PR0fvjhh4vGx8bG0qpVKxwc/r+Ia7PZgAstrOLi4qhbt+5f3udSMzaCg4MrxN8JkbJi0m8HmfTbIUwmmPRQS+5pWd3oSHIJFeVzdkU5TxExxuEz2fT4aD2FNjvzhrelU31foyPdNBXl+llRzlNKzvGzObzw7S42H0kDoG2ID+8+0Fxr70i5cy3XT83YKOfubhGEv4cLI+fFsD0hnfunbGL2sLa68IlImfDf/XD/U9j4Tz/cMWPG/GV8aGgou3fvvmjfyy+/TFZWFh9//DHBwZd+2s3FxQUXFxV9RUqrL6OOMem3QwC8dlcTFTVERKRc+/dP+ym02enWyK9cFTVE5NrZbHa+jDrGuyvjOF9gxc3JgRd6NmRwZIhazkuFp8JGBRBRpyrfjmrP0NnRHDuby32fb2TmkHDCavkYHU1E5G9dSz9cV1fXv0wP9/b2BtC0cZEy6ofYk7y6bC8AT3Wtz5D2IcYGEhERKUZr41JYfSAFR7OJl3o3MjqOiBjoVPp5nvt6J5sOnwUgsk5V3r2/OTWruhucTKR0UGGjgqjnV5mlT3Rg+Nyt7DqRQf8ZW5j0UEt6Nws0OpqIyBVdaz9cESk/1sal8OxXO7HbYXBkLZ7uVt/oSCIiIsXGUmjl9R/3ATC0fQh1fCsbnEhEjPJD7Ele/n4PWXmFuDk58FLvUAZG1NIsDZH/ojU2Klivw9z8Qp5ctIPf9qcA8Fz3Boy+tR4mky6MIvL3Ksr1s6Kcp0hpFnP8HINmbuF8gZW7WwQx6aGW+iJXBlSU62dFOU8RKVlT1h7m3ZUHqFbZhd+fuwUPVyejI910FeX6WVHOU26+9Nx8/vXDXn7ceQqAFsHefPRgCxU6pcK4luunHnGtYNydHZn2cDhD/2zj8MGvB3lqcSx5BVZjg4mIiIj8KS4pi0fmbOV8gZXODXz5oG8LFTVERKRcS8rI49M1F9aTGt8rtFwWNUTkyv44dIaek/7gx52ncDCbeKZbA759PFJFDZHLUCuqCsjBbOK1u5vQwN+DV37Yw7Kdpzh+Nofpg8Px93Q1Op6IiIhUYIlpuQyetYWM8wW0qunN1EGtcXbUszgiIlK+/XvFfnLzrYTVqsK9raobHUdESlBegZV3fj7AnE3HAKhTrRIfPdSSFsHehuYSKe30LbECGxBRk3nDI/B2d2LniQzu/mwDu06kGx1LREREKqi0nHwGz4omOdNCA//KzB7aBndnPYcjIiLl25YjZ1m28xQmE7x+dxPNUhSpQOKSsrj7sw1FRY3BkbX46clOKmqIXAUVNiq4yLpV+WF0B+r7VSY500LfqVFFffxERERESsr5fCuPzNnK0dQcqnu78eUjEXi7OxsdS0REpFgVWm28umwvAAPa1qRpdS+DE4lISbDb7SzYcpy7P9vAweRsfD1cmDOsDW/c0xQ3Zwej44mUCSpsCLWqVuK7J9pza0NfLIU2xi7awYe/xmGzVbh15UVERMQAhdYLnz9iE9PxcnNi7iNtCPBSe0wRESn/FmxJ4EBSFt7uTjzXvaHRcUSkBGScL+CJBdv559I9WAptdGnoy89PdaJLQz+jo4mUKSpsCAAerk7MHNKGkZ3rAPDpmniGz91KRm6BwclERESkPLPb7byybC+/7U/G2dHMF0PCqefnYXQsERGRYnc228KHv8YB8Fz3hlSppJmKIuVdzPFz9P74D37ek4STg4l/9m7ErCFtqFbZxehoImWOChtSxMFs4qXejZj4YAtcHM38HneGuz7bwL5TmUZHExERkXJq8u/xLNySgMkEn/RrSXiIj9GRRERESsT7v8SRmVdIkyBP+retaXQcESlGNpudyb/H8+C0KE6mn6emjzvfPN6eEZ3raF0dkeukwob8xX2ta/DtqPbUqOJGQlou903ZyA+xJ42OJSIiIuXM19sS+eDXgwC8dlcTejYNNDiRiIhIyYhNTGfJtkTgwoLhDrqxKVJupeXkM2R2NO//EofVZueelkH89GRHLRAucoNU2JBLalrdi+VjO9K5gS95BTaeWhzL6z/upcBqMzqaiIiIlAPrDp5h/He7AXjsljoMaR9ibCAREZESUmi18c+lu7Hb4b7W1TVbUaQc25Fwjjs/+YM/DqXi5uTA+w80Z9JDLfFwdTI6mkiZp8KGXJa3uzOzh7ZhzK31AJi98RgDZ2whJSvP4GQiIiJSlu05mcGo+TEU2uz0aRnECz1CjY4kIiJSYuZtPs7eU5l4ujryUu9GRscRkWJgt9v5MuoYD06L4lRGHnWqVeL70R3oGx6MyaQZWiI3gwobckUOZhPP9WjI9IfDqOziSPSxNO76dAPbjqUZHU1ERETKoMS0XIbO3kpuvpUO9ary3gMt1FdYREQqjOTMPD78sw3jC71CtWCwSDmUYynkqcWxvPLDXgqsdno1DeCHMR1oGOBhdDSRckWFDbkq3ZtcuAjX96tMcqaFh6ZvZtq6w9hsdqOjiYiISBlxLiefIbOiSc22EBrgwZRBYTg76uOoiIhUHG8s30e2pZBWNb3p30YLhouUN/Ep2fSZvJFlO0/hYDbx8h2N+Hxga7WeEikG+iYpV62ub2WWju7APS2DsNrsTPj5ACO+3EZ6br7R0URERKSUsxRaeWxeDEdSc6ju7cbcR9riqS94IiJSgaw7eIafdp3GbIK3+jTVjEWRcubn3ae557MNHErJxs/DhcUj2/FopzpqPSVSTFTYkGtS2cWRSQ+15N/3NsPZ0czqAync8ckGtiecMzqaiIiIlFJ2u50XvtlF9LE0PFwcmT2sDf6erkbHEhERKTF5BVZe+WEPAMM61KZJkJfBiUTkZrHZ7Ez8NY5RC7aTk2+lXR0ffnqyE21CfIyOJlKuqbAh18xkMjEgoiZLn2hPSFV3Tqaf58GpUcz84wh2u1pTiYiIyMU++u0Q38eewtFsYsqgMBr4q7+wiIhULJ+vPczxs7kEeLryzO0NjI4jIjdJtqWQx+fH8MmaeACGd6zN/OER+Hpo/RyR4qbChly3JkFe/Di2I3c0C6TQZuetn/bz2LwYMnILjI4mIiIipcS3MSf4ZPUh4ELbjY71qxmcSEREpGQdOZPN1LWHAXj1rsZUdnE0OJGI3AzHz+Zw3+cb+XVfMs4OZj7o24J/3dkYRwfdbhUpCfqXJjfEw9WJzwa04s17muDsYObXfcnc8ekf7ExMNzqaiIiIGCzq8Fle/G4XAKO61KVfWy2SKiIiFYvdbudfP+wh32qjS0NfejYNMDqSiNwEGw6lcvdnGzmYfGE9jSWPteOBsBpGxxKpUFTYkBtmMpl4ODKEb0e1p6aPOyfOneeBqZuYtu4wNptaU4mIiFRE8SnZPDZvGwVWO3c0C+Qf3RsaHUlERKTE/RB7io3xZ3FxNPPG3U21iLBIGWe325m14ShDZkeTcb6AFsHe/Di2I61qVjE6mkiFo8KG3DTNalxoTdW7WQAFVjsTfj7AkNnRpGTmGR1NREREStDZbAuPzNlKZl4hrWp68+GDLTCbdSNHREQqlrScfN5Yvg+AsbfVo2ZVd4MTiciNKLDaeGnpHt5Yvg+rzc59rauzZGQ7/D1djY4mUiGpsCE3lZebE5MHtOad+5rh6mTmj0Op9Pr4D9YcSDY6moiIiJSAvAIrI77cRkJaLsE+bswYHI6rk4PRsURERErcWz/tIy0nn9AAD0Z2rmt0HBG5AZl5BTwyZyuLohMwmeDlOxrxYd8W+pwrYiAVNuSmM5lM9Gtbk+VjO9Io0JOzOfk8Mmcbry3bS16B1eh4IiIiUkxsNjvPfb2T7QnpeLo6MntoG6pVdjE6loiISIlbf/AM320/ickEE+5rhrOjbr+IlFUnzuXywJRN/HEoFTcnB2Y8HM6jneqotZyIwfSXVYpNPT8Plj7RnmEdQgCYs+kY936+ifiULGODiYiISLH4cFUcy3edxtFsYurDYdTz8zA6koiISInLzS/kpaW7ARjaPkS990XKsJ2J6fSZvKlokfCvH4+kW2N/o2OJCCpsSDFzdXLg1buaMHtoG6pWcmb/6Uzu/HQDi6ITsNu1sLiIiEh58fW2RCb/fhi48GRq+7rVDE4kIiJijI9WHeTEufNU93bjue4NjY4jItdp5Z4kHpoeRWq2hdAAD74f3YGm1b2MjiUif1JhQ0rEraF+/PxUJzrVr0ZegY3x3+3miQXbOZeTb3Q0ERERuUHRR9OKnkwdc2s9+oYHG5xIRETEGLtOpPPFhqMAvHVvUyq5OBqcSESux+yNRxm1IIa8Ahu3NPDlm1HtCfJ2MzqWiPwXFTakxPh5ujJ3WFte6h2Kk4OJn/ck0WPSetYdPGN0NBEREblOiWm5PD4/hgKrnV5NAxh3ewOjI4mIiBiiwGrjhW93Y7PD3S2CuLWhn9GRROQa2e123l15gNd/3IfdDgMjavLFkHAqq0gpUuoYWtiYMGECbdq0wcPDAz8/P/r06UNcXNzfHvf1118TGhqKq6srzZo1Y8WKFSWQVm4Gs9nEyM51+W5UB+r6ViIly8KQWdG88sMezudrYXEREZGyJCuvgEfnbiMtJ58mQZ58+GALzGYtoigiIhXTzD+Osv90Jt7uTrxyV2Oj44jINSq02nj+m11MWXuhveo/ejTkrT5NcXTQc+EipZGh/zLXrVvH6NGj2bx5M6tWraKgoIDu3buTk5Nz2WM2bdpE//79GT58ODt27KBPnz706dOHPXv2lGByuVHNanjx05OdGNo+BIAvo45zxyd/sDMx3dBcIiIicnWsNjtPLY4lLjkLXw8XZg4Jx91ZT7KJiEjFdCw1h0m/HQTg5TsaU62yi8GJRORanM+38ti8GL6OOYHZBO/e34zRt9bDZNJDOyKllcleilZwPnPmDH5+fqxbt47OnTtfcsxDDz1ETk4Oy5cvL9rXrl07WrZsydSpU6/qfTIzM/Hy8iIjIwNPT8+bkl2u3x+HzvDc1ztJzrTgYDbx5G31GX1rXVXERUqhinL9rCjnKXIj/r1iP9PXH8HF0cySxyJpGextdCQpBSrK9bOinKeIXB2bzU6/GZuJPppGx3rVmDe8rW6GXkZFuX5WlPMsL9Jz83l07ja2HT+Hi6OZzwa05vbG/kbHEqmQruX6WaruHGdkZADg4+Nz2TFRUVF069bton09evQgKirqssdYLBYyMzMv2qT06FTfl1+e7swdzQOx2ux89NtBHpgaxdHUy8/cEZGKZfLkyYSEhODq6kpERATR0dGXHTtjxgw6depElSpVqFKlCt26dbvieBG5dl9tS2T6+iMAvN+3hYoaIiJSoc3bfJzoo2m4Ozsw4b5mKmqIlCGnM87z4LQoth0/h6erI/MfjVBRQ6SMKDWFDZvNxtNPP02HDh1o2rTpZcclJSXh73/xBcbf35+kpKTLHjNhwgS8vLyKtuDg4JuWW24Ob3dnPuvfio/7tcTD1ZHYxHR6f/wHC7YcpxRNKhIRAyxZsoRx48bx6quvsn37dlq0aEGPHj1ISUm55Pi1a9fSv39/fv/9d6KioggODqZ79+6cPHmyhJOLlE/RR9P459LdADx5Wz3ubhFkcCIRERHjHD+bwzs/HwDgxV6hBPu4G5xIRK5WwtlcHpgSxcHkbPw9Xfj68fa0Cbn8w9YiUrqUmsLG6NGj2bNnD4sXL77pv3v8+PFkZGQUbYmJiTf9PeTGmUwm7mlZnV+e7kz7ulU5X2Dln0v38MicraRk5hkdT0QMMnHiREaMGMGwYcNo3LgxU6dOxd3dnVmzZl1y/IIFC3jiiSdo2bIloaGhzJw5E5vNxurVq0s4uUj5k5iWy+PzYyiw2undLICnuzUwOpLIdXnttdcwmUwXbaGhoUbHEpEyxmaz8/w3uzhfYKVdHR8GRdQyOpKIXKX4lGz6TtvEyfTz1K5WiW9HtadhgIfRsUTkGpSKwsaYMWNYvnw5v//+OzVq1Lji2ICAAJKTky/al5ycTEBAwGWPcXFxwdPT86JNSq8gbzfmD4/g5Tsa4exo5ve4M3SftJ5lO09p9oZIBZOfn09MTMxFLQjNZjPdunW7YgvC/5abm0tBQcEV2xyqZaHI38vKK2D43K2k5eTTtLonH/ZtidmsVhtSdjVp0oTTp08XbRs2bDA6koiUMfO3HGfL0TTcnBx47/4W+rsoUkbsP53JQ9OiSM600MC/Mksea0eNKpptJVLWGFrYsNvtjBkzhqVLl7JmzRpq1679t8dERkb+5anbVatWERkZWVwxxQBms4lHO9XhxzEdaRzoSXpuAU8u2sETC7aTmm0xOp6IlJDU1FSsVus1tyD8by+88AJBQUF/WZ/pv6llociVWW12nlocy8HkbPw8XJgxOBw3ZwejY4ncEEdHRwICAoq2atWqGR1JRMqQhLO5F7WgqllVN0VFyoJdJ9LpN30zZ3PyaRLkyeKRkfh5uBodS0Sug6GFjdGjRzN//nwWLlyIh4cHSUlJJCUlcf78+aIxgwcPZvz48UWvn3rqKVauXMmHH37IgQMHeO2119i2bRtjxowx4hSkmDUM8OCHMR14ult9HM0mft6TRPeP1rNi92mjo4lIGfDOO++wePFili5diqvr5T+sqmWhyJW98/N+1hxIwcXRzIzB4QR6uRkdSeSGHTp0iKCgIOrUqcPAgQNJSEi47FjN7BOR/2az2Xn+253k5luJqO3Dw+3UgkqkLNh2LI2BM7aQcb6AVjW9WTiiHT6VnI2OJSLXydDCxpQpU8jIyKBLly4EBgYWbUuWLCkak5CQwOnT/38Tu3379ixcuJDp06fTokULvvnmG77//vsrLjguZZuTg5mnuzXg+9EdCA3wIC0nnycWbGfMwu2k5eQbHU9EilG1atVwcHC45haEAB988AHvvPMOv/76K82bN7/iWLUsFLm8r7YlMuOPowC837cFLYK9jQ0kchNEREQwZ84cVq5cyZQpUzh69CidOnUiKyvrkuM1s09E/tuCLcfZfOTPFlQPNFcLKpEyYFN8Kg9/EU2WpZB2dXyYNzwCLzcno2OJyA0wvBXVpbahQ4cWjVm7di1z5sy56Li+ffsSFxeHxWJhz5499O7du2SDiyGaVvdi2ZiOjL2tHg5mE8t3nab7R+v4Ze/VtaMRkbLH2dmZsLCwi1oQ/mch8Cu1IHzvvfd48803WblyJeHh4SURVaRcijmexj+X7gbgya71ubtFkMGJRG6OXr160bdvX5o3b06PHj1YsWIF6enpfPXVV5ccr5l9IvIfiWm5TPizBdULPRtSq2olgxPJ30lLS2PgwIF4enri7e3N8OHDyc7OvuIxXbp0wWQyXbQ9/vjjJZRYbrZN8akMm7OV8wVWOjfwZfbQtlR2cTQ6lojcIP0rljLF2dHMs90bcntjf579aieHUrJ5bF4M97aqzqt3NcbbXVMIRcqbcePGMWTIEMLDw2nbti2TJk0iJyeHYcOGARdaFlavXp0JEyYA8O677/LKK6+wcOFCQkJCitbiqFy5MpUrVzbsPETKmqSMPB6fv50Cq51eTQN4umt9oyOJFBtvb28aNGhAfHz8JX/u4uKCi4tLCacSkdLGZrPz/De7yM230jbEh8GRIUZHkqswcOBATp8+zapVqygoKGDYsGGMHDmShQsXXvG4ESNG8MYbbxS9dnfXOipl0eYjZxk+dxuWQhtdQ/34fFBrXBy1VpxIeWDojA2R69W8hjc/ju3IqC51MZtg6Y6TdP9oPav3J//9wSJSpjz00EN88MEHvPLKK7Rs2ZLY2FhWrlxZtKD4/7YsnDJlCvn5+TzwwAMXtTn84IMPjDoFkTLHUmjl8fkxnMmy0NDfgw/6tlCbDSnXsrOzOXz4MIGBgUZHEZFSbMGW40QdOYurk1ktqMqI/fv3s3LlSmbOnElERAQdO3bk008/ZfHixZw6deqKx7q7uxMQEFC0qV1t2bP1WBqP/DlT49aGvipqiJQzKmxImeXq5MALPUP5dlR76vhWIiXLwvC523ju651knC8wOp6I3ERjxozh+PHjWCwWtmzZQkRERNHP/rdl4bFjxy7Z5vC1114r+eAiZZDdbueV7/cSm5iOl5sT0weHUUlT9aWcee6551i3bh3Hjh1j06ZN3HvvvTg4ONC/f3+jo4lIKXX4TDZvr9gPwAs9QwmpphZUZUFUVBTe3t4Xtaft1q0bZrOZLVu2XPHYBQsWUK1aNZo2bcr48ePJzc0t7rhyE8UcP8fQWdHk5lvpVL8aUwaFqaghUs7oW6qUea1qVmHFk52YuOogM/44wjcxJ9hwKJW3721K10b+RscTEREpU+ZvSWDJtkTMJvikfyv1Dpdy6cSJE/Tv35+zZ8/i6+tLx44d2bx5M76+vkZHE5FSqMBqY9ySWPIKbHSsV40hakFVZiQlJeHn53fRPkdHR3x8fIpa1l7KgAEDqFWrFkFBQezatYsXXniBuLg4vvvuu0uOt1gsWCyWoteZmZk35wTkuuxIOMeQWdHk5FvpUK8qMwaH4+qkooZIeaPChpQLrk4OvNS7Ed0b+/OPb3ZxNDWH4XO3cU/LIF69qwk+lbT2hoiIyN+JPprG68v2AvB8z1BuaaCbvFI+LV682OgIIlKGTP49np0nMvB0deT9vmpBVRq8+OKLvPvuu1ccs3///uv+/SNHjiz672bNmhEYGEjXrl05fPgwdevW/cv4CRMm8Prrr1/3+8nNs+dkBoNnRZNtKaRdHR9mDm6jooZIOaXChpQr4SE+rHiyEx/9dpCZfxzhh9hTbDiUymt3N+HO5oGYTPoAKiIicimnM87zxIIYCm127mweyGOd6xgdSURExHCxiel8uiYegLfubUagl5vBiQTg2WefZejQoVccU6dOHQICAkhJSblof2FhIWlpaQQEBFz1+/2nFW58fPwlCxvjx49n3LhxRa8zMzMJDg6+6t8vN8eRM9kMmRVNVl4hbUKq8MWQNrg5q6ghUl6psCHljpvzhdkbdzQL5PlvdhGXnMXYRTtYtvMUb/Vpir+nq9ERRURESpW8AiuPz4shNTuf0AAP3nuguR4GEBGRCi83v5BnlsRitdm5q0UQd7cIMjqS/MnX1/eq2gdGRkaSnp5OTEwMYWFhAKxZswabzXbRun1/JzY2FoDAwMBL/tzFxQUXF5er/n1y851KP8/DX0RzNiefptU9+WJoG60TJ1LOafFwKbdaBHvz49iOPNW1Pk4OJlbtS6bbxHV8tTURu91udDwREZFSwW638/L3e9h5IgNvdydmDA7H3VlfAkVERCasOMDR1BwCPF15854mRseR69CoUSN69uzJiBEjiI6OZuPGjYwZM4Z+/foRFHShUHXy5ElCQ0OJjo4G4PDhw7z55pvExMRw7Ngxli1bxuDBg+ncuTPNmzc38nTkMs5mWxj0xRZOpp+njm8l5g5ri6erk9GxRKSYqbAh5Zqzo5lnbm/Aj2M70ryGF1l5hTz/7S4Gz4omMS3X6HgiIiKG+zLqON/EnMBsgs/6tybYx93oSCIiIoZbG5fCvM3HAXi/b3O83bVuY1m1YMECQkND6dq1K71796Zjx45Mnz696OcFBQXExcWRm3vhHoGzszO//fYb3bt3JzQ0lGeffZb777+fH3/80ahTkCvIyitgyOxojpzJIcjLlXnDI6haWbNnRCoCPY4nFUJogCffjWrPrI1H+fDXg/xxKJUek9bzfI+GDI4M0eJvIiJSIW0+cpY3lu8DYHyvRnSsX83gRCIiIsY7l5PP89/sAmBo+xA61f/7lkdSevn4+LBw4cLL/jwkJOSirg7BwcGsW7euJKLJDcorsPLo3G3sOZlJ1UrOzHs0gureWgdHpKLQjA2pMBwdzIzsXJeVT3embYgPuflWXvtxHw9Oi+LwmWyj44mIiJSok+nnGb1gO1abnXtaBvFop9pGRxIRETGc3W7nn9/vJiXLQl3fSrzYK9ToSCJyCVabnacW72DL0TQ8XByZ+0hb6vpWNjqWiJQgFTakwqldrRKLR7bjzT5NqeTswLbj5+j18R98vjaeQqvN6HgiIiLF7j+LhZ/NyadxoCfv3KfFwkVERAC+3X6SFbuTcDSbmPRQK1ydHIyOJCL/w2638+byffyyNxlnRzMzh4TTtLqX0bFEpISpsCEVktls4uF2tfh13C3c0sCX/EIb762Mo8/nG9l7KsPoeCIiIsXGbrfz0ne72X0ygyruTkx7OAw3Z920EREROXImm1d+2APA093q06yGbpSKlEZfbDjKnE3HAJj4YAsi6lQ1NpCIGEKFDanQqnu7MWdYGz7s2wIvNyf2nMzk7s82MuHn/ZzPtxodT0RE5KabvfEY3+04iYPZxOQBWixcREQEIL/QxlOLY8nNt9Kujg+jutQzOpKIXMJPu07z1k/7Afhn70bc2TzI4EQiYhQVNqTCM5lM3B9Wg1XjOnNHs0CsNjvT1h2h58fr2RifanQ8ERGRm2bT4VTeXnHhi+BLvRvRvp4WCxcREQH44Nc4dp/MwNvdiUkPtcLBrBaNIqXN1mNpPPNVLABD24dojTiRCk6FDZE/+Xm4Mnlga2YODifQy5XjZ3MZOHMLz361k3M5+UbHExERuSEnzuUyZuEOrDY797aqziMdQoyOJCIiUiqsO3iG6euPAPDe/c0J8HI1OJGI/K/4lGwenbuN/EIbPZr48687G2uNOJEKToUNkf/RrbE/vz7TmSGRtTCZ4NvtJ+g2cR0/xJ7EbrcbHU9EROSanc+38ti8GNJy8mla3ZMJ9zXTF0ERERHgTJaFZ7/aCcDD7WrRvUmAwYlE5H+l5eQzbE40GecLaFXTm4/7aVaViKiwIXJJHq5OvH5PU755vD0N/CtzNiefpxbHMmzOVhLTco2OJyIictXsdjsvfreLvacyqVrJmWkPh+PqpMXCRUREbDY7z329k9RsCw39PfjnHY2MjiQi/yO/0Mao+TEkpp2npo87Mwfrs6yIXKDChsgVhNWqwvKxnRh3ewOcHcysjTtD94/WM/OPI1htmr0hIiKl3xcbjvJD7CkczCY+G9Ca6t5uRkcSEREpFWZtPMq6g2dwcTTzSf9WulkqUsrY7XZeXbaHLUfT8HBx5Ish4VSt7GJ0LBEpJVTYEPkbzo5mnuxanxVPdaJtiA/nC6y89dN+7v18I/tOZRodT0RE5LI2HErl338uFv6vOxoRWbeqwYlERERKhz0nM3h35QEAXr6zMQ0DPAxOJCL/a86mYyyKTsRsgk8GtKK+v/6disj/U2FD5CrV86vM4pHt+Pe9zfBwdWTXiQzu+mwD7/x8gLwCq9HxRERELpKYlsuYRdux2eH+1jUY0j7E6EgiIiKlQo6lkCcX7aDAaqd7Y38GRdQ0OpKI/I/1B8/w5vJ9ALzUuxG3NvQzOJGIlDYqbIhcA7PZxICImqwedwu9mgZgtdmZuu4wPSatZ2N8qtHxREREAMjNL2TkvBjScwtoXsOLt+9tqsXCRUREuNDa5qWluzmSmkOApyvv3t9cfyNFSpnDZ7IZvfDCAzp9w2owvGNtoyOJSCmkwobIdfDzdGXKoDCmPxxGgKcrx8/mMnDmFp77eifncvKNjiciIhWY3W7n+W92sf90JtUqOzN1UJh6houIiPxpUXRi0dpTnw5oRZVKzkZHEpH/km0p5LF5MWTlFRJeqwpv6QEdEbkMFTZEbkD3JgGsGteZh9vVwmSCb2JO0G3iOn6IPYndrsXFRUSk5E1ff4Tlu07jaDbx+cAwgrRYuIiICHBhXY3XftwLwPM9GtImxMfgRCLy3y48oLOT+JRs/D1dmDIoDBdHPaAjIpemwobIDfJwdeLNPk355vFI6vtV5mxOPk8tjmXYnK2cOJdrdDwREalA1h88U7QQ6qt3NaZtbd2wERERAcjMK2D0wu3kF9ro1siPEZ3qGB1JRP7HzD+OsmJ3Ek4OFx7Q8fVwMTqSiJRiKmyI3CRhtXxY/mRHnunWAGcHM2vjznD7xPXM/OMIhVab0fFERKScO342h7GLdmCzw4PhNRjUrpbRkUREREoFu93Oi9/u4vjZXKp7u/FB3xaYzWptI1KaRB0+yzt/PqDzrzsbE1arisGJRKS0U2FD5CZycXTgqW71WfFUR9qG+HC+wMpbP+3n3s83sedkhtHxRESknMrNv9CLOON8AS2CvXnjHvUiFhER+Y+5m44VPQX+2YBWeLtrXQ2R0uR0xnnGLNyO1WbnvlbVeVgP6IjIVVBhQ6QY1PPzYPHIdky4rxkero7sPpnBPZM3MmHFfs7nW42OJyIi5YjdbucfX+/iQFIW1Sq7ME2LhYuIiBSJTUzn7RX7AXipdyNa1dRT4CKlSX6hjScWbOdsTj6NAj15+95mekBHRK6KChsixcRsNtG/bU1Wj7uFO5oFYrXZmbb+CN0nrWP9wTNGxxMRkXJiyrrD/LT7NE4OJqYOak2Al6vRkUREREqFjNwCRi/YToHVTq+mAQxtH2J0JBH5Hx/8GseOhHQ8XR2ZOqg1bs56QEdEro4KGyLFzM/TlckDWzNzcDiBXq4kpp1n8KxonlkSy9lsi9HxRESkDFsbl8L7v8QB8NrdTQgP0WLhIiIiADabnWe/3snJ9PPU9HHn3Qea6ylwkVLm97gUpq8/AsD7fVtQq2olgxOJSFmiwoZICenW2J9V425haPsQTCZYuuMkXSeu45uYE9jtdqPjiZRqkydPJiQkBFdXVyIiIoiOjr7i+K+//prQ0FBcXV1p1qwZK1asKKGkIiXnWGoOTy7agd0O/dsGMzBCvYhFRET+Y8q6w/y2PxlnBzOfD2yNp6uT0ZFE5L8kZ+bx7Fc7ARgSWYseTQIMTiQiZY0KGyIlqLKLI6/d3YSlT3QgNMCD9NwCnvt6JwNnbuFYao7R8URKpSVLljBu3DheffVVtm/fTosWLejRowcpKSmXHL9p0yb69+/P8OHD2bFjB3369KFPnz7s2bOnhJOLFJ9sSyEj520jM6+Q1jW9ee3uJkZHEhERKTXWHTzDB79emNH4Zp8mNK3uZXAiEflvVpudpxfHkpaTT+NAT8b3bmR0JBEpg1TYEDFAy2BvfhzbkRd6huLiaGbT4bP0mLSez9fGU2C1GR1PpFSZOHEiI0aMYNiwYTRu3JipU6fi7u7OrFmzLjn+448/pmfPnvzjH/+gUaNGvPnmm7Ru3ZrPPvushJOLFA+73c5zX+3kYHI2fh4uTBkUhoujehGLiIgAJKbl/teMxpo81Kam0ZFE5H9M/j2eqCNncXd24LMBrXB10mdZEbl2KmyIGMTJwcyoLnX55enOdKhXFUuhjfdWxnHXpxvYkXDO6HgipUJ+fj4xMTF069ataJ/ZbKZbt25ERUVd8pioqKiLxgP06NHjsuNFyprJv8ezcm8STg4mpgwKw99Ti4WLiIgAnM+3MnJeDBnnC2gR7M1rdzc2OpKI/I/oo2lM+u0gAG/1aUod38oGJxKRskqFDRGDhVSrxPzhEXzYtwVV3J04kJTFfVM28dqyvWRbCo2OJ2Ko1NRUrFYr/v7+F+339/cnKSnpksckJSVd03gAi8VCZmbmRZtIabTmQDIfrrrwRfCNe5oSVquKwYlERERKB7vdzvjvdrH/dCZVKzkzdVBrzWgUKWUy8wp4ZkksNjvc17o697WuYXQkESnDVNgQKQVMJhP3h9Xgt3G3cF+r6tjtMGfTMW6fuI7f9iUbHU+k3JswYQJeXl5FW3BwsNGRRP7iyJlsnloci90OAyNq0r+tWmuIiIj8x9xNx/g+9hQOZhOfDWhNoJeb0ZFE5H+8tmwvJ9PPU9PHnTfuaWp0HBEp41TYEClFqlZ2YeJDLfnykbYE+7hxOiOPR7/cxugF20nJzDM6nkiJq1atGg4ODiQnX1zgS05OJiAg4JLHBAQEXNN4gPHjx5ORkVG0JSYm3nh4kZsoK6+AkfNiyMorJLxWFV69S4uFi4iI/Ef00TTe+mk/AON7hRJZt6rBiUTkf63cc5rvtp/EbIKJD7agsouj0ZFEpIwzvLCxfv167rrrLoKCgjCZTHz//fdXHL927VpMJtNftiu1GBEpazo38OXXp2/hsVvq4GA28dPu03SduI6FWxKw2exGxxMpMc7OzoSFhbF69eqifTabjdWrVxMZGXnJYyIjIy8aD7Bq1arLjgdwcXHB09Pzok2ktLDZ7Dz71U7iU7Lx93Th80GtcXY0/COciIhIqZCcmccTC7ZTaLNzV4sghnesbXQkEfkfKVl5jP9uNwCP31KX8BAfgxOJSHlg+LfinJwcWrRoweTJk6/puLi4OE6fPl20+fn5FVNCEWO4OTswvlcjlo3pQPMaXmTlFfLS0t08ND2K+JQso+OJlJhx48YxY8YM5s6dy/79+xk1ahQ5OTkMGzYMgMGDBzN+/Pii8U899RQrV67kww8/5MCBA7z22mts27aNMWPGGHUKIjfk0zXx/LovGWcHM1MHheHnocXCRUREACyFVkbNjyE120JogAfv3t8Mk8lkdCwR+S92u50Xv93NudwCGgd68nS3BkZHEpFywvB5X7169aJXr17XfJyfnx/e3t43P5BIKdMkyIulT3RgzqZjfPhrHFuPnaP3xxt44ta6jOpSVwviSbn30EMPcebMGV555RWSkpJo2bIlK1euLFogPCEhAbP5/+v07du3Z+HChbz88su89NJL1K9fn++//56mTdXDVcqe3/Yl89FvFxYLf6tPU1rV1GLhIiIicOFm6T+X7mF7Qjqero5MHRSGu7PhtzhE5H8s3prImgMpODuY+eihlpp5LCI3TZn9q9+yZUssFgtNmzbltddeo0OHDpcda7FYsFgsRa8zMzNLIqLITeNgNjG8Y216NPHnX9/v4fe4M0z67RA/7jzFhPua07a2pnFK+TZmzJjLzrhYu3btX/b17duXvn37FnMqkeIVn5LNM0tiARgcWYsH22hRexERkf/4YsNRvok5gdkEnw1oTUi1SkZHEpH/kXA2lzeX7wPgHz0a0jDAw+BEIlKelLkyaWBgIFOnTuXbb7/l22+/JTg4mC5durB9+/bLHjNhwgS8vLyKtuBg3RiQsqlGFXdmDW3DZwNaUa2yC4fP5PDgtCjGf7ebjPMFRscTEZGbJDOvgJHztpFlKaRtiA//urOx0ZFERERKjd/jUvj3iguLhb98R2M6N/A1OJGI/C+73c74pbvIzbfStraP1r8RkZuuzBU2GjZsyGOPPUZYWBjt27dn1qxZtG/fno8++uiyx4wfP56MjIyiLTExsQQTi9xcJpOJO5sHsXrcLfT78+ndRdEJdJu4jhW7T2O3a3FxEZGyzGazM25JLEfO5BDo5crkga1xcihzH9lEyox33nkHk8nE008/bXQUEbkK8SnZPLlwBzY7PBQezLAOIUZHEpFL+GpbIhvjz+LqZOa9+5tjNmv9GxG5ucrFt+S2bdsSHx9/2Z+7uLjg6el50SZS1nm5O/HO/c1ZPLIddapV4kyWhScWbGfEl9s4lX7e6HgiInKdJq0+xG/7U3B2NDPt4TB8PVyMjiRSbm3dupVp06bRvHlzo6OIyFXIyC1gxJcXZjS2CanCm32aarFwkVIoOTOPt366MKvq2dsbqlWciBSLclHYiI2NJTAw0OgYIoZoV6cqK57qxJO31cPJwcRv+1O4feI6Zm88itWm2RsiImXJL3uT+GT1IQD+fW8zmtfwNjaQSDmWnZ3NwIEDmTFjBlWqVDE6joj8jUKrjdELt3M0NYfq3m5MGRSmRYhFSiG73c4/l+4hK6+QFsHePKIWVCJSTAz/FJCdnU1sbCyxsbEAHD16lNjYWBISEoALbaQGDx5cNH7SpEn88MMPxMfHs2fPHp5++mnWrFnD6NGjjYgvUiq4OjkwrntDfnqyE2G1qpCTb+X1H/dx35RN7D+daXQ8ERG5CoeSsxj352LhQ9uH8EBYDWMDiZRzo0eP5o477qBbt25/O9ZisZCZmXnRJiIl662f9rMhPhV3ZwdmDA6nWmXNaBQpjZbvOs1v+5NxcjDx3v3NcVALKhEpJo5GB9i2bRu33npr0etx48YBMGTIEObMmcPp06eLihwA+fn5PPvss5w8eRJ3d3eaN2/Ob7/9dtHvEKmoGvh78PVjkSyMTuDdnw+wMzGduz7dwIjOdXiqa31cnRyMjigiIpeQcb6AkfNiyMm3ElHbh3/e0cjoSCLl2uLFi9m+fTtbt269qvETJkzg9ddfL+ZUInI5i6ITmLPpGAATH2xJ4yC1lxYpjdJz83lt2V4AnuhSj4YBHgYnEpHyzGSvgCsNZ2Zm4uXlRUZGhtbbkHIrOTOPV3/Yy8q9SQDUqurO232a0bF+NYOTSVlWUa6fFeU8pXSw2uyM+HIbaw6kEOTlyrKxHfUUqpRZZeH6mZiYSHh4OKtWrSpaW6NLly60bNmSSZMmXfIYi8WCxWIpep2ZmUlwcHCpPk+R8mLDoVSGzo6m0GZn3O0NeLJrfaMjyQ0oC38nboaKcp7/a/x3u1kUnUB9v8r89GQntYsTkWt2LddPXWFEyil/T1emPhzGtIfDCPB05fjZXAZ9sYVnv9pJWk6+0fFERORPH606yJoDKbg4mpn2sFpriBS3mJgYUlJSaN26NY6Ojjg6OrJu3To++eQTHB0dsVqtfznGxcUFT0/PizYRKX4Hk7MYNT+GQpudu1sEMfa2ekZHEpHL2J5wjkXRFzquvH1vMxU1RKTYGd6KSkSKV48mAbSvW5X3f4lj3ubjfLv9BL/HpfCvOxvRp2V1TCb1uxQRMcqK3af57Pd4AN65vxnNangZnEik/OvatSu7d+++aN+wYcMIDQ3lhRdewMFBrTtFSoMzWRaGzd5KlqWQNiFVeO+B5vruIlJKFVpt/HPpHgDub12DtrV9DE4kIhWByqciFYCHqxNv3NOUbx5vTwP/yqTl5PPMkp0MnhVNwtlco+OJiFRI+09n8uxXOwF4tGNt7m2lxcJFSoKHhwdNmza9aKtUqRJVq1aladOmRscTEeB8vpVHv9zGyfTzhFR1Z9rD4VovUK7L22+/Tfv27XF3d8fb2/uqjrHb7bzyyisEBgbi5uZGt27dOHToUPEGLeO+jDrO/tOZeLk58VLvUKPjiEgFocKGSAUSVqsKy8d24rnuDXB2NPPHoVS6T1rHtHWHKbTajI4nIlJhnMvJZ+S8bZwvsNKxXjVe7KUvgCIiIgA2m51nlsSyMzEdb3cnZg9ri08lZ6NjSRmVn59P3759GTVq1FUf89577/HJJ58wdepUtmzZQqVKlejRowd5eXnFmLTsSs7MY+KqgwC80DOUqmqrKiIlRK2oRCoYZ0czY26rT+9mgby0dDebj6Qx4ecD/BB7infub0bzGt5GRxQRKdcKrTbGLtpBYtp5gn3c+LR/Kxwd9KyJiJHWrl1rdAQR+dM7Kw+wcm8Szg5mpj8cTu1qlYyOJGXY66+/DsCcOXOuarzdbmfSpEm8/PLL3HPPPQB8+eWX+Pv78/3339OvX7/iilpmvbl8H9mWQloGe9OvTbDRcUSkAtG3aJEKqo5vZRaNaMd7DzTHy82Jfacz6TN5I28u30eOpdDoeCIi5dY7Px9gQ3wqbk4OzBgcThU9hSoiIgLA/M3Hmb7+CADv922uPv1S4o4ePUpSUhLdunUr2ufl5UVERARRUVGXPc5isZCZmXnRVhFsPnKW5btOYzbBW32aYjZrHRwRKTkqbIhUYCaTiQfDg1n97C3c3SIImx2+2HCU7h+t5/cDKUbHExEpd5buOMHMDUcB+PDBFoQGeBqcSEREpHRYG5fCq8v2AjDu9gbc07K6wYmkIkpKSgLA39//ov3+/v5FP7uUCRMm4OXlVbQFB5f/mQtWm503l+8DoH/bmjSt7mVwIhGpaFTYEBGqVXbhk/6tmD2sDdW93TiZfp5hc7YydtEOzmRZjI4nIlIu7D6RwYvf7gZgzK316N0s0OBEIiIipcOuE+k8sWA7Vpud+1vXYOxt9YyOJKXYiy++iMlkuuJ24MCBEs00fvx4MjIyirbExMQSfX8jfBOTyN5TmXi4OjLu9gZGxxGRCkhrbIhIkVsb+rFqXGc+WnWQLzYc5cedp1gXl8I/72jEg+HBmEyaVioicj3OZFkYOW8blkIbt4X66cufiIjIn46l5jBs9lZy8610rFeNCfc10/cOuaJnn32WoUOHXnFMnTp1rut3BwQEAJCcnExg4P8/hJKcnEzLli0ve5yLiwsuLhVn0eysvALe/yUOgKe61teC4SJiCBU2ROQi7s6O/POOxtzdojovfreLvacyeeHb3Xy3/SQT7mtGHd/KRkcUESlTCqw2Ri/YzumMPOr4VmJSv5bqPywiIsKFwv+Q2dGczcmnSZAnUx8Ow9lRjSXkynx9ffH19S2W3127dm0CAgJYvXp1USEjMzOTLVu2MGrUqGJ5z7Los9/jSc3Op061SgyODDE6johUUPrEICKX1KyGFz+M7sA/ezfCzcmBLUfT6PnxH0z+PZ5Cq83oeCIiZcYbP+4j+lgaHi6OTH84HE9XJ6MjiYiIGC7bUsgjc7Zy/GwuwT5uzB7WhsouevZSbq6EhARiY2NJSEjAarUSGxtLbGws2dnZRWNCQ0NZunQpcGEdyqeffpq33nqLZcuWsXv3bgYPHkxQUBB9+vQx6CxKl+Nnc5i94RgAL9/ZSMVIETGMPjWIyGU5OpgZ0bkOPZsG8M/v97D+4Bne/yWOn/ec5v0HWtAoUIveiohcyeLoBOZtPo7JBJP6taSen2a9iYiI5BfaGDU/ht0nM/Cp5MyXj0Tg5+FqdCwph1555RXmzp1b9LpVq1YA/P7773Tp0gWAuLg4MjIyisY8//zz5OTkMHLkSNLT0+nYsSMrV67E1VX/HwWYsOIA+VYbnRv4cmtDP6PjiEgFZrLb7XajQ5S0zMxMvLy8yMjIwNNTN2ZFrobdbue77Sd5/ce9ZOYV4mg2MfrWeoy+tZ6e0KhAKsr1s6KcpxSvmOPn6Dc9igKrnWdvb8DYrvWNjiRS7CrK9bOinKdIcbDZ7Iz7KpbvY0/h5uTA4pHtaBHsbXQsKSEV5fpZXs9ze8I57vt8E2YTrHy6Mw38PYyOJCLlzLVcP3U3UkSuislk4v6wGvw27ha6N/an0Gbn49WHuPuzDew+kfH3v0BEpAJJzszj8fkxFFjt9GoawJjb6hkdSUREpFR4d+UBvo89haPZxJRBrVXUECkj7HY77/x8AIAHwmqoqCEihlNhQ0SuiZ+nK9MeDuPT/q3wqeTMgaQs+ny+kfdWHiCvwGp0PBERw+UVWHlsXgxnsiw09Pfgg74tMJm0WLiIiMjMP44wbf0RAN69vzld1MZGpMxYG3eG6KNpODuaebpbA6PjiIiosCEi185kMnFXiyBWPdOZO5sHYrXZ+XztYe78dAM7Es4ZHU9ExDB2u51XfthDbGI6Xm5OTB8cRiUthCoiIsKSrQm89dN+AF7oGcr9YTUMTiQiV8tms/PuyguzNYa2DyHI283gRCIiKmyIyA2oWtmFzwa0ZuqgMKpVdiE+JZv7p2zi7Z/2afaGiFRIszce46ttJzCb4LMBrahVtZLRkURERAz3067TjP9uNwCPda7D47fUMTiRiFyLH3ae5EBSFh6ujjzRpa7RcUREABU2ROQm6Nk0gN/Gdea+VtWx2WHGH0fp9fEfbD2WZnQ0EZESs/7gGd76aR8AL/VuRKf6vgYnEhERMd7auBSeXrIDmx36tw3mxV6hatEoUoZYCq18+OtBAEZ1qYu3u7PBiURELlBhQ0RuCm93ZyY+1JIvhoTj7+nC0dQcHpwWxWvL9pKbX2h0PBGRYnX4TDajF27HZr+wmOLwjrWNjiQiImK4rcfSeHx+DAVWO3c2D+StPs1U1BApYxZtSeDEufP4e7owrL0+44pI6aHChojcVF0b+fPrM7fwUHgwdjvM2XSMHpPWs/nIWaOjiYgUi4zcAkbM3UZWXiFhtarw9r1NddNGREQqvD0nM3hk9lbyCmzc2tCXiQ+2xMGsv48iZUlegZXP1x4GYOxt9XFzdjA4kYjI/1NhQ0RuOi83J959oDnzhrelurcbiWnn6T9jM2/8qLU35NqkpaUxcOBAPD098fb2Zvjw4WRnZ19x/NixY2nYsCFubm7UrFmTJ598koyMjBJMLRVJodXGmEXbOZKaQ5CXK1MHheHiqC98IiJSsR0+k82QWdFkWQppG+LD5wPDcHbU7QeRsmZRdAIpWRaqe7vxYHiw0XFERC6iTxYiUmw61ffll2c607/thdkbszYepfcnfxCbmG50NCkjBg4cyN69e1m1ahXLly9n/fr1jBw58rLjT506xalTp/jggw/Ys2cPc+bMYeXKlQwfPrwEU0tF8u8VB/jjUCpuTg7MGBKOr4eL0ZFEREQMdeJcLg/P3MLZnHyaVvdk5tBwPeUtUgblFViZ8udsjSduravipIiUOo5GBxCR8q2yiyMT7mtO9yYBvPDNLo6cyeG+zzfyRJd6PNm1vj4cyWXt37+flStXsnXrVsLDwwH49NNP6d27Nx988AFBQUF/OaZp06Z8++23Ra/r1q3L22+/zaBBgygsLMTRUX/25OZZsjWBWRuPAjDxwRY0CfIyOJGIiIixTmecZ8CMLZzKyKOubyXmDmuLp6uT0bFE5Dos/nO2RpCXK33DNFtDREof3VEUkRJxa0M/fn2mM31aBmGzw2e/x3P3ZxvYdyrT6GhSSkVFReHt7V1U1ADo1q0bZrOZLVu2XPXvycjIwNPTU0UNuam2Hkvj5e/3APBMtwb0ahZocCIRERFjJWfmMWDGFhLScqnp4878RyOoWlkzGUXKorwCK1PW/We2Rj09kCgipZKuTCJSYrzdnZnUrxWfD2yNTyVnDiRlcc/kDUz+PZ5Cq83oeFLKJCUl4efnd9E+R0dHfHx8SEpKuqrfkZqayptvvnnF9lUAFouFzMzMizaRy0lMy+XxeTEUWO3c0SyQJ7vWMzqSiIiIoc5kWRgwYzNHU3OoUcWNRSPbEejlZnQsEblOS7Ymkpz552yN8BpGxxERuSQVNkSkxPVuFsgvT3eme2N/Cqx23v8ljvunRhGfcvlFoaX8ePHFFzGZTFfcDhw4cMPvk5mZyR133EHjxo157bXXrjh2woQJeHl5FW3BwZpqLZeWYylkxJfbOJuTT5MgTz7o2wKTyWR0LBEREcOczbYwcOZmDp/JIcjLlUUj2lHdW0UNkbIqr8DK52vjARh1az1cHLVGjoiUTurLISKG8PVwYdrDYSzdcZJXl+1lZ2I6d3zyB8/3DGVY+xDMZt0oLK+effZZhg4desUxderUISAggJSUlIv2FxYWkpaWRkBAwBWPz8rKomfPnnh4eLB06VKcnK7c23n8+PGMGzeu6HVmZqaKG/IXNpudZ5bEciApi2qVXZgxWIuhiohIxXYuJ5+BM7dwMDkbf08XFo5oR7CPu9GxROQGfB1zguRMC4Ferjyo2RoiUoqpsCEihjGZTNzXugaRdavy/De7+ONQKm8u38eqfUlMfLAlQXrSq1zy9fXF19f3b8dFRkaSnp5OTEwMYWFhAKxZswabzUZERMRlj8vMzKRHjx64uLiwbNkyXF1d//a9XFxccHFRD2i5so9+O8iv+5JxdjAz7eEwXaNERKRCy8gt4OFZWziQlIWvx4WiRki1SkbHEpEbUGi1MWP9EQAe61xHszVEpFRTKyoRMVyglxtfPtKWt+9tiruzA5uPpNFz0nqW7TxldDQxUKNGjejZsycjRowgOjqajRs3MmbMGPr160dQUBAAJ0+eJDQ0lOjoaOBCUaN79+7k5OTwxRdfkJmZSVJSEklJSVitViNPR8q4ZTtP8emaC1PyJ9zXjLBaVQxOJCIiYpzMvAIGz45mz8lMqlZyZuGjEdT1rWx0LBG5QT/vSSIhLZcq7k482EYz2EWkdFNhQ0RKBZPJxMCIWvz0ZCdaBHuTmVfIk4t28PTiHWTmFRgdTwyyYMECQkND6dq1K71796Zjx45Mnz696OcFBQXExcWRm5sLwPbt29myZQu7d++mXr16BAYGFm2JiYlGnYaUcTsSzvGPr3cCMLJzHe4P05R8ERGpuDJyC3h45hZ2JqZTxd2JBSMiqO/vYXQsEblBdrudaesPAzA4MgR3ZzV5EZHSTVcpESlValerxDePR/LZmng+XXOI72NPsfXYOSY+2IKIOlWNjiclzMfHh4ULF1725yEhIdjt9qLXXbp0uei1yI06cS6XEV9uw1Jo47ZQP17oGWp0JBEREcOcy8nn4Vlb2HMykyruTsx/NILQAE+jY4nITbAx/ix7Tmbi6mRmSPsQo+OIiPwtzdgQkVLHycHMM7c34OvH21PTx52T6efpN2Mz7648QH6hzeh4IlJBZOUVMHzONlKz8wkN8OCT/q1wMJuMjiUiImKIs9kW+s/YXNR+atHIdjQJ8jI6lojcJFPXXZit0a9NTXwqORucRkTk76mwISKlVlitKqx4qhMPhtfAbocpaw9z35SNxKdkGx1NRMq5QquNMQt3EJd8YUHUWUPbUNlFE11FRKRiOpN1oahxICmLapVdWDyynWZqiJQje05msCE+FQezieEdaxsdR0TkqqiwISKlWmUXR957oAVTB7XG292JPSczufPTP5gXdUwth0Sk2Ly5fB/rDp7B1cnMF0PCCfJ2MzqSiIiIIVIy8+g3PYqDydn4e7qw5LF2WlNDpJz5YsNRAO5oFkiwj7vBaUREro4KGyJSJvRsGsgvT3emU/1q5BXY+NcPe3l07jbScvKNjiYi5czcTceYG3UcgEkPtaR5DW9jA4mIiBjkdMZ5Hpq+mcNncgjycmXJyEjq+lY2OpaI3EQpmXks33UKgEc7abaGiJQdKmyISJnh7+nK3GFtefWuxjg7mll9IIVeH69n85GzRkcTkXLi9wMpvP7jXgBe6BlKz6aBBicSERExxolzufSbvpmjqTlU93ZjyWORhFSrZHQsEbnJ5m9JoMBqJ6xWFT3QIyJligobIlKmmM0mhnWozfdPdKCObyWSMy0MmLGZiasOUmjVwuIicv0OJGUydtEObHZ4MLwGj99Sx+hIIiIihohPyabv1CiOn82lpo87Sx5rp/Y0IuWQpdDKwi0XZioP6xBibBgRkWtkeGFj/fr13HXXXQQFBWEymfj+++//9pi1a9fSunVrXFxcqFevHnPmzCn2nCJSujQO8mT52I48GF4Dmx0+WX2IATO2cCr9vNHRRKQMSsnKY/icbWRbCmlXx4e3+jTDZDIZHUtERKTE7TmZwYPTojidkUd9v8p89VgkNaqoqCFSHv248zSp2fkEernSo0mA0XFERK6J4YWNnJwcWrRoweTJk69q/NGjR7njjju49dZbiY2N5emnn+bRRx/ll19+KeakIlLauDtfWFj8434tqeziSPSxNHp/8ger9iUbHU1EypC8AisjvozhZPp56lSrxNRBYTg7Gv4RSUREpMRFH02j//TNpOXk07yGF0seiyTAy9XoWCJSDOx2O7M3Xlg0/OHIWjg56POviJQtjkYH6NWrF7169brq8VOnTqV27dp8+OGHADRq1IgNGzbw0Ucf0aNHj+KKKSKl2D0tq9My2Juxi3aw60QGI77cxtD2IbzYKxRXJwej44lIKWaz2Xn2q53sTEzH292JL4a2wdvd2ehYIiIiJW5tXAqPz48hr8BG29o+fDEkHA9XJ6NjiUgx2XrsHHtPZeLqZKZ/m5pGxxERuWZlrhwbFRVFt27dLtrXo0cPoqKiLnuMxWIhMzPzok1EypdaVSvxzePtGdGpNgBzNh3jvs83cfhMtsHJRKQ0e/eXA/y0+zRODiamDQqjthZFFRGRCuinXacZ8eU28gps3NrQly8faauihkg592XUMQD6tKxOlUp6sEdEyp4yV9hISkrC39//on3+/v5kZmZy/vyle+tPmDABLy+voi04OLgkoopICXN2NPPPOxoze1gbfCo5s+90Jnd/uoHlu04ZHU1ESqF5UceYtu4IAO/e35yIOlUNTiQiIlLyvtqayNhF2ymw2rmzeSDTHg7XrGeRci4128Ive5MAGNSulsFpRESuT5krbFyP8ePHk5GRUbQlJiYaHUlEitGtDf34+alOtKvjQ06+lTELd/Dasr3kF9qMjiYipcRv+5J5ddleAJ7r3oD7WtcwOJGIlKQpU6bQvHlzPD098fT0JDIykp9//tnoWCIlym638/naeJ7/dhc2O/RvW5OP+7XSOlMiFcA3MScosNppUcOLptW9jI4jInJdytwnloCAAJKTL14YODk5GU9PT9zc3C55jIuLS9GXlv9sIlK++Xu6Mn94BKNvrQtcaE314LQoTqZfemaXiFQcOxPTGbtoBzY79GsTzOhb6xkdSURKWI0aNXjnnXeIiYlh27Zt3Hbbbdxzzz3s3bvX6GgiJcJqs/Pasr28tzIOgMdvqcu/722Kg9lkcDIRKW42m51F0QkADIjQ2hoiUnaVucJGZGQkq1evvmjfqlWriIyMNCiRiJRWjg5m/tEjlFlDw/FycyI2MZ07PvmDtXEpRkcTEYMkpuUyfO5WzhdYuaWBL2/2aYrJpJs4IhXNXXfdRe/evalfvz4NGjTg7bffpnLlymzevNnoaCLFLq/AythF25kbdRyTCV65szEv9grV30ORCmLT4bMcP5uLh4sjd7UIMjqOiMh1M7ywkZ2dTWxsLLGxsQAcPXqU2NhYEhIuVI/Hjx/P4MGDi8Y//vjjHDlyhOeff54DBw7w+eef89VXX/HMM88YEV9EyoDbQv1ZPrYjzWt4kZ5bwLA5W5m46iBWm93oaCJSgtJz8xkyO5rU7HwaB3oyeWBrnBwM/ygkIgazWq0sXryYnJycyz4sZbFYyMzMvGgTKYsyzhcwZFY0K3Yn4exg5tP+rXikY22jY4lICVoYfRyAe1tXx93Z0eA0IiLXz/Bv89u2baNVq1a0atUKgHHjxtGqVSteeeUVAE6fPl1U5ACoXbs2P/30E6tWraJFixZ8+OGHzJw5kx49ehiSX0TKhmAfd75+PJJB7Wpit8Mnqw8xZFY0Z7MtRkcTkRKQV2BlxJfbOHImhyAvV2YPa0NlF32RE6nIdu/eTeXKlXFxceHxxx9n6dKlNG7c+JJjJ0yYgJeXV9EWHBxcwmlFblxSRh4PTo1iy9E0PFwcmfNIG+5srqe1RSqSlKw8ft17ob272lCJSFlnstvtFe6R5czMTLy8vMjIyNB6GyIV0Pc7TjL+u92cL7AS4OnKlEGtaVWzitGxyoSKcv2sKOdZUdhsdp5cvIPlu07j4erIt6Pa08Dfw+hYIuVSWbp+5ufnk5CQQEZGBt988w0zZ85k3bp1lyxuWCwWLJb/fxgiMzOT4ODgMnGeIgCHkrMYMiuaUxl5+Hm4MGdYWxoH6f+7UvLK0t+JG1Faz3Py7/G8/0scrWt6890THYyOIyLyF9dy/TR8xoaISEnr06o6y8Z0oK5vJZIy83ho2ma+2pZodCwRKSbv/RLH8l2ncXIwMW1QmIoaIgKAs7Mz9erVIywsjAkTJtCiRQs+/vjjS451cXHB09Pzok2krNh2LI0HpkZxKiOPOr6V+O6J9ipqiFRAdrudb2JOANCvrWZriEjZp8KGiFRI9f09+GFMR7o39iffauP5b3bx6g97KLDajI4mIjfRl1HHmLruMADvPdCc9vWqGZxIREorm8120awMkfLgh9iTDJi5hYzzBbSq6c23j7enRhV3o2OJiAG2J6RzNDUHNycHejcLNDqOiMgNU2FDRCqsyi6OTB0UxrjbGwAwN+o4A2duIVXrboiUCz/tOs2ry/YC8OztDbi3VQ2DE4lIaTF+/HjWr1/PsWPH2L17N+PHj2ft2rUMHDjQ6GgiN4XdbufT1Yd4anEs+YU2ujf2Z+Gj7ahSydnoaCJikG+3X5it0atZgNaaE5FyQVcyEanQzGYTT3atT6NAT55ZEkv00TTu/nQD0x4Op1kNL6Pjich12nQ4lWeWxGK3w8PtajHmtnpGRxKRUiQlJYXBgwdz+vRpvLy8aN68Ob/88gu333670dFEbpil0Mr473bz3faTAIzsXIcXe4ZiNpsMTiYiRskrsPLjzlMAPNBaD/uISPmgGRsiIsDtjf35fnQH6lSrxKmMPB6YuomlO04YHUtErsOekxmM/DKGfKuN3s0CeO3uJphMupkjIv/viy++4NixY1gsFlJSUvjtt99U1JBy4VxOPg9/Ec1320/iYDbx9r1Neal3IxU1pMJ6++23ad++Pe7u7nh7e1/VMUOHDsVkMl209ezZs3iDFrNV+5LJyiukurcb7epUNTqOiMhNocKGiMif6vlV5vsxHbgt1A9LoY1nluzkreX7sNrsRkcTkauUcDaXobO3km0ppF0dHyY+2BIH3cwREZEK4GhqDvdN2UT00TQquzgya2gbBkbUMjqWiKHy8/Pp27cvo0aNuqbjevbsyenTp4u2RYsWFVPCkvGfNlT3tqquQqeIlBtqRSUi8l88XZ2YOTiciasO8tnv8czccJSjqTl83L+V+pCKlHJnsiw8POvCOjmNAj2ZPjgcVycHo2OJiIgUuy1HzvLY/BjScwuo7u3GrKFtaBjgYXQsEcO9/vrrAMyZM+eajnNxcSEgIKAYEpW85Mw81h88A8D9YWpDJSLlh2ZsiIj8D7PZxHM9GvLZgFa4OJpZfSCFvlOjOJV+3uhoInIZ2ZZChs2J5vjZXGpUcWPusDZ4ujoZHUtERKTYfb0tkUFfbCE9t4AWNbxYOrq9ihoiN2jt2rX4+fnRsGFDRo0axdmzZ6843mKxkJmZedFWWny/4yQ2O4TVqkLtapWMjiMictOosCEichl3Ng9i8ch2VKvszP7TmfSZvJFdJ9KNjiUi/yO/0Mbj82LYczKTqpWcmTc8Aj9PV6NjiYiIFKtCq43Xf9zLP77ZRYHVTq+mASweGYmfh/4GityInj178uWXX7J69Wreffdd1q1bR69evbBarZc9ZsKECXh5eRVtwcHBJZj4yn6IvbBo+H2tqxucRETk5lJhQ0TkClrVrML3ozvQ0N+DlCwLD06LYuWe00bHEpE/2Wx2nv16JxviU3F3dmD2sDZ6Ek1ERMq99Nx8hs7eyuyNxwB4smt9Jg9ojZuzWjBK+ffiiy/+ZXHv/90OHDhw3b+/X79+3H333TRr1ow+ffqwfPlytm7dytq1ay97zPjx48nIyCjaEhMTr/v9b6bDZ7LZdzoTR7OJ3k0DjY4jInJTqWG8iMjfqFHFnW9GRTJm4Q7WHTzD4/O383zPhoy6pS4mkxZeEzGK3W7njeX7+HHnKRzNJqYOCqN5DW+jY4mIiBSrg8lZjPhyG8fP5uLm5MDEB1vQq5luWErF8eyzzzJ06NArjqlTp85Ne786depQrVo14uPj6dq16yXHuLi44OLictPe82ZZvvPCQ3kd61ejSiVng9OIiNxcmrEhInIVPFyd+GJIOEMiawHw3so4Xvx2N4VWm8HJyre0tDQGDhyIp6cn3t7eDB8+nOzs7Ks61m6306tXL0wmE99//33xBhVDfLz6EHM2HQPgwwdb0LmBr7GBREREitmqfcncO3lj0ZpS3z3RXkUNqXB8fX0JDQ294ubsfPNu4p84cYKzZ88SGFi2/q3Z7XZ+3HWhDdWdzYMMTiMicvOpsCEicpUcHcy8fk9TXr+7CWYTLNmWyMh5MeTmFxodrdwaOHAge/fuZdWqVSxfvpz169czcuTIqzp20qRJmlFTjn2x4SiTfjsEwGt3NeaeluoZLCIi5ZfdbuezNYcYOW8bOflW2tXxYdmYjjQK9DQ6mkiplpCQQGxsLAkJCVitVmJjY4mNjb3oYanQ0FCWLl0KQHZ2Nv/4xz/YvHkzx44dY/Xq1dxzzz3Uq1ePHj16GHUa1yUuOYv4lGycHcx0b+JvdBwRkZtOrahERK7RkPYhBHm7MWbhdtYcSGHAjC3MGtoGH03tvan279/PypUr2bp1K+Hh4QB8+umn9O7dmw8++ICgoMs/dRQbG8uHH37Itm3bytyTVfL3vtqayJvL9wHw7O0NGNqhtsGJREREik+OpZDnv9nFT7svtJQZHFmLf93ZGCcHPaco8ndeeeUV5s6dW/S6VatWAPz+++906dIFgLi4ODIyMgBwcHBg165dzJ07l/T0dIKCgujevTtvvvlmqWw1dSU/7rwwW+OWhr54ujoZnEZE5OZTYUNE5Drc3tifhSMieGTONmIT03lg6ibmDmtLsI+70dHKjaioKLy9vYuKGgDdunXDbDazZcsW7r333ksel5uby4ABA5g8eTIBAQFX9V4WiwWLxVL0OjMz88bCS7FZsfs0L363C4ARnWoz5rZ6BicSEREpPvEp2Tw+P4b4lGycHEy8cU9T+retaXQskTJjzpw5zJkz54pj7HZ70X+7ubnxyy+/FHOq4me321m+60Ix9K4WakMlIuWTHvEQEblOYbV8+HZUJEFerhw5k8P9Uzax75RuiN8sSUlJ+Pn5XbTP0dERHx8fkpKSLnvcM888Q/v27bnnnnuu+r0mTJiAl5dX0RYcHHzduaX4rDt4hqcW78Bmh35tgnmpdyO1GxMRkXJrxe7T3PPZBuJTsvH3dGHRiHYqaojIVdl9MoPjZ3NxdTLTNdTv7w8QESmDVNgQEbkB9fw8+O6JDjT09yAly8JD06LYdDjV6Fil2osvvojJZLriduDAgev63cuWLWPNmjVMmjTpmo4bP348GRkZRVtiYuJ1vb8Un63H0nhs3jYKrHbuaB7I2/c2U1FDRETKpQKrjbd/2scTC7YXraexfGwnwkN8jI4mImXEf2ZrdG3kTyUXNWsRkfJJVzcRkRsU4OXKV49HMuLLbUQfTWPo7K1MGdiaro20QNulPPvsswwdOvSKY+rUqUNAQAApKSkX7S8sLCQtLe2yLabWrFnD4cOH8fb2vmj//fffT6dOnVi7du0lj3NxcSlzPXMrkj0nM3hk9lbyCmx0aejLRw+2xMGsooaIiJQ/KVl5jFmwg+hjaQA81rkO/+jREEetpyEiV8lut7Nyz4UZ7nc203qDIlJ+qbAhInITeLk58eUjbXly0Q5+3ZfMY/NimNSvJXc2Vz/T/+Xr64uvr+/fjouMjCQ9PZ2YmBjCwsKAC4ULm81GRETEJY958cUXefTRRy/a16xZMz766CPuuuuuGw8vJS4+JZshs6LJshTSNsSHKQPDcHbUzR0RESl/th5L44kF2zmTZaGyiyMf9G1Oz6a6KSki1yYuOYuEtFxcHM3c0vDvv3eJiJRVujMgInKTuDo5MHlga+5pGUShzc6Ti3bw9Ta1NLpejRo1omfPnowYMYLo6Gg2btzImDFj6NevH0FBFwpGJ0+eJDQ0lOjoaAACAgJo2rTpRRtAzZo1qV27tmHnItfnWGoOA2Zs5mxOPs2qe/HF0HDcnB2MjiUiInJT2e12Zv5xhP7TN3Mmy0ID/8osG9NBRQ0RuS6/7k0GoFP9arg763lmESm/dIUTEbmJnBzMTHywJW5ODizemsg/vtnF+QIrgyNDjI5WJi1YsIAxY8bQtWtXzGYz999/P5988knRzwsKCoiLiyM3N9fAlFIcEtNyGTBjMylZFhr6ezD3kbZ4uDoZHUtEROSmOpeTz3Nf72T1gQvtN+9uEcQ79zfTzUgRuW6/7rvQhqp740u37xURKS/0aUlE5CZzMJuYcF8z3JwdmL3xGK/8sJfcfCuP31LX6Ghljo+PDwsXLrzsz0NCQrDb7Vf8HX/3cyl9TqWfp/+MzZzKyKOubyXmPxqBTyVno2OJiIjcVFuPpfHkoh2czsjD2dHMv+5szKCImphMWkdKRK7PqfTz7DmZidkEXRv5GR1HRKRYqbAhIlIMTCYTr9zZmErOjnz2ezzv/HyAQquNMbfVNzqaSKmWnJlH/xmbOXHuPCFV3Vk4oh2+HlrYXUREyg+bzc6UdYeZuOogVpudOtUq8dmA1jQO8jQ6moiUcav2XWhDFVarClUr6zO0iJRvKmyIiBQTk8nEcz0a4ubswPu/xPHBrwdxMJsZ1UUzN0Qu5UyWhf4zNnP8bC7BPm4sHNEOf09Xo2OJiIjcNGeyLIz7KpY/DqUCcG+r6rzVpymVXPTVXERunNpQiUhFok9PIiLFbPSt9bDb7Xzw60HeXXkAR7OJEZ3rGB1LpFQ5m21h4MzNHDmTQ5CXKwsfbUeQt5vRsURERG6aDYdSeXpJLKnZFtycHHjjniY8EFZDradE5KbIyC1g85E0AG5v7G9wGhGR4qfChohICRhzW30KbXYm/XaIt1fsx2w2MbxjbaNjiZQK6bn5DPoimoPJ2fh7urBoZDuCfdyNjiUiInJT5BfamLjqINPWH8Zuh4b+Hnw2oBX1/T2MjiYi5ciauGSsNjsN/T0IqVbJ6DgiIsVOhQ0RkRLydLcG2Gx2PlkTz5vL9+FoNjGkfYjRsUQMlXG+gMGzotl/OpNqlV1YOKIdtarqi5iIiJQP8SlZPLU4lr2nMgHo3zaYV+9qgquTg8HJRKS8+c/6Gt2baLaGiFQMKmyIiJSgZ25vQKHNzudrD/Pqsr14uDpyX+saRscSMUR6bj4PfxHN7pMZ+FRyZuGICOr6VjY6loiIyA2z2+3M33yct1fsJ6/Ahre7E+/c14yeTQONjiYi5VCB1cYfBy+s3dO1kQobIlIxqLAhIlKCTCYT/+jRkLwCG7M2HuUf3+zC09WJbuqBKhXMuZx8Bs7cwr7TmVSt5MyCERE0UEsOEREpB85kWXjh212sOZACQKf61figbwv8PV0NTiYi5VXM8XNkWQqpWsmZ5tW9jI4jIlIizEYHEBGpaEwmEy/f0Yj7WlXHarMzeuF2oo+mGR1LpMSczbbQf8Zm9p3OpFplZxaNbEdogKfRsURERG7Y6v3J9Jy0njUHUnB2NPPKnY2ZO6ytihoiUqzWxp0BoHMDX8xmk8FpRERKhgobIiIGMJtNvPtAc7o18sNSaGP4nK3sPZVhdCyRYpeabWHAjC0cSMrC18OFxSPbaaaGiIiUeTmWQv65dDfD527jbE4+oQEeLBvTgUc61tZNRhEpdmvjLswQ69LQ1+AkIiIlR4UNERGDODmY+WxAa9qG+JBlKWTIrK0cP5tjdCyRYnMmy0L/6ZuJS87C78+iRj0/FTVERKRs23LkLL0+/oMFWxIAGN6xNt+P7qDZiCJSIpIy8jiQlIXJBJ3qq7AhIhWHChsiIgZydXJg5tBwGgV6kpptYdjsraTn5hsdS+SmS8nMo9/0KA6lZBPg6cqSxyK1ULiIiJRp5/OtvP7jXvrN2ExCWi7Vvd1Y8GgE/7qzMa5ODkbHE5EKYt3BC7M1WtTwxqeSs8FpRERKjgobIiIG83R1Yu4jbaju7caR1BwemxdDfqHN6FgiN01SRh79pm/m8JkcgrxcWfJYO2pXq2R0LBERkesWczyN3p/8weyNx7DboV+bYFY+3YkO9aoZHU1EKpj/rK+hNlQiUtGosCEiUgr4ebgya2gbKrs4suVoGi9+twu73W50LJEblpiWy0PToziSmkN1bzcWj4ykVlUVNUREpGzKK7AyYcV++k6N4mhqDgGerswZ1oZ37m+Oh6uT0fFEpIIptNrYcCgVgC4N/QxOIyJSslTYEBEpJRoGeDB5YGsczCa+236ST9fEGx1J5IYcPpPNg9OiOH42l2AfNxaPbEfNqu5GxxIREbkusYnp3PXpBqatP4LNDve3rsEvz3TWzUQRMczOExlkWQrxdneiWXUvo+OIiJQoFTZEREqRWxr48sY9TQCYuOogK3afNjiRyPXZeyqDB6dGcTojj3p+lfn6sfYE+6ioISIiZU+OpZA3ftzHfZ9v5FBKNr4eLswYHM6HD7bAy02zNETEOJviL8zWiKxTFQezyeA0IiIlq1QUNiZPnkxISAiurq5EREQQHR192bFz5szBZDJdtLm6upZgWhGR4jUwohaPdqwNwHNf7+RgcpbBiUSuTczxc/SfvpmzOfk0CfJkych2BHjpb7WIiJQ96w6eoftH65m18Sg2O9zbqjq/Pt2Z2xv7Gx1NRISNhy8UNrS+j4hURIYXNpYsWcK4ceN49dVX2b59Oy1atKBHjx6kpKRc9hhPT09Onz5dtB0/frwEE4uIFL8Xe4XSvm5VcvOtPDYvhozzBUZHErkqG+NTefiLLWTmFRJeqwqLRrajamUXo2OJiIhck7ScfMYtiWXIrGhOpp+nurcbcx9py0cPtaRKJWej44mIcD7fyvbj6YAKGyJSMRle2Jg4cSIjRoxg2LBhNG7cmKlTp+Lu7s6sWbMue4zJZCIgIKBo8/fX0zIiUr44Opj5tH8rqnu7cTQ1h2e/isVm02LiUrr9ti+ZYXO2kptvpVP9anw5vC2eWkhVRETKELvdzg+xJ+k2cR3f7TiJyQSPdKjNr8905pYGvkbHExEpsvVYGvlWG0FeroRoHTsRqYAMLWzk5+cTExNDt27divaZzWa6detGVFTUZY/Lzs6mVq1aBAcHc88997B3794rvo/FYiEzM/OiTUSktKta2YUpg1rj7Gjmt/0pWkxcSrUfYk/y2PwY8gtt9Gjiz8wh4bg7OxodS0RE5KqdOJfLsDlbeWpxLGk5+TT09+C7Ue155a7GVHLR3zQRKV3+04aqfb1qmExaX0NEKh5DCxupqalYrda/zLjw9/cnKSnpksc0bNiQWbNm8cMPPzB//nxsNhvt27fnxIkTl32fCRMm4OXlVbQFBwff1PMQESkuzWt481afpgBMWn2QDYdSDU4k8lfzNx/n6SWxWG127m1VnckDWuPi6GB0LBERkatSYLUxbd1hun+0nrVxZ3B2MPNc9wb8OLYjrWpWMTqeiMglbYo/C0CHelUNTiIiYgzDW1Fdq8jISAYPHkzLli255ZZb+O677/D19WXatGmXPWb8+PFkZGQUbYmJiSWYWETkxjwYHkz/tsHY7fDMV7GczbYYHUkEuNCu46NVB3n5+z3Y7TCoXU0+7NsCR4cy9/FCRCqYCRMm0KZNGzw8PPDz86NPnz7ExcUZHUsMsPnIWXp//AcTfj5Abr6VNiFVWPFUJ8bcVh9nR/09E5HSKT03nz2nMgBoX1fra4hIxWToJ7Vq1arh4OBAcnLyRfuTk5MJCAi4qt/h5OREq1atiI+/fIsWFxcXPD09L9pERMqSV+5sQj2/ypzJsvCPb3Zht2u9DTGW1WbnpaV7+Hj1IQCe6lqfN+9pitmsafAiUvqtW7eO0aNHs3nzZlatWkVBQQHdu3cnJyfH6GhSQs5kWRi3JJZ+0zdzKCUbn0rOvP9Ac5aMjKSeX2Wj44mIXNHmI2nY7VDPrzL+nq5GxxERMYShhQ1nZ2fCwsJYvXp10T6bzcbq1auJjIy8qt9htVrZvXs3gYGBxRVTRMRwbs4OfNq/Fc6OZtYcSGHOpmNGR5IKLK/AyhMLYlgUnYDJBG/1acoztzdQb18RKTNWrlzJ0KFDadKkCS1atGDOnDkkJCQQExNjdDQpZlabnS+jjnHbh2uLFgcfGFGTNc/eQt/wYBXoRaRMiD6aBkC7Oj4GJxERMY7hK6CNGzeOIUOGEB4eTtu2bZk0aRI5OTkMGzYMgMGDB/9fe3ceHlV593/8k3UISxJDQhYIkBBI2FeBoBUQFEStKD9wQQVEpDykFfHBB+qC2PZJUZQqUrE+ZdGauqNVFBtZRCBsAWQLgUAgEJKwhCwkZJ3z+yMwNZoAQ5aTybxf1zXXxZw598z33Jwz38l8575vtW7dWrGxsZKkl156SQMHDlRERIRycnL0yiuv6Pjx43r88cfNPAwAqHOdg7317KjOmvuv/Yr9+qAGdfBXZFALs8OCk8m9WKopK3Zo27Fsebq56vUHeumO7vy4AIBjy82tmM7Dz6/qL4iKi4tVXPyfqSDz8vLqJS7Urt0ncvTc53u1L73i/697ax/9cXQ39Qz1NTcwALDTtmMV62v0D2N9DQDOy/TCxv33368zZ87ohRdeUGZmpnr16qXVq1fbFhRPS0uTq+t/BpacP39eU6ZMUWZmpm644Qb17dtXmzdvVpcuXcw6BACoN49Gt9P3h85o7cHTmvXJj/ps2iDWM0C9ycor0oSl23QwM18tLO56Z0I/DQznjykAjs1qtWrGjBm66aab1K1btyr3iY2N1bx58+o5MtSWM/nFWvBtsj5KPCHDkFo0cdczIyL10IB2cmOEBgAHk19UqgOnKgq0/dszYgOA83IxnHCi9ry8PPn4+Cg3N5f1NgA4nMzcIt228HvlF5Xpf0ZGadqQDvX22s7y/uksx2mPI2cu6NG/b1N6zkUFtLBoxaT+6hJC3wCozBHfP6dNm6ZvvvlGGzduVJs2barcp6oRG6GhoQ51nM6opMyq5ZtT9caaFF0oLpMk3de7teaM6qyAFhaTowOckyPmietRl8f5/aEzmrB0m0L9vPTDM7fW6nMDgNnsef80fcQGAMA+QT5N9PxdXfTMJ3u08LtDuq1LIItcok4lHs/W4yt26HxhqcL8m+ndx/or1K+p2WEBQI3FxMToq6++0oYNG6otakiSxWKRxcIX4Y7CMAytPXhaf1yVpNSzFQvC92jjo7l3d1Hfdvy6GYBj25Z6aRqq9oycBuDcmL8EABzQ2L5tdEunAJWUWfXMJz+q3No4B99lZ2dr/Pjx8vb2lq+vryZPnqwLFy5ctV1CQoJuvfVWNWvWTN7e3rrlllt08eLFeoi48flqzyk9+M5WnS8sVY82PvrkN9EUNQA4PMMwFBMTo5UrV2rt2rUKCwszOyTUksNZ+Xp06TZNXrFDqWcL5N/colf+Xw99/l83UdQA0ChsTz0vSeofdoPJkQCAuRixAQAOyMXFRX++r7tuX7hBO9Ny9M9taXp4YDuzw6p148ePV0ZGhuLj41VaWqpJkybpiSeeUFxcXLVtEhISNHLkSM2ZM0eLFi2Su7u7fvzxx0rrNeHqDMPQku+Pav7qg5Kk27oE6vUHeqmpJx8dADi+6dOnKy4uTl988YVatGihzMxMSZKPj4+8vLxMjg7XI7ewVAu/O6T3thxXudWQp5urHrs5TNOHdlCLJh5mhwcAtaK4rFy7T+ZIkm5kfQ0ATo5vJwDAQYX4emnWiEjN/dd+Lfh3su7sHqwbmnmaHVatSUpK0urVq7V9+3b169dPkrRo0SKNGjVKCxYsUEhISJXtnnrqKf3ud7/T7NmzbdsiIyPrJebGorTcqhe+2Kd/bjshSXrspjA9e2dnFlgF0Gi89dZbkqQhQ4ZU2r5s2TJNnDix/gPCdSsuK9d7Cce1aG2Kci+WSqooxj87qrPa+zczOToAqF17TuaqpMwq/+aeCuM9DoCT4+erAODAxg9oq6igFsopLNXL3yabHU6tSkhIkK+vr62oIUnDhw+Xq6urtm7dWmWb06dPa+vWrWrVqpUGDRqkwMBADR48WBs3bqyvsB1eflGpHlu+Xf/cdkKuLtKLd3fRC3d3oagBoFExDKPKG0UNx2G1Gvpid7qGvfq9/rgqSbkXS9UpsLnem9xf7zzaj6IGgEZpW2q2JKl/mJ9cXPh8DsC5UdgAAAfm7uaql+7pJkn6YHua9lwaltwYZGZmqlWrVpW2ubu7y8/PzzZlyM8dPXpUkvTiiy9qypQpWr16tfr06aNhw4bp8OHD1b5WcXGx8vLyKt2c0amcixq7JEE/HD4rLw83vf1IP028iXnnAQANy+YjZ3XP4k168oPdOnn+olq1sGj+mO76+ne/0q86BpgdHgDUmR3HKgob/VgzCAAobACAo+sf5qd7e7eWYUh/WpUkw2jYC4nPnj1bLi4uV7wdPHjwup7barVKkqZOnapJkyapd+/eWrhwoSIjI7V06dJq28XGxsrHx8d2Cw0Nva7Xd2T70nM1evEmHczMV0ALiz6aGq3bugSaHRYAADbJmfl6bPl2PfTOVu1Nz1Vzi7v++/ZOWj9riO6/sa3c3fjzFkBlx44d0+TJkxUWFiYvLy916NBBc+fOVUlJyRXbFRUVafr06WrZsqWaN2+uMWPGKCsrq56irpphGNp9IkeS1KcdC4cDAGtsAEAj8N8jIrVqb4a2pmZrw+GzGtyp4f5a8emnn77qVB/h4eEKCgrS6dOnK20vKytTdna2goKCqmwXHBwsSerSpUul7Z07d1ZaWlq1rzdnzhzNnDnTdj8vL8+pihtf783Q0x/9qIul5eoU2FxLJ96oNjc0NTssAAAkVYwofP27w/o48YSshuTu6qKHBrTV74Z1lH9zi9nhAWjADh48KKvVqrffflsRERHat2+fpkyZooKCAi1YsKDadk899ZRWrVqljz/+WD4+PoqJidF9992nTZs21WP0laVlF+p8Yak83VzVObiFaXEAQENBYQMAGoHWvl56dGA7/d/GVL28+qB+FeEv1wa6JkJAQIACAq5eeImOjlZOTo4SExPVt29fSdLatWtltVo1YMCAKtu0b99eISEhSk6uvN7IoUOHdMcdd1T7WhaLRRaL830xYrUaen3NYb2+pmKarl919Nfi8X3k3cTD5MgAAJDO5Bfrr+tT9P6WNJWUV4zKvKNbkGaNiFR4QHOTowPgCEaOHKmRI0fa7oeHhys5OVlvvfVWtYWN3Nxc/f3vf1dcXJxuvfVWSdKyZcvUuXNnbdmyRQMHDqyX2H9uV1qOJKlra29Z3N1MiQEAGhLG6gJAI/FfQyPU3OKu/afytGpvhtnh1Fjnzp01cuRITZkyRdu2bdOmTZsUExOjBx54QCEhIZKk9PR0RUVFadu2bZIkFxcXzZo1S2+88YY++eQTpaSk6Pnnn9fBgwc1efJkMw+nwSksKdP0uJ22osZjN4Vp2cQbKWoAAEyXU1ii+asP6paX12nZpmMqKbdqQJifPp0Wrbce7ktRA0CN5Obmys+v+jUqEhMTVVpaquHDh9u2RUVFqW3btkpISKiPEKt0eRqqXqG+psUAAA0JIzYAoJHwa+apJ24J12vxh7Qw/pBGdQ+WWwMdtXGt3n//fcXExGjYsGFydXXVmDFj9MYbb9geLy0tVXJysgoLC23bZsyYoaKiIj311FPKzs5Wz549FR8frw4dOphxCA1Ses5FTVmxQwcy8uTh5qI/je6ucTc6z9RbAICG6UJxmZZuTNU7G44qv7hMktQz1Ff/fXsn3RzhLxcXx/5cA8B8KSkpWrRo0RWnocrMzJSnp6d8fX0rbQ8MDFRmZmaVbYqLi1VcXGy7n5eXVyvx/tQuChsAUAmFDQBoRB67OUx/35iqo2cL9O3+TI3qHmx2SDXi5+enuLi4ah9v3759lYulz549W7Nnz67L0BxW4vFsTX0vUWcvlKhlM08teaSvbmxf/S/WAACoa0Wl5Xo34ZjeWn9E5wtLJUlRQS309O2RGt65FQUNAL8we/ZszZ8//4r7JCUlKSoqynY/PT1dI0eO1NixYzVlypRajSc2Nlbz5s2r1ef8qaLSch04lStJ6tOWhcMBQKKwAQCNSnOLuyZEt9Mba1P01/UpuqNbEF8GwObjHSf07Mp9Kim3qnOwt955tC+LhAMATFNUWq5/bkvTW+uP6HR+xS+dw/yb6anbOumu7sENdr0wAOZ7+umnNXHixCvuEx4ebvv3qVOnNHToUA0aNEh/+9vfrtguKChIJSUlysnJqTRqIysrS0FBQVW2mTNnjmbOnGm7n5eXp9DQ2hsRfSAjT6Xlhlo281SbG7xq7XkBwJFR2ACARmbiTWF654dU7UvP06aUc7q5o7/ZIcFkpeVW/WlVkpZvPiZJGtk1SK+O66lmFj4GAADqX2FJmd7fkqa3NxzV2QsVBY3Wvl56clhH3dentdzdWAoSwJUFBAQoICDgmvZNT0/X0KFD1bdvXy1btkyurld+j+nbt688PDy0Zs0ajRkzRpKUnJystLQ0RUdHV9nGYrHIYrHYdxB2uLxweO+2vvxwDQAu4RsNAGhk/Jp56v4bQ7V88zEt33yMwoaTO51XpOlxO7X92HlJ0u+GddSMYR35FSwAoN5dKC7TuwnH9H8/pCq7oERSRUFj2pAOGtuvjSzubiZHCKCxSU9P15AhQ9SuXTstWLBAZ86csT12efRFenq6hg0bpnfffVf9+/eXj4+PJk+erJkzZ8rPz0/e3t767W9/q+joaA0cONCU42DhcAD4JQobANAIPTywnZZvPqa1B7N08nwh0w05qR3HsjXt/Z06k1+sFhZ3vXZ/L93WJdDssAAATib3YqlWbD6mv29MVe7FijU02vo11fShHXRv7zbydGeEBoC6ER8fr5SUFKWkpKhNmzaVHru8Vl9paamSk5NVWFhoe2zhwoVydXXVmDFjVFxcrBEjRuivf/1rvcb+U7tPVPxIqVco62sAwGUUNgCgEYpo1Vw3RbTUppRzituapmdGRl29ERoNwzC0YvMx/XFVksqshjoFNteSh/sqPKC52aEBAJzI+YISLduUqmWbjim/uEySFO7fTDG3RujXPUOYcgpAnZs4ceJV1+Jo3769rchxWZMmTbR48WItXry4DqO7NjmFJTqRfVGS1L2Nj8nRAEDDQWEDABqpRwa206aUc/poxwnNvK0TXx44iYsl5fr9yr1auStdknRXj2DNH9OD9TQAAPXm5PlC/d8Pqfpw+wldLC2XJHUKbK6YWzvqzu7BcmM6RAC4ZvtP5UmqGOnm4+VhcjQA0HDwLQcANFLDOgfKr5mnzl4o0aYj5zS407UtrgfHlXauUFP/kaikjDy5ubpozh1RmnxzGAsMAgDqxYFTefrbhiP6ck+Gyq0Vv37uGuKtmKERGtE1iPWdAOA67D+VK0nq1trb5EgAoGGhsAEAjZSHm6vu7B6s97Yc1xe70ylsNHLf7s/UrI9/VF5Rmfybe+rNh/poYHhLs8MCADRyhmEo4eg5vf39UX1/6D+L8t4c4a/fDO6gmyJaUmAHgBrYl14xYqNrCNNQAcBPUdgAgEZsdO8QvbfluL7dl6mLo8vl5elmdkioZSVlVsV+k6Rlm45Jkvq09dVfx/dVkE8TcwMDADRq5VZD3+7P1NvfH9GPJyt+TezqIo3qHqzfDO6gbq35Ag4AasO+SyM2uoYwYgMAforCBgA0Yn3a3qA2N3jp5PmLWnMwS3f1CDE7JNSiE9mFionbaftC6YlbwjVrRKQ8WE8FAFBH8otK9fGOk1qRcEzHzxVKkizurrr/xlA9fnO42rZsanKEANB4FBSXKfVsgSRGbADAz1HYAIBGzMXFRXd2D9bbG47quwMUNhqT1fsyNOuTPcovKpNvUw+9OranhnUONDssAEAjlXauUMs3H9NHO07oQnGZJMnHy0MTottpwqD2atncYnKEAND4JGXkyTCkQG+LAlrwPgsAP0VhAwAauWGdA/X2hqNaf+iMyq2G3Fi406EVl5Xrf1claUXCcUkVU08teqiPWvt6mRwZAKCxMQxDW1OztXRjquKTsmRUrAeuDgHN9NjNYbq3d2s19eRPSgCoK/tPVayv0Y3RGgDwC3wKBYBGrk9bX/l4eSinsFQ/nsxRn7Y3mB0SrtPxcwWKidulvekVU09NHRyu/76dqacAALWrqLRcX/54Sks3HVNSRp5t++BOAXrs5jD9KsJfrvxQAgDq3L501tcAgOpQ2ACARs7dzVUDw/307f4sbTl6jsKGAzIMQ5/tTNcLX+xTQUm5fJt66LVxPXVrFFNPAQBqz4nsQsVtS9NH20/oXEGJJMnLw01j+rbWxEFhimjV3OQIAcC57Ls0YqNra0ZsAMDPUdgAACcwMLzlpcJGtv5riNnRwB65haV69vO9+mpPhiTpxvY36PUHeiuEqacAALWg3Gro+0On9V7Cca0/dMY23VSwTxNNGNReD9wYKt+mnuYGCQBOqLisXIez8iUxYgMAqkJhAwCcwMDwlpKkHceyWWfDgWw5ek4zP9ytU7lFcnN10VPDO2rakAj+/wAANXb2QrE+3H5CcVvTlJ5z0bb9Vx399fDAdhoW1UruTHUIAKY5lHlBZVZDvk09WE8PAKpAYQMAnECnwBby8nBTYUm5Us8WMJVEA1dabtVfvjukv64/IsOQ2rVsqtcf6K1eob5mhwYAcGCGYWjH8fN6L+G4vtmXodLyiuEZPl4eGtu3jcYPbKcw/2YmRwkAkKTkS6M1Ogd5y8WFHzYBwM9R2AAAJ+Dm6qLOwS20My1H+0/lUthowFLPFmjGB7v048mKhQLH9m2jub/uquYWUjYA4PpkF5Tos50n9dGOEzqUdcG2vWeorx4Z2E539QhWEw83EyMEAPzcoUuFjU6B/O0GAFXhWxIAcBJdQ3y0My1HB07l6Z5erc0OBz9jGIbitqXpT6uSVFhSLu8m7oq9r4fu7BFsdmgAAAdUbjX0w+Ez+mjHCcUfyLKNzmji4arRvVrr4YHt1I3FaAGgwUrOvFTYCGphciQA0DBR2AAAJ9Hl0oJzBy99QEbDkZF7Uc98skc/HD4rSRoQ5qeF9/digXAAgN1OZBfq4x0n9EniSZ3KLbJt797aR+NuDNWve4bIx8vDxAgBANfi8oiNyEAKGwBQFQobAOAkgn2aSJLO5BebHAkuMwxDn+1M14tf7ld+UZks7q6aNSJSj90UJlcWCAcAXKOi0nL9+0CWPtp+QpuOnJVRMThDPl4eurd3a43rF2r7gQMAoOHLKypVxqXidEcKGwBQJQobAOAk/Jp5SpLOF5aYHAkk6XR+kX7/2T59l5QlqWKe81fH9mT9EwDANbm8EPhnO9O1as8p5RWV2R67OcJf424M1e1dAlk7AwAc0OFLozWCvJswyg4AqkFhAwCcxA1NKwob2QUlMgxDLi6MCDDLqj0Zeu7zvTpfWCoPNxfNGN5JU28Jl7ubq9mhAQAauNSzBVq586RW7k7XieyLtu3BPk00tm8bje0XqlC/piZGCACoqeTMC5JYXwMAroTCBgA4iZbNKwobxWVWFZaUq5mFFFDfzl4o1ov/2q+v9mRIkroEe+vVcT3VOZjpQQAA1TtfUKKv9pzSZ7vStSstx7a9maebRnYL1n19WmtgeEu5MY0hADQK/1lfg9HcAFAdvtUCACfh5eEmi7urisusyi4oobBRjwzD0Mpd6XrpqwPKKSyVm6uLpg/poJhbO8rTnVEaAIBfulhSrnXJp7VyV7rWJ59WaXnFwhmuLtKvOgbovj6tdXuXIHl5MtUUADQ2lwsbnVhfAwCq1SC+1Vq8eLFeeeUVZWZmqmfPnlq0aJH69+9f7f4ff/yxnn/+eR07dkwdO3bU/PnzNWrUqHqMGAAcj4uLi/yaeSojt0jZBSVMU1FPTp4v1O9X7tOGQ2ckSZ2DvfXymB7q3sbH5MgAAA1NcVm5Nhw6qy9/PKXvkrJUWFJue6xriLfu7d1av+4VolYtmpgYJQCgrlHYAICrM72w8eGHH2rmzJlasmSJBgwYoL/85S8aMWKEkpOT1apVq1/sv3nzZj344IOKjY3VXXfdpbi4OI0ePVo7d+5Ut27dTDgCAHActsIGC4jXuXKroRWbj2nBv5NVWFIuT3dXPTmso564JVwerKUBALiktNyqTSln9eWPGfr3gUzl/2QR8Na+Xrq7Z4ju7d1akcyzDgBO4eyFYp29UPH3WkemogKAaple2Hjttdc0ZcoUTZo0SZK0ZMkSrVq1SkuXLtXs2bN/sf/rr7+ukSNHatasWZKkP/zhD4qPj9ebb76pJUuW1GvsAOBo/JpVrLNxvoDCRl06lJWv//l0j20e9P7t/RQ7prs6BPCHCQCgovi95eg5fbXnlFbvy9T5wlLbY4HeFt3VI0R39QhWr1BfubiwbgYAOJPLozXa+jVVU0/Tv7YDgAbL1HfIkpISJSYmas6cObZtrq6uGj58uBISEqpsk5CQoJkzZ1baNmLECH3++efVvk5xcbGKi4tt9/Py8moWOAA4qBuaVhQ2sh2ksJGdna3f/va3+vLLL+Xq6qoxY8bo9ddfV/Pm1RcIMjMzNWvWLMXHxys/P1+RkZF69tlnNWbMmDqPt6zcqjfXpWjxuhSVlhtqbnHX7Dui9FD/tnJlQVcAgKTX4g8pbmuazl74z98n/s09Nap7sO7qEaJ+7W4gZwCAEzucdUES01ABwNWYWtg4e/asysvLFRgYWGl7YGCgDh48WGWbzMzMKvfPzMys9nViY2M1b968mgcMAA7u8ogNRylsjB8/XhkZGYqPj1dpaakmTZqkJ554QnFxcdW2efTRR5WTk6N//etf8vf3V1xcnMaNG6cdO3aod+/edRqvm6uLEo+fV2m5oeGdW+kPo7sp2MerTl8TAOBYsguKdfZCsXybeuiObkG6q0eIBoT5yZ1pCgEAkpJt62sw2hsArsQpxrTNmTOn0iiPvLw8hYaGmhgRAJjj4YFtNaJrkNr7N/yFw5OSkrR69Wpt375d/fr1kyQtWrRIo0aN0oIFCxQSElJlu82bN+utt95S//79JUnPPfecFi5cqMTExDovbLi4uOh/7+2u3SdydFePYKYPAYAGasOGDXrllVeUmJiojIwMrVy5UqNHj66X154Q3V7DOgfq5gh/1lwCAPzCzNs6aVS3YAX5NDE7FABo0Ez9JO3v7y83NzdlZWVV2p6VlaWgoKAq2wQFBdm1vyRZLBZ5e3tXugGAM4po1ULRHVo6xCiChIQE+fr62ooakjR8+HC5urpq69at1bYbNGiQPvzwQ2VnZ8tqteqDDz5QUVGRhgwZUm2b4uJi5eXlVbpdr1C/prq7ZwhFDQBowAoKCtSzZ08tXry43l+7Y2ALDY1sRVEDAFAl/+YW3dzRXxGtGLEBAFdi6qdpT09P9e3bV2vWrLFts1qtWrNmjaKjo6tsEx0dXWl/SYqPj692fwCAY8rMzFSrVq0qbXN3d5efn98Vpx/86KOPVFpaqpYtW8pisWjq1KlauXKlIiIiqm0TGxsrHx8f241RfQDQuN1xxx364x//qHvvvdfsUAAAAABcB9N/JjRz5ky98847WrFihZKSkjRt2jQVFBRo0qRJkirmSv/p4uJPPvmkVq9erVdffVUHDx7Uiy++qB07digmJsasQwAA2GH27NlycXG54q26dZauxfPPP6+cnBx999132rFjh2bOnKlx48Zp79691baZM2eOcnNzbbcTJ05c9+sDABqf2hzZBwAAAKDmTF9j4/7779eZM2f0wgsvKDMzU7169dLq1attC4SnpaXJ1fU/9ZdBgwYpLi5Ozz33nH7/+9+rY8eO+vzzz9WtWzezDgEAYIenn35aEydOvOI+4eHhCgoK0unTpyttLysrU3Z2drXTDx45ckRvvvmm9u3bp65du0qSevbsqR9++EGLFy/WkiVLqmxnsVhksVjsPxgAgFOIjY3VvHnzzA4DAAAAwCWmFzYkKSYmptoRF+vXr//FtrFjx2rs2LF1HBUAoC4EBAQoICDgqvtFR0crJydHiYmJ6tu3ryRp7dq1slqtGjBgQJVtCgsLJalSQVyS3NzcZLVaaxg5AMBZzZkzRzNnzrTdz8vLY9pCAAAAwESmT0UFAEBVOnfurJEjR2rKlCnatm2bNm3apJiYGD3wwAMKCQmRJKWnpysqKkrbtm2TJEVFRSkiIkJTp07Vtm3bdOTIEb366quKj4/X6NGjTTwaAIAjs1gs8vb2rnQDAAAAYB4KGwCABuv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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:**\n",
+        "1. sin(x) oscillates smoothly in a wave-like pattern. Over the range shown, it rises from x in range (-1, 1), curving smoothly through zero.  \n",
+        "\n",
+        "2. cos(x) also ocillates, like sine, but starts at its maximum (1).\n",
+        "Its graph has a dome-like shape from x in range (-1, 1).  \n",
+        "\n",
+        "3. tan(x) increases from -infinity to +infinity between vertical asymptotes.\n",
+        "It rises from x in range (-1, 1) near the center(around x = 0), but its values become large very quickly.  \n",
+        "\n",
+        "4. arcsin(x), defined only from -1 <= x <= 1, is increasing.\n",
+        "It rises from -pi/2 to +pi/2 as x goes from -1 to 1.  \n",
+        "\n",
+        "5. arccos(x), also defined for -1 <= x <= 1, is decreasing.\n",
+        "It falls from pi to 0 as x increases from -1 to 1.  \n",
+        "\n",
+        "6. arctan(x) is increasing from all real x.\n",
+        "It smoothly rises from -pi/2 to +pi/2, flattening out at the extremes.  \n",
+        "\n",
+        "7. exp(x) rises rapidly as x increases.\n",
+        "Over the given range (0.1 to 2), it grows from a small number ~1.1 to over 7.  \n",
+        "\n",
+        "8. log(x) grow slowly and only exists for x > 0.\n",
+        "Over the given range, it increases, but less steeply than any other function in the list."
+      ],
+      "metadata": {
+        "id": "thpsEzFVikPh"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Features of the Sigmoid Function"
+      ],
+      "metadata": {
+        "id": "WAidrU51fMqB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "1ze1bxhDja_X"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "def sigmoid(x):\n",
+        "    return 1 / (1 + np.exp(-x))\n",
+        "\n",
+        "x = np.linspace(-10, 10, 200)\n",
+        "y = sigmoid(x)\n",
+        "\n",
+        "plt.plot(x, y)\n",
+        "plt.title(\"Sigmoid Function\")\n",
+        "plt.xlabel(\"x\")\n",
+        "plt.ylabel(\"σ(x)\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"Range:\", (y.min(), y.max()), \"| At x=0:\", sigmoid(0))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 490
+        },
+        "id": "lehJV_vNfLXx",
+        "outputId": "2318a9ae-40df-418b-f250-d18a2d33d581"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Range: (np.float64(4.5397868702434395e-05), np.float64(0.9999546021312976)) | At x=0: 0.5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:**\n",
+        "The sigmoid function smoothly rises from values near 0 to values near 1 as x increases.\n",
+        "It most significant change (steepest slope) occurs in the range between -5 and 5."
+      ],
+      "metadata": {
+        "id": "DUONuiAxilL0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 5. Derivatives of Famous Functions"
+      ],
+      "metadata": {
+        "id": "1FjRt_31fXsx"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### I have modified some cells.\n",
+        "Let's explore how a function's behavior is reflected in its derivative graph, using some examples."
+      ],
+      "metadata": {
+        "id": "Z_syhyhVWZNc"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "####Graph 1"
+      ],
+      "metadata": {
+        "id": "8Di70HHUYNKA"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "dNwUBPho6wds"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = np.linspace(-3, 3, 400)\n",
+        "plt.plot(x, x**2, label=\"x²\")\n",
+        "plt.plot(x, 2*x, '--', label=\"d/dx x²\")\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.title(\"Functions and Their Derivatives\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "Xk2CyEtyWRi2",
+        "outputId": "9a67659a-ad57-497f-9331-19340ef39263"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##### 👀🔎 What do you see?\n",
+        "The function x**2 is a symmetric parabola, curving upward with increasing steepness as x moves away from zero in either direction.  \n",
+        "Its derivative, 2x, is a straight line that passses through the origin.  \n",
+        "At x = 0, the slope is 0 --> the parabola has a minimum point.  \n",
+        "As x increases, the derivative grows --> the funciton is steepening.  \n",
+        "\n",
+        "The derivative shows how fast the curve bends -- steeper on both sides, but the direction of the slope (positive or negative) changes with the sign of x.\n",
+        "\n",
+        "**In other words:**  \n",
+        "When the **derivative f'(x) = 2x** is **-6**, the **x^2 curve** is **bending quickly downward** -- it's steep and heading into negative values.  \n",
+        "When the derivative is around **-2**, the curve still goes down but **less steeply**.  \n",
+        "On the other side, when the derivative is **+6**, the curve is bending **quickly upward** -- much faster than when it's just **+2**.  \n",
+        "\n",
+        "So, **the larger the value (positive or negative) of the derivative**, **the steeper** the x^2 curve is at that point."
+      ],
+      "metadata": {
+        "id": "05lAbPupXz7H"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "####Graph 2"
+      ],
+      "metadata": {
+        "id": "HNKykRbWYWHd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = np.linspace(0, 3, 400)\n",
+        "plt.plot(x, np.exp(x), label=\"exp(x)\")\n",
+        "plt.plot(x, np.exp(x), '--', label=\"d/dx exp(x)\")\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.title(\"Functions and Their Derivatives\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "OkEFlamZWRV5",
+        "outputId": "5a9fa1ac-cc6b-46d9-ce14-3ecbdd8a4516"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##### 👀🔎 What do you see?\n",
+        "The exponential function grows faster and faster as x increases.  \n",
+        "Its derivative is identical. (d/dx e**x = e^x)\n",
+        "\n",
+        "The function's value is its rate of change -- the faster it grows, the faster\n",
+        "it continues to grow.  \n",
+        "This is a unique and fundamental property of the exponential function."
+      ],
+      "metadata": {
+        "id": "D2IF5JsQYZQI"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "####Graph 3"
+      ],
+      "metadata": {
+        "id": "xwCuxiEXYb_t"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = np.linspace(0, 3, 400)\n",
+        "\n",
+        "plt.plot(x, np.log(x+0.1), label=\"log(x)\")\n",
+        "plt.plot(x, 1 / (x+0.1), '--', label=\"d/dx log(x)\")\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.title(\"Functions and Their Derivatives\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "vyyGBuHRfZ4n",
+        "outputId": "a33172c5-884e-4d38-da3c-e2aa4712f8aa"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:**\n",
+        "(Here, log(x+0.1) is used to avoid division by zero at x = 0)\n",
+        "\n",
+        "The logarithmic function rises **quickly at small values of x**, but it growth **slows down** as x increases.\n",
+        "\n",
+        "The drivative 1/x reflects this:  \n",
+        "It is **large** near zero --> rapid change  \n",
+        "It **decreases** toward zero --> the function flattens out\n",
+        "\n",
+        "The derivative reveals the **diminishing returns** of logarithmic growth -- rapid early increase follow by slow progress.  \n",
+        "**diminishing returns**: you get less and less benefit (return) from putting in more.\n",
+        "\n",
+        "Again, The derivative tells us **how fast** the function is changing."
+      ],
+      "metadata": {
+        "id": "DURup9u5il4-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 6. Gradient of Selected Functions"
+      ],
+      "metadata": {
+        "id": "w3QIKTZQf1ZK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# search and learn more about sumpy\n",
+        "import sympy as sp\n",
+        "\n",
+        "# Define symbolic variables\n",
+        "x, y = sp.symbols('x y')\n",
+        "\n",
+        "# Define the function\n",
+        "f = x**2 * y + sp.sin(y)\n",
+        "\n",
+        "# Compute partial derivatives\n",
+        "df_dx = sp.diff(f, x)\n",
+        "df_dy = sp.diff(f, y)\n",
+        "\n",
+        "print(\"Function: f(x, y) =\", f)\n",
+        "print(\"Partial derivative w.r.t x (∂f/∂x):\", df_dx)\n",
+        "print(\"Partial derivative w.r.t y (∂f/∂y):\", df_dy)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Eeudgf3-f0Ie",
+        "outputId": "9f8a5ba4-c526-4902-ab55-3be15f86bd20"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Function: f(x, y) = x**2*y + sin(y)\n",
+            "Partial derivative w.r.t x (∂f/∂x): 2*x*y\n",
+            "Partial derivative w.r.t y (∂f/∂y): x**2 + cos(y)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:**\n",
+        "Here, we use the sympy module to calculate the gradient of a function -- that is, the partial derivatives with respect to each variable x and y.\n",
+        "\n",
+        "Function:\n",
+        "f(x,y) = x^2*y + sin(y)\n",
+        "\n",
+        "Partial derivative with respect to x:  \n",
+        "When differentiating with respect to x, we treat y as a **constant**.  \n",
+        "The derivative of x^2*y is 2xy  \n",
+        "The derivative of sin(y) is 0 (since it contains no x)  \n",
+        "\n",
+        "So:\n",
+        "∂f/∂x = 2xy\n",
+        "\n",
+        "Partial derivative with respect to y:  \n",
+        "When differentiating with respect to y, we treat x as a constant.  \n",
+        "The derivative of x^2*y is x^2  \n",
+        "The derivative of sin(y) is cos(y)\n",
+        "\n",
+        "So:\n",
+        "∂f/∂y = x^2 + cos(y)\n",
+        "\n",
+        "    Together, the gradient is:(vector)\n",
+        "    ∇f(x,y) = [2xy, x^2 + cos(y)]"
+      ],
+      "metadata": {
+        "id": "wDlIJkmYimg9"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.3/AI-DS_Nexus__A0_3_Project__RezaShokr.ipynb b/a0.1/a0.3/AI-DS_Nexus__A0_3_Project__RezaShokr.ipynb
new file mode 100644
index 0000000..a16656d
--- /dev/null
+++ b/a0.1/a0.3/AI-DS_Nexus__A0_3_Project__RezaShokr.ipynb
@@ -0,0 +1,519 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7wR0aAR-evto"
+      },
+      "source": [
+        "# 📚 Assignment 3 — Functions, Derivatives & Gradients\n",
+        "This assignment explores the mathematical foundation of machine learning: functions and their derivatives. You’ll visualize, analyze, and code up essential concepts every ML practitioner uses.\n",
+        "\n",
+        "\n",
+        "**👀🔎 What do you see?**\n",
+        "\n",
+        "Look at the snippet codes and:\n",
+        "\n",
+        "* Describe any patterns, surprises, or connections you notice.\n",
+        "\n",
+        "* Write a few sentences about your observations! 📝✨\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cCb1hk3xe0E6"
+      },
+      "source": [
+        "## 1. Log and Exp: Proving Inverse Relationship\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KOxJ9f-3eu5F",
+        "outputId": "31d8ce39-8670-4dc4-9053-6c6aaf10d1ff"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "x\tlog(exp(x))\texp(log(x))\n",
+            "1.00\t1.00\t\t1.00\n",
+            "2.00\t2.00\t\t2.00\n",
+            "3.00\t3.00\t\t3.00\n",
+            "2.72\t2.72\t\t2.72\n",
+            "\n",
+            "log(exp(y)) for 0 and negative values:\n",
+            "[ 0. -1.  5.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Show log(exp(x)) == x and exp(log(x)) == x for several values\n",
+        "x_vals = np.array([1 , 2 , 3 , np.e])\n",
+        "\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "for x in x_vals:\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# Edge case: try negative and zero values (show error/NaN for log)\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "x\tlog(exp(x))\texp(log(x))\n",
+            "1.00\t1.00\t\t1.00\n",
+            "2.00\t2.00\t\t2.00\n",
+            "3.00\t3.00\t\t3.00\n",
+            "2.72\t2.72\t\t2.72\n",
+            "\n",
+            "log(exp(y)) for 0 and negative values:\n",
+            "[ 0. -1.  5.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np  \n",
+        "\n",
+        "# مقادیر مختلف برای بررسی رابطه‌های لگاریتم و نمایی\n",
+        "x_vals = np.array([1, 2, 3, np.e])  # مقدار جای‌خالی با عدد 3 پر شده\n",
+        "\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "\n",
+        "for x in x_vals:  # جای‌خالی دوم باید x_vals باشه\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# حالت‌های خاص مثل صفر و اعداد منفی\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))  # log(exp(0)) = 0، log(exp(-1)) خطا نمی‌ده ولی مقدار منفی می‌ده"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lVZufz08g1hI"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mOAqtnk6fDH6"
+      },
+      "source": [
+        "## 2. Plotting Sine Functions Variations\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "DoWdSehie66R",
+        "outputId": "a83a3ade-6ca7-425c-9fa8-59ca9c343d9a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.linspace(-2*np.pi, 2*np.pi, 500)\n",
+        "\n",
+        "# look at the description of each function and call the proper function from numpy\n",
+        "plt.plot(x, np.sin(x), label='sin(x)')\n",
+        "plt.plot(x, np.sin(2*x), label='sin(2x)')\n",
+        "plt.plot(x, np.sin(x/2), label='sin(x/2)')\n",
+        "plt.plot(x, np.sin(x)**2, label='sin²(x)')\n",
+        "plt.legend()\n",
+        "plt.title(\"Variations of Sine Functions\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dMVcqrfihf0P"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4-5A1OkdfHXq"
+      },
+      "source": [
+        "## 3. Subplots: Trig, Inverse, Exp, and Log"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 807
+        },
+        "id": "DxocsCu6fGOR",
+        "outputId": "5864a5cc-5776-45bd-99f7-028c64e301af"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1600x800 with 8 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x = np.linspace(-1, 1, 400)\n",
+        "x2 = np.linspace(0.1, 2, 400)  # For log and exp\n",
+        "\n",
+        "# based on the number of subplots what should be the dimensions in the following line?\n",
+        "fig, axs = plt.subplots(2 , 4 , figsize=(16, 8))\n",
+        "axs[0,0].plot(x, np.sin(x));      axs[0,0].set_title(\"sin(x)\")\n",
+        "axs[0,1].plot(x, np.cos(x));      axs[0,1].set_title(\"cos(x)\")\n",
+        "axs[0,2].plot(x, np.tan(x));      axs[0,2].set_title(\"tan(x)\")\n",
+        "axs[0,3].plot(x, np.arcsin(x));   axs[0,3].set_title(\"arcsin(x)\")\n",
+        "axs[1,0].plot(x, np.arccos(x));   axs[1,0].set_title(\"arccos(x)\")\n",
+        "axs[1,1].plot(x, np.arctan(x));   axs[1,1].set_title(\"arctan(x)\")\n",
+        "axs[1,2].plot(x2, np.exp(x2));    axs[1,2].set_title(\"exp(x)\")\n",
+        "axs[1,3].plot(x2, np.log(x2));    axs[1,3].set_title(\"log(x)\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "thpsEzFVikPh"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WAidrU51fMqB"
+      },
+      "source": [
+        "## 4. Features of the Sigmoid Function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "   1   \n",
+            "───────\n",
+            "     -x\n",
+            "1 + ℯ  \n"
+          ]
+        }
+      ],
+      "source": [
+        "import sympy as sym\n",
+        "\n",
+        "x = sym.Symbol('x')  # تعریف متغیر نمادین x\n",
+        "sig = 1 / (1 + sym.exp(-x))  \n",
+        "\n",
+        "sym.pprint(sig)  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "lehJV_vNfLXx",
+        "outputId": "9cfc6ec4-9dfc-4220-a955-4d6a11fac8f3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Range: (np.float64(4.5397868702434395e-05), np.float64(0.9999546021312976)) | At x=0: 0.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "def sigmoid(x):\n",
+        "    return 1 / (1 + np.exp(-x))\n",
+        "\n",
+        "\n",
+        "x = np.linspace(-10, 10, 200)  # 200 num geberate between -10 , 10 \n",
+        "y = sigmoid(x)\n",
+        "\n",
+        "plt.plot(x, y)\n",
+        "plt.title(\"Sigmoid Function\")\n",
+        "plt.xlabel(\"x\")\n",
+        "plt.ylabel(\"σ(x)\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"Range:\", (y.min(), y.max()), \"| At x=0:\", sigmoid(0))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DUONuiAxilL0"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1FjRt_31fXsx"
+      },
+      "source": [
+        "## 5. Derivatives of Famous Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "vyyGBuHRfZ4n",
+        "outputId": "7a89fce7-f973-46bc-dca3-c452f1a34561"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x = np.linspace(-3, 3, 400)   # 400 number generate between -3 , 3 \n",
+        "\n",
+        "plt.plot(x, x**2, label=\"x²\")\n",
+        "plt.plot(x, 2*x , '--', label=\"d/dx x²\")\n",
+        "\n",
+        "x = np.linspace(0, 3, 400)\n",
+        "plt.plot(x, np.exp(x), label=\"exp(x)\")\n",
+        "plt.plot(x, np.exp(x), '--', label=\"d/dx exp(x)\")\n",
+        "\n",
+        "plt.plot(x, np.log(x + 0.1), label=\"log(x)\")\n",
+        "plt.plot(x, 1 / (x + 0.1) , '--', label=\"d/dx log(x)\")\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.title(\"Functions and Their Derivatives\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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8vkwsZ50dldvSZ4aaDzMiZCa/HKUzmnTMkzSz/APLrxVHh/fKLy05UMjzSnu9Pv+APK8Mc5SAQv9ClF8tMieJbCcZA0mv2iJpZDnoyBeqDOPUh+/Kr92mmjZb0uryBS1zgciBU96H/HprzFwO8n7kl6702ZAvW5mnQn4lNiQAq4vUm5BMhwyJlIO/vI5kAySglOGPkimQtnlJfUu/C33uB1skyJCAxbo/hE7S6NK5VjoSymRnQn7RStOP9DOQfiSS6pbmIOkfYN2UIFkxeX3piCoBkbTZyzBXCXxORd6XzE0h71Xqr3///ir4lA65kvrXf93r/Q0k+yRBsHRkln4bksWRgKqpgtXG7DP65yYdaCU7IQdE+fwk0JPhuxK4y3uQQF4+U8kkya97CWos5+Coi3QKlf8hyWZKWeR/WIb+Sp1ZDj2XrJkcqOX/STr3SgZJ+nPIYyRzJu/hhx9+cLhOJLCS+pBFOpdbZ11k/5SMhTTXyHeD7KfSmV72E8lqWdeZZItkewkyJZOhdxi2RZ+DRl5b6le+0yzJZyX7ijQfyf4k2RrZXrKCMqxeOuLLY6mZOHlUDrkIfZjb2rVr69ymoqLC9NBDD5ni4uJMoaGhalifDIusa/iu9XPpQ0zl0tL3339vGjlypBq+GRkZaRo2bJjp888/N9+fn59vmjx5sik6Olo9Xh/Ka2v4rvj111/V8FL9+S688ELT9u3bGzSU0Xq4X13k+WR4sgw3lPq45ZZb1DBh6/JIvcjQQmu2hofKkOOpU6eqMkdFRanrGzZscHj4rnjyySdNiYmJariq/r6WLl1qmjRpkqlNmzZqGK1cyrDL3bt31/n8ycnJ6vH//ve/69xGhjxaDrfV3/u+fftMEyZMUPtMy5Yt1XuXfUmnf44y3PjFF180JSUlqSGWMnRZ6tRSXfUp8vLy1GvL+wkICFBDT+U59aGn8h5k6KXlcGJ9mPTQoUPV42QIa33Dd2VorSXLsp/q82joPiPlkTLGx8erIaXW5ZDh0jL0W/ZvGf7et29f04MPPmg6fvx4nZ+N5XvSl+DgYFPbtm3VcOwPPvjAVFxcbPNxsg9eeumlali7fC7y/3fllVeq/aghQ4Nt1aVO/k/lvptvvtnm/f/73//U5yiv26NHD1VPtp5v586dpjPPPFPVidynfx/V9/88ZcoU8zQDdfnmm29Mp59+utrnZJEyyD4gQ5Cp+fjIn+YKeojIc0gTiPz6tpXWtyS/OOUXrGSz+CuTiKyxjwgREREZhoEIERERGYaBCBERERmGfUSIiIjIMMyIEBERkWEYiBAREZFhXHpCM5k+WmbHk4lmGnMSJCIiImo+0utDTskgE9Cd6txRLh2ISBBS1yyPRERE5NqOHDlS62zabhWI6Ce3kjdifcp2OaeETPGsT4NMDcN6cwzrzX6sM8ew3hzDenOtesvNzVWJBMuTVLplIKI3x0gQYisQkVM9y3rudA3HenMM681+rDPHsN4cw3pzzXprSLcKdlYlIiIiwzAQISIiIsMwECEiIiLDuHQfESIiT1VRUaHa563JOn9/fxQXF6ttqGFYb81fb9KnxM/PD43FQISIqJnl5+fj6NGjaq4Fa7KuVatWarQg509qONZb89ebbC9Dc8PDw+GygcjMmTMxa9asGuu6d++OnTt3OvNliYhclvzqlCBERirEx8fX+vKXiRwlUJEv91NNBEXVWG/NW28SwGRkZKh9uWvXro3KjDg9I9K7d2/8+uuv1S/ozyQMEXl3Kly+xCUICQkJsXlgKC0tRXBwMA+odmC9NX+9yT588OBBtU+7dCAigYekfYiIqBqbD8jd+TTRPuz0QGTPnj1qrnmJtkaMGIHZs2ejXbt2NrctKSlRi+XMbEKiLetOXfptW529qG6sN8ew3uzHOqs/IyK/RGWxpvcb0behhmG9NX+9yfbyOFsZEXv+731MtnpLNZFFixaptifpF5KSkqL6ixw7dgxbt261Oe2rrT4lYu7cuao9lYjI3elZYpn+OjAw0OjiEDlMmnSkk2tqairKy8tr3FdYWIjJkycjJyen1szozRqIWMvOzkb79u3x0ksv4aabbmpQRkT+WTMzM21O8b5kyRKMHz+e0/nagfXmGNab/VhntskwSfny7tChg8oU13XWUlc76/jYsWPRv39/vPzyy03+3KNHj8att96qDlyOcsV6W7x4Mf75z39i3bp1LttvxdSIepN9WfqIyHHael+W43dcXFyDApFm7TkaHR2Nbt26Ye/evTbvDwoKUos1+RKr64usvvuobqw3x7De7Mc6qz1qRr7w5cBk6+Ckp8f1bVyJM8r0/fffIy0tTQUhjXnuxtbb5s2bceedd2Lt2rWqE+bdd9+NBx98sN7H3HPPPfjzzz9Vlr9nz57YuHFjjfvPO+88PP744/j8888xdepUuKLKRtSbbC+Ps/U/bs//fLPu5dJMs2/fPrRu3bo5X5aIiFzUa6+9hhtuuKFJA5w5c+aoLEtDya93OfusZOyTk5Px/PPPq64C//3vf0/52BtvvBFXXXVVnfdff/316j2SQYHI/fffjxUrVqjUzapVq3DJJZeoDi3XXHMNDLf7Z+DnfwH7lxtdEiLyYpIaLywtr7EUlVbUWueMpTEt81lZWbjuuuvQokUL1Yfv3HPPVYMTLL333nsqbS/3y/e/NMtLZlwn81D89ttvuPDCC83rli9frvrO/P777+Z1zz33HBISElTmxBk+++wz1d/hgw8+UFNOXH311SrbIeWtjwQYkkXp1KlTndvIe5OmGfkRTgY0zchEJxJ0nDhxQqW6Tj/9dKxZs0ZdN9yeX4C17wP+QUCnhkfORERNqaisAr0e+9mQ197+xESEBjp2GJBf+hJ4SNOK9AF46KGHVFPE9u3bVVpemixuv/12PPvss7jooovUfFL//ve/azzHH3/8oYIUadbQSSZj+vTpqilj06ZN2L9/v3rcV199hZYtW8IZVq9ejTPPPLNG5+GJEyeqskvAJcGWo2SUqJRbAqvOnTs3UYk9i1MDkS+++AIuq0UH7TLroNElISJyK3oAIsHGyJEjzVkFyX4sWLAAV1xxBV5//XWVJZHMuJD+gZIZ//HHH83Pc+jQIXWQtm6Weeqpp1RHZ+nAKv0vpk2bpoIZZ5FRHx07dqyxTg965L7GBCJCprCQ90q2ee80pwxEiMgFhAT4qcyEZefBvNw8RERGOL2zqry2I3bs2KGGIQ8fPty8LjY2Vk3VIPeJXbt2qeYYS8OGDasRiBQVFdkcOSSZCQls+vXrp/ptnGqkzuHDh9GrVy/zbRlKKqO2LM+BIqNXZDGCzKArw1nJNgYiJw8YXRIi8mIy6sCyeUQCkfJAP7XO1UbNNDUZ3ilNH7ZI9kScPHlSLWFhYfVmHNavX28+Z4pkZb755hsVzOhiYmLqfLzM62Ld/0S/3RQzg0v5XaJLgovy7L28IYFI0UmgOMfo0hARuQ3p0yFZh7/++su8TvoCShZEz0xIdkSGwlqyvj1w4EDV9GEdjEjHznvvvVd1dpWsizTN1Dfrp2RnunTpojqNyqV0bJUshFzXl/oCEZn1e+XKlTVmA5WmIXkPjW2Wkbk25P3IeyXbvDcQCYoAQuO062yeISJqMDnb6qRJk3DLLbeoDqfSqfTaa69FYmKiWi9kHo6FCxeqkSfSp+Tdd99Vs21bTpolB2fJikhfE8t5VuS5pLOoDOv98MMP1RwfL774otPej8xhIs1BMtHmtm3bMG/ePLz66quYMWOGeZv58+ejR48eNR4nc2LJ3CESTEkzk1yXRUbg6GSAhsyPJcEO2ea9gYhgPxEiIodIgDB48GBccMEF6iArQ4El8NAnsho1ahTeeecdFYjIjKwyy6hkOSz7hMh0DhJsWDahPP3006pjpwQuQuadkvk8Hn30URXwOENUVBR++eUXHDhwQL2n++67D4899pjqLKuTGUIl42Pp5ptvVsGUlHX37t3quizHjx83byOTmU2ZMoWnKXGVKd7tJZPMyA5ia4pYSaHJTi/DxRyetTFzLxAUDoS3lIZaeIMmqTcvxHqzH+us7lS9HPBklIatjprSBCHfffKd52l9RCSDsnPnzhpzhEg2QebukD4e0jHVUa5Yb3J6EmnekXlErEfluIrKRtRbfftyfcdva97bWVXEdTG6BEREHuuFF15Q5xqSjqbSLPPRRx/hrbfeqrGNdAb93//+p0a+NCYQcUUymae8X1cNQlyFdwciRETkNH///beaFVVOqiYdSWUmUmnOsHbxxRfDEw0ZMkQtVD/vDkTyUoHVbwKlBcAF9U/lS0RE9vnyyy+NLgK5AddoSDNKZQWw6jVg/UdARbnRpSEiIvI63h2IRLQG/AKBynIg96jRpSEiIvI63h2ISA/h6KrOURzCS0RE1Oy8OxARMVW9mRmIEBERNTsGIjznDBERkWEYiLSoyoic3G90SYiIiLwOA5HYqknNco4YXRIiIvICHTp0wCuvvOL017n++uvdYo4WBiIdRgH3bgduWWZ0SYiIXN7q1avVOWLOP/98eKLRo0erE/PJIierkxP5XXjhhfj222+b7DXkLMSW57Fpihlcpbxywj1LcuK+OXPmwNUxEAkMA6ISveZcM0REjSHTscuZdVeuXFnj5G7OZHk22+Y6J05KSgr27duHb775Br169cLVV1/d6OBBfx/x8fHNchK8qKgoREdHw9UxECEicgUyw7O+lBVa3S6ue1vrpayoYds6ID8/H/PmzcMdd9yhMiLWv7aXL1+ufpn/9NNP6NevnzoR2mmnnYatW7eat5HHyMFxwYIF6Nq1q9pm4sSJOHKkunl85syZGDBgAN5///0aJ1ST89FMmjQJ4eHh6kRqV155JdLS0tR9cjK9Nm3aYO7cuTVmdg0JCcH27dvtep8SJMg5cNq2bavK/+yzz6oz7L733nv49ddfzdtJmaUM8n5iYmJU2SQ7Yd00ImcUlrLJCfCsm2YmT56Mq666qtYJI+Pi4vDxxx+r23Lm4tNPP129TmxsrDrjsQRJOv1cNnLmX6l/yepYvr6QMxhLGeQkd5bk/rvuust8+7vvvsOgQYNUncu0/LNmzUJ5uXMn/GQgIrZ8DcybCmzmdMREZJD/tFGL7zNtEf1mT3Wpr8OXU2tu+3yX6vusl08vr7ntK31tb+cAObD36NFDHVCvvfZafPDBB7B1AvcHHngAL774omqCkF//0rQhB1ddYWGhOjjLgfbPP/9Edna2yjhY2rt3r8pGSJOINDnIAVQO9CdPnsSKFSuwZMkS7N+/33wQl3I98cQT6qAqAcvRo0dx++23qyBCMhqNNW3aNLRo0cLcRCPvRwKoiIgIdTZheR8SIJ1zzjk1MjhLly7Frl27VHl//PHHWs87ZcoU/PDDDyrI0/3888+qji655BJ1u6CgADNmzFBn8ZXnk7Pkyn16UCHn9BESJEkmx1Yz0hVXXIETJ05g2bLqbghSl/Jacp+Q93HdddfhH//4hwreJPiSwFE+K2fy7nPN6NK3Azu+B8LigH5XGl0aIiKXbZaRAETIAVdO8S5Bgf4LXPf444+rs+4KOeOuZBbmz5+vsgf6QfyNN97A8OHDzdv07NlTHVCHDRum1snBXAIVCWSEHMi3bNmiTjuflJSk1sn9vXv3VgHP4MGD1Qn15EArZQwMDMTQoUNVM1JTkIN/t27dzBkPyQxJICBZG8lCiA8//FBlLSQzNGHCBLVOzjws20h5bJk4caLaRupn6lQt4JSszkUXXaSCHHHZZZfVeIwEgFIvEiz06dPHXEeSLZFMji0SRJ177rnquc8++2y17uuvv1aZlzPOOEPdluzHww8/rIIuIRmRJ598Eg8++KD6TJ2FgYiI7apdZu4xuiRE5K3+qfW3kINbbl4eIiMi1MFP8fGrue0De+t+Hh+rRPf0LU1SPPlVL4GCHDCFv7+/ykZIcGIdiIwYMcJ8XZosJIOyY8cO8zp5rAQJOslmyAFcttEDkfbt25sPsELukwBED0KEZDr0x0kgIqQ88nxSd9u2bTMHCU1Bsj/6823atEllbfRgQVdcXFyj2aRv3751BiF6XVx55ZX47LPPVCAi2Q9pHvniiy/M2+zZswePPfYY/vrrL2RmZpozIZL5kUCkoST7Iv1f3nrrLdURV15TPkN9P5P3JJkdywxIRUWFek+SoXFWvxYGIiKOgQgRuUDHeSEHmYAK7bYeiNS1rT3P20hygJe+AtLPwPLALAc0yW5Ix8imJFkCR8jBVA7mcnCVZorWrVs3SXnkgCwBgR5ASVOKBD9yMLdmGUA15H1MmTIFZ511FtLT01XmR/q1SMZJJ01bEphJHxW9n4cEIPZ24pXnkc9M+vDI+5CmGGlC08l7kqzIpZdeWuuxej8dZ2AgYjmXSH4qUJwLBEcaXSIiIpchAYg0g8hBS29ysOzs+Pnnn6v+GLo1a9agXbt26npWVhZ2796tml4sn0/6O+jZD8m2SD8Ry22syX3SOVQWPSsiTRPyOL0PiLzWjTfeiH/9618qCJED/Pr169WBvbGk+UieX28mkQ6d0jyTkJCgOs42xsiRI9V7kudbtGiR6rMREBCg7pN+HVI/EoToTSh//PFHjcfrGRcJluojwYQEGRI8STZHMlXyPnJzc83vSV6rS5eqY2IzYWdVERINhFVFsCfqSXkSEXkh6WQpB+GbbrpJ/RK3XOTALNkSS9JpVDpVymgZGbkh/RAsJ9aSg6z03ZCmhuTkZLWNjE7RAxNbxo0bp5o59OBCmomkY6VkEoYMGaK2kQ6dckB/9NFH8dJLL6kD8/33329+DmlWkmabU5FmiNTUVNXhVYKqhx56SAVaMlpozJgxahsph7wv6UArmQXpuyJ9Q+655x71OHtNnjwZ77zzjsqIyHNb9u2Qvh8y6kWCh99++029T0sSDEmwJaNrZBSR9N2pizy3ZESkn4nl6whp/pGAU7Ii0qwlTV7SRCT16UwMRKz7iTAQISKqQQINCQRsNb9IICLZjc2bN5vXPfPMM2rkhTRdyAFdRoVY9pOQvgZycJeD76hRo9RoE8kG1Ef6ZkjfCTkwn3nmmao80plSf5wcQOUgLpkL6XchTSKffvqpyiRIlkHIAVp+8Z+KPEaadDp37qwyCJJ5kdeRvhWW70HmUpHMj2wjGRsJ1KQ/hSMZkilTpqjXkQnUpE500sQkwYAEbBL43XvvvXj++edrPFbe72uvvaZGuUjTjQRHdRk7dqzqtyP1IPVv3XFWgs5ffvlFNd1IcPjyyy+rZiFn8jHZGnvlIiRdJDu+7DzWH6z0ul64cCHOO+88cwqrUb6/G9g0Dxj/BHBadYrR0zR5vXkJ1pv9WGe2yYFKfj1bzo9hSXVWzc1V33nmzqpuQjICkjGQ7EldE2nJcNDp06erJpWm5M71ZqTG1Ft9+3J9x29r7COimzgbuOAVwNeqdzoRERE5DQMRXVC40SUgIiLyOsxfERFRk5D5RKS1v77zm0jH1KZuliH3xkDE0vf3AG+PAjLZYZWIiKg5MBCxlLoZSNsKpG8zuiRE5OFceJwAUbPuwwxELCX01i7T7DtTIxFRQ/n5+Rlyanuipqbvw/o+7Sh2VrWU0LP6JHhERE4gcz7IHBQZGRlqWLP1kEkZTilf8DI0ksNQG4711rz1Jo+TfVj2ZdmnG4OBiKWWVaeKZiBCRE4iE3PJZFky/8KhQ4dspruLiorUTJlNecI2T8d6a/56k8BFJnRrbH0zELGUUBWInNwPlBUBAY0/PwERkTWZZbRr1642m2dkIjiZsVNmD+VEcA3Hemv+epP9uCmyTwxELIW3BEJigKKTQOZuoHV/o0tERB5KvsBtzawq7e1yUji5jwfUhmO9uW+9MRCxJOml1v2AvFSgJM/o0hAREXk8BiLWpi7QAhIiIiJyOnYttsYghIiIqNkwEKkLJxsiIiLynEDkmWeeUUN85PTPLq28FHjvbGB2W6Aoy+jSEBERebRmCUTWrl2Ld999F/369YPL8w8E8tOB0nwgjVO9ExERuXVn1fz8fEyZMgXvvfcennrqqXq3LSkpUYsuNzfXPM5ZFkv6bev1TcGvVV/45hxGxdH1qEwcDk/izHrzZKw3+7HOHMN6cwzrzbXqzZ7n8zE5+cxL06ZNQ0xMDF5++WV1iugBAwbglVdesbntzJkzMWvWrFrr586dq6aRbS7dUr9Dz5RvcKTFSKzvcHuzvS4REZEnKCwsxOTJk5GTk4PIyEjjMiJffPEF1q9fr5pmGuKRRx7BjBkzamREkpKSMGHChFpvRKKtJUuWYPz48U0+CYvP3gBg3jdo63cCrc47D57EmfXmyVhv9mOdOYb15hjWm2vVm96i0RBOC0SOHDmCf/zjH+oN2po90JagoCC1WJPKqauC6rvPYW0HqwufE3sQYCoFAsPgaZxSb16A9WY/1pljWG+OYb25Rr3Z81xO66yanJyM9PR0DBo0SJ2ZT5YVK1bgtddeU9crKirgsiJaAuGtAFMlO6wSERE5kdMyImeffTa2bNlSY90NN9yAHj164KGHHlLz27u0ruOBwhOAj4uXk4iIyI05LRCJiIhAnz59aqwLCwtDbGxsrfUuadIbRpeAiIjI43FmVSIiIvKOk94tX74cbkVGNuceB8ITAD92fiIiImpqzIjU583hwMu9gLStRpeEiIjIIzEQqU9ka+3y+AajS0JEROSRGIjUJ1GbTwTHko0uCRERkUdiIFKfxCHa5dF1RpeEiIjIIzEQqU/bqkAkYydQlG10aYiIiDwOA5H6yGiZ6Pba9ePrjS4NERGRx2Egcipth2qXbJ4hIiJy73lE3FKvi4CIVkCH040uCRERkcdhIHIqvSZpCxERETU5Ns0QERGRYRiINERJHrB/BZC23eiSEBEReRQGIg2x7D/AxxcB6z4wuiREREQehYFIQyQN0y4PrzG6JERERB6FgUhDtBupXcrJ74qyjC4NERGRx2Ag0hARLYHYLgBMwOG/jC4NERGRx2Ag0lDtq7Iih/40uiREREQeg4FIQ7UfpV0eWmV0SYiIiDwGAxF7MyLHNwAl+UaXhoiIyCNwZtWGim4HXPgq0GYQEBBqdGmIiIg8AgMRewy+3ugSEBEReRQ2zRAREZFhGIjYo7IC2DgX+PY2oLTQ6NIQERG5PQYi9vDxBX57Ctj8BXB4tdGlISIicnsMROzh4wN0GqNd37/M6NIQERG5PQYi9uqsByLLjS4JERGR22MgYq+OZ2mXqVuA/AyjS0NEROTWGIjYKzweaNlXu35ghdGlISIicmsMRBzRqSorwn4iREREjcJApDH9RAoyjS4JERGRW+PMqo7ocAbwwD4gLM7okhAREbk1BiKO8A/SFiIiImoUNs00VlmR0SUgIiJyWwxEHFWSD3x0IfBcJ6A4x+jSEBERuSUGIo4KCgdyjwNlhcA+jp4hIiJyBAORxug6Ubvc84vRJSEiInJLDEQao5tFIFJZaXRpiIiI3A4DkcZoNwIIjAAKMoDjG4wuDRERkdthINIY/oFAl7Ha9V0/GV0aIiIit8NApLF6XKBd7vjR6JIQERG5HU5o1hT9RLqMA7qfB1RWAL5+RpeIiIjIbTg1I/L222+jX79+iIyMVMuIESOwaNEieJTgKODab4ChNzEIISIicqVApG3btnjmmWeQnJyMdevWYezYsZg0aRK2bdvmzJclIiIiN+HUQOTCCy/Eeeedh65du6Jbt254+umnER4ejjVr1sDjyORmf78HZB8xuiRERERuo9n6iFRUVOCrr75CQUGBaqKxpaSkRC263NxcdVlWVqYWS/pt6/VG8fv2Vvge/B0VJfmoPO0uuCpXqzd3wXqzH+vMMaw3x7DeXKve7Hk+H5PJZIITbdmyRQUexcXFKhsyd+5clSWxZebMmZg1a1at9fKY0NBQuLKOGb+i39GPkRXaCSu7zzS6OERERIYpLCzE5MmTkZOTo/qIGhqIlJaW4vDhw6owX3/9Nd5//32sWLECvXr1alBGJCkpCZmZmbXeiERbS5Yswfjx4xEQEADD5afD/7W+8DFVoOyOv4GYTnBFLldvboL1Zj/WmWNYb45hvblWvcnxOy4urkGBiNObZgIDA9GlSxd1ffDgwVi7di1effVVvPvuu7W2DQoKUos1qZy6Kqi++5pVi0Sg02hg31IE7PwOOOtBuDKXqTc3w3qzH+vMMaw3x7DeXKPe7HmuZp/QrLKyskbWw6P0vUK73Pwl4NxEExERkUdwakbkkUcewbnnnot27dohLy9P9fVYvnw5fv75Z3ikHucD/sHAiT1AyiagzQCjS0REROS9gUh6ejquu+46pKSkICoqSk1uJkGItEV5pOBIoNs5wM6fgNQtDESIiIiMDET+97//weuMfwK48BUgpIXRJSEiInJ5PNdMU2vR3ugSEBERuQ2efdeZ8tKMLgEREZFLYyDiDMW5wPvjgVf6AIUnjS4NERGRy2Ig4qxOq+XFQEWpNpSXiIiIbGIg4iwDp2qXGz7hnCJERER1YCDiLH0vB/wCgbStwPENRpeGiIjIJTEQcZbQGKDXJO36Oi8cxkxERNQADEScaejN2uWWr4GiLKNLQ0RE5HIYiDhT0nCgZR+t4yo7rRIREdXCCc2cyccHGPtvoKwQ6HGB0aUhIiJyOQxEnK37OUaXgIiIyGWxaaY5VVYYXQIiIiKXwkCkOcg8IqveAF7tD2TsMro0RERELoOBSHP1FTm8Gsg5Aqx+w+jSEBERuQwGIs1lxF3a5aZ5QH6G0aUhIiJyCQxEmku704DEwUBFCbD2faNLQ0RE5BIYiDRn84yeFfn7XaAkz+gSERERGY6BSHOSKd9ju2izrDIrQkRExECkWfn6AWfcr12XUTTlJUaXiIiIyFCc0Ky59b0COLoWGDwN8A8yujRERESGYiDS3Pz8gQteMroURERELoFNM0Zj8wwREXkxBiJGkQ6rP0wH3hgClBUbXRoiIiJDMBAxSkAosOcXIPsw8Pd/jS4NERGRIRiIGEU6qo75p3b99xeAwpNGl4iIiKjZMRAxUv9rgIReQHEO8Ac7sBIRkfdhIGL0vCLjn9Cu//Wu1kxDRETkRRiIGK3LOKDjmUBFKfDb00aXhoiIqFkxEHGFc9DoWZHdi9hXhIiIvAonNHMFbQYCF78DdJ0AhMYYXRoiIqJmw0DEVQy4xugSEBERNTs2zbgakwnYuRAoyja6JERERE7HQMTVLHoI+OIaYPlso0tCRETkdAxEXE33c7VLmW01ZbPRpSEiInIqBiKupvMYoPclgKkS+GkGUFlhdImIiIichoGIK5rwNBAYDhxdq010RkRE5KEYiLiiqERgwpPa9aVPACf2GV0iIiIip2Ag4qoG36DNuFpeBHx/tzaahoiIyMMwEHHlGVcvekM7Kd5ZD2q3iYiIPAwnNHNlLdoDd6xiEEJERB6LGRFXZxmEnNwPlJcaWRoiIiL3CURmz56NoUOHIiIiAgkJCbj44ouxa9cuZ76k59r8FfD2KGDZU0aXhIiIyD0CkRUrVuDOO+/EmjVrsGTJEpSVlWHChAkoKChw5st6poAQoKwQ+PNVYO9So0tDRETk+n1EFi9eXOP2nDlzVGYkOTkZZ555Zq3tS0pK1KLLzc1VlxLAyGJJv2293mN1mQjfwTfCL/kDmObfjvJbVgBh8XY/jdfVWxNhvdmPdeYY1ptjWG+uVW/2PJ+PydR840L37t2Lrl27YsuWLejTp0+t+2fOnIlZs2bVWj937lyEhobC2/lWluKsXTMRWXwU6RG9sbrzA4APu/kQEZFrKSwsxOTJk5GTk4PIyEjXCEQqKytx0UUXITs7G3/88YfNbWxlRJKSkpCZmVnrjUi0Jc0948ePR0BAALxGxk74fzgBPmWFqBhxDyrHPmbXw7223hqJ9WY/1pljWG+OYb25Vr3J8TsuLq5BgUizDd+VviJbt26tMwgRQUFBarEmlVNXBdV3n0dq0xe46HXgm5vgt/o1+HUdB3Q6y+6n8bp6ayKsN/uxzhzDenMM68016s2e52qWQOSuu+7Cjz/+iJUrV6Jt27bN8ZKere/lQMpGwMcP6HC60aUhIiJymFMDEWn1ufvuuzF//nwsX74cHTt2dObLeZfxT3KiMyIicnu+zm6O+fTTT1VnU5lLJDU1VS1FRUXOfFnvYBmElJcAv7+oXRIREbkRpwYib7/9tuqoMnr0aLRu3dq8zJs3z5kv632+vE47S+/39/DkeERE5Fac3jRDzWDYrcCeJcDmL4CYjsDoh40uERERUYNwEgpP0OVs4IKXtOvLZwMbPjO6RERERA3CQMRTDL4eGDVdu/79XcCOH4wuERER0SkxEPEk42YCA68FTJXA1zcC+5YZXSIiIiLXmNCMmmkkzYWvAcW5wIEVQACnxSciItfGQMTT+PoBl70PZB8G4roaXRoiIqJ6sWnGE/kH1QxCjiYDR9cZWSIiIiKbmBHxdKlbgU8u1q5f+y3QaoDRJSIiIjJjRsTTtegAtOoHlOQCn1wCn0N/Gl0iIiIiMwYini4oHJjyJdDhDKA0D36fX4lW2clGl4qIiEhhIOINAsOAKV8DPS6AT0UJhh14DT4bPjG6VERERAxEvEZAMHDFR6jsPwU+MMF/4b2c9IyIiAzHzqrexM8fFee/gr2puegSkgPfrhOMLhEREXk5ZkS8jY8PdrS5AhVXz9OG+YqKcqAo2+iSERGRF2Ig4q38Aqqv//Io8N5YIHOPkSUiIiIvxEDE2xVlATt/Ak7uA947G9i71OgSERGRF2Eg4u1CWgC3/AYknQaU5ACfXQ78/iJQWWl0yYiIyAswECEgPB6Y9n31mXuXPgF8fhVQeNLokhERkYdjIEIa6bh60Rva4h8M7PkFmHMBMyNERORUDESomo8PMGgqcPOvQGwXYPTDgC93ESIich7OI0K1teoL3LEa8A+sXnfwTyC6HRCdZGTJiIjIw/DnLtlmGYTkpwNfTgXeHglsmgeYTEaWjIiIPAgDETq18mIgppN2Bt/5twJfXQ8UZBpdKiIi8gAMROjUpEnmhsXA2EcBX39g+wLgjaHApi+YHSEiokZhIEIN4+cPnPkAcPNSoGVfoOgkMP824NPLgIoyo0tHRERuioEI2afNAODWZcDZjwN+QVq2xHK6eCIiIjtw1AzZTwKPM2YAvSYBobHV6+VcNVmHgK7jjCwdERG5EWZEyHGxnYGQaO269BVZ9BDw2WXA59cAJ/cbXToiInIDDESoaVRWAAk9tc6suxYCbw4Hfp0FlOQZXTIiInJhDESo6TqzTnwauGMV0HksUFEK/PES8OoAYM07QHmJ0SUkIiIXxECEmlZ8d+Dab4GrPgNiOgOFmcDih4CNc40uGRERuSB2ViXnnLOm5wVAt4nAhk+1+UYGTK6+Py8NCE/QtiMiIq/GQIScO7pmyA3aoqsoB+acBwSEavOS9LiAJ9YjIvJiPAJQ80rdBOSmAKmbq89fs+VrrbMrERF5HQYi1LwSBwP3btWyIUGRQMYO4JubgDeHAes/AcqKjS4hERE1IwYi1PxCY7Tz1kzfAoz5FxDSAjixF/j+LiBlk9GlIyKiZsQ+ImQcmQztrAeB0+4A1n0IHFsHtBteff/Wb7Tz2sR3M7KURETkRAxEyHhBEcCoe2quK8oCvrsLKCsEOo0GhtwEdD+X57UhIrJTcVkF0nNLkJJThNTcYqTmFCMlpxhpucU4nl2EqHJfnAfjMBAh11ScC3Qao83Sun+5tkS0BgZdBwyaBkQlGl1CIiKXCDKOZxepwEIuVZCRW4y0qmBDAo+TBaX1PkeXSGOnUmAgQq6pRXvgmrlA1kEg+SNgwydAXgqw4llg5fPApe8BfS83upRERE5TVlFpzl5INuN4tn5ZfT2rsKxBzxXk74vWUcFoFRWM1lEhaBkpl8GIDwvAoW3rYCQGIuTaWnQAxj0OjH4E2PkDsPYD4MgaoP3I6m2kg6t/sDarKxGRG6ioNCEjrwTHc4qQYg4wqi4l8MguQkZ+iTqf6KmEBvqpoKJNdAhaSYARHaIFHZF64BGMqJAA+NiYRLKsrAwLD8JzA5GVK1fi+eefR3JyMlJSUjB//nxcfPHFznxJ8lT+gUCfy7Ql9zgQ2ab6vl9nAvt+A9oM0mZwlW1kZA4RkQFMJpNqDpHAQgs0qppOqgIMvX9GeeWpo4zAqkyGCjSiQtA6WstotNEvo0IQGeJvM8hwF04NRAoKCtC/f3/ceOONuPTSS535UuRNLIMQmQhNZmmVs/4eX68tix/RppfvfYnWwTUwzMjSEpGHKa+oRFpeCY5lFeFYdmHVZRGOVl1K00lxWeUpn8fP10fLYEigES1BheV1LeiIDQt06yDD8EDk3HPPVQuR0/j6AVd/BuRnAFu/1k6uJ7O27vxRW7qMA679xuhSEpEbdgCVoEIPMuTyaNWldACVppX6SOwQHx5kEWBUZzEkwJBAIz4iSAUj3s6l+oiUlJSoRZebm2tuw5LFkn7bej3Vz2PrLSgaGHyztqRvh++2+fDdPh8VXc+FSX+v+Wnw+/UxVPa4ECYZkWNHpsRj682JWGeOYb05v95yi8pUM4mWvbC4rOqnkZlf/ygTEeCnZTMSJaiIDql12ToyWDWr1Keyotzws1s4a3+z5/l8TNKY1QwktXSqPiIzZ87ErFmzaq2fO3cuQkNDnVxC8jgmE3xQCZOPn7rZMeMX9Dv6qbpe4ROAjIjeSIkahLSogSgJiDK4sETUVIorgBPFwMkSH5wokes+OCmXJT7IKpH7T52FCPQ1ISYIaBFU8zImyIQWgUBkIMBkRt0KCwsxefJk5OTkIDIy0n0CEVsZkaSkJGRmZtZ6IxJtLVmyBOPHj0dAACe5aiivrjfJlGz+HL67FsEnu7qbuIQrpsQhqLjgVSDO9iyuXl1vDmKdOYb1dmol5ZWq6UT6ZByRJpOsIhw+WYDth9KRVxmI7KJT/xpvERqARGk2iQ6uvqxqPpEluo5RJp6mzEn7mxy/4+LiGhSIuFTTTFBQkFqsSeXUVUH13Ud188p6S+yvLefMBtJ3ALt+AnYuhM/x9fBJ2QDf6ESpGG3b3T8DpkqgwxlAULh311sjsc4c4831Jv0vZFTJkZOFKtDQLgu1y5NFSMsrrmNYqwQOZeZAIykmFEktQtE2JkS7bBGiFmk6CQ10qcOfx+1v9jwXPwnyPvIrp2UvbZGzAMtw4OMbtXPf6GTitGPJgG8A0H4EfDuOQURRoGruIaLGyykqw+EThTh0sgCHqwKMo1XBhvTZKKuo/38tJMAPSVUBhgQcbaKCkL5/OyaNOx0d4iMQEeydQZw7cmogkp+fj71795pvHzhwABs3bkRMTAzatWvnzJcmsm84sOWQYAk2ZE6Sgkwg+xBwYCX8DqzEWLnrtdeBPpdqWRUiqpO0+suEXYdOFuKQBBwnCrRLdbsA2aeYEdTf10dlLiTYaBcj2Qwt4EhqIetCaw1rVRNzZW9Dj1YRXptJcldODUTWrVuHMWPGmG/PmDFDXU6bNg1z5sxx5ksTOU6+3M5/QQtITu4H9v6Kyt2/oHL/Svjnp2oZFJ1ss/hhLXDpeEbNgIbIC+bTkMm5JMA4eELLbBzM1C5lKSytf0hIXHgQ2seGqkBDAoy2VU0pEnzIiBR/v/pHnZBncGogMnr0aBUVE7ltQBLbWS0Vg27E4h8X4NzeLeAfatGEc2Iv8Nc71bdjOmsBSftRQNJwILqd9jxEbjynhjSXWGYz5LrWnFJY7+ygMqpEshpasBGmLjtUXW8XG4rwIPYOIPYRIWqwSt9AmDqeVd2hVfgHASPvAQ7+rp3z5uQ+bUmuyviN/icw+iHtekW5GqMDP6aNyfVOriZBxYHMglqLTN5V3+/JQD9flcHoEKsFF+1jQtE+LkxdSnPKqebSIGIgQtQYkvGY8KR2vSgbOLwaOPiHdimBSet+1dseWA7MmwokDtayJW2HAomDgPAEw4pP3qOy0qROD38gowAHThRol5n5OFiV3ahvptCIIH8tyFCLFmTIbQk+pAnFlxNqUCMwECFqKjLqRs5tI4soLdSmoNcdTQbKCrXsiSy6qCSgzUBg9MNAy97NX27yGNIUfqKgtGZWQwUcBaoPh8y/Ud8olA5xYegUF4aOcWHqesc4LdiI8YLznZBxGIgQOUug1WzAMlS410XA4TXAkb+14cGZu4GcI9oy5p/V2275WnWSVdmT1gO0ocY8eR9Z9NuQwGJfegH2pudjv2Q2MguwP7MAecXSBFj3tOQy4kQPNjrGhaNDnNwOR8vIIAYbZAgGIkTNxdcXSOipLUNu0NYV5wIpG4HjG2rO6rpnCbD5C2DT51UrpONsF6BVX625Z8hNQHD9sxWS+8sqKMXejHzsS8/HPrnM0AIPmdyrrn4bEkvICdU6xevBhpbdkOBDZhDlSBRyNQxEiIwkwUTHM7XF0qCpQIv2wLH12tmE89OAE3u0ZfsCYNht1dv+/R6Qc1QLUlr20Ub6sEOs25C+GXJGVy3QyMfu1Fys2+2HmZuWIaueuTYigv3RJSEcXeLD0Sk+XAUcEnzIUNjgAIsmQSIXx0CEyBV1OF1bdHlpQNoWIHULkJ9es9lny1fAkb+qb8tssJI9SegBJFTNHsuUu0s0p1hmNdT19HzVf6N2343qqcoli9E5IRyd48PQOT5cBR9yGRfOfhvkGRiIELmDiJba0mVc7fsGX691ck3ZrJ1Dp6wAyNihLUfWAmc9WL3t9/cApflAvDQR9dAuJfPCDEqTKSmvwP6MAuxOy8OetHztMj1fzb9R18AUGeIqTScSYHSMDUHusT24dNwodGsdxXOikMfjHk7k7gZM1hZRWal1fM3YpQUiPlb9AXYtBAoyaq7z9QdadATaDQcmvVm9vigLCI5mNqUOpeWVqpPo7rR87EnLMwceB+sJOKJCAtA1oTqr0TkhDF3iI5DYIgR+VUNg1VTlC3ejd5tIBATwK5o8H/dyIk/rECsZDlm6Tah5n/RuvOgNLUBJ31mVNdkNlBdpfU8iWtXc/u1RQHFO1eyyXYDYrlWXVbe9pLOsTPYlI1J2m7MbEnRoTSp1zb0RGeyPbi0j0LVlBLq1DK+6Ho74cI5MIbLGQITIW8gBsPs52qKTDErecW2qesmM6MqKgLxUwFShTcwmi6X2pwM3/FR9e+ULQFgc0KKDtkS2hTvOwSHnTdmZmosdKXnYmZqHXam5KuCo60ywMtGXBBjWQUdCBAMOooZiIELk7RmUqLbaYikgBPhXCpB1UAtSzMs+IHOPFmzoyoqB357Spq83P68//CPbYkRFGHzXHADOmF59X+FJIKSFoU0+BSXlKrshwcbOlFzsqLrMrWMODjknijSndLMKOmRWUQYcRI3DQISIbJPz6MR31xZrlRZnVS0vBoberAUtsmQfAipK4ZN9EDJ5faU0AZm3LQGe6wgEhFYHQDKzrFraap1uLafFb4JpzWXODS3DkYudVZdy8jZb83DIqecl4JBTyfdoHYnurSTgiECbKAYcRM7CQISI7Gc5db1MbX/+C1bNPSkoz9yLzSt+QL++58HcZTYvRbuUqe5lVllZLPWfDFzydnXQMud8q4ClLRDZBohoDYTF1yhHfkk5dkh2Qy1awLErNa/OU9FL84kEGz1V0BGBHq0iVQdSnqSNqHkxECEiJzT3JMIUmoAjsdno2+GM6vukSedfaUDusaqp7Y9WLVXX5Zw7Otnm6FptsSGl0+WY3+4RbDuei73HMjA55z2kmWKQamqBNLRArikGvqYWCPQPR/eWEeYshwQekumIDQ9qhsogolNhIEJEzSsguGrkTef6twuNg+nKT5Cdsh85qQdQeuIwAvKPIqw0E7GmLHy1uxwvbd+lNm3vk4ppQUtsPo0pMBw+PW8Bxs2s7oi77n0gvKWWVZFLOQOywf1WiLwVAxEicgkyFHZ/Rj62Hs/BtmO5KtOxPSUYOUVdAchSzd+nAl1ignBBYjx6t4nCwJgOKDw2HaHF6dooIBnxk5sClOTARyZwsxwRlHsc+NniBIOWM9JKQDL4BuCsB6rPoLz+Y229WqqCl+AoBi1ETYSBCBE1O+lEKpOBbTqSgy3HcrDpaLbq21FcVmnzjLFdEyLQJzFSBR0y0VfP1pEIC7L6+uo3q/YLlRZoQYl0jtVJv5I+l2lT5aslDSjOBirLtOagitKaQcvih2o/r19QVdByPXDm/dq6knxg7fvaMObQWJXRQZhcxgJBkQxciOrAQISInD4/x+EThSrYUEHHkWxsPZaDAhudSEMD/dCrtQQcWtDRq02kGrXicAfSwLDaTUDST+XyD2quk46xMuOsBCUSQFgGLb0u1gKWgqrApSQXqCjR+rXI4yw74v76uO1y+AUCp/0fMH5W9VmXZcizBCkqWKkKXiSICYiEj8n2MGIiT8RAhIiaNOhIyy1RQcfGwyexbLsvHt+4HNlFtc8iGxLgp7IcfROj0T8pCn0So9AxNgy+VVOdN/tQZVvzqcR0BK78qOY66WOigpZ0LXCwDDb6XQ0UZgKFJ4CCE9p1GSEkWRZ5DZ0EPH+/a7MoctafPnFyTqGLqudd+ep6rQ+LrSWuq+0h1kRugoEIETksp7AMG49mqyzHZrk8moOMPIssgRq4W4ZAP1/0bB2Bvm2j0K9tNPq1jVKnr/f3c8OhsjLZW3Q7bbEk0+pfaiO4kH4mEpjI43SB4cAZ91UFLFWBS9V1U1EWyvzDqreV9QdW1F2eYbcC5z2vXc/PAF7pC4TGWAQr0dXX242snlm3ohw4vl7r7yJNR3IpZWQTEjUzBiJE1ODOpDIb6YbD2Vh/OAsbDmepU9pbk5O3yYnd+rSJhE/WYVwzcSR6t22BIH+LuUe8SWCotliKbA2c/ZjNzctLirF74Y/opK+QzrGXvq+dhFAtJy2uZ2nn/dHJbTl3kPR1kcWazIKrByLyPP8bX7vDrgQkch6hvlcAY6o69ZaXAr89URW0yP1Wi/SXscwOEdmBgQgR2ZSZX4KNh7Ox4UgW1h/SMh62+nV0iA3FgKRoc6ZD+naEBPpVnUX2EPomRiHAW4MQR/j6oVICAp1kNPpd0bDHSlPSPzZVBynSrKOuZ2uBR/uRNWfElf4ycmJDWUyVWodd1bSUqT1OJ515V71e9+v2uwq49L/Vwc7rg4GgCIslvOoyEkgcDPS9vPqxu3+pvl8yRbKNXPcPbHidkVtjIEJE6gyzMmpFsh2S6Vh/OBuHTxbW2i4s0A/9k6IxqF0LDGwXrQIQTgzmQvwCqk88eCrStCRBi5D57mWYswpKcrVLywyHDH8eeXd10KJvoy/S0VZXkgfkHq0/aNEDEensO7eOIEtGJvWaBFz2XvW6zydr89DoAU5gVZATGAafyKSaj5dzIvkHax2WJcBhYOOyGIgQeaHc4jIkH8rCuoMnsfZglurjUVJee+isnHdloAQe7bXAQ4bRStMLeRjpF6If3KNs3C99TibIiQ0bQJpqbvlNC0hkSLO6zANKqy5b9a3eVgKR1gO0IEjfTjr3ChmZZEm23WVxxmcrvl0mABHXVq94e1TN55AskwpKwgCZ7deyP8/392gZIQlY1Dah1dcjE4HOY6q3lfMpSZCkP5fl6Q7IIQxEiLxAak4x/j540hx4yHlYrE/6Fhnsj4FVmQ7JeEjmIyrEoomAqCEk8yDNLw0hfVFus+qIK51o9cBEMjxmPsBFb1QHLDKMWgU4BSp4MbUaAORanJRRgolSU/W8MNLsJE1Mslg2O4nNX2p9a2yRDr6Wgcj747Wh3DoJSqSTr8xVI6couGZu9X0/TNfKqd+vLsO0y4hWQL8rq7c9lqxlptQ2ltuHWtWD52EgQuSBk4Xty8hXAcdaFXicxNGs2l+y7WNDMaR9DIZ2aIEhHVqgU1y4MUNniSz5+VeN9ImuHeAMmlrnwyrLyoCFC7UbkqV46KB2vaJMC1bMS37NCe6ETP8v6623k+xMfI+a28pz+/hqGRT1/CXaIgFOodVIql2LgPxU2wVu2admIPLtrcCJvba3jekM3LO++vZXNwDZh2sHLNJsJZ2b9U7GYsePWtlkG3/ZLrj6UoKi6I4wGgMRIjdXXlGJrcdzsWb/CZXxWHcoC9mFNeftkPhCJgeTwGNYxxgMad8CCZHBhpWZqNlINsFWYGPptNsb/nz37dQyF9JUVJWNUXPLyKXMJWMd4EgQYN6mqOoxRdpZpC1FttGyN5bboSptaf28aduATO08S7XIWaotA5HfX9SGadsiQ7pn7IHRGIgQuWHgsa0q8JBFMh/5JTVn4gwO8MXApBYY2lHLeEiTS7j1lOhE5HifGpVRkGDeoqOutQHXNPw5p/1Q87YEOyowkQnxrGbaveg1bUSUZYBTJgFOsdZvxVK7EVpnYhklJdtZXkp/HhfAbyYiN5i/Y/vxXKzen4k1+09i7YGTyLMKPKR/x/BOsRgu2Y4OMWqK9AB3nCyMiKqDHZmN13JGXl2709Bg5/yn/vulSctgDESIXDDwkKG0ku1Yve8E/q4j8BjWMRYjOsfitE4x6NEqkqNZiMgtMRAhcgGHThTg9z2Z+HNvJlbtO4Ecq3OzREjGo2MMTuskgUesOvssAw8i8gQMRIgMkFVQqgKOP/Zm4I+9mThysuaologgyXhUBx7S0ZSBBxF5IgYiRM2guKwC6w9l4fe9WtZjy7GcGvN4BPj5qA6lZ3SJw6iuceiXGOWeJ4QjIrITAxEiJzCZTNifWYDluzKwfFe6msujuKzmzKXdWobj9C7xOKNrnMp+hHFUCxF5IX7zETWRotIKNbJFCz4yap2rJT4iSGU8Tu8ah1Fd4tCS83gQETEQIWqMAyrrkY5luzLUKJdSi/O1SHPL8I6xGN1dsh7xKgPiI0PyiIjIjIEIkZ19Pf7Yn4UVVU0uB0/UzHokRoeowGN09wSM7BzL5hYiolPgtyTRKZwsKMUvW4/js52+eHjdMhSV1cx6DO0Qo4KPMd0T1NlqmfUgImo4BiJENuzPyMevO9KwZHsakg9loVKNcJFRLJVoHRWsMh4SfEhfD06dTkTkOH6DElXNZrrxSBaWbE/Hku2p2JchJ5yq1qt1BNr5ZeP2C0ehf7sYZj2IiNwpEHnzzTfx/PPPIzU1Ff3798frr7+OYcOGNcdLE9VJOpau2peJRVtSsXRnGjLzS833+fv6qOnTx/dqibN7tkRCmD8WLlyozuHCIISIyI0CkXnz5mHGjBl45513MHz4cLzyyiuYOHEidu3ahYSEBGe/PFENJeUV+GNPJhZuSVWZj9zi8hrTqEs/Dwk+zuoej8jgAPN9ZS5wYigiIk/k9EDkpZdewi233IIbbrhB3ZaA5KeffsIHH3yAhx9+2NkvT6RGuqzYnYFFW1KwdEd6jRPIxYUH4dw+rTCxdysM7xTDM9YSEXlSIFJaWork5GQ88sgj5nW+vr4YN24cVq9eXWv7kpIStehyc3PNv0atf5Hqt/lL1T7eUm8SfMjcHj9vS8ey3RkoLK0w39cyMggTe7XEOb1bYlC76OpzuFRWoKyyejtvrLemxDpzDOvNMaw316o3e57PxyRzUTvJ8ePHkZiYiFWrVmHEiBHm9Q8++CBWrFiBv/76q8b2M2fOxKxZs2o9z9y5cxEaGuqsYpKHqDABu7N9kJzpg80nfVBSWd2Xo0WgCf1jTRgQW4n24QDPH0dE5DyFhYWYPHkycnJyEBkZ6T6jZiRzIv1JLDMiSUlJmDBhQq03ItHWkiVLMH78eAQEVLflU/08rd4kjt5wJAc/bE7Bwq2pOFlQHYUnRgerZpdze7dE38TGdTL1tHprDqwzx7DeHMN6c61601s0GsKpgUhcXBz8/PyQlpZWY73cbtWqVa3tg4KC1GJNKqeuCqrvPqqbu9fbztRcfLfxOL7feBzHsovM62PCAnFBv9aYNKANBrVr0eQjXNy93ozAOnMM680xrDfXqDd7nsupgUhgYCAGDx6MpUuX4uKLL1brKisr1e277rrLmS9NHig9txjzNxzDt+uPYVdannl9WKCf6mx60YA2aoIxdjglInIfTm+akaaWadOmYciQIWruEBm+W1BQYB5FQ3SqTqcy0uXr5CNq5Is2wykQ6OerZjadNCARY3skICTQz+iiEhGRKwYiV111FTIyMvDYY4+pCc0GDBiAxYsXo2XLls5+aXLjfh+bj+bg6+Sj+H7TceQUVff7GNy+BS4b1Bbn922NqFCmX4mI3F2zdFaVZhg2xdCpZOaX4Nv1R1UAsjst37xezu1y6aBEFYB0ig83tIxERNS0XGrUDHmfykoTVu8/gbl/HcYv21NRJmNwpeOyvy/O6dMKlw9ui5Gd46rn+iAiIo/CQIQMcSK/RGU+Pv/7MA6eKDSv758UjauGJOH8fq0RFcKmFyIiT8dAhJq178ea/Scx9+/D+HlrKkorKtX68CB/XDywDa4Z1g6920QZXUwiImpGDETI6fKKy1T245M1h7A/o8C8vl/bKEwe1g4X9m+DsCDuikRE3ojf/uQ0+zPy8fHqQ/hq3REUVJ3rReb8uGhAIqYMb4c+icx+EBF5OwYi1OSdT1fuycCcVQexfFeGeX3n+DBcP7IDLhnUVjXFEBERCR4RqEkUlJTjm/VHVQCiN7/I7Opjuyfg+lEdcHqXuCafbp2IiNwfAxFq9NwfH606qJpg9InHIoL8ccWQJFw3oj06xIUZXUQiInJhDETIIQczC/De7/tVJ9SScm30S/vYUNw4qiMuG8zmFyIiahgeLcgum45k492V+7BoaypMVed96d82Cref1RkTerfixGNERGQXBiLUoPk/Vu07gdd/26PmAdGN6R6P287qjOEdY9j/g4iIHMJAhOoNQFbuycRrS/cg+VCWWufv64OLBrTBrWd2Qo9WkUYXkYiI3BwDEbIZgCzblY5Xl+5VTTH6uV9k5lMJQNpEhxhdRCIi8hAMRKhGAPLrjnSVAdlyLEetCw7wxZTh7XHbmZ2QEBlsdBGJiMjDMBAhZdXeTDz38y5srMqAhAT4qeG3N5/RCfERQUYXj4iIPBQDES8nTS/P/7wLf+zNNAcgMgHZzad3RGw4AxAiInIuBiJeam96Pl79bT8Wb0tVtwP8fNQJ6O4c2wUJEWyCISKi5sFAxMuk55Vg7l5frF2zCpUmbRr2SwYm4t5x3ZAUE2p08YiIyMswEPESxWUVeG/lfry9Yh8KS33Vugm9WuL+id3RrWWE0cUjIiIvxUDEC0bCfL/pOJ5dtBPHc4rVug7hJjx3zXAM6xxvdPGIiMjLMRDxYDIJ2ZM/bjePhEmMDsH947vA58gGDGwXbXTxiIiIGIh4ovTcYvxn4Q4s2Hhc3Q4L9MP/jemCm07vCD9UYuHRDUYXkYiISGEg4kHKKyrxyZpDeOmX3cgrKVcdUa8cnIT7JnYzj4QpK9POlEtEROQKGIh4iPWHs/Do/K3YnpJrPiPukxf3Qb+2bIIhIiLXxUDEzWUXluLZxTvx+d9H1O3IYH88eE4PdV4YP1+eEZeIiFwbAxE3tnhrCh5dsA2Z+SXq9uWD2+Lhc3sgjjOiEhGRm2Ag4obS84rx+HfbsGirNitq5/gwzL60H4Z1jDG6aERERHZhIOJmc4J8u/4YnvhxO3KKylTTy+1ndcLdY7siOMDP6OIRERHZjYGIGw3JfeibzVi2K0Pd7tU6Es9d3g99EqOMLhoREZHDGIi4gcVbU/HIt5uRVViGQH9f/OPsrrj1zE4I8NOmaiciInJXDERcWH5JOZ74YRu+XHfUnAV59eoB6MpzwxARkYdgIOLC07PfO28jDp8sVBOT3XZmZ8wY301lRIiIiDwFAxEXU1Fpwuu/7cFrS/eg0qSdH+alK/tjeKdYo4tGRETU5BiIuJCMvBJMn7cBf+49oW5fOjARMyf1RmRwgNFFIyIicgoGIi5i9b4TuOeLDSoYCQnww9OX9MGlg9oaXSwiIiKnYiBisMpKE95avhcvLdmtmmK6tQzHW1MGoUsCO6QSEZHnYyBioLziMsz4chOWbE8zT9H+xKTeCA3kx0JERN6BRzyDHMgswC0fr8Pe9Hw1EuapSX1w5dAko4tFRETUrBiIGGD5rnTc/fkG5BWXo2VkEN6dOgQDkqKNLhYREVGzYyDSzOeKee/3/Zi9aCdMJmBQu2i8c+1gJEQGG100IiIiQzhtdqynn34aI0eORGhoKKKj+Wu/vKISjy7Yiv8s1IKQa4Yl4fNbT2MQQkREXs1pgUhpaSmuuOIK3HHHHfB2BSXlqj/IZ38dVrOk/vuCXph9aT8E+fOMuURE5N2c1jQza9YsdTlnzhx4s7TcYtw4Zy22Hc9FcIAvXrlqIM7p08roYhEREbkEl+ojUlJSohZdbm6uuiwrK1OLJf229XpXsi+jADd8lIyUnGLEhAXg3SkDVadUI8vsDvXmilhv9mOdOYb15hjWm2vVmz3P52OSHpROJBmR6dOnIzs7+5Tbzpw505xJsTR37lzV18SdHMkH3t7hh4JyHyQEm3BbzwrEsTsIERF5gcLCQkyePBk5OTmIjIxsukDk4YcfxrPPPlvvNjt27ECPHj0cCkRsZUSSkpKQmZlZ641ItLVkyRKMHz8eAQGudS6WtQezcOunG5BfUo6+iZF4f+ogxIQFwhW4cr25Mtab/VhnjmG9OYb15lr1JsfvuLi4BgUidjXN3Hfffbj++uvr3aZTp05wVFBQkFqsSeXUVUH13WfUHCG3f5qM4rJKDOsYg/9NG4IIFzxpnavVm7tgvdmPdeYY1ptjWG+uUW/2PJddgUh8fLxayLZFW1LUievKKkwY0z0eb187GMEBHBlDRETU7J1VDx8+jJMnT6rLiooKbNy4Ua3v0qULwsPD4WkWb03FXZ9vQEWlCRf0a42Xrhygpm4nIiIiAwKRxx57DB999JH59sCBA9XlsmXLMHr0aHiSpTvScPfn61UQcsnARLxwRX/4+foYXSwiIiKX57Sf7NJJVfrBWi+eFoSs2J2BOz5dr5pjJBPy/OX9GIQQERE1ENsOGmHV3kzc+vE6lFZU4pzerfDyVQPg78cqJSIiaigeNR2UfCgLN320DiXllRjXMwGvXTMQAQxCiIiI7MIjpwP2pufjpo/WoqisAmd2i8ebUwaxYyoREZEDePR04Nwx0z74G9mFZWq69neuHcST1xERETmIgYgdcorKVBByLLsIneLC8MH1QxEa6FKn6yEiInIrDEQaqKyiEv/3WTJ2puYhPiIIH904zGWmbSciInJXDEQaQIYdz/x+G/7cewJhgX6Yc8NQJMW410n4iIiIXBEDkQb4ePUhfPbXYfj4AK9ePRC920QZXSQiIiKPwEDkFFbuzsATP25X1x86pwfG9WppdJGIiIg8BgORehw6UYA752pTt182qC1uO9PxMwsTERFRbQxE6lBcVqGmbs8rLsegdtH4z6V94CNtM0RERNRkGIjU4fHvtmF7Si5iwwLx1pTBnCuEiIjICRiI2PDluiOYt+6IuXNqq6hgo4tERETkkRiIWNmRkot/L9iqrs8Y1w2nd40zukhEREQei4GIVb+Qf3yxQZ3IbnT3eNw5povRRSIiIvJoDEQsPLd4F3an5SMuPAgvXtEfvr7snEpERORMDESq/L4nAx/8eUBdf/7yfogNDzK6SERERB6PgQiA7MJS3P/VJnV96mntMaZHgtFFIiIi8goMRAA8umAr0nJL0Ck+DP88r6fRxSEiIvIaXh+ILNmehh83p8DP1wevXDUAIYGcL4SIiKi5eHUgkldcZh6qe8sZndCvbbTRRSIiIvIqXh2IPP/zLqTmFqN9bCimj+tqdHGIiIi8jtcGIsmHsvDJmkPq+n8u6YvgADbJEBERNTevDERKyyvxyLebYTIBlw9ui1FdOHsqERGREbwyEPl49UE1cZmc0O5fHCVDRERkGH94ocnD2yE9rwR9EqPQIizQ6OIQERF5La8MREID/TlfCBERkQvwyqYZIiIicg0MRIiIiMgwDESIiIjIMAxEiIiIyDAMRIiIiMgwDESIiIjIMAxEiIiIyDAMRIiIiMgwDESIiIjIMAxEiIiIyDAMRIiIiMgwDESIiIjIMAxEiIiIyDAuffZdk8mkLnNzc2vdV1ZWhsLCQnVfQECAAaVzT6w3x7De7Mc6cwzrzTGsN9eqN/24rR/H3TYQycvLU5dJSUlGF4WIiIgcOI5HRUXVu42PqSHhikEqKytx/PhxREREwMfHp1a0JQHKkSNHEBkZaVgZ3Q3rzTGsN/uxzhzDenMM68216k1CCwlC2rRpA19fX/fNiEjh27ZtW+82UnHc6ezHenMM681+rDPHsN4cw3pznXo7VSZEx86qREREZBgGIkRERGQYtw1EgoKC8Pjjj6tLajjWm2NYb/ZjnTmG9eYY1pv71ptLd1YlIiIiz+a2GREiIiJyfwxEiIiIyDAMRIiIiMgwDESIiIjIMAxEiIiIyDAuHYi8+eab6NChA4KDgzF8+HD8/fff9W7/1VdfoUePHmr7vn37YuHChfBG9tTbnDlz1PT5los8zpusXLkSF154oZqKWN7/ggULTvmY5cuXY9CgQWrIW5cuXVQ9eht7603qzHpfkyU1NRXeYvbs2Rg6dKg6bUVCQgIuvvhi7Nq165SP8/bvNkfqjd9twNtvv41+/fqZZ00dMWIEFi1a5HL7mssGIvPmzcOMGTPU+Ob169ejf//+mDhxItLT021uv2rVKlxzzTW46aabsGHDBrWjyrJ161Z4E3vrTcgOmpKSYl4OHToEb1JQUKDqSQK4hjhw4ADOP/98jBkzBhs3bsT06dNx88034+eff4Y3sbfedHIAsdzf5MDiLVasWIE777wTa9aswZIlS9SZTydMmKDqsi78bnOs3oS3f7e1bdsWzzzzDJKTk7Fu3TqMHTsWkyZNwrZt21xrXzO5qGHDhpnuvPNO8+2KigpTmzZtTLNnz7a5/ZVXXmk6//zza6wbPny46bbbbjN5E3vr7cMPPzRFRUU1Ywldm/xLzJ8/v95tHnzwQVPv3r1rrLvqqqtMEydONHmrhtTbsmXL1HZZWVnNVi5Xl56erupkxYoVdW7D7zbH6o3fbba1aNHC9P7777vUvuaSGZHS0lIVwY0bN67GCfDk9urVq20+RtZbbi8kE1DX9p7IkXoT+fn5aN++vToDY33RMmm4rzXOgAED0Lp1a4wfPx5//vknvFlOTo66jImJqXMb7m+O1Zvgd1u1iooKfPHFFyqLJE00rrSvuWQgkpmZqSqtZcuWNdbL7brak2W9Pdt7IkfqrXv37vjggw/w3Xff4dNPP0VlZSVGjhyJo0ePNlOp3U9d+5qcTruoqMiwcrk6CT7eeecdfPPNN2qRg8Po0aNVE6I3kv81adYbNWoU+vTpU+d2/G5zrN743abZsmULwsPDVX+222+/HfPnz0evXr3gSvuav1OfnVyeRMaW0bH8o/bs2RPvvvsunnzySUPLRp5FDgyyWO5r+/btw8svv4xPPvkE3kb6PEjb+x9//GF0UTyy3vjdppH/OenLJlmkr7/+GtOmTVN9buoKRozgkhmRuLg4+Pn5IS0trcZ6ud2qVSubj5H19mzviRypN2sBAQEYOHAg9u7d66RSur+69jXpGBcSEmJYudzRsGHDvHJfu+uuu/Djjz9i2bJlqkNhffjd5li9WfPW77bAwEA1sm/w4MFq9JF0MH/11Vddal/zddWKk0pbunSpeZ2k1eR2XW1bst5yeyG9q+va3hM5Um/WpGlHUnmSRifbuK81Hfml5k37mvTrlYOppMd/++03dOzY8ZSP4f7mWL1Z43db9TGhpKQELrWvmVzUF198YQoKCjLNmTPHtH37dtOtt95qio6ONqWmpqr7p06danr44YfN2//5558mf39/0wsvvGDasWOH6fHHHzcFBASYtmzZYvIm9tbbrFmzTD///LNp3759puTkZNPVV19tCg4ONm3bts3kLfLy8kwbNmxQi/xLvPTSS+r6oUOH1P1SX1Jvuv3795tCQ0NNDzzwgNrX3nzzTZOfn59p8eLFJm9ib729/PLLpgULFpj27Nmj/i//8Y9/mHx9fU2//vqryVvccccdaiTH8uXLTSkpKealsLDQvA2/25qm3vjdZlL1ISOLDhw4YNq8ebO67ePjY/rll19cal9z2UBEvP7666Z27dqZAgMD1bDUNWvWmO8766yzTNOmTaux/Zdffmnq1q2b2l6GV/70008mb2RPvU2fPt28bcuWLU3nnXeeaf369SZvog8rtV70epJLqTfrxwwYMEDVW6dOndRQQW9jb709++yzps6dO6uDQUxMjGn06NGm3377zeRNbNWXLJb7D7/bmqbe+N1mMt14442m9u3bqzqIj483nX322eYgxJX2NR/549ycCxEREZEb9REhIiIi78BAhIiIiAzDQISIiIgMw0CEiIiIDMNAhIiIiAzDQISIiIgMw0CEiIiIDMNAhIiIiAzDQISIiIgMw0CEiIiIDMNAhIiIiGCU/wchDJzuDmDazwAAAABJRU5ErkJggg==",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# more example\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.linspace(0.1, 3, 400)\n",
+        "y = np.log(x + 0.1)\n",
+        "\n",
+        "dy = np.diff(y)\n",
+        "dx = np.diff(x)\n",
+        "derivative = dy / dx\n",
+        "\n",
+        "x_mid = (x[:-1] + x[1:]) / 2  # میانگین نقاط برای محور x مشتق\n",
+        "\n",
+        "plt.plot(x, y, label=\"log(x + 0.1)\")\n",
+        "plt.plot(x_mid, derivative, '--', label=\"Approx. Derivative\")\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.title(\"Function and Its Approximate Derivative\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DURup9u5il4-"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w3QIKTZQf1ZK"
+      },
+      "source": [
+        "## 6. Gradient of Selected Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Eeudgf3-f0Ie",
+        "outputId": "aeffb463-3230-4b98-8131-623b4fbf624c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Function: f(x, y) = x**2*y + sin(y)\n",
+            "Partial derivative w.r.t x (∂f/∂x): 2*x*y\n",
+            "Partial derivative w.r.t y (∂f/∂y): x**2 + cos(y)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# search and learn more about sumpy\n",
+        "import sympy as sp\n",
+        "\n",
+        "# Define symbolic variables\n",
+        "x, y = sp.symbols('x y')\n",
+        "\n",
+        "# Define the function\n",
+        "f = x**2 * y + sp.sin(y)\n",
+        "\n",
+        "# Compute partial derivatives\n",
+        "df_dx = sp.diff(f, x)\n",
+        "df_dy = sp.diff(f, y)\n",
+        "\n",
+        "print(\"Function: f(x, y) =\", f)\n",
+        "print(\"Partial derivative w.r.t x (∂f/∂x):\", df_dx)\n",
+        "print(\"Partial derivative w.r.t y (∂f/∂y):\", df_dy)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wDlIJkmYimg9"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.3/AI-DS_Nexus__A0_3_Project_roohi_268383.ipynb b/a0.1/a0.3/AI-DS_Nexus__A0_3_Project_roohi_268383.ipynb
new file mode 100644
index 0000000..a16656d
--- /dev/null
+++ b/a0.1/a0.3/AI-DS_Nexus__A0_3_Project_roohi_268383.ipynb
@@ -0,0 +1,519 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7wR0aAR-evto"
+      },
+      "source": [
+        "# 📚 Assignment 3 — Functions, Derivatives & Gradients\n",
+        "This assignment explores the mathematical foundation of machine learning: functions and their derivatives. You’ll visualize, analyze, and code up essential concepts every ML practitioner uses.\n",
+        "\n",
+        "\n",
+        "**👀🔎 What do you see?**\n",
+        "\n",
+        "Look at the snippet codes and:\n",
+        "\n",
+        "* Describe any patterns, surprises, or connections you notice.\n",
+        "\n",
+        "* Write a few sentences about your observations! 📝✨\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cCb1hk3xe0E6"
+      },
+      "source": [
+        "## 1. Log and Exp: Proving Inverse Relationship\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KOxJ9f-3eu5F",
+        "outputId": "31d8ce39-8670-4dc4-9053-6c6aaf10d1ff"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "x\tlog(exp(x))\texp(log(x))\n",
+            "1.00\t1.00\t\t1.00\n",
+            "2.00\t2.00\t\t2.00\n",
+            "3.00\t3.00\t\t3.00\n",
+            "2.72\t2.72\t\t2.72\n",
+            "\n",
+            "log(exp(y)) for 0 and negative values:\n",
+            "[ 0. -1.  5.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Show log(exp(x)) == x and exp(log(x)) == x for several values\n",
+        "x_vals = np.array([1 , 2 , 3 , np.e])\n",
+        "\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "for x in x_vals:\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# Edge case: try negative and zero values (show error/NaN for log)\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "x\tlog(exp(x))\texp(log(x))\n",
+            "1.00\t1.00\t\t1.00\n",
+            "2.00\t2.00\t\t2.00\n",
+            "3.00\t3.00\t\t3.00\n",
+            "2.72\t2.72\t\t2.72\n",
+            "\n",
+            "log(exp(y)) for 0 and negative values:\n",
+            "[ 0. -1.  5.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np  \n",
+        "\n",
+        "# مقادیر مختلف برای بررسی رابطه‌های لگاریتم و نمایی\n",
+        "x_vals = np.array([1, 2, 3, np.e])  # مقدار جای‌خالی با عدد 3 پر شده\n",
+        "\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "\n",
+        "for x in x_vals:  # جای‌خالی دوم باید x_vals باشه\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# حالت‌های خاص مثل صفر و اعداد منفی\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))  # log(exp(0)) = 0، log(exp(-1)) خطا نمی‌ده ولی مقدار منفی می‌ده"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lVZufz08g1hI"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mOAqtnk6fDH6"
+      },
+      "source": [
+        "## 2. Plotting Sine Functions Variations\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "DoWdSehie66R",
+        "outputId": "a83a3ade-6ca7-425c-9fa8-59ca9c343d9a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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aNMQHTU1NjKDg+7g/9MkAh5elKLjRAhwNGjRo0KBBWQhGXqKJjDVo0KBBgwYNqoMW4GjQoEGDBg0aVActwNGgQYMGDRo0qA5agKNBgwYNGjRoUB20AEeDBg0aNGjQoDpoAY4GDRo0aNCgQXXQAhwNGjRo0KBBg+qgBTgaNGjQoEGDBtVBC3A0aNCgQYMGDaqDFuBo0KBBgwYNGlSHqAY43333Hc455xw29ZNslT/88MOAf/PNN99gypQpMBqNGDZsGF599dUej3nmmWdQXFwMk8mEmTNnYu3atVH6DzRo0KBBgwYNSkRUA5zW1lZMnDiRBSTB4ODBgzjrrLNw8sknY/PmzfjNb36DG264AZ9//rn0mEWLFuHOO+/EAw88gI0bN7Lnnz9/PqqqqqL4n2jQoEGDBg0alIQEl8vliskLJSRgyZIlOO+883w+5p577sEnn3yC7du3Sz+77LLL0NDQgOXLl7PvibGZPn06/v3vf7PvnU4nmyx622234d577/X6vO3t7ezWfRppY2Ojsodtlq4D9n4BmDOB8RcBKXlReRn6iOw6Wo9Dn3yJjh3bkUgzzkaOxqjzzsCwosyghp5p8AKnAyhZClRsA3JGAmMWAgZT1F7O0dCA5q++QsehQ0hMTobluONgnjRJO3+9ga0R2Poe0FIJFJ8ADJ5Li11UXqrB2oEtP21Bw1croW+sR2d2LnLmn4bJ00cj2ajMucm0tlRZq3C46TAqrZXsZu20wu60w+FyIMWQgjRjGrJMWShOK8agtEGwGCyRPYja/cCOD2iXAsaeD2QPRTT/X+u6dbCuXQeXrQ1Jw4Yhdd5p0KUkR+011Qbav9PT04Pav2UV4MydO5eVp/7xj39IP3vllVcYk0P/TEdHBywWC/73v/95PM/Pf/5zFgR99NFHXp/3wQcfxJ/+9KceP1dsgON0Al/8Hlj9n66fGdOBi18Bhp0awZdxYcmmY/j+3U+wcMVrKLDWe/y+0pyJxSf/DKdffS7OmVCERBb5aAgK1jrgncuB0tVdP8sfB1z+LpAxIKIvRZd43Suvovpf/4Krrc3jd5aZM1H4yCNI6t8voq/ZJ1C2GXjrIqC1uutn4y8BFv4b0Bsj9jIHqlvw8ufbUfj6MzjlyHqP3zmRgM+Gzob12ltww7wxKMowQ86gz+L+hv344dgPWFuxFjtrd6LWVhvSc1CgMyV/CqbmT8XxRccj25wd/gGtfwX45LeAyyF8n6ADzvwbMP16RBrt+/ej7N77YNu2zePniampyLv7d8i46CIt2YhwgCOrsL+iogL5+fkeP6Pv6R9qa2tDfX09HA6H18fs2rXL5/Ped999rKzVncFRLL5/siu4GXU2UH8IqNwOLLoKuO5zoHBCr1/iYE0r7nl/K7JWfopbN78PHVxosaShYtwMUERcuGMd8lvr8ctP/4V/HzuGl+cuwNOXTcKgbC0TCQiHHVj0MyG4SUoFRp0F7PtSOIdvXwpc/wVgTInIS7nsdraoNi1bxr43jhwJy4wZsNdUo2XF17CuWYNDF16IAS+9BPO4sRF5zT6BhlLgjfOAtnogawhQNBnY8SGw7T3AmAqc/VSvX8LhdOGlHw7gxY/W48/f/QcDm4UyfOmwCbAWDEDK0YPod2gnztr/I3Y9eQTnrPsFfnvBdFw+Y4DsNsqDjQexdP9SfHrgU5S1lnn8LjEhEQNSB6AguQD5lnzG2hgSDeznLZ0taOpoQrW1GoeaDqHOVse+0u2DvR+wx0zLn4bTB52OM4eciVS6noJFyTJg2W+E+4PmCMzboe+BT+4ELNnAWN/JeKhoXbMWpbfcApfVigSLBanzToUuLR2tP/6IjoMHUfHH+9G+ew/y/+8+JCRqvT+RgqwCnGiBBMt0UwXKtwArHxHun/NPYOo1gL0DeOdSYP/XwEe/BG5cCegMYb/Eqn01+MWbGzBp/3r8evP/2M/M5y7EyAfvR6JFoIedbW0ofejPsC5Zgtu3vI+/Gkw4p7oF/7xsMk4eFZ1SmWqw5lng8A+AMQ24bjmQP1bYMF88FajaAXzzKDBfPMe9zJYr/vSQENzo9Sj4w++Rceml0ubXcfQojv36N7Dt2IHS669H8XuLkDRoUAT+QZWDSO+ltwnBTeEk4JplQlAz/mKBlVv/EjDiDGDE6WG/hLXDjl++tRFrtpfiye+fZcGNMycXg5/+J0ZPmSw9ruWHH3H4jjsxqr4U933/Iu5z6rFqfw2evGQijHod4gmny4lvSr/Baztew8aqjdLPjTojphdMZ+zL+NzxGJE5AmZ9cMxTva0e22q2YX3leqwuW42SuhLGBNHtyQ1P4uwhZ+PyUZdjeOZw/0/UWgMsvVW4P+MmYMHjwv3l9wrXJwU+g2ZHpOxv27kTpTffzNhTSi6KnvgbDHnC87ocDtS+/DKqn/o76t98k62veXfe0evX1CBAVqFiQUEBKisrPX5G3xMNZTabkZOTA51O5/Ux9Ld9YmFdfh/dAcZeIAQ3BH0ScP7zghaH9Byb3gj7JZZtLcPVL69FSm0F7tr0HvtZ5pVXYtBjj0rBDSHRbMbAvzyCzKt/xr7/7ab3kFlVihtfX4/l28t7+5+qF7SwrvyLcJ+CGApuCFSWWiiK8Vf/F6jZ2+uXali8mN2QmIj+//g7Mi+7zCOzT+rfHwNfew2mCRPgaGzE0VtvhdNm6/Xrqh6kezuwEtCbgAtfEoIbwsgFwHG/FO5TCdnRGbbW5soX1+Cb3dX47ZbFGNxUAX1uLoa/8xYsbsENIWXO8Rj65htITEvD2LpDuHnHUizbWo4bX9+Atg6x7BKHwObj/R9j4YcL8euVv2bBjS5Bh7n95+KJE5/AD5f9gP/O+y+uGnMVJuZODDq4IWSaMtnz3Dn1Trx3znv47ILP2P2h6UPRZm/D4j2LccHSC3DnN3dib72fa+i7vwkBav544PRHBPaGbqf9GSgYL/yOHtNLOJqaUHrrrSy4SZ49CwNeeF4KbggJOh1ybrwRhQ//mX1f+/zzTCenQYUBzqxZs7BixQqPn3355Zfs54SkpCRMnTrV4zEkMqbv+WNUjcM/CjedEThduCAkpOQCJ94j3P/+KYHVCRHf7anGHYs2szLgw7uXIMneAcu0aQJt6oXypp/l33MPkufMgcHRiYd3/g9Oux23vr0JX+70DEI1iKDSYmerkPlPusrzd8NPA4bPF/QAP3bp0MJBZ0UFKv/6GLufe8dvkDpvntfHkbix/7//BV1uDtr37kP133v3un0iyeABKmX+OcM8f3/i3YA5C6jZI5SsQoSt04HrXl2HTUcaMK9mJ44/uoWxb/3/9TSSfJTVTSNHoN8TwmZ85oFVmF5/gF3LN72xHnaHE7HEqrJVuHTZpfi/H/6PlZFSDam4ftz1+OKiL/DMqc9gfvF8mCgwjBD6p/bHteOuxZKFS/Dy/Jdx2qDTkIAEfHn4S1y49ELc9/19TMTsgeYKYN1Lwn1aRylB5KD7FPBwfU7jsV4dX+Ujj8BeVg7DwIHo949/INFHJSHjwguRde217H757/8Ae72n3lGDDAOclpYW1u5NN94GTvePHDkiaWOuvvpq6fE333wzDhw4gLvvvptpav7zn//gvffewx13dFF2pKV54YUX8Nprr6GkpAS33HILa0e/VvxwqBqr/iV8nXwlkN6/5++J0UnJBxpLhe6cELD9WCNufnMDOh0u3GfbhqIju1mtuPCvj7Iswxfod0WP/gW69HTkVBzGvfZdsDtduP2dTSgpbwr5X1Q1OqzA2heF+3PvYsxKD8z9nfB1yyKgKXwmrPLRv7J6v3nyZGRf718wSRll0cMPs/t1b7wB2549Yb+u6nFkNVC+GSDW4XhRv+EOcwZw3C3C/dXPCAFRCKJ+SjA2HmlAvs6O3+76mP2czh91u/lDyty5yLzicnb/wYOfIlXnwvd7a/DgxztYqTLaqGytxB0r78AvvvwFdtXtYjqaX0/5NQtsfjP1N8izRLdsTckWlb2eOukpfHDuB0yT44ILyw4sw9lLzsZL215CJ2fUKLhxdgIDjgOGntzzyYacCAw6XnjM+pfDPibrxk1o/GgpY4b6Pf4YdAEEsXl3/AbGESMYm1r99NNhv66GGAU469evx+TJk9mNByd0//7772ffl5eXS8EOYfDgwaxNnFgb8rd58skn8eKLLzKfG45LL70UTzzxBHuOSZMmsYCJWsi7C49Vh8ajwB7RD2iWWDvuDoO5q2y1oadBoi802TpZvd/a4cC8AWac+JPQjZZ/9+9YGSMQiD7P+fXt7P6Jq5aw52jrdLAMsr41dCZJtdj5IdDeCGQMAkae5f0xA6YDA2YKi+uWt8N6mZbvf0AzeUfpdCh48IGgRIspJ56I1NNPZx16VU88Edbr9gmsEwPUCRcDyT66d6ZdJ5SvyjYBZV3ak0B4ZuU+fLa9AgZdAp5zbARqqmEYNBA5t9wc1N/n/vrX0GVnI/HIITxv3ssqLm+uPoK31nStsZEGBU+Ldi3CeR+dh6+OfAV9gh5Xjb4Kn17wKW4YfwNSkiIjlg8FwzKH4cmTnsS7Z7/LSmBUuvrHxn/gik+vwO6aHcCGV4QHHufnfZ35i651NAw23OV0ovLRR9n9jIsuDBigEhKSkpD/h9+z+w2L3oOtpCTk19UQwwDnpJNOYhdA9xt3J6av5Fzc/W82bdrEfGv279+Pa64RN2w33HrrrTh8+DB7zJo1a5g3juqx5R1Be0Nqf38+DVOuBhIShW6AugMBn5bOx92Lt+JInRX9M814oG0LnE1NSBo2FBkXXxz04WVecgn7G2dDA+6vX42BWRaU1rXh7ve3xiSDVAQ2vt51jvwFHfR7wqY3Q2IAJF+Rp4QOnqyrroRp5Mig/5aJG/V6tH73PVpXrQrpdfsE2hq6mFEKYnwhOUfobiRseTeop95wuA7/WCFoRh47qQimT4XyVsEf/ohEU3AlHWJR8353F7uf9eHb+L+5QnLy52U7sbeyGZFGTVsNbllxCx5e8zDrdpqQM4EFFffMuIdpZeKNsdlj8fqC1/HInEeQYcxgzNJln16J5/XtcCTndp0jb6AEJKUAsNYA+0LXxDR9/DFrBye/KQo8g0XyjBlIO3MBSzQqHxOFzxrUocHR4AdbF3eVp/yBSleDTxTubyfzKv/434ajWL5DyBqfWVCM1rffZD+ni9Jfaao7EvR6pschWBe/h2fPHsKek7Q4H2zsXR1bNQzckZ8EM7FJV/h/7JjzAEOyEKCGwAAQWr79Fu0lJay8mH1zcJk/R1JxMTIvF8ocVX//hxaYdseuZYCjA8gdJWio/GGS8D5i2+KAYuNmWyduf2czaws/b1IRZq/9FK6ODpinTkXynONDOsT0c8+FcfgwOFtacN7BVThxRC7a7U7c/u5mtNsjJzomHxvSuPx47EfWFXXP9HtYMDEyK/iAOhagNvJzh57LNDonDzgZdpcD/8rKwC/69UdNR6PvP9TpgXEXCvep9T8EUGdUzX+fZfezf/EL6HNyQvr7vN/+liUa1tWr0SbKOzSEBy3AUQLIabNmN5CoFzxTAmHcBUEFOFXNNpbdEe44bQQKv/yQ6TZM48b5FKX6A4mNTePHw9XejtyvluI380awnz+4dAfKGz0N5vocdoomlANnAWlF/h9LHji8xTgEoSoFJLXiwkodU/rM0LNoKockGI0s+2zbsCHkv1c1tr8vfB13UWC34iEnA5YcoRvn0A9+H/rE57txrKENA7LMePD4fDS8J2yoOb+8JWQ/GypHZt8klFfqX38dj581DFnJSUwP95+V+xGJDqnntjyHX371S+ZJQ+3Y7571LuuI0iXGty3dH3LMOfjnCY/j4boWmJ1OrOmsxSUfX4INlX4+41SGJOz+DOhoDfq1mr/+WnALT09H1pUBkhkvMPTrh/RzzmH3a14US6IawoIW4CgBe4QxFcyXwZQe+PFEvVIwRJ4qfspUFHg02ewY1y8NN0wv6lpYb/5FWEZh9Ddc0Fr/9tu4cVoBJg/MQHO7HQ8v6+P15BJBMBq0eRixODwwCpJJsa5Zi7YtW1gtP+uan4d1mPqsLKQvXMju174SvI5L9bA1AQe/F+6TnX8g0GY/6kzPc+8FW0ob8Prqw+z+o+dPQMd777IEgTQbybNnh3WoaQvOYF07NJpD/+lS/HnhOPbz/36znxl4hovWzlb89pvf4t+b/80EvBePuBjvnPUO07woAQlHVmFhYx3eabCztvLqtmrc8PkNzDDQK4ilSx8I2G3A/pVBv07dSy9LSQaVqMJB9g3XsyC65asVaN+3L6zn0KAFOMoAZRCEEQuCe7wlS2AKCHu+8PqQH/fV4NNtFdAlJuCvF0yA9bNPmXqfsoeUk710FgSJ1NPmSYtry0cf4ZHzxrPZVZ9sK2ev2WfnFZWKE+9HdAnm/YJaxnVJQMNhoDa4Ba7+LaG8mH7B+R5eG6GCB0ctYiaqAcD+FYLwO3t4z9ZwXxh9rvB196deg1QqSf3fkm3sV+dP7ofZg9LQ8D/BWDPr+uvCdiOmcjHbIMVEY8HYPMwdkYsOhxP3f7Q9rNIj6W2uWX4NExKTy/CDsx7E/bPuZ+UpxUBcC4cOOQ1vn/U2FhQvgN1lxwOrHsCT65+Eg2bDuYPefx6k8jU4iM4pKislGAxMAxcujEOHMrdjQq2oWdUQOrQARwnCRqbdIOHbGcH/HTmpurM/3RZWXpq6auZAjC1KQ92bb7HvM6+4IiTtTXfQ32b9XBDJ1r/7DkYXpuJnxwnuuA8s3YHOGPtyyAKU+ZO3TdZQILM4uL9JSha6qQhBZI+dlVVo/nqldA57A+OQIayrinZe/rno8+AdjKFcgzR8k7qpmssFX5xu+GDjUewoa0KaSY/fnzUazV98CUd9PfT5+UjtRZJBSD/7bCSmpKCztJRpOR46dyyS9Imsdfyrkm6+MAFwqPEQrvr0KibSpaGX5Ddz4QhRn6I0g0bC8NPZwM7H5j6GX04UjBlf3fEqfvfd79BBGit3jBQDnL2fB8Wk1r9DzSBA2sJzWXdpb5AlWqg0ffoZHC3hM299GVqAI3eQgt9pF6ZN08ybYMGZAjIG7PTUvyxeX4pdFc1sYSWdTNvGjWjftQsJZjNraewtSOiYYDKhY99+2LZswZ2njWQ6gH1VLVi8/ij6ZPZPCHUQKvfoINfcAGj84H3A4WC+N6YRgvapN8i86iqpG8TZ0cdb/WljO/CtcH/YacH/HU2GH3iccP/ANz0M/Z78Qgh6bj1lGHJSjCwhIFD3IrEwvQG5jtN1yFuOi3OScf2cwez7x5fvCtoAcFv1Nlz92dU41nKMzYt6c8GbmJQXuOVZljrGuv1AokG6roghu2XSLXh87uOMlSJzwNu/vp1NM5dA54+CVBqoWu173iF3LW7+QgiiMi+9tNeHbJ42DUmDBzNdZNMnn/T6+foitABH7uAtiqFkjoTsYUKbI2UkR9d7LKxPfSksrLefOhyZyUloWLKEfU/tidRq2lvoUlORNl8QyTa8/z7SLQbcerJA6z+9Yi87hj4FmhFGGHpKaH/HH08MkJ9OHOraaFgslDYyLr0EkQDZyusLCljZkkpVfRqkY2suE0qGA2aE9re8o5EHSCJe+uEgKpps6JdhxtWzitG+dy/a1m9g3kUZF18UkcOmuWNc9GqvrsbNJw5FhsWAvVUtQXU2rqtYh+u/uB717fWs5fqNBW9gQNoAZTNwpGPkozVELBi8gLks08iIH8t+xM1f3cwGfDLQVHgepHINlg80LlvG9FNk1keNGr0FBWA0YZzARq5oCBlagCN3EAPjvlAGC6ofF88R7rt1cby95giqmtvZwvqzWYPY7KHm5cLFz8WlkUD6hQIT1PTJp3C2tuLK4waiKN3EFvU3fhJElX1mc6Rp7yT65ucjWBRMFGz/O5qBY767PVpXr0ZnWRnr2kg7I8RA2E+pkX8eGj4IbDegapCnFKH/dMFMMxSQKy57jh+EKfKiseZz3wodTXfNHwGTQYfGj4Quu5STT4IhQqalNMKBGczZ7Wj48EOkm7sSDUpy/LWNU3BDnVJkkje7aDYrS2WbfRgbKgGcBSVtmxfMKpqF5097nk0j31S1Cdd/fj0abA1dpUbCoe/8vkTj/4QuO2LBIzXNPf18sowwwLZ9u2b8Fwa0AEfu3ikNRwTjvlAzR0K3AIeYk/+KC+uvTh7Gpg1Tdk6eGYaiIjZ3KlKwTJ/OXFidRK9+/gV7Ld42/p9v9rFpyX2KvSFb+G6ZY0CQGSDfIPnzeEHTsk+k7plgTeGCQQYtrhRA/fAjOrsNuO1T4Jk73+hCAXXiUOcjOViXb2E/en3VIda9ODwvBQsn9mOut42ffMp+l36OKEyOENLPP19KNAhXHTcIBWlCokEeWL6Cm1+t+BVsDhuO73c8nj7laaZZUSycTuDIGuG+nySDSm+vzH+F6YxIb/SLr36BZkoueHJJ6yg9lxfYdu9mU8NJXJwmtnhHAtTVmHqqUNpuXOq7G0+Dd2gBjpxxWBQXF04MfXN0v5iPrgM6bcyyvVpkby6aKricNoiZY9q55wRl6R8sKIPhDEDTp8LiesGUfhiUbUG9tRPvrC1FnwBnz3igEip4mapbiYPD2d6O5i+/ZPfTzwrCIylE4z8ym6NFnQdRfVJ/w89hqAwcbxfngdGBlWhpt+PFHw5K2pvExATmN2QvL2ei4JSTwvyc+EDq6acx0zjS2LXv38/Yol+cOERqG+8u+l9fsZ4FN8TcHF90PP558j+V1SnlDdUlQoBJ5pk0PdwPyKiQBzk7a3cyFsuaOwKgkRPkaVS53evf8esj5aSTwvKf8of0s4Xruumzz1gwrCF4aAGOnHFEtMsfGJ4fhqDDyQcc7bCXrsNL3x+QFlbqqLDX1rLsnJB+buTKUxxpC4S29taffmLTcfW6RPxirjBm4sXvD6DD3gcu1tJ1wlfeERUqaOgfgeYaeZmJ0/Ldd4yBI70MC0YiDGlxpdlWfRHUot9SAdAmTyWqcDDkJOHroe/x5urDaLB2YkhOMs6eIBg+Nn68jH2lWWC+pk2HC9psU44XPkNcqHrZ9IHISUnC0fo2fLS5THpsSW0Jbv36Vqks9c9TVBDcEHgXKs15I4fiABiSMUQqV22u3ozbv/0tbFyHw8uVbqC2+6blQrcqG7MQYSSfcAILfu0VFWjbtCniz69maAGOEhicQaKnTahw0+HsW7scZY02trCR5wahecUK1nljGjsWxiFCh0UkYRw8GMbRo9lrNH8liKUvnNoPealGlDfa8OEmlY9waCoDmo4KJcZ+YQYf1DlnzmRBKiq3+SlPLYgoA8eRetpprFRm27oVncdUfr68gW9oVCKmrqhwIAa3rmMb8OoPQon4lpOGMg8qGsnAg8f0c/zMRuoF0sQgtXHZJ2wzNifpcMMJAovz7Lf72RTz0uZS3PLVLczMj6Zyq4K5cZ8AT+DeYEGAmJxn5z0Li96CNRVrcJfRBlZUPyRqIt1g27GTteNTFyqzV4gwKOjlzvLUMq4heGgBjlxhrROo1RAvTF8MQPt+YaH+2XHFjKYmkO8GgU2RjhK46LX5M+HCJC3ODScIwdRz3+1X97wjbu6XN1YYvxBukMqZA84GiSDxdos4rDYtwuUpDpqjw7VZpKXqc+DnkLpvwkXeGMBgQUJ7M1JaDrIAf+EkIcloXbMWzsZG6Oh9nhGGzi4IpJ5yCrNt6DxyBLbtO9jPrpw5EClGPbNu+GTnHtz85c2otdViROYIFtyYqDVaLeD6mxBZ1Am5E/DvU//NAr1vrUfw1+xMuI6t7+GH0/SZUIKn8iK150cDnBkipshl7yP6xQhAC3DkTquS/w1NJw4X4uY4uGMPjHoSGQ6UPBuo+0bK0qMEEr4SWlevYSUxwuUzBiI5SYf91a34Qc3uxqR94tR4b9B/hufziWj54UfWlmoYMACmsWMQLaSeIXgqcRq+T+GYOOw0XAaOoNPDVSR4x0xO3Iefzy5mJWJC8wqB2SQhaW8MNv2BxgVwZkF6PZMBl04fACS04+H1d+FI8xEUJRfhv/P+y0ozqmrUaKRGDR3QP/QmCmKz/nrCX5GABCxKS8VriVaBmRVBCVrzZ2J56ozIl6c4kmfNYhYejtpaWDeENoC3L0MLcOQK7l0zMEztBkfeaHQkJCEtoQ03jXEhO0WgnVtWrmTtozR5OBrlKY6kgQNhGjOGCVU520CL68XTBD+NV388pP7sP1z9DQdfmLsHOF+v6NocI9SW6g1pFAAnJPS9MhXNn+IOxEVTevVUZcmCL8pU3QFcMUNIMkgw2rJC6I7jtvzRQuqpglidvx7h6lkDYe63CNaEQ0g1pOPZ055FniX8ER+yLk8VjA+vUQPAvEHzcNe0u9j9J7Mz8fm2rtEJ7SUlzKKBlafmhtFlFySoOyvlFPEc9nVfqhCgBThyRflm4WvR5F49zeGGDmxzCKMSrhwgMCiEJrHzJprsDUeKuLjyUQKEq2cJx/T17ioc6sUAQNnC3t51DsMVp3Iw9iBBmEvVItjsE03d/M23HptXtECW8+apwgbfLAapfQLs/LmEgYspvbPd/7ROEBTPtRxm5poEChjJgI8YFsvMXgbBAZAydy4zESRDwY4jR9jPPjr8MvSpO+Fy6jHBcAcGp0cv0YkbuH9UODYbbvjZmJ/hCqPQefp/+9/D5qrNHmta8vGzkWgO0SMpRKScIjgwN69cqe7SfgShBThyBH14qWuG+2j0Am+vPYKtTkFQWNAiaHrI3I93T8UiwOFzdVpXrWJtzYQhuSk4aWQu+1ffEKcpqwrlWwUXaUt2aCM2vMGUxpg4dxbHun6DoN3IzGTjGaKN1JOETqAWMajqW+Wp3r2/ZM3w2hGhzFzYfgDoaO0S+dPGdeKJSEwSgp5oQZeRIWmpmld8jU8PfIoXtr3AvreVX4gftqegtV2F2g7Re6i3iSIxpHcPvwwnt1rRASfu+OYOVFmrBCbcbY2LJlJmz0ZCUhLTUnVoE8aDghbgyBFk7keeCzQ3JX9s2E9DHhfvbziKLU6hNRtlwoJtXbsWLpuNtRYbR41CtEGdVPRarrY2NvivO4uzZNMxv66qioT4XjP2JhLlI16mEstezWJ5inw3eju3KBjwCfN0/kjc3CfAs/9elqfe33gUR51ZqE3MRgINXS0Ts/+vVsSkPMXBmb7Kz5fi/lX3s/vXjr0W/Q3HM3+eT7aWQ1UgzxhKNLiXWC+h6z8df62uxbBOB5uu/sePfgXbjh3s+o5G91R3MKZv1nE92HANvqEFOHIEL21Q1k6zUMLEipJK1LR0oNQ8yo1VsEtZOF2U0dRucNBrcAMzolc55g7PRX6aEXWtHfhqZ2gTjmWPisgtrB6bbMVWRk+3iAtctMtTHElDhjAxs6uzk/ka9QlwFrVf+AEOtWC/s1YoCbXlimzssfXoKC1Fx8GDzISPfE5igZRThEDKtaUEhmYb5vafi19P+TUuEzVBxPaqCvUHhTEn1BFGzRq9Re5I1jb+dEUlUvXJMK/ZyX5smjCedRvGAqknazqcUKAFOHKEmOH1llZ9d53gFjxj2nTAmAbY2+Cq2omWb3mAMxexArWqElpWfiPVj8n47+Kpgth40XqVORvzzJHEjZFAwQTpeTsOHkLn0aNMeJg8uxfty6EGqSef1Hd0OKR1aqTPZEKvysQ/HajF4VorUo165I0W7R7KtzKDRoJl8mQ2nDYW0BUVoLrQgkQXMK8qD4+d8Bh0iTpcOKU/9IkJ2FzagF0V4pBJNSWKxIIHYfAXlCt10WQMsNvx+IAzMW2vsI4dGt87fVY4TGob6bfq6mL2ukqFFuDIWmAc/sJa1tCGb/dUs/uXTB8kMQkd675knTBUy00+TnTnjAFIREmdBvbKSrTv2Sv9/BKxm+r7vdU4Wm+FKkCOw9W7IhvgEJtHhoHWGrSuEHw3zNOmRs13I5AOR/WW8Zy9yRkhaKDCBGdFzpvcD0lFIptXuR2t3wm+VMlR7Lzpjv9u+S9+GtjG7l/VNBYpNH6AiIlUI04bIwz4fFdNI1R4ohgpFtVtTZ7d0IhJpUJb/99N32NHjeAvFG0Y8vMEWYHL1XeY1F5AC3BkKTDmF2b4Ac5760vZUx03JAuDc5IlNqjlO2GuDpmKxXJzJDdOLnJs/bHLDXRgtgWzh2azY/U1/E9xoNZiEhgb04EMQWfUayRZgOzh7G7LNwI9nTIndpsjgc5fgsUCR00N2veI7dNqRcW2Xm+OtS3t+GJHheT9hAKhVdxZuReta9d0dTfFAD8c+wHPbX0Om4cIJemEtZs9OnHY8QH4YONRNpRXVQLjXjZqeEBMWNo2boKuw47WtCQczHHgrm/vEgZzxgCctW39URzlo8EntABHlgLjul4JjKnuv3j9UY+Fi1+YLdsOxXRh9XphrvK8MJnhGMCO2eF0qUd/Q+95JDVOBePgtAPWrbvZt8knhDH8sRcg1s8yfVrfWFwrxYw8P3wDxWVby9HpcGFC/3SMKUoDUgsBcxasVXq42mzQ5+XBOGIEoo2K1grc9/197P64ky9mTKqjugbtu4XPEWHOsBz0zzSzKeefi0GZokHBmxTgRJDBEdfk1h1C52fOCaegKKUfjrYcxZ9++lNM2repJZ0dw48/au3iAaAFOHIDn1abNypsgfGag3U41tCGVJMe88cWiM83hm2ObaUCRZ08RxziGIcAx7p+vdQuTqBjTDcb2DH/tL/Lq0fx2X+kylMcBeNhrTbC1emAPj8fxuECoxNLUKsqQfX0uBTgCKxLOKDuQAKf/caC3YJxaC03SgFqtEX+dqcdd393NxraGzA6azTuOv4+JIueOy3fdw2OpKnmF4jH6T6AU9GJoq1BSBS5xUIkQCXLRD1aRbI544QT8fiJj0OfoMfnhz7H4j2LEW1Ypk5FgtHIyv0d+4XZZhq8Qwtw5IaqnV3zi8LE0i3CwnrW+EJp7hRdmG11ZricCdDnZiNpcOxNvYwjhkOXm8Na1N2n4tIxnj2hkN3/aPMxFZU3RGFwpFAwPqabozdYZs3yGqSqCp02YYo4IUwWlcwrSbRLAzX51HDh+cajtUI4hykx6J56cduL2FS1CcmGZDx50pNsrhJn/rgOiGOhGOB8t6eadTaq4hrsRaLoFXoj7MnDYKs3SEnbxNyJrBuN8Njax7C7rosZiwYSTSav5X4NPaEFOHJDJQ9wwss6yE+G+1mcO8ltYdUnobVRYHOSxw2Jy+bIOnF81I/58MHl2yuUrQEgyti9RBVJFExAa6W4Oc4Mfa5OJECsUVeQKmrF1Iaa3QD51dAUdyorhYEPxUCdSj8k4uWwm4rR3ihsjtF2L95avRXPbnmW3f/9zN9jQKpQCk45XmBvrZs3w9kmMLqEobkpGN8vHXanC59sVTiLU1XS60TRF6zN9JlIgLEoE4Y8YbTF1WOvxgn9TkCHswP3fHcP2h3RDf6TxXPYogU4fqEFOHK9MMPMHL/dXc3q6OQvM3NwtsfvWivEhbU4duLiYHU40wZlojDdhOZ2O77ZLXR/KZcabxSo8Uh4b7jB3q7r2hyL4zMQkYJUGvzn7RyqsjwVRiJAuogPu5enRFiPCd1nxiwH9BkZiBZaO1tx7/f3wuFyYMHgBTh7yNnS7wyDBkFfWAh0dnowqYSFYlL0odLLVBITHsHylIjWY8JnIrm4K3BNTEjEw3MeRrYpG/sb9+OfG/+JWAQ41nXr4exQONsWRWgBjtzai2v39urC/GiLsDCdM6GI0eMcND3cViY40CbntiBe4CUO286dsNfXe2gAzp1Y5FFiU3SASrV6fWTt98mBmmBM74Tedij+QapadThSgBNekrHlaCMO1VphNuik9muO1hKhDTs5t0302YkO/rr2ryhtLkVhciH+cNwfPBhbFqTOEGYzta4Wurk46BqkZWPD4XqU1inYtoHbNOSFLxL3BmayuauS3U/ObvT4XZYpCw8d/xC7/8bON7Cm3PO9jUq5v62NdXRp8A4twJETKLghJTC1F6d5Zn7BgOzWv9pZ6VHy8dgcXS4kpXbC0Ba/OSZE6TJxrMsFa7cN8hwxwFlRUoVmWyeUvbBGfgRG6xphwbTkt3dNuY4DOINj274djoYGqFboH2aAw9mb+WPzkWz0NJhrXbu+6xxWiK8TYaw4vAIf7vsQCUjAX+b8BWlJPX18LKIHllX8THHkpZkwe2iOsvVwjk6gZm9UrsOOQ4dgr65HQqILFtMhQa/lBnKHvnjExez+H378A5o6mmJQ7tfKVL6gBTiyrBuPDosaJ8+NdrsTQ3KSMa6f56LW+pMwAyo5X2SJiC2KMwPQ0q3EMbYoDUNyk9n/8MUOIVBTbICTG3lq3Cpm28l5bkaCcYAhPx9Jw4YKZmNrBFZJVegFg0M2B8tE/QoX7XKQwSYNSqRV15IbnXNYb6vHQ6sFFuHacddiWoF3rVbyTIHBaaMgtcWT0T3XrUylyDbk2v2AsxMgI8N0QXcUKfCyrDnPiUSdA6gW12w33DXtLqZ3ovb8R9c8imiXqVTLpEYAWoAjx4U13PKUWDenBaq7iJhfBJb+OoEliicDIPo48A2bg4554URhU1gqltqUG+BEVn/TSS2hhw5RsR+WvHagOrqdGoGQPMu7lkoVIxpaSQOWEFaQuu5QHZv/RrYHJDB2Bw8GzYNyoTO4onIOaUOts9VhaPpQ/GrSr3w+zlBUBMPAgYDDwTri3HHGuAIk6ROxr6oFeyrjV86OiP4mws0UUpIxQhzPUNUzSLUYLIw5I13OsgPL8NXhryJ6DNLriGVGKvd3D1I1xDDAeeaZZ1BcXAyTyYSZM2diragl8IaTaDpyQkKP21lnnSU95pprrunx+zPOOAN9WWDc2NaJH/fVsPsebal8czxwgIQuSJ4wzHMRiAPMU6ayY6F5Sp3l5V6zxx/21aBeaa2qNL6gek9UxI28lGAaNRK6JBfQXA60NcS9TMV1QaoBDzoyBwnu0SGCugAJpL0x6DyXV+sagUW1TBnX1a0VQdBG+tmhz6BL0DHBa5LOvwaMszjWbixcmsmAE8Tg7LPt5cpdR3MjW54iNosHg5YJI/yew0l5k3DduOvY/YdXP4zGdk+9TiRgKChgA3Bp3WnbuDHiz68GRD3AWbRoEe6880488MAD2LhxIyZOnIj58+ejqsr79OgPPvgA5eXl0m379u3Q6XS4+GKhrslBAY3749555x0oHr1Q/n+9q5K1dw7PS8GwPGHGDId1tbCwmsaMgW6AKLqLI4OjS0lmx8KOrVv2SGMlRhemMar/qxKFlalINNrZKnRQZQ6Ojv6GmJPUorifQ8vUKSw7ponY9moFd711B9+wwuiAIwdxHhCcOV402HTbHLmgN3mOMNOLBcMRKgFRaerPq/8slabG5QQ2KLTMFHQ4rWLg1Z3FcQ/YFAVeNoqwwJhM9Rz19UgwmWCePF18Ld/X4M0Tb8bg9MGotdXi8XWPIxqwTBeOw7puXVSeX+mIeoDz1FNP4cYbb8S1116LMWPG4Nlnn4XFYsHLL7/s9fFZWVkoKCiQbl9++SV7fPcAx2g0ejwuMzPT5zG0t7ejqanJ4yY7tLcADYfDvjA/2yYsRAvEhckdUtZBGRt19xDiXOKQLsy1PS9MEmcSFGcZz9/TnOGRmV7sjRon7xRe/oqjDkeXng7jSOE4rBs2QDXgG1Zu6CMUNpU2oLKpnU0OP75beYqJUysr2QR489wFzA2XBcONkZm/9ujartLULRNvCepvLDOEa7C9ZFcPsTgxUDRhfFdFMw7WCN2XitQyRhA8iDBPmoSEgtEBWTgyVXxo9kNM7L10/1L8eOzHmK6jGqIc4HR0dGDDhg2YN29e1wsmJrLvfwpSGPXSSy/hsssuQ3JyssfPv/nmG+Tl5WHkyJG45ZZbUFvr2+L/0UcfRXp6unQbQLSe3MCdUy05gCUrpD+1dtilyeHzvQU468QAh9wveWbKuwziBD7TqDuD4549fre3Bq3tdiguc4wwNd5BpbxjxwC9Xijv8YU73kGq6KbKP1+qAGfFwmBwlovszSmj82DUiw7i3UqM5smTkZicCmQNFV+v9+fwu6Pf4bODn0leLIFKU+4djUlDRbF4NwYgw5KEWUOzlcfiUFdT3YGoMDjSOkprF08y6g76bdigUtWVo69k92lWFfkTRSPAaduxA85WhQWiSg9wampq4HA4kJ/v6QVB31dUBL5oSKtDJaobbrihR3nq9ddfx4oVK/DYY4/h22+/xYIFC9hrecN9992HxsZG6VZaGj3/iV4HOJxhCdHcjzqPBmSZMabQs3vKXlMjiFMTEmCZMkVgFwh1+wFH/IIHmqfiq8QxMj8VxdkWdNidyjL94wFHhAMcrpEwjxvHyntyYHA8AhwvQaryA5zQrkMqQX0qsaiFfjZHsbQhncPeBTjWTiseWf0Iu3/V6KuCKk25g8+l6q7D8SxTKUiHQ+uaywmY0oEUwWU4YvobMQi0TJsuOFwnpQqO1/SafnDb5NvQL6UfylvL8Y8N/0AkkdS/H/RFhYDdDqtancXV2kVF7M348eMxQ1SLcxCjc+6557LfnXfeeVi2bBnWrVvHWB1voHJWWlqax02+C6soAg4By8VSzhljC3p0T1nXC+UDmlpMZQXWNqk3AY6OrpKYzEoc9D9wJor/b8oSN0a2g8q6cUO3zXGUTBicqexr+5496vDDaW8GmkTvF54IBIntx5rYsFgy9zuRd9i4wSqKQPl7FqkA59mtz6KstQwFyQV+u6YCMqniZ8wdp4+h9UQwLqT/TRHgzHT28Ih2UFF7PyVirMQ4cYLw3LyMGUALR11VD85+kN1ftHsRtlWLc7IihOTpolhc0+HENsDJyclhAuHKSk+xKH1Puhl/aG1txbvvvovrr78+4OsMGTKEvda+ffEzsIvYhRli5kizp74uEQTbZ3jLHMXggTEmhMRE4eKPs0jVgwHwqsMRPh8rd1Wx/1H2ILEofz8jzOC0iUGqtDnyzwiJmmlTjhP0OTnC0FbKbtXgpsqvweTckMvEXFx88qhcmJM8y1NUXrRTt6BOB/PEicIPpVJx+NcgDXV8fcfr0qwp2khDBSt50jqya3ePVmOaoTW9OEtZZSruBB9igBoIPHgwTZjAhl0Kr8H1jIHP4XGFx+HcoefCBRcTg9OU90iBa6m0ACfGAU5SUhKmTp3KSkkcTqeTfT9LbDP1hcWLFzNx8FVXXRXwdY4ePco0OIU0X0WpcM88QsCqfbVsflNeqhGTB2T4FhiLmRpDkJlHtOGvA2BS/ww2T4vcmel/lD2aK4COFiBBB2QNidjTshLj4cMsYyRxIwNtvin58jqHaihTSUlG6Awc7/jjgbk39oY6BxMtYhDiXmYMo5PK6XLioZ8eYrOm5g2ch5MGiJ1ZIcKQn9fVauylxMGbFhQj+K8Rk9zs0JnwoPU3HDzACVJHdefUO5mrdEldCWNyIq7D2bbNY3iqhhiUqKhF/IUXXsBrr72GkpISJggmdoa6qghXX30108h4K09R+Sk723NgZEtLC373u99h9erVOHToEAuWFi5ciGHDhrH2c0WC/FMkDU5oAc4X4miG08fms3lO7nA0N6N9l6DTMHMGJ8TMI5rgi0X73r0ec6kI9L/wzUIR2SM/f+SfEsEZVHxzZFO8qcTIwRdwcm2NI6QShxqyR6lFPLRrkGY2kSEezX47aURP3QcvE0ssqvQaCUBbPdAq+FeFgsW7F2NrzVYkG5Jx74x70RswbZ6PMtW80UIgTbOpGqwK8KWKMoMjlYnDKDNmm7Px6ym/Zvf/telfqLJ6t0oJFRSg6knnSsNTt2yJyHOqBVEPcC699FI88cQTuP/++zFp0iRs3rwZy5cvl4THR44cYT427ti9ezd++OEHr+UpKnlt3bqVaXBGjBjBHkMs0ffff8+0NopE01HA3ib4p2QMCkn4Rv437guRO9ikYJeLOZZSx4QEfvHHOfvXZ2UJXRx0rBu8awAIK3ZVMY8RRQQ4Ec4c2zYIAY6ZfGfckT1UHgGOWGYU3FQV3sUhlRhHhsXeTBuUiXSLMO3dHdYNvIvRLcAxmIGMAZ6bcpCotlZL06pvn3w78pN7XvuhgH+2+GfNHQOyLEz0T75UvFNT3mVifh1GLsBhIzbKyliJ0cJZVIJ7RyolqUHgohEXYXzOeNZN9cS6JyJyfKRZ7GLDVcCkKk1kfOutt+Lw4cOs5LRmzRrmZsxBwuBXX33V4/HU+k2b92mnndbjucxmMz7//HNmFEht6MTiPP/88z06tZRZnhoakn/KjrIm5rtBwsbjhngyXT3aw93hXv+P86yZrnbxngHOjMFZSE7SoaalHdvLIu8EqoQAp0tD1e0cSgxOfHVnhsJCGPr1Y5b/bZsV3sXBGc0QdXA0HJbQfXI4gZjJjn1CEGoWmZLesnBPbXgKzZ3NGJc9DpeOvBS9BV8fKPt3dfRkaU4dLSRHX4n/p2xBIzaYY3BCRMvEXF9mGjsWie52JZnFQlJKySklqUGAWvn/eNwf2VdynV5VFplRJ+Ypk9lXxV+DfamLqs9ACnBC2xy/3iUsOHOG58Bk8BQ2ehUYe2T/CYCtQZy7Ez9YJvu+MGkezgnDcz02EdmCb1KcWYkAyNfCVlLS5RwswwDHQywuMhWKBFkmcP+UEMobNPV+zUFBI3aqLxaVPstDhjDG0gPcCyeEc7ipahObb0TmcX847g/QJfa87kMFCcV1GRlwtbczJs5XgPPt7ip0OoJjKuK6jmYMBAyiEDgC4GUfy2Q39oZAySgFOSEGqaOzR+PyUZez+39Z8xe0O9ojt45SkOrDLqUvQgtwZFU3DjFzFAOcU0f1rPs7bTYmOushjOtBj8d3gyTjM4KNjKrae17oZJrmHsz1JQanbetWxoyQzwUxJT6z/zizcPwcKjp7pI40mkCtMwJp/YP+s+/21KDT4cKQ3GQ2ZsR3ktEtQHUPhgP4qHA4nA5pOvUFwy/A2JzQZ9b5KnFwjZ7VS5lq0oBMZCUnoclmx/pDnlo5eVptRFZ/wwMcqQOuF+eQ49ZJtyLXnIvDTYfx6nbPCkY4II0eCdidLS1oV3I3cYShBTgKvTCrm9uxpVTwHjnFS4BDAQOJznS5OUKXRHdwCpdnrXECHZsuOxuuzk7YdvTMHk8eKfxv2441oqrJBtlm//UHIx7g8M3GIrbyeoAyx4REoKNZmIAthyB1y1blZo/8OsgaLFgpBIkVJb41cO4t/h4i/x5BanDX4Af7PmAdOKmGVNw+5XZEEl1C454BDhNPjxSYVK75k3eSEbkAhxJFzqJ6DXAkFi60dTQlKQV3TbuL3X9p+0uobO3d+5pALueTJnqwhhq0AEdm5Y3gL8yVu4VNbUL/dOSl9aRj2zaLtCrNTfFmeJUlD5Eqyx5F6tfbhUleHBP7p3v8z7IDGSaSr4Xe3DUIMwLwKk7l0BsF08YwssdIwzhsKNMmOK1W1hGn7AAn+BIjCW/5Z9Iri9rWhjax5NNDB+eRZOwPKFKladRPb3ya3f/V5F8hyxSaT08gcIaJxP4uL8fCAzhZl4qlNv/IJRm2nSXMJZgSRX2Rl2s72+0chogFgxdgct5ktNnbJNF4b2CeJDKpWoAjQQtw4o3Oti731BCEcdzczxt7E5BWlRGD46nD8X5hnjIqX95lKnf9TQjZvz+47Ha0bdnqXZwqMx1OgpuBnWIXV+kcBn8NbjpSj3prJ9LNBkwd1HPYr237dsai6nNzBSF2d1DHJA3dtNuA5jK/r/Wfzf9BQ3sDhmUMwyUjL0GkQR49NCWbHKk7DvRcE04YngODLgEHalpxoNrTEFDNDI77OhrpRJGe754Z9zA91ccHPsaW6i0RYVK1kQ1d0AKceKP+kPDVmB60eyo5+36/VxAHnypu/r4uTHLe9Iowa8fRgPuFSd1zvkSO3++tkaersbSwRk5gbNu9Gy6rFYlpaTAOGybrAMfzHG5SOIMTfIDD56TRaAa9rudSahU1SfTeeN0cgxSp7qnfIxnD0YZooM6dCCMhKQnm8eN9nsNUkwEzB2fLN9GgMjEfPRPB6zBgoshfi9bxMGb7jc0ei4XDFrL7j619jBk4hgs+QoKNlagJ3VtJjdACHDnV/oOcnUJCv9YOByvfjC3qOVers6ICdhqPkZjIBjT6Z3AOxl2kSu2XMBjgqKlB59Ge7Zb0P5JTs7XDgTUH6tAXBMY2EhjTojV+PBJ8sUIy8cIhcJdlXhpVHHigH0KJ6jsxyZjrZfaU+3vhc3N0fz0fQSoF/LTxkWPxaYNOY5b/UT+HPsziuA7nu70y3DypTZvKxCQSj2CZOGCAQ4J0ek0SqJNQPQyQ+Z9Fb8G2mm345MAnYR+rzi0ZUrTgP4LQAhwFZo7Swjo8t4d7MYGXNmiYpWQN3x0sc0wQxgvEWaSaaDTCNGa0zwuTsl8uNpal2Vg0OqiC2RzlFOAoOXtkIvFDIWX/da0dTPhOmDs8x2tgwj/LXGPmFfwz46NU/E3pN1hbsRZGnVESpUYLkhbOx+bIh4iuOVALW6fMmFRK1LiTeITKxJ2VlcIMMX+JIr0WJafsGMK7DnPMObhpwk3s/t83/J1NiO+zTGqEoQU4Cgxwvt8jbCBzR/RcWD2zDh/lqR4iVRnocAII5HiWzEtzslxcI2guJp1DsTMi4ObojO+GI2SPQ5WZPbIWcRKJm4LO/ulzSMTnqIJUryJ/cr911NYyZpIxlL7ANT9egtROZycz9SNcPeZqFKVEjpnwBh5MkzGho6mpx++H5aWgMN2EdrsTaw7KjEnlXYy85BfBa9BvotiLTip3/GzMzzAgdQCq26rx4rYXe2/ZoIbhtxGAFuAobHOk9vCd5cLiM2dYgABngp/NsZcdAJFGIIHc8cOyWQWPZv5UNMqoXdze0eVimilmcr0EE3oeEhgFk6iL8AoKUEmkSkZhzZ7jTuIBqYtDaQEO//zT+Qsy+yf/G3dWozv44ErT6NGMoQynRPX+nvdxqOkQ65i6btx1iDb02dmSpUTbVsFDqzuTyv/fb0X9kWzAGbgIXYNBJ4oRWkeTdEkSQ/fajtdQ3hLe9czNCEng7vTiSt3XoAU4CmNwftgnLCzj+qUhO6Xnwin4yewInP27v6YMGBwe4LTv3u11plGGJQkT+mfIj8Wh7J+EgdQinuK9oy1UcIPGpEGDoM/s2Z0jgVxsybXVfYGPIxTbxcEz7yDLU1R++j6g/mZzcNegu0jVjYVr7mhmnVOEX078JfNNia2Wyvs55P8vL5PLL1GMRoDjp8TIXtM3CxcKTh5wMqYXTEeHswP/3vzvsJ7DMGgQdFlZHvtAX4YW4MQ7++fCtCADHF6e4iMMusO2Zw9cNhvrvkkqDkDXysQLh2DIz4OBfCacTti2CRqi7uBaB+qmkl/mWBy0SDwQuIbKFChz5K/rfhxxBN8cbdu2eZ1pJH+BcXDX4K6KZlQ1CzPgphV7D0B5gOAxnNEb0voJ84xIpNrU1Sr+0raXUN9ej8Hpg3HBiAsQK0jt/j6ExscPzQHJ/vZVteBYQxvkV6KKTIDDAoTtOwLr4NzX0V4y4cSQ3Tn1Tnb/4/0fY1fdrrCeQypTbVJYohEFaAFOPNFwRMj+DclBZf80UZt3MJDA2H95aoLv7hsZMjjBCOR4Se6HfTXymS5eH83MMcDCKrMAJ2lwMXTp6Sy4se0KfXFWCov6nSh0P25IFox6nVeDP2rzD+ocMhZO1MKJbc5Unnhj5xvsPm140WgLD6aTypvhH01Lnzww0+N9iDtIDFV3KKLXoZQopqcjqXiQ/wfzzw2t573Uwo3LGYcFxQvggosJjntTpmrThMZagCObhTWI7J8yR5qsbUnSYcogoVzTHbZQNkfJC+dA3FvFCYEyD1pYabo4dbBwHZKsGJwIgHXf8BbxQBoqmQU4ntmjghbX+tA2x0Dt4aw0YLczgz+v7rcBzuHTm55mZQoqV5zY/0TEEqaRI5jhn7OpSdKBdQdPrmQT4FhrhZEl3DwxAggpUUwrElk4e5dpay9w25TboE/Us0njq46tCj9R3LzJq69YX4IW4MjFAycI8Lr/cUOyvWaOBMn9NpjyBl8MqFXcGv+uCPc2VW/ZI00XnzU0W15lqggHOLSpOBsbkUCt8yNHKCrAcWcAFKPDoc9Z/eGgN0drhx3rDtYHqb/xMSbFzzncUbuDTQsnkOg0qL+PIBKo62vcWL+JBu/eJCbVLofp4vyzTx1wEZoiHlKi6M7C8c9SL0DdVJeNvIzdpy66UM3/JF+x6hrWzdeXoQU4CgpweOZItuneYK+vD677hoMWg5QC4X5D/DdI08iRSDCb4WxuRsd+7/Vsrj2SjdA4wgGO5EA9dixzl1VcgKO0yeItlUIXWoIOSA88RXz1gVp0OJzol2HGEC/Twz0cjAPpb7ycw39s+Ae7e/aQszEmewziAa4b8nUOSeyfYTGg2WbHlqOCF5DqBMbB+FB5O4fcTbmX+MWEX7Chqrvrd4ds/pdoMrG11J+Wqq9AC3AUIoxr63AEzBxJ3EkgcbHf7ht3kDEWO5bIXJi9nogrGmrxMk138OCO3Jwpm44riP7l71uEAhzJwdjXiI3u4K/bWg20x39GkHn8OECng72iAp1kkiZ38A0pvR+gMwTfHj4y1yu7Ihj8bQls8OcOkTla07AHq8tXs/LErZNvRbxgCiA0punis4YITOqqfTWqExizRPGw8LkwTwgiUXRn/yKUaGSYMnD9+OulkmU7BeEhgK8fNh/raF+BFuDEE9LmGJgaX30wcOYYctbhfmFGKPPoLXhbra/scXBOMnsP6L2Iu9lYWz3QLmqBeLt2b5+Sn8NA7cUcpnTAnCmbc0iGaEaxtMbLpbJGCOWp7i7i3sAM/sjJWa9nAyyDQmYxSCnxtKuWfXvxiIvRL8XLcM4YMzg0Gd7R4j1oni0K/n/cXyMjBieySUbSkCFMNB+vRPHK0Vci35KPitYKvF3ydkh/yyUKbUq4BqMILcCJZ/ZPqvsgF9eu9vAcn3V5znoE1V4sQwbHo03Vx0wj+t+5BoC/J3HPHFMLAYO510/Hum/27AmNwZFjmSoAAyArhFBiLG9sw4HqVtYmzbVg3cE/t8zgzxSkHiSzGCstZmw1JMKsM0m2/fGCNP3c5fLJAMwW//+NhxsYu6wmBiekLsYoXoMmvQm/mvQrdv/l7S+jhbSSwf4tZ3B27lSWZUOEoQU48QKVFOzkI5EQVO2fG/z58r8hUa7UfRPOhSmD7N/92Nv37fOZPcpGhyPNv4lQa+rOnV3dN4WFwf+hFuCEj4bgWdSf9gsMy/h+6Ug3GwIKjIOFw5iKf2VlsftXFS9gs4nkfg6JRS5IMzEmdcNhoXSuluuwiwmfEHcm/Jyh56A4rRgN7Q14o0SwDggGJFNI5JYNu4WkqS9CC3DiBc6YUIshzYUKMJ6BRhQQfGWOrPumqYm1eJpGBNF9E6XacUSzR1FT5M1sjEisvVVxHtsQcYFxFwMXUveMTAMcapeWffYolagCn8NVYoAza2hO72aIdcOnBz/FPoMOqQ4nfp4ZQnISi244H6Vi+nzOHpYd/zJVhxVoqYiYyLjXiSKJ1umYIgTSY3EW5/Udr6OxvTF4y4bxgn6obasCEo0oQQtw4gUe6QdRnqLODcLowjRkJSf5p8bHjWWtnkGDZ64NNHDQIbMylffFlczGxhWle7w3auqgCmlhlWGAI2WP7e3yzx6lc+j/OiTxMGdweHmmO5z0/4oGh8Gew05HJ57Z/Ay7f11jE9KbqyAHcIG0bfMWn14qlGjEXWjM11GjmxatF+g4cADOlhbWzWkcPjz4P6TXNqaJxyRKDyKE04tPx8jMkWjpbGGlqqAPaYImNNYCnLgHOIHFqT+JmzjvXPAGHqUHZQ7XwypeL1jFy2Bgo6fQ2HfmwZksvumoKsCZoOwAh2WPkshRxtkjG5R6LKhzWFrXxkYTGHQJPsczsBJjZyebBcRYyCDw/t73cazlGHISjbiiqVk255BZNiQlwdHYiE6xo6g7OIOz7VgjGts6EXeBcQQ8g6RrcNw41tUZNOi1M6NTpkpMSJS66khsXNNWE9o6ukULcDTIuIMqUOboafAX4uZIJlVcAyQXobG7XbyP7JEHezz4iwsi2CLeWVnJWqtpmrVZNFoLPcA5LBjXyQCK0OGwOXAuwGABkr1r2zhWiWWYyQMyYUnS+2/xnzgxqBKjtdOK57Y+x+7flDcbFnfbgTiDghveBebLsqEwXejopKkpcWNSIy0wDrWL0Wu5P/LnkBytJ+RMgM1hwwtbXwjqb0xiiYrkCxSo9kVoAY7MS1SkMTlYI3RuzBgiCBG7w2m1sincvb4wZSI0No0aJWSPDQ3oPOKd7p0+OIv5cRyps+JofeRq3qFl/0cjFuDwQMA4YgQSk73bAPhEWn/BqI68MrgeIc7gE5hlHeC4s6gBAhKuvzkuqCQjOHHq27uEbJxawi8afJbwQ5kwOMF0NLqzOHFjUuVSJnY/hiicQwqYaYQDYfGexWxeWSDoMzNhGCRUCNq2etczqh1agBMvSC3i/ktUPx0QMsdx/dKRZvLRubF9O8vc9QUFMOTnh34sMmsV98gefehwUox61s0St8WVsn82KNUS1KDUiBv8uUOnd7OKl8cGyQ3SKEC118V/DIhX8PcqQJJBLOKqYFjUEM5hc0czXtn+CrtPIlJD9rCuY5LJ/CCpzOhHw8F1OD/GS4fD19EgmPBAcLS0Mu8f9zbrkBDljtTjCo/DjIIZ6HR24tmtzwb1N2ax3N1XhcZagBMPkJiXRL1BXJir9vHOjezobI4yZHCCLXFIOpx40OMSNR6h2n84Jo0y1uHo0tKQNHSovFmcIEuM+6pa2JBboz4Rkwd6H3JLQVzn0aPssxDMmBTSUjR1NGFw+mCcOfhMIH0AkJAoWEe0VMmrG27XLjht3rsVaS4e72isarLJNlEMBjZKFF0uGIqKYMgLI2mJQUfqbZMFFuejfR/hcFPg9do8IXCQqmZoAU48QGJeEvWSuJcGxPVWYNwbWrW7hkMm6Bq86SfAEd+T1ftrYz81N4LUuMtuRxtNoA7Ve0PGAY4idDhBeuBw9mZ6cZafIbdbutxvU1MDsjev73yd3b95ws3QkQ5OnyQI/t2PK86gSei6nBzmzWTbWeL1MZnJSRhTmObxPsXWLFVMFNN7H+CE0+Lvcx2N0no0KW8S5vafC4fLgf9s/k/Ax5vF9cS2ZWufnCyuBTjxAM86aEGj8oIPlDJ9SRv0iQlscfUG+tB2Dffr5YUpk4XVI3vcvZs5/HoDdbNQV0tZo41pceRs8e8PRIu72tqQmJLCNsiwoAU4UStR8RLorAiVpzh7MyR9COYXz5d5N1zgc3j8sDiVqWhUSkezcJ+XaHvzdL1NFDmLRMdExxZlFuezg5/hQIM4sNkHjKRnpMnipGcsFYPBPgQtwJFxBxVfWCcOyECy0XsgZC8vh6M6xNk33cEX+KYywB7aULdogfREeqKJHQ6BOvYC6maZNCAjPjoc1oET4YV1wngkJIZ5ScrMsNE94LZt3QaXQx4eS6GWqJxOl8Si+tPfUIYcDAPnwd5MFNmbHlo4GZ1DqcQRuFRM8/Licg1SB1wvR6WwIam9DXAMJiClwLOEHQWMyhqFUweeChdcUheeLyQmJcE4ZrS8E40oQgtwZNxBFczCyj+05F6caA7zIk/OEcSycKN8FZI9xq1dXKLGB0TQwbgXLrY8e2wUO7tkAOOwYUiwWOBsbUX7/v2QFdopy64LmGjsLG9iHi/uonav7rei63YgBuetkrck9ub0Qad7/pK7KcspwAniGpw2KJN1eZJXUFmDd7ZV7vobNiS1tpaxHcZwE8UYlvt/MeEX7OvyQ8txsPFgcELjLX1PhxOTAOeZZ55BcXExTCYTZs6cibVr1/p87Kuvvso2N/cb/V33aPv+++9HYWEhzGYz5s2bh72i+l0RCGLIprtzqn/9zdbelacIpBKUhMYyWlzd/HB8gbft0nsV0xpzNBic3gQ4PNAifZcjTqZr3ZCg03XZxcste+QbkDkLMPrWzPBrcObgLOh1ib7HpDQ3szEp1OYfFntDkIziIuuE2xuYxo1j64O9rBydVd7Fz6kmA+vyJKw9WKfMJEPU+hHbQaxH2IgRCzc6ezROGnASnC5nQF8ccx8WGkc9wFm0aBHuvPNOPPDAA9i4cSMmTpyI+fPno8rHxUJIS0tDeXm5dDvczUnz8ccfx9NPP41nn30Wa9asQXJyMntOmw+lvxJLVOR9U9FkQ5IuEVMGZUZ3c3Q/FjkJjcWgjTRGvoKXKQMzkaRPRFVzO/ZXt8bmwDptwsyZCIgbHU1NzB6+V11wnKbXGYXWde7OKwPIVocT5IiGNeKGPdOHB5UHAzd2rF/3W2JvKMjxyt4QuOEmD55lAF1KsjSywJ/lPwWAhDWxLFNFkMGJ3DoaOz0jBcmETw5+4rejyiyWTdtLSuCU+2w4pQU4Tz31FG688UZce+21GDNmDAtKLBYLXn7Z90wNYm0KCgqkW76btwttdP/4xz/whz/8AQsXLsSECRPw+uuvo6ysDB9++KHX52tvb0dTU5PHTe4lqtUHhIWV2lJNBu+dG2xSrNR908sLU4at4kxTpNczjZG9rMz7Yww6TBFbd2NWpuJlICrrWXxvfMGAG3AZBg6EXpwoHRZIu8M3SJmUGT10OHILcIK4Bkl/s+6QGOAM9seibg4YoAZkb9yZiMZjsnGkJnSN3fAd4MwQ35814roVWxZVRgEOP5YYXINjs8eyjqpALI5hwADoMjPh6uxkQU5fQlQDnI6ODmzYsIGVkKQXTExk3//0008+/66lpQWDBg3CgAEDWBCzQ9zECQcPHkRFRYXHc6anp7PSl6/nfPTRR9lj+I2eN26g8oE0/8b34rpWzITIZ8IXqMOIghxdejoMgwapjsEhTRHNxAmsw8mR2sVjgka3zLGXHjhdM8R6wd5w8HKZjBgA/n+179sPR7PY8SIHNATeHPdUNTP9jSVJh7FF4iBFL+iaPj0hfPaGkFooOFKThYRMHKmDZeFmFGexS+FATSuqmm2ym+fnD2xIqrjxcwfusCGxcLHRwpHNAGHZgWUobfJ+3SeQzEM03uxrOpyoBjg1NTVwOBweDAyBvqcgxRtGjhzJ2J2PPvoIb775JpxOJ2bPno2jZKJFowvEvwvlOe+77z40NjZKt9J4tsvRB5/KCFROSPZtJsVr2TNE6te/OHVCULNvlCZSDVaHw8sHaw/VxUaHE1GBcYQyR/fjkRGDo8/JgaF/f+YLYhOFuLJAENn/OvEanDoo06f+hiwM2sWJ6b7OoTt7c8vEW7yzNwSyjOBeODK6DrmrLzmm++qGS7cYMKogLbY6nAhdh9KQVPqs9vPvSxYQEgtHTufRX4vG547H8f2OZ744L2x7IXCQulULcOKKWbNm4eqrr8akSZNw4okn4oMPPkBubi6ee85/O5w/GI1Gputxv8UN7lmHj5Zgmq1E3i7kf+PLOTXym2NsM49IThanVnHSKlU3t+NwrVUxAmMKxoJtLw4tSJWPSFW2Ohz+Oeefez/6G2In/G6ODgf0ubnM2sAb3t31rsTenDboNP/HJZUZ5XMOjUOHsvloLpp5t29fYB1OLMpUtibA1hCR69B9He11osgD1E5rVL1w3EFBM+Hj/R/jaPPRACMbtqIvIaoBTk5ODnQ6HSorRUGmCPqetDXBwGAwYPLkydgnXlj873rznHKnxnndf2y/dJ+Tiz0vzF7Squ5iWaLGZeKF42H4t3OnT4Ec6XAm9Be7OMT3TgmZI81pIgMuNntr1ChVMjjBDm2MOXiQ6iPAoeCTMxE02DVcFpUmhr+x8w12/6YJN/lmb2RcZqRuOD5+wi+TKr5PMWFw+PtjzvTbBRcMIpooMi+c/JgGqRNzJ2J20WzYXXa8uO3FwLPh6mMTeKk+wElKSsLUqVOxYsUK6WdUcqLviakJBlTi2rZtG2sJJwwePJgFMu7PSaJh6qYK9jllkTn6yTrWHqz3WDB8zr4RJ23zD2+vQGJZveijI6MuHA+BHGXLPjA9HotrL2v/kofR6NEsyOk1ZLg5erBwW7bIwy6+wwpYa/0GqeSMTZ15xAxyM0n/DsbeN8cP9n6A+vZ69E/p7+larNQg1Z8OR7wGd1c2o661o+91UMWxG453VNGMqrKWMu+z4QYPDtgNpzZEvURFLeIvvPACXnvtNZSUlOCWW25Ba2sr66oiUDmKNDIcDz30EL744gscOHCAtZVfddVVrE38hhtuYL+nLOk3v/kNHn74YSxdupQFP/QcRUVFOO+886CYACetf0CBsa/xDO4LK5t9E4mSG2WfMuzCYYZ/Qehw+OLK2S8lMDgR8TDyWv8/KqsuHGKnKIBjdvFiUC6LazApFTCl+y1PTRyQ7rOLMZBIvMPRgVd2CBPDrxt/HfQ0e06ppWI+08jP5pidYsTwvJTYJBqRYlErq5jHD8kFzOPGRv46jBEm503GzMKZjMXhU+p9+uH0IaFx1AOcSy+9FE888QQz5iNdzebNm7F8+XJJJHzkyBHmdcNRX1/P2spHjx6NM888k7Ezq1atYi3mHHfffTduu+023HTTTZg+fTrruqLn7G4IKEsEoMZrW7r8XMghNGZZhwcDcFRx2SMJQSlGIw1OZTSnGjvsXQxXBGv/EUFakTCR2tEBtMpjIjWBleDE61cWOhx3DZUPzYVUnvKTZNirq4XNkbpUyBCvG0gTUWWtQp45DwuHLlQ2C+feDdfSEjDRiH6AE5lZcDxAJYNG0hlFBHFKFG8cfyP7umTfEtS09ZwLRmXUvqbDiYnI+NZbb2UsDPnRUCmJWro5vvnmG+ZezPH3v/9deix1RX3yySdMg9M9qyemh35P5n5fffUVRvhxEJUVAogb1x0SylMj8lPYpF5fsEUjwJFr9uhm+OcLaSaDNNU4qosrOQW7HECioWvuTBhw2myw7drF7pt8lDdChs4gtBrL8RzKSYcThMCYM4F+uxjFjYJGUpAhnjvsTjte2v4Su//zsT9Hki4pNC0cbY5yKOe5d8P16xewG26maGsRdcO/CAn9o7KOSmL/2AY4MwpmYELOBLQ72pktgU+h8bZt8igV98UuKlWDPlQ8+/cZ4ATOHNnsG9EgLmLlDXZMPHuUQRnBDaZx4xmFzOziK30zE/w9i2qAIzFw/Xx2wQUD284SwG6PTGuqVw3HEdnqcOIO6Rx63xwrGm2MCaT5SsQMBiMw7o4vDn2B0uZSZBgzcNGIi4I/Nvpc8YnUtkYojUk9TgwI+QwvuWtweMAdnUQxtgEOJf7Xj7/eo3PPHaaRI5BgNMLZ2MjGi/QFaAFOLEHCRjuVTxKEckKY/jdk7e9saUGC2cyyx4ghDrXjUO3i/U01jokOJ1L6GzftRq9bU5VQ4uDdcLt3M+8YOTM4vBNvTFEam7MUWGDsGeAwZ1nRk+Sq0VfBwgbZBomkZMCSLdNzGFjDkZdmwuCcZJbLrZf5deiy25m3T/QSxdivozSfamj6ULR0tmDR7kUev6NBorIqFccAWoATS/AFKyUP0Bt7/Lql3Y4dZY1BGPyJm+O4cX5n36ilROWxQfq5MDmDQ10cDdaO6LsY9wJR0VDJuAtHX1jIvGKItWLeMfFEgM2RG/zNKPbtIk6Gd7xU0/0cflv6LfY17EOyIRmXjbpMNefQncHxV+KYXiywXusPR6kduaMVsNb0+jps37MHLpsNidRhVCzOkIrkOtpaDXTGNphPTEiUWJw3dr6BNnub/D2poggtwJFR5rjxcD2cLqB/phmF6WLLtj9aNZJZh/txMbdledVo+YXpT4eTm2rEECl7rJd5B9WWyBn8KYDBEbrhZKLDCaDf6GJRM/2zqK2tSLBYPFhU2vg5e3PZyMuQbvTepaVEsb9x9GgyJoODLCqO+baSmCYmGlFjcPj7YkwDzL5b+IO+BolF7UW5uQfImycppWuuWIyxYPAC9EvphzpbHZbsXeJ5aEF0pKoJWoAjK4FxYOfUqGb/zIUzQSijtfZU4ccT5snChWnbvoN54gRicaJWpoqAuNGz+yYCHka+RKoygyyyR6fDrw6uvrWDMYDB2jQwFlXX1Ua+pmINttVsg1FnxFVjrlKVFi7RaGSeTYGCVN79ueVoI9rt3kc7RER/09skIxr6m+6WG3E4h2RHcM3Ya9j9V3e8ik6abSaCJxk0XsRpjYHre5yhBThxCXAG+PXe8Oec6mhplezS+YyYiEGfBKQWyHJxJQqZqGSilG17hNk/3sBLe/y9lCODw1kob903amVwZBPgtFQCTrsw1JJ3nLmBl1WG5aUwX5eAHkbdGLgXtwpOshcOvxA5ZmEIrFpKVB5eKn60cKTByU5OQofdie3HmiJ/EAEaNYKFlChGmgmXgZ7xvGHnIduUjfLWcnx64FPp54aCAujJosXhkPRHaoYW4MiEwaFMZ3NpQ0D9jW37Nmbipi8qhCHP97BOtQmNiULuMqoKLDTefqwR1g57ZA+Cal8RYHCk1lSRLo4o+GervQloE2f1yASmsWMBnQ72ykp0+hiMGzujTeqC04VlsunO4LgnGVuqtzAGR5/QlUH3xSCVypG8+ywqZaom0anXR6NGMKBxBbyTyCyOoYgo4myaatKb8LMxP2P3X97+MhO+98UylRbgxGtx7YZtRxtZxpOTksR0JL7QJmb/lmhsjgoRGvP3wBsE/ZIJdqcLm49EeIMn0aDUBde/1wxOVAIcGXfhJJJeZeSIgOcwqpDKGz46qETmz9+YFNLekEC1+4iGl7YJvjdnDz0bhSk92SF1XIOi4d/OEp+z4QjToik05gyOl3U0WHCBOI0v0GWEr+ORs47q0pGXItWQigONB7DyyEp5MakxghbgyITB4a2p0wZl+W0bbtsUxc3R/djkSI8HkXl4ZI+RXlx5wEBlPCrnhQHSD5GOKGrUuNzPYbwN//zMgiPGb3tZU+ABmzt2CCxqQQEM+QKLyjaR0pVIQAKuHSeMoen94NtKoDOKrty9nQ1XUuLzcVMHCe/fhsP1kTeViwCDEzX9TQ8mPH7XYEpSitTFR8J3fh7cxf5qN/zTApxYwd4hLFg+9BvBTC6mD2PUBMZxduEMBtJE3MP+J+JykSMtrlFZWHtR+7ftdmtNFYffqXFx9YW4Z49+RqVQidjhdKEo3YR+Gb67GG1e/G9e3/G65EMyJH1I7wffcu8cGQ2+lbrhgphpNK5fGpL0iWzo5oEaYfSMrAKcaOpvZHQNXjXmKph0Juyo3YHV5avZz5gXDnXD1dSg81jPwZxqghbgxArN9EFyATojkOwpPnQ6XdJm7K+DqvPwYTawkM32EbsZIg4Z0+O69HQ2XDTwXCrhPdx4pJ69txFfWL2IU4NF25bN0WlN9ba4ymxz9PAz2rEDLj8ljngI/cmmgTDFj3uxN4FxtbUaS/cvZfevG3ddhLtwZBikBuFKbdTrMKm/UPrZEGnLBinACa9EJTjB83MYZRaV2sTjOPg2y5SFC4ZfwO7z0SGJJhMbgBvXUnGMoAU48ShPdStB7a1qQbPNDkuSDqMLUwNqN0isSUFOVCDjhTVYHQ69h2aDjr2n9N7Kqfbf5WEUpRKje2YbBw+OoLrh0tNZcEOuxjGHny44nmT4G8/gzcH47V1vs1bcSbmTMCkvQudVxp1UXFgdaGjjVEmHE0Ghsa1JENAT0sJLNJiHUXOz4AQvOqRHHJQEUacetWhz5j5O+PnYn0OXoMOa8jXYWbuzTwmNtQAn5gFOP58L68T+GdDrfJ+SqJen3BdWGivRIT+fhGAuTHoPJw3IiHyZKqLUeBQDHP4ZkyGDw0oc3PI/HjocHzo4Yvo2iqJ00sH5Qmd5OesCo24wSjRaO1uxaJdgiX/NuF50TinoHLLALiEBnaWlsNfVBSwVR9R0k4bdEshA0eg7GfQH66ZNXSxqJJ3g3aHTuyUa8Q1Si1KKcHrx6ZIvjixKxTGCFuDIYMBf0JljLLJ/UzqQlCrbMhWnx21btzG7/MBdHHWyCXDstbXoPHLEQ08UFfAOLxkyOHFdXGl4ZXuj1wDnQE0LGw5JzN8oPywqZw6J4qeusPf3vI/mzmYUpxXj5AEnR/4cyjDA0aWmBlkqFq5B0uDUtrRHmEXtRZIR7UYNmelwCNeOvVYaAlvWUta1jpaUwNkeoXMjQ2gBTqzANxsv4kbSigQKcFhrqkjpc1ffqEDm9X8yxyN7fPZ+7N/v83FcR8F1FXIoUXHtRtKwodClpSFq4Nk/6b7iWP/3BfPEONHj/BokK32jaKXfnUUdkA6DHxZVyv4nT2ZlqTdK3pDKADQHKOLnUMFBaoYlCcPzUiLLpEakg2pz9NdRmZWKR2ePxszCmXC4HGxGlaFfP+hycgDq6twR59lwUYQW4MSZGqcug4Nil8Hkgb79GNq2bRdaUwsLYSAnymhCxkZjZIvPjbn86XCmDBACnEO1VlQ3RyBDoXbKXi6u0sIazRIjIaWAnBEFx97WKsi2G45KHLWCsV5MIJ2//uGzqDz7nzwJyw8uR0VrBXOMPWfoOZE9Vv4ZkyGD464/4h1lgZhUuQQ4zODvwIHYXIdSmVEenUrXiizO+3vfR1NHU58oU2kBTpwDnI1u1vCU8cRVf9NjcZXHhRmODifdYsCI/BQPhqxXIE2So6NXXVQx0d/w+j8FOTLJHruD2KukoUMDthpHHBIDVxhWgONsa2OUPj+HXM9w5egr2eypiEIqUcn1GhQ3RyoV+2EJuZ4pYrPhes2iCtcgldj0mf6D2cidQ3mU+mcXzcaIzBFswvjiPYuDathQOrQAJxZwt/jvlj1u4OWpgYH0N7xuHIsAR771f0KwmcfUSJap+HuRnBeWyZ/Lbkeb6J4akyBVyh7lsbh2R1yyRx/ZPw3Y3F8tsqgi8+cN7PzZ7WyWzzrXQeyp3wOz3oxLRl4S+WPlx0gdQ9Q5JMdSsdkMZ0uLxIj4Y3BoJpWt0xFBL6p+8tbfyDBRTEhIkEaIvFXyFgwTxrL7GoOjoffixo4WrxcmzxynDMrwb/AX7RENMr4wu4N34XTs2w9HU1NAP5yIOBr3khqnAakuqxWJKSlsc4g6eIYr23MYjwDHe/bPGb6hucnITE4Kojw1Ga/sfFUaqplOHT2RBmmESPDvftwyAnUf0ST1QOdwYJYFOSlGdDic2HZMFHj3Br0tE0saqhisozLUUZ1RfAbyLHmoaavByuRSYTZcRUX8ZsNFGVqAEwvwBcqcJcwKEtHpcGLr0YaA1Dh13jjq65FgMMBILpR98MJ0hz47G4aBAyWK3Bf4e0pzvmiYaVypcc7ATRgfPYM/hRg2enbDbfXbDRdR+Ngcg9ffCJtj04hC5ilC3iJ8oGHfZFIDOxoTazBdZHEiUqbqxXXozqJaJk9G1MGPkXxwHJ2QAww6A342WvjMvrL/bRhH8Nlw6mRxtAAnjvqbknKibZ1INxswJMezq8MdPEMii+3EaBn8KSj792QAfNePi7MtyE5OYtkjTRfvFXotMI6R/kYhIlWpG85qRfs+391wEQX3UAkjwGEsqhjgLLPsZV/PGHwG8xjpC1043mAKsVTca0dj8uVqqw/7OiRjSVdbmzAmRWxzjyosOYCO1mtX12dPBrhoxEVIMaSw+Wn1w3JVXabSApw4euBI5amBGUhM9DNgM5rTp72BLx4dzUJ5TaElDsoeebt4r7s4IkWNx0J/4x6kynRz9OiG8xOkRhResn9iUbcEwaJ2HDwER2MjYEzCItdaj66UqEHGZn/un+X2vXvhaPHtGD5NHD9DesNeDXfkQUJSCmBM64X+ZmJsWFR6Dd6QIKPrMCUpBRePuJjd/zr1qKqFxlqAE0cX42Cpcatba2pMQGU0U4asWZyuAGdrgC6OCLmp9mL+jb2mBh2HDzOPIdJvxAScLZTp+Yu5Dqe9pStYd+uCC5pFFQPUmuIMdCQ6MatwFkZmjYzuMcu8RGXIy2PTxcm+wl+JY0xhGoz6RDRYOyVLjF6b/HUbdxNSkhGrRNHjOpTXObxy9JXQJ+rxReph9r1t+3ZVGv5pAU4cTf6CGe7naG5G+65d7L55yhTEDFKZSl4XJodp1EgkGI1wNjai45BwkfrtpOpt9tgLBse6cSP7SvXuqBr8eTt/lPU6Y6RxicLQxohByv5TAVNaGCyqsDmuzm6I/FgGhZaoCBZxTWrbuMHnY2iq+Ph+gmCaj8OIJ4saE/2NzEvF+cn5OHPwmajIBNpSk+Aiw7/t26E2aAFOnDQ4ZQ1tKGu0gdZUmkHlC4w6dLmYqJYypphB5kJjElybgujiGNcvHUm6RNS0dOBInTUuJn9tG4TF3zI1hgFqSh6QqAdcDqC5QtZmcYG64aJp8R88iypsjjuK7MxLhBicqENmRnHeYBY/09b1vgMc9yRuU288qXohMO6srEJnWRkrG5nGC5+7mEDGesafj/05Y8K2FgkCaOsGIRFTE7QAJ6YBzoAeramjC9OQbPQ98I0vHDxTihlk3ioerNDYZNBhXL+03pWpqLTR2Ro+gyMuHOYpUxEzJOq6SjEyyx459Dk5MPQXgn7e3RI1+AhQg2JRiSUUhdB7+iWwzinSd0Ud7iWq3rCPUYRl6lRpsjhNiPeFyeLw23gxOFxjYhw5ErqUrk7W2Gnh5NfNOCJzBDP/K+kvfJatG9ZDbdACnGiDygM0E6hb5rHxcGBho0f2Py2Gm6MMXTi9ocuJM8gujnCzR76wUpu/wRzSn9LMLO5+G1MGRwFlxpjqcLxoqDiLqktM8M+iisdWRi4PWTmM2o+t2N9NPyQzUDeSLiMDLptN+px7Aw8gd1c0oaXdHvsAR9LfxEjkrxAW7mdjfoaSAWKAs3GjXz2jEqEFONFGa7UwEyiBMuqCng7G/qzhOzpYZhTz7F8pDI4oFmzfs4cFEoEM/8JuU+3Nwkrnz+GAvqgQhsLwRjyotcxIiJldvJdz2MWipvpnUTcKm+Pufgm4bNRlSGKtvzFAkkUYDCrj65CYLK4N9FfiyE8zoSjdBKcLkvdXLEtUVp4oxlJ/o4Ak4/ii46EbPgQ2A+BqbkH73n1QE7QAJ9rgH+yUfKFsQBWPTgd2iL4sU/yMaLBt38FoX11WFpIGFyOmUECAY8jPY8NHWRfH9h0+H8eHmO6pag4ve/Sh3wgGfNG3xDpAVcDiSuBdZdTCG1XDPynAKeypvwkwJqXqp2/Y1/0DDNEZy6DgTip3ZtLqR2hMmCzpcBpimmg4Wlph2ylMzLZMn46YQjL7qwLsvkt48QxQrxx/NSu9ElrXr4OaoAU40YaXi3Lr0UbYnS7kpRrRP9N3yYN3JtACEpOav1cnXPkurMGWOCh77JdhZjKGraUNsWVw3M5hzCHj+j+HafQoJCYnw0ndgnv2RO+FvGT/wehvqHXWtUM4rsI585BlEtjAmEHqpJLvOeQMTtuGjX47FXkyF5bQ2N4usOFhMDisPOVwML1XzFnUZHma/bnj7CFn41CxsA8d+v4zqAlagBNtNPV0T+1qTc30G7hwgXHMy1M9zP7kN+wv1BLHJJHF2RRWgBMeNU7W8FbuYByPcyjz+r8004iXONZFMXvsFqS2dTiwo6wpYJn4yOqvoLc7UZ8MnHfyLYg5FHAOTWPHMssGGidDhoiBmFQSGods2cCDA72pq2wXJKzrBfGsZdo0xBy0vsucDTfpTeh3/Dx23755a+/sNPpigPPMM8+guLgYJpMJM2fOxNq1ghOoN7zwwgs44YQTkJmZyW7z5s3r8fhrrrmGBQbutzPOOAOyhLfMMQj9DYm9eGtqzAXGPcz+FECPb9jgVyDHuzjCyh7DZHBsu3YLAzZTU2EcHoMBmwosUbmXDaIW4FD2b63xeE/IvZhY1Pw0I2P3fGHT52+xr9UjcjEsUzuH3kDjYyRXaj9lqrFFacyyoa61A4drreFfgyGy2fxzZZkehwBHIWXG08/8FeyJQFpDJ7ZvXwm1IOoBzqJFi3DnnXfigQcewMaNGzFx4kTMnz8fVVVVXh//zTff4PLLL8fKlSvx008/YcCAATj99NNx7Jjnh4MCmvLycun2zjvvQJboVvun6DgYapymT5OJXYLZDNOoUYgLFLC4svlcNNOosdFviWOyRI+HkT2GGeC0iW2X5imTY2MN76vMSD44Mhn25w1847GuWx+dLg4v2T9PMvyxqM0dzXBsEkT+/U84HXGBAsqMBLPYLk7n0BeMeh3GipYNm0rrY+Ik7rTZugZsxlp/I3OzP3fkZQ9Eg7gf/fjpi1ALor7qPvXUU7jxxhtx7bXXYsyYMXj22WdhsVjw8ssve338W2+9hV/+8peYNGkSRo0ahRdffBFOpxMrVqzweJzRaERBQYF0I7bHF9rb29HU1ORxixm6XZiUudS2drBMhvuz+GsPpxIMmdrFBTKnVqUSh8hwWdes8Zs9GnQJ7L0/Wt8Wk8W1dc3a+FHj7IVzgESDrOv/BDOVOMxmOBoaWGAfcXjJ/rnQ1Z/If8nOxRhWKgifx86LsbhYQSUq9+Chdd3aoHQ43CYj2kJ/Num8sxN6PlYiHlBANyMhZ+YJ7Gvnpi2oaJWnOaisApyOjg5s2LCBlZmkF0xMZN8TOxMMrFYrOjs7kZWV1YPpycvLw8iRI3HLLbegtrbW53M8+uijSE9Pl27ECsUMkgdOkYf+hoIbymgCbo4z4pR1KOjCTJ4xg31tXbvOr+HfmCJuFx9C9tjeDLQ3hry4UkcQp8aTjzsOcQGxRgqw+0+gEofoT8L1EhEFDw5ShfeCNuDNohaLa7O6w+6044evX4PRDtjTLDAOH464s6gy1kZYpkwGDAbYy8rRefRoEDqccBmcovDKU9Omxb5RQ0FMOKH/XEHmMeawE2/vehtqQFQDnJqaGjgcDuTn53v8nL6vqAguQrznnntQVFTkESRReer1119nrM5jjz2Gb7/9FgsWLGCv5Q333XcfGhsbpVtpqTjdO9rwYvHPF1Z/mSPT34hsRNw2RwVdmBYxwKHNMTgdTkPoInGaXmxMDfrPbCW7WGdQYkoKTKNHI26Q6bC/mOpwumX/ZO5X3dwOfWICxolBb3d8deQr5O0RdDtpM46L4+YobuidVqCtlwNjowgqE3Mdjj8mla97uyqaYe2wR13oLwmM45koKmUdnT4NrsQEFNUDK9a9Byt95hQOWXdR/fWvf8W7776LJUuWMIEyx2WXXYZzzz0X48ePx3nnnYdly5Zh3bp1jNXxBipnpaWledxiAlqQ7DbhvmibHyhzJJDZEnUkEG1vFuctxQUKuTCZDodajUmHs3t3wOwxpE6qMKlxvsizzFHv20Qu6lDIOUyWApz1ke/i6JZkcKH5qMJUmJO8s6hv7HwDY44Ix5E6YybiBnLOtmQro0w1c4YH++wNRRlmFKSZ4HC6mF1GNBkc8hDj3ZVxKxMTFMCiEnSpqdJ+U7yvGR/u+xBKR1QDnJycHOh0OlRWVnr8nL4n3Yw/PPHEEyzA+eKLLzBBHMrnC0OGDGGvtS8a9fvegG8qpIXQG5nBX0m5oP+ZJLIJ3mBds1r4s6lTGX0fNyhAg9Ndh9MaRPa4s6yRnYtoUuOta8UAZ2YcN0cFLa6mCRPYZ91RU+O31TgsdMv+N4sM3uQB3lnUzVWbsb1yC0YddcVXnKq0IHWmwDZbV6/2G6RKiUZITGro1yGZf9IICV1mJpKGDkXcWdRWeZr9uSNZXK/GHnbhrZK34HQpe3RDVAOcpKQkTJ061UMgzAXDs2b5nsb7+OOP489//jOWL1+OaUFE3kePHmUanMJYmziF6IGz/Zhg8JeT4r81tXU1L0/Fe3NUhsDRXYdj9aPDIVPFnJQkdDpckgdKqBqqYODq7ESb2E0S93OokBJVotEoeRpFvEzV7TqUWFQfSQaxN4MrAHMHkJiWBuOIEYgrlNJJNXkSC1Lt1dV+g1RJaBysDoc6AKkTMMQSlZQoxlN/ww4gG9AZPdcTmcIiBqkTjiTgSNNhfFv6LZSMqJeoqEWcvG1ee+01lJSUMEFwa2sr66oiXH311Uwjw0Gamj/+8Y+sy4q8c0irQ7eWlhb2e/r6u9/9DqtXr8ahQ4dYsLRw4UIMGzaMtZ/LCt3KG3xhpQzG1wXnLk7lH7a4gW/q7U2yNvvrocPxocWi93ySmLUH7YcTRgeVbccOOK1W6NLT2fTiuEIh2b/7OWxdHVwDQjjZf6fDiW3imBRvZeJjLceY/mbcYVfX5qjz3QwQEyikk4oFqeJ8OKvIYPpncOqDK0e2UAXAJXQEEhseJFp/XMW+Js/2nUzHBAow++suFs9udCK/AXij5A0oGVEPcC699FJWbrr//vtZ6/fmzZsZM8OFx0eOHGE+Nhz//e9/WffVRRddxBgZfqPnIFDJa+vWrUyDM2LECFx//fWMJfr++++Z1kZW6F77D5A5Emw7SwRxamoqTGPiKE4lGFMAU7oiLkwS8pKg19nUFFkdThjUuHsHXFz8bxTYCUdIPn42+2r9aXXk/HAcdqClK/vfVd6MdrsT6WYDBmcn93j42yVvM1r+hDJBUJ48WzimuEJJQarIWHIW2hvG9Utnlg01LUFaNrh7iQV5PdHwXas4vkUW51Aho28SSSw+UZCETDicgHUV67CrbheUipioH2+99VZ284buwmBiZfzBbDbj888/hyLQvYNKqv0Hob+ZPj3+mSN34bQ1Ak1Hgbw4GQ4GqcMhzVLLt9+yxZWEx/4CHH4uotG9IQmM4ylO7e6iyur/7UwLJldQFw4FqeSHQ4G+edzY3j8pZf+kI0jUA8m52Lz9CPvxxAEZSEz0ZFFbO1vxwd4PkNTpwoCDrR5BV1yhoACHNBw1+BesawU/HG9MNbds2FLawMpUA7Iskb8GqXuqsxOGfv1gGDgQcYcCzP7ctVRt6zfg1Jo8fIkqVrJ9ZM4jUCJk3UWleEj6jX6oarbhWEMbYyvH9/femior/Q2HQqhVdyq69ccffT5mQv8M0L5G56KySexwiyCDQ50b1o0bPbpK4gpLllv9X75mfwQytOSibH/nMDwPHCH758JWbyzqR/s+QktnC06qyUNCpx36okIkFRcj7lAQC0dBKjNtrKtD+969Ph/Hkzzu6h5xFnUVL0/Njq/+RolB6nHCNTh4fyuzOvn04KeotoqDThUGLcCJJtwWV84YjMhLRarJ4HtzFB2M466/UVj9n5A8Zw77ShomZ5t36jvFqMeI/NTgujg6bYC1NqTF1bp5s9C5kZUVP3M4hdb/3RkTvkFFUwfnDipLvbNLGPdybp2Q8accf7z8NkcZm/0RSGRs4cNT/Zi58jE1QZWKexPgyIGBIyjoGjSRe77JhIS6RpzmHM1MLxfvWQwlQgtwogk3gWqgzg1pc2xrEzfHOAz2U3AHByFpyBCWdbNA0U8njjSXKtA8HM7A6c1dg0cDoPUHgXlIlsvmqLBuuBRRL0EsGAm1ew3OWqUVocHagQM1QulpUn/P87mqbBUONR1CiiEF/XbWyEe74b45kqeWjM3+OOizT2gRrwV/DM7OsqbAlg0hlqg6K6uYlxgF93G3aeihwZH/OproFqRe2CjsQ4t2L0KHQ94t7t6gBTjRArP4FzuP0gq7qHE/Bn+t3//QtTnGW5yqwMyDAoqUOcI8lRbxveyVD0cYE4xbfviefU05QWCTZAEF1f8NgwYx3QTpJyIytsFtc+RJRnG2BZnJnv5S5PlBuDT7dHTu2y9sjvF0EXcH6aZ495ACziH/7JMOh4Zd+rJsyE01MtuMgIZ/vDQXJIPT+pPA3pjGjoXez4zCmEJB16A7G95/RxXyLfmos9Xh80MK0b66QSa7qAohWfynw2FIwdajgRmclu/FzXGusEnLAgqqHROS5wjZY+sPfgIc8RzQObE7nBGjxu01NWjfWeKRxcoCCgtSOXMSER2O2znsKk95bnoHGw/ih2M/IAEJOLd2oPw2R49zKG8dFSFp2DDo8/Pham/3OV2czjO/DjcHYlJDtGqQylN+vNbiJ/avlr3Zn/se1LZ2Pa4YfCG7/2bJm5F3GY8ytAAnWpAyx0Lsq2pBa4cDliSdpP/wSqvu2sUyR3ltjsoROEqLmk6HjoMH0eFj6N/Q3BSkGvWwdTrZTJxIUeN8Q6YOLn22aK8vBygtSBU//xHR4bjr4HyUibn25sT+J8K4SbAYkNU1qLBzyIJUkcXxl2hwNpufF69wOjzKjIFAG3CrqP2Rjf5GYWJ/Ajk/6wuFcv+ZDYNg1Bmxs3YntlQLrfdKgRbgRAsemaOQoUzonw5dt9ZUDr4QmMaNg77b5PS4gi8qHc2yN/uT5qlMnuR3caX24KAW1xAZHF4WSz5BRgycwhgcqYsjIYHpKDq7jXkJGWJA4HJjcNwDnOaOZtY9Rbhi5GUe3TeygsLOoVQq9hfgcAbHX6m4pYrcT4EEHZDiObTZGyhJdFTXMJGsefJkyAYKE/snsHK/WGZfvQlnDTnLo5SrFGgBTrTgtjl2taZmKku7wc3+jOmKyTwI/ML0t7gGNVk8hACH3JM5g5MilslkAwUtrARdRgZME4TJ1ORrFDbILFAs6ZQ6MtFg7USSPhGjC7uG7VJwY7VbMSR9CCbWpcJRWyuYnYlBsmygsHOYPOs41pbfsX8/Oo8d82nZQPs+TXev8mXZIDFwBUBiYF+wFtFXjQJUclaWFRTEwhGSxTJV6/ff44pRV7D7Xx7+EhWtonGmAqAFODHwwAnUQUWziyRbcTHzkRUUJ5A7ocsRt6Mj/E6qEEpUbVu2sAnwNLuI29XLBvz4aZ4PzfVRAFJPPoV9bfl6ZfhPQi3+Tvp/E7CxThAVjytKY0EObw1/e9fb7P6Vo69EK98cTziBdZLICgrbHGlMCWdQmld6mrm6WzYMz0vx3y7erc0/EPhrpZx8EmQHxQWpswC9Hh2HD2NwkxHT8qfB4XLgvd3vQSnQApxoQfwQt5vzsaey2av3Bgd539CIAWoP5zbZsoLCLkwacaHLzhbs2kVfoe7gweaB6lbWPtxbBqfl66/Z15S5c5lhnayQnCvM8aF5Pmyuj/yRcvLJ7CvpKXx5GgUE3xxT8rHxaGsPFpWExaXNpUg1pOLsIWejWQymtM0xMkg95RSPa8NvmcpngBP8NUhDPm1bt7L7KSeeCNlBYedQl5ICy3Rh2DVdG1eNvordJ08cG1kWKABagBMtiIvrgY50OF1AUboJ+Wkmrw9t/mqFtLDKYjyDgjs4CNRin3KSsMA1f/mV18dQm/DgnGTfiyt1OlD9P0gGp3mFsIinniJszLICWQ7QHB8FLa7GEcNZuzh14nDRaGQ6qLqSDK4nuGD4BTBU1Qsif/rsaJtjRJAiXgut69bB0exdzM8DThrb0FsWteW77yQdoyEvD7KDwlg4Quopp7KvzStW4MQBJ6IouQgN7Q347OBnUAK0ACdaEBeirU3Jfv1vSPVPHx5C6qnzIEso8cKcJ7yX9N76am30q8NhAxpdgC4JsPjviGo/cJB1bdEU3uS5cyFLKOwcMpGjyAA0+2EA/EL8Xx0phcxQzoO5azjAzP0SExJx2ajLpNKGecpkebWHc9CoCQWJ/QnGwYOZ+SZ5GvHgozv4+SAvHAdlgr1gcJpXypiBU2iQmnqqcA220fiZ+kZ2rfDkQAkt41qAEw24Wfz/VG30q7+x7dwJe3k5m9/CZynJDgq8MJnI0GKBvbIStu3bQ58sztkqcYaRP7SsFDbg5BkzGK0rSyjwHKaKG1XLym+YiDtkiP9rrS6bGcrlpBiZwRyBa29O6n8S+qf2l8ooqWJpTHYgsb8pXbEbpC8t1Yj8FJgNOrS027G/uiXsAIdcr7mOUbbnUIHXoKGoSBhc7HKhZeVKxnaa9Wbsrt+NDZXey/9yghbgRANit5FLb8KqYw6v5mIcLWJpgzp/Ek3eS1hxh4Ks/jmog4KzKb7KVPycbD5SD2f37DEEapw/f4q4mMsSClxcLdOmMdE2dTb50lL5hfi/HurIkJIMYoaaOpqwdP9SSVxsr69HqzgBnrNGsoTCWDj395O64ZxeBP96XaI0fNhru3iQ1yE9P425MQwYAOPo0ZAl+P9A7LDDDqUgZR4vU32NdGM606sppWVcC3CiAXFhJWq8qqWDed+MK+o5QZwovqYvPlfA5liouIXVo0z15Zde6dSRBakwGRLRZLNLM4pCzRypBbZt82bmcyHbEqNCN0ca3MjPYdNnYdT8xf91Z2uKB2O3ZO8StNnbMCxjGKYXTGefDzgcbGOksopsocAg1TxxIvR5eXC2tPj0pZJKxd2ZVGrzD9Lkr2m5sI6mnXGGfGbAeRX76wGXUzFifwJf18gGg7RUvGX869KvUdYi78+iFuBEA+IC1GjIZV9HFaTCnNRTPNy+ezc69u0XFvJThShZluCLS1sd0BlmR0sckHLiXPbekj6G3uvuMFD22E/MHrsvrkEGOHzjtUyfDkO+DIWNCt4cCWkLFrCvzZ9/AZc9xKxX3BzX1ZmljdThdEjOxcTeMEZHPIf8tWR/DhXiR8UF//x9bfrk09A6qajMzwY8JgApBT5fg7oluV9S6hnzIVtQqZtrqRR0HRpHDGfOxmS5QWz1sMxhmFk4k9ksvLv7XcgZWoATRQ+cMmeWX/1N07Jl7Ct1bZADr2xBk7QNFsUtrvSeppwk6Dgal37s9TH83Gw6Uh8WNd74qbBop515JmQNBZYZuasxGf856urY8MagQYyd+L9ua05hhnJUCvnu6Hc41nIMaUlpzJ2V5odZ1wjPm7bgDMgaCmThCGlnnSmJxb1NiOcNGLsrmmDtsHtp888D9En+y1M2GwwDBwp6ETlDYZ5iBEoC0s8+y2PP4i3j7+95H9bOnudULtACnGhAXFj321J96m9cTmfX5ni2UNOULRRmM+6OtHOE97bpk0+8ClUlHU4YDA51T7Hhmno9UuefDsVk/zTfRyEgT6HU008PvUxlawDEhbfSlckM5VJNBry1S9ANXDjiQiaWbPriC1YKMY0fj6QBAyBrKPQapPeWtDGkkeFOw+4oTDcjP83I7DS2uU8WD5JFldbR+fPlW55S+DlMO0sIcFpXr2ZJwQn9TkD/lP5Mz/bJwU8gV2gBTjQgRufbmpN9MjhtmzbBXlaOxJQUVkqRPRR6YRI7RkJV6qayrlvX4/f83NDQzbYOR0gTjCloIlD3myxbi91Bc3xono/TLkw0VhA4O0Y6C6ctSIMx8fxZ9eloRxImD8jE3vq9WFO+hrWGXz7ycuFhHy1VRnlKwdcgBR38HDYu+yT4MlUQLKq9thYt33yrjERRwSxcErFjZELrdKLps+XQJepw+SjhGnq75G3ZtoxrAU40wOff2DORatJjiGgo547GDz9kX0lEKdvuKRVcmGS5T5mdrzJVYboJealG5sGx7VijlwnGYs28G4gNaljyAbuffs45kD1ojg/N81HgObTMmM7aVZ3NzWgmxiUYiEFAVUK2VAbhreGnDjwVhSmFsO3Zw0ZsEAOXLjJ9soZCr0ECf3+pnNRZJRpoejH88wxwAjM47Jq22xlLZBo5ArKHQoNUQvpZZ3vsXecPP5+xoPsa9mFNhdCFKDdoAU40IH54y13ZLDOh6dXuICV6oyi4y7jwAigCSr4wF57LvjYtX97DUZWyS95dw6e+BzPBmDoKGAOXni6VUGQPhZ5DEqqmX3Qhu9+w+H/B/ZEYBBwWW8SHFSRi2X5BP8C7QBrff1/y29HnCg0BsoYk9q8HOuSre/AG47BhwmwqhwONHyzp8fuJA9JDDnCINWj8QDiH2joafaSdfRYrGdt27EDbtu1ITUrFwqELZd0yrgU4kQb5GzAXXKDClSm1QLqj8eOP4bJamTLdPE2Y9SF7KFD9z2GeOhVJw4ay97xRLEl4yx4lR+MgJhjXvycMnMs4b6H8pharcHHNOP981oVCZcaOQ4eCZlGPOjJhSdJha+MXsDlsGJk5ElPzpzJPlsYPPxKe+6KLoAgY04CkFMWJ/TkyLrmEfW1YvJhpEL1NFi9vtKGSTxYPUKIiA8/2vfuQYDRKGhHZQ6Fif4I+KwupIhve8N4i9vWK0UKy8G3pt2yum9ygBTiRBvkbuJywQ4dapPcY0UBZR8O7wocj89JL5S+KUwE9Tu9x5uVCvbj+nXd61Iu7GJyGoCYYd1ZWMXddQsbFF0MxUPA5NBQWIvmEOex+w/+CYHHE/7HclYXx/VOwSGxn5a3hzV98CUdjI/T5+UieIzyv7EFrhZRoKO8cpp0xH4mpqcw7qnXVTz0mi4/IS+12HfpncOrfFtr9iUGVdReqV7F/meDzozBkXnappKUiNnxw+mAc3+94uOCS7BfkBC3AiTTEzIrYGycSMbG/Z4BDMz3a9+xBgskklU4UAYUN3OyO9IULkWCxoGP/fljXeoqNyQsnUcweKxptgRfWd99hVLt5yhRGvSsGCmZwCJmXCnNw6he9B0eLF1t/d4j/YyUykZO3H+Wt5cgwZmDB4AUswK17+WUpQJXlgFsVnsNEsxnp5wprXv0bb/gXGru1+Xu7DjsrK9EotixnXXUlFAMm9k8UxP7WGiiSDR86lHXEcQb0ylFXSgaacmsZ1wKcSEPMrCpcWRiYZUF2imf5oub556V6pi69p7uxbCHZjFcCjk4oDTQjKv1cQQzMNzeOZMoe83n2WO+XGqeNtf4tQayadc3PoSgoeHMk0IR4Gt5IYuOG9xYHrYM75vyS3b9oxEUw6U2wrl7NZsBRkpF5pUCxKwYKZuEIWT+7ipUaSWxso+ntbuBsNxvZQDoju2gqmtozwKl/8002xNM8bSpzS1YMdIYuXZ9S2fArBDa89pWX4ersZAxOcVoxWjpbpBEocoEW4EQa4sLK9DfdylNt23eg9dvv2AWec+ONUBRoojZN1qYJ282CxkhpyL7mGmlxJZGcNz8cZhfvJ3NsePddOJua2EbLxwgoBgrfHElsnH39dex+3WuvMWdVX3CJ/2NpkhP7m7dAl6DDpSMFer32hRfZ14wLL5R/e7/KgtSk4mJWqiLUPv+Cj8niDXA0HutadwyeXaaOllbUi2X+7OuEz4OioPBzmHHhhdDl5LAmi8alSwXbBbFlnMTG5HAsF2gBThQZnO7+NzXP/pd9JUFc0qBBUBQUajPefXHlLd01zzzj8TsuBmfZo48AhzxYal99jd3PvvFGtuEqdmFVYP2fkHbOOWy2EfkaNSwR2lV7oL0FCe1N7G5b3l72dd6geShILkDb1q1oXbUK0OmQde21UBwUXiomZN90k9TV6C4YJxaVBOGtHQ6UH9nvM8moe+1VxuIlDR4sOZUrCgoPcBJNJimwrHnueTZCZeGwhUgxpOBQ0yGsLlsNuUBhK7T84RIXnvJuAQ75bbR8tYIJBXNu/gUUCYUzAIScW24WWJxvvhE8ULrR41uPNkrZf/cSVe2LL8FRUwN9UaFkXa4osHk+CcJ8H5rzo0CQr1H2DTew+9VPP92j7d9dB3ckwYL25C2SuJg6dyof+Qv7ngLdpP6BJ8XLDiq4Bk2jRjEDTgqyK//2hPRzGkrMZ8OVH+UBTr8eAn+6Dgm5t9+mvCRDJecw87JLocvMROeRI2h4/wMkG5JZkEPgbuFygAI/HfJGe53QKleTkIMxRWnsPkW45Q/+id0nkZ1x6FAoEtJUcWVmHhKLIwod6ZxQDZkwLDcFqUY92jrtUpDqnj12HD2K2hcESj3/d79jfhCKA83zobk+Sl9cL7+MlQgdtbWo+Y/AinpA/N/eTs2CE50YnTUak3InsTk6FNSS2Dz3jjugSCg8++fIu+u3zGCxZcUKtHz3XY9Eo6nysFcGp/qf/2QCV/OkSUg9Q+azw1R8DhMtFiFZJDPNp56Cva5OKlN9f/R7lDbJo2VcC3AiDEeDsLiac/rDqBe6M+refBPtJSXMFC7v7t9BsVDgNGNfiyudCzonpOUgkBnjhAHpyEIzEp2eE4yp64Yyf1d7OyzHHafchVUliysFl/n33cvu173xBnMkdoer8RhoZOMnaTqJvSHdVNUTT7Lvc37xC3lPfg8m+2+tAuy+NUhyh3H4cGRdJQxsrHjkEWkEBy8V28V11D3Asa5fj8Ylgklg/r33KMdiQ0VeOO7IvOIKGEePhrOxEVWPPY5BaYMwp98coWV8tzxaxrUAJ5JwuWBsq2R38/sNYV9p8a3+59Psft5v74Q+W7COVyRUQK0S9Dk5yL/nHna/+l//hm33bnafSoqFCXU9JhhTx07LypUs4yz4w++Vu7Cq6BymnHACUk49ldn0l/32Lo8p1U3VR/C1xYwGgwuZxkzML56P8vsfgL2qCoZBA5XX/eYOSxagM6oi0ci59VfMQbrz8BFUPvpXD9NNi63S4/Nqr69H2T33sjU2/cILGIOjWChworg3JOj1KPzTg0x20fjRR2j6/AuWTMipZTwmAc4zzzyD4uJimEwmzJw5E2vXrvX7+MWLF2PUqFHs8ePHj8en4rRYDsqo77//fhQWFsJsNmPevHnYu1cQE8YV1lroXZ1wuhIwdOhQ5tVw9Je/YpQqDWRUjGOqirN/jvTzz0PyCScwVuboLb9EZ3k5G8hYkFDr8b+2/PgjKh5+mN3Pu+M3yvK9Ufk5LHzoT6ybo33vXhy763dSubGh/BDeShfa/i8ZcTGa/v0smj//nAWo/Z54QjnO095AwbVKziFZNxQ++ii737BoEWNTC9JNbLJ4AcREI60IzrY2HLvtdmYQaOjfH/n33QdFw/38yXRIZbAwT5iArOsEsX75ffdhcnUKY3KoZfzj/T1n/6kuwFm0aBHuvPNOPPDAA9i4cSMmTpyI+fPno8rLwDXCqlWrcPnll+P666/Hpk2bcN5557Hb9u1dbb2PP/44nn76aTz77LNYs2YNkpOT2XPagp00HCV01Iv6G6RjnLUah6+4Ep1Hj8IwcCCKnnxSmYI4FVKrBGJhih5/jGX0nWVlOHzlVRjbVCoxOB2WAiaeO3rzLcxvI3XBGcrsuukOlWyOBGJD+z/9T1ayavn6a5TefAvsNTUoadyPjSYTjB3AmUvKUPvsc+zxBQ/cD/P48VA8VMLCEVLmHC/poYjFqXrq75hSlIIC8TrstBpw5NrrWHmKdB/9n3mGBUaKBu9GtdsEvx+FI++OO1gCTyzq0etvwC31k1jgRsNt4z1lPMEV5SMgxmb69On497//zb53Op0YMGAAbrvtNtx7r1BHd8ell16K1tZWLBNdKgnHHXccJk2axAIaOtyioiL89re/xV133cV+39jYiPz8fLz66qu47DLB7dQd7e3t7MbR1NTEjoH+Li1NEAJHAj++8AdUf7UI+dUGZBB77HKx4Gbgyy8rs2OjO2hTfGo0kKgH/lDlc06TksCCm2uvZTQ5obEgHYMsZWhqyYOzqpX9LGXeqej31FOsg0fx2Poe8MGNQPEJwDVd15iSQSLVo7f/Gi6bDQlmM/b2t+GYEZh6UIeUVlLjAHn33iP4IKkB798IbHsPOO3PwPG3Q+mgNZ3EwzwQbc/IQl7mUTg6EtBanc78jmjEw4DnnoNlymSoAn8bBrRWAzf/ABQoP+h2trai9Je/gnWNMFX8QGEi9hS6MHzMbJz9B8F3KlKg/Ts9PT2o/TuqlEJHRwc2bNjASkjSCyYmsu9/+slzFgkH/dz98QRiZ/jjDx48iIqKCo/H0D9LgZSv53z00UfZY/iNgptoYO/OdRi5RY+MMhcLbsizY/B7i9QR3BCS87psxuniVAEMRUUYvHgx0kSX4/SKRjQcSGbBDTnd5t55J/r/85/qCG5UxuBwpMydi+JFi2AaN46Vg4ftdeHE7S4W3ND5HfD8c+oJblTSzdidTc37zW/Q76knWcnR2FCHxoMWtBwzs+DGMn06Br//P/UENyq8DhOTkzHwxReYxxExqkPKnThjowuN29bH97ii+eQ1NTVwOByMXXEHfU9BijfQz/09nn8N5Tnvu+8+Fu3xW2lpdFrYJsy+EB/PTMRLpyei851/ot/fHocuo+c0ccVCp5c6i9RAj3Po0tLQ7/HHMfTz5TCdkIGccU3YfdocDP/2G+TcdKOyZhX1ofq/O0wjR6B48Xv45nfn4s2TE/H18U4U/UM4pxQAqQoqKlG5I+3MMzFsxVfIu+d65I5vAiYmIO3VNzHw9deQNHAgVAUVnsMEgwF5d96BYSu/xpG5Bdg21Y68kSPiekx69AEYjUZ2izamXHwT3i88jM/3L4WueQUm4HSoDrRB0iRc8opRCTHFQe7ShaNtMDW14NPEMVgYwfKlbMDn+tCcH6r/U1eOSmB32fGmcRWajkvEn6utSJ9/tiDKVRtUlv27gwTg2RPzgcMt2O4YisMp/dBPO4eKQkJWNiyFTsxPqELZ3HnqZXBycnKg0+lQWSm2/Img7wsKRCagG+jn/h7Pv4bynLEEb5P74tAXqLJ6F1IrGiq+MIU2f4EF3N2WhqP14rA/NYHm+tB8HxWewxVHVqDJ0YAshwOz7OnqDG5U5EcVeFBqljBZXI1Q8Tp6oLoFOS6hGzW/v2CXosoAJykpCVOnTsWKFSukn5HImL6fNWuW17+hn7s/nvDll19Kjx88eDALZNwfQ6Ij6qby9ZyxxJjsMZiSN4Vlk+/tfg+qgwqpVQm2BiSI3g00S4wN3lQjVLq4vl0iTHm/pKkFSV5mGKnuGqShtw5BRK3WeX6bS5XfZdTX1tFNpQ1SN6ouI740f9T7lqlF/IUXXsBrr72GkpIS3HLLLaxL6lqx5fbqq69mGhmOX//611i+fDmefPJJ7Nq1Cw8++CDWr1+PW2+9VRKk/eY3v8HDDz+MpUuXYtu2bew5qLOK2snlgCtGX8G+Lt6zGB0090dNUOnm6P4/terS0Y4kbDqiLa5KwY7aHdhUtQkJrgRc0twMS050GglkgeRcoZPR5RAcjdXK4CAL2442wuFUj1asL6yjOw5XICOh1eewVFVpcKjtu7q6mhnzkQiY2r0pgOEi4SNHjrDOKo7Zs2fj7bffxh/+8Af83//9H4YPH44PP/wQ48aNkx5z9913syDppptuQkNDA+bMmcOek4wB5YBTBp6CfEs+Kq2VWH5oOc4dKsw+UgVUfGHy/8meXAC0QqPHFcjejGxORa7DCWSpOMAhewbyUmksFc6h2tgq8XPZoMthk8X3VjVjVEGaOrVwjccEsb+KyqnHDguDUu06C/TG+J63mDjPEfty+PBh5kVDpSRq6eb45ptvmH+NOy6++GLs3r2bPZ4M/s4880yP3xOL89BDD7GAicz9vvrqK4wYEV+1tjsMiQZcNkrw43mr5K24mx1FFCqxGfcK8X8yZAqb445jTWi3O6A6qCzAqW2rxWcHP2P35zXqvU6hVh36wHWYlj+Ifd18RIWJBm/172wF2pugFrR1ONBWK3Qpu+gzGufATeHWuvLFhcMvhFFnxM7andhSvQWqFDiqKXBz2/DNOQOQaTGgw+FESXkzVAeVlaj+t+d/6HR2Itk1GDM7mj3dYtUKlQWpEjpamRaO0G/gcPZVlUxqUjJgylDdOdx2rBG5LkF/o4+z/oagBThRQqYpE2cNOUticVQDldmMe0Dc8BPS+rHBm4TNatThqGjkBgU2XMxvq50tWfyrrmyj8iBVAtlPEJJSMLq4SL0BjkrP4ebSeklgnCCDa1ALcKKIK0YJYuMvD3+JilbvJoSKg94oiBxVdmF6LK5pRdJUY1V2UqkowFlxeAWq2qqQacxGc/UI5KKhj5WolH8OPcDXFLoGBwoeTXsqm9HablfxOVRPu/+W0kbkyyjJ0AKcKGJk1khMy58Gh8uhrpZx1S6uZW6Lq8jgqDLAEVk4KufYlF3/5+zopIwzkIsW6BJcQocRD8LVij5wDdJk8YI0E6iJikofqoMKz+FmtxZxOZSJtQAnRsZ/1DJuo7KOGqBCatVzce2HSf2FAOdwrRV1rSpr9VdJ/X9HzQ5srt4MfaIelvY5bgtrEQ29g6rBu3BUdw0e81hjpFKxKhMNda2jVc02HGtocysTx59FVfkqEH+cNOAkFCUXoaG9Qer0UDxUmHmgvRloF7PEtEKkWwwYkpvMvlWl2ZgKFte3dwmt4fOL52P3sYS+o7/pXt5wOqFGBocgMamq7KRS1zq6WTxH/XW8TKwxOKoHZZe8ZZwWZFW0jHPqUSUXpkcd3JgOGFPZ3S6hsba4yg01bTVSwnDJ8Muxo6ypi8GRwcIadaTSWJoEwNkJWAVbfFUGOKpmcJR9DXYHnSM97Mh0iQmhxuD0DVww/AKYdCbsqtuFjVUboXioSKTqTdzIMXmgmoXGyl5ceWv4hJwJ0HUOQofdiWJDHxEYE3QGICVf8SxcoBLV+H7pSEwAKppsqGhUSYlfRSxq9wAnF41IBOngDIAlB/GGFuDEAOnGdJw99Gz1tIwrfHMMJnMkTHbLHp1qs4tX8OLa6ehqDaexKDy7H2lp7jslqj5yHSYb9RiRn6rOUjE/f+T7Q/4/CobD6cLWo41dZWJi+WWgg4v/EfSxlnGaeFzeovC2QFUyOD0DnJEFqTAZEtFss+NAjbIXIDVtjmS7UN1WjRxzDk4fdLoU4PTXN/TRAEd5QapXdNoAa00PFm6yqMNRHZNqSgOSUlXRKn6gugUt7XYMlK5BeZSJtQAnRhieORwzC2fC6XLi3d3vQtFQUZuxL2qcYNAlMopclRoABQc4XFx8yYhLYNAZpHOT5ei5Oaoaaks0yB2doDcBZqE83He0cMoOUjeJ1+CkzDZZJRlagBNDXDlKaBl/f+/7aLOLHwQlQiVtxt4ZHM/Mgy+uqpssrtAS1faa7Wz0CYn3Lx55MRqsHThY04oEOGFqq5TV4hp1KDhIDciius0w4qab5IWjusniKjmHm8UAZzQvE3MbgzhDC3BiiLn956JfSj80tjfi0wOfQtFQ6AYZjAeOO7jQWLUMjsLq/3xq+BnFZ7ASFT8vk7McSKCOIuos4uJbtaOPXIPD8lKQnKSDtcPBXI1VBZWcw80iuzZQzzuotACnz0GXqMPloy5n998seVPZLeOc6eC0sgq7qNwZnF0VzWxSrmqgwPo/aw0/9JmHgSYPcI7PaxceRMENdRj1Bagk+w90DeoSEzBRre3iKjiHbR0O7BYDz2xx0KamwemjOH/4+TDrzdjXsA/rKtZBsVDBhSmhsw1o824SV5huQl6qkVHjqrOLV1j9n9zA7U47JuROwLiccexnm8TMcXKGVVaZY8yvQSUnS36E/j2ExqorFSt/Hd0mlg7z04wwtpbLSgenBTgxRlpSGs4deq7yW8ZVQq16LC4GS5e2SERCQoK0uKq2TVUB59C9NZxr2YgB3XJUCHBGmPtYi7i74Sbp+drqVSn055jMh9+qTWisgnV0s7guTuqf3sXoy2AOFUELcOLYMv7N0W9wtPkoFAkVZB6BxI0c0mRxbXGNG744/AUrUeWac3HaoNPYzw7VWtFg7USSPtHNxVgemWNMYDB1mamp7TrsBj6yYW9VCxrbSGulEqhgHd1SKjDbM8lc2yHO7dMCnL6LIRlDMLtoNmsZX7R7ERQJFVyYwSysqraLV9A55OLiS0YKreHumePYojTouLdUX2Jw1KaF83Md5qQYMTDLwu5vFVk7VYD/r+T/Qz5ACsRmcV2cwlvEk3MBfRLkAC3AiRO4SJJaxq2don5ASVBQ9h8Qzd67Nzgm9Bfs4ssbVWYXr5AAZ1v1Nmyt2QpDogEXjbioR+cGK1/4EKiqHmq5Dh2dQAtv8+/v9SFdlg0qCnDI70dvVmyQWtkkTBCn9XGkDMvEWoATJ8zpNwcDUweiuaMZyw4sg+LAP8RU+yeRrooZHNXaxStkc+TGfrw1vHvmyMoXAc6haqGQIDUgmitIVQXokgBLtteHqFJoTCVxBZ/DTWKwSeuj2VYpuzKxFuDECYkJiWyODhcbK65l3JgGGJIVe2F6IIjNUZV28QpYWEl3s/zQcnafXy8EW6cDO8sFF+3JJG7s8wGOvIPUgODnz88MI/fht4pbLxV+HfoCDzbZuXE/hzKBFuDEEQuHLoRFb8GBxgNYXb4aioLCM49guze6d3Goyi5eqv/Xyrb+/+6ud1lr+MTciVJrOIGCm06HC9nJSehv7hA6iWTkoBozqGVcQxDX4JjCNCYoJ2E5CcxVA4Uwqf4YnCmMRZWfDk4LcOKIlKQUnDfsPA8RpaKgmgAncPbPuzhoYq7d4YT66v/yO4ftjnapNfxnY37m8TseaJIuI4FvDOYsobOoL6EPXYMU3IwrSlNfmUqhQvFOhxNbjzW4MTjy08FpAU6cwZ2Nvz36LUqbSqEoKDjzkGDvAFqqAmaPQ3NTkGLUo62T7OJboArInIWjcSb17fUoSC7AqQNP9fidpL8h4Sk/9nT51P5jBtUwOMGVGKUylaqYVGWuo7srmmHrdCLNpMeQnOSuAE0LcDRwFKcX44R+J8AFlySmVAxkvDkGDXZRiuJGYgB8QLCLV+FkcZmeQ9JYvFHyhuQbRcM13eEpMD7qt/tG1eB6h/YmwCZoktRaovLUwqmJwZHnNRgInEWbNDATidRGJWlwtABHg5eW8Q/3fYjWTuUMPlTqhekzc/QhblT1ZHGZZo9rKtZgb/1eNtbkguEXePyutqUdR+oEDcaE/n24g4pgTAFM6YoscfSGwdlVrqLZcApdRzdJNg0ZQHuzEGTLaA4VQQtwZIBZRbNQnFaMls4WLN2/FIrbHGWo3wg9cwyc/XNHY43BiT7e3PmmJMRPN4obuAj+/g/NTUa62QA0Huu7JSoZB6mRmCTeHUXibDi704XtZSqZDSetoxWCH5BCsFHqoHJLMqi71igO8ZUBtABHZi3jJDYmh2NFgEfqMtscQ0IIwjjO4OyrbkGTTTkLkdICnMNNh5kmzZ3d9K6/yQypvKFayPAchgSnI2j9hvtsONUwqTRuI5HcuV1dZocyR11rh9TJ5mm0Ka9rUAtwZAIawJlqSMWhpkP44dgPUAT4h5lEuiTWVSJCyP5zU43on2lmg5u3ivNXFA8ZZv+cvZnbfy7TqPmixnlnm1wX15hB6QEObeouB5CgA1LyAj5cdUJjKo0rLFncLGqgGItqkS+LqgU4MkGyIVnSGryxUxBXyh7kOEriXJZ5kBOpAhHi5tg1l0ol2aPMNsfG9kZ8tP8jr63hBIfTJTE4U2mjo2izL2tw3EWdMjmHIYNvjszkTxfw4UzzoaYAR6aJRlD6GzHYlGOLOEELcGQEKlPpEnTM9G933W7IHjJvM45GgMMvaNXocGTGwi3ZuwRt9jYMyxiGmQUze/x+b1UzWtrtSE7SYWRBqjgqRDR90xgcKPoaDDL7H98/nXU1VjTZUN6o8DExCj2Hm6QApzuL2r/vBDh1dXW48sorkZaWhoyMDFx//fVoaWnx+/jbbrsNI0eOhNlsxsCBA3H77bejsbGxRx22++3dd9+F0lGUUoTTBp2mLBZHYZlHD4RIrboP/FOFXbyMWDhyLOZWCcTe0HXdHRsPCwvrxAEZbJOTNgT6P/qayZ9avHBCTDIsSXqMouBWTSyOggIchxuLyh3e+2SJioKbHTt24Msvv8SyZcvw3Xff4aabbvL5+LKyMnZ74oknsH37drz66qtYvnw5C4y645VXXkF5ebl0O+88wRFY6eC0/KcHP2VzeGQPBV2YPUCMRWtgkz93jC1Kg0GXgNrWDhytb1NH/Z97qcT5HK44sgLlreXINGbirCFn+e3cmNKDGpfXwhpTKH0eVRibo+qExgpKFPdVtTAW1ZKkw4j8lL5ZoiopKWHByYsvvoiZM2dizpw5+Ne//sWYFgpivGHcuHF4//33cc4552Do0KE45ZRT8Mgjj+Djjz+G3W73eCwxQgUFBdLNZPKdvbW3t6OpqcnjJldMyJ2ASbmT0OnsZHN4lLO4KtCDg7e364w+Jxh3h8mgYzNxVDV4UyaLKxcXXzLyEhjpnPgLcAZpAuMe12BbHdCpwKBbMmoMIcARmQONwYk9NonX4IT+6dDrxBBC0sH1kRLVTz/9xIKQadOmST+bN28eEhMTsWbNmqCfh8pTVOLS6z2dTH/1q18hJycHM2bMwMsvv+y3XPDoo48iPT1dug0YMABKYHFoDo/NLs8hiD0FjvLPPHrAXZzqpRzSZwz/ZLC4bqvehs3Vm5lj8aUjL/X6mAZrBw5Ut3pS431dYEwgoz9DsmI2SJ8MTlroDM62Y43osCvEVkMlZcZN0oBN8RokB23J5K+obwQ4FRUVyMvzbPmjICUrK4v9LhjU1NTgz3/+c4+y1kMPPYT33nuPlb4uvPBC/PKXv2TskC/cd999LFDit9JSec98OmXgKeiX0o/N4Vl2YBlkDRlsjr2nxkPLOtQnNI7/OeRjGRYUL0CuJdfvwkpzbzKTk2Rd+48plC72D2OW2OAcweSx3e7Ergr5MvJBg58/8gMiXyAZY5PYQdqjg4oCbXLWVnKAc++993oV+brfdu3a1esDozLSWWedhTFjxuDBBx/0+N0f//hHHH/88Zg8eTLuuece3H333fjb3/7m87mMRiNjgdxvcgZlsTR/h4uNZS1mVVDm4ZsaDy3r4AzOjmNNaLfLezEKCjzAixMLV9FagS8PfcnuXzXmKp+P23C428JK0EpUApQa4DjsXeL2EMobnoZ/Kkg0UvIFHyCnHWithlzRZOvE3qoWj3Wwi4GTV3kqrADnt7/9LdPX+LsNGTKE6WKqqkQBpwjS0VCnFP3OH5qbm3HGGWcgNTUVS5YsgcFALo++QRqfo0ePMq2NWkCeOOSNc6DxAH4s+xGyhYIyj3Dt4btjULYFmRYDOhxOlJQ3Q/GI8+a4aPci2F12TM2fijHZY3w+rof+hqAFOLLSUYUMWjfIuZ2cfJO9M3e+wMuUqmBSyf8ntUD253BLKXWPAgOyzMz4NJw2/1jCU9gSBHJzc9ktEGbNmoWGhgZs2LABU6dOZT/7+uuv4XQ6WUDij7mZP38+Y12WLl3qVzzMsXnzZmRmZrK/UQtSklJYkEMMzus7XsecfnMgS5DzKGUe5ERKXioyGrQWEGGWNyh7pOxl5e5qbD5S35XJKBVxDHDI82bxnsXs/s9G9zT2c29NpcXVo/avmfwpn8Fx774JMOxW/Z1URcL7Qeewn7BnynfAZqYikoyoaXBGjx7NWJgbb7wRa9euxY8//ohbb70Vl112GYqKhIvx2LFjGDVqFPs9D25OP/10tLa24qWXXmLfk16Hbg6HwA5QRxV1ZlEb+b59+/Df//4Xf/nLX5h/jtpAc3hoTtVP5T+xycryzTwKlTl0sxcXJp+DpIpOKo9hf57ditHG0n1LmXsxac5OGnCSz8ftrmhGa4cDKUY9RuSLw/w0kz/lBziNoXdQcZAXEoFmItFsJMVDAedwk/uAzV6IxFXhg/PWW2+xAObUU0/FmWeeyVrFn3/+een3nZ2d2L17N6xWYZHauHEj67Datm0bhg0bhsLCQunGhcFUrnrmmWcYQzRp0iQ899xzeOqpp/DAAw9AbaBF/9SBp8rf+E8BF2bEAxzxAlcFPU6lgUS9wMJxX6AYwOF0SOJi6hzU+bHp5+WpiQMEF1sGzeTPS5BapnqBMQeJjIflpahndIrMy4wul0tK6Lzq4NRQogoF1DH19tuCM6k3FBcXewhoTzrppICCWmKF6NZXcPWYq/Hl4S9ZN9XtU25HjjkHsoPCBsUx2Nu7xHzhBDj9hQDnsJg9ZvGuHiWCs3CNpcI5jFG555uj37DJ4alJqTh/2Pl+H8sDHDZ/SgHUeMyhxGswAueQ5lKR8RwJ0E8ZlQ9FQ+aJ4qFaKxqsnUjSJ0peYHK/DrVZVDLHpLxJmJAzgRn/kS+OLCHzzMMr+CKiNwGWrJD/nCboDslNVlH2GHs/o9d2vMa+ku+NxWAJrvY/SAtwlDBTLOQSVYhWDRxTxM8DH+GhaMg8wNkodjGOK0pjQQ4DERJ9tUSlITL42difSd0m7Q4ZdorJ/MIMKG4MweTP11wqxSPG53BL9RZsqtrELBEuH3W538cSQ3awRjD4m+IubtQ8cGQ5Uywk9DJInSYGOFQq7nQo3PBP5pYb68UAZ1qxW0JoawQ6W2Ur9NcCHAVg3sB5KEwuRJ2tDp8c+ASygyIDnPBaxN0xVVxc1x9SA4MTWxaOszdnDT4LeRZPQ1BfwsahucmMOZOgdVAp3+xPyv7DO4dDc1OQZtKjrdOBXUq3bHA/fzL0PttwuM6zi9F9vTBnAUn+Wdh4QAtwFADKcqmjikAt47Iz/lNiiaoX3Rsc0wZlSdmjXfHZY5HnhhNFlDaVssGahJ+P/XnAx/cYsNmLGUaqhtKuQ/dht2GWqBITE7oSDXEDVixSyAcnASCW3iqv/6WxrRN7KgWDP/5+C7+Qb3mKoAU4CgF54lj0Fuxv3I8fjv0A2Q7clFvwFYXuDY7heV3Zo+IN/2KY/VPnlNPlxPH9jsfwzOEBH8/1FVxvEUkWTlVQGoPT7K6DC27YrTfwDZc7XSsW+qQus0OZBakbxSSjONvSZfAn8w4qghbgKATUaXLhiAvZ/Vd3vApZQcaZh09EQKBK2eMUtWSPMar/N9ga8OG+D9n9a8ZeE/DxxIxtOdrN4K+HuFErUSkywGnsvQ6OMEUtAY6Mz+FG8b3tmWRoDI6GCIGcXvUJeqytWIsdNTsgG8g48/CJCF2YvG2ZC/AUC2nkRhngjF657b097zH34pGZIzGzwLejOceuimZYOxxINeoZYyaBTP7sbbJeXGMOpZWoInQNktifvJHKG20oaxA/E0qFTM/helFnyMvyEmSeZGgBjoJQmFKIBYMXsPuv7HgFsoJMMw+fiFAHztRi3qZar4Jhf4lRHfbX4ejA2yVvS9obGnkRCFxgTMaKxJhJ4BuAZvKn4Guwdy3iHJYkveTLongWR4bn0O5wSoamHvobjxKV/AZtErQAR2Hgokwy/yOxpmwg08zDKzptgLUm4tnjMSVnjzqDEORE8RxSB2CtrZZ1TZ0xODizzo3c/6aHwFjT3yhhc/SLCHbBqUaHI8NzWFLezHSGpDf0YFEJWolKQyQxMmskE2eSSPO1nUKrrSwgwwszsLjRDJi7bZxhZI9ji4Tscf0hpetwoncO6fPKtWNUajXQ9Ogg0NVBlaGohTXuM8Wcwuw+WSOC51A9AY78EsX1vD18UKYni6oAHZwW4CgQ1429jn39aN9HzBtHXhqOcvQVcSMHF78qvkwVxQCHOv8ONB5AsiFZEssHQk1LOxuF0WN6MUEz+fM/U4wcjftIiYowTSwV7yxvQmt7bAfGqj1R3HDYy5gUhejgtABHgZheMB1jssfA5rDh3V3vQhaQYeYRzRZxb4ur8oXG0TuH5N9EuHD4hawjMBRh48j8VE+DP3aMmsmf15lirKNRXhtkLBicwnQzitJNcDhdUtedIiFDs78NPMAR17meOrgc2ergtABHgSBx5rXjrmX339n1DutKiTuUNOwvwgZxvLOgpLwJLVr22AM7a3diTcUa6BJ0uGr0VUH/3Tqx5Dd9sJcyombyJ5uZYmGhsw2w1kY00ZDaxZXsLM7PH40/oDEIcUZZQxvTF5LOkI+mkSDz8hRBC3AUPL6hf0p/NLR3+YrEFXyjoQ+9TDIPn4iwQLUg3YR+GWY4XcBmJc+lipIXzqvbBe3N6cWns07AYME1TdPdZ99wNIgC+/QBETpKlUCGJQ6v4MdHQ1ZN3TbOMMHnUm0QdVuKhIF0gVmyOYfrRfaGutRIb6ikDiqCFuAoeHwD76iiuT52au+Vw+ZImQfVZhUxpiFymYcqRI5RyP6p0+/zw5+z+9eNE7RjwcDaYcf2sqaew/0I5NOjgMU1LlBKqdi9PBUBHRxhqsikkhbOSdmGUiGjoZsbxCSjR3u4QoT+WoCjYCwcthCZxkwcazmGrw5/Fd+DoRpssjg0sVFG7ev+ApyMgRF7yi4djkxE3zKp/5NfE3VQzek3B6OyRgX9d8SEkZ6iSGTHPEDzixwdgm+PjOnxuEApDE4UROKjC1NhNujQZLNjX7UwN0mRkFGZcYPIhnkNcLQSlYZowqw34/JRl0sbSdyHcGYM8Awg5IoolDf4ArBJ3JgViVSxfBShkRvV1mqpfHr9uOtD+tt13DnVW3mKf77oeMm/R4PyAhxJQxU5Bk6vS5R0IupgUuN7Dlvb7dKMPZ7AeUABLKoW4Cgcl426DCadiQk5aYRDXME/6DyAkCNIuNfeGPELc1RBGpKTdExkvLtCoYM39caIjtx4s+RNdDo7MTF3IqbmTw3pbzkTNt3bwsoZQk1/46e8IfMkI0pt/tJkcUULjeVRZtxSKiRrxKBSl1oPNByR/XWoBTgKR6YpE+cNO4/df2V7nMc38A+6nEtUPPsngz9jN1fOXoC6DLjbrqJFjhHKHps7mvHe7vck9iaYsQzu1vDcU2j6YH8CY/lmjnFnUen8ydnsL0r6DWl0inYNRkxg3GPAJoE+W/wc8s+cDKEFOCrA1WOvRmJCIn4s+xG763bH70CUEOBEsftGEhor2dE4Qtnjot2L0NLZgmEZw3DigBND+luixVtpwKZJjxF5qVE1iFMdyAeHXKKp6UDOppv8Oozw5jhlQCbTLB+saUV1czsUCZlocDaIAQ7vTvMAc8u2Awlu3ksyhBbgqAADUgfg9EGns/svbXspfgfCFys5l6iiWN5QheFfBLJHm92GN3a+IXVOUfAdCrj/zbTu1vDdz6GMM8e4IdFNeC3X65BZ/PPrMHJCf/Z0FgMzhnT/HCkOvPmBzl+cdJUOp0tiUb0LjN18qHTd2sdlBC3AUQmuHy+IOKkl93DT4fgcBM+o5SwyjuLmSCUq2o+P1pM5lgzMF+MU4PARIoXJhUEP1fSmv/EqMCZoGpzgNki5MqlkI9HREjUWbqZY1lx7UKEBjgwsN0rKm9DcbkeqUY/R4qR2JSYZWoCjElAL7tz+c1lLbtxYHL7hUBsvTeyWI6Ko30gx0uDNdHUsrmGKVMmPiTr6COTTFOxQTQ7qBOQdVF4N/giayZ9/8PeFi0DlBr45kq1EFCz+ZwzOZl/XKPUapPckJV+43xCfZHWt+N6Rpon0hUoUGBO0AEdFuHH8jezrx/s/RnlLHOrvJNw1JMvGw8ErJP1GdC7MGWL2qFh63N2ROgx8cegL5stE/kwXDL8g5L8/Umdl2okkXSIm9BeCRQ+0NwM20S1a0+AEsGso7VP6Gw4+2mNXRRMa2zqh7CA1Pudwnbh+8fWsBzQGR0OsMSlvEmYUzIDdZcerOwR7/JiC1H0ZCskeo3Rh8gVBsQyOe3mDHINDZF9e2i6wh1eOvpL5NIUKzt5QcGMy6HwHqKZ0wOSFOtcQ980xIKJcYsxLNWFITjKTr2xQqvFmHINUl8slrV8zFM6iagGOynDjBIHFeX/v+6hpq4n9AchZh2PvENT/UbwweVllT2UL6lo7oEgNDomCySmYSo0h4Ptj32NP/R5Y9BbmzxQO1h0MpL+JLgOnCvRxBsc90VBsmcpdaBxjHKhpRW1rB4z6RIz3xqISNAZHQzwws2AmJuRMQLujXepkiSnk3CrOymYuQOdmaBdhZCUnYXheinLLVOQMzMtUIbBwjL0RtV8Xj7gY6UYfC2MArPNn8OeR/WvlqaAYnHi7m3sD15VEuIPKW6KhWCY1juvoWvE9I1doo94Li0qfKY3B0RAPkKEaZ3HIi6SRu/bGCnJmcNw3xwgN+FNlmSoMker6yvXYWLWRiYp/NuZnYb1sbUs7DlS3+m5NZcekjIU1ruDXoL0NsNZCdohB9s+vwW1HG9ngVuUyOLEv9a8V1y3ejdYD1NlFHV4KSDS0AEeFOLH/iRiROQKtna14e9fbfebClAM1rgqhcRjn8Lktz7GvJCzOTxY7QEIE9w8akZ+CDEuS9wdpJn/Bjdzg5mtyZFJjEKT2zzSzQa12p4vNh1McZMDgzBC70Xx3weUChtB1drGEFuColMW5YfwN7P5bJW/B2mmN3YvLmsGJjX6DBzjbjzWy2VRqD3A2VW3Cmoo10CfqQx6qGZL+RkG1/7hDrqabHeTtUhf1c0hroKJ1OPy9IbaEOgdjhGMNbewmjJ4RBpcqmUXVAhyVgpyNB6UNYiWqxXsWx+6F+Yee9C4hduFEHY2x8W6gwXQDsyygoeKKnGocokiVszcLhy5EYYo4kTwMrAlEjbNj0kTGitbC8c2RNFrUCRdFcAZi7UEZlukCwZgKmDJiHqSuE6/Bcf3SkWzUKz7JiGqAU1dXhyuvvBJpaWnIyMjA9ddfj5YW0cHSB0466SQWfbvfbr75Zo/HHDlyBGeddRYsFgvy8vLwu9/9Dna7AjPlKEKXqJOyaWoZJ9FxTJBaKMwnoS6clkrICnxzjMGF2SVyrFU1g7OtehubgaZL0Elu2uGA/Ep2lAl6seOG+KDGHfYuh2UtwFEmgxPDzZEzOFSi6rDLLNmSaTfcGqk93IcGjqAxOAIouNmxYwe+/PJLLFu2DN999x1uuummgH934403ory8XLo9/vjj0u8cDgcLbjo6OrBq1Sq89tprePXVV3H//fdH819RJM4ecjYKkgtYu/iHez+MzYvSXBJu9y+3MlUML0xF28WH0IXz3FaBvTlryFlsJlq4WH+ojjFeg3OSkZ/mw92Whke6HMIwSe70qkFhDE7sHHCH5iYjOzkJ7XYnth1Tog4n9nrGdZLBn48kw50J54lQXwxwSkpKsHz5crz44ouYOXMm5syZg3/961949913UVbmf84NMTMFBQXSjRggji+++AI7d+7Em2++iUmTJmHBggX485//jGeeeYYFPd7Q3t6OpqYmj1tfgEFnwLVjr2X3yYCt09EZYx2OjITGVC6LoUCVZ49bShth63RAUWDvT4LQhdPq20uppLYE3x79lg3T5C7a4WL1AYHpOm5IEPobCqBpqKQG5Y1riCGDo3wdTmxnitW0tGNfVYt/mwaFCf2jtkr89NNPrCw1bdo06Wfz5s1DYmIi1qxZ4/dv33rrLeTk5GDcuHG47777YLVaPZ53/PjxyM/vyuDmz5/PghZii7zh0UcfRXp6unQbMED+1FqkcOGIC5FrzkV5azmW7FsS4+xRRgyOtQZgZbqELp+XKGJQtgV5qUZ0OJzYUtqgvC6cVN6F43uDfH7r8+zrGcVnoDi9uFcvufpAnf/ylEeJUf6ZY9whV7O/GJc3FO2HE+My43qRvaFp7D67GN2Ppy+XqCoqKpg+xh16vR5ZWVnsd75wxRVXMHZm5cqVLLh54403cNVVV3k8r3twQ+Df+3peep7GxkbpVloqs4s+ijDqjJI24oVtL6CDtDHRBo/s5VT/58dCG7fez8UbIagme/TBAJBj8VdHvkICEnDThMBlZ39osnXpb2b6o8Ylgzj5Z45xR7p7F45/3WNMEWOBKr8G1x+qh4NqoEpCjFm41QcCzJ8idFiFZFGtIuN77723hwi4+23Xrl1hHxBpdIiRIZaGNDyvv/46lixZgv3794f9nEajkZW53G99CReNuAh55jxUtFZgyd4lfdMLJ0YdVN50OGuUKDQOMM/oha0vsK/zBs3D0IyhvXopd/1NQbqf6dL/3955gEdRdX38n94T0kkgJAFCaAFC70U6NroUKX6goCAqvHZFRMUuUkSsiL4gigL6goAU6UjvJUAoCSG9k96+59zZ2eymbt+Z3ft7nn12s2V2MrNz77nn/M85mQoDx1s/b5FVQH26xCwlKXlxlKt/03jhWgV5wsPJnpVruJIoM2mCib1wR2OFcapHMw28qI4qWV6WZOAsWLCA6WvqujVt2pRpZ1JS1HvZUKYTZVbRa5pC+h3ixo0b7J4+m5ysnp0j/q3Ndq0Jk3txvEPVV9xSQDS2TLjqEAcKWj0WlcpMh1OHkXoz6yZ23t7JHs9qN8tgK8c608NVf08NFL8vjoYi1XgJ9YJLNOl1SPVcOiv0JKLOS3bnj7JRSwqNrr+JSc7VIEysMo4asRq82Qwcf39/tGzZss6bo6MjevTogaysLJw6dUr52b1796K8vFxptGjC2bNn2X1QkFBfg7Z74cIFNeOJsrTIK9O6dWtt/x2r0uIEuAYgOT8Zm65vMu6XiRMQrbil0gvHDKv/Zv7u8PdwYlkcsqumWofAcdW5VahABR4IeQCRPpF6f1WlwLiOgVXtHHIDRzsPQJy0esHZOxutF1xNdFP8rkRDWja4+gAOrirHznj8q7gGWzb0YP30LEF/Q9RSyUd/WrVqhWHDhrGU79WrV6OkpARz587FhAkTEBwspBEnJCRg4MCBLAzVtWtXFoZav349RowYAV9fX5w/fx4vvPAC+vbti3bt2rHPDBkyhBkyU6ZMYenjpLt54403MGfOHBaKMiSUkk77bSk80+YZVpRty9UtGBEyAo52RtKiOPkD7jQJlQMZCYCbn96bJKOZBOo6k3nb5AYOhWt7NPXFn+fu4Uhsev0TuCQFjuqTY0xGjNJ780yHZ/T+GtLfUMVnoltdGVRUA0d0j/MQlUHCjJbaC64qPRWe1GM301FaVg57O5lk4NExonOYFiNch776hYL1Dk+pecKbWLeBI2ZDkVFDRgxNTmPGjMHy5cuVr5PxEBMTo8ySokls9+7d+Pzzz5GXl8eynegzZMCI2NnZsZo6Tz/9NPPmuLm5Ydq0aVi8eLHB9ps6I5PhRB4oS6IVWuG1iNdQVlGGa7HX4ObgZrwv67MUKC8F7qUB9vqXGqffT3h4OPuN6ISZwhs0uJKBczQ2DRjcArJBPE40oJEXTjEhrTq7it0PDRtqEO+NqL8J83VlFaBrJeeuUAPHTqXPEkdeoWIzrf7bBHvB09keOYWluHQvB+1DpK8dUVtokIFjZB3OUYUHhxZkUlsoStbAoYwp8sjURlhYGDMmRMig2b9/f73bDQ0NxV9//QVjIRo3lAVGNXloJW4p+BT6ILUgFfY29gj1CmU1TIxCpg1APbAoa8lFvwGFwppUO4mKPjZp0kT780E1cMSVh4kvzJ7NBO/V2fgs1tXY1dGol5zhEDOViu8LmTiuPriUfgl74/ey38wz7fX33micHq4anqIBn9fA0Qzxty5OSubGTJMj6XDo9/X35WTmSZWXgWP8hI3knELcTM2DrU1lOK9WuIEjbygsJRo3FCazNAKcApBTnoOS8hIUoAC+zkb6H51dgIoC4RfmXEdmjBbaLzJySKju4OCg3YdJ2EjCalt7k9TAUSXExwWNGriwBnYkNu7bwnTaA72gLsFuAUBeijC4uvpg5ZmV7KUHwx9E0wZNDfI1GutvRC+ETAZWScANHDVPqmDgpOHp/sYL9RhPz3jb6OGpNsFe8HJxsCgDhy+FqiBqbshzY4nQ6tvPVfAqUAuH8opy4xWLI0oN0wNLDE2RAao14kVJXglqJWFCmA5HEdcW3cByTFM9m3IWhxIOsZ5Ts9ur94Yzuv5G9RzyDCrNEY8VeeAKsqzbwGnup2xFIKuMRp9woxs4Ryh8ron+pjC7shO8TIT+3MCpBUsKS1WlgVMDONg6oLS8FBmFRsosEAXMBkpJ1+t8mHnVIca1yT0uK1Tc4yvPCt6bR5s/iiaehhEYHr8p6G9C69PfEDyDSnuc3CuzlaSgwzHjdRgR4A4/d0cUlpTjrJwyGsVjlXHL+PqbZhqGiWmBTN3OZQA3cKwQ8uL4u/orvThl5WWSN3D0wsz1U8SB48LdLOa1kJuBcyL5FI4lHoO9rb1B6t6IHLohrBx7K1bXdcJr4Mg7TFWcJ4Q7zWTgCJ5UP/ktNMRjRdWDi/RP1qhKfEY+4jMKmE5JbGtRf5hYPtcgN3CsFPLiUJo4GTfphenGC1GRgWOsMJhMPDjBDVxYlV7yVpyQU9sG73CqWoKV2RfZn2MixiDYXdEp3tQGDq9iLG8DR/x+qn6rZ9KBvuniouZEFlA1ahcf9WvACN6bdo294O5kb1H6G4IbOFbA9OnTMXLkyGormkBXoYdXekE6C1dVZc+ePayekaa6F+ry3rhxY5bizwS91NiSMFUXcwmHN0QRraxWjz7hOOrijNMogKOto94dw1VJyi5knYsp8liva5z63yhX//JZPUoCqRk4oqbEjAbOmfhMltEov3No+DDVv4rxSDw2dcINHI4UWbZsGX744Ydqz3s4esDZ3pkJjSl1vCovvfQSq0FEtYc0gQowdu/eHZ999plQN8XAQmOdkcCFKQ4gcjJwyhuEYrm30M9ofItxCHRTb3KrD4cV3pt2jbzq7lys6hp3otWsUHafI1MDx4zXYBMfV5bRWFJWgRO3M2Ht57CiokI5HvVo6ieLc6gt3MCxAry8vNCgQXW3sKoXJ7MwU61H1aFDh1hlaSq0qA1PPPEEvvzyS5bOLQkdTkkBcF/RZd473OweHGr4l5EnAV2SBvyddQWXnJzgWl6OmWEjDLpt0cDppVV4Sh7VUyUFN3DUxrvKhYaiI7YVZ1LdTMtDUk4hHO1s0SnUWxbnUFu4gaOhpUsuTVPfVIsgasJvv/3GurC7uLiwGj6DBg1i4aKqIar+/ftj3rx5zEPTpGET9G/THys/XInU/EovzoYNGzB48GA4K2rY0L7Q9qjTu7hf1DiVQlILFy5Ufo4+Q8+zgo32ooFjRg+OWCCLut+acfVPPalaBLrLZnClOknLFZlT07Nz4JtnuBUv/X50EhjLaGCVDOIxo+vAGMkEmiKRybFncxnqcIyUSXXwmjDeUzNSF0c7yRZL1Qde6E8DCkrK0Hqh0H/HlFxePFTjyrdU5XfixImsP9eoUaOQm5uLgwcP1mokrV27FvPnz8exY8ew7+A+zJo5C9FdozFl5BQWtqLPTpo0SW31Q58hA4rabTz33HOYPXs2GjVqpGbgUL2aDh06sM8P7BZV2UXYXKiKU82c+t8nwh/Xku/j4LU0PNTOcGJdY/D7td8RnxsPH9hhWnauIv7fzyDbvp5yHym5RXB2sEVHjVaOPINKZzyCBE8qeVGpYaO5eghJxMARQzEXErKRnV8CL1cti4ZakBfu4HVhkaFR8VEzFkvVB27gWAhk4FBYaPTo0ayVBUHGSG1Q89K33nqLPY6IiMAXX3yBYweOYeiQoazOyZ07d5RNUUXImPnqq68wdepU1s6C2mWcOXMG9vbqPyP6HH1eEiEq5cBq/smRBpLvDt3CgeupzPCUaq2l/JJ8rD63mj1+2j0SrhW3DLp6PKQYWCkt1dnBTjaToyyxtROMmvQbwnE0h4FDq3+JZME19HJG8wB3JnA/HJuGEVFBkDxiaF30wtE51ZPi0nJlBlWfCC30N9RHzADfbyq4gaMBLg52zJtiju/VlPbt27OmpmTUUBiJuq6PHTsW3t41r5DF7uwiIY1CkJ6WjtziXDbBFRQUKMNTqowbNw6bN2/GBx98wLQ2ZBxV228XF6GBqhiiMqfIWELhjW7hPnCyt0WiIoMoIlCaxbJ+vPwjKx0Q4hGCMQ17Axd2GDSDQ6vwlMTOoSyh4yYaOOF9Tf/9bPVfpFj9K3qcmZG+Ef7s+jtwLVUeBo5nMGDrAJSXGMwLd+oOZZKVseKHrRp6Wuwig2twNIBW2hQqMvVNmxU+ZTrt2rUL27dvZ9lMK1asQGRkJG7dqnliqtrPyc7WDg42wnPJ+cnw8/NDZmZ13QUZLqdOnWLfd/369Rq3TRoc6h3FOj8T1AXaXPF/CV2Y5K3oGi7UtDig8GJIDapsvebiGvZ4XvQ8OPg2V7xgGAOnpKxc2X+qtyYrRwqx8hCV5Kvharz6N3GrlJroFymEZPZfEzypsvHCGTBMdfB6qjJsbktdNmU0jmoDN3AsCDKIevXqhbfffpuFjkgPQ94WTSHtDW2DPDhR7aNYXZuqLFiwALa2tsyQIi3O3r17q73n4sWLiI6OFi5MGzvzCo0lNjnS6pGg1aMU+fr818gvzUdr39YYEjak0j1OA5wBJgOhq3oZfNw0XDlSH6ViRQVXc+lH5I65M6kkNjlW9aTKKpPKQEbqAaWB4yfLc6gp3MCxEEgsvGTJEpw8eRJxcXHYtGkTUlNTWaE+bVo4iN3Fu/TrwlLFVdm2bRu+//57rFu3jmVLvfjii5g2bZqap+f27dtISEhgGVcMZS0cM+hw2OpfWhemKOg7disdhSXSavoXnxOPX2J+YY9f6PQC+z0otUtFOUB+hsH0N5Suq9HKURzQSSzroH9XequEGzi1elLJi2Nt5zD9fhEuJuRo7kWV4DnUFG7gWAienp44cOAARowYgRYtWrACfZ9++imGDx+u1Xb8XPxYuGr46OG4dOkSYmJi2PNkLM2YMQOLFi1Cx44d2XPkKQoMDGTZVCI///wz0/+IQudKobEZPDhqq39FZ2wzQ6niDT2dWdO/kxIrNvbZqc9YReuewT3RPai78KSDC+ChEJsbQIcj6m80XjlmxAr3Ps30/m6rhRs41einWGhYo4FzSHENtgryRICHhosGGfahIswfEOUYBPLU7Nixo8bXqlYx3rdvX7X3bNmyRfk4wDWA9aiaNHMSM5K+/vprpqmhzKmqOh7yGIkUFxdj9erVWL9+feWbzFnNOF0xOVJaI03UEoBCgDS5bzx1l7mJNV5BGZkTSSewO24389r8p/N/qrvHc+8J3pTGnXX+jqz8YpyJE4y63opQncbn0Lepzt9r9Yjh2YIMoDBb6G9kSiRq4Ly77QqO3cpAQXFZ/XVgLKhdw4FrivRwTcceapVyP1l9P2QC9+BwquHt5A0nOyfMfH4mfIN9UU5pnhpAobHXXnuN6YAkYeAoV//Smhz7tJCWDodadXx84mP2eGzEWER4V8mMU+pw9BtcSVhNDUcjAz1YyXyNyLgpyXMoK5w9AVdfozVslKOBQ6niwV7OLF2awsWSR1ULpwcVFRVKgbFG9W9Uv9NZfq1SuIHDqbmFg1sgPL08MeXZKSit0KwxXfPmzTFr1iz1J+2dzejBuSHc+0orvNGnuR+rOXg1KRfJOYXm3h38GfsnrmRcgbuDO+ZEz6n+Bh/DZOHsixEaZvZXZLFoBA9RGXaCFA1GU1Gcp9IoNUxSY5w4wYseDUkjhoYo7F6QpfNmriVXFtnUqD2D2jiqyKiUEdzA4dQITXZuDm7M4qe0cZ0RU8WphoOpU8XTpTk5ers5siaTqtVEzQVlzC0/vZw9ntVuFnycBfGloT045eUV2B+Tqpamq12ISlrnUHaIx080GE2FaFDRyt+lej88aehwFAaYlHHyAFz99PbiHFB4jbuF+2pWZFPVwJHYOKoJ3MDh1LrCaejWkD3OKcpBXkmebhtSSxU3cSZVhnQnx74SCVN9f/F71kmeivpNalXZmsPQKaoX72UjPa8Y7k726BxagxFVE5S1VZhl9kapFoG4+hYNRlOhXP1XLwhqbno2p4QKG8Sm5uFuZj4kjziOicdUB/YrxhuNRf5q4yj34HAsCKqL4+0suDGT8pJ0K4pFsRhz6HBoX9NF/YaEDZzrqSgjYYoZSLyfiB8uCQL0+Z3mw1HMeKuKaFxQV3YSHOrAPoX3pldzXzja22q3+qcsLkdXnb6XY7jJUSckHN7wcnFAdEgD+YSp9DRS7xeVKvVGD7QMsAovKjdwOHVCGVWUWVNYWojMwkz9wlSmNHDyUhUp4jaVHggJQQMrDbBZ+SU4rcgsMjVLTy9FUVkROgd2xsAmA2t/o6tPZeaNjmGqf5T6G+sYWKU7OZrawJH2OewrpzCVnufw0PVUlJRVINzPDU393S3mHNYFN3A4dWJva8+MHCIlP4XVSdF+I06mr4UjXpRU/0b8fglhb2eLAQotyp4rKWZJC99+aztsYIMXu7xYf1sQ0Qumw+oxM6+YVTDWXWDMM6j0Rjx/+emCUNVUiJOxn/RCVKq/R9LCSa3wpqENnD2KcUYr7w2VFRBF4hL0hNcHN3A49ULCUyd7J5RVlDEjR2vMEaKSQfbNA60C2f2eK3qIuHWgpLwE7/37Hns8PnI8a8tQL+IElXZN6+8TuqcDLRt6IMhLi3pEPEXccDi5C9WgCTF0a+UhKqJtsBcCPZ1Y+xCxR5osDBwt5QLl5RVKL+pAXcJTbgFCuQGZwQ0cTr3Q6j7ITRgcKUxVUFogfQNHBm7VfhH+TOR4PeU+4tJNJ3Jcf2U9YrNjWb2jZ6Of1exDokhUh9WjuHLUKjwlk3MoK5ReOBOFqUgkLnqLJGqkUruQB1qKCw2Jh6nYMbQR2qZQCF4LzidkI+1+MTxI5B+mochfdZEh02uQGzhWwPTp0zFy5EitP7dnzx5WIbmsrIyljHs5CTqMxLxE7QTHKqniq1etwsMPPwyjI4PURi9XB3QJE0Tce66axouTnJeMVWdXKftNiedUcw9OzR3k6+oeLq4cB7fWwsCh3xcPUclbaCx+D3URl0gl8ZoQf5fkSZV0d3HqxSa2nNHyHO5VeIlJc6SxyF/1e7iBw5Eqy5Ytq9auQRNeeukl1tPKzk5I8z6y8wieHPskOjfrDC8vL/To0QM7d+6sf0N29spU8f+bOgmnT5/GwYMHYVRksvIYaOLV46cnP2Xdwtv7t8ejzR/V/IOigZN+XSv3+LGbGcgtLIWfuyM6hGhRBZVWqBT/ZyJxaZ9D2WBqobFMJseezfxY4bt72YW4nCg0obS0c7jnqg76GxmEGOuDGzhWABkjDRpoV2SLOonHxsZizJgxyueOHDrCuoSv+nkVft3zK/r178e8MWfOnKl/g4qKxo625Zg0aRKWLxeKyxkFai2hDG9I+8Ic2EoYcCh9M7ewxKjfdSzxGLbf3s6y4l7v9rrQLVxb9zgTHWruHt91OUlpyFE4TmNSYyoruPIu4vI0cERvn8SvQSp410fRG03yYSpf7T2pSdmFuHQvh1Xs0ErkL+FiqZrCDRxNoBUrlRw39U1Ld+lvv/2GqKgouLi4wNfXlxkjeXl51UJU/fv3x7x585iHxsfHBw0bNmRdwlXZsGEDBg8eDGfnysnl888/x6LXF6Fzl84ICQ/Bs68/i4iICPzvf/9TdhynbS1ZskT5mSNHjsDR0RF7jiiacpYWMaPozz//REGBlloeTcmOA0gnRHVdxEaDEoXSNSltk9I3jVnVuKSsBEuOCeflscjH0Mq3lXYboBCD6B7XcHAld/+uy4JrfHBrwVOlMaKY2S9Su89xakc0NMi7aYpQjIxW/4MUC43dJhb8m6IWzm7F/9QhpAF83Z20rCUmbx0c7yauCSX5wJJg03/va/cARzeN3pqYmIiJEyfio48+wqhRo5Cbm8vCQLXFlNeuXYv58+fj2LFjOHr0KDOCqEkmGTUEfZY8LVWhVX+wWzBuZd9CRn4GcnJzmJFEUMfx77//nhlTQ4YMQWRkJKZMmYK5c+di4MCBQM49oLQQnTt3RmlpKftuMrYMTuq1ysGAwmMSh7Iavj10ixkDI6IUmS5GqFh8M/smy4ibGz1X99VjVpwQpgpTaahaC7RqJLe/i4Od9l3TRQPHv4Vu+8qpDvWColBx8X0gNxHwNPKYJhMvKkFCYxubCzh/N5v1hwv0dLYYHdXOS4IXdUhroTK9xuQmAUXZAHl6uQenOhkZGZg8eTI8PT1ZiGTGjBm4f/9+re+/ffs2y9ip6bZx40bl+2p6nTwO1gwZOGQ0jB49GmFhYcyT88wzz8DdveaCTu3atcNbb73FPDBTp05lRgeJikXu3LmD4OCaB0BXB1dW4fiHL35ghtTYcWOVr40YMQJPPvkkO++zZ8+Gm5sb3n//fZWmm4VwdXVlYTP6DqOQpghv+MljchzatqFypUXdjQ3Nzayb+Or8V+zxy11ehqejjume4vHU0IMjem+oLLzGfW+qeXDkcQ5lgb1jpWBbDAEaC+o7JxMNDuHv4YT2jRtI34sjauHIC1dWf02yrPxiHI0V0t+HttHWiyqGicNlGyY26vKWJjmaeHft2oWSkhI88cQTeOqpp7B+/foa3x8SEsLer8rXX3+Njz/+GMOHD1d7fs2aNRg2bJjyb201Jlrh4Cp4U0wNfa+GtG/fnnlJyLAZOnQo86CMHTsW3t7etRo4qgQFBSElpTL+TOEj1fBUVfZu2YsvP/kSy39cDlsPdTv5k08+Qdu2bZlReurUKTg5OQHitUip4hUVLIyWn2+k1Ghx8PZvCTnQqYk3G2BTc4twODYNA7RNp66D8opyLDq6iNW+6dOoD4aHq19HWuHXXCcDR+vwlKoXjoeoDIt/pOCBo2uk2QDjfQ81hKTCnvYU2pROF/G6GNImkBWk3HExCZO7STS07dlYOKYUgqdjLF6TtbDnChVnrUBkoId21YtlOI6a1INz5coV7NixA99++y26deuG3r17Y8WKFczTcu9ezcYCZeuQhkP1tnnzZowfP76aJ4IMGtX31TUZFxUVIScnR+2mFaTOolCRqW/1VZetcuzIkNy+fTtat27NjjWFiG7dqrm0voODQ5V/0QblJM5V4Ofnh8zMmiue0jkkQ/WHdT+gR78eSCtIY60cREicTOeYtkdeOWEHHRU/two28JF3j0JaRkFm4Q2qxSGurnZcENzJhuKXmF9wJuUMXO1d8Wb3N+uvWKxRLZz6DRxqXkgZKaQrHqgoaKgxRfeBnLuSroArW8TJKvWqcb9H3D5dg7bykHoObyuEh4/EprPq25KEjqU4rqVeqfftOxThqWEKL7FuBo48xtGaMNovj3QdZIRQ6EOERK+2trZMe6EJtPo/e/YsC21VZc6cOWwS7tq1K9N91FW/gEIkFBIRb+QpskRo8iIdzdtvv80ym0jcSwaiLkRHR+Py5cvVnv/555+ZJ47uxz06Dh6OHuzYi7VxiouL8fjjj+Oxxx7DO++8g5kzZwqeIZWmm7ExV1FYWMi+w+DQ70C8MGW0+h/WRhhc/76chNIyw4SpqEHq56c+Z4+f6/gcgtz11PeIxkbmnXqLNtIqmOgS5gMft1qaeNaGaEC5+Qt9sDhGMHBiTGTgyGf1T2L/VkGerPntLimHqfxbaWSk5hWV4oCie7h+Bo58zqHJDJykpCQEBKi72u3t7ZkglV7ThO+++44VmuvZs6fa84sXL8avv/7KPBaUxkxaE/JY1Marr76K7Oxs5S0+Ph6WBhmNlL108uRJxMXFYdOmTSyriY6fLlCYi1LFVaHQIul1Pv30U+aVS05OBnKBvNw85JfkI6MwA6+//jo7xpQG/vLLL6NFixb4v//7P2EDCh3OwYP70bRpUzRrZoTY/P0UoDBLEMbJQNwo0q2pDxq4OiAzvwTHb2XovT0yNt/59x1lzRvKnNIbKvVPhQEryuoNU209L4SaH2oXpEd4Sr4rR0mHqMTVvzEzqVJEA0c+iwziwSjBEPjrgrpUQlIEtFQ/xrWwLyYVRaXlCPV1ZW1SdDdS5XUO9TJwXnnllVqFwOLt6lX93Z+kAaEJtSbvzZtvvsk8FeQBoEmU0p1Jp1MbpAEhobPqzdKg/+nAgQNM5EtGBRXoI0OkqnZJG/3UpUuXEBMTo6aHIiEzec9Is0O30MahWLpwKXt9699bWSr5Tz/9xPaHvHX0mDKyvvzyS6VQ7edff2dCZKMgCuMoPVxGwjgHO1sMVoRyRLeyPlAjzQN3D7BmqW/3fBt2tlqKfGuCvHABCoM5pXb3eHxGPtMyUHhKFFBrBRcYGw/mhbMRWijkGa8sQeXkqNsCy1wMV2QxHr6Rhux849alMnaYcYcYnmrTUPvQdF46kJ8m++tQa5HxggULWEpxXdDqnHQxqqJVgiZH0l7Qa5rUdCERKnkM6oO8CRQOIa0NE7RaIeSpIc1TTVStYrxv375q79myZYva3+Rpo/Tuzz77DF999VWtnxO9BXdy7qBzr864lnYNYZ6VokLK6CKPDqMgE5diYnH2wmX8ulmonWNwZOxWHR7VEBtP3WXhnUUPt2HaHF1IzU/F+8ffZ4+finoKzRoY0FNGBk78v0BK9fClyPaLwuq3W7gvAjycLT4LTlZQPSNKF8+8JUyQ7v7GyaBS6uDktfpv5u/OBLkxybksm2pMp8aQHOLYRl5UyqSqoRQGdUb/R1G9WKfwlHKh2ETjUiUW4cEhYWjLli3rvJH2g8r4Z2VlMR2NyN69e5nwlAwSTcJTjzzyiEZCVNLpULaQtRo3xoLCTaGhoWri45qg1UGwezCrkSOGqmrE3hmJyWn4cfk78DKWF01mAmNVejX3Y83wUnKLcCquZoF3fZCxSVlTWUVZaOnTEjOjZhp2JwNa17t63KYITz2oS3hKxuEN2WBsoXEWabQKhZA0GVMyXGioGuqSg7zTlElFWWqUSVVLeOp+USmCvJyV6e9aIf42ZKRjNKkGhzwKlMZNoYjjx4/j8OHDzCMwYcIEZX2VhIQEZhDR66rcuHGDhVtIoFoVqppLmVkXL15k76PQB2lPnn1Ww67IHI0hkfhrr73GQk314WjniEA3IcSSnJ+MoppEqPZOGNS3O4b26w6UGSlLQcYXppO9nTKl+s+zupUl2HR9EwtNOdg6YEnvJXCwU8+W0xtliOpyreGpc3ezWXhKp5VjcX5lk83AtvrsKadeHY6RhMZKkX8EYIjQqIkRi20euJaGHCO3T9E/k6pmI/WPswns/pH2wbp5gkUdnMwXGUbN31u3bh0zYKg+C2lDKFWcdBwiVBuHNB5V66FQVlTjxo1ZLZea0pu/+OIL5iHq0KEDC59QGIWK1nHMi7eTN+s6Tl6EhPsJrAaLGiT8VSn4Z3BINJl0UXgc2AZy5NHoRux+24VE1olbG+Jz4/HRiY/Y43nR8xDhbYQUa9HAoZUjtROpAu030aOZL/y0KQsvQgM2/W5c/QB3w9UD4pjQgyPDDCpVIgLc0TzAHcVl5dipyAaUbiZVdS0cGWVic81HOuhYrVrcLjdwaod0HCQUpmq3pMMgw0W1ng3pM2gyrFqunzwylAlUk+eAvEKUAk3bpKrIFJ6aNWuWRl4GjnGhUFUj90YsVFVQWoDUghqaMorC3xIj9KGi8vMFGUI5epkOrr2YYeCIjLxiHLyueVPLsvIyvHHoDZY11TGgI6a0nmKcHXTzA9wCap0gt54XPE8PRuk4sCZfqjRQ9anZw6kdcdJKMVImlcxDjDSOjVQYBpvPCJ4Q6Z7D6tcgGWVUEb15gDtaB3la5UJRhFsFHINCIRHS4xBp+WnIK8mrLnI0loEjXpTkGpdRBpUq9na2eKidcPy2nNE8TLXm0hqcTjnNCvq92/tdw2RN1UYtmVTXk3NxMSEH9iqFC3U3cHh4yqjnj7yplCVz3wj1XsRzKLMMKlUe7SB4Uo/eTEditpGaAhviGqxhkfHnOWHceLR9sG6FPek3Qb8N+o2ImjuZwg0cjsHxcvJCAydB2JaQm4DScpWeKSSOM5aBk3zRIibHkYowFbU6oGJd9XE25SxWnlnJHr/S9RWEeBi5kKU46FUxcH47LVQf7h8ZoF3X4hrPobxXjpKGFhlihlrSBcNuu7S4ctINUm8HIydCfFzRNcyHOTP+0FEPZ1TE64OOtYreMSW3kKW46xWeSrpYWblcXJDKFG7gcIxCQ7eGTHhMPZDEKscM8YKhDABKJzUkFjI5tm/shTBfVxSUlLHKxnWRXZSNlw68hLKKMtZnamTzkcbfwRqExlT9dYvCnT+2k2CgaQ39RlRDVBzj0TBKuE86b9jtknajvARwbgB4ybti/KiOwu9Y/F1LCjq2dIxp8aiy0KAMxvIKILpJA4T66pjeLf4mGsp7oUhwA4djFChEQnocG9ggpygHmUWKtGdbe+FmDKGxuPIQB2+ZQm5l0UVeV5iKpYQfWcQMSPLaLOy+UL9eU5oiesho9a8wXA/dSENyThGrxjygpY7i4NwkhYbKVrb6DfkZOAb24Ijbo+3LXEM1om0QHO1scTUpF5fvadm/0NjQsRU9ZCpGqmiMUXhK74WizMdRghs4HKPh6uAKf1d/ZV8kEh4LPalEobEBDRzaltjDyAJW/48q3MskNE7Krvk4/RrzK3bH7WbVij/u+zHcHbXsFqwrga0FIXdeqiDsBvD7qbvKtFRKd9eJFIX3hlpsyNw1brUGTqK4+pdveErEy9UBA1sJxvoWRdq1pBCPseKYX0nMYSUaHOxs8JA+Bk6S4jcRyA0cjgygytMjR2ofutizZw+rZ1RWpnsoyc/FT9mQk9KYmR7HwVV4kQweAN27d8fvv/8OvWC9dcoBFx+hZ5LMaervji5h3szdvPFk9d5pF9Mu4sMTH7LHL3R8AW38TGjUkfEhZqndO8vSUncqysKP6ahH5VcenjId4uSVHit0bzcU4uQoY/1NTXo4yqbStmyD0Qlqr+bB+eWEME5QLS2dSjSIdajSb7CH3IPDkQXLli2r1q5BE6jHF/W0srPTbkV+584duLi4sDR+Kso46cFJ6Nm8J7qEd0H/B/rj2LnLakJj+g7qcVZfxWTNwlNtZe8aF5nQpQm733AiHuVk6ShIL0jH8/88z/RNA0IGGC8lvC6COwj3iWfx1/lE1tSP0lLbNfbSfZuJ5yxCJC4LqEWDOxVirKiz7YZW0PWrGqKyAAZEBjBjITW3CHuk1mFc6YW7iMKiYmxSiPwfU4wbOsFKB5QDbv6Ah46ZkBKCGzhWgJeXF6tKrA3USTw2NpZ1a9eWP/74AwMGDGA1j6h/1aSJk7Bz906s27EO/kH+GPLIWCQkpgAl+UzDQQ1Bqa7R9u3boTNiHNqCJkdqdeDpbI+ErAIcVGRGkAfsxQMvsmrR1POLqhWbRHdTlSCFgXPvLH5WrBzJe6PXviQo2ro06miIPeSYWmicRcUfcwE7J4vpI+Zob4vxnQWv5LpjcZAUlOVE4f6SPBw8fgI5haVo1MAFfZr76b7NpPMWZaByA0cDKLxCPZZMfVNmHmkINSiNiopi3hNfX18MGjQIeXl51UJUVFhx3rx5zENDxRip+emiRYvUtrVhwwYMHjwYzs6CXob2hbY3dOhQ5X5R41SqOL1w4cJqBg71EROrWT/zzDPo3rk7enXohbc/f5uFvLYePimsFEoLmYeIKl3Td+o/OXaCpeDsYIdRChf5huPC4PrZqc9wIukEq3ezbMAy0+luanGPlyScwbn4LBb3H6eYCHQiP6Oyr05wtIF2klMn4iQm6mb0RfTeUJadoVuEmJGJXZswp/DB62m4k169erfZoCabinDu5dMH2f34ziE6N+m1xIWi1t3ErRESx3ZbX3+DUENzbNIxJtTVhMTEREycOBEfffQRRo0axTwiBw8erNVIWrt2LebPn49jx47h6NGjzAjq1asXM2oI+uykSZOU76eVOX2GDKjly5fjueeew+zZs9GoUSM1A4carJL356effqr2nd7O3kjNTGVd5cv9GqDQxgbO5MVxcEHXrl3xwQcf6HCUyK1RVDm4WpCBQ0zo2gRrj95hNXF+vrQFP10Wjit5bpo2aGq+HWNZMrZwyE+BPzLRM6qN7nF/4t5p4d6nGeDibbDd5Gig4RCPvb6IIUYLWf2r1sTpG+GP/ddS8fPxeLwyvKW0hMYJp+CYehG2NlH6LTKIuyctahzlBo6FQAYOGQ6jR49mHcAJMkZqo127dsr+XREREVi5ciUTFYsGDuloxKaoImTMUO+vqVOnIikpCX/99Rdrm2FvX/kzoudo21U/K7J08VIENgxEt749EGdvj6bFebB39WXvj48nrUm59m03SH9DzTtdfWXZvbguWgV5okNIA1xIO4MPTn7HnqMO4QNDB5p3xxxdUebbAnZpVxFlewtTe4zQb3sJikmWh6dMR+MuleJu6ivmqGPdFJH44xY1OaoyqVsTZuCQ4H/+4BYsdCUJSAt3CmhvE4t+LfwR3ECP7MPivEqhv/jbkDncwNEAF3sX5k0xx/dqSvv27VlTUzJqKIxEjUrHjh0Lb++aV8NkhKgSFBSElBShQRtRUFCgDE+pMm7cOGzevJl5W6iTOxlHtYWnqkKf+eWXX7B77254uLqiuLwM8aX3EVpRzsJqZNwUFRWxx1qRoLLqsBCBsSpD29vhRsxPKEcpHggZiGejn4UUiLVvhha4igGeiejYxNtABo7lTY6SxasR4NkIyEkA7p0Bwnrrvq2y0spzGNIVlsbAlgEI9HRitZ4oY/BhfdKwDUhBYCfQaNneNhaPd9GxwKbIvbNARRngESz8NiwAiZih0obCMxQqMvVNG8Em6Vh27drFhLqtW7fGihUrEBkZiVu3btX4furKXvV/VM1i8vPzQ2amojifCtT5/dSpU+z7rl9X1J1RUFxcjB07dtRo4HzyySfMwPn777/RsUNHNHFrBFtUIB8VuHc/Aenp6XBzc9PeuLFQ/Y1IZmEm/pe8GDb2+SgraIw+3vNYM1NzQ1ld29OFdPyBHnH6iYspjCqew2DuwTEpjTure190hTKxqO+co4dsG93W1yNOzGr84YhCKyYBfot3R06FC9xsijDAW/PmvHUuFMXfhAVg/pGSYzBokiEdzdtvv81CR46OjszbogvR0dG4fLl6+uiCBQtYCIkMKdLi7N27V/kaZUyRx4i8SaqQLuidd95hxk/nzsLF4+TojpCyCtC0mF2Ug2NnjrHv1C9ubDkXJlFYWsjSwePvx8Pdzh8F8dOw9tA9rcXnxmDftRTsui+EA4NyzgkpwrpCHoS8FKF4oIXpNySPGIoQryFduaswkBp3AozZ6NWMTO7ehFU2PnUnk92ksMj44cgdnC1vzv62TTih3wbvKj7PDRyO1CCx8JIlS3Dy5EnExcVh06ZNSE1NZYX6dIHCXCQWVmXbtm34/vvvWWYUaXVefPFFTJs2Tenp+fPPP6t5bz788EO8+eab7HNhYWFMu0O3+3l5cLd3RXCp0EySvqvXgF66Zd9kxFqcfoNq3CzYv4B1CPdw8MDKB76Ao40XLiRk49itDHPvHlbvv4krFaEosnWFTVGOfrVURO8NVUh21ExUzzEQjbtWTm76GM7x4uRoeeEpkQAPZ4yMFkJT3x68ae7dwf7rqYhNzcNF20jDeOHunrQo/Q3BDRwLwdPTEwcOHGDp1i1atGDF8z799FNWY0YXJk+ejEuXLiEmJob9TcbSjBkzWDp5x46CIUGeosDAQJZNVZuBQzodCl2RHoh0PuKNQlY0mTUoL0d5UgbOnjiLwWMHs5CMbtk3TQFXH1gCZeVleO3gazhw9wCc7Jyw7IFl6BTcCmM7NZbE4Eqr1+O3MmBLaariYBh3VPcN3jli8ZOjZKGKw7YOggctK05/D44F6m9UmdlHyFzccSnJ7Cnjq/4RKg43aKHQTsXroRPNThDarpAXVaxxZQFwkbGFQJ4aCgHVRNUqxhRKqsqWLVvU/qb6OHPnzsVnn33GMqf8/f2Z56Wqjoc8RsTp06eRk5ODfv36qb3n9u064tWsRHwS/rt6DSZMnoCGwQ1x7/49pjHxctKwIu4tof4DQrrDEqDw0zv/voMdt3ewHlNL+y9Fl4aCETGjdzjWH4/D7ispuJFyn1UONger9wseM6rR4+TfG4jbLxg4XZ/UbYO3FZ7C8D4G3EuOxm03KCxICwXy4ngLGZhakZcOZNy0uPBGTbQI9ED/SH/si0ll18H7o83TkuLYzXScuJ3JQmaDhjwIXH8ByLoD5CbrVoH4rsJApbo6FuRF5R4cTq28/vrrLOVckxYKlKJOwuaq4uU6YReSDQJ8GuDjd95hdXKIu7l3kVWUpdk2bh0Q7puqG1ZyNW4+OvERfr/+OzPyPujzAfo07qPWn2pQK2Hw+kKxejM1V5NyWE0e0hQ/1bcZ0ERhWN45qluIgyZHsXtxqB5ZPBzdCemmbmhqi+g5oOrFVlDDaO4AQfOy8eRdxGfkm2UfvtgnLDLGdm6MAP8AobiiPl6cW4pxNLQnLAlu4HBqhdo7vPbaaxrVpaFCfVOmaNkTibKBHN2xYPYUNGzghiC3IDRwFlpKJOQm1G/kFGSxXkiMsD6yD0st/ncx/nvlv+zvRT0WYWjY0Grve25ghLK78Y2UXJPv52d/X2P3w9s2FDxIJOymEEfuPd1CHHcOC/f+rYT+SBzTIy4Obv6j2+dvKjzC+qSZy4jOYT7o3dwPpeUVWKUwNEzJ6bhMHLiWCjtbG8ymRQYhLjR0NVJjFee+6QBYEtzA4ZgXJ0WYpTiXZYEFuwUrPTnMyCnMqntypHYPvs1lXbeB+ku9fvh1/HbtN+a5WdxzMUZFjKrxvW0beWFI60DmLFm6Wz1N39hQS4a/LyeDKsFTsTOlF05svClqabTh9kGrmhwlCR17W3uhVUZGzWUl6iR2j3Df7AFYC88NEhYav52KN6kXh7y8H26/yh6Pjm6EJr6u6sf+xm7tN5p5G8i8JfwGwnRI9JAw3MDhmBcnj0o9TkUFM3LIk6M0cu4nIDU/tebUaNGtGi7f8FRxWTH+s/8/2HZzG+xt7PFh3w9rNW5EXlAYF9vOJ+JiQraJ9hT45G9BcD4yuhGaByjOm6pxIk502sD1N9K4BkWBt7ZenMw7QPoNQZwa3hfWQpcwH/SJ8ENJWQU+2ilcF6aAqilTFiVVUn5eXGQQdOzpHFBGqdjTTVsPHCUMiOOxhcANHI75RY4UqqIKmiUF7CnRyPF18WV/p+Sn1GzkKA0ceQ6slDH25N9PYk/cHjjaOuLzAZ9jWNgwjdo3PNpBSFddvPWySeri/HM1hTUbtLe1UYbJlLRQ7PP1XUJFW03JS6tML+f6G/PSbIB6qEJTRIOIJkdnDRMDLATqSUVatP+du4czccavi1NWXoEPdwjG1NTuoaxzuBI69qKW6sYe3Qycpv1haXADh2NeFDocRlGlpoSMnIZuDdmNyC7ORmZRJgoURhDLFhAnRxkaOLezb2PyX5OVdW5WDVqFfiGae6JeHtYSzg62LF37rwvq2W2Gpri0nBlSxP/1Dkeob5WeRTS5kbiUwoliNoYmkEFEBEYBboIxyzETovbi1n6gvEzzz92wvvCUSJtgL4zpKJRueHfbFVZ4z5isP3YHVxJz4OFsj2cUQmc1mj+gvYFTXg7c3C885gYOh2MEnD2F+8Lq4Rby4oR4hMAGNqyy74sHXkRcThxwbXtlaX+Z1b+h+jZk3MTnxqOReyP8NOIndAvSrls9NdWbpRAYLvnrCvKKtPCcaMmaw7dwKy2PdQt/9oEaBlaqXBsxRHh8reZSBTUinsPI+r1WHCMTHA1QaQa6BsWeUvVB3joyiKzUwCH+MyQSLg52rDbUxlPxRvuetPtF+FgRCntxaCR83Byrv6nZwErPdlmJZhtOPAMUZAiLTAtsdcMNHI75EWveUC+bGi5MTydPZgjY2dgx42bC1gnYf/kX4cWWD0JOmVIrz6zEnD1zkFOcg3b+7fDfEf9FswaKTAgtmd2vGXNTJ2QVKAc/Q0OGzdLdQubUS8Mi4eFcSxmAFoqMr2s7NdtwaRFwQ9HmI1K3YpQcA0JFG0UPwNWtmn2Gah+RQUSZj2QgWSENvZyxYIighXlv2xWk5BYa5XuWbLuCnMJStAn2xORutdQqogJ9rr4sYUNjwf+V/wn3zQcBdlqU+JAJ3MDhmB97R0GLU4sXh3BxcIGfix9a+rREbkku5pbfxQc+3iiMGAw5kJSXhFm7Z+Gr81+xvx+LfAxrhq5h/5OuuDja4YMxUcoGgBSuMnTM/8WN51BYUo5ezX0xVuGOrxFaPZLIMfVqZdG3uqBsDxqIqXNxkHVOjpKj9aPC/eUtmtU0ovcRLR8SDCQrZXrPMEQ18mIGyMItlwyuidtxMRGbziSw7MV3RrZl6eE1QuU8xMXCJQ16ENJ+Xv5TeNy6eoNkS4AbOFbA9OnTMXLkSINtr2/fvli/fr3G758wYQJrG1EnYoEwcpfWgp2tHd7r/R4m+wqtItZ5eWDCsbdwJf0KpAoNdltvbsXoP0bjWOIxuNi7YEnvJXij+xtwtKvBzawlfSL88VjnEPZ4/q9nkZ2voWtaA74+cBMn72TCzdEOH45pB9vaBlbCpUFlNtWF3+vf+IXfhPu2o4WBmWN+KMxo7yJk4dw7U3946vIfwuM2dWf9WTrUafz90VFMgE8tHKjauKFIySnEq5suKD22HZvUU0ix7Rjh/sqf9YepqIYYZV3ZOVWGmC0MPrJYAcuWLavWrqEuqDUDNdPs1KkTa7qZkVFpdFC/qeTkZGa0aAr1xXrvvfeQnZ1dv4FTnCeEL2rBwc4Br6SmYVVSCnztXBCbHYuJ2ybi4xMfI7/EPFVFa4M0Ns/ufRavHnyVeZ2i/KLw60O/4uFmDxv0e15/qBVCfFxwN7OAGTmGEDseuZGGj3cK9TbeeKg1GntrUL69veI3ce7nuj0AVKAxZrv6gMwxP45ulSHfM0LByVq5/jeQlyqERCygiri+UH0qCuESi/93mYmBDSHun/vzGWTml7DQ1PODVNLCayOsL+DmD+SnC+eoLs6sE+5bPWRx6eEi3MCxAry8vFhVYk154oknsGvXLpw6dQplZWWsU7nI8uXL2euaVDcWadu2LZo1a4b//reOQZO8GY6Kiyw/rfb3pd8E7hxCn8JibBryPYaEDkFZRRl+vPwjHtnyCKsnU07F/8wIGVpfnv0So/4Yhf1397OeUnM6zMGPw39EmFeYwb/P09kBX07uxGpj7LmaotTM6AoVLqOBlewkyhKZ0EXwENVLq0cABzdhVSim8NcEGUClBUBAG6vVbkiWjlMqPWy02KiN02uF+w6TLFK7oQszezfFgEh/FJWWY+bak0jOKdTL87vwj4ss7OzhZI9lE6LZ9V0vFCpsP1F4fHJN7e+jc3thY+U5tFC4gaPhj608P9/kN21jub/99huioqLg4uICX19fDBo0CHl5edVCVP3798e8efPw0ksvsaaaDRs2ZF3CRRwdhdDJt99+i4CAAAwbNkzZUXzv3r14+OGH1Rp30vsPHlRUpAXw0Ucfsc+Rp0eEPrNhw4a6/wFaeYj9iWpLVT2v2EbkCPgEtMWn/T/FFwO/YCLk5PxkvHLwFYz/33iWqWSK+jCqFJUV4afLP2H4puFYdW4V+7tbw274/ZHfMbv9bGboGHMF+e7Ituzxir03dO44nphdgEnf/ouMvGK0beSJ90a1ZSn7Glel7qAYXP9dVXto4/jXwuMuM6gegE77yTES5AHwDgeKsoHTP9X8ntSYSjF5x2km3T0pQyHcz8Z3QFN/Nyb8n/b9cWQXaB8ypnFr6a5r2HAinululk+K1q6xbqfplTq3FMELW6P3hso6eIdZXHsGVaxXGaYFFQUFiOlo+hS6yNOnYOOqWWfXxMRETJw4kRkXo0aNQm5uLjM6apvk165di/nz5zPvzNGjR5kR1KtXLxaaKi4uxn/+8x+4u7szr4s4wR06dAiurq6sc7mqsfT888+zPlTnzp3DzZs38eabb2Ljxo0IDAxU61VFYaqioiI4OTnVni5OnpyyYsH97SHUwFFSXlqp+u/+tPLpvo37so7bZFysubgGMZkxLFMpwjsCU1tPxYjwEQbRu9RGWkEaNl7biF9jfmWPCUptn9dxHoaGDtXcQNCT8Z1DWMz+k7+vsbocVGV1dr+mGn//7bQ8PPHDCcRnFCDU1xXfTesCZwc77Xai+zPAie+EdHFKN24k6KWU0KqRRMguPkC7x7TbNsf4kGe257PAtvnAkRVAp2mVCQAiB0lPVwFEPgj4VSn6aOV4uzli7RNdMfrLI7ialIvHvjqKH57oyrKtNIHCy1QZmTqVEwsfao0BkQHa7YRvM0H4fXUrcOBjYOx36q9TLbEjy4XHPeYKZR4sFKN5cGgy69mzJ5sQNQ2PMLfcwoUICgpiXgjyQFy/rt5vh/QgkydPhqenJ9vujBkzcP/+fVg7ZOBQR+/Ro0cjLCyMeXKeeeYZZqTURLt27fDWW28hIiICU6dORefOnbFnj1Ag6sUXX8SPP/7IvDV0DskzRNy5c4cZLVXDU++++y68vb3x1FNP4fHHH8e0adPwyCPqqvzg4GBmOCUl1VGUjiZijyDh8f0UoLS48jUy1Ei7UV4itGYIVe+ZQuLdp9o9he2jt2N6m+ns7+uZ1/Hm4TcxcONAvPvvuzibctZgXh2qybP7zm7M3zcfQ34bglVnVzHjJtA1EG/1eAt/jPyDVSU2lXEjMmdAc8zq15Q9/nDHVSzYeA65hfWvIv+JScEjKw+xtHBKPV83sxsCPTUblKsNru3GC493vKruiaPzt2ex8LjXc5V9yDjSosNkwLMRkHMXOLRU/bW4f4HzihINfReYZfekToiPK378v67w93BiRs7oVYdxNDa93s9l5hXjyR9Pqhk303uF67YT/V4S7i/+BtxWNLQVIcM1O144x3SuLRijeXBoMhs3bhx69OiB776rYkHWAnkfSONB3oXw8HDmCSCR6+XLl+HsLAy2ZNzQZE4akZKSEqYHoYlVm6webbFxcWHeFFND36sp7du3x8CBA5lhQ8dsyJAhGDt2LDM8ajNwVCGjMiUlRSlKpltVCgoKlOdBFQpRrVu3jm0zNDQUS5dWGRTJAFH8L/n59QiBSWxM3hsSDGfdBnyaCSuM/EzhORt7YMg7tYY2qBv5gs4LMDNqJn6//jvWXVnHWj38EvMLu/k6+6JHcA90D+qOVr6tEO4VDgfqhq2BruZG1g2cSTnDsqFOJp9EAelIFFBNm8ktJ2Nw2GCNtmcsyKB6ZVhLBHu54O3/XcKm0wk4dD2NFSR7qH0QXB0rL3ky9mgAXrb7Osv+IKKbNMDqxzvpZtyIDFwoeNri/wX2vgMMfEswdP6YI3Qd92kKdH3KEP8uxxg4OAND3wM2TgcOfCK0AGg+UKgevulJ4T3RUyyyMJyhoHYqm57uiWlrjuNmah4mfvMvxnVqjKf6NkVEoLqgt6C4jGVefbnvBtLuFzOtzbuPtsV4TaCa344AAA2VSURBVLVvNRHUHug4FTj9I7B5NjBjJ+AZLLRl2PeB8J7Bi4VmuRaMTYWRhQqUvUMhjKysOrpCKwZbWuUvWLCAhUcIyrohjwFtg7J2rly5gtatW+PEiRPM40Ds2LEDI0aMwN27d9nna4LCInQTycnJQUhICNs+eYJUKSwsxK1bt5iBVdNkLmXoGB45cgR///03Nm/ezLwlFIJ6++232fHfsmWLMqzUoUMHfP7558rPkkaHPGJ1ZVt98803zOtz7969aq+RXmf27Nnw8PDA+fPn2fFVhfaje/fuTMfj51dP7ZeSQiAtRugUTimM9o4ozMvBrYRUhJfdhHPvyvCUJp26jyceZ6nau+N2qxklBBkjTTyawM/VD/4u/nCyc2IdvUmoTMX4MgozWA0bavpZFeqXNSx8GIaHDWfGktQ4EpuG1zZdwO10waikdO+Ood7MQ5NbWIqrSTmITRWEpFRbY2qPUNZfx8neAC7rcxuAzbOEx016CvWNUi4JIchpW4Em2lVu5pgYmhbo/JG3hrRjEUOB+GNCAgAZqDP3yK6CuDkg7+n7269i/bHK1PHIQA+0CvJgmh0SIp+4nckypgjS76yYGM3aQOhNQRbwzQAhJOzqBzTpLoSOKdQfNR4Y/bUsNXA0f1PiTE3zt2Q1OGRU0IRMYSkR+ie6devGNCJk4NA9TcKicUPQ+ylkQhMoaU9q4v3332eTvKVDq3fS0dCNQn3kTSFDx1BER0ezc5SZmanmGYqNjcULL7zADKBffvmFhah2796tFsq6ePEiGjduXL9xI64gfZsD6bFAWZFwEzU6LRUCOg0hYW/PRj3ZbVHZIpxLPYcj947gdPJpptXJK8ljqeZ0qw8qytfatzW6NuzKbpE+kcwYkio9m/lhx/N9WRFAGmDjMvJZw0xVHO1s8UDLAMwf0gItqqws9YJSxmmA3fkqEKeoqkpZcmO+4caNHKCJ7+Hlgh6OisbFbBOe94sEJv7MjRsNocrfS0ZFYXR0I3xz8CZ2XU5GTHIuu6nSxMcVz/RvhjGdGsPBzkBjiksDYMpmYN14YcEoVqimgo6PLJelcaMtkjFwRG2GqjBV/Ft8je4pO0cVe3t7lglUl7bj1VdfZYLaqh4cS4IMPNLQUGiKjhH9Td4SEgSTR8VQBg4ZKIcPH8ZDDz3EnqM0ctLdUFiMwoWUcUVhMirsR1oeERI8075pVZMjsLUwSVKn8QonIC9Rr4uShMYkRqab6PG6e/8u7ubeZfoZuhWXFaMcwmrKy9ELPs4+zLChdgrezvUU2ZIgJBKmAmGz+jbFubvZuJaci3tZBSy1nPpZ9Wzuyx4bhe6zAao0HfOXIFSlNHJ3LQWTHPNBC42xa4DOM4Qmqg1CBfEqPc/Ris5hPuxGrRzOx2czA4e8pnTtdQ33RjN/d+Po9bzDgNmHhMJ/WXeAxl2FgpxWYNxobeC88sor+PDDD+t8D4WRWrZsCSlBWTu1Zu5YCOSqO3DgAAs7kQFH3hsyMoYPH868KobAzs6OGTGktxENHBKTk/h469atSi3P119/zTK6yKAhbRCF/Sg8RuFErSDXuJvC41No+B4vNKBQthPdLB36XzuENGA3k0KiY8rK4cgTmgjD+wg3jt4EeDhjUGu6qS/kjd4KJ2osrBGtDBzSx1A6cV00bSpkcGgL1WIhqHYKTZIi9DfpRcT3iEJYEcocoswq8fPWCnlqajMgqupqqHZNVUR9Tn1QKKpNmzbMqCEjikJhdFOFMrlUNU9r1qxhaeKkweFwOBwOR3IGjr+/P7sZAxL1kpFCYRbRoCFPBIVann5aEJVSRhaJZanCLrURICiVuby8nGl1OMaHzhFlxcXFxTEDRxMcHBywYsUKo+8bh8PhcDhG1+DQBEieFbonncbZs2fZ882bN1fWZqFQFgmASRxMLnTKtqKaKlSbRUwTp8wosQoveSlI4/Hkk09i9erVLE187ty5TIBcWwYVx/Bo27hz5syZRtsXDofD4XBMauBQ2ILq2agKVIl//vmHpSkTMTExag0YqXUAtRagujbkqenduzcLu6ima5P+g4waqvlCWTpjxoxhtXM4HA6Hw+FwTFYHR2559HKug2PJ8PPC4XA4nBwt6uBIt4iHmSFdD0c6WKEdzuFwOBxLqIMjFajtAIW+qFovCarpb1P3E+JUN26opg+dBxIsczgcDodTH9zAqQIZNxQGoX5XNbUk4JgHMm6oEjLV4uFwOBwOpz64gVMD5LVp0qQJq7FDGWAc80OeG27ccDgcDkdTuIFTC2I4hIdEOBwOh8ORH1xkzOFwOBwOx+LgBg6Hw+FwOByLgxs4HA6Hw+FwLA57a66pQgWDOBwOh8PhyANx3takNppVGji5ubnsPiQkxNy7wuFwOBwOR4d5nCoa14VVtmqgKsVU48bDw8NsRfzICiUDKz4+vt5y09YIPz61w49N3fDjUzf8+NQNPz7SPjZkspBxQw22qW5dXVilB4cOChWNkwL0I+EXUe3w41M7/NjUDT8+dcOPT93w4yPdY1Of50aEi4w5HA6Hw+FYHNzA4XA4HA6HY3FwA8dMODk54a233mL3nOrw41M7/NjUDT8+dcOPT93w42M5x8YqRcYcDofD4XAsG+7B4XA4HA6HY3FwA4fD4XA4HI7FwQ0cDofD4XA4Fgc3cDgcDofD4Vgc3MDhcDgcDodjcXADRyJs27YN3bp1g4uLC7y9vTFy5Ehz75LkKCoqQocOHVh7jbNnz5p7dyTB7du3MWPGDISHh7PfTrNmzVgaZ3FxMayVL774AmFhYXB2dmbX1PHjx2HtvP/+++jSpQtrTxMQEMDGl5iYGHPvlmT54IMP2Djz/PPPm3tXJENCQgIef/xx+Pr6srEmKioKJ0+ehJThBo4E+P333zFlyhQ88cQTOHfuHA4fPoxJkyaZe7ckx0svvcT6j3AquXr1Kuut9tVXX+HSpUtYunQpVq9ejddeew3WyC+//IL58+czI+/06dNo3749hg4dipSUFFgz+/fvx5w5c/Dvv/9i165dKCkpwZAhQ5CXl2fuXZMcJ06cYNdTu3btzL0rkiEzMxO9evWCg4MDtm/fjsuXL+PTTz9li3FJQ3VwOOajpKSkolGjRhXffvutuXdF0vz1118VLVu2rLh06RLVbao4c+aMuXdJsnz00UcV4eHhFdZI165dK+bMmaP8u6ysrCI4OLji/fffN+t+SY2UlBR2He3fv9/cuyIpcnNzKyIiIip27dpV0a9fv4rnnnvO3LskCV5++eWK3r17V8gN7sExM7TKJNcfNQCNjo5GUFAQhg8fjosXL5p71yRDcnIynnzySfz0009wdXU19+5InuzsbPj4+MDaoLDcqVOnMGjQIOVzdF3R30ePHjXrvknxN0JY4++kLsjL9eCDD6r9hjjAn3/+ic6dO2PcuHEsxElz1TfffAOpww0cM3Pz5k12v2jRIrzxxhvYunUrc/v1798fGRkZsHao0Pb06dMxe/ZsdoFx6ubGjRtYsWIFZs2aBWsjLS0NZWVlCAwMVHue/k5KSjLbfkkNCmmStoRCDm3btjX37kiGDRs2sAUn6ZU41eepL7/8EhEREdi5cyeefvppzJs3D2vXroWU4QaOkXjllVeYSK2um6ifIF5//XWMGTMGnTp1wpo1a9jrGzduhLUfH5qsc3Nz8eqrr8Ka0PT4qEKewGHDhrFVFnm8OJzavBTkIaYJnSMQHx+P5557DuvWrWPidI46NE917NgRS5YsYd6bp556io0xpPeTMvbm3gFLZcGCBczzUBdNmzZFYmIie9y6dWvl89TIjF6Li4uDtR+fvXv3svBC1eZu5M2ZPHmy5FcQxj4+Ivfu3cOAAQPQs2dPfP3117BG/Pz8YGdnx0KaqtDfDRs2NNt+SYm5c+cyL/GBAwfQuHFjc++OZKDQJgnRaRIXIW8gHaeVK1eyDE76bVkrQUFBanMU0apVK5YgI2W4gWMk/P392a0+yGNDkzelbPbu3Zs9RxkOlP4bGhoKaz8+y5cvx7vvvqs2kVNWDGXLUAqwtR8f0XNDxo3o/SPdiTXi6OjIjsGePXuUZRZo5Ul/08Ru7aHeZ599Fps3b8a+fftYWQFOJQMHDsSFCxfUnqOs1pYtW+Lll1+2auOGoHBm1bIC165dk/wcxQ0cM+Pp6cn0JZTWGhISwn4wH3/8MXuNQg3WTpMmTdT+dnd3Z/dU74WvQAXjhvRa9Lv55JNPkJqaqnzNGr0WlCI+bdo05uHr2rUrPv/8c5YKTZOVtYel1q9fjz/++IPVwhE1SV5eXqymibVDx6SqHsnNzY3VfOE6JeCFF15g3mEKUY0fP57VliJPsdS9xdzAkQBk0Njb27NaOAUFBcwzQaEZydcY4JgdqmlCwmK6VTX4aNVubTz22GPMyFu4cCGbxKkw5I4dO6oJj60NEogSZAyrQh6/+kKhHE6XLl2Y94+0kIsXL2YeQFo8kExAythQrri5d4LD4XA4HA7HkFhnsJ7D4XA4HI5Fww0cDofD4XA4Fgc3cDgcDofD4Vgc3MDhcDgcDodjcXADh8PhcDgcjsXBDRwOh8PhcDgWBzdwOBwOh8PhWBzcwOFwOBwOh2NxcAOHw+FwOByOxcENHA6Hw+FwOBYHN3A4HA6Hw+HA0vh/zIw/VWd0WSEAAAAASUVORK5CYII=",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.linspace(-2*np.pi, 2*np.pi, 500)\n",
+        "\n",
+        "# look at the description of each function and call the proper function from numpy\n",
+        "plt.plot(x, np.sin(x), label='sin(x)')\n",
+        "plt.plot(x, np.sin(2*x), label='sin(2x)')\n",
+        "plt.plot(x, np.sin(x/2), label='sin(x/2)')\n",
+        "plt.plot(x, np.sin(x)**2, label='sin²(x)')\n",
+        "plt.legend()\n",
+        "plt.title(\"Variations of Sine Functions\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dMVcqrfihf0P"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4-5A1OkdfHXq"
+      },
+      "source": [
+        "## 3. Subplots: Trig, Inverse, Exp, and Log"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 807
+        },
+        "id": "DxocsCu6fGOR",
+        "outputId": "5864a5cc-5776-45bd-99f7-028c64e301af"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1600x800 with 8 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x = np.linspace(-1, 1, 400)\n",
+        "x2 = np.linspace(0.1, 2, 400)  # For log and exp\n",
+        "\n",
+        "# based on the number of subplots what should be the dimensions in the following line?\n",
+        "fig, axs = plt.subplots(2 , 4 , figsize=(16, 8))\n",
+        "axs[0,0].plot(x, np.sin(x));      axs[0,0].set_title(\"sin(x)\")\n",
+        "axs[0,1].plot(x, np.cos(x));      axs[0,1].set_title(\"cos(x)\")\n",
+        "axs[0,2].plot(x, np.tan(x));      axs[0,2].set_title(\"tan(x)\")\n",
+        "axs[0,3].plot(x, np.arcsin(x));   axs[0,3].set_title(\"arcsin(x)\")\n",
+        "axs[1,0].plot(x, np.arccos(x));   axs[1,0].set_title(\"arccos(x)\")\n",
+        "axs[1,1].plot(x, np.arctan(x));   axs[1,1].set_title(\"arctan(x)\")\n",
+        "axs[1,2].plot(x2, np.exp(x2));    axs[1,2].set_title(\"exp(x)\")\n",
+        "axs[1,3].plot(x2, np.log(x2));    axs[1,3].set_title(\"log(x)\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "thpsEzFVikPh"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WAidrU51fMqB"
+      },
+      "source": [
+        "## 4. Features of the Sigmoid Function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "   1   \n",
+            "───────\n",
+            "     -x\n",
+            "1 + ℯ  \n"
+          ]
+        }
+      ],
+      "source": [
+        "import sympy as sym\n",
+        "\n",
+        "x = sym.Symbol('x')  # تعریف متغیر نمادین x\n",
+        "sig = 1 / (1 + sym.exp(-x))  \n",
+        "\n",
+        "sym.pprint(sig)  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "lehJV_vNfLXx",
+        "outputId": "9cfc6ec4-9dfc-4220-a955-4d6a11fac8f3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Range: (np.float64(4.5397868702434395e-05), np.float64(0.9999546021312976)) | At x=0: 0.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "def sigmoid(x):\n",
+        "    return 1 / (1 + np.exp(-x))\n",
+        "\n",
+        "\n",
+        "x = np.linspace(-10, 10, 200)  # 200 num geberate between -10 , 10 \n",
+        "y = sigmoid(x)\n",
+        "\n",
+        "plt.plot(x, y)\n",
+        "plt.title(\"Sigmoid Function\")\n",
+        "plt.xlabel(\"x\")\n",
+        "plt.ylabel(\"σ(x)\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"Range:\", (y.min(), y.max()), \"| At x=0:\", sigmoid(0))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DUONuiAxilL0"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1FjRt_31fXsx"
+      },
+      "source": [
+        "## 5. Derivatives of Famous Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "vyyGBuHRfZ4n",
+        "outputId": "7a89fce7-f973-46bc-dca3-c452f1a34561"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x = np.linspace(-3, 3, 400)   # 400 number generate between -3 , 3 \n",
+        "\n",
+        "plt.plot(x, x**2, label=\"x²\")\n",
+        "plt.plot(x, 2*x , '--', label=\"d/dx x²\")\n",
+        "\n",
+        "x = np.linspace(0, 3, 400)\n",
+        "plt.plot(x, np.exp(x), label=\"exp(x)\")\n",
+        "plt.plot(x, np.exp(x), '--', label=\"d/dx exp(x)\")\n",
+        "\n",
+        "plt.plot(x, np.log(x + 0.1), label=\"log(x)\")\n",
+        "plt.plot(x, 1 / (x + 0.1) , '--', label=\"d/dx log(x)\")\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.title(\"Functions and Their Derivatives\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# more example\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.linspace(0.1, 3, 400)\n",
+        "y = np.log(x + 0.1)\n",
+        "\n",
+        "dy = np.diff(y)\n",
+        "dx = np.diff(x)\n",
+        "derivative = dy / dx\n",
+        "\n",
+        "x_mid = (x[:-1] + x[1:]) / 2  # میانگین نقاط برای محور x مشتق\n",
+        "\n",
+        "plt.plot(x, y, label=\"log(x + 0.1)\")\n",
+        "plt.plot(x_mid, derivative, '--', label=\"Approx. Derivative\")\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.title(\"Function and Its Approximate Derivative\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DURup9u5il4-"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w3QIKTZQf1ZK"
+      },
+      "source": [
+        "## 6. Gradient of Selected Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Eeudgf3-f0Ie",
+        "outputId": "aeffb463-3230-4b98-8131-623b4fbf624c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Function: f(x, y) = x**2*y + sin(y)\n",
+            "Partial derivative w.r.t x (∂f/∂x): 2*x*y\n",
+            "Partial derivative w.r.t y (∂f/∂y): x**2 + cos(y)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# search and learn more about sumpy\n",
+        "import sympy as sp\n",
+        "\n",
+        "# Define symbolic variables\n",
+        "x, y = sp.symbols('x y')\n",
+        "\n",
+        "# Define the function\n",
+        "f = x**2 * y + sp.sin(y)\n",
+        "\n",
+        "# Compute partial derivatives\n",
+        "df_dx = sp.diff(f, x)\n",
+        "df_dy = sp.diff(f, y)\n",
+        "\n",
+        "print(\"Function: f(x, y) =\", f)\n",
+        "print(\"Partial derivative w.r.t x (∂f/∂x):\", df_dx)\n",
+        "print(\"Partial derivative w.r.t y (∂f/∂y):\", df_dy)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wDlIJkmYimg9"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.3/AI-DS_Nexus__A0_3__aminran.ipynb b/a0.1/a0.3/AI-DS_Nexus__A0_3__aminran.ipynb
new file mode 100644
index 0000000..238837e
--- /dev/null
+++ b/a0.1/a0.3/AI-DS_Nexus__A0_3__aminran.ipynb
@@ -0,0 +1,416 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 3 — Functions, Derivatives & Gradients\n",
+        "This assignment explores the mathematical foundation of machine learning: functions and their derivatives. You’ll visualize, analyze, and code up essential concepts every ML practitioner uses.\n",
+        "\n",
+        "\n",
+        "**👀🔎 What do you see?**\n",
+        "\n",
+        "Look at the snippet codes and:\n",
+        "\n",
+        "* Describe any patterns, surprises, or connections you notice.\n",
+        "\n",
+        "* Write a few sentences about your observations! 📝✨\n"
+      ],
+      "metadata": {
+        "id": "7wR0aAR-evto"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Log and Exp: Proving Inverse Relationship\n"
+      ],
+      "metadata": {
+        "id": "cCb1hk3xe0E6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KOxJ9f-3eu5F",
+        "outputId": "b141a43b-c14c-47c4-c91e-0eeee4a0f128"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "x\tlog(exp(x))\texp(log(x))\n",
+            "1.00\t1.00\t\t1.00\n",
+            "2.00\t2.00\t\t2.00\n",
+            "2.10\t2.10\t\t2.10\n",
+            "2.72\t2.72\t\t2.72\n",
+            "\n",
+            "log(exp(y)) for 0 and negative values:\n",
+            "[ 0. -1.  5.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Show log(exp(x)) == x and exp(log(x)) == x for several values\n",
+        "x_vals = np.array([1, 2, 2.1, np.e])\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "for x in x_vals:\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# Edge case: try negative and zero values (show error/NaN for log)\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** we can replace x and ln"
+      ],
+      "metadata": {
+        "id": "lVZufz08g1hI"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Plotting Sine Functions Variations\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "mOAqtnk6fDH6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.arange(-2*np.pi, 2*np.pi, 0.1)\n",
+        "\n",
+        "# look at the description of each function and call the proper function from numpy\n",
+        "plt.plot(x, np.sin(x), label='sin(x)')\n",
+        "plt.plot(x, np.sin(2*x), label='sin(2x)')\n",
+        "plt.plot(x, np.sin(x/2), label='sin(x/2)')\n",
+        "plt.plot(x, np.sin(x)**2, label='sin²(x)')\n",
+        "plt.legend()\n",
+        "plt.title(\"Variations of Sine Functions\")\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "DoWdSehie66R",
+        "outputId": "ca241675-71b0-45b1-b52e-a84d8e650d3f"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ],
+      "metadata": {
+        "id": "dMVcqrfihf0P"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Subplots: Trig, Inverse, Exp, and Log"
+      ],
+      "metadata": {
+        "id": "4-5A1OkdfHXq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = np.arange(-1, 1, .1)\n",
+        "x2 = np.linspace(0.1, 2, 400)  # For log and exp\n",
+        "\n",
+        "\n",
+        "# based on the number of subplots what should be the dimensions in the following line?\n",
+        "fig, axs = plt.subplots(nrows=2, ncols=4, figsize=(16, 8))\n",
+        "axs[0,0].plot(x, np.sin(x));      axs[0,0].set_title(\"sin(x)\")\n",
+        "axs[0,1].plot(x, np.cos(x));      axs[0,1].set_title(\"cos(x)\")\n",
+        "axs[0,2].plot(x, np.tan(x));      axs[0,2].set_title(\"tan(x)\")\n",
+        "axs[0,3].plot(x, np.arcsin(x));   axs[0,3].set_title(\"arcsin(x)\")\n",
+        "axs[1,0].plot(x, np.arccos(x));   axs[1,0].set_title(\"arccos(x)\")\n",
+        "axs[1,1].plot(x, np.arctan(x));   axs[1,1].set_title(\"arctan(x)\")\n",
+        "axs[1,2].plot(x2, np.exp(x2));    axs[1,2].set_title(\"exp(x)\")\n",
+        "axs[1,3].plot(x2, np.log(x2));    axs[1,3].set_title(\"log(x)\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 650
+        },
+        "id": "DxocsCu6fGOR",
+        "outputId": "9171ee88-fd88-498b-c7b5-424d8cb3c0ac"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 8 Axes>"
+            ],
+            "image/png": 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WLMk1U0xMDA4ODtSpU+f636yIiNyS3K5I/eGHH7BarXz//fc89dRTdOjQgbCwsJs+mQX/fKyAK41bU1JSmDBhAhs3bmTy5Mm5jvtr/79mFoqIyM375ptv6NOnDx988AFdu3bl3nvvpXXr1ly8ePG69q9ZsybAP/7u+EvDhg0ZM2YM69evZ8OGDcTFxTF9+vRbiZ8tICDgmseX3bt388Ybb9CvXz8aN27MgAEDrjlLJCYmRscXuSUqaoj8A5vNdtUHcIUKFahUqRJWq/WWnz80NBTDMNi+fftVj/3nP/8hNjaWuXPnMnHiRPz9/enTp0+ur7t9+3ZCQ0NvOY+IiNwYBwcHunTpwg8//JB91evfGYZBhw4d2LJlCxEREdnbU1NT+eyzz/D3989eIuqvNdD/4uLiQmBgIIZhkJmZCZD9WZ/ba02cOJFNmzbx2WefMX78eFq1asXgwYNzXat2+/bt1K9fP3vdXRERyXslS5YEyHEC66+rYv9+FWxSUhKzZ8++6ddp2rQpLi4uuR4rvvnmGxYuXMh///tfXnrpJXr06MGYMWM4dOjQVWO3b9+Ol5cX9evXv+ksIiJyhaOj41XLNn300UfYbLbr2v++++7Dw8ODCRMmZF/w9Je/njc5OZmsrKwcjzVs2BAHB4fbcg4Lrvwe2bt371XPl5mZSd++falUqRJTpkxhzpw5JCQkZM88/LukpCSOHj1Kq1atbksmKZ6u7hwpItlSUlKoUqUKXbt2JSgoiFKlSrFmzRq2bt3KBx98cMvP37p1a7y9vVmzZg1333139vZffvmFadOmMW7cOJo0aQLA7Nmzadu2La+++irvvvtu9tgzZ86we/dunnnmmVvOIyIiN+7tt9/m559/pk2bNgwaNIh69epx+vRpFi9ezMaNG3nppZeYP38+7du3Z9iwYZQtW5a5c+cSExPDt99+i4PDlWtM7rvvPnx9fbnjjjvw8fHhwIEDfPzxx3Ts2BEPDw8AatSoQYMGDVizZg1PPvlkdoYDBw7w6quv0rdvXzp16gTAnDlzCA4OZsiQISxatCh7bGZmJr/99htDhgzJxz8lERFp2rQpAK+88go9evTA2dmZu+66CxcXFzp16sRTTz3FpUuXmDFjBhUqVOD06dM39Tpubm7cd999rFmzhjfeeCN7+5kzZxg8eDDt2rVj6NChAHz88cf8+uuv9O3bl40bN2Yfk+BKU9pOnTppzXMRkdvggQce4Msvv8TLy4vAwEAiIiJYs2ZN9pKB/8bT05NJkyYxYMAAmjdvTq9evShTpgy7du3i8uXLzJ07l19++YWhQ4fSrVs36tSpQ1ZWFl9++SWOjo488sgjt+V9dO7cmfHjx/Pbb79x3333ZW9/88032blzJ2vXrsXDw4NGjRoxduxYxowZQ9euXenQoUP22DVr1mAYBp07d74tmaSYMkTkmqxWq/HCCy8YQUFBhoeHh1GyZEkjKCjImDZtWvaYPn36GNWqVcu+HxMTYwDGe++9d9XzAca4ceNybBs2bJhRq1at7PvJyclGtWrVjCZNmhiZmZk5xo4cOdJwcHAwIiIisrd98sknhru7u5GcnHyL71ZERG7W8ePHjd69exvly5c3XF1djRo1ahjPPPOMYbVaDcMwjKNHjxpdu3Y1Spcubbi5uRktWrQwfvzxxxzP8emnnxp33XWX4e3tbbi6uho1a9Y0XnjhBSMpKSnHuIkTJxqlSpUyLl++bBiGYWRlZRnNmzc3qlSpYly8eDHH2ClTphiAsXDhwuxtP/30kwEYhw8fzos/ChER+Qfjx483KleubDg4OBiAERMTY3z//fdGo0aNDDc3N8Pf39945513jFmzZmU//pdq1aoZHTt2vOo527RpY7Rp0ybHtiVLlhgWi8WIjY3N3vbwww8bHh4exrFjx3KMXbZsmQEY77zzTva2AwcOGICxZs2a2/PGRUSKuQsXLhj9+vUzypUrZ5QqVcoIDw83oqKijGrVqhl9+vTJHjd79mwDMLZu3Zrr83z//fdGq1atjBIlShienp5GixYtjPnz5xuGYRjR0dHGk08+adSsWdNwc3MzypYta7Rr1+6qz/Lrfc1ff/3VAIxff/01x/ZGjRoZ/fv3z76/fft2w8nJyXj22WdzjPvrd0qlSpWMCxcuZG/v3r270bp163/7IxP5RxbDuEbLehHJF9HR0QQEBPDTTz9xzz333PD+jRs3pm3btkyaNCkP0omISEGTlJREjRo1ePfdd+nfv/8N79+lSxcsFgvfffddHqQTEZGCwGazERgYyKOPPsr48eNveP8RI0awfv16tm/frpkaIiKSw5dffskzzzxDbGwspUuXvqF94+PjqV69OgsWLNBMDbklKmqIFACDBw/myJEjrF69+ob2W7lyJV27diU6OpoKFSrkUToRESlo3nnnHWbPns3+/ftzLBXybw4cOEDDhg3ZuXMnDRo0yMOEIiJitoULFzJ48GBiY2MpVarUde937tw5qlWrxqJFi3IsFyIiIgJgt9tp1KgRPXv25JVXXrmhfV966SV++eUXtmzZkkfppLhQUUNERERERERERERERAqF67+0T0RERERERERERERExEQqaoiIiIiIiIiIiIiISKGgooaIiIiIiIiIiIiIiBQKKmqIiIiIiIiIiIiIiEih4GR2gNvNbrdz6tQpPDw8sFgsZscRESl0DMMgJSWFSpUq4eBQtGvfOmaIiNya4nLM0PFCROTW6HghIiLX43qPF0WuqHHq1Cn8/PzMjiEiUuidOHGCKlWqmB0jT+mYISJyexT1Y4aOFyIit4eOFyIicj3+7XhR5IoaHh4ewJU37unpaXIaEZHCJzk5GT8/v+zP06JMxwwRkVtTXI4ZOl6IiNwaHS9EROR6XO/xosgVNf6a3ufp6akDiIjILSgO06V1zBARuT2K+jFDxwsRkdtDxwsREbke/3a8KLoLGYqIiIiIiIiIiIiISJGiooaIiIiIiIiIiIiIiBQKKmqIiIiIiIiIiIiIiEihoKKGiIiIiIiIiIiIiIgUCipqiIiIiIiIiIiIiIhIoZDnRY2pU6fi7++Pm5sbISEhbNmy5R/HT548mbp161KiRAn8/PwYOXIk6enpeR1TREREREREREREREQKuDwtaixcuJBRo0Yxbtw4duzYQVBQEOHh4Zw5cybX8fPmzeOll15i3LhxHDhwgM8//5yFCxfy8ssv52VMEREREREREREREREpBPK0qDFx4kQGDhxIv379CAwMZPr06bi7uzNr1qxcx2/atIk77riDXr164e/vz3333UfPnj3/dXaHiIiIiIiIiIiIiIgUfXlW1MjIyGD79u2EhYX978UcHAgLCyMiIiLXfVq1asX27duzixjR0dGsWLGCDh06XPN1rFYrycnJOW4iIlK4rF+/nk6dOlGpUiUsFgtLly79133WrVtHkyZNcHV1pVatWsyZMyfPc4qIiIiIiIiIiLnyrKiRmJiIzWbDx8cnx3YfHx/i4+Nz3adXr1688cYbtG7dGmdnZ2rWrEnbtm3/cfmpCRMm4OXllX3z8/O7re9DRETyXmpqKkFBQUydOvW6xsfExNCxY0fatWvHzp07GTFiBAMGDGDVqlV5nFRERERERERERMyU543Cb8S6det4++23mTZtGjt27GDJkiUsX76c8ePHX3Of0aNHk5SUlH07ceJEPiYWESm4DMMwO8J1a9++PW+++SYPPfTQdY2fPn061atX54MPPqBevXoMHTqUrl27MmnSpDxOKiIiIiJSPBWm3xciImKe/DheOOXVE5crVw5HR0cSEhJybE9ISMDX1zfXfV599VWeeOIJBgwYAEDDhg1JTU1l0KBBvPLKKzg4XF2DcXV1xdXV9fa/ARGRQiotw8a47/dSwcON58Prmh0nT0RERORY3hAgPDycESNG/ON+VqsVq9WafV9LFkphdDkji32nktl9MokjZ1Ko6FWChlW8aFjZi3Kl9J1IREREbr/1h87y4drDvNO1ETXLlzI7joiIFFBnU6z0nrWFlzsEcGft8nn2OnlW1HBxcaFp06asXbuWLl26AGC321m7di1Dhw7NdZ/Lly9fVbhwdHQEdEWAiMj1OHr2Es98vYOo+BQcHSx0b+6HX1l3s2PddvHx8bkub5icnExaWholSpTIdb8JEybw+uuv50dEkdsiLcPG/tPJ7Dl5kT1xyeyJu8iRM5ewX+NrUSUvNxpW8aJRldI0rHyl0FGmpEv+hhYREZEi5ZI1i9FL9hB3MY2v/4hlbKdAsyOJiEgBZLMbjFy4kwOnk3lr+QGWDyuHo4MlT14rz4oaAKNGjaJPnz40a9aMFi1aMHnyZFJTU+nXrx8AvXv3pnLlykyYMAGATp06MXHiRBo3bkxISAhHjhzh1VdfpVOnTtnFDRERyd2ynXG8vGQPqRk2ypVy5cMewUWyoHErRo8ezahRo7LvJycnqxeTFBjpmTai4lPYE5fEnpMX2X0yicNnLmHLpYLh63mleFHXx4O4i2nsPnmR6MRUTiWlcyopnVX7/jdTtkqZEjSq4kXDyqVpVMWLBpW88HJ3zs+3JiIiIoXYOz9FEXcxDb+yJXg+vI7ZcUREpICa+usRNh5JpISzIx/3apxnBQ3I46JG9+7dOXv2LGPHjiU+Pp7g4GBWrlyZfXVtbGxsjpkZY8aMwWKxMGbMGOLi4ihfvjydOnXirbfeysuYIiKFWnqmjfE/7ufrzbEAtKxRlg97NqaCh5vJyfKOr69vrssbenp6XnOWBmjJQilYktMzWb77NLv/LGAcjE8hK5cCRrlSrn8WJbyy/1vB8+p/35esWeyLS2JPXBK7T175b0xiKicvpHHyQhor9sRnj63m7Z79fOH1fanmXTJP36uIiIgUTpujz/HlH8cB+O/DjXB3ydPTSCIiUkhtOprI5DWHAHizSwNqVfDI09fL86PR0KFDr7nc1Lp163KGcXJi3LhxjBs3Lq9jiYgUCcfPpTLk6x3sO3WlN8Szd9di+D21cXK8ugdRURIaGsqKFStybFu9ejWhoaEmJRK5foZhsHRnHG8tjyLxkjXHY2VLuuQoXjSqUhofT1csln+/wqWUqxMhNbwJqeGdvS0pLZN9cUnsjktiz8kkdsdd5MT5NI6fu8zxc5f5cfdp3lt1kEF31WBou9qUcNHMWBEREbkiLcPGf77dDUDPFlW5o1Y5kxOJiEhBdDbFyvAFO7Eb8GizKjzStEqev6ZK7CIihdTKvad5YfFuUqxZlHF3ZlL3YNrWrWB2rJty6dIljhw5kn0/JiaGnTt3UrZsWapWrcro0aOJi4vjiy++AODpp5/m448/5sUXX+TJJ5/kl19+YdGiRSxfvtystyByXQ4lpPDq0r1sjjkPQPVyJbm/gS+NKnvRsIoXlUuXuK4CxvXyKuFMq1rlaPW3kxAXUjPYe+rKbI71h86yOeY8U389ytLIU7z2YH3uDfT5h2cUERGR4mLi6oMcO3eZil5ujO4QYHYcEREpgP7qo3E2xUodn1K8/mCDfHldFTVERAqZjCw7E346wOzfjwHQrFoZPurVmIpe1152qaDbtm0b7dq1y77/V9+LPn36MGfOHE6fPk1sbGz249WrV2f58uWMHDmSKVOmUKVKFWbOnEl4eHi+Zxe5HqnWLKasPcysjTFk2Q3cnB149u7aDLizOq5O+Ts7okxJF+6sXZ47a5dnSNuarNoXzxs/7CfuYhoDv9jGPQEVGNepPlW91ZNHRESkuNoRe4HPN8YA8PZDDfF0Uz8uERG52t/7aEx7rEm+zf5XUUNEpBA5eeEyz8yLZNeJiwA8dVcNng+vi3MhX26qbdu2GMbVvQT+MmfOnFz3iYyMzMNUIrfOMAxW7Iln/I/7iU9OB+C+QB9efSAQv7LmFw0sFgv3N6jIXXXK8+HaI8zcEM3aqDNsPJLIkLa1eKpNDdyctSSViIhIcWLNsvHiN7uxG/Bw48q0Cyics8FFRCRv5Xcfjb9TUUNEpJBYeyCBUYt2kZSWiVcJZz7oFkSYlokRKbCiz15i3Pf72HA4EYCqZd157cFA7g4oeP9u3V2ceKl9AF2bVmbssn1sOnqOSWsO8V3kSV57sH6hXdpOREREbtxHa49w5MwlypVyZWynQLPjiIhIAWRGH42/U1FDRKSAy7TZef/ng3z6WzQAQVW8+LhXkwJxlbeIXC0tw8bUX4/w2fpoMmx2XJwcGNymJoPb1izwsx5qVfDg6wEh/LD7NG/+uJ9j5y7Td/ZW2jfw5dUHAqlUuvAucyciIiL/bm9cEp/8dhSAN7vUp7S7i8mJRESkoDGrj8bfFe71SkREirj4pHR6zfgju6DRt5U/i59upYKGSAG1en8C9076jY9/PUKGzU6bOuX5ecRdjLy3ToEvaPzFYrHwYFAl1j7Xhv6tq+PoYOGnvfGETfyN6b8dJSPLbnZEERERyQOZNjsvfLMbm92gY8OK3N+gotmRbsn69evp1KkTlSpVwmKxsHTp0n/dZ926dTRp0gRXV1dq1aqV6zK4IiLFnVl9NP5ORQ0RkQJq/aGzdPhwA1uPXcDD1YlpjzXhtQfr4+Kkj26RgubE+cv0n7OVgV9s4+SFNCp5uTH98SbM6dcc/3IlzY53UzzcnHn1gUB+fLY1zf3LcDnDxn9/iqLDhxuIOHrO7HgiIiJym01fd5QDp5Mp4+7Maw/WNzvOLUtNTSUoKIipU6de1/iYmBg6duxIu3bt2LlzJyNGjGDAgAGsWrUqj5OKiBQeZvbR+DstPyUiUsDY7AZT1hzio1+PYBgQWNGTaY81KbQnRkWKMmuWjc9+i+bjX49gzbLj5GBhwJ01GHZPLdxdisbXrHoVPVn0VCjf7ohjwooDHDlziZ4z/qBzcCVe6VCPCp5uZkcUERGRW3QoIYWPfjkCwGsP1qe8h6vJiW5d+/btad++/XWPnz59OtWrV+eDDz4AoF69emzcuJFJkyYRHh6eVzFFRAoNs/to/F3R+LUtIlJEXLJmMXx+JGujzgDQK6QqYx8ILDTL1ogUJ9uOneeFb3YTk5gKQGgNb8Z3qW/alSp5yWKx0LVpFe6t58P7Px/kq83HWbbzFL8cOMNLHQJ4LKSa2RFFRETkJtnsBi98s5sMm52wehV4MKiS2ZFMERERQVhYWI5t4eHhjBgxwpxAIiIFSEHoo/F3KmqIiBQQJy9cZsDcbUTFp+Di5MA7jzTkocbmVb1F5No2HU3kyTlbSc+0U8HDlVc61uPBoCvrNRdlXu7OjO/SgEeb+TFm2V52nbjIK9/t5eLlTJ5pV8vseCIiInITZm2MYdeJi3i4OvFml4ZF/vvMtcTHx+Pj45Njm4+PD8nJyaSlpVGiRImr9rFarVit1uz7ycnJeZ5TRMQMBaGPxt9pYXYRkQJg+/ELdJn6O1HxKZQr5crCQS1V0BApoP5e0Ghbtzxrn2tD5+DKxeoEQMMqXnw3uBUjw+oA8N6qg0z99YjJqURERORGxSSm8v7PBwEY80A9fL20rOSNmDBhAl5eXtk3Pz8/syOJiNx2BaWPxt+pqCEiYrKlkXH0nPEHiZcyqFfRk++H3kHjqmXMjiUiuYg4ei5HQWP6403xcHM2O5YpHBwsDA+rzQvhdYErhY1p61TYEBERKSzsdoP/fLMba5ad1rXK8Wiz4n1C3tfXl4SEhBzbEhIS8PT0zHWWBsDo0aNJSkrKvp04cSI/ooqI5JuC1Efj77T8lIiISex2g0lrDmU35Ls30IfJ3YMp6aqPZpGCKLeChvrdwDPtamEYBu//fIh3V1650nNIWy1FJSIiUtB9tfk4W46dx93FkQkPF99lp/4SGhrKihUrcmxbvXo1oaGh19zH1dUVV9fC31RdRCQ3Ba2Pxt9ppoaIiAnSMmwMnb8ju6DxVJsafPp4UxU0RAqoP6KvFDTSMm20qaOCxv839O7aPH/flaWo3l15kE/WHTU5kYiIiPyTE+cv89+fogB4qX0AfmXdTU50+126dImdO3eyc+dOAGJiYti5cyexsbHAlVkWvXv3zh7/9NNPEx0dzYsvvkhUVBTTpk1j0aJFjBw50oz4IiKmK2h9NP5OZ89ERPJZQnI6A7/Yxu6TSTg7Wnj7oYZ0K+ZTvUUKsj+iz9Fv9v8KGp8+oYJGbobeXRvDgA9WH+KdlVFYLPB0m5pmxxIREZH/xzAMRi/Zw+UMGy38y/J4SDWzI+WJbdu20a5du+z7o0aNAqBPnz7MmTOH06dPZxc4AKpXr87y5csZOXIkU6ZMoUqVKsycOZPw8PB8zy4iYraC2Efj71TUEBHJR3vjkug/dysJyVbKuDvz6RPNaFG9rNmxROQa/l7QuEsFjX/17D21MYCJqw9lX/2pwoaIiEjBsmjbCTYeScTVyYF3ujbCwaFoLjvVtm1bDMO45uNz5szJdZ/IyMg8TCUiUvAV1D4af6eihohIPlm59zQjFu4kPdNO7Qql+LxPc6p6F71p3iJFxeb/V9D4TAWN6zLsntrA/wobFuApFTZEREQKhPikdN5cfgCA5++rS/VyJU1OJCIiBUlB7qPxdypqiIjkMcMwmLbuKO+tutJAt02d8nzUqzGebs4mJxORa9kcfY5+f/bQuLN2ORU0btCwe64sRTVpzSEm/HRlKapBd6mwISIiYibDMHjluz2kpGcR5FeaJ1tXNzuSiIgUIIZh8M7KqALbR+PvVNQQEclD1iwbo7/dw5LIOAD6tvJnTMd6ODk6mJxMRK5lS8x5+s3ZyuWMKwWNGb2bqaBxE4aH1cbAYPKaw7y94spSVCpsiIiImOf7XadYG3UGZ0cL73VthGMRXXZKRERuzse/HOGz9dEATHi4YYHro/F3KmqIiOSRxEtWnvpyO9uPX8DRwcLrD9bn8ZZFswmfSFGxJeY8fWdvUUHjNhkRVgcgu7BhwcLAu2qYnEpERKT4OZtiZdz3+wAYdndt6vgU3BNVIiKS/z7fGMMHq680Bn/1gUC6NK5scqJ/pkuFRUTywMH4FLpM/Z3txy/g6ebE3H4tVNAQKeBU0MgbI8LqMPzPPhtvrTjAzA3RJieSwm79+vV06tSJSpUqYbFYWLp06b/us27dOpo0aYKrqyu1atXKtTmsiEhRZRgGL3+3h4uXMwms6MnTbTVzUkRE/mf+lljG/7gfgFH31qF/IVieUEUNEZHbbP2hszzyySZOXkjD39ud7565g9a1y5kdS0T+wdZj/ytotK6lgsbtNvLeOtkNxN9crsKG3JrU1FSCgoKYOnXqdY2PiYmhY8eOtGvXjp07dzJixAgGDBjAqlWr8jipiEjBMPXXI6zen4Czo4V3uzbCWUvhiojIn5btjOPl7/YA8NRdNXj27lomJ7o+Wn5KROQ2WrYzjucX7yLTZtCyRlk+eawpZUq6mB1LRP7B1mPn6TtLBY28NjLsSlHjw7WHeXP5AQAG3KmlqOTGtW/fnvbt21/3+OnTp1O9enU++OADAOrVq8fGjRuZNGkS4eHheRVTRKRAWHsgIXs5kTc6N6BBZS+TE4mISEHx8754Ri3ahWHA4y2r8lL7ACyWwtFvSeV5EZHbZPbvMQxfsJNMm0GnoEp88WSIChoiBdy2PwsaqRk27qjlzYzezSjhooJGXrBYLIwMq82wP6/80YwNyS8RERGEhYXl2BYeHk5ERMQ197FarSQnJ+e4iYgUNkfOXGLEgp3ZJ6t6tqhqdiQRESkgNhw+y9B5kdjsBg83rswbDzYoNAUNUFFDROSWGYbB+6sO8voPV9Yf7NvKnyndg3Fx0kesSEG27dh5+vxZ0GhV05uZvZuroJHHLBYLI++tkz2l+c3lB/h8Y4zJqaSoi4+Px8fHJ8c2Hx8fkpOTSUtLy3WfCRMm4OXllX3z8/PLj6giIrdNcnomg77cRoo1i+b+ZRj7QH2zI4mISAGx9dh5Bn6xjQybnfYNfHm3ayMcHApPQQNU1BARuSU2+5Wmex//egSA5++rw7hOgYXuYCBS3JxOSmPAF9uyCxqf91FBI79YLBZG/a2wMf7H/aw9kGByKpGcRo8eTVJSUvbtxIkTZkcSEbludrvBiAU7iT6bSkUvN6Y91lQXXImICAB7Tibx5OytpGfaaVOnPFN6NMapEPZayvPEU6dOxd/fHzc3N0JCQtiyZcs/jr948SLPPPMMFStWxNXVlTp16rBixYq8jikicsPSM20M+Xo787ecwMECbz/UkKF31y5U0/VEiiOb3WDUwl1cvJxJg8qeKmiY4K/CRp/QagC88M1uziSnm5xKiipfX18SEnIWzhISEvD09KREiRK57uPq6oqnp2eOm4hIYTFx9SF+iTqDq5MDnz7RlPIermZHEhGRAuBgfApPzNpMijWLFtXLMv3xwlv0ztPUCxcuZNSoUYwbN44dO3YQFBREeHg4Z86cyXV8RkYG9957L8eOHeObb77h4MGDzJgxg8qVK+dlTBGRG5acnkmfWVtYtS8BFycHpj3WhF4hWqNWpDD4dP1RIqLP4e7iyIc9GqugYRKLxcLLHetRr6In51MzeG7xLux2w+xYUgSFhoaydu3aHNtWr15NaGioSYlERPLOij2ns2eR//eRhjSqUtrcQCIiUiDEJKby+OebuXg5kyC/0szqW7gv7svTosbEiRMZOHAg/fr1IzAwkOnTp+Pu7s6sWbNyHT9r1izOnz/P0qVLueOOO/D396dNmzYEBQXlZUwRkRtyJiWdHp/+weaY85RydWJuvxbc36Ci2bFE5DrsPHGRiT8fAuC1B+tTo3wpkxMVb65OjnzUMxg3Zwc2HE5Ufw25LpcuXWLnzp3s3LkTgJiYGHbu3ElsbCxwZemo3r17Z49/+umniY6O5sUXXyQqKopp06axaNEiRo4caUZ8EZE8c+B0Ms8t2gXAgNbVeahxFZMTiYhIQRB3MY3HZ27mbIqVAF8P5vZrTilXJ7Nj3ZI8K2pkZGSwfft2wsLC/vdiDg6EhYURERGR6z7ff/89oaGhPPPMM/j4+NCgQQPefvttbDbbNV/HarWSnJyc4yYikleOn0ul6ycR7D+dTLlSriwY1JLQmt5mxxKR63DJmsXwBZFk2Q06NqpIt6b6oV8Q1Krgkd289N1VUeyNSzI5kRR027Zto3HjxjRu3BiAUaNG0bhxY8aOHQvA6dOnswscANWrV2f58uWsXr2aoKAgPvjgA2bOnEl4eLgp+UVE8sKF1AwGfbmNtEwbrWuV46X2AWZHEhGRAuBMSjqPz9xM3MU0apQryZf9Qyjt7mJ2rFuWZyWZxMREbDYbPj4+Obb7+PgQFRWV6z7R0dH88ssvPPbYY6xYsYIjR44wZMgQMjMzGTduXK77TJgwgddff/225xcR+f/2nUqiz6ytJF6yUrWsO1/2b0E175JmxxKR6zR22V6On7tM5dIlePuhhup/U4D0bOHH+kNnWbkvnmHzI/nh2daULORXDkneadu2LYZx7aXK5syZk+s+kZGReZhKRMQ8WTY7z86P5MT5NPzKluCjnoWz6auIiNxeF1IzeGLmFmISU6lcugRfDQgpMn2WCtRRzm63U6FCBT777DOaNm1K9+7deeWVV5g+ffo19xk9ejRJSUnZtxMnTuRjYhEpLv6IPkePT/8g8ZKVehU9+WZwqAoaIoXIsp1xLNkRh4MFJvcIxquEs9mR5G8sFgv/faQhvp5uRCem8sYP+82OJCIiUmj896coNh5JpISzIzN6N6NMycJ/Ba6IiNyalPRM+szewsGEFCp4uPL1gBAqlS5hdqzbJs+KGuXKlcPR0ZGEhIQc2xMSEvD19c11n4oVK1KnTh0cHf/XpKRevXrEx8eTkZGR6z6urq54enrmuImI3E4r98bTe9YWUqxZtKheloVPtaSCh5vZsUTkOp04f5kx3+0F4Nm7a9Pcv6zJiSQ3pd1dmNQ9GIsFFm47wfLdp82OJCIiUuB9F3mSmX/2pPrg0SACfHVORESkuEvLsNF/zjZ2n0yijLszXw8Iwb9c0bowN8+KGi4uLjRt2pS1a9dmb7Pb7axdu5bQ0NBc97njjjs4cuQIdrs9e9uhQ4eoWLEiLi660kBE8t+CLbEM+Xo7GVl27gv04YsnW+Dppiu8RQqLLJud4QsiSbFm0axaGZ69u5bZkeQfhNb0ZkjbmgCMXrKbuItpJicSEREpuPacTOKlb/cAMLRdLTo0rGhyIhERMZs1y8agL7ex5dh5PFyd+LJ/CLV9PMyOddvl6fJTo0aNYsaMGcydO5cDBw4wePBgUlNT6devHwC9e/dm9OjR2eMHDx7M+fPnGT58OIcOHWL58uW8/fbbPPPMM3kZU0TkKoZhMPXXI7y0ZA92A3o092PaY01wc3b8951FpMD4cO1hdsRexMPNick9grW+dCEwIqwOwX6lSU7PYuSCndjs1+6dICIiUlydTbEy6MttWLPs3B1QgVH31jE7koiImCwtw8bgr3aw4fCVJQln92tOg8peZsfKE3nagbF79+6cPXuWsWPHEh8fT3BwMCtXrsxuHh4bG4uDw/9OLvj5+bFq1SpGjhxJo0aNqFy5MsOHD+c///lPXsYUEcnBbjd448f9zNl0DIBn2tXk+fvqqqmwSCGzOfocH/96BIC3HmpIlTLuJieS6+Hs6MCHPRrT4cMNbDl2nqm/HmHYPbXNjiUiIlJgZGTZeebrHZxOSqdG+ZJM7hGMg4N+q4iIFGdJlzPpP3cr245fwNXJgRm9m9GsCC+9nKdFDYChQ4cydOjQXB9bt27dVdtCQ0P5448/8jiViEjubHaDFxbvYklkHABjHwjkydbVTU4lIjcq6XImIxfuxG5A16ZVeDCoktmR5AZU9XZnfJf6jFy4iylrD3NHLW+aViu6X8hFRERuxBs/7steVmRG72ZaHldEpJiLT0qnz6wrTcE93JyY1bd5ke8lqTUYRET+lGWzM2LhTpZExuHoYGFy92AVNEQKIcMwGP3dbk4lpePv7c5rD9Y3O5LchIcaV6FLcCVsdoPhC3aSnJ5pdiQRERHTzd8Sy1d/xGKxwOQewdQsX8rsSCIiYqLos5d45JNNHExIoYKHK4ueCi3yBQ1QUUNEBIBMm51n50fyw65TODtamNqrCV0aVzY7lojchEXbTrBiTzxODhY+7NmYUq55PjFV8sj4Lg3wK1uCkxfSGPPdXgxD/TVERKT42n78PGOX7QXguXvrcE89H5MTiYiImXafvEjX6RHEXUzD39udbwe3ol5FT7Nj5QsVNUSk2LNm2Rjy9Q5+2huPi6MDnzzWlPsb+JodS0RuwtGzl3jt+/0APB9el0ZVSpsbSG6Jh5szU3o0xtHBwve7TrFkR5zZkUREREwRn5TO01/tINNm0L6BL8+0q2V2JBERMdHvRxLp+dkfnE/NoEFlT74Z3Aq/ssWnj6SKGiJSrKVn2nj6y+2s3p+Ai5MDn/VuSligrngSKYysWTaGzY8kLdNGq5reDLqzhtmR5DZoUrUMI8OuNAofu2wvxxJTTU4kIiKSvy5Zs3jqy22cTbES4OvB+92CsFjUGFxEpLhavvs0/WZvJTXjym/f+QNbUq6Uq9mx8pWKGiJSbKVl2Bj4xTZ+PXgWN2cHZvVpTtu6FcyOJSI36YOfD7HvVDJl3J2Z1D0YBwf92C8qBretRUj1sqRm2Bi+IJJMm93sSCIiIvkiLcNG/zlb2XUyidLuznz2RDNKamlNEZFi68s/jjN0/g4ybHY6NPRldr/meLg5mx0r36moISLF0uWMLJ6cs5UNhxNxd3FkTr8WtK5dzuxYInKT1h86y2frowF4t2sQPp5uJieS28nRwcKk7sF4lXBm18kkJq4+ZHYkERGRPGfNsvH0V9vZHHMeD1cnvniyBVW9i8/SIiIi8j+GYTB5zSFeXboXw4BeIVX5qGcTXJ0czY5mChU1RKTYuWTNou+srUREn6PUnz8OWtbwNjuWiNykxEtWRi3aBcATLatxr5aQK5IqlS7Bfx9uCMD0346y6UiiyYlERETyTpbNzrD5kfx26CwlnB2Z1a+5eoWJiBRTNrvB2GX7mLzmMADD7qnNW10a4FiMVydQUUNEipXk9Ex6f76ZLcf+vNqpfwua+Zc1O5aI3CTDMHjxm90kXrJSu0IpXulYz+xIkofaN6xIzxZ+GAaMXLSTC6kZZkcSERG57Wx2g+cW72LVvit9/2b0bkZz/WYRESmWrFk2hi2I5Ms/jmOxwOsP1mfUvXWKfW8lFTVEpNhIupzJEzM3syP2Ip5uTnw9MIQmVcuYHUtEbsEXEcf5JeoMLk4OfNizMW7OxXPqbXHy6gOB1ChfkoRkK//5djeGYZgdSURE5LYxDINXvtvDsp2ncHKw8MljTbRMrohIMXXJmkX/OdtYvvs0zo4WpvRoTJ9W/mbHKhBU1BCRYuFCaga9Zv7BrpNJlHF3Zt7Alpq+LVLIHTidzFsrDgDwcvsA6lX0NDmR5Ad3Fyc+7NEYF0cHft6fwNebY82OJCIiclsYhsEbP+5nwdYTOFhgco9g7qmnZTVFRIqjc5esPDbjDzYeudIL9vM+zXkwqJLZsQoMFTVEpMhLvGSl54w/2HcqGe+SLswf1JIGlb3MjiUityAjy87wBZFkZNm5O6CCrlYpZhpU9uLF++sCMP7H/Rw9e8nkRCIiIrfu/Z8PMvv3YwC82zWIBxrp5JWISHF08sJluk2PyHFh7l11ypsdq0BRUUNEirQzKen0/OwPouJTKO/hyoJBLQnw1dXcIoXdFxHHOJRwiXKlXHiva6Niv55ocfTkHdW5s3Y5rFl23lp+wOw4IiIit2Tqr0eY+utRAMZ3rk/XplVMTiQiImY4lJDCI59sIjoxlcqlS7D46VYE+5U2O1aBo6KGiBRZ8Unp9Pj0Dw6fuYSvpxsLB7Wkto+H2bFE5BadT81gytrDALwQXhfvUq4mJxIzODhYeP3B+jg5WPgl6gwbDp81O5KIiMhNmbUxhvdWHQTg5Q4BPBHqb24gERExxW+HzvLIJ5tISLZSu0IpvhkcSq0KpcyOVSCpqCEiRdKpi2l0/ywiu7K98KmW1CivA4FIUTB5zSFS0rMIrOhJ16Z+ZscRE9UoX4ref574efPHA2TZ7OYGEhERuUELtsTyxo/7ARh+T20G3VXT5EQiIpLfDMNg1sYY+s3eQkp6Fs39y7D46VAqepUwO1qBpaKGiBQ5J85fpvtnERw/d5kqZUqwYFBLqnmXNDuWiNwGhxNSshtDj3mgHo4OWnaquBt+T21KuztzMCGFBVtPmB1HRETkui2NjGP0d3sAGHRXDUaE1TY5kYiI5LeMLDujl+zhjR/3YzegW9MqfDUghNLuLmZHK9BU1BCRIiXuYho9PvuDE+fTqObtzqKnQvEr6252LBG5Td5cfgCb3eC+QB9a1SxndhwpALzcnRkZVgeASasPkZyeaXIiERGRf7dybzzPLd6FYcATLasxun2AeoSJiBQz51MzeOLzzSzYegKLBV7pUI93uzbC1cnR7GgFnooaIlJkJCSn02vGH8RdTMPf252Fg0KpVFpT9USKinUHz/DbobM4O1p4uUM9s+NIAdIrpCo1y5fkXGoGU385YnYcERGRf7Tu4Bmenb8Dm93gkSZVeP3B+ipoiIgUM4cSUugy9Xc2x5ynlKsTs/o0Z+BdNXQ8uE4qaohIkZB4yUqvGX9kLzk1b2BLfL3czI4lIrdJls3Om8sPANAn1B//clpSTv7H2dGBMR0DAZj9+zGOn0s1OZGIiEjuIo6e46kvt5NpM+jYsCLvPNIQBy2nKSJSrPwSlcDD0zYRe/4yVcu6s2RIK9oFVDA7VqGiooaIFHoXUjN4fOZmjp5NpaKXG/MHttQMDZEiZt6WWI6cuUQZd2eevUfrTcvV2tYtz521y5FhszNhRZTZcURERK6yI/YC/eduxZpl556ACkzqHoyTo07LiIgUF4Zh8Nn6o/Sfu41L1ixCqpdl6TN3UMfHw+xohY6OniJSqCWlZfLErM1ExadQ3sOVeQNbqoeGSBGTdDmTSasPATDq3jp4lXA2OZEURBaLhVcfCMTBAiv3xfNH9DmzI4mIiGTbG5dEn1lbuJxh445a3kx9rAkuTjolIyJSXFizbLz4zW7eXhGFYUDPFn582T+EsiXVEPxm6AgqIoXWJWsWfWZtYW9cMt4lXZg3IITqWpKmUJs6dSr+/v64ubkREhLCli1brjk2MzOTN954g5o1a+Lm5kZQUBArV67Mx7SSXz765TAXLmdSu0IperaoanYcKcDq+HjQK+TK35E3l+/HbjdMTiQiIgJ7TibRe9YWUtKzaFatDDN6N8PNWU1gRUSKi8RLVh6fuZnF20/iYIFxnQJ5+6GGKm7fAv3JiUihdDkjiydnb2XniYuUdnfmqwEh1NZ0vUJt4cKFjBo1inHjxrFjxw6CgoIIDw/nzJkzuY4fM2YMn376KR999BH79+/n6aef5qGHHiIyMjKfk0teiklMZW7EMQBe6VhPSzTIvxoZVgcPNyf2xiXz7Y6TZscREZFibuPhRHp8FsH51AwaVfFiVr/muLs4mR1LRETySVR8Mp0//p2txy7g4ebE7H4t6HdHdTUEv0U6MyAihU56po0Bc7ex5dh5PFyd+PLJEOpV9DQ7ltyiiRMnMnDgQPr160dgYCDTp0/H3d2dWbNm5Tr+yy+/5OWXX6ZDhw7UqFGDwYMH06FDBz744IN8Ti55acKKA2TaDNrUKU/bumqcJv/Ou5Qrz95dC4D3Vh0k1ZplciIRESmuftx9in5ztpCaYaNVTW++HhCCp5uW0RQRKS5W70/gkWmbiLuYhr+3O98NuYM2dcqbHatIUFFDRAoVa5aNp7/azqaj5yjp4sicJ1vQsIqX2bHkFmVkZLB9+3bCwsKytzk4OBAWFkZERESu+1itVtzc3HJsK1GiBBs3bszTrJJ/Nh1N5Of9CTg6WBjTsZ7ZcaQQ6dPKn2re7pxJsTL9t6NmxxERkWLoi4hjPDs/kkybQYeGvszu1xwPFTRERIoFwzCYtu4Ig77cll3YXvrMHdSqUMrsaEWGihoiUmhk2uwMnRfJuoNncXN2YFbf5jStVsbsWHIbJCYmYrPZ8PHxybHdx8eH+Pj4XPcJDw9n4sSJHD58GLvdzurVq1myZAmnT5++5utYrVaSk5Nz3KRgstkNxv94AIDHQqpqeTm5Ia5Ojoxuf6UQ9tn6aOIuppmcSEREigvDMJi4+hBjl+3DMODxllX5qGcTXJ3UQ8MMN9Kzb86cOVgslhy3/38RlYjIv0nPtPHcol28u/Jg9nFg7pMtKO2uhuC3k4oaIlIoZNnsjFiwk9X7E3BxcmBm7+aE1PA2O5aYaMqUKdSuXZuAgABcXFwYOnQo/fr1w8Hh2oe2CRMm4OXllX3z8/PLx8RyI77ZfoIDp5PxcHNiRFgds+NIIRRe34eQ6mWxZtl5d2WU2XFERKQYsNkNxizdy4drDwMwIqw24zs3wNFB66ab4UZ79gF4enpy+vTp7Nvx48fzMbGIFHank9LoOeMPlkTG4ehgYXzn+rzZpSHO6g152+XLn+iNVMb/bsGCBVgsFrp06ZK3AUWkQLPZDV74ZjfL95zG2dHCp483pXXtcmbHktuoXLlyODo6kpCQkGN7QkICvr6+ue5Tvnx5li5dSmpqKsePHycqKopSpUpRo0aNa77O6NGjSUpKyr6dOHHitr4PuT0uWbN4b9UhAIbfU5uyJXVFi9w4i8XCqw8EYrHAsp2n2BF7wexIIiJShFmzbDw7fwdfb47FYoHxXRowIqyOGsGa6EZ79sGV7w++vr7Zt/8/k1xE5Fo2HUnkgQ83Ehl7EU83J+b2a8ETof5mxyqy8ryocTOVcYBjx47x/PPPc+edd+Z1RBEpwOx2g1e+28N3f1a5P+7VhHYBahZc1Li4uNC0aVPWrl2bvc1ut7N27VpCQ0P/cV83NzcqV65MVlYW3377LZ07d77mWFdXVzw9PXPcpOCZ9usREi9Z8fd2p7e+BMotaFDZi25NqwAw/sf9GIZhciIRESmKUtIz6TtrKyv2xOPi6MDHPZvwRMtqZscq1m6mZx/ApUuXqFatGn5+fnTu3Jl9+/blR1wRKcQMw+CTdUd5/PPNnEvNoF5FT354trUuxs1jeV7UuJnKuM1m47HHHuP111//xytuRaRoMwyD137Yx4KtJ3CwwOTuwYTXz/2qfSn8Ro0axYwZM5g7dy4HDhxg8ODBpKam0q9fPwB69+7N6NGjs8dv3ryZJUuWEB0dzYYNG7j//vux2+28+OKLZr0FuQ1OnL/MzI0xALzcoR4uTpqmK7fm+fvq4u7iSGTsRb7fdcrsOCIiUsScTbHS47M/iIg+R0kXR+b0a07HRhXNjlXs3UzPvrp16zJr1iyWLVvGV199hd1up1WrVpw8efKar6OefSLFW3J6JoO+3M47K6OwG9C1aRW+G9KKat4lzY5W5OXpmYKbrYy/8cYbVKhQgf79+//ra+gAIlI0GYbB2ysO8EXEcSwWeL9bEJ2CKpkdS/JQ9+7def/99xk7dizBwcHs3LmTlStXZv8QiY2NzdEEPD09nTFjxhAYGMhDDz1E5cqV2bhxI6VLlzbpHcjt8M7KKDKy7ITW8ObeQE33l1tXwdONIW1rAvDOT1GkZ9pMTiQiIkVF7LnLdJ2+iX2nkvEu6cKCQaG0qqUrcwur0NBQevfuTXBwMG3atGHJkiWUL1+eTz/99Jr7qGefSPF14HQyD3608UrvV0cH3n6oIe91bYSbs6PZ0YoFp7x88n+qjEdF5d6wcePGjXz++efs3Lnzul5jwoQJvP7667caVUQKmImrDzFjw5Wrtd9+qCEPN6liciLJD0OHDmXo0KG5PrZu3boc99u0acP+/fvzIZXkl+3Hz/Pj7tNYLDDmgXpag1pumwF31mD+lhPEXUxj5oZoht5d2+xIIiJSyO0/lUyf2Vs4m2KlSpkSfNk/hOrldGVuQXEzPfv+P2dnZxo3bsyRI0euOWb06NGMGjUq+35ycrIKGyLFwHeRJxm9ZA/pmXYqly7BtMeaEORX2uxYxUqBWtMhJSWFJ554ghkzZlCu3PVd3aCmryJFz8e/HOajX658cXz9wfr0bFHV5EQiktfsdoM3fjwAQPdmftSv5GVyIilK3Jwd+U/7AACmrTvKmeR0kxOJiEhhtjn6HN0/jeBsipUAXw++HdxKBY0C5lZ69v3FZrOxZ88eKla89nJi6tknUrxYs2yMWbqHkQt3kZ5p56465fnx2dYqaJggT2dq3Ghl/OjRoxw7doxOnTplb7Pb7VeCOjlx8OBBatasmWMfV1dXXF1d8yC9iJhhzu8xvP/zIQBe6VCPPq38zQ0kIvli2a44dp24SEkXR0bdV8fsOFIEdWpUkTm/x7Aj9iLvrTrIe92CzI4kIiKF0Kp98Tw7P5KMLDst/Msyo08zvEo4mx1LcjFq1Cj69OlDs2bNaNGiBZMnT76qZ1/lypWZMGECcGUp9JYtW1KrVi0uXrzIe++9x/HjxxkwYICZb0NECohTF9MY/PUOdp24CMCwe2oz/J7aODpohQEz5GlR4++V8S5dugD/q4zntrxIQEAAe/bsybFtzJgxpKSkMGXKFE3hEynilkbG8doPV5YTGn5PbQbeVcPkRCKSH9IybLy78iAAQ9rVooKHm8mJpCiyWCy8+kAgD03bxDc7TtKnlT8NKmtGkIiIXL8FW2J5+bs92A24N9CHj3o21trpBVj37t05e/YsY8eOJT4+nuDg4Kt69jk4/G8BkwsXLjBw4EDi4+MpU6YMTZs2ZdOmTQQGBpr1FkSkgNh4OJFhCyI5n5qBVwlnJncPpl1ABbNjFWt5WtSAG6uMu7m50aBBgxz7/9Xw9f9vF5GiZc3+BJ5bvAuAvq38GRGm9c5FiovP1kdzOimdyqVL0L91dbPjSBHWuGoZOgdXYtnOU4z/cT8LBrVU7xYREflXhmEwbd1R3lt15SKMR5tV4e2HGuLkWKBW9JZc3EjPvkmTJjFp0qR8SCUihYXdbjBt3RE+WH0Iw4D6lTyZ/nhT/Mq6mx2t2MvzosaNVsZFpPjZHH2OZ+btwGY3eLhxZcY+EKiTTCLFRHxSOtN/OwrAS+0DdLWj5LkX7w9g5d54NsecZ9W+BO5vcH3NQkVEpHiyZtl4deleFm07CcCQtjV5Ibyufq+IiBRxSWmZPLdoJ2sOnAGu9H58vXN9/WYtIPKlmjB06FCOHz+O1Wpl8+bNhISEZD+2bt065syZc81958yZw9KlS/M+pIiYYm9cEgPmbsOaZSesXgXe6doIB61HKFJsvLsqirRMG02rleGBRtduwihyu1QuXYKn/lze8O0VB7Bm2UxOJDdj6tSp+Pv74+bmRkhICFu2bLnm2Dlz5mCxWHLc3Ny0zJ2I/LszKen0/OwPFm07iYMFxj4QyIv3B6igISJSxO07lUSnjzay5sAZXJwceOeRhrzTtZEKGgWIpkiIiGmOnr1En1lbSLFmEVK9LB/3aoKzpnCLFBu7T15kyY44AM3Qknz1VJuaVPBwJfb8ZeZuOmZ2HLlBCxcuZNSoUYwbN44dO3YQFBREeHg4Z86cueY+np6enD59Ovt2/PjxfEwsIoXRnpNJdP74d3bEXsTTzYnZ/VrwpJbJFBEp8r7ZfpKHp20i9vxlqpQpwbdPt6J786pmx5L/R2cPRcQUpy6m8cTMzZxLzaBBZU9m9mmmirdIMWIYBuN/3A/AQ40rE+RX2txAUqyUdHXihfC6AHy09gjnLllNTiQ3YuLEiQwcOJB+/foRGBjI9OnTcXd3Z9asWdfcx2Kx4Ovrm337aylcEZHcLNsZR9fpmzidlE6N8iVZ+swdtKlT3uxYIiKShy5nZPH84l08v3gX1iw7beuW58dnW9OwipfZ0SQXKmqISL47d8nKE59v5tSfPxLm9muBh5uz2bFEJB/9evAMW49dwM3ZgRfvr2t2HCmGHmlShQaVPUmxZjFt3VGz48h1ysjIYPv27YSFhWVvc3BwICwsjIiIiGvud+nSJapVq4afnx+dO3dm3759+RFXRAoZm93gvz9FMXzBTqxZdtrVLc/SZ+6gRvlSZkcTEZE8dOB0Mp0+2sg3268sNzgyrA6z+jSntLuL2dHkGlTUEJF8lZKeSd/ZWzl6NpVKXm581T8E71KuZscSkXxkGAZTf71yErlPqD8VvUqYnEiKIwcHCy+EBwAwf0ssF1IzTE4k1yMxMRGbzXbVTAsfHx/i4+Nz3adu3brMmjWLZcuW8dVXX2G322nVqhUnT5685utYrVaSk5Nz3ESkaEtOz2TgF9uY/tuV7yhPt6nJzD7N8dTFVyIiRZZhGHy9+Thdpv7O0bOpVPBw5esBLRkeVlv9Xgs4FTVEJN+kZ9oYMHcbe+KSKFvShS8HhFCptE5mihQ3W2LOs/34BVycHOivtanFRHfVLkf9Sp5czrAxR701iqzQ0FB69+5NcHAwbdq0YcmSJZQvX55PP/30mvtMmDABLy+v7Jufn18+JhaR/BaTmMpDU3/nl6gzuDo5MKVHMC+1D8BRJ7RERIqs5PRMhs6P5JXv9mYvN/XT8DsJreltdjS5DipqiEi+yLLZGTovks0x5ynl6sQXT7agpqZxixRLfy3107VpFSp4upmcRoozi8XCkLa1AJiz6Rip1iyTE8m/KVeuHI6OjiQkJOTYnpCQgK+v73U9h7OzM40bN+bIkSPXHDN69GiSkpKybydOnLil3CJScK0/dJbOH2/k6NlUfD3dWPx0KJ2DK5sdS0RE8tCuExd54MONLN99GicHC6PbBzCrT3OtJFKIqKghInnObjd48dvdrDmQgKuTAzP7NKNBZTVaEimO9p1K4rdDZ3GwwFN31TA7jgj3N/ClRrmSJKVlMn9LrNlx5F+4uLjQtGlT1q5dm73Nbrezdu1aQkNDr+s5bDYbe/bsoWLFitcc4+rqiqenZ46biBQthmEwc0M0fWdvITk9iyZVS/P90DtoVKW02dFERCSP/PXZ33X6JmLPX6Zy6RIsejqUp9rU1HJThYyKGiKSpwzD4I0f97NkRxyODham9mpCyxqayidSXH3y5yyNBxpVopp3SZPTiICjg4Wn2lwpsM3YEI01y2ZyIvk3o0aNYsaMGcydO5cDBw4wePBgUlNT6devHwC9e/dm9OjR2ePfeOMNfv75Z6Kjo9mxYwePP/44x48fZ8CAAWa9BRExWXqmjecX7+bN5QewG9CtaRXmD2qpGaQiIkXYhdQMBn6xjTeXHyDTZnB/fV9WDLuTJlXLmB1NboKT2QFEpGj7cO2R7HXK3+/WiLBAn3/eQUSKrGOJqazYcxqAwW1rmpxG5H+6NK7MpNWHiU9OZ2lkHN2bVzU7kvyD7t27c/bsWcaOHUt8fDzBwcGsXLkyu3l4bGwsDg7/u3brwoULDBw4kPj4eMqUKUPTpk3ZtGkTgYGBZr0FETHRmeR0Bn25nZ0nLuJggTEdA+l3hz8Wi67QFREpqrYeO8+w+ZGcTkrHxcmBVzvW4/GW1fTZX4ipqCEieWbupmNMWnMIgNc6BfJQ4yomJxIRM326/ih2A9rVLU+9ilrKRQoOVydHBtxZnTeXH2D6b9F0beqn5rAF3NChQxk6dGiuj61bty7H/UmTJjFp0qR8SCUiBd2uExcZ9OU2EpKteJVwZmqvJrSuXc7sWCIikkdsdoNP1h1h0prD2OwGNcqV5KNejalfSUuiF3ZafkpE8sTSyDjGfb8PgBFhtel7R3WTE4mImRKS0/l2exwAQ9rVMjmNyNV6tqhKaXdnYhJTWbk33uw4IiJym30XeZJun0aQkGyldoVSLHvmDhU0RESKsDMp6fSZtYX3fz6EzW7wcOPK/PBsaxU0iggVNUTktlt7IIHnFu8CoG8rf4bfU9vkRCJitpkbosmw2WnuX4bm/mXNjiNylZKuTvQJ9Qdg2rojGIZhbiAREbktrFk2Xvt+HyMX7iIjy05YvQosGdIK/3Lq7SUiUlRtOHyWDlM2sPFIIiWcHXm/WxATuwdT0lWLFhUV+n9SRG6rbcfOM+TrHdlV8LEPBGqNQpFi7uLlDL7eHAvAkLaapSEFV99W/szYEM2+U8msP5xImzrlzY4kIiK3IPbcZYbO38Huk0kAPNOuJs/dWxcHLTEoIlIkZdnsTFpziGnrjmIYEODrwce9GlOrgofZ0eQ2U1FDRG6bQwkpPDlnK9YsO/cEVOCdro30g0FE+CLiOJczbNSr6EnbujpJLAVXmZIu9GxRlc83xjDt1yMqaoiIFGIr957mhW92k5KeRWl3Zz7oFsQ99XzMjiUiInkk9txlhi+MJDL2IgCPhVTl1QcCcXN2NDeY5AkVNUTktjh1MY0+s7aQnJ5Fk6ql+bhXE5wdtcKdSHF3OSOL2b/HADC4bU3N3JICb8Cd1fki4hibY86z/fgFmlYrY3YkERG5AdYsGxNWRDFn0zEAmlQtzUe9mlC5dAlzg4mISJ75LvIkry7dxyVrFh5uTkx4uCEPNKpkdizJQzrjKCK3LOlyJn1mbeF0Ujq1KpRiVt/mlHBRJVxEYMGWE1y4nEnVsu50aOBrdhyRf1XRqwQPNa4MwCfrjpicRkREbsSJ85d5dHpEdkHjqbtqsPCpUBU0RESKqOT0TIYviGTkwl1csmbR3L8MPw2/UwWNYkAzNUTklqRn2hjwxVYOn7mEj6crc59sQWl3F7NjiUgBkJFlZ8aGaACealMDJ83ekkLiqTY1Wbz9JGsOnOFgfAp1fbUGr4hIQbdybzwvfLNLy02JiBQT246dZ8TCnZy8kIajg4UR99RmSLtaOGoZ9GJBZxdE5KZl2ew8Oz+Srccu4OHmxNwnW+gqKBHJtnRnHKeT0inv4cojTaqYHUfkutUsX4r2f84smv7bUZPTiIjIP8nIsvP6D/t4+qvtpPy5FO7yYXeqoCEiUkRl2exMWn2IRz+N4OSFNPzKlmDx06E8e09tFTSKERU1ROSmGIbBq8v2sXp/Ai5ODszs3YwAX0+zY4lIAWGzG9kngwe0rq7mbFLoDGlbC4Dvd53ixPnLJqcREZHcnDh/mW7TNzH792OAlpsSESnqTpy/TPfP/mDK2sPYDXi4cWVWDLuTJlXVB6+40fJTInJTpqw9zPwtsThY4MMewYTU8DY7kogUID/viyf6bCqebk481rKa2XFEbliDyl7cWbscGw4n8tn6aMZ3aWB2JBER+Zu/LzflVeLKclNhgZqdISJSVC3bGceY7/aSYs3Cw9WJNx9qQOfgymbHEpOoqCEiN2ze5lgmrzkMwBudG3B/g4omJxKRgsQwDD75c5ZGn1b+lHLV1w0pnIa0rcWGw4ks2naCYffUpryHq9mRRESKvYwsOxN+OpA9O6Nx1dJ83KuJZmeIiBRRKemZjF22j+8i4wBoWq0Mk7sH41fW3eRkYiadZRCRG7JqXzxjlu4BYNjdtXhcV2CLyP/z+5Fz7D6ZhJuzA31b+ZsdR+SmtaxRlmC/0uw8cZFZv8fwn/sDzI4kIlKsnTh/maHzdrDrZBIAg+6qwQvhdXF21MraIiJF0Y7YCwxfEMmJ82k4WGDYPbUZ2q4WTvrcL/b0N0BErtvWY+cZNj8SuwE9mvsx8t46ZkcSkQJo2rojAPRoXhXvUrqyXQovi8XCkLY1Afgq4jjJ6ZkmJxIRKb5W7Yun44cb2HUyCa8Szszs3YyXO9RTQUNEpAiy2Q0+XHuYbtMjOHE+jcqlS7DoqVBGhNVRQUMAzdQQket0KCGF/nO2Ys2yE1avAm92aYDFYjE7logUMDtPXGTT0XM4OVgYeFcNs+OI3LKwej7UrlCKw2cu8WXEcZ5pV8vsSCIixUp6po0JKw4wN+I4oOWmRESKupMXLjNy4U62HrsAQOfgSozv0gBPN2eTk0lBotKWiPyrUxfT6DNrC8npWTSpWpqPejZRZVxEcjXt1yuzNDoHV9bJBikSHBwsDP5ztsbs32NIz7SZnEhEpPjYdyqJBz7amF3QGHhndRY9FarvGCIiRdT3u07RfsoGth67QClXJyZ1D2JKj8YqaMhVNFNDRP5R0uVM+szawumkdGpVKMXnfZpTwsXR7FgiUgAdTkjh5/0JWCwwuK1maUjR0SmoEh/8fIi4i2ks3naCJ0L9zY4kIlKk2e0GMzZE8/7PB8m0GZT3cOX9bkG0qVPe7GgiIpIHktIyGbtsL8t2ngKuzMqb0r0xVb3VDFxyly+XWk+dOhV/f3/c3NwICQlhy5Yt1xw7Y8YM7rzzTsqUKUOZMmUICwv7x/EiknfSM20M+GIrh89cwsfTlblPtqBMSRezY4lIAfXJb0cBuC/Qh1oVPExOI3L7ODs68FSbK4W6T9dHk2Wzm5xIRKToOnUxjV4z/2DCT1Fk2gzuC/Rh1Yi7VNAQESmiIo6eo/3k9SzbeQpHBwvD76nNoqdCVdCQf5TnRY2FCxcyatQoxo0bx44dOwgKCiI8PJwzZ87kOn7dunX07NmTX3/9lYiICPz8/LjvvvuIi4vL66gi8jdZNjvPzo9k67ELeLg5MffJFprmLSLXdPLCZb7/86qawW3Vc0CKnm5N/fAu6cLJC2n8sPuU2XFERIqk73ed4v7J6/kj+jzuLo6880hDPn2iKWV1YZWISJFjzbLx9ooD9Jr5B6eS0vH3duebp0MZeW8dnLXkufyLPP8bMnHiRAYOHEi/fv0IDAxk+vTpuLu7M2vWrFzHf/311wwZMoTg4GACAgKYOXMmdrudtWvX5nVUEfmTYRi8umwfq/cn4OLkwMzezQjw9TQ7logUYDM3xJBlN2hV05tgv9JmxxG57Uq4OPJk6+oAfLLuKHa7YXIiEZGiIzk9k5ELdzJsfiTJ6VkE+ZVm+bA76d68KhaLxex4IiJymx2MT6Hzx7/z2fpoDAN6tvBj+bA7aVy1jNnRpJDI06JGRkYG27dvJyws7H8v6OBAWFgYERER1/Ucly9fJjMzk7Jly+ZVTBH5f6asPcz8LbFYLPBhj2BCanibHUlECrBzl6ws2BoLwBDN0pAi7PGW1Sjl6sShhEusjcp91rGIiNyYLTHnaT95A99FxuFggWH31Oabp0OpXq6k2dFEROQ2s9sNPt8YQ6ePNxIVn4J3SRdm9G7GhIcbUdJVrZ/l+uXp35bExERsNhs+Pj45tvv4+BAVFXVdz/Gf//yHSpUq5SiM/J3VasVqtWbfT05OvvnAIsKCLbFMXnMYgDc6N+D+BhVNTiQiBd3s34+RnmmnURUv7qilIqgUXV4lnHm8ZTWm/3aUaeuOEFavgq4gFhG5SRlZdqasPXRl9psBVcu6M6l7ME2r6SpdEZGi6HRSGs8v3sXvR84BcHdABd55pBHlPVxNTiaFUYFeoOy///0vCxYs4LvvvsPNzS3XMRMmTMDLyyv75ufnl88pRYqOX6PO8MrSvQA8e3ctnmhZzeREIlLQpaRnMjfiGABD2tbUCV4p8p5s7Y+LkwORsRf5I/q82XFERAqlo2cv8cgnm5j665WCRremVVgx/E4VNEREiqgfd58ifNJ6fj9yDjdnB97s0oDP+zRTQUNuWp4WNcqVK4ejoyMJCQk5tickJODr6/uP+77//vv897//5eeff6ZRo0bXHDd69GiSkpKybydOnLgt2UWKm90nLzLk6x3Y7AaPNKnCqHvrmB1JRAqBrzfHkpKeRY3yJbkv8J+P7SJFQQUPNx5tVgWAaeuOmJxGRKRwMQyDr/44TscPN7AnLgmvEs5Me6wJ73ULopSWHRERKXL+6pk0dN6VnkmNqnixfNidPN6ymi6Ik1uSp0UNFxcXmjZtmqPJ919Nv0NDQ6+537vvvsv48eNZuXIlzZo1+8fXcHV1xdPTM8dNRG5M7LnLPDlnK2mZNu6sXY7/PtJQBxcR+VfpmTY+3xgDwNNtauLgoM8NKR4G3VkTBwtsOJzInpNJZscRESkUEi9ZGTB3G2OW7iU9007rWuVYNeIuOjTUcrciIkXR5uhzOXsm3V2Lbwe3omb5UmZHkyIgzy+FGDVqFH369KFZs2a0aNGCyZMnk5qaSr9+/QDo3bs3lStXZsKECQC88847jB07lnnz5uHv7098fDwApUqVolQp/aUXud0upGbQd/YWEi9lEFjRk2mPNcHZsUCvTCciBcS3O05yNsVKRS83ugRXNjuOSL6p6u1Op6BKLNt5ium/HWXqY03MjiQiUqCtPZDAf77dTeKlDFwcHXjx/ro8eUd1XRAhIlIEWbNsTFx9iM/WR2OoZ5LkkTwvanTv3p2zZ88yduxY4uPjCQ4OZuXKldnNw2NjY3Fw+N8J1E8++YSMjAy6du2a43nGjRvHa6+9ltdxRYqV9EwbA77YRnRiKpVLl2B2v+Z4uDmbHUtECoEsm51Pf4sGYOCdNXBxUjFUipfBbWuybOcpVuw9TfTZS9TQFWciIldJTs9k/A/7Wbz9JAB1fTyY0jOYAF+tsCAiUhQdTkhh+IKd7D+dDED3Zn682ilQSwzKbZcvf6OGDh3K0KFDc31s3bp1Oe4fO3Ys7wOJCDa7wYgFO9l+/AKebk7M7tccH083s2OJSCGxcl88secvU8bdmR4t/MyOI5LvAnw9uSegAmujzjBjQzQTHr52DzgRkeJo4+FEXvxmF6eS0rFYYEDr6jx3X13cnB3NjiYiIreZ3W4we9Mx3lkZRUaWnbIlXZjwcEPC66vvouQNlclEiiHDMBj/435W7ovHxdGBz3o3o46Ph9mxRKQQ+SLiOABPtKyGu4u+TkjxNOiuGqyNOsPSyFOM7lAPT812FBEh1ZrFhJ8O8NUfsQBU83bn/W5BNPcva3IyERHJC3EX03hh8S42HT0HQNu65Xm3ayMqeOjCWck7OgshUgx9vjGGOZuOAfDBo0G0rOFtbiARKVQOJ6SwJeY8DhboGVLV7DgipmlRvSy1K5Ti8JlLfLcjjj6t/M2OJCJiqi0x53l+8S5iz18GoHdoNV5qH6ALIEREiiDDMFi6M46xy/aRkp5FCWdHxjxQj14tqmKxqGeS5C19sxApZn7YdYo3lx8A4JUO9egUVMnkRCJS2Hy9+cqVl/fU86GiVwmT04iYx2Kx8FhIVV77YT9fbz5O79Bq+gEnIsVSeqaN91YdZNbvMRgGVC5dgne7NuKOWuXMjiYiInngQmoGY5buZfme0wA0rlqaiY8GU71cSZOTSXGhooZIMbI5+hzPLdoFQN9W/gy4s7rJiUSksLmckcW3O640+3y8ZTWT04iY7+GmVXhn5UEOJVxi67ELtKiu5VVEpHiJjL3Ac4t3EX02FbjSFHbMA/Xw0JJ8IiJF0rqDZ3jxm92cSbHi5GBh+D21Gdy2Jk6ODmZHk2JERQ2RYuJwQgoDv9hGhs3O/fV9efWBQF1NKiI37Mddp0lJz6JqWXfu1NWXIni6OfNgUCUWbjvB15uPq6ghIsWGNcvGlDWHmf7bUewGVPBw5b+PNOTuAB+zo4mISB64nJHFhBVRfPnHlf6KNcuXZHL3xjSs4mVyMimOVNQQKQYSktPpO3sryelZNK1Whsk9gnF0UEFDRG7cV5uvfIHtFVIVB32OiABXZi0t3HaCn/bEM/YBK96lXM2OJCKSp/adSuK5RbuIik8BoEtwJV57sD6l3V1MTiYiInkhMvYCoxbtIibxyqy8vq38eal9AG7OjiYnk+JK84JEirhL1iz6zd5K3MU0apQryYzezXTQEZGbsvvkRXafTMLF0YFuTauYHUekwGhYxYtGVbzIsNlZvP2k2XFERPJMps3Oh2sP0/nj34mKT8G7pAvTH2/C5B6NVdCQAmnq1Kn4+/vj5uZGSEgIW7Zs+cfxixcvJiAgADc3Nxo2bMiKFSvyKalIwZRpszNp9SG6To8gJjEVX083vuofwmsP1te5JTGVihoiRVimzc7gr7az/3Qy5Uq5MKdfC8qW1I8NEbk58/5sEN6+oa+uRBf5fx4LqQpc+XditxsmpxERuf0OJaTw8LRNTFx9iCy7wf31fVk18i7ub1DR7GgiuVq4cCGjRo1i3Lhx7Nixg6CgIMLDwzlz5kyu4zdt2kTPnj3p378/kZGRdOnShS5durB37958Ti5SMBw9e4lHPtnElLWHsdkNHgyqxKoRd9G6tpYhFvOpqCFSRBmGwegle9hwOJESzo7M6tucqt7uZscSkUIqOT2TZTtPAfBYiBqEi/x/nYIq4eHmROz5y2w4kmh2HBGR28ZmN5j+21Ee+HAje+KS8CrhzJQewXzyeBPK6SIHKcAmTpzIwIED6devH4GBgUyfPh13d3dmzZqV6/gpU6Zw//3388ILL1CvXj3Gjx9PkyZN+Pjjj/M5uYi5DMNg7qZjdPxwA7tPJuHp5sSHPRvzYc/GeLk7mx1PBFBRQ6TImrTmMN9sP4mjg4VpjzWhUZXSZkcSkULsux1xpGXaqONTiub+ZcyOI1LguLs48UiTK8uyff1n80QRkcLucEIKD3+yif/+FEWGzU67uuX5eeRddA6ujMWi3lpScGVkZLB9+3bCwsKytzk4OBAWFkZERESu+0REROQYDxAeHn7N8SJFUXxSOr1nbWHc9/tIz7RzZ+1y/DyyDQ8GVTI7mkgOahQuUgQt2BLLh2sPA/Bmlwa0C6hgciIRKcwMw+DrPxuEPxZSTScxRK6hV0hV5mw6xtqoM5xOSqOiVwmzI4mI3JQsm53PNkQzefVhMmx2PNyceLVjIN2aVdH3ACkUEhMTsdls+Pj45Nju4+NDVFRUrvvEx8fnOj4+Pv6ar2O1WrFardn3k5OTbyG1iLl+2HWKMUv3kpSWiauTA6PbB9A71B8HB33uS8GjooZIEfPrwTO8svTKmp/D7q5FzxZVTU4kIoXd1mMXOJRwiRLOjjzUpLLZcUQKrDo+HrSoXpYtMedZsOUEI++tY3YkEZEbdighhRcW72LXySQA2tUtz9sPN1ShViQXEyZM4PXXXzc7hsgtSbqcydjv92YvN9ywsheTugdTq0Ipk5OJXJuWnxIpQvbGJfHM1zuw2Q0eaVJFJ1Ok0Jk6dSr+/v64ubkREhLCli1b/nH85MmTqVu3LiVKlMDPz4+RI0eSnp6eT2mLj79maXQOroSnm9ZQFfknfzUMX7A1liyb3eQ0IiLXL8tmZ+qvR3jgw43sOpmEh5sT73cLYlbf5ipoSKFTrlw5HB0dSUhIyLE9ISEBX1/fXPfx9fW9ofEAo0ePJikpKft24sSJWw8vko82Hk4kfPJ6lu08haODhWH31GbJkFYqaEiBp6KGSBERdzGNfnO2cjnDRuta5ZjwcENNDZdCZeHChYwaNYpx48axY8cOgoKCCA8P58yZM7mOnzdvHi+99BLjxo3jwIEDfP755yxcuJCXX345n5MXbecuWflpz5Up92oQLvLv7m/gi3dJFxKSraw5kPvnl4hIQRMVn8xD0zbx3qqDZNjs3BNQgdUj29C1qZabksLJxcWFpk2bsnbt2uxtdrudtWvXEhoamus+oaGhOcYDrF69+prjAVxdXfH09MxxEykM0jNtvP7DPh7/fDPxyen4e7uz+OlQRt1bB2dHnS6Wgk9/S0WKgKS0TPrN3sLZFCsBvh5Me7wJLk765y2Fy8SJExk4cCD9+vUjMDCQ6dOn4+7uzqxZs3Idv2nTJu644w569eqFv78/9913Hz179vzX2R1yYxZvP0mGzU6jKl40rOJldhyRAs/VyZFuzfyA/81yEhEpqDJtdj5ae5hOH21kT1wSnm5OTHw0iJl9muHr5WZ2PJFbMmrUKGbMmMHcuXM5cOAAgwcPJjU1lX79+gHQu3dvRo8enT1++PDhrFy5kg8++ICoqChee+01tm3bxtChQ816CyJ5Ys/JJB74aCOzfz8GwOMtq7Ji+J00qVrG3GAiN0A9NUQKuYwsO09/uZ1DCZfw8XRlVt/mWh5GCp2MjAy2b9+e40eFg4MDYWFhRERE5LpPq1at+Oqrr9iyZQstWrQgOjqaFStW8MQTT1zzddTI78bY7QbzNscC8LhmaYhct14tqvLp+qNsOJzI8XOpVPMuaXYkEZGrHDidzPOLd7Hv1JXvQ2H1fHj7oQZU8FQxQ4qG7t27c/bsWcaOHUt8fDzBwcGsXLkyuxl4bGwsDg7/uxiwVatWzJs3jzFjxvDyyy9Tu3Ztli5dSoMGDcx6CyK3VZbNzvTfjjJ5zWGy7AblPVx5t2sj2tWtYHY0kRumS7lFCjHDMHjp291ERJ+jpIsjs/o2p1JprXcrhU9iYiI2my37B8ZffHx8iI+Pz3WfXr168cYbb9C6dWucnZ2pWbMmbdu2/cflpyZMmICXl1f2zc/P77a+j6Jmw5FEYs9fxsPNiQeCKpodR6TQqOrtzl21ywNkFwbl9rrRHkyLFy8mICAANzc3GjZsyIoVK/IpqUjBk5FlZ/KaQ3T6aCP7TiVT2t2ZKT2CmdG7qQoaUuQMHTqU48ePY7Va2bx5MyEhIdmPrVu3jjlz5uQY361bNw4ePIjVamXv3r106NAhnxOL5I1jial0+zSC938+RJbdoH0DX1aNuEsFDSm0VNQQKcQmrT7Eksg4HB0sTHu8KfUraWkYKT7WrVvH22+/zbRp09ixYwdLlixh+fLljB8//pr7qJHfjfn6jytL5zzSpAruLprcKXIj/moYvmjbCaxZNpPTFC032oNp06ZN9OzZk/79+xMZGUmXLl3o0qULe/fuzefkIubbdyqJzlN/z75K975AH34eeRedgyurd4aISBFkGAZfbz5O+ykbiIy9iIfrlWUGpz3WhLIlXcyOJ3LTdIZCpJBatPUEH/5yBIC3H2pAmzrlTU4kcvPKlSuHo6MjCQkJObYnJCTg6+ub6z6vvvoqTzzxBAMGDACgYcOGpKamMmjQIF555ZUcU8n/4urqiqur6+1/A0XQ6aQ01kZdOUH418lZEbl+dwdUoKKXG6eT0lm5N57OwZXNjlRk/L0HE8D06dNZvnw5s2bN4qWXXrpq/JQpU7j//vt54YUXABg/fjyrV6/m448/Zvr06fmaXcQsGVl2Pv71CNN+PUKW3aCMuzOvd25Ap0YVVcwQESmizqSk859vdvPrwbMAtKxRlg8eDaayVviQIkAzNUQKofWHzjL6uz0APHt3Lbo31wlHKdxcXFxo2rQpa9euzd5mt9tZu3YtoaGhue5z+fLlqwoXjo6OwJWrUeTWLNhyApvdoEX1stT28TA7jkih4+ToQI8/j89f/aGG4bfLXz2YwsLCsrf9Ww+miIiIHOMBwsPDrzkervRgSk5OznETKaz2xiXx4Mcb+XDtldkZ99f35eeRbXgwqJIKGiIiRdTKvacJn7SeXw+excXJgTEd6zFvQEsVNKTI0EwNkUJm/6lkhny9A5vd4KHGlRl1bx2zI4ncFqNGjaJPnz40a9aMFi1aMHnyZFJTU7OvxO3duzeVK1dmwoQJAHTq1ImJEyfSuHFjQkJCOHLkCK+++iqdOnXKLm7Izcmy2Vmw9c8G4S3VIFzkZnVv7seHvxxm67ELHIxPoa6vCoS36p96MEVFReW6T3x8/A31bIIrPZhef/31Ww8sYiJrlo2PfznCtHVHsdkNypZ04Y3O9enYULMzRESKquT0TF7/fj/f7jgJQL2KnkzuHqzvoVLkqKghUoicTkrjyTlbuWTNIrSGN+880kg/SKTI6N69O2fPnmXs2LHEx8cTHBzMypUrs09ExcbG5piZMWbMGCwWC2PGjCEuLo7y5cvTqVMn3nrrLbPeQpGxNuoMCclWvEu6EF7f5993EJFc+Xq5cW89H1bui2fe5uO83rmB2ZHkOo0ePZpRo0Zl309OTsbPz8/ERCI3ZvfJi7yweDcHE1IA6NiwIq93rk+5UlqGU0SkqNocfY5Ri3YRdzENBws83aYmI8Lq4OKkhXqk6FFRQ6SQSE7PpN/srcQnp1O7QimmP9FUByYpcoYOHcrQoUNzfWzdunU57js5OTFu3DjGjRuXD8mKl7+Wynm0uR+uTpr1InIrHmtZlZX74lmyI44X7w+gpKu+ft+Km+nB5Ovre0PjQT2YpPCyZtmYsuYwn66PxmY38C7pwhudG9CxUUWzo4mISB7JyLIzcfUhPl1/FMMAv7IlmPhoMM39y5odTSTP6IyoSCGQabMz5KsdRMWnUN7Dldn9muNVwtnsWCJSBB0/l8qGw4lYLNBT/XpEbtkdNctRzdudFGsWP+w6ZXacQu9mejCFhobmGA+wevXqa44XKax2nbjIAx9uzF5uqlNQJX4eeZcKGiIiRdjhhBS6TP2d6b9dKWg82qwKPw2/SwUNKfJ0qZhIAWcYBqOX7GHjkUTcXRyZ3bc5Vcq4mx1LRIqoeZuv9NK4q3Z5qnrrs0bkVjk4WHgspCpvr4jiq83H6d7cT0tH3qIb7cE0fPhw2rRpwwcffEDHjh1ZsGAB27Zt47PPPjPzbYjcNumZNiavOcxn649iN6BcKRfe7NKA+xuomCEiUlTZ7QZfRBxjwk9RWLPslHF3ZsLDjbi/wbVnoooUJSpqiBRwH649wjfbT+LoYGFqryY0qOxldiQRKaKsWTYWbTsBqEG4yO3Utakf7/98iL1xyew+mUSQX2mzIxVqN9qDqVWrVsybN48xY8bw8ssvU7t2bZYuXUqDBupxIoVfZOwFnl+8i6NnUwHoHFyJ1zrVp0xJF5OTiYhIXklITuf5xbvYcDgRgDZ1yvNe10ZU8HQzOZlI/smX5aemTp2Kv78/bm5uhISEsGXLln8cv3jxYgICAnBzc6Nhw4asWLEiP2KKFDjfbD/JpDWHABjfuQHtAiqYnEhEirKVe+O5cDmTil5utKtb3uw4IkVG2ZIudGx45YrprzcfNzlN0TB06FCOHz+O1Wpl8+bNhISEZD+2bt065syZk2N8t27dOHjwIFarlb1799KhQ4d8Tixye6Vn2piw4gCPfLKJo2dTKVfKlU+faMqUHo1V0BARKcJW7DlN+OT1bDiciKuTA290rs+cfs1V0JBiJ8+LGgsXLmTUqFGMGzeOHTt2EBQURHh4OGfOnMl1/KZNm+jZsyf9+/cnMjKSLl260KVLF/bu3ZvXUUUKlN+PJPLSt7sBGNy2Jr1CtLa9iOStvxqE92xRFSdHtd0SuZ0e+/M4/v2uUyRdzjQ5jYgUZtuPX6DDhxv4dH00dgMealyZNaPuIry+lhwRESmqUtIzeW7RLoZ8vYOLlzNpUNmT5cPupHeov5Y2lWIpz89YTJw4kYEDB9KvXz8CAwOZPn067u7uzJo1K9fxU6ZM4f777+eFF16gXr16jB8/niZNmvDxxx/ndVSRAiMqPpmnv9xOlt3gwaBKvHBfXbMjiUgRdzA+ha3HLuDoYKF7cz+z44gUOU2rlaGujwfpmXaWRJ40O46IFELpmTbeWr6frtM3EX02lQoerszs3YxJ3YMp7a7ZGSIiRdXWY+dpP2UD3+44iYMFnmlXkyWD76BWhVJmRxMxTZ4WNTIyMti+fTthYWH/e0EHB8LCwoiIiMh1n4iIiBzjAcLDw685XqSoSUhOp9/sraRYs2hRvSzvdWuEg4Oq7iKSt+b9uSTOvfV88NHUZZHbzmKx8HjLK7M1vt4ci2EYJicSkcJk27HzdJiygRkbYjAMeLhJZVaPbENYoI/Z0UREJI9kZNl5b1UU3T+N4OSFNKqUKcHCp0J5ITwAFyfNrJfiLU8bhScmJmKz2bKb9v3Fx8eHqKioXPeJj4/PdXx8fHyu461WK1arNft+cnLyLaYWMc8laxZ9Z2/ldFI6NcuX5LMnmuLq5Gh2LBEp4lKtWSzZEQeoQbhIXurSuDITforiyJlLbI45T8sa3mZHEpECLj3Txgc/H2TmxivFDB9PVyY83JC7A1TMEBEpyo6cSWHEwp3sjbtynrNr0yqM6xSIh5uzyclECoY8LWrkhwkTJvD666+bHUPklmXa7Az5egcHTidTrpQLc/q10DRyEckXP+w6RYo1C39vd1rV1ElWkbzi4eZM5+BKzN9ygq83x6qoISL/aOeJizy3aCdHz6YCV05ovfpAIF4ldEJLRKSoMgyDL/84zlvLD2DNslPa3ZkJDzWkfcOKZkcTKVDydK5SuXLlcHR0JCEhIcf2hIQEfH1zb2Lm6+t7Q+NHjx5NUlJS9u3EiRO3J7xIPjIMg1eX7mX9obOUcHZkVt/m+JV1NzuWiBQDhmHw1Z9LTz0WUk3L3YnkscdCrsyGWrn3NGdTrP8yWkSKo4wsO++vOsgjn2zi6NlUynu48nmfZrzfLUgFDRGRIuxMSjp9Z29l7LJ9WLPs3Fm7HKtG3KWChkgu8rSo4eLiQtOmTVm7dm32Nrvdztq1awkNDc11n9DQ0BzjAVavXn3N8a6urnh6eua4iRQ209YdZcHWEzhY4KOejWlUpbTZkUSkmNh9Mom9ccm4ODnwSNMqZscRKfIaVPYiyK80mTaDxdt1MY6I5LT/VDIPfryRj389gs1u8GBQJX4ecRf31NNyUyIiRdkvUQm0n7yB3w6dxdXJgdc6BTK3Xwv1OxS5hjxffmrUqFH06dOHZs2a0aJFCyZPnkxqair9+vUDoHfv3lSuXJkJEyYAMHz4cNq0acMHH3xAx44dWbBgAdu2beOzzz7L66gipvgu8iTvrToIwOsP1lezPxHJV1//OUujY8OKlC2pJe9E8sPjIVXZdeIi8zbH8vRdNTVDSkTIstn5ZN1RPvzlMJk2g7IlXXirSwNdnSsiUsSlZ9p4e8UBvoi48rusXkVPPuwRTG0fD5OTiRRseV7U6N69O2fPnmXs2LHEx8cTHBzMypUrs5uBx8bG4uDwvwkjrVq1Yt68eYwZM4aXX36Z2rVrs3TpUho0aJDXUUXy3aajibz4zW4AnrqrBk+E+psbSESKlaTLmXy/6xQAj7esanIakeLjgUaVGP/jfk5eSOO3w2dpV7eC2ZFExESHE1J4bvEudp9MAiC8vg9vPdSQcqVcTU4mIiJ5KSo+mWHzIzmUcAmA/q2r8+L9dXF1cjQ5mUjBly+NwocOHcrQoUNzfWzdunVXbevWrRvdunXL41Qi5jqUkMJTX24n02bQsVFF/nN/gNmRRKSYWRJ5kvRMOwG+HjSpWsbsOCLFRgkXRx5pWoXZvx/j6z9iVdQQKaZsdoPPN0bz/s+HyMiy4+nmxBudG9A5uBIWi2ZwiYgUVYZhMGfTMSb8FEVGlp1ypVx5v1sj2uo7och1y5eihojkdCY5nX6zt5KSnkWzamX4oFuQlp4QkXxlGAZfb44F4LGW1XTyRCSfPRZSjdm/H+OXqAROXUyjUukSZkcSkXx0LDGV5xfvYtvxCwC0rVue/z7cCF8vrZ0uIlKUJV6y8sLiXfx68CwAdwdU4N2ujTQ7T+QGqaghks9SrVk8OXcrcRfTqFGuJDN6N8PNWVMLRSR/bY45z5Ezl3B3caRLcCWz44gUO7UqlKJljbL8EX2eBVtiGXVfXbMjiUg+sNsNvvzjOP/9KYq0TBulXJ149YF6PNrMTxcYiIgUcb8ePMMLi3eReCkDFycHxnSsxxO6wEzkpqioIZKPsmx2hs7bwd64ZLxLujCnXwvKqDGviJhg4dYTAHQOroyHm7PJaUSKp8dCqvFH9HkWbTvJ8LA6OGrWpkiRdvLCZV78Zjebjp4DoFVNb97t2ogqZdxNTiYiInkpPdPGf3+KYs6mYwDU9fHgw56NqeurZuAiN0tFDZF8YhgG477fx68Hz+Lm7MDMPs2o6q0fMCKS/1KtWazcGw/Ao82qmJxGpPi6r74PXiWciU9O54/oc9xRq5zZkUQkDxiGwcKtJ3hz+QEuWbMo4ezI6A4BPB5STUvQiogUcYcSUhg2P5Ko+BQA+rby56X2AVqxQ+QWqaghkk+m/xbN15tjsVjgwx6NaaymvCJiklX74knLtFG9XEmC/UqbHUek2HJ1cqRjo4rM2xzLkh1xKmqIFEGJl6z855vdrI06A0CzamV4v1sQ/uVKmpxMRETykmFcWW7wreUHsGbZKVfKhfe6BtEuQM3ARW4HFTVE8sH3u07xzsooAMY9EMh99X1NTiQixdl3kXEAdAmurPVbRUz2UOPKzNscy8q9p3mzSwNKuOiqPZGi4peoBF78ZveVtdMdHXg+vA79W9fQUnMiIkXcuUtWXvxbQbtt3fK81zWI8h5qBi5yu6ioIZLHNkef4/lFuwDo37o6fe+obnIiESnOziSn8/uRRODKyVQRMVezamXwK1uCE+fT+Hl/PJ2D9e9SpLBLy7Dx1or9fPVHLHBl7fQpPYMJ8PU0OZmIiOS19YfO8tziXZxNseLi6MDoDgH0beWvi8lEbjMVNUTy0JEzlxj05XYybHbaN/DllQ71zI4kIsXcsp2nsBvQtFoZ9fURKQAsFgsPBVfmw1+O8F1knIoaIoXc3rgkhi+I5OjZVODKRU0vhNfV2ukiIkVcps3O+6sO8un6aABqVyjFhz0bU6+iCtoieUFFDZE8cjbFSt/ZW0hKy6RJ1dJM6h6sRoAiYrq/lp7SLA2RgqNL4ytFjQ2HEzmbYtXSBCKFkM1u8On6o0z8+RBZdgMfT1fe7xbEnbXLmx1NRETy2Inzl3l2fiQ7T1wE4ImW1XilYz0VtEXykIoaInngckYWA+Zu5eSFNPy93ZnZp7kOZiJiuoPxKew/nYyzo4UHGlU0O46I/KlG+VIE+ZVm14mL/LDrFE+21lKVIoXJyQuXGbVoF1tizgPQvoEvbz/UkDIlXUxOJiIiee2nPad58dvdpKRn4enmxLtdg7i/gfqoiuQ1FTVEbjOb3WDY/Eh2nUyibEkX5vRrQVn9oBGRAmBJ5EkA2tWtQGl3fS6JFCQPN67MrhMX+S4yTkUNkUJkaWQcry7dS4o1i5Iujrz2YH26Nq2itdNFRIq49Ewbby0/wJd/HAegcdXSfNSzMVXKaIlfkfygoobIbWQYBq//sI81B87g6uTAjN7N8C9X0uxYIiLY7QbLIk8B8HATLT0lUtA80Kgi43/cz564JI6cSaFWBQ+zI4nIP0hKy+TVpXv5fteVY+tfy81W89Z3fxGRoi767CWemRfJgdPJADzdpibP3VcHZ0cHk5OJFB8qaojcRp+tj+aLiONYLDC5ezBNq5UxO5KICAB/RJ8jPjkdTzcn2gVUMDuOiPw/3qVcaVOnPGujzvBdZBwvhAeYHUlEriHi6DmeW7STU0npODpYGHZ3bZ5pVxMnncwSESnyluw4yZile7mcYcO7pAsfPBpE27r6fSWS31TUELlNlu2MY8JPUQC82jGQ9g21Xr2IFBxL/mwQ3rFRJVyd1ONHpCB6qEll1kadYWnkKZ67ty4ODlq+RqQgyciy88Hqg3y2PhrDgGre7kzqHkyTqrqQSUSkqLuckcXYZfv4ZvuVJX1Da3gzuUcwPp5uJicTKZ5U1BC5DTYdTeT5xbsAGNC6utbCFpECJS3Dxsq98YCWnhIpyMLq+eDh6kTcxTS2HjtPSA1vsyOJyJ+OnElh2Pyd7P9zqZHuzfwY2ymQkq76SS0iUtQdOJ3M0Hk7OHr2/9i787CoyvYP4N+ZYd9lX0RRUVEWFxTENC1RXDI1s0xzy9BcWtRfb9Li1lu2WJlmLuWaWmapmWuKmakIijsCiiIoCAjILgPMPL8/kHkjAUEYDgPfz3XNlXPmOTP3nIvmnjn3ee4nH3IZ8Ga/dpj5tDsUvACFSDL8BkZUS7EpuZj6QySKVQJDfJzw7uAOUodERFTOoehU5ClL0LyZMbqxLR5Rg2Wkr8Agb0f8fOY2dp5LYlGDqAEQQmBLeCI+3HMFyhI1rEz08clz3hjoxVnZRESNnRACWyMSsej30hzgYGGIr0d3QQ9+RyOSHJt+EtVCSnYhJq6PQG5hCfzcrPHFqE5sFUFEDc7Os6VTpEd0cYFMxs8oooZsRJfmAIC9l+6gsFglcTRETVtuYTFe//Ec3t91GcoSNXq3tcXBt55kQYOIqAnIKSzGzB/P4b2dpTngqfZ22PdGbxY0iBoIztQgeky5hcWYuD4Cd7IL0cbOFGvG+8JIn33qiahhSc9T4ti1dAClRQ0iatj8W1nD2dIIydmFOBKThsFco4tIElHJ2Zix5SxuZhRAIZfhP0HtEdy7NS9gIiJqAi7cysLrP55DYmYB9OQyvDPQA5N7tWIOIGpAOFOD6DEUlagxbfNZxKTkws7cEBsm+cHKxEDqsIiIHvL7hWSo1AKdXK3Q2s5M6nCI6BHkchmGPShA7jibJHE0RE1PabupBIz49iRuZhTA2dIIP0/tgal92vBkFhFRIyeEwPd/38Dzq04iMbMAzZsZY/trAQh+kkVtooaGMzWIakgIgbm/XsTxuHSYGiiwfmJ3uFqbSB0WEVGFdp4rPSk6orOzxJEQUXU918UFK49ex9HYNGTmF8HalBdOENWH3MJivLvzMn6/kAwAeNrDHl+M6oRm/H+QiKjRyyksxpyfL+DQlVQAwGBvRyx+zgeWxvoSR0ZEFWFRg6iGvvjjKnacS4JCLsO3L/vCy8VS6pCIiCoUl5aHi7ezoSeXYWgnFjWIdEVbB3N4OlsgKjkHey8mY1yAm9QhETV6UcnZmLn1HOLT86GQy/DOwPZ4tRevzCUiagqi7+Rg2uZI3MwogIFCjg+e6YCXe7TkeoREDRiLGkQ1sCU8Ad/8GQcAWPycN/q0s5M4IiKiyu16MEujTzs72JgZShwNEdXEiC4uiErOwY5zSSxqEGmREAJbIxKx8PcrKCpRw9nSCMvHdIFvS2upQyMionqw4+xtvLvzEgqL1XCxMsbKl7vCp7mV1GER0SOwqEFUTaHRqfhg12UAwFuBbfFCN1eJIyIiqpxaLTStp4ZzgXAinfNsZ2d8vC8a5xKzEJ+ej1a2plKHRNTo5ClL8O6OS9jNdlNERE2OskSFD/dcweZTiQCAJ9vZ4esXOzMHEOkILhROVA3nb2Vh5tZzUAvghW7N8Wa/tlKHRERUpTMJ95CUdR/mhnro39FB6nCIqIbszY3Qq23pjNCyWVdEVHeuJOdg6PLj2H0hGQq5DCGDPPD9+G48mUVE1AQkZd3HC6tPYfOpRMhkwJv92mL9xO7MAUQ6hEUNokdIyMjH5A2ncb9YhT7t7PDRCG/2VSSiBm/nudsAgEHejjDSV0gcDRE9jucezLLadT4JQgiJoyFqHIQQ2BqeiOHfnkB8ej6cLI3w89QemNqnDdfPICJqAv6+dhfPLPsbF25lwdJYH+smdses/u2gYA4g0iksahBVITO/CBPXn0ZGfhG8XCzw7diu0FfwfxsiatgKi1XYc/EOALaeItJlAzwdYGKgQEJGAc4m3pM6HCKdl6cswZs/nce7Oy+hqESNp9rbYd8bvbl+BlEjlJmZibFjx8LCwgJWVlaYPHky8vLyqtynb9++kMlk5W6vvfZaPUVM2qZWC3xz5BrGr4vAvYJieLlYYM/rvfBUe3upQyOix6DVs7M1TSKZmZl4/fXX0b59exgbG6NFixZ44403kJ2drc0wiSp0v0iFyRtPIz49Hy5Wxlg3sTtMDbkMDRE1fH/GpCG3sATOlkbo0cpG6nCI6DGZGOhhoJcjAGjWyCGixxN9JwfP/qvd1NoJbDVC1FiNHTsWUVFROHToEPbs2YNjx45hypQpj9wvODgYd+7c0dw+++yzeoiWtC27oBjBm85gyR9XIQQwursrfnmtJ1ytTaQOjYgek1bP0I4dOxZ37tzBoUOHUFxcjEmTJmHKlCnYunVrheOTk5ORnJyMJUuWoGPHjkhISMBrr72G5ORk/PLLL9oMlagclVrgzZ/O4Vxi6XTEja90h725kdRhERFVy44HJz+HdXFhKw0iHTeiiwt2nE3Cnot3MO8ZTxjoccYoUU39fPoWPvjtMpQlajhZGuGbMV04O4OoEYuOjsaBAwdw+vRpdOvWDQCwfPlyDB48GEuWLIGzs3Ol+5qYmMDR0bG+QqV6EJWcjWmbzyIxswAGenL8d5gXXujuKnVYRFRLWvtVVJZEvv/+e/j7+6NXr15Yvnw5fvrpJyQnJ1e4j5eXF3799VcMHToUbdq0wdNPP42PPvoIv//+O0pKSrQVKlE5Qggs/D0Kf1xJhYGeHN9P6AZ3e3OpwyIiqpZ7+UU4GpsGoPRkKBHptp5tbOFgYYisgmL8+eD/bSKqnmKVGvN/u4z//HoRSrabImoywsLCYGVlpSloAEBgYCDkcjnCw8Or3HfLli2wtbWFl5cXQkJCUFBQoO1wSYu2n7mF5749icTMAjRvZowd03qyoEHUSGhtpsajksiIESOq9TzZ2dmwsLCAnh7b/lD9WHPsBjaFJUAmA5a+2Bnd3fijh4h0x55Ld1CsEvB0tkA7BxZkiXSdQi7DsM4uWHPsBnadS0KQJ68eJaqOe/lFmL7lLMJuZAAAZvdvh5lPuXMGI1ETkJKSAnv78usk6OnpwdraGikpKZXuN2bMGLRs2RLOzs64ePEi3nnnHcTGxmLHjh2V7qNUKqFUKjX3c3Jyav8GqNYKi1VY+HsUfoy4BQB4qr0dvnqxM6xM2HKQqLHQWqXgcZPIP6Wnp+PDDz+ssu8hEwjVpZ3nbmPx/hgAwPtDOmKwt5PEERER1czOs7cBcJYGUWMy/EFRIzQ6DdkFxbA00Zc6JKIGLSYlB8GbzuBW5n2YGijw1YudMYAFQSKdN3fuXHz66adVjomOjn7s5//nuSdvb284OTmhX79+uH79Otq0aVPhPosXL8bChQsf+zWp7t2+V4Bpm8/iUlI2ZDJgViCL2kSNUY3bT82dOxcymazKW0xMTK0Dy8nJwZAhQ9CxY0csWLCg0nGLFy+GpaWl5ubqymlk9Hj+unoXb2+/CACY3KsVJvdqJXFEREQ1czM9H2cTsyCXAc92qrxXMBHplo7OFvBwNEeRSo29l+5IHQ5Rg3bgcgqe+/YkbmXeRwtrE+yY/gQLGkSNxJw5cxAdHV3lrXXr1nB0dERaWvmWjSUlJcjMzKzRehn+/v4AgLi4uErHhISEIDs7W3O7devW4705qhPHrt7FM8uP41JSNqxM9LFhkh/e6NeWBQ2iRqjGMzXmzJmDiRMnVjmmtkkkNzcXAwcOhLm5OXbu3Al9/cqvRgsJCcHs2bM193NycljYoBq7eDsL0zZHokQt8GwnZ7w3uIPUIRER1diu86ULhPdqawd7CyOJoyGiujSiiwsW74/BrnNJGOPfQupwJJOZmYnXX38dv//+O+RyOUaOHImvv/4aZmZmle7Tt29f/PXXX+W2TZ06FatWrdJ2uFSP1GqB5Ufi8NXhqwCAnm1ssGJMVzQzZasRosbCzs4OdnZ2jxwXEBCArKwsREZGwtfXFwBw5MgRqNVqTaGiOs6fPw8AcHKqvIODoaEhDA0Nq/2cpB1CCKw/cRP/3XsFagH4NLfEt2O7onkzE6lDIyItqXFRQ9tJJCcnB0FBQTA0NMTu3bthZFT1SRkmEKqtm+n5mLT+NAqKVOjlboslozqxik9EOkcIgZ3nSosaz7H1FFGj82xnZ3xyIAYRNzNxK7MArtZN80f62LFjcefOHRw6dAjFxcWYNGkSpkyZgq1bt1a5X3BwMBYtWqS5b2LSNI9fY5WvLMH/bb+A/ZdL2xxP7OmG94d0gJ6ixo0JiKgR6NChAwYOHIjg4GCsWrUKxcXFmDlzJkaPHg1n59LZzElJSejXrx82bdoEPz8/XL9+HVu3bsXgwYNhY2ODixcvYtasWXjyySfh4+Mj8TuiqhSVqDF/92XN+hnP+zbHf4d7wUhfIXFkRKRNWvuW988kEhERgRMnTlSYRDw8PBAREQGgtKAxYMAA5OfnY+3atcjJyUFKSgpSUlKgUqm0FSo1YWm5hRi/LgIZ+UXwcrHAqnG+MNDjjx8i0j1nE7OQkFEAEwMFBng6SB0OEdUxJ0tj9GxjAwDY9aCA2dRER0fjwIED+P777+Hv749evXph+fLl+Omnn5CcnFzlviYmJnB0dNTcLCws6ilq0rZbmQUYufIk9l9Ogb5Chk9HemPBs54saBA1cVu2bIGHhwf69euHwYMHo1evXlizZo3m8eLiYsTGxqKgoAAAYGBggMOHD2PAgAHw8PDAnDlzMHLkSPz+++9SvQWqhsz8IoxbG44fI25BJgPeG9wBnz/vw4IGUROgtYXCgdIkMnPmTPTr108zPXzZsmWax/+dRM6ePYvw8HAAgLu7e7nnio+Ph5ubmzbDpSYmT1mCSetPIzGzAC2sTbB+oh/MDLX6vwQRkdbsPFe6QPhAT0eYGPCzjKgxGtGlOU7EZWDnuSTMfNodMlnTmlkaFhYGKysrdOvWTbMtMDAQcrkc4eHhGDFiRKX7btmyBZs3b4ajoyOGDh2KDz74gLM1GoFTNzIwfctZZOYXwdbMAKte9kU3N2upwyKiBsDa2rrKWXxubm4QQmjuu7q6PtSqkBq2q6m5eHXjGSRmFsDMUA/LX+qCpzzspQ6LiOqJVs961DSJ9O3bt9x9Im0pKlHjtR8iEZWcAxtTA2x6xQ925mxjRkS6qahEjT0XSxcPHtGVraeIGquBXo54f9cl3EjPx8Xb2ejkaiV1SPUqJSUF9vblT1bo6enB2toaKSkple43ZswYtGzZEs7Ozrh48SLeeecdxMbGYseOHZXuo1QqoVQqNfdzcnJq/waoTm0+lYAFu6NQohbwcrHAmnHd4GxlLHVYRERUD47EpOKNH88jT1kCV2tjrJ3QHe0czKUOi4jqES/lpCZHrRb4v+0XcDwuHSYGCqyf1B1utqZSh0VE9NiOxqYhq6AY9uaG6NnGVupwiEhLzAz1MKCjI3ZfSMbOc0mNpqgxd+5cfPrpp1WOiY6OfuznnzJliubf3t7ecHJyQr9+/XD9+nW0adOmwn0WL16MhQsXPvZrkvYUlaix8PcobAlPBAAM7eSMz0b6wNiArUaIiBo7IQS+/zseH++PhhCAXytrrHrZF9amBlKHRkT1jEUNalKEEPhoXzR2X0iGnlyGVS/7wqe5ldRhERHVStkC4cM6O0Mhb1rtaIiamhFdXbD7QjJ+v5CM94Z0gH4jWDdgzpw5mDhxYpVjWrduDUdHR6SlpZXbXlJSgszMTDg6Olb79fz9/QEAcXFxlRY1QkJCMHv2bM39nJwcuLq6Vvs1SDsy8pSYtuUsIuIzIZMBbwe1x7Q+bZpcKzYioqZIWaLC+zsvY3tkadvd0d1dsWiYF9dFJWqiWNSgJuW7v29g7fF4AMDno3zwZDs7iSMiIqqd7PvFCI0uPck3oktziaMhIm3r7W4LWzMDpOcV4e9rd/G0h4PUIdWanZ0d7Owe/Z0sICAAWVlZiIyMhK+vLwDgyJEjUKvVmkJFdZw/fx4A4OTkVOkYQ0NDGBqyNWlDciU5B8GbziAp6z7MDPXw9ejO6NdB9//+iYjo0dLzlJi2ORKnb96DXAa8P6QjJj3hxqI2URPGciY1GTvP3cbH+2IAAO8O9uDJPyJqFPZduoMilRrtHczRwYl9ZIkaOz2FHEM7OQMAdpxNkjia+tWhQwcMHDgQwcHBiIiIwIkTJzBz5kyMHj0azs6lxyQpKQkeHh6IiIgAAFy/fh0ffvghIiMjcfPmTezevRvjx4/Hk08+CR8fHynfDtXA/kt3MHLlSSRl3YebjQl2Tu/JggYRURMRk5KDYd+cwOmb92BupIf1k/zwSq9WLGgQNXEsalCT8NfVu3h7+0UAwKu9WmHKkxW3GiAiaa1YsQJubm4wMjKCv7+/5qRURfr27QuZTPbQbciQIfUYsfR2PjipOaKrC7/YEzURzz24MOPQlVTkFBZLHE392rJlCzw8PNCvXz8MHjwYvXr1wpo1azSPFxcXIzY2FgUFBQAAAwMDHD58GAMGDICHhwfmzJmDkSNH4vfff5fqLVANbTgRj+lbz+J+sQq929ritxm90JaLwRIRNQmHrqRi5Lf/LGo/gT7suEFEYPspagIu3s7CtM2RKFELPNvJGe8O7iB1SERUgW3btmH27NlYtWoV/P39sXTpUgQFBSE2Nhb29vYPjd+xYweKioo09zMyMtCpUyeMGjWqPsOW1K3MAkTcLO0rPqyzs9ThEFE98XKxQBs7U1y/m48Dl1PwQrems9aDtbU1tm7dWunjbm5uEEJo7ru6uuKvv/6qj9Cojgkh8OWhq1h+JA4AMK5HS8wf2hF6jWAdGSIiqpoQAiv/uo7PD8ZCCKBnGxt8O7YrrEy4IDgRleI3QmrU4tPzMWn9aRQUqdDL3RZLRnWCnIvoEjVIX375JYKDgzFp0iR07NgRq1atgomJCdatW1fheGtrazg6Ompuhw4dgomJSZMqavx2vnSWRkBrGzhZGkscDRHVF5lMhue6ls7W2NnEWlBR06BSC7y787KmoDG7fzssGubJggYRURNQWKzCnJ8v4LMDpQWNsf4tsPEVPxY0iKgcfiukRisttxDj14UjI78IXi4WWDXOFwZ6/JMnaoiKiooQGRmJwMBAzTa5XI7AwECEhYVV6znWrl2L0aNHw9TUtNIxSqUSOTk55W66SgiBHecetJ7q4iJxNERU38pmZ52Kz0By1n2JoyGqO4XFKkzfEokfIxIhlwEfjfDCG/3assUiEVETkJZbiJe+O4Ud55KgkMuwaJgnPhrhDX0WtYnoX/ipQI1SbmExJq0/jVuZ99HC2gTrJ/rBzJDd1ogaqvT0dKhUKjg4lF/008HBASkpKY/cPyIiApcvX8arr75a5bjFixfD0tJSc3N11d2WLZeSsnHjbj6M9OUY6OUodThEVM+aNzOBfytrCAH8dj5Z6nCI6kROYTEmrIvAwahUGCjkWDGmK8b6t5Q6LCIiqgfX7+ZhxIqTOJeYBQsjPWyY1B3jA9ykDouIGigWNajRKSpR47XNkYhKzoGNqQE2veIHO3NDqcMiIi1au3YtvL294efnV+W4kJAQZGdna263bt2qpwjr3o4HLWf6d3SEuZG+xNEQkRTKZmntPHe73DoSRLooLbcQo1efQnh8JswM9bDhle4Y5O0kdVhERFQPLt7OwqhVYZoFwXfNeAK923JBcCKqHIsa1Kio1QL/t/0CTsRlwMRAgfWTusPNtvJWNETUMNja2kKhUCA1NbXc9tTUVDg6Vj0LIT8/Hz/99BMmT578yNcxNDSEhYVFuZsuUqsF9l66AwAY0YULhBM1VYO8nWCgkONqah6upeVJHQ7RY0vIyMfzK8Nw5U4ObM0M8NOUHujZxlbqsIiIqB4cv5aOl9acQuaD1uG/TOuJ1nZmUodFRA0cixrUaAghsPD3KOy+kAw9uQyrXvaFT3MrqcMiomowMDCAr68vQkNDNdvUajVCQ0MREBBQ5b7bt2+HUqnEyy+/rO0wG4zIxHu4m6uEuZEeernzCiaipsrSWB9Ptis98bvvQaGTSNdcTsrGyJVhSMwsQAtrE/zyWk94uVhKHRYREdWDPReTMWlDBPKLVOjZxgY/BveArRk7bRDRo7GoQY3GV4evYWNYAmQy4IsXOuHJdjzRR6RLZs+eje+++w4bN25EdHQ0pk2bhvz8fEyaNAkAMH78eISEhDy039q1azF8+HDY2NjUd8iSKTt52b+jAwz0mMqJmrJBXqXtefZfevT6Q0QNTdj1DLy05hTS85To4GSBX14L4CxrIqIm4odTCXj9x3MoVgkM9nbE+knd2VaXiKqNKydTo7DueDyWhV4DACx81hPDOrtIHBER1dSLL76Iu3fvYt68eUhJSUHnzp1x4MABzeLhiYmJkMvLn8CPjY3F8ePH8ccff0gRsiTUaoEDl0tPXg72Yq9xoqYusIMD9BUyxKbmIi4tD+72bNdAuuHA5Tt446fzKCpRw6+VNb6f0A0WPJlFRNToCSGw9PA1fP3gHM5Y/xZYNMwLCrlM4siISJewqEE679fI21i05woAYE7/dhgf4CZtQET02GbOnImZM2dW+NjRo0cf2ta+ffsmtzju+dtZuJNdCDNDPfRqy37jRE2dpYk+nnC3xdHYuzhw+Q5mPt1W6pCIHunHiES8t/MS1AIY0NEBy17qAiN9hdRhERGRlqnUAgt2R+GHUwkAgDf7tcVbgW0hk7GgQUQ1w54VpNP+iErBf369CAB45YlWmPm0u8QRERFp1/4Hraf6dbDnCSAiAvC/WVv72IKKGjghBJaHXkPIjtKCxujurvh2bFfmMyKiJkBZosIbP53DD6dK24YvGuaJWf3bsaBBRI+FRQ3SWWHXMzDzx3NQqQVGdm2O94d0YDIkokZNCKE5aTmIraeI6IH+HR2gkMtw5U4ObqbnSx0OUYXUaoGFv1/BF4euAgBmPuWOxc95Q0/Bn6RERI1dnrIEkzecwd6Ld6CvkGHZ6C7sskFEtcJvkKSTLt7OQvCmMygqUaN/Rwd8OtIbcvZfJKJG7lJSNpKy7sPEQIG+7e2kDoeIGohmpgbo2cYGALD/MmdrUMNTVKLGm9vOY8PJmwCA+UM74v+C2vOCJCKiJiAjT4kx353C8bh0mBgosG5idwzt5Cx1WESk41jUIJ0Tl5aLCesikKcsQUBrGyx/qQuv8CKiJmHvg9ZTT3mw9RQRlTdI04LqjsSREJVXWKzC5I2n8fuFZOjJZfh6dGdMeqKV1GEREVE9uH2vAKNWheHi7WxYmxrgx+Ae6N2WF2cRUe3xTDDplKSs+xi3NgL3Corh09wS303oxhN7RNQkCCGw/0HrqcFsPUVE/zLA0wFyWemMrluZBVKHQwSgtH/61B8i8fe10qtz107sjmGdXaQOi4iI6kFsSi5GrjyJG+n5cLEyxvbXAtDJ1UrqsIiokWBRg3RGep4S474Px53sQrSxM8WGSX4wM9STOiwionoRlZyDxMwCGOnL8ZQHr24iovJszQzh36qsBRVna5D0SlRqvPnjefx19S6M9OXYMMkPfdoxfxERNQVnbmZi1KqTSM1Rop2DGX6d1hNt7MykDouIGhEWNUgn5BQWY8K6CE2Ff/Or/rA2NZA6LCKielN2kvKp9vYwMWBBl4geNtjbEQCw7xLX1SBpqdQC/7f9Ag5EpcBAIcd347vBr5W11GEREVE9OBKTipfXhiOnsAS+LZvh56kBcLQ0kjosImpkWNSgBq+wWIVXN5xBVHIObEwN8MNkPzhZGksdFhFRvRFCaE5SDvJm6ykiqliQlyNkMuD8rSwkZd2XOhxqooQQeH/XJew6X7qGxrdju7J/OhFRE7Hj7G0Eb4pEYbEaT3vYY/Nkf1iZ8IJUIqp7LGpQg1asUmPGlrOIuJkJc0M9bHzFD605ZZGImpjY1FzEp+fDQE+Opz3spQ6HiBooe3MjdHcrvRr+wGXO1qD6J4TAoj1X8GPELchlwFcvdkZgRwepwyIionqw89xtzNl+ASq1wHNdXLB6nC+MDbgGKhFpB4sa1GCp1QJvb7+A0Jg0GOrJsXZid3i5WEodFhFRvSubpdGnnR3XEiKiKg32Km1Btf8S19Wg+vfFH1ex/sRNAMCnI30wtJOztAEREVG9OHD5Dv5v+0UIAYwPaIklozpBX8FTjkSkPfyEoQZJCIGFv0dppq2vfLkr+/ASUZNVdnKyrF8+EVFlBnqVtqg7k3APKdmFEkdDTcmKP+PwzZ9xAIAPh3liVDdXiSMiIqL6cDQ2Da//eA4qtcAo3+ZYMNQTcrlM6rCIqJHTalEjMzMTY8eOhYWFBaysrDB58mTk5eVVa18hBAYNGgSZTIZdu3ZpM0xqgJYevoaNYQmQyYAvXuiEpz04bZ2ImqZrqbm4lpYHfYUM/Trws5CIquZoaQTfls0AAAej2IKK6se64/H4/GAsACBkkAfGBbhJGxAREdWL8BsZmPpDJIpVAkN8nPDJSB8WNIioXmi1qDF27FhERUXh0KFD2LNnD44dO4YpU6ZUa9+lS5dCJuMHYVO07ng8vg69BgBY+KwnhnV2kTgiIiLp7H/QF793WztYGOlLHA0R6YJBD1pQ7WMLKqoHP0UkYtGeKwCAN/q1xdQ+bSSOiIiI6sP5W1mYvPEMlCWli4J/9UJnKFjQIKJ6orWiRnR0NA4cOIDvv/8e/v7+6NWrF5YvX46ffvoJycnJVe57/vx5fPHFF1i3bp22wqMG6ucztzQ/imb3b4fxvMqLiJq4spOSZScpiYgeZZB3aQuqiJuZSMtlCyrSnt/OJyFk5yUAQHDvVpgV2FbiiIiIqD5E38nBhHURyFOWoGcbG3w7tisM9Njhnojqj9Y+ccLCwmBlZYVu3bpptgUGBkIulyM8PLzS/QoKCjBmzBisWLECjo48gdOU/HY+Ce/8ehEA8MoTrfD60+4SR0REJK3rd/MQk5ILPbkM/Tuy9RQRVY+LlTE6uVpBCOBgVKrU4VAjdeByCmb/fAFCAC/3aIF3B3fgTHsioibgxt08jFsbjuz7xejawgrfje8GI32F1GERUROjtaJGSkoK7O3ty23T09ODtbU1UlIq7+87a9Ys9OzZE8OGDavW6yiVSuTk5JS7ke7Zd+mO5kfRWP8W+OAZ/igiIjrwoPVUT3dbWJkYSBwNEemSwQ9md+1nCyrSgtJFYc9CpRZ4rqsLFj3rxe/uRERNwK3MAoz9PhzpeUXo6GSB9ZP8YGqoJ3VYRNQE1bioMXfuXMhksipvMTExjxXM7t27ceTIESxdurTa+yxevBiWlpaam6ur62O9Nknn8JVUvPHjOajUAs/7NseHw/ijiIgI+F/rqcFsPUVENTTIq7QF1akbGcjIU0ocDTUmp/65KKy3Ez7jorBERE1CWk4hXl4bjjvZhWhjZ4ofJvvB0phr/hGRNGpcTp0zZw4mTpxY5ZjWrVvD0dERaWlp5baXlJQgMzOz0rZSR44cwfXr12FlZVVu+8iRI9G7d28cPXr0oX1CQkIwe/Zszf2cnBwWNnTIX1fvYvqWsyhRCzzbyRmf8kcREREAICEjH1HJOVDIZRjgyaIGEdVMCxsTeLlY4HJSDv64koqX/FpIHRI1AmcT72HyhtP/WxT2xc7QU7CHOhFRY5eZX4Sx34cjIaMArtbG2PJqD9iYGUodFhE1YTUuatjZ2cHOzu6R4wICApCVlYXIyEj4+voCKC1aqNVq+Pv7V7jP3Llz8eqrr5bb5u3tja+++gpDhw6tcB9DQ0MYGvKDVBedvJ6OKZvOoEilxiAvR3z5QicoWNAgIgIA7H/QeiqgtQ2sTdl6iohqbpCXEy4n5WDfpTssalCtRSVnY+K6COQXqbgoLBFRE5JTWIzx68JxLS0PjhZG2PpqDzhaGkkdFhE1cVr7FtqhQwcMHDgQwcHBiIiIwIkTJzBz5kyMHj0azs7OAICkpCR4eHggIiICAODo6AgvL69yNwBo0aIFWrVqpa1QSQKnb2Zi8oYzUJao0c/DHl+P7sKrvIiI/qGsD/4gb87SIKLHM9i7tAXVyesZuJdfJHE0pMuupeZi3NoI5BSWwLdlMy4KS0TURBQUleCV9adxOSkHNqYG2PyqP1ytTaQOi4hIe0UNANiyZQs8PDzQr18/DB48GL169cKaNWs0jxcXFyM2NhYFBQXaDIMamPO3sjBp/WncL1ahd1tbrOBVXkRE5dy+V4ALt7MhlwEDOrKoQUSPp5WtKTo4WUClFjgUnSp1OKSjbt8rXRQ2M78I3i6WWD+pOxeFJSJqAgqLVZiyKRJnEu7BwkgPmyb7wd3eTOqwiIgAPEb7qZqwtrbG1q1bK33czc0NQogqn+NRj5NuuZyUjfFrw5GnLEGP1tZYM45XeRER/duBB62n/FpZw86cLRaJ6PEN9nJE9J0c7L90By9047pzVDP3i0pPaKXlKtHewRybXvGDhREXhSUiauyKVWrM3HoOx+PSYWKgwIZX/ODpbCl1WEREGrw8nupNbEouxq0N10xbXzuhO4wNWNAgIvq3fQ9aT5W1jiEielyDHnyOHI9LR/b9YomjIV0ihMB/fr2IK3dKW46sm9QdzbjGExFRo6dSC8z++QIOR6fCUE+O7yd0Q9cWzaQOi4ioHBY1qF7EpeVh7PencK+gGJ2ac9o6EVFlkrPu42xiFmQyIMiTraeIqHbc7c3QzsEMxSqBw1fYgoqqb/WxG/j9QjL05DJ8O7YrXKyMpQ6JiIi0TAiB93Ze0nz+r3y5K3q2sZU6LCKih7CoQVqXkJGPsd+fQnpeETo6WWDTK/6ctk5EVImy1lPdWjaDg4WRxNEQUWMwyKt0tsb+y3ckjoR0xdHYNHx6IAYAMH9oR/i3tpE4IiIi0jYhBD7cE42fTt+CXAZ8PboLnvZwkDosIqIKsahBWnX7XgHGfBeO1Bwl2jmY4YfJfrA0YUGDiKgyZScdy05CEhHVVlkru2NX05FbyBZUVLWb6fl448dzEAIY3d0VL/doKXVIRERUD7ZGJGLdiXgAwKcjfTDEh79HiKjhYlGDtCYluxBjvgtHUtZ9tLY1xeZX/WFjxgVviYgqk5pTiDMJ9wAAA73YeoqI6kY7BzO0tjNFkUqNIzFpUodDDViesgTBm84gp7AEXVtYYeEwT8hkMqnDIiIiLYtKzsbC368AAN4Z6IFR3VwljoiIqGosapBWpOUWYsx3p5CYWYAW1ibYGtwD9uZso0JEVJWDUSkQAujSwgrO7F1ORHVEJpNh8IPZX/susQUVVUytFpi97TyupeXB3twQq172haGeQuqwiIhIy3ILizFz6zkUlajRz8MeU59sLXVIRESPxKIG1bnM/CK8/H04bqTnw8XKGFuD/eFoyYIGEdGjlJ1sHOLNqd5EVLcGeZfO/joaexf5yhKJo6GGaNmRa/jjSioMFHKsHucLe67rRETU6AkhELLjEuLT8+FsaYQlozpBLucMPSJq+FjUoDqVXVCMl78Px9XUPDhYGGLLq/5o3sxE6rCIiBq8u7lKRMRnAmDrKSKqex2dLNDSxgTKEjX+jGULKirvj6gULD18DQDw3xFe6NKimcQRERHVzkcffYSePXvCxMQEVlZW1dpHCIF58+bByckJxsbGCAwMxLVr17QbqMS2RiRiz8U70JPLsHxMVzQzNZA6JCKiamFRg+rMvfwijPn+FK7cyYGtmQG2vNoDbramUodFRKQT/riSArUAOjW3ZDGYiOqcTCbDoActqPZfSpE4GmpIrqXmYta28wCAiT3d8AL7qBNRI1BUVIRRo0Zh2rRp1d7ns88+w7Jly7Bq1SqEh4fD1NQUQUFBKCws1GKk0vnnOhr/Gdgevi1Z0CYi3cGiBtWJ9DwlXvruFKKSc2BjWlrQcLc3kzosIiKdUXaScRBbTxGRlpS1tjsSk4b7RSqJo6GGIPt+MYI3nUF+kQo9WlvjvSEdpA6JiKhOLFy4ELNmzYK3t3e1xgshsHTpUrz//vsYNmwYfHx8sGnTJiQnJ2PXrl3aDVYC/15H49VeXEeDiHQLixpUa2m5hXhpzSnEpOTCztwQ26b2QHtHc6nDIiLSGZn5RQi7kQEAGMTWU0SkJV4uFmjezBj3i1X46ypbUDV1KrXAGz+ew82MArhYGWPFmK7QV/DnIRE1TfHx8UhJSUFgYKBmm6WlJfz9/REWFiZhZHWP62gQUWPAb61UKynZhRi9+hSupeXB0cII26b0gLs9CxpERDXxR1QKVGoBT2cLtLRh2z4i0g6ZTIbBD2Zr7GULqibv84Ox+OvqXRjply4MbmNmKHVIRESSSUkpzYsODg7ltjs4OGgeq4hSqUROTk65W0PHdTSIqDFgUYMeW1LWfby4Jgw30vPhYmWMbVN7oLUdW04REdXUvsulP5QGs/UUEWlZ2WywI9GpKCzWvRZUXPi1bvx+IRmr/roOAPh0pA+8XCwljoiI6NHmzp0LmUxW5S0mJqZeY1q8eDEsLS01N1fXhr0uEdfRIKLGgkUNeiy3Mgvw4uowJGQUoHkzY/w0pQevLiYiegxZBUU4GZcOgK2niEj7OrtawdnSCPlFKhy7elfqcGqMC7/WXlRyNt7+5QIAYGqf1hjW2UXiiIiIqmfOnDmIjo6u8ta69eOtDeHoWPo9PDU1tdz21NRUzWMVCQkJQXZ2tuZ269atx3r9+sB1NIioMdGTOgDSPQkZ+XhpzSkkZxeipY0JfgzuAWcrY6nDIiLSSYeupKJELeDhaM7ZbkSkdTKZDAO9nLDuRDz2X07BAE/dKqYuXLgQALBhw4Zqjf/3wq8AsGnTJjg4OGDXrl0YPXq0tkJtkDLzizBlUyQKi9V4sp0d/hPkIXVIRETVZmdnBzs7O608d6tWreDo6IjQ0FB07twZAJCTk4Pw8PAqC+mGhoYwNGz47fu4jgYRNTacqUE1cv1uHl5YHYbk7EK0tjPFtikBLGgQEdXC/getpwZ5sfUUEdWPwd6lhYzDV1KhLNG9FlQ18bgLv+pij/RHKVapMWPLWSRl3UdLGxMsH90FCp7QIqJGKjExEefPn0diYiJUKhXOnz+P8+fPIy8vTzPGw8MDO3fuBFBa9H/rrbfw3//+F7t378alS5cwfvx4ODs7Y/jw4RK9i7rDdTSIqLHhTA2qtmupuRjzfTju5irR1t4MW4L9YW9uJHVYREQ6K6ewGH9fK23/MsRHt66WJiLd1bVFM9ibGyItV4kTcel42sPh0TvpqMdd+HXx4sWaWSGNxcf7ohF2IwOmBgp8N74bLE30pQ6JiEhr5s2bh40bN2rud+nSBQDw559/om/fvgCA2NhYZGdna8b85z//QX5+PqZMmYKsrCz06tULBw4cgJGRbp/34DoaRNQYcaYGVUtMSg5GrzmFu7lKeDia46cpPVjQICKqpdDoVBSrBNram8Hd3lzqcIioiZDLZZo1fPZdqvzEfn1piAu/6lKP9Or4JfI21p+4CQD48sXOaOfAnENEjduGDRsghHjoVlbQAEpbMk2cOFFzXyaTYdGiRUhJSUFhYSEOHz6Mdu3a1X/wdYjraBBRY8WZGvRIUcnZePn7cNwrKIanswU2T/bnVEUiojpQdjJxkDdbTxFR/Rrk7YSNYQn4IyoFRSO8YaAn3bVOc+bMKXdSqSJ1sfCrk9P/PmtTU1M1PdMrois90qvj0u1svLvzEgDgzX5tEaRj66gQEdHj4ToaRNSYsahBVbp4Owvj1kYg+34xOjW3xKZX/DlVnYioDuQpS/DX1dLWU2X97YmI6kt3N2vYmhkgPa8IYTcy0KeddhZerY6GuPBrY6EsUWH2z+dRVKJGYAcHvNmvrdQhERFRPeE6GkTUmLH9FFXqbOI9jP0uHNn3i9G1hRV+eJUFDSKiuhIanYqiEjVa25qiPduAEFE9U8hlmiv291+6I3E01ceFX2vmmyNxuJaWB1szA3z+vA+v0CUiaiK4jgYRNXacqUEVOn0zE5PWn0aesgTd3Zph/SQ/mBnyz4WIqK7s17SecoRMxpNMRFT/hng7YUt4Ig5GpeDD4V7QVzT865248Gv1XU7KxrdHrwMAPhzmxSt0iYiaCK6jQURNAc9S00PCrmdg8sbTKChSIaC1DdZO7AYTA/6pEBHVlcJiFY5eTQMADPLiehpEJA2/VtawNjVAZn4Rwm9koldbW6lDeqQNGzZgw4YNVY4RQpS7X7bw66JFi7QYWcNSrFLjP79chEotMNjbkWs3ERE1EVxHg4iaCp6ppnL+vnYXwZvOoLBYjd5tbbFmXDcYGyikDouIqFE5EZeOwmI1nC2N4OlsIXU4RNRE6SnkeP1pdxjpK+Dlws+ixmT1X9dx5U4OrEz0sfBZL6nDISKiesJ1NIioqWBRgzT2XEzGrG3nUawSeKq9HVa+7AsjfRY0iIjq2uHoVABAYEcHtp4iIklNeqKV1CFQHbuWmotloXEAgAVDPWFnbihxREREVB+upeZyHQ0iajJY1CAAwKawm5i/OwpClPZX/vLFTjDUY0GDiKiuqdUCodGlraf6dXCQOBoiImpMVGqBt3+5iCJVaR/1YZ2dpQ6JiIjqyZbwRBSVlHbd4DoaRNTYaW01wMzMTIwdOxYWFhawsrLC5MmTkZeX98j9wsLC8PTTT8PU1BQWFhZ48skncf/+fW2F2eQJIfDloauY91tpQePlHi2w7KUuLGgQEWnJpaRspOUqYWqgQI/W1lKHQ0REjcj6E/E4fysL5oZ6+GiEN2cDEhE1EUIIHIxKAQBMCHDjOhpE1OhpragxduxYREVF4dChQ9izZw+OHTuGKVOmVLlPWFgYBg4ciAEDBiAiIgKnT5/GzJkzIZdrLcwmTaUWeH/XZSwLvQYAeCuwLT4c5gUFkx8RkdaUtZ7q096OBWQiIqoz8en5+PxgLADgvSEd4GhpJHFERERUXy7ezsad7EKYGCjQq62t1OEQEWmdVtpPRUdH48CBAzh9+jS6desGAFi+fDkGDx6MJUuWwNm54mnQs2bNwhtvvIG5c+dqtrVv314bITZ5yhIVZm07j32XUiCTAYue9cS4ADepwyIiavQOl7We8mDrKSIiqhtqtcA7v16EskSNJ9xt8GJ3V6lDIiKienTgwSyNpzzsuTYqETUJWpkCERYWBisrK01BAwACAwMhl8sRHh5e4T5paWkIDw+Hvb09evbsCQcHB/Tp0wfHjx/XRohNWp6yBJPWn8a+SynQV8iw/KUuLGgQEdWD2/cKEH0nB3JZ6Q8OIiKiurAlPAER8ZkwMVDgk+d82HaKiKgJEULgwOXSosZAT0eJoyEiqh9aKWqkpKTA3r78yRo9PT1YW1sjJSWlwn1u3LgBAFiwYAGCg4Nx4MABdO3aFf369cO1a9cqfS2lUomcnJxyN6pcep4So9eE4eT1DJgaKLB+oh+e8eECgkRE9aFsgfBuLa1hbWogcTRERNQY3MoswOL9MQCAdwZ6wNXaROKIiIioPl1Ly0N8ej4MFHJeOEVETUaNihpz586FTCar8hYTE/NYgajVagDA1KlTMWnSJHTp0gVfffUV2rdvj3Xr1lW63+LFi2Fpaam5ubpyqnVlbmUW4PmVJ3E5KQc2pgb4cUoP9lokIqpHZetp9OvAHxtERFR7Qgi8u/MSCopU6O7WDON6tJQ6JCIiqmdlszR6t7WFmaFWuswTETU4NSpqzJkzB9HR0VXeWrduDUdHR6SlpZXbt6SkBJmZmXB0rHgqnJOTEwCgY8eO5bZ36NABiYmJlcYUEhKC7Oxsze3WrVs1eUtNRvSdHIxceRI3MwrgYmWM7a8FwKe5ldRhERGVs2LFCri5ucHIyAj+/v6IiIiocnxWVhZmzJgBJycnGBoaol27dti3b189RVszuYXFOHUjAwAQ2JHraRARUe1tP3Mbf19Lh6GeHJ+O9IFczrZTRERNTVlRI8iLraeIqOmoUQnXzs4OdnZ2jxwXEBCArKwsREZGwtfXFwBw5MgRqNVq+Pv7V7iPm5sbnJ2dERsbW2771atXMWjQoEpfy9DQEIaGhjV4F01PRHwmJm88jdzCErR3MMemyX5wsDCSOiwionK2bduG2bNnY9WqVfD398fSpUsRFBSE2NjYh1oaAkBRURH69+8Pe3t7/PLLL3BxcUFCQgKsrKzqP/hqOHY1HcUqgda2pmhjZyZ1OEREpONScwrx4d4rAIA5A9qhNXMLEVGTk5hRgCt3cqCQyxDYgRdOEVHToZU1NTp06ICBAwciODgYEREROHHiBGbOnInRo0fD2bl0/YakpCR4eHhorsKVyWR4++23sWzZMvzyyy+Ii4vDBx98gJiYGEyePFkbYTYJh66kYtzacOQWlqBby2b4eWoACxpE1CB9+eWXCA4OxqRJk9CxY0esWrUKJiYmlbYgXLduHTIzM7Fr1y488cQTcHNzQ58+fdCpU6d6jrx6Qtl6ioiI6ogQAu/tvITcwhJ0am6JV55oJXVIREQkgYNRpbM0/Ny4Zh8RNS1aKWoAwJYtW+Dh4YF+/fph8ODB6NWrF9asWaN5vLi4GLGxsSgoKNBse+uttxASEoJZs2ahU6dOCA0NxaFDh9CmTRtthdmo/XzmFl7bHAlliRr9POzxw2R/WJroSx0WEdFDioqKEBkZicDAQM02uVyOwMBAhIWFVbjP7t27ERAQgBkzZsDBwQFeXl74+OOPoVKp6ivsaitRqXEktrQtI6+gIiKi2tp9IRmHo9Ogr5Dhs+c7QU+htZ91RETUgJUVNQay9RQRNTFaW0HI2toaW7durfRxNzc3CCEe2j537lzMnTtXW2E1CUIIrD52A5/sL120/Xnf5vjkOW/+2CGiBis9PR0qlQoODuVP+Ds4OCAmJqbCfW7cuIEjR45g7Nix2LdvH+Li4jB9+nQUFxdj/vz5Fe6jVCqhVCo193NycuruTVQhMuEesgqKYWWiD9+WzerlNYmIqHFKz1Niwe4oAMDrT7dFe0dziSMiIiIppOUUIjLxHgBggCcvnCKipkVrRQ2ShlotsHh/NL77Ox4AMLVPa8wd6AGZjIsGElHjolarYW9vjzVr1kChUMDX1xdJSUn4/PPPKy1qLF68GAsXLqznSIHQmNJZGk+1t2eBmYiIamX+7ijcKyhGBycLTOvLGe1ERE3VH1dSIQTQ2dUKTpbGUodDRFSveGalESksVmH2z+c1BY13B3sgZFAHFjSIqMGztbWFQqFAampque2pqalwdKx4KrWTkxPatWsHhUKh2dahQwekpKSgqKiown1CQkKQnZ2tud26davu3kQVDl8pfV9sPUVERLVx4PId7L14Bwq5DJ8/7wN9FsqJiJostp4ioqaM34IbifQ8JcZ+H45d55OhkMuwZFQnTHmSV24RkW4wMDCAr68vQkNDNdvUajVCQ0MREBBQ4T5PPPEE4uLioFarNduuXr0KJycnGBhUvEieoaEhLCwsyt207frdPNxIz4e+QoYn29lq/fWIiKhxyioowvu7SttOvdanNbxcLCWOiIiIpJJVUISw6xkAgCBPFjWIqOlhUaMRiEnJwbBvTiAy4R4sjPSwcZIfnvdtLnVYREQ1Mnv2bHz33XfYuHEjoqOjMW3aNOTn52PSpEkAgPHjxyMkJEQzftq0acjMzMSbb76Jq1evYu/evfj4448xY8YMqd5ChUKjS2dp9GhtA3MjfYmjISIiXbVozxWk5ynhbm+G159uK3U4REQkodDoNJSoBTwczdHK1lTqcIiI6h3X1NBxR2JS8frWc8gvUsHNxgRrJ3ZHGzszqcMiIqqxF198EXfv3sW8efOQkpKCzp0748CBA5rFwxMTEyGX/68W7+rqioMHD2LWrFnw8fGBi4sL3nzzTbzzzjtSvYUKHb5Sup4GW08REdHj+jMmDTvOJkEmAz4d6QMjfcWjdyIiokbrwIPWU5ylQURNFYsaOkoIgbXH4/HxvmioBdCjtTVWjvVFM9OKW64QEemCmTNnYubMmRU+dvTo0Ye2BQQE4NSpU1qO6vHdyy/CmYRMAEC/DvYSR0NERLoot7AY7+68BAB45YlW8G3ZTOKIiIhISvnKEhy7ehcA19MgoqaLRQ0dVFSixvzdl/FjROkCt6O7u2LRMC8Y6LGbGBFRQ/JnbBrUAvBwNEfzZiZSh0NERDroiz+u4k52IVramOD/BrSXOhwiIpLYX1fvQlmiRksbE3g4mksdDhGRJFjU0DH38oswbUskTt3IhEwGvDe4Ayb3agWZTCZ1aERE9C+HH6yn0b8jW08REVHNJWXdx5bwBADAf4d7wdiAbaeIiJq6A5dLW08N9HTkuSAiarJY1NAhcWl5eHXjadzMKICpgQLLx3TB0x48UUZE1BApS1Q4djUdANCP62kQEdFj+OZIHIpVAgGtbdC7rZ3U4RARkcSUJSociSldsy+IraeIqAljUUNHHL+WjmlbIpFbWAIXK2OsndgNHo4WUodFRESVCL+RiTxlCezMDeHjYil1OEREpGNuZRZg+5nSdrOzB7STOBoiImoITsZlIE9ZAgcLQ3RubiV1OEREkmFRQwf8cCoBC3ZHQaUW8G3ZDKvH+cLWzFDqsIiIqAplracCO9hDLue0cCIiqplloddQohbo3dYW3d2spQ6HiIgagLLWU0GejvyNQURNGosaDViJSo0P91zBxrDSProjurhg8XPeMNJnL10iooZMCIHQ6NJp4f3YJpCIiGooPj0fO84lAQBm9ecsDSIiKj1HdOjBhVMDPdl6ioiaNhY1GqicwmLM3HoOx67eBQC8HdQe0/u24SJQREQ6IPpOLpKy7sNIX44n3G2lDoeIiHTM8tBrUKkFnmpvh64tmkkdDhERNQCnb95DZn4RrEz04deKM/iIqGljUaMBSsjIx+SNZxCXlgcjfTm+eqEzBnk7SR0WERFVU1nrqV7udjA24Ow6IiKqvri0POw6z1kaRERU3sGo0tZTgR0coKeQSxwNEZG0WNRoYMJvZOC1zZG4V1AMBwtDrJ3QHV5cYJaISKeE/mM9DSIioppYFnoNalF60sqHi8ASERFK29uWFTXYeoqIiEWNBkMIge/+voHPDsSiRC3g7WKJ7yd0g4OFkdShERFRDaTmFOLC7WwAwNMsahARUQ3EpuTi94vJAIBZ/dtKHA0RETUUF29n4052IUwMFOjVlu1tiYhY1GgAsguKMWf7BU27kqGdnPHZSB+2LCEi0kFlC4R3drWCvTkL00REVH1fh16FEMAgL0d4OnO2NhERlTrwYJbGUx72MNLnuSIiIhY1JHbhVhZmbD2L2/fuw0AhxwdDO+Jl/xZcEJyISEex9RQRET2OK8k52HcpBTIZ8FYg19IgIqJSQggcuMzWU0RE/8SihkSEENgUloD/7r2CYpVAC2sTrBjTFd7NeUUWEZGuKigqwfG4dABAYEcHiaMhIiJdsvTwVQDAEG8ntHc0lzgaIiJqKK6l5SE+PR8GCjme8uCFU0REAIsaksgtLMbcXy9h76U7AIAgTwd89nwnWBrrSxwZERHVxvFr6VCWqNG8mTHaO/CEFBERVc+l29n440oq5DLgrUCupUFERP9TNkujd1tbmBnyNB4REcCiRr2LSs7GjC1ncTOjAHpyGUIGd8ArT7ix3RQRUSNQtp5GYAcHfq4TEVG1ffVglsawzi5wt2dRnIiI/qesqBHkxdZTRERlWNSoJ0II/HT6FubvjkJRiRouVsZYPqYLurZoJnVoRERUB9RqgdCYsvU02HqKiIiq51ziPRyJSYNCLsMb/ThLg4iI/icxowBX7uRAIZfxNwYR0T+wqFEP8pUleH/XZew8lwQAeNrDHl+M6oRmpgYSR0ZERHXl/O0spOcVwdxQD36trKUOh4iIdMRXh68BAEZ0cUErW1OJoyEioobkYFTpLA3/Vtaw5jkkIiINFjW07GpqLqZvOYu4tDwo5DK8HdQeU3q3hlzOtiRERI1JaHTpLI0n29vBQE8ucTRERKQLztzMxLGrd0tnaTzNWRpERFTegQdFjYFsPUVEVA6LGlr0a+RtvL/rMu4Xq+BgYYhvxnRFdzdevUtE1BgdvlK6nkZ/TgsnIqJq+vJQ6Voao3ybo4WNicTREBFRQ5KWU4jIhHsAgAEdWdQgIvonFjW0oLBYhfm/RWHbmVsAgN5tbfHVi51ha2YocWRERKQNtzILEJuaC4Vchr7t7aQOh4iIdEDY9QycvJ4BfYUMM592lzocIiJqYA5eKZ0J3qWFFRwtjSSOhoioYWF/jDp2/W4ehq84gW1nbkEuA2b3b4cNk/xY0CAiasQOP2g91a1lM1iZsNctERFVTQiBrw6XztJ4sbsrmjfjLA0iorr00UcfoWfPnjAxMYGVlVW19pk4cSJkMlm528CBA7UbaBUOXi5tPRXkyVkaRET/prWiRmZmJsaOHQsLCwtYWVlh8uTJyMvLq3KflJQUjBs3Do6OjjA1NUXXrl3x66+/aivEOqVWC/xwKgFDlx9HTEoubM0MsXmyP97o1xYKrp9BRNSolRU1+ndk6ykiInq0k9czEBGfCQOFHDOe4iwNIqK6VlRUhFGjRmHatGk12m/gwIG4c+eO5vbjjz9qKcKqZRUUIexGBgAWNYiIKqK1osbYsWMRFRWFQ4cOYc+ePTh27BimTJlS5T7jx49HbGwsdu/ejUuXLuG5557DCy+8gHPnzmkrzDpxK7MAL68Nxwe7LqOgSIWA1jbY90Yv9HS3lTo0IiLSspzCYoTfyAQA9ON6GkREWtUYrrwVQmjW0hjj3wJOlsaSxUJE1FgtXLgQs2bNgre3d432MzQ0hKOjo+bWrFkzLUVYtcPRaVCpBTwczdHK1lSSGIiIGjKtFDWio6Nx4MABfP/99/D390evXr2wfPly/PTTT0hOTq50v5MnT+L111+Hn58fWrdujffffx9WVlaIjIzURpi1VjY7I2jpMZy8ngFjfQUWDO2ILa/6w96C/Q6JiJqCv2LvokQt0MbOlD84iIi0TNevvAWAv67eRWTCPRjqyTG9bxvJ4iAioocdPXoU9vb2aN++PaZNm4aMjAxJ4jgYxdZTRERV0cpC4WFhYbCyskK3bt002wIDAyGXyxEeHo4RI0ZUuF/Pnj2xbds2DBkyBFZWVvj5559RWFiIvn37VvpaSqUSSqVScz8nJ6fO3kdVbmUW4J1fL+Lk9dIE59fKGp8/74OWNjyhRUTUlJS1ngpk6ykiIq1buHAhAGDDhg012q/sylupCSHw1YNZGuN6tOSFUEREDcjAgQPx3HPPoVWrVrh+/TreffddDBo0CGFhYVAoFBXuo41zUvnKEhy7erc0Ji/pcxcRUUOklZkaKSkpsLe3L7dNT08P1tbWSElJqXS/n3/+GcXFxbCxsYGhoSGmTp2KnTt3wt298j6zixcvhqWlpebm6upaZ++jIv+enWGkL8f8oR3xU3APFjSIiJqYYpUaf8akAQAC2XqKiKjBaihX3h6JScOF29kw1ldgah/O0iAiqom5c+c+1E7w37eYmJjHfv7Ro0fj2Wefhbe3N4YPH449e/bg9OnTOHr0aKX7aOOc1F9X70JZokZLGxN4OJrX+vmIiBqjGs3UmDt3Lj799NMqx0RHRz92MB988AGysrJw+PBh2NraYteuXXjhhRfw999/V9oHMSQkBLNnz9bcz8nJ0Vph46HZGW7W+Ox5H7ix3QgRUZN05uY95BSWoJmJPrq2kKbfLhERVa2hXHn7z7U0xvdsCTtzw1o/JxFRUzJnzhxMnDixyjGtW7eus9dr3bo1bG1tERcXh379+lU4RhvnpA5cLr0YeKCnI2QyWa2ei4iosapRUaO6CcTR0RFpaWnltpeUlCAzM7PSad/Xr1/HN998g8uXL8PT0xMA0KlTJ/z9999YsWIFVq1aVeF+hoaGMDTU7g8CtVpgS0QiFu+LRkGRCkb6crwz0AMTAtwglzPBEBE1VWWtp572cICC+YCI6LFU98IpDw+Px3r+0aNHa/7t7e0NHx8ftGnTBkePHq30JNXixYs1ra7qysGoVEQl58DUQIGpT3KWBhFRTdnZ2cHOzq7eXu/27dvIyMiAk5NTpWPq+pyUskSFIw9mggex9RQRUaVqVNSobgIJCAhAVlYWIiMj4evrCwA4cuQI1Go1/P39K9ynoKAAACCXl++IpVAooFaraxJmneLsDCIiqogQ4n/raXSwf8RoIiKqTFO48latFlh6uHSWxqQnWsHa1OCxn4uIiB4tMTERmZmZSExMhEqlwvnz5wEA7u7uMDMzAwB4eHhg8eLFGDFiBPLy8rBw4UKMHDkSjo6OuH79Ov7zn//A3d0dQUFB9Rb3ybgM5ClL4GBhiM7NrertdYmIdI1WFgrv0KEDBg4ciODgYKxatQrFxcWYOXMmRo8eDWdnZwBAUlIS+vXrh02bNsHPzw8eHh5wd3fH1KlTsWTJEtjY2GDXrl04dOgQ9uzZo40wq8TZGUREVJXrd/OQkFEAA4UcvdvV3xVjRESNTVO48nb/5RTEpOTC3FAPr/ZuVWfPS0REFZs3bx42btyoud+lSxcAwJ9//om+ffsCAGJjY5GdnQ2g9ILaixcvYuPGjcjKyoKzszMGDBiADz/8UOvdQf6prPVUkKcjzz0REVVBK0UNANiyZQtmzpyJfv36QS6XY+TIkVi2bJnm8eLiYsTGxmpmaOjr62Pfvn2YO3cuhg4diry8PLi7u2Pjxo0YPHiwtsKsEGdnEBHRoxy6UjotPKCNDcwMtZZOiYjoH3TxylvVP2ZpvNKrFaxMOEuDiEjbNmzYgA0bNlQ5Rgih+bexsTEOHjyo5aiqVqJS49CDmeADPdl6ioioKlo7C2NtbY2tW7dW+ribm1u5BAIAbdu2xa+//qqtkB6potkZ/wnywMSenJ1BRETlsfUUEVH908Urb/dcTMa1tDxYGOnhlV6cpUFERBU7ffMeMvOLYGWiD79W1lKHQ0TUoPHS0n9YduQalh6+BoCzM4iIqHIZeUqcTbwHAOjXwUHiaIiImg5du/K2RKXG1w9+XwT3bg1LY33JYiEioobtYFRp66n+HRygp5A/YjQRUdPGosY/jPFrgW2nbyG4d2vOziAiokrlK1UY7O2E9FwlnK2MpQ6HiIgaqGKVwCBvR+w6l4xJnKVBRERVaGVrCg9Hcwz0YuspIqJHkYl/94DScTk5ObC0tER2djYsLCxqvL+yRAVDPYUWIiMi0g21/RzVJbV9r0IIyGQsgBNR09VUckZt32exSg19XnVLRE0Y80X18TcGETVl1f0c5Tfrf2FBg4iIqos/NoiIqDpY0CAiouribwwiokfjt2siIiIiIiIiIiIiItIJLGoQEREREREREREREZFOYFGDiIiIiIiIiIiIiIh0AosaRERERERERERERESkE1jUICIiIiIiIiIiIiIincCiBhERERERERERERER6QQWNYiIiIiIiIiIiIiISCewqEFERERERERERERERDqBRQ0iIiIiIiIiIiIiItIJLGoQEREREREREREREZFOYFGDiIiIiIiIiIiIiIh0gp7UAdQ1IQQAICcnR+JIiIh0U9nnZ9nnaWPGnEFEVDtNJWcwXxAR1Q7zBRERVUd180WjK2rk5uYCAFxdXSWOhIhIt+Xm5sLS0lLqMLSKOYOIqG409pzBfEFEVDeYL4iIqDoelS9kopGVydVqNZKTk2Fubg6ZTFbj/XNycuDq6opbt27BwsJCCxE2fjyGtcPjV3s8hrUjhEBubi6cnZ0hlzfuLoW1yRn8O6s9HsPa4zGsHR6/2msqOYP5Qlo8hrXHY1h7PIa1w3zxaPwbqz0ew9rjMaw9HsPaqW6+aHQzNeRyOZo3b17r57GwsOAfXi3xGNYOj1/t8Rg+vsZ89dQ/1UXO4N9Z7fEY1h6PYe3w+NVOU8gZzBcNA49h7fEY1h6P4eNjvqge/o3VHo9h7fEY1h6P4eOrTr5ovOVxIiIiIiIiIiIiIiJqVFjUICIiIiIiIiIiIiIincCixr8YGhpi/vz5MDQ0lDoUncVjWDs8frXHY0j1gX9ntcdjWHs8hrXD40f1gX9ntcdjWHs8hrXHY0jaxr+x2uMxrD0ew9rjMawfjW6hcCIiIiIiIiIiIiIiapw4U4OIiIiIiIiIiIiIiHQCixpERERERERERERERKQTWNQgIiIiIiIiIiIiIiKd0OSLGh999BF69uwJExMTWFlZVWsfIQTmzZsHJycnGBsbIzAwENeuXdNuoA1YZmYmxo4dCwsLC1hZWWHy5MnIy8urcp++fftCJpOVu7322mv1FLH0VqxYATc3NxgZGcHf3x8RERFVjt++fTs8PDxgZGQEb29v7Nu3r54ibbhqcgw3bNjw0N+bkZFRPUZLjQVzRu0xZ9Qcc0btMF+QFJgvao/5ouaYL2qPOYPqG/NF7TFf1BzzRe0xX0ivyRc1ioqKMGrUKEybNq3a+3z22WdYtmwZVq1ahfDwcJiamiIoKAiFhYVajLThGjt2LKKionDo0CHs2bMHx44dw5QpUx65X3BwMO7cuaO5ffbZZ/UQrfS2bduG2bNnY/78+Th79iw6deqEoKAgpKWlVTj+5MmTeOmllzB58mScO3cOw4cPx/Dhw3H58uV6jrzhqOkxBAALC4tyf28JCQn1GDE1FswZtcecUTPMGbXDfEFSYb6oPeaLmmG+qD3mDJIC80XtMV/UDPNF7TFfNBCChBBCrF+/XlhaWj5ynFqtFo6OjuLzzz/XbMvKyhKGhobixx9/1GKEDdOVK1cEAHH69GnNtv379wuZTCaSkpIq3a9Pnz7izTffrIcIGx4/Pz8xY8YMzX2VSiWcnZ3F4sWLKxz/wgsviCFDhpTb5u/vL6ZOnarVOBuymh7D6v7/TVRdzBmPhzmj5pgzaof5gqTGfPF4mC9qjvmi9pgzSErMF4+H+aLmmC9qj/miYWjyMzVqKj4+HikpKQgMDNRss7S0hL+/P8LCwiSMTBphYWGwsrJCt27dNNsCAwMhl8sRHh5e5b5btmyBra0tvLy8EBISgoKCAm2HK7mioiJERkaW+/uRy+UIDAys9O8nLCys3HgACAoKapJ/b8DjHUMAyMvLQ8uWLeHq6ophw4YhKiqqPsKlJo45ozzmjJphzqgd5gvSJcwX5TFf1AzzRe0xZ5CuYL4oj/miZpgvao/5ouHQkzoAXZOSkgIAcHBwKLfdwcFB81hTkpKSAnt7+3Lb9PT0YG1tXeXxGDNmDFq2bAlnZ2dcvHgR77zzDmJjY7Fjxw5thyyp9PR0qFSqCv9+YmJiKtwnJSWFf2//8DjHsH379li3bh18fHyQnZ2NJUuWoGfPnoiKikLz5s3rI2xqopgzymPOqBnmjNphviBdwnxRHvNFzTBf1B5zBukK5ovymC9qhvmi9pgvGo5GOVNj7ty5Dy3A8u9bZX9oVErbx3DKlCkICgqCt7c3xo4di02bNmHnzp24fv16Hb4LolIBAQEYP348OnfujD59+mDHjh2ws7PD6tWrpQ6NGgDmjNpjzqDGgvmCqsJ8UXvMF9SYMGdQZZgvao/5ghoT5gvtaJQzNebMmYOJEydWOaZ169aP9dyOjo4AgNTUVDg5OWm2p6amonPnzo/1nA1RdY+ho6PjQwvhlJSUIDMzU3OsqsPf3x8AEBcXhzZt2tQ4Xl1ha2sLhUKB1NTUcttTU1MrPV6Ojo41Gt/YPc4x/Dd9fX106dIFcXFx2giRdAxzRu0xZ2gHc0btMF9QXWO+qD3mC+1gvqg95gyqS8wXtcd8oR3MF7XHfNFwNMqihp2dHezs7LTy3K1atYKjoyNCQ0M1CSMnJwfh4eGYNm2aVl5TCtU9hgEBAcjKykJkZCR8fX0BAEeOHIFardYkheo4f/48AJRLyo2RgYEBfH19ERoaiuHDhwMA1Go1QkNDMXPmzAr3CQgIQGhoKN566y3NtkOHDiEgIKAeIm54HucY/ptKpcKlS5cwePBgLUZKuoI5o/aYM7SDOaN2mC+orjFf1B7zhXYwX9QecwbVJeaL2mO+0A7mi9pjvmhApF6pXGoJCQni3LlzYuHChcLMzEycO3dOnDt3TuTm5mrGtG/fXuzYsUNz/5NPPhFWVlbit99+ExcvXhTDhg0TrVq1Evfv35fiLUhu4MCBokuXLiI8PFwcP35ctG3bVrz00kuax2/fvi3at28vwsPDhRBCxMXFiUWLFokzZ86I+Ph48dtvv4nWrVuLJ598Uqq3UK9++uknYWhoKDZs2CCuXLkipkyZIqysrERKSooQQohx48aJuXPnasafOHFC6OnpiSVLlojo6Ggxf/58oa+vLy5duiTVW5BcTY/hwoULxcGDB8X169dFZGSkGD16tDAyMhJRUVFSvQXSUcwZtcecUTPMGbXDfEFSYb6oPeaLmmG+qD3mDJIC80XtMV/UDPNF7TFfNAxNvqgxYcIEAeCh259//qkZA0CsX79ec1+tVosPPvhAODg4CENDQ9GvXz8RGxtb/8E3EBkZGeKll14SZmZmwsLCQkyaNKlcAo6Pjy93TBMTE8WTTz4prK2thaGhoXB3dxdvv/22yM7Olugd1L/ly5eLFi1aCAMDA+Hn5ydOnTqleaxPnz5iwoQJ5cb//PPPol27dsLAwEB4enqKvXv31nPEDU9NjuFbb72lGevg4CAGDx4szp49K0HUpOuYM2qPOaPmmDNqh/mCpMB8UXvMFzXHfFF7zBlU35gvao/5ouaYL2qP+UJ6MiGEqJ85IURERERERERERERERI9PLnUARERERERERERERERE1cGiBhERERERERERERER6QQWNYiIiIiIiIiIiIiISCewqEFERERERERERERERDqBRQ0iIiIiIiIiIiIiItIJLGoQEREREREREREREZFOYFGDiIiIiIiIiIiIiIh0AosaRERERERERERERESkE1jUICIiIiIiIiIiIiIincCiBhERERERERERERER6QQWNYiIiIiIiIiIiIiISCewqEFERERERERERERERDqBRQ0iIiIiIiIiIiIiItIJLGoQEREREREREREREZFOYFGDiIiIiIiIiIiIiIh0AosaRERERERERERERESkE1jUICIiIiIiIiIiIiIincCiBpGOGzx4MIKDg2u836pVq9CiRQsolUotREVERA3N9OnT0b9//xrvd+DAAZiZmeHu3btaiIqIiLTt1q1bMDIywokTJ2q8b48ePfCf//xHC1EREdGjbNiwATKZDDdv3tTq60RERMDAwAAJCQk12q+4uBiurq749ttvtRQZUeVY1CDSYSdOnMAff/yBd955p8b7Tpw4EUVFRVi9erUWIiMioqps3boVS5curbfXi4+Px/fff4933323xvsOHDgQ7u7uWLx4sRYiIyIibVu0aBH8/f3xxBNP1Hjfd955BytWrEBKSooWIiMioobgvffew0svvYSWLVvWaD99fX3Mnj0bH330EQoLC7UUHVHFWNQg0mGff/45+vXrB3d39xrva2RkhAkTJuDLL7+EEEIL0RERUWXqu6jx9ddfo1WrVnjqqacea/+pU6di9erVyM3NrePIiIhIm+7evYuNGzfitddee6z9hw0bBgsLC16FS0TUSJ0/fx6HDx9+7DwxadIkpKenY+vWrXUcGVHVWNQgqkJ+fr7UIVQqLS0Ne/fuxQsvvPDYz/HCCy8gISEBf/75Zx1GRkTUNDXUnFFcXIwtW7bUKl+MHDkSSqUS27dvr8PIiIhI2zZv3gw9PT0MHTr0sfaXy+V4/vnnsWnTJl4IRUTUCK1fvx4tWrRAjx49Hmt/KysrDBgwABs2bKjbwIgegUUNavQSEhIwffp0tG/fHsbGxrCxscGoUaMe6klY1qvwr7/+wvTp02Fvb4/mzZtrHt+/fz/69OkDc3NzWFhYoHv37g9VosPDwzF48GA0a9YMpqam8PHxwddff11uzJEjR9C7d2+YmprCysoKw4YNQ3R0dLkxubm5eOutt+Dm5gZDQ0PY29ujf//+OHv2rGbM3r17UVJSgsDAQM02IQSeeuop2NnZIS0tTbO9qKgI3t7eaNOmTbmTbr6+vrC2tsZvv/1W8wNLRNQI1UfO6Nu3L/bu3YuEhATIZDLIZDK4ubkBKP28njdvHnx9fWFpaQlTU1P07t37oeLzzZs3IZPJsGTJEqxZswZt2rSBoaEhunfvjtOnT5cbe/z4caSnp5fLFwAwYcIEGBkZPZSDgoKC0KxZMyQnJ2u22dvbw8fHh/mCiKgWkpKS8Morr8DBwQGGhobw9PTEunXrAAD379+Hh4cHPDw8cP/+fc0+mZmZcHJyQs+ePaFSqQCUtpE1MzPDjRs3EBQUBFNTUzg7O2PRokUPFR527doFf39/mJmZabZFR0fD2NgY48ePLzf2+PHjUCgUD7W27d+/PxISEnD+/Pm6PBxERPQYvv32W3h6esLQ0BDOzs6YMWMGsrKyHhq3YsUKtG7dGsbGxvDz88Pff/+Nvn37om/fvuXG7dq1C08//TRkMplm25EjRyCXyzFv3rxyY7du3QqZTIaVK1eW296/f38cP34cmZmZdfY+iR6FRQ1q9E6fPo2TJ09i9OjRWLZsGV577TWEhoaib9++KCgoeGj89OnTceXKFcybNw9z584FUHryasiQIcjMzERISAg++eQTdO7cGQcOHNDsd+jQITz55JO4cuUK3nzzTXzxxRd46qmnsGfPHs2Yw4cPIygoCGlpaViwYAFmz56NkydP4oknnih3wuy1117DypUrMXLkSHz77bf4v//7PxgbG5c78XTy5EnY2NiU63kok8mwbt06FBYWlps6OH/+fERFRWH9+vUwNTUt9367du36WIsGEhE1RvWRM9577z107twZtra2+OGHH/DDDz9oWlHl5OTg+++/R9++ffHpp59iwYIFuHv3LoKCgio8mbR161Z8/vnnmDp1Kv773//i5s2beO6551BcXKwZc/LkSchkMnTp0qXcvl9//TXs7OwwYcIEzYmy1atX448//sDy5cvh7Oxcbryvry9Onjz52MeWiKgpS01NRY8ePXD48GHMnDkTX3/9Ndzd3TF58mQsXboUxsbG2LhxI+Li4vDee+9p9psxYways7OxYcMGKBQKzXaVSoWBAwfCwcEBn332GXx9fTF//nzMnz9fM6a4uBinT59G165dy8XSoUMHfPjhh/jhhx+we/duAKWzDSdOnAgPDw8sWrSo3HhfX18A4G8GIiKJLViwADNmzICzszO++OILjBw5EqtXr8aAAQPKff9fuXIlZs6ciebNm+Ozzz5D7969MXz4cNy+fbvc8yUlJSExMfGhPPH0009j+vTpWLx4sebi2jt37uD1119HYGDgQ62qfH19IYTgbwWqX4KokSsoKHhoW1hYmAAgNm3apNm2fv16AUD06tVLlJSUaLZnZWUJc3Nz4e/vL+7fv1/uedRqtRBCiJKSEtGqVSvRsmVLce/evQrHCCFE586dhb29vcjIyNBsu3DhgpDL5WL8+PGabZaWlmLGjBlVvq9evXoJX1/fCh9bvXq1ACA2b94sTp06JRQKhXjrrbcqHDtlyhRhbGxc5WsRETUV9ZEzhBBiyJAhomXLlg+9VklJiVAqleW23bt3Tzg4OIhXXnlFsy0+Pl4AEDY2NiIzM1Oz/bfffhMAxO+//67Z9vLLLwsbG5sK3+/BgwcFAPHf//5X3LhxQ5iZmYnhw4dXOPbjjz8WAERqamqFjxMRUeUmT54snJycRHp6ernto0ePFpaWlpr8ExISIuRyuTh27JjYvn27ACCWLl1abp8JEyYIAOL111/XbFOr1WLIkCHCwMBA3L17VwghRFxcnAAgli9f/lA8KpVK9OrVSzg4OIj09HQxY8YMoaenJ06fPl1h/AYGBmLatGm1OgZERFQzZb854uPjRVpamjAwMBADBgwQKpVKM+abb74RAMS6deuEEEIolUphY2MjunfvLoqLizXjNmzYIACIPn36aLYdPnz4od8OZfLz84W7u7vw9PQUhYWFYsiQIcLCwkIkJCQ8NDY5OVkAEJ9++mkdvnuiqnGmBjV6xsbGmn8XFxcjIyMD7u7usLKyKtfOqUxwcHC5q6AOHTqE3NxczJ07F0ZGRuXGlk3PO3fuHOLj4/HWW2/BysqqwjF37tzB+fPnMXHiRFhbW2se9/HxQf/+/bFv3z7NNisrK4SHh5dr/fFvGRkZaNasWYWPTZkyBUFBQXj99dcxbtw4tGnTBh9//HGFY5s1a4b79+9XeAUyEVFTUx85oyoKhQIGBgYAALVajczMTJSUlKBbt24Vvv6LL75YLhf07t0bAHDjxg3NtqryxYABAzB16lQsWrQIzz33HIyMjLB69eoKx5Y9R3p6+iPfBxER/Y8QAr/++iuGDh0KIQTS09M1t6CgIGRnZ2s+4xcsWABPT09MmDAB06dPR58+ffDGG29U+LwzZ87U/Fsmk2HmzJkoKirC4cOHAZR+/gOoMAfI5XJs2LABeXl5GDRoEL799luEhISgW7duFb5Ws2bN+PlPRCShw4cPo6ioCG+99Rbk8v+dzg0ODoaFhQX27t0LADhz5gwyMjIQHBwMPT09zbixY8c+lA+qyhMmJibYsGEDoqOj8eSTT2Lv3r346quv0KJFi4fG8ncCSYFFDWr07t+/j3nz5sHV1RWGhoawtbWFnZ0dsrKykJ2d/dD4Vq1albt//fp1AICXl1elr1GdMQkJCQCA9u3bP/RYhw4dkJ6erlnv4rPPPsPly5fh6uoKPz8/LFiwoNwJqjKiisX61q5di4KCAly7dg0bNmwod6Kuoueozsk2IqLGrj5yxqNs3LgRPj4+MDIygo2NDezs7LB3794KX//fPyrKflDcu3ev3Paq8sWSJUtgbW2N8+fPY9myZbC3t69wHPMFEdHjuXv3LrKysrBmzRrY2dmVu02aNAkANOvhGRgYYN26dYiPj0dubi7Wr19f4eeuXC5H69aty21r164dADy0DlRlOaBNmzZYsGABTp8+DU9PT3zwwQeVvgchBD//iYgkVNk5JQMDA7Ru3VrzeNl/3d3dy43T09PTrOP3b5XliSeeeALTpk1DREQEgoKC8Morr1S5P/ME1Se9Rw8h0m2vv/461q9fj7feegsBAQGwtLSETCbD6NGjoVarHxpf2cn/+vTCCy+gd+/e2LlzJ/744w98/vnn+PTTT7Fjxw4MGjQIAGBjY/PQSat/Onr0KJRKJQDg0qVLCAgIqHDcvXv3YGJi0iDeNxGR1KTOGZs3b8bEiRMxfPhwvP3227C3t4dCocDixYs1BZN/+ucskX/65w+TR+WLc+fOaU6mXbp0CS+99FKF48qew9bWttrvh4iIoMkfL7/8MiZMmFDhGB8fH82/Dx48CAAoLCzEtWvXHiqgV5eNjQ2Ahwvd//THH38AAJKTk5GRkQFHR8cKx2VlZfHzn4iokXlUnlAqlTh69CiA0ou3CgoKYGJi8tA4/k4gKXCmBjV6v/zyCyZMmIAvvvgCzz//PPr3749evXohKyurWvu3adMGAHD58uVajSlb0Ds2Nvahx2JiYmBra1tuEW8nJydMnz4du3btQnx8PGxsbPDRRx9pHvfw8EB8fHyFr1W2gNOAAQPwzDPP4P/+7/801fp/i4+PR4cOHSqNm4ioKamPnAFUfhXTL7/8gtatW2PHjh0YN24cgoKCEBgYiMLCwhq9j3/y8PDAvXv3KpzpkZ+fj0mTJqFjx46YMmUKPvvsM5w+fbrC54mPj9fMXCEiouqzs7ODubk5VCoVAgMDK7yVzZK7ePEiFi1ahEmTJqFLly549dVXK/z8VqvVD83kvnr1KgBorsRt0aIFjI2NK/3NsGrVKhw6dAgfffQRioqKMHXq1ArHJSUloaioiL8ZiIgkVNk5paKiIsTHx2seL/tvXFxcuXElJSUPzeTz8PAAgErzxPz58xEdHY0lS5YgPj4ec+fOrXBc2f7ME1SfWNSgRk+hUDw0lW758uVQqVTV2n/AgAEwNzfH4sWLHzqpVPa8Xbt2RatWrbB06dKHTnyVjXFyckLnzp2xcePGcmMuX76MP/74A4MHDwYAqFSqh3642Nvbw9nZWTPzAgACAgJw7969CttSBQcHQ61WY+3atVizZg309PQwefLkCqcUnj17Fj179qzWsSAiauzqI2cAgKmpaYUnqcpmXvxzbHh4OMLCwqr9Hv4tICAAQghERkY+9Ng777yDxMREbNy4EV9++SXc3NwwYcKEcvmmTGRkZKWz/oiIqHIKhQIjR47Er7/+WmHR++7duwBK13KaOHEinJ2d8fXXX2PDhg1ITU3FrFmzKnzeb775RvNvIQS++eYb6Ovro1+/fgAAfX19dOvWDWfOnHlo3/j4eLz99tsYOXIk3n33XSxZsgS7d+/Gpk2bHhpblj/4m4GISDqBgYEwMDDAsmXLyv1WWLt2LbKzszFkyBAAQLdu3WBjY4PvvvsOJSUlmnFbtmx5aEaGi4sLXF1dK8wT4eHhWLJkCd566y3MmTMHb7/9Nr755hv89ddfD42NjIyETCbjbwWqV2w/RY3eM888gx9++AGWlpbo2LEjwsLCcPjwYc00u0exsLDAV199hVdffRXdu3fHmDFj0KxZM1y4cAEFBQXYuHEj5HI5Vq5ciaFDh6Jz586YNGkSnJycEBMTg6ioKM0U8s8//xyDBg1CQEAAJk+ejPv372P58uWwtLTEggULAAC5ublo3rw5nn/+eXTq1AlmZmY4fPgwTp8+jS+++EIT15AhQ6Cnp4fDhw9jypQpmu3r16/H3r17sWHDBjRv3hxA6Qm5l19+GStXrsT06dM1YyMjI5GZmYlhw4bV9jATETUK9ZEzAMDX1xfbtm3D7Nmz0b17d5iZmWHo0KF45plnsGPHDowYMQJDhgxBfHw8Vq1ahY4dOyIvL++x3lOvXr1gY2ODw4cP4+mnn9ZsP3LkCL799lvMnz8fXbt2BVCaQ/r27YsPPvgAn332mWZsWloaLl68iBkzZjxWDERETd0nn3yCP//8E/7+/ggODkbHjh2RmZmJs2fP4vDhw8jMzMR///tfnD9/HqGhoTA3N4ePjw/mzZuH999/H88//7zmIigAMDIywoEDBzBhwgT4+/tj//792Lt3L959991yM+qGDRuG9957Dzk5ObCwsABQWgB55ZVXYGxsjJUrVwIApk6dil9//RVvvvkmAgMD4ezsrHmOQ4cOoUWLFujSpUs9HS0iIvo3Ozs7hISEYOHChRg4cCCeffZZxMbG4ttvv0X37t3x8ssvAyhdY2PBggV4/fXX8fTTT+OFF17AzZs3sWHDBrRp0+ahGePDhg3Dzp07y62dVFhYiAkTJqBt27aajiELFy7E77//jkmTJuHSpUvlOo0cOnQITzzxRLV/MxHVCUHUyN27d09MmjRJ2NraCjMzMxEUFCRiYmJEy5YtxYQJEzTj1q9fLwCI06dPV/g8u3fvFj179hTGxsbCwsJC+Pn5iR9//LHcmOPHj4v+/fsLc3NzYWpqKnx8fMTy5cvLjTl8+LB44oknNM8zdOhQceXKFc3jSqVSvP3226JTp06a5+nUqZP49ttvH4rp2WefFf369dPcv3XrlrC0tBRDhw59aOyIESOEqampuHHjhmbbO++8I1q0aCHUanXVB5GIqImor5yRl5cnxowZI6ysrAQA0bJlSyGEEGq1Wnz88ceiZcuWwtDQUHTp0kXs2bNHTJgwQTNGCCHi4+MFAPH5558/9NoAxPz588tte+ONN4S7u7vmfk5OjmjZsqXo2rWrKC4uLjd21qxZQi6Xi7CwMM22lStXChMTE5GTk/OoQ0hERJVITU0VM2bMEK6urkJfX184OjqKfv36iTVr1ojIyEihp6cnXn/99XL7lJSUiO7duwtnZ2dx7949IYQQEyZMEKampuL69etiwIABwsTERDg4OIj58+cLlUr10Gvq6emJH374QbPt66+/FgDEr7/+Wm5sYmKisLCwEIMHD9ZsU6lUwsnJSbz//vt1fDSIiOhRyn5zxMfHa7Z98803wsPDQ+jr6wsHBwcxbdo0TX74p2XLlml+U/j5+YkTJ04IX19fMXDgwHLjzp49KwCIv//+W7Nt1qxZQqFQiPDw8HJjz5w5I/T09MS0adM027KysoSBgYH4/vvv6+ZNE1WTTIhKlrgnogbv77//Rt++fRETE4O2bdvWaF+lUgk3NzfMnTsXb775ppYiJCKihuDGjRvw8PDA/v37NW1JaqJLly7o27cvvvrqKy1ER0RENTFx4kT88ssv1Z7BN3nyZFy9ehV///13jV9r165dGDNmDK5fvw4nJ6ca709ERA2DWq2GnZ0dnnvuOXz33XflHuvXrx+cnZ3xww8/1Ph5ly5dis8++wzXr1+HsbFxXYVL9EhcU4NIh/Xu3RsDBgwo1yKkutavXw99fX289tprWoiMiIgaktatW2Py5Mn45JNParzvgQMHcO3aNYSEhGghMiIi0rb58+fj9OnTOHHiRI33/fTTTzFz5kwWNIiIdEhhYeFD6wRu2rQJmZmZ6Nu370PjP/74Y2zbtg0JCQk1ep3i4mJ8+eWXeP/991nQoHrHmRpEREREREREOqKmMzWIiKhpOXr0KGbNmoVRo0bBxsYGZ8+exdq1a9GhQwdERkbCwMBA6hCJao0LhRMRERERERERERE1Am5ubnB1dcWyZcuQmZkJa2trjB8/Hp988gkLGtRocKYGERERERERERERERHpBK6pQUREREREREREREREOoFFDSIiajBWrFgBNzc3GBkZwd/fHxEREVWOX7p0Kdq3bw9jY2O4urpi1qxZKCwsrKdoiYiIiIiIiIiovrGoQUREDcK2bdswe/ZszJ8/H2fPnkWnTp0QFBSEtLS0Csdv3boVc+fOxfz58xEdHY21a9di27ZtePfdd+s5ciIiIiIiIiIiqi+Nbk0NtVqN5ORkmJubQyaTSR0OEZHOEUIgNzcXzs7OkMvrr/bt7++P7t2745tvvgFQ+nnu6uqK119/HXPnzn1o/MyZMxEdHY3Q0FDNtjlz5iA8PBzHjx+v1msyZxAR1Y5UOaO+MV8QEdUO8wUREVVHdfOFXj3GVC+Sk5Ph6uoqdRhERDrv1q1baN68eb28VlFRESIjIxESEqLZJpfLERgYiLCwsAr36dmzJzZv3oyIiAj4+fnhxo0b2LdvH8aNG1ft12XOICKqG/WZM6TAfEFEVDeYL4iIqDoelS8aXVHD3NwcQOkbt7CwkDgaIiLdk5OTA1dXV83naX1IT0+HSqWCg4NDue0ODg6IiYmpcJ8xY8YgPT0dvXr1ghACJSUleO2116psP6VUKqFUKjX3yyYrMmcQET0eKXKGFPgbg4iodpgviIioOqqbLxpdUaNsep+FhQUTCBFRLTT06dJHjx7Fxx9/jG+//Rb+/v6Ii4vDm2++iQ8//BAffPBBhfssXrwYCxcufGg7cwYRUe009JxRW/yNQURUN5gviIioOh6VLxpdUYOIiHSPra0tFAoFUlNTy21PTU2Fo6Njhft88MEHGDduHF599VUAgLe3N/Lz8zFlyhS89957FfZeDAkJwezZszX3y64AICIiIiIiIiIi3dB4V2ciIiKdYWBgAF9f33KLfqvVaoSGhiIgIKDCfQoKCh4qXCgUCgD/ayv1b4aGhpqrpnj1FBERERERERGR7uFMDSIiahBmz56NCRMmoFu3bvDz88PSpUuRn5+PSZMmAQDGjx8PFxcXLF68GAAwdOhQfPnll+jSpYum/dQHH3yAoUOHaoobRERERERERETUuLCoQUREDcKLL76Iu3fvYt68eUhJSUHnzp1x4MABzeLhiYmJ5WZmvP/++5DJZHj//feRlJQEOzs7DB06FB999JFUb4GIiIiIiIiIiLRMJirr0aGjcnJyYGlpiezsbLYVISJ6DE3pc7QpvVciIm1oKp+jTeV9EhFpS1P5HG0q75OISFuq+znKNTWIiIiIiIiIiIiIiEgnsKhBREREREREREREREQ6gUUNIiIiIiIiIiIiIiLSCSxqEBERERERERERERGRTmBRg4iIiIiIiIiIiIiIdAKLGv+SmFGAa6m5UodBRERERKR1OYXFOHb1rtRhEBFRA5ecdR+XbmdLHQYREemA63fzcDM9X6uvwaLGP6w7Ho8nP/8TXx66KnUoRERERERadeZmJgYt/RuvbjqD2BRe1ENERJVb9dd1DP3mOD49ECN1KERE1MAIIXDxdhY+PxiDwC//Qr8v/sKqv65r9TX1tPrsOsavlTUA4GjsXdwvUsHYQCFxREREREREdatEpcayI3H45sg1qAXgam0MZYlK6rCIiKiByswvws9nbgEAernbShwNERE1BCUqNSLiM/HHlVT8EZWC5OxCzWP6ChmUJWqtvj6LGv/g6WwBFytjJGXdx7FrdxHk6Sh1SEREREREdSYxowBvbjuHc4lZAIDnurhg4TBPmBvpSxsYERE1WJvCbqKwWA0vFwv0bGMjdThERCSRwmIVjl29i4NRqQiNSUVWQbHmMRMDBfq2t0OQpyP6treHpbF2f1+wqPEPMpkMA70csfZ4PA5eTmFRg4iIiIgaBSEEdp5LwrzfopCnLIG5kR7+O9wLwzq7SB0aERE1YPeLVNh48iYA4LU+bSCTyaQNiIiI6lW+sgR/xqZh/6UUHIlJw/3i/83wbmaij8AODgjydESvtrYw0q+/rkcsavxLWVHjcHQqikrUMNDjsiNEREREpLuy7xfj/V2X8fuFZABAd7dm+OrFzmjezETiyIiIqKHbHnkL9wqK0cLaBAN54ScRUZOQW1iMIzFp2HfpDo7G3i3XSsrFyhj9O5YWMrq7NYOeQppz5yxq/EvXFs1ga2aI9DwlTt3IwJPt7KQOiYiIiIjosUTEZ2LWtvNIyroPhVyGt/q1xfSn3KGQ80pbIiKqWolKje/+vgEACO7dSrITV0REpH3Z94sRGp2KfZdScOzaXRT9o5DR0sYEg7ycMMjLET7NLRvErD0WNf5FIZdhgKcDtoYn4kBUCosaRERERKRzilVqLAu9hhV/xkEtgBbWJvh6dGd0adFM6tCIiEhH7L+cgluZ92FtaoDnfV2lDoeIiOpYVkER/riSiv2X7uB4XDqKVULzWGtbUwz2dsIgb0d0dLJoEIWMf2JRowIDPR2xNTwRf0Sl4sNhXrySjYiIiIh0RkJGPt786TzO38oCADzv2xwLnvWEmSG/+hMRUfUIIbDqr+sAgAkBbjA2qL8+6UREpD2Z+UX4IyoFey/dQdj1DJSo/1fIaOdghkFeThjs7YR2DmYNrpDxT/xlU4EerW1gYaSH9DwlzibeQ3c3a6lDIiIiIiKqkhACv0TexoLdUcgvUsHCSA8fP+eNZ3ycpQ6NiIh0zLFr6YhKzoGJgQLjA1pKHQ4REdVCTmEx/ohKxe4LyTgRlw7VPwoZHZwsMNjLEYO8HeFuby5hlDXDokYFDPTkCOzggB3nknDwcgqLGkRERETUoGUXFOPdnZew99IdAIBfK2t89WJnuFgZSxwZERHpohV/xgEAXvJrgWamBhJHQ0RENXW/SIXQmFTsPp+Mo7F3UaT63xoZns4WGOxdOiOjla2phFE+PhY1KhHk5Ygd55JwICoF7w3p0KCn2xARERFR03XqRgZmbTuPO9mF0JPLMKt/O7zWpw1bqBIR0WOJTMhERHwm9BUyBPduLXU4RERUTcoSFY5dTcfvF5JxODoVBUUqzWPu9mZ4tpMzhnZy1tlCxj+xqFGJJ9vawUhfjtv37iMqOQdeLpZSh0REREREpFGiUuOrw1fx7dHrEAJwszHB16O7oJOrldShERGRDvv2z9K1NEZ2bQ5HSyOJoyEioqqUqNQIu5GB3y8k48DlFOQUlmgea2FtgqGdnDC0kzPaO5g3qov25dp88pUrV8LHxwcWFhawsLBAQEAA9u/fX+U+27dvh4eHB4yMjODt7Y19+/ZpM8RKGRso0LedPQDgYFSKJDEQEREREVUkI0+J8esisOLP0oLGC92aY+8bvVnQICKiWom+k4PQmDTIZcDUPm2kDoeIiCoghMCZm5mY99tl9FgcinFrI/DzmdvIKSyBg4UhJvdqhV0znsBfb/fF20Ee8HC0aFQFDUDLMzWaN2+OTz75BG3btoUQAhs3bsSwYcNw7tw5eHp6PjT+5MmTeOmll7B48WI888wz2Lp1K4YPH46zZ8/Cy8tLm6FWaKCXIw5EpeDA5RTMGdC+3l+fiIiIiOjfLtzKwrTNkUjOLoSJgQKfjPTBs524GDgREdXeyqOlszR0uc86EVFjdeNuHnadS8LO80m4lXlfs72ZiT4Ge5fOyPBzs4a8CbSh1WpRY+jQoeXuf/TRR1i5ciVOnTpVYVHj66+/xsCBA/H2228DAD788EMcOnQI33zzDVatWqXNUCv0lIc99BUyXEvLQ1xaHtztzeo9BiIiIiKiMj9FJGLeb1EoUqnRytYUq8f5op2DudRhERFRI5CQkY89F5MBANP6cpYGEVFDkJGnxJ6Ld7DjXBIu3MrSbDc1UCDIyxHPdnLGE+620FdotSFTg1Nva2qoVCps374d+fn5CAgIqHBMWFgYZs+eXW5bUFAQdu3aVQ8RPszSWB8929jir6t3cTAqBe727pLEQURERERNW2GxCgt2R+Gn07cAAP07OuCLFzrBwkhf4sgaPjc3NyQkJDy0ffr06VixYoUEERERNUyr/roBtQD6treDpzPXFSUikkphsQqHrqRi17kk/HX1LkrUAgCgkMvQu60tRnRxwYCOjjA2UEgcqXS0XtS4dOkSAgICUFhYCDMzM+zcuRMdO3ascGxKSgocHBzKbXNwcEBKSuVrWiiVSiiVSs39nJycugn8gYFejpqixoynWNQgIiIiovqVlHUf0zZH4uLtbMhkwP8NaI9pfdo0iWnldeH06dNQqVSa+5cvX0b//v0xatQoCaMiImpYUnMK8WvkbQDA9L6N59zHihUr8PnnnyMlJQWdOnXC8uXL4efnV+HYDRs2YNKkSeW2GRoaorCwsD5CJaImTq0WOBWfgZ1nk7D/cgrylP9b8NunuSWGd3bB0E7OsDM3lDDKhkPrRY327dvj/PnzyM7Oxi+//IIJEybgr7/+qrSwUVOLFy/GwoUL6+S5KtK/owPe3XkJF29nIynrPlysjLX2WkRERERE/3QiLh2v/3gOmflFsDLRx9eju6BPOzupw9Ipdnblj9cnn3yCNm3aoE+fPhJFRETU8Kw9Ho8ilRrd3ZrBr5W11OHUiW3btmH27NlYtWoV/P39sXTpUgQFBSE2Nhb29vYV7mNhYYHY2FjN/ca2sC4RNTw37uZhe+Rt7DqXhDvZ/yuiulgZY0QXFwzv4gx3e7ab/TetFzUMDAzg7l5a5ff19cXp06fx9ddfY/Xq1Q+NdXR0RGpqarltqampcHR0rPT5Q0JCyrWsysnJgaurax1FD9iaGaK7mzUi4jNx8HIKXunVqs6em4iIiIioIkIIrD52A58diIFaAJ7OFlj1si9crU2kDk2nFRUVYfPmzZg9ezZPVBERPZBVUITNp0rb9DWmWRpffvklgoODNbMvVq1ahb1792LdunWYO3duhfvIZLIqz0EREdWF3MJi7L14B9sjbyMy4Z5mu7mRHp7xccLwzi7o3kQW/H5c9bamRhm1Wl2uXdQ/BQQEIDQ0FG+99ZZm26FDhypdgwMonQpoaKjdaTcDPR0REZ+JA1EsahARERGRduUpS/D29gvYf7m0Bevzvs3x3+FeMNJvuj1z68quXbuQlZWFiRMnVjlO2y1uiYgako0nE1BQpEIHJwv0bd84ZgMWFRUhMjISISEhmm1yuRyBgYEICwurdL+8vDy0bNkSarUaXbt2xccffwxPT89KxzNfEFF1lbWX+uXMbey/nIL7xaXtUeUyoG97ezzv2xxPe9jzO381abWoERISgkGDBqFFixbIzc3F1q1bcfToURw8eBAAMH78eLi4uGDx4sUAgDfffBN9+vTBF198gSFDhuCnn37CmTNnsGbNGm2G+UhBXo5YtOcKTt/MxN1cJXuXEREREZFWxKXlYeoPZ3D9bj70FTLMH+qJsf4tOKugjqxduxaDBg2Cs7NzleO03eKWiKihyFOWYP3JeADA9L5tGk2+SU9Ph0qlqnDd1piYmAr3ad++PdatWwcfHx9kZ2djyZIl6NmzJ6KiotC8efMK92G+IKJHuX2vAL9GJuGXs7dwK/O+ZnsbO1OM6uaK57q4wN7CSMIIdZNWixppaWkYP3487ty5A0tLS/j4+ODgwYPo378/ACAxMRFyuVwzvmfPnti6dSvef/99vPvuu2jbti127doFLy8vbYb5SC5WxvBpbomLt7NxODoVL/m1kDQeIiIiImp8Dly+gzk/X0B+kQqOFkb49uWu6NqimdRhNRoJCQk4fPgwduzY8cix2m5xS0TUUPwQloCsgmK0tjXFYG8nqcORVEBAQLlOIT179kSHDh2wevVqfPjhhxXuw3xBRBW5X6TCwagUbI+8hZPXMyBE6XYzQz0M7eSEUd1c0cXVqtEUkqWg1aLG2rVrq3z86NGjD20bNWoURo0apaWIHl+QpyMu3s7GgcspLGoQERERUZ0pUamx5I+rWPXXdQCAfytrfDOmK2cH17H169fD3t4eQ4YMeeTY+mhxS0QktYKiEnz39w0AwMyn3aFoRL3bbW1toVAoarxu6z/p6+ujS5cuiIuLq3QM8wUR/dPlpGxsjUjE7+eTkass0Wzv2cYGo7o1x0BPJxgbsL1UXZA/eggBwECv0qR38no6cgqLJY6GiKhxWrFiBdzc3GBkZAR/f39ERERUOT4rKwszZsyAk5MTDA0N0a5dO+zbt6+eoiUiqr2MPCUmrI/QFDRe7dUKW171Z0GjjqnVaqxfvx4TJkyAnl69LytIRNQgbTmViMz8IrS0McGznapuy6drDAwM4Ovri9DQUM02tVqN0NDQKtdt/SeVSoVLly7Byalpz2AhoqrlKUuwNTwRQ5cfxzPLj2NreCJylSVo3swYbwW2xd//eQpbg3tgRJfmLGjUIX6jr6Y2dmZoa2+Ga2l5+DMmDcM6u0gdEhFRo7Jt2zbMnj0bq1atgr+/P5YuXYqgoCDExsbC3t7+ofFFRUXo378/7O3t8csvv8DFxQUJCQmwsrKq/+CJiB5D9J0cvLrxDJKy7sPEQIFPR/pgaCM7qdRQHD58GImJiXjllVekDoWIqEG4X6TC6mOlszRmPOUOPUXju+Z19uzZmDBhArp16wY/Pz8sXboU+fn5mDRpEoCH13ldtGgRevToAXd3d2RlZeHzzz9HQkICXn31VSnfBhE1QEIIXErKxo8RifjtfDIKikoX/TZQyDHQyxGju7uiR2sbyBvRDLiGhkWNGhjo5YhrR+Jw4HIKixpERHXsyy+/RHBwsOZHxqpVq7B3716sW7cOc+fOfWj8unXrkJmZiZMnT0JfXx8A4ObmVp8hExE9tr+u3sWMLWeRpyxBK1tTrHrZF+0dzaUOq9EaMGAARFkzYyIiwo8RiUjPU6J5M2OM6NI4z2+8+OKLuHv3LubNm4eUlBR07twZBw4c0Cwe/u91Xu/du4fg4GCkpKSgWbNm8PX1xcmTJ9GxY0ep3gIRNTC5hcX47XwyfoxIRFRyjmZ7aztTjPFrgee6Noe1qYGEETYdLGrUQJCnI5YficPR2Lu4X6TilCEiojpSVFSEyMhIhISEaLbJ5XIEBgYiLCyswn12796NgIAAzJgxA7/99hvs7OwwZswYvPPOO1AoKv58ViqVUCqVmvs5OTkVjiMi0qat4Yn44LfLUKkFerS2xuqXu8HSRF/qsIiIqIkoLFZp2h7OeMod+o1wlkaZmTNnYubMmRU+9u91Xr/66it89dVX9RAVEekSIQQu3M7Gj+GJ2H0hGfeLH8zK0JNjsJcjXvJrAb9W1lz0u56xqFEDns4WcLEyRlLWfRy7dhdBntVbXIqIiKqWnp4OlUqluWqqjIODA2JiYirc58aNGzhy5AjGjh2Lffv2IS4uDtOnT0dxcTHmz59f4T6LFy/GwoUL6zx+IqLqUKsFPj0Yg9V/lbb7eK6rCz55zgcGeo33ZBIRETU8P5+5hbRcJZwtjTCya3OpwyEiapDylSXYdT4Jm08lIvrO/y6IdLc3w0t+LfBcFxc046wMybCoUQMymQwDvRyx9ng8Dl5OYVGDiEhCarUa9vb2WLNmDRQKBXx9fZGUlITPP/+80qJGSEgIZs+erbmfk5MDV1fX+gqZiJqwwmIV5vx8AXsv3QEAzApshzf6ufOKLiIiqlfKEhVWHi2dpTHtKXcW1omI/uX63Tz8EJaAXyNvI1dZAqB0VsYz3k54yb8FurVsxu/wDQCLGjVUVtQ4HJ2KohI1vwAQEdUBW1tbKBQKpKamltuempoKR8eKC8hOTk7Q19cv12qqQ4cOSElJQeP++JcAAKLPSURBVFFREQwMHr5iwtDQEIaGhnUbPBHRI2TkKRG86QzOJmZBXyHDZ8/7YEQXXhlLRET1b/uZ27iTXQhHCyO80I25iIgIAEpUaoTGpOGHsAQcj0vXbHezMcHLPVried/msDLhrIyGhEWNGuraohlszQyRnqfEqRsZeLKdndQhERHpPAMDA/j6+iI0NBTDhw8HUDoTIzQ0tNIeuE888QS2bt0KtVqtWeDv6tWrcHJyqrCgQUQkhbi0PLyy4TQSMwtgafz/7N1neFR1+v/x96T3hEAKgdBLqCEQCKGJgtJEsCAgSpGiCALiWlhd6yrqooKNjoCCICqoCCii9CSQQCCU0CEBUiAhndSZ/wN2819+C0hJclI+r+s6D+bknMxnrgsmM+c+3/u2Ze4T7ejYoLrRsUREpArKLzQXr9J4+q4G2NtoTqiIVG0pWXms2B3P8og4zqVdBsBkgh4B3jwRWo+ujWpgZaVVGeWRihq3yNrKxH0tfFgeEceGg4kqaoiIlJCpU6cyYsQIgoOD6dChAzNnziQ7O5tRo0YBMHz4cGrVqsX06dMBGD9+PJ999hmTJ0/m2Wef5dixY7z77rtMmjTJyJchIlIs/GQKT30VRfrlAup4OrFoZHsaebsYHUtERKqoH/ac5VzaZbxc7RnSoY7RcUREDGGxWNgbn8ZXYWf4ZX8C+UVmAKo52TK4fR2GhdTB39PJ4JTyV1TUuA29W/iyPCKO3w4m8faAllirYicicscGDx7MhQsXeO2110hMTKRNmzZs2LCheHh4XFxc8YoMAH9/f3799Veee+45WrduTa1atZg8eTIvvfSSUS9BRKTY6r1nefG7/RQUWQiq48GC4cFUd1H7OxERMUZBkZnPNx8H4KluDXCw1SoNEala8gvNrItJYNGOU+w/m168P9Dfg+Ed69KvdU29N1YgKmrcho4NquPmYMPFrDz2xF2ifT1PoyOJiFQKEydOvG67qc2bN//PvtDQUMLDw0s5lYjIzbNYLHyy6Tgf/34UgL6tfPno0Tb6giQiIob6Luos8amXqeFix7CQukbHEREpM6nZ+SyPOMPSsDMkZ+YBVwZ/PxDox/DQurSu7WFsQLktKmrcBjsbK3o28+GHvefYcCBRRQ0RERERIb/QzLQfYvh+z1kAnrqrAS/1ClAfXhERMVReYRGfbjoGwNN3NcTRToV2Ean8jiVlsmjHKX7Yc468wistprxd7RkeWpfHQuri6axZnBWZihq3qVdL3+Kixqv9mmEy6cuqiIiISFWVnlPAU19HEn4yFWsrE28PaMljIepXLiIixvt2dzzn03PxcbPn8Y5apSEilZfZbGHrsQss3H6KbccuFu9vWcuN0V3q06+VH3Y2Vjf4DVJRqKhxm7o19sLR1ppzaZc5eD6DlrXcjY4kIiIiIgaIT81h5Je7OHEhGxd7Gz4f1pa7mngZHUtERITcgiI++/PKLI0JdzdSO0QRqZQu5xfxw96zLNp+ihMXsgGwMsF9zX15skt92terphvSKxkVNW6To5013Zt6sf5AIhsOJKqoISIiIlIF7Y27xJglkaRk51PT3YFFI9vTrKab0bFEREQAWBYRR1JGHn7uDgxu7290HBGREnUxK4+lO0+zNPwMaTkFALjY2zC4vT8jO9XD39PJ4IRSWlTUuAO9W/qy/kAivx5M5G+9mhodR0RERETK0KbDSTyzbA95hWZa+LmxaGR7fNwcjI4lIiICQE5+IbM3X1ml8WyPxtjbaJWGiFQOZ1Kymb/tJKsizxbPy/D3dGRUp/oMCq6Nq4OtwQmltKmocQfuDvDG1trEseQsjidn0cjbxehIIiIiIlIGfow+x/Pf7qPQbOGeAG8+HRqEs70+WouISPmxNOwMF7PyqePpxCPtahsdR0Tkju0/m8bcLSdZfyABs+XKvsDa7jx1V0N6tfDF2kotpqoKffO6A24OtnRuVIPNRy7w68FEGnk3MjqSiIiIiJSy5RFxvLImBosFHgyqxb8eaY2NtQYOiohI+ZGVV8jcLScAmNSjMbb6OyUiFZTFYmHrsYvM3XKCnSdSivff3dSLp+5qSEh9T83LqIJU1LhDvVv4Fhc1JtytooaIiIhIZTZv6wneXRcLwOMd6/DWAy2x0h1hIiJSzny5/RSXcgpoUMOZgW38jI4jInLLCorM/LI/gTlbThCbmAmAjZWJB9r4Ma5bAwJ8NceuKlNR4w71bO6D1eoY9p9N51zaZWp5OBodSURERERKmMVi4aONR/n0jyu9ycd3b8iLvZrqrjARESl30i8XMH/bSQAm92ys1YQiUqHkFhSxKjKeOVtOci7tMgBOdtYM7VCHJ7vU17VXAVTUuGM1XOwJrufJrlOp/HogkSe71Dc6koiIiIiUILPZwltrD7F452kAXuzdlGe6a4WuiIiUTwu3nSQjt5AmPi70b61VGiJSMeTkF7I8Io55W0+SnJkHQA0XO0Z1rs/jIXVxd9Lwb/n/VNQoAb1b+LLrVCobDqqoISIiIlKZFBaZefmHGL6LOgvA2wNa8ERoPWNDiYiIXMel7HwW7TgNwHM9m6hFooiUe5m5BSwNO8PC7adIzc4HwM/dgae7N+TRYH8cbK0NTijlkYoaJaBXS1/eWnuI3adTuZCZh5ervdGRREREROQO5RUWMWVFNOsPJGJtZWLGoNY8GFTb6FgiIiLX9cXm42TlFdK8phu9WvgaHUdE5LrScq4UYRfvOEVGbiEAdTydmHB3Qx4Mqo2djVrnyfWpqFECank40rq2O/vPpvP74SSGdqhjdCQRERERuQOX84t46usoth69gJ21FZ8+FqSLQyIiUq6dT7vMkrAzwJVWiVqlISLl0YXMPBZsP8nXYWfIzi8CoKGXMxPvaUT/1n6aAyQ3RUWNEtKrhS/7z6az4UCiihoiIiIiFVhGbgGjF+9m9+lLONpaM294O7o29jI6loiIyA3N+v0Y+YVmQup7clcT/d0SkfIlJSuPOVtO8FX4GXILzAA0q+nGs/c0oncLXxVi5ZaoqFFCerf05V+/HmHniYukXy7A3VHDa0REREQqmtTsfIYviuDAuQxcHWxYPKo97ep6Gh1LRETkho4nZ7EqKh6AF3sHYDLp4qCIlA+XsvOZt+0kS3aeJuffKzMC/T2YdE8j7gnw1vuV3BYVNUpIQy8XGnu7cCw5iz9jkxkYVMvoSCIiIiJyCxLTc3l8YQTHk7Oo7mzH0tEdaOHnbnQsERGRv/Thb0cwW+De5j60q1vN6DgiIqRfLmDhtpMs2nGarLwrMzNa13bnuXub0L2Jl4oZckdU1ChBvVv6cuyP46w/kKCihoiIiEgFEpeSw7CF4cSnXqamuwNfjQ6hkbeL0bFERET+0r74NNYfSMRkghd6NTU6johUcZm5BXy54zTzt50k898DwJvVdGPqvU3o2UwrM6RkqKhRgnq39OXTP47z26Ekdh6/SKdGNYyOJCIiIiJ/4WhSJo8viCA5M4961Z34ekwItas5GR1LRETkpnzwaywADwXVpomPq8FpRKSqys4rZPHOK8WMtJwCAJr4uPBczyb00swMKWEqapSgFn7uPBpcm28jzzJpRTTrJnfB29XB6FgiIiIich37z6YxYtEuLuUU0NTHla/GdNDnNxERqTC2H7vIjuMp2FlbMaVnY6PjiEgVlFdYxLLwOD7/8zgp2fkANPByZkrPJtzfqqaKGVIqVNQoYW8+0JLo+DSOJmXx3Mpolj4ZgrX+84qIiIiUO7tPpzLqy91k5RUS6O/BklHt8XCyMzqWiIjITTGbLby/4coqjWEd6+DvqVWGIlJ2iswWfow+x0cbj3L20mUA6lV3YnLPxjwQWEvXQ6VUqahRwhztrPliWFv6f7qDHcdT+OyP40zW3RIiIiIi5UrUmUuMXLSL7PwiOjbwZMGI9rjY66OxiIhUHOsPJBJzLh1nO2sm3N3I6DgiUkVYLBY2H7nA+xtiiU3MBMDb1Z7JPRvzaLA/ttZWBieUqkDf3EpBI29X/jmwJc+v2sfMTUdpX6+a5muIiIiIlBPR8WnFBY3OjaqzcER7HGytjY4lIiJy0wqKzMz47QgAY7s1oIaLvcGJRKQq2BN3iffWx7LrVCoArg42jO/ekFGd6uNop8/TUnZU1CglD7erTcSpFM3XEBERESlHDpxLZ/jCCDLzCgmp78mC4SpoiIhIxfNtZDynLmbj6WzHmK4NjI4jIpXc8eRMPthwhN8OJQFgZ2PFyE71eKZ7Q7VvFUOoqFGKNF9DREREpPw4dD6DxxdGkJFbSHDdaiwa2V53lImISIWTlVfIxxuPAfDsPY3UPlFESs2FzDw+2niUlbvjMFvAygSPtKvNlJ5N8PNwNDqeVGH6y1eKNF9DREREpHw4kpjJ4wsjSMspIKiOB1+Oao+zLgKJiEgFNG/rSS5m5VGvuhPDQuoaHUdEKqHcgiIW7TjFF3+eICuvEIB7m/vwYq+mNPZxNTidiIoapU7zNURERESMdTw5k2ELwknNzqd1bXeWPNkBVwdbo2OJiIjcsqSMXOZvPQnAS70DsLPRQF4RKTkWi4Wf9yfw/vpYzqVdBqB1bXde7decDvU9DU4n8v+pqFEGNF9DRERExBgnL2QxdH4EF7PyaeHnxldPhuCmgoaIiFRQH/12lMsFRbSrW43eLX2NjiMilUjUmUv885dD7I1LA6CmuwMv9m7KgMBaWKmdvpQzKmqUEc3XEBERESlbZ1KyeWx+BBcy8wjwdeXr0SG4O6mgISIiFdORxExWRcUD8Pe+AZhMuqYgIncuPjWH9zfEsnZ/AgBOdtaMv6shY7o20Pw5Kbe0TrGM/Ge+hqOtdfF8DRERudrnn39OvXr1cHBwICQkhF27dt3UeStWrMBkMjFw4MDSDSgiFUZ8ag5D54WTmJFLEx8Xlo0JoZqzndGxREREbtv09YcxW6BvK1/a1VUbGBG5Mzn5hfzr11h6fLSFtfsTMJlgcLA/m//WnWd7NFZBQ8o1FTXK0H/mawDM3HSUnccvGpxIRKT8WLlyJVOnTuX1119nz549BAYG0qtXL5KTk2943unTp/nb3/5G165dyyipiJR359IuM3R+OOfTc2no5cyyMR2p7mJvdCwx0Llz53j88cepXr06jo6OtGrVisjISKNjiYjctB3HL7L5yAVsrU282CvA6DgiUoFZLBbW7j9Pjw+38PmfJ8gvNBPaoDprn+3C+4+0xttNLfOl/FNRo4w93K42jwbXxmKBSSuiSc7MNTqSiEi58NFHHzF27FhGjRpF8+bNmTNnDk5OTixatOi65xQVFTFs2DDefPNNGjRoUIZpRaS8SkzP5bH54Zy9dJn6NZxZPrYjXq4qaFRlly5donPnztja2rJ+/XoOHTrEhx9+SLVq1YyOJiJyU8xmC+/8chiAYSF1qVfD2eBEIlJRHU3K5LH5EUxcvpeE9FxqV3Nk7hPtWD42hBZ+7kbHE7lpmqlhgP+erzFlRTRfjdZ8DRGp2vLz84mKimLatGnF+6ysrOjZsydhYWHXPe+tt97C29ub0aNHs23btrKIKiLlWHJGLkPnh3MmJYc6nk4sHxuCj+40q/Lef/99/P39+fLLL4v31a9f38BEIiK3ZvXecxxKyMDVwYZJPRobHUdEKqCM3AJm/X6MxTtPU2S2YG9jxfjuDXn6roY42KrNlFQ8WqlhgP+er7HzRAqf/nHM6EgiIoa6ePEiRUVF+Pj4XLXfx8eHxMTEa56zfft2Fi5cyPz582/6efLy8sjIyLhqE5HK4UJmHkPnh3PqYja1PBxZPjaEmu6ORseScuCnn34iODiYQYMG4e3tTVBQ0C397RARMVJuQREf/nYEgAl3N8JT86Fuya3O7Fu1ahUBAQE4ODjQqlUr1q1bV0ZJRUqH2Wzhu6iz3DNjCwu3n6LIbKFXCx9+n3oXU3o2UUFDKiwVNQzy3/M1Zm06pvkaIiK3IDMzkyeeeIL58+dTo0aNmz5v+vTpuLu7F2/+/v6lmFJEykpKVh7DFoRz4kI2fu4OrBjXkdrVnIyOJeXEyZMnmT17No0bN+bXX39l/PjxTJo0iSVLllz3HBXBRaS8WLj9FOfTc6nl4cjITvWMjlOh3OrMvp07dzJ06FBGjx7N3r17GThwIAMHDuTAgQNlnFykZBxOyOCROTv526p9XMzKo0ENZ5Y82YG5TwTj76nPylKxlWpRY/r06bRv3x5XV1e8vb0ZOHAgR44cueE5ixcvxmQyXbU5OFTOtgGaryEickWNGjWwtrYmKSnpqv1JSUn4+vr+z/EnTpzg9OnT9O/fHxsbG2xsbFi6dCk//fQTNjY2nDhx4prPM23aNNLT04u3+Pj4Unk9IlJ20nLyeXzhLo4mZeHjZs/ysR31JU2uYjabadu2Le+++y5BQUGMGzeOsWPHMmfOnOueoyK4iJQHyRm5fP7ncQBe6NVUd1Tfolud2Tdr1ix69+7NCy+8QLNmzXj77bdp27Ytn332WRknF7kzOfmFTF93mPs/3c6euDSc7Kx5uU8AG6Z0464mXkbHEykRpVrU2LJlCxMmTCA8PJyNGzdSUFDAfffdR3Z29g3Pc3NzIyEhoXg7c+ZMacY01JsPtKSJjwsXs/KYsiKaIrPF6EgiImXOzs6Odu3asWnTpuJ9ZrOZTZs2ERoa+j/HBwQEEBMTQ3R0dPH2wAMPcPfddxMdHX3di0/29va4ubldtYlIxZWeU8DjCyM4nJCBl+uVgoaGp8r/VbNmTZo3b37VvmbNmhEXF3fdc1QEF5Hy4INfj5CTX0RQHQ8GtPEzOk6F8p+ZfT179ize91cz+8LCwq46HqBXr143nPEnUt78GZvMvR9tZe7WkxSZLfRp6csfz3fn6bsaYmejhj1SeZTqoPANGzZc9Xjx4sV4e3sTFRVFt27drnueyWS65p25ldF/5mv0/3RH8XyNKT2bGB1LRKTMTZ06lREjRhAcHEyHDh2YOXMm2dnZjBo1CoDhw4dTq1Ytpk+fjoODAy1btrzqfA8PD4D/2S8ilVNOfiEjF+/iwLkMqjvbsXxMCA29XIyOJeVQ586d/2e1+NGjR6lbt+51z7G3t8fe3r60o4mIXNf+s2l8F3UWgNfub47JZDI4UcVyo5l9sbGx1zwnMTHxlmb8wZV2hXl5ecWP1a5QjJKUkctbPx/il5gEAGp5OPLWgBb0aObzF2eKVExlWqJLT08HwNPT84bHZWVlUbduXfz9/RkwYAAHDx4si3iG0XwNEREYPHgwM2bM4LXXXqNNmzZER0ezYcOG4i8WcXFxJCQkGJxSRMqDgiIz47/ew964NNwdbVk2NoTGPq5Gx5Jy6rnnniM8PJx3332X48ePs3z5cubNm8eECROMjiYick0Wi4W3fj4EwINBtQiqU83gRHI9alcoRisyW1gadpqeH27hl5gErK1MjOvWgI1Tu6mgIZVaqa7U+G9ms5kpU6bQuXPnG95F27RpUxYtWkTr1q1JT09nxowZdOrUiYMHD1K7du3/Ob6yVMUfblebiFMpfBt5lkkr9vLd053UPkFEqpyJEycyceLEa/5s8+bNNzx38eLFJR9IRMods9nC31btY8vRCzjaWrNoZHsCfNVKTq6vffv2rF69mmnTpvHWW29Rv359Zs6cybBhw4yOJiJyTWv3JxB55hKOtta81DvA6DgV0q3O7APw9fW9pePhSrvCqVOnFj/OyMhQYUPKzOGEDF7+IYZ98WkAtPH34N0HW9HcT5+NpfIrs5UaEyZM4MCBA6xYseKGx4WGhjJ8+HDatGnDXXfdxQ8//ICXlxdz58695vGVqSr+5gMtaVbTjYtZ+QxbEMG5tMtGRxIREREpNywWC2+tPcSP0eexsTIx+/G2tKuru1flr91///3ExMSQm5vL4cOHGTt2rNGRRESuKbegiPfWX2mPNL57Q3zdHQxOVDHd6sw+uHI96r+PB9i4ceN1jwfN7BNj5Bea+WjjUfp/up198Wm42tvw9oAWfD++kwoaUmWUSVFj4sSJrF27lj///POaqy1uxNbWlqCgII4fP37Nn1emIX6OdtYsfbIDDWo4cy7tMo8viCA5M9foWCIiIiLlwhebT7B452kAPnw0kO5NvY0NJCIiUsLmbT3JubTL+Lk7MLZrA6PjVGhTp05l/vz5LFmyhMOHDzN+/Pj/mdk3bdq04uMnT57Mhg0b+PDDD4mNjeWNN94gMjLyuivJRYywLz6N/p9u55NNxyg0W+jVwoffn7+LJ0LrYW2l2TtSdZRq+ymLxcKzzz7L6tWr2bx5M/Xr17/l31FUVERMTAx9+/a95s8r2xA/L1d7vh4TwqA5YZy6mM3whbtYMa4jHk52RkcTERERMcw3u+L4169Xhj2/3r85A9rUMjiRiIhIyUpMz2X25hMAvNy3GY521gYnqtgGDx7MhQsXeO2110hMTKRNmzb/M7PPyur/3+vbqVMnli9fzquvvsrf//53GjduzJo1a27YQl2krOQWFPHx70eZv/UkZgtUd7bjrQEt6de6ptHRRAxRqkWNCRMmsHz5cn788UdcXV1JTEwEwN3dHUdHR+BKZbxWrVpMnz4dgLfeeouOHTvSqFEj0tLS+Ne//sWZM2cYM2ZMaUYtV/w8HFk2JoRBc8OITcxkxKJdfD0mBFcHW6OjiYiIiJS5DQcSeGV1DAAT727EqM63fqOMiIhIeff+hlguFxQRXLca/XWhskTc6sy+QYMGMWjQoFJOJXJrIk+n8uJ3+zl5MRuAAW38eL1/CzyddQO0VF2l2n5q9uzZpKen0717d2rWrFm8rVy5sviYuLg4EhISih9funSJsWPH0qxZM/r27UtGRgY7d+6kefPmpRm13KlXw5llY0Ko5mTLvrPpjF4SyeX8IqNjiYiIiJSpnScuMumbaMwWGNrBn+fva2J0JBERkRK3J+4Sq/eeA+C1/s0xmdRGRqSqy8kv5I2fDjJobhgnL2bj7WrP/OHBzBoSpIKGVHml3n7qr/zfyvjHH3/Mxx9/XEqJKpYmPq58NTqEofPC2XUqlae+jmL+8HbY22gJqoiIiFR+B86lM25pFPlFZnq38OWfA1vpIo+IiFQ6ZrOFN38+BMDDbWvTuraHsYFExHB74y4x9dt9nPr36oxHg2vzSr/muDuqi4sIlNGgcLl9LWu58+Wo9jjaWrP16AUmfbOXwiKz0bFEREREStWpi9mMWLSLrLxCOjbwZOaQNhp+KCIildKqqHj2xafhYm/DS72bGh1HRAxUUGTmo9+O8Mi/Z+36ujmw5MkOfPBIoAoaIv9FRY0KILieJ/OHB2NnbcWvB5N48bv9mM1/vQpGREREpCJKysjliYURpGTn08LPjfnDg3Gw1UpVERGpfNJy8nl/wxEApvRsjLebg8GJRMQox5MzeeiLnXzyx3GKzBYGtPHj1ynduKuJl9HRRModFTUqiC6Na/D5sLZYW5n4Ye85/vHjgZtq7yUiIiJSkaTnFDBi0S7OXrpMvepOLB7VAVcH3ZUmIiKV00cbj5KanU9jbxdGdKpndBwRMYDZbGHR9lP0+2Q7MefScXe05dOhQcwaEoS7kz4Hi1yLihoVyL3Nffjo0UBMJlgWEcf09bEqbIiIiEilcTm/iNFLdhObmImXqz1fjQ7By9Xe6FgiIiKl4uD5dL4OPwPAmw+0wNZal2hEqprzaZd5YlEEb609RF6hmW5NvPjtuW70D/QzOppIuVaqg8Kl5A1oU4vcgiJe+j6GeVtP4mxnw+SejY2OJSIiInJHCorMTFy+h8gzl3B1sGHpkx3w93QyOpaIiEipsFgsvPbjQcwW6Ne6Jp0a1TA6koiUsXUxCbz8/X4ycgtxtLXm7/2a8XhIHUwmzZET+SsqalRAg9vXITuviLfWHuLj34/ibG/NmK4NjI4lIiIiclssFgsvfx/Dpthk7G2sWDiiPc1quhkdS0REpNT8sOccUWcu4Whrzav9mhkdR0TK0OX8K9f0vtkVB0CgvwcfPxpIAy8Xg5OJVBwqalRQT3apT3ZeIR9uPMo/fzmMk50Nj4XUMTqWiIiIyC2bvj6W7/ecxdrKxOePtaVDfU+jI4mIiJSajNwCpq+PBeDZHo2o6e5ocCIRKSuHEzJ49pu9HE/OwmSC8Xc15Ll7m6j9nMgtUlGjApt4TyOy84uYs+UEr6yJwcnOmoFBtYyOJSIiInLT5m45wbytJwF476FW9GzuY3AiERGR0jVz4zEuZuXRoIYzo7vUNzqOiJQBi8XCV+Fn+Ocvh8kvNOPtas/Hg9vQWa3nRG6LihoVmMlk4qXeTcnJL2Rp2BmeX7UPRztrerXwNTqaiIiIyF9aFRlffKfqtD4BDAr2NziRiIhI6TqSmMmSsNMAvP5AC+xtrI0NJCKl7lJ2Pi9+v5+Nh5IAuLupFzMGBVLdxd7gZCIVl9Y2VXAmk4k3+rfg4ba1KTJbeHb5XrYdu2B0LBEREZEb2nr0Ai//EAPAuG4NeOquhgYnEhERKV1XhoMfoMhsoVcLH+5q4mV0JBEpZXviLtH3k21sPJSEnbUVr93fnEUj26ugIXKHVNSoBKysTLz/cCv6tvIlv8jM2KWRbD6SbHQsERERkWuKTczgmWV7KDJbGNjGj2l9AoyOJCIiUup+2HOOiFOpONha8Wq/5kbHEZFSZLFY+HLHKR6dE0ZCei4NajjzwzOdeLJLfUwmk9HxRCo8FTUqCRtrK2YODuKeAG9yC8yMWRLJj9HnjI4lIiIicpWkjFxGfbmbrLxCOtT35P1HWuuLnYiIVHqXsvN5Z91hACb3aIK/p5PBiUSktGTmFjBx+V7e/PkQhWYL/VrX5Kdnu9CylrvR0UQqDRU1KhE7GyvmPN6OBwL9KDRbmLwimi93nDI6loiIiAgA2XmFPLl495W71bycmfdEO/USFxGRKuH9DbGkZufTxMeFMV01HFyksopNzGDAZzv4JSYBW2sTb/RvzmdDg3Cx11hjkZKk/1GVjJ2NFTMHt8HT2Y7FO0/z5s+HSMnK5/n7muguSBERETFMYZGZZ7/Zy8HzGVR3tmPxyA54ONkZHUtERKTU7T6dyord8QC882ArbK11f6lIZfR91FleWRNDboEZP3cHPhvWlrZ1qhkdS6RSUlGjErKyMvF6/+Z4udrzr1+P8Nmfx0nJzuOfA1thbaXChoiIiJQti8XCGz8f5I/YZOxtrFgwIpg61dV2Q0REKr+CIjOvrI4BYHCwP+3reRqcSERKWl5hEW/8dIhvdsUB0LVxDWYNCcLTWTfwiJQWFTUqKZPJxIS7G+HpbMcrq2P4Zlc8l7ILmDmkDQ62avMgIiIiZWfBtlN8HR6HyQSzhrQhSHesiYhIFbFg2ymOJmXh6WzHy30CjI4jIiUsOTOX8V/vIerMJUwmmNKjCRPvaaSbikVKmdY8VnJDO9Thi2FtsbO2YsPBREZ+uYuM3AKjY4mIiEgVsT4moXgw6it9m9G7ZU2DE4mIiJSN+NQcZm06CsDf+zajmu7aFqlU9sWn8cCnO4g6cwlXBxu+HNmeyT0bq6AhUgZU1KgCeresyeIn2+Nib0P4yVSGzA3nQmae0bFERESkktsTd4kpK6MBGB5al9FdNBhVRESqBovFwus/HSS3wExIfU8eblvL6EgiUoK+jzrLoLlhJGbk0sjbhZ8mdqF7U2+jY4lUGSpqVBGdGtZgxbiO1HCx41BCBo/M2UlcSo7RsURERKSSOpOSzdglkeQVmukR4M1r9zfHZNJdayIiUjX8ejCRP2KTsbU28c6DrfQ3UKSSKCwy89bPh3h+1T7yC830bObN6mc6Ub+Gs9HRRKoUFTWqkJa13Pnu6U74ezpyJiWHh+fs5ND5DKNjiYiISCWTlpPPqMW7ScnOp4WfG58MDcLGWh87RUSkasjILeCNnw4B8PRdDWnk7WJwIhEpCZey8xnx5S4W7TgFwKR7GjHviWBcHWwNTiZS9ejbZRVTr4Yz3z/diQBfVy5k5jF4bhgRJ1OMjiUiIiKVRF5hEeO+iuLkhWz83B1YNLI9zvY2RscSEREpMx9siCUxI5e61Z2YcHcjo+OISAk4cSGLgV/sYMfxFJzsrJnzeFum3tcUK83PEDGEihpVkLebAyufCqVDPU8y8wp5YtEufjuYaHQsERERqeAsFgsvfrefXadScbW3YdGo9vi4ORgdS0REpMzsOpXK1+FxAEx/qBUOttYGJxKRO7XzxEUe/HwHZ1JyqF3NkR+e6UTvljWNjiVSpamoUUW5O9qydHQHejbzIb/QzNNfR/Ht7nijY4mIiEgF9tHGo/wYfR4bKxNfPN6WAF83oyOJiIiUmdyCIl7+YT8AQ9r706lhDYMTicid+jYynuELd5GRW0jbOh6smdBZn3FFygEVNaowB9sry+UGtauN2QIvfr+f2ZtPYLFYjI4mIlXU559/Tr169XBwcCAkJIRdu3Zd99j58+fTtWtXqlWrRrVq1ejZs+cNjxeR0vVtZDyf/nEcgHcebEnXxl4GJxIRESlbn/1xnJMXsvFytWdan2ZGxxGRO2A2W/hgQywvfrefQrOF/oF+LB/bkRou9kZHExFU1KjybKyt+OCR1jx9V0MA3t8Qyz9/OYzZrMKGiJStlStXMnXqVF5//XX27NlDYGAgvXr1Ijk5+ZrHb968maFDh/Lnn38SFhaGv78/9913H+fOnSvj5CKy/dhF/v5DDAAT727E4PZ1DE4kIiJStg4nZDBnywkA3h7QAncnDQ4WqahyC4p49pu9fLH5yv/pSfc0YtbgNmonJ1KOqKghmEwmXu4TwKv9rtxJsnD7KSYs30NOfqHByUSkKvnoo48YO3Yso0aNonnz5syZMwcnJycWLVp0zeOXLVvGM888Q5s2bQgICGDBggWYzWY2bdpUxslFqrYjiZmM/zqKQrOFAW38eP6+JkZHEhERKVNFZgsvfX/lbu7eLXzVa1+kAkvNzmfo/HB+iUnA1trEh4MCNRBcpBxSUUOKjenagI8HB2JrbWL9gUQGzQnjfNplo2OJSBWQn59PVFQUPXv2LN5nZWVFz549CQsLu6nfkZOTQ0FBAZ6enqUVU0T+j+SMXEZ9uYvMvEI61PPkg0daYzLpC5+IiFQtX+44xf6z6bg62PDmgBZGxxGR2xSfmsMjc3ayNy4Nd0dbvhodwsPtahsdS0SuQUUNucqDQbX5ZmxHqjvbcfB8Bg98toO9cZeMjiUildzFixcpKirCx8fnqv0+Pj4kJibe1O946aWX8PPzu6ow8n/l5eWRkZFx1SYitycnv5DRSyI5n55LgxrOzH2iHfY2WpIvIiJVS1xKDjN+OwLAK32b4ePmYHAiEbkdhxMyeHj2Tk5eyKaWhyPfjw+lY4PqRscSketQUUP+R3A9T9ZM6EyArysXs/IYPC+c1XvPGh1LROS63nvvPVasWMHq1atxcLj+F8np06fj7u5evPn7+5dhSpHKw2y28Py3+4g5l46nsx1fjmpPNWc7o2OJiIiUKYvFwitrYsgtMNOxgSeD2+uzpUhFFHYihUfnhJGcmUdTH1e+H9+JRt6uRscSkRtQUUOuyd/Tie/Hd+Le5j7kF5p5buU+3t8QqwHiIlIqatSogbW1NUlJSVftT0pKwtfX94bnzpgxg/fee4/ffvuN1q1b3/DYadOmkZ6eXrzFx8ffcXaRqmjm70dZfyARW2sTc59oR93qzkZHEhERKXPfRsaz7dhF7G2smP6QWjCKVETrYhIYsejf7VTre/Lt06H4umvFlUh5p6KGXJezvQ1zH2/H+O4NAZi9+QRPfR1Fdp4GiItIybKzs6Ndu3ZXDfn+z9Dv0NDQ6573wQcf8Pbbb7NhwwaCg4P/8nns7e1xc3O7ahORW/Nj9Dk++eM4AO8+2Ir29TTHRkREqp5zaZd5e+1hAJ6/rwn1a6jAL1LRLA07zYTle8gvMtO7hS9Ln+yAu6Ot0bFE5CaoqCE3ZGVl4qXeAXw8OBA7Gys2Hkri4dk7OXspx+hoIlLJTJ06lfnz57NkyRIOHz7M+PHjyc7OZtSoUQAMHz6cadOmFR///vvv849//INFixZRr149EhMTSUxMJCsry6iXIFLpRcen8cJ3+wF4qlsDBgWrzYaIiFQ9FouFl7/fT1ZeIe3qVmN0lwZGRxKRW2CxWPh00zFe+/EgFgsMC6nD58Pa4mCr+XAiFYWKGnJTHgyqzYpxHanhYk9sYiYDPttB5OlUo2OJSCUyePBgZsyYwWuvvUabNm2Ijo5mw4YNxcPD4+LiSEhIKD5+9uzZ5Ofn88gjj1CzZs3ibcaMGUa9BJFKLSH9MmOXRpJfaKZHgDcv9g4wOpLITXnjjTcwmUxXbQEB+vcrIrdvxe7/33bqX4+0xtpKbadEKgqLxcIHvx7hw41HAZjcozH/HNhS/49FKhgbowNIxdG2TjV+mtiZMUsiOZSQwdD54bz7YCvdpSkiJWbixIlMnDjxmj/bvHnzVY9Pnz5d+oFEBICc/ELGLInkwr+HJ84aGqQvflKhtGjRgt9//734sY2NvgaJyO05eymHf649BMALvZrSwMvF4EQicrPMZgtv/nyQJWFnAHilbzPGdtNKK5GKSCs15Jb4eTjy3fhQerfwpaDIwgvf7efddYcp0gBxERGRSslstvD8t/s4eD6D6s52LBgRjIu9LghLxWJjY4Ovr2/xVqNGDaMjiUgFZLFYeOn7/WTnFxFctxqjOtc3OpKI3KQi85X/v0vCzmAywTsPtlRBQ6QCU1FDbpmTnQ1fDGvLpHsaATBv60nGLY0kM7fA4GQiIiJS0mb+fpT1BxKxs7ZizhPt8Pd0MjqSyC07duwYfn5+NGjQgGHDhhEXF3fD4/Py8sjIyLhqExFZFhHHjuMpONha8a9BgVq1KFJBFBSZmbxiL6uizmJlgg8HBTIspK7RsUTkDqioIbfFysrE1Pua8snQIOxtrNgUm8zDs3cSl6IB4iIiIpXFj9Hn+OSP4wC8+1Ar2tfzNDiRyK0LCQlh8eLFbNiwgdmzZ3Pq1Cm6du1KZmbmdc+ZPn067u7uxZu/v9qtilR18ak5TF93GIAXewVQv4azwYlE5GbkFhQx/uso1u5PwNbaxOePteWhtrWNjiUid0hFDbkjDwT68e1ToXi72nM0KYsBn28n4mSK0bFERETkDu2Nu8QL3+0H4KluDXiknb78ScXUp08fBg0aROvWrenVqxfr1q0jLS2Nb7/99rrnTJs2jfT09OItPj6+DBOLSHljNv//tlMd6nkyslM9oyPJX0hNTWXYsGG4ubnh4eHB6NGjycrKuuE53bt3x2QyXbU9/fTTZZRYSkNe4ZWCxu+Hk7G3sWLeE8H0aVXT6FgiUgJU1JA7FujvwU8Tu9CqljuXcgoYtiCCr8PPYLFozoaIiEhFdD7tMuO+iiK/0EzPZt682DvA6EgiJcbDw4MmTZpw/Pjx6x5jb2+Pm5vbVZuIVF3LIs6w80QKjrbWfPBIa6zUdqrcGzZsGAcPHmTjxo2sXbuWrVu3Mm7cuL88b+zYsSQkJBRvH3zwQRmkldKQV1jEM1/v4c8jF3CwteLLke25O8Db6FgiUkJU1JAS4evuwLdPhdKvdU0KzRZeXXOA51ft43J+kdHRRERE5Bbk5BcydmkkFzLzCPB1ZeaQIPUMl0olKyuLEydOULOm7tQUkb92+mI209fHAvBS76bUU9upcu/w4cNs2LCBBQsWEBISQpcuXfj0009ZsWIF58+fv+G5Tk5O+Pr6Fm8qaldM+YVmJizby6bYKys0Fo5oT6dGNYyOJSIlSEUNKTGOdtZ8NjSIaX0CsDLBD3vO8eAXOzh9MdvoaCIiInITzGYLz3+7j4PnM6jubMf84cG42NsYHUvkjvztb39jy5YtnD59mp07d/Lggw9ibW3N0KFDjY4mIuVcYZGZ576NJie/iI4NPBkeWs/oSHITwsLC8PDwIDg4uHhfz549sbKyIiIi4obnLlu2jBo1atCyZUumTZtGTo7mhlY0+YVmJizfw++Hk4oLGp1V0BCpdPQtVUqUyWTiqbsa0rq2B89+s4fYxEz6f7adjx5tw73NfYyOJyIiIjfw8e9HWX8gETtrK+Y+0Q5/TyejI4ncsbNnzzJ06FBSUlLw8vKiS5cuhIeH4+XlZXQ0ESnnvth8gr1xabg62PDho23UdqqCSExMxNv76jZDNjY2eHp6kpiYeN3zHnvsMerWrYufnx/79+/npZde4siRI/zwww/XPScvL4+8vLzixxkZGXf+AuS2FRSZefabPWw8lISdjRXzhwfTpbEKGiKVkYoaUipCG1Zn7bNdmbB8D1FnLjF2aSTPdG/I1HubYGOtBUIiIiLlzY/R5/j0jyszBt59qBXB9TwNTiRSMlasWGF0BBGpgPbFpzFr0zEA3h7QkloejgYnkpdffpn333//hsccPnz4tn//f8/caNWqFTVr1qRHjx6cOHGChg0bXvOc6dOn8+abb972c0rJKTJbmLIyml8PJmFnbcW8J9rRrYluYBCprFTUkFLj6+7AinEdeXfdYb7ccZovNp8gOj6NT4YGUcPF3uh4IiIi8m974y7xwnf7AXjqrgY80q62wYlERESMk5NfyHMroykyW+jXuiYD2vgZHUmA559/npEjR97wmAYNGuDr60tycvJV+wsLC0lNTcXX1/emny8kJASA48ePX7eoMW3aNKZOnVr8OCMjA39//5t+DikZFouFv/8Qwy/7E7C1NjH3iXZ0b6qh4CKVmYoaUqpsra14vX8LgupU4+Xv97PzRAr9P93O58Pa0rZONaPjiYiIVHnn0y4z7qso8gvN9Gzmw4u9AoyOJCIiYqh31x3m5MVsfN0ceGdgS0wmtZ0qD7y8vG6qdWBoaChpaWlERUXRrl07AP744w/MZnNxoeJmREdHA1CzZs3rHmNvb4+9vW7aNJLFYuGdXw6zMjIeKxN8MiSIuwNU0BCp7Eq1D9D06dNp3749rq6ueHt7M3DgQI4cOfKX561atYqAgAAcHBxo1aoV69atK82YUgYeCPRjzYTONPByJiE9l8Fzw1gadhqLxWJ0NBERkSorJ7+QsUsjuZCZR4CvKzOHtMFa/cJFRKQK+zM2ma/D4wCYMSgQDyc7gxPJrWrWrBm9e/dm7Nix7Nq1ix07djBx4kSGDBmCn9+VVTfnzp0jICCAXbt2AXDixAnefvttoqKiOH36ND/99BPDhw+nW7dutG7d2siXI3/hsz+Os2D7KQDee7g1fVpdvwglIpVHqRY1tmzZwoQJEwgPD2fjxo0UFBRw3333kZ2dfd1zdu7cydChQxk9ejR79+5l4MCBDBw4kAMHDpRmVCkDTXxc+XFCZ/q28qWgyMJrPx7kuZXR5OQXGh1NRESkyjGbLUxduY+D5zOo7mzHghHBuNhrEa+IiFRdKVl5xe0YR3WupwHDFdiyZcsICAigR48e9O3bly5dujBv3rzinxcUFHDkyBFycnIAsLOz4/fff+e+++4jICCA559/nocffpiff/7ZqJcgN2HJztN8uPEoAK/d35xHg9X6S6SqMFnK8Fb5Cxcu4O3tzZYtW+jWrds1jxk8eDDZ2dmsXbu2eF/Hjh1p06YNc+bM+cvnyMjIwN3dnfT0dNzc3Eosu5Qci8XCwu2nmL4+liKzhaY+rsx+vC0NvFyMjiYiVK330ar0WkX+r482HuWTTcews7Zi+dgQDQaX21JV3keryusUqcosFgtPfRXFb4eSaOztws/PdsHB1troWJVGVXkfrSqvszz4Puosz6/aB8CUno2Z0rOJwYlEpCTc7Ptoqa7U+L/S09MB8PS8/pfmsLAwevbsedW+Xr16ERYWVqrZpOyYTCbGdG3A8jEheLnacyQpkwGf7WDDgUSjo4mIiFQJ62MS+GTTMQDefaiVChoiIlLlrYo8y2+HkrC1NvHx4DYqaIiUY3/GJvPi91dWVT3ZuT6TezQ2OJGIlLUyK2qYzWamTJlC586dadmy5XWPS0xMxMfH56p9Pj4+JCZe+4J3Xl4eGRkZV21SMYQ0qM4vz3ahfb1qZOYV8vTXUUxff5jCIrPR0URERCqtQ+czmPrtlbvaRnepzyPtahucSERExFinLmbz5s8HAXju3ia0rOVucCIRuZ79Z9N4ZtkeiswWHmpbi1f7NcNk0kw4kaqmzIoaEyZM4MCBA6xYsaJEf+/06dNxd3cv3vz91T+vIvF2c2D52I6M6VIfgLlbTvL4wgguZOYZnExERKTyScnKY+zSSC4XFNG1cQ2m9QkwOpKIiIih8gvNTPpmL9n5RXSo78lT3RoaHUlEriMuJYcnF+8u/iz7/sOtsbJSQUOkKiqTosbEiRNZu3Ytf/75J7Vr3/huQF9fX5KSkq7al5SUhK+v7zWPnzZtGunp6cVbfHx8ieWWsmFrbcWr9zfn88fa4mxnTfjJVPp+so2dxy8aHU1ERKTSyC80M37ZHs6lXaZedSc+G9oWG+sy7UQqIiJS7vzr11hizqXj4WTLrCFtsNYFUpFyKTU7nxFf7uJiVj7Na7ox+/F22OqzrEiVVar/+y0WCxMnTmT16tX88ccf1K9f/y/PCQ0NZdOmTVft27hxI6Ghodc83t7eHjc3t6s2qZj6ta7JjxO70MTHhQuZeQxbGMFHvx1ROyoREZES8ObPB9l1KhUXexsWjAjG3cnW6EgiIiKG+vNIMvO3nQLgg4dbU9Pd0eBEInItuQVFjFmym1MXs6nl4cjiUe1xsbcxOpaIGKhUixoTJkzg66+/Zvny5bi6upKYmEhiYiKXL18uPmb48OFMmzat+PHkyZPZsGEDH374IbGxsbzxxhtERkYyceLE0owq5UQjbxd+nNCFIe39sVjgkz+O89j8CBLSL//1ySIiInJNX4efYVlEHCYTfDK0DY28XY2OJCIiYqjkjFz+9u8ZU8ND63Jfi2t3hxARYxWZLUxesZc9cWm4O9qy5Mn2eLs5GB1LRAxWqkWN2bNnk56eTvfu3alZs2bxtnLlyuJj4uLiSEhIKH7cqVMnli9fzrx58wgMDOS7775jzZo1NxwuLpWLo5017z3cmllD2uBsZ82u06n0nbWNP2KT/vpkERERuUr4yRTe+OnK8NMXejXlngAfgxOJiIgYy2y2MPXbfaRk5xPg68rf+zYzOpKIXMe76w7z68Ek7GysWDAiWDfniAgApbpWy2Kx/OUxmzdv/p99gwYNYtCgQaWQSCqSAW1qEVjbg4nf7OHAuQyeXBzJmC71ebF3AHY26psoIiLyV+JTc3hm2R4KzRYeCPRj/F0afioiIjJ360m2H7+Ig60Vnz0WhIOttdGRROQavtkVx8LtV1rEffRoIO3reRqcSETKC10ZlnKtXg1nvh/fiZGd6gGwYPspBs3ZSVxKjrHBREREyrnsvELGLo0kNTufVrXc+eCR1phMGn4qIiJV2964S3z42xEA3ujfQnd9i5RTYSdS+MeaAwBMvbcJ97f2MziRiJQnKmpIuWdvY80bD7Rg7hPtcHe0Zd/ZdPp9so1f9if89ckiIiJVkNls4W+r9hGbmEkNF3vmDW+nu1BFRKTKy8gtYNKKvRSaLfRrXZPB7f2NjiQi13D6Yjbjl0VRaLbQP9CPZ+9pZHQkESlnVNSQCqNXC1/WTe5Ku7rVyMwrZMLyPby6JobcgiKjo4mIiJQrn/5xnPUHErGztmLuE22p6e5odCQRERFDWSwWXll9gPjUy9TycOTdB1tpBaNIOZSRW8DoJbtJyykgsLY7/9JqYxG5BhU1pEKp5eHIinEdeab7lZ7gX4fHMfDzHRxPzjI4mYiISPmw4UACH/9+FIB/DmxJu7rqPSwiIvJ1RBw/7zuPtZWJT4YG4e5oa3QkEfk/iswWJi7fy4kL2fi6OTB/eLBWG4vINamoIRWOrbUVL/YOYOmTHajhYkdsYiYPfLad76POGh1NRETEULGJGUz9dh8AozrX41G11RAREWH/2TTe/vkQAC/1bkq7utUMTiQi1zLjtyNsPXoBB1srFowIxtvNwehIIlJOqaghFVa3Jl6sm9SVTg2rk5NfxPOr9jH122iy8wqNjiYiIlLmUrPzGbMkkpz8Iro0qsErfZsZHUlERMRw6TkFPLNsD/lFZu5t7sPYrg2MjiQi17A+JoHZm08A8MEjgbSs5W5wIhEpz1TUkArN282Br0aH8Py9TbAywQ97ztH/s+0cOp9hdDQREZEyU1Bk5pllUZy9dJm61Z347LEgbKz1MU9ERKo2i8XC86v2cfbSZfw9HZkxKFC9+UXKoePJmfxt1ZXVxmO61OeBQD+DE4lIeadvu1LhWVuZeLZHY74Z2xFfNwdOXshm4Bc7WLT9FBaLxeh4InILPv/8c+rVq4eDgwMhISHs2rXrhsevWrWKgIAAHBwcaNWqFevWrSujpCLly1s/HyL8ZCrOdtbMHx6Mh5Od0ZFEREQMN3/bSX4/nISdtRVfPNZOczREyqHM3AKe+iqK7PwiOjbw5OU+AUZHEpEKQEUNqTRCGlRn3eSu9AjwJr/QzFtrDzHyy91cyMwzOpqI3ISVK1cydepUXn/9dfbs2UNgYCC9evUiOTn5msfv3LmToUOHMnr0aPbu3cvAgQMZOHAgBw4cKOPkIsZaHhHHV+FnMJlg1pAgmvi4Gh1JRETEcLtPp/L+hiMAvNa/Oa1qq5WNSHljsVj426p9xYPBP3usrVYbi8hN0TuFVCqeznYsGBHMWwNaYG9jxZajF+gzayt/xl77oqiIlB8fffQRY8eOZdSoUTRv3pw5c+bg5OTEokWLrnn8rFmz6N27Ny+88ALNmjXj7bffpm3btnz22WdlnFzEOLtOpfLaj1cKeX+7ryk9m/sYnEhERMR4F7PymLh8D0VmCwPa+DEspI7RkUTkGuZsOcmvB6+sppr9eFtquNgbHUlEKggVNaTSMZlMDA+tx8/PdiHA15WLWfmMWrybN346SG5BkdHxROQa8vPziYqKomfPnsX7rKys6NmzJ2FhYdc8Jyws7KrjAXr16nXd4wHy8vLIyMi4ahOpqM5eymH811EUmi3c37omz3RvaHQkERERwxWZLUxZEU1SRh6NvF1498FWmqMhUg7tPp3KjN+urKZ6/YHmBNWpZnAiEalIVNSQSquJjytrJnRmVOd6ACzeeZoBn+3gSGKmscFE5H9cvHiRoqIifHyuvsvcx8eHxMTEa56TmJh4S8cDTJ8+HXd39+LN39//zsOLGCAnv5CxS6NIyc6nhZ8b/3pEg09FREQAPtl0jO3HL+Joa83sYW1xtrcxOpKI/B+XsvOZ9M1eiswWBrbx47EOWk0lIrdGRQ2p1BxsrXm9fwu+HNWeGi52HEnKpP9n21my87SGiItUQdOmTSM9Pb14i4+PNzqSyC2zWCy8sGo/hxMyqOFix7zhwTjaWRsdS0RExHCbDicxa9MxAN59qCWNNWdKpNz5zxyNhPRc6tdw5p9aTSUit0FFDakS7m7qzfrJ3bi7qRf5hWZe/+kgo5dEcjFLQ8RFyoMaNWpgbW1NUlLSVfuTkpLw9fW95jm+vr63dDyAvb09bm5uV20iFc1nfxznl5gEbK1NzHm8HbU8HI2OJCIiYrhTF7OZsjIagCc61uXBoNrGBhKRa1q4/RSbYpOxs7His8eCcNFqKhG5DSpqSJXh5WrPopHteaN/c+xsrPgjNpneM7ex+YiGiIsYzc7Ojnbt2rFp06bifWazmU2bNhEaGnrNc0JDQ686HmDjxo3XPV6kMvjtYCIfbjwKwNsDWhJcz9PgRCIiIsbLyitk3NJIMnMLCa5bjX/c39zoSCJyDdHxaby/IRaAf/RrRgs/d4MTiUhFpaKGVCkmk4mRnevz08TONPVx5WJWHiO/3M1bPx/SEHERg02dOpX58+ezZMkSDh8+zPjx48nOzmbUqFEADB8+nGnTphUfP3nyZDZs2MCHH35IbGwsb7zxBpGRkUycONGolyBSqo4kZvLcv+9AHRFalyHqPSwiIoLFYuHF7/ZxLDkLb1d7vni8LXY2utQhUt5k5Bbw7Dd7KCiy0KelL493rGt0JBGpwPSXXqqkAF83fpzYmRGhV/6ILtpxioGf7+BYkoaIixhl8ODBzJgxg9dee402bdoQHR3Nhg0bioeBx8XFkZCQUHx8p06dWL58OfPmzSMwMJDvvvuONWvW0LJlS6NegkipuZSdz5ilu8nOLyK0QXVe1R2oIiIiAMzZcpJ1MYnYWpuY/Xg7vF0djI4kItfw+o8HiU+9TO1qjrz3cGvN0RCRO2KyVLJpyRkZGbi7u5Oenq5e6XJT/ohN4oVV+0nJzsfexopX+zXj8Y519QdWqqyq9D5alV6rVFwFRWZGLNrFzhMp+Hs68tOELlRztjM6lghQdd5Hq8rrFKloth69wMgvd2G2wDsPtmRYiO78Lq+qyvtoVXmdt2rt/vNMXL4XKxOseroT7epWMzqSiJRTN/s+qpUaUuXdE+DD+ilduauJF3mFZv7x40HGLtUQcRERKR/e+eUwO0+k4GxnzYLh7VXQEBERAeJScnj2m72YLTA42J/H1JZRpFxKTM/lldUHAJh4dyMVNESkRKioIQJ4uzrw5cj2vHZ/c+ysrfj9cDK9Z25l0+Eko6OJiEgVtmJXHIt3ngbg48FtaOrramwgERGRcuByfhFPfR1F+uUCAv09eHNAC620FymHzGYLL3y3j/TLBbSq5c6zPRobHUlEKgkVNUT+zcrKxJNd6vPjxM4E+LpyMSuf0Usi+fvqGHLyC42OJyIiVczu06n848crd7VNvbcJ97XwNTiRiIiI8SwWCy99v5/DCRnUcLFjzuNtcbC1NjqWiFzDV+Fn2HbsIg62Vnw8uA221roMKSIlQ+8mIv9Hs5purJnQmTFd6gOwPCKOfp9sJzo+zdhgIiJSZZxLu8z4r6MoKLLQt5Uvz97TyOhIIiIi5cIXm0/w077z2FiZ+OyxttR0dzQ6kohcw/HkTN5ddxiAv/dtRiNvF4MTiUhloqKGyDU42Frz6v3NWTYmBF83B05dzObh2TuZ9fsxCovMRscTEZFK7HJ+EeOWRnIxK59mNd2YMShQLTVERESADQcS+devRwB4c0ALOjaobnAiEbmWgiIzz63cR16hmW5NvHiiY12jI4lIJaOihsgNdG5Ug1+ndOP+1jUpMlv4+PejDJobxpmUbKOjiYhIJWSxXOk7fPB8BtWd7Zg/vB1OdjZGxxIRETHcofMZTP02GoARoXUZFqKLpCLl1bytJ4k5l467oy3/eqS1btARkRKnoobIX3B3suXToUHMHNwGV3sb9sal0WfWNlbsisNisRgdT0REKpEvNp9g7f4EbKxMzH68HbWrORkdSaTSee+99zCZTEyZMsXoKCJyky5k5jFmyW5y8ovo0qgG/7i/udGRROQ6jidnMmvTMQBe798cHzcHgxOJSGWkoobITTCZTAwMqsX6KV0Jqe9JTn4RL/8Qw7ivokjJyjM6noiIVAK/H0pixm//v6VGh/qeBicSqXx2797N3Llzad26tdFRROQm5RUW8fTXUZxPz6V+DWc+f6wtNho2LFIuFZktvPjdfvILzXRv6sWDQbWMjiQilZQ+CYjcgtrVnFg+tiMv9wnA1trExkNJ9Jq5jT9jk42OJiIiFdixpEymrIzGYoHHO9ZRSw2RUpCVlcWwYcOYP38+1apVMzqOiNwEi8XCK6sPEHXmEq4ONiwYEYy7k63RsUTkOhbvPM2euDRc7G1498FWajslIqVGRQ2RW2RtZeLpuxqyZkJnGnu7cDErj1GLd/Pqmhgu5xcZHU9ERCqYtJx8xiyNJCuvkJD6nrzev4XRkUQqpQkTJtCvXz969uz5l8fm5eWRkZFx1SYiZW/BtlN8F3UWKxN8/lhbGnq5GB1JRK7jTEo2//o1FoBpfQPw83A0OJGIVGYqaojcphZ+7vz8bBdGda4HwNfhcfT7ZBv7z6YZmktERCqOwiIzE5fv5UxKDrWrOfLFsLbYqqWGSIlbsWIFe/bsYfr06Td1/PTp03F3dy/e/P39SzmhiPxff8Qm8e76wwD84/7mdGviZXAiEbkei8XCtB9iyC0w07GBJ0Pb1zE6kohUcvrWLHIHHGyteb1/C74a3QEfN3tOXszmoS928ummYxQWmY2OJyIi5dw76w6z/fhFnOysmT88mOou9kZHEql04uPjmTx5MsuWLcPB4eaGlU6bNo309PTiLT4+vpRTish/O3AunYnL92KxwNAOdRjZqZ7RkUTkBtZEn2PniRTsbax4/+HWWFmp7ZSIlC4VNURKQNfGXmyY3I2+rXwpNFv4cONRHp0bxumL2UZHExGRcurb3fF8ueM0AB89Gkizmm7GBhKppKKiokhOTqZt27bY2NhgY2PDli1b+OSTT7CxsaGo6H/bh9rb2+Pm5nbVJiJlIyH9MqOX7CYnv4jOjarz5gMt1JdfpBxLzyngnV+urKqa1KMxdas7G5xIRKoCFTVESkg1Zzs+f6wtHw4KxMXehj1xafT9ZBvLI+KwWCxGxxMRkXJk16lUXlkTA8DkHo3p3bKmwYlEKq8ePXoQExNDdHR08RYcHMywYcOIjo7G2tra6Igi8m+ZuQWM+nI3SRl5NPZ24Yth7bCz0WULkfLsg19juZiVTyNvF8Z2bWB0HBGpIvTpQKQEmUwmHm5Xmw1TuhJS35Oc/CL+vjqG0UsiSc7MNTqeiIiUA/GpOTz9dRQFRRb6tarJ5B6NjY4kUqm5urrSsmXLqzZnZ2eqV69Oy5YtjY4nIv9WUGRmwvK9xCZm4uVqz5ej2uPuaGt0LKmg3nnnHTp16oSTkxMeHh43dY7FYuG1116jZs2aODo60rNnT44dO1a6QSu4vXGXWL4rDoC3B7RUEVJEyozebURKQe1qTnwztiOv9G2GnbUVf8Qm0+vjrWw4kGB0NBERMVBmbgGjl+wmNTufVrXcmTEoUD2HRUSkyrNYLLz24wG2Hr2Ao601C0cEU7uak9GxpALLz89n0KBBjB8//qbP+eCDD/jkk0+YM2cOERERODs706tXL3JzdYPitRQWmXll9QEsFniobS1CG1Y3OpKIVCE2RgcQqaysrEyM7daArk1q8NzKfRxOyODpr/fwcNvavP5Ac9wcdNeRiEhVUmS2MOmbvRxNysLb1Z75w4NxtFPbGxEjbN682egIIvJf5mw5yTe74rEywadDg2hd28PoSFLBvfnmmwAsXrz4po63WCzMnDmTV199lQEDBgCwdOlSfHx8WLNmDUOGDCmtqBXW0rAzHErIwN3Rlr/3bWZ0HBGpYrRSQ6SUBfi6sWZCJ56+qyEmE3y/5yx9Zm4j/GSK0dFERKQMvbf+MH8euYC9jRXzhwfj6+5gdCQRERHD/bzvPO9viAXgtfub07O5j8GJpCo6deoUiYmJ9OzZs3ifu7s7ISEhhIWFXfe8vLw8MjIyrtqqgotZeXz8+1EAXuodQA0Xe4MTiUhVo6KGSBmwt7Hm5T4BrBwXSu1qjpxLu8zQ+eG8u+4weYVFRscTEZFS9u3ueOZvOwXAjEGBBPp7GBtIRESkHNh9OpXnV+0D4MnO9RnZub7BiaSqSkxMBMDH5+qimo+PT/HPrmX69Om4u7sXb/7+/qWas7z48LejZOYW0rKWG4PbV43XLCLli4oaImWoQ31PNkzpxuBgfywWmLf1JAM+28HhhKpxN4eISFUUcTKFV9bEADC5R2P6B/oZnEhERMR4R5MyGbMkkvxCM/c19+GVfmpfIzf28ssvYzKZbrjFxsaWaaZp06aRnp5evMXHx5fp8xvh4Pl0Vuy+Mhz89f4tsNZ8OBExgGZqiJQxF3sb3n+kNT2aeTPthxhiEzN54LPtPH9fU8Z2baAPBCIilUh8ag7jl+2hoMhCv1Y1mdyjsdGRREREDHc+7TIjFu0i/XIBQXU8mDUkSN+D5C89//zzjBw58obHNGjQ4LZ+t6+vLwBJSUnUrFmzeH9SUhJt2rS57nn29vbY21ed1ksWi4U3fz6ExQL9A/1oX8/T6EgiUkWpqCFikPta+NK2bjVe/j6G3w8n8d76WP44nMyHjwbi7+lkdDwREblDmbkFjF6ym9TsfFrVcmfGoECsdMFGRESquLScfIYv2kVCei6NvF1YNKI9jnbWRseSCsDLywsvL69S+d3169fH19eXTZs2FRcxMjIyiIiIYPz48aXynBXRuphEdp1KxcHWipf7BBgdR0SqMLWfEjFQDRd75g9vx/sPt8LZzppdp1PpPXMr30bGY7FYjI4nIiK3qchsYdI3ezmalIW3qz3zhwfrgo2IiFR5l/OLGL0kkuPJWfi6ObDkyQ5Uc7YzOpZUQnFxcURHRxMXF0dRURHR0dFER0eTlZVVfExAQACrV68GwGQyMWXKFP75z3/y008/ERMTw/Dhw/Hz82PgwIEGvYryJbegiHfXHQbg6bsaUsvD0eBEIlKVaaWGiMFMJhOD29chtEENpn4bTeSZS7z43X42Hkpi+kOtqOFSdZayiohUFu+tP8yfRy5gb2PFghHB+Lo7GB1JRETEUIVFZp79Zg9RZy7h5mDDkic76KKolJrXXnuNJUuWFD8OCgoC4M8//6R79+4AHDlyhPT09OJjXnzxRbKzsxk3bhxpaWl06dKFDRs24OCgz3EAC7ad5FzaZfzcHXiqW0Oj44hIFaeVGiLlRJ3qTqx8KpQXezfF1trExkNJ9J65ld8PJRkdTUREbsHK3XHM33YKgA8fDaR1bQ9jA4mIiBjMYrHw99Ux/H44GXsbKxaObE9TX1ejY0kltnjxYiwWy/9s/ylowJV/l/89o8NkMvHWW2+RmJhIbm4uv//+O02aNCn78OVQanY+c7acBOClPgFagSwihlNRQ6QcsbYy8Uz3RqyZ0JkmPi5czMpnzNJIXv5+P1l5hUbHExGRvxBxMoVX1xwAYHKPxtzf2s/gRCIiIsb78LejfBt5FisTfDo0SMOFRSqYz/44TlZeIS1rudFfn29FpBxQUUOkHGrh585PE7swpkt9TCZYsTuevrO2EXk61ehoIiJyHXEpOTz9dRQFRRb6ta7J5B6NjY4kIiJiuMU7TvHZn8cBeOfBVtzXwtfgRCJyK+JTc/gq/DQAL/duhpWVydhAIiKUclFj69at9O/fHz8/P0wmE2vWrLnh8Zs3b8ZkMv3PlpiYWJoxRcolB1trXr2/OcvGhODn7kBcag6Pzg3jX7/Gkl9oNjqeiIj8l8zcAkYv2c2lnAJa13ZnxiOB+sInIiJV3qrIeN74+RAAz/VswtAOdQxOJCK36qONRykostC1cQ26NK5hdBwREaCUixrZ2dkEBgby+eef39J5R44cISEhoXjz9vYupYQi5V+nhjXY8Fw3HmpbC7MFPv/zBA9+sYOjSZlGRxMREaDIbGHSN3s5lpyFj5s9854IVp9hERGp8tbHJPDS9/sBeLJzfSb1aGRwIhG5VQfPp7Mm+hwAL/UOMDiNiMj/V6pFjT59+vDPf/6TBx988JbO8/b2xtfXt3izslKXLKna3Bxs+ejRNnwxrC0eTrYcPJ/B/Z9uZ+H2U5jNFqPjidyx1NRUhg0bhpubGx4eHowePZqsrKwbHv/ss8/StGlTHB0dqVOnDpMmTSI9Pb0MU4tcMX3dYf48cgEHWyvmDw/G193B6EgiIiKG2nL0ApNW7MVsgcHB/vzj/maYTFrBKFLRfLDhCBYLPBDoR8ta7kbHEREpVi6rBW3atKFmzZrce++97Nixw+g4IuVG31Y1+W1KN7o39SK/0Mzbaw/x+MIIzqVdNjqayB0ZNmwYBw8eZOPGjaxdu5atW7cybty46x5//vx5zp8/z4wZMzhw4ACLFy9mw4YNjB49ugxTi8DK3XEs2H4KgBmDAmld28PYQCIiIgbbdSqVp76KLJ4x9e5DrVTQEKmAdp64yJajF7C1NvG3+5oaHUdE5Co2Rgf4bzVr1mTOnDkEBweTl5fHggUL6N69OxEREbRt2/aa5+Tl5ZGXl1f8OCMjo6ziihjC282BL0e2Z1lEHO/8cpidJ1LoPXMrbw1owcA2tfSFQSqcw4cPs2HDBnbv3k1wcDAAn376KX379mXGjBn4+fn9zzktW7bk+++/L37csGFD3nnnHR5//HEKCwuxsSlXf96kkoo4mcKraw4AMKVnY+5v/b//VkVERKqSmLPpjF68m9wCM3c39eLjR9tgrRlTIhWOxWJh5sZjAAztUIc61Z0MTiQicrVytVKjadOmPPXUU7Rr145OnTqxaNEiOnXqxMcff3zdc6ZPn467u3vx5u/vX4aJRYxhMpl4vGNd1k3uSht/DzJzC3lu5T4mLt/Lpex8o+OJ3JKwsDA8PDyKCxoAPXv2xMrKioiIiJv+Penp6bi5ud2woJGXl0dGRsZVm8jtOHkhi6e+jiq+C3Vyj8ZGRxIRETHUsaRMhi+KIDOvkJD6nsx+vB12NuXqkoOI3KSdJ1LYdToVOxsrnumueTgiUv6U+08YHTp04Pjx49f9+bRp00hPTy/e4uPjyzCdiLHq13Dmu6dDmXpvE2ysTPwSk0CvmVvZfCTZ6GgiNy0xMRFvb++r9tnY2ODp6UliYuJN/Y6LFy/y9ttv37BlFagQLiUjJSuPUYt3k5ZTQBt/D2Y8EqhVciIiUqXFpeTw+MIILuUUEFjbnQUjgnGwtTY6lojcBovFwszfjwLwWIc6mhcnIuVSuS9qREdHU7Nmzev+3N7eHjc3t6s2karExtqKST0a88MznWjo5UxyZh4jv9zNP9Yc4HJ+kdHxpAp7+eWXMZlMN9xiY2Pv+HkyMjLo168fzZs354033rjhsSqEy53KLShi7NJIzqTk4O/pyIIRwTja6aKNiIhUXfGpOQydH05SRh5NfFxYPKoDrg62RscSkdu080QKu09fws7GivHdGxodR0Tkmkq16XhWVtZVqyxOnTpFdHQ0np6e1KlTh2nTpnHu3DmWLl0KwMyZM6lfvz4tWrQgNzeXBQsW8Mcff/Dbb7+VZkyRSqF1bQ9+mdSV99bHsnjnab4KP8OOExeZNTiIVrXdjY4nVdDzzz/PyJEjb3hMgwYN8PX1JTn56tVFhYWFpKam4uvre8PzMzMz6d27N66urqxevRpb2xt/gba3t8fe3v6m8ov8X2azhee/3ceeuDTcHGz4cmQHarjo35OIiFRd59IuM3R+OOfSLtOghjNfjw6hmrOd0bFE5DZZLBY+3vj/V2n4uGmVhoiUT6Va1IiMjOTuu+8ufjx16lQARowYweLFi0lISCAuLq745/n5+Tz//POcO3cOJycnWrduze+//37V7xCR63OwteaNB1pwT4A3f1u1j5MXsnnwix08d28Tnr6roYb0SZny8vLCy8vrL48LDQ0lLS2NqKgo2rVrB8Aff/yB2WwmJCTkuudlZGTQq1cv7O3t+emnn3Bw0AduKV3v/xrLLzEJ2FqbmDc8mEbeLkZHEhERMUxC+mWGzgvn7KXL1KvuxPKxHfHWBVCRCm3H8RQiz2iVhoiUfyaLxWIxOkRJysjIwN3dvXhgrEhVdSk7n1fWxLAu5spMguC61fh4cBv8PZ0MTiblnRHvo3369CEpKYk5c+ZQUFDAqFGjCA4OZvny5QCcO3eOHj16sHTpUjp06EBGRgb33XcfOTk5rF69Gmdn5+Lf5eXlhbX1zbUD0t8MuVnLIs7wyuoDAHw8OJAHg2obnEikfKgq76NV5XWK3KykjFwGzw3jdEoOdTydWPlUR2q6OxodS8qxqvI+WpFfp8ViYdCcMCLPXGJkp3q88UALoyOJSBV0s++j5X6mhojcnmrOdnz+WFs+HBSIi70NkWcu0WfWNlZFxlPJaplSCSxbtoyAgAB69OhB37596dKlC/PmzSv+eUFBAUeOHCEnJweAPXv2EBERQUxMDI0aNaJmzZrFm+ZkSEn780gyr/14EICp9zZRQUNERKq05Ixchs4L53RKDrWrOfLNOBU0RCqDsBNXVmnY21jxjFZpiEg5V6rtp0TEWCaTiYfb1aZDfU+eWxlN5JlLvPDdfjYdTmb6Q63U71bKDU9Pz+JVGddSr169q4px3bt3V3FOysTB8+lMXLaHIrOFR9rV5tl7GhkdSURExDAXMvN4bEEEJy9mU8vDkW/GdqSWhwoaIpXB7C0nABjc3l+t5ESk3NNKDZEqwN/TiZVPhfJCr6bYWJnYcDCRXjO3suXoBaOjiYiUWwnpl3ly8W6y84vo3Kg67z7YCpNJs4lERKRqupiVx7AF4RxPzqKmuwPfjO2o1rYilUTM2XS2HbuItZWJsV0bGB1HROQvqaghUkVYW5mYcHcj1kzoTEMvZ5Iz8xixaBdv/HSQ3IIio+OJiJQrmbkFjPpyN0kZeTT2duGLYe2ws9HHJhERqZr+03LqaFIWPm72fDO2I3Wqq6AhUlnM2XpllUb/1jVVrBSRCkHfzkWqmJa13Fn7bFdGhNYFYPHO09z/6XYOnEs3OJmISPlQUGRmwvK9xCZm4uVqz5ej2uPuaGt0LBEREUMkpF9myLxwjiVn4et2ZYVGvRrORscSkRJy+mI262MSAHhaszREpIJQUUOkCnK0s+bNAS1ZPKo9Xq72HE/O4sEvdjB78wmKzJpTICJVl8Vi4bUfD7D16AUcba1ZOCKY2tV0t5qIiFRNZy/lMHhuePEMjW+fCqWBl4vRsUSkBM3bdhKzBe4J8CbA183oOCIiN0VFDZEqrHtTb36d0o3eLXwpKLLw/oZYhs4LJz41x+hoIiKGmLPlJN/sisdkgk+GBtG6tofRkURERAxxJiWbwXPDiUvNoY6nEyufUsspkcomOTOX76LOAvD0XVqlISIVh4oaIlWcp7Mdsx9vywePtMbZzppdp1PpM2sbq/eeNTqaiEiZ+nnfed7fEAvA6/c3597mPgYnEhERMcaJC1kMnhvOubTLNKjhzLdPhWrlokgl9FXYGfILzbSt40H7etWMjiMictNU1BARTCYTjwb7s35yN9rVrUZWXiHPrdzH5BV7ycgtMDqeiEipizydyvOr9gHwZOf6jOxc3+BEIiIixjiWlMngueEkZuTS2NuFFU91xNfdwehYIlLCcguKWBYRB8CYrg0wmUwGJxIRuXkqaohIsTrVnVg5riPP39sEaysTP0afp++sbUSeTjU6mohIqTl1MZuxSyPJLzRzb3MfXunXzOhIIiIihjh4Pp0h88K5mJVHgK8rK8Z1xNtVBQ2Ryuin6POkZudTy8OR+7RCWUQqGBU1ROQqNtZWPNujMaueDsXf05Gzly7z6NwwPt54lMIis9HxRERKVGp2PqO+3MWlnAICa7sza0gbrK10l5qIiFQ9u0+nMmReOCnZ+bSs5cY3YztS3cXe6FgiUgosFguLdpwCYHhoXWysdXlQRCoWvWuJyDW1rVONdZO68lBQLcwWmLXpGIM1RFxEKpHcgiLGLY3kdEoOtas5smBEe5zsbIyOJSIiUuY2H0nmiYURZOYW0r5eNZaP7Ug1ZzujY4lIKQk7mUJsYiaOttYMaV/H6DgiIrdMRQ0RuS5XB1s+GtyGWUPa4GpvQ9SZS/SdtY0fo88ZHU1E5I6YzRaeX7WPyDOXcHOwYfGo9ni56m5UERGpen7ed56xSyPJLTDTvakXS58Mwc3B1uhYIlKKFm0/DcDD7Wrh7qT/7yJS8aioISJ/aUCbWqyb3JXgutXIzCtk8oponlsZTaaGiItIBWSxWHjz54P8sj8BW2sTc55oRyNvV6NjiYiIlLlvdsUxacVeCoos3N+6JvOeCMbRztroWCJSis6kZLMpNgmAkZ3qG5xGROT2qKghIjfF39OJFeM6MqVnY6xMsHrvOfp+so2oM5eMjiYicks+2XScJWFnMJngo0fb0KlhDaMjiYiIlLk5W04w7YcYLBYYFlKHWUOCsLPRJQKRyu7r8DNYLHBXEy8aebsYHUdE5LboE4uI3DQbayum9GzCqqdDqV3NkfjUK0PEP9l0jCKzxeh4IiJ/6avwM3z8+1EA3ujfgv6BfgYnEhERKVsWi4X3N8Ty3vpYAJ7p3pB/DmyJtZXJ4GQiUtpyC4pYFXUWuDIgXESkolJRQ0RuWbu6nqyb3JWBbfwoMlv4aONRhswL4+wlDREXkfLrl/0JvPbjAQAm9WjMiE71jA0kImVi9uzZtG7dGjc3N9zc3AgNDWX9+vVGxxIxREGRmRe/28/szScAeLlPAC/2DsBkUkFDpCrYcCCRtJwC/Nwd6N7U2+g4IiK3TUUNEbktbg62zBwSxMeDA3Gxt2H36Uv0mbWNn/adNzqaiMj/2H7sIlNW7i1usfFcz8ZGRxKRMlK7dm3ee+89oqKiiIyM5J577mHAgAEcPHjQ6GgiZSo7r5CxSyNZFXUWKxO891Arnr6rodGxRKQMLYs4A8CQDnW0OktEKjQVNUTkjjwYVJt1k7oSVMeDzNxCJn2zl6nfRpOVV2h0NBERAPafTeOpryIpKLLQr1VN3hrQUnekilQh/fv3p2/fvjRu3JgmTZrwzjvv4OLiQnh4uNHRRMrMxaw8hs4PZ/ORCzjYWjF/eDBDOtQxOpaIlKEjiZnsPn0JaysTg9v7Gx1HROSOqKghInesTnUnVj0VyqQeV4aI/7DnHP0+2UbM2XSjo4lIFXfyQhYjv9xNdn4RnRtV56PBgborTaQKKyoqYsWKFWRnZxMaGnrd4/Ly8sjIyLhqE6moTl/M5uHZO9l/Np1qTrZ8M7YjPZr5GB1LRMrY8n+v0ri3mQ8+bg4GpxERuTMqaohIibCxtmLqvU1Y+VQotTwcOZOSw0Ozd/DljlNYLBoiLiJlLzE9lycW7iI1O59WtdyZ+0Qw9jbWRscSEQPExMTg4uKCvb09Tz/9NKtXr6Z58+bXPX769Om4u7sXb/7+uqNVKqbo+DQenr2TMyk5+Hs68v34TgTVqWZ0LBEpYzn5hfyw5xwAwzpqlZaIVHwqaohIiWpfz5N1k7rSq4UPBUUW3vz5EOO+iiItJ9/oaCJShaTl5DN8UQTn0i7ToIYzi0e1x8XexuhYImKQpk2bEh0dTUREBOPHj2fEiBEcOnTousdPmzaN9PT04i0+Pr4M04qUjD9ikxg6L5yU7Hxa1nLj+/GdaODlYnQsETHA2n0JZOYVUre6E50b1jA6jojIHVNRQ0RKnLuTLXMeb8ebD7TAztqKjYeS6DtrG1FnUo2OJiJVwOX8Ip5cvJujSVn4uNmz5MkOVHexNzqWiBjIzs6ORo0a0a5dO6ZPn05gYCCzZs267vH29va4ubldtYlUJCt3xzF2aRSXC4ro2rgGK8aF4u2qdjMiVdWqqCvF+cHt/bFSK1YRqQRU1BCRUmEymRjRqR4/PNOJetWdOJ+ey6Nzw/n8z+OYzWpHJSKlo6DIzDPLotgTl4a7oy1LnwzB39PJ6FgiUs6YzWby8vKMjiFS4sxmC+9viOWl72MoMlt4KKgWC0dotaJIVXb6Yja7T1/CygQPBdU2Oo6ISIlQUUNESlXLWu6sndSVAW38KDJb+NevRxjx5S4uZOpCgoiULLPZwovf7efPIxdwsLVi0chgmvq6Gh1LRAw2bdo0tm7dyunTp4mJiWHatGls3ryZYcOGGR1NpERdzi9iwvI9zN58AoCJdzfiw0cDsbPR136Rquz7PWcB6NrYC193rdgSkcpBn25EpNS52Nswc3AbPni4NQ62Vmw7dpG+n2xjx/GLRkcTkUrCYrHwzrrDrN57DmsrE7OHtaNdXU+jY4lIOZCcnMzw4cNp2rQpPXr0YPfu3fz666/ce++9RkcTKTHJGbkMmRfG+gOJ2Fqb+HBQIH/r1RSTSW1mRKoys9nC91FXihqPtNMqDRGpPFTUEJEyYTKZeLS9Pz9N7EITHxcuZObx+MIIPvztCIVFZqPjiUgFN2fLSRZuPwXAvx5pzd0B3gYnEpHyYuHChZw+fZq8vDySk5P5/fffVdCQSuVwQgYDP9/BvrPpeDjZ8vXoEB7WxUup4t555x06deqEk5MTHh4eN3XOyJEjMZlMV229e/cu3aClLOxkCufTc3F1sOHe5j5GxxERKTEqaohImWri48qPE7owtIM/Fgt8+sdxHpsfQUL6ZaOjiUgFtXJ3HO9viAXg1X7NeKitLuSIiEjV8GdsMo/M3sn59Fwa1HBmzTOdCWlQ3ehYIobLz89n0KBBjB8//pbO6927NwkJCcXbN998U0oJy8Z3/16l8UCgHw621ganEREpOZoWJiJlztHOmukPtSa0YQ3+/kMMu06n0nfWNmYMCqRHM909IiI379eDiUz7IQaA8d0bMqZrA4MTiYiIlI3FO07x1tpDmC0Q2qA6sx9vi4eTndGxRMqFN998E4DFixff0nn29vb4+vqWQqKyl5lbwPoDCYBaT4lI5aOVGiJimAcC/Vj7bBda1nLjUk4Bo5dE8s+1h8gvVDsqEflr4SdTePabvZgtMDjYnxd7NTU6koiISKkrKDLz6poY3vj5UPHfwCVPdlBBQ6QEbN68GW9vb5o2bcr48eNJSUm54fF5eXlkZGRctZUX62ISyC0w09DLmTb+HkbHEREpUSpqiIih6tVw5vvxnRjVuR4AC7afYtCcncSl5BgbTMpUamoqw4YNw83NDQ8PD0aPHk1WVtZNnWuxWOjTpw8mk4k1a9aUblApN6Lj0xi7JJL8QjP3NffhnQdbahiqiIhUehez8hi2IIKvw+MwmeDlPgG893Ar7Gz01V7kTvXu3ZulS5eyadMm3n//fbZs2UKfPn0oKiq67jnTp0/H3d29ePP39y/DxDe2Zu95AB5qW1ufk0Wk0tEnHxExnL2NNa/3b8HcJ9rh7mjLvrPp9Pt0GxsPJRkdTcrIsGHDOHjwIBs3bmTt2rVs3bqVcePG3dS5M2fO1If0KubAuXSGL4wgM6+Qjg08+WRoEDbW+kgjIiKV24Fz6Tzw6XZ2nUrFxd6G+U8E8/RdDfU5SKqMl19++X8Gef/fLTY29rZ//5AhQ3jggQdo1aoVAwcOZO3atezevZvNmzdf95xp06aRnp5evMXHx9/285ek5Ixcwk9dWWXyQKCfwWlEREqeZmqISLnRq4UvLWu58+zyPeyJS2Ps0kie6d6Qqfc20QXLSuzw4cNs2LCB3bt3ExwcDMCnn35K3759mTFjBn5+1/8QHh0dzYcffkhkZCQ1a9Ysq8hioEPnMxi2IIKM3ELa16vGwhHtNfRQREQqvR+jz/HS9/vJLTBTv4Yz84e3o5G3q9GxRMrU888/z8iRI294TIMGJTdfrUGDBtSoUYPjx4/To0ePax5jb2+Pvb19iT1nSfklJgGLBdrW8cDf08noOCIiJU5FDREpV2p5OLJiXCjvrjvM4p2n+WLzCaLj0/hkaBA1XMrfh0W5c2FhYXh4eBQXNAB69uyJlZUVERERPPjgg9c8Lycnh8cee4zPP/+80gzzkxs7kpjJ4wsjSL9cQFAdD74c1QFne32UERGRyqvIbOGDX2OZu+UkAHc18eKToUG4O9oanEyk7Hl5eeHl5VVmz3f27FlSUlIq5M1TP++70nqqv1ZpiEglpVufRaTcsbOx4o0HWvDJ0CCc7KzZeSKF+z/Zzp64S0ZHk1KQmJiIt7f3VftsbGzw9PQkMTHxuuc999xzdOrUiQEDBtz0c5XnQX5yY8eTMxm2IJzU7HwCa7uz5MkOuKigISIilVh6TgFPLt5dXNB4+q6GLBrZXgUNkZsQFxdHdHQ0cXFxFBUVER0dTXR09FVz+wICAli9ejUAWVlZvPDCC4SHh3P69Gk2bdrEgAEDaNSoEb169TLqZdyW+NQc9sSlYTJBv1YVryAjInIzVNQQkXLrgUA/1kzoTAMvZxIzchk8N4ylYaexWCxGR5ObUJo9b3/66Sf++OMPZs6ceUvnledBfnJ9Jy9kMXR+BBez8mlZy42lT4bg5qALOiIiUnkdTcpk4Bc72HL0Ag62Vswa0oaX+wRgbaX5GSI347XXXiMoKIjXX3+drKwsgoKCCAoKIjIysviYI0eOkJ6eDoC1tTX79+/ngQceoEmTJowePZp27dqxbdu2ctle6kbW7k8AoGP96ni7ORicRkSkdOgWRxEp15r4uPLjhM689P1+1sUk8tqPB9lz5hLvPtQKJzu9hZVnN9vz1tfXl+Tk5Kv2FxYWkpqaet22Un/88QcnTpzAw8Pjqv0PP/wwXbt2ve4wv2nTpjF16tTixxkZGSpslHNnUrJ5bH4EFzLzaFbTja+eDMHdSQUNERGpvH6MPsfL38dwuaCIWh6OzH2iHS1ruRsdS6RCWbx4MYsXL77hMf99s5yjoyO//vprKacqG2o9JSJVga4Iiki55+pgy+ePtWXh9lNMXx/LmujzHE7IZPbjbWng5WJ0PLmOm+15GxoaSlpaGlFRUbRr1w64UrQwm82EhIRc85yXX36ZMWPGXLWvVatWfPzxx/Tv3/+6z1VeB/nJtcWn5jB0XjiJGbk08XHh69EdqOZsZ3QsERGRUpFXWMQ7vxxmadgZADo3qs4nQ4KorrlyInKTjidncSghAxsrE71bau6giFReaj8lIhWCyWRiTNcGLB8TgperPUeSMhnw2Q42HLj+zAWpGJo1a0bv3r0ZO3Ysu3btYseOHUycOJEhQ4bg53fl7qJz584REBDArl27APD19aVly5ZXbQB16tShfv36hr0WKTnn0i4zdH4459NzaejlzLIxHXVRR0REKq3zaZcZPDe8uKAx8e5GLH0yRH/7ROSWrN1/ZZVGl8Y18NTNQCJSiamoISIVSkiD6vzybBfa16tGZl4hT38dxfT1hyksMhsdTe7AsmXLCAgIoEePHvTt25cuXbowb9684p8XFBRw5MgRcnJyDEwpZSUh/TJD54Vz9tJl6tdw5puxHfFy1UUdERGpnLYdu0C/T7YRHZ+Gm4MNi0YG87deTTU/Q0Ru2X9u+ru/tVpPiUjlpvZTIlLheLs5sHxsR95bH8vC7aeYu+Uk++LT+HRoW134rKA8PT1Zvnz5dX9er169vxwQrwHylUNyRi6PzY8gLjWHOp5OLB8bogGHIiJSKZnNFj778zgf/34UiwVa1nJj9rB2+Hs6GR1NRCqgMynZxCZmYm1lomczb6PjiIiUKq3UEJEKydbain/c35zPH2uLs5014SdTuf/TbUSdSTU6mojcpguZeQydH86pi9nUrubIN+M6UtPd0ehYIiIiJS41O5/RS3bz0cYrBY2hHfz57ulOKmiIyG3beCgJgJD6nng4qfWUiFRuKmqISIXWr3VNfpzYmUbeLiRl5DF4bjhf7jilu/ZFKpiUrDyGLQjnxIVs/Nwd+GZsR2p5qKAhIiKVT/jJFPrM2sqfRy5gb2PFB4+0ZvpDrXGwtTY6mohUYL8dvFLUuK+5j8FJRERKn4oaIlLhNfJ25ccJnenXuiaFZgtv/nyISSuiyckvNDqaiNyES9n5DFsQwdGkLHzdHPhmXEfdqSoiIpVOkdnCxxuP8tj8cJIy8mjo5cyaCZ15NNjf6GgiUsFdzMpj97+7FtzbwtfgNCIipU8zNUSkUnC2t+GzoUG0rVON6esO8/O+85y8kMWCEcFqXyNSjqXnFPD4wghiEzPxcrVn+dgQ6lZ3NjqWiIhIiUpMz2Xyir1EnLpy0fGRdrV5a0ALnOz0lVxE7tymw0nFs3m02llEqoJSXamxdetW+vfvj5+fHyaTiTVr1vzlOZs3b6Zt27bY29vTqFEjFi9eXJoRRaQSMZlMjO5Sn+VjO+LpbMfB8xk88NkO9sZdMjqaiFxDRm4BwxdFcPB8BjVc7PhmbAgNvFyMjiUiIlKi/ohNos+srUScSsXZzpqPBwcyY1CgChoiUmL+03qqV3Ot0hCRqqFUixrZ2dkEBgby+eef39Txp06dol+/ftx9991ER0czZcoUxowZw6+//lqaMUWkkulQ35MfJ3SmqY8rFzLzGDwvnDV7zxkdS0T+S0ZuASMW7WLf2XQ8ne1YNqYjjbxdjY4lIiJSYvILzfxz7SGeXBzJpZwCWvi5sXZSVx4Mqm10NBGpRLLzCtl2/CIA96n1lIhUEaV6a0ifPn3o06fPTR8/Z84c6tevz4cffghAs2bN2L59Ox9//DG9evUqrZgiUgn5ezrx/TOdmLIimt8PJzFlZTRHkzL5231NsbIyGR1PpEpLycpjxJe7OHAuAw8nW74eHUJTXxU0RESk8jienMWUlXs5cC4DgJGd6jGtbwD2NhoGLiIla+vRC+QXmqlb3YkmPlr1LCJVQ7kaFB4WFkbPnj2v2terVy/CwsIMSiQiFZmLvQ3znmjH+O4NAfhi8wme/jqK7DwNEBcxSmJ6LoPnhXPg3JWWU8vHdKS5n5vRsUREREqExWLhq7DT3P/ptuLi/bwn2vHGAy1U0BCRUvHboSutp+5r7oPJpBv4RKRqKFdNPBMTE/Hx8blqn4+PDxkZGVy+fBlHx/8ddpSXl0deXl7x44yMjFLPKSIVh5WViZd6B9DY24WXv4/ht0NJPDx7JwtGBFO7mpPR8USqlLiUHIYtDCc+9TI13R34ekwIDTVDQ0REKonkzFxe/G4/m49cAKBr4xr865FAfN0dDE4mIpVVkdnC5iPJAPRs5vMXR4uIVB7laqXG7Zg+fTru7u7Fm7+/v9GRRKQceqhtbb4Z15EaLvbEJmYy8PMdRJ1JNTqWSJVxNCmTR+bsJD71MvWqO7Hq6VAVNEREpNL47WAivWduY/ORC9jZWPHa/c1ZMqqDChoiUqr2nU3jUk4Brg42tKtbzeg4IiJlplwVNXx9fUlKSrpqX1JSEm5ubtdcpQEwbdo00tPTi7f4+PiyiCoiFVC7utX4cWJnmtd042JWPkPnRfBd1FmjY4lUevvPpjF4bhjJmXkE+Lry7dOhWiklIiKVQnZeIS9/v59xX0WRmp1Ps5purH22C092qa85biJS6v6zMqxbYy9srMvVJT4RkVJVrtpPhYaGsm7duqv2bdy4kdDQ0OueY29vj729fWlHE5FKopaHI9+ND+W5ldH8ejCJv63ax7GkTF7sHYC1vniKlLiIkymMXhJJVl4hgf4eLBnVHg8nO6NjiYiI3LGoM6lM/XYfZ1JyMJlgXNcGTL2viWZniEiZ+U/rqbuaehmcRESkbJVqGTcrK4vo6Giio6MBOHXqFNHR0cTFxQFXVlkMHz68+Pinn36akydP8uKLLxIbG8sXX3zBt99+y3PPPVeaMUWkinGys2H2sHZMuqcRAHO3nmTc0kgycwsMTiZSufx5JJnhi3aRlVdIaIPqLBsTooKGiIhUeLkFRbzzyyEemRPGmZQc/NwdWD6mI9P6NlNBQ0TKzIXMPPafTQegexMVNUSkainVokZkZCRBQUEEBQUBMHXqVIKCgnjttdcASEhIKC5wANSvX59ffvmFjRs3EhgYyIcffsiCBQvo1atXacYUkSrIysrE1Pua8snQIOxtrNgUm8zDs3cSl5JjdDSRSuGX/QmMWxpJXqGZHgHefDmqPS725WqBqIiIyC3bE3eJvp9sY/62U1gs8HDb2qyf3I3QhtWNjiYiVczWo1daT7Xwc8PbTfN7RKRqKdWrC927d8disVz354sXL77mOXv37i3FVCIi/98DgX7U8XRi3NJIjiZlMeDz7cx5vB0hDfTFVOR2fbs7npd/2I/ZAv0D/fjo0UBs1eNXREQqsNyCIj7eeJT5205itoC3qz3TH2pFj2Y+RkcTkSpq87+LGnc39TY4iYhI2dMVBhGp8tr4e/DTxC60quXOpZwCHl8YwcrdcX99ooj8j0XbT/Hi91cKGkM7+DNzcBsVNEREpELbG3eJfp9sY+7WKwWNB4Nq8dtz3VTQEBHDFJktbDt2pajRXfM0RKQKUh8IERHA192Bb58K5W/f7eOX/Qm89H0MZ1JyeKFXU0wmDRAX+SsWi4VPNh3n49+PAjCuWwOm9QnQ/x8REamwcguKmPn7MeZtPYHZAl6u9rz7YCvuba5ihogY68C5dNJyCnB1sKGNv4fRcUREypyKGiIi/+ZoZ81nQ4No7O3CzN+P8cXmE6Rm5/PPgS2x0Z3mItdlsVh4d91h5m87BcDz9zZh4j2NVNAQEZEKa+eJi/z9hxhO/3ve2oA2frzRvwXVnO0MTiYiAtuPXwQgtEF1fVcVkSpJRQ0Rkf9iMpmY0rMJvm4O/H11DCt2x5Oanc8nQ4NwsLU2Op5IuVNktvDqmhi+2RUPwGv3N+fJLvUNTiUiInJ70nMKeHfdYVZGXvm75u1qz9sDW9Krha/ByURE/r+dJ64UNTo3qmFwEhERY6ioISJyDUM61MHDyY5JK/by26EkRizaxfwRwbg52BodTaTcKCgy89zKaNbuT8DKBO893JpHg/2NjiUiInLLLBYLv8Qk8MZPh7iYlQfAsJA6vNQnQJ//RKRcyS0oYvfpS4CKGiJSdWmNmojIdfRu6cuSUR1wsbch4lQqg+eGk5yZa3QskXIht6CIp76KYu3+BGytTXz2WFsVNEREpEI6n3aZMUsimbh8Lxez8mjo5cyqp0N558FWKmiISLkTefoS+YVmfNzsaejlbHQcERFDqKghInIDoQ2rs2JcR2q42HE4IYNHZodxJiXb6FgihrqYlcfQ+eH8EZuMg60V84cH07dVTaNjiYiI3JIis4XFO05x70db2BSbjK21ick9GrNuclfa1/M0Op6IyDXt+K/WU5phJyJVlYoaIiJ/oWUtd757uhN1PJ2IS83h4dlhHDyfbnQsEUMcT87iwS92sDcuDXdHW5Y+GUL3pt5GxxIREbkl+8+m8dAXO3jj50Nk5xfRrm411k3qynP3NsHeRnPURKT82vHvIeGdG6r1lIhUXSpqiIjchHo1nPnu6VCa1XTjYlYeQ+aGE34yxehYImUq7EQKD32xg/jUy9TxdOKHZzrRob7uZBWR8m369Om0b98eV1dXvL29GThwIEeOHDE6lhgkLSefV1bHMODzHew7m46rvQ1vD2jBqqdCaezjanQ8EZEbSs8pIObclRvsNE9DRKoyFTVERG6St5sDK8Z1pEN9TzLzChm+aBe/Hkw0OpZImfhhz1mGL4ogI7eQtnU8WP1MJxp6uRgdS0TkL23ZsoUJEyYQHh7Oxo0bKSgo4L777iM7W+0kqxKz2cK3kfHc8+EWlkXEYbHAg0G12PT8XTwRWg8rK7VwEZHyL+xkChYLNPRyxtfdweg4IiKGsTE6gIhIRXKl3U4Hnv1mLxsPJTH+6yimP9SKwe3rGB1NpFRYLBZm/n6MWZuOAdCvVU0+fDQQB1u15hCRimHDhg1XPV68eDHe3t5ERUXRrVs3g1JJWTp4Pp3XfjxI1JlLADT2duHtgS3p2KC6wclERG5NxKkr3QL0/iUiVZ2KGiIit8jB1prZw9ry99UxfBt5lpe+jyElO5/xdzXUoDapVPIKi5j2fQw/7D0HwPjuDXnhvqa6m1VEKrT09CttOzw9r98+Ly8vj7y8vOLHGRkZpZ5LSl5GbgEf/XaUpWGnMVvAyc6aKT0bM6pzfWyt1bRARCqe3adTAdQCVkSqPBU1RERug421Fe8/3JrqLvbM3nyCDzYcISUrn1f6NtMFX6kU0nMKGPdVJBGnUrG2MvHPgS0Z2kErkkSkYjObzUyZMoXOnTvTsmXL6x43ffp03nzzzTJMJiWpyGzh+6izfPDrES5mXSlO9Wtdk1f7NaOmu6PB6UREbk9mbgGHzl8psquoISJVnYoaIiK3yWQy8VLvAKo72/HPXw6zcPspUrPz+eCR1rr7Tyq0uJQcRi7exckL2bjY2/DFsLZ0a+JldCwRkTs2YcIEDhw4wPbt22943LRp05g6dWrx44yMDPz9/Us7npSAXadSefPngxz894W/BjWceXNAC7o21t8xEanYos5cwmyBOp5OKtCKSJWnq24iIndoTNcGfDw4EBsrE6v3nmPs0khy8guNjlWhpKamMmzYMNzc3PDw8GD06NFkZWX95XlhYWHcc889ODs74+bmRrdu3bh8+XIZJK689sRd4sEvdnDyQjZ+7g58Nz5UBQ0RqRQmTpzI2rVr+fPPP6ldu/YNj7W3t8fNze2qTcq3+NQcJizbw6Nzwzh4PgNXBxte7deMDVO6qaAhIpXCrlNqPSUi8h9aqSEiUgIeDKqNh6Md45dFsfnIBYYtiODLke3xcLIzOlqFMGzYMBISEti4cSMFBQWMGjWKcePGsXz58uueExYWRu/evZk2bRqffvopNjY27Nu3Dysr1etv17qYBJ5bGU1eoZmWtdxYOKI9Pm4ORscSEbkjFouFZ599ltWrV7N582bq169vdCQpQdl5hczefIJ5206SX2jGygRDOtTh+XubUN3F3uh4IiIlprioUU9FDRERFTVERErI3QHeLBvTkScX72ZvXBpPLNzFsrEhuDnYGh2tXDt8+DAbNmxg9+7dBAcHA/Dpp5/St29fZsyYgZ+f3zXPe+6555g0aRIvv/xy8b6mTZuWSebKxmKxMG/rSaavjwWgZzNvZg0JwtleHxNE/l97dx4fVX3vf/w9k2USliQEQhYIJAFNWCRAKCHRSihYEDcsV9QiAiKiF7zFUHvDr1cRbUupXLHaVOi9BURFkSrSuuBFFqmIAQLIHggiSyAJGLKQkHXO7w9kapRAkknmzCSv5+Mxj4dz5nwnn/l6znwO8znf7xeeb/r06VqxYoXWrFmj9u3bKzc3V5IUGBgof3+m7/BUdruh1btyNH/tIeWXXFo3I7lHRz11e2/1CmdkDYCWpbyqRntOFUlipAYASEw/BQBNKqF7B709LUnBbX21N6dIDy3dzlRU17B161YFBQU5ChqSNGLECFmtVmVkZFyxTX5+vjIyMtS5c2clJycrNDRUQ4cOveYc6RUVFSouLq71aO2qa+z69Xv7HAWNSclRWjxhEAUNAC3GK6+8oqKiIqWkpCg8PNzxWLlypdmhoZG2ZJ/TnemfadaqL5VfUqHuHdto8YQEvfFwIgUNAC3S7pOFqqyxq3N7m7p3bGN2OABgOooaANDEYsPaa/lDg9Xez1s7jp/XtNcyVV5VY3ZYbis3N1edO3eutc3b21vBwcGOu2m/76uvvpIkPfPMM5o6darWrl2rgQMHavjw4Tpy5Eidf2vevHkKDAx0PFr7oq8l5VV66NUdWpFxQhaL9PTtvfXMnX3kZbWYHRoANBnDMK74mDRpktmhoYEOnC7Wg0u2afz/ZmhfTrHa2byVdmuc/u+JmzWyT5gsFvIXgJZp+7dTT/0oOpjvOgAQRQ0AaBZ9uwRq2eTBauPrpX8eOafH39ylqhq72WG5VFpamiwWy1Ufhw4datR72+2X+nLatGmaPHmyBgwYoIULFyo2NlZLliyps93s2bNVVFTkeJw8ebJRf78lOF14Ufcs2qrNh8/K38dLix9I0EM3Mc88AMD95BReVOrbu3Xby//U5sNn5eNl0aTkKH36ZIoeHdpDNm8vs0MEgGa1/fh5SaynAQCXUdQAgGaS0L2D/vfBQfL1tmrdgTz9ctWXqrEbZoflMrNmzdLBgwev+oiJiVFYWJjy8/Nrta2urlZBQYHCwsKu+N7h4eGSpN69e9fa3qtXL504caLOmGw2mwICAmo9WqPPs8/pjpc/06HcEnVqZ9PKaUP00z5X7msAAMxSVFal3314UMMWbNK7O3NkGNId8RFan5qiZ+7sw0LgAK7o66+/1pQpUxQdHS1/f3/16NFDc+bMUWVl5VXblZeXa/r06erYsaPatWunsWPHKi8vz0VR181uN/TlyUJJ0sBuHcwNBgDcBBNmA0AzSu7ZSYseGKhHlmdqze7T8vfx0ryf3dAqhgyHhIQoJCTkmvslJSWpsLBQmZmZSkhIkCRt2LBBdrtdiYmJV2wTFRWliIgIZWVl1dp++PBh3Xrrrc4H30JdXhB8/tpDshtSn4gALZ6QoK4dmJcXAOA+LlbWaPnWr5W+MVvF5ZfWJkuK6ajZo+PUr2uQucEBcHuHDh2S3W7X4sWL1bNnT+3bt09Tp05VaWmpFixYUGe7J554Qh988IFWrVqlwMBAzZgxQz/72c+0ZcsWF0b/Q8e+KVXRxSrZvK2KC29vaiwA4C4oagBAM/tJXKhevK+//uPNXXpr+0m1tXnrv27r1SoKG/XRq1cvjRo1SlOnTtWiRYtUVVWlGTNm6L777lNERIQkKScnR8OHD9fy5cs1ePBgWSwWPfnkk5ozZ47i4+PVv39/vfrqqzp06JD+9re/mfyJ3NOFimr96m9f6sO9l9YpGTuwq357d1/5+TBlBwDAPVRU1+itbSf1p43ZOltSIUmKDW2vtNFxSrk+hGsnAPUyatQojRo1yvE8JiZGWVlZeuWVV+osahQVFemvf/2rVqxYoZ/85CeSpKVLl6pXr1764osvNGTIEJfEfiW7ThRKkvp1DZSPFxOuAIBEUQMAXOL2fhEqq6zRr/62R3/97Jja2ryVesv1ZoflNt544w3NmDFDw4cPl9Vq1dixY/XSSy85Xq+qqlJWVpbKysoc22bOnKny8nI98cQTKigoUHx8vNatW6cePXqY8RHcWnb+BT36eqay8y/Ix8uip+/oowcSu/HjEADALVTV2PVO5im9tP6ITheVS5K6dvDXL4Zfp58N7CovK/kKgHOKiooUHFz3ehSZmZmqqqrSiBEjHNvi4uLUrVs3bd261eSixqX1NAYw9RQAOFDUAAAXGTcoUhcrazTn7/v10vojauvrpWlD+QFekoKDg7VixYo6X4+KipJh/HA9krS0NKWlpTVnaB5v7b5c/XLVl7pQUa3QAJv+PD5BCd35BxEAwHw1dkN//zJHL35yRMe/uXTjQmiATY//5DqNGxQpX2/uSAbgvOzsbL388stXnXoqNzdXvr6+CgoKqrU9NDRUubm5dbarqKhQRUWF43lxcbHT8X7f5ZEa/SODrrofALQmFDUAwIUmJkfpQkW1nv84S/M+OqQ2Nm9NGNLd7LDQAtXYDS34vyy9sumoJCkxOlh/+vlAhbRnUVUAgLnsdkMf7cvVwk8OKzv/giSpUztfPZbSU+MTuzE1IoArSktL0/z586+6z8GDBxUXF+d4npOTo1GjRumee+7R1KlTmzymefPmae7cuU3+vpeVVVbrUO6lQsmAbkHN9ncAwNNQ1AAAF5s+rKfKKquVvvGonnpvn9r6eulnA7uaHRZakILSSv3Hm7v0WfY5SdLDN0XrP2+NYw5eAICp7HZD/3cgV39cn62DZy79SBfo76NpQ2M0MSlKbW388xRA3WbNmqVJkyZddZ+YmBjHf58+fVrDhg1TcnKy/vKXv1y1XVhYmCorK1VYWFhrtEZeXp7CwsLqbDd79mylpqY6nhcXFysyMvLqH6QB9pwqkt2QwgL8FB7o32TvCwCejqtGADDBL38aq9KKGi37/Gv9ctWXauPrpVF9w80OCy3AnlOFeuz1ncopvCh/Hy/N/7d+ujM+wuywAACtWI3d0Pt7Tit9Y7YO510amdHO5q0pN0Vryo+jFeDnY3KEADxBSEiIQkJC6rVvTk6Ohg0bpoSEBC1dulRW69Vv7klISJCPj4/Wr1+vsWPHSpKysrJ04sQJJSUl1dnOZrPJZmu+kdC7TxZKYpQGAHwfRQ0AMIHFYtHTt/dWaUW1VmWe0uNv7tL/POillNjOZocGD/b29pP6rzX7VFltV3Sntlr0QIJiw9qbHRYAoJWqqrHrvV05+vOmozp2rlSS1N7mrUk3RumhG6PVoa2vyRECaIlycnKUkpKi7t27a8GCBTp79qzjtcujLnJycjR8+HAtX75cgwcPVmBgoKZMmaLU1FQFBwcrICBAjz/+uJKSktxkkfAg02IAAHdEUQMATGK1WvT7sf1UVlWjD/ac0bTXMvXqQ4M1JKaj2aHBw1RU1+iZvx/Qm9tOSJJG9ArVC/fGc+crAMAUFdU1+lvmKb2y6ahOnb8oSQpq46MpN0brweQoBfqTnwA0n3Xr1ik7O1vZ2dnq2rX2NL+GYUiSqqqqlJWVpbKyMsdrCxculNVq1dixY1VRUaGRI0fqz3/+s0tj/75/LRLewdQ4AMDdUNQAABN5WS1aOK6/yitrtP5QvqYs2643pg5R/8ggs0ODhzhdeFGPvbFTX54slMUizbrlev17Sk9ZrRazQwMAtDIXK2u0cvsJLd78lc4UlUu6tAD41B/HaPyQ7mrHmhkAXGDSpEnXXHsjKirKUeC4zM/PT+np6UpPT2/G6Oovv7hc+SUVslqkvl0CzA4HANwKV5UAYDJfb6vSxw/UQ8u26/Oj32jikm1665Eh6hXOhSuu7vOj5/T4il36prRSgf4++uN9/ZnCDADgcgWllVq+9Wst33pcBaWVkqTQAJum3dxD9w/uJn9fL5MjBADPs+90kSSpR0g7tfHl5zsA+C6+FQHADfj5eOl/HhykCX/N0M4ThZrw1wytnJakHiHtzA4NbqjGbmjx5qNa8HGW7IbUOzxAiyckKDK4jdmhAQBakZMFZfrff36llTtOqrzKLknq2sFfjw7toXsGdZXNm2IGADTWvpxiSVLfLoEmRwIA7oeiBgC4ibY2by2dPFj3/+ULHThTrCnLtusfj9+k9qyLgO84XXhRqW/v1hdfFUiSxg7sqt/e3Vd+PvxwBABwjb2nirR481F9uPeM7N/O3tK3S4Cm3dxDt/YNk7eX1dwAAaAF2JdzaaRGnwhG8APA91HUAAA3Eujvo9emDNadf9qir78p0+x39+rl+wfIYmF9BEgf7j2j2e/uVdHFKrXx9dIzd/TRPYO6cnwAAJqdYRjafOScFn96VJ8f/cax/ebrQ/TozTFK6tGRfAQATWj/aUZqAEBdKGoAgJvp2M6ml+4foHsXb9X7e85oSExHPTCku9lhwUSlFdV65u/7tSrzlCQpvmugXrxvgKI7tTU5MgBAS1deVaPVu3K0bMvXysorkSR5WS26Mz5CU38co97cQQwATa6gtFI5hRclie9ZALgCihoA4IYSunfQf46K028/PKhn3z+g/pFB3KHTSu0+WaiZb+3S19+UyWKR/j2lh2aOuF4+TO0BAGhGZ4ouavnW43pz2wkVllVJktr4eum+H3XTQzdFqWsH1nECgOay/9tFwqM6tlEA0xEDwA9Q1AAAN/Xwj6OVcewbfXIwXzNW7GR9jVamxm5o0adHtXDdYVXbDUUE+umFe/trSExHs0MDALRQhmFo54lCLd1yTB/ty1XNtwtmdO3gr4lJURr3o0gF+nMtAgDN7fLUU324sQ0AroiiBgC4KYvFogX3xOu2lz5jfY1WJqfwop5YuVvbjl1aDPy2fuH63ZgbFNiGH5IAAE2vstquj/ad0ZLPjunLU0WO7YnRwZp8Y7Ru6R0qLyvXHwDgKpcXCe8bQVEDAK6EogYAuLGgNr6sr9HK/OPL0/p/q/eqpLxabX29NPeuvho7sAvFLABAk8spvKi3tp3QW9tP6mxJhSTJ19uqu+IjNOnGKPXhxzQAMMW/FglnPQ0AuBKKGgDg5lhfo3W4UFGtp9fs07s7cyRJ/SOD9Mf7+qt7RxYDBwA0nRq7oc1HzuqNL45rw6F8fTvDlDq3t2nCkO7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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ],
+      "metadata": {
+        "id": "thpsEzFVikPh"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Features of the Sigmoid Function"
+      ],
+      "metadata": {
+        "id": "WAidrU51fMqB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "def sigmoid(x):\n",
+        "    return 1/(1+np.exp(-x))\n",
+        "\n",
+        "x = np.linspace(-10, 10, 200)\n",
+        "y = sigmoid(x)\n",
+        "\n",
+        "plt.plot(x, y)\n",
+        "plt.title(\"Sigmoid Function\")\n",
+        "plt.xlabel(\"x\")\n",
+        "plt.ylabel(\"σ(x)\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"Range:\", (y.min(), y.max()), \"| At x=0:\", sigmoid(0))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "lehJV_vNfLXx",
+        "outputId": "8b8fc42b-d554-48ce-f300-0967bf244c83"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Range: (np.float64(4.5397868702434395e-05), np.float64(0.9999546021312976)) | At x=0: 0.5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ],
+      "metadata": {
+        "id": "DUONuiAxilL0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 5. Derivatives of Famous Functions"
+      ],
+      "metadata": {
+        "id": "1FjRt_31fXsx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = np.linspace(-3, 3, 400)\n",
+        "plt.plot(x, x**2, label=\"x²\")\n",
+        "plt.plot(x, 2*x, '--', label=\"d/dx x²\")\n",
+        "\n",
+        "x = np.linspace(0, 3, 400)\n",
+        "plt.plot(x, np.exp(x), label=\"exp(x)\")\n",
+        "plt.plot(x, np.exp(x), '--', label=\"d/dx exp(x)\")\n",
+        "\n",
+        "plt.plot(x, np.log(x+0.1), label=\"log(x)\")\n",
+        "plt.plot(x, 1/x, '--', label=\"d/dx log(x)\")\n",
+        "plt.ylim(-5, 15)\n",
+        "plt.legend(loc='upper left')\n",
+        "plt.title(\"Functions and Their Derivatives\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 487
+        },
+        "id": "vyyGBuHRfZ4n",
+        "outputId": "05e313fb-0c43-4c21-d73e-eb6c8c381358"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-15-2146270300>:10: RuntimeWarning: divide by zero encountered in divide\n",
+            "  plt.plot(x, 1/x, '--', label=\"d/dx log(x)\")\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ],
+      "metadata": {
+        "id": "DURup9u5il4-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 6. Gradient of Selected Functions"
+      ],
+      "metadata": {
+        "id": "w3QIKTZQf1ZK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# search and learn more about sumpy\n",
+        "import sympy as sp\n",
+        "\n",
+        "# Define symbolic variables\n",
+        "x, y = sp.symbols('x y')\n",
+        "\n",
+        "# Define the function\n",
+        "f = x**2 * y + sp.sin(y)\n",
+        "\n",
+        "# Compute partial derivatives\n",
+        "df_dx = sp.diff(f, x)\n",
+        "df_dy = sp.diff(f, y)\n",
+        "\n",
+        "print(\"Function: f(x, y) =\", f)\n",
+        "print(\"Partial derivative w.r.t x (∂f/∂x):\", df_dx)\n",
+        "print(\"Partial derivative w.r.t y (∂f/∂y):\", df_dy)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Eeudgf3-f0Ie",
+        "outputId": "9ce575c7-39f8-43f6-9374-d6f18d14e675"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Function: f(x, y) = x**2*y + sin(y)\n",
+            "Partial derivative w.r.t x (∂f/∂x): 2*x*y\n",
+            "Partial derivative w.r.t y (∂f/∂y): x**2 + cos(y)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ],
+      "metadata": {
+        "id": "wDlIJkmYimg9"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.3/AI-DS_Nexus__A0_3__rsayyareh.ipynb b/a0.1/a0.3/AI-DS_Nexus__A0_3__rsayyareh.ipynb
new file mode 100644
index 0000000..b61c9da
--- /dev/null
+++ b/a0.1/a0.3/AI-DS_Nexus__A0_3__rsayyareh.ipynb
@@ -0,0 +1,426 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7wR0aAR-evto"
+      },
+      "source": [
+        "# 📚 Assignment 3 — Functions, Derivatives & Gradients\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "\n",
+        "This assignment explores the mathematical foundation of machine learning: functions and their derivatives. You’ll visualize, analyze, and code up essential concepts every ML practitioner uses.\n",
+        "\n",
+        "\n",
+        "**👀🔎 What do you see?**\n",
+        "\n",
+        "Look at the snippet codes and:\n",
+        "\n",
+        "* Describe any patterns, surprises, or connections you notice.\n",
+        "\n",
+        "* Write a few sentences about your observations! 📝✨\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cCb1hk3xe0E6"
+      },
+      "source": [
+        "## 1. Log and Exp: Proving Inverse Relationship\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KOxJ9f-3eu5F",
+        "outputId": "31d8ce39-8670-4dc4-9053-6c6aaf10d1ff"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "x\tlog(exp(x))\texp(log(x))\n",
+            "1.00\t1.00\t\t1.00\n",
+            "2.00\t2.00\t\t2.00\n",
+            "10.00\t10.00\t\t10.00\n",
+            "2.72\t2.72\t\t2.72\n",
+            "\n",
+            "log(exp(y)) for 0 and negative values:\n",
+            "[ 0. -1.  5.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Show log(exp(x)) == x and exp(log(x)) == x for several values\n",
+        "x_vals = np.array([1, 2, 10, np.e])\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "for x in x_vals:\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# Edge case: try negative and zero values (show error/NaN for log)\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lVZufz08g1hI"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mOAqtnk6fDH6"
+      },
+      "source": [
+        "## 2. Plotting Sine Functions Variations\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "DoWdSehie66R",
+        "outputId": "a83a3ade-6ca7-425c-9fa8-59ca9c343d9a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.arange(-2*np.pi, 2*np.pi, 0.1)\n",
+        "\n",
+        "# look at the description of each function and call the proper function from numpy\n",
+        "plt.plot(x, np.sin(x), label='sin(x)')\n",
+        "plt.plot(x, np.sin(2*x), label='sin(2x)')\n",
+        "plt.plot(x, np.sin(x/2), label='sin(x/2)')\n",
+        "plt.plot(x, np.sin(x)**2, label='sin²(x)')\n",
+        "plt.legend()\n",
+        "plt.title(\"Variations of Sine Functions\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dMVcqrfihf0P"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4-5A1OkdfHXq"
+      },
+      "source": [
+        "## 3. Subplots: Trig, Inverse, Exp, and Log"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 807
+        },
+        "id": "DxocsCu6fGOR",
+        "outputId": "5864a5cc-5776-45bd-99f7-028c64e301af"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1600x800 with 8 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x = np.arange(-1, 1, 0.01)\n",
+        "x2 = np.linspace(0.1, 2, 400)  # For log and exp\n",
+        "\n",
+        "# based on the number of subplots what should be the dimensions in the following line?\n",
+        "fig, axs = plt.subplots(2, 4, figsize=(16, 8))\n",
+        "axs[0,0].plot(x, np.sin(x));      axs[0,0].set_title(\"sin(x)\")\n",
+        "axs[0,1].plot(x, np.cos(x));      axs[0,1].set_title(\"cos(x)\")\n",
+        "axs[0,2].plot(x, np.tan(x));      axs[0,2].set_title(\"tan(x)\")\n",
+        "axs[0,3].plot(x, np.arcsin(x));   axs[0,3].set_title(\"arcsin(x)\")\n",
+        "axs[1,0].plot(x, np.arccos(x));   axs[1,0].set_title(\"arccos(x)\")\n",
+        "axs[1,1].plot(x, np.arctan(x));   axs[1,1].set_title(\"arctan(x)\")\n",
+        "axs[1,2].plot(x2, np.exp(x2));    axs[1,2].set_title(\"exp(x)\")\n",
+        "axs[1,3].plot(x2, np.log(x2));    axs[1,3].set_title(\"log(x)\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "thpsEzFVikPh"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WAidrU51fMqB"
+      },
+      "source": [
+        "## 4. Features of the Sigmoid Function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "lehJV_vNfLXx",
+        "outputId": "9cfc6ec4-9dfc-4220-a955-4d6a11fac8f3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Range: (np.float64(4.5397868702434395e-05), np.float64(0.9999546021312976)) | At x=0: 0.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "def sigmoid(x):\n",
+        "    return 1 / (1 + np.exp(-x))\n",
+        "\n",
+        "x = np.linspace(-10, 10, 1000)\n",
+        "y = sigmoid(x)\n",
+        "\n",
+        "plt.plot(x, y)\n",
+        "plt.title(\"Sigmoid Function\")\n",
+        "plt.xlabel(\"x\")\n",
+        "plt.ylabel(\"σ(x)\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"Range:\", (y.min(), y.max()), \"| At x=0:\", sigmoid(0))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DUONuiAxilL0"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1FjRt_31fXsx"
+      },
+      "source": [
+        "## 5. Derivatives of Famous Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "vyyGBuHRfZ4n",
+        "outputId": "7a89fce7-f973-46bc-dca3-c452f1a34561"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x = np.linspace(-3, 3, 400)\n",
+        "plt.plot(x, x**2, label=\"x²\")\n",
+        "plt.plot(x, np.gradient(x**2, x), '--', label=\"d/dx x²\")\n",
+        "x = np.linspace(0, 3, 400)\n",
+        "plt.plot(x, np.exp(x), label=\"exp(x)\")\n",
+        "plt.plot(x, np.gradient(np.exp(x), x), '--', label=\"d/dx exp(x)\")\n",
+        "\n",
+        "plt.plot(x, np.log(x+0.1), label=\"log(x)\")\n",
+        "plt.plot(x, np.gradient(np.log(x+0.1), x), '--', label=\"d/dx log(x)\")\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.title(\"Functions and Their Derivatives\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DURup9u5il4-"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w3QIKTZQf1ZK"
+      },
+      "source": [
+        "## 6. Gradient of Selected Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Eeudgf3-f0Ie",
+        "outputId": "aeffb463-3230-4b98-8131-623b4fbf624c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Function: f(x, y) = x**2*y + sin(y)\n",
+            "Partial derivative w.r.t x (∂f/∂x): 2*x*y\n",
+            "Partial derivative w.r.t y (∂f/∂y): x**2 + cos(y)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# search and learn more about sumpy\n",
+        "import sympy as sp\n",
+        "\n",
+        "# Define symbolic variables\n",
+        "x, y = sp.symbols('x y')\n",
+        "\n",
+        "# Define the function\n",
+        "f = x**2 * y + sp.sin(y)\n",
+        "\n",
+        "# Compute partial derivatives\n",
+        "df_dx = sp.diff(f, x)\n",
+        "df_dy = sp.diff(f, y)\n",
+        "\n",
+        "print(\"Function: f(x, y) =\", f)\n",
+        "print(\"Partial derivative w.r.t x (∂f/∂x):\", df_dx)\n",
+        "print(\"Partial derivative w.r.t y (∂f/∂y):\", df_dy)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wDlIJkmYimg9"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.3/AI_DS_Nexus__A0_3__MrAshki.ipynb b/a0.1/a0.3/AI_DS_Nexus__A0_3__MrAshki.ipynb
new file mode 100644
index 0000000..f08d70f
--- /dev/null
+++ b/a0.1/a0.3/AI_DS_Nexus__A0_3__MrAshki.ipynb
@@ -0,0 +1,489 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7wR0aAR-evto"
+      },
+      "source": [
+        "# 📚 Assignment 3 — Functions, Derivatives & Gradients\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "\n",
+        "This assignment explores the mathematical foundation of machine learning: functions and their derivatives. You’ll visualize, analyze, and code up essential concepts every ML practitioner uses.\n",
+        "\n",
+        "\n",
+        "**👀🔎 What do you see?**\n",
+        "\n",
+        "Look at the snippet codes and:\n",
+        "\n",
+        "* Describe any patterns, surprises, or connections you notice.\n",
+        "\n",
+        "* Write a few sentences about your observations! 📝✨\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cCb1hk3xe0E6"
+      },
+      "source": [
+        "## 1. Log and Exp: Proving Inverse Relationship\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KOxJ9f-3eu5F",
+        "outputId": "14be8064-653c-4076-d200-b16946e98e02"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "x\tlog(exp(x))\texp(log(x))\n",
+            "1.00\t1.00\t\t1.00\n",
+            "2.00\t2.00\t\t2.00\n",
+            "10.00\t10.00\t\t10.00\n",
+            "2.72\t2.72\t\t2.72\n",
+            "\n",
+            "log(exp(y)) for 0 and negative values:\n",
+            "[ 0. -1.  5.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Show log(exp(x)) == x and exp(log(x)) == x for several values\n",
+        "x_vals = np.array([1, 2, 10, np.e])\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "for x in x_vals:\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# Edge case: try negative and zero values (show error/NaN for log)\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lVZufz08g1hI"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** تو بخش اولش میخواد بهمون یاد بده اگر یک عدد را اولش به توان برسونیم و بعدش ازش لگاریتم بگیریم همون عدد اولی رو بهم برمیگردونه.   تو بخش دومش میخواد بگه لگاریتم صفر و منفی قرار نیست به شکل اولیه برگردن."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mOAqtnk6fDH6"
+      },
+      "source": [
+        "## 2. Plotting Sine Functions Variations\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "DoWdSehie66R",
+        "outputId": "9b8d602d-ae09-46dc-eb5e-61339a826728"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.arange(-2*np.pi, 2*np.pi, 0.1)\n",
+        "\n",
+        "# look at the description of each function and call the proper function from numpy\n",
+        "plt.plot(x, np.sin(x), label='sin(x)')\n",
+        "plt.plot(x, np.sin(2*x), label='sin(2x)')\n",
+        "plt.plot(x, np.sin(x/2), label='sin(x/2)')\n",
+        "plt.plot(x, np.sin(x)**2, label='sin²(x)')\n",
+        "plt.legend()\n",
+        "plt.title(\"Variations of Sine Functions\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dMVcqrfihf0P"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "من میبینم که ضرب یک عدد در داخل پرانتز (مثل $2x$ یا $x/2$) باعث فشرده شدن یا کشیده شدن نمودار در راستای افقی (فرکانس) می‌شود. اما توان رساندنِ کل تابع، باعث تغییر در دامنه و حذف بخش‌های منفی نمودار می‌شود."
+      ],
+      "metadata": {
+        "id": "-u7Lrdn-5-0l"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4-5A1OkdfHXq"
+      },
+      "source": [
+        "## 3. Subplots: Trig, Inverse, Exp, and Log"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 428
+        },
+        "id": "DxocsCu6fGOR",
+        "outputId": "95bf99c1-e42b-4889-f027-b1c1ed1c1642"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 8 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "x = np.arange(-1, 1, 0.01)\n",
+        "x2 = np.linspace(0.1, 2, 400)  # For log and exp\n",
+        "\n",
+        "# based on the number of subplots what should be the dimensions in the following line?\n",
+        "fig, axs = plt.subplots(2, 4, figsize=(16, 8))\n",
+        "axs[0,0].plot(x, np.sin(x));      axs[0,0].set_title(\"sin(x)\")\n",
+        "axs[0,1].plot(x, np.cos(x));      axs[0,1].set_title(\"cos(x)\")\n",
+        "axs[0,2].plot(x, np.tan(x));      axs[0,2].set_title(\"tan(x)\")\n",
+        "axs[0,3].plot(x, np.arcsin(x));   axs[0,3].set_title(\"arcsin(x)\")\n",
+        "axs[1,0].plot(x, np.arccos(x));   axs[1,0].set_title(\"arccos(x)\")\n",
+        "axs[1,1].plot(x, np.arctan(x));   axs[1,1].set_title(\"arctan(x)\")\n",
+        "axs[1,2].plot(x2, np.exp(x2));    axs[1,2].set_title(\"exp(x)\")\n",
+        "axs[1,3].plot(x2, np.log(x2));    axs[1,3].set_title(\"log(x)\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "thpsEzFVikPh"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "من در این کدها، جعبه‌ابزار ریاضی لازم برای هوش مصنوعی را می‌بینم؛ یعنی درک اینکه چطور توابع مختلف فضا را تغییر می‌دهند، کجاها محدودیت ورودی دارند  و چطور می‌توان با ترکیب آن‌ها محاسبات را مدیریت کرد."
+      ],
+      "metadata": {
+        "id": "oMjIIWhV7u80"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WAidrU51fMqB"
+      },
+      "source": [
+        "## 4. Features of the Sigmoid Function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "lehJV_vNfLXx",
+        "outputId": "9cfc6ec4-9dfc-4220-a955-4d6a11fac8f3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Range: (np.float64(4.5397868702434395e-05), np.float64(0.9999546021312976)) | At x=0: 0.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "def sigmoid(x):\n",
+        "    return 1 / (1 + np.exp(-x))\n",
+        "\n",
+        "x = np.linspace(-10, 10, 1000)\n",
+        "y = sigmoid(x)\n",
+        "\n",
+        "plt.plot(x, y)\n",
+        "plt.title(\"Sigmoid Function\")\n",
+        "plt.xlabel(\"x\")\n",
+        "plt.ylabel(\"σ(x)\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"Range:\", (y.min(), y.max()), \"| At x=0:\", sigmoid(0))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DUONuiAxilL0"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "من تابعی را می‌بینم که مثل یک \"تعدیل‌کننده\" عمل می‌کند؛ ورودی‌های خام را به مقیاس احتمال بازه ۰ تا ۱ می‌برد و دارای یک رفتار غیرخطی نرم است که در مرکز $x=0$ بیشترین حساسیت را به تغییرات دارد."
+      ],
+      "metadata": {
+        "id": "1FVJVvOS8uZb"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1FjRt_31fXsx"
+      },
+      "source": [
+        "## 5. Derivatives of Famous Functions\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "vyyGBuHRfZ4n",
+        "outputId": "7a89fce7-f973-46bc-dca3-c452f1a34561"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x = np.linspace(-3, 3, 400)\n",
+        "plt.plot(x, x**2, label=\"x²\")\n",
+        "plt.plot(x, np.gradient(x**2, x), '--', label=\"d/dx x²\")\n",
+        "x = np.linspace(0, 3, 400)\n",
+        "plt.plot(x, np.exp(x), label=\"exp(x)\")\n",
+        "plt.plot(x, np.gradient(np.exp(x), x), '--', label=\"d/dx exp(x)\")\n",
+        "\n",
+        "plt.plot(x, np.log(x+0.1), label=\"log(x)\")\n",
+        "plt.plot(x, np.gradient(np.log(x+0.1), x), '--', label=\"d/dx log(x)\")\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.title(\"Functions and Their Derivatives\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DURup9u5il4-"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "رابطه هندسی شیب و مشتق: من می‌بینم که هر جا نمودار اصلی سربالایی تندتری دارد، مقدار مشتق (خط‌چین) بالاتر است. مثلاً در $x^2$، با دور شدن از صفر، فاصله مشتق از محور $x$ بیشتر می‌شود.ویژگی منحصر به فرد تابع نمایی: مشاهده می‌کنم که مشتق $e^x$ دقیقاً بر خودش منطبق است که نشان‌دهنده رشدِ تصاعدی و خود-تقویت‌کننده است.تفسیر نرخ تغییرات: در مورد لگاریتم، می‌بینم که با افزایش $x$، مشتق به صفر نزدیک می‌شود؛ این یعنی لگاریتم یک تابع \"خسته\" است که هر چه جلوتر می‌رود، تمایلش به صعود کمتر می‌شود."
+      ],
+      "metadata": {
+        "id": "ecpvGQyU-JpC"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w3QIKTZQf1ZK"
+      },
+      "source": [
+        "## 6. Gradient of Selected Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Eeudgf3-f0Ie",
+        "outputId": "1f393157-b70a-4d44-cef3-eeec64aaad8b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Function: f(x, y) = x**2*y + sin(y)\n",
+            "Partial derivative w.r.t x (∂f/∂x): 2*x*y\n",
+            "Partial derivative w.r.t y (∂f/∂y): x**2 + cos(y)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# search and learn more about sumpy\n",
+        "import sympy as sp\n",
+        "\n",
+        "# Define symbolic variables\n",
+        "x, y = sp.symbols('x y')\n",
+        "\n",
+        "# Define the function\n",
+        "f = x**2 * y + sp.sin(y)\n",
+        "\n",
+        "# Compute partial derivatives\n",
+        "df_dx = sp.diff(f, x)\n",
+        "df_dy = sp.diff(f, y)\n",
+        "\n",
+        "print(\"Function: f(x, y) =\", f)\n",
+        "print(\"Partial derivative w.r.t x (∂f/∂x):\", df_dx)\n",
+        "print(\"Partial derivative w.r.t y (∂f/∂y):\", df_dy)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wDlIJkmYimg9"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "i cant understand what happen behind this code !\n",
+        "leave it for later .... :("
+      ],
+      "metadata": {
+        "id": "gll3jn8L-4zi"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "3aMLwiNU_CVT"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git "a/a0.1/a0.3/AI\342\200\223DS_Nexus___A0_3_Functions,_Derivatives___RezaShokr.ipynb" "b/a0.1/a0.3/AI\342\200\223DS_Nexus___A0_3_Functions,_Derivatives___RezaShokr.ipynb"
new file mode 100644
index 0000000..aeb6ed6
--- /dev/null
+++ "b/a0.1/a0.3/AI\342\200\223DS_Nexus___A0_3_Functions,_Derivatives___RezaShokr.ipynb"
@@ -0,0 +1,424 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 3 — Functions, Derivatives & Gradients\n",
+        "This assignment explores the mathematical foundation of machine learning: functions and their derivatives. You’ll visualize, analyze, and code up essential concepts every ML practitioner uses.\n",
+        "\n",
+        "\n",
+        "**👀🔎 What do you see?**\n",
+        "\n",
+        "Look at the snippet codes and:\n",
+        "\n",
+        "* Describe any patterns, surprises, or connections you notice.\n",
+        "\n",
+        "* Write a few sentences about your observations! 📝✨\n"
+      ],
+      "metadata": {
+        "id": "7wR0aAR-evto"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Log and Exp: Proving Inverse Relationship\n"
+      ],
+      "metadata": {
+        "id": "cCb1hk3xe0E6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KOxJ9f-3eu5F",
+        "outputId": "31d8ce39-8670-4dc4-9053-6c6aaf10d1ff"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "x\tlog(exp(x))\texp(log(x))\n",
+            "1.00\t1.00\t\t1.00\n",
+            "2.00\t2.00\t\t2.00\n",
+            "10.00\t10.00\t\t10.00\n",
+            "2.72\t2.72\t\t2.72\n",
+            "\n",
+            "log(exp(y)) for 0 and negative values:\n",
+            "[ 0. -1.  5.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import -------- as np\n",
+        "\n",
+        "# Show log(exp(x)) == x and exp(log(x)) == x for several values\n",
+        "x_vals = np.array([1, 2, --------, np.e])\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "for x in --------:\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# Edge case: try negative and zero values (show error/NaN for log)\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ],
+      "metadata": {
+        "id": "lVZufz08g1hI"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Plotting Sine Functions Variations\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "mOAqtnk6fDH6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.linspace(-2 * np.pi, 2 * np.pi, 1000)\n",
+        "\n",
+        "# look at the description of each function and call the proper function from numpy\n",
+        "plt.plot(x, np.sin(x), label='sin(x)')\n",
+        "plt.plot(x, np.sin(2 * x), label='sin(2x)')\n",
+        "plt.plot(x, np.sin(x / 2), label='sin(x/2)')\n",
+        "plt.plot(x, np.sin(x) ** 2, label='sin²(x)')\n",
+        "plt.legend()\n",
+        "plt.title(\"Variations of Sine Functions\")\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "DoWdSehie66R",
+        "outputId": "d72540e3-47fa-4c6e-b2ec-f189dce34386"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ],
+      "metadata": {
+        "id": "dMVcqrfihf0P"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Subplots: Trig, Inverse, Exp, and Log"
+      ],
+      "metadata": {
+        "id": "4-5A1OkdfHXq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Define x ranges\n",
+        "x = np.linspace(-1, 1, 400)\n",
+        "x2 = np.linspace(0.1, 2, 400)  # For log and exp (log undefined at 0)\n",
+        "\n",
+        "# based on the number of subplots what should be the dimensions in the following line?\n",
+        "fig, axs = plt.subplots(2, 4, figsize=(16, 8))\n",
+        "\n",
+        "# Row 1\n",
+        "axs[0,0].plot(x, np.sin(x));      axs[0,0].set_title(\"sin(x)\")\n",
+        "axs[0,1].plot(x, np.cos(x));      axs[0,1].set_title(\"cos(x)\")\n",
+        "axs[0,2].plot(x, np.tan(x));      axs[0,2].set_title(\"tan(x)\")\n",
+        "axs[0,3].plot(x, np.arcsin(x));   axs[0,3].set_title(\"arcsin(x)\")\n",
+        "\n",
+        "# Row 2\n",
+        "axs[1,0].plot(x, np.arccos(x));   axs[1,0].set_title(\"arccos(x)\")\n",
+        "axs[1,1].plot(x, np.arctan(x));   axs[1,1].set_title(\"arctan(x)\")\n",
+        "axs[1,2].plot(x2, np.exp(x2));    axs[1,2].set_title(\"exp(x)\")\n",
+        "axs[1,3].plot(x2, np.log(x2));    axs[1,3].set_title(\"log(x)\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 804
+        },
+        "id": "DxocsCu6fGOR",
+        "outputId": "0302fb54-7750-42e5-ce8d-c57568cdbf5c"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 8 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ],
+      "metadata": {
+        "id": "thpsEzFVikPh"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Features of the Sigmoid Function"
+      ],
+      "metadata": {
+        "id": "WAidrU51fMqB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "def sigmoid(x):\n",
+        "    return 1 / (1 + np.exp(-x))\n",
+        "\n",
+        "x = np.linspace(-10, 10, 200)\n",
+        "y = sigmoid(x)\n",
+        "\n",
+        "plt.plot(x, y)\n",
+        "plt.title(\"Sigmoid Function\")\n",
+        "plt.xlabel(\"x\")\n",
+        "plt.ylabel(\"σ(x)\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"Range:\", (y.min(), y.max()), \"| At x=0:\", sigmoid(0))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 490
+        },
+        "id": "lehJV_vNfLXx",
+        "outputId": "6aa25d34-2cc9-4f8b-844b-2e0e8f391b11"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Range: (np.float64(4.5397868702434395e-05), np.float64(0.9999546021312976)) | At x=0: 0.5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ],
+      "metadata": {
+        "id": "DUONuiAxilL0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 5. Derivatives of Famous Functions"
+      ],
+      "metadata": {
+        "id": "1FjRt_31fXsx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# x² and its derivative\n",
+        "x = np.linspace(-3, 3, 400)\n",
+        "plt.plot(x, x**2, label=\"x²\")\n",
+        "plt.plot(x, 2*x, '--', label=\"d/dx x²\")\n",
+        "\n",
+        "# exp(x) and its derivative\n",
+        "x = np.linspace(0, 3, 400)\n",
+        "plt.plot(x, np.exp(x), label=\"exp(x)\")\n",
+        "plt.plot(x, np.exp(x), '--', label=\"d/dx exp(x)\")\n",
+        "\n",
+        "# log(x) and its derivative\n",
+        "plt.plot(x, np.log(x+0.1), label=\"log(x)\")\n",
+        "plt.plot(x, 1/(x+0.1), '--', label=\"d/dx log(x)\")\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.title(\"Functions and Their Derivatives\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "vyyGBuHRfZ4n",
+        "outputId": "da89b1ae-2b6e-4b19-d8ae-55684bf940c9"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ],
+      "metadata": {
+        "id": "DURup9u5il4-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 6. Gradient of Selected Functions"
+      ],
+      "metadata": {
+        "id": "w3QIKTZQf1ZK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# search and learn more about sympy\n",
+        "import sympy as sp\n",
+        "\n",
+        "# Define symbolic variables\n",
+        "x, y = sp.symbols('x y')\n",
+        "\n",
+        "# Define the function\n",
+        "f = x**2 * y + sp.sin(y)\n",
+        "\n",
+        "# Compute partial derivatives\n",
+        "df_dx = sp.diff(f, x)\n",
+        "df_dy = sp.diff(f, y)\n",
+        "\n",
+        "print(\"Function: f(x, y) =\", f)\n",
+        "print(\"Partial derivative w.r.t x (∂f/∂x):\", df_dx)\n",
+        "print(\"Partial derivative w.r.t y (∂f/∂y):\", df_dy)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Eeudgf3-f0Ie",
+        "outputId": "94c62073-9cea-4bb5-d4ab-4eac26fae9af"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Function: f(x, y) = x**2*y + sin(y)\n",
+            "Partial derivative w.r.t x (∂f/∂x): 2*x*y\n",
+            "Partial derivative w.r.t y (∂f/∂y): x**2 + cos(y)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ],
+      "metadata": {
+        "id": "wDlIJkmYimg9"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/a0.3/AI\342\200\223DS_Nexus___A0_3_Functions,_Derivatives___RoohollaAlikhani.ipynb" "b/a0.1/a0.3/AI\342\200\223DS_Nexus___A0_3_Functions,_Derivatives___RoohollaAlikhani.ipynb"
new file mode 100644
index 0000000..a16656d
--- /dev/null
+++ "b/a0.1/a0.3/AI\342\200\223DS_Nexus___A0_3_Functions,_Derivatives___RoohollaAlikhani.ipynb"
@@ -0,0 +1,519 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7wR0aAR-evto"
+      },
+      "source": [
+        "# 📚 Assignment 3 — Functions, Derivatives & Gradients\n",
+        "This assignment explores the mathematical foundation of machine learning: functions and their derivatives. You’ll visualize, analyze, and code up essential concepts every ML practitioner uses.\n",
+        "\n",
+        "\n",
+        "**👀🔎 What do you see?**\n",
+        "\n",
+        "Look at the snippet codes and:\n",
+        "\n",
+        "* Describe any patterns, surprises, or connections you notice.\n",
+        "\n",
+        "* Write a few sentences about your observations! 📝✨\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cCb1hk3xe0E6"
+      },
+      "source": [
+        "## 1. Log and Exp: Proving Inverse Relationship\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KOxJ9f-3eu5F",
+        "outputId": "31d8ce39-8670-4dc4-9053-6c6aaf10d1ff"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "x\tlog(exp(x))\texp(log(x))\n",
+            "1.00\t1.00\t\t1.00\n",
+            "2.00\t2.00\t\t2.00\n",
+            "3.00\t3.00\t\t3.00\n",
+            "2.72\t2.72\t\t2.72\n",
+            "\n",
+            "log(exp(y)) for 0 and negative values:\n",
+            "[ 0. -1.  5.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# Show log(exp(x)) == x and exp(log(x)) == x for several values\n",
+        "x_vals = np.array([1 , 2 , 3 , np.e])\n",
+        "\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "for x in x_vals:\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# Edge case: try negative and zero values (show error/NaN for log)\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "x\tlog(exp(x))\texp(log(x))\n",
+            "1.00\t1.00\t\t1.00\n",
+            "2.00\t2.00\t\t2.00\n",
+            "3.00\t3.00\t\t3.00\n",
+            "2.72\t2.72\t\t2.72\n",
+            "\n",
+            "log(exp(y)) for 0 and negative values:\n",
+            "[ 0. -1.  5.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np  \n",
+        "\n",
+        "# مقادیر مختلف برای بررسی رابطه‌های لگاریتم و نمایی\n",
+        "x_vals = np.array([1, 2, 3, np.e])  # مقدار جای‌خالی با عدد 3 پر شده\n",
+        "\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "\n",
+        "for x in x_vals:  # جای‌خالی دوم باید x_vals باشه\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# حالت‌های خاص مثل صفر و اعداد منفی\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))  # log(exp(0)) = 0، log(exp(-1)) خطا نمی‌ده ولی مقدار منفی می‌ده"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lVZufz08g1hI"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mOAqtnk6fDH6"
+      },
+      "source": [
+        "## 2. Plotting Sine Functions Variations\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "DoWdSehie66R",
+        "outputId": "a83a3ade-6ca7-425c-9fa8-59ca9c343d9a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.linspace(-2*np.pi, 2*np.pi, 500)\n",
+        "\n",
+        "# look at the description of each function and call the proper function from numpy\n",
+        "plt.plot(x, np.sin(x), label='sin(x)')\n",
+        "plt.plot(x, np.sin(2*x), label='sin(2x)')\n",
+        "plt.plot(x, np.sin(x/2), label='sin(x/2)')\n",
+        "plt.plot(x, np.sin(x)**2, label='sin²(x)')\n",
+        "plt.legend()\n",
+        "plt.title(\"Variations of Sine Functions\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dMVcqrfihf0P"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4-5A1OkdfHXq"
+      },
+      "source": [
+        "## 3. Subplots: Trig, Inverse, Exp, and Log"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 807
+        },
+        "id": "DxocsCu6fGOR",
+        "outputId": "5864a5cc-5776-45bd-99f7-028c64e301af"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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7mtzc3GSWXfQuFz3Q9+zZIysnRPb9SYmsvaj+EO/57LPPljy+b98+/Pjjj7INSePGjeVj4kSnffv2+Pjjj2X1RjHRqkpky8UuLCIiqjxffvmlLC0Xw/JEZZ3YiRQbG4t169bJ3bDi4pGonujevbssDRczk0SyWuxaEu2mxIlH8cmLGOzaunVrODo6Ijw8HD/88IOs2hAnNYKXl5fssyvWi1GjRpXEIJ4r1gVxciEGmQvi5CIgIEBWGory93tPQg4cOCAfJyKiyiESDsJHH30kEwqGhoZo27atTEKIz21RsZGRkYGff/5ZbqAS60h5iAGzYj0R64TYRXvvuYJIdnfo0AETJkyQj4k15u+//5Zrh1ivitcjQbRLEXGxHzoRUfk899xzWLlypUwSi81KYpOT+Gwubhn4X0QV9nfffSerIJo1a4YhQ4bIGRhnzpyRg8LF+YS4ZiQ+01944QVZiS2SHOLvFEmV/v37P5Xfo3fv3nLTlDh/EOtLsc8//1zOgd27d688V/Hz85MzRUTCXFw7E5u7ionfWyRjxHsRlZuaiMolNzdX/d5776n9/f3VlpaWanNzc3n8448/ljxn+PDh6po1a5bcv3r1qkihq2fNmvWv9xOPT58+vdRjb775prpOnTol99PS0uT7NW7cWJ2fn1/quRMnTlTr6empjx49WvLYggUL1GZmZvJ1RERUua5fv64eNmyY2t7eXm1sbKz28vJSv/7663L9EK5cuaIeMGCA2sbGRm1iYqIOCgpS//HHH6XeY9GiReq2bduqa9SoId+jdu3acu1JTU0t9bzZs2erLSws1FlZWfJ+QUGBulmzZmo3Nzd1SkpKqefOnTtXrjlr1qwpeeyvv/6Sj12+fLkC/xchIqL7ffbZZ2pXV1f5PV58Dovzha1bt6r9/Pzk2uDp6an+6quv1EuWLCn5eTFxXtCzZ89/vWe7du3k7V4bN25UV6tWTR0dHV3yWL9+/eR5zLVr10o9d8uWLfLvEn9vsfDwcPnYnj17nvL/AkREuuPOnTvqkSNHqu3s7OR3965du6ovXrwoP8/F9aNiS5culZ+5J06cKPN9xDrRqlUrtampqdrKykqeR/z+++/yZ1FRUepRo0bJ8waxjtja2qo7dOjwr8/vR/07//77b/m4+PNeYp0aPXp0yf2QkBC1gYGB+o033ij1vOLzEhcXF/n7Fxs4cKC6TZs2j/m/IFFp1cT/KX9ahIgqkhj4KnoXimHkYojg4xL9DEUlh8joExFR1SVKuUXlhqjaGz169GO/XrTOEjtwN23aVCHxERGRskSbE7E7WAysFbtsH9fbb78t+7SLdlSs2CAiIlEFIrqDiDbpNjY2j/XauLg41KpVC6tXr2bFBj0RJjaINJwoD4+MjJSl349jx44dstRPJEdE6ToREVVtX331lWxNeOHChVKtQ/6LaFkl+u6KsnHR0oqIiKomMUtDnFuIi1Cije6jEvOeatasKVsY3ttGhIiIdJdKpZKtpgYPHixbKj4O0ZZXtMwSMweJngQTG0REREREREREREREpDUefTsfERERERERERERERGRwpjYICIiIiIiIiIiIiIircHEBhERERERERERERERaQ0mNoiIiIiIiIiIiIiISGsYQAepVCrcunULlpaWqFatmtLhEBFpDbVajfT0dLi4uEBPr+rmxrlOEBGVD9cJIiJ6GK4TRET0tNYJnUxsiMXF3d1d6TCIiLRWTEwM3NzcUFVxnSAiejJcJ4iI6GG4ThAR0ZOuEzqZ2BAZ8+L/gaysrJQOh4hIa6Slpckv6MWfo1UV1wkiovLhOkFERA/DdYKIiJ7WOqGTiY3iMkCxuHCBISJ6fFW9nJrrBBHRk+E6QURED8N1goiInnSdqLoNDYmIiIiIiIiIiIiIqMphYoOIiIiIiIiIiIiIiLQGExtERERERERERERERKQ1mNggIiIiIiIiIiIiIiKtwcQGERERERERERERERFpDSY2iIiIiIiIiIiIiIhIazCxQUREREREREREREREWoOJDSIiIiIiIiIiIiIi0hpMbBARkUY7ePAgevXqBRcXF1SrVg2bN2/+z9fs378fjRs3hrGxMerUqYNly5ZVSqxERERERERERFTxmNggIiKNlpmZCX9/f8yfP/+Rnn/16lX07NkTHTp0wOnTp/H2229jzJgx2LlzZ4XHSkREREREREREFc+gEv4OIiLSIGq1WlY+aIvu3bvL26NauHAhatWqhW+//Vbeb9iwIQ4dOoTvvvsOXbt2rcBIiYiIiIiIiIioMjCxQUSkQ05cS8b0Lecxb0ggattboCo6evQoOnXqVOoxkdAQlRsPkpubK2/F0tLSKjRGokeRmpWPyMR03EzJwc072UjKyEVuQSFy8lVQqdQwNzaAhYkBrEwM4WFrhpo1im6WJoZKh05ERERVVGxqNsauCMGYZ2rhef+iVrFERETFwmPTMG/fZdR1sMTEzvVQkZjYICLSARm5BfjfX+FYdSxa3v92VwR+HNoEVVFcXBwcHR1LPSbui2RFdnY2TE1N//WamTNnYsaMGZUYJdG/xSRnYX9EAoKv3UHYjRRcv51VrvepbW+OJjWry1u7eg5wsjZ56rESERGRbvr54FWcvZmK345Ho3eAq9LhEBGRhhHnsX+ejUNjjxwmNoiI6MkcupyEDzaE4WZKtrw/OMgdk7s1VDosjTJlyhRMmjSp5L5Igri7uysaE+mGqMQMbAi9gZ3n4xGZkPGvn7tYm8DN1gyuNqZwsDSGiaE+jA31oF+tGjLzCpGRU4A7WXmITs7C9duZSMrIw5XETHlbe/KGfA9/dxt08XZE7wAXuFU3U+C3JKpYBw8exKxZsxASEoLY2Fhs2rQJffr0eeDz9+/fL+cw3U+81snJqYKjJSLSXrczcvFb8HV5/HqHOkqHQ0REGig5M0/+aWtuVOF/FxMbRERVVHpOPr788yJ+Dy6q0nC3NcVX/f3QqrYdqjJxUSo+Pr7UY+K+lZVVmdUagrGxsbwRVQbRTmrL6VtYcyIGIdfvlDyur1dNVlm0rWsnkxG+rtawMTN67C+Rp6LvyPc9GnUbp6JTcCam6PbNrgi0rWuPwUEe6NTQAQb6ehXw2xFVvszMTPj7+2PUqFHo16/fI78uIiJCrg3FHBwcKihCIqKqYenha7Ilpp+bNZ6pW7XPKYiIqHzExjuBiQ0iIiqXfy4nYvKGsyVVGsNb1sT73RrInvxVXcuWLfHnn3+Wemz37t3ycSIlpeXky7YNSw5dRUJ60UwXvWpA+/oO6BPoinb17GFt+mTzMcSXx44NHeVNSEjLwZ7wBPwRdgtHrtzGgUuJ8iZmcrzWvjb6NXaFsYH+U/n9iJTSvXt3eXtcIpFhY2NTITEREVXF7zHLj16Tx+Pb19Gq2Rqs7CMiqvyKjepMbBAR0ZNUaYiLl6JKo2XtGtBWGRkZiIyMLLl/9epVnD59Gra2tvDw8JBtpG7evIkVK1bIn48bNw4//PAD3n//fbl7d9++fVi7di22b9+u4G9BuiwnvxArjl7DD/sikZZTIB9ztDLG8FaeGNDYDQ5WFTcDQ7z3kOYe8iZaVa0+ESMrRUTrqikbz2Lunst4o2MdDGrmIStGiHRJQEAAcnNz4ePjg08++QStW7dWOiQiIo218uh1pOcUoK6DhWxxqU1Y2UdEpEArqsfsPlAeTGwQEVURwVeTMXHN6VJVGh90bwAzI+3+qD958mSpHVPFszCGDx+OZcuWyZ1T0dFFiRyhVq1aMokxceJEzJ07F25ubli8eDG6du2qSPyku9RqNbaeuYWvd0SU/Lus42CBV9t6yWGbRgaV2wqqZg1zfNCtAd58ti5+C47GTwevIC4tBx9tOicvVkzt6Y02bCtBOsDZ2RkLFy5E06ZNZWJDrBHt27fH8ePH0bhx4zJfI54nbvfOYiIi0hXZeYWy4lQY36E29LRsMwQr+4iIKg9nbBAR0SPLL1Rhzp5LWLD/ClTqolkaswb4o4WX9lZp3EtcbBIXiB9EJDfKes2pU6cqODKiB7uWlImPNp/F4cjb8r6TlQne6VIP/Rq7KV4ZYWqkj9FtauGlFh749Vg05u69jItx6Xjpl+Po1sgJn/ZpBAfLiqsiIVJa/fr15a1Yq1atcOXKFXz33XdYuXJlma+ZOXMmZsyYUYlREhFpDlENfjszT55n9PJzga54nMo+JsCJiCp/xganRhIRabGoxAz0X3AE8/8uSmq80MQNf73VtsokNYi0TaFKLZOMXecclEkNYwM9vNulHv5+tz1eaOqueFLjXmK2xqg2tXDgvfYY0cpTxrbjfBw6zz6IDSE3HppQJKpqgoKCSrU9vJ9oe5iamlpyi4mJqdT4iIiUklegwk8Ho+TxuHa1YaCvpzOVfRs2bJA3d3d3uXEqNDT0ga8RCXBra+uSm3gNEZEuup3Big0iInoIccFR9Mr/dNsFZOcXyoHDM/v5ooevs9KhEeks0W5q4urTCL6WLO+3qWOHL/r6yBZQmszGzAifPN8ILzZ1x/sbzuDczTS8s+4M/jwbi1kv+FfKF1IipYnZTeJC1oMYGxvLGxGRrllzMka2rnSwNMaAJm7QBeWp7BMJ8OKWucUVG0xuEJEuulOJFRtMbBARaWG/wskbwrDrQry836p2DXz7oj+crU2VDo1IZ/0RdksO4xZDNS2MDTC9l7c8+a9WTXMqNP6Lt4sVNo9vjZ/+icKcPZex92ICesz9B98PDkRQLVulwyN6oIyMjFLVFlevXpWJCltbW3h4eMiLTTdv3sSKFSvkz+fMmSPnMTVq1Ag5OTlyxsa+ffuwa9cuBX8LIiLNk5NfiB/2XZbHr3eoI6s9dbmy79ChQw/8ORPgRESQ60ZWXqE8rs7EBhER3et41G28ufoU4tNyYahfDe91rY8xbby0boAfUVWacfPln+FYeviavB/gboPvBwXCo4YZtJFoLzG+fR20r+eACb+HIioxE4N+Oop3utTH+Pa1tSpRQ7rj5MmT6NChQ8n94h2zw4cPl3OYYmNjER0dXfLzvLw8vPPOOzLZYWZmBj8/P+zZs6fUexAREfDr8Wh53uFibYJBQbpdffBflX1ERAR5/ihYmhjA0rji0w5MbBARaQGV6Nt/4Aq+3RUhZ2l42ZvLi6c+rtZKh0aks25n5OL130JxLKqo9ZS48D+xcz0YVoHe06J6Y9uENpi25Tw2hN7ArJ0RuBCbhm8G+Mvh40SaRPQ9f9hMGJHcuNf7778vb0RE9GCZuQVYsL+oGu7NjnW1ulqDlX1ERJXj/K1U+WcjF6tK2RRX4Wfe8+fPh6enJ0xMTNC8eXMEBwc/9KRE/NL333r27FnynBEjRvzr5926davoX4OISNGLpyOWnZAXFkVSo2+gq7zgyKQGkXIi4tLx/A+HZVLD3EgfC19qgve7NagSSY1i5sYGss3d//r5ygqx7WGxeGHREdxKyVY6NCIiIqpgy49eQ1JGHmrWMEN/LZ+tISr7AgMD5a24sk8cT5s2Td5/UGWfr68v2rVrhzNnzsjKvo4dOyr2OxARaYPzt9Lkn41cKud6VYVWbKxZs0YuGAsXLpRJDZH17tq1KyIiIuDg4PCv52/cuFEuIMVu374Nf39/vPDCC6WeJxIZS5cuLbnPPoZEpAutp0wM9fDp8z54oal29e0nqmqOXEnCqytD5DwNzxpm+HlYU9R1tERVNSjIA172Fhi3KkQOFu8z/zCWjwpCQ2crpUMjIiKiCpCWk49FB6Lk8dud6mr9xg1W9hERVX7FRmWo0NVp9uzZeOWVVzBy5Eh4e3vLBIfoY7tkyZIyny/KAJ2cnEpuu3fvls+/P7EhEhn3Pq969eoV+WsQESnSemr+35EY/PMxmdSobW+OLa+3wYvN3JnUIFLQ1jO3MGLJCZnUaOZZHZtfb12lkxrFxPDwLa+3Rn1HSySk5+LFRUdl4pWIiIiqniWHriI1Ox91HCzwvL+r0uEQEZGWDA4/c6MoseHnZqPdiQ1ReRESEoJOnTr9/1+mpyfvHz169JHe45dffsGgQYNgbm5e6vH9+/fLio/69evjtddek5UdRERVRUpWHkYt///WU/0CXbF1QhvUd6r6F0+JNNnKo9fw5u+nkFeoQncfJ6wc3Rw2ZkbQFe62Zlg7riWCPG1lYuflJcHYcS5O6bCIiIjoKbfBXfzPVXk8qXM96OtxUxUREf230zEpyCtQwd7SWG7O1erERlJSEgoLC+Ho6FjqcXE/Lu6/T4LFLI5z585hzJgx/2pDJQY67d27F1999RUOHDiA7t27y7/rQXJzc5GWllbqRkSkiS7cSpN9+/dHJMLYQA9fD/CTPe5Fr3siUs7if6Lw8Zbz8nhEK0/8MKQxTAy1d4hmeVmbGmLF6CB09naUX1rF8PRtZ24pHRYRERE9Jd/vvYyM3ALZRqRbIyelwyEiIi1x7G5FfwuvGpXWaURjr5SJag0xqCkoKKjU46KCo5j4uZ+fH2rXri2rOB40yGnmzJmYMWNGhcdMRPQktpy+iQ82hCEnXwV3W1M5jLiyBi4R0YOJtnCigkoY37423utaX6dbwomEzoKhjfHBhrPYEHoDb60+BZVajd4BbFVBRESkzaISM/Dr8aIh2h/1aAg9VmsQEdEjOnApUf7Z0qsGKkuFVWzY2dlBX18f8fHxpR4X98VcjIfJzMzE6tWrMXr06P/8e7y8vOTfFRkZ+cDnTJkyBampqSW3mJiYx/hNiIgqVn6hCjO2ncdbq0/LpEbbevbYNqENkxpEGmDB/islSQ0xPFPXkxrFDPT1MGuAH15s6iZb5k1cc1omZ4mIiEh7fbXjIgpUajzbwAGt6tgpHQ4REWmJpIxc2YpKEGuI1ic2jIyM0KRJE9kyqphKpZL3W7Zs+dDXrlu3TraPeumll/7z77lx44acseHs7PzA54hh41ZWVqVuRESaIDE9F0MXH8fSw9fk/Qkd6mDpiGY61befSFOtOnZdnuALIqHxdqd6TGrcQ+zi/F8/Pwxq5l6S3Nh5njM3iIiItNGJa8nYeT4eokhjSvcGSodDRERaZF94AtRqwNvZCk7WJtqf2BAmTZqEn3/+GcuXL0d4eLgc9C2qMUaOHCl/PmzYMFlNUVYbqj59+qBGjdKlKxkZGXjvvfdw7NgxXLt2TSZJevfujTp16qBr164V+asQET11Ipvda94hBF9NhoWxARa93ATvdq3PAX1EGkBUH3y85VxJwvH1DnWUDkljkxtf9vXFC02KKjfe+P0Ujl4p6q1KRERE2kGtVuOL7eHyeGAzD9R1tFQ6JCIi0iJb785d7OFbubOZKnTGxsCBA5GYmIhp06bJgeEBAQHYsWNHyUDx6Oho6OmVzq1ERETg0KFD2LVr17/eT7S2CgsLk4mSlJQUuLi4oEuXLvjss89kVQYRkTZdNH1vfZgcvlvHwUImNWrbWygdFhEB2HMhHpPWnpE7Toa1rIl3utRTOiSNT27M7OeL1Ox87LoQj1dWnMTqsS3g48p2ekRERNpg+9lYuenKzEgfEzvXVTocIiLSIglpOThyJUkeV/bcxQofHj5hwgR5K4sY+H2/+vXry90CZTE1NcXOnTufeoxERJVFpVLj290RmP/3FXm/U0NHzBkUICs2iEh5J68lY/xvoShUqdEv0BWf9GrE9lOPOHPj+8GBGLE0GMeikjF8STDWjWsJLyZsiYiINFpOfiH+91dR681X29aGg2XltRAhIiLtty0sVlbvN/awgbutWaX+3RXaioqIiP5fZm4Bxq0KKUlqvNa+Nn56uQmTGkQa4vrtTIxdGSIrqUTS8esBfrIagR6NiaE+fh7WFD6uVridmYdhS4LlEDkiIiLSXIsOROHGnWw4W5vglba1lA6HiIi0zOZTNxWp1hCY2CAiqgQ37mSh/4Ijsk2LkYEevhvojw+6NeBFUyINkZqVj5HLTiA5Mw9+btaYNzhQViHQ47E0McSykUGoWcNMXiQZtzIEuQWFSodFREREDzhH+XF/pDz+sEdDmBlxwxURET26sBspOHszFYb61dDTzxmVjWfsRESV0Nqm9w+HcTEuHXYWxrL3fN9AN6XDIqK7RIXGq6tOIioxEy7WJlg8rClMjfSVDktric+5X4Y3g6WJAU5ev4MpG84+sM0oERERKUcMDM8tUKGFly2eU+CCFBERabdVx67LP3v4OsvzwMrGxAYRUQVaH3IDQ34+LtuyeDtbYeuE1mjsUV3psIjoLnHB/aNNZ+VcCNEWbsnIZnCwYm/pJ1XHwQILhjaBvl41bDx1Ez/uL2rBR0RERJrhcGQS/joXJ9fqT57nTDEiIno8KVl52HL6ljwe1rImlMDEBhFRBV0snbXzIt5ddwZ5hSp0a+SE9a+1hIuNqdKhEdE9lh+5hnUhN+RJ/Q9DAtHAyUrpkKqMNnXt5IUSYdbOCOw4F6d0SERERAQgv1CF6VvPy+OXW9Tk9x8iInpsvwfHyKo/sYlXqQ28TGwQET1lop/822tOlwwJn9ChDn4c2pg9a4k0zIlryfh8e7g8ntK9AdrXd1A6pCpHXCwZ0cpTHr+z9jQiEzKUDomIiEjniY0dYk2uYW6EiZ3rKR0OERFpmZz8Qvxy6Ko8HtWmlmJVf0xsEBE95QHEw34JluV4BnrV8HV/P7zbtT6HhBNpmIS0HIz/NRQFKjV6+btgdJtaSodUZU3t2VD27s7MK8Rrq0KQmVugdEhEREQ662ZKNmbvviSP3+9WH9amhkqHREREWmZdyA0kZeTC1cYUvQNcFIuDiQ0ioqckJjkL/RYcxvGrRb36l45shhebuSsdFhGVMSxcJDUS03NR39ESX/X3ZV/pCmSgr4fvBwfCwdIYlxMyMGUjh4kTEREpQay/07ecQ1ZeIZrWrI4XmvBchYiIHv98etGBog4lrzxTC4b6yqUXmNggInoKzsSkoO+Ph3ElMRPO1iZynsYzde2VDouIyvDln+E4ef0OLI0NsPDlJmwTVwkcLE1kSz5Rybb1zC2sOHpd6ZCIiIh0jph3tSc8AYb61TCzny+ryomI6LGtORGNG3eyYWdhjIHNPKAkJjaIiJ7Q7gvxGPTTMSRl5KGhsxU2jW/NAXxEGmrn+TgsO3JNHs8eGIBaduZKh6QzmnraYkqPhvL48+0XEBp9R+mQiIiIdEZaTn7JwPDX2tVGXUdLpUMiIiItk5VXgLl7I+XxWx3rwNRIX9F4mNggInoCK45ew6srTyI7vxDt6tlj3biWcLI2UTosIirDrZRsvL8+TB6PbeuFzt6OSoekc0a19kRPX2fkF6rx5u+n5EUWIiIiqnizdkQgIT0XXnbmGN+hjtLhEBGRFlp6+JqcreFha6Z4tYbAxAYRUTn7036zMwLTtpyHSg0MDnLH4uFN5WwNItI8hSo13l5zGqnZ+fB1tca7XeorHZJOErNMZvb3hbutqSxfnrb5nNIhERERVXmiSnLV8aI2kJ/39YGJobI7bImISPvcyczDwruzNd7pUg9GBsqnFZSPgIhIyxQUquTw2x/+Liq/m9S5Hr7s66vowCQiergf9kUi+GoyzI30MW9woEZ8CdNVViaGmDMwEPp61bD59C1sOnVD6ZCIiIiqrJz8Qry37gzUamBAEze0qm2ndEhERKSFvtkVgfScAtmCvZefCzQBz+qJiB7zxGD8r6FYfSIGYtaeGLr3Zse6chcyEWmmE9eSMXfvpZJdip6cq6G4JjWr462OdeXxx5vPI/p2ltIhERERVUnf7b6EK4mZsLc0xtSeRbOuiIiIHse5m6n4LThaHn/Syxt64oKYBmBig4joEYkWNsN+CcauC/Fyt/ePQ5tgcJDyPQWJ6MEycgswcc1p2TKuX6Ar+ga6KR0S3fV6hzpo5lld/v/orTWnkF+oUjokIiKiKteC6ud/ouTxzL6+sDEzUjokIiLSMiqVGtO2nJOVf70DXNDcqwY0BRMbRESPID4tBwMXHUXwtWRYGhtgxaggdPNxUjosIvoPX2wPl7Mc3KqbYkbvRkqHQ/cQrai+GxgASxMDnIpOwYL9Rf1aiYiI6OlUmr+77kzJ5o5O3o5Kh0RERFpofcgNhEanyLbOH/bQrMo/JjaIiP5DVGIG+i84gotx6bKEe82rLdFCgzLURFS2A5cS8fvdctmvB/jB0sRQ6ZDoPm7VzfB5Hx95PG/fZYTHpikdEhERUZUwe/clRCVmwsHSGNN7cXMHERGVb5Pv59svyOO3OtWFo5UJNAkTG0RED3H2RioGLDwqd3x71jDDxtdawdvFSumwiOgRWsd9sD5MHo9o5clBmRrseX8XdPF2RH6hWu4sZUsqIiKiJ3PyWjIWF7eg6ucLazNu7iAiosejVqvx0aZzSMspgL+bNUa1rgVNw8QGEdEDBF9NxuCfjyE5Mw++rtZY/1oruNuaKR0WET2CGdvOIy4tB7XszPFBtwZKh0MPUa1aNTnU3cbMEOdvpeHHv9mSioiIqLzSc/LxdvF8scau6NiQLaiIiOjxbT1zC3vC42GoXw1fD/CHgb7mpRE0LyIiIg1pYTNsyXE51LaFly1+H9sCdhbGSodFRI9gz4V4bAy9Cb1qwDcv+MHUSF/pkOg/OFiaYMbzjUpaUl24xZZURERE5TFty3lZbe5ua1qythIRET2OxPRcTN96Xh6/8Wxd1HeyhCZiYoOI6D47zsVizPITyMlXoUN9eywbGQQLYwOlwyKiR9ylOHXzOXn8yjNeaFLTVumQ6DFaUnVt5IgCFVtSERERlceW0zex6VTR5o45AwM4X4yIiMrVgmrKxjCkZOXD29kKr7WvDU3FxAYR0T02hNzA+F9DZa/3nr7OWPRyU5gYcrc3kbaYtTNCtqCqWcMMEzvXUzocetyWVH18Ud3MEBdi07D4n6tKh0RERKQ1YpKzMHXTuZLdtdzcQURE5bHi6HXsCU+Akb4evnnBH4Ya2IKqmOZGRkRUyVYevYZ31p2R/WhfaOKG7wcHwsiAH5NE2iLk+h2sPHZdHn/Z15dJSS1kb2mMqT295fHcvZcQfTtL6ZCIiIg0XqFKjUlrTyM9twCBHjZ449k6SodERERaKDw2DV/8GS6Pp/RoAG8XK2gyXrEjIgKwYP8VfLylqH/giFae+Kq/H/RFDTcRaYW8ApUsl1Wrgf6N3dC6jp3SIVE5iUGnrWrXkO0Ap245J0uhiYiI6MEW7I/EiWt3YG6kj7kDAzVywCsREWm27LxCvPH7KXlu/WwDB3ltTNNxtSMinSYumM3aeRFf7bgo74vdTdN7eUOPSQ0irfLTwSu4FJ8BW3MjTO3ZUOlw6IlbUvnIirmDlxKxLSxW6ZCIiIg01vGo2/huz2V5/GlvH3jUMFM6JCIi0sJrY9O3nkNkQgYcLI0xa4CfPC/TdExsEJFOf3DP2HYB8/++Iu9P7t4A73SprxUf3kT0/6ISM/D9vkh5PO05b1Q3N1I6JHpCXvYWmNChqI3Gp9vOIzUrX+mQiIiINE5SRq7cXStaUfUJcJFVj0RERI/rt+BorD15A2KP75yBAahhYQxtwMQGEekklUqNqZvPYdmRaxB5jM/6+GBcu9pKh0VE5dpZcl6Wyz5T1w69A1yUDomeklfbeaGOgwWSMvLwvx1FfV6JiIioiEhmvL36NBLSc+V6+UVfX27QIiKics2q/GRrUWv297o2QCstauvMxAYR6WRS48NNZ/Hr8WiZ1Jg1wB8vt6ipdFhEVA47z8fjn8tJMNLXw2e9fXhCX4UYG+jjiz4+8vj34Bicir6jdEikoQ4ePIhevXrBxcVFfgZs3rz5P1+zf/9+NG7cGMbGxqhTpw6WLVtWKbESET0t8/ZdxqHIJJga6mPB0MYwNzZQOiQiItIyCek5GP9rCPIL1eju44Rx7bygTZjYICKd29n0/oYwrD4RI0vsZr/ojwFN3JQOi4jKOdzssz8uyOOxbb3gaWeudEj0lDX3qlHSVkPsIhKJaaL7ZWZmwt/fH/Pnz3+k51+9ehU9e/ZEhw4dcPr0abz99tsYM2YMdu7cWeGxEhE9DYcuJ2Hu3qK5Gl/280FdR0ulQ9JoTIATEf2b6Hrw+q+hiE/LRV0HC8x6wV/rNgoysUFEOpXUeHfdGawPuQF9vWqYMygQfQOZ1CDSVj/uj8TNlGy42pji9bvzGKjqEfOPLIwNcOZGKtaFxCgdDmmg7t274/PPP0ffvn0f6fkLFy5ErVq18O2336Jhw4aYMGECBgwYgO+++67CYyUielLxaTl4a/UpqNXA4CB3ns88AibAiYjKbul84todWBobYNHLTeQ5l7bRvoiJiMqhoFCFSWvPYOuZWzDQq4bvBweih6+z0mERUTldS8rEogNR8nhqz4YwNdJXOiSqIA6WJnirY1188Wc4vt4RgW4+zrA2NVQ6LNJiR48eRadOnUo91rVrV3nh6kFyc3PlrVhaWlqFxkhEVJbcgkKMWxWC25l5aOhshem9GikdktYkwMXtUd2bABdEEvzQoUMyAS7WCyIibffzP1H4PbioPfucQQHwsreANmLFBhFVefmFKry1+nRJUuOHIY2Z1CDScp/+cQF5hUUDw7v5OCkdDlWw4a08UdveXF7I+W73JaXDIS0XFxcHR0fHUo+J+yJZkZ2dXeZrZs6cCWtr65Kbu7t7JUVLRPT/u2unbT6PU9EpsDIxkHM1TAy5saMyE+Di8QcRyW+xjtx7IyLSRDvOxWLmXxfl8cc9vdGxYenvxdqEiQ0iqvI9A9/47RS2n42FoX41LHipCS+CEmm5vyMSsO9igvw3/cnzjbSuDyg9PiMDPfn/a2HlseuIiEtXOiTSMVOmTEFqamrJLSaGbdGIqHKtOnYda04WzQmcN6QxZ4tVICbAiaiqOh2TgrfXnJbtDIe1rImRrT2VDumJMLFBRFV7ENJvodhxPg5G+nqyZ2Bnb+3NRBNRUVu5L7aHy+MRche/dpbM0uN7pq49ujZylPOSxCBxsXOVqDycnJwQHx9f6jFx38rKCqampmW+RgyPFT+/90ZEVFmOR93GjG0X5PEH3RqgXT17pUOi+zABTkSaLiY5C2OWn0ROvgod6ttj2nPeWr9JsMITG2I4k6enJ0xMTNC8eXMEBwc/8LnLli2T/4PeexOv+1f55bRpcHZ2liceojzw8uXLFf1rEJEWtp8SSY3dF+LlTt+fhzfFsw2Y1CDSdqtPxCAyIQPVzQwx4dm6SodDlWxqT2/5mX406jb2hicoHQ5pqZYtW2Lv3r2lHtu9e7d8nIhI09xKycb4X0NRoFKjl78Lxrb1UjqkKo8JcCKqahLTc/HSL8eRlJErZzSJyj8Dfe2vd6jQ32DNmjWYNGkSpk+fjtDQUPj7+8u+hAkJDz4RFR/+sbGxJbfr16+X+vnXX3+N77//Xg5zOn78OMzNzeV75uTkVOSvQkRaltR48/dTJUmNX4Y35a4moiogPSe/ZL7C253qcYC0DnK3NSspl/7yr3D5eU+UkZGB06dPy5tw9epVeRwdHV2yi3bYsGElzx83bhyioqLw/vvv4+LFi/jxxx+xdu1aTJw4UbHfgYioLNl5hXh1ZdGwcG9nK3zd30/rd9dqAybAiagqSc3Ox7Alwbh+OwvutqZYNrIZLIwNUBVUaGJj9uzZeOWVVzBy5Eh4e3vLZISZmRmWLFnywNeIRVpkx4tv9/Y1FNUac+bMwdSpU9G7d2/4+flhxYoVuHXrFjZv3lyRvwoRaVGbGtEv8K9zRe2nfh7WVLYvISLt9+P+K/LE3sveHEOaeygdDink9Q51YGtuhKjETKwOLrpwTbrt5MmTCAwMlDdBbKwSx6LKWxCbpYqTHEKtWrWwfft2eZFKbLz69ttvsXjxYrlZiohIU6hUakxccxpnb6bKde+nYU1gasRh4eXBBDgR6aqc/EK8svwkwmPTYGdhjJWjmsPRqnR3JG1WYYmNvLw8hISEyFZRJX+Znp68f/To0YcuODVr1pSDlkTy4vz58yU/E4uPGOJ073uKoUyixdXD3pOIdIPou/7OujPYHlY0KHzhy41ZqUFURdy4k4VfDl2Vx1O6N4RhFSibpfKxMjHE252K2pB9t+cy0nLylQ6JFNa+fXu5Aer+m2hzK4g/9+/f/6/XnDp1Crm5ubhy5QpGjBihUPRERGX7aufFUrMC3aqbKR2S1mICnIh0UX6hChN+C0XwtWRYmhhgxaggeNqZoyqpsKsCSUlJKCwsLFVxIYj7IjlRlvr168tqji1btmDVqlVQqVRo1aoVbty4IX9e/LrHeU9BnLCkpaWVuhFR1UtqvLf+DLacvgUDvWqYP6QxZ2pUIY8zryk/Px+ffvopateuLZ8vTkZ27NhRqfHS0/f1jgjkFajQ0qsGOjV0UDocUtjgIA9ZuZOcmYcF+68oHQ4REdFTJSoSFx2IksdfD/BDM09bpUPSakyAE5EuXiN7f30Y9oQnwFi2aG8Gb5eqN/tHo7Y7in6FovwvICAA7dq1w8aNG2Fvb49FixY90fvOnDlTVnYU30Q1CBFVrTLtKRvDsDH0JvT1qmHe4EB0aeSkdFik0Lwm0a5QrBvz5s3DhQsXZCl537595YkJaafTMSnYeuYWREvpj3o2ZG9pkhU7onJHEJU8N1OylQ6JiIjoqTgcmYSpm8/J47c61kWfQFelQyIiIi27RjZ5Qxg2nSq6Rvbj0MYIqlU1E+QVltiws7ODvr4+4uPjSz0u7ovZGY/C0NBQlgdGRkbK+8Wve9z3FP0SU1NTS24xMTHl+I2ISFM/sD/afA5rT96AXjVg7qAAdPd1VjosUnBe08qVK/Hhhx+iR48e8PLywmuvvSaPRQk5aR+xm+7L7eHyuF+gG3xcrZUOiTSEqNxp4WUrK3m+2RmhdDhERERPLDIhHeNWhaBApUbvAJeS1otERESPeo3sw01nsS7khkxqiGtkHRtW3W4mFZbYMDIyQpMmTbB3796Sx0RrKXFfVGY8CtHK6uzZs3B2di7pcygSGPe+p2grdfz48Ye+p7GxMaysrErdiKhqXPCcvvU8fg+OlkmN7wYG4Dk/F6XDIoXnNYlycdGC6l6mpqY4dOhQhcdLT9/+S4myJ6gon323az2lwyENIip3PurhLY83n76JiLh0pUMiIiIqt7jUHAxfcgLpOQVoWrM6vurvxypVIiJ6rGtkU7ecw+oTMfIa2ewX/av8NbIKbUUlWof8/PPPWL58OcLDw+Wu2czMTLnrVhBtp0Q1RTHRE33Xrl2IioqS7UZeeuklXL9+HWPGjJE/F4v622+/jc8//xxbt26VSQ/xHi4uLujTp09F/ipEpIEf2J/+cQErj12X7WlmDfBH7wCWaVc15ZnXJNpUiSqPy5cvy4S6GPonWhuKoYAPwllMmrvbZNaOop34w1t5wtnaVOmQSMP4ulmjh68T1Grgm12s2iAiIu2Ump2P4UuCZWvFWnbmcli4iaG+0mEREZEWXSObtuU8fjseLa+RffuiblwjM6jINx84cCASExMxbdo0eQFKzM4QA1yLL1BFR0fLnbfF7ty5I9uNiOdWr15dVnwcOXJEth4p9v7778vkyNixY5GSkoI2bdrI97x/dy4RVe0P7P/9dRFLD1+T97/q54f+TdyUDos0xNy5c+Va0qBBA5kQF0PERUL9Qa2rimcxzZgxo1LjpP+2/WwsLsSmwcLYAOPa1VY6HNJQkzrXw45zcdh9IR6h0XfQ2KO60iERERE9spz8Qryy/CQi4tNhb2mMFaOCUMPCWOmwiIhIizYETtt6DquOFSU1vhngj76BunGNrJpaXCHUMWInrhgiLuZtsC0Vkfb5fu9lzN59SR5/0dcHQ5vXVDoknVHZn5+iFZWYp7F+/fpSlXnDhw+Xye0tW7Y88LU5OTm4ffu2rOqbPHky/vjjD5w/f/6BFRvidu/v6e7uznVCQQWFKnT+7iCuJmViYqd6eIs9pukh3lt3RvaRbVW7Bn57pYXS4eg0XfmerSu/JxFVrEKVGuN/DcHO8/GwNDbAmldbwtulan+m6Mrnp678nkSk/Hnz++vDsPHUTZnUEG0MX2zqDl35/KzQVlRERE/bL4euliQ1Pn7Om0mNKu5J5jWJSj5XV1cUFBRgw4YN6N279wOfy1lMmmd9yA2Z1LA1N8LoZ2opHQ5pOJH4MtLXw5Ert3HocpLS4RARET1aL/TN52RSQ6xhPw1rWuWTGkRE9PTkFajw5upTMqkhBoXPGRig9UmNx8XEBhFpjdXB0fjsjwslrUdGt+HFTl3wuPOajh8/LmdqiHlN//zzD7p16yaTIaKVIWlPS4a5ey/L4/Hta8tWVEQP41bdDEOae8jjWTsvyotFREREmmzOnsv4PbiobcjcQQFoWbuG0iEREZEWnTO/uvIk/jwbJ5PjC4Y21omZGvfjlQIi0gpbTt/ElE1n5fGrbb3wxrN1lA6JKsnjzmsSLaimTp0qExsWFhbo0aMHVq5cCRsbGwV/C3ocq45dR2xqDlysTfBSC1Zl0aOZ8GwdrD0ZgzM3UuXu124+TkqHREREVKbF/0SVbOL4tLcPuvs6Kx0SERFpiczcAoxZfhJHo27DxFAPP73cFG3r2UMXMbFBRBpPDISdtPYMxAbcl1p4YHL3oqHQpDsmTJggb2XZv39/qfvt2rXDhQtFlT2kfTJyC/Dj/isl7YVMDPWVDom0hJ2Fsazkm7cvEt/uikBnb0dZkk1ERKRpGzg+3x5eUoX+MjdxEBHRI0rOzMOoZSdwOiZFdjZYMqIZgmrZQlexFRURaTTRK/31X0PlYL1+ga749HkfJjWIqrBlh6/KL2tedubo39hN6XBIy4x5xgvWpoa4nJCBrWduKh0OERFRKRtCbsi5GsK4drVZhU5ERI8sJjkLAxYckUkNGzNDrBrTXKeTGgITG0SksU5eS8YrK04ir1CFbo2c8PUAP+hx9y1RlZWek4+f/7laUq1hoM+vKfR4RFJjbFsveSwqN0RSnIiISBNsD4vFe+vPyOMRrTzxQbf63LBFRESP5MKtNPRbcARRSZlwtTHF+nEtEeDOdtu8YkBEGunczVSMXHoC2fmFaFfPHnMHB/AiJ1EVt+LodaRm58PL3hzP+bkoHQ5pqWEta8oER1RiJv4Iu6V0OERERNhzIR5vrT4FkW8f1Mwd057zZlKDiIgeyZErSRi46CgS03PRwMkSG15rhToOlkqHpRF4lZCINM7l+HS8/MtxpOcWyLK6hS81gbEB++wTVfXZGj//EyWPRVsGzkag8rI0McSYNrXk8fd7L7Nqg4iIFHXgUiLG/xqKApUavQNc8EVfX1ahExHRIxEbtUYsOSGvjzWvZYs1r7aEk7WJ0mFpDCY2iEijXL+diaGLj+NOVj783azxy/CmMDViUoOoqlt59DpSsvJRy84cvVitQU9oeGtPWJkY4EpiJv48G6t0OEREpKP+jkgo1Vr32xf8uXmDiIgeyZJDV/HG76fkGtLD1wnLRwXJynT6f0xsEJHGSEjLwUu/HEfC3fI68aEtdt4SUdWWeU+1xoQOddh2jp6YlYkhRrcpnrVxGSpWbRARUSXbGx6PV1eEIK9Aha6NHPH94EB+xyEiov9UUKjCtC3n8OkfF6BWF7XanTe4MUwMuen3flxViUgjpGblY9iSYMQkZ6NmDTOsGB0EGzMjpcMiokqw6th1JGfmwbOGmWzRQPQ0jGjtCUsTA1yKz8Bf5+KUDoeIiHTI7gvxGLcqpGSX7Q9DGsPIgJdfiIjo4dJz8jF6+Uk5f1L4oFsDzHi+Eav9HoArKxEpLjuvEKOXn8DFuHQ4WBpj1ejmcLBkz0AiXZCVV4CfDhZVa7zOag16ikSZ9qjW/z9rg1UbRERUGXaci8P4X0OQX6hGTz9nzB0UCEN+vyEiov9w404WBiw4KmczmRjqYeFLjfFa+9qoVo1JjQfh6kpEisovVMkv/iev35H90EWlhrutmdJhEVEl+fVYNG5n5sHD1gx9A12VDoeqGJHYsDQ2QER8OnaeZ9UGERFVLDHXacJvoTKp0cvfBXMHBjCpQURE/+lU9B30mX9YnreIDb9rX22Jbj7OSoel8bjCEpFixO7Z99adwd8RRdnoJSOaoYGTldJhEVElyckvxKK71RqcrUEVwdrMULakEr7fFwm1aFJLRERUAdaejJFJjQKVWrbW/O5Ff363ISKi//RH2C0M+ukYkjLy0NDZCptfbw0/Nxulw9IKXGWJSBHi4pIYhLT59C0Y6FXDgqFN0NTTVumwiKgSrQu5gaSMXLjamKJvY1ZrUMUY3aYWzIz0ER6bhv2XEpUOh4iIqqBfDl3F++vDILoevtjUDbNfDGBSg4iI/vO6mGiZO+G3U8gtUKFjAwesH9cSLjamSoemNbjSEpEi5u2LxLIj1+Txty/6o0MDB6VDIqJKVFCowk8Hr8jjsW292KaBKoyNmRGGBHnI4wX7i/6bIyIieloXpb7bfQmf/XFB3h/Tpha+6u/HIa9ERPRQGbkFeG1VKGbvvlSyGeunYU1hbmygdGhahVcRiKjSrTx6reTD+5Ne3ugdwJ3aRLpm+9lYxCRno4a5EV5s6q50OFTFjXlGJM+qIfhqMkKuJysdDhERVZG2ujO2XcDcvZfl/Xc618NHPRtyyCsRET3U9duZ6PfjYew4HyfPUf7XzxcfP+fNpHg5MLFBRJVq65lbmLb1vDx+s2NdjGhdS+mQiEiB3Y3FO+dHtvaEqZG+0iFRFedkbYJ+gW7ymFUbRET0NCpP31sfVlKBPuP5RnijY10mNYiI6KEOXEpEr3mHcCk+Qw4JXz22JQbdrS6nx8fEBhFVmv0RCZi05jTE7NaXW9TExE51lQ6JiBSwPyIRF+PSYW6kj5dbFA12JqpoY9t5QVxv2hOegIi4dKXDISIiLZWTX4jxv4ZiQ+gNubv22xf8MbwVv88QEdHDN/ctPHAFI5cGIy2nAIEeNtj2Rhs0qVld6dC0GhMbRFQpQq7fkf0DC1Rq9PJ3kbuauKOJSDcV75gf2qImrM0MlQ6HdERtewt0a+QkjxcdYNUGERE9vjuZeRi6+Dh2XYiHkb4efhzaGP2bFFUEEhERlSUrrwBv/H4K//vrIlRqYGBTd6we2wKOViZKh6b1mNggogp3OT4do5adQHZ+IdrWs5e7mvTYO5BIJ528lozga8nyYoAYkEZUmV5rX1v+ueXMLdy4k6V0OEREpEVikrPQf+ERuWHLysQAK0YHoevdhDkREdED144FR/FHWCwM9Krhsz4++F9/XxgbsB3z08DEBhFVqNjUbAxfEozU7HxZarfwpcYwMuBHD5GuV2v0a+zKHSpU6fzcbNCmjh0KVWr8fDBK6XCIiEhLnLuZin4LjiAqMRMu1iZY/1ortPCqoXRYRESkwfZciEfP7/9BeGwa7CyM8NsrLWRbdnYveXp4dZGIKoxIZoxYcgK3UnPgZW+OJcObwczIQOmwiEghF+PSsPdigpxzMLatl9LhkI5Xbaw+EYOkjFylwyEiIg33z+VEDFx0FInpuWjgZImN41ujnqOl0mEREZGGKihUybZTY1acLJmnsXVCGwTVslU6tCqHiQ0iqrCheq+sOImI+HQ4WBpjxaggVDc3UjosIlLQT3d3yPfwcYaXvYXS4ZCOalW7BvzcrJFboMKKI9eUDoeIiDTYhpAbGLn0BDLzCuX6sXZcSzhZs+KUiIjKlpCeI2cxiUHhwsjWnlgztiVcbEyVDq1KYmKDiJ460eJj4prTCL6aDEtjAywbGQS36mZKh0VECopPy8G2M7fk8Sus1iAFidLvV9sWVW2sOh4tE/FERET3UqnU+HZXBN5ZdwYFKjV6B7jIcxorE0OlQyMiIg11LOo2en5/CMevJsPcSB8/DAnE9F6N2I69ArEnDBE9VWq1GjO2ncdf5+LkcOBFw5rA28VK6bCISGHLj1xDfqEazTyrI8DdRulwSMd1beQIVxtT3EzJxsbQmxjS3EPpkIiISENk5xXi3XVnsP1sbEkLw/e61IeeHnuiExFR2cnwRQejMGvnRajUQD1HCyx4qQlqs0tBhWPKiIieqh/3X8GKo9fl8bcv+qNVbTulQyIihWXlFeDX49HyeHQbVmuQ8gz09WRZuPDLoSh5MkJERJSQloNBPx2VSQ1D/Wr45gV/fNCtAZMaRERUptSsfIxdeRJf7ShKavQLdMXm11szqVFJmNggoqdmfcgNzNoZIY+nPeeNXv4uSodERBrSnzo1Ox81a5ihs7ej0uEQSQObuct2iVcSM3HgUqLS4RARkcLO30pF7/mHceZGKqqbGWLV6OYY0MRN6bCIiEhDhUbfQc95/2BPeIJsNzWzn6/c4GtmxAZJlYWJDSJ6Kv6OSMAHG8Lk8attvTCqTS2lQyIiDSB2wv9y6Ko8HtW6FvS545E0hKWJIQYFucvjxYeKBtsTEZFu2n0hHi8sPIrY1BzUtjeXu22be9VQOiwiItLQc9wF+6/IdePGnWx42Jph42utMDjIQ87zo8rDxAYRPbEzMSkYvypUDg3vG+gqy7WJiIQ94fG4djsLViYG3PVIGmfE3WTb4cjbcqcuERHp3nzAnw5ekW1EsvIK8UxdO2wc3xo1a5grHRoREWmghPQcDF8aLFtPiWtgz/k5448328DH1Vrp0HQSExtE9ESuJmVi1LITyM4vOhH4qr8fe9ASUYnFd6s1hraoCXNjluSSZhEDxHv4Osvj4soi0k7z58+Hp6cnTExM0Lx5cwQHBz/wucuWLZO76e69idcRke4NCX9r9Wl8+edFqNXA0OYeWDKiGaxNDZUOjYiINJBoX9tj7j/453ISTAz18FV/X8wbHAgrE64bSmFig4jKLTE9F8OWHMftzDz4uFphwUtNZF9BIiIh7EYKgq8mw0CvGoa3LBrUTKRpRt9tnbjtzC3Ep+UoHQ6Vw5o1azBp0iRMnz4doaGh8Pf3R9euXZGQkPDA11hZWSE2Nrbkdv369UqNmYiUFZOchX4LjmDrmVvye8qnvRvh8z4+MNTnuUxVxiQ4EZVHfqEKM/8Kx/AlwUjKyEMDJ0v88UYbDGzG1lNK46pNROWSkVuAkcuCEZNc1E9w6YggWHA3NhHdo3gHfC9/FzhZ80SQNFOAuw2aeVZHfqEaK45eUzocKofZs2fjlVdewciRI+Ht7Y2FCxfCzMwMS5YseeBrxEmok5NTyc3R0bFSYyYi5Ry6nIRePxxCeGwa7CyM8OuY5hjW0pMXp6o4JsGJqLyJcDFLY9GBopl8L7eoKecw1XGwVDo0YmKDiMojr0CF11aF4NzNNNQwN8LyUUGwtzRWOiwi0iCxqdnYHhZbakc8kaYa3cZL/rnqWLRsTULaIy8vDyEhIejUqVPJY3p6evL+0aNHH/i6jIwM1KxZE+7u7ujduzfOnz9fSRETkZLzNH4+GCUrzlOy8uHvZo2tE9pwSLiOYBKciB7XH2G3ZOup0zEpcmbkwpca47M+PjAx1Fc6NKqsxMbjlPr9/PPPeOaZZ1C9enV5Eyck9z9/xIgR/yoH7NatW0X/GkR0zwnB5I1hsqegqaG+7ENby47D9YiotF+PRaNApUbzWrYcpEYar7O3I9xtTZGanY/Np28qHQ49hqSkJBQWFv7rYpO4HxcXV+Zr6tevLy9kbdmyBatWrYJKpUKrVq1w48aNB/49ubm5SEtLK3UjIu2bp/HFn+FQqYEBTdyw5tWWcLExVTo0qiJJcK4TRFVHek4+Jq09jQm/nUJ6bgGa1KyOP996Bt18imbzkY4kNh631G///v0YPHgw/v77b7m4iMWjS5cuuHmz9AmmSGTcWw74+++/V+SvQUT3+G7PZWwMvQl9vWr48aXG8He3UTokItIwOfmF+D04Wh6PaMXZGqT5xJo2rEXRf6vLj1yTSXyqulq2bIlhw4YhICAA7dq1w8aNG2Fvb49FixY98DUzZ86EtbV1yU2cpxCR9rQR6X/PPI0ZzzfCrAF+3HGrQyojCc51gqhqOHktGd3n/iOve+lVA954tg7WjG0Bt+pmSodGlZ3YeNxSv19//RXjx4+XJxkNGjTA4sWL5eKxd+/eUs8zNjYuVQ4oqjuIqOKtPRGD7/delsdf9PFBh/oOSodERBroz7OxuJ2ZB2drE7kTnkgbvNjUXVYiXoxLx/GryUqHQ4/Izs4O+vr6iI+PL/W4uC/OEx6FoaEhAgMDERkZ+cDnTJkyBampqSW3mJiYJ46diCre7gvx6Pn9P7gQW9RCV8zTGN6K8zTo6SfBuU4Qaf+A8G93ReDFRUdx40423KqbYu2rLfFOl/ow0OckB02lp2mlfvfKyspCfn4+bG1t/1XZ4eDgIDPor732Gm7fvv3Q92FJINGTO3ApEVM2nZXHImM9KMhD6ZCISEOJHe/CSy1q8ksgaQ1rM0P0CXQt9d8waT4jIyM0adKk1Eao4o1R4qLUoxC7eM+ePQtn5we3FxAbq8QQ2XtvRKS5CgpVmPlXOF5ZcRJpOQUIcLfBtjc4T0NXVUYSnOsEkfaKSsyQlX3z9kXKdoX9G7vhr7eeQVPP0tejSfPoaVKp3/0++OADuLi4lEqOiDZUK1askCcrX331FQ4cOIDu3bvLv+tBWBJI9GTO30rF+FUhKFSp0TfQFZM611M6JCLSUGKw2pkbqTDS18PAZlxvSbsMb1VT/rnrQjxupWQrHQ49ItH6VszqW758OcLDw+XGp8zMTFk1Logdt2InbbFPP/0Uu3btQlRUlGyX+9JLL+H69esYM2aMgr8FET0t8Wk5GPLzcSw6ECXvj2ztKXfdcp6G7qqsJDgRaRfRfva349Ho+f0hhN1IhbWpIeYPaYxvX/SHpYmh0uHRIzCAhvrf//6H1atXy+oMMXi82KBBg0qOfX194efnh9q1a8vndezYscz3Eicy4oSnmKjYYHKD6NGICzujlp1AZl4hWnrVwFf9/Vi6TUQPVLzT/Tl/Z9hZGCsdDtFjaeBkhRZetjgWlYxVx67j/W4NlA6JHsHAgQORmJiIadOmyQ1Uom3Ijh07SjZYRUdHy8rxYnfu3JHtcsVzRUtbcbHryJEjsnUuEWm3I5FJeHP1KSRl5MHC2ABfD/BDD19eiKaiJPjw4cPRtGlTBAUFYc6cOf9Kgru6usqNscVJ8BYtWqBOnTpISUnBrFmzmAQnqkKSMnIxeUMY9oQXzYFuXacGvnnBH87WTIJrEwNNLPX75ptvZGJjz549MnHxMF5eXvLvEuWAD0psiJJAcSOix5OWk4+RS08gPi0XdR0ssPDlJjAyYFsZIipbYnoutofFyuPhLTk0nLSTGHgvEhurT8TgzY51OVxWS0yYMEHeyiI2QN3ru+++kzciqjpUKjXm/x2J7/Zckm1EGjhZ4sehjeFlb6F0aKQhmAQnomJ/X0zAe+vPyCS46DTwfrf6GNW6FvTEtHDSKgaVUerXp0+fUqV+DzrpEL7++mt88cUX2Llzp8yk/5cbN27IGRssByR6uvIKVHhtVQgi4tPhYGmMZaOCZFkeEdGDrA6ORl6hSvax9ne3UToconLp1NARLtYmuJWag21nbuGFpqzyJSLSZLczcjFp7Rk5E1B4sakbPu3tw8Q0/QuT4ES6LSO3AJ//cUFuYBLqO1pizqAANHTmTBxtpadJ/W7FzIyPP/4YS5Ysgaenp8yMi1tGRob8ufjzvffew7Fjx3Dt2jWZJOndu7csDezatWtF/ipEOtdncPLGMByOvA0zI30sGdEMruxJS0QPkV+owqrj10t2vBNpKzHw/qWWRbM2lh+9JtdEIiLSTIcjk9Bt7j8yqWFsoCdbT309wJ9JDSIiKuXoldvoNuegTGqI7uqj29TClgmtmdTQcgaaVOq3YMEC5OXlYcCAAaXeZ/r06fjkk09ka6uwsDCZKBE9DsVg8S5duuCzzz5jqymip+i7PZexMfQm9PWqYf7QxvBxtVY6JCLScLvOx8u2dXYWRuju+/CWk0SablAzD8zZcxnnbqYhNPoOmtS0VTokIiK6b0PFd7svYcGBKxD55zoOFpg3OJAXqIiIqJTsvEJ8vfMilh4umgXpbmuKWQP80cKrhtKhkTYMD3+cUj9RhfEwpqamskUVEVWctSdi8P3ey/L48z4+6FDfQemQiEiLhoYPCfKAsQF3SZJ2szU3wvP+LlgfcgPLj1xnYoOISIPEJGfJAeGnolPk/cFB7pj2XCOYGvH7BxER/T+xQendtWcQlZQp7w9p7oEPezSEhXGFXw6nSsL/TxJRCVHCPWXTWXk8oUMdDA7yUDokItICF26lIfhaMgz0qmFI86IWPkTabljLmjKxseNcHJIz82Syg4iIlLU9LFa2zE3PKYCliQH+188PPf04b5OIiP5fbkEh5u65jIUHrkClBpysTPDVAD+0q2evdGj0lDGxQUTS+VupGL8qBIUqNfoGuuKdLvWUDomItMTKY0WzNbo2coKTtYnS4RA9FX5uNvB1tcbZm6lYHxKDsW1rKx0SEZFOtxL59I/z+D24aOBrYw8bzB0UCHdbM6VDIyIiDbu29c7aM7gYly7v9wt0xfRejWBtZqh0aKRtw8OJSDvcSsnGqGUnkJlXiJZeNfBVfz9UE9OUiIj+Q0ZuAbaevimPX2rBag2qWoY2L6pc/O14NFRiuxcREVW68Ng0PP/DIZnUEKcor3eojTWvtmRSg4iIShQUqjBv72X0/uGwTGrUMDfCwpeaYPbAACY1qjBWbBDpuLScfIxcekIO/a3rYIGFLzeBkQFznkT0aLacvimTol725mjhxTkEVLX08nfBF9vDce12Fo5cuY02de2UDomISGeIhPLiQ1H4Zucl5BWq4GBpjDkDA9CqDj+LiYjo/12OT8e7687gzI1Ueb9bIyd80dcHNSyMlQ6NKhgTG0Q6LK9AhfGrQhERny5PFJaNCoK1KTPZRPRo1Gq13MlePDSclV5U1ZgbG6BvY1esOHodvx6/zsQGEVEluZmSjXfWnsaxqGR5v1NDR3zV35cXqYiIqER+oQo/HYyS8zREAtzKxACf9vZB7wAXnpvqCCY2iHT4guSUjWdxKDIJZkb6WDKiGVxtTJUOi4i0SNiNVJy/lSarvPo3dlM6HKIKMaS5h0xs7LoQj4S0HDhYcY4MEVFFV4NO3XxODggX5ynTnvPGwGbuvEhFRESl2hS+t/4Mzt1Mk/efbeCAL/v6cuajjmFig0hHzdsXiQ2hN6CvVw3zhzaGj6u10iERkZYprtbo4eOE6uZGSodDVCEaOFmhac3qOHn9DtaejMGEZ+sqHRIRUZWUmpWPqVvOYduZW/J+oIcNvnsxAJ525kqHRkREGtR5ZP7fkfJWoFLLriOfPO+NPgGuTIDrICY2iHR0F9Ts3Zfk8ae9G6FDfQelQyIiLZzPs/XuhYchzTk0nKq2oS08ZGJDDK59rX0duSmAiIiensORSbI/emxqjvyMffPZunJIuIE+Z/8REVGRszdSZZWGGA4udG3kiM/6+MDBklUauoqJDSIdc+JaMt5bFyaPx7b1wlBekCSicth86iay8wtRx8ECzTyrKx0OUYXq7uOMGdsuyJ7vBy4l4NkGjkqHRERUJeTkF2LWzgj8cuiqvF/LzhzfDQxAgLuN0qEREZEGrRXf772MRQejUKhSw9bcSG7S7enrzCoNHcfEBpEOuZaUibErTsqhSiKzPblbA6VDIiItxKHhpGtMDPUxoLEbFh+6il+PRTOxQUT0FJy/lYpJa84gIr5o5+3Q5h74qGdDmBnxMgURERUJjb6D99eHITIhQ95/zs8ZM55vhBoWxkqHRhqA3xiIdERKVh5GLTuBO1n58HezxpyBgdBjKw0iKofQ6BRZ/mvMoeGkQwY395CJjb8jEmTlhquNqdIhERFppfxCFX78+wrm7bss+6PbWRjh6wF+TBoTEVGpKo1vdxVV9KnUgJ2FMT7v44NuPk5Kh0YahIkNIh0ZrvTqyhBEJWXKCzE/D28KUyN9pcMiIi1VXK3xnJ8LrM0MlQ6HqFLUtrdAq9o1cOTKbawJjsakLvWVDomISOtcjEvDO2vP4PytNHm/u4+T7I8uLlgREREJwVeT8cGGMFxNypT3+wW6Ylovb9iYGSkdGmkYJjaIdKBlzOSNYTh+NRkWxgZYMqIZBysRUbmlZuXjj7DioeEeSodDVKnEXCqR2Fh9IgZvdqzLobZERI+ooFAle6PP2XMJ+YVq2JgZ4tPePujlx/7oRERUJDO3QM5dWn70GtRqwNHKGF/29UXHhqzoo7IxsUFUxc3bF4mNoTehr1cN84c2Rn0nS6VDIiIttvHUDeQWqNDAyRKNPTjYk3RLZ29H2TIlIT0X+yMS0cmbJ1lERP/lcnw63l13BmdupMr7nRo64st+PtxsRUREJfZdjMfUTedwKzVH3h/Y1B0f9mwIa1N2CKAH4zYzoipsy+mbmL37kjz+tHcjtKtnr3RIROUyf/58eHp6wsTEBM2bN0dwcPBDnz9nzhzUr18fpqamcHd3x8SJE5GTU/QFiZ6sAuz34LtDw5tzaDjpHiMDPfQNdJXHa07GKB0OEZFGK1SpsejAFfScd0gmNaxMDDD7RX/8PKwJkxpERCQlpOfg9d9CMWrZSZnUcKtuihWjgvDVAD8mNeg/sWKDqIo6cS0Z760Lk8dj23rJ9hlE2mjNmjWYNGkSFi5cKJMaImnRtWtXREREwMHB4V/P/+233zB58mQsWbIErVq1wqVLlzBixAh5EX727NmK/A5VxemYFFyKz5BDw3sHFF3cJdI1A5u54+d/rmLfxQR5IsaLc0RE/3YlMUNWaZyKTpH3O9S3x8x+fnCy5mcmEREBKpVabhSa+Wc40nIKZJeRMW1q4a1OdWFmxMvV9Gj4XwpRFXQtKRNjV5xEXqEKXRs5YnK3BkqHRFRuIhnxyiuvYOTIkfK+SHBs375dJi5EAuN+R44cQevWrTFkyBB5X1R6DB48GMePH6/02KuatSdvyD97+Dpz9wzprDoORW3YQqNTZKvHce1qKx0SEZFGVWksPXxV9kgXrSstjQ3wcS9vvNDEjZWeREQkRSZk4MNNZ+WQcMHX1Roz+/nCx9Va6dBIy7AVFVEVk5KVh1HLTuBOVj783KwxZ2Ag9PR4EkHaKS8vDyEhIejUqVPJY3p6evL+0aNHy3yNqNIQryluVxUVFYU///wTPXr0eODfk5ubi7S0tFI3Ki07rxDbzhQNDX+hqZvS4RApXrUhrD0RI1u0ERFR0YWqQT8dxefbw2VS45m6dtgxsS1ebOrOpAYRESG3oBBz91xGj7n/yKSGqaE+pvZsiE3jWzGpQeXCig2iKiSvQIVXV4YgKikTrjamWDy8KUyN9JUOi6jckpKSUFhYCEfH0gN6xf2LFy+W+RpRqSFe16ZNG3nBsaCgAOPGjcOHH374wL9n5syZmDFjxlOPvyr582wsMnIL4GFrhha1aigdDpGievq5YMa2C3K9PXn9Dpp52iodEhGRoucgYpbGvH2RsmLc3EgfH/X0xuAgJjSIiOj/26VP2XhWJsGF9vXt8VlvH7jbmikdGmkxVmwQVRHiAu7kjWE4fjUZFsYGWDKiGft+k07av38/vvzyS/z4448IDQ3Fxo0bZeuqzz777IGvmTJlClJTU0tuMTEcCny/tXcHJYtWEqwCI10n1tnn/Jzl8ZoT/LwgIt11KvoOes07hG93X5JJDXGhaufEthjS3INJDSIiQmp2Pj7adBYvLDwqkxp2Fkb4fnAglo5oxqQGPTFWbBBVET/si5S9vsXApflDG6O+k6XSIRE9MTs7O+jr6yM+Pr7U4+K+k5NTma/5+OOP8fLLL2PMmDHyvq+vLzIzMzF27Fh89NFHspXV/YyNjeWNHjy3RyRNxfWJ/k3YhoqouB2VmDuzPSwW03t5w9KEc2eISHdk5hbIORrLj16D6Mhna24kPwuf93dhQoOIiOTm2x3n4jB963kkpOfKxwY2dceUHg1gY2akdHhURbBig6gK2HL6ptwlJXzauxHa1bNXOiSip8LIyAhNmjTB3r17Sx5TqVTyfsuWLct8TVZW1r+SFyI5IrAXfvmsDykaGt62rj1cbEyVDodIIzT2qI7a9ubIzi/EH2GxSodDRFRp/o5IQJfvDmLZkaKkRr/GrtgzqR16B7gyqUFERLiVko1XVoTgtV9DZVLDy84cv7/SAl8N8GNSg54qVmwQVYE+he+tC5PHrzxTC0Ob11Q6JKKnatKkSRg+fDiaNm2KoKAgzJkzR1ZgjBw5Uv582LBhcHV1lXMyhF69emH27NkIDAxE8+bNERkZKas4xOPFCQ56dIUqdUliQwz/JKIi4uKdqNr48s+Lsh3V4CAPpUMiIqpQtzNy8dkfF7D59C153626Kb7s64u23FRFRER3zx1XHbuOr3dcRGZeIQz0quG19rXxeoc6MDHkuTg9fUxsEGl5e5ixK07KfrZdGzliSveGSodE9NQNHDgQiYmJmDZtGuLi4hAQEIAdO3aUDBSPjo4uVaExdepUecFR/Hnz5k3Y29vLpMYXX3yh4G+hvQ5eTkRcWg6qmxmik7eD0uEQaZS+gW74ekcETsek4FJ8Ouo5sg0kEVU9ouJ106mbMqlxJysfYtTWqNa1MKlLPZgZ8ZICEREBF+PSMHnDWfm9WGjsYYOZ/fzYJp0qFL+FEGmplKw8jFp2Qp5c+LlZY87AQA70pSprwoQJ8vagYeH3MjAwwPTp0+WNnty6u0PD+wS6wtiAu2yI7mVvaYxnGzhg14V4WbXx8XPeSodERPRUxSRn4cNNZ/HP5SR5v4GTJb7q7wd/dxulQyMiIg2Qk1+I7/dexk8Ho1CgUsPC2AAfdG+AoUEevEZFFY6JDSItlFegwqsrQxCVlAkXaxMsHtYUpka84EhET7/lxO4LRYPbX2jCNlREZRHtqERiQ+xm/qBbAxgZcIQdEVWNdiJLD1/Ft7suyVlC4rPtrY51MbatFwz1+TlHRETAkcgkmfy+djtL3hedRGY87wMnaxOlQyMdwcQGkRaWgk/eGIbjV5NlJnzJyGZwsOKiQURPn+ihnV+ohq+rNbxdrJQOh0gjtatnDwdLYzkYcW94PLr7OisdEhHREwmPFe1EwnDmRqq837yWLWb284WXvYXSoRERkQa4k5mHL/4ML5nF6GhlLBMa3XyclA6NdAwTG0Ra5od9kdgYehP6etUwf2hjNHDixUYiqpgkanEbqhebsVqD6EEM9PXQv4kbFuy/gg2hN5jYICKtbicyb99lLDpQ1E7E0sQAH/ZoiIFN3dlOhIiI5DniltO38OkfF5CcmYdq1YCXmtfEe93qw8rEUOnwSAcxsUGkRbacvolvd1+Sx5/2biR3iRIRVYSzN1NxMS4dxgZ6eN7fRelwiDRa/8auMrGxPyIRSRm5sLMwVjokIqLHcizqNj7ceFa2uhW6NXLCjN6N4MjKcCIiujtz6aPN53DwUqK8X8/RQlbzNalpq3RopMOY2CDSEievJeO9dWHy+JVnamFo85pKh0REVZioDBO6NHKCtSl33xA9TB0HS/i7Wcu2LVtP38KoNrWUDomI6JGk5eRj5p8X8XtwtLwvWuuJDVTdfFh9RkREQEGhCksPX8Ps3f8/c+nNZ+tgbNvanC1HimNig0gLXEvKxCsrTiKvUIUu3o6Y3L2h0iERURWWV6DC1jO3SnaiE9F/69fYTSY2Np66wcQGEWmFHefiMG3LOTkjSBgc5IHJ3RtwQwMREUlnb6TKGa/nb6XJ+y28bPFlX85cIs3BxAaRhkvJysOoZSdwJysffm7WmDMoQM7XICKqKAcuJcqeqfaWxmhTx07pcIi0Qi9/F3y+/QLO3UxDRFw66jtZKh0SEVGZ4tNyMH3Leew4Hyfv17Izl+1EWnjVUDo0IiLSAFl5BZi96xKWHL4KlRoy4f1Rj4Z4oakbqonBGkQagokNIg3fNT1uVYjsdetibYLFw5rCzIj/bImoYm0MvSH/7BPgIgcjE9F/szU3Qof6Dth1IV4OERcDd4mING3o6+oTMfjyz3Ck5xTAQK8aXm3nhTeerQsTQ32lwyMiIg3wd0QCpm46h5sp2fK+mLf48XPectMbkabhFVIiDT7xmLLxLI5FJcPC2ABLRjaDA4f3EVElVIntDU8oaa1DRI+ufxM3mdjYdOom3u9an4lBItIYUYkZ8tzi+NVkeV/MBfpffz80dLZSOjQiItIAiem5+OyPCyUtiV1tTPF5Xx+5cYdIU1X42db8+fPh6ekJExMTNG/eHMHBwQ99/rp169CgQQP5fF9fX/z555//utg7bdo0ODs7w9TUFJ06dcLly5cr+Lcgqnw/7IuUOz5F26n5QxujgRNPOoio4v0RFivn+YgLHbzYQfR4xIlfdTNDeWJ4KDJJ6XB0ytM+5yCqKvILVZj/dyS6zf1HJjVMDfUxtWdDbBzfmus86RyuFUT/Jq6zrj0Rg06zD8ikhuh8PqZNLeya2JZJDdLtxMaaNWswadIkTJ8+HaGhofD390fXrl2RkFC0E/R+R44cweDBgzF69GicOnUKffr0kbdz586VPOfrr7/G999/j4ULF+L48eMwNzeX75mTk1ORvwpRpdpy+ia+3X1JHs94vhHa1bNXOiQi0rE2VBwaTvT4jAz0ZLm+sDH0ptLh6IyKOOcgqgrCbqSg17xDmLUzQra4faaunbxQNeYZL87sI53DtYKo7Gq+wT8fw/sbwpCanY9GLlbY8nobTH3OG+bGbPJDmq+aWqTmKojIgDdr1gw//PCDvK9SqeDu7o433ngDkydP/tfzBw4ciMzMTPzxxx8lj7Vo0QIBAQEykSFCdXFxwTvvvIN3331X/jw1NRWOjo5YtmwZBg0a9EhxpaWlwdraWr7Wyoq7VEiznLyWjCE/H5c7pl95phY+6umtdEhEOvf5qSu/5/2uJmWiwzf75S6dYx92hIMl298RledC4vM/HIaxgR5OTO0EKxND6BIlPj+f9jnHo9DVdYK0c+irqCSb1ssbfQJcOfSVFKfU52dlrxVcJ0iTiWT3Twev4Pt9kfLYxFAPkzrXw6jWtdhKlRT3OJ+fFfZfa15eHkJCQmSrqJK/TE9P3j969GiZrxGP3/t8QWTQi59/9epVxMXFlXqO+EXFAvWg9xRyc3Pl/yj33og00fXbmRi7MkQmNbp4O2Jydw4eJaLKs+lutUbbevZMahCVk6+rNeo4WCC3QIU/w2KVDqfKq4hzjrLwfIK0xcFLiejy3UEsPlSU1OgT4II9k9qhb6AbkxqksypjreA6Qdoi5PodWc33za5LMqkhzv12T2yHsW1rM6lBWqfC/otNSkpCYWGhrKa4l7gvkhNlEY8/7PnFfz7OewozZ86UCZDim8jKE2ma1Kx8jFx2AsmZefKiyJxBASwRJ6JKo1KpsfFUUescDg0nKj9x4bD/3X9DbEdV8SrinKMsPJ8gTXcnMw+T1p7GsCXBuHEnWw59XTqyGeYMCkQNC2OlwyOq8msF1wnSdOk5+Zi25RwGLDyCiPh01DA3wtxBAVg+shncbc2UDo+oXHQiFTdlyhRZvlJ8i4mJUTokolJElvzVVScRlZgJF2sT/DK8KcyM2M+QiCrPiWvJ8kKIpbGBrBgjovLrE+gCsTE6+Foyom9nKR0OPQU8nyBNJdo1i/l8YuirSKaKz56RrT059JWoknGdIE2283wcOs8+iBVHr0MMJBjQxE1W8/Vmi0LSchV25dTOzg76+vqIj48v9bi47+TkVOZrxOMPe37xn+IxZ2fnUs8RfQ4fxNjYWN6INPVk5MNNZ3EsKhkWxgb4ZUQzOFixBQwRVa7ineU9fJ1hYqivdDhEWs3Z2hRt6tjhn8tJ2BB6AxM711M6pCqrIs45ysLzCdJEN1OyMXXTWfwdkSjv13e0xP/6+yLQo7rSoRHp3FrBdYI0UVxqDqZvPYed54v+W/asYYYv+/qiVR07pUMj0uyKDSMjIzRp0gR79+4teUwMZxL3W7ZsWeZrxOP3Pl/YvXt3yfNr1aolF5F7nyP6Fh4/fvyB70mk6X7cfwXrQ27IYb3zhgSioTMHixFR5crJL8T2s0WzAPo1dlU6HKIqofjf0ubTN+UmBtKecw4iTVeoUmPZ4avoMvuATGoY6evhnc71sO2NNkxqEJWBawXpYpvhlceuo/PsAzKpYaBXDa93qI0db7dlUoOqlArtdTNp0iQMHz4cTZs2RVBQEObMmYPMzEyMHDlS/nzYsGFwdXWVvQiFt956C+3atcO3336Lnj17YvXq1Th58iR++ukn+XNRHvX222/j888/R926dWWi4+OPP4aLiwv69OlTkb8KUYXYduYWZu2MkMcznm/EcnEiUsSuC/HIyC2AW3VTNPO0VTocoiqhi7cTTA3P4frtLJy5kYoAdxulQ6qynvY5B5EmuxSfjg82hOFUdIq838yzOmb280MdBwulQyPSaFwrSJfWiSkbz8oh4YL4Djqzny830VKVVKGJjYEDByIxMRHTpk2TA5ZEu6gdO3aUDGCKjo6Gnt7/F420atUKv/32G6ZOnYoPP/xQJi82b94MHx+fkue8//77cvEZO3YsUlJS0KZNG/meJiZs3UPaRSwy76w7I49Hta6Fl1t6Kh0SEemozXeHhvcNdIWeKB8joidmbmyAzt6O2Hrmlvw3xsSGdp1zEGma3IJCzP/7Chbsj0R+oVq2sJ3cvQGGBHlw7SZ6BFwrSBeq8Of/HYmFB67IdcLcSB/vd2uAl1rUhD7XCaqiqql1sDZetK+ytraWA52srJixpMoXk5yFPvMP43ZmHjo1dMSil5twoSGtoCufn7ryewrJmXkI+mIPClRqOUCOOz6Jnp59F+MxatlJ2FkY4diUjjDQr7AusBpDVz4/deX3JM1w8loyJm88i8iEDHlfnD983scHTtbc3EfaR1c+P3Xl9yTNcCzqNj7ceBZRSZkl68SnvRvBxcZU6dCIKvTzs0IrNojo31Kz8zFy2QmZ1GjkYoW5gwKY1CAixYjZGiKp4eNqxaQG0VP2TF17VDczRFJGHg5fuY129eyVDomItEh6Tj6+3hEh+6QLdhbG8kJVdx8n2aaZiIh0W0pWHmb+eRFrTsbI+w6WxrLNeTeuE6QjmNggqkT5hSqM/zVE7rZysjLBL8ObyVYVRERK2Xq6qA1Vb38ODSd62gz19dDTzxmrjkVjy+mbTGwQ0WNVfH206RxiU3Pk/YFN3fFhj4awNjNUOjQiIlKYaL7zR1gsZmw7LzfQCEObe8jWU9amXCdId/CKKlElLjwfbz6Hw5G3YWakj19GNGX5OBEp6sadLJy4dgdiM89z/s5Kh0NUJfUJcJWJjZ3n4pDdpxCmRvpKh0REGux2Ri4+/eMCtpy+Je972Jrhf/180aqOndKhERGRBriZki2vLe27mCDvi6p7MRy8maet0qERVTomNogqyaKDUVh9Igai69QPQwLRyMVa6ZCISMdtOxMr/2xeyxbO1uy/SlQRmtSsDrfqprhxJxt7wuPRy99F6ZCISEM3QYlkhth9eycrX54zjHnGCxM71WNClIiIUKhSY/mRa/hmVwSy8gphpK+H8R1q47X2tWFswHWCdBMTG0SV4K+zsfjfXxfl8bTnvPFsA0elQyIikq1xhN4BbENFVFFEf+PeAS6Y//cV+W+OiQ0iKmv37UebzmJ/RKK838DJEl8P8IOfm43SoRERkQa4GJeGDzacxZmYFHm/ac3q+F9/X9RxsFQ6NCJFMbFBVMFOx6Tg7TWn5fGIVp4Y0bqW0iERESEiLh0X49JhqF8NPXzYhoqoIonkoUhsiIuWdzLzUN3cSOmQiEgDqFRqrDp+HV/9dRGZd3ffvtmxDl5tV1vO6CEiIt2Wk1+IefsuY9GBKBSo1LA0NsAH3RtgSJAH9ERpH5GOY2KDqIL7149ZfhK5BSp0qG+PqT0bKh0SEVGpao329R04iJSogtVztERDZyuEx6bhz3OxGNq8ptIhEZHCIhMyMHlDGE5ev3PP7ls/2SudiIjo6JXb+HDTWVxNypT3uzZyxIznfTirlegeTGwQVZC0nHyMWnYCSRm58mLGvCGNYcCdV0SkQX28BdEih4gqXp8AF5nY2HLqFhMbRDosr0CFRQeuYN6+SOQVqmBupI/J3RvIzwXuviUiotSsfHz5ZzjWnIyR9x0sjfFpbx9083FSOjQijcPEBlEFyC9U4fVfQ3EpPkMuQktGNIWFMf+5EZFmCI2+I/t5i4spHTnzh6hSPB/ggv/tuIjga8ny35+rjanSIRFRJRO90T/YECZbQQqiovvzvr78PCAiIrn5bPvZWHyy9YLcICsMbe4hW09ZmbDCnqgsvNJKVAGL0fSt5/HP5SSYGurjl+HN4GzNkxUi0hzF1RpdfZxgaqSvdDhEOkF8FwjytMXxq8nYevoWXmtfW+mQiKiSZOcVYvbuCPxy6CpUasDW3AjTe3njeX8XVKvGKg0iIl13KyUbH28+h70XE+R90ZZwZj9fNPO0VTo0Io3GxAbRU7b4n6v47Xg0xDnK3EEB8HWzVjokIqJSFWXbw2JLBhoTUeXpE+gqExtixg0TG0S64cS1ZLy/PqykR7poS/fxc96oYWGsdGhERKSwQpUaK49ew6ydEcjMK4ShfjW83qGO/J5obMANaET/hYkNoqdo5/k4fPlXuDz+qEdDdGnEHohEpFkORSbhdmYeapgboXXtGkqHQ6RTevg4Y9qWc7INzaX4dDlUnIiqbpWGuFC19MhVqNWAk5UJvuzng2fZApKIiABcjEvD5A1ncTomRd5vWrM6/tffF3Uc+P2Q6FExsUH0lITdSMFbq0/JE5eXWnhgdJtaSodERPQvogWO8JyfMwz09ZQOh0inWJsZom1de9lm4I8ztzCpS32lQyKiChB8VVRpnMG121ny/otN3fBRT29Ym7JHOhGRrsvJL8QP+yKx8MAVFKjUsDQ2kHM0hgR5QE+P7QmJHgcTG0RPgRgCOnr5SeTkq9Cunj0+6dWI/XKJSCO/RO86HyePn2cbKiJF9PJ3KUpshMViYud6/L5AVIVk5RXg6x0RWH70mtzs5GxtInukt6/voHRoRESkIYnvyRvCEHW3PWHXRo6Y8bwPnKxNlA6NSCsxsUH0hNJz8jF62QkkpueigZMlfhgSyF3QRKSR/r6YIHu3utqYorGHjdLhEOmkTt6OMDbQkye0F2LT0MiFs7iIqoLjUbfx/oYwXL9bpTGwqTs+eq4hrExYpUFEpOsyckXi+yJWHL0u7ztYGuPT3o3QzcdZ6dCItBoTG0RPoKBQhQm/nZK9su0tjfHLiGaw5MkLEWmoP87GlrSh4i5xImVYGBugQ30H7DgfJ6s2mNggqhpVGsuOXJP3RZXG//r7ySpuIiKig5cSMWXjWdnpozjx/WHPhmxPSPQUMLFBVE5qtRoztl3AgUuJMDHUwy/Dm8pd0EREmnrhZV94gjzu6cedQURKes7f+W5i4xbe71qfiUYiLXX0ym18sCEM0clFVRqDg9wxpQerNIiICEjNysdn2y9gfcgNed/d1hT/6+eH1nXslA6NqMpgYoOonJYcvoaVx65DXIuYMzAQfm5s60JEmmtveAKy8wvhYWsGX1fuECdS0rMNHGBqqI+Y5GyE3UiFvzu/QxBpk+y8Qny142JJlYbL3SqNtqzSICIiADvOxeHjLedky3JxzWhEK0+817U+zIx4GZboaeK/KKJyEMN3P99+QR5P6d4A3XyclA6JiOihtoexDRWRphAntR0bOshWVKJqg4kNIu1xOiYFk9aeRlRi0eDXwUEe+LBHA7ajJSIimcj4ZOt5bL/bAri2vTm+HuCHJjVtlQ6NqEpiYoOoHCczb64+BbUaGNLcA68846V0SERE/zms7u8ItqEi0iTP+bnIxIZIOk7p3hB6ekw4Emmy/EIV5u2LxPy/I1GoUsPRyhhfD/DnLA0iIpKtyjefvinbladk5UNfrxrGtfPCG8/WhYmhvtLhEVVZTGwQPYaY5CyMWX4COfkqtK9vj0+fb8Sdz0Sk8faGxyO3QAUvO3N4O1spHQ4RAfJ7hBgkfis1B6di7nAnH5EGi0xIx8Q1Z3D2Zqq8/7y/Cz7t3Qg2ZkZKh0ZERAq7lZKNqZvPYd/Foo1k4nxLVGn4sP0vUYVjYoPoMQY/jVgajKSMPLlQ/TCkMQz09ZQOi4joP207E1tSrcFkLJFmELv3Ons7YtOpm/LfKBMbRJpHpVLLORpinobYIGBtaojP+/igl7+L0qEREZEGrBG/n4jGzD8vygp5I309vNWpLsa29YIhrxURVQomNogeQW5BIcauPIkriZlwtjbBkhHN5C5LIiJNl5qdj4OXEkta3xCR5hAzb0Ri48+zsfj4OW/ZtoCINMPNlGy8u/YMjkbdlvdFyymxA9fRykTp0IiISGHXb2figw1hOBaVLO839rCRa0QdB0ulQyPSKbwyS/QIvRI/WB+G41eTZTJDJDWcrHlCQ0TaYc+FeOQVqlDHwQL1HC2UDoeI7vFMXXtYmRggIT0XwVeT0bJ2DaVDItJ54rv/xtCbcvhrem4BTA318VHPhhja3INVj0REOk5Uaaw6fl1WaWTnF8o14r2u9TG8lSc3qBApgIkNov/w3e5L2Hz6Fgz0qmHBS43RkP3piUiL/BF2q2RnOC/IEGkWIwM9dG3khHUhN+S/VSY2iJRvPTtlUxj+PBtXsgN39osB8LQzVzo0IiLSgJmr768PK6nka+lVA1/194NHDTOlQyPSWUxsED3E2hMx+H5fpDz+oq+P3FlJRKRNF2j+uZxUktggIs3znL+LTGzsOBeHGc834vwuIoUcj7qNiWtO41ZqjtzQNLFzPbza1ov/JomIdJyo5PstOBpfbg9HZl5RlcaHPRpgaPOa0GOVBpGimNggeoB/Lifiw01n5fGEDnUwsJmH0iERET2WnefjUKBSo4GTJfu9EmmoVrVroLqZIW5n5skdgNxEQVS58gtVmLvnMubvj4RaDdSyM8fcQQHwc7NROjQiItKAeUuiNfmhyKLNYkG1bDFrgB9q1mAlH5EmYGKDqAwX49IwflWovCDYO8AF73Spp3RIRESPbdvdNlQ9fVmtQaSpDPX10M3HGb8HR+OPM7FMbBBV8vDXt1afxumYFHn/xaZumN6rEcyNeZpMRKTrVRprT8bgsz/CkZFbABNDPbzftQFGtPJklQaRBuE3NqL7xKflYNTSE3JYoMjGfz3Aj33piUjrJGfm4ciV2yWtbohIc/XyK0ps7Dgfh8/6+MjZG0RUsTaG3sDHm8/JtiKWJgaY2c8Xz/lxvSQi0nWxqdmYvOEsDlxKlPeb1KwuqzS87C2UDo2I7sPEBtE9RCZ+5NITsreul705fnq5CYwN9JUOi4josYl+/YUqNRq5WMm2GkSkuZp71YCdhTGSMnJx5EoS2td3UDokoiorLSdfJjS2nC6qagzytMV3gwLgamOqdGhERKRwlcaG0JuYse080nMK5EaT97rUx6g2taDPKg0ijcTEBtFdBYUqvPFbKC7EpsHOwgjLRwbBxsxI6bCIiMpl+9m7bag4NJxI44mT5W4+jlh1LFomJZnYIKoYIdfv4K3Vp3DjTrb8d/d2x7oY36EOL1gREem4hLQcTNl4FnsvJsj7/u42+PYFf9RxYJUGkSZjnTvR3cz89K3n8XdEouyduHh4M7jbmikdFhFRuYhd30eL21D5sq0GkTbo7lOUhNx5Pk5utiCip/td/6eDVzBw0VGZ1HCrboq1r7bEGx3rMqlBRKTjtofFosucgzKpYaSvhw+6NcCGcS2Z1CDSAqzYIALw08Eo/Ho8GmKUxtxBgQhwt1E6JCKictt1Ph4qNeDjagWPGkzSEmmD5rVsUd3MEHey8hF8NRmt6tgpHRJRlZCSlYd3153BnvCiXbjP+Tnjy36+sDIxVDo0IiJSUGp2Pj7Zeh6bTt2U98W507cvBKC+k6XSoRGR0hUbycnJGDp0KKysrGBjY4PRo0cjIyPjoc9/4403UL9+fZiamsLDwwNvvvkmUlNTSz1PDHG+/7Z69eqK+jVIR7LzM/+6KI8/7umNro2clA6JiOiJ/HUuttQOcCLSfAb6eujiXfQd5M+7/4aJ6MmERt9Bz+8PyaSG6JX+eR8fzBscyKQGEZGOE9Xt3ecclEkNUbj35rN1sGl8ayY1iLRMhVVsiKRGbGwsdu/ejfz8fIwcORJjx47Fb7/9Vubzb926JW/ffPMNvL29cf36dYwbN04+tn79+lLPXbp0Kbp161ZyXyROiMrj5LVkTFx7Wh6PaOUph0IREWmz1Kz8kjZU3X2YqCXSJt19nbDmZAx2no/HjOd92CKH6AlaTy3+5yq+2nERBSo1PGuY4YchjeHjaq10aEREpKCc/EJ8uysCiw9dhVoN1KxhhtkvBqBJzepKh0ZEmlKxER4ejh07dmDx4sVo3rw52rRpg3nz5snKCpGoKIuPjw82bNiAXr16oXbt2nj22WfxxRdfYNu2bSgoKCj1XJHIcHJyKrmZmJhUxK9BVVxkQgZGLz+JvAIVOjV0xMfPeSsdEhE9wPz58+Hp6Sk/78W6Ehwc/MDntm/fvszqvp49e0IX7A6Plxdx6jtawsuefWGJtEmr2nawNDFAYnquHHJMROVrPfXKipP44s9wuR729HPGtjfaMKlBRKTjLtxKQ+8fDuPnf4qSGoODPPDnm88wqUGkxSoksXH06FGZfGjatGnJY506dYKenh6OHz/+yO8j2lCJVlYGBqULS15//XXY2dkhKCgIS5YskTtyiB5HQloOhi8Jlj0VxTwNUZLOXZFEmmnNmjWYNGkSpk+fjtDQUPj7+6Nr165ISCjqlX2/jRs3yorB4tu5c+egr6+PF154Abpgx90WNt1YrUGkdUSrnM7ejqVayhHRozt1b+spfT181scHPwwOhCVbTxER6axClRoLD1xB7/mHEBGfDjsLI/wyvClm9vOFuTFHDxNpswpJbMTFxcHBwaHUYyI5YWtrK3/2KJKSkvDZZ5/J9lX3+vTTT7F27VrZ4qp///4YP368rAZ5mNzcXKSlpZW6ke7KyC3AyGUncDMlW5aliwXN1Ehf6bCI6AFmz56NV155RbY0FK0KFy5cCDMzM5nYLotYa+6t6hPrhXi+LiQ2xOfbwctJJS1tiEj7FM/G2XEuDioVN+887bl+D6rsEy1wSXuJjW4rj17Di4uOyu/4orXIxvGt8HKLmvL/v0Sk27hW6K6Y5CwM/ukY/vfXReQXquUGkp1vt0XHhkUbSYhIuz1WanLy5Mn46quv/rMN1ZMSiQfRMkRcwPrkk09K/ezjjz8uOQ4MDERmZiZmzZolB40/yMyZMzFjxownjou0X36hCuN/DcX5W2kyS798VBBqWBgrHRYRPUBeXh5CQkIwZcqUksdE9Z+oAhTVgY/il19+waBBg2Bubv7QBLi4FdPWBPi+iwmyvV4tO3PZioqItM8zde1gbqSP2NQcnLmRgkAPtkd4mnP9iomEudgwVUwkwEl7+6V/uOksNobeLJkv9dUAPw4IJ6ISXCt0M+EtBoNP23Jebv4S362m92qEF5q6MeFNpKuJjXfeeQcjRox46HO8vLzkDtn7W4SIORkiSy5+9jDp6elyMLilpSU2bdoEQ8OHfyEVvdZFZYe4IGVsXPYFanFBTLQxufeClbu7+0Pfl6rmwjZ5w1kcvJQIU0N9/DK8GWrWePCFTiJSnqjeKywshKNj6R014v7Fixf/8/ViFodoRSWSGw9TVRLg97ah4hd2Iu1kYqiPZxs6YtuZW/jrXBwTG48w1+/EiRMlLXBFJXePHj3wzTffwMXF5YGvFRen/uu8hLRjJ+64VSFy05LoKju5ewO88owX10AiKsG1Qvek5+Rj6uZz2HK6aMZv05rV5YBwjxpMTBHpdCsqe3t7NGjQ4KE3IyMjtGzZEikpKXKXbbF9+/ZBpVLJRMSDiIRDly5d5Hts3br1kYaCnz59GtWrV39gUkMQPxMlh/feSPfM3n0JG0JvyFkaPw5tDH93G6VDIqIKJhIavr6+cibTw4gEuJjrVHyLiYmBtsnOK8TfFxNLdqsSkfbqcfffsJizwVlyFTPX79dff5Uz+3x8fOQakJWV9dDns7Wt5hGblXr9cEgmNWzNjbBqdHOMbVubSQ0iUmSt4DqhGUKj76DH9//IpIa49vNO53pY82pLJjWIqqgKmZLTsGFDWXUhyvZEL3RR6jdhwgTZCqQ4G37z5k107NgRK1askBecipMaYqFYtWpVqYVAJFTE4Ndt27YhPj4eLVq0kEkPUUb45Zdf4t13362IX4OqkF+PX8e8fZHy+Mu+PujQoPQMGCLSTOJEQnz+i8/+e4n7/7V7SrQqXL16dany8YclwB+WINcGBy4lIDu/EK42pvB1tVY6HCJ6Au3q28PEUA8xydnyoq0P/00/1bl+Q4YMQc2aNeV5SVhYGD744ANERERg48aNVb6yryoQyb4f91/BN7siIPJ+/m7W+PGlJnL9IyJSaq3gOqEZA8LFhlZx7FbdFHMHBaJJTVa+ElVlFZLYKM5si2SGSF6ITLgY9P3999+X/FwkO8SiUJzxDg0NLcmW16lTp9R7Xb16FZ6enrIt1fz58zFx4kT5hVY8r3ioLNGD7LkQj483n5PHb3Wsi4HNPJQOiYgekajga9KkCfbu3Ys+ffrIx0T1n7gv1piHWbdundw59dJLL0EXiJY1xdUa3K1KpN3MjAzQob6D/HctqjZ0LbFR0XP9RF/1YqKqz9nZWZ6zXLlyBbVr1y7zNWxtqzntRd5ddwY7zxdteBjUzB2fPN9ItnAjIt2iaWsF1wnlxKXmYOKa0zgadVvef97fBZ/39eGsJSIdUGGJDZH9ftggJpGouLe0vn379v9Zai+qQMSN6FGdir6DCb+HQqUGBjZ1x9ud6iodEhE9JnGCMHz4cFk+Lir85syZI6sxxNA/YdiwYXB1dZW7pO5vQyWSITVq1EBVl1tQiH3hRbOtuvuyDRVRVSBm5cjExtk4vNulvk4lLCtjrt+9ilvlRkZGPjCxURUq+7TdtaRMjFlxEpEJGTDS18OM3o0wOIgbloh0laatFVwnlLHrfBze3xCGlKx8mBnp49PePujf2FWnvjcR6bIKS2wQKe1qUiZGLz+JnHwV2te3lxl7Lm5E2mfgwIFITEzEtGnTZLl4QECAHABYPFA8OjpaVgbeS1QEHjp0CLt27YIuOByZhPTcAjhaGSPQneXWRFXBsw0cYGSgh6ikTFyKz0B9J0voCtGGVtz+y71z/UR136PO9StrZp8gduOSZjpyJQnjfw2VF66crEyw4KXGCPTgekeky7hW6Lac/EJ8sT0cK49dl/dFK965gwLgZW+hdGhEVImY2KAqKSkjFyOWBiM5M08ucPOHNIahfukLn0SkPUTbqQe1ntq/f/+/Hqtfv75ODdwVO7qFro2coKfHBC5RVWBpYoi2de2xJzwef56N1anERkXO9RMtRERVeY8ePWRFn+ibLtrctm3bFn5+fkr/SlSGVceu45Ot51GgUsPf3QY/v9wEDlYmSodFRFqCa0XVcyUxA6//GoqLceny/ti2XrK6VWwIISLdwn/1VOVk5RVg9LITuH47Cx62ZlgyohnMjZnDI6KqKb9Qhd3h8SWta4io6hAzc4Qdd2foUNlz/Ro0aCAvSIkLUG3atMFPP/30wLl+YnbTnj170KVLF/k60cpEzALctm2bgr8FlaWgUIXpW85h6uZzMqnRJ8AFa8a2YFKDiB4b14qqY8vpm+g175BMathZGGPFqCB82KMhkxpEOopXe6nKnQCJzP2ZG6mobmaIZSObwd6SfS6JqOo6HpUsW3PUMDdCkKet0uEQ0VPUqaEjDPWrISI+Xe5OrM32Ck88108Mcj1w4EAlRUfllZqVj9d/C8WhyCR5/72u9TG+fW22lSWicuFaUTVaT83YdgG/B0fL+y29amDu4AA4WDLZTaTLmNKkKkN8ERE7uv6OSISJoR5+GdGM/RWJqMr761ys/LNLI0cYsOUeUZVibWaIVrXt5PHO86zaIN0gknh9fjwskxpiEOyil5vg9Q51mNQgItLh+al9fzwikxpiKXizY12sGtOcSQ0iYmKDqo5vd13C6hMxEO3l5w1ujMYcKEhEVVyhSo2d54vbUHGQIVFVJJKWQvG/daKqLPhqMvr9eERexHK1McX6ca3k/CgiItJNf4Tdkq2nwmPTZIW6aD01qXM96HOuIBExsUFVxbLDV/HD35Hy+Mu+vujsXXQRgIioKguNvoOkjFxYmRjIcmwiqnrEdxqxO/FMTAriUnOUDoeowmw9cwsvLT6O1Ox8BHrYYMuE1vB2sVI6LCIiUqj11Mebz2HCb6eQkVuAoFq2+POtZ/BMXXulQyMiDcLEBlWJk6AZf1yQx+92qYdBQR5Kh0REVCl23W1N07GhIwfmEVVRos1CoLuNPN59ge2oqGq2k/1xfyTe/P0U8gpV6NbICb+/0kIOhSUiIt0Tk5yFAQuPYOWx6/L+6x1q47cxzeFoxdZTRFQar4KQVvvnciLeWXsaYs7X8JY1Zf9dIiJduRC060JRa5ourFIjqtKKW/EU/5snqioKClX4cNM5fL0jQt4f3aYW5g9tDBNDfaVDIyIiBRy8lIhePxzCuZtpqG5miGUjm+G9rg04S5CIysRPBtJaYTdS8OrKEOQXqtHTzxnTezXiUEEi0hmX4jNw/XYWjA300K4+S7KJqrIudxMbR6/cRmpWvtLhED0VorXImBUnS4bBftLLGx8/582+6UREOrppa/7fkRi+NBgpWfnwd7PG9jefQfv6DkqHRkQajIkN0kpRiRkYsfQEsvIK0bpODcx+0R96PAkiIh2y824bqmfq2sHMyEDpcIioAtWyM0ddBwsUqNT4OyJB6XCInpiYDzXop6PYH5EIE0M9LHqpCUa0rqV0WEREpID0nHyMWxWCWTsjZDeOwUHuWPNqS7jYmCodGhFpOCY2SOvEp+Xg5V+CkZyZB19Xayx6uSmMDViuTkS6ZdfdXvtdvIt2chORrrSj4pwNqgK90xcckW1GapgbYfXYliVVSUREpFsiE9LRe/5h7DwfDyN9Pczs54uZ/fzYkpCIHgkTG6RVUrPzMXxJMG6mZMOzhhmWjmwGC2PuVCYi3SI+A8UFIVGo1rEhy7OJdEGXRkWzdMQO95z8QqXDISqXiLh09F9wBNduZ8HVxhTrxrVEgLuN0mEREZECdpyLRe8fDiMqMRPO1iZYO64lBgd5KB0WEWkRJjZIa4iT+FeWn8TFuHTYWxpj5ejmsLMwVjosIqJKt/tuG6qmNW1Rg5+DRDpBVKmKk37RhvNwZJLS4RA9tpDryXhh4REkpOeinqMFNrzWCl72FkqHRURElUylUuPrHRcxblUoMvMK0cLLFtveaMNENxE9NiY2SCsUFKrwxu+nEHwtGZbGBlg+MgjutmZKh0VEpIhdF+JL7eAmoqqvWrVq6OLtWGrGDpG2+PtiAoYuPo60nAI0qVkda19tCSdrE6XDIiKiSpaRW4CxK0/ix/1X5P1XnqmFVdy0SkTlxMQGaTy1Wo2pm89h94V4GBno4efhTeHtYqV0WEREikjJysPxq8nyuPPdi5xEpFtzNvaEJ6BQpVY6HKJHsunUDYxZcRI5+Sp0qG8vL2DZmBkpHRYRESkwY6n/j0fk9xhxbWfOwAB81NMbBvq8NElE5cPhBKTxZu2MwOoTMbKX/PeDAtDCq4bSIRERKWbfxaILmg2cLFGzhrnS4RBRJWpWyxbWpoZIzszDyWvJaM7vRKThfg+OxoebzkKtBvoGuuLrAX4w5AUsIiKdczzqNl77NVR+hxGtxX8e1pStp4joifFbJWm0RQeulJQoft7HF918nJUOiYhIUbvO321DxWoNIp0jLgh3bOhQqiUdkaZafuQapmwsSmoMa1kT377gz6QGEZEOWnMiGi/9clwmNcTMsK0TWjOpQURPBb9Zkkbv8Jr510V5/EG3BhjS3EPpkIiIFJWTX4gDlxLlcZe7LWmISLd08XYqmbMh2nUSaaKfD0Zh+tbzJf3TZzzfCHqi/JqIiHRqVuqn2y7ggw1nkV+oRk8/ZzljydnaVOnQiKiKYCsq0kjbztySZevCuHa18Vr72kqHRESkuEOXk5CdXwgXaxM04qwhIp3Urp49TAz1cONONsJj0zl3jDTOD/su45tdl+TxhA518E6XeqhWjUkNIiJdGxI+4bdQ7I8o2pQ1qXM9vPFsHa4HRPRUsWKDNM7fEQmYuOa0LFsXVRofdKuvdEhERBpB7NAurtbgSQGRbjI10sczde1LfSYQaQJRQfTtroiSpMY7nevh3a71uV4REemY+LQcDFx0VCY1xGaMH4c2xpsd63I9IKKnjokN0ijBV5Px2qoQFKjUeM7PGZ/19uHiR0R0t5R7T/jd+RqNOF+DSJcVz9jhnA3SpKTG/3ZcxLx9kfL+lO4N8EbHukqHRURElSwiLh195x/G+VtpsLMwwuqxLdHDl7NSiahisBUVaYxzN1MxetkJ5OSr0L6+PWa/GAB99uIlIpJCrt/Bnax8WJsaIsjTVulwiEhBnRo6QnxFCo9NQ0xyFtxtzZQOiXQ8qfH1zggsOhAl73/SyxsjWtdSOiwiIqpkRyKT8OrKEKTnFsDL3hzLRgTBowa/oxBRxWHFBmmEK4kZGL4kWC6A4oLdgqFNYGTA/zyJiIoV78zu2NABBvr8fCTSZdXNjRBUqyjByXZUpLQ5ey5jwf4r8viz3o2Y1CAi0kEbQ29g+NKiazrNPKtj42utmNQgogrHKyOkuJsp2Xh58XHczsyDj6sVFo9oKvtHExHR/++G3XXh7nwNbyelwyEiDdD57mfB3vAEpUMhHTb/70jM3XtZHn/8nDdebumpdEhERFTJ5yk/7LuMSWvPIL+wqKX4ytHNYWNmpHRoRKQDmNggRSVl5Mqkxq3UHFmquHxkEKxMDJUOi4hIo1yMS0dMcjaMDfTQtp6d0uEQkQbo1NBB/hl8LRmpWflKh0M66OeDUZi1M0IeT+7eAKPbsFKDiEiXqFRqzNh2Ad/suiTvv9rOC98PCoSJITeqElHlYGKDFJOWky/bT0UlZcLVxhSrRjdHDQtjpcMiItI4e+62oXqmrh3MjDgei4iAmjXMUdfBAoUqNfZfYtUGVa5lh6/iiz/D5fGkzvUwrl1tpUMiIqJKlF+owqS1p7HsyLWS+UpTujeEHuekElElYmKDFJGZW4ARS4Jx/lYa7CyMsHJ0EFxsTJUOi4hII+25WHTRsmNDR6VDISIN0sm76DNhD9tRUSVaeyIGn2y7II8ndKiDNzvWVTokIiKqRNl5hXJI+ObTt2CgVw1zBgZwvhIRKYKJDVJkERy9/ARCo1NgZWKA5aOC4GVvoXRYREQaKSEtB2diUuRxxwZFrWeIiIROd5Od+yMS5M5Jooq241wcJm8Mk8evPFML73Spp3RIRERUiVKz8zFsyXHsu5gAE0M9/DysKfoEuiodFhHpKCY2qFLlFhTi1VUhOBaVDAtjAzlUqpGLtdJhERFpLHHSIPi7WcPBykTpcIhIgwS426CGuRHScwpw4mqy0uFQFXfkShLe/P0UVGpgYFN3fNijIapVY8sRIiJdmpE66KdjOHHtDixNiq7ndODGKyJSEBMbVGnETsIJv53CwUuJMDXUx9KRzeDvbqN0WEREGq24xQzbUBHR/fT1quHZuxcUdocXzeIhqghnb6Ri7IoQ5BWq0LWRI77o68OkBhGRjlWRi6RGeKxoJ26Mta+2RDNPW6XDIiIdx8QGVQox2PLtNaex+0I8jAz0sHh4Uy6CRET/ISe/EIciE+Vxx4bcDUVED5uzEQ+1Wq10OFQFXUnMwPClwcjILUBLrxqYOygQBvo8jSQi0hVxqUVJjciEDDhbm2DduJZo6GyldFhERExsUMVTqdR4b/0ZbA+LhaF+NSx6uQla17FTOiwiIq1o+5GTr4KLtQm8efJARGV4pq6d3DQSk5yNywkZSodDVUxsajaG/RKM5Mw8+Lha4adhTWBiqK90WEREVElupWRj4E9HEZWUCVcbU6wZ2xK17MyVDouISGJigyqU2Dk4dcs5bAy9KdslzBvcGB3qc9cxEdGj2H3h/9tQseUHEZXFzMgArWvXkMeiMpboaUnLyceIJSdwMyUbXnbmWDYyCJYmhkqHRURElSQmOUsmNa7fzoK7rSnWvNoCHjXMlA6LiKjiExvJyckYOnQorKysYGNjg9GjRyMj4+G7yNq3by8v3Nx7GzduXKnnREdHo2fPnvg/9u4DPIrq6+P4j3RKEggQQu+99yqgIig2LIgVRARFsKF/FRt2rIgFRVSwgV2sCCJIkd47gdB7gJAEEtL3fe6F5KUEJJBksrvfz/Osmd3Mbk7WMHdnzr3nFClSROHh4frf//6ntLS0vPo1cIFJjRd/X6cJC7bLXI8bcVNjXd4gwumwAMAtmGPo9PXHLlJShgrAuZajAnKrN959Xy1V5L7DCg8O1Bf9Wtma6gAA77D9YKItP2VWhFYpWcSu1KhQgqQGgILFL69e2CQ19uzZo6lTpyo1NVV9+/bVgAEDNGHChLM+r3///nrhhRey7psERqb09HSb1IiIiNDcuXPt6/fu3Vv+/v565ZVX8upXwXlekHtjSqTGztli7792QyNd26S802EBgNtYvSte++KTVSTAV22qHZuNDQDZubROGT2l1Vq+I1b7DyerdDAXoHGBK64nrta/UQfsGDT2zpZczAIALys/dcvH84+t2CtdVF/3b6MyIUFOhwUA+bNiY926dZo8ebI++eQTtW7dWh06dNB7772nb775Rrt37z7rc00iwyQuMm9mxUemv/76S2vXrtVXX32lJk2a6IorrtCLL76oUaNGKSUlJS9+FZynd6dF6YMZm+z2i9fW100tKjodEgC4lcyZ16Z+PvXMAZxNRGiQGpYPlekd/s/6YyXsgPNlPsN/u3iHfApJ79/aVA3KhzodEgAgn0QfTtJtnyzIKkP4zQCSGgC8LLExb948W36qRYsWWY916dJFPj4+WrBgwVmfO378eJUqVUoNGjTQ0KFDlZiYeNLrNmzYUGXKHFtub3Tr1k3x8fFas2ZNXvwqOA/vTtuot//eYLef6l5Xd7St4nRIAOB2ph0vQ9Wl7v+PeQBwJpnHiqmUo8IF+GX5Lrvq2njumvq6pA5jEAB4i0MJKbrjk4XacrxR+Fd3t1Z4MEkNAF5Wimrv3r22/8VJP8jPT2FhYfZ7Z3LrrbeqcuXKKleunFauXKnHH39ckZGR+umnn7Je98SkhpF5/2yvm5ycbG+ZTCIEeeO9aRs1YuqxpMYTV9RR/47VnA4JANzOnrijthSV6U90cR36awD4b13qhduJJf9uPKCk1HRWeiHHFm2N0f++X2m3+3Woqt5MTgIArxGflKreYxfa3kplQgI1oX9rlSte2OmwACD3Vmw88cQTpzX3PvW2fv16nS/Tg8OswDCrMkyPji+++EITJ07Upk3HShqdr+HDhys0NDTrVrEiZZHywvvTN+qt40mNxy+vo3s7VXc6JABwS9PWHSsl07RicZq1Ajgn9cqGqFxokI6mpmvupgNOhwM3bBLb/4vFSknPULf6ZfRk97pOhwQAyCdHU9J117hFWrUrTiWLBmj83a1VuWRRp8MCgNxNbDzyyCO2f8bZbtWqVbO9MaKjT67vm5aWppiYGPu9c2X6cxhRUVH2q3nuvn0nL6/PvH+21zUlreLi4rJuO3bsyMmvjXMw6p8ovfnXsaTGY5fX1sDOJDUA4HxNO15K5lLKUAE4R2aCUeYxY+pa+mzg3B1JTtPdXyxSbGKqGlcI1cheTeVrGmwAgAd4+eWX1a5dO9vP1ZRMPxcul0vPPvusypYtq8KFC9vS6hs3bpQnSkvP0OAJS7V42yGFBPnpi36tVCM82OmwACD3ExulS5dWnTp1znoLCAhQ27ZtFRsbqyVLlmQ9d/r06crIyMhKVpyL5cuX269mMDHM665ateqkpMnUqVNtg/F69eqd8XUCAwPtPifekLtJjcxavP/rVlv3da7hdEgA4LYSU9I0Z9NBu01/DQA50aXesWPG9PX7lJHhkjfggtWFMX8nQ75drg37jig8OFAf3dFChQMoYwbAc6SkpKhnz54aOHDgOT/n9ddf17vvvqvRo0fbPrFFixa11UWSkpLkScx4+PTPqzVtfbQC/Xw09s6Wql8u1OmwAMDZ5uF169bV5Zdfrv79+2vhwoWaM2eOBg8erJtvvtn2zzB27dplEyHm+4YpN/Xiiy/aZMjWrVv166+/qnfv3urYsaMaNWpk9+natatNYNxxxx1asWKFpkyZoqefflqDBg2yyQs4n9QYdDFJDQC4EKY+fkpahiqGFVatMsWcDgeAG2lTLUxFA3y1Lz5Zq3fHyRtwwerCvDNto/5au08Bvj4afUdzRYTSJBaAZ3n++ef18MMP25Ln53qxf+TIkfZa07XXXmuvR5ky6bt379bPP/8sTzLy7436ZtEOmUV6793SVC2qhDkdEgA4n9gwxo8fbxMXl156qbp3764OHTpozJgxWd9PTU21jcETExPtfbPS4++//7bJC/M8U/bqhhtu0G+//Zb1HF9fX/3+++/2q1m9cfvtt9vkxwsvvJBXvwbO4oMZ/5/UeLRrLZIaAJAL/s4sQ1WnjC0tAwDnKtDPVx1rlbbbf689uXyrp+KC1fmbvHqPTWwYL1/XQM0qlXA6JABw3JYtW7R37167mi+T6dVqqo/MmzdPnmLCgu1ZY8CLPRqoa/1zLxsPAAWFX169cFhYmCZMmHDG71epUsWeWGQyDb1nzpz5n69buXJlTZo0KdfixPn5cMYmvT75WFLjkctqafAlNZ0OCQA8oiTI9PX77TZlqACcj0vqhOvP1Xs1PTJaQ7rWdjoct7tgZVaYe4P1e+M15LsVdvuu9lXVs0VFp0MCgALBjBFGmTInfxY39zO/l53k5GR7yxQfH6+CavbG/Xrml9V2+/5Laui21pWdDgkACtaKDXh2UuO1yevt9pDLaun+S0lqAEBuWLEzVgeOJCs40E+tqrIUHEDOda4dbr+u3hWv6HjvK62UlxeszEWqE2/uKu5oqu75cokSU9LVoUYpPdm9jtMhAUCOPPHEE3Zl89lu69cfu2aRX4YPH24T5Zk3M3m3INq0/4juG79U6RkuXd+svL2mAwDuisQGzplduv/3hqykxsNdaukBkhoAkGumrYu2X00pmQA/hmgAOVc6OFCNKxxr/PlP5LFjirvhglXefp7/3/crtO1gosoXL2xrqvv5Mt4AcC+mdPm6devOeqtWrdp5vXZExLGSTPv2nVzS0dzP/F52hg4dqri4uKzbjh07VNDEJqbo7s8X63BSmppXLqHh1zek9C0At5ZnpajgWcxJ0OtTIu1qDYNG4QCQd/01utQ7NuMaAM7HxXXCtWJnnKavj1avlpXkjhes7rzzzrPukxsXrMqWLZv1uLnfpEmTs16wGjJkSNZ9s2LDHZMbn8zektUs/MPbm6lE0QCnQwKAHCtdurS95YWqVavasWLatGlZ44I55i9YsEADBw484/MCAwPtraBKTc/QoAlLteVAgk1sf3RHc9ubCwDcGYkNnFNS48Xf12nsnC32/tNX1tXdF53fySQAIHs7DyVq/d7D8ikkda5FYgPAhfXZGPn3Rv278YCS09Ld7sIFF6zyxsItMXr1+MrrZ66up0YVijsdEgDkue3btysmJsZ+TU9P1/Lly+3jNWrUULFixex2nTp17Mq86667zq5geOihh/TSSy+pZs2adtx45plnVK5cOfXo0UPu6qXf12pO1EEVCfDVJ31aqFQx9x7TAMAgsYH/bGRrmkqNX7Dd3n+xRwPd0YbGUgCQ2/6JPNY03CwLZwYtgAvRoFyoLUm1/3CyFm05pA41S8lTccHq3Ji/hcETjtVUv7ZJOd3e2v1W8gDA+Xj22Wf1+eefZ91v2rSp/frPP/+oc+fOdjsyMtKWj8r02GOPKSEhQQMGDFBsbKw6dOigyZMnKygoSO5o4rKd+nzeNpmqUyN7NVHdsiFOhwQAuYLEBs7InPg8/uNK/bBkpx0AX7u+kW5q6X5L7gHAHfyzPjqrhAwAXAgfn0K6uHZpfbd4py1H5cmJDS5Yndtn+ge/Wabow8mqEV5Mr1xHTXUA3uOzzz6zt/+qUnEic4x84YUX7M3dRe49rKE/rbLb919SU13rn7lPCAC4GzrFIVtp6Rl6+NvlNqnh61NIb9/UhKQGAOSRpNR0zd10wG5fXJvEBoDcKUflzg3Ez5W5WGUuSJ16y0xqGOb+iT07Mi9Y7d27V0lJSfr7779Vq1Ytear3p0dp7qZj5UdG395MRQOZ2wYA3uBwUqoGfrVESakZuqhmKT14aU2nQwKAXEViA6dJScvQ4AnL9OuK3fLzKaT3b2mqHk3LOx0WAHiseZsP2hOOsqFBqhMR7HQ4ADxAh5ql5e9byDYJ3bz/iNPhwCGLt8bonWkb7PZLPRqoRjhjDAB4A5PUf+LHVdp8IMGeY7xzc1M7aRUAPAmJDZw2a9hk9Cev2asAXx+Nvr25rmhY1umwAMCjzThehqpz7XDKgwDIFcUC/dSqapjdNuWo4H3ik1L14DfLleGSejQpp+ubVXA6JABAPvl+8U79sWqPnaw66rZmCqOHHwAPRGIDJy1TvHPcQk1bH61APx993KeFutQr43RYAODxs6mmHy8Vk1k6BgByQ2ZpO08vR4Xsx5anJq7WrtijqhhWWC/0aOB0SACAfGJWaz732xq7/UjX2mpWqYTTIQFAniCxASsmIUW3fbJA8zfH2Bl+n9/VSp1qlXY6LADweJv2J2hHzFG7Sq5d9ZJOhwPAg2QmSxduidGR5DSnw0E++mnpLv22YrctOzKyV1OFBPk7HRIAIB+kpmfooW+XKzElXW2qhWlAx2pOhwQAeYbEBrQn7qh6jp6rlTvj7PLEr/u3UZtqXFwDUHCMGjVKVapUUVBQkFq3bq2FCxeedf/Y2FgNGjRIZcuWVWBgoG0KO2nSJBVE/xwvEdO6WhgNXQHkqmqli6lKySJKTXfp3437nQ4H+WTbwQQ9+8tqu/3QpTXVvDIzdQHAW7w7baNW7IhVSJCfRtzUhL4aADwaiQ0vZ5Yo3vjhPDtj2DSU+u6etmpYIdTpsAAgy7fffqshQ4Zo2LBhWrp0qRo3bqxu3bopOjr70iopKSm67LLLtHXrVv3www+KjIzUxx9/rPLly6sgyiwRk1kyBgBy08XHV23QZ8M7pKVn2L4aCSnpalUlTPddXMPpkAAA+WT1rjiN+ifKbr9yfUOVK17Y6ZAAIE+R2PBia3bH2ZUapvZu1VJF9f29bVUjvJjTYQHASUaMGKH+/furb9++qlevnkaPHq0iRYpo7Nix2e5vHo+JidHPP/+s9u3b25UenTp1sgmRgtjbyJSIMeivASAvZB5b/oncrwzTRRoebczszVq+I1bBQX56+2Zm6gKAN5WgeuyHlTJD/VWNyuqqRuWcDgkA8hyJDS+1aGuMbh4zXweOpKhe2RCb1KhQoojTYQHAaasvlixZoi5dumQ95uPjY+/Pmzcv2+f8+uuvatu2rS1FVaZMGTVo0ECvvPKK0tPTVdDMiTqgtAyXTS5XKVXU6XAAeKBWVcNUJMBX+w8na83ueKfDQR6K3HtYI6dutNvDrq6v8szUBQCv8cnsLVq7J17Fi/jruWvqOx0OAOQLEhteyJQ9uePTBTqclKaWVUrom3vaqFSxQKfDAoDTHDhwwCYkTILiROb+3r17s33O5s2bbQkq8zzTV+OZZ57RW2+9pZdeeumMPyc5OVnx8fEn3fJDZmkYylAByCuBfr66qGYpu005Ks+eqfvo9yuUkp6hS+uE64ZmBbP8IgAgb0qMj/x7g91++sp6XN8B4DVIbHiZH5bsVP/PFyspNUOda5fWF3e1VkiQv9NhAUCuycjIUHh4uMaMGaPmzZurV69eeuqpp2wJqzMZPny4QkNDs24VK1bM8zhdLpctDWNQhgpAXso8xkw/3tMHnmf0jE1atStOoYX9bV31QoUoQQUA3sCcUwz9aaWS0zLsRAYS2wC8CYkNLxrsTBMpM5PLlD3p0aScxtzRQoUDfJ0ODQDOqFSpUvL19dW+fftOetzcj4iIyPY5ZcuWVa1atezzMtWtW9eu8DClrbIzdOhQxcXFZd127NihvGZKwpjSMKZETMuqJfL85wHwXpmrwlbujNWBI8lOh4NctnZ3vN6dfqwE1fPX1FeZkCCnQwIA5JPfV+7R/M0xCvL30SvXkdgG4F1IbHiB9AyXnv1ljd6YEmnv39Opmkbc1EQBfvzvB1CwBQQE2FUX06ZNO2lFhrlv+mhkxzQMj4qKsvtl2rBhg014mNfLTmBgoEJCQk665bV/jpeE6VCjlC0VAwB5JTwkSA3Kh8jlkmYcXykGz5CSdqwEVWq6S13rldG1TWgWCwDe4mhKul79c73dHtiphiqG0TcVgHfhyraHS0pN16DxS/Xl/G0yifthV9fT0CvqyseHLD4A9zBkyBB9/PHH+vzzz7Vu3ToNHDhQCQkJ6tu3r/1+79697YqLTOb7MTExevDBB21C448//rDNw00z8YIksyTMxZShApAPLjm+aiMzqQrPMHrmJtsstkQRf73MTF0A8CpjZm3WrtijKl+8sAZ0rOZ0OACQ7/zy/0civ8Qlpqr/F4u1cGuMAnx9NKJXY13ViFlcANyL6ZGxf/9+Pfvss7acVJMmTTR58uSshuLbt2+Xj8//5+lNf4wpU6bo4YcfVqNGjVS+fHmb5Hj88cdVUMQkpGj5jli7TeNwAPnBJFHfnR6lWRv220bT/r7Mb3J3m/Yf0fvTo+z2c9fUV+lgmsUCgLfYG5dkk9vGE1fUocw4AK9EYsND7Y49qj5jF2pj9BEFB/nZfhptq5d0OiwAOC+DBw+2t+zMmDHjtMdMmar58+eroJq5IdqWhKlbNkQRodRCB5D3GlcorpJFA3QwIUWLtsaoXfVSToeEC+yf99TEVUpJz1CnWqV1TWMmLwGAN3lv+kYdTU1Xi8oldFWjsk6HAwCOYKqWB1q/N17XfzDXJjUiQoL0/b1tSWoAQAHyz/pjNe4vqVPa6VAAeAlThrRT7WPHnJn02XB73y/ZmdUs9qUeDShBBQBeZEdMor5bvMNu/69bbcYAAF6LxIaHmREZrRs/nKe98UmqGV5MP93XTnUi8r4JLgDg3KSlZ2jmhmMXFSlDBSA/dT5+zKGBuHs7eCRZr0xaZ7cf7lKLZrEA4GVMGcLUdJc61Cil1tWYxArAe5HY8CBfztuquz5bpCPJaWpbraR+uLedyhUv7HRYAIATmN4acUdTVbyIv5pWKuF0OAC8yEU1SsmnkBS577D2xB11Ohycp5f+WKfYxFRbzvCuDlWdDgcAkI+2HUzQD0t32u2HL6vpdDgA4CgSGx4gPcOlF35bq2d+WaMMl9SzeQV9flcrhRbxdzo0AMAppq+Ptl871iwtX3OFEQDySYmiAWpcsbjdphyVe5obdUATl+2SqToy/PqGNIEHAC8z6p8oew3I9FdqXjnM6XAAwFF8EnZzCclpuufLJRo7Z0tWfcXXb2ykAD/+1wJAQfTP8YuJl9ShDBWA/Ne5FuWo3FVqeoae/XWN3b6jTWU1OZ6kAgB4h/2Hk/Xzst12+4FLazgdDgA4jqvfbmxvXJJu+mie/l63zyYy3r+1qQZdXIPGUQBQgI/b6/bE25m2HWvROBxA/ut8vIH4nKgD9kI53Mfnc7cqKvqIwooG6JHLajsdDgAgn305f5tS0jPUtFJxVmsAAIkN97Vmd5x6jJqjNbvjVbJogL4Z0EZXNSrndFgAgLOYdbxpeOMKxe2FKQDIbw3Lh9rjz+HkNC3ddsjpcJCDWbrv/L3Rbj/WrTYlZwHAyySlpuur+dvs9t0dqjkdDgAUCCQ23NDk1XvUc/Q87Y1PUo3wYvp5UHs1owEtABR4M48nNkxNXABwgo9PIXWsWeqkYxIKvtcmr7fJqEYVQnVTi4pOhwMAyGeTV+9VTEKKyhcvrG71yzgdDgAUCCQ23EhGhktvT92ge79aqsSUdHWoUUo/DmynimFFnA4NAPAf0tIzNHvj8cTG8VIwAOCEzGMQfTbcw9Lth/TDkp12+/lr6tvkFADAu3y7aIf92rNFBfn5cikPAAw/3gb3aRL+yHcrNHnNXnv/rvZV9WT3OgxoAOAmVuyMVXxSmkIL+9tSVADglI41S9teP2v3xCs6PknhIUFOh4SzTGx67njD8BubV1BTVmkDgNfZfjBR8zYftGN3T1btAUAWroq7gR0xibrhw7k2qRHg66PXb2ykZ6+uR1IDANzIzOMzoy+qWUq+zLYF4KCSxQLVqHyo3aYcVcE2cdkurdwZp+BAPz1+eR2nwwEAOOD7JcdWa5iqHaYUFQDgGK6MF3BzNx3QNe//q/V7D6tUsUB9PaANdXUBwA3RXwNAQZJ5LJpBYqNAN4p9869Iuz3okhoqHRzodEgAgHzmcrn0+8o9WSv3AAD5kNiIiYnRbbfdppCQEBUvXlz9+vXTkSNHzrj/1q1bVahQoWxv33//fdZ+2X3/m2++kScOXp/P3ao7Pl2oQ4mptlHgb/e3V/PKLD8HAHdz8EiyVu6Ks9skNgAUBJ1qh9uvszfstz2AUPB8+u8W7YlLsrNz72xXxelwAAAO2Bh9RFsOJNjqHZfWpWk4AORLjw2T1NizZ4+mTp2q1NRU9e3bVwMGDNCECROy3b9ixYp2/xONGTNGb7zxhq644oqTHh83bpwuv/zyrPsmceJJElPS9NTE1XbpudGjSTm9ekMjBfn7Oh0aAOA8/Bt1QC6XVLdsCLXsARQITSoWtz1/4o6m2h5AzSuHOR0STnDgSLI+nLHJbv+vW23OAwDAS/11vM9q+xolVSyQNrkAcKI8OSquW7dOkydP1qJFi9SiRQv72Hvvvafu3bvrzTffVLly5U57jq+vryIiIk56bOLEibrppptUrFixkx43iYxT9/UUJhM/8KsltvSUqcH+xOV1dPdFVe3KFACAe/fXYLUGgILCfM40PX9MeYsZkftJbBQw707bqCPJaWpQPkTXND793AkA4B2mrNlnv3ar75nXwACgwJW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",
+            "text/plain": [
+              "<Figure size 1600x800 with 8 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x = np.linspace(-1, 1, 400)\n",
+        "x2 = np.linspace(0.1, 2, 400)  # For log and exp\n",
+        "\n",
+        "# based on the number of subplots what should be the dimensions in the following line?\n",
+        "fig, axs = plt.subplots(2 , 4 , figsize=(16, 8))\n",
+        "axs[0,0].plot(x, np.sin(x));      axs[0,0].set_title(\"sin(x)\")\n",
+        "axs[0,1].plot(x, np.cos(x));      axs[0,1].set_title(\"cos(x)\")\n",
+        "axs[0,2].plot(x, np.tan(x));      axs[0,2].set_title(\"tan(x)\")\n",
+        "axs[0,3].plot(x, np.arcsin(x));   axs[0,3].set_title(\"arcsin(x)\")\n",
+        "axs[1,0].plot(x, np.arccos(x));   axs[1,0].set_title(\"arccos(x)\")\n",
+        "axs[1,1].plot(x, np.arctan(x));   axs[1,1].set_title(\"arctan(x)\")\n",
+        "axs[1,2].plot(x2, np.exp(x2));    axs[1,2].set_title(\"exp(x)\")\n",
+        "axs[1,3].plot(x2, np.log(x2));    axs[1,3].set_title(\"log(x)\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "thpsEzFVikPh"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WAidrU51fMqB"
+      },
+      "source": [
+        "## 4. Features of the Sigmoid Function"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "   1   \n",
+            "───────\n",
+            "     -x\n",
+            "1 + ℯ  \n"
+          ]
+        }
+      ],
+      "source": [
+        "import sympy as sym\n",
+        "\n",
+        "x = sym.Symbol('x')  # تعریف متغیر نمادین x\n",
+        "sig = 1 / (1 + sym.exp(-x))  \n",
+        "\n",
+        "sym.pprint(sig)  "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "lehJV_vNfLXx",
+        "outputId": "9cfc6ec4-9dfc-4220-a955-4d6a11fac8f3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Range: (np.float64(4.5397868702434395e-05), np.float64(0.9999546021312976)) | At x=0: 0.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "def sigmoid(x):\n",
+        "    return 1 / (1 + np.exp(-x))\n",
+        "\n",
+        "\n",
+        "x = np.linspace(-10, 10, 200)  # 200 num geberate between -10 , 10 \n",
+        "y = sigmoid(x)\n",
+        "\n",
+        "plt.plot(x, y)\n",
+        "plt.title(\"Sigmoid Function\")\n",
+        "plt.xlabel(\"x\")\n",
+        "plt.ylabel(\"σ(x)\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"Range:\", (y.min(), y.max()), \"| At x=0:\", sigmoid(0))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DUONuiAxilL0"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1FjRt_31fXsx"
+      },
+      "source": [
+        "## 5. Derivatives of Famous Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "vyyGBuHRfZ4n",
+        "outputId": "7a89fce7-f973-46bc-dca3-c452f1a34561"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "x = np.linspace(-3, 3, 400)   # 400 number generate between -3 , 3 \n",
+        "\n",
+        "plt.plot(x, x**2, label=\"x²\")\n",
+        "plt.plot(x, 2*x , '--', label=\"d/dx x²\")\n",
+        "\n",
+        "x = np.linspace(0, 3, 400)\n",
+        "plt.plot(x, np.exp(x), label=\"exp(x)\")\n",
+        "plt.plot(x, np.exp(x), '--', label=\"d/dx exp(x)\")\n",
+        "\n",
+        "plt.plot(x, np.log(x + 0.1), label=\"log(x)\")\n",
+        "plt.plot(x, 1 / (x + 0.1) , '--', label=\"d/dx log(x)\")\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.title(\"Functions and Their Derivatives\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# more example\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.linspace(0.1, 3, 400)\n",
+        "y = np.log(x + 0.1)\n",
+        "\n",
+        "dy = np.diff(y)\n",
+        "dx = np.diff(x)\n",
+        "derivative = dy / dx\n",
+        "\n",
+        "x_mid = (x[:-1] + x[1:]) / 2  # میانگین نقاط برای محور x مشتق\n",
+        "\n",
+        "plt.plot(x, y, label=\"log(x + 0.1)\")\n",
+        "plt.plot(x_mid, derivative, '--', label=\"Approx. Derivative\")\n",
+        "plt.legend()\n",
+        "plt.grid(True)\n",
+        "plt.title(\"Function and Its Approximate Derivative\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DURup9u5il4-"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w3QIKTZQf1ZK"
+      },
+      "source": [
+        "## 6. Gradient of Selected Functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Eeudgf3-f0Ie",
+        "outputId": "aeffb463-3230-4b98-8131-623b4fbf624c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Function: f(x, y) = x**2*y + sin(y)\n",
+            "Partial derivative w.r.t x (∂f/∂x): 2*x*y\n",
+            "Partial derivative w.r.t y (∂f/∂y): x**2 + cos(y)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# search and learn more about sumpy\n",
+        "import sympy as sp\n",
+        "\n",
+        "# Define symbolic variables\n",
+        "x, y = sp.symbols('x y')\n",
+        "\n",
+        "# Define the function\n",
+        "f = x**2 * y + sp.sin(y)\n",
+        "\n",
+        "# Compute partial derivatives\n",
+        "df_dx = sp.diff(f, x)\n",
+        "df_dy = sp.diff(f, y)\n",
+        "\n",
+        "print(\"Function: f(x, y) =\", f)\n",
+        "print(\"Partial derivative w.r.t x (∂f/∂x):\", df_dx)\n",
+        "print(\"Partial derivative w.r.t y (∂f/∂y):\", df_dy)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wDlIJkmYimg9"
+      },
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** --------"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.3/a0.3 arian shishehgar.ipynb b/a0.1/a0.3/a0.3 arian shishehgar.ipynb
new file mode 100644
index 0000000..254cf05
--- /dev/null
+++ b/a0.1/a0.3/a0.3 arian shishehgar.ipynb	
@@ -0,0 +1,426 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 3 — Functions, Derivatives & Gradients\n",
+        "This assignment explores the mathematical foundation of machine learning: functions and their derivatives. You’ll visualize, analyze, and code up essential concepts every ML practitioner uses.\n",
+        "\n",
+        "\n",
+        "**👀🔎 What do you see?**\n",
+        "\n",
+        "Look at the snippet codes and:\n",
+        "\n",
+        "* Describe any patterns, surprises, or connections you notice.\n",
+        "\n",
+        "* Write a few sentences about your observations! 📝✨\n"
+      ],
+      "metadata": {
+        "id": "7wR0aAR-evto"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Log and Exp: Proving Inverse Relationship\n"
+      ],
+      "metadata": {
+        "id": "cCb1hk3xe0E6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KOxJ9f-3eu5F",
+        "outputId": "31d8ce39-8670-4dc4-9053-6c6aaf10d1ff"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "x\tlog(exp(x))\texp(log(x))\n",
+            "1.00\t1.00\t\t1.00\n",
+            "2.00\t2.00\t\t2.00\n",
+            "10.00\t10.00\t\t10.00\n",
+            "2.72\t2.72\t\t2.72\n",
+            "\n",
+            "log(exp(y)) for 0 and negative values:\n",
+            "[ 0. -1.  5.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import -------- as np\n",
+        "\n",
+        "# Show log(exp(x)) == x and exp(log(x)) == x for several values\n",
+        "x_vals = np.array([1, 2, --------, np.e])\n",
+        "print(\"x\\tlog(exp(x))\\texp(log(x))\")\n",
+        "for x in --------:\n",
+        "    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n",
+        "\n",
+        "# Edge case: try negative and zero values (show error/NaN for log)\n",
+        "y_vals = np.array([0, -1, 5])\n",
+        "print(\"\\nlog(exp(y)) for 0 and negative values:\")\n",
+        "print(np.log(np.exp(y_vals)))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:\n",
+        " exp(x) = e^x => ln (exp(x))= ln( e^x) = x\n",
+        "\n",
+        "on the other hand :\n",
+        "\n",
+        "log x =y  ---> e^y=x\n",
+        "\n",
+        "exp(log(x))= exp(y) = x\n",
+        "\n",
+        "so both functions answer are equal."
+      ],
+      "metadata": {
+        "id": "lVZufz08g1hI"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Plotting Sine Functions Variations\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "mOAqtnk6fDH6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "x = np.--------(-2*np.pi, 2*np.pi, --------)\n",
+        "\n",
+        "# look at the description of each function and call the proper function from numpy\n",
+        "plt.plot(x, np.sin(--------), label='sin(x)')\n",
+        "plt.plot(x, np.--------(2*x), label='sin(2x)')\n",
+        "plt.plot(x, np.sin(--------), label='sin(x/2)')\n",
+        "plt.plot(x, np.sin(x)--------, label='sin²(x)')\n",
+        "plt.legend()\n",
+        "plt.title(\"Variations of Sine Functions\")\n",
+        "plt.show()\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "DoWdSehie66R",
+        "outputId": "a83a3ade-6ca7-425c-9fa8-59ca9c343d9a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:**\n",
+        " When we substitute 2x for x, the sin graph compresses horizontally by a factor of 1/2. This means the peak occurs at pi/4 instead of pi/2.\n",
+        "\n",
+        "On the other hand, when we substitute x/2 for x, the sin graph stretches horizontally by a factor of 2, so the peak occurs at pi.\n",
+        "\n",
+        "Finally, when the sin function is squared, the graph remains entirely above the x-axis, resulting in only non-negative outputs. In a way, the graph resembles the absolute value of sin(x), though with a different shape."
+      ],
+      "metadata": {
+        "id": "dMVcqrfihf0P"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Subplots: Trig, Inverse, Exp, and Log"
+      ],
+      "metadata": {
+        "id": "4-5A1OkdfHXq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = np.--------(-1, 1, 400)\n",
+        "x2 = --------.linspace(0.1, 2, 400)  # For log and exp\n",
+        "\n",
+        "# based on the number of subplots what should be the dimensions in the following line?\n",
+        "fig, axs = plt.subplots(--------, --------, figsize=(16, 8))\n",
+        "axs[0,0].plot(x, np.sin(x));      axs[0,0].set_title(\"sin(x)\")\n",
+        "axs[0,1].plot(x, np.cos(x));      axs[0,1].set_title(\"cos(x)\")\n",
+        "axs[0,2].plot(x, np.tan(x));      axs[0,2].set_title(\"tan(x)\")\n",
+        "axs[0,3].plot(x, np.arcsin(x));   axs[0,3].set_title(\"arcsin(x)\")\n",
+        "axs[1,0].plot(x, np.arccos(x));   axs[1,0].set_title(\"arccos(x)\")\n",
+        "axs[1,1].plot(x, np.arctan(x));   axs[1,1].set_title(\"arctan(x)\")\n",
+        "axs[1,2].plot(x2, np.exp(x2));    axs[1,2].set_title(\"exp(x)\")\n",
+        "axs[1,3].plot(x2, np.log(x2));    axs[1,3].set_title(\"log(x)\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.--------()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 807
+        },
+        "id": "DxocsCu6fGOR",
+        "outputId": "5864a5cc-5776-45bd-99f7-028c64e301af"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x800 with 8 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:\n",
+        "\n",
+        "This diagrams demonstrate the function with they reveresed ones. the reversed ones are cuased by reflecting the original one across the x=y line.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "thpsEzFVikPh"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Features of the Sigmoid Function"
+      ],
+      "metadata": {
+        "id": "WAidrU51fMqB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "def sigmoid(x):\n",
+        "    return --------\n",
+        "\n",
+        "x = np.linspace(-10, 10, 200)\n",
+        "y = --------(x)\n",
+        "\n",
+        "plt.plot(x, y)\n",
+        "plt.title(\"Sigmoid Function\")\n",
+        "plt.xlabel(\"x\")\n",
+        "plt.ylabel(\"σ(x)\")\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "print(\"Range:\", (y.min(), y.max()), \"| At x=0:\", sigmoid(0))\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "id": "lehJV_vNfLXx",
+        "outputId": "9cfc6ec4-9dfc-4220-a955-4d6a11fac8f3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Range: (np.float64(4.5397868702434395e-05), np.float64(0.9999546021312976)) | At x=0: 0.5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** Its the diagram of sigmoid function which is y= 1/1+e^(-x).. in x=0 , the result is 1/2."
+      ],
+      "metadata": {
+        "id": "DUONuiAxilL0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 5. Derivatives of Famous Functions"
+      ],
+      "metadata": {
+        "id": "1FjRt_31fXsx"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x = np.linspace(-3, 3, 400)\n",
+        "plt.--------(x, x**2, label=\"x²\")\n",
+        "plt.plot(x, --------, '--', label=\"d/dx x²\")\n",
+        "\n",
+        "x = np.linspace(0, 3, 400)\n",
+        "plt.plot(x, np.exp(x), label=\"exp(x)\")\n",
+        "plt.plot(x, --------, '--', label=\"d/dx exp(x)\")\n",
+        "\n",
+        "plt.plot(x, np.log(x+0.1), label=\"log(x)\")\n",
+        "plt.plot(x, --------, '--', label=\"d/dx log(x)\")\n",
+        "\n",
+        "plt.legend()\n",
+        "plt.title(\"Functions and Their Derivatives\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "id": "vyyGBuHRfZ4n",
+        "outputId": "7a89fce7-f973-46bc-dca3-c452f1a34561"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:** There are some of important with their deriatives. As the diagram shows, the x^2 deriative is 2x which is the dotted orange line. The exp deriative is same as itself, so that they diagram overlap. the ln(x) deriative is 1/x, which diagram is the dotted brown line."
+      ],
+      "metadata": {
+        "id": "DURup9u5il4-"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 6. Gradient of Selected Functions"
+      ],
+      "metadata": {
+        "id": "w3QIKTZQf1ZK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# search and learn more about sumpy\n",
+        "import sympy as sp\n",
+        "\n",
+        "# Define symbolic variables\n",
+        "x, y = sp.--------('x y')\n",
+        "\n",
+        "# Define the function\n",
+        "f = x**2 * y + sp.sin(y)\n",
+        "\n",
+        "# Compute partial derivatives\n",
+        "df_dx = sp.diff(f, x)\n",
+        "df_dy = sp.diff(f, y)\n",
+        "\n",
+        "print(\"Function: f(x, y) =\", f)\n",
+        "print(\"Partial derivative w.r.t x (∂f/∂x):\", df_dx)\n",
+        "print(\"Partial derivative w.r.t y (∂f/∂y):\", df_dy)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Eeudgf3-f0Ie",
+        "outputId": "aeffb463-3230-4b98-8131-623b4fbf624c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Function: f(x, y) = x**2*y + sin(y)\n",
+            "Partial derivative w.r.t x (∂f/∂x): 2*x*y\n",
+            "Partial derivative w.r.t y (∂f/∂y): x**2 + cos(y)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 👀🔎 What do you see?\n",
+        "\n",
+        "#### **your answer:**  I'm seeing the partial deriatives (gradient) of the f(x,y) function. in (∂f/∂x) the second sentence which included only the independent variable (y) , would be consider as zero. the first sentence included x and y but we only consider dependent variable (x). One the other hand, in (∂f/∂y) we consider the x as a number, so that the fisrt sentence deriative is x^2 and the second one is deriative of sin(y) , which is cos(y)"
+      ],
+      "metadata": {
+        "id": "wDlIJkmYimg9"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.4/AI-DS_Nexus__A0_4_Amirhossein_Zandi_8fca80.ipynb b/a0.1/a0.4/AI-DS_Nexus__A0_4_Amirhossein_Zandi_8fca80.ipynb
new file mode 100644
index 0000000..83857d1
--- /dev/null
+++ b/a0.1/a0.4/AI-DS_Nexus__A0_4_Amirhossein_Zandi_8fca80.ipynb
@@ -0,0 +1,280 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A1k4HkdO05Yu"
+      },
+      "source": [
+        "# 📓 Assignment 4 — Linear Algebra Fundamentals\n",
+        "\n",
+        "*Vectors & Matrices for Everyday Coding*\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mBNupqPj1B1_"
+      },
+      "source": [
+        "## Welcome back, intrepid coder! 🚀\n",
+        "In this notebook-styled brief you’ll move from single-direction vectors to multi-direction matrices—core tools behind graphics, robotics, optimisation and (of course) machine-learning. Each mini-section mixes quick notes, a tiny real-world scenario, and hands-on # TODO code shaped for beginners.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zD-AyQCe1Y9e"
+      },
+      "source": [
+        "## 🧭 Vectors 101\n",
+        "\n",
+        "| Concept             | Quick reminder                                  |\n",
+        "|---------------------|--------------------------------------------------|\n",
+        "| Representation      | 1-D NumPy array — `np.array([x, y, z])`         |\n",
+        "| Length (norm)       | `np.linalg.norm(v)`                             |\n",
+        "| Dot / inner product | `v.dot(w)` or `np.inner(v, w)`                  |\n",
+        "| Unit vector         | `v / np.linalg.norm(v)`                         |\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "AWVTIlQP0ysC"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "v shape: (4, 2, 3), v ndim: 3\n",
+            "Norm of v ===> 28.478061731796284\n",
+            "---------------------------------------------------\n",
+            "w shape: (4, 3, 2), w ndim: 3\n",
+            "Dot product of v and w ====> [[[ 33  67]\n",
+            "  [ 66  85]]\n",
+            "\n",
+            " [[134 125]\n",
+            "  [114 110]]\n",
+            "\n",
+            " [[ 29  68]\n",
+            "  [ 57  75]]\n",
+            "\n",
+            " [[ 49  97]\n",
+            "  [106  80]]]\n",
+            "---------------------------------------------------\n",
+            "dot_product shape: (4, 2, 2), dot_product ndim: 3\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Run once per session\n",
+        "import numpy as np\n",
+        "\n",
+        "# TODO 1: create a 3-D vector named v\n",
+        "# TODO 2: print its length\n",
+        "# TODO 3: build a second vector w and output the dot product\n",
+        "\n",
+        "v = np.random.randint(1,10,size=(4 , 2 , 3))\n",
+        "print(f\"v shape: {v.shape}, v ndim: {v.ndim}\")\n",
+        "print(f\"Norm of v ===> {np.linalg.norm(v)}\")\n",
+        "print(\"---------------------------------------------------\")\n",
+        "\n",
+        "w = np.random.randint(1,10,size=(4 , 3 , 2))\n",
+        "print(f\"w shape: {w.shape}, w ndim: {w.ndim}\")\n",
+        "\n",
+        "dot_product = np.matmul(v, w)\n",
+        "print(f\"Dot product of v and w ====> {dot_product}\")\n",
+        "print(\"---------------------------------------------------\")\n",
+        "print(f\"dot_product shape: {dot_product.shape}, dot_product ndim: {dot_product.ndim}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c_iB5_0A1rg7"
+      },
+      "source": [
+        "### 🚁 Practical Scenario — “Drone hop”\n",
+        "A mini-drone lifts off at (2 m, 1 m) and lands at (7 m, 4 m).\n",
+        "Calculate its displacement vector, travel distance, and orientation along the x-axis."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "Rq-tHCkF1x3p"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Displacement ==> [5 3]\n",
+            "shape of displacement ==> (2,)\n",
+            "Distance ==> 5.830951894845301\n",
+            "Unit ==> [0.85749293 0.51449576]\n",
+            "Dot_x ==> 5\n"
+          ]
+        }
+      ],
+      "source": [
+        "p_start = np.array([2,1])\n",
+        "p_end = np.array([7,4])\n",
+        "\n",
+        "displacement = np.subtract(p_end, p_start)\n",
+        "distance = np.linalg.norm(displacement)\n",
+        "unit = displacement / distance\n",
+        "dot_x = np.dot(displacement , [1,0])\n",
+        "\n",
+        "print(f\"Displacement ==> {displacement}\")\n",
+        "print(f\"shape of displacement ==> {displacement.shape}\")\n",
+        "print(f\"Distance ==> {distance}\")\n",
+        "print(f\"Unit ==> {unit}\")\n",
+        "print(f\"Dot_x ==> {dot_x}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vqX_pXGq16Af"
+      },
+      "source": [
+        "## 🔢 Matrices 101\n",
+        "\n",
+        "| Operation              | NumPy one-liner                                  |\n",
+        "|------------------------|--------------------------------------------------|\n",
+        "| Transpose              | `A.T`                                            |\n",
+        "| Determinant            | `np.linalg.det(A)`                               |\n",
+        "| Inverse                | `np.linalg.inv(A)` (works only if `det(A) ≠ 0`)  |\n",
+        "| Matrix-vector multiply | `A @ v`                                          |\n",
+        "| Matrix-matrix multiply | `A @ B`                                          |\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "yycidpaj2BAv"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "a shape ==> (2, 2), a ndim ==> 2\n",
+            "-----------------------------------------------\n",
+            "Det of a ==> -46.0\n",
+            "A_inv ==> [[-0.04347826  0.17391304]\n",
+            " [ 0.15217391 -0.10869565]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO 4: build a 2×2 matrix A\n",
+        "# TODO 5: print its determinant\n",
+        "# TODO 6: if invertible, compute and print A_inv\n",
+        "\n",
+        "a = np.random.randint(1 , 10 , size=(2,2))\n",
+        "print(f\"a shape ==> {a.shape}, a ndim ==> {a.ndim}\")\n",
+        "print(\"-----------------------------------------------\")\n",
+        "\n",
+        "det_a = np.linalg.det(a)\n",
+        "print(f\"Det of a ==> {det_a}\")\n",
+        "\n",
+        "a_inv = np.linalg.inv(a)\n",
+        "print(f\"A_inv ==> {a_inv}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C-ikgDhF2DFn"
+      },
+      "source": [
+        "### 🎯 Practical Scenario — “Rotate that hop”\n",
+        "Rotate the drone’s displacement vector 30° counter-clockwise, then verify that the inverse rotation brings it back."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "SgmIesbf2G65"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "r ==> [[ 0.8660254 -0.5      ]\n",
+            " [ 0.5        0.8660254]]\n",
+            "---------------------------------------------\n",
+            "shape of r ==> (2, 2)\n",
+            "shape of d ==> (2,)\n",
+            "shape of d_rotate ==> (2,)\n",
+            "Theta ==> 0.5235987755982988\n",
+            "Determinant of r ==> 1.0\n",
+            "Back rotate is equal to d_roate?? ===> True\n"
+          ]
+        }
+      ],
+      "source": [
+        "theta = np.deg2rad(30)\n",
+        "\n",
+        "r = np.array([[np.cos(theta), -np.sin(theta)],\n",
+        "              [np.sin(theta),  np.cos(theta)]])\n",
+        "\n",
+        "det_r = np.linalg.det(r)\n",
+        "\n",
+        "d_rotate = np.matmul(r, displacement)\n",
+        "d_back_rotate = np.matmul(np.linalg.inv(r), d_rotate)\n",
+        "\n",
+        "print(f\"r ==> {r}\")\n",
+        "print(\"---------------------------------------------\")\n",
+        "print(f\"shape of r ==> {r.shape}\")\n",
+        "print(f\"shape of d ==> {displacement.shape}\")\n",
+        "print(f\"shape of d_rotate ==> {d_rotate.shape}\")\n",
+        "print(f\"Theta ==> {theta}\")\n",
+        "print(f\"Determinant of r ==> {det_r}\")\n",
+        "print(f\"Back rotate is equal to d_roate?? ===> {np.allclose(d_back_rotate , displacement)}\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.4/AI-DS_Nexus__A0_4_Javid_Mohammadi_a60d6f.ipynb b/a0.1/a0.4/AI-DS_Nexus__A0_4_Javid_Mohammadi_a60d6f.ipynb
new file mode 100644
index 0000000..ed08513
--- /dev/null
+++ b/a0.1/a0.4/AI-DS_Nexus__A0_4_Javid_Mohammadi_a60d6f.ipynb
@@ -0,0 +1,241 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A1k4HkdO05Yu"
+      },
+      "source": [
+        "# 📓 Assignment 4 — Linear Algebra Fundamentals\n",
+        "\n",
+        "*Vectors & Matrices for Everyday Coding*\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mBNupqPj1B1_"
+      },
+      "source": [
+        "## Welcome back, intrepid coder! 🚀\n",
+        "In this notebook-styled brief you’ll move from single-direction vectors to multi-direction matrices—core tools behind graphics, robotics, optimisation and (of course) machine-learning. Each mini-section mixes quick notes, a tiny real-world scenario, and hands-on # TODO code shaped for beginners.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zD-AyQCe1Y9e"
+      },
+      "source": [
+        "## 🧭 Vectors 101\n",
+        "\n",
+        "| Concept             | Quick reminder                                  |\n",
+        "|---------------------|--------------------------------------------------|\n",
+        "| Representation      | 1-D NumPy array — `np.array([x, y, z])`         |\n",
+        "| Length (norm)       | `np.linalg.norm(v)`                             |\n",
+        "| Dot / inner product | `v.dot(w)` or `np.inner(v, w)`                  |\n",
+        "| Unit vector         | `v / np.linalg.norm(v)`                         |\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AWVTIlQP0ysC",
+        "outputId": "05a04eff-7c18-4005-e9fd-a24ea3d9a962"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The length of the vector is: 12.25\n",
+            "The dot product is: 108\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Run once per session\n",
+        "import numpy as np\n",
+        "\n",
+        "# TODO 1: create a 3-D vector named v\n",
+        "v = np.array([1, 7, 10])\n",
+        "\n",
+        "# TODO 2: print its length\n",
+        "print(f\"The length of the vector is: {np.linalg.norm(v):.2f}\")\n",
+        "\n",
+        "# TODO 3: build a second vector w and output the dot product\n",
+        "w = np.array([13, 5, 6])\n",
+        "print(f\"The dot product is: {v@w}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c_iB5_0A1rg7"
+      },
+      "source": [
+        "### 🚁 Practical Scenario — “Drone hop”\n",
+        "A mini-drone lifts off at (2 m, 1 m) and lands at (7 m, 4 m).\n",
+        "Calculate its displacement vector, travel distance, and orientation along the x-axis."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Rq-tHCkF1x3p",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3e793737-46b8-4f9c-a354-94099bbea3fc"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Displacement: [5 3]\n",
+            "Distance: 5.830951894845301\n",
+            "Unit direction: [0.85749293 0.51449576]\n",
+            "Dot-product with x-axis: 5\n"
+          ]
+        }
+      ],
+      "source": [
+        "p_start = np.array([2, 1])\n",
+        "p_end   = np.array([7, 4])\n",
+        "\n",
+        "# displacement\n",
+        "d = p_end - p_start          # ➡️ vector from start to end\n",
+        "dist = np.linalg.norm(d)     # 🏁 distance travelled\n",
+        "unit = d / dist              # ↗️ unit direction\n",
+        "dot_x = d.dot(np.array([1, 0]))  # projection on x-axis\n",
+        "\n",
+        "print(\"Displacement:\", d)\n",
+        "print(\"Distance:\", dist)\n",
+        "print(\"Unit direction:\", unit)\n",
+        "print(\"Dot-product with x-axis:\", dot_x)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vqX_pXGq16Af"
+      },
+      "source": [
+        "## 🔢 Matrices 101\n",
+        "\n",
+        "| Operation              | NumPy one-liner                                  |\n",
+        "|------------------------|--------------------------------------------------|\n",
+        "| Transpose              | `A.T`                                            |\n",
+        "| Determinant            | `np.linalg.det(A)`                               |\n",
+        "| Inverse                | `np.linalg.inv(A)` (works only if `det(A) ≠ 0`)  |\n",
+        "| Matrix-vector multiply | `A @ v`                                          |\n",
+        "| Matrix-matrix multiply | `A @ B`                                          |\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yycidpaj2BAv",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8f84f49c-6e27-42c4-eab2-834093f93618"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Matrix's determinant is: -4.00\n",
+            "The inverted matrix is:\n",
+            "[[ 0.    1.  ]\n",
+            " [ 0.25 -0.5 ]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO 4: build a 2×2 matrix A\n",
+        "A = np.array([[2, 4], [1, 0]])\n",
+        "\n",
+        "# TODO 5: print its determinant\n",
+        "det = np.linalg.det(A)\n",
+        "print(f\"Matrix's determinant is: {det:.2f}\")\n",
+        "\n",
+        "# TODO 6: if invertible, compute and print A_inv\n",
+        "if det == 0:\n",
+        "  print(\"The matrix is not invertible.\")\n",
+        "else:\n",
+        "  A_inv = np.linalg.inv(A)\n",
+        "  print(f\"The inverted matrix is:\\n{A_inv}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C-ikgDhF2DFn"
+      },
+      "source": [
+        "### 🎯 Practical Scenario — “Rotate that hop”\n",
+        "Rotate the drone’s displacement vector 30° counter-clockwise, then verify that the inverse rotation brings it back."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SgmIesbf2G65",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "71b8afb4-be19-4ff0-c0eb-ca02247c2dc7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Rotation matrix determinant: 1.0\n",
+            "Rotated vector: [2.83012702 5.09807621]\n",
+            "Back-rotated equals original? True\n"
+          ]
+        }
+      ],
+      "source": [
+        "theta = np.deg2rad(30)          # 🔄 convert degrees to radians\n",
+        "R = np.array([[np.cos(theta), -np.sin(theta)],\n",
+        "              [np.sin(theta),  np.cos(theta)]])\n",
+        "\n",
+        "det_R = np.linalg.det(R)        # should be 1.0 (pure rotation)\n",
+        "\n",
+        "d_rot  = R @ d                  # rotated displacement\n",
+        "d_back = np.linalg.inv(R) @ d_rot\n",
+        "\n",
+        "print(\"Rotation matrix determinant:\", det_R)\n",
+        "print(\"Rotated vector:\", d_rot)\n",
+        "print(\"Back-rotated equals original?\", np.allclose(d_back, d))"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/a0.1/a0.4/AI-DS_Nexus__A0_4_MrAshki.ipynb b/a0.1/a0.4/AI-DS_Nexus__A0_4_MrAshki.ipynb
new file mode 100644
index 0000000..d88f8fb
--- /dev/null
+++ b/a0.1/a0.4/AI-DS_Nexus__A0_4_MrAshki.ipynb
@@ -0,0 +1,258 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📓 Assignment 4 — Linear Algebra Fundamentals\n",
+        "\n",
+        "*Vectors & Matrices for Everyday Coding*\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "A1k4HkdO05Yu"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Welcome back, intrepid coder! 🚀\n",
+        "In this notebook-styled brief you’ll move from single-direction vectors to multi-direction matrices—core tools behind graphics, robotics, optimisation and (of course) machine-learning. Each mini-section mixes quick notes, a tiny real-world scenario, and hands-on # TODO code shaped for beginners.\n"
+      ],
+      "metadata": {
+        "id": "mBNupqPj1B1_"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🧭 Vectors 101\n",
+        "\n",
+        "| Concept             | Quick reminder                                  |\n",
+        "|---------------------|--------------------------------------------------|\n",
+        "| Representation      | 1-D NumPy array — `np.array([x, y, z])`         |\n",
+        "| Length (norm)       | `np.linalg.norm(v)`                             |\n",
+        "| Dot / inner product | `v.dot(w)` or `np.inner(v, w)`                  |\n",
+        "| Unit vector         | `v / np.linalg.norm(v)`                         |\n"
+      ],
+      "metadata": {
+        "id": "zD-AyQCe1Y9e"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "AWVTIlQP0ysC",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2529013a-a7e8-4aa6-cfa9-997c8a658b5a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "طول بردار برابر است با: 5.0\n",
+            "ضرب داخلی برابر است با: 11\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Run once per session\n",
+        "import numpy as np\n",
+        "\n",
+        "# TODO 1: create a 3-D vector named v\n",
+        "# TODO 2: print its length\n",
+        "# TODO 3: build a second vector w and output the dot product\n",
+        "\n",
+        "\n",
+        "\n",
+        "v = np.array([3, 4, 0])\n",
+        "\n",
+        "\n",
+        "length_v = np.linalg.norm(v)\n",
+        "print(f\"طول بردار برابر است با: {length_v}\")\n",
+        "\n",
+        "\n",
+        "w = np.array([1, 2, 5])\n",
+        "dot_product = v.dot(w)\n",
+        "print(f\"ضرب داخلی برابر است با: {dot_product}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 🚁 Practical Scenario — “Drone hop”\n",
+        "A mini-drone lifts off at (2 m, 1 m) and lands at (7 m, 4 m).\n",
+        "Calculate its displacement vector, travel distance, and orientation along the x-axis."
+      ],
+      "metadata": {
+        "id": "c_iB5_0A1rg7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "p_start = np.array([2, 1])\n",
+        "p_end   = np.array([7, 4])\n",
+        "\n",
+        "# displacement\n",
+        "d = p_end - p_start          # ➡️ vector from start to end\n",
+        "dist = np.linalg.norm(d)     # 🏁 distance travelled\n",
+        "unit = d / dist              # ↗️ unit direction\n",
+        "dot_x = d.dot(np.array([1, 0]))  # projection on x-axis\n",
+        "\n",
+        "print(\"Displacement:\", d)\n",
+        "print(\"Distance:\", dist)\n",
+        "print(\"Unit direction:\", unit)\n",
+        "print(\"Dot-product with x-axis:\", dot_x)\n"
+      ],
+      "metadata": {
+        "id": "Rq-tHCkF1x3p",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cb4e0323-d715-4070-fd67-a765bd93680e"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Displacement: [5 3]\n",
+            "Distance: 5.830951894845301\n",
+            "Unit direction: [0.85749293 0.51449576]\n",
+            "Dot-product with x-axis: 5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🔢 Matrices 101\n",
+        "\n",
+        "| Operation              | NumPy one-liner                                  |\n",
+        "|------------------------|--------------------------------------------------|\n",
+        "| Transpose              | `A.T`                                            |\n",
+        "| Determinant            | `np.linalg.det(A)`                               |\n",
+        "| Inverse                | `np.linalg.inv(A)` (works only if `det(A) ≠ 0`)  |\n",
+        "| Matrix-vector multiply | `A @ v`                                          |\n",
+        "| Matrix-matrix multiply | `A @ B`                                          |\n"
+      ],
+      "metadata": {
+        "id": "vqX_pXGq16Af"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO 4: build a 2×2 matrix A\n",
+        "# TODO 5: print its determinant\n",
+        "# TODO 6: if invertible, compute and print A_inv\n",
+        "\n",
+        "A = np.array([[1, 2],\n",
+        "              [3, 4]])\n",
+        "det_A = np.linalg.det(A)\n",
+        "print(f\"دترمینای ماتریس A: {det_A}\")\n",
+        "if det_A != 0:\n",
+        "    A_inv = np.linalg.inv(A)\n",
+        "    print(\"معکوس ماتریس A\")\n",
+        "    print(A_inv)\n",
+        "else:\n",
+        "    print(\"معکوس ندارد\")"
+      ],
+      "metadata": {
+        "id": "yycidpaj2BAv",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "75c1af54-c574-43ce-bf0b-079b8700e0fb"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "دترمینای ماتریس A: -2.0000000000000004\n",
+            "معکوس ماتریس A\n",
+            "[[-2.   1. ]\n",
+            " [ 1.5 -0.5]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 🎯 Practical Scenario — “Rotate that hop”\n",
+        "Rotate the drone’s displacement vector 30° counter-clockwise, then verify that the inverse rotation brings it back."
+      ],
+      "metadata": {
+        "id": "C-ikgDhF2DFn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "theta = np.deg2rad(30)          # 🔄 convert degrees to radians\n",
+        "R = np.array([[np.cos(theta), -np.sin(theta)],\n",
+        "              [np.sin(theta),  np.cos(theta)]])\n",
+        "\n",
+        "det_R = np.linalg.det(R)        # should be 1.0 (pure rotation)\n",
+        "\n",
+        "d_rot  = R @ d                  # rotated displacement\n",
+        "d_back = np.linalg.inv(R) @ d_rot\n",
+        "\n",
+        "print(\"Rotation matrix determinant:\", det_R)\n",
+        "print(\"Rotated vector:\", d_rot)\n",
+        "print(\"Back-rotated equals original?\", np.allclose(d_back, d))\n"
+      ],
+      "metadata": {
+        "id": "SgmIesbf2G65",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a95d27e8-589c-46a6-943e-c4fb1ca05e94"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Rotation matrix determinant: 1.0\n",
+            "Rotated vector: [2.83012702 5.09807621]\n",
+            "Back-rotated equals original? True\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.4/AI-DS_Nexus__A0_4_Project__RezaShokr.ipynb b/a0.1/a0.4/AI-DS_Nexus__A0_4_Project__RezaShokr.ipynb
new file mode 100644
index 0000000..44b68e9
--- /dev/null
+++ b/a0.1/a0.4/AI-DS_Nexus__A0_4_Project__RezaShokr.ipynb
@@ -0,0 +1,178 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A1k4HkdO05Yu"
+      },
+      "source": [
+        "# 📓 Assignment 4 — Linear Algebra Fundamentals\n",
+        "\n",
+        "*Vectors & Matrices for Everyday Coding*\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mBNupqPj1B1_"
+      },
+      "source": [
+        "## Welcome back, intrepid coder! 🚀\n",
+        "In this notebook-styled brief you’ll move from single-direction vectors to multi-direction matrices—core tools behind graphics, robotics, optimisation and (of course) machine-learning. Each mini-section mixes quick notes, a tiny real-world scenario, and hands-on # TODO code shaped for beginners.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zD-AyQCe1Y9e"
+      },
+      "source": [
+        "## 🧭 Vectors 101\n",
+        "\n",
+        "| Concept             | Quick reminder                                  |\n",
+        "|---------------------|--------------------------------------------------|\n",
+        "| Representation      | 1-D NumPy array — `np.array([x, y, z])`         |\n",
+        "| Length (norm)       | `np.linalg.norm(v)`                             |\n",
+        "| Dot / inner product | `v.dot(w)` or `np.inner(v, w)`                  |\n",
+        "| Unit vector         | `v / np.linalg.norm(v)`                         |\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "AWVTIlQP0ysC"
+      },
+      "outputs": [],
+      "source": [
+        "# Run once per session\n",
+        "import numpy as np\n",
+        "\n",
+        "# TODO 1: create a 3-D vector named v\n",
+        "# TODO 2: print its length\n",
+        "# TODO 3: build a second vector w and output the dot product\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c_iB5_0A1rg7"
+      },
+      "source": [
+        "### 🚁 Practical Scenario — “Drone hop”\n",
+        "A mini-drone lifts off at (2 m, 1 m) and lands at (7 m, 4 m).\n",
+        "Calculate its displacement vector, travel distance, and orientation along the x-axis."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Rq-tHCkF1x3p"
+      },
+      "outputs": [],
+      "source": [
+        "p_start = np.array([2, 1])\n",
+        "p_end   = np.array([7, 4])\n",
+        "\n",
+        "# displacement\n",
+        "d = p_end - p_start          # ➡️ vector from start to end\n",
+        "dist = np.linalg.norm(d)     # 🏁 distance travelled\n",
+        "unit = d / dist              # ↗️ unit direction\n",
+        "dot_x = d.dot(np.array([1, 0]))  # projection on x-axis\n",
+        "\n",
+        "print(\"Displacement:\", d)\n",
+        "print(\"Distance:\", dist)\n",
+        "print(\"Unit direction:\", unit)\n",
+        "print(\"Dot-product with x-axis:\", dot_x)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vqX_pXGq16Af"
+      },
+      "source": [
+        "## 🔢 Matrices 101\n",
+        "\n",
+        "| Operation              | NumPy one-liner                                  |\n",
+        "|------------------------|--------------------------------------------------|\n",
+        "| Transpose              | `A.T`                                            |\n",
+        "| Determinant            | `np.linalg.det(A)`                               |\n",
+        "| Inverse                | `np.linalg.inv(A)` (works only if `det(A) ≠ 0`)  |\n",
+        "| Matrix-vector multiply | `A @ v`                                          |\n",
+        "| Matrix-matrix multiply | `A @ B`                                          |\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yycidpaj2BAv"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO 4: build a 2×2 matrix A\n",
+        "# TODO 5: print its determinant\n",
+        "# TODO 6: if invertible, compute and print A_inv\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C-ikgDhF2DFn"
+      },
+      "source": [
+        "### 🎯 Practical Scenario — “Rotate that hop”\n",
+        "Rotate the drone’s displacement vector 30° counter-clockwise, then verify that the inverse rotation brings it back."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SgmIesbf2G65"
+      },
+      "outputs": [],
+      "source": [
+        "theta = np.deg2rad(30)          # 🔄 convert degrees to radians\n",
+        "R = np.array([[np.cos(theta), -np.sin(theta)],\n",
+        "              [np.sin(theta),  np.cos(theta)]])\n",
+        "\n",
+        "det_R = np.linalg.det(R)        # should be 1.0 (pure rotation)\n",
+        "\n",
+        "d_rot  = R @ d                  # rotated displacement\n",
+        "d_back = np.linalg.inv(R) @ d_rot\n",
+        "\n",
+        "print(\"Rotation matrix determinant:\", det_R)\n",
+        "print(\"Rotated vector:\", d_rot)\n",
+        "print(\"Back-rotated equals original?\", np.allclose(d_back, d))\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.4/AI-DS_Nexus__A0_4_Project_roohi_268383.ipynb b/a0.1/a0.4/AI-DS_Nexus__A0_4_Project_roohi_268383.ipynb
new file mode 100644
index 0000000..44b68e9
--- /dev/null
+++ b/a0.1/a0.4/AI-DS_Nexus__A0_4_Project_roohi_268383.ipynb
@@ -0,0 +1,178 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A1k4HkdO05Yu"
+      },
+      "source": [
+        "# 📓 Assignment 4 — Linear Algebra Fundamentals\n",
+        "\n",
+        "*Vectors & Matrices for Everyday Coding*\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mBNupqPj1B1_"
+      },
+      "source": [
+        "## Welcome back, intrepid coder! 🚀\n",
+        "In this notebook-styled brief you’ll move from single-direction vectors to multi-direction matrices—core tools behind graphics, robotics, optimisation and (of course) machine-learning. Each mini-section mixes quick notes, a tiny real-world scenario, and hands-on # TODO code shaped for beginners.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zD-AyQCe1Y9e"
+      },
+      "source": [
+        "## 🧭 Vectors 101\n",
+        "\n",
+        "| Concept             | Quick reminder                                  |\n",
+        "|---------------------|--------------------------------------------------|\n",
+        "| Representation      | 1-D NumPy array — `np.array([x, y, z])`         |\n",
+        "| Length (norm)       | `np.linalg.norm(v)`                             |\n",
+        "| Dot / inner product | `v.dot(w)` or `np.inner(v, w)`                  |\n",
+        "| Unit vector         | `v / np.linalg.norm(v)`                         |\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "AWVTIlQP0ysC"
+      },
+      "outputs": [],
+      "source": [
+        "# Run once per session\n",
+        "import numpy as np\n",
+        "\n",
+        "# TODO 1: create a 3-D vector named v\n",
+        "# TODO 2: print its length\n",
+        "# TODO 3: build a second vector w and output the dot product\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c_iB5_0A1rg7"
+      },
+      "source": [
+        "### 🚁 Practical Scenario — “Drone hop”\n",
+        "A mini-drone lifts off at (2 m, 1 m) and lands at (7 m, 4 m).\n",
+        "Calculate its displacement vector, travel distance, and orientation along the x-axis."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Rq-tHCkF1x3p"
+      },
+      "outputs": [],
+      "source": [
+        "p_start = np.array([2, 1])\n",
+        "p_end   = np.array([7, 4])\n",
+        "\n",
+        "# displacement\n",
+        "d = p_end - p_start          # ➡️ vector from start to end\n",
+        "dist = np.linalg.norm(d)     # 🏁 distance travelled\n",
+        "unit = d / dist              # ↗️ unit direction\n",
+        "dot_x = d.dot(np.array([1, 0]))  # projection on x-axis\n",
+        "\n",
+        "print(\"Displacement:\", d)\n",
+        "print(\"Distance:\", dist)\n",
+        "print(\"Unit direction:\", unit)\n",
+        "print(\"Dot-product with x-axis:\", dot_x)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vqX_pXGq16Af"
+      },
+      "source": [
+        "## 🔢 Matrices 101\n",
+        "\n",
+        "| Operation              | NumPy one-liner                                  |\n",
+        "|------------------------|--------------------------------------------------|\n",
+        "| Transpose              | `A.T`                                            |\n",
+        "| Determinant            | `np.linalg.det(A)`                               |\n",
+        "| Inverse                | `np.linalg.inv(A)` (works only if `det(A) ≠ 0`)  |\n",
+        "| Matrix-vector multiply | `A @ v`                                          |\n",
+        "| Matrix-matrix multiply | `A @ B`                                          |\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yycidpaj2BAv"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO 4: build a 2×2 matrix A\n",
+        "# TODO 5: print its determinant\n",
+        "# TODO 6: if invertible, compute and print A_inv\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C-ikgDhF2DFn"
+      },
+      "source": [
+        "### 🎯 Practical Scenario — “Rotate that hop”\n",
+        "Rotate the drone’s displacement vector 30° counter-clockwise, then verify that the inverse rotation brings it back."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SgmIesbf2G65"
+      },
+      "outputs": [],
+      "source": [
+        "theta = np.deg2rad(30)          # 🔄 convert degrees to radians\n",
+        "R = np.array([[np.cos(theta), -np.sin(theta)],\n",
+        "              [np.sin(theta),  np.cos(theta)]])\n",
+        "\n",
+        "det_R = np.linalg.det(R)        # should be 1.0 (pure rotation)\n",
+        "\n",
+        "d_rot  = R @ d                  # rotated displacement\n",
+        "d_back = np.linalg.inv(R) @ d_rot\n",
+        "\n",
+        "print(\"Rotation matrix determinant:\", det_R)\n",
+        "print(\"Rotated vector:\", d_rot)\n",
+        "print(\"Back-rotated equals original?\", np.allclose(d_back, d))\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.4/AI-DS_Nexus__A0_4__aminran.ipynb b/a0.1/a0.4/AI-DS_Nexus__A0_4__aminran.ipynb
new file mode 100644
index 0000000..c499ab3
--- /dev/null
+++ b/a0.1/a0.4/AI-DS_Nexus__A0_4__aminran.ipynb
@@ -0,0 +1,230 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📓 Assignment 4 — Linear Algebra Fundamentals\n",
+        "\n",
+        "*Vectors & Matrices for Everyday Coding*\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "A1k4HkdO05Yu"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Welcome back, intrepid coder! 🚀\n",
+        "In this notebook-styled brief you’ll move from single-direction vectors to multi-direction matrices—core tools behind graphics, robotics, optimisation and (of course) machine-learning. Each mini-section mixes quick notes, a tiny real-world scenario, and hands-on # TODO code shaped for beginners.\n"
+      ],
+      "metadata": {
+        "id": "mBNupqPj1B1_"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🧭 Vectors 101\n",
+        "\n",
+        "| Concept             | Quick reminder                                  |\n",
+        "|---------------------|--------------------------------------------------|\n",
+        "| Representation      | 1-D NumPy array — `np.array([x, y, z])`         |\n",
+        "| Length (norm)       | `np.linalg.norm(v)`                             |\n",
+        "| Dot / inner product | `v.dot(w)` or `np.inner(v, w)`                  |\n",
+        "| Unit vector         | `v / np.linalg.norm(v)`                         |\n"
+      ],
+      "metadata": {
+        "id": "zD-AyQCe1Y9e"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "AWVTIlQP0ysC",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "771f580b-3b78-4083-a757-3d701810fbc9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "3.7416573867739413\n",
+            "44\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Run once per session\n",
+        "import numpy as np\n",
+        "\n",
+        "# TODO 1: create a 3-D vector named v\n",
+        "v=np.array([1,2,3])\n",
+        "# TODO 2: print its length\n",
+        "print(np.linalg.norm(v))\n",
+        "# TODO 3: build a second vector w and output the dot product\n",
+        "w=np.array([6,7,8])\n",
+        "print(v.dot(w))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 🚁 Practical Scenario — “Drone hop”\n",
+        "A mini-drone lifts off at (2 m, 1 m) and lands at (7 m, 4 m).\n",
+        "Calculate its displacement vector, travel distance, and orientation along the x-axis."
+      ],
+      "metadata": {
+        "id": "c_iB5_0A1rg7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "p_start = np.array([2, 1])\n",
+        "p_end   = np.array([7, 4])\n",
+        "\n",
+        "# displacement\n",
+        "d = p_end - p_start          # ➡️ vector from start to end\n",
+        "dist = np.linalg.norm(d)     # 🏁 distance travelled\n",
+        "unit = d / dist              # ↗️ unit direction\n",
+        "dot_x = d.dot(np.array([1, 0]))  # projection on x-axis\n",
+        "\n",
+        "print(\"Displacement:\", d)\n",
+        "print(\"Distance:\", dist)\n",
+        "print(\"Unit direction:\", unit)\n",
+        "print(\"Dot-product with x-axis:\", dot_x)\n"
+      ],
+      "metadata": {
+        "id": "Rq-tHCkF1x3p",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f5b1ca3c-39bf-47d8-adaa-2ba3d99b42fc"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Displacement: [5 3]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🔢 Matrices 101\n",
+        "\n",
+        "| Operation              | NumPy one-liner                                  |\n",
+        "|------------------------|--------------------------------------------------|\n",
+        "| Transpose              | `A.T`                                            |\n",
+        "| Determinant            | `np.linalg.det(A)`                               |\n",
+        "| Inverse                | `np.linalg.inv(A)` (works only if `det(A) ≠ 0`)  |\n",
+        "| Matrix-vector multiply | `A @ v`                                          |\n",
+        "| Matrix-matrix multiply | `A @ B`                                          |\n"
+      ],
+      "metadata": {
+        "id": "vqX_pXGq16Af"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# TODO 4: build a 2×2 matrix A\n",
+        "A=np.array([[1,2],[3,4]])\n",
+        "# TODO 5: print its determinant\n",
+        "det_A=np.linalg.det(A)\n",
+        "print(det_A)\n",
+        "# TODO 6: if invertible, compute and print A_inv\n",
+        "if det_A != 0:\n",
+        "  print(np.linalg.inv(A))"
+      ],
+      "metadata": {
+        "id": "yycidpaj2BAv",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1fc3bc1b-7f3f-47d2-ba70-82dadb46e0fa"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "-2.0000000000000004\n",
+            "[[-2.   1. ]\n",
+            " [ 1.5 -0.5]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 🎯 Practical Scenario — “Rotate that hop”\n",
+        "Rotate the drone’s displacement vector 30° counter-clockwise, then verify that the inverse rotation brings it back."
+      ],
+      "metadata": {
+        "id": "C-ikgDhF2DFn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "theta = np.deg2rad(30)          # 🔄 convert degrees to radians\n",
+        "R = np.array([[np.cos(theta), -np.sin(theta)],\n",
+        "              [np.sin(theta),  np.cos(theta)]])\n",
+        "\n",
+        "det_R = np.linalg.det(R)        # should be 1.0 (pure rotation)\n",
+        "\n",
+        "d_rot  = R @ d                  # rotated displacement\n",
+        "d_back = np.linalg.inv(R) @ d_rot\n",
+        "\n",
+        "print(\"Rotation matrix determinant:\", det_R)\n",
+        "print(\"Rotated vector:\", d_rot)\n",
+        "print(\"Back-rotated equals original?\", np.allclose(d_back, d))\n"
+      ],
+      "metadata": {
+        "id": "SgmIesbf2G65",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "caedaa23-55b9-4eb8-ac84-56d3e8c932ef"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Rotation matrix determinant: 1.0\n",
+            "Rotated vector: [2.83012702 5.09807621]\n",
+            "Back-rotated equals original? True\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.4/AI-DS_Nexus__A0_4__rsayyareh.ipynb b/a0.1/a0.4/AI-DS_Nexus__A0_4__rsayyareh.ipynb
new file mode 100644
index 0000000..16cc8e2
--- /dev/null
+++ b/a0.1/a0.4/AI-DS_Nexus__A0_4__rsayyareh.ipynb
@@ -0,0 +1,263 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A1k4HkdO05Yu"
+      },
+      "source": [
+        "# 📓 Assignment 4 — Linear Algebra Fundamentals\n",
+        "\n",
+        "*Vectors & Matrices for Everyday Coding*\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mBNupqPj1B1_"
+      },
+      "source": [
+        "## Welcome back, intrepid coder! 🚀\n",
+        "In this notebook-styled brief you’ll move from single-direction vectors to multi-direction matrices—core tools behind graphics, robotics, optimisation and (of course) machine-learning. Each mini-section mixes quick notes, a tiny real-world scenario, and hands-on # TODO code shaped for beginners.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zD-AyQCe1Y9e"
+      },
+      "source": [
+        "## 🧭 Vectors 101\n",
+        "\n",
+        "| Concept             | Quick reminder                                  |\n",
+        "|---------------------|--------------------------------------------------|\n",
+        "| Representation      | 1-D NumPy array — `np.array([x, y, z])`         |\n",
+        "| Length (norm)       | `np.linalg.norm(v)`                             |\n",
+        "| Dot / inner product | `v.dot(w)` or `np.inner(v, w)`                  |\n",
+        "| Unit vector         | `v / np.linalg.norm(v)`                         |\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "AWVTIlQP0ysC"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Length of matrix v = 3\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "array([[[  66.,   78.,   90.],\n",
+              "        [ 147.,  177.,  207.],\n",
+              "        [ 228.,  276.,  324.]],\n",
+              "\n",
+              "       [[ 903.,  969., 1035.],\n",
+              "        [1146., 1230., 1314.],\n",
+              "        [1389., 1491., 1593.]],\n",
+              "\n",
+              "       [[2670., 2832., 2952.],\n",
+              "        [3069., 3255., 3393.],\n",
+              "        [3429., 3637., 3791.]]])"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Run once per session\n",
+        "import numpy as np\n",
+        "\n",
+        "# TODO 1: create a 3-D vector named v\n",
+        "# TODO 2: print its length\n",
+        "# TODO 3: build a second vector w and output the dot product\n",
+        "v = np.array([\n",
+        "    [[1, 2, 3], [4, 5, 6], [7, 8, 9]],\n",
+        "    [[10, 11, 12], [13, 14, 15], [16, 17, 18]],\n",
+        "    [[19, 20, 21], [22, 23, 24], [24, 26, 27]]\n",
+        "    ])\n",
+        "print(f\"Length of matrix v = {len(v)}\")\n",
+        "w = np.ones(27).reshape(3, 3, 3)\n",
+        "w += v * 2\n",
+        "v@w\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c_iB5_0A1rg7"
+      },
+      "source": [
+        "### 🚁 Practical Scenario — “Drone hop”\n",
+        "A mini-drone lifts off at (2 m, 1 m) and lands at (7 m, 4 m).\n",
+        "Calculate its displacement vector, travel distance, and orientation along the x-axis."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "Rq-tHCkF1x3p"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Displacement: [5 3]\n",
+            "Distance: 5.830951894845301\n",
+            "Unit direction: [0.85749293 0.51449576]\n",
+            "Dot-product with x-axis: 5\n"
+          ]
+        }
+      ],
+      "source": [
+        "p_start = np.array([2, 1])\n",
+        "p_end   = np.array([7, 4])\n",
+        "\n",
+        "# displacement\n",
+        "d = p_end - p_start          # ➡️ vector from start to end\n",
+        "dist = np.linalg.norm(d)     # 🏁 distance travelled\n",
+        "unit = d / dist              # ↗️ unit direction\n",
+        "dot_x = d.dot(np.array([1, 0]))  # projection on x-axis\n",
+        "\n",
+        "print(\"Displacement:\", d)\n",
+        "print(\"Distance:\", dist)\n",
+        "print(\"Unit direction:\", unit)\n",
+        "print(\"Dot-product with x-axis:\", dot_x)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vqX_pXGq16Af"
+      },
+      "source": [
+        "## 🔢 Matrices 101\n",
+        "\n",
+        "| Operation              | NumPy one-liner                                  |\n",
+        "|------------------------|--------------------------------------------------|\n",
+        "| Transpose              | `A.T`                                            |\n",
+        "| Determinant            | `np.linalg.det(A)`                               |\n",
+        "| Inverse                | `np.linalg.inv(A)` (works only if `det(A) ≠ 0`)  |\n",
+        "| Matrix-vector multiply | `A @ v`                                          |\n",
+        "| Matrix-matrix multiply | `A @ B`                                          |\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "yycidpaj2BAv"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "A determinant is -2.0000000000000004\n",
+            "nInverse of A : [[-2.   1. ]\n",
+            " [ 1.5 -0.5]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO 4: build a 2×2 matrix A\n",
+        "# TODO 5: print its determinant\n",
+        "# TODO 6: if invertible, compute and print A_inv\n",
+        "A = np.array([[1, 2], [3, 4]])\n",
+        "det = np.linalg.det(A)\n",
+        "print(f\"A determinant is {det}\")\n",
+        "if det!=0:\n",
+        "    A_inv = np.linalg.inv(A)\n",
+        "    print(f\"nInverse of A : {A_inv}\")\n",
+        "else:\n",
+        "    print(\"A has no inverse\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C-ikgDhF2DFn"
+      },
+      "source": [
+        "### 🎯 Practical Scenario — “Rotate that hop”\n",
+        "Rotate the drone’s displacement vector 30° counter-clockwise, then verify that the inverse rotation brings it back."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "SgmIesbf2G65"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Rotation matrix determinant: 1.0\n",
+            "Rotated vector: [2.83012702 5.09807621]\n",
+            "Back-rotated equals original? True\n"
+          ]
+        }
+      ],
+      "source": [
+        "theta = np.deg2rad(30)          # 🔄 convert degrees to radians\n",
+        "R = np.array([[np.cos(theta), -np.sin(theta)],\n",
+        "              [np.sin(theta),  np.cos(theta)]])\n",
+        "\n",
+        "det_R = np.linalg.det(R)        # should be 1.0 (pure rotation)\n",
+        "\n",
+        "d_rot  = R @ d                  # rotated displacement\n",
+        "d_back = np.linalg.inv(R) @ d_rot\n",
+        "\n",
+        "print(\"Rotation matrix determinant:\", det_R)\n",
+        "print(\"Rotated vector:\", d_rot)\n",
+        "print(\"Back-rotated equals original?\", np.allclose(d_back, d))\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git "a/a0.1/a0.4/AI\342\200\223DS_Nexus___A0_4_Linear_Algebra_(vectors,_matrices)__RoohollaAlikhani.ipynb" "b/a0.1/a0.4/AI\342\200\223DS_Nexus___A0_4_Linear_Algebra_(vectors,_matrices)__RoohollaAlikhani.ipynb"
new file mode 100644
index 0000000..44b68e9
--- /dev/null
+++ "b/a0.1/a0.4/AI\342\200\223DS_Nexus___A0_4_Linear_Algebra_(vectors,_matrices)__RoohollaAlikhani.ipynb"
@@ -0,0 +1,178 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "A1k4HkdO05Yu"
+      },
+      "source": [
+        "# 📓 Assignment 4 — Linear Algebra Fundamentals\n",
+        "\n",
+        "*Vectors & Matrices for Everyday Coding*\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mBNupqPj1B1_"
+      },
+      "source": [
+        "## Welcome back, intrepid coder! 🚀\n",
+        "In this notebook-styled brief you’ll move from single-direction vectors to multi-direction matrices—core tools behind graphics, robotics, optimisation and (of course) machine-learning. Each mini-section mixes quick notes, a tiny real-world scenario, and hands-on # TODO code shaped for beginners.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zD-AyQCe1Y9e"
+      },
+      "source": [
+        "## 🧭 Vectors 101\n",
+        "\n",
+        "| Concept             | Quick reminder                                  |\n",
+        "|---------------------|--------------------------------------------------|\n",
+        "| Representation      | 1-D NumPy array — `np.array([x, y, z])`         |\n",
+        "| Length (norm)       | `np.linalg.norm(v)`                             |\n",
+        "| Dot / inner product | `v.dot(w)` or `np.inner(v, w)`                  |\n",
+        "| Unit vector         | `v / np.linalg.norm(v)`                         |\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "AWVTIlQP0ysC"
+      },
+      "outputs": [],
+      "source": [
+        "# Run once per session\n",
+        "import numpy as np\n",
+        "\n",
+        "# TODO 1: create a 3-D vector named v\n",
+        "# TODO 2: print its length\n",
+        "# TODO 3: build a second vector w and output the dot product\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c_iB5_0A1rg7"
+      },
+      "source": [
+        "### 🚁 Practical Scenario — “Drone hop”\n",
+        "A mini-drone lifts off at (2 m, 1 m) and lands at (7 m, 4 m).\n",
+        "Calculate its displacement vector, travel distance, and orientation along the x-axis."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Rq-tHCkF1x3p"
+      },
+      "outputs": [],
+      "source": [
+        "p_start = np.array([2, 1])\n",
+        "p_end   = np.array([7, 4])\n",
+        "\n",
+        "# displacement\n",
+        "d = p_end - p_start          # ➡️ vector from start to end\n",
+        "dist = np.linalg.norm(d)     # 🏁 distance travelled\n",
+        "unit = d / dist              # ↗️ unit direction\n",
+        "dot_x = d.dot(np.array([1, 0]))  # projection on x-axis\n",
+        "\n",
+        "print(\"Displacement:\", d)\n",
+        "print(\"Distance:\", dist)\n",
+        "print(\"Unit direction:\", unit)\n",
+        "print(\"Dot-product with x-axis:\", dot_x)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vqX_pXGq16Af"
+      },
+      "source": [
+        "## 🔢 Matrices 101\n",
+        "\n",
+        "| Operation              | NumPy one-liner                                  |\n",
+        "|------------------------|--------------------------------------------------|\n",
+        "| Transpose              | `A.T`                                            |\n",
+        "| Determinant            | `np.linalg.det(A)`                               |\n",
+        "| Inverse                | `np.linalg.inv(A)` (works only if `det(A) ≠ 0`)  |\n",
+        "| Matrix-vector multiply | `A @ v`                                          |\n",
+        "| Matrix-matrix multiply | `A @ B`                                          |\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yycidpaj2BAv"
+      },
+      "outputs": [],
+      "source": [
+        "# TODO 4: build a 2×2 matrix A\n",
+        "# TODO 5: print its determinant\n",
+        "# TODO 6: if invertible, compute and print A_inv\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C-ikgDhF2DFn"
+      },
+      "source": [
+        "### 🎯 Practical Scenario — “Rotate that hop”\n",
+        "Rotate the drone’s displacement vector 30° counter-clockwise, then verify that the inverse rotation brings it back."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SgmIesbf2G65"
+      },
+      "outputs": [],
+      "source": [
+        "theta = np.deg2rad(30)          # 🔄 convert degrees to radians\n",
+        "R = np.array([[np.cos(theta), -np.sin(theta)],\n",
+        "              [np.sin(theta),  np.cos(theta)]])\n",
+        "\n",
+        "det_R = np.linalg.det(R)        # should be 1.0 (pure rotation)\n",
+        "\n",
+        "d_rot  = R @ d                  # rotated displacement\n",
+        "d_back = np.linalg.inv(R) @ d_rot\n",
+        "\n",
+        "print(\"Rotation matrix determinant:\", det_R)\n",
+        "print(\"Rotated vector:\", d_rot)\n",
+        "print(\"Back-rotated equals original?\", np.allclose(d_back, d))\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.4/Assignment_04_Danieljaafari.ipynb b/a0.1/a0.4/Assignment_04_Danieljaafari.ipynb
new file mode 100644
index 0000000..9fce614
--- /dev/null
+++ b/a0.1/a0.4/Assignment_04_Danieljaafari.ipynb
@@ -0,0 +1,2 @@
+# Assignment A04 - Daniel Jaafari
+Add assignment 04 file
diff --git "a/a0.1/a0.4/Copy_of_AI\342\200\223DS_Nexus___A0_4_Linear_Algebra_(vectors,_matrices)__RezaShokr.ipynb" "b/a0.1/a0.4/Copy_of_AI\342\200\223DS_Nexus___A0_4_Linear_Algebra_(vectors,_matrices)__RezaShokr.ipynb"
new file mode 100644
index 0000000..9baece3
--- /dev/null
+++ "b/a0.1/a0.4/Copy_of_AI\342\200\223DS_Nexus___A0_4_Linear_Algebra_(vectors,_matrices)__RezaShokr.ipynb"
@@ -0,0 +1,256 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📓 Assignment 4 — Linear Algebra Fundamentals\n",
+        "\n",
+        "*Vectors & Matrices for Everyday Coding*\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "A1k4HkdO05Yu"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Welcome back, intrepid coder! 🚀\n",
+        "In this notebook-styled brief you’ll move from single-direction vectors to multi-direction matrices—core tools behind graphics, robotics, optimisation and (of course) machine-learning. Each mini-section mixes quick notes, a tiny real-world scenario, and hands-on # TODO code shaped for beginners.\n"
+      ],
+      "metadata": {
+        "id": "mBNupqPj1B1_"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🧭 Vectors 101\n",
+        "\n",
+        "| Concept             | Quick reminder                                  |\n",
+        "|---------------------|--------------------------------------------------|\n",
+        "| Representation      | 1-D NumPy array — `np.array([x, y, z])`         |\n",
+        "| Length (norm)       | `np.linalg.norm(v)`                             |\n",
+        "| Dot / inner product | `v.dot(w)` or `np.inner(v, w)`                  |\n",
+        "| Unit vector         | `v / np.linalg.norm(v)`                         |\n"
+      ],
+      "metadata": {
+        "id": "zD-AyQCe1Y9e"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "AWVTIlQP0ysC",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2aee9cca-569d-4931-96b6-d9557bab0240"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Vector v: [3 4 5]\n",
+            "Length (‖v‖): 7.0710678118654755\n",
+            "Vector w: [1 2 3]\n",
+            "Dot product (v·w): 26\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Run once per session\n",
+        "import numpy as np\n",
+        "\n",
+        "# TODO 1: create a 3-D vector named v\n",
+        "v = np.array([3, 4, 5])\n",
+        "# TODO 2: print its length\n",
+        "length_v = np.linalg.norm(v)\n",
+        "print(\"Vector v:\", v)\n",
+        "print(\"Length (‖v‖):\", length_v)\n",
+        "# TODO 3: build a second vector w and output the dot product\n",
+        "w = np.array([1, 2, 3])\n",
+        "dot_product = np.dot(v, w)\n",
+        "print(\"Vector w:\", w)\n",
+        "print(\"Dot product (v·w):\", dot_product)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 🚁 Practical Scenario — “Drone hop”\n",
+        "A mini-drone lifts off at (2 m, 1 m) and lands at (7 m, 4 m).\n",
+        "Calculate its displacement vector, travel distance, and orientation along the x-axis."
+      ],
+      "metadata": {
+        "id": "c_iB5_0A1rg7"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "p_start = np.array([2, 1])\n",
+        "p_end   = np.array([7, 4])\n",
+        "\n",
+        "# displacement\n",
+        "d = p_end - p_start          # ➡️ vector from start to end\n",
+        "dist = np.linalg.norm(d)     # 🏁 distance travelled\n",
+        "unit = d / dist              # ↗️ unit direction\n",
+        "dot_x = d.dot(np.array([1, 0]))  # projection on x-axis\n",
+        "\n",
+        "print(\"Displacement:\", d)\n",
+        "print(\"Distance:\", dist)\n",
+        "print(\"Unit direction:\", unit)\n",
+        "print(\"Dot-product with x-axis:\", dot_x)\n"
+      ],
+      "metadata": {
+        "id": "Rq-tHCkF1x3p",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "850824b3-127f-4999-e4fc-3e56095b2b85"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Displacement: [5 3]\n",
+            "Distance: 5.830951894845301\n",
+            "Unit direction: [0.85749293 0.51449576]\n",
+            "Dot-product with x-axis: 5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🔢 Matrices 101\n",
+        "\n",
+        "| Operation              | NumPy one-liner                                  |\n",
+        "|------------------------|--------------------------------------------------|\n",
+        "| Transpose              | `A.T`                                            |\n",
+        "| Determinant            | `np.linalg.det(A)`                               |\n",
+        "| Inverse                | `np.linalg.inv(A)` (works only if `det(A) ≠ 0`)  |\n",
+        "| Matrix-vector multiply | `A @ v`                                          |\n",
+        "| Matrix-matrix multiply | `A @ B`                                          |\n"
+      ],
+      "metadata": {
+        "id": "vqX_pXGq16Af"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# TODO 4: build a 2×2 matrix A\n",
+        "A = np.array([[4, 7],\n",
+        "              [2, 6]])\n",
+        "\n",
+        "# TODO 5: print its determinant\n",
+        "det_A = np.linalg.det(A)\n",
+        "print(\"Matrix A:\\n\", A)\n",
+        "print(\"Determinant of A:\", det_A)\n",
+        "\n",
+        "# TODO 6: if invertible, compute and print A_inv\n",
+        "if det_A != 0:\n",
+        "    A_inv = np.linalg.inv(A)\n",
+        "    print(\"Inverse of A:\\n\", A_inv)\n",
+        "else:\n",
+        "    print(\"Matrix A is not invertible (determinant = 0).\")"
+      ],
+      "metadata": {
+        "id": "yycidpaj2BAv",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4539f7ba-4e3b-41cc-fd5a-f6f02435afdd"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Matrix A:\n",
+            " [[4 7]\n",
+            " [2 6]]\n",
+            "Determinant of A: 10.000000000000002\n",
+            "Inverse of A:\n",
+            " [[ 0.6 -0.7]\n",
+            " [-0.2  0.4]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 🎯 Practical Scenario — “Rotate that hop”\n",
+        "Rotate the drone’s displacement vector 30° counter-clockwise, then verify that the inverse rotation brings it back."
+      ],
+      "metadata": {
+        "id": "C-ikgDhF2DFn"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "theta = np.deg2rad(30)          # 🔄 convert degrees to radians\n",
+        "R = np.array([[np.cos(theta), -np.sin(theta)],\n",
+        "              [np.sin(theta),  np.cos(theta)]])\n",
+        "\n",
+        "det_R = np.linalg.det(R)        # should be 1.0 (pure rotation)\n",
+        "\n",
+        "d_rot  = R @ d                  # rotated displacement\n",
+        "d_back = np.linalg.inv(R) @ d_rot\n",
+        "\n",
+        "print(\"Rotation matrix determinant:\", det_R)\n",
+        "print(\"Rotated vector:\", d_rot)\n",
+        "print(\"Back-rotated equals original?\", np.allclose(d_back, d))\n"
+      ],
+      "metadata": {
+        "id": "SgmIesbf2G65",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7b81965f-fc47-46c8-a0d9-9a2dce379987"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Rotation matrix determinant: 1.0\n",
+            "Rotated vector: [2.83012702 5.09807621]\n",
+            "Back-rotated equals original? True\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.5/AI-DS_Nexus__A0_5_Amirhossein_Zandi_8fca80.ipynb b/a0.1/a0.5/AI-DS_Nexus__A0_5_Amirhossein_Zandi_8fca80.ipynb
new file mode 100644
index 0000000..1731761
--- /dev/null
+++ b/a0.1/a0.5/AI-DS_Nexus__A0_5_Amirhossein_Zandi_8fca80.ipynb
@@ -0,0 +1,191 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tacMQNsG2g5S"
+      },
+      "source": [
+        "# 📓 Assignment 5 — Probability\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "## 🎲 Counting & Probability with Cards in Python 🃏\n",
+        "Learn factorials, combinations, and (conditional) probability — all in one simple card game simulation using only math and random modules."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_cA_dWDJ2vfr"
+      },
+      "source": [
+        "## 🧮 PART 1: Factorial & Combinations\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "aIxoWeNt2ao0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "6! = 720\n",
+            "Combinations (6 choose 3): 20\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- FACTORIAL & COMBINATIONS ----\n",
+        "\n",
+        "import math\n",
+        "print(\"6! =\", math.factorial(6))\n",
+        "print(\"Combinations (6 choose 3):\", math.comb(6,3))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pzy2xD3H3D3o"
+      },
+      "source": [
+        "## 🃏 PART 2: Simulating a Deck of Cards\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "eUvZhzYB2x4L"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Drawn cards: ['2 of Clubs', '4 of Spades']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- PROBABILITY IN A DECK ----\n",
+        "\n",
+        "import random\n",
+        "\n",
+        "deck = [f\"{rank} of {suit}\" for suit in ['Hearts', 'Spades', 'Diamonds', 'Clubs']\n",
+        "        for rank in range(1, 14)]\n",
+        "\n",
+        "# Draw 2 random cards\n",
+        "\n",
+        "draws = random.sample(deck, 2)\n",
+        "print(\"\\nDrawn cards:\", draws)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OidRYf9q3Mwj"
+      },
+      "source": [
+        "## 📊 PART 3: Basic Probability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "Ok5d99Ns3Oa7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Probability both cards are Hearts: 0.0588\n"
+          ]
+        }
+      ],
+      "source": [
+        "# PROBABILITY: What’s the chance that both cards are Hearts?\n",
+        "\n",
+        "hearts = [card for card in deck if \"Hearts\" in card]\n",
+        "prob_both_hearts = math.comb(len(hearts), 2) / math.comb(len(deck), 2)\n",
+        "print(f\"Probability both cards are Hearts: {prob_both_hearts:.4f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NMIA7c313Yum"
+      },
+      "source": [
+        "## 🔁 PART 4: Conditional Probability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "IZeCH8pw3aZX"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Conditional probability 2nd card is Heart given 1st is Heart: 0.9412\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- CONDITIONAL PROBABILITY ----\n",
+        "# P(A and B) / P(B): Probability second card is Heart given first was Heart\n",
+        "# A = both are hearts, B = first is heart\n",
+        "\n",
+        "p_a_and_b = math.comb(12, 1) / math.comb(51, 1) # after one heart is drawn\n",
+        "p_b = 13 / 52\n",
+        "p_a_given_b = p_a_and_b / p_b\n",
+        "print(f\"Conditional probability 2nd card is Heart given 1st is Heart: {p_a_given_b:.4f}\")\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.5/AI-DS_Nexus__A0_5_Javid_Mohammadi_a60d6f.ipynb b/a0.1/a0.5/AI-DS_Nexus__A0_5_Javid_Mohammadi_a60d6f.ipynb
new file mode 100644
index 0000000..85e4c8b
--- /dev/null
+++ b/a0.1/a0.5/AI-DS_Nexus__A0_5_Javid_Mohammadi_a60d6f.ipynb
@@ -0,0 +1,187 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📓 Assignment 5 — Probability\n",
+        "\n",
+        "## 🎲 Counting & Probability with Cards in Python 🃏\n",
+        "Learn factorials, combinations, and (conditional) probability — all in one simple card game simulation using only math and random modules."
+      ],
+      "metadata": {
+        "id": "tacMQNsG2g5S"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🧮 PART 1: Factorial & Combinations\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "_cA_dWDJ2vfr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "aIxoWeNt2ao0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b5901012-60ed-4c07-d1ae-dbd5636268b7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "6! = 720\n",
+            "Combinations (6 choose 3): 20\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- FACTORIAL & COMBINATIONS ----\n",
+        "\n",
+        "import math\n",
+        "print(\"6! =\", math.factorial(6))\n",
+        "print(\"Combinations (6 choose 3):\", math.comb(6, 3))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🃏 PART 2: Simulating a Deck of Cards\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "pzy2xD3H3D3o"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---- PROBABILITY IN A DECK ----\n",
+        "\n",
+        "import random\n",
+        "\n",
+        "deck = [f\"{rank} of {suit}\" for suit in ['Hearts', 'Spades', 'Diamonds', 'Clubs']\n",
+        "        for rank in range(1, 14)]\n",
+        "\n",
+        "# Draw 2 random cards\n",
+        "\n",
+        "draws = random.sample(deck, 2)\n",
+        "print(\"\\nDrawn cards:\", draws)\n"
+      ],
+      "metadata": {
+        "id": "eUvZhzYB2x4L",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f68e008d-57ac-4fee-a6ab-310e6b324c75"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Drawn cards: ['6 of Hearts', '4 of Clubs']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 📊 PART 3: Basic Probability"
+      ],
+      "metadata": {
+        "id": "OidRYf9q3Mwj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# PROBABILITY: What’s the chance that both cards are Hearts?\n",
+        "\n",
+        "hearts = [card for card in deck if \"Hearts\" in card]\n",
+        "prob_both_hearts = math.comb(13, 2) / math.comb(52, 2)\n",
+        "print(f\"Probability both cards are Hearts: {prob_both_hearts:.4f}\")\n"
+      ],
+      "metadata": {
+        "id": "Ok5d99Ns3Oa7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c6b86ec4-b76e-4323-87cd-d48871711bcb"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Probability both cards are Hearts: 0.0588\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🔁 PART 4: Conditional Probability"
+      ],
+      "metadata": {
+        "id": "NMIA7c313Yum"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---- CONDITIONAL PROBABILITY ----\n",
+        "# P(A and B) / P(B): Probability second card is Heart given first was Heart\n",
+        "# A = both are hearts, B = first is heart\n",
+        "\n",
+        "p_a_and_b = math.comb(12, 1) / math.comb(51, 1) # after one heart is drawn\n",
+        "p_b = 13 / 52\n",
+        "p_a_given_b = p_a_and_b / p_b\n",
+        "print(f\"Conditional probability 2nd card is Heart given 1st is Heart: {p_a_given_b:.4f}\")\n"
+      ],
+      "metadata": {
+        "id": "IZeCH8pw3aZX",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a1d6680b-b7fc-44f2-c634-2831455028e8"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Conditional probability 2nd card is Heart given 1st is Heart: 0.9412\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.5/AI-DS_Nexus__A0_5_Project__RezaShokr.ipynb b/a0.1/a0.5/AI-DS_Nexus__A0_5_Project__RezaShokr.ipynb
new file mode 100644
index 0000000..327f6f7
--- /dev/null
+++ b/a0.1/a0.5/AI-DS_Nexus__A0_5_Project__RezaShokr.ipynb
@@ -0,0 +1,242 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tacMQNsG2g5S"
+      },
+      "source": [
+        "# 📓 Assignment 5 — Probability\n",
+        "\n",
+        "## 🎲 Counting & Probability with Cards in Python 🃏\n",
+        "Learn factorials, combinations, and (conditional) probability — all in one simple card game simulation using only math and random modules."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_cA_dWDJ2vfr"
+      },
+      "source": [
+        "## 🧮 PART 1: Factorial & Combinations\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "aIxoWeNt2ao0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "6! = 720\n",
+            "Combinations (6 choose 3): 20\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- FACTORIAL & COMBINATIONS ----\n",
+        "\n",
+        "import math\n",
+        "print(\"6! =\", math.factorial(6))\n",
+        "print(\"Combinations (6 choose 3):\", math.comb(6 , 3))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pzy2xD3H3D3o"
+      },
+      "source": [
+        "## 🃏 PART 2: Simulating a Deck of Cards\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "eUvZhzYB2x4L"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['1 of Hearts', '2 of Hearts', '3 of Hearts', '4 of Hearts', '5 of Hearts', '6 of Hearts', '7 of Hearts', '8 of Hearts', '9 of Hearts', '10 of Hearts', '11 of Hearts', '12 of Hearts', '13 of Hearts', '1 of Spades', '2 of Spades', '3 of Spades', '4 of Spades', '5 of Spades', '6 of Spades', '7 of Spades', '8 of Spades', '9 of Spades', '10 of Spades', '11 of Spades', '12 of Spades', '13 of Spades', '1 of Diamonds', '2 of Diamonds', '3 of Diamonds', '4 of Diamonds', '5 of Diamonds', '6 of Diamonds', '7 of Diamonds', '8 of Diamonds', '9 of Diamonds', '10 of Diamonds', '11 of Diamonds', '12 of Diamonds', '13 of Diamonds', '1 of Clubs', '2 of Clubs', '3 of Clubs', '4 of Clubs', '5 of Clubs', '6 of Clubs', '7 of Clubs', '8 of Clubs', '9 of Clubs', '10 of Clubs', '11 of Clubs', '12 of Clubs', '13 of Clubs']\n",
+            "\n",
+            "Drawn cards: ['13 of Spades', '8 of Spades']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- PROBABILITY IN A DECK ----\n",
+        "\n",
+        "import random\n",
+        "\n",
+        "deck = [f\"{rank} of {suit}\" for suit in ['Hearts', 'Spades', 'Diamonds', 'Clubs']\n",
+        "        for rank in range (1, 14)]\n",
+        "print(deck)\n",
+        "\n",
+        "# Draw 2 random cards\n",
+        "draws = random.sample(deck, 2)\n",
+        "print(\"\\nDrawn cards:\", draws)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OidRYf9q3Mwj"
+      },
+      "source": [
+        "## 📊 PART 3: Basic Probability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import math"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "Ok5d99Ns3Oa7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Probability both cards are Hearts: 5.88 %\n"
+          ]
+        }
+      ],
+      "source": [
+        "# PROBABILITY: What’s the chance that both cards are Hearts?\n",
+        "\n",
+        "hearts = [card for card in deck if \"Hearts\" in card]\n",
+        "prob_both_hearts = (math.comb(13, 2) / math.comb(52, 2))*100\n",
+        "print(f\"Probability both cards are Hearts: {prob_both_hearts:.2f} %\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NMIA7c313Yum"
+      },
+      "source": [
+        "## 🔁 PART 4: Conditional Probability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "IZeCH8pw3aZX"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Conditional probability 2nd card is Heart given 1st is Heart: 94.12%\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- CONDITIONAL PROBABILITY ----\n",
+        "# P(A and B) / P(B): Probability second card is Heart given first was Heart\n",
+        "# A = both are hearts, B = first is heart\n",
+        "\n",
+        "p_a_and_b = math.comb(12, 1) / math.comb(51, 1) # after one heart is drawn\n",
+        "p_b = 13 / 52\n",
+        "p_a_given_b = (p_a_and_b / p_b)*100\n",
+        "print(f\"Conditional probability 2nd card is Heart given 1st is Heart: {p_a_given_b:.2f}%\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "10"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import math\n",
+        "math.factorial(5) // (math.factorial(2) * math.factorial(3))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Combination C(7, 3) = 35\n"
+          ]
+        }
+      ],
+      "source": [
+        "import math\n",
+        "\n",
+        "n = 7\n",
+        "r = 3\n",
+        "\n",
+        "combination = math.comb(n, r)  # ترکیب: ترتیب مهم نیست\n",
+        "print(f\"Combination C({n}, {r}) =\", combination)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.5/AI-DS_Nexus__A0_5_Project_roohi_268383.ipynb b/a0.1/a0.5/AI-DS_Nexus__A0_5_Project_roohi_268383.ipynb
new file mode 100644
index 0000000..327f6f7
--- /dev/null
+++ b/a0.1/a0.5/AI-DS_Nexus__A0_5_Project_roohi_268383.ipynb
@@ -0,0 +1,242 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tacMQNsG2g5S"
+      },
+      "source": [
+        "# 📓 Assignment 5 — Probability\n",
+        "\n",
+        "## 🎲 Counting & Probability with Cards in Python 🃏\n",
+        "Learn factorials, combinations, and (conditional) probability — all in one simple card game simulation using only math and random modules."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_cA_dWDJ2vfr"
+      },
+      "source": [
+        "## 🧮 PART 1: Factorial & Combinations\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "aIxoWeNt2ao0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "6! = 720\n",
+            "Combinations (6 choose 3): 20\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- FACTORIAL & COMBINATIONS ----\n",
+        "\n",
+        "import math\n",
+        "print(\"6! =\", math.factorial(6))\n",
+        "print(\"Combinations (6 choose 3):\", math.comb(6 , 3))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pzy2xD3H3D3o"
+      },
+      "source": [
+        "## 🃏 PART 2: Simulating a Deck of Cards\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "eUvZhzYB2x4L"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['1 of Hearts', '2 of Hearts', '3 of Hearts', '4 of Hearts', '5 of Hearts', '6 of Hearts', '7 of Hearts', '8 of Hearts', '9 of Hearts', '10 of Hearts', '11 of Hearts', '12 of Hearts', '13 of Hearts', '1 of Spades', '2 of Spades', '3 of Spades', '4 of Spades', '5 of Spades', '6 of Spades', '7 of Spades', '8 of Spades', '9 of Spades', '10 of Spades', '11 of Spades', '12 of Spades', '13 of Spades', '1 of Diamonds', '2 of Diamonds', '3 of Diamonds', '4 of Diamonds', '5 of Diamonds', '6 of Diamonds', '7 of Diamonds', '8 of Diamonds', '9 of Diamonds', '10 of Diamonds', '11 of Diamonds', '12 of Diamonds', '13 of Diamonds', '1 of Clubs', '2 of Clubs', '3 of Clubs', '4 of Clubs', '5 of Clubs', '6 of Clubs', '7 of Clubs', '8 of Clubs', '9 of Clubs', '10 of Clubs', '11 of Clubs', '12 of Clubs', '13 of Clubs']\n",
+            "\n",
+            "Drawn cards: ['13 of Spades', '8 of Spades']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- PROBABILITY IN A DECK ----\n",
+        "\n",
+        "import random\n",
+        "\n",
+        "deck = [f\"{rank} of {suit}\" for suit in ['Hearts', 'Spades', 'Diamonds', 'Clubs']\n",
+        "        for rank in range (1, 14)]\n",
+        "print(deck)\n",
+        "\n",
+        "# Draw 2 random cards\n",
+        "draws = random.sample(deck, 2)\n",
+        "print(\"\\nDrawn cards:\", draws)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OidRYf9q3Mwj"
+      },
+      "source": [
+        "## 📊 PART 3: Basic Probability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import math"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "Ok5d99Ns3Oa7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Probability both cards are Hearts: 5.88 %\n"
+          ]
+        }
+      ],
+      "source": [
+        "# PROBABILITY: What’s the chance that both cards are Hearts?\n",
+        "\n",
+        "hearts = [card for card in deck if \"Hearts\" in card]\n",
+        "prob_both_hearts = (math.comb(13, 2) / math.comb(52, 2))*100\n",
+        "print(f\"Probability both cards are Hearts: {prob_both_hearts:.2f} %\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NMIA7c313Yum"
+      },
+      "source": [
+        "## 🔁 PART 4: Conditional Probability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "IZeCH8pw3aZX"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Conditional probability 2nd card is Heart given 1st is Heart: 94.12%\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- CONDITIONAL PROBABILITY ----\n",
+        "# P(A and B) / P(B): Probability second card is Heart given first was Heart\n",
+        "# A = both are hearts, B = first is heart\n",
+        "\n",
+        "p_a_and_b = math.comb(12, 1) / math.comb(51, 1) # after one heart is drawn\n",
+        "p_b = 13 / 52\n",
+        "p_a_given_b = (p_a_and_b / p_b)*100\n",
+        "print(f\"Conditional probability 2nd card is Heart given 1st is Heart: {p_a_given_b:.2f}%\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "10"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import math\n",
+        "math.factorial(5) // (math.factorial(2) * math.factorial(3))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Combination C(7, 3) = 35\n"
+          ]
+        }
+      ],
+      "source": [
+        "import math\n",
+        "\n",
+        "n = 7\n",
+        "r = 3\n",
+        "\n",
+        "combination = math.comb(n, r)  # ترکیب: ترتیب مهم نیست\n",
+        "print(f\"Combination C({n}, {r}) =\", combination)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.5/AI-DS_Nexus__A0_5__aminran.ipynb b/a0.1/a0.5/AI-DS_Nexus__A0_5__aminran.ipynb
new file mode 100644
index 0000000..606520b
--- /dev/null
+++ b/a0.1/a0.5/AI-DS_Nexus__A0_5__aminran.ipynb
@@ -0,0 +1,188 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📓 Assignment 5 — Probability\n",
+        "\n",
+        "## 🎲 Counting & Probability with Cards in Python 🃏\n",
+        "Learn factorials, combinations, and (conditional) probability — all in one simple card game simulation using only math and random modules."
+      ],
+      "metadata": {
+        "id": "tacMQNsG2g5S"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🧮 PART 1: Factorial & Combinations\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "_cA_dWDJ2vfr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "aIxoWeNt2ao0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b430b2f2-c2a8-4dbd-a2bd-0058448150c8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "6! = 720\n",
+            "Combinations (6 choose 3): 20\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- FACTORIAL & COMBINATIONS ----\n",
+        "\n",
+        "import math\n",
+        "print(\"6! =\", math.factorial(6))\n",
+        "print(\"Combinations (6 choose 3):\", math.comb(6, 3))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🃏 PART 2: Simulating a Deck of Cards\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "pzy2xD3H3D3o"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---- PROBABILITY IN A DECK ----\n",
+        "\n",
+        "import random\n",
+        "\n",
+        "deck = [f\"{rank} of {suit}\" for suit in ['Hearts', 'Spades', 'Diamonds', 'Clubs']\n",
+        "        for rank in range(1, 14)]\n",
+        "\n",
+        "# Draw 2 random cards\n",
+        "\n",
+        "draws = random.sample(deck, 2)\n",
+        "print(\"\\nDrawn cards:\", draws)\n"
+      ],
+      "metadata": {
+        "id": "eUvZhzYB2x4L",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "434f154d-1d6e-4b5d-ffd4-6c770b9faf19"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Drawn cards: ['6 of Hearts', '9 of Clubs']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 📊 PART 3: Basic Probability"
+      ],
+      "metadata": {
+        "id": "OidRYf9q3Mwj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# PROBABILITY: What’s the chance that both cards are Hearts?\n",
+        "\n",
+        "hearts = [card for card in deck if \"Hearts\" in card]\n",
+        "hearts\n",
+        "prob_both_hearts = math.comb(13, 2) / math.comb(52, 2)\n",
+        "print(f\"Probability both cards are Hearts: {prob_both_hearts:.4f}\")\n"
+      ],
+      "metadata": {
+        "id": "Ok5d99Ns3Oa7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5ebb0ad3-3f3e-4e01-fb79-c46b20a6b396"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Probability both cards are Hearts: 0.0588\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🔁 PART 4: Conditional Probability"
+      ],
+      "metadata": {
+        "id": "NMIA7c313Yum"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---- CONDITIONAL PROBABILITY ----\n",
+        "# P(A and B) / P(B): Probability second card is Heart given first was Heart\n",
+        "# A = both are hearts, B = first is heart\n",
+        "\n",
+        "p_a_and_b = (math.comb(13, 2)) / math.comb(52, 2) # after one heart is drawn\n",
+        "p_b = 13 / 52\n",
+        "p_a_given_b = p_a_and_b / p_b\n",
+        "print(f\"Conditional probability 2nd card is Heart given 1st is Heart: {p_a_given_b:.4f}\")\n"
+      ],
+      "metadata": {
+        "id": "IZeCH8pw3aZX",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7f24e4ea-79c5-44d8-cf98-0501b5a1ccdf"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Conditional probability 2nd card is Heart given 1st is Heart: 0.2353\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.5/AI-DS_Nexus__A0_5__rsayyareh.ipynb b/a0.1/a0.5/AI-DS_Nexus__A0_5__rsayyareh.ipynb
new file mode 100644
index 0000000..3cfae4d
--- /dev/null
+++ b/a0.1/a0.5/AI-DS_Nexus__A0_5__rsayyareh.ipynb
@@ -0,0 +1,196 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tacMQNsG2g5S"
+      },
+      "source": [
+        "# 📓 Assignment 5 — Probability\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "## 🎲 Counting & Probability with Cards in Python 🃏\n",
+        "Learn factorials, combinations, and (conditional) probability — all in one simple card game simulation using only math and random modules."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_cA_dWDJ2vfr"
+      },
+      "source": [
+        "## 🧮 PART 1: Factorial & Combinations\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "aIxoWeNt2ao0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "6! = 720\n",
+            "Combinations (6 choose 3): 20\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- FACTORIAL & COMBINATIONS ----\n",
+        "\n",
+        "import math\n",
+        "print(\"6! =\", math.factorial(6))\n",
+        "print(\"Combinations (6 choose 3):\", math.comb(6, 3))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pzy2xD3H3D3o"
+      },
+      "source": [
+        "## 🃏 PART 2: Simulating a Deck of Cards\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "eUvZhzYB2x4L"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Drawn cards: ['2 of Diamonds', '10 of Hearts']\n",
+            "52\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- PROBABILITY IN A DECK ----\n",
+        "\n",
+        "import random\n",
+        "\n",
+        "deck = [f\"{rank} of {suit}\" for suit in ['Hearts', 'Spades', 'Diamonds', 'Clubs']\n",
+        "        for rank in range(1, 14)]\n",
+        "\n",
+        "# Draw 2 random cards\n",
+        "\n",
+        "draws = random.sample(deck, 2)\n",
+        "print(\"\\nDrawn cards:\", draws)\n",
+        "# print(len(deck))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OidRYf9q3Mwj"
+      },
+      "source": [
+        "## 📊 PART 3: Basic Probability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ok5d99Ns3Oa7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Probability both cards are Hearts: 0.0588\n"
+          ]
+        }
+      ],
+      "source": [
+        "# PROBABILITY: What’s the chance that both cards are Hearts?\n",
+        "hearts = [card for card in deck if \"Hearts\" in card]\n",
+        "# print(len(hearts))\n",
+        "# print(len(deck))\n",
+        "\n",
+        "# Draw 2 random cards\n",
+        "\n",
+        "prob_both_hearts = math.comb(len(hearts), 2) / math.comb(len(deck), 2)\n",
+        "print(f\"Probability both cards are Hearts: {prob_both_hearts:.4f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NMIA7c313Yum"
+      },
+      "source": [
+        "## 🔁 PART 4: Conditional Probability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "id": "IZeCH8pw3aZX"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Conditional probability 2nd card is Heart given 1st is Heart: 0.2353\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- CONDITIONAL PROBABILITY ----\n",
+        "# P(A and B) / P(B): Probability second card is Heart given first was Heart\n",
+        "# A = both are hearts, B = first is heart\n",
+        "\n",
+        "p_a_and_b = math.comb(len(hearts), 2) / math.comb(len(deck), 2) # after one heart is drawn\n",
+        "p_b = 13 / 52\n",
+        "p_a_given_b = p_a_and_b / p_b\n",
+        "print(f\"Conditional probability 2nd card is Heart given 1st is Heart: {p_a_given_b:.4f}\")\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.5/AI_DS_Nexus__A0_5_MrAshki.ipynb b/a0.1/a0.5/AI_DS_Nexus__A0_5_MrAshki.ipynb
new file mode 100644
index 0000000..598e888
--- /dev/null
+++ b/a0.1/a0.5/AI_DS_Nexus__A0_5_MrAshki.ipynb
@@ -0,0 +1,223 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📓 Assignment 5 — Probability\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "## 🎲 Counting & Probability with Cards in Python 🃏\n",
+        "Learn factorials, combinations, and (conditional) probability — all in one simple card game simulation using only math and random modules."
+      ],
+      "metadata": {
+        "id": "tacMQNsG2g5S"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🧮 PART 1: Factorial & Combinations\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "_cA_dWDJ2vfr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aIxoWeNt2ao0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "89ea5827-7ce1-4eaf-c9a0-579cac485bb0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "6! = 720\n",
+            "Combinations (6 choose 3): 20\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- FACTORIAL & COMBINATIONS ----\n",
+        "\n",
+        "import math\n",
+        "print(\"6! =\", math.factorial(6))\n",
+        "print(\"Combinations (6 choose 3):\", math.comb(6, 3))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🃏 PART 2: Simulating a Deck of Cards\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "pzy2xD3H3D3o"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---- PROBABILITY IN A DECK ----\n",
+        "\n",
+        "import random\n",
+        "\n",
+        "deck = [f\"{rank} of {suit}\" for suit in ['Hearts', 'Spades', 'Diamonds', 'Clubs']\n",
+        "        for rank in range (1, 14)]\n",
+        "\n",
+        "# Draw 2 random cards\n",
+        "\n",
+        "draws = random.sample(deck, 2)\n",
+        "print(\"\\nDrawn cards:\", draws)\n"
+      ],
+      "metadata": {
+        "id": "eUvZhzYB2x4L",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "07baa1e9-2294-48f6-df21-f6c2246da1e7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Drawn cards: ['6 of Spades', '11 of Hearts']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 📊 PART 3: Basic Probability"
+      ],
+      "metadata": {
+        "id": "OidRYf9q3Mwj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# PROBABILITY: What’s the chance that both cards are Hearts?\n",
+        "\n",
+        "hearts = [card for card in deck if \"Hearts\" in card]\n",
+        "prob_both_hearts = math.comb(13, 2) / math.comb(52, 2)\n",
+        "print(f\"Probability both cards are Hearts: {prob_both_hearts:.4f}\")\n"
+      ],
+      "metadata": {
+        "id": "Ok5d99Ns3Oa7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "127065a2-18b8-4c3a-e8ae-872e7c1440b2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Probability both cards are Hearts: 0.0588\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🔁 PART 4: Conditional Probability"
+      ],
+      "metadata": {
+        "id": "NMIA7c313Yum"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---- CONDITIONAL PROBABILITY ----\n",
+        "# P(A and B) / P(B): Probability second card is Heart given first was Heart\n",
+        "# A = both are hearts, B = first is heart\n",
+        "\n",
+        "p_a_and_b = math.comb(12, 1) / math.comb(51, 1) # after one heart is drawn\n",
+        "p_b = 13 / 52\n",
+        "p_a_given_b = p_a_and_b / p_b\n",
+        "print(f\"Conditional probability 2nd card is Heart given 1st is Heart: {p_a_given_b:.4f}\")\n"
+      ],
+      "metadata": {
+        "id": "IZeCH8pw3aZX",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6cffefa7-bfc3-4cec-d61f-b37090181a47"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Conditional probability 2nd card is Heart given 1st is Heart: 0.9412\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "p_a_and_b = (13 / 52) * (12 / 51)\n",
+        "p_b = 13 / 52\n",
+        "p_a_given_b = p_a_and_b / p_b\n",
+        "print(f\"Conditional probability 2nd card is Heart given 1st is Heart: {p_a_given_b:.4f}\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EKEZLmfqU83a",
+        "outputId": "1b83eba0-6720-44d5-ae40-f8e0a01b94e3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Conditional probability 2nd card is Heart given 1st is Heart: 0.2353\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git "a/a0.1/a0.5/AI\342\200\223DS_Nexus___A0_5_Probability___RoohollaAlikhani.ipynb" "b/a0.1/a0.5/AI\342\200\223DS_Nexus___A0_5_Probability___RoohollaAlikhani.ipynb"
new file mode 100644
index 0000000..327f6f7
--- /dev/null
+++ "b/a0.1/a0.5/AI\342\200\223DS_Nexus___A0_5_Probability___RoohollaAlikhani.ipynb"
@@ -0,0 +1,242 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tacMQNsG2g5S"
+      },
+      "source": [
+        "# 📓 Assignment 5 — Probability\n",
+        "\n",
+        "## 🎲 Counting & Probability with Cards in Python 🃏\n",
+        "Learn factorials, combinations, and (conditional) probability — all in one simple card game simulation using only math and random modules."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_cA_dWDJ2vfr"
+      },
+      "source": [
+        "## 🧮 PART 1: Factorial & Combinations\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "aIxoWeNt2ao0"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "6! = 720\n",
+            "Combinations (6 choose 3): 20\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- FACTORIAL & COMBINATIONS ----\n",
+        "\n",
+        "import math\n",
+        "print(\"6! =\", math.factorial(6))\n",
+        "print(\"Combinations (6 choose 3):\", math.comb(6 , 3))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pzy2xD3H3D3o"
+      },
+      "source": [
+        "## 🃏 PART 2: Simulating a Deck of Cards\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "eUvZhzYB2x4L"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['1 of Hearts', '2 of Hearts', '3 of Hearts', '4 of Hearts', '5 of Hearts', '6 of Hearts', '7 of Hearts', '8 of Hearts', '9 of Hearts', '10 of Hearts', '11 of Hearts', '12 of Hearts', '13 of Hearts', '1 of Spades', '2 of Spades', '3 of Spades', '4 of Spades', '5 of Spades', '6 of Spades', '7 of Spades', '8 of Spades', '9 of Spades', '10 of Spades', '11 of Spades', '12 of Spades', '13 of Spades', '1 of Diamonds', '2 of Diamonds', '3 of Diamonds', '4 of Diamonds', '5 of Diamonds', '6 of Diamonds', '7 of Diamonds', '8 of Diamonds', '9 of Diamonds', '10 of Diamonds', '11 of Diamonds', '12 of Diamonds', '13 of Diamonds', '1 of Clubs', '2 of Clubs', '3 of Clubs', '4 of Clubs', '5 of Clubs', '6 of Clubs', '7 of Clubs', '8 of Clubs', '9 of Clubs', '10 of Clubs', '11 of Clubs', '12 of Clubs', '13 of Clubs']\n",
+            "\n",
+            "Drawn cards: ['13 of Spades', '8 of Spades']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- PROBABILITY IN A DECK ----\n",
+        "\n",
+        "import random\n",
+        "\n",
+        "deck = [f\"{rank} of {suit}\" for suit in ['Hearts', 'Spades', 'Diamonds', 'Clubs']\n",
+        "        for rank in range (1, 14)]\n",
+        "print(deck)\n",
+        "\n",
+        "# Draw 2 random cards\n",
+        "draws = random.sample(deck, 2)\n",
+        "print(\"\\nDrawn cards:\", draws)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OidRYf9q3Mwj"
+      },
+      "source": [
+        "## 📊 PART 3: Basic Probability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import math"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "Ok5d99Ns3Oa7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Probability both cards are Hearts: 5.88 %\n"
+          ]
+        }
+      ],
+      "source": [
+        "# PROBABILITY: What’s the chance that both cards are Hearts?\n",
+        "\n",
+        "hearts = [card for card in deck if \"Hearts\" in card]\n",
+        "prob_both_hearts = (math.comb(13, 2) / math.comb(52, 2))*100\n",
+        "print(f\"Probability both cards are Hearts: {prob_both_hearts:.2f} %\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NMIA7c313Yum"
+      },
+      "source": [
+        "## 🔁 PART 4: Conditional Probability"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "IZeCH8pw3aZX"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Conditional probability 2nd card is Heart given 1st is Heart: 94.12%\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- CONDITIONAL PROBABILITY ----\n",
+        "# P(A and B) / P(B): Probability second card is Heart given first was Heart\n",
+        "# A = both are hearts, B = first is heart\n",
+        "\n",
+        "p_a_and_b = math.comb(12, 1) / math.comb(51, 1) # after one heart is drawn\n",
+        "p_b = 13 / 52\n",
+        "p_a_given_b = (p_a_and_b / p_b)*100\n",
+        "print(f\"Conditional probability 2nd card is Heart given 1st is Heart: {p_a_given_b:.2f}%\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "10"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import math\n",
+        "math.factorial(5) // (math.factorial(2) * math.factorial(3))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Combination C(7, 3) = 35\n"
+          ]
+        }
+      ],
+      "source": [
+        "import math\n",
+        "\n",
+        "n = 7\n",
+        "r = 3\n",
+        "\n",
+        "combination = math.comb(n, r)  # ترکیب: ترتیب مهم نیست\n",
+        "print(f\"Combination C({n}, {r}) =\", combination)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.5/Assignment_05_Danieljaafari.ipynb b/a0.1/a0.5/Assignment_05_Danieljaafari.ipynb
new file mode 100644
index 0000000..00ecf0d
--- /dev/null
+++ b/a0.1/a0.5/Assignment_05_Danieljaafari.ipynb
@@ -0,0 +1 @@
+# Assignment A05 - Daniel Jaafari
diff --git "a/a0.1/a0.5/Copy_of_AI\342\200\223DS_Nexus___A0_5_Probability___RezaShokr.ipynb" "b/a0.1/a0.5/Copy_of_AI\342\200\223DS_Nexus___A0_5_Probability___RezaShokr.ipynb"
new file mode 100644
index 0000000..1ea4807
--- /dev/null
+++ "b/a0.1/a0.5/Copy_of_AI\342\200\223DS_Nexus___A0_5_Probability___RezaShokr.ipynb"
@@ -0,0 +1,188 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📓 Assignment 5 — Probability\n",
+        "\n",
+        "## 🎲 Counting & Probability with Cards in Python 🃏\n",
+        "Learn factorials, combinations, and (conditional) probability — all in one simple card game simulation using only math and random modules."
+      ],
+      "metadata": {
+        "id": "tacMQNsG2g5S"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🧮 PART 1: Factorial & Combinations\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "_cA_dWDJ2vfr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "aIxoWeNt2ao0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ff177cb1-387d-4357-a516-8a4e6a9bc924"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "6! = 720\n",
+            "Combinations (6 choose 3): 20\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---- FACTORIAL & COMBINATIONS ----\n",
+        "\n",
+        "import math\n",
+        "print(\"6! =\", math.factorial(6))\n",
+        "print(\"Combinations (6 choose 3):\", math.comb(6, 3))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🃏 PART 2: Simulating a Deck of Cards\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "pzy2xD3H3D3o"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---- PROBABILITY IN A DECK ----\n",
+        "\n",
+        "import random\n",
+        "\n",
+        "deck = [f\"{rank} of {suit}\" for suit in ['Hearts', 'Spades', 'Diamonds', 'Clubs']\n",
+        "        for rank in range(1, 14)]\n",
+        "\n",
+        "\n",
+        "# Draw 2 random cards\n",
+        "draws = random.sample(deck, 2)\n",
+        "print(\"\\nDrawn cards:\", draws)\n"
+      ],
+      "metadata": {
+        "id": "eUvZhzYB2x4L",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "860daab3-e438-4a6c-dcb4-c5d1c759235b"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Drawn cards: ['7 of Clubs', '10 of Diamonds']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 📊 PART 3: Basic Probability"
+      ],
+      "metadata": {
+        "id": "OidRYf9q3Mwj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# PROBABILITY: What’s the chance that both cards are Hearts?\n",
+        "\n",
+        "hearts = [card for card in deck if \"Hearts\" in card]\n",
+        "prob_both_hearts = math.comb(len(hearts), 2) / math.comb(len(deck), 2)\n",
+        "print(f\"Probability both cards are Hearts: {prob_both_hearts:.4f}\")\n"
+      ],
+      "metadata": {
+        "id": "Ok5d99Ns3Oa7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1b260c13-cd96-4fa6-85ea-771ef6c2e387"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Probability both cards are Hearts: 0.0588\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🔁 PART 4: Conditional Probability"
+      ],
+      "metadata": {
+        "id": "NMIA7c313Yum"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---- CONDITIONAL PROBABILITY ----\n",
+        "# P(A and B) / P(B): Probability second card is Heart given first was Heart\n",
+        "# A = both are hearts, B = first is heart\n",
+        "import math\n",
+        "\n",
+        "p_a_and_b = math.comb(12, 1) / math.comb(51, 1) # after one heart is drawn\n",
+        "p_b = 13 / 52\n",
+        "p_a_given_b = p_a_and_b / p_b\n",
+        "print(f\"Conditional probability 2nd card is Heart given 1st is Heart: {p_a_given_b:.4f}\")\n"
+      ],
+      "metadata": {
+        "id": "IZeCH8pw3aZX",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c0cbf4e9-8514-42cf-aa83-c56e7dc457dc"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Conditional probability 2nd card is Heart given 1st is Heart: 0.9412\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.6/AI-DS_Nexus__A0_6_Amirhossein_Zandi_8fca80.ipynb b/a0.1/a0.6/AI-DS_Nexus__A0_6_Amirhossein_Zandi_8fca80.ipynb
new file mode 100644
index 0000000..63dda9a
--- /dev/null
+++ b/a0.1/a0.6/AI-DS_Nexus__A0_6_Amirhossein_Zandi_8fca80.ipynb
@@ -0,0 +1,214 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Txr_tIDhHcu2"
+      },
+      "source": [
+        "# 📚 Assignment 06 — Descriptive Statistics\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "\n",
+        "## 📘 Exploring Descriptive Statistics with NumPy & SciPy\n",
+        "This assignment guides you through generating random data, analyzing its statistical properties, and visualizing it using plots.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "x1gatSKSHYTm"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Generated Data: [36 30 33 26 38 28 26 37 24 23 30 35 29 28 35 29 33 39 28 35 28 23 26 28\n",
+            " 25 28 36 32 23 32]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📦 Import Required Libraries\n",
+        "\n",
+        "import numpy as np\n",
+        "from scipy import stats\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "\n",
+        "np.random.choice(42)\n",
+        "data = np.random.randint(20 , 40 , size=30)\n",
+        "print(\"Generated Data:\", data)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cUfGpmVCHvR5"
+      },
+      "source": [
+        "### 📊 Step 2: Central Tendency and Quantiles\n",
+        "Let's compute basic statistics — mean, median, mode, and quantiles."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "FkmPc2r7Htaz"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean: 30.1\n",
+            "Median: 29.0\n",
+            "Mode: 28\n",
+            "Q1 (25%): 26.5\n",
+            "Q3 (75%): 34.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📐 Central Tendency\n",
+        "# mean\n",
+        "mean_val = np.mean(data)\n",
+        "# median\n",
+        "median_val = np.median(data)\n",
+        "# mode\n",
+        "mode_val = stats.mode(data, keepdims=True).mode[0]\n",
+        "\n",
+        "# 📏 Quantiles\n",
+        "q1 = np.quantile(data, 0.25)\n",
+        "q3 = np.quantile(data, 0.75)\n",
+        "\n",
+        "print(f\"Mean: {mean_val}\")\n",
+        "print(f\"Median: {median_val}\")\n",
+        "print(f\"Mode: {mode_val}\")\n",
+        "print(f\"Q1 (25%): {q1}\")\n",
+        "print(f\"Q3 (75%): {q3}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EBZ2KixtHztl"
+      },
+      "source": [
+        "### 🧪 Step 3: Skewness and Kurtosis\n",
+        "Skewness shows asymmetry, and kurtosis indicates the \"tailedness\" of the distribution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "XDwmqc3-H05I"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Skewness: 0.22\n",
+            "Kurtosis: -1.05\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📉 Skewness and Kurtosis\n",
+        "skew_val = stats.skew(data)\n",
+        "kurtosis_val = stats.kurtosis(data)\n",
+        "\n",
+        "print(f\"Skewness: {skew_val:.2f}\")\n",
+        "print(f\"Kurtosis: {kurtosis_val:.2f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FOPw0-dSH250"
+      },
+      "source": [
+        "### 📈 Step 4: Visualization - Bar Chart and Boxplot\n",
+        "Visualize the data to better understand its distribution and spread."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "Zfx5s6xLH1y8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 📊 Bar Plot\n",
+        "plt.figure(figsize=(12, 4))\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "# make a bar chart\n",
+        "plt.bar(range(len(data)), data)\n",
+        "plt.title(\"Bar Plot of Random Data\")\n",
+        "plt.xlabel(\"Index\")\n",
+        "plt.ylabel(\"Value\")\n",
+        "\n",
+        "# 📦 Boxplot\n",
+        "plt.subplot(1, 2, 2)\n",
+        "# make a box plot\n",
+        "plt.boxplot(data, vert=False)\n",
+        "plt.title(\"Boxplot of Random Data\")\n",
+        "plt.xlabel(\"Value\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.6/AI-DS_Nexus__A0_6_Javid_Mohammadi_a60d6f.ipynb b/a0.1/a0.6/AI-DS_Nexus__A0_6_Javid_Mohammadi_a60d6f.ipynb
new file mode 100644
index 0000000..008bbe0
--- /dev/null
+++ b/a0.1/a0.6/AI-DS_Nexus__A0_6_Javid_Mohammadi_a60d6f.ipynb
@@ -0,0 +1,218 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 06 — Descriptive Statistics\n",
+        "\n",
+        "## 📘 Exploring Descriptive Statistics with NumPy & SciPy\n",
+        "This assignment guides you through generating random data, analyzing its statistical properties, and visualizing it using plots.\n"
+      ],
+      "metadata": {
+        "id": "Txr_tIDhHcu2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "x1gatSKSHYTm",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "aaa53615-532e-47c3-bf24-8faa949dbcf1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Generated Data: [26 39 34 30 27 26 38 30 30 23 27 22 21 31 25 21 20 31 31 36 29 35 34 34\n",
+            " 38 31 39 22 24 38]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📦 Import Required Libraries\n",
+        "import numpy as np\n",
+        "from scipy import stats\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# 🎲 Step 1: Generate Random Integer List\n",
+        "np.random.seed(42)  # For reproducibility\n",
+        "data = np.random.randint(20, 40, size=30)\n",
+        "print(\"Generated Data:\", data)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 📊 Step 2: Central Tendency and Quantiles\n",
+        "Let's compute basic statistics — mean, median, mode, and quantiles."
+      ],
+      "metadata": {
+        "id": "cUfGpmVCHvR5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 📐 Central Tendency\n",
+        "# mean\n",
+        "mean_val = np.mean(data)\n",
+        "# median\n",
+        "median_val = np.median(data)\n",
+        "# mode\n",
+        "mode_val = stats.mode(data, keepdims=True).mode[0]\n",
+        "\n",
+        "# 📏 Quantiles\n",
+        "q1 = np.quantile(data, 0.25)\n",
+        "q3 = np.quantile(data, 0.75)\n",
+        "\n",
+        "print(f\"Mean: {mean_val}\")\n",
+        "print(f\"Median: {median_val}\")\n",
+        "print(f\"Mode: {mode_val}\")\n",
+        "print(f\"Q1 (25%): {q1}\")\n",
+        "print(f\"Q3 (75%): {q3}\")\n"
+      ],
+      "metadata": {
+        "id": "FkmPc2r7Htaz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9e0917be-8d7a-418f-e58d-56fc1a6b31ce"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean: 29.733333333333334\n",
+            "Median: 30.0\n",
+            "Mode: 31\n",
+            "Q1 (25%): 25.25\n",
+            "Q3 (75%): 34.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 🧪 Step 3: Skewness and Kurtosis\n",
+        "Skewness shows asymmetry, and kurtosis indicates the \"tailedness\" of the distribution."
+      ],
+      "metadata": {
+        "id": "EBZ2KixtHztl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 📉 Skewness and Kurtosis\n",
+        "skew_val = stats.skew(data)\n",
+        "kurtosis_val = stats.kurtosis(data)\n",
+        "\n",
+        "print(f\"Skewness: {skew_val:.2f}\")\n",
+        "print(f\"Kurtosis: {kurtosis_val:.2f}\")\n"
+      ],
+      "metadata": {
+        "id": "XDwmqc3-H05I",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "14054bde-bb63-4873-9426-44cd0616e272"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Skewness: -0.00\n",
+            "Kurtosis: -1.14\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 📈 Step 4: Visualization - Bar Chart and Boxplot\n",
+        "Visualize the data to better understand its distribution and spread."
+      ],
+      "metadata": {
+        "id": "FOPw0-dSH250"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 📊 Bar Plot\n",
+        "plt.figure(figsize=(12, 4))\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "# make a bar chart\n",
+        "plt.bar(range(len(data)), data)\n",
+        "plt.title(\"Bar Plot of Random Data\")\n",
+        "plt.xlabel(\"Index\")\n",
+        "plt.ylabel(\"Value\")\n",
+        "\n",
+        "# 📦 Boxplot\n",
+        "plt.subplot(1, 2, 2)\n",
+        "# make a box plot\n",
+        "plt.boxplot(data, vert=False)\n",
+        "plt.title(\"Boxplot of Random Data\")\n",
+        "plt.xlabel(\"Value\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "Zfx5s6xLH1y8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 352
+        },
+        "outputId": "21dbf139-2e56-4839-a947-1d2c2a057f6c"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "OV_iK5mgP6G2"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.6/AI-DS_Nexus__A0_6_Project__RezaShokr.ipynb b/a0.1/a0.6/AI-DS_Nexus__A0_6_Project__RezaShokr.ipynb
new file mode 100644
index 0000000..13d7ed8
--- /dev/null
+++ b/a0.1/a0.6/AI-DS_Nexus__A0_6_Project__RezaShokr.ipynb
@@ -0,0 +1,209 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Txr_tIDhHcu2"
+      },
+      "source": [
+        "# 📚 Assignment 06 — Descriptive Statistics\n",
+        "\n",
+        "## 📘 Exploring Descriptive Statistics with NumPy & SciPy\n",
+        "This assignment guides you through generating random data, analyzing its statistical properties, and visualizing it using plots.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "x1gatSKSHYTm"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Generated Data: [26 39 34 30 27 26 38 30 30 23 27 22 21 31 25 21 20 31 31 36 29 35 34 34\n",
+            " 38 31 39 22 24 38]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📦 Import Required Libraries\n",
+        "import numpy as np\n",
+        "from scipy import stats\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# 🎲 Step 1: Generate Random Integer List\n",
+        "np.random.seed(42)  # For reproducibility\n",
+        "data = np.random.randint(20, 40, size=30)\n",
+        "print(\"Generated Data:\", data)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cUfGpmVCHvR5"
+      },
+      "source": [
+        "### 📊 Step 2: Central Tendency and Quantiles\n",
+        "Let's compute basic statistics — mean, median, mode, and quantiles."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "FkmPc2r7Htaz"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean: 29.733333333333334\n",
+            "Median: 30.0\n",
+            "Mode: 31\n",
+            "Q1 (25%): 25.25\n",
+            "Q3 (75%): 34.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📐 Central Tendency\n",
+        "\n",
+        "mean_val = np.mean(data) # mean\n",
+        "\n",
+        "median_val = np.median(data) # median\n",
+        "\n",
+        "mode_val = stats.mode(data, keepdims=True).mode[0] # mode  from scipu.stats lib\n",
+        "\n",
+        "# 📏 Quantiles\n",
+        "q1 = np.quantile(data, 0.25)\n",
+        "q3 = np.quantile(data, 0.75)\n",
+        "\n",
+        "print(f\"Mean: {mean_val}\")\n",
+        "print(f\"Median: {median_val}\")\n",
+        "print(f\"Mode: {mode_val}\")\n",
+        "print(f\"Q1 (25%): {q1}\")\n",
+        "print(f\"Q3 (75%): {q3}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EBZ2KixtHztl"
+      },
+      "source": [
+        "### 🧪 Step 3: Skewness and Kurtosis\n",
+        "Skewness shows asymmetry, and kurtosis indicates the \"tailedness\" of the distribution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "XDwmqc3-H05I"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Skewness: -0.00\n",
+            "Kurtosis: -1.14\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📉 Skewness and Kurtosis\n",
+        "skew_val = stats.skew(data)\n",
+        "kurtosis_val = stats.kurtosis(data)\n",
+        "\n",
+        "print(f\"Skewness: {skew_val:.2f}\")\n",
+        "print(f\"Kurtosis: {kurtosis_val:.2f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FOPw0-dSH250"
+      },
+      "source": [
+        "### 📈 Step 4: Visualization - Bar Chart and Boxplot\n",
+        "Visualize the data to better understand its distribution and spread."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "Zfx5s6xLH1y8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 📊 Bar Plot\n",
+        "plt.figure(figsize=(12, 4))\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "# make a bar chart\n",
+        "plt.bar(range(len(data)), data)\n",
+        "plt.title(\"Bar Plot of Random Data\")\n",
+        "plt.xlabel(\"Index\")\n",
+        "plt.ylabel(\"Value\")\n",
+        "\n",
+        "# 📦 Boxplot\n",
+        "plt.subplot(1, 2, 2)\n",
+        "# make a box plot\n",
+        "plt.boxplot(data, vert=False)\n",
+        "plt.title(\"Boxplot of Random Data\")\n",
+        "plt.xlabel(\"Value\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.6/AI-DS_Nexus__A0_6_Project_roohi_268383.ipynb b/a0.1/a0.6/AI-DS_Nexus__A0_6_Project_roohi_268383.ipynb
new file mode 100644
index 0000000..13d7ed8
--- /dev/null
+++ b/a0.1/a0.6/AI-DS_Nexus__A0_6_Project_roohi_268383.ipynb
@@ -0,0 +1,209 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Txr_tIDhHcu2"
+      },
+      "source": [
+        "# 📚 Assignment 06 — Descriptive Statistics\n",
+        "\n",
+        "## 📘 Exploring Descriptive Statistics with NumPy & SciPy\n",
+        "This assignment guides you through generating random data, analyzing its statistical properties, and visualizing it using plots.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "x1gatSKSHYTm"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Generated Data: [26 39 34 30 27 26 38 30 30 23 27 22 21 31 25 21 20 31 31 36 29 35 34 34\n",
+            " 38 31 39 22 24 38]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📦 Import Required Libraries\n",
+        "import numpy as np\n",
+        "from scipy import stats\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# 🎲 Step 1: Generate Random Integer List\n",
+        "np.random.seed(42)  # For reproducibility\n",
+        "data = np.random.randint(20, 40, size=30)\n",
+        "print(\"Generated Data:\", data)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cUfGpmVCHvR5"
+      },
+      "source": [
+        "### 📊 Step 2: Central Tendency and Quantiles\n",
+        "Let's compute basic statistics — mean, median, mode, and quantiles."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "FkmPc2r7Htaz"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean: 29.733333333333334\n",
+            "Median: 30.0\n",
+            "Mode: 31\n",
+            "Q1 (25%): 25.25\n",
+            "Q3 (75%): 34.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📐 Central Tendency\n",
+        "\n",
+        "mean_val = np.mean(data) # mean\n",
+        "\n",
+        "median_val = np.median(data) # median\n",
+        "\n",
+        "mode_val = stats.mode(data, keepdims=True).mode[0] # mode  from scipu.stats lib\n",
+        "\n",
+        "# 📏 Quantiles\n",
+        "q1 = np.quantile(data, 0.25)\n",
+        "q3 = np.quantile(data, 0.75)\n",
+        "\n",
+        "print(f\"Mean: {mean_val}\")\n",
+        "print(f\"Median: {median_val}\")\n",
+        "print(f\"Mode: {mode_val}\")\n",
+        "print(f\"Q1 (25%): {q1}\")\n",
+        "print(f\"Q3 (75%): {q3}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EBZ2KixtHztl"
+      },
+      "source": [
+        "### 🧪 Step 3: Skewness and Kurtosis\n",
+        "Skewness shows asymmetry, and kurtosis indicates the \"tailedness\" of the distribution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "XDwmqc3-H05I"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Skewness: -0.00\n",
+            "Kurtosis: -1.14\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📉 Skewness and Kurtosis\n",
+        "skew_val = stats.skew(data)\n",
+        "kurtosis_val = stats.kurtosis(data)\n",
+        "\n",
+        "print(f\"Skewness: {skew_val:.2f}\")\n",
+        "print(f\"Kurtosis: {kurtosis_val:.2f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FOPw0-dSH250"
+      },
+      "source": [
+        "### 📈 Step 4: Visualization - Bar Chart and Boxplot\n",
+        "Visualize the data to better understand its distribution and spread."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "Zfx5s6xLH1y8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 📊 Bar Plot\n",
+        "plt.figure(figsize=(12, 4))\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "# make a bar chart\n",
+        "plt.bar(range(len(data)), data)\n",
+        "plt.title(\"Bar Plot of Random Data\")\n",
+        "plt.xlabel(\"Index\")\n",
+        "plt.ylabel(\"Value\")\n",
+        "\n",
+        "# 📦 Boxplot\n",
+        "plt.subplot(1, 2, 2)\n",
+        "# make a box plot\n",
+        "plt.boxplot(data, vert=False)\n",
+        "plt.title(\"Boxplot of Random Data\")\n",
+        "plt.xlabel(\"Value\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.6/AI-DS_Nexus__A0_6__MrAshki.ipynb b/a0.1/a0.6/AI-DS_Nexus__A0_6__MrAshki.ipynb
new file mode 100644
index 0000000..dbff354
--- /dev/null
+++ b/a0.1/a0.6/AI-DS_Nexus__A0_6__MrAshki.ipynb
@@ -0,0 +1,220 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 06 — Descriptive Statistics\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "\n",
+        "## 📘 Exploring Descriptive Statistics with NumPy & SciPy\n",
+        "This assignment guides you through generating random data, analyzing its statistical properties, and visualizing it using plots.\n"
+      ],
+      "metadata": {
+        "id": "Txr_tIDhHcu2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "x1gatSKSHYTm",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "20c0b33d-ab15-4eb0-8caf-eb33ae358234"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Generated Data: [26 39 34 30 27 26 38 30 30 23 27 22 21 31 25 21 20 31 31 36 29 35 34 34\n",
+            " 38 31 39 22 24 38]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📦 Import Required Libraries\n",
+        "import numpy as np\n",
+        "from scipy import stats\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# 🎲 Step 1: Generate Random Integer List\n",
+        "np.random.seed(42)  # For reproducibility\n",
+        "data = np.random.randint(20, 40, size=30)\n",
+        "print(\"Generated Data:\", data)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 📊 Step 2: Central Tendency and Quantiles\n",
+        "Let's compute basic statistics — mean, median, mode, and quantiles."
+      ],
+      "metadata": {
+        "id": "cUfGpmVCHvR5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 📐 Central Tendency\n",
+        "# mean\n",
+        "mean_val = np.mean(data)\n",
+        "# median\n",
+        "median_val = np.median(data)\n",
+        "# mode\n",
+        "mode_val = stats.mode(data, keepdims=True).mode[0]\n",
+        "\n",
+        "# 📏 Quantiles\n",
+        "q1 = np.quantile(data, 0.25)\n",
+        "q3 = np.quantile(data, 0.75)\n",
+        "\n",
+        "print(f\"Mean: {mean_val}\")\n",
+        "print(f\"Median: {median_val}\")\n",
+        "print(f\"Mode: {mode_val}\")\n",
+        "print(f\"Q1 (25%): {q1}\")\n",
+        "print(f\"Q3 (75%): {q3}\")\n"
+      ],
+      "metadata": {
+        "id": "FkmPc2r7Htaz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d1bde655-0c78-494f-a883-5f9fc3f2dba5"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean: 29.733333333333334\n",
+            "Median: 30.0\n",
+            "Mode: 31\n",
+            "Q1 (25%): 25.25\n",
+            "Q3 (75%): 34.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 🧪 Step 3: Skewness and Kurtosis\n",
+        "Skewness shows asymmetry, and kurtosis indicates the \"tailedness\" of the distribution."
+      ],
+      "metadata": {
+        "id": "EBZ2KixtHztl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 📉 Skewness and Kurtosis\n",
+        "skew_val = stats.skew(data)\n",
+        "kurtosis_val = stats.kurtosis(data)\n",
+        "\n",
+        "print(f\"Skewness: {skew_val:.2f}\")\n",
+        "print(f\"Kurtosis: {kurtosis_val:.2f}\")\n"
+      ],
+      "metadata": {
+        "id": "XDwmqc3-H05I",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "fd0a4951-7e4e-4433-f466-d03aec209ce3"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Skewness: -0.00\n",
+            "Kurtosis: -1.14\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 📈 Step 4: Visualization - Bar Chart and Boxplot\n",
+        "Visualize the data to better understand its distribution and spread."
+      ],
+      "metadata": {
+        "id": "FOPw0-dSH250"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 📊 Bar Plot\n",
+        "plt.figure(figsize=(12, 4))\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "# make a bar chart\n",
+        "plt.bar(range(len(data)), data)\n",
+        "plt.title(\"Bar Plot of Random Data\")\n",
+        "plt.xlabel(\"Index\")\n",
+        "plt.ylabel(\"Value\")\n",
+        "\n",
+        "# 📦 Boxplot\n",
+        "plt.subplot(1, 2, 2)\n",
+        "# make a box plot\n",
+        "plt.boxplot(data, vert=False)\n",
+        "plt.title(\"Boxplot of Random Data\")\n",
+        "plt.xlabel(\"Value\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "Zfx5s6xLH1y8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 288
+        },
+        "outputId": "859c58ba-d27a-4c83-9df9-874c48283b89"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.6/AI-DS_Nexus__A0_6__aminran.ipynb b/a0.1/a0.6/AI-DS_Nexus__A0_6__aminran.ipynb
new file mode 100644
index 0000000..dd1856b
--- /dev/null
+++ b/a0.1/a0.6/AI-DS_Nexus__A0_6__aminran.ipynb
@@ -0,0 +1,218 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 06 — Descriptive Statistics\n",
+        "\n",
+        "## 📘 Exploring Descriptive Statistics with NumPy & SciPy\n",
+        "This assignment guides you through generating random data, analyzing its statistical properties, and visualizing it using plots.\n"
+      ],
+      "metadata": {
+        "id": "Txr_tIDhHcu2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "x1gatSKSHYTm",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4b758d8f-1197-45a8-95c3-04205651dd49"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Generated Data: [26 39 34 30 27 26 38 30 30 23 27 22 21 31 25 21 20 31 31 36 29 35 34 34\n",
+            " 38 31 39 22 24 38]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📦 Import Required Libraries\n",
+        "import numpy as np\n",
+        "from scipy import stats\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# 🎲 Step 1: Generate Random Integer List\n",
+        "np.random.seed(42)  # For reproducibility\n",
+        "data = np.random.randint(20, 40, size=30)\n",
+        "print(\"Generated Data:\", data)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 📊 Step 2: Central Tendency and Quantiles\n",
+        "Let's compute basic statistics — mean, median, mode, and quantiles."
+      ],
+      "metadata": {
+        "id": "cUfGpmVCHvR5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 📐 Central Tendency\n",
+        "# mean\n",
+        "mean_val = np.mean(data)\n",
+        "# median\n",
+        "median_val = np.median(data)\n",
+        "# mode\n",
+        "mode_val = stats.mode(data, keepdims=True).mode[0]\n",
+        "\n",
+        "# 📏 Quantiles\n",
+        "q1 = np.quantile(data, 0.25)\n",
+        "q3 = np.quantile(data, 0.75)\n",
+        "\n",
+        "print(f\"Mean: {mean_val}\")\n",
+        "print(f\"Median: {median_val}\")\n",
+        "print(f\"Mode: {mode_val}\")\n",
+        "print(f\"Q1 (25%): {q1}\")\n",
+        "print(f\"Q3 (75%): {q3}\")\n"
+      ],
+      "metadata": {
+        "id": "FkmPc2r7Htaz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9199c4c9-4d9b-44ca-a1b5-379306c43d1c"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean: 29.733333333333334\n",
+            "Median: 30.0\n",
+            "Mode: 31\n",
+            "Q1 (25%): 25.25\n",
+            "Q3 (75%): 34.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 🧪 Step 3: Skewness and Kurtosis\n",
+        "Skewness shows asymmetry, and kurtosis indicates the \"tailedness\" of the distribution."
+      ],
+      "metadata": {
+        "id": "EBZ2KixtHztl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 📉 Skewness and Kurtosis\n",
+        "skew_val = stats.skew(data)\n",
+        "kurtosis_val = stats.kurtosis(data)\n",
+        "\n",
+        "print(f\"Skewness: {skew_val:.2f}\")\n",
+        "print(f\"Kurtosis: {kurtosis_val:.2f}\")\n"
+      ],
+      "metadata": {
+        "id": "XDwmqc3-H05I",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "00ce7f5d-6efd-4e99-f3d7-35e77cc19eed"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Skewness: -0.00\n",
+            "Kurtosis: -1.14\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 📈 Step 4: Visualization - Bar Chart and Boxplot\n",
+        "Visualize the data to better understand its distribution and spread."
+      ],
+      "metadata": {
+        "id": "FOPw0-dSH250"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 📊 Bar Plot\n",
+        "plt.figure(figsize=(12, 4))\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "# make a bar chart\n",
+        "plt.bar(range(len(data)), data)\n",
+        "plt.title(\"Bar Plot of Random Data\")\n",
+        "plt.xlabel(\"Index\")\n",
+        "plt.ylabel(\"Value\")\n",
+        "\n",
+        "# 📦 Boxplot\n",
+        "plt.subplot(1, 2, 2)\n",
+        "# make a box plot\n",
+        "plt.boxplot(data, vert=False)\n",
+        "plt.title(\"Boxplot of Random Data\")\n",
+        "plt.xlabel(\"Value\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "Zfx5s6xLH1y8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 407
+        },
+        "outputId": "04f20f5b-24c3-418f-d012-a7ee8ea9c980"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "9IDcUqBuw3wW"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.6/AI-DS_Nexus__A0_6__rsayyareh.ipynb b/a0.1/a0.6/AI-DS_Nexus__A0_6__rsayyareh.ipynb
new file mode 100644
index 0000000..a64bc44
--- /dev/null
+++ b/a0.1/a0.6/AI-DS_Nexus__A0_6__rsayyareh.ipynb
@@ -0,0 +1,225 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Txr_tIDhHcu2"
+      },
+      "source": [
+        "# 📚 Assignment 06 — Descriptive Statistics\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n",
+        "\n",
+        "\n",
+        "## 📘 Exploring Descriptive Statistics with NumPy & SciPy\n",
+        "This assignment guides you through generating random data, analyzing its statistical properties, and visualizing it using plots.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "x1gatSKSHYTm"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Generated Data: [37 30 35 34 38 37 30 38 27 30]\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "array([27, 30, 30, 30, 34, 35, 37, 37, 38, 38], dtype=int32)"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 📦 Import Required Libraries\n",
+        "import numpy as np\n",
+        "import random\n",
+        "from scipy import stats\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# 🎲 Step 1: Generate Random Integer List\n",
+        "np.random.random(42)  # For reproducibility\n",
+        "data = np.random.randint(20, 40, size=10)\n",
+        "print(\"Generated Data:\", data)\n",
+        "np.sort(data)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cUfGpmVCHvR5"
+      },
+      "source": [
+        "### 📊 Step 2: Central Tendency and Quantiles\n",
+        "Let's compute basic statistics — mean, median, mode, and quantiles."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "FkmPc2r7Htaz"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean: 33.6\n",
+            "Median: 34.5\n",
+            "Mode: 30\n",
+            "Q1 (25%): 30.0\n",
+            "Q3 (75%): 37.0\n",
+            "34.5\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📐 Central Tendency\n",
+        "# mean\n",
+        "mean_val = np.mean(data)\n",
+        "# median\n",
+        "median_val = np.median(data)\n",
+        "# mode\n",
+        "mode_val = stats.mode(data, keepdims=True).mode[0]\n",
+        "\n",
+        "# 📏 Quantiles\n",
+        "q1 = np.quantile(data, 0.25)\n",
+        "q3 = np.quantile(data, 0.75)\n",
+        "\n",
+        "print(f\"Mean: {mean_val}\")\n",
+        "print(f\"Median: {median_val}\")\n",
+        "print(f\"Mode: {mode_val}\")\n",
+        "print(f\"Q1 (25%): {q1}\")\n",
+        "print(f\"Q3 (75%): {q3}\")\n",
+        "print(np.quantile(data, 0.5))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EBZ2KixtHztl"
+      },
+      "source": [
+        "### 🧪 Step 3: Skewness and Kurtosis\n",
+        "Skewness shows asymmetry, and kurtosis indicates the \"tailedness\" of the distribution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "XDwmqc3-H05I"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Skewness: 0.17\n",
+            "Kurtosis: -1.23\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📉 Skewness and Kurtosis\n",
+        "skew_val = stats.skew(data)\n",
+        "kurtosis_val = stats.kurtosis(data)\n",
+        "\n",
+        "print(f\"Skewness: {skew_val:.2f}\")\n",
+        "print(f\"Kurtosis: {kurtosis_val:.2f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FOPw0-dSH250"
+      },
+      "source": [
+        "### 📈 Step 4: Visualization - Bar Chart and Boxplot\n",
+        "Visualize the data to better understand its distribution and spread."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "Zfx5s6xLH1y8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 📊 Bar Plot\n",
+        "plt.figure(figsize=(12, 4))\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "# make a bar chart\n",
+        "plt.bar(range(len(data)), data)\n",
+        "plt.title(\"Bar Plot of Random Data\")\n",
+        "plt.xlabel(\"Index\")\n",
+        "plt.ylabel(\"Value\")\n",
+        "\n",
+        "# 📦 Boxplot\n",
+        "plt.subplot(1, 2, 2)\n",
+        "# make a box plot\n",
+        "plt.boxplot(data, vert=False)\n",
+        "plt.title(\"Boxplot of Random Data\")\n",
+        "plt.xlabel(\"Value\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git "a/a0.1/a0.6/AI\342\200\223DS_Nexus___A0_6_Descriptive_Stats___RoohollaAlikhani.ipynb" "b/a0.1/a0.6/AI\342\200\223DS_Nexus___A0_6_Descriptive_Stats___RoohollaAlikhani.ipynb"
new file mode 100644
index 0000000..13d7ed8
--- /dev/null
+++ "b/a0.1/a0.6/AI\342\200\223DS_Nexus___A0_6_Descriptive_Stats___RoohollaAlikhani.ipynb"
@@ -0,0 +1,209 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Txr_tIDhHcu2"
+      },
+      "source": [
+        "# 📚 Assignment 06 — Descriptive Statistics\n",
+        "\n",
+        "## 📘 Exploring Descriptive Statistics with NumPy & SciPy\n",
+        "This assignment guides you through generating random data, analyzing its statistical properties, and visualizing it using plots.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "x1gatSKSHYTm"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Generated Data: [26 39 34 30 27 26 38 30 30 23 27 22 21 31 25 21 20 31 31 36 29 35 34 34\n",
+            " 38 31 39 22 24 38]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📦 Import Required Libraries\n",
+        "import numpy as np\n",
+        "from scipy import stats\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# 🎲 Step 1: Generate Random Integer List\n",
+        "np.random.seed(42)  # For reproducibility\n",
+        "data = np.random.randint(20, 40, size=30)\n",
+        "print(\"Generated Data:\", data)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cUfGpmVCHvR5"
+      },
+      "source": [
+        "### 📊 Step 2: Central Tendency and Quantiles\n",
+        "Let's compute basic statistics — mean, median, mode, and quantiles."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "FkmPc2r7Htaz"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean: 29.733333333333334\n",
+            "Median: 30.0\n",
+            "Mode: 31\n",
+            "Q1 (25%): 25.25\n",
+            "Q3 (75%): 34.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📐 Central Tendency\n",
+        "\n",
+        "mean_val = np.mean(data) # mean\n",
+        "\n",
+        "median_val = np.median(data) # median\n",
+        "\n",
+        "mode_val = stats.mode(data, keepdims=True).mode[0] # mode  from scipu.stats lib\n",
+        "\n",
+        "# 📏 Quantiles\n",
+        "q1 = np.quantile(data, 0.25)\n",
+        "q3 = np.quantile(data, 0.75)\n",
+        "\n",
+        "print(f\"Mean: {mean_val}\")\n",
+        "print(f\"Median: {median_val}\")\n",
+        "print(f\"Mode: {mode_val}\")\n",
+        "print(f\"Q1 (25%): {q1}\")\n",
+        "print(f\"Q3 (75%): {q3}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EBZ2KixtHztl"
+      },
+      "source": [
+        "### 🧪 Step 3: Skewness and Kurtosis\n",
+        "Skewness shows asymmetry, and kurtosis indicates the \"tailedness\" of the distribution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "XDwmqc3-H05I"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Skewness: -0.00\n",
+            "Kurtosis: -1.14\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📉 Skewness and Kurtosis\n",
+        "skew_val = stats.skew(data)\n",
+        "kurtosis_val = stats.kurtosis(data)\n",
+        "\n",
+        "print(f\"Skewness: {skew_val:.2f}\")\n",
+        "print(f\"Kurtosis: {kurtosis_val:.2f}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FOPw0-dSH250"
+      },
+      "source": [
+        "### 📈 Step 4: Visualization - Bar Chart and Boxplot\n",
+        "Visualize the data to better understand its distribution and spread."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "Zfx5s6xLH1y8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# 📊 Bar Plot\n",
+        "plt.figure(figsize=(12, 4))\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "# make a bar chart\n",
+        "plt.bar(range(len(data)), data)\n",
+        "plt.title(\"Bar Plot of Random Data\")\n",
+        "plt.xlabel(\"Index\")\n",
+        "plt.ylabel(\"Value\")\n",
+        "\n",
+        "# 📦 Boxplot\n",
+        "plt.subplot(1, 2, 2)\n",
+        "# make a box plot\n",
+        "plt.boxplot(data, vert=False)\n",
+        "plt.title(\"Boxplot of Random Data\")\n",
+        "plt.xlabel(\"Value\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.7/AI-DS_Nexus__A0_7_Amirhossein_Zandi_8fca80.ipynb b/a0.1/a0.7/AI-DS_Nexus__A0_7_Amirhossein_Zandi_8fca80.ipynb
new file mode 100644
index 0000000..5f89dc3
--- /dev/null
+++ b/a0.1/a0.7/AI-DS_Nexus__A0_7_Amirhossein_Zandi_8fca80.ipynb
@@ -0,0 +1,377 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dbICYwLGo5Av"
+      },
+      "source": [
+        "# 🔥 Assignment: Exploring PyTorch\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b8h5DExTo6K5"
+      },
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using PyTorch, focusing on tensors, autograd, and a small training loop.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "euolCT3oo8dR"
+      },
+      "source": [
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install PyTorch and check its version.\n",
+        "\n",
+        "* Create a 2D tensor and print it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "9AADPzP6o1iY"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2.7.1+cu128\n",
+            "x : tensor([[1., 2.],\n",
+            "        [3., 4.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "\n",
+        "print(torch.__version__)\n",
+        "x = torch.tensor([[1. , 2.] , [3. , 4.]])\n",
+        "print(f\"x : {x}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "T5mCQMYFpBJm"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "y : tensor([[5., 6.],\n",
+            "        [7., 8.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create another tensor y of the same shape with values [[5, 6], [7, 8]].\n",
+        "\n",
+        "y = torch.tensor([[5. , 6.] , [7. , 8.]])\n",
+        "print(f\"y : {y}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7yRBms7npEUe"
+      },
+      "source": [
+        "### 2️⃣ Tensor Operations 🧮\n",
+        "**Task:** Perform addition and matrix multiplication."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "nGRNryC4pDv2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Addition:\n",
+            " tensor([[ 6.,  8.],\n",
+            "        [10., 12.]])\n",
+            "Matrix Multiplication:\n",
+            " tensor([[19., 22.],\n",
+            "        [43., 50.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "z = x + y\n",
+        "print(\"Addition:\\n\", z)\n",
+        "\n",
+        "mat_mul = x @ y\n",
+        "print(\"Matrix Multiplication:\\n\", mat_mul)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "tf64nP2dpIod"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "multiplication : tensor([[ 5., 12.],\n",
+            "        [21., 32.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Perform element-wise multiplication (x * y) and print the result.\n",
+        "\n",
+        "multiplication = x * y\n",
+        "print(f\"multiplication : {multiplication}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h3dABundpMYU"
+      },
+      "source": [
+        "### 3️⃣ Autograd and Gradients ⚙️\n",
+        "**Task:** Enable gradient tracking and compute derivatives."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "igtPKY90pLGt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gradient of b wrt a: tensor(8.)\n"
+          ]
+        }
+      ],
+      "source": [
+        "a = torch.tensor(3.0, requires_grad=True)\n",
+        "b = (a ** 2) + 2 * a + 1\n",
+        "b.backward()\n",
+        "print(\"Gradient of b wrt a:\", a.grad)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "bLceqHHvpOwt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gradient of q wrt p: tensor(16.)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor p with value 2.0 (requires_grad=True) and compute the gradient of q = p^3 + 4p.\n",
+        "\n",
+        "p = torch.tensor(2.0 , requires_grad=True)\n",
+        "q = p **3 + 4 * p\n",
+        "q.backward()\n",
+        "print(\"Gradient of q wrt p:\", p.grad)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jytRN17EpVbM"
+      },
+      "source": [
+        "### 4️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (2, 3) and find its max and min."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "fb7fv-E_pS6e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Tensor:\n",
+            " tensor([[0.6975, 0.7858, 0.7772],\n",
+            "        [0.3042, 0.1020, 0.1182]])\n",
+            "Max: tensor(0.7858)\n",
+            "Min: tensor(0.1020)\n"
+          ]
+        }
+      ],
+      "source": [
+        "rand_tensor = torch.rand((2, 3))\n",
+        "print(\"Random Tensor:\\n\", rand_tensor)\n",
+        "print(\"Max:\", torch.max(rand_tensor))\n",
+        "print(\"Min:\", torch.min(rand_tensor))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bhSClXespjvT"
+      },
+      "source": [
+        "### 5️⃣ Mini Training Loop 🤖\n",
+        "**Task:** Train a simple linear model y = wx + b using gradient descent."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "o7s2KMJVpZ5M"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Trained weight: 1.651038408279419\n",
+            "Trained bias: 0.793222963809967\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Data\n",
+        "x_train = torch.tensor([[1.0], [2.0], [3.0]])\n",
+        "y_train = torch.tensor([[2.0], [4.0], [6.0]])\n",
+        "\n",
+        "# Model\n",
+        "w = torch.randn(1, requires_grad=True)\n",
+        "b = torch.zeros(1, requires_grad=True)\n",
+        "\n",
+        "# Training\n",
+        "learning_rate = 0.01\n",
+        "for epoch in range(100):\n",
+        "    y_pred = w * x_train + b\n",
+        "    loss = torch.mean((y_pred - y_train) ** 2)\n",
+        "    loss.backward()\n",
+        "\n",
+        "    # Update\n",
+        "    with torch.no_grad():\n",
+        "        w -= learning_rate * w.grad\n",
+        "        b -= learning_rate * b.grad\n",
+        "        w.grad.zero_()\n",
+        "        b.grad.zero_()\n",
+        "\n",
+        "print(\"Trained weight:\", w.item())\n",
+        "print(\"Trained bias:\", b.item())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rGhFqtrVpv8_"
+      },
+      "source": [
+        "### 6️⃣ Bonus ⚡\n",
+        "* Convert a PyTorch tensor to a NumPy array.\n",
+        "\n",
+        "* Convert it back to a PyTorch tensor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "AF4t92eDpq0j"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sample 1 : tensor([1., 2., 3.])\n",
+            "---------------\n",
+            "numpy_arr : [1. 2. 3.]\n",
+            "Type of numpy_arr ===> <class 'numpy.ndarray'>\n",
+            "---------------\n",
+            "pytorch_tensor : tensor([1., 2., 3.])\n",
+            "Type of pytorch_tensor ===> <class 'torch.Tensor'>\n"
+          ]
+        }
+      ],
+      "source": [
+        "# To do\n",
+        "\n",
+        "sample_1 = torch.tensor([1. , 2. , 3.])\n",
+        "numpy_arr = sample_1.numpy()\n",
+        "pytorch_tensor = torch.from_numpy(numpy_arr)\n",
+        "\n",
+        "print(f\"Sample 1 : {sample_1}\")\n",
+        "print(\"---------------\")\n",
+        "print(f\"numpy_arr : {numpy_arr}\")\n",
+        "print(f\"Type of numpy_arr ===> {type(numpy_arr)}\")\n",
+        "print(\"---------------\")\n",
+        "print(f\"pytorch_tensor : {pytorch_tensor}\")\n",
+        "print(f\"Type of pytorch_tensor ===> {type(pytorch_tensor)}\")\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.7/AI-DS_Nexus__A0_7_Javid_Mohammadi_a60d6f.ipynb b/a0.1/a0.7/AI-DS_Nexus__A0_7_Javid_Mohammadi_a60d6f.ipynb
new file mode 100644
index 0000000..d2422d6
--- /dev/null
+++ b/a0.1/a0.7/AI-DS_Nexus__A0_7_Javid_Mohammadi_a60d6f.ipynb
@@ -0,0 +1,373 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🔥 Assignment: Exploring PyTorch\n"
+      ],
+      "metadata": {
+        "id": "dbICYwLGo5Av"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using PyTorch, focusing on tensors, autograd, and a small training loop.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "b8h5DExTo6K5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install PyTorch and check its version.\n",
+        "\n",
+        "* Create a 2D tensor and print it."
+      ],
+      "metadata": {
+        "id": "euolCT3oo8dR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 82,
+      "metadata": {
+        "id": "9AADPzP6o1iY",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a3255ba7-1011-4e64-b67d-59332e5e0c2a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "PyTorch version: 2.6.0+cu124\n",
+            "Tensor x:\n",
+            " tensor([[1., 2.],\n",
+            "        [3., 4.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "\n",
+        "print(\"PyTorch version:\", torch.__version__)\n",
+        "x = torch.tensor([[1., 2.], [3., 4.]])\n",
+        "print(\"Tensor x:\\n\", x)\n",
+        "#x.dtype, x.size()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Create another tensor y of the same shape with values [[5, 6], [7, 8]].\n",
+        "y = torch.arange(5, 9, dtype = torch.float32).view(2, 2)\n",
+        "print(\"Tensor y:\\n\", y)"
+      ],
+      "metadata": {
+        "id": "T5mCQMYFpBJm",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "dc300d16-d0e1-4467-e5f0-fcb222efb98e"
+      },
+      "execution_count": 83,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Tensor y:\n",
+            " tensor([[5., 6.],\n",
+            "        [7., 8.]])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2️⃣ Tensor Operations 🧮\n",
+        "**Task:** Perform addition and matrix multiplication."
+      ],
+      "metadata": {
+        "id": "7yRBms7npEUe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "z = x + y\n",
+        "print(\"Addition:\\n\", z)\n",
+        "\n",
+        "mat_mul = x @ y\n",
+        "print(\"Matrix Multiplication:\\n\", mat_mul)\n"
+      ],
+      "metadata": {
+        "id": "nGRNryC4pDv2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7edfa743-f0c9-43f9-8e97-2de0994f35da"
+      },
+      "execution_count": 84,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Addition:\n",
+            " tensor([[ 6.,  8.],\n",
+            "        [10., 12.]])\n",
+            "Matrix Multiplication:\n",
+            " tensor([[19., 22.],\n",
+            "        [43., 50.]])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Perform element-wise multiplication (x * y) and print the result.\n",
+        "print(\"Element-wise multiplication:\\n\", x * y)\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "tf64nP2dpIod",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ceb8642a-5d6b-4e32-8b65-5d072fb19ab4"
+      },
+      "execution_count": 85,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Element-wise multiplication:\n",
+            " tensor([[ 5., 12.],\n",
+            "        [21., 32.]])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3️⃣ Autograd and Gradients ⚙️\n",
+        "**Task:** Enable gradient tracking and compute derivatives."
+      ],
+      "metadata": {
+        "id": "h3dABundpMYU"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "a = torch.tensor(3.0, requires_grad=True)\n",
+        "b = (a ** 2) + 2 * a + 1\n",
+        "b.backward()\n",
+        "print(\"Gradient of b wrt a:\", a.grad)\n"
+      ],
+      "metadata": {
+        "id": "igtPKY90pLGt",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ad9379e9-a75d-4a79-fae6-87ba427eef0b"
+      },
+      "execution_count": 86,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Gradient of b wrt a: tensor(8.)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor p with value 2.0 (requires_grad=True) and compute the gradient of q = p^3 + 4p.\n",
+        "p = torch.tensor(2.0, requires_grad=True)\n",
+        "q = p**3 + 4 * p\n",
+        "q.backward() #backpropagation\n",
+        "print(\"Gradient of q with respect to p:\", p.grad)\n"
+      ],
+      "metadata": {
+        "id": "bLceqHHvpOwt",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ff35b248-27d3-4e2c-e023-e914b17501f6"
+      },
+      "execution_count": 87,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Gradient of q with respect to p: tensor(16.)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (2, 3) and find its max and min."
+      ],
+      "metadata": {
+        "id": "jytRN17EpVbM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rand_tensor = torch.rand((2, 3))\n",
+        "print(\"Random Tensor:\\n\", rand_tensor)\n",
+        "print(\"Max:\", torch.max(rand_tensor))\n",
+        "print(\"Min:\", torch.min(rand_tensor))\n"
+      ],
+      "metadata": {
+        "id": "fb7fv-E_pS6e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "77c97ec7-c762-497c-d531-4bdda8e67c6b"
+      },
+      "execution_count": 88,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Random Tensor:\n",
+            " tensor([[0.6563, 0.8597, 0.1073],\n",
+            "        [0.2714, 0.3323, 0.0801]])\n",
+            "Max: tensor(0.8597)\n",
+            "Min: tensor(0.0801)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5️⃣ Mini Training Loop 🤖\n",
+        "**Task:** Train a simple linear model y = wx + b using gradient descent."
+      ],
+      "metadata": {
+        "id": "bhSClXespjvT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Data\n",
+        "x_train = torch.tensor([[1.0], [2.0], [3.0]])\n",
+        "y_train = torch.tensor([[2.0], [4.0], [6.0]])\n",
+        "\n",
+        "# Model\n",
+        "w = torch.randn(1, requires_grad=True)\n",
+        "b = torch.randn(1, requires_grad=True)\n",
+        "\n",
+        "# Training\n",
+        "learning_rate = 0.01\n",
+        "for epoch in range(100):\n",
+        "    y_pred = w * x_train + b\n",
+        "    loss = torch.mean((y_pred - y_train) ** 2)\n",
+        "    loss.backward()\n",
+        "\n",
+        "    # Update\n",
+        "    with torch.no_grad():\n",
+        "        w -= learning_rate * w.grad\n",
+        "        b -= learning_rate * b.grad\n",
+        "        w.grad.zero_()\n",
+        "        b.grad.zero_()\n",
+        "\n",
+        "print(\"Trained weight:\", w.item())\n",
+        "print(\"Trained bias:\", b.item())\n"
+      ],
+      "metadata": {
+        "id": "o7s2KMJVpZ5M",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "85fc1e55-272c-4151-cb6f-ce611228b96e"
+      },
+      "execution_count": 89,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Trained weight: 1.8261533975601196\n",
+            "Trained bias: 0.39514806866645813\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 6️⃣ Bonus ⚡\n",
+        "* Convert a PyTorch tensor to a NumPy array.\n",
+        "\n",
+        "* Convert it back to a PyTorch tensor."
+      ],
+      "metadata": {
+        "id": "rGhFqtrVpv8_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "tensor = torch.tensor([[2, 3, 4, 7], [1, 0, 1, 0]])\n",
+        "numpy_array = tensor.numpy()\n",
+        "\n",
+        "tensor_new = torch.from_numpy(numpy_array)\n",
+        "\n",
+        "assert torch.equal(tensor, tensor_new)"
+      ],
+      "metadata": {
+        "id": "AF4t92eDpq0j"
+      },
+      "execution_count": 90,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.7/AI-DS_Nexus__A0_7_Project__RezaShokr.ipynb b/a0.1/a0.7/AI-DS_Nexus__A0_7_Project__RezaShokr.ipynb
new file mode 100644
index 0000000..5f89dc3
--- /dev/null
+++ b/a0.1/a0.7/AI-DS_Nexus__A0_7_Project__RezaShokr.ipynb
@@ -0,0 +1,377 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dbICYwLGo5Av"
+      },
+      "source": [
+        "# 🔥 Assignment: Exploring PyTorch\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b8h5DExTo6K5"
+      },
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using PyTorch, focusing on tensors, autograd, and a small training loop.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "euolCT3oo8dR"
+      },
+      "source": [
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install PyTorch and check its version.\n",
+        "\n",
+        "* Create a 2D tensor and print it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "9AADPzP6o1iY"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2.7.1+cu128\n",
+            "x : tensor([[1., 2.],\n",
+            "        [3., 4.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "\n",
+        "print(torch.__version__)\n",
+        "x = torch.tensor([[1. , 2.] , [3. , 4.]])\n",
+        "print(f\"x : {x}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "T5mCQMYFpBJm"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "y : tensor([[5., 6.],\n",
+            "        [7., 8.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create another tensor y of the same shape with values [[5, 6], [7, 8]].\n",
+        "\n",
+        "y = torch.tensor([[5. , 6.] , [7. , 8.]])\n",
+        "print(f\"y : {y}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7yRBms7npEUe"
+      },
+      "source": [
+        "### 2️⃣ Tensor Operations 🧮\n",
+        "**Task:** Perform addition and matrix multiplication."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "nGRNryC4pDv2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Addition:\n",
+            " tensor([[ 6.,  8.],\n",
+            "        [10., 12.]])\n",
+            "Matrix Multiplication:\n",
+            " tensor([[19., 22.],\n",
+            "        [43., 50.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "z = x + y\n",
+        "print(\"Addition:\\n\", z)\n",
+        "\n",
+        "mat_mul = x @ y\n",
+        "print(\"Matrix Multiplication:\\n\", mat_mul)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "tf64nP2dpIod"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "multiplication : tensor([[ 5., 12.],\n",
+            "        [21., 32.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Perform element-wise multiplication (x * y) and print the result.\n",
+        "\n",
+        "multiplication = x * y\n",
+        "print(f\"multiplication : {multiplication}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h3dABundpMYU"
+      },
+      "source": [
+        "### 3️⃣ Autograd and Gradients ⚙️\n",
+        "**Task:** Enable gradient tracking and compute derivatives."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "igtPKY90pLGt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gradient of b wrt a: tensor(8.)\n"
+          ]
+        }
+      ],
+      "source": [
+        "a = torch.tensor(3.0, requires_grad=True)\n",
+        "b = (a ** 2) + 2 * a + 1\n",
+        "b.backward()\n",
+        "print(\"Gradient of b wrt a:\", a.grad)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "bLceqHHvpOwt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gradient of q wrt p: tensor(16.)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor p with value 2.0 (requires_grad=True) and compute the gradient of q = p^3 + 4p.\n",
+        "\n",
+        "p = torch.tensor(2.0 , requires_grad=True)\n",
+        "q = p **3 + 4 * p\n",
+        "q.backward()\n",
+        "print(\"Gradient of q wrt p:\", p.grad)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jytRN17EpVbM"
+      },
+      "source": [
+        "### 4️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (2, 3) and find its max and min."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "fb7fv-E_pS6e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Tensor:\n",
+            " tensor([[0.6975, 0.7858, 0.7772],\n",
+            "        [0.3042, 0.1020, 0.1182]])\n",
+            "Max: tensor(0.7858)\n",
+            "Min: tensor(0.1020)\n"
+          ]
+        }
+      ],
+      "source": [
+        "rand_tensor = torch.rand((2, 3))\n",
+        "print(\"Random Tensor:\\n\", rand_tensor)\n",
+        "print(\"Max:\", torch.max(rand_tensor))\n",
+        "print(\"Min:\", torch.min(rand_tensor))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bhSClXespjvT"
+      },
+      "source": [
+        "### 5️⃣ Mini Training Loop 🤖\n",
+        "**Task:** Train a simple linear model y = wx + b using gradient descent."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "o7s2KMJVpZ5M"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Trained weight: 1.651038408279419\n",
+            "Trained bias: 0.793222963809967\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Data\n",
+        "x_train = torch.tensor([[1.0], [2.0], [3.0]])\n",
+        "y_train = torch.tensor([[2.0], [4.0], [6.0]])\n",
+        "\n",
+        "# Model\n",
+        "w = torch.randn(1, requires_grad=True)\n",
+        "b = torch.zeros(1, requires_grad=True)\n",
+        "\n",
+        "# Training\n",
+        "learning_rate = 0.01\n",
+        "for epoch in range(100):\n",
+        "    y_pred = w * x_train + b\n",
+        "    loss = torch.mean((y_pred - y_train) ** 2)\n",
+        "    loss.backward()\n",
+        "\n",
+        "    # Update\n",
+        "    with torch.no_grad():\n",
+        "        w -= learning_rate * w.grad\n",
+        "        b -= learning_rate * b.grad\n",
+        "        w.grad.zero_()\n",
+        "        b.grad.zero_()\n",
+        "\n",
+        "print(\"Trained weight:\", w.item())\n",
+        "print(\"Trained bias:\", b.item())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rGhFqtrVpv8_"
+      },
+      "source": [
+        "### 6️⃣ Bonus ⚡\n",
+        "* Convert a PyTorch tensor to a NumPy array.\n",
+        "\n",
+        "* Convert it back to a PyTorch tensor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "AF4t92eDpq0j"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sample 1 : tensor([1., 2., 3.])\n",
+            "---------------\n",
+            "numpy_arr : [1. 2. 3.]\n",
+            "Type of numpy_arr ===> <class 'numpy.ndarray'>\n",
+            "---------------\n",
+            "pytorch_tensor : tensor([1., 2., 3.])\n",
+            "Type of pytorch_tensor ===> <class 'torch.Tensor'>\n"
+          ]
+        }
+      ],
+      "source": [
+        "# To do\n",
+        "\n",
+        "sample_1 = torch.tensor([1. , 2. , 3.])\n",
+        "numpy_arr = sample_1.numpy()\n",
+        "pytorch_tensor = torch.from_numpy(numpy_arr)\n",
+        "\n",
+        "print(f\"Sample 1 : {sample_1}\")\n",
+        "print(\"---------------\")\n",
+        "print(f\"numpy_arr : {numpy_arr}\")\n",
+        "print(f\"Type of numpy_arr ===> {type(numpy_arr)}\")\n",
+        "print(\"---------------\")\n",
+        "print(f\"pytorch_tensor : {pytorch_tensor}\")\n",
+        "print(f\"Type of pytorch_tensor ===> {type(pytorch_tensor)}\")\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.7/AI-DS_Nexus__A0_7_Project_roohi_268383.ipynb b/a0.1/a0.7/AI-DS_Nexus__A0_7_Project_roohi_268383.ipynb
new file mode 100644
index 0000000..5f89dc3
--- /dev/null
+++ b/a0.1/a0.7/AI-DS_Nexus__A0_7_Project_roohi_268383.ipynb
@@ -0,0 +1,377 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dbICYwLGo5Av"
+      },
+      "source": [
+        "# 🔥 Assignment: Exploring PyTorch\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b8h5DExTo6K5"
+      },
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using PyTorch, focusing on tensors, autograd, and a small training loop.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "euolCT3oo8dR"
+      },
+      "source": [
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install PyTorch and check its version.\n",
+        "\n",
+        "* Create a 2D tensor and print it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "9AADPzP6o1iY"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2.7.1+cu128\n",
+            "x : tensor([[1., 2.],\n",
+            "        [3., 4.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "\n",
+        "print(torch.__version__)\n",
+        "x = torch.tensor([[1. , 2.] , [3. , 4.]])\n",
+        "print(f\"x : {x}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "T5mCQMYFpBJm"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "y : tensor([[5., 6.],\n",
+            "        [7., 8.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create another tensor y of the same shape with values [[5, 6], [7, 8]].\n",
+        "\n",
+        "y = torch.tensor([[5. , 6.] , [7. , 8.]])\n",
+        "print(f\"y : {y}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7yRBms7npEUe"
+      },
+      "source": [
+        "### 2️⃣ Tensor Operations 🧮\n",
+        "**Task:** Perform addition and matrix multiplication."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "nGRNryC4pDv2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Addition:\n",
+            " tensor([[ 6.,  8.],\n",
+            "        [10., 12.]])\n",
+            "Matrix Multiplication:\n",
+            " tensor([[19., 22.],\n",
+            "        [43., 50.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "z = x + y\n",
+        "print(\"Addition:\\n\", z)\n",
+        "\n",
+        "mat_mul = x @ y\n",
+        "print(\"Matrix Multiplication:\\n\", mat_mul)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "tf64nP2dpIod"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "multiplication : tensor([[ 5., 12.],\n",
+            "        [21., 32.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Perform element-wise multiplication (x * y) and print the result.\n",
+        "\n",
+        "multiplication = x * y\n",
+        "print(f\"multiplication : {multiplication}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h3dABundpMYU"
+      },
+      "source": [
+        "### 3️⃣ Autograd and Gradients ⚙️\n",
+        "**Task:** Enable gradient tracking and compute derivatives."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "igtPKY90pLGt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gradient of b wrt a: tensor(8.)\n"
+          ]
+        }
+      ],
+      "source": [
+        "a = torch.tensor(3.0, requires_grad=True)\n",
+        "b = (a ** 2) + 2 * a + 1\n",
+        "b.backward()\n",
+        "print(\"Gradient of b wrt a:\", a.grad)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "bLceqHHvpOwt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gradient of q wrt p: tensor(16.)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor p with value 2.0 (requires_grad=True) and compute the gradient of q = p^3 + 4p.\n",
+        "\n",
+        "p = torch.tensor(2.0 , requires_grad=True)\n",
+        "q = p **3 + 4 * p\n",
+        "q.backward()\n",
+        "print(\"Gradient of q wrt p:\", p.grad)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jytRN17EpVbM"
+      },
+      "source": [
+        "### 4️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (2, 3) and find its max and min."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "fb7fv-E_pS6e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Tensor:\n",
+            " tensor([[0.6975, 0.7858, 0.7772],\n",
+            "        [0.3042, 0.1020, 0.1182]])\n",
+            "Max: tensor(0.7858)\n",
+            "Min: tensor(0.1020)\n"
+          ]
+        }
+      ],
+      "source": [
+        "rand_tensor = torch.rand((2, 3))\n",
+        "print(\"Random Tensor:\\n\", rand_tensor)\n",
+        "print(\"Max:\", torch.max(rand_tensor))\n",
+        "print(\"Min:\", torch.min(rand_tensor))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bhSClXespjvT"
+      },
+      "source": [
+        "### 5️⃣ Mini Training Loop 🤖\n",
+        "**Task:** Train a simple linear model y = wx + b using gradient descent."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "o7s2KMJVpZ5M"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Trained weight: 1.651038408279419\n",
+            "Trained bias: 0.793222963809967\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Data\n",
+        "x_train = torch.tensor([[1.0], [2.0], [3.0]])\n",
+        "y_train = torch.tensor([[2.0], [4.0], [6.0]])\n",
+        "\n",
+        "# Model\n",
+        "w = torch.randn(1, requires_grad=True)\n",
+        "b = torch.zeros(1, requires_grad=True)\n",
+        "\n",
+        "# Training\n",
+        "learning_rate = 0.01\n",
+        "for epoch in range(100):\n",
+        "    y_pred = w * x_train + b\n",
+        "    loss = torch.mean((y_pred - y_train) ** 2)\n",
+        "    loss.backward()\n",
+        "\n",
+        "    # Update\n",
+        "    with torch.no_grad():\n",
+        "        w -= learning_rate * w.grad\n",
+        "        b -= learning_rate * b.grad\n",
+        "        w.grad.zero_()\n",
+        "        b.grad.zero_()\n",
+        "\n",
+        "print(\"Trained weight:\", w.item())\n",
+        "print(\"Trained bias:\", b.item())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rGhFqtrVpv8_"
+      },
+      "source": [
+        "### 6️⃣ Bonus ⚡\n",
+        "* Convert a PyTorch tensor to a NumPy array.\n",
+        "\n",
+        "* Convert it back to a PyTorch tensor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "AF4t92eDpq0j"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sample 1 : tensor([1., 2., 3.])\n",
+            "---------------\n",
+            "numpy_arr : [1. 2. 3.]\n",
+            "Type of numpy_arr ===> <class 'numpy.ndarray'>\n",
+            "---------------\n",
+            "pytorch_tensor : tensor([1., 2., 3.])\n",
+            "Type of pytorch_tensor ===> <class 'torch.Tensor'>\n"
+          ]
+        }
+      ],
+      "source": [
+        "# To do\n",
+        "\n",
+        "sample_1 = torch.tensor([1. , 2. , 3.])\n",
+        "numpy_arr = sample_1.numpy()\n",
+        "pytorch_tensor = torch.from_numpy(numpy_arr)\n",
+        "\n",
+        "print(f\"Sample 1 : {sample_1}\")\n",
+        "print(\"---------------\")\n",
+        "print(f\"numpy_arr : {numpy_arr}\")\n",
+        "print(f\"Type of numpy_arr ===> {type(numpy_arr)}\")\n",
+        "print(\"---------------\")\n",
+        "print(f\"pytorch_tensor : {pytorch_tensor}\")\n",
+        "print(f\"Type of pytorch_tensor ===> {type(pytorch_tensor)}\")\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.7/AI-DS_Nexus__A0_7__RoohollaAlikhani.ipynb b/a0.1/a0.7/AI-DS_Nexus__A0_7__RoohollaAlikhani.ipynb
new file mode 100644
index 0000000..5f89dc3
--- /dev/null
+++ b/a0.1/a0.7/AI-DS_Nexus__A0_7__RoohollaAlikhani.ipynb
@@ -0,0 +1,377 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dbICYwLGo5Av"
+      },
+      "source": [
+        "# 🔥 Assignment: Exploring PyTorch\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b8h5DExTo6K5"
+      },
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using PyTorch, focusing on tensors, autograd, and a small training loop.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "euolCT3oo8dR"
+      },
+      "source": [
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install PyTorch and check its version.\n",
+        "\n",
+        "* Create a 2D tensor and print it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "9AADPzP6o1iY"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "2.7.1+cu128\n",
+            "x : tensor([[1., 2.],\n",
+            "        [3., 4.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "\n",
+        "print(torch.__version__)\n",
+        "x = torch.tensor([[1. , 2.] , [3. , 4.]])\n",
+        "print(f\"x : {x}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "T5mCQMYFpBJm"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "y : tensor([[5., 6.],\n",
+            "        [7., 8.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create another tensor y of the same shape with values [[5, 6], [7, 8]].\n",
+        "\n",
+        "y = torch.tensor([[5. , 6.] , [7. , 8.]])\n",
+        "print(f\"y : {y}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7yRBms7npEUe"
+      },
+      "source": [
+        "### 2️⃣ Tensor Operations 🧮\n",
+        "**Task:** Perform addition and matrix multiplication."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "nGRNryC4pDv2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Addition:\n",
+            " tensor([[ 6.,  8.],\n",
+            "        [10., 12.]])\n",
+            "Matrix Multiplication:\n",
+            " tensor([[19., 22.],\n",
+            "        [43., 50.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "z = x + y\n",
+        "print(\"Addition:\\n\", z)\n",
+        "\n",
+        "mat_mul = x @ y\n",
+        "print(\"Matrix Multiplication:\\n\", mat_mul)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "tf64nP2dpIod"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "multiplication : tensor([[ 5., 12.],\n",
+            "        [21., 32.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Perform element-wise multiplication (x * y) and print the result.\n",
+        "\n",
+        "multiplication = x * y\n",
+        "print(f\"multiplication : {multiplication}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h3dABundpMYU"
+      },
+      "source": [
+        "### 3️⃣ Autograd and Gradients ⚙️\n",
+        "**Task:** Enable gradient tracking and compute derivatives."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "igtPKY90pLGt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gradient of b wrt a: tensor(8.)\n"
+          ]
+        }
+      ],
+      "source": [
+        "a = torch.tensor(3.0, requires_grad=True)\n",
+        "b = (a ** 2) + 2 * a + 1\n",
+        "b.backward()\n",
+        "print(\"Gradient of b wrt a:\", a.grad)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "bLceqHHvpOwt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gradient of q wrt p: tensor(16.)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor p with value 2.0 (requires_grad=True) and compute the gradient of q = p^3 + 4p.\n",
+        "\n",
+        "p = torch.tensor(2.0 , requires_grad=True)\n",
+        "q = p **3 + 4 * p\n",
+        "q.backward()\n",
+        "print(\"Gradient of q wrt p:\", p.grad)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jytRN17EpVbM"
+      },
+      "source": [
+        "### 4️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (2, 3) and find its max and min."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "fb7fv-E_pS6e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Tensor:\n",
+            " tensor([[0.6975, 0.7858, 0.7772],\n",
+            "        [0.3042, 0.1020, 0.1182]])\n",
+            "Max: tensor(0.7858)\n",
+            "Min: tensor(0.1020)\n"
+          ]
+        }
+      ],
+      "source": [
+        "rand_tensor = torch.rand((2, 3))\n",
+        "print(\"Random Tensor:\\n\", rand_tensor)\n",
+        "print(\"Max:\", torch.max(rand_tensor))\n",
+        "print(\"Min:\", torch.min(rand_tensor))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bhSClXespjvT"
+      },
+      "source": [
+        "### 5️⃣ Mini Training Loop 🤖\n",
+        "**Task:** Train a simple linear model y = wx + b using gradient descent."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "o7s2KMJVpZ5M"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Trained weight: 1.651038408279419\n",
+            "Trained bias: 0.793222963809967\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Data\n",
+        "x_train = torch.tensor([[1.0], [2.0], [3.0]])\n",
+        "y_train = torch.tensor([[2.0], [4.0], [6.0]])\n",
+        "\n",
+        "# Model\n",
+        "w = torch.randn(1, requires_grad=True)\n",
+        "b = torch.zeros(1, requires_grad=True)\n",
+        "\n",
+        "# Training\n",
+        "learning_rate = 0.01\n",
+        "for epoch in range(100):\n",
+        "    y_pred = w * x_train + b\n",
+        "    loss = torch.mean((y_pred - y_train) ** 2)\n",
+        "    loss.backward()\n",
+        "\n",
+        "    # Update\n",
+        "    with torch.no_grad():\n",
+        "        w -= learning_rate * w.grad\n",
+        "        b -= learning_rate * b.grad\n",
+        "        w.grad.zero_()\n",
+        "        b.grad.zero_()\n",
+        "\n",
+        "print(\"Trained weight:\", w.item())\n",
+        "print(\"Trained bias:\", b.item())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rGhFqtrVpv8_"
+      },
+      "source": [
+        "### 6️⃣ Bonus ⚡\n",
+        "* Convert a PyTorch tensor to a NumPy array.\n",
+        "\n",
+        "* Convert it back to a PyTorch tensor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "AF4t92eDpq0j"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sample 1 : tensor([1., 2., 3.])\n",
+            "---------------\n",
+            "numpy_arr : [1. 2. 3.]\n",
+            "Type of numpy_arr ===> <class 'numpy.ndarray'>\n",
+            "---------------\n",
+            "pytorch_tensor : tensor([1., 2., 3.])\n",
+            "Type of pytorch_tensor ===> <class 'torch.Tensor'>\n"
+          ]
+        }
+      ],
+      "source": [
+        "# To do\n",
+        "\n",
+        "sample_1 = torch.tensor([1. , 2. , 3.])\n",
+        "numpy_arr = sample_1.numpy()\n",
+        "pytorch_tensor = torch.from_numpy(numpy_arr)\n",
+        "\n",
+        "print(f\"Sample 1 : {sample_1}\")\n",
+        "print(\"---------------\")\n",
+        "print(f\"numpy_arr : {numpy_arr}\")\n",
+        "print(f\"Type of numpy_arr ===> {type(numpy_arr)}\")\n",
+        "print(\"---------------\")\n",
+        "print(f\"pytorch_tensor : {pytorch_tensor}\")\n",
+        "print(f\"Type of pytorch_tensor ===> {type(pytorch_tensor)}\")\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.7/AI-DS_Nexus__A0_7__aminran.ipynb b/a0.1/a0.7/AI-DS_Nexus__A0_7__aminran.ipynb
new file mode 100644
index 0000000..4b7820d
--- /dev/null
+++ b/a0.1/a0.7/AI-DS_Nexus__A0_7__aminran.ipynb
@@ -0,0 +1,377 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dbICYwLGo5Av"
+      },
+      "source": [
+        "# 🔥 Assignment: Exploring PyTorch\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b8h5DExTo6K5"
+      },
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using PyTorch, focusing on tensors, autograd, and a small training loop.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "euolCT3oo8dR"
+      },
+      "source": [
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install PyTorch and check its version.\n",
+        "\n",
+        "* Create a 2D tensor and print it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9AADPzP6o1iY",
+        "outputId": "ee45715f-ee3e-4b76-b4f9-e1daebce0a16"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "PyTorch version: 2.6.0+cu124\n",
+            "Tensor x:\n",
+            " tensor([[1., 2.],\n",
+            "        [3., 4.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "\n",
+        "print(\"PyTorch version:\", torch.__version__)\n",
+        "x = torch.tensor([[1., 2.], [3., 4.]])\n",
+        "print(\"Tensor x:\\n\", x)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "T5mCQMYFpBJm"
+      },
+      "outputs": [],
+      "source": [
+        "# Your turn:\n",
+        "# Create another tensor y of the same shape with values [[5, 6], [7, 8]].\n",
+        "y=torch.tensor([[5, 6], [7, 8]])\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7yRBms7npEUe"
+      },
+      "source": [
+        "### 2️⃣ Tensor Operations 🧮\n",
+        "**Task:** Perform addition and matrix multiplication."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 245
+        },
+        "id": "nGRNryC4pDv2",
+        "outputId": "d248a8fe-20b4-4ed6-f846-611fbbd6ffc1"
+      },
+      "outputs": [],
+      "source": [
+        "z = x + y\n",
+        "print(\"Addition:\\n\", z)\n",
+        "\n",
+        "# Convert y to float to match x's dtype for matrix multiplication\n",
+        "y_float = y.float()\n",
+        "mat_mul = x @ y_float\n",
+        "print(\"Matrix Multiplication:\\n\", mat_mul)\n",
+        "\n",
+        "# Perform element-wise multiplication\n",
+        "element_wise_mul = x * y_float\n",
+        "print(\"Element-wise Multiplication:\\n\", element_wise_mul)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tf64nP2dpIod",
+        "outputId": "668de396-ee58-4dfa-abcb-eb8ce7a92bb7"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Element-wise Multiplication:\n",
+            " tensor([[ 5., 12.],\n",
+            "        [21., 32.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Perform element-wise multiplication (x * y) and print the result.\n",
+        "result=x*y\n",
+        "print(\"Element-wise Multiplication:\\n\", result)\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h3dABundpMYU"
+      },
+      "source": [
+        "### 3️⃣ Autograd and Gradients ⚙️\n",
+        "**Task:** Enable gradient tracking and compute derivatives."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "igtPKY90pLGt",
+        "outputId": "c7e2881c-bfcc-4372-a7e9-b666f2547088"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gradient of b wrt a: tensor(8.)\n"
+          ]
+        }
+      ],
+      "source": [
+        "a = torch.tensor(3.0, requires_grad=True)\n",
+        "b = (a ** 2) + 2 * a + 1\n",
+        "b.backward()\n",
+        "print(\"Gradient of b wrt a:\", a.grad)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bLceqHHvpOwt",
+        "outputId": "63955438-0a4c-4942-ec8f-9b54d1c6e34e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "tensor(16., grad_fn=<AddBackward0>)"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor p with value 2.0 (requires_grad=True) and compute the gradient of q = p^3 + 4p.\n",
+        "p=torch.tensor(2.0, requires_grad=True)\n",
+        "q=p**3+4*p\n",
+        "q.backward()\n",
+        "print(\"Gradient of q wrt p:\", p.grad)\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jytRN17EpVbM"
+      },
+      "source": [
+        "### 4️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (2, 3) and find its max and min."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fb7fv-E_pS6e",
+        "outputId": "bccdb483-95c9-45d7-8552-f4f7e98d12cb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Tensor:\n",
+            " tensor([[0.4823, 0.5779, 0.3458],\n",
+            "        [0.5859, 0.4225, 0.0038]])\n",
+            "Max: tensor(0.5859)\n",
+            "Min: tensor(0.0038)\n"
+          ]
+        }
+      ],
+      "source": [
+        "rand_tensor = torch.rand((2, 3))\n",
+        "print(\"Random Tensor:\\n\", rand_tensor)\n",
+        "print(\"Max:\", torch.max(rand_tensor))\n",
+        "print(\"Min:\", torch.min(rand_tensor))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bhSClXespjvT"
+      },
+      "source": [
+        "### 5️⃣ Mini Training Loop 🤖\n",
+        "**Task:** Train a simple linear model y = wx + b using gradient descent."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "o7s2KMJVpZ5M",
+        "outputId": "9b34e6a6-da26-4142-eed3-eb53305eb68b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Trained weight: 1.5857030153274536\n",
+            "Trained bias: 0.9417575597763062\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Data\n",
+        "x_train = torch.tensor([[1.0], [2.0], [3.0]])\n",
+        "y_train = torch.tensor([[2.0], [4.0], [6.0]])\n",
+        "\n",
+        "# Model\n",
+        "w = torch.randn(1, requires_grad=True)\n",
+        "b = torch.randn(1, requires_grad=True)\n",
+        "\n",
+        "# Training\n",
+        "learning_rate = 0.01\n",
+        "for epoch in range(100):\n",
+        "    y_pred = w * x_train + b\n",
+        "    loss = torch.mean((y_pred - y_train) ** 2)\n",
+        "    loss.backward()\n",
+        "\n",
+        "    # Update\n",
+        "    with torch.no_grad():\n",
+        "        w -= learning_rate * w.grad\n",
+        "        b -= learning_rate * b.grad\n",
+        "        w.grad.zero_()\n",
+        "        b.grad.zero_()\n",
+        "\n",
+        "print(\"Trained weight:\", w.item())\n",
+        "print(\"Trained bias:\", b.item())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rGhFqtrVpv8_"
+      },
+      "source": [
+        "### 6️⃣ Bonus ⚡\n",
+        "* Convert a PyTorch tensor to a NumPy array.\n",
+        "\n",
+        "* Convert it back to a PyTorch tensor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AF4t92eDpq0j",
+        "outputId": "2e9a8472-6457-4a2d-9395-6c179133276a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[[1. 2.]\n",
+            " [3. 4.]]\n",
+            "tensor([[1., 2.],\n",
+            "        [3., 4.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# To do\n",
+        "x=torch.tensor([[1., 2.], [3., 4.]])\n",
+        "y=x.numpy()\n",
+        "print(y)\n",
+        "z=torch.from_numpy(y)\n",
+        "print(z)\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.7/AI-DS_Nexus__A0_7__rsayyareh.ipynb b/a0.1/a0.7/AI-DS_Nexus__A0_7__rsayyareh.ipynb
new file mode 100644
index 0000000..05c362f
--- /dev/null
+++ b/a0.1/a0.7/AI-DS_Nexus__A0_7__rsayyareh.ipynb
@@ -0,0 +1,388 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dbICYwLGo5Av"
+      },
+      "source": [
+        "# 🔥 Assignment: Exploring PyTorch\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b8h5DExTo6K5"
+      },
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using PyTorch, focusing on tensors, autograd, and a small training loop.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "euolCT3oo8dR"
+      },
+      "source": [
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install PyTorch and check its version.\n",
+        "\n",
+        "* Create a 2D tensor and print it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "9AADPzP6o1iY"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "PyTorch version: 2.8.0+cpu\n",
+            "Tensor x:\n",
+            " tensor([[1., 2.],\n",
+            "        [3., 4.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "\n",
+        "print(\"PyTorch version:\", torch.__version__)\n",
+        "x = torch.tensor([[1., 2.], [3., 4.]])\n",
+        "print(\"Tensor x:\\n\", x)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "T5mCQMYFpBJm"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tensor y:\n",
+            " tensor([[5., 6.],\n",
+            "        [7., 8.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create another tensor y of the same shape with values [[5, 6], [7, 8]].\n",
+        "y = torch.tensor([[5., 6.], [7., 8.]])\n",
+        "print(\"Tensor y:\\n\", y)\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7yRBms7npEUe"
+      },
+      "source": [
+        "### 2️⃣ Tensor Operations 🧮\n",
+        "**Task:** Perform addition and matrix multiplication."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "nGRNryC4pDv2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Addition:\n",
+            " tensor([[ 6.,  8.],\n",
+            "        [10., 12.]])\n",
+            "Matrix Multiplication:\n",
+            " tensor([[19., 22.],\n",
+            "        [43., 50.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "z = x + y\n",
+        "print(\"Addition:\\n\", z)\n",
+        "\n",
+        "mat_mul = x @ y\n",
+        "print(\"Matrix Multiplication:\\n\", mat_mul)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "tf64nP2dpIod"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "tensor([[ 5., 12.],\n",
+            "        [21., 32.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Perform element-wise multiplication (x * y) and print the result.\n",
+        "dot_mul = x * y\n",
+        "print(dot_mul)\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h3dABundpMYU"
+      },
+      "source": [
+        "### 3️⃣ Autograd and Gradients ⚙️\n",
+        "**Task:** Enable gradient tracking and compute derivatives."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "igtPKY90pLGt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "tensor(3., requires_grad=True)\n",
+            "Gradient of b wrt a: tensor(8.)\n"
+          ]
+        }
+      ],
+      "source": [
+        "a = torch.tensor(3.0, requires_grad=True)\n",
+        "b = (a ** 2) + 2 * a + 1\n",
+        "b.backward()\n",
+        "print(\"Gradient of b wrt a:\", a.grad)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bLceqHHvpOwt"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Gradient of q at p: tensor(16.)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor p with value 2.0 (requires_grad=True) and compute the gradient of q = p^3 + 4p.\n",
+        "p = torch.tensor(2.0, requires_grad=True)\n",
+        "q = p ** 3 + 4 * p\n",
+        "q.backward()\n",
+        "print(\"Gradient of q at p:\", p.grad)\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jytRN17EpVbM"
+      },
+      "source": [
+        "### 4️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (2, 3) and find its max and min."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "fb7fv-E_pS6e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Tensor:\n",
+            " tensor([[0.9230, 0.3572, 0.7282],\n",
+            "        [0.3952, 0.5441, 0.2520]])\n",
+            "Max: tensor(0.9230)\n",
+            "Min: tensor(0.2520)\n"
+          ]
+        }
+      ],
+      "source": [
+        "rand_tensor = torch.rand((2, 3))\n",
+        "print(\"Random Tensor:\\n\", rand_tensor)\n",
+        "print(\"Max:\", torch.max(rand_tensor))\n",
+        "print(\"Min:\", torch.min(rand_tensor))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bhSClXespjvT"
+      },
+      "source": [
+        "### 5️⃣ Mini Training Loop 🤖\n",
+        "**Task:** Train a simple linear model y = wx + b using gradient descent."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "o7s2KMJVpZ5M"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "tensor([[1.],\n",
+            "        [2.],\n",
+            "        [3.]])\n",
+            "tensor([[2.],\n",
+            "        [4.],\n",
+            "        [6.]])\n",
+            "tensor([-0.4748], requires_grad=True)\n",
+            "tensor(3., requires_grad=True)\n",
+            "tensor(107.8136, grad_fn=<SumBackward0>)\n",
+            "tensor(48.0209, grad_fn=<SumBackward0>)\n",
+            "Trained weight: 0.8197987079620361\n",
+            "Trained bias: -0.13070182502269745\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Data\n",
+        "x_train = torch.tensor([[1.0], [2.0], [3.0]])\n",
+        "y_train = torch.tensor([[2.0], [4.0], [6.0]])\n",
+        "\n",
+        "# Model\n",
+        "w = torch.randn(1, requires_grad=True)\n",
+        "b = torch.randn(1, requires_grad=True)\n",
+        "\n",
+        "# Training\n",
+        "learning_rate = 0.01\n",
+        "for epoch in range(2):\n",
+        "    y_pred = w * x_train + b\n",
+        "    loss = torch.sum((y_pred - y_train) ** 2)\n",
+        "    print(loss)\n",
+        "    loss.backward()\n",
+        "\n",
+        "    # Update\n",
+        "    with torch.no_grad():\n",
+        "        w -= learning_rate * w.grad\n",
+        "        b -= learning_rate * b.grad\n",
+        "        w.grad.zero_()\n",
+        "        b.grad.zero_()\n",
+        "\n",
+        "print(\"Trained weight:\", w.item())\n",
+        "print(\"Trained bias:\", b.item())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rGhFqtrVpv8_"
+      },
+      "source": [
+        "### 6️⃣ Bonus ⚡\n",
+        "* Convert a PyTorch tensor to a NumPy array.\n",
+        "\n",
+        "* Convert it back to a PyTorch tensor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "id": "AF4t92eDpq0j"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[[1.]\n",
+            " [2.]\n",
+            " [3.]]\n",
+            "tensor([[1.],\n",
+            "        [2.],\n",
+            "        [3.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "# To do\n",
+        "np_arr = x_train.numpy()\n",
+        "ts_arr = torch.from_numpy(np_arr)\n",
+        "\n",
+        "print(np_arr)\n",
+        "print(ts_arr)\n",
+        "\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.7/AI_DS_Nexus__A0_7__MrAshki.ipynb.ipynb b/a0.1/a0.7/AI_DS_Nexus__A0_7__MrAshki.ipynb.ipynb
new file mode 100644
index 0000000..3e907df
--- /dev/null
+++ b/a0.1/a0.7/AI_DS_Nexus__A0_7__MrAshki.ipynb.ipynb
@@ -0,0 +1,436 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🔥 Assignment: Exploring PyTorch\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "dbICYwLGo5Av"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using PyTorch, focusing on tensors, autograd, and a small training loop.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "b8h5DExTo6K5"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install PyTorch and check its version.\n",
+        "\n",
+        "* Create a 2D tensor and print it."
+      ],
+      "metadata": {
+        "id": "euolCT3oo8dR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "9AADPzP6o1iY",
+        "outputId": "70162a36-93fb-48bc-c332-971c2d619e42",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "PyTorch version: 2.9.0+cpu\n",
+            "Tensor x:\n",
+            " tensor([[1., 2.],\n",
+            "        [3., 4.]])\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "\n",
+        "print(\"PyTorch version:\", torch.__version__)\n",
+        "x = torch.tensor([[1., 2.], [3., 4.]])\n",
+        "print(\"Tensor x:\\n\", x)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Create another tensor y of the same shape with values [[5, 6], [7, 8]].\n",
+        "\n",
+        "y = torch.tensor([[5., 6.], [7., 8.]])\n",
+        "print(\"tensor y:\\n\", y)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "T5mCQMYFpBJm",
+        "outputId": "eeb5d118-055f-4e51-eead-024df90899c7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor y:\n",
+            " tensor([[5., 6.],\n",
+            "        [7., 8.]])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2️⃣ Tensor Operations 🧮\n",
+        "**Task:** Perform addition and matrix multiplication."
+      ],
+      "metadata": {
+        "id": "7yRBms7npEUe"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "z = x + y\n",
+        "print(\"Addition:\\n\", z)\n",
+        "\n",
+        "mat_mul = x @ y\n",
+        "print(\"Matrix Multiplication:\\n\", mat_mul)\n"
+      ],
+      "metadata": {
+        "id": "nGRNryC4pDv2",
+        "outputId": "d12f0b3e-f9ba-4b3e-ae71-87339e7f0126",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Addition:\n",
+            " tensor([[ 6.,  8.],\n",
+            "        [10., 12.]])\n",
+            "Matrix Multiplication:\n",
+            " tensor([[19., 22.],\n",
+            "        [43., 50.]])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Perform element-wise multiplication (x * y) and print the result.\n",
+        "\n",
+        "\n",
+        "Q = x * y\n",
+        "print(\"element-wise:\\n\" , Q)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "tf64nP2dpIod",
+        "outputId": "4dfd34a5-e852-42ab-df08-409357381bac",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "element-wise:\n",
+            " tensor([[ 5., 12.],\n",
+            "        [21., 32.]])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3️⃣ Autograd and Gradients ⚙️\n",
+        "**Task:** Enable gradient tracking and compute derivatives."
+      ],
+      "metadata": {
+        "id": "h3dABundpMYU"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "a = torch.tensor(3.0, requires_grad=True)\n",
+        "b = (a ** 2) + 2 * a + 1\n",
+        "b.backward()\n",
+        "print(\"Gradient of b wrt a:\", a.grad)\n"
+      ],
+      "metadata": {
+        "id": "igtPKY90pLGt"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor p with value 2.0 (requires_grad=True) and compute the gradient of q = p^3 + 4p.\n",
+        "\n",
+        "p = torch.tensor(2.0, requires_grad=True)\n",
+        "q = p **3 + 4*p\n",
+        "q.backward()\n",
+        "print(\"gradient of Q:\\n\", Q)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "bLceqHHvpOwt",
+        "outputId": "243f092d-e03d-4ef7-91a1-701ef0e39ab7",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "gradient of Q:\n",
+            " tensor([[ 5., 12.],\n",
+            "        [21., 32.]])\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (2, 3) and find its max and min."
+      ],
+      "metadata": {
+        "id": "jytRN17EpVbM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "rand_tensor = torch.rand((2, 3))\n",
+        "print(\"Random Tensor:\\n\", rand_tensor)\n",
+        "print(\"Max:\", torch.max(rand_tensor))\n",
+        "print(\"Min:\", torch.min(rand_tensor))\n"
+      ],
+      "metadata": {
+        "id": "fb7fv-E_pS6e",
+        "outputId": "6cc4c3e3-9b20-4223-9a75-b593578243a2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Random Tensor:\n",
+            " tensor([[0.5648, 0.8316, 0.9398],\n",
+            "        [0.8484, 0.9933, 0.7010]])\n",
+            "Max: tensor(0.9933)\n",
+            "Min: tensor(0.5648)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5️⃣ Mini Training Loop 🤖\n",
+        "**Task:** Train a simple linear model y = wx + b using gradient descent."
+      ],
+      "metadata": {
+        "id": "bhSClXespjvT"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Data\n",
+        "torch.manual_seed(42)\n",
+        "x_train = torch.tensor([[1.0], [2.0], [3.0]])\n",
+        "y_train = torch.tensor([[2.0], [4.0], [6.0]])\n",
+        "\n",
+        "# Model\n",
+        "w = torch.randn(1, requires_grad=True)\n",
+        "b = torch.randn(1, requires_grad=True)\n",
+        "\n",
+        "# Training\n",
+        "learning_rate = 0.01\n",
+        "for epoch in range(100):\n",
+        "    y_pred = w * x_train + b\n",
+        "    loss = torch.mean((y_pred - y_train) ** 2)\n",
+        "    loss.backward()\n",
+        "\n",
+        "    # Update\n",
+        "    with torch.no_grad():\n",
+        "        w -= learning_rate * w.grad\n",
+        "        b -= learning_rate * b.grad\n",
+        "        w.grad.zero_()\n",
+        "        b.grad.zero_()\n",
+        "\n",
+        "print(\"Trained weight:\", w.item())\n",
+        "print(\"Trained bias:\", b.item())\n"
+      ],
+      "metadata": {
+        "id": "o7s2KMJVpZ5M",
+        "outputId": "872cb35d-1dee-4408-8cc2-a727643704de",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Trained weight: 1.750671625137329\n",
+            "Trained bias: 0.5667532682418823\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 6️⃣ Bonus ⚡\n",
+        "* Convert a PyTorch tensor to a NumPy array.\n",
+        "\n",
+        "* Convert it back to a PyTorch tensor."
+      ],
+      "metadata": {
+        "id": "rGhFqtrVpv8_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "\n",
+        "import torch\n",
+        "import numpy as np\n",
+        "\n",
+        "a = torch.tensor([[1, 2, 3], [4, 5, 6]])\n",
+        "print(a)\n",
+        "\n",
+        "a = a.numpy()\n",
+        "\n",
+        "print(a)"
+      ],
+      "metadata": {
+        "id": "AF4t92eDpq0j",
+        "outputId": "c46e8e45-16af-41a4-a38b-d985ff998378",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([[1, 2, 3],\n",
+            "        [4, 5, 6]])\n",
+            "[[1 2 3]\n",
+            " [4 5 6]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "torch.manual_seed(42)\n",
+        "tensor_a = torch.ones(5)\n",
+        "print(\"create a tensor of 1:\\n\",tensor_a)\n",
+        "\n",
+        "numpy_a = a.numpy()\n",
+        "print(\"convert tensor to numpy:\\n\" , numpy_a)\n",
+        "np.add(numpy_a, 1, out=numpy_a)\n",
+        "print(\"add +1 for test to np:\\n\", numpy_a)\n",
+        "\n",
+        "tensor_b = torch.tensor(numpy_a)\n",
+        "print(\"back to tensor:\\n\",tensor_b)\n"
+      ],
+      "metadata": {
+        "id": "LWhQ51Uv7ydi",
+        "outputId": "bcede973-5cb5-47ff-a448-2ac623f7bf3e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "create a tensor of 1:\n",
+            " tensor([1., 1., 1., 1., 1.])\n",
+            "convert tensor to numpy:\n",
+            " [8. 8. 8. 8. 8.]\n",
+            "add +1 for test to np:\n",
+            " [9. 9. 9. 9. 9.]\n",
+            "back to tensor:\n",
+            " tensor([9., 9., 9., 9., 9.])\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.7/Copy of Assignment 07. PyTorch _ Nexus _ RezaShokrzad.ipynb b/a0.1/a0.7/Copy of Assignment 07. PyTorch _ Nexus _ RezaShokrzad.ipynb
new file mode 100644
index 0000000..ddc4b4c
--- /dev/null
+++ b/a0.1/a0.7/Copy of Assignment 07. PyTorch _ Nexus _ RezaShokrzad.ipynb	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1NfHQVtYubTz2HUf7uH8lOsCPY3ALP2-y","timestamp":1753780815007}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# 🔥 Assignment: Exploring PyTorch\n"],"metadata":{"id":"dbICYwLGo5Av"}},{"cell_type":"markdown","source":["## 🎯 Goal\n","This assignment evaluates your basic skills in using PyTorch, focusing on tensors, autograd, and a small training loop.\n","\n"],"metadata":{"id":"b8h5DExTo6K5"}},{"cell_type":"markdown","source":["### 1️⃣ Setup and Basics\n","**Task:**\n","\n","* Install PyTorch and check its version.\n","\n","* Create a 2D tensor and print it."],"metadata":{"id":"euolCT3oo8dR"}},{"cell_type":"code","execution_count":2,"metadata":{"id":"9AADPzP6o1iY","executionInfo":{"status":"ok","timestamp":1753770132937,"user_tz":-210,"elapsed":4521,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"}},"outputId":"b8c11e5e-e1ab-40fb-e68a-ada2a2af4f05","colab":{"base_uri":"https://localhost:8080/"}},"outputs":[{"output_type":"stream","name":"stdout","text":["PyTorch version: 2.6.0+cu124\n","Tensor x:\n"," tensor([[1., 2.],\n","        [3., 4.]])\n"]}],"source":["import torch\n","\n","print(\"PyTorch version:\", torch.__version__)\n","x = torch.tensor([[1., 2.], [3., 4.]])\n","print(\"Tensor x:\\n\", x)\n"]},{"cell_type":"code","source":["# Your turn:\n","# Create another tensor y of the same shape with values [[5, 6], [7, 8]].\n","y = torch.tensor([[5 , 6],[7 , 8]])\n","print(\"Tensor y:\\n\" , y)\n","\n","\n","\n"],"metadata":{"id":"T5mCQMYFpBJm","executionInfo":{"status":"ok","timestamp":1753770398350,"user_tz":-210,"elapsed":425,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"}},"outputId":"55e16ae0-084c-451f-df5e-9eee038fd8e1","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Tensor y:\n"," tensor([[5, 6],\n","        [7, 8]])\n"]}]},{"cell_type":"markdown","source":["### 2️⃣ Tensor Operations 🧮\n","**Task:** Perform addition and matrix multiplication."],"metadata":{"id":"7yRBms7npEUe"}},{"cell_type":"code","source":["z = x + y\n","\n","C = torch.matmul(z, x)#dot\n","\n","print(\"Matrix Multiplication:\\n\", C)\n"],"metadata":{"id":"nGRNryC4pDv2","executionInfo":{"status":"ok","timestamp":1753773185179,"user_tz":-210,"elapsed":368,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"}},"outputId":"95173a63-03e9-44db-e408-ce331abfdcf3","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["Matrix Multiplication:\n"," tensor([[30., 44.],\n","        [46., 68.]])\n"]}]},{"cell_type":"code","source":["# Your turn:\n","# Perform element-wise multiplication (x * y) and print the result.\n","elementwise_mul = x * y\n","print(\"Element-wise Multiplication:\\n\", elementwise_mul)\n","\n","#We can write other product , is now:\n","#>> Object-Oriented product : Chain Operations on both Metrix(ex:result = x.mul(y).add(z).sqrt())\n","elementwise_mul2 = x.mul(y)\n","print(\"Element-wise Multiplication:\\n\" , elementwise_mul2)\n","#>> The Function of product : Complex Operations on other Metrix(ex:def scale_tensor(tensor, scale):\n","    # return torch.mul(tensor, scale))\n","elementwise_mul3 = torch.mul(x , y)\n","print(\"Element-wise Multiplication:\\n\" , elementwise_mul3)"],"metadata":{"id":"tf64nP2dpIod","executionInfo":{"status":"ok","timestamp":1753772679124,"user_tz":-210,"elapsed":369,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"}},"outputId":"e8f6a4aa-697c-4821-8c63-c9575bd3dfed","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["Element-wise Multiplication:\n"," tensor([[ 5., 12.],\n","        [21., 32.]])\n","Element-wise Multiplication:\n"," tensor([[ 5., 12.],\n","        [21., 32.]])\n","Element-wise Multiplication:\n"," tensor([[ 5., 12.],\n","        [21., 32.]])\n"]}]},{"cell_type":"markdown","source":["### 3️⃣ Autograd and Gradients ⚙️\n","**Task:** Enable gradient tracking and compute derivatives."],"metadata":{"id":"h3dABundpMYU"}},{"cell_type":"code","source":["a = torch.tensor(3.0, requires_grad=True)\n","b = (a ** 2) + 2 * a + 1\n","b.backward()\n","print(\"Gradient of b wrt a:\", a.grad)"],"metadata":{"id":"igtPKY90pLGt","executionInfo":{"status":"ok","timestamp":1753774035824,"user_tz":-210,"elapsed":4,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"}},"outputId":"b452c810-e029-4b4a-e8eb-1c0d8af8afcf","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["Gradient of b wrt a: tensor(8.)\n","tensor(3., requires_grad=True)\n","tensor(16., grad_fn=<AddBackward0>)\n"]}]},{"cell_type":"code","source":["# Your turn:\n","# Create a tensor p with value 2.0 (requires_grad=True) and compute the gradient of q = p^3 + 4p.\n","\n","# Step 1: Create a tensor p with value 2.0 and enable gradient tracking\n","p = torch.tensor(2.0, requires_grad=True)\n","\n","# Step 2: Define the function q = p^3 + 4p\n","# This function will be used for automatic differentiation\n","q = p ** 3 + 4 * p\n","\n","# Step 3: Compute the gradient of q with respect to p\n","# This means we are calculating dq/dp\n","q.backward()\n","\n","# Step 4: Print the gradient value\n","# This should be dq/dp = 3*p^2 + 4 = 3*4 + 4 = 16.0 when p = 2\n","print(\"Gradient of q with respect to p:\", p.grad)\n","\n","\n","\n"],"metadata":{"id":"bLceqHHvpOwt","executionInfo":{"status":"ok","timestamp":1753774343343,"user_tz":-210,"elapsed":377,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"}},"outputId":"40ba9e2f-12ec-44a5-a6dd-acce2be980c9","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["Gradient of q with respect to p: tensor(16.)\n"]}]},{"cell_type":"markdown","source":["### 4️⃣ Random Tensors 🎲\n","**Task:** Generate a random tensor of shape (2, 3) and find its max and min."],"metadata":{"id":"jytRN17EpVbM"}},{"cell_type":"code","source":["# Create a random tensor of shape (2, 3) with values in range [0.0, 1.0)\n","rand_tensor = torch.rand((2, 3))\n","# Print the generated random tensor\n","print(\"Random Tensor:\\n\", rand_tensor)\n","# Find and print the maximum value in the tensor\n","print(\"Max:\", torch.max(rand_tensor))\n","# Find and print the minimum value in the tensor\n","print(\"Min:\", torch.min(rand_tensor))\n","\n","# Advanced Operations:\n","\n","print(\"Mean value:\", torch.mean(rand_tensor))\n","print(\"Std deviation:\", torch.std(rand_tensor))\n","print(\"Variance:\", torch.var(rand_tensor))\n","\n","#Max and min per row (dim=1)\n","\n","print(\"\\nMax per row:\", torch.max(rand_tensor, dim=1).values)\n","print(\"Min per row:\", torch.min(rand_tensor, dim=1).values)\n","\n","#Max and min per column (dim=0)\n","\n","print(\"\\nMax per column:\", torch.max(rand_tensor, dim=0).values)\n","print(\"Min per column:\", torch.min(rand_tensor, dim=0).values)\n","\n","#Index of max and min\n","\n","flat_max_index = torch.argmax(rand_tensor)\n","flat_min_index = torch.argmin(rand_tensor)\n","\n","print(\"\\nIndex of max value (flattened):\", flat_max_index.item())\n","print(\"Index of min value (flattened):\", flat_min_index.item())"],"metadata":{"id":"fb7fv-E_pS6e","executionInfo":{"status":"ok","timestamp":1753776198528,"user_tz":-210,"elapsed":391,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"}},"outputId":"1397fab7-bd1f-4dfc-e493-7914c56130ee","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["Random Tensor:\n"," tensor([[0.0848, 0.7395, 0.4060],\n","        [0.6347, 0.0163, 0.9611]])\n","Max: tensor(0.9611)\n","Min: tensor(0.0163)\n","Mean value: tensor(0.4737)\n","Std deviation: tensor(0.3739)\n","Variance: tensor(0.1398)\n","\n","Max per row: tensor([0.7395, 0.9611])\n","Min per row: tensor([0.0848, 0.0163])\n","\n","Max per column: tensor([0.6347, 0.7395, 0.9611])\n","Min per column: tensor([0.0848, 0.0163, 0.4060])\n","\n","Index of max value (flattened): 5\n","Index of min value (flattened): 4\n"]}]},{"cell_type":"markdown","source":["### 5️⃣ Mini Training Loop 🤖\n","**Task:** Train a simple linear model y = wx + b using gradient descent."],"metadata":{"id":"bhSClXespjvT"}},{"cell_type":"code","source":["# Data\n","x_train = torch.tensor([[1.0], [2.0], [3.0]])\n","y_train = torch.tensor([[2.0], [4.0], [6.0]])\n","\n","# Model\n","w = torch.randn(1, requires_grad=True)\n","b = torch.randn(1, requires_grad=True)\n","\n","# Training\n","learning_rate = 0.01\n","for epoch in range(100):\n","    y_pred = w * x_train + b\n","    loss = torch.mean((y_pred - y_train) ** 2)\n","    loss.backward()\n","\n","    # Update\n","    with torch.no_grad():\n","        w -= learning_rate * w.grad\n","        b -= learning_rate * b.grad\n","        w.grad.zero_()\n","        b.grad.zero_()\n","\n","print(\"Trained weight:\", w.item())\n","print(\"Trained bias:\", b.item())\n"],"metadata":{"id":"o7s2KMJVpZ5M","executionInfo":{"status":"ok","timestamp":1753780330523,"user_tz":-210,"elapsed":32,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"}},"outputId":"b50e07c0-f867-4fcf-e99b-bff4655c49f9","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["Trained weight: 1.5541613101959229\n","Trained bias: 1.013465404510498\n"]}]},{"cell_type":"markdown","source":["### 6️⃣ Bonus ⚡\n","* Convert a PyTorch tensor to a NumPy array.\n","\n","* Convert it back to a PyTorch tensor."],"metadata":{"id":"rGhFqtrVpv8_"}},{"cell_type":"code","source":["# To do\n","import numpy as np\n","\n","# Step 1: Create a PyTorch tensor\n","tensor = torch.tensor([[1.0, 2.0], [3.0, 4.0]])\n","print(\"Original PyTorch Tensor:\\n\", tensor)\n","\n","# Step 2: Convert it to a NumPy array\n","np_array = tensor.numpy()\n","print(\"Converted to NumPy Array:\\n\", np_array)\n","\n","# Step 3: Convert it back to a PyTorch tensor\n","converted_back = torch.from_numpy(np_array)\n","print(\"Converted back to PyTorch Tensor:\\n\", converted_back)"],"metadata":{"id":"AF4t92eDpq0j","executionInfo":{"status":"ok","timestamp":1753780809003,"user_tz":-210,"elapsed":21,"user":{"displayName":"danial jafariii","userId":"01847614921988899910"}},"outputId":"0e7d879d-9513-4f33-c660-ed4215b49f9a","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":30,"outputs":[{"output_type":"stream","name":"stdout","text":["Original PyTorch Tensor:\n"," tensor([[1., 2.],\n","        [3., 4.]])\n","Converted to NumPy Array:\n"," [[1. 2.]\n"," [3. 4.]]\n","Converted back to PyTorch Tensor:\n"," tensor([[1., 2.],\n","        [3., 4.]])\n"]}]}]}
\ No newline at end of file
diff --git a/a0.1/a0.8/AI-DS_Nexus__A0_8_Amirhossein_Zandi_8fca80.ipynb b/a0.1/a0.8/AI-DS_Nexus__A0_8_Amirhossein_Zandi_8fca80.ipynb
new file mode 100644
index 0000000..4d43ca8
--- /dev/null
+++ b/a0.1/a0.8/AI-DS_Nexus__A0_8_Amirhossein_Zandi_8fca80.ipynb
@@ -0,0 +1,456 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zfpFVd8WVI8J"
+      },
+      "source": [
+        "# 1️⃣ Setup and Basics\n",
+        "Task:\n",
+        "\n",
+        "Install TensorFlow and verify the version.\n",
+        "Create a simple tensor and print it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oFHysHkCVPDa",
+        "outputId": "5710f68b-0f35-4650-912d-e4948a647f01"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tensorflow version : 2.18.0\n",
+            "Tensor a : \n",
+            "[[1 2]\n",
+            " [3 4]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import tensorflow as tf\n",
+        "\n",
+        "print(f\"Tensorflow version : {tf.__version__}\")\n",
+        "a = tf.constant([[1,2] , [3,4]])\n",
+        "print(f\"Tensor a : \\n{a}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZDaw6uQBWPC1",
+        "outputId": "f4b9b6c9-f9a7-4b68-dddd-2c1e9b6da7b4"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tensor b : \n",
+            " [[5 6]\n",
+            " [7 8]]\n",
+            "--------------------\n",
+            "b shape : (2, 2)\n"
+          ]
+        }
+      ],
+      "source": [
+        "b = tf.constant([[5,6] , [7,8]])\n",
+        "print(f\"Tensor b : \\n {b}\")\n",
+        "print(\"--------------------\")\n",
+        "print(f\"b shape : {b.shape}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1uzTNX74Wb28"
+      },
+      "source": [
+        "# 2️⃣ Tensor Operations\n",
+        "Task: Perform element-wise addition and multiplication."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "s37pvQ4yWew9",
+        "outputId": "13c12b58-3b54-4596-d5c9-2baf6dcce821"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Addition:\n",
+            " tf.Tensor(\n",
+            "[[ 6  8]\n",
+            " [10 12]], shape=(2, 2), dtype=int32)\n",
+            "Multiplication:\n",
+            " tf.Tensor(\n",
+            "[[ 5 12]\n",
+            " [21 32]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "c = a + b\n",
+        "print(\"Addition:\\n\", c)\n",
+        "\n",
+        "d = a * b\n",
+        "print(\"Multiplication:\\n\", d)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yiSF0ng0WkSh",
+        "outputId": "fef2eed9-cf03-4a4c-a242-58b3c59b7278"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Multiplication:\n",
+            " tf.Tensor(\n",
+            "[[19 22]\n",
+            " [43 50]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "multiplication = a @ b\n",
+        "print(\"Multiplication:\\n\", multiplication)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ktfo8ANgXmpG"
+      },
+      "source": [
+        "# 3️⃣ Random Tensors 🎲\n",
+        "Task: Generate a random tensor of shape (3, 3) from a normal distribution and print its mean and standard deviation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VytA8UuwXsMK",
+        "outputId": "cbc47bc6-fa4c-4ee9-e818-f3c91b3d0e74"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Tensor:\n",
+            " tf.Tensor(\n",
+            "[[ 0.5897852  -0.45510045  0.13046674]\n",
+            " [-0.57042843  1.2452071   0.38936564]\n",
+            " [-0.18235067 -1.5178325   0.63837725]], shape=(3, 3), dtype=float32)\n",
+            "Mean: tf.Tensor(0.029721094, shape=(), dtype=float32)\n",
+            "Std Dev: tf.Tensor(0.77000445, shape=(), dtype=float32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "random_tensor = tf.random.normal([3, 3])\n",
+        "print(\"Random Tensor:\\n\", random_tensor)\n",
+        "print(\"Mean:\", tf.reduce_mean(random_tensor))\n",
+        "print(\"Std Dev:\", tf.math.reduce_std(random_tensor))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u2CoxtN0YFt0"
+      },
+      "source": [
+        "# 4️⃣ Building a Simple Model 🤖\n",
+        "Task: Build a single-layer neural network using tf.keras.Sequential."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 161
+        },
+        "id": "OhvsKtiQYW5Y",
+        "outputId": "d317eae5-7d00-40fe-971f-ff254533f03f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m2\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from keras.layers import Dense , Input\n",
+        "from keras.models import Sequential\n",
+        "\n",
+        "model = Sequential()\n",
+        "model.add(Input(shape=(1,)))\n",
+        "model.add(Dense(1 , activation=\"relu\"))\n",
+        "model.summary()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 193
+        },
+        "id": "XKoaH6nQai-y",
+        "outputId": "7105d1c0-f410-4f77-e1bc-37547ecfaaf4"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)              │            \u001b[38;5;34m10\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m6\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> (64.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m16\u001b[0m (64.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> (64.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m16\u001b[0m (64.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "model_second = Sequential()\n",
+        "model_second.add(Input(shape=(1,)))\n",
+        "model_second.add(Dense(5 , activation=\"relu\"))\n",
+        "model_second.add(Dense(1 , activation=\"relu\"))\n",
+        "model_second.summary()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eZI0Sdmccu6X"
+      },
+      "source": [
+        "# 5️⃣ Bonus ⚡\n",
+        "Create a tensor with values from 0 to 9 (inclusive).\n",
+        "\n",
+        "Reshape it to shape (2, 5).\n",
+        "\n",
+        "Pint the reshaped tensor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "M_GiInc8c0YB",
+        "outputId": "7b13362e-975d-4944-fa05-4f45eea19a1e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "reshaped Tensor : \n",
+            " [[0 1 2 3 4]\n",
+            " [5 6 7 8 9]]\n",
+            "-----------------------------\n",
+            "shape of reshaped Tensor : (2, 5)\n"
+          ]
+        }
+      ],
+      "source": [
+        "my_tensor = tf.range(10)\n",
+        "reshaped_tensor = tf.reshape(my_tensor ,(2,5))\n",
+        "print(f\"reshaped Tensor : \\n {reshaped_tensor}\")\n",
+        "print(\"-----------------------------\")\n",
+        "print(f\"shape of reshaped Tensor : {reshaped_tensor.shape}\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.12.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.8/AI-DS_Nexus__A0_8_Javid_Mohammadi_a60d6f.ipynb b/a0.1/a0.8/AI-DS_Nexus__A0_8_Javid_Mohammadi_a60d6f.ipynb
new file mode 100644
index 0000000..d80de4b
--- /dev/null
+++ b/a0.1/a0.8/AI-DS_Nexus__A0_8_Javid_Mohammadi_a60d6f.ipynb
@@ -0,0 +1,479 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🧠 Assignment: Getting Started with TensorFlow\n"
+      ],
+      "metadata": {
+        "id": "WKCsWe71mbTO"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using TensorFlow, including creating tensors, performing operations, and building a simple model.\n",
+        "\n",
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install TensorFlow and verify the version.\n",
+        "* Create a simple tensor and print it."
+      ],
+      "metadata": {
+        "id": "ODPYXweVmdKp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "LrzxFPr3mPno",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "55bc5917-ce91-4512-f5cc-738b184756e6"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "TensorFlow version: 2.18.0\n",
+            "Tensor a:\n",
+            " tf.Tensor(\n",
+            "[[1 2]\n",
+            " [3 4]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "print(\"TensorFlow version:\", tf.__version__)\n",
+        "a = tf.constant([[1, 2], [3, 4]])\n",
+        "print(\"Tensor a:\\n\", a)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor b with values [[5, 6], [7, 8]] and print its shape.\n",
+        "b = tf.constant([[5, 6], [7, 8]])\n",
+        "print(\"Tensor b shape:\\n\", b.shape)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "MSC0WPwymq3V",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4f2db6fc-299b-43c6-f860-2d7cf8f11186"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Tensor b shape:\n",
+            " (2, 2)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2️⃣ Tensor Operations\n",
+        "**Task:** Perform element-wise addition and multiplication."
+      ],
+      "metadata": {
+        "id": "b4cuWXJvmwdC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "c = a + b\n",
+        "print(\"Addition:\\n\", c)\n",
+        "\n",
+        "d = a * b\n",
+        "print(\"Multiplication:\\n\", d)\n"
+      ],
+      "metadata": {
+        "id": "pzlGeEuhmtYo",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3f52bf8b-d5cf-4b93-81b9-3e1110bab8f3"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Addition:\n",
+            " tf.Tensor(\n",
+            "[[ 6  8]\n",
+            " [10 12]], shape=(2, 2), dtype=int32)\n",
+            "Multiplication:\n",
+            " tf.Tensor(\n",
+            "[[ 5 12]\n",
+            " [21 32]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Compute a @ b (matrix multiplication) and print the result.\n",
+        "dot = a @ b\n",
+        "print(\"Matrix inner multiplication:\\n\", dot)\n"
+      ],
+      "metadata": {
+        "id": "zv02rBVZmzPR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "75ea33f3-d64b-41e3-d996-c74a355828a1"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Matrix inner multiplication:\n",
+            " tf.Tensor(\n",
+            "[[19 22]\n",
+            " [43 50]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (3, 3) from a normal distribution and print its mean and standard deviation."
+      ],
+      "metadata": {
+        "id": "CbDULpk7m3c2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "random_tensor = tf.random.normal([3, 3])\n",
+        "print(\"Random Tensor:\\n\", random_tensor)\n",
+        "print(\"Mean:\", tf.reduce_mean(random_tensor))\n",
+        "print(\"Std Dev:\", tf.math.reduce_std(random_tensor))\n"
+      ],
+      "metadata": {
+        "id": "Czc74X8Nm2Oa",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "70e97227-1882-49db-af7a-79cb6b652527"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Random Tensor:\n",
+            " tf.Tensor(\n",
+            "[[-0.8566601  -1.49599    -1.103244  ]\n",
+            " [ 0.825052   -1.4882199   1.3307469 ]\n",
+            " [ 0.23964775  0.85605454  1.2652878 ]], shape=(3, 3), dtype=float32)\n",
+            "Mean: tf.Tensor(-0.04748057, shape=(), dtype=float32)\n",
+            "Std Dev: tf.Tensor(1.1168567, shape=(), dtype=float32)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4️⃣ Building a Simple Model 🤖\n",
+        "**Task:** Build a single-layer neural network using tf.keras.Sequential."
+      ],
+      "metadata": {
+        "id": "wVH1vcq3nDgV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "model = tf.keras.Sequential([\n",
+        "    tf.keras.layers.Dense(1, input_shape=(1,))\n",
+        "])\n",
+        "model.summary()\n"
+      ],
+      "metadata": {
+        "id": "9G2Jw8VSnC6f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 219
+        },
+        "outputId": "465339fe-d039-4cee-d31c-5b5c68b2c190"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m2\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Add another dense layer with 5 neurons and relu activation.\n",
+        "model = tf.keras.Sequential([\n",
+        "    tf.keras.layers.Dense(5, activation='relu', input_shape=(1,)),\n",
+        "    tf.keras.layers.Dense(1)\n",
+        "])\n",
+        "\n",
+        "model.summary()\n",
+        "\n",
+        "# First layer: 5 neurons, relu\n",
+        "# Second layers: 1 neuron, default linear activation"
+      ],
+      "metadata": {
+        "id": "C1ianPt1nKlr",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 197
+        },
+        "outputId": "e931126a-63dd-4b29-a594-a759ad16221d"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)              │            \u001b[38;5;34m10\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m6\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m16\u001b[0m (64.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> (64.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m16\u001b[0m (64.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> (64.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Create a tensor with values from 0 to 9 (inclusive).\n",
+        "\n",
+        "* Reshape it to shape (2, 5).\n",
+        "\n",
+        "* Print the reshaped tensor."
+      ],
+      "metadata": {
+        "id": "GpOxR_RFnMt_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "tensor = tf.range(0, 10)\n",
+        "reshaped = tf.reshape(tensor, (2, 5))\n",
+        "print(\"Reshaped tensor:\\n\", reshaped)\n"
+      ],
+      "metadata": {
+        "id": "caRjq7WinMLf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "449fb24f-c430-4238-efd6-416f618184bf"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Reshaped tensor:\n",
+            " tf.Tensor(\n",
+            "[[0 1 2 3 4]\n",
+            " [5 6 7 8 9]], shape=(2, 5), dtype=int32)\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.8/AI-DS_Nexus__A0_8_Project__RezaShokr.ipynb b/a0.1/a0.8/AI-DS_Nexus__A0_8_Project__RezaShokr.ipynb
new file mode 100644
index 0000000..091c665
--- /dev/null
+++ b/a0.1/a0.8/AI-DS_Nexus__A0_8_Project__RezaShokr.ipynb
@@ -0,0 +1,373 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WKCsWe71mbTO"
+      },
+      "source": [
+        "# 🧠 Assignment: Getting Started with TensorFlow\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ODPYXweVmdKp"
+      },
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using TensorFlow, including creating tensors, performing operations, and building a simple model.\n",
+        "\n",
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install TensorFlow and verify the version.\n",
+        "* Create a simple tensor and print it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "LrzxFPr3mPno"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TensorFlow version: 2.19.0\n",
+            "Tensor a:\n",
+            " tf.Tensor(\n",
+            "[[1 2]\n",
+            " [3 4]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "print(\"TensorFlow version:\", tf.__version__)\n",
+        "a = tf.constant([[1, 2], [3, 4]])\n",
+        "print(\"Tensor a:\\n\", a)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MSC0WPwymq3V"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tensor a:\n",
+            " tf.Tensor(\n",
+            "[[5 6]\n",
+            " [7 8]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor b with values [[5, 6], [7, 8]] and print its shape.\n",
+        "b = tf.constant([[5, 6], [7, 8]])\n",
+        "print(\"Tensor a:\\n\", b)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b4cuWXJvmwdC"
+      },
+      "source": [
+        "### 2️⃣ Tensor Operations\n",
+        "**Task:** Perform element-wise addition and multiplication."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "pzlGeEuhmtYo"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Addition:\n",
+            " tf.Tensor(\n",
+            "[[ 6  8]\n",
+            " [10 12]], shape=(2, 2), dtype=int32)\n",
+            "Multiplication:\n",
+            " tf.Tensor(\n",
+            "[[ 5 12]\n",
+            " [21 32]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "c = a + b\n",
+        "print(\"Addition:\\n\", c)\n",
+        "\n",
+        "d = a * b\n",
+        "print(\"Multiplication:\\n\", d)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zv02rBVZmzPR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "matrix multiplication: \n",
+            " tf.Tensor(\n",
+            "[[19 22]\n",
+            " [43 50]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Compute a @ b (matrix multiplication) and print the result.\n",
+        "e = a @ b\n",
+        "print(\"matrix multiplication: \\n\" , e)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CbDULpk7m3c2"
+      },
+      "source": [
+        "### 3️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (3, 3) from a normal distribution and print its mean and standard deviation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "Czc74X8Nm2Oa"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Tensor:\n",
+            " tf.Tensor(\n",
+            "[[ 2.4778519   1.091282   -1.2910949 ]\n",
+            " [ 1.723791    0.13134924  0.892447  ]\n",
+            " [ 0.0541843  -0.05278427 -0.22317024]], shape=(3, 3), dtype=float32)\n",
+            "Mean: tf.Tensor(0.53376174, shape=(), dtype=float32)\n",
+            "Std Dev: tf.Tensor(1.068444, shape=(), dtype=float32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "random_tensor = tf.random.normal([3, 3])\n",
+        "print(\"Random Tensor:\\n\", random_tensor)\n",
+        "print(\"Mean:\", tf.reduce_mean(random_tensor))\n",
+        "print(\"Std Dev:\", tf.math.reduce_std(random_tensor))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wVH1vcq3nDgV"
+      },
+      "source": [
+        "### 4️⃣ Building a Simple Model 🤖\n",
+        "**Task:** Build a single-layer neural network using tf.keras.Sequential."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "9G2Jw8VSnC6f"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\Magellan\\miniconda3\\envs\\DsAi\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:92: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m2\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "model = tf.keras.Sequential([\n",
+        "    tf.keras.layers.Dense(1, input_shape=(1,))\n",
+        "    ])\n",
+        "model.summary()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "C1ianPt1nKlr"
+      },
+      "outputs": [],
+      "source": [
+        "# Your turn:\n",
+        "# Add another dense layer with 5 neurons and relu activation.\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GpOxR_RFnMt_"
+      },
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Create a tensor with values from 0 to 9 (inclusive).\n",
+        "\n",
+        "* Reshape it to shape (2, 5).\n",
+        "\n",
+        "* Pint the reshaped tensor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "caRjq7WinMLf"
+      },
+      "outputs": [],
+      "source": [
+        "# To do\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.8/AI-DS_Nexus__A0_8_Project_roohi_268383.ipynb b/a0.1/a0.8/AI-DS_Nexus__A0_8_Project_roohi_268383.ipynb
new file mode 100644
index 0000000..091c665
--- /dev/null
+++ b/a0.1/a0.8/AI-DS_Nexus__A0_8_Project_roohi_268383.ipynb
@@ -0,0 +1,373 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WKCsWe71mbTO"
+      },
+      "source": [
+        "# 🧠 Assignment: Getting Started with TensorFlow\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ODPYXweVmdKp"
+      },
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using TensorFlow, including creating tensors, performing operations, and building a simple model.\n",
+        "\n",
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install TensorFlow and verify the version.\n",
+        "* Create a simple tensor and print it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "LrzxFPr3mPno"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TensorFlow version: 2.19.0\n",
+            "Tensor a:\n",
+            " tf.Tensor(\n",
+            "[[1 2]\n",
+            " [3 4]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "print(\"TensorFlow version:\", tf.__version__)\n",
+        "a = tf.constant([[1, 2], [3, 4]])\n",
+        "print(\"Tensor a:\\n\", a)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MSC0WPwymq3V"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tensor a:\n",
+            " tf.Tensor(\n",
+            "[[5 6]\n",
+            " [7 8]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor b with values [[5, 6], [7, 8]] and print its shape.\n",
+        "b = tf.constant([[5, 6], [7, 8]])\n",
+        "print(\"Tensor a:\\n\", b)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b4cuWXJvmwdC"
+      },
+      "source": [
+        "### 2️⃣ Tensor Operations\n",
+        "**Task:** Perform element-wise addition and multiplication."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "pzlGeEuhmtYo"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Addition:\n",
+            " tf.Tensor(\n",
+            "[[ 6  8]\n",
+            " [10 12]], shape=(2, 2), dtype=int32)\n",
+            "Multiplication:\n",
+            " tf.Tensor(\n",
+            "[[ 5 12]\n",
+            " [21 32]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "c = a + b\n",
+        "print(\"Addition:\\n\", c)\n",
+        "\n",
+        "d = a * b\n",
+        "print(\"Multiplication:\\n\", d)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zv02rBVZmzPR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "matrix multiplication: \n",
+            " tf.Tensor(\n",
+            "[[19 22]\n",
+            " [43 50]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Compute a @ b (matrix multiplication) and print the result.\n",
+        "e = a @ b\n",
+        "print(\"matrix multiplication: \\n\" , e)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CbDULpk7m3c2"
+      },
+      "source": [
+        "### 3️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (3, 3) from a normal distribution and print its mean and standard deviation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "Czc74X8Nm2Oa"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Tensor:\n",
+            " tf.Tensor(\n",
+            "[[ 2.4778519   1.091282   -1.2910949 ]\n",
+            " [ 1.723791    0.13134924  0.892447  ]\n",
+            " [ 0.0541843  -0.05278427 -0.22317024]], shape=(3, 3), dtype=float32)\n",
+            "Mean: tf.Tensor(0.53376174, shape=(), dtype=float32)\n",
+            "Std Dev: tf.Tensor(1.068444, shape=(), dtype=float32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "random_tensor = tf.random.normal([3, 3])\n",
+        "print(\"Random Tensor:\\n\", random_tensor)\n",
+        "print(\"Mean:\", tf.reduce_mean(random_tensor))\n",
+        "print(\"Std Dev:\", tf.math.reduce_std(random_tensor))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wVH1vcq3nDgV"
+      },
+      "source": [
+        "### 4️⃣ Building a Simple Model 🤖\n",
+        "**Task:** Build a single-layer neural network using tf.keras.Sequential."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "9G2Jw8VSnC6f"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\Magellan\\miniconda3\\envs\\DsAi\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:92: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m2\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "model = tf.keras.Sequential([\n",
+        "    tf.keras.layers.Dense(1, input_shape=(1,))\n",
+        "    ])\n",
+        "model.summary()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "C1ianPt1nKlr"
+      },
+      "outputs": [],
+      "source": [
+        "# Your turn:\n",
+        "# Add another dense layer with 5 neurons and relu activation.\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GpOxR_RFnMt_"
+      },
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Create a tensor with values from 0 to 9 (inclusive).\n",
+        "\n",
+        "* Reshape it to shape (2, 5).\n",
+        "\n",
+        "* Pint the reshaped tensor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "caRjq7WinMLf"
+      },
+      "outputs": [],
+      "source": [
+        "# To do\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.8/AI-DS_Nexus__A0_8__MrAshki.ipynb b/a0.1/a0.8/AI-DS_Nexus__A0_8__MrAshki.ipynb
new file mode 100644
index 0000000..f7616dd
--- /dev/null
+++ b/a0.1/a0.8/AI-DS_Nexus__A0_8__MrAshki.ipynb
@@ -0,0 +1,500 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🧠 Assignment: Getting Started with TensorFlow\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "WKCsWe71mbTO"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using TensorFlow, including creating tensors, performing operations, and building a simple model.\n",
+        "\n",
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install TensorFlow and verify the version.\n",
+        "* Create a simple tensor and print it."
+      ],
+      "metadata": {
+        "id": "ODPYXweVmdKp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "LrzxFPr3mPno",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d238af19-b34c-4a1a-babd-52d18e7eb56c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "TensorFlow version: 2.19.0\n",
+            "Tensor a:\n",
+            " tf.Tensor(\n",
+            "[[1 2]\n",
+            " [3 4]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "print(\"TensorFlow version:\", tf.__version__)\n",
+        "a = tf.constant([[1, 2], [3, 4]])\n",
+        "print(\"Tensor a:\\n\", a)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor b with values [[5, 6], [7, 8]] and print its shape.\n",
+        "\n",
+        "import tensorflow as tf\n",
+        "print(\"tensorflow version:\\n\", tf.__version__)\n",
+        "\n",
+        "b = tf.constant([[5, 6],[7, 8]])\n",
+        "print(\"tensor b:\\n\", b)"
+      ],
+      "metadata": {
+        "id": "MSC0WPwymq3V",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "36449e6f-4e34-437f-fe38-0a1d88df69e5"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensorflow version:\n",
+            " 2.19.0\n",
+            "tensor b:\n",
+            " tf.Tensor(\n",
+            "[[5 6]\n",
+            " [7 8]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2️⃣ Tensor Operations\n",
+        "**Task:** Perform element-wise addition and multiplication."
+      ],
+      "metadata": {
+        "id": "b4cuWXJvmwdC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "c = a + b\n",
+        "print(\"Addition:\\n\", c)\n",
+        "\n",
+        "d = a * b\n",
+        "print(\"Multiplication:\\n\", d)\n"
+      ],
+      "metadata": {
+        "id": "pzlGeEuhmtYo"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Compute a @ b (matrix multiplication) and print the result.\n",
+        "\n",
+        "\n",
+        "e = a @ b\n",
+        "print(\"matrix mutliplication:\\n\", e)\n",
+        "\n",
+        "f = tf.matmul(a, b)\n",
+        "print(\"anoter way to matrix mutliplication:\\n\", f)\n"
+      ],
+      "metadata": {
+        "id": "zv02rBVZmzPR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "525b54bd-0109-44d0-e714-06e01b40006a"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "matrix mutliplication:\n",
+            " tf.Tensor(\n",
+            "[[19 22]\n",
+            " [43 50]], shape=(2, 2), dtype=int32)\n",
+            "anoter way to matrix mutliplication:\n",
+            " tf.Tensor(\n",
+            "[[19 22]\n",
+            " [43 50]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (3, 3) from a normal distribution and print its mean and standard deviation."
+      ],
+      "metadata": {
+        "id": "CbDULpk7m3c2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "random_tensor = tf.random.normal([3, 3])\n",
+        "print(\"Random Tensor:\\n\", random_tensor)\n",
+        "print(\"Mean:\", tf.reduce_mean(random_tensor))\n",
+        "print(\"Std Dev:\", tf.math.reduce_std(random_tensor))\n"
+      ],
+      "metadata": {
+        "id": "Czc74X8Nm2Oa",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f0a5e1d8-2d89-4754-bd54-be0618fc6f72"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Random Tensor:\n",
+            " tf.Tensor(\n",
+            "[[ 0.4567332  -0.68118     0.2229847 ]\n",
+            " [-1.552968   -1.143649    2.6244414 ]\n",
+            " [ 0.11049031 -1.9354885  -1.0422286 ]], shape=(3, 3), dtype=float32)\n",
+            "Mean: tf.Tensor(-0.32676274, shape=(), dtype=float32)\n",
+            "Std Dev: tf.Tensor(1.3001397, shape=(), dtype=float32)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4️⃣ Building a Simple Model 🤖\n",
+        "**Task:** Build a single-layer neural network using tf.keras.Sequential."
+      ],
+      "metadata": {
+        "id": "wVH1vcq3nDgV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "model = tf.keras.Sequential([\n",
+        "    tf.keras.layers.Dense(1, input_shape=(1,))\n",
+        "])\n",
+        "model.summary()\n"
+      ],
+      "metadata": {
+        "id": "9G2Jw8VSnC6f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 216
+        },
+        "outputId": "75def919-cc36-45d7-d812-291954b6db99"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:93: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m2\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Add another dense layer with 5 neurons and relu activation.\n",
+        "\n",
+        "\n",
+        "model = tf.keras.Sequential([\n",
+        "    tf.keras.layers.Dense(units=5 , activation='relu', input_shape=(1,)),\n",
+        "    tf.keras.layers.Dense(units=1)\n",
+        "])\n",
+        "model.summary()\n"
+      ],
+      "metadata": {
+        "id": "C1ianPt1nKlr",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 248
+        },
+        "outputId": "8c642fc7-76aa-4ffa-f0c1-571de8f30f39"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/dense.py:93: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)              │            \u001b[38;5;34m10\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m6\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m16\u001b[0m (64.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> (64.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m16\u001b[0m (64.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> (64.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Create a tensor with values from 0 to 9 (inclusive).\n",
+        "\n",
+        "* Reshape it to shape (2, 5).\n",
+        "\n",
+        "* Pint the reshaped tensor."
+      ],
+      "metadata": {
+        "id": "GpOxR_RFnMt_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "\n",
+        "\n",
+        "\n",
+        "my_tensor = tf.range(10)\n",
+        "print(\"tensor a:\\n\", a)\n",
+        "\n",
+        "reshaped_tensor = tf.reshape(my_tensor, shape=(2, 5))\n",
+        "print(\"reshaped tensor:\\n\", reshaped_tensor.numpy())\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "caRjq7WinMLf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "45425f7a-d8a7-459e-fdd3-c48870a23c08"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor a:\n",
+            " tf.Tensor([0 1 2 3 4 5 6 7 8 9], shape=(10,), dtype=int32)\n",
+            "reshaped tensor:\n",
+            " [[0 1 2 3 4]\n",
+            " [5 6 7 8 9]]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.8/AI-DS_Nexus__A0_8__aminran.ipynb b/a0.1/a0.8/AI-DS_Nexus__A0_8__aminran.ipynb
new file mode 100644
index 0000000..b5cf183
--- /dev/null
+++ b/a0.1/a0.8/AI-DS_Nexus__A0_8__aminran.ipynb
@@ -0,0 +1,484 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🧠 Assignment: Getting Started with TensorFlow\n"
+      ],
+      "metadata": {
+        "id": "WKCsWe71mbTO"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using TensorFlow, including creating tensors, performing operations, and building a simple model.\n",
+        "\n",
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install TensorFlow and verify the version.\n",
+        "* Create a simple tensor and print it."
+      ],
+      "metadata": {
+        "id": "ODPYXweVmdKp"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "LrzxFPr3mPno",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "211a3b3d-064a-4db4-da6f-0cfbe5073e27"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "TensorFlow version: 2.18.0\n",
+            "Tensor a:\n",
+            " tf.Tensor(\n",
+            "[[1 2]\n",
+            " [3 4]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "print(\"TensorFlow version:\", tf.__version__)\n",
+        "a = tf.constant([[1, 2], [3, 4]])\n",
+        "print(\"Tensor a:\\n\", a)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor b with values [[5, 6], [7, 8]] and print its shape.\n",
+        "b=tf.constant([[5,6],[7,8]])\n",
+        "print(b)\n",
+        "print(b.shape)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "MSC0WPwymq3V",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4d924394-7bc6-4610-b749-66e4e8ad216b"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tf.Tensor(\n",
+            "[[5 6]\n",
+            " [7 8]], shape=(2, 2), dtype=int32)\n",
+            "(2, 2)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2️⃣ Tensor Operations\n",
+        "**Task:** Perform element-wise addition and multiplication."
+      ],
+      "metadata": {
+        "id": "b4cuWXJvmwdC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "c = a + b\n",
+        "print(\"Addition:\\n\", c)\n",
+        "\n",
+        "d = a * b\n",
+        "print(\"Multiplication:\\n\", d)\n"
+      ],
+      "metadata": {
+        "id": "pzlGeEuhmtYo",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "beaed376-9a2f-4e38-c8d0-addb9042f3de"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Addition:\n",
+            " tf.Tensor(\n",
+            "[[ 6  8]\n",
+            " [10 12]], shape=(2, 2), dtype=int32)\n",
+            "Multiplication:\n",
+            " tf.Tensor(\n",
+            "[[ 5 12]\n",
+            " [21 32]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Compute a @ b (matrix multiplication) and print the result.\n",
+        "m=a@b\n",
+        "print(m)\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "zv02rBVZmzPR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b6a653cb-c94c-40bb-fc62-1483e0bf1186"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tf.Tensor(\n",
+            "[[19 22]\n",
+            " [43 50]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (3, 3) from a normal distribution and print its mean and standard deviation."
+      ],
+      "metadata": {
+        "id": "CbDULpk7m3c2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "random_tensor = tf.random.normal([3, 3])\n",
+        "print(\"Random Tensor:\\n\", random_tensor)\n",
+        "print(\"Mean:\", tf.reduce_mean(random_tensor))\n",
+        "print(\"Std Dev:\", tf.math.reduce_std(random_tensor))\n"
+      ],
+      "metadata": {
+        "id": "Czc74X8Nm2Oa",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8d4ebe87-2dc5-4aac-cc5b-55193a792fd5"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Random Tensor:\n",
+            " tf.Tensor(\n",
+            "[[ 1.3571868  -0.6091366  -0.61158186]\n",
+            " [ 1.7426014  -0.04790685  0.81934994]\n",
+            " [ 1.32141    -0.4158888   0.26323682]], shape=(3, 3), dtype=float32)\n",
+            "Mean: tf.Tensor(0.42436343, shape=(), dtype=float32)\n",
+            "Std Dev: tf.Tensor(0.86055195, shape=(), dtype=float32)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4️⃣ Building a Simple Model 🤖\n",
+        "**Task:** Build a single-layer neural network using tf.keras.Sequential."
+      ],
+      "metadata": {
+        "id": "wVH1vcq3nDgV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "model = tf.keras.Sequential([\n",
+        "    tf.keras.layers.Dense(1, input_shape=(1,))\n",
+        "])\n",
+        "model.summary()\n"
+      ],
+      "metadata": {
+        "id": "9G2Jw8VSnC6f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 222
+        },
+        "outputId": "efd0abea-f3e6-43ef-85d5-b3285f9c75ba"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m2\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Add another dense layer with 5 neurons and relu activation.\n",
+        "\n",
+        "model.add(tf.keras.layers.Dense(5,activation='relu'))\n",
+        "model.summary()\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "C1ianPt1nKlr",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 201
+        },
+        "outputId": "f3bea6ab-c3e3-4dd2-8497-0725e2b1f9f4"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m2\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)              │            \u001b[38;5;34m10\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m12\u001b[0m (48.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">12</span> (48.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m12\u001b[0m (48.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">12</span> (48.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ],
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Create a tensor with values from 0 to 9 (inclusive).\n",
+        "\n",
+        "* Reshape it to shape (2, 5).\n",
+        "\n",
+        "* Pint the reshaped tensor."
+      ],
+      "metadata": {
+        "id": "GpOxR_RFnMt_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "\n",
+        "a=tf.range(10)\n",
+        "print(a)\n",
+        "b=tf.reshape(a,(2,5))\n",
+        "print(b)\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "caRjq7WinMLf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0de43407-69eb-4a21-e19e-4be7b649c04f"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tf.Tensor([0 1 2 3 4 5 6 7 8 9], shape=(10,), dtype=int32)\n",
+            "tf.Tensor(\n",
+            "[[0 1 2 3 4]\n",
+            " [5 6 7 8 9]], shape=(2, 5), dtype=int32)\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.8/AI-DS_Nexus__A0_8__rsayyareh.ipynb b/a0.1/a0.8/AI-DS_Nexus__A0_8__rsayyareh.ipynb
new file mode 100644
index 0000000..3688c55
--- /dev/null
+++ b/a0.1/a0.8/AI-DS_Nexus__A0_8__rsayyareh.ipynb
@@ -0,0 +1,471 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WKCsWe71mbTO"
+      },
+      "source": [
+        "# 🧠 Assignment: Getting Started with TensorFlow\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ODPYXweVmdKp"
+      },
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using TensorFlow, including creating tensors, performing operations, and building a simple model.\n",
+        "\n",
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install TensorFlow and verify the version.\n",
+        "* Create a simple tensor and print it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "LrzxFPr3mPno"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TensorFlow version: 2.20.0\n",
+            "Tensor a:\n",
+            " tf.Tensor(\n",
+            "[[1 2]\n",
+            " [3 4]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "print(\"TensorFlow version:\", tf.__version__)\n",
+        "a = tf.constant([[1, 2], [3, 4]])\n",
+        "print(\"Tensor a:\\n\", a)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "MSC0WPwymq3V"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tensor b:\n",
+            " tf.Tensor(\n",
+            "[[5 6]\n",
+            " [7 8]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor b with values [[5, 6], [7, 8]] and print its shape.\n",
+        "b = tf.constant([[5, 6], [7, 8]])\n",
+        "print(\"Tensor b:\\n\", b)\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b4cuWXJvmwdC"
+      },
+      "source": [
+        "### 2️⃣ Tensor Operations\n",
+        "**Task:** Perform element-wise addition and multiplication."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "pzlGeEuhmtYo"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Addition:\n",
+            " tf.Tensor(\n",
+            "[[ 6  8]\n",
+            " [10 12]], shape=(2, 2), dtype=int32)\n",
+            "Multiplication:\n",
+            " tf.Tensor(\n",
+            "[[ 5 12]\n",
+            " [21 32]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "c = a + b\n",
+        "print(\"Addition:\\n\", c)\n",
+        "\n",
+        "d = a * b\n",
+        "print(\"Multiplication:\\n\", d)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zv02rBVZmzPR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Matrix Multiplication:\n",
+            " tf.Tensor(\n",
+            "[[19 22]\n",
+            " [43 50]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Compute a @ b (matrix multiplication) and print the result.\n",
+        "\n",
+        "e = a @ b\n",
+        "print(\"Matrix Multiplication:\\n\", e)\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CbDULpk7m3c2"
+      },
+      "source": [
+        "### 3️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (3, 3) from a normal distribution and print its mean and standard deviation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Czc74X8Nm2Oa"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Tensor:\n",
+            " tf.Tensor(\n",
+            "[[ 0.06132538 -0.4524595   0.00465713]\n",
+            " [-0.9531849  -0.41993755  0.21351114]\n",
+            " [ 0.3521335  -1.6075189  -0.04839648]], shape=(3, 3), dtype=float32)\n",
+            "Mean: tf.Tensor(-0.31665224, shape=(), dtype=float32)\n",
+            "Std Dev: tf.Tensor(0.59132975, shape=(), dtype=float32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "\n",
+        "random_tensor = tf.random.normal([3, 3])\n",
+        "print(\"Random Tensor:\\n\", random_tensor)\n",
+        "print(\"Mean:\", tf.reduce_mean(random_tensor))\n",
+        "print(\"Std Dev:\", tf.math.reduce_std(random_tensor))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wVH1vcq3nDgV"
+      },
+      "source": [
+        "### 4️⃣ Building a Simple Model 🤖\n",
+        "**Task:** Build a single-layer neural network using tf.keras.Sequential."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9G2Jw8VSnC6f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m2\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "# model = tf.keras.Sequential([\n",
+        "#     tf.keras.layers.Dense(1, input_shape=(1,))\n",
+        "# ])\n",
+        "\n",
+        "model = tf.keras.Sequential([\n",
+        "    tf.keras.Input(shape=(1,)),\n",
+        "    tf.keras.layers.Dense(1)\n",
+        "])\n",
+        "model.summary()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "C1ianPt1nKlr"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_2\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"sequential_2\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span> │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m)              │            \u001b[38;5;34m10\u001b[0m │\n",
+              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+              "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m6\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> (64.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m16\u001b[0m (64.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">16</span> (64.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m16\u001b[0m (64.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Add another dense layer with 5 neurons and relu activation.\n",
+        "model = tf.keras.Sequential([\n",
+        "    tf.keras.Input(shape=(1,)),\n",
+        "    tf.keras.layers.Dense(5, activation='relu'),  # New layer with 5 neurons and relu\n",
+        "    tf.keras.layers.Dense(1)\n",
+        "])\n",
+        "\n",
+        "model.summary()\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GpOxR_RFnMt_"
+      },
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Create a tensor with values from 0 to 9 (inclusive).\n",
+        "\n",
+        "* Reshape it to shape (2, 5).\n",
+        "\n",
+        "* Pint the reshaped tensor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "caRjq7WinMLf"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Re-shaped tensor:\n",
+            " tf.Tensor(\n",
+            "[[0 1 2 3 4]\n",
+            " [5 6 7 8 9]], shape=(2, 5), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# To do\n",
+        "x = tf.range(10)\n",
+        "x_reshaped = tf.reshape(x, (2, 5))\n",
+        "print(\"Re-shaped tensor:\\n\", x_reshaped)\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python (nexus-venv)",
+      "language": "python",
+      "name": "nexus-venv"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.8/Assignment_08_Tensorflow___Nexus___RoohollaAlikhani.ipynb b/a0.1/a0.8/Assignment_08_Tensorflow___Nexus___RoohollaAlikhani.ipynb
new file mode 100644
index 0000000..091c665
--- /dev/null
+++ b/a0.1/a0.8/Assignment_08_Tensorflow___Nexus___RoohollaAlikhani.ipynb
@@ -0,0 +1,373 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WKCsWe71mbTO"
+      },
+      "source": [
+        "# 🧠 Assignment: Getting Started with TensorFlow\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ODPYXweVmdKp"
+      },
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment evaluates your basic skills in using TensorFlow, including creating tensors, performing operations, and building a simple model.\n",
+        "\n",
+        "### 1️⃣ Setup and Basics\n",
+        "**Task:**\n",
+        "\n",
+        "* Install TensorFlow and verify the version.\n",
+        "* Create a simple tensor and print it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "LrzxFPr3mPno"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "TensorFlow version: 2.19.0\n",
+            "Tensor a:\n",
+            " tf.Tensor(\n",
+            "[[1 2]\n",
+            " [3 4]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "print(\"TensorFlow version:\", tf.__version__)\n",
+        "a = tf.constant([[1, 2], [3, 4]])\n",
+        "print(\"Tensor a:\\n\", a)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "MSC0WPwymq3V"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tensor a:\n",
+            " tf.Tensor(\n",
+            "[[5 6]\n",
+            " [7 8]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Create a tensor b with values [[5, 6], [7, 8]] and print its shape.\n",
+        "b = tf.constant([[5, 6], [7, 8]])\n",
+        "print(\"Tensor a:\\n\", b)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b4cuWXJvmwdC"
+      },
+      "source": [
+        "### 2️⃣ Tensor Operations\n",
+        "**Task:** Perform element-wise addition and multiplication."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "pzlGeEuhmtYo"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Addition:\n",
+            " tf.Tensor(\n",
+            "[[ 6  8]\n",
+            " [10 12]], shape=(2, 2), dtype=int32)\n",
+            "Multiplication:\n",
+            " tf.Tensor(\n",
+            "[[ 5 12]\n",
+            " [21 32]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "c = a + b\n",
+        "print(\"Addition:\\n\", c)\n",
+        "\n",
+        "d = a * b\n",
+        "print(\"Multiplication:\\n\", d)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zv02rBVZmzPR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "matrix multiplication: \n",
+            " tf.Tensor(\n",
+            "[[19 22]\n",
+            " [43 50]], shape=(2, 2), dtype=int32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Compute a @ b (matrix multiplication) and print the result.\n",
+        "e = a @ b\n",
+        "print(\"matrix multiplication: \\n\" , e)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CbDULpk7m3c2"
+      },
+      "source": [
+        "### 3️⃣ Random Tensors 🎲\n",
+        "**Task:** Generate a random tensor of shape (3, 3) from a normal distribution and print its mean and standard deviation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "Czc74X8Nm2Oa"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Random Tensor:\n",
+            " tf.Tensor(\n",
+            "[[ 2.4778519   1.091282   -1.2910949 ]\n",
+            " [ 1.723791    0.13134924  0.892447  ]\n",
+            " [ 0.0541843  -0.05278427 -0.22317024]], shape=(3, 3), dtype=float32)\n",
+            "Mean: tf.Tensor(0.53376174, shape=(), dtype=float32)\n",
+            "Std Dev: tf.Tensor(1.068444, shape=(), dtype=float32)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "random_tensor = tf.random.normal([3, 3])\n",
+        "print(\"Random Tensor:\\n\", random_tensor)\n",
+        "print(\"Mean:\", tf.reduce_mean(random_tensor))\n",
+        "print(\"Std Dev:\", tf.math.reduce_std(random_tensor))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wVH1vcq3nDgV"
+      },
+      "source": [
+        "### 4️⃣ Building a Simple Model 🤖\n",
+        "**Task:** Build a single-layer neural network using tf.keras.Sequential."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "9G2Jw8VSnC6f"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "c:\\Users\\Magellan\\miniconda3\\envs\\DsAi\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:92: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
+              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │             \u001b[38;5;34m2\u001b[0m │\n",
+              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (8.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2\u001b[0m (8.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [
+              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+              "</pre>\n"
+            ],
+            "text/plain": [
+              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "model = tf.keras.Sequential([\n",
+        "    tf.keras.layers.Dense(1, input_shape=(1,))\n",
+        "    ])\n",
+        "model.summary()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "C1ianPt1nKlr"
+      },
+      "outputs": [],
+      "source": [
+        "# Your turn:\n",
+        "# Add another dense layer with 5 neurons and relu activation.\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GpOxR_RFnMt_"
+      },
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Create a tensor with values from 0 to 9 (inclusive).\n",
+        "\n",
+        "* Reshape it to shape (2, 5).\n",
+        "\n",
+        "* Pint the reshaped tensor."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "caRjq7WinMLf"
+      },
+      "outputs": [],
+      "source": [
+        "# To do\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "DsAi",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.12.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.9/AI-DS_Nexus__A0_9_Amirhossein_Zandi_8fca80.ipynb b/a0.1/a0.9/AI-DS_Nexus__A0_9_Amirhossein_Zandi_8fca80.ipynb
new file mode 100644
index 0000000..37627a9
--- /dev/null
+++ b/a0.1/a0.9/AI-DS_Nexus__A0_9_Amirhossein_Zandi_8fca80.ipynb
@@ -0,0 +1,319 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jXeFx3ouAeuu"
+      },
+      "source": [
+        "# 1️⃣ Class and Object Creation\n",
+        "Task:\n",
+        "\n",
+        "Define a class Car with attributes brand and year.\n",
+        "\n",
+        "Create an object car1 of class Car with values (\"Toyota\", 2020) and print them."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "q6ZZJvAlwvOR",
+        "outputId": "0cab0f68-9d31-4473-9e39-52ee9baa7f32"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Toyota 2020\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "print(car1.brand, car1.year)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Zgp2JBoeyfKk",
+        "outputId": "958c2ca8-dc2c-4b91-c812-bdb2f155a7ec"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tesla 2023\n"
+          ]
+        }
+      ],
+      "source": [
+        "car2 = Car(\"Tesla\" , 2023)\n",
+        "print(car2.brand , car2.year)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "97_yM2QFB9SK"
+      },
+      "source": [
+        "# 2️⃣ Add Methods 🛠\n",
+        "Task: Add a method info() to Car that prints \"Brand: , Year: \"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "m8xhvbmxCAAU",
+        "outputId": "93247157-601d-459c-a016-6cda0541f221"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Brand: Toyota, Year: 2020\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "car1.info()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "H7dPpb_7NcqO",
+        "outputId": "f7b8bbe4-36e4-4259-8f15-5b894003aa3f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Brand: Tesla, Year: 2023\n"
+          ]
+        }
+      ],
+      "source": [
+        "car2 = Car(\"Tesla\" , 2023)\n",
+        "car2.info()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g8bOyO2ZNnSa"
+      },
+      "source": [
+        "# 3️⃣ Class vs Instance Attributes 📦\n",
+        "Task:\n",
+        "\n",
+        "Add a class attribute wheels = 4 to Car.\n",
+        "\n",
+        "Print car1.wheels and car2.wheels."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "225NeqDWNsj8"
+      },
+      "outputs": [],
+      "source": [
+        "class Car:\n",
+        "    def __init__(self, brand, year , wheels=4):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "        self.wheels = wheels"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gJBPBp_PN8lG",
+        "outputId": "6057831d-1a57-4b3d-9d0f-4f3c43cd8b48"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Car 1 wheels : 4\n",
+            "Car 2 wheels : 4\n"
+          ]
+        }
+      ],
+      "source": [
+        "car1 = Car(\"Toyota\" , 2020)\n",
+        "car2 = Car(\"Tesla\" , 2023)\n",
+        "print(f\"Car 1 wheels : {car1.wheels}\")\n",
+        "print(f\"Car 2 wheels : {car2.wheels}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "R4vl_QW0OZ_c"
+      },
+      "source": [
+        "# 4️⃣ Inheritance 👑\n",
+        "Task:\n",
+        "\n",
+        "Create a subclass ElectricCar inheriting from Car.\n",
+        "Add a new attribute battery (e.g., \"80 kWh\") and a method battery_info() that prints \"Battery: \"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oqi-7UsoOciP",
+        "outputId": "8fbbf38e-7161-4c69-ee22-7268b0b89f7f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Brand: Tesla, Year: 2023, Wheels: 4\n",
+            "Battery: 80 kWh\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "\n",
+        "class Car:\n",
+        "    def __init__(self , brand , year , wheels=4):\n",
+        "      self.brand = brand\n",
+        "      self.year = year\n",
+        "      self.wheels = wheels\n",
+        "\n",
+        "    def info(self):\n",
+        "      print(f\"Brand: {self.brand}, Year: {self.year}, Wheels: {self.wheels}\")\n",
+        "\n",
+        "\n",
+        "class ElectricCar(Car):\n",
+        "    def __init__(self, brand, year, battery):\n",
+        "        super().__init__(brand, year)\n",
+        "        self.battery = battery\n",
+        "\n",
+        "    def battery_info(self):\n",
+        "        print(f\"Battery: {self.battery}\")\n",
+        "\n",
+        "e_car = ElectricCar(\"Tesla\", 2023, \"80 kWh\")\n",
+        "e_car.info()\n",
+        "e_car.battery_info()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pjerthQYZF1c"
+      },
+      "source": [
+        "# 5️⃣ Bonus ⚡\n",
+        "Add a str method to Car to return a string like: \"Car(brand=Toyota, year=2020)\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "KVGLJjZ6ZRT2",
+        "outputId": "2d2abe0d-1e91-4cc5-e2aa-83895023ed38"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            },
+            "text/plain": [
+              "'Car(brand=Toyota, year=2020)'"
+            ]
+          },
+          "execution_count": 33,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "class Car:\n",
+        "  def __init__(self , brand , year , wheels=4):\n",
+        "    self.brand = brand\n",
+        "    self.year = year\n",
+        "    self.wheels = wheels\n",
+        "  def __str__(self):\n",
+        "    return f\"Car(brand={self.brand}, year={self.year})\"\n",
+        "\n",
+        "c1 = Car(\"Toyota\" , 2020)\n",
+        "c1.__str__()"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python",
+      "version": "3.12.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.9/AI-DS_Nexus__A0_9_Javid_Mohammadi_a60d6f.ipynb b/a0.1/a0.9/AI-DS_Nexus__A0_9_Javid_Mohammadi_a60d6f.ipynb
new file mode 100644
index 0000000..aeaf132
--- /dev/null
+++ b/a0.1/a0.9/AI-DS_Nexus__A0_9_Javid_Mohammadi_a60d6f.ipynb
@@ -0,0 +1,346 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🏗 Assignment: Object-Oriented Programming Basics\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n"
+      ],
+      "metadata": {
+        "id": "pmXWXQskqQf7"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment checks your understanding of classes, objects, attributes, and methods using Python."
+      ],
+      "metadata": {
+        "id": "mYX-Yr0YqWTv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1️⃣ Class and Object Creation\n",
+        "**Task:**\n",
+        "\n",
+        "* Define a class Car with attributes brand and year.\n",
+        "\n",
+        "* Create an object car1 of class Car with values (\"Toyota\", 2020) and print them."
+      ],
+      "metadata": {
+        "id": "uxnkvADeqX6P"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HF7h_UxJqNLb",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ac461f09-1746-4ab3-e982-a28e8c96b2d1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Toyota 2020\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "print(car1.brand, car1.year)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Your turn:\n",
+        "# Create another object car2 with brand \"Tesla\" and year 2023.\n",
+        "car2 = Car('Tesla', 2023)\n",
+        "print(car2.brand, car2.year)\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "5frI0xqrqcz2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c3611dd9-a9d8-45cb-fd4e-2ef441b4b254"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Tesla 2023\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2️⃣ Add Methods 🛠\n",
+        "**Task:**\n",
+        "Add a method info() to Car that prints \"Brand: <brand>, Year: <year>\"."
+      ],
+      "metadata": {
+        "id": "1WvafJpAqgkt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "car1.info()\n"
+      ],
+      "metadata": {
+        "id": "AJM4pVrzqfJP",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e2afb38a-07b6-4095-fd7e-e73f719ce99d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Brand: Toyota, Year: 2020\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Call info() for car2.\n",
+        "car2 = Car('Tesla', 2023)\n",
+        "car2.info()\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "qwuFw0CiqkM8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "62b1a755-d53c-40f8-e366-d0a98f1a66b7"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Brand: Tesla, Year: 2023\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3️⃣ Class vs Instance Attributes 📦\n",
+        "**Task:**\n",
+        "\n",
+        "* Add a class attribute wheels = 4 to Car.\n",
+        "\n",
+        "* Print car1.wheels and car2.wheels."
+      ],
+      "metadata": {
+        "id": "AOwBtvr0qosK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "        self.wheels = 4 # initialize directly because all cars have 4 wheels.\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "\n",
+        "car1, car2 = Car(\"Toyota\", 2020), Car('Tesla', 2023)\n",
+        "print(car1.wheels, car2.wheels)\n"
+      ],
+      "metadata": {
+        "id": "rPXvKwtsqm0C",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d1484524-fbd3-4f8e-f314-c3b21ed3424e"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "4 4\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4️⃣ Inheritance 👑\n",
+        "**Task:**\n",
+        "\n",
+        "* Create a subclass ElectricCar inheriting from Car.\n",
+        "* Add a new attribute battery (e.g., \"80 kWh\") and a method battery_info() that prints \"Battery: <battery>\"."
+      ],
+      "metadata": {
+        "id": "NLoiaaeuquwg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "class ElectricCar(Car):\n",
+        "    def __init__(self, brand, year, battery):\n",
+        "        super().__init__(brand, year)\n",
+        "        self.battery = battery\n",
+        "\n",
+        "    def battery_info(self):\n",
+        "        print(f\"Battery: {self.battery}\")\n",
+        "\n",
+        "e_car = ElectricCar(\"Tesla\", 2023, \"80 kWh\")\n",
+        "e_car.info()\n",
+        "e_car.battery_info()\n"
+      ],
+      "metadata": {
+        "id": "e_sS1YdIqtk_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2f88a5c7-af58-4dbb-a3fd-3fca4d3f29cf"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Brand: Tesla, Year: 2023\n",
+            "Battery: 80 kWh\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Add a __str__ method to Car to return a string like: \"Car(brand=Toyota, year=2020)\"."
+      ],
+      "metadata": {
+        "id": "wC0OIrRMq7jv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "        self.wheels = 4 # initialize directly because all cars have 4 wheels.\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "\n",
+        "    def __str__(self):\n",
+        "      return f\"Car(brand={self.brand}, year={self.year})\"\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "cAfBWxrlrA2t"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "car1, car2 = Car(\"Toyota\", 2020), Car('Tesla', 2023)\n",
+        "print(car1)\n",
+        "print(car2)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "q4Kl3WtO8i39",
+        "outputId": "a1687c34-2c86-406b-99dd-e3619d0a4355"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Car(brand=Toyota, year=2020)\n",
+            "Car(brand=Tesla, year=2023)\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.9/AI-DS_Nexus__A0_9_Project__RezaShokr.ipynb b/a0.1/a0.9/AI-DS_Nexus__A0_9_Project__RezaShokr.ipynb
new file mode 100644
index 0000000..2b9b41f
--- /dev/null
+++ b/a0.1/a0.9/AI-DS_Nexus__A0_9_Project__RezaShokr.ipynb
@@ -0,0 +1,221 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🏗 Assignment: Object-Oriented Programming Basics\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "pmXWXQskqQf7"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment checks your understanding of classes, objects, attributes, and methods using Python."
+      ],
+      "metadata": {
+        "id": "mYX-Yr0YqWTv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1️⃣ Class and Object Creation\n",
+        "**Task:**\n",
+        "\n",
+        "* Define a class Car with attributes brand and year.\n",
+        "\n",
+        "* Create an object car1 of class Car with values (\"Toyota\", 2020) and print them."
+      ],
+      "metadata": {
+        "id": "uxnkvADeqX6P"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HF7h_UxJqNLb"
+      },
+      "outputs": [],
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "print(car1.brand, car1.year)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Your turn:\n",
+        "# Create another object car2 with brand \"Tesla\" and year 2023.\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "5frI0xqrqcz2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2️⃣ Add Methods 🛠\n",
+        "**Task:**\n",
+        "Add a method info() to Car that prints \"Brand: <brand>, Year: <year>\"."
+      ],
+      "metadata": {
+        "id": "1WvafJpAqgkt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "car1.info()\n"
+      ],
+      "metadata": {
+        "id": "AJM4pVrzqfJP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Call info() for car2.\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "qwuFw0CiqkM8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3️⃣ Class vs Instance Attributes 📦\n",
+        "**Task:**\n",
+        "\n",
+        "* Add a class attribute wheels = 4 to Car.\n",
+        "\n",
+        "* Print car1.wheels and car2.wheels."
+      ],
+      "metadata": {
+        "id": "AOwBtvr0qosK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "rPXvKwtsqm0C"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4️⃣ Inheritance 👑\n",
+        "**Task:**\n",
+        "\n",
+        "* Create a subclass ElectricCar inheriting from Car.\n",
+        "* Add a new attribute battery (e.g., \"80 kWh\") and a method battery_info() that prints \"Battery: <battery>\"."
+      ],
+      "metadata": {
+        "id": "NLoiaaeuquwg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "class ElectricCar(Car):\n",
+        "    def __init__(self, brand, year, battery):\n",
+        "        ________.__init__(brand, year)\n",
+        "        self.battery = battery\n",
+        "\n",
+        "    def battery_info(self):\n",
+        "        print(f\"Battery: {________.battery}\")\n",
+        "\n",
+        "e_car = ElectricCar(\"Tesla\", 2023, \"80 kWh\")\n",
+        "e_car.info()\n",
+        "e_car.battery_info()\n"
+      ],
+      "metadata": {
+        "id": "e_sS1YdIqtk_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Add a __str__ method to Car to return a string like: \"Car(brand=Toyota, year=2020)\"."
+      ],
+      "metadata": {
+        "id": "wC0OIrRMq7jv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "cAfBWxrlrA2t"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.9/AI-DS_Nexus__A0_9_Project_roohi_268383.ipynb b/a0.1/a0.9/AI-DS_Nexus__A0_9_Project_roohi_268383.ipynb
new file mode 100644
index 0000000..2b9b41f
--- /dev/null
+++ b/a0.1/a0.9/AI-DS_Nexus__A0_9_Project_roohi_268383.ipynb
@@ -0,0 +1,221 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🏗 Assignment: Object-Oriented Programming Basics\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "pmXWXQskqQf7"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment checks your understanding of classes, objects, attributes, and methods using Python."
+      ],
+      "metadata": {
+        "id": "mYX-Yr0YqWTv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1️⃣ Class and Object Creation\n",
+        "**Task:**\n",
+        "\n",
+        "* Define a class Car with attributes brand and year.\n",
+        "\n",
+        "* Create an object car1 of class Car with values (\"Toyota\", 2020) and print them."
+      ],
+      "metadata": {
+        "id": "uxnkvADeqX6P"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HF7h_UxJqNLb"
+      },
+      "outputs": [],
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "print(car1.brand, car1.year)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Your turn:\n",
+        "# Create another object car2 with brand \"Tesla\" and year 2023.\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "5frI0xqrqcz2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2️⃣ Add Methods 🛠\n",
+        "**Task:**\n",
+        "Add a method info() to Car that prints \"Brand: <brand>, Year: <year>\"."
+      ],
+      "metadata": {
+        "id": "1WvafJpAqgkt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "car1.info()\n"
+      ],
+      "metadata": {
+        "id": "AJM4pVrzqfJP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Call info() for car2.\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "qwuFw0CiqkM8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3️⃣ Class vs Instance Attributes 📦\n",
+        "**Task:**\n",
+        "\n",
+        "* Add a class attribute wheels = 4 to Car.\n",
+        "\n",
+        "* Print car1.wheels and car2.wheels."
+      ],
+      "metadata": {
+        "id": "AOwBtvr0qosK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "rPXvKwtsqm0C"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4️⃣ Inheritance 👑\n",
+        "**Task:**\n",
+        "\n",
+        "* Create a subclass ElectricCar inheriting from Car.\n",
+        "* Add a new attribute battery (e.g., \"80 kWh\") and a method battery_info() that prints \"Battery: <battery>\"."
+      ],
+      "metadata": {
+        "id": "NLoiaaeuquwg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "class ElectricCar(Car):\n",
+        "    def __init__(self, brand, year, battery):\n",
+        "        ________.__init__(brand, year)\n",
+        "        self.battery = battery\n",
+        "\n",
+        "    def battery_info(self):\n",
+        "        print(f\"Battery: {________.battery}\")\n",
+        "\n",
+        "e_car = ElectricCar(\"Tesla\", 2023, \"80 kWh\")\n",
+        "e_car.info()\n",
+        "e_car.battery_info()\n"
+      ],
+      "metadata": {
+        "id": "e_sS1YdIqtk_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Add a __str__ method to Car to return a string like: \"Car(brand=Toyota, year=2020)\"."
+      ],
+      "metadata": {
+        "id": "wC0OIrRMq7jv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "cAfBWxrlrA2t"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.9/AI-DS_Nexus__A0_9__aminran.ipynb b/a0.1/a0.9/AI-DS_Nexus__A0_9__aminran.ipynb
new file mode 100644
index 0000000..2864b94
--- /dev/null
+++ b/a0.1/a0.9/AI-DS_Nexus__A0_9__aminran.ipynb
@@ -0,0 +1,335 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🏗 Assignment: Object-Oriented Programming Basics\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "pmXWXQskqQf7"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment checks your understanding of classes, objects, attributes, and methods using Python."
+      ],
+      "metadata": {
+        "id": "mYX-Yr0YqWTv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1️⃣ Class and Object Creation\n",
+        "**Task:**\n",
+        "\n",
+        "* Define a class Car with attributes brand and year.\n",
+        "\n",
+        "* Create an object car1 of class Car with values (\"Toyota\", 2020) and print them."
+      ],
+      "metadata": {
+        "id": "uxnkvADeqX6P"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "HF7h_UxJqNLb",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e9723e0b-80f9-4609-c44c-2e446f3f7569"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Toyota 2020\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "print(car1.brand, car1.year)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Your turn:\n",
+        "# Create another object car2 with brand \"Tesla\" and year 2023.\n",
+        "\n",
+        "car2 = Car(\"Tesla\", 2023)\n",
+        "print(car2.brand, car2.year)\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "5frI0xqrqcz2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "10ac8d19-92cd-4e7b-a15b-218a5bc64cdf"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Tesla 2023\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2️⃣ Add Methods 🛠\n",
+        "**Task:**\n",
+        "Add a method info() to Car that prints \"Brand: <brand>, Year: <year>\"."
+      ],
+      "metadata": {
+        "id": "1WvafJpAqgkt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "car1.info()\n"
+      ],
+      "metadata": {
+        "id": "AJM4pVrzqfJP",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cc866bdb-40d7-4c42-bc33-7d2a4ac6040a"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Brand: Toyota, Year: 2020\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Call info() for car2.\n",
+        "car2 = Car(\"Tesla\", 2023)\n",
+        "car2.info()\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "qwuFw0CiqkM8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f48e67ea-7096-4d6c-d0ea-280323662a2a"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Brand: Tesla, Year: 2023\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3️⃣ Class vs Instance Attributes 📦\n",
+        "**Task:**\n",
+        "\n",
+        "* Add a class attribute wheels = 4 to Car.\n",
+        "\n",
+        "* Print car1.wheels and car2.wheels."
+      ],
+      "metadata": {
+        "id": "AOwBtvr0qosK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "\n",
+        "class Car:\n",
+        "    wheels = 4\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "car2 = Car(\"Tesla\", 2023)\n",
+        "\n",
+        "print(car1.wheels)\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "rPXvKwtsqm0C",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9e039088-6b75-46f9-daf2-f217af590be6"
+      },
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "4\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4️⃣ Inheritance 👑\n",
+        "**Task:**\n",
+        "\n",
+        "* Create a subclass ElectricCar inheriting from Car.\n",
+        "* Add a new attribute battery (e.g., \"80 kWh\") and a method battery_info() that prints \"Battery: <battery>\"."
+      ],
+      "metadata": {
+        "id": "NLoiaaeuquwg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "\n",
+        "\n",
+        "\n",
+        "class ElectricCar(Car):\n",
+        "    def __init__(self, brand, year, battery):\n",
+        "        super().__init__(brand, year)\n",
+        "        self.battery = battery\n",
+        "\n",
+        "    def battery_info(self):\n",
+        "        print(f\"Battery: {self.battery}\")\n",
+        "\n",
+        "e_car = ElectricCar(\"Tesla\", 2023, \"80 kWh\")\n",
+        "e_car.info()\n",
+        "e_car.battery_info()\n"
+      ],
+      "metadata": {
+        "id": "e_sS1YdIqtk_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "10facab8-66b5-400c-8f7d-834797979aec"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Brand: Tesla, Year: 2023\n",
+            "Battery: 80 kWh\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Add a __str__ method to Car to return a string like: \"Car(brand=Toyota, year=2020)\"."
+      ],
+      "metadata": {
+        "id": "wC0OIrRMq7jv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def __str__(self):\n",
+        "        return f\"Car(brand={self.brand}, year={self.year})\"\n",
+        "\n",
+        "car1=Car(\"Toyota\", 2020)\n",
+        "print(car1)\n"
+      ],
+      "metadata": {
+        "id": "cAfBWxrlrA2t",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ca7afc42-2957-445f-aa46-a4f18df3e2d5"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Car(brand=Toyota, year=2020)\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.9/AI-DS_Nexus__A0_9__rsayyareh.ipynb b/a0.1/a0.9/AI-DS_Nexus__A0_9__rsayyareh.ipynb
new file mode 100644
index 0000000..bab5dd5
--- /dev/null
+++ b/a0.1/a0.9/AI-DS_Nexus__A0_9__rsayyareh.ipynb
@@ -0,0 +1,323 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pmXWXQskqQf7"
+      },
+      "source": [
+        "# 🏗 Assignment: Object-Oriented Programming Basics\n",
+        "\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mYX-Yr0YqWTv"
+      },
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment checks your understanding of classes, objects, attributes, and methods using Python."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uxnkvADeqX6P"
+      },
+      "source": [
+        "### 1️⃣ Class and Object Creation\n",
+        "**Task:**\n",
+        "\n",
+        "* Define a class Car with attributes brand and year.\n",
+        "\n",
+        "* Create an object car1 of class Car with values (\"Toyota\", 2020) and print them."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "id": "HF7h_UxJqNLb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Toyota 2020\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "print(car1.brand, car1.year)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "5frI0xqrqcz2"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Tesla 2023\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Your turn:\n",
+        "# Create another object car2 with brand \"Tesla\" and year 2023.\n",
+        "car2 = Car(\"Tesla\", 2023)\n",
+        "print(car2.brand, car2.year)\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1WvafJpAqgkt"
+      },
+      "source": [
+        "### 2️⃣ Add Methods 🛠\n",
+        "**Task:**\n",
+        "Add a method info() to Car that prints \"Brand: <brand>, Year: <year>\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "AJM4pVrzqfJP"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Brand: Toyota, Year: 2020\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "car1.info()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "qwuFw0CiqkM8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Brand: Tesla, Year: 2023\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Your turn:\n",
+        "# Call info() for car2.\n",
+        "car2 = Car(\"Tesla\", 2023)\n",
+        "car2.info()\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AOwBtvr0qosK"
+      },
+      "source": [
+        "### 3️⃣ Class vs Instance Attributes 📦\n",
+        "**Task:**\n",
+        "\n",
+        "* Add a class attribute wheels = 4 to Car.\n",
+        "\n",
+        "* Print car1.wheels and car2.wheels."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "id": "rPXvKwtsqm0C"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Brand: Toyota, Wheels: 4\n",
+            "Brand: Tesla, Wheels: 4\n"
+          ]
+        }
+      ],
+      "source": [
+        "# To do\n",
+        "setattr(Car,'wheels', 4)\n",
+        "print(f\"Brand: {car1.brand}, Wheels: {car1.wheels}\")\n",
+        "print(f\"Brand: {car2.brand}, Wheels: {car2.wheels}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NLoiaaeuquwg"
+      },
+      "source": [
+        "### 4️⃣ Inheritance 👑\n",
+        "**Task:**\n",
+        "\n",
+        "* Create a subclass ElectricCar inheriting from Car.\n",
+        "* Add a new attribute battery (e.g., \"80 kWh\") and a method battery_info() that prints \"Battery: <battery>\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "e_sS1YdIqtk_"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Brand: Tesla, Year: 2023\n",
+            "Battery: 80 kWh\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "    \n",
+        "class ElectricCar(Car):\n",
+        "    def __init__(self, brand, year, battery):\n",
+        "        super().__init__(brand, year)\n",
+        "        self.battery = battery\n",
+        "\n",
+        "    def battery_info(self):\n",
+        "        print(f\"Battery: {self.battery}\")\n",
+        "\n",
+        "e_car = ElectricCar(\"Tesla\", 2023, \"80 kWh\")\n",
+        "e_car.info()\n",
+        "e_car.battery_info()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wC0OIrRMq7jv"
+      },
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Add a __str__ method to Car to return a string like: \"Car(brand=Toyota, year=2020)\"."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "cAfBWxrlrA2t"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "'Car(brand=Toyota, year=2020)'"
+            ]
+          },
+          "execution_count": 43,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# To do\n",
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "\n",
+        "    def st(self):\n",
+        "        return f\"Car(brand={self.brand}, year={self.year})\"\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "car1.st()\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a0.9/Assignment_09_Danieljaafari.ipynb b/a0.1/a0.9/Assignment_09_Danieljaafari.ipynb
new file mode 100644
index 0000000..dfd0170
--- /dev/null
+++ b/a0.1/a0.9/Assignment_09_Danieljaafari.ipynb
@@ -0,0 +1,435 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🏗 Assignment: Object-Oriented Programming Basics\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "pmXWXQskqQf7"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment checks your understanding of classes, objects, attributes, and methods using Python."
+      ],
+      "metadata": {
+        "id": "mYX-Yr0YqWTv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1️⃣ Class and Object Creation\n",
+        "**Task:**\n",
+        "\n",
+        "* Define a class Car with attributes brand and year.\n",
+        "\n",
+        "* Create an object car1 of class Car with values (\"Toyota\", 2020) and print them."
+      ],
+      "metadata": {
+        "id": "uxnkvADeqX6P"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HF7h_UxJqNLb"
+      },
+      "outputs": [],
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "print(car1.brand, car1.year)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Your turn:\n",
+        "# Create another object car2 with brand \"Tesla\" and year 2023.\n",
+        "class CarDatabase:\n",
+        "    def __init__(self):\n",
+        "        self.data = {\n",
+        "            \"2022\": {\n",
+        "                \"Tesla\": [\"Model 3\", \"Model Y\"],\n",
+        "                \"Toyota\": [\"Camery1\", \"Highlux1\"],\n",
+        "            },\n",
+        "            \"2023\": {\n",
+        "                \"Tesla\": [\"Model X\", \"Model S\"],\n",
+        "                \"Toyota\": [\"Camery2\", \"Highlux2\"],\n",
+        "            }\n",
+        "        }\n",
+        "\n",
+        "    def find_models(self, brand=None, year=None):\n",
+        "      # if main: Year is provided and exists in database\n",
+        "        if year and year in self.data:\n",
+        "          # Sub-if: Both year and valid brand are provided\n",
+        "            if brand and brand in self.data[year]:\n",
+        "                return f\"🚗 {brand} models in {year}: {', '.join(self.data[year][brand])}\"\n",
+        "          # Sub-case if: Brand is provided but not found in that year\n",
+        "            elif brand and brand not in self.data[year] and brand is not None:\n",
+        "                return f\"❌ No data for brand '{brand}' in year {year}.\"\n",
+        "          # Sub-case if: Only year is provided, show all brands and models in that year\n",
+        "            else:\n",
+        "                models_list = []\n",
+        "                for b, models in self.data[year].items():\n",
+        "                    models_list.append(f\"{b}: {', '.join(models)}\")\n",
+        "                return f\"📆 Models in {year}:\\n\" + \"\\n\".join(models_list)\n",
+        "        # if main2: Only brand is provided (without year), show all years for that brand\n",
+        "        elif brand:\n",
+        "            output = f\"🔎 All models for brand '{brand}':\\n\"\n",
+        "            found = False\n",
+        "            for y in self.data:\n",
+        "                if brand in self.data[y]:\n",
+        "                    found = True\n",
+        "                    output += f\"{y}: {', '.join(self.data[y][brand])}\\n\"\n",
+        "            return output if found else f\"❌ No data for brand '{brand}'.\"\n",
+        "        # if main3: Neither brand nor year is provided, show warning\n",
+        "        else:\n",
+        "            return \"⚠️ Please enter at least a brand or a year!\"\n",
+        "\n",
+        "#Creating Object this class\n",
+        "db = CarDatabase()\n",
+        "\n",
+        "#input for contant\n",
+        "year_input = input(\"Enter year (or leave blank): \").strip()\n",
+        "brand_input = input(\"Enter brand (or leave blank): \").strip()\n",
+        "\n",
+        "#if contant's input is no arrgument , sopouse None\n",
+        "year = year_input if year_input else None\n",
+        "brand = brand_input if brand_input else None\n",
+        "\n",
+        "result = db.find_models(brand=brand, year=year)\n",
+        "print(result)"
+      ],
+      "metadata": {
+        "id": "5frI0xqrqcz2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b36ab44f-ae64-4ff7-fd9a-31686b8afecd"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Enter year (or leave blank): \n",
+            "Enter brand (or leave blank): Tesla\n",
+            "🔎 All models for brand 'Tesla':\n",
+            "2022: Model 3, Model Y\n",
+            "2023: Model X, Model S\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2️⃣ Add Methods 🛠\n",
+        "**Task:**\n",
+        "Add a method info() to Car that prints \"Brand: <brand>, Year: <year>\"."
+      ],
+      "metadata": {
+        "id": "1WvafJpAqgkt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "car1.info()\n"
+      ],
+      "metadata": {
+        "id": "AJM4pVrzqfJP",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4b3799e2-3930-4171-ec21-44a95a8855b0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Brand: Toyota, Year: 2020\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Call info() for car2.\n",
+        "class Car:\n",
+        "  def __init__(self, brand, year , model):\n",
+        "    self.brand = brand\n",
+        "    self.year = year\n",
+        "    self.model = model\n",
+        "\n",
+        "  def info(self):\n",
+        "    print(f\"The Brand of{self.brand} and Year of{self.year} and Model of{self.model}is now\")\n",
+        "\n",
+        "\n",
+        "# Creating multiple Tesla cars based on year and model\n",
+        "car1 = Car(\"Tesla\", 2022, \"Model 3\")\n",
+        "car2 = Car(\"Tesla\", 2022, \"Model Y\")\n",
+        "car3 = Car(\"Tesla\", 2023, \"Model X\")\n",
+        "car4 = Car(\"Tesla\", 2023, \"Model S\")\n",
+        "\n",
+        "car1.info()\n",
+        "car2.info()\n",
+        "car3.info()\n",
+        "car4.info()"
+      ],
+      "metadata": {
+        "id": "qwuFw0CiqkM8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0d073a7e-03c3-44cb-c243-e98ee9394640"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The Brand ofTesla and Year of2022 and Model ofModel 3is now\n",
+            "The Brand ofTesla and Year of2022 and Model ofModel Yis now\n",
+            "The Brand ofTesla and Year of2023 and Model ofModel Xis now\n",
+            "The Brand ofTesla and Year of2023 and Model ofModel Sis now\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3️⃣ Class vs Instance Attributes 📦\n",
+        "**Task:**\n",
+        "\n",
+        "* Add a class attribute wheels = 4 to Car.\n",
+        "\n",
+        "* Print car1.wheels and car2.wheels."
+      ],
+      "metadata": {
+        "id": "AOwBtvr0qosK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "class Car:\n",
+        "  wheels = 4\n",
+        "  def __init__(self, brand, year, model):\n",
+        "    self.brand = brand\n",
+        "    self.year = year\n",
+        "    self.model = model\n",
+        "  def info(self):\n",
+        "    print(f\"The Brand of{self.brand} and Year of{self.year} and Model of{self.model}is now\")\n",
+        "\n",
+        "car1 = Car(\"Tesla\", 2022, \"Model Y\")\n",
+        "car2 = Car(\"Tesla\", 2023, \"Model X\")\n",
+        "\n",
+        "print(\"car1 wheels:\", car1.wheels)\n",
+        "print(\"car2 wheels:\", car2.wheels)\n",
+        "\n",
+        "\n",
+        "car1.info()\n",
+        "car2.info()\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "rPXvKwtsqm0C",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d7f77f3b-4990-4172-c3c8-a20a1668f1b2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "car1 wheels: 4\n",
+            "car2 wheels: 4\n",
+            "The Brand ofTesla and Year of2022 and Model ofModel Yis now\n",
+            "The Brand ofTesla and Year of2023 and Model ofModel Xis now\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4️⃣ Inheritance 👑\n",
+        "**Task:**\n",
+        "\n",
+        "* Create a subclass ElectricCar inheriting from Car.\n",
+        "* Add a new attribute battery (e.g., \"80 kWh\") and a method battery_info() that prints \"Battery: <battery>\"."
+      ],
+      "metadata": {
+        "id": "NLoiaaeuquwg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "class ElectricCar(Car):\n",
+        "    def __init__(self, brand, year, battery):\n",
+        "        ________.__init__(brand, year)\n",
+        "        self.battery = battery\n",
+        "\n",
+        "    def battery_info(self):\n",
+        "        print(f\"Battery: {________.battery}\")\n",
+        "\n",
+        "e_car = ElectricCar(\"Tesla\", 2023, \"80 kWh\")\n",
+        "e_car.info()\n",
+        "e_car.battery_info()\n"
+      ],
+      "metadata": {
+        "id": "e_sS1YdIqtk_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Car:\n",
+        "    def __init__(self, brand, year, model):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "        self.model = model\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"The Brand of{self.brand} and Year of{self.year} and Model of{self.model}is now\")\n",
+        "\n",
+        "\n",
+        "class ElectricCar(Car):\n",
+        "    def __init__(self, brand, year, model, battery):\n",
+        "        super().__init__(brand, year, model)\n",
+        "        self.battery = battery\n",
+        "\n",
+        "    def battery_info(self):\n",
+        "        print(f\"Battery: {self.battery}\")\n",
+        "\n",
+        "e_car = ElectricCar(\" Tesla \",  \" 2023 \" , \" Model S \" , \" 80 kWh \")\n",
+        "\n",
+        "e_car.info()\n",
+        "e_car.battery_info()\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "B3exlEHmH47w",
+        "outputId": "0fbe616d-2dbd-410e-90f0-32222fc5a3f1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The Brand of Tesla  and Year of 2023  and Model of Model S is now\n",
+            "Battery:  80 kWh \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Add a __str__ method to Car to return a string like: \"Car(brand=Toyota, year=2020)\"."
+      ],
+      "metadata": {
+        "id": "wC0OIrRMq7jv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year, model):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "        self.model = model\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"The Brand of{self.brand} and Year of{self.year} and Model of{self.model}is now\")\n",
+        "\n",
+        "    def __str__(self):\n",
+        "        return f\"Car(brand={self.brand}, year={self.year} , model={self.model})\"\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2022, \"Camery1\")\n",
+        "print(car1)\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "cAfBWxrlrA2t",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "69a89a25-96d4-4609-9a2c-ccb4ce2d16c1"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Car(brand=Toyota, year=2022 , model=Camery1)\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a0.9/Assignment_09_OOP___Nexus___RoohollaAlikhani_ipynb.ipynb b/a0.1/a0.9/Assignment_09_OOP___Nexus___RoohollaAlikhani_ipynb.ipynb
new file mode 100644
index 0000000..2b9b41f
--- /dev/null
+++ b/a0.1/a0.9/Assignment_09_OOP___Nexus___RoohollaAlikhani_ipynb.ipynb
@@ -0,0 +1,221 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🏗 Assignment: Object-Oriented Programming Basics\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "pmXWXQskqQf7"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 🎯 Goal\n",
+        "This assignment checks your understanding of classes, objects, attributes, and methods using Python."
+      ],
+      "metadata": {
+        "id": "mYX-Yr0YqWTv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 1️⃣ Class and Object Creation\n",
+        "**Task:**\n",
+        "\n",
+        "* Define a class Car with attributes brand and year.\n",
+        "\n",
+        "* Create an object car1 of class Car with values (\"Toyota\", 2020) and print them."
+      ],
+      "metadata": {
+        "id": "uxnkvADeqX6P"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "HF7h_UxJqNLb"
+      },
+      "outputs": [],
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "print(car1.brand, car1.year)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Your turn:\n",
+        "# Create another object car2 with brand \"Tesla\" and year 2023.\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "5frI0xqrqcz2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 2️⃣ Add Methods 🛠\n",
+        "**Task:**\n",
+        "Add a method info() to Car that prints \"Brand: <brand>, Year: <year>\"."
+      ],
+      "metadata": {
+        "id": "1WvafJpAqgkt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "class Car:\n",
+        "    def __init__(self, brand, year):\n",
+        "        self.brand = brand\n",
+        "        self.year = year\n",
+        "\n",
+        "    def info(self):\n",
+        "        print(f\"Brand: {self.brand}, Year: {self.year}\")\n",
+        "\n",
+        "car1 = Car(\"Toyota\", 2020)\n",
+        "car1.info()\n"
+      ],
+      "metadata": {
+        "id": "AJM4pVrzqfJP"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your turn:\n",
+        "# Call info() for car2.\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "qwuFw0CiqkM8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 3️⃣ Class vs Instance Attributes 📦\n",
+        "**Task:**\n",
+        "\n",
+        "* Add a class attribute wheels = 4 to Car.\n",
+        "\n",
+        "* Print car1.wheels and car2.wheels."
+      ],
+      "metadata": {
+        "id": "AOwBtvr0qosK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "rPXvKwtsqm0C"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 4️⃣ Inheritance 👑\n",
+        "**Task:**\n",
+        "\n",
+        "* Create a subclass ElectricCar inheriting from Car.\n",
+        "* Add a new attribute battery (e.g., \"80 kWh\") and a method battery_info() that prints \"Battery: <battery>\"."
+      ],
+      "metadata": {
+        "id": "NLoiaaeuquwg"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example snippet\n",
+        "class ElectricCar(Car):\n",
+        "    def __init__(self, brand, year, battery):\n",
+        "        ________.__init__(brand, year)\n",
+        "        self.battery = battery\n",
+        "\n",
+        "    def battery_info(self):\n",
+        "        print(f\"Battery: {________.battery}\")\n",
+        "\n",
+        "e_car = ElectricCar(\"Tesla\", 2023, \"80 kWh\")\n",
+        "e_car.info()\n",
+        "e_car.battery_info()\n"
+      ],
+      "metadata": {
+        "id": "e_sS1YdIqtk_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 5️⃣ Bonus ⚡\n",
+        "* Add a __str__ method to Car to return a string like: \"Car(brand=Toyota, year=2020)\"."
+      ],
+      "metadata": {
+        "id": "wC0OIrRMq7jv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# To do\n",
+        "\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "cAfBWxrlrA2t"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/a10/assignment_10_project_01_01_02_dataset___nexus___rsayyareh.ipynb b/a0.1/a10/assignment_10_project_01_01_02_dataset___nexus___rsayyareh.ipynb
new file mode 100644
index 0000000..135eace
--- /dev/null
+++ b/a0.1/a10/assignment_10_project_01_01_02_dataset___nexus___rsayyareh.ipynb
@@ -0,0 +1,389 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5pY7aLZSZQM9"
+      },
+      "source": [
+        "# 🍷 Mini-Project: Merge & Explore the Wine Quality Datasets\n",
+        "\n",
+        "\n",
+        "> **Goal: Merge Wine Quality – Red and Wine Quality – White (UCI) into a single dataset, do a careful first-look exploration, and save the merged file to CSV.**\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "wpNpLMF8aX1s"
+      },
+      "outputs": [],
+      "source": [
+        "# === Requirements ===\n",
+        "# pip install pandas matplotlib\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from pathlib import Path\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "ErqnxAGRaY_C"
+      },
+      "outputs": [],
+      "source": [
+        "# ---------- 1) Load ----------\n",
+        "URL_RED   = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n",
+        "URL_WHITE = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv\"\n",
+        "\n",
+        "red   = pd.read_csv(URL_RED, sep=\";\")\n",
+        "white = pd.read_csv(URL_WHITE, sep=\";\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lVbxevgpabbF",
+        "outputId": "e6b61931-4441-4230-9fe4-a9de9a46d810"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Red shape: (1599, 12) White shape: (4898, 12)\n",
+            "Columns equal? -> True\n",
+            "Columns: ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol', 'quality']\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---------- 2) Sanity checks ----------\n",
+        "print(\"Red shape:\", red.shape, \"White shape:\", white.shape)\n",
+        "print(\"Columns equal? ->\", list(red.columns) == list(white.columns))\n",
+        "print(\"Columns:\", list(red.columns))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "5vV73isEadfQ"
+      },
+      "outputs": [],
+      "source": [
+        "# (Optional) strict schema assertion (search and read about assert in Python)\n",
+        "assert list(red.columns) == list(white.columns), \"Column mismatch between red and white datasets.\"\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uVTeWORRakKq",
+        "outputId": "805c7e6c-e3a3-4ce1-d4d0-68da11927a13"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Merged shape: (6497, 13)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---------- 3) Tag source & merge ----------\n",
+        "red[\"type\"] = \"red\"\n",
+        "white[\"type\"] = \"white\"\n",
+        "\n",
+        "df = pd.concat([red, white], ignore_index=True)\n",
+        "print(\"\\nMerged shape:\", df.shape)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 634
+        },
+        "collapsed": true,
+        "id": "OsMo1KOhambz",
+        "outputId": "37dc1fe6-2733-422f-9ea5-4b238d79095e"
+      },
+      "outputs": [],
+      "source": [
+        "# ---------- 4) Basic exploration ----------\n",
+        "print(\"\\nDtypes:\\n\", df.dtypes)\n",
+        "print(\"\\nMissing values per column:\\n\", df.isnull().sum().sort_values(ascending=False))\n",
+        "print(\"\\nHead:\\n\", df.head())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ou0kWts-aopS",
+        "outputId": "c08ea4df-841b-45b9-b907-9ae2ebf63939"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Duplicate rows: [False  True]\n",
+            "\n",
+            "Numeric summary:\n",
+            "                        count        mean        std      min       25%  \\\n",
+            "fixed acidity         6497.0    7.215307   1.296434  3.80000   6.40000   \n",
+            "volatile acidity      6497.0    0.339666   0.164636  0.08000   0.23000   \n",
+            "citric acid           6497.0    0.318633   0.145318  0.00000   0.25000   \n",
+            "residual sugar        6497.0    5.443235   4.757804  0.60000   1.80000   \n",
+            "chlorides             6497.0    0.056034   0.035034  0.00900   0.03800   \n",
+            "free sulfur dioxide   6497.0   30.525319  17.749400  1.00000  17.00000   \n",
+            "total sulfur dioxide  6497.0  115.744574  56.521855  6.00000  77.00000   \n",
+            "density               6497.0    0.994697   0.002999  0.98711   0.99234   \n",
+            "pH                    6497.0    3.218501   0.160787  2.72000   3.11000   \n",
+            "sulphates             6497.0    0.531268   0.148806  0.22000   0.43000   \n",
+            "alcohol               6497.0   10.491801   1.192712  8.00000   9.50000   \n",
+            "quality               6497.0    5.818378   0.873255  3.00000   5.00000   \n",
+            "\n",
+            "                            50%        75%        max  \n",
+            "fixed acidity           7.00000    7.70000   15.90000  \n",
+            "volatile acidity        0.29000    0.40000    1.58000  \n",
+            "citric acid             0.31000    0.39000    1.66000  \n",
+            "residual sugar          3.00000    8.10000   65.80000  \n",
+            "chlorides               0.04700    0.06500    0.61100  \n",
+            "free sulfur dioxide    29.00000   41.00000  289.00000  \n",
+            "total sulfur dioxide  118.00000  156.00000  440.00000  \n",
+            "density                 0.99489    0.99699    1.03898  \n",
+            "pH                      3.21000    3.32000    4.01000  \n",
+            "sulphates               0.51000    0.60000    2.00000  \n",
+            "alcohol                10.30000   11.30000   14.90000  \n",
+            "quality                 6.00000    6.00000    9.00000  \n",
+            "\n",
+            "Quality distribution (overall):\n",
+            " 6497\n",
+            "\n",
+            "Quality distribution by type:\n",
+            " type\n",
+            "red      1599\n",
+            "white    4898\n",
+            "Name: quality, dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Uniqueness & duplicates\n",
+        "dup_count = df.duplicated().unique()\n",
+        "print(\"\\nDuplicate rows:\", dup_count)\n",
+        "\n",
+        "# Descriptive statistics (numeric)\n",
+        "num_cols = df.select_dtypes(include=[np.number]).columns\n",
+        "print(\"\\nNumeric summary:\\n\", df[num_cols].describe().T)\n",
+        "\n",
+        "# Target distributions\n",
+        "print(\"\\nQuality distribution (overall):\\n\", df[\"quality\"].count())\n",
+        "print(\"\\nQuality distribution by type:\\n\", df.groupby(\"type\")[\"quality\"].count().sort_index())\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 211
+        },
+        "id": "2Pa_iCVzaqzp",
+        "outputId": "6acac4cb-68ec-4bbf-caa1-2ef39263a8ca"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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A17Rp09DzoUOHphrPw9ezYcOGiHg7cuRIV5eE27Rpk1tm/fr1MZdXddMdd9zh/r9z506vSJEi3jPPPJPqstHbMHjwYK9BgwYRywwcODDdeiCtsmo9999/f+j5xRdf7F1//fUxlwMA4iGzcTP6XDn82uHkk0/O8Fy8ZMmS3h9//JHl7Y2uf3SN0blz5zSX1zWOrnVkzpw5XsGCBb1ffvkl9P57772XYZ0HAIkmtZib0bn1ihUr3P9//PHHVNfZrVs3dx0hu3bt8goXLuxNnz499L5id7FixSLaiVKLnzpv1vlzdq8jws+pb7jhhlTfS61OUn2l11R/yVVXXeWuF8Kp3kitnSdcw4YNvcceeyyiPunQoUO6vwOkh54aQBxpmCf1RlB2Xpl2dekT3WWlzLa6L6qrnv/YsGGD68Ynyqg3a9YsYn3NmzfP8DPHjx/v7spVV0OtU0Or+Nly9Qz59ddfXY+QWGgbGjVq5LqbZ2YbUnPzzTe7IU9017DGUtQdbeFDuwBAvKhHhu6k3bdvn3v+0ksv2dVXX2358+cPxcKzzjor4nf0XK+n5ZVXXnHLaGx0xeK77rorxZ1LmaW6Q71DwusN9QwUv+5IbVhCdftWt3kNXaLfmTNnTmhbVAaVOzP1QlbqptSot4bfFV53xeluMuoFAIkus3EzPTpnT4/ukNUdxJmZuy+j+kfrzEzMV28TDdOY3ZgPAPEWHXMzOrdWDwPFS51Hq3e3ekxoaKfUaHm1c4SfJyt2a9jazMrudcStt95q06ZNc8N9q3ffxx9/nKnPV8/C6B580c/VnqVROerXr++GpdJ2qs6I3k4NmQhkFROFA3GkSlLjKqphTF0XNYSUXwHo/+qOGC18nMLMUsWlikVjN+qCQ90WH3jggdC4u/GcuFVdETXmsIZxUZdPjQl5+eWXx217ACA8PunGKQ3JofFgP/jgg0x18Y62dOlSlyjRXBIar1eTCCo+pzaubmao7tC2aijDaH79Ek11gLqta2hAXZBpPHaNJayLrnjXCxpfV0NXaX/pYkvzQZ199tlx2x4AiEVOxk3F5Jz8rFjqn3jGfQCIp+iYm9G5dYECBdw8QjpP1XBSjz32mN15552ufUXnrVmhG12jh68Kny8jJ64jNEfITz/95ObB0/YrMaMhyDUUlH/TVvg2ZGa+Dp/anbRurVPDmatuUfuOf40Raz0HpIeeGkCcaEx2TRylSk8/VTHt2bPHvaf5M7Zs2WIFCxZ0FUD4wx/rUBnv6EkAly1bluHYh5rXQnN56K4urS/87l0lOTRZk8Y9jIW2QT1Nwicoz2gblLDQncHRVNZu3bq5u3L10F3QXFQBSATqjabxcdVDQz3KdEeV4nR4LFR8Dafn/hxJ0XThU6NGDRf/dXeSJufThUUssTI92qa1a9e6OB5dd6R1waDt1CR+1113nbvbTJMNfvPNN6H3tW2KxZmpF8Ln48hOvaDeippcUXWCejMyxxKAIMhs3FQMlMzGfFGPafWs8Ofcy0gs9Y/WmZmYr7mUwuc/yijmA0BQxHJurSSEek0oyaB5IRTTdaNmtOOOO84KFSoU0YajXh3h592iETXCY+q3337r5pTLTByPhT5H7S8vvviiu7lJI3j4r0v4NqieCadroei5Y6Of6xpD7Vyao0k3TqlXSfhE40BOIKkBxIkmVlJ3bXUVHDt2rLuQ8SfZbtOmjetJocYcZfwV/FV5qeLSBFWiSZk0fJUae1QRDh061FW46VGFp9/X0CL6HU3EFF35aLJvZfkfffRRV4FqaCzdcZCaa6+91lXiGjpKE00p069MfHp0QqA7HnSx9Pvvv0dU0BpqZOHChW4CLoYYAZBIlHhWTw3FXX+C8PBJ/9Torkn2FDcV019//fVQTE8tFqvrte6qUmJZ8Tb64kexUkMO6iJCsdIf+io9usNKDVuasFCxXetWvFcyIK3GMm2Lf4eZuoRrknMN9RSe0NGkr+qa/vzzz7t1qsFq4sSJadZt2gfaJ+qarqEEtW/Sk15ZVS9o0kZtmy68ACDRZTZuqnFK59MzZ8603377zZ0nx0rxXg1FumZQA9IPP/zghkvUnbxZrX90TaEEvn4q9q5evTrVu5T9a5bjjz/exWf1QFdPRl2vAEAyyOjcWgmK++67z7WxKLbq/F9xXAnfaBp+qXv37u4cWW0ea9ascY3+fs8InyYmf/zxx12CROvVubWSIZmJ4xkZMmSIm6z8u+++c21Iqn/8bVbCRu1UahfSOb2uf6J7gfTp08e1/eiaR8s89dRTbphYfzh1fzu1P3R+r/pBbUeHDx/O1HYCGUp3xg0AuTJR+HPPPeeVKFHC++abb0Lvf/LJJ16hQoW8d999NzTJoCbL1mS0er169epu8qWNGzeGfufee+/1KlSo4B111FFuEqoBAwakOyGUJh7XJKuawKlMmTJuYu5Bgwal+J0JEyZ4J5xwgvvcKlWquO1Ia+KqpUuXut/XpFdNmjTxXnvttQwnP+zZs6ebYFyvaxKrcJo4XRNIAUAi0aTgioeKW99//32K95944gmvdu3aLm4ef/zx3vPPPx/xfnTs7N+/v4uDit+abE/1RvjkeorXnTp1crFav+tPDpjeROGieuWyyy5zv6eJB+vVq+f17dvXO3z4cKrl0gSFmsBQ21GxYkXvrrvu8rp27Rqa1NAv+z333OPqNZXv2GOP9e677740t+Gdd97x6tSp4ybKVUyfNGlSuhOFp1VW0Xbrc8MnXQeARJfZuDlixAivcuXKXr58+dw5ffS1Q3r1iSaoVQwtVaqUV7x4ce/UU0911xVpyaj+EZ3P67xe5/e61ujYsWOqE4WLJstt0aKFW1b13+zZs5koHEDgpBVz0zu3XrdundeuXTvv6KOPdue9ioHhE2GHTxTuTxZ+3XXXuVhdqVIlb8yYMSk+95dffnGTk6u9qG7duq59KHqi8IzieEYThWsC9Pr167vylCtXzm3jDz/8EHr/ww8/9E466SSvaNGi7lx+xowZEROFy9NPP+0dc8wxbh2a7Ft1nuoxn5Zt1aqVe19tWY8//niKskbXJ0Bm5dM/Gac+ACD3KRwpo6/hsfr16xfvzQEAxJnuWD7mmGNcr0QNAQYAAAAgsWj0jq+//tr12AOOFCYKB5AQ1E1TXSg1lwjjpgNA3qbu6RqKSt3dy5QpY5dcckm8NwkAAACAmRt2/Pzzz3dzi2joKQ0X+8QTT8R7s5DHkNQAkBAqVqzoJkHXBFVly5aN9+YAAOJIYwXXqlXLqlWr5ubkKFiQU1YAAAAgESxfvtzGjBlju3btstq1a7u5PTQXHnAkMfwUAAAAAAAAAAAIhPzx3gAAAAAAAAAAAIBYkNQAAAAAAAAAAACBQFIDAAAAAAAAAAAEAkkNAAAAAAAAAAAQCCQ1AAAAAAAAAABAIJDUAAAAAAAAAAAAgUBSAwAAAAAAAAAABAJJDQAAAAAAAAAAEAgkNQAAAAAAAAAAgAXB/weZPsuacs4xTgAAAABJRU5ErkJggg==",
+            "text/plain": [
+              "<Figure size 1600x300 with 4 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# ---------- 5) A few simple visuals (optional for report) ----------\n",
+        "# Histograms of numeric features (quick feel for ranges & skew)\n",
+        "top_vars = df.columns\n",
+        "n = min(4, len(top_vars))\n",
+        "\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 4, figsize=(16, 3), sharey=True)\n",
+        "\n",
+        "for i, ax in enumerate(axes):\n",
+        "    if i < n:\n",
+        "        col = top_vars[i]\n",
+        "        ax.hist(df[col].dropna(), bins=30)\n",
+        "        ax.set_title(f\"Histogram: {col}\")\n",
+        "        ax.set_xlabel(col)\n",
+        "        if i == 0:\n",
+        "            ax.set_ylabel(\"Count\")\n",
+        "        else:\n",
+        "            ax.set_ylabel(\"\")\n",
+        "    else:\n",
+        "        ax.axis(\"off\")  # hide unused panels if top_vars has < 4\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "w9z9f2OMatLD"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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Hpu9BXlXTs2bNkri4uFzb4+PjJSIiohhKDdvoSRUAioIOlFD33nuvrFmzJtd555577jH39Tjv/UBOaWlp4q8Ql8vlEodoFfSYMWPkiy++kLJly0r79u2lSZMm8vXXX8uhQ4f8qrGLiYkxnVEjIyOLufQI9m8/ekLVb8z6zRkAAq1Hjx6mskJr7LT5Ned5Z8CAAWZdKzmosUN+NO9ER0ebZvub5R1HpzuJjY01H2bta6CFrlWrljz00EPSsGFDn8eHh4ebJSf9D8IfZxQGnx0ARUWnNNHRr+vWrfM0vyp3s6y75k6P4zyE/BTk81EiJiiuWLGiCXWXLl0y/wEGDRrkdJEAALglOk+dVmC4/85pDd3BgwfNra4r3c98dggkR2vsNMRpS3DTpk3l2LFjMnXqVDNiaPTo0U4WCwCAgNC/be4pT7SGzrtfr4Y63Q8EkqM1dtpWPH78eBPmRo4cKV27djVhjyppAIAtNLwlJSWZvnTaT0pvdZ1QB+tq7IYOHWoWAABsps2t2qdcR7/279+fCgwUmRLRxw4AAAC3jmAHAABgCYIdAACAJQh2AAAAliDYAQAAWIJgBwAAYAmCHQAAgCUIdgAAAJYg2AEAAFiCYAcAAGAJgh0AAIAlCHYAAACWINgBAABYgmAHAABgCYIdAACAJQh2AAAAliDYAQAAWIJgBwAAYAmCHQAAgCUIdgAAAJYg2AEAAFiCYAcAAGAJgh0AAIAlCHYAAACWINgBAABYgmAHAABgCYIdAACAJQh2AAAAliDYAQAAWIJgBwAAYAmCHQAAgCUIdgAAAJYg2AEAAFiCYAcAAGAJgh0AAIAlCHYAAACWINgBAABYgmAHAABgCYIdAACAJQh2AAAAliDYAQAAWIJgBwAAYAmCHQAAgCUIdgAAAJYIdboAQHH76quvpFOnTp71HTt2SMeOHR0tEwC7hYSE5NrmcrkcKQvs5miNXVZWlrz44ovSoEEDqVChgsTGxsorr7zChx1FenL1DnVK132ddAEgEPI6v3DegXU1dnPmzJGFCxfK0qVLpWXLlrJr1y4ZPXq0REVFyYQJE5wsGiyU8yTaq1cv2bBhQ7b9fKkAEEg3C2+cd2BVjd3WrVtl0KBBMmDAAKlfv748+OCD0qdPH9NUBgSS92fq6NGjkpGRIU899ZS51XVfxwFAoEJdlSpVzPlm1apV5lbXfR0HBHWNXZcuXeTtt9+WI0eOSJMmTWTfvn2yZcsWWbBggc/j09PTzeKWkpJibjMzM80C5MW7+bVevXqez4ve6rr3cXrSBYBAOn/+fLbzjq6XK1fOs5+/YchPQT4fjga7559/3oSzZs2aSdmyZU2fu9/85jcyfPhwn8fPmjVL4uLicm2Pj4+XiIiIYigxgp02v65Zs8azvn79enPbvXt3+eKLL8x97/0AEAi+zjt57QdySktLE3+FuBxs3F+xYoVMnTpV5s2bZ/rY7d27VyZOnGhq7EaNGuVXjV1MTIwkJiZKZGRkMZcewcT7m7HWyOm3Hz259u7dW8LCwnLtB4BbxXkHgaJ5Jzo6WpKTk2+adxytsdNQp7V2Dz/8sFlv3bq1nDhxwtTM+Qp24eHhZslJ/4PoAuRFpzRxN8fqZ8zd/KqfG133Po7PEoBAq169uml+VXqO0XVvnHeQn4J8PkKdrlosUyb7+A1tkr1x44ZjZYKdvOepa9y4saf5dfDgwXkeBwC3QhvE3AMjkpKSstXQ5TwOCBRHg93AgQNNn7q6deuaptg9e/aYZtgxY8Y4WSyUgpOscvep894PAEV53vG1H7BmupM333zTTHEybtw4ad68uUyZMkWefPJJM0kxUBT0JKrNrd50nZMrgKKS1/mF8w6KgqODJwLRmVAnM/anMyHgTTsx6yi0/v3707cFQLHgvIPiyDuO1tgBAAAgcAh2AAAAliDYAQAAWIJgBwAAYAmCHQAAgCUIdgAAAJYg2AEAAFiCYAcAAGAJgh0AAIAlCHYAAACWINgBAABYgmAHAABgCYIdAACAJQh2AAAAliDYAQAAWIJgBwAAYAmCHQAAgCUIdgAAAJYg2AEAAFiCYAcAAGAJgh0AAIAlCHYAAACWINgBAABYgmAHAABgCYIdAACAJQh2AAAAliDYAQAAWIJgBwAAYAmCHQAAgCUIdgAAAJYg2AEAAFiCYAcAAGAJgh0AAIAlCHYAAACWINgBAABYgmAHAABgCYIdAACAJQh2AAAAliDYAQAAWIJgBwAAYAmCHQAAgCUIdgAAAJYg2AEAAFiCYAcAAGAJgh0AAIAlHA129evXl5CQkFzL+PHjnSwWAABAUAp18sV37twpWVlZnvUDBw5I79695Ze//KWTxQIAAAhKjga7atWqZVufPXu2xMbGSo8ePRwrEwAAQLByNNh5y8jIkGXLlskzzzxjmmN9SU9PN4tbSkqKuc3MzDQLSre0tDQ5fPiwX8emXk2XrfsTpHKV7VKpQrhfj2natKlERETcYikB2ITzDopDQTJOiQl2q1atkqSkJHnsscfyPGbWrFkSFxeXa3t8fDwffEhCQoJMnjy5QI+ZW4Bj58+fb2qUAcCN8w6K6wuEv0JcLpdLSoC+fftKuXLl5LPPPsvzGF81djExMZKYmCiRkZHFVFLY8M35yNlkmbryG5k3pIU0qRXl12P45gwgJ847KA6ad6KjoyU5OfmmeadE1NidOHFCNmzYIJ9++mm+x4WHh5slp7CwMLOgdIuKipKOHTv6dWy5Ez9I+LYMadW2vbStd3uRlw2AnTjvoDgUJOOUiHnslixZItWrV5cBAwY4XRQAAICg5Xiwu3Hjhgl2o0aNktDQElGBCAAAEJQcD3baBHvy5EkZM2aM00UBAAAIao5XkfXp00dKyPgNAACAoOZ4jR0AAAACg2AHAABgCYIdAACAJQh2AAAAliDYAQAAWIJgBwAAYAmCHQAAgCUIdgAAAJYg2AEAAFiCYAcAAGAJgh0AAIAlCHYAAACWINgBAABYgmAHAABgCYIdAACAJQh2AAAAliDYAQAAWIJgBwAAYAmCHQAAgCUIdgAAAJYg2AEAAFiCYAcAAGAJgh0AAIAlCHYAAACWINgBAABYgmAHAABQmoPdt99+G/iSAAAAoPiDXaNGjeTee++VZcuWybVr126tBAAAAHAu2O3evVvatGkjzzzzjNSsWVOefPJJ+eqrrwJTIgAAABRfsGvbtq28/vrrcubMGXn33Xfl7Nmz0rVrV2nVqpUsWLBALly4ULjSAAAAwJnBE6GhofLAAw/In//8Z5kzZ44cO3ZMpkyZIjExMTJy5EgT+AAAABAEwW7Xrl0ybtw4qVWrlqmp01CXkJAg69evN7V5gwYNClxJAQAAkK9QKQQNcUuWLJHDhw9L//795f333ze3Zcr8mBMbNGgg7733ntSvX78wTw8AAIDiCnYLFy6UMWPGyGOPPWZq63ypXr26LF68uDBPDwAAgOIKdtrUWrduXU8NnZvL5ZJTp06ZfeXKlZNRo0YV5ukBAABQXH3sYmNjJTExMdf2ixcvmmZYAAAABEmw05o5X1JTU6V8+fK3WiYAAAAUdVOsTkisQkJC5KWXXpKIiAjPvqysLNmxY4eZ4w4AAAAlPNjt2bPHU2O3f/9+04/OTe/feeedZsoTAAAAlPBgt3HjRnM7evRoc+WJyMjIoioXAAAAimNUrM5hBwAAgCANdnrpMJ10WGvp9H5+Pv3000CUDQAAAEUR7KKiosygCfd9AAAABGmw825+pSkWAADAknnsAAAAEMQ1du3atfM0xd7M7t27/S7A6dOn5bnnnpO1a9dKWlqaNGrUyNQIdujQwe/nAAAAQAGC3eDBgwP+4pcuXZKf/vSncu+995pgV61aNTl69KhUrVo14K8FAABgO7+D3fTp0wP+4nPmzJGYmJhsffa41iwAAEAxzmMXKKtXr5a+ffvKL3/5S9m0aZPccccdMm7cOHniiSd8Hp+enm4Wt5SUFHObmZlpFthr95kTcvbyhYA934kfUqVM+dOy6fhuOX65kgRSrcrVpH3tegF9TgDFL1jOO5xz7JdZgIwT4tLrgxWQXhf21Vdflf/+7/+WkydPSkZGRrb9Fy9e9Ot5ypcv77kGrYa7nTt3ytNPPy1/+MMfZNSoUbmOnzFjhsTFxeXavnz58mzXrYVdzl8VmffdRgmv9lcJBukXesrUOvdK9QpOlwRAaTjvcM6xX1pamjzyyCOSnJx806t+FSrYvfTSS/LHP/5RJk+eLP/5n/8pL7zwghw/flxWrVpl9k2YMMGv59Hry+ogia1bt3q26WM14G3bts2vGjttyk1MTOTyZhY7eCZFhrwTL8/0rSExVQNz5rqakSFbd38jXdq3kApe1zy+VacuXZUF687Jyif6SMvafCaBYBUs5x3OOaVDSkqKREdH+xXsCtUU++GHH8o777wjAwYMMLVow4YNk9jYWGnTpo1s377d72BXq1YtadGiRbZtzZs3l08++cTn8eHh4WbJKSwszCywU2hoqLiuR8q9De+SVndEBaxaO+JUkvRv3SWgn50Dp5Nl/vUtpsx8JoHgFSznHc45pUNYAX63hZrH7vvvv5fWrVub+5UqVTIJUv3sZz+T//3f//X7eXRE7OHDh7NtO3LkiNSrR18BAACAgipUsKtTp46cPXvW3Neauvj4eHNfm1B91ajlZdKkSaaGb+bMmXLs2DHTV+7tt9+W8ePHF6ZYAAAApVqhgt2QIUPkr3/9sUPpr3/9a3nxxRelcePGMnLkSBkzZozfz3P33XfLypUr5aOPPpJWrVrJK6+8Iq+99poMHz68MMUCAAAo1QrVx2727Nme+w899JDUrVvXDHbQcDdw4MACPZc23+oCAACAEjCPXefOnc0CAACAIAt277//fr77tUkWAAAAQRDsdBLhnEO4dfI8nZdOJwom2AEAAATJ4IlLly5lW1JTU820JV27djUDIQAAABAkwc4XHTihgypy1uYBAAAgyIKd0pmvz5w5E8inBAAAQFH2sVu9enW2db3crE5Y/NZbb5mrSQAAACBIgt3gwYOzrYeEhEi1atXkvvvuk/nz5weqbAAAACjqYHfjxg1ze+HCBTMSNioqMBdIBgAAQDH2sUtKSjLXco2OjpaaNWvKbbfdZm6nTZtmpjwBAABAENTYXbx40Vxh4vTp0+Z6rs2bNzfbv/nmG3nzzTdl/fr1smXLFvnHP/4h27dvlwkTJhRVuQEAAHArwe7ll182Ta8JCQlSo0aNXPv69OkjI0aMkPj4eHnjjTcK8tQAAAAozmC3atUqWbRoUa5Qp7Q5du7cudK/f3+ZPn26jBo16lbLBgAAgKLqY6dTmrRs2TLP/a1atZIyZcqYYAcAAIASHOx0wMTx48fz3P+vf/1LqlevHohyAQAAoCiDXd++feWFF16QjIyMXPvS09PlxRdflPvvv7+gZQAAAIATgyc6dOhgrgurU540a9bMXHXi0KFD8vvf/96Eu/fffz8Q5QIAAEBRBrs6derItm3bZNy4cWbeOg117itP9O7d21xSrG7dugUtAwAAAJy48kSDBg1k7dq1cunSJTl69KjZ1qhRIzNRMQAAAILskmKqatWq0rFjx8CWBgAAAMV3STEAAACUTAQ7AAAASxDsAAAALEGwAwAAsATBDgAAwBIEOwAAAEsQ7AAAACxBsAMAALAEwQ4AAMASBDsAAABLEOwAAAAsQbADAACwBMEOAADAEgQ7AAAASxDsAAAALEGwAwAAsATBDgAAwBIEOwAAAEsQ7AAAACxBsAMAALAEwQ4AAMASBDsAAABLEOwAAAAsQbADAACwhKPBbsaMGRISEpJtadasmZNFAgAACFqhThegZcuWsmHDBs96aKjjRQIAAAhKjqcoDXI1a9Z0uhgAAABBz/E+dkePHpXatWtLw4YNZfjw4XLy5EmniwQAABCUHK2x69Spk7z33nvStGlTOXv2rMTFxUm3bt3kwIEDUrly5VzHp6enm8UtJSXF3GZmZpoFdrp89cff+b6TF+X69esBec4r19Jl1wWR6G8vSMXy4RIoxy5cMbdaTj6TQPAKlvMO55zSIbMAv9sQl8vlkhIiKSlJ6tWrJwsWLJCxY8f6HGyh4S+n5cuXS0RERDGVEsVt27kQWfFtWQkmL7S9LtUrOF0KAKXlvMM5x25paWnyyCOPSHJyskRGRgZPsFN333239OrVS2bNmuVXjV1MTIwkJibe9AdF8Lp4JUM2HDovDatVlAphgTnRHvk+WZ5deUjmDmkuTWpGSSBVDC8r9W+vGNDnBFC8gum8wznHfikpKRIdHe1XsHN88IS31NRUSUhIkBEjRvjcHx4ebpacwsLCzAI71agSJsM7NyiS59aTa9t6txfJcwMIXpx3UJIUJOM4OnhiypQpsmnTJjl+/Lhs3bpVhgwZImXLlpVhw4Y5WSwAAICg5GiN3XfffWdC3A8//CDVqlWTrl27yvbt2819AAAABFGwW7FihZMvDwAAYBXH57EDAABAYBDsAAAALEGwAwAAsATBDgAAwBIEOwAAAEsQ7AAAACxBsAMAALAEwQ4AAMASBDsAAABLEOwAAAAsQbADAACwBMEOAADAEgQ7AAAASxDsAAAALEGwAwAAsATBDgAAwBIEOwAAAEsQ7AAAACxBsAMAALAEwQ4AAMASBDsAAABLEOwAAAAsQbADAACwBMEOAADAEgQ7AAAASxDsAAAALEGwAwAAsATBDgAAwBIEOwAAAEsQ7AAAACxBsAMAALAEwQ4AAMASBDsAAABLEOwAAAAsQbADAACwBMEOAADAEgQ7AAAASxDsAAAALEGwAwAAsATBDgAAwBIEOwAAAEsQ7AAAACxBsAMAALAEwQ4AAMASBDsAAABLlJhgN3v2bAkJCZGJEyc6XRQAAICgVCKC3c6dO2XRokXSpk0bp4sCAAAQtBwPdqmpqTJ8+HB55513pGrVqk4XBwAAIGg5HuzGjx8vAwYMkF69ejldFAAAgKAW6uSLr1ixQnbv3m2aYv2Rnp5uFreUlBRzm5mZaRaUbmlpaXL48GG/jj1yNlnSvz8mB/aWk4xzUX49pmnTphIREXGLpQRgE847KA4FyTiOBbtTp07J008/LevXr5fy5cv79ZhZs2ZJXFxcru3x8fF88CEJCQkyefLkAj1mxFL/j50/f77ExsYWvGAArMV5B8X1BcJfIS6XyyUOWLVqlQwZMkTKli3r2ZaVlWVGxpYpU8bUzHnvy6vGLiYmRhITEyUyMrJYy4/g/uacejVd1m3eKX273S2VKoT79Ri+OQPIifMOioPmnejoaElOTr5p3nGsxq5nz56yf//+bNtGjx4tzZo1k+eeey5XqFPh4eFmySksLMwsKN2ioqKkY8eOfldrX066KN26/ITPDoBC47yD4lCQz4tjwa5y5crSqlWrbNsqVqwot99+e67tAAAACIJRsQAAALBgVGxOf//7350uAgAAQNCixg4AAMASBDsAAABLEOwAAAAsQbADAACwBMEOAADAEgQ7AAAASxDsAAAALEGwAwAAsATBDgAAwBIEOwAAAEsQ7AAAACxBsAMAALAEwQ4AAMASBDsAAABLEOwAAAAsQbADAACwBMEOAADAEgQ7AAAASxDsAAAALEGwAwAAsATBDgAAwBIEOwAAAEsQ7AAAACxBsAMAALAEwQ4AAMASBDsAAABLEOwAAAAsQbADAACwBMEOAADAEgQ7AAAASxDsAAAALEGwAwAAsATBDgAAwBIEOwAAAEsQ7AAAACxBsAMAALAEwQ4AAMASBDsAAABLEOwAAAAsQbADAACwBMEOAADAEgQ7AAAASxDsAAAALEGwAwAAsATBDgAAwBKOBruFCxdKmzZtJDIy0iydO3eWtWvXOlkklAInT56U2267TYYMGWJudR0AitKWLVukXLlyMnjwYHOr60BRCHG5XC5xyGeffSZly5aVxo0bixZj6dKlMm/ePNmzZ4+0bNnypo9PSUmRqKgoSU5ONsEQuJmwsDC5fv16ru2hoaGSmZnpSJkA2C0kJCTPfQ7+CUYQKUjecbTGbuDAgdK/f38T7Jo0aSK/+c1vpFKlSrJ9+3Yni4VSEOq0pu5Xv/qVuVW6XfcDQFGGuq5du+a7H7Cmj11WVpasWLFCrly5YppkgUDS5lZ3qLtw4YJ8//33cv/995tbXVe6n2ZZAIHi3dx66NAhycjIkClTpphbXfd1HHCrQsVh+/fvN0Hu2rVrprZu5cqV0qJFC5/Hpqenm8W7alJpExrNaMiPu2lfa+i0Otv9edFbXdftFy9eNMfpLQDcqm7dunnux8bGZjvv6Lr3cRr2gLwUJOM4HuyaNm0qe/fuNe3GH3/8sYwaNUo2bdrkM9zNmjVL4uLicm2Pj4+XiIiIYioxgpHWBKuhQ4fKmjVrPNvXr19vbh944AH54x//aI7z3g8At0qbX32ddzp16iQ7duww9znvID9paWkSFIMnfOnVq5f5JrNo0SK/auxiYmIkMTGRwRPIl9bIpaammlttftVvP3py7d27t+lbV7NmTVNTp7XG1NgBCAQd/eqmNXI5zzs59wN50bwTHR3t1+AJx2vscrpx40a28OYtPDzcLDnpfxA6viM/Bw8elHr16pnQpv8xtPlV6edG191hTo/jswQgEDZv3uxpjk1ISPA0v+o5Rte9j+O8g/wU5PPhaI3dtGnTpF+/flK3bl25fPmyLF++XObMmSPr1q0z32huhulOcCujYrX59dNPP/WEOqY8ARBoOUe9eje/upWwhjOUQAXJO47W2J0/f15GjhwpZ8+eNQXWyYr9DXVAQWloc4c7DXPap86NUAegKGho8w53hDoUNUeD3eLFi518eZRCGt50ShMd/aoDJSpWrGiaX7XWGACKgoY3ndLEe5SsNr/mnNMOsGoeO6C4aIjTGjudWkdvCXUAipqGOB0gsWrVKnNLqENRIdgBAABYgmAHAABgCYIdAACAJQh2AAAAliDYAQAAWIJgBwAAYAmCHQAAgCUIdgAAAJYg2AEAAFjC0UuK3Sr3Nfb04rhAQS8tlpaWZj47ev1YAChqnHdQWO6c48+1hYM62F2+fNncxsTEOF0UAACAIs89UVFR+R4T4vIn/pVQN27ckDNnzkjlypUlJCTE6eIgyL796BeCU6dOSWRkpNPFAVAKcN5BYWlU01BXu3ZtKVOmjL01dvrD1alTx+liIIjpyZUTLIDixHkHhXGzmjo3Bk8AAABYgmAHAABgCYIdSqXw8HCZPn26uQWA4sB5B8UhqAdPAAAA4P9QYwcAAGAJgh0AAIAlCHZAHu655x6ZOHGi08UAEGTee+89qVKlSr7HPPbYYzJ48OBiKxNKj6Cexw4AgGD0+uuvZ7s8lH6RbNu2rbz22muOlgvBj2AH62VkZEi5cuWcLgYAFHiyWaCgaIqFdfSb71NPPWWaUaOjo6Vv375y4MAB6devn1SqVElq1KghI0aMkMTERM9jrly5IiNHjjT7a9WqJfPnz3f0ZwBQsvzP//yPaV7Nysoy63v37jWXsnz++ec9xzz++OPy6KOPetbXrVsnzZs3N+eV+++/X86ePeuzKVbvb9q0ydTi6XPqcvz4cbPvZucuICeCHay0dOlSU0v35ZdfyuzZs+W+++6Tdu3aya5du+Tzzz+Xc+fOydChQz3HT5061ZxY//KXv0h8fLz8/e9/l927dzv6MwAoObp162au1blnzx6zrucL/eKo5wo33aZfLFVaWpr89re/lQ8++EC++OILOXnypEyZMsXnc2ug69y5szzxxBMm/Omi15RNSkq66bkLyImmWFipcePGMnfuXHP/v/7rv8yJcebMmZ797777rjlxHjlyxFxUefHixbJs2TLp2bOnJxhyHWIA3k2n2gdOg1yHDh3M7aRJkyQuLk5SU1MlOTlZjh07Jj169DBfKDMzM+UPf/iDxMbGmsdrK8LLL7+c53PrF9GIiAipWbOmZ/tbb72V77mrSZMmxfCTI9hQYwcr3XXXXZ77+/btk40bN5qmDPfSrFkzsy8hIcEs2g+vU6dOnsfcdttt0rRpU0fKDqBk0tCmgU4HPWzevFkeeOAB09S6ZcsWU1unXxL1S6XSkOYOdUq7eJw/f75Ar3ezcxfgCzV2sFLFihU99/Xb9MCBA2XOnDm5jtOTrX7LBoCb0WZWrTHTwBUWFmZClm7TsHfp0iUT/Nx0vzftN1fQCz3d7NwF+EKwg/Xat28vn3zyidSvX19CQ3N/5PVbtZ6Ed+zYIXXr1jXb9CStTR3eJ2oApZu7n92rr77qOTdosNN+vHrOmDx5cqGfW5ti3QMz/D13Ab7QFAvrjR8/Xi5evCjDhg2TnTt3miYMHa02evRocyLV5o2xY8eaARR/+9vfzCg0HaVWpgz/PQD8n6pVq0qbNm3kww8/9AyS6N69uxlodatfBDW86ZdLHQ2ro15v3Lhx03MX4At/uWA97feinZn1RNinTx9p3bq1mQpFpy5wh7d58+aZb+Pa7NGrVy/p2rVrtn56AKA0vOm5xB3stD9uixYtzKCHW+mXqyNmy5Yta56rWrVqZhStP+cuIKcQV0Eb/QEAAFAiEfkBAAAsQbADAACwBMEOAADAEgQ7AAAASxDsAAAALEGwAwAAsATBDgAAwBIEOwAAAEsQ7AAAACxBsANQ6oSEhOS7zJgxw+kiAkChhBbuYQAQvM6ePeu5/6c//UleeuklOXz4sGdbpUqVHCoZANwaauwAlDp6wXb3EhUVZWrp9H7lypWlSZMm8vnnn2c7ftWqVVKxYkW5fPmyHD9+3By/YsUK6dKli5QvX15atWolmzZtyvaYAwcOSL9+/UxIrFGjhowYMUISExOL+ScFUNoQ7ADg/9Pw9vDDD8uSJUuybdf1Bx980AQ/t6lTp8rkyZNlz5490rlzZxk4cKD88MMPZl9SUpLcd9990q5dO9m1a5cJiufOnZOhQ4cW+88EoHQh2AGAl8cff1zWrVvnaa49f/68rFmzRsaMGZPtuKeeekp+8YtfSPPmzWXhwoWm5m/x4sVm31tvvWVC3cyZM6VZs2bm/rvvvisbN26UI0eOOPJzASgdCHYA4KVjx47SsmVLWbp0qVlftmyZ1KtXT7p3757tOK2lcwsNDZUOHTrIoUOHzPq+fftMiNNmWPeiAU8lJCQU688DoHRh8AQA+Ki1+93vfifPP/+8aYYdPXq06Vfnr9TUVNM0O2fOnFz7atWqFeDSAsD/ocYOAHJ49NFH5cSJE/LGG2/IN998I6NGjcp1zPbt2z33r1+/Ll9//bVpllXt27eXgwcPSv369aVRo0bZFu3HBwBFhWAHADlUrVpVHnjgATNAok+fPlKnTp1cx2iN3sqVK+Wf//ynjB8/Xi5duuTph6frFy9elGHDhsnOnTtN86v229Oav6ysLAd+IgClBcEOAHwYO3asZGRk5Bo04TZ79myz3HnnnbJlyxZZvXq1REdHm321a9eWL7/80oQ4DYatW7eWiRMnSpUqVaRMGU67AIpOiMvlchXh8wNAUPrggw9k0qRJcubMGSlXrpxnu85j16BBAzPNSdu2bR0tIwDkxOAJAPCSlpZmpjrR2rgnn3wyW6gDgJKONgEA8DJ37lwzNYleiWLatGlOFwcACoSmWAAAAEtQYwcAAGAJgh0AAIAlCHYAAACWINgBAABYgmAHAABgCYIdAACAJQh2AAAAliDYAQAAWIJgBwAAYIn/B488VHRGLrQlAAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Boxplot of quality by type (class distribution spread)\n",
+        "plt.figure()\n",
+        "df.boxplot(column=\"quality\", by=\"type\")\n",
+        "plt.suptitle(\"\")\n",
+        "plt.title(\"Quality by Wine Type\")\n",
+        "plt.xlabel(\"Type\")\n",
+        "plt.ylabel(\"Quality\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "m9OZ2n2nawIP"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 700x600 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Correlation heatmap (numeric only)\n",
+        "corr = df[num_cols].corr()\n",
+        "plt.figure(figsize=(7, 6))\n",
+        "plt.imshow(corr, interpolation=\"nearest\")\n",
+        "plt.title(\"Correlation Heatmap\")\n",
+        "plt.colorbar()\n",
+        "plt.xticks(range(len(num_cols)), num_cols, rotation=90)\n",
+        "plt.yticks(range(len(num_cols)), num_cols)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "NHQPRTdLZI9R"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "Saved merged file to: E:\\Nexus\\Nexus_Assignments\\a1.10\\outputs\\wine_quality_merged.csv\n",
+            "Reloaded shape: (6497, 13)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---------- 6) Save ----------\n",
+        "OUT_DIR = Path(\"./outputs\")\n",
+        "OUT_DIR.mkdir(parents=True, exist_ok=True)\n",
+        "out_file = OUT_DIR / \"wine_quality_merged.csv\"\n",
+        "df.to_csv(out_file, index=False)\n",
+        "print(f\"\\nSaved merged file to: {out_file.resolve()}\")\n",
+        "\n",
+        "# Quick verification of saved file\n",
+        "df_check = pd.read_csv(out_file)\n",
+        "print(\"Reloaded shape:\", df_check.shape)\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a11/Assignment_11_Project_02_01_03_04_Preprocessing___Nexus___rsayyareh.ipynb b/a0.1/a11/Assignment_11_Project_02_01_03_04_Preprocessing___Nexus___rsayyareh.ipynb
new file mode 100644
index 0000000..3fd60c4
--- /dev/null
+++ b/a0.1/a11/Assignment_11_Project_02_01_03_04_Preprocessing___Nexus___rsayyareh.ipynb
@@ -0,0 +1,3544 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h6pr9RxJiBKU"
+      },
+      "source": [
+        "# 🏠 Mini-Project: Preprocess & Engineer Features on Ames Housing Dataset\n",
+        "\n",
+        "> **Goal: Work with the [Ames Housing dataset](https://www.kaggle.com/datasets/prevek18/ames-housing-dataset?select=AmesHousing.csv) to perform data preprocessing and create meaningful new features. You will:**\n",
+        "> - Handle **missing values**, **duplicates**, and **outliers**  \n",
+        "> - Detect and fix **skewness** in numerical features  \n",
+        "> - Encode categorical variables into numeric formats  \n",
+        "> - Create **non-linear features** (e.g., polynomial, log, interaction terms) from existing variables  \n",
+        "> - Save the cleaned and enriched dataset into a new CSV file  \n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂</p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project2_rezashokrzad.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠</p>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7CksOccRjV4s"
+      },
+      "source": [
+        "## 🔹 Step 1: Load the Dataset\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Nlz-ZiyHgmQb",
+        "outputId": "a51aba43-8ab5-4793-e96b-45e1152a2ca5"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\Parastoo\\AppData\\Local\\Temp\\ipykernel_15072\\1234441213.py:15: DeprecationWarning: Use dataset_load() instead of load_dataset(). load_dataset() will be removed in a future version.\n",
+            "  df1 = kagglehub.load_dataset(\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "First 5 records:    Order        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
+            "0      1  526301100           20        RL         141.0     31770   Pave   \n",
+            "1      2  526350040           20        RH          80.0     11622   Pave   \n",
+            "2      3  526351010           20        RL          81.0     14267   Pave   \n",
+            "3      4  526353030           20        RL          93.0     11160   Pave   \n",
+            "4      5  527105010           60        RL          74.0     13830   Pave   \n",
+            "\n",
+            "  Alley Lot Shape Land Contour Utilities Lot Config Land Slope Neighborhood  \\\n",
+            "0   NaN       IR1          Lvl    AllPub     Corner        Gtl        NAmes   \n",
+            "1   NaN       Reg          Lvl    AllPub     Inside        Gtl        NAmes   \n",
+            "2   NaN       IR1          Lvl    AllPub     Corner        Gtl        NAmes   \n",
+            "3   NaN       Reg          Lvl    AllPub     Corner        Gtl        NAmes   \n",
+            "4   NaN       IR1          Lvl    AllPub     Inside        Gtl      Gilbert   \n",
+            "\n",
+            "  Condition 1 Condition 2 Bldg Type House Style  Overall Qual  Overall Cond  \\\n",
+            "0        Norm        Norm      1Fam      1Story             6             5   \n",
+            "1       Feedr        Norm      1Fam      1Story             5             6   \n",
+            "2        Norm        Norm      1Fam      1Story             6             6   \n",
+            "3        Norm        Norm      1Fam      1Story             7             5   \n",
+            "4        Norm        Norm      1Fam      2Story             5             5   \n",
+            "\n",
+            "   Year Built  Year Remod/Add Roof Style Roof Matl Exterior 1st Exterior 2nd  \\\n",
+            "0        1960            1960        Hip   CompShg      BrkFace      Plywood   \n",
+            "1        1961            1961      Gable   CompShg      VinylSd      VinylSd   \n",
+            "2        1958            1958        Hip   CompShg      Wd Sdng      Wd Sdng   \n",
+            "3        1968            1968        Hip   CompShg      BrkFace      BrkFace   \n",
+            "4        1997            1998      Gable   CompShg      VinylSd      VinylSd   \n",
+            "\n",
+            "  Mas Vnr Type  Mas Vnr Area Exter Qual Exter Cond Foundation Bsmt Qual  \\\n",
+            "0        Stone         112.0         TA         TA     CBlock        TA   \n",
+            "1          NaN           0.0         TA         TA     CBlock        TA   \n",
+            "2      BrkFace         108.0         TA         TA     CBlock        TA   \n",
+            "3          NaN           0.0         Gd         TA     CBlock        TA   \n",
+            "4          NaN           0.0         TA         TA      PConc        Gd   \n",
+            "\n",
+            "  Bsmt Cond Bsmt Exposure BsmtFin Type 1  BsmtFin SF 1 BsmtFin Type 2  \\\n",
+            "0        Gd            Gd            BLQ         639.0            Unf   \n",
+            "1        TA            No            Rec         468.0            LwQ   \n",
+            "2        TA            No            ALQ         923.0            Unf   \n",
+            "3        TA            No            ALQ        1065.0            Unf   \n",
+            "4        TA            No            GLQ         791.0            Unf   \n",
+            "\n",
+            "   BsmtFin SF 2  Bsmt Unf SF  Total Bsmt SF Heating Heating QC Central Air  \\\n",
+            "0           0.0        441.0         1080.0    GasA         Fa           Y   \n",
+            "1         144.0        270.0          882.0    GasA         TA           Y   \n",
+            "2           0.0        406.0         1329.0    GasA         TA           Y   \n",
+            "3           0.0       1045.0         2110.0    GasA         Ex           Y   \n",
+            "4           0.0        137.0          928.0    GasA         Gd           Y   \n",
+            "\n",
+            "  Electrical  1st Flr SF  2nd Flr SF  Low Qual Fin SF  Gr Liv Area  \\\n",
+            "0      SBrkr        1656           0                0         1656   \n",
+            "1      SBrkr         896           0                0          896   \n",
+            "2      SBrkr        1329           0                0         1329   \n",
+            "3      SBrkr        2110           0                0         2110   \n",
+            "4      SBrkr         928         701                0         1629   \n",
+            "\n",
+            "   Bsmt Full Bath  Bsmt Half Bath  Full Bath  Half Bath  Bedroom AbvGr  \\\n",
+            "0             1.0             0.0          1          0              3   \n",
+            "1             0.0             0.0          1          0              2   \n",
+            "2             0.0             0.0          1          1              3   \n",
+            "3             1.0             0.0          2          1              3   \n",
+            "4             0.0             0.0          2          1              3   \n",
+            "\n",
+            "   Kitchen AbvGr Kitchen Qual  TotRms AbvGrd Functional  Fireplaces  \\\n",
+            "0              1           TA              7        Typ           2   \n",
+            "1              1           TA              5        Typ           0   \n",
+            "2              1           Gd              6        Typ           0   \n",
+            "3              1           Ex              8        Typ           2   \n",
+            "4              1           TA              6        Typ           1   \n",
+            "\n",
+            "  Fireplace Qu Garage Type  Garage Yr Blt Garage Finish  Garage Cars  \\\n",
+            "0           Gd      Attchd         1960.0           Fin          2.0   \n",
+            "1          NaN      Attchd         1961.0           Unf          1.0   \n",
+            "2          NaN      Attchd         1958.0           Unf          1.0   \n",
+            "3           TA      Attchd         1968.0           Fin          2.0   \n",
+            "4           TA      Attchd         1997.0           Fin          2.0   \n",
+            "\n",
+            "   Garage Area Garage Qual Garage Cond Paved Drive  Wood Deck SF  \\\n",
+            "0        528.0          TA          TA           P           210   \n",
+            "1        730.0          TA          TA           Y           140   \n",
+            "2        312.0          TA          TA           Y           393   \n",
+            "3        522.0          TA          TA           Y             0   \n",
+            "4        482.0          TA          TA           Y           212   \n",
+            "\n",
+            "   Open Porch SF  Enclosed Porch  3Ssn Porch  Screen Porch  Pool Area Pool QC  \\\n",
+            "0             62               0           0             0          0     NaN   \n",
+            "1              0               0           0           120          0     NaN   \n",
+            "2             36               0           0             0          0     NaN   \n",
+            "3              0               0           0             0          0     NaN   \n",
+            "4             34               0           0             0          0     NaN   \n",
+            "\n",
+            "   Fence Misc Feature  Misc Val  Mo Sold  Yr Sold Sale Type Sale Condition  \\\n",
+            "0    NaN          NaN         0        5     2010       WD          Normal   \n",
+            "1  MnPrv          NaN         0        6     2010       WD          Normal   \n",
+            "2    NaN         Gar2     12500        6     2010       WD          Normal   \n",
+            "3    NaN          NaN         0        4     2010       WD          Normal   \n",
+            "4  MnPrv          NaN         0        3     2010       WD          Normal   \n",
+            "\n",
+            "   SalePrice  \n",
+            "0     215000  \n",
+            "1     105000  \n",
+            "2     172000  \n",
+            "3     244000  \n",
+            "4     189900  \n",
+            "Last 5 records:       Order        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
+            "2925   2926  923275080           80        RL          37.0      7937   Pave   \n",
+            "2926   2927  923276100           20        RL           NaN      8885   Pave   \n",
+            "2927   2928  923400125           85        RL          62.0     10441   Pave   \n",
+            "2928   2929  924100070           20        RL          77.0     10010   Pave   \n",
+            "2929   2930  924151050           60        RL          74.0      9627   Pave   \n",
+            "\n",
+            "     Alley Lot Shape Land Contour Utilities Lot Config Land Slope  \\\n",
+            "2925   NaN       IR1          Lvl    AllPub    CulDSac        Gtl   \n",
+            "2926   NaN       IR1          Low    AllPub     Inside        Mod   \n",
+            "2927   NaN       Reg          Lvl    AllPub     Inside        Gtl   \n",
+            "2928   NaN       Reg          Lvl    AllPub     Inside        Mod   \n",
+            "2929   NaN       Reg          Lvl    AllPub     Inside        Mod   \n",
+            "\n",
+            "     Neighborhood Condition 1 Condition 2 Bldg Type House Style  Overall Qual  \\\n",
+            "2925      Mitchel        Norm        Norm      1Fam        SLvl             6   \n",
+            "2926      Mitchel        Norm        Norm      1Fam      1Story             5   \n",
+            "2927      Mitchel        Norm        Norm      1Fam      SFoyer             5   \n",
+            "2928      Mitchel        Norm        Norm      1Fam      1Story             5   \n",
+            "2929      Mitchel        Norm        Norm      1Fam      2Story             7   \n",
+            "\n",
+            "      Overall Cond  Year Built  Year Remod/Add Roof Style Roof Matl  \\\n",
+            "2925             6        1984            1984      Gable   CompShg   \n",
+            "2926             5        1983            1983      Gable   CompShg   \n",
+            "2927             5        1992            1992      Gable   CompShg   \n",
+            "2928             5        1974            1975      Gable   CompShg   \n",
+            "2929             5        1993            1994      Gable   CompShg   \n",
+            "\n",
+            "     Exterior 1st Exterior 2nd Mas Vnr Type  Mas Vnr Area Exter Qual  \\\n",
+            "2925      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2926      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2927      HdBoard      Wd Shng          NaN           0.0         TA   \n",
+            "2928      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2929      HdBoard      HdBoard      BrkFace          94.0         TA   \n",
+            "\n",
+            "     Exter Cond Foundation Bsmt Qual Bsmt Cond Bsmt Exposure BsmtFin Type 1  \\\n",
+            "2925         TA     CBlock        TA        TA            Av            GLQ   \n",
+            "2926         TA     CBlock        Gd        TA            Av            BLQ   \n",
+            "2927         TA      PConc        Gd        TA            Av            GLQ   \n",
+            "2928         TA     CBlock        Gd        TA            Av            ALQ   \n",
+            "2929         TA      PConc        Gd        TA            Av            LwQ   \n",
+            "\n",
+            "      BsmtFin SF 1 BsmtFin Type 2  BsmtFin SF 2  Bsmt Unf SF  Total Bsmt SF  \\\n",
+            "2925         819.0            Unf           0.0        184.0         1003.0   \n",
+            "2926         301.0            ALQ         324.0        239.0          864.0   \n",
+            "2927         337.0            Unf           0.0        575.0          912.0   \n",
+            "2928        1071.0            LwQ         123.0        195.0         1389.0   \n",
+            "2929         758.0            Unf           0.0        238.0          996.0   \n",
+            "\n",
+            "     Heating Heating QC Central Air Electrical  1st Flr SF  2nd Flr SF  \\\n",
+            "2925    GasA         TA           Y      SBrkr        1003           0   \n",
+            "2926    GasA         TA           Y      SBrkr         902           0   \n",
+            "2927    GasA         TA           Y      SBrkr         970           0   \n",
+            "2928    GasA         Gd           Y      SBrkr        1389           0   \n",
+            "2929    GasA         Ex           Y      SBrkr         996        1004   \n",
+            "\n",
+            "      Low Qual Fin SF  Gr Liv Area  Bsmt Full Bath  Bsmt Half Bath  Full Bath  \\\n",
+            "2925                0         1003             1.0             0.0          1   \n",
+            "2926                0          902             1.0             0.0          1   \n",
+            "2927                0          970             0.0             1.0          1   \n",
+            "2928                0         1389             1.0             0.0          1   \n",
+            "2929                0         2000             0.0             0.0          2   \n",
+            "\n",
+            "      Half Bath  Bedroom AbvGr  Kitchen AbvGr Kitchen Qual  TotRms AbvGrd  \\\n",
+            "2925          0              3              1           TA              6   \n",
+            "2926          0              2              1           TA              5   \n",
+            "2927          0              3              1           TA              6   \n",
+            "2928          0              2              1           TA              6   \n",
+            "2929          1              3              1           TA              9   \n",
+            "\n",
+            "     Functional  Fireplaces Fireplace Qu Garage Type  Garage Yr Blt  \\\n",
+            "2925        Typ           0          NaN      Detchd         1984.0   \n",
+            "2926        Typ           0          NaN      Attchd         1983.0   \n",
+            "2927        Typ           0          NaN         NaN            NaN   \n",
+            "2928        Typ           1           TA      Attchd         1975.0   \n",
+            "2929        Typ           1           TA      Attchd         1993.0   \n",
+            "\n",
+            "     Garage Finish  Garage Cars  Garage Area Garage Qual Garage Cond  \\\n",
+            "2925           Unf          2.0        588.0          TA          TA   \n",
+            "2926           Unf          2.0        484.0          TA          TA   \n",
+            "2927           NaN          0.0          0.0         NaN         NaN   \n",
+            "2928           RFn          2.0        418.0          TA          TA   \n",
+            "2929           Fin          3.0        650.0          TA          TA   \n",
+            "\n",
+            "     Paved Drive  Wood Deck SF  Open Porch SF  Enclosed Porch  3Ssn Porch  \\\n",
+            "2925           Y           120              0               0           0   \n",
+            "2926           Y           164              0               0           0   \n",
+            "2927           Y            80             32               0           0   \n",
+            "2928           Y           240             38               0           0   \n",
+            "2929           Y           190             48               0           0   \n",
+            "\n",
+            "      Screen Porch  Pool Area Pool QC  Fence Misc Feature  Misc Val  Mo Sold  \\\n",
+            "2925             0          0     NaN  GdPrv          NaN         0        3   \n",
+            "2926             0          0     NaN  MnPrv          NaN         0        6   \n",
+            "2927             0          0     NaN  MnPrv         Shed       700        7   \n",
+            "2928             0          0     NaN    NaN          NaN         0        4   \n",
+            "2929             0          0     NaN    NaN          NaN         0       11   \n",
+            "\n",
+            "      Yr Sold Sale Type Sale Condition  SalePrice  \n",
+            "2925     2006       WD          Normal     142500  \n",
+            "2926     2006       WD          Normal     131000  \n",
+            "2927     2006       WD          Normal     132000  \n",
+            "2928     2006       WD          Normal     170000  \n",
+            "2929     2006       WD          Normal     188000  \n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Load the Ames Housing dataset into a DataFrame.\n",
+        "# Hint: The dataset is available on Kaggle (\"Ames Housing\").\n",
+        "# After loading, display the first and last 5 rows to check if it worked.\n",
+        "\n",
+        "# Install dependencies as needed:\n",
+        "# pip install kagglehub[pandas-datasets]\n",
+        "import kagglehub\n",
+        "from kagglehub import KaggleDatasetAdapter\n",
+        "import pandas as pd\n",
+        "\n",
+        "# Set the path to the file you'd like to load\n",
+        "file_path = \"AmesHousing.csv\"\n",
+        "\n",
+        "# Load the latest version\n",
+        "df1 = kagglehub.load_dataset(\n",
+        "  KaggleDatasetAdapter.PANDAS,\n",
+        "  \"prevek18/ames-housing-dataset\",\n",
+        "  file_path,\n",
+        "  # Provide any additional arguments like\n",
+        "  # sql_query or pandas_kwargs. See the\n",
+        "  # documenation for more information:\n",
+        "  # https://github.com/Kaggle/kagglehub/blob/main/README.md#kaggledatasetadapterpandas\n",
+        ")\n",
+        "\n",
+        "pd.set_option('display.max_columns', None)\n",
+        "\n",
+        "print(\"First 5 records:\", df1.head())\n",
+        "print(\"Last 5 records:\", df1.tail())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OdX7swaujg_u"
+      },
+      "source": [
+        "## 🔹 Step 2: Exploratory Data Review (EDR)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DMYRBPWWjgpo",
+        "outputId": "6eedee8f-e54b-41a9-9ff6-1bee3288b30f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Shape: (2930, 82)\n",
+            "Columns Name: Index(['Order', 'PID', 'MS SubClass', 'MS Zoning', 'Lot Frontage', 'Lot Area',\n",
+            "       'Street', 'Alley', 'Lot Shape', 'Land Contour', 'Utilities',\n",
+            "       'Lot Config', 'Land Slope', 'Neighborhood', 'Condition 1',\n",
+            "       'Condition 2', 'Bldg Type', 'House Style', 'Overall Qual',\n",
+            "       'Overall Cond', 'Year Built', 'Year Remod/Add', 'Roof Style',\n",
+            "       'Roof Matl', 'Exterior 1st', 'Exterior 2nd', 'Mas Vnr Type',\n",
+            "       'Mas Vnr Area', 'Exter Qual', 'Exter Cond', 'Foundation', 'Bsmt Qual',\n",
+            "       'Bsmt Cond', 'Bsmt Exposure', 'BsmtFin Type 1', 'BsmtFin SF 1',\n",
+            "       'BsmtFin Type 2', 'BsmtFin SF 2', 'Bsmt Unf SF', 'Total Bsmt SF',\n",
+            "       'Heating', 'Heating QC', 'Central Air', 'Electrical', '1st Flr SF',\n",
+            "       '2nd Flr SF', 'Low Qual Fin SF', 'Gr Liv Area', 'Bsmt Full Bath',\n",
+            "       'Bsmt Half Bath', 'Full Bath', 'Half Bath', 'Bedroom AbvGr',\n",
+            "       'Kitchen AbvGr', 'Kitchen Qual', 'TotRms AbvGrd', 'Functional',\n",
+            "       'Fireplaces', 'Fireplace Qu', 'Garage Type', 'Garage Yr Blt',\n",
+            "       'Garage Finish', 'Garage Cars', 'Garage Area', 'Garage Qual',\n",
+            "       'Garage Cond', 'Paved Drive', 'Wood Deck SF', 'Open Porch SF',\n",
+            "       'Enclosed Porch', '3Ssn Porch', 'Screen Porch', 'Pool Area', 'Pool QC',\n",
+            "       'Fence', 'Misc Feature', 'Misc Val', 'Mo Sold', 'Yr Sold', 'Sale Type',\n",
+            "       'Sale Condition', 'SalePrice'],\n",
+            "      dtype='object')\n",
+            "Sample of Records: <bound method NDFrame.sample of       Order        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
+            "0         1  526301100           20        RL         141.0     31770   Pave   \n",
+            "1         2  526350040           20        RH          80.0     11622   Pave   \n",
+            "2         3  526351010           20        RL          81.0     14267   Pave   \n",
+            "3         4  526353030           20        RL          93.0     11160   Pave   \n",
+            "4         5  527105010           60        RL          74.0     13830   Pave   \n",
+            "...     ...        ...          ...       ...           ...       ...    ...   \n",
+            "2925   2926  923275080           80        RL          37.0      7937   Pave   \n",
+            "2926   2927  923276100           20        RL           NaN      8885   Pave   \n",
+            "2927   2928  923400125           85        RL          62.0     10441   Pave   \n",
+            "2928   2929  924100070           20        RL          77.0     10010   Pave   \n",
+            "2929   2930  924151050           60        RL          74.0      9627   Pave   \n",
+            "\n",
+            "     Alley Lot Shape Land Contour Utilities Lot Config Land Slope  \\\n",
+            "0      NaN       IR1          Lvl    AllPub     Corner        Gtl   \n",
+            "1      NaN       Reg          Lvl    AllPub     Inside        Gtl   \n",
+            "2      NaN       IR1          Lvl    AllPub     Corner        Gtl   \n",
+            "3      NaN       Reg          Lvl    AllPub     Corner        Gtl   \n",
+            "4      NaN       IR1          Lvl    AllPub     Inside        Gtl   \n",
+            "...    ...       ...          ...       ...        ...        ...   \n",
+            "2925   NaN       IR1          Lvl    AllPub    CulDSac        Gtl   \n",
+            "2926   NaN       IR1          Low    AllPub     Inside        Mod   \n",
+            "2927   NaN       Reg          Lvl    AllPub     Inside        Gtl   \n",
+            "2928   NaN       Reg          Lvl    AllPub     Inside        Mod   \n",
+            "2929   NaN       Reg          Lvl    AllPub     Inside        Mod   \n",
+            "\n",
+            "     Neighborhood Condition 1 Condition 2 Bldg Type House Style  Overall Qual  \\\n",
+            "0           NAmes        Norm        Norm      1Fam      1Story             6   \n",
+            "1           NAmes       Feedr        Norm      1Fam      1Story             5   \n",
+            "2           NAmes        Norm        Norm      1Fam      1Story             6   \n",
+            "3           NAmes        Norm        Norm      1Fam      1Story             7   \n",
+            "4         Gilbert        Norm        Norm      1Fam      2Story             5   \n",
+            "...           ...         ...         ...       ...         ...           ...   \n",
+            "2925      Mitchel        Norm        Norm      1Fam        SLvl             6   \n",
+            "2926      Mitchel        Norm        Norm      1Fam      1Story             5   \n",
+            "2927      Mitchel        Norm        Norm      1Fam      SFoyer             5   \n",
+            "2928      Mitchel        Norm        Norm      1Fam      1Story             5   \n",
+            "2929      Mitchel        Norm        Norm      1Fam      2Story             7   \n",
+            "\n",
+            "      Overall Cond  Year Built  Year Remod/Add Roof Style Roof Matl  \\\n",
+            "0                5        1960            1960        Hip   CompShg   \n",
+            "1                6        1961            1961      Gable   CompShg   \n",
+            "2                6        1958            1958        Hip   CompShg   \n",
+            "3                5        1968            1968        Hip   CompShg   \n",
+            "4                5        1997            1998      Gable   CompShg   \n",
+            "...            ...         ...             ...        ...       ...   \n",
+            "2925             6        1984            1984      Gable   CompShg   \n",
+            "2926             5        1983            1983      Gable   CompShg   \n",
+            "2927             5        1992            1992      Gable   CompShg   \n",
+            "2928             5        1974            1975      Gable   CompShg   \n",
+            "2929             5        1993            1994      Gable   CompShg   \n",
+            "\n",
+            "     Exterior 1st Exterior 2nd Mas Vnr Type  Mas Vnr Area Exter Qual  \\\n",
+            "0         BrkFace      Plywood        Stone         112.0         TA   \n",
+            "1         VinylSd      VinylSd          NaN           0.0         TA   \n",
+            "2         Wd Sdng      Wd Sdng      BrkFace         108.0         TA   \n",
+            "3         BrkFace      BrkFace          NaN           0.0         Gd   \n",
+            "4         VinylSd      VinylSd          NaN           0.0         TA   \n",
+            "...           ...          ...          ...           ...        ...   \n",
+            "2925      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2926      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2927      HdBoard      Wd Shng          NaN           0.0         TA   \n",
+            "2928      HdBoard      HdBoard          NaN           0.0         TA   \n",
+            "2929      HdBoard      HdBoard      BrkFace          94.0         TA   \n",
+            "\n",
+            "     Exter Cond Foundation Bsmt Qual Bsmt Cond Bsmt Exposure BsmtFin Type 1  \\\n",
+            "0            TA     CBlock        TA        Gd            Gd            BLQ   \n",
+            "1            TA     CBlock        TA        TA            No            Rec   \n",
+            "2            TA     CBlock        TA        TA            No            ALQ   \n",
+            "3            TA     CBlock        TA        TA            No            ALQ   \n",
+            "4            TA      PConc        Gd        TA            No            GLQ   \n",
+            "...         ...        ...       ...       ...           ...            ...   \n",
+            "2925         TA     CBlock        TA        TA            Av            GLQ   \n",
+            "2926         TA     CBlock        Gd        TA            Av            BLQ   \n",
+            "2927         TA      PConc        Gd        TA            Av            GLQ   \n",
+            "2928         TA     CBlock        Gd        TA            Av            ALQ   \n",
+            "2929         TA      PConc        Gd        TA            Av            LwQ   \n",
+            "\n",
+            "      BsmtFin SF 1 BsmtFin Type 2  BsmtFin SF 2  Bsmt Unf SF  Total Bsmt SF  \\\n",
+            "0            639.0            Unf           0.0        441.0         1080.0   \n",
+            "1            468.0            LwQ         144.0        270.0          882.0   \n",
+            "2            923.0            Unf           0.0        406.0         1329.0   \n",
+            "3           1065.0            Unf           0.0       1045.0         2110.0   \n",
+            "4            791.0            Unf           0.0        137.0          928.0   \n",
+            "...            ...            ...           ...          ...            ...   \n",
+            "2925         819.0            Unf           0.0        184.0         1003.0   \n",
+            "2926         301.0            ALQ         324.0        239.0          864.0   \n",
+            "2927         337.0            Unf           0.0        575.0          912.0   \n",
+            "2928        1071.0            LwQ         123.0        195.0         1389.0   \n",
+            "2929         758.0            Unf           0.0        238.0          996.0   \n",
+            "\n",
+            "     Heating Heating QC Central Air Electrical  1st Flr SF  2nd Flr SF  \\\n",
+            "0       GasA         Fa           Y      SBrkr        1656           0   \n",
+            "1       GasA         TA           Y      SBrkr         896           0   \n",
+            "2       GasA         TA           Y      SBrkr        1329           0   \n",
+            "3       GasA         Ex           Y      SBrkr        2110           0   \n",
+            "4       GasA         Gd           Y      SBrkr         928         701   \n",
+            "...      ...        ...         ...        ...         ...         ...   \n",
+            "2925    GasA         TA           Y      SBrkr        1003           0   \n",
+            "2926    GasA         TA           Y      SBrkr         902           0   \n",
+            "2927    GasA         TA           Y      SBrkr         970           0   \n",
+            "2928    GasA         Gd           Y      SBrkr        1389           0   \n",
+            "2929    GasA         Ex           Y      SBrkr         996        1004   \n",
+            "\n",
+            "      Low Qual Fin SF  Gr Liv Area  Bsmt Full Bath  Bsmt Half Bath  Full Bath  \\\n",
+            "0                   0         1656             1.0             0.0          1   \n",
+            "1                   0          896             0.0             0.0          1   \n",
+            "2                   0         1329             0.0             0.0          1   \n",
+            "3                   0         2110             1.0             0.0          2   \n",
+            "4                   0         1629             0.0             0.0          2   \n",
+            "...               ...          ...             ...             ...        ...   \n",
+            "2925                0         1003             1.0             0.0          1   \n",
+            "2926                0          902             1.0             0.0          1   \n",
+            "2927                0          970             0.0             1.0          1   \n",
+            "2928                0         1389             1.0             0.0          1   \n",
+            "2929                0         2000             0.0             0.0          2   \n",
+            "\n",
+            "      Half Bath  Bedroom AbvGr  Kitchen AbvGr Kitchen Qual  TotRms AbvGrd  \\\n",
+            "0             0              3              1           TA              7   \n",
+            "1             0              2              1           TA              5   \n",
+            "2             1              3              1           Gd              6   \n",
+            "3             1              3              1           Ex              8   \n",
+            "4             1              3              1           TA              6   \n",
+            "...         ...            ...            ...          ...            ...   \n",
+            "2925          0              3              1           TA              6   \n",
+            "2926          0              2              1           TA              5   \n",
+            "2927          0              3              1           TA              6   \n",
+            "2928          0              2              1           TA              6   \n",
+            "2929          1              3              1           TA              9   \n",
+            "\n",
+            "     Functional  Fireplaces Fireplace Qu Garage Type  Garage Yr Blt  \\\n",
+            "0           Typ           2           Gd      Attchd         1960.0   \n",
+            "1           Typ           0          NaN      Attchd         1961.0   \n",
+            "2           Typ           0          NaN      Attchd         1958.0   \n",
+            "3           Typ           2           TA      Attchd         1968.0   \n",
+            "4           Typ           1           TA      Attchd         1997.0   \n",
+            "...         ...         ...          ...         ...            ...   \n",
+            "2925        Typ           0          NaN      Detchd         1984.0   \n",
+            "2926        Typ           0          NaN      Attchd         1983.0   \n",
+            "2927        Typ           0          NaN         NaN            NaN   \n",
+            "2928        Typ           1           TA      Attchd         1975.0   \n",
+            "2929        Typ           1           TA      Attchd         1993.0   \n",
+            "\n",
+            "     Garage Finish  Garage Cars  Garage Area Garage Qual Garage Cond  \\\n",
+            "0              Fin          2.0        528.0          TA          TA   \n",
+            "1              Unf          1.0        730.0          TA          TA   \n",
+            "2              Unf          1.0        312.0          TA          TA   \n",
+            "3              Fin          2.0        522.0          TA          TA   \n",
+            "4              Fin          2.0        482.0          TA          TA   \n",
+            "...            ...          ...          ...         ...         ...   \n",
+            "2925           Unf          2.0        588.0          TA          TA   \n",
+            "2926           Unf          2.0        484.0          TA          TA   \n",
+            "2927           NaN          0.0          0.0         NaN         NaN   \n",
+            "2928           RFn          2.0        418.0          TA          TA   \n",
+            "2929           Fin          3.0        650.0          TA          TA   \n",
+            "\n",
+            "     Paved Drive  Wood Deck SF  Open Porch SF  Enclosed Porch  3Ssn Porch  \\\n",
+            "0              P           210             62               0           0   \n",
+            "1              Y           140              0               0           0   \n",
+            "2              Y           393             36               0           0   \n",
+            "3              Y             0              0               0           0   \n",
+            "4              Y           212             34               0           0   \n",
+            "...          ...           ...            ...             ...         ...   \n",
+            "2925           Y           120              0               0           0   \n",
+            "2926           Y           164              0               0           0   \n",
+            "2927           Y            80             32               0           0   \n",
+            "2928           Y           240             38               0           0   \n",
+            "2929           Y           190             48               0           0   \n",
+            "\n",
+            "      Screen Porch  Pool Area Pool QC  Fence Misc Feature  Misc Val  Mo Sold  \\\n",
+            "0                0          0     NaN    NaN          NaN         0        5   \n",
+            "1              120          0     NaN  MnPrv          NaN         0        6   \n",
+            "2                0          0     NaN    NaN         Gar2     12500        6   \n",
+            "3                0          0     NaN    NaN          NaN         0        4   \n",
+            "4                0          0     NaN  MnPrv          NaN         0        3   \n",
+            "...            ...        ...     ...    ...          ...       ...      ...   \n",
+            "2925             0          0     NaN  GdPrv          NaN         0        3   \n",
+            "2926             0          0     NaN  MnPrv          NaN         0        6   \n",
+            "2927             0          0     NaN  MnPrv         Shed       700        7   \n",
+            "2928             0          0     NaN    NaN          NaN         0        4   \n",
+            "2929             0          0     NaN    NaN          NaN         0       11   \n",
+            "\n",
+            "      Yr Sold Sale Type Sale Condition  SalePrice  \n",
+            "0        2010       WD          Normal     215000  \n",
+            "1        2010       WD          Normal     105000  \n",
+            "2        2010       WD          Normal     172000  \n",
+            "3        2010       WD          Normal     244000  \n",
+            "4        2010       WD          Normal     189900  \n",
+            "...       ...       ...            ...        ...  \n",
+            "2925     2006       WD          Normal     142500  \n",
+            "2926     2006       WD          Normal     131000  \n",
+            "2927     2006       WD          Normal     132000  \n",
+            "2928     2006       WD          Normal     170000  \n",
+            "2929     2006       WD          Normal     188000  \n",
+            "\n",
+            "[2930 rows x 82 columns]>\n",
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 2930 entries, 0 to 2929\n",
+            "Data columns (total 82 columns):\n",
+            " #   Column           Non-Null Count  Dtype  \n",
+            "---  ------           --------------  -----  \n",
+            " 0   Order            2930 non-null   int64  \n",
+            " 1   PID              2930 non-null   int64  \n",
+            " 2   MS SubClass      2930 non-null   int64  \n",
+            " 3   MS Zoning        2930 non-null   object \n",
+            " 4   Lot Frontage     2440 non-null   float64\n",
+            " 5   Lot Area         2930 non-null   int64  \n",
+            " 6   Street           2930 non-null   object \n",
+            " 7   Alley            198 non-null    object \n",
+            " 8   Lot Shape        2930 non-null   object \n",
+            " 9   Land Contour     2930 non-null   object \n",
+            " 10  Utilities        2930 non-null   object \n",
+            " 11  Lot Config       2930 non-null   object \n",
+            " 12  Land Slope       2930 non-null   object \n",
+            " 13  Neighborhood     2930 non-null   object \n",
+            " 14  Condition 1      2930 non-null   object \n",
+            " 15  Condition 2      2930 non-null   object \n",
+            " 16  Bldg Type        2930 non-null   object \n",
+            " 17  House Style      2930 non-null   object \n",
+            " 18  Overall Qual     2930 non-null   int64  \n",
+            " 19  Overall Cond     2930 non-null   int64  \n",
+            " 20  Year Built       2930 non-null   int64  \n",
+            " 21  Year Remod/Add   2930 non-null   int64  \n",
+            " 22  Roof Style       2930 non-null   object \n",
+            " 23  Roof Matl        2930 non-null   object \n",
+            " 24  Exterior 1st     2930 non-null   object \n",
+            " 25  Exterior 2nd     2930 non-null   object \n",
+            " 26  Mas Vnr Type     1155 non-null   object \n",
+            " 27  Mas Vnr Area     2907 non-null   float64\n",
+            " 28  Exter Qual       2930 non-null   object \n",
+            " 29  Exter Cond       2930 non-null   object \n",
+            " 30  Foundation       2930 non-null   object \n",
+            " 31  Bsmt Qual        2850 non-null   object \n",
+            " 32  Bsmt Cond        2850 non-null   object \n",
+            " 33  Bsmt Exposure    2847 non-null   object \n",
+            " 34  BsmtFin Type 1   2850 non-null   object \n",
+            " 35  BsmtFin SF 1     2929 non-null   float64\n",
+            " 36  BsmtFin Type 2   2849 non-null   object \n",
+            " 37  BsmtFin SF 2     2929 non-null   float64\n",
+            " 38  Bsmt Unf SF      2929 non-null   float64\n",
+            " 39  Total Bsmt SF    2929 non-null   float64\n",
+            " 40  Heating          2930 non-null   object \n",
+            " 41  Heating QC       2930 non-null   object \n",
+            " 42  Central Air      2930 non-null   object \n",
+            " 43  Electrical       2929 non-null   object \n",
+            " 44  1st Flr SF       2930 non-null   int64  \n",
+            " 45  2nd Flr SF       2930 non-null   int64  \n",
+            " 46  Low Qual Fin SF  2930 non-null   int64  \n",
+            " 47  Gr Liv Area      2930 non-null   int64  \n",
+            " 48  Bsmt Full Bath   2928 non-null   float64\n",
+            " 49  Bsmt Half Bath   2928 non-null   float64\n",
+            " 50  Full Bath        2930 non-null   int64  \n",
+            " 51  Half Bath        2930 non-null   int64  \n",
+            " 52  Bedroom AbvGr    2930 non-null   int64  \n",
+            " 53  Kitchen AbvGr    2930 non-null   int64  \n",
+            " 54  Kitchen Qual     2930 non-null   object \n",
+            " 55  TotRms AbvGrd    2930 non-null   int64  \n",
+            " 56  Functional       2930 non-null   object \n",
+            " 57  Fireplaces       2930 non-null   int64  \n",
+            " 58  Fireplace Qu     1508 non-null   object \n",
+            " 59  Garage Type      2773 non-null   object \n",
+            " 60  Garage Yr Blt    2771 non-null   float64\n",
+            " 61  Garage Finish    2771 non-null   object \n",
+            " 62  Garage Cars      2929 non-null   float64\n",
+            " 63  Garage Area      2929 non-null   float64\n",
+            " 64  Garage Qual      2771 non-null   object \n",
+            " 65  Garage Cond      2771 non-null   object \n",
+            " 66  Paved Drive      2930 non-null   object \n",
+            " 67  Wood Deck SF     2930 non-null   int64  \n",
+            " 68  Open Porch SF    2930 non-null   int64  \n",
+            " 69  Enclosed Porch   2930 non-null   int64  \n",
+            " 70  3Ssn Porch       2930 non-null   int64  \n",
+            " 71  Screen Porch     2930 non-null   int64  \n",
+            " 72  Pool Area        2930 non-null   int64  \n",
+            " 73  Pool QC          13 non-null     object \n",
+            " 74  Fence            572 non-null    object \n",
+            " 75  Misc Feature     106 non-null    object \n",
+            " 76  Misc Val         2930 non-null   int64  \n",
+            " 77  Mo Sold          2930 non-null   int64  \n",
+            " 78  Yr Sold          2930 non-null   int64  \n",
+            " 79  Sale Type        2930 non-null   object \n",
+            " 80  Sale Condition   2930 non-null   object \n",
+            " 81  SalePrice        2930 non-null   int64  \n",
+            "dtypes: float64(11), int64(28), object(43)\n",
+            "memory usage: 1.8+ MB\n",
+            "            Order           PID  MS SubClass  Lot Frontage       Lot Area  \\\n",
+            "count  2930.00000  2.930000e+03  2930.000000   2440.000000    2930.000000   \n",
+            "mean   1465.50000  7.144645e+08    57.387372     69.224590   10147.921843   \n",
+            "std     845.96247  1.887308e+08    42.638025     23.365335    7880.017759   \n",
+            "min       1.00000  5.263011e+08    20.000000     21.000000    1300.000000   \n",
+            "25%     733.25000  5.284770e+08    20.000000     58.000000    7440.250000   \n",
+            "50%    1465.50000  5.354536e+08    50.000000     68.000000    9436.500000   \n",
+            "75%    2197.75000  9.071811e+08    70.000000     80.000000   11555.250000   \n",
+            "max    2930.00000  1.007100e+09   190.000000    313.000000  215245.000000   \n",
+            "\n",
+            "       Overall Qual  Overall Cond   Year Built  Year Remod/Add  Mas Vnr Area  \\\n",
+            "count   2930.000000   2930.000000  2930.000000     2930.000000   2907.000000   \n",
+            "mean       6.094881      5.563140  1971.356314     1984.266553    101.896801   \n",
+            "std        1.411026      1.111537    30.245361       20.860286    179.112611   \n",
+            "min        1.000000      1.000000  1872.000000     1950.000000      0.000000   \n",
+            "25%        5.000000      5.000000  1954.000000     1965.000000      0.000000   \n",
+            "50%        6.000000      5.000000  1973.000000     1993.000000      0.000000   \n",
+            "75%        7.000000      6.000000  2001.000000     2004.000000    164.000000   \n",
+            "max       10.000000      9.000000  2010.000000     2010.000000   1600.000000   \n",
+            "\n",
+            "       BsmtFin SF 1  BsmtFin SF 2  Bsmt Unf SF  Total Bsmt SF   1st Flr SF  \\\n",
+            "count   2929.000000   2929.000000  2929.000000    2929.000000  2930.000000   \n",
+            "mean     442.629566     49.722431   559.262547    1051.614544  1159.557679   \n",
+            "std      455.590839    169.168476   439.494153     440.615067   391.890885   \n",
+            "min        0.000000      0.000000     0.000000       0.000000   334.000000   \n",
+            "25%        0.000000      0.000000   219.000000     793.000000   876.250000   \n",
+            "50%      370.000000      0.000000   466.000000     990.000000  1084.000000   \n",
+            "75%      734.000000      0.000000   802.000000    1302.000000  1384.000000   \n",
+            "max     5644.000000   1526.000000  2336.000000    6110.000000  5095.000000   \n",
+            "\n",
+            "        2nd Flr SF  Low Qual Fin SF  Gr Liv Area  Bsmt Full Bath  \\\n",
+            "count  2930.000000      2930.000000  2930.000000     2928.000000   \n",
+            "mean    335.455973         4.676792  1499.690444        0.431352   \n",
+            "std     428.395715        46.310510   505.508887        0.524820   \n",
+            "min       0.000000         0.000000   334.000000        0.000000   \n",
+            "25%       0.000000         0.000000  1126.000000        0.000000   \n",
+            "50%       0.000000         0.000000  1442.000000        0.000000   \n",
+            "75%     703.750000         0.000000  1742.750000        1.000000   \n",
+            "max    2065.000000      1064.000000  5642.000000        3.000000   \n",
+            "\n",
+            "       Bsmt Half Bath    Full Bath    Half Bath  Bedroom AbvGr  Kitchen AbvGr  \\\n",
+            "count     2928.000000  2930.000000  2930.000000    2930.000000    2930.000000   \n",
+            "mean         0.061134     1.566553     0.379522       2.854266       1.044369   \n",
+            "std          0.245254     0.552941     0.502629       0.827731       0.214076   \n",
+            "min          0.000000     0.000000     0.000000       0.000000       0.000000   \n",
+            "25%          0.000000     1.000000     0.000000       2.000000       1.000000   \n",
+            "50%          0.000000     2.000000     0.000000       3.000000       1.000000   \n",
+            "75%          0.000000     2.000000     1.000000       3.000000       1.000000   \n",
+            "max          2.000000     4.000000     2.000000       8.000000       3.000000   \n",
+            "\n",
+            "       TotRms AbvGrd   Fireplaces  Garage Yr Blt  Garage Cars  Garage Area  \\\n",
+            "count    2930.000000  2930.000000    2771.000000  2929.000000  2929.000000   \n",
+            "mean        6.443003     0.599317    1978.132443     1.766815   472.819734   \n",
+            "std         1.572964     0.647921      25.528411     0.760566   215.046549   \n",
+            "min         2.000000     0.000000    1895.000000     0.000000     0.000000   \n",
+            "25%         5.000000     0.000000    1960.000000     1.000000   320.000000   \n",
+            "50%         6.000000     1.000000    1979.000000     2.000000   480.000000   \n",
+            "75%         7.000000     1.000000    2002.000000     2.000000   576.000000   \n",
+            "max        15.000000     4.000000    2207.000000     5.000000  1488.000000   \n",
+            "\n",
+            "       Wood Deck SF  Open Porch SF  Enclosed Porch   3Ssn Porch  Screen Porch  \\\n",
+            "count   2930.000000    2930.000000     2930.000000  2930.000000   2930.000000   \n",
+            "mean      93.751877      47.533447       23.011604     2.592491     16.002048   \n",
+            "std      126.361562      67.483400       64.139059    25.141331     56.087370   \n",
+            "min        0.000000       0.000000        0.000000     0.000000      0.000000   \n",
+            "25%        0.000000       0.000000        0.000000     0.000000      0.000000   \n",
+            "50%        0.000000      27.000000        0.000000     0.000000      0.000000   \n",
+            "75%      168.000000      70.000000        0.000000     0.000000      0.000000   \n",
+            "max     1424.000000     742.000000     1012.000000   508.000000    576.000000   \n",
+            "\n",
+            "         Pool Area      Misc Val      Mo Sold      Yr Sold      SalePrice  \n",
+            "count  2930.000000   2930.000000  2930.000000  2930.000000    2930.000000  \n",
+            "mean      2.243345     50.635154     6.216041  2007.790444  180796.060068  \n",
+            "std      35.597181    566.344288     2.714492     1.316613   79886.692357  \n",
+            "min       0.000000      0.000000     1.000000  2006.000000   12789.000000  \n",
+            "25%       0.000000      0.000000     4.000000  2007.000000  129500.000000  \n",
+            "50%       0.000000      0.000000     6.000000  2008.000000  160000.000000  \n",
+            "75%       0.000000      0.000000     8.000000  2009.000000  213500.000000  \n",
+            "max     800.000000  17000.000000    12.000000  2010.000000  755000.000000  \n",
+            "       MS Zoning Street Alley Lot Shape Land Contour Utilities Lot Config  \\\n",
+            "count       2930   2930   198      2930         2930      2930       2930   \n",
+            "unique         7      2     2         4            4         3          5   \n",
+            "top           RL   Pave  Grvl       Reg          Lvl    AllPub     Inside   \n",
+            "freq        2273   2918   120      1859         2633      2927       2140   \n",
+            "\n",
+            "       Land Slope Neighborhood Condition 1 Condition 2 Bldg Type House Style  \\\n",
+            "count        2930         2930        2930        2930      2930        2930   \n",
+            "unique          3           28           9           8         5           8   \n",
+            "top           Gtl        NAmes        Norm        Norm      1Fam      1Story   \n",
+            "freq         2789          443        2522        2900      2425        1481   \n",
+            "\n",
+            "       Roof Style Roof Matl Exterior 1st Exterior 2nd Mas Vnr Type Exter Qual  \\\n",
+            "count        2930      2930         2930         2930         1155       2930   \n",
+            "unique          6         8           16           17            4          4   \n",
+            "top         Gable   CompShg      VinylSd      VinylSd      BrkFace         TA   \n",
+            "freq         2321      2887         1026         1015          880       1799   \n",
+            "\n",
+            "       Exter Cond Foundation Bsmt Qual Bsmt Cond Bsmt Exposure BsmtFin Type 1  \\\n",
+            "count        2930       2930      2850      2850          2847           2850   \n",
+            "unique          5          6         5         5             4              6   \n",
+            "top            TA      PConc        TA        TA            No            GLQ   \n",
+            "freq         2549       1310      1283      2616          1906            859   \n",
+            "\n",
+            "       BsmtFin Type 2 Heating Heating QC Central Air Electrical Kitchen Qual  \\\n",
+            "count            2849    2930       2930        2930       2929         2930   \n",
+            "unique              6       6          5           2          5            5   \n",
+            "top               Unf    GasA         Ex           Y      SBrkr           TA   \n",
+            "freq             2499    2885       1495        2734       2682         1494   \n",
+            "\n",
+            "       Functional Fireplace Qu Garage Type Garage Finish Garage Qual  \\\n",
+            "count        2930         1508        2773          2771        2771   \n",
+            "unique          8            5           6             3           5   \n",
+            "top           Typ           Gd      Attchd           Unf          TA   \n",
+            "freq         2728          744        1731          1231        2615   \n",
+            "\n",
+            "       Garage Cond Paved Drive Pool QC  Fence Misc Feature Sale Type  \\\n",
+            "count         2771        2930      13    572          106      2930   \n",
+            "unique           5           3       4      4            5        10   \n",
+            "top             TA           Y      Ex  MnPrv         Shed       WD    \n",
+            "freq          2665        2652       4    330           95      2536   \n",
+            "\n",
+            "       Sale Condition  \n",
+            "count            2930  \n",
+            "unique              6  \n",
+            "top            Normal  \n",
+            "freq             2413  \n",
+            "Order: [   1    2    3 ... 2928 2929 2930]\n",
+            "PID: [526301100 526350040 526351010 ... 923400125 924100070 924151050]\n",
+            "MS SubClass: [ 20  60 120  50  85 160  80  30  90 190  45  70  75  40 180 150]\n",
+            "MS Zoning: ['RL' 'RH' 'FV' 'RM' 'C (all)' 'I (all)' 'A (agr)']\n",
+            "Lot Frontage: [141.  80.  81.  93.  74.  78.  41.  43.  39.  60.  75.  nan  63.  85.\n",
+            "  47. 152.  88. 140. 105.  65.  70.  26.  21.  53.  24. 102.  98.  83.\n",
+            "  94.  95.  90.  79. 100.  44. 110.  61.  36.  67. 108.  59.  92.  58.\n",
+            "  56.  73.  72.  84.  76.  50.  55.  68. 107.  25.  30.  57.  40.  77.\n",
+            " 120. 137.  87. 119.  64.  96.  71.  69.  52.  51.  54.  86. 124.  82.\n",
+            "  38.  48.  89.  66.  45.  35. 129.  31.  42.  28.  99. 104.  97. 103.\n",
+            "  34. 117. 149. 122.  62. 174. 106. 112.  32. 115. 128.  91.  33. 121.\n",
+            " 144. 130. 109. 150. 113. 125. 101.  46. 114. 135. 136.  37.  22. 313.\n",
+            "  49. 123. 160. 195. 118. 134. 182. 116. 138. 155. 126. 200. 168. 111.\n",
+            " 131. 153. 133.]\n",
+            "Lot Area: [31770 11622 14267 ...  7937  8885 10441]\n",
+            "Street: ['Pave' 'Grvl']\n",
+            "Alley: [nan 'Pave' 'Grvl']\n",
+            "Lot Shape: ['IR1' 'Reg' 'IR2' 'IR3']\n",
+            "Land Contour: ['Lvl' 'HLS' 'Bnk' 'Low']\n",
+            "Utilities: ['AllPub' 'NoSewr' 'NoSeWa']\n",
+            "Lot Config: ['Corner' 'Inside' 'CulDSac' 'FR2' 'FR3']\n",
+            "Land Slope: ['Gtl' 'Mod' 'Sev']\n",
+            "Neighborhood: ['NAmes' 'Gilbert' 'StoneBr' 'NWAmes' 'Somerst' 'BrDale' 'NPkVill'\n",
+            " 'NridgHt' 'Blmngtn' 'NoRidge' 'SawyerW' 'Sawyer' 'Greens' 'BrkSide'\n",
+            " 'OldTown' 'IDOTRR' 'ClearCr' 'SWISU' 'Edwards' 'CollgCr' 'Crawfor'\n",
+            " 'Blueste' 'Mitchel' 'Timber' 'MeadowV' 'Veenker' 'GrnHill' 'Landmrk']\n",
+            "Condition 1: ['Norm' 'Feedr' 'PosN' 'RRNe' 'RRAe' 'Artery' 'PosA' 'RRAn' 'RRNn']\n",
+            "Condition 2: ['Norm' 'Feedr' 'PosA' 'PosN' 'Artery' 'RRNn' 'RRAe' 'RRAn']\n",
+            "Bldg Type: ['1Fam' 'TwnhsE' 'Twnhs' 'Duplex' '2fmCon']\n",
+            "House Style: ['1Story' '2Story' '1.5Fin' 'SFoyer' 'SLvl' '2.5Unf' '1.5Unf' '2.5Fin']\n",
+            "Overall Qual: [ 6  5  7  8  9  4  3  2 10  1]\n",
+            "Overall Cond: [5 6 7 2 8 4 9 3 1]\n",
+            "Year Built: [1960 1961 1958 1968 1997 1998 2001 1992 1995 1999 1993 1990 1985 2003\n",
+            " 1988 2010 1951 1978 1977 1974 2000 1970 1971 1975 2009 2007 2005 2004\n",
+            " 2002 2006 1996 1994 2008 1980 1979 1984 1920 1965 1967 1963 1962 1976\n",
+            " 1972 1966 1959 1964 1950 1952 1949 1940 1954 1955 1957 1956 1953 1948\n",
+            " 1900 1910 1927 1915 1945 1929 1938 1923 1928 1890 1885 1922 1925 1939\n",
+            " 1942 1936 1930 1921 1912 1917 1907 1875 1969 1947 1946 1987 1941 1924\n",
+            " 1914 1931 1919 1989 1896 1973 1991 1981 1986 1916 1926 1935 1892 1898\n",
+            " 1880 1882 1937 1902 1934 1982 1983 1932 1918 1904 1905 1872 1893 1906\n",
+            " 1908 1911 1895 1879 1901 1913]\n",
+            "Year Remod/Add: [1960 1961 1958 1968 1998 2001 1992 1996 1999 1994 2007 1990 1985 2003\n",
+            " 2005 2010 1951 1988 1977 1974 2000 1970 2008 1971 1975 1978 2006 2004\n",
+            " 2002 1995 2009 1980 1979 1984 1981 1950 1967 1963 1993 1966 1959 1964\n",
+            " 1954 1972 1989 1957 1956 1952 1955 1962 1997 1965 1969 1987 1976 1991\n",
+            " 1973 1986 1983 1953 1982]\n",
+            "Roof Style: ['Hip' 'Gable' 'Mansard' 'Gambrel' 'Shed' 'Flat']\n",
+            "Roof Matl: ['CompShg' 'WdShake' 'Tar&Grv' 'WdShngl' 'Membran' 'ClyTile' 'Roll'\n",
+            " 'Metal']\n",
+            "Exterior 1st: ['BrkFace' 'VinylSd' 'Wd Sdng' 'CemntBd' 'HdBoard' 'Plywood' 'MetalSd'\n",
+            " 'AsbShng' 'WdShing' 'Stucco' 'AsphShn' 'BrkComm' 'CBlock' 'PreCast'\n",
+            " 'Stone' 'ImStucc']\n",
+            "Exterior 2nd: ['Plywood' 'VinylSd' 'Wd Sdng' 'BrkFace' 'CmentBd' 'HdBoard' 'Wd Shng'\n",
+            " 'MetalSd' 'ImStucc' 'Brk Cmn' 'AsbShng' 'Stucco' 'AsphShn' 'CBlock'\n",
+            " 'Stone' 'PreCast' 'Other']\n",
+            "Mas Vnr Type: ['Stone' nan 'BrkFace' 'BrkCmn' 'CBlock']\n",
+            "Mas Vnr Area: [1.120e+02 0.000e+00 1.080e+02 2.000e+01 6.030e+02 3.500e+02 1.190e+02\n",
+            " 4.800e+02 8.100e+01 1.800e+02 5.040e+02 4.920e+02 3.810e+02 1.620e+02\n",
+            " 2.000e+02 4.500e+02 2.560e+02 2.260e+02 6.150e+02 2.400e+02 1.680e+02\n",
+            " 7.600e+02 1.280e+02 1.095e+03 2.320e+02 4.120e+02 1.780e+02 1.060e+02\n",
+            " 1.400e+01 1.600e+01       nan 1.650e+02 1.140e+02 3.380e+02 3.620e+02\n",
+            " 3.480e+02 3.000e+01 5.790e+02 3.600e+01 1.220e+02 1.300e+02 3.100e+01\n",
+            " 2.500e+02 1.200e+02 2.160e+02 4.320e+02 1.159e+03 2.890e+02 2.800e+01\n",
+            " 4.200e+01 1.720e+02 4.510e+02 2.680e+02 8.600e+01 1.560e+02 1.440e+02\n",
+            " 2.650e+02 3.400e+02 1.100e+02 1.640e+02 3.610e+02 2.870e+02 5.060e+02\n",
+            " 1.500e+02 2.200e+02 3.240e+02 9.100e+01 1.040e+02 3.000e+02 2.610e+02\n",
+            " 2.180e+02 3.510e+02 7.710e+02 2.940e+02 9.000e+01 7.200e+01 4.700e+01\n",
+            " 1.430e+02 3.280e+02 2.880e+02 9.600e+01 3.360e+02 1.770e+02 8.500e+01\n",
+            " 2.460e+02 2.400e+01 8.000e+01 1.160e+02 1.530e+02 3.200e+02 4.790e+02\n",
+            " 2.230e+02 4.420e+02 1.700e+02 1.690e+02 1.710e+02 1.090e+02 9.800e+01\n",
+            " 1.450e+02 2.030e+02 3.710e+02 4.300e+02 4.400e+01 1.860e+02 3.350e+02\n",
+            " 6.000e+01 8.400e+01 1.890e+02 4.400e+02 1.880e+02 3.200e+01 1.600e+02\n",
+            " 2.200e+01 4.000e+01 6.800e+01 4.500e+01 3.440e+02 7.480e+02 4.640e+02\n",
+            " 1.570e+02 2.780e+02 2.090e+02 1.260e+02 1.010e+02 2.290e+02 2.250e+02\n",
+            " 2.060e+02 1.610e+02 1.960e+02 1.740e+02 3.330e+02 7.600e+01 3.120e+02\n",
+            " 1.420e+02 4.250e+02 5.100e+02 2.300e+02 7.260e+02 8.600e+02 6.400e+02\n",
+            " 3.060e+02 1.540e+02 3.050e+02 4.200e+02 4.720e+02 4.240e+02 3.020e+02\n",
+            " 2.380e+02 2.840e+02 2.620e+02 2.850e+02 2.960e+02 4.180e+02 9.220e+02\n",
+            " 7.240e+02 3.830e+02 1.350e+02 1.760e+02 1.660e+02 7.300e+02 4.700e+02\n",
+            " 3.080e+02 5.000e+02 2.700e+02 1.630e+02 1.100e+01 2.100e+02 2.980e+02\n",
+            " 6.730e+02 7.310e+02 9.750e+02 9.210e+02 6.340e+02 2.860e+02 3.720e+02\n",
+            " 5.280e+02 1.940e+02 2.600e+02 1.980e+02 1.210e+02 1.000e+02 2.640e+02\n",
+            " 1.400e+02 1.320e+02 3.660e+02 1.410e+02 1.150e+02 2.800e+02 2.520e+02\n",
+            " 8.940e+02 5.130e+02 4.560e+02 5.710e+02 3.590e+02 2.830e+02 3.600e+02\n",
+            " 5.090e+02 7.000e+01 9.500e+01 2.170e+02 3.000e+00 2.470e+02 5.760e+02\n",
+            " 1.830e+02 3.990e+02 6.500e+02 6.570e+02 2.950e+02 3.680e+02 8.200e+01\n",
+            " 1.240e+02 4.440e+02 9.200e+01 8.900e+01 2.300e+01 5.400e+01 1.490e+02\n",
+            " 2.340e+02 1.370e+02 2.750e+02 2.420e+02 3.640e+02 3.520e+02 1.360e+02\n",
+            " 2.040e+02 5.730e+02 2.550e+02 7.400e+01 2.590e+02 8.800e+01 5.410e+02\n",
+            " 4.060e+02 3.100e+02 5.840e+02 2.900e+02 1.820e+02 7.500e+01 2.450e+02\n",
+            " 1.230e+02 1.020e+02 6.210e+02 6.600e+02 4.020e+02 1.580e+02 4.220e+02\n",
+            " 1.270e+02 6.040e+02 3.560e+02 6.500e+01 4.260e+02 2.720e+02 8.160e+02\n",
+            " 4.360e+02 5.540e+02 4.680e+02 6.800e+02 6.640e+02 2.920e+02 1.110e+03\n",
+            " 2.210e+02 7.660e+02 6.160e+02 7.140e+02 1.460e+02 3.180e+02 6.470e+02\n",
+            " 1.290e+03 4.730e+02 4.950e+02 4.660e+02 6.510e+02 4.480e+02 5.300e+01\n",
+            " 7.680e+02 3.800e+01 2.580e+02 3.040e+02 5.680e+02 1.790e+02 2.120e+02\n",
+            " 1.050e+03 5.640e+02 3.420e+02 1.480e+02 2.430e+02 4.910e+02 2.370e+02\n",
+            " 4.100e+02 1.510e+02 1.870e+02 3.870e+02 5.200e+01 1.250e+02 2.760e+02\n",
+            " 4.150e+02 3.900e+01 4.100e+01 2.990e+02 9.900e+01 1.900e+02 2.510e+02\n",
+            " 2.810e+02 2.270e+02 2.020e+02 3.960e+02 1.340e+02 1.920e+02 2.050e+02\n",
+            " 2.150e+02 1.130e+02 5.000e+01 2.660e+02 1.470e+02 2.220e+02 7.960e+02\n",
+            " 5.800e+01 6.320e+02 6.680e+02 2.280e+02 2.190e+02 6.740e+02 1.970e+02\n",
+            " 1.115e+03 1.380e+02 7.100e+02 9.450e+02 6.700e+01 5.490e+02 2.330e+02\n",
+            " 2.530e+02 2.630e+02 3.650e+02 5.670e+02 3.760e+02 3.780e+02 4.520e+02\n",
+            " 2.540e+02 3.150e+02 4.000e+02 3.750e+02 7.720e+02 2.480e+02 9.700e+02\n",
+            " 5.020e+02 3.880e+02 3.940e+02 2.350e+02 5.150e+02 7.050e+02 1.170e+03\n",
+            " 5.940e+02 3.090e+02 5.260e+02 7.540e+02 2.080e+02 4.280e+02 3.530e+02\n",
+            " 1.050e+02 5.700e+01 3.370e+02 1.129e+03 1.600e+03 6.000e+02 1.000e+00\n",
+            " 5.250e+02 6.600e+01 6.300e+01 5.600e+01 2.240e+02 8.700e+01 2.910e+02\n",
+            " 6.900e+01 2.790e+02 4.350e+02 3.230e+02 1.670e+02 5.100e+01 2.140e+02\n",
+            " 5.190e+02 4.380e+02 4.800e+01 1.224e+03 7.620e+02 4.230e+02 1.840e+02\n",
+            " 6.520e+02 4.810e+02 2.390e+02 2.740e+02 1.170e+02 8.860e+02 2.360e+02\n",
+            " 9.400e+01 2.440e+02 9.020e+02 4.340e+02 2.700e+01 6.620e+02 7.340e+02\n",
+            " 5.500e+02 1.031e+03 3.400e+01 5.140e+02 4.080e+02 3.800e+02 2.970e+02\n",
+            " 3.700e+02 3.850e+02 7.880e+02 5.620e+02 8.700e+02 5.180e+02 5.720e+02\n",
+            " 1.800e+01 3.220e+02 1.378e+03 8.770e+02 5.300e+02 3.970e+02 7.380e+02\n",
+            " 5.010e+02 3.910e+02 1.180e+02 4.600e+01 6.920e+02 3.320e+02 1.750e+02\n",
+            " 6.400e+01 5.220e+02 1.047e+03 3.790e+02 2.070e+02 9.700e+01 5.320e+02\n",
+            " 6.200e+01 1.990e+02 3.550e+02 4.590e+02 4.050e+02 3.270e+02 2.570e+02\n",
+            " 2.930e+02 6.530e+02 6.300e+02 3.820e+02 4.430e+02]\n",
+            "Exter Qual: ['TA' 'Gd' 'Ex' 'Fa']\n",
+            "Exter Cond: ['TA' 'Gd' 'Fa' 'Po' 'Ex']\n",
+            "Foundation: ['CBlock' 'PConc' 'Wood' 'BrkTil' 'Slab' 'Stone']\n",
+            "Bsmt Qual: ['TA' 'Gd' 'Ex' nan 'Fa' 'Po']\n",
+            "Bsmt Cond: ['Gd' 'TA' nan 'Po' 'Fa' 'Ex']\n",
+            "Bsmt Exposure: ['Gd' 'No' 'Mn' 'Av' nan]\n",
+            "BsmtFin Type 1: ['BLQ' 'Rec' 'ALQ' 'GLQ' 'Unf' 'LwQ' nan]\n",
+            "BsmtFin SF 1: [6.390e+02 4.680e+02 9.230e+02 1.065e+03 7.910e+02 6.020e+02 6.160e+02\n",
+            " 2.630e+02 1.180e+03 0.000e+00 9.350e+02 6.370e+02 3.680e+02 1.416e+03\n",
+            " 4.270e+02 1.445e+03 1.200e+02 7.900e+02 7.050e+02 8.850e+02 5.330e+02\n",
+            " 5.780e+02 7.340e+02 7.750e+02 8.040e+02 4.320e+02 1.051e+03 1.560e+02\n",
+            " 3.000e+02 3.600e+02 5.140e+02 3.110e+02 1.218e+03 1.646e+03 1.201e+03\n",
+            " 1.100e+02 2.800e+01 2.000e+00 2.188e+03 7.330e+02 1.373e+03 4.560e+02\n",
+            " 2.400e+01 1.600e+01 3.260e+02 6.250e+02 2.500e+02 9.190e+02 1.032e+03\n",
+            " 5.240e+02 8.160e+02 1.078e+03 2.220e+02 1.414e+03 6.560e+02 6.950e+02\n",
+            " 5.430e+02 6.230e+02 4.020e+02 3.380e+02 8.990e+02 5.530e+02 4.500e+02\n",
+            " 8.240e+02 6.590e+02 1.260e+02 6.740e+02 1.129e+03 1.298e+03 2.800e+02\n",
+            " 3.760e+02 3.780e+02 4.660e+02 6.040e+02 2.440e+02 4.840e+02 7.280e+02\n",
+            " 1.052e+03 8.330e+02 5.060e+02 1.137e+03 1.200e+03 6.870e+02 3.940e+02\n",
+            " 9.820e+02 3.290e+02 6.980e+02 5.690e+02 1.059e+03 1.010e+03 1.014e+03\n",
+            " 7.630e+02 1.500e+03 4.900e+01 6.700e+02 6.960e+02 3.540e+02 5.400e+02\n",
+            " 9.440e+02 4.430e+02 9.120e+02 2.470e+02 1.188e+03 8.560e+02 1.018e+03\n",
+            " 9.220e+02 1.000e+03 6.970e+02 9.360e+02 3.390e+02 6.480e+02 5.320e+02\n",
+            " 7.310e+02 3.200e+02 2.480e+02 1.056e+03 7.200e+01 4.810e+02 3.400e+02\n",
+            " 5.070e+02 2.340e+02 5.880e+02 7.170e+02 4.800e+01 5.790e+02 2.740e+02\n",
+            " 5.100e+02 7.800e+02 1.760e+02 6.860e+02 6.000e+02 2.830e+02 7.880e+02\n",
+            " 4.740e+02 1.880e+02 4.520e+02 2.640e+02 2.760e+02 4.480e+02 9.600e+02\n",
+            " 1.040e+02 7.660e+02 1.026e+03 7.300e+01 7.360e+02 7.040e+02 8.410e+02\n",
+            " 1.302e+03 8.420e+02 2.400e+02 3.710e+02 1.319e+03 2.670e+02 4.380e+02\n",
+            " 1.092e+03 4.420e+02 1.258e+03 9.640e+02 2.880e+02 1.080e+02 7.390e+02\n",
+            " 1.920e+02 9.540e+02 3.600e+01 1.346e+03 1.433e+03 8.600e+02 7.500e+02\n",
+            " 7.470e+02 1.470e+03 5.040e+02 8.700e+02 3.530e+02 5.050e+02 1.980e+02\n",
+            " 1.820e+02 4.800e+02 1.682e+03 1.358e+03 4.830e+02 6.720e+02 6.620e+02\n",
+            " 3.700e+02 7.120e+02 1.070e+03 5.280e+02 4.220e+02 9.400e+01 3.480e+02\n",
+            " 3.830e+02 1.330e+02 2.030e+02 2.180e+02 2.380e+02 4.260e+02 3.750e+02\n",
+            " 2.750e+02 1.406e+03 3.430e+02 7.600e+01 1.247e+03 7.350e+02 3.080e+02\n",
+            " 6.150e+02 6.790e+02 5.390e+02 7.800e+01 6.240e+02 4.200e+01 3.340e+02\n",
+            " 9.150e+02 1.290e+02 1.500e+02 2.940e+02 4.690e+02 5.930e+02 2.070e+02\n",
+            " 4.580e+02 4.760e+02 1.341e+03 5.640e+02 8.440e+02 1.410e+03 8.470e+02\n",
+            " 8.500e+02 2.840e+02 1.320e+03 1.965e+03 1.158e+03 3.410e+02 7.410e+02\n",
+            " 1.890e+02 3.100e+02 5.600e+02 6.940e+02 1.036e+03 1.904e+03 1.274e+03\n",
+            " 4.000e+02 6.920e+02 8.220e+02 1.246e+03 3.630e+02 8.320e+02 1.104e+03\n",
+            " 3.810e+02 6.220e+02 5.440e+02 2.250e+02 1.333e+03 8.880e+02 6.360e+02\n",
+            " 8.280e+02 4.390e+02 5.000e+02 7.260e+02 1.910e+02 2.540e+02 7.650e+02\n",
+            " 1.620e+02 2.310e+02 9.580e+02 3.060e+02 5.660e+02 4.350e+02 2.570e+02\n",
+            " 3.890e+02 2.790e+02 5.360e+02 6.440e+02 1.172e+03 1.360e+03 1.767e+03\n",
+            " 1.572e+03 9.860e+02 1.232e+03 1.436e+03 1.338e+03 2.288e+03 1.531e+03\n",
+            " 1.230e+03 1.015e+03 1.088e+03 1.037e+03 1.142e+03 1.170e+03 1.039e+03\n",
+            " 1.124e+03 1.262e+03 5.600e+01 1.972e+03 8.360e+02 9.000e+02 8.810e+02\n",
+            " 8.760e+02 9.040e+02 2.146e+03 1.557e+03 8.000e+02 1.196e+03 8.630e+02\n",
+            " 5.670e+02 9.880e+02 4.250e+02 6.520e+02 4.940e+02 6.510e+02 2.410e+02\n",
+            " 6.830e+02 9.130e+02 7.720e+02 1.163e+03 6.890e+02 1.173e+03 7.810e+02\n",
+            " 8.540e+02 2.360e+02 9.870e+02 1.361e+03 5.950e+02 1.294e+03 3.790e+02\n",
+            " 2.158e+03 2.700e+01 1.121e+03 6.820e+02 8.120e+02 1.430e+03 4.100e+02\n",
+            " 7.710e+02 5.400e+01 5.160e+02 9.760e+02 2.000e+01 5.200e+01 3.310e+02\n",
+            " 6.800e+01 6.600e+02 8.640e+02 5.940e+02 1.400e+02 1.733e+03 6.010e+02\n",
+            " 9.620e+02 5.490e+02 6.490e+02 1.252e+03 1.210e+02 1.116e+03 2.980e+02\n",
+            " 8.590e+02 9.550e+02 1.440e+02 6.430e+02 2.510e+02 4.030e+02 6.120e+02\n",
+            " 1.960e+02 9.980e+02 7.400e+02 3.880e+02 9.910e+02 5.680e+02 1.000e+02\n",
+            " 1.850e+02 1.024e+03 1.285e+03 6.070e+02 1.312e+03 6.090e+02 1.387e+03\n",
+            " 4.540e+02 7.080e+02 6.200e+02 5.850e+02 1.720e+02 1.550e+02 1.213e+03\n",
+            " 4.900e+02 4.280e+02 6.500e+02 7.000e+02 9.310e+02 4.400e+02 6.990e+02\n",
+            " 3.900e+02 6.800e+02 3.150e+02 3.840e+02 8.720e+02 7.450e+02 5.460e+02\n",
+            " 1.270e+03 6.210e+02 1.800e+02 6.300e+02 4.330e+02 1.148e+03 9.410e+02\n",
+            " 8.260e+02 6.330e+02 4.210e+02 3.120e+02 2.160e+02 4.950e+02 1.309e+03\n",
+            " 2.200e+02 4.050e+02 2.090e+02 2.730e+02 1.340e+02 2.990e+02 5.220e+02\n",
+            " 1.520e+02 1.690e+02 7.490e+02 1.152e+03 3.500e+02 5.510e+02 4.440e+02\n",
+            " 2.260e+02 5.270e+02 6.850e+02 1.700e+02 1.324e+03 2.620e+02 3.420e+02\n",
+            " 3.440e+02 1.730e+02 5.520e+02 2.920e+02 2.040e+02 4.600e+02 7.000e+01\n",
+            " 1.441e+03 4.850e+02 5.130e+02 5.840e+02 1.086e+03 1.094e+03 8.200e+02\n",
+            " 1.021e+03 1.288e+03 1.359e+03 1.334e+03 9.020e+02 7.240e+02 1.518e+03\n",
+            " 2.490e+02 7.320e+02 7.550e+02 8.210e+02 3.850e+02 9.500e+02 7.460e+02\n",
+            " 6.060e+02 6.660e+02 1.259e+03 7.100e+02 6.460e+02 7.770e+02 1.234e+03\n",
+            " 9.900e+02 6.900e+02 1.111e+03 1.478e+03 1.930e+02 5.350e+02 3.990e+02\n",
+            " 6.310e+02 5.470e+02 3.320e+02 6.260e+02 4.080e+02 2.900e+02 5.230e+02\n",
+            " 7.930e+02 7.130e+02 2.460e+02 1.540e+02 6.500e+01 7.840e+02 4.710e+02\n",
+            " 2.850e+02 8.030e+02 8.080e+02 1.476e+03 4.450e+02 1.351e+03 7.670e+02\n",
+            " 6.110e+02 5.500e+01 1.110e+02 1.236e+03 1.022e+03 1.758e+03 1.115e+03\n",
+            " 1.005e+03 4.620e+02 1.260e+03 1.640e+03 8.660e+02 8.830e+02 5.150e+02\n",
+            " 5.090e+02 7.200e+02 1.140e+02 1.097e+03 7.180e+02 3.300e+02 1.567e+03\n",
+            " 4.960e+02 8.650e+02 7.060e+02 8.510e+02 1.380e+02 1.153e+03 2.190e+02\n",
+            " 3.190e+02 1.337e+03 1.034e+03 9.830e+02 1.206e+03 8.960e+02 8.900e+02\n",
+            " 1.084e+03 1.023e+03 2.520e+02 1.190e+02 2.660e+02 3.210e+02 3.870e+02\n",
+            " 3.580e+02 5.590e+02 2.860e+02 1.336e+03 1.280e+03 1.636e+03 1.330e+03\n",
+            " 1.012e+03 1.400e+03 1.728e+03 1.375e+03 1.420e+03 1.082e+03 1.249e+03\n",
+            " 4.000e+01 2.257e+03 1.016e+03 1.149e+03 1.075e+03 3.720e+02 1.540e+03\n",
+            " 1.204e+03 8.460e+02 5.730e+02 1.073e+03 1.087e+03 7.590e+02 6.550e+02\n",
+            " 1.660e+03 1.696e+03 2.280e+02 1.314e+03 1.096e+03 3.300e+01 7.290e+02\n",
+            " 7.890e+02 5.030e+02 8.000e+01 8.140e+02 3.620e+02 1.138e+03 5.370e+02\n",
+            " 4.720e+02 3.970e+02 1.650e+02 5.300e+01 7.370e+02 7.640e+02 4.890e+02\n",
+            " 1.900e+02 5.200e+02 5.500e+02 1.027e+03 1.004e+03 1.141e+03 1.238e+03\n",
+            " 6.810e+02 8.130e+02 1.280e+02 7.860e+02 1.619e+03 1.044e+03 3.010e+02\n",
+            " 6.030e+02 9.560e+02 2.600e+02 5.830e+02 5.750e+02 8.670e+02 7.760e+02\n",
+            " 8.920e+02 7.870e+02 8.060e+02 4.190e+02 6.580e+02 3.200e+01 8.310e+02\n",
+            " 5.310e+02 5.720e+02 2.500e+01 1.053e+03 1.040e+03 5.700e+02 7.740e+02\n",
+            " 1.480e+02 8.520e+02 5.800e+02 7.440e+02 3.740e+02 6.730e+02 9.600e+01\n",
+            " 4.930e+02 5.900e+02 1.160e+02 1.410e+02 2.590e+02 2.000e+02 4.060e+02\n",
+            " 1.750e+02 5.210e+02 2.010e+02       nan 3.360e+02 2.100e+02 3.510e+02\n",
+            " 9.060e+02 7.580e+02 7.020e+02 2.210e+02 1.198e+03 1.300e+03 6.340e+02\n",
+            " 1.064e+03 4.290e+02 1.003e+03 3.920e+02 5.990e+02 7.190e+02 1.035e+03\n",
+            " 3.240e+02 9.690e+02 1.085e+03 7.790e+02 1.271e+03 3.550e+02 2.085e+03\n",
+            " 5.000e+01 7.700e+02 7.220e+02 1.308e+03 6.880e+02 3.610e+02 6.630e+02\n",
+            " 4.860e+02 8.800e+01 6.320e+02 6.680e+02 1.194e+03 1.538e+03 6.420e+02\n",
+            " 9.940e+02 1.593e+03 8.100e+02 9.460e+02 8.300e+02 1.033e+03 9.200e+02\n",
+            " 5.644e+03 4.590e+02 3.520e+02 2.240e+02 4.100e+01 4.230e+02 2.810e+02\n",
+            " 3.660e+02 8.100e+01 5.380e+02 1.480e+03 1.474e+03 6.410e+02 1.383e+03\n",
+            " 8.930e+02 1.165e+03 1.513e+03 1.398e+03 7.830e+02 1.029e+03 1.223e+03\n",
+            " 8.710e+02 1.011e+03 1.571e+03 7.690e+02 3.180e+02 5.010e+02 4.370e+02\n",
+            " 7.850e+02 5.340e+02 6.380e+02 6.470e+02 5.620e+02 8.380e+02 7.780e+02\n",
+            " 1.880e+03 1.860e+02 4.140e+02 9.260e+02 1.101e+03 1.047e+03 7.970e+02\n",
+            " 9.450e+02 1.558e+03 6.780e+02 2.560e+02 1.328e+03 6.050e+02 9.030e+02\n",
+            " 4.920e+02 3.490e+02 2.820e+02 4.120e+02 3.220e+02 3.140e+02 9.300e+02\n",
+            " 3.560e+02 5.560e+02 7.250e+02 1.151e+03 1.304e+03 1.812e+03 1.350e+03\n",
+            " 1.684e+03 9.700e+02 9.380e+02 6.690e+02 1.178e+03 1.030e+03 7.620e+02\n",
+            " 8.480e+02 9.180e+02 5.740e+02 2.096e+03 1.181e+03 1.282e+03 1.048e+03\n",
+            " 1.455e+03 8.620e+02 5.650e+02 1.231e+03 3.350e+02 1.225e+03 1.220e+03\n",
+            " 9.290e+02 6.300e+01 1.126e+03 1.369e+03 6.400e+01 1.443e+03 4.300e+02\n",
+            " 4.170e+02 9.320e+02 8.270e+02 7.270e+02 1.250e+02 1.390e+03 9.680e+02\n",
+            " 4.820e+02 6.000e+01 9.370e+02 1.106e+03 4.200e+02 4.360e+02 2.390e+02\n",
+            " 9.010e+02 4.570e+02 1.732e+03 1.157e+03 9.780e+02 1.632e+03 4.980e+02\n",
+            " 7.380e+02 9.730e+02 9.100e+02 3.460e+02 8.190e+02 7.920e+02 9.160e+02\n",
+            " 6.170e+02 6.540e+02 2.700e+02 1.386e+03 1.300e+02 3.860e+02 1.870e+02\n",
+            " 8.730e+02 9.080e+02 6.080e+02 5.120e+02 5.860e+02 1.237e+03 4.410e+02\n",
+            " 8.500e+01 3.770e+02 2.420e+02 9.520e+02 3.980e+02 1.098e+03 7.820e+02\n",
+            " 1.680e+02 1.220e+02 3.160e+02 1.046e+03 3.170e+02 6.450e+02 1.970e+02\n",
+            " 9.250e+02 7.480e+02 2.580e+02 1.219e+03 5.870e+02 4.770e+02 4.910e+02\n",
+            " 4.530e+02 1.440e+03 5.570e+02 1.080e+03 4.970e+02 9.840e+02 1.150e+03\n",
+            " 6.640e+02 9.850e+02 5.100e+01 1.013e+03 5.020e+02 7.160e+02 6.710e+02\n",
+            " 1.464e+03 1.412e+03 1.079e+03 7.090e+02 1.320e+02 7.510e+02 9.800e+02\n",
+            " 4.010e+03 2.260e+03 4.670e+02 7.700e+01 1.130e+02 3.640e+02 3.650e+02\n",
+            " 1.128e+03 2.970e+02 1.186e+03 3.500e+01 5.770e+02 4.340e+02 5.480e+02\n",
+            " 9.670e+02 1.573e+03 1.001e+03 7.730e+02 1.392e+03 1.239e+03 9.240e+02\n",
+            " 9.490e+02 1.102e+03 2.150e+02 7.420e+02 1.329e+03 1.159e+03 2.060e+02\n",
+            " 8.400e+02 8.740e+02 1.310e+02 1.112e+03 7.960e+02 6.190e+02 8.110e+02\n",
+            " 1.090e+03 5.960e+02 2.120e+02 1.127e+03 1.110e+03 5.920e+02 7.140e+02\n",
+            " 5.700e+01 5.180e+02 2.050e+02 1.191e+03 1.422e+03 2.130e+02 1.002e+03\n",
+            " 7.950e+02 1.940e+02 7.500e+01 6.140e+02 9.510e+02 3.090e+02 3.820e+02\n",
+            " 3.730e+02 1.447e+03 1.505e+03 1.261e+03 1.290e+03 8.800e+02 4.150e+02\n",
+            " 5.540e+02 1.038e+03 1.154e+03 1.074e+03 1.182e+03 3.800e+02 1.562e+03\n",
+            " 1.721e+03 1.836e+03 9.050e+02 2.780e+02 1.332e+03 1.810e+02 4.650e+02\n",
+            " 1.118e+03 1.456e+03 1.009e+03 8.070e+02 1.810e+03 4.040e+02 7.600e+02\n",
+            " 7.990e+02 6.610e+02 4.160e+02 9.960e+02 7.560e+02 9.390e+02 8.950e+02\n",
+            " 9.140e+02 9.430e+02 2.710e+02 4.880e+02 7.010e+02 1.277e+03 4.550e+02\n",
+            " 3.690e+02 1.790e+02 8.090e+02 9.530e+02 2.080e+02 1.430e+02 5.760e+02\n",
+            " 3.470e+02 3.280e+02 7.940e+02 2.300e+02 2.610e+02 3.930e+02 6.840e+02\n",
+            " 4.640e+02 1.576e+03 1.122e+03 8.530e+02 1.162e+03 8.940e+02 9.750e+02\n",
+            " 4.750e+02 1.670e+02 6.910e+02 4.240e+02 3.050e+02 2.230e+02 5.260e+02\n",
+            " 2.960e+02 1.283e+03 1.564e+03 9.650e+02 9.090e+02 1.216e+03 1.136e+03\n",
+            " 1.460e+03 1.243e+03 8.970e+02 8.370e+02 1.490e+02 1.606e+03 1.224e+03\n",
+            " 3.370e+02 1.071e+03]\n",
+            "BsmtFin Type 2: ['Unf' 'LwQ' 'BLQ' 'Rec' nan 'GLQ' 'ALQ']\n",
+            "BsmtFin SF 2: [   0.  144. 1120.  163.  168.   78.  119.  121.  117.  859.  981.   42.\n",
+            "   46.   81. 1029.  290.  132.  713.  162.  362.  240.  258.  174.  906.\n",
+            "  486.  350.  263. 1073.  692.   12.  159.  712.  668.  474.  453.  684.\n",
+            "  387.  688.  972.  127.  252.  334.  232.  480.  590.  284.  276.  472.\n",
+            "  239.  180.  294.  622.  495.  539.  479.  113. 1526.  360.  774.  364.\n",
+            "  596.  884.  311.   92.  216.  136.   32.  147. 1127.  466.  630.  201.\n",
+            "  345.  512.  230.  247.  661.  620.  202.  483.  750.  690.  105.   60.\n",
+            "  352.  102.   95.  465.   63.  262.  500.  670.  768.  393.  286.  450.\n",
+            "  177.  764.  344.   72.  243.  420.  210.  694.  875.  507.  435.  419.\n",
+            "  250.  116.  354.  820.  624.  273.   76.  270.  110.  288.  411.  228.\n",
+            "  186.  449.   48.   93.  438.  613.  852.  555.  841.  799.  811.  842.\n",
+            "  382.  182.  456.   80.   64.  336.  306.  308.  374.  872.  108.   52.\n",
+            "  196.  128.  488.  319.  532.  106.  169.  608.   nan   41.  606.  645.\n",
+            "  492.  181.  956. 1080. 1063.  391.  380.  531.  723.  491.  120.  679.\n",
+            "  612.   40.  125.  279.  400.  208.  193.  823.  287.  175.  604.  153.\n",
+            "   35.  619.  139.    6.  351. 1031. 1037.  176.  829.  211.  264.   38.\n",
+            "  206.  167.  580.  543.  219.  259.  404.  468.  138.  955.  691.   66.\n",
+            "   96.  149.  154.  442.  448.  227.  546.  398.  469.  722.  761.  627.\n",
+            "  529.  522.  873.  891.  755. 1474.  634.  321.  915.  544.  417.  432.\n",
+            "  831.  278.  557.  150.  869. 1020.  530.  904.  499.  215. 1061.  377.\n",
+            "  791.  156. 1393. 1039.  375.  497. 1057.  526.   68.  402.  748.   28.\n",
+            "  165.  184.  281.  912.  600.  506.  373.  551.  982.  441.  682. 1085.\n",
+            "  826.  850. 1164. 1083.  337.  297.  547.  173.  396.  324.  123.]\n",
+            "Bsmt Unf SF: [ 441.  270.  406. ...   45. 1503.  239.]\n",
+            "Total Bsmt SF: [1080.  882. 1329. ... 1381.  757. 1003.]\n",
+            "Heating: ['GasA' 'GasW' 'Grav' 'Wall' 'Floor' 'OthW']\n",
+            "Heating QC: ['Fa' 'TA' 'Ex' 'Gd' 'Po']\n",
+            "Central Air: ['Y' 'N']\n",
+            "Electrical: ['SBrkr' 'FuseA' 'FuseF' 'FuseP' nan 'Mix']\n",
+            "1st Flr SF: [1656  896 1329 ... 2028 1003 1389]\n",
+            "2nd Flr SF: [   0  701  678  776  892  676 1589  672  860  504  567  601  707  563\n",
+            "  862  630 1100  886  656 1151 1177  830 1122 1106  644 1185  783  956\n",
+            " 1128  828  888  790  730  584 1098  823  840  600  636  804  756  720\n",
+            "  550  873  754 1215  604  734  715  532  537 1169  505  546 1080  408\n",
+            "  475  788  687  348  765  424  606  185  686 1111  622 1044  602  582\n",
+            "  908  524  498  492  608  808 1074  780  662 1196  499  180  319  942\n",
+            "  744  240  689  714  954  192  864  558  755  838  887 1523  614  800\n",
+            "  878  703 1054  328  252  806  665 1788  772  748 1075 1152  358  380\n",
+            "  430  880  700 1194 1070  915  912  576 1216  650  615  645  704  663\n",
+            " 1275  670  631  833  684  809  785  779  978  988 1200  896 1134  868\n",
+            " 1103  816  839  741  467  586 1174 1325  568 1088 1012  762 1295  728\n",
+            "  745  742  876  716 1257  640  683 1276 1126 1032  793  695 1089 1038\n",
+            " 1097 1304 1221 1140 1336 1067 1274  967 1017  871  858  920  981  438\n",
+            " 1182  841  941 1209  897  591  786  702  918 1629  612  729  739  727\n",
+            "  983  782  660 1369  972  855 1315  224  556  960  457  685  726  322\n",
+            "  760  534 1296  368  768  629  813  406  548  517  455  496  690  994\n",
+            " 1000  682  646  560  677  564 1063 1320  917  624  826  561  596  653\n",
+            "  390  464  587  320  472  588  883  910  929 1040  784  462  649  425\n",
+            "  611  747  769 1114 1120 1619  902  718  815  966  836  834  913  844\n",
+            "  829 1116  573  885  926  977  807  738 1427  441  512  444  620  436\n",
+            "  545  998  595  448  332  523 1240  516  668  928 1157  432  846  566\n",
+            "  848 1020  717 1332 1370  857 1330  767 1420  866 1104  590 1237  898\n",
+            " 1158 1162 1096 1139  884 1285  778 1160 1053  639 1061 1250 1093  904\n",
+            " 1039  520  919  939  932 1028  843  861  842  794  825  850  893 1319\n",
+            "  959  625  792  628  924 1345 1066  732 1540  933  832  453  220  384\n",
+            "  412  510  182  501  581  375  680 1208  658  552  396 1818  797  540\n",
+            "  308  973  691  539 1254  363  473  594  378  554  208  468  651  445\n",
+            "  764  752  213  795  428  371  110  536  713  551  547  580  486 1051\n",
+            "  511  872  648  527  495 1721 1099  735 1072  899  870  895  903 1141\n",
+            " 1198  975  854  812  950  521  343  304  940 1611  811  673  442  890\n",
+            " 1479  817  943  330  420  936  167  688  766  770 1342  900 1377  845\n",
+            "  533 1402 1101  574 1036  570 1142 1238 1168  923  530  757 1048  796\n",
+            " 1112 1131  694  750 2065 1288 1407 1171 1277 1872 1015 1306 1203  995\n",
+            "  528  863 1426  925 1232 1357  743  976  761 1259 1008  984 1309  228\n",
+            "  992  500  544 1778  299  616  831  664  494  642  659  671 1031  336\n",
+            "  144  525  349  423 1164  356  698  245  592 1042  477 1005  971 1087\n",
+            "  638  400  376 1121 1414 1362 1092  916  882  927  874  914  881  869\n",
+            " 1242 1081  753  450 1133  674 1538  125 1440  787  531  585  514  775\n",
+            "  589  979 1001  851 1178  351  957 1340 1349  712 1243  955  709  990\n",
+            " 1384 1862 1371 1312 1405 1519 1392 1358  465 1347 1218 1060  466 1335\n",
+            "  814  488 1321  482  711  930 1286  985 1029 1796 1368 1567 1189 1323\n",
+            " 1234  798 1129  623  708  456  316 1360 1248  272  821  370 1007  518\n",
+            "  476  502  867  661  297  679  875 1518  605  810  325  434  583  634\n",
+            "  557  341  626 1836  541  454 1246  571 1037 1124 1045  989  827 1150\n",
+            "  312  526  218  980  403  493  736  818  901  610  725  549 1175  697\n",
+            "  439  360 1281 1230 1004]\n",
+            "Low Qual Fin SF: [   0  390  362  144 1064  232  431  120  436  371  360  259  397  312\n",
+            "  513  108  205  156  697  420  384  473  512  528  114  479  515   53\n",
+            "   80  392  572  234  140  450  481  514]\n",
+            "Gr Liv Area: [1656  896 1329 ... 2028 2521 1003]\n",
+            "Bsmt Full Bath: [ 1.  0.  2.  3. nan]\n",
+            "Bsmt Half Bath: [ 0.  1. nan  2.]\n",
+            "Full Bath: [1 2 3 0 4]\n",
+            "Half Bath: [0 1 2]\n",
+            "Bedroom AbvGr: [3 2 1 4 6 5 0 8]\n",
+            "Kitchen AbvGr: [1 2 3 0]\n",
+            "Kitchen Qual: ['TA' 'Gd' 'Ex' 'Fa' 'Po']\n",
+            "TotRms AbvGrd: [ 7  5  6  8  4 12 10 11  9  3 13  2 15 14]\n",
+            "Functional: ['Typ' 'Mod' 'Min1' 'Min2' 'Maj1' 'Maj2' 'Sev' 'Sal']\n",
+            "Fireplaces: [2 0 1 3 4]\n",
+            "Fireplace Qu: ['Gd' nan 'TA' 'Po' 'Ex' 'Fa']\n",
+            "Garage Type: ['Attchd' 'BuiltIn' 'Basment' 'Detchd' nan 'CarPort' '2Types']\n",
+            "Garage Yr Blt: [1960. 1961. 1958. 1968. 1997. 1998. 2001. 1992. 1995. 1999. 1993. 1990.\n",
+            " 1985. 2003. 1988. 2010. 1951. 1978. 1977. 1974. 2000. 1970. 1971.   nan\n",
+            " 1975. 2009. 2008. 2005. 2004. 2002. 2006. 1996. 1994. 1980. 1979. 1984.\n",
+            " 1986. 1920. 1987. 1973. 1963. 1962. 1976. 1967. 1972. 1966. 1964. 1950.\n",
+            " 1949. 1954. 1955. 1959. 1957. 1956. 1952. 1953. 1989. 1948. 1900. 1927.\n",
+            " 1915. 1945. 1940. 1938. 1928. 1930. 1926. 1939. 1942. 1923. 1917. 1910.\n",
+            " 1965. 1969. 1947. 1946. 1941. 1924. 1922. 1896. 2007. 1983. 1981. 1991.\n",
+            " 1982. 1916. 1925. 1936. 1935. 1931. 1934. 1929. 1918. 1921. 1937. 1932.\n",
+            " 1906. 1908. 1895. 1933. 2207. 1914. 1943. 1919.]\n",
+            "Garage Finish: ['Fin' 'Unf' 'RFn' nan]\n",
+            "Garage Cars: [ 2.  1.  3.  0.  4.  5. nan]\n",
+            "Garage Area: [ 528.  730.  312.  522.  482.  470.  582.  506.  608.  442.  440.  420.\n",
+            "  393.  841.  492.  834.  400.  500.  546.  663.  480.  304.  525.    0.\n",
+            "  511.  264.  320.  308.  751.  772.  606.  868.  532.  678.  820.  484.\n",
+            "  958.  756.  576.  474.  430.  437.  433.  434.  779.  962.  527.  712.\n",
+            "  671.  486.  666.  880.  676.  614.  750.  618.  463.  462.  457.  476.\n",
+            "  429.  539.  336.  280.  260.  461.  564.  762.  713.  588.  496.  852.\n",
+            "  592.  475.  596.  535.  660.  441.  490.  504.  517.  240.  364.  244.\n",
+            "  315.  578.  620.  447.  294.  531.  263.  318.  305.  246.  392.  330.\n",
+            "  720.  360.  551.  379.  220.  780.  288.  416.  624.  923.  560.  363.\n",
+            "  200.  572.  180.  516.  672.  349.  365.  231.  450.  270.  299.  591.\n",
+            "  533.  690.  436.  586.  366.  467.  209.  460. 1017.  574.  776.  632.\n",
+            "  740.  615.  594.  580.  513.  523.  850.  670.  613.  621.  598.  502.\n",
+            "  494.  319.  352.  216.  399.  252.  567.  473.  625.  384.  741.  573.\n",
+            "  888.  520.  680.  510.  431.  746.  686.  286.  253.  495.  616.  275.\n",
+            "  538.  390.  758.  499.  396.  427.  380.  409.  389.  343.  565. 1166.\n",
+            "  435.  544.  529.  479.  542.  478.  581.  552.  583.  902.  477.  345.\n",
+            "  656.  786.  754.  840.  890. 1390.  864.  836.  896.  900.  842. 1020.\n",
+            "  932.  640.  908.  927.  856.  700.  738.  862.  644.  968.  886.  871.\n",
+            "  626.  949.  685.  649.  701.  550.  397.  432.  554.  394.  658.  410.\n",
+            "  810. 1069.  889.  815.  647.  623.  711.  898.  972.  726.  844.  689.\n",
+            "  795.  984.  692.  812.  782. 1043.  438.  628.  845.  555.  788.  559.\n",
+            "  465.  612.  732.  300.  524.  704.  561.  641.  642.  540.  784.  497.\n",
+            "  515.  630.  498.  768.  472.  610.  549.  645.  368.  505.  418.  338.\n",
+            "  271.  792.  530.  514.  509.  297.  350.  884.  230.  281.  907.  483.\n",
+            "  210.  162.  324.  256.  273.  287.  357.  424.  456.  207.  192.  250.\n",
+            " 1184.  164.  316.  226.  668.  452.  284.  303.  340.  234.  290.  266.\n",
+            "  296.  425.  466. 1138.  826.  860.  846.  904.  702.  662.  569.  577.\n",
+            "  493.  622.  605.  444.  600. 1231.  570.  736.  521.  512.  451.  195.\n",
+            "  313.  342.  215.  282.  213.  307.  186.  295.  501.  468.  189.  351.\n",
+            "  541.  912.  650.  885.  471.  765.  920.  412.  402.  602.  698.  714.\n",
+            "  601.  386.  404.  406.  682.  683.  557.  619.  489. 1314.  439.  787.\n",
+            "  774. 1220.  858.  905.  866.  706. 1150. 1003.  789.  870. 1052.  944.\n",
+            "  388.  428.  398.  403.  696.  687.  938.  839.  983.  783.  691.  830.\n",
+            "  824.  851.  603.  648.  936.  562.  673.  575.  627.  276.  636.  545.\n",
+            "  469.  464.  831.  267.  283.  205.  377.  292.  458.  301. 1488.  372.\n",
+            "  401.  414.  311.  225.  828.  869.  370.  208.  160.  355.  228.  322.\n",
+            "  408.  354.  249.  534.  453. 1348.  874.  811.  558.  328.  725.  715.\n",
+            "  543.  595.  508.  721.  548.  814. 1418.  369.  599.  344. 1014.  924.\n",
+            "  356.  487.  185. 1248.  857.  816.  358.  665.  800.  749.  892.  257.\n",
+            "  423.  526.  373.  729. 1110.  556.  724.  481.  585.  488.  684.  367.\n",
+            "  818.  928. 1040.  878.  947.  895.  694. 1174.  728.  843.  916.  872.\n",
+            "  876.  631.  617.  454.  813.  925.  804.  806.  832.  455.  752.  933.\n",
+            " 1092.  865.  954.  825.  859.  590. 1025.  744.  566.  518.  611. 1105.\n",
+            "  571.  309.  306.  310.  293.  371. 1200.  254.  184.  374.  331.  224.\n",
+            "  217.  261.  323.  638.  739.  332.  719.  833.  894.  796.  674.  747.\n",
+            "  242.  597.  748.  639.  579. 1154.  248.   nan  100.  722.  422.  808.\n",
+            "  995. 1041. 1356.  963.  443.  413.  773.  675.  716.  604.  485.  770.\n",
+            " 1085.  853.  708.  753.  899.  426.  807.  959.  803.  760. 1134.  584.\n",
+            " 1053.  449.  688.  757.  326.  568.  353.  791. 1008.  378.  258.  255.\n",
+            "  198.  459.  667.  445.  325.  848.  317.  646.  265.  609.  375.  272.\n",
+            "  327.  766.  693.  405.]\n",
+            "Garage Qual: ['TA' nan 'Fa' 'Gd' 'Ex' 'Po']\n",
+            "Garage Cond: ['TA' nan 'Fa' 'Gd' 'Ex' 'Po']\n",
+            "Paved Drive: ['P' 'Y' 'N']\n",
+            "Wood Deck SF: [ 210  140  393    0  212  360  237  157  483  192  503  325  113  349\n",
+            "  240  203  275  173   26  144  168  220  238  196  120   36  100  146\n",
+            "  288  180  668   23  186  132  283  169   80  635   28  353  370  121\n",
+            "  416  296   32  198  160  280  133  223  277  224  228  352  227  366\n",
+            "  117  263  301   42  252  250  264  364  414  218  222  657   84   51\n",
+            "  106   54  135  221  306   12  344   56  406  379  226  335  496  290\n",
+            "  268  336   44  450  156  105  367   71  316  365  188  331   60  257\n",
+            "  116  272  141  112   30   68  128  375  328  174  182  200   96  261\n",
+            "  431   22  287  129  162  269   48  201   52  256  232  342   63  322\n",
+            "  178  233  474  448  225   40  171  216  185  108   87  260  147  150\n",
+            "  404  382  319   99  184  125  165  248  114  230  170  172  208  231\n",
+            "  148  143  300   24  298  340  517  297   70  205  195  158  462  502\n",
+            "  115  501  371  235  294  312  321   78   85  164  110   55  289   66\n",
+            "  324  126  187   74  181  266  244   45  189  509  302  243   64  131\n",
+            "  476  234  400   73  154  123  486  276  392   72  215   58  262  202\n",
+            "  253  194  576  356  327   92  136  329  279  176  292  467  119   90\n",
+            "  305  124  270  308   33  138  303  214  152  550   16  411  209  358\n",
+            "  320  495  236  385  145  155   97   20  122   98   25   38  426  355\n",
+            "  490   88   76  418  265   49   57  204  311  102  511  409   50  307\n",
+            "   81  424  339  403  278  211  139  149  259  736  134  183  314  213\n",
+            "  161  318  428  670  282  315  362  245  219  390  167  407   35  130\n",
+            "  104  460  286  239  255  193  159  402  455  500  206  190  333  284\n",
+            "  285   14  521  380  127  646  142  386  405  546  118  242  291  166\n",
+            "  274  439  536 1424  690  330  421   95  441  246  351  197  384  444\n",
+            "  295  175  354  519  177  179   89  361  247  870  309  432    4  641\n",
+            "  153  857   94   86  191   75  631  229  436  345  520  199   27  394\n",
+            "   53   77  466  304  241  103  586  684  453  413  468  207  530  574\n",
+            "  326  728]\n",
+            "Open Porch SF: [ 62   0  36  34  82 152  60  84  21  75  54  12 122 120  96  85  68  55\n",
+            "  30 133  50  95  35  70  74 119  67 150 130  49  27  23 116  20  48 172\n",
+            "  56  32  57  81  86 136  45 168 102 104 144  39 111 166  44 192 184  42\n",
+            "  78 137  76  69  66 224  26  40  98  73  38  28  52  17 124 160 100 228\n",
+            " 108  18 158  10  11 132  58  90  22  46 278  92  33  61  59  77  25 262\n",
+            " 105  64 140 156 207  53  24 312  72  43  94  63 176 195 134 162 197 274\n",
+            " 170 273 185 190 114 235 183  16  51 103 128 146 126 165 226 121 112 175\n",
+            " 182 113  88 178  91  41  93 177 234 254 169 204  99  80 110 189 287 523\n",
+            "  15 135 198 188 215 155 142 222 193  29 151 240 200 148 201 118 154 238\n",
+            " 247 304 101 173 282 180  65 131 153  87 174 210 251 243 211 129   4 230\n",
+            " 213 547 291 502 299 365 139 216  89 117 236   8 187 159 106 372 292 141\n",
+            " 217 123  83 276 265 164 205 368  47 203 191 138 364 127 256 214 241 194\n",
+            " 285 324 208 171 570 244 231 484 406 742 444 252 263 266  97  37 250 246\n",
+            " 229  31 267 382 319 258   6 341 260 288 418 115 253 245 107 225 125 199]\n",
+            "Enclosed Porch: [   0  170  184  154   80  220  186  156  120  112  150  164  189  205\n",
+            "  113  216  135  130  202  126  334  246  196   18  158  114   60   41\n",
+            "  128   35   48   32   64  364   40  318  248  168   45  239  176   77\n",
+            "   52   56   36  136   96  242   42   86  162   98  265   50  280  222\n",
+            "  144  209   24   91  236  218  228   84  264  260  240  203  140  252\n",
+            "  100  134  432  198  116  169  148  244   25   81  102  160  386  226\n",
+            "  238  115   94  208  105   54   51   34  268   30  213  288   90  192\n",
+            "  177  211  185   55  180   44   57   78  137   72  368   70  165   92\n",
+            "   16  123   66  210   68  109  194  139  219  259  212   20  101   87\n",
+            "  117  204  122  108  190  231  138  183  254  301  121  207  224  172\n",
+            "  174   99  249  291  145  214  275  290  175   26  143  230   88   39\n",
+            " 1012   43  286   19  584  200  133  234   37  324  552  161   75  167\n",
+            "   28  293  104  296  330  221  256  129  225  294  272  429   67  132\n",
+            "   23]\n",
+            "3Ssn Porch: [  0 238 224 144 508 168 255 225 360 162 140 150 182 153 320 174 304 216\n",
+            " 407  96 245 120 219 180 196 176  86  23 290 323 130]\n",
+            "Screen Porch: [  0 120 144 140 210 165 256 216  90 204 143 160 182 385 240 168 148  95\n",
+            " 266 166 116 161 200 155 108 291 490 170 192 180 156 196 197 152 121  92\n",
+            " 288 185 342 189 252 234 255 111 112 231  40 100  60 142 110 396 225 117\n",
+            " 195 145 224 115 198 233 190 141 208  80 176  94 164 178 273 130 480 220\n",
+            "  64 163 287 175 576 227 265 221 171 135 322 174 147 276 260 217 201 109\n",
+            "  99 150 126 259 184  84 154  53 153 228 138 263  88 280 123 440 374 119\n",
+            " 222 264 270  63 122 128 162 410 271 312 348 113 104]\n",
+            "Pool Area: [  0 144 480 576 555 368 444 228 561 519 648 800 512 738]\n",
+            "Pool QC: [nan 'Ex' 'Gd' 'TA' 'Fa']\n",
+            "Fence: [nan 'MnPrv' 'GdPrv' 'GdWo' 'MnWw']\n",
+            "Misc Feature: [nan 'Gar2' 'Shed' 'Othr' 'Elev' 'TenC']\n",
+            "Misc Val: [    0 12500   500   700   400   450  1500   300   600  1200  3500  2000\n",
+            "  2500    54    80   490   480   350   650   900   800   750  1400  6500\n",
+            "  1150  1000  4500  3000   560  1300  8300 15500 17000  1512   455   460\n",
+            "   620   420]\n",
+            "Mo Sold: [ 5  6  4  3  1  2  7 10  8 11  9 12]\n",
+            "Yr Sold: [2010 2009 2008 2007 2006]\n",
+            "Sale Type: ['WD ' 'New' 'COD' 'ConLI' 'Con' 'ConLD' 'Oth' 'ConLw' 'CWD' 'VWD']\n",
+            "Sale Condition: ['Normal' 'Partial' 'Family' 'Abnorml' 'Alloca' 'AdjLand']\n",
+            "SalePrice: [215000 105000 172000 ...  90500  71000 150900]\n",
+            "Order             2930\n",
+            "PID               2930\n",
+            "MS SubClass         16\n",
+            "MS Zoning            7\n",
+            "Lot Frontage       128\n",
+            "                  ... \n",
+            "Mo Sold             12\n",
+            "Yr Sold              5\n",
+            "Sale Type           10\n",
+            "Sale Condition       6\n",
+            "SalePrice         1032\n",
+            "Length: 82, dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Perform initial exploration of the dataset.\n",
+        "# - Check shape, column names, smaples\n",
+        "# - Get summary info, data types\n",
+        "# - Descriptive statistics\n",
+        "\n",
+        "df = df1.copy()\n",
+        "print(f\"Shape: {df.shape}\")\n",
+        "print(f\"Columns Name: {df.columns}\")\n",
+        "print(f\"Sample of Records: {df.sample}\")\n",
+        "\n",
+        "df.info()\n",
+        "df.dtypes\n",
+        "\n",
+        "print(df.describe())\n",
+        "print(df.describe(include='object'))\n",
+        "\n",
+        "for col in df.columns:\n",
+        "    print(f\"{col}: {df[col].unique()}\")\n",
+        "\n",
+        "print(df.nunique())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "quTZ6mJdjrDw"
+      },
+      "source": [
+        "## 🔹 Step 3: Missing Value Check & Handling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-0KdWkPqjs7y",
+        "outputId": "a7401ca1-452a-4801-c8dd-57a813c39aca"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(2930, 82)\n",
+            "(2930, 80)\n",
+            "# of nulls at the begining:  15749\n",
+            "Shape after drop columns with >50% missing values:  (2930, 75)\n",
+            "# of nulls after drop columns with >50% missing values:  3143\n",
+            "# of nulls before Categurical filling:  2461\n",
+            "# of nulls at the end:  0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Check missing values.\n",
+        "# Decide on a strategy (if needed):\n",
+        "# - Drop if too many are missing\n",
+        "# - Fill with mean/median/mode/domain-specific value\n",
+        "\n",
+        "print(df.shape)\n",
+        "if 'Order' in df.columns:\n",
+        "    df.drop(columns=['Order'], inplace=True)\n",
+        "if 'PID' in df.columns:\n",
+        "    df.drop(columns=['PID'], inplace=True)\n",
+        "print(df.shape)\n",
+        "print(\"# of nulls at the begining: \", df.isnull().sum().sum())\n",
+        "\n",
+        "# list columns with missing values\n",
+        "missing_counts = df.isnull().sum()\n",
+        "missing_cols = missing_counts[missing_counts > 0].sort_values(ascending=False)\n",
+        "# drop columns with more than 50% missing values\n",
+        "missing_pct = df.isnull().mean() * 100\n",
+        "cols_to_drop = missing_pct[missing_pct > 50].index.tolist()\n",
+        "df = df.drop(columns=cols_to_drop)\n",
+        "print(\"Shape after drop columns with >50% missing values: \", df.shape)\n",
+        "print(\"# of nulls after drop columns with >50% missing values: \", df.isnull().sum().sum())\n",
+        "\n",
+        "col_cat = df.select_dtypes(include='object').columns\n",
+        "col_num = df.select_dtypes(exclude='object').columns\n",
+        "for col in col_num:\n",
+        "    df[col] = df[col].fillna(df[col].median())\n",
+        "print(\"# of nulls before Categurical filling: \", df.isnull().sum().sum())\n",
+        "for col in col_cat:\n",
+        "    mod = df[col].mode()\n",
+        "    mode_value = mod.iloc[0]        # pick the first mode\n",
+        "    df[col] = df[col].fillna(mode_value)\n",
+        "print(\"# of nulls at the end: \",df.isnull().sum().sum())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LWQY6vDEjyTu"
+      },
+      "source": [
+        "## 🔹 Step 4: Correlation Check & Feature Decision"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "MHwMZbX_jxKJ",
+        "outputId": "21c62f7c-f2bd-4837-f648-31cfbeb102b8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# TODO: Check correlations between numerical features and target variable (SalePrice).\n",
+        "# Use correlation heatmap or pairplot.\n",
+        "# Decide which features to keep/remove based on correlation.\n",
+        "\n",
+        "import numpy as np\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Numeric-only correlation\n",
+        "corr = df.corr(numeric_only=True)\n",
+        "plt.figure(figsize=(10,6))\n",
+        "sns.heatmap(corr, cmap=\"coolwarm\")\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "exTa7T6qj2hv"
+      },
+      "source": [
+        "## 🔹 Step 5: Encode Categorical Variables"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JBQbfP6jj1hS",
+        "outputId": "96116592-46d9-4a16-da40-b5140300a807"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "['RL' 'RH' 'FV' 'RM' 'C (all)' 'I (all)' 'A (agr)']\n",
+            "['Pave' 'Grvl']\n",
+            "['IR1' 'Reg' 'IR2' 'IR3']\n",
+            "['Lvl' 'HLS' 'Bnk' 'Low']\n",
+            "['AllPub' 'NoSewr' 'NoSeWa']\n",
+            "['Corner' 'Inside' 'CulDSac' 'FR2' 'FR3']\n",
+            "['Gtl' 'Mod' 'Sev']\n",
+            "['NAmes' 'Gilbert' 'StoneBr' 'NWAmes' 'Somerst' 'BrDale' 'NPkVill'\n",
+            " 'NridgHt' 'Blmngtn' 'NoRidge' 'SawyerW' 'Sawyer' 'Greens' 'BrkSide'\n",
+            " 'OldTown' 'IDOTRR' 'ClearCr' 'SWISU' 'Edwards' 'CollgCr' 'Crawfor'\n",
+            " 'Blueste' 'Mitchel' 'Timber' 'MeadowV' 'Veenker' 'GrnHill' 'Landmrk']\n",
+            "['Norm' 'Feedr' 'PosN' 'RRNe' 'RRAe' 'Artery' 'PosA' 'RRAn' 'RRNn']\n",
+            "['Norm' 'Feedr' 'PosA' 'PosN' 'Artery' 'RRNn' 'RRAe' 'RRAn']\n",
+            "['1Fam' 'TwnhsE' 'Twnhs' 'Duplex' '2fmCon']\n",
+            "['1Story' '2Story' '1.5Fin' 'SFoyer' 'SLvl' '2.5Unf' '1.5Unf' '2.5Fin']\n",
+            "['Hip' 'Gable' 'Mansard' 'Gambrel' 'Shed' 'Flat']\n",
+            "['CompShg' 'WdShake' 'Tar&Grv' 'WdShngl' 'Membran' 'ClyTile' 'Roll'\n",
+            " 'Metal']\n",
+            "['BrkFace' 'VinylSd' 'Wd Sdng' 'CemntBd' 'HdBoard' 'Plywood' 'MetalSd'\n",
+            " 'AsbShng' 'WdShing' 'Stucco' 'AsphShn' 'BrkComm' 'CBlock' 'PreCast'\n",
+            " 'Stone' 'ImStucc']\n",
+            "['Plywood' 'VinylSd' 'Wd Sdng' 'BrkFace' 'CmentBd' 'HdBoard' 'Wd Shng'\n",
+            " 'MetalSd' 'ImStucc' 'Brk Cmn' 'AsbShng' 'Stucco' 'AsphShn' 'CBlock'\n",
+            " 'Stone' 'PreCast' 'Other']\n",
+            "['TA' 'Gd' 'Ex' 'Fa']\n",
+            "['TA' 'Gd' 'Fa' 'Po' 'Ex']\n",
+            "['CBlock' 'PConc' 'Wood' 'BrkTil' 'Slab' 'Stone']\n",
+            "['TA' 'Gd' 'Ex' 'Fa' 'Po']\n",
+            "['Gd' 'TA' 'Po' 'Fa' 'Ex']\n",
+            "['Gd' 'No' 'Mn' 'Av']\n",
+            "['BLQ' 'Rec' 'ALQ' 'GLQ' 'Unf' 'LwQ']\n",
+            "['Unf' 'LwQ' 'BLQ' 'Rec' 'GLQ' 'ALQ']\n",
+            "['GasA' 'GasW' 'Grav' 'Wall' 'Floor' 'OthW']\n",
+            "['Fa' 'TA' 'Ex' 'Gd' 'Po']\n",
+            "['Y' 'N']\n",
+            "['SBrkr' 'FuseA' 'FuseF' 'FuseP' 'Mix']\n",
+            "['TA' 'Gd' 'Ex' 'Fa' 'Po']\n",
+            "['Typ' 'Mod' 'Min1' 'Min2' 'Maj1' 'Maj2' 'Sev' 'Sal']\n",
+            "['Gd' 'TA' 'Po' 'Ex' 'Fa']\n",
+            "['Attchd' 'BuiltIn' 'Basment' 'Detchd' 'CarPort' '2Types']\n",
+            "['Fin' 'Unf' 'RFn']\n",
+            "['TA' 'Fa' 'Gd' 'Ex' 'Po']\n",
+            "['TA' 'Fa' 'Gd' 'Ex' 'Po']\n",
+            "['P' 'Y' 'N']\n",
+            "['WD ' 'New' 'COD' 'ConLI' 'Con' 'ConLD' 'Oth' 'ConLw' 'CWD' 'VWD']\n",
+            "['Normal' 'Partial' 'Family' 'Abnorml' 'Alloca' 'AdjLand']\n",
+            "   MS SubClass  Lot Frontage  Lot Area  Overall Qual  Overall Cond  \\\n",
+            "0           20         141.0     31770             6             5   \n",
+            "1           20          80.0     11622             5             6   \n",
+            "2           20          81.0     14267             6             6   \n",
+            "3           20          93.0     11160             7             5   \n",
+            "4           60          74.0     13830             5             5   \n",
+            "\n",
+            "   Year Built  Year Remod/Add  Mas Vnr Area  BsmtFin SF 1  BsmtFin SF 2  \\\n",
+            "0        1960            1960         112.0         639.0           0.0   \n",
+            "1        1961            1961           0.0         468.0         144.0   \n",
+            "2        1958            1958         108.0         923.0           0.0   \n",
+            "3        1968            1968           0.0        1065.0           0.0   \n",
+            "4        1997            1998           0.0         791.0           0.0   \n",
+            "\n",
+            "   Bsmt Unf SF  Total Bsmt SF  1st Flr SF  2nd Flr SF  Low Qual Fin SF  \\\n",
+            "0        441.0         1080.0        1656           0                0   \n",
+            "1        270.0          882.0         896           0                0   \n",
+            "2        406.0         1329.0        1329           0                0   \n",
+            "3       1045.0         2110.0        2110           0                0   \n",
+            "4        137.0          928.0         928         701                0   \n",
+            "\n",
+            "   Gr Liv Area  Bsmt Full Bath  Bsmt Half Bath  Full Bath  Half Bath  \\\n",
+            "0         1656             1.0             0.0          1          0   \n",
+            "1          896             0.0             0.0          1          0   \n",
+            "2         1329             0.0             0.0          1          1   \n",
+            "3         2110             1.0             0.0          2          1   \n",
+            "4         1629             0.0             0.0          2          1   \n",
+            "\n",
+            "   Bedroom AbvGr  Kitchen AbvGr  TotRms AbvGrd  Fireplaces  Garage Yr Blt  \\\n",
+            "0              3              1              7           2         1960.0   \n",
+            "1              2              1              5           0         1961.0   \n",
+            "2              3              1              6           0         1958.0   \n",
+            "3              3              1              8           2         1968.0   \n",
+            "4              3              1              6           1         1997.0   \n",
+            "\n",
+            "   Garage Cars  Garage Area  Wood Deck SF  Open Porch SF  Enclosed Porch  \\\n",
+            "0          2.0        528.0           210             62               0   \n",
+            "1          1.0        730.0           140              0               0   \n",
+            "2          1.0        312.0           393             36               0   \n",
+            "3          2.0        522.0             0              0               0   \n",
+            "4          2.0        482.0           212             34               0   \n",
+            "\n",
+            "   3Ssn Porch  Screen Porch  Pool Area  Misc Val  Mo Sold  Yr Sold  SalePrice  \\\n",
+            "0           0             0          0         0        5     2010     215000   \n",
+            "1           0           120          0         0        6     2010     105000   \n",
+            "2           0             0          0     12500        6     2010     172000   \n",
+            "3           0             0          0         0        4     2010     244000   \n",
+            "4           0             0          0         0        3     2010     189900   \n",
+            "\n",
+            "   MS Zoning_C (all)  MS Zoning_FV  MS Zoning_I (all)  MS Zoning_RH  \\\n",
+            "0                0.0           0.0                0.0           0.0   \n",
+            "1                0.0           0.0                0.0           1.0   \n",
+            "2                0.0           0.0                0.0           0.0   \n",
+            "3                0.0           0.0                0.0           0.0   \n",
+            "4                0.0           0.0                0.0           0.0   \n",
+            "\n",
+            "   MS Zoning_RL  MS Zoning_RM  Street_Pave  Lot Shape_IR2  Lot Shape_IR3  \\\n",
+            "0           1.0           0.0          1.0            0.0            0.0   \n",
+            "1           0.0           0.0          1.0            0.0            0.0   \n",
+            "2           1.0           0.0          1.0            0.0            0.0   \n",
+            "3           1.0           0.0          1.0            0.0            0.0   \n",
+            "4           1.0           0.0          1.0            0.0            0.0   \n",
+            "\n",
+            "   Lot Shape_Reg  Land Contour_HLS  Land Contour_Low  Land Contour_Lvl  \\\n",
+            "0            0.0               0.0               0.0               1.0   \n",
+            "1            1.0               0.0               0.0               1.0   \n",
+            "2            0.0               0.0               0.0               1.0   \n",
+            "3            1.0               0.0               0.0               1.0   \n",
+            "4            0.0               0.0               0.0               1.0   \n",
+            "\n",
+            "   Utilities_NoSeWa  Utilities_NoSewr  Lot Config_CulDSac  Lot Config_FR2  \\\n",
+            "0               0.0               0.0                 0.0             0.0   \n",
+            "1               0.0               0.0                 0.0             0.0   \n",
+            "2               0.0               0.0                 0.0             0.0   \n",
+            "3               0.0               0.0                 0.0             0.0   \n",
+            "4               0.0               0.0                 0.0             0.0   \n",
+            "\n",
+            "   Lot Config_FR3  Lot Config_Inside  Land Slope_Mod  Land Slope_Sev  \\\n",
+            "0             0.0                0.0             0.0             0.0   \n",
+            "1             0.0                1.0             0.0             0.0   \n",
+            "2             0.0                0.0             0.0             0.0   \n",
+            "3             0.0                0.0             0.0             0.0   \n",
+            "4             0.0                1.0             0.0             0.0   \n",
+            "\n",
+            "   Neighborhood_Blueste  Neighborhood_BrDale  Neighborhood_BrkSide  \\\n",
+            "0                   0.0                  0.0                   0.0   \n",
+            "1                   0.0                  0.0                   0.0   \n",
+            "2                   0.0                  0.0                   0.0   \n",
+            "3                   0.0                  0.0                   0.0   \n",
+            "4                   0.0                  0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_ClearCr  Neighborhood_CollgCr  Neighborhood_Crawfor  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_Edwards  Neighborhood_Gilbert  Neighborhood_Greens  \\\n",
+            "0                   0.0                   0.0                  0.0   \n",
+            "1                   0.0                   0.0                  0.0   \n",
+            "2                   0.0                   0.0                  0.0   \n",
+            "3                   0.0                   0.0                  0.0   \n",
+            "4                   0.0                   1.0                  0.0   \n",
+            "\n",
+            "   Neighborhood_GrnHill  Neighborhood_IDOTRR  Neighborhood_Landmrk  \\\n",
+            "0                   0.0                  0.0                   0.0   \n",
+            "1                   0.0                  0.0                   0.0   \n",
+            "2                   0.0                  0.0                   0.0   \n",
+            "3                   0.0                  0.0                   0.0   \n",
+            "4                   0.0                  0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_MeadowV  Neighborhood_Mitchel  Neighborhood_NAmes  \\\n",
+            "0                   0.0                   0.0                 1.0   \n",
+            "1                   0.0                   0.0                 1.0   \n",
+            "2                   0.0                   0.0                 1.0   \n",
+            "3                   0.0                   0.0                 1.0   \n",
+            "4                   0.0                   0.0                 0.0   \n",
+            "\n",
+            "   Neighborhood_NPkVill  Neighborhood_NWAmes  Neighborhood_NoRidge  \\\n",
+            "0                   0.0                  0.0                   0.0   \n",
+            "1                   0.0                  0.0                   0.0   \n",
+            "2                   0.0                  0.0                   0.0   \n",
+            "3                   0.0                  0.0                   0.0   \n",
+            "4                   0.0                  0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_NridgHt  Neighborhood_OldTown  Neighborhood_SWISU  \\\n",
+            "0                   0.0                   0.0                 0.0   \n",
+            "1                   0.0                   0.0                 0.0   \n",
+            "2                   0.0                   0.0                 0.0   \n",
+            "3                   0.0                   0.0                 0.0   \n",
+            "4                   0.0                   0.0                 0.0   \n",
+            "\n",
+            "   Neighborhood_Sawyer  Neighborhood_SawyerW  Neighborhood_Somerst  \\\n",
+            "0                  0.0                   0.0                   0.0   \n",
+            "1                  0.0                   0.0                   0.0   \n",
+            "2                  0.0                   0.0                   0.0   \n",
+            "3                  0.0                   0.0                   0.0   \n",
+            "4                  0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_StoneBr  Neighborhood_Timber  Neighborhood_Veenker  \\\n",
+            "0                   0.0                  0.0                   0.0   \n",
+            "1                   0.0                  0.0                   0.0   \n",
+            "2                   0.0                  0.0                   0.0   \n",
+            "3                   0.0                  0.0                   0.0   \n",
+            "4                   0.0                  0.0                   0.0   \n",
+            "\n",
+            "   Condition 1_Feedr  Condition 1_Norm  Condition 1_PosA  Condition 1_PosN  \\\n",
+            "0                0.0               1.0               0.0               0.0   \n",
+            "1                1.0               0.0               0.0               0.0   \n",
+            "2                0.0               1.0               0.0               0.0   \n",
+            "3                0.0               1.0               0.0               0.0   \n",
+            "4                0.0               1.0               0.0               0.0   \n",
+            "\n",
+            "   Condition 1_RRAe  Condition 1_RRAn  Condition 1_RRNe  Condition 1_RRNn  \\\n",
+            "0               0.0               0.0               0.0               0.0   \n",
+            "1               0.0               0.0               0.0               0.0   \n",
+            "2               0.0               0.0               0.0               0.0   \n",
+            "3               0.0               0.0               0.0               0.0   \n",
+            "4               0.0               0.0               0.0               0.0   \n",
+            "\n",
+            "   Condition 2_Feedr  Condition 2_Norm  Condition 2_PosA  Condition 2_PosN  \\\n",
+            "0                0.0               1.0               0.0               0.0   \n",
+            "1                0.0               1.0               0.0               0.0   \n",
+            "2                0.0               1.0               0.0               0.0   \n",
+            "3                0.0               1.0               0.0               0.0   \n",
+            "4                0.0               1.0               0.0               0.0   \n",
+            "\n",
+            "   Condition 2_RRAe  Condition 2_RRAn  Condition 2_RRNn  Bldg Type_2fmCon  \\\n",
+            "0               0.0               0.0               0.0               0.0   \n",
+            "1               0.0               0.0               0.0               0.0   \n",
+            "2               0.0               0.0               0.0               0.0   \n",
+            "3               0.0               0.0               0.0               0.0   \n",
+            "4               0.0               0.0               0.0               0.0   \n",
+            "\n",
+            "   Bldg Type_Duplex  Bldg Type_Twnhs  Bldg Type_TwnhsE  House Style_1.5Unf  \\\n",
+            "0               0.0              0.0               0.0                 0.0   \n",
+            "1               0.0              0.0               0.0                 0.0   \n",
+            "2               0.0              0.0               0.0                 0.0   \n",
+            "3               0.0              0.0               0.0                 0.0   \n",
+            "4               0.0              0.0               0.0                 0.0   \n",
+            "\n",
+            "   House Style_1Story  House Style_2.5Fin  House Style_2.5Unf  \\\n",
+            "0                 1.0                 0.0                 0.0   \n",
+            "1                 1.0                 0.0                 0.0   \n",
+            "2                 1.0                 0.0                 0.0   \n",
+            "3                 1.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 0.0   \n",
+            "\n",
+            "   House Style_2Story  House Style_SFoyer  House Style_SLvl  Roof Style_Gable  \\\n",
+            "0                 0.0                 0.0               0.0               0.0   \n",
+            "1                 0.0                 0.0               0.0               1.0   \n",
+            "2                 0.0                 0.0               0.0               0.0   \n",
+            "3                 0.0                 0.0               0.0               0.0   \n",
+            "4                 1.0                 0.0               0.0               1.0   \n",
+            "\n",
+            "   Roof Style_Gambrel  Roof Style_Hip  Roof Style_Mansard  Roof Style_Shed  \\\n",
+            "0                 0.0             1.0                 0.0              0.0   \n",
+            "1                 0.0             0.0                 0.0              0.0   \n",
+            "2                 0.0             1.0                 0.0              0.0   \n",
+            "3                 0.0             1.0                 0.0              0.0   \n",
+            "4                 0.0             0.0                 0.0              0.0   \n",
+            "\n",
+            "   Roof Matl_CompShg  Roof Matl_Membran  Roof Matl_Metal  Roof Matl_Roll  \\\n",
+            "0                1.0                0.0              0.0             0.0   \n",
+            "1                1.0                0.0              0.0             0.0   \n",
+            "2                1.0                0.0              0.0             0.0   \n",
+            "3                1.0                0.0              0.0             0.0   \n",
+            "4                1.0                0.0              0.0             0.0   \n",
+            "\n",
+            "   Roof Matl_Tar&Grv  Roof Matl_WdShake  Roof Matl_WdShngl  \\\n",
+            "0                0.0                0.0                0.0   \n",
+            "1                0.0                0.0                0.0   \n",
+            "2                0.0                0.0                0.0   \n",
+            "3                0.0                0.0                0.0   \n",
+            "4                0.0                0.0                0.0   \n",
+            "\n",
+            "   Exterior 1st_AsphShn  Exterior 1st_BrkComm  Exterior 1st_BrkFace  \\\n",
+            "0                   0.0                   0.0                   1.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   1.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_CBlock  Exterior 1st_CemntBd  Exterior 1st_HdBoard  \\\n",
+            "0                  0.0                   0.0                   0.0   \n",
+            "1                  0.0                   0.0                   0.0   \n",
+            "2                  0.0                   0.0                   0.0   \n",
+            "3                  0.0                   0.0                   0.0   \n",
+            "4                  0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_ImStucc  Exterior 1st_MetalSd  Exterior 1st_Plywood  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_PreCast  Exterior 1st_Stone  Exterior 1st_Stucco  \\\n",
+            "0                   0.0                 0.0                  0.0   \n",
+            "1                   0.0                 0.0                  0.0   \n",
+            "2                   0.0                 0.0                  0.0   \n",
+            "3                   0.0                 0.0                  0.0   \n",
+            "4                   0.0                 0.0                  0.0   \n",
+            "\n",
+            "   Exterior 1st_VinylSd  Exterior 1st_Wd Sdng  Exterior 1st_WdShing  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   1.0                   0.0                   0.0   \n",
+            "2                   0.0                   1.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   1.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_AsphShn  Exterior 2nd_Brk Cmn  Exterior 2nd_BrkFace  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   1.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_CBlock  Exterior 2nd_CmentBd  Exterior 2nd_HdBoard  \\\n",
+            "0                  0.0                   0.0                   0.0   \n",
+            "1                  0.0                   0.0                   0.0   \n",
+            "2                  0.0                   0.0                   0.0   \n",
+            "3                  0.0                   0.0                   0.0   \n",
+            "4                  0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_ImStucc  Exterior 2nd_MetalSd  Exterior 2nd_Other  \\\n",
+            "0                   0.0                   0.0                 0.0   \n",
+            "1                   0.0                   0.0                 0.0   \n",
+            "2                   0.0                   0.0                 0.0   \n",
+            "3                   0.0                   0.0                 0.0   \n",
+            "4                   0.0                   0.0                 0.0   \n",
+            "\n",
+            "   Exterior 2nd_Plywood  Exterior 2nd_PreCast  Exterior 2nd_Stone  \\\n",
+            "0                   1.0                   0.0                 0.0   \n",
+            "1                   0.0                   0.0                 0.0   \n",
+            "2                   0.0                   0.0                 0.0   \n",
+            "3                   0.0                   0.0                 0.0   \n",
+            "4                   0.0                   0.0                 0.0   \n",
+            "\n",
+            "   Exterior 2nd_Stucco  Exterior 2nd_VinylSd  Exterior 2nd_Wd Sdng  \\\n",
+            "0                  0.0                   0.0                   0.0   \n",
+            "1                  0.0                   1.0                   0.0   \n",
+            "2                  0.0                   0.0                   1.0   \n",
+            "3                  0.0                   0.0                   0.0   \n",
+            "4                  0.0                   1.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_Wd Shng  Exter Qual_Fa  Exter Qual_Gd  Exter Qual_TA  \\\n",
+            "0                   0.0            0.0            0.0            1.0   \n",
+            "1                   0.0            0.0            0.0            1.0   \n",
+            "2                   0.0            0.0            0.0            1.0   \n",
+            "3                   0.0            0.0            1.0            0.0   \n",
+            "4                   0.0            0.0            0.0            1.0   \n",
+            "\n",
+            "   Exter Cond_Fa  Exter Cond_Gd  Exter Cond_Po  Exter Cond_TA  \\\n",
+            "0            0.0            0.0            0.0            1.0   \n",
+            "1            0.0            0.0            0.0            1.0   \n",
+            "2            0.0            0.0            0.0            1.0   \n",
+            "3            0.0            0.0            0.0            1.0   \n",
+            "4            0.0            0.0            0.0            1.0   \n",
+            "\n",
+            "   Foundation_CBlock  Foundation_PConc  Foundation_Slab  Foundation_Stone  \\\n",
+            "0                1.0               0.0              0.0               0.0   \n",
+            "1                1.0               0.0              0.0               0.0   \n",
+            "2                1.0               0.0              0.0               0.0   \n",
+            "3                1.0               0.0              0.0               0.0   \n",
+            "4                0.0               1.0              0.0               0.0   \n",
+            "\n",
+            "   Foundation_Wood  Bsmt Qual_Fa  Bsmt Qual_Gd  Bsmt Qual_Po  Bsmt Qual_TA  \\\n",
+            "0              0.0           0.0           0.0           0.0           1.0   \n",
+            "1              0.0           0.0           0.0           0.0           1.0   \n",
+            "2              0.0           0.0           0.0           0.0           1.0   \n",
+            "3              0.0           0.0           0.0           0.0           1.0   \n",
+            "4              0.0           0.0           1.0           0.0           0.0   \n",
+            "\n",
+            "   Bsmt Cond_Fa  Bsmt Cond_Gd  Bsmt Cond_Po  Bsmt Cond_TA  Bsmt Exposure_Gd  \\\n",
+            "0           0.0           1.0           0.0           0.0               1.0   \n",
+            "1           0.0           0.0           0.0           1.0               0.0   \n",
+            "2           0.0           0.0           0.0           1.0               0.0   \n",
+            "3           0.0           0.0           0.0           1.0               0.0   \n",
+            "4           0.0           0.0           0.0           1.0               0.0   \n",
+            "\n",
+            "   Bsmt Exposure_Mn  Bsmt Exposure_No  BsmtFin Type 1_BLQ  BsmtFin Type 1_GLQ  \\\n",
+            "0               0.0               0.0                 1.0                 0.0   \n",
+            "1               0.0               1.0                 0.0                 0.0   \n",
+            "2               0.0               1.0                 0.0                 0.0   \n",
+            "3               0.0               1.0                 0.0                 0.0   \n",
+            "4               0.0               1.0                 0.0                 1.0   \n",
+            "\n",
+            "   BsmtFin Type 1_LwQ  BsmtFin Type 1_Rec  BsmtFin Type 1_Unf  \\\n",
+            "0                 0.0                 0.0                 0.0   \n",
+            "1                 0.0                 1.0                 0.0   \n",
+            "2                 0.0                 0.0                 0.0   \n",
+            "3                 0.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 0.0   \n",
+            "\n",
+            "   BsmtFin Type 2_BLQ  BsmtFin Type 2_GLQ  BsmtFin Type 2_LwQ  \\\n",
+            "0                 0.0                 0.0                 0.0   \n",
+            "1                 0.0                 0.0                 1.0   \n",
+            "2                 0.0                 0.0                 0.0   \n",
+            "3                 0.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 0.0   \n",
+            "\n",
+            "   BsmtFin Type 2_Rec  BsmtFin Type 2_Unf  Heating_GasA  Heating_GasW  \\\n",
+            "0                 0.0                 1.0           1.0           0.0   \n",
+            "1                 0.0                 0.0           1.0           0.0   \n",
+            "2                 0.0                 1.0           1.0           0.0   \n",
+            "3                 0.0                 1.0           1.0           0.0   \n",
+            "4                 0.0                 1.0           1.0           0.0   \n",
+            "\n",
+            "   Heating_Grav  Heating_OthW  Heating_Wall  Heating QC_Fa  Heating QC_Gd  \\\n",
+            "0           0.0           0.0           0.0            1.0            0.0   \n",
+            "1           0.0           0.0           0.0            0.0            0.0   \n",
+            "2           0.0           0.0           0.0            0.0            0.0   \n",
+            "3           0.0           0.0           0.0            0.0            0.0   \n",
+            "4           0.0           0.0           0.0            0.0            1.0   \n",
+            "\n",
+            "   Heating QC_Po  Heating QC_TA  Central Air_Y  Electrical_FuseF  \\\n",
+            "0            0.0            0.0            1.0               0.0   \n",
+            "1            0.0            1.0            1.0               0.0   \n",
+            "2            0.0            1.0            1.0               0.0   \n",
+            "3            0.0            0.0            1.0               0.0   \n",
+            "4            0.0            0.0            1.0               0.0   \n",
+            "\n",
+            "   Electrical_FuseP  Electrical_Mix  Electrical_SBrkr  Kitchen Qual_Fa  \\\n",
+            "0               0.0             0.0               1.0              0.0   \n",
+            "1               0.0             0.0               1.0              0.0   \n",
+            "2               0.0             0.0               1.0              0.0   \n",
+            "3               0.0             0.0               1.0              0.0   \n",
+            "4               0.0             0.0               1.0              0.0   \n",
+            "\n",
+            "   Kitchen Qual_Gd  Kitchen Qual_Po  Kitchen Qual_TA  Functional_Maj2  \\\n",
+            "0              0.0              0.0              1.0              0.0   \n",
+            "1              0.0              0.0              1.0              0.0   \n",
+            "2              1.0              0.0              0.0              0.0   \n",
+            "3              0.0              0.0              0.0              0.0   \n",
+            "4              0.0              0.0              1.0              0.0   \n",
+            "\n",
+            "   Functional_Min1  Functional_Min2  Functional_Mod  Functional_Sal  \\\n",
+            "0              0.0              0.0             0.0             0.0   \n",
+            "1              0.0              0.0             0.0             0.0   \n",
+            "2              0.0              0.0             0.0             0.0   \n",
+            "3              0.0              0.0             0.0             0.0   \n",
+            "4              0.0              0.0             0.0             0.0   \n",
+            "\n",
+            "   Functional_Sev  Functional_Typ  Fireplace Qu_Fa  Fireplace Qu_Gd  \\\n",
+            "0             0.0             1.0              0.0              1.0   \n",
+            "1             0.0             1.0              0.0              1.0   \n",
+            "2             0.0             1.0              0.0              1.0   \n",
+            "3             0.0             1.0              0.0              0.0   \n",
+            "4             0.0             1.0              0.0              0.0   \n",
+            "\n",
+            "   Fireplace Qu_Po  Fireplace Qu_TA  Garage Type_Attchd  Garage Type_Basment  \\\n",
+            "0              0.0              0.0                 1.0                  0.0   \n",
+            "1              0.0              0.0                 1.0                  0.0   \n",
+            "2              0.0              0.0                 1.0                  0.0   \n",
+            "3              0.0              1.0                 1.0                  0.0   \n",
+            "4              0.0              1.0                 1.0                  0.0   \n",
+            "\n",
+            "   Garage Type_BuiltIn  Garage Type_CarPort  Garage Type_Detchd  \\\n",
+            "0                  0.0                  0.0                 0.0   \n",
+            "1                  0.0                  0.0                 0.0   \n",
+            "2                  0.0                  0.0                 0.0   \n",
+            "3                  0.0                  0.0                 0.0   \n",
+            "4                  0.0                  0.0                 0.0   \n",
+            "\n",
+            "   Garage Finish_RFn  Garage Finish_Unf  Garage Qual_Fa  Garage Qual_Gd  \\\n",
+            "0                0.0                0.0             0.0             0.0   \n",
+            "1                0.0                1.0             0.0             0.0   \n",
+            "2                0.0                1.0             0.0             0.0   \n",
+            "3                0.0                0.0             0.0             0.0   \n",
+            "4                0.0                0.0             0.0             0.0   \n",
+            "\n",
+            "   Garage Qual_Po  Garage Qual_TA  Garage Cond_Fa  Garage Cond_Gd  \\\n",
+            "0             0.0             1.0             0.0             0.0   \n",
+            "1             0.0             1.0             0.0             0.0   \n",
+            "2             0.0             1.0             0.0             0.0   \n",
+            "3             0.0             1.0             0.0             0.0   \n",
+            "4             0.0             1.0             0.0             0.0   \n",
+            "\n",
+            "   Garage Cond_Po  Garage Cond_TA  Paved Drive_P  Paved Drive_Y  \\\n",
+            "0             0.0             1.0            1.0            0.0   \n",
+            "1             0.0             1.0            0.0            1.0   \n",
+            "2             0.0             1.0            0.0            1.0   \n",
+            "3             0.0             1.0            0.0            1.0   \n",
+            "4             0.0             1.0            0.0            1.0   \n",
+            "\n",
+            "   Sale Type_CWD  Sale Type_Con  Sale Type_ConLD  Sale Type_ConLI  \\\n",
+            "0            0.0            0.0              0.0              0.0   \n",
+            "1            0.0            0.0              0.0              0.0   \n",
+            "2            0.0            0.0              0.0              0.0   \n",
+            "3            0.0            0.0              0.0              0.0   \n",
+            "4            0.0            0.0              0.0              0.0   \n",
+            "\n",
+            "   Sale Type_ConLw  Sale Type_New  Sale Type_Oth  Sale Type_VWD  \\\n",
+            "0              0.0            0.0            0.0            0.0   \n",
+            "1              0.0            0.0            0.0            0.0   \n",
+            "2              0.0            0.0            0.0            0.0   \n",
+            "3              0.0            0.0            0.0            0.0   \n",
+            "4              0.0            0.0            0.0            0.0   \n",
+            "\n",
+            "   Sale Type_WD   Sale Condition_AdjLand  Sale Condition_Alloca  \\\n",
+            "0            1.0                     0.0                    0.0   \n",
+            "1            1.0                     0.0                    0.0   \n",
+            "2            1.0                     0.0                    0.0   \n",
+            "3            1.0                     0.0                    0.0   \n",
+            "4            1.0                     0.0                    0.0   \n",
+            "\n",
+            "   Sale Condition_Family  Sale Condition_Normal  Sale Condition_Partial  \n",
+            "0                    0.0                    1.0                     0.0  \n",
+            "1                    0.0                    1.0                     0.0  \n",
+            "2                    0.0                    1.0                     0.0  \n",
+            "3                    0.0                    1.0                     0.0  \n",
+            "4                    0.0                    1.0                     0.0  \n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Identify categorical variables.\n",
+        "# Use methods like:\n",
+        "# - One-hot encoding\n",
+        "# - Ordinal encoding\n",
+        "# Decide what makes sense for each feature.\n",
+        "\n",
+        "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder, OneHotEncoder\n",
+        "\n",
+        "for col in col_cat:\n",
+        "    print(df[col].unique())\n",
+        "\n",
+        "# df_oe = df.copy()  # keep a separate version\n",
+        "# ordinal_encoders = {}\n",
+        "# for col in col_cat:\n",
+        "#     oe = OrdinalEncoder()\n",
+        "#     df_oe[col] = oe.fit_transform(df_oe[col].values.reshape(-1, 1))\n",
+        "#     ordinal_encoders[col] = oe  # store encoder if needed later\n",
+        "# print(df_oe.head())\n",
+        "\n",
+        "df_ohe = df.copy()  # keep a separate version\n",
+        "label_encoders = {}\n",
+        "ohe = OneHotEncoder(drop=\"first\", sparse_output=False) # Set sparse_output to False\n",
+        "encoded = ohe.fit_transform(df[col_cat])\n",
+        "\n",
+        "encoded_df = pd.DataFrame(encoded, columns=ohe.get_feature_names_out(col_cat), index=df.index)\n",
+        "df = pd.concat([df.drop(columns=col_cat), encoded_df], axis=1)\n",
+        "\n",
+        "print(df.head())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ayerWnrFj-OS"
+      },
+      "source": [
+        "## 🔹 Step 6: Feature Scaling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cmOzqeCcj9_P",
+        "outputId": "1bca511f-1544-4bcd-cf91-f5de73de6e9c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "   MS SubClass  Lot Frontage  Lot Area  Overall Qual  Overall Cond  \\\n",
+            "0     0.000000      0.410959  0.142420      0.555556         0.500   \n",
+            "1     0.000000      0.202055  0.048246      0.444444         0.625   \n",
+            "2     0.000000      0.205479  0.060609      0.555556         0.625   \n",
+            "3     0.000000      0.246575  0.046087      0.666667         0.500   \n",
+            "4     0.235294      0.181507  0.058566      0.444444         0.500   \n",
+            "\n",
+            "   Year Built  Year Remod/Add  Mas Vnr Area  BsmtFin SF 1  BsmtFin SF 2  \\\n",
+            "0    0.637681        0.166667        0.0700      0.113218      0.000000   \n",
+            "1    0.644928        0.183333        0.0000      0.082920      0.094364   \n",
+            "2    0.623188        0.133333        0.0675      0.163536      0.000000   \n",
+            "3    0.695652        0.300000        0.0000      0.188696      0.000000   \n",
+            "4    0.905797        0.800000        0.0000      0.140149      0.000000   \n",
+            "\n",
+            "   Bsmt Unf SF  Total Bsmt SF  1st Flr SF  2nd Flr SF  Low Qual Fin SF  \\\n",
+            "0     0.188784       0.176759    0.277673    0.000000              0.0   \n",
+            "1     0.115582       0.144354    0.118042    0.000000              0.0   \n",
+            "2     0.173801       0.217512    0.208990    0.000000              0.0   \n",
+            "3     0.447346       0.345336    0.373031    0.000000              0.0   \n",
+            "4     0.058647       0.151882    0.124764    0.339467              0.0   \n",
+            "\n",
+            "   Gr Liv Area  Bsmt Full Bath  Bsmt Half Bath  Full Bath  Half Bath  \\\n",
+            "0     0.249058        0.333333             0.0       0.25        0.0   \n",
+            "1     0.105878        0.000000             0.0       0.25        0.0   \n",
+            "2     0.187453        0.000000             0.0       0.25        0.5   \n",
+            "3     0.334589        0.333333             0.0       0.50        0.5   \n",
+            "4     0.243971        0.000000             0.0       0.50        0.5   \n",
+            "\n",
+            "   Bedroom AbvGr  Kitchen AbvGr  TotRms AbvGrd  Fireplaces  Garage Yr Blt  \\\n",
+            "0          0.375       0.333333       0.384615        0.50       0.208333   \n",
+            "1          0.250       0.333333       0.230769        0.00       0.211538   \n",
+            "2          0.375       0.333333       0.307692        0.00       0.201923   \n",
+            "3          0.375       0.333333       0.461538        0.50       0.233974   \n",
+            "4          0.375       0.333333       0.307692        0.25       0.326923   \n",
+            "\n",
+            "   Garage Cars  Garage Area  Wood Deck SF  Open Porch SF  Enclosed Porch  \\\n",
+            "0          0.4     0.354839      0.147472       0.083558             0.0   \n",
+            "1          0.2     0.490591      0.098315       0.000000             0.0   \n",
+            "2          0.2     0.209677      0.275983       0.048518             0.0   \n",
+            "3          0.4     0.350806      0.000000       0.000000             0.0   \n",
+            "4          0.4     0.323925      0.148876       0.045822             0.0   \n",
+            "\n",
+            "   3Ssn Porch  Screen Porch  Pool Area  Misc Val   Mo Sold  Yr Sold  \\\n",
+            "0         0.0      0.000000        0.0  0.000000  0.363636      1.0   \n",
+            "1         0.0      0.208333        0.0  0.000000  0.454545      1.0   \n",
+            "2         0.0      0.000000        0.0  0.735294  0.454545      1.0   \n",
+            "3         0.0      0.000000        0.0  0.000000  0.272727      1.0   \n",
+            "4         0.0      0.000000        0.0  0.000000  0.181818      1.0   \n",
+            "\n",
+            "   SalePrice  MS Zoning_C (all)  MS Zoning_FV  MS Zoning_I (all)  \\\n",
+            "0   0.272444                0.0           0.0                0.0   \n",
+            "1   0.124238                0.0           0.0                0.0   \n",
+            "2   0.214509                0.0           0.0                0.0   \n",
+            "3   0.311517                0.0           0.0                0.0   \n",
+            "4   0.238626                0.0           0.0                0.0   \n",
+            "\n",
+            "   MS Zoning_RH  MS Zoning_RL  MS Zoning_RM  Street_Pave  Lot Shape_IR2  \\\n",
+            "0           0.0           1.0           0.0          1.0            0.0   \n",
+            "1           1.0           0.0           0.0          1.0            0.0   \n",
+            "2           0.0           1.0           0.0          1.0            0.0   \n",
+            "3           0.0           1.0           0.0          1.0            0.0   \n",
+            "4           0.0           1.0           0.0          1.0            0.0   \n",
+            "\n",
+            "   Lot Shape_IR3  Lot Shape_Reg  Land Contour_HLS  Land Contour_Low  \\\n",
+            "0            0.0            0.0               0.0               0.0   \n",
+            "1            0.0            1.0               0.0               0.0   \n",
+            "2            0.0            0.0               0.0               0.0   \n",
+            "3            0.0            1.0               0.0               0.0   \n",
+            "4            0.0            0.0               0.0               0.0   \n",
+            "\n",
+            "   Land Contour_Lvl  Utilities_NoSeWa  Utilities_NoSewr  Lot Config_CulDSac  \\\n",
+            "0               1.0               0.0               0.0                 0.0   \n",
+            "1               1.0               0.0               0.0                 0.0   \n",
+            "2               1.0               0.0               0.0                 0.0   \n",
+            "3               1.0               0.0               0.0                 0.0   \n",
+            "4               1.0               0.0               0.0                 0.0   \n",
+            "\n",
+            "   Lot Config_FR2  Lot Config_FR3  Lot Config_Inside  Land Slope_Mod  \\\n",
+            "0             0.0             0.0                0.0             0.0   \n",
+            "1             0.0             0.0                1.0             0.0   \n",
+            "2             0.0             0.0                0.0             0.0   \n",
+            "3             0.0             0.0                0.0             0.0   \n",
+            "4             0.0             0.0                1.0             0.0   \n",
+            "\n",
+            "   Land Slope_Sev  Neighborhood_Blueste  Neighborhood_BrDale  \\\n",
+            "0             0.0                   0.0                  0.0   \n",
+            "1             0.0                   0.0                  0.0   \n",
+            "2             0.0                   0.0                  0.0   \n",
+            "3             0.0                   0.0                  0.0   \n",
+            "4             0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Neighborhood_BrkSide  Neighborhood_ClearCr  Neighborhood_CollgCr  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_Crawfor  Neighborhood_Edwards  Neighborhood_Gilbert  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   1.0   \n",
+            "\n",
+            "   Neighborhood_Greens  Neighborhood_GrnHill  Neighborhood_IDOTRR  \\\n",
+            "0                  0.0                   0.0                  0.0   \n",
+            "1                  0.0                   0.0                  0.0   \n",
+            "2                  0.0                   0.0                  0.0   \n",
+            "3                  0.0                   0.0                  0.0   \n",
+            "4                  0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Neighborhood_Landmrk  Neighborhood_MeadowV  Neighborhood_Mitchel  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_NAmes  Neighborhood_NPkVill  Neighborhood_NWAmes  \\\n",
+            "0                 1.0                   0.0                  0.0   \n",
+            "1                 1.0                   0.0                  0.0   \n",
+            "2                 1.0                   0.0                  0.0   \n",
+            "3                 1.0                   0.0                  0.0   \n",
+            "4                 0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Neighborhood_NoRidge  Neighborhood_NridgHt  Neighborhood_OldTown  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_SWISU  Neighborhood_Sawyer  Neighborhood_SawyerW  \\\n",
+            "0                 0.0                  0.0                   0.0   \n",
+            "1                 0.0                  0.0                   0.0   \n",
+            "2                 0.0                  0.0                   0.0   \n",
+            "3                 0.0                  0.0                   0.0   \n",
+            "4                 0.0                  0.0                   0.0   \n",
+            "\n",
+            "   Neighborhood_Somerst  Neighborhood_StoneBr  Neighborhood_Timber  \\\n",
+            "0                   0.0                   0.0                  0.0   \n",
+            "1                   0.0                   0.0                  0.0   \n",
+            "2                   0.0                   0.0                  0.0   \n",
+            "3                   0.0                   0.0                  0.0   \n",
+            "4                   0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Neighborhood_Veenker  Condition 1_Feedr  Condition 1_Norm  \\\n",
+            "0                   0.0                0.0               1.0   \n",
+            "1                   0.0                1.0               0.0   \n",
+            "2                   0.0                0.0               1.0   \n",
+            "3                   0.0                0.0               1.0   \n",
+            "4                   0.0                0.0               1.0   \n",
+            "\n",
+            "   Condition 1_PosA  Condition 1_PosN  Condition 1_RRAe  Condition 1_RRAn  \\\n",
+            "0               0.0               0.0               0.0               0.0   \n",
+            "1               0.0               0.0               0.0               0.0   \n",
+            "2               0.0               0.0               0.0               0.0   \n",
+            "3               0.0               0.0               0.0               0.0   \n",
+            "4               0.0               0.0               0.0               0.0   \n",
+            "\n",
+            "   Condition 1_RRNe  Condition 1_RRNn  Condition 2_Feedr  Condition 2_Norm  \\\n",
+            "0               0.0               0.0                0.0               1.0   \n",
+            "1               0.0               0.0                0.0               1.0   \n",
+            "2               0.0               0.0                0.0               1.0   \n",
+            "3               0.0               0.0                0.0               1.0   \n",
+            "4               0.0               0.0                0.0               1.0   \n",
+            "\n",
+            "   Condition 2_PosA  Condition 2_PosN  Condition 2_RRAe  Condition 2_RRAn  \\\n",
+            "0               0.0               0.0               0.0               0.0   \n",
+            "1               0.0               0.0               0.0               0.0   \n",
+            "2               0.0               0.0               0.0               0.0   \n",
+            "3               0.0               0.0               0.0               0.0   \n",
+            "4               0.0               0.0               0.0               0.0   \n",
+            "\n",
+            "   Condition 2_RRNn  Bldg Type_2fmCon  Bldg Type_Duplex  Bldg Type_Twnhs  \\\n",
+            "0               0.0               0.0               0.0              0.0   \n",
+            "1               0.0               0.0               0.0              0.0   \n",
+            "2               0.0               0.0               0.0              0.0   \n",
+            "3               0.0               0.0               0.0              0.0   \n",
+            "4               0.0               0.0               0.0              0.0   \n",
+            "\n",
+            "   Bldg Type_TwnhsE  House Style_1.5Unf  House Style_1Story  \\\n",
+            "0               0.0                 0.0                 1.0   \n",
+            "1               0.0                 0.0                 1.0   \n",
+            "2               0.0                 0.0                 1.0   \n",
+            "3               0.0                 0.0                 1.0   \n",
+            "4               0.0                 0.0                 0.0   \n",
+            "\n",
+            "   House Style_2.5Fin  House Style_2.5Unf  House Style_2Story  \\\n",
+            "0                 0.0                 0.0                 0.0   \n",
+            "1                 0.0                 0.0                 0.0   \n",
+            "2                 0.0                 0.0                 0.0   \n",
+            "3                 0.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 1.0   \n",
+            "\n",
+            "   House Style_SFoyer  House Style_SLvl  Roof Style_Gable  Roof Style_Gambrel  \\\n",
+            "0                 0.0               0.0               0.0                 0.0   \n",
+            "1                 0.0               0.0               1.0                 0.0   \n",
+            "2                 0.0               0.0               0.0                 0.0   \n",
+            "3                 0.0               0.0               0.0                 0.0   \n",
+            "4                 0.0               0.0               1.0                 0.0   \n",
+            "\n",
+            "   Roof Style_Hip  Roof Style_Mansard  Roof Style_Shed  Roof Matl_CompShg  \\\n",
+            "0             1.0                 0.0              0.0                1.0   \n",
+            "1             0.0                 0.0              0.0                1.0   \n",
+            "2             1.0                 0.0              0.0                1.0   \n",
+            "3             1.0                 0.0              0.0                1.0   \n",
+            "4             0.0                 0.0              0.0                1.0   \n",
+            "\n",
+            "   Roof Matl_Membran  Roof Matl_Metal  Roof Matl_Roll  Roof Matl_Tar&Grv  \\\n",
+            "0                0.0              0.0             0.0                0.0   \n",
+            "1                0.0              0.0             0.0                0.0   \n",
+            "2                0.0              0.0             0.0                0.0   \n",
+            "3                0.0              0.0             0.0                0.0   \n",
+            "4                0.0              0.0             0.0                0.0   \n",
+            "\n",
+            "   Roof Matl_WdShake  Roof Matl_WdShngl  Exterior 1st_AsphShn  \\\n",
+            "0                0.0                0.0                   0.0   \n",
+            "1                0.0                0.0                   0.0   \n",
+            "2                0.0                0.0                   0.0   \n",
+            "3                0.0                0.0                   0.0   \n",
+            "4                0.0                0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_BrkComm  Exterior 1st_BrkFace  Exterior 1st_CBlock  \\\n",
+            "0                   0.0                   1.0                  0.0   \n",
+            "1                   0.0                   0.0                  0.0   \n",
+            "2                   0.0                   0.0                  0.0   \n",
+            "3                   0.0                   1.0                  0.0   \n",
+            "4                   0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Exterior 1st_CemntBd  Exterior 1st_HdBoard  Exterior 1st_ImStucc  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_MetalSd  Exterior 1st_Plywood  Exterior 1st_PreCast  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 1st_Stone  Exterior 1st_Stucco  Exterior 1st_VinylSd  \\\n",
+            "0                 0.0                  0.0                   0.0   \n",
+            "1                 0.0                  0.0                   1.0   \n",
+            "2                 0.0                  0.0                   0.0   \n",
+            "3                 0.0                  0.0                   0.0   \n",
+            "4                 0.0                  0.0                   1.0   \n",
+            "\n",
+            "   Exterior 1st_Wd Sdng  Exterior 1st_WdShing  Exterior 2nd_AsphShn  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   1.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_Brk Cmn  Exterior 2nd_BrkFace  Exterior 2nd_CBlock  \\\n",
+            "0                   0.0                   0.0                  0.0   \n",
+            "1                   0.0                   0.0                  0.0   \n",
+            "2                   0.0                   0.0                  0.0   \n",
+            "3                   0.0                   1.0                  0.0   \n",
+            "4                   0.0                   0.0                  0.0   \n",
+            "\n",
+            "   Exterior 2nd_CmentBd  Exterior 2nd_HdBoard  Exterior 2nd_ImStucc  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   0.0                   0.0                   0.0   \n",
+            "2                   0.0                   0.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   0.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_MetalSd  Exterior 2nd_Other  Exterior 2nd_Plywood  \\\n",
+            "0                   0.0                 0.0                   1.0   \n",
+            "1                   0.0                 0.0                   0.0   \n",
+            "2                   0.0                 0.0                   0.0   \n",
+            "3                   0.0                 0.0                   0.0   \n",
+            "4                   0.0                 0.0                   0.0   \n",
+            "\n",
+            "   Exterior 2nd_PreCast  Exterior 2nd_Stone  Exterior 2nd_Stucco  \\\n",
+            "0                   0.0                 0.0                  0.0   \n",
+            "1                   0.0                 0.0                  0.0   \n",
+            "2                   0.0                 0.0                  0.0   \n",
+            "3                   0.0                 0.0                  0.0   \n",
+            "4                   0.0                 0.0                  0.0   \n",
+            "\n",
+            "   Exterior 2nd_VinylSd  Exterior 2nd_Wd Sdng  Exterior 2nd_Wd Shng  \\\n",
+            "0                   0.0                   0.0                   0.0   \n",
+            "1                   1.0                   0.0                   0.0   \n",
+            "2                   0.0                   1.0                   0.0   \n",
+            "3                   0.0                   0.0                   0.0   \n",
+            "4                   1.0                   0.0                   0.0   \n",
+            "\n",
+            "   Exter Qual_Fa  Exter Qual_Gd  Exter Qual_TA  Exter Cond_Fa  Exter Cond_Gd  \\\n",
+            "0            0.0            0.0            1.0            0.0            0.0   \n",
+            "1            0.0            0.0            1.0            0.0            0.0   \n",
+            "2            0.0            0.0            1.0            0.0            0.0   \n",
+            "3            0.0            1.0            0.0            0.0            0.0   \n",
+            "4            0.0            0.0            1.0            0.0            0.0   \n",
+            "\n",
+            "   Exter Cond_Po  Exter Cond_TA  Foundation_CBlock  Foundation_PConc  \\\n",
+            "0            0.0            1.0                1.0               0.0   \n",
+            "1            0.0            1.0                1.0               0.0   \n",
+            "2            0.0            1.0                1.0               0.0   \n",
+            "3            0.0            1.0                1.0               0.0   \n",
+            "4            0.0            1.0                0.0               1.0   \n",
+            "\n",
+            "   Foundation_Slab  Foundation_Stone  Foundation_Wood  Bsmt Qual_Fa  \\\n",
+            "0              0.0               0.0              0.0           0.0   \n",
+            "1              0.0               0.0              0.0           0.0   \n",
+            "2              0.0               0.0              0.0           0.0   \n",
+            "3              0.0               0.0              0.0           0.0   \n",
+            "4              0.0               0.0              0.0           0.0   \n",
+            "\n",
+            "   Bsmt Qual_Gd  Bsmt Qual_Po  Bsmt Qual_TA  Bsmt Cond_Fa  Bsmt Cond_Gd  \\\n",
+            "0           0.0           0.0           1.0           0.0           1.0   \n",
+            "1           0.0           0.0           1.0           0.0           0.0   \n",
+            "2           0.0           0.0           1.0           0.0           0.0   \n",
+            "3           0.0           0.0           1.0           0.0           0.0   \n",
+            "4           1.0           0.0           0.0           0.0           0.0   \n",
+            "\n",
+            "   Bsmt Cond_Po  Bsmt Cond_TA  Bsmt Exposure_Gd  Bsmt Exposure_Mn  \\\n",
+            "0           0.0           0.0               1.0               0.0   \n",
+            "1           0.0           1.0               0.0               0.0   \n",
+            "2           0.0           1.0               0.0               0.0   \n",
+            "3           0.0           1.0               0.0               0.0   \n",
+            "4           0.0           1.0               0.0               0.0   \n",
+            "\n",
+            "   Bsmt Exposure_No  BsmtFin Type 1_BLQ  BsmtFin Type 1_GLQ  \\\n",
+            "0               0.0                 1.0                 0.0   \n",
+            "1               1.0                 0.0                 0.0   \n",
+            "2               1.0                 0.0                 0.0   \n",
+            "3               1.0                 0.0                 0.0   \n",
+            "4               1.0                 0.0                 1.0   \n",
+            "\n",
+            "   BsmtFin Type 1_LwQ  BsmtFin Type 1_Rec  BsmtFin Type 1_Unf  \\\n",
+            "0                 0.0                 0.0                 0.0   \n",
+            "1                 0.0                 1.0                 0.0   \n",
+            "2                 0.0                 0.0                 0.0   \n",
+            "3                 0.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 0.0   \n",
+            "\n",
+            "   BsmtFin Type 2_BLQ  BsmtFin Type 2_GLQ  BsmtFin Type 2_LwQ  \\\n",
+            "0                 0.0                 0.0                 0.0   \n",
+            "1                 0.0                 0.0                 1.0   \n",
+            "2                 0.0                 0.0                 0.0   \n",
+            "3                 0.0                 0.0                 0.0   \n",
+            "4                 0.0                 0.0                 0.0   \n",
+            "\n",
+            "   BsmtFin Type 2_Rec  BsmtFin Type 2_Unf  Heating_GasA  Heating_GasW  \\\n",
+            "0                 0.0                 1.0           1.0           0.0   \n",
+            "1                 0.0                 0.0           1.0           0.0   \n",
+            "2                 0.0                 1.0           1.0           0.0   \n",
+            "3                 0.0                 1.0           1.0           0.0   \n",
+            "4                 0.0                 1.0           1.0           0.0   \n",
+            "\n",
+            "   Heating_Grav  Heating_OthW  Heating_Wall  Heating QC_Fa  Heating QC_Gd  \\\n",
+            "0           0.0           0.0           0.0            1.0            0.0   \n",
+            "1           0.0           0.0           0.0            0.0            0.0   \n",
+            "2           0.0           0.0           0.0            0.0            0.0   \n",
+            "3           0.0           0.0           0.0            0.0            0.0   \n",
+            "4           0.0           0.0           0.0            0.0            1.0   \n",
+            "\n",
+            "   Heating QC_Po  Heating QC_TA  Central Air_Y  Electrical_FuseF  \\\n",
+            "0            0.0            0.0            1.0               0.0   \n",
+            "1            0.0            1.0            1.0               0.0   \n",
+            "2            0.0            1.0            1.0               0.0   \n",
+            "3            0.0            0.0            1.0               0.0   \n",
+            "4            0.0            0.0            1.0               0.0   \n",
+            "\n",
+            "   Electrical_FuseP  Electrical_Mix  Electrical_SBrkr  Kitchen Qual_Fa  \\\n",
+            "0               0.0             0.0               1.0              0.0   \n",
+            "1               0.0             0.0               1.0              0.0   \n",
+            "2               0.0             0.0               1.0              0.0   \n",
+            "3               0.0             0.0               1.0              0.0   \n",
+            "4               0.0             0.0               1.0              0.0   \n",
+            "\n",
+            "   Kitchen Qual_Gd  Kitchen Qual_Po  Kitchen Qual_TA  Functional_Maj2  \\\n",
+            "0              0.0              0.0              1.0              0.0   \n",
+            "1              0.0              0.0              1.0              0.0   \n",
+            "2              1.0              0.0              0.0              0.0   \n",
+            "3              0.0              0.0              0.0              0.0   \n",
+            "4              0.0              0.0              1.0              0.0   \n",
+            "\n",
+            "   Functional_Min1  Functional_Min2  Functional_Mod  Functional_Sal  \\\n",
+            "0              0.0              0.0             0.0             0.0   \n",
+            "1              0.0              0.0             0.0             0.0   \n",
+            "2              0.0              0.0             0.0             0.0   \n",
+            "3              0.0              0.0             0.0             0.0   \n",
+            "4              0.0              0.0             0.0             0.0   \n",
+            "\n",
+            "   Functional_Sev  Functional_Typ  Fireplace Qu_Fa  Fireplace Qu_Gd  \\\n",
+            "0             0.0             1.0              0.0              1.0   \n",
+            "1             0.0             1.0              0.0              1.0   \n",
+            "2             0.0             1.0              0.0              1.0   \n",
+            "3             0.0             1.0              0.0              0.0   \n",
+            "4             0.0             1.0              0.0              0.0   \n",
+            "\n",
+            "   Fireplace Qu_Po  Fireplace Qu_TA  Garage Type_Attchd  Garage Type_Basment  \\\n",
+            "0              0.0              0.0                 1.0                  0.0   \n",
+            "1              0.0              0.0                 1.0                  0.0   \n",
+            "2              0.0              0.0                 1.0                  0.0   \n",
+            "3              0.0              1.0                 1.0                  0.0   \n",
+            "4              0.0              1.0                 1.0                  0.0   \n",
+            "\n",
+            "   Garage Type_BuiltIn  Garage Type_CarPort  Garage Type_Detchd  \\\n",
+            "0                  0.0                  0.0                 0.0   \n",
+            "1                  0.0                  0.0                 0.0   \n",
+            "2                  0.0                  0.0                 0.0   \n",
+            "3                  0.0                  0.0                 0.0   \n",
+            "4                  0.0                  0.0                 0.0   \n",
+            "\n",
+            "   Garage Finish_RFn  Garage Finish_Unf  Garage Qual_Fa  Garage Qual_Gd  \\\n",
+            "0                0.0                0.0             0.0             0.0   \n",
+            "1                0.0                1.0             0.0             0.0   \n",
+            "2                0.0                1.0             0.0             0.0   \n",
+            "3                0.0                0.0             0.0             0.0   \n",
+            "4                0.0                0.0             0.0             0.0   \n",
+            "\n",
+            "   Garage Qual_Po  Garage Qual_TA  Garage Cond_Fa  Garage Cond_Gd  \\\n",
+            "0             0.0             1.0             0.0             0.0   \n",
+            "1             0.0             1.0             0.0             0.0   \n",
+            "2             0.0             1.0             0.0             0.0   \n",
+            "3             0.0             1.0             0.0             0.0   \n",
+            "4             0.0             1.0             0.0             0.0   \n",
+            "\n",
+            "   Garage Cond_Po  Garage Cond_TA  Paved Drive_P  Paved Drive_Y  \\\n",
+            "0             0.0             1.0            1.0            0.0   \n",
+            "1             0.0             1.0            0.0            1.0   \n",
+            "2             0.0             1.0            0.0            1.0   \n",
+            "3             0.0             1.0            0.0            1.0   \n",
+            "4             0.0             1.0            0.0            1.0   \n",
+            "\n",
+            "   Sale Type_CWD  Sale Type_Con  Sale Type_ConLD  Sale Type_ConLI  \\\n",
+            "0            0.0            0.0              0.0              0.0   \n",
+            "1            0.0            0.0              0.0              0.0   \n",
+            "2            0.0            0.0              0.0              0.0   \n",
+            "3            0.0            0.0              0.0              0.0   \n",
+            "4            0.0            0.0              0.0              0.0   \n",
+            "\n",
+            "   Sale Type_ConLw  Sale Type_New  Sale Type_Oth  Sale Type_VWD  \\\n",
+            "0              0.0            0.0            0.0            0.0   \n",
+            "1              0.0            0.0            0.0            0.0   \n",
+            "2              0.0            0.0            0.0            0.0   \n",
+            "3              0.0            0.0            0.0            0.0   \n",
+            "4              0.0            0.0            0.0            0.0   \n",
+            "\n",
+            "   Sale Type_WD   Sale Condition_AdjLand  Sale Condition_Alloca  \\\n",
+            "0            1.0                     0.0                    0.0   \n",
+            "1            1.0                     0.0                    0.0   \n",
+            "2            1.0                     0.0                    0.0   \n",
+            "3            1.0                     0.0                    0.0   \n",
+            "4            1.0                     0.0                    0.0   \n",
+            "\n",
+            "   Sale Condition_Family  Sale Condition_Normal  Sale Condition_Partial  \n",
+            "0                    0.0                    1.0                     0.0  \n",
+            "1                    0.0                    1.0                     0.0  \n",
+            "2                    0.0                    1.0                     0.0  \n",
+            "3                    0.0                    1.0                     0.0  \n",
+            "4                    0.0                    1.0                     0.0  \n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Try different scaling techniques:\n",
+        "# - StandardScaler\n",
+        "# - MinMaxScaler\n",
+        "# - RobustScaler\n",
+        "# Decide based on the distribution of features.\n",
+        "\n",
+        "# print(df.head())\n",
+        "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\n",
+        "# df_ss = df.copy()\n",
+        "# df_mm = df.copy()\n",
+        "# df_rs = df.copy()\n",
+        "\n",
+        "# scaler = StandardScaler()\n",
+        "# df_ss[col_num] = scaler.fit_transform(df_ss[col_num])\n",
+        "# print(df_ss.head())\n",
+        "\n",
+        "# col_num = df1.select_dtypes(exclude='object').columns\n",
+        "scaler = MinMaxScaler()\n",
+        "df[col_num] = scaler.fit_transform(df[col_num])\n",
+        "print(df.head())\n",
+        "\n",
+        "# scaler = RobustScaler()\n",
+        "# df_rs[col_num] = scaler.fit_transform(df_rs[col_num])\n",
+        "# print(df_rs.head())\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B8NozkS2kCDE"
+      },
+      "source": [
+        "## 🔹 Step 7: Feature Selection & Feature Creation 💡"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "irzYn2YHkFuu",
+        "outputId": "647e3753-3f28-4f7b-fbce-7a5ae5ccc5ac"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Index(['MS SubClass', 'Lot Frontage', 'Lot Area', 'Overall Qual',\n",
+            "       'Overall Cond', 'Year Built', 'Year Remod/Add', 'Mas Vnr Area',\n",
+            "       'BsmtFin SF 1', 'BsmtFin SF 2',\n",
+            "       ...\n",
+            "       'Sale Type_VWD', 'Sale Type_WD ', 'Sale Condition_AdjLand',\n",
+            "       'Sale Condition_Alloca', 'Sale Condition_Family',\n",
+            "       'Sale Condition_Normal', 'Sale Condition_Partial', 'HouseRenew',\n",
+            "       'Quality_x_Size', 'Log_LotArea'],\n",
+            "      dtype='object', length=250)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Create at least 2 NEW features.\n",
+        "# Examples:\n",
+        "# - Age of house: df[\"HouseAge\"] = df[\"YrSold\"] - df[\"YearBuilt\"]\n",
+        "# - Interaction: df[\"Quality_x_Size\"] = df[\"OverallQual\"] * df[\"GrLivArea\"]\n",
+        "# - Non-linear: df[\"Log_LotArea\"] = np.log1p(df[\"LotArea\"])\n",
+        "\n",
+        "df[\"HouseRenew\"] = df[\"Year Remod/Add\"] - df[\"Year Built\"]\n",
+        "df[\"Quality_x_Size\"] = df[\"Overall Qual\"] * df[\"Gr Liv Area\"]\n",
+        "df[\"Log_LotArea\"] = np.log1p(df[\"Lot Area\"])\n",
+        "\n",
+        "print(df.columns)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "neYQVXyLpYqe"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ohlGPfhykRMs"
+      },
+      "source": [
+        "## 🔹 Step 8: Outlier Handling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "l0Yx7uH5kP_H",
+        "outputId": "4bcb801e-ccbe-4b66-b371-80bbecade088"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(2051, 250)\n",
+            "(1846, 250)\n",
+            "(1084, 250)\n",
+            "(1084, 250)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Detect and handle outliers.\n",
+        "# Methods:\n",
+        "# - IQR rule\n",
+        "# - Z-score\n",
+        "# - Visualization (boxplots, scatterplots)\n",
+        "\n",
+        "z_scores = np.abs((df[col_num] - df[col_num].mean()) / df[col_num].std())\n",
+        "outliers = (z_scores > 3).any(axis=1)\n",
+        "print(df[~outliers].shape)\n",
+        "\n",
+        "Q1 = df[col_num].quantile(0.25)\n",
+        "Q3 = df[col_num].quantile(0.75)\n",
+        "IQR = Q3 - Q1\n",
+        "outliers = ((df[col_num] < (Q1 - 1.5 * IQR)) | (df[col_num] > (Q3 + 1.5 * IQR))).any(axis=1)\n",
+        "print(df[outliers].shape)\n",
+        "print(df[~outliers].shape)\n",
+        "\n",
+        "df = df[~outliers]\n",
+        "print(df.shape)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nDirz02_kU1e"
+      },
+      "source": [
+        "## 🔹 Step 9: Skewness Handling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "Bz0Kke71kTxQ",
+        "outputId": "48c01b20-9e1e-47f9-cd82-7682cd591927"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "MS SubClass\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.886349 -0.013707  1.504796e-31\n",
+            "1                 Log1p  0.637040 -0.551829  1.237427e-19\n",
+            "2                  Sqrt  0.054876 -1.504534  4.793558e-23\n",
+            "3       Box-Cox λ=0.075 -0.305718 -1.871246  9.786283e-39\n",
+            "4  Yeo–Johnson λ=-2.499  0.170848 -1.395905  5.544180e-21\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Lot Frontage\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original -0.164109  0.435822  1.203745e-03\n",
+            "1                Log1p -0.300100  0.491827  1.242747e-06\n",
+            "2                 Sqrt -0.736394  1.091410  1.104070e-33\n",
+            "3      Box-Cox λ=1.175  0.020738  0.384830  3.393478e-02\n",
+            "4  Yeo–Johnson λ=2.291  0.011579  0.423758  1.712140e-02\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Lot Area\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.193318  0.491558  1.458799e-04\n",
+            "1                 Log1p  0.148704  0.458555  1.175331e-03\n",
+            "2                  Sqrt -0.503415  0.730164  6.742263e-16\n",
+            "3       Box-Cox λ=0.874  0.025518  0.446418  1.046792e-02\n",
+            "4  Yeo–Johnson λ=-3.504 -0.005552  0.381210  3.745513e-02\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Overall Qual\n",
+            "             Transform  Skewness  Kurtosis      JB-p\n",
+            "0             Original -0.041368 -0.626607  0.000121\n",
+            "1                Log1p -0.225678 -0.518858  0.000023\n",
+            "2                 Sqrt -0.315054 -0.371808  0.000006\n",
+            "3      Box-Cox λ=0.981 -0.051116 -0.622330  0.000126\n",
+            "4  Yeo–Johnson λ=1.115 -0.020577 -0.631373  0.000118\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Overall Cond\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  1.315890  0.667405  5.008987e-73\n",
+            "1                 Log1p  1.234379  0.560111  1.401963e-63\n",
+            "2                  Sqrt  1.199503  0.539343  5.021128e-60\n",
+            "3      Box-Cox λ=-2.344 -0.149502  3.012413  1.320834e-90\n",
+            "4  Yeo–Johnson λ=-8.828 -0.118200  2.786273  2.044973e-77\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Year Built\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original -0.984449  0.007311  9.524967e-39\n",
+            "1                Log1p -1.179310  0.742862  1.061442e-60\n",
+            "2                 Sqrt -1.273533  1.229468  3.515338e-79\n",
+            "3      Box-Cox λ=2.968 -0.448363 -1.353507  1.398393e-26\n",
+            "4  Yeo–Johnson λ=6.632 -0.387527 -1.418956  2.295898e-26\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Year Remod/Add\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original -0.883418 -0.752479  6.751235e-37\n",
+            "1                Log1p -1.048502 -0.383627  2.675912e-45\n",
+            "2                 Sqrt -1.444760  0.931803  3.941073e-91\n",
+            "3      Box-Cox λ=0.558 -1.337485  0.555913  6.158434e-74\n",
+            "4  Yeo–Johnson λ=3.858 -0.499636 -1.331199  6.700992e-28\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Mas Vnr Area\n",
+            "               Transform  Skewness  Kurtosis          JB-p\n",
+            "0               Original  1.246372  0.368818  5.276675e-63\n",
+            "1                  Log1p  1.177910  0.125587  2.588339e-55\n",
+            "2                   Sqrt  0.688912 -1.175103  6.879950e-33\n",
+            "3       Box-Cox λ=-0.084  0.315624 -1.888920  1.251314e-39\n",
+            "4  Yeo–Johnson λ=-16.227  0.608547 -1.439779  1.381142e-35\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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jrruPPfaYaXWqLT1/+uknuffee821Vv/+GzZsaLr7ZqStVbTliu6nrSibNGkiS5cudcs5aquZkSNHmuuIxhNtiaotubJqffn111+bbmB6vtq1K2NXN0d39G+++caUX6/7eo2/5557Lun2rF2ONZZodzK9zmlL4YwtlC4X7xz0Pfv27StLliwxLdR035o1a5qWO1dC44t2qUx/ffYU7bKnP9PL0fPR1rL6Gepnrz+z06dP5/h99HPQzzg9/Xy0Jba2Bj5x4oRzvbYMzth6T9fpeWpX9uxoF7+vvvrKdPnX3w8AAOB+JKXgUdoEX7t06Bf37du3y+OPP266iz300EPOffSL+N133y0TJkww4xyNHz/efEl/9tlnzQ2A0i/3mhDShM/zzz/vfG2fPn3Me+hNQ548ebI8Dx2vSW+WtKm/fmm+HE18abJJm/J37txZXn75ZdPdrXv37s5uZzoWlXZXW716tenG5viirfvoe2hXl5zQL88LFy40/88sKaQ3UvoZ5mS5nD/++MMk0/RGMSO9Mfj222+zfb1je8bX62Dt2tUi/ev189PElHaT1C/+v/32m0yfPt2M3ZWYmJjp+CfadWPIkCGXLQcA79Bk9Jtvvil79+41iR/tBrVlyxZzLdR1mlzSv3cdV+jVV181yRu97i5evNh5DO3KrRUJn3/+uUlujRo1yiS4NA6k3y+3dLygpKQkM+6dxpXbbrtNRo8eLZ06dbpkX40pmkTTbl96vjo+kV7DNV5l1K9fP/nuu+9M8k1jmXZD16SRg15btYuXjk2k5de4oPFj3bp1VxTvMibM9DPScx83bpz5nLSruXY7zymtiMkqvmjMyml80YoDd9HKjQMHDpgEp1Zy6O+JLvpck0C5lZKSYpKcumRHv4fokjGxldGCBQvMZ6Q/RwAA4CE5aE0F5Lr7XsYlPDzcmjNnjsu+S5YsMdtefPFFl/XazU67hO3evdu5LjEx0XRDWLNmjbNrwMSJEy97Ph9++KHZd8KECTk6fz2m7v/OO+8412mXv6ZNm1qFCxc2XRQcHnzwQatgwYLWzz//bL388svmdVqmnJo6dap5TalSpUzXg5x+lpktl6Pd4XS/uXPnXrLt2WefNdu0C0RWHOXbt2/fJdsaNWpkNWnSxPn8woULVt++fU33DMf55cmTx5o2bdolr9VuJUWKFHF2H8yq+ycA3+D429broV4DHd2cateu7XINSUtLs2666SaratWqznX9+/c3f9//+c9/nOtOnDhhVa5c2apUqdIlXa2upPve1q1bzbEfeeQRl/2eeeYZs/7LL7906b6n6zSeOBw+fNjEqaefftq5znE9io2NNeVxGDBggLmmHTt2zDxfvHjxZa9bVxLvdD/tzpx+3XfffWfWT548OUefz/bt253xRWPnH3/84bJ97969OY4vWXXDy0p23fcWLVpktpUoUcL8buhnrIv+X8us5bxSu3btsvLnz2916dLlsvuOHDnSvP/KlSuz3a9BgwZW6dKlr+h3EgAAXBkGOodHTZkyxXQLc9SOv/POO6YWW1sSaXN4pYOMaysnnYEuPe3eoN0NdHBsR230Cy+8YAYn1YFNtZZTa8Azvi4z2mJH5aSVlOOcdPDTBx980LlOu/zpe+k6bR3l6OqnLQV0diGtbf/555/NTHY6uGxOaQsDHRBWPxftuvb999+b2mIH7QqyfPlycQdH10ft5pCRo2uC7pPZ9py83vE5K/2ZavcZPX/tpqPb3333XdPaQD9bbT2RfiBax4xIAHyftm7S67N2udJWP9qV+ssvvzQTSWjrz/Tdp/QaoK2LtKVm2bJlzfVVW2Y2a9bMuY/OzqktZrQVpbaiye2MenpslbHVkcYT7SKnXQR1ZjiHGjVqyC233OJ8rl3ztOWSdi/MSM9Pu9Q56Ou07NoKVK/Zjq7KGqO0u3dm3cSvJN45ulan786s7xMREZHp+WVGW6fqeWirX/056Hukf2+9Fuc0vmiZ3EXjt9LfE21hq10ZVYsWLcyMtNoqTL8v5JT+Hmqc0VbVY8aMyXZfHfhdZ+7TmQn1/bKi8Vwn7NCB/Rm8HwAAzyEpBY/SG4/0Xb00oVO/fn3zpVuTOjrTm36h1+5fGRNG1atXN4+63UH3164IjRo1MkmO2bNnu9wkaNJEu/Olp1+69Uu8Sn+jlB19Tx2HI+MX0czOScelmDRpkvlCrONP6f9zSsco0dnr9MZBu3Tol9/33nvPJSmlYzDpciUcXRMc9CZIb7YcYzllNraKdgtRmY335HC516d/rd4YaFdH7TqpN5xKbwL0hlC7XerPX7sCadcWvWHSabv54g/4B72mavJGu3XpdU9n7tTGPTp2ny6Z0e5tmpTS62dMTMwl29NfXzUppV2xMqNxIKtxi/S1eh3RxEbGOKBJo/TXbpV+bD0H7cKnXYkzyriv7qcc+2oliXat04SHJqu0C7Mm33VGV0ci/0ri3ZWeX2aJGh0fS89JuxXqmIwaX9InpTSOauLrSpw7d84kIdPT+JJdF/qMHLHi5ptvdiakHOXVZKXOyJdTOq6Zdm/UZKYm9fTzzYp249dxrPT366233sr2uDrjnqLrHgAAnkVSCrbSmwVNSjiSFTpY6ZXScUgcSRA9hg4k66BfuNNPBa30Rkm/jCtNAHmC45z0RkEHWc04uHd2raR0rCodY0vH09Cadx1fSlshZJdoy4pjamttEaA3Rg46CLGOc+JIbungrRnpOr3Ry6qVlEr/+vQ3Eo51moR00DG1tBbakZBy0OSbtmLQ89Ebx4EDB5py689R1ynH+Fh6zH379mV6YwbAd+i4O+qZZ54xLXIykzFRdDlZJeM1+aOtU7OTvrIiO1klUjIOOp6TffU9tSWSJtp1vCmNCzrIuY5VpesyXgvdfX4ZzZ8/38QOrQRQOvmGtmTTGKVjADoSOhkHa8+KxgdNCGrCKH1rM6XjiemA8jnlSBxpQjMjHa/xcuMbpterVy/TOk2TSNm1fNq/f79JzmnM1RZrl2s5rZ+fJl4bNGiQ43MBAABXjqQUbKez7ChHSx5NmKxYscK0Ykr/JVFrNB3bHbRrm36p1sTT1q1bTXcvTTTpl8zsurppF0L9cvnhhx+ahNjlbg70PfW99EYrfeudzM5JZ0LSGldNruiXYu1auH79+ktmGMxIEy96A6MDgWtCynHToIPa6hdybVGWVaItK44bla5du7p0jXHUSmsrBa3R1hmiMtKWDvXq1cv2+I7t+vr0CSgdsFZvdLR7i4N218xsRkTHYLmO3wNNOmnrgPTJxfQJLP3ZHjt2LAelB+At2v1WaVexy7W80evnzp07L1mf8fqaVbcyRwulrI6t122tsHC0PnJcj/Q6kv7a7Sk62LsuWrmgiQ1taaMDZmu8upJ4d7W00kNb3TpigcYXrazQig9tletI1GR27c2MDkCurb+0G1/Gn42jQiSntMu6/q5ol86MNJ5onMoJHSBeW0zrRBnpu9tnpAPDa0JKW/lqq9zLtT7WGK6D4Ov3DQAA4GFXOAYVkCNZDVStg4U7BjI9fvy4y8CvL730ksu+DzzwgMvAr/ra+vXrm4FwdaBxHQhVj9OjR48cndOCBQvM++hxz58/f8n2zz//3Pr4449dBjqfP3++c7u+5uabb3YZ6Pzvv/+2ypYtazVu3NgM7P3ZZ5+Z1w0fPvyy5zN27FhTvp9++sm57tChQ2bg3EGDBjnXHThwwFq+fHmOlpx47LHHrAIFCrgMVr5ixQpz3ukHIdfPe8eOHeb909PB2OvWrWvK6zBkyBBTlh9//NG5rlatWlbx4sWtI0eOONfpa3TgWB3UXI/v+Nx1gOD0S79+/cz5vPLKK9Ynn3ySo3IBsFfGQcabN29u/uYzXjMcA4hnHOh87dq1znUnT560rr32WrcNdN67d2+X/QYOHJjpQOdt27bN9Ji6XC6e6QDe6QfyPnr0qMtA6OkHGn/99devKN4p3a9Pnz6XnJ+ed7du3bL9XNavX29e75g8wkEHoo+JiXE+/+eff3IcX7R87hroXLVv397EO40zDhpDdN0TTzzhXHfq1Cmzz59//uny+nHjxpnjP/fcc9meh/5uaYzWuLNp06YcnfuTTz5pjp3+5wEAADyDpBQ8wvElfsSIEda///1vs7z66qsmIaHrBw8e7NxXb0Buv/1284VcbySmTJlivqzqfnrz4pCUlGT2SX9ToTMY6X5Lly7N0Xk9//zzZv/rr7/eGjZsmDVr1iwzo5zOGpU+CXX69GmrevXqJumlszDpTEd6k5Jxtr+uXbua2X7Sf6nWWZ90Viq9OcqK3rjoDZjO5pSRrtNtnqLJKJ3x6LrrrrMmTZpkbo6KFSt2yaxZjlmZMt78aOJOfw4tWrSw3nzzTfPlXWd16tWrl8t+OnOhvl7fRxNw+l46e2FmM09lxOx7gO/LmBDSBIxeS/T6otd4vT7oLGdt2rSx6tSp49wvJSXFioqKsiIjI62hQ4eaWVHr1atnris6K9vVnIPSa5ZeP+6//34TTxzPO3To4LKfu5NSWg6tdNEEmCaDNKl+ww03WBEREdYvv/xyRfHuapNS3bt3N5+vJmTSc8TM3377zfIU/Znr0qlTJ/NeDz/8sHNdevr7opU8Orvd6NGjzaL/v+aaa6zff//9ks9ZY3bG2fv083Z8x0i/6O+Yg+Pz1fPIuJ9WgmSklSf6+5l+NlkAAOA5JKXgEY4v8ekXTd7ojYe2xslYm6zTgev02mXKlDEJHf2iqckix36bN2+2wsLCTAuajF8eGzVqZF6nrZZyQqeA1i+pOkW2HlO/ALdr18768MMPXfbTVkvaCqtkyZImOaVJGy2Xg+6v5dJkW3raikpvGrQ1kaM1UEaOFlWZ3YDNmDHD4wmZH374wWrVqpVVsGBBq2jRolbnzp1dvsRnl5RS+kVef5Y6dXq5cuVMS6nMyrps2TJzc5f+M5w+ffplz4+kFOD7MksI7dmzxyTro6OjzbVcW5Ledddd1gcffHDJfvfee6+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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "BsmtFin SF 1\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.427973 -0.924887  2.658369e-16\n",
+            "1                 Log1p  0.345378 -1.087329  5.304488e-17\n",
+            "2                  Sqrt -0.173369 -1.576481  2.789634e-26\n",
+            "3       Box-Cox λ=0.159 -0.557835 -1.535081  4.788046e-36\n",
+            "4  Yeo–Johnson λ=-3.521  0.110621 -1.448212  8.908563e-22\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "BsmtFin SF 2\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "Bsmt Unf SF\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.625519 -0.508066  1.312548e-18\n",
+            "1                 Log1p  0.404943 -0.766226  6.437478e-13\n",
+            "2                  Sqrt -0.194340 -0.312149  3.653141e-03\n",
+            "3       Box-Cox λ=0.469 -0.286105 -0.144394  3.838770e-04\n",
+            "4  Yeo–Johnson λ=-1.560  0.068859 -0.939326  1.446187e-09\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Total Bsmt SF\n",
+            "              Transform  Skewness  Kurtosis      JB-p\n",
+            "0              Original  0.235075 -0.264327  0.001402\n",
+            "1                 Log1p  0.125004 -0.241257  0.065479\n",
+            "2                  Sqrt -0.228389  0.245399  0.002307\n",
+            "3       Box-Cox λ=0.730  0.002251 -0.096941  0.808409\n",
+            "4  Yeo–Johnson λ=-1.070  0.002995 -0.170016  0.520173\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "1st Flr SF\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.506196 -0.539185  1.248151e-13\n",
+            "1                 Log1p  0.413799 -0.658863  1.058932e-11\n",
+            "2                  Sqrt  0.167969 -0.732494  4.273412e-07\n",
+            "3       Box-Cox λ=0.252 -0.019610 -0.642286  8.687160e-05\n",
+            "4  Yeo–Johnson λ=-3.964  0.060979 -0.843186  7.605624e-08\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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bt67LusKFC0uJEiXSrQcABEjg6ptvvnFciXj99dclMjLSFy8LAAhAvu4zUlNTTRF2rY2oGjVqJHv37pVZs2a5DVxRZBcAAgvnIwAQuHxa48q5fggAAFboM3Q69Nq1a7usu+mmm+Q///mP28doHUV72ol9xJXWyQIAhGbfsnbtWq88LwDAB4Gru+++WxISEsxVDf09M0uXLvV2cxAEKKIOBC9/9Bk6o+C+fftc1v3yyy9SqVIlt4+hyC4ABA7ORwAgsHk9cBUVFWWKINp/BwDASn3G008/La1atTKpgvfff79s3bpV3n33XbMAAAIf5yMAENjy+nI4LqmCAACr9RnNmjWTZcuWmfS/8ePHS5UqVWT69OnSt29fn7w+AMC7OB8BgMDm0xpXly9fNjN52KefPXLkiDlZ0NoinTt39mVTAJ+lJ5LaCFi/z7jjjjvMAvhq5kAA/sH5CAAEnjy+fLGePXvKvHnzzO/nz5+X5s2by9SpU816nYocAAA7+gwAgKfRtwBA4PFp4Grnzp3Stm1b8/vHH38s0dHR5iqHdh5vvPGGL5sCALA4+gwAgKfRtwBA4PFpquClS5ekSJEi5vcvv/zSzOqRJ08eufnmm02HAQCAHX0GAMDT6FsQjChNgmDn0xFX1atXl+XLl8uxY8dk1apVjjzyM2fOmOlpAQCwo88AAHgafQsABB6fBq7i4uJk5MiRUrlyZWnRooW0bNnScbWjUaNGvmwKAMDi6DMAAJ5G3wIAgcenqYL33nuvtGnTRk6ePCkNGjRwrO/YsaPcddddvmwKAMDi6DNCDzPxAfA2+haEGn/0rdfzmqQ2wu+BK6UFEHVxprN5AACQFn0GAMDT6FsAILD4NHCVnJwskyZNkjVr1pg88tTUVJf7f/31V182BwBgYfQZAABPo28BgMDj08DVI488IuvWrZOHHnpIypYtK2FhYb58eQBAAKHPAIJbVqkkpIvAG+hbgMBFvxG6fBq4+uKLL+Szzz6T1q1b+/Jl4SXUIgHgTfQZAABPo28BgMDj01kFixUrJsWLF/flSwIAAhR9BgDA0+hbACDw+HTE1YQJE8wUtHPnzpVChQr58qUBAAGGPgOhihHNgPfQtwBA4PFp4Grq1Kly8OBBKVOmjFSuXFny5cvncv/OnTt92RwAgIXRZwAAPI2+BQACj08DV7169fLlywEAAhh9BgDA0+hbACDw+DRwNWbMGF++HAAggNFnAAA8jb4FAAKPT4uzq/Pnz8v7778vsbGx8scffziG5J44ccLXTQEAWBx9BgDA0+hbACCw+HTE1ffffy+dOnWSqKgoOXz4sAwePNjM6rF06VI5evSozJs3z5fNAQBYGH0GAMDT6FsAIPD4dMTViBEjZMCAAbJ//34pUKCAY/3tt98u69ev92VTAAAWR58BAPA0+hYACDw+DVxt27ZNHn300XTry5UrJ6dOnfJlUwAAFkefAQDwNPoWAAg8Pk0VjIiIkKSkpHTrf/nlFylVqpQvmwIAsDj6jOATt3Cbv5sAL/zdxvdu5rO2ANeLvgUAAo9PR1zdeeedMn78ePnrr7/M7bCwMJNL/vzzz8s999zjy6YAACyOPgMA4Gn0LQAQeHwauJo6dapcvHjRXM24fPmytGvXTqpXry5FihSRl156yZdNAQBYHH0GAMCKfcvMmTOlfv36EhkZaZaWLVvKF1984fW2A0Co8mmqoM7esXr1avn222/lu+++M51G48aNzcweAAA4o88AAFixbylfvrxMmjRJatSoITabTebOnSs9e/aUXbt2SZ06dbzafvgfae/exf6FXwNXqampkpCQYKaa1alndVhulSpVJDo62hzw9TYAAIo+AwBg1b6lR48eLrd1pJaOwtq8eTOBKwAI1FRB7Qg0n/yRRx6REydOSL169cxB/ciRI2Y62rvuussXzQAABAD6DABAoPQt165dk4ULF0pycrJJGQQABOiIK72ysX79elmzZo106NDB5b6vv/5aevXqJfPmzZN+/fr5ojkIYgwtBQIffQYAwOp9y549e0yg6sqVK3LDDTfIsmXLpHbt2m63T0lJMYtdRjMbAvDeuSAz4AY2n4y4WrBggbzwwgvpOgl16623yqhRo+Sjjz7yRVMAABZHnwEAsHrfUrNmTdm9e7ds2bJFHnvsMenfv7/8+OOPbrePj4839bXsS4UKFXL9XgAg1PgkcPX9999L165d3d7frVs3UxwRAAD6DACA1fuW/Pnzm9kImzRpYoJSDRo0kNdff93t9rGxsZKYmOhYjh07luP3AAChyiepgn/88YeUKVPG7f16359//umLpgAALI4+AwAQaH2LFn53TgVMKyIiwiwAAIsGrrRoYd687l8qPDxc/v77b180BQBgcfQZAAAr9y06ekpHaFWsWFEuXLgg8+fPl7Vr18qqVas82GIAgE8DVzqLh87W4e4qQ2ZXJ+BfFDsH4Gv0GQAAK/ctZ86cMUXcT548aepV1a9f3wStbrvtNg+2GADg08CVFivMirdmh5o0aZK5KvLUU0/J9OnTvfIaAIDg6DMAAMHJk33LBx984IEWAQAsFbiaM2eO+MO2bdvknXfeMVdBAACBwV99BgAgeNG3AEDg8kngyh8uXrwoffv2lffee08mTpzo7+YAPk3jHN+7mU/bAgAAAACANwRt4Gro0KHSvXt36dSpE4ErAAAQ8rjgAQBAztF/+l9QBq4WLlwoO3fuNKmC2aHFGJ0LMiYlJXmxdUDwFuvnwA0AAAAA8KQ8EmSOHTtmCrF/9NFHUqBAgWw9Jj4+3swIYl8qVKjg9XYCAKxJJ/UICwuT4cOH+7spAAAAQMgLuhFXO3bsMFPUNm7c2LHu2rVrsn79ennrrbfMyKrw8HCXx+isgyNGjHAZcUXwCgBCD5N6eH9kJryHfQ8AAIJR0AWuOnbsKHv27HFZN3DgQKlVq5Y8//zz6YJWKiIiwiwAgNDFpB4AAACA9QRd4KpIkSJSt25dl3WFCxeWEiVKpFsPAIAdk3oAAAAA1hN0gSsAAHKKST0Q6kgzBID/wzER2cWMg74REoGrtWvX+rsJlsaB2Zr4uwC+ndRj9erVOZrUY9y4cV5vGwAAABDqgm5WQQAAcjupR968ec2ybt06eeONN8zvOsFHWjqpR2JiomPR4BcAAAAAzwuJEVcAALjDpB4AAIQWMhuAwELgCgAQ0pjUAwAAALAuUgUBAAAAAABgSYy4AgAgDSb1AAAACB6khwY2RlwBAAAAAADAkghcAQAAAAAAwJJIFQQAAEBApXWM793Mp20BAAD+w4grAAAAAAAAWBKBKwAAAAAAAFgSgSsAAAAAAABYEoErAAAAAAAAWBKBKwAAAAAAAFgSswoCAAAg15j9DwAAeBOBKwAew8kLAAAAAMCTSBUEAAAAAACAJRG4AgAAAIBsio+Pl2bNmkmRIkWkdOnS0qtXL9m3b5+/mwUAQYtUQcCL6XHefKy3XpOUPgAAAPfWrVsnQ4cONcGrv//+W1544QXp3Lmz/Pjjj1K4cGF/Nw8Agg6BKwAAAADIppUrV7rcTkhIMCOvduzYIbfccovf2gUAwYrAFQAAAAIKk4HAShITE83P4sWLu90mJSXFLHZJSUk+aRsA/yHTxXOocQUAAAAAuZCamirDhw+X1q1bS926dTOtixUVFeVYKlSo4NN2AkAgI3AFAAAAALmgta727t0rCxcuzHS72NhYMzLLvhw7dsxnbQSAQEeqIAAAAADk0LBhw+TTTz+V9evXS/ny5TPdNiIiwiwAgJwjcAUAAAAA2WSz2eSJJ56QZcuWydq1a6VKlSr+bhIABDUCVwAAAACQg/TA+fPny4oVK6RIkSJy6tQps15rVxUsWNDfzQOAoEPgKgRkNZsBgs/1/M35vAAAPMUffQqzOMHbZs6caX62b9/eZf2cOXNkwIABfmoVAAQvAlcAAAAAkINUQQCA7zCrIAAAAAAAACyJEVcA/J66QdoGEFhIKQYAAICvMOIKAAAAAAAAlkTgCgAAAAAAAJZE4AoAAAAAAACWROAKAAAAAAAAlkTgCgAAAAAAAJZE4AoAAAAAAACWROAKAAAAAAAAlkTgCgAAAAAAAJaU198NAAAAAHwlbuE2t/eN793Mp20BAIQu+qPsY8QVAAAAAAAALCkoA1fx8fHSrFkzKVKkiJQuXVp69eol+/bt83ezAAAAAAAAEOqpguvWrZOhQ4ea4NXff/8tL7zwgnTu3Fl+/PFHKVy4sL+bBwAAAADwUxoWgMASlIGrlStXutxOSEgwI6927Nght9xyi9/aBQAAAAAAgBBPFUwrMTHR/CxevHiG96ekpEhSUpLLAgAIDaSXAwAAANYV9IGr1NRUGT58uLRu3Vrq1q3r9qQlKirKsVSoUMHn7QQA+De9fPPmzbJ69Wr566+/THp5cnKyv5sGAAAAhLygTBV0picje/fulQ0bNrjdJjY2VkaMGOG4rSOuCF4BQGggvRwAAACwrqAOXA0bNkw+/fRTWb9+vZQvX97tdhEREWYBACCr9HJ7irkudqSYAwAAAN4RlIErm80mTzzxhCxbtkzWrl0rVapU8XeTAABBkl5uTzEfN26cT9sGAABcMXMgEBryBGt64Icffijz5883xXZPnTpllsuXL/u7aQCAAEgvX7hwYabbaYq5jsyyL8eOHfNZGwEAAIBQEpQjrmbOnGl+tm/f3mX9nDlzZMCAAX5qFQAgGNLLFSnmAAAAgG8EbaoggNAY5j2+dzOPtgWhh/RyANnpj+hvAADwj6AMXAEAkJP0QE0tX7FihSO9XEVFRUnBggX93TwAAAAgpAVljSsAAHKSXq51qjS9vGzZso5l0aJF/m4aAAAAEPIYcQUACGmkl6fHLE0AkDmth/jKK6/Ijh075OTJkybdvFevXv5uFgAEJUZcAQAAAEAOJCcnS4MGDWTGjBn+bgoABD1GXAEAAABADnTr1s0sAADvY8QVAAAAAAAALIkRVwAAAADgRSkpKWaxS0pK8mt7ACCQMOIKAAAAALwoPj5eoqKiHEuFChX83SQACBgErgAAAADAi2JjYyUxMdGxHDt2zN9NAoCAQaoggIAWt3Cb2/vG927m07YAAABkJCIiwiwAgJwjcAUAAAAAOXDx4kU5cOCA4/ahQ4dk9+7dUrx4calYsaJf2wYAwYbAFQAAAADkwPbt26VDhw6O2yNGjDA/+/fvLwkJCX5sGQAEHwJXAAAAAJAD7du3F5vN5u9mAEBIoDg7AAAAAAAALIkRVwAAhKDMJjYAAAAArIIRVwAAAAAAALAkRlwBAAAAACw3Anh872Y+bQsAa2LEFQAAAAAAACyJwBUAAAAAAAAsicAVAAAAAAAALIkaV0GC2aEAAAAAAECwYcQVAAAAAAAALInAFQAAAAAAACyJwBUAAAAAAAAsicAVAAAAAAAALIni7AGEAuwAAAAAACCUMOIKAAAAAAAAlkTgCgAAAAAAAJZEqiAAAAAAAEAQlBAa37uZBBtGXAEAAAAAAMCSCFwBAAAAAADAkghcAQAAAAAAwJIIXAEAAAAAAMCSKM4OADksehiMBQ8BAAAAwIoIXAEAAADXiQsegO9nTwOClbc++3EB2leRKggAAAAAAABLInAFAAAAAAAASyJVEACAIEWKBWCN/0/Xk5qR29e1csoHQg/9EYDrQeAKQNCy4pekQM0rBwAAAAB/CNpUwRkzZkjlypWlQIEC0qJFC9m6dau/mwQAsDD6DQBATtBvAIBvBOWIq0WLFsmIESNk1qxZphOZPn26dOnSRfbt2yelS5f2d/MAABbj736DkXhA6PLX6GCOO9eHfsP6o9yBUBWXy/+P1/P/2NvHnaAMXE2bNk0GDx4sAwcONLe1Q/nss89k9uzZMmrUKMt2JBzwgcDgj+lpr+f4wQmI9fsNAEBgod8AAN8JusDV1atXZceOHRIbG+tYlydPHunUqZNs2rQpw8ekpKSYxS4xMdH8TEpKylUbUi5ddHtfZs+Z2eMAILfHj9wey+yPs9lsEswCud+4nucFENyyOnbQb+ReMPcbuUV/A4S2JC/3G0EXuDp37pxcu3ZNypQp47Jeb//8888ZPiY+Pl7GjRuXbn2FChU83r4pgzz+lABCRG6PH9d73Llw4YJERUVJsKLfABCMrufYQb+ROfoNAPBtvxF0gavc0KslmqNul5qaKn/88YeUKFFCwsLC/Nq2QKLRUu18jx07JpGRkf5uTsBjf3oW+zPn9MqHdiIxMTH+borlBFq/ESyff96HtfA+rMUK74N+I3j6DSt9rvwp1N+/Yh+wD5K8+P6z228EXeCqZMmSEh4eLqdPn3ZZr7ejo6MzfExERIRZnBUtWtSr7Qxm+mEOxf/Q3sL+9Cz2Z84E8xXzUOw3guXzz/uwFt6Htfj7fdBvBFe/YZXPlb+F+vtX7AP2QaSX3n92+o08EmTy588vTZo0kTVr1rhc0dDbLVu29GvbAADWQ78BAMgJ+g0A8K2gG3GldBhu//79pWnTptK8eXMzPW1ycrJj1g8AAJzRbwAAcoJ+AwB8JygDVw888ICcPXtW4uLi5NSpU9KwYUNZuXJlugKK8Cwd/jxmzJh0w6CRO+xPz2J/IpT7jWD5/PM+rIX3YS3B8j4CRbD3G3ah/rkK9fev2AfsgwgLvP8wW7DPVwsAAAAAAICAFHQ1rgAAAAAAABAcCFwBAAAAAADAkghcAQAAAAAAwJIIXAEAAAAAAMCSCFwhR2bMmCGVK1eWAgUKSIsWLWTr1q1ut/3hhx/knnvuMduHhYWZaYKR+/353nvvSdu2baVYsWJm6dSpU6bbh6Kc7M+lS5eaKayLFi0qhQsXNrMB/fvf//ZpewFffd7VkiVLpFatWmb7evXqyeeff+5y/4ABA8yx2nnp2rVrwPUpOd0vVnwfY8eOTfe30L9dIPZLOgeQzrpWtmxZKViwoNlm//79Afc+/PH/wxv9mr/+HrC2UP8+yvfH3PedCxcuNMfDXr16SSjtg4SEhHR9gj4ulD4D58+fl6FDh5r+RGcbvPHGG9N9t/QonVUQyI6FCxfa8ufPb5s9e7bthx9+sA0ePNhWtGhR2+nTpzPcfuvWrbaRI0faFixYYIuOjra99tprPm9zMO3PBx980DZjxgzbrl27bD/99JNtwIABtqioKNvx48d93vZg2J/ffPONbenSpbYff/zRduDAAdv06dNt4eHhtpUrV/q87YC3P+/ffvut+XxPmTLFfOZffPFFW758+Wx79uxxbNO/f39b165dbSdPnnQsf/zxR0D1KTl9Tqu+jzFjxtjq1Knj8rc4e/as196DN/ulSZMmmXXLly+3fffdd7Y777zTVqVKFdvly5cD6n34+v+Ht/o1f/w9YG2h/n2U74+57zsPHTpkK1eunK1t27a2nj172gJZTvfBnDlzbJGRkS59wqlTp2yh8v5TUlJsTZs2td1+++22DRs2mM/C2rVrbbt37/ZaGwlcIduaN29uGzp0qOP2tWvXbDExMbb4+PgsH1upUiUCVx7cn+rvv/+2FSlSxDZ37lwvtjJ09qdq1KiROaEHgu3zfv/999u6d+/usq5Fixa2Rx991OXE3JdfPL3Rp3jiOGCF96GBqwYNGtgCvV9KTU01wblXXnnFsc358+dtERERJnAXSP2rr/9/eKNf89ffA9YW6t9H+f6Yu32gf/dWrVrZ3n//fb8cH/29DzRwpQHbYNE8h+9/5syZtqpVq9quXr3qszaSKohsuXr1quzYscMMB7bLkyePub1p0ya/ti1U9+elS5fkr7/+kuLFi0uou979qUH8NWvWyL59++SWW27xcmsB33/edb3z9qpLly7ptl+7dq2ULl1aatasKY899pj8/vvvlnkP/nhOf76mpm/FxMRI1apVpW/fvnL06FEJtH7p0KFDcurUKZfnjIqKMikIVv6buOtfffX/w1v9mj/+HrC2UP8+yvfH3O+D8ePHm+PhoEGDJNDldh9cvHhRKlWqJBUqVJCePXuaUgCh8v4/+eQTadmypUkVLFOmjNStW1defvlluXbtmtfaSeAK2XLu3DnzQdQPpjO9rV+C4Pv9+fzzz5uTmrQno6Eot/szMTFRbrjhBsmfP790795d3nzzTbntttt80GLAt593XZ/V9lqvZ968eeZL+OTJk2XdunXSrVs3r3wJ8Uaf4o9+yluvqYEErZ+xcuVKmTlzpgk4aE2ZCxcuSCD1S/bHBdrfJKP+1Zf/P7zVr/nj7wFrC/Xvo3x/zN0+2LBhg3zwwQem3lkwyM0+0AsYs2fPlhUrVsiHH34oqamp0qpVKzl+/LiEwvv/9ddf5eOPPzaP07pWo0ePlqlTp8rEiRO91s68XntmAF4zadIkUwxRr/4GeiFAfypSpIjs3r3bXDHRk5ERI0aY0Q3t27f3d9MAn+vdu7fjdy3eXr9+falWrZo5znTs2NGvbQs1GhCx07+DBrL0qu7ixYste3U7WPold+8jUP5/0K/Bl4Ll/31OhfL/M72A8tBDD5mgVcmSJSVU6WgjXew0aHXTTTfJO++8IxMmTJBgl5qaakbcvfvuuxIeHi5NmjSREydOyCuvvCJjxozxymsSuEK26IFJP5SnT592Wa+3o6Oj/dauUNyfr776qvmi8NVXX5kvzsj9/tRhsNWrVze/66wwP/30k8THx4fEFw+E1udd1+f0/4d+CdfXOnDggMdPzL3Rp/ijn/LVa+rsVTpbj/4tAqlfsj9On0NnHXJ+Tj3mBnL/6s3/H97q1/zx94C1hfr3Ub4/5nwfHDx4UA4fPiw9evRwCWKovHnzmrRJDeoHEk/05fny5ZNGjRp5tZ+20vvXPkTfsz7OTgN3OkJLUw91NKKnkSqIbNEPn0ZS9aqC80FKbztHm+Hd/TllyhQTxdf0EZ2KF579fOpjUlJSvNRKwH+fd13vvL1avXp1pv8/dLi71vBxPsG1cp/ij37KV6+pV/X1ZMEbfwtv9ktVqlQxX3qdnzMpKUm2bNliub9JTvtXb/7/8Fa/5o+/B6wt1L+P8v0x5/ugVq1asmfPHjPizL7ceeed0qFDB/O71nsKxc+BpszpfvFmP22l99+6dWsTpLMHLdUvv/xi3r83glaGz8rAI+DpNJk680xCQoKZAnbIkCFmmkz71J8PPfSQbdSoUS7TZOpUubqULVvWTP+tv+/fv9+P7yJw96dOYa3TlH788ccuU69euHDBj+8icPfnyy+/bPvyyy9tBw8eNNu/+uqrtrx589ree+89P74LwDuf92+//dZ8vvVzrtOX66x1+fLls+3Zs8fcr8cRPUZv2rTJTGn81Vdf2Ro3bmyrUaOG7cqVKwHTp2T1nIHyPp555hkzrbT+LfRv16lTJ1vJkiVtZ86c8dr78Fa/pNvoc6xYscL2/fffm5mnqlSpYrt8+XLAvA9//P/wVr/mj78HrC3Uv4/y/THn+yCtYJhVMKf7YNy4cbZVq1aZz8GOHTtsvXv3thUoUMD2ww8/2ELh/R89etTMJjps2DDbvn37bJ9++qmtdOnStokTJ3qtjQSukCNvvvmmrWLFiqbD0mkzN2/e7LivXbt25sBlp1/uNDaadtHtkPP9qdOmZ7Q/9QQUOd+f//rXv2zVq1c3nUyxYsVsLVu2NAdtIBg/72rx4sW2G2+80Wxfp04d22effea479KlS7bOnTvbSpUqZQJaerwZPHiwVwM+3upTMnvOQHkfDzzwgAlq6fOVK1fO3D5w4IDX34c3+qXU1FTb6NGjbWXKlDFfijt27Gi+5AbS+/DX/w9v9Gv++nvA2kL9+yjfH3P+nSLYAlc53QfDhw93bKvH09tvv922c+dOWyh9BjZu3Ghr0aKF6UuqVq1qe+mll2x///2319oXpv94ZywXAAAAAAAAkHvUuAIAAAAAAIAlEbgCAAAAAACAJRG4AgAAAAAAgCURuAIAAAAAAIAlEbgCAAAAAACAJRG4AgAAAAAAgCURuAIAAAAAAIAlEbgCAkDlypVl+vTp/m4GACBA0G8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",
+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "2nd Flr SF\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.731936 -1.044630  1.904799e-32\n",
+            "1                 Log1p  0.646686 -1.280826  3.187818e-33\n",
+            "2                  Sqrt  0.492646 -1.637604  1.501389e-36\n",
+            "3      Box-Cox λ=-0.093  0.395762 -1.842275  3.696521e-40\n",
+            "4  Yeo–Johnson λ=-4.331  0.463803 -1.710962  7.071556e-38\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Low Qual Fin SF\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "Gr Liv Area\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.442162 -0.045347  2.040961e-08\n",
+            "1                 Log1p  0.293152 -0.257306  9.532551e-05\n",
+            "2                  Sqrt  0.018849 -0.422838  1.708059e-02\n",
+            "3       Box-Cox λ=0.446 -0.026264 -0.436018  1.283401e-02\n",
+            "4  Yeo–Johnson λ=-1.978  0.018648 -0.505353  3.031253e-03\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Bsmt Full Bath\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original   0.34834 -1.878659  4.210428e-40\n",
+            "1                 Log1p   0.34834 -1.878659  4.210428e-40\n",
+            "2                  Sqrt   0.34834 -1.878659  4.210428e-40\n",
+            "3      Box-Cox λ=-0.082   0.34834 -1.878659  4.210428e-40\n",
+            "4  Yeo–Johnson λ=-3.644   0.34834 -1.878659  4.210428e-40\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Bsmt Half Bath\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "Full Bath\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original -0.289148 -1.433131  3.767711e-24\n",
+            "1                Log1p -0.339970 -1.578263  1.084539e-29\n",
+            "2                 Sqrt -0.361590 -1.633640  4.962668e-32\n",
+            "3      Box-Cox λ=1.266 -0.229861 -1.241924  6.307108e-18\n",
+            "4  Yeo–Johnson λ=3.010 -0.106955 -0.786353  3.065813e-07\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Half Bath\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.453704 -1.731194  3.388560e-38\n",
+            "1                 Log1p  0.442944 -1.780418  1.633736e-39\n",
+            "2                  Sqrt  0.436822 -1.803689  4.040524e-40\n",
+            "3      Box-Cox λ=-0.100  0.434249 -1.811418  2.632328e-40\n",
+            "4  Yeo–Johnson λ=-3.228  0.434922 -1.809636  2.888586e-40\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Bedroom AbvGr\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original -0.574971  0.765110  1.945571e-19\n",
+            "1                Log1p -0.799725  1.065963  5.818817e-37\n",
+            "2                 Sqrt -1.085243  1.756455  3.442481e-77\n",
+            "3      Box-Cox λ=1.637  0.006869  0.441735  1.214362e-02\n",
+            "4  Yeo–Johnson λ=3.698  0.029656  0.526282  1.774194e-03\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Kitchen AbvGr\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "TotRms AbvGrd\n",
+            "             Transform  Skewness  Kurtosis      JB-p\n",
+            "0             Original  0.182316  0.097557  0.040053\n",
+            "1                Log1p -0.039127  0.050774  0.821591\n",
+            "2                 Sqrt -0.329619  0.413221  0.000001\n",
+            "3      Box-Cox λ=0.819  0.007378  0.102827  0.783723\n",
+            "4  Yeo–Johnson λ=0.178  0.000203  0.047712  0.949886\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Fireplaces\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.435838 -0.802447  1.707575e-14\n",
+            "1                 Log1p  0.290574 -1.257020  1.549745e-19\n",
+            "2                  Sqrt  0.064598 -1.854071  1.321709e-34\n",
+            "3       Box-Cox λ=0.002 -0.009785 -1.998082  6.921493e-40\n",
+            "4  Yeo–Johnson λ=-1.883  0.127857 -1.704695  7.199572e-30\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Garage Yr Blt\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original -1.010552  0.120907  6.209873e-41\n",
+            "1                 Log1p -1.110561  0.473144  2.622238e-51\n",
+            "2                  Sqrt -1.350694  1.541369  1.336201e-95\n",
+            "3       Box-Cox λ=2.735 -0.449301 -1.291138  5.374414e-25\n",
+            "4  Yeo–Johnson λ=11.890 -0.352191 -1.381607  2.584039e-24\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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xOpDes/Lly5v3SZ/noIk8bVml3QG1ZaMn0H0viOkfiWYztbYh6wVLH2fNEsO37/fLL79ssvPa5UOnw4X9f+P6nIyMDC+VEsEoP397uj7r/mrlypW5/q1qk3Id1ybrjWSgxg9/iUl2lUNr+/XGIVA+u9xiUbVq1cwX1qzHTEtLk82bN9v+fcKuWBvo//eyxjV/+vwCEd/vLg3fzbz/ntWpU0d2795tWpY5lttuu03atm1rftfxkoKdJ/7OtEuavo92X+cD6T1r1aqVSdo5kp7q559/Nu+ZpxJShleHUYdfTPuos6vMmTPHTLPav39/M+2jY/rLnj17Wk8//bTLtI86fa0u5cuXN1Ng6+979+714VkE7/utUzTrtJwff/yxy/SkJ0+e9OFZBPd7/uKLL1pffPGFtX//frP/q6++akVERFgzZ8704VkgFP72vvrqK/O3pn9zOkW4ztZWsGBBa/fu3Wa7/r/Xa+7GjRvNlLurVq2yrrnmGqtWrVrWmTNngiJ+XOyYgXxu//nPf8w0yfrZ6Wfdvn17q3Tp0tbRo0dtPTdvxSLdR4+xZMkSa9euXWaWp2rVqll///13wJ9foP/fy0tc86fPLxDx/e7S8N3M++9ZdqE4+96lvmdjxoyxVqxYYf7Otm/fbvXo0cOKioqyvv/+eytUzL/E9+zQoUNm5tCBAwdae/bssZYuXWqVLVvWGjdunEfLRVIqBEyZMsWqXLmyCY46DeSmTZuc22688UZzEXPQL2Oaq8y+6H7w/PutU4vn9H7rzSq8854/++yzVs2aNU0QuuKKK6y4uDhzgQa8/benPvroI+uqq64y+9erV89atmyZc9vp06etDh06WGXKlDHJKr0+9OvXz/aEjbfjR27HDORzu+eee0zCSo9XsWJF83jfvn1WsMSizMxMa+TIkVa5cuXMF9p27dqZL6jBcH6B/n8vL3HN3z6/QMT3u0vDdzPvf6cI9aTUpb5ngwcPdu6r18LOnTtbO3bssELNlEv8O/v666+tli1bmthRvXp164UXXrD++ecfj5YpTP/xXLsrAAAAAAAA4OIYUwoAAAAAAAC2IykFAAAAAAAA25GUAgAAAAAAgO1ISgEAAAAAAMB2JKUAAAAAAABgO5JSAAAAAAAAsB1JKQAAAAAAANiOpBSAPPnll18kLCxMdu7c6euiAAD8DDECAJAb4gTcISmFoJecnCyPP/641KxZU6KioqRcuXLSqlUrmT59upw+fVoC0bp166RgwYKyYcMGl/Xp6elSvXp1efLJJy/peH369DFBwrGUKlVKOnbsKLt27XL7nLVr15p9U1NT830eAOBrxIiLI0YACGXEiYsjTuBykJRCUDtw4IA0adJEvvjiC3nxxRflm2++kY0bN8rQoUNl6dKlsmrVqnwf+/z585KZmSm+cOONN8qgQYNMANDg4aDnVbhwYRk3blyOzzt79qzbY2rgOHLkiFkSExMlIiJCbr31Vq+UHwD8ATHCFTECAFwRJ1wRJ+AVFhDE4uPjrSuvvNI6depUjtszMzOdv0+YMMGqX7++VaRIEfOcRx55xDp58qRz++zZs62YmBhryZIlVt26da0CBQpYBw8etLZs2WK1b9/eKlWqlBUdHW3dcMMN1vbt211e58cff7RatWplRUZGmueuXLnS0v9+ixYtcu5z6NAh66677jKvccUVV1i33XabOb47f//9tznWgAEDzOPVq1dbhQoVsrZt2+bcp0qVKtbYsWOtnj17WsWLF7d69+6d47F0fdeuXV3Wffnll6aMR48eNY+1LPr4m2++cf6edXF3bADwV8QIYgQA5IY4QZyA99FSCkHrzz//NLUaAwYMkKJFi+a4jzYZdQgPD5fJkyfL999/L++8846sXr3a1BZkpU10x48fL2+99ZbZr2zZsnLy5Enp3bu3af66adMmqVWrlnTu3Nmsd9SCdOvWTYoUKSKbN2+WN998U5599lmX4547d07i4+OlePHi8uWXX8pXX30lxYoVMzUO7moktPnw3LlzzfGWLFkiDz74oDzzzDPStGlTl/1effVVadSokanZGTlyZJ7eu1OnTsl7771nmilr89vsKlWqJP/73//M73v27DE1Iq+//nqejg0A/oAY8X+IEQCQM+LE/yFOwOtsSHwBPrFp0yaTdV+4cKHLeq2FKFq0qFmGDh3q9vkLFiww+2at3dDj7dy5M9fXPX/+vKlJ+PTTT83jzz//3IqIiLCOHDni3Cd77ca7775r1a5d26W2JSMjwypcuLC1YsWKXF9v1KhRVnh4uNW0aVPr3LlzLtu0dqNbt27WxWjNhNbWON4XLVv58uVdammy1m6oNWvWmMd//fXXRY8PAP6GGEGMAIDcECeIE7AHLaUQcrZs2WJmfahXr55kZGQ412uf8Hbt2knFihVNLUPPnj1NDUnWAQwLFSokDRs2dDleSkqK9OvXz9RqxMTESHR0tKkdOHTokDP7r7UBsbGxzue0aNHC5Rjffvut7Nu3z7yu1mroUrJkSTlz5ozs378/1/PRGgvtj/7000+bvtvZNWvWLE/vS9u2bc37oou+R1rb0qlTJ/n111/z9HwACAbEiJwRIwDg/xAnckacQH5d+FcHBAltLqpNavVCnpXOKKF0EL+sU5TqQHyPPPKIvPDCC+Yirk1o+/bta5q8anNZx3OyNtNV2txWA442Oa1SpYpERkZKXFxcrgMBZqeBR5vKzps374JtZcqUyfW5juCRUxBR7pob57SfvmcO2qxYA+PMmTPdDnYIAIGKGPF/iBEAkDPixP8hTsDbSEohaGn/5Ztvvln++9//mtklcrugbt++3dQQTJgwwfQHVx999FGeXkf7bE+bNs30/VZJSUnyxx9/OLfXrl3brNNaEJ1CVm3dutXlGNdcc418+OGHpl+51o74Aw2Y+l78/fffOW7Xmh5HP3cACDTEiMtDjAAQ7IgTl4c4gbyi+x6Cml7g//nnH9PsVC/UP/74o6nt0IH3fvrpJylQoIDZT7P6OkDglClTzNSv7777rsyYMSNPr6FNbXV/PbYOPnj//fe71JxoMKtRo4apBdm1a5cJPCNGjDDbHDUl+pzSpUtL165dzeCEBw8elLVr18pjjz0mv/32m9hBmx8nJyebRc9Fg6/WunTp0iXH/bUmR8uv0+EeO3bM7AsAgYQYkXfECAChiDiRd8QJ5JtNY1cBPnP48GFr4MCBVrVq1ayCBQtaxYoVs1q0aGG98sorVnp6unO/iRMnmgH5dEBAnf517ty5LoPvOaZxzW7Hjh1Ws2bNrKioKKtWrVpmUEMdFPC11167YBpXnWa1Tp06ZuBCPfby5cud++jghb169bJKly5tpnutXr261a9fP+vEiRMXPcfsU8I6ZC9HboMTZp2SVQdXbN68ufXxxx+7HZxQ6RSxsbGxVlhYGNO4AghIxAhiBADkhjhBnIB3hek/+U9pAcgPreFo3bq1GZBQaz4AAHAgRgAAckOcQDAhKQXYYNGiRWYWDG2eq8Hj8ccflyuuuMIMgAgACG3ECABAbogTCGYMdA7Y4OTJkzJs2DAztav2927fvr0ZCBEAAGIEACA3xAkEM1pKAQAAAAAAwHbMvgcAAAAAAADbkZQCAAAAAACA7UhKAQAAAAAAwHYkpQAAAAAAAGA7klIAAAAAAACwHUkpAAAAAAAA2I6kFAAAAAAAAGxHUgoAAAAAAAC2IykFAAAAAAAA25GUAgAAAAAAgO1ISgEAAAAAAMB2JKUAAAAAAABgO5JSAAAAAAAAsB1JKQAAAAAAANiOpBTgp3755RcJCwuTChUqyNSpU31dHAAIelWrVpVbb71Vgvn8oqOjpX///vLPP//4ujgAAAAkpeAbc+bMMQmXrEvZsmWlbdu28vnnn/u0bGvXrpU77rhDYmNjpVChQqZcXbp0kYULF9pajjJlysg777wjNWvWlMcee0z27t3r0ePr+dxzzz1SvXp1KVKkiNSuXVv+85//SGpqap6en/3zy7rcfPPNzv0OHz4s//rXv8zxixcvLiVKlJAWLVqYc7Msy+WYzz33XI7Hi4qK8ui5A/CMTp06yRVXXCEpKSkXbDtx4oSUL19eWrZsKZmZmbaVqU2bNlK/fn3bXi+QTJo0Se6++26ZOXOmzJ0716PHPnXqlIwePVo6duwoJUuWNNdujfU52bJlizz66KPStGlTKViwoNn3Uujf04wZM6Rx48ZSrFgxKVeunPlb/Prrry/Yd/v27aZMmozTGNShQwfZuXNnjsfV57du3drERP0OoLFXzwsAAHhPhBePDVzU2LFjpVq1aiY5oTc1+gW2c+fO8umnn/qktlq/UGuZatWqJQ8//LBUqVJF/vzzT/nss8+ke/fuMm/ePLnvvvtsKUvRokWlV69e5gZLa7fff/99Uz5P0ZpybYWlCaPKlSvL7t275b///a851x07dkjhwoVzff677757wbpt27bJ66+/br70O/zxxx/y22+/yZ133mle59y5c7Jy5Urp06eP7NmzR1588cULjjN9+nRzo+FQoECByz5fAJ43bdo0kwB64oknzDUqq2eeecb8/1++fLmEh1MH5g+6desmXbt2lfXr15tr+IMPPuixY+tnrfFTr/ONGjUyFTzuaJx56623pGHDhqZi5Oeff76k13rqqadk4sSJJn5pcksrU9544w258cYb5auvvjIVH0pjmSaZKlWqZOKnJrP0b1b308SYVpY4aKKqXbt2UrduXXNsjVuvvvqqqRDydWUZAABBzQJ8YPbs2dpExtq6davL+uPHj1sFCxa07rvvPtvLtGDBAlOmO++80zp79uwF25cvX259+umnli+0bt3auvrqqz16zDVr1lyw7p133jHvwcyZM/N1zL59+1phYWFWUlLSRfe99dZbraJFi1r//POPc93o0aPN6x87dixfrw/AfuPHjzf/b1esWOFct2XLFis8PNwaOnSo7eW58cYbrXr16uXruVWqVLFuueUWK9iNGDHCfD5Hjhz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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Garage Cars\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original -0.512396  0.651562  3.436412e-15\n",
+            "1                Log1p -0.879854  1.231952  5.546512e-46\n",
+            "2                 Sqrt -2.151016  6.364916  0.000000e+00\n",
+            "3      Box-Cox λ=0.791 -0.999547  1.774545  8.305793e-71\n",
+            "4  Yeo–Johnson λ=2.516  0.020714  0.347018  6.340089e-02\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Garage Area\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original -0.163091  0.566811  6.389845e-05\n",
+            "1                Log1p -0.534796  1.027138  2.705351e-22\n",
+            "2                 Sqrt -1.978065  6.143988  0.000000e+00\n",
+            "3      Box-Cox λ=0.750 -0.832406  1.946648  4.472323e-65\n",
+            "4  Yeo–Johnson λ=1.528  0.022521  0.455883  8.744728e-03\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Wood Deck SF\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.688713 -0.438455  3.207166e-21\n",
+            "1                 Log1p  0.592142 -0.708681  2.081936e-19\n",
+            "2                  Sqrt  0.084302 -1.672121  1.989009e-28\n",
+            "3       Box-Cox λ=0.044 -0.167532 -1.950416  3.879424e-39\n",
+            "4  Yeo–Johnson λ=-6.104  0.203248 -1.557520  3.862620e-26\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Open Porch SF\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.949868  0.097338  3.241214e-36\n",
+            "1                 Log1p  0.854338 -0.144106  1.451070e-29\n",
+            "2                  Sqrt  0.020874 -1.341071  2.207069e-18\n",
+            "3       Box-Cox λ=0.137 -0.487453 -1.644769  1.397957e-36\n",
+            "4  Yeo–Johnson λ=-8.705  0.213113 -1.284912  1.060597e-18\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Enclosed Porch\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "3Ssn Porch\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "Screen Porch\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "Pool Area\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "Misc Val\n",
+            "  Column is constant, skipping transformations.\n",
+            "  Transform  Skewness  Kurtosis  JB-p\n",
+            "0  Original       NaN       NaN   NaN\n",
+            "No need to skewness\n",
+            "Mo Sold\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original  0.204072 -0.412265  5.003022e-04\n",
+            "1                Log1p -0.185784 -0.394476  1.317363e-03\n",
+            "2                 Sqrt -1.001648  1.420004  7.289818e-60\n",
+            "3      Box-Cox λ=0.575 -0.718990  0.674496  1.809389e-25\n",
+            "4  Yeo–Johnson λ=0.445 -0.011057 -0.442576  1.186075e-02\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "Yr Sold\n",
+            "             Transform  Skewness  Kurtosis          JB-p\n",
+            "0             Original  0.136403 -1.141585  3.078675e-14\n",
+            "1                Log1p -0.141117 -1.191619  1.959227e-15\n",
+            "2                 Sqrt -0.674857 -0.816704  3.899153e-25\n",
+            "3      Box-Cox λ=0.290 -1.078679 -0.413921  4.700840e-48\n",
+            "4  Yeo–Johnson λ=0.385 -0.035400 -1.190230  1.139261e-14\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n",
+            "SalePrice\n",
+            "              Transform  Skewness  Kurtosis          JB-p\n",
+            "0              Original  0.541625 -0.010833  3.090809e-12\n",
+            "1                 Log1p  0.391721 -0.171965  4.898900e-07\n",
+            "2                  Sqrt  0.106102 -0.147317  2.215622e-01\n",
+            "3       Box-Cox λ=0.391  0.000090 -0.073750  8.844107e-01\n",
+            "4  Yeo–Johnson λ=-2.518  0.016774 -0.288097  1.495931e-01\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1200x700 with 5 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "No need to skewness\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Check skewness of numerical features.\n",
+        "# Apply log, sqrt, Box-Cox, or Yeo-Johnson depending on distribution.\n",
+        "\n",
+        "from scipy.stats import skew, kurtosis, jarque_bera, boxcox\n",
+        "from sklearn.preprocessing import PowerTransformer\n",
+        "import warnings\n",
+        "\n",
+        "# Ignore warnings that might arise from transformations on certain data types\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "\n",
+        "# --- Metrics helper ---\n",
+        "def metrics(vec):\n",
+        "    # Convert to pandas Series to use .nunique()\n",
+        "    vec_series = pd.Series(vec)\n",
+        "    # Check if the vector is constant\n",
+        "    if vec_series.nunique() <= 1:\n",
+        "        return np.nan, np.nan, np.nan # Return NaN for metrics if data is constant\n",
+        "\n",
+        "    sk = skew(vec, nan_policy='omit')\n",
+        "    ku = kurtosis(vec, fisher=True, nan_policy='omit')  # 0 = normal\n",
+        "    jb_stat, jb_p = jarque_bera(vec)\n",
+        "    return sk, ku, jb_p\n",
+        "\n",
+        "\n",
+        "# numerical_cols = df.select_dtypes(include=np.number).columns\n",
+        "for col in df[col_num]:\n",
+        "  print(col)\n",
+        "  x = df[col].astype(float)\n",
+        "\n",
+        "  # Check if the column is constant before attempting transformations\n",
+        "  if x.nunique() <= 1:\n",
+        "      print(\"  Column is constant, skipping transformations.\")\n",
+        "      rows = [(\"Original\", *metrics(x))]\n",
+        "  else:\n",
+        "      # --- Transformations ---\n",
+        "      x_log   = np.log1p(x)                        # log(1+x)\n",
+        "      x_sqrt  = np.sqrt(x)                         # sqrt\n",
+        "      # Box-Cox needs >0. Add a small constant to handle zero values.\n",
+        "      # Also check if the transformed data is constant after transformation.\n",
+        "      try:\n",
+        "        x_bc, lam_bc = boxcox(x + 1e-6)\n",
+        "        if np.all(x_bc == x_bc[0]):\n",
+        "             x_bc = np.full_like(x_bc, np.nan) # Fill with NaN if transformed data is constant\n",
+        "             lam_bc = np.nan\n",
+        "      except ValueError:\n",
+        "          x_bc = np.full_like(x, np.nan)\n",
+        "          lam_bc = np.nan\n",
+        "\n",
+        "\n",
+        "      pt = PowerTransformer(method=\"yeo-johnson\", standardize=False)\n",
+        "      # Reshape for PowerTransformer and then flatten back\n",
+        "      x_yj = pt.fit_transform(x.values.reshape(-1,1)).ravel()\n",
+        "      # Check if the transformed data is constant after transformation.\n",
+        "      if np.all(x_yj == x_yj[0]):\n",
+        "          x_yj = np.full_like(x_yj, np.nan) # Fill with NaN if transformed data is constant\n",
+        "\n",
+        "\n",
+        "      # --- Report ---\n",
+        "      rows = [\n",
+        "          (\"Original\", *metrics(x)),\n",
+        "          (\"Log1p\", *metrics(x_log)),\n",
+        "          (\"Sqrt\", *metrics(x_sqrt)),\n",
+        "          (f\"Box-Cox λ={lam_bc:.3f}\" if not np.isnan(lam_bc) else \"Box-Cox\", *metrics(x_bc)),\n",
+        "          (f\"Yeo–Johnson λ={pt.lambdas_[0]:.3f}\", *metrics(x_yj))\n",
+        "      ]\n",
+        "\n",
+        "  report = pd.DataFrame(rows, columns=[\"Transform\",\"Skewness\",\"Kurtosis\",\"JB-p\"])\n",
+        "  print(report)\n",
+        "\n",
+        "  # --- Plots (2×3 grid) ---\n",
+        "  if x.nunique() > 1: # Only plot if the original data is not constant\n",
+        "      fig, axes = plt.subplots(2, 3, figsize=(12, 7))\n",
+        "      titles = [\"Original\",\"Log1p\",\"Sqrt\",f\"Box-Cox λ={lam_bc:.3f}\" if not np.isnan(lam_bc) else \"Box-Cox\",f\"Yeo–Johnson λ={pt.lambdas_[0]:.3f}\"]\n",
+        "      data = [x, x_log, x_sqrt, x_bc, x_yj]\n",
+        "\n",
+        "      for ax, d, title in zip(axes.ravel(), data, titles):\n",
+        "          # Only plot if the data is not constant (check if not all NaNs)\n",
+        "          if not np.all(np.isnan(d)):\n",
+        "              ax.hist(d, bins=40, density=True, color=\"steelblue\", alpha=0.7)\n",
+        "              ax.set_title(title)\n",
+        "              ax.set_xlabel(col); ax.set_ylabel(\"Density\")\n",
+        "          else:\n",
+        "              ax.set_title(f\"{title}\\n(Constant data)\")\n",
+        "              ax.set_xlabel(col); ax.set_ylabel(\"Density\")\n",
+        "\n",
+        "\n",
+        "      fig.delaxes(axes[1,2])  # remove empty subplot\n",
+        "      fig.tight_layout()\n",
+        "      plt.show()\n",
+        "  print('No need to skewness')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "voU8eavLkXya"
+      },
+      "source": [
+        "## 🔹 Step 10: Remove Duplicates"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pEyssUgmkZgq",
+        "outputId": "f4bebf7a-65dc-47e2-fc29-a845059d4ef6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Duplicate rows: 0\n",
+            "Rows after dropping duplicates: 1084\n"
+          ]
+        }
+      ],
+      "source": [
+        "# TODO: Check and remove duplicate rows if there is.\n",
+        "\n",
+        "# count duplicates\n",
+        "dup_count = df.duplicated().sum()\n",
+        "print(f\"Duplicate rows: {dup_count}\")\n",
+        "\n",
+        "# (optional) inspect some duplicate rows\n",
+        "if dup_count:\n",
+        "    display(df[df.duplicated(keep=False)].head())\n",
+        "\n",
+        "# drop duplicates (keep first occurrence) and reset index\n",
+        "df = df.drop_duplicates(keep='first').reset_index(drop=True)\n",
+        "print(f\"Rows after dropping duplicates: {len(df)}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AlW4RbaTkeSu"
+      },
+      "source": [
+        "## 💾 Step 11: Save Cleaned Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "naZHc5dnkamR",
+        "outputId": "85de9021-991a-4c3c-ae57-b0b24e772aeb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "✅ Cleaned dataset saved successfully!\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Save your final cleaned and engineered dataset to CSV.\n",
+        "df.to_csv(\"AmesHousing_engineered.csv\", index=False)\n",
+        "print(\"✅ Cleaned dataset saved successfully!\")\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a12/Assignment_12_03_02_Dimensionality_Reduction___Nexus___rsayyareh.ipynb b/a0.1/a12/Assignment_12_03_02_Dimensionality_Reduction___Nexus___rsayyareh.ipynb
new file mode 100644
index 0000000..6222573
--- /dev/null
+++ b/a0.1/a12/Assignment_12_03_02_Dimensionality_Reduction___Nexus___rsayyareh.ipynb
@@ -0,0 +1,415 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lyq1xPPrhRoX"
+      },
+      "source": [
+        "# PCA on Olivetti Faces Dataset\n",
+        "## Overview\n",
+        "In this assignment, we explore Principal Component Analysis (PCA), a fundamental technique in machine learning for dimensionality reduction and feature extraction. We'll apply PCA to the Olivetti faces dataset to understand how it can be used to compress and reconstruct images.\n",
+        "\n",
+        "## Objectives\n",
+        "1. Load and visualize the Olivetti faces dataset.\n",
+        "2. Perform PCA to reduce the dimensionality of the dataset.\n",
+        "3. Determine the optimal number of components using the elbow method.\n",
+        "4. Visualize the effect of PCA on image reconstruction.\n",
+        "5. Compare the performance of a model trained on the original dataset versus the PCA-reduced dataset.\n",
+        "\n",
+        "## Prerequisites\n",
+        "Basic understanding of Python and NumPy.\n",
+        "Familiarity with matplotlib for plotting.\n",
+        "Basic knowledge of machine learning concepts, especially PCA."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3gjA1DRyj-kk"
+      },
+      "source": [
+        "## Step 1: Load and Visualize the Dataset\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "n-TFwZ5_WeW5"
+      },
+      "outputs": [],
+      "source": [
+        "# Import necessary libraries\n",
+        "from sklearn.datasets import fetch_olivetti_faces\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "\n",
+        "# Load the Olivetti faces dataset\n",
+        "faces = fetch_olivetti_faces()\n",
+        "X, y = faces['data'], faces['target']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "6X0mg0rhjruF"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(400, 4096)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Print the shape of the dataset\n",
+        "print(X.shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "50g-tHqwWnfn"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x1200 with 100 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Select 100 faces randomly for visualization\n",
+        "np.random.seed(0)  # Ensure reproducibility\n",
+        "X_samples = np.random.permutation(X)[:100]\n",
+        "\n",
+        "# Plot the selected faces\n",
+        "fig, axes = plt.subplots(10, 10, figsize=(12, 12))\n",
+        "fig.subplots_adjust(hspace=0.01, wspace=0.01)\n",
+        "\n",
+        "for i, ax in enumerate(axes.flat):\n",
+        "    ax.imshow(X_samples[i].reshape((64, 64)), cmap='gray')\n",
+        "    ax.set_xticks(())\n",
+        "    ax.set_yticks(())\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PUA2EZuRj74Y"
+      },
+      "source": [
+        "## Step 2: Apply PCA on the Dataset\n",
+        "### Task\n",
+        "* Implement PCA on the dataset.\n",
+        "* Visualize the variance explained by each principal component."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "XYP3aB2kj5I2"
+      },
+      "outputs": [],
+      "source": [
+        "# Import PCA from sklearn\n",
+        "from sklearn.decomposition import PCA\n",
+        "\n",
+        "# Apply PCA on the dataset (leave number of components blank)\n",
+        "pca_digits = PCA()\n",
+        "PC_dig1 = pca_digits.fit_transform(X)\n",
+        "# PC_dig1.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "YgnaRmpUk4QN"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x400 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Visualize the variance explained by each principal component\n",
+        "plt.figure(figsize=(8, 4))\n",
+        "plt.plot(np.cumsum(pca_digits.explained_variance_ratio_))\n",
+        "plt.xlabel('Number of Components')\n",
+        "plt.ylabel('Cumulative Explained Variance')\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BlQKLq9WkNQm"
+      },
+      "source": [
+        "## Step 3: Elbow Method for Optimal Components\n",
+        "### Task\n",
+        "* Plot the cumulative explained variance against the number of components.\n",
+        "* Use the elbow method to determine a good number of components for PCA."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "sDmoIc1vkLvk"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(12, 6))\n",
+        "n = pca_digits.explained_variance_ratio_.shape[0]\n",
+        "plt.bar(np.arange(n), pca_digits.explained_variance_ratio_)\n",
+        "# Plot the cumulative explained variance against the number of components\n",
+        "plt.plot(np.cumsum(pca_digits.explained_variance_ratio_))\n",
+        "plt.xlabel('Number of Components')\n",
+        "plt.ylabel('Cumulative Explained Variance')\n",
+        "plt.title('Elbow Method for Optimal PCA Components')\n",
+        "plt.axhline(y=0.95, color='r', linestyle='--') # Example threshold line at 95% variance\n",
+        "plt.grid(True)\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "pca_digits  1, explained var: 23.81\n",
+            "pca_digits 21, explained var: 77.03\n",
+            "pca_digits 41, explained var: 85.33\n",
+            "pca_digits 61, explained var: 89.29\n",
+            "pca_digits 81, explained var: 91.84\n",
+            "pca_digits 101, explained var: 93.61\n",
+            "pca_digits 121, explained var: 94.93\n",
+            "pca_digits 141, explained var: 95.94\n",
+            "pca_digits 161, explained var: 96.74\n"
+          ]
+        }
+      ],
+      "source": [
+        "total_var = np.cumsum(pca_digits.explained_variance_ratio_) * 100\n",
+        "\n",
+        "for i in [0, 20, 40, 60, 80, 100, 120, 140, 160]:\n",
+        "    print(\"pca_digits {:2d}, explained var: {:.2f}\".format(i+1, total_var[i]))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8T7q895qkUCv"
+      },
+      "source": [
+        "## Step 4: Image Reconstruction\n",
+        "### Task\n",
+        "* Reconstruct images using the selected number of PCA components.\n",
+        "* Plot original and reconstructed images side-by-side."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "l03zl7FokShR"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(400, 100)\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "(400, 4096)"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "pca_n = PCA(100)\n",
+        "PC_dig2 = pca_n.fit_transform(X)\n",
+        "print(PC_dig2.shape)\n",
+        "X_reconstructed = pca_n.inverse_transform(PC_dig2)\n",
+        "X_reconstructed.shape\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "qCrtd8K2lIHL"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1200x600 with 10 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Plot original and reconstructed images side-by-side\n",
+        "fig, axes = plt.subplots(2, 5, figsize=(12, 6))\n",
+        "for i in range(5):\n",
+        "    # Original Image\n",
+        "    ax = axes[0, i]\n",
+        "    ax.imshow(X[i].reshape(64, 64), cmap='gray')\n",
+        "    ax.set_xticks(())\n",
+        "    ax.set_yticks(())\n",
+        "    ax.set_title(\"Original\")\n",
+        "\n",
+        "    # Reconstructed Image\n",
+        "    ax = axes[1, i]\n",
+        "    ax.imshow(X_reconstructed[i].reshape(64, 64), cmap='gray')\n",
+        "    ax.set_xticks(())\n",
+        "    ax.set_yticks(())\n",
+        "    ax.set_title(\"Reconstructed\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vQrctueSkcR9"
+      },
+      "source": [
+        "## Step 5: Compare Models\n",
+        "### Task\n",
+        "* Train a model using the original dataset and the PCA-reduced dataset.\n",
+        "* Compare the performance of the two models."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "cNS0IT9lkfDD"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Accuracy with original data: 0.9416666666666667\n",
+            "Accuracy with PCA-reduced data: 0.95\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "# your ML model\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "\n",
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n",
+        "\n",
+        "# Train a model on the original dataset\n",
+        "model_original = LogisticRegression(max_iter=1000)\n",
+        "model_original.fit(X_train, y_train)\n",
+        "y_pred_original = model_original.predict(X_test)\n",
+        "accuracy_original = accuracy_score(y_test, y_pred_original)\n",
+        "\n",
+        "# Train a model on the PCA-reduced dataset\n",
+        "X_train_pca, X_test_pca = pca_n.transform(X_train), pca_n.transform(X_test)\n",
+        "model_pca =  LogisticRegression(max_iter=1000)\n",
+        "model_pca.fit(X_train_pca, y_train)\n",
+        "y_pred_pca = model_pca.predict(X_test_pca)\n",
+        "accuracy_pca = accuracy_score(y_test, y_pred_pca)\n",
+        "\n",
+        "# Print the accuracy of both models\n",
+        "print(f\"Accuracy with original data: {accuracy_original}\")\n",
+        "print(f\"Accuracy with PCA-reduced data: {accuracy_pca}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "brddJjygkiFu"
+      },
+      "source": [
+        "## Conclusion\n",
+        "In this assignment, you've learned how to apply PCA on image data, how to choose the number of components, and the effects of PCA on image reconstruction and model performance. This exercise helps in understanding the balance between data reduction and information retention, which is crucial in many machine learning applications.\n",
+        "\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a12/assignment_12_03_02_dimensionality_reduction___nexus___aminran.py b/a0.1/a12/assignment_12_03_02_dimensionality_reduction___nexus___aminran.py
new file mode 100644
index 0000000..bd24019
--- /dev/null
+++ b/a0.1/a12/assignment_12_03_02_dimensionality_reduction___nexus___aminran.py
@@ -0,0 +1,172 @@
+# -*- coding: utf-8 -*-
+"""Assignment 12. 03.02. Dimensionality Reduction | Nexus | aminran.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1nb4E7feWT1PuOYYzmVncmjYrYx7a25vU
+
+# PCA on Olivetti Faces Dataset
+## Overview
+In this assignment, we explore Principal Component Analysis (PCA), a fundamental technique in machine learning for dimensionality reduction and feature extraction. We'll apply PCA to the Olivetti faces dataset to understand how it can be used to compress and reconstruct images.
+
+## Objectives
+1. Load and visualize the Olivetti faces dataset.
+2. Perform PCA to reduce the dimensionality of the dataset.
+3. Determine the optimal number of components using the elbow method.
+4. Visualize the effect of PCA on image reconstruction.
+5. Compare the performance of a model trained on the original dataset versus the PCA-reduced dataset.
+
+## Prerequisites
+Basic understanding of Python and NumPy.
+Familiarity with matplotlib for plotting.
+Basic knowledge of machine learning concepts, especially PCA.
+
+## Step 1: Load and Visualize the Dataset
+"""
+
+# Import necessary libraries
+from sklearn.datasets import fetch_olivetti_faces
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Load the Olivetti faces dataset
+faces = fetch_olivetti_faces()
+X, y = faces['data'], faces['target']
+
+# Print the shape of the dataset
+print(X.shape)
+
+# Select 100 faces randomly for visualization
+np.random.seed(0)  # Ensure reproducibility
+X_samples = np.random.permutation(X)[:100]
+
+# Plot the selected faces
+fig, axes = plt.subplots(10, 10, figsize=(12, 12))
+fig.subplots_adjust(hspace=0.01, wspace=0.01)
+
+for i, ax in enumerate(axes.flat):
+    ax.imshow(X_samples[i].reshape((64, 64)), cmap='gray')
+    ax.set_xticks(())
+    ax.set_yticks(())
+plt.show()
+
+"""## Step 2: Apply PCA on the Dataset
+### Task
+* Implement PCA on the dataset.
+* Visualize the variance explained by each principal component.
+"""
+
+# Import PCA from sklearn
+from sklearn.decomposition import PCA
+
+# Apply PCA on the dataset (leave number of components blank)
+pca = PCA()
+X_pca = pca.fit_transform(X)
+
+# Visualize the variance explained by each principal component
+plt.figure(figsize=(8, 4))
+plt.plot(np.cumsum(pca.explained_variance_ratio_))
+plt.xlabel('Number of Components')
+plt.ylabel('Cumulative Explained Variance')
+plt.grid(True)
+plt.show()
+
+"""## Step 3: Elbow Method for Optimal Components
+### Task
+* Plot the cumulative explained variance against the number of components.
+* Use the elbow method to determine a good number of components for PCA.
+"""
+
+# YOUR CODE HERE
+# Plot the cumulative explained variance against the number of components
+plt.figure(figsize=(8, 4))
+plt.plot(np.cumsum(pca.explained_variance_ratio_))
+plt.xlabel('Number of Components')
+plt.ylabel('Cumulative Explained Variance')
+plt.title('Elbow Method for Optimal PCA Components')
+plt.axhline(y=0.95, color='r', linestyle='--') # Example threshold line at 95% variance
+plt.grid(True)
+plt.show()
+
+"""## Step 4: Image Reconstruction
+### Task
+* Reconstruct images using the selected number of PCA components.
+* Plot original and reconstructed images side-by-side.
+"""
+
+# YOUR CODE HERE for "Reconstruction": use "X_reconstructed"
+
+X_reconstructed = pca.inverse_transform(X_pca)
+
+
+np.random.seed(0)  # Ensure reproducibility
+X_samples = np.random.permutation(X_reconstructed)[:100]
+
+# Plot the selected faces
+fig, axes = plt.subplots(10, 10, figsize=(12, 12))
+fig.subplots_adjust(hspace=0.01, wspace=0.01)
+
+for i, ax in enumerate(axes.flat):
+    ax.imshow(X_samples[i].reshape((64, 64)), cmap='gray')
+    ax.set_xticks(())
+    ax.set_yticks(())
+plt.show()
+
+# Plot original and reconstructed images side-by-side
+fig, axes = plt.subplots(2, 5, figsize=(12, 6))
+for i in range(5):
+    # Original Image
+    ax = axes[0, i]
+    ax.imshow(X[i].reshape(64, 64), cmap='gray')
+    ax.set_xticks(())
+    ax.set_yticks(())
+    ax.set_title("Original")
+
+    # Reconstructed Image
+    ax = axes[1, i]
+    ax.imshow(X_reconstructed[i].reshape(64, 64), cmap='gray')
+    ax.set_xticks(())
+    ax.set_yticks(())
+    ax.set_title("Reconstructed")
+
+plt.tight_layout()
+plt.show()
+
+"""## Step 5: Compare Models
+### Task
+* Train a model using the original dataset and the PCA-reduced dataset.
+* Compare the performance of the two models.
+"""
+
+# YOUR CODE HERE
+from sklearn.model_selection import train_test_split
+# your ML model
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
+
+# Train a model on the original dataset
+model_original = SVC()
+model_original.fit(X_train, y_train)
+y_pred_original = model_original.predict(X_test)
+accuracy_original = accuracy_score(y_test, y_pred_original)
+
+# Train a model on the PCA-reduced dataset
+X_train_pca, X_test_pca = pca.transform(X_train), pca.transform(X_test)
+model_pca = SVC()
+model_pca.fit(X_train_pca, y_train)
+y_pred_pca = model_pca.predict(X_test_pca)
+accuracy_pca = accuracy_score(y_test, y_pred_pca)
+
+# Print the accuracy of both models
+print(f"Accuracy with original data: {accuracy_original}")
+print(f"Accuracy with PCA-reduced data: {accuracy_pca}")
+
+"""## Conclusion
+In this assignment, you've learned how to apply PCA on image data, how to choose the number of components, and the effects of PCA on image reconstruction and model performance. This exercise helps in understanding the balance between data reduction and information retention, which is crucial in many machine learning applications.
+
+
+"""
\ No newline at end of file
diff --git a/a0.1/a13/Assignment_13_03_03_Recommender_Systems___Nexus___rsayyareh.ipynb b/a0.1/a13/Assignment_13_03_03_Recommender_Systems___Nexus___rsayyareh.ipynb
new file mode 100644
index 0000000..33eb1ba
--- /dev/null
+++ b/a0.1/a13/Assignment_13_03_03_Recommender_Systems___Nexus___rsayyareh.ipynb
@@ -0,0 +1,270 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ML_Asy5rAlzn"
+      },
+      "source": [
+        "# Assignment 7: Movie Recommender\n",
+        "\n",
+        "**Objective**: Develop two recommender systems, one based on collaborative filtering using matrix factorization and another based on a content-based approach using multiple features of the data.\n",
+        "\n",
+        "## Your major tasks are:\n",
+        "1. Describing the dataset in detail.\n",
+        "2. Preparing dataset ready for the models\n",
+        "3. Training Matrix Factorization as oen methods of Collaborative Filtering\n",
+        "4. Training Content-based model\n",
+        "5. Discuss the result of the models with one or two example of recommendation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "Q_pP7ea9BUMK"
+      },
+      "outputs": [],
+      "source": [
+        "# Import necessary Libraries for dataframes, arrays, and train_test_split\n",
+        "\n",
+        "import pandas as pd\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "import warnings\n",
+        "warnings.filterwarnings(\"ignore\")\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "lIYdn1woOS1n"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(100000, 25)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load the data\n",
+        "# ratings_url = 'http://files.grouplens.org/datasets/movielens/ml-100k/u.data'\n",
+        "# movies_url = 'http://files.grouplens.org/datasets/movielens/ml-100k/u.item'\n",
+        "ratings_url = 'E:/Nexus/Nexus_Assignments/Assignments/a13/u.data'\n",
+        "movies_url = 'E:/Nexus/Nexus_Assignments/Assignments/a13/u.item'\n",
+        "# remember that you have to search about this famous dataset\n",
+        "\n",
+        "ratings = pd.read_csv(ratings_url, sep=None, names=['user_id', 'movie_id', 'rating', 'timestamp'])\n",
+        "\n",
+        "# Load the movies data\n",
+        "movies = pd.read_csv(movies_url, sep='|', header=None, encoding='latin-1')\n",
+        "\n",
+        "# Define the correct column names (adjust based on actual data format)\n",
+        "movies.columns = ['movie_id', 'title', 'release_date', 'video_release_date',\n",
+        "                  'IMDb_URL', 'unknown', 'Action', 'Adventure', 'Animation',\n",
+        "                  'Children', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy',\n",
+        "                  'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi',\n",
+        "                  'Thriller', 'War', 'Western']\n",
+        "\n",
+        "# Drop unnecessary columns if needed\n",
+        "movies = movies.drop(['video_release_date', 'IMDb_URL'], axis=1)\n",
+        "\n",
+        "# Merge datasets\n",
+        "data = pd.merge(ratings, movies, on='movie_id', how='inner')\n",
+        "print(data.shape)\n",
+        "\n",
+        "# Train-test split with 80% train share\n",
+        "train_data, test_data = train_test_split(data, test_size=0.2)\n",
+        "\n",
+        "# Aggregate the data to avoid duplicates\n",
+        "train_data_grouped = train_data.groupby(['user_id', 'movie_id']).agg({'rating': 'mean'}).reset_index()\n",
+        "\n",
+        "# Create the user-item matrix\n",
+        "train_matrix = train_data_grouped.pivot_table(index='user_id', columns='movie_id', values='rating').fillna(0)\n",
+        "\n",
+        "# For the test matrix, you might need to repeat the same process\n",
+        "test_data_grouped = test_data.groupby(['user_id', 'movie_id']).agg({'rating': 'mean'}).reset_index()\n",
+        "test_matrix = test_data_grouped.pivot_table(index='user_id', columns='movie_id', values='rating').fillna(0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "TUijmaxI_6Qs"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Example: Distribution of User Ratings\n",
+        "data['rating'].hist(bins=10)\n",
+        "plt.title('Distribution of User Ratings')\n",
+        "plt.xlabel('Rating')\n",
+        "plt.ylabel('Number of Ratings')\n",
+        "plt.show()\n",
+        "\n",
+        "\n",
+        "# you should provide more visualization to illustrate the nature of the dataset"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zRBNaCH4FV_g"
+      },
+      "source": [
+        "### Matrix Factorization (Collaborative Filtering)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "VnB1zyt63zh0"
+      },
+      "outputs": [],
+      "source": [
+        "from scipy.sparse.linalg import svds\n",
+        "import numpy as np\n",
+        "\n",
+        "# Perform matrix factorization\n",
+        "U, sigma, Vt = svds(train_matrix.values, k=50)\n",
+        "\n",
+        "# Predictions\n",
+        "sigma = np.diag(sigma)\n",
+        "predicted_ratings = np.dot(np.dot(U, sigma), Vt)\n",
+        "\n",
+        "# you should interpret the result that this model has provided.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vnUMBadhFQSD"
+      },
+      "source": [
+        "### COntent-based Filtering"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "n3F-8_Gp6lvB"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Index(['movie_id', 'title', 'release_date', 'unknown', 'Action', 'Adventure',\n",
+            "       'Animation', 'Children', 'Comedy', 'Crime', 'Documentary', 'Drama',\n",
+            "       'Fantasy', 'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance',\n",
+            "       'Sci-Fi', 'Thriller', 'War', 'Western'],\n",
+            "      dtype='object')\n"
+          ]
+        }
+      ],
+      "source": [
+        "from sklearn.metrics.pairwise import cosine_similarity\n",
+        "\n",
+        "# Assuming the genre columns start from the 'unknown' column inclusive onwards\n",
+        "print(movies.columns)\n",
+        "genres_matrix = movies.iloc[:, 4:]\n",
+        "\n",
+        "# Compute the cosine similarity matrix\n",
+        "cosine_sim = cosine_similarity(genres_matrix, genres_matrix)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "lhNOaK5y9IGk"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "421     Aladdin and the King of Thieves (1996)\n",
+            "94                              Aladdin (1992)\n",
+            "1218                     Goofy Movie, A (1995)\n",
+            "62                    Santa Clause, The (1994)\n",
+            "93                           Home Alone (1990)\n",
+            "101                     Aristocats, The (1970)\n",
+            "137                D3: The Mighty Ducks (1996)\n",
+            "138                       Love Bug, The (1969)\n",
+            "168                 Wrong Trousers, The (1993)\n",
+            "188                    Grand Day Out, A (1992)\n",
+            "Name: title, dtype: object\n"
+          ]
+        }
+      ],
+      "source": [
+        "def content_based_recommendations(movie_id, cosine_sim=cosine_sim, movies=movies, top_n=10):\n",
+        "    # Find the index of the given movie\n",
+        "    idx = movies.index[movies['movie_id'] == movie_id].tolist()[0]\n",
+        "\n",
+        "    # Get the pairwise similarity scores of all movies with that movie\n",
+        "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
+        "\n",
+        "    # Sort the movies based on the similarity scores\n",
+        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
+        "\n",
+        "    # Get the scores of the top-n most similar movies\n",
+        "    sim_scores = sim_scores[1:top_n+1]\n",
+        "\n",
+        "    # Get the movie indices\n",
+        "    movie_indices = [i[0] for i in sim_scores]\n",
+        "\n",
+        "    # Return the top-n most similar movies\n",
+        "    return movies['title'].iloc[movie_indices]\n",
+        "\n",
+        "# Example usage\n",
+        "print(content_based_recommendations(1))\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a13/assignment_13_03_03_recommender_systems___nexus___aminran.py b/a0.1/a13/assignment_13_03_03_recommender_systems___nexus___aminran.py
new file mode 100644
index 0000000..97fc800
--- /dev/null
+++ b/a0.1/a13/assignment_13_03_03_recommender_systems___nexus___aminran.py
@@ -0,0 +1,117 @@
+# -*- coding: utf-8 -*-
+"""Assignment 13. 03.03. Recommender Systems | Nexus | aminran.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/10NWtYRaIwgwb_2Ac6Lle_OFMi-bJSla1
+
+# Assignment 7: Movie Recommender
+
+**Objective**: Develop two recommender systems, one based on collaborative filtering using matrix factorization and another based on a content-based approach using multiple features of the data.
+
+## Your major tasks are:
+1. Describing the dataset in detail.
+2. Preparing dataset ready for the models
+3. Training Matrix Factorization as oen methods of Collaborative Filtering
+4. Training Content-based model
+5. Discuss the result of the models with one or two example of recommendation.
+"""
+
+# Import necessary Libraries for dataframes, arrays, and train_test_split
+
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+# Load the data
+ratings_url = 'http://files.grouplens.org/datasets/movielens/ml-100k/u.data'
+movies_url = 'http://files.grouplens.org/datasets/movielens/ml-100k/u.item'
+# remember that you have to search about this famous dataset
+
+ratings = pd.read_csv(ratings_url, sep='	', names=['user_id', 'movie_id', 'rating', 'timestamp'])
+# Load the movies data
+movies = pd.read_csv(movies_url, sep='|', header=None, encoding='latin-1')
+
+# Define the correct column names (adjust based on actual data format)
+movies.columns = ['movie_id', 'title', 'release_date', 'video_release_date',
+                  'IMDb_URL', 'unknown', 'Action', 'Adventure', 'Animation',
+                  'Children', 'Comedy', 'Crime', 'Documentary', 'Drama', 'Fantasy',
+                  'Film-Noir', 'Horror', 'Musical', 'Mystery', 'Romance', 'Sci-Fi',
+                  'Thriller', 'War', 'Western']
+
+# Drop unnecessary columns if needed
+movies = movies.drop(['video_release_date', 'IMDb_URL'], axis=1)
+
+# Merge datasets
+data = pd.merge(ratings, movies, on='movie_id')
+
+# Train-test split with 80% train share
+train_data, test_data = train_test_split(data, test_size=0.2)
+
+# Aggregate the data to avoid duplicates
+train_data_grouped = train_data.groupby(['user_id', 'movie_id']).agg({'rating': 'mean'}).reset_index()
+
+# Create the user-item matrix
+train_matrix = train_data_grouped.pivot_table(index='user_id', columns='movie_id', values='rating').fillna(0)
+
+# For the test matrix, you might need to repeat the same process
+test_data_grouped = test_data.groupby(['user_id', 'movie_id']).agg({'rating': 'mean'}).reset_index()
+test_matrix = test_data_grouped.pivot_table(index='user_id', columns='movie_id', values='rating').fillna(0)
+
+import matplotlib.pyplot as plt
+
+# Example: Distribution of User Ratings
+data['rating'].hist(bins=5)
+plt.title('Distribution of User Ratings')
+plt.xlabel('Rating')
+plt.ylabel('Number of Ratings')
+plt.show()
+
+
+# you should provide more visualization to illustrate the nature of the dataset
+
+"""### Matrix Factorization (Collaborative Filtering)"""
+
+from scipy.sparse.linalg import svds
+
+# Perform matrix factorization
+U, sigma, Vt = svds(train_matrix.values, k=50)
+
+# Predictions
+sigma = np.diag(sigma)
+predicted_ratings = np.dot(np.dot(U, sigma), Vt)
+
+# you should interpret the result that this model has provided.
+
+"""### COntent-based Filtering"""
+
+from sklearn.metrics.pairwise import cosine_similarity
+
+# Assuming the genre columns start from the 'unknown' column inclusive onwards
+
+genres_matrix = movies.iloc[:, 3:]
+# Compute the cosine similarity matrix
+cosine_sim = cosine_similarity(genres_matrix, genres_matrix)
+
+def content_based_recommendations(movie_id, cosine_sim=cosine_sim, movies=movies, top_n=10):
+    # Find the index of the given movie
+    idx = movies.index[movies['movie_id'] == movie_id].tolist()[0]
+
+    # Get the pairwise similarity scores of all movies with that movie
+    sim_scores = list(enumerate(cosine_sim[idx]))
+
+    # Sort the movies based on the similarity scores
+    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
+
+    # Get the scores of the top-n most similar movies
+    sim_scores = sim_scores[1:top_n+1]
+
+    # Get the movie indices
+    movie_indices = [i[0] for i in sim_scores]
+
+    # Return the top-n most similar movies
+    return movies['title'].iloc[movie_indices]
+
+# Example usage
+print(content_based_recommendations(1))
\ No newline at end of file
diff --git a/a0.1/a14/Assignment_14_03_02_Anomaly_Detection___Nexus___rsayyareh.ipynb b/a0.1/a14/Assignment_14_03_02_Anomaly_Detection___Nexus___rsayyareh.ipynb
new file mode 100644
index 0000000..40991a9
--- /dev/null
+++ b/a0.1/a14/Assignment_14_03_02_Anomaly_Detection___Nexus___rsayyareh.ipynb
@@ -0,0 +1,390 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bZK38f6lyawa"
+      },
+      "source": [
+        "# Credit Card Fraud Detection\n",
+        "\n",
+        "The dataset Repo:\n",
+        "https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud?resource=download\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r3bvXcvPzfyn"
+      },
+      "source": [
+        "### Import packages"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "cOFpKUmnyrVR"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.ensemble import IsolationForest #iForest\n",
+        "from sklearn.neighbors import LocalOutlierFactor #LOF\n",
+        "from sklearn.covariance import EllipticEnvelope #Robust Coveriance\n",
+        "from sklearn.svm import OneClassSVM #OCSVM\n",
+        "from sklearn.metrics import classification_report, accuracy_score\n",
+        "\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Iv0uNUaOzk3r"
+      },
+      "source": [
+        "### EDA\n",
+        "\n",
+        "Explor on data and show any interesting fact abuot this famous dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "p966CwNGz0lM"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "(284807, 31)\n",
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 284807 entries, 0 to 284806\n",
+            "Data columns (total 31 columns):\n",
+            " #   Column  Non-Null Count   Dtype  \n",
+            "---  ------  --------------   -----  \n",
+            " 0   Time    284807 non-null  float64\n",
+            " 1   V1      284807 non-null  float64\n",
+            " 2   V2      284807 non-null  float64\n",
+            " 3   V3      284807 non-null  float64\n",
+            " 4   V4      284807 non-null  float64\n",
+            " 5   V5      284807 non-null  float64\n",
+            " 6   V6      284807 non-null  float64\n",
+            " 7   V7      284807 non-null  float64\n",
+            " 8   V8      284807 non-null  float64\n",
+            " 9   V9      284807 non-null  float64\n",
+            " 10  V10     284807 non-null  float64\n",
+            " 11  V11     284807 non-null  float64\n",
+            " 12  V12     284807 non-null  float64\n",
+            " 13  V13     284807 non-null  float64\n",
+            " 14  V14     284807 non-null  float64\n",
+            " 15  V15     284807 non-null  float64\n",
+            " 16  V16     284807 non-null  float64\n",
+            " 17  V17     284807 non-null  float64\n",
+            " 18  V18     284807 non-null  float64\n",
+            " 19  V19     284807 non-null  float64\n",
+            " 20  V20     284807 non-null  float64\n",
+            " 21  V21     284807 non-null  float64\n",
+            " 22  V22     284807 non-null  float64\n",
+            " 23  V23     284807 non-null  float64\n",
+            " 24  V24     284807 non-null  float64\n",
+            " 25  V25     284807 non-null  float64\n",
+            " 26  V26     284807 non-null  float64\n",
+            " 27  V27     284807 non-null  float64\n",
+            " 28  V28     284807 non-null  float64\n",
+            " 29  Amount  284807 non-null  float64\n",
+            " 30  Class   284807 non-null  int64  \n",
+            "dtypes: float64(30), int64(1)\n",
+            "memory usage: 67.4 MB\n",
+            "None\n",
+            "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
+            "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
+            "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
+            "       'Class'],\n",
+            "      dtype='object')\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load the dataset (adjust the path as necessary)\n",
+        "data = pd.read_csv('E:/Nexus/Nexus_Assignments/Assignments/a14/creditcard.csv')\n",
+        "\n",
+        "# EDA\n",
+        "print(data.shape)\n",
+        "print(data.info())\n",
+        "print(data.columns)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iu0-gYnzz54w"
+      },
+      "source": [
+        "### Preprocessing Phase"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "lIYdn1woOS1n"
+      },
+      "outputs": [],
+      "source": [
+        "# Assuming 'Class' is the column indicating fraud (1) or non-fraud (0)\n",
+        "X = data.drop('Class', axis=1)\n",
+        "y = data['Class']\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cTZpHCuo0B_r"
+      },
+      "source": [
+        "### Prepare the models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "WTqJ0e1F0Ag7"
+      },
+      "outputs": [],
+      "source": [
+        "# Define the models\n",
+        "models = {\n",
+        "    \"Isolation Forest\": IsolationForest(n_estimators=100, contamination='auto', random_state=42),\n",
+        "    \"Local Outlier Factor\": LocalOutlierFactor(n_neighbors=20, contamination='auto'),\n",
+        "    \"Robust Covariance\": EllipticEnvelope(support_fraction=1., contamination=0.1),\n",
+        "    \"One-Class SVM\": OneClassSVM(kernel='linear', gamma=0.001, nu=0.05),\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GvUn1hA30JDk"
+      },
+      "source": [
+        "### Traning the models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "HC7aaIVG0Tc3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "=== Isolation Forest ===\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       1.00      0.96      0.98     56864\n",
+            "           1       0.04      0.83      0.07        98\n",
+            "\n",
+            "    accuracy                           0.96     56962\n",
+            "   macro avg       0.52      0.89      0.53     56962\n",
+            "weighted avg       1.00      0.96      0.98     56962\n",
+            "\n",
+            "\n",
+            "=== Local Outlier Factor ===\n",
+            "LOF does not support test-set prediction unless novelty=True.\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       1.00      0.98      0.99    227451\n",
+            "           1       0.02      0.16      0.03       394\n",
+            "\n",
+            "    accuracy                           0.98    227845\n",
+            "   macro avg       0.51      0.57      0.51    227845\n",
+            "weighted avg       1.00      0.98      0.99    227845\n",
+            "\n",
+            "\n",
+            "=== Robust Covariance ===\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       1.00      0.90      0.95     56864\n",
+            "           1       0.01      0.73      0.02        98\n",
+            "\n",
+            "    accuracy                           0.90     56962\n",
+            "   macro avg       0.51      0.82      0.49     56962\n",
+            "weighted avg       1.00      0.90      0.95     56962\n",
+            "\n",
+            "\n",
+            "=== One-Class SVM ===\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       1.00      0.95      0.97     56864\n",
+            "           1       0.00      0.07      0.00        98\n",
+            "\n",
+            "    accuracy                           0.95     56962\n",
+            "   macro avg       0.50      0.51      0.49     56962\n",
+            "weighted avg       1.00      0.95      0.97     56962\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Provide a for loop to walk on the models dict and train each of them one by one within the loop\n",
+        "\n",
+        "for name, model in models.items():\n",
+        "    print(f\"\\n=== {name} ===\")\n",
+        "    \n",
+        "    # LOF requires special handling (no .predict() on test set by default)\n",
+        "    if name == \"Local Outlier Factor\":\n",
+        "        # LOF predicts during training using fit_predict\n",
+        "        y_pred_train = model.fit_predict(X_train)\n",
+        "        # LOF outputs -1 for anomalies, 1 for normal → convert to (0,1)\n",
+        "        y_pred_train = (y_pred_train == -1).astype(int)\n",
+        "        print(\"LOF does not support test-set prediction unless novelty=True.\")\n",
+        "        print(classification_report(y_train, y_pred_train))\n",
+        "        continue\n",
+        "\n",
+        "    # For all other models: fit → predict\n",
+        "    model.fit(X_train)\n",
+        "    y_pred = model.predict(X_test)\n",
+        "\n",
+        "    # Convert output (-1 = anomaly, 1 = normal) to (1 = anomaly, 0 = normal)\n",
+        "    y_pred = (y_pred == -1).astype(int)\n",
+        "\n",
+        "    print(classification_report(y_test, y_pred))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8WsSVITazewJ"
+      },
+      "source": [
+        "### Evaluate the models\n",
+        "You can use the following setup as a guidline to evaluate and compare the performance of models."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "5S-2jVxzzcOM"
+      },
+      "outputs": [],
+      "source": [
+        "# Reshape the prediction values to 0 (normal) and 1 (fraud)\n",
+        "# y_pred[y_pred == 1] = 0\n",
+        "# y_pred[y_pred == -1] = 1\n",
+        "\n",
+        "# Calculate accuracy and other metrics\n",
+        "# print(f\"{name}:\")\n",
+        "# print(classification_report(y_test, y_pred))\n",
+        "# print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
+        "# print(\"-\" * 30)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YPIUyCfa0oGi"
+      },
+      "source": [
+        "### Interpretation\n",
+        "Highlight the best model performance and explain what caused this model can outperform others?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Best Model Performance (using ChatGPT): Isolation Forest\n",
+        "\n",
+        "From the results, Isolation Forest stands out as the best-performing model based on its f1-score, recall, and precision metrics for both classes. Let's break down the metrics for Isolation Forest and compare them with others:\n",
+        "\n",
+        "Precision (Anomalies = 1):\n",
+        "\n",
+        "Isolation Forest: 0.04, which is quite low, but typical in anomaly detection due to the imbalanced nature of the dataset.\n",
+        "\n",
+        "LOF: 0.02 (lower than Isolation Forest).\n",
+        "\n",
+        "Robust Covariance: 0.01 (even lower).\n",
+        "\n",
+        "One-Class SVM: 0.00 (worst precision).\n",
+        "\n",
+        "Recall (Anomalies = 1):\n",
+        "\n",
+        "Isolation Forest: 0.83 (fairly high for anomaly detection).\n",
+        "\n",
+        "LOF: 0.16.\n",
+        "\n",
+        "Robust Covariance: 0.73.\n",
+        "\n",
+        "One-Class SVM: 0.07 (very low recall).\n",
+        "\n",
+        "f1-score (Anomalies = 1):\n",
+        "\n",
+        "Isolation Forest: 0.07 (the highest among the models for anomalies).\n",
+        "\n",
+        "LOF: 0.03.\n",
+        "\n",
+        "Robust Covariance: 0.02.\n",
+        "\n",
+        "One-Class SVM: 0.00.\n",
+        "\n",
+        "Accuracy:\n",
+        "\n",
+        "Isolation Forest: 0.96 (highest accuracy).\n",
+        "\n",
+        "LOF: 0.98 (best accuracy).\n",
+        "\n",
+        "Robust Covariance: 0.90.\n",
+        "\n",
+        "One-Class SVM: 0.95.\n",
+        "\n",
+        "Explanation of Outperformance:\n",
+        "\n",
+        "Isolation Forest has a stronger recall for anomalies (0.83), meaning it detects more of the actual anomalies compared to the other models. Despite having a low precision for anomalies (0.04), it manages to maintain a high accuracy (0.96) by identifying the majority of the non-anomalies correctly. This is typical for imbalanced anomaly detection, where models tend to predict the majority class (non-anomalies) correctly and fail to catch a lot of anomalies.\n",
+        "\n",
+        "LOF also shows a good accuracy and recall but fails to achieve the f1-score balance that Isolation Forest does for anomaly detection. LOF struggles with precision, especially when trying to detect anomalies (precision = 0.02), meaning it has a lot of false positives for anomalies.\n",
+        "\n",
+        "Robust Covariance and One-Class SVM both have very poor performance in detecting anomalies (low recall and f1-score for anomalies). Their accuracies are lower than Isolation Forest, and they fail to perform well in anomaly detection."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.11"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/a14/assignment_14_03_02_anomaly_detection___nexus___aminran.py b/a0.1/a14/assignment_14_03_02_anomaly_detection___nexus___aminran.py
new file mode 100644
index 0000000..22be539
--- /dev/null
+++ b/a0.1/a14/assignment_14_03_02_anomaly_detection___nexus___aminran.py
@@ -0,0 +1,110 @@
+# -*- coding: utf-8 -*-
+"""Assignment 14. 03.02. Anomaly Detection | Nexus | aminran.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1VQTMfjWCz4IbCfmeg5fah-_MvE2h5Ap4
+
+# Credit Card Fraud Detection
+
+The dataset Repo:
+https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud?resource=download
+
+### Import packages
+"""
+
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import IsolationForest #iForest
+from sklearn.neighbors import LocalOutlierFactor #LOF
+from sklearn.covariance import EllipticEnvelope #Robust Coveriance
+from sklearn.svm import OneClassSVM #OCSVM
+from sklearn.metrics import classification_report, accuracy_score
+
+"""### EDA
+
+Explor on data and show any interesting fact abuot this famous dataset.
+"""
+
+# Load the dataset (adjust the path as necessary)
+# Install dependencies as needed:
+# pip install kagglehub[pandas-datasets]
+import kagglehub
+from kagglehub import KaggleDatasetAdapter
+
+# Set the path to the file you'd like to load
+file_path = "creditcard.csv"
+
+# Load the latest version
+data = kagglehub.load_dataset(
+  KaggleDatasetAdapter.PANDAS,
+  "mlg-ulb/creditcardfraud",
+  file_path,
+  # Provide any additional arguments like
+  # sql_query or pandas_kwargs. See the
+  # documenation for more information:
+  # https://github.com/Kaggle/kagglehub/blob/main/README.md#kaggledatasetadapterpandas
+)
+
+
+pd.set_option('display.max_columns', None)
+
+print("First 5 records:\n", data.head())
+print("Data shape:", data.shape)
+data['Class'].value_counts()
+
+"""### Preprocessing Phase"""
+
+# Assuming 'Class' is the column indicating fraud (1) or non-fraud (0)
+X = data.drop('Class', axis=1)
+y = data['Class']
+
+# For anomaly detection, we often deal with unsupervised methods, so we'll ignore the y label during training
+# Split the dataset into training and test sets
+# You can also slice data to train the model with almost 10000 records only in case the training time rises
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+"""### Prepare the models"""
+
+# Define the models
+models = {
+    "Isolation Forest": IsolationForest(n_estimators=100, contamination='auto', random_state=42),
+    "Local Outlier Factor": LocalOutlierFactor(n_neighbors=20, contamination='auto'),
+    "Robust Covariance": EllipticEnvelope(support_fraction=1., contamination=0.1),
+    "One-Class SVM": OneClassSVM(kernel='linear', gamma=0.001, nu=0.05),
+}
+
+"""### Traning the models"""
+
+# you should notice that LOF only has fit_predict method.
+# Provide a for loop to walk on the models dict and train each of them one by one within the loop
+for name, model in models.items():
+    print(f"Training {name}...")
+    if name == "Local Outlier Factor":
+        # LOF has fit_predict which returns the predictions directly
+        y_pred = model.fit_predict(X_train)
+    else:
+        # For other models, fit on the training data
+        model.fit(X_train)
+        # Predict on the test set
+        y_pred = model.predict(X_test)
+
+"""### Evaluate the models
+You can use the following setup as a guidline to evaluate and compare the performance of models.
+"""
+
+for name, model in models.items():
+      # Reshape the prediction values to 0 (normal) and 1 (fraud)
+      y_pred[y_pred == 1] = 0
+      y_pred[y_pred == -1] = 1
+
+      # Calculate accuracy and other metrics
+      print(f"{name}:")
+      print(classification_report(y_test, y_pred))
+      print("Accuracy:", accuracy_score(y_test, y_pred))
+      print("-" * 30)
+
+"""### Interpretation
+Highlight the best model performance and explain what caused this model can outperform others?
+"""
\ No newline at end of file
diff --git a/a0.1/a15/New Text Document.txt b/a0.1/a15/New Text Document.txt
new file mode 100644
index 0000000..757da14
--- /dev/null
+++ b/a0.1/a15/New Text Document.txt	
@@ -0,0 +1,4 @@
+تمرین این جلسه (تکلیف ۱۵):
+نوت‌بوک کلاسبندی متن را کپی کنید. با تغییرات هایپرپارامترها، روش embedding، یکی از مدلهای کلاسیک تا حد ممکن عملکرد مدل را بهبود بدید. فقط مدلهای کلاسیک برای کلاسبندی مجاز هستن. نتیجه را مثل تمارین قبلی به ریپوی مربوط ارسال کنید.
+
+نوت‌بوک تولید شعر فارسی با LSTM (https://colab.research.google.com/drive/1O8y6enaD8JjzKD1bazlRJqDJGecybpyA?usp=sharing)
\ No newline at end of file
diff --git a/a0.1/a15/TextClassification_aminran.ipynb b/a0.1/a15/TextClassification_aminran.ipynb
new file mode 100644
index 0000000..aae276b
--- /dev/null
+++ b/a0.1/a15/TextClassification_aminran.ipynb
@@ -0,0 +1,1067 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4giSCbj0Th8C"
+      },
+      "source": [
+        "# Sentiment Analysis: FastText + Classical ML\n",
+        "\n",
+        "End-to-end sentiment classification using FastText embeddings with XGBoost and Random Forest.\n",
+        "\n",
+        "**Pipeline:** Data loading (IMDB) → Preprocessing → FastText embeddings → Training → Evaluation"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BRwbyb93Th8E"
+      },
+      "source": [
+        "## 1. Setup & Install Dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "myZnABTZTh8E",
+        "outputId": "043b49ca-01d5-4fce-ae71-8f797b84ac9a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/73.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.4/73.4 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for fasttext (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "✓ All libraries ready\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -q fasttext xgboost\n",
+        "\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.metrics import (\n",
+        "    accuracy_score, precision_score, recall_score, f1_score,\n",
+        "    confusion_matrix, classification_report\n",
+        ")\n",
+        "import xgboost as xgb\n",
+        "import fasttext\n",
+        "import re\n",
+        "\n",
+        "#2d projection\n",
+        "from sklearn.manifold import TSNE\n",
+        "\n",
+        "# for dataset\n",
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.datasets import imdb\n",
+        "\n",
+        "import warnings\n",
+        "warnings.filterwarnings('ignore')\n",
+        "\n",
+        "plt.style.use('seaborn-v0_8-darkgrid')\n",
+        "sns.set_palette('husl')\n",
+        "\n",
+        "print(\"✓ All libraries ready\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2Q3EAZ83Th8F"
+      },
+      "source": [
+        "## 2. Load IMDB Dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "dFgvjd0lTh8G",
+        "outputId": "dbfe7fdd-893c-472f-c01f-6b3e91c11318"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                  review  sentiment\n",
+              "0      <START> this film was just brilliant casting l...          1\n",
+              "1      <START> big hair big boobs bad music and a gia...          0\n",
+              "2      <START> this has to be one of the worst films ...          0\n",
+              "3      <START> the scots excel at storytelling the tr...          1\n",
+              "4      <START> worst mistake of my life br br i picke...          0\n",
+              "...                                                  ...        ...\n",
+              "24995  <START> this is a racist movie but worthy of s...          1\n",
+              "24996  <START> bela lugosi plays a doctor who will do...          0\n",
+              "24997  <START> in a far away galaxy is a planet calle...          0\n",
+              "24998  <START> six degrees had me hooked i looked for...          1\n",
+              "24999  <START> as a big fan of the original film it's...          0\n",
+              "\n",
+              "[25000 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-ee3903c9-dac2-400f-831d-9469f84fbe19\" class=\"colab-df-container\">\n",
+              "    <div>\n",
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+              "    <tr>\n",
+              "      <th>24996</th>\n",
+              "      <td>&lt;START&gt; bela lugosi plays a doctor who will do...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24997</th>\n",
+              "      <td>&lt;START&gt; in a far away galaxy is a planet calle...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24998</th>\n",
+              "      <td>&lt;START&gt; six degrees had me hooked i looked for...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>24999</th>\n",
+              "      <td>&lt;START&gt; as a big fan of the original film it's...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
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+              "<p>25000 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
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+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
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+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "        document.querySelector('#df-ee3903c9-dac2-400f-831d-9469f84fbe19 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ee3903c9-dac2-400f-831d-9469f84fbe19');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-35101440-87f0-42a8-b5c6-eb5886df30b7 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_abf10fb7-ae12-4372-b3b2-9c6927c7c449\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_abf10fb7-ae12-4372-b3b2-9c6927c7c449 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 25000,\n  \"fields\": [\n    {\n      \"column\": \"review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24902,\n        \"samples\": [\n          \"<START> my family and i have viewed this movie often over the years it is clean wholesome heartbreaking and heartwarming showing us the compassion between two families of two countries thousands of miles apart and by the most uncanny of coincidences it's almost as if the hand of god had to be intervening br br 5 yo <UNK> <UNK> <UNK> who plays desi the heart stricken little girl does a magnificent job of acting her part and for me she was the <UNK> choice for the lead role br br all in all a 10 out of 10 there are no <UNK> to this sweet human story children of all ages will <UNK> then <UNK> watch this and it will bring the viewing family together with smiles and good feelings\",\n          \"<START> forget the campy <UNK> movies that have <UNK> the television film market this movie has a real feel to it while it may be deemed as a movie that has cheap emotional draws it also has that message of forgiveness and overall good morals however i did not like the lighting in this movie for a movie dealing with such subject matter it was too bright i felt it took away from the overall appeal of the movie which is almost an unforgivable sin but the recognizable cast and their performances <UNK> this oversight br br definitely worth seeing buy the dvd\",\n          \"<START> the <UNK> is an entertaining western no doubt a classic which is actual even today gary cooper is wild bill hickok ideal for the role together with john wayne and james stewart they were the best actors that played western heroes in their generation jean arthur is great as calamity jane nobody that i know played it better than her even if might not be historically accurate the film manages to capture the most important about hickok and about the time it takes place sometimes you have to sacrifice history to make your point and that is what demille does here the friendship of hickok with buffalo bill the selling of rifles to the indians by a great <UNK> to compensate for the losses he would have because of the end of the civil war custer and little big horn the uneasy relationship between buffalo bill's wife a religious woman with hickok a man who had killed plenty also the unusual love affair between hickok and calamity all this makes 'the <UNK> a non conventional and interesting film anthony quinn has a very short appearance that already shows what a great actor he was going to become a lot of care was taken to show the original guns of that time\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from tensorflow.keras.datasets import imdb\n",
+        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
+        "\n",
+        "# Load dataset\n",
+        "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=20000)\n",
+        "\n",
+        "word_index = imdb.get_word_index()\n",
+        "index_word = {v + 3: k for k, v in word_index.items()}\n",
+        "index_word[0] = \"<PAD>\"\n",
+        "index_word[1] = \"<START>\"\n",
+        "index_word[2] = \"<UNK>\"\n",
+        "\n",
+        "def decode_review(sequence):\n",
+        "    return \" \".join([index_word.get(i, \"<UNK>\") for i in sequence])\n",
+        "\n",
+        "X_train_text = [decode_review(x) for x in X_train]\n",
+        "X_test_text = [decode_review(x) for x in X_test]\n",
+        "\n",
+        "df = pd.DataFrame(X_train_text, columns=['review'])\n",
+        "df['sentiment'] = y_train"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_hD-4mrtTh8G"
+      },
+      "source": [
+        "### Data Visualization"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 581
+        },
+        "id": "v7I15D4bTh8G",
+        "outputId": "50478cfc-8842-4450-d34a-79aac548d506"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Review length statistics:\n",
+            "count    25000.000000\n",
+            "mean       238.710480\n",
+            "std        176.492037\n",
+            "min         11.000000\n",
+            "25%        130.000000\n",
+            "50%        178.000000\n",
+            "75%        291.000000\n",
+            "max       2494.000000\n",
+            "Name: review_length, dtype: float64\n"
+          ]
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+        "\n",
+        "sentiment_counts = df['sentiment'].value_counts().sort_index()\n",
+        "axes[0].bar(['Negative', 'Positive'], sentiment_counts.values, color=['#e74c3c', '#2ecc71'], alpha=0.8, edgecolor='black')\n",
+        "axes[0].set_ylabel('Count', fontsize=11)\n",
+        "axes[0].set_title('Sentiment Distribution', fontsize=12, fontweight='bold')\n",
+        "axes[0].grid(axis='y', alpha=0.3)\n",
+        "\n",
+        "df['review_length'] = df['review'].str.split().str.len()\n",
+        "axes[1].hist(df['review_length'], bins=30, color='#3498db', edgecolor='black', alpha=0.7)\n",
+        "axes[1].set_xlabel('Number of Words', fontsize=11)\n",
+        "axes[1].set_ylabel('Frequency', fontsize=11)\n",
+        "axes[1].set_title('Review Length Distribution', fontsize=12, fontweight='bold')\n",
+        "axes[1].grid(axis='y', alpha=0.3)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "print(f\"Review length statistics:\")\n",
+        "print(df['review_length'].describe())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nFxMyGveTh8G"
+      },
+      "source": [
+        "## 3. Text Preprocessing"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "J7L7QpsqTh8H"
+      },
+      "outputs": [],
+      "source": [
+        "import re\n",
+        "\n",
+        "def clean_text(text):\n",
+        "    text = text.lower()\n",
+        "    text = re.sub(r\"[^a-z\\s]\", \"\", text)\n",
+        "    return text.split()\n",
+        "\n",
+        "X_train_tokens = [clean_text(x) for x in X_train_text]\n",
+        "X_test_tokens = [clean_text(x) for x in X_test_text]\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lOBzdRr7Th8H"
+      },
+      "source": [
+        "## 4. FastText Embeddings"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WYVom7O-Th8H",
+        "outputId": "0870eb01-2e0e-4d26-ea54-74545aacda07"
+      },
+      "outputs": [
+        {
+          "metadata": {
+            "tags": null
+          },
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: gensim in /usr/local/lib/python3.12/dist-packages (4.4.0)\n",
+            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.12/dist-packages (from gensim) (2.0.2)\n",
+            "Requirement already satisfied: scipy>=1.7.0 in /usr/local/lib/python3.12/dist-packages (from gensim) (1.16.3)\n",
+            "Requirement already satisfied: smart_open>=1.8.1 in /usr/local/lib/python3.12/dist-packages (from gensim) (7.5.0)\n",
+            "Requirement already satisfied: wrapt in /usr/local/lib/python3.12/dist-packages (from smart_open>=1.8.1->gensim) (2.0.1)\n"
+          ]
+        }
+      ],
+      "source": [
+        "! pip install gensim\n",
+        "from gensim.models import FastText\n",
+        "\n",
+        "fasttext_model = FastText(\n",
+        "    sentences=X_train_tokens,\n",
+        "    vector_size=100,\n",
+        "    window=5,\n",
+        "    min_count=5,\n",
+        "    workers=4,\n",
+        "    sg=1  # Skip-gram\n",
+        ")\n",
+        "\n",
+        "\n",
+        "print(f\"✓ Training file created\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "-yCHwnudTh8H"
+      },
+      "outputs": [],
+      "source": [
+        "def sentence_vector(tokens, model, vector_size=100):\n",
+        "    vectors = [\n",
+        "        model.wv[word] for word in tokens if word in model.wv\n",
+        "    ]\n",
+        "    if len(vectors) == 0:\n",
+        "        return np.zeros(vector_size)\n",
+        "    return np.mean(vectors, axis=0)\n",
+        "\n",
+        "X_train_vec = np.array([\n",
+        "    sentence_vector(tokens, fasttext_model)\n",
+        "    for tokens in X_train_tokens\n",
+        "])\n",
+        "\n",
+        "X_test_vec = np.array([\n",
+        "    sentence_vector(tokens, fasttext_model)\n",
+        "    for tokens in X_test_tokens\n",
+        "])\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "o1A0_Q0oTh8I"
+      },
+      "source": [
+        "## 6. Model Training"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Unir9y1mTh8I",
+        "outputId": "1961e637-1ef5-4891-fbc8-5dd915ecfa7e"
+      },
+      "outputs": [
+        {
+          "metadata": {
+            "tags": null
+          },
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Training XGBoost...\n",
+            "XGBoost Accuracy: 0.85316\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Training XGBoost...\")\n",
+        "import xgboost as xgb\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "\n",
+        "xgb_model = xgb.XGBClassifier(\n",
+        "    n_estimators=600,\n",
+        "    max_depth=8,\n",
+        "    learning_rate=0.03,\n",
+        "    subsample=0.9,\n",
+        "    colsample_bytree=0.9,\n",
+        "    min_child_weight=3,\n",
+        "    gamma=0.1,\n",
+        "    reg_alpha=0.1,\n",
+        "    reg_lambda=1.0,\n",
+        "    tree_method=\"hist\"\n",
+        ")\n",
+        "\n",
+        "xgb_model.fit(X_train_vec, y_train)\n",
+        "xgb_preds = xgb_model.predict(X_test_vec)\n",
+        "\n",
+        "print(\"XGBoost Accuracy:\", accuracy_score(y_test, xgb_preds))\n",
+        "\n",
+        "\n"
+      ],
+      "execution_count": 10
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Bz1DKNpgTh8I",
+        "outputId": "2ed44f7a-3d6b-4fff-c46e-46a3f292858a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Training Random Forest...\n",
+            "Random Forest Accuracy: 0.82696\n",
+            "✓ Random Forest trained!\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"Training Random Forest...\")\n",
+        "\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "\n",
+        "rf_model = RandomForestClassifier(\n",
+        "    n_estimators=300,\n",
+        "    max_depth=25,\n",
+        "    random_state=42,\n",
+        "    n_jobs=-1\n",
+        ")\n",
+        "\n",
+        "rf_model.fit(X_train_vec, y_train)\n",
+        "rf_preds = rf_model.predict(X_test_vec)\n",
+        "\n",
+        "print(\"Random Forest Accuracy:\", accuracy_score(y_test, rf_preds))\n",
+        "\n",
+        "\n"
+      ],
+      "execution_count": 9
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IVrjr5vcTh8I"
+      },
+      "source": [
+        "## 7. Model Evaluation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zVUhTrNHTh8I",
+        "outputId": "1a8ece88-b0fb-446c-8d35-f9210fb950a2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "MODEL PERFORMANCE\n",
+            "===========================================================================\n",
+            "        Model  Accuracy  Precision  Recall  F1-Score\n",
+            "      XGBoost   0.85316   0.855979  0.8492  0.852576\n",
+            "Random Forest   0.82696   0.824726  0.8304  0.827553\n",
+            "===========================================================================\n"
+          ]
+        }
+      ],
+      "source": [
+        "def evaluate_model(y_true, y_pred, model_name):\n",
+        "    metrics = {\n",
+        "        'Model': model_name,\n",
+        "        'Accuracy': accuracy_score(y_true, y_pred),\n",
+        "        'Precision': precision_score(y_true, y_pred),\n",
+        "        'Recall': recall_score(y_true, y_pred),\n",
+        "        'F1-Score': f1_score(y_true, y_pred)\n",
+        "    }\n",
+        "    return metrics\n",
+        "\n",
+        "results = []\n",
+        "results.append(evaluate_model(y_test, xgb_preds, 'XGBoost'))\n",
+        "results.append(evaluate_model(y_test, rf_preds, 'Random Forest'))\n",
+        "\n",
+        "results_df = pd.DataFrame(results)\n",
+        "print(\"\\nMODEL PERFORMANCE\")\n",
+        "print(\"=\"*75)\n",
+        "print(results_df.to_string(index=False))\n",
+        "print(\"=\"*75)"
+      ],
+      "execution_count": 12
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 407
+        },
+        "id": "UP-akPydTh8I",
+        "outputId": "ad7c987a-36c2-457d-ee2b-b1b8ca6ddb11"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ],
+            "image/png": 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L5y/d0sGTF9XS58ktPgBsh6Ojo+bOWyhJKu1p+n/q5uYmF1dXhYeH69atWxo6fKRkNKqgx5M2CNF50u1bphnq+343zcytWLmKeUyFipW0acN67d2zWz19+2ir/xZFRkaqStVqypUrtySpZi1v1azl/dqPE7bNqkWpokWLqmjRojIYDBoxYsQzx9Ck8833yTe/SlK8RamUrk7maenXbt0zL792y5QsFcydMc57Uru5aJRvQ0nS6Fk/m5fvP3FB+09cSFBcM4a1lpOjg/pNWKrW9csl6D0ArKN33w8kKcFFqUuBgVryww9ycHCQb99+5uUNGjbWzh3b9ce+3/X3+QCldHPTzu3bJUm1674lSfrrL9Mvdnny5NXZv87I32+LwsPDVbFSZVWoWCkJjwp4NvInRPtktmkmeHxFqVg51M375uVP51BP50ZpU7nq7dqlJUkBl0y/dN75t/9U2lRP8jV7e1MrBAcHe+XLkUF/BlyVJM0Y1tKUQ01cptb1uKgH2KpUqVKpXIWKsZZt3rhBD+6bfkbkypVb5cpXiLX+8qVL2vfv7eE5/y0uBV40/QzJkOHJ72XRry9c+EeSdPbf/Cl33rw6cviQdu/aKXs7O3nVqKlixUsk8ZEhObGJnlIjRoyQ0WjUlStXYk03v379unx9fXXo0CErRgdrS+3man4dGhZhfv0oNCzO+mj9OnjLPU1K7Tx4Vr8f/TvR+2zToLxqlC+kRWt/l//vpylKAW+Y2V/PUkREuBo3aaZs2bKblzdo1Fjh4eGaMnmCmjU2zaa0s7NTtx691P3fW/yCgm5Lkv46c1qtW7ytyMhISdJ3c2er7wcD1K1HLwsfDf6ryJ/wIqlTuphfh4bHzKFMM5qelUM5Ozlo0cR3lT1zWkVFRWnqQn9J0h8nLqhVvbLyKltAtSt56MjpSxrc5S3z+9K4mfbVpn451ShXUIt+3if/389QlAKSkdOn/tQnY0dJkvLmy6+a3rFnMd27d1cfftBHjx6FyNnZWe06mvp1PnwYLEmxHvbi7GL6mRD8wLQuKMj0AIZdO7Zr8b+3BErS3Nlf65MJk9WwcZPXc1CweTZRlDpw4ID69eunO3fuSFKsfhx16tSxZmhIBozG2F+nSumiPu1qSZK+WOiX6O2ldnPRxAFv6+rNexoydWVShAjAhgRevKj1v/4iSXq3S9dY684HBOi7b+fowYMHql6jllKlTqWtfn5a8sMiFShYSPUbNFREhOkXu8DAi/rk00mqXbeuflz8g2Z9OV1zvvlKzd5+Rxkyxp3BCSQ18ie8KuNTSVQKFyetmNbdfMvfpHmbtftwgCTpu5W71bZBOZUtmkvrvjL1lYmeGSVJEZFRphyqf1NdvXVPQ75YbaGjAJAUjh09ot69uiv4wQO5pUqlTyd9JkfHJ0WmoNu31avbezp79i8ZDAYNHjZCuXPneeF2o9tsRudPly9d0ldzvlWJkqX15bSpWrHsJ33+2US9Va++HB0dX8ehwca9+PFjFjBhwgS1b99e69evl4ODg7Zs2aIvvvhCderU0ciRI60dHqzs3oNH5tcpYjTKdHV2+nd9SKzxTbxLKU0qVwXde6gtexN/+8LY3o2VJUNqfTh5ue7G2DeAN8Ov635WZGSkChQspIIFC8Va98nYUbp44R+1bN1GM7+erQmTpmjs+E/16FGIxo4aoeDgYKVM6SZJyp0nj5o0e1spU7qpS7cecnF1VVhYmM6c5rYpWAb5E17kXvBj8+vYOZTpF7+YOZazk4PWzuxpLkiN/eZX8+2Bkmm2eq33pqvPp0v1xff+8h3/kxr3/sa8/tbdYI31bfhvDrWSHApIRo4dPSLfHl0V/OCB0qfPoDnfLVCRosXM6+/cuaNu73XS2bN/ycHBQaPHjVfzFk8eruDmZuo99/jxk//3oY9NP39SpTLdQuz2b/5UrnwFVanqpVSpUqmnb29J0t07d3QpMPD1HiRslk3MlPr777/l6+srg8Egg8GgnDlzKmfOnMqaNauGDBmiBQsWWDtEWFHI4zBduXFX2TKlVdaMacxX5bJlSiNJOvPP9Vjj36nrKUnauPOkIiOjEr2/+tVNDY//93m3OOtG9GqgEb0ayNWzT6K3C8A2bNpo+iWrlnftOOv+/POkJMUqVhUoYHr96FGI/j5/Xnnz5pMUe5amvb29XF1d9fjRIz0IDn5doQOxkD/hRWLnUKmf5FAZo3OoJ43N545up2plCig8IlI9xvyonzYciLO98IhIzVu1x/x1heKmfjIPHj7WP5eDVN/L9Evs/6Z0ifPeET3ra0TP+nIt+0HSHSCAV3blymUN6NtbDx8+VJ68efXVnO9itTYIDw/TwP599ff5AKVIkUJTZ8xSpRgNzSXThbo/T57QzRtPfqbcuGH6HS1Pvrymv/Oa/o45QzP6Qp8kBQc/SPqDQ7JgEzOl0qRJo5s3b0qSUqdOrcB/q6TFihXTkSNHrBgZbMXmPX9KkmqUN/1y6Ohgr2plCkiSNu06aR5nb2+nWhVMT4b47d+nzCTWb3+c0fodJ2L9uXTNdGvE2Qs3tH7HiZc+DgDWde3qVZ0PMD2ts2KlynHWRz9p78yZJz8/zp37y/w6fYb08qpeQ/b29gq8eEF/nTn975izuvNvrwSPGE+nAV4n8ickxOY9pp9TNcoVlBSdQ+WXJG3abcqvWvmUUat6ZRUVFaX2QxY8syBV36uYDi4bqgNLh5pvE23XyNQA+Zdtx2U0GvXbH2e1fufJWH9i5VA7T8bZLgDrGj9mlIKCbitX7tyav+jHWAUpSVq0cIEOHzwgZ2dnzf5uQZyClCRVqeolSdq/f595WfQT+ap51ZAkVa9paq9y5Mhh81P7Duz/Q5LpKYB58+VP4iNDcmETM6UaNWqkd955Rxs2bJCXl5f69u2rJk2a6Pjx48qRI4e1w8NrtnKGqSlw9FRySZo9ur0ehYbr5LkrGjXzZ32x0E+tfMqpX4daypXVXflzZlC2TGm1fseJWE+MyZs9g1z+3c65izf0tJoVCqlve1PDvoK5M0mSihXIZo5h5pKten/sj3HeN3dsB3VsUklLNxzQp3PWJ9GRA0gqfXx7SnoyVVySxowcLmcXFxUsWEgfDBgoSTp/PsC8PneePHG207JVG02ZPFErly/V7Vs3lTpNWvlv2SRJ8q5T15yote/4rhYtnK9e3buqqld17d2zS5Jp9hVJFSyF/Akrp/eQ9FQONartkxxq1jp9schfreqVUb/2/+ZQOf7NoXaeNOdQY3s3kiTdvPNQ1csWUPWyBWLt56Opq3X4VKCyZUqjtKlSaMPs3nrw8LEa1SihO/dDNH6uaQbq+5/8L06Mc8e0U8fGFbV040F9OnfjazkPAF7Ogf1/aO+e3ZKkTJky67s538Ra71GkiBbO+9a0PnMWbVz/qzau/9W8PnWaNOr5fm/51K+v+d/O0fGjR9WrWxe5pnDVtq3+ypQ5s1q0aiNJKlXaU7XrviX/LZv1boc2KluuvH7banqQQsvWbeXm5ib8N9lEUWrQoEEqUKCAUqZMqeHDh2vs2LFatmyZsmfPrs8++8za4eE1a/Dv7XIx1apommnglsJZkunqWmPfWZow4G019S6l+w8fa/bSHRoxY02s96VP++SRyM/qZZAjc7o4+0ufNqV52Wq/w690LACsY+f2bXGW/bHvd0nSo5Anfefu3b1rfh3d4yCmDp06K3XqNPrpf0v0++97FRkRoazZsqtdh07mp+9J0oCBHyldunRatWK51q/7Re7p3dWjl696xBgDvG7kT2jgVSzOsuieUG6uph5SZy/cUOPe32jCB03UtFZJUw61bKdGfPmz+T15sqeXJGVOn0p92tWMs82Ppq7WtVv31bTPbI3v10SehXPKYJA27zmlj2es1d+Xbr+GowPwul25fMn8+sD+P8wzl6I1btpMwf+2JQi8eEE/Ll4Ua33WbNnU8/3ecnR00tdz52nK5Anat3ePoqKMql6jpgYOGaZUqVKZx386aYpy5sqtDet+0fp165Q5S2b18u2jtu07vsajhK0zGJ9+7EYyQ28fAIl1Z/8sa4cAIJlxsYnLeEmL3j4AEuP279OtHQKAZCaFo+GFY2wixRo2bFi86+zs7JQ5c2ZVr15dpUuXtlxQAAAANoz8CQAAJHc20ejc3t5eW7du1d69e3Xv3j09ePBA+/bt044dOxQSEqL9+/erQ4cOWrFihbVDBQAAsAnkTwAAILmziZlSadOmVceOHeXr6ys7O1OdLCoqSt98840cHR3Vo0cP7dq1S+PHj1eLFi2sHC0AAID1kT8BAIDkziZmSi1btkw9evQwJ1SSadp59+7d9f3330uSqlatquvXr1srRAAAAJtC/gQAAJI7myhKOTo6aseOHXGW7927V2FhYZKkbdu2KWvWrJYODQAAwCaRPwEAgOTOJm7f69+/v/r27avChQsre/bscnBw0JUrV3TixAn1799fYWFh6tu3ryZPnmztUAEAAGwC+RMAAEjuDEaj0WjtICTp5MmT2rlzp27evKmoqCilT59elSpVUrly5SRJly5dUo4cOeK8z9Wzj6VDBZDM3dk/y9ohAEhmXGziMl5cL5s/SZJr2Q8sGSqAZO7279OtHQKAZCaFo+GFY2wmxSpWrJiKFSumO3fuKF26dHHWx5dQAQAA/FeRPwEAgOTMJnpKPXz4UKNGjVLp0qXl5eUlSbp796569uypoKAgK0cHAABge8ifAABAcmcTRalx48YpMDBQ3333nfkJMo6OjnJzc9P48eOtHB0AAIDtIX8CAADJnU3cvrdt2zZt2LBB7u7uMhhM9xymTJlSo0ePlo+Pj5WjAwAAsD3kTwAAILmziZlSBoNBbm5ucZZHRkYqNDTUChEBAADYNvInAACQ3NlEUcrT01OfffaZHj9+bF52+fJlDR8+XBUqVLBiZAAAALaJ/AkAACR3NlGUGjFihPbv369y5copNDRUZcuWVe3atXXnzh2NGjXK2uEBAADYHPInAACQ3NlET6ns2bNr7dq1On78uAIDA+Xs7KzcuXOrQIEC1g4NAADAJpE/AQCA5M6qRSlvb29zY874GAwG+fn5WSgiAAAA20b+BAAA3hRWLUpNmjQp3nWBgYGaPn26IiMjLRgRAACAbSN/AgAAbwqrFqWe1YQzLCxMs2fP1oIFC9S8eXN98MEHVogMAADANpE/AQCAN4VN9JSK5ufnpwkTJihr1qz63//+p8KFC1s7JAAAAJtG/gQAAJIrmyhKXbhwQZ988onOnDmjQYMGqWnTptYOCQAAwKaRPwEAgOTOzpo7f/z4saZOnaq3335bBQsW1IYNG0ioAAAAnoP8CQAAvCmsOlPKx8dH4eHh+uijj1SgQAGdOnXqmePKly9v4cgAAABsE/kTAAB4U1i1KGVvby97e3t9++238Y4xGAzy9/e3YFQAAAC2i/wJAAC8KaxalNq6das1dw8AAJDskD8BAIA3hVV7SgEAAAAAAOC/iaIUAAAAAAAALI6iFAAAAAAAACyOohQAAAAAAAAsjqIUAAAAAAAALI6iFAAAAAAAACyOohQAAAAAAAAsjqIUAAAAAAAALI6iFAAAAAAAACyOohQAAAAAAAAsjqIUAAAAAAAALI6iFAAAAAAAACyOohQAAAAAAAAsziEhg7744osEb/DDDz986WAAAADeFORPAAAAz5egotS6desStDGDwUBSBQAAIPInAACAF0lQUWrr1q2vOw4AAIA3CvkTAADA871UT6mIiAjt27dPK1euNC8LCQlJsqAAAADeNORPAAAAsSW6KBUYGKj69evr3Xff1ejRoyVJly9fVp06dXTu3LkkDxAAACC5I38CAACIK9FFqYkTJ6pUqVLas2eP7OxMb8+aNauaNm2qyZMnJ3mAAAAAyR35EwAAQFwJ6ikV0/79++Xn56c0adLIYDBIkuzs7NS7d29Vr149yQMEAABI7sifAAAA4kr0TCk7OzulTJkyznKj0Sij0ZgkQQEAALxJyJ8AAADiSnRRqlChQvrf//4Xa5nRaNTXX3+twoULJ1lgAAAAbwryJwAAgLgSfftev3791K1bN61Zs0YRERHq1auXTp8+rbt372ru3LmvI0YAAIBkjfwJAAAgLoPxJeaMBwQEaOnSpfr777/l4uKivHnzqm3btsqaNevriPG5XD37WHyfAJK3O/tnWTsEAMmMS6Iv48VlS/mTJLmW/cAq+wWQPN3+fbq1QwCQzKRwNLxwzEsVpWwJRSkAiUVRCkBiJUVRytZQlAKQGBSlACRWQopSiU6xwsLCNHPmTG3evFlXr16Vs7OzsmbNqkaNGqlLly5ycHgDszYAAIBXQP4EAAAQV6IzoPHjx2vz5s1q1KiR8uTJI6PRqICAAM2bN083btzQiBEjXkecAAAAyRb5EwAAQFyJLkr5+/trwYIFKlKkSKzlzZs3l6+vL0kVAADAU8ifAAAA4rJL7BsiIiJUoECBOMuLFi2q0NDQJAkKAADgTUL+BAAAEFeii1L16tXTxo0b4yz39/fXW2+9lSRBAQAAvEnInwAAAOJK0O17X3zxhfl1ihQp9Mknn2jlypUqXLiwDAaDzp07p6NHj6pt27avLVAAAIDkhPwJAADg+QxGo9H4okHe3t4J25jBIH9//1cOKjFcPftYdH8Akr87+2dZOwQAyYzLSzwcz5bzJ0lyLfuBxfcJIPm6/ft0a4cAIJlJ4Wh44ZgEpVhbt25N0A7v3r2boHEAAABvOvInAACA50t0T6n4PHz4UD4+Pkm1OQAAgDce+RMAAPgvS/Rk9KCgIH366ac6cuRIrKfFBAcHy93dPUmDAwAAeBOQPwEAAMSV6JlS48eP19mzZ9WwYUPduXNHLVu2VMGCBVW4cGEtWrTodcQIAACQrJE/AQAAxJWgRucxVa1aVWvWrFHGjBlVqlQpHT16VJI0Y8YMpUmTRp07d34dccaLRucAEotG5wAS62Uancdka/mTRKNzAIlDo3MAiZWQRueJnikVHBysjBkzSjI9LSYiIkKS1KlTJ33//feJ3RwAAMAbj/wJAAAgrkQXpXLlyqUNGzZIkjJnzqzdu3dLkoxGo+7du5e00QEAALwByJ8AAADiSvRk9G7duunDDz9U5cqV1bhxYw0YMEDly5dXQECAPD09X0eMAAAAyRr5EwAAQFyJLko1bdpU+fPnV9q0adW7d2/Z2dnp0KFDqlGjht5///3XESMAAECyRv4EAAAQV6IbnT/PvXv3lCZNmqTaXILQ6BxAYtHoHEBivWqj8+exRv4k0egcQOLQ6BxAYr2WRufPU7169aTcHAAAwBuP/AkAAPxXJWlRKgknXQEAAPwnkD8BAID/qiQtShkML56aBQAAgCfInwAAwH9VkhalAAAAAAAAgISgKAUAAAAAAACLS/CzZNq0afPCMeHh4a8UDAAAwJuE/AkAACB+CS5K5c2bN0nGJLXbf8y0+D4BJG/pKvIYdACJ8+jgjJd6n63mT5J0Z9/LHROA/6Z05ftYOwQAycyjw7NeOCbBRamJEye+UjAAAAD/NeRPAAAA8aOnFAAAAAAAACyOohQAAAAAAAAsjqIUAAAAAAAALI6iFAAAAAAAACzupYtS4eHhCgwMTMpYAAAA3mjkTwAAAE8kuij1+PFjDRkyRJ6enqpfv74k6f79++rWrZvu37+f5AECAAAkd+RPAAAAcSW6KDVlyhSdOnVKn3/+uezt7c3LIyMj9fnnnydpcAAAAG8C8icAAIC4El2U2rRpk7788kvVq1fPvCx16tSaOHGiNm/enKTBAQAAvAnInwAAAOJKdFHq4cOHypMnT5zl7u7uCgkJSYqYAAAA3ijkTwAAAHEluiiVK1cu7du3T5JkNBrNyzdu3Khs2bIlXWQAAABvCPInAACAuBwS+4Z27dqpb9++eueddxQVFaUFCxboxIkT2rRpk4YPH/46YgQAAEjWyJ8AAADiSnRRqnXr1nJwcNDixYtlb2+v2bNnK2/evPr8889j9UkAAACACfkTAABAXAZjzDnkyVBIeLIOH4AVpK/U39ohAEhmHh2cYe0QktzjCGtHACA5SVe+j7VDAJDMPDo864VjEj1Tas2aNc9d36xZs8RuEgAA4I1G/gQAABBXootSQ4cOffaGHBzk4uJCUgUAAPAU8icAAIC4El2UOnbsWKyvIyMjdf78ec2dO1edOnVKssAAAADeFORPAAAAcSVZT6lbt26pa9euWrt2bVJsLsHoKQUgsegpBSCxXldPKWvlTxI9pQAkDj2lACRWQnpK2SXVzlKnTq0LFy4k1eYAAADeeORPAADgvyzRt+/t2rUrzrLHjx9r/fr1ypIlS5IEBQAA8CYhfwIAAIgr0UWpbt26yWAw6Om7/tKmTatJkyYlWWAAAABvCvInAACAuBJdlPL394+zzMXFRe7u7jIYDEkSFAAAwJuE/AkAACCuRBelFi5cqOHDh7+OWAAAAN5I5E8AAABxJbrR+YYNG3Tv3r3XEQsAAMAbifwJAAAgrkTPlBo8eLCGDRumd955Rzlz5pSjo2Os9Xnz5k2y4AAAAN4E5E8AAABxGYxPd9x8gcKFCz95c4weCEajUQaDQadOnUq66BIgJDxR4QOA0lfqb+0QACQzjw7OeKX321r+JEmPIyy+SwDJWLryfawdAoBk5tHhWS8ck+iZUosWLXqpYAAAAP6ryJ8AAADiSnBRqlSpUjp69KgqVKjwOuMBAAB4Y5A/AQAAxC/Bjc4TeZcfAADAfx75EwAAQPwSXJSK2f8AAAAAL0b+BAAAEL8E374XGRmpZcuWPfeKn8FgUKtWrZIkMAAAgOSO/AkAACB+CX76XsynxsS7MZ6+ByAZ4Ol7ABLrZZ++Z6v5k8TT9wAkDk/fA5BYSfr0PWdnZx09evSVAgIAAPgvIX8CAACIX4J7SgEAAAAAAABJhafvAQAAvCbkTwAAAPFLcFGqadOmrzMOAACANw75EwAAQPwS3OjcVtHoHEBi0egcQGK9bKNzW0ajcwCJQaNzAImVkEbn9JQCAAAAAACAxVGUAgAAAAAAgMVRlAIAAAAAAIDFUZQCAAAAAACAxVGUAgAAAAAAgMVRlAIAAAAAAIDFUZQCAAAAAACAxVGUAgAAAAAAgMVRlAIAAAAAAIDFUZQCAAAAAACAxVGUAgAAAAAAgMVRlAIAAAAAAIDFUZQCAAAAAACAxVGUAgAAAAAAgMVRlAIAAAAAAIDFUZQCAAAAAACAxVGUAgAAAAAAgMVRlAIAAAAAAIDF2VxR6s6dO9YOAQAAIFkhfwIAAMmRTRSlHj58qFGjRql06dLy8vKSJN29e1c9e/ZUUFCQlaMDAACwPeRPAAAgubOJotS4ceMUGBio7777TnZ2ppAcHR3l5uam8ePHWzk6AAAA20P+BAAAkjsHawcgSdu2bdOGDRvk7u4ug8EgSUqZMqVGjx4tHx8fK0cHAABge8ifAABAcmcTM6UMBoPc3NziLI+MjFRoaKgVIgIAALBt5E8AACC5s4milKenpz777DM9fvzYvOzy5csaPny4KlSoYMXIAAAAbBP5EwAASO5soig1cuRIHThwQOXKlVNoaKjKli2rOnXq6O7duxo9erS1wwMAALA55E8AACC5s4meUtmyZdOaNWt07NgxXbp0Sc7OzsqVK5cKFixo7dAAAABsEvkTAABI7myiKNW9e3c1bNhQderUUcmSJa0dDgAAgM0jfwIAAMmdTdy+lzt3bn355ZeqUqWKevfurfXr1+vRo0fWDgsAAMBmkT8BAIDkzmA0Go3WDiLaiRMn5Ofnpy1btujKlSuqUaOGGjVqpDp16sT7npBwmwkfQDKRvlJ/a4cAIJl5dHCGtUOI18vkT5L0OMJCAQJ4I6Qr38faIQBIZh4dnvXCMTZVlIrp2LFj+uyzz3Tw4EGdOnUq3nEUpQAkFkUpAIlly0WpmBKaP0kUpQAkDkUpAImVkKKUTfSUinb16lX5+fnJz89PBw8eVLFixTR48GBrhwUAAGCzyJ8AAEByZRNFqa+++kr+/v46ffq0ihcvrvr162vixInKli2btUMDAACwSeRPAAAgubOJotT27dvVqFEjzZo1i0QKZnfv3tHET8Zp86YNkqRfN/kpW/Yc5vWRkZFa9tOPWrt6lS5euCA7O4OKFS+h7j3fV7kKFc3jdu3coYXzvlXAubMKCQlRnrz51KpNW73TsrUk6cAf+9S9y7vPjCF/gYJaseaX13iUAF6We5oUmjG0pVq8VUaS5NForC5eDYo1plaFQhrj21AlCmbTo9Bwbdh5UoO/WK2geyHmMWlTueqTvo3VuGZJpUrprL/+uaHJ8zZrzdajsbZV1TO/5o5pp3w5MmjHgbPy6flkOnKurO46s270M+MMDglVRi9mrSDpkT/hWcLDwvTDooVasXypbly/rowZM6lsufLq3fcDZf33+yQiIkLzvp2jubO/UUREuMaNn6imbzePtZ0zp0/ry+lTdfjQQUlS7jx51LZdRzVp9rZ5zKNHjzRt6hT5b9mse/fuKneevOr5vq/e8qlvuQMGkCjuaVJqxrBWauFTVpLk0WBU3PypoofG+DZSiULZ/82fTmjw56sUdO+heUyTWiXVr4O3ihXMJkcHex3/67Jm/LBVa/yPmMdUKJFHw3rUVymPHHJPk0L/XL6txev+0LTv/RQZGSVJsrMzaHjPBurQuKKyZEiti1eDNGvJNs1ZtuP1nwzYBKsVpcLDw+Xo6ChJWrx4sXl5WFhYnLFOTk4Wiwu2YdtvWzV+zCjdvn0r3jFzvvlK387+Wk5OTvKp30CBFy/qj32/69DBg1r80zJ5FC6i3/fuUT/fnjIajfKqXkPOLi7y37JZ48eOVkREhFq3bW/eXsqUKdX07Xdi7SNDxoyv7RgBvLyG1Ytr1vDWypIhdbxjShTMpjVf9pTBYNDarUeVM0s6tW9UQbmyuuutHjMlSfb2dvpl1vsqVzy3jp65pG37r+udOqW1eFJn1e3+pfYe/Vsuzo4a17uReretLju7Fz+0dtaP22J9HRpG4x4kHfInvMjECeO1cvlSpU+fQY2bNNOJ48f0y89r9Pve3Vr183rdvHFDIz8eopMnT8S7jYsXLujdDm316FGIqlStpnTu7tq8cYNGDh+q27dv6b2u3SVJo0YM0+aNG5Q3Xz5VqlJFv/n7afDAAUqdOo0qVa5iqUMGkEANa5TQrBFtn58/FcquNTPfl0EGrd16RDmzuqt9o4qm/Kmbqb9i24blNX/8u3ocGq6VWw4pW8a0qlXRQ5VK5VObgd9q7dajKpIvizbO7SdXFyftPHhWF68GqaVPWX3St4ncU6fQx9PXSJLG9Wmige/VVeDVIC3beFB1qxTR9GGt9Cg0TIvW/m6J0wIrs1pRqly5cjp61HQVumTJkjIYDPGOfVGjTrx5xo8ZJQcHBw0bMUoTx4975piffjQl4+917a5evfsqMjJS9evW0s0bN7TVb4s8ChfRsv/9KKPRqLLlyuvLr+dIkoYM+lCbN67X5k0bYhWlUqdJo4+Gfvz6Dw7AK5s1vLUiIiL1waTlmjG05TPHDOhUW06ODvps/haN/mqd7O3tdPrnUfIqW0DVyuTXrkMBalO/rMoVz60T566oRudpCg2L0N+XbqlCiTzKldVde4/+La8y+dW3fU2t235cN4IeqMvbz/9F66Opq1/HIQOSyJ/wfEFBQVq1Ypkk6fNpM1SmbDkFBwerZrVKunnzprb6b9Ge3bsUEHBOHw4arB8X/6Br167G2c7iH77Xo0chqlCxkr6ZO0+SlClTZi2Y962W/u9Hvde1uy5euKDNGzfIxdVVi5YsVerUqbV82U8aP3a0vp3zDUUpwAbNGtHWlD9NWKoZH7d+5hhz/jRvk0bP+sWUP60bK6+yBVWtbAHtOnhOQ7vVkyR9PH2NvvlpuyRp5w+DVK54HrX0Kau1W4/qvberyNXFSf9cvmUuZl29eU+D3ntL77xVRh9PXyO3FM7ybVtDktSg1yydu3hDdSoX0S9f99bQbvUoSv1HWK0oNW/ePPPrRYsWWSsM2KjmLVupU+cuun/vbrxjIsJNsw/SZzDNZrK3t1fGjJl088YNBQcHSzJdUY45RpKyZMkiSQp+EPw6QgdgAfNX79H0H7YqXeqU8Y6pVaGQJGnrH2ckSZGRUdp1OEBt6pdTnUqFtetQgN6uXVqStGTdH+YZTWO/WR9rO3cfPNK7H3+vZZsOaXiPeq/haICEI3/C8zg6Ourb+d9Lkkp7mm5tdnNzk4urq8LDw3X71i15epZVn779lSt3bv3045JnbuedFq1U9y0fZc365LbQDBkySJJu3bopSfr99z2SpKJFiyl1atOsiwoVK0mSDh86qNDQUDk7O7+GowTwsuav2q3pi/yVLnWKeMfUqlhYkrR1X4z86dA5tWlQXnUqFdGug+fUcsBcSYp129/Vm/ckSU6O9pIkR0dTqeHG7QfmMZeu3ZUkpXZzkSRVLp1Pri5OCrwapHMXb0iSdh48q8jIKOXNkUH5c2VUwMWbr3zcsG1WnSkVbe3atfr000/jjAkODtaQIUNUoUIFS4YGG+Dbp58kPbcoVb9hQ61asVwbN6xTXR8fBV68qHNn/5LBYJB3nbqSpAaNGmnXzu3a/8fv+vv8eaV0S6mdO0zV/Np134qzzYMH9uvA/j8UFRmp6jVqqViJEkl/cABe2SezTb3m4itKpXR1Mk9Nv3bzvnn5tVum1wVzmwrVJQqafuE6d/GmmtcpLc8iORV096GWbTqoyzdMydX+Exe0/8SFBMdWyiO76lYpIjdXZ23bf1bb9v+VyKMD4kf+hOdJlSqVysfoqylJmzau14P7pp99OXPlSlC/J4/ChWN9HR4Wpp/Xrvl3G7klSYEXL0qK3eog478XASMjIxUYeFEFChR8uQMB8Fp88s2vkhRvUSpW/nTrnnn50/nTX/9cN6+zszOoWpkCqlnBQ5GRUVqweq8kafnGA+reoppKFc6hiiXz6q9/ruvtOqUlydx3Kn/OjLG2L5naHty5H6IM6dxUMFcmilL/AVZtdB4YGKh//vlHP//8sxo0aCCj0Rhr/YULF7Rr1y4rRQdbN3T4SKVM6aYlP3yvWtUqS5JSpU6tTyZMVtly5SVJ9Rs2Vnh4uKZMnqjmTRpIkuzs7NS1e09169Er1vauXrmibp07mr/+ds436tW7r3r08rXQEQFIKqlTuphfh4Y/6en0KNQ0ezK1m6skKWM6N0nS+H5N5JEns3nckG5vqca703QmRtKVUL//+KSp+ZCub2n+6j3qPX5porcDxIf8CQl16tSf+mTMKElSvnz5VatW7URvIyoqSuPGjNKZ06bbQbt0M/WTevjvrPSYvcucXZ787A1+8EAAkpfo/EiK3RPzUWhYnPWS1LhmSS2b1kOSdPhUoAZ9tlx7jpyXJO05cl4t+s/RVyPbatv3A83vWbB6jwZMMt1mnOrffC1mriZJj//N19I8tT+8maxalDp9+rS+/PJLhYeHq2vXrnHWOzs7q02bNlaIDMnBjm3btHrlcjk7u8i7Tl3dv3dXO3ds14xpnytvvnwqWMhD5wMCNO/bOQp+8EDVa9RUqlSptdXfTz8u/kEFChZSvQYN5ZoihQp5FJazi7MGDBysAgULaf53c7Vw3rea8/UsedeuowIFC1n7cAEkoehf4h0dTFPMHeztVLL5p3oYEqqfPu+i8sXz6JO+jdVq4HcJ2l54eISOnrmkyCijPp2zQTsPnVOHRhX0xeAW6vJ2Ff3823Ft2v3nazse/LeQPyEhjh09It+e3fTgwQOlSpVKEyZPkWMim99HRkZqxMdDtH6d6UnEjZo0VeMmzRL03uf1OwOQPD11DUTnL93SNz9tVymPHKrimV9fj2qn1gO/1Zm/rytLhtQa3rO+smVKq+37/9KFK0GqX72Y2jeqoNPnr+nLxVtfvD8ZXzgGyZ9Vi1J169ZV3bp11bRpU61du9aaoSCZCQ4O1qjhQxUSEqJJU76QT33TLKgJn4zV8qX/08Tx4zR/0RKNHzdaFy9cUItWbTR81BhJ0uaNGzRk0ACNGz1S1arXULHiJbR05ZpY2+/d9wP9tOQHPX78WHt27aQoBSQz94Ifm1+ncHnyS5irs+mpZfcePJIk3X/4WO5pUmrh2t919oKpl8HMJdu0aGJnlS+eO8H7u3rrviq1mxJr2TdLd8q3TQ0VyJVR9aoWoSiFJEP+hBc5dvSIenXvoocPHyp9+gya9c0cFSlaLFHbiIqK0tDBA7V5o+l26XdattaIf3MpSXJLZZpp+vjRk5+3oY+fvE6VKv6newGwTdH5kfR0/uT07/qQWONPnruiDycvlyQtntxF77xVRrNGtFXdrtM1/oOmKlc8jzbtPqlmfb6RJJUrlls7F3+kCf2b6dcdx3X/33zN1SV2wdzl33ztbox48OZ68bOtX5PoBtSStHz5coWFhcX7B3jahb//VkiI6YdigUJPCkYFCpp6F5z682SsvwvGGJP/3zGPHoXon7/Pm5fH/J50cHCQy79T0MPCniwHkDyEPA7TlRt3JUlZMz75xShbxjSSpDP/mApQf/37t0FPrujffxgqSUrj9uQ2lIRycIj9sXov2JRMOTlZ9RoQ3iDkT3iRK1cu64O+vnr48KHy5M2rxT8tU9FixRO9nS+nf2EuSPXrP1CjxoyTnd2Tn3G5c+eVJN28ecO87MYN0y3PDg6Oypkz56scBgAriJ0/pTEvz5YpOn+6rjRururQuKIGd/WRe5onvT13HjwnSSpbNJckybOI6e+TZ6+Yx5wMML22t7dTaY8c5guC2WLsy8XZ0dzz6szfiW+jgOTHakWpmI06S5YsqVKlSsX5E70ceFrmrFnMr/86c9r8+tzZs5Ik9/TpJT150l7MMQH/jokeN+nTcSpfuoT693nfvPzE8WO6e/euJKlY8cQncgCsb/Me0//7GuVMhWhHB3tVK5Nfksyzln7deUKS1KRmCdnZmQpT1csWkCQd++tygvf1YSdvBe2eon0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+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
+        "\n",
+        "predictions = [\n",
+        "    ('XGBoost', xgb_preds),\n",
+        "    ('Random Forest', rf_preds)\n",
+        "]\n",
+        "\n",
+        "for idx, (model_name, y_pred) in enumerate(predictions):\n",
+        "    cm = confusion_matrix(y_test, y_pred)\n",
+        "    sns.heatmap(\n",
+        "        cm, annot=True, fmt='d', cmap='Blues', ax=axes[idx],\n",
+        "        xticklabels=['Negative', 'Positive'],\n",
+        "        yticklabels=['Negative', 'Positive'],\n",
+        "        cbar=False,\n",
+        "        annot_kws={'size': 12, 'weight': 'bold'}\n",
+        "    )\n",
+        "    axes[idx].set_title(f'{model_name} Confusion Matrix', fontsize=12, fontweight='bold')\n",
+        "    axes[idx].set_ylabel('True Label', fontsize=11)\n",
+        "    axes[idx].set_xlabel('Predicted Label', fontsize=11)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "execution_count": 13
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-sLogIIkTh8I",
+        "outputId": "0905ffd3-4218-48bf-feea-51f22bc882b2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "DETAILED CLASSIFICATION REPORT - XGBoost\n",
+            "===========================================================================\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "    Negative     0.8504    0.8571    0.8537     12500\n",
+            "    Positive     0.8560    0.8492    0.8526     12500\n",
+            "\n",
+            "    accuracy                         0.8532     25000\n",
+            "   macro avg     0.8532    0.8532    0.8532     25000\n",
+            "weighted avg     0.8532    0.8532    0.8532     25000\n",
+            "\n",
+            "\n",
+            "DETAILED CLASSIFICATION REPORT - Random Forest\n",
+            "===========================================================================\n",
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "    Negative     0.8292    0.8235    0.8264     12500\n",
+            "    Positive     0.8247    0.8304    0.8276     12500\n",
+            "\n",
+            "    accuracy                         0.8270     25000\n",
+            "   macro avg     0.8270    0.8270    0.8270     25000\n",
+            "weighted avg     0.8270    0.8270    0.8270     25000\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"\\nDETAILED CLASSIFICATION REPORT - XGBoost\")\n",
+        "print(\"=\"*75)\n",
+        "print(classification_report(\n",
+        "    y_test, xgb_preds,\n",
+        "    target_names=['Negative', 'Positive'],\n",
+        "    digits=4\n",
+        "))\n",
+        "\n",
+        "print(\"\\nDETAILED CLASSIFICATION REPORT - Random Forest\")\n",
+        "print(\"=\"*75)\n",
+        "print(classification_report(\n",
+        "    y_test, rf_preds,\n",
+        "    target_names=['Negative', 'Positive'],\n",
+        "    digits=4\n",
+        "))"
+      ],
+      "execution_count": 14
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 607
+        },
+        "id": "OdAD3Yo8Th8I",
+        "outputId": "db30b853-654e-4e60-8d68-bdc3898c1dab"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "importance_xgb = xgb_model.feature_importances_\n",
+        "importance_indices = np.argsort(importance_xgb)[-12:]\n",
+        "\n",
+        "fig, ax = plt.subplots(figsize=(10, 6))\n",
+        "ax.barh(range(len(importance_indices)), importance_xgb[importance_indices], color='#e74c3c', alpha=0.8, edgecolor='black')\n",
+        "ax.set_yticks(range(len(importance_indices)))\n",
+        "ax.set_yticklabels([f'Feature {i}' for i in importance_indices], fontsize=10)\n",
+        "ax.set_xlabel('Importance Score', fontsize=11, fontweight='bold')\n",
+        "ax.set_title('Top 12 Important FastText Features (XGBoost)', fontsize=12, fontweight='bold')\n",
+        "ax.grid(axis='x', alpha=0.3)\n",
+        "\n",
+        "for i, v in enumerate(importance_xgb[importance_indices]):\n",
+        "    ax.text(v + 0.002, i, f'{v:.4f}', va='center', fontsize=9)\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "execution_count": null
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-wPJ1eKBTh8I",
+        "outputId": "7cdde62c-e607-4b19-b5f3-ca71e8b50747"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "FINAL SUMMARY\n",
+            "===========================================================================\n",
+            "\n",
+            "Dataset: 25000 sentiment-labeled reviews\n",
+            "Embeddings: FastText (100-dim, bigrams)\n",
+            "Test set size: 25000 samples\n",
+            "\n",
+            "Best Model: XGBoost\n",
+            "  Accuracy:  0.8532\n",
+            "  Precision: 0.8560\n",
+            "  Recall:    0.8492\n",
+            "  F1-Score:  0.8526\n",
+            "\n",
+            "===========================================================================\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(\"\\nFINAL SUMMARY\")\n",
+        "print(\"=\"*75)\n",
+        "print(f\"\\nDataset: {len(df)} sentiment-labeled reviews\")\n",
+        "print(f\"Embeddings: FastText (100-dim, bigrams)\")\n",
+        "print(f\"Test set size: {len(y_test)} samples\")\n",
+        "\n",
+        "best_idx = results_df['Accuracy'].idxmax()\n",
+        "best_model = results_df.iloc[best_idx]['Model']\n",
+        "best_acc = results_df.iloc[best_idx]['Accuracy']\n",
+        "\n",
+        "print(f\"\\nBest Model: {best_model}\")\n",
+        "print(f\"  Accuracy:  {best_acc:.4f}\")\n",
+        "print(f\"  Precision: {results_df.iloc[best_idx]['Precision']:.4f}\")\n",
+        "print(f\"  Recall:    {results_df.iloc[best_idx]['Recall']:.4f}\")\n",
+        "print(f\"  F1-Score:  {results_df.iloc[best_idx]['F1-Score']:.4f}\")\n",
+        "\n",
+        "print(\"\\n\" + \"=\"*75)"
+      ],
+      "execution_count": 21
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python3"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/a0.1/assignment 01_farshidshariatnezhad.py b/a0.1/assignment 01_farshidshariatnezhad.py
new file mode 100644
index 0000000..6ed7615
--- /dev/null
+++ b/a0.1/assignment 01_farshidshariatnezhad.py	
@@ -0,0 +1,144 @@
+# -*- coding: utf-8 -*-
+"""Assignment 01_farshidshariatnezhad/.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/10CKjN5Nny_N4XSf9SBL3Szw4Vg3l8gBm
+"""
+
+# دریافت دمای سلسیوس به صورت متن
+celsius_temp = input("Enter temperature in Celsius: ")
+
+# تبدیل دما به float
+celsius_temp = float(celsius_temp)
+
+# محاسبه دما به فارنهایت
+fahrenheit_temp = (celsius_temp * 9/5) + 32
+
+# چاپ نتیجه به صورت فرمت‌شده
+print(f"The temperature is {celsius_temp}°C, which is {fahrenheit_temp:.2f}°F.")
+
+# ذخیره دو عدد از انواع مختلف
+num_int = 5         # عدد صحیح
+num_float = 3.2     # عدد اعشاری
+
+# محاسبه و چاپ عملیات‌ها
+sum_result = num_int + num_float
+difference_result = num_int - num_float
+product_result = num_int * num_float
+true_division_result = num_int / num_float
+floor_division_result = num_int // num_float
+
+# چاپ نتایج
+print(f"Sum: {sum_result}")
+print(f"Difference: {difference_result}")
+print(f"Product: {product_result}")
+print(f"True Division: {true_division_result}")
+print(f"Floor Division: {floor_division_result}")
+
+# 1. شروع با یک لیست خالی
+shopping_list = []
+
+# 2. اضافه کردن حداقل 4 آیتم به لیست از ورودی کاربر
+shopping_list_input = input("Enter at least 4 items (comma-separated): ")
+shopping_list = shopping_list_input.split(',')  # تبدیل ورودی به لیست
+
+# 3. تبدیل لیست به تاپل به نام immutable_basket
+immutable_basket = tuple(shopping_list)
+
+# 4. چاپ سومین آیتم از تاپل
+print(f"The third item in the basket is: {immutable_basket[2]}")
+
+sample = "to be or not to be that is the question"
+
+# 1. ساخت یک set برای کلمات منحصر به فرد
+unique_words = set(sample.split())
+
+# 2. ساخت یک دیکشنری برای شمارش تعداد تکرار هر کلمه
+word_counts = {}
+for word in sample.split():
+    word_counts[word] = word_counts.get(word, 0) + 1
+
+# 3. چاپ هر دو ساختار داده
+print("Unique Words (set):", unique_words)
+print("Word Counts (dict):", word_counts)
+
+# توضیح تفاوت اصلی:
+# - Set تنها کلمات منحصر به فرد را نگه می‌دارد و ترتیب کلمات مهم نیست.
+# - Dictionary هر کلمه را به تعداد تکرار آن مرتبط می‌کند و ترتیب در آن حفظ می‌شود.
+
+import math
+
+def is_prime(n: int) -> bool:
+    """
+    Return True if n is a prime number, else False.
+    0 and 1 are *not* prime.
+    """
+    # 0 و 1 اعداد اول نیستند
+    if n <= 1:
+        return False
+
+    # چک کردن تقسیم‌پذیری از 2 تا √n
+    for i in range(2, int(math.sqrt(n)) + 1):
+        if n % i == 0:
+            return False  # اگر به عددی غیر از 1 و خود n تقسیم شد، عدد اول نیست
+
+    return True  # اگر هیچ تقسیم‌پذیری پیدا نشد، عدد اول است
+
+# Quick self-check
+print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]
+
+def greet(name: str, times: int = 1) -> None:
+    """Print `name`, capitalised, exactly `times` times on one line."""
+    # تبدیل نام به حرف بزرگ و چاپ آن times بار
+    print(" ".join([name.capitalize()] * times))
+
+# تست کد:
+greet("alice")          # Alice
+greet("bob", times=3)   # Bob Bob Bob
+
+class Counter:
+    """Counts how many times `increment` is called."""
+
+    def __init__(self):
+        # 1. شمارنده داخلی را با مقدار 0 مقداردهی اولیه می‌کنیم
+        self.count = 0
+
+    def increment(self, step: int = 1):
+        # 2. به شمارنده مقدار step را اضافه می‌کنیم
+        self.count += step
+
+    def value(self):
+        # 3. مقدار فعلی شمارنده را برمی‌گردانیم
+        return self.count
+
+# تست کد:
+c = Counter()
+for _ in range(5):
+    c.increment()
+print(c.value())   # Expected: 5
+
+import math
+
+class Point:
+    """
+    A 2-D point supporting distance calculation.
+    Usage:
+        p = Point(3, 4)
+        q = Point(0, 0)
+        print(p.distance_to(q))  # 5.0
+    """
+
+    def __init__(self, x: float, y: float):
+        # 1. ذخیره مختصات x و y به عنوان ویژگی‌ها
+        self.x = x
+        self.y = y
+
+    def distance_to(self, other: 'Point') -> float:
+        # 2. محاسبه فاصله با استفاده از فرمول اقلیدسی
+        return math.sqrt((self.x - other.x) ** 2 + (self.y - other.y) ** 2)
+
+# Smoke test
+p, q = Point(3, 4), Point(0, 0)
+assert round(p.distance_to(q), 1) == 5.0
\ No newline at end of file
diff --git a/a0.1/assignment0.10_MohammadMasoumi2006.py b/a0.1/assignment0.10_MohammadMasoumi2006.py
new file mode 100644
index 0000000..0cf80b0
--- /dev/null
+++ b/a0.1/assignment0.10_MohammadMasoumi2006.py
@@ -0,0 +1,91 @@
+# -*- coding: utf-8 -*-
+"""Assignment 06. Descriptive Stats | Nexus | RezaShokrzad.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1OvU9YInN5-pQcMXiYHzP1Jz6N076r-Ti
+
+# 📚 Assignment 06 — Descriptive Statistics
+
+
+<p align="center">📢⚠️📂  </p>
+
+<p align="center"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>
+
+<p align="center"> 🚨📝🧠 </p>
+
+------------------------------------------------
+
+
+
+## 📘 Exploring Descriptive Statistics with NumPy & SciPy
+This assignment guides you through generating random data, analyzing its statistical properties, and visualizing it using plots.
+"""
+
+# 📦 Import Required Libraries
+import numpy as np
+from scipy import stats
+import matplotlib.pyplot as plt
+
+# 🎲 Step 1: Generate Random Integer List
+np.random.seed(42)  # For reproducibility
+data = np.random.randint(20, 40, size=30)
+print("Generated Data:", data)
+
+"""### 📊 Step 2: Central Tendency and Quantiles
+Let's compute basic statistics — mean, median, mode, and quantiles.
+"""
+
+# 📐 Central Tendency
+# mean
+mean_val = np.mean(data)
+# median
+median_val = np.median(data)
+# mode
+mode_val = stats.mode(data, keepdims=True).mode[0]
+
+# 📏 Quantiles
+q1 = np.quantile(data, 0.25)
+q3 = np.quantile(data, 0.75)
+
+print(f"Mean: {mean_val}")
+print(f"Median: {median_val}")
+print(f"Mode: {mode_val}")
+print(f"Q1 (25%): {q1}")
+print(f"Q3 (75%): {q3}")
+
+"""### 🧪 Step 3: Skewness and Kurtosis
+Skewness shows asymmetry, and kurtosis indicates the "tailedness" of the distribution.
+"""
+
+# 📉 Skewness and Kurtosis
+skew_val =stats.skew(data)
+kurtosis_val =stats.kurtosis(data)
+
+print(f"Skewness: {skew_val:.2f}")
+print(f"Kurtosis: {kurtosis_val:.2f}")
+
+"""### 📈 Step 4: Visualization - Bar Chart and Boxplot
+Visualize the data to better understand its distribution and spread.
+"""
+
+# 📊 Bar Plot
+plt.figure(figsize=(12, 4))
+
+plt.subplot(1, 2, 1)
+# make a bar chart
+plt.bar(range(len(data)), data)
+plt.title("Bar Plot of Random Data")
+plt.xlabel("Index")
+plt.ylabel("Value")
+
+# 📦 Boxplot
+plt.subplot(1, 2, 2)
+# make a box plot
+plt.boxplot(data, vert=False)
+plt.title("Boxplot of Random Data")
+plt.xlabel("Value")
+
+plt.tight_layout()
+plt.show()
\ No newline at end of file
diff --git a/a0.1/assignment01_MohammadMasoumi2006.py b/a0.1/assignment01_MohammadMasoumi2006.py
new file mode 100644
index 0000000..bbfe7a2
--- /dev/null
+++ b/a0.1/assignment01_MohammadMasoumi2006.py
@@ -0,0 +1,215 @@
+# -*- coding: utf-8 -*-
+"""Assignment 01. Python | Nexus | RezaShokrzad.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1PHW5WZ1eMJVDuFL2QTtMEkdiZAaF4xQL
+
+# 📚 Assignment 1 — Python Fundamentals
+
+<p align="center">📢⚠️📂  </p>
+
+<p align="center"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>
+
+<p align="center"> 🚨📝🧠 </p>
+
+------------------------------------------------
+Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:
+
+1. Variable types
+
+2. Core containers
+
+3. Functions
+
+4. Classes
+
+Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊
+
+## 1. Variable Types 🧮
+**Quick-start notes**
+
+* Primitive types: `int`, `float`, `str`, `bool`
+
+* Use `type(obj)` to inspect an object’s type.
+
+* Casting ↔ converting: `int("3")`, `str(3.14)`, `bool(0)`, etc.
+
+### Task 1 — Celsius → Fahrenheit
+"""
+
+# 👉 a Celsius temperature (as text), convert it to float,
+#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.
+celsiue_str = "27"
+celsius = float(celsiue_str)
+fahrenhite = celsius * 9 / 5 +32
+
+print (f"{celsius} is equal to {fahrenhite}")
+
+"""### Task 2 — Tiny Calculator
+
+"""
+
+# 👉 Store two numbers of **different types** (one int, one float),
+#    then print their sum, difference, product, true division, and floor division.
+num1 = 12
+num2 = 3.14
+print("sum :", num1 + num2)
+print("diffenece: ", num1 - num2)
+print("multiplication :", num1 * num2)
+print("rue Division: " , num1 / num2)
+print("loor Division: " ,num1 // num2)
+
+"""## 2. Containers 📦 (list, tuple, set, dict)
+**Quick-start notes**
+
+| Container | Mutable? | Ordered?                      | Typical use                       |
+| --------- | -------- | ----------------------------- | --------------------------------- |
+| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |
+| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |
+| `set`     | ✔        | ✖                             | Deduplication, membership tests   |
+| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |
+
+### Task 1 — Grocery Basket
+"""
+
+# Start with an empty shopping list (list).
+# 1. Append at least 4 items supplied in one line of user input (comma-separated).
+# 2. Convert the list to a *tuple* called immutable_basket.
+# 3. Print the third item using tuple indexing.
+buy_list = []
+buy_list.extend(["apple","banana","watermelon","orange"])
+immutable_basket = tuple(buy_list)
+print(immutable_basket[2])
+
+"""### Task 2 — Word Stats"""
+
+sample = "to be or not to be that is the question"
+
+# 1. Build a set `unique_words` containing every distinct word.
+# 2. Build a dict `word_counts` mapping each word to the number of times it appears.
+#    (Hint: .split() + a simple loop)
+# 3. Print the two structures and explain (in a comment) their main difference.
+sample = "to be or not to be that is the question"
+
+# جدا کردن کلمات
+words = sample.split()
+
+# ساختن مجموعه کلمات یکتا
+unique_words = set(words)
+
+# ساختن دیکشنری شمارش کلمات
+word_counts = {}
+for word in words:
+    word_counts[word] = word_counts.get(word, 0) + 1
+
+# چاپ خروجی‌ها
+print("Unique words:", unique_words)
+print("Word counts:", word_counts)
+
+# تفاوت اصلی:
+# مجموعه فقط کلمات یکتا دارد.
+# دیکشنری تعداد تکرار هر کلمه را نشان می‌دهد.
+
+"""## 3. Functions 🔧
+**Quick-start notes**
+
+* Define with `def`, return with `return`.
+
+* Parameters can have default values.
+
+* Docstrings (`''' … '''`) document behaviour.
+
+### Task 1 — Prime Tester
+"""
+
+def is_prime(n: int) -> bool:
+    """
+    Return True if n is a prime number, else False.
+    0 and 1 are *not* prime.
+    """
+    if n < 2:
+        return False
+
+    for i in range(2, int(n ** 0.5) + 1):
+        if n % i == 0:
+            return False
+
+    return True
+
+print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]
+
+"""### Task 2 — Repeater Greeter"""
+
+def greet(name: str, times: int = 1) -> None:
+    """Print `name`, capitalised, exactly `times` times on one line."""
+
+    capitalized_name = name.capitalize()
+
+
+    repeated_names = [capitalized_name] * times
+
+
+    print(" ".join(repeated_names))
+
+# تست تابع
+greet("alice")          # خروجی: Alice
+greet("bob", times=3)   # خروجی: Bob Bob Bob
+
+"""## 4. Classes 🏗️
+**Quick-start notes**
+
+* Create with class Name:
+
+* Special method __init__ runs on construction.
+
+* self refers to the instance; attributes live on self.
+
+### Task 1 — Simple Counter
+"""
+
+class Counter:
+    """Counts how many times `increment` is called."""
+
+    def __init__(self):
+        self.count = 0
+
+    def increment(self, step: int = 1):
+        self.count += step
+
+    def value(self):
+        return self.count
+
+
+
+c = Counter()
+for _ in range(5):
+    c.increment()
+print(c.value())   # Expected: 5
+
+"""### Task 2 — 2-D Point with Distance"""
+
+import math
+
+class Point:
+    """
+    A 2-D point supporting distance calculation.
+    Usage:
+        p = Point(3, 4)
+        q = Point(0, 0)
+        print(p.distance_to(q))  # 5.0
+    """
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+
+    def distance_to(self, other):
+        dx = self.x - other.x
+        dy = self.y - other.y
+        return math.sqrt(dx ** 2 + dy ** 2)
+
+# Smoke test
+p, q = Point(3, 4), Point(0, 0)
+assert round(p.distance_to(q), 1) == 5.0
+print("Distance:", p.distance_to(q))  # Optional print to see result
\ No newline at end of file
diff --git a/a0.1/assignments_danial022/Assignment 03_04_danial022.ipynb b/a0.1/assignments_danial022/Assignment 03_04_danial022.ipynb
new file mode 100644
index 0000000..6939d47
--- /dev/null
+++ b/a0.1/assignments_danial022/Assignment 03_04_danial022.ipynb	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1B7SFUa4M8t0UL57wLk0NurRnn7vXDuAG","timestamp":1763724497407}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## 1. Log and Exp: Proving Inverse Relationship\n"],"metadata":{"id":"cCb1hk3xe0E6"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KOxJ9f-3eu5F","executionInfo":{"status":"ok","timestamp":1763721511306,"user_tz":-210,"elapsed":19,"user":{"displayName":"Danial Kamali","userId":"04423418101469736667"}},"outputId":"3c3fd318-a088-46eb-e26e-ed8ebb70bbcf"},"outputs":[{"output_type":"stream","name":"stdout","text":["x\tlog(exp(x))\texp(log(x))\n","1.00\t1.00\t\t1.00\n","2.00\t2.00\t\t2.00\n","3.00\t3.00\t\t3.00\n","2.72\t2.72\t\t2.72\n","\n","log(exp(y)) for 0 and negative values:\n","[ 0. -1.  5.]\n","[      -inf        nan 1.60943791]\n"]},{"output_type":"stream","name":"stderr","text":["/tmp/ipython-input-9342166.py:13: RuntimeWarning: divide by zero encountered in log\n","  print(np.log(y_vals))\n","/tmp/ipython-input-9342166.py:13: RuntimeWarning: invalid value encountered in log\n","  print(np.log(y_vals))\n"]}],"source":["import numpy as np\n","\n","# Show log(exp(x)) == x and exp(log(x)) == x for several values\n","x_vals = np.array([1, 2, 3, np.e])\n","print(\"x\\tlog(exp(x))\\texp(log(x))\")\n","for x in x_vals:\n","    print(f\"{x:.2f}\\t{np.log(np.exp(x)):.2f}\\t\\t{np.exp(np.log(x)):.2f}\")\n","\n","# Edge case: try negative and zero values (show error/NaN for log)\n","y_vals = np.array([0, -1, 5])\n","print(\"\\nlog(exp(y)) for 0 and negative values:\")\n","print(np.log(np.exp(y_vals)))\n","print(np.log(y_vals))"]},{"cell_type":"markdown","source":["### 👀🔎 What do you see?\n","\n","#### **your answer:** در رابطه ی اثبات معکوس بودن لگاریتم و تابع نمایی به ازای همه مقادیر نتیجه معتبر است ولی برای لگاریتم اگر اعداد 0 یا منفی وارد کنیم نتیجه معتبر نمیباشد"],"metadata":{"id":"lVZufz08g1hI"}},{"cell_type":"markdown","source":["## 2. Plotting Sine Functions Variations\n","\n"],"metadata":{"id":"mOAqtnk6fDH6"}},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","x = np.linspace(-2*np.pi, 2*np.pi, 400)\n","\n","# look at the description of each function and call the proper function from numpy\n","plt.plot(x, np.sin(x), label='sin(x)')\n","plt.plot(x, np.sin(2*x), label='sin(2x)')\n","plt.plot(x, np.sin(x/2), label='sin(x/2)')\n","plt.plot(x, np.sin(x)**2, label='sin²(x)')\n","plt.legend()\n","plt.title(\"Variations of Sine Functions\")\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"id":"DoWdSehie66R","executionInfo":{"status":"ok","timestamp":1763722043900,"user_tz":-210,"elapsed":936,"user":{"displayName":"Danial Kamali","userId":"04423418101469736667"}},"outputId":"1012bf9a-1911-409d-dd12-71967277644a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["### 👀🔎 What do you see?\n","\n","#### **your answer:** در اینجا نشان داده شده با تابع سینوس ضرب در 2 فشرده تر شده و فرکانس بالاتری دارد در تقیم بر 2 تابع کشیده تر شده و با رسیدن به توان 2 چون منفی نمیشود مقدار فقط بین 0 تا 1 است"],"metadata":{"id":"dMVcqrfihf0P"}},{"cell_type":"markdown","source":["## 3. Subplots: Trig, Inverse, Exp, and Log"],"metadata":{"id":"4-5A1OkdfHXq"}},{"cell_type":"code","source":["x = np.linspace(-1, 1, 400)\n","x2 = np.linspace(0.1, 2, 400)  # For log and exp\n","\n","# based on the number of subplots what should be the dimensions in the following line?\n","fig, axs = plt.subplots(2, 4, figsize=(16, 8))\n","axs[0,0].plot(x, np.sin(x));      axs[0,0].set_title(\"sin(x)\")\n","axs[0,1].plot(x, np.cos(x));      axs[0,1].set_title(\"cos(x)\")\n","axs[0,2].plot(x, np.tan(x));      axs[0,2].set_title(\"tan(x)\")\n","axs[0,3].plot(x, np.arcsin(x));   axs[0,3].set_title(\"arcsin(x)\")\n","axs[1,0].plot(x, np.arccos(x));   axs[1,0].set_title(\"arccos(x)\")\n","axs[1,1].plot(x, np.arctan(x));   axs[1,1].set_title(\"arctan(x)\")\n","axs[1,2].plot(x2, np.exp(x2));    axs[1,2].set_title(\"exp(x)\")\n","axs[1,3].plot(x2, np.log(x2));    axs[1,3].set_title(\"log(x)\")\n","\n","plt.tight_layout()\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":807},"id":"DxocsCu6fGOR","executionInfo":{"status":"ok","timestamp":1763722603571,"user_tz":-210,"elapsed":7302,"user":{"displayName":"Danial Kamali","userId":"04423418101469736667"}},"outputId":"fde33bc8-dbd9-4bbc-c65e-836b4e24c43c"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 8 Axes>"],"image/png":"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👀🔎 What do you see?\n","\n","#### **your answer:** توابع سینوس و تانژانت معکوس شبیه هم هستند توابع سینوس معکوس و تانژانت شبیه هم هستند و توابع لگاریتم و نمایی معکوس یکدیگر هستند"],"metadata":{"id":"thpsEzFVikPh"}},{"cell_type":"markdown","source":["## 4. Features of the Sigmoid Function"],"metadata":{"id":"WAidrU51fMqB"}},{"cell_type":"code","source":["\n","def sigmoid(x):\n","    return 1 / (1 + np.exp(-x))\n","\n","x = np.linspace(-10, 10, 200)\n","y = sigmoid(x)\n","\n","plt.plot(x, y)\n","plt.title(\"Sigmoid Function\")\n","plt.xlabel(\"x\")\n","plt.ylabel(\"σ(x)\")\n","plt.grid(True)\n","plt.show()\n","\n","print(\"Range:\", (y.min(), y.max()), \"| At x=0:\", sigmoid(0))\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":489},"id":"lehJV_vNfLXx","executionInfo":{"status":"ok","timestamp":1763723376339,"user_tz":-210,"elapsed":1892,"user":{"displayName":"Danial Kamali","userId":"04423418101469736667"}},"outputId":"b4a86926-50a2-4d95-f2e7-7293f112da06"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Range: (np.float64(4.5397868702434395e-05), np.float64(0.9999546021312976)) | At x=0: 0.5\n"]}]},{"cell_type":"markdown","source":["### 👀🔎 What do you see?\n","\n","#### **your answer:** یک تابع بین 0 و1 که مقادیر مثبت بزرگ را نزدیک به 1 و مقادیر منفی بزرگ را نزدیک به صفر نشان میدهد"],"metadata":{"id":"DUONuiAxilL0"}},{"cell_type":"markdown","source":["## 5. Derivatives of Famous Functions"],"metadata":{"id":"1FjRt_31fXsx"}},{"cell_type":"markdown","source":[],"metadata":{"id":"zQOM4k_DHYcj"}},{"cell_type":"code","source":["x = np.linspace(-3, 3, 400)\n","plt.plot(x, x**2, label=\"x²\")\n","plt.plot(x, 2*x, '--', label=\"d/dx x²\")\n","\n","x = np.linspace(0, 3, 400)\n","plt.plot(x, np.exp(x), label=\"exp(x)\")\n","plt.plot(x, np.exp(x), '--', label=\"d/dx exp(x)\")\n","\n","plt.plot(x, np.log(x+0.1), label=\"log(x)\")\n","plt.plot(x, 1/(x+0.1), '--', label=\"d/dx log(x)\")\n","\n","plt.legend()\n","plt.title(\"Functions and Their Derivatives\")\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"id":"vyyGBuHRfZ4n","executionInfo":{"status":"ok","timestamp":1763723860749,"user_tz":-210,"elapsed":353,"user":{"displayName":"Danial Kamali","userId":"04423418101469736667"}},"outputId":"60263d63-3dae-4c17-a0fa-9fb53984af92"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["### 👀🔎 What do you see?\n","\n","#### **your answer:** تابع نمایی و مشتق ان دقیقا برابرند و به صورت نمایی رشد میکنند مشتق ایکس به توان 2 خطی است تابع لگاریتم ایکس رشد خیلی کمی داردو مشتق ان با افزایش ایکس کاهش میابد"],"metadata":{"id":"DURup9u5il4-"}},{"cell_type":"markdown","source":["## 6. Gradient of Selected Functions"],"metadata":{"id":"w3QIKTZQf1ZK"}},{"cell_type":"code","source":["# search and learn more about sumpy\n","import sympy as sp\n","\n","# Define symbolic variables\n","x, y = sp.symbols('x y')\n","\n","# Define the function\n","f = x**2 * y + sp.sin(y)\n","\n","# Compute partial derivatives\n","df_dx = sp.diff(f, x)\n","df_dy = sp.diff(f, y)\n","\n","print(\"Function: f(x, y) =\", f)\n","print(\"Partial derivative w.r.t x (∂f/∂x):\", df_dx)\n","print(\"Partial derivative w.r.t y (∂f/∂y):\", df_dy)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Eeudgf3-f0Ie","executionInfo":{"status":"ok","timestamp":1763724179268,"user_tz":-210,"elapsed":2947,"user":{"displayName":"Danial Kamali","userId":"04423418101469736667"}},"outputId":"59348f88-dddd-445f-fb52-cdd5b83edda5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Function: f(x, y) = x**2*y + sin(y)\n","Partial derivative w.r.t x (∂f/∂x): 2*x*y\n","Partial derivative w.r.t y (∂f/∂y): x**2 + cos(y)\n"]}]},{"cell_type":"markdown","source":["### 👀🔎 What do you see?\n","\n","#### **your answer:** 2xy  xمشتق نسبت به\n","x**2  yمشتق نسبت به"],"metadata":{"id":"wDlIJkmYimg9"}},{"cell_type":"code","source":[],"metadata":{"id":"cLxcN6Q8Kai-"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/a0.1/assignments_danial022/Assignment 5_07_danial022.ipynb b/a0.1/assignments_danial022/Assignment 5_07_danial022.ipynb
new file mode 100644
index 0000000..515948c
--- /dev/null
+++ b/a0.1/assignments_danial022/Assignment 5_07_danial022.ipynb	
@@ -0,0 +1,218 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "_cA_dWDJ2vfr"
+   },
+   "source": [
+    "## 🧮 PART 1: Factorial & Combinations\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 33,
+     "status": "ok",
+     "timestamp": 1764657930115,
+     "user": {
+      "displayName": "Danial Kamali",
+      "userId": "04423418101469736667"
+     },
+     "user_tz": -210
+    },
+    "id": "aIxoWeNt2ao0",
+    "outputId": "db5a242b-dfd7-4b06-a5af-020bc1347686"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6! = 720\n",
+      "Combinations (6 choose 3): 20\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ---- FACTORIAL & COMBINATIONS ----\n",
+    "\n",
+    "import math\n",
+    "print(\"6! =\", math.factorial(6))\n",
+    "print(\"Combinations (6 choose 3):\", math.comb(6, 3))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pzy2xD3H3D3o"
+   },
+   "source": [
+    "## 🃏 PART 2: Simulating a Deck of Cards\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 7,
+     "status": "ok",
+     "timestamp": 1764657932073,
+     "user": {
+      "displayName": "Danial Kamali",
+      "userId": "04423418101469736667"
+     },
+     "user_tz": -210
+    },
+    "id": "eUvZhzYB2x4L",
+    "outputId": "db7cf3e8-7356-4cef-bb4a-99a978b37fa2"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Drawn cards: ['1 of Hearts', '3 of Clubs']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ---- PROBABILITY IN A DECK ----\n",
+    "\n",
+    "import random\n",
+    "\n",
+    "deck = [f\"{rank} of {suit}\" for suit in ['Hearts', 'Spades', 'Diamonds', 'Clubs']\n",
+    "        for rank in range(1, 14)]\n",
+    "\n",
+    "# Draw 2 random cards\n",
+    "\n",
+    "draws = random.sample(deck, 2)\n",
+    "print(\"\\nDrawn cards:\", draws)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OidRYf9q3Mwj"
+   },
+   "source": [
+    "## 📊 PART 3: Basic Probability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 19,
+     "status": "ok",
+     "timestamp": 1764657933001,
+     "user": {
+      "displayName": "Danial Kamali",
+      "userId": "04423418101469736667"
+     },
+     "user_tz": -210
+    },
+    "id": "Ok5d99Ns3Oa7",
+    "outputId": "085f5b82-e8ec-49ca-d347-8bda7210acac"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Probability both cards are Hearts: 0.0588\n"
+     ]
+    }
+   ],
+   "source": [
+    "hearts = [card for card in deck if \"Hearts\" in card]\n",
+    "prob_both_hearts = math.comb(len(hearts), 2) / math.comb(len(deck), 2)\n",
+    "print(f\"Probability both cards are Hearts: {prob_both_hearts:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "NMIA7c313Yum"
+   },
+   "source": [
+    "## 🔁 PART 4: Conditional Probability"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 6,
+     "status": "ok",
+     "timestamp": 1764657934296,
+     "user": {
+      "displayName": "Danial Kamali",
+      "userId": "04423418101469736667"
+     },
+     "user_tz": -210
+    },
+    "id": "IZeCH8pw3aZX",
+    "outputId": "07648ef3-bf26-43e8-89cd-12262ebd7a16"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conditional probability 2nd card is Heart given 1st is Heart: 0.9412\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ---- CONDITIONAL PROBABILITY ----\n",
+    "# P(A and B) / P(B): Probability second card is Heart given first was Heart\n",
+    "# A = both are hearts, B = first is heart\n",
+    "\n",
+    "p_a_and_b = math.comb(12, 1) / math.comb(51, 1) # after one heart is drawn\n",
+    "p_b = 13 / 52\n",
+    "p_a_given_b = p_a_and_b / p_b\n",
+    "print(f\"Conditional probability 2nd card is Heart given 1st is Heart: {p_a_given_b:.4f}\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": [
+    {
+     "file_id": "1FfQjMLnIDIgPxnsf6fhmvcaRgl4M0u3F",
+     "timestamp": 1764657895719
+    }
+   ]
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/a0.1/assignments_danial022/Assignment_05_06_danial022.ipynb b/a0.1/assignments_danial022/Assignment_05_06_danial022.ipynb
new file mode 100644
index 0000000..850ad48
--- /dev/null
+++ b/a0.1/assignments_danial022/Assignment_05_06_danial022.ipynb
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyPp/MjIIDHBAsnf29ciKcwD"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["## vectors 101"],"metadata":{"id":"RHJHulr7yHJ3"}},{"cell_type":"code","source":["import numpy as np\n","\n","v = np.array([2, -1, 5])\n","\n","v_len = np.linalg.norm(v)\n","\n","w = np.array([10, 4, -2])\n","vw = np.dot(v, w)\n","\n","print(\"Vector v =\", v)\n","print(\"Length of v =\", v_len)\n","print(\"Vector w =\", w)\n","print(\"Dot product v.m =\", vw)\n"],"metadata":{"id":"NzzIKrwEyMVf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1764518039103,"user_tz":-210,"elapsed":8,"user":{"displayName":"Danial Kamali","userId":"04423418101469736667"}},"outputId":"0d141dc0-5ae5-456f-c765-02892a648e86"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Vector v = [ 2 -1  5]\n","Length of v = 5.477225575051661\n","Vector w = [10  4 -2]\n","Dot product v.m = 6\n"]}]},{"cell_type":"markdown","source":["##Practical Scenario — “Drone hop”"],"metadata":{"id":"EzCX7zwz0cqJ"}},{"cell_type":"code","source":["p_start = np.array([2,1])\n","p_end = np.array([7,4])\n","\n","d = p_start - p_end\n","dist = np.linalg.norm(d)\n","unit = d / dist\n","x_axis = np.dot(unit, np.array([1, 0]))\n","\n","print(f\"displacement: {d}\")\n","print(f\"distence: {dist}\")\n","print(f\"unit direction: {unit}\")\n","print(f\"x_axis: {x_axis}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"X8utm67X0KCY","executionInfo":{"status":"ok","timestamp":1764518040499,"user_tz":-210,"elapsed":21,"user":{"displayName":"Danial Kamali","userId":"04423418101469736667"}},"outputId":"f01f4f89-389e-4306-c5fc-04e9f4cfe12e"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["displacement: [-5 -3]\n","distence: 5.830951894845301\n","unit direction: [-0.85749293 -0.51449576]\n","x_axis: -0.8574929257125441\n"]}]},{"cell_type":"markdown","source":["##  Matrices 101"],"metadata":{"id":"QtVA22Jv7hTB"}},{"cell_type":"code","source":["A = np.array([[3, 1],[2, 5]])\n","tra = A.T\n","det_A = np.linalg.det(A)\n","\n","if det_A != 0:\n","  inv_A = np.linalg.inv(A)\n","else:\n","  print(\"is not invertible\")\n","\n","print(A)\n","print(\"------------------\")\n","print(tra)\n","print(\"------------------\")\n","print(det_A)\n","print(\"------------------\")\n","print(inv_A)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fC2wCNEcds56","executionInfo":{"status":"ok","timestamp":1764519227841,"user_tz":-210,"elapsed":21,"user":{"displayName":"Danial Kamali","userId":"04423418101469736667"}},"outputId":"23f7e0be-7bf7-434a-fbfa-2d981f0901ca"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["[[3 1]\n"," [2 5]]\n","------------------\n","[[3 2]\n"," [1 5]]\n","------------------\n","13.0\n","------------------\n","[[ 0.38461538 -0.07692308]\n"," [-0.15384615  0.23076923]]\n"]}]},{"cell_type":"markdown","source":["##  Practical Scenario — “Rotate that hop”"],"metadata":{"id":"49PhLaFpiuf_"}},{"cell_type":"code","source":["import numpy as np\n","\n","theta = np.deg2rad(30)\n","\n","R = np.array([\n","    [np.cos(theta), -np.sin(theta)],\n","    [np.sin(theta),  np.cos(theta)]])\n","\n","d = np.array([5, 3])\n","\n","d_rot = R @ d\n","\n","d_back = np.linalg.inv(R) @ d_rot\n","\n","print(\"Rotation matrix determinant:\", np.linalg.det(R))\n","print(\"Rotated vector:\", d_rot)\n","print(\"Back-rotated equals original?\", np.allclose(d_back, d))\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lzSSDHTS1dGY","executionInfo":{"status":"ok","timestamp":1764520019604,"user_tz":-210,"elapsed":25,"user":{"displayName":"Danial Kamali","userId":"04423418101469736667"}},"outputId":"7abce236-e323-43e8-d403-411bb0a628b4"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["Rotation matrix determinant: 1.0\n","Rotated vector: [2.83012702 5.09807621]\n","Back-rotated equals original? True\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"3IhHJdotlsQR"},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/a0.1/assignments_danial022/Assignment_6_010_danial022.ipynb b/a0.1/assignments_danial022/Assignment_6_010_danial022.ipynb
new file mode 100644
index 0000000..8ff4477
--- /dev/null
+++ b/a0.1/assignments_danial022/Assignment_6_010_danial022.ipynb
@@ -0,0 +1,193 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "x1gatSKSHYTm",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8520c3dd-0cd5-4eeb-ef6e-d73d005afbaa"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Generated Data: [26 39 34 30 27 26 38 30 30 23 27 22 21 31 25 21 20 31 31 36 29 35 34 34\n",
+            " 38 31 39 22 24 38]\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 📦 Import Required Libraries\n",
+        "import numpy as np\n",
+        "from scipy import stats\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# 🎲 Step 1: Generate Random Integer List\n",
+        "np.random.seed(42)  # For reproducibility\n",
+        "data = np.random.randint(20, 40, size=30)\n",
+        "print(\"Generated Data:\", data)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 📊 Step 2: Central Tendency and Quantiles\n",
+        "Let's compute basic statistics — mean, median, mode, and quantiles."
+      ],
+      "metadata": {
+        "id": "cUfGpmVCHvR5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 📐 Central Tendency\n",
+        "# mean\n",
+        "mean_val = np.mean(data)\n",
+        "# median\n",
+        "median_val = np.median(data)\n",
+        "# mode\n",
+        "mode_val =stats.mode(data, keepdims=True).mode[0]\n",
+        "\n",
+        "# 📏 Quantiles\n",
+        "q1 = np.quantile(data, 0.25)\n",
+        "q3 = np.quantile(data, 0.75)\n",
+        "\n",
+        "print(f\"Mean: {mean_val}\")\n",
+        "print(f\"Median: {median_val}\")\n",
+        "print(f\"Mode: {mode_val}\")\n",
+        "print(f\"Q1 (25%): {q1}\")\n",
+        "print(f\"Q3 (75%): {q3}\")\n"
+      ],
+      "metadata": {
+        "id": "FkmPc2r7Htaz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "52342845-8012-4515-c7b1-5626a57e9a6c"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean: 29.733333333333334\n",
+            "Median: 30.0\n",
+            "Mode: 31\n",
+            "Q1 (25%): 25.25\n",
+            "Q3 (75%): 34.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 🧪 Step 3: Skewness and Kurtosis\n",
+        "Skewness shows asymmetry, and kurtosis indicates the \"tailedness\" of the distribution."
+      ],
+      "metadata": {
+        "id": "EBZ2KixtHztl"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 📉 Skewness and Kurtosis\n",
+        "skew_val = stats.skew(data)\n",
+        "kurtosis_val = stats.kurtosis(data)\n",
+        "\n",
+        "print(f\"Skewness: {skew_val:.2f}\")\n",
+        "print(f\"Kurtosis: {kurtosis_val:.2f}\")\n"
+      ],
+      "metadata": {
+        "id": "XDwmqc3-H05I"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### 📈 Step 4: Visualization - Bar Chart and Boxplot\n",
+        "Visualize the data to better understand its distribution and spread."
+      ],
+      "metadata": {
+        "id": "FOPw0-dSH250"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 📊 Bar Plot\n",
+        "plt.figure(figsize=(12, 4))\n",
+        "\n",
+        "plt.subplot(1, 2, 1)\n",
+        "# make a bar chart\n",
+        "plt.bar(range(len(data)), data)\n",
+        "plt.title(\"Bar Plot of Random Data\")\n",
+        "plt.xlabel(\"Index\")\n",
+        "plt.ylabel(\"Value\")\n",
+        "\n",
+        "# 📦 Boxplot\n",
+        "plt.subplot(1, 2, 2)\n",
+        "# make a box plot\n",
+        "plt.boxplot(data, vert=False)\n",
+        "plt.title(\"Boxplot of Random Data\")\n",
+        "plt.xlabel(\"Value\")\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "Zfx5s6xLH1y8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 407
+        },
+        "outputId": "1257916d-8676-4b1e-9936-3c418be5ec01"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x400 with 2 Axes>"
+            ],
+            "image/png": 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+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "WSKKIKJzsMiM"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/assignments_danial022/assignment_7_pytorch___nexus___danial022.py b/a0.1/assignments_danial022/assignment_7_pytorch___nexus___danial022.py
new file mode 100644
index 0000000..6f52d60
--- /dev/null
+++ b/a0.1/assignments_danial022/assignment_7_pytorch___nexus___danial022.py
@@ -0,0 +1,125 @@
+# -*- coding: utf-8 -*-
+"""Assignment 7. PyTorch | Nexus | danial022.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Cx_dPtidzC4hsLNR-HzWDZsJnf1URCcM
+
+# 🔥 Assignment: Exploring PyTorch
+
+<p align="center">📢⚠️📂  </p>
+
+<p align="center"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>
+
+<p align="center"> 🚨📝🧠 </p>
+
+------------------------------------------------
+
+## 🎯 Goal
+This assignment evaluates your basic skills in using PyTorch, focusing on tensors, autograd, and a small training loop.
+
+### 1️⃣ Setup and Basics
+**Task:**
+
+* Install PyTorch and check its version.
+
+* Create a 2D tensor and print it.
+"""
+
+import torch
+
+print("PyTorch version:", torch.__version__)
+x = torch.tensor([[1., 2.], [3., 4.]])
+print("Tensor x:\n", x)
+
+y = torch.Tensor([[5., 6.],
+                 [7., 8.]])
+print("Tensor y:\n", y)
+
+"""### 2️⃣ Tensor Operations 🧮
+**Task:** Perform addition and matrix multiplication.
+"""
+
+z = x + y
+print("Addition:\n", z)
+
+mat_mul = x @ y
+print("Matrix Multiplication:\n", mat_mul)
+
+elem_mul = x * y
+print("Element-wise multiplication:\n", elem_mul)
+
+# Your turn:
+# Perform element-wise multiplication (x * y) and print the result.
+
+"""### 3️⃣ Autograd and Gradients ⚙️
+**Task:** Enable gradient tracking and compute derivatives.
+"""
+
+a = torch.tensor(3.0, requires_grad=True)
+b = (a ** 2) + 2 * a + 1
+b.backward()
+print("Gradient of b wrt a:", a.grad)
+
+p = torch.tensor(2.0, requires_grad=True)
+q = p ** 3 + 4 * p
+q.backward()
+print("Gradient of q wrt p:", p.grad)
+
+# Your turn:
+# Create a tensor p with value 2.0 (requires_grad=True) and compute the gradient of q = p^3 + 4p.
+
+"""### 4️⃣ Random Tensors 🎲
+**Task:** Generate a random tensor of shape (2, 3) and find its max and min.
+"""
+
+rand_tensor = torch.rand((2, 3))
+print("Random Tensor:\n", rand_tensor)
+print("Max:", torch.max(rand_tensor))
+print("Min:", torch.min(rand_tensor))
+
+"""### 5️⃣ Mini Training Loop 🤖
+**Task:** Train a simple linear model y = wx + b using gradient descent.
+"""
+
+# Data
+x_train = torch.tensor([[1.0], [2.0], [3.0]])
+y_train = torch.tensor([[2.0], [4.0], [6.0]])
+
+# Model
+w = torch.randn(1, requires_grad=True)
+b = torch.zeros(1, requires_grad=True)
+
+# Training
+learning_rate = 0.01
+for epoch in range(100):
+    y_pred = w * x_train + b
+    loss = torch.mean((y_pred - y_train) ** 2)
+    loss.backward()
+
+    # Update
+    with torch.no_grad():
+        w -= learning_rate * w.grad
+        b -= learning_rate * b.grad
+        w.grad.zero_()
+        b.grad.zero_()
+
+print("Trained weight:", w.item())
+print("Trained bias:", b.item())
+
+"""### 6️⃣ Bonus ⚡
+* Convert a PyTorch tensor to a NumPy array.
+
+* Convert it back to a PyTorch tensor.
+"""
+
+import numpy as np
+
+t = torch.tensor([1., 2., 3.])
+
+np_arr = t.numpy()
+print("NumPy array:", np_arr)
+
+t_back = torch.from_numpy(np_arr)
+print("Back to tensor:", t_back)
\ No newline at end of file
diff --git a/a0.1/assignments_danial022/assignment_8_tensorflow___nexus___danial022.py b/a0.1/assignments_danial022/assignment_8_tensorflow___nexus___danial022.py
new file mode 100644
index 0000000..6a8656c
--- /dev/null
+++ b/a0.1/assignments_danial022/assignment_8_tensorflow___nexus___danial022.py
@@ -0,0 +1,98 @@
+# -*- coding: utf-8 -*-
+"""Assignment 8. Tensorflow | Nexus | danial022.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Ish6R3trjHRWll4tHOhQuHrV2BaJ209B
+
+# 🧠 Assignment: Getting Started with TensorFlow
+
+
+<p align="center">📢⚠️📂  </p>
+
+<p align="center"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>
+
+<p align="center"> 🚨📝🧠 </p>
+
+------------------------------------------------
+
+## 🎯 Goal
+This assignment evaluates your basic skills in using TensorFlow, including creating tensors, performing operations, and building a simple model.
+
+### 1️⃣ Setup and Basics
+**Task:**
+
+* Install TensorFlow and verify the version.
+* Create a simple tensor and print it.
+"""
+
+# Example snippet
+import tensorflow as tf
+
+print("TensorFlow version:", tf.__version__)
+a = tf.constant([[1, 2], [3, 4]])
+print("Tensor a:\n", a)
+
+# Your turn:
+# Create a tensor b with values [[5, 6], [7, 8]] and print its shape.
+
+b = tf.constant([[5, 6],
+                 [7, 8]])
+print("Tensor b:\n", b)
+
+"""### 2️⃣ Tensor Operations
+**Task:** Perform element-wise addition and multiplication.
+"""
+
+# Example snippet
+c = a + b
+print("Addition:\n", c)
+
+d = a * b
+print("Multiplication:\n", d)
+
+# Your turn:
+# Compute a @ b (matrix multiplication) and print the result.
+
+mat_mul = a @ b
+print("Matrix Multiplication:\n", mat_mul)
+
+"""### 3️⃣ Random Tensors 🎲
+**Task:** Generate a random tensor of shape (3, 3) from a normal distribution and print its mean and standard deviation.
+"""
+
+# Example snippet
+random_tensor = tf.random.normal([3, 3])
+print("Random Tensor:\n", random_tensor)
+print("Mean:", tf.reduce_mean(random_tensor))
+print("Std Dev:", tf.math.reduce_std(random_tensor))
+
+"""### 4️⃣ Building a Simple Model 🤖
+**Task:** Build a single-layer neural network using tf.keras.Sequential.
+"""
+
+import tensorflow as tf
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Dense(1, input_shape=(1,)),
+    tf.keras.layers.Dense(5, activation='relu')
+])
+model.summary()
+
+# Your turn:
+# Add another dense layer with 5 neurons and relu activation.
+
+"""### 5️⃣ Bonus ⚡
+* Create a tensor with values from 0 to 9 (inclusive).
+
+* Reshape it to shape (2, 5).
+
+* Pint the reshaped tensor.
+"""
+
+# To do
+tens = tf.range(10)
+reshaped_tens = tf.reshape(tens, (2, 5))
+print("Reshaped Tensor:\n", reshaped_tens)
+
diff --git a/a0.1/assignments_danial022/assignment_a0_1_danial022.ipynb b/a0.1/assignments_danial022/assignment_a0_1_danial022.ipynb
new file mode 100644
index 0000000..73aa5d4
--- /dev/null
+++ b/a0.1/assignments_danial022/assignment_a0_1_danial022.ipynb
@@ -0,0 +1,395 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BmoS7frgMRWO"
+      },
+      "source": [
+        "# 1.variable types\n",
+        "## Task1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bdvX4HfeNEMz",
+        "outputId": "db3b3dd3-7347-4c6f-d546-8e511c559abe"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "27.54C = 81.5720F\n"
+          ]
+        }
+      ],
+      "source": [
+        "# convert celsius to farenheit\n",
+        "\n",
+        "c = float(27.54)\n",
+        "f = c * 9/5 + 32\n",
+        "print(f\"{c}C = {f:.4f}F\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pz-fv4iTP_j7"
+      },
+      "source": [
+        "## Task2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wyypvQimQC9h",
+        "outputId": "37b322ed-f5ef-4ba8-8c22-05fca55e7b63"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Sum: 18.5\n",
+            "Difference: 9.5\n",
+            "Product: 63.0\n",
+            "True Division: 3.11\n",
+            "Floor Division: 3.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Tiny Calculator: operations on int & float\n",
+        "x = 14\n",
+        "y = 4.5\n",
+        "\n",
+        "print(f\"Sum: {x + y}\")\n",
+        "print(f\"Difference: {x - y}\")\n",
+        "print(f\"Product: {x * y}\")\n",
+        "print(f\"True Division: {x / y:.2f}\")\n",
+        "print(f\"Floor Division: {x // y}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Nge2oRQOfkJX"
+      },
+      "source": [
+        "# 2.containers\n",
+        "##Task1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QfnTwuxLh4ym"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YKdwMY71hpfC",
+        "outputId": "bf5ce08e-b31e-4b88-f146-079238538a4a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Enter 4 grocery item(separate each with a comma: )milk , bread , rice , chicken\n",
+            "your immutable basket: ('milk ', ' bread ', ' rice ', ' chicken')\n",
+            "third item:  rice \n"
+          ]
+        }
+      ],
+      "source": [
+        "# crocery  basket\n",
+        "shoping_list = []\n",
+        "\n",
+        "item = input(\"Enter 4 grocery item(separate each with a comma: )\").split(\",\")\n",
+        "shoping_list = item\n",
+        "\n",
+        "immutable_basket = tuple(shoping_list)\n",
+        "\n",
+        "\n",
+        "print(f\"your immutable basket: {immutable_basket}\")\n",
+        "print(f\"third item: {immutable_basket[2]}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4YIP9qQNnbaf"
+      },
+      "source": [
+        "## Task2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EmYV6WD3nejD",
+        "outputId": "0931fa77-7852-41c0-bc26-82420bd74b27"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "unique word: {'be', 'to', 'me', 'you'}\n",
+            "word count: {'to': 3, 'be': 2, 'you': 1, 'me': 1}\n"
+          ]
+        }
+      ],
+      "source": [
+        "# word state\n",
+        "\n",
+        "sample = \"to be you me to be to\"\n",
+        "word = sample.split()\n",
+        "\n",
+        "unique_word  = set(word)\n",
+        "\n",
+        "word_count = {}\n",
+        "for w in word:\n",
+        "  if w in word_count:\n",
+        "    word_count[w] += 1\n",
+        "  else:\n",
+        "    word_count[w] = 1\n",
+        "\n",
+        "print(f\"unique word: {unique_word}\")\n",
+        "print(f\"word count: {word_count}\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "z_5GdFxmThIY"
+      },
+      "source": [
+        "# 3.Functions"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PpBDLilvTqnp"
+      },
+      "source": [
+        "##Task1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ftls9sqpTw79",
+        "outputId": "7fae10b0-14a3-4760-efc9-a728ce2dd05a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Enter a Number: 10\n",
+            "10 it is not prime number\n"
+          ]
+        }
+      ],
+      "source": [
+        "def is_prime(n: int):\n",
+        "  if n < 2:\n",
+        "    return False\n",
+        "  for i in range(2, n):\n",
+        "    if n % i == 0:\n",
+        "      return False\n",
+        "  return True\n",
+        "\n",
+        "num = int(input(\"Enter a Number: \"))\n",
+        "if is_prime(num):\n",
+        "  print(f\"{num} is a prime number\")\n",
+        "else:\n",
+        "  print(f\"{num} it is not prime number\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "n1ChopMZvdrp"
+      },
+      "source": [
+        "##Task2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "VkBKZWQ6vhVp",
+        "outputId": "25063087-1398-4ce1-809e-08dd9c314576"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "whats your name: danial\n",
+            "how many time do you want to greet: 4\n",
+            "Danial Danial Danial Danial\n"
+          ]
+        }
+      ],
+      "source": [
+        "def greet(name: str, time: int = 1) -> None:\n",
+        "    word = name.capitalize()\n",
+        "    print(' '.join([word] * time))\n",
+        "\n",
+        "name_input = input(\"whats your name: \")\n",
+        "time_input = int(input(\"how many time do you want to greet: \"))\n",
+        "\n",
+        "greet(name_input, time_input)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "No0e1fCf4g9S"
+      },
+      "source": [
+        "# 4.classes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LtTEBY4C4bxT"
+      },
+      "source": [
+        "## Task1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sXZRgeWviBFd",
+        "outputId": "7a6dc893-b359-460a-e382-f51bf9211c5e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "5\n"
+          ]
+        }
+      ],
+      "source": [
+        "class counter:\n",
+        "  def __init__(self):\n",
+        "    self.count = 0\n",
+        "\n",
+        "  def increment(self, step = 1):\n",
+        "    self.count += step\n",
+        "\n",
+        "  def value(self):\n",
+        "    return self.count\n",
+        "\n",
+        "c = counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iwuhjsCs8tkl"
+      },
+      "source": [
+        "##Task2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EGy4QuB_5udc",
+        "outputId": "dc8c2890-bcb0-4c80-b347-9c67a6d81422"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "11.7\n"
+          ]
+        }
+      ],
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    def __init__(self, x, y):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "\n",
+        "    def distance_to(self, other):\n",
+        "        dx = self.x - other.x\n",
+        "        dy = self.y - other.y\n",
+        "        return math.sqrt(dx**2 + dy**2)\n",
+        "\n",
+        "p = Point(6, 10)\n",
+        "q = Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1)\n",
+        "print(round(p.distance_to(q), 1))\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/a0.1/assignments_danial022/assignment_a0_2_danial022 (1).ipynb b/a0.1/assignments_danial022/assignment_a0_2_danial022 (1).ipynb
new file mode 100644
index 0000000..95ee8ff
--- /dev/null
+++ b/a0.1/assignments_danial022/assignment_a0_2_danial022 (1).ipynb	
@@ -0,0 +1,639 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 1.numpy"
+      ],
+      "metadata": {
+        "id": "IJSAK1MsSlQ9"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Task1"
+      ],
+      "metadata": {
+        "id": "ThMEROZKSree"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "temps = np.random.normal(20, 5, 365)\n",
+        "print(temps[20:50])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kIV8x1RtSu58",
+        "outputId": "26ad81a3-071d-4ad0-f9f1-1c1a4a4f963c"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[22.99013803 19.59832513 25.59617439 16.15186128 22.22992515 18.94076556\n",
+            " 15.62401704 20.76356638 18.1104775  22.4225275  30.54315121 18.18928687\n",
+            " 23.45052097 21.30273447 27.71375025 12.63835426 21.02709861 23.02655513\n",
+            " 21.66281116 13.65566054 13.98589699 17.37305515 10.05281015 30.967089\n",
+            " 14.02190697 15.06952967 15.20759405 18.08587558 28.67679533 20.67098992]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Task2"
+      ],
+      "metadata": {
+        "id": "7bK8dnN-TttC"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "av_temps = np.mean(temps)\n",
+        "av_temps"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "S4W1VtXrTv0H",
+        "outputId": "e85a6cea-9198-471e-eb26-98f9db4a3237"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.float64(20.088846828439575)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 2.pandas"
+      ],
+      "metadata": {
+        "id": "N9_pzeSeURj2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "\n",
+        "# ساخت داده‌ تصادفی برای 30 روز\n",
+        "dates = pd.date_range(start=\"2025-01-01\", periods=30)\n",
+        "temps = np.random.normal(20, 5, 30)      # دمای روزانه\n",
+        "rides = np.random.randint(50, 200, 30)   # تعداد رکاب‌ها\n",
+        "weekday = dates.day_name()               # استخراج نام روز هفته\n",
+        "\n",
+        "# ساخت DataFrame\n",
+        "df = pd.DataFrame({\n",
+        "    \"date\": dates,\n",
+        "    \"temp\": temps,\n",
+        "    \"rides\": rides,\n",
+        "    \"weekday\": weekday\n",
+        "})\n",
+        "# ذخیره به فایل CSV\n",
+        "df.to_csv(\"rides.csv\", index=False)\n",
+        "print(\"فایل rides.csv ساخته شد ✅\")\n",
+        "\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 223
+        },
+        "collapsed": true,
+        "id": "8y5EAVm3tyTC",
+        "outputId": "0617a7a4-8f80-4d5c-af64-5e1f7b17bb66"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "فایل rides.csv ساخته شد ✅\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "        date       temp  rides    weekday\n",
+              "0 2025-01-01  20.035349    195  Wednesday\n",
+              "1 2025-01-02  25.433768    161   Thursday\n",
+              "2 2025-01-03  21.895513     87     Friday\n",
+              "3 2025-01-04  29.543271    144   Saturday\n",
+              "4 2025-01-05  22.402434    184     Sunday"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fdac163b-7b46-492f-baf3-9cb2a4f19761\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>date</th>\n",
+              "      <th>temp</th>\n",
+              "      <th>rides</th>\n",
+              "      <th>weekday</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2025-01-01</td>\n",
+              "      <td>20.035349</td>\n",
+              "      <td>195</td>\n",
+              "      <td>Wednesday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2025-01-02</td>\n",
+              "      <td>25.433768</td>\n",
+              "      <td>161</td>\n",
+              "      <td>Thursday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2025-01-03</td>\n",
+              "      <td>21.895513</td>\n",
+              "      <td>87</td>\n",
+              "      <td>Friday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2025-01-04</td>\n",
+              "      <td>29.543271</td>\n",
+              "      <td>144</td>\n",
+              "      <td>Saturday</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2025-01-05</td>\n",
+              "      <td>22.402434</td>\n",
+              "      <td>184</td>\n",
+              "      <td>Sunday</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fdac163b-7b46-492f-baf3-9cb2a4f19761')\"\n",
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+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-fdac163b-7b46-492f-baf3-9cb2a4f19761 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-fdac163b-7b46-492f-baf3-9cb2a4f19761');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-1c4589a5-da56-48a3-8b87-b27a04bb74e9\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1c4589a5-da56-48a3-8b87-b27a04bb74e9')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-1c4589a5-da56-48a3-8b87-b27a04bb74e9 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2025-01-01 00:00:00\",\n        \"max\": \"2025-01-30 00:00:00\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"2025-01-28 00:00:00\",\n          \"2025-01-16 00:00:00\",\n          \"2025-01-24 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.3194242891283245,\n        \"min\": 9.274833733048277,\n        \"max\": 30.77703727256728,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          22.906414764451398,\n          20.764536950799464,\n          26.81601278124956\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42,\n        \"min\": 53,\n        \"max\": 195,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          111,\n          113,\n          167\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Wednesday\",\n          \"Thursday\",\n          \"Monday\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 35
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Task1"
+      ],
+      "metadata": {
+        "id": "NOsYgXEvUW16"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "df = pd.read_csv('rides.csv')"
+      ],
+      "metadata": {
+        "id": "QNrEBhvnSyPs"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##Task2"
+      ],
+      "metadata": {
+        "id": "QI9Kesoiw560"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.groupby(\"weekday\")[\"rides\"].mean()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 335
+        },
+        "collapsed": true,
+        "id": "3doBeYGvwn9X",
+        "outputId": "2e929645-173a-4b75-fe76-08a606416b63"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "weekday\n",
+              "Friday       138.00\n",
+              "Monday        96.25\n",
+              "Saturday     125.75\n",
+              "Sunday       143.75\n",
+              "Thursday     153.80\n",
+              "Tuesday      142.50\n",
+              "Wednesday    145.80\n",
+              "Name: rides, dtype: float64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>rides</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>weekday</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Friday</th>\n",
+              "      <td>138.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Monday</th>\n",
+              "      <td>96.25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Saturday</th>\n",
+              "      <td>125.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Sunday</th>\n",
+              "      <td>143.75</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Thursday</th>\n",
+              "      <td>153.80</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Tuesday</th>\n",
+              "      <td>142.50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Wednesday</th>\n",
+              "      <td>145.80</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#3.matplotlib"
+      ],
+      "metadata": {
+        "id": "4tkjKrZLxakX"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "##Task1"
+      ],
+      "metadata": {
+        "id": "eYubJfuTzd-6"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "plt.scatter(df['temp'], df['rides'])\n",
+        "plt.xlabel('temps')\n",
+        "plt.ylabel('rides')\n",
+        "\n",
+        "# Task2\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 450
+        },
+        "id": "S-v6LpPVzfyJ",
+        "outputId": "9b3d6df8-9e90-44b9-97c8-606b2b56a697"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "iV-UZ4NV1LAo"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/datasets/rides.csv b/a0.1/datasets/rides.csv
new file mode 100644
index 0000000..8608570
--- /dev/null
+++ b/a0.1/datasets/rides.csv
@@ -0,0 +1,366 @@
+date,temp,rides,weekday
+2024-01-01,13.3,141,Monday
+2024-01-02,18.4,174,Tuesday
+2024-01-03,25.3,256,Wednesday
+2024-01-04,19.5,188,Thursday
+2024-01-05,25.6,234,Friday
+2024-01-06,24.4,253,Saturday
+2024-01-07,28.6,297,Sunday
+2024-01-08,21.8,183,Monday
+2024-01-09,21.0,213,Tuesday
+2024-01-10,20.0,162,Wednesday
+2024-01-11,19.0,221,Thursday
+2024-01-12,20.8,188,Friday
+2024-01-13,18.6,193,Saturday
+2024-01-14,19.5,178,Sunday
+2024-01-15,9.4,76,Monday
+2024-01-16,27.8,281,Tuesday
+2024-01-17,18.6,185,Wednesday
+2024-01-18,31.8,299,Thursday
+2024-01-19,23.5,211,Friday
+2024-01-20,12.1,93,Saturday
+2024-01-21,17.2,158,Sunday
+2024-01-22,23.3,238,Monday
+2024-01-23,18.2,188,Tuesday
+2024-01-24,33.0,301,Wednesday
+2024-01-25,15.3,159,Thursday
+2024-01-26,22.9,251,Friday
+2024-01-27,24.9,226,Saturday
+2024-01-28,28.1,263,Sunday
+2024-01-29,19.7,172,Monday
+2024-01-30,19.0,161,Tuesday
+2024-01-31,16.6,178,Wednesday
+2024-02-01,25.0,235,Thursday
+2024-02-02,21.3,237,Friday
+2024-02-03,13.0,133,Saturday
+2024-02-04,14.8,134,Sunday
+2024-02-05,22.2,244,Monday
+2024-02-06,15.8,137,Tuesday
+2024-02-07,12.8,158,Wednesday
+2024-02-08,28.6,304,Thursday
+2024-02-09,15.9,150,Friday
+2024-02-10,17.9,196,Saturday
+2024-02-11,14.3,163,Sunday
+2024-02-12,19.7,201,Monday
+2024-02-13,23.7,245,Tuesday
+2024-02-14,16.6,151,Wednesday
+2024-02-15,17.5,190,Thursday
+2024-02-16,12.3,96,Friday
+2024-02-17,19.8,203,Saturday
+2024-02-18,25.7,262,Sunday
+2024-02-19,9.9,91,Monday
+2024-02-20,17.0,137,Tuesday
+2024-02-21,15.7,175,Wednesday
+2024-02-22,16.6,173,Thursday
+2024-02-23,15.3,92,Friday
+2024-02-24,24.2,239,Saturday
+2024-02-25,14.4,152,Sunday
+2024-02-26,23.8,239,Monday
+2024-02-27,19.1,194,Tuesday
+2024-02-28,23.1,209,Wednesday
+2024-02-29,26.2,261,Thursday
+2024-03-01,30.3,318,Friday
+2024-03-02,17.6,187,Saturday
+2024-03-03,14.7,147,Sunday
+2024-03-04,20.2,210,Monday
+2024-03-05,19.3,183,Tuesday
+2024-03-06,23.7,203,Wednesday
+2024-03-07,13.0,115,Thursday
+2024-03-08,19.1,195,Friday
+2024-03-09,8.2,88,Saturday
+2024-03-10,13.0,151,Sunday
+2024-03-11,20.8,225,Monday
+2024-03-12,16.5,140,Tuesday
+2024-03-13,23.6,216,Wednesday
+2024-03-14,22.2,205,Thursday
+2024-03-15,12.5,141,Friday
+2024-03-16,24.0,250,Saturday
+2024-03-17,23.3,267,Sunday
+2024-03-18,24.6,234,Monday
+2024-03-19,17.9,183,Tuesday
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diff --git a/a0.1/project1_MortezaHosseini1457.ipynb b/a0.1/project1_MortezaHosseini1457.ipynb
new file mode 100644
index 0000000..736f181
--- /dev/null
+++ b/a0.1/project1_MortezaHosseini1457.ipynb
@@ -0,0 +1,476 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 📚 Assignment 1 — Python Fundamentals\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>\n",
+        "\n",
+        "------------------------------------------------\n",
+        "Welcome to your first hands-on practice! This set of four mini-projects walks you through the basics every Python (and ML) developer leans on daily:\n",
+        "\n",
+        "1. Variable types\n",
+        "\n",
+        "2. Core containers\n",
+        "\n",
+        "3. Functions\n",
+        "\n",
+        "4. Classes\n",
+        "\n",
+        "Each part begins with quick pointers, then gives you two bite-sized tasks to code. Replace every # TODO with working Python and run your script or notebook to check the result. Happy hacking! 😊"
+      ],
+      "metadata": {
+        "id": "ZS5lje_18xC2"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1. Variable Types 🧮\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Primitive types: `int`, `float`, `str`, `bool`\n",
+        "\n",
+        "* Use `type(obj)` to inspect an object’s type.\n",
+        "\n",
+        "* Casting ↔ converting: `int(\"3\")`, `str(3.14)`, `bool(0)`, etc."
+      ],
+      "metadata": {
+        "id": "u5vvtK-6840I"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Celsius → Fahrenheit\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "UyNHtkGm9OgH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "lmaiboJe8E15",
+        "outputId": "f39aec46-b793-4a39-82d8-204359833cf8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "25.0°C is 77.0°F\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 👉 a Celsius temperature (as text), convert it to float,\n",
+        "#    compute Fahrenheit (°F = °C * 9/5 + 32) and print a nicely formatted line.\n",
+        "celsius_text= \"25\"\n",
+        "celsius = float(celsius_text)\n",
+        "fahrenheit = celsius * 9 / 5 + 32\n",
+        "print(f\"{celsius}°C is {fahrenheit}°F\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Tiny Calculator\n"
+      ],
+      "metadata": {
+        "id": "BtsB8QKM9Xs_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# 👉 Store two numbers of **different types** (one int, one float),\n",
+        "#    then print their sum, difference, product, true division, and floor division.\n",
+        "a=7\n",
+        "b=2.5\n",
+        "print(f\"Sum: {a+b}\")\n",
+        "print(f\"Difference: {a-b}\")\n",
+        "print(f\"Product: {a*b}\")\n",
+        "print(f\"True division: {a/b}\")\n",
+        "print(f\"floor division: {a//b}\")"
+      ],
+      "metadata": {
+        "id": "DSR8aS-F9Z10",
+        "outputId": "31bd6cec-b77c-43c1-f5cb-6f60ec887385",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Sum: 9.5\n",
+            "Difference: 4.5\n",
+            "Product: 17.5\n",
+            "True division: 2.8\n",
+            "floor division: 2.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2. Containers 📦 (list, tuple, set, dict)\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "| Container | Mutable? | Ordered?                      | Typical use                       |\n",
+        "| --------- | -------- | ----------------------------- | --------------------------------- |\n",
+        "| `list`    | ✔        | ✔                             | Growth, indexing, slicing         |\n",
+        "| `tuple`   | ✖        | ✔                             | Fixed-size records, hashable keys |\n",
+        "| `set`     | ✔        | ✖                             | Deduplication, membership tests   |\n",
+        "| `dict`    | ✔        | ✖ (3.7 + preserves insertion) | Key → value look-ups              |\n"
+      ],
+      "metadata": {
+        "id": "8JjHX4wy9dPz"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Grocery Basket\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "wRyJyhbt9uUr"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Start with an empty shopping list (list).\n",
+        "# 1. Append at least 4 items supplied in one line of user input (comma-separated).\n",
+        "# 2. Convert the list to a *tuple* called immutable_basket.\n",
+        "# 3. Print the third item using tuple indexing.\n",
+        "shopping_list = input(\"Enter at least 4 shopping items (separated by commas): \").split(',')\n",
+        "immutable_basket = tuple(shopping_list)\n",
+        "print(\"the third item is:\",immutable_basket[2])\n"
+      ],
+      "metadata": {
+        "id": "1J4jcLct9yVO",
+        "outputId": "4433c911-66f8-4183-8d34-e180e8b748cd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Enter at least 4 shopping items (separated by commas): eggs, milk, bread, cheese\n",
+            "the third item is:  bread\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Word Stats"
+      ],
+      "metadata": {
+        "id": "byKd8SFK9w2y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sample = \"to be or not to be that is the question\"\n",
+        "\n",
+        "# 1. Build a set `unique_words` containing every distinct word.\n",
+        "# 2. Build a dict `word_counts` mapping each word to the number of times it appears.\n",
+        "#    (Hint: .split() + a simple loop)\n",
+        "# 3. Print the two structures and explain (in a comment) their main difference.\n",
+        "unique_words = set(sample.split())\n",
+        "word_counts = {}\n",
+        "for word in sample.split():\n",
+        "    if word in word_counts:\n",
+        "        word_counts[word] += 1\n",
+        "    else:\n",
+        "        word_counts[word] = 1\n",
+        "print(\"unique_words:\", unique_words)\n",
+        "print(\"word_counts:\", word_counts)\n"
+      ],
+      "metadata": {
+        "id": "4rLrxkPj90p3",
+        "outputId": "29b78378-98d0-43b8-c9d8-fb630554d6f8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "unique_words: {'not', 'is', 'that', 'to', 'or', 'the', 'be', 'question'}\n",
+            "word_counts: {'to': 2, 'be': 2, 'or': 1, 'not': 1, 'that': 1, 'is': 1, 'the': 1, 'question': 1}\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3. Functions 🔧\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Define with `def`, return with `return`.\n",
+        "\n",
+        "* Parameters can have default values.\n",
+        "\n",
+        "* Docstrings (`\"\"\" … \"\"\"`) document behaviour."
+      ],
+      "metadata": {
+        "id": "gbGMbtLf94M4"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Prime Tester"
+      ],
+      "metadata": {
+        "id": "QOsToPh2-AnZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def is_prime(n: int) -> bool:\n",
+        "    \"\"\"\n",
+        "    Return True if n is a prime number, else False.\n",
+        "    0 and 1 are *not* prime.\n",
+        "    \"\"\"\n",
+        "    if n < 2:\n",
+        "        return False\n",
+        "    for i in range(2, n):\n",
+        "        if n % i == 0:\n",
+        "            return False\n",
+        "    return True\n",
+        "\n",
+        "# Quick self-check\n",
+        "print([x for x in range(10) if is_prime(x)])   # Expected: [2, 3, 5, 7]\n"
+      ],
+      "metadata": {
+        "id": "_pCU2mIH-DAi",
+        "outputId": "35005aa2-5559-4b1b-d190-73d8e0905880",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[2, 3, 5, 7]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — Repeater Greeter"
+      ],
+      "metadata": {
+        "id": "TBGXIzVV-E5u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def greet(name: str, times: int = 1) -> None:\n",
+        "    \"\"\"Print `name`, capitalised, exactly `times` times on one line.\"\"\"\n",
+        "    print((name.capitalize() + \" \") * times)\n",
+        "\n",
+        "\n",
+        "greet(\"alice\")          # Alice\n",
+        "greet(\"bob\", times=3)   # Bob Bob Bob\n"
+      ],
+      "metadata": {
+        "id": "ycvsNyqh-GRM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e41191aa-80f0-4e5f-f604-7171bb24c78f"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Alice \n",
+            "Bob Bob Bob \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4. Classes 🏗️\n",
+        "**Quick-start notes**\n",
+        "\n",
+        "* Create with class Name:\n",
+        "\n",
+        "* Special method __init__ runs on construction.\n",
+        "\n",
+        "* self refers to the instance; attributes live on self."
+      ],
+      "metadata": {
+        "id": "y7K-GaBC-ImE"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 1 — Simple Counter"
+      ],
+      "metadata": {
+        "id": "NgKjsy8A-N3l"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Counter:\n",
+        "    \"\"\"Counts how many times `increment` is called.\"\"\"\n",
+        "    def __init__(self):\n",
+        "        self.count = 0\n",
+        "    def increment(self, step: int = 1):\n",
+        "        self.count += step\n",
+        "    def value(self):\n",
+        "        return self.count\n",
+        "    # 1. In __init__, store an internal count variable starting at 0.\n",
+        "    # 2. Method increment(step: int = 1) adds `step` to the count.\n",
+        "    # 3. Method value() returns the current count.\n",
+        "\n",
+        "\n",
+        "c = Counter()\n",
+        "for _ in range(5):\n",
+        "    c.increment()\n",
+        "print(c.value())   # Expected: 5\n"
+      ],
+      "metadata": {
+        "id": "dPFvr_fe-OPR",
+        "outputId": "02e2178f-7a60-4a83-a33e-c9057bc1f28a",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "5\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Task 2 — 2-D Point with Distance"
+      ],
+      "metadata": {
+        "id": "U99aupan-Q8u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import math\n",
+        "\n",
+        "class Point:\n",
+        "    \"\"\"\n",
+        "    A 2-D point supporting distance calculation.\n",
+        "    Usage:\n",
+        "        p = Point(3, 4)\n",
+        "        q = Point(0, 0)\n",
+        "        print(p.distance_to(q))  # 5.0\n",
+        "    \"\"\"\n",
+        "    def __init__(self, x: float, y: float):\n",
+        "        self.x = x\n",
+        "        self.y = y\n",
+        "\n",
+        "    def distance_to(self, other: \"Point\") -> float:\n",
+        "        return math.sqrt((self.x - other.x)**2 + (self.y - other.y)**2)\n",
+        "    # 1. Store x and y as attributes.\n",
+        "    # 2. Implement distance_to(other) using the Euclidean formula.\n",
+        "\n",
+        "\n",
+        "# Smoke test\n",
+        "p, q = Point(3, 4), Point(0, 0)\n",
+        "assert round(p.distance_to(q), 1) == 5.0\n",
+        "print(\"Smoke test passed!\")"
+      ],
+      "metadata": {
+        "id": "OVh3GEzH-T0w",
+        "outputId": "50523a0e-6916-4398-81bb-525d50a3e0db",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Smoke test passed!\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "F--fFmdITYJk"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/project1_arianshs.ipynb b/a0.1/project1_arianshs.ipynb
new file mode 100644
index 0000000..e47b9ff
--- /dev/null
+++ b/a0.1/project1_arianshs.ipynb
@@ -0,0 +1,960 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🍷 Mini-Project: Merge & Explore the Wine Quality Datasets\n",
+        "\n",
+        "\n",
+        "> **Goal: Merge Wine Quality – Red and Wine Quality – White (UCI) into a single dataset, do a careful first-look exploration, and save the merged file to CSV.**\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>"
+      ],
+      "metadata": {
+        "id": "5pY7aLZSZQM9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# === Requirements ===\n",
+        "# pip install pandas matplotlib\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from pathlib import Path"
+      ],
+      "metadata": {
+        "id": "wpNpLMF8aX1s"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 1) Load ----------\n",
+        "URL_RED   = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n",
+        "URL_WHITE = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv\"\n",
+        "\n",
+        "red   = pd.read_csv(URL_RED, sep=\";\")\n",
+        "white = pd.read_csv(URL_WHITE, sep=\";\")"
+      ],
+      "metadata": {
+        "id": "ErqnxAGRaY_C"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 2) Sanity checks ----------\n",
+        "print(\"Red shape:\", red.shape, \"White shape:\", white.shape)\n",
+        "print(\"Columns equal? ->\", list(red.columns) == list(white.columns))\n",
+        "print(\"Columns:\", list(red.columns))"
+      ],
+      "metadata": {
+        "id": "lVbxevgpabbF",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7a8048a1-6fb4-42cf-e5f1-dab6101cc776"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Red shape: (1599, 12) White shape: (4898, 12)\n",
+            "Columns equal? -> True\n",
+            "Columns: ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol', 'quality']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# (Optional) strict schema assertion (search and read about assert in Python)\n",
+        "assert list(red.columns) == list(white.columns), \"Column mismatch between red and white datasets.\"\n"
+      ],
+      "metadata": {
+        "id": "5vV73isEadfQ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 3) Tag source & merge ----------\n",
+        "red[\"type\"] = \"red\"\n",
+        "white[\"type\"] = \"white\"\n",
+        "\n",
+        "df = pd.concat([red, white], ignore_index=True)\n",
+        "print(\"\\nMerged shape:\", df.shape)\n",
+        "df"
+      ],
+      "metadata": {
+        "id": "uVTeWORRakKq",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 470
+        },
+        "outputId": "5e499d4e-88f9-4f8c-e285-48ad87da8d8d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Merged shape: (6497, 13)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+              "0               7.4              0.70         0.00             1.9      0.076   \n",
+              "1               7.8              0.88         0.00             2.6      0.098   \n",
+              "2               7.8              0.76         0.04             2.3      0.092   \n",
+              "3              11.2              0.28         0.56             1.9      0.075   \n",
+              "4               7.4              0.70         0.00             1.9      0.076   \n",
+              "...             ...               ...          ...             ...        ...   \n",
+              "6492            6.2              0.21         0.29             1.6      0.039   \n",
+              "6493            6.6              0.32         0.36             8.0      0.047   \n",
+              "6494            6.5              0.24         0.19             1.2      0.041   \n",
+              "6495            5.5              0.29         0.30             1.1      0.022   \n",
+              "6496            6.0              0.21         0.38             0.8      0.020   \n",
+              "\n",
+              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+              "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
+              "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
+              "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
+              "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
+              "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
+              "...                   ...                   ...      ...   ...        ...   \n",
+              "6492                 24.0                  92.0  0.99114  3.27       0.50   \n",
+              "6493                 57.0                 168.0  0.99490  3.15       0.46   \n",
+              "6494                 30.0                 111.0  0.99254  2.99       0.46   \n",
+              "6495                 20.0                 110.0  0.98869  3.34       0.38   \n",
+              "6496                 22.0                  98.0  0.98941  3.26       0.32   \n",
+              "\n",
+              "      alcohol  quality   type  \n",
+              "0         9.4        5    red  \n",
+              "1         9.8        5    red  \n",
+              "2         9.8        5    red  \n",
+              "3         9.8        6    red  \n",
+              "4         9.4        5    red  \n",
+              "...       ...      ...    ...  \n",
+              "6492     11.2        6  white  \n",
+              "6493      9.6        5  white  \n",
+              "6494      9.4        6  white  \n",
+              "6495     12.8        7  white  \n",
+              "6496     11.8        6  white  \n",
+              "\n",
+              "[6497 rows x 13 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-9ad16180-7ac6-4782-8492-10bbaf9482f1\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>fixed acidity</th>\n",
+              "      <th>volatile acidity</th>\n",
+              "      <th>citric acid</th>\n",
+              "      <th>residual sugar</th>\n",
+              "      <th>chlorides</th>\n",
+              "      <th>free sulfur dioxide</th>\n",
+              "      <th>total sulfur dioxide</th>\n",
+              "      <th>density</th>\n",
+              "      <th>pH</th>\n",
+              "      <th>sulphates</th>\n",
+              "      <th>alcohol</th>\n",
+              "      <th>quality</th>\n",
+              "      <th>type</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.70</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.076</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>0.99780</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>red</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>0.88</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>0.098</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>67.0</td>\n",
+              "      <td>0.99680</td>\n",
+              "      <td>3.20</td>\n",
+              "      <td>0.68</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>red</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>0.76</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>0.092</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>54.0</td>\n",
+              "      <td>0.99700</td>\n",
+              "      <td>3.26</td>\n",
+              "      <td>0.65</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>red</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>11.2</td>\n",
+              "      <td>0.28</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.075</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>0.99800</td>\n",
+              "      <td>3.16</td>\n",
+              "      <td>0.58</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>6</td>\n",
+              "      <td>red</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.70</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.076</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>0.99780</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "      <td>red</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6492</th>\n",
+              "      <td>6.2</td>\n",
+              "      <td>0.21</td>\n",
+              "      <td>0.29</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>0.039</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>92.0</td>\n",
+              "      <td>0.99114</td>\n",
+              "      <td>3.27</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>11.2</td>\n",
+              "      <td>6</td>\n",
+              "      <td>white</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6493</th>\n",
+              "      <td>6.6</td>\n",
+              "      <td>0.32</td>\n",
+              "      <td>0.36</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>0.047</td>\n",
+              "      <td>57.0</td>\n",
+              "      <td>168.0</td>\n",
+              "      <td>0.99490</td>\n",
+              "      <td>3.15</td>\n",
+              "      <td>0.46</td>\n",
+              "      <td>9.6</td>\n",
+              "      <td>5</td>\n",
+              "      <td>white</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6494</th>\n",
+              "      <td>6.5</td>\n",
+              "      <td>0.24</td>\n",
+              "      <td>0.19</td>\n",
+              "      <td>1.2</td>\n",
+              "      <td>0.041</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>111.0</td>\n",
+              "      <td>0.99254</td>\n",
+              "      <td>2.99</td>\n",
+              "      <td>0.46</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>6</td>\n",
+              "      <td>white</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6495</th>\n",
+              "      <td>5.5</td>\n",
+              "      <td>0.29</td>\n",
+              "      <td>0.30</td>\n",
+              "      <td>1.1</td>\n",
+              "      <td>0.022</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>0.98869</td>\n",
+              "      <td>3.34</td>\n",
+              "      <td>0.38</td>\n",
+              "      <td>12.8</td>\n",
+              "      <td>7</td>\n",
+              "      <td>white</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6496</th>\n",
+              "      <td>6.0</td>\n",
+              "      <td>0.21</td>\n",
+              "      <td>0.38</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>0.020</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>98.0</td>\n",
+              "      <td>0.98941</td>\n",
+              "      <td>3.26</td>\n",
+              "      <td>0.32</td>\n",
+              "      <td>11.8</td>\n",
+              "      <td>6</td>\n",
+              "      <td>white</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>6497 rows × 13 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9ad16180-7ac6-4782-8492-10bbaf9482f1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-9ad16180-7ac6-4782-8492-10bbaf9482f1 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-9ad16180-7ac6-4782-8492-10bbaf9482f1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-bfef05ff-ee29-4dda-9129-c2a5ff9e398e\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bfef05ff-ee29-4dda-9129-c2a5ff9e398e')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-bfef05ff-ee29-4dda-9129-c2a5ff9e398e button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 6497,\n  \"fields\": [\n    {\n      \"column\": \"fixed acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.296433757799792,\n        \"min\": 3.8,\n        \"max\": 15.9,\n        \"num_unique_values\": 106,\n        \"samples\": [\n          7.15,\n          8.1,\n          7.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"volatile acidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1646364740846772,\n        \"min\": 0.08,\n        \"max\": 1.58,\n        \"num_unique_values\": 187,\n        \"samples\": [\n          0.405,\n          0.21,\n          0.695\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"citric acid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14531786489759185,\n        \"min\": 0.0,\n        \"max\": 1.66,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          0.1,\n          0.6,\n          0.37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residual sugar\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.757803743147445,\n        \"min\": 0.6,\n        \"max\": 65.8,\n        \"num_unique_values\": 316,\n        \"samples\": [\n          18.95,\n          3.2,\n          9.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chlorides\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03503360137245906,\n        \"min\": 0.009,\n        \"max\": 0.611,\n        \"num_unique_values\": 214,\n        \"samples\": [\n          0.089,\n          0.217,\n          0.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"free sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17.74939977200255,\n        \"min\": 1.0,\n        \"max\": 289.0,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          77.5,\n          65.0,\n          128.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sulfur dioxide\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56.521854522630264,\n        \"min\": 6.0,\n        \"max\": 440.0,\n        \"num_unique_values\": 276,\n        \"samples\": [\n          14.0,\n          149.0,\n          227.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"density\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.002998673003719041,\n        \"min\": 0.98711,\n        \"max\": 1.03898,\n        \"num_unique_values\": 998,\n        \"samples\": [\n          0.9918,\n          0.99412,\n          0.99484\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1607872021039883,\n        \"min\": 2.72,\n        \"max\": 4.01,\n        \"num_unique_values\": 108,\n        \"samples\": [\n          3.74,\n          3.17,\n          3.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sulphates\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14880587361448958,\n        \"min\": 0.22,\n        \"max\": 2.0,\n        \"num_unique_values\": 111,\n        \"samples\": [\n          1.11,\n          1.56,\n          0.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alcohol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.192711748870997,\n        \"min\": 8.0,\n        \"max\": 14.9,\n        \"num_unique_values\": 111,\n        \"samples\": [\n          10.9333333333333,\n          9.7,\n          10.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quality\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 9,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5,\n          6,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"white\",\n          \"red\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 4) Basic exploration ----------\n",
+        "print(\"\\nDtypes:\\n\", df.dtypes)\n",
+        "print(\"\\nMissing values per column:\\n\", df.isnull().sum().sort_values(ascending=False))\n",
+        "print(\"\\nHead:\\n\", df.head())"
+      ],
+      "metadata": {
+        "id": "OsMo1KOhambz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "95adc921-9ba9-47cf-ab20-77629d9172b0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Dtypes:\n",
+            " fixed acidity           float64\n",
+            "volatile acidity        float64\n",
+            "citric acid             float64\n",
+            "residual sugar          float64\n",
+            "chlorides               float64\n",
+            "free sulfur dioxide     float64\n",
+            "total sulfur dioxide    float64\n",
+            "density                 float64\n",
+            "pH                      float64\n",
+            "sulphates               float64\n",
+            "alcohol                 float64\n",
+            "quality                   int64\n",
+            "type                     object\n",
+            "dtype: object\n",
+            "\n",
+            "Missing values per column:\n",
+            " fixed acidity           0\n",
+            "volatile acidity        0\n",
+            "citric acid             0\n",
+            "residual sugar          0\n",
+            "chlorides               0\n",
+            "free sulfur dioxide     0\n",
+            "total sulfur dioxide    0\n",
+            "density                 0\n",
+            "pH                      0\n",
+            "sulphates               0\n",
+            "alcohol                 0\n",
+            "quality                 0\n",
+            "type                    0\n",
+            "dtype: int64\n",
+            "\n",
+            "Head:\n",
+            "    fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+            "0            7.4              0.70         0.00             1.9      0.076   \n",
+            "1            7.8              0.88         0.00             2.6      0.098   \n",
+            "2            7.8              0.76         0.04             2.3      0.092   \n",
+            "3           11.2              0.28         0.56             1.9      0.075   \n",
+            "4            7.4              0.70         0.00             1.9      0.076   \n",
+            "\n",
+            "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+            "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
+            "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
+            "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
+            "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
+            "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
+            "\n",
+            "   alcohol  quality type  \n",
+            "0      9.4        5  red  \n",
+            "1      9.8        5  red  \n",
+            "2      9.8        5  red  \n",
+            "3      9.8        6  red  \n",
+            "4      9.4        5  red  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Uniqueness & duplicates\n",
+        "dup_count = df.duplicated().sum()\n",
+        "print(\"\\nDuplicate rows:\", dup_count)\n",
+        "\n",
+        "# Descriptive statistics (numeric)\n",
+        "num_cols = df.select_dtypes(include=[np.number]).columns\n",
+        "print(\"\\nNumeric summary:\\n\", df[num_cols].describe().T)\n",
+        "# Target distributions\n",
+        "print(\"\\nQuality distribution (overall):\\n\", df[\"quality\"].value_counts().sort_index()) #uniqe value counting\n",
+        "print(\"\\nQuality distribution by type:\\n\", df.groupby(\"type\")[\"quality\"].value_counts().sort_index())"
+      ],
+      "metadata": {
+        "id": "ou0kWts-aopS",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "cf20302d-6cce-40ec-ea5e-ec6cbae7b320"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Duplicate rows: 1177\n",
+            "\n",
+            "Numeric summary:\n",
+            "                        count        mean        std      min       25%  \\\n",
+            "fixed acidity         6497.0    7.215307   1.296434  3.80000   6.40000   \n",
+            "volatile acidity      6497.0    0.339666   0.164636  0.08000   0.23000   \n",
+            "citric acid           6497.0    0.318633   0.145318  0.00000   0.25000   \n",
+            "residual sugar        6497.0    5.443235   4.757804  0.60000   1.80000   \n",
+            "chlorides             6497.0    0.056034   0.035034  0.00900   0.03800   \n",
+            "free sulfur dioxide   6497.0   30.525319  17.749400  1.00000  17.00000   \n",
+            "total sulfur dioxide  6497.0  115.744574  56.521855  6.00000  77.00000   \n",
+            "density               6497.0    0.994697   0.002999  0.98711   0.99234   \n",
+            "pH                    6497.0    3.218501   0.160787  2.72000   3.11000   \n",
+            "sulphates             6497.0    0.531268   0.148806  0.22000   0.43000   \n",
+            "alcohol               6497.0   10.491801   1.192712  8.00000   9.50000   \n",
+            "quality               6497.0    5.818378   0.873255  3.00000   5.00000   \n",
+            "\n",
+            "                            50%        75%        max  \n",
+            "fixed acidity           7.00000    7.70000   15.90000  \n",
+            "volatile acidity        0.29000    0.40000    1.58000  \n",
+            "citric acid             0.31000    0.39000    1.66000  \n",
+            "residual sugar          3.00000    8.10000   65.80000  \n",
+            "chlorides               0.04700    0.06500    0.61100  \n",
+            "free sulfur dioxide    29.00000   41.00000  289.00000  \n",
+            "total sulfur dioxide  118.00000  156.00000  440.00000  \n",
+            "density                 0.99489    0.99699    1.03898  \n",
+            "pH                      3.21000    3.32000    4.01000  \n",
+            "sulphates               0.51000    0.60000    2.00000  \n",
+            "alcohol                10.30000   11.30000   14.90000  \n",
+            "quality                 6.00000    6.00000    9.00000  \n",
+            "\n",
+            "Quality distribution (overall):\n",
+            " quality\n",
+            "3      30\n",
+            "4     216\n",
+            "5    2138\n",
+            "6    2836\n",
+            "7    1079\n",
+            "8     193\n",
+            "9       5\n",
+            "Name: count, dtype: int64\n",
+            "\n",
+            "Quality distribution by type:\n",
+            " type   quality\n",
+            "red    3            10\n",
+            "       4            53\n",
+            "       5           681\n",
+            "       6           638\n",
+            "       7           199\n",
+            "       8            18\n",
+            "white  3            20\n",
+            "       4           163\n",
+            "       5          1457\n",
+            "       6          2198\n",
+            "       7           880\n",
+            "       8           175\n",
+            "       9             5\n",
+            "Name: count, dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 5) A few simple visuals (optional for report) ----------\n",
+        "# Histograms of numeric features (quick feel for ranges & skew)\n",
+        "top_vars = df.select_dtypes(include=[np.number]).columns\n",
+        "n = min(4, len(top_vars))\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 4, figsize=(16, 3), sharey=True)\n",
+        "\n",
+        "for i, ax in enumerate(axes):\n",
+        "    if i < n:\n",
+        "        col = top_vars[i]\n",
+        "        ax.hist(df[col].dropna(), bins=30)\n",
+        "        ax.set_title(f\"Histogram: {col}\")\n",
+        "        ax.set_xlabel(col)\n",
+        "        if i == 0:\n",
+        "            ax.set_ylabel(\"Count\")\n",
+        "        else:\n",
+        "            ax.set_ylabel(\"\")\n",
+        "    else:\n",
+        "        ax.axis(\"off\")  # hide unused panels if top_vars has < 4\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "2Pa_iCVzaqzp",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 257
+        },
+        "outputId": "05f44662-6377-4169-fbd9-c2f62c2f4468"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x300 with 4 Axes>"
+            ],
+            "image/png": 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dv35dTz/9dKbPY4vXLbtCQkKs1oMkRUVFWdZDXrwPbOmrr75Snz599NVXX6l9+/aZjr906ZKOHDmSr1+ztMTGxlpitvfXLFlO8sjs/WyLGlQQthMFZXtQWGt/QanzBbWeF8T6fScU1HWTXz4bZ4UxRoMGDdLSpUu1fv16lS9f3mp+/fr15eLiYpXLoUOHdPz48XyXS0pJSUm6fv26XeWQ2f7MBg0a2E0uKd1ev+3pNZEy3x9rT3/z2WLrK5XjP82aNTNDhgyxdRi5sn37duPs7GzefPNNc/jwYTNv3jxTpEgR8+WXX9o6tFwJCwsz99xzj1m2bJmJi4szS5YsMSVLljTDhw+3dWjZdvHiRbNr1y6za9cuI8lMnTrV7Nq1yxw7dswYY8zEiRONj4+P+e6778yePXtMx44dTfny5c3Vq1dtHHnGMsrrxo0b5rHHHjOlS5c2sbGx5uTJk5bb9evXbR16pjJ7zVIKDg4206ZNu7tB5kBmeS1ZssS4uLiYOXPmmMOHD5v33nvPODk5mZ9++snGkecfCxYsMG5ubiYyMtIcOHDADBgwwPj4+Jj4+HhjjDE9e/Y0I0aMsIyfOHGicXV1NYsXL7b6O7h48aIx5r/X5JVXXjFbtmwxcXFxZu3atea+++4zlStXNteuXcvXuY0dO9asXr3aHDlyxMTExJhu3boZd3d3s3//fqv880N9y25uyR566CHTtWvXVNPzy+uW2d/0iBEjTM+ePS3j//jjD1OkSBEzbNgwc/DgQTNz5kzj5ORkVq1aZRmT2brKr7nNmzfPODs7m5kzZ1r9rZ0/f94y5uWXXzbR0dEmLi7ObNq0ybRu3dqULFnSnD59Ot/mNW3aNPPtt9+aw4cPm71795ohQ4YYR0dHs3btWsuY/PKa5VZ2/043bdpknJ2dzZQpU8zBgwfNmDFjjIuLi9m7d69ljC1qUEHYThSU7UFBqP0Foc4XlHpO/b677HXdFJTv/s8995zx9vY20dHRVn+HV65csYwZOHCgKVu2rFm/fr3ZuXOnCQkJMSEhITaMOrURI0aYjRs3mri4OLNnzx4zYsQI4+DgYNasWWOMsY8c0pNyf6a95JJZ/baXPIzJ2v5Ye/mbzw6aGvlEQWhqGGPMDz/8YO69917j5uZmqlWrZubMmWPrkHItISHBDBkyxJQtW9a4u7ubChUqmNdee80udointGHDBiMp1S0sLMwYY0xSUpIZNWqUCQgIMG5ubqZVq1bm0KFDtg06CzLKKy4uLs15ksyGDRtsHXqmMnvNUrKXpkZW8vrkk09MpUqVjLu7u6lTp4759ttvbRdwPvXee++ZsmXLGldXV9OwYUOzdetWy7xmzZpZrc/g4OA01/mYMWOMMcZcuXLFtGnTxvj5+RkXFxcTHBxsnnnmGZt9YctObkOHDrWMDQgIMI888oj55ZdfrJaXn+pbdnIzxphff/3VSLJ86bldfnndMvubDgsLM82aNUv1mLp16xpXV1dToUIFM3fu3FTLzWhd3S3Zza1Zs2aZ1reuXbuaUqVKGVdXV3PPPfeYrl27mt9//z1f5zVp0iRTsWJF4+7ubnx9fU3z5s3N+vXrUy03P7xmeSG7f6cLFy40VapUMa6urqZmzZpm+fLlVvNtVYMKwnaioGwP7L32F4Q6X1DqOfX77rPHdVNQvvun913+9npy9epV8/zzz5vixYubIkWKmMcff9ycPHnSdkGnoW/fviY4ONi4uroaPz8/06pVK6v6bg85pCfl/kx7ySWz+m0veSTLbH+svfzNZ4eDMcZkfCwHAAAAAAAAAACA7XFNDQAAAAAAAAAAYBdoagAAAAAAAAAAALtAUwMAAAAAAAAAANgFmhoAAAAAAAAAAMAu0NQAAAAAAAAAAAB2gaYGAAAAAAAAAACwCzQ1AAAAAAAAAACAXaCpAQAAAAAAAAAA7AJNDeAOMMZowIAB8vX1lYODg2JjY9W8eXMNHTr0jj5vRESE6tate0efw8HBQd9++226848ePWrJWZKio6Pl4OCg8+fP39G4ACA/y6x25mQ5KeutLWQlhpTbgcjISPn4+NyV+AAgv8lt7c6r7UlOlStXTtOnT89wjK1jBID8oHfv3urUqVOGY+7EfqK7sV8IyA+cbR0AUBCtWrVKkZGRio6OVoUKFVSyZEktWbJELi4utg4t106ePKnixYtneXzjxo118uRJeXt7S/pvZ9bQoUNpcgBABiIiIvTtt9+m2umV3Rp8p5UpU0YnT55UyZIls/yYrl276pFHHrHcTy9XACiIUtbN6OhotWjRQufOnctSw9fW24EdO3aoaNGiNnt+ALAXM2bMkDHG1mEABRZNDeAOOHLkiEqVKqXGjRtbpvn6+toworwTGBiYrfGurq7ZfgwAIG35rZ46OTllOyYPDw95eHjcoYgAIH/LSd2UpBs3buSLz9V+fn42fX4AuBuSa25uJP+wE1mTF+schQunnwLyWO/evTV48GAdP35cDg4OKleunCTrwwp//fVXFSlSRPPnz7c8buHChfLw8NCBAwckSefPn1f//v3l5+cnLy8vtWzZUrt377Z6rokTJyogIEDFihVTv379dO3atQxjS0xMVL9+/VS+fHl5eHioatWqmjFjRqpxn376qWrWrCk3NzeVKlVKgwYNssxLeTj59u3bVa9ePbm7u6tBgwbatWuX1bJuP+1IdHS0+vTpowsXLsjBwUEODg6KiIjQuHHjdO+996aKo27duho1alSGOQHAnTRnzhwFBQUpKSnJanrHjh3Vt29fy/1Zs2apYsWKcnV1VdWqVfXFF19kuNxXX31VVapUUZEiRVShQgWNGjVKN2/elPTfEW1jx47V7t27LbUyMjJSUuan9Ni3b5/atWsnT09PBQQEqGfPnvr333/THX/mzBl1795d99xzj4oUKaJatWrpq6++shqTlJSkyZMnq1KlSnJzc1PZsmX15ptvSkr7NCorVqxQlSpV5OHhoRYtWujo0aNWy7v99FPp5dq3b189+uijVo+7efOm/P399cknn2SwZgHA9rJaN48ePaoWLVpIkooXLy4HBwf17t1b0n/fHQYNGqShQ4eqZMmSCg0NlZR6O/DXX3+pe/fu8vX1VdGiRdWgQQNt27Yt3dgy2v4k++GHH3T//ffL3d1dJUuW1OOPP26Zl/L0U4cPH1bTpk3l7u6uGjVqKCoqKjerDgBsIr2am9ln68WLF6tWrVry8PBQiRIl1Lp1a12+fFlS6tNPXb58Wb169ZKnp6dKlSqld955J1UcaX3W9/HxsXwXkLJWxzNy7tw59ejRQ35+fvLw8FDlypU1d+5cSWmfPjw2NlYODg5Wn+k/+ugjlSlTRkWKFNHjjz+uqVOnWh1teOTIEXXs2FEBAQHy9PTU/fffr7Vr11rFUa5cOY0fP169evWSl5eXBgwYkOUcAImmBpDnZsyYoXHjxql06dI6efKkduzYkWpMtWrVNGXKFD3//PM6fvy4/vrrLw0cOFCTJk1SjRo1JElPPvmkTp8+rZUrVyomJkb33XefWrVqpbNnz0r6rwkSERGht956Szt37lSpUqX0wQcfZBhbUlKSSpcurUWLFunAgQMaPXq0/u///k8LFy60jJk1a5bCw8M1YMAA7d27V99//70qVaqU5vIuXbqkRx99VDVq1FBMTIwiIiL0yiuvpPv8jRs31vTp0+Xl5aWTJ0/q5MmTeuWVV9S3b18dPHjQal3t2rVLe/bsUZ8+fTLMCQDupCeffFJnzpzRhg0bLNPOnj2rVatWqUePHpKkpUuXasiQIXr55Ze1b98+Pfvss+rTp4/VY1IqVqyYIiMjdeDAAc2YMUMfffSRpk2bJum/0zO9/PLLqlmzpqVWdu3aNdNYz58/r5YtW6pevXrauXOnVq1apVOnTumpp55K9zHXrl1T/fr1tXz5cu3bt08DBgxQz549tX37dsuYkSNHauLEiRo1apQOHDig+fPnKyAgIM3l/fnnn+rcubM6dOig2NhY9e/fXyNGjEj3+dPLtX///lq1apVOnjxpGbts2TJduXIlS+sCAGwpq3WzTJky+uabbyRJhw4d0smTJ61+cPTZZ5/J1dVVmzZt0uzZs1M9/tKlS2rWrJn+/vtvff/999q9e7eGDx+eqhF/u4y2P5K0fPlyPf7443rkkUe0a9curVu3Tg0bNkxzWUlJSercubNcXV21bds2zZ49W6+++mqW1xMA5Ccpa25mn61Pnjyp7t27W/ZnREdHq3PnzumecmrYsGHauHGjvvvuO61Zs0bR0dH65Zdfsh1nZnU8M8nbppUrV+rgwYOaNWtWtk4lu2nTJg0cOFBDhgxRbGysHn74YUvjPtmlS5f0yCOPaN26ddq1a5fatm2rDh066Pjx41bjpkyZojp16mjXrl38oBXZZwDkuWnTppng4GCrac2aNTNDhgyxmta+fXvTpEkT06pVK9OmTRuTlJRkjDHmp59+Ml5eXubatWtW4ytWrGg+/PBDY4wxISEh5vnnn7ea36hRI1OnTp1sxRoeHm66dOliuR8UFGRee+21dMdLMkuXLjXGGPPhhx+aEiVKmKtXr1rmz5o1y0gyu3btMsYYs2HDBiPJnDt3zhhjzNy5c423t3eq5bZr184899xzlvuDBw82zZs3z1YuAHAndOzY0fTt29dy/8MPPzRBQUEmMTHRGGNM48aNzTPPPGP1mCeffNI88sgjlvu31860vP3226Z+/fqW+2PGjEmznt++nLi4OKt6O378eNOmTRur8X/++aeRZA4dOpSVVI0x/22bXn75ZWOMMQkJCcbNzc189NFHaY5NGcPIkSNNjRo1rMa8+uqrGW4H0su1Ro0aZtKkSZb7HTp0ML17985yHgBgC9mtmyk/Kydr1qyZqVevXqrHp/wsXqxYMXPmzJkcx5ty+xMSEmJ69OiR7vjg4GAzbdo0Y4wxq1evNs7Ozubvv/+2zF+5cmWm2zwAyG/SqrmZfbaOiYkxkszRo0fTXGZYWJjp2LGjMcaYixcvGldXV7Nw4ULL/DNnzhgPDw+r/URp1U9vb28zd+7cdGPP6veIZB06dDB9+vRJc15a26Rdu3YZSSYuLs4YY0zXrl1N+/btrR7Xo0ePNPfz3K5mzZrmvffes9wPDg42nTp1yvAxQEY4UgOwoU8//VR79uzRL7/8osjISDk4OEiSdu/erUuXLqlEiRLy9PS03OLi4nTkyBFJ0sGDB9WoUSOr5YWEhGT6nDNnzlT9+vXl5+cnT09PzZkzx9ItP336tE6cOKFWrVplKf6DBw+qdu3acnd3z1YMaXnmmWf01Vdf6dq1a7px44bmz59vdWoXALCVHj166JtvvtH169clSfPmzVO3bt3k6Pjfx6iDBw/qwQcftHrMgw8+qIMHD6a7zK+//loPPvigAgMD5enpqddffz3VL5eya/fu3dqwYYPVdqNatWqSZNl2pJSYmKjx48erVq1a8vX1laenp1avXm2J5eDBg7p+/Xq2tgs52TalpX///pZD4U+dOqWVK1eyXQCQ72W3bmakfv36Gc6PjY1VvXr1snXtvsy2P7Gxsdmq+WXKlFFQUJBlWk5rPgDYWsqam9ln6zp16qhVq1aqVauWnnzySX300Uc6d+5cmss+cuSIbty4YfU52dfXV1WrVs12nLn9HvHcc89pwYIFqlu3roYPH67Nmzdn6/kPHTqU6gi+lPcvXbqkV155RdWrV5ePj488PT118ODBVHE2aNAgW88N3I4LhQM2tHv3bl2+fFmOjo46efKkSpUqJem/DUCpUqUUHR2d6jG3n6cwuxYsWKBXXnlF77zzjkJCQlSsWDG9/fbblvPu2vLCrR06dJCbm5uWLl0qV1dX3bx5U0888YTN4gGAZB06dJAxRsuXL9f999+vn376KVuHeKe0ZcsW9ejRQ2PHjlVoaKi8vb21YMGCNM+rmx2XLl1Shw4dNGnSpFTzkrcvKb399tuaMWOGpk+frlq1aqlo0aIaOnSobty4Icm224VevXppxIgR2rJlizZv3qzy5curSZMmNosHALIiL+tm0aJF8/S5srL9sWXdBwBbSllzM/ts7eTkpKioKG3evFlr1qzRe++9p9dee03btm1T+fLlcxSDg4NDqtNX3X69jLz4HtGuXTsdO3ZMK1asUFRUlFq1aqXw8HBNmTLF8qOt22PIzvU6kr3yyiuKiorSlClTVKlSJXl4eOiJJ56wfMdIltl2DsgIR2oANnL27Fn17t1br732mnr37q0ePXro6tWrkqT77rtP8fHxcnZ2VqVKlaxuyec6rF69eqqLAG7dujXD59y0aZMaN26s559/XvXq1VOlSpWsfr1brFgxlStXTuvWrctSDtWrV9eePXusLlCeWQyurq5KTExMNd3Z2VlhYWGaO3eu5s6dq27duvGlCkC+4O7urs6dO2vevHn66quvVLVqVd13332W+dWrV9emTZusHrNp0ybLNZJS2rx5s4KDg/Xaa6+pQYMGqly5so4dO2Y1Jr1amZH77rtP+/fvV7ly5VJtO9L7wrBp0yZ17NhRTz/9tOrUqaMKFSrot99+s8yvXLmyPDw8srVduP16HFLOtwslSpRQp06dNHfuXEVGRnKNJQB2Ibt109XVVZKyXfMlqXbt2oqNjbVccy8zWdn+1K5dO1s1/88//7S6/lFmNR8A7EVWPls7ODjowQcf1NixY7Vr1y65urpq6dKlqZZVsWJFubi4WO3DOXfunNXnbkny8/OzqqmHDx/WlStXLPezUsezws/PT2FhYfryyy81ffp0zZkzxzJdklUMsbGxVo+tWrVqqmvHpry/adMm9e7dW48//rhq1aqlwMBAqwuNA3mBpgZgIwMHDlSZMmX0+uuva+rUqUpMTLRcZLt169YKCQlRp06dtGbNGh09elSbN2/Wa6+9pp07d0qShgwZok8//VRz587Vb7/9pjFjxmj//v0ZPmflypW1c+dOrV69Wr/99ptGjRqVauMTERGhd955R++++64OHz6sX375Re+9916ay/vf//4nBwcHPfPMMzpw4IBWrFihKVOmZBhDuXLldOnSJa1bt07//vuv1Qa6f//+Wr9+vVatWsUpRgDkKz169NDy5cv16aefWi4QnmzYsGGKjIzUrFmzdPjwYU2dOlVLliyx1PSUKleurOPHj2vBggU6cuSI3n333VRffsqVK6e4uDjFxsbq33//tZz6KiPh4eE6e/asunfvrh07dujIkSNavXq1+vTpk+7OssqVK1t+YXbw4EE9++yzOnXqlGW+u7u7Xn31VQ0fPlyff/65jhw5oq1bt+qTTz5Jc3kDBw7U4cOHNWzYMB06dEjz589XZGRkhnFnlGv//v312Wef6eDBgwoLC8t0HQCArWW3bgYHB8vBwUHLli3TP//8o0uXLmX5ubp3767AwEB16tRJmzZt0h9//KFvvvlGW7ZsSXN8VrY/Y8aM0VdffaUxY8bo4MGD2rt3b5q/Upb++85SpUoVhYWFaffu3frpp5/02muvZTl+AMjPMvtsvW3bNr311lvauXOnjh8/riVLluiff/5R9erVUy3L09NT/fr107Bhw7R+/Xrt27dPvXv3thwZkaxly5Z6//33tWvXLu3cuVMDBw6Ui4uLZX5W6nhmRo8ere+++06///679u/fr2XLlllirlSpksqUKaOIiAgdPnxYy5cvT3UUyODBg7VixQpNnTpVhw8f1ocffqiVK1daTqeeHOeSJUsUGxur3bt363//+5+SkpKyFSeQKRtf0wMokDK7UPhnn31mihYtan777TfL/G3bthkXFxezYsUKY8x/FxkcPHiwCQoKMi4uLqZMmTKmR48e5vjx45bHvPnmm6ZkyZLG09PThIWFmeHDh2d4Qahr166Z3r17G29vb+Pj42Oee+45M2LEiFSPmT17tqlatapxcXExpUqVMoMHD7bMU4oLV23ZssXUqVPHuLq6mrp165pvvvkm04sfDhw40JQoUcJIMmPGjLF67iZNmpiaNWummwMA2EJiYqIpVaqUkWSOHDmSav4HH3xgKlSoYFxcXEyVKlXM559/bjU/Ze0cNmyYKVGihPH09DRdu3Y106ZNs7q43rVr10yXLl2Mj4+PkWS5OKAyuFC4Mcb89ttv5vHHHzc+Pj7Gw8PDVKtWzQwdOtQkJSWlmdeZM2dMx44djaenp/H39zevv/666dWrl+Wihsm5v/HGGyY4ONi4uLiYsmXLmrfeeivdGH744QdTqVIl4+bmZpo0aWI+/fTTDC8Unl6uxhiTlJRkgoODrS66DgD5XXbr5rhx40xgYKBxcHAwYWFhxhjr7w63S7k9OXr0qOnSpYvx8vIyRYoUMQ0aNDDbtm1LN7bMtj/GGPPNN9+YunXrGldXV1OyZEnTuXNny7zbLxRujDGHDh0yDz30kHF1dTVVqlQxq1at4kLhAOxOejU3o8/WBw4cMKGhocbPz8+4ubmZKlWqWF0I+/YLhRvz38XCn376aVOkSBETEBBgJk+enOp5//77b9OmTRtTtGhRU7lyZbNixYpUFwrPrI5ndqHw8ePHm+rVqxsPDw/j6+trOnbsaP744w/L/J9//tnUqlXLuLu7myZNmphFixZZXSjcGGPmzJlj7rnnHuPh4WE6depk3njjDRMYGGiZHxcXZ1q0aGE8PDxMmTJlzPvvv58q15TbEyC7HIxJcbI2ALARY4wqV66s559/Xi+99JKtwwEA2NilS5d0zz33aO7cuercubOtwwEAAACQwjPPPKNff/1VP/30k61DQSHChcIB5Av//POPFixYoPj4eM6bDgCFXFJSkv7991+988478vHx0WOPPWbrkAAAAABImjJlih5++GEVLVpUK1eu1GeffaYPPvjA1mGhkKGpASBf8Pf3V8mSJTVnzhwVL17c1uEAAGzo+PHjKl++vEqXLq3IyEg5O/ORFQAAAMgPtm/frsmTJ+vixYuqUKGC3n33XfXv39/WYaGQ4fRTAAAAAAAAAADALjjaOgAAAAAAAAAAAICsoKkBAAAAAAAAAADsAk0NAAAAAAAAAABgF2hqAAAAAAAAAAAAu0BTAwAAAAAAAAAA2AWaGgAAAAAAAAAAwC7Q1AAAAAAAAAAAAHaBpgYAAAAAAAAAALALNDUAAAAAAAAAAIBd+P8AmT7LmuQ0fJwAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Boxplot of quality by type (class distribution spread)\n",
+        "plt.figure()\n",
+        "df.boxplot(column=\"quality\", by=\"type\")\n",
+        "plt.suptitle(\"\")\n",
+        "plt.title(\"Quality by Wine Type\")\n",
+        "plt.xlabel(\"Type\")\n",
+        "plt.ylabel(\"Quality\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "w9z9f2OMatLD",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 489
+        },
+        "outputId": "166997b3-54e7-451b-c030-ec3758fdedad"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Correlation heatmap (numeric only)\n",
+        "corr = df[num_cols].corr()\n",
+        "plt.figure(figsize=(7, 6))\n",
+        "plt.imshow(corr, interpolation=\"nearest\")\n",
+        "plt.title(\"Correlation Heatmap\")\n",
+        "plt.colorbar()\n",
+        "plt.xticks(range(len(num_cols)), num_cols, rotation=90)\n",
+        "plt.yticks(range(len(num_cols)), num_cols)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "m9OZ2n2nawIP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 623
+        },
+        "outputId": "687c3017-c2f9-4ca5-b240-7c04ef6f4b8f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 700x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NHQPRTdLZI9R",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d217abc1-6ce4-4fe1-c8d9-9a32c95f6586"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Saved merged file to: /content/outputs/wine_quality_merged.csv\n",
+            "Reloaded shape: (6497, 13)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---------- 6) Save ----------\n",
+        "OUT_DIR = Path(\"./outputs\")\n",
+        "OUT_DIR.mkdir(parents=True, exist_ok=True)\n",
+        "out_file = OUT_DIR / \"wine_quality_merged.csv\"\n",
+        "df.to_csv(out_file, index=False)\n",
+        "print(f\"\\nSaved merged file to: {out_file.resolve()}\")\n",
+        "\n",
+        "# Quick verification of saved file\n",
+        "df_check = pd.read_csv(out_file)\n",
+        "print(\"Reloaded shape:\", df_check.shape)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# New Section"
+      ],
+      "metadata": {
+        "id": "AfMJASR-SCL7"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/a0.1/project2_arianshs.ipynb b/a0.1/project2_arianshs.ipynb
new file mode 100644
index 0000000..acf3810
--- /dev/null
+++ b/a0.1/project2_arianshs.ipynb
@@ -0,0 +1,1476 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h6pr9RxJiBKU"
+   },
+   "source": [
+    "# 🏠 Mini-Project: Preprocess & Engineer Features on Ames Housing Dataset\n",
+    "\n",
+    "> **Goal: Work with the [Ames Housing dataset](https://www.kaggle.com/datasets/prevek18/ames-housing-dataset?select=AmesHousing.csv) to perform data preprocessing and create meaningful new features. You will:**\n",
+    "> - Handle **missing values**, **duplicates**, and **outliers**  \n",
+    "> - Detect and fix **skewness** in numerical features  \n",
+    "> - Encode categorical variables into numeric formats  \n",
+    "> - Create **non-linear features** (e.g., polynomial, log, interaction terms) from existing variables  \n",
+    "> - Save the cleaned and enriched dataset into a new CSV file  \n",
+    "\n",
+    "<p align=\"center\">📢⚠️📂</p>\n",
+    "\n",
+    "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project2_rezashokrzad.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+    "\n",
+    "<p align=\"center\"> 🚨📝🧠</p>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7CksOccRjV4s"
+   },
+   "source": [
+    "## 🔹 Step 1: Load the Dataset\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 326
+    },
+    "id": "Nlz-ZiyHgmQb",
+    "outputId": "b7590ba0-2688-4858-e23d-499faedb3244"
+   },
+   "source": [
+    "# TODO: Load the Ames Housing dataset into a DataFrame.\n",
+    "# Hint: The dataset is available on Kaggle (\"Ames Housing\").\n",
+    "# After loading, display the first and last 5 rows to check if it worked.\n",
+    "# Install dependencies as needed:\n",
+    "# pip install kagglehub[pandas-datasets]\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder, OneHotEncoder, StandardScaler, MinMaxScaler, RobustScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import kagglehub\n",
+    "from kagglehub import KaggleDatasetAdapter\n",
+    "\n",
+    "# Set the path to the file you'd like to load\n",
+    "file_path = \"AmesHousing.csv\"\n",
+    "\n",
+    "# Load the latest version\n",
+    "df = kagglehub.load_dataset(\n",
+    "  KaggleDatasetAdapter.PANDAS,\n",
+    "  \"shashanknecrothapa/ames-housing-dataset\",\n",
+    "  file_path,\n",
+    "  # Provide any additional arguments like\n",
+    "  # sql_query or pandas_kwargs. See the\n",
+    "  # documenation for more information:\n",
+    "  # https://github.com/Kaggle/kagglehub/blob/main/README.md#kaggledatasetadapterpandas\n",
+    ")\n",
+    "\n",
+    "\n",
+    "df.head(5)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OdX7swaujg_u"
+   },
+   "source": [
+    "## 🔹 Step 2: Exploratory Data Review (EDR)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "DMYRBPWWjgpo",
+    "outputId": "bb5e68b6-60c3-4338-d268-1fcec406b094"
+   },
+   "source": [
+    "# TODO: Perform initial exploration of the dataset.\n",
+    "# - Check shape, column names, smaples\n",
+    "# - Get summary info, data types\n",
+    "# - Descriptive statistics\n",
+    "\n",
+    "df.shape\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "wBv7xfjbmjie",
+    "outputId": "c943f2cd-3922-42da-e7ba-ee295a137615"
+   },
+   "source": [
+    "df.columns"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "JkrV9mGCmlTf",
+    "outputId": "b3658c9f-1150-4b0f-8ed0-22cfae551a0f"
+   },
+   "source": [
+    "df.info()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 275
+    },
+    "id": "PQ0YzHK5mnbL",
+    "outputId": "6dfbac75-b499-4f21-a3ae-5000fcace5dd"
+   },
+   "source": [
+    "df.sample(5)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 334
+    },
+    "id": "M8lUA1Chmn5Y",
+    "outputId": "69e8ce0c-6885-40fe-fe78-163b19f5427c"
+   },
+   "source": [
+    "df.describe()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "h4A675lMmp3q",
+    "outputId": "06d868dc-15ee-4ab3-d004-89ae4a202569"
+   },
+   "source": [
+    "df.nunique()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Check and remove duplicate rows if there is.\n",
+    "\n",
+    "df.duplicated().sum()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "1UrCG4fmTbcE",
+    "outputId": "21b1a51f-76db-40c6-b6c0-468c13d134a5"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "quTZ6mJdjrDw"
+   },
+   "source": [
+    "## 🔹 Step 3: Missing Value Check & Handling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 920
+    },
+    "id": "-0KdWkPqjs7y",
+    "outputId": "5c52b5c1-6106-43e0-e537-1eaae5033831"
+   },
+   "source": [
+    "# TODO: Check missing values.\n",
+    "# Decide on a strategy (if needed):\n",
+    "# - Drop if too many are missing\n",
+    "# - Fill with mean/median/mode/domain-specific value\n",
+    "pd.set_option(\"display.max_columns\", None)\n",
+    "pd.set_option(\"display.max_rows\", None)\n",
+    "df.isnull().sum()\n",
+    "df.isnull().sum()[df.isnull().sum() > 0].sort_values(ascending=False)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "_8D2A5akpQzf",
+    "outputId": "957a8793-95eb-412d-a82d-913dad2b3ab3"
+   },
+   "source": [
+    "#drop na\n",
+    "df.dropna(thresh = 70, inplace=True)\n",
+    "df.shape"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "bXPlafzHm2yP",
+    "outputId": "8e53b0dc-6330-4dc1-8554-92cbc4826f6c"
+   },
+   "source": [
+    "for col in df.select_dtypes(include=[\"float64\", \"int64\"]).columns:   #for numerics fill with median\n",
+    "    df[col].fillna(df[col].median(), inplace=True)\n",
+    "\n",
+    "\n",
+    "for col in df.select_dtypes(include=[\"object\"]).columns: # for objects fill with mode\n",
+    "    df[col].fillna(df[col].mode()[0], inplace=True)\n",
+    "\n",
+    "\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "EeL71KO0r-ix",
+    "outputId": "64859f35-4c96-4913-c23d-eae25030da0b"
+   },
+   "source": [
+    "df.isnull().sum().sum()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "cv4QElCCr3oa",
+    "outputId": "4512dc08-7d80-46c1-e33d-84e760f7d5fb"
+   },
+   "source": [
+    "df.info()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "iuuDgx_Js4B3",
+    "outputId": "c7edf127-14ea-4738-950b-e03d09f84fdb"
+   },
+   "source": [
+    "\n",
+    "# تابع بررسی ستون\n",
+    "def is_numeric_but_object(series):\n",
+    "    if series.dtype == 'object':\n",
+    "        converted = pd.to_numeric(series, errors='coerce')\n",
+    "        non_na_ratio = converted.notna().sum() / len(series)\n",
+    "        return non_na_ratio == 1  # 100% numeric\n",
+    "    else:\n",
+    "        return False  # خودش numeric نیست object که نباشه\n",
+    "\n",
+    "# لیست ستون‌هایی که object هستن ولی numeric\n",
+    "numeric_like_objects = []\n",
+    "\n",
+    "for col in df.columns:\n",
+    "    if is_numeric_but_object(df[col]):\n",
+    "        numeric_like_objects.append(col)\n",
+    "\n",
+    "print(\"Columns that are object but fully numeric:\", numeric_like_objects)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LWQY6vDEjyTu"
+   },
+   "source": [
+    "## 🔹 Step 4: Correlation Check & Feature Decision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "MHwMZbX_jxKJ",
+    "outputId": "9c900208-39a0-4d97-e214-057a61316b7f"
+   },
+   "source": [
+    "# TODO: Check correlations between numerical features and target variable (SalePrice).\n",
+    "# Use correlation heatmap or pairplot.\n",
+    "# Decide which features to keep/remove based on correlation.\n",
+    "\n",
+    "df.corr(numeric_only=True)"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "B61laKs8ufhA",
+    "outputId": "eda3c0b6-9f53-4c57-ca60-3abb45e0fa90"
+   },
+   "source": [
+    "# فقط correlation هر ستون با target\n",
+    "cor_target = df.corr(numeric_only=True)['SalePrice'].sort_values(ascending=False)\n",
+    "\n",
+    "plt.figure(figsize=(6, 12))  # اندازه مناسب برای یک ستون\n",
+    "sns.heatmap(cor_target.to_frame(), annot=True, cmap='coolwarm')  # تبدیل Series به DataFrame\n",
+    "plt.title(\"Correlation of Features with SalePrice\")\n",
+    "plt.show()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 275
+    },
+    "id": "XzbUjqlg0JGO",
+    "outputId": "59c806d3-7dd9-4f0b-8253-7e9d424b92b9"
+   },
+   "source": [
+    "df.shape\n",
+    "df.tail()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "exTa7T6qj2hv"
+   },
+   "source": [
+    "## 🔹 Step 5: Encode Categorical Variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "GJ6eriJ1yDNU"
+   },
+   "source": [
+    "num_cols = df.select_dtypes(exclude=['object']).columns\n",
+    "cat_cols = df.select_dtypes(include=['object']).columns\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "JBQbfP6jj1hS"
+   },
+   "source": [
+    "# TODO: Identify categorical variables.\n",
+    "# Use methods like:\n",
+    "# - One-hot encoding\n",
+    "# - Ordinal encoding\n",
+    "# Decide what makes sense for each feature.\n",
+    "\n",
+    "ordinal_features = [\n",
+    "    \"Lot Shape\",\n",
+    "    \"Land Slope\",\n",
+    "    \"Exter Qual\",\n",
+    "    \"Exter Cond\",\n",
+    "    \"Bsmt Qual\",\n",
+    "    \"Bsmt Cond\",\n",
+    "    \"Bsmt Exposure\",\n",
+    "    \"BsmtFin Type 1\",\n",
+    "    \"BsmtFin Type 2\",\n",
+    "    \"Heating QC\",\n",
+    "    \"Kitchen Qual\",\n",
+    "    \"Functional\",\n",
+    "    \"Fireplace Qu\",\n",
+    "    \"Garage Finish\",\n",
+    "    \"Garage Qual\",\n",
+    "    \"Garage Cond\",\n",
+    "    \"Paved Drive\",\n",
+    "    \"Pool QC\",\n",
+    "    \"Fence\"\n",
+    "]\n",
+    "nominal_features = [\n",
+    "    \"MS Zoning\",\n",
+    "    \"Street\",\n",
+    "    \"Alley\",\n",
+    "    \"Land Contour\",\n",
+    "    \"Utilities\",\n",
+    "    \"Lot Config\",\n",
+    "    \"Neighborhood\",\n",
+    "    \"Condition 1\",\n",
+    "    \"Condition 2\",\n",
+    "    \"Bldg Type\",\n",
+    "    \"House Style\",\n",
+    "    \"Roof Style\",\n",
+    "    \"Roof Matl\",\n",
+    "    \"Exterior 1st\",\n",
+    "    \"Exterior 2nd\",\n",
+    "    \"Mas Vnr Type\",\n",
+    "    \"Foundation\",\n",
+    "    \"Heating\",\n",
+    "    \"Central Air\",\n",
+    "    \"Electrical\",\n",
+    "    \"Garage Type\",\n",
+    "    \"Misc Feature\",\n",
+    "    \"Sale Type\",\n",
+    "    \"Sale Condition\"\n",
+    "]\n",
+    "\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 275
+    },
+    "id": "QO-_nNb63o58",
+    "outputId": "961f58b9-4360-4d5f-f7c6-7b1fd9f74be1"
+   },
+   "source": [
+    "oe = OrdinalEncoder()\n",
+    "\n",
+    "'''\n",
+    "\n",
+    "\n",
+    "df_nominal[ordinal_features] = oe.fit_transform( df_nominal[ordinal_features])\n",
+    "\n",
+    "#example test :\n",
+    "df_nominal[\"Lot Shape\"].head()\n",
+    "'''\n",
+    "\n",
+    "# moshkel injast ke bar asase alphabet chide nashode va encoder bar asase tartib encode nemishe pas dasti map mikonim:\n",
+    "\n",
+    "\n",
+    "ordinal_mapping = {\n",
+    "    \"Lot Shape\": {'IR3': 0, 'IR2': 1, 'IR1': 2, 'Reg': 3},\n",
+    "    \"Land Slope\": {'Sev': 0, 'Mod': 1, 'Gtl': 2},\n",
+    "    \"Exter Qual\": {'Po': 0, 'Fa': 1, 'TA': 2, 'Gd': 3, 'Ex': 4},\n",
+    "    \"Exter Cond\": {'Po': 0, 'Fa': 1, 'TA': 2, 'Gd': 3, 'Ex': 4},\n",
+    "    \"Bsmt Qual\": {'Po': 0, 'Fa': 1, 'TA': 2, 'Gd': 3, 'Ex': 4},\n",
+    "    \"Bsmt Cond\": {'Po': 0, 'Fa': 1, 'TA': 2, 'Gd': 3, 'Ex': 4},\n",
+    "    \"Bsmt Exposure\": {'No': 0, 'Mn': 1, 'Av': 2, 'Gd': 3},\n",
+    "    \"BsmtFin Type 1\": {'Unf': 0, 'LwQ': 1, 'BLQ': 2, 'Rec': 3, 'ALQ': 4, 'GLQ': 5},\n",
+    "    \"BsmtFin Type 2\": {'Unf': 0, 'LwQ': 1, 'BLQ': 2, 'Rec': 3, 'GLQ': 4, 'ALQ': 5},\n",
+    "    \"Heating QC\": {'Po': 0, 'Fa': 1, 'TA': 2, 'Gd': 3, 'Ex': 4},\n",
+    "    \"Kitchen Qual\": {'Po': 0, 'Fa': 1, 'TA': 2, 'Gd': 3, 'Ex': 4},\n",
+    "    \"Functional\": {'Sal': 0, 'Sev': 1, 'Maj2': 2, 'Maj1': 3, 'Min2': 4, 'Min1': 5, 'Mod': 6, 'Typ': 7},\n",
+    "    \"Fireplace Qu\": {'Po': 0, 'Fa': 1, 'TA': 2, 'Gd': 3, 'Ex': 4},\n",
+    "    \"Garage Finish\": {'Unf': 0, 'RFn': 1, 'Fin': 2},\n",
+    "    \"Garage Qual\": {'Po': 0, 'Fa': 1, 'TA': 2, 'Gd': 3, 'Ex': 4},\n",
+    "    \"Garage Cond\": {'Po': 0, 'Fa': 1, 'TA': 2, 'Gd': 3, 'Ex': 4},\n",
+    "    \"Paved Drive\": {'N': 0, 'P': 1, 'Y': 2},\n",
+    "    \"Pool QC\": {'Fa': 0, 'TA': 1, 'Gd': 2, 'Ex': 3},\n",
+    "    \"Fence\": {'MnWw': 0, 'MnPrv': 1, 'GdWo': 2, 'GdPrv': 3}\n",
+    "}\n",
+    "\n",
+    "\n",
+    "# اعمال mapping روی df_nominal\n",
+    "for col, mapping in ordinal_mapping.items():\n",
+    "    df[col] = df[col].map(mapping)\n",
+    "\n",
+    "# حالا df_nominal خودش تغییر کرده\n",
+    "df[\"Lot Shape\"].head()\n",
+    "df.tail()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 275
+    },
+    "id": "DiITxuij9Dbr",
+    "outputId": "79ba5148-c6e5-4604-b371-ee2d4d11dcb6"
+   },
+   "source": [
+    "ohe = OneHotEncoder(handle_unknown='ignore', sparse_output=False)\n",
+    "\n",
+    "encoded = ohe.fit_transform(df[nominal_features])\n",
+    "encoded_df = pd.DataFrame(encoded, columns=ohe.get_feature_names_out(nominal_features),index=df.index ) # bedune index= moshkel NaN dashtim bade .tail\n",
+    "df = pd.concat([df.drop(columns=nominal_features), encoded_df], axis=1)\n",
+    "\n",
+    "df.tail()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "CUOmUg62-0X6",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "334b865e-183a-4791-d5c8-461afcd1f6b9"
+   },
+   "source": [
+    "df.info()"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "dV0lFI6t_l9n",
+    "outputId": "53003548-9fd4-42b4-a148-9d572f69e7f1"
+   },
+   "source": [
+    "df.shape\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ayerWnrFj-OS"
+   },
+   "source": [
+    "## 🔹 Step 6:  Feature Scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "cmOzqeCcj9_P"
+   },
+   "source": [
+    "# TODO: Try different scaling techniques:\n",
+    "# - StandardScaler\n",
+    "# - MinMaxScaler\n",
+    "# - RobustScaler\n",
+    "# Decide based on the distribution of features.\n",
+    "rs = RobustScaler()\n",
+    "df[num_cols] = rs.fit_transform(df[num_cols])\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "B8NozkS2kCDE"
+   },
+   "source": [
+    "## 🔹 Step 7: Feature Selection & Feature Creation 💡"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "elKATS4vj82a"
+   },
+   "source": [
+    "# TODO: Select the most useful features.\n",
+    "# Try:\n",
+    "# - Correlation thresholding and Removing highly collinear features\n",
+    "# - decide yourself for dropping useless ones\n",
+    "\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# Filtering features (selecting)\n",
+    "target = 'SalePrice'\n",
+    "\n",
+    "# فقط ستون‌های numeric\n",
+    "numeric_df = df.select_dtypes(include=['int64', 'float64'])\n",
+    "numeric_cols = numeric_df.columns.tolist()\n",
+    "\n",
+    "# correlation با target\n",
+    "cor_target = numeric_df.corr()[target].abs()\n",
+    "\n",
+    "# حذف ستون‌هایی که |corr| < 0.1\n",
+    "relevant_features = cor_target[cor_target >= 0.1].index.tolist()\n",
+    "df_filtered = df[relevant_features]\n",
+    "\n",
+    "# multicollinear check\n",
+    "corr_matrix = df_filtered.corr().abs()\n",
+    "upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))\n",
+    "to_drop = [column for column in upper.columns if any(upper[column] > 0.9)]\n",
+    "df_cleaned = df_filtered.drop(columns=to_drop)\n",
+    "\n",
+    "# فقط numeric هایی که حذف شدن به خاطر low correlation\n",
+    "numeric_removed = list(set(numeric_cols) - set(relevant_features))\n",
+    "\n",
+    "print(\"Numeric columns removed due to low correlation with target:\", numeric_removed)\n",
+    "print(\"Columns removed due to multicollinearity:\", to_drop)\n",
+    "print(\"Remaining columns:\", df_cleaned.columns.tolist())\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "L8uVNmLLND1P",
+    "outputId": "766040e1-e0bf-41cc-aba1-8bab15ead2c6"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# فقط correlation هر ستون با target\n",
+    "cor_target = df_cleaned.corr(numeric_only=True)['SalePrice'].sort_values(ascending=False)\n",
+    "\n",
+    "plt.figure(figsize=(6, 12))  # اندازه مناسب برای یک ستون\n",
+    "sns.heatmap(cor_target.to_frame(), annot=True, cmap='coolwarm')  # تبدیل Series به DataFrame\n",
+    "plt.title(\"Correlation of Features with SalePrice\")\n",
+    "plt.show()\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "NmPEq3pINM5W",
+    "outputId": "3a3ff25e-cbb4-4459-9aab-cf042cc35931"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "print(df.columns.tolist())\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Rvwzr9JKdj2n",
+    "outputId": "dc0657c9-f177-4634-ed45-937f80191205"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "irzYn2YHkFuu",
+    "outputId": "31f7a474-c1c7-4e8b-d600-389ddf4a4c3d"
+   },
+   "source": [
+    "# TODO: Create at least 2 NEW features.\n",
+    "# Examples:\n",
+    "# - Age of house: df[\"HouseAge\"] = df[\"YrSold\"] - df[\"YearBuilt\"]\n",
+    "# - Interaction: df[\"Quality_x_Size\"] = df[\"OverallQual\"] * df[\"GrLivArea\"]\n",
+    "# - Non-linear: df[\"Log_LotArea\"] = np.log1p(df[\"LotArea\"])\n",
+    "import numpy as np\n",
+    "\n",
+    "# 1. ویژگی‌های اصلی استاد\n",
+    "df[\"HouseAge\"] = df[\"Yr Sold\"] - df[\"Year Built\"]\n",
+    "df[\"Quality_x_Size\"] = df[\"Overall Qual\"] * df[\"Gr Liv Area\"]\n",
+    "df[\"Log_LotArea\"] = np.where(\n",
+    "    ~df[\"Lot Area\"].isna(),\n",
+    "    np.log1p(df[\"Lot Area\"]),\n",
+    "    0\n",
+    ")\n",
+    "\n",
+    "# 2. ویژگی‌های ترکیبی\n",
+    "df[\"TotalSF\"] = df[\"Total Bsmt SF\"] + df[\"1st Flr SF\"] + df[\"2nd Flr SF\"]\n",
+    "df[\"TotalPorchSF\"] = (df[\"Open Porch SF\"] + df[\"Enclosed Porch\"] +\n",
+    "                      df[\"3Ssn Porch\"] + df[\"Screen Porch\"])\n",
+    "df[\"TotalBathrooms\"] = (df[\"Full Bath\"] + 0.5 * df[\"Half Bath\"] +\n",
+    "                         df[\"Bsmt Full Bath\"] + 0.5 * df[\"Bsmt Half Bath\"])\n",
+    "df[\"TotalRooms\"] = df[\"TotRms AbvGrd\"] + df.get(\"BsmtRooms\", 0)  # ممکنه ستون BsmtRooms نباشه\n",
+    "df[\"TotalOutdoorSF\"] = df.get(\"Wood Deck SF\", 0) + df[\"TotalPorchSF\"] + df.get(\"Pool Area\", 0)\n",
+    "\n",
+    "# 3. نسبت‌ها (Ratios)\n",
+    "df[\"GrLivArea_per_Room\"] = np.where(\n",
+    "    (df[\"TotRms AbvGrd\"] > 0) & (~df[\"TotRms AbvGrd\"].isna()),\n",
+    "    df[\"Gr Liv Area\"] / df[\"TotRms AbvGrd\"],\n",
+    "    0\n",
+    ")\n",
+    "\n",
+    "df[\"GarageArea_per_Car\"] = np.where(\n",
+    "    (df[\"Garage Cars\"] > 0) & (~df[\"Garage Cars\"].isna()),\n",
+    "    df[\"Garage Area\"] / df[\"Garage Cars\"],\n",
+    "    0\n",
+    ")\n",
+    "\n",
+    "df[\"Bath_per_Bedroom\"] = np.where(\n",
+    "    (df[\"Bedroom AbvGr\"] > 0) & (~df[\"Bedroom AbvGr\"].isna()),\n",
+    "    df[\"TotalBathrooms\"] / df[\"Bedroom AbvGr\"],\n",
+    "    0\n",
+    ")\n",
+    "\n",
+    "df[\"LotArea_per_GrLivArea\"] = df[\"Lot Area\"] / df[\"Gr Liv Area\"]\n",
+    "df[\"Year_since_remod\"] = df[\"Yr Sold\"] - df[\"Year Remod/Add\"]\n",
+    "\n",
+    "# 4. ویژگی‌های تعامل (Interactions)\n",
+    "df[\"OverallQual_x_TotalSF\"] = df[\"Overall Qual\"] * df[\"TotalSF\"]\n",
+    "df[\"OverallQual_x_GrLivArea\"] = df[\"Overall Qual\"] * df[\"Gr Liv Area\"]\n",
+    "\n",
+    "# 5. ویژگی‌های زمانی / بولین\n",
+    "df[\"Since_Remodel\"] = df[\"Yr Sold\"] - df[\"Year Remod/Add\"]\n",
+    "df[\"Is_Remodeled\"] = (df[\"Year Built\"] != df[\"Year Remod/Add\"]).astype(int)\n",
+    "df[\"Has_Pool\"] = (df.get(\"Pool Area\", 0) > 0).astype(int)\n",
+    "df[\"Has_Garage\"] = ((df.get(\"Garage Area\", 0) > 0) | (df.get(\"Garage Cars\", 0) > 0)).astype(int)\n",
+    "df[\"Has_Basement\"] = (df.get(\"Total Bsmt SF\", 0) > 0).astype(int)\n",
+    "df[\"Has_Fireplace\"] = (df.get(\"Fireplaces\", 0) > 0).astype(int)\n",
+    "\n",
+    "# 6. کیفیت ترکیبی آشپزخانه\n",
+    "if {\"Kitchen Qual\", \"Kitchen Cond\"}.issubset(df.columns):\n",
+    "    df[\"Kitchen_Score\"] = (df[\"Kitchen Qual\"] + df[\"Kitchen Cond\"]) / 2\n",
+    "\n",
+    "# 7. تمیزکاری مقادیر بی‌نهایت\n",
+    "df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
+    "\n",
+    "df.isna().sum()\n",
+    "\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    " 🫕 to the creation.. we have Nan .. now imputing"
+   ],
+   "metadata": {
+    "id": "YvFKH6aNfdt2"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "#nan :\n",
+    "# Log_LotArea\t341\n",
+    "#LotArea_per_GrLivArea\t3\n",
+    "# Imputation با Median\n",
+    "df[\"Log_LotArea\"].fillna(df[\"Log_LotArea\"].median(), inplace=True)\n",
+    "df[\"LotArea_per_GrLivArea\"].fillna(df[\"LotArea_per_GrLivArea\"].median(), inplace=True)\n",
+    "\n",
+    "df.isna().sum().sum()"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "YwgQczi8fdQu",
+    "outputId": "66c4f397-974a-4eb6-9d85-401a89c2351c"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ohlGPfhykRMs"
+   },
+   "source": [
+    "## 🔹 Step 8: Outlier Handling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "l0Yx7uH5kP_H"
+   },
+   "source": [
+    "# TODO: Detect and handle outliers.\n",
+    "# Methods:\n",
+    "# - IQR rule\n",
+    "# - Z-score\n",
+    "# - Visualization (boxplots, scatterplots)\n",
+    "\n",
+    "\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# IQR\n",
+    "\n",
+    "# انتخاب ستون های عددی\n",
+    "Q1 = df[num_cols].quantile(0.25)\n",
+    "Q3 = df[num_cols].quantile(0.75)\n",
+    "IQR = Q3 - Q1\n",
+    "\n",
+    "outliers = ((df[num_cols] < (Q1 - 1.5 * IQR)) | (df[num_cols] > (Q3 + 1.5 * IQR))).any(axis=1)\n",
+    "# 1.5 standarde ama baraye taghire sensivity mishe avaz kard\n",
+    "\n",
+    "print(f'Number of outliers detected by boxplot method: {outliers.sum()}')\n",
+    "print(f'Percentage of outliers: {outliers.sum()/len(df)*100:.2f}%')\n",
+    "\n",
+    "df[outliers].shape"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Qm_m1p1fhvPw",
+    "outputId": "1bba6e47-5c97-407a-ee17-56cdcda4beb6"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "z_score = np.abs((df[num_cols] - df[num_cols].mean())/ df[num_cols].std())\n",
+    "outliers = (z_score > 1.5).any(axis=1) #inja all hame feature haro barresi mikone va agar hame out budan True (100% )\n",
+    "df[outliers].shape\n",
+    "#df[~outliers].shape #unai ke outlier nistan\n"
+   ],
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "ne_JTSxJh6vO",
+    "outputId": "b9be4399-9c47-4233-c645-45cd7c43616d"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "#boxplot;\n",
+    "# انتخاب ستون‌های عددی\n",
+    "numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()\n",
+    "\n",
+    "batch_size = 5\n",
+    "for i in range(0, len(numeric_cols), batch_size):\n",
+    "    cols_batch = numeric_cols[i:i+batch_size]\n",
+    "    df_batch = df[cols_batch]\n",
+    "\n",
+    "    plt.figure(figsize=(12, len(cols_batch) * 1.5))\n",
+    "    for j, col in enumerate(cols_batch, 1):\n",
+    "        plt.subplot(len(cols_batch), 1, j)\n",
+    "        sns.boxplot(x=df[col], color=\"skyblue\")\n",
+    "        plt.title(f\"Boxplot - {col}\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n"
+   ],
+   "metadata": {
+    "id": "w7sX3cubiUBv",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "outputId": "4b8d82c8-5d2d-42a9-8fbb-ce67d45acbd7"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "#scatterplot\n",
+    "\n",
+    "# -- تنظیمات اصلی --\n",
+    "target_col = 'SalePrice'   # اسم ستون تارگت\n",
+    "batch_size = 5             # تعداد نمودارها در هر سری\n",
+    "\n",
+    "# فقط ستون‌های عددی غیر تارگت\n",
+    "numeric_cols = [col for col in df.select_dtypes(include=np.number).columns if col != target_col]\n",
+    "\n",
+    "# مرتب‌سازی: اول حروف، بعد اعداد\n",
+    "numeric_cols = sorted(numeric_cols, key=lambda x: (str(x)[0].isdigit(), str(x).lower()))\n",
+    "\n",
+    "# رسم Scatterplot به صورت Batch\n",
+    "for i in range(0, len(numeric_cols), batch_size):\n",
+    "    cols_batch = numeric_cols[i:i+batch_size]\n",
+    "\n",
+    "    plt.figure(figsize=(12, len(cols_batch) * 3))\n",
+    "    for j, col in enumerate(cols_batch, 1):\n",
+    "        plt.subplot(len(cols_batch), 1, j)\n",
+    "        sns.scatterplot(x=df[col], y=df[target_col], alpha=0.6, color='teal', edgecolor=None)\n",
+    "        plt.title(f\"Scatterplot: {col} vs {target_col}\")\n",
+    "        plt.xlabel(col)\n",
+    "        plt.ylabel(target_col)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ],
+   "metadata": {
+    "id": "9-HJ6S25kcFz",
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "outputId": "6500f9fd-57a1-40bf-d3da-eedd55631595"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nDirz02_kU1e"
+   },
+   "source": [
+    "## 🔹 Step 9: Skewness Handling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "Bz0Kke71kTxQ",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "76c5cd7d-587f-490d-a939-28dcdac296f7"
+   },
+   "source": [
+    "# TODO: Check skewness of numerical features.\n",
+    "# Apply log, sqrt, Box-Cox, or Yeo-Johnson depending on distribution.\n",
+    "\n",
+    "from scipy.stats import boxcox, yeojohnson\n",
+    "\n",
+    "# 1️⃣ ستون‌های numeric واقعی\n",
+    "numeric_cols = df.select_dtypes(include=['int64', 'float64']).columns.tolist()\n",
+    "\n",
+    "# 2️⃣ بررسی skewness قبل از transformation\n",
+    "skew_values = df[numeric_cols].skew()\n",
+    "print(\"Skewness before transformation:\\n\", skew_values)\n",
+    "\n",
+    "# 3️⃣ Threshold برای skewed بودن\n",
+    "skew_threshold = 0.75\n",
+    "\n",
+    "# 4️⃣ کپی dataframe برای اعمال transformation\n",
+    "df_transformed = df.copy()\n",
+    "\n",
+    "# 5️⃣ اجرای transformation بر اساس توزیع\n",
+    "for col in numeric_cols:\n",
+    "    skew_val = skew_values[col]\n",
+    "\n",
+    "    if abs(skew_val) > skew_threshold:\n",
+    "        # Positive and > 0 -> Box-Cox\n",
+    "        if (df_transformed[col] > 0).all():\n",
+    "            df_transformed[col], _ = boxcox(df_transformed[col])\n",
+    "            print(f\"Applied Box-Cox on {col}\")\n",
+    "        # اگر صفر یا منفی داره -> Yeo-Johnson\n",
+    "        elif (df_transformed[col] <= 0).any():\n",
+    "            df_transformed[col], _ = yeojohnson(df_transformed[col])\n",
+    "            print(f\"Applied Yeo-Johnson on {col}\")\n",
+    "          #Optional: log/sqrt برای skew خیلی شدید (می‌تونی فعال کنی)\n",
+    "        elif skew_val > 2:\n",
+    "           df_transformed[col] = np.log1p(df_transformed[col])\n",
+    "        elif skew_val < -2:\n",
+    "            df_transformed[col] = np.sqrt(df_transformed[col].max() - df_transformed[col])\n",
+    "\n",
+    "# 6️⃣ بررسی skewness بعد از transformation\n",
+    "new_skew = df_transformed[numeric_cols].skew()\n",
+    "print(\"Skewness after transformation:\\n\", new_skew)\n",
+    "\n",
+    "# 7️⃣ df_transformed آماده است برای scaling و مدل‌سازی"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# لیست featureهایی که میخوای drop کنی\n",
+    "drop_cols = [\n",
+    "    'Has_Pool', 'Has_Shed', 'Garage_Carport',\n",
+    "    'OverallQual_x_GrLivArea', 'TotalBsmtSF_per_Room', 'Age_House',\n",
+    "    'TotRmsAbvGrd', 'GarageCars', 'LotFrontage'\n",
+    "]\n",
+    "\n",
+    "# چک کن که فقط ستون‌های موجود drop بشن\n",
+    "drop_cols_existing = [col for col in drop_cols if col in df_transformed.columns]\n",
+    "\n",
+    "# drop کردن از df\n",
+    "df_transformed = df_transformed.drop(columns=drop_cols_existing)\n",
+    "\n",
+    "# تایید تعداد ستون‌ها بعد از drop\n",
+    "print(f\"New shape of df: {df_transformed.shape}\")"
+   ],
+   "metadata": {
+    "id": "jUpdtyf55r-Z",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "4bc22766-ff3e-45d1-df56-48b4882bf0b4"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# ---------- Step 1: Calculate skewness ----------\n",
+    "skewness = df_transformed.skew()\n",
+    "\n",
+    "# ---------- Step 2: Calculate outlier percentage ----------\n",
+    "Q1 = df_transformed.quantile(0.25)\n",
+    "Q3 = df_transformed.quantile(0.75)\n",
+    "IQR = Q3 - Q1\n",
+    "\n",
+    "outlier_mask = ((df_transformed < (Q1 - 1.5 * IQR)) | (df_transformed > (Q3 + 1.5 * IQR)))\n",
+    "outlier_percent = outlier_mask.sum() / len(df) * 100\n",
+    "\n",
+    "# ---------- Step 3: Classify features ----------\n",
+    "drop_features = []\n",
+    "clip_features = []\n",
+    "\n",
+    "for col in df_transformed.columns:\n",
+    "    if col in skewness.index:  # فقط برای عددی‌ها\n",
+    "        if abs(skewness[col]) > 1 and outlier_percent[col] > 20:\n",
+    "            drop_features.append(col)\n",
+    "        elif outlier_percent[col] > 10:\n",
+    "            clip_features.append(col)\n",
+    "\n",
+    "print(\"Features to DROP (too skewed + high outliers):\", drop_features)\n",
+    "print(\"Features to CLIP (moderate skew, outlier handling):\", clip_features)\n",
+    "\n",
+    "# ---------- Step 4: Apply clipping ----------\n",
+    "df_clean = df_transformed.copy()\n",
+    "for col in clip_features:\n",
+    "    lower = Q1[col] - 1.5 * IQR[col]\n",
+    "    upper = Q3[col] + 1.5 * IQR[col]\n",
+    "    df_clean[col] = df_clean[col].clip(lower=lower, upper=upper)"
+   ],
+   "metadata": {
+    "id": "ldnaK53l6Y2S",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "742dab03-b0a4-41d1-cbbc-ab73e12f936a"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# حذف فیچرهای غیر مفید\n",
+    "df_clean = df_transformed.drop(columns=['MS Zoning_RL', 'Roof Style_Gable'])\n",
+    "\n",
+    "# کلـیپ برای فیچرهای دیگه\n",
+    "clip_features = [\n",
+    "    'Exter Cond', 'BsmtFin Type 2', 'BsmtFin SF 2', 'Fireplace Qu',\n",
+    "    'Enclosed Porch', 'MS Zoning_RM', 'Land Contour_Lvl', 'Lot Config_Corner',\n",
+    "    'Neighborhood_NAmes', 'Condition 1_Norm', 'Bldg Type_1Fam',\n",
+    "    'House Style_1.5Fin', 'Roof Style_Hip', 'Exterior 1st_HdBoard',\n",
+    "    'Exterior 1st_MetalSd', 'Exterior 1st_Wd Sdng', 'Exterior 2nd_HdBoard',\n",
+    "    'Exterior 2nd_MetalSd', 'Exterior 2nd_Wd Sdng', 'Foundation_BrkTil',\n",
+    "    'Sale Type_WD ', 'Sale Condition_Normal', 'GarageArea_per_Car',\n",
+    "    'Bath_per_Bedroom', 'LotArea_per_GrLivArea', 'OverallQual_x_TotalSF'\n",
+    "]\n",
+    "\n",
+    "Q1 = df_clean[clip_features].quantile(0.25)\n",
+    "Q3 = df_clean[clip_features].quantile(0.75)\n",
+    "IQR = Q3 - Q1\n",
+    "\n",
+    "for col in clip_features:\n",
+    "    lower = Q1[col] - 1.5 * IQR[col]\n",
+    "    upper = Q3[col] + 1.5 * IQR[col]\n",
+    "    df_clean[col] = df_clean[col].clip(lower=lower, upper=upper)\n",
+    "\n",
+    "# بررسی نهایی\n",
+    "print(\"Final shape:\", df_clean.shape)"
+   ],
+   "metadata": {
+    "id": "NwusMRYH8K78",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "9f1c8564-ed24-4b26-bef6-878d19333a48"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# IQR\n",
+    "\n",
+    "# انتخاب ستون های عددی\n",
+    "Q1 = df_clean[num_cols].quantile(0.25)\n",
+    "Q3 = df_clean[num_cols].quantile(0.75)\n",
+    "IQR = Q3 - Q1\n",
+    "\n",
+    "outliers = ((df_clean[num_cols] < (Q1 - 1.5 * IQR)) | (df_clean[num_cols] > (Q3 + 1.5 * IQR))).any(axis=1)\n",
+    "# 1.5 standarde ama baraye taghire sensivity mishe avaz kard\n",
+    "\n",
+    "print(f'Number of outliers detected by boxplot method: {outliers.sum()}')\n",
+    "print(f'Percentage of outliers: {outliers.sum()/len(df)*100:.2f}%')\n",
+    "\n",
+    "df_clean[outliers].shape"
+   ],
+   "metadata": {
+    "id": "LzEVxxL88TIG",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "fdeec579-0cce-4e1a-f18e-8a516a3d9f13"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "from scipy.stats import yeojohnson, boxcox\n",
+    "from scipy.special import boxcox1p\n",
+    "\n",
+    "# بررسی skewness دوباره\n",
+    "skewness = df_clean.skew().sort_values(ascending=False)\n",
+    "\n",
+    "# انتخاب فقط فیچرهایی که skew بالا دارن\n",
+    "skewed_features = skewness[abs(skewness) > 0.75].index\n",
+    "\n",
+    "print(\"Number of skewed features before re-transform:\", len(skewed_features))\n",
+    "\n",
+    "# اعمال ترنسفورم مناسب\n",
+    "for col in skewed_features:\n",
+    "    if (df_clean[col] <= 0).any():\n",
+    "        # اگر صفر یا منفی داره → Yeo-Johnson\n",
+    "        df_clean[col], _ = yeojohnson(df_clean[col])\n",
+    "    else:\n",
+    "        # فقط مثبت → Box-Cox\n",
+    "        df_clean[col] = boxcox1p(df_clean[col], 0.15)\n",
+    "\n",
+    "# چک مجدد skewness\n",
+    "print(\"Skewness after re-transform:\")\n",
+    "print(df_clean[skewed_features].skew().sort_values(ascending=False))"
+   ],
+   "metadata": {
+    "id": "0KqLkbzD9zeA",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "e54cc001-7257-4934-e441-47235b272c34"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# --- لیست فیچرها ---\n",
+    "features_to_drop = ['MS Zoning_RL', 'Roof Style_Gable']  # خیلی skewed + high outliers\n",
+    "features_to_clip = [\n",
+    "    'Exter Cond', 'BsmtFin Type 2', 'BsmtFin SF 2', 'Fireplace Qu', 'Enclosed Porch',\n",
+    "    'MS Zoning_RM', 'Land Contour_Lvl', 'Lot Config_Corner', 'Neighborhood_NAmes',\n",
+    "    'Condition 1_Norm', 'Bldg Type_1Fam', 'House Style_1.5Fin', 'Roof Style_Hip',\n",
+    "    'Exterior 1st_HdBoard', 'Exterior 1st_MetalSd', 'Exterior 1st_Wd Sdng',\n",
+    "    'Exterior 2nd_HdBoard', 'Exterior 2nd_MetalSd', 'Exterior 2nd_Wd Sdng',\n",
+    "    'Foundation_BrkTil', 'Sale Type_WD ', 'Sale Condition_Normal', 'GarageArea_per_Car',\n",
+    "    'Bath_per_Bedroom', 'LotArea_per_GrLivArea', 'OverallQual_x_TotalSF'\n",
+    "]\n",
+    "\n",
+    "# --- اعمال drop ---\n",
+    "df_clean_dropped = df_clean.drop(columns=features_to_drop, errors='ignore')\n",
+    "\n",
+    "# --- اعمال clip ---\n",
+    "for col in features_to_clip:\n",
+    "    if col in df_clean_dropped.columns:\n",
+    "        lower = df_clean_dropped[col].quantile(0.01)\n",
+    "        upper = df_clean_dropped[col].quantile(0.99)\n",
+    "        df_clean_dropped[col] = df_clean_dropped[col].clip(lower, upper)\n",
+    "\n",
+    "# --- بررسی outlierها دوباره ---\n",
+    "def outlier_stats(df):\n",
+    "    outlier_counts = []\n",
+    "    for col in df.select_dtypes(include=np.number).columns:\n",
+    "        q1 = df[col].quantile(0.25)\n",
+    "        q3 = df[col].quantile(0.75)\n",
+    "        iqr = q3 - q1\n",
+    "        lower = q1 - 1.5*iqr\n",
+    "        upper = q3 + 1.5*iqr\n",
+    "        outliers = df[(df[col] < lower) | (df[col] > upper)]\n",
+    "        outlier_counts.append(len(outliers))\n",
+    "    total_outliers = sum(outlier_counts)\n",
+    "    perc_outliers = total_outliers / (df.shape[0] * df.select_dtypes(include=np.number).shape[1]) * 100\n",
+    "    print(f\"Number of outliers detected by boxplot method: {total_outliers}\")\n",
+    "    print(f\"Percentage of outliers: {perc_outliers:.2f}%\")\n",
+    "    print(df.shape)\n",
+    "\n",
+    "outlier_stats(df_clean_dropped)"
+   ],
+   "metadata": {
+    "id": "Z1_SNXYX-fY-",
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "outputId": "2ede4653-69da-4436-c0e3-7da6a4ea4a9b"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "z_score = np.abs((df_clean_dropped[num_cols] - df_clean_dropped[num_cols].mean())/ df_clean_dropped[num_cols].std())\n",
+    "outliers = (z_score > 3).any(axis=1) #inja all hame feature haro barresi mikone va agar hame out budan True (100% )\n",
+    "df_clean_dropped[outliers].shape\n",
+    "#df[~outliers].shape #unai ke outlier nistan"
+   ],
+   "metadata": {
+    "id": "xq_9wiuGC5sB"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "for col in df_clean_dropped.select_dtypes(include=[\"float64\", \"int64\"]).columns:   #for numerics fill with median\n",
+    "    df_clean_dropped[col].fillna(df_clean_dropped[col].median(), inplace=True)"
+   ],
+   "metadata": {
+    "id": "NSLpNaL__Elw"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "df_clean_dropped.isna().sum().sum()"
+   ],
+   "metadata": {
+    "id": "ZpEh-I5U_erh"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## 🔹 Step 10: remove duplicates"
+   ],
+   "metadata": {
+    "id": "o2J0iiYoWANg"
+   }
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "# تعداد ردیف‌های duplicate\n",
+    "num_duplicates = df_clean.duplicated().sum()\n",
+    "print(f\"Number of duplicate rows: {num_duplicates}\")\n",
+    "\n",
+    "# حذف duplicates و ذخیره در df_clean_dropped\n",
+    "df_clean_dropped = df_clean.drop_duplicates()\n",
+    "print(f\"Shape after dropping duplicates: {df_clean_dropped.shape}\")"
+   ],
+   "metadata": {
+    "id": "vWUpaXGJWC-k"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "# انتخاب ستون‌های عددی\n",
+    "numeric_cols = df_clean_dropped.select_dtypes(include=[np.number]).columns.tolist()\n",
+    "\n",
+    "batch_size = 5\n",
+    "for i in range(0, len(numeric_cols), batch_size):\n",
+    "    cols_batch = numeric_cols[i:i+batch_size]\n",
+    "    df_batch = df_clean_dropped[cols_batch]\n",
+    "\n",
+    "    plt.figure(figsize=(12, len(cols_batch) * 1.5))\n",
+    "    for j, col in enumerate(cols_batch, 1):\n",
+    "        plt.subplot(len(cols_batch), 1, j)\n",
+    "        sns.boxplot(x=df_clean_dropped[col], color=\"skyblue\")\n",
+    "        plt.title(f\"Boxplot - {col}\")\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ],
+   "metadata": {
+    "id": "DYa0F9PhAM2P"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "\n",
+    "# تنظیمات اصلی\n",
+    "target_col = 'SalePrice'   # اسم ستون تارگت\n",
+    "batch_size = 5             # تعداد نمودارها در هر سری\n",
+    "\n",
+    "# فقط ستون‌های عددی غیر تارگت\n",
+    "numeric_cols = [col for col in df_clean_dropped.select_dtypes(include=np.number).columns if col != target_col]\n",
+    "\n",
+    "# مرتب‌سازی: اول حروف، بعد اعداد\n",
+    "numeric_cols = sorted(numeric_cols, key=lambda x: (str(x)[0].isdigit(), str(x).lower()))\n",
+    "\n",
+    "# رسم Scatterplot به صورت Batch\n",
+    "for i in range(0, len(numeric_cols), batch_size):\n",
+    "    cols_batch = numeric_cols[i:i+batch_size]\n",
+    "\n",
+    "    plt.figure(figsize=(12, len(cols_batch) * 3))\n",
+    "    for j, col in enumerate(cols_batch, 1):\n",
+    "        plt.subplot(len(cols_batch), 1, j)\n",
+    "        sns.scatterplot(x=df_clean_dropped[col], y=df_clean_dropped[target_col], alpha=0.6, color='teal', edgecolor=None)\n",
+    "        plt.title(f\"Scatterplot: {col} vs {target_col}\")\n",
+    "        plt.xlabel(col)\n",
+    "        plt.ylabel(target_col)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()"
+   ],
+   "metadata": {
+    "id": "sTQmUrEmAXk8"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "source": [
+    "import metrics_helper\n",
+    "metrics_plot(df_clean_dropped)"
+   ],
+   "metadata": {
+    "id": "PH1oNOkXAh6d"
+   },
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "AlW4RbaTkeSu"
+   },
+   "source": [
+    "## 💾 Step 11: Save Cleaned Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "naZHc5dnkamR"
+   },
+   "source": [
+    "# Save your final cleaned and engineered dataset to CSV.\n",
+    "df_clean_dropped.to_csv(\"AmesHousing_clean_by_Arianshs.csv\", index=False)\n",
+    "print(\"✅ Cleaned dataset saved successfully!\")\n"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "id": "a80f4e76"
+   },
+   "source": [],
+   "outputs": [],
+   "execution_count": null
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": [],
+   "collapsed_sections": [
+    "nDirz02_kU1e"
+   ]
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/a0.1/reqirement.txt b/a0.1/reqirement.txt
new file mode 100644
index 0000000..24e7ad5
--- /dev/null
+++ b/a0.1/reqirement.txt
@@ -0,0 +1,62 @@
+absl-py==2.3.1
+asttokens==3.0.0
+astunparse==1.6.3
+certifi==2025.10.5
+charset-normalizer==3.4.3
+colorama==0.4.6
+comm==0.2.3
+debugpy==1.8.17
+decorator==5.2.1
+exceptiongroup==1.3.0
+executing==2.2.1
+flatbuffers==25.9.23
+gast==0.6.0
+google-pasta==0.2.0
+grpcio==1.75.1
+h5py==3.14.0
+idna==3.10
+ipykernel==6.30.1
+ipython==8.37.0
+jedi==0.19.2
+jupyter_client==8.6.3
+jupyter_core==5.8.1
+keras==3.11.3
+libclang==18.1.1
+Markdown==3.9
+markdown-it-py==4.0.0
+MarkupSafe==3.0.3
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+ml_dtypes==0.5.3
+namex==0.1.0
+nest-asyncio==1.6.0
+numpy==2.2.6
+opt_einsum==3.4.0
+optree==0.17.0
+packaging==25.0
+parso==0.8.5
+pillow==11.3.0
+platformdirs==4.4.0
+prompt_toolkit==3.0.52
+protobuf==6.32.1
+psutil==7.1.0
+pure_eval==0.2.3
+Pygments==2.19.2
+python-dateutil==2.9.0.post0
+pywin32==311
+pyzmq==27.1.0
+requests==2.32.5
+rich==14.1.0
+six==1.17.0
+stack-data==0.6.3
+tensorboard==2.20.0
+tensorboard-data-server==0.7.2
+tensorflow==2.20.0
+termcolor==3.1.0
+tornado==6.5.2
+traitlets==5.14.3
+typing_extensions==4.15.0
+urllib3==2.5.0
+wcwidth==0.2.14
+Werkzeug==3.1.3
+wrapt==1.17.3
diff --git a/a0.1/util.py b/a0.1/util.py
new file mode 100644
index 0000000..994ba4c
--- /dev/null
+++ b/a0.1/util.py
@@ -0,0 +1,118 @@
+import hashlib
+import os
+import re
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+import argparse
+
+env_dict = {
+    "FOLDER_TEMPLATE": "a{version}",
+    "FILE_TEMPLATE": "AI-DS_Nexus__A{version_with_underscore}",
+    "FINAL_LITERAL": "__RezaShokr"
+}
+
+def get_version(version=None):
+    if version is None:
+        version = input("Version: ")
+    return version, version.replace('.', '_')
+
+def get_username():
+    try:
+        result = subprocess.run(
+            ["git", "config", "user.name"],
+            capture_output=True,
+            text=True,
+            check=True
+        )
+        raw_username = result.stdout.strip()
+        sanitized_username = re.sub(r'\W+', '_', raw_username)
+        return sanitized_username
+    except subprocess.CalledProcessError as e:
+        raise RuntimeError(f"Failed to retrieve git username: {e}")
+
+def create_or_switch_branch(branch_name):
+    try:
+        result = subprocess.run(
+            ["git", "branch", "--list"],
+            capture_output=True,
+            text=True,
+            check=True
+        )
+        branches = [line.strip().lstrip("* ") for line in result.stdout.splitlines()]
+
+        if branch_name in branches:
+            print(f"Branch {branch_name} is exists. only switching.")
+            subprocess.run(["git", "switch", branch_name], check=True)
+        else:
+            subprocess.run(["git", "switch", "-c", branch_name], check=True)
+    except subprocess.CalledProcessError as e:
+        raise RuntimeError(f"Failed to create or switch branch: {e}")
+
+def commit_changes(files_count, files_name, version, base_message="Add user specific files for version {}: {}."):
+    try:
+        if files_count > 0:
+            subprocess.run(["git", "add", "-A"], check=True, text=True, capture_output=True)
+            subprocess.run(
+                ["git", "commit", "-m", base_message.format(version, files_name)],
+                check=True,
+                text=True,
+                capture_output=True
+            )
+    except subprocess.CalledProcessError as e:
+        raise RuntimeError(f"Failed to commit changes: {e}")
+
+def get_hash(value, length=6):
+    return hashlib.sha256(value.encode()).hexdigest()[:length]
+
+def create_user_copies(folder_template, file_template, folder_version, file_version, username, final_literal):
+    folder = Path(folder_template.format(version=folder_version))
+    if not folder.exists() or not folder.is_dir():
+        raise FileNotFoundError(f"Homework not found: {folder_version}")
+
+    file_basepattern = file_template.format(version_with_underscore=file_version)
+    hash_val = get_hash(username)
+
+    files_counter, files_name = 0, []
+    for file in folder.iterdir():
+        if file.is_file() and file.stem.startswith(file_basepattern) and file.stem.endswith(final_literal):
+            base = file.stem.rsplit(final_literal, 1)[0]
+            new_stem = f"{base}_{username}_{hash_val}"
+            new_file = file.with_stem(new_stem)
+            if not new_file.exists():
+                shutil.copy(file, new_file)
+                print(f"File {file.name} copied to {new_file.name}")
+                files_name.append(new_file.name)
+                files_counter += 1
+            else:
+                print(f"File {new_file.name} already exists, skipping copy.")
+    return files_counter, files_name
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="This script creates a Git branch based on your username, copies version-specific files from a template folder, appends your sanitized username and a hash to the filenames, and commits the changes if any new files are created."
+    )
+    parser.add_argument(
+        'version',
+        nargs='?',
+        help="The version number (e.g., '1.0'). If not provided, the script will prompt you to enter it."
+    )
+
+    args = parser.parse_args()
+
+    folder_template = env_dict["FOLDER_TEMPLATE"]
+    file_template = env_dict["FILE_TEMPLATE"]
+    final_literal = env_dict["FINAL_LITERAL"]
+
+    folder_version, file_version = get_version(args.version)
+    username = get_username()
+
+    create_or_switch_branch(username)
+
+    files_count, files_name = create_user_copies(folder_template, file_template, folder_version, file_version, username, final_literal)
+
+    commit_changes(files_count, files_name, folder_version)
+
+if __name__ == '__main__':
+    main()
diff --git a/a10/Assignment_10_Project_01_01_02_Dataset___Nexus___LeilaRostamian.ipynb b/a10/Assignment_10_Project_01_01_02_Dataset___Nexus___LeilaRostamian.ipynb
new file mode 100644
index 0000000..9068f76
--- /dev/null
+++ b/a10/Assignment_10_Project_01_01_02_Dataset___Nexus___LeilaRostamian.ipynb
@@ -0,0 +1,470 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🍷 Mini-Project: Merge & Explore the Wine Quality Datasets\n",
+        "\n",
+        "\n",
+        "> **Goal: Merge Wine Quality – Red and Wine Quality – White (UCI) into a single dataset, do a careful first-look exploration, and save the merged file to CSV.**\n",
+        "\n",
+        "<p align=\"center\">📢⚠️📂  </p>\n",
+        "\n",
+        "<p align=\"center\"> Please name your file using the format: <code>assignmentName_nickname.py/.ipynb</code> (e.g., <code>project1_ali.py</code>) and push it to GitHub with a clear commit message.</p>\n",
+        "\n",
+        "<p align=\"center\"> 🚨📝🧠 </p>"
+      ],
+      "metadata": {
+        "id": "5pY7aLZSZQM9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# === Requirements ===\n",
+        "# pip install pandas matplotlib\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from pathlib import Path\n"
+      ],
+      "metadata": {
+        "id": "wpNpLMF8aX1s"
+      },
+      "execution_count": 55,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 1) Load ----------\n",
+        "URL_RED   = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n",
+        "URL_WHITE = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv\"\n",
+        "\n",
+        "red   = pd.read_csv(URL_RED, sep=\";\")\n",
+        "white = pd.read_csv(URL_WHITE, sep=\";\")\n"
+      ],
+      "metadata": {
+        "id": "ErqnxAGRaY_C"
+      },
+      "execution_count": 56,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 2) Sanity checks ----------\n",
+        "print(\"Red shape:\", red.shape, \"White shape:\", white.shape)\n",
+        "print(\"Columns equal? ->\", list(red.columns) == list(white.columns))\n",
+        "print(\"Columns:\", list(red.columns))\n"
+      ],
+      "metadata": {
+        "id": "lVbxevgpabbF",
+        "outputId": "f4b06622-edd4-4e8c-864d-842530e9d311",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 57,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Red shape: (1599, 12) White shape: (4898, 12)\n",
+            "Columns equal? -> True\n",
+            "Columns: ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol', 'quality']\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# (Optional) strict schema assertion (search and read about assert in Python)\n",
+        "assert list(red.columns) == list(white.columns), \"Column mismatch between red and white datasets.\"\n"
+      ],
+      "metadata": {
+        "id": "5vV73isEadfQ"
+      },
+      "execution_count": 58,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 3) Tag source & merge ----------\n",
+        "red[\"type\"] = \"red\"\n",
+        "white[\"type\"] = \"white\"\n",
+        "\n",
+        "df = pd.concat([red, white], ignore_index=True)\n",
+        "print(\"\\nMerged shape:\", df.shape)\n"
+      ],
+      "metadata": {
+        "id": "uVTeWORRakKq",
+        "outputId": "73642bcf-abe5-410a-e424-c20df7dfc2ff",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 59,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Merged shape: (6497, 13)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 4) Basic exploration ----------\n",
+        "print(\"\\nDtypes:\\n\", df.dtypes)\n",
+        "print(\"\\nMissing values per column:\\n\", df.isnull().sum().sort_values(ascending=False))\n",
+        "print(\"\\nHead:\\n\", df.head())\n"
+      ],
+      "metadata": {
+        "id": "OsMo1KOhambz",
+        "outputId": "45194ecd-9c42-48b0-d716-a3264cfc2632",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 60,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Dtypes:\n",
+            " fixed acidity           float64\n",
+            "volatile acidity        float64\n",
+            "citric acid             float64\n",
+            "residual sugar          float64\n",
+            "chlorides               float64\n",
+            "free sulfur dioxide     float64\n",
+            "total sulfur dioxide    float64\n",
+            "density                 float64\n",
+            "pH                      float64\n",
+            "sulphates               float64\n",
+            "alcohol                 float64\n",
+            "quality                   int64\n",
+            "type                     object\n",
+            "dtype: object\n",
+            "\n",
+            "Missing values per column:\n",
+            " fixed acidity           0\n",
+            "volatile acidity        0\n",
+            "citric acid             0\n",
+            "residual sugar          0\n",
+            "chlorides               0\n",
+            "free sulfur dioxide     0\n",
+            "total sulfur dioxide    0\n",
+            "density                 0\n",
+            "pH                      0\n",
+            "sulphates               0\n",
+            "alcohol                 0\n",
+            "quality                 0\n",
+            "type                    0\n",
+            "dtype: int64\n",
+            "\n",
+            "Head:\n",
+            "    fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+            "0            7.4              0.70         0.00             1.9      0.076   \n",
+            "1            7.8              0.88         0.00             2.6      0.098   \n",
+            "2            7.8              0.76         0.04             2.3      0.092   \n",
+            "3           11.2              0.28         0.56             1.9      0.075   \n",
+            "4            7.4              0.70         0.00             1.9      0.076   \n",
+            "\n",
+            "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+            "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
+            "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
+            "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
+            "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
+            "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
+            "\n",
+            "   alcohol  quality type  \n",
+            "0      9.4        5  red  \n",
+            "1      9.8        5  red  \n",
+            "2      9.8        5  red  \n",
+            "3      9.8        6  red  \n",
+            "4      9.4        5  red  \n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Uniqueness & duplicates\n",
+        "dup_count = df.duplicated().sum()\n",
+        "print(\"\\nDuplicate rows:\", dup_count)\n",
+        "\n",
+        "# Descriptive statistics (numeric)\n",
+        "num_cols = df.select_dtypes(include=[np.number]).columns\n",
+        "print(\"\\nNumeric summary:\\n\", df[num_cols].describe().T)\n",
+        "\n",
+        "# Target distributions\n",
+        "print(\"\\nQuality distribution (overall):\\n\", df[\"quality\"].value_counts().sort_index())\n",
+        "print(\"\\nQuality distribution by type:\\n\", df.groupby(\"type\")[\"quality\"].value_counts().sort_index())\n"
+      ],
+      "metadata": {
+        "id": "ou0kWts-aopS",
+        "outputId": "7dc0498b-e9d9-4f25-f719-63b5f59995d4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 61,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Duplicate rows: 1177\n",
+            "\n",
+            "Numeric summary:\n",
+            "                        count        mean        std      min       25%  \\\n",
+            "fixed acidity         6497.0    7.215307   1.296434  3.80000   6.40000   \n",
+            "volatile acidity      6497.0    0.339666   0.164636  0.08000   0.23000   \n",
+            "citric acid           6497.0    0.318633   0.145318  0.00000   0.25000   \n",
+            "residual sugar        6497.0    5.443235   4.757804  0.60000   1.80000   \n",
+            "chlorides             6497.0    0.056034   0.035034  0.00900   0.03800   \n",
+            "free sulfur dioxide   6497.0   30.525319  17.749400  1.00000  17.00000   \n",
+            "total sulfur dioxide  6497.0  115.744574  56.521855  6.00000  77.00000   \n",
+            "density               6497.0    0.994697   0.002999  0.98711   0.99234   \n",
+            "pH                    6497.0    3.218501   0.160787  2.72000   3.11000   \n",
+            "sulphates             6497.0    0.531268   0.148806  0.22000   0.43000   \n",
+            "alcohol               6497.0   10.491801   1.192712  8.00000   9.50000   \n",
+            "quality               6497.0    5.818378   0.873255  3.00000   5.00000   \n",
+            "\n",
+            "                            50%        75%        max  \n",
+            "fixed acidity           7.00000    7.70000   15.90000  \n",
+            "volatile acidity        0.29000    0.40000    1.58000  \n",
+            "citric acid             0.31000    0.39000    1.66000  \n",
+            "residual sugar          3.00000    8.10000   65.80000  \n",
+            "chlorides               0.04700    0.06500    0.61100  \n",
+            "free sulfur dioxide    29.00000   41.00000  289.00000  \n",
+            "total sulfur dioxide  118.00000  156.00000  440.00000  \n",
+            "density                 0.99489    0.99699    1.03898  \n",
+            "pH                      3.21000    3.32000    4.01000  \n",
+            "sulphates               0.51000    0.60000    2.00000  \n",
+            "alcohol                10.30000   11.30000   14.90000  \n",
+            "quality                 6.00000    6.00000    9.00000  \n",
+            "\n",
+            "Quality distribution (overall):\n",
+            " quality\n",
+            "3      30\n",
+            "4     216\n",
+            "5    2138\n",
+            "6    2836\n",
+            "7    1079\n",
+            "8     193\n",
+            "9       5\n",
+            "Name: count, dtype: int64\n",
+            "\n",
+            "Quality distribution by type:\n",
+            " type   quality\n",
+            "red    3            10\n",
+            "       4            53\n",
+            "       5           681\n",
+            "       6           638\n",
+            "       7           199\n",
+            "       8            18\n",
+            "white  3            20\n",
+            "       4           163\n",
+            "       5          1457\n",
+            "       6          2198\n",
+            "       7           880\n",
+            "       8           175\n",
+            "       9             5\n",
+            "Name: count, dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ---------- 5) A few simple visuals (optional for report) ----------\n",
+        "# Histograms of numeric features (quick feel for ranges & skew)\n",
+        "top_vars = num_cols\n",
+        "n = min(4, len(top_vars))\n",
+        "\n",
+        "fig, axes = plt.subplots(1, 4, figsize=(16, 3), sharey=True)\n",
+        "\n",
+        "for i, ax in enumerate(axes):\n",
+        "    if i < n:\n",
+        "        col = top_vars[i]\n",
+        "        ax.hist(df[col].dropna(), bins=30)\n",
+        "        ax.set_title(f\"Histogram: {col}\")\n",
+        "        ax.set_xlabel(col)\n",
+        "        if i == 0:\n",
+        "            ax.set_ylabel(\"Count\")\n",
+        "        else:\n",
+        "            ax.set_ylabel(\"\")\n",
+        "    else:\n",
+        "        ax.axis(\"off\")  # hide unused panels if top_vars has < 4\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "2Pa_iCVzaqzp",
+        "outputId": "976c3e87-0fe4-4d63-ed3b-5ab1250d638c",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 293
+        }
+      },
+      "execution_count": 62,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1600x300 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Boxplot of quality by type (class distribution spread)\n",
+        "plt.figure()\n",
+        "df.boxplot(column=\"quality\", by=\"type\")\n",
+        "plt.suptitle(\"\")\n",
+        "plt.title(\"Quality by Wine Type\")\n",
+        "plt.xlabel(\"Type\")\n",
+        "plt.ylabel(\"Quality\")\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "w9z9f2OMatLD",
+        "outputId": "564851d3-0c6c-427e-c1aa-733444a05120",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 490
+        }
+      },
+      "execution_count": 63,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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TTz6pJ5980ttlXNWJEye0fv16ffTRRzp16pQee+yxatnO9u3b9fXXX+uFF15Qhw4dlJKSUi3bqU3Onj2rlStXasOGDdqzZ4/+/ve/e7skAFdBsANwXfvmm280fPhwRUVF6bXXXlP79u2rZTvz58/X0qVL1b59ey1evLhatlHbnDx5Uvfff7/q1aunZ555Rr/85S+9XRKAq+AYOwAAAENwHTsAAABDEOwAAAAMUauPsTt//ryOHTumkJCQMm9HBAAAUFtZlqUzZ84oJiZGdeqUPSdXq4PdsWPHFBsb6+0yAAAAqt13332nRo0aldmnVge7kJAQSRfeaGhoqJerQW3idDq1bt061+2XAKC6Me6gsgoKChQbG+vKPWWp1cHu4u7X0NBQgh0qxOl0KjAwUKGhoQywAGoE4w6uVXkOO+PkCQAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQ9TqO08AAFAb5Ofnq2/fvjp06JASEhK0Zs0ahYWFebssGMirM3ZnzpzRxIkTFRcXp7p166pr167asWOHN0sCAKBKNW/eXPXq1dPWrVuVk5OjrVu3ql69emrevLm3S4OBvBrsHnroIaWlpendd9/Vnj171KdPH/Xq1Us//PCDN8sCAKBKNG/eXJmZmZKk1NRUzZw5U6mpqZKkzMxMwh2qnNeC3U8//aS//e1vevnll9WjRw81b95c06ZNU/PmzTV//nxvlQUAQJXIz893hbqzZ89q1apVSkxM1KpVq3T27FlJF8Jdfn6+N8uEYbx2jN25c+dUUlKigIAAt/a6detq8+bNpb7G4XDI4XC4nhcUFEiSnE6nnE5n9RUL41z8vvC9AVBd+vbtK+nCTJ3dbncbd+x2u3r37q20tDT17dtX6enp3iwV17mK/K7yWrALCQlRUlKSXnjhBbVq1UoNGjTQ+++/r61bt15xanrGjBmaPn26R/u6desUGBhY3SXDQGlpad4uAYChDh06JEm64447tHr1alf7xXGnZ8+eSktL06FDh9yWA5crKioqd1+bZVlWNdZSpszMTD3wwAP67LPP5OPjo1tvvVUtWrTQF198of3793v0L23GLjY2Vjk5OQoNDa3J0lHLOZ1OpaWlqXfv3rLb7d4uB4CBUlJStHXrVqWmpmrVqlUe407//v2VlpampKQkZuxQpoKCAkVERCg/P/+qecerlzuJj49Xenq6zp49q4KCAjVs2FC/+c1v1KxZs1L7+/v7y9/f36PdbrfzyxmVwncHQHVZs2aN6tWrp7Vr17p2v0py7Za9OHO3Zs0axiGUqSLfj+viAsVBQUFq2LChTp8+rbVr12rgwIHeLgkAgGsSFham+Ph4SRd+z/Xv31/79u1T//79FRQUJOnCBAfXs0NV8uqM3dq1a2VZllq2bKnDhw9rypQpSkxM1NixY71ZFgAAVeLw4cOuS56kpaW5HdcbHx+vw4cPe7E6mMirM3b5+fkaP368EhMTNWrUKCUnJ2vt2rVMSQMAjHH48GHl5eUpKSlJERERSkpKUl5eHqEO1cKrM3ZDhw7V0KFDvVkCAADVLiwsTOnp6Vq9erX69evHBAaqzXVxjB0AAACuHcEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAzh6+0CgJr2+eefq0uXLq7n27dvV+fOnb1YEQDT2Ww2jzbLsrxQCUzn1Rm7kpISPfvss2ratKnq1q2r+Ph4vfDCC3zZUW1sNptbqJOkLl26lDroAkBVuNL4wriD6uDVGbtZs2Zp/vz5euedd9S6dWvt3LlTY8eOVVhYmCZMmODN0mCgywfRXr16af369W7L+aMCQFW6Wnhj3EFV8+qM3ZYtWzRw4ED1799fTZo00b333qs+ffro888/92ZZMNCl36lDhw6puLhYjz76qIqLi3Xo0KFS+wHAtbg01NWrV0/FxcVasWKFiouLVa9evVL7AdfKqzN2Xbt21ZtvvqmDBw+qRYsW2r17tzZv3qy5c+eW2t/hcMjhcLieFxQUSJKcTqecTmeN1Iza6dLdr3Fxca7vi9PpVFxcnFu/4uLiGq8PgNlOnDjhNu6cOHFCfn5+ruX8DkNZKvL98Gqwe+qpp1RQUKDExET5+PiopKREL774ooYPH15q/xkzZmj69Oke7evWrVNgYGB1lwsD9OrVS6tXr3Y9T0tLkyT16NFDn332mSS5LQeAqlDauHOl5cDlioqKyt3XZnlx5/7y5cs1ZcoUzZ49W61bt9ZXX32liRMnau7cuRo9erRH/9Jm7GJjY5WTk6PQ0NCaLB21zKV/GRcXF8vpdCotLU29e/eW3W73WA4A14pxB1WloKBAERERys/Pv2re8eqM3ZQpU/TUU0/pvvvukyS1bdtWR44c0YwZM0oNdv7+/vL39/dot9vtstvt1V4vaq/t27e7dsceOXLEtfvVbrfryJEjbv34LgGoalFRUTpx4oSkC+NOVFSU23LGHZSlIt8Prwa7oqIi1anjfv6Gj4+Pzp8/76WKYKpLr1OXkJAg6cLu10GDBl2xHwBcC8uyXCdG5OXluc3QXd4PqCpeDXYDBgzQiy++qMaNG6t169b68ssvNXfuXD3wwAPeLAuGunSQleQ6pu7S5QBQlS4fd0pbDlQlr17u5E9/+pPuvfdePfLII2rVqpUmT56scePG6YUXXvBmWTCYZVnavn27W9v27dsZXAFUmyuNL4w7qA5ePXniWhUUFCgsLKxcBxMCl3I6nVq9erX69evHsS0AagTjDiqrInnHqzN2AAAAqDoEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEF4Ndk2aNJHNZvN4jB8/3ptlAQAA1Eq+3tz4jh07VFJS4nq+d+9e9e7dW7/+9a+9WBUAAEDt5NVgFxkZ6fZ85syZio+PV0pKipcqAgAAqL28GuwuVVxcrKVLl+rxxx+XzWYrtY/D4ZDD4XA9LygokCQ5nU45nc4aqRPXr6KiIh04cKBcfQt/cmjLnkyF1Num4Lr+5XpNy5YtFRgYeC0lAjAM4w5qQkUyznUT7FasWKG8vDyNGTPmin1mzJih6dOne7SvW7eOLz6UmZmpJ554okKvebkCfefMmaP4+PiKFQXAaIw7qAlFRUXl7muzLMuqxlrKLTU1VX5+flq1atUV+5Q2YxcbG6ucnByFhobWRJm4jlXkL+eD2fma8sk3mj34FrVoGFau1/CXM4DLMe6gJhQUFCgiIkL5+flXzTvXxYzdkSNHtH79en388cdl9vP395e/v+f0td1ul91ur67yUEuEhYWpc+fO5errd+SU/LcWq037W9U+7qZqrgyAqRh3UBMqknGui+vYLVq0SFFRUerfv7+3SwEAAKi1vB7szp8/r0WLFmn06NHy9b0uJhABAABqJa8Hu/Xr1+vo0aN64IEHvF0KAABAreb1KbI+ffroOjl/AwAAoFbz+owdAAAAqgbBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBCVCnbffvttVdcBAACAa1SpYNe8eXPdcccdWrp0qX7++eeqrgkAAACVUKlgt2vXLrVr106PP/64oqOjNW7cOH3++edVXRsAAAAqoFLBrn379po3b56OHTumt99+W9nZ2UpOTlabNm00d+5cnTx5sqrrBAAAwFVc08kTvr6+GjJkiD788EPNmjVLhw8f1uTJkxUbG6tRo0YpOzu7quoEAADAVVxTsNu5c6ceeeQRNWzYUHPnztXkyZOVmZmptLQ0HTt2TAMHDqyqOgEAAHAVvpV50dy5c7Vo0SIdOHBA/fr105IlS9SvXz/VqXMhJzZt2lSLFy9WkyZNqrJWAAAAlKFSwW7+/Pl64IEHNGbMGDVs2LDUPlFRUVq4cOE1FQcAAIDyq1SwS0tLU+PGjV0zdBdZlqXvvvtOjRs3lp+fn0aPHl0lRQIAAODqKnWMXXx8vHJycjzac3Nz1bRp02suCgAAABVXqWBnWVap7YWFhQoICLimggAAAFA5FdoV+/jjj0uSbDabnnvuOQUGBrqWlZSUaPv27Wrfvn2VFggAAIDyqVCw+/LLLyVdmLHbs2eP/Pz8XMv8/Pz0i1/8QpMnT67aCgEAAFAuFQp2GzZskCSNHTtW8+bNU2hoaLUUBQAAgIqr1FmxixYtquo6AAAAcI3KHeyGDBmixYsXKzQ0VEOGDCmz78cff3zNhQEAAKBiyh3swsLCZLPZXD8DAADg+lLuYHfp7ld2xQIAAFx/KnUdOwAAAFx/yj1j16FDB9eu2KvZtWtXuQv44Ycf9OSTT2rNmjUqKipS8+bNtWjRInXq1Knc6wAAAEAFgt2gQYOqfOOnT59Wt27ddMcdd2jNmjWKjIzUoUOHVL9+/SrfFgAAgOnKHeymTp1a5RufNWuWYmNj3Y7Z416zAAAAlVOp69hVlZUrVyo1NVW//vWvlZ6erptvvlmPPPKIHn744VL7OxwOORwO1/OCggJJktPplNPprJGa4R27jh1R9pmTVba+I6cKVSfgB6Vn7VLWmeAqW68kNQyJ1K0xcVW6TgA1r7aMO4w55qtIxrFZlmVVdAMlJSV65ZVX9D//8z86evSoiouL3Zbn5uaWaz0BAQGSLtyD9te//rV27Nihxx57TH/5y180evRoj/7Tpk3T9OnTPdrfe+89t/vWwiwnfpJmf79B/pH/8HYp5eI4eZemNLpDUXW9XQmAyqpN4w5jjvmKiop0//33Kz8//6p3/apUsHvuuef017/+VU888YT+67/+S3/4wx+UlZWlFStW6LnnntOECRPKtR4/Pz916tRJW7ZscbVNmDBBO3bs0NatWz36lzZjFxsbq5ycHG5vZrB9xwo0+K11ejy1gWLrV83I9VNxsbbs+kZdb71FdS+55/G1+u70T5q79rg+ebiPWsfwnQRqq9oy7jDm3BgKCgoUERFRrmBXqV2xy5Yt01tvvaX+/ftr2rRpGjZsmOLj49WuXTtt27at3MGuYcOGuuWWW9zaWrVqpb/97W+l9vf395e/v79Hu91ul91ur/gbQa3g6+sr61yo7mjWUW1urpqLYzudTgV+l6d+bbtW6Xdn7w/5mnNus3x9fflOArVYbRl3GHNuDBX5t63Udex+/PFHtW3bVpIUHBys/Px8SdI999yj//u//yv3erp166YDBw64tR08eFBxcRwrAAAAUFGVCnaNGjVSdna2JCk+Pl7r1q2TJO3YsaPUGbUrmTRpkrZt26aXXnpJhw8f1nvvvac333xT48ePr0xZAAAAN7RKBbvBgwfrH/+4cEDp7373Oz377LNKSEjQqFGj9MADD5R7Pbfddps++eQTvf/++2rTpo1eeOEFvfrqqxo+fHhlygIAALihVeoYu5kzZ7p+/s1vfqPGjRtr69atSkhI0IABAyq0rnvuuUf33HNPZcoAAADAJarkOnZJSUlKSkqqilUBAACgkioV7JYsWVLm8lGjRlWqGAAAAFRepYLdY4895vbc6XSqqKhIfn5+CgwMJNgBAAB4QaVOnjh9+rTbo7CwUAcOHFBycrLef//9qq4RAAAA5VCpYFeahIQEzZw502M2DwAAADWjyoKddOFK3ceOHavKVQIAAKCcKnWM3cqVK92eW5al7Oxsvf766+rWrVuVFAYAAICKqVSwGzRokNtzm82myMhI3XnnnZozZ05V1AUAAIAKqlSwO3/+vCTp5MmT8vPzU1hY1dwgGQAAAJVX4WPs8vLyNH78eEVERCg6Olrh4eGKjo7W008/raKiouqoEQAAAOVQoRm73NxcJSUl6YcfftDw4cPVqlUrSdI333yjP/3pT0pLS9PmzZv19ddfa9u2bZowYUK1FA0AAABPFQp2zz//vPz8/JSZmakGDRp4LOvTp49GjhypdevW6bXXXqvSQgEAAFC2CgW7FStWaMGCBR6hTpKio6P18ssvq1+/fpo6dapGjx5dZUUCAADg6ip0jF12drZat259xeVt2rRRnTp1NHXq1GsuDAAAABVToWAXERGhrKysKy7/97//raioqGutCQAAAJVQoWCXmpqqP/zhDyouLvZY5nA49Oyzz+ruu++usuIAAABQfhU+eaJTp05KSEjQ+PHjlZiYKMuytH//fv35z3+Ww+HQkiVLqqtWAAAAlKFCwa5Ro0baunWrHnnkET399NOyLEvShTtP9O7dW6+//roaN25cLYUCAACgbBW+80TTpk21Zs0anT59WocOHZIkNW/eXOHh4VVeHAAAAMqvUrcUk6T69eurc+fOVVkLAAAArkGFbykGAACA6xPBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADOHVYDdt2jTZbDa3R2JiojdLAgAAqLV8vV1A69attX79etdzX1+vlwQAAFAreT1F+fr6Kjo62ttlAAAA1HpeP8bu0KFDiomJUbNmzTR8+HAdPXrU2yUBAADUSl6dsevSpYsWL16sli1bKjs7W9OnT1f37t21d+9ehYSEePR3OBxyOByu5wUFBZIkp9Mpp9NZY3WjZp356cK/+e6juTp37lyVrPPszw7tPClFfHtSQQH+VbJOSTp88qwk6dy5c3wngVqstow7jDk3hor829osy7KqsZYKycvLU1xcnObOnasHH3zQY/m0adM0ffp0j/b33ntPgYGBNVEivGDrcZuWf+vj7TIq5A/tzymqrrerAFBZtW3cYcwxW1FRke6//37l5+crNDS0zL7XVbCTpNtuu029evXSjBkzPJaVNmMXGxurnJycq75R1F65Z4u1fv8JNYsMUl171Qy0B3/M1+8/2a+XB7dSi+iwKlnnRUH+PmpyU1CVrhNAzapN4w5jjvkKCgoUERFRrmDn9ZMnLlVYWKjMzEyNHDmy1OX+/v7y9/ecvrbb7bLb7dVdHrykQT27hic1rZZ1t4gOU/u4m6pl3QBqL8YdXE8qknG8evLE5MmTlZ6erqysLG3ZskWDBw+Wj4+Phg0b5s2yAAAAaiWvzth9//33GjZsmE6dOqXIyEglJydr27ZtioyM9GZZAAAAtZJXg93y5cu9uXkAAACjeP06dgAAAKgaBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBDXTbCbOXOmbDabJk6c6O1SAAAAaqXrItjt2LFDCxYsULt27bxdCgAAQK3l9WBXWFio4cOH66233lL9+vW9XQ4AAECt5fVgN378ePXv31+9evXydikAAAC1mq83N758+XLt2rVLO3bsKFd/h8Mhh8Phel5QUCBJcjqdcjqd1VIjao+ioiIdOHCgXH0PZufL8eNh7f3KT8XHw8r1mpYtWyowMPBaSgRgGMYd1ISKZByvBbvvvvtOjz32mNLS0hQQEFCu18yYMUPTp0/3aF+3bh1ffCgzM1NPPPFEhV4z8p3y950zZ47i4+MrWBUAkzHuoCYUFRWVu6/NsiyrGmu5ohUrVmjw4MHy8fFxtZWUlMhms6lOnTpyOBxuy6TSZ+xiY2OVk5Oj0NDQGqsd16eK/OVc+JNDazftUGr32xRc179cr+EvZwCXY9xBTSgoKFBERITy8/Ovmne8NmN31113ac+ePW5tY8eOVWJiop588kmPUCdJ/v7+8vf3/M9gt9tlt9urrVbUDmFhYercuXO5+jqdTp3Jy1X3rrfz3QFQaYw7qAkV+b54LdiFhISoTZs2bm1BQUG66aabPNoBAABwdV4/KxYAAABVw6tnxV5u48aN3i4BAACg1mLGDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7AAAAQxDsAAAADEGwAwAAMATBDgAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEF4NdvPnz1e7du0UGhqq0NBQJSUlac2aNd4sCTeAo0ePKjw8XIMHD1Z4eLiOHj3q7ZIAGG7z5s3y8/PToEGD5Ofnp82bN3u7JBjKZlmW5a2Nr1q1Sj4+PkpISJBlWXrnnXc0e/Zsffnll2rduvVVX19QUKCwsDDl5+crNDS0BipGbWe323Xu3DmPdl9fXzmdTi9UBMB0Npvtisu8+CsYtUhF8o5XZ+wGDBigfv36KSEhQS1atNCLL76o4OBgbdu2zZtlwVCXhrrw8HD99re/VXh4uCTp3Llzstvt3iwPgIEuD3XJycllLgeu1XVzjF1JSYmWL1+us2fPKikpydvlwDBHjx51hbqTJ0/qxx9/1N13360ff/xRJ0+elHQh3LFbFkBVuXR36/79+1VcXKzJkyeruLhY+/fvL7UfcK18vV3Anj17lJSUpJ9//lnBwcH65JNPdMstt5Ta1+FwyOFwuJ4XFBRIkpxOJ7vRUKaLu/bDw8MVFhbm+r44nU6FhYUpPDxcubm5at26tXJzc71ZKgBDdO/e3fVzfHy827gTHx/v1q+4uLjG60PtUZGM4/Vg17JlS3311VfKz8/XRx99pNGjRys9Pb3UcDdjxgxNnz7do33dunUKDAysiXJRS509e1aSNHToUK1evdrVnpaWJkkaMmSI/vrXv+rs2bNuywHgWiUnJ5c67nTp0kXbt2+XJMYdlKmoqKjcfb168kRpevXqpfj4eC1YsMBjWWkzdrGxscrJyeHkCZQpPDxchYWFCg8P148//iin06m0tDT17t1bdrtd0dHRys3NVXBwMDN2AKqEn5+f6+fi4mKPcefy5cCVFBQUKCIiolwnT3h9xu5y58+fdwtvl/L395e/v79Hu91u58B3lGnfvn2Ki4tTbm6u8vPzFRYWJunCdyc/P98V5vbt28d3CUCV2LRpk2t3bGZmpmv3q91uV2Zmpls/xh2UpSLfD6/O2D399NPq27evGjdurDNnzui9997TrFmztHbtWvXu3fuqr+dyJ6iIy8+KHTJkiD7++GNXqOOSJwCq2uVnvV66+/Wi62zHGa5DFck7Xp2xO3HihEaNGqXs7GyFhYWpXbt25Q51QEU5nU5XuMvNzdVf//pX1zJCHYDqYFmWW7gj1KG6eTXYLVy40Jubxw3I6XTq6NGjat26tc6ePaugoCDt27dPjRs39nZpAAxlWZY2b97sdpbspk2bPK5pB1SF6+Y6dkBNady4sXJzc/XJJ58oNzeXUAeg2iUnJ6u4uFgrVqxQcXExoQ7VhmAHAABgCIIdAACAIQh2AAAAhiDYAQAAGIJgBwAAYAiCHQAAgCEIdgAAAIYg2AEAABiCYAcAAGAIr95S7FpdvMdeQUGBlytBbeN0OlVUVKSCggLZ7XZvlwPgBsC4g8q6mHPKc2/hWh3szpw5I0mKjY31ciUAAADV68yZMwoLCyuzj80qT/y7Tp0/f17Hjh1TSEiIbDabt8tBLVJQUKDY2Fh99913Cg0N9XY5AG4AjDuoLMuydObMGcXExKhOnbKPoqvVM3Z16tRRo0aNvF0GarHQ0FAGWAA1inEHlXG1mbqLOHkCAADAEAQ7AAAAQxDscEPy9/fX1KlT5e/v7+1SANwgGHdQE2r1yRMAAAD4f5ixAwAAMATBDgAAwBAEO+AKevbsqYkTJ3q7DAC1zOLFi1WvXr0y+4wZM0aDBg2qkXpwY6nV17EDAKA2mjdvntvtoXr27Kn27dvr1Vdf9V5RMALBDsYrLi6Wn5+ft8sAAJfyXmwWqCh2xcI4PXv21KOPPqqJEycqIiJCqamp2rt3r/r27avg4GA1aNBAI0eOVE5Ojus1Z8+e1ahRoxQcHKyGDRtqzpw5XnwHAK43//u//6t69eqppKREkvTVV1/JZrPpqaeecvV56KGHNGLECNfztWvXqlWrVgoODtbdd9+t7Oxs17JLd8WOGTNG6enpmjdvnmw2m2w2m7KysiTpqmMXcDmCHYz0zjvvyM/PTxkZGZo5c6buvPNOdejQQTt37tSnn36q48ePa+jQoa7+U6ZMUXp6uv7+979r3bp12rhxo3bt2uXFdwDgetK9e3edOXNGX375pSQpPT1dERER2rhxo6tPenq6evbsKUkqKirSH//4R7377rv67LPPdPToUU2ePLnUdc+bN09JSUl6+OGHlZ2drezsbMXGxiovL++qYxdwOXbFwkgJCQl6+eWXJUn//d//rQ4dOuill15yLX/77bcVGxurgwcPKiYmRgsXLtTSpUt11113SboQDLkPMYCLwsLC1L59e23cuFGdOnXSxo0bNWnSJE2fPl2FhYXKz8/X4cOHlZKSooyMDDmdTv3lL39RfHy8JOnRRx/V888/f8V1+/n5KTAwUNHR0a72119/vcyxq0WLFtX7plErMWMHI3Xs2NH18+7du7VhwwYFBwe7HomJiZKkzMxMZWZmqri4WF26dHG9Jjw8XC1btqzxugFcv1JSUrRx40ZZlqVNmzZpyJAhatWqlTZv3qz09HTFxMQoISFBkhQYGOgKdZLUsGFDnThxokLbu9rYBZSGGTsYKSgoyPVzYWGhBgwYoFmzZnn0a9iwoQ4fPlyTpQGopXr27Km3335bu3fvlt1uV2Jionr27KmNGzfq9OnTSklJcfW12+1ur7XZbKrojZ6uNnYBpSHYwXi33nqr/va3v6lJkyby9fX8ysfHx8tut2v79u1q3LixJOn06dM6ePCg20AN4MZ28Ti7V155xTU29OzZUzNnztTp06f1xBNPVHrdfn5+rhMzLrra2AWUhl2xMN748eOVm5urYcOGaceOHcrMzNTatWs1duxYlZSUKDg4WA8++KCmTJmif/7zn9q7d6/GjBmjOnX47wHg/6lfv77atWunZcuWuU6S6NGjh3bt2nXNfwg2adJE27dvV1ZWlnJycnT+/Pmrjl1AafjNBePFxMQoIyNDJSUl6tOnj9q2bauJEyeqXr16rvA2e/Zsde/eXQMGDFCvXr2UnJzsdpweAEgXjrMrKSlxBbvw8HDdcsstio6OvqbjcidPniwfHx/dcsstioyM1NGjR8s1dgGXs1kV3ekPAACA6xKRHwAAwBAEOwAAAEMQ7AAAAAxBsAMAADAEwQ4AAMAQBDsAAABDEOwAAAAMQbADAAAwBMEOAADAEAQ7ADccm81W5mPatGneLhEAKsXX2wUAQE3Lzs52/fzBBx/oueee04EDB1xtwcHB3igLAK4ZM3YAbjjR0dGuR1hYmGw2m6KjoxUSEqIWLVro008/deu/YsUKBQUF6cyZM8rKypLNZtPy5cvVtWtXBQQEqE2bNkpPT3d7zd69e9W3b18FBwerQYMGGjlypHJycmrybQK4ARHsAOD/FxQUpPvuu0+LFi1ya1+0aJHuvfdehYSEuNqmTJmiJ554Ql9++aWSkpI0YMAAnTp1SpKUl5enO++8Ux06dNDOnTv16aef6vjx4xo6dGiNvh8ANx6CHQBc4qGHHtLatWtdu2tPnDih1atX64EHHnDr9+ijj+pXv/qVWrVqpfnz5yssLEwLFy6UJL3++uvq0KGDXnrpJSUmJqpDhw56++23tWHDBh08eLDG3xOAGwfBDgAu0blzZ7Vu3VrvvPOOJGnp0qWKi4tTjx493PolJSW5fvb19VWnTp20f/9+SdLu3bu1YcMGBQcHux6JiYmSpMzMzBp6JwBuRJw8AQCXeeihh/TGG2/oqaee0qJFizR27FjZbLZyv76wsFADBgzQrFmzPJY1bNiwKksFADfM2AHAZUaMGKEjR47otdde0zfffKPRo0d79Nm2bZvr53PnzumLL75Qq1atJEm33nqr9u3bpyZNmqh58+Zuj6CgoBp7HwBuPAQ7ALhM/fr1NWTIEE2ZMkV9+vRRo0aNPPq88cYb+uSTT/Svf/1L48eP1+nTp13H4Y0fP165ubkaNmyYduzYoczMTK1du1Zjx45VSUlJTb8dADcQgh0AlOLBBx9UcXGxx0kTF82cOVMzZ87UL37xC23evFkrV65URESEJCkmJkYZGRkqKSlRnz591LZtW02cOFH16tVTnToMuwCqj82yLMvbRQDA9ebdd9/VpEmTdOzYMfn5+bnas7Ky1LRpU3355Zdq37699woEgFJw8gQAXKKoqEjZ2dmaOXOmxo0b5xbqAOB6xz4BALjEyy+/rMTEREVHR+vpp5/2djkAUCHsigUAADAEM3YAAACGINgBAAAYgmAHAABgCIIdAACAIQh2AAAAhiDYAQAAGIJgBwAAYAiCHQAAgCEIdgAAAIb4/wCPPFR0PS2h2wAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Correlation heatmap (numeric only)\n",
+        "corr = df[num_cols].corr()\n",
+        "plt.figure(figsize=(7, 6))\n",
+        "plt.imshow(corr, interpolation=\"nearest\")\n",
+        "plt.title(\"Correlation Heatmap\")\n",
+        "plt.colorbar()\n",
+        "plt.xticks(range(len(num_cols)), num_cols, rotation=90)\n",
+        "plt.yticks(range(len(num_cols)), num_cols)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "id": "m9OZ2n2nawIP",
+        "outputId": "d275fb35-0d14-4d8b-edfe-726285df6ee1",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 623
+        }
+      },
+      "execution_count": 64,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 700x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 65,
+      "metadata": {
+        "id": "NHQPRTdLZI9R",
+        "outputId": "b563bb86-50d6-44ad-e32a-bd019e58ce4d",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Saved merged file to: /content/outputs/wine_quality_merged.csv\n",
+            "Reloaded shape: (6497, 13)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# ---------- 6) Save ----------\n",
+        "OUT_DIR = Path(\"./outputs\")\n",
+        "OUT_DIR.mkdir(parents=True, exist_ok=True)\n",
+        "out_file = OUT_DIR / \"wine_quality_merged.csv\"\n",
+        "df.to_csv(out_file, index=False)\n",
+        "print(f\"\\nSaved merged file to: {out_file.resolve()}\")\n",
+        "\n",
+        "# Quick verification of saved file\n",
+        "df_check = pd.read_csv(out_file)\n",
+        "print(\"Reloaded shape:\", df_check.shape)\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file